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PMC9604423 | Mauricio Peñuela,Jenny Johana Gallo-Franco,Jorge Finke,Camilo Rocha,Anestis Gkanogiannis,Thaura Ghneim-Herrera,Mathias Lorieux | Methylation in the CHH Context Allows to Predict Recombination in Rice | 19-10-2022 | epigenetic,DNA methylation,bisulfite sequencing,machine learning,modeling | DNA methylation is the most studied epigenetic trait. It is considered a key factor in regulating plant development and physiology, and has been associated with the regulation of several genomic features, including transposon silencing, regulation of gene expression, and recombination rates. Nonetheless, understanding the relation between DNA methylation and recombination rates remains a challenge. This work explores the association between recombination rates and DNA methylation for two commercial rice varieties. The results show negative correlations between recombination rates and methylated cytosine counts for all contexts tested at the same time, and for CG and CHG contexts independently. In contrast, a positive correlation between recombination rates and methylated cytosine count is reported in CHH contexts. Similar behavior is observed when considering only methylated cytosines within genes, transposons, and retrotransposons. Moreover, it is shown that the centromere region strongly affects the relationship between recombination rates and methylation. Finally, machine learning regression models are applied to predict recombination using the count of methylated cytosines in the CHH context as the entrance feature. These findings shed light on the understanding of the recombination landscape of rice and represent a reference framework for future studies in rice breeding, genetics, and epigenetics. | Methylation in the CHH Context Allows to Predict Recombination in Rice
DNA methylation is the most studied epigenetic trait. It is considered a key factor in regulating plant development and physiology, and has been associated with the regulation of several genomic features, including transposon silencing, regulation of gene expression, and recombination rates. Nonetheless, understanding the relation between DNA methylation and recombination rates remains a challenge. This work explores the association between recombination rates and DNA methylation for two commercial rice varieties. The results show negative correlations between recombination rates and methylated cytosine counts for all contexts tested at the same time, and for CG and CHG contexts independently. In contrast, a positive correlation between recombination rates and methylated cytosine count is reported in CHH contexts. Similar behavior is observed when considering only methylated cytosines within genes, transposons, and retrotransposons. Moreover, it is shown that the centromere region strongly affects the relationship between recombination rates and methylation. Finally, machine learning regression models are applied to predict recombination using the count of methylated cytosines in the CHH context as the entrance feature. These findings shed light on the understanding of the recombination landscape of rice and represent a reference framework for future studies in rice breeding, genetics, and epigenetics.
Meiotic recombination is recognized as a key process in genetics. During this process, maternally and paternally inherited homologous chromosomes exchange information by gene conversion or crossing over to create novel allelic combinations. Recombination is widely recognized for its role in promoting diversity to respond to continually shifting environments, in addition to preventing the build-up of genetic load by decoupling linked deleterious and beneficial variants [1]. However, meiotic recombination between homologous chromosomes is restricted by the number and location of crossover sites per chromosome. The crossover distribution and frequency along the genome are uneven, especially in plants [2]. Sites with high recombination rates have been linked to subtelomeric regions that are generally hypomethylated and have high gene and DNA transposon frequencies. In contrast, recombination is suppressed in the centromeric region, which is characterized by high frequencies of long terminal repeat retroelements and few genes [3]. The role of chromatin structure and DNA methylation in determining recombination rates has been previously reported. For example, high levels of histone H3 acetylation in Arabidopsis mutants were associated with changes in the crossover frequencies [4]. Likewise, studies using met1 and ddm1 mutants, which are globally hypomethylated, showed regional remodeling of crossover frequencies with increased recombination in chromosome arms and decreased recombination in the pericentromeric region [5,6]. However, understanding how the DNA methylation patterns affect the recombination rates remains an open challenge. In plants, DNA methylation occurs at cytosine nucleotides in all the sequence contexts CG, CHG, and CHH (H = C, T, or A). DNA methylation is a stable mark inherited from generation to generation and a crucial factor for plant development [7]. DNA methylation, in combination with histone and non-histone protein modifications, defines chromatin structure and accessibility, which helps to regulate gene expression, transposon silencing, chromosome interactions, and trait inheritance [8]. Several studies have shown that sexual reproduction in plants involves the reprogramming of DNA methylation patterns [9]. The methylation dynamics for each sequence context is determined by different mechanisms and related to specific biological functions [8]. The maintenance mechanism of plant DNA methylation depends on the context and is mediated by different enzymes. For example, in Arabidopsis thaliana, CG cytosine methylation is maintained by MET1, in a semiconservative manner in the DNA replication process, while CHG methylation is maintained by CMT3 and CMT2, which enables the propagation of methylation through a positive feedback loop together with the H3K9me2 in the cell division process. Meanwhile, CHH methylation is maintained by DRM2 or CMT2, depending on the genomic region [8]. De novo methylation is carried out by CMT2 for CHG and CHH context [9], and the RdDM pathway for all sequence contexts [9]. This process is not the same for all plants. In rice, CG cytosine methylation is carried out by two related genes OsMET1-1 and OsMET1-2, with a possible redundant function, while OsCMT3a is the only functional ortholog of CMT3 involved in CHG methylation during replication. For CHH methylation, no associated gene has yet been reported. There is some evidence that suggests that OsCMT2 is closely related to CMT2 and may play a role in CHH methylation [10]. More research on methylation and demethylation events and their precursors will be necessary to clarify these mechanisms. Identifying factors influencing the meiotic recombination rates is important for breeders interested in transferring genes from one variety to another through crosses. Thus, developing new allelic combinations that allow breeders to meet the needs present in agricultural systems. Recently, a number of studies have addressed this issue and have developed different types of strategies to discover where crossovers occur most frequently and try to predict them. For example, Liu et al. [11] developed a predictor of recombination hot/cold spots in yeast using a machine learning approach combined with principal component analysis. Moreover, Demirci et al. [12] explored DNA sequence and shape features to train machine learning models for predicting crossover occurrence in Arabidopsis, maize, tomato, and rice. Moreover, Adrion et al. [13] used recurrent neural networks, a deep learning method for estimating genome-wide recombination in a natural population of African Drosophila melanogaster. In recent years, rice has been a model monocotyledonous plant for understanding the methylation process because it is highly homozygous and self-pollinating. In addition, rice is of great importance in food security since half of the world’s population depends on it as daily food [14]. However, few studies have analyzed methylation patterns in relation to recombination rates in rice. For instance, Habu et al. [15] developed an experiment crossing methylated and unmethylated rice varieties and concluded that the position and frequency of meiotic recombination in rice centromeric heterochromatin are regulated by the epigenetic state of the chromatin. Likewise, Choi et al. [16] explore how transposable elements interact with host plant epigenetics. They suggest that high levels of methylation at these elements have a role in suppressing deleterious ectopic recombination. Nevertheless, none of these studies have explored in detail how the methylation contexts are related with recombination rates. In this work, the relationship between chromosomal recombination rates and different methylation contexts is explored by using Oryza sativa as a model. The focus is on the following objectives: (1) To estimate the correlation between recombination and methylation in all contexts, (2) to describe the effect of methylation within genes, transposons, and retrotransposons with respect to recombination, and (3) to implement a machine learning model to predict recombination based on methylation data. The results provide evidence that recombination can be described by methylation in the context of CHH, regardless of whether it is outside or inside genes, transposons, and retrotransposons. Consequently, the use of machine learning models to predict chromosomal recombination rates in rice cultivars using CHH methylation is proposed.
In this study, the correlation between recombination rates and the methylated cytosine counts for all chromosomes in two rice cultivars is evaluated (Figure 1 and Figure 2). The correlation values are, on average, −0.44 ± 0.17 for all chromosomes of both varieties, with higher values in the centromere region. Similar results in rice were previously described by Yan et al. [17], revealing that DNA methylation patterns in the centromere are shaped by the DNA sequence and the centromeric domains. Habu et al. [15] described how artificial chromatin modification can vary the frequency of meiotic recombination. Overall, high levels of methylation in heterochromatin regions near the centromeres have been reported as a common pattern, where meiotic recombination is repressed. In the same way, recombination-free regions around centromeres are likely to be important for normal centromere function during meiosis [15,18]. By evaluating the CG and CHG methylation contexts independently, a decrease in recombination rates with increasing methylated cytosines is reported. On the contrary, methylated cytosines in the CHH context increase with recombination rates showing a positive correlation (Figure 3). The opposite relationship between the methylation contexts of CG and CHH has been reported in rice by Li et al. [19], who identified the tendency towards hypermethylation in CG context, but hypomethylation in CHH. The positive correlation between methylated cytosine count and recombination rates observed in the context of CHH is not clear when all methylation contexts are assessed together because the total number of methylated cytosines in the CG and CHG contexts was higher. This trend is observed for both varieties, IR64 and Azucena, where the methylation data and the alignment process have been obtained independently. The positive relationship between the CHH methylated cytosine count and recombination rates has been reported by Rodgers-Melnick et al. [1], who include the CHH methylation as a feature of a linear model to predict recombination in maize. It is unclear what role methylated cytosines play in the CHH context with respect to recombination. Variability in DNA methylation can be heritable or reversible, and this can allow for phenotypic variation and rapid response to environmental changes. Even the degree of intraspecies epigenomic diversity can be correlated with climate and geographic origin [10]. It has been reported that CHH methylation could be related to fruit size in apples [20] and silencing transposons in sugar beets [21]. A potential role in A. thaliana seed dormancy, with increases in CHH methylation in seeds during seed development and a decrease during germination, has also been reported in [8]. These observations suggest the multiple roles that CHH methylation can play in plant genomes. Recently, Wang et al. [22] reported that CHH methylation levels are higher in rice reproductive organs, such as panicles and pistils, than in seedlings, suggesting a positive feedback loop between DNA methylation and RNA-directed DNA methylation activity involved in sexual reproduction. The functional analysis performed with annotation data of genes, transposons, and retrotransposons for each variety, shows that the increment in the number of genes per window is correlated with recombination rates in the chromosomes of both varieties (Figure 4 and Figure 5). This positive trend has been previously evidenced in Drosophila, A. thaliana, yeast, finches, monkeyflowers, and dogs, with recombination hotspots typically located near the promoter regions of genes [23] and observed in the euchromatic regions of maize [24]. In contrast, a negative correlation between the number of transposons and retrotransposons has been found with respect to recombination rates across all chromosomes for both rice varieties. This can be explained by the abundance of such elements near the centromere where recombination rates are low. Similar results have been found by Tian et al. [25], who suggested that the rice genome is organized along recombinational gradients due to the negative correlation of recombination with transposable elements and positive one with gene densities. Recombination tends to occur within and near genes and away from transposable elements. This may reflect the passive effects of recombination initiating in open chromatin [23]. Recent analyses of the localization of recombination at the fine scale tend to show negative correlations with local densities of repetitive elements. Actually, strong recombination suppression and a large accumulation of transposable elements are usual in pericentromeric regions [23]. For rice, this pattern is shared between japonica and indica groups [25]. There remains uncertainty about the directionality of cause and effect, the extent to which the correlation is driven by associations of both recombination and transposable elements with other factors, or why patterns differ among species and types of repetitive elements [23]. The count of methylated cytosines is assessed within genes, transposons, and retrotransposons and compared to recombination rates (Supplementary Materials Figures S1 and S2). The analysis shows that methylated cytosine count in genes, transposons, and retrotransposons is negatively correlated with recombination rates when evaluated for all contexts together (Supplementary Materials Figure S3). This indicates that methylation inside these entities is higher when recombination is lower. The same negative trend is observed when methylated cytosines are analyzed in CG and CHG contexts. Methylation events in transposons and retrotransposons are associated with the prevention of their expression and movement in chromosomes, which can be damageable to the organism and even deleterious [23,26]. It should be noted that these methylation events can also affect surrounding genomic regions [26], potentially influencing the methylation status of nearby genes. In genes, methylation usually occurs at the promoters or within the body of the transcribed gene, inhibiting their expression [8]. However, the methylated cytosines in the CHH context are also positively correlated with the recombination rates. This is a consequence of low CHH methylation near the centromere region and greater presence in the chromosome arms. Gallo-Franco et al. [27] reported high CHH methylation levels of transposable elements close to genes in rice, which supports the conclusion of Martin et al. [28] for grass species that long genes and genes close to transposable elements tend to have CHH islands more frequently. It could be hypothesized that the presence of these CHH islands is promoting the positive correlation between methylation and recombination in gene-rich regions. Chromosomal regions close to the centromere have a high incidence on methylation. When only the chromosome arms are evaluated, correlation trends change, from being high negative to being negative, for all contexts evaluated together and for the CG and CHG contexts evaluated independently (Figure 6). For CHH methylation, the markedly positive correlation also decreases but is still positive. In the context centromere regions are evaluated, negative correlations are evidenced in all contexts when they are evaluated together and for CG and CHG contexts independently. These results are in agreement with the reported importance of DNA methylation for plant chromosomal interactions in pericentromeric regions [9]. They also agree with the results obtained by Habu et al. [15], who indicate that the position and frequency of meiotic recombination in the centromeric heterochromatin of rice are regulated by the epigenetic state of the chromatin. With respect to methylation in CHH contexts, the correlation of the centromere region is positive but weaker than that of the whole chromosome (Figure 6). The contributions of methylation in CG, CHG, and CHH contexts to predict recombination as features of machine learning models are assessed using the Shapley package. The results show a great contribution of CHH for the prediction of recombination and a low contribution of CG and CHG for both varieties (Figure 7). This agrees with the fact that the CHH context has the highest correlation values with respect to chromosome recombination rates, while the CG and CHG contexts have lower correlations. The Shap summary plot also shows the same trend, evidencing the strongest effect on recombination when the CHH values are higher. Subsequently, the methylated cytosine count in the CHH context is used as a unique feature to evaluate regression algorithms of machine learning, because the performance of the model decreases when the other features are considered. The evaluation is carried out independently for each variety using the Lazy Predict package. The results show that the Extra Trees algorithm performed the best prediction (R2 = 0.57, RMSE = 0.01 for IR64; R2 = 0.69, RMSE = 0.01 for Azucena). Thus, this algorithm is used to develop the training and subsequent predictions. Predictions on Azucena’s chromosomes, by training the Extra Trees algorithm with information from IR64, give an R2 of 0.32 ± 0.13 and an MSE of 0.02 ± 0.00, on average. Meanwhile, predictions on IR64’s chromosomes by training the Extra Trees algorithm with information from Azucena give an R2 of 0.21 ± 0.21 and an MSE of 0.03 ± 0.00, on average. In both cases, the average correlation values between predictions and recombination rates are 0.67 ± 0.06 for Azucena and 0.65 ± 0.07 for IR64, evidencing a positive trend (Table 1, Figure 8). Several studies have focused on predicting recombination using machine learning. For example, Liu et al. [11] combined support vector machines with consensus feature dinucleotide-based autocross covariance to predict the recombination of hot/cold spots in yeast. Demirci et al. [12] used features, such as gene annotation, propeller, and helical twist, AT/TA dinucleotides, and CA dinucleotides to train machine learning models for predicting crossover occurrences in Arabidopsis, maize, rice, and tomato. More recently, Adrion et al. [13] proposed an approach to predict the recombination landscape in African populations of Drosophila melanogaster using deep learning with recurrent neural networks. For all cases, the results have been satisfactory according to the specific objective of each study, which demonstrates the power of machine learning approaches to predict complex traits such as chromosomal recombination. The Extra Trees regression model makes it possible to predict chromosomal recombination using a single feature: The CHH methylated cytosine count. It is possible due to the high correlation between this feature and the recombination rates, which behaved similarly in all chromosomes. The model was trained on a dataset of one variety and was tested it on the other, performing two independent tests and finding that results were consistent (Figure 8). This opens a door for future studies. The evidence suggests that these models can be used to predict chromosomal recombination rates in any variety of Oryza sativa rice. This is because the two varieties used in this study, IR64 and Azucena, are highly distant genetically, belonging to the indica and japonica groups, respectively.
The recombination rates are estimated from an inter-subspecific segregating population of 212 F11 recombinant inbred lines (RIL). They are obtained by single seed descent, derived from the cross between the rice varieties IR64 (indica group) and Azucena (tropical japonica group), and genotyped using shallow Illumina sequencing (~2×) followed by imputation with NOISYmputer. Local recombination rates in cM/bp are calculated in sliding windows of 100 kb using MapDisto.
Seeds of rice varieties IR64 and Azucena were germinated and grown in a growth chamber at 30 °C and 12:12 dark/light conditions for 10 days. Seedlings were transferred to a hydroponic medium with a Kimura B solution (pH 7) and Arnon micronutrients. Roots from three weeks-old seedlings were collected and stored at −80 °C. Total genomic DNA was extracted from frozen root tissue by CTAB 2X protocol with modifications [29]. Genomic DNA quality was evaluated on agarose gels, and DNA quantity was measured using a Nanodrop spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA).
Bisulfite-seq (BS-seq) libraries were made from genomic DNA isolated from IR64 and Azucena seedling roots. DNA from three independent seedlings for each genotype was pooled as one sample and sequenced. Bisulfite conversion of DNA, library construction, and sequencing were performed by CD Genomics (CD Genomics Inc., Shirley, New York, NY, USA). Raw data are available in the GenBank repository for IR64 (Accession number: SRR20325840) and Azucena (Accession number: SRR20325842). Basic statistics on the quality of the raw reads was done with the FastQC tool (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (accessed on 5 September 2021)). Sequencing adapters and low-quality data of the sequencing data were removed by Trimmomatic (http://www.usadellab.org/cms/?page=trimmomatic (accessed 21 November 2021)). Cleaned data were aligned to the reference genomes reported in the GenBank repository for IR64 (Accession number: RWKJ00000000) and Azucena (Accession number: PKQC000000000) using Bismark v.0.16.3 [30] with default parameters. Only uniquely aligned reads were maintained. Methylation calling data obtained from Bismark were used for further analysis.
To compare the methylation patterns with the local recombination rates, the genomes were divided into 100 kb windows, in which the number of cytosines with a methylation level greater than 75% was calculated for each of the CG, CHG, and CHH contexts. Exponential smoothing with α = 0.1 was applied to the recombination and methylation data to remove noise associated with the abrupt change in the count of methylated cytosines in adjacent windows. Subsequently, a Pearson correlation analysis per chromosome was developed to evaluate the linear relationships between the recombination rates and the methylation patterns of both varieties.
Gene, transposon, and retrotransposon annotation information from both varieties was used (Supplementary Materials Tables S1 and S2). Pearson correlation analyses were carried out between the number of genes, transposons, and retrotransposons with respect to recombination of the chromosome to investigate their relationship with the recombination landscape. Later, the start and end coordinates of these elements were used to extract the count of methylated cytosines inside them. New correlation analyses were performed to learn the trends between methylated cytosines for each context within these functional elements with respect to recombination. A differentiation between the centromere and non-centromere regions was also included.
To assess the usefulness of methylation in predicting chromosome recombination, different machine learning approaches were explored. The total counts of methylated cytosines in windows of 100 kb belonging to the CG, CHG, and CHH contexts for each variety were evaluated as features for machine learning modeling using the Shapley package (https://shap.readthedocs.io/en/latest/index.html (accessed on 2 February 2022)). Subsequently, the performance of different machine learning models was evaluated using the LazyPredict package (https://pypi.org/project/lazypredict/ (accessed on 2 February 2022)). Exponential smoothing with α = 0.1 was applied to the data input before training the model and another one to the model output with α = 0.3. The coefficient of determination R2 and the root of the mean square error RMSE were used to evaluate the performance of the models. MSE was used for predictions. Pearson correlation analyses were also performed to discover general linear trends between the predictions and the experimental data. The resulting best model was fitted, and the information from the twelve chromosomes of one variety was used as a training dataset to predict the recombination rates in each of the twelve chromosomes of the other variety. All these analyses and the previous ones were run in Python.
This study reported on how methylated cytosines in the CHH context positively correlate with recombination rates in the twelve rice chromosomes of two genetically distant rice varieties: IR64 and Azucena. However, a negative correlation was found between methylation and recombination rates when only CG and CHG contexts were tested, as well as in the three methylation contexts together. For this case, the positive correlation of CHH was hidden due to the high number of methylated cytosines from the CG and CHG contexts. In addition, functional analysis showed that genes were positively correlated with recombination rates, unlike transposons and retrotransposons, which showed a negative correlation. The correlation between methylation and recombination suggests the same trends for the entire genome with respect to only methylation in genes, transposons, and retrotransposons. The influence of the centromere on methylation patterns and its correlation with recombination rates was evident, supporting the hypothesis that the position and frequency of meiotic recombination in rice centromeric heterochromatin are regulated by the epigenetic state of the chromatin. Finally, a machine learning model was proposed and trained using the CHH methylated cytosine count to predict recombination rates, which obtained consistent results in two independent data sets. This suggests that the extraction of methylation data and the use of machine learning models in future studies is a promising path to focus on predicting recombination rates using the count of CHH-methylated cytosines in rice as a feature. Colleagues are invited to explore how the counting of CHH-methylated cytosines in other species behaves with respect to chromosomal recombination. | true | true | true |
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PMC9604520 | 36288306 | Joëlle Magné,Douglas R. Green | LC3-associated endocytosis and the functions of Rubicon and ATG16L1 | 01-10-2022 | LC3-associated endocytosis (LANDO) is a noncanonical function of the autophagy machinery, in which LC3 (microtubule-associated protein light chain) is conjugated to rab5-positive endosomes, using a portion of the canonical autophagy pathway. LANDO was initially discovered in a murine model of Alzheimer’s disease as a critical regulator of amyloid-β receptor recycling in microglial cells, playing a protective role against neuronal loss and memory impairment. Recent evidence suggests an emerging role of LANDO in cytokine receptor signaling and innate immunity. Here, we discuss the regulation of two crucial effectors of LANDO, Rubicon and ATG16L1, and their impact on endocytosis, autophagy, and phagocytosis. | LC3-associated endocytosis and the functions of Rubicon and ATG16L1
LC3-associated endocytosis (LANDO) is a noncanonical function of the autophagy machinery, in which LC3 (microtubule-associated protein light chain) is conjugated to rab5-positive endosomes, using a portion of the canonical autophagy pathway. LANDO was initially discovered in a murine model of Alzheimer’s disease as a critical regulator of amyloid-β receptor recycling in microglial cells, playing a protective role against neuronal loss and memory impairment. Recent evidence suggests an emerging role of LANDO in cytokine receptor signaling and innate immunity. Here, we discuss the regulation of two crucial effectors of LANDO, Rubicon and ATG16L1, and their impact on endocytosis, autophagy, and phagocytosis.
Macroautophagy, henceforth referred to as autophagy, is a conserved process involving the nonspecific engulfment of cytoplasmic material or specific engulfment of intracellular misfolded proteins, aggregates, or damaged organelles in a double-membrane vesicle called the autophagosome. The autophagosome fuses with lysosomes for cargo degradation (1). A sequential activation cascade of autophagy-related (ATG) proteins allows the lipidation of microtubule-associated protein light chain 3 (LC3) family members and related molecules (herein, collectively called LC3) and their recruitment to a pre-autophagosomal structure known as the phagophore. The generation of gene-edited organisms revealed the role of ATG genes in stress responses, intracellular quality control, development, and cell differentiation, and studies have implicated autophagy in human diseases such as neurodegeneration, inflammatory disorders, and cancer (1). Schematically, in canonical autophagy, a cascade of three sequentially engaged multiprotein complexes leads to autophagosome biogenesis: (i) a serine-threonine kinase ULK complex, (ii) a phosphoinositide 3-kinase (PI3K) complex containing VPS34, and (iii) ubiquitin conjugation–like systems for conjugation of ATG12 to ATG5 and conjugation of LC3 to phospholipids. In response to amino acid starvation, the activation of adenosine monophosphate–activated kinase (AMPK) and inactivation of mechanistic target of rapamycin complex 1 (mTORC1) result in the engagement of canonical autophagy by activation of a complex composed of the ULK1 or ULK2 serine-threonine kinase and the adaptor proteins ATG13, ATG101, and FIP200 (also called RB1CC1). ULK complex phosphorylation at the cytosolic face of the endoplasmic reticulum (ER) subsequently recruits the VPS34 complex (2, 3). Phosphatidylinositol 3-phosphate (PI3P) is locally generated by the VPS34 complex consisting of a minimal tetrameric catalytic unit, the class III PI3K lipid kinase VPS34, Beclin 1, and VPS15. Two complexes known as complex I and complex II have been described wherein the minimal catalytic unit is either associated with the adaptor proteins ATG14 for complex I or UVRAG for complex II (4). PI3P-enriched membranes recruit, in turn, a complex of ATG12-ATG5 and ATG16L1, which acts as an E3 ligase to ligate LC3 to phosphoethanolamine. LC3 conjugation is crucial for cargo recognition, phagophore elongation, and its completion into a double-membrane vesicle organelle. The autophagosome ultimately fuses to lysosomes, in which the cargo is degraded by lysosomal hydrolases (Fig. 1). Over the past 15 years, increasing evidence has supported roles of several autophagy-related proteins in nonautophagic processes such as phagocytosis, endocytosis, entosis, and micropinocytosis, in which LC3 is lipidated at single membranes (5). LC3-associated phagocytosis (LAP) was the first such nonautophagic pathway to be described (6). In murine macrophages, extracellular particles including killed yeast (zymosan), beads coated with lipopolysaccharide, PAM3csk4 or antibodies, Escherichia coli, or DNA–anti-DNA immune complexes that stimulate Fc receptors to mediate Toll-like receptor 9 (TLR9) and type I interferon signaling trigger LC3 recruitment on mature phagosomes engulfing those particles (6). A VPS34 complex containing UVRAG and Rubicon (RUN domain and cysteine-rich domain containing, Beclin 1 interacting protein) and the ATG12-ATG5-ATG16L1 conjugation system are necessary for LAP; however, all components of the ULK complex (including FIP200) and the VPS34 complex I adaptor protein ATG14 are dispensable (Fig. 1). Ablation of components of LAP in murine myeloid compartments demonstrated that LAP deficiency promotes an antitumor effect due to an improved T cell response to dying tumor cells (7). The role of LAP in efferocytosis and innate immunity has been reviewed elsewhere (8, 9). Endocytic pathways allow cells to sense their environment and regulate their signal transmission by internalizing various extracellular or surface resident molecules, which are either degraded, recycled back to the cell surface, or targeted to the trans-Golgi network (10). Endocytic transport consists of several sets of vesicles including early endosomes, recycling endosomes, late endosomes, and lysosomes, which dynamically interact with each other by fusion and fission events. To exercise this complex trafficking of molecules, each organelle is marked by specific Rab–guanosine triphosphatases (GTPases) and tether proteins. Beyond its role in autophagy, VPS34 lipid kinase activity is essential for endocytic vesicle trafficking (11–14). VPS34 complex II is recruited to Rab5-positive early endosomes and is involved in the maturation to Rab7-positive late endosomes and endolysosomal fusion (15). While multiple lines of evidence suggest that several proteins involved in the endocytic pathway are essential for efficient autophagy at different stages of the degradative process (16, 17), here, we review emerging functions of a recent noncanonical function of the autophagy pathway called LC3-associated endocytosis (LANDO), in which LC3 is lipidated on early endosomes. Here, we focus on the molecular machinery and signaling pathways shared between different noncanonical functions of autophagy proteins, and we discuss recent findings that highlight LC3 conjugation on single membranes.
LANDO was initially characterized in microglia cells, in which endocytic uptake of oligomerized amyloid-β (Aβ) peptide leads to conjugation of LC3 to the membrane to rab5-positive, clathrin-positive endosomes (18). Ablation of Rubicon or Atg5 in microglial cells had no effect on primary Aβ uptake or lysosomal degradation but led to a marked reduction in the recycling of putative Aβ receptors, including TLR4, TREM2, and CD36. It was only after a secondary exposure that Aβ uptake was diminished in vitro in microglial cells lacking Rubicon or ATG5. Previous results in microglial cells showed a role for Beclin 1 and VPS34 in the recycling of TREM2 and CD36 by the retromer complex, a conserved complex responsible for endosome to Golgi retrograde transport and consisting of VPS26, VPS29, VPS35, and sorting nexin subunits (19). Recycling of TLR4 and CD36 was also observed to be dependent on LANDO in macrophages, as was that of CD36 and transferrin receptor in mouse embryonic fibroblasts (MEFs) (18). In vivo, microglial- or myeloid-specific deletion of Rubicon or Atg5 in the 5xFAD genetic mouse model of Alzheimer’s disease resulted in an early-onset accumulation of neurotoxic Aβ plaques, microgliosis, and an increase in proinflammatory cytokines within the cortex and the hippocampus (18). Similar results were observed in aged mice with whole-body deletion of the WD domain of Atg16L1, which is dispensable for canonical autophagy but essential for LANDO (20), notably without expression of any transgene. 5xFAD mice lacking microglial Rubicon or Atg5 or mice lacking the Atg16L1 WD domain display substantial neurodegeneration, including neuronal apoptosis, impaired long-term potentiation, and severe short-term memory impairment in behavioral tests. Reduced levels of Rubicon, Atg16L1, Atg5, and Beclin 1 protein expression were observed in human Alzheimer’s disease brains, suggesting that these results may be relevant to human disease. In a similar manner to LAP, Beclin 1 and Rubicon in the VPS34 complex are required for LANDO, as are the proteins of the LC3 conjugation system, while ULK, FIP200, and the VPS34 complex I adaptor protein ATG14 are dispensable for LANDO and receptor recycling. Indeed, during LAP and LANDO, single-membranes vesicles with extracellular cargo are enriched for PI3P and decorated with LC3 (Fig. 1). However, LANDO appears to be distinguished from LAP by one main feature; LANDO is not required for lysosomal degradation of the engulfed material in the endosomes, while LAP facilitates such degradation in phagosomes. In microglial cells, phagocytosis of zymosan induces LAP, which promotes lysosomal degradation, while Aβ oligomers induce LANDO, in which lysosomal degradation is unaffected by disruption of LC3 lipidation. This suggests that regulatory pathways differ between LAP and LANDO. Given the crucial roles of receptor recycling in human physiology, it is likely that LANDO will emerge as an important pathway in physiopathology of human diseases. Can LANDO be induced by other stimuli and occur in other cell types? A critical distinction between canonical autophagy and the processes of LAP and LANDO is the regulation of the latter by two key effectors, Rubicon and the WD domain of ATG16L1. While LANDO is not required for acidification of endocytosed Aβ oligomers (18), the LC3 lipidation machinery is required for acidification of endocytosed DNA–anti-DNA antibody complexes (21) and activation of the endosomal TLR9 to signal type I interferon responses. Interestingly, LC3 was shown to interact with an LC3-interacting region in IκB kinase α, necessary for signaling in this pathway. It is possible that this interaction functions in signaling events under other conditions of LANDO.
Rubicon not only is essential for LANDO and LAP but also acts as a negative regulator of autophagy. Multiple functional domains of Rubicon are involved in protein-protein interactions with several binding partners, which dictate its downstream signaling (Fig. 2). Rubicon consists of an N-terminal RUN domain, a middle region that includes its PI3K-binding domain (PIKBD), a coiled-coil domain (CCD), a serine-rich region (S-R), a helix-coil–rich domain (H-C), and a C-terminal Rubicon homology domain (RH). Here, we discuss the mechanisms by which Rubicon may regulate canonical and noncanonical functions of the autophagy pathway, with an emphasis on its role in mammalian physiology and pathophysiology.
Rubicon was initially discovered as a Beclin 1 binding partner in a VPS34 complex in mouse brains and livers as well as in human epithelial mammary tumor cells overexpressing Beclin 1 (22, 23). The presence of Rubicon in the VPS34 complex reduces its lipid kinase activity and inhibits autophagy induced by starvation, as observed by PI3P generation and LC3 puncta formation, respectively. Knockdown of Rubicon in human A549 carcinoma cells up-regulated autophagosome formation, as observed by ATG16L1 puncta formation. In human embryonic kidney (HEK) cells overexpressing Rubicon, Rubicon directly interacts with the catalytic unit of VPS34 via its RUN domain, which is necessary for autophagy inhibition (24). Structural studies also suggest that the C-terminal RH domain may participate in autophagy inhibition (25). Using hydrogen-deuterium exchange coupled mass spectrometry, giant unilamellar assays, and cryo–electron microscopy, the Rubicon PIKBD domain was found to bind to the Beclin 1 C-terminal BARA domain (previously known as evolutionary conserved domain). Acetylation of Beclin 1 at multiple sites regulates further Beclin 1-Rubicon interaction and VPS34 kinase activity in HEK cells overexpressing Beclin 1 or in murine hepatocytes (26–28). In HEK cells coexpressing tagged Rubicon and tagged VPS34, the Rubicon-Beclin 1 protein-protein interaction blocks the recruitment of VPS34 complex to membranes, inhibits VPS34 kinase activity, and leads to an inhibition of autophagosome maturation (29). Clearly, this inhibition does not apply to conditions of conjugation of LC3 to single membranes, raising the possibility that Rubicon and ATG14 compete in the formation of VPS34 complexes relevant to autophagy (VPS34 complex I) and single membranes such as in LAP and LANDO (Rubicon-containing VPS34 complex). Studies analyzing PI3P generation, ATG16L1 localization, and LC3 puncta formation are, so far, mainly based on either endogenous protein staining or fluorescent sensors analyzed by classical confocal microscopy. The use of modern approaches such as correlative light electron microscopy, structured illuminated microscopy, photoactivated localization microscopy, and other superresolution techniques will provide a better appreciation of the dynamic nature of ATG-related proteins and their diverse distribution depending on the cell context and mode of stimulation.
Rubicon regulates endosome maturation via a mechanism of protein-protein interaction competition with either Rab7 or UVRAG (30). Highly enriched in rab5-positive endosomes in human osteosarcoma U2OS cells, Rubicon directly interacts with rab7, the late endosome marker, through its C-terminal RH domain (previously described as FYVE-like domain). The Rubicon PIKBD domain also directly binds the C-terminal BARA2 domain of UVRAG, the VPS34 complex II subunit (29). When Rubicon and UVRAG are in the same complex, it prevents UVRAG interaction with the C-VPS/HOPS (homotypic fusion and vacuole protein sorting) complex, a tethering protein multiplex involved in the endolysosomal fusion in Hela cells coexpressing Rubicon and UVRAG or in HCT116 upon epidermal growth factor (EGF) stimulation (31, 32). In the presence of its active guanosine triphosphate (GTP)–bound form, Rab7 competes with UVRAG for Rubicon binding and allows the release of UVRAG, which can now bind to C-VPS/HOPS as a feed-forward mechanism. Nutrient availability regulates this protein-protein interaction cascade. Under nutrient-rich conditions, mTORC1 phosphorylates UVRAG at its Ser498 residue, which facilitates Rubicon-UVRAG interactions. Under nutrient-deprived conditions, UVRAG is dephosphorylated, is released from Rubicon, and can interact with C-VPS/HOPS (33). In HEK cells depleted of Rubicon, epidermal growth factor receptor (EGFR) uptake is accelerated and transferrin receptor recycling from endosomes to the plasma membrane is reduced, suggesting that Rubicon acts as a negative regulator of endocytic trafficking (22, 30). These observations are consistent with the role of Rubicon in LANDO observed in microglial cells, macrophages, and fibroblasts (18). To what extent other domains of Rubicon and protein binding partners have a specific function in LANDO remains to be elucidated.
Several Rubicon domains are associated with its role in LAP and the clearance of extracellular particles (34). Upon zymosan stimulation in Raw264.7 or bone marrow–derived macrophages (BMDMs), Rubicon directly interacts via its serine-rich region (S-R) with two subunits of the nicotinamide adenine dinucleotide phosphate (NADPH) oxidase complex, p22phox, the common integral protein subunit, and gp91phox, the catalytic subunit of NOX2. The Rubicon-NOX2 interaction stabilizes the NADPH oxidase complex and facilitates the production of reactive oxygen species (ROS) against microbes (34). Hence, depletion of Rubicon in macrophages reduces the production of ROS and inflammatory cytokines, resulting in a defective response toward TLR2 stimuli such as zymosan and an impairment of bacterial killing after infection with Listeria monocytogenes or Mycobacterium bovis BCG. Interestingly, Rubicon directly interacts via its helix-coiled region (H-C) with CARD9, an adaptor protein forming the CBM complex (CARD9, BCL10, MALT1), which is critical for signaling between the activation of pathogen pattern recognition receptors, such as Dectin-1 and RIG-I, and the downstream immune response in macrophages (35, 36). Upon fungal, bacterial, or viral infection, Rubicon acts as a negative regulator of the CBM complex. Patients with chronic hepatitis B virus (HBV) or hepatitis C virus show a substantial up-regulation of Rubicon in peripheral blood mononuclear cells and serum at both mRNA and protein expression levels compared to healthy individuals (37, 38). Upon HBV infection of hepatic hepG2 cells, a direct interaction between Rubicon via its RUN domain and NEMO, the nuclear factor κB (NF-κB) essential modulator, leads to an inhibition of type I interferon signaling, as observed by the phosphorylation of TBK1 and IRF3. Similar results are observed in BMDMs infected with several other stimuli such as influenza virus PR8, vesicular stomatitis virus, polyinosinic: polycytidylic acid [poly(I:C)], and double-stranded RNA, emphasizing the role of Rubicon as a negative regulator of the type I interferon (IFN) response (39). By dampening the inflammatory response, Rubicon seems to maintain immune homeostasis upon acute viral infection but can also provide a mechanism that favors viral immune evasion and viral replication. While the pathways involved in signaling Rubicon and LAP to an immune response are still largely unknown, L. monocytogenes or fungal melanin can escape and inhibit LAP by up-regulating calcium signaling, which increase bacterial and fungal survival (40, 41). Acetylation of Rubicon by acetyl–coenzyme A at the NOX2 interaction decreases LAP formation (41). Together, depending on the stimuli and its binding partners, Rubicon acts as a decision-making hub that either facilitates bacterial clearance in a LAP-dependent manner or dampens the downstream immune response, with a potential for viral escape. Since NOX2, CARD9, and NEMO are also activated in sterile environments such as hypoxia or hyperlipidemia, it will be interesting to understand whether those binding domains in Rubicon play a role in LAP or LANDO under nonpathogenic conditions.
Although the factors that specifically determine whether Rubicon is engaged in LAP, LANDO, or canonical autophagy are still under investigation, the cell type and several environmental factors including nutrient availability, inflammatory status, and cell stress may be informative. In retinal pigment epithelial cells (RPE), the clearance of photoreceptor outer segments (POS) engages LAP, which facilitates its retinal reutilization (42). In the morning, when POS degradation is at its highest to support vision, Rubicon is also at its highest level of protein expression and colocalizes mainly with phagosomes (43). We can speculate that Rubicon protein availability is important for the induction of LAP or LANDO in (patho-)physiological settings. It may be that high levels of Rubicon compete with ATG14 for formation of VPS34 complexes, limiting canonical autophagy and thereby facilitating LAP and LANDO (see above). How promotion of canonical autophagy may limit LAP and LANDO remains unclear. While autophagy activity and lysosomal function decline during aging in most cell types, recent evidence demonstrates an increase in Rubicon protein expression in flies and in mouse kidneys during aging (44). Whole-body and specific genetic depletion of Rubicon in neurons substantially extends worm and fly life span, suggesting that autophagy inhibition might represent a major function of Rubicon in neurons. Further beneficial effects of genetic depletion of Rubicon in mice against hepatic steatosis induced by high-fat diet (45), kidney fibrosis, α-synuclein accumulation in the brain (44), lipopolysaccharide-induced stroke (46), doxorubicin-induced cardiotoxicity (47), cardiac pressure overload (48), or cardiac ischemia/reperfusion injury (49) identify Rubicon depletion as a potential therapeutic target against age-associated phenotypes. However, the mechanisms by which aging dampens autophagy activity and up-regulates Rubicon are largely unknown. Intriguingly, in contrast with other tissues, in aged murine white adipose tissue, autophagy activity is up-regulated, and Rubicon expression is down-regulated at both protein and mRNA levels. Specific genetic ablation of Rubicon in adipose tissue leads to fat atrophy and hepatic lipid accumulation (50), emphasizing the critical need to better understand in which tissue, cell type, physiological, and pathological context the functions of Rubicon in canonical autophagy inhibition versus LAP or LANDO induction are most prominent.
ATG16L1 is a protein at the core of ATG12-ATG5-ATG16L E3 ligase that leads to LC3 lipidation of phagophores, endosomes, and phagosomes (51, 52). ATG16L1 consists of an N-terminal domain necessary for ATG5 binding, a middle region containing the CCD, and a C-terminal domain with seven WD repeats (Fig. 3). This C-terminal WD domain is lacking in some species, including Saccharomyces cerevisiae (in which canonical autophagy was originally described). While the mechanisms are still poorly understood, the WD domain of ATG16L1 is required for the recruitment of ATG16L1 complex and the lipidation of LC3 at single membranes including phagosomes and endosomes (53). Several protein-lipid and protein-protein interactions with ATG16L1 domains are crucial for its downstream signaling (54).
In higher eukaryotes, ATG16L1 is not observed in its monomeric form but in large homodimer complexes consisting of ATG12-ATG5-ATG16L1 dimerized through the ATG16L1 CCD. ATG7, an E1 ligase, and ATG10, an E2 ligase, catalyze the conjugation of ATG12 to ATG5. Once conjugated to ATG12, ATG5 interacts with ATG16L1 through its N-terminal helix 1 region (55). In canonical autophagy, ATG12-ATG5-ATG16L1 complexes exert E3 ligase activity that catalyzes LC3 recruitment to double-membrane phagophores and activates the E2 ligase, ATG3, mediating LC3 conjugation to phosphatidylethanolamine (PE) (Fig. 4). In contrast, during LAP and LANDO, ATG7, ATG3, and ATG12-ATG5-ATG16L1 conjugate LC3 to both PE and phosphatidylserine (PS) (56).
Ablation of adjacent residues of the ATG16L1 middle region, which are involved in the interaction with FIP200, the ULK1 adaptor protein, or with WIPI2, a PI3P-binding molecule, suppresses ATG16L1 recruitment to the phagophore and dampens canonical autophagy induced by amino acid starvation in knockout cell lines stably expressing tagged proteins (57–60). The middle region of ATG16L1 as well as residues within the CCD (I171, K179, and R193) mediate direct binding to PI3P that allows ATG16L1 complex to be targeted at the phagophore membrane. Sustaining the lipid binding of ATG16L1 to the phagophore by mutating the CCD residues to negatively charged ones also suppresses canonical autophagy, emphasizing the need to unravel the mechanisms underlying the removal of ATG16L1 complex from the phagophore as well as the delipidation of LC3 (61). Interestingly, in murine embryonic fibroblast NIH3T3 cells, under both nutrient-rich and amino acid starvation conditions, ATG16L1 interacts through its CCD domain with Rab33b, a Golgi resident small GTPase, known to be involved in the retrograde transport between Golgi and ER (62, 63). Although the function of the ATG16L1-Rab33 interaction remains poorly understood, overexpression of Rab33b or its binding partner OATL1 suppresses autophagosome formation and maturation as observed by LC3 puncta and colocalization of LC3 with LAMP1-positive lysosome under amino acid starvation (62, 64). Recent findings in Hela cells and MEF cells show that depletion of Rab33b binding sites in ATG16L1 disrupts the recruitment of the ATG12-ATG5-ATG16L1 complex to the phagophore and the transfer of Rab33b from the Golgi to the phagophore under starvation, suggesting that Rab33b+ Golgi vesicles may provide a source of membrane for autophagosome formation (65, 66). While in rat adrenal gland PC12 cells, the Rab33b binding domain of ATG16L1 is involved in neuropeptide Y sequestration in dense core vesicles and its secretion in an autophagy-independent manner, to what extent the Rab33b-ATG16L1 interaction participates in endocytosis and LANDO is not yet known (65).
In contrast to the FIP200 domain, the WD repeat–containing C-terminal domain of ATG16L1 is completely dispensable for canonical autophagy (53, 57). In ATG16L1-deficient HCT116 cells, the reconstitution of ATG16L1 depleted of its WD repeat domain (ΔWD) allows the recruitment of LC3 on double-membrane phagophores induced by starvation but dampens LAP induced by the engulfment of apoptotic cells or zymosan (53), indicating a specific role for the ATG16L1 WD domain in noncanonical autophagy. Determination of the ATG16L1 C-terminal WD domain crystal structure reveals several known and putative binding interaction partners, all involved in regulation of inflammatory responses and xenophagy (67). During Salmonella infection in Hela cells, the ATG16L1 WD domain interacts directly with ubiquitin-decorated endosomes containing bacteria (68). Furthermore, cells expressing ATG16L1 lacking the C-terminal region show an increased inflammatory response upon Listeria or Shigella infection in an autophagy-independent manner (69). Mechanistically, a direct interaction between ATG16L1 and Rip2, an adaptor protein of intracellular pattern recognition molecules Nod1 and Nod2, may partially explain the function of ATG16L1 C-terminal domain in dampening the cytokine response during bacterial infection. These studies are consistent with the association between a coding variant of human ATG16L1 (T300A) and an increased risk for developing intestinal inflammation in Crohn’s disease (70–72). Interestingly, the T300A variant of ATG16L1 suppresses the interaction between the ATG16L1 WD domain and a transmembrane protein, TMEM59, and further impairs xenophagy induced by Staphylococcus aureus (73). In addition, after being endocytosed, Chlamydia trachomatis secretes a bacterial effector, Taip, which prevents the interaction between the ATG16L1 WD domain and TMEM59 and diverts the bacteria-containing endosome toward Rab6-positive vesicles, known to be involved in retrograde trafficking, trans-Golgi network, and recycling to the plasma membrane (74). This latter result highlights the idea that disruption of interactions with the ATG16L1 WD domain can provide a mechanism that facilitates bacterial and viral immune evasion and virulence. On the basis of a CRISPR screen comparing Hela cells depleted in either FIP200 or ATG5, coupled with mass spectrometry analysis, a recent study found that the interaction between the ATG16L1 WD domain and vacuolar adenosine triphosphatase (vATPase) is essential for initiating xenophagy and LC3 recruitment to single-membrane vacuoles containing Salmonella (75). vATPase is a multi-subunit proton pump that plays a crucial role in organelle acidification (76). SopF is a robust bacterial effector that disrupts the ATG16L1 WD domain–vATPase interaction and inhibits LC3 lipidation on single membranes upon Salmonella infection (75, 77), detection of foreign double-stranded DNA inducing the cGAS-STING pathway (78), and induction of the viral M2 proton channel of influenza A virus (79). Further studies are needed to understand whether the ATG16L1 WD domain and its binding partners can sense pathogens when they are at the plasma membrane surface or already engulfed in endosomes or phagosomes.
While mice with whole-body depletion of the ATG16L1 WD domain do not show obvious defects in postnatal growth, fertility, or tissue homeostasis (80), an increase sensitivity to influenza A virus was associated with lung inflammation and a decrease in survival rate (81). Further studies of mice lacking the ATG16L1 WD domain in different tissues and under several conditions of xenophagy or sterile inflammation will be instrumental in our understanding of the functions of LANDO and LAP in mammalian physiology and pathophysiology. Using a peptide microarray strategy to evaluate the amino acid motifs that preferentially interact with a human recombinant glutathione S-transferase (GST)–hemagglutinin (HA) tagged ATG16L WD domain, a recent study identified two intracellular domains of cytokine receptors, interleukin-10 receptor (IL-10R) and IL-2Rγ. Upon cytokine stimulation, the cytokine receptors bound to its substrate and interacted with the ATG16L1 WD repeat C-terminal domain. Early colocalization of IL-10–IL-10R with EEA1-positive endosome was reduced by the depletion of the ATG16L1 WD domain in HEK cells, THP1 cells, and BMDMs (82). These results are consistent with a reduced major histocompatibility complex class II (MHCII) antigen presentation (83) and a lower production of tumor necrosis factor–α (TNF-α) and IL-1β (84) in bone marrow–derived dendritic cells isolated from mice lacking the ATG16L1 WD domain upon S. cerevisiae stimulation. We can only speculate at this point that LANDO-dependent recycling of ligated, stimulatory receptors (such as TLRs) may act to limit the engagement of inflammatory pathways. Since the activation of the NLRP3 inflammasome has been associated with a defect in canonical autophagy proteins in microglial cells (85), it will be valuable to know whether this effect is due to defective autophagy or defective LANDO (or both). Indeed, neuroinflammation observed in LANDO-deficient mice lacking the WD domain of ATG16L1 is reversed by inhibition of NLRP3 in vivo (20). It is also intriguing that IκB kinase α (IKKα) contains LC3-interacting domains that bind to lipidated LC3 (21) and promote interferon signaling in ligated TLR9-containing endosomes. Whether this plays any role in LANDO-dependent effects on inflammatory signaling in other contexts is unknown. Various mechanisms including leukocyte infiltration, PECAM1 expression at endothelial junctions following ischemia-reperfusion injury (86), and primary cilium formation in MEF cells (87) have been linked to a depletion in ATG16L1 WD domain, suggesting that, in such settings, defective LANDO may contribute to the effects.
Beyond the key role of ATG16L1 and Rubicon, several layers of regulatory pathways differ between canonical autophagy, LAP, or LANDO. Here, we highlight major evidence ranging from the physicochemical properties of single and double membranes, LC3 conjugation, to liquid-liquid phase separation interface.
Although PI3P generation is essential for canonical autophagy, LANDO, and LAP, it is remarkable that, thus far, VPS34 complex I is only observed on double-membrane phagophore and the Rubicon-containing VPS34 complex is localized only on single membrane early endosome or phagosome (15). Membrane-protein interaction represents key mechanisms by which VPS34 complexes become activated on specific membranes (Fig. 4A). Recent in vitro studies of both human recombinant VPS34 complex I and the Rubicon-containing complex in giant unilamellar assays, combined with hydrogen-deuterium exchange coupled mass spectrometry, mutagenesis, and lipid kinase assay, demonstrated that the three physicochemical properties of membranes as defined by lipid saturation, membrane curvature, and electrostatic charge can differentially regulate VPS34 kinase activity and critically affect the selection of the complexes (28, 88). Indeed, the amphipathic lipid packing sensor motif of ATG14L BATS domain present in VPS34 complex I is essential in sensing highly unsaturated lipids, curved double membranes such as ER (88, 89). In contrast, the aromatic fingers (AF1/2) and the hydrophobic loop motifs of Beclin 1 BARA domain in VPS34 complex II are key in detecting single membranes high in negatively charged PS and containing Rab5 such as early endosomes (88). Including Rubicon in the VPS34 complex II in similar in vitro studies of single membranes and using different stimuli will be necessary to further understand the precise mechanisms that dictate LAP or LANDO activity. In addition, while Rab5a activates VPS34 complex II on early endosomes, Rab1a is an exclusive activator of VPS34 complex I on phagophore, suggesting that protein-protein interactions between specific small GTPases and VPS34 complexes represent additional regulatory mechanisms of membrane selection and canonical autophagy, LANDO, and LAP activity (90). Notably, in a VPS34-independent manner, PI3P generation by PI3K class II (PI3K-C2a) has also been involved in recycling endocytosis in MEF cells (91, 92) and canonical autophagy in human kidney cells HK2 when stimulated with shear stress (93). Further investigation of canonical and noncanonical autophagy activation by other PI3K isoforms in different cell types and physiological context may reveal additional mechanisms involved in selection of single membranes versus double membranes.
Beyond the role of ATG16L1 WD domains, alternative LC3 lipid conjugation into PS on single-membrane endosomes or phagosomes represents an important molecular signature of noncanonical autophagy processes LAP and LANDO (56). In canonical autophagy, only LC3 lipidation to PE is observed (Fig. 4B). Although the mechanisms are still not clear, ATG4B and ATG4D, two isoforms of ATG4, the enzyme that catalyzes LC3 proteolytic priming and delipidation, might specifically contribute to the delipidation of LC3-PS in LAP and LANDO (56). Developing pharmacological or genetical tools that modulate LC3 lipidation or delipidation into PS or PE will provide attractive approaches for regulating noncanonical autophagy.
Emerging evidence reported that liquid-liquid phase separation that is defined by the compartmentalization of macromolecules and cellular materials into membraneless condensates or droplet-like structures regulates at a very early stage both canonical and noncanonical autophagy [reviewed in (94, 95)]. Phase separation was initially observed in the selection of cargos during canonical autophagy. In yeast S. cerevisiae, the formation of gel-like condensates of aminopeptidase 1 (Ape1) is essential for the selective autophagy-like cytoplasm to vacuole pathway (96). In C. elegans, P-granule proteins are first assembled in condensates before their canonical autophagic degradation during embryogenesis (97). In mammals, phase separation of p62-polyubiquitiniated protein aggregates induces droplet deformation by adhesion at the submillimeter scale, also known as wetting, which supports double-membrane phagophore biogenesis (96, 98, 99). In C. elegans and Hela cells, the inositol polyphosphate multikinase (IMPK) has shown to directly inhibit the phase separation of TFEB, a transcription factor that controls gene expression involved in canonical autophagy and lysosome biogenesis (97). Changes in the physical properties of TFEB condensates, such as tension, viscosity, and elasticity, seem to regulate canonical autophagy (100). Interestingly, Ede1, the yeast homolog of mammalian Esp15 that usually serves as an early-acting scaffold protein for clathrin-mediated endocytosis, can oligomerize and condense by liquid-liquid phase separation. This leads to selective canonical autophagic degradation of this protein, thus acting as a quality control mechanism of endocytosis (101, 102). Several conditions of adaptive stress response observed during aging and neurodegenerative diseases have been associated with alterations in droplet properties (103, 104). Posttranslational modifications of amyloid-like structures observed in Alzheimer’s disease appear to facilitate liquid-liquid phase separation of tau repeats (105). New techniques and tools are urgently necessary to probe and quantify the liquid-liquid separation interface in cells and the interactions of condensates with single- and double-membrane organelles.
During the past 5 years, LANDO has emerged as a noncanonical process characterized by Rab5-positive, single-membrane endosomes interacting with VPS34 complex II and ATG16L1 WD domains and decorated with LC3-PS and LC3-PE. While specific domains in Rubicon, VPS34 complexes, and ATG16L1 are critical for LAP or LANDO activity, the elucidation of the key genes essential only for LANDO is still necessary. In vitro studies of the functions of LANDO in different stimulatory conditions in nonprofessional phagocytes such as endothelial cells and epithelial cells, where we expect a more marginal role for LAP, will be invaluable. Thus far, the generation of animals lacking Rubicon and the Atg16L1 WD domain supports a crucial role for LANDO in endocytic MHCII, cytokine receptors, and Aβ receptor recycling and human diseases such as neurodegeneration, inflammatory disorders, and metabolic diseases. Recent data reported a role for Rubicon in LC3-associated macropinocytosis, another noncanonical autophagy process in which damaged plasma membranes restore membrane integrity with the help of large endocytic vesicles that contain Rab5 (106). Further investigation of knockout mammals and model organisms such as C. elegans and Drosophila with an emphasis on cell-specific approaches will extend our knowledge of LANDO functions in physiological and pathological contexts. The progress in delineating the role of LANDO relies on the development of methods for monitoring LANDO activity in vitro and in vivo. Last, drug-screening approaches that will define specific inducers and inhibitors for LANDO compared to LAP or canonical autophagy will be required for further progress in this emerging area (107). | true | true | true |
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PMC9604538 | 36288298 | Yingxue Wang,Maria Ramos,Matthew Jefferson,Weijiao Zhang,Naiara Beraza,Simon Carding,Penny P. Powell,James P. Stewart,Ulrike Mayer,Thomas Wileman | Control of infection by LC3-associated phagocytosis, CASM, and detection of raised vacuolar pH by the V-ATPase-ATG16L1 axis | 01-10-2022 | The delivery of pathogens to lysosomes for degradation provides an important defense against infection. Degradation is enhanced when LC3 is conjugated to endosomes and phagosomes containing pathogens to facilitate fusion with lysosomes. In phagocytic cells, TLR signaling and Rubicon activate LC3-associated phagocytosis (LAP) where stabilization of the NADPH oxidase leads to sustained ROS production and raised vacuolar pH. Raised pH triggers the assembly of the vacuolar ATPase on the vacuole membrane where it binds ATG16L1 to recruit the core LC3 conjugation complex (ATG16L1:ATG5-12). This V-ATPase-ATG16L1 axis is also activated in nonphagocytic cells to conjugate LC3 to endosomes containing extracellular microbes. Pathogens provide additional signals for recruitment of LC3 when they raise vacuolar pH with pore-forming toxins and proteins, phospholipases, or specialized secretion systems. Many microbes secrete virulence factors to inhibit ROS production and/or the V-ATPase-ATG16L1 axis to slow LC3 recruitment and avoid degradation in lysosomes. | Control of infection by LC3-associated phagocytosis, CASM, and detection of raised vacuolar pH by the V-ATPase-ATG16L1 axis
The delivery of pathogens to lysosomes for degradation provides an important defense against infection. Degradation is enhanced when LC3 is conjugated to endosomes and phagosomes containing pathogens to facilitate fusion with lysosomes. In phagocytic cells, TLR signaling and Rubicon activate LC3-associated phagocytosis (LAP) where stabilization of the NADPH oxidase leads to sustained ROS production and raised vacuolar pH. Raised pH triggers the assembly of the vacuolar ATPase on the vacuole membrane where it binds ATG16L1 to recruit the core LC3 conjugation complex (ATG16L1:ATG5-12). This V-ATPase-ATG16L1 axis is also activated in nonphagocytic cells to conjugate LC3 to endosomes containing extracellular microbes. Pathogens provide additional signals for recruitment of LC3 when they raise vacuolar pH with pore-forming toxins and proteins, phospholipases, or specialized secretion systems. Many microbes secrete virulence factors to inhibit ROS production and/or the V-ATPase-ATG16L1 axis to slow LC3 recruitment and avoid degradation in lysosomes.
Autophagy provides a powerful means of removing pathogens from cells (Fig. 1) by degrading them in lysosomes. The best studied pathway is called “canonical” autophagy that uses a series of autophagy (ATG) proteins to generate autophagosomes that capture pathogens in the cytoplasm (1). Pathogens are killed and degraded when autophagosomes fuse with lysosomes, allowing microbial components to be exposed to innate and acquired immune systems. Many pathogens enter cells by endocytosis or phagocytosis, and during this early phase of infection, they are protected from canonical autophagy by the endosome/phagosome membrane. Viruses use this opportunity to release genomes into the cell, and several bacteria and parasites modify the phagosome/endosome membranes to provide vacuoles specialized for replication. In many cases, this gap in microbial defense has been filled by alternative pathways that use a subset of ATG proteins to target endosomes and phagosomes containing microbes. A key feature of both pathways is the conjugation of autophagy protein LC3/ATG8 (LC3) to the membranes surrounding the pathogen to facilitate fusion with lysosomes.
The terminology used for alternative pathways of LC3 conjugation can be rationalized by considering the origins of the material being delivered to the lysosome (Fig. 1). In the context of infection, classical canonical autophagy delivers pathogens directly from the cytosol to lysosomes and involves conjugation of LC3 to double-membrane autophagosomes. In contrast, the alternative pathways result in conjugation of LC3 to single-membrane endosomes and/or phagosomes, allowing pathogens to be delivered to lysosomes before they reach the cytosol. Alternative LC3 conjugation pathways were discovered during early studies that noticed the recruitment of LC3 from the cytosol to phagosomes generated in macrophages ingesting killed yeast, Escherichia coli bacteria, or incubated with ligands for Toll-like receptors (TLRs) (2, 3). LC3 recruitment was dependent on proteins essential for autophagy such as ATG5 and ATG7, but unexpectedly, LC3 was recruited to single-membrane phagosomes rather than double-membrane autophagosomes. This alternative pathway was called LC3-associated phagocytosis (LAP) to indicate a pathway operating in phagocytic cells to eliminate pathogens and apoptotic cells. Similar studies showed that recruitment of LC3 to phagosomes in phagocytic cells and to vacuoles containing Salmonella in epithelial cells was dependent on NADPH (reduced form of nicotinamide adenine dinucleotide phosphate) oxidase (4). This showed that recruitment of LC3 to endocytic membranes was not unique to phagocytic cells and could take place in many different cell types. LC3 recruitment could also be induced by a wide range of stimuli. Examples now include perturbation of endolysosome membranes when the osmotic balance within endocytic vesicles is perturbed by lysosomotropic drugs, by inhibitors of the vacuolar adenosine triphosphatase (V-ATPase), by stimulation of lysosome Ca2+ channel TRPML1, or during uptake of transfection reagents and apoptotic cells or following uptake of exocytic zymogen granules by pancreatic acinar cells (5–10). For this reason, an umbrella term CASM has been proposed to describe the conjugation of ATG8 to endolysosomal single membranes (11). LC3 recruitment also influences endocytic trafficking and has been called LANDO to describe LC3-associated endocytosis. LANDO is implicated in the clearance of β-amyloid and pathogenesis of Alzheimer’s disease, and during endocytosis and signaling of cytokine receptors (12–14). The main differences between canonical autophagy and alternative LAP, LANDO, and CASM pathways are summarized in Fig. 2. In both cases, conjugation of LC3 to phospholipids in lipid bilayers involves the core LC3/ATG8 lipidation complex composed of ATG5-ATG12, ATG16L1, and ATG3/ATG7, but they differ in upstream signaling. During canonical autophagy (Fig. 2A), activation of the ULK1 complex (ULK1, FIP200, ATG13, and ATG101) in response to a fall in amino acids leads to activation of the “PI3K complex” containing VPS15, VPS34, Beclin1, and ATG14 (15, 16). The phosphatidylinositol 3-kinase (PI3K) activity of VPS34 generates phosphatidylinositol 3,4,5-trisphosphate (PIP3) in lipid bilayers to generate a binding site for WIPI2, which, in turn, binds the ATG16L1:ATG5-ATG12 complex to initiate conjugation of LC3 to phosphatidylethanolamine (PE) (17). In contrast, LAP is activated by TLR signaling and mediated by a complex of proteins containing Rubicon, Beclin1, UVRAG, VPS15, and VPS34 (Fig. 2B). TLR signaling induces binding of Rubicon to the p22phox subunit of the NADPH oxidase complex to increase production of reactive oxygen species (ROS) within the phagosome. At the same time, generation of PIP3 by VPS34 generates a binding site for the N-terminal phox-homology (PX) domain in p40phox to stabilize the NADPH complex. Sustained production of ROS increases the pH within the phagosome, and this is thought to provide a signal for recruitment of the ATG16L1:ATG5-ATG12 complex to the phagosome to initiate conjugation of LC3 to the amino residues of PE and phosphatidylserine (PS), which is abundant in phagosome membranes (11, 18–20). ATG4 is a cysteine protease able to cleave LC3 at the C-terminal glycine-120 used for attachment to PE or PS to recycle LC3 back to the cytosol. Production of ROS during LAP inhibits ATG4 to slow removal of LC3 from the phagosome membrane and increase processing and presentation of antigens on class II major histocompatibility complex (MHC) (21).
The canonical autophagy and alternative pathways use different domains of ATG16L1 to recruit the ATG16L1:ATG5-ATG12 complex to membranes (Fig. 2). Canonical autophagy requires the N-terminal coiled coil domain (CCD) of ATG16L1, which binds ATG5-ATG12 and WIPI2. This directs ATG16L1:ATG5-ATG12 to sites enriched for PIP3 generated in autophagosome membranes by VPS34. The C terminus of ATG16L1 contains a WD repeat domain that is not required for autophagy but is required for recruitment of ATG16L1:ATG5-ATG12 during LAP/CASM (22, 23). The WD domain contains a lysine residue at position 490 that is crucial for lipidation of LC3 on endolysosome compartments (22). The WD domain also has several amino acids that can bind phospholipids “in vitro” and are required for binding ATG16L1 to perturbed endosome membranes (24).
Recent studies show that recruitment of the ATG16L1:ATG5-ATG12 complex onto endolysosome compartments is triggered by the assembly of V-ATPase. V-ATPase is anchored in endolysosome compartments by the integral membrane subunits of the Vo domain that make a pore to transport protons into the lumen of the vacuole, while the ATPase activity required for proton transport is provided by the V1 domain recruited from the cytosol. Recruitment of the V1 domain is triggered by a rise in vacuolar pH (Fig. 2B). Assembly of V-ATPase provides a binding site for ATG16L1, which recruits the LC3 conjugation machinery (ATG16L1:ATG5-ATG12) onto vacuoles. This pathway has been called the V-ATPase-ATG16L1 axis and/or VAIL for V-ATPase-ATG16L1–induced LC3B lipidation and was first identified during a study of recruitment of LC3 to vacuoles during Salmonella infection (25, 26). Recruitment of LC3 during Salmonella infection required the core LC3 conjugation machinery (ATG3, ATG5:ATG12, ATG7, and ATG16L1) but was independent of FIP200, suggesting a pathway similar to CASM or LAP. This was confirmed by a CRISPR-Cas9 screen showing that recruitment of LC3 to the vacuole required V-ATPase, and that this involved binding of ATG16L1 to V-ATPase on the vacuole membrane (26). Furthermore, binding to V-ATPase was dependent on the ATG16L1 WD domain. Subsequent work has demonstrated the importance of the interaction between the WD domain of ATG16L1 and V-ATPase for other conjugation pathways described under the CASM umbrella (20). Importantly, this has provided a link between the V-ATPase-ATG16L1 axis and ROS production by the NADPH oxidase (Fig. 2B) where the raised vacuolar pH induced by ROS drives assembly of the Vo-V1 complex and subsequent recruitment of ATG16L1 (20, 27). ATG16L1 can also be recruited to endocytic compartments by overexpression of TMEM59, a type 1 membrane glycoprotein found in late endosomes and lysosomes (28). The cytoplasmic domain of TMEM59 contains a short 19–amino acid domain that binds directly to the WD domain of ATG16L1 and is required for recruitment of LC3 to endosomes containing Staphylococcus aureus.
Infection by DNA viruses or bacteria can introduce DNA into the cytosol where it is recognized by a DNA sensor called cGAS [cyclic guanosine monophosphate (GMP)–adenosine monophosphate (AMP) synthase], which uses GMP and AMP to generate cyclic dinucleotide GMP-AMP (cGAMP) (29). cGAMP acts as second messenger that activates STING to induce type 1 interferon (IFN) signaling and the formation of LC3 puncta (Fig. 3) (25, 30, 31). Activation of STING by cGAMP increases clearance of herpes simplex virus 1 (HSV1) and pseudotyped HIV from cells independently of IFN, suggesting a role for STING-mediated LC3 lipidation in controlling infection. Activation of STING by cGAMP releases STING from the endoplasmic reticulum (ER), leading to COPII-dependent movement into Golgi membranes and from there to perinuclear vesicles close to the Golgi apparatus. The vesicles have a single membrane and recruit the ATG5-ATG12:ATG16L1 complex to conjugate LC3 to PE independently of ULK1/2:FIP200 complex, suggesting recruitment through CASM/LAP (25, 30). Interestingly, recent work shows that, in common with CASM, activation of STING signaling leads to recruitment of the V1 complex of V-ATPase from the cytosol to the perinuclear vesicles containing LC3 by a pathway dependent on the WD domain of ATG16L1 (25). Precisely how recruitment of LC3 to vesicles by STING controls infection has not been resolved (20, 25). The perinuclear vacuoles can capture DNA from the cytosol and could therefore engulf microbes in the cytoplasm; alternatively, it is possible that the perinuclear vesicles are derived from the endolysosome system, allowing capture of viruses entering cells and their delivery to lysosomes (30). Interestingly, STING-mediated conjugation of LC3 to membranes can be demonstrated in Nematostella vectensis, a small sea anemone that evolved 500 million years ago (30). It is possible that the ability of STING to drive LC3 conjugation to membranes is very ancient and evolved before the emergence of type 1 IFN pathways in vertebrates.
Infection of cells with viruses invariably leads to formation of LC3 puncta, indicating recruitment of LC3 to membrane compartments. Recent work using a genome-wide small interfering RNA (siRNA) screen shows that production of LC3 puncta in response to a broad range of viruses is reduced following silencing of sorting nexin 5 (SNX5) (32). Loss of SNX5 increased virus replication in tissue culture and increased susceptibility of neonatal Snx5−/− mice to lethal infection by several viruses. Interestingly, silencing of Snx5 did not affect induction of canonical autophagy induced by starvation or following inhibition of mammalian target of rapamycin (mTOR). Furthermore, the recruitment of LC3 to vacuoles containing bacteria such as group A Streptococcus, or latex beads, or to membranes exposed to osmotic stress was independent of SNX5. Together, the results suggested that SNX5-dependent recruitment of LC3 to membranes is separate from LAP/CASM and represents a previously unknown pathway specialized to use LC3 conjugation to single-membrane vesicles (endosomes) as a defense against virus infection (Fig. 4). Mechanistically, SNX5 binds endosomes through an N-terminal PX domain that recognizes PIP3 and a C-terminal concave BAR domain that binds to and/or induces curved membranes. Pull-down experiments show that SNX5 binds to ATG14/Barkor, an autophagy protein that forms part of the PI3K complex (VPS15, VPS34, Beclin1, and ATG14) required for canonical autophagy. Experiments using artificial lipids resembling endosomes showed that SNX5 increases generation of PIP3 by the PI3K complex containing ATG14, and this may involve the use of the rigid SNX5 BAR domain to increase membrane curvature, possibly providing a binding site for ATG14. The local production of PIP3 would then provide a binding site for WIPI2 and recruit the ATG16L1:ATG5-ATG12 LC3 conjugation machinery to endosome (Fig. 4). How viruses signal assembly of the SNX5:ATG14:PI3K complex on the cytosolic face of the endosome remains to be resolved. One possibility is that SNX5 binds to the cytoplasmic tails of transmembrane proteins that are used as receptors, or otherwise rearranged, during virus entry. If so, this would fit with the way that SNX5 regulates receptor trafficking during endocytosis.
Figure 1 shows that LAP and CASM pathways attach LC3 to single-membrane endosomes and phagosomes, while canonical autophagy attaches LC3 to double-membrane autophagosomes. This difference in membrane topology, and the ATG proteins required for conjugation of LC3 (Fig. 2), has proved very valuable in unraveling the roles played by different autophagy pathways during infection. Studies aimed at identifying LAP and CASM, for example, can use electron microscopy to demonstrate recruitment of LC3 to vesicles with a single membrane. LC3 recruitment should be independent of the unique components of the ULK1 initiation complex (ULK1, FIP200, and ATG13) required for autophagy, but require Rubicon or UVRAG, and this can be tested by gene silencing or isolation of cells [mouse embryonic fibroblasts (MEFs) and bone marrow–derived macrophages (BMDMs)] from knockout mice. Canonical autophagy and LAP/CASM can be further distinguished by establishing the requirement of the WD domain of ATG16L1 and the V-ATPase-ATG16L axis, and ROS. This can be achieved by expression of the SopF virulence factor from Salmonella, which blocks binding of the WD domain of ATG16L1 to V-ATPase, and drugs that promote (saliphenylhalamide) or inhibit (bafilomycin) assembly of the Vo-V1 complex, and using diphenyleneiodonium (DPI) to inhibit NADPH oxidase (20, 26).
Thus far, two mouse models have been developed to study the consequences of LAP/CASM on pathogen survival in vivo. Mice lacking the WD and linker domains of ATG16L1 (ΔWD) are defective in LAP and CASM but preserve canonical autophagy through expression of the CCD of ATG16L1 that binds to WIPI2 (23). The ΔWD mice survive postnatal starvation, maintain tissue homeostasis, and grow at the same rate as littermate controls. Importantly, for infection studies, the frequencies of B cells, T cells, and macrophages are the same as littermate controls, and ΔWD mice do not show the proinflammatory phenotype and increased interleukin-1β (IL-1β) production seen following complete loss of ATG16L1 (33). ΔWD mice have been used to study influenza virus infection and for production of BMDMs to study antigen presentation and responses to commensal yeasts (22, 34, 35). Furthermore, cre recombinase technology can be used to direct tissue-specific loss of the WD domain to determine the role played by LAP and CASM in different tissue types during infection (34). Mice lacking Rubicon are also defective in LAP and show defects in control of Aspergillus fumigatus (19). Rubicon is a multidomain adaptor protein upstream of ATG16L1 and has multiple roles in addition to stabilizing the NADPH/NOX2 complex (18, 19). These include inhibition of canonical autophagy and inhibition of endocytosis and inflammatory cytokine production following TLR signaling (18). Rubicon deletion has also been used in zebrafish embryo model to study LAP during Salmonella infection (36).
The ways that recruitment of LC3 to vacuoles by LAP/CASM can affect a variety of pathogens are summarized in Fig. 5. This is intended to provide a guide to the more detailed accounts that follow. The pathogens are presented on a background where TLR signaling has stabilized the NADPH oxidase complex through Rubicon. In most cases, it is thought that pathogens can further activate LAP/CASM when they raise vacuolar pH by generating pores in endosome or phagosome membranes. Bacteria assemble specialized secretion systems, e.g., the type 3 secretion system (T3SS) of Salmonella, to deliver the products of pathogenicity islands into the cytosol of the host. These translocons may generate pores in endosome membranes that can activate the V-ATPase-ATG16L1 axis to recruit LC3. At the same time, TLR signaling can lead to assembly of the NOX2 complex (p91, p40, and p47) to generate ROS, which, in turn, can increase vacuolar pH and activate the V-ATPase-ATG16L1 axis to recruit LC3. Many microbes have evolved ways to inhibit LC3 recruitment, and this generally involves inhibition of the NADPH/NOX2 complex and/or inhibition of the V-ATPase-ATG16L1 axis. Details are given in the figure legends and in the text below. Release of pore-forming toxins and phospholipases into endosomes and phagosomes can cause further membrane damage, allowing release of Ca2+ into the cytosol to activate membrane repair pathways. These pathways recruit damage sensors, such as galectins that bind sugars that would not normally be exposed to the cytosol, and ubiquitin-conjugating enzymes (37). The sensors bind LC3 in autophagosome membranes to direct damaged membranes, and exposed pathogens to lysosomes for degradation. These damage repair pathways often run in parallel with LAP/CASM, making the interpretation of LC3 recruitment to vacuoles difficult, particularly when reading some early studies when the potential role of LAP/CASM in the control of pathogens was less well recognized. Furthermore, many pathogens make virulence factors to inhibit LAP/CASM, making it difficult to follow LAP/CASM pathways until mutant strains lacking the virulence factors become available. A general theme is, however, emerging where LAP/CASM pathways are important early during infection when pathogens are within the endolysosome system. Canonical autophagy is reserved for the removal of pathogens that escape into the cytosol.
Respiratory viruses such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) [coronavirus disease 2019 (COVID-19)] and influenza A virus (IAV) cause pandemic infections where lung inflammation, cytokine storm syndrome, and pneumonia lead to high mortality. IAV infects airway and lung epithelial cells and is cleared by innate and acquired immune responses; however, excessive cytokine production during infection leads to inflammation and lung injury. Delivery of viruses to lysosomes provides an important defense against infection that can reduce virus yields and attenuate inflammatory responses. In common with many viruses, IAV activates the SNX5-ATG14 axis described above (Fig. 4, i to iv) (32). Infection of cells by IAV also induces redistribution of LC3 to intracellular vesicles and the plasma membrane (38). This redistribution is mediated by the viral M2 protein that acts as the proton channel to raise the pH of endosomes (Fig. 4, v to viii). Redistribution of LC3 by M2 requires the WD domain of ATG16L1 and the V-ATPase-ATG16L1 axis, and by implication LAP/CASM (22, 27). The role played by LAP/CASM during IAV infection in vivo has been tested using ΔWD mice that lack the WD domain of ATG16L1 (34). Mice with systemic loss of the WD domain in all tissues were highly sensitive to infection with low-pathogenicity murine-adapted IAV, leading to extensive IAV replication in the lung, cytokine storm, and high mortality. Tissue-specific targeting of the ΔWD mutation to myeloid cells and adoptive transfer of immune cells showed that LAP/CASM in lung epithelial cells, rather immune cells and phagocytes, protected mice from lethal IAV infection. Parallel in vitro studies showed that the WD domain of ATG16L1 reduced fusion of IAV with endosome membranes and reduced cytokine production by delaying recognition of viral RNA by IFN sensors (34). Together, these studies establish LAP/CASM in airway epithelial (rather than professional phagocytes) as a previously unidentified innate defense that can restrict IAV infection and lethal cytokine storm.
Infection of cells with picornaviruses induces the formation of LC3 puncta and double-membrane vesicles (DMVs) (39). The DMVs were first discovered during early electron microscopy of cells infected with viruses in the 1960s, and their precise origins still remain unclear (40). It is, however, generally believed that the DMVs provide a platform to concentrate the viral replicase proteins that generate new genomes from viral RNA templates (41). This increases the efficiency of replication and protects double-stranded RNA intermediates from recognition by IFN signaling pathways. The double-membrane topology of the picornavirus DMVs suggests that they might be formed by canonical autophagy pathways, and the observation that virus yields can increase 10-fold following activation of autophagy suggests that autophagy can facilitate replication (39, 42). Parallel studies have, however, suggested that the membranes required for picornavirus replication may be generated by manipulation of proteins involved in trafficking membranes within the secretory pathway. The 3A nonstructural proteins of poliovirus and coxsackievirus B3 (CB3), for example, bind to Golgi membranes and activate the Arf1 guanosine triphosphatase (GTPase) (43–45). This leads to activation of phosphatidylinositol 4-kinase IIIβ (PI4-IIIβ) and increased production of PI4P that facilitates binding of the RNA-dependent RNA polymerase. CB3 is a (+) single-stranded RNA virus of the Picornaviridae family. Inhibition of autophagy reduces CB3 replication (46). Systematic study of the role played by autophagy pathways in formation of DMVs showed formation of LC3 puncta, and lipidated LC3II required ATG5 and ATG16L1 but was independent of the ATG13 and FIP200 components of the ULK1 initiation complex (47). ULK1-independent LC3 lipidation has also been reported for poliovirus (48). Analysis of downstream autophagy pathways showed that LC3 lipidation was also independent of WIPI2 and ATG9A. Next, the investigators studied the role played by phosphatidylinositol 4-kinase IIIβ (PI4-IIIβ) that is recruited to the ER by the 3A nonstructural protein of CB3 and found that LC3 lipidation was markedly reduced following silencing of PI4-IIIβ expression. The precise role played by PI4-IIIβ and PI4P in virus-mediated LC3 lipidation is not clear, but it is known that the CCD domain of ATG16L1 binds PI4P directly and this may allow the virus to recruit the LC3 conjugation machinery directly to the sites of virus replication. Generation of LC3 puncta by CB3 is also reduced following knockdown of Snx5 expression, suggesting that lipidation is facilitated by recruitment of ATG14 and the PI3K complex to endosome membranes (32).
Studies on the role played by autophagy during foot and mouth disease virus (FMDV) infection show induction of LC3 puncta and LC3 lipidation very early during infection. This occurs at a time when capsids could be seen colocalized with LC3 in endocytic compartments, suggesting activation of LC3 lipidation during cell entry (49). Furthermore, the LC3 puncta could be induced by ultraviolet-inactivated virus and by empty capsids independently of replication. These results suggest that entry of FMDV into cells triggers recruitment of LC3 to single-membrane endosomes, but a systematic analysis of the role played by the ULK1 complex or the WD domain of ATG16L1 has not been reported, so it is not possible to rule out activation of autophagy. It will be interesting to determine whether FMDV induces binding of SNX5 to ATG14 to induce LC3 lipidation of endosomes, and whether this is activated by the integrins used for FMDV cell entry.
Salmonella serovars are foodborne pathogens that cause a range of gastrointestinal diseases. Oral ingestion of contaminated water or food results in delivery of bacteria to the small intestine where they enter gastrointestinal epithelial cells by endocytosis. Key stages in bacterial entry rely on T3SS that inject effector proteins encoded by pathogenicity islands across host cell membranes. Effectors encoded by pathogenicity island 1 (SPI-1) are injected across the plasma membrane and stimulate actin rearrangements to facilitate invasion of the cell. Salmonella enter endosomes that recruit markers for early and late endosomes (Rab5, Rab7, and Rab9) and lysosomes (LAMP1). The endosomes also recruit V-ATPase, which lowers the pH in the lumen of the endosome, and this activates SPI-2. The effectors encoded by SPI-2 are injected into the cytosol and modify the endosome to produce Salmonella-containing vacuoles (SCVs), which are thought to protect the bacteria from recognition by cytosolic sensors of infection and degradation in lysosomes through autophagy. The SPI-2 effector SifA, for example, interacts with endosome trafficking proteins to protect salmonella by slowing delivery of lysosomal enzymes to the SCV, and SifA is required for the formation of membrane tubules that extend from the SCV along microtubules. These Salmonella-induced tubules may connect with endocytic pathways to bring nutrients into the SCV (50). Salmonella infection triggers the formation of LC3 puncta that results from the formation of autophagosomes and from the translocation of LC3 to single-membrane endolysosome compartments. Early on during infection, a small number (10 to 20%) of Salmonella escape into the cytosol. This occurs as early as 15 min after invasion and continues for an hour (Fig. 6, i). In epithelial cells, the delivery of Salmonella to nutrient-rich cytosol leads to hyperreplication (>100 bacteria per cell) and host cell death (Fig. 6, ii). Hyperreplication does not occur in fibroblasts or macrophages. Damage to endosomes during Salmonella entry and escape has the potential to recruit LC3 onto membranes via autophagy, and this may stabilize the endosome and slow release of bacteria into the cytosol (Fig. 6, iii). Endosomes damaged by Salmonella can, for example, be ubiquitinated and recruit p62 and LC3 (51–54). Studies of endosomes containing latex beads coated with cationic polymers used as surrogate bacteria (51) showed ubiquitin-dependent recruitment of ULK1, WIPI2, and ATG14L, suggesting canonical autophagy; however, part of the mechanism involves direct binding of ubiquitinated proteins to the WD domain of ATG16L1, rather than binding of WIPI2 to PIP3. Later work using an RNA interference (RNAi) screen showed that early membrane repair during Salmonella entry requires autophagy proteins such as ATG5-ATG12 and ATG16L1 required for LC3 conjugation (55). LC3 recruitment to Rab5-positive structures required activation of SPI-1 (T1) and occurred early at 20 min and peaked at 40 to 60 min and dissociated following induction of SPI-2. Together with the observation that SCV maturation was slowed in the absence of ATG5, it is likely that autophagy-dependent membrane repair stabilizes the endosome and slows release of Salmonella into the cytosol. This, in turn, favors progression to the SCV and expression of SPI-2 (T2) effectors. Interestingly, silencing of FIP200 required for canonical autophagy did not affect membrane repair (55), suggesting that LC3 recruitment to the SCV may be through LAP/CASM; however, a requirement for ATG9 and ULK1 argues against this. Much work has focused on the role played by canonical autophagy in the control of Salmonella that enter the cytosol from damaged endosomes. Lipopolysaccharides exposed on the surface of cytosolic Salmonella are ubiquitinated by the E3 ubiquitin ligase RNF213 (Fig. 6, iv), leading to recruitment of autophagy cargo receptors p62/SQSTM1, optineurin, and NDP52, which bind to LC3 in autophagosome membranes (56). Vacuolar damage caused by Salmonella is recognized by a cytosolic lectin called galectin-8 (37) that facilitates autophagy by recruiting NDP52 to vacuole remnants associated with bacteria (Fig. 6, iv). Silencing RNF213, Atg5, Atg7, Atg16L1, or galectin or pharmacological inhibition of autophagy increases Salmonella replication, suggesting that autophagy and degradation in lysosomes protect cells from infection (37, 53, 54, 56). For Salmonella that are retained in endosome assembly of the T3SS protein, translocation machinery generates pores between 2 and 5 nm diameter that can alter the balance of ions across the SCV and endosome. The entry of Ca2+ into the cytosol triggers lysosome-mediated membrane repair where lysosomes fuse with membranes to seal the pore (Fig. 6, v). Synaptotagmin VII senses Ca2+ entering cells through the T3SS translocon and triggers fusion of lysosomes with endosomes and SCV containing Salmonella (57). As with recruitment of LC3 and autophagosomes (55), lysosome fusion is thought to stabilize the SCV and protect cells from infection by slowing entry of Salmonella into the cytosol. In epithelial cell lines, LC3 is directed to vacuoles containing Salmonella by a pathway dependent on ROS, again suggesting involvement of LAP/CASM (4). As with autophagy-dependent membrane repair, LC3 recruitment is seen within 60 min of cell entry, suggesting translocation of LC3 to endosomes damaged by SPI-1 before expression of SPI-2 effectors. Epithelial cells do not express NOX2 but do express p22phox. LC3 translocation was inhibited by inhibition of NADPH oxidase activity by DPI and siRNA for p22phox. Silencing p22phox resulted in increased replication at 6 to 8 hours, consistent with LC3 slowing escape of Salmonella into the cytosol from damaged endosomes. This raises the possibility that LC3 is recruited to endosomes containing Salmonella by a LAP/CASM pathway activated by TLR signaling. Recruitment of LC3 to Salmonella has been demonstrated in cells lacking ATG9, a membrane protein that is essential for autophagosome formation (58). Live cell imaging of control Atg9+/+ cells showed recruitment of the PI3K complex (ATG4), WIPI-1, ATG5, and LC3 to Salmonella in the cytosol, consistent with a role for canonical autophagy in engulfment of Salmonella by autophagosomes. Interestingly, analysis of Atg9−/− MEFs showed that ATG5, ATG16L1, and green fluorescent protein (GFP)–LC3 could be recruited to Salmonella independently of ATG9 and the PI3K. Correlative light electron microscopy showed Salmonella surrounded by a single membrane in Atg9−/− cells, and in cells lacking the FIP200 protein also required for canonical autophagy. LC3 became associated with Salmonella contained within a membrane vacuole within 10 min of infection, consistent with recruitment of LC3 to endosomes and/or SCV damaged by SP1 effectors, rather than by cytosolic bacteria. Together, these results again show that LC3 can be recruited to single membranes surrounding Salmonella early during infection by pathways independent of canonical autophagy.
Quantitative secretome profiling by liquid chromatography–tandem mass spectrometry (LC-MS/MS) identified SopF (STM1239) as SPI-1 effector protein secreted into the cytoplasm by Salmonella through T3SS (59). SopF facilitates replication in macrophages and virulence in mice. SopF binds phosphoinositides and locates to membranes in cells where it concentrates in ruffles during bacterial entry, and around intracellular bacteria (60). ΔsopF Salmonella show increased exposure of glycans recognized by galectin-8 and increased colocalization with p62 and LC3 early during infection (1 hour), suggesting that SopF stabilizes the SCV. Parallel work using an unbiased genetic screen identified SopF as an effector protein able to inhibit recruitment of LC3 to SCVs (26). Inhibition of LC3 recruitment to vacuoles was independent of bacterial species, and ectopic expression of SopF inhibited recruitment of LC3 to endosomes damaged by polystyrene beads. ΔsopF Salmonella were used in a CRISPR-Cas9 screen to identify host factors required for LC3 translocation to vacuoles (26). Interestingly, LC3 recruitment did not require FIP200, which is essential for canonical autophagy, but did require the LC3 conjugation machinery (ATG3, ATG5:ATG12, ATG7, and ATG16L1). Incubation of cells with impermeable dyes showed that LC3 recruitment was coincident with release of the dye from vacuoles and was dependent on T3SS. As with a previous study (55), this suggested that LC3 recruitment is triggered by vacuolar damage induced by assembly of T3SS. Further work showed that vacuolar damage induced binding of ATG16L1 to V-ATPase in the membrane of the SCV (Fig. 6, v and vi) (26). This provided a pathway for recruiting LC3 conjugation machinery to the cytoplasmic face of vacuoles containing bacteria. Importantly, binding of ATG16L1 to V-ATPase required the WD domain of ATG16L1 (26), again indicating parallels with recruitment of LC3 to membranes by LAP/CASM. The crystal structure of Sop-F (61) revealed close similarity with adenosine diphosphate (ADP)–ribosyl transferases, which add ADP-ribose to proteins. ADP-ribose pull-down experiments demonstrated that Sop-F transferred ADP-ribose to Gln124 in the ATP6VOC subunit of V-ATPase, and that this blocked binding of the WD domain of ATG16L1 (26). Mechanistically, it is thought that membrane pores resulting from assembly of the T3SS translocon induce a conformational change in V-ATPase and/or raise vacuolar pH allowing V-ATPase to bind the WD domain of ATG16L. This, in turn, recruits the LC3 conjugation machinery to the vacuole independently of the ULK1 and PI3K complexes required for canonical autophagy. A product of T3SS encoded by Burkholderia pseudomallei also inhibits LAP to slow killing in phagolysosomes (62). It will be interesting to see whether this involves inhibition of the V-ATPase-ATG16L1 axis.
Murine infection models show that Salmonella induce the formation of LC3 puncta in intestinal epithelial cells (63, 64). LC3 puncta were dependent on the innate immune adaptor protein MyD88 that is downstream of many TLRs that recognize Pathogen-associated molecular patterns (PAMPs) expressed by bacteria (64). The observation that dissemination of Salmonella and enteric bacteria to spleen and liver increases following cre-lox–mediated loss of Atg5 or Atg16L1 from intestinal epithelial cells raises the possibility that recognition of Salmonella by TLRs may activate autophagy pathways to protect against infection in vivo (63, 64). The detection of double-membrane vacuoles surrounding Salmonella, rather than single membranes, suggests autophagy; however, these electron micrographs may represent the removal of Salmonella by autophagy late during the infectious cycle, rather than early recruitment of LC3 triggered by LAP/CASM (64). The role played by LAP/CASM during Salmonella infection in vivo has been tested directly using zebrafish embryos (36). Live cell imaging of embryos expressing GFP-LC3 showed recruitment of LC3 to vacuoles containing Salmonella expressing mCherry. The vacuoles were formed predominantly in macrophages and neutrophils and, as judged by electron microscopy, were surrounded by single rather than double membranes. A morpholino knockdown demonstrated that embryo survival, and recruitment of LC3 to vacuoles following infection, required ATG5 but did not require ATG13, a component of the ULK1 complex required to initiate canonical autophagy. Interestingly, knockdown of Rubicon or the p22 subunit of NADPH oxidase reduced LC3 recruitment to vacuoles and increased susceptibility of embryos to infection. Together, the results show that LAP in phagocytic cells plays an important role in protection against Salmonella infection in vivo.
Listeria are Gram-positive bacteria that can cause severe foodborne infections, particularly in immunocompromised individuals. Listeria are primarily controlled during phagocytosis by macrophages, allowing bacteria to be killed following delivery of lysosomal enzymes to phagosomes. Listeria can escape from the phagosome by generating the pore-forming toxin listeriolysin O (LLO) and two phospholipase enzymes PI-PLC and PC-PLC. Listeria entering the nutrient-rich cytosol replicate rapidly and then use ActA-mediated polymerization of actin to generate actin “rockets” at one end of the bacteria that propel them into neighboring cells. In fruit flies, recognition of Listeria cell wall peptidoglycan by pattern recognition receptor PGRP-LE leads to Atg5-dependent formation of LC3-positive autophagosomes, and intracellular bacteria are held within double-membrane vacuoles, suggesting capture by canonical autophagy (65). Atg5 silencing increases intracellular growth of Listeria and decreases survival of flies, showing that in flies autophagy plays a protective role during Listeria infection. Similar experiments show increased sensitivity to Listeria infection when mice lack atg5 in myeloid cells, again suggesting a protective role for autophagy. In vitro studies using RAW264.7 macrophages show peak association of LC3 with intracellular bacteria 1 hour after cell entry. Close examination of phagosomes showed that vacuoles containing Listeria lacked ubiquitin and p62 and have a single membrane suggesting capture by LAP/CASM (66, 67). LC3 association is lost once bacteria enter the cytosol where they become targets for canonical autophagy. LC3 recruitment may be prevented by ActA, which inhibits host E3 ligases to reduce subsequent recruitment of ubiquitin and autophagy adaptor proteins p62 and NDP52. These results suggest that, in mammalian cells, LC3 recruitment to Listeria can be directed by autophagy and LAP/CASM, but as seen for Salmonella, LAP/CASM is the predominant pathway for recruitment to phagosomes early during entry while canonical autophagy targets Listeria in the cytosol (Fig. 7). LC3 recruitment to vacuoles early during infection is independent of ULK1, requires expression of pore-forming toxin LLO, and can lead to the formation of large vacuoles called SLAPs (spacious Listeria containing phagosomes) (66, 67). LC3 recruitment to vacuoles and SLAP maturation were greatly reduced when NADPH oxidase was inhibited by DPI and was reduced in BMDMs lacking NADPH oxidase activity (67, 68). LC3 recruitment early during infection also required generation of diacylglycerol (DAG) by host phospholipase D (PLD) and phosphatidic acid phosphatase (66). Studies using cybb−/− BMDM lacking the NOX2 required for ROS production have revealed a ROS-independent pathway for recruitment of LC3 to vacuoles damaged by LLO or pore-forming toxins from S. aureus (69). This has been called PINCA for pore-forming toxin–induced noncanonical autophagy. Interestingly, LC3 conjugation in response to PINCA increases fusion of phagosomes with lysosomes but does not make a major contribution to killing of Listeria. This could be because of reduced ROS production. Killing of Listeria by tissue-resident macrophages requires expression of Mac-1 (Fig. 7) (67). Mac-1 is an amb2 integrin composed of CD11b and CD18 that can bind extracellular matrix and complement (70). Peritoneal macrophages from mice deficient in the CD11b subunit of Mac-1 produce low levels of ROS when incubated with Listeria and show reduced recruitment of LC3 to phagosomes containing Listeria (67). Further work shows that recognition of Listeria by Mac-1, rather than other pattern recognition receptors such as MyD88-dependent TLRs or NOD2, was required for activation of NOX2 and ROS production. Mac-1 induces ROS by activating acid sphingomyelinase, which generates ceramide in the phagosome membrane to recruit NOX2 and also to promote fusion with lysosomes. Mice with loss of ATG7 from myeloid cells showed increased bacterial burden, indicating that activation of CASM/LAP by Mac-1 is important for elimination of Listeria in vivo. LLO is a pore-forming toxin that enhances growth of Listeria in vacuoles and allows the bacteria to escape into the cytosol. Similarly, pneumolysin toxin from Streptococcus pneumoniae generates pores by binding cholesterol in membranes and can activate CASM/LAP (71). As with Salmonella, the flux of Ca2+ into the cell induced by the toxin stimulates membrane repair pathways where lysosomes fuse with the plasma membrane or damaged endosomes. Recent work shows that this process requires autophagy protein ATG16L1 (72), and interestingly, membrane repair required the WD domain of ATG16L1 and was compromised in cells expressing ATG16L1 carrying the T300A risk allele for Crohn’s disease. At first glance, this would suggest involvement of LC3 recruitment via CASM/LAP, but repair did not require Rubicon and did not result in recruitment of LC3 or ATG16L1 to sites of repair. Instead, the WD domain of ATG16L1 appears to be required for maintaining the correct distribution of cholesterol in cells, and this may be important for lysosome exocytosis during membrane repair. This noncanonical role for ATG16L1 inhibits damage inflicted by LLO and slows cell to cell spread of Listeria monocytogenes (72).
Legionella are Gram-negative bacteria that are parasites of phagocytic protozoa such as amoebae, but they can infect humans if they get access to the respiratory tract where they infect alveolar macrophages leading to a pneumonia called Legionnaire’s disease. Survival in phagocytes is dependent on a type 4 secretion system (T4SS) (Dot/Icm) that assembles over 20 different proteins into a pore able to span the inner and outer envelopes of the bacteria allowing secretion of virulence factors into the cell. Legionella entering alveolar macrophages by phagocytosis are surrounded by a membrane compartment called the Legionella-containing vacuole (LCV). The vacuole protects the bacteria from the phagosome-lysosome system by recruiting small GTPases such as Rab1 and Arf1, which recruit ER- and Golgi-derived membrane vesicles to deliver ER proteins to the vacuole. The LCV also acquires autophagy markers ATG7 and LC3 possibly as a result of pore formation by type IV virulence factors (73). Legionella are, however, able to inhibit autophagy and degradation in lysosomes by secreting the RavZ protein (Fig. 5, v), which acts an LC3 deconjugation enzyme to remove LC3 from autophagosome membranes (74). Interestingly, studies of the Legionella dumoffi strain, which does not encode RavZ, show recruitment of LC3 to vacuoles containing bacteria by LAP in macrophage cell lines (75). Unlike the Legionella pneumophila strain, the LCVs formed around L. dumoffi had single membranes and were negative for ubiquitin, galectin-8, NDP52, and p62. In common with LAP, recruitment of LC3 was independent of ULK1 but did require NADPH oxidases and DAG and was facilitated by TLR signaling and Rubicon. Live cell imaging showed that LC3 recruitment to LCVs was followed by acidification and degradation of bacteria, indicating that LAP provides a defense against infection that may be subverted by deconjugation of LC3 by the RavZ protein of L. pneumophila (74).
It has been known for many years that Mycobacterium tuberculosis (M.tb) survives in macrophages by evading degradation in lysosomes (76). The mycobacterium activates pathogen recognition receptors, but very few phagosomes recruit LC3, suggesting that wild-type M.tb can inhibit CASM/LAP. A yeast two-hybrid screen using the reading frames of proteins predicted to be secreted by M.tb identified CpsA as a protein able to bind autophagy cargo protein NDP52 and might therefore modulate autophagy (77). CpsA is secreted by type VII secretion (ESX-1 translocon) that creates a 5-nm pore in the inner membrane of the bacteria, allowing virulence factors to leave the bacteria through the cell wall and the outer hydrophobic mycomembrane. Simultaneous secretion of membrane destabilizing factors is thought to damage the phagosome, allowing delivery of CpsA into the cytosol of the macrophage (Fig. 5, ii). At the same time, release of DNA, either from M.tb or mitochondria responding to infection, can activate the cGAS/STING pathway, leading to type 1 IFN production and noncanonical recruitment of LC3 to endolysosome membranes via the V-ATPase-Atg16L1 axis (78). Interestingly, M.tb strains lacking CpsA (ΔcpsA) show increased recruitment of LC3 to phagosomes containing bacteria and increased delivery of M.tb to LAMP1-positive compartments for degradation. The degradation of mutant M.tb did not require ULK1 or ATG14 or cGAS but was dependent on Rubicon and NADPH oxidase, showing that CASM/LAP was responsible for clearing the ΔcpsA mutant. Further analysis showed that exogenous expression of CpsA in macrophages inhibited recruitment of p47phox and p40phox to phagosomes containing zymosan or wild-type M.tb. Crucial in vivo experiments showed that ΔcpsA strains failed to establish infections in mice unless mice carried mutations in NOX2, demonstrating that CpsA protects M.tb from CASM/LAP in vivo by inactivating NADPH oxidase (77).
Shigella flexneri are Gram-negative bacteria causing bacterial dysentery and are one of the leading causes of deaths of infants in developing countries. Shigella reaching the colon and rectum translocate across epithelial barriers where they are ingested by macrophages, leading to rapid replication and cell death. Shigella released from macrophages enter epithelial cells by phagocytosis/endocytosis where expression of T3SS damages the vacuole, allowing escape into the cytosol. Expression of outer membrane protein VirG (IcsA) seeds actin assembly at one pole of the bacteria through interactions with N-WASP and the Arp2/3 actin polymerization complex to propel the bacteria into neighboring cells. At the same time, VirG binds ATG5 and this may initiate capture of cytosolic Shigella by canonical autophagy. This process is inhibited by the IcsB protein that is secreted into the cytosol via T3SS to compete with ATG5 for binding to VirG (79). More recent work has shown recruitment of LC3 to Shigella during cell-to-cell spread. Shigella are carried into neighboring cells within membrane protrusions generated by actin polymerization. The protrusions are engulfed by the endolysosome system of the recipient cells where acidification switches on T3SS, leading to membrane damage that triggers recruitment of LC3, possibly by LAP/CASM. At the same time, expression of IcsB and VirA is thought to destabilize the new vacuole to allow escape of Shigella into the cytosol of the recipient cell, before they are killed by fusion with lysosomes (80).
A. fumigatus is a major airborne fungal pathogen where predisposition to infection is increased by steroid treatment, by chemotherapy, or through mutations to NADPH oxidase as seen in chronic granulomatous disease. Aspergillus is transmitted following inhalation of dormant conidia (spores) into the airways where they are controlled through phagocytosis by alveolar macrophages and subsequent activation of proinflammatory responses. The cell walls of dormant conidia are covered by hydrophobic rodlet proteins that conceal PAMPs. The rodlets are held in the cell wall by a glycosylphosphatidylinositol anchor that is degraded during germination, allowing the subsequent exposure of PAMPs to activate strong innate immune responses. Exposed β-glucan is recognized by Dectin-1, and Syk kinase/NADPH signaling activates recruitment of LC3 to phagosomes by LAP (81). Detailed studies have shown that conidia unable to express rodlet proteins (Δrod) expose β-glycan but do not activate LAP/CASM unless the conidia carry a second mutation, Δpksp, that prevents expression of cell wall melanin (82). The combined loss of rodlets and melanin from conidia increased killing in lysosomes and reduced virulence in immunocompromised mice, suggesting that melanin prevents innate immune recognition and elimination of conidia by LAP/CASM. A comparison of cell signaling pathways induced by mutant and wild-type conidia showed that cell wall melanin greatly reduced production of ROS following Dectin-1/Syk signaling from macrophage phagosomes by preventing assembly of p22phox into the NADPH-oxidase complex. The loss of ROS impairs subsequent recruitment of LC3 via LAP/CASM. The topological problem of how melanin inside the phagosome would affect assembly of the NADPH oxidase complex on the cytosolic side of the vacuole was solved by the discovery that, in Δrod conidia lacking melanin, calmodulin (CaM) is recruited from the cytosol to phagosomes following activation of LAP/CASM (83). Recruitment of LC3 to phagosomes and killing of Aspergillus were inhibited by inhibitors of CaM or calcium/CaM-dependent kinase II (CAMKII). A comparison of Ca2+ release from phagosomes showed that phagosomes containing wild-type strains released less of Ca2+ than melanin−/− strains. This suggested that melanin sequestered Ca2+ within the phagosome, blocked recruitment of CaM, and reduced Ca2+-CaM signaling. Remarkably, a single-nucleotide polymorphism (CALM1 CC) that reduces the activity of the CaM promoter has been found in patients at risk of Aspergillus infection, suggesting a role for impaired Ca2+-CaM signaling in preventing human fungal infection (83). LAP has also been shown to target yeast (84). Invasive candidiasis caused by Candida albicans is a leading cause of hospitalization and death in immunocompromised patients. In common with A. fumigatus, C. albicans is controlled by phagocytic cells, and activation of Dectin-1 by exposed β-1,3 glucan recruits LC3 to the phagosome membrane (84). Phosphorylation of Dectin by Syk leads to activation of the NADPH oxidase on the phagosome and recruitment of LC3 via LAP/CASM. LAP/CASM pathways also respond to recognition of α-mannan in the cell walls of commensal yeast such as Saccharomyces cerevisiae and Kazachstania unispora in the gut microbiota where they facilitate Dectin-2 signaling and proinflammatory responses (35).
Leishmania are unicellular eukaryotes responsible for leishmaniasis. Promastigotes use flagella to facilitate entry into phagocytic cells and transform into a nonmotile amastigote form that can replicate by cell division. Promastigotes internalized into phagocytes use a zinc metalloprotease called GP63 to inhibit phagolysosome fusion and prevent degradation in lysosomes. GP63 is secreted from the Leishmania in exosomes that fuse with the limiting membrane of the vacuole and deliver GP63 into the cytosol (Fig. 5, iii). Recent studies using mutants lacking GP63 show that GP63 prevents recruitment of LC3 to phagosomes containing Leishmania during early stages of cell entry (85). Earlier work had shown that vesicle-associated membrane protein 8 (VAMP8) controlled recruitment of the NOX2 complex on phagosomes and that VAMP8 was cleaved by GP63 (86). This allows GP63 to prevent LAP/CASM by preventing production of ROS and consequent rise in vacuolar pH.
Plasmodium species are unicellular eukaryotic parasites that are transferred between hosts by insects such as mosquitoes. Plasmodium vivax infects humans where it causes malaria. It is not as lethal as Plasmodium falciparum but has widespread global distribution and can live in the liver in a dormant hypnozoite stage for many years. It has been known for a long time that IFN-γ can prevent liver stage infection (87). IFN-γ also inhibits the intrahepatocytic development of malaria parasites in vitro, but the mechanisms were unclear (87). IFN-γ induces LC3 lipidation and formation of LC3 puncta, suggesting a role for LC3 in clearance of Plasmodium (88). IFN-γ induces the formation of relatively small numbers of LC3 puncta per cell (between 1 and 4) that require expression of Beclin1 and ATG5 (89). Cells lacking Beclin1 and ATG5 were less able to eliminate parasites in response to IFN-γ, but elimination was unaffected following loss of ULK1, implicating LAP/CASM (89). Plasmodium do not enter cells by phagocytosis but use an active parasite-driven pathway that leads to the formation of a parasitophorous vacuole in the cytosol (90). IFN-γ does, however, increase recruitment of LC3 to parasitophorous vacuoles, and this may be through LAP/CASM. This is supported by studies of Plasmodium berghei where LC3 is recruited to parasitophorous vacuoles formed in hepatocytes within 20 min of cell entry. Recruitment of LC3 required ATG5 but was independent of FIP200 (88).
The identification of pathways such as LAP and CASM, which conjugate LC3 to endosomes and phagosomes containing pathogens as they enter cells, has given a new perspective on how autophagy proteins can control infection. It is known that LC3 recruitment during infection is triggered by TLR signaling through Rubicon and ROS production and can be mediated by the V-ATPase-ATG16L1 axis, which senses a rise in vacuolar pH. It is now appreciated that pathogens can further activate LC3 recruitment when they raise vacuolar pH following membrane damage or generation of pores in endosomes or phagosomes before they gain access to the cytosol. This information will underpin the search for new pathogen-associated signals able to activate these alternative pathways of LC3 conjugation, and systematic studies of the pathways involved. There will also be a search for unknown virulence factors generated by pathogens to slow LC3 recruitment by inhibition of defined pathways such as ROS production and/or the V-ATPase-ATG16L1 axis, or through inhibition of previously unidentified pathways. Animal models that are defective in recruiting LC3 to membranes through LAP/CASM will be valuable for determining the importance of LAP and CASM in controlling infection in vivo. Key questions center on the role played by LAP and CASM at epithelial surfaces and within phagocytic cells and leukocytes, and how these influence innate and acquired immune responses. | true | true | true |
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PMC9604557 | Dongrong Liu,Yan Liu,Yun Hu,Ye Ming,Xuehuan Meng,Hao Tan,Leilei Zheng | MiR-134-5p/Stat3 Axis Modulates Proliferation and Migration of MSCs Co-Cultured with Glioma C6 Cells by Regulating Pvt1 Expression | 20-10-2022 | MSCs,proliferation,migration,miR-134-5p,Stat3,Pvt1 | Mesenchymal stem cells (MSCs) are critical in regenerating tissues because they can differentiate into various tissue cells. MSCs interact closely with cells in the tissue microenvironment during the repair of damaged tissue. Although regarded as non-healing wounds, tumors can be treated by MSCs, which showed satisfactory treatment outcomes in previous reports. However, it is largely unknown whether the biological behaviors of MSCs would be affected by the tumor microenvironment. Exploring the truth of tumor microenvironmental cues driving MSCs tumor “wound” regeneration would provide a deeper understanding of the biological behavior of MSCs. Therefore, we mimicked the tumor microenvironment using co-cultured glioma C6 cells and rat MSCs, aiming to assess the proliferation and migration of MSCs and the associated effects of Stat3 in this process. The results showed that co-cultured MSCs significantly exhibited enhanced tumorigenic, migratory, and proliferative abilities. Both up-regulation of Stat3 and down-regulation of miR-134-5p were detected in co-cultured MSCs. Furthermore, miR-134-5p directly regulated Stat3 by binding to the sequence complementary to microRNA response elements in the 3′-UTR of its mRNA. Functional studies showed that both the migration and proliferation abilities of co-cultured MSCs were inhibited by miR-134-5p, whereas Stat3 gain-of-function treatment reversed these effects. In addition, Pvt1 was confirmed to be regulated by miR-134-5p through Stat3 and the suppression of Pvt1 reduced the migration and proliferation abilities of co-cultured MSCs. To sum up, these results demonstrate a suppressive role of miR-134-5p in tumor-environment-driven malignant transformation of rat MSCs through directly targeting Stat3, highlighting a crucial role of loss-of-function of miR-134-5p/Stat3 axis in the malignant transformation, providing a reference to the potential clinic use of MSCs. | MiR-134-5p/Stat3 Axis Modulates Proliferation and Migration of MSCs Co-Cultured with Glioma C6 Cells by Regulating Pvt1 Expression
Mesenchymal stem cells (MSCs) are critical in regenerating tissues because they can differentiate into various tissue cells. MSCs interact closely with cells in the tissue microenvironment during the repair of damaged tissue. Although regarded as non-healing wounds, tumors can be treated by MSCs, which showed satisfactory treatment outcomes in previous reports. However, it is largely unknown whether the biological behaviors of MSCs would be affected by the tumor microenvironment. Exploring the truth of tumor microenvironmental cues driving MSCs tumor “wound” regeneration would provide a deeper understanding of the biological behavior of MSCs. Therefore, we mimicked the tumor microenvironment using co-cultured glioma C6 cells and rat MSCs, aiming to assess the proliferation and migration of MSCs and the associated effects of Stat3 in this process. The results showed that co-cultured MSCs significantly exhibited enhanced tumorigenic, migratory, and proliferative abilities. Both up-regulation of Stat3 and down-regulation of miR-134-5p were detected in co-cultured MSCs. Furthermore, miR-134-5p directly regulated Stat3 by binding to the sequence complementary to microRNA response elements in the 3′-UTR of its mRNA. Functional studies showed that both the migration and proliferation abilities of co-cultured MSCs were inhibited by miR-134-5p, whereas Stat3 gain-of-function treatment reversed these effects. In addition, Pvt1 was confirmed to be regulated by miR-134-5p through Stat3 and the suppression of Pvt1 reduced the migration and proliferation abilities of co-cultured MSCs. To sum up, these results demonstrate a suppressive role of miR-134-5p in tumor-environment-driven malignant transformation of rat MSCs through directly targeting Stat3, highlighting a crucial role of loss-of-function of miR-134-5p/Stat3 axis in the malignant transformation, providing a reference to the potential clinic use of MSCs.
Mesenchymal stem cells (MSCs) exert an important function in wound healing and regeneration of tissues, because they are a pluripotent, heterogeneous cell population with multiple differentiation potentials [1]. Tumors have long been regarded as non-healing wounds [2], and tumor tissue sites enable MSCs homing, that is to say, MSCs are considered to have a natural tumor-homing ability [3]. MSCs often become an integral part of the tumor microenvironment, usually responding to signals from tumor cells after being recruited and incorporated [4]. Evidence from previous studies suggests that MSCs may promote tumor growth or, conversely, inhibit tumor growth [5,6]. Some findings even lend credence to the new intriguing notion that tumors may arise from stem cells and that MSCs might represent a potential source of malignancy [7,8,9]. With the development of high-throughput technology, increasing evidence has revealed that the progression of tumors is accompanied by dysregulation of noncoding RNAs (ncRNAs) [10], which can be sorted into long noncoding RNAs (lncRNAs), microRNAs (miRNAs) and small interfering RNAs (siRNAs) [10]. To date, one of the most widely studied noncoding RNAs is miRNAs, which are 18–25 nucleotides in length [11] and play crucial roles in such biological functions as cell differentiation, metabolism, organogenesis, embryogenesis and apoptosis [12]. Mature miRNAs are guided to bind to the 3′-UTR region of the mRNAs, causing their destabilization or inhibition of translation [13]. MiR-134 is considered to be an antioncogene that is down-regulated in renal cell carcinoma, osteosarcoma, lung cancer and regulates cell growth, apoptosis, metastasis, angiogenesis by attenuating signal pathways such as VEGFA/VEGFR1 pathway, ERK1/2 pathway and MAPK/ERK pathway [14,15,16]. Signal transducer and activator of transcription (STAT) 3 is often persistently activated in various malignant tumors [17]. In tumor cells, STAT3 not only affects tumor microenvironment to provide a favorable condition for tumor development, but also regulates cell proliferation, metastasis and angiogenesis by acting as a transcription factor, which controls the transduction of numerous target genes, including noncoding genes [18,19,20]. Previous study reported that STAT3 in tumor microenvironment can reduce the activity of NK cells to help tumors evade immune recognition [21]. Activated STAT3 can directly bind to the promoter of MMP2 and VEGF to upregulate their expression, thus promoting tumor metastasis and angiogenesis [22,23]. Therefore, STAT3 may be a potential therapeutic target of many tumors. It has been reported that miRNAs could inhibit tumor progression by targeting STAT3 in different tumors. For example, in the squamous cell carcinoma of skin, miR-125b inhibits cell proliferation while also promoting apoptosis by targeting STAT3 [24]. In breast cancer, miR-124 directly regulates STAT3 expression to reduce breast cancer stem cell resistance to doxorubicin [25]. In order to observe the MSCs biological behavior in tumor microenvironment, we co-cultured rat MSCs and glioma C6 cells to simulate the microenvironment and analyzed the biological behaviors of the co-cultured MSCs. Results showed that the proliferation, soft agar colony formation and migration abilities of co-cultured MSCs in vitro and their oncogenic activity in vivo were altered. Previous studies reported that Stat3 was up-regulated in rat MSCs after co-cultured with glioma cells [26]. So Stat3 was opted for study in the present research. At the same time, miR-134-5p was selected as a regulatory gene of Stat3 through online prediction software. Both upregulation of Stat3 and downregulation of miR-134-5p were found in co-cultured MSCs. In this study, we investigated the biological behaviors of MSCs after co-cultured with glioma C6 cells and the role of miR-134-5p/Stat3 axis in the process of MSCs transformation, intending to provide a reference to the potential clinic use of MSCs and novel targets for therapeutic intervention of malignant diseases.
All animal experiments met the requirements of ARRIVE Guidelines and Guidelines for the Care and Use of Laboratory Animals, were reviewed and then authorized by Bioethics Committee for Animal Research of Chongqing Medical University (BCAR-CQMU) (#2020037) in Chongqing Key Laboratory of Oral Diseases and Biomedical Sciences (CKLODBS) (Chongqing, China).
Male 4-week-old SD rats were bought from Experimental Animal Center of CQMU (Chongqing, China). Before extraction of cells, rats were anesthetized with 3% isoflurane for 2 to 3 min and sacrificed. Rat MSCs were separated immediately from the thigh and shin bones of male SD rats in the biosafety cabinet (AIRTECH, Tianjin, China). Rat glioma C6 cells were donated by the Children’s Hospital of CQMU (Chongqing, China). 1% penicillin-streptomycin and 10% FBS (Lonsera, Uruguay) were added into the DMEM/F12 (HyClone, Logan, UT, USA) medium for cell culture. All of the cells were kept in an incubator at 37 degrees centigrade under 5% CO2.
A number of 1 × 106 P3 MSCs were collected and incubated with anti-CD29-PE (582154), anti-CD90-FITC (561973), anti-CD31-PE (555027) and anti-CD45-PE (554878) by 1:100, respectively, at 4 degrees centigrade for 30 min in the dark. Then, necessary surface markers were distinguished by flow cytometer (BD, Influx, The Franklin Lake, NJ, USA). Oil red O and Alizarin red were used to stain and detect osteogenic and adipogenic capacity of MSCs [27]. Briefly, 3 × 104/well P3 MSCs were seeded to six-well plates and cultured with adipogenic or osteogenic induction medium for 21 days. After the induction, oil red O (Sigma-Aldrich, Saint Louis, MO, USA) or alizarin red (Solarbio, Beijing, China) were adopted to stain the cells.
The P3 MSCs (1.5 × 105/well) were cultivated in DMEM/F12 and were indirectly co-cultured with glioma C6 cells (1.5 × 105/well) in a 6-well transwell chamber (0.4 µm pore-sized, Corning Costar, Cambridge, MA, USA). The cells were co-cultured for 3 days before passaging. After 2-week co-culture with C6 cells, the MSCs were collected.
PcDNA3.1-Stat3 plasmids were constructed by GenePharma (Shanghai, China), which also synthesized the negative control (si-NC) and the siRNA against Pvt1 (si-Pvt1). MiR-134-5p mimics/inhibitor, and all controls were supplied by Sangon Biotech Co., Ltd. (Shanghai, China). 2 × 105 MSCs were seeded into 6-well plates and grown to a confluence of 50−70% before transfection using Lipofectamine 3000 (Invitrogen, Waltham, MA, USA). Further experiments were performed after transfection for the indicated times. The sequences of siRNA, miR-134-5p mimics and inhibitor are listed in Table 1 and Table 2.
Cell Counting Kit-8 (CCK-8) (manufactured by: Dojindo, Japan) was utilized to determine cell viability. In brief, normal MSCs, glioma C6 cells, and co-cultured MSCs (2 × 103 each) were cultured in 96-well plates. Each group of cells were provided with three wells. Cell viability was detected for 7 days. CCK-8 solution 10 µL mixed with fresh complete medium 100 µL was added into each well and the cells were incubated at 37 °C for 4 h. After incubation, the spectrophotometric absorbance of the samples was measured at 450 nm using a microtiter plate reader. In total, 3 × 103 transfected cells were plated in 96-well plates, and cell viability was assessed for 4 days as described above.
Cell Cycle and Apoptosis Analysis kits were used in the flow cytometry assay (Beyotime, Beijing, China) to determine the cell cycle. The cells collected were put into EP tubes (1.5 mL) and fixed with 70% ethanol for 12 to 24 h. The sample cells were washed with PBS and stained with PI solution at 37 °C in the dark for 30 min. FACS Calibur instrument (BD, Influx, Burlington, MA, USA) and Modfit software were used to determine and analyze cell cycle distribution. Each experiment was independently carried out for 3 times.
The bottom 1.2% and top 0.7% low-melting-point agarose (Biotopped, Beijing, China) mixed with an equal volume culture medium supplemented with 2% penicillin-streptomycin and 20% FBS was adopted to culture cells in 60 mm dishes. The cells were cultured under the atmosphere with 5% CO2 at 37 °C for two to three weeks, stained with 0.05% crystal violet, and then the formed colonies were numbered under a light microscope. Each experiment was independently carried out 3 times.
All cells collected were cultured in the 6-well plates. A pipette tip (volume: 200-µL) was used to create scratches in cell monolayers grown to a 90% confluence. The cells were washed by PBS for three times and were cultured in the serum-free DMEM/F12. At 0 and 48 h after incubation, a microscope (Nikon, Japan) was used to image and observe the scratched areas of the cells. Each experiment was independently carried out 3 times.
In total, 3 × 104 cells were seeded in upper chamber of a 24-well transwell plate (8 µm) with 200 µL serum-free DMEM/F12 medium. 600 µL DMEM/F12 containing 10% FBS was added in the lower chamber. At 24 h after incubation, the uninvaded cells in the upper chamber were removed, the migrated cells were then stained with 0.05% crystal violet and observed under a microscope in 3 randomly chosen fields of view. Each experiment was independently carried out 3 times.
First of all, 6-week-old athymic nude mice were obtained from EAC-CQMU and maintained in a pathogen-free facility in CKLODBS. A total of 6 nude mice were divided into 3 groups. The subcutaneous injection was carried out in the mice’s flanks with 1 × 106 cells without anesthesia after being sterilized with 75% ethanol in the biosafety cabinet of animal laboratory. The size of the tumors was measured every week, and after 8 weeks, the mice were anesthetized with inhalation of 3% isoflurane for 2–3 min and killed by cervical dislocation to obtain the tumor tissue.
Hematoxylin and eosin (H&E) staining was conducted according to reported method [28]. Briefly, after fixation with 10% formalin, the harvested xenografts were dehydrated with graded ethanol and then embedded in the paraffin. Afterwards, 4–5 µm tissue sections were stained with H&E.
Total RNA was extracted with a TRIzol reagent (Takara, Nogihigashi, Japan), and was transcribed reversely in cDNA with the GoScript™ Reverse Transcription System (Promega, Madison, WI, USA). RT-PCR analysis was conducted using a GoTaq® qPCR Master Mix system (Promega, Madison, WI, USA). Three wells were repeated in each sample. With internal control of Gapdh, the 2−ΔΔCt method was utilized to calculate the relative expression levels of genes. The specific primers for miRNAs were designed using stem-loop RT-PCR. U6 was used as the miRNA reference. All of the primers are shown in Table 3 and Table 4.
The RIPA buffer containing 1% PMSF was used to lyse the cells to obtain the protein samples. BCA protein assay kits (Beyotime, Beijing, China) were applied for the determination of the protein concentration. Proteins were separated on 8% SDS-PAGE gels and electrophoretically transferred to the membranes of PVDF (0.22 µm pore size; Millipore, Boston, MA, USA). The PVDF membranes were blocked with 5% BSA at room temperature for one hour and then incubated with an antibody against Stat3 (1:2000; 79D7, CST, Danvers, MA, USA) at 4 °C overnight. Subsequently, the blots were incubated with a secondary antibody (1:5000; Beyotime, Beijing, China) for 2 h at room temperature. Immunoreactivity was detected using Enhanced Chemiluminescence (ECL) (Beyotime, Beijing, China) and Quantity One software (Bio-Rad, Hercules, California, USA). Gapdh (1:2000; D16H11, CST, Danvers, MA, USA) was utilized as an internal control. The experiment was performed in 3 replicates.
A Stat3 reporter bearing either a predicted wild-type or mutant miR-134-5p-binding site was generated by inserting the sequences into GP-miRGLO (GenePharma, Shanghai, China). Co-cultured MSCs were co-transfected with GP-miRGLO-Stat3-WT, GP-miRGLO-Stat3-MUT and mimics-NC or miR-134-5p mimics in the 96-well plates using the Lipofectamine 3000 (Invitrogen, Waltham, MA, USA). At 48 h after co-transfection, the relative luciferase activity was determined by the Dual-Luciferase Reporter Assay System (Promega, Madison, WI, USA) using a luminometer with Firefly luciferase data normalized to Renilla.
GraphPad Prism (version 8.0, La Jolla, CA, USA) was used for statistical data analyses. All data were described by mean ± standard deviation. Student’s t-test was applied for the intergroup comparison. p < 0.05 is considered as statistically significant.
To determine that the cells were bone marrow MSCs, surface markers were identified using a flow cytometry. The results showed that CD29, CD90 were highly expressed, whereas CD31 and CD45 were expressed at a lower level in the cells examined (Figure 1A). Alizarin red staining exhibited mineralized nodules (Figure 1B), indicating that the cells possess the potential of osteogenesis. After oil red O staining, red lipid was seen under microscopy (Figure 1C), indicating that the cells have the capability of adipogenic differentiation after adipogenic induction. The above results demonstrated that the cells were bone marrow MSCs in the present study.
To explore the effect of tumor microenvironment on MSCs, we co-cultured MSCs with glioma C6 cells to simulate this microenvironment. The morphology, proliferation, migration and tumorigenesis abilities of co-cultured MSCs were analyzed, and the results were shown as follows. The morphology of the MSCs significantly changed after two-week indirect co-culture with C6 cells, exhibiting thinner and longer shapes that were similar to those of C6 cells (Figure 1D). Colony formation assay showed that colonies were observed in the co-culture and C6 groups but not in the normal MSC group (Figure 2A). The flow cytometry assay showed an evidently higher S and G2/M phase cell percentage and lower G0/G1 phase cell percentage in co-cultured MSCs compared with normal MSCs (Figure 2B). The CCK-8 assay revealed that co-cultured MSCs were higher than normal MSCs in terms of proliferation rate (Figure 2C). The migration ability of co-cultured MSCs was also greatly enhanced compared to that of normal MSCs (Figure 2D). At 8 weeks after the subcutaneous injection, xenograft tumors were established in the nude mice with co-cultured MSCs and C6 cells but not those with normal MSCs, and the histological analysis revealed that the harvested tumors exhibited atypia (Figure 2E). Altogether, these results suggested that at two weeks after indirect co-culture with C6 cells, MSCs experienced a tumor-like transformation.
It was reported that changes in MSCs in tumor microenvironment may associate with Stat3 [26], so Stat3 was selected as a biomarker to study the cause of co-cultured MSCs transformation. Stat3 mRNA (Figure 3A) and protein (Figure 3B) levels were both up-regulated in the co-cultured MSCs relative to the normal MSCs. To find the reason for Stat3 up-regulation, miRNAs caught our attention due to the numerous reports of miRNAs in various biological behaviors. MiRwalk (http://mirwalk.umm.uni-heidelberg.de/) and TargetScan (http://www.targetscan.org/mamm_31/) (accessed on 30 October 2006) were then applied to predict potential miRNAs silencing Stat3. Six miRNAs (miR-30b-5p, miR-30a-5p, miR-376c-3p, miR-26b-5p, miR-134-5p, miR-381-3p) had the possibility to target Stat3. Among the miRNAs tested, only miR-134-5p was down-regulated in co-cultured MSCs in comparison to that in normal MSCs (Figure 3C–F, miR-376c-3p and miR-381-3p were not expressed in the two kinds of cells). The above data indicated that the transformation of MSCs may be partly due to the expression changes of Stat3 and miR-134-5p.
To verify whether Stat3 is a target of miR-134-5p, we designed and performed luciferase reporter assay. The luciferase reporter plasmids for Stat3 of wild type (Stat3-WT) and mutant type (Stat3-MUT) were constructed (Figure 3G). We observed the reduced luciferase activity in co-cultured MSCs co-transfected with Stat3-WT and miR-134-5p mimics (Figure 3H), whereas the reduction in luciferase activity was completely abolished by co-transfection with either Stat3-MUT or mimics NC. Furthermore, inhibition of miR-134-5p enhanced the expression of Stat3 at both mRNA and protein levels in normal MSCs (Figure 3I). Conversely, ectopic overexpression of miR-134-5p attenuated the expression of Stat3 at both mRNA and protein levels in co-cultured MSCs (Figure 3J). Taken together, these data suggested that Stat3 is a direct target of miR-134-5p.
To further elucidate whether proliferation and migration of the transformed co-cultured MSCs might be regulated by miR-134-5p/Stat3 pathway, the co-cultured MSCs were transfected with miR-134-5p alone or in combination with Stat3. Colony formation assay showed that the colony count decreased in the miR-134-5p mimics group, which was rescued by Stat3 plasmids (Figure 4A). The CCK-8 and flow cytometry assays indicated that the Stat3 plasmid reversed the inhibited proliferation of co-cultured MSCs caused by miR-134-5p mimics (Figure 4B,C). Furthermore, wound healing and transwell migration assays showed that the ectopic Stat3 overexpression reversed the migration inhibition of co-cultured MSCs induced by miR-134-5p mimics (Figure 4D,E). These results indicated that the tumor-like changes of co-cultured MSCs was partly regulated by miR-134-5p/Stat3 axis.
Previous studies reported that STAT3 promoted PVT1 transcription by binding to PVT1 promoter [20]. However, it is not clear whether Pvt1 could be facilitated by miR-134-5p targeted Stat3 in the present study. Co-cultured MSCs were co-transfected with Stat3 plasmids and miR-134-5p mimics (Figure 5A,B). Consistent with previous report [20], the miR-134-5p-mediated down-regulation of Pvt1 mRNA level was restored by ectopic Stat3 overexpression in co-cultured MSCs (Figure 5C). In addition, we detected the expression of Pvt1 in normal and co-cultured MSCs, and discovered that Pvt1 was up-regulated in co-cultured MSCs (Figure 5D).
We next looked at the contributing role of Pvt1 in the process of MSCs malignant transformation through a series of loss-of-function assays. Co-cultured MSCs were transfected with si-Pvt1 to inhibit Pvt1 expression (Figure 6A). Pvt1 knockdown profoundly inhibited colony formation and proliferation of co-cultured MSCs (Figure 6B,C). Flow cytometry assay showed that G0/G1 cell proportion significantly increased, and the S and G2/M phase significantly decreased in Pvt1 knockdown group (Figure 6D). Pvt1 knockdown also significantly attenuated migration of co-cultured MSCs (Figure 6E,F). These results suggested that Pvt1 may play a crucial role in mediating Stat3-induced proliferation and migration of co-cultured MSCs.
MSCs can self-renew and rapidly proliferate [29]. Tissue regeneration results in complete restoration of damaged tissue structure and function [30]. Accumulating data suggested that tumor microenvironment sites have tropism for MSCs, and the way that they interact closely with tumor cells is paracrine signaling. Therefore, an issue associated with MSCs is their ability to alter the biological characteristics in the tumor microenvironment or inflammatory microenvironment [31]. MSCs underwent a malignant transformation with smaller morphology and abnormal mitosis, and tumors generated in nude mice when stimulated by inflammatory factors such as INF-γ and TNF-α for a long time [32]. Tumor-like masses were formed by MSCs in the nude mice when the MSCs were cultured with a conditioned medium from breast cancer cells [33]. The MSCs injected to the brain could also be transformed when surrounded by glioma C6 cells [34]. Therefore, the risks of iatrogenic tumor formation should be highly valued. In this paper, we demonstrated that rat MSCs exhibited similar changes after being co-cultured with rat glioma C6 cells and showed a significantly faster proliferation rate, increased migration ability and greater tumor formation ability in nude mice. Thereafter, we have carefully studied the mechanism by which the malignant transformation of MSCs occurred and found that Stat3 was significantly up-regulated in co-cultured MSCs. Recently STAT3 was discovered to play a crucial role in tumor progression and prognosis of different types of cancers. For example, STAT3 is involved in the process of proliferation, migration and invasion of cancers [18,35]. At the same time, high expression of STAT3 is corrected with an advanced tumor grade and poor prognosis [36,37,38]. Therefore, the inhibition of STAT3 has become a new idea for treating malignant diseases. With improved understanding of noncoding RNA function, numerous studies have demonstrated that miRNAs can regulate mRNAs at the post-transcriptional level and inhibit mRNA translation [13]. STAT3 has been reported to be regulated by tumor suppressor miRNAs in numerous cancers. In the squamous cell carcinoma of skin, STAT3 is regulated by miR-125b [24]. In the breast cancer, STAT3 is modulated by miR-124 [25]. In the colorectal cancer, STAT3 is suppressed by miR-124-3p [39]. In this study, for the first time, we revealed that miR-134-5p could directly target Stat3 with luciferase assay. MiR-134 has been demonstrated to be a suppressor of tumor progression and is down-regulated in numerous cancers [40]. Furthermore, the expression level of miR-134-5p, relative to normal MSCs, was significantly decreased in co-cultured MSCs. miR-134-5p inhibition led to up-regulation of Stat3 expression, whereas miR-134-5p overexpression triggered down-regulation of Stat3 expression. Proliferation and migration of co-cultured MSCs could be inhibited by overexpression of miR-134-5p via inhibiting Stat3. STAT3, an important member of STAT family, is an important transcription factor participating in multiple biological processes by regulating the transcription of various genes. Previous studies demonstrated that STAT3 can directly or indirectly interact with the promoters of cyclin D1, Twist, MMP2, MMP7, MMP9, VEGF, upregulate their expression and regulate cell proliferation, tumor metastasis and angiogenesis [18,41,42]. With the deepening of research on the human genome, more and more studies found that STAT3 also functions to transcribe non-coding RNAs. In gastric cancer, STAT3 occupies the promoter of PVT1 and stimulates PVT1 expression [20]. In gallbladder cancer, the expression of lncRNA-HEGBC is activated by STAT3 through STAT3 bound to the promoter of lncRNA-HEGBC [43]. In hepatocellular carcinoma, STAT3 acts on HOXD-AS1 promoter and activates HOXD-AS1 transcription [19]. This study showed that in co-cultured MSCs, miR-134-5p regulated Pvt1 expression via silencing Stat3. Pvt1 expression was decreased by the overexpression of miR-134-5p, whereas it was up-regulated by co-transfection with Stat3. However, whether it was due to the transcriptional effect of Stat3 or another regulatory mechanism needs further investigation. PVT1 is a long noncoding RNA located on 8q24.21 which is lowly expressed in normal cells and tissues while being abnormally up-regulated in malignant tumor tissues and cells [44]. Thus, PVT1 is considered to be an oncogene. According to reports, the biological activity of many cancer cells can be modulated by PVT1 [20,45]. In this study, we noted that Pvt1 was up-regulated in co-cultured MSCs, Pvt1 knockdown inhibited the proliferation and migration of co-cultured MSCs, indicating that Pvt1 exerts a promoting function in the tumor-like transformation of MSCs. In the present study, Pvt1 was regulated by miR-134-5p through Stat3. As a mediator of tumor progression, PVT1 also has multiple regulatory mechanisms. PVT1 has the ability to impair miRNA activity on its target gene by acting as competing endogenous RNA [46]. Apart from affecting mRNA translation via miRNA, PVT1 can also directly interact with proteins and regulate the stability of proteins. In gastric cancer cells, PVT1 interacts with STAT3 and protects STAT3 from poly-ubiquitination and proteasome-dependent degradation to sustain the stability of p-STA3 [20]. Through recruiting Enhancer from Zeste homolog 2, PVT1 can epigenetically regulate miR-200b, miR-195 [47,48]. The above results suggested that the action of PVT1 is complex. In the current study, whether Pvt1 regulates miR-134-5p and Stat3 needs further study.
In summary, as shown in Figure 7, the proliferative and migratory capacity of MSCs in vitro and their oncogenic activity in vivo are increased after having been co-cultured with glioma C6 cells. MiR-134-5p, which directly target Stat3, is down-regulated in co-cultured MSCs, leading to tumor-like transformation of MSCs by enhancing Pvt1 expression, representing novel targets for therapeutic intervention of malignant diseases. | true | true | true |
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PMC9604878 | Xiaoyu Zhang,Xiao Ge,Fangyuan Shen,Jinjuan Qiao,Yubo Zhang,Heng Li | Diagnostic efficiency of RPA/RAA integrated CRISPR-Cas technique for COVID-19: A systematic review and meta-analysis | 26-10-2022 | Objective To evaluate the diagnostic value of recombinase polymerase/ aided amplification (RPA/RAA) integrated clustered regularly interspaced short palindromic repeats (CRISPR) in the diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Methods We searched relevant literature on CRISPR technology for COVID-19 diagnosis using "novel coronavirus", "clustered regularly interspaced short palindromic repeats" and "RPA/RAA" as subject terms in PubMed, Cochrane, Web of Science, and Embase databases. Further, we performed a meta-analysis after screening the literature, quality assessment, and data extraction. Results The pooled sensitivity, specificity and a rea under the summary receiver operator characteristic curve (AUC) were 0.98 [95% confidence interval (CI):0.97–0.99], 0.99 (95% CI: 0.97–1.00) and 1.00 (95% CI: 0.98–1.00), respectively. For CRISPR-associated (Cas) proteins-12, the sensitivity, specificity was 0.98 (95% CI: 0.96–1.00), 1.00 (95% CI: 0.99–1.00), respectively. For Cas13, the sensitivity and specificity were 0.99 (95% CI: 0.97–1.00) and 0.95 (95% CI: 0.91–1.00). The positive likelihood ratio (PLR) was 183.2 (95% CI: 28.8, 1166.8); the negative likelihood ratio (NLR) was 0.02 (95% CI: 0.01, 0.03). Conclusion RPA/RAA integrated with CRISPR technology is used to diagnose coronavirus disease-19 (COVID-19) with high accuracy and can be used for large-scale population screening. | Diagnostic efficiency of RPA/RAA integrated CRISPR-Cas technique for COVID-19: A systematic review and meta-analysis
To evaluate the diagnostic value of recombinase polymerase/ aided amplification (RPA/RAA) integrated clustered regularly interspaced short palindromic repeats (CRISPR) in the diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2).
We searched relevant literature on CRISPR technology for COVID-19 diagnosis using "novel coronavirus", "clustered regularly interspaced short palindromic repeats" and "RPA/RAA" as subject terms in PubMed, Cochrane, Web of Science, and Embase databases. Further, we performed a meta-analysis after screening the literature, quality assessment, and data extraction.
The pooled sensitivity, specificity and a rea under the summary receiver operator characteristic curve (AUC) were 0.98 [95% confidence interval (CI):0.97–0.99], 0.99 (95% CI: 0.97–1.00) and 1.00 (95% CI: 0.98–1.00), respectively. For CRISPR-associated (Cas) proteins-12, the sensitivity, specificity was 0.98 (95% CI: 0.96–1.00), 1.00 (95% CI: 0.99–1.00), respectively. For Cas13, the sensitivity and specificity were 0.99 (95% CI: 0.97–1.00) and 0.95 (95% CI: 0.91–1.00). The positive likelihood ratio (PLR) was 183.2 (95% CI: 28.8, 1166.8); the negative likelihood ratio (NLR) was 0.02 (95% CI: 0.01, 0.03).
RPA/RAA integrated with CRISPR technology is used to diagnose coronavirus disease-19 (COVID-19) with high accuracy and can be used for large-scale population screening.
The World Health Organization (WHO) declared COVID-19 a Public Health Emergency of international concern on January 30, 2020, and a pandemic on March 11, 2020 [1, 2]. COVID-19 is extremely harmful to the economy and public health of countries around the world, and we urgently need an efficient and accurate diagnostic method for large-scale population screening. Real-time reverse transcriptase-polymerase chain reaction (RT-PCR) of nasopharyngeal swabs is commonly used to confirm the clinical diagnosis [3]. However, RT-PCR requires dedicated equipment and well-trained professional technicians, making them expensive and difficult to apply in areas with poor medical resources [4]. Therefore, it is particularly important to develop diagnostic test assays that are more rapid and easier to implement than RT-PCR. Recently developed Clustered Regularly Interspaced Short Palindromic Repeat (CRISPR) diagnostic technology has shown high sensitivity, specificity, rapidity, and convenience in pathogen technology [5, 6]. At present, the methods of combining recombinase polymerase/ aided amplification (RPA/RAA) with CRISPR/Cas systems (mostly Cas9, Cas12, and Cas13) have been used to detect SARS-CoV-2. We synthesized the relevant studies on the application of RPA/RAA integrated with CRISPR in the diagnosis of COVID-19 and evaluated its diagnostic value for COVID-19 to provide a theoretical basis for rapid large-scale screening of COVID-19.
The CRISPR-Cas system establishes adaptive immunity in three stages: adaptation, expression, and interference [7]. When exogenous genes invade bacteria, bacteria cut exogenous genes into several spacer sequences and insert them into the bacterial genome. Guide RNAs encoded in CRISPR repeat region sequences are transcribed and processed into single CRISPR RNAs (crRNAs) that guide Cas proteins to recognize and cleave invading DNA or RNA in a sequence-dependent manner, which is the cis-cleavage activity of Cas proteins [8]. In addition, there is a trans-cleavage activity in Cas12 or Cas13 proteins, which can non-specifically cleave all single-stranded DNA (ssDNA) or single-stranded ribonucleic acid (ssRNA) in the system [9]. The complete genome of SARS-CoV-2 is about 30 kb, of which the first 2/3 close to the 5 ’end contain open reading frame 1ab (ORF1ab) genes encoding 16 non-structural proteins, and the last 1/3 contain genes encoding structural proteins including envelope protein (E), spike protein (S), nucleocapsid protein (N) and membrane protein (M) genes [10]. The cleavage activity of the Cas protein is provoked by exogenous delivery of crRNA that can specifically bind to the gene sequence in SARS-CoV-2. In the Cas12/Cas13-based systems, the presence of a cleavage reaction can be detected with a fluorescence reader or lateral flow strips by placing single-stranded DNA reporters labeled with fluorophore and biotin/quenching group [11, 12]; other Cas proteins can accurately detect the crRNA sequence of SARS-CoV-2 using their activity of targeted binding [13].
Azhar et al [14] developed a method FELUDA (FnCas9 editor linked uniform detection assay) for the detection of SARS-CoV-2 based on the CRISPR-Cas9 system using FnCas9, a homolog of Cas9. FELUDA does not require trans-cleavage of reporter molecules, directly uses the digestion of Cas9 for nucleic acid detection, has a specificity and sensitivity of 98% and 96%, and is also highly accurate in detecting pathogenic single nucleotide variants and nucleic acid sequence analysis. Using the collateral single-stranded DNA cleavage activity of Cas3, Yoshimi et al developed a rapid (within 40 min), low-cost, instrument-free SARS-CoV-2 detection method, named CONAN [15] (Cas3-operated nucleic acid detection). CONAN not only detects SARS-CoV-2 in clinical samples but also offers specific detection of single-base-pair mutations in influenza virus A variants.
In 2020, based on the trans-cleavage of Cas12, Zhang Feng et al [16] developed STOP (SHERLOCK Testing in One Pot) for the detection of new-crowned viruses. STOP integrated sample processing, nucleic acid amplification, and detection in one tube, avoiding the effect of false positives caused by repeated uncapping. STOP is also combined with loop-mediated isothermal amplification technology (LAMP), which improves the sensitivity of the product, with viral loads as low as 10–100 copies/μL. Broughton et al [17] combined the previously established CRISPR-Cas12a-based targeting DETECTR (DNA endonuclease-targeted CRISPR trans reporter) and RT-LAMP technologies to detect SARS-CoV-2 N and E genes within forty minutes. The AIOD-CRISPR (all-in-one dual CRISPR-Cas12a) detection technology developed by Ding et al [18] uses a pair of Cas12a-crRNA complexes to bind different sites of the target sequence for specific recognition by targeting the nucleoprotein (N) gene of SARS-CoV-2. By adding a single-stranded DNA fluorophore-quencher (ssDNA-FQ) reporter, the detection results can be directly read by naked eyes in the background of blue LED lamp.
Jennifer Doudna et al [19] combined two different CRISPR enzymes, Cas13 and Csm6, to create a method that can detect a small amount of RNA virus within one hour, known as FIND-IT (Fast Integrated Nuclease Detection In Tandem), and this amplification-free technology provides a rapid and inexpensive diagnostic strategy for COVID-19. To address the need for reagents as well as expensive equipment, Rauch et al [20] developed CREST (Cas13-based, Rugged, Equitable, Scalable Testing) using the advantage of widely available enzymes (Cas13), low-cost thermocyclers (DIY-Bio and mini PCR/mini 16), and easy-to-use fluorescent visualizers, providing a bedside solution for COVID-19. The LOD of the CREST protocol was up to 10 copies of a target RNA molecule per microliter which unveiled that CREST is as sensitive as the corresponding RT-qPCR. At the same time, the authors also proposed PEARL [21] (Precipitation Enhanced Analyte Retrieval), which can avoid a convenient RNA extraction method using commercial kits, and the scalability of CREST can be further improved if it is used in combination. We summarized various CRISPR/Cas based detection platforms (Table 1).
Article screening and data extraction in our work were followed by the proposal of the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA-P) 2009 (S1 Checklist).
We used "COVID-19" or "SARS-CoV-2" or "coronavirus disease-19" or " severe acute respiratory syndrome coronavirus 2" and "CRISPR" or "clustered regularly interspaced short palindromic repeats" as subject terms or keywords to search the literature published in PubMed, Embase, Cochrane Library, and Web of Science before April 2, 2022 (S1 File).
All articles were screened according to the inclusion and exclusion criteria. The inclusion criteria were as follows: (1) The study subjects were suspected or confirmed COVID-19 cases; (2) both peer-reviewed and preprint original articles on RPA/RAA integrated CRISPR/Cas technology; (3) The purpose of the research is to evaluate the accuracy of CRISPR diagnostic method; (4) The extracted or calculated data could be used to obtain true-positive (TP), false-positive (FP), false-negative (FN), and true-negative (TN) values. The exclusion criteria were as follows: (1) Non-English Literature; (2) Non-Clinical research literature consisting of conference abstracts, reviews, and case reports; (3) studies based only on either RPA/RAA or CRISPR-Cas assays related to COVID-19.
We made a self-made scale to extract relevant information for inclusion in the study, including: (1) study first author;(2) year of publication;(3) TP, FP, FN, and TN;(4) location of study;(5) types of specimens;(6) targeted genes;(7) the type of Cas protein. When controversial results were encountered, they were negotiated by a third researcher.
QUADAS-2 (Quality Assessment of Diagnostic Accuracy Studies 2) is currently the most recommended quality evaluation tool for diagnostic accuracy tests and is mainly composed of four parts: case selection, test to be evaluated, gold standard, case process and progress. Literature quality evaluation was performed by two investigators independently according to QUADAS-2 to evaluate the included literature, and when inconsistent evaluation was encountered, it was solved by negotiation.
The Spearman correlation coefficient and I2 test reflect the heterogeneity of threshold and non-threshold effects, respectively. We assessed the sensitivity, specificity, and diagnostic odds ratio (DOR), positive likelihood ratio (PLR), and negative likelihood ratio (NLP) of the 95% confidence interval (CIs) between RPA/RAA combined with CRISPR and the reference standard. We assessed diagnostic accuracy by summary receiver operating characteristic (SROC) curves and area under the curve (AUC), with high accuracy at AUC above 0.9. We used Deeks ’ funnel plot to determine whether this article had publication bias. The meta-analysis was undertaken using STATA17.0 and Meta-Disc 1.4. Quality assessments of included studies were carried out with RevMan 5.4 and SPSS 26. Differences with P < 0.05 were considered statistically significant.
Following title, abstract, and full text screening according to inclusion and exclusion criteria, a final 25 articles were included for descriptive and numerical analysis in this scoping review, as outlined in the PRISMA flow diagram (Fig 1). The general information on the included studies is presented in Table 2.
Risk of bias assessment and applicability concerns for each study was carried out using the QUADAS-2 tool. In terms of case selection, because COVID-19 is more special and case selection has some limitations, studies have a high risk of bias; In terms of diagnostic tests to be evaluated, because there is no clear experimental design or unclear expression, some studies do not perform diagnosis by blind method, so the risk of bias is mostly unclear; In terms of reference standard and case process, real-time PCR was used for all reference standard tests, so the risk of study bias was a low risk of bias. In terms of applicability, except for case selection, which has some limitations, the other aspects have a high evaluation, so the overall quality of the literature included in the study is good.
The results showed no heterogeneity caused by the threshold effect as the spearman correlation coefficient was 0.327 and the P value was 0.111 (> 0.05). However, it was observed that there was significant heterogeneity caused by non-threshold effects. The sensitivity of I2 was 42.54, and the specificity of I2 was 72.20, indicating that there was overall heterogeneity, but the sensitivity and specificity of most studies are close to 1 (Fig 2 and Table 3). And meta-regression was conducted to perform sources of heterogeneity.
Diagnostic efficacy parameters for RPA/RAA combined with CRISPR were derived by pooling effect sizes. The pooled sensitivity, specificity, PLR, NLR and DOR were 0.98 (0.97,0.99), 0.99 (0.97,1.00), 183.2 (28.8,1166.8), 0.02 (0.01,0.03) and 10702 (1765,64903) (Table 3), respectively. In addition, the SROC curve showed that AUC was 1.00 (0.98–1.00) (Fig 3).
We performed the meta-regression based on the variables including publication country, sample type, Cas type, and number of Genes, to explain this heterogeneity. Meta-regression analysis indicated that region and Cas type were the sources of heterogeneity for sensitivity and specificity. Additionally, the P value of the sample type was less than 0.05 indicating that it might be a significant source of heterogeneity in specificity (Table 4).
Individual articles were eliminated one by one, and then recalculated and analyzed to observe I2, P values, and the combined effect size and 95% CI after individual removal. The results suggest that a single article has little effect on the above indicators, indicating that the study results are stable. Deeks’ funnel plot was used to evaluate whether there was a publication bias in the included studies; P < 0.05 indicates significant publication bias (Fig 4).
The results of this study indicate that the detection of COVID-19 by RPA/RAA integrated CRISPR technology is a technique with high diagnostic efficacy. In this meta-analysis, we have included only those articles which were solely based on RPA/RAA integrated CRISPR technique used for COVID-19 diagnosis. After the implementation of several inclusion and exclusion criteria as reported above, finally, 25 articles have been thoroughly assessed and selected for further analysis. Through QUADAS-2 analysis of the included studies, the quality of the articles generally performed well and more reliable results could be obtained. Our included literature was performed using RT-qPCR as the gold standard, and RPA/RAA combined with CRISPR showed an accuracy of the approximate gold standard for the diagnosis of COVID-19. Whether CRISPR can be used as an alternative to RT-qPCR, we compared the two assays by self-scale (Table 5). We pooled RNA extraction approach and the CT values of RT-qPCR from 25 articles, and the CT values of positive specimens were not the same in each article, but all floated within the same range (< 40) and met the "Diagnosis and Treatment Protocol for Novel Coronavirus Pneumonia (Trial Eighth Edition)" criteria (S2 File). Factors such as disease severity, sample collection time, and extraction approach will cause differences in positive CT values, and 25 laboratories cannot ensure that the above factors are completely consistent, so positive CT values in the range of less than 40 are reasonable and will not have much impact on the gold standard RT-qPCR. The pooled AUC of CRISPR in the diagnosis of COVID-19 was 1.00 (0.98–1.00). AUC is an important approach to assessing the accuracy of diagnostic tests, when the AUC is ≥ 0.9, it indicates an excellent diagnostic test [44]. Generally, a PLR higher than 10 indicates that the test under consideration is sufficient to confirm the diagnosis, while an NLR less than 0.10 means that the negative index test is sufficient to rule out the target disease. The pooled PLR and NLR were 183.2 (28.8,1166.8) and 0.02 (0.01,0.03), respectively. The range of DOR is from 0 to infinity, reflecting the connection between the results of diagnostic tests and diseases; a higher value suggests that the diagnostic test has a stronger discriminatory ability between patients and healthy people. The pooled DOR was 10702 (1765,64903). The results show that CRISPR has good diagnostic performance and high diagnostic value. Meta-regression showed that the region and Cas type were the causes of heterogeneity in sensitivity and specificity (P < 0.05). Regarding regional classification, because the number of studies we included is not large enough, and it is only a simple classification of regions into domestic and foreign types, this result is not of great significance. Small but statistically significant differences (p < 0.05) were found for Cas type in terms of sensitivity and specificity. Sixteen studies used Cas12, five used Cas13, and four others used Cas9. Cas13 directly acts on the genetic material of coronaviruses, that is RNA, so the detection step is relatively simple and the detection time is shorter compared with Cas12. Both Cas12/Cas13 combined with RPA/RAA techniques show extremely high sensitivity and specificity in diagnosing COVID-19, and the same conclusion was made in Wang’s study [45]. In sample type analysis, to unify the criteria, we did not include the literature on multiple types of samples containing nasopharyngeal swabs in the analysis. We classified the literature on other sample types (saliva, blood, bronchoalveolar lavage fluid specimens, etc.) as the "Other" group. The specificity of a single nasopharyngeal swab was higher than the "Other" group by meta-regression analysis. The conclusion is supported by other studies that suggest that nasopharyngeal swabs have a higher diagnostic effect value and a better diagnostic effect than saliva, but given the difference in sampling time, we believe that saliva sampling is a reasonable alternative to nasopharyngeal swabs [46]. RPA/RAA is the commonly used amplification technique, but the pre-amplification process increases detection time, LAMP combined with CRISPR technology for the diagnosis of COVID-19 is another commonly used detection method [47], but compared with RPA/RAA, LAMP requires two pairs of primers and is more prone to false positives [48], so more and more unamplified CRISPR technologies are being developed for nucleic acid detection [49]. At present, amplification-free techniques are divided into the following three strategies [50]: (I) Reduce the reaction volume to increase the target concentration and improve the LOD (limit of detection); (II) Combined electrochemical biosensors; (III) Cas-mediated cascade amplification. We analyzed the amplification-free CRISPR technology for COVID-19, but due to the insufficient number of articles to combine effect sizes, the specificity and sensitivity of each study were simply extracted for discussion. The specificity of amplification-free CRISPR technology is high, but in Liang’s [51] study in terms of sensitivity, the detection sensitivity was as low as 0.66 and the performance was suboptimal. This shows that areas with high requirements for detection time can use amplification-free for initial screening and reduce time costs, but in terms of accuracy, amplification-free CRISPR technology still needs to continue to be optimized. We summarized the CRISPR/Cas-based amplification-free platforms (Table 6). This study has three objective factors that warrant discussing: (I) the number of included studies is small, and although the use of the single literature elimination method to verify that single literature has little effect on the pooled effect size results, the more included studies, the sample size of the study subjects, the more persuasive the results; (II) different target genes, sample types, sampling methods, storage conditions, transportation methods may cause bias in the results, and these factors cannot be controlled; (III) our analysis indicated significant publication bias in included studies. Given that CRISPR technology is a newly developed nucleic acid detection technology and the detection applied to new crown viruses is also in the early development stage, many of the platforms developed based on the CRISPR system and evaluation of diagnostic accuracy for new crown viruses are performed by the same research group. Thus, there is likely to be bias toward reporting higher diagnosis performance.
The rapid spread of the COVID-19 pandemic has severely affected thousands of people’s health and has had a significant impact on the global economy. At present, although RT-qPCR can be used as the gold standard method for COVID-19 detection, due to the lack of reagents and equipment, COVID-19 cannot be found and diagnosed in a timely manner in areas with less developed medical conditions. The use of RPA/RAA integrated CRISPR-Cas in COVID-19 diagnosis has made a significant impact that should be endorsed for further optimization and improvement. Especially in resource-limited areas, patients can get their COVID-19 test reports in a short period, which is crucial in the event of an outbreak. Based on the results we analyzed, it can be concluded that RPA/RAA integrated CRISPR performs well and has great potential as an alternative to RT-qPCR for the diagnosis of COVID-19 in the resource-poor area.
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PMC9605075 | Sheila da Silva,Fernanda Alves de Freitas Guedes,João Ricardo Vidal Amaral,José Roberto de Assis Ribeiro,Yuri Pinheiro Alves de Souza,Ângela Correa de Freitas-Almeida,Fabiano Lopes Thompson,Rommel Thiago Jucá Ramos,Andrew Steven Whiteley,Andrew Macrae,Selma Soares de Oliveira | Aeromonas allosaccharophila Strain AE59-TE2 Is Highly Antagonistic towards Multidrug-Resistant Human Pathogens, What Does Its Genome Tell Us? | 26-09-2022 | Aeromonas allosaccharophila,antimicrobial activity,bacteriocins,antimicrobial resistance,genomics | Multidrug-resistant bacteria are of critical importance and a problem for human health and food preservation; the discovery of new antimicrobial substances to control their proliferation is part of the solution. This work reports on 57 antagonistic Aeromonas strains, of which 38 strains were antagonistic towards problematic human pathogens. The genome of the most antagonistic strain was sequenced and identified as Aeromonas allosaccharophila. Its genome was fully annotated and mined for genes that might explain that activity. Strain AE59-TE was antagonistic toward clinically relevant gram-negative and gram-positive multidrug-resistant bacteria, including Klebsiella pneumoniae KPC, Escherichia coli ESBL, Salmonella typhimurium, and Staphylococcus aureus MRSA. Strain AE59-TE2 was identified by multilocus sequence analysis. Genome mining identified four genes homologous to the bacteriocin, zoocin A from Streptococcus equi and a gene 98% similar to cvpA linked to colicin V production. A. allosaccharophila strain AE59-TE2 produced antimicrobial activity against a broad range of bacteria, including important gram-negative bacteria, not typically targeted by bacteriocins. Herewere described novel zoocin genes that are promising for industrial applications in the food and health sectors. Interesting and important antagonistic activity is described combined with the first detailed genomic analysis of the species Aeromonas allosaccharophila. | Aeromonas allosaccharophila Strain AE59-TE2 Is Highly Antagonistic towards Multidrug-Resistant Human Pathogens, What Does Its Genome Tell Us?
Multidrug-resistant bacteria are of critical importance and a problem for human health and food preservation; the discovery of new antimicrobial substances to control their proliferation is part of the solution. This work reports on 57 antagonistic Aeromonas strains, of which 38 strains were antagonistic towards problematic human pathogens. The genome of the most antagonistic strain was sequenced and identified as Aeromonas allosaccharophila. Its genome was fully annotated and mined for genes that might explain that activity. Strain AE59-TE was antagonistic toward clinically relevant gram-negative and gram-positive multidrug-resistant bacteria, including Klebsiella pneumoniae KPC, Escherichia coli ESBL, Salmonella typhimurium, and Staphylococcus aureus MRSA. Strain AE59-TE2 was identified by multilocus sequence analysis. Genome mining identified four genes homologous to the bacteriocin, zoocin A from Streptococcus equi and a gene 98% similar to cvpA linked to colicin V production. A. allosaccharophila strain AE59-TE2 produced antimicrobial activity against a broad range of bacteria, including important gram-negative bacteria, not typically targeted by bacteriocins. Herewere described novel zoocin genes that are promising for industrial applications in the food and health sectors. Interesting and important antagonistic activity is described combined with the first detailed genomic analysis of the species Aeromonas allosaccharophila.
Multidrug-resistant bacteria cause persistent hospital infections that increase morbidity and mortality, especially in developing countries [1,2]. Their impact on health care systems is mostly due to the unavailability of effective antibiotics [1]. The main nosocomial antibiotic-resistant pathogens are Acinetobacter baumannii, Pseudomonas aeruginosa, extended-spectrum beta-lactamase-producing Escherichia coli, methicillin-resistant Staphylococcus aureus (MRSA), Klebsiella pneumoniae, carbapenem-resistant Enterobacterales (CRE), and vancomycin-resistant Enterococci (VRE) [1,3,4]. Antimicrobial resistance is the ability of microorganisms to inactivate or decrease the effectiveness of antibiotics. Resistance can occur spontaneously due to genetic modifications; nonetheless, this process can be accelerated by the inappropriate use of antibiotics, resulting in evolutionary pressures for genetic mutations and the exchange of genetic material between bacteria and phages [5]. Since the discovery of antibiotics, between 1930–1962, more than 20 new classes have been described. However, resistance continues to evolve, and the search for new antimicrobial compounds is an urgent challenge [5]. Only three new classes of antibiotics against gram-positive bacteria have been described recently: the Oxazolidinones class with Linezolid (2001) and tedizolid (2014); the daptomycin class, consisting of cyclic lipopeptides, discovered in 2006; and the fidaxomicin class, a macrocycle drug, discovered in 2011 [5]. Bacteria are a source of many antimicrobial compounds. They produce lipopeptide, comprising non-ribosomal peptides synthetases (NRPSs), such as circular lipopeptides (surfactin, iturine, and phengycine families), polyketide (PKS) compounds, and siderophores [6]. Some of these compounds are products of secondary metabolism, such as antibiotics, while others are bioactive molecules ribosomally synthesized, such as antimicrobial peptides and bacteriocins. Bacteriocins are viable alternatives to antibiotics that are no longer effective due to antimicrobial resistance [7,8]. Traditional bioprospecting strategies for new antibiotics are not efficient in finding new substances [9]. Since traditional methods for screening antimicrobial substances can last a long time and have high costs, genomic analyses provide a new opportunity to search these substances in a more practical and less expensive way. Genome sequencing, gene annotation, and the activation of silent gene clusters constitute the basis of new methods for massive screening and new antibiotics discovery and yet success is limited [9]. Aeromonas strains are known to produce several antimicrobial substances with the potential to become new antibiotics and therefore are worthy of detailed genomic investigations. The Aeromonas genus comprises gram-negative, facultative anaerobic bacteria often found in aquatic environments [10], the human gastrointestinal tract, and other animals, including fish, reptiles, and amphibians [10,11,12]. Aeromonas species can cause several animal diseases. Furunculosis, for example, is a condition observed in fish [13], which is associated with significant economic losses in pisciculture [11,12,14]. Many Aeromonas’ virulence genes have already been reported: vapA (layer A); act, alt, and ast (cytotonic enterotoxins); ahyB (elastase); exu (DNases) [15,16]. Aeromonas strains are considered opportunistic pathogens, infecting mainly immunosuppressed patients [11,12,17]. Although there are reports correlating Aeromonas sp. to gastroenteritis and few cases of more severe infections, the etiological role of the genus in this pathogenicity remains controversial [17,18]. The World Health Organization’s “One-World-One Health” concept highlights that healthiness is based on a balance of human, animal, microbe and environmental interactions [19]. In this manner, solutions to these problems are likely to be found in nature. Antagonistic interactions are continually observed and are a part of nature, and are in natural environments. This concept was a guide for the research presented in this article. Bacteriocins receive special focus because they possess great potential in preventing the spread of infectious bacteria, controlling spoilage of industrialized products, and mitigating the indiscriminate and excessive use of other antibiotics [20]. There are numerous reports on Aeromonas strains producing bacteriocin-like substances (BLS) [21,22]. However, to date, the activity and presence of BLS has not been linked to its genetic origin. Antimicrobial peptides are important compounds for microorganisms, which grant competitiveness in different environments [23]. These molecules are synthesized by several organisms for their defense. Amongst these are peptides called bacteriocins, which can kill or inhibit the growth of other microorganisms [7]. Bacteriocins from gram-positive bacteria are frequently described as inhibiting other gram-positive strains [24]. However, important gram-negative pathogens, such as Salmonella and Escherichia coli, have not yet been targeted by bacteriocins [25,26]. Thus, there is a need to discover and report on bacteriocins that target gram-negative disease-causing bacteria. Biotechnological applications of bacteriocins include their use as antibiotics, food preservatives and bacteriocin, such as Nisin, used by food industries [8,23]; and as probiotics [27]. Bacteriocins may also have applications as anticancer agents [7,24]. There are new assays that use bacteriocins in agriculture for the biocontrol of phytopathogens [8]. Colicin is used for the biocontrol of pests in tobacco plants and considered an efficient strategy that meets GRAS (FDA) safety protocols for controlling bacteria [28]. Bacteria from aquatic environments have already been described as great candidates for the production of antimicrobial substances. The Aeromonas genus has been reported as capable of producing bacteriocins. This is an interesting genus, since it is found either in animal, water, or human, which requires a certain level of adaptation to different niches, where bacteriocins and toxins may play a role in the competition and maintenance of those species in respective niches [29]. Therefore, our work aimed to isolate Aeromonas bacteria from fish to investigate the antimicrobial substances’ production and to perform the genomic characterization of the producing strain. Here are described the bioprospecting and screening of a wide range of wild Aeromonas strains looking for novel antagonistic behavior, followed by genomic mining to search for genes related to bacteriocins and antimicrobial activity.
Aeromonas strains were isolated from healthy fish branchiae, scales, and cloaca. Two replicates were taken for Mugil brasiliensis (popular names: Tainha/Mullet) and three replicates for Caranx latus (popular name: Xerelete), which were purchased at a street market located in Rio de Janeiro city, RJ, Brazil (−22.910468, −43.240857). A total of 200 mL of water was collected from six different lagoon points at the Rodrigo de Freitas Lagoon (latitudes 43°11′09″ N and 43°13′03″ S, and longitudes 022°57′02″ E and 022°58′09″ W). Water samples were centrifuged at 12,100× g for 15 min and the pellet was used to isolate Aeromonas. One Aeromonas strain previously isolated from lettuce leaves [30] was also screened for antagonistic activity in this bioprospection. All the samples collected were incubated in alkaline peptone water (APA) at 30 °C for 24 h and were inoculated at 30 °C for 48 h in selective medium glutamate starch phenol (GSP) red agar (Merck, Darmstadt, Germany). The Aeromonas sp. strains were then examined and classified into phenospecies using the criteria described in the literature [12,31].
A total of 57 Aeromonas strains were screened for antimicrobial activity by either the agar well diffusion assay [32] or the chloroform method [33] with modifications (Supplementary Figure S1). Both experiments were performed in triplicate. Bacteria were grown on nutrient agar and were incubated at 28 °C. Inhibition halos greater than 1 cm were considered as positive results for antimicrobial activity. Klebsiella pneumoniae KPC (Klebsiella pneumoniae carbapenemase), K. pneumoniae ESBL (extended-spectrum β-lactamase-producing), K. pneumoniae ATCC 13883, Escherichia coli ESBL, Enterobacter cloacae NDM (New Delhi metallo-beta-lactamase), Acinetobacter baumannii, Salmonella typhimurium ATCC 14028, Pseudomonas aeruginosa, and P. aeruginosa strains SPM (São Paulo metallo-β-lactamase) from Laboratory of Medical Investigation; E. coli, Staphylococcus aureus ATCC 6538 and P. aeruginosa ATCC 15422 from Laboratory of Food Microbiology and K. pneumoniae 19ae, Enterococcus faecalis 5ae, S. aureus HIV 86a, and S. aureus HIV 87a from the Laboratory of Nosocomial Infection were used as indicator strains. These strains were selected because they can be etiological agents of severe diseases and are multidrug resistant. The presence (value 1) or absence (value 0) of antimicrobial activity was converted into a table and used to build a hierarchical clustering with the GenePattern online tool [34] using its default values. Strain AE59-TE2 exhibited the broadest antimicrobial activity spectrum and was selected for genome sequencing and mining.
Genomic DNA was extracted and purified using the CTAB method [35]. The AE59-TE2 paired-end (2 × 300 bp) library was constructed from approximately 1 µg of gDNA using the Nextera XT DNA Sample Preparation Kit (Illumina, San Diego, CA, USA) and sequenced with the MiSeq Illumina platform (Rio de Janeiro, Brazil). Trimmomatic v0.36 [36] was used for quality control and to trim the sequences using default parameter values to remove adaptors and N-containing reads, as well as small (<36 bp) reads. SPAdes v3.10.1 [37] was used for a de novo assembly, and contigs were mapped twice with the MeDuSa v.1.6 server [38] using 6 A. veronii complete genomes (GCA_001634325.1, GCA_001593245.1, GCA_001634345.1, GCA_002803925.1, GCA_002803945.1, and GCA_000204115.1) as reference, being the closest species and with the most complete genomes deposited on NCBI. To evaluate the assembly’s quality, filtered reads were aligned to the AE59-TE2 scaffolds using BWA v0.7.75a [39] and the statistics were obtained with Qualimap v2.2.1 [40]. CheckM v1.4.0 [41] was used to verify genome completeness and contamination, and QUAST v.5.0.2 [42] was used to verify the genome quality. A flowchart summarizing the assembly steps is shown in Supplementary Figure S2. The AE59-TE2 sequenced library and the genome final assembly were deposited at the NCBI database under BioSample accession numbers SAMN08436981.
Multiple methods were used for species identification: 16S rRNA gene sequencing and phylogenetic analysis with BLAST server v.2.12.0 [43], multilocus sequence analysis (MLSA), in silico DDH using the Genome-to-Genome Distance Calculator (GGDC) v2.1 server [44] were performed. In silico DDH analyses, using 33 Aeromonas Type strains (Supplementary Table S1) obtained from the EZBio Cloud database (https://www.ezbiocloud.net/ accessed on 18 September 2022) were made with a cutoff value of 70% of similarity. MLSA was conducted with six housekeeping genes (recA, gyrB, gltA, metG, groL, and 16S rRNA) [45] from A. allosaccharophila CECT 4199, A. aquatica AE235, A. australiensis CECT 8023, A. bestiarum CECT 424227, A. bivalvium CECT 7113, A. caviae CECT 838, A. dhakensis CIP 1077500, A. diversa CECT 4254, A. encheleia CECT 4342, A. enteropelogenes CECT 4487, A. eucrenophila CECT 4224, A. finlandensis 4287, A. fluvialis LMG 24681, A. hydrophila subsp. hydrophila ATCC 7966, A. jandaei CECT 4228, A. lacus AE122, A. media CECT 4232, A. piscicola LMG 24783, A. popoffii CIP 105493, A. rivuli DSM 22539, A. salmonicida subsp. masoucida NBRC 13784, A. salmonicida subsp. pectinolytica 34mel, A. sanarellii LMG 24682, A. simiae CIP 107798, A. sobria CECT 4245, A. taiwanensis LMG 24683, A. tecta CECT 7082, and A. veronii CECT 4257. Oceanimonas doudoroffi ATCC 27123 was used as an outgroup. All genomes were annotated with the Rapid Prokaryotic Genome Annotation (PROKKA) tool v1.14.6 [46]. For the multilocus sequence analysis, sequences of each gene were concatenated to construct “supergenes” (approximately 14 kbp). They were then multiple aligned and gaps were removed using BioEdit v7.2.5 [47]. Phylogenetic analyses were conducted using MEGA11 software [48]. A phylogenetic tree was inferred using the Maximum Likelihood (ML) method and General Time Reversible (GTR) model. A discrete Gamma distribution was used to model evolutionary rate differences among sites, allowing for some sites to be evolutionarily invariable. The bootstrap test was performed using 1000 replicates. The taxonomic classification was corroborated by the Average Nucleotide Identity (ANI) v3.8.3 and Tetra Correlation Search (TCS) v3.8.3 analyses using the AE59-TE2 genome against GenomesDB in JSpeciesWS server v3.8.3 [49]. ANIb result was shown by heatmap using R tool [50].
The search for virulence factors is important, as there are already reports of bacteriocins with typical characteristics of virulence factors, thus making dissemination and replication in the host cell easier [29,51]. The virulence potential of the AE59-TE2 strain was evaluated using the virulence factors database (VFDB) from the ABRicate tool version 1.0.1 [52]. NCBI Antimicrobial Resistance Gene Finder Plus (AMRFinderPlus) tool v3.10.23 [53], ResFinder v 4.0 [54], and Resistance Gene Identifier (RGI) v.5.2.0 from Comprehensive Antibiotic Resistance Database (CARD) v. 3.1.4 [55] were used to verify the antibiotic resistance profile.
Gene prediction and functional annotation were carried out using the classic RAST v2.0 server [56] and Rapid Prokaryotic Genome Annotation (PROKKA) tool v1.14.6 [46]. To search for more genes related to bacteriocin production, BAGEL (class III), Bactibase, and DoBiscuit-Database of BioSynthesis cluster CUrated and InTegrated (https://www.nite.go.jp/nbrc/pks/ accessed on 18 September 2022) [57] and some genes for the Colicin V production protein (cvaC) and Zoocin production protein (ZooA) from UniProt were used with BLASTp v.2.11.0+ [42] against the PROKKA genome annotation. Four zoocin-like sequences from the AE59-TE2 genome and 1 zoocin A sequences from UniProt (accession number: O54308) was multiple aligned with Clustal Omega v1.2.4 [58]. Analysis with AntiSMASH v6.0 was performed to verify secondary metabolism and search for bacteriocins [59]. The GO FEAT tool [60] was used for functional annotation and enrichment of genomic data. The KEGG Automatic Annotation Server (Kaas) [61] and KEGG Mapper Reconstruction were used to make orthology assignments and pathway mapping. To perform a better characterization of the genome, the Pathosystems Resource Integration Center (PATRIC) v.3.6.9 was used [62], and the GO Feat tool was used to search for keywords that are related to antimicrobial activities, such as the words: Bacteriocin, Antibiotic, Colicin, Microcin, Endopeptidase, Endonuclease, polyketides (PKS), and Rhamnolipid. The circular genome plot was made with the Circular Genome Viewing (CGView) tool [63].
Forty-one Aeromonas strains were isolated from fish samples. Preliminary biochemical tests identified the samples as A. hydrophila (n = 21), A. caviae (n = 14), and A. veronii bv sobria (n = 6). Fifteen Aeromonas strains were isolated from the water sample and identified as A. caviae (n = 8), A. hydrophila (n = 4), A. veronii bv sobria (n = 2), and A. salmonicida (n = 1). Another Aeromonas strain previously isolated from lettuce was identified as A. caviae [30]. Among the 57 strains tested, 38 demonstrated differing levels of antimicrobial activity towards at least one highly pathogenic bacterial strain (Figure 1). A hierarchical cluster analysis was performed to visualize the results for antimicrobial activity. The analysis revealed a group of seven strains (AE04, AE34, AE43, AE31, AE45, AE54, and AE59-TE2) with a similar profile, broadly inhibiting both gram-negative and gram-positive multidrug-resistant pathogens. Strain AE59-TE2 exhibited antimicrobial activity towards 14 of the 16 indicator strains, namely K. pneumoniae (KPC, ESBL, 19ae ATCC 13883), E. coli and E. coli ESBL, E. cloacae NDM, A. baumannii, S. typhimurium ATCC 14028, S. aureus (ATCC 6538, HIV 86a, and HIV 87a), E. faecalis 5ae, and P. aeruginosa (Figure 1). Supplementary Figure S1 shows the inhibition zones produced against the E. coli ESBL strain.
Illumina MiSeq paired-end sequencing of the AE59-TE2 library yielded 532,201 raw reads. After preprocessing steps, 365,719 (68.72%) quality reads were obtained (Supplementary Table S2). De novo genome assembly generated 109 contigs (Table 1) and the mapping genome assembly resulted in one scaffold with a total sequence length of 4,498,261 bp and 58.68% G + C content in the QUAST result (Figure 2). CheckM analysis resulted in 100% completeness and 0.29% of contamination. More than 99% of the reads aligned to the assembled AE59-TE2 scaffolds, with a mean coverage of 23.42. Six AE59-TE2 scaffolds (scaffold_10, scaffold_20, scaffold_22, scaffold_26, scaffold_28, and scaffold_35) generated a consensus sequence (549 bp) that putatively encodes for a transposase (data not shown).
Via BLAST and the nr database at the NCBI, the AE59-TE 16S rRNA sequence was highly similar to Aeromonas allosaccharophila (accession number: FJ940841.1; 100% identity and 99% query coverage) and A. veronii (accession number: CP024933.1; 99% identity and 100% query coverage). Since 16S rRNA analyses are not gold standard for species-level identification in the Aeromonas genus, MLSA, DDH in silico, and ANI analyses were performed. The MLSA phylogenetic tree inference grouped the AE59-TE2 strain and A. allosaccharophila together (Figure 3). DDH in silico analysis demonstrated that the A. allosaccharophila reference genome from EZBio Cloud database (Supplementary Table S3) was the most similar to the AE59-TE2 strain, DDH 62.6%. ANI analysis resulted in a score of 95.01% for type strain A. allosaccharophila CECT 4199 (Figure 4). TCS analysis resulted in a score of 0.99965 for A. allosaccharophila TTU2014-159ASC and a score of 0.99943 for type strain A. allosaccharophila CECT 4199. Analyses of the 16S rRNA gene, MLSA, DDH in silico, and ANI strongly indicate that the AE59-TE2 strain belongs to the A. allosaccharophila species cluster.
The virulence factors database (VFDB) identified 36 genes with identities above 90% and 88 genes with identities between 80–89% (Supplementary Table S4). Type III secretion system (T3SS) structural genes were identified (ascV and ascC genes), but only one gene of the main effectors was found (aopH). The ResFinder database did not identify any resistance genes in the AE59-TE2 genome. However, the AMRFinderPlus tool identified three genes: blaOXA (OXA-12 family class D beta-lactamase) with 98.11% identity and 100% coverage; arsD (arsenite efflux transporter metallochaperone) with 47.89% identify and 100% coverage; and arsC (glutaredoxin-dependent arsenate reductase) with 77.86% identity and 99.29% coverage. CARD/RGI annotated four strict hits. Two genes related to the resistance-nodulation-cell division (RND) antibiotic efflux pump: the rsmA gene with 92.73% identity; and adeF with 48.56% identity. A gene related to OXA beta-lactamase (antibiotic inactivation): the OXA-726 with 95.45% identity. A gene related to elfamycin antibiotic (antibiotic target alteration), Escherichia coli gene EF-Tu mutants conferring resistance to Pulvomycin with 88.8% identity. This latter gene was annotated as a tuf1 gene with 99.5% identity in the UniProt database and functions promoting the GTP-dependent binding of aminoacyl-tRNA to the A-site of ribosomes during protein biosynthesis.
Gene annotation with the PROKKA software resulted in 4075 coding sequences, 10 rRNA, and 105 tRNA. The RAST server annotated 4173 features, including 4050 protein-coding sequences and 123 non-coding RNAs including tRNAs and rRNAs, with 2177 (52.17%) of them being categorized in at least one RAST-defined functional category (Figure 5). Five sequences associated with “Phages, Prophages, Transposable elements, Plasmids” were identified that included two phage tail proteins, two proteins linked to phage replication, and one linked to DNA synthesis. Furthermore, 43 non-assembled sequences were also annotated with the RAST server and compared to the nr database. Among these, six high-identity (>90%) matches with the Aeromonas pS68-1 plasmid (CP022182.1) were observed. The AntiSMASH tool identified one homoserine lactone cluster (647,196–667,849 nt), a 20 kb region comprising one core gene, three biosynthetic genes, and three regulatory genes.
The RAST server revealed 101 matches for the virulence, disease, and defense category (Supplementary Table S5). Five of these sequences were annotated into the tolerance to colicin E2 subsystem and nine into the colicin V and bacteriocin production cluster subsystem (Table 2). One predicted sequence (peg.850, 162 aa) exhibited homology to the colicin V production protein (CvpA). Genomic enrichment with the GO FEAT tool revealed four sequences associated with antimicrobial substances: a bacteriocin production protein (CvpA); a Tol-Pal system protein TolQ; a cell envelope integrity protein TolA; and an outer membrane receptor for ferrienterochelin and colicins (Supplementary Table S6). The AE59-TE2 CvpA protein sequence was correlated with a bacteriocin production protein (CvpA) predicted from A. veronii (UniProtKB accession number: A0A0T6U8X2) with 98.78% (162/164) coverage and 100% identity. BLAST analysis showed 100% coverage and 100% identity with Aeromonas CvpA family protein (accession number: WP_005337086.1), confirming the presence of an important gene related to the production of bacteriocin in strain AE59-TE2. The BLAST analysis against CvpA colicin V production protein (UniProtKB/SwissProt accession number: P08550.1) from Escherichia coli str. K-12 substr. MG1655 showed 99% coverage and 64.59% identity. BLASTp analyses found four zooA-like sequences in the AE59-TE2 genome. The zooA gene encodes a Zoocin A protein, a peptidase, from the M23/M37 family from Streptococcus equi subsp. zooepidemicus (UniProtKB/SwissProt accession number: O54308). One sequence with 50% identity, was annotated as MepM_2 (Murein DD-endopeptidase); another with 46.73% identity, annotated as MepM_1 (Murein DD-endopeptidase); two sequences with 45% and 34.21% identity, were annotated as hypothetical proteins (Table 3). Multiple alignments demonstrated that the most conserved region was between positions 375–500 aa, a region that contains the peptidase M23 domain (Figure 6). The DoBiscuit database was used to search for more sequences related to antimicrobial activity and found six sequences with more than 60% identity (Table 4). The Functional annotation of orthologous groups with Kaas and KEGG Mapper Reconstruction tools annotated six categories of functional groups (Figure 7). The Metabolism category has 12 subcategories with 2700 genes, amongst these subcategories, the most important for this work are: Metabolism of terpenoids and polyketides with 25 genes and biosynthesis of other secondary metabolites with 44 genes. The biosynthesis of other secondary metabolites subcategories was presented in more detail in Table 5. Using PROKKA and PATRIC, searches for keywords endopeptidase, endonuclease, polyketide, antibiotic, colicin and microcin unraveled 38 proteins (Table 6). PATRIC identified 129 metabolic pathways within the genome. The most important pathways related to antimicrobial activity were: Biosynthesis of secondary metabolites and Biosynthesis of polyketides and Nonribosomal peptides. In the Biosynthesis of the secondary metabolites category and puromycin Biosynthesis subcategory, a sequence was identified as an xdhD gene, a possible hypoxanthine oxidase.
Messi et al., 2003 [22], had previously reported on the potential of Aeromonas strains to produce antimicrobial substances. Following their rationale, a screening for antimicrobial activity was performed and confirmed that strains of the Aeromonas genus are widely antagonistic, as observed in Figure 1. Thirty-eight of the 57 strains tested demonstrated some type of antagonistic activity towards at least one highly pathogenic bacterial strain. Screening analysis detected a group of seven strains, which can inhibit both gram-positive and gram-negative bacteria, with a different profile from others described in the literature, this is unusual and important for bacteriocin research. A lack of bacteriocin patents suggests they have perhaps been neglected and are an opportunity for novel discoveries. A glance further back in the literature reveals that bacteriocin-producing strains have been described to inhibit Yersinia ruckeri, Listonella anguillarum, and Photobacterium damselae [64]; fish pathogens, such as Vibrio tubiashii [65]; and strains associated with food contamination, such as Staphylococcus sp. and Lactobacillus sp. [21,22]. Strain AE59-TE2 stands out for being able to inhibit 14 of the 16 indicator strains tested. This strain exhibited antagonistic activity towards K. pneumoniae KPC (Klebsiella pneumoniae carbapenemase). KPC-producing bacteria are a group of microorganisms with elevated resistance to various antibiotics, which causes infections that commonly available antibiotics can no longer effectively treat [66]. This result is highlighted, since the majority of bacteriocins come from gram-positive bacteria and are not reported as being antagonistic towards gram-negative pathogenic microorganisms. There is a need for bacteriocins that target gram-negative food-spoilage strains, such as those from the genera Salmonella and Escherichia [25,26]. These findings shine a light on possible new solutions for medical, pharmaceutical, and food sectors. In this work, the characterization of the Aeromonas AE59-TE2 strain was proposed. Species identification and taxonomy within the Aeromonas genus is controversial, even with the contribution of genomic analyses. Based on our data and as described in the literature, 16S rRNA gene sequences are highly conserved and do not contain enough genetic signal to separate A. veronii from A. allosaccharophila [12]. Separating these taxons requires more than 16S rRNA sequence and biochemical tests [67]. DDH analysis and MLSA [45] phylogenetic inference were used with multiple housekeeping genes, including rpoD, for taxonomic identification, and the strain was classified as Aeromonas allosaccharophila AE59-TE2. Since Aeromonas strains are described as opportunistic pathogens, an equally important factor was assessing the strain’s virulence potential, which could impair its biotechnological applications in the future [11,12]. Aerolysin (aerA) [68], toxin A(rtxA) [69], layer A (vapA) and secretion systems types II (T2SS) (exeAB and exeC-N operons), T3SS (ascV, aopP, aopH, ascC and aexT genes), T4SS (traB, traC, traD, traE, trbJ, traA, traF, traG, traH, traI, traJ and traK genes, with traA, traF-traI as core components) and T6SS (hcp (haemolysin), vgrG2 (valine), vgrG (glycine), vgrG1 (ADP-ribosyltransferase activity), vasH (transcription regulator) and the vasK (unknown function genes) altogether make up for the major virulence factors identified in the Aeromonas genus [70,71,72,73]. The virulence factors database (VFDB), from the ABRicate tool, identified the ascV and ascC genes, which are type III secretion system (T3SS) structural genes, and aopH, one of the main effector genes. Vanden Bergh and Frey (2014) [74] demonstrated that, due to several mutations and genetic rearrangements, changes may occur in the type III secretion system. Thus, to affirm its integrity, one must analyze whether the structural genes (ascV and ascC) are intact and whether the main effector genes (aopH, aexT, ati2, aopO, aopP and aopS) are present [74]. The T3SS is a complex structure used by gram-negative bacteria, which is capable of injecting effector proteins directly into the host cell cytoplasm. Only one effector gene was found in the AE59-TE2 genome. Furthermore, a progressive loss of virulence potential in A. salmonicida is observed as constant genetic deletions and additions occur due to horizontal gene transfer with environmental bacteria. This is especially observed in strains grown in laboratories that do not undergo the selective pressures of natural environments [74]. It is worth mentioning that some genes found in the AE59-TE2 genome may not be functional because they are truncated, as has already been described for Aeromonas virulence mechanisms [75]. Further analyses found the genes blaOXA (a beta-lactamase), arsD (an arsenite efflux transporter metallochaperone), arsC (glutaredoxin-dependent arsenate reductase), rsmA and adeF (antibiotic efflux pump), OXA-726 (beta-lactamase/antibiotic inactivation), and EF-Tu (resistance to Pulvomycin). These genes are mostly related to antibiotic resistance. Concerns about virulence with this strain are founded but can be circumvented by using bacteriocins in a purified form. There is increasing interest in the pharmaceutical industry for the use of purified bacteriocins [76]. The antiSMASH tool identified a homoserine lactone cluster in the AE59-TE2 genome. The N-acyl homoserine-lactone (AHL) is a “signal” molecule in gram-negative bacteria and is responsible for the regulation of several biological processes, such as biofilm formation, antibiotic production, and motility [10]. Thus, this is a vital cluster that may be related to the antimicrobial activity observed in our analyses. RAST server annotation uncovered a CvpA protein in the AE59-TE2 genome. This protein is required for colicin V production and was originally identified in plasmid pColV-K30 from Escherichia coli. Nonetheless, this is not the structural gene for the colicin V bacteriocin [77]. The cvpA gene is chromosomal and is required for colicin V production and secretion 77]. It encodes an inner membrane protein that is involved in the colicin V export machinery [77]. The colicin V structural cvaC gene and the cvaA and cvaB genes are required for toxin processing and export. The protein that confers immunity on the host cell is encoded by the cvi gene [78]. Gene clusters similar to known bacteriocins have been described in other Aeromonas genomes [79], and the receptor for ferrienterochelin and colicins was identified in A. salmonicida subsp. pectinolytica 34melT genome [80]. However, no correlation between the presence of these clusters and bacteriocin activity has been reported until now. BLAST analysis between a CvpA protein identified in the AE59-TE2 genome and a CvpA colicin V production protein from Escherichia coli str. K-12 substr. MG1655 revealed a 64.59% identity. This result suggests that the gene could be associated with the production and secretion of colicin V peptide, cvaC gene, or a similar structural peptide gene [78]. However, no significant homology to the E. coli cvaC gene was found. Blast analyses identified four sequences with similarities to the zooA gene. This gene encodes a Zn-metalloprotease called zoocin A, belonging to the M23/M37 family, and isolated initially from Streptococcus equi subsp. zooepidemicus. This protein functions as an enzybiotic that is active against gram-positive bacteria, cleaving peptides from their cell wall [81]. The AE59-TE2 strain was able to inhibit the growth of gram-positive bacteria, namely Enterococcus sp. and Staphylococcus sp., which is not a common feature for gram-negative bacteriocin-producing strains. These data suggest that the AE59-TE2 strain might use different mechanisms to inhibit gram-negative and gram-positive bacteria. Multiple alignments between the four sequences found in the AE59-TE2 genome and other zooA sequences from the UniProt database demonstrated a highly conserved region for a peptidase M23 domain, suggesting that they may be new sequences related to the production of bacteriocins similar to Zoocin A. The BLAST against DoBiscuit Database resulted in six sequences with more than 60% identity with sequences related to antibiotics. These sequences were annotated in the PROKKA software as RpoC, InfA (translation initiation factor IF-1), ThiC (phosphomethylpyrimidine synthase), FadH (2,4-dienoyl-CoA reductase), and MetK (S-adenosylmethionine synthase). Proteins RpoC and InfA could be related with resistance to Ansamycin and Rubradirin, respectively. The ThiC protein is associated with thiamine biosynthesis. The FadH protein is a NADPH-dependent 2,4-dienoyl-CoA reductase, and MetK protein catalyzes the formation of S-adenosylmethionine (AdoMet) from methionine and ATP and is associated with tylosin production [82]. Tylosin is a macrolide antibiotic that is used as a feed additive in veterinary medicine. KEGG analysis provided a general characterization of the genome and highlighted several important pathways to be studied. These results were further explored by investigating and comparing sequences with genes of known important antimicrobial compounds. For instance, the peptidase family C39 contains bacteriocin processing endopeptidases. In this genome, the mepM gene was identified and is related to peptidoglycan synthesis [14]. Also identified was the nlpC gene, which is related to cell wall remodeling, cell separation during division, and cleaving non-canonical peptide bonds [83]. Zoocin A is a D-alanyl-L-alanyl endopeptidase, which hydrolyses cross bridges in the peptidoglycan structure of susceptible streptococci [84]. The PROKKA software identified two sequences as D-alanyl-D-alanine endopeptidases (GAJHKBHP_00392 and GAJHKBHP_03927), corroborating with previous results of Zoocin A sequences. One protein containing an HNH endonuclease domain (Peg.1792) was annotated by the PATRIC server. HNH-type endonucleases are known as Nuclease Bacteriocins (NB) [85]. Polyketides (PKS) were also pursued due to their antimicrobial activity, as described in the literature. Kegg analysis identified the rfb operon, which comprises four genes (rfbABCD) and is involved in dTDP-rhamnose biosynthesis. Genes rfbAB transform D-glucose-1-phosphate into dTDP-4-oxo-6-deoxy-D-glucose, an essential substance in polyketide sugar unit biosynthesis. This substance is further processed by genes rfbCD, resulting in dTDP-l-rhamnose. This latter substance can be involved in the biosynthesis of enediyne antibiotics and streptomycin. Streptomycin, for instance, is an aminoglycoside that possesses antimicrobial activity towards many bacteria, such as Bacillus subtilis, E. coli, certain strains of Salmonella, B. mycoides, B. cereus, and P. aeruginosa [86]. Several enzyme complexes can be produced by the secondary metabolism of bacteria. Type I PKSs, known as modular/iterative, are multicatalytic enzymes, which give rise to known natural products, such as macrolides (erythromycin) and polyenes (nystatin). On the other hand, type II aromatic PKSs are mono and bifunctional enzymes that interact during the synthesis of polycyclic aromatic compounds, such as tetracycline or doxorubicin [87]. Polyketide synthase modules and other related proteins were annotated in the PATRIC server (peg.1909). Antibiotic biosynthesis monooxygenase (ABM) is a protein superfamily that is involved in the production of several antibiotics, playing an important role in the biosynthesis of aromatic polyketides. ABM leads to a significant increase in antibiotic production [88]. The PATRIC server identified an antibiotic biosynthesis monooxygenase (peg.705), demonstrating another important sequence related to antimicrobial activity and how rich is the genome. The PATRIC server also demonstrated several important pathways related to antimicrobial activity to be further explored in the future. These genomic analyses of an A. allosaccharophila strain fill in a knowledge gap for this species, which has not been studied in such detail before. Furthermore, the A. allosaccharophila AE59-TE2 genome has similarities with the enzybiotic zoocin A endopeptidase sequences from streptococci bacteria. AE59-TE2 possesses a broad spectrum of inhibitory activity, targeting gram-positive and gram-negative multidrug resistant pathogens. Genomic analyses revealed important sequences associated with antimicrobial activity. Further analyses are required to better elucidate this antimicrobial substance, since it holds promising biotechnological use for the health and food sectors. | true | true | true |
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PMC9605147 | Andrei Marian Niculae,Maria Dobre,Vlad Herlea,Teodora Ecaterina Manuc,Bogdan Trandafir,Elena Milanesi,Mihail Eugen Hinescu | Let-7 microRNAs Are Possibly Associated with Perineural Invasion in Colorectal Cancer by Targeting IGF Axis | 19-10-2022 | insulin-like growth factor,let-7 microRNAs,colorectal cancer,perineural invasion | Increased insulin-like growth factor (IGF) axis activity is associated with the development and progression of different types of malignancies, including colorectal cancer (CRC). MicroRNAs (miRNAs) belonging to the let-7 family have been reported to target genes involved in this axis and are known as tumor suppressors. In this study, in silico bioinformatic analysis was performed to assess miRNA–mRNA interactions between eight miRNAs belonging to the let-7 family and genes involved in the IGF signaling pathway, coding for receptors and substrates. miRNAs’ expression analysis revealed that hsa-let-7a-5p, hsa-let-7b-5p, hsa-let-7c-5p, hsa-let- 97 7d-5p, hsa-let-7e-5p, hsa-let-7f-5p, and hsa-let-7g-5p were significantly down-regulated in 25 CRC tumoral tissues (T) compared to the corresponding adjacent peritumoral tissues (PT). Moreover, our results showed an upregulation of miR-let-7e-5p in CRC tissues with mutations in KRAS codon 12 or 13, and, for the first time, found a specific dysregulation of let-7a-5p, let-7b-5p, let-7c-5p, let-7d-5p, and let-7i-5p in CRC with perineural invasion. Our results sustain the relationship between the IGF axis, let-7 miRNAs, and CRC and suggest an association between the expression of these miRNAs and perineural invasion. | Let-7 microRNAs Are Possibly Associated with Perineural Invasion in Colorectal Cancer by Targeting IGF Axis
Increased insulin-like growth factor (IGF) axis activity is associated with the development and progression of different types of malignancies, including colorectal cancer (CRC). MicroRNAs (miRNAs) belonging to the let-7 family have been reported to target genes involved in this axis and are known as tumor suppressors. In this study, in silico bioinformatic analysis was performed to assess miRNA–mRNA interactions between eight miRNAs belonging to the let-7 family and genes involved in the IGF signaling pathway, coding for receptors and substrates. miRNAs’ expression analysis revealed that hsa-let-7a-5p, hsa-let-7b-5p, hsa-let-7c-5p, hsa-let- 97 7d-5p, hsa-let-7e-5p, hsa-let-7f-5p, and hsa-let-7g-5p were significantly down-regulated in 25 CRC tumoral tissues (T) compared to the corresponding adjacent peritumoral tissues (PT). Moreover, our results showed an upregulation of miR-let-7e-5p in CRC tissues with mutations in KRAS codon 12 or 13, and, for the first time, found a specific dysregulation of let-7a-5p, let-7b-5p, let-7c-5p, let-7d-5p, and let-7i-5p in CRC with perineural invasion. Our results sustain the relationship between the IGF axis, let-7 miRNAs, and CRC and suggest an association between the expression of these miRNAs and perineural invasion.
The insulin and insulin-like growth factor (IGF) signaling system is one of the main actors of growth and energy metabolism and plays a significant role in the pathogenesis and progression of many cancers. Although insulin and IGF serve different physiological functions, they share signaling pathways that involve phosphoinositide 3-kinase (PI3K), Akt or Ras, and MAP kinase [1]. Through these pathways, they mediate responses to a variety of other cellular stimuli, promoting cell proliferation and inhibiting apoptosis [2]. Therefore, this axis is a potential target for multiple lines of therapy, being incriminated also in tumor resistance and invasiveness. Silencing the IGF pathway has shown promising results in preclinical trials, albeit with significant challenges [3,4]. IGFs are small peptides isolated in human plasma which were named due to their resemblance to proinsulin. IGF-1 is secreted by multiple organs and tissues and can act both as endocrine, exocrine, or autocrine hormones [5]. The connection between the IGF-1 signaling pathway and colorectal cancer (CRC) has been suspected more than 10 years due to observation of the association between CRC and lifestyle factors (mainly physical inactivity and obesity) mediated by insulin resistance and hyperinsulinemia via the IGF axis [6]. Notably, one of the hallmarks of CRC initiation and progression is the Warburg effect—enhanced glucose uptake and aerobic glycolysis even in the presence of mitochondria functioning. However, the exact mechanisms involved in this type of metabolic reprogramming are still unknown [7]. Studies in CRC patients have shown that plasma levels of IGF-1 are higher in CRC patients compared to healthy controls [8], but they are not correlated to disease burden or post-operative progression [4]. Moreover, in CRC tissues, a wide range of positivity of IGF-1, IGF-2, and IGF-1R [9,10,11,12] as well as an impairment of these transcript levels [9,11,13] have been found. The let-7 mi-RNA family is known as a tumor suppressor, and some studies have proposed it as a biomarker and prognostic factor in multiple fields of oncology and other diseases [14]. IGF1/IGF1R are potentially targeted by the let-7 miRNA family. The liaison between let-7 family miRNAs, CRC, and IGF1 pathways has previously been reported [15]. However, the association between let-7 family miRNAs’ expression and mechanisms leading to perineural invasion has not been deeply studied. Perineural invasion (PNI) is a possible course for metastatic spread in a variety of cancers, including CRC, and is usually a poor prognostic factor [16]. Moreover, PNI presence has been associated with a higher T and N stage, histological features of adenocarcinoma, and higher tumor grade [17]. In the last available review highlighting the connection between miRNAs and PNI in different malignancies, miR-128-3p, miR-3679-5p, and miR-145 were tied to PNI in CRC. Surprisingly, miRNAs belonging to the let-7 family have been associated with PNI in multiple other types of cancer [18], but no studies in CRC are reported. Evidently, research in this narrow field has been sparse. This study started by bioinformatics analysis to assess the relationship between miRNAs hsa-let-7a-5p, hsa-let-7b-5p, hsa-let-7c-5p, hsa-let-7d-5p, hsa-let-7e-5p, hsa-let-7f-5p, hsa-let-7g-5p, hsa-let-7i-5p, and genes involved in the IGF1 signaling pathway, coding for receptors and substrates. This analysis was followed by the comparison of the expression level of these miRNAs in 25 CRC tumoral and in the corresponding adjacent peritumoral tissues. Furthermore, the association between miRNAs’ expression and tumoral features was evaluated. The results of this work confirmed the expression impairment of these miRNAs in CRC and suggested their association with perineural invasion.
Twenty-five patients with confirmed primary CRC who underwent curative surgical intervention at the Fundeni Clinical Institute, Bucharest, Romania were enrolled in the study. Tumoral (T) and the corresponding adjacent peritumoral mucosa (PT) tissues were collected from patients. The tissues were formalin fixed and paraffin embedded (FFPE) for histological, immunohistochemical evaluation and DNA isolation. Part of the T and PT tissue was preserved in RNA protect Tissue Reagent (Qiagen, Hilden, Germany) and then stored at −80 °C until RNA isolation. Informed consent was obtained from all of the patients, and the study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the “Fundeni” Clinical Institute (11 December 2019) and Victor Babes National Institute of Pathology (approval number 78 of 3 December 2019). In Table 1 the clinical features of the patients and the characteristics of the analyzed tumor specimens are presented.
DNA isolation from FFPE tissues and the detection of KRAS codon 12 and codon 13 mutations, as well as the evaluation of microsatellite instability (MSI), were performed as previously described [19]. BRAF mutations in codon 600 and codon 601 were detected using the BRAF 600/601 StripAssay (ViennaLab Diagnostic GmbH, Vienna, Austria) according to the manufacturer’s protocol.
The interactions between the miRNAs hsa-let-7a-5p, hsa-let-7b-5p, hsa-let-7c-5p, hsa-let-7d-5p, hsa-let-7e-5p, hsa-let-7f-5p, hsa-let-7g-5p, and hsa-let-7i-5p and the genes involved in the IGF1 signaling pathway, coding for receptors and substrates (IGF1R, IGF2R, INSR, IRS1, and IRS2) were identified using miRTargetLink 2.0 [20]. This tool provides miRNA–target interactions experimentally validated (weak and strong) and computationally predicted.
Total RNA, including miRNAs, was isolated using a miRNeasy Mini Kit (Qiagen, Hilden, Germany) according to the manufacturer’s protocol. The reverse transcription of 10 ng RNA was performed with a miRCURY LNA RT Kit (Qiagen), and the expression of the selected 8 miRNAs involved in the IGF-1 signaling pathway was evaluated using a miRCURY LNA SYBR Green PCR Kit and a miRCURY LNA miRNA PCR Assay (Qiagen). For each sample, two technical replicates were evaluated, and the difference in the Ct value between the duplicates was <0.4 cycles. The Ct data were normalized against the geometric mean of SNORD38B and SNORD49A. In the comparison of T vs. PT, the miRNA expression data are presented as 2 −∆∆Ct values (fold change—FC values) using the values of PT tissues as control. In the other comparisons, showing a stratification of the PT and T samples according to the presence of PNI and KRAS mutations, data are presented as 2 −∆Ct values.
The non-parametric Wilcoxon signed-rank test was applied in order to assess the differences between paired tumoral and the adjacent peritumoral mucosa, since the values of miRNA levels were not normally distributed (Shapiro–Wilk test, p < 0.05). The Mann–Whitney test was used to compare the tumoral miRNAs levels in the other comparisons. The differences in miRNA levels between the groups were considered significant when p < 0.05 and 0.65 ≥ FC ≥ 1.5. The Statistical Package for the Social Sciences (SPSS version 20.0, IBM, New York, NY, USA) and GraphPad Prism 8.4.3 (GraphPad Software, San Diego, CA, USA). were used to perform statistical analysis and generate the graphs.
The in silico analysis to assess the miRNA–mRNA interaction between the eight let-7 miRNAs and the genes involved in the IGF1 signaling pathway, coding for receptors and substrates (IGF1R, IGF2R, INSR, IRS1, and IRS2), showed that all of the miRNAs were predicted or validated to target the selected genes, except IGF2R and IRS1. The interaction graph is reported in Figure 1, and the types of interactions (predicted or validated) are reported in supplementary data (Table S1). The differential expression between tumoral (T) and the paired adjacent peritumoral mucosa (PT) showed that seven out of the eight analyzed let-7 miRNAs were significantly downregulated in T: let-7a-5p (FC = 0.44, p = 0.001), let-7b-5p (FC = 0.48, p = 0.002), let-7c-5p (FC = 0.63, p = 0.001), let-7d-5p (FC = 0.65, p = 0.007), let-7e-5p (FC = 0.50, p = 0.004), let-7f-5p (FC = 0.53, p < 0.001), and let-7g-5p (FC = 0.59, p = 0.001). Let-7i-5p was moderately less expressed in T tissue without reaching statistical significance (FC = 0.80, p = 0.115) (Figure 2). Considering the comparison between tumoral tissues with perineural invasion (PNI+) vs. those without perineural invasion (PNI-), we found an upregulation in the PNI+ samples of the following miRNAs: let-7a-5p (FC = 2.30, p = 0.014), let-7b-5p (FC = 2.87, p = 0.006), let-7c-5p (FC = 3.65, p = 0.011), let-7d-5p (FC = 2.71, p = 0.009), and let-7i-5p (FC = 2.63, p = 0.036) (Figure 3A–E). When comparing the miRNAs’ expression levels between the 12 T with KRAS mutation (codon 12 or codon 13) vs. the 13 T without KRAS mutation, we found that miR-let-7e-5p was upregulated (FC = 1.78, p = 0.040). This increase was also observed when comparing the PT tissues of the two groups (FC = 2.15; p = 0.004) even though none of the PT tissues presented mutations (Figure 3F). A downregulation of miRNAs let-7c-5p, let-7d-5p, and let-7i-5p was found when comparing T with BRAF mutation vs. those without BRAF mutation (FC = 0.21 p = 0.027; FC = 0.32 p = 0.027; FC = 0.30 p = 0.040, respectively). This result cannot be suggestive, since only two T tissues reported BRAF mutation. No significant difference in miRNAs expression was observed in relation to the other characteristics of the tumors or patient features which includes localization, grade, histologic subtype, lymphovascular invasion, microsatellite instability, sex, family history of cancer, and reported moderate alcohol consumption (p > 0.05)
Insulin and insulin-like growth factor 1 (IGF-1) act on the tyrosine kinase receptors INSR and IGF-1R. INSR and IGF-1R are frequently overexpressed in cancer cells [21,22], where they activate a variety of intracellular signaling cascades that inhibit apoptosis and promote cell cycle progression [22,23]. Studies conducted on colonic tissues aimed at the detection of the IGF-1R gene and protein expression, found that the IGF-1R mRNA levels are significantly higher in CRC tissue compared with adjacent normal mucosa [13,15]. Moreover, the level of IGF-1R protein is associated with tumor localization, grading, tumor growth, lymphatic vessel invasion, and mismatch repair protein expression status [12]. Investigations on INSR have shown that the isoform A is significantly higher in CRC than in normal tissues [24], while isoform B expression has been indicated to be reduced in adenomas compared to normal colon tissue [25]. Using bioinformatics analysis, we observed the miRNA–mRNA interaction between eight let-7 miRNAs and three genes coding for the receptors IGF-1R and INSR, and the insulin receptor substrate IRS2. Through qRT-PCR we found that let-7a-5p, let-7b-5p, let-7c-5p, let-7d-5p, let-7e-5p, let-7f-5p, and let-7g-5p were downregulated in the tumoral tissues. These results partially reproduced those obtained by Li and collaborators, which found a significant downregulation of let-7a, let-7b, and let-7e in CRC tissues, whereas no changes in the expression levels of let-7c, let-7d, let-7f, and let-7g were observed in their study [15]. Notably, in our study let-7a, let-7b, and let-7e were identified as the most downregulated miRNAs. The involvement of the let-7 family in CRC has been investigated in other studies. Results on the expression of let-7a has indicated that this miRNA shows low expression in human CRC cell lines [26], and that its levels could be upregulated by cisatracurium [27], a compound which inhibits the proliferation and induces apoptosis of the cancer cells [28]. Studies in human tissues have indicated let-7a-5p to be downregulated in CRC compared to normal tissue from non-CRC controls [29,30], whereas no difference has been found in a study comparing let-7a-5p between adjacent normal and tumor tissues [31]. Interestingly, a negative correlation between this miRNA and tumor size, stage and lymph node metastasis in CRC patients has been observed [32]. In line with our results, a downregulation of let-7b was observed in tumoral tissues [15,33] and in the plasma [34] of CRC patients, and, along with a panel of other miRNAs, it has been indicated as a biomarker able to distinguish between normal and tumoral colonic tissues [35]. In contrast, in a study performed on FFPE samples, the expression of let-7b was found to be upregulated in CRC [29]. This difference can be explained by the fact that comparing miRNA expression profiles of paired fresh frozen and FFPE samples; only 27–38% of the differentially expressed miRNAs overlapped between the two source systems [36]. Let-7c has been indicated as a hub miRNA related to CRC prognosis [37], and, contrasting with our results, its level has been found to be increased in CRC tissue samples as compared to normal colonic tissues [29]. Literature data on the expression of let-7d-5p are also contrasting, with some works reporting its diminished level in CRC tissues [38] as in our study, and other studies detecting its upregulation in tumoral tissues [29] and cell lines [39]. Regarding the expression of let-7e-5p, a study on TCGA datasets found its expression levels to be lower in colorectal tumor tissues than in normal tissues [40], as confirmed by Li et al. and our data, which detected the same changes by qRT-PCR [15]. Finally, our study revealed the under-expression of two other miRNAs, let-7f-5p and let-7g-5p. The results on the involvement of these miRNAs in CRC are not clear, with one study showing let-7g-5p upregulation in tumoral tissues [29] and one, in agreement with our findings, showing decreased expression of let-7f in CRC cell lines [41]. The discrepancies among the studies on the expression of let-7 miRNAs in CRC could be due to the different characteristics of tumoral tissues in terms of the grade, stage, tumor locations, presence of perineural invasion, and KRAS or BRAF mutation status. Indeed, our results showed an upregulation of let-7e-5p in CRC tissues with mutation in KRAS codon 12 or 13 and, for the first time, suggested an association between let-7a-5p, let-7b-5p, let-7c-5p, let-7d-5p, and let-7i-5p and perineural invasion in CRC. Among the identified miRNAs, only let-7a and let-7d have so far been discussed in the literature as being involved in PNI, reporting a downregulation in oral squamous cell carcinoma [42] and an upregulation in head and neck squamous carcinoma [43] in PNI condition, respectively. Our study suggests a possible association between the let-7 miRNAs/IGF axis and PNI in CRC patients, without reporting functional validation. Due to the importance of PNI as a negative prognostic factor in CRC, further exploration in a larger cohort of patients is necessary, as well as functional studies to clarify the intricate mechanisms by which miRNAs are involved in PNI.
In conclusion, our results showed a general downregulation of let-7 miRNAs in tumoral CRC tissues. The let-7 miRNA profile in CRC is related to specific clinicopathological characteristics of tumors, mainly KRAS or BRAF mutations and the presence of PNI. These characteristics must be considered when conducting studies for the identification of miRNAs as prognostic and predictive biomarkers, as well as for the application of let-7-targeting therapy against CRC. | true | true | true |
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PMC9605465 | Anna Paszkowska,Tomasz Kolenda,Kacper Guglas,Joanna Kozłowska-Masłoń,Marta Podralska,Anna Teresiak,Renata Bliźniak,Agnieszka Dzikiewicz-Krawczyk,Katarzyna Lamperska | C10orf55, CASC2, and SFTA1P lncRNAs Are Potential Biomarkers to Assess Radiation Therapy Response in Head and Neck Cancers | 11-10-2022 | C10orf55,CASC2,SFTA1P,lncRNA,ncRNA,HNSCC,biomarker,radiotherapy,TCGA | Long non-coding RNAs have proven to be important molecules in carcinogenesis. Due to little knowledge about them, the molecular mechanisms of tumorigenesis are still being explored. The aim of this work was to study the effect of ionizing radiation on the expression of lncRNAs in head and neck squamous cell carcinoma (HNSCC) in patients responding and non-responding to radiotherapy. The experimental model was created using a group of patients with response (RG, n = 75) and no response (NRG, n = 75) to radiotherapy based on the cancer genome atlas (TCGA) data. Using the in silico model, statistically significant lncRNAs were defined and further validated on six HNSCC cell lines irradiated at three different doses. Based on the TCGA model, C10orf55, C3orf35, C5orf38, CASC2, MEG3, MYCNOS, SFTA1P, SNHG3, and TMEM105, with the altered expression between the RG and NRG were observed. Analysis of pathways and immune profile indicated that these lncRNAs were associated with changes in processes, such as epithelial-to-mesenchymal transition, regulation of spindle division, and the p53 pathway, and differences in immune cells score and lymphocyte infiltration signature score. However, only C10orf55, CASC2, and SFTA1P presented statistically altered expression after irradiation in the in vitro model. In conclusion, the expression of lncRNAs is affected by ionization radiation in HNSCC, and these lncRNAs are associated with pathways, which are important for radiation response and immune response. Potentially presented lncRNAs could be used as biomarkers for personalized radiotherapy in the future. However, these results need to be verified based on an in vitro experimental model to show a direct net of interactions. | C10orf55, CASC2, and SFTA1P lncRNAs Are Potential Biomarkers to Assess Radiation Therapy Response in Head and Neck Cancers
Long non-coding RNAs have proven to be important molecules in carcinogenesis. Due to little knowledge about them, the molecular mechanisms of tumorigenesis are still being explored. The aim of this work was to study the effect of ionizing radiation on the expression of lncRNAs in head and neck squamous cell carcinoma (HNSCC) in patients responding and non-responding to radiotherapy. The experimental model was created using a group of patients with response (RG, n = 75) and no response (NRG, n = 75) to radiotherapy based on the cancer genome atlas (TCGA) data. Using the in silico model, statistically significant lncRNAs were defined and further validated on six HNSCC cell lines irradiated at three different doses. Based on the TCGA model, C10orf55, C3orf35, C5orf38, CASC2, MEG3, MYCNOS, SFTA1P, SNHG3, and TMEM105, with the altered expression between the RG and NRG were observed. Analysis of pathways and immune profile indicated that these lncRNAs were associated with changes in processes, such as epithelial-to-mesenchymal transition, regulation of spindle division, and the p53 pathway, and differences in immune cells score and lymphocyte infiltration signature score. However, only C10orf55, CASC2, and SFTA1P presented statistically altered expression after irradiation in the in vitro model. In conclusion, the expression of lncRNAs is affected by ionization radiation in HNSCC, and these lncRNAs are associated with pathways, which are important for radiation response and immune response. Potentially presented lncRNAs could be used as biomarkers for personalized radiotherapy in the future. However, these results need to be verified based on an in vitro experimental model to show a direct net of interactions.
Head and neck squamous cell carcinomas (HNSCC) are the sixth most common among all cancers worldwide. The mortality rate of HNSCC patients can be as high as 50% [1]. On this basis, two groups were distinguished among HNSCC: cancers caused by carcinogens, such as alcohol and smoking, which account for up to 75% of the incidence of HNSCC, and those associated with a viral infection, such as HPV [2,3,4,5]. A patient’s treatment is determined after evaluating factors, such as the size of the tumor, the presence and number of metastases, and their location. Despite medical advances, the mortality rate among HNSCC patients is still high [6]. Due to the low clinically apparent precancerous changes, in most cases, cancer is diagnosed at a late stage, which reduces the chances of recovery [7]. Current strategies for treating HNSCC include surgical and non-surgical approaches, such as radiation therapy. For patients with head and neck cancers, conventional radiation therapy is used, during which patients are subjected to radiation at a fraction of 2 Gy once a day until the total radiation dose received is 70 Gy [8]. Molecular mechanisms of HNSCC pathogenesis, however, have still not been fully clarified, so it is crucial to investigate their genetic basis and to improve diagnostic methods and treatment [9]. Among HNSCC patients, radiation therapy is a widely used treatment method. As studies have shown; however, it has proven to be more effective in HPV-positive patients than in HPV-negative patients [10,11,12]. The susceptibility of this group of patients is due to abnormalities in signaling and repair mechanisms within DNA strands. On this basis, much of the current research focuses on the protein-coding genes and proteins themselves, which are responsible for repair mechanisms and inhibitors associated with cell cycle regulation. One of the consequences of ionizing radiation, with a profound impact on cell function, is DNA damage, and consequently a whole cascade of repair systems. Following irradiation, signaling pathways are activated to repair the damage or apoptotic mechanisms are triggered [13]. DNA damage can affect one or both DNA strands, and thus single-strand breaks (SSBs) and double-strand breaks (DSBs) are distinguished. DSBs are characterized by impaired DNA repair kinetics and numerous oxidative base impairment, resulting in genome destabilization. Additionally, an important effect of radiation on the cell is water ionization, which causes the formation of free radicals in the cell, which are harmful to the genetic material of the cell and can lead to cell death [14]. However, it is possible that after a dose of infrared radiation, DNA repair cascade effectively and largely restores the functionality of cancer cells, and as a result, these cells can acquire radioresistance [15,16]. It was shown that under the influence of ionizing radiation and chemotherapeutics expression not only of protein-coding genes but also of non-coding RNAs is changed as a natural response and leading to molecular changes, which can overcome harmful factors [17]. Although long non-coding RNAs (lncRNAs) do not encode proteins, they have been recognized as valuable and significant molecules over time. Their sequence consists of more than 200 nucleotides and very often contains elements typical of mRNA, namely poly-A tails and regulatory elements, such as miRNAs binding sites. The process of lncRNA biogenesis is regulated by RNA polymerase II and is similar to mRNA formation [18]. The activity of lncRNAs is characterized not only by interaction with proteins and other RNAs but also by regulation of transcription and expression of genes through changes in chromatin structure [19,20]. lncRNAs appear to be important in functions related to the regulation of gene transcription in the nucleus or subsequent post-transcriptional modifications in the cytoplasm [21]. Abnormalities in the activity or biogenesis mechanisms of lncRNAs can appear in states of pathological conditions and indicate cancer progression by affecting not only the structure of the chromatin but also several transcription factors [22]. It has been demonstrated that lncRNAs play a key role in cancer biology. Although the function and activity of lncRNAs are still under investigation, in the future they may become important tools for predicting the development and possible treatment of cancer [23]. Moreover, dysregulated lncRNAs are closely associated with the regulation of a cellular pathway associated with the response to irradiation [16]. A better understanding of the mechanisms of tumor cell response to radiation therapy, and characterization of the genes, including lncRNAs, which can be used as irradiation markers, gives the possibility of more effective treatment. In this study, we used The Cancer Genome Atlas (TCGA) data of HNSCC patients to define the lncRNAs’ panel with a high ability to distinguish patients in response to implemented radiotherapy during the treatment. Based on this, nine selected lncRNAs were validated using HNSCC cell lines and different doses of irradiation. The overview of the experimental approach with analysis steps used in this study is presented in Figure 1.
Publicly available data from the TCGA was downloaded from the website of the University of Santa Cruz in California (https://xenabrowser.net/datapages/, accessed on 12 March 2022). Files included gene expression and clinical presentation of patients with head and neck cancers: TCGA.HNSC.sampleMap/HiSeqV2 was used (with expression unit: estimated gene level transcription, log2(x + 1) transformed RSEM normalized count, number of patients: 566). lncRNAs of interest were selected using the tool BioMart available at: https://www.ensembl.org/index.html, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridgeshire, United Kingdom, accessed on 12 March 2022. ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) data for immune cell analysis were downloaded from https://bioinformatics.mdanderson.org/estimate/disease.html (University of Texas MD Anderson Cancer Center Houston, Texas, United States of America, accessed on 25 August 2022), platform RNA-Seq-V2 [24]. Data presented by Thorsson et al. [25] (accessed on 25 August 2022) was used for the analysis of lymphocyte infiltration signature score. All the data are available online with unrestricted access and do not require the patients’ consent or other permissions. The use of the data does not violate the right of any person or any institution.
To build the model, 297 patients with radiation treatment were selected to obtain two groups of patients who were separated based on their overall survival (OS). These two marginal groups of patients were named further as the responding group with OS longer than 1049 days (RG) and the non-responding group with OS shorter than 854 days (NRG). The rest of the 147 patients with OS time less than 1049 days and higher with 854 were excluded (percentile 25–50% and 50–75%). Information about patients included in RG and NRG groups is enclosed in Supplementary Table S1. The obtained model of 150 patients (RG with n = 75 and NRG with n = 75) was characterized based on the clinical and pathological information in terms of criteria, such as location: oral cavity vs. pharynx vs. larynx; tumor cell differentiation grade: G1 vs. G2 vs. G3; tumor size: T1 vs. T2 vs. T3 vs. T4; HPV p16 status: positive vs. negative; alcohol consumption history: yes vs. no; smoking tobacco products history: yes vs. no, as described in the statistical section.
Gene set enrichment analysis (GSEA) of a group of genes was conducted using software from www.gsea-msigdb.org, accessed on 10 May 2022. The tested 150 patients were divided into two groups with low and high expression of a particular lncRNA with the median expression used as a cutoff. The analysis took into account the expression profile of all genes for a given patient. Analysis of oncogenic signatures (C) from MSigDB collection with 1000 gene set permutations and with nominal p-value ≤ 0.05 and FDR q-value ≤ 0.27 were considered significant. Next, the enriched gene sets for all significant results from GSEA were analyzed using the GeneMANIA online tool (https://genemania.org, accessed on 20 August 2022) for deeper prediction of their biological network integration for gene prioritization and function and visualization of possible interactions [26].
Infiltration of immune cells into tumor tissues and inference of the tumor purity in patients’ groups depending on the high and low lncRNA expression level (cut off based on the median expression of a specific gene) was analyzed using the dataset ESTIMATE [24]. Next, lymphocyte infiltration signature scores were estimated depending on the high and low expression levels of specified lncRNA in the whole (RG and NRG) group of patients using deconvolution data about specific immune cells presented by Thorsson et al. [25] as described in the statistical method section.
Commercially available head and neck squamous cell lines DOK, SCC-25, SCC-040, FaDu, CAL-27, and Detroit 562 were used for the analysis of changes in the expression level of specified lncRNAs selected based on the TCGA analysis. All cell lines were cultured in DMEM (BioWest, Nuaille, France) supplemented with 10% FBS (BioWest) and 4.5 g/L gentamicin antibiotic (Krka, Poland) in culture bottles and incubated in a 37 °C and 5% CO2 atmosphere. The culture bottles were filled with PBS and the cells were exposed to ionizing radiation at doses of 2, 4, and 8 Gy, similar to those described previously by Lindell Jonsson E et al. [27], using Gammacell® 3000 Elite (Theratronics, Canada) with the Cesium-137 isotope as the radiation source with the emission of α, β+ and β−, and gamma-penetrating radiation. As controls, cell lines that were identically grown but not subjected to ionizing radiation (0 Gy) were used. All irradiation experiments were made with a minimum of three independent biological replicates. After irradiation, the PBS was removed, and cells were cultured for 24 h as described above. After that, total RNA was isolated using TRI Reagent® (Sigma-Aldrich, Munich, Germany) according to the manufacturer’s protocol. The quality and quantity of RNA were estimated by observation of 28S and 18S rRNA bands using electrophoresis in 1% agarose with TEA (Tris-acetate-EDTA (Ethylenediaminetetraacetic acid)) buffer and NanoDrop 2000 spectrophotometer (Thermo Scientific, Waltham, MA, USA).
Reverse transcription was performed using EvoScript cDNA synthesis reaction kit (Roche, Basel, Switzerland) according to the manufacturer’s protocol and using 1 µg of total RNA. Obtained cDNA was diluted (10×) and used for a quantitative reverse transcription-polymerase chain reaction (qRT-PCR) using LightCycler 96 (Roche) and SYBR Green I Master buffer (Roche) as described previously [28]. The primers for quantification of C10orf55, C3orf35, C5orf38, CASC2, MEG3, MYCNOS, SFTA1P, SNHG3, and TMEM105 were described previously [28,29,30,31,32,33,34] and are presented in Table 1. The compatibility of all primers and their complementarity to the target sequence was checked using the Basic Local Alignment Search Tool (BLAST), National Center for Biotechnology Information (NCBI), Bethesda, Maryland, United States of America, available at: https://blast.ncbi.nlm.nih.gov/Blast.cgi, accessed on 15 May 2022. All real-time PCR data were analyzed by calculating the 2−∆Ct method and normalized against the GAPDH expression as described previously [29,30,31,32,33,34,35].
All statistical analyses of the data extracted from TCGA-based databases were performed using the GraphPad Prism 5 software. For each of the analyzed groups, the distribution was checked using the Shapiro–Wilk test. Analysis of differences between groups was performed using the Student’s t-test or Mann–Whitney U test, depending on the distribution. Differences in clinical and pathological parameters in the groups of patients responding and non-responding to ionizing radiation were analyzed using the Chi-square test. Differences in the expression levels for individual lncRNAs in cell lines undergoing irradiation were analyzed using the ANOVA test. Previously, it was checked whether the normal distribution was present in each group. For the primary analysis, the Kruskal-Wallis ANOVA test was used, and for the post hoc analysis, the Dunn’s test. For all analyses, the test probability value (p-value) was assumed to be statistically significant, when it is less than 0.05. For each analysis, it was assumed that the value of the test probability (p-value) is statistically significant when it is less than 0.05.
First, using the data available from the TCGA project the overall survival (OS) time of HNSCC patients with radiation therapy and without this treatment was assessed. It was observed that patients who received radiation therapy displayed prolonged OS time with a median survival of 2570 days in comparison to the group without this treatment with a median survival assessed at 1134 days (p = 0.0006 and p < 0.0001) (Figure 2A). Next, from 297 patients who received radiation therapy, two distinct groups named responders’ (RG) and non-responders’ groups (NRG) were separated based on OS. The responding group was the group of patients with the longest survival time (5480 days, with undefined median survival) after the procedure was performed and consisted of 75 patients. The non-responders’ group was the group of patients with the shortest survival time (69 days with median survival assessed of 379 days) after the procedure was performed and included 75 patients (Figure 2B). None of the 150 patients had a history of neoadjuvant treatment. Moreover, information about additional pharmaceutical therapy (unknown type) was presented only for 9 patients from RG (3 patients received therapy and 6 patients without therapy) and 31 from NRG groups (14 patients received therapy and 17 patients without therapy). Based on Chi-square analysis, no differences between groups were observed (p = 0.5274). The lack of information about additional pharmaceutical therapy is for 94% of RG and 79% of NRG patients in the TCGA data. Moreover, no differences in the number of patients undergoing targeted molecular therapy among RG and NRG patients were observed (p = 0.3813); see Figure 2C. Additionally, no differences in tumor localization sites in the RG and NRG group of patients were noticed (p = 0.1231); see Figure 2D. The differences between the responding and non-responding groups to radiotherapy were examined based on selected clinical and pathological parameters. It was observed that there were no differences in several parameters: gender (female vs. male, p = 0.3344), alcohol consumption (yes vs. no, p = 0.4253), smoking of tobacco products (yes vs. no, p = 0.2424), stage of tumor (I + II vs. III + IV, p = 0.4142), tumor cell differentiation stage (G1 + G2 vs. G3 + G4, p = 0.1241), tumor size (T1 + T2 vs. T3 + T4, p = 0.0761), the presence of tumor cells in lymph nodes (N0 vs. N1 + N2 + N3, p = 0.8621), invasion of the perineural space (yes vs. no, p = 0.0816), degree of spread of tumor cells (I + II vs. III + IV, p = 0.1241), excision of cervical lymph nodes (yes vs. no, p = 0.4253), and HPV infection status (positive vs. negative, p = 0.1982). All results are shown in Figure 2E and Supplementary Table S2.
Significantly different (p < 0.05) expression between RG and NRG patients was observed for twenty lncRNA. Next, C10orf55, C3orf35, C5orf38, CASC2, MEG3, MYCNOS, SFTA1P, SNHG3, and TMEM105 were selected for further analysis as the most changed between the group of RG and NRG patients. Increased expression in the NRG was observed for C10orf55 (p = 0.0002), C3orf35 (p = 0.0054), C5orf38 (p = 0.0002), TMEM105 (p = 0.0005), MEG3 (p = 0.0043), SFTA1P (p = 0.001), and SNHG3 (p = 0.0039), and lower expression in the case of MYCNOS (p = 0.0001), as well as CASC2 (p = 0.01). (Figure 3A and Supplementary Table S3). Analysis of the receiver-operating characteristic (ROC) curve was performed to describe lncRNAs’ ability to differentiate groups of patients (RG vs. NRG). It was observed that 17 lncRNAs displayed AUC higher than 0.6 and in this group MYCNOS (AUC = 0.6544; p = 0.0013), SFTA1P (AUC = 0.6577; p = 0.001), TMEM105 (AUC = 0.672; p = 0.0003), C5orf38 (AUC = 0.6777; p = 0.0002), and C10orf55 (AUC = 0.6784; p = 0.0002) had the higher ability to differentiate patients with the response to radiotherapy (Figure 3B and Supplementary Table S4). Moreover, the expression levels of these twenty lncRNAs were compared between tumor (n = 522) and healthy samples (n = 44) using all HNSCC data taken from the TCGA. It was indicated that expression levels were up-regulated for SNHG3, C10orf55, C2orf27A, C5orf38, HCP5, SFTA1P, SNHG10 (for all p < 0.0001), C5orf60 (p = 0.0002), PVT1 (p = 0.0013), C6orf223 (p = 0.0135), and TMEM105 (p = 0.0357) and downregulated for MYCNOS (p = 0.0472) in tumor compared to healthy samples. No differences were observed for NEAT1 (p = 0.0841), MEG3 (p = 0.1824), SMCR5 (p = 0.5756), SNHG7 (p = 0.5826), C3orf35 (p = 0.6029), CASC2 (p = 0.7752), HHLA3 (p = 0.8326), and RFPL1S (p = 0.9121); see Figure 3C and Supplementary Table S5.
Next, pathways and cellular processes analysis for the nine lncRNAs showing the greatest differences in the RG and NRG groups was carried out. Gene set enrichment analysis (GSEA) was performed depending on the high and low expression levels of specified lncRNA using the set of 150 patients (RG and NRG groups). Based on the GSEA analysis altered peroxisome-associated pathways were observed for lncRNA CASC2 associated with the P53 pathway (190 genes, FDR = 0.030, p < 0.0001); for C3orf35 associated with response to androgen hormones (96 genes, FDR = 0.254, p = 0.019), UV response (137 genes, FDR = 0.264, p = 0.016), and the mitotic spindle (196 genes, FDR = 0.088, p = 0.018); for SFTA1P associated with coagulation (136 genes, FDR = 0.251, p = 0.008), epithelial-to-mesenchymal transition (194 genes, FDR = 0.122, p < 0.0001), and angiogenesis (36 genes, FDR = 0.226, p = 0.002); for MEG3 associated with the G2M checkpoint (184 genes, FDR = 0.108, p = 0.0367), mitotic spindle (196 genes, FDR = 0.030, p = 0.004); for SNHG3 associated with DNA repair (139 genes, FDR = 0.144, p = 0.041). All results are shown in Figure 4A and Supplementary Table S6. Next, using the GeneMANIA tool, further analysis of genes included in GSEA results (FDR < 0.27 and p < 0.05) was performed and included the following processes: epithelial-to-mesenchymal transition process and angiogenesis for SFTA1 and DNA repair for SNHG3 observed in the group of patients with higher levels of these lncRNAs. The same analysis was conducted for enriched processes in the patients with lower expression of C3orf35 (mitotic spindle, UV response DN), CASC2 (TP53 pathway), and MEG3 (mitotic spindle and G2M checkpoint). For SFTA1, additional detected processes included: extracellular matrix organization, angiogenesis, cellular migration, response to UV, apoptosis, and connection with leukocyte migration, extracellular matrix organization, angiogenesis, response to UV, or regulation of cell proliferation. In the case of SNHG3 were noticed processes associated with DNA repair (e.g., nucleotide-excision repair, telomere maintenance, DNA synthesis involved in DNA repair) as well as gene silencing by miRNA. Analysis for lower expression of C3orf35 indicated changes in processes associated with Golgi apparatus and vesicle transport. Moreover, with this lncRNA processes strictly connected with division, cytoskeleton, and cell cycle were observed. These processes were also noticed with MEG3 and additional processes associated with DNA damage response and signal transduction caused by changes in DNA, cell–cell junction, as well as processes linked with antigen processing and presentation were also noticed. Patients with lower levels of MEG3 displayed also changes in processes, such as a mitotic spindle, cell cycle regulation, signal transduction in response to DNA damage, DNA damage checkpoint, and double-strand break repair via homologous recombination. The last lncRNA, CASC2, was connected with the regulation of proliferation, signal transduction, apoptosis, cell cycle arrest, response to external stimulus, signal transduction involved in mitotic DNA damage checkpoint, as well as response to oxygen levels. All results are presented in Figure 4B,C, and Supplementary Table S7. Infiltration of immune cells into tumor tissues and infer tumor purity depending on the lncRNA expression level was analyzed using the dataset ESTIMATE because immune processes were observed for some of the pathways associated with selected lncRNAs. No changes between high and low expression levels of C3orf35, MEG3, MYCNOS, TMEM105, CASC2, C10orf55, and SFTA1P (p > 0.05) were observed. However, for C5orf38 and SNHG3, differences in tumor purity between patients with low and high levels of these two lncRNAs were indicated (p = 0.0005 and p = 0.0013, respectively). Only in the case of MEG3 differences in stromal cells were noticed (p = 0.0122). Lower levels of immune score and lower levels of lymphocyte infiltration signature score in the group of patients with higher levels of C5orf38 (p < 0.0001 and p < 0.0001, respectively), SNHG3 (p = 0.0093 and p = 0.0243, respectively) as well as TMEM105 (p = 0.0268 and p = 0.0002, respectively) in comparison to patients with lower levels of these lncRNAs were observed. Moreover, in the case of patients with lower levels of MEG3 and higher levels of MYCNOS, a higher lymphocyte infiltration signature score was observed (p = 0.0334 and p = 0.0475, respectively). All results are shown in Figure 5 and Supplementary Table S8.
Finally, the C10orf55, C3orf35, C5orf38, CASC2, MEG3, MYCNOS, SFTA1P, SNHG3, and TMEM105 lncRNAs, were validated using HNSCC cell line models, which were irradiated using 2, 4, and 8 Gy of doses and compared to the non-irradiated controls (0 Gy). Changes in the expression were assessed using the 2−∆Ct method and normalized to the GAPDH reference gene [28,29,30,31,32,33,34]. We observed that the expression level of C10orf55 was downregulated in SCC-25 cells after irradiation (p = 0.0146). In the case of CASC2 lncRNA in Detroit 562 cells downregulation of this lncRNA was observed (p = 0.0482). Opposite results were determined for SFTA1P, whose expression levels were up-regulated in CAL-27 (p = 0.0057) and FaDu cells (p = 0.0043) after irradiation. For the rest of the cell lines and specified lncRNAs, no significant differences were noticed (p > 0.05). All results are presented in Figure 6.
Because the survival rate of the HNSCC patient group is still poor, understanding the genetic basis is crucial to finding personalized treatment and potential biomarkers for HNSCC. The present work focuses on investigating the relevance of specific lncRNAs in the pathomechanisms of head and neck cancers after ionizing radiation. In the context of tumorigenesis, the literature data on the role of lncRNAs in head and neck area cancers are limited. It should be mentioned that none of the genes identified in this work are described in the context of the effects of ionizing radiation on the HNSCC. This study used available data from the TCGA database of both transcriptome and clinical parameters to create a research model to identify potential lncRNAs. In silico analyses showed significant differences in the expression of twenty genes between the groups of patients responding and non-responding to ionizing radiation. The model consisted of patients who received radiation therapy during treatment, and among these patients, a responding and non-responding group was separated based on survival length. Based on comparative analysis, differences were shown for lncRNAs; for the responding group versus the non-responding group, increased expression was observed for C5orf60, C6orf223, MYCNOS, RFPL1S, CASC2, as well as SMCR5 and decreased expression for C10orf55, C2orf27A, C3orf35, C5orf38, TMEM105, HCP5, HHLA3, MEG3, NEAT1, PVT1, SFTA1P, SNHG10, SNHG3, and SNHG7. In order to verify that the model itself did not affect the results obtained, i.e., specific clinical and pathological parameters, a comparative analysis was performed for the two groups compared. No statistically significant differences in clinical and pathological parameters were observed except for differences in the presence of tumor cells in lymph nodes. This indicates that the established research model appears to be correct. The results were not affected by variables, such as gender, age, tumor stage, and tumor size. It should be noted that TCGA data are often used to create a research model, which is then validated against another database, such as the Gene Expression Omnibus (GEO), patient samples collected at a given center, or cell lines [3,36]. It has been shown that for the genes C10orf55, C3orf35, C5orf38, CASC2, MEG3, MYCNOS, SFTA1P, SNHG3, and TMEM105, there are the greatest differences in expression levels between the group responding and non-responding to radiotherapy. Verification of these data on samples derived from cell lines confirmed this relationship only for genes C10orf55, CASC2, and SFTA1P. Similar studies have already been conducted, but different results were observed than those presented here [37,38]. Guglas et al. analyzed lncRNA expression in SCC-040, SCC-25, FaDu, and Cal27 cell lines treated with radiation doses of 5, 10, and 20 Gy. It was observed that lncRNA expression is dose-dependant and for a dose of 5 Gy the expression level of HOTAIR, HOXA3as SNHG5, and Zfhx2as were changed; for a dose of 10 Gy, CAR Intergenic 10, Dio3os, HAR1A, Zfhx2as, and HAR1B were changed, and HOXA6as, Zfhx2as, and PTENP1 lncRNAs were changed after a dose of 20 Gy. However, in that study, a defined set of lncRNAs were used for qRT-PCR analysis [37]. In this study, from all available lncRNA transcripts indicated in the RNAseq data, we selected those with the highest differences in expression level between RG and NRG group. Moreover, it should be noted that the radiosensitivity of HNSCC depends on the clinical-pathological features of this heterogeneous group of cancer. For example, ncRNAs encoded by viral particles have modulating ability on the radio-susceptibility in the case of nasopharyngeal carcinoma (NPC) connected with Epstein-Barr virus (EBV) infection [38]. In our opinion, the selection of potential genetic biomarkers based on the TCGA data is a better methodological approach than studies based on cell lines [39]. However, in both cases, this data should be validated in a large number of patients, what is our future goal. The first indicated lncRNA is C10orf55 (chromosome 10 putative open reading frame 55). The importance of this lncRNA in HNSCC has been demonstrated. Studies have confirmed the close association of C10orf55 with plasminogen activator (PLAU), higher levels of which were unequivocally associated with a worse prognosis of HNSCC. The in vivo and in vitro results suggest an involvement of C10orf55 in tumor cell proliferation and migration [40]. It has also been investigated in acute myeloid leukemia and in complete remission of the disease, following treatment with chemotherapeutics [41]. Dysregulation of this lncRNA after successful treatment with both chemotherapy and radiotherapy offers hope for new diagnostic methods and creates room for more accurate studies. Another cancer in which C10orf55 plays an important role is esophageal adenocarcinoma. A group of researchers has created a compilation of four genes that are targeted by miR-3648 and whose expression closely correlates with the OS time of patients [42]. Unfortunately, based on GSEA analysis, no signaling pathways have been related to the response to ionizing radiation. The next lncRNA identified in this work is CASC2 (cancer susceptibility 2). Present studies do not indicate an association of CASC2 expression with radiation, although its importance in HNSCC has already been noted. Oral squamous cell carcinoma (OSCC) owes its resistance to chemotherapy to CASC2 [43]. As reported in the literature, the role of this lncRNA in the defense response to tumorigenesis is already known, and its involvement in the apoptotic process has also been noted [44]. CASC2 is also revealed in other cancers, such as gastric, colorectal, and endometrial, always with reduced expression [45]. It should be mentioned that the effect of cisplatin on cells with reduced CASC2 expression in esophageal squamous cell carcinoma was analyzed. It was shown that CASC2 promotes the anti-tumor effect of cisplatin in cancer cells [46]. Based on the results of this study, which also noted CASC2 dysregulation in cells derived from the oral cavity and pharynx, it is suggested that this lncRNA may represent a future biomarker in response to both chemotherapy and radiotherapy. The analysis of changes in signaling pathways performed indicates that patients with low CASC2 expression have increased expression of genes related to the p53 pathway. It was also observed that HNSCC patients responding to radiotherapy have an increased expression of this lncRNA. DNA damage is known to stabilize p53 in part through the DNA damage signaling pathway, which involves sensory kinases, including ATM and ATR, and effector kinases, such as Chk1/2 and Wee1, which lead to post-transcriptional regulation of various genes involved in DNA repair, cell cycle control, apoptosis, and aging [47]. The results obtained and the literature data indicate a role for lncRNA CASC2 in the radiation response and its likely importance in the p53 pathway, which should be further analyzed. The last lncRNA whose expression changes under the influence of ionizing radiation is SFTA1P (surfactant associated 1 pseudogene). As numerous studies have shown, SFTA1P is closely associated with lung diseases [48]. In cell lines of non-small cell lung cancer, it is transcriptionally activated, which is responsible for the inhibition of cell proliferation or induction of apoptosis [48]. However, in 2020 and 2021, two studies were conducted, demonstrating its importance also in HNSCC [49,50]. SFTA1P has potential prognostic significance and can be used to assess survival outcomes [50]. Based on an in vitro model, a change in the expression of SFTA1P after treatment of lung squamous cell carcinoma cells with cisplatin. It was found that SFTA1P, due to its correlation with the response to cisplatin, would be in the future a good biomarker in predicting response to chemotherapy [50]. Analysis of signaling pathways showed that in a group of patients with high lncRNA expression SFTA1P showed enhanced expression of genes related to the epithelial-to-mesenchymal transition. It should be noted that the expression level of this lncRNA is also high in the group of patients undergoing radiotherapy and with a short survival period (non-responders). It is known that cells that undergo a change in phenotype from epithelial to mesenchymal are characterized by greater aggressiveness, ability to metastasize, and resistance to both ionizing radiation and exposure to chemotherapeutics. This resistance is closely related to the alteration of the cellular program [51]. SFTA1P appears to be one of the lncRNAs associated with acquiring a more malignant cellular phenotype. There is no literature data in relation to changes in the expression of this lncRNA under the influence of ionizing radiation, and the demonstrated potential importance of this lncRNA in the presented results of this work provides new opportunities for understanding the role of this lncRNA in HNSCC. We also checked the immune profile of HNSCC patients depending on the expression levels of C10orf55, C3orf35, C5orf38, CASC2, MEG3, MYCNOS, SFTA1P, SNHG3, and TMEM105 lncRNAs. We indicated that with higher levels of C5orf38, SNHG3 and TMEM105 patients displayed lower levels of immune score and lymphocyte infiltration signature score. Moreover, higher levels of C5orf38, SNHG3, and TMEM105 were characteristic for patients from non-responders’ groups (NRG), which displayed shorter OS time. Chen et al., based on the analysis of lncRNAs in glioma patients, indicated that C5orf38 (chromosome 5 open reading frame 38) is one of the necroptosis-related lncRNAs and was one of the protective factors [52]. It is known that SNHG3 (small nucleolar RNA host gene 3) regulates EZH2, which in turn influences the promoter methylation of KLF2 (Krüppel-like Factor 2) and p21 genes. KLF2 is a zinc-finger transcription factor responsible for activation of CD4+ T cells [53]. Unfortunately, there is no literature information about the potential role and significance of TMEM105 lncRNA (TMEM105 long non-coding RNA) in cancer immunology, and it is difficult to discuss the observed results for HNSCC patients. Similar results were in the case of MEG3 and MYCNOS. MEG3 is downregulated in patients responding to radiotherapy, and in these patients, a lower level of MEG3 is associated with a higher level of lymphocyte infiltration signature score. Xu et al. investigated the role of MEG3 (maternally expressed gene 3) as a prognostic factor and its immune-related role in gliomas. They observed that lower levels of MEG3 were associated with shorter patients’ survival. In low-grade glioma, MEG3 was negatively correlated with infiltrating B cells, CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells, but in the case of glioblastoma multiforme, MEG3 was positively correlated with infiltrating CD8+ T cells and negatively correlated with infiltrating dendritic cells [54]. The immunomodulatory role of MEG3 was also observed by Wang et al., and they showed that MEG3 was downregulated in CD4+ T cells derived from aplastic anemia patients. They proposed that regulation of CD4+ T cell activation depended on MEG3, which in turn regulated miR-23a expression level and finally influenced TIGIT (T cell immunoreceptor with Ig and ITIM domains) [55]. The last lncRNA associated with the immune profile was MYCNOS (MYCN opposite strand). Higher levels of lymphocytes were associated with MYCNOS lncRNAs, which are upregulated in the responding group (RG) and display longer survival in comparison to the non-responders’ group (NRG). It is known that MYCNOS lncRNA regulates the expression of MYCN by binding to its promoter and influencing cancer cell phenotype [56], but its role in the regulation of immune cells has not been indicated. C5orf38, SNHG3, TMEM105, MEG3, and MYCNOS lncRNAs seem to be potential biomarkers describing the immune profile of HNSCC patients in response to radiotherapy. However, it should be verified based on more data from in vitro and in vivo models. In conclusion, ionizing radiation is certainly an important factor affecting the expression of long non-coding RNAs in head and neck cancers. For their full understanding, however, more extensive analyses need to be conducted using more samples tested, both in vitro and in vivo. The small number of relevant genes obtained in this study may be due to the juxtaposition of results derived from only six cell lines with results from one hundred and fifty patients. It is also worth noting that cell lines do not always reflect the cellular phenotype of cancer patients, which is why it is important to conduct further research. In light of the above evidence, it can be suggested that the expression levels of C10orf55, CASC2, and SFTA1P in the future may be a prognostic factor in assessing the patient’s response to radiotherapy. | true | true | true |
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PMC9605792 | 36312516 | Supitchaya Phirom,Jeerath Phannajit,Watsamon Jantarabenjakul,Leilani Paitoonpong,Thidarat Kitrungphaiboon,Nuchjarnun Choktaweesak,Pawinee Kupatawintu,Salin Wattanatorn,Wisit Prasithsirikul,Somchai Eiam-Ong,Yingyos Avihingsanon,Pokrath Hansasuta,Jakapat Vanichanan,Natavudh Townamchai | Comparison of the Immune Response After an Extended Primary Series of COVID-19 Vaccination in Kidney Transplant Recipients Receiving Standard Versus Mycophenolic Acid–sparing Immunosuppressive Regimen | 25-10-2022 | Background. Two doses of coronavirus disease 2019 vaccination provide suboptimal immune response in transplant patients. Mycophenolic acid (MPA) is one of the most important factors that blunts the immune response. We studied the immune response to the extended primary series of 2 doses of AZD1222 and a single dose of BNT162b2 in kidney transplant patients who were on the standard immunosuppressive regimen compared to those on the MPA-sparing regimen. Methods. The kidney transplant recipients who were enrolled into the study were divided into 2 groups based on their immunosuppressive regimen. Those on the standard immunosuppressive regimen received tacrolimus (TAC), MPA, and prednisolone (standard group). The patients in the MPA-sparing group received mammalian target of rapamycin inhibitors (mTORi) with low dose TAC plus prednisolone (MPA-sparing group). The vaccination consisted of 2 doses of AZD1222 and a single dose of BNT162b2. Results. A total of 115 patients completed the study. There were 76 (66.08%) patients in the standard group and 39 (33.91%) patients in the MPA-sparing group. The overall median anti–severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) S antibody level at 4 wk after vaccine completion was 676.64 (interquartile range = 6.02–3644.03) BAU/mL with an 80% seroconversion rate. The MPA-sparing group achieved higher anti–SARS-CoV-2 S antibody level compared to the standard group (3060.69 and 113.91 BAU/mL, P < 0.001). The seroconversion rate of MPA-sparing and standard groups were 97.4% and 71.1%, respectively (P < 0.001). The anti-HLA antibodies did not significantly increase after vaccination. Conclusions. The extended primary series of 2 doses of AZD1222 and a single dose of BNT162b2 provided significant humoral immune response. The MPA-sparing regimen with mTORi and low dose TAC had a higher ant–SARS-CoV-2 S antibody level and seroconversion rate compared to the participants in the standard regimen. | Comparison of the Immune Response After an Extended Primary Series of COVID-19 Vaccination in Kidney Transplant Recipients Receiving Standard Versus Mycophenolic Acid–sparing Immunosuppressive Regimen
Two doses of coronavirus disease 2019 vaccination provide suboptimal immune response in transplant patients. Mycophenolic acid (MPA) is one of the most important factors that blunts the immune response. We studied the immune response to the extended primary series of 2 doses of AZD1222 and a single dose of BNT162b2 in kidney transplant patients who were on the standard immunosuppressive regimen compared to those on the MPA-sparing regimen.
The kidney transplant recipients who were enrolled into the study were divided into 2 groups based on their immunosuppressive regimen. Those on the standard immunosuppressive regimen received tacrolimus (TAC), MPA, and prednisolone (standard group). The patients in the MPA-sparing group received mammalian target of rapamycin inhibitors (mTORi) with low dose TAC plus prednisolone (MPA-sparing group). The vaccination consisted of 2 doses of AZD1222 and a single dose of BNT162b2.
A total of 115 patients completed the study. There were 76 (66.08%) patients in the standard group and 39 (33.91%) patients in the MPA-sparing group. The overall median anti–severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) S antibody level at 4 wk after vaccine completion was 676.64 (interquartile range = 6.02–3644.03) BAU/mL with an 80% seroconversion rate. The MPA-sparing group achieved higher anti–SARS-CoV-2 S antibody level compared to the standard group (3060.69 and 113.91 BAU/mL, P < 0.001). The seroconversion rate of MPA-sparing and standard groups were 97.4% and 71.1%, respectively (P < 0.001). The anti-HLA antibodies did not significantly increase after vaccination.
The extended primary series of 2 doses of AZD1222 and a single dose of BNT162b2 provided significant humoral immune response. The MPA-sparing regimen with mTORi and low dose TAC had a higher ant–SARS-CoV-2 S antibody level and seroconversion rate compared to the participants in the standard regimen.
Coronavirus disease 2019 (COVID-19), a pandemic infectious disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has different clinical presentations, which range from no symptom to pneumonia and respiratory failure, resulting in high morbidity and mortality rates. The risk of developing severe disease is high, especially in kidney transplant recipients who have to take immunosuppressive drugs to prevent graft rejection. These immunosuppressive drugs do not only impair the natural immune response against infection but also diminish humoral and cellular-mediated immune responses to COVID-19 vaccination. Maintenance immunosuppressive regimens in kidney transplant recipients commonly include a calcineurin inhibitor (CNI) such as tacrolimus (TAC), an antimetabolite such as mycophenolic acid (MPA), and mammalian target of rapamycin inhibitors (mTORi) such as sirolimus. Currently, these medications are widely used in 2 combination regimens. First, the standard regimen consisted of TAC, MPA, and prednisolone. The second regimen, the CNI reduction regimen or also can be called as the MPA-sparing regimen, consisted of mTORi, low dose CNI, and prednisolone. These immunosuppressive drugs are crucial in preventing donor-specific anti-HLA antibody (DSA) production and allograft rejection. Previous observational studies conducted in kidney transplant recipient population have shown that 2 doses of mRNA, viral vector, or inactivated COVID-19 vaccines provided only suboptimal immunogenicity. The vaccine response rate after 2 doses of mRNA vaccine was around 30%–60% in solid organ transplant recipients. A third dose of mRNA vaccine can provide another 50% seroconversion among those patients who did not have an immune response to the first 2 doses of the mRNA vaccine. Moreover, the vector-based vaccine yielded lower serological response compared to the mRNA vaccination. Therefore, in August 2021, the US Food and Drug Administration authorized the administration of a third dose of SARS-CoV-2 mRNA vaccine to immunocompromised patients, including kidney transplant recipients. Switching the types of vaccines used can improve the serological response in healthy and organ transplant patients. In an observational study of solid organ transplant patients, a heterologous vaccination of 2 doses of AZD1222 with a single booster dose of BNT162b2 showed that the immune response was comparable to people who received 3 doses of mRNA vaccine. Most but not all previous studies showed that MPA, which inhibits the proliferation of both T and B cells is one of the most important factors that blunts the immune response. Studies of comparing the immune response after receiving 3 doses of SARS-CoV-2 vaccine consisting of 2 doses of viral vector vaccines (AZD1222) followed by a single booster dose of mRNA vaccine (BNT162b2) between kidney transplant recipients who used MPA-sparing regimen (mTORi, low-dose TAC, and prednisolone) and the standard immunosuppressive regimen of TAC, MPA, and prednisolone have never been published. In the present study, we prospectively examined the immune response of our kidney transplant recipients on either one of these 2 immunosuppressive regimens who received this heterogenous extended primary series of SARS-CoV-2 vaccine with 2 doses of AZD1222 and a single dose of BNT162b2. There is evidence that after the vaccination, there is an increase of anti-HLA antibody and mRNA vaccine can induce very strong immunogenicity. Therefore, we assessed the anti-HLA antibody, including panel-reactive anti-HLA antibody (PRA) and DSA before and after BNT162b2 vaccination.
This is a single-center, prospective, cohort study that was conducted at the King Chulalongkorn Memorial Hospital, Bangkok, Thailand, from July 2021 to February 2022. At the time of the study, there was an outbreak of the delta variant. The inclusion criteria were kidney transplant recipients older than 18 y of age who underwent kidney transplantation for more than 6 mo with stable allograft function and were at least 6 wk on either one of the 2 immunosuppressive regimens, the standard regimen (TAC, MPA, and prednisolone) or the MPA-sparing regimen (mTORi, low dose TAC, and prednisolone). Kidney transplant recipients with active rejection or infection within 3 mo before screening or with a history of SARS-CoV-2 infection were excluded from the study. Most of the enrolled patients were included in the ongoing ODKT trial (Thai clinical trial registry; TCTR20190228005) which is an open label, randomized clinical trial comparing the outcomes between standard immunosuppressive regimen (TAC, MPA, and prednisolone) and CNI reduction (mTORi, low dose TAC, and prednisolone, also called the MPA-sparing regimen). All of the patients in this ODKT trial were ABO-compatible kidney transplant recipients without preexisting or presence of DSA at the time of enrollment. Patients who did not participate in the ODKT trial have been selected to receive immunosuppressive regimens based on many reasons such as risk of rejection, history of cytomegalovirus, or polyomavirus (BK) infection, and CNI nephrotoxicity proven by surveillance allograft biopsy.
The standard immunosuppressive regimen consists of TAC (Prograf or Advagraf, Astellas, Tokyo, Japan) with a trough level of 4–7 ng/mL, MPA (mycophenolate mofetil [MMF], Cellcept, Roche, Basel, Switzerland; or enteric-coated mycophenolate sodium, Myfortic, Novartis, Basel, Switzerland) 1000–1500 mg/day, and prednisolone (standard group). The MPA-sparing regimen comprised mTORi (sirolimus, Rapamune, Pfizer, New York, NY; or everolimus, Certican, Novartis, Basel, Switzerland) with a trough level of 5–10 ng/mL, low dose TAC (Prograf or Advagraf, Astellas, Tokyo, Japan) with a trough level of 2–4 ng/mL, and prednisolone (MPA-sparing group). The immunosuppressive regimens were not changed for at least 6 wk before entering the study and continued throughout the study period. The trough level of TAC, everolimus, and sirolimus were measured every 1–3 mo in every outpatient follow-up visit.
Patients were randomly tested for baseline anti–SARS-CoV-2 S antibody to screen for unrecognized asymptomatic COVID-19 infection before entering the study. Forty-four patients were tested and none of them had anti–SARS-CoV-2 S antibody. After enrollment, 2 doses of AZD1222 (AstraZeneca, Cambridge, United Kingdom) were administered 12 wk apart. Four weeks after receiving 2 doses of AZD1222, a full single dose of BNT162b2 (Pfizer, New York, NY) was administered to all recipients. Blood was collected at 4 wk after 2 doses of AZD1222 were administered, and at 4 wk after BNT162b2 was administered. The blood samples were tested for anti–SARS-CoV-2 S antibody level (Elecsys, by Cobas e 411 analyzer; Roche Diagnostics, Basel, Switzerland). According to the cut-off index of the test, anti–SARS-CoV-2 S antibody level ≥ 0.823 binding antibody units (BAU)/mL was considered reactive or seroconverted. Patients were screened for COVID-19 symptoms. The risk factors were assessed from questionnaires and medical history of the patients at every visit. Patients who had either symptomatic or asymptomatic COVID-19 infection during the study period, which was confirmed by a positive SARS-CoV-2 reverse transcription polymerase chain reaction (RT-PCR), were excluded from the study. After all patients were vaccinated, they were required to remain in the waiting area for observation for 30 min. All serious side effects were reported directly to their transplant nurse coordinator or nephrologist.
The anti-HLA antibody was evaluated in all participants by solid phase (Luminex bead-based assays) before and 4 wk after receiving BNT162b2. PRA was calculated using Luminex phenotype beads. DSA was determined by matching between anti-HLA antibody and donor HLA typing using a molecular method.
Categorical data were presented as counts and percentages. Continuous data were presented as mean ± SD or median and interquartile range (IQR) as appropriate. The differences between the groups were tested using Chi-square test for categorical data and independent t test or one-way ANOVA for continuous data. Antibody levels were log-transformed before t-test due to non-Gaussian distribution of the data. All analyses and visualizations were performed using GraphPad Prism version 9.0 for Windows (GraphPad Software, San Diego, CA) and SPSS statistical analysis package (version 28.00; SPSS Inc, Chicago, IL).
All participants provided written informed consent prior to their enrollment in this study and medical records were thoroughly reviewed. The study was approved by the Institutional Review Board of the Research Ethics Review Committee for Research Involving Human Research Participants, Health Sciences Group, Chulalongkorn University (Institutional Review Board number 477/64), and was conducted according to the Declaration of Helsinki 1983. The study was registered in the Thai Clinical Trials Registry (TCTR20220402001).
A total of 138 kidney transplant recipients were enrolled, of which 19 patients did not complete the 3 doses of the vaccination. There were 4 patients who developed SARS-CoV-2 infection, which was confirmed by polymerase chain reaction (PCR) before completing the study (Figure 1). Therefore, 115 patients completed the study, of which 39 (33.91%) and 76 (66.08%) patients were in the MPA-sparing and standard groups, respectively. The mean age (± SD) of the recipients was 50.65 ± 11.40 y, which was not different between the 2 groups (Table 1). The mean (±SD) transplant vintage of the standard and the MPA-sparing groups were 6.13 ± 5.68 y and 6.52 ± 4.21 y (P = 0.122), respectively. There were lower proportion of female recipients in the MPA-sparing group (35.8%) compared to the standard group (55.2%) (P = 0.049). The mean PRA (±SD) of the standard group was higher than the MPA-sparing group (11.74 ± 3.06% and 1.41 ± 0.81%, P < 0.001). The mean (±SD) total white blood cell counts was comparable between the 2 groups. However, the lymphocyte count of the MPA-sparing group was higher than the standard group (2332.10 ± 1235.91 cells/μL and 1899.55 ± 1284.59 cells/μL, respectively, P = 0.043).
The median anti–SARS-CoV-2 S antibody level 4 wk after 2 doses of AZD1222 was 8.85 (IQR = 00.00–180.81) BAU/mL and significantly increased to 676.64 (IQR = 6.02–3644.03) BAU/mL at 4 wk after receiving BNT162b2 (Table 2 and Figure 2). The overall seroconversion rates were 69.6% after receiving 2 doses of AZD1222 and 80% after receiving BNT162b2 (Table 3). The MPA-sparing group had a higher anti–SARS-CoV-2 S antibody level after receiving AZD1222 and higher anti–SARS-CoV-2 S antibody level after receiving BNT162b2 compared to the standard group (Table 2 and Figure 3). The seroconversion rate after receiving 3 doses of the vaccines was 97.4% in the MPA-sparing group and 71.1% in the standard group. The anti–SARS-CoV-2 S antibody level among the MPA-sparing group was higher than the standard group (P < 0.001, Table 3 and Figure 4). We also evaluated the seroconversion rate after receiving the third dose of BNT162b2 in 35 patients who had negative anti–SARS-CoV-2 S antibody after receiving 2 doses of AZD1222. Twelve (34.3%) patients have seroconverted after receiving the third dose; 7 of 8 (87.5%) patients from the MPA-sparing group and 5 of 27 (18.5%) patients from the standard group seroconverted (P < 0.001, Table 3).
The anti-HLA antibody was measured before and 4 wk after receiving BNT162b2. Eleven of 115 patients were positive for anti-HLA antibody (PRA > 0%) before BNT162b2 vaccination (10 patients were in the standard group and one patient was in the MPA-sparing group), of which 4 patients had DSA and all of them were in the standard group (Table S1, SDC, http://links.lww.com/TXD/A467). None of the patients developed de novo DSA after BNT162b2 vaccination. Out of 104 patients, one patient was negative for anti-HLA antibody after receiving 2 doses of AZD1222 but later developed anti-HLA antibody after receiving BNT162b2.
There were no serious local or systemic adverse events such as bruising, bleeding, chest discomfort, severe headache, vomiting, seizure, or stroke-like symptoms, within 30 min after each vaccination. During a 3-mo follow-up period, 7 (6.1%) of 115 patients developed COVID-19 infection of which 2 (5.1%) were in the MPA-sparing group and 5 (6.6%) patients were in the standard group (Table S2, SDC, http://links.lww.com/TXD/A467). One of the 5 patients in the standard group who had no seroconversion after receiving 3 doses of the vaccines experienced mild pneumonia while the remaining 4 patients only presented with upper respiratory tract symptoms. None had serious COVID-19 infection. There were no morbidity and mortality in this cohort.
The results in the present prospective study demonstrated that the overall seroconversion rate of the extended primary series of 2 doses of AZD1222 followed by a single dose of BNT162b2 in kidney transplant recipients was 80%. The MPA-sparing immunosuppressive regimen group had a higher seroconversion rate compared to the standard regimen group (Table 3). The MPA-sparing regimen group had a higher anti–SARS-CoV-2 S antibody level compared to the standard regimen group. The seroconversion rate of only the third dose of BNT162b2 was 34.3% of which 87.5% were from the MPA-sparing regimen group and 18.5% were from the standard group. In the standard group, the baseline PRA was higher. However, the anti-HLA antibody, PRA, and DSA of both groups remained unchanged after receiving BNT162b2. Seven of 115 patients experienced SARS-CoV-2 infection after completing the vaccination. Only one patient had mild pneumonia. Immunization is crucial for posttransplant recipients especially in the COVID-19 era. Many vaccines, including COVID-19 vaccine, yielded poor response in immunocompromised and kidney transplant recipients. The third dose of mRNA COVID-19 vaccine improved immune response compared to the standard 2 doses. The heterologous COVID-19 vaccination with vector-based and mRNA vaccines improved the immune response in transplant recipients. The priming with vector-based vaccine prior to mRNA vaccination might result in higher SARS-CoV-2-specific CD4 and CD8 T-cell levels in healthy individual compared to mRNA vaccination alone. However, the distinction of these cellular immunity activation between vector-based and mRNA vaccines could not be demonstrated in the organ transplant patients. Further studies are needed to understand the mechanism of the immune response to heterologous vaccination in transplant recipients. The present study demonstrated the efficacy of this extended primary series of 3 doses of heterologous vaccination, which had an 80% seroconversion rate compared to previous homologous vaccination using 3 doses of mRNA regimen. The 3-doses homologous vaccination had a seroconversion rate between 62.3% and 68.0%. Immunosuppressive drugs have been considered as the major factor that blunt the immune response; MPA is the most recognized agent to reduce immune response to vaccination. The present prospective cohort study enrolled patients on standard and MPA-sparing regimens. We found that patients from the MPA-sparing regimen group had significantly higher seroconversion rate (97.4% versus 71.1%) and median anti–SARS-CoV-2 S antibody level (3060.69 BAU/mL versus 113.91 BAU/mL) compared to the standard group. A study from Osmanodja et al had similar results. Patients with reduced MPA dose or had temporary stopped using MPA during the fourth dose of the vaccination provided better immune response compared to patients with unchanged MPA dose. There were 2 recent studies published comparing the immune response between CNI + MPA and CNI + mTORi. However, there were some differences in the levels of the immunosuppressive drugs between the present study and the 2 previous studies. In addition, the protocol of vaccination for both previous studies was 2 doses of mRNA vaccine. A study from Netti et al showed that recipients who received 2 doses of BNT162b2 and were on immunosuppressive regimen of TAC (trough level 5–7 ng/mL) + everolimus (trough level 3–5 ng/mL) + prednisolone had a higher anti–SARS-CoV-2 IgG, higher percentages of anti–SARS-CoV-2 S1/RBD Ig, and SARS-CoV-2-specific T cell–derived IFN-γ release compared to the standard regimen group of TAC (5–7 ng/mL) + MMF (1000 mg/day) + prednisolone. A randomized study conducted by de Boer et al compared the immune response between 16 patients on TAC (5–8 ng/mL) + MMF (1000 mg/day) + prednisolone and 16 patients on low dose TAC (1.5–4 ng/mL) + everolimus (3–6 ng/mL) + prednisolone; SARS-CoV-2 anti-spike receptor binding domain IgG antibody level after receiving 2 doses of mRNA vaccine was significantly higher in the low dose TAC + everolimus + prednisolone group. However, there were no differences in the T-cell response to SARS-CoV-2 in both subgroup of patients who had tested for T-cell response. These findings support the benefit of MPA-sparing regimen in both humoral and cellular immune responses to vaccination. The difference in lymphocyte counts between the 2 groups may contribute to the difference in the immune response to vaccination. The lower lymphocyte count of the standard group may lead to lower anti–SARS-CoV-2 S antibody level. The difference in white blood cell numbers between the 2 immunosuppressive regimens has been previously reported in the randomized control trials. A higher incidence of leukopenia has been found in the MPA with CNI group. The present study also found that none of the previously anti-HLA antibody negative patients developed anti-HLA antibody during the follow-up period. Moreover, the PRA levels in anti-HLA antibody positive patients remained stable without developing any DSA. This finding shows that this vaccination strategy was safe among patients using different immunosuppressive regimens. This vaccine strategy is also effective. Only one out of 115 patients developed mild COVID-19 pneumonia. There were certain strengths in the present study. The 2 specific immunosuppressive regimens used in this study were strictly controlled for target Ctrough level. Because the low intensity immunosuppression can lead to de novo DSA, our study monitored for anti-HLA antibody, PRA, and DSA. We found that there were no elevated levels of anti-HLA in our transplant patients. More than one-third of our patients were randomly tested for unrecognized asymptomatic COVID-19 infection by screening for anti–SARS-CoV-2 S antibody prior entering the study. Furthermore, the patients who had COVID-19 infection during the study were also excluded from the final analysis. As a result of this, we were able to minimize the confounder of anti–SARS-CoV-2 S antibody level. Admittedly, there were some limitations in the present work. This study only measured the anti–SARS-CoV-2 S antibody level. However, it has been shown that anti–SARS-CoV-2 S antibody level together with neutralizing antibody could prevent the acquisition of SARS-CoV-2 infection and had a good correlation. As one-third of the patients have been screened for anti–SARS-CoV-2 S antibody before entering the study, we should keep in mind that two-thirds of the patients may acquire asymptomatic COVID-19 infection before enrollment. Since vaccination against COVID-19 infection is crucial for our vulnerable transplant patients during the outbreak, we used the cohort study design instead of the randomized controlled study, in which patients may require a washout period of the immunosuppressive regimen before vaccination. Further studies of immunosuppressive regimen switching from the standard TAC with MPA to mTORi with low dose TAC during immunization should be conducted. As the PRA of the MPA-sparing group was lower, long-term administer of the regimen in high immunological risk patients cannot be recommended. The long-term efficacy and outcome of the heterologous vaccination and the MPA-sparing regimen should be further studied.
The present study demonstrated the efficacy and safety of the extended primary series of the 2 doses of AZD1222 and a single dose of BNT162b2 vaccination in kidney transplant recipients. The MPA-sparing regimen (mTORi, low dose TAC, and prednisolone) provides favorable humoral immune response. Studies with a greater number of patients and randomized controlled studies should be carried out in the future to confirm the benefit of the regimen.
The authors are grateful to Nisarat Runesawang, Manatchaya Sangpraphan, Ratime Chanchaidechachai, and Pornsawan Piamsom for vaccinating all patients in this study. | true | true | true |
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PMC9605833 | Peng Ma,Wen Han,Cunying Meng,Xiaohong Tan,Pengfei Liu,Lei Dong | LINC02389/miR-7-5p Regulated Cisplatin Resistance of Non-Small-Cell Lung Cancer via Promoting Oxidative Stress | 19-10-2022 | Background Non-small-cell lung cancer (NSCLC) is one of the most common malignancies worldwide, and cisplatin-based chemotherapy is the main treatment for NSCLC. However, cisplatin resistance of NSCLC cells is a major challenge for NSCLC treatment. Materials and Methods qRT-PCR and Western blot were performed to detect the expression of LINC02389 and miR-7-5p in NSCLC tissues and cell lines. Cell counting kit-8 (CCK-8) assay and flow cytometry assay were applied to exam cell proliferation and apoptosis rate of NSCLC cells. The interaction between LINC02389 and miR-7-5p was verified by dual luciferase reporter gene assay, RNA pull-down assay, and RNA immunoprecipitation (RIP) assay. Additionally, cisplatin-resistant NSCLC cells were generated to assess the biological function of LINC02389 and miR-7-5p in cisplatin resistance of NSCLC. Results LINC02389 was highly expressed in NSCLC tissues and was correlated with poor prognosis of NSCLC patients. Knockdown of LINC02389 inhibited cell proliferation and promoted cell apoptosis of NSCLC, whereas miR-7-5p knockdown exerted the opposite effects. Moreover, LINC02389 negatively regulated the expression of miR-7-5p. In addition, LINC02389 was overexpressed, yet miR-7-5p was downregulated in cisplatin-resistant NSCLC cells compared with their parental cells. Moreover, oxidative stress biomarkers were overexpressed in cisplatin-resistant cells and were regulated by LINC02389. Besides, LINC02389 could reverse the inhibitory effect of cisplatin on NSCLC cells, which was partially reversed by attenuating the expression of miR-7-5p. Conclusion Our research firstly demonstrated that lncRNA LINC02389 acted as an oncogene to promote progression, oxidative stress, and cisplatin resistance through sponging miR-7-5p and may provide therapeutic targets for NSCLC. | LINC02389/miR-7-5p Regulated Cisplatin Resistance of Non-Small-Cell Lung Cancer via Promoting Oxidative Stress
Non-small-cell lung cancer (NSCLC) is one of the most common malignancies worldwide, and cisplatin-based chemotherapy is the main treatment for NSCLC. However, cisplatin resistance of NSCLC cells is a major challenge for NSCLC treatment.
qRT-PCR and Western blot were performed to detect the expression of LINC02389 and miR-7-5p in NSCLC tissues and cell lines. Cell counting kit-8 (CCK-8) assay and flow cytometry assay were applied to exam cell proliferation and apoptosis rate of NSCLC cells. The interaction between LINC02389 and miR-7-5p was verified by dual luciferase reporter gene assay, RNA pull-down assay, and RNA immunoprecipitation (RIP) assay. Additionally, cisplatin-resistant NSCLC cells were generated to assess the biological function of LINC02389 and miR-7-5p in cisplatin resistance of NSCLC.
LINC02389 was highly expressed in NSCLC tissues and was correlated with poor prognosis of NSCLC patients. Knockdown of LINC02389 inhibited cell proliferation and promoted cell apoptosis of NSCLC, whereas miR-7-5p knockdown exerted the opposite effects. Moreover, LINC02389 negatively regulated the expression of miR-7-5p. In addition, LINC02389 was overexpressed, yet miR-7-5p was downregulated in cisplatin-resistant NSCLC cells compared with their parental cells. Moreover, oxidative stress biomarkers were overexpressed in cisplatin-resistant cells and were regulated by LINC02389. Besides, LINC02389 could reverse the inhibitory effect of cisplatin on NSCLC cells, which was partially reversed by attenuating the expression of miR-7-5p.
Our research firstly demonstrated that lncRNA LINC02389 acted as an oncogene to promote progression, oxidative stress, and cisplatin resistance through sponging miR-7-5p and may provide therapeutic targets for NSCLC.
Lung cancer is the cancer with the highest mortality and morbidity worldwide, causing a huge medical burden for cancer treatment worldwide [1, 2]. However, deteriorating air conditions and a booming tobacco economy have placed people at risk of a variety of lung cancer causes and different types of cancer [3, 4]. Among them, non-small-cell lung cancer (NSCLC) is the most common lung cancer. For perioperative or unresectable NSCLC, adjuvant therapies such as chemotherapy, radiotherapy, and targeted therapy play an important role [5, 6]. Among them, cisplatin-based chemotherapy has become the first-line treatment for unresectable NSCLC. Interestingly, cisplatin exerts its anticancer effects by damaging nuclear DNA [7, 8], but DNA repair mechanisms will facilitate the rapid acquisition of cisplatin resistance in cancer cells [9]. Therefore, cisplatin resistance has become a difficult problem in current clinical treatment, and it is necessary to further explore how to reverse this drug resistance. Recently, lncRNAs (long noncoding RNA) have been identified to be involved in mediating cisplatin resistance [10]. lncRNA is a group of RNA which does not encode proteins but plays a vital role in epigenetic regulation and modification [11]. lncRNA always exerts their functions by interacting with mRNA, miRNA, proteins, and even DNA [12]. Many biochemistry intracellular processes such as epithelia mesenchymal transition (EMT) and oxidative stress are regulated by lncRNA through its upstream effects [13]. In NSCLC initiation and progression, lncRNA MALAT1 is able to cause chemoresistance by the miR-197-3p/p120 catenin axis [14]. Otherwise, lncRNA functions in a way called compete endogenous RNA (ceRNA). lncRNA PTAR can sponge miRNA101 like a sponge that results in the miRNA downregulation and promote NSCLC cell proliferation, migration, and invasion [15]. LINC02389 is reported to be a significant prognostic factor in lung squamous cancer in a genome-wide analysis [16]. However, the function of LINC02389 in NSCLC remains buried. miRNA exhibits inhibitive effects in gene transcription initiation and the stability and translation of mRNA in recent studies [17]. As a member of miRNA family, miR-7 plays a crucial role in posttranscriptional modification. Moreover, substantial studies of miR-7-5p have revealed that miR-7-5p is a tumor suppressor in hepatocellular carcinoma, color rectal cancer, breast cancer, and glioblastoma. Downregulation of miR-7-5p causes OGT overexpression in colorectal cancer resulting in disease progression [18]. It is also reported in colorectal cancer that miR-7-5p is negatively regulated by ZFAS1 inducing cancer cell progression [19]. miR-7-5p is also involved in glioma tumorigenesis via the miR-7-5p/EGFR/PI3K/AKT/c-MYC feedback loop [20]. Intriguingly, miR-7-5p downregulates KLF4 in colorectal cancer as well as in esophageal cancer, but this biological downregulation inhibits tumorigenesis of esophagus cancer [21, 22]. As discussed before, miR-7-5p is negatively related both in gastric cancer and glioblastoma coincidentally [23, 24]. In addition, miR-7-5p is determined to be related with tumor recurrence and metastasis in a cohort study [25]. Considering the cancer cell chemoresistance, this miRNA promotes resistance of cervical cancer cells but attenuates doxorubicin resistance of small-cell lung cancer cells [26, 27]. Among the NSCLC, Li et al. found that miR-7-5p induces apoptosis, cell growth inhibition, and cell cycle arrest via regulating PAK2 [28]. Generally, miR-7-5p has already been widely reported in cancer. In this study, we prove a novel mechanism of miR-7-5p regulation in NSCLC. The underlying mechanisms in LINC02389/miR-7-5p-regulated cisplatin resistance are still obscure. Herein, we conducted experiments in vitro to analyze the role of LINC02389/miR-7-5p alteration which will provide a novelty in NSCLC treatment.
From 2016 to 2018, 257 patients admitted to the Second Affiliated Hospital of Xi'an Jiaotong University were recruited into the experiment after being diagnosed with NSCLC by imaging and histopathological examinations. All patients have been fully informed and signed the written consent. Cancer and adjacent normal tissues were obtained during the surgery. Our study meets the requirements of declaration of Helsinki and was approved by the ethics committee of the Second Affiliated Hospital of Xi'an Jiaotong University (no. 2015-ms-45).
Five cisplatin-based chemotherapy-resistant and 5 cisplatin-based chemotherapy-sensitive patients' cancer and adjacent tissues were collected and frozen for RNA sequencing to identify the differentially expressed lncRNA between resistant and sensitive cancer tissues.
A549, HCC827, and MRC-5 cell lines were obtained from American type culture collection (ATCC, Genetimes, Shanghai, China). A549 was cultivated with Ham's F-12K (Kaighn's) medium and HCC827 was treated with RPMI 1640 medium. MRC-5 cells were cultured with DMEM. All the medium was supplemented with 10% FBS and 1% penicillin and streptomycin. Cells were incubated at 37°C with 5% CO2 and proper humidity. To establish the LINC02389 and miR-7-5p knockdown and miR-7-5p overexpression cell model, lentivirus was constructed and generated by GeneChem (Shanghai, China). Cells were seeded in a 6-well plate before the day of transfection and cultivated to 50-80% confluency. Afterwards, treat the lentivirus with prepared cells following the introductions and amplify the stable transfected cells for couple of days. The efficiency could be visualized by fluorescence microscope. qRT-PCR was performed to detect the expression level.
Each group of cells was washed with PBS three times and extracted with TRIzol reagent. Isolate the total RNA from the cytoplasm and nucleus by centrifugation. The Advantage RT-for-PCR Kit (TaKaRa, Japan) was applied for reverse transcription and PCR assay. Reversely transcribe the RNA into cDNA following the instruction. The thermal cycler was programmed and heated already before the experiment. Place each group of template RNA into the system and set up the reaction. Collect the relative expression level of each group and analyze the final data subsequently.
Each group of cells was washed with PBS three times and extracted with RIPA supplemented with protease inhibitor and phosphatase inhibitor on ice for 10 mins. Vortex and centrifuge the solution at 4°C and pipette the supernatant for BCA quantitative analysis to identify the protein concentration. BCA kits were provided by Beyotime Biotechnology (Shanghai, China). Each protein sample was supplemented with 5x loading buffer and boiled for 10 mins. Then, samples were electrophoretically separated on sodium dodecyl sulfate-polyacrylamide gel (SDS-PAGE) and transferred onto polyvinylidene fluoride (PVDF) membrane via semidry assay afterwards. Block the membrane in 5% skim milk for 1 h and wash with TBST slightly. Incubate the membrane with antibody against H3K27ac in tube at 4°C and shake overnight. Histone H3 was set as internal control. Wash the membrane with TBST three times for 10 mins each and then incubate the corresponding secondary antibody with H3K27ac and Histone H3 for 1-2 h at room temperature. Eventually, wash the membrane three times with TBST for 10 mins each before detection.
The ChIP assay was performed with the Chromatin Immunoprecipitation (ChIP) Assay Kit (Beyotime, China). Different groups of cells were crosslinked with 1% formaldehyde for about 10 mins at room temperature and quenched with glycine. After ultrasonication, the cell lysates were incubated with primary antibody anti-H3K27ac overnight. The immunoprecipitated DNA fragments were subjected to qPCR analysis with LINC02389-specific primers described before.
For the miR-7-5p target LINC02389 dual luciferase reporter gene assay, synthesized PmirGLO-LINC02389-wt/mut vector and miR-7-5p-overexpression/NC vector were cotransfected with A549 and A549-R cells. PmirGLO-LINC02389-wt/mut vector and miR-7-5p-overexpression/NC vector were designed and synthesized by GeneChem (Shanghai, China). The luciferase activity was measured with the Dual Luciferase Assay system, and the luciferase activity was normalized via Renilla luciferase activity.
RNA pull-down assay was performed to explore the internal crosstalk between LINC02389 and miR-7-5p. Magnetic beads with specific binding sites to LINC02389-wt/mut were incubated with cells overnight, followed by elute and purification. The resultant was subjected to qRT-PCR to detect the miR-7-5p expression.
The ROS/superoxide detection assay kit (cell-based) (Abcam, UK, ab139476) was utilized to detect the ROS level in each group. Seed the cells into polystyrene tissue culture plates one day before the experiment and make sure a 50-70% confluency on the day of experiment. Then, follow the instruction of the assay kit and handle the different group of cells properly. Detect the results via microplate reader and collect the data for further analysis.
To detect the MDA and GSH and SOD expressions in cells, ELISA was performed using a relevant ELISA kit from Abcam (MDA, ab238537; GSH, ab193767; SOD, ab277415), following the manufacturer protocol. Specific antibody to MDA and GSH and SOD linked with enzyme was incubated with cell lysates. The activity of enzyme demonstrates the expression level of each protein.
The cell migration capacity was detected by Transwell assay. Different groups of cells were seeded onto small chambers added serum-free medium place on the 12-well plate. The lower wells were loaded with specific medium supplemented with FBS. After 24 h cultivation at 37°C, remove the upper chamber and fix with paraformaldehyde and stain with crystal violet. Image the results via a microscope.
Cell counting kit 8 (CCK-8, Abcam, Shanghai, China) was utilized to detect the proliferation rate and IC50 of A549 and A549-R cells. For both experiment processes, cells were plated into a 96-well plate and cultivated to 50% confluency approximately. Add CCK-8 reagent to each well and incubate for a proper time following the instructions, and the cell proliferation rate curves should be plotted by measuring the optimal density (OD) at 450 nm. Besides, for the IC50 detection, different concentrations of cisplatin should be added to each well to form a concentration gradient.
Caspase 3/7 detection reagent (GeneChem, Shanghai, China) was applied to detect the apoptosis rate. Briefly speaking, add diluted reagent to cells and incubate 30 mins, and then, measure the fluorescence at 502-530 nm approximately.
All the results were replicated at least three times, and the most representative figure was demonstrated. All data are demonstrated as the mean ± S.D. Student's t-test was performed to identify the difference between two independent samples, and the difference among the groups was determined by two-way ANOVA using GraphPad Prism 8.2 (GraphPad Prism Software, San Diego, CA, USA). Highlighted in the pictures with a horizontal line means P value of <0.05 was considered statistically significant.
LINC02389 was identified by RNA sequence; heat map for top 20 differentially expressed genes between cisplatin-based chemotherapy-resistant (n = 5) and chemotherapy-sensitive groups (n = 5) are shown in Figure 1(a). In 257 NSCLC patients, LINC02389 was overexpressed in cancer tissues compared with normal tissues (Figure 1(b)). Survival analysis showed that LINC02389 correlated with poor prognosis in 257 NSCLC patients (Figure 1(d)). In addition, in 93 local recurrent patients, we observed higher expression of LINC02389, indicating its potential role in mediating cisplatin-based chemotherapy resistance (Figure 1(c)). Moreover, we have found increased MDA level in local recurrent patients (Figure 1(e)) and decreased GSH expression in local recurrent patients (Figure 1(f)), suggesting that oxidative stress plays important roles in regulating cisplatin-based chemotherapy resistance. Furthermore, we have found increased MDA level and decreased GSH expression in LINC02389 high expressed patients (Figures 1(g) and 1(h)).
We have found aberrantly high expression of LINC02389 in A549 and HCC827 compared with MRC5 (Figure 2(a)). Then, we have knocked down LINC02389 in A549 (Figure 2(b)) and HCC827 (Figure 2(c)). CCK-8 assay showed that LINC02389 knockdown led to impaired cell proliferation rate (Figures 2(d) and 2(e)). Then, we have applied Transwell assay and found that cell migration assay was decreased by LINC02389 knockdown (Figures 2(f) and 2(g)). Caspase 3/7 assay indicated that LINC02389 knockdown resulted in enhanced apoptosis (Figures 2(h) and 2(i)).
We have constructed cisplatin-resistant A549 cell line (A549-R); the RI was 4.79/0.89 = 5.38 (Figure 3(a)). We found that LINC02389 was overexpressed in A549-R compared with A549 (Figure 3(b)). After knocking down LINC02389 in A549-R (Figure 3(c)), we have noticed decreased IC50 of A549-R (2.21 μg/ml), suggesting that LINC02389 was essential in mediating cisplatin resistance of A549 (Figure 3(d)). Based on these results, we assumed that LINC02389 expression was activated in A549-R. According to UCSC genome browser, we found that H3K27ac was important in promoting A549 expression (Figure 3(e)). In addition, we have detected overexpression of H3K27ac in A549-R compared with A549 (Figure 3(f)). By treating with C646, we have found decreased expression of H3K27ac in A549-R (Figure 3(g)); and further, LINC02389 expression can be decreased by C646 (Figure 3(h)). Then, we have found that H3K27ac was enriched in the promoter region of LINC02389 by ChIP (Figure 3(i)), and this enrichment can be suppressed by C646 (Figure 3(j)).
We have found that LINC02389 was predominately expressed in the cytoplasm of A549-R (Figure 4(a)); therefore, we assumed that LINC02389 might regulate cisplatin resistance by sponging microRNAs. Based on starBase, we have predicted potential microRNA targets for LINC02389. By qRT-PCR, we have found that only miR-7-5p was significantly downregulated in NSCLC cells (Figure 4(b)). Moreover, in NSCLC patients, we have found that miR-7-5p was downregulated in NSCLC patients (Figure 4(c)) and in LINC02389 high expressed patients as well (Figure 4(d)). In addition, miR-7-5p was suppressed in A549-R (Figure 4(e)). Therefore, we assumed that LINC02389 might sponge miR-7-5p to regulate cisplatin resistance. In A549-R, we have downregulated the expression of miR-7-5p (Figure 4(f)) and found that IC50 of A549-R was impaired (2.16 μg/ml) (Figure 4(g)); however, enhanced IC50 of A549-R was detected after miR-7-5p overexpressed (6.36 μg/ml) (Figures 4(h) and 4(i)).
After knocking down LINC02389, we have found aberrantly high expression of miR-7-5p in A549 and A549-R (Figures 5(a) and 5(b)). Then, dual luciferase reporter gene assay was applied and showed that miR-7-5p might interact with LINC02389 in A549 and A549-R (Figures 5(c) and 5(d)). Then, RNA pull-down assay confirmed the crosstalk between LINC02389 and miR-7-5p in A549 and A549-R (Figures 5(e) and 5(f)). Then, we knocked down LINC02389 and miR-7-5p simultaneously and found that IC50 was not significantly influenced compared with the control group (Figure 5(g)).
Previously, we have found that oxidative stress was alleviated in cisplatin-based chemotherapy-resistant patients. In this part, we have detected decreased ROS and MDA and increased GSH and SOD in A549-R compared with A549 (Figures 6(a)–6(d)). Then, we found that by knocking down LINC02389 in A549-R, ROS and MDA were increased and GSH and SOD were decreased (Figures 6(e)–6(h)). Moreover, we knocked down LINC02389 and miR-7-5p simultaneously and found that ROS, MDA, GSH, and SOD levels were not significantly influenced (Figures 6(i)–6(l)).
The mechanism by which chemotherapy induces drug resistance in lung cancer is not fully understood. At present, a variety of mechanism theories have been formed, such as DNA damage repair, cancer stem cells, antiapoptosis, and immune escape [29]. However, various molecular mechanisms need to act by affecting RNA or protein expression. miR-7-5p has been extensively studied in NSCLC due to its tumor suppressive effect, and it can affect cell proliferation and apoptosis [28, 30, 31]. LINC02389 has also been suggested to play an important role in NSCLC in previous studies, but its specific mechanism is unclear. We performed qRT-PCR and Western blot analysis of NSCLC and its adjacent tissues. The results showed that LINC02389 expression was higher in NSCLC cell, whereas miR-7-5p was the opposite. Besides, the expression of LINC02389 was significantly reduced in local recurrence patients. These confirm that LINC02389 and miR-7-5p are indeed differentially expressed in NSCLC. We further explored their effects on proliferation and apoptosis in NSCLC cell lines. Knockdown of LINC02389 inhibited apoptosis by CCK-8 and flow cytometry assays, whereas knockdown of miR-7-5p showed the opposite effect. This is similar to the study by Li [28]. In addition, we also found that both LINC02389 and miR-7-5p can promote cancer cell proliferation. In subsequent cisplatin-resistant lung cancer cells, we found that LINC02389 was overexpressed and miR-7-5p was suppressed. Meanwhile, only miR-7-5p was significantly downregulated in cisplatin-resistant cells by qRT-PCR. The effects of LINC02389 and miR-7-5p on tumor cells were significantly different. This change in the drug-resistant cancer cells was induced in vitro and appears to be related. Therefore, we further verified the relationship between them. Firstly, we found that in patients with high LINC02389 expression, miR-7-5p expression was low, and they were negatively correlated. In contrast, the expression of miR-7-5p was significantly increased after LINC02389 knockdown. Subsequently, we used the dual-luciferase reporter gene assay to determine the negative correlation between LINC02389 and miR-7-5p in NSCLC cancer cells in vitro, and RIP and RNA pull-down assays suggested their binding activity. In other words, LINC02389 can act as a ceRNA to reduce the expression of miR-7-5p. We elucidate the expression of LINC02389 and miR-7-5p in cisplatin-resistant cells. Furthermore, the inhibitory effect of cisplatin was reversed when LINC02389 was overexpressed in NSCLC cells. And this reversal could be attenuated by the expression of miR-7-5p. This implies that LINC02389 promotes drug resistance by downregulating miR-7-5p. Our study is the first to demonstrate the role of LINC02389 as an upstream regulator in the downregulation of miR-7-5p. Oxidative stress is an imbalance of free radicals and their metabolism [32]. It causes DNA damage in nucleotides [33]. Many signaling pathways can trigger signaling cascades leading to ROS, such as the NF-κB pathway [34]. In recent years, oxidative stress has also been considered a possible underlying mechanism for tumor cells to develop chemoresistance. We observed significant differences in the expression of MDA and GSH in local recurrence patients. In in vitro experiments, we investigated the expression of many ROS biomarkers at the protein and RNA levels. We found that ROS and MDA were reduced and GSH and SOD were increased in cisplatin-resistant cells, which were reversed by the knockout of LINC02389. However, when LINC02389 and miR-7-5p were simultaneously knocked out, we did not observe significant changes in any of all oxidative stress indicators. This suggests that LINC02389 can promote oxidative stress in cisplatin-resistant cells, and this promotion is achieved by regulating the expression of miR-7-5p. | true | true | true |
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PMC9606244 | Xiaoxia Tian,Junping Lu,Kathleen Nanding,Linzhe Zhang,Yanrong Liu,Mailisu Mailisu,Morigen Morigen,Lifei Fan | The antihyperlipidemic drug potassium piperonate impairs the migration and tumorigenesis of breast cancer cells via the upregulation of miR-31 | 13-10-2022 | potassium piperate,miR-31,breast cancer,cell migration,drug combination | Background Breast cancer is the second cause of cancer death in women, and tumor metastasis is the primary cause of mortality. Due to the involvement of many regulatory molecules and signaling pathways, the occurrence and development of metastases needs to be further studied. MicroRNAs (miRNAs) are ubiquitously expressed small non-coding RNAs that have been shown to play an important role in the diagnosis and treatment of many diseases, as well as representing an attractive candidate for metastasis control. In this study, we investigated the mechanism of potassium piperonate (GBK) in impairing breast cancer cell invasion and metastasis by targeting miR-31. Methods Breast cancer cells, either treated with GBK or left untreated, were assessed for migration and invasion capacities using wound healing and transwell assays. GBK-targeted miRNAs were identified and verified using RT-qPCR. Western blotting was used to validate the changes in expression levels of miR-31-targeted genes. Methylation specific PCR was performed to detect the effect of GBK on the methylation levels of the lncRNA LOC554202 host gene. The synergistic effect of GBK and the chemotherapy drug cisplatin (DDP) on breast cancer cells was verified using cell proliferation, colony formation, and RT-qPCR assays in vitro, and the tumor xenograft model in vivo. Results We found that miR-31 was the main target of GBK. GBK treatment affected the epigenetic modification at CpG sites by downregulating DNA methyltransferases. Thus, the CpG-associated methylation levels of lncRNA LOC554202 decreased significantly, and in turn upregulated both miR-31 and its host gene LOC554202 in breast cancer cells. We also observed the significant inhibition of miR-31-targeted genes following GBK treatment, including RHOA, WAVE3, and SATB2, with functions closely related to cancer cell invasion, migration, and proliferation. Furthermore, we revealed that the combination of GBK and DDP had a synergistic effect on inhibiting the proliferation of breast cancer cells in vitro and in vivo, especially in triple negative breast cancer (TNBC). Conclusions This study investigated the target of GBK in the inhibition of breast cancer migration and invasion, and the underlying mechanisms involved, providing theoretical support for the development of GBK as an auxiliary drug for clinical treatment. | The antihyperlipidemic drug potassium piperonate impairs the migration and tumorigenesis of breast cancer cells via the upregulation of miR-31
Breast cancer is the second cause of cancer death in women, and tumor metastasis is the primary cause of mortality. Due to the involvement of many regulatory molecules and signaling pathways, the occurrence and development of metastases needs to be further studied. MicroRNAs (miRNAs) are ubiquitously expressed small non-coding RNAs that have been shown to play an important role in the diagnosis and treatment of many diseases, as well as representing an attractive candidate for metastasis control. In this study, we investigated the mechanism of potassium piperonate (GBK) in impairing breast cancer cell invasion and metastasis by targeting miR-31.
Breast cancer cells, either treated with GBK or left untreated, were assessed for migration and invasion capacities using wound healing and transwell assays. GBK-targeted miRNAs were identified and verified using RT-qPCR. Western blotting was used to validate the changes in expression levels of miR-31-targeted genes. Methylation specific PCR was performed to detect the effect of GBK on the methylation levels of the lncRNA LOC554202 host gene. The synergistic effect of GBK and the chemotherapy drug cisplatin (DDP) on breast cancer cells was verified using cell proliferation, colony formation, and RT-qPCR assays in vitro, and the tumor xenograft model in vivo.
We found that miR-31 was the main target of GBK. GBK treatment affected the epigenetic modification at CpG sites by downregulating DNA methyltransferases. Thus, the CpG-associated methylation levels of lncRNA LOC554202 decreased significantly, and in turn upregulated both miR-31 and its host gene LOC554202 in breast cancer cells. We also observed the significant inhibition of miR-31-targeted genes following GBK treatment, including RHOA, WAVE3, and SATB2, with functions closely related to cancer cell invasion, migration, and proliferation. Furthermore, we revealed that the combination of GBK and DDP had a synergistic effect on inhibiting the proliferation of breast cancer cells in vitro and in vivo, especially in triple negative breast cancer (TNBC).
This study investigated the target of GBK in the inhibition of breast cancer migration and invasion, and the underlying mechanisms involved, providing theoretical support for the development of GBK as an auxiliary drug for clinical treatment.
Breast cancer is now the most frequently diagnosed cancer and the second leading cause of cancer-related deaths in women worldwide (1). Strategies targeting the primary tumor have markedly improved, including surgery, chemotherapy, radiation therapy, hormone therapy, and targeted therapy. However, metastasis remains the greatest clinical challenge in breast cancer. The mechanisms implicated in tumor metastasis need to be further investigated in order to improve the long-term control of breast cancer progression. Previous research has shown that the deregulated expression of miRNAs is intimately associated with breast tumor invasion and metastasis (2). In recent years, miRNAs have emerged as key players in the processes of gene expression regulation. Ubiquitously expressed miRNAs are approximately 19-24 nucleotides in length, and function by binding to the complementary sequences of their target mRNAs, leading to mRNA degradation as well as the subsequent downregulation or suppression of protein synthesis. miRNAs play a pivotal role in various cellular processes in vivo, such as proliferation, migration, cell death, and cell cycle regulation (3). The miRNA expression patterns differ among different subtypes of breast cancer: the let-7c, miR-10a, and let-7f miRNAs are associated with luminal type A breast cancer; miR-18a, miR-135b, miR-93, and miR-155 have been shown to be related to the basal cell subtype; while miR-142-3p and miR-150 have been shown to be associated with the HER2-positive subtype (4); and miR-10b, miR-26a, and miR-153 have been used as potential biomarkers for triple negative breast cancer (TNBC) 5). About 70% of breast cancers are estrogen receptor (ER) or progesterone receptor (PgR) positive. ER controls the expression of multiple genes and proteins through genomic and non-genomic pathways, whereas PgR is induced by ER, and PgR-related signal transduction pathways are closely related to the occurrence and development of breast cancer (4). The tumor suppressor miRNAs that have been identified in breast cancer include miR-206, miR-17-5p, miR-125a, miR-125b, miR-200, let-7, miR-34, and miR-31. On the contrary, the expression of miR-21, miR-155, miR-10b, miR-373, and miR-520c are positively correlated with the occurrence of breast cancer (6). Piper longum L. (also called long pepper) is a plant used in traditional Chinese medicine, as a source of the antihyperlipidemic agents piperine, piperlonguminine, and pipernonaline (7). Potassium piperonate (GBK) is a derivative of piperine. GBK has the functions of reducing blood lipid levels and cholesterol, with efficacy comparable to that of the commercial antihyperlipidemic drug statins (8). In addition, previous studies have shown that GBK exerts an anti-tumor effect, especially in breast cancer. GBK can specifically inhibit the viability of a variety of breast cancer cells by arresting the cell cycle in G1 phase and inhibiting cell proliferation. Furthermore, GBK can induce breast cancer cell apoptosis through the mitochondria-dependent pathway. However, the potential of GBK to inhibit breast cancer cell invasion and metastasis has not been previously investigated. In this study, we set out to uncover the mechanisms employed by GBK to impair breast cancer cell invasion and metastasis, and aimed to identify the main target of GBK by analyzing miRNA expression pattern changes after GBK treatment. Furthermore, we explored whether the growth and migration of breast cancer cells could be more efficiently inhibited when chemotherapy drugs were used in combination with GBK in vitro and in vivo. In summary, this study further assessed the underlying mechanisms involved in the GBK-mediated inhibition of breast cancer progression. Our finding provides theoretical support for the development of GBK as an auxiliary drug for the clinical treatment of breast cancer.
MCF-10A, MCF-7 and SUM-159 were purchased from National Infrastructure of Cell Line Resource of China. MCF-10A cells were maintained in F12 medium (Gibco) supplemented with 5% horse serum (Gibco), 1%(vol/vol) penicillin/streptomycin/L-Glutamin (Gibco), 10 mg/mL insulin, 20 mg/mL EGF, 100 mg/mL cholera toxin and 0.5 mg/mL hydrocortisone. SUM-159 cells were maintained in F12 medium (Gibco) supplemented with 5% fetal bovine serum (GEMINI), 1% (vol/vol) penicillin/streptomycin/L-Glutamin (Gibco), 5 mg/mL insulin and 10 mg/mL dexamethasone. MCF-7 cells were maintained in Dulbecco’s Modified Eagle Medium (Gibco) supplemented with 10% (vol/vol) fetal bovine serum (GEMINI) and 1% (vol/vol) penicillin/streptomycin/L-Glutamin (Gibco). All cells were cultured at 37°C, 5% CO2 in a humidified atmosphere.
GBK is a generous gift from Professor Gereltu Borjihan of Inner Mongolia University. The purity of Piperine is 99% detected by high pressure liquid chromatography ( Supplementary Figure 1 ). miRNeasy® Serum/Plasma Kit (Qiagen, USA); TransZol Up Kit (TransGene Biotechnology Co., Ltd.); Mir-XTM miRNA First-Strand Synthesis Kit (TaKara); TransScript One-Step gDNA Removal and cDNA Synthesis SuperMix Kit (TransGene Biotechnology Co., Ltd.); SYBR® Premix Ex TaqTM II Kit (TaKara); CCK-8 Kit (Beyotime Biotechnology Co., Ltd.); Crystal Violet (SIGMA); FITC Annexin V Apoptosis Detection Kit I (BD Pharmingen); Protein Antibody (Absin); Tubulin Antibody (TransGene Biotechnology Co., Ltd.); PierceTM ECL Western Blotting Substrate Kit (Thermo, USA); ELISA Kit (Wuhan Xinqidi Biotechnology Co., Ltd.); TransStart FastPfu DNA Polymerase (TransGene Biotechnology Co., Ltd., Lot: M10524); Cisplatin (DDP, Meilun Bio, Lot: D0921A); Etoposide (VP-16), United Laboratories).
The primers used in the real-time quantitative PCR (RT-qPCR) or PCR were designed by Primer 5 software and NCBI Primer-BLAST, synthesized by Shanghai Biotech (Sangon) Beijing Primer Synthesis Company, and Tm value fluctuated at 60°C. Specific primers are shown in Supplementary Tables 1–3 .
miR-31 mimics and inhibitor were synthesized by Biomics biotechnologies (China, Jiangsu) and the sequences are shown in Supplementary Table 4 .
Cells were seeded in 6-well plates. The cells were harvested 2 days after GBK added, washed once in PBS, and lysed in sample buffer (2% SDS, 0.25 M pH 6.8 Tris-HCl, 20 mM dithiothreitol, 10% glycerol, 0.1% Bromophenol blue). 20 μg of protein was separated on 12% Polyacrylamide gel and transferred to a nitrocellulose membrane. Membrane was blocked for 1 hour at room temperature in blocking buffer (5% skim milk in PBS containing 0.05%Tween-20) and then incubated with primary antibodies and peroxidase (HRP)-conjugated secondary antibody. Tubulin or GAPDH was served as a reference protein.
5×103/100 μL cells were seeded in a 96-well plate and incubated at 37°C, 4% CO2. After 24 hours, the cells were treated with GBK. After 48 hours, 10 μL CCK-8 solution was added into each well and returned to the incubator for further 1.5 hours. The viability of the cells was measured using a microplate reader at the wavelength of OD450nm.
The NOD/SCID mice used in this experiment were purchased from Boai Biotechnology Co., Ltd. 12 female NOD/SCID mice aged 3-4 weeks were used to establish heterogeneous tumor models with MCF-7 breast cancer cells. Five mice were selected as the control group, and the other seven as the experimental group. After the tumor volume reached 8 mm3, the experimental group was injected with GBK 10 mg/(kg.1D), while the control group was injected with 0.9% normal saline. Serum samples were taken 21 days after treatment. Extraction of miRNA from mouse serum samples was performed exactly according to miRNeasy® Serum/Plasma Kit (Qiagen, USA) instructions. 200 μL of serum sample were taken from each mouse, diluted in 5x volume of lysate, and after incubated for 5 mins at room temperature, 3.5 μL of the “control” solution was added. Next, 200 μL of chloroform was added and incubated at room temperature for 3 min. The clear supernatant was then transferred to a new EP tube, mixed with 1.5x volume of absolute ethanol and vortexed to mix. All the solution was added to the adsorption column, centrifuged at room temperature, 12000 rpm for 1 min. 700 μL of RWT buffer was added to the adsorption column, centrifuged. 500 μL of RPE buffer was added to the adsorption column, centrifuged. 500 μL of 80% ethanol was added to the adsorption column, centrifuged. The adsorption column was placed on a new collection tube and centrifuged. 14 μL of RNase-free water was added to the adsorption column and incubated for 1 min, then centrifuged. The extracted RNA was stored at -80°C until use.
Extraction of total RNA from cell lines was carried out according to the TransZol Up Kit instruction. Reverse transcription PCR was performed using TransScript One-Step gDNA Removal and cDNA Synthesis SuperMix Kit (TransGene Biotechnology Co., Ltd.), and quantitative real-time PCR was performed using SYBR® Premix Ex TaqTM II Kit (TaKara). GAPDH gene was served as a reference. Gene expression was measured in triplicates.
Cells were seeded into 6-well tissue culture plate. When the cell density reached 100%, scratching and photographing were performed. Replaced old medium with the 1% low-serum medium and continue to culture cells in incubator after drugs were added. Photos were taken every 12 hours on an inverted microscope. The gap distance can be evaluated using Image J software to calculate the cell migration rate.
The subpackaged matrix glue (Corning, US) was removed from -20°C and quickly diluted ten times with serum-free medium on ice. 100 μL diluted matrix glue was applied to the upper and lower surfaces of the transwell chamber in a 24-well plate, and then incubated at 37°C for 1 hour. 5 × 105 cells/mL suspension was prepared and 200 μL was added to each chamber. Simultaneously, 600 μL medium with 5% FBS was added into the lower layer of the transwell chamber. The bottom of the upper chamber was checked for absence of bubbles and the whole plate was incubated at 37°C for 24 hours. A cotton swab was used to gently wipe the upper chamber and carefully remove the sidewall cells. 600 μL 4% PFA was added into each lower chamber and the cells fixed at room temperature for 20 mins. After the PFA was washed off using PBS, 600 μL 0.1% crystal violet was added into the bottom chamber, and the cells stained in the dark for 30 mins at room temperature. The chamber was then taken out and washed with PBS, and the number of cells transferred to the lower surface of the chamber was counted under the microscope.
1×103 cells were seeded into 6-well plate. After being cultured for 24 hours in an incubator, potassium piperonate (GBK), cisplatin (DDP) and etoposide (VP-16) were added with a concentration gradient for further 7-14 days. Then, 1 mL 4% PFA was added to each well for 15 minutes, cells were rinsed with 1 ml PBS, and 500 μL 0.01% crystal violet was added to each well for 15 minutes. The plates were dried and the colonies were counted.
Preparation of bisulfite-modified DNA for methylation analysis was performed according to the EZ DNA Methylation-Gold™ kit (Zymo Research) instructions. In brief, the CT Conversion Reagent and M-Wash Buffer were prepared prior to use. Genomic DNA (200 ng in 20 μL) was converted using 130 μL of CT Conversion Reagent in a PCR cycler, with the following cycle program: 98°C for 10 mins, 64°C for 2.5 hours, 4°C storage up to 20 hours. 600 μL of M-Binding Buffer was added, followed by a single washing step with M-Wash Buffer. 200 μL M-Desulphonation Buffer was added, and then incubated at RT for 20 mins and washed twice with M-Wash Buffer. 10 μL of double-distilled water was added, the samples was centrifuged, and then the DNA was resuspended. MSP was performed on bisufite-converted DNA using the special primer pairs described in Supplementary Table 3 . Every genomic DNA sample was amplified using either the unmethylated or the methylated primer pairs. The PCR products were next separated by agarose electrophoresis according to their densities, which corresponded to the intensities of the PCR products between the methylated and unmethylated primer-pairs.
The mouse model was derived from Jennio Biotechnology company. Twenty immunodeficient female mice between 3 and 4 weeks were used to establish a NOD/SCID mouse xenograft model. 100 μl of prepared 2×106/100 μL MCF-7 cells suspension was injected into the hind leg of NOD/SCID mice, 4 nude mice were selected randomly as the control group, and the other 12 nude mice were divided into 3 experimental groups randomly. The body weight of mice was measured every 3 days after injection. When the tumor mass grew to 8 mm3, the drugs or 0.9% saline was injected into nude mice. Weight and the tumor volume measured with a vernier caliper were recorded every 3 days. The tumor volume was calculated according to the following formula: V=(ab2)/2 (“a” represents the longest diameter of the tumor and “b” represents the shortest). After 20 days, the tumor-bearing mice were killed, the tumor lumps were removed and weighed.
In order to investigate the effect of GBK on the migration and invasion of breast cancer cells, would healing and transwell cell invasion assays were performed. The TNBC cell line SUM-159, the ER-positive breast cancer cell line MCF-7, and the normal human epithelial mammary cell line MCF-10A were scratched and photographed, prior to treatment with different concentrations of GBK. After 24 hours of culture, cell images were captured, and the cell motility changes were analyzed. In order to rule out the influence of cell proliferation, we switched to 1% low serum medium after the scratch treatment and took pictures within one cell cycle. The results showed that the wound closure ability of both SUM-159 and MCF-7 cells was significantly inhibited by GBK ( Figures 1A, B ). We observed a 59% reduction in cell migration in the 150 μg/mL (IC50 of SUM-159 cells) GBK treatment group, which further increased to 71% in the 300 μg/mL GBK treatment group ( Figure 1B ). Moreover, for the SUM-159 cells, GBK treatment inhibited invasion (in the transwell invasion assay) by 64% and 92% after treatment with 150 μg/mL or 300 μg/mL GBK, respectively ( Figures 1C, D ). The administration of GBK to the MCF-7 human non-invasive breast cancer cells also impaired cell invasion, but to a lesser extent ( Figures 1C, D ). GBK treatment had no significant influence on the migration and invasion of normal human epithelial mammary cell line MCF-10A ( Figures 1A–D ). Taken together, these data indicate the anti-migration and anti-invasion roles of GBK in breast cancer, especially in invasive breast cancer cells.
miRNAs have been shown to function as tumor suppressors, implicated directly in the inhibition of cancer progression. Consequently, certain miRNAs may act as specific drug targets in cancer treatment. For instance, miR-21 is involved in the regulation of apoptosis in breast cancer cells (9), miR-151 is found to affect the development of breast cancer by modulation of DNA repair processes, and miR-421 can inhibit the migration and invasion of breast cancer by targeting MTA1 (10, 11). On the basis of such research, we selected nine miRNAs closely associated with the occurrence and development of breast cancer. Of the nine miRNAs selected, miR-22, miR-31, miR-41, and miR-421 function as tumor suppressors, while miR-21, miR-145, miR-150, miR-182, and miR-217 have been shown to promote cancer development (10, 12–21). The downregulation of the tumor suppressor miR-22 in metastatic cancer cells has been shown to be associated with a disproportionately poor prognosis (16, 17). miR-31 inhibits the cell cycle by suppressing the expression of multiple factors involved in the regulation of DNA replication and cell cycle progression. In addition, increasing miR-31 or miR-421 levels have been shown to significantly inhibit the migration and invasion of MDA-MB-231 and MCF-7 cells (10, 15, 22). miR−411 downregulation in breast cancer is associated with lymph node metastasis and histological grade (22). miR-21 overexpression facilitates breast cancer cell proliferation and metastasis in vivo, and plasma miR-21 level is an important biomarker for breast cancer diagnosis 13). miR-145 reduces breast cancer cell migration and inhibits epithelial-mesenchymal transition (14). The upregulation of miR-150 in breast cancer is inversely associated with P2X7 receptor expression levels, which regulates cell growth through apoptosis (12, 23). In TNBC, miR-182 promotes cell proliferation and metastasis by targeting FOXF2, while miR-217 inhibits cell growth, migration, and invasion by targeting KLF5 (18, 20). These miRNAs mainly affect the proliferation, migration, and invasion of breast cancer cells during the development of breast cancer. GBK can specifically inhibit the proliferation and migration of breast cancer cells. Therefore, we wanted to test the effect of GBK on the nine miRNAs described above. Initially, in order to determine the potential miRNA target of GBK, RT-qPCR was used to detect miRNA expression in serum samples derived from the breast cancer xenograft model mice after GBK treatment ( Supplementary Figure 2 ). We found that the differential expression of miRNAs in serum were not significant in GBK-treated mice compared to that in the control group. We then investigated the effect of GBK treatment on the expression of candidate miRNAs in the breast cancer cell lines MCF-7 and SUM-159. Different concentrations of GBK were added to the cells for 48 hours and miRNA expression was detection by RT-qPCR. The expression of miR-31 was consistently upregulated after GBK treatment in the two breast cancer cell lines tested ( Figure 2A ). The differential expression pattern of other miRNAs were not consistent after GBK treatment and thus were excluded from further investigation ( Supplementary Figure 3 ). By analyzing the expression of nine breast cancer related miRNAs, our preliminary results indicated that the tumor suppression effect of GBK may be closely related to miR-31.
In previous studies, the function of miR-31 has been shown to be highly associated with the progression and metastasis of breast cancer. miR-31 plays a fundamental role in the regulation of the invasion-metastasis cascade by targeting critical genes, such as those involved in cytoskeletal rearrangement of cancer-associated fibroblasts (CAFs). RHOA, which participates in the regulation of the actin cytoskeleton, was shown to be a direct target of miR-31 (24, 25). Moreover, another member of the Rho family, RHOBTB1, was shown to be a target of miR-31 in colon cancer (26). WAVE3, an actin remodeling protein, was shown to be overexpressed in invasive breast cancer cells due to miR-31 downregulation, and its expression promoted cancer cell migration and invasion (27). The homeobox gene SATB2 was shown to be a direct target of miR-31 in CAFs and is involved in promoting tumor cell migration and invasion (4). We screened nine target genes closely related to the anti-metastatic function of miR-31, monitoring their expression in MCF-7 and SUM-159 cells after GBK treatment. The expression levels of RHOA, SATB2, and WAVE3 were all downregulated after 48 hours of GBK treatment, indicating that the tumor suppression effect exerted by GBK may be related to invasion/metastasis-associated signaling pathways ( Figures 2B–D , Supplementary Figure 4 ). UBC13, which has a regulatory role in cell death (28), was also downregulated, although its function in tumor invasion is poorly understood. Western blot analysis was also performed to further validate the effect of GBK on the expression of invasion-metastatic related genes. It was demonstrated that the expression levels of RHOA, WAVE3, and SATB2 were all reduced after GBK treatment in MCF-7 and SUM-159 cells ( Figures 3F, G NC group). In order to demonstrate the direct involvement of miR-31 in the regulation of these genes in cancer cells, we tested the effect of miR-31 mimics and inhibitor on the expression of STAB2, RHOA, WAVE3 and other relative genes after transfection into MCF-7 and SUM-159 cells ( Figures 3A, C ; Supplementary Figure 5 ). We found that miR-31 mimcs downregulated STAB2, RHOA, WAVE3 expression, by contrast, miR-31 inhibitor upregulated STAB2, RHOA, WAVE3 expression in both MCF-7 and SUM-159 cells ( Figures 3A–E ). Moreover, inhibition of miR-31 prevented the degradation of these proteins, orchestrated by GBK gradient ( Figures 3F, G inhibitor group). Thus, we can speculate that GBK inhibits the migration and invasion of breast cancer cells by promoting the expression of miR-31, which in turn impaires the expression of miR-31 target genes, such as RHOA, WAVE3, and SATB2.
It has been documented that miR-31 is located in the intronic sequence of long non-coding RNA (lncRNA) LOC554202, and its transcriptional activity is regulated by LOC554202 ( Figure 4A ) (27). It was also demonstrated that the major mechanisms for silencing miR-31 in TNBC is hypermethylation of the CpG island of the LOC554202 promoter region, which may become a new entry point for TNBC treatment (27). We tested the expression of LOC554202 in the MCF-7 and SUM-159 cell lines and found that the expression of LOC554202 was upregulated after GBK treatment ( Figures 4B, C ). Next, we selected two CpG sites within LOC554202, and methylation-specific PCR (MSP) technology was used to detect changes in the methylation level of these sites after GBK treatment ( Supplementary Figure 6 ). We found that the methylation levels of CpG MSP Set2 decreased significantly under GBK treatment in a dose-dependent manner in the two cell lines ( Figures 4D, E ). These data indicate that GBK treatment affects the epigenetic modification at the CpG sites and plays an important role in the upregulation of both LOC554202 and miR-31.
The chemotherapy drugs cisplatin (DDP) and Vepeside (VP-16) are widely used as first-line therapy for metastatic breast cancer and small cell lung cancer 29, 30). In our previous study, we demonstrated that GBK exerted specific inhibitory effects on breast cancer cells but not on normal breast cells or cancer cell types (31). In this preclinical study, we want to evaluate the effect of GBK when used in combination with DDP/VP-16 to treat breast cancer cells. The sensitivity of SUM-159 and MCF-7 cell lines to growth inhibition was determined after a 48 hour incubation with GBK and DDP/VP-16, which was used as a single agent, or in combination at six different concentrations between 0.1× and 4× their respective IC50 for DDP, as well as 0.2× and 4× their respective IC50 for VP-16. The effect of the combined treatment on cell growth inhibition was cell type dependent. The combination of DDP/VP-16 with GBK at all concentrations led to greater growth inhibition compared to either agent alone in both SUM-159 ( Figures 5A–E ) and MCF-7 ( Figures 5F–J ) cell lines, although the increase was smaller for MCF-7 cells. These results demonstrated that chemotherapy drugs DDP/VP-16 combination with GBK has synergistic and dose reduction potential in the proliferation of breast cancer cells SUM-159 and MCF-7, indicating a potential guiding significance for clinical combination treatment.
In colony formation experiments, the colony forming rate represents independent cell survival. The representative images after crystal violet staining showed that GBK can inhibit the formation of cell colonies in TNBC SUM-159. Moreover, this effect is further enhanced when GBK is used in combination with DDP, and this inhibitory ability is positively correlated with drug concentration ( Figure 6A ). The quantitative data also indicated that a combination of GBK and DDP treatment had a synergistic inhibitory effect on breast cancer cell colony formation ( Figure 6B ). The combination treatment with DDP and GBK has a more obvious and stable inhibitory effect on the colony formation than that in VP-16 and GBK treatment group(data not shown), therefore DDP and GBK strategy was chosen to conduct tumor xenograft model experiments in nude mice.
It has been shown that GBK treatment causes the upregulation of miR-31 and its host gene lncRNA LOC554202 in breast cancer cells when acting alone. In order to study whether the combination of GBK and DDP could also have the same synergistic function, RT-qPCR was performed to detect the expression of miR-31-targeted genes in SUM-159 and MCF-7 cells after drug treatment. It was revealed that when SUM-19 cells were treated with DDP and high dose of GBK combination, the expression of RHOBTB1 ( Figure 7A ), ITGA5 ( Figure 7C ), SATB2 ( Figure 7D ), WAVE3 ( Figure 7G ) and RDX ( Figure 7I ) decreased more significantly than those using DDP alone. Meanwhile, changes in the expression of other genes were not significant ( Figures 7B, E, F, H ). The synergistic effect of GBK and DDP combination therapy was more pronounced in SUM-159 cells then that in MCF-7 cells, indicating that the combination treatment has better effect on inhibition of gene expression in the more aggressive breast cancer cell line.
To further evaluate whether the combination treatment with GBK and DDP had a clear inhibitory effect on breast cancer progression in vivo, NOD/SCID immunodeficient mice were used to construct a human breast cancer cell xenograft model by subcutaneously inoculating mice with SUM-159 cells. All of the mice developed subcutaneous xenografts, prior to being divided into four groups and treated intraperitoneally with GBK 10 mg/(kg.1 Day) and DDP 1 mg/(kg.2 Days) in group A, GBK 5 mg/(kg.1 Day) and DDP 1 mg/(kg.2 Days) in group B, 0.9% saline in group C, and DDP 1 mg/(kg.2 Days) in group D, when the tumor volume reached 8 mm3. Mice were monitored for the next 24 days of continuous injection, and the notable anti-tumor effects were observed. We found that DDP alone (group D) could inhibit tumor proliferation in the xenograft model. The combined treatment of GBK and DDP resulted in a further reduction in the tumor volume and the average tumor weight of mice in groups A and B, in comparison to group D. In day 24, we observed a significant decrease in the tumor weight and tumor volume in group A compared with group D ( Figures 8B, C ), indicating that DDP and GBK worked synergistically in a dose dependent manner in inhibiting breast cancer cell proliferation in vivo ( Figures 8A–C ). In addition, the body weights of mice in group C (which were given saline) increased slightly, while the body weights of mice in group D (receiving DDP alone) decreased slightly. Interestingly, the body weights of mice in groups A and B (receiving GBK and DPP in combination) decreased markedly, with group A mice experiencing more pronounced weight loss compared to group B ( Figure 8D ). It was indicated that GBK could also perform as an antihyperlipidemic drug alone with its anti-tumor properties.
In this study, we investigated the mechanisms underlying the GBK-mediated impairment of breast cancer cell invasion and metastasis at the cellular and molecular levels. GBK exerts a significant inhibitory effect on the proliferation and migration of breast cancer cells, in a dose dependent manner. We also identified the main target of GBK by analyzing breast cancer-associated miRNA expression pattern changes during GBK treatment. We found that the expression of both miR-31 and its host gene LOC554202 was upregulated following GBK treatment. Meanwhile, the miR-31 target genes, associated with cell migration and metastasis, were downregulated, indicating that GBK may inhibit cell migration by promoting the expression of tumor suppressor miR-31. Moreover, we demonstrated that the actions of GBK in inhibiting the growth and migration of TNBC cells were further enhanced when used in combination with the chemotherapy drug DDP, both in vitro and in vivo. Given the large number of studies reporting the link between abnormal miRNA expression and a number of human diseases, it is evident that these molecules are key regulators of many biological processes. In addition, miRNAs play role in the regulation of cancer growth. While some miRNAs can be used as prognostic markers of malignant tumors, others are potential targets for cancer treatment 32). Considering that the expression of miRNAs affects almost every stage of malignant tumor formation (occurrence, development, metastasis, and drug resistance) (33) and that it is relatively stable and can be detected in a variety of biological fluids (such as blood, urine, cerebrospinal fluid and saliva) (34), miRNAs may become valuable biomarkers in cancer therapy. One especially interesting cancer-related miRNA is miR-31, which is frequently altered in a large variety of cancers (35, 36). For example, in breast cancer, the loss of miR-31 expression is associated with a high risk of metastases (35). Existing studies have demonstrated that miR-31 is a tumor suppressor with an important role in the occurrence and development of cancer. miR-31 directly acts on the 3’UTR region of multiple target genes such as RHOA, SATB2, or WAVE3, which are implicated in cell invasion, migration, and proliferation. RhoA is required for the motility and migration of breast cancer cells, through its involvement in actin and microfilament skeleton polymerization (37). The RhoA/Rho-associated coiled coil-forming protein kinase (ROCK) signaling pathway plays an important role in this process. In addition to directly affecting cell microfilament skeleton polymerization, it also affects the degradation of the extracellular matrix (ECM) (38), while activated ROCK can promote tumor cell invasion and metastasis 39). Special AT-rich sequence-binding protein-2 (SATB2) is an important nuclear matrix protein that participates in actin cytoskeleton regulation (40). The main targets of SATB2 are matrix metalloproteinase-3 (MMP3) and TIMP3 (41). The extracellular domain of cell adhesion factor E-cadherin can be cleaved by MMP3 directly, which facilitates the metastasis of cancer cells (42). SATB2 also acts on the MEK5/ERK5 signaling pathway (43). Extracellular signal-regulated kinase 5 (ERK5) is a mitogen-activated protein kinase that can induce actin cytoskeleton remodeling and promote cell migration. WAVE3 has multiple downstream effectors. It was found that WAVE3 has an intricate regulatory relationship with the Akt-associated and NF-κB signaling pathways. The NF-κB signaling pathway is mainly involved in the degradation of the ECM (44). Based on existing experimental data, we postulated that the targets of miR-31 mentioned above act as potential downstream effectors of GBK. Thus, we propose a possible signaling network whereby GBK acts by inhibiting the migration and invasion of breast cancer cells ( Figure 9 ). In this study, we have shown that GBK affects the expression of RHOA, SATB2, and WAVE3. Whether the target proteins and signaling pathways regulated by these three genes are also affected by GBK needs to be further studied. miR-31 is located in the intronic sequence of lncRNA LOC554202, and its transcriptional activity is regulated by LOC554202. It has been documented that the major mechanism responsible for silencing miR-31 in TNBC is the hypermethylation of the CpG island in the LOC554202 promoter region (27). We demonstrated previously that the transcription of DNA methyltransferase DNMT1, DNMT3A, DNMT3B, and COQ3 genes were downregulated in GBK-treated MCF-7 cells (31). In this study, we found that the methylation levels at the CpG of lncRNA LOC554202 decreased significantly under GBK treatment in a dose-dependent manner in breast cancer cell lines (45). Therefore, GBK treatment affects the epigenetic modification at the CpG sites by downregulating DNA methyltransferases and plays an important role in the upregulation of both the LOC554202 and miR-31 ( Figure 9 ). In addition, miRNAs are known to affect many cellular processes via their ability to post-transcriptionally control gene expression. It is therefore important to identify other miRNA target genes, examine how miRNAs are regulated, and study their involvement in cellular functions. High-throughput bioinformatics analysis could offer a better method for studying the mechanism of action of GBK, and also benefit clinical applications. DDP is an alkylating agent classified as an anti-neoplastic drug that has been extensively used in the treatment of advanced breast cancer, especially in metastatic breast cancer and TNBC. However, several adverse side effects limit its long-term usage. Combination treatment with other anti-tumor agents is an effective way to solve this problem. The combination of GBK and DDP treatments appear to work synergistically to inhibit the proliferation of breast cancer cells (especially in TNBC) in vitro and in vivo, and could be used to potentially reduce the individual doses of these drugs required to achieve the same effect in the clinic. Moreover, the expression of the miR-31 target genes SATB2 and RHOA was significantly reduced in SUM-159 cells after combination treatment with GBK and DDP, indicating that GBK could impair breast cancer cell migration and invasion.
In conclusion, in this study we identified the target of GBK and explored the underlying mechanisms involved in its inhibition of breast cancer migration and invasion, especially in TNBC, thus providing theoretical support for the development of GBK as an auxiliary drug in the clinical treatment.
The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding author.
The animal study was reviewed and approved by Institutional Animal Care and Use Committee at Inner Mongolia University.
Data curation, LF and MMo; funding acquisition, LF; investigation, XT, JL, KN, LZ, and YL; methodology, MMa. All authors contributed to the article and approved the submitted version.
This work was supported by grants from the National Natural Science Foundation of China (NSFC Grant no. 31960162 to LF), and the Program for Young Talents of Science and Technology in Universities of Inner Mongolia Autonomous Region (Grant no. NJYT-17-B03 to LF),
GBK is a generous gift from Professor Gereltu Borjihan of Inner Mongolia University.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. | true | true | true |
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PMC9606334 | Ying Shi,Jinying Li,Ming Tang,Jingwen Liu,Yalu Zhong,Wei Huang | CircHADHA-augmented autophagy suppresses tumor growth of colon cancer by regulating autophagy-related gene via miR-361 | 13-10-2022 | circHADHA,colon cancer,asymptomatic polyp,tumor growth,autophagy | Colon cancer undergoes a traditional pathway from colon polyps to colon cancer. It is of great significance to investigate the key molecules involved in carcinogenesis from polyps to malignancies. Circular RNAs (circRNAs) are stably expressed in human body fluids such as plasma. Here, we demonstrated a differential expression pattern of plasma circRNAs in healthy individuals, colon polyp patients and colon cancer patients using circRNA Arraystar microarray. We explored that circRNA HADHA (circHADHA) was upregulated in plasma from polyp patients, whereas it was downregulated in plasma from colon cancer patients. Overexpression of circHADHA promoted autophagy in colon epithelial cells. Moreover, in colon cancer cells, overexpression of circHADHA promoted autophagy, whereas it inhibited cell proliferation and colony formation. CircHADHA increased the expression of ATG13 via miR-361 in both colon epithelial and cancer cells. ATG13 knockdown reduced autophagy even in the presence of circHADHA in colon cancer cells. Furthermore, the growth of circHADHA-overexpressing colon cancer cell-derived xenograft tumors was significantly decreased compared with control tumors in nude mice. In conclusion, circHADHA was differentially expressed in the plasma of healthy individuals, colon polyp patients and colon cancer patients. CircHADHA promoted autophagy by regulating ATG13 via miR-361 in both colon epithelial and cancer cells. CircHADHA suppressed tumor growth by inducing cell autophagy in colon cancer cells. CircHADHA potentially serves as a biomarker for screening of precursor colon cancer and a therapeutic target for colon cancer treatment. | CircHADHA-augmented autophagy suppresses tumor growth of colon cancer by regulating autophagy-related gene via miR-361
Colon cancer undergoes a traditional pathway from colon polyps to colon cancer. It is of great significance to investigate the key molecules involved in carcinogenesis from polyps to malignancies. Circular RNAs (circRNAs) are stably expressed in human body fluids such as plasma. Here, we demonstrated a differential expression pattern of plasma circRNAs in healthy individuals, colon polyp patients and colon cancer patients using circRNA Arraystar microarray. We explored that circRNA HADHA (circHADHA) was upregulated in plasma from polyp patients, whereas it was downregulated in plasma from colon cancer patients. Overexpression of circHADHA promoted autophagy in colon epithelial cells. Moreover, in colon cancer cells, overexpression of circHADHA promoted autophagy, whereas it inhibited cell proliferation and colony formation. CircHADHA increased the expression of ATG13 via miR-361 in both colon epithelial and cancer cells. ATG13 knockdown reduced autophagy even in the presence of circHADHA in colon cancer cells. Furthermore, the growth of circHADHA-overexpressing colon cancer cell-derived xenograft tumors was significantly decreased compared with control tumors in nude mice. In conclusion, circHADHA was differentially expressed in the plasma of healthy individuals, colon polyp patients and colon cancer patients. CircHADHA promoted autophagy by regulating ATG13 via miR-361 in both colon epithelial and cancer cells. CircHADHA suppressed tumor growth by inducing cell autophagy in colon cancer cells. CircHADHA potentially serves as a biomarker for screening of precursor colon cancer and a therapeutic target for colon cancer treatment.
Colon cancer is the third leading cause of cancer-related new cases and death (1, 2). In China, colon cancer is also one of the most common cancers (3). Colon cancer undergoes a series of processes from normal colon epithelial cells to aberrant crypt foci and finally to malignancy. Colon polyps are precursor lesions of colon cancer in the conventional adenoma-to-carcinoma pathway, in which oncogenic transformation is driven by mutations in APC, KRAS, SMAD4, and TP53 (4–6). Except colonoscopy, conventional screening methods for screening and early diagnosis of colon cancer include blood tests, fecal occult blood testing (FOBT), fecal immunochemical test (FIT), and DNA or RNA stool tests (7). However, novel noninvasive and economical technologies and biomarkers remain to be explored to combine colonoscopy diagnosis for the prediction of the malignant transformation from premalignancy to colon cancer (8–10). Circular RNAs (circRNAs) are a class of endogenous RNAs with a special circular covalently bonded structure (11). Unlike linear RNA, circRNAs exhibit resistance to digestion by ribonucleases, such as RNase R, due to lack of 3’ and 5’ terminals (12, 13). CircRNAs also have a longer half-life (12). With the development of RNA sequencing technologies and bioinformatics, the dynamic expression patterns and diversity of circRNAs were identified in a variety of diseases, including cancer (14, 15). In recent years, circRNA-based liquid biopsy biomarkers have gained much attention (16). CircRNAs are highly abundant in blood and enriched in plasma exosomes, serving as potential biomarkers for the prediction and diagnosis of colon cancer (17–19). In addition, more biological functions of circRNA were revealed, such as acting as microRNA (miRNA) sponges (20), modulating the expression of parental genes (21), regulating alternative splicing (22), being protein scaffolds, and being involved in RNA–protein interactions (23). Here, we investigated the dynamic expression pattern of plasma circRNA in the malignant transformation from colon polyps to colon cancers and revealed the biological behavior of circHADHA in colon epithelial and cancer cells. Our results indicated that circHADHA may serve as a biomarker for premalignancy prediction and potential therapeutics for colon cancer patients.
All participants were adults (≥18 years of age). Healthy individuals were eligible if they were excluded from colon polyps by colonoscopy and had no other diseases by medical checkups. The colon polyp group included asymptomatic populations that were diagnosed with colorectal polyps by colonoscopy screening, excluding patients with colorectal cancer and other comorbidities by medical checkups. The histological diagnosis of the enrolled patients with colon polyps included hyperplastic polyps, tubular adenoma, villous adenoma, and tubulovillous adenoma. The colon cancer group included patients with primary colorectal cancer diagnosed for the first time by colonoscopy and histology who had not yet undergone surgical resection and drug treatment, excluding patients with other comorbidities.
Blood samples were collected from colon cancer and polyp patients and healthy individuals from the First Affiliated Hospital, Jinan University. All samples from colon cancer and polyp patients were collected before medical treatment. Plasma was isolated from blood by centrifuging at RCF 1,500g for 10 min. Total RNA was extracted from each plasma sample and prepared according to the Arraystar’s standard protocols. The concentrations of the RNA samples were measured by NanoDrop ND-1000. The integrity of RNA was assessed by electrophoresis on a denaturing agarose gel. RNA from each sample was treated with Rnase R to degrade the abundant linear RNAs and enrich circRNAs. The enriched circRNAs were amplified and transcribed using a random priming (Arraystar Super RNA Labeling Kit; Arraystar). After complementary RNA (cRNA) was purified (RNeasy Mini Kit, Qiagen), the hybridization was performed on Human circRNA Array (Arraystar Inc.). Agilent Scanner G2505C was used for array scanning.
Fold changes were computed between the groups for each circRNA. The statistical significance of the difference may be conveniently estimated by Student’s t-test. Fold changes >1.5 and P < 0.05 were statistical significance. R software/limma package (24) was used for differential expression of the microarray data.
The potential interaction of messenger RNA (mRNA) and miRNA with circRNA was predicted (Arraystar’s home-made miRNA target prediction software) based on TargetScan and miRanda databases. The competing endogenous RNA (ceRNA) network was illustrated by Cytoscape 3.0.
Human colon epithelial cells HCoEpiC purchased from ScienCell Research Laboratories (Carlsbad, CA, USA) were cultured in Dulbecco’s modified Eagle’s medium (Gibco) supplemented with 10% fetal bovine serum (Gibco) and 1% penicillin G/streptomycin (Gibco). NCM460 cells (25) were cultured in M3Base medium (INCELL) supplemented with 10% fetal bovine serum (Gibco). LoVo cells purchased from American Type Culture Collection (ATCC) (Manassas, VA, USA) were cultured in Ham’s F-12K (Kaighn’s) Medium (Gibco) supplemented with 10% fetal bovine serum (Gibco) and 1% penicillin G/streptomycin (Gibco). Cells were cultured at 37°C in an atmosphere of 95% air and 5% CO2.
The human circHADHA-overexpressing construct was generated based on modified pLCDH-ciR vector (Geneseed Biotech). The head-to-tail splice junction in circHADHA was predicted and designed as ggtggaacccctgGCatgttagccgcttgcaaga. Stable circHADHA-overexpressing HCoEpiC, NCM460, and LoVo cells were selected by puromycin (Invitrogen). The expression level of circHADHA was measured using real-time PCR.
Tissue cells were homogenized in TRIzol Reagent (Invitrogen). Chloroform was added to separate the homogenate into a clear upper aqueous layer, an interphase, and an organic layer. RNA was precipitated from the aqueous layer with isopropanol. First-strand cDNA was synthesized using Geneseed II First Strand cDNA Synthesis Kit (Geneseed Biotech). Real-time PCR was performed using Geneseed qPCR SYBR® Green Master Mix (Geneseed Biotech). Divergent primers of circHADHA: forward primer, 5’-tggtggaacccctggcatgt-3’, and reverse primer, 5’-caggcaggatccattgatggc-3’.
Cells were cultured on coverslips with 1.0 μg/ml of lipopolysaccharide (LPS) treatment. Cells were removed from the medium and rinsed with phosphate buffered saline (PBS). After incubating with 0.5% TritonX-100 at room temperature for 15 min, cells were fixed in 4% paraformaldehyde. The cells were washed in phosphate buffered saline (PBS), treated with 100% ethanol, and then air-dried. Briefly, digoxin-labeled probes against circHADHA (5’, 3’ fluorescein isothiocyanate (FITC)-labeled) and miR-361 (5’, 3’ Cy3-labeled) targets (Geneseed Biotech) were denatured at 85°C for 5 min and hybridized at 37°C overnight. On the following day, the slides were washed in 2× saline-sodium citrate buffer (SSC, Sigma-Aldrich). Subsequently, blocking was performed with 3% bovine serum albumin (BSA) at 37°C for 30 min, and the anti-digoxigenin fluorescence-conjugated antibodies were added to the slides at 37°C for 1 h. After washing in PBS, the cell nuclei were stained by 50 µl DAPI/Antifade solution (Sigma-Aldrich). Rubber cement (MP Biomedicals) was used for sealing coverslips, which were observed under the laser scanning confocal microscope afterward.
The sequence of LC3B-h was inserted into modified pmCherry vector. Cells were cultured on coverslips with LPS (1.0 μg/ml) treatment. Cells were removed from the medium and rinsed with PBS. After incubating with 0.5% TritonX-100 at room temperature for 15 min, cells were fixed in 4% paraformaldehyde. The cells were washed in PBS, treated with 100% ethanol, and then air-dried. Cell nuclei were stained by 50 µl DAPI/Antifade solution (Sigma-Aldrich). Rubber cement (MP Biomedicals) was used for sealing coverslips, which were observed under the laser scanning confocal microscope afterward.
Briefly, circHADHA dual-luciferase reporter constructs were generated by inserting the total length of circHADHA or the mutations of the miRNA target sites in circHADHA fragment into psiCHECK-2 dual-luciferase vector (Promega). Two mutation fragments (MUT_1 and MUT_2) were designed for the target sites of hsa-miR26a-1, hsa-miR-26a-2, hsa-miR-361, and hsa-miR-214. All miRNA mimics, miRNA inhibitors, and corresponding controls were purchased from GenePharma. HCoEpiC were seeded in 24-well plates at a density of 6 × 104/well. The dual-luciferase reporter constructs with wild-type or mutant circHADHA gene were cotransfected with miRNA mimic, inhibitor, or corresponding controls, respectively. Then, 48 h after cotransfection, the luminescence activity of both firefly and Renilla luciferase was analyzed using Dual-Luciferase Reporter Assay System (Promega).
Briefly, ATG13-3’UTR dual-luciferase reporter constructs were generated by inserting the total length of ATG13-3’UTR segments or the mutations of the miR-361 target sites in the ATG13-3’UTR fragment into psiCHECK-2 dual-luciferase vector (Promega). HCoEpiC were seeded in 24-well plates at a density of 6 × 104/well. The dual-luciferase reporter constructs with wild-type or mutant 3’UTR in ATG13 gene were cotransfected with miR-361 mimic, inhibitor, or corresponding controls (GenePharma), respectively. Then, 48 h after cotransfection, the luminescence activity of both firefly and Renilla luciferase was analyzed using Dual-Luciferase Reporter Assay System (Promega).
HCoEpiC with or without circHADHA overexpression were seeded in six-well plates at a density of 2 × 105 cells/well. miR-361 mimic, inhibitor, or corresponding controls were transfected by HiPerFect Transfection Reagent (Qiagen). Then, 48 h after transfection, cells were homogenized in TRIzol Reagent (Invitrogen) and RNA was extracted by RNeasy Kits (Qiagen) according to the manufacturer’s instructions. First-strand cDNA was synthesized using the miScript II RT Kit (Qiagen). Real-time PCR was performed using QuantiTect SYBR Green PCR Kits (Qiagen). The primers target ATG13: forward primer: 5’-GGCAATTTGAGAGGACCCCA-3’; reverse primer: 5’-CAGTGTCCTCACCAGCAGTT-3’. The primers target GAPDH: forward primer: 5’-AGAAGGCTGGGGCTCATTTG-3’; reverse primer: 5’-GCAGGAGGCATTGCTGATGAT-3’.
Total proteins were extracted from cells and tissues using RIPA buffer (Thermo Scientific). The lysate was centrifuged, and the supernatant was immediately transferred to a fresh tube. The protein concentration was determined using the BCA Protein Assay kit (Thermo Scientific). The prepared cell lysate was added into 4× NuPAGE LDS sample buffer (Invitrogen) and boiled for 10 min. Samples were loaded into Mini-Protean TGX Precast Gels (4%–15%, Bio-Rad). The samples were run on a Mini-Protean TGX Precast Gel (4%–15%, Bio-Rad) and then transferred to polyvinylidene fluoride (PVDF) membranes in protein transfer buffer for 60 min. Following transfer, non-specific binding on the membrane was blocked, and the membrane was incubated with primary antibodies at 4°C overnight. After washing three times with TBST, the membrane was incubated with secondary antibodies at room temperature for 1 h. Antibodies against ATG13 (E1Y9V, 13468) and mTOR (7C10, 2983) were purchased from Cell Signaling Technology (Danvers, MA, USA). Antibodies against LC3B (ab48394), Beclin 1 (EPR19662, ab207612), p62 (EPR4844), and Bcl-2 (ab196495) were purchased from Abcam (Boston, MA, USA).
shRNA sequence (5’-GCCATGTTTGCTCCCAAGAAT-3’) for the Atg13 gene was designed by the algorithm of ThermoFisher (http://rnaidesigner.thermofisher.com/rnaiexpress/). shATG13 plasmid was generated and was transfected in LoVo cells by Lipofectamine 3000 (Thermo Fisher Scientific). The knockdown efficiency of shATG13 was measured by real-time PCR and Western blot analysis.
Cells with stable circHADHA overexpression and corresponding control cells were seeded into 96-well plates at a density of 2 × 103 cells/well in the presence or absence of LPS (1.0 μg/ml). After 24, 48, and 72 h, cells were incubated with 10 μl of Cell Counting Kit-8 solution (DoJinDo) for 4 h. The absorbance was measured using a microplate reader at a wavelength of 450 nm. Three independent experiments were performed in triplicate.
Briefly, cells were seeded in six-well plates at a density of 500 cells/well and treated with or without LPS (1.0 μg/ml). The colonies were fixed in 4% paraformaldehyde and then stained in 1% crystal violet after a 14-day culture. The colonies containing over 50 cells were counted. Three independent experiments were performed in triplicate.
Apoptosis was evaluated using the Annexin V-FITC Apoptosis Detection Kit I (BD Biosciences), according to the manufacturer’s instructions. Cells were cultured in low-glucose DMEM (Gibco, Thermo Fisher Scientific) and treated with or without LPS (1.0 μg/ml) for 24 h. Then, cells were suspended in 500 μl of binding buffer and stained with 5 μl of Annexin V-fluorescein isothiocyanate and 2.5 μl of propidium iodide for 10 min at room temperature. The samples were subjected to flow cytometry. The data were analyzed using Summit software (FlowJo).
TdT-mediated dUTP nick-end labeling (TUNEL) assays were performed using the one-step TUNEL kit (Beyotime Institute of Biotechnology) following the manufacturer’s instructions. Cells were cultured on poly-(L-lysine)-coated coverslips in 12-well plates in low-glucose DMEM (Gibco, Thermo Fisher Scientific) and treated with or without LPS (1.0 μg/ml) for 24 h. Cells were fixed in 4% paraformaldehyde and then permeabilized with 0.1% Triton X-100 before photophobic incubation in 50 μl TUNEL reaction mixture for 1 h at 37°C. Cell nuclei were stained with DAPI for 2 min at room temperature.
HCoEpiC and NCM460 cells with or without circHADHA overexpression were seeded in six-well plates at a density of 2 × 105 cells/well with LPS (1.0 μg/ml) treatment. miR-361 was transfected by HiPerFect Transfection Reagent (Qiagen). Then, 24 h after transfection, cells were homogenized in TRIzol Reagent (Invitrogen), and RNA was extracted by RNeasy Kits (Qiagen) according to the manufacturer’s instructions. First-strand cDNA was synthesized using the miScript II RT Kit (Qiagen). Real-time PCR was performed using QuantiTect SYBR Green PCR Kits (Qiagen). The primers target IL1β: forward primer: 5’-AGGAAGATGCTGGTTCCCTG-3’; reverse primer: 5’-GCATCGTGCACATAAGCCTC-3’. The primers target IL17a: forward primer: 5’-CAAGAACTTCCCCCGGACTG-3’; reverse primer: 5’-CTCTCAGGGTCCTCATTGCG-3’. The primers target Toll-like receptor 4 (TLR4): forward primer: 5’-GCCATTGCTGCCAACATCAT-3’; reverse primer: 5’-ACTGCCAGGTCTGAGCAATC-3’. The primers target GAPDH: forward primer: 5’-AGAAGGCTGGGGCTCATTTG-3’; reverse primer: 5’-GCAGGAGGCATTGCTGATGAT-3’.
LoVo control (Ctrl, 5 × 106) and LoVo-circHADHA (circHADHA, 5 × 106) cells were subcutaneously injected into nude mice. Three days later, solid tumors were observed in mice that received cell injections. The size of xenograft tumors was measured every 3 days using a Vernier caliper {[length (mm) × width (mm)2]/2}. The xenograft tumors were dissected and weighed after mice were sacrificed.
Xenograft tumor tissues were fixed in formalin and embedded in paraffin before being sectioned. Antigen was retrieved by Citrate Antigen Retrieval solution (Maxim Biotech). Tissue sections were deparaffinized and rehydrated. The slides were treated with peroxidase and blocked with 10% serum for 2 h at room temperature. The slides were incubated with antibody against ATG13 (ab105392, Abcam) overnight at 4°C. On the following day, the sections were rinsed and then incubated with secondary antibodies (Maxim Biotech). DAB Detection Kit (Maxim Biotech) was applied to the slides before counterstaining with hematoxylin.
Statistical analysis was performed using SPSS 21.0 software (SPSS Inc.). Data between two groups were compared by using Student’s t-test. Two-way ANOVA analysis was used for the comparison between multiple groups. The values are expressed as the mean ± standard deviation of at least three independent experiments performed in triplicate. P < 0.05 was considered to be statistically different. Graphs were plotted using GraphPad Prism 9.0 (GraphPad Software Inc.).
We collected plasma from healthy individuals and colon polyp and colon cancer patients confirmed by endoscopic diagnosis ( Figure 1A ) and analyzed 2,162 human circRNAs by Arraystar ( Figure 1B ). Pairwise comparison indicated that 52 circRNAs were upregulated and 38 circRNAs were downregulated in the colon polyp group compared with healthy individuals ( Figure 1C ). In addition, 38 circRNAs were upregulated and 81 circRNAs were downregulated in the colon cancer group compared with colon polyps ( Figure 1D ). Among them, 29 circRNAs were upregulated while 37 circRNAs were downregulated in colon cancer groups compared with healthy individuals and colon polyps ( Figures 1D, E ). Most detectable candidates were predicated as exonic circRNAs ( Figures 1F–H ). Then, we performed a further analysis between upregulated and downregulated circRNAs in plasma from different groups to explore potential indicators that evaluate the malignant transformation of colon cancer. Venn diagram demonstrated that five overlapping circRNAs were upregulated in the colon cancer group (colon cancer vs. colon polyp), while they were downregulated in the colon polyp group (colon polyp vs. healthy individuals) ( Figure 1I ; Supplementary Table S1 ). Thirty-three overlapping circRNAs were downregulated in colon cancer (colon cancer vs. colon polyp), whereas they were upregulated in the colon polyp group (colon polyp vs. healthy individuals) ( Figure 1J ; Supplementary Table S1 ). We validated 10 circRNA candidates with the most significance and performed real-time PCR to identify potential biomarkers ( Figure 1K ). Relative expression of candidate circRNAs demonstrated that hsa_circ_0053063 was upregulated in plasma from colon polyp patients compared with healthy individuals (P < 0.0001), whereas it was downregulated in plasma from colon cancer patients compared with colon polyp (P < 0.0001) ( Figures 1K, L ). In addition, the expression of plasma hsa_circ_0001013 (P < 0.0001), hsa_circ_0005758 (P < 0.0001), and hsa_circ_0007422 (P < 0.0001) was increased in colon cancer compared with colon polyp ( Figure 1K ). Since gene symbol of circ_0053063 is HADHA (circBase database: http://www.circbase.org), we termed it as circHADHA. As a result, circHADHA may be a potential indicator for premalignant colon cancer.
In order to elucidate the roles of circHADHA in colon epithelial cells, we generated circHADHA-overexpressing HCoEpiC (P < 0.01) and NCM460 (P < 0.01) cells ( Figures 2A, B ). LPS was used to induce injury in colon epithelial cells with circHADHA overexpression or corresponding control. We expressed LC3B with mCherry in circHADHA-overexpressing and control cells to perform LPS-induced autophagy assays. We found that the overexpression of circHADHA significantly promoted mCherry-labeled LC3B-positive autophagosomes induced by LPS in HCoEpiC compared with controls ( Figure 2C ). We also performed CCK-8 and colony formation assays to explore the proliferation ability mediated by circHADHA in colon epithelial cells. We found that overexpression of circHADHA did not contribute to cell viability in HCoEpiC ( Figure 2D ) and LPS-injured HCoEpiC ( Figure 2E ). In addition, circHADHA overexpression did not affect colony formation in HCoEpiC ( Figure 2F ) and LPS-injured HCoEpiC ( Figure 2G ). We examined cell apoptosis by flow cytometry and found that circHADHA overexpression did not mediate the alteration of apoptosis in HCoEpiC ( Figures 2H, I ) and LPS-injured HCoEpiC ( Figures 2H, J ). Moreover, TUNEL assays also demonstrated that circHADHA overexpression did not significantly regulate apoptosis compared with control in HCoEpiC ( Figures 2K, L ) and LPS-injured HCoEpiC ( Figures 2M, N ).
Subsequently, we generated circHADHA-overexpressing LoVo colon cancer cells to investigate its behavior in colon cancer. The relative expression of circHADHA was significantly increased in circHADHA-overexpressing LoVo in comparison with control cells ( Figure 3A , P < 0.01). We performed autophagy assays and found that overexpression of circHADHA increased mCherry-labeled LC3B-positive autophagosomes in LPS-induced LoVo compared with control cells ( Figure 3B ). We examined cell viability by CCK-8 assay and found that overexpression of circHADHA inhibited proliferation in LoVo and in LPS-induced LoVo compared with corresponding control cells after 48 h (P < 0.01), 72 h (P < 0.05), and 96 h (P < 0.01) ( Figure 3C ). Furthermore, we detected apoptosis and found that circHADHA overexpression did not significantly regulate apoptosis compared with control in LoVo and LPS-injured LoVo cells ( Figure 3D ).
We analyzed ceRNAs to predict the candidates of RNAs that potentially interacted with circHADHA. Interactome analyses indicated that miR-26a-1, miR-26a-2, miR-361, and miR-214 were most related to circHADHA, and ATG13 related to both circHADHA and autophagy ( Figure 4A ; Supplementary Table S2 ). We transfected mimics of candidate miRNAs in circHADHA-overexpressing HCoEpiC and control cells and performed real-time PCR to measure ATG13 expression. We found that miR-26a-1, miR-26a-1, and miR-361 mimics increased the expression level of ATG13 in circHADHA-overexpressing HCoEpiC compared to control cells ( Figure 4B ). Among them, miR-361 resulted in the most significant increase of ATG13 expression in the presence of circHADHA in HCoEpiC ( Figure 4B ). Furthermore, we generated dual-luciferase (dual-LUC) reporter system with full-length or mutants of circHADHA and cotransfected with miRNAs. By performing the dual-LUC reporter assays, we found that the luciferase reporter activities of full-length circHADHA were significantly decreased by transfection with mimics of miR-26a-1 (P < 0.01), miR-361 (P < 0.01), and miR-214 (P < 0.01) compared to the transfection of control mimic in colon epithelial cells ( Figure 4C ), whereas the luciferase reporter activities of mutant circHADHA did not change by transfection with mimics of miRNA candidates compared to the control mimic ( Figure 4D ). On the contrary, the luciferase reporter activities of full-length circHADHA were significantly increased by transfection with inhibitors of miR-26a-1 (P < 0.01), miR-361 (P < 0.01), and miR-214 (P < 0.01) compared to the transfection of control inhibitor in colon epithelial cells ( Figure 4E ). While the luciferase reporter activities of mutant circHADHA did not change by transfection with inhibitors of miRNA candidates compared to the control inhibitor ( Figure 4F ). Moreover, we performed FISH assay and demonstrated a colocalization of circHADHA and miR-361 in HCoEpiC ( Figure 4G ). As a result, circHADHA is a sponge that binds to miR-361 directly.
Potential binding site analysis implied that circHADHA (7mer-m8 position) and ATG13 shared the same complementary seed region of miR-361 at the 5’ end ( Figure 5A ). We inserted complementarity binding sites at 3’UTR of ATG13 into dual-luciferase reporter vectors and measured the luciferase reporter activities with miR-361 cotransfection. Figure 5B demonstrated that miR-361 mimic reduced (P < 0.01), whereas miR-361 inhibitor increased (P < 0.01), the luciferase activities of ATG13 compared with corresponding controls ( Figure 5C ). However, there were no alterations of luciferase reporter activities in the presence of miR-361 mimic or inhibitor cotransfected with the control ( Figure 5B ) or mutant of 3’UTR of ATG13 ( Figure 5D ), respectively. Then, we measured ATG13 expression transfected by miR-361 with or without circHADHA overexpression ( Figure 5E ). miR-361 mimic significantly inhibited the expression of ATG13 in HCoEpiC (P < 0.01). While circHADHA overexpression increased ATG13 compared with corresponding control (P < 0.01), which was consistent with miR-361 inhibitor transfection in HCoEpiC (P < 0.01). However, the expression of ATG13 was elevated in the presence of circHADHA overexpression regardless of the transfection with miR-361 (P < 0.01) or control mimic (P < 0.05). These results revealed that circHADHA competitively inhibited the combination between miR-361 and 3’UTR of ATG13. Then, we measured the expression of ATG13 and LC3B at the protein level in LPS-injured HCoEpiC. Western blot demonstrated that the expression of ATG13 and LC3B (II/I) was increased in circHADHA-overexpressing HCoEpiC compared to pLCDHciR control cells ( Figure 5F ). As a result, the expression of ATG13 was negatively regulated by the binding of circHADHA to miR-361 competitively in colon epithelial cells.
In order to validate the intermediate regulation of miR-361 and ATG13 in circHADHA-induced autophagy, we performed LC3B-based autophagy assays with transfection of miR-361 in HCoEpiC ( Figure 6A ). In pLCDHciR control cells, miR-361 significantly suppressed autophagy by reducing the production of LC3B-positive autophagosomes with LPS treatment. However, circHADHA overexpression promoted LC3B-positive autophagosomes even in the presence of miR-361 treated with LPS in HCoEpiC. Then, we measured ATG13 and LC3B expression at the protein level in LPS-injured HCoEpiC ( Figure 6B ). Western blot demonstrated that miR-361 inhibited ATG13 ( Figure 6C , P < 0.01) and LC3B ( Figure 6D , P < 0.05) compared to miR-control in the absence of circHADHA. However, circHADHA overexpression increased the expression of ATG13 ( Figure 6C , P < 0.05) and LC3B ( Figure 6D , P < 0.05) compared to pLCDHciR control by transfection with miR-361. Additionally, the expression of mTOR did not show significant changes at the protein level with circHADHA/miR-361 treatment ( Figure 6E ). These results showed that miR-361 declined autophagy by inhibiting ATG13, whereas the presence of circHADHA rescued autophagy by competitively recruiting miR-361 from ATG13 in LPS-injured colon epithelial cells. The inflammatory cytokines were validated in colon epithelial cells with LPS treatment. Overexpression of circHADHA decreased the expression of interleukin (IL)-1β ( Figure 6F , P < 0.01, P < 0.01) and IL-17A ( Figure 6G , P < 0.01, P < 0.05) compared with control in miR-361-treated HCoEpiC and NCM460 cells. Whereas the expression of TLR4 was upregulated in circHADHA-overexpressing HCoEpiC (P < 0.01) and NCM460 (P < 0.05) cells transfected with miR-361 ( Figure 6H ). Then, we knocked down ATG13 by shRNA in LoVo ( Figure 6I , P < 0.01). In circHADHA-overexpressing LoVo cells, shATG13 decreased ATG13 expression at the protein level compared with control and miR-361 inhibitor transfection ( Figure 6J ). Autophagy assays demonstrated that shATG13 reduced autophagy in circHADHA-overexpressing LoVo cells compared with control and miR-361 inhibitor transfection with LPS treatment ( Figure 6K ). We also performed CCK-8 assay to evaluate the cell viability in LPS-induced LoVo cells ( Figure 6L ). In the presence of circHADHA, shATG13 increased proliferation compared with shControl (P < 0.01). In addition, the inhibitor of miR-361 significantly decreased cell viability compared with knockdown of ATG13 in circHADHA-overexpressing LoVo (P < 0.01). Thus, circHADHA promoted autophagy regulated by miR-361 and ATG13 in colon epithelial and cancer cells. Moreover, circHADHA-augmented autophagy impeded cell proliferation mediated by miR-361/ATG13 in colon cancer cells.
We generated xenograft-bearing nude mice by subcutaneously transplanting with circHADHA-overexpressing or control LoVo cells ( Figure 7A ). The tumor size was measured every 3 days. Figure 7B showed that circHADHA significantly suppressed xenograft tumor growth (P < 0.01). After 12 days, mice were sacrificed, and xenograft tumors were collected ( Figure 7C ). circHADHA significantly reduced the average weight of xenograft tumors ( Figure 7D , P < 0.01). Immunohistochemical staining was performed and demonstrated increasing expression of ATG13 and LC3B in circHADHA-overexpressing xenograft tumors ( Figures 7E, F ). Autophagy-related proteins were measured by Western blot and showed that Beclin1 was expressed more in xenografts with circHADHA overexpression than in control tumors. While Bcl-2 was decreased and p62 was degraded in circHADHA-derived xenografts compared with control tumors ( Figure 7G ). Therefore, we provided insights into circHADHA as a potential biomarker and therapeutic target for colon cancer from colon polyps ( Figure 8 ). The dynamic expressions of circHADHA in the plasma from healthy individuals and colon polyp and colon cancer patients imply a possibility of a novel noninvasive marker in the early detection of colon cancer. In addition, circHADHA improved autophagy regulated by miR-361 and ATG13 in both colon epithelial and cancer cells, and circHADHA-augmented autophagy impeded cell proliferation in colon cancer cells and colon cancer cell-derived xenograft tumors. These indicates that circHADHA plays an important role in protecting intestinal epithelial cells from injury and may be a target for the treatment of colon cancer.
The conventional oncogenic transformation of colon cancer undergoes a series process from asymptomatic polyp to adenoma-carcinoma. Efficient screening administrations are of benefit to the reduction of the colon cancer-related mortality. Colonoscopy and fecal-based screening, such as FOBT, FIT, and DNA or RNA stool tests, are common clinical applications for the screening and early diagnosis of colon cancer (26). Colonoscopy examination is a standard approach for the detection and therapy of both early cancer and cancer precursor lesions, which is invasive and usually implied after abnormal stool-based screening (27). Fecal screening tests currently lack high sensitivity for precursor lesions of colon cancer, although they are inexpensive and easy to operate (7, 28). Thus, novel noninvasive and economical technologies and biomarkers remain to be explored to combine colonoscopy diagnosis for the prediction of the malignant transformation from premalignancy to colon cancer (8–10). The oncogenic transformation via the traditional adenoma-carcinoma pathway is usually driven by mutations in APC, KRAS, SMAD4, and TP53 (4–6). The sessile serrated polyps with high-level microsatellite instability (MSI-H) phenotype are premalignant lesions (29). In order to implement fast and convenient diagnostic strategy to distinguish the potential premalignant risk, more and more tumor markers were identified (30). However, the biomarkers for the prediction of the transformation from normal epithelial-polyps-colon cancer still need to be explored (9). circRNA is a novel type of noncoding RNA, which is characterized by its circular shape and stable expression (31). circRNA acted as a miRNA sponge, involved in cancer progression. Most circRNAs stem from self-splicing introns of pre-ribosomal RNA (32, 33). In our present study, we found that the significant differentially expressed circRNAs were classified into intronic, exonic, antisense, and intragenic types. Exonic circRNA constitutes the majority. Exons of different genes produce fusion circRNAs that associate with cancerous chromosomal translocations, which are involved in cell transformation, tumor progression, and therapy resistance (34). Indispensably, molecular events are involved in the development of polyp-adenoma-adenocarcinoma progression. The gene mutation and epigenetic regulation are indicated in the whole sequence. circRNA ciRS-7 spatially resolved cellular expression patterns in colon cancer and is highly expressed in stromal cells within the tumor microenvironment (35). The biomarker ciRS-7 reduces epidermal growth factor receptor (EGFR-RAF1) activity in colon cancer patients and promotes growth and metastasis of esophageal squamous cell carcinoma via miR-7/HOXB13 (36, 37). In colon cancer tissues, hsa_circ_001988 expression reduced (38) and hsa_circ_001569 negatively correlated with miR-145 as a sponge by attenuating BAG4, E2F5, and FMNL2 expressions (39). In colon cancer cells, hsa_circ_000984 competitively combined with miR-106b as a ceRNA and increased CDK6 expression effectively (40). circRNAs represent a potential implication in medical practice, which are stably enriched in plasma exosomes and have been reported to be biomarkers for malignant diseases (41–43). circRNAs are enriched in blood much more than corresponding linear RNAs (42). A large-scale identification of metastasis-related circRNAs in colon cancer has been performed to diagnose and investigate the development and metastasis of colon cancer (44). According to published reports, certain circRNAs have been reported as tumor biomarkers in colon cancer by next-generation sequencing. The significant differential expression patterns of circRNAs have been identified between colon cancer and normal cells (44). However, only a few studies reported the candidate circRNAs in precancerous diseases. In the present study, we have analyzed a total of 2,162 human circRNAs and found that the expression pattern of circRNA was altered in colon polyp and colon cancer plasma compared with that in healthy individuals. Potential circRNA candidates were selected as biomarkers to predict malignant progression from colon polyps to cancer. circRNAs regulate splicing and transcription and act as miRNA sponges or interactors with RNA‐binding proteins (RBPs) (45). circRNAs have been identified as specific targets for the diagnosis and prognosis of colon cancer, involved in the molecular mechanisms of the development and progression of colon cancer (46, 47). Guarnerio et al. (34) reported that well-established cancer-associated chromosomal translocations gave rise to fusion circRNAs, having tumor-promoting properties. circRNAs can also arise from protein-coding genes and act as ceRNAs or miRNA sponges to regulate gene expression (20, 32). In malignant diseases, miRNA sponges have potential effects on oncogenesis and pathway regulation (45). In our present study, we found that the dynamic alterations of circHADHA in colon polyp and colon cancer plasma as well as in healthy individuals have a great potential in predicting colon malignant transformation. We induced LPS injury and performed different assays to confirm the biological function of circHADHA in colon epithelial cells. We found that circHADHA did not have effects on cell viability, colony formation, and apoptosis in colon epithelial cells. However, circHADHA mediated autophagy in colon epithelial cells. We performed an integrative analysis of the ceRNA network between circHADHA, miRNAs, and mRNAs and found a potential interaction between circHADHA, miR-361, and ATG13, which was consistent with autophagy regulation. Luciferase reporter assay, real-time PCR, and competitive inhibition assay demonstrated that circHADHA complementarily bound miR-361 to negatively regulate ATG13 expression, leading to the alteration of autophagy in LPS-injured colon epithelial cells. Furthermore, we performed autophagy assay in colon cancer cells with present or absent circHADHA and showed that circHADHA regulated autophagy via the miR-361/ATG13 axis. Therefore, circHADHA acts as a sponge to competitively bind miR-361 and regulate ATG13 expression and autophagy in colon epithelial and cancer cells. It is interesting that circHADHA overexpression did not affect cell proliferation in normal colon epithelial cells. However, cell viability was significantly inhibited in circHADHA-overexpressing colon cancer cells. And the growth of xenograft tumors was suppressed by circHADHA overexpression. Due to the dynamic alteration of circHADHA expression in plasma from healthy individuals, colon polyp patients, and cancer patients, circHADHA may be a potential candidate for early diagnosis and treatment of colon cancer. We demonstrated a dynamic alteration of circHADHA in the oncogenic process. circHADHA was upregulated in colon polyp patients compared with healthy individuals, which competitively recruited miR-361 to promote autophagy by releasing ATG13. Whereas it was lowly expressed in colon cancer patients compared with polyp patients, which led to the inhibition of ATG13 by binding miR-361. Although the dynamic expression of circHADHA implies the progress from colon polyps to colon cancers, the findings of this study still have to be seen in light of some limitations. In the present study, we only included patients with sporadic colon polyps and colon cancer, but no patients with familial adenomatous polyposis, Lynch syndrome, and secondary colon cancer. Sporadic colon cancer is a multistep and polygenic disease (48); thus, we cannot rely on a single genetic abnormality or mutation to diagnose colon cancer. The continuous discovery of novel tumor markers, as well as more mechanism studies, will promote the early diagnosis and treatment of colon cancer. In summary, circHADHA augmented autophagy and suppressed the progression of colon cancer by regulating the autophagy-related gene via miR-361. CircHADHA may play important roles in preventing colon polyps from developing into colon cancer.
The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author.
The studies involving human participants were reviewed and approved by Ethics Committee of First Affiliated Hospital, Jinan University, Guangzhou, China. The patients/participants provided their written informed consent to participate in this study. The animal study was reviewed and approved by Ethics Committee for Animal Research of Jinan University, Guangzhou, China.
YS and WH designed the experiments. YS, JLi, MT, and YZ performed experiments and analyzed data. YS and JLi performed statistical analyses. YS, JLi, and JLiu collected clinical samples. YS and WH wrote the manuscript. All authors contributed to the article and approved the submitted version.
The study was supported by the Medical Science and Technology Foundation of Guangdong Province (A2021306).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. | true | true | true |
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PMC9606464 | Junchao Wu,Sijie Yu,Yalan Wang,Jie Zhu,Zhenhua Zhang | New insights into the role of ribonuclease P protein subunit p30 from tumor to internal reference | 13-10-2022 | RPP30,protein structure,tumor,internal reference gene,PCR diagnosis | Ribonuclease P protein subunit p30 (RPP30) is a highly conserved housekeeping gene that exists in many species and tissues throughout the three life kingdoms (archaea, bacteria, and eukaryotes). RPP30 is closely related to a few types of tumors in human diseases but has a very stable transcription level in most cases. Based on this feature, increasing number of studies have used RPP30 as an internal reference gene. Here, the structure and basic functions of RPP30 are summarized and the likely relationship between RPP30 and various diseases in plants and human is outlined. Finally, the current application of RPP30 as an internal reference gene and its advantages over traditional internal reference genes are reviewed. RPP30 characteristics suggest that it has a good prospect of being selected as an internal reference; more work is needed to develop this research avenue. | New insights into the role of ribonuclease P protein subunit p30 from tumor to internal reference
Ribonuclease P protein subunit p30 (RPP30) is a highly conserved housekeeping gene that exists in many species and tissues throughout the three life kingdoms (archaea, bacteria, and eukaryotes). RPP30 is closely related to a few types of tumors in human diseases but has a very stable transcription level in most cases. Based on this feature, increasing number of studies have used RPP30 as an internal reference gene. Here, the structure and basic functions of RPP30 are summarized and the likely relationship between RPP30 and various diseases in plants and human is outlined. Finally, the current application of RPP30 as an internal reference gene and its advantages over traditional internal reference genes are reviewed. RPP30 characteristics suggest that it has a good prospect of being selected as an internal reference; more work is needed to develop this research avenue.
The ribonuclease P protein subunit P30 (RPP30) gene is included in the National Center for Biotechnology Information (NCBI ID#10556) database. RPP30 has been shown to be highly conserved in gene pool data, and many studies have shown that there are 16 homologous genes of RPP30 contained in many species from the three life kingdoms (archaea, bacteria, and eukaryotes) (1, 2). As a housekeeping gene, the protein encoded by RPP30 is one of shared protein subunits of ribonuclease P (RNase P) and ribonuclease MRP (RMRP), which are widely expressed in various tissues and participate in many life processes of microscopic and macroscopic organisms. It should be noted that as a protein subunit, detection of RPP30 in different tissues is not uniform and stable, possibly because of the complex modification process after translation (3–6). In this review, the diseases associated with RPP30 and the factors that may influence its expression are introduced for reference in further studies and in quality control. Abnormal gene expression or mutation studies have made some progress with regard to botanical diseases (5–7). At present, studies on human diseases mainly involve tumors, but only a few of them have demonstrated RPP30 overexpression (8). In addition, RPP30 is associated with glioblastoma (GBM) pathogenesis and low bone mineral density (LBMD) (9) Reports that RPP30 expression level is affected by other factors are very limited, such as aging (10). Given the relatively high and stable ribose nucleic acid (RNA) expression of RPP30 in human tissues, increasing studies have recently used RPP30 as an internal reference gene in reverse transcription-polymerase chain reaction (RT-PCR) protocols. Thus, the use of RPP30 as an internal reference gene for many applications, including detection of pathogens, calculation of the number of tumor cells, diagnosis of tumors, and some childhood diseases are discussed. In particular, the application of this gene in nucleic acid detection of SARS-CoV-2 demonstrates its great value as an internal reference (11). Finally, the advantages of RPP30 over conventional reference genes, such as β-actin and glyceraldehyde-3-phosphate dehydrogenase (GAPDH) are discussed. Ideal reference genes should be stably expressed in different tissues and different life cycles. With increased research and more applications, the expression of β-actin and GAPDH has been observed to be related to physiological/pathological states, experimental conditions, and tissue type (12–14). In contrast, changes in RPP30 expression seem less likely to be reported in the many conditions described above. These data suggest RPP30 may be used as an internal reference gene in further studies. Of course, the reliability of RPP30 as an internal reference is required to be verified by more comprehensive experiments.
The highly conserved RPP30 genome sequence is located on human chromosome 10 (10Q23.31) at 90,871,974–90,908,556 and is 36,582 nucleotides in length, with 14 exons (https://www.ncbi.nlm.nih.gov/gene/). There are 16 homologous genes in primates, canine, bovine, Rodentia, Amphibia, Drosophila, Arthropoda, and Saccharomycetes. These data are obtained from NCBI (ID#10556). The highly conserved sequence and other characteristics of RPP30 are illustrated in a gene evolutionary tree in Figure 1A . Human nuclear RNase P consists of 1 RNA subunit H1 and 10 conserved proteins, and the complex has a slender conformation similar to the overall shape of yeast RNase P shown by cryo-negative staining electron microscopy (2). The human RNase P protein consists of a single protein, Pop1, and three subcomplexes, which include the RPP20-RPP25 heterodimer, Pop5-RPP14-(RPP30)2-RPP40 heteropentamer, and RPP21-RPP29-RPP38 heterotrimer (2). The proteins are tightly attached to each other, forming a structure similar to a right-handed clip with three modules: finger, palm, and wrist. The POP5-RPP14-(RPP30)2-RPP40 heteropentamer becomes the palm module of the protein clamp. Two copies of the RPP30 molecule bind to the central POP5-RPP14 from opposite sides, forming a typical trisose phosphate isomerase (TIM) barrel fold. The molecule of RPP30 that interacts with RPP40 is called RPP30B, and the other molecule is called RPP30A. The secondary and tertiary structures of RPP30 have not been analyzed at this stage (2). The RPP30 protein subunit is homologous to the RNase P protein subunit of archaea and other eukaryotes, as shown in Figure 1B . By comparing the amino acid sequence of homologous genes of RPP30, the domain and conserved site of the RPP30 protein may be identified, which will illustrate how conserved this protein is.
RPP30 mainly functions in catalysis, nuclear localization, assembly, and/or regulation of holoenzyme activity (3). The GeneCards (https://www.genecards.org/) and Gene Ontology (GO; http://geneontology.org/) databases were searched for the RPP30 gene to identify its basic functions. The gene’s molecular functions include binding proteins, catalyzing reactions, and so on (15). In archaea, two RPP30 copies bind with ribonuclease P/MRP protein subunit Pop5 dimers to form the Pop5•RPP30 heterodimer. The Pop5•RPP30 heterodimer is anchored on the catalytic domain of RNase P RNA(RPR), which is necessary for pre-tRNA cleavage (16). Cellular components encoded by the gene include RNase P complex, RNase MRP complex, and the multimeric ribonuclease P complex. Biological processes mediated by the gene include rRNA and tRNA processing (15). RPP30 gene encodes a type of ribonuclease that achieves RNaseP RNA binding activity, contributes to ribonuclease P activity, and participates in the removal of tRNA5′- precursor, as well as the formation of polynuclease P complex, and ribonuclease MRP complex, which is necessary for the gene transcription of RNA polymerase III (17). RPP30 also facilitates immunity in rice and reproduction in Arabidopsis and Drosophila (5–7). Therefore, as the most conserved gene in various types of organisms, RPP30 is also involved in the most basic life processes, which drive almost all life functions and activities.
RNase P and RMRP are both small nucleolar ribonucleoprotein complexes (snoRNPs) that are classified into three major classes (box H/ACA snoRNPs, box C/D snoRNPs, RNase P and RnaseMRP) (18). RMRP has only been found in eukaryotes, located mostly in the nucleolus, and has many functions, including cleaving the pre-rRNA at site A3 in vivo and in vitro to mature the 5′ end of the 5.8S rRNA, cleaving an RNA transcript to generate RNA primers for mitochondrial DNA duplication, cleaving the B-type cyclin, Clb2, mRNA, recognizing and cutting pre-tRNA, and is required to turn over cell cycle mRNA (19–21). RNase P, located both in the nucleoplasm and nucleolus, is necessary for Mg2+ dependent 5′ maturation of tRNAs in archaeal, bacterial, and eukaryotic kingdoms (22). RNase P may also act in the stress response and be a transcription factor that regulates polymerase I and III (23). RNAs containing N6 methyladenosine (m6A), 4.5S pre-rRNA, operon mRNAs, box C/D small nucleolar RNAs that reassemble tRNAs are also substrates of RNase P (15, 24–30). RNase P and RMRP have similar functional and structural characteristics (18, 31). These two enzymes share at least ten protein subunits, including RPP14, RPP20, RPP21, RPP25, RPP29, RPP30, RPP38, RPP40, Pop1, and Pop5 (4, 15). RPP30, with a highly conserved amino acid sequence, has an important role in joining the RNase P and RMRP complexes (32). RPP30, as one of the common subunits between the RNase P and RMRP complexes, contributes to an increased number of RNA substrates and atypical functions of eukaryotes (4, 33). RNase P H1 and RMRP RNAs may crosstalk with miRNAs that are related to stability and translation of mRNAs (34). Stolc and Altman reveal that reduction RPP1 (homologous to human RPP30) in S. cerevisiae causes disruptions in both RNase P and RMRP by inhibiting correct cleavage of the internal transcribed spacer I of rRNA surrounding the A3 site (35).
Although RNase P RNA (RPR) is suggested to have activity in vitro, its activity in vivo requires protein cofactors (36). In 2006, Welting et al. used glycerol gradient sedimentation and coimmunoprecipitation to determine that RPP30 is related to the RNA subunit of RNase P and RMRP (18). UV–crosslinking studies also show that RPP30 interacts directly with H1 RNA, an RNA subunit of RNase P (37, 38). Isothermal titration calorimetry has been used to explore interactions among the protein subunits of RNase P and RMRP (22, 39, 40). In archaea, bacteria, and yeast, RPP30/RPP30 paired with Pop5/Pop5, may be functionally reconstituted with the phylogenetically-conserved core catalytic domain (C domain) of the RNA subunit to promote the assembly of RNase P providing substrate RNA binding sites and activating the RNA subunit (probably by RNA annealing and strand displacement (41) and stabilize ionic interactions with the RNA subunit or the substrate pre-tRNA at a relatively lower salt concentration (1, 22, 42–44). In the hyperthermophilic archaeon Pyrococcushorikoshii, PhoRPP30 is homologous to human RPP30 and acts as a molecular chaperone of PhoPop5, which recognizes the stem-loop containing the P3 helix in PhopRNA (45). RPP30-Pop5 is a tight heterotetrameric complex that increases the affinity of the holoenzyme for Mg2+ and protects the RNase P M1 RNA’s C domain from RNase T1 cleavage, especially near conserved nucleotides of RNase P in archaea whose RNase P protein is homologous to eukaryotic counterparts (36, 46–48). The RPP30-Pop5 complex also increases the RPR cleavage rate of pre-tRNA and may be activated by the RPP21-RPP29 complex reflecting indirect effects (36). In Dictyosteliumdiscoideum, RPP30 adopts a TIM-barrel fold that stabilizes the structure and enhances the affinity of pre-tRNA of RNase P to promote the formation of a native fold (46, 49, 50). In humans, RPP30 interacts with RPP14, RPP40, RPP20, RPP21, Pop1, RPP29, 4pp38, and RPP30 itself (15, 37, 51). Moreover, Stolc and Altman have shown that the RPP30 and RPP38 cDNA code for proteins related to catalytic complexes of RNase P from HeLa cells (35). Additionally, RPP30 may interact with other RNAs; as an important subunit of RNase P, RPP30 may be involved in the cleavage of hepatitis C virus RNA (52).
In Arabidopsis, the RPP30 domain is present from 98–248 amino acids in gametophyte defective 1 (GAF1), which is important in female gametophyte development and male competence and has a universal contribution to plant development (5). In Drosophila, RPP30 is necessary for female oogenesis because of its relationship with tRNA processing, DNA replication, and piRNA transcription (7). RPP30 also positively regulates rice immunity by interacting with histone deacetylase 701 (HDT701, RPP30 may be a substrate of HDT701), which functions in suppressing innate immunity in rice and may upregulate expression of defense genes (6). Although many functions of RPP30 have recently been identified, the specific role of RPP30 in basic life processes requires further research.
RNase P and RMRP play an important role in RNA or non-RNA processing that are universal programs closely related to many life activities. As an important subunit, the mutation and abnormal expression of RPP30 leads to many diseases.
In Arabidopsis, GAF1 mutations result in decreased RPP30 levels that induce defects in mitosis during female gametophyte development, arrest embryo sacs at stages FG1–FG7 and also cause defects in male competence (5). In Drosophila, an isolated mutation that inserts the P-element P(lacW)k01901 into RPP30 leads to complete sterility in females (7, 49). The pathogenic mechanisms that have been uncovered include a mutation in RPP30 that arrests oogenesis by decreasing tRNA processing, which leads to transcription-replication conflicts (7). This includes decreases in transposon expression, accumulation of the polymerase III subunit Brf, and the collapse of Proliferating Cell Nuclear Antigen (PCNA), which increases DNA replication stress and gene defense by small RNAs and activates several DNA duplication checkpoint proteins, including p53, claspin, and checkpoint kinase 2 that decrease piRNA transcription and piRNAclusterpopulations (7). piRNAs are native defenders of germline cell genomes whose mature structure called a “nuage” surrounds the nurse cells that provide nutrients to oocytes (53). Additionally, downregulation of piRNA levels leads to derepression of transposable elements and activates DNA checkpoints to promote positive feedback of defective oogenesis (7, 53–56).
Li et al. have identified OsRPP30, a cellular protein that may regulate the biological function of rice HDT701 (6). HDT701 negatively regulates defense mechanisms in rice by increasing histone H4 deacetylation and increasing the sensitivity to Magnaporthe grisea and Xanthomonas oryzaepv.oryzae (57). When rice is infected with Pyriculariaoryzae (syn. Magnaportheoryzae), RPP30 expression increases, which activates the transcription of defense genes (6). The overexpression of OsRPP30 in genetically modified rice increases expression of defense genome and the production of reactive oxygen species, resulting in resistance to Magnaporthe grisea and Xanthomonas oryzae. OsRPP30 is located at the top of the immune pathway triggered by HDT701-mediated pathogen-associated molecular patterns, which may overcome the negative effects of HTD701 and provide a new direction for the cultivation of pathogen-resistant food in the future (6).
Anti-Th/To is one of the rarer antinuclear antibodies identified in patients with systemic sclerosis (SSc) and is composed of hPOP1, RPP25, RPP30, and RPP40 (58, 59). Researchers refer to “anti-Th” and “anti-To” in the cases of RNase MRP and RNase P, respectively (60, 61). Recombinant RPP30 and RPP38 cross-react with anti-Th/To antibodies of patients afflicted with SSc (3, 32). In addition, people with positive anti-RPP30 antibodies have a lower risk of tendon friction rubs and cancer, but more likely to have severe lung diseases and pulmonary hypertension (59, 62). However, the positivity of anti-RPP30 antibodies only represents the antigenicity of RPP30 protein, and does not suggest the existence of abnormal expression or a RPP30 gene defect, which requires further research.
The nucleophosmin (NPM1) gene, located at human chromosome 5Q35, contains 12 exons and encodes a multifunctional shuttling protein that shuttles between the nucleolus and cytoplasm. NPM1 mutations happen in approximately one-third of acute myeloid leukemias (AMLs) (63). Martelli et al. have shown that the NPM1/RPP30 complex serves as one of three NPM1 rearrangements that have been found and analyzed in 13,979 AML samples (64). In patients with AML that have a NPM1 rearrangement, RPP30 is rearranged with NPM1 at exon 11, whereas the rearrangement of NPM1 with RPP30 is at the end of exon 9 (64). These data indicate RPP30 may help detect AML and monitor NPM1-mutated AML. A new study found that RPP30 may be a transcriptional regulator in glioblastoma (GBM) and the decreased RPP30 expression in elderly people could be a risk factor for GBM (10). This study showed that RPP30 was related to RNA and post-transcriptional modification in non-tumor tissues, and RNA modification in GBM. RPP30 regulates protein expression in GBM by affecting post-transcriptional modification of proteins and functional accumulation of these proteins indicates that these proteins are mainly involved in the activation of cancer signaling pathways (10). In addition, downregulation of RPP30 expression in human astrocyte (HA) cells promotes the proliferation of HA cells, while overexpression inhibits the activation of tumor-related pathways and the proliferation of HA cells, further confirming the close relationship between RPP30 and the occurrence and development of GBM (10). Correlation analysis of RPP30 expression levels with gene expression in cancer-related pathways, such as cancer, Wnt, and mitogen-activated protein kinase pathways in the Chinese Glioma Genome Atlas and The Cancer Genome Atlas databases show significant correlation (10). The Gene Expression Profiling Interactive Analysis (GEPIA2) database has been used to obtain broad knowledge of the relationship between RPP30 (Ensembl ID: ENSG00000148688.13) and tumors ( Figure 2 ) (8). RPP30 expression was significantly different in tumor tissues (higher) and non-tumor tissues in diffuse large B-cell lymphoma, pancreatic adenocarcinoma (PAAD) and thymoma (THYM) ( Figure 3 ). These data also show that there is no significant difference in RPP30 expression levels in different stages of those tumors while high expression of RPP30 is correlated with lower overall survival in PAAD using data from the GEPIA2 public database (http://gepia2.cancer-pku.cn/#index). RPP30 gene expression is high under epidermal development, cell differentiation, and keratinocyte differentiation processes, which play important roles in the differentiation of gastric epithelial cells. Recently, Kan et al. used TGCA RNA-seq to explore the role of RPP30 expression in gastric cancer. They found that RPP30 protein expression was positively correlated with the number of T helper 2 cells, active dendritic cells, and T helper 1 cells, and negatively correlated with the number of T helper 17 cells. They also found that RPP30 RNA expression in gastric cancer (GC) tissue is higher than that in normal tissue and higher RPP30 RNA expression is related to worse overall survival (OS) at the T1, T2, and N0 stages of the tumor. The mechanism may be that RPP30 RNA expression is upregulation via the G alpha S signaling pathway, neuronal system, and olfactory transduction, in addition to increasing cAMP levels, which are tightly correlated with GC histopathology. RPP30 could regulate tRNA modification, transcriptional replication, DNA repair, replication fork stagnation, and protein expression, which are correlated with cancer cell proliferation (65, 66).
Lee et al. Have found that rpp30 may be related to genetic factors of LBMD through genome-wide association studies involving two signaling pathways of eight related diseases (9). No further association between rpp30 and LBMD has been reported. RPP30 is indirectly related to some diseases, including lung diseases and pulmonary hypertension, secondary to autoimmune diseases (62). Currently, the research on genes and diseases is extremely in-depth and making rapid progress. Although rpp30 is involved in basic life activities, only a few human diseases have been confirmed to be related to rpp30, and even fewer have been confirmed to have abnormal expression. These results further reflect the stable expression of rpp30 and how well conserved it is.
Although RPP30 is associated with appellate disease, the expression of RPP30 in normal tissues and most tumor cells is stable. Approximately 3647 species have RPP30 subunits and 424 organisms have orthologs of human RPP30. RPP30 RNA is widely expressed in 27 human tissues, including testis, heart, kidney, lung, thymus, and lymph nodes, and more, among which testes and lymph nodes show the most expression and pancreas shows the least expression using data from the NCBI, InterPro (https://www.ebi.ac.uk/interpro/), and GeneCards public databases. In Figure 4A , although there are differences in RPP30 RNA expression levels calculated by different databases, RPP30 RNA expression levels of different organizations calculated by the same database are basically the same, which is consistent with the results obtained by Bgee involving gene expression data in animals. However, the expression of human RPP30 protein is not as stable as RPP30 RNA. There are differences in the expression of RPP30 protein among different tissues or cells and the RPP30 protein has weak expression in some tissues or cells, such as lymph node, brain, spinal cord, ovary, bone, colon, and liver secretion ( Figure 4B ). There have also been no reports using RPP30 protein as an internal reference for western blotting. To summarize, the expression level of RPP30 RNA in human tissue is relatively high and stable and is suitable to be used as an internal reference gene (11, 67, 68). Mouse RPP30 RNA is widely expressed in the central nervous system, bladder, brain, liver, and testis, etc., with higher expression in the central nervous system and lower expression in the adrenal gland and stomach. At present, there are relatively few reports on factors affecting RPP30 expression levels. Li, Zhai (10) have found that RPP30 expression is affected by age-related factors. Using analysis of age-related genes, RPP30 expression was negatively correlated with increased age, indicating that the change in RPP30 expression may be related to cell senescence. Li, Xiong (6) have found that RPP30 expression is upregulated after rice has been infected with fungal and bacterial pathogens. Mattijssen, Welting (69) have speculated that the expression of housekeeping genes may be altered in the growth plates of patients with cartilage-hair hypoplasia.
RPP30 has been used as an internal reference gene in the detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Coronavirus disease 2019 (COVID-19) broke out in Wuhan, China in December 2019 and then spread widely around the world, with strong infectivity (70). Despite its reputation as the gold standard for the detection of SARS-CoV-2, RT-PCR often produces false negative results in detection and diagnosis (71). This may be related to sample quality or changes of primer/probe binding site sequences, but the latter is less likely (72, 73). RPP30 is a single copy sequence gene stably expressed in the human genome, which has a good amplification efficiency, shows 100% sensitivity and specificity, and is not affected by swabs and methodology (74). Compared to other internal parameters, only RPP30 exists in all types of SARS-COV-2 infection samples (67). Figure 5A is the flow of RT-PCR. Both RPP30 RNA and viral RNA were present in epithelial cells ( Figure 5B ), and RPP30 RNA levels were closely related to SARS-CoV-2 RNA levels in respiratory tract samples ( Figure 5C ). Thus, RPP30 RNA may be used to control sample quality and the RPP30 Ct cutoff value may effectively identify false negative results (11, 72), which may increase sensitivity and reduce the spread of SARS-CoV-2. In addition to evaluating the quality of the sample, RPP30 may also determine whether mRNA has been extracted successfully and whether there is inhibition in the PCR (75). RPP30 is used as an internal reference gene to determine the best effective drug concentration for tumor treatment (76). The efficacy of traditional antineoplastic drugs is evaluated by calculating the lethality of drugs to all cells in vitro, so it is impossible to measure the lethality of antineoplastic drugs to normal cells, which is often accompanied by unpredictable side effects. Because RPP30 is stably expressed in the vast majority of tumor cells and non-tumor cells, while the neurofibromatosis type 1 (NF1) gene loses heterozygosity in tumor cells, the number of tumor cells may be evaluated by the quantitative RT-PCR ratio of NF1 to RPP30, which may be used to evaluate the efficacy and side effects of tumor drugs, and may also be used in personalized adjuvant chemotherapy. Due to the different behavior of cells in vivo and in vitro, this method has some limitations (76–78). As an internal reference gene, RPP30 may also accurately and effectively evaluate the concentration of antiretroviral drugs in cells (79). RPP30 is used as an internal reference gene to analyze the feasibility of HIV DNA detection in cerebrospinal fluid (CSF) (80). RPP30, as a housekeeper gene, is highly conserved and widely expressed in human tissues and may be used as an internal reference gene to detect the number of leukocytes in CSF. Using droplet digital PCR (dd-PCR) detection, the level of HIV DNA in CSF cells is not correlated with RPP30 levels, indicating that the detectability of HIV DNA does not depend entirely on the number of cells available in each sample. Then, the correlation between the level of HIV DNA in the CSF and the level of HIV RNA in peripheral blood cells, as well as the relationship between the virus inhibition and non-inhibition subgroups, may be analyzed to explore the detectability of HIV DNA level in CSF. RPP30 is used as an internal reference gene in diagnostic experiments (68). At least 5.5% of all pathogenic genetic changes in humans are large genome deletions or duplicates (81). With the discovery of disease-related genes, dd-PCR has been used to quantify the copy number of genes to diagnose diseases (82). Because of its conserved sequence and stable expression in almost all cells, RPP30 is widely used as an internal reference gene (83). For example, RPP30 has been used as an internal reference in real-time fluorescent PCR or dd-PCR to quantify the survival of motor neuron 1 gene, T-cell receptor excision circles, and Kappa-deleting recombination excision circles; to screen neonatal spinal muscular atrophy, severe combined immunodeficiency disease, and detect immune remodeling of the thymus and bone marrow (84–86); and to quantify the sex-determining region Y gene to detect male/female chimerism, which may track chimerism after hematopoietic stem cell transplantation (87). RPP30 has also been used as an internal reference in single-cell dd-PCR to evaluate the genomic DNA of rare circulating fetal cells in peripheral blood samples of pregnant women with male fetuses and validate the concept of non-invasive prenatal diagnosis (88). There are also many reports on the use of RPP30 as an internal reference in different molecular biology techniques for the diagnosis of human diseases, as shown in Table 1 . Dyavar et al. (79) used human and rhesus macaque (RM) gDNA templates to quantitate RPP30 copies, and found a low coefficient of variation and strong correlation between human and RM gDNA templates and the number of RPP30 copies in intra-laboratory (R2 = 0.996, p < 0.001; R2 = 0.975, p < 0.001), inter-laboratory (R2 = 0.997, p < 0.001; R2 = 0.989, p < 0.001), and inter-operational (R2 = 0.994, p < 0.001; R2 = 0.986, p < 0.001) studies, which confirms the high accuracy and precision of the RPP30 dd-PCR assay. In addition, Profaizer and Slev (86) observed that RPP30 dd-PCR could detect 2 copies/µL of genes, which is more accurate than the previous 24 copies/µL for qPCR.
Housekeeping genes are mainly involved in the maintenance of basic cell functions and are thought to be expressed in all cells (98), They are widely used as internal controls to standardize the expression of genes in western blotting, northern blotting, and RT-PCR. The ideal housekeeping gene should be expressed at the same level in all tissues (99). At present, frequently used housekeeping genes are β-actin and GAPDH, in which β-actin has a molecular weight of approximately 42–43 kDa and is composed of 375 amino acids. It is widely distributed in the cytoplasm and is involved in cell movement, structure, and integrity (100), whereas GAPDH is an enzyme with a molecular weight of approximately 37 kDa and is involved in glycolysis, DNA repair, tRNA output, membrane fusion, and transport (101). However, there are increasing reports that the RNA expression level of these genes is affected by the physiological/pathological state, experimental conditions, and tissue types (12–14). Thus, the factors affecting mRNA expression levels of RPP30, GAPDH, and β-actin were compared. Table 2 shows that the length of the RPP30 amplification product is smaller than that of β-actin and GAPDH, which reduces errors and improves efficiency during the process of RPP30 amplification. Table 3 lists the pseudogenes found in β-actin and GAPDH, but, to date, no pseudogenes have been found in RPP30. The existence of pseudogenes reduces the amplification efficiency of genes and reduces the accuracy of their use as internal reference genes for standardization (122, 123). Therefore, using RPP30 as the internal reference gene may be more accurate. Numerous reports suggest that gene expression levels of β-actin and GAPDH are affected by many factors under different pathological conditions, such as tumor cells and non-tumor cells (103–105, 124), steatosis and alcoholic hepatitis (106), and Alzheimer’s disease (107). Under different experimental conditions, expression levels of the traditional internal reference genes, β-actin and GAPDH, vary greatly, such as in serum-stimulated fibroblasts (108), miR-644a (109), dietary conditions (125), and other conditions (110–114, 119–121). In addition, β-actin has extensive variation in mouse lymphocytes and is not appropriate for use as an internal reference gene for the quantitative PCR analysis of mouse lymphocytes (126), since such changes may lead to data divergence and inaccuracy. At present, there are few reports on factors affecting RPP30 expression levels, which may be related to the existence of RPP30 in all three fields of life (archaea, bacteria, and eukaryotes), and because it is widely expressed in different tissues whose gene sequences are conserved and homologous, such as in humans, chimpanzees, rhesus monkeys, mice, fruit flies, Saccharomyces cerevisiae, and archaea (127). In addition, currently, research on RPP30 is scant. The mRNA of β-actin and GAPDH are not highly expressed in all cell types or tissues of chicken embryos, and the expression levels are different in different tissues (115), which is similar to the 15-fold difference between the highest and lowest expression levels of GAPDH in different human tissues observed by Barber et al. (128). GAPDH expression levels also vary in different varieties of the same plant (129). Furthermore, β-actin and GAPDH expression levels fluctuate significantly at different stages of lymphocyte activation (116), which may be related to their participation in other cellular biology functions. RPP30 mRNA expression in different tissues is more stable than those of β-actin and GAPDH. In addition, RPP30 is widely expressed in 27 human tissues, is relatively conserved in structure and function, is not correlated with DNA content in the sample, and is not affected by the content of genes to be tested, resulting in high application value in a series of samples with scarce and uneven DNA content (91). Currently, to reduce the inaccurate data caused by differences in the expression of internal reference genes among different tissue types, RPP30 has become the main internal reference gene for quantitative detection of genes (130). Moreover, the expression level of the three genes is affected by age (10, 117); their expression level decrease with age, but it is not known whether the specific mechanism is the same. There are also differences in the expression level of β-actin at different developmental stages (131). The factors affecting the expression level of RPP30 RNA in different pathological states, experimental conditions, and tissue types is lower than that of the commonly used internal reference genes, β-actin and GAPDH. RPP30 has good amplification efficiency and may be better used in RT-PCR experiments. Currently, there is no housekeeping gene that has stable expression, is abundant, and consistent under any condition (132). Therefore, specific reference genes should be verified and selected according to experimental conditions and sample type (102, 118).
RPP30 is a highly conserved gene that has homologous genes in 16 species. Although RPP30 is a housekeeping gene and its encoding protein is a key subunit that maintains basic life activities, it is rarely reported to be associated with human diseases, and is overexpressed only in a few patients with cancer. In addition, compared to traditional reference genes, RPP30 has advantages of short sequence length, is widely and uniformly expressed in various tissues, and its expression level is rarely disturbed by external factors. Overall, RPP30 has great prospects and value as an internal reference gene. To date, RPP30 has been used as an internal reference for nucleic acid tests of Sars-CoV-2, evaluation of therapeutic drugs and drug side effects, analysis of the feasibility of HIV detection, and many other diagnostic experiments. However, due to the unstable detection results of the RPP30 protein, there are no studies that have used RPP30 as a reference in western blotting. In such conditions, RPP30 may not be the first choice as a reference gene for these tests. Regardless of which kind of reference that is chosen, it may be affected by a few inevitable conditions. Thus, the correct reference to be used for these tests should be further explored.
JW, SY and YW collected literature and wrote the original draft and prepared the figures and tables. JZ revised the original draft. ZZ conceived the idea and revised the manuscript. All authors contributed to the article and approved the submitted version.
The study was supported by Anhui Provincial Natural Science Foundation (grant number 2108085MH298) and the Scientific research project of Anhui Medical University (grant number 2019GMFY02, 2021lcxk027). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.
We would like to thank Editage (www.editage.cn) for English language editing.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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PMC9606830 | Eryong Chen,Xiaobei Yang,Ruie Liu,Mengke Zhang,Meng Zhang,Feng Zhou,Dongxiao Li,Haiyan Hu,Chengwei Li | GhBEE3-Like gene regulated by brassinosteroids is involved in cotton drought tolerance | 13-10-2022 | GhBEE3-Like,bHLH transcription factor,brassinosteroids (BRs),GhBZR1,drought tolerance,cotton (Gossypium hirsutum L.) | Brassinosteroids (BRs) are important phytohormones that play a vital role in plant drought tolerance, but their mechanisms in cotton (Gossypium hirsutum L.) are poorly understood. Numerous basic helix-loop-helix (bHLH) family genes are involved in the responses to both BRs and drought stress. GhBEE3-Like, a bHLH transcription factor, is repressed by both 24-epi-BL (an active BR substance) and PEG8000 (drought simulation) treatments in cotton. Moreover, GhBZR1, a crucial transcription factor in BR signaling pathway, directly binds to the E-box element in GhBEE3-Like promoter region and inhibits its expression, which has been confirmed by electrophoretic mobility shift assay (EMSA) and dual luciferase reporter assay. Functional analysis revealed that Arabidopsis with GhBEE3-Like overexpression had drought sensitive phenotype, while GhBEE3-Like knock-down cotton plants obtained by virus-induced gene silencing (VIGS) technology were more tolerant to drought stress. Furthermore, the expression levels of three stress-related genes, GhERD10, GhCDPK1 and GhRD26, were significantly higher in GhBEE3-Like knock-down cotton than in control cotton after drought treatment. These results suggest that GhBEE3-Like is inhibited by BRs which elevates the expressions of stress-related genes to enhance plant drought tolerance. This study lays the foundation for understanding the mechanisms of BR-regulated drought tolerance and establishment of drought-resistant cotton lines. | GhBEE3-Like gene regulated by brassinosteroids is involved in cotton drought tolerance
Brassinosteroids (BRs) are important phytohormones that play a vital role in plant drought tolerance, but their mechanisms in cotton (Gossypium hirsutum L.) are poorly understood. Numerous basic helix-loop-helix (bHLH) family genes are involved in the responses to both BRs and drought stress. GhBEE3-Like, a bHLH transcription factor, is repressed by both 24-epi-BL (an active BR substance) and PEG8000 (drought simulation) treatments in cotton. Moreover, GhBZR1, a crucial transcription factor in BR signaling pathway, directly binds to the E-box element in GhBEE3-Like promoter region and inhibits its expression, which has been confirmed by electrophoretic mobility shift assay (EMSA) and dual luciferase reporter assay. Functional analysis revealed that Arabidopsis with GhBEE3-Like overexpression had drought sensitive phenotype, while GhBEE3-Like knock-down cotton plants obtained by virus-induced gene silencing (VIGS) technology were more tolerant to drought stress. Furthermore, the expression levels of three stress-related genes, GhERD10, GhCDPK1 and GhRD26, were significantly higher in GhBEE3-Like knock-down cotton than in control cotton after drought treatment. These results suggest that GhBEE3-Like is inhibited by BRs which elevates the expressions of stress-related genes to enhance plant drought tolerance. This study lays the foundation for understanding the mechanisms of BR-regulated drought tolerance and establishment of drought-resistant cotton lines.
Brassinosteroids (BRs) are a specific class of steroidal hormones in plants, and were initially found in the pollen of Brassica napus (Grove et al., 1979). At present, there are more than 70 compounds of BRs identified in different plant species. BRs play vital roles in regulating multiple physiological processes such as photomorphogenesis, seed germination, root development, cell division and elongation/differentiation, reproductive processes, guard cell development, senescence, and biotic/abiotic stress responses (Tang et al., 2016; Liu et al., 2018a; Ahammed et al., 2020; Lin, 2020; Manghwar et al., 2022; Xiong et al., 2022). BR signal transduction has been extensively studied in recent years. BRs are perceived by the extracellular domain of BR insensitive-1 (BRI1), a plasmatic membrane receptor-like kinase (RLK) (Shiu et al., 2004). The BRI1 dimerizes with BRI1-associated receptor kinase 1 (BAK1) or somatic embryogenesis receptor kinase 1 (SERK1) after binding with BRs (Li et al., 2002; Russinova et al., 2004). Sequential trans-phosphorylation between BAK1 and BRI1 activates them, and the kinases further phosphorylate the inhibitor of BRI1 (BKI1), leading to its association with 14-3-3 proteins (Wang and Chory, 2006; Jaillais et al., 2011). Concomitantly, the activated BRI1 phosphorylates constitutive differential growth 1 (CDG1) and BR-signaling kinase 1 (BSK1), which both phosphorylate and active BRI1-supressor 1 (BSU1) phosphatase (Mora-García et al., 2004; Tang et al., 2008; Kim and Wang, 2010; Kim et al., 2011). Additionally, BSK3 (BRI1-associated receptor kinase 3) functions as a BR signaling scaffold and upregulates BSU1 protein levels (Ren et al., 2019). BSU1 subsequently dephosphorylates the GSK3-like kinase BR insensitive 2 (BIN2), which is posteriorly inhibited by Kink suppressed in bzr1-1D (KIB1) (Ryu et al., 2010; Zhu et al., 2017). Upon BIN2 inactivation, the two homologous transcription factors (TFs) in BR signaling pathway, bri1-EMS-suppressor 1 (BES1) and brassinazole resistant 1 (BZR1), are dephosphorylated by phosphatase 2 (PP2A) and consequently dissociated from 14-3-3 proteins, resulting in the binding to E-Box (CANNTG) and BRRE elements (CGTGT/CG) in the promoters of BR-responsive genes (Wang et al., 2006; Li and Jin, 2007; Wang et al., 2014). In the absence of BR, BKI1 can bind to the intracellular domain of BRI1 and prevent its association with BAK1, thus resulting in the inhibition of phosphorylation cascade (Wang and Chory, 2006). In turn, BIN2 may not be inactivated, and 14-3-3 proteins associate with BZR1 and BES1, which retain their dephosphorylation form and inhibit their ability to modulate the expression of BR responsive genes (Jaillais et al., 2011), and stimulate their degradation (Kim et al., 2019). The basic helix-loop-helix (bHLH) protein family is the second largest family of TFs in plants (Feller et al., 2011). The family is defined by its typical feature, bHLH domain which holds about 60 amino acids, including two diverse regions: the basic region (b region) and the HLH region (Murre et al., 1989a; Jones, 2004). The basic domain located at the N-terminal end is liable for recognizing and binding to the DNA regulatory motif in the promoters of their target genes (Toledo-Ortiz et al., 2003; Li et al., 2006). bHLH proteins are typically divided into two groups based on different functions of the basic region: DNA- and non-DNA-binders (Jones, 2004). The HLH domain at the C-terminus carries two amphipathic α-helices joined by a loop with variable sequences and involves in homo- or heterodimerization with other bHLH proteins (Toledo-Ortiz et al., 2003; Feller et al., 2011). Several reports have mentioned that bHLH proteins are involved in the processes of morphogenesis (De Lucas and Prat, 2014; Li et al., 2016a; Gangappa and Kumar, 2017), iron homeostasis (Li et al., 2016b), light signaling (Leivar et al., 2012), flowering time (Ito et al., 2012), biotic and abiotic stresses (Waseem et al., 2019; Verma et al., 2020), and hormonal signals (Liu et al., 2018b). A number of bHLH family proteins are involved in BR signaling pathway (Nemhauser et al., 2004; Vert et al., 2005). The reasonable explanation for this may be that BES1 and BZR1, the key TFs in BR signaling pathway, contain bHLH domains, and their recognized elements are found in the promoter of many bHLH genes (Yin et al., 2005). BR Enhanced Expression (BEE) genes, such as BEE1, BEE2 and BEE3, are BR early-response genes and have redundant function in BR signaling pathway, which belong to bHLH family genes (Friedrichsen et al., 2002). Here, we identified a bHLH family gene, namely, GhBEE3-Like. Quantitative RT-PCR (qPCR), electrophoretic mobility shift assay (EMSA) and dual luciferase reporter assay demonstrated that GhBZR1, the key TF in BR signaling pathway, can bond to the promoter of GhBEE3-Like to inhibit its expression. Moreover, the GhBEE3-Like transgenic Arabidopsis was more sensitive to drought stress, and the cotton with GhBEE3-Like gene knock-down by virus-induced gene silencing (VIGS) exhibited drought resistance phenotype. The results of water loss rate, stomatal aperture and the expression of stress-related genes (GhERD10, GhCDPK1 and GhRD26) also indicated the similar plant phenotypes under drought stress conditions. Our study reveals the mechanism of BRs repressing the expression level of GhBEE3-Like to enhance cotton drought tolerance, which may lay a foundation for the establishment of drought resistance cotton lines.
Wild-type Arabidopsis thaliana, Col-0 (Columbia-0), was employed as the control. The methods of sowing and cultivating Arabidopsis, water loss assay and drought experiments of Col-0 and transgenic plants were performed according to a previous report (Chen et al., 2017a). CCRI24 cotton (G. hirsutum L.) was supplied by the Institute of Cotton Research, Chinese Academy of Agricultural Sciences, and was used as a research material. To detect the gene expression of GhBEE3-Like, the samples of root, stem, leaf, petal, anther, sepal, ovules of -1 days post-anthesis (dpa) and 0 dpa, fibers (1, 3, 6, 9, 12, 15, 18, 21, 24, 27 and 30 dpa) were harvested and frozen in liquid nitrogen for RNA isolation. The methods of cotton culture, as well as sample handling and collection were conducted as described previously (Chen et al., 2017a). To clone GhBEE3-Like gene, the leaf samples from CCRI24 cotton was used for RNA isolation and cDNA synthesis. To analyze whether GhBEE3-Like gene in cotton can respond to osmotic stress, the specific methods of CCRI24 cotton germination and treatment were performed according to a previous study (Chen et al., 2017a). PEG8000 (10%) was used to simulate drought stress to treat the cotton seedlings.
GhBEE3-Like (Gh_A12G0489) was obtained through TBLASTN method using the protein sequences of BEE3 (AT1G73830) as query to retrieve gene sequences from the cotton (G. hirsutum L.) database (https://www.cottongen.org/). Subsequently, the SMART program (http://smart.embl-heidelberg.de/) was employed to analyze the domains containing in GhBEE3-Like protein. To assess the phylogenetic relationships among GhBEE3-Like and its homologous proteins, the BLAST program in NCBI (http://www.ncbi.nlm.nih.gov/) was applied to search all homologous genes, except BEE3 gene. The phylogenetic tree was generated in MEGA software (version 7.0) by adopting the neighbor-joining (NJ) algorithm, and the parameter setting was adopted from the previous study (Chen et al., 2021).
Total RNA was isolated by RNAprep Pure Plant Kit (TIANGEN, Beijing, China), and cDNA synthesis was performed using PrimeScript® RT Reagent Kit with gDNA Eraser (TaKaRa, Dalian, China). The cDNAs were used for semi-quantitative RT-PCR and qPCR.
To gain the overexpression vector and GFP vector of GhBEE3-Like gene, the full-length coding sequence of GhBEE3-Like (738 bp) was amplified using two sets of specific primers, and then ligated into the pMD19-T simple vector (TaKaRa, Dalian, China), respectively. Subsequently, GhBEE3-Like was digested with BamHI/SacI and SalI/BamHI enzymes in the pMD19-T-GhBEE3-Like recombination vector (TaKaRa, Dalian, China), respectively. The coding sequence of GhBEE3-Like was cloned in the BamHI and SacI sits of p6MYC vector to produce overexpression vector 35S::GhBEE3-Like, and cloned in the SalI and BamHI sits of pEZR (K)-LC to obtain the GFP vector 35S::GFP-GhBEE3-Like. The primers used for constructing the above-mentioned vectors were GhBEE3-Like-OV-F/GhBEE3-Like-OV-R and GhBEE3-Like-GFP-F/GhBEE3-Like-GFP-R ( Supplementary Table 1 ). GhBEE3-Like gene fragment (377 bp) was amplified by PCR technology to produce the VIGS vector of GhBEE3-Like, while the above-mentioned 35S::GhBEE3-Like recombination vector was used as a template. The GhBEE3-Like gene fragment was digested with XbaI and SacI (TaKaRa, Dalian, China), and cloned into the same sites of TRV2 vector to produce the recombination vector TRV2:GhBEE3-Like. The primer sets GhBEE3-Like-VIGS-F and GhBEE3-Like-VIGS-R were used for the construction of VIGS vector ( Supplementary Table 1 ).
To determine the subcellular localization of GhBEE3-Like, Agrobacterium tumefaciens-mediated transient transformation method was used to transform the recombination vector 35S::GFP-GhBEE3-Like into Nicotiana benthamiana leaves (Wroblewski et al., 2005). A confocal laser scanning microscope (ZEISS, LSM 780) was employed to record the green fluorescence images 48 h after transformation.
To analyze the functions of GhBEE3-Like gene under drought stress. VIGS technology was applied to produce TRV2 (control) and TRV2:GhBEE3-Like (GhBEE3-Like gene knock-down) cottons according to previous studies (Pang et al., 2013; Chen et al., 2021). In the VIGS technology system, GhPDS was used as a marker gene to evaluate the presence of albino phenotype in TRV2:GhPDS cotton after knock-down. Trefoil stage cotton seedlings were water withheld for 1 week and then rewatered for 1 week to calculate the survival rates. Moreover, the trefoil stage cotton leaves were collected for RNA isolation and expression analysis of GhBEE3-Like gene.
To examine whether GhBEE3-Like is highly expressed in Arabidopsis, the leaves of Col-0 and transgenic lines were collected for total RNA extraction and cDNA synthesis. PCR was carried out in a heated-lid thermal cycler (Applied Biosystems, USA) as previously described (Chen et al., 2021). The primers of the internal reference AtUBQ10 (At4g05320) are listed in Supplementary Table 1 . To analyze the tissue expression patterns of GhBEE3-Like gene and whether GhBEE3-Like can respond to drought stress or BR treatment, the expression levels of GhBEE3-Like and stress-related genes were detected using the Applied Biosystems QuantStudio 6 Flex system according to a previous report (Chen et al., 2021). GhHIS3 gene was employed as an internal reference. The primers used for qPCR are presented in Supplementary Table 2 .
The coding region of GhBZR1 (CotAD_50537) was cloned into the pET30a vector to form GhBZR1-His, in which a His tag was fused into the C-terminal of GhBZR1. The reconstruction vector was transformed into Escherichia coli BL21 (DE3). The biotin probe labeled at both ends (3′-end and 5′-end) was prepared (Tsingke Biotechnology Co., Ltd., China). The competitor probe was lacking a biotin label. The ESMA experiment was conducted using the LightShift Chemiluminescent EMSA Kit (Thermo Fisher Scientific, USA). The primer sequences for each probe are shown in Supplementary Table 1 .
To further confirm GhBEE3-Like expression regulated by GhBZR1 protein, dual luciferase reporter assay were performed. For transcription activity analysis, the coding region of GhBZR1 was cloned into the restriction sites of KpnI and BamHI in pCambia1300-35S-3×FLAG vector as an effector, with the empty vector of pCambia1300-35S-3×FLAG as control plasmid. The promoter sequence (1980bp) of GhBEE3-Like was inserted into the restriction sites of KpnI and HindIII that upstream of firefly LUC gene in pGreenII 0800-LUC vector to generate a reporter plasmid. REN in the pGreenII 0800-LUC vector under the CaMV35S promoter was used as internal reference (Hellens et al., 2005). The effector plasmid and reporter plasmid were cotransformed into 30 days old tobacco leaves using the Agrobacterium-mediated method (Zhou et al., 2018). A dual-LUC reporter assay kit (Catalog No.E1910; Promega, USA) was used to measure LUC and REN activities and the binding efficiency was reported as ratio of LUC to REN.
The qPCR reaction of each sample was performed in triplicate, and three biological replicates were applied. The qPCR results were calculated using the 2-ΔΔCT method. For survival rate analysis of Arabidopsis and cotton, three biological replicates consisting of 18 plants and 30 cotton plants were analyzed for each group, respectively, and the survival rate was calculated as the percentage of surviving plants to total plants. For the assay of water loss rate, three biological replicates which consist 3 leaves in each group were used, and the water loss rate was calculated as the percentage of leave water reduction to total weight of leaves at each time point we checked. To analyze the stomatal aperture, 27 stomata in each group were analyzed. For the dual luciferase reporter assay, five biological replicates were applied for analyzing. Error bars in our study were obtained using mean with SD. The significant differences in all experiments were analyzed by Student’s t-test.
GhBEE3-Like sequence was obtained through BLAST program in the G. hirsutum L. cotton database (https://www.cottongen.org/) using BEE3 (AT1G73830) protein sequence as query, and then GhBEE3-Like was cloned from CCRI24 cotton using a pair of specific primers. Sequence analysis revealed that GhBEE3-Like contained a signature domain, namely, HLH domain ( Figure 1A ). This indicates that GhBEE3-Like is a member of bHLH protein family. A phylogenetic tree was built according to the protein sequences of GhBEE3-Like and its homologs to analyze their evolutionary relationships. The results showed that GhBEE3-Like clustered together with its paralog XP_017636924.1 in G. arboreum (A-genome species) ( Figure 1B ). Moreover, GhBEE3-lik had much closer evolutionary relationship with its ortholog XP_022759934.1 from Durio zibethinus ( Figure 1B ). This implies that GhBEE3-Like in G. hirsutum (allotetraploid cotton) is originated from XP_017636924.1 in A-genome cotton and share a common ancestor with XP_022759934.1 in D. zibethinus.
To determine the subcellular localization of GhBEE3-Like protein, GhBEE3-Like gene was cloned into the C-terminus of a green fluorescent protein (GFP) vector, PEZR(K)-LC. The reconstruction vector was transiently transferred into tobacco leaves and the GhBEE3-Like localization was observed via confocal laser scanning microscopy. Unlike the control signals (PEZR(K)-LC vector) that distributed throughout the cells, GFP-GhBEE3-Like was specifically localized in the nucleus ( Figure 2 ). These results indicate that GhBEE3-Like can act as a TF due to its localization in the nucleus.
Determination of gene expression in various tissues helps to understand the gene function in plants. Thus, qPCR was applied to determine the expression patterns of GhBEE3-Like in different tissues which include roots, shoots, leaves, sepals, petals, anthers, ovules at -1 dpa and 0 dpa, and fibers at 1 dpa, 3 dpa, 6 dpa, 9 dpa, 12 dpa, 15 dpa, 18 dpa, 21 dpa, 24 dpa, 27 dpa and 30 dpa. The data indicated that GhBEE3-Like was highly expressed in shoots, leaves and sepals, especially in shoots, but lowly expressed in other tissues and could not even be detected in fiber samples at 15 dpa (days post-anthesis), 18 dpa, 21 dpa, 27 dpa and 30 dpa ( Figure 3A ). These findings demonstrate that GhBEE3-Like can play vital roles in shoot growth and development. BEE3 is a gene with BR-enhanced expression (Friedrichsen et al., 2002). Thus, BR substance 24-epi-BL was used to treat trefoil-stage cotton seedlings, and the expression profile of GhBEE3-Like was detected. GhBEE3-Like showed a down-regulated patterns and had significantly lower expression level in 24-epi-BL-treated cotton than that in untreated cotton (0 h), and the lowest expression level occurred at 6 h ( Figure 3B ). These findings indicate that GhBEE3-Like is a BRs repressing gene in cotton. To analyze whether GhBEE3-Like can respond to drought stress, 10% PEG8000 was used to treat trefoil-stage cotton seedlings. qPCR data revealed that the expression levels of GhBEE3-Like were decreased in the PEG8000-treated samples compared to the untreated sample (0 h) ( Figure 3C ). These results suggest that the expression of GhBEE3-Like is repressed by drought stress, and may be involved in drought tolerance.
BZR1 is the critical TF of BR signal transduction pathway, which binds to E-box (CANNTG) and BR response elements (BRREs, CGTGT/CG) in BR-responsive gene promoters to regulate their expression (He et al., 2005; Yin et al., 2005). The EMSA experiment was conducted to explore whether GhBZR1 can bind to the promoter of GhBEE3-Like. The results demonstrate that GhBZR1 binds to the biotin probe, GhBEE3-Like Probe, which contains an E-box ( Figure 4 ). Moreover, the Competitor Probe, which has the same sequences with GhBEE3-Like Probe without labeling biotin, could significantly reduce the combination of GhBZR1 protein and GhBEE3-Like Probe ( Figure 4 ). However, GhBZR1 protein was not bound to the Mutant Probe ( Figure 4 ). This indicates that GhBZR1 can bind to the promoter of GhBEE3-Like to affect its function in cotton via BR signaling pathway.
To further verify the effect of GhBZR1 on the promoter activity of GhBEE3-Like, dual-luciferase (LUC) reporter assay was conducted. The promoter sequences from the upstream start codon of GhBEE3-Like were used to drive the firefly LUC gene expression and as a reporter, and the REN in the vector was used as an internal control to normalize the transformation efficiency ( Figure 5A ). GhBZR1 under the CaMV35S promoter was used as the effector, whereas the empty vector with the FLAG labels was served as a negative control ( Figure 5A ). After co-transformation of the GhBZR1effector and reporter in the leaf cells of N. benthamiana, the LUC/REN ratio of co-expression of GhBZR1 and GhBEE3-Like promoter was significantly lower than the FLAG control at 2 days post-inoculation (dpi) ( Figure 5B ). The results suggest that GhBZR1, the key TF in BR signaling pathway, efficiently inhibits the expression of GhBEE3-Like.
BRs are a troop of steroidal hormones that regulate multiple physiological functions, including abiotic stresses. BEE3 is the early response TF that required for BR action. To investigate the function of GhBEE3-Like gene on drought stress, the homozygous transgenic Arabidopsis plants were chosen and further ensured by semi-quantitative RT-PCR ( Figure 6A ). The transgenic plants and Col-0 plants were grown and withheld water for 3 weeks in the same pot. After water recovery for 1 week, the transgenic lines were more sensitive to drought stress, and the survival rates of GhBEE3-Like overexpression lines were remarkably lower compared to Col-0 plant ( Figures 6B,C ). Moreover, the GhBEE3-Like overexpression lines showed higher water loss rates compared with Col-0, especially the lines 37-4 and 49-2 ( Figure 6D ). Stomatal aperture is the important factor of plant water loss. The analysis results showed that the transgenic lines had lager stomatal aperture than Col-0 after ABA treatment, and the statistical data were consistent with the phenotypic observation of stomata ( Figures 6E, F ). These findings imply that GhBEE3-Like is involved in drought stress and increases the plant sensitivity to drought stress.
To further clarify the function of GhBEE3-Like in drought tolerance, VIGS technology was used to knock down GhBEE3-Like gene in cotton. Here, GhPDS was used as a marker gene to verify the VIGS system on gene knock-down in cottons, and TRV2:GhPDS cotton showed an albino phenotype after two weeks of transformation ( Supplementary Figure 1 ). The results demonstrated that VIGS technology could be used for cotton gene knock-down. In addition, the leaves of WT, TRV2 and TRV2:GhBEE3-Like cottons were collected at trefoil stage and qPCR was applied to analyze the knock-down efficiency of GhBEE3-Like in WT cotton. Notably, the expression levels of GhBEE3-Like in TRV2:GhBEE3-Like cotton plants were significantly lower compared to TRV2 cotton, indicating that GhBEE3-Like knocked-down cotton was successfully obtained ( Figure 7A ). Moreover, the WT, TRV2 and TRV2:GhBEE3-Like cottons were withheld water for one week, and then rewatered for growth. One week later, the TRV2:GhBEE3-Like cotton showed more tolerance against drought stress than the WT and TRV2 cottons, and the statistical data of survival rates were consistent with the phenotypic findings ( Figures 7B, C ). These results indicate that GhBEE3-Like negatively regulates drought tolerance in cotton.
GhBEE3-Like is a TF member of bHLH protein family, which can affect the expression levels of many genes. To further comprehend the molecular control of GhBEE3-Like on drought stress, the expression profiles of GhERD10, GhCDPK1 and GhRD26 were analyzed by qPCR. Under normal conditions, no obvious differences in the expression levels of these three genes were found among the WT, TRV2 and TRV2:GhBEE3-Like cottons. However, these three stress-related genes showed higher expression levels in the TRV2:GhBEE3-Like cotton than in the TRV2 cotton under drought conditions ( Figure 8 ). What’s more, the expression levels of these three genes in TRV2:GhBEE3-Like cotton were also higher than those in WT cotton ( Figure 8 ). These findings indicate that GhBEE3-Like affects many stress-related genes to regulate drought tolerance in cotton.
The bHLH TF family of genes is one of the largest families in eukaryotes and plays significant roles in plant developmental and physiological processes (Ledent and Vervoort, 2001; Liu et al., 2015). The bHLH proteins comprise a basic region (b) followed by a helice-loop-helice (HLH) domain (Ferré-D’amaré et al., 1993). The basic region functions for DNA binding and the HLH domain promotes dimerization with other bHLH proteins (Murre et al., 1989b; Ferré-D’amaré et al., 1993). In upland cotton (Gossypium hirsutum) genome, 437 bHLH proteins were identified (Lu et al., 2018). In this study, we cloned a gene, namely, GhBEE3-Like. Domain analysis revealed that GhBEE3-Like possessed a typical HLH domain ( Figure 1A ). Subcellular localization analysis revealed that GhBEE3-Like was localized in the nucleus ( Figure 2 ). These results indicate that GhBEE3-Like is a member of bHLH TF family. BEE genes (BEE1, BEE2, and BEE3) are BR early-response genes and their expression levels were strongly induced by BRs (Friedrichsen et al., 2002). The findings are different with our study in the respect that the expression of GhBEE3-Like is decreased after BR treatment ( Figure 3B ). The reason for this may be that different concentrations of BL (an active BR substance) and different culture media were used to treat different plant species. Many studies on the function of BEE genes in plants have been reported. Mutations in one or two BEE1 genes could not result in a distinct phenotype (Friedrichsen et al., 2002). However, bee1/bee2/bee3, triple mutants of these genes, showed the typical phenotypes of BR mutants regardless of at seedling or floral stage (Friedrichsen et al., 2002). BEE1 interacts with another bHLH protein CESTA to regulate CPD, a BR biosynthesis gene, for activating the biosynthesis of BR in plants through binding to the G-box motif in CPD promoter (Poppenberger et al., 2011). In Arabidopsis, BEE could affect plant shade-avoidance syndrome by forming a network with BES1-interacting Myc-like1 (BIM) and phytochrome rapidly regulated 1 (PAR1) (Cifuentes-Esquivel et al., 2013). Moreover, inhibiting the activities of BEE and BIM results in a defect of shade avoidance and a dwarf rosette phenotype (Cifuentes-Esquivel et al., 2013). BEE1, BEE3 and HAF (HALF FILLED) have similar expression patterns in the reproductive organs, and their triple mutant has defective pollen tube growth (Crawford and Yanofsky, 2011). In cotton, the uncontrolled expression of GhBEE1-Like could result in anther indehiscence (Chen et al., 2017b). PagBEE3L, a BEE3-Like gene in poplar, enhances the proliferation of xylem cells in stems to promote biomass production (Noh et al., 2015). The bee1/bee2 double mutant and bee1/bee2/bee3 triple mutant were used to assess the tolerance to drought stress, and the data showed that bee1/bee2/bee3 mutant plants were more tolerant to drought stress than Col-0 plants, but the survival rates between bee1/bee2 double mutant and Col-0 had no significant difference (Moreno et al., 2018). These findings imply that BEE3 may play a vital role on drought resistance in plants. Here, we found that transgenic Arabidopsis overexpressing GhBEE3-Like were more vulnerable to drought stress than the control plant, Col-0, and the statistical results of survival rates were consistent with the phenotypic findings ( Figures 6A–C ). Furthermore, the water loss rates of GhBEE3-Like transgenic Arabidopsis lines were relatively higher compared to Col-0 plants, and the stomatal aperture of Col-0 was smaller than that of GhBEE3-Like transgenic Arabidopsis ( Figures 6D–F ). Additionally, GhBEE3-Like gene knock-down cotton obtained by VIGS technology had drought resistant phenotype ( Figure 7 ). These results indicate that GhBEE3-Like can act as a negative regulator in cotton drought tolerance. BRs, the sixth class of phytohormones, play vital roles in regulating multiple physiological processes, including biotic and abiotic stress responses (Peres et al., 2019). BR signal transduction pathway is that BRs are perceived and bound by the plasmatic membrane receptor-like kinase BRI1 (Shiu et al., 2004). Through a series of transmembrane signal transduction process to activate the key TFs in BR signaling pathway, BES1 and BZR1, to regulate the functions of BR responsive genes (Jaillais et al., 2011). The bHLH proteins are involved in various biological processes, including drought and osmotic stress signaling (Liu et al., 2014) and BR signaling pathway (Zhang et al., 2009). BEE3 is a bHLH family gene and responses to BRs at early stage (Friedrichsen et al., 2002). In previous study, our results found that BR deficiency caused higher sensitivity to drought stress in cotton (Chen et al., 2019). So, does BRs regulate BEE3 gene to affect plant drought tolerance? Here, GhBEE3-Like, a bHLH TF family gene in cotton, is repressed by BRs at transcriptional level ( Figures 1 - 3 ). EMSA results demonstrated that GhBZR1, the key TF in BR signaling pathway, could bind to the E-box motif in GhBEE3-Like gene promoter ( Figure 4 ). Additionally, the results of dual-luciferase reporter assay showed that GhBZR1 repressed the gene expression of GhBEE3-Like ( Figure 5 ). All the results confirmed that GhBEE3-Like was a BR-regulated gene in cotton. Moreover, Arabidopsis with GhBEE3-Like overexpression were more sensitive to drought stress, and knocking down the GhBEE3-Like gene in cotton with VIGS technology could enhance cotton drought tolerance and induce the expression of many stress-related genes (GhERD10, GhCDPK1 and GhRD26) under drought conditions ( Figures 6 – 8 ). Hence, we speculate that a regulatory mechanism is involved in cotton drought resistance by which BRs active GhBZR1 to repress the expression of GhBEE3-Like, which in turn induces the expression of stress-related genes to enhance cotton resistance under drought stress ( Figure 9 ). Overall, we propose a molecular mechanism of cotton drought resistance regulated by GhBEE3-Like gene via BR signaling pathway. GhBEE3-Like, a bHLH TF family gene, is repressed by the key TF of BR signaling pathway, GhBZR1. Moreover, transgenic Arabidopsis overexpressing GhBEE3-Like were more sensitive to drought stress, and knocking down GhBEE3-Like gene with VIGS technology could enhance cotton drought resistance. The expression levels of three stress-related genes, GhERD10, GhCDPK1 and GhRD26, were upregulated in cotton plants after GhBEE3-Like knock-down under drought conditions. These results deepen our understanding of BR-regulated mechanisms on cotton drought resistance mediated by GhBEE3-Like.
The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.
CL and HH conceived and designed the research; EC and XY performed the experiments; RL, MKZ, and MZ analyzed the data; FZ and DL read and provided helpful discussion; EC wrote the original draft; CL and HH reviewed and edited the manuscript. All authors read and approved to publish the final manuscript.
This research was supported by the National Natural Science Foundation of China (Grant No. 32101679), the Natural Science Foundation of Henan Province (Grant No. 202300410162), the Doctoral Research Start-up Fund of Henan Institute of Science and Technology (Grant No. 2017006) and the National Natural Science Foundation of China (Grant No. 31900394).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. | true | true | true |
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PMC9606903 | Yuehua Qi,Chunjing Zhang,Di Wu,Yue Zhang,Yunfeng Zhao,Wenjuan Li | Indole-3-Carbinol Stabilizes p53 to Induce miR-34a, Which Targets LDHA to Block Aerobic Glycolysis in Liver Cancer Cells | 13-10-2022 | aerobic glycolysis,LDHA,I3C,miRNA,liver cancer | Certain cancer cells prefer aerobic glycolysis rather than oxidative phosphorylation for energy supply. Lactate dehydrogenase A (LDHA) catalyzes the reduction of pyruvate to lactate and regains NAD+ so that glycolysis is continued. As a pivotal enzyme to promote smooth glycolysis, LDHA plays an important role in carcinogenesis. Indole-3-carbinol (I3C) has displayed antitumor activity, but the exact mechanism remains to be identified. In this study, we treated liver cancer cells with I3C, performed colony formation and cell migration, measured the expression of glycolysis-related proteins, and predicted and validated LDHA-targeting miRNA from the databases. In addition, the mRNA and protein levels of p53, glycolysis-related genes and miRNAs that regulate glycolysis were detected after I3C and siRNA-p53 treatment alone or in combination. Next, the expression and colocalization of p53 and MDM2 in liver cancer cells were evaluated after I3C treatment, and the effect of I3C on p53 protein stability was examined. The results showed that I3C inhibited cell proliferation, migration, and the expression levels of glycolysis-related gene LDHAs. MiR-34a was predicted to target LDHA, and I3C downregulated its expression. Furthermore, the combined I3C and siRNA-p53 treatment demonstrated that I3C regulated the expression of LDHA via miR-34a in a p53-dependent manner. Finally, I3C inhibited MDM2 expression and its colocalization with p53 and stabilized p53 expression. In summary, I3C inhibited the degradation of p53 by MDM2 in liver cancer cells; stable p53 induced miR-34a, which targeted LDHA, a key enzyme for aerobic glycolysis, suggesting cancer metabolism is an important target for I3C in liver cancer cells. | Indole-3-Carbinol Stabilizes p53 to Induce miR-34a, Which Targets LDHA to Block Aerobic Glycolysis in Liver Cancer Cells
Certain cancer cells prefer aerobic glycolysis rather than oxidative phosphorylation for energy supply. Lactate dehydrogenase A (LDHA) catalyzes the reduction of pyruvate to lactate and regains NAD+ so that glycolysis is continued. As a pivotal enzyme to promote smooth glycolysis, LDHA plays an important role in carcinogenesis. Indole-3-carbinol (I3C) has displayed antitumor activity, but the exact mechanism remains to be identified. In this study, we treated liver cancer cells with I3C, performed colony formation and cell migration, measured the expression of glycolysis-related proteins, and predicted and validated LDHA-targeting miRNA from the databases. In addition, the mRNA and protein levels of p53, glycolysis-related genes and miRNAs that regulate glycolysis were detected after I3C and siRNA-p53 treatment alone or in combination. Next, the expression and colocalization of p53 and MDM2 in liver cancer cells were evaluated after I3C treatment, and the effect of I3C on p53 protein stability was examined. The results showed that I3C inhibited cell proliferation, migration, and the expression levels of glycolysis-related gene LDHAs. MiR-34a was predicted to target LDHA, and I3C downregulated its expression. Furthermore, the combined I3C and siRNA-p53 treatment demonstrated that I3C regulated the expression of LDHA via miR-34a in a p53-dependent manner. Finally, I3C inhibited MDM2 expression and its colocalization with p53 and stabilized p53 expression. In summary, I3C inhibited the degradation of p53 by MDM2 in liver cancer cells; stable p53 induced miR-34a, which targeted LDHA, a key enzyme for aerobic glycolysis, suggesting cancer metabolism is an important target for I3C in liver cancer cells.
Metabolic reprogramming has attracted widespread attention because it runs through the courses of carcinogenesis [1]. Even under normoxia, certain cancer cells tend to obtain energy via glycolysis, known as the “Warburg effect” or “Aerobic glycolysis” [2]. Aerobic glycolysis inhibitors that target deregulated cellular energetics have been studied for cancer treatment [1]. For instance, lactate dehydrogenase (LDH) has been proposed as a possible target for cancer diagnosis, therapy, and prevention [3]. LDH catalyzes a reversible reaction between pyruvate and lactate, whereas LDH isoform A (LDHA) tends to metabolize pyruvate to lactate, and LDH isoform B (LDHB) catalyzes the reverse reaction [3]. Studies have found that LDHA overexpression is inversely correlated with survival in solid tumors and also associated with drug resistance [4]; downregulated LDHA promotes oxidative phosphorylation (OXPHOS), which is reversed by electron transport chain inhibitors [5]. The regulation of LDHA expression involves multiple levels [6]; one of these is post-transcriptional processing, which affects mRNA stability. MicroRNAs (miRNAs) are short, endogenously initiated, noncoding RNAs that repress gene translation by inducing mRNA degradation [7]. Studies have shown that compounds inhibit tumor proliferation, invasion, and migration through miR-34a in gastric cancer cells [8]; in breast cancer cells, glycolysis and cell proliferation can be directly inhibited by miR-34a [9]. p53, coded by the gene p53, is often referred to as the “guardian of the genome” as it protects the integrity of DNA in tissue cells and regulates cell cycle arrest, DNA damage repair, and apoptosis [10]. In addition, p53 regulates the expression of noncoding RNA in addition to protein-coding genes, and microRNAs (miRNAs) are one of them [11]. Furthermore, the stability and expression of p53 are also subject to various regulations. p53 is modified by post-translational modifications such as acetylation, phosphorylation, and ubiquitination, etc. [12]. The ubiquitination of p53 is predominantly mediated by murine double minute 2 (MDM2), an E3 ubiquitin ligase involved in the proteasome pathway [13]. Regulating metabolism is an important function of p53. p53 directly inhibits the transcription of glucose transporter 1 (GLUT1) and GLUT4, thereby reducing glucose uptake [14]. Glycolysis could be inhibited by p53-downregulated hexokinase 2 (HK2) in prostate cancer cells [15]; p53 induces the expression of E3 ubiquitin ligase Parkin to promote hypoxia inducible factor-1α (HIF-1α) degradation through ubiquitination and subsequently inhibit the expression of glycolysis enzymes LDHA and GLUT1 in breast cancer MCF7 cells [16]. As a compound found in cruciferous vegetables, Indole-3-carbinol (I3C) is derived from the breakdown of glucobrassicin [17]. Its safety in normal cells has been proven [18], and it has displayed plenty of anticancer activities [19]. For example, I3C inhibits the proliferation of breast cancer cells through the NF–κB pathway [20]; I3C regulates the expression of apoptosis-related proteins and induces G1 phase cell-cycle arrest in lung cancer cells [21]; in liver cancer cells, I3C induces DNA damage and increases the permeability of mitochondria and the release cytochrome C from mitochondria into the cytoplasm [22]. In addition, I3C exerts a potential role in the energetic metabolism of cells. In HeLa cells, I3C reduces intracellular glucose and lactate concentrations and inhibits glucose metabolism [23]. I3C could activate p53, thereby directly or indirectly regulating the expression of glycolysis-related proteins such as GLUT, LDHA, and PKM2 [24]. More importantly, I3C is a natural inhibitor of E3 ubiquitin ligase WWP1, which in turn triggers the ubiquitination of PTEN and regulates PKM2 during aerobic glycolysis [25]. The latest global cancer (GLOBOCAN) statistics has shown that primary liver cancer ranks sixth in incidence and third in mortality [26]. In China, the incidence and mortality of liver cancer are fifth and second, respectively [27]. Liver is the main metabolic organ, and studies have suggested that combinational therapies targeting autophagy and aerobic glycolysis may be effective in the treatment of liver cancer [28]. Other studies have also demonstrated that downregulation of Forkhead box protein K1 (FOXK1) regulates aerobic glycolysis, thereby inhibiting liver cancer cell proliferation [29]. The metabolic reprogramming has been confirmed throughout the development of cancer [30]. It is worth exploring the exact molecular mechanism of I3C in this process and investigating the therapeutic potential of I3C in human liver cancers, which may provide more effective theoretical and experimental bases for liver cancer diagnosis and treatment.
To validate whether I3C has an effect on liver cancer cell growth, the CCK8 assay was performed on liver cancer HepG2 cells, and a dose- and time-dependent suppression of cell proliferation were observed (Figure 1A). Based on the IC50 (949 μM for 12 h, 282 μM for 24 h, 235 μM for 48 h), 200 μM I3C and 12 h treatment were chosen for subsequent studies. Unlimited division of cancer cells is a key feature, which was measured using a clonogenic assay in HepG2 cells treated with I3C for 12 h. The data showed that, compared with the control group, I3C significantly inhibited the colony formation of HepG2 cells (Figure 1B,C). To detect cell migration ability, another feature of cancer cells, scratch wound assay was utilized, and, as shown in Figure 1D,E, I3C treatment retarded the healing of the scratch area in a time- and dose-dependent manner in HepG2 cells. Next, whether I3C regulates glycolysis in liver cancer cells was studied. The results demonstrated that I3C treatment significantly inhibited lactate production and glucose consumption (Figure 1F,G), suggesting that I3C is effective to impede the glycolytic phenotype of liver cancer cells. Glycolysis includes multiple chemical reactions, and some enzymes are involved in this process [31]. To address whether I3C directly affects glycolysis, the mRNA and protein expression levels of glycolysis-related enzymes were measured using qPCR and Western blot, respectively. The results showed that I3C treatment increased the mRNA levels of PKM2 and LDHA, both important glycolysis-related genes (Figure 2A). However, the proteins level of PKM2 and LDHA were decreased after I3C treatment (Figure 2B,C). Meanwhile, we detected the change in mitochondrial membrane potential (MMP) as a marker for mitochondrial function [32]. As shown in Figure 2D, the levels of MMP in the I3C treatment group were significantly higher than that in the control group. These results suggest that I3C affects the expression of glycolysis-related proteins, not at the RNA level, and these changes negatively correlate with MMP. To study how I3C regulates the expression of glycolysis-related genes at the post-transcriptional level, in the following experiments we focused on LDHA because it plays a key role in the regeneration of nicotinamide adenine dinucleotide (NAD+), which is required to maintain glycolysis [33].
It is known that miRNAs are key molecules in the post-transcriptional regulation of LDHA [9]. We found that both miR-34a and miR-449a can target LDHA using three online tools (miRcode, starbase, and targetscan) (Figure 3A). RNAlocate was used to validate our prediction (Figure 3B). The relationship between the expression levels of miR-34a and LDHA and liver cancer patient survival rate was examined using the Kaplan–Meier Plotter tool. The results showed that patients with low miR-34a expression had lower overall survival and poor prognosis (Figure 3C), whereas patients with high LDHA expression had worse survival (Figure 3D). Next, we studied the effect of I3C treatment on miR-34a. The results showed that I3C treatment increased the miR-34a level (Figure 3E). To figure out how miRNA is regulated by I3C treatment, we utilized the STITCH tool to predict the regulators being involved. The result showed that p53 can be the molecule that plays a regulatory role (Figure 3F), which is consistent with several other studies [34]. Indeed, our results showed that I3C treatment increased the protein (Figure 3G,H) and mRNA (Figure 3I) levels of p53. Next, how I3C regulates LDHA-targeted miRNA via p53 was studied in p53 knockdown HepG2 cells. As shown in Figure 4A,B, p53 knockdown upregulated LDHA expression, which was reversed after I3C treatment. As for the mRNA levels of p53 and LDHA, the former was in line with the changes in protein levels, whereas the latter was different, as expected (Figure 4C). The results in Figure 4D demonstrated that I3C treatment reversed the downregulation of miR-34a caused by knockdown of p53. We then performed colony formation and wound scratch assays for phenotype evaluation. The results showed that, compared with the control group, siRNA-p53 promoted colony formation (Figure 4E,F) and migration (Figure 4G,H) of HepG2 cells, whereas I3C treatment attenuated these promotional effects. These results demonstrate that I3C inhibits LDHA expression via the p53/miRNA-34a axis. PFT-α, as an inhibitor of p53 transcription activity, was used to further confirm the relationship between p53, miRNA, and LDHA. Based on CCK8 analysis (Figure S1), we chose 20 µM of PFT-α for the 12 h treatment for the following experiments. Firstly, PFT-α treatment enhanced the protein and mRNA levels of LDHA with no significant effects on that of p53; I3C treatment attenuated the effect of PFT-α on the protein levels (Figure 5A,B), not mRNA levels, of LDHA (Figure 5C). PFT-α treatment suppressed the expression of miR-34a, which was inhibited by I3C (Figure 5D). These data further suggest that I3C regulates the expression of LDHA via the p53/miR-34a axis.
Since the ubiquitination by MDM2 is a primary passion in the post-translational regulation of p53, we investigated whether I3C plays a role here. We first detected MDM2 expression, and I3C treatment reduced the protein levels of MDM2 (Figure 6A,B). To further assess the effect of I3C on the interaction of p53 and MDM2, we extracted the nuclear and cytoplasmic fractions of HepG2 cells after I3C treatment. As shown in Figure 7C,D, the protein levels of MDM2 were increased in the nucleus but decreased in the cytoplasm, while the protein levels of p53 were increased in both nucleus and cytoplasm after I3C treatment. Meanwhile, the results from the immunofluorescence assay showed the mean fluorescence intensity of p53 was increased, whereas the mean fluorescence intensity of MDM2 was decreased after I3C treatment (Figure 6E,F); furthermore, the colocalization coefficiency of p53 and MDM2 was reduced after I3C treatment (Figure 6G,H). These findings suggest that: (1) I3C upregulates p53 expression and promotes its transcriptional activity in the nucleus; (2) I3C might inhibit the activity of MDM and thereby stabilize p53. Finally, we studied the effect of I3C treatment on the degradation of p53. HepG2 cells were treated with the proteasome inhibitor MG-132 and I3C alone or in combination; the results showed increased p53 expression when MG-132 was combined with I3C (Figure 7A,B). Next, we measured the protein levels of MDM2 in siRNA-p53 transfected HepG2 cells and observed that knockdown of p53 inhibited the expression levels of MDM2 (Figure 7C,D), suggesting that there is feedback regulation between p53 and MDM2, which is consistent with other studies [35]. Altogether, these results indicate that I3C stabilizes p53 expression by inhibiting its degradation to ensure its transcriptional activity.
Due to the rapid growth and division of cancer cells, the metabolic mode has been changed to gain energy through glycolysis irrespective of oxygen availability, which is known as “metabolic reprogramming” [2]. So far, scientists have proposed 14 key hallmarks of cancer, including limitless replicative potential [36], deregulating cellular energetics [1], senescent cells [37], etc., which all well explain the mechanism of carcinogenesis and therapeutic response. Plants as a source of medicines have been used clinically in the treatment of various diseases including cancer. I3C is a natural agent widely present in cruciferous vegetables, and its safety on normal cells has been confirmed [18]. I3C has shown great potential in cancer prevention and chemotherapy [19]. Studies have pointed out that I3C inhibits cell proliferation, induces apoptosis, and promotes cell cycle arrest to inhibit cancer development through various pathways [21,38]. Based on our results, metabolic reprogramming may serve as a new mechanism for I3C’s anticancer activity. Through a series of experiments, our data have demonstrated that I3C inhibits the glycolytic phenotype as well as the clonal proliferation, migration of liver cancer cells, glucose consumption, and lactate release (Figure 1). These data suggest that I3C may function as an aerobic glycolysis inhibitor, which can deregulate cellular energetics to suppress cancer cell growth. Glycolysis is a multistep process requiring the activities of a number of enzymes including LDHA. LDHA contributes to the regeneration of NAD+ during the reduction of pyruvate to lactate, which is critical for ongoing glycolysis; meanwhile, elevated lactate level is associated with tumor cell proliferation, angiogenesis, and invasion [39,40]. Therefore, LDHA may serve as a potential target for cancer diagnosis and treatment. Our results show that I3C inhibits the protein levels of LDHA, but not the mRNA levels (Figure 2), suggesting I3C regulates LDHA at the post-transcriptional level. Through database prediction and verification, we identified miR-34 as the miRNA involved in the regulation of LDHA, and we experimentally confirmed the effect of I3C on miR-34a expression (Figure 3A–E). p53 regulates the expression and processing of miRNAs; the latter are involved in carcinogenesis through mediating multiple processes such as cell migration, metabolism, and cell survival [11]. Based on our results, we propose that I3C may regulate miR-34 through p53, as I3C increased the protein levels of p53 (Figure 3F–H). Next, we suppressed the expression of p53 in HepG2 cells by two approaches: siRNA-p53 and a p53 transcriptional activity inhibitor. siRNA-p53 increased the protein, but not the mRNA levels of LDHA (Figure 4). Similar results were obtained using PFT-α, an inhibitor of transcriptional activity of p53 (Figure 5). All these data suggest that the inhibition of LDHA expression by I3C is mediated through p53-upregulated miR-34a. Subsequently, how I3C regulates p53 was studied. The stability and intracellular localization of p53 have been focal in the molecular mechanisms of cancer inhibition [41]. Building on the previous results, we proposed that I3C may enhance the transcriptional activity of p53. MDM2 binds to the carboxyl-terminal of p53, which induces its nuclear export [10]. We found that the protein expression of MDM2 was suppressed after I3C treatment as expected. We also observed a change in p53 localization: the expression of p53 in the cytoplasm and nucleus increased after I3C treatment. As for MDM2, the expression levels were decreased in the cytoplasm but increased in the nucleus, possibly because I3C indirectly inhibited the expression of phosphorylated MDM2 in the nucleus, reducing the ubiquitination of p53, thereby promoting the expression of p53 [42]. In addition, immunofluorescence colocalization results showed that the colocalization of p53 and MDM2 was decreased, therefore reducing the interaction of both (Figure 6). Meanwhile, the protein level of p53 was further increased after the combined treatment of I3C and MG-132. The inhibition of MDM2 expression by siRNA-p53 further confirmed that MDM2 is a downstream gene of p53, and I3C can affect the feedback inhibition between p53 and MDM2 (Figure 7). These results demonstrate that I3C stabilizes the activity of p53 by inhibiting its degradation by MDM2.
The human liver cancer HepG2 cells were purchased from the Cell Bank of Type Culture Collection of the Chinese Academy of Sciences (Beijing, China) and cultured in DMEM (C11995500BT, Gibco, Waltham, MA, USA) containing 10% fetal bovine serum (FBS, 04-001-1A, BI, Herzliya, Israel) and 1% penicillin and streptomycin (P1400, Solarbio, Beijing, China) under 37 °C and humidified 5% CO2. Indole-3-carbinol (I3C), dissolved in dimethyl sulfoxide (DMSO, D5879, Sigma-Aldrich, St. Louis, MO, USA) was purchased from Sigma-Aldrich (I7256, St. Louis, MO, USA). Pifithrin-α (PFT-α, S1816) was purchased from Beyotime Biotechnology (Shanghai, China).
HepG2 cells were seeded into a 96-well plate at 5000 cells per well. After 24 h, the cells were treated with DMSO or different doses of I3C for 12, 24, and 48 h, respectively. After I3C treatment, 10 μL of CCK8 reagent (CA1210, Solarbio, Beijing, China) was added to each well and incubated for 1.5 h at 37 °C. The absorbance was measured at 450 nm by a microplate reader (Synergy H1, BioTek, Winooski, VT, USA). The values were recorded as mean ± SD, n = 6; the blank growth medium was used as the negative control. The cell survival percentage was calculated by a ratio of recorded value to the control.
HepG2 Cells (2000 cells per well) were seeded in 35 mm plates and incubated at 37 °C [43]. After overnight incubation, cells were treated with 200 μM I3C for 12 h. After replacing the medium, cells were incubated for 10 days. Plates were rinsed with phosphate-buffered saline (PBS, P1022, Solarbio, Beijing, China) and 2 mL of fixation reagent methanol was added for 15 min. Next, the cells were washed with PBS again and stained with 0.1% crystal violet (G1063, Solarbio, Beijing, China) for 10 min at room temperature. Plates were imaged using a digital camera (D7500, Nikon, Tokyo, Japan).
Cell migration was measured by wound scratch assay. HepG2 (7 × 105 cells/plate) cells were seeded into six-well plates and incubated in a normal growth medium. After 24 h, scratched wounds were gently created in the monolayer with a sterile 200 µL pipette tip across the center of the well. After removal of floating cells, cells were cultured with 1% serum medium and then treated with 200 μM I3C. Cell migration into the wound space was measured at 0, 24, and 48 h after treatment. Photographs were taken with the microscope (magnification, ×100; DS-Fi2, Nikon, Tokyo, Japan), and images were analyzed using the ImageJ software. Wound closure was determined according to the wound area.
Cells were seeded into six-well plates at a density of 5 × 105 cells in 2 mL DMEM per well and cultured overnight. Cells were then treated with or without 200 μM I3C for 12 h. Culture media were collected and centrifuged at 1000 rpm for 5 min. Glucose consumption and lactate release in the supernatant were detected using a commercially available glucose assay kit (BC2505, Solarbio, Beijing, China) and lactate assay kit (BC2235, Solarbio, Beijing, China), respectively, according to the manufacturer’s instruction with minor modifications. Glucose consumption was calculated by deducting the glucose level in the cultured medium from the glucose level of the fresh medium. Lactate release was calculated by plotting the standard curve using the standard solution with different dilution to calculate the content of lactate in the culture media. All data were normalized by the cell numbers.
Total RNA was extracted from HepG2 cells using the TRIzol reagent (DP424, TIANGEN, Beijing, China) following the manufacturer’s instructions. For miRNA analysis, reverse transcription was performed using the miRNA 1st Strand cDNA Synthesis Kit (by stem-loop) (MR101, Vazyme, Nanjing, China). For mRNA analysis, 2 µL of the total RNA was reverse transcribed using the HiScript II First Strand cDNA Synthesis kit (R223-01, Vazyme, Nanjing, China). The levels of mRNA or miRNA were determined by a real-time PCR system (Lightcycler 96, Roche, Basel, Switzerland) using qPCR SYBR Green Master mix (Q711-02, Vazyme, Nanjing, China), which were normalized by the internal control, β-actin, and U6, respectively. Each sample was analyzed in triplicate using the 2−ΔΔCt-based fold change method. The primer sequences are shown in Table 1 and were synthesized by Sangon (Shanghai, China).
The whole-cell lysate was extracted from cultured HepG2 cells using RIPA lysis buffer (P0013C, Solarbio, Beijing, China) containing PMSF (P0100, Solarbio, Beijing, China). The protein concentration of the lysate was detected by BCA assay (E112, Vazyme, Nanjing, China). Thirty micrograms of whole-cell lysate were separated by 10% sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and transferred onto Polyvinylidene Fluoride (PVDF) membranes. Antibodies against β-actin (AF7018, 1:1000), LDHA (DF6280; 1:1000), Lamin B1 (AF5161, 1:1000), and p53 (AF0879, 1:1000) were purchased from Affinity Biosciences (Changzhou, China). Antibodies against PKM2 (4053, 1:1000) were purchased from Cell Signaling Technology (USA). Antibodies against MDM2 (1:200) and HSP90 (sc-13119, 1:1000) were purchased from Santa Cruz (Dallas, TX, USA). The membranes were incubated with HRP conjugated antirabbit (AS014, 1:5000, Abclonal, Wuhan, China) or antimouse secondary antibodies (AS003, 1:5000, Abclonal, Wuhan, China). The blots were detected by Biokit Technology Maxilumin™-WB Pico Chemiluminescence Substrate reagent (WB001, Baizhi, Beijing, China) and exposed to ChemiDoc (Tanon -4600SF, Shanghai, China). The gray value analysis was carried out by GraphPad Prism.
The nuclear protein extraction kit (R0050, Solarbio, Beijing, China) was employed to isolate nuclear and cytoplasmic protein from HepG2 cells according to the manufacturer’s guidelines. Briefly, cells were resuspended in the cytoplasmic extract reagent, vortexed for 10 s, and placed on ice for 5 min, then centrifuged at 16,000× g for 5 min at 4 °C. The supernatant was collected as the cytoplasmic extract. The pellet was resuspended in the nuclear extract reagent, vortexed for 15 s, and placed on ice for 10 min, then centrifuged at 16,000× g for 10 min at 4 °C. The supernatant was collected as the nuclear extract.
The levels of the mitochondrial membrane potential (MMP) were monitored using the mitochondrial membrane potential assay kit with JC-1 (C2006, Beyotime, Shanghai, China). JC-1 aggregates (red fluorescence) in healthy mitochondria, whereas it exerts in monomers (green fluorescence) in unhealthy mitochondria. Therefore, the red/green ratio is used as a sensitive measure of changes in MMP. HepG2 cells (5 × 103/well) were seeded into a 96-well culture plate and incubated at 37 °C. After 24 h, the cells were treated with 200 μM I3C. After six more hours, cells were rinsed and added with the JC-1 working solution. Cells were then incubated at 37 °C for 20 min. After incubation, cells were rinsed twice and the level of MMP was determined by the ratio of the intensity of red fluorescence to green fluorescence (Synergy H1, BioTek, Gene Company Limited, Winooski, VT, USA).
Twenty thousand HepG2 cells were seeded in a 6-well tissue culture plate, and transfection was started when the cell density reached 60–80%. Preparation of solution A: 2 μL of p53 siRNA (sc-29435, Santa Cruz, Dallas, TX, USA) or a negative control (sc-37007, Santa Cruz, Dallas, TX, USA) was added into 100 μL of siRNA transfection medium (sc-36868, Santa Cruz, Dallas, TX, USA); solution B: 2 μL of transfection reagent (sc-29528, Santa Cruz, Dallas, TX, USA) was added into 100 μL of siRNA transfection medium. Solutions A and B were mixed gently and incubated for 45 min at room temperature. Cells were rinsed once using DMEM medium; for each transfection, 0.8 mL of siRNA transfection medium was added into the solution of the A/B mixture, mixed well, and added to the cells. After 6 h of incubation, 2 mL of medium was added into each well. Cells were cultured for an additional 24 h. All of the reagents were purchased from Santa Cruz Biotechnology (Santa Cruz, Dallas, TX, USA).
Seventy-five thousand HepG2 cells (per well) were seeded on glass coverslips pretreated with TC (YA0350, Solarbio, Beijing, China). The following day, cells were treated with DMSO and I3C as explained previously for 12 h. Next, cells were rinsed twice with PBS and fixed in 4% paraformaldehyde (P1110, Solarbio, Beijing, China) for 15 min at room temperature. Then, cell permeation was performed with 0.5% Triton X-100 (T8200, Solarbio, Beijing, China) for 20 min. After cells were blocked with 1% bovine serum albumin (BSA, A8020, Solarbio, Beijing, China) for 30 min, the cell climbing slices were incubated with anti-p53 (1:200) and anti-MDM2 (1:50) overnight at 4 °C. After washing with PBS, we added goat antirabbit lgG (H+L) Fluor488-conjugate (S0018, Affinity Biosciences, Changzhou, China) and antirabbit lgG (H+L) Fluor647-conjugate (S0014, Affinity Biosciences, Changzhou, China) for 1 h at room temperature. 4′,6-diamidino-2-phenylindole (DAPI, C0065, Solarbio, Beijing, China) was used for staining for 5 min at room temperature, and a 3 µL antifluorescence quenching solution was used to seal the slides. The mounted cells were analyzed using confocal microscopy (FV3000, OLYMPUS, Tokyo, Japan).
TargetScan (http://www.targetscan.org (accessed on 6 June 2021)) [44], miRcode (http://www.mircode.org/ (accessed on 6 June 2021)) [45], and starbase (http://starbase.sysu.edu.cn (accessed on 6 June 2021)) [46] databases were used to predict the target miRNAs of LDHA. RNAlocate (http://www.rna-society.org/rnalocate/index.html (accessed on 17 June 2021)) [47] was used to further validate the predictions.
The online database Kaplan–Meier Plotter (http://kmplot.com (accessed on 17 July 2021)) [48] was employed to evaluate the prognostic value of hepatocellular carcinoma samples of LDHA and miR-34a expression. Patient samples were categorized into high- and low-expression groups according to the following software’s auto setting of the best cutoff value. Logrank p-value < 0.05 was considered as statistically significant.
The interaction network between drugs and their targeted proteins was predicted using the STITCH database (http://stitch.embl.de/ (accessed on 8 July 2021)) [49]. The inputs for analysis were composed of I3C to predict molecular targets.
For multigroup comparisons, one-way analysis of variance (ANOVA) was used in conjunction with the Newman–Keuls post-test. All of the experiments were repeated more than three times. p < 0.05 indicates a significant difference.
Taken together, we first demonstrated that I3C induces miR-34a, which then targets LDHA, a vital enzyme for aerobic glycolysis, by inhibiting the degradation of p53 by MDM2, thereby downregulating the expression of LDHA and suppressing aerobic glycolysis, leading to growth inhibition of liver cancer cells. Our study provides a theoretical and experimental basis for the plant source of I3C in the treatment of liver cancers (Figure 8). Our results are the first to demonstrate the therapeutic potential of I3C for human liver cancer. Subsequent studies will focus on the in-depth mechanisms of action and the pharmacal kinetics of I3C using mouse models of liver cancer. | true | true | true |
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PMC9606912 | Lixia Wang,Chunhui Ji,Xianzhu Xia,Xuepeng Cai,Qingling Meng,Jun Qiao | A Regulatory SRNA Rli43 Is Involved in the Modulation of Biofilm Formation and Virulence in Listeria monocytogenes | 30-09-2022 | Listeria monocytogenes,Rli43,biofilm formation,virulence | Small RNAs (sRNAs) are a kind of regulatory molecule that can modulate gene expression at the post-transcriptional level, thereby involving alteration of the physiological characteristics of bacteria. However, the regulatory roles and mechanisms of most sRNAs remain unknown in Listeria monocytogenes (L. monocytogenes). To explore the regulatory roles of sRNA Rli43 in L. monocytogenes, the rli43 gene deletion strain LM-Δrli43 and complementation strain LM-Δrli43-rli43 were constructed to investigate the effects of Rli43 on responses to environmental stress, biofilm formation, and virulence, respectively. Additionally, Rli43-regulated target genes were identified using bioinformatic analysis tools and a bacterial dual plasmid reporter system based on E. coli. The results showed that the intracellular expression level of the rli43 gene was significantly upregulated compared with those under extracellular conditions. Compared with the parental and complementation strains, the environmental adaptation, motility, biofilm formation, adhesion, invasion, and intracellular survival of LM-Δrli43 were significantly reduced, respectively, whereas the LD50 of LM-Δrli43 was significantly elevated in BALB/c mice. Furthermore, the bacterial loads and pathological damages were alleviated, suggesting that sRNA Rli43 was involved in the modulation of the virulence of L. monocytogenes. It was confirmed that Rli43 may complementarily pair with the 5′-UTR (−47–−55) of HtrA mRNA, thereby regulating the expression level of HtrA protein at the post-transcriptional level. These findings suggest that Rli43-mediated control was involved in the modulation of environmental adaptation, biofilm formation, and virulence in L. monocytogenes. | A Regulatory SRNA Rli43 Is Involved in the Modulation of Biofilm Formation and Virulence in Listeria monocytogenes
Small RNAs (sRNAs) are a kind of regulatory molecule that can modulate gene expression at the post-transcriptional level, thereby involving alteration of the physiological characteristics of bacteria. However, the regulatory roles and mechanisms of most sRNAs remain unknown in Listeria monocytogenes (L. monocytogenes). To explore the regulatory roles of sRNA Rli43 in L. monocytogenes, the rli43 gene deletion strain LM-Δrli43 and complementation strain LM-Δrli43-rli43 were constructed to investigate the effects of Rli43 on responses to environmental stress, biofilm formation, and virulence, respectively. Additionally, Rli43-regulated target genes were identified using bioinformatic analysis tools and a bacterial dual plasmid reporter system based on E. coli. The results showed that the intracellular expression level of the rli43 gene was significantly upregulated compared with those under extracellular conditions. Compared with the parental and complementation strains, the environmental adaptation, motility, biofilm formation, adhesion, invasion, and intracellular survival of LM-Δrli43 were significantly reduced, respectively, whereas the LD50 of LM-Δrli43 was significantly elevated in BALB/c mice. Furthermore, the bacterial loads and pathological damages were alleviated, suggesting that sRNA Rli43 was involved in the modulation of the virulence of L. monocytogenes. It was confirmed that Rli43 may complementarily pair with the 5′-UTR (−47–−55) of HtrA mRNA, thereby regulating the expression level of HtrA protein at the post-transcriptional level. These findings suggest that Rli43-mediated control was involved in the modulation of environmental adaptation, biofilm formation, and virulence in L. monocytogenes.
Listeria monocytogenes (L. monocytogenes) is a facultative intracellular gram-positive zoonotic pathogen that is ubiquitous bacterium in the natural environment, including soil and sewage [1]. As an important food-borne pathogen, this bacterium has not only raised a serious concern for worldwide public health [2], but also has posed a grave threat to the food industry [3]. Existing studies have shown that the expression of virulence genes can be finely regulated at the transcriptional level by regulatory molecules such as PrfA, Sigma B, and VirR in L. monocytogenes, whereby it may adapt to stressful environments rapidly [4]. Recently, a large number of non-coding RNAs (ncRNAs) have been identified in bacteria using bioinformatics, gene chips, and transcriptomic sequencing. Many researchers have revealed that some ncRNAs played important regulatory roles in bacterial adaptation to environmental stress, biofilm formation, motility, quorum sensing, glucose metabolism, and iron homeostasis [5]. As a type of bacterial ncRNAs, small RNA (sRNA) was verified to be involved in the regulation of bacterial virulence and environmental adaptations at the post-transcriptional level through binding to the mRNAs of their target genes [6]. Mraheil et al. found that ncRNA species RliB, Rli33-1, Rli38, and Rli50 are only present in L. monocytogenes [7]. Several studies revealed that the absence of rli31, rli33-1, and rli50 could significantly decrease L. monocytogenes survival and proliferation in mouse macrophages, whereas the absence of RliB could enhance L. monocytogenes proliferation in the mouse liver, which indicated that ncRNA played an important role in regulating L. monocytogenes virulence [7,8,9]. Furthermore, Peng et al. confirmed that rli60 may regulate the adaptability of L. monocytogenes to environmental stresses, such as low temperature, high temperature, and alkaline and alcoholic conditions [10]. Currently, more than 150 sRNAs have been identified in the L. monocytogenes genome [11]; however, the regulatory functions and mechanisms of most sRNAs remain unknown. Rli43 was identified as an sRNA in L. monocytogenes by Toledo-Arana et al. (2009) [8], while its molecular characteristics and regulatory roles in L. monocytogenes environmental stress, motility, biofilm formation, and virulence are still unclear. The main purpose of this study was to characterize the intracellular and extracellular expression profiles of sRNA Rli43, to explore its regulatory roles in L. monocytogenes environmental stress, motility, biofilm, and virulence, and to identify the target genes regulated by sRNA Rli43. The disclosure of regulatory roles of sRNA Rli43 will provide new insights into the regulatory mechanisms of sRNAs for the environmental adaptation and intracellular parasitism of L. monocytogenes.
LM EGD-e strain (kindly donated by W. Goebel, University of Wurzburg, Germany) was used as wild-type strain to generate the Δrli43 mutant and complementation strains. The plasmids pHoss1, pHT304, and pMD19-T were employed for the construction of recombinant vectors [12,13], and pUT18 C and pMR-LacZ were employed for the dual plasmid reporter system [14]. These strains were routinely cultured in BHI liquid medium at 37 °C with vigorous shaking or on agar plates containing 1.5% (wt/vol) agar. For the analysis of bacterial growth, overnight cultures of these strains were diluted 100-fold, inoculated into BHI liquid medium, and cultured under different conditions. The primers were designed using Primer Premier 6.0 software (Premier Canada Assurance Managers Ltd., Laval, QC, Canada) based on the LM EGD-e genome sequence deposited in GenBank (accession number: AL591824) (Table 1). The restriction sites were added to the 5′ end of primers. E. coli DH5α was used for plasmid construction, while E. coli H101 was used for validation of the interaction between sRNA and target mRNA. All E. coli strains were grown in Luria Bertani (LB) solid or broth medium at 37 °C.
In brief, LM EGD-e strain was inoculated in BHI liquid medium (Sigma, Burlington, MA, USA) and grown to approximately 106 CFU/mL at 37 °C. Following this, mouse RAW264.7 cells cultured in 6-well microplates were infected with LM EGD-e at a macrophage/bacteria ratio of 1:10 for 2 h, and total RNA of LM EGD-e was extracted from the extracellular culture and infected RAW264.7 cells using Trizol (Invitrogen by Life Technologies, Carlsbad, CA, USA), respectively. This was then reversely transcribed into cDNA using an AMV reverse transcription kit (TaKaRa, Shiga, Japan). The Rli43 gene was amplified using cDNA as template, and 16S rRNA gene was used as an internal reference gene to profile the differential expression of Rli43 under intra- and extracellular conditions by qRT-PCR.
The rli43 deletion mutant and complementation strains were constructed using the homologous recombination technique. Briefly, the rli43 gene was amplified from the genome of the LM EGD-e strain, and the Δrli43 mutant gene was generated using the SOE-PCR technique [15]. Following this, the Δrli43 mutant gene was cloned into the pMD19-T simple vector (TaKaRa, Japan) to generate the recombinant plasmid pMD19-T-Δrli43. The Δrli43 mutant gene was then ligated with pHoss1 to generate the recombinant shuttle plasmid pHoss1-Δrli43. pHoss1-Δrli43 was then electro-transferred (2.5 kv, 5.0 ms) into LM EGD-e competent cells, and these clones were subjected to homologous recombination at 42 °C and erythromycin resistance (10 μg/mL) to obtain Δrli43 deletion of the mutant strain. For the construction of the complementation strain, the pHT304-rli43 vector was transferred into LM-Δrli43 competent cells, and the positive transformant was screened to obtain the complementation strain LM-Δrli43-rli43.
The adaptability to environmental stress of LM EGD-e, LM-Δrli43, and LM-Δrli43-rli43 was examined as previously described [16]. In brief, LM EGD-e, LM-Δrli43, and LM-Δrli43-rli43 were inoculated with BHI liquid medium (Sigma, St. Louis, Missouri, USA) and cultured at 30 °C, 37 °C, and 42 °C, respectively, with 200 r/min shaking. The OD600 nm values were measured by full wavelength microplate (Thermo Multiskan SkyHigh, Singapore) at 1.5 h intervals for 12 h, and the bacterial growth curves were plotted. Subsequently, the bacterial solution was inoculated in BHI (Sigma, St. Louis, Missouri, USA) at pH 4 and 9 and containing 3.8% alcohol, respectively, and its OD600 nm value was measured at different times. The growth curve was plotted, and the experiment was repeated 3 times.
Bacterial motility was determined using semi-solid agar puncture inoculation and a plate diffusion assay as previously described [17]. Briefly, LM EGD-e, LM-Δrli43, and LM-Δrli43-rli43 were punctured and inoculated with 0.5% semi-solid BHI medium for 5 days at 25 °C, respectively, and their growths were observed and photographed daily. Their motility was also assayed by measuring the diameter of the growth zone compared to control bacteria on 0.5% soft agar plates inoculated with 10 μL of a broth culture and incubated overnight at 25 °C. Meanwhile, 200 μL of these strains was added to 96-well microplates containing polystyrene. The formed biofilms were subsequently prepared by crystalline violet staining [17], followed by the assay of OD570 nm values using a spectrophotometer (Shimadzu, Japan) [18]. Following this, the structure of the biofilm was observed under an inverted microscope. Meanwhile, bacteria were cultured on 316 stainless steel sampling plates (Sigma, St. Louis, MO, USA) at 37 °C for 24 h, and observed under a scanning electron microscope (Hitachi SU8010, Japan) for the observation of the morphological structure of biofilm.
Briefly, mouse RAW264.7 cells were cultured in 6-well microplates with DMEM supplemented with 10% fetal bovine serum at 37 °C with 5% CO2 for 36 h. The cells were infected with LM EGD-e, LM-Δrli43, and LM-Δrli43-rli43 with a macrophage/bacteria ratio of 1:10. Following this, the adhesion, invasion, and intracellular proliferation of RAW264.7 were examined according to the previously described protocol [18,19]. In brief, LM EGD-e, LM-Δrli43, and LM-Δrli43-rli43 were added to the monolayer and incubated at 37 °C with 5% CO2 for 1 h. RAW264.7 cell monolayers were washed with DMEM 3 times, followed by 1 h incubation with gentamycin (50 µg/mL) in each well. The monolayers were washed with DMEM thrice. To enumerate the adhered and invading bacterial cells, cell monolayers were subsequently treated with 0.1% Triton X-100, followed by incubation at 37 °C for 10–15 min, and then appropriate dilutions were plated on BHI agar. Plates were incubated at 37 °C for 18–24 h and bacterial cell counts were expressed as percentage adhesion and invasion. Three independent repeats were set up for each group and each experiment was repeated 3 times.
The experiments with mice were performed following the ethical principles in animal research adopted by the National Council for the Control of Animal Experimentation (CONCEA, Shanghai, China), and the protocol was approved by the Research and Ethical Committee of Shihezi University (No. A2019186). The study was carried out in compliance with the ARRIVE guidelines. All experiments were performed in accordance with relevant guidelines and regulations. At the end of the study, all mice were euthanized by intraperitoneal injection of an overdose of 2, 2, 2-tribromoethanol (500 mg/kg, CAS: 75–80-9 Sigma-Aldrich Chemie GmbH, Steinheim, Germany) and cervical dislocation was performed. Two hundred 8-week-old BALB/c mice were randomly divided into 1 control group and 3 infection groups. Each group was divided into 5 subgroups with 10 mice each. The mice in each subgroup were injected intraperitoneally with LM EGD-e, LM-Δrli43, and LM-Δrli43-rli43 at 0.5 mL/each, respectively, while the control group was injected with a PBS buffer at 0.5 mL/each. After inoculation, the mental status and mortality of mice in each group were recorded daily for 7 days. The LD50 in mice was determined using the Karber method [20], and a Kaplan–Meier survival curve was plotted. For studies measuring bacterial colonization of the liver and spleen, mice were subjected to an intraperitoneal injection of 0.5 mL LM EGD-e, LM-Δrli43, and LM-Δrli43-rli43 at a sublethal dose (5.25 × 104 CFU). At day 1 to day 6 time points post-inoculation, 6 mice in each group were sacrificed by intraperitoneal injection of 2, 2, 2-tribromoethanol (125 mg/kg), which was repeated 3 times. The liver, spleen, and kidney of infected mice were collected and fixed in 4% formaldehyde solution, followed by the preparation of tissue sections. Following this, histopathological changes were observed and recorded.
The transcriptional levels of motility-related (flaA and motB), biofilm-associated (flgE and degU), and virulence-related genes (PrfA, inlA, inlB, lap and actA) were determined by qRT-PCR, respectively [21]. Briefly, LM EGD-e, LM-Δrli43, and LM-Δrli43-rli43 were cultured in BHI liquid medium at 25 °C and 37 °C, respectively, which facilitated bacteria to form flagella and biofilm. Total RNA was extracted using Trizol (Invitrogen by Life Technologies, USA) and was reversely transcribed into cDNA using the AMV reverse transcription kit (TaKaRa, Japan). Following this, the relative transcriptional levels of genes mentioned above were analyzed on a LightCycler 480 instrument (Roche, Switzerland), and the 16S rRNA gene was used as an internal reference gene [10,14]. The experiment was repeated 3 times. The relative transcript levels were calculated according to the 2-ΔΔCT method [22].
Genetic localization and molecular characteristics of the rli43 gene were analyzed using the online software Softberry, fruitfly, and RNAfold, respectively. The phylogenetic tree-based rli43 gene was then constructed using MEGA10.0 software (NJ method, Bootstrap of 1 000) to reveal the relationships between different strains of L. monocytogenes. The genes with low P-values and long consecutive pairings with rli43 were screened and recognized as target genes by combining TargetRNA2 (http://cs.wellesley.edu/~btjaden/TargetRNA2/) with IntaRNA (http://rna.informatik.uni-freiburg.de/IntaRNA/Input.jsp) (accessed on 21 October 2008) predicting results.
To understand the interaction between the sRNA Rli43 and htrA mRNA, the dual plasmid reporter system based on E. coli was employed [14]. Briefly, the pUT18C-rli43 and pMR-LacZ-htrA recombinant vectors were constructed by inserting the rli43 gene and the promoter sequences of target gene htrA into pUT18C (Ampicillin resistance) and pMR-LacZ plasmid (Kanamycin and Ampicillin resistance), respectively. The recombinant vectors were co-electrotransferred into E. coli BTH101 receptor cells, followed by cultivation in LB liquid medium (containing 100 mg/mL Kanamycin and 100 mg/mL Ampicillin) for the screening of positive clones. The obtained bacteria were further incubated in LB solid medium containing X-gal and IPTG at 37 °C overnight. The color change of the lawn was observed, and the OD470 nm of bacterial solution rinsed from the plates was determined [23]. Furthermore, the mRNA level of the htrA gene was determined by qRT-PCR according to the method described above.
The protein level of the target gene regulated by Rli43 was detected using SDS-PAGE gels and semi-dry mobile blotting according to the protocols from a previous report [24] with a few changes. Briefly, LM EGD-e, LM-Δrli43, and LM-Δrli43-rli43 protein samples were separated by SDS-PAGE gels and transferred into nitrocellulose membranes (Sigma, St. Louis, MO, USA) using semi-dry mobile blotting (GE Healthcare, Germany). A Western blot was then performed using mouse anti-HtrA recombinant protein antibody as the primary antibody (1: 2000) and goat anti-mouse IgG-HPR (TaKaRa, Japan) (1:4500) as the secondary antibody. Glyceraldehyde-3-phosphate dehydrogenase (GapA) was used as an internal reference protein to analyze the effects of rli43 on the expression of the target protein HtrA. Finally, ImageJ 1.8.0 software (NIH, USA) was applied to quantify the protein bands of the Western blot.
Each experiment was conducted in triplicate, and all data were presented as mean ± standard deviation (SD). The statistical analysis was performed using GraphPad Prism 5.0 software (GraphPad Software, Inc., San Diego, CA, USA). Independent t-tests were used to analyze differences between 2 groups, and a 1-way analysis of variance (ANOVA, London, UK) was used to analyze differences between multiple groups. A value of p < 0.05 was considered significant, while p < 0.01 was considered extremely significant.
Given that small RNA (sRNA) is a kind of regulatory molecule that plays an important role in bacteria, the role of sRNA rli43 in the response to environmental changes in L. monocytogenes was explored. The sRNA Rli43 was successfully amplified from LM EGD-e by RT-PCR amplification and was verified by sequencing (Figure S1). Following this, we profiled the differential expression of Rli43 under intra- and extracellular conditions by qRT-PCR. The qRT-PCR assay revealed that the transcript level of Rli43 was up-regulated 15.49-fold during cell infection compared with the extracellular culture (Figure 1).
To investigate the regulatory roles of Rli43 in L. monocytogenes, the rli43 deletion mutant and its complemented strains were constructed by homologous recombination and the overlap-extension PCR technique. The generation of LM-Δrli43 deletion strain was confirmed by PCR amplification and sequencing verification (Figure S2a). Meanwhile, the genetic stability of the LM-Δrli43 and LM-Δrli43-rli43 strains were analyzed by the PCR method, respectively (Figure S2b,c). To evaluate the effects of rli43 gene deletion on adaptability, responses to different environmental stresses of L. monocytogenes were determined. As shown in Figure 2, the growth of LM-Δrli43 significantly declined in the logarithmic phase at 30 °C and 42 °C when compared with LM EGD-e and LM-Δrli43-rli43 (p < 0.05) (Figure 2a,b), while the difference in growth among the three strains was not significant (p > 0.05) at 37 °C (Figure 2c). Similarly, LM-Δrli43 grew significantly lower than LM EGD-e and LM-Δrli43-rli43 under pH 9 and pH 4 conditions (p < 0.05) (Figure 2d,e). However, the difference in growth among the three strains was not significant (p > 0.05) under the 3.8% alcohol condition (Figure 2f). Together, these results suggested that deletion of the rli43 gene could reduce adaptation to environmental stresses of L. monocytogenes.
To evaluate the motility of LM EGD-e, LM-Δrli43, and LM-Δrli43-rli43, a semi-solid agar puncture inoculation and a plate diffusion assay were performed. As shown in Figure 3, when grown in semi-solid agar by punctured inoculation, LM-Δrli43 formed a significantly smaller inverted umbrella structure than that of LM EGD-e and LM-Δrli43-rli43 (Figure 3a). Furthermore, when grown in swarming agar plates, the motility of the LM-Δrli43 strain was markedly reduced compared with LM EGD-e and LM-Δrli43-rli43 (Figure 3b). The movement diameter of LM-Δrli43 in swarming agar plates was also significantly decreased compared with LM EGD-e and LM-Δrli43-rli43 (p < 0.05) (Figure 3c). To assess biofilm formation, these strains were subsequently grown statically in 96-well microplates containing polystyrene and 316 stainless steel sampling plates in BHI liquid medium at 37 °C for 24 h and 48 h, respectively. The staining of bacterial cells with crystal violet (CV) showed that LM-Δrli43 formed a looser biofilm structure when compared to LM EGD-e and LM-Δrli43-rli43 (Figure 4a). We also quantitatively measured biofilm formation ability and the results indicated that biofilm formation in LM-Δrli43 (OD570 nm) was significantly lower than that of LM EGD-e and LM-Δrli43-rli43 (p < 0.05) (Figure 4c). In addition, TEM results indicated that the biofilm formation for LM-Δrli43 on the stainless-steel surface was also significantly lower than that of LM EGD-e and LM-Δrli43-rli43 (Figure 4b). The data of the above motility and biofilm formation assays indicates that deletion of the rli43 gene weakens L. monocytogenes motility and biofilm formation.
To probe the regulatory role of Rli43 during the infection of a macrophage, the adhesion, invasion, and intracellular proliferation of RAW264.7 were examined by bacterial cell counts. As shown in Figure 5, the adhesion, invasion, and intracellular survival of LM-Δrli43 in RAW264.7 cells were significantly lower when compared with LM EGD-e and LM-Δrli43-rli43 (p < 0.05) (Figure 5a–d), respectively, suggesting that rli43 gene deletion impaired the adhesion, invasion, and intracellular survival in a macrophage.
To investigate the regulatory roles of rli43 in the virulence of L. monocytogenes, we compared the lethality of L. monocytogenes in LM EGD-e, LM-Δrli43, and LM-Δrli43-rli43 mice. The mice in each subgroup were injected intraperitoneally with LM EGD-e, LM-Δrli43, and LM-Δrli43-rli43 at 0.5 mL/each, respectively. After inoculation, the mental status and mortality of mice in each group were recorded daily for seven days. As shown in Supplementary Table S1, the LD50 of LM EGD-e, LM-Δrli43, and LM-Δrli43-rli43 on BALB/c mice were 105.56 CFU, 107.32 CFU, and 105.76 CFU, respectively (Table S1). Compared with LM EGD-e and LM-Δrli43-rli43, the LD50 of LM-Δrli43 was elevated by 1.76 and 1.56 logarithmic orders of magnitude, and mice in the LM-Δrli43-infected group survived significantly longer (Figure 6a). Moreover, bacterial loads in both liver and spleen were significantly declined (p < 0.01) (Figure 6b,c). The pathological changes in the livers, spleens, and kidneys of L. monocytogenes-infected mice were further investigated. Compared with the normal control mice (Figure 7a–c), the pathological changes in the livers of LM EGD-e-infected (Black arrows in Figure 7d) and LM-Δrli43-rli43-infected (black arrows in Figure 7g) mice were mainly manifested as partial hepatocyte necrosis and hepatic lobular inflammatory cell infiltration; the tissue structure was unclear and contained a large amount of tissue debris. Transparent degeneration and lymphoid tissue necrosis occurred in spleen reticular fibers, and spleen nodules increased in size. Lymphocytes at the site of lymph nodes were necrotic and lymphoid tissue was bleeding (black arrows in Figure 7e,h). Renal venular hemorrhage occurred, inflammatory cells appeared, and the interstitial volume was enlarged (black arrows in Figure 7f,i). However, compared with LM EGD-e-infected and LM-Δrli43-rli43-infected mice, histopathological damage to the liver, spleen, and kidney was alleviated to some extent in LM-Δrli43-infected mice (Figure 7j–l), indicating that rli43 gene deletion lessened the virulence of L. monocytogenes.
Based on the results which suggested that the deletion of rli43 could weaken L. monocytogenes motility, biofilm formation, and virulence, we quantified the relative transcription levels of motility-related (flaA and motB), biofilm-associated (flgE and degU), and virulence-related genes (PrfA, inlA, inlB, lap and actA) by qRT-PCR, respectively. As shown in Figure 8, the mRNA levels of motility-related genes flaA and motB genes (Figure 8a), and biofilm-associated genes flgE and degU (Figure 8b), were significantly declined in the rli43 gene deletion strain (p < 0.05). Notably, the transcription levels of virulence-related genes (inlA, inlB, lap, actA, PrfA) were also significantly decreased (p < 0.05) in the LM-Δrli43 strain (Figure 8c). These findings were in agreement with the bacterial phenotype assay, indicating that the deletion of the rli43 gene can impair the survival and proliferation of L. monocytogenes in RAW264.7 cells.
To reveal how Rli43 regulated the phenotypes of motility, biofilm formation, and virulence, we used online software to analyze the genetic localization, molecular characteristics, and target gene of sRNA Rli43. Sequence analysis showed that the rli43 gene was located at the spacer region between the inl C and rplS gene in the genome of LM EGD-e, with a length of 254 bp. Moreover, its promoter region contained an FIS transcription factor binding site (Figure S3a). It was shown that the transcription of sRNA Rli43 owns six stem-loop domains in the secondary structure (Figure 9a). The genetic evolutionary analysis showed that the rli43 gene was highly conserved in L. monocytogenes serotypes ½ a, ½c, 3c, and 4b (Figure S3b). By combining the predicted results of TargetRNA2 and IntaRNA, it was revealed that sRNA Rli43 may complementarily pair with the 5′-UTR (−47–−55) due to the high temperature requirement of htrA mRNA (Figure 9b), suggesting that the htrA gene was one of the potential target genes regulated by Rli43. To verify the interaction between rli43 and htrA mRNA, a dual plasmid reporter system based on E. coli was employed, and the pUT18C-rli43 and pMR-LacZ-htrA plasmids were successfully constructed and verified by PCR and double enzyme digestion, respectively (Figure S4). As shown in Figure 9, the lawn of E. coli strain co-transformed, as pUT18C-rli43 and pMR-LacZ-htrA was displayed as darker blue on the plates containing X-gal compared with those strains of E. coli transformed by pUT18C, pMR-LacZ-htrA, or its co-transformation. The OD470 nm values of bacterial suspensions differed significantly (p < 0.05) (Figure 9c–g), suggesting that Rli43 can interact with the mRNA of target gene htrA. The mRNA and protein levels of the htrA gene were significantly lower (p < 0.05) in LM-Δrli43 when compared with LM EGD-e and LM-Δrli43-rli43 (Figure 9h,i), indicating that Rli43 may modulate the stability of mRNA in the htrA gene, so that sRNA Rli43 can positively regulate the expression of the htrA gene (Figure 9j).
As a class of ncRNAs, bacterial sRNAs are one of the most important regulators of gene expression and they perform a broad range of physiological functions. Commonly, bacterial sRNAs range from 50–300 nucleotides, and are complementarity with their targeting mRNAs. Currently, a number of sRNAs have been identified in bacteria. However, biological functions have been studied in detail for only a small proportion of sRNAs. Generally speaking, sRNAs are recognized as one of the most important regulators, and they are involved in posttranscriptional or translational control of gene expression through a variety of mechanisms [25]. In L. monocytogenes, some sRNAs, together with PrfA, Sigma B, and VirR regulators, form a complex regulatory network to facilitate its survival and infection. As a newly identified sRNA in L. monocytogenes [8], however, the regulatory roles and mechanism of Rli43 have not yet been revealed. To investigate the regulatory roles of rli43 in L. monocytogenes, the relative transcription levels of rli43 were profiled under extracellular and intracellular conditions. It was shown that the transcriptional level of Rli43 was significantly upregulated in RAW264.7 cells compared with extracellular conditions, which suggested that Rli43 is involved in the processes of infection and intracellular survival. An in silico analysis revealed that the high temperature requirement (htrA) gene was one of the potential target genes regulated by Rli43. To date, it has been proven that HtrA is an important regulator with a dual function of molecular chaperone and protease activity. It has a critical role in the prevention of severe cellular malfunctions owing to the accumulation of mislocalized or misfolded proteins under physiological and stressful conditions [26]. Currently, HtrA has been proven to be involved in responses to environmental stresses (e.g., high and low temperatures, oxidative stress, high salt, or extreme pH) in Streptococcus pneumonia [27], Lactococcus lactis [28], E. coli [29], and H. pylori [30]. Furthermore, Rebecca and Laura et al. confirmed that the survival capacity of the htrA gene mutant strains of L. monocytogenes is significantly reduced under high temperatures and oxidation conditions [31,32]. Herein, we revealed that Rli43 may facilitate the expression of the htrA gene to modulate the adaptation of L. monocytogenes in response to environmental changes, which is similar to those bacteria mentioned above. Several studies have shown that HtrA also played important roles in the regulation of motility and biofilm formation in bacteria [33]. Zhang et al. (2019) confirmed that HtrA is a serine protease from B. burgdorferi that regulates the conversion of FlaB [34]. Notably, the motility and the mRNA levels of flaA and motB genes were significantly reduced in LM-Δrli43, suggesting that Rli43 may indirectly regulate motility-related genes through modifying the expression of htrA gene. Furthermore, it has been shown that HtrA also plays important roles in biofilm formation [35] in L. monocytogenes [10], Streptococcus pyogenes [36], E. coli [26], and pneumococcal [37], respectively. In this study, we verified that the biofilm-forming ability was significantly reduced, and the transcriptional levels of flgE and degU genes related to biofilm formation were significantly downregulated in the deletion strain. However, the detailed mechanism of LM Rli43-modulating motility and biofilm formation should be further unraveled in the future. It was confirmed that HtrA also played important regulatory roles in the expression of bacterial virulence genes [26,27,38,39,40,41,42,43], thereby influencing bacterial pathogenicity [31]. In Campylobacter jejuni, it was confirmed that HtrA could modify the processes of cell adhesion, invasion, and migration ability [44]. In particular, the deficiency of the htrA gene could lessen the survival capacities of Salmonella, Brucella, and Yersinia in macrophages or mice [45]. In L. monocytogenes, htrA gene deletion may result in the accumulation of misfolded proteins on the bacterial membrane, thus impairing intracellular survival [46,47]. In the present study, we verified that the deficiency of the rli43 gene impeded adhesion, invasion, and intracellular survival in a macrophage. In addition, the qRT-PCR results further verified that these findings were in agreement with the results of the bacterial phenotype assay, indicating that the deletion of the rli43 gene can impair the survival of L. monocytogenes in RAW264.7 cells. Current studies have revealed that the molecular mechanisms underlying sRNA-mediated control are obvious diversity and flexibility in bacteria [3,6]. Commonly, sRNAs may regulate the expression of target genes by base pairing them with the 5′-untranslated region (5′-UTR) of the mRNA, thereby modulating multiple physiological processes, such as virulence and biofilm formation of bacteria [48]. On the one hand, the binding of trans-encoded sRNAs in the vicinity of the ribosomal binding site (RBS) may result in the inhibition of translation initiation or mRNA decay. Alternatively, sRNAs are likely to promote translation or prevent mRNA degradation, especially by base pairing to the far upstream from the RBS [6]. In this study, we verify the interaction between rli43 and target gene htrA mRNA using a two-plasmid system based on E. coli, which suggested that rli43 may bind to the 5′-UTR of htrA mRNA. To date, at least five RNases have been identified in bacteria, namely RNase A, RNase P, RNase E, RNase R, and RNase III. Among them, RNase III is mainly involved in the degradation of double-stranded RNA, while RNase JI is mainly involved in the degradation of single-stranded RNA in gram-positive bacteria. Combined with the results of the qRT-PCR and the Western blot, it is reasonable to infer that Rli43 may protect htrA mRNA from RNase JI degradation, thereby maintaining the stability of its mRNA. This regulatory mechanism mediated by rli43 is similar to the regulatory mode reported by Dutta et al. [48], which further highlights the similarities of sRNA-mediated control in various species of bacteria. However, this detailed regulatory motif in Rli43 should be further experimentally validated by employing site-directed mutagenesis.
This study revealed that Rli43 was involved in posttranscriptional control of htrA gene expression by modulating the stability of its mRNA. The regulatory mechanisms of sRNA rli43 provided new insights into the understanding of the diversity and flexibility of sRNA-mediated control in biofilm formation and virulence in L. monocytogenes. | true | true | true |
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PMC9606995 | Katharina Sydow,Elias Eger,Michael Schwabe,Stefan E. Heiden,Jürgen A. Bohnert,Sören Franzenburg,Christoph Jurischka,Peter Schierack,Katharina Schaufler | Geno- and Phenotypic Characteristics of a Klebsiella pneumoniae ST20 Isolate with Unusual Colony Morphology | 19-10-2022 | Enterobacterales,K. pneumoniae,virulence,next-generation sequencing,biofilm,exopolysaccharides | Klebsiella pneumoniae is a common member of the intestinal flora of vertebrates. In addition to opportunistic representatives, hypervirulent (hvKp) and antibiotic-resistant K. pneumoniae (ABR-Kp) occur. While ABR-Kp isolates often cause difficult-to-treat diseases due to limited therapeutic options, hvKp is a pathotype that can infect healthy individuals often leading to recurrent infection. Here, we investigated the clinical K. pneumoniae isolate PBIO3459 obtained from a blood sample, which showed an unusual colony morphology. By combining whole-genome and RNA sequencing with multiple in vitro and in vivo virulence-associated assays, we aimed to define the respective Klebsiella subtype and explore the unusual phenotypic appearance. We demonstrate that PBIO3459 belongs to sequence type (ST)20 and carries no acquired resistance genes, consistent with phenotypic susceptibility tests. In addition, the isolate showed low-level virulence, both at genetic and phenotypic levels. We thus suggest that PBIO3459 is an opportunistic (commensal) K. pneumoniae isolate. Genomic comparison of PBIO3459 with closely related ABR-Kp ST20 isolates revealed that they differed only in resistance genes. Finally, the unusual colony morphology was mainly associated with carbohydrate and amino acid transport and metabolism. In conclusion, our study reveals the characteristics of a Klebsiella sepsis isolate and suggests that opportunistic representatives likely acquire and accumulate antibiotic resistances that subsequently enable their emergence as ABR-Kp pathogens. | Geno- and Phenotypic Characteristics of a Klebsiella pneumoniae ST20 Isolate with Unusual Colony Morphology
Klebsiella pneumoniae is a common member of the intestinal flora of vertebrates. In addition to opportunistic representatives, hypervirulent (hvKp) and antibiotic-resistant K. pneumoniae (ABR-Kp) occur. While ABR-Kp isolates often cause difficult-to-treat diseases due to limited therapeutic options, hvKp is a pathotype that can infect healthy individuals often leading to recurrent infection. Here, we investigated the clinical K. pneumoniae isolate PBIO3459 obtained from a blood sample, which showed an unusual colony morphology. By combining whole-genome and RNA sequencing with multiple in vitro and in vivo virulence-associated assays, we aimed to define the respective Klebsiella subtype and explore the unusual phenotypic appearance. We demonstrate that PBIO3459 belongs to sequence type (ST)20 and carries no acquired resistance genes, consistent with phenotypic susceptibility tests. In addition, the isolate showed low-level virulence, both at genetic and phenotypic levels. We thus suggest that PBIO3459 is an opportunistic (commensal) K. pneumoniae isolate. Genomic comparison of PBIO3459 with closely related ABR-Kp ST20 isolates revealed that they differed only in resistance genes. Finally, the unusual colony morphology was mainly associated with carbohydrate and amino acid transport and metabolism. In conclusion, our study reveals the characteristics of a Klebsiella sepsis isolate and suggests that opportunistic representatives likely acquire and accumulate antibiotic resistances that subsequently enable their emergence as ABR-Kp pathogens.
Klebsiella pneumoniae belongs to the Enterobacterales and is divided into opportunistic (commensal), hypervirulent (hvKp) and generally antibiotic-resistant (ABR-Kp) subtypes [1]. While the latter mostly affects immunocompromised patients in healthcare settings and causes pneumonia, bacteremia, and urinary tract infections, hvKp can infect healthy individuals leading to severe diseases including liver abscesses and meningitis [1]. Specific virulence characteristics allow hvKp to cause “metastatic” infection in multiple body sites [2]. HvKp are usually classified by hypermucoviscosity, historically characterized by a positive string test, and high siderophore production [3]. However, typing based on specific genetic biomarkers has been shown to be more accurate [4]. Russo et al. proposed peg-344 (metabolite transporter), iroB (salmochelin), iucA (aerobactin), and the plasmid-based genes prmpA and prmpA2 (mucoid phenotype regulators) as particular biomarkers [4]. ABR-Kp isolates usually act as opportunistic pathogens but are increasingly difficult to treat due to the exhibition of multiple antibiotic resistance (ABR) features [5]. In addition, in recent years, multiple studies have reported on the emergence of convergent strains combining both hypervirulence and ABR [2,6]. As K. pneumoniae are ubiquitous in soil and water, the environment likely represents a reservoir for colonization or infection of humans and animals with these potentially harmful opportunists [7,8,9,10]. Interestingly, several studies have shown that environmental K. pneumoniae isolates share high similarities with clinical representatives [11,12,13,14], although capsule types might differ [15]. In addition, K. pneumoniae from the environment are usually antibiotic-susceptible, reflecting the broad use of antibiotics in the clinical setting [11]. Once acquired by humans, K. pneumoniae may colonize the gastrointestinal tract especially in hospitalized patients, from where it can disseminate into the bloodstream upon epithelial cell wall damage [16]. Following E. coli, K. pneumoniae is the second leading reason for bloodstream infections [17,18], often in patients suffering from cancer or diabetes mellitus [19]. Here, we investigated a clinical K. pneumoniae isolate (PBIO3459) from a blood culture that showed an unusual colony morphology. As it is known that particular phenotypes, e.g., small colony variants [20], reflect bacterial fitness and/or virulence and thus affect potential treatment [21,22], we decided to further investigate our isolate with the altered morphology. To classify PBIO3459, it was phenotypically compared to an archetypal hvKp and a multidrug-resistant (MDR) K. pneumoniae isolate with some hypervirulence features (convergent type). Additionally, the isolate was investigated at genomic, transcriptomic and phylogenetic levels.
The sample of interest (PBIO3459) was isolated from a blood culture during routine diagnostic procedures at the University Medicine Greifswald (Germany). The bacterial species K. pneumoniae was confirmed by MALDI-TOF MS (VITEK MS, bioMérieux, Marcy l’Etoile, France) and phenotypic antibiotic susceptibility testing (AST) was performed using the VITEK 2 system (bioMérieux, Marcy l’Etoile, France). Other isolates were used as reference and control for the performed assays: a multidrug-resistant K. pneumoniae ST307 isolate from a clonal outbreak in the same university hospital (PBIO1953, [6]), a hypervirulent K. pneumoniae ST86 isolate (hvKP1, [23,24]), and an Escherichia coli K12 (ST10) (W3110, [25]), as well as an E. coli ST131 (IMT18399, [26]). All isolates were stored at −80 °C in lysogeny broth (LB; Carl Roth, Karlsruhe, Germany) containing 20% (v/v) glycerol (anhydrous; Merck, Darmstadt, Germany). For initial characterization, PBIO3459 was plated on chromogenic agar (CHROMagar, Paris, France), MacConkey agar (Carl Roth, Karlsruhe, Germany) and on Simmons citrate agar (Carl Roth, Karlsruhe, Germany) supplemented with 5 g/L bile salts (Sigma-Aldrich, St. Louis, MO, USA), 10 g/L myo-inositol (Carl Roth, Karlsruhe, Germany) and 10 g/L tryptophan (Sigma-Aldrich, St. Louis, MO, USA). The supplemented Simmons agar showed reliable results for detecting Klebsiella species [27].
A randomly selected single colony from LB agar (LB and 1.5% agar [Carl Roth, Karlsruhe, Germany]) was cultured overnight in 5 mL of LB under shaking conditions (130 rpm) at 37 °C. Total DNA was extracted using the MasterPure DNA Purification Kit for Blood, v.2 (Lucigen, Middleton, WI, USA), according to the manufacturer’s instructions. The isolated DNA was quantified fluorometrically using the Qubit 4 fluorometer and the corresponding dsDNA HS Assay Kit (Thermo Fisher Scientific, Waltham, MA, USA). DNA was sent to the Microbial Genome Sequencing Center (MiGS), now SeqCenter (Pittsburgh, PA, USA), and after library preparation using the Illumina DNA Prep Kit and IDT 10 bp UDI indices (Illumina, San Diego, CA, USA), sequenced on an Illumina NextSeq 2000, producing 2 × 151 bp reads. In addition, DNA was sequenced on the Oxford Nanopore platform to obtain long reads.
Short-read data were trimmed (adapter-trimming, contaminant-filtering, quality-trimming, polymer-trimming) using BBDuk from BBTools v.38.90 (https://sourceforge.net/projects/bbmap/, accessed on 3 March 2022). Read QC of raw and trimmed reads was assessed using FastQC v.0.11.9 (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/, accessed on 3 March 2022). A short-read-only assembly of the trimmed reads was conducted by using the assembly pipeline shovill v.1.1.0 (https://github.com/tseemann/shovill, accessed on 3 March 2022) in combination with SPAdes v.3.15.2 [28] and polished by first mapping the trimmed reads to the assembly using BWA v.0.7.17 [29]. After SAMtools v.1.12-processing (SAM-to-BAM conversion, sorting, duplicate-marking) [30], the draft contigs were corrected using Pilon v.1.23 [31]. Hybrid assembly with short- and long-read data was conducted using Unicycler v.0.4.9 [32] in combination with SPAdes v.3.13.0 [28]. Assemblies were checked for peculiarities in the assembly metrics and additionally assessed using CheckM v.1.1.3 [33] to estimate genome completeness and contamination. Automatic annotation was performed using Prokka v.1.14.6 [34]. In silico multi-locus sequence typing (MLST) and antibiotic resistance and virulence detection were carried out using mlst v.2.19.0 (https://github.com/tseemann/mlst, accessed on 4 March 2022; using the PubMLST database [35]), ABRicate v.1.0.0 (https://github.com/tseemann/abricate, accessed on 4 March 2022; using the ResFinder [36], PlasmidFinder [37], and VFDB [38] databases), and Kleborate v.2.2.0 [39] with Kaptive v.2.0.0 [40]. According to Kleborate, a resistance score of 0 describes the absence of ESBL and carbapenemase genes (a potentially colistin-resistant genotype is not considered in this case). A resistance score of 1 corresponds to the presence of ESBL genes and the absence of carbapenemase genes, whereas a score of 2 indicates the presence of carbapenemase genes without colistin resistance genes (ESBL genes or OmpK mutations are not considered). Accordingly, a resistance score of 3 is characterized by the presence of carbapenemase genes in combination with a colistin-resistant genotype. A virulence score of 0 describes the absence of the siderophores yersiniabactin (ybt), colibactin (clb), and aerobactin (iuc), whereas a score of 1 indicates the presence of only yersiniabactin. A virulence value of 2 corresponds to the presence of yersiniabactin and colibactin (or colibactin only) and 3 describes the presence of aerobactin (without yersiniabactin or colibactin). To identify genomes of ABR-Kp closely related to PBIO3459, all K. pneumoniae GenBank assemblies were downloaded from the National Center for Biotechnology Information (NCBI) Assembly site (accessed on 25 August 2022) using “NCBI:txid573” as the search term. Sequence type (ST) was determined using mlst v.2.19.0. All ST20 genomes (n = 293) were analyzed with Kleborate v.2.2.0 to identify genomes with a resistance score ≥ 1. In addition, Mash v.2.3 [41,42] was used to create a sketch archive (sketch size 1,000,000) of PBIO3459 and all ST20 genomes. The pairwise distance from PBIO3459 to all sequences in the sketch archive was then calculated. The Kleborate and Mash results were combined, and the two genomes most closely related to PBIO3459 with a resistance score ≥ 1 (GCA_021917825.1 and GCA_022235845.1) were selected for synteny analysis. Contigs of the two isolates were ordered according to the alignment position to the genome of PBIO3459. Reordered genomes of GCA_021917825.1 and GCA_022235845.1 were annotated using Prokka v.1.14.6. The synteny plot was generated using the gbk files as input for pyGenomeViz v.0.2.1 (mode: pgv-mummer; identity threshold of 50%; https://github.com/moshi4/pyGenomeViz, accessed on 25 August 2022). Genome coverage was calculated based on the align_coords.tsv file. PIRATE v.1.0.4 [43] was used to build a pangenome of the three genomes and to identify the presence and absence of genes. The downloaded ST20 genomes from NCBI were used to generate a core single-nucleotide polymorphism (SNP)-based phylogeny. Genomes were used as input for snippy v.4.6.0 (https://github.com/tseemann/snippy, accessed on 4 October 2022) with PBIO3459 as reference to create a whole-genome alignment. The alignments were filtered using Gubbins v.3.2.1 [44] to remove recombinations and snp-sites v.2.5.1 [45] was used to extract core SNPs. A maximum likelihood (ML) tree was inferred with RAxML-NG v.1.1.0 [46] using GTR + R. Here, 30 genomes were excluded because they were identical to another sequence. Clustering of the tree was performed using fastbaps [47], with the final multiple sequence alignment and the best-scoring ML tree (mid-point rooted with FigTree [https://github.com/rambaut/figtree, accessed on 4 October 2022]) as input. The clustered tree was midpoint-rooted in iTOL v.6.5.8 [48] and visualized with metadata that was retrieved from the NCBI Assembly and BioSample websites and Kleborate resistance and virulence scores.
Randomly selected colonies were picked from chromogenic, LB and brain heart infusion (BHI; Merck, Darmstadt, Germany) agar plates, respectively, and placed into 1 mL of phosphate buffered saline (PBS) each until the optical density measured at λ = 600 nm (OD600) reached 0.2. Total RNA was extracted using the RNeasy Micro Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. The isolated RNA was checked for purity and quantified using a Qubit 4 fluorometer. RNA was sent on dry ice to the Competence Centre for Genomic Analysis (CCGA, Kiel, Germany) and prepared using the Illumina stranded total RNA kit with Ribozero Plus (Illumina, San Diego, CA, USA). Resulting library preparations were paired-end sequenced (2 × 100 bp reads) using Illumina NovaSeq 6000 (Illumina, San Diego, CA, USA).
Adapter and quality trimming of the raw sequencing reads was performed using Trim Galore v.0.6.7 (https://github.com/FelixKrueger/TrimGalore, accessed on 2 May 2022). The assembly of PBIO3459 was used as reference for mapping the trimmed reads using Bowtie 2 v.2.4.4 [49]. Then, the number of genes was calculated using featureCounts v.2.0.1 [50] based on the annotation of PBIO3459. Differential gene expression was calculated using DESeq2 v.1.36.0 [51] in R v.4.2.0 (https://www.R-project.org/, accessed on 2 May 2022). The analysis was performed in default mode with one exception; genes with rowSums of <10 in the count table were removed prior to the analysis. Genes were considered differentially expressed if the absolute log2 fold change was greater than or equal to 2 and the adjusted p value was less than or equal to 0.05.
Growth kinetics were measured using a microplate reader (CLARIOstar Plus, BMG LABTECH, Ortenberg, Germany) and performed in LB at 37 °C and 40 °C. Single colonies were picked from LB agar plates and incubated in 5 mL of LB on a rotary shaker at 130 rpm and 37 °C overnight. The bacterial suspension was set to 0.5 McFarland standard turbidity in the respective medium. Kinetics were measured on 96-well plates (Nunc™, Thermo Fisher Scientific, Waltham, WA, USA) and therefore 200 µL of the adjusted bacterial suspension was added to each well. For the kinetics, OD600 was measured every 30 min for 24 h, with double-orbital shaking at 200 rpm between the measurements. Each kinetic was performed with three biological and three technical replicates.
Hypermucoviscosity was tested using the string test. Therefore, a sterile inoculation loop was placed on a single colony on an agar plate and picked up. If a string of 5 mm or longer formed, the test was defined as positive [52]. The hypermucoviscosity was also tested with a sedimentation assay as described previously [53]. A bacterial suspension with a turbidity according to the 0.5 McFarland standard was prepared in 0.9% (w/v) NaCl solution. From this suspension, 50 µL were added to 5 mL of LB and incubated on a rotary shaker at 130 rpm and 37 °C for 24 h. After incubation, 1.5 mL of the cultures were given in 2 mL reaction tubes (Carl Roth, Karlsruhe, Germany) and centrifuged at 1000× g for 5 min at room temperature (RT). Then, 200 µL of the supernatant and 200 µL of the initial culture were transferred separately into a 96-well plate and the OD600 was measured. The assay was only performed with K. pneumoniae isolates. The ability to produce exopolysaccharides was tested using BHI agar plates supplemented with 5% (w/v) sucrose (Carl Roth, Karlsruhe, Germany) and 0.08% (w/v) congo red (Carl Roth, Karlsruhe, Germany) as previously described [54]. A single colony was taken from an agar plate, streaked onto the BHI agar plates, and then incubated overnight at 37 °C. The assay was done with the K. pneumoniae isolates with addition of PBIO1961 (K. pneumoniae) and PBIO2010 (K. pneumoniae) as positive and negative control, respectively. Exopolysaccharide producers would show black colonies while non-producers would show orange to white colonies.
To determine the survival in human blood, serum resistance was tested in 50% human serum as described elsewhere [53]. Overnight cultures were diluted 1:100 with fresh LB and incubated on a rotary shaker at 130 rpm and 37 °C until the OD600 reached 0.5 McFarland standard turbidity. From this suspension, 1 mL was given into a 1.5 mL reaction tube (Carl Roth, Karlsruhe, Germany) and centrifuged at 7500× g for 5 min at RT. The supernatant was discarded and the pellet was resuspended in 1 mL of PBS. On a 96-well plate, 100 µL of the bacterial suspension were mixed with 100 µL of human serum (US origin, Sigma-Aldrich, St. Louis, MO, USA). Then, 10 µL were taken from each well and serial dilutions were plated on LB agar plates to quantify the colony forming units (CFUs) per mL in the inoculum. The 96-well plate was incubated at 37 °C for 4 h. A second serial dilution was performed to determine the number of survived CFUs/mL. All LB agar plates were incubated overnight at 37 °C and formed colonies were counted the next day. Serum-resistant hvKP1 (K. pneumoniae ST86) was used as positive control and serum-sensitive W3110 (E. coli K12 ST10) was used as a negative control.
The ability to produce siderophores was tested using a previously described method [53]. Briefly, bacterial cultures were again set to 0.5 McFarland standard turbidity in 0.9% (w/v) NaCl solution and diluted 1:100 in 5 mL of iron-chelated M9 medium (200 μM 2,2′-dipyridyl [Carl Roth, Karlsruhe, Germany] added M9 minimal salt medium [MP Biomedicals, Irvine, CA, USA]) supplemented with 0.3% (w/v) casamino acids (c-M9-CA, BD, Franklin Lakes, NJ, USA) in sterile 15 mL tubes (Sarstedt, Nürnbrecht, Germany). After incubation on a rotary shaker at 130 rpm and 37 °C for 24 h, 1 mL of sample was given into 1.5 mL reaction tubes and centrifuged for 20 min at 4900× g and RT. A 96-well plate was prepared with 100 µL of Chromazurol S (CAS) shuttle solution (composited according to [55]) and 100 µL of the supernatant were added. Fresh media served as blank and 15 mM aqueous EDTA solution (Carl Roth, Karlsruhe, Germany) as a positive control. After incubation in the dark for 30 min at RT, the OD was measured at λ = 630 nm. Secretion of siderophores was calculated as previously described [56] and expressed as a percentage unit of siderophore production. The non-siderophore producing E. coli isolate (W3110) was used as a negative control.
To test the production of cellulose and/or curli fimbriae, a long-term colony assay was performed as described previously [57]. Briefly, 20 mL of dye (containing 50 mg congo red and 25 mg Coomassie-Brilliant-Blue G-250 [Carl Roth, Karlsruhe, Germany] dissolved in deionized water and ethanol [99%, v/v; 1:7]) was filter sterilized into 1 L of LB Lennox (Carl Roth, Karlsruhe, Germany) containing 1.8% (w/v) span agar (Hellmuth Carroux, Hamburg, Germany). From overnight cultures, 5 µL were dropped onto the agar plates. The plates were hermetically sealed and incubated at 28 °C for 5 days followed by visual evaluation. Purple colonies would mark the production of curli fimbriae, structured areas indicated the production of cellulose and white colonies did not show any of the structures [58]. Cellulose production was also tested using a calcofluor assay in 24-well microtiter plates (Sarstedt, Nürnbrecht, Germany). Calcofluor has a high affinity for cellulose and allows the quantification of cellulose production [59,60]. For this purpose, LB Lennox was prepared with 1.8% (w/w) span agar. An aqueous 0.2% (w/v) calcofluor solution (Sigma-Aldrich, St. Louis, MO, USA) was sterile filtered and 2 mL were added to 100 mL of the span agar solution. Of this solution, 1 mL was transferred into each well of the 24-well plate and allowed to harden. Colonies from agar plates were taken and added to sterile PBS until OD600 = 0.5 was reached and 5 µL of the suspension were dropped onto the center of each well. After 5 days of incubation at 28 °C, the fluorescence intensity was measured using the microplate reader (excitation 400–415 nm, emission 480–520 nm). Biofilm formation was tested with a crystal-violet (CV) assay [26]. From overnight cultures in LB, a 1:100 dilution in M9 medium supplemented with 1 mM magnesium sulfate (Carl Roth, Karlsruhe, Germany) and 0.4% glucose (Carl Roth, Karlsruhe, Germany) was prepared and incubated on a rotary shaker at 130 rpm and 37 °C until 0.5 McFarland standard turbidity was reached. The OD was then set to 0.01 in the supplemented M9 medium and 200 µL of this suspension were placed into a 96-well plate, which was incubated at 28 °C for 24 h. Additionally, triplicates of 200 µL of sterile medium were used as controls and blanks. The OD600 was then measured using the microplate reader. The 96-well plate was washed thrice with deionized water to remove planktonic cells and air-dried for 10 min. Then, the cells were fixed in 250 µL of methanol (Merck, Darmstadt, Germany) for 15 min. After air-drying, cells were stained with 250 µL of a 0.1% (w/v) aqueous CV solution (Sigma-Aldrich, St. Louis, MO, USA) for 30 min, before the plate was washed three times with deionized water and dried for 10 min. Subsequently, the bound CV was dissolved in 300 µL of a mixture of 80 parts ethanol (99.8% (v/v); Carl Roth, Karlsruhe, Germany) and 20 parts acetone (Merck, Darmstadt, Germany) at RT with horizontal shaking at 200 rpm for 30 min. Finally, 125 µL of this solution were transferred to a new 96-well plate and the OD was measured at λ = 570 nm using the microplate reader. The strength of biofilm formation was expressed as specific biofilm formation (SBF). The SBF was calculated according to the following formula [61]: SBF = (B − NC)/G, where B is the OD570 of the stained bacteria, NC is the OD570 of the stained control wells to eliminate the fraction of CV adhering to the polystyrene surface due to abiotic factors, and G is the OD600 representing the density of cells grown in the media. W3110 and the biofilm-producing IMT18399 were used as controls. W3110 was used as a negative control for cellulose production, while both isolates were used as positive controls for the other assays.
The mortality of the K. pneumoniae isolates (PBIO1953, hvKP1, PBIO3459) was tested using larvae of the wax moth Galleria mellonella as model organisms, as described previously [62]. Several single colonies were suspended in PBS until an OD600 of 1.0 was reached (approx. 2 × 109 CFU/mL). The bacterial suspensions were then centrifuged at 12,000× g and RT for 5 min and washed twice with PBS. The suspension was diluted to 2 × 107 CFU/mL. Larvae (proinsects, Minden, Germany) were randomly divided into groups of 10 individuals each and 10 µL of the adjusted bacterial suspensions were injected into the left proleg. In addition, 10 µL of PBS was injected into a group of larvae to ensure that death was not due to trauma from the injection. Each group was placed in 90 mm glass Petri dishes, kept at 37 °C in the dark, and death was recorded every 24 h. Individuals were considered dead when they no longer responded to physical stimuli and showed pigmentation. The assay was repeated three times, results for each isolate were pooled and Kaplan–Meier plots were generated to show mortality rates [63].
Statistical analyses were performed using GraphPad Prism v.7.05 for Windows (GraphPad Software, San Diego, CA, USA). All phenotypic experiments were performed with three biological replicates. Unless otherwise indicated, data were expressed as mean and standard deviation. Statistical significance was assessed by analysis of variation (ANOVA) with Dunnett’s multiple comparison post hoc test. p values of less than 0.05 were used to indicate significant statistical differences among the results.
PBIO3459 was isolated from a blood sample of a patient with diabetes mellitus and gastroenteritis and exhibited colony morphology unusual for Klebsiella (Figure 1a). On blood, MacConkey, and chromogenic agar, colonies demonstrated a rough, dry, and elevated surface (Figure 1a), whereas on LB and supplemented Simmons agar, colonies with a smooth and even surface were observed. Species identification using MALDI-TOF MS revealed PBIO3459 as K. pneumoniae. Whole-genome sequence (WGS) analysis showed that PBIO3459 belonged to ST20 and capsule biosynthesis (KL) and lipopolysaccharide antigen (O) loci were KL28 and O1/O2v2, respectively. Interestingly, the insertion sequence (IS) ISKPn74, previously described in a hvKp isolate [53], was found within the K locus (identity: 1050/1056 bp, 99%). In addition, we identified IS903B (identity: 1029/1057 bp, 97%) within the O locus and in the region between both loci. PBIO3459 carried a plasmid with incompatibly group (Inc)FIA and Col-plasmids but no acquired antibiotic resistance genes, which was confirmed by AST as only intrinsic ampicillin and amoxicillin phenotypic resistances were detected. Using VFDB to assign virulence factors, overall 109 different genes were identified. The most abundant genes were associated with metabolic factors (n = 29), immune modulation (n = 22), adherence (n = 19), and effector delivery systems (n = 18). The metabolic factors were mainly related to iron uptake and transport such as the two siderophore systems yersiniabactin (fyuA, irp1, irp2, ybtAEPQSTUX), and enterobactin (entABCDF, fepABCDG, fes). In addition, some genes encoding for salmochelin (iroEN) and aerobactin (iutA) siderophores were present, but we did not detect the complete systems. Adherence genes were mainly related to fimbriae and pili and all genes associated with effector delivery systems encoded for the type VI (14/18) and type II secretion systems (4/18).
To gain further insights regarding the assignation of PBIO3459 to a particular subtype, it was tested in phenotypic fitness-, virulence-, and resilience-associated assays (Figure 1) and compared to different reference strains, i.e., an archetypical hypervirulent (hvKP1) and an extensively drug-resistant K. pneumoniae with enhanced virulence (convergent strain PBIO1953). Regarding basic growth at two different temperatures, aiming at testing healthy (37 °C) as well as diseased (fever) conditions (40 °C), the three isolates showed similar kinetics in LB medium (Figure 1b,c). However, comparing the area under the curve (AUC) at 37 °C, the AUC of PBIO1953 (62.05, p < 0.0001) was significantly lower than that of PBIO3459 (69.73), while it was not significant for hvKP1 (69.1) compared to PBIO3459 (p = 0.37). Comparison of the AUCs at 40 °C showed that the AUC of PBIO3459 (70.96) was significantly higher than those of hvKP1 (66.31, p < 0.0001) and PBIO1953 (59.48, p < 0.0001) (Figure 1b). Next, we tested whether PBIO3459 demonstrated any typical hypervirulence-associated features. In the hypermucoviscosity string test, only hvKP1 colonies formed a string longer than 5 mm and were thus defined positive. For PBIO1953 and PBIO3459, the string test was negative. Second, we performed a sedimentation experiment. Mucus production affects the sedimentation behavior of cells as mucus is more viscous than the surrounding medium. During centrifugation, mucus-producing cells sediment more slowly due to the viscous properties. The ratio between the supernatant and the OD600 of the complete culture can be calculated and compared with the different isolates to assess hypermucoviscosity. The highest mean ratio was observed for hvKP1 (0.39). Ratios of PBIO3459 (0.25; p = 0.30) and PBIO1953 (0.20; p = 0.15) were lower but not statistically significant (Figure 1d). Similar results were obtained for siderophore production, where high values were measured for PBIO1953 and hvKP1, while PBIO3459 produced a significantly lower amount (p < 0.0001) (Figure 1e). This matches the WGS data, where only yersiniabactin and enterobactin siderophore systems were found, while aerobactin, the dominant siderophore in hvKp [64], was absent. When we challenged the isolates with 50% human serum, both PBIO3459 (p = 0.0085) and PBIO1953 (p = 0.0143) showed significantly lower CFU levels than hvKP1 after 4 hours of incubation (Figure 1f). Finally, to explore the isolates’ ultimate virulence potential, we performed in vivo mortality in Galleria mellonella larvae (Figure 1g). As expected, hvKP1 showed the highest mortality rate (50.0% after 24 h; 60.0% after 48 h; 70.0% after 72 h) whereas PBIO3459 killed significantly lower numbers of larvae (10.0% after 24 h, p = 0.0004; 10.0% after 48 h, p < 0.0001; 10.0% after 72 h, p < 0.0001). Except for the time point after 24 h, the mortality rate of PBIO3459 was also significantly lower than that of PBIO1953 (16.7% after 24 h, p = 0.69; 46.7% after 48 h, p = 0.0010; 53.3% after 72 h, p = 0.0002). As the unusual rough appearance of PBIO3459 on certain nutrient media might be associated with modified capsular polysaccharides, the production of exopolysaccharides was investigated using BHI agar supplemented with sucrose and congo red (Figure 2a). Upon exopolysaccharides production, the respective bacterium grows in grayblackish colonies, whereas orange and white colonies indicate no exopolysaccharide production. PBIO1953 was tested negative for exopolysaccharides showing orange colonies; hvKP1 grew in gray-blackish colonies and was therefore tested positive. The result of PBIO3459 could not be determined clearly, as colonies showed a red-grayish color. In addition to capsular components, many Klebsiella isolates form biofilms, which have been previously related to intestinal colonization and infection. Here, we tested the production of important biofilm-associated cellulose and curli fimbriae structures in long-term colonies. PBIO1953 and hvKP1 were white and unstructured and thus classified negative for curli and cellulose production, whereas colonies of PBIO3459 could not be assigned to any particular phenotype as they appeared differently than the positive control (Figure 2b). To further explore the biofilm-associated results, we performed a crystal violet experiment that tested the isolates’ ability to adhere to plastic surfaces, revealing that PBIO1953 and PBIO3459 had similar specific biofilm formation capacities whereas hvKP1 had a lower adhesion affinity. However, note that these differences were not statistically significant (Figure 2c). Cellulose production was tested additionally, using the fluorescent dye calcofluor. In summary, the assay suggests PBIO3459 as an extensive cellulose producer (Figure 2d).
To address the genomic relatedness of antibiotic-resistant ST20 isolates and PBIO3459, we selected two publicly available K. pneumoniae ST20 genomes based on the following two criteria: (i) a Kleborate resistance score of ≥1 and (ii) the most shared k-mers using a Mash-approach. Like PBIO3459, the two identified genomes GCA_021917825.1 and GCA_022235845.1 demonstrated the K/O loci KL28 (for the former unknown KL28) and O1/O2v2, respectively, and a virulence score of 1. Kleborate revealed a resistance score of 1 for GCA_021917825.1 with 14 resistance genes conferring resistance to six antibiotic classes (aminoglycosides, quinolones, sulfonamides, trimethoprim, beta-lactams, including third-generation cephalosporins and monobactams). In contrast, GCA_022235845.1 had a resistance score of 2 and exhibited seven resistance genes to three antibiotic classes (quinolones, beta-lactams, including carbapenems). When comparing the two genomes to PBIO3459, we noticed high sequence similarities, reflected by high genomic coverage (Figure 3). GCA_021917825.1 and GCA_022235845.1 sequences both covered over 96% of the complete genome of PBIO3459 whereas, in contrast, PBIO3459 covered approximately 93% of the draft genomes of GCA_021917825.1 and GCA_022235845.1 (Figure 3) suggesting that all three genomes possessed unique genomic areas, e.g., mobile genetic elements. To investigate these slight differences at the gene level, we performed group clustering using PIRATE. Most groups (83.77%; 4739/5657) were found in all three genomes, while we detected only 1.77% (100/5657) exclusively in PBIO3459, and 4.40% (249/5657) and 6.15% (348/5657) were present in GCA_021917825.1 and GCA_022235845.1 only. Most unique genes were hypothetical proteins (HP). Interestingly, in addition to HP, heavy metal resistance (HMR) and heat shock proteins were found in the GCA genomes. Twenty-four percent (24/100) of the unique groups in PBIO3459 were of plasmid origin and another 25% (25/100) were associated with prophages, which are not uncommon in human-associated, clinical strains [65]. In summary, the investigated ST20 genomes only differed in the presence of ABR and HMR genes. Then, to investigate our isolate in a global context, we performed a phylogenetic analysis for which we used all publicly available ST20 genomes and PBIO3459 (Figure 4). The dataset included 264 international genomes from 35 countries. The majority originated from Europe (37.5%; 98/264), North America (23.11%; 61/264), and Asia (18.56%; 49/264) and was isolated from humans (211/264). The phylogenetic tree, which is based on 8904 core SNP sites, contained three main clades. While clade 3, as the largest, consisted of 110 genomes, clade 2 contained 92, and clade 1 62 genomes, respectively. Most genomes with a resistance score of ≥1 and low virulence levels belonged to clades 1 and 3. Interestingly, two clade 1-genomes (GCA_021973675.1 and GCA_022470475.1) from North America demonstrated high resistance (score = 2) and virulence (score = 3) levels, suggesting convergent Klebsiella types. Within clade 3, nine partially unrelated genomes showed an extensively drug-resistant genotype (resistance score 3, virulence score 0). The intra-clade distance, i.e., the number of SNPs between the two closest as well as the most distant genomes within one clade, was 2–291 (median distance: 187) for clade 1 and 1–303 (median distance: 182) for clade 3. Clade 2, containing PBIO3459, had an intra-clade distance of 1–190 (median distance: 104). Also note that clade 2 divided into two sub-clusters, both with genomes demonstrating resistance scores between 0 and 2. The first cluster consisted of genomes with a virulence score of 0, whereas the second mainly contained genomes with a virulence score of 1. PBIO3459 belonged to the latter and was closely related to a ST20 genome from North America (GCA_008082375.1) that is also human-derived.
Finally, to explore potential underlying mechanisms of the two different phenotypes, RNA sequencing was performed. As PBIO3459 showed the same (smooth) phenotype on LB and BHI agar, both conditions were compared with the other phenotype (rough) that occurred on chromogenic agar. Of particular interest were those genes that were down-regulated in the rough phenotype whilst up-regulated in the smooth phenotype and vice versa. Therefore, a double comparison was done. Genes that were differentially expressed on chromogenic agar vs. LB agar were compared with chromogenic agar vs. BHI agar. We identified nine down-regulated and 48 up-regulated genes on chromogenic agar compared with both LB and BHI (Figure S1). According to the database of clusters of orthologous groups [66], the most abundant groups within the down-regulated genes were associated with amino acid transport and metabolism (3/9) and transcription (2/9). Most up-regulated genes were related to carbohydrate transport and metabolism (14/48) or energy production and conversion (7/48). The differentially expressed genes were further investigated using the Kyoto encyclopedia of genes and genomes (KEGG) database [67,68,69]. The down-regulated genes did not show clear clustering regarding particular pathways but some of the genes were related to lysine metabolism (mdcR, lysA, cadB). For the up-regulated genes, the KEGG database detected many genes potentially involved in starch and sucrose metabolism. In particular, genes associated with the degradation of extracellular sugars and glucose production were found to be up-regulated. In this context, chbA, celAB, gmuB, bglBCF (degradation of extracellular cellobiose to glucose) and treBC (degradation of extracellular trehalose to glucose-6-phosphate) were particularly noteworthy. A similar pattern of up-regulated genes was also seen when only comparing the data of chromogenic agar with LB agar. Many sugar-related genes, e.g., associated with maltoporins, mannose, galactosidases, glucosidases and cellobiose, were up-regulated, whereas amino acid-related genes were down-regulated, e.g., associated with lysine, leucine, cysteine, and methionine. However, it was not possible to identify unique genes associated with either the smooth or the rough phenotype when comparing only two media, as pure media effects could not be excluded.
To investigate the virulence and ABR potential of PBIO3459 and provide a subsequent classification, several phenotypic assays were performed in addition to whole-genome and RNA sequencing. The rough colonies of PBIO3459 on chromogenic media had a sponge-like appearance, similar to rugose colony phenotypes of Nocardia spp. [70,71] or Vibrio cholerae [72]. To our knowledge, such morphologies have not been previously reported for K. pneumoniae. While PBIO3459 showed low virulence in hypermucoviscosity (string and sedimentation tests), siderophore production and serum resistance assays, as well as in vivo mortality, which are all traits typical for hvKp, good production of exopolysaccharides and cellulose as well as biofilm formation revealed characteristics putatively important for intestinal colonization and thus opportunistic pathogens [73]. WGS analysis revealed that only two minor siderophore systems (yersiniabactin and enterobactin) were present, resulting in the observed low siderophore production unusual for hvKp isolates [74,75]. Serum resistance of PBIO3459 was also low, which again seems logical when classifying it as opportunistic pathogen that lacks the appropriate virulence features. Indeed, hvKP1 demonstrated the opposite phenotype. It has been previously reported that the lipopolysaccharide composition of outer membrane components [76] but also the siderophore aerobactin [64] play important roles in bacterial serum resistance. Changes in those lipopolysaccharides [76] or deletion of aerobactin can lead to reduced growth in serum [64], supporting our results. Overall, concluding our phenotypic results and the absence of specific genetic biomarkers, PBIO3459 seemingly does not belong to the hvKp pathotype. Since it also does not meet the criteria for being classified as MDR representative, as supported by an antibiotic resistance score of 0 and phenotypic AST, the opportunistic/commensal character of PBIO3459 is highly likely. We speculate that PBIO3459 disseminated to the bloodstream from the intestinal tract, enabled by the patient’s severe gastroenteritis and subsequent potentially disrupted blood–intestinal barrier. Interestingly, PBIO3459 belongs to a phylogenetic background (ST20) that can cause fatal infections. Outbreaks with MDR ST20 isolates have been reported in several countries such as South Korea [77], Spain [78,79], Canada [80], New Zealand [81], Greece [82], Brazil [83], and China [84,85,86,87,88,89,90,91], and isolates without ABR have occasionally been reported [77,82,83,91]. In a few cases, K. pneumoniae ST20 were isolated from healthy individuals [92] or those without an underlying infection [82]. For example, Lepuschitz et al. investigated the colonization pattern of K. pneumoniae in healthy individuals [92]. They found a variety of different STs, including ST20 isolates; one ST20 KL102 O2v2 and one ST20 KL28 O1v2 isolate [92], with the latter demonstrating the same K and O loci as PBIO3459, again supporting its classification. Mavrodi et al. reported an outbreak in neonates caused mainly by K. pneumoniae ST20 [82]. They identified ST20 isolates in infected but also colonized neonates [82]. It is well known that ABR can spread through mobile genetic elements and that commensal microorganisms acquire such traits in the intestinal tract [93]. For example, a ST20-K28 K. pneumoniae isolate carrying a hybrid plasmid with the potential for horizontal gene transfer has been previously reported [90]. Additionally, here, we revealed high genome sequence identities of exemplary ST20 isolates, only differing in the presence of ABR and HMR genes. The latter follows in the context of their frequently described co-localization on mobile genetic elements [94]. In addition, we revealed PBIO3459′s close relationship (median intra-clade distance: 104) to ST20 genomes from different European locations and worldwide. Of the 14 genomes most closely related to PBIO3459, eleven were of human, and two from wastewater origin (Table S1). In addition, note that the combination of genomic resistance and virulence features present in the two isolates from North America might suggest their convergent character. However, this will have to be further explored in the future. Taken together, our results suggest that ABR-Kp isolates may descend from intestinal commensals by acquiring resistance, possibly then leading to difficult-to-treat infections. Finally, our RNA sequencing analysis to explore the underlying mechanisms of PBIO3459′s two different phenotypes revealed several pitfalls. Since the exact composition of chromogenic agar is unknown, it was not possible to confidently relate the results to meaningful biological processes and exclude media effects. LB agar only contains amino acids as nutrients and no sugars [95]. Therefore, when LB is compared to another medium which contains sugars as nutrients, the up-regulation of sugar-associated genes is a logical consequence, while noticing a down-regulation of amino acid-associated genes. Some sugar-associated genes, e.g., for mannose, fucose, rhamnose or galactofuranose, could be related to capsular polysaccharide synthesis [96], but these genes could either be phenotype- or simply medium-related. However, as PBIO3459 showed the same phenotype (smooth) on LB and BHI agar, while rough colonies appeared on chromogenic medium, we investigated genes that were up- or down-regulated on both, LB and BHI agar, in comparison to chromogenic agar to receive actually meaningful data. Therefore, the up-regulated genes associated with the degradation of extracellular sugars and glucose production might be responsible for the different phenotypes indeed. As extracellular polysaccharides can be used as nutrient reservoirs [97], depletion or lack of available nutrients might lead to the degradation of PBIO3459′s extracellular sugars. Cellulose, as an architectural polysaccharide, might also contribute to the phenotype [97]. Cellulose is, i.a., involved in the expression of the multicellular rdar (rough, dry, and red) morphotype of Salmonella typhimurium and some E. coli isolates [97,98]; however, these phenotypes do not exactly match our isolate. Another architectural polysaccharide potentially leading to rough colony morphologies is colanic acid, which is frequently formed at low temperatures [97,99]. Interestingly, deletion mutants of E. coli have shown the importance of colanic acid in the formation of complex three-dimensional structures [100]. As we have performed all biofilm-related assays at 28 °C, colanic acid might also be a contributing factor. However, this issue needs to be addressed prospectively.
Here, we investigated a K. pneumoniae isolate with unusual colony morphology from a blood sample and revealed its low-level antibiotic resistance and virulence features and thus opportunistic character. In addition, we discussed potential underlying mechanisms contributing to the phenotype and the probability that ABR-Kp originate from opportunistic representatives through the acquisition of antibiotic resistance traits. | true | true | true |
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PMC9607084 | Yasmin M. Ahmed,Raha Orfali,Nada S. Abdelwahab,Hossam M. Hassan,Mostafa E. Rateb,Asmaa M. AboulMagd | Partial Synthetic PPARƳ Derivative Ameliorates Aorta Injury in Experimental Diabetic Rats Mediated by Activation of miR-126-5p Pi3k/AKT/PDK 1/mTOR Expression | 22-09-2022 | type 2 diabetes mellitus (T2D),peroxisome proliferator-activated receptor (PPAR),intracellular adhesion molecule 1 (ICAM-1),endothelial nitric oxide synthase (eNOS),endothelin-1 (ET-1) | Type 2 diabetes mellitus (T2D) is a world wild health care issue marked by insulin resistance, a risk factor for the metabolic disorder that exaggerates endothelial dysfunction, increasing the risk of cardiovascular complications. Peroxisome proliferator-activated receptor PPAR) agonists have therapeutically mitigated hyperlipidemia and hyperglycemia in T2D patients. Therefore, we aimed to experimentally investigate the efficacy of newly designed synthetic PPARα/Ƴ partial agonists on a High-Fat Diet (HFD)/streptozotocin (STZ)-induced T2D. Female Wistar rats (200 ± 25 g body weight) were divided into four groups. The experimental groups were fed the HFD for three consecutive weeks before STZ injection (45 mg/kg/i.p) to induce T2D. Standard reference PPARƳ agonist pioglitazone and the partial synthetic PPARƳ (PIO; 20 mg/kg/BW, orally) were administered orally for 2 weeks after 72 h of STZ injection. The aorta tissue was isolated for biological ELISA, qRT-PCR, and Western blotting investigations for vascular inflammatory endothelial mediators endothelin-1 (ET-1), intracellular adhesion molecule 1 (ICAM-1), E-selectin, and anti-inflammatory vasoactive intestinal polypeptide (VIP), as well as microRNA126-5p and p-AKT/p-Pi3k/p-PDK-1/p-mTOR, endothelial Nitric Oxide Synthase (eNOS) immunohistochemical staining all are coupled with and histopathological examination. Our results revealed that HFD/STZ-induced T2D increased fasting blood glucose, ET-1, ICAM-1, E-selectin, and VIP levels, while decreasing the expression of both microRNA126-5p and p-AKT/p-Pi3k/p-PDK-1/p-mTOR phosphorylation. In contrast, the partial synthetic PPARƳ derivative evidenced a vascular alteration significantly more than reference PIO via decreasing (ET-1), ICAM-1, E-selectin, and VIP, along with increased expression of microRNA126-5p and p-AKT/p-Pi3k/p-PDK-1/p-mTOR. In conclusion, the partial synthetic PPARƳ derivative significantly affected HFD/STZ-induced T2D with vascular complications in the rat aorta. | Partial Synthetic PPARƳ Derivative Ameliorates Aorta Injury in Experimental Diabetic Rats Mediated by Activation of miR-126-5p Pi3k/AKT/PDK 1/mTOR Expression
Type 2 diabetes mellitus (T2D) is a world wild health care issue marked by insulin resistance, a risk factor for the metabolic disorder that exaggerates endothelial dysfunction, increasing the risk of cardiovascular complications. Peroxisome proliferator-activated receptor PPAR) agonists have therapeutically mitigated hyperlipidemia and hyperglycemia in T2D patients. Therefore, we aimed to experimentally investigate the efficacy of newly designed synthetic PPARα/Ƴ partial agonists on a High-Fat Diet (HFD)/streptozotocin (STZ)-induced T2D. Female Wistar rats (200 ± 25 g body weight) were divided into four groups. The experimental groups were fed the HFD for three consecutive weeks before STZ injection (45 mg/kg/i.p) to induce T2D. Standard reference PPARƳ agonist pioglitazone and the partial synthetic PPARƳ (PIO; 20 mg/kg/BW, orally) were administered orally for 2 weeks after 72 h of STZ injection. The aorta tissue was isolated for biological ELISA, qRT-PCR, and Western blotting investigations for vascular inflammatory endothelial mediators endothelin-1 (ET-1), intracellular adhesion molecule 1 (ICAM-1), E-selectin, and anti-inflammatory vasoactive intestinal polypeptide (VIP), as well as microRNA126-5p and p-AKT/p-Pi3k/p-PDK-1/p-mTOR, endothelial Nitric Oxide Synthase (eNOS) immunohistochemical staining all are coupled with and histopathological examination. Our results revealed that HFD/STZ-induced T2D increased fasting blood glucose, ET-1, ICAM-1, E-selectin, and VIP levels, while decreasing the expression of both microRNA126-5p and p-AKT/p-Pi3k/p-PDK-1/p-mTOR phosphorylation. In contrast, the partial synthetic PPARƳ derivative evidenced a vascular alteration significantly more than reference PIO via decreasing (ET-1), ICAM-1, E-selectin, and VIP, along with increased expression of microRNA126-5p and p-AKT/p-Pi3k/p-PDK-1/p-mTOR. In conclusion, the partial synthetic PPARƳ derivative significantly affected HFD/STZ-induced T2D with vascular complications in the rat aorta.
Type 2 diabetes (T2D) is a worldwide concern that establishes a significant influence on patient mortality and morbidity [1], affecting about 463 million adult people aged 20–79 years [2], which is expected to increase by 51% to 700 million by 2045 [2,3]. T2D is characterized by peripheral insulin resistance [4,5], which diminishes glucose reuptake in skeletal muscle and adipose tissue, leads to defective hepatic glucose output, and impairs insulin production from pancreatic B-cells [6,7]. Prolonged insulin resistance develops micro- and macrovascular problems predisposing to vascular risk factors, elevated blood pressure, obesity, diminished glucose metabolism, and dyslipidemia [8,9,10], consequently leading to microangiopathy in multiple organs, retina, kidney, and neurons, endothelial dysfunction, and risk of cardiovascular disease [11,12]. This metabolic syndrome increases free fatty acids; oxidative stress mediators induce the breakdown of mitochondrial functions [13,14] and impair endothelial nitric oxide synthase (eNOS) activity [15]. Elevated levels of endothelin-1 (ET-1) are produced as a result of decreased eNOS expression and increased vascular oxidative stress [16,17], as well as adhesion molecules resembling P-selectin and E-selectin [18,19]. Endothelial dysfunction is a hallmark of type 2 diabetes and a precursor to the development and worsening of atherosclerotic plaques [20], characterized by inflammation of the arterial wall controlled by vascular smooth muscle cells (VSMCs), macrophages, and endothelial cells (ECs) [21]. The lipotoxicity of saturated long-chain fatty acids in cardiomyocytes [22] is related to many factors such as reactive oxygen species (ROS) [23], peroxisome proliferator-activated receptors (PPARs) [24,25], and phosphoglycerate cofactor 1 (PGC-1) [26]. Furthermore, endothelial short noncoding microRNAs (miRNAs) have essential roles in vascular formation, hemodynamic stress, progression of atherosclerosis, and inflammation [27,28]. The most abundant meta-regulators for endothelial gene expression miRNAs are miR-126-3p and miR-126-5p [28]; the aberration for gene-encoding pre-miR-126 impacts vascular integrity and angiogenesis [29]. On the other hand, the significant presence of ischemic neovascularization in the Mir126−/− mice model and the transmission of miR-126-3p via microparticles released from apoptotic ECs inhibits atherosclerosis [30,31,32], demonstrating that miR-126 is essential for the endothelium stress response. Furthermore, the endothelial cell death inhibitor miR-126-5p works by directly targeting the transient receptor potential channel (TRPC6) [28,33]. Tang et al. [34] revealed that the overexpression of miR-126-5p triggers the Phosphatidylinositol-3-Kinase/Serine-Threonine Kinase/Mammalian/Mechanistic Target of Rapamycin (PI3K/Akt/mTOR) pathway by restoring autophagy, reflecting the antiatherogenic effect of miR-126-5p [35]. Consequently, cell proliferation, migration, and survival of endothelium and VSMCs are all improved by activating PI3K/Akt/mTOR [36,37]. The upregulation of adenosine monophosphate-activated protein kinase–mammalian/mechanistic target of rapamycin (AMPK/mTOR) and hypoxia-inducible factor alpha (HIFα) is related to the induction of autophagy [38], while PI3K/Akt/PDK1/mTOR, peroxisome proliferator-activated receptors gamma (PPARƳ), and nuclear factor kappa B (NF-κB) are significant to reserve autophagy [39]. Dong et al. [40] recently revealed that miRNA-126-5P significantly interacted with peroxisome proliferator-activated receptor alpha (PPARα), ATP-binding cassette transporter (ABCA1), and cholesterol 7α-hydroxylase (CYP7A1) genes, ameliorating dyslipidemia and atherosclerosis. The nuclear receptor superfamily includes ligand-activated transcription factors called peroxisome proliferator-activated receptors (PPARs) [28]; the PPAR family is subdivided into three isotypes PPARα, PPAR β/δ, and PPARƳ [41]. Activation of the PPARs subtype is essential for controlling cell proliferation, differentiation, apoptosis [42], enhancing cell development [43], and wound healing [44], raising high-density lipoprotein (HDL) levels [45], reducing triglyceride levels [46], and improving insulin sensitivity [47]. However, PPARα is widely distributed throughout the body tissues such as cardiac [48], renal, liver [49], muscles, and adipose tissue [50], which is essential for regulation of angiogenesis, inflammation, and free fatty acid catabolism [51]. Recent studies indicate that the ECs, VSMCs, and macrophages co-expressed both isotypes PPARα and PPARƳ [52], which function in endothelial cell survival and proliferation [53]. It has been reported in individuals with T2D exposed to high-fat meals that protein, lipid, and carbohydrate load was connected to increased ROS generation and impaired endothelium-dependent vasodilation [54], lowering the endothelial function [55,56]. The activation of vascular endothelial cells results in the release of pro-inflammatory adhesion molecules such as Intracellular Adhesion Molecule 1 (ICAM-1), Vascular Cell Adhesion Protein 1 (VCAM-1), and E-selectin expression [57], as well as an increase in pro-inflammatory cytokines such as tumor necrosis factor-, interleukins, and platelet-derived growth factor [58]. The simultaneous activation of dual alpha and gamma PPARs agonists may provide superior glucose and lipid regulation compared to single subtype-selective drugs [59,60]. In addition, growth factors and cytokines that promote endothelial cell migration also regulate angiogenesis [61,62], proliferation [63], and survival to promote revascularization and tissue ischemia affected by T2D [64,65]. Currently, thiazolidinedione (TZDs), such as pioglitazone, ciglitazone, troglitazone, and rosiglitazone and their composites are essential drugs promoting favorable effects in modulating endothelial dysfunction in T2D comorbidity due to their anti-inflammatory and anticancer effects, as well as antihyperlipidemic activity [66,67]. They act on PPARα and PPARƳ to ameliorate hyperlipidemia and hyperglycemia in T2D patients [68]. Consequently, they may downregulate the activation of proinflammatory mediators via Pi3k, AKT, and mTOR signaling pathways [69,70] by promoting favorable effects in modulating endothelial dysfunction in diabetes comorbidity due to their anti-inflammatory and anticancer effects, as well as antihyperlipidemic activity [71,72]. Additionally, PPARs are expressed in adipose tissue and endothelial cell lining [73], modulating chemokines and adhesion molecules (ICAM, VCAM), as well as downregulating ROS [74]. Indeed, PPARƳ enhances nitric oxide (NO) production in the endothelium and retracts ET-1 expression, promoting endothelial relaxation [75,76]. Ahmet et al. [77] reported that pioglitazone analogue significantly regulates Streptozotocin-Induced T2D through stimulating local angiotensin-converting enzyme 2/angiotensin 1-7 axis with the aid of PI3K/AKT/mTOR Signaling pathway in the hepatic tissues, thereby regulating glycogen deposition and enhancing lipolysis. However, Molavi et al. [78] found that PPARƳ ligand rosiglitazone protects against myocardial ischemia/reperfusion injury via an effect on AT2 receptor upregulation and p42/44 MAPK inhibition. Thus, the greater abundance of PPARs in different body organs may be promising to protect T2D patients from cardiovascular comorbidity. Even though many previous studies demonstrated the beneficial effect of PPAR ligands in the treatment of T2D patients with cardiovascular complications and endothelial damage, to date, few studies have examined the beneficial effect of PPARƳ ligand agents on miR-126-5p and Pi3k/AKT/PDK1/mTOR expression in T2D-induced vascular damage. Our study designed a new partial synthetic PPARƳ ligand derivative to assess its protective effect on tissue-induced vascular changes in the aorta of diabetic rats; the aorta vascular tissue levels were estimated for ET-1, ICAM-1, E-selectin, and VIP, qRT-PCR microRNA126-5p gene expression and Western blotting expression of p-AKT/p-Pi3k/p-PDK-1/p-mTOR, coupled with immunohistochemical examination for endothelial nitric oxide synthase (eNOS) and histopathological examination using hematoxylin and eosin. Our findings revealed that synthetic derivatives upregulate miR-126-5p, enhancing p-Pi3k, p-AKT, p-PDK, and p-mTOR signaling pathway activation coupled with suppressing proinflammatory molecules ET-1, ICAM-1, E-selectin, and the anti-inflammatory vasoactive intestinal polypeptide (VIP) in diabetic rats. Furthermore, immunohistochemical estimations of eNOS and histopathological examination using hematoxylin and eosin for aortic tissues enhanced the role of partial synthetic PPARƳ derivatives in correcting diabetes-induced vascular complications.
The mean values of the normal control group regarding serum fasting blood glucose (mg/dL) were 101.66 ± 4.73. Rats subjected to STZ showed significantly higher fasting blood glucose serum levels, reaching 289.33 ± 12.90 (284.60% increase) compared to normal control rats. However, rats subjected to STZ + PIO as a standard treatment and STZ + P-PPARƳ synthetic derivative groups showed significantly decreased mean values of fasting blood glucose levels (129.66 ± 8.08 and 96.33 ± 0.8.14, respectively), respectively, when compared to those in the STZ group (Table 1).
The mean values of the normal control group regarding tissue protein intracellular adhesion molecule 1 (ICAM-1) (ng/mL) and E-selectin (pg/mL) were 14.50 ± 0.53 and 1.55 ± 0.30, respectively. The T2D group significantly increased ICAM-1 and E-selectin in tissue (688.69% and 718.71% increases, respectively), compared with normal rats. On the other hand, the standard PIO group represented an improvement in ICAM-1 level by 21.23% and E-selectin by 27.83% regarding the STZ positive control group. While the rats received P-PPAR γ synthetic derivative treatment significantly improved tissue ICAM-1 level to 28.70% and E-selectin to 26.30 compared with diabetic comorbidity rats. Treatment with P-PPAR γ synthetic derivative improved ICAM-1 and E-selectin substantially better than the reference standard PIO (Figure 1A,B).
The mean values of normal control group regarding tissue VIP (pg/mL) and ET-1 (pg/mL) were 2.63 ± 0.11 and 2.88 ± 0.26, respectively. Rats subjected to the STZ positive control group exposed to a significant increase in the tissue levels of ET-1 and P-selectin, increasing by 767.30% and 2310.42%, respectively. However, the PIO standard group improved tissue levels of VIP by 16.60% and ET-1 by 12.73% compared to the STZ group, while the P-PPARƳ synthetic derivative revealed a significant improvement in VIP by 20.71% and ET-1 by 14.74% compared to STZ group that showed a better improvement of the derivative when compared to the reference standard PIO group (Figure 2A,B).
To determine miR-126-5p contribution in vascular repair induced by P-PPARƳ derivatives treatment against T2D in experimental rats, we used qRT-PCR to evaluate miR-126-5p gene expression. Notably, unlike the normal control rats group, diabetic rats significantly decreased miR-126-5p expression to 14.99%, compared to the normal control group. Oral treatment with pioglitazone and P-PPARƳ synthetic derivative (20 mg/kg, p.o) significantly upregulated miR-126-5p expression to 588.79% and 641.50%, respectively, compared to the diabetic positive control group. However, P-PPARƳ synthetic derivative treatments restored miR-126-5p gene expression back to normal. These results indicate that P-PPARƳ synthetic derivatives counteracted STZ-induced apoptosis and endothelial damage suggesting a functional involvement in regulating miR-126-5p expression-induced vascular endothelial repair (Figure 3).
Restoring the phosphorylation of the p-AKT/p-Pi3k/p-PDK/p-mTOR signaling pathways triggers the endothelium defense mechanism. Western blot analysis showed a significantly diminished expression of p-AKT/p-Pi3k/p-PDK 1/p-mTOR to 56.41%, 51.39%, 61.54%, and 43.82%, respectively, in diabetic rats than in normal control animals Alternatively, the treatment of rats with the standard PIO represented an improvement in p-AKT/p-Pi3k/p-PDK 1/p-mTOR expression, increasing by 162.16%, 154.55%, 145.83%, and 176.92%; additionally, rats receiving P-PPARƳ synthetic derivative significantly increased the expression of p-AKT/p-Pi3k/p-PDK 1/p-mTOR signaling pathways by 115.90%, 135.13%, 124.68%, and 138.56%, respectively, compared to the positive control group. Our results indicate that, with P-PPARƳ synthetic derivative treatment, p-AKT/p-Pi3k/p-PDK 1/p-mTOR signaling pathway expression was restored to normal levels (Figure 4A–D).
Histopathological examination was indicated to detect STZ-induced aortic endothelial blood vessel abrasions and the ability of P-PPARƳ synthetic derivatives to modulate endothelial texture against injury in comparison with standard group PIO. The aorta strip section revealed a normal endothelium and smooth muscle, regarding the normal control group. Additionally, it showed elongated nuclei with an eosinophilic cytoplasm-enhanced marked elastic tissue (Figure 5A). By contrast, the STZ positive control group showed average endothelial lining coupled with marked clefts in the media with cytoplasmic vacuoles in smooth muscle cells and sub-medial separation (Figure 5B). PIO standard treatment group showed minimal endothelial layer clefting (Figure 5C). In contrast, the P-PPARƳ synthetic derivative group decreased vascular endothelial pathological changes by returning endothelial blood vessels to their normal form with a slight smooth muscle clefting and restoring elastic tissue activity (Figure 5D).
An endothelial nitric oxide synthase (eNOS) expression assay investigated endothelial oxidative stress following STZ-induced vascular endothelial injury. Our data revealed that rats subjected to STZ showed a weak eNOS reaction in the endothelial lining and smooth muscle cytoplasm (Figure 6(Ab)) compared to the normal control group (Figure 6(Aa)). PIO standard treatment group endothelial cells showed a mild cytoplasmic reactivity to eNOS, with no reactivity on smooth muscles (Figure 6(Ac)), while P-PPARƳ synthetic derivative re-established eNOS expression on the cytoplasm and endothelial smooth muscles (Figure 6(Ad)) compared to the positive control group. The immunohistochemical findings reveal that eNOS expression increased after treatment with P-PPAR Ƴ synthetic derivative reaching normal control levels, demonstrating the role of tested drugs as antioxidants and ROS scavengers in modifying blood vessel activity (Figure 6A,B).
Diabetes mellitus forms progressive diseases of the blood vessels and cardiomyopathy [79], as well as increases the rate of cardiac hypertrophy [5]. DM is characterized by the presence of elevated oxidative stress [80], elevated inflammatory and vascular biomarkers [81], disturbance in lipid metabolism [82], fibrosis, and elevated serum cardiac injury muscle biomarkers [83,84]. In the current study, we investigated the role of pioglitazone and a new naturally inspired P-PPARƳ synthetic derivative that improves endothelial enhancement through alleviating the inflammatory cascade and vascular endothelial modulation via upregulating endothelial miR-126-5p gene expression. The STZ model is a well-established method for inducing type 1 or 2 diabetes in rats and, subsequently, diabetic complications [85]. For this purpose, our results represent that STZ aortic tissue levels significantly elevated VIP, E-selectin, endothelin-1, and ICAM-1 levels compared to the normal control group (Figure 1A). Moreover, severe endothelium smooth muscle histological abnormalities were evidenced by endothelial lining clefts with cytoplasmic vacuoles in smooth muscle proliferation, as well as eNOS in the smooth muscle (Figure 5A,B and Figure 6A,B).In agreement with our data results, previous studies revealed that STZ-induced DM endothelial complications in the experimental rats significantly increased tissue levels of VIP, E-selectin, endothelin-1, and ICAM-1 [86,87]. Additionally, earlier data proved that vascular damage is induced as a secondary complication to metabolic syndrome-induced insulin resistance in diabetic patients [88], enhancing immunological disorders [89,90]. Consequently, endothelial damage triggers systemic inflammation by increasing the production of proinflammatory molecules and vasoconstrictor agents such as VIP, E-selectin, endothelin-1, and ICAM-1 [91,92], together with an imbalance between endothelial eNOS and iNOS [93,94]. These results agree with our data that DM induction via different mechanisms mediates STZ action. Moreover, numerous studies have shown that hyperglycemia causes severe inflammation [86,87]. Researchers have outlined thiazolidinediones, especially pioglitazone and rosiglitazone, to manage endothelial problems [95]. Treatment with a PPARƳ agonist inhibits LPS-induced endothelial inflammation by reducing IL-6, VCAM, TNF-α, and mRNA expression [96]. In turn, this reduces the production of inflammatory mediators, adhesion molecules, and atherosclerosis in endothelial cells [97,98]. Similarly, a model of diabetic nephropathy suggested that treatment with pioglitazone reduces glomerular sclerosis, fibrosis, and hypertrophy by lowering ICAM-1, E-selectin, and albuminuria [98,99]. However, a study on women with polycystic ovarian syndrome found that pioglitazone treatment for insulin resistance dramatically improved endothelial-independent function, adipokines, and ET-1 [100]. Furthermore, VIP modulation played a significant role in carbohydrate and lipid metabolism [101], in addition to being a potent anti-inflammatory and neuroendocrine vasodilator [102]. Consequently, in an Alzheimer’s disease animal model, a new action on glial cell polypeptide was revealed, which shielded neurons against toxins and memory loss coupled with inhibiting oxidative stress production in the vascular compartment [103]. Additionally, in an STZ-induced diabetes mellitus rat model, pioglitazone administration for four weeks in a row restored ET-1, superoxide dehydrogenase (SOD), and NAD(P)H oxidase activity, thereby restoring aortic function [100,103]. Pitocco et al. [104] reported pioglitazone’s effectiveness in treating pulmonary hypertension rats via inhibiting cellular remodeling, proliferation, and inflammation of VSMCs. Correspondingly, the efficacy of thiazolidinediones in treating human endothelium by decreased inflammatory, pro-inflammatory, and vasoconstrictor agents has been reported [105]. Our findings are consistent with the findings of the prior study. These studies validated our results and confirmed the anti-inflammatory and anti-oxidant properties of the investigated agents. Atherosclerosis-induced hypercholesterolemia is a cardiovascular progression coupled with type 2 diabetes mellitus [106], which increases reactive oxygen species (ROS), and subsequent eNOS degradation, releasing endothelin 1-induced vasoconstriction [107]. Changes in cholesterol levels due to glucose intolerance induce dysregulation of ICAM-1 and VCAM-1 [108]. miR-126-5p downregulation has been reported in elevated serum levels of ICAM-1, VCAM-1, and E-selectin-induced coronary syndrome [109]. Recent studies have revealed the critical role PI3K/AKT/mTOR axis in governing cell survival [82]. Additionally, Jia et al. [21] reported the crosstalk of the circRNA/PI3K/AKT axis, particularly regarding its protective effect against atherosclerosis, oxidative stress, and apoptosis via the regulating impact of tumor cell biological activities. In particular, miR-126-5p maintains a key function in the integrity of endothelial cells, inflammation, angiogenesis, and vascular repair [110]. Otherwise, recent data represent that miR-126 overexpression significantly increases the protein expression of the PI3K, Akt, GSK3β, and ERK1/2 signaling pathways and attenuates ROS vascular content [111]. Another research project reported that miR-126 negatively regulates vascular endothelial growth factor expression in hypoxia-induced monkey chorioretinal vessel endothelial cells [112]. Furthermore, it was discovered that pioglitazone plays a key role in reducing ventricular hypertrophy via ERK activation and increased phosphorylation of the AMPK axis in an experimentally induced hypertensive rat model [113]. It was recently reported that the STZ/HFD-induced insulin resistance [114] significantly suppresses p-AMPK, p-Pi3k, p-AKT, p-PDK, and p-mTOR axis levels in HepG2 cells [115], demonstrating that miRNAs play a role in heart illness as fundamental regulators of gene expression [116]. Additionally, pioglitazone targets miR-126-5p gene expression, which is involved in inflammatory processes, adhesion molecules, cell-cycle events such as proliferation and migration, apoptosis, and NO signaling in endothelial cell health, leading to the development of atherosclerosis [117,118,119]. Our study results revealed that treating diabetic rats with pioglitazone and P-PPARƳ synthetic derivatives associated with the significant upregulation of miR-126-5p expression (Figure 3) activated the expression and phosphorylation of the p-Pi3k, p-AKT, p-PDK 1, and p-mTOR axis (Figure 4), coupled with histopathological endothelial lining healing (Figure 5) and eNOS restoration in the endothelium (Figure 6).
Streptozotocin (Catalog number MFCD00006607) and pioglitazone hydrochloride (Catalog number MFCD04975446) were purchased from Sigma–Aldrich Chemical Company (St. Louis, MO, USA). Enzyme-linked immunosorbent assay (ELISA) kits for rat ET-1 (catalog number MBS5704215), E-selectin (Catalog number ERA14RB), ICAM-1 (Catalog number RAB0221-1KT), and eNOS (Catalog number PA5-17917) vasoactive intestinal peptide (VIP; catalog number MBS5031002) were obtained from ThermoFisher Scientific (Rockford, IL, USA). The Western blotting assay had different primer antibodies against P-pi3k (Catalog number sc-293115), P-AKT (Catalog number # 200-301-268), p-PDK-1 (Catalog number sc-515944), and P-mTOR (Catalog number # PA1-518). The qRT-PCR monoclonal antibody for miR-126-5p (Catalog number 217004) was obtained from Qiagen (Germantown, MD, USA). All materials were obtained from authorized sources in analytical grade.
The Nahda University animal house in Beni-Suef, Egypt, provided adult female albino rats weighing 200–220 g. Before starting the experiment, the animals were held in controlled conditions for 12/12 h with food and water access at the optimal temperature and humidity conditions. The treatment and care of the animals were conducted following the National Institutes of Health (NIH) Guide for the Care and Use of Laboratory Animals (Publication No. 85-23, revised 1985).
Adult female forty albino rats were divided into four groups: Normal control group: receiving vehicle in Tween-80, 2%; STZ (T2D) positive control group: receiving intraperitoneal STZ injection (45 mg/kg) [120] after being subjected to three consecutive weeks of HFD feeding [77]; Pioglitazone-treated group: receiving STZ + HFD as with positive control group, as well as pioglitazone (20 mg/kg/14 day, p.o) dissolved in Tween-80, 2% [76,121]; P-PPAR Ƴ synthetic derivative-treated group: receiving STZ + HFD as with positive control group, as well as the same treatment dose as reference pioglitazone (20 mg/kg/14 days, p.o) dissolved in Tween-80, 2%. The two tested drugs were administrated for two consecutive weeks starting from the 24th day after the STZ injection. The doses depended on previous reference studies and a determined effective pilot study. Synthesis and elucidation of P-PPAR Ƴ synthetic derivative; The procedure is fully described in Supplementary material (S1); STZ-induced diabetes mellitus type 2 model; The procedure and mode of induction for diabetes mellites are fully described in Supplementary material (S2).
After the rats received the last dose of treatment drugs, they were sacrificed by cervical dislocation under anesthesia. The aorta was gently freed of any adjacent tissues and lipids before undergoing midline thoracotomy. The exposed aorta tissue was divided into two portions: one was preserved at −80 °C until the assay time for endothelial tissue biomarker biochemical ELISA, qRt-PCR estimation for ET-1, E-selectin, VIP, and ICAM-1, and Western blotting estimation for p-Pi3k, p-AKT, p-PDK-1, p-mTOR, and qRT-PCR miR-126-5p; The second portion was preserved in formalin 10% isotonic solution for 48 h to adequate fixation before the histopathological examination, and an immunohistochemical assessment of eNOS expression was performed.
Previous cardioprotective studies indicated that increased endothelial content and leakage are valuable markers representing a pathological condition in the aortic strip tissues ET-1, E-selectin, VIP, and ICAM-1. These biomarkers were evaluated using ELISA chemical kits according to the instructions of the assay kit. According to the sandwich technique described previously, the assay depends on the colorimetric measurement of a microplate reader at 450 nm (Model Spectra Max Plus-384 Absorbance Microplate Reader, Molecular devices LLC (San Jose, CA, USA) to test the parameter levels [122].
Aortic cell lysis was performed using RIPA buffer (Beyotime Institute of Biotechnology) to evaluate p-AMPK, p-Pi3k, p-AKT, p-PDK, and p-mTOR expression; cell lysates were centrifuged at 10,000× g at 4 °C for 15 min. Using a bicinchoninic acid protein kit (Beyotime Institute of Biotechnology), protein quantification is as follows. Proteins were loaded on PVDF membranes after 10% SDS-PAGE separation; each lane contained 40 µg of protein. During this time, the membranes were soaked in a blocking solution of 5% nonfat milk in PBST (0.1% Tween-20) for 1 h at room temperature. Then, the samples were left overnight and incubated at 4 °C against the primary antibody. The membrane was then incubated against the secondary antibody at room temperature for an hour after being washed three times with PBST. Lastly, the protein bands were visualized using 5-bromo-4-chloro-3-indolyphosphate (BCIP)/nitro-blue tetrazolium (NBT). The quantification analysis of the detected bands was performed using Image-J/ NIH software and the BioRad microarray protein electrophoresis separation machine (Model 1658004, Sinorica International Patent and Trademark, Germantown, MD, USA). The assessment methodology is provided according to a previously described method [123].
The aortic tissue strip slides were prepared for staining after being fixed for 24 h in a 10% formalin saline solution. Afterward, fixed tissues were transferred to hardening via paraffin blocks. Then, the aorta sections were cut and stained with standard hematoxylin and eosin (H&E) for histopathological investigations under a Nikon microscope at 400× magnification using Bancroft and Steven’s previously published method [120]. The slides were examined by a skilled pathologist.
The eNOS immunohistochemical investigation was performed using a previously described method [124]. The deparaffinized and rehydrated aortic tissues were washed with a buffer solution for 20 min. Then, the tissue was injected with an adequate digestive enzyme. Afterward, sections were exposed to 0.3% H2O2 for 10 min to decrease tissue endogenous peroxidase activity. At that point, the slides were incubated overnight at 4 °C with primary antibodies against eNOS. Following incubation, the slides were washed with buffer, reincubated with secondary antibody HRP for 10 min, and then washed with deionized water. Sections cleaned with deionized water were visualized by adding the DAB Quanto chromogen drop to 1 mL of DAB Quanto substrate. The slides were restained with hematoxylin. Finally, a professional observer used a light microscope (Leica microsystem, Wetzlar, Germany) to monitor the dehydration of slides in xylene and positive dye to identify the samples.
We used a Qiagen tissue extraction kit (Qiagen, Germantown, MD, USA) for aorta RNA extraction. Each stage was performed according to the manufacturer’s instructions. A NanoDrop® ND-8000 UV–Vis spectrophotometer was used to determine the total RNA yield (NanoDrop Technologies, Wilmington, DE, USA). The full RNA isolation and identification are described in the Supplementary data (S3), along with the primer sequences.
The mean and standard error of the mean (SEM) were used to depict the data in this study (eight participants). An ANOVA test followed by a Tukey–Kramer test on biochemical data was conducted using SPSS (version 19.0) computer software (SPSS Inc., Chicago, IL, USA). A p-value of 0.05 was considered statistically significant. Image J was used to measure the intensity of the bands on the Western blot (NIH, USA).
In conclusion, our results indicate that the newly designed partial PPAR Ƴ synthetic derivative, in addition to its anti-hypoglycemic potential, reduces the severity of vascular damage induced due to T2D through upregulating expression of microRNA126-5p, p-AKT/p-Pi3k/p-PDK 1/p-mTOR, and eNOS. In addition, the P-PPAR Ƴ synthetic derivative decreases endothelial inflammatory and vascular integrity parameters ET-1, ICAM-1, E-selectin, and VIP. The PPAR Ƴ synthetic derivative might become an alternative approach to improving diabetes vascular complications caused by metabolic syndrome insults mediated by the antioxidant, anti-inflammatory, and antiapoptotic signaling pathways. Further clinical trials are needed to confirm such findings clinically. | true | true | true |
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PMC9607375 | Qing Sun,Yixuan Xie,Zhixin Guan,Yan Zhang,Yuhao Li,Yang Yang,Junjie Zhang,Zongjie Li,Yafeng Qiu,Beibei Li,Ke Liu,Donghua Shao,Jiaxiang Wang,Zhiyong Ma,Jianchao Wei,Peng Li | Seroprevalence of Getah virus in Pigs in Eastern China Determined with a Recombinant E2 Protein-Based Indirect ELISA | 30-09-2022 | serological survey,Getah virus,ELISA,China | Getah virus (GETV), in the genus Alphavirus and the family Togaviridae, has been detected throughout the world. GETV causes high morbidity and mortality in newborn piglets, entailing serious economic losses. Therefore, the experimental work on GETV detection is necessary. However, due to the influence of a variety of unavoidable factors, the ELISA test for the primary screening of animal diseases has low accuracy in detection results. Therefore, we optimized a recombinant E2 (rE2) protein-based enzyme-linked immunosorbent assay (ELISA) for the detection of GETV antibodies in pig serum. The E2 protein was successfully expressed and purified with SDS-PAGE. A Western blotting analysis of sera from infected pigs showed strong reaction with a viral antigen of ~46 KDa corresponding to the E2 glycoproteins. By using chessboard titration and comparing the P/N values, we found that the optimal concentration of coated antigen was found to be 24.5 μg/mL, and the optimal dilution of serum specimens was 1:100. The best working dilution of the horseradish peroxidase (HRP)-conjugated goat anti-pig immunoglobulin (IgG) was 1:5000. The optimal coating conditions were 12 h at 4 °C. The optimal incubation conditions for serum specimens, blocking, and reaction with the secondary antibody were all 1 h at 37 °C. We also investigated the seroprevalence of GETV in 133 serum specimens collected in Eastern China, and 37.59% of the samples tested positive for anti-GETV IgG antibodies, indicating that the seroprevalence of GETV is high in pig populations in China. The seroprevalence was significantly lower in spring (April; 24.24%, 16/66) than in autumn (October; 50.75%, 34/67), which suggested that the presence of anti-GETV antibodies in pigs was seasonal. In conclusion, we improved an rE2 ELISA that detected pig antibodies against GETV after experimental and natural infections. This should be useful in the diagnosis and surveillance of GETV infections. | Seroprevalence of Getah virus in Pigs in Eastern China Determined with a Recombinant E2 Protein-Based Indirect ELISA
Getah virus (GETV), in the genus Alphavirus and the family Togaviridae, has been detected throughout the world. GETV causes high morbidity and mortality in newborn piglets, entailing serious economic losses. Therefore, the experimental work on GETV detection is necessary. However, due to the influence of a variety of unavoidable factors, the ELISA test for the primary screening of animal diseases has low accuracy in detection results. Therefore, we optimized a recombinant E2 (rE2) protein-based enzyme-linked immunosorbent assay (ELISA) for the detection of GETV antibodies in pig serum. The E2 protein was successfully expressed and purified with SDS-PAGE. A Western blotting analysis of sera from infected pigs showed strong reaction with a viral antigen of ~46 KDa corresponding to the E2 glycoproteins. By using chessboard titration and comparing the P/N values, we found that the optimal concentration of coated antigen was found to be 24.5 μg/mL, and the optimal dilution of serum specimens was 1:100. The best working dilution of the horseradish peroxidase (HRP)-conjugated goat anti-pig immunoglobulin (IgG) was 1:5000. The optimal coating conditions were 12 h at 4 °C. The optimal incubation conditions for serum specimens, blocking, and reaction with the secondary antibody were all 1 h at 37 °C. We also investigated the seroprevalence of GETV in 133 serum specimens collected in Eastern China, and 37.59% of the samples tested positive for anti-GETV IgG antibodies, indicating that the seroprevalence of GETV is high in pig populations in China. The seroprevalence was significantly lower in spring (April; 24.24%, 16/66) than in autumn (October; 50.75%, 34/67), which suggested that the presence of anti-GETV antibodies in pigs was seasonal. In conclusion, we improved an rE2 ELISA that detected pig antibodies against GETV after experimental and natural infections. This should be useful in the diagnosis and surveillance of GETV infections.
Getah virus (GETV) causes one of the most severe worldwide infectious diseases in swine of all ages [1]. GETV was originally isolated from Culex gelidus mosquitoes in Malaysia in 1955, and has since been reported in East Eurasia and South-East Asian countries [2,3,4,5,6], including Far East Russia, Mongolia, China, South Korea, Japan, Thailand, the Philippines, and Australia [4,7]. GETV infects domestic animals through the bites of mosquitoes, causing symptomatic disease [8,9]. The major mosquito vector of GETV is known to be C. tritaeniorhynchus Giles, which is also a major vector of Japanese encephalitis virus (JEV; family Flaviviridae, genus Flavivirus) [10]. Infection with GETV, a member of the genus Alphavirus, has been related to diarrhea and death in piglets, reproductive failure, and abortion in sows [11,12]. As with JEV, pigs infected with GETV become viremic, suggesting that pigs are the main amplifying host of GETV in nature [12]. A serological survey detected anti-GETV antibodies in animals such as birds, cattle, goats, and humans [13]. GETV infection can cause high morbidity and mortality in newborn piglets, leading to serious economic losses. Therefore, a rapid and effective method to evaluate GETV infection in pigs is necessary for its prevention and control. The GETV genome is a single-positive-stranded RNA of 11–12 kb, two-thirds of which encodes nonstructural proteins (NSP1–4) involved in viral RNA transcription, replication, polyprotein cleavage, and RNA capping; the remainder encodes structural proteins (C, E3, E2, 6K, and E1) [14]. Among these proteins, E2 occurs on the surface of the viral particle. E2 is the immunodominant region of the GETV E protein and plays a crucial role in the early steps of infection [15,16]. It mediates viral binding to the cell membrane and the subsequent fusion of the virus and cell [17]. The E2 region is on the outer surface of the virion envelope and induces immune protection against GETV infection in its host. Therefore, the E2 protein has potential utility as a diagnostic antigen to develop a specific and sensitive serological test. Among the various serological methods currently used to detect GETV antibodies, the enzyme-linked immunosorbent assay (ELISA) is simple, effective, rapid, and economic, making it suitable for clinical applications. Compared with other detection methods, such as the fluorescent focus assay or immunofluorescent assay (IFA), ELISA is less expensive and less time-consuming and requires no additional laboratory apparatus. Various anti-GETV antibodies have been used to establish an ELISA for its detection. A recombinant E2 protein was used as the antigen for an indirect ELISA for horse GETV [18,19]. In this study, we expressed the GETV E2 protein in a prokaryotic expression system and improved a new ELISA based on the recombinant E2 protein (rE2). Our method can be used for the detection of anti-GETV immunoglobulin (IgG) in infected or inoculated pigs.
A total of 133 clinical pig serum samples were collected from finishing pigs (24–28 weeks old) in slaughterhouses or sows (more than 28 weeks old) on pig farms in six provinces (Shandong, Hebei, Zhejiang, Shanghai, Jiangsu, Guangdong) in Eastern China, in April (n = 66) and in October (n = 67) of 2018. A total of 20 sera samples (GETV-P1~P10 and GETV-N1~N10) from naturally infected pigs, which were tested by VN [13], IFA [20], qRT-PCR [21], were used to establish and optimize the ELISA protocol (Supplementary Table S1). Positive sera of Japanese encephalitis virus (JEV, JE-P1~P5), porcine reproductive and respiratory syndrome (PRRSV, PRRS-P1~P5), classical swine fever virus (CSFV, CSF-P1~P5), Pseudorabies virus (PRV, PR-p1~p5), and porcine epidemic diarrhea virus (PEDV, PED-P1~P5) verified by VN and IFA (Supplementary Table S2) were from experimentally infected pigs provided by the China Animal Health and Epidemiology Center (Shanghai Branch) and were used to optimize ELISA protocols.
We referred to the complete gene sequence of structural protein E2 of GETV (SH05-6) in GenBank (EU015066), whose length is 1278bp, and optimized and synthesized the gene sequence according to the codon of E. coli. The recombinant plasmid pCold I-E2 was constructed (and is maintained by the Shanghai Veterinary Research Institute, Shanghai, China). The recombinant vector was confirmed with SDS-PAGE and the expressed protein with a Western blotting assay based on GETV-positive serum (GETV-P1) and a His-tagged antibody. The expressed protein was purified on the Ni-column using the His-Bind Purification Kit (Bio-Rad, Hercules, CA, USA), according to the instructions of the manufacturer, and was confirmed with SDS-PAGE.
An indirect ELISA was carried out and optimized with positive (GETV-P2~P4) and negative (GETV-N1~N3) control serum samples. The purified rE2 protein was diluted with 0.05 mol/L carbonate buffer (pH 9.6). Then, 100 µL of the diluted antigen was added to each well of a 96-well ELISA plate and incubated at 4 °C for 12 h. After the plate was washed three times with phosphate-buffered saline (PBS) containing 0.05% Tween 20 (PBST), it was blocked at 37 °C for 1 h with 200 µL of 5% bovine serum albumin (BSA) dissolved by PBST. After blocking, the plates were washed three times with PBST and air-dried. Serum samples (100 μL/well) diluted with PBST containing 5% BSA were added to the plates and incubated for 60 min at 37 °C. After the plate was washed three times with PBST, 100 µL of diluted horseradish peroxidase (HRP)-conjugated goat anti-swine IgG antibody (Sigma-Aldrich, Burlington, MA, USA) was added and incubated at 37 °C for 1 h. The wells were then washed three times with 100 µL of PBST. The peroxidase reaction was visualized with 3,3’,5,5’-tetramethylbenzidine (TMB) solution as the substrate (KPL, Gaithersburg, MD, USA). The reaction was terminated by the addition of 100 µL of 2 M sulfuric acid to each well and the optical density at a wavelength of 450 nm (OD450) of each well was read with a microplate reader (Thermolab System, Helsinki, Finland). Under these basic conditions, the antigen coating concentration, the serum dilution ratio, the coating conditions, the blocking conditions, and the serum conditions were optimized with a checkerboard serial-dilution analysis. The HRP-conjugated antibody dilutions tested were 1:2000, 1:4000, 1:5000, and 1:10,000, under the set incubation conditions described above. The conditions that produced the largest positive/negative ratio (P/N) of OD450 (OD450 of positive serum/OD450 of negative serum) were deemed the best reaction conditions. Then, 100 sera samples which were randomly selected from 133 sera were tested simultaneously. The sample/positive control (S/P) values were calculated as: (sample OD450, negative control OD450)/(positive control OD450). The cut-off value was calculated with a receiver operating characteristic (ROC) analysis.
A total of 35 specific sera samples of different viruses (GETV-P6~P10,GETV-N4~N8,JE-P1~P5,PRRS-P1~P5,CSF-P1~P5,PR-P1~P5,PED-P1~P5, Supplementary Tables S1 and S2) were used to evaluate the specificity of the ELISA.
GETV serum (GETV-P1~P3, GETV-P4~P6, GETV-P7~P9, GETV-N8~N10, Supplementary Table S1) was diluted 1:100, 1:200, 1:400, 1:800, 1:1600, 1:3200, 1:6400, 1:12,800, 1:25,600, or 1:51,200, and analyzed with the established ELISA to determine its limit of detection.
The reproducibility of the ELISA was evaluated with six serum samples. The coefficient of variation (CV) was used to evaluate the intra- and interassay variation. Each sample was tested on three plates on different occasions to determine the interassay CV, and three replicates within the same plate were used to calculate the intra-assay CV. The mean S/P ratios and standard deviations (SD) were also calculated.
A total of 133 clinical sera samples from pig farms were tested with the ELISA developed here, and the results were compared with those of IFA to determine the coincidence rate.
All results are presented as the means ± standard errors of the means (SE) of triplicate experiments. The data were analyzed with GraphPad Prism 8.4.3 (GraphPad Software, San Diego, CA, USA) and SPSS 22.0 (IBM Corp, Armonk, NY, USA). Statistical significance was evaluated with one-way analysis of variance (ANOVA).
The rE2 protein was purified by elution against an imidazole gradient, and yielded a purified protein concentration of 1.96 μg/μL. An SDS-PAGE analysis demonstrated that the rE2 protein had an approximate molecular mass of 46 kDa (Figure 1A: lanes 4 and 5). However, Western blotting with an anti-His monoclonal antibody and known GETV-positive pig serum showed the shifted band to be the rE2 protein with the monoclonal antibody (Figure 1B,C)
The conditions for antigen coating were optimized at different temperatures and times. The optimal ELISA result was obtained when the wells were coated with antigen at 4 °C for 12 h. The optimal working concentration of antigen was 24.5 μg/mL and the appropriate serum dilution was 1:100. These conditions were determined with checkerboard assays using serial dilutions of the antigen and sera (Table 1). The optimum conditions were antigen coating at 4 °C for 12 h, blocking at 37 °C for 1 h, and incubation with GETV-positive serum at 37 °C for 1 h. The HRP-conjugated goat anti-pig IgG antibody was optimally diluted 1:5000, and the best reaction conditions were 37 °C for 1 h. All these results were based on the principle that the P/N ratio should be >2.1 [22].
One hundred pig serum samples collected in the field were analyzed with IFA, and the results of this analysis were compared with those of the ELISA developed in this study based on an rE2 protein. Using analyze -roc curve in SPSS 22.0, the ROC analysis showed that the area under the curve (AUC) for the ELISA was 0.964 (95% confidence interval [CI], 0.940−0.989; Figure 2A), and the sensitivity and specificity were 97.1% and 93.3%, respectively. The cut-off value was determined to be 0.344 [23,24].
To test the cross-reactivity of the rE2-based indirect ELISA, anti-sera from other common porcine viruses including JEV, PRRSV, CSFV, PEDV, and PRV were examined. The average resultant S/P of the JEV (S/P = 0.220), PRRSV (S/P = 0.152), CSFV (S/P = 0.137), PEDV (S/P = 0.220), and PRV (S/P = 0.227) anti-sera were lower than the positive cut-off value (0.344), confirming that the established rE2 indirect ELISA was non-cross-reactive with these samples. Thus, the indirect ELISA proved to possess high specificity (Figure 2B).
Different dilutions of positive serum were tested. The results showed that the limit of detection with this method was a dilution of 1:12,800 (Figure 2C).
Six pig sera were tested three times in duplicate using three established batches of ELISA-coated plates. The results showed that the intra-assay CV of this method was 2.25–6.16% and the interassay CV was 3.29–5.81%, so both were <7% (Table 2), indicating that the method has a high degree of repeatability. It is therefore highly accurate and can be used for the routine detection of GETV.
When 133 samples were tested with ELISA and IFA, the ELISA identified three false-positive samples and four false-negative samples (Table 3). Therefore, the coincidence rate of detection with these two methods was 94.74% (126/133).
Antibody testing of pig sera from Eastern China (Shandong, Hebei, Zhejiang, Shanghai, Jiangsu, and Guangdong Provinces) showed that the positive rates in Shandong, Hebei, and Shanghai were <30.00%, <35.00%, and <35.00%, respectively, whereas the positive rates in Zhejiang, Jiangsu and Guangdong were 45.00%, 43.48%, and 36.67%, respectively (Table 4). However, the differences among the provinces were not significant (p > 0.05).
October is the breeding season for mosquitoes in Eastern China, and it is also the epidemic period for GETV. A statistical analysis of the numbers of GETV-antibody-positive samples in April and October showed that the average positivity rate in spring (April) was 24.24%, which was significantly lower than the average positivity rate in autumn (October, 50.75%), after the epidemic season. The difference was significant (p < 0.05; Table 4), indicating that the rate of GETV antibody positivity in pigs in Eastern China has a certain seasonality.
GETV is mainly distributed in southern cities of China [12,13,25], and the epidemic period extends from July to September each year, coinciding with the period of high mosquito incidence. GETV causes death in piglets and abortion in sows, resulting in economic losses, and detection methods for this virus must be established. The alphavirus E2 glycoprotein is a surface protein and is highly immunogenic. It has three functional domains (A, B, and C). Domains A and B are exposed on the viral membrane and are responsible for viral attachment to the host cell [15,26,27]. Because these domains contain antigenic epitopes for virus-neutralizing antibodies, they have been used as targets for the development of serodiagnostic tests for Chikungunya virus [28,29]. Virus isolation and identification is the most commonly used pathogen detection method, but is complicated and time-consuming [30]. PCR and real-time PCR instruments are expensive and difficult to use. ELISAs have the advantages of simple operation, high sensitivity, and suitability for large-scale sample identification, and they are commonly used in laboratories. In the present study, we improved an ELISA based on the rE2 protein of GETV. A ROC curve analysis of the GETV-E2 ELISA results showed that its sensitivity was 97.1% and its specificity 93.3%. The AUC was 0.964, indicating a high level of diagnostic accuracy. This was confirmed in a reproducibility test that showed that all CVs (for intra-assay, interassay, and interlaboratory comparisons) were <7%. No cross-reactivity was observed with antisera against PEDV, JEV, CSFV, PRV, or PRRSV. These results suggested that we re-confirmed the usefulness of the rE2-ELISA, providing appropriate references for both the diagnosis of GETV infection and seroepidemiological surveys. In 2017–2018, the rate of GETV infection in pigs in Thailand was 23.1%, and two different GETVs were circulating [7]. GETV was detected in Foshan City, Guangdong Province, China in 2018, and was very similar (99.7%) to swine virus strain HNJZ-S2 in Henan [25]. Therefore, GETV has been detected in various countries, so a reliable test is required. When 133 serum specimens collected from six provinces in Eastern China in 2018 were clinically tested, 50 (37.59%) of the specimens tested positive for anti-GETV IgG antibodies with the GETV-E2 ELISA. Zhejiang Province had the highest positivity rate of 45.00%. The positivity rates for Zhejiang, Jiangsu, and Guangdong Provinces were higher than those for Shandong, Hebei, and Shanghai Provinces. Therefore, GETV may spread more easily in southern China than in the north because the climate in the south is humid, with, consequently, more mosquitoes. In October, China has a high temperature, high humidity, and high mosquito density. The seroprevalence of GETV was significantly lower in April (24.24%, 16/66) than in October (50.75%, 34/67). The rate of GETV antibody positivity is related to the epidemic season of GETV, so the positive rate of GETV antibodies in autumn (October) after the mosquito epidemic season was significantly higher than in April, before the epidemic season, which means that the incidence of GETV was related to the season. Our data indicated that the prevalence of GETV was relatively high in Eastern China. Further research on the circulation of GETV in China will extend our understanding of the epidemiology of the disease associated with this virus. | true | true | true |
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PMC9607422 | Armin Ezzati,Sara K. Rosenkranz,Benjamin D. Horne | Importance of Intermittent Fasting Regimens and Selection of Adequate Therapy on Inflammation and Oxidative Stress in SARS-CoV-2 Infection | 14-10-2022 | SARS-CoV-2,dietary supplements,intermittent fasting,COVID-19,inflammation,oxidative stress,time-restricted eating,vitamins,nutraceutical,chronic diseases | The unpredictable nature of new variants of coronavirus 2 (SARS-CoV-2)—highly transmissible and some with vaccine-resistance, have led to an increased need for feasible lifestyle modifications as complementary therapies. Systemic inflammation is the common hallmark of communicable diseases like severe coronavirus disease 2019 (COVID-19) and non-communicable chronic diseases (NCDs) such as obesity, cardiovascular diseases (CVD), diabetes mellitus, and cancers, all for which mitigation of severe outcomes is of paramount importance. Dietary quality is associated with NCDs, and intermittent fasting (IF) has been suggested as an effective approach for treatment and prevention of some NCDs, similar to that of caloric restriction. There is a paucity of high-quality data from randomized controlled trials regarding the impact of IF and the intake of specific nutrients on inflammation and post-infection outcomes in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The current review of recent literature was performed to explore the immunomodulatory roles of IF regimens and supplements involving the intake of specific nutrients including vitamins (A, B, C, D, and E), zinc, and nutraceuticals (n-3 polyunsaturated fatty acids, quercetin, and probiotics) on inflammatory and oxidative stress markers, with consideration of how they may be related to SARS-CoV-2. | Importance of Intermittent Fasting Regimens and Selection of Adequate Therapy on Inflammation and Oxidative Stress in SARS-CoV-2 Infection
The unpredictable nature of new variants of coronavirus 2 (SARS-CoV-2)—highly transmissible and some with vaccine-resistance, have led to an increased need for feasible lifestyle modifications as complementary therapies. Systemic inflammation is the common hallmark of communicable diseases like severe coronavirus disease 2019 (COVID-19) and non-communicable chronic diseases (NCDs) such as obesity, cardiovascular diseases (CVD), diabetes mellitus, and cancers, all for which mitigation of severe outcomes is of paramount importance. Dietary quality is associated with NCDs, and intermittent fasting (IF) has been suggested as an effective approach for treatment and prevention of some NCDs, similar to that of caloric restriction. There is a paucity of high-quality data from randomized controlled trials regarding the impact of IF and the intake of specific nutrients on inflammation and post-infection outcomes in patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The current review of recent literature was performed to explore the immunomodulatory roles of IF regimens and supplements involving the intake of specific nutrients including vitamins (A, B, C, D, and E), zinc, and nutraceuticals (n-3 polyunsaturated fatty acids, quercetin, and probiotics) on inflammatory and oxidative stress markers, with consideration of how they may be related to SARS-CoV-2.
A new wave of a subvariant of concern of the “omicron” variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)—which poses very high risk of infection—is spreading worldwide, continuing the evolving pandemic that is now in its third year [1]. The efficacy of current vaccines against this heavily mutated variant is still unclear [2]. The unpredictable nature of new variants of SARS-CoV-2 and the poorly characterized and poorly understood post-acute sequelae (PASC) of coronavirus disease 2019 (COVID-19) have led to an increased need for feasible lifestyle modifications as complementary therapies to vaccines to reduce disease severity, particularly given that those effects wane over a period of months [3]. Identification of effective and feasible complementary therapies is critical to provide protection against the severity of both acute and PASC outcomes, since vaccinating a majority of the world population every 6 months or even more frequently is unlikely to be financially or logistically possible. A key characteristic of severe presentations of acute SARS-CoV-2 infection involves overactive host inflammatory responses, with a substantial proportion of severe outcomes such as hospitalizations and deaths from COVID-19 linked to hyper-inflammation [4]. Inflammation and oxidative stress play pivotal roles in the progression of infectious diseases including COVID-19 [5]. Evidence suggests that high sensitivity (hs) C-reactive protein (CRP), interleukin (IL)-6, and matrix metalloproteinases (MMPs) are among the most important biomarkers of COVID-19 severity, similar to the chronic conditions involved in vascular aging [6,7,8,9]. Lactate dehydrogenase (LDH) and hsCRP are also biomarkers of respiratory failure in patients with COVID-19 [10]. Furthermore, it is well established that elevated levels of other inflammatory markers are common in COVID-19 patients. These markers include IL-1β, IL-7, IL-8, IL-18, interferon (IFN)-γ, tumor necrosis factor (TNF)-α, procalcitonin (PCT), serum ferritin, and erythrocyte sedimentation rate (ESR) [11,12]. Interestingly, hs-CRP, IL-6, and many of the other inflammatory biomarkers mentioned above, are also markers of risk in NCDs such as coronary artery disease and diabetes [13,14,15,16]. A wealth of evidence indicates that treatments that reduce inflammation, assessed using these biomarkers, also protect against major adverse cardiovascular events [17,18,19]. Some of these treatments may include simple nutritional therapies such as vitamin D supplementation in patients with vitamin D deficiency [20]. Many studies suggest that a high-quality diet, calorie restriction-induced weight loss (>5%), and intermittent fasting (IF), lower the risk of NCDs including diabetes and coronary artery disease [21,22,23,24,25,26,27,28,29,30,31,32], which may be due in part to reduction of chronic inflammation. If so, it may also be that IF and some simple specific nutrients may play a role in boosting immunity in response to SARS-CoV-2 infection, as has been hypothesized in previous reviews [33,34,35,36]. Yet, there is a paucity of comprehensive data from randomized controlled trials (RCTs) regarding the impact of IF and other simple nutritional therapies on inflammation generally, and specifically related to their effects on responses to SARS-CoV-2. While weight loss is known to reduce inflammation and to control risk factors related to both COVID-19 outcomes and NCDs, some dietary regimens, including IF, may impact inflammation and other pathways independently of weight loss [30,37,38]. The current review of recent literature was performed to explore the immunomodulatory roles of dietary supplements (including some vitamins, minerals, and nutraceuticals) and IF on inflammatory and oxidative stress markers that may be related to SARS-CoV-2 or host responses to infection, and also have some connection to reduction of chronic inflammation linked to NCDs.
A literature search of the PubMed and Google Scholar databases was conducted to identify peer-reviewed articles published from January 2017 to March 2022 that involved assessments of markers of inflammation or oxidative stress. The search terms for dietary supplementation articles included the following: “vitamin”, “nutrient”, “mineral”, “antioxidant”, “nutraceuticals”, “polyphenols”, “resveratrol”, “quercetin”, “probiotic”, “omega-3”, combined with “infection”, “inflammation”, “inflammatory markers”, “immune response”, “immunity”, “oxidative stress”, “CVD”, “obese adults”, “obesity”, “overweight”, “‘SARS-CoV-2′”, and “COVID-19.” The search terms for intermittent fasting articles included the following: “intermittent fasting”, “alternate-day fasting”, “time-restricted eating”, “5:2 diet”, “Ramadan fasting”, combined with “infection”, “inflammation”, “inflammatory markers”, “immune response”, “immunity”, “oxidative stress”, “CVD”, “obese adults”, “obesity”, “overweight”, “SARS-CoV-2”, and “COVID-19.” Filters were set to allow only “human” studies, and studies written in the “English” language.
Supplementation with vitamins such as A, B, C, D, and E is thought to play a significant role in the severity of COVID-19 infection by reducing inflammation, time to recovery, and preventing lung fibrosis [39,40]. To our knowledge, no RCTs have tested the effects of supplementation with vitamin A, B, or C alone, on COVID-19 severity; however, in a 7-day randomized placebo-controlled trial of 60 COVID-19 patients admitted to intensive care unit (ICU), those who received a combination of vitamins A (25,000 IU daily), D (600,000 IU; one dose), E (300 IU twice daily), C (500 mg four times daily), and a B-complex ampule (daily), demonstrated significant reductions in the duration of hospitalization, TNF-α, Il-6, erythrocyte sedimentation and hs-CRP, but not IFN-γ, as compared to a placebo [41]. In contrast, 10-days of standard treatment plus high doses of vitamin C (2 g), Melatonin (6 mg), and Zinc (50 mg), was not effective for lowering inflammatory markers or length of hospitalization in 20 patients with severe COVID-19 [42]. Accumulating data highlight the immunomodulatory role of vitamin D, and link hypovitaminosis D (25 OHD ≤ 20 ng/mL) with hyperinflammation (i.e., the so-called “cytokine storm”) and elevated risk of mortality in patients with COVID-19 [43,44,45,46]. Therefore, supplementation with vitamin D is suggested to attenuate the risk of the cytokine storm, and the severity of COVID-19 [43,47]. The impact of vitamin D on inflammatory markers and in response to SARS-CoV-2 infection has been explored in several RCTs (see Table 1). The current state of evidence from RCTs indicates that higher doses of daily intake of vitamin D can reduce the levels of inflammatory markers like IL-6, hsCRP, and time to recovery in COVID-19 patients [48,49]. Ten days of supplementation with 60,000 IU/day of vitamin D in combination with standard treatment significantly reduced IL-6, hsCRP, LDH, ferritin, and Neutrophil/Lymphocyte ratio, in 87 hospitalized COVID-19 patients with vitamin D deficiency (D < 30 ng/m) as compared with the controls, who received standard treatment for 8 to 10 days [49]. Similarly, in a 2-week trial, oral intakes of two different doses of vitamin D3 (5000 IU vs. 1000 IU) resulted in significant decreases in plasma IL-6 versus baseline, with no between group differences, while hsCRP levels remained unchanged in both groups [48]. Furthermore, time required for resolving cough symptoms with D3 supplementation (5000 IU) was significantly shorter compared to the comparison group [48]. Rastogi et al. investigated the effects of high-dose vitamin D supplementation (60,000 IU) as compared with control in 40 SARS-CoV-2 RNA positive individuals. Patients with vitamin D deficiency (25(OH) D < 20 ng/mL) who were positive for SARS-CoV-2 RNA, with mild or no symptoms, significantly improved with regard to viral SARS-CoV-2 RNA clearance (62.5% vs. 20.8%) and fibrinogen levels [50]. In contrast, a single dose of vitamin D was ineffective for lowering inflammatory markers and for the treatment of patients with severe COVID-19 [51,52,53]. Evidence for the potential role of zinc supplementation for COVID-19 infection is inconclusive [54,55,56]; with only one 28-day RCT of 191 patients with COVID-19 having investigated the effects of zinc supplementation (50 mg of zinc twice daily) combined with chloroquine/hydroxychloroquine (CQ/HCQ) on inflammatory markers. The study results indicated no significant changes in hs-CRP levels or clinical recovery time [57].
It is well known that n-3 PUFAs including eicosatetraenoic acid (EPA) and docosahexaenoic acid (DHA) have anti-inflammatory properties [64,65,66]. The current state of evidence from RCTs suggests that supplementation with n-3 fatty acids appears to be effective for alleviating clinical symptoms and inflammatory responses in patients with COVID-19 (see Table 1). Three of the trials included in this review investigated the efficacy of n-3 PUFAs for COVID-19 outcomes [59,60,62]. Supplementation with daily omega-3 capsules (1000 mg; 400 mg EPAs and 200 mg DHAs) for 2 weeks resulted in significantly higher 1-month survival rates in 128 ICU patients with COVID-19 when compared to the control patients [59]. The study also reported meaningful improvements in respiratory and renal function parameters such as arterial PH, bicarbonate, and other base excess in the treatment group. Similarly, DHA and EPA supplementation (2 g) plus hydroxychloroquine, significantly reduced hs-CRP, body pain, and fatigue compared to hydroxychloroquine alone in 30 patients with COVID-19 [60]. In agreement with those results, a 7-day trial in 43 patients with COVID-19 who were treated with oral immuno-nutrient supplements containing arginine, omega-3 fatty acids, and nucleotides (Two 200 mL units over 24 h), indicated meaningful reductions in hs-CRP when compared with the patients who were given only high-protein nutritional supplements (Two 200 mL units) [62].
Quercetin—a polyphenol with antioxidant and anti-inflammatory properties—is widely known to have favorable effects on inflammation and infection [67,68,69]. Di Pierro et al. reported that in 42 outpatients with COVID-19, two weeks of quercetin therapy (500–1000 mg daily) significantly diminished the rate (−68.2%) and length (−76.8%) of hospitalization, the need for non-invasive oxygen therapy (−93.3%), and the mortality rate (although events were very limited: none vs. 3 people) compared to the control (standard of care) [58]. Furthermore, significant reductions in LDH and ferritin levels were reported in the treatment group vs. control, while hs-CRP remained unchanged [58]. Unlike the 2-week trial by Di Pierro et al., a shorter 7-day RCT including 60 patients with severe COVID-19, treated with daily quercetin (1000 mg) along with antiviral drugs, compared with control (only antiviral drugs), did not alter mortality or ICU-admission duration significantly. Notably, significant reductions in inflammatory markers such as TNF-α, IL-1β, IL-6, hs-CRP, ALP, and LDH were shown in the treatment group as compared to the control or baseline [61].
High quality evidence from systematic reviews and meta-analyses of RCTs, indicates that probiotics may have a favorable role for responses to infections [70]. In a recent one-month RCT of 293 outpatients with COVID-19, supplementation with four-strain probiotics consisting of Lactiplantibacillus plantarum KABP033 (CECT30292), L. plantarum KABP022 (CECT7484), L. plantarum KABP023 (CECT7485), and Pediococcus acidilactici KABP021 (CECT7483), produced significant reductions in remission rates vs. placebo (53.1% in probiotic group vs. 28.1% in placebo [63].
To date, no RCTs have investigated the effects of IF on SARS-CoV-2 infection or severity of COVID-19 outcomes, although epidemiologic evidence regarding periodic fasting suggests that fasting in some forms may prevent severe COVID-19 outcomes such as hospitalization and death [71], perhaps in part, through the control of hyper-inflammation. The impacts of IF on markers of inflammation and oxidative stress are summarized below:
Current evidence from RCTs reveals that time-restricted eating (TRE) may be effective for reducing interleukins IL-1β, IL-6 and IL-8 (see Table 2.) Of the 11 TRE trials included in this review, only one trial assessed IL-1β levels. This 12-month trial of 20 healthy athletes reported significant reductions in both IL-1β and IL-6 levels in the TRE group (3 meals; 1 p.m., 4 p.m., and 8 p.m.) compared with normal diet control group (3 meals; 8 a.m., 1 p.m., and 8 p.m.) [72]. However, IL-6 levels have remained unchanged in other TRE studies [30,32,73,74]. Xie et al. conducted a 5-week trial examining plasma IL-8 levels in 82 healthy participants who had normal weight, when following early TRE (6 a.m.–3 p.m.), mid-day TRE (11 a.m.–8 p.m.), and as compared with an ad libitum intake control group, and showed a significant reduction in IL-8 levels vs. control when following early TRE [75]. Similarly, results of observational and clinical trials from Ramadan IF (fasting from sunrise to sunset) studies suggest that this form of IF may be effective for counterbalancing inflammatory interleukins such as IL-1β, IL-2, Il-6, IL-8, and IL-10 [76,77,78,79,80,81,82,83,84,85] (see Table 3). In a one-month RCT in 28 men with obesity conducted by Zouhal et al., Ramadan IF produced meaningful reductions in plasma IL-6 levels vs. control. Unlike the results from TRE and Ramadan IF trials, other popular types of IF, including alternate-day fasting (ADF) and twice-weekly 24-h fasting (commonly called “5:2 diets”) have not shown effects on inflammatory interleukins including IL-1β, IL-6, and IL-10 [28,86,87,88,89,90,91,92] (see Table 4). The effects of 24-h prolonged fasting on inflammatory markers has been tested in two single-arm trials by Han et al. with results indicating significant improvements in IL-1β, IL-2, IL-4, IL-5, IL-13, IL-17 and IL-22 as compared to postprandial responses measured 3 h after refeeding with isocaloric breakfast meal (500 kcal) [93,94].
Studies of the impact of different IF regimens on TNF-α have reported inconsistent results (see Table 2, Table 3 and Table 4). Early TRE and Ramadan IF have been shown to significantly improve TNF-α levels vs. control and/or baseline, while mid-day TRE has not shown such improvements in two out of the three TRE trials included in this review [32,72]. Likewise, ADF appears to be ineffective for reducing plasma TNF-α, as two ADF trials in adults with overweight or obesity and metabolic syndrome, which tested changes in TNF-α have not demonstrated significant improvements following ADF regimens [86,91]. Of the two twice-weekly fasting studies which assessed TNF-α, one RCT in 43 adults with central obesity showed significant decreases (−18%) vs. baseline, but no between group differences when compared with a caloric restriction arm [90].
There has also been inconsistency in the results of various trials of IF regimens on hs-CRP levels, with most studies indicating no effects (see Table 2, Table 3 and Table 4). Of the seven TRE studies included in this review, only two trials (4 to 5 week durations) reported significant improvements in hs-CRP levels (−45% and −42%) in overweight and obese participants vs. a control or as compared with baseline [96,97]. In two TRE studies, only hs-CRP was examined and was unchanged [95,98]. Hs-CRP levels were not altered in short-term (8 weeks) ADF studies, whereas longer duration trials (17 and 24 weeks; with a high protein diet) have significantly decreased hs-CRP (−48% and −19%) versus CR or baseline, respectively [87,89,91]. In addition, twice-weekly fasting did not appear to affect hs-CRP levels [28,38,88,90]. Furthermore, only one observational Ramadan IF study in 83 patients with non-alcoholic fatty liver disease, has shown significant decreases in hs-CRP levels vs. control and baseline following Ramadan IF after 30 days [76].
IGF-1 levels remain unchanged with most IF trials that recruited participants with overweight and obesity. In athletes, however, two RCTs conducted by Moro et al. reported significant reductions in IGF-1 levels with TRE as compared to control and baseline after both 4 weeks and 12 months [72,74]. IGF-1 significantly increased (34%) in a single arm trial of early TRE (8 a.m.–4 p.m.) in 15 overweight and obese women with Polycystic ovary syndrome [97]. There is lack of consistency in IGF-1 results of observational Ramadan IF studies (see Table 3).
Interferon gamma (IFN-γ), a proinflammatory cytokine that plays a significant role in immune responses, has been investigated in only two 24-h prolonged fasting studies by Han and colleagues. These studies indicate that IFN-γ levels were significantly reduced when compared with 3 h after refeeding with isocaloric breakfast meal (500 kcal) in a total of adults who fasted for 24 h [93,94]. Matrix metalloproteinase 9 (MMP-9) was assessed in one observational Ramadan IF study in 34 healthy adults [85]. The levels MMP-9 were determined at five different time periods within 27–29 days of fasting during the month of Ramadan. While MMP-9 levels remained unchanged from mid-month (days 14–16) to one week after Ramadan, they were significantly lower vs. baseline at the one month after Ramadan timepoint (see Table 3). Changes in CD40 ligand have also been tested in two studies, with only one 8-week RCT of twice-per-week fasting in 39 adults with metabolic syndrome, demonstrating a meaningful reduction in CD40 ligand vs. control after 8 weeks [92].
8-Isoprostane is a marker of oxidative stress that is associated with COVID-19 [99]. To date, only two trials have examined 8-Isoprostane levels following TRE [30,32], with significant decreases in 8-Isoprostane reported in both studies compared to both control and baseline. Early TRE (eTRE; 6-hr eating window, last meal before 3 pm) showed a 14% reduction in 8-Isoprostane in the eTRE arm versus control (12-hr. eating window) in 12 prediabetic men after 5 weeks. In addition, restricting eating windows to four (3–7 p.m.) and six hours (1–7 p.m.) significantly reduced 8-Isoprostane levels in both groups (37% and 34%, respectively) vs. control (ad libitum intake) in adults with obesity following 8 weeks [32]. In contrast, short-term (6 weeks) eucaloric TRE with self-selected eating windows did not significantly enhance plasma oxidized LDL or the oxidized total LDL ratio in non-obese healthy older adults [73].
Evidence suggests that intermittent fasting and supplementation with vitamin D, and nutraceuticals including quercetin, n-3 fatty acids, and probiotics, impact inflammation in canonical pathways related to both NCDs and infectious diseases including COVID-19 and, thus, should reduce COVID-19 severity. A lack of high-quality evidence exists for the support of such efficacy of supplementation with vitamins A, B, and C, as well as zinc in COVID-19 patients. Further investigation of these fasting and supplementation effects is indicated. Observational studies report that some diets (e.g., the Mediterranean diet, plant-based diets) may reduce SARS-CoV-2 severity, potentially—at least in part—because the foods contain antioxidants and other components with anti-inflammatory properties [100,101,102,103], but further research is needed on such diets. Ultimately, the impact of intermittent energy restriction methods on inflammation during energy restriction may be acute but transient, contrasting with longer-term persistent changes to basal levels of inflammation. Future randomized controlled trials are needed to elucidate the effects of intermittent fasting on NCDs and on inflammatory host responses to infection. | true | true | true |
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PMC9607534 | Marcia S. C. Melhem,Vivian C. Coelho,Claudia A. Fonseca,Lidiane de Oliveira,Lucas X. Bonfietti,Maria. W. Szeszs,Marcello M. C. Magri,Francine S. Dorneles,Hideaki Taguchi,Daniel V. S. Moreira,Adriana L. Motta,Marjorie V. Batista,Katsuhiko Kamei,Maria A. Shikanai-Yasuda | Evaluation of the Sensititre YeastOne and Etest in Comparison with CLSI M38-A2 for Antifungal Susceptibility Testing of Three Azoles, Amphotericin B, Caspofungin, and Anidulafungin, against Aspergillus fumigatus and Other Species, Using New Clinical Breakpoints and Epidemiological Cutoff Values | 11-10-2022 | gradient diffusion assays,Aspergillus fumigatus,Etest,CLSI,Sensititre YeastOne,azole antifungals | Aspergillosis is an invasive fungal disease associated with high mortality. Antifungal susceptibility testing (AFST) is receiving increasing consideration for managing patients, as well as for surveilling emerging drug resistance, despite having time-consuming and technically complex reference methodologies. The Sensititre YeastOne (SYO) and Etest methods are widely utilized for yeasts but have not been extensively evaluated for Aspergillus isolates. We obtained Posaconazole (POS), Voriconazole (VCZ), Itraconazole (ITC), Amphotericin B (AMB), Caspofungin (CAS), and Anidulafungin (AND) minimum inhibitory concentrations (MICs) for both the Etest (n = 330) and SYO (n = 339) methods for 106 sequenced clinical strains. For 84 A. fumigatus, we analyzed the performance of both commercial methods in comparison with the CLSI-AFST, using available cutoff values. An excellent correlation could be demonstrated for Etest-AMB and Etest-VCZ (p < 0.01). SYO-MICs of AMB, VCZ, and POS resulted in excellent essential agreement (>93%), and >80% for AMB, VCZ, and ITC Etest-MICs. High categoric agreement was found for AMB, ITC, and CAS Etest-MICs (>85%) and AMB SYO-MICs (>90%). The considerable number of major/very major errors found using Etest and SYO, possibly related to the proposed cutoffs and associated with the less time-consuming processes, support the need for the improvement of commercial methods for Aspergillus strains. | Evaluation of the Sensititre YeastOne and Etest in Comparison with CLSI M38-A2 for Antifungal Susceptibility Testing of Three Azoles, Amphotericin B, Caspofungin, and Anidulafungin, against Aspergillus fumigatus and Other Species, Using New Clinical Breakpoints and Epidemiological Cutoff Values
Aspergillosis is an invasive fungal disease associated with high mortality. Antifungal susceptibility testing (AFST) is receiving increasing consideration for managing patients, as well as for surveilling emerging drug resistance, despite having time-consuming and technically complex reference methodologies. The Sensititre YeastOne (SYO) and Etest methods are widely utilized for yeasts but have not been extensively evaluated for Aspergillus isolates. We obtained Posaconazole (POS), Voriconazole (VCZ), Itraconazole (ITC), Amphotericin B (AMB), Caspofungin (CAS), and Anidulafungin (AND) minimum inhibitory concentrations (MICs) for both the Etest (n = 330) and SYO (n = 339) methods for 106 sequenced clinical strains. For 84 A. fumigatus, we analyzed the performance of both commercial methods in comparison with the CLSI-AFST, using available cutoff values. An excellent correlation could be demonstrated for Etest-AMB and Etest-VCZ (p < 0.01). SYO-MICs of AMB, VCZ, and POS resulted in excellent essential agreement (>93%), and >80% for AMB, VCZ, and ITC Etest-MICs. High categoric agreement was found for AMB, ITC, and CAS Etest-MICs (>85%) and AMB SYO-MICs (>90%). The considerable number of major/very major errors found using Etest and SYO, possibly related to the proposed cutoffs and associated with the less time-consuming processes, support the need for the improvement of commercial methods for Aspergillus strains.
The incidence of invasive aspergillosis has increased considerably in the past few decades. Many factors have contributed to this increase such as the increasing number of patients who undergo organ transplants and corticosteroids therapy, and the spread of the COVID-19 pandemic [1,2,3]. The disease is associated with high mortality and antifungal susceptibility testing (AFST) has been receiving increasing consideration as a valuable tool for managing patients as well as for epidemiological surveillance of emerging drug resistance [4]. The Clinical and Laboratory Standards Institute (CLSI) and European Committee on Antimicrobial Susceptibility Testing (EUCAST) have established standard procedures for testing the susceptibility of most prevalent molds and yeasts of clinical relevance to antifungal agents and have proposed some species-specific clinical breakpoints (CBPs) for interpreting MIC results of some antifungal agents [4]. AFST plays a significant role in patient management by aiding the prescription of an appropriate antifungals; however, the reference methodologies are time-consuming and technically complex, leading laboratories to adopt commercially available alternatives [5]. An increasing number of hospital microbiology laboratories are performing AFST. The medical demand for timely AFST results prevents the routine use of the laborious reference broth microdilution method and promotes interest in the available commercial AST systems. An alternative is the use of gradient diffusion strips, Etest being one of the most adopted worldwide. In this context, the method’s performance should be extensively evaluated using clinical isolates from all over the world. Etest has acted as a valuable alternative for the detection of emerging non-wild-type (non-WT) resistance as epidemiological cutoff values (ECVs) for this method have been recently defined for several drug-species combinations. Otherwise, studies evaluating the agreement between the Etest method and the reference methods for filamentous fungi showed variable results depending on the antifungal agent, species, and incubation time [4,6,7]. In addition, the colorimetric broth microdilution SYO panel is widely utilized for the AST of Candida in the clinical laboratory and less utilized with Aspergillus isolates for research purposes [8,9]. Previous data were limited by the small number of tested Aspergillus clinical isolates from the South American region, thus precluding an evaluation of Etest’s role for determining local resistance. While susceptibility testing with antifungal agents against molds such as A. fumigatus isolates has been determined extensively in reference methods, it has not been widely evaluated by commercial methods. The role of AFST systems in the detection of resistance in A. fumigatus has not been extensively assessed as few isolates have been tested to date [8,10,11,12]. Therefore, we chose to assess the Etest and SYO’s ability in performing AFST for 106 clinical isolates of molecularly identified Aspergillus clinical isolates. For commercial methods, there are no suitable clinical data to distinguish susceptible or resistant isolates, and available epidemiological cutoff points were applied to interpret the MIC results. We then used the Etest method and SYO panel to determine antifungal agents’ MICs for the isolates to calculate correlations, and essential and categorical agreements with the reference broth microdilution method according to the CLSI M38-A2 document [13]. Species-specific CBPs for interpreting MIC results serve as predictors of clinical success of treatment. CLSI has established CBPs for the more prevalent Aspergillus species, A. fumigatus, and VCZ in the M59-ED3 document (CLSI M59-ED3, 2020) [14]. In the absence of BPs, ECVs should identify non-WT isolates (MIC ≤ ECV) with reduced susceptibility to the drug under analysis [15]. We identified errors in both commercial methods for determining the profile of susceptibility. This enabled us to advise laboratories to use Etest and SYO systems for azole-MIC and Amphotericin B-MIC and to prevent them from using SYO on a routine basis for testing echinocandins against Aspergillus isolates.
The highest possible commercially available grade of Itraconazole (ITC), Voriconazole (VCZ), Amphotericin B (AMB), Caspofungin (CAS), and Anidulafungin (AND) was purchased from Sigma-Aldrich (St. Louis, MO, USA). The organic solvent for the drugs, dimethyl sulfoxide in analytical grade, was obtained from Sigma (St. Louis, MO, USA). Further drug dilutions were made in Roswell Park Memorial Institute (RPMI) 1640 with L-glutamine, without bicarbonate, buffered with 0.165 morpholine propane sulfonic acid to pH 7.0 (RPMI; Sigma Chemical Co., St. Louis, MO, USA). Etest strips containing ITC, VCZ, AMB, CAS, and AND were used (bioMérieux, Marcy L’Etoile, France). Sensititre YeastOne YO10 was purchased from TREK (Diagnostic Systems Ltd., West Sussex, UK). The fungal conidia inoculums were prepared in RPMI 1640 Medium (Sigma, USA) supplemented with glucose (2% final concentration). Potato dextrose agar (PDA; Becton Dickinson, Sparks, MD, USA), Bacto agar (Difco Laboratories, Detroit, MI, USA), and Sabouraud dextrose agar (SDA; Becton Dickinson, Sparks, MD, USA) were used in the study.
Isolates were received from the Laboratory of Microbiology—Division of Central Laboratory (Laboratory of Medical Investigation—LIM 03) of the Hospital das Clínicas, Faculdade de Medicina, University of São Paulo (HC-FMUSP), Brazil, and were originally recovered from 106 patients of HC-FMUSP, Brazil. This laboratory is accredited by the American College of Pathology and follows all that provider’s guidelines for performing fungal cultures. The isolates were identified as 84 A. fumigatus, 9 A. niger, 7 A. flavus, 3 A. clavatus, 1 A. terreus, 1 A. awamori, and 1 A. welwitschiae by the Laboratory of Medical Investigation in Immunology (LIM 48) of HC-FMUSP, Brazil. For molecular identification, the segment of the b-tubulin gene was amplified using primers bT2a and bT2b [16]. Similarly, a region of the rodlet gene was amplified using the primers rodA [17]. The calmodulin gene was also amplified as previously described [18]. The ITS regions (ITS-1 and ITS-2) of the ribosomal RNA (rRNA) gene complex were amplified [19] when the other gene fragments were discordant with each other. The sequences obtained were compared with sequences deposited in the GenBank (http://www.ncbi.nlm.nih.gov/BLAST (accessed on 15 April 2015). DNA sequences representing ITS regions, b-tubulin, and calmodulin genes were aligned using ClustalX and visually edited in the Genedoc version 2.6. MEGA (MAC version 6) program to generate and edit the phylogenetic trees, and the similarity/dissimilarity amongst the sequences of various Aspergilli was studied.
AFST by CLSI and Etest and SYO methods were initially performed at the Laboratory of Medical Investigation in Immunology (LIM 48), HCFMUSP, and 15% were also analyzed by CLSI at the Medical Mycology Research Center, Chiba, Japan. All isolates were submitted from the LIM to the Reference Laboratory Adolfo Lutz Institute (IAL, São Paulo, Brazil) for antifungal susceptibility duplicated testing through CLSI (n = 106) and Etest (n = 84) methods. SYO was performed at the Laboratory of Medical Investigation (LIM 48), HCFMUSP, and partially (17%) in duplicate at the Infectious and Parasitic Diseases Laboratory (LabDIP) of the Federal University of Mato Grosso do Sul (MS, BR). Two quality control isolates (QC), Candida krusei ATCC 6258 and Candida parapsilosis ATCC 22019 were included as controls in all experiments (CLSI M61-ED2, 2020) [20].
The AFST was performed as outlined in CLSI document M38-3rd Ed. (CLSI. Reference Method for Broth Dilution Antifungal Susceptibility Testing of Filamentous Fungi. 3rd ed. CLSI standard M38. Wayne, PA: Clinical and Laboratory Standards Institute; 2017) [13]. Briefly, the susceptibilities of the isolates to ITC, VCZ, POS, AMB, CAS, and AND were assayed by the broth microdilution method. Isolates were grown on potato dextrose agar (PDA) at 35 °C up to 48–72 h to maximize conidial harvest, and the conidia were counted with a Neubauer chamber and adjusted to a concentration of 106 CFU/mL. The microdilution was performed with twofold dilutions of the drugs at concentrations ranging from 0.03 to 16 mg/mL. The triazole-MIC and AMB-MIC were defined as the lowest drug concentrations that showed a complete reduction in fungal growth. For CAS and AND, the minimal effective concentration (MEC) was defined as the minimum concentration of drug that produced abnormal hyphal growth with highly branched tips. Susceptibilities were determined by duplicate measures in the LIM 48 and IAL laboratories. We first determined the MIC of the routinely used drugs in cases of Aspergillosis, namely three triazole agents and the polyene Amphotericin B as well as two echinocandins (Caspofungin and Anidulafungin) against 106 clinical strains of Aspergillus spp. The MIC results were then assessed to classify the isolates into different susceptibility categories, according to the existing clinical breakpoints for each drug-species pair.
Etest (biomérieux, Marcy-l’Étoile, France) consists of a predefined gradient of antifungal agent concentrations on a plastic strip which, after placement and incubation in an inoculated agar plate, results in an ellipse of growth inhibition that is used to determine the MIC of the drug being tested. Numerous previous studies have examined the susceptibility of yeasts by the Etest gradient strip method with consistent excellent results, as reviewed by Espinel-Ingroff, 2022 [21], but data on its performance with molds are still lacking [22]. All the isolates were subcultured for three to four days on PDA at 35 °C before testing. Inoculum of up to 106 isolates of conidial suspensions were successively prepared in sterile saline, adjusted to a concentration of 106 CFU/mL (68 to 82% transmittance at 530 nm), except for A. nigri isolates suspensions, which were counted with a Neubauer chamber, adjusted to a concentration of 106 CFU/mL, and swabbed onto RPMI 1640 agar in three directions. The agar version of the medium was obtained by using RPMI 1640 medium solidified with 1.5% Bacto agar. The strips of ITC, VCZ, AMB, CAS, and AND (concentrations ranging from 0.002 to 32 mg/L) were applied to the inoculated agar. The MIC was determined at 100% inhibition for all tested antifungals at the interception of the elliptical growth inhibition halo to the scale of the antifungal strip. MICs were determined after 24 and 48 h of incubation at 35 °C.
The MICs of POS, VCZ, ITC, AMB, CAS, and AND (concentrations between 0.0015 and 8 mg/L) were determined at 100% inhibition for all tested antifungals and read at the lowest drug concentration that produced a color change. The growth medium contained resazurin, an indicator of cell viability that turns from blue to pink when oxidized by viable fungi. Inoculum concentration was adjusted at a McFarland standard of 0.5 (0.5–5 × 106 UFC/mL), except for A. nigri suspensions that were counted with a Neubauer chamber. MICs were determined after 24 h of incubation as the lowest antifungal concentrations at which the wells remained blue (no growth) and interpreted according to the CLSI breakpoints [23]. MICs were confirmed in different experiments executed in both the LIM 48 and LabDIP laboratories. Unfortunately, approximately 2/3 of the samples analyzed by SYO for echinocandins showed growth in all tested concentrations (up to 8 mg/L), while for the control isolates the expected MIC of CAS, AND, and micafungin were achieved. MIC values ≥ 8 mg/L were not registered for this study. The data presented for SYO refers to seven isolates of A. fumigatus and one isolate of A. flavus.
The antifungal susceptibility of all isolates was determined according to the current available CLSI CBPs or ECVs (CLSI M59-ED3, 2020) [14,24]. CBPs were divided into resistant and susceptible isolates of Aspergillus according to certain species and antifungal agents. ECVs were applied to classify isolates non-WT with decreased susceptibility and having probable resistance mechanisms, and wild-type (WT) isolates were defined as those that do not harbor any acquired resistance to the drug being examined [14,25]. Regarding A. fumigatus and AND, neither CBP nor ECV was defined by CLSI or elsewhere. For the remaining drugs, we adopted the existing CBP or ECV values as shown in Table 1 below.
All experiments were carried out in duplicates, or triplicates, and results were indicated as the modal value when distinct values were found. Etest MIC endpoints were raised to the nearest twofold dilution value that matched the CLSI concentration ranges to facilitate comparisons of results. MIC ranges were obtained for each species-drug combination by each method tested. The MIC50s and MIC90s, which represent MICs at which 50% and 90% of the isolates tested are inhibited, respectively, were determined for species for which at least seven isolates were available. Differences between MIC values of no more than two log2-dilutions were used to calculate the percentages of essential agreements (EAs) between Etest and CLSI and SYO and CLSI. Essential agreements of ≥90% between the two methods were considered acceptable [24]. These analyses were performed for results obtained with A. fumigatus due to the robust number of isolates. Categorical agreements (CAs) between the susceptibility category of each isolate according to method dependent ECVs were calculated. Errors were categorized as very major errors (VMEs) or false susceptible when the commercial methods classified an isolate as susceptible/wild type for a given agent and the CLSI reference method classified it as resistant/non-WT. They were categorized as major errors (MEs) or false resistance when an isolate was classified as non-WT by commercial method and susceptible/WT by the CLSI gold standard method. A result was deemed to be a minor error (MiE) when, for a given agent, it was classified as wild-type/non-wild-type by any of the commercial methods studied but was determined to be intermediary by the reference CLSI method.
The correlation among the susceptibility methods was determined by Pearson coefficients. Fisher’s exact test was used to determine the association between the CAs, according to the methods. The statistical analyses were performed using the Stata® program (version 11.0, Stata Corp. LP, College Station, TX, USA). A p-value of <0.05 was considered significant. All graphs have been generated and analyzed using Prism nonlinear regression software (GraphPad Software, San Diego, CA, USA).
The CLSI-MICs for the QC strains were within the recommended 24-h MIC/MEC limits (Supplementary Materials Table S1). According to the available CBP and ECV in tests with A. fumigatus, we included in this study one VCZ resistant isolate and four non-WT isolates for POS (n = 1), for CAS (n= 1), and for ITC (n = 2). For AMB, all isolates were of the wild-type. Summarized in Table 2 are the MIC ranges (MIC50 and MIC90 values) of the six antifungal drugs tested against 106 isolates of Aspergillus spp. determined by the CLSI M38-A2, Etest, and for up to 58 isolates by colorimetric SYO methods.
The essential agreement within ±2 Log2 dilutions for the comparison of Etest or SYO with the CLSI reference broth dilution method and categorical agreement results, when applied, are shown in Table 3. All agreements were over 82.1%, except for SYO-ITC (78.3%), Etest-AND (66.7%), and Etest-CAS (35.3%). For AMB, the EA, as well as CA, were high in both commercial methods, ranging from 82.1% to 95.2%. For VCZ, the EA of SYO was superior (95.7%) to the EA of Etest (87.3%) compared with the CLSI results. Considering the CBP for VCZ, a single VCZ-resistant A. fumigatus isolate was correctly identified as non-WT by Etest and by SYO. In the identification of VCZ-intermediary isolates (n = 4), we verified more correct results (non-WT classification) by the SYO method (75%) than by Etest (33.3%). Among 41 susceptible isolates, Etest barely categorized 14.9% (4 out 27 tests) as wild-type category, similar to SYO (22%; 9 out 41). For testing ITC by Etest, we verified a good EA (82.8%) and CA (90.6%). As for SYO, a single significant correlation was found between SYO and CLSI (r = 0.2958; p = 0.0460) for ITC MICs. The EA found for SYO testing of POS was high (93.5%). Considering the proposed ECV, SYO was able to identify the single non-WT-POS isolate. However, it identified only 5 (11.1%) out of 45 WT-POS tested isolates, resulting in low CA (13.4%) due to the high amount of ME (93.5%).
We determined the Etest MICs for antifungal agents used as first-choice and salvage therapy for invasive Aspergillosis. We additionally categorized the 84 isolates of A. fumigatus according to the proposed ECVs or CBPs of VCZ [14,20]. To our knowledge, most studies focus on A. fumigatus and evaluate MICs obtained by commercial methods with CLSI M38 methodology as a reference, and therefore, preclude robust comparison of our MIC findings with other Aspergillus species. We will first discuss the MIC results of VCZ and AMB against A. fumigatus since both drugs are the backbone of antifungal therapy in cases of invasive Aspergillosis and information on in vitro susceptibility of Aspergillus clinical isolates to those drugs is relevant for validating CBPs and ECVs. In suspected or proven azole-resistant A. fumigatus cases, AMB remains the first-line therapy, and a reliable simple commercial AST is needed to provide the fungal susceptibility profile in a timely way to help with therapeutic decisions. As in several other studies, we found good EAs and CAs for Etest-MICs in comparison with AMB CLSI-MICs, showing values mostly between 80% and 100%, with the highest percentages observed at 24 h of reaction incubation [22,29]. Unlike Meletiadis et al., 2002 [30] and Martín-Mazuelos et al., 2003 [31], we obtained good agreement of results between the Etest and CLSI methods. Indeed, we observed increases in Etest-MIC values up to six-step dilutions for some isolates as cited in these studies [30,31]. The unique established CLSI-CBP for A. fumigatus is for VCZ, and the Etest performed very well, giving comparable results to the reference methodology. Otherwise, the ECV warrants improvement since the CA was unacceptably low, although Etest did reliably detect the resistance to VCZ in the single isolate of A. fumigatus included in this study. Very few isolates showing intermediary susceptibility profiles were correctly identified as non-WT to the agent, resulting in MEs for this commercial method. Notably, there was a high number of MiE of incorrectly identified non-WT isolates to VCZ. We should stress that an ECV corresponds to the MIC that captures ≥ 97.5% of the statistically modeled WT population and represented the probability for an isolate to be a WT isolate if its MIC was lower or equal to the ECV value. Consequently, low ECVs may overlook potentially susceptible isolates (WT), which could justify the high percentage of non-WT isolates identified by the Etest method. The poor CA observed for Etest-VCZ in comparison with the CLSI reference method suggests a need for improvement before routine employment in daily practice. While the values for EAs remained >80% for Etest-VCZ, our MIC values for Aspergillus spp. tended to be lower (p 0.3114) by the Etest method compared with the CLSI reference method, as previously described [29,32]. The differences remained in the acceptable range of ±2 Log2-dilutions, resulting in high EA values of >90% [24,33]. For Etest and Aspergillus species, the best predictor of non-WT isolates, confirmed through assessments for mutants, was the proposed ECV-ITC of 2 mg/L [27]. To our knowledge, method-dependent Etest ECVs for AMB, CAS, and AND have not been extensively studied on the basis of proven mechanisms of resistance, since ECVs are based on in vitro data (either MICs or minimal effective concentration results). There is a need to improve method-dependent ECV studies with extensive analyses in different environments and regions. The performance of Etest in AFST with ITC and A. fumigatus was good in terms of EA and in CA, despite four VMEs and one ME. Previously, it was noted for Aspergillus spp. that the EA between ITC Etest MICs read at 24 h and reference microdilution MICs read at 48 h was 100% with RPMI agar medium, the conditions followed in this study. Conversely, for ITC, the overall agreement between Etest and M38-A for Aspergillus species could be as low as 67.2% [25]. This low agreement could also be due to the low reproducibility of the Etest-ITC, as previously described [30]. Although we have not studied the reproducibility, we observed, in general, MICs by the Etest to be higher, as verified in other studies [30,31,34] Despite a substantial number of studies, including at least 25 comparative studies performed for Aspergillus species, with more than 3000 isolates tested against antifungal agents [22], the endpoint reading of echinocandins MECs has been found to be subjective, time-consuming, and has been associated with VME [30,35]. We confirm these issues and stress the need for studies with CAS-MICs using Etest, as well as with AND, as previously recommended [36]. The EA observed with Etest and AND was low and contrasts with published data [11,29]. In our work, the CA result for Etest and CLSI in testing CAS appeared to be not so strong in comparison with the Etest-azole results. Given the inadequate EA shown here, we could not conclude for its use in routine practice. However, in a previous study, the echinocandins EA values found for A. fumigatus were excellent [24]. Regarding the SYO assay for AFST of filamentous fungi, no recommendation has been released to date. Otherwise, authors have investigated the performances of the SYO to determine the MICs of filamentous fungi [10,37]. In general, excellent EA (>93.5%) with AMB, POS, or VCZ has been reported in a comprehensive review [4]. In general, the colorimetric assay performed well for Aspergillus species, with high overall essential agreements (≥95%) with the CLSI reference method to assess the susceptibility to triazoles and polyene drugs [10,38,39,40]. Contrary to previous data showing the lower performance of SYO for EA to test AMB [41], we found good EA, similar to Wang et al., 2018 [38]. We observed, accordingly, high categorical agreement for detecting wild type isolates, and although we verified a few MEs, SYO-AMB could not be analyzed for detection of non-WT as described previously [27], since no WT isolate was classified as non-WT by CLSI in our work. Importantly, lower EAs for SYO-ITC than the previous (90.2–95.2%) values reported [10,31] were observed in our study, precluding us from considering SYO-ITC tests for predicting WT isolates of A. fumigatus, as recommended [27]. We also found unacceptable CAs due to a high level of MEs in SYO-VCZ, although SYO was able to detect the VCZ-resistant A. fumigatus isolate. To date, no data on such categorical agreement has been found in the literature for comparison, and the SYO method should probably not be used for routine testing in the clinical laboratory for this species/agent combination till a more feasible ECV is determined. Ideally, only one ECV should be established for each drug-bug combination. Up to now, only two multicenter studies have been performed to determine the Etest ECVs of antifungal drugs in A. fumigatus species and data should be rationally combined to achieve a consensus [42]. The combination of echinocandin with high-dose salvage posaconazole in cases of invasive Aspergillosis may be attempted and results from AFSTs could provide useful information for validating future CBPs or contribute to the improvement of data banks in establishing or implementing ECVs for these antifungal agents [35]. Up to now, the evaluation of the SYO for POS-MIC distributions in a large multicenter study indicated that this method provides less reliable and much lower MICs than those yielded by the CLSI method, possibly due to the different MIC determination criteria used by the laboratories [26]. We found exceptionally low CAs for SYO-POS and no literature data to compare the results. Surprisingly, an unacceptable high number of MEs were verified for POS-susceptible isolates classified as non-WT by the SYO method. The CA between results obtained by SYO with the CLSI-M38 depends on the ECV adopted. In our study, we utilized one tentative value of ECV, which may be a cause of such discrepant results. In addition, we observed a particularly good EA between results obtained by SYO and by the reference method as outlined previously [10]. Given that only very few data supported SYO MIC distributions for the tentative establishment of ECV [27], at this time, our data do not allow us to recommend its application in laboratory routines as a POS susceptibility test for Aspergillus. Given the importance of POS in the therapeutic arsenal for the management of invasive aspergillosis, prospective studies with larger samples of WT and non-WT isolates are necessary to establish reliable conclusions for their routine application. Because there are no CLSI ECVs for AND and A. fumigatus, comparable data were not available for this study. Using the SYO method, only seven AND-MECs were readable, making impossible any robust evaluation of essential agreement. Moreover, the lack of established CLSI-ECVs and Etest-ECVs, for AND and A. fumigatus, prohibited the categorical agreement calculi [42,43,44]. Finally, we verified for a few isolates of A. fumigatus unusual phenotypes presenting trailing effects or paradoxical growth, that could create difficulties and errors in the reading of MEC endpoints. Notably, the majority of CAS as well as AND plates in the SYO experiments did not produce conclusive results given the absolute frequency of the MIC above or at the highest tested concentration (data not shown). Accepting such results led to an unreal frequency of non-WT isolates. The superior performance of the SYO plates with the other antifungal agents and correct MIC values for the control strains lead to the hypothesis of low stability of echinocandins or other factors resulting in the inactivation of these drugs. Further investigation into the usefulness of this assay for candins and A. fumigatus is warranted [37,43,44]. One limitation of our study is the lack of reference CBPs to determine more feasible CA percentages since the ECVs do not categorize an isolate as susceptible or resistant to certain target agents as CBPs do. Moreover, ECV carries some problems with some proposals of ECVs that still present overlapping of non-WT and WT isolates [15,16]. Another question is the small number of isolates, especially for the SYO-echinocandins tests, due to the difficulty described above in reading most reactions. Moreover, we found no previous studies regarding SYO using ITC against A. fumigatus, which precludes appropriate discussion of our results.
We have studied Etest and SYO for testing the susceptibilities of an ample collection of A. fumigatus clinical isolates, among other species, against first-line drugs used in the management of Aspergillosis cases. Additionally, we observed general agreement between the commercial methods for A. fumigatus and the CLSI reference broth microdilution reference M38-A2 method. A correlation between SYO-MICs and CLSI-MICs was demonstrated only in tests with ITC based on absolute values of MICs. More extensive studies to better assess the usefulness of SYO tests using ITC are warranted. We stress the usefulness of the colorimetric assay for detecting AMB-WT isolates. The Etest yielded EAs and CAs for testing AMB and ITC, as well as SYO for AMB. Both commercial methods presented major errors in identifying wild-type isolates as non-wild-type, capable of harboring underlying mechanisms of antifungal resistance. High EAs, but unacceptable low CAs with the SYO panel results using VCZ or PCZ were observed, thus confirming the need for better ECVs for the commercial method. Being more practical and less time-consuming for routine use, the Etest and SYO methods have potential value for the performance of susceptibility tests of A. fumigatus. Considering the relevance of new drugs in the therapeutic arsenal for the management of invasive aspergillosis, we recommend robust multicentric research with many WT and non-WT isolates aimed at improving these methods for further application in daily laboratory routines. | true | true | true |
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PMC9607676 | Changmei Yang,Tianxiang Wang,Songbiao Zhu,Zhaoyun Zong,Chengting Luo,Yujiao Zhao,Jing Liu,Ting Li,Xiaohui Liu,Chongdong Liu,Haiteng Deng | Nicotinamide N-Methyltransferase Remodeled Cell Metabolism and Aggravated Proinflammatory Responses by Activating STAT3/IL1β/PGE2 Pathway | 12-10-2022 | Nicotinamide N-methyltransferase (NNMT) is a cytosolic methyltransferase, catalyzing N-methylation of nicotinamide (NAM) to form 1-methylnicotinamide (1-MNAM), in which S-adenosyl- l -methionine (SAM) is the methyl donor. It has been well documented that NNMT is elevated in multiple cancers and promotes tumor aggressiveness. In the present study, we investigated the effects of NNMT overexpression on cellular metabolism and proinflammatory responses. We found that NNMT overexpression reduced NAD+ and SAM levels, and activated the STAT3 signaling pathway. Consequently, STAT3 activation upregulated interleukin 1β (IL1β) and cyclooxygenase-2 (COX2), leading to prostaglandin E2 (PGE2) accumulation. On the other hand, NNMT downregulated 15-hydroxyprostaglandin dehydrogenase (15-PGDH) which catalyzes PGE2 into inactive molecules. Moreover, secretomic data indicated that NNMT promoted secretion of collagens, pro-inflammatory cytokines, and extracellular matrix proteins, confirming NNMT aggravated inflammatory responses to promote cell growth, migration, epithelial-mesenchymal transition (EMT), and chemoresistance. Taken together, we showed that NNMT played a pro-inflammatory role in cancer cells by activating the STAT3/IL1β/PGE2 axis and proposed that NNMT was a potential therapeutic target for cancer treatment. | Nicotinamide N-Methyltransferase Remodeled Cell Metabolism and Aggravated Proinflammatory Responses by Activating STAT3/IL1β/PGE2 Pathway
Nicotinamide N-methyltransferase (NNMT) is a cytosolic methyltransferase, catalyzing N-methylation of nicotinamide (NAM) to form 1-methylnicotinamide (1-MNAM), in which S-adenosyl-l-methionine (SAM) is the methyl donor. It has been well documented that NNMT is elevated in multiple cancers and promotes tumor aggressiveness. In the present study, we investigated the effects of NNMT overexpression on cellular metabolism and proinflammatory responses. We found that NNMT overexpression reduced NAD+ and SAM levels, and activated the STAT3 signaling pathway. Consequently, STAT3 activation upregulated interleukin 1β (IL1β) and cyclooxygenase-2 (COX2), leading to prostaglandin E2 (PGE2) accumulation. On the other hand, NNMT downregulated 15-hydroxyprostaglandin dehydrogenase (15-PGDH) which catalyzes PGE2 into inactive molecules. Moreover, secretomic data indicated that NNMT promoted secretion of collagens, pro-inflammatory cytokines, and extracellular matrix proteins, confirming NNMT aggravated inflammatory responses to promote cell growth, migration, epithelial-mesenchymal transition (EMT), and chemoresistance. Taken together, we showed that NNMT played a pro-inflammatory role in cancer cells by activating the STAT3/IL1β/PGE2 axis and proposed that NNMT was a potential therapeutic target for cancer treatment.
Nicotinamide N-methyltransferase (NNMT) is a S-adenosyl-l-methionine (SAM)-dependent intracellular enzyme which takes SAM as the methyl donor and catalyzes N-methylation of nicotinamide (NAM) to generate 1-methylnicotinamide (1-MNAM) and S-adenosyl-l-homocysteine (SAH). Growing evidence demonstrated that NNMT levels were upregulated in lung adenocarcinoma, kidney renal clear cell carcinoma, pancreatic adenocarcinoma, glioblastoma multiforme, oral squamous cell carcinoma, gastric cancer, ovarian cancer, and in cancer-associated fibroblasts (CAFs) of high-grade serous carcinoma (HGSC). According to the data from International Agency for Research on Cancer (IARC), lung cancer has been the leading cause of tumor-related deaths in the world and accounts for 11.4% of the total newly diagnosed cancer cases in 2020. Lung adenocarcinoma is the most common subtype and the only subtype nonsmokers develop. NNMT is not only elevated in lung adenocarcinoma tissues compared to adjacent normal tissues, but it is also increased in the serum of nonsmall cell lung cancer (NSCLC) patients. In addition, it was reported that NNMT mRNA and protein levels were elevated in gefitinib-resistant NSCLC tissues and cell lines. By contrast, the downregulation of NNMT significantly reduced in vitro tumorigenicity of A549 cells. Cancer stem cells in bladder cancer (T24), lung cancer (A549), colorectal cancer (CaCo-2), and osteosarcoma (MG63) cell lines were associated with high NNMT expression. However, the precise mechanisms underlying NNMT-promoted tumor progression remain elusive. As a substrate of NNMT, NAM is one of the most abundant molecules in cells and one of the precursors for synthesizing NAD+. It was reported that NNMT knockdown significantly increased intracellular NAD+ levels in mouse adipocytes and HT-29 cells, whereas NNMT overexpression in SW480 cells led to a 30% decrease in NAD+ levels. Our recent studies showed that NAD+ decline promoted epithelial-mesenchymal transition (EMT) by activating STAT3 signaling pathway and degrading 15-hydroxyprostaglandin dehydrogenase (15-PGDH). Further, 15-PGDH degradation led to prostaglandin E2 (PGE2) accumulation and excretion to tumor microenvironment, resulting in aggravating inflammation. However, it remains to be elucidated whether NNMT can promote EMT and PGE2 production in cancer cells by depleting NAD+. SAM is the other substrate of NNMT, which is the methyl donor for histone and DNA methylation, and for polyamine biosynthesis. It has been reported that NNMT overexpression in 769P cells resulted in a decrease in overall histone H3 methylation, while silencing NNMT in SKOV3 cells resulted in an increase in overall histone H3 methylation. However, little is known about how NNMT regulates gene expression through consuming SAM and NAM. In the present study, we investigated the effects of NNMT overexpression on cellular metabolism and inflammatory response. The results showed that NNMT promoted secretion of collagens, pro-inflammatory cytokines, and extracellular matrix proteins to enhance proinflammatory responses, which drove cell growth, migration, epithelial-mesenchymal transition (EMT), and chemoresistance. All these data suggested that NNMT aggravated inflammation in cancer cells and is a potential therapeutic target in cancer treatment.
A549 cells (purchased from the cell bank of the Chinese Academy of Sciences) were cultured in RPMI-1640 medium (Wisent, Montreal, Canada). The culture medium was supplemented with 10% fetal bovine serum (FBS) (PAN-Biotech, Germany) and 1% penicillin/streptomycin (Wisent, Montreal, Canada). 293T cells were cultured in DMEM medium with 10% FBS and 1% penicillin/streptomycin. Cells were cultured in an incubator.
Human NNMT cDNA was obtained from HepG2 cell line. A Flag tag was added at the C-terminus and NNMT cDNA was then cloned into PLVX-IRES-ZsGreen1 lentiviral transfer vector. The 293T cell line was transfected with PLVX-IRES-ZsGreen1 or PLVX-NNMT-IRES-ZsGreen1 using lentiviral packaging vectors and polyethylenimine (Sigma, St Louis, MO, USA). Supernatants were harvested after 72 h and concentrated with PEG6000. Precipitated lentiviral particles were resuspended in phosphate buffered saline (PBS). A549 cells were infected in the presence of 5 μg/mL Polybrene (Sigma, St Louis, MO, USA). Infected cells (Ctrl and NNMT-OE cells) with green fluorescence were sorted by flow cytometry and used for further analysis.
The shRNA-containing plasmids (pLKO.1) for IL1β knockdown were purchased from the shared instrument facility at the Center for Biomedical Analysis of Tsinghua University. The shRNA-containing plasmids were cotransfected with pLP2, pLP/VSVG, and pLP1 into 293T cells by polyethylenimine. After 72 h, cell culture supernatants were collected and concentrated using PEG6000. The lentiviral particles were resuspended using PBS. NNMT-OE cells were transfected with lentiviral particles for 10 h in the presence of 5 μg/mL Polybrene. Cells were selected in the presence of 2 μg/mL puromycin to obtain stable knockdown cell lines, which were then verified by qPCR and Western blotting.
Total RNA was isolated from cells with Trizol reagent (TIANGEN, Beijing, China). cDNA was synthesized from 2 μg total RNA by using the reverse transcription kit (CWBIO, Beijing, China) according to the manufacturer’s instructions. qPCR was performed with the Roche LightCycler 96 System (Roche, Basel, Switzerland) by using an SYBR green reaction mixture (CWBIO, Beijing, China). β-Actin was used as the internal control for relative quantification. Primers used in qPCR are listed in Supplementary Table 1.
Cells were lysed in RIPA lysis buffer (Beyotime, Beijing, China) added with a Protease inhibitors cocktail. Equal volumes of proteins were separated on 12% SDS-PAGE gel and electro-transferred onto a polyvinylidence difluoride (PVDF) membrane. Primary anti-NNMT, anti-ZO-1, anti-Snail, anti-Cytokeratin-18, anti-Stat3, anti-Stat3 pS727, anti-Stat3 AcK685, anti-TGFβ2 (Proteintech, Rosemont, USA), anti-HPGD (Bioworld), anti-COX2, anti-E-cadherin, anti-N-cadherin (Cell Signaling Technology), and secondary antirabbit HRP-IgG antibodies (CST, Danvers, MA) were used for immunoblotting.
Equal numbers of cells were seeded and cultured in 96-well plates. The cell proliferation rate was determined with CCK-8 (Dojindo, Kumamoto, Japan) reagent. The plates were incubated in a cell incubator containing 5% CO2 at 37 °C for 2 h. Then the absorbance at 450 nm was measured.
Ctrl and NNMT-OE A549 cells were seeded into 96-well plates with 4000 cells/well. After 48 h of incubation, cells were treated with cisplatin (Selleck, Houston, TX) in five replicates for 24 h. CCK-8 reagent was added to treated cells and incubated at 37 °C for 2 h. Then the absorbance at 450 nm was measured. Cell viability was represented as the percentage of viable cells compared to untreated cells.
Cells were seeded in a 6-well plate at 1000 cells/well. A549 Ctrl and NNMT-OE cells were cultured at 37 °C. The colonies were fixed with 1 mL of 4% paraformaldehyde solution for 15 min at room temperature. The colonies were then rinsed with PBS. A 500 μL aliquot of 1% crystal violet staining solution (Solarbio, Beijing, China) was added into each well and incubated for 30 min at room temperature. Excessive crystal violet staining solution was removed, and the colonies were imaged by using the Image Lab software (Bio-Rad Laboratories, CA, USA).
Proteomic analysis was performed as described previously. Briefly, a total of 100 μg of protein was extracted from cells with 8 M urea containing a 1% protease inhibitors cocktail. Then, the proteins were digested with trypsin at 37 °C overnight. Tryptic peptides were desalted and labeled with the TMT 6-plex reagent. Then, the mixed labeled peptides were subjected to liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis.
Secretomic analysis was performed as described previously. Briefly, cells were cultured in medium supplemented with 10% FBS until they reached 70% confluency. Then cells were washed with PBS three times and further incubated in serum and phenol red-free medium at 37 °C for 24 h. Medium was collected and centrifuged at 10,000 rpm for 10 min to remove large debris. Supernatants were collected, and five volumes of ice-cold acetone added, followed by overnight precipitation at −20 °C. The pellets were dissolved in 8 M urea, and protein concentration was determined using a BCA protein assay kit (Solarbio, Beijing, China). Equal protein amounts were used for the following experiment the same as quantitative proteomic analysis.
Labeled peptides were injected on an Ultimate 3000 HPLC system that was directly interfaced with a Thermo Orbitrap Fusion Lumos mass spectrometer and separated by a 120 min gradient elution with a flow rate of 0.300 μL/min. The analytical column was a C-18 (300 Å, 5 μm, Thermo Fisher Scientific, USA) resin packed fused silica capillary column (75 μm inner-diameter, 150 mm length, Thermo Fisher Scientific, USA). Mobile phase A consisted of 0.1% formic acid, and mobile phase B consisted of 100% acetonitrile and 0.1% formic acid. The mass spectrometer was operated in the data-dependent acquisition (DDA) mode using Xcalibur 4.1.31.9 software, and MS1 spectra were acquired at a mass range of 300–1800 m/z with a resolution of 60,000. The spray voltage was 3800 V, and the Automatic Gain Control (AGC) target was 3e6. For MS2 scans, the top 20 most intense precursor ions were fragmented in the HCD collision cell at a normalized collision energy of 32% using a 0.7 Da isolation window, and the dynamic exclusion duration was 15 s. The AGC target was 1e5 whereas the maximum injection time was 100 ms.
The raw mass spectrometry data were searched against the Homo sapiens database by the Proteome Discoverer 2.3 software. The following parameters were used for database searching: static modifications of TMT 6-plex on lysine or peptide N terminus (+229.13 Da) and carbamidomethylation (+57.021 Da) on cysteine; dynamic modification of oxidation (+15.995 Da) on methionine; two missed cleavages were allowed; the mass tolerances of precursors and fragments were 10 ppm and 0.02 Da, respectively; at least one unique peptide for quantification of proteins; only peptides with false discovery rate (FDR) below 1% were considered as high-confidence hits.
Cold methanol extraction was used for the collection of cell metabolic products. Briefly, the cells were washed twice with ice cold PBS and were extracted using prechilled 80% methanol (−80 °C). Macromolecules and debris were removed by centrifugation, and the metabolites within the supernatant were concentrated by drying completely using a Speedvac for mass spectroscopy analysis. The chromatographic peak area was used to represent the relative abundance of a metabolite, and the protein content was used for normalization.
The cellular reactive oxygen species were detected by using CellROX Deep Red Reagents (Invitrogen, NY, USA). Briefly, cells were washed twice with PBS and stained with 5 μM CellROX Deep Red reagent. After staining for 30 min at 37 °C, the fluorescence of 20,000 cells was analyzed by using a BD Flow Cytometer (BD Biosciences, NJ, USA). Relative quantification was determined by the mean emission fluorescence intensity at 665 nm.
A wound healing assay was performed according to the method described in our previous study. Briefly, monolayer cells with 95% confluence were wounded by scratching the surface of each well in a 6-well plate as uniformly as possible with a 200 μL pipet tip. The wells were then rinsed with PBS three times, and the well was replenished with 1640 medium without FBS, incubated at 37 °C for 72 h. Images of the initial wound, and the movement of cells into the scratched area, were captured using a Nikon microscope (Olympus Corporation, Tokyo, Japan) at 0 and 72 h. This assay was done in triplicates.
A mice experiment was performed in the animal facility of Tsinghua University (Beijing, China) with approval of the Institutional Animal Care and Use Committee of Tsinghua University. A total of 10 male nude mice (6 weeks old, 20–22 g) were purchased from an external source with a health certificate (Sterling, China) and were housed under specific pathogen-free conditions at Laboratory Animal Research Center, Tsinghua University. A549 Ctrl and NNMT-OE cells (2 × 106 cells) in 200 μL of prechilled PBS were subcutaneously injected into the right underarm of mice. Tumor growth was measured every other day using a digital caliper. All mice were sacrificed 35 days after injection, and the tumors were harvested, washed, and photoed.
R (version 4.3) and GraphPad Prism software (version 9.0) were used for statistical analysis and plotting. Student’s t test and one-way ANOVA test were used to determine the significance of differences, and p-values less than 0.05 were considered to be significant.
To investigate the effects of NNMT on cell metabolism, we stably constructed a cell line by overexpressing NNMT in A549 cells using an established protocol. NNMT-overexpressing (NNMT-OE) A549 cells were confirmed by Western blotting and qPCR (Figure 1A, 1B). NAM is the precursor for NAD+ synthesis from the salvage pathway. To examine NNMT activity, we detected NAM, 1-MNAM, SAM, and SAH levels by using targeted metabolomics. As expected, 1-MNAM was 9-fold higher in NNMT-OE cells than that in control (Ctrl) cells, confirming that NNMT was successfully overexpressed in A549 cells with enzyme activity (Figure 1C). Additionally, SAM levels decreased dramatically while SAH levels increased by 2-fold in NNMT-OE cells when compared with Ctrl cells (Figure 1D, 1E). Unexpectedly, NAM was not altered by NNMT overexpression (Figure 1F). Given that NAM was not changed, we measured NAD+ levels by targeted metabolomics and found NAD+ levels reduced by approximately 25% (Figure 1G), suggesting that the breakdown of NAD+ was responsible for the unchanged NAM levels. In addition to metabolites closely related to NNMT functions, we also analyzed other metabolites in NNMT-OE cells by using untargeted metabolomics. As Figure 2A shows, glucose was elevated in NNMT-OE cells while the intermediates in glycolysis such as fructose-1,6-PP, 3-P-glycerate, and phosphoenolpyruvate were reduced and lactate was not altered, indicating that glycolysis may be suppressed. Moreover, metabolites in TCA cycle were significantly accumulated (Figure 2B), implying that NNMT promoted oxidative phosphorylation in cancer cells. Downregulation of proteins associated with glycolysis and upregulation of proteins in oxidative phosphorylation were confirmed by the subsequent proteomic analysis. Interestingly, we found tryptophan was markedly decreased accompanied by increased kynurenine and 3-hydroxyanthranilic acid (3-HAA) (Figure 2C). Specifically, kynurenine was increased by 2-fold while 3-HAA was increased by 6-fold. NNMT-overexpression also led to the lower levels of amino acids except for glutamic acid, glutamine, and aspartic acid (Figure 2D). Taken together, NNMT reduced NAD+ levels and remodeled cell metabolism in cancer cells with enhanced oxidative phosphorylation.
Our previous studies indicated that NAD+ decline activated the STAT3 signaling pathway and drove the EMT process. Similarly, NNMT overexpression also reduced NAD+ levels and activated STAT3 by upregulating STAT3 and promoting S727 phosphorylation and K685 acetylation of STAT3 (Figure 3A), which were crucial in the activation of STAT3 and regulated cancer-associated inflammation. To comprehensively investigate the underlying mechanisms of NNMT in cancer-associated inflammation, proteomic analysis was performed by using Ctrl and NNMT-OE cells. Experiments were conducted in biological triplicates, with a total of 8115 proteins identified and 6502 proteins identified in all three replicates (Supplementary Table 2). Figure 3B is the abundance boxplot of each sample labeled with TMT. Based on fold change (>1.3 or <0.77) and p values (p < 0.05), 1016 proteins were considered as differentially expressed proteins (DEPs) between Ctrl and NNMT-OE cells, in which 461 proteins were down-regulated and 555 were up-regulated (Supplementary Table 3). Blue and red dots in the volcano plot indicate the downregulated and upregulated proteins with a significant difference, respectively (Figure 3C). Heatmap clustering analysis showed hierarchical patterns and correlations of differentially expressed proteins in Ctrl and NNMT-OE cells (Figure 3D). Furthermore, Ingenuity pathway analysis (IPA) was conducted to analyze these DEPs, and the results strongly suggested that NNMT activated oxidative phosphorylation, fatty acid β-oxidation, NAD signaling pathway, ERK/MAPK signaling, and ILK signaling which resulted in phosphorylation of AKT, GSK-3, and other signaling proteins that regulated gene expression for cell proliferation (Figure 3E). Conversely, NNMT inhibited the pentose phosphate pathway, glycolysis, remodeling of epithelial adherens junctions, EIF2 signaling, and tRNA charging (Figure 3E). Consistent with metabolic analysis, the proteomic data supported that NNMT attenuated glycolysis and enhanced oxidative phosphorylation in cancer cells. Among those DEPs, COX2 was increased by 2.6-fold while 15-PGDH was decreased by 4-fold, respectively (Supplementary Table 3). Then we confirmed elevated COX2 protein levels and reduced 15-PGDH protein levels in NNMT-OE cells by Western blotting and qPCR (Figure 4A–4C). COX2 is an inducible enzyme catalyzing the formation of PGE2 from arachidonic acid which is normally absent in most tissues, and its expression is induced by a wide range of inflammatory stimuli. Inversely, 15-PGDH catalyzes NAD+-linked oxidation of PGE2 and is the key enzyme responsible for the biological inactivation of these eicosanoids. Consequently, we found that PGE2 increased by almost 10-fold in NNMT-OE A549 cells (Figure 4D). COX2 gene expression is usually promoted by pro-inflammatory cytokine IL1β. NNMT overexpression resulted in enhanced IL1β production (Figure 4E). To test whether COX2 was induced by IL1β, we knocked down IL1β gene expression by using shRNA and found that IL1β silencing caused a marked COX2 decline in both mRNA and protein levels (Figure 4F, 4G). Notably, it is known that IL1β, COX2, IL-6, IL-8, IL11, MMP2, and MMP9 are STAT3 target genes. Thus, the upregulation and activation of STAT3 by NNMT promoted IL1β, COX2, and PGE2 production. In summary, NNMT decreased 15-PGDH, activated the STAT3/IL1β/PGE2 pathway, and resulted in inflammatory PGE2 accumulation.
NNMT overexpression activated the STAT3 signaling pathway and up-regulated DEPs between Ctrl and NNMT-OE A549 cells, which were closely related to extracellular structure and matrix organization. By secretomic analysis, we identified 1396 authentically secretory proteins by searching the Uniprot proteome database annotated for “Signal” and “Extracellular” proteins (Figure 5A, Supplementary Table 4). By using the cutoff fold change values (<0.77 or >1.3) and p values (<0.05), we found that 134 proteins were upregulated while 145 proteins were downregulated (Supplementary Table 5). Heatmap clustering analysis listed the significantly changed proteins such as IL11, IL6, MMP2, VCAM1, TGM2, ADAM19, multiple collagens, and CXCLs in Figure 5B. It is worth noting that IL6 and IL11 are activators for STAT3. Moreover, IPA analysis demonstrated that NNMT overexpression activated various pathways that promoted cancer progression (Figure 5C). Combined with the proteomic data, we found that intracellular transforming growth factor β2 (TGFβ2) increased by 2-fold while secreted TGFβ2 increased by 1.5-fold (Figure S1A, S1B). Quantitative PCR and Western blotting analysis showed remarkable production of TGFβ2 in NNMT-OE cells (Figure S1C, S1D). It is increasingly recognized that TGFβ2 has strong immunosuppressive properties and regulates key mechanisms of carcinogensis, metastasis, and chemoresistance. From secretomic data, histograms displayed the enhanced secretion of collagens, extracellular matrix proteins as well as interleukins, chemokines (Figure 5D–5F). We detected some matrix metalloproteinases (MMPs) by qPCR, and the results showed that the mRNA levels of MMP2, MMP3, and MMP9 were significantly upregulated (Figure S1E–S1G). In agreement with the activation of the STAT3 pathway, NNMT promoted the secretion of multiple pro-inflammatory cytokines, extracellular matrix proteins, PGE2 and TGFβ2, which changed the tumor microenvironment. Together, high NNMT levels changed the secretome atlas of cancer cells and aggravated cancer-associated inflammation.
STAT3 signaling is a major pathway for inflammation. Cytokines, chemokines, and other mediators like IL6, IL1β, and COX2, are important for inducing and maintaining a cancer-promoting inflammatory environment, and STAT3 is essential for regulating their expressions. The proliferation ability of NNMT-OE cells was measured by growth curve and colony-forming assay, and the results showed that NNMT-OE cells grew faster than Ctrl cells and possessed stronger colony-forming abilities (Figure 6A, 6B). Wound healing assay indicated that NNMT promoted cancer cell migration (Figure 6C). These data are consistent with a previous observation using the NNMT knockdown A549 cell line, NNMT overexpression significantly promoted cell proliferation, migration, and colony-forming capacity of cancer cells. Furthermore, we monitored protein levels of EMT markers such as E-cadherin, ZO-1, Cytokeratin-18, N-cadherin, and Snail (Figure 6D). These results suggested that NNMT promoted the EMT process. In terms of cell morphology, NNMT-OE cells became more elongated, similar to the shape of intermediate cells during EMT process (Figure S2A). In addition, to assess the pro-proliferation activity of NNMT in vivo, Ctrl and NNMT-OE cells (2 × 106 cells) were subcutaneously injected into mice. Consistent with in vitro data, NNMT-OE cells possessed stronger oncogenicity and proliferative ability in vivo (Figures 6E, S2B, S2C). A genome-wide analysis identified NNMT as one of eight invasion-related genes and chemo-resistant genes which can be utilized to predict the relapse-free survival time in the lung cancer cohorts. Thus, we measured the chemotherapy sensitivity of NNMT-OE cells after cisplatin treatment for 24 h. It is obvious that NNMT-OE cells were more chemoresistant than Ctrl cells after cisplatin treatment (Figure 6F). NNMT overexpression aggravated the progression of cancer cells by promoting cell proliferation, migration, EMT, colony formation ability, and chemoresistance.
It has been shown that NNMT mRNA and protein are both highly expressed in various cancers and NNMT is considered as a potential biomarker and therapeutic target. A few studies indicated that NNMT was more sensitive than carcinoembryonic antigen (CEA) for diagnosis of lung adenocarcinoma and colorectal cancer (CRC), and combined testing of NNMT and CEA in serum can improve the diagnosis accuracy. The present study explored the underlying mechanisms of NNMT on cell metabolism and inflammatory responses by performing proteomics, secretomics, and metabolomics. Herein, we reported that NNMT overexpression in cancer cells reduced NAD+ levels and remodeled cell metabolism. Specifically, glycolysis was attenuated while oxidative phosphorylation was enhanced in NNMT-OE cells and NNMT rendered cells more addicted to glutamine. Reliance on glutamine has long been considered to be a hallmark of cancer cell metabolism and this phenomenon is known as glutamine addiction which depicts an increased dependency of cancer cells on other nutrients to feed the TCA cycle. Of note, NNMT promoted the accumulation of kynurenine and 3-HAA. The kynurenine pathway (KP) of tryptophan metabolism in cancer cells has emerged as a key metabolic pathway contributing to immune escape. Among the KP metabolites, 3-HAA appears to have the strongest capacity to modulate the immune functions. A recent report showed that compared with the control subjects, the patients with NSCLC exhibited significantly lower levels of tryptophan and significantly higher levels of 3-HAA. Besides, the patients with low 3-HAA had remarkably longer progression free survival (PFS) than those with high 3-HAA. Accordingly, NNMT may promote tumor aggressiveness through activation of tryptophan metabolism and accumulation of KP metabolites. Intriguingly, intracellular NAM levels were not changed after NNMT overexpression which may be attributed to the quick uptake of NAM from culture medium or the NAD+ breakdown by Sirtuins, PARPs or CD38. Indeed, CD38 was upregulated by 2-fold in the proteomic data (Supplementary Table 3) which may be a factor affecting NAD+ levels in NNMT-OE cells. Our previous paper showed that CD38 overexpression caused 15-PGDH degradation and the EMT process which was consistent with the current findings. COX2 was induced by IL1β which is a target gene of STAT3. The reciprocal regulation of COX2 and 15-PGDH was reported in lung adenocarcinoma before. However, this is the first report that COX2 and 15-PGDH were inversely regulated by NNMT in lung adenocarcinoma cells. Moreover, it was reported that the COX2/PGE2 signaling pathway induced EMT and COX2 can potentiate cisplatin resistance in lung cancer cells. Given that NNMT overexpression activated the pro-inflammatory response, we carried out secretomic analysis and found NNMT promoted the secretion of various pro-inflammatory cytokines, collagens, chemokines, and ECM-related proteins. All these results supported the conclusion that NNMT promoted tumor progression and NNMT secreted pro-inflammatory factors like PGE2, interleukins, and MMPs to promote progression of cancer cells, suggesting that NNMT is a potential therapeutic target. However, how NNMT affected the expression of STAT3 is unresolved. NNMT may alter SAM levels, leading to histone or DNA hypomethylation. In addition, it is unknown which genes will be altered in their expression when SAM levels are reduced by NNMT. It is worth exploring how NNMT-OE cells interplay with immune cells or stromal cells in tumor microenvironment by secreting 1-MNAM, IL1β, IL6, IL11, and TGFβ2. In conclusion, our multiomic analysis demonstrated that NNMT aggravated tumor inflammatory responses and promoted cancer progression via the STAT3/IL-1β/PGE2 pathway, and NNMT remodeled cell metabolism with enhanced oxidative phosphorylation and tryptophan metabolism. These results indicate that NNMT is a potential therapeutic target for cancer treatment. | true | true | true |
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PMC9608039 | Long Feng,Shihui Fu,Yao Yao,Yulong Li,Longhe Xu,Yali Zhao,Leiming Luo | Roles for c-Abl in postoperative neurodegeneration | 28-09-2022 | Alzheimer's disease,Nonreceptor tyrosine kinase,Oxidative stress,Parkinson's disease,Postoperative cognitive dysfunction,Postoperative neurodegeneration | The nonreceptor tyrosine kinase c-Abl is inactive under normal conditions. Upon activation, c-Abl regulates signaling pathways related to cytoskeletal reorganization. It plays a vital role in modulating cell protrusion, cell migration, morphogenesis, adhesion, endocytosis and phagocytosis. A large number of studies have also found that abnormally activated c-Abl plays an important role in a variety of pathologies, including various inflammatory diseases and neurodegenerative diseases. c-Abl also plays a crucial role in neurodevelopment and neurodegenerative diseases, mainly through mechanisms such as neuroinflammation, oxidative stress (OS), and Tau protein phosphorylation. Inhibiting expression or activity of this kinase has certain neuroprotective and anti-inflammatory effects and can also improve cognition and behavior. Blockers of this kinase may have good preventive and treatment effects on neurodegenerative diseases. Cognitive dysfunction after anesthesia is also closely related to the abovementioned mechanisms. We infer that alterations in the expression and activity of c-Abl may underlie postoperative cognitive dysfunction (POCD). This article summarizes the current understanding and research progress on the mechanisms by which c-Abl may be related to postoperative neurodegeneration. | Roles for c-Abl in postoperative neurodegeneration
The nonreceptor tyrosine kinase c-Abl is inactive under normal conditions. Upon activation, c-Abl regulates signaling pathways related to cytoskeletal reorganization. It plays a vital role in modulating cell protrusion, cell migration, morphogenesis, adhesion, endocytosis and phagocytosis. A large number of studies have also found that abnormally activated c-Abl plays an important role in a variety of pathologies, including various inflammatory diseases and neurodegenerative diseases. c-Abl also plays a crucial role in neurodevelopment and neurodegenerative diseases, mainly through mechanisms such as neuroinflammation, oxidative stress (OS), and Tau protein phosphorylation. Inhibiting expression or activity of this kinase has certain neuroprotective and anti-inflammatory effects and can also improve cognition and behavior. Blockers of this kinase may have good preventive and treatment effects on neurodegenerative diseases. Cognitive dysfunction after anesthesia is also closely related to the abovementioned mechanisms. We infer that alterations in the expression and activity of c-Abl may underlie postoperative cognitive dysfunction (POCD). This article summarizes the current understanding and research progress on the mechanisms by which c-Abl may be related to postoperative neurodegeneration.
Neurodegenerative diseases, such as Alzheimer's disease (AD), Parkinson's disease (PD), amyotrophic lateral sclerosis (ALS) and frontotemporal dementia, are common central nervous system (CNS) diseases 1-5. These diseases lead to the gradual impairment of CNS functions, such as activity, memory, learning, judgement and coordination. Besides, it often leads to postoperative cognitive dysfunction (POCD) after surgery with anesthesia in the elderly, which is more common after cardiac surgery than other types of surgery 6. Developmental neurotoxicity induced by general anesthetics can cause acute widespread neuronal cell death affecting long-term memory and learning defects of the infants and young children 7-8. The incidence of POCD decreases over time, with the rate being the highest (30% to 70%) at hospital discharge, 12% to 21% 3 months after anesthesia and noncardiac surgery, 20% to 30% 6 months after surgery, and 15% to 25% after 12 months of follow-up 9-13. POCD can prolongs hospitalization and rehabilitation time, increases the incidence of disability, and decreases life quality and survival, thus resulting in a heavy economic burden on the health care system 5,14-15. Compared with common neurodegenerative diseases and POCD, not only do they share the same characteristic symptoms, at the same time brain structural changes are similar, including cerebral white matter changes, central neuroinflammation, neuronal apoptosis, and so on 16-20. Brain aging is a complex process that affects everything from the subcellular level to the organ level, starting early in life and accelerating with aging process 21. Morphologically, brain aging is mainly characterized by brain volume loss, ventricular enlargement, cortical thinning, vitrification loss and white matter degradation. Pathophysiologically, brain aging is associated with neuron atrophy, dendritic degeneration, metabolic slowing, microglial activation, demyelinating disease, small vessel disease, and formation of white matter lesions (WML) 21. WML is caused by cerebral hypoperfusion, observed in the elderly, related to cognitive decline, and prevalent in AD patients 22. Multiple sclerosis (MS) is a chronic inflammatory demyelinating disease of CNS that causes focal VML and diffuse neurodegeneration throughout the brain. Range of MS lesions is in relation to inflammatory processes 23. Furthermore, apoptosis is a programmed cell death that plays a key role in nervous system development and chronic neurodegenerative diseases, including AD and MS 24-26. Neuroinflammation and neurodegeneration were caused by surgery and general anesthesia. Patients with POCD had significantly more white matter lesions and greater gray matter loss (medial temporal lobe) 27. Different anesthetics and neuroinflammation will cause the apoptosis of neurons and increase the incidence of POCD 28-30. c-Abl was first discovered in Aberson murine leukemia virus by Ozanne et al. in 1982 31. c-Abl (ABL, Abl1) and Abelson (ABL)-related genes (Arg, Abl2) have been identified as members of the c-Abl family of tyrosine kinases. c-Abl family members are highly conserved among different species and are involved in many cell regulatory processes, including regulation of actin cytoskeleton, cell cycle, stress-induced apoptosis and cycle arrest 32. Under normal circumstances, apoptosis is an important physiological process that maintains the stability of internal environment and ensures normal tissue development and growth. Previous research has found that activated c-Abl can participate in apoptosis by interacting with multiple factors 33. The gene is inactive under normal conditions. Upon activated and overexpression, this kinase can cause cell apoptosis, cycle alterations and pathological changes that may be related to neurodegenerative diseases 32-40. Other studies have shown that Abl family kinases, especially c-Abl, play a critical role in neurodevelopmental and neurodegenerative diseases. Selective inhibition of c-Abl expression and activity has neuroprotective effects 32-39. In addition, abnormally activated c-Abl is associated with a variety of neurodegenerative diseases. In studies using AD and PD models, it was found that c-Abl inhibitors can promote amyloid clearance and reduce the neural inflammation, which are two key drivers of nerve cell death 41. Therefore, a large amount of literature has proposed that this kinase is crucial in regulating neurodegenerative diseases. The main mechanism of anesthesia-induced POCD is very similar to the mechanism by which c-Abl is involved in the pathogenesis of neurodegenerative diseases. c-Abl activation regulates neuronal death response to Aβ fibrils. Intraperitoneal administration of imatinib rescued cognitive decline, Tau phosphorylation, and caspase-3 activation in neurons surrounding Aβ deposits 42. Imatinib reduces plasma β-Oligomers and brain features, such as Oligomer accumulation, neural inflammation and cognitive deficits. The results support the role of c-Abl in Aβ accumulation of neurodegenerative diseases, and the efficacy of imatinib in the treatment of these diseases 43. Besides, the c-Abl-drp1 signaling pathway regulates oxidative stress-induced mitochondrial fragmentation and cell death, which may be a potential target for the treatment of neurodegenerative diseases 44. Furthermore, previous studies have found that intravenous anesthetic propofol significantly reduces c-Abl expression, but reducing c-Abl expression by propofol did not impair learning or memory function 45. This study illustrates the involvement of c-Abl in the POCD process. Therefore, this paper hypothesized that the expression and activation of c-Abl are abnormally elevated in various neurodegenerative diseases (AD, PD, ALS, MS, POCD, etc.), mainly as a result of abnormality in the Tau and Aβ proteins caused by neuroinflammation and oxidative stress (OS). In addition, various c-Abl blockers (imatinib, nilotinib, dasatinib, ladotinib and other drugs) can effectively reduce abnormal proteins and ameliorate cognitive dysfunction. As shown in Figure 1, this article summarizes the current understanding and research progress on the mechanisms by which c-Abl may be related to neurodegenerative diseases.
ABL and Arg tyrosine kinases play an important role in the development and function of neuronal systems. Abl1 and Abl2 are downstream targets of ABL family to regulate cell growth and transformation. They may have unique and common functions in the development of CNS. Changed phosphorylation and molecular weight of ABL protein that occur during the maturation of CNS suggest that Abl1 and Abl2 may be involved in the signaling events that are responsible for regulating neural cell development 46. Koleske and others found that ABL and Arg kinases are expressed at the highest levels in the neuroepithelium at Embryos day9 (E9) 47. Arg is more abundant in the adult mouse brain, especially in the synaptic-rich regions. Arg deficient mice develop normally but show several behavioral abnormalities, indicating the existence of brain defects in these mice 47. Embryos lacking both ABL and Arg develop neurodevelopmental defects 47. ABL kinase-mediated signal transduction from different cell surface receptors also regulates cell proliferation and survival during cell development and homeostasis 48. In addition, during normal axonal development, axonal growth is promoted by the binding of kinesin-1 to c-Abl and their interaction. The c-Jun interacting protein-1 (JIP1) is an important regulator of axonal development and a key target of c-Abl-dependent pathway in controlling axonal growth 49. In addition, during neurite growth, c-Abl binds to and activates cyclin-dependent kinase 5 (CDK5), affecting neuronal migration and growth 35. In addition to its typical functions in the pathogenesis of leukemia, c-Abl is also considered to play an important role in neuronal development, neuronal migration, axon extension and synaptic plasticity 37-39. Miller et al found that c-Abl and ataxia-telangiectasia mutation (ATM) are very important for development and survival, especially after genotoxic stress, and that they have obvious selectivity for the developing nervous system 50. In addition, c-Abl functionally interacts with p53 during cell development, and mice lacking them are unlikely to be viable 51. Therefore, c-Abl is associated with a variety of cellular processes, including the regulation of cell growth and survival, as c-Abl deficient mice are embryonic or neonatal lethal 52,53.
A large number of studies have found that c-Abl is activated in human neurodegenerative diseases, especially AD 32-41. Bowser's team found that the phosphorylation of c-Abl at Y412 and granulovacuolar degeneration (GVD) in the brain are common in patients with AD 54. Some studies have also found in PD that c-Abl expression is increased in the striatum and that the tyrosine phosphorylation is also increased 55. However, the pathogenesis of AD and POCD is still unclear. The possible mechanisms underlying the diseases may involve the following processes. First, some scholars believe that amyloid cascade leads to the formation of Aβ fibrils. Increased aggregation of these fibrils leads to neuroinflammation, which in turn alters the physiology of neurons and induces oxidative stress in these cells, ultimately leading to kinase activation, the formation of tangles and reduced nerve cells. In addition, a study suggested that the mechanism of AD may be related to an abnormal neuroinflammatory response caused by initial injury 56. The signaling pathway activated by c-Abl may be related to growth factors, cell adhesion and OS 46-47.
Upon aging and disease development, the body's ability to deal with OS and deoxyribonucleic acid (DNA) damage during normal cell processes is weakened, resulting in the accumulation of oxygen free radicals and DNA damage. OS can regulate neurodegeneration through various mechanisms, including protein and lipid-related processes, Aβ deposition, cytokine production, mitochondrial dysfunction, proteasome dysfunction, and the formation of advanced glycation products, oxidation of nucleic acids and activation of glial cells 47. c-Abl generally exists in cells in inactive and activated states, with the activation of c-Abl being tightly regulated by intramolecular bonds. c-Abl protein complexes can also be linked to the membrane through an amino-terminal myristoyl group. Reactive oxygen species (ROS) may activate c-Abl by triggering ATM kinase activation through OS. In addition, a subtype of protein kinase C can activate and phosphorylate c-Abl under hydrogen peroxide (H2O2) stimulation 41,46,49. Some authors even believe that ROS can directly activate c-Abl 40. c-Abl can eventually cause cell dysfunction through DNA damage and increase the level of free radicals under stress conditions 32. Alvarez et al. found that c-Abl expression is upregulated in response to OS and the presence of Aβ fibrils in cultured neurons 57. In addition, c-Abl can be activated under OS, dopaminergic stress, and genotoxic stress, and exposure to these stresses leads to reduced and destructed neuronal cells 58-59. Therefore, OS is considered a potential therapeutic target for the prevention and treatment of neurodegenerative diseases through regulation of oxygen free radicals or alleviation of their harmful effects 60.
Tau is a microtubule-associated protein and a major component of neurofibrillary tangles (NFTs). It is normally involved in the formation of microtubules and cytoskeletal dynamics, and abnormal phosphorylation of Tau protein can cause microtubule entanglement and instability. Hyperphosphorylation of wild-type Tau protein results in the formation of NFTs, which is one of the hallmark pathologies of individuals with AD 61. Excessive accumulation of toxic Tau protein can cause death and dysfunction of nerve cells and glial cells, thereby causing disease symptoms. Studies have shown that the exposure of cultured neuronal cells to various Aβ peptides can activate tyrosine kinases, in turn causing tyrosine phosphorylation of Tau protein. The E3 ubiquitin ligase is a common player to play a neuroprotective role in AD and PD through scavenging misfolded proteins, such as Aβ peptides and phosphorylated Tau protein 41. However, during OS response in neurodegenerative diseases, ABL translocates to mitochondria, where it phosphorylates Parkin, causing activity loss of E3 ubiquitin ligase. This causes abnormal accumulation of Aβ peptides and Tau protein and is responsible for neuronal apoptosis and cognitive dysfunction 41. Furthermore, previous studies have found that oligomeric Aβ peptides are present in the brain cells of AD patients during the induction of Tau protein hyperphosphorylation 62. In addition, Derkinderen and colleagues pointed out that c-Abl phosphorylates Tau protein at Y394 63. It was also found in AD brain that activated ABL is present in granular structures of hippocampal neurons.
Inflammatory mediators are detected in brain sections from AD and PD patients, and neuroinflammation may be one of the causes of these neurodegenerative diseases 64. Numerous studies have also found that the etiology of AD and PD may be related to chronic neuroinflammation 65-66. In the study of two transgenic mice (AblPP/tTA mice and ArgPP/tTA mice), it was found that c-Abl overexpression can lead to neuronal loss and neuroinflammatory response 65. Neuroinflammation can be triggered by a variety of biological mechanisms, including OS and glial response. Neuroinflammatory mediators, such as cytokines and prostaglandins, play an important role in the development of neurodegenerative diseases 67-69. In the early stage of neuroinflammation in AD, a vicious cycle of microglial activation, proinflammatory factor release and neuronal damage may occur 70. Some authors have pointed out that c-Abl activation by neuronal cells can lead to neurodegenerative changes and neuroinflammatory changes 66. Treatment of AD model mice with a c-Abl blocker led to the clearance of Aβ peptides, reduced the number of astrocytes and dendritic cells, and regulated the distribution of cytokines and chemokines 71. It has also been indicated that continuous overexpression of c-Abl in neurons can cause the degeneration of neuronal cells in the hippocampal region. In a follow-up study, it was found that this pathophysiological process is mainly caused by transient and obvious changes in the cell cycle that are associated with protein and DNA replication in the olfactory bulb and activation of transcription 1 (STAT1) signaling pathway, which is crucial for the regulation of c-Abl-induced neuroinflammation and neurodegenerative diseases 66, in the hippocampal region. Wu et al. found that c-Abl can abnormally activate P38 and lead to neuronal death in the neuroinflammatory environment 72. It is known that P38 is a member of mitogen activated protein kinases (MAPKs) family and plays an important role in inflammation, neurodegeneration and cell death. Therefore, according to all above these findings, we speculate that the activation of c-Abl in neurons leads to pathological changes related to neuroinflammation.
Imatinib, nilotinib, and bosutinib have been shown to inhibit proinflammatory cytokines (TNF-α, IL-1β, IL-6, iNOS, COX-2, NLRP3) production, thereby reducing the recruitment of inflammatory cells to central nervous system 71-74. AD is the most common neurodegenerative disease and the main cause of dementia. Its main neuropathological hallmarks are mainly the accumulation of extracellular neurotrophic plaques comprising Aβ peptides and the formation of intracellular and extracellular NFTs in brain regions. Neuroinflammation also plays an important role in the pathogenesis and progression of AD 74. Infection, trauma, ischemia, and toxins increase levels of proinflammatory cytokines, including TNF-α, IL-1β, IL-6, IL-18, and chemokines such as C-C motif chemokine ligand 1 (CCL1), CCL5 and C-X-C motif chemokine ligand 1 (CXCL1). The release of pro-inflammatory molecules can lead to synaptic dysfunction, neuronal death and neurogenesis inhibition. During the progression of AD, memory loss and cognitive decline are accompanied by the degradation of specific neurons in the hippocampus and cerebral cortex. Studies have shown that the phosphorylation of tyrosine kinases at T412 is significantly increased in the hippocampus and entorhinal cortex in the brains of AD patients 75. Moreover, Aβ can enhance the activity of c-Abl, and this kinase is involved in regulating the death of neurons 57. c-Abl blockers have also been shown to significantly reduce Tau protein phosphorylation in transgenic AD animal models. It has also been found in vitro that Aβ can significantly increase c-Abl expression and phosphorylation of Tau protein in nerve cells 76. This indicates that the expression of c-Abl is significantly increased during the pathogenesis of AD and that kinase blockers can significantly reduce the accumulation and expression of abnormal proteins. In addition, similar studies have also shown that c-Abl blocker imatinib can significantly reduce plasma Aβ protein levels 77. Intraperitoneal injection of the drug into AD model mice can not only ameliorate cognitive decline but also reduce neuronal apoptosis and Tau protein phosphorylation 78. In addition, a study on AD-related diseases in humans showed that the level of c-Abl in the brains of AD patients was obviously higher than that in the brains of controls. The Abl-py412, an activated form of c-Abl phosphorylated on tyrosine residue 412, has an increased level in the early stage of AD. In the late stage of the disease, the level of abl-py412 is mainly increased in the hippocampal area. The Abl-pt735, an alternative phosphorylated form of c-Abl on threonine residue 735, has an increased level specifically in the hippocampal region. Furthermore, c-Abl and phosphorylated Tau protein interact in the brains of AD patients, and there is a certain correlation between the levels of these two proteins. The above results show that functional state of c-Abl is different at different stages of AD and that phosphorylated c-Abl and Tau protein interact in the pathogenesis of AD 54. PD is the second most common neurodegenerative disease in the elderly. Studies have found that Parkin is a substrate of c-Abl and that c-Abl can specifically phosphorylate Parkin at Y143 79-80. The main pathway of this disease is the direct phosphorylation of α-synuclein at Y39 by c-Abl, which leads to abnormal aggregation of α-synuclein and a reduction in Parkin expression. In addition, c-Abl can phosphorylate E3 ubiquitin ligase at Y143 and inactivate it 81-82. These changes can lead to an abnormal increase in the level of toxic protein zinc finger protein 746 (PARIS) and aminoacyl tRNA synthetase complex-interacting multifunctional protein 2 (AIMP2) 81-82. In addition, previous studies have demonstrated that virus induced PARIS transgenic mice can lead to c-Abl activity dependent on PD characteristics such as dyskinesia, dopaminergic neuron loss and neuroinflammation 83. Nilotinib, a c-Abl blocker, can significantly reduce c-Abl activity and Parkin levels, and improve neuronal apoptosis and cognitive function 84. Wu et al. 85 found that in the study of BV2 microglia and PD model of mouse brain neuroinflammation induced by lipopolysaccharide (LPS), nilotinib can reduce TNF-α, IL-1β, IL-6, iNOS, COX-2, and other proinflammatory factors in BV2 cells. And it can significantly inhibit LPS induced neuroinflammation. In addition, tyrosine hydroxylase (TH) is an important player in PD, and nilornib can increase the number of TH- and Nissl-positive neurons in PD patients. It has also been found that c-Abl blockers can significantly reduce dopaminergic neuron loss in c-Abl knockout animals 80. In vitro studies, ladotinib can protect against mitochondrial function impairment caused by α-synuclein and reduce the formation of α-synuclein inclusions. Furthermore, in vivo studies, the drug can effectively reduce dopaminergic neuron loss and neuroinflammation and improve cognitive function 86. The intracellular inflammasome complex is involved in the recognition and execution of host inflammatory responses. Studies using pharmacological inhibition of c-Abl found that dasatinib reduced inflammasome activation, mitochondrial oxidative stress and LPS induced microglial activation 87. Besides, the relate vitro study also indicated that c-Abl mediated microglia activation may be an important source of inflammatory mediators 88. In addition, many studies have shown that the activity and expression of c-Abl are significantly increased in the brains of PD patients. Kinase blockers can improve brain function, reduce neuron death, inhibit CDK5 phosphorylation, regulate α-synuclein elimination, and inhibit Parkin phosphorylation 89-93. In addition to animal experiments, clinical studies have also found that the treatment of patients with moderate or severe PD with nilotinib for 6 months can significantly alleviate cognitive symptoms 94. In addition, some studies have found that c-Abl kinase inhibitor PD180970 can reduce Toll like receptor-4 mediated NF-κB and inhibits the release of pro-inflammatory cytokines such as IL-6 and monocyte chemoattractant protein-1 (MCP-1) 95. In addition to the above common neurodegenerative diseases, c-Abl is also associated with Niemann-Pick C (NPC), a fatal autosomal recessive disease characterized by the accumulation of free cholesterol and sphingolipids in the endolysosomal system. c-Abl is activated and triggers neuronal apoptosis in vitro and in vivo nasopharyngeal carcinoma models 96-97. Klein et al. found that the c-Abl/p73 pathway is related to neurodegeneration in NPC and that c-Abl blocker can delay neurodegeneration in this disease 96. In addition, in other studies of NPC models in neurons, the c-Abl/Histone deacetylases (HDAC2) signaling pathway was found to be involved in the regulation of neurons. Inhibition of c-Abl may be a pharmacological strategy for preventing the adverse effects of elevated HDAC2 levels in nasopharyngeal carcinoma patients 98. In addition, c-Abl is involved in ALS, which is characterized by neuron death. In ALS, c-Abl signaling is triggered through mitochondrial alteration-mediated ROS production 99. Some researches suggest that c-Abl is a treatment target for ALS, and it has been found that the c-Abl blocker dasatinib has neuroprotective effects against this disease in vitro and in vivo 100. Small interfering RNA (siRNA)-mediated c-Abl gene knockout attenuated the production of proinflammatory mediators in LPS induced glial cell culture 101.
The expression and activation of c-Abl are abnormally elevated in various neurodegenerative diseases (AD, PD, NPC, ALS, etc.), mainly as a result of neuroinflammation, OS, and abnormal Aβ and Tau protein. In addition, various c-Abl blockers (nilotinib, imatinib, dasatinib, ladotinib and other drugs) can effectively reduce abnormal protein levels and ameliorate cognitive dysfunction. The main mechanism of anesthesia-induced POCD is very similar to the mechanism by which c-Abl is involved in the pathogenesis of neurodegenerative diseases. Therefore, we hypothesized that anesthesia-induced POCD may also be related to abnormal activation or increased expression of c-Abl following the exposure to anesthetic drugs. These drugs, which may have certain preventive effects against POCD, may be new options for the treatment of postoperative neurological dysfunction caused by surgery and anesthesia. However, high-quality studies confirming specific role and potential mechanism of c-Abl in dementia and cognitive decline after anesthesia are lacking. c-Abl is a very promising target for perioperative intervention and treatment of POCD.
c-Abl plays a crucial role in neurodegenerative diseases, mainly through mechanisms such as neuroinflammation, oxidative stress (OS) and Tau protein phosphorylation. Blockers of c-Abl may have a good preventive and treatment effects on postoperative neurodegeneration. This article summarizes the current understanding and research progress on the mechanisms by which c-Abl may be related to postoperative neurodegeneration.
Does c-Abl inhibition have anti-inflammatory and neuroprotective effects? Does c-Abl inhibition alleviate postoperative cognitive dysfunction (POCD)? Through which mechanisms is c-Abl related to POCD? | true | true | true |
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PMC9608056 | 36285472 | Yuelan Qin,Renba Liang,Pingan Lu,Lin Lai,Xiaodong Zhu | Depicting the Implication of miR-378a in Cancers | 26-10-2022 | miR-378a,tumorigenesis,migration,invasion,proliferation,targets,clinical applications | MicroRNA-378a (miR-378a), including miR-378a-3p and miR-378a-5p, are encoded in PPARGC1B gene. miR-378a is essential for tumorigenesis and is an independent prognostic biomarker for various malignant tumors. Aberrant expression of miR-378a affects several physiological and pathological processes, including proliferation, apoptosis, tumorigenesis, cancer invasion, metastasis, and therapeutic resistance. Interestingly, miR-378a has a dual functional role in either promoting or inhibiting tumorigenesis, independent of the cancer type. In this review, we comprehensively summarized the role and regulatory mechanisms of miR-378a in cancer development, hoping to provide a direction for its potential use in cancer therapy. | Depicting the Implication of miR-378a in Cancers
MicroRNA-378a (miR-378a), including miR-378a-3p and miR-378a-5p, are encoded in PPARGC1B gene. miR-378a is essential for tumorigenesis and is an independent prognostic biomarker for various malignant tumors. Aberrant expression of miR-378a affects several physiological and pathological processes, including proliferation, apoptosis, tumorigenesis, cancer invasion, metastasis, and therapeutic resistance. Interestingly, miR-378a has a dual functional role in either promoting or inhibiting tumorigenesis, independent of the cancer type. In this review, we comprehensively summarized the role and regulatory mechanisms of miR-378a in cancer development, hoping to provide a direction for its potential use in cancer therapy.
Micro RNAs (miRNAs) are small non-coding RNAs (19-24 nucleotides) produced from an endogenous transcript having a local hairpin structure with the help of RNase III-type enzyme. miRNAs can repress the translation of target proteins by cleaving or clinging to the 3′ untranslated region (UTR) of the corresponding messenger RNAs (mRNAs). The first miRNA was discovered over 20 years ago in mammals. Based on their genomic origin, miRNAs are divided into intergenic, intronic, and exonic. They are essential regulators of various critical biological processes, such as cell differentiation, apoptosis, proliferation, cell division, protein secretion, and viral infection. Recently, several studies have shown that the aberrant expression of miRNA is tightly associated with the progression of human diseases, especially cancers, as they can suppress or promote tumor growth by regulating the target gene mRNA. The miRNA miR-378a is one such essential tumor-regulating miRNA. It is located on chromosome 5q32 and embedded within the first intron of the PPARGC1β gene, which encodes the PGC1β protein. miR-378a is divided into miR-378a-3p (usually identified as miR-378) and miR-378a-5p (usually identified as miR-378*), which are the guide and passenger strands of miR-378a, respectively. Both miR-378a-3p and miR-378a-5p are synchronously transcribed with PGC1β, although there is an exception. Recently, it has been demonstrated that the over-expression (Table 1) or under-expression (Table 2) of miR-378a regulates the expression of its target genes and affects the proliferation, invasion, and metastasis of cancers. For example, low miR-378a expression is usually correlated to a significantly poor overall survival (OS) in colorectal cancer patients and associated with significantly decreased OS and disease-free survival (DFS) in gastric cancer patients. However, its expression and role in different cancers, such as lung and cervical cancers, is still controversial. miR-378a was identified as an onco-RNA in various tumors, including lung cancer, ovarian cancer, cervical cancer, nasopharyngeal carcinoma, melanoma, osteosarcoma, cholangiocarcinoma, acute myeloid leukemia, chronic myeloid leukemia, and Burkitt lymphoma. In contrast, it acted as an tumor suppressor in gastric cancer, colorectal cancer, liver cancer, glioblastoma, prostate cancer, breast cancer (BC), medulloblastoma (MB), pituitary adenoma (PA), oral squamous cell carcinomas, bladder cancer, esophageal carcinoma, rhabdomyosarcoma (RMS), and retinoblastoma (RB). Compared with normal tissues and no lymph node metastasis cancer tissues, miR-378a was upregulated in cervical cancer tissues, especially in CIN III and lymph node metastasis cervical cancer. Additionally, a study of 120 cholangiocarcinoma tissues and adjacent noncancerous tissues found that miR-378a expression increased with the development of the TNM (T-tumor, N-nodes, M-metastasis) stages. Positive miR-378a expression is also tightly linked to a low OS in cholangiocarcinoma patients. However, in colorectal cancer patients, miR-378a is downregulated and low miR-378a correlated with poor OS, suggesting an inhibiting effect of miR-378a. Thus, miR-378a can drive cancer cell proliferation, invasion, and migration in some tumors, while inhibiting these processes in other ftumors. Further, a study on the radiation response in a glioblastoma xenograft model found that the overexpression of miR-378a was associated with enhanced local tumor response to radiotherapy by increasing the vascular density and perfusion, thereby prolonging the survival of tumor-bearing hosts and acting as a novel therapeutic intervention. To summarize, the abnormal expression of miR-378a is crucial for the occurrence and development of malignant tumors. Therefore, it is important to understand the functions of miR-378a in tumor biology to enhance the efficiency of early screening and diagnosis of cancer, strengthen anticancer therapy, and develop novel targeted methods for tumor therapy. Hence, this review focuses on the roles and targets of miR-378a in different cancers to elucidate the specific molecular mechanisms of miR-378a during tumor progression.
Growing research studies have elucidated the critical role of miR-378a in cancer progression, and most studies focused on the specific targets of miR-378a in tumor cell proliferation, migration, invasion, and regulatory pathways. Here, we summarize the roles and targets of miR-378a in tumorigenesis. Interestingly, both oncogenic (Table 1) and tumor-suppressive (Table 2) roles have been reported for miR-378a, implicating its dual function in cancer progression. By regulating downstream target genes, miR-378a promotes (Table 3) or inhibits tumors (Table 4), while it is regulated by its upstream regulators (Figure 1).
Lung cancer is a common malignant tumor with a high mortality rate. Despite advances in cancer therapy, the OS rate of patients with advanced lung cancer is low. The expression of miR-378a both in tumor tissues and cell lines was significantly higher than in adjacent normal tissues or cells, and upregulation of miR-378a promoted the tumor progression. Further, miR-378a was significantly overexpressed in serum exosomes of non-small cell lung cancer (NSCLC) patients. The aberrant expression of miR-378a was closely associated with an advanced TNM stage, positive metastasis, negative therapeutic response, and poor OS. miR-378a can promote NSCLC cell migration, invasion, and tumor angiogenesis. Moreover, the circular RNA has-circ-0007059 was identified as an upstream regulatory factor of miR-378a, which can inhibit the proliferation and epithelial-mesenchymal transition (EMT) of A549 and H1975 cells via inhibiting miR-378a. The lncRNA ACTA2-AS1 suppressed malignancy by decoying miR-378a-3p, thus enhancing SOX7 expression. As for the downstream regulation, miR-378a works on different targets and pathways to produce a plethora of effects on lung cancer progression. Studies on stimulating miR-378a for promoting lung cancer progression and constructing miR-378a overexpression cell lines have shown that miR-378a can strengthen metastasis by regulating EMT. Further, miR-378a targets RBX1, and miR-378 upregulation boosts NSCLC cell invasion by downregulating RBX1, but it did not have any major effect on the angiogenesis signaling pathway. Likewise, HMOX1 was another target of miR-378a, and the interplay between HMOX1 and miR-378a modulated the miRNA transcriptome and the 3′ UTR of HMOX1 in tumors. Further experiments in NCI-H292 cells with stable overexpression of miR-378a indicated that miR-378a enhanced the proliferation, migration, and angiogenic capabilities of the cells both in vitro and in vivo by downregulating HMOX1 mRNA and protein expression. However, when HMOX1 was diminished, miR-378a could not modulate the cell's resistance to chemotherapeutic drugs and oxidative stress. Another study noted a reverse correlation between miR-378a and FOXG1. Silencing FOXG1 led to tumor suppression in NSCLC cells by downregulating miR-378a, thereby inhibiting NSCLC cell proliferation, promoting apoptosis, and increasing the length of the G0/G1 phase in the cell cycle. Hence, all the above studies show a positive relation between miR-378a and tumorigenesis. However, some studies have shown the opposite result. Cisplatin (CDDP)-sensitive patients have high expression of miR-378a and low expression of small cell lung cancer (SCLU). Forced upregulation of miR-378a inhibits neoplasm growth and SCLU expression and makes cells more sensitive to CDDP. Overexpression of miR-378a-3p can inhibit cell proliferation and decrease the expression of proliferation-related proteins CDK4 and CDK6. Since lncRNA OIP5-AS1 functioned as a competing endogenous RNA of miR-378a-3p, overexpressed wild-type OIP5-AS1 increased CDK4 and CDK6 expression, thereby promoting tumor growth. In addition, SCP inhibits tumorigenesis by upregulating TUSC2 by targeting miR-378a-5p. Thus, the aberrant miR-378a expression in lung cancer significantly affects tumors. However, the controversy over whether it is the low or high expression of miR-378a that increases lung cancer progression is yet to be resolved.
Ovarian cancer (OC) and cervical cancer (CC) are the most common gynecological tumors. In OC and CC, miR-378a is overexpressed both in the tumor tissues and cancer cell lines. Among recurrent OC patients treated with bevacizumab, higher miR-378a correlated to longer PFS. miR-378a and its downstream targets ALCAM and EHD1 can be used as markers for anti-angiogenic therapy. In OC tissues and cells, it has been shown that circATRNL1 is under-expressed while miR-378a is overexpressed. Further study revealed that circATRNL1 could adhere to miR-378a while miR-378a directly acts on SMAD4. High circATRNL1 expression also reduces miR-378a, which, in turn, inhibits SMAD4 expression, resulting in the suppression of angiogenesis, cell proliferation, invasion, and migration, both in vitro and in vivo. Hence, in OC, circATRNL1 acts as a miR-378a sponge, thereby accelerating SMAD4 signaling and attenuating angiogenesis and metastasis. miR-378a is also a target of circ-LOPD2. circ-LOPD2 expression is high in both OC tissue and cells and is responsible for promoting OC cell proliferation. Several studies have revealed that circ-RNAs worked as molecule sponges of miR-378a to regulate cancer development. As for CC, miR-378a acts as an oncogene since high miR-378a expression significantly enhances cancer migration and invasion in vitro, and metastasis in vivo, while downregulating miR-378a produces the opposite effect in vitro. The lncRNA LINC00641 inhibits CC progression by diminishing miR-378a-3p. Further, knockout of CPEB3, a downstream target of miR-378a-3p, can reverse the effects induced by LINC00641 overexpression. Likewise, ATG12 targets miR-378a and their expression is inversely correlated, thereby promoting tumor cell progression. Moreover, overexpressed miR-378a facilitates cell cycle progression, reduces apoptosis, and promotes cell growth by attenuating ST7L expression. miR-378a upregulation also activates the Wnt/β-catenin pathway to regulate tumor progression. However, miR-378a-3p is lower in the serum and tissues of CC patients than in the serum and tissues of healthy control subjects and normal tissues, leading to the poor prognosis of patients with CC. In this study, high miR-378a-3p expression reduced the proliferation and migration of CC cells both in vitro and in vivo. In short, miR-378a functions as an onco-miRNA by being regulated by circ-RNAs and then regulating its downstream targets and pathways, which is crucial in the malignancy of gynecological tumors. However, due to some inconsistent results, more studies are needed to elucidate the exact function of miR-378a in gynecological tumors.
Melanoma is the most aggressive type of skin cancer with high mortality. The miRNA miR-378a shows the highest upregulation in advanced melanoma patients and those with lymph node metastasis and miR-378a-5p is upregulated in metastatic melanoma patients, especially those resistant to targeted therapy. High miR-378a-5p expression enhances cell invasion, migration, and activates EMT in melanoma cells, and promotes angiogenesis. Further, several studies implementing various approaches have demonstrated that miR-378a-5p plays a carcinogenic role by regulating downstream targets such as SUFU, FUS-1, KLF9, STAMBP, and HOXD10. However, when miR-378a targets the 3′ UTR of FOXN3, it mitigates the stimulation of miR-378a for proliferation, migration, and invasion. Moreover, FOXN3 interacts with β-catenin to downregulate the Wnt/β-catenin signaling proteins, and this pathway is crucial for cell proliferation, self-regeneration, differentiation, and tissue homeostasis.
Apart from the positive function of miR-378a in the tumors mentioned above, similar results have been reported in nasopharyngeal carcinoma (NPC), osteosarcoma, cholangiocarcinoma, renal carcinoma, Burkitt lymphoma (BL), acute lymphoblastic leukemia (AML), and chronic lymphoblastic leukemia (CML). In NPC, miR-378a is upregulated in NPC tissues and cell lines. Elevating the expression of miR-378a dramatically promotes the capability of NPC to proliferate, migrate, and invade in vitro, as well as grow in vivo by restraining the expression of the TOB2 transducer. TOB2 is a potential tumor suppressor which inhibits cell proliferation by arresting the progression of the G0/G1 phase cells to the S phase. In osteosarcoma cells and patient-derived tumor specimens, overexpression of miR-378a promotes osteosarcoma cell proliferation by diminishing the levels of KLF9. Forced upregulation of KLF9 extraneously reverses the tumor proliferation-promoting effect of miR-378a. miR-378a-3p upregulation can repress cell invasion and suppress EMT by suppressing BYSL. Elevated miR-378a exerts a similar promoting effect in cholangiocarcinoma, which is significantly associated with an advanced TNM stage, positive lymph node metastasis, and short OS, and a miR-378a knockout inhibits cell proliferation, migration, and invasion. Similarly, miR-378a was identified as a diagnostic biomarker when renal carcinoma cancer tissues and cells showed increased proliferation, and increased miR-378a inhibited POLR2A and RUNX2 expression and subsequently promoted cell apoptosis. In BL, miR-378a-3p can facilitate tumor growth by targeting IRAK4 and MNT. miR-378a dysregulation is also detected in non-solid tumors, such as AML and CML. The upregulation of miR-378a is positively correlated with poor survival in AML patients. The 5′ flanking region of miR-378a is hypomethylated in AML. Thus, miR-378a can be reactivated by demethylation after 5-aza-dC treatment, but this is unlikely to provide helpful prognostic information in AML patients. Knockdown of LINC00641 inhibited cell malignancy and promoted apoptosis by downregulating miR-378a by promoting ZBTB20 expression in AML. Besides, lower FUS-1 expression, whose expression inversely correlates with miR-378a, is linked to the poor prognosis of AML patients. In CML, enhanced expression of miR-378a in leukemia cell line K562 facilitates cell proliferation, clonality, and drug resistance to 5-FU, boosts expression of stem cell self-renewal markers OCT4 and c-Myc, and suppresses apoptosis. As in AML, FUS-1 was also identified as a target of miR-378a in CML, but its role has not been elucidated yet. Together, miR-378a overexpression exerts a stimulative effect on tumors mentioned above by regulating its different targets.
Unlike the cancers overexpressing miR-378a, it is worth noting that miR-378a is downregulated in many cancers. An inverse relationship between miR-378a expression and tumors progression has been reported in various carcinomas, including gastric cancer (GC), colorectal cancer (CRC), liver cancer, glioma, prostate cancer (PCa), BC, MB, pituitary cancer, oral squamous carcinoma (OSCC), bladder cancer, esophageal carcinoma (ESCC), RMS, and RB.
GC has a high incidence worldwide and severely affects the quality of life. Several studies have explored the underlying molecular alterations of miR-378a mediating tumorigenesis and GC development. A marked reduction of miR-378a was detected in GC tissues and cells compared to their controls. Underexpression of miR-378a led to poor prognosis and malignant clinicopathologic features in GC patients. Circulating miR-378a level is considered the best biomarker for GC detection since it has a higher sensitivity and specificity than that of the serum levels of CEA and CA199. Low miR-378a expression in GC patients is also associated with an advanced TNM stage, poor tumor differentiation, high lymph node metastasis rate, and poor OS and DFS. Further, GC cells are arrested in the G0/G1 or G2/M phase after miR-378 overexpression. miR-378a regulates various targets to exert its function as a tumor suppressor. For example, miR-378a targets BMP2, which has a high expression in GC patients with a shorter OS and DFS. Reverse-validation experiment confirmed that miR-378a overexpression inhibits invasion, migration, and EMT of GC by modulating BMP2, suggesting that components of the miR-378a/BMP2 axis might be potential therapeutic targets of GC. Additionally, two other targets of miR-378a in GC—MAPK1 and VEGF—are closely associated with cancer malignancy. miR-378 inhibits MAPK1 in GC. Depletion of MAPK1 by RNA interference in MC-803 cells reduces cell growth, increases apoptosis, and suppresses cell migration and invasion, which corresponded to the tumor-inhibiting effects caused by miR-378a restoration. Furthermore, miR-378a can directly bind to GAPLINC and reduce its expression, thereby repressing MAPK1 expression. Upregulated miR-378a suppresses MAPK1 expression, cell proliferation, and cell cycle progression in GC cells, while these effects are reversed by increasing the expression of the lncRNA GAPLINC. Another miR-378 target in GC is VEGF, as confirmed by the evidence that VEGF is downregulated by exogenously overexpressed miR-378. Together, these findings suggest that miR-378 regulates GC development and can act as a promising biomarker for the treatment of GC patients.
Decreased miR-378a expression significantly correlates with shorter OS in CRC patients. Hence, miR-378a is a potential biomarker for the early detection and diagnosis of patients with CRC. Interestingly, miR-378a might also function as a novel biomarker to predict the efficacy of vaccines against CRC. The overexpression of miR-378a in CRC repressed cell growth, induced apoptosis, and inhibited migration and invasion by restraining EMT. Studies also showed that miR-378a mitigated the malignant phenotypes of CRC cells by inhibiting the Wnt/ β-catenin pathway. Two lncRNAs were found to regulate miR-378a-3p. SP1-activated LINC00339 decreased miR-378a-3p but enhanced MED19 expression, contributing to cell proliferation, cell cycle progression, migration, and invasion in CRC cells. Further, the lncRNA CYTOR decreased miR-378a-5p and led to the increase in SERPINE1, which inhibited L-OHP resistance and EMT. Several other genes are the downstream targets of miR-378a, including IGF1R, CDC40, vimentin, BRAF, SDAD1, and KISS1. IGF1R protein expression significantly negatively correlated with miR-378a in CRC tissues. Further, luciferase reporter assays confirmed CDC40 as a direct target of miR-378a. miR-378a represses cell growth and G1/S transition in CRC cells, and consistent with the inhibitory effect of miR-378a on tumor growth, CDC40 knockdown reduces proliferation and represses cell cycle progression by regulating G1/S and G2/M phases and pre-mRNA splicing. High miR-378a expression restrains CRC cell growth and invasion and downregulates vimentin while silencing miR-378a produces the inverse effects. miR-378 diametrically targets the 3′ UTR of vimentin to inhibit tumor progression. Similarly, BRAF and SDAD1 were significantly reduced by miR-378a-5p overexpression in CRC. SDAD1 expression increased upon blocking miR-378a, suggesting that miR-378a and SDAD1 have a direct but negative regulatory relationship. Further, the expression of KiSS1 is enhanced in CRC cells with miR-378a elevation, leading to decreased proliferation, migration, and invasion abilities of the cancer cells.
Low expression of miR-378a is related to a poor prognosis of liver cancer. Enhanced miR-378a expression inhibits the proliferation and invasion but increases the apoptosis in liver cancer. Moreover, miR-378a elevated the sensitivity of sorafenib treatment by targeting VEGFR, PDGFRβ, and c-Raf. miR-378a also enhances hepatocellular carcinoma (HCC) development by downregulating FUS expression, while miR-378a-5p targets VEGF. Moreover, miR-378a targets TRAF1 and weakens NF-κB signaling, consequently downregulating VEGF. IGF1R was also identified as a novel target of miR-378a in sorafenib-resistant HCC cells, with high miR-378a expression activating the PI3K/AKT and the Ras/Raf/MAP kinase pathways to mediate cell survival and IGF1R knockout significantly restrained the miR-378a-mediated sorafenib resistance. miR-378a also suppresses HCC tumorigenesis by downregulating PD-L1 and STAT3 expression. Moreover, the overexpressed miR-378a suppressed HCC cell proliferation by arresting G2/M and inhibited tumor growth in vivo. Interestingly, in HCC, circCRIM1 acts as an upstream regulator of miR-378a that upregulates SKP2 by acting as a sponge for miR-378a and facilitates cell proliferation, angiogenesis, and the transition from the G1 to the S phase in the cell cycle.
Even with the advancement of therapeutic strategies, glioma still shows a poor prognosis due to its high tumor aggressiveness and unlimited proliferation. Serval clinical studies in glioma have shown that miR-378a is remarkably decreased in cancer tissue and can potentially act as a diagnostic biomarker. Glioma patients with lower miR-378a expression showed poorer OS, and miR-378a overexpression restrained cell migration and invasion. Downregulated miR-378a increased the target IRG1 expression, which enhanced glioma cell growth, invasion, migration, and EMT. Another study showed that TSPAN17 was a direct target of miR-378a and correlated with poor prognosis in glioblastoma patients. Overexpressed miR-378a weakened TSPAN17 expression, consequently promoting apoptosis and reducing proliferation, migration, and invasion. These effects were attenuated in rescue experiments increasing TSPAN17 expression. Interestingly, interfilamentous vimentin can also mediate miR-378a self-renewal by regulating SOX2 transcription factor expression. Further, miR-378a promotes cell survival, tumor growth, and angiogenesis by targeting SUFU and FUS-1 expression.
miR-378a is notably underexpressed in PCa tissues. The reduction of miR-378a levels resulted in higher Gleason score, larger diameter tumors, and elevated serum prostate-specific antigen in PCa. miR-378a improved risk stratification based on the Gleason score or tumor stage and led to a higher risk of recurrence in patients with low miR-378a levels. Thus, miR-378a is a promising independent predictor of short-term recurrence in patients at high and very high risk of recurrence. CircRNA PDHX boosts PCa progression by sponging miR-378a. MiR-378a also inhibits prostate cell proliferation and glucose metabolism by repressing GLUT1. Forced miR-378a overexpression represses prostate cancer cell migration and invasion but promotes cell apoptosis in vitro. Further, studies have shown that KLK2, KLK4, and MAPK1 are the targets of miR-378a in PCa, and the ectopic expression of MAPK1 rescues miR-378a-inhibited cell migration and invasion capacity.
Even in BC, miR-378a shows a low expression and correlates with the unfavorable prognosis of BC patients upon tamoxifen treatment. GOLT1A is regulated by miR-378a and is critical for the mechanisms underlying BC endocrine resistance. The m6A-mediated increase in LINC00958 expression enhanced tumorigenesis through the miR-378a/YY1 pathway. Further, DCTPP1 is modulated by miR-378a, and decreased miR-378a expression elevates the DCTPP1 level, which facilitates cell proliferation by activating DNA repair signaling. RUNX1 is significantly increased upon inhibition of miR-378a expression, which also enhances the invasion and migration of the triple-negative breast cancer (TNBC) cells. Further, the regulation of RUNX1 is mediated by the PPARGC1Β/miR-378a/RUNX1 regulatory pathway. It has also been revealed that lncRNA GAS5 and miR-378a bind to each other, and the target of miR-378a-5p, SUFU, promotes GAS5-induced apoptosis of TNBC cells. This implicates that GAS5 stimulates apoptosis in TNBC cells by regulating miR-378a-5p/SUFU signaling. Moreover, although no significant effect of miR-378a was found on KLK4 expression, both miR-378a and KLK4 represent unfavorable prognostic markers in TNBC patients.
In addition to the above-mentioned tumors, decreased miR-378a expression has also been reported in other cancers. miR-378a is significantly reduced in MB. miR-378a negatively regulates UHRF1 expression in MB by binding to the 3′ UTR of its transcript. Therefore, miR-378a overexpression inhibits UHRF1, but increased expression of UHRF1 reverses the miR-378a-induced suppression of cell proliferation and promotion of apoptosis. In PA, RNF31 is highly expressed, and its expression is negatively regulated by miR-378a. Knocking out RNF31 inhibits the proliferation and migration of the pituitary tumor cell line GH3. Similarly, miR-378a plays an inhibitory role in oral squamous cell carcinoma (OSCC). miR-378a is downregulated in OSCC and also targets KLK4. Overexpressed KLK4 reverses the miR-378a-induced suppression of migration and invasion by enhancing MMP-9, MMP-2, and N-cadherin expression while decreasing E-cadherin levels. CircRNA-100290 can also rescue the miR-378a-induced suppression of GLUT1 by functioning as a competing endogenous RNA (ceRNA), thereby accelerating cell proliferation and glycolysis in OSCC. NCAPG, a target of miR-378a in OSCC, promotes cell proliferation and cell cycle progression but inhibits apoptosis by activating the GSK-3β/β-catenin signaling in OSCC. In bladder cancer, miR-378a is downregulated and can act as an independent prognostic factor for the OS and recurrence. LINC00958, an upstream regulator of miR-378, promotes the proliferation and metastasis of bladder cancer cells by increasing IGF1R, a downstream target of miR-378a, by sponging miR-378a. miR-378a is lower in esophageal squamous cell carcinoma (ESCC) cells and tissues than in normal cells and adjacent tissues. Low miR-378a expression leads to an unfavorable prognosis and shorter OS in ESCC patients. Further, low miR-378a levels elevate cell proliferation, migration, and invasion. However, miR-378a overexpression exerts the opposite effect by inhibiting Rab10. Mechanistically, the lncRNAs SLC2A1-AS1 and LINC00514 are essential in ESCC progression by acting as miR-378a sponges. SLC2A1-AS1 inhibits miR-378a-5p by regulating glycolysis, thereby restoring GLUT1 expression and enhancing ESCC. LINC00514, acting as a ceRNA, upregulates SPHK1 by sponging miR-378a-5p and activates adipogenesis-related pathways, thereby promoting the proliferation and invasion of ESCC cells. In a RMS-derived cell line, miR-378a upregulation inhibited IGF1R expression and impacted phosphorylated-Akt protein levels. Moreover, ectopic expression of miR-378a modulated apoptosis, cell migration, cytoskeleton organization, and the expression of the muscle markers MyoD1, MyoR, desmin, and MyHC in RMS. DNA demethylation by 5-aza-2 -deoxycytidine (5-aza-dC) also elevates miR-378a levels which correlate with increased apoptosis, cell viability reduction, and cell cycle arrest in G2-phase. In RB, upregulated miR-378a-3p inhibits cell proliferation by targeting FOXG1.
Unfortunately, cancer cells cumulatively get resistant to miscellaneous anti-tumor therapies, leading to disease recurrence and metastasis. Hence, understanding the internal molecular mechanism of cancer drug resistance is essential for finding new therapeutic approaches. Given that miR-378 is involved in various biological processes, the abnormal expression of miR-378a might be responsible for the anti-cancer resistance. For instance, miR-378a expression is preternatural in chemotherapy resistance in esophageal cancer cells. Many studies have also shown miR-378a as a promising candidate for anti-tumor treatment. In lung cancer, elevated miR-378a recovered CDDP chemosensitivity by targeting SCLU and downregulating Bcl-2, pCas-3, p-Erk1/2, and p-Akt. In ovarian cancer, low miR-378a expression can elevate the efficacy of bevacizumab treatment in patients with recurrent ovarian cancer, thereby lengthening PFS. Further, multivariate analysis showed miR-378a level as an efficient predictor of PFS after anti-angiogenic therapy. In CRC cells with upregulated miR-378a, sensitivity to cetuximab was restored in all BRAF mutants and half of KRAS mutants, and lauric acid improved the sensitization of Cetuximab in KRAS/BRAF mutated CRC cells by regaining miR-378a expression. Moreover, EPA-induced upregulation of miR-378 led to the significant restoration of sensitivity to cetuximab in the KRAS-mutant cells.In glioma, the inhibitory effect of curcumin was enhanced in miR-378a-expressing stable U87 cells. Likewise, increased miR-378a expression enhances radiation response by promoting radiation-induced TGD in glioblastoma cells. However, reduced miR-378a expression was found in sorafenib-resistant HCC cells than in the sorafenib-sensitive group. Further, LXR stimulated miRNA-378a-3p transcription and hence can be used as a potential combinable treatment strategy with sorafenib to suppress HCC progression. Metformin also induces miR-378a to downregulate CDK1, leading to suppression of cell proliferation and induction of the G2/M cell cycle arrest in HCC. Moreover, the biologically active component isolated from the fungus Ganoderma lucidum can also overcome the drug resistance conferred by miR-378.
Cancer is the leading cause of death worldwide. The high mortality rate of cancers can be ascribed to its inefficient early detection, inherent or acquired therapeutic resistance, and poor predictive power of conventional screening techniques and clinicopathological parameters. Hence, searching for novel molecular targets is necessary to solve this problem. The miRNA miR-378a is a promising biomarker and therapeutic target for cancers. As discussed in this paper, miR-378a expression is aberrant in more than 23 cancers and is responsible for affecting the cell's capability to proliferate, invade, metastasize, and resist multiple anti-tumor treatment regimens. However, there is still some controversy and ambiguity over its expression and roles in some cancers. miR-378a plays a dual role in tumors and can produce different impacts on different tumors based on whether it is overexpressed or under-expressed. The fact that it acts as an onco-miRNA makes it an attractive therapeutic target in lung cancer, CC, OC, NPC, melanoma, osteosarcoma, cholangiocarcinoma, AML, CML, renal carcinoma, and BL. However, it acts as a tumor suppressor in GC, CRC , liver cancer, glioma, PCa, BC, MB, PA, OSCC, bladder cancer, ESCC, RMS, and RB. Notably, the expression and role of miR-378a are still controversial in malignant tumors, including lung cancer and CC. All these effects may be due to the different expression of the two strands of miR-378a and it is also due to the different specific expression of miR-378a in tissues and cells of different tumors. Further, the differences and limitations in the source of cell and tissue samples might be the key influencing factors. The expression of circulating miR-378a can be used for cancer screening and diagnosis. The identification of the signaling pathways involved in cell proliferation, migration, invasion, and apoptosis also provides evidence for the different functions of miR-378a. miR-378a has also shown value as a therapy sensitizer and drug target. All the results mentioned above might be due to the regulation of miR-378a by various factors, such as circRNA and lncRNA, and the interaction between miR-378a and its targets that constitute complex regulatory networks. Depending on the cellular environment, miR-378a might have various biological functions and clinical applications and can even be expressed differently and have varied roles in different tumors. Because of the dual function of miR-378a in cancer development and whether targeting miR-378a is an effective and feasible approach for individual tumor patient-based treatment, more detailed investigations and understanding of the mechanisms underlying how miR-378a regulates tumor progression and therapy resistance are required in the future. Different analysis methods and intervention strategies for the role of the miRNA in different cancers are essential. Further, considering that miR-378a can effectively regulate the response of cancer cells to chemotherapy and radiotherapy, a combination of miR-378a targeted therapy and chemotherapy or radiotherapy might achieve better therapeutic effects in some cancer types. Thus, in this review, we provide insights into the rationale underlying the dual functions of miRNAs in tumor onset and progression. We show that miR-378 is critical for key biological and pathological processes via its complex network regulation mechanism in human cancers. In the future, miR-378 has great potential to become a clinically effective approach for cancer diagnosis and prognosis by refining cancer types and subtypes and an effective therapeutic strategy against cancer. | true | true | true |
PMC9608077 | Lorena Alexandra Lisencu,Andrei Roman,Simona Visan,Eduard-Alexandru Bonci,Andrei Pașca,Emilia Grigorescu,Elena Mustea,Andrei Cismaru,Alexandru Irimie,Cosmin Lisencu,Loredana Balacescu,Ovidiu Balacescu,Oana Tudoran | The Role of miR-375-3p, miR-210-3p and Let-7e-5p in the Pathological Response of Breast Cancer Patients to Neoadjuvant Therapy | 20-10-2022 | breast cancer,neoadjuvant therapy,pathological complete response,miR-375-3p,let-7e-5p,MiR-210-3p | Background and Objectives: Prediction of response to therapy remains a continuing challenge in treating breast cancer, especially for identifying molecular tissue markers that best characterize resistant tumours. Microribonucleic acids (miRNA), known as master modulators of tumour phenotype, could be helpful candidates for predicting drug resistance. We aimed to assess the association of miR-375-3p, miR-210-3p and let-7e-5p in breast cancer tissues with pathological response to neoadjuvant therapy (NAT) and clinicopathological data. Material and methods: Sixty female patients diagnosed with invasive breast cancer at The Oncology Institute “Ion Chiricuță”, Cluj-Napoca, Romania (IOCN) were included in this study. Before patients received any treatment, fresh breast tissue biopsies were collected through core biopsy under echographic guidance and processed for total RNA extraction and miRNA quantification. The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) database was used as an independent external validation cohort. Results: miR-375-3p expression was associated with more differentiated tumours, hormone receptor presence and lymphatic invasion. According to the Miller–Payne system, a higher miR-375-3p expression was calculated for patients that presented with intermediate versus (vs.) no pathological response. Higher miR-210-3p expression was associated with an improved response to NAT in both Miller–Payne and RCB evaluation systems. Several druggable mRNA targets were correlated with miR-375-3p and miR-210-3p expression, with upstream analysis using the IPA knowledge base revealing a list of possible chemical and biological targeting drugs. Regarding let-7e-5p, no significant association was noticed with any of the analysed clinicopathological data. Conclusions: Our results suggest that tumours with higher levels of miR-375-3p are more sensitive to neoadjuvant therapy compared to resistant tumours and that higher miR-210-3p expression in responsive tumours could indicate an excellent pathological response. | The Role of miR-375-3p, miR-210-3p and Let-7e-5p in the Pathological Response of Breast Cancer Patients to Neoadjuvant Therapy
Background and Objectives: Prediction of response to therapy remains a continuing challenge in treating breast cancer, especially for identifying molecular tissue markers that best characterize resistant tumours. Microribonucleic acids (miRNA), known as master modulators of tumour phenotype, could be helpful candidates for predicting drug resistance. We aimed to assess the association of miR-375-3p, miR-210-3p and let-7e-5p in breast cancer tissues with pathological response to neoadjuvant therapy (NAT) and clinicopathological data. Material and methods: Sixty female patients diagnosed with invasive breast cancer at The Oncology Institute “Ion Chiricuță”, Cluj-Napoca, Romania (IOCN) were included in this study. Before patients received any treatment, fresh breast tissue biopsies were collected through core biopsy under echographic guidance and processed for total RNA extraction and miRNA quantification. The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) database was used as an independent external validation cohort. Results: miR-375-3p expression was associated with more differentiated tumours, hormone receptor presence and lymphatic invasion. According to the Miller–Payne system, a higher miR-375-3p expression was calculated for patients that presented with intermediate versus (vs.) no pathological response. Higher miR-210-3p expression was associated with an improved response to NAT in both Miller–Payne and RCB evaluation systems. Several druggable mRNA targets were correlated with miR-375-3p and miR-210-3p expression, with upstream analysis using the IPA knowledge base revealing a list of possible chemical and biological targeting drugs. Regarding let-7e-5p, no significant association was noticed with any of the analysed clinicopathological data. Conclusions: Our results suggest that tumours with higher levels of miR-375-3p are more sensitive to neoadjuvant therapy compared to resistant tumours and that higher miR-210-3p expression in responsive tumours could indicate an excellent pathological response.
Breast cancer (BC) is the most common cause of cancer among women worldwide, accounting for 15–20% of all cancer deaths in women [1]. Due to advances in diagnosis and treatment modalities, the prognosis of this disease has improved in recent years, with a 5-year survival rate of almost 90%. However, around 30% of breast cancer patients fail to respond to conventional treatments, leading to tumour progression [1]. Breast cancer is a highly heterogeneous disease; therefore, its treatment depends on multiple clinical and pathological factors such as tumour grade and hormone receptor (HR) status. Tumour characteristics and the extent of the disease direct the choice and timing of systemic treatments (chemotherapy, endocrine therapy or HER2-directed therapy). In the case of high-risk primary tumours or locally advanced breast cancer, neoadjuvant (preoperative) therapy is a frequently practical therapeutic approach as it offers the advantage of reducing the extent of surgery [2,3]. Furthermore, a tumour’s response to NAT can be used to guide adjuvant treatment selection and offer prognostic information regarding patient outcome [4]. The response to neoadjuvant therapy (NAT) is usually assessed clinically and pathologically. Pathological evaluation is the gold standard, as clinical evaluation can often misevaluate the response to NAT. While several systems have been proposed to evaluate the pathological response to NAT, the most used system for prognosis prediction are the Miller–Payne (MP) and the residual cancer burden (RCB) systems [5,6]. The MP system evaluates the changes in tumour cellularity between biopsy and surgery tissue. It has five grades as follows: 1 (no change), 2 (minor reduction in tumour cells, but 30%), 3 (reduction in tumour cells by 30–90%), 4 (reduction in tumour cells with >90%) and 5 (no detectable tumour cells). Grades 1–4 correspond to partial pathological response (pPR) while grade 5 means pathological complete response (pCR) [7,8]. The RCB system measures the primary tumour’s bidimensional size and cellularity and assesses lymph nodes’ involvement. The RCB index is classified as 0 (pCR), 1 (minimal residual disease), 2 (moderate residual disease) and 3 (extensive residual disease) [8,9]. The goal of NAT is pCR, as it plays an important prognostic role in BC patients. PCR is associated with improved overall survival and disease-free survival in comparison with those that do not achieve pCR and who have an unfavourable prognosis [3]. Due to its role in the prognosis of BC patients, predicting pCR is important in order to identify those patients that would benefit from NAT. While routine HR, HER2 receptors, grading and Ki-67 assessment remain essential for treatment guidance, non-coding ribonucleic acids (RNA) have increased in popularity as their dysregulation has been associated with breast cancer pathogenesis. Microribonucleic acids (miRNA) are small, non-coding RNAs that regulate gene expression at the post-transcriptional level by binding to target messenger RNAs (mRNAs) and triggering their degradation. One of the first papers about the role of miRNA in cancer pathology demonstrated that miRNA is a better classifier than mRNA profiling when investigating poorly differentiated tumours, opening the way for using miRNA expression as a reliable marker for cancer diagnosis, prognosis and treatment response [10]. Since then, overwhelming data have indicated that miRNAs are involved in the regulation of processes such as proliferation, apoptosis and migration of cancer cells [3], having the potential of being oncogenic (oncomirs), tumour suppressors or both [1,11]. As key modulators of oncogenesis, miRNAs have been reported to have clinical utility in the diagnostic, prognostic and therapeutic approach of breast cancer patients [1,11], making them highly attractive as biomarkers for personalized medicine [1]. Several miRNAs have been reported to have predictive power in pathological response following NAT in breast cancer [12,13,14], with most of these studies being focused on neoadjuvant chemotherapy (NACT). However, recent treatment guidelines [15] encourage the administration of endocrine as well as targeted therapies concurrent with, or instead of, NACT to increase tumours’ sensitivity to treatment. Thus, there is an increasing need to further explore the role of these miRNAs as biomarkers of NAT response. Based on the existing literature, we have identified conflicting data regarding the prognostic role of several miRNAs. Of interest, miR-375-3p, miR-210-3p and let-7e have shown discrepancies regarding the clinical significance as prognostic biomarkers [16,17,18,19,20], being reported to have both increased and decreased expression associations with BC patients’ response to NAT. In this study, we aimed to assess the prognostic value of these highly controversial miRNAs, miR-375-3p, miR-210-3p and let-7e-5p in breast cancer tissues by investigating their expression association with patients’ pathological response to neoadjuvant therapy and clinicopathological features.
Sixty female patients diagnosed with invasive breast cancer at The Oncology Institute “Ion Chiricuță”, Cluj-Napoca, Romania (IOCN) were included in this study. The study was approved by the IOCN ethical committee (Approval No. 59/29.11.2016) and by the University of Medicine and Pharmacy Iuliu Hatieganu, Cluj-Napoca, Romania (Approval No. 290/09.09.2020). All patients were informed and gave their written consent for participation in the study following the Declaration of Helsinki. Before patients received any treatment, fresh breast tissue biopsies were collected through core biopsy under echographic guidance. The first core biopsy was sent for pathologic analysis, while a second biopsy was collected in RNAlater (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA) and stored in liquid nitrogen for transcriptomic studies.
Frozen biopsies were homogenized in TriReagent Solution (Ambion, Thermo Fisher Scientific, Waltham, MA, USA) using a Miccra D-1 (Miccra GmbH, Mullheim, Germany) polytron and processed for total RNA extraction using the classic phenol–chloroform method. The RNAs were quantified using NanoDrop ND-1000 (Thermo Scientific, Wilmington, DE, USA) and 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA).
Fifty nanograms (ng) of total RNAs were pre-amplified using universal RT miRNA primers to generate cDNAs following the TaqMan Advanced miRNA cDNA Synthesis Kit protocol (Thermo Fisher Scientific, Waltham, MA, USA). Next, 1:10 v/v diluted cDNAs and specific miRNA advanced assays were amplified with TaqMan Fast Advanced Master Mix (2X) (Thermo Fisher Scientific, Waltham, MA, USA) using the Light Cycler 480 device (Roche, Basel, Switzerland) with the following PCR settings: 55 °C for 2 min to remove RNA contaminants; 95 °C for 20 s for Taq polymerase amplification; and 40 cycles of 95 °C for 3 s followed by 60 °C for 30 s for PCR amplification. The ∆∆Ct method was used for miRNA relative quantification by reporting the Ct values of the miRNAs of interest to miR-16-5p Ct values.
The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) expression data (miRNA and mRNA) and their clinical information were obtained from National Cancer Institute Genomic Data Commons (NCI GDC) data portal (https://portal.gdc.cancer.gov/, accessed on 19 April 2019) and cBioPortal for Cancer Genomics (https://www.cbioportal.org/, accessed on 19 April 2019). The miRNA-seq data, expressed as reads per million and fragments per kilobase millions mRNA-seq data, were filtered and log2(x + 1)-transformed. After processing, a miRNA dataset containing 916 tumoral samples and 93 standard samples and an mRNA dataset of 983 tumoral samples were retained for subsequent analysis. Pearson correlation was used to test potential miRNAs–mRNA associations and intersected with validated miRNA–target interactions retrieved from the miRTarBase. The Ingenuity Pathway Analysis (IPA, Qiagen, Redwood City, CA, USA) upstream analysis module was used to interrogate for possible targeting drugs.
The correlation between clinicopathological characteristics and tissue miRNAs expression was evaluated with the Mann–Whitney U test for two categorical variables or the Kruskal–Wallis test. It was followed by Dunn’s multiple comparison post hoc test in the case of three or more categorical variables based on the data distribution. A p-value less than 0.05 was considered statistically significant. Fold regulation (FR) was calculated as the ratio between mean value of the interest group and the reference group.
The clinicopathological features of the 60 included patients are summarized in Table 1. The median age of the patients was 60 (29–77), with most of the patients (73.33%) being over 50 years old at the time of diagnosis. Over 85% of the patients had moderately to poorly differentiated carcinomas, and 61.67% presented Ki-67 higher than 20. Most of the patients had luminal tumours (76.67%), with luminal B being the predominant subtype (46.67%). Over 85% of the patients were already in advanced clinical stages at diagnosis (>II). Of the 60 investigated patients, 49 received NAT: 32 received chemotherapy alone, 7 received hormonal therapy, 4 also received Her2 targeted therapy, while 5 received combinations of regimens. TNM staging was retained for prognostic information (primary and post-NAT surgery), while the Miller–Payne and RCB systems were used to evaluate the pathological response of the patients to NAT. According to the Miller–Payne evaluation, 14 patients did not respond to NAT, 4 presented a minor response, 13 had an intermediate response, and 13 had almost complete pathological response. According to the RCB classification system, 13 patients reached a high pathological response, 16 were therapy-resistant, and 15 had a partial response.
The association between tissue miRNA expression and the clinicopathological data of the patients included is presented in Table 2. No correlations were observed between the investigated miRNA expression and the age of the patients at a 50-years-of-age cut-off value. Additionally, no significant clinicopathological associations were found for let-7e-5p expression. Lower miR-375-3p expressions were associated with higher tumour grading and KI67 proliferation index. ER- and PR-positive tumours had a higher miR-375-3p expression, with significantly decreased expression in TNBC compared to luminal subtypes. Except for miR-375-3p expression with lymphatic positivity, no other significant correlations were demonstrated between tissue miRNAs expression and TNM staging, neither for clinical nor pathological evaluations. According to the Miller–Payne system, patients with a high pathological response to NAT had lower miR-375-3p and higher miR-210-3p expressions compared to intermediate- and low-responding patients. Higher miR-210-3p expression was observed for high responders versus partial responders in the RCB evaluation system. Analysis of the miRNAs expression in the TCGA database was performed as an independent external validation cohort. Increased miRNA expression was observed between tumour and standard samples (Supplementary Figure S1A), with luminal tumours having significantly higher miR-375-3p and lower miR-210-3p expression (Figure 1, Supplementary Table S1 and Figure S1B–D) compared to the basal-like subtype. No significant miRNA expression differences (FR cut-off > 1.5) were observed for patients with positive lymph nodes (Supplementary Figure S2A) or metastatic diseases (Supplementary Figure S2B). Patients with advanced diseases had higher miR-210-3p expression (Figure 2, Table S1), while a slight decrease in miR-210-3p expression was associated with a better survival (Figure 3, Table S1). In order to explore the possible mechanisms mediated by the investigated miRNAs, a correlation analysis between miRNA and mRNA expression in the TCGA cohort was undertaken. The significantly correlated genes were intersected with the validated mRNA targets downloaded from the miRTarBase. Only the validated target genes that were inversely correlated with miRNA expression and with a correlation coefficient under −0.3 were considered of interest (Table S2). A total of 17 possible mRNA targets for miR-375-3p and 9 for miR-210-3p were identified (Figure 4A). No significantly correlated genes were observed for let-7e-5p. The number of identified genes was too small to run a GSEA analysis; therefore, no specific molecular mechanisms could be attributed to either miRNA. Upstream analysis using the IPA knowledge base identified a list of 127 possible chemical and biological targeting drugs (Figure 4B).
Different miRNA signatures have been associated with response to chemo-endocrine or radiotherapy in breast cancer [11], emerging as valuable biomarkers for a personalized therapeutic approach of the disease [1,23]. This study reports the expression profiles of three miRNAs involved in the treatment response of BC patients. The analysis explored the association of miRNA expression with the pathological response to NAT and their correlation with the clinicopathological data of the patients. The patients’ pathological response to NAT was assessed using both MP and RCB systems. However, both of them have limitations. The MP system ignores the involvement of the axillary lymph nodes; therefore, the prognosis can be overrated in lymph-node-positive patients [8]. The RCB classification showed a better performance than the MP system, especially for the TNBC subtype [8], but it is limited to anatomical factors without considering the biological ones [8]. To improve its value, assessment of Ki-67 expression after treatment in combination with the RCB index might improve the prediction of survival outcomes [24]. Of the three investigated miRNAs, miR-375-3p and miR-210-3p were significantly associated with MP response, while miR-210-3p expression was also significantly associated with RCB. MiR-375-3p is dysregulated in various types of cancer. It is involved in epithelial-to-mesenchymal transition (EMT), and is associated with increased invasiveness potential while also being correlated with refractory response to chemotherapy [25]. MiR-375 is a known tumour suppressor. In hepatocellular carcinoma (HCC), it inhibits the autophagy and tumour growth. Moreover, miR-375 promotes the release of mitochondrial apoptotic proteins, reducing the viability of HCC cells in hypoxic conditions. In HCC, miR-375 was downregulated. Autophagy is an adaptive mechanism of the tumour cells that helps them to survive in the tumour microenvironment conditions by reducing apoptosis and enhancing the elimination of the injured mitochondria [26]. Although miR-375 could be related to treatment response by mitochondria reprogramming, to date, there are no data presenting evidence about the role of mir-375 in inducing NAT response through mitochondria reprogramming in breast cancer. Despite the well-documented role as a tumour suppressor, in breast cancer, miR-375-3p is upregulated [16,27,28] and is highly expressed in hormone-receptor-positive breast tumours [29] and lymph-node-positive patients [29]. The present results are in line with these findings. The upregulated expression of miR-375-3p in breast cancer suggests a potential oncogenic activity [16]. Predicted miR-375-3p targets were downloaded from the miRTarBase and intersected with inversely correlated miRNA-mRNA genes from TCGA. Using GSEA, we interrogated Reactome, Kyoto Encyclopedia of Genes and Genomes (KEEG), and Gene Ontology (GO) databases to explore gene functionality. However, the gene list was too small to generate any significant signalling pathways associated with the identified genes. Thus, the regulatory role of miR-375-3p in breast cancer remains unclear. Multiple miR-375-3p-mediated therapy resistance mechanisms have been described. Generally, miR-375-3p expression is downregulated in drug-resistant BC cells, while its overexpression has been shown to increase cells’ sensitivity to chemo-endocrine or targeted therapy. Mir-375-3p-mediated targeting of YBX1 [30] and JAK2 genes [31] has led to increased sensitivity to Adriamycin and paclitaxel first-line treatments. In fulvestrant-resistant BC cells, the overexpression of miR-375-3p inhibited cell growth and autophagy by silencing autophagy-related proteins [30]. Furthermore, by targeting HOXB3 (17) or MDTH gene expression [32], miR-375-3p has decreased EMT, stem features and resistance to tamoxifen in ER-positive BC cells. Moreover, epigenetic silencing of miR-375-3p induced trastuzumab resistance in HER2-positive BC by targeting IGF1R [33]. All this evidence suggests that miR-375-3p might serve as a potential therapeutic approach for the treatment of resistant breast cancer and as a prognostic marker of therapy. According to the MP evaluation system, in the present cohort, higher miR-375-3p expression was calculated for patients with intermediate response to NAT compared to nonresponders or good responders. Consistent with previous reports, lower miR-375-3p expression levels were observed in the resistant-to-NAT tumour group, while higher expression levels were associated with an improved response. Notably, low levels of miR-375 were also observed in the high-responsive group of patients. The existing literature regarding the prognostic potential of miR-375-3p is controversial. Furthermore, most reports are based on circulating miR-375-3p levels. According to a three-year follow-up study, patients with relatively higher tumour miR-375-3p expression had a worse survival rate and less survival time, namely, a worse prognosis [34]. On the other hand, a lower expression of circulating miR-375-3p was associated with incomplete response to NAT, while increased expression was noticed in patients achieving pCR after NAT [35]. Similarly, Wu et al. [36] reported that miR-375-3p prevalence in circulation was associated with better clinical outcomes, complete response to NAC, and an absence of relapse. Simultaneously, lower levels of miR-375-3p were noted in therapy-resistant HER2-positive patients. In nonresponder luminal B HER2- patients, NAT can induce the upregulation of circulating miR-375-3p, and this change might be associated with a good response to NAT [35]. Moreover, miR-375-3p association with NAT response seems to be subtype-specific. A lower expression of miR-375-3p was correlated with an increased risk of disease relapse in luminal B patients, while in luminal A, patients with lower miR-375-3p expression were found to be more sensitive to NAC [37]. However, when comparing circulating miRNA levels with tumour levels, it should be considered that the cellular source of circulating miR-375-3p remains unknown; their prevalence may not reflect expression in the primary tumour but rather a combination with other cell types, such as immune cells. High expression of miR-210-3p in human cancers has become a predictive marker of tumour hypoxia, increasing experimental evidence supporting its clinical relevance. In BC, miR-210-3p is overexpressed in tumour tissues, specifically in triple-negative and HER2+ tumours compared [38], while miR-210-3p upregulation was associated with drug-resistant breast cancer cells [18,39]. The literature data regarding the association of miR-210-3p expression and patient response to neoadjuvant treatment are conflicting, highlighting that both increased and decreased miR-210-3p expression have shown discrepancies regarding the clinical significance as prognostic biomarkers. In HER2-positive BC patients, higher circulating miR-210-3p levels were noticed before surgical excision in patients with residual disease and lymph node metastasis [18]. Muller et al. reported increased miR-210-3p circulating levels following NAT. However, no association between miR-210-3p levels and pCR was observed [40]. Conversely, higher circulating miR-210-3p levels have been associated with residual disease following trastuzumab-combined NAC in BC patients [18,41]. Similar results were also described in ER-positive BC patients treated with tamoxifen; miR-210-3p expression was linked with poor clinical outcomes and an increased risk of relapse [42]. A meta-analysis revealed that miR-210-3p overexpression correlated with poorer survival in TNBC patients and associated circulating miR-210 expression with resistance to doxorubicin, cyclophosphamide, cisplatin and paclitaxel [43]. When quantified in tissue samples, no clinical association with NAT response was observed in miR-210-3p expression levels in BC formalin-fixed paraffin-embedded tissues [40]. However, miR-210-3p expression was suggested to represent a marker for predicting metastasis development and a worse prognosis in patients treated with taxanes in adjuvant settings [44]. Bioinformatic analysis identified microtubule regulation, drug efflux pathways and NRF2-mediated oxidative stress response as the most significant associated pathways between miR-210-3p signalling and docetaxel resistance [44]. Analysis of the miR-210-3p expression in the present cohort was performed to further elucidate the controversy surrounding this miR’s ability to predict cPCR. Patients with almost complete or partial pathological responses had significantly higher miR-210-3p expression levels than nonresponders. MiR-210-3p’s role as an oncogene is well-characterized; however, it was also suggested to act as a tumour suppressor [19]. For example, in oesophageal squamous cell carcinoma, miR-210-3p has been shown to inhibit cancer cell survival and proliferation by inducing cell death and cell cycle arrest in G(1)/G(0) and G(2)/M through FGFRL1 downregulation [19]. More recently, Bar I et al. [45] showed that in TNBC patients, miR-210-3p is expressed by both tumour cells and the tumour microenvironment (TME) that is more likely to be regulated by a mechanism independent of HIF-1 alpha. MiR-210-3p has multiple functions, including the regulation of the immune response and increasing data support the concept that immunologically “hot” tumours are more responsive to chemotherapy [46]. Let-7e-5p is one of the first discovered miRNAs [47]. This is a tumour suppressor gene that targets essential pathways involved in tumorigeneses such as Janus protein tyrosine kinase (JAK), c-Myc and signal traducer and activator of transcription 3 (STAT3). The literature reports about let-7e-5p expression during NAT are scarce. In patients with a lower Ki-67 and pCR, a decrease in let-7e-5p expression was noticed after NAT [48]. Lv. J. et al. showed that let-7e-5p expression is down-regulated in chemoresistant tumours, while decreased expression was associated with a worse prognosis [20]. The present analysis did not find any significant association between let-7e-5p expression and pathological response to NAT or with the clinicopathological features of the patients, neither in the given cohort nor in the TCGA database. Our study is limited by the patients’ heterogeneity and the relatively small sample size cohort and, thus, the present analysis has low statistical power. However, to the best of our knowledge, this is the first study that illustrates a putative relationship between miR-375-3p and miR-210-3p and breast tumours’ pathological response to neoadjuvant therapy. Increasing the number of patients would allow for more homogeneous groups and subsequent differential analysis based on the administered type of therapy. Of great interest, prognostic markers for patients’ response to combined regimens are largely unexplored.
Based on documented mechanistic actions of the two miRNAs, our results suggest that tumours with higher levels of miR-375-3p are more sensitive to neoadjuvant therapy compared to resistant tumours, and that higher miR-210-3p expression in responsive tumours could indicate immunologically “hot” tumours. These findings suggest the potential role of these two miRNAs in stratifying BC patients that will respond to NAT. However, as data regarding these two miRNAs are controversial, further studies are needed to elucidate their complex role in mediating BC patients’ response to neoadjuvant therapy. | true | true | true |
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PMC9608165 | 36185028 | Ngoc Tung Quach,Thi Hanh Nguyen Vu,Thi Lien Bui,Anh Tuan Pham,Thi Thu An Nguyen,Thi Thanh Xuan Le,Thi Thu Thuy Ta,Pravin Dudhagara,Quyet-Tien Phi | Genome-Guided Investigation Provides New Insights into Secondary Metabolites of Streptomyces Parvulus SX6 from Aegiceras Corniculatum | 24-09-2022 | Aegiceras corniculatum,Streptomyces parvulus,genome mining,plant-derived compounds,secondary metabolites | Abstract Whole-genome sequencing and genome mining are recently considered an efficient approach to shine more light on the underlying secondary metabolites of Streptomyces. The present study unearths the biosynthetic potential of endophytic SX6 as a promising source of biologically active substances and plant-derived compounds for the first time. Out of 38 isolates associated with Aegiceras corniculatum (L.) Blanco, Streptomyces parvulus SX6 was highly active against Pseudomonas aeruginosa ATCC® 9027™ and methicillin-resistant Staphylococcus epidermidis (MRSE) ATCC® 35984™. Additionally, S. parvulus SX6 culture extract showed strong cytotoxicity against Hep3B, MCF-7, and A549 cell lines at a concentration of 30 μg/ml, but not in non-cancerous HEK-293 cells. The genome contained 7.69 Mb in size with an average G + C content of 72.8% and consisted of 6,779 protein-coding genes. AntiSMASH analysis resulted in the identification of 29 biosynthetic gene clusters (BGCs) for secondary metabolites. Among them, 4 BGCs showed low similarity (28–67% of genes show similarity) to actinomycin, streptovaricin, and polyoxypeptin gene clusters, possibly attributed to antibacterial and anticancer activities observed. In addition, the complete biosynthetic pathways of plant-derived compounds, including daidzein and genistein were identified using genome mining and HPLC-DAD-MS analysis. These findings portray an exciting avenue for future characterization of promising secondary metabolites from mangrove endophytic S. parvulus. | Genome-Guided Investigation Provides New Insights into Secondary Metabolites of Streptomyces Parvulus SX6 from Aegiceras Corniculatum
Whole-genome sequencing and genome mining are recently considered an efficient approach to shine more light on the underlying secondary metabolites of Streptomyces. The present study unearths the biosynthetic potential of endophytic SX6 as a promising source of biologically active substances and plant-derived compounds for the first time. Out of 38 isolates associated with Aegiceras corniculatum (L.) Blanco, Streptomyces parvulus SX6 was highly active against Pseudomonas aeruginosa ATCC® 9027™ and methicillin-resistant Staphylococcus epidermidis (MRSE) ATCC® 35984™. Additionally, S. parvulus SX6 culture extract showed strong cytotoxicity against Hep3B, MCF-7, and A549 cell lines at a concentration of 30 μg/ml, but not in non-cancerous HEK-293 cells. The genome contained 7.69 Mb in size with an average G + C content of 72.8% and consisted of 6,779 protein-coding genes. AntiSMASH analysis resulted in the identification of 29 biosynthetic gene clusters (BGCs) for secondary metabolites. Among them, 4 BGCs showed low similarity (28–67% of genes show similarity) to actinomycin, streptovaricin, and polyoxypeptin gene clusters, possibly attributed to antibacterial and anticancer activities observed. In addition, the complete biosynthetic pathways of plant-derived compounds, including daidzein and genistein were identified using genome mining and HPLC-DAD-MS analysis. These findings portray an exciting avenue for future characterization of promising secondary metabolites from mangrove endophytic S. parvulus.
Streptomyces is a well-known genus of actinobacteria, capable of producing various bioactive compounds widely used in medicinal and pharmaceutical industries. Terrestrial Streptomyces are known to be producers of secondary metabolites with significant biological activities, including antibacterial, anticancer, antioxidant, and anti-inflammatory, contributing to nearly 45% of commercially available antibiotics used by humans (Azman et al. 2017). Since the opportunity of finding novel metabolites has been limited to common terrestrial Streptomyces species in the last decades, extreme environmental Streptomyces have gained more attention (Lee et al. 2018; Girão et al. 2019). The mangrove is known for its dynamic environment with high salinity, temperature, pH, and fluctuating nutrient availability, from which various Streptomyces strains possess a wide array of therapeutic drugs that have already been isolated (Tan et al. 2017; Chandrakar and Gupta 2019; Quach et al. 2021). It is believed that actinobacteria can adapt highly to harsh mangrove conditions by developing unique metabolic pathways, which can provide novel secondary metabolites (Tan et al. 2017). Of note, one of the less explored niches in the mangrove environments is the mangrove plants such as Aegiceras corniculatum. A previous study reported that Streptomyces sp. GT-20026114 from A. corniculatum produced four novel cyclopentene derivatives; however, antimicrobial, anticancer, and antiviral activities were not detected (Wang et al. 2010). It raises the possibility of finding new bioactive compounds from endophytic Streptomyces. Instead of traditional methods that have considerably slowed the chance of finding new compounds, whole-genome sequencing and genome mining have paved a new way to exploit the biosynthetic potential of bioactive Streptomyces. Comparative genome studies demonstrated that Streptomyces species had an open genome in which biosynthetic gene clusters (BGCs) accounted for 15% of the genome size (Tian et al. 2016; Chevrette and Currie 2019). Interestingly, Streptomyces fildesensis and Streptomyces bingchenggensis devoted 22% of their genomes to BGCs (Núñez-Montero et al. 2019; Belknap et al. 2020). In addition, most BGCs remain poorly characterized and are silent under laboratory culture conditions. A novel anti-HIV compound streptoketides from soil Streptomyces sp. Tü 6314 was recently discovered by identifying a cryptic type II PKS cluster predicted by antiSMASH (Qian et al. 2020). In addition, genome analysis of S. coelicolor A3(2) found bacterial homologous genes of a plant-derived enzyme involved in isoflavonoid synthesis (Moore et al. 2002). Isoflavonoids such as genistein and daidzein are polyphenolic secondary metabolites in plants, which are believed to have anticancer, antibacterial, and antioxidant activities (Liu et al. 2021; Sohn et al. 2021). Surprisingly, endophytic Streptomyces spp. such as Streptomyces variabilis LCP18, Streptomyces sp. YIM 65408, and Streptomyces cavourensis YBQ59 also produced either active genistein or daidzein in the cultural broth (Yang et al. 2013; Vu et al. 2018; Quach et al. 2021). However, genes encoding functional proteins involved in the biosynthesis of these plant-derived compounds have not been exploited yet. More and more Streptomyces genomes publicly available would increase opportunities for identifying novel and existing BGCs, avoiding time-consuming and labor-intensive experiments. In this study, we characterized biological activities and sequenced the genome of Streptomyces parvulus SX6 associated with A. corniculatum collected in the mangrove forest area of Quang Ninh province, northern Vietnam, where studies on actinobacteria and their bioactive metabolites are scanty. Given that only three terrestrial S. parvulus are available from the NCBI, this is the first genomic report of mangrove endophyte S. parvulus showing BGCs attributed to remarkable antibacterial and anticancer activities. In addition, the biosynthetic pathway of plant-derived compounds, including daidzein and genistein, was proposed using comparative genomic and HPLC-DAD-MS analysis. These findings highlight the capability of endophytic Streptomyces from mangrove plants to produce novel agents and plant-derived compounds with therapeutic applications.
Collection and isolation of endophytic actinobacteria. The roots, stems, and leaves of healthy mangrove plants A. corniculatum were collected from different sites in Quang Ninh province (21.0064°N, 107.2925°E), Vietnam, in June 2020. These samples were placed in sterile plastic bags, transported to the laboratory, and used for isolation procedures within 48 h. The obtained plants were then identified as A. corniculatum species by the Institute of Ecology and Biological Resources, Vietnam Academy of Science and Technology. The samples were washed with tap water and distilled water. The surface sterilization procedure was carried out as described previously to eliminate unwanted microorganisms (Musa et al. 2020; Vu et al. 2020). The sterilized samples were frozen at –80°C for 2 weeks and spread onto 8 media, including humic acid-vitamin B agar (humic acid 1.0 g/l, Na2HPO4 0.5 g/l, KCl 1.7 g/l, MgSO4 · 7H2O 0.05 g/l, CaCl2 1.0 g/l, vitamins mixture 1.0 g/l, agar 15.0 g/l, pH 7.0), raffinose-histidine agar (histidine 0.5 g/l, raffinose 2.5 g/l, K2HPO4 1.0 g/l, MgSO4 · 7H2O 0.5 g/l, FeSO4 · 7H2O 0.01 g/l, CaCl2 0.02 g/l, agar 15.0 g/l, pH 7.0), tap water-yeast agar (yeast 0.25 g/l, K2HPO4 0.5 g/l, agar 15.0 g/l, pH 7.2), trehalose-proline agar (trehalose 5.0 g/l, proline 1.0 g/l, (NH4)2SO4 1.0 g/l, NaCl 1.0 g/l, CaCl2 2.0 g/l, K2HPO4 1.0 g/l, MgSO4 · 7H2O 1.0 g/l, agar 15.0 g/l, pH 7.0), sodium succinate-asparagine agar (sodium succinate 1.0 g/l, L-asparagine 1.0 g/l, KH2PO4 0.9 g/l, K2HPO4 0.6 g/l, MgSO4 · 7H2O 0.1 g/l, CaCl2 0.2 g/l, KCl 0.3 g/l, FeSO4 · 7H2O 0.001 g/l, agar 15.0 g/l, pH 7.2), starch agar (starch 20 g/l; KNO3 2 g/l, K2HPO4, 1.0 g/l, MgSO4 · 7H2O 0.5 g/l, NaCl 0.5 g/l, CaCO3 3.0 g/l, FeSO4 · 7H2O 0.01 g/l, agar 15.0 g/l, pH 7.0), citrate acid agar (citric acid 0.12 g/l, NaNO3 1.5 g/l, K2HPO4 0.4 g/l, MgSO4 · 7H2O 0.1 g/l, CaCl2 0.05 g/l, EDTA 0.02 g/l, Na2CO3 0.2 g/l, agar 15.0 g/l, pH 7.2), and sodium propionate agar (sodium propionate 1.0 g/l, L-asparagine 0.2 g/l, KH2PO4 0.9 g/l, K2HPO4 0.6 g/l, MgSO4 · 7H2O 0.1 g/l, CaCl2 0.2 g/l, agar 15.0 g/l, pH 7.0) as described previously (Qin et al. 2009; Musa et al. 2020; Vu et al. 2020). Each medium was amended with 50 mg/ ml nystatin, 25 mg/ ml K2Cr2O7, and 25 mg/ ml nalidixic acid to inhibit the growth of Gram-negative bacteria and fungi. All plates were incubated for one month at 30°C. Once observed, actinobacteria colonies were purified by repeated streaking onto International Streptomyces Project (ISP) 2 medium (Quach et al. 2021) and then stored in 15% (v/v) glycerol at –80°C. Morphological characteristics and molecular identification by 16S rRNA phylogenetic analysis. Morphological and physical characteristics of the bioactive isolate were studied using a series of ISP1-ISP7 agar media. The morphological features were observed using a scanning electron microscope (SEM) JSM-5410 (JEOL, Japan). To evaluate the effect of pH, strain SX6 were grown in ISP2 medium at pH 2.0–10.0 adjusted with different buffer systems including 0.1 M KCl/0.02M HCl pH 2.0; 0.1 M citric acid/0.1 M sodium citrate pH 3.0–5.0; 0.1 M KH2PO4/0.1 M NaOH pH 6.0–8.0; 0.1 M NaHCO3/0.1 M Na2CO3 pH 9.0–10.0 (Singh et al. 2019). Growth at different NaCl concentrations (0–10%, w/v) and varying temperature conditions (15–45°C) was performed as described previously (Quach et al. 2021). The ability to utilize sole carbon and nitrogen sources was assessed using the basal medium described previously (Williams et al. 1983). The enzymatic tests such as amylase, cellulase, chitinase, protease, and xylanase were performed on the ISP2 agar medium (Quach et al. 2021). Following the manufacturer’s protocol, the genomic DNA of strain SX6 was extracted using G-spin™ Total DNA Extraction Mini Kit (Intron Bio, Korea). PCR amplification for the 16S rRNA gene was performed as described previously (Quach et al. 2021). The identification of phylogenetic neighbors and calculation of pairwise 16S rRNA gene sequence similarities were carried out on the EzTaxon server (Chun et al. 2007). The phylogenetic tree was built by the maximum-likelihood method using Molecular Evolutionary Genetics Analysis (MEGA) software version 7 with Kimura-2-parameter distances. Nocardia farcinica ATCC® 3318™ (NR_115831) was used as an outgroup branch. The obtained 16S rRNA gene sequence was deposited at GenBank (NCBI) under accession number OL468549. Inhibitory effects of strain SX6 on pathogenic bacteria. All endophytic actinobacteria were cultivated in an ISP2 medium at 30°C with shaking at 180 rpm for 8 days. Agar-well dilution assay was used to evaluate antimicrobial activity against 6 pathogenic bacteria, including Bacillus cereus ATCC® 11778™, Pseudomonas aeruginosa ATCC® 9027™, methicillin-resistant Staphylococcus epidermidis (MRSE) ATCC® 35984™, Enterobacter aerogenes ATCC® 13048™, Escherichia coli ATCC® 11105™, Salmonella typhimurium ATCC® 14028™ (Holder and Boyce 1994). All test bacteria were grown in Luria-Bertani (LB) medium and then spread on the entire surface of LB agar plates. Six mm (diameter) wells were perforated in the agar, in which 100 μl of cell-free supernatant was added to each well. The experiment was performed in triplicates, and diameters of inhibition zones were determined after 12–16 h of incubation at 37°C. Heatmap illustrating the antibacterial activity of endophytic isolates was generated through the online software Heatmapper (Babicki et al. 2016). Ethyl acetate was used to extract secondary metabolites from strain SX6 following the procedure described previously (Nguyen et al. 2019b). In brief, the mixture of cell-free supernatant:ethyl acetate (1:1 ratio) was vigorously shaken for 30 min and kept stationary 60 min until the separation of aqueous and organic phases. The organic phase was evaporated on the rotary evaporator (Scilogex RE100-Pro, USA) at 55°C and 80 × g. The dried crude extract was weighed and dissolved in DMSO or 70% ethanol, depending on the experiments. The crude extract of SX6 was evaluated for its antibacterial activity using the minimum inhibitory concentration (MIC) (Andrews 2001). MIC values were recorded as the lowest concentration with no visible growth of pathogenic bacteria after 12–16 h of incubation. Cytotoxic activity. Cytotoxicity against human hepatoma Hep3B, breast cancer MCF-7, lung cancer A549, and non-cancerous HEK-293 cell lines was assessed by 3-(4,5-dimethythiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay (Salam et al. 2017). Briefly, the Hep3B, MCF-7, A549, and HEK-293 cell lines were seeded in 96-well plates containing RMPI medium supplemented with 10% fetal bovine serum, 100 U/ ml penicillin, and 100 μg/ml streptomycin at a density of ~2.5 × 104 cells/well and then incubated at 37°C, 5% CO2 for 48 h. Then, the cells were exposed to 30 μg/ml and 100 μg/ml of SX6 extract. After 24 h of incubation, 20 μl of MTT (5 mg/ ml in PBS) was added to each well, followed by incubation at 37°C, 5% CO2 for 4 h. The medium was discarded by gentle aspiration, and the formazan crystals were dissolved in DMSO. About 10 μg/ml ellipticine was employed as a positive control, while 10% DMSO (v/v) was considered a negative control. The absorbance of each well was measured at 570 nm, and the test was performed in three independent experiments. The hydroxyl radical scavenging activity. The hydroxyl radical scavenging activity of SX6 extract was evaluated with slight modifications (Liu et al. 2009; Vu et al. 2021). About 1.0 ml of 70% ethanol extract was added to the mixture containing 1.0 ml of 0.75 mM 1,10-phenanthroline, 1.0 ml of 0.75 mM FeSO4, 1.0 ml of 0.01% H 2O 2, and 1.5 ml of 0.15 M sodium phosphate buffer (pH 7.4). The absorbance was measured at 536 nm, and hydroxyl radical scavenging activity was calculated as follows: where Asample is the absorbance of the mixture containing SX6 extract, A0 is the absorbance of the reaction mixture without SX6 extract and H2O2 (SX6 extract and H2O2 were replaced by the same volume of 70% ethanol and distilled water, respectively), and Ablank is the absorbance of 70% ethanol. DPPH-radical scavenging activity. The 2-diphenyl-1-picrylhydrazyl (DPPH) assay was carried out as in the previous studies (Kadaikunnan et al. 2015; Vu et al. 2021). The crude extract dissolved in 70% ethanol was reacted with 0.2 ml of 0.1 mM 2,2-diphenyl-1-picrylhydrazyl (DPPH) followed by 2.0 ml of deionized water. The reaction was incubated in the dark for 30 min, and absorbance was subsequently measured at 517 nm. The following formula was used to calculate the percentage DPPH radical scavenging activity of SX6 extract: where Asample is the absorbance of the mixture comprising SX6 extract, A0 is the absorbance of 70% ethanol and 0.1 mM DPPH solution, and Ablank is the absorbance of 70% ethanol. Whole-genome sequencing, de novo assembly, and annotation. The whole genome was sequenced with the Illumina Miseq sequencing platform (Illumina, USA). The quality control was performed by FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc) and read trimming was implemented using Trimmomatic 3.0 (Bolger et al. 2014). SPAdes 3.13 was used for de novo assembly with default parameters and k-mer = 21, 33, 55, 77 (Bankevich et al. 2012). The completeness of the assembled genome was evaluated using the Benchmarking Universal Single-Copy Orthologous (BUSCO) v3.0 (https://gitlab.com/ezlab/busco) The SX6 genome was annotated by Prokaryotic Genomes Annotation Pipeline (http://www.ncbi.nlm.nih.gov/genome/annotation_prok) Prokka (Seemann 2014), and Rapid Annotation using Subsystem Technology (RAST) (Aziz et al. 2008). Orthologous genes were analyzed using clusters of orthologous genes (COGs) (Galperin et al. 2015). A whole-genome-based taxonomic analysis (https://tygs.dsmz.de) was utilized to calculate in silico digital DDH (dDDH), branch lengths, and genome BLAST distance phylogeny (Meier-Kolthoff and Göker 2019). The genome sequence of S. parvulus SX6 were deposited at GenBank under accession number JAJJMU010000000. Identification of genetic determinants involved in biological activities. Genome mining for BGCs encoding secondary metabolites was performed using antiSMASH 5.1.2 with default parameters and all features selected (Blin et al. 2017). BLASTP and TBLASTN were utilized to determine homologous protein-coding sequences present in SX6 and other S. parvulus genomes available on GenBank, including 2297 (CP015866), JCM 4068 (BMRX00000000), and LP03 (JAIWPL000000000). HPLC-DAD-MS analysis of plant-derived compounds. The SX6 extract samples were analyzed on a Thermo Dionex Ultimate 3000 HPLC system (Thermo Fisher Scientific, USA) consisting of a vacuum degasser, a quaternary mixing pump, an autosampler, a column oven, and a diode-array detector (DAD), which was coupled to a Thermo MSQ Plus single quadrupole mass spectrometer (Thermo Fisher Scientific, USA). A Hypersil GOLD HPLC column (150 mm × 4.6 mm, 5 μm) was used at 35°C in which 0.1% formic acid in HPLC grade water (Fisher Scientific, USA) and acetonitrile (Fisher Scientific, USA) were set as solvent channels A and B, respectively. The crude SX6 extract (5 mg/ml) dissolved in methanol HPLC grade was injected at a flow rate of 400 μl/min with an injection volume of 2 μl and a UV detector at 254 nm. Daidzein and genistein present in the crude SX6 extract were detected by comparing the retention times and UV spectra with the reference compounds of daidzein and genistein (Sigma, USA) under the same HPLC condition. Moreover, MS spectra were used to confirm the ion of daidzein at m/z 255 ([M+H]+) and genistein at m/z 271 ([M+H]+) as described previously (Shrestha et al. 2021).
Screening of bioactivity and identification of the isolate SX6. A total of 38 actinobacteria with different morphological characteristics were isolated from the mangrove plant A. corniculatum. Primary screening of antibacterial activity against six selective pathogens revealed that 27 isolates were active against at least one tested bacterium. Using the 16S rRNA sequence analysis, 21 out of 27 isolates were affiliated with the genus Streptomyces (Table SI). Among them, isolate SX6 displayed significant broad-spectrum antibacterial effects on five tested pathogens (Fig. 1). Indeed, SX6 isolated from stems depicted inhibition zones against S. typhimurium ATCC® 14028™ (16.0 ± 0.4 mm), E. coli ATCC® 11105™ (7.9 ± 0.1 mm), P. aeruginosa ATCC® 9027™ (23.6 ± 0.6 mm), MRSE ATCC® 35984™ (32.5 ± 0.1 mm), and E. aerogenus ATCC® 13048™ (12.4 ± 0.1 mm). When grown on ISP1-7 media, the aerial mycelium of isolate SX6 formed monopodial branched hyphae and was well-developed with white color, while substrate mycelium was pale yellow (Table SII). The yellow pigment was observed in the ISP2 agar on which this isolate grew at the maximum level under cultivation temperature of 30°C, pH 7.0, and 1% NaCl. Spiral spore chain and warty spore surface were observed by SEM (Fig. 2A). In addition, the isolate SX6 assimilated various carbon sources such as glucosamine, fructose, sorbitol, trehalose, mannose but not myo-inositol, mannitol, and raffinose. Enzymatic tests revealed the production of cellulase, chitinase, protease, and xylanase (Table SII). BLAST search of the 16S rRNA gene sequence of SX6 showed the highest similarity to S. parvulus NBRC 13193 (100%) and S. parvulus JCM 4068 (99.9%). In addition, the neighbor-joining phylogenetic tree indicated that isolate SX6, S. parvulus NBRC 13193 and S. parvulus JCM 4068 were located on the same branch of the tree (Fig. 2B). Morphological, biochemical characteristics and 16S rRNA gene sequence analyses confirmed the mangrove endophytic strain SX6 as Streptomyces parvulus SX6. Evaluation of antibacterial, antioxidant, and anticancer activities of S. parvulus SX6 extract. In the antibacterial assay, the SX6 extract displayed superior activity against only P. aeruginosa ATCC® 9027™ and MRSE ATCC® 35984™ with MIC values of 4 μg/ml and 16 μg/ml, respectively (Fig. 3A). Regarding in vitro cytotoxicity effects on human cell lines, the SX6 extract had significant inhibition at both concentrations with the viability of 3 cell lines ranging from 25.6–54.2% (Fig. 3B). Specifically, 30 μg/ml extract displayed the highest cytotoxic activity against MCF7 and Hep3B with cell viability recorded at 31.1 ± 0.8% and 32.7 ± 0.8%, respectively. Increasing extract concentration to 100 μg/ml did not significantly enhance cytotoxic activity against MCF7 and Hep3B cell lines. In contrast, A549 was resistant to 30 μg/ml extract but not to 100 μg/ml at which concentration cell viability decreased to less than 40%. Meanwhile, the SX6 extract showed low cytotoxicity against non-cancerous cell line HEK-293 with cell viability of 72.9 ± 2.6% and 42.5 ± 1.8% at 30 μg/ml and 100 μg/ml extract, respectively. DPPH and hydroxyl radical scavenging assays in vitro presented significant antioxidant effects of the SX6 extract (Fig. 3C). To be specific, the SX6 extract proved the most potent antioxidant activity against DPPH free radicals (90.0 ± 1.4%) at 1.6 mg/ml, which was comparable to that of the ascorbic acid (p > 0.05). Moreover, the hydroxyl radical scavenging activity of the SX6 extract was determined to be 89.1 ± 0.9% at 9.6 mg/ml, similar to the activity of ascorbic acid (p > 0.05) (Fig. 3D). General genomic features and comparative genomes. To deeper understand the biological properties of S. parvulus SX6, this strain was sequenced by the Illumina platform. A total of 5,733,880 high-quality reads were generated, yielding a 7.69 Mb linear chromosome with an average G + C content of 72.8% (Table I). The chromosome contained 48 contigs encoding for 6,779 protein-coding genes (CDSs). The genome assembly was validated using BUSCO, leading to 135 complete single-copy (91.22%), seven duplicated (4.73%), five missing (3.38%), and one fragmented (0.68) BUSCOs. For functional annotation, 6,779 CDSs (99.2%) were assigned to 21 functional categories. Almost all CDSs were associated with functions, including general function (1,090 genes), transcription (553 genes), amino acid transport and metabolism (427 genes), carbohydrate transport and metabolism (408 genes), and energy production and conversion (314 genes) (Fig. 4A). The species status of strain SX6 was further confirmed by in silico dDDH and G + C difference values. Among 18 reference strains, SX6 showed the highest similarity to S. parvulus JCM 4068 with in silico dDDH and G + C difference values of 94.7% and 0.02%, respectively (Fig. 4B). This finding was in agreement with 16S rRNA sequence analysis, which concluded that the studied strain was S. parvulus. Biosynthetic gene clusters for secondary metabolites of S. parvulus SX6. AntiSMASH analysis resulted in the identification of 29 gene clusters encoding secondary metabolites with multiple clusters encoding terpenes (5); non-ribosomal peptide synthetases (NRPS) (9); lantipeptide (1); type II polyketide synthase (T2PKS) (1); type III polyketide synthase (T3PKS) (1); ectoine (1); ribosomally synthesised and post-translationally modified peptide (RiPP) (2); bacteriocin (1); indole (1); siderophore (3); melanin (1); and clusters with a hybrid character (3) (Table SIII). Six gene clusters, including geosmin, albaflavenone, isorenieratene, hopene, sapB, and ectoine were identified comprising 100% of the genes from the know cluster. Clusters with 60–90% similarity included melanin (60%), citrulassin (60%), spore pigment (66%), desferrioxamin (83%), and coelichelin (90%) (Table SIII). Notably, cluster 2 with a predicted similarity of 67% to BGC of actinomycin D, a well-known antibiotic with high antibacterial and cytotoxic activities (Liu et al. 2016), was a hybrid cluster containing 11 genes homologous to acmB, acnT, acmF, acmG, acmH, acmI, acmJ, acmU, acmW, acmX, and acmrC (Fig. 5). Another predicted actinomycin D cluster with a lower similarity of only 28% was cluster 20, which consisted of five genes homologous to acmB, acmA, acmD, acmR, acnT (24,1 kb) (Fig. 5). Despite being classified as S. parvulus, actinomycin D cluster was only found in the genome of soil-derived strain 2297 with 82% similarity, but not in JCM 4068. In addition, the largest cluster in the SX6 genome, cluster 19, showed moderate similarity at 48% to the BGC of streptovaricin from S. spectabilis CCTCC M2017417, encoding 10 PKS and 1 NRPS proteins, and a dozen of other enzymes such as cytochrome P450, transporters, and regulatory proteins (Fig. 5). It is worthy to note that cluster 19 had two repeats of the PKS core biosynthesis genes. Comparative genomics analysis revealed that streptovaricin cluster was not present in the 2297 genome. Cluster 25 was predicted as a complex of NRPS, T1PKS, and other genes that also exhibited a similarity of 48% with the known polyoxypeptin BGC of Streptomyces sp. MK498-98F14 (Fig. 5). Different to polyoxypeptin cluster from Streptomyces sp. MK49898 F14, only 30 genes in cluster 25 were not annotated to the known BGCs. Meanwhile, three cytochrome P450 genes were also found. A polyoxypeptin cluster of 390,736 bp was also predicted in the genome of soil-derived S. parvulus 2297 with 51% similarity consisting of three major PKS regions and two NRPS regions. Compared to S. parvulus 2297, cluster 25 was around three times smaller and only showed 39% similarity to corresponding BGC. The biosynthesis of plant-derived compounds. The search for critical enzymes involved in the biosynthesis of plant-derived compounds found nine putative homologous proteins in the SX6 genome, that are not clustered in an operon (Fig. 6A). In the phenylpropanoid pathway, phenylalaline as a precursor for the biosynthesis of daidzein and genistein, is initially catalyzed by phenylalaline ammonia-lyase HutH (orf_4806, orf_6344), cinnamate 4-hydrolase C4H (orf_2463), 4-coumarate-CoA ligase 4CL (orf_5768) yielding p-coumaroyl-CoA . After that, chalcone synthase CHS (orf_6560) is responsible for further condensation of p-coumaroyl-CoA to naringenin chalcone in the genistein pathway, while the addition of 3X malonyl-CoA and chalcone reductase CHR (orf_01094) result in the conversion of p-coumaroyl-CoA to liquiritigenin that is critical for the daidzein pathway. These intermediates are modified by chalcone isomerase (orf_3461) and converted to 2, 7, 4’-trihydroxyl-isoflavanone or 2, 5, 7, 4’-tetrahydroxy-isoflavanone. Finally, isoflavones daidzein and genistein are synthesized under activation of 2-hydroxyisoflavanone dehydratase HID (orf_01718) (Fig. 6A). Comparative genome analysis revealed that these genes are also conserved among S. parvulus species including JCM 4068, 2297 and LP03. The only exception was S. parvulus 2297 in which genes chr and hid were not found (Fig. 6B). Of note, only SX6 possesses two copies of hutH, unlike a single copy of huth in the other S. parvulus genomes. In supporting of the genomic finding, HPLC-DAD-MS analysis revealed the presence of daidzein in the extract of SX6 at the retention time of 9.103 min, which was relatively similar to the retention time of the standard daidzein compound (8.897 min) (Fig. 6C). Additionally, genistein was detected based on a 12.323-min retention time. Further confirmation by MS analysis showed that MS spectra of SX6 extract revealed two distinct MS peaks [M + H]+ = 255.44 m/z and [M + H] + = 271.40 m/z, corresponding to standard daidzein and genistein (Fig. SI and SII). The UV absorption spectra for the SX6 extract also displayed similar peaks with standard daidzein and genistein.
The strain SX6 belongs to the S. parvulus, as our results demonstrate. Phylogenetic tree based on 16S rRNA gene and whole-genome sequence showed that SX6 formed a distinct cluster with the reference sequences of S. parvulus species. S. parvulus is mainly isolated from soils, which a producer of actinomycin D with broad-spectrum activities against bacteria, fungi, viruses, and cancer cells (Shetty et al. 2014; Chandrakar and Gupta 2019). The earlier genomic analysis claimed that S. parvulus 03 from mangrove plant Kandelia candel potentially secreted friulimicin, lobophorin, laspartomycin, colabomycin, borrelidin, pristinamycin, kanamycin, desferrioxamin, and melanin (Hu et al. 2018). However, only desferrioxamin and melanin were identified in the crude extract using LC-MS analysis. Desferrioxamin and melanin biosynthesis clusters of S. parvulus SX6 had a high homology (≥ 60%) to the existing clusters available in AntiSMASH database. These clusters were previously shown to be associated with antioxidant and anticancer activities (El-Naggar and El-Ewasy 2017; Hu et al. 2018). In contrast, friulimicin, lobophorin, laspartomycin, colabomycin, borrelidin, pristinamycin, and kanamycin clusters were not predicted in the SX6 genome. It leads to speculation that despite being classified into the same species, the BGCs can differ due to environmental niche adaptations. Genomic analysis of S. parvulus SX6 revealed clusters 2 and 20 with moderate to low similarities with the cluster of actinomycin D known as a highly effective chemotherapeutic and antimicrobial agents (Khieu et al. 2015; Cai et al. 2016; Liu et al. 2016). In soil-derived S. parvulus, actinomycin D cluster in S. parvulus 2297, but not JCM 4068, shared 82% similarity to the reference actinomycin D from S. anulatus available on AntiSMASH database. It suggested that clusters 2 and 20 of SX6 could be involved in synthesizing new secondary metabolites instead of actinomycin D. In addition, clusters 19 and 25 were identified as hybrid BGCs with low similarities with streptovaricin and polyoxypeptin BGCs, respectively. Compared to the reference streptovaricin cluster, a structurally-related macrolide antibiotic with a cluster size around 95 kb comprising 41 open reading frames (Liu et al. 2020; Luo et al. 2022), cluster 19 contained duplicates of five genes encoding type I modular PKSs responsible for the streptovaricin backbone. Likewise, cluster 25 only contained core genes involved in the production of polyoxypeptin a potent apoptosis inducer (Balachandran et al. 2014). The genome-wide comparison revealed that streptovaricin was only predicted in the S. parvulus JCM 4068 genome, while S. parvulus 2297 genome comprised polyoxypeptin. At the phenotypic level, the SX6 extract showed selective activity against P. aeruginosa ATCC® 9027™ and MRSE ATCC® 35984™. In addition, compared to the cytotoxicity against the non-cancerous HEK-293 cell line, the SX6 extract exhibited more significant cytotoxicity towards A549, Hep3B, and MCF-7 cell lines. The substantial antibacterial and anticancer activities shown by S. parvulus SX6 could be related to BGCs 19 and 25. Since most BGCs remain inactive under normal laboratory fermentation conditions, analysis of secondary metabolites under different culture conditions would be a fascinating subject for future studies. A highlight of this study was the presence of the biosynthetic pathways of plant-derived compounds, including daidzein and genistein. Daidzein and genistein are representative compounds of isoflavones found in plants, especially legumes (Sohn et al. 2021). Various in vitro and in vivo reports have described the beneficial effects of these compounds in treating human diseases such as cancer, pathogenic infection, cardiovascular conditions, and diabetic complications (Yamasaki et al. 2007; Liu et al. 2021; Sohn et al. 2021). Previous investigations demonstrated that these plant-derived compounds were only produced by a few endophytic actinobacteria such as S. variabilis LCP18, S. cavourensis YBQ59, and Streptomyces sp. SS52 (Vu et al. 2018; Nguyen et al. 2019a; Quach et al. 2021). In this study, nine homologous enzymes building complete daidzein and genistein pathways similar to that in plants were identified. It was partly in agreement with the biosynthesis of daidzein found in the genome of Streptomyces sp. SS52 (Nguyen et al. 2019b). Comparative analysis showed that most of these genes are conserved across S. parvulus species. It is worth noting that huth is duplicated in S. parvulus SX6, which may convert the phenylalanine to yield cinnamic acid. A recent study demonstrated that many duplicated genes, such as ABC transporters, contributed to fitness improvements of Streptomyces albidoflavus DEF1AK in planta conditions (Kunova et al. 2021). Thus, this finding supports the well-known assumption that gene duplication acts as a mechanism of genomic adaptation to a changing environment (Bratlie et al. 2010). HPLC-DAD-MS analysis further confirmed the presence of daidzein and genistein in the SX6 extract. It inferred that these isoflavones might also contribute to antioxidant, antibacterial, and cytotoxic activities obtained in the S. parvulus SX6 extract. The current finding further highlighted the potential of S. parvulus as the producer of plant-derived compounds.
In this study, we reported for the first time a broad range of biological activities and the complete genome information for S. parvulus SX6 associated with A. corniculatum. S. parvulus SX6 showed significant inhibitory effects against bacterial pathogens, cancer cell lines, and free radicals. The genomic data comparison and analysis revealed the presence of 4 cryptic secondary metabolite BGCs likely contributing to observed bioactivities. In addition, the most significant finding represented here was the proposed biosynthetic pathways of plant-derived compounds such as daidzein and genistein. This study suggested that mangrove endophytic S. parvulus has the potential to produce novel metabolites and could be an effective platform for daidzein and genistein production. | true | true | true |
PMC9608387 | Rowena Rubim Couto,Francyne Kubaski,Marina Siebert,Têmis Maria Félix,Ana Carolina Brusius-Facchin,Sandra Leistner-Segal | Increased Serum Levels of miR-125b and miR-132 in Fragile X Syndrome A Preliminary Study | 26-10-2022 | Background and Objectives Fragile X syndrome (FXS) is a neurodevelopmental disorder, identified as the most common cause of hereditary intellectual disability and monogenic cause of autism spectrum disorders (ASDs), caused by the loss of fragile X mental retardation protein (FMRP). FMRP is an RNA-binding protein, a regulator of translation that plays an important role in neurodevelopment, and its loss causes cognitive and behavioral deficits. MicroRNAs (miRNAs) are small molecules that regulate gene expression in diverse biological processes. Previous studies found that the interaction of FMRP with miR-125b and miR-132 regulates the maturation and synaptic plasticity in animal models and miRNA dysregulation plays a role in the pathophysiology of FXS. The present study aimed to analyze the expression of miR-125b-5p and miR-132-3p in the serum of patients with FXS. Methods The expressions of circulating miRNAs were studied in the serum of 10 patients with FXS and 20 controls using the real-time quantitative retrotranscribed method analyzed by relative quantification. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were generated to assess the diagnostic values of the miRNAs. Results We found that both miR-125b and miR-132 were increased in the serum of patients with FXS compared with controls and likely involved with FMRP loss. The AUC (95% confidence interval) of miR-125b and miR-132 was 0.94 (0.86–1.0) and 0.89 (0.77–1.0), respectively. Databases allowed for the identification of possible target genes for miR-125b and miR-132, whose products play an important role in the homeostasis of the nervous system. Discussion Our results indicate that serum miR-125b and miR-132 may serve as potential biomarkers for FXS. The increased expression of circulating miR-125b and miR-132 seems to be associated with the genotype of FXS. Predicted gene targets of the differentially regulated miRNAs are involved in cognitive performance and ASD phenotype. Classification of Evidence This study provides Class III evidence that miR-125b and miR-132 distinguish men with FXS from normal controls. | Increased Serum Levels of miR-125b and miR-132 in Fragile X Syndrome A Preliminary Study
Fragile X syndrome (FXS) is a neurodevelopmental disorder, identified as the most common cause of hereditary intellectual disability and monogenic cause of autism spectrum disorders (ASDs), caused by the loss of fragile X mental retardation protein (FMRP). FMRP is an RNA-binding protein, a regulator of translation that plays an important role in neurodevelopment, and its loss causes cognitive and behavioral deficits. MicroRNAs (miRNAs) are small molecules that regulate gene expression in diverse biological processes. Previous studies found that the interaction of FMRP with miR-125b and miR-132 regulates the maturation and synaptic plasticity in animal models and miRNA dysregulation plays a role in the pathophysiology of FXS. The present study aimed to analyze the expression of miR-125b-5p and miR-132-3p in the serum of patients with FXS.
The expressions of circulating miRNAs were studied in the serum of 10 patients with FXS and 20 controls using the real-time quantitative retrotranscribed method analyzed by relative quantification. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were generated to assess the diagnostic values of the miRNAs.
We found that both miR-125b and miR-132 were increased in the serum of patients with FXS compared with controls and likely involved with FMRP loss. The AUC (95% confidence interval) of miR-125b and miR-132 was 0.94 (0.86–1.0) and 0.89 (0.77–1.0), respectively. Databases allowed for the identification of possible target genes for miR-125b and miR-132, whose products play an important role in the homeostasis of the nervous system.
Our results indicate that serum miR-125b and miR-132 may serve as potential biomarkers for FXS. The increased expression of circulating miR-125b and miR-132 seems to be associated with the genotype of FXS. Predicted gene targets of the differentially regulated miRNAs are involved in cognitive performance and ASD phenotype.
This study provides Class III evidence that miR-125b and miR-132 distinguish men with FXS from normal controls.
MicroRNAs (miRNAs) are short noncoding RNAs involved in posttranscriptional gene regulation by promoting messenger RNA (mRNA) silencing and influencing protein translation. Several studies have provided evidence that miRNAs are involved in the pathogenesis of different disorders, and disease-specific miRNA profiles have been identified being isolated from bodily fluids (circulating miRNAs), including those selectively expressed in the brain. miRNAs play a role during neurodevelopment to regulate essential biological processes in the functioning of the CNS. The deregulation expression of miRNAs has been found to play a major role in the pathogenesis of neurodisorders. Because of the stability and easy accessibility of circulating miRNAs, their use as diagnostic and prognostic biomarkers for patient stratification is highlighted and the efficacy of targeted treatments for neurodisorders, such as fragile X syndrome (FXS), is increased. FXS is the most common cause of inherited intellectual disability (ID) and the most prevalent monogenic cause of autism spectrum disorders (ASDs). FXS has an estimated incidence of 1 in 4,000 men and 1 in 8,000 women without known incidence in Brazil. This neurodevelopmental disorder is characterized by a broad spectrum of behaviors, such as delayed neuropsychomotor development (ADNPM), anxiety, aggression, self-injury, attention deficit disorder, social withdrawal, and physical comorbidities, such as facial dysmorphisms, macroorchidism, otitis, and seizures, resulting in a large phenotypic heterogeneity across the FXS population. The syndrome is caused by the loss of fragile X mental retardation protein (FMRP), a consequence of the full mutation (FM), more than 200 cytosine-guanine-guanine (CGG) repeats in the 5′ untranslated region of the fragile X mental retardation 1 (FMR1) gene (OMIM# 309550) that leads to hypermethylation of the promoter region, and consequently the absence of the FMRP. FXS mosaicism has been described as the coexistence of the FM and the premutation (CGG repeats between 55 and 200), and the clinical manifestations of FXS mosaicism may vary according to the presence of different methylation levels of the FM allele leading to differential FMRP expression within tissues. FMRP is an RNA-binding protein highly expressed in the CNS with essential functions for normal development and maintenance of neuronal synaptic function and plasticity through the role as a regulator of translation of neuronal mRNAs in response to synaptic activity. Reduced expression of FMRP leads to abnormalities in neurodevelopmental spines and disturbed neuronal processes, observed in FXS. The RNA binding capacity of FMRP is central to its molecular function, it has 2 K homology domains and 1 arginine-glycine-glycine box, also both nuclear localization and exportation signals for transport target RNA between the nucleus and cytoplasm. FMRP is associated with polyribosomes to form ribonucleoprotein complexes that regulate translation of certain proteins involved in neuronal development and plasticity, and FMRP functions as a translation repressor at synapsis due to its binding to miRNAs and interactions with proteins, including Argonaute (Ago2), incorporated into a multiprotein complex called an RNA-induced silencing complex (RISC), resulting in the regulation of synaptic structure and plasticity. Studies have provided evidence of miRNA involvement in the pathogenesis of FXS by identifying and isolating several r(CGG)-derived miRNAs, as miR-125b and miR-132, required for maintaining neuronal connectivity and synaptic plasticity, in the zebrafish FXS model. Furthermore, microarray analyses of miRNAs associated with FMRP in the mouse brain identified miR-125b and miR-132 and the dysregulation of both miRNA-mediated protein translation resulting in early neural development and synaptic physiology. A recent study showed that FMRP binds with some miRNAs, specifically miR-125-family, in regions outside of the seed sequence to modulate the RISC complex through specific interaction with Ago2 protein. In addition, a recent miRNA profiling performed in the urine of boys with FXS has identified an increase of miR-125a and its potential as an FXS biomarker. In this study, these miRNAs were chosen and analyzed in the serum samples of patients with FXS (n = 10) compared with healthy controls (n = 20) with the aim of investigating whether FXS pathogenesis is associated with miR-125b-5p and miR-132-3p and whether those could be used as diagnostic and prognostic biomarkers of patients with FXS.
Samples were collected from patients with FXS from the Medical Genetics Service at Hospital de Clínicas de Porto Alegre (HCPA) between March 2018 and September 2019. In total, blood samples of 10 patients with FXS and 20 controls at HCPA were collected, which included 30 males with ages ranging from 11 to 26 years. All blood donors were clinically examined and interviewed and had not been diagnosed with FXS and neuropathologies at the time of blood collection. Acknowledging the influence of other diseases, we also excluded donors who were diagnosed with any other medical comorbidities or with a family history of neurologic disorders. Individuals with a maximum age of 26 years were included as controls in the study to avoid a significant age difference between patient and control groups. Developmental delays, such as ADNPM, ID, ASD, and other commodities, were observed based on medical history.
This study was approved by the Ethics Committee of the HCPA (project number 20180264), and informed written consent was previously obtained from all subjects or guardians in the clinical setting.
Blood samples were collected in the serum separator tube and used to derive serum samples. Within 30 minutes after blood collection, samples were centrifuged at 1,900g for 10 minutes at 4°C. After centrifugation, the serum was separated and stored at −80°C immediately until RNA isolation. miRNAs were extracted from 200 μL of serum in each sample using the miRNeasy Serum/Plasma Kit (Qiagen Cat. No. 217184), according to the manufacturer's instructions. Extracted and purified miRNAs were eluted into 12 μL of RNase-free water per sample. The purity of RNA was detected by measuring its absorbance at 260–280 nm using the NanoDrop 2000 (Thermo Fisher Scientific). The miRNAs were stored at −80°C until use.
The reverse transcription into complementary DNA (cDNA) was performed using the StepOne (Thermo Fisher Scientific) and the TaqMan Advanced miRNA cDNA Synthesis Kit (Cat. No. A28007), according to the manufacturer's instructions. Quantitative real-time PCR was performed using the Applied Biosystems QuantStudio 3 PCR system (Applied Biosystems; Thermo Fisher Scientific, Inc), and all miR-specific primers and universal adaptor PCR miRNA primers were purchased from commercial TaqMan miRNA assays (Life Technologies; miR-24-3p [ID 477992_mir], miR-132-3p [477900_mir], and miR-125-5p [477885_mir]). The TaqMan Fast Advanced Master Mix (Cat. No. 4444605) was used for the qPCR. All the reactions were performed in duplicates, the reaction mixtures were incubated in a 96-well plate at 95°C for 20 seconds, and then 40 cycles of reaction were performed at 95°C for 1 second and at 60°C for 20 seconds. Fluorescent signals were measured during the extension phase. The relative expression levels were calculated using the comparative Ct method (∆∆Ct) with mean values of controls set as a calibrator. The Ct values of miRNAs were standardized and calculated using the 2−ΔΔCt method for the fold change between patients with FXS and controls. For normalization, miR-24-3p was used as an endogenous control for further statistical analysis.
All analyses were performed using R software, version 4.0.5 (R Core Team 2020, R-project.org/). As data were not normally distributed, nonparametric statistical tests were used. miRNA expression levels between the patients and controls were analyzed using a 2-sided Mann-Whitney test. Differences in the miRNA expressions between the groups were studied using the same test to evaluate the association between miRNA expressions and clinical parameters (genotypes, age, ID, ASD, comorbidities, and behavioral alterations). Receiver operating characteristic (ROC) curve analysis was constructed, and the area under the ROC curve (AUC) was generated to assess its diagnostic values for evaluating the diagnostic accuracy of circulating miRNAs. p < 0.05 was considered statistically significant.
The mirPath database, which uses experimentally validated miRNA interactions derived from DIANA Tools mirPath v.3 (microrna.gr/miRPathv3), was used to investigate in which pathways the miRNAs of this study are already determined to be involved. Using the Human MicroRNA Disease Database (HMDD V3.0) (cuilab.cn/hmdd), we searched for interaction targets involved in human diseases. We also used the R platform, multiMiR package (R Core Team, R-project.org/), and we searched for interaction targets of miR-125b-5p and miR-132-3p involving neurodevelopmental diseases. Targets with experimental validation were chosen through miRwalk (mirwalk.umm.uni-heidelberg.de/) and DIANA Tools TarBase v.8 (microrna.gr/tarbase).
The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available as the data contain information that could compromise the privacy of research participants.
We analyzed the expression of circulating miR-125b-5p and miR-132-3p in the serum obtained from 10 patients with FXS and 20 healthy controls (HCs). miR-125b-5p and miR-132-3p were identified by qRT-PCR. Analysis of the miRNA expressions between the groups revealed overexpression in patients with FXS of miR-125b-5p and miR-132-3p (fold change, FC > 1.5; p < 0.05) (Table 1) in comparison to controls. miR-125b-5p (p = 1.79 × 10−5) (Figure 1A) and miR-132-3p (p = 2.58 × 10−4) (Figure 1B) were increased in the FXS serum. ROC curves were constructed, and the AUC was generated to assess the diagnostic values of both selected miRNAs. As shown in Figure 2, ROC analysis revealed that the AUC of miR-125b and miR-132 was 0.94 (0.86–1) and 0.89 (0.77–1.0), respectively. Detailed information about the ability of both miRNAs to diagnose the FXS in patients is shown in Table 2. High values for sensitivity and specificity were observed, 90% and 80% for miR-125b-5p and 90% and 75% for miR-132-3p, respectively, suggesting them as potential predictors of FXS.
In 5 cases, an FM (CGG> 200) was observed, in 5 cases, a mosaic was found, and in 4, the length of the CGG repeat was quantified (177/>200, 114/>200, 56/>200, and 38/>200). Patient characteristics are summarized in Table 3 according to their genotype. Differences between clinical characteristics were not detected in FM and mosaic patients. Significant differences were not found between ages and CGG length repeat. In the whole FM group, the expression of miR-125b-5p and miR-132-3p was strongly increased (Figure 3A and Figure 3B). The FM group showed significant overexpression of miR-125b-5p (p = 1.5 × 10−4) and miR-132-3p (p = 7.15 × 10−4) in comparison with controls, and mosaic patients showed significant overexpression of miR-125b-5p (p = 4.25 × 10−3) and miR-132-3p (p = 2.36 × 10−2) in comparison with controls. The expression of miR-125b-5p and miR-132-3p in the FM genotype was upregulated in comparison with mosaic patients, but statistical significance was not observed (miR-125b-5p, p = 0.0952; miR-132-3p, p = 0.1508).
Using the miRSystem tool, more than 600 pathways were found. The miRNAs were involved separately, such as the mitogen‐activated protein kinase (MAPK) signaling pathway, pathways in cancer, neurotrophic signaling, and immune adaptive system. The pathway ranking summary revealed that both miRNAs were involved in 13 pathways: signaling by transforming growth factor beta (TGF-β) signaling pathway, regulation of the actin cytoskeleton, bacterial invasion of epithelial cells, hippo signaling pathway, lysine degradation, proteoglycans in cancer, other types of O-glycan biosynthesis, adherens junction, cell cycle, arrhythmogenic right ventricular cardiomyopathy, biosynthesis of unsaturated fatty acids, fatty acid biosynthesis, and fatty acid metabolism. Figure 4 represents a heatmap of union pathways of both miRNAs derived from experimentally validated data using miRPath v.3, DIANA Tools Database. There are 925 predicted targets for miR-125b-5p and 673 predicted targets for miR-132-3p in miRDB. Using the HMDD, we found that for both miRNAs investigated, there are at least 4 confirmed target genes in humans. Using the multimiR platform, it was possible to generate a list of target genes for miR-125b-5p and miR-132-3p that are related to neurodevelopmental diseases. We identified 4 possible target genes by miR-125b-5p including insulin-like growth factor 1 receptor (IGF1R), tumor protein p53 (TP53), glutamate ionotropic receptor NMDA type subunit 2A (GRIN2A), and single-minded 1 (SIM1) and 5 genes targeted by miR-132-3p, including SRY-box 5 (SOX5), SRY-box 6 (SOX6), mitogen-activated protein kinase 1 (MAPK1), brain-derived neurotrophic factor (BDNF), and phosphatase and tensin homolog (PTEN) (Table 4).
The search for noninvasive biomarkers for early diagnosis and disease prognosis is currently one of the most rapidly growing areas in neurodevelopmental research. In the serum of patients with FXS, circulating miRNAs could be used as prognostic neuropathologic biomarkers. Recently, the blood serum was found to contain circulating miRNAs, which are stable, reproducible, and have already been proposed as novel noninvasive biomarkers for the diagnosis and prognosis of many neurodisorders. In this study, FXS-specific changes in serum miRNAs were identified. The levels of miR-125b and miR-132 were found increased in the FXS serum, compared with the serum samples of HCs (Figure 1), suggesting an important role of these miRNAs in the pathophysiology of FXS. Previous studies have demonstrated that miR-125b and miR-132 are associated with FMRP in the mouse brain, and this association is necessary to affect dendritic spine morphology. In addition, miR-125b and miR-132 regulation is affected in the absence of the FMRP, which is in agreement with our results. Through the phosphorylation state of FMRP, influenced by mGluR and when phosphorylated, FMRP is associated with miR-125b and miR-132 to form the RISC complex, which, when induced by those miRNAs, regulates postsynaptic density protein 95 (PSD-95) translation, which is critical for the synaptic function. In the brain of the FMR1-KO mouse, the levels of miR-125 and miR-132 were reduced, explaining the inability of miR-125– and miR-132–guided RISC complex to regulate PSD-95 mRNA, resulting in dendritic abnormalities. PSD-95 is a postsynaptic scaffolding protein that modulates the synaptic formation, maintenance, and localization by forming related signals with the NMDA receptor, which plays an important role in synaptic transmission. The mGluR5 receptor binds to the NMDA receptor through PSD-95, which directly affects synaptic plasticity. It was demonstrated that miR-125b targets the NR2A subunit of the NDMAR that influences synaptic plasticity and memory consolidation and the association between miR-125-family and mGluR5 signaling to the inhibitory complex on PSD-95 mRNA. miR-132 is also linked to NMDA receptors. Pharmacologic studies showed that miR-132 induction requires the NMDA receptor activation, and recently, miR-132 was shown to participate in regulating the expression of PSD-95 through target genes. Possibly, miR-125b and miR-132 interact with FMRP by other sequential signaling pathways, such as mGluR and NMDA receptors signaling, to mediate the PSD-95 translation. Thus, any destabilization of these molecules may lead to abnormal synaptic plasticity, but the molecular mechanisms by which this regulation is accomplished are still not clear. The phenotype analysis failed to reveal a significant correlation between the miRNA levels, considering that the sample size is a limitation in this study. Studies performed using miRNA have been related to many neuropsychiatric diseases, such as anxiety disorder, ASD, and hyperactivity. In our study, the stratification of the patients with FXS by clinical characteristics only evidenced the phenotypic heterogeneity beyond the disease. Moreover, the severity of the FXS phenotype varies individually and depends on both the dosage and the length of the CGG repeat. The behavioral phenotype that includes a majority of attention disorders such as ID, hyperactivity, mood instability, aggressivity, and abnormalities in sensory stimuli as in ADNPM was found in most of our patients. Although epilepsy is associated with FXS in about 15%–20% of males with FXS, the patients included in this study did not present this disorder. In addition, miR-132 was found significantly higher in children with attention deficit and hyperactivity, and miR-125b was identified to be upregulated in 20 patients with ASD compared with controls. Whereas those are common characteristics in FXS, any correlation between them would be solely a hypothesis. A regulation, whereas FM patient samples showed higher serum expression levels of miR-125b and miR-132, which underline the absence of FMRP protein may further directly influence the gene expression of these miRNAs, since mosaic patients express low levels of FMRP. Previously, miR-125b and miR-132 were reported to be severely decreased in the mouse brain due to FMRP knockdown. Recently, miR-125a was increased in the urine of 9 patients with FXS, compared with HCs. The FM genotype patients with higher levels of miR-125b and miR-132 in the serum may present a subgroup whose cellular homeostasis differs from the mosaic genotype subgroup, with lower levels of miR-125b and miR-132. The higher serum miR-125b and miR-132 levels in FM patients could be a consequence of the lack of FMRP interaction and may reflect increased production and/or brain secretion of these miRNAs in corporal fluids, whereas the expression of FMRP could decrease the high serum levels of miR-125b and miR-132. However, it is not possible to make any specific directly correlation between the expression of circulating miRNAs in human fluids and previous findings in mouse brain miRNAs levels. Thus, it becomes necessary to invest in further studies that seek to better understand the deregulation of the levels of these miRNAs observed in FXS. Moreover, these miRNAs are found related to different neuropathologies, which demonstrates their involvement with several pathways' regulation. Previously, serum miR-125b was found to be increased in patients with multiple sclerosis and Alzheimer disease, associated with severe cognitive decline, and described as a potential biomarker for these neuropathologies. miR-125b transfection into neuronal cells caused hyperphosphorylation of tau, induced oxidative stress, inflammation, and apoptosis, and inhibited cell proliferation, exemplifying how the dysregulation of this miRNA can affect the biochemistry of the brain, contributing to the onset of neuropathologies. Of interest, a study described the inhibitory effect of miR-125b results in the upregulation of multiple miR-125b target genes, including IGF1R. IGF1R is a transmembrane tyrosine kinase receptor essential for neuronal development, has been found to regulate changes in neuronal polarity, and has neuroprotective effects after brain injury. miR-132 is the most studied miRNA linked to brain function, predominantly observed dysregulated in neuropathologies and regulated in response to neuronal activity. Many studies have shown that miR-132 increased in the blood components of neuropathologies, such as Alzheimer disease, Parkinson disease, MS, and amyotrophic lateral sclerosis, which is in accordance with the higher levels found in our study, and it highlights its overexpression related to neuropathologic processes and its potential as a biomarker of neuropathologies. The experimental heatmap showed miR-125b-5p and miR-132-3p connected to cancers and cycle cellular pathways (Figure 4). Both miRNAs are involved with oncogenic aspects demonstrating the potential of biomarkers for the screening of different types of cancer. miR-132-3p could be a novel biomarker for screening of hepatocellular, lung, and nasopharyngeal carcinoma, while miR-125b-5p could be the same for lung cancer and breast cancer. The validated target TP53 gene for miR-125b plays a key role in cell death, DNA repair, and cell proliferation, and the overexpression of miR-125b represses the endogenous level of the P53 protein. A TP53 review highlighted a key role for the gene in the regulation of neurons, which highlights the complexity of this target in neuropathologies. miR-125b-5p and miR-132-3p are related to signaling pathways of fatty acid biosynthesis and fatty acid metabolism, and circulating levels of both miRNAs are related to body weight. Obesity has already been demonstrated to affect gene expression and was associated with cognitive decline and dementia. Another related pathway between the miR-125b-5p and miR-132-3p is the TGF-β signaling, which play essential roles in every stage of neural development, showing the importance of both miRNAs in the pathways of TGF-β members in the CNS at various developmental stages. In fact, miRNAs can regulate hundreds of different targets involved in different pathways, and these are just some routes involving both miRNAs. The experimentally validated gene targets showed that the miRNAs have been connected to genes involved in cognitive performance and ASD (Table 3). MiR-125b is involved with GRIN2A downregulation, and this gene encodes an important subunit of NR2A, a subunit of NMDA receptor. Variants of GRIN2A have been described as associated with several neurodevelopmental disorders, including epilepsy and ASD. Another target gene for miR-125b is SIM1, involved with behavioral disorders, ASD, and obesity in humans. miR-132 was found to reduce SOX5 mRNA and protein expression. Subsequent to this finding, SOX5 had been identified as a novel candidate gene for ASD. Furthermore, miR-132-3p was found to target SOX6 and downregulated its protein expression. SOX6 variants have been reported to cause a neurodevelopmental syndrome associated with attention deficit and hyperactivity disorder. Another gene target and downregulated by miR-132 is MAPK1. MAPK signaling regulates intracellular functions, and perturbations to MAPK signaling are related to contribute to the pathogenesis of ASD. Therefore, miR-132 was found to improve the cognitive function of rats by directly inhibiting MAPK1 expression. BDNF is indirectly targeted by miR-132 through methyl CpG-binding protein 2, a known regulator of neuronal maturation and synapse formation, which triggers the induction of BDNF. A BDNF meta-analysis study has detailed the effects of gene expression in ASD. Higher peripheral BDNF in ASD is concordant with several neurologic and psychological causes and symptoms of this condition, and the lower levels of BDNF were found in schizophrenia, bipolar disorder, and depression. Recently, miR-132 overexpression demonstrated their downregulated expression of PTEN. PTEN protein is involved in the regulation of the cell cycle and is important in synaptic plasticity, neuronal function, and development. PTEN variants have been associated with ASD phenotypes, and PTEN-KO mice models showed macrocephaly, loss of neuronal polarity, and behavioral anomalies associated with ASD, such as anxiety, convulsions, and decreased social interest. Of interest, TGF-β plays important roles in the maintenance of neuron and spine homeostasis, and there are different studies describing the association of the TGF-β signaling pathway with neuronal development and neurologic disorders. Recently, the transcript of the type 2 BMP receptor (BMPR2) gene, a member of the TGF-β superfamily, has been identified as a novel target of FMRP. In the brains and neurons of patients with FXS and Fmr1-KO mice, the amount of BMPR2 protein is increased. Another pathway involved with both miRNAs and correlated with FXS is fatty acid biosynthesis. Fatty acid biosynthesis was found significantly altered in Fmr1 KO2 mice. Additional studies are needed to elucidate and better understand the correlation between the aberrant activation of this pathway and the FXS pathogenesis. The AUC values of miR-125b and miR-132-3p in discrimination of patients with FXS from HCs were 0.94 and 0.89, respectively, which demonstrate the ideal diagnostic value to distinguish patients with FXS from healthy individuals. The increased levels may indicate the severity of FXS in patients, contribute to clinical diagnosis, and further patient prognosis; because FXS still has no prognostic tools, developing them is an urgent requirement because the neurodevelopmental disorder is accompanied by uncertain wide-ranging comorbidities. Although the present study still has some limitations, as the samples recruited in our study are relatively small, our findings elucidate the clinical potential biomarkers of serum miR-125b and miR-132 for patients with FXS, and the genetic miRNA associations have shown that targets of miR-125b and miR-132 can determine the onset of neurodisorders and ASD phenotypes. A multicenter collaborative study would be of great value to confirm our findings and to allow correlations to the patient's phenotype to improve the understanding of the FXS regulation by miRNAs. | true | true | true |
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PMC9608439 | Manabu Kitano,Seiya Fukuoka,Naoki Adachi,Tadashi Hisamitsu,Masataka Sunagawa | Shoseiryuto Ameliorated TDI-Induced Allergic Rhinitis by Suppressing IL-33 Release from Nasal Epithelial Cells | 29-09-2022 | Shoseiryuto,TDI-induced allergic rhinitis,IL-33 release | Toluene diisocyanate (TDI) is a major cause of occupational asthma and rhinitis. Shoseiryuto (SST) is one of the traditional herbal medicines (Kampo medicine) and has long been used as a natural medicine for allergic diseases such as allergic rhinitis (AR) and asthma. Recent studies have shown that the expression and release of IL-33, which regulates the TH2 cytokine response in epithelial cells, is an important step in developing the inflammatory response of the nasal mucosa. In this study, we investigated whether SST may ameliorate the TDI-induced AR-related symptoms in rats and inhibit IL-33 release from nasal epithelial cells. An AR rat model was generated by sensitization and induction with TDI. SST was administered during the sensitization period. AR-related symptoms in rats were evaluated, and IL-33 release was measured both in vivo and in vitro. SST suppressed symptoms appearing in TDI-induced AR model rats, such as elevated serum histamine and IL-33 levels in nasal lavage fluid (NLF)/serum, which were suppressed by SST administration. TDI-induced IL-33 release from the nasal epithelial cell nuclei was also observed and suppressed in SST-treated rats and cultured nasal epithelial cells. These results suggest that SST ameliorates the symptoms of TDI-induced AR at least partially by inhibiting IL-33 release from nasal epithelial cells. | Shoseiryuto Ameliorated TDI-Induced Allergic Rhinitis by Suppressing IL-33 Release from Nasal Epithelial Cells
Toluene diisocyanate (TDI) is a major cause of occupational asthma and rhinitis. Shoseiryuto (SST) is one of the traditional herbal medicines (Kampo medicine) and has long been used as a natural medicine for allergic diseases such as allergic rhinitis (AR) and asthma. Recent studies have shown that the expression and release of IL-33, which regulates the TH2 cytokine response in epithelial cells, is an important step in developing the inflammatory response of the nasal mucosa. In this study, we investigated whether SST may ameliorate the TDI-induced AR-related symptoms in rats and inhibit IL-33 release from nasal epithelial cells. An AR rat model was generated by sensitization and induction with TDI. SST was administered during the sensitization period. AR-related symptoms in rats were evaluated, and IL-33 release was measured both in vivo and in vitro. SST suppressed symptoms appearing in TDI-induced AR model rats, such as elevated serum histamine and IL-33 levels in nasal lavage fluid (NLF)/serum, which were suppressed by SST administration. TDI-induced IL-33 release from the nasal epithelial cell nuclei was also observed and suppressed in SST-treated rats and cultured nasal epithelial cells. These results suggest that SST ameliorates the symptoms of TDI-induced AR at least partially by inhibiting IL-33 release from nasal epithelial cells.
Allergic rhinitis (AR) is induced by allergens such as ragweed and other pollens, mites, and fungi. It is one of the most common allergic inflammatory diseases, affecting more than 600 million people worldwide [1,2]. The immune response in the nasal cavity can be divided into two phases: an early IgE-dependent response and a late TH2 cytokine-dependent response [2,3,4]. Early-phase reactions are symptoms such as rhinorrhea and sneezing that occur within 30 min of allergen exposure, whereas late-phase reactions are described as congestion and fatigue that occur up to 24 h [2]. Airway epithelial cells provide an important barrier function in the superficial layer of the airways, preventing the entry of harmful substances from the environment. In addition to this barrier function, the airway epithelium also plays a role in controlling inflammatory conditions by producing and secreting cytokines and chemokines that regulate various inflammatory responses [5]. Among the inflammatory cytokines involved in the late phase of AR, IL-33 has been highlighted as one of the key cytokines. IL-33 belongs to the IL-1 family and activates the same intracellular signaling pathway as IL-1 and IL-18, with ST2 as a receptor [2,6,7]. It has been suggested that IL-33 may induce allergic inflammation by inducing TH2 cytokine production in various immune cells, including TH2 and mast cells [2,8]. Importantly, Haenuki and colleagues revealed that IL-33 knockout mice showed significant reductions in the frequency of sneezing, total IgE levels and IgE-related responses, and infiltration of eosinophils and basophils into the nasal mucosa after ragweed challenge [2]. IL-33 protein is expressed in the nuclei of nasal epithelial cells in the normal condition and is rapidly released into the nasal fluid after exposure to allergens [2,9]. In humans, it has also been shown that IL-33 protein level was markedly reduced in nasal epithelial cells of the patients with AR compared to healthy controls [2]. Toluene diisocyanate (TDI) is a leading cause of occupational asthma (OA) and is also known to induce allergic rhinitis [5]. Regarding TDI-induced immune responses, induction of interleukins (ILs), including IL-4, 13, 25, and 33, in human bronchial epithelial cells has been reported [5]. Shoseiryuto (SST), called Xiao-quinglong-tang in China and So-cheong-ryong-tang in Korea, is one of the traditional herbal medicines (Kampo medicine) that has long been used as a natural medicine for allergic diseases, such as AR and asthma. SST has been used to improve both acute symptoms, including sneezing and rhinorrhea, and chronic symptoms, including nasal obstruction, in AR patients [10]. In relation to the mechanisms underlying the effects of SST, it has been reported that SST inhibited histamine release from mast cells in rats [11] and decreased the serum concentration of IgE AR patients [12]. Furthermore, it has been reported that SST improves the symptoms of nasal mucosa in a rat AR model by suppressing the transcription of mRNAs of TH2 cytokines, including IL-4, 5, and histamine H1 receptor (H1R) [9]. In the present study, we show an ameliorative effect of SST on TDI-induced AR symptoms, which has not yet been clarified, and examined the relationship between the effect of SST and the IL-33 release from nasal epithelial cells.
Sprague-Dawley (SD) male rats at the age of 5 weeks (120–130 g) were purchased from Nippon Bio-Supp. Center (Tokyo, Japan). Three or 4 rats were housed in a cage in the conditioned room (12:12 h light/dark cycle at 25 °C ± 1 °C, with 45% ± 5% humidity). The rats accessed food (CLEA Japan, CE-2, Tokyo, Japan) and water ad libitum. All efforts were made to minimize animal suffering and to reduce the number of animals used. This study was conducted with the approval of the Committee of Animal Care and Welfare of Showa University and in accordance with the guidelines established with the Committee of Animal Care and Welfare of Showa University (certificate number: 07064; date of approval: 1 April 2017). Each experiment was repeated two times.
Eighteen rats were randomly assigned to tree groups. Control: distilled water; Allergic rhinitis (AR): TDI; AR + SST: TDI + SST. The AR and AR + SST groups were sensitized with a modified protocol of Zheng et al. [13] by dropping 5 μL of 10% TDI (89870; Sigma-Aldrich, St. Louis, MO, USA) dissolved in a 1:4 mixture of ethyl acetate and olive oil (FUJIFILM Wako Pure Chemical Co., Osaka, Japan) into each nostril for the 1st 5 days. Control group rats were treated with an intranasal mixture of ethyl acetate and olive oil instead of TDI. After 2 days of rest, the rats were resensitized in the same way for 5 days. Ten days after the 2nd sensitization, the rats were provoked with an intranasal dose of TDI (5 μL of 10% TDI). SST was administered to the AR + SST group rats from the 1st day of the initial day of sensitization until the day of the last provocation with TDI (a total of 22 days; Figure 1).
SST (Lot. 2140019010) was kindly gifted by Tsumura and Co. (Tokyo, Japan) and prepared using the same procedures as previously reported [14]. Five grams of dried SST powder was prepared from extraction with boiling water of 8 herbs: 6.0 g of Pinellia tuber (Pinellia ternata, Breitenbach), 3.0 g of Asiasarum root (Asiasarum sieboldi, F. Maekawa), 3.0 g of Cinnamon bark (Cinnamomum cassia, Blume), 3.0 g of Ephedra herb (Ephedra sinica Stapf), 3.0 g of Glycyrrhiza (Glycyrrhiza uralensis, Fischer), 3.0 g of Schisandra fruit (Schisandra chinensis Baillon), 3.0 g of Paeony root (Paeonia lactiflora, Pallas), and 3.0 g of Processed ginger (Zingiber officinale, Roscoe).
After the last TDI stimulation, each animal was observed for 10 min, and the number of sneezes and the time spent nose scratching were scored.
Nasal lavage fluid (NLF) was collected from rats anesthetized with intraperitoneal administration of pentobarbital sodium (50 mg/kg; Somnopentyl, Kyoritsu Seiyaku Co., Tokyo, Japan). The trachea was incised, a polyethylene catheter with a diameter of 1 mm was inserted into the nasopharyngeal cavity, and 1 mL of PBS flushed from the nostrils was collected. The NLF was centrifuged at 400× g for 10 min, and the supernatant was collected, stored at −80 °C, and used for measurement. Subsequently, blood was collected from the inferior vena cava after laparotomy.
IL-33 concentrations in NLF and serum were examined in triplicate using an ELISA test kit (SEB980Ra; Cloud-Clone, Houston, TX, USA) according to the manufacturer’s protocol.
Blood was centrifuged at 400× g for 10 min. The serum was withdrawn, and the histamine content was measured using an ELISA kit (A05890; Bertin Bioreagent, Montigny-le-Bretonneux, France) according to the manufacturer’s protocol.
After sampling NLF, the anesthetized rats were perfused intracardially with cold PBS (pH 7.4) and then perfused with 4% paraformaldehyde/PBS. The tissue, including the nasal cavity, was excised and cut into 20 μm thickness by a cryostat (CM1860; Leica Biosystems, Wetzlar, Germany). Then immunostaining process was conducted: rinse with PBS 3 times, blocking (10% goat serum with 0.3% Triton X-100 in PBS) for 2 h at room temperature, incubation with primary antibody (rabbit anti-IL-33, 1:200, bs-2633R, Bioss, Woburn, MA, USA) for 48 h, washing with PBS 3 times, incubation with 2nd antibody (donkey anti-rabbit Alexa Fluor 555, 1:1000, #A31572, Thermo Fisher Scientific, Waltham, MA, USA). Nuclei were counter-stained with DAPI (4′,6-diamidino-2-phenylindole, 1:1000, Thermo Fisher Scientific). Immunofluorescent images were obtained with a confocal microscope (FV1000D, Olympus, Tokyo, Japan).
TDI-HSA was prepared as reported previously [15]. Briefly, 2.4 g of TDI was reacted with 90 mL of 1% human serum albumin (HSA)/PBS with constant stirring for 30 min. To terminate the reaction, 2 M ammonium carbonate was added to the solution. As for control-HSA, 1% HSA solution was used. To remove unreacted TDR, reacted TDI-HSA and control-HSA solutions were centrifuged (3000× g, 20 min). Then, TDI-HSA and control-HSA solutions were dialyzed with a dialysis tube (Spectra/Por, MWCO 12-14,000, Repligen, Waltham, MA, USA) for 3 days with 4 L of 0.1 M ammonium carbonate. TDI-HSA and control-HSA were precipitated with an equal volume of 20% trichloroacetic acid and then redissolved with 1 M sodium hydroxide. The solution was dialyzed again (overnight, 3 times with 4 L of distilled water). Synthesized TDI-BSA was visualized by Coomassie blue staining after native SDS-PAGE.
Human Nasal Epithelial Cells (HNEpCs) (Promocell, Rockville, MD, USA) were cultured on poly-L-lysine coated cover glasses (Matsunami, Osaka, Japan) in Airway Epithelial Cell Growth Medium (Promocell) and maintained in 5% CO2 and 95% air at 37 °C. Twelve hours after stimulation with TDI-HSA, HNEpCs were fixed with 4% sucrose-containing 4% paraformaldehyde (Sigma Chemical Co.) for 20 min. The fixed cells were permeabilized with 0.2% Triton X-100/PBS (Sigma Chemical Co.) for 5 min and blocked with 10% goat serum/PBS for 1 h. Then, an anti-IL-33 antibody (Nessy-1, 1:250, Enzo Life Sciences, Farmingdale, NY, USA) was applied overnight at 4 °C. IL-33 was visualized by isotype-specific secondary antibody conjugated with Alexa 488 (1:200, Molecular Probes, Eugene, OR, USA). A fluorescent microscope (Axio Observer, Carl Zeiss, Oberkochen, Germany) was used to obtain fluorescent images.
All the values are expressed as means and standard error of the mean (SEM). Each data set was firstly analyzed using the Kolmogorov–Smirnov test to determine whether the data were normally distributed or not. One-way ANOVA followed by Tukey’s multiple comparisons or Kruskal-Wallis tests was used for normally distributed or non-normally distributed data, respectively, using EZR (Saitama Medical Center, Jichi Medical University, Saitama, Japan) [16]. p-value < 0.05 was considered statistically significant.
Rats were sensitized to/provoked with TDI and administered SST in the schedule, as shown in Figure 1. Provocation with TDI after sensitization to the reagent clearly induced apparent allergic symptoms such as reddening and swelling in the nose of rats (Figure 2A). Rats treated with SST, however, had a similar nasal appearance to control rats (Figure 2A). Next, the number of sneezes and duration of nose scratching for 10 min immediately after provocation were determined. In TDI-sensitized AR rats, the total number of sneezes was 12.3 ± 2.58 while 0.17 ± 0.17 in the control rats (Figure 2B). Administration of SST significantly suppressed sneezing (3.50 ± 1.02; Figure 2B). SST also reduced the increased duration of scratching nose observed in AR rats (162 ± 20.7 s in AR rats; 15.1 ± 11.2 s in AR + SST rats; Figure 2C).
Serum histamine levels were also measured as a marker of inflammatory levels in rats. As expected, serum histamine levels were dramatically raised in TDI-sensitized rats, which was suppressed by SST treatment (14.3 ± 2.04 nM in control rats; 71.5 ± 22.8 nM in AR rats; 15.0 ± 2.19 nM in AR + SST rats) (Figure 2D). These results clearly showed that SST treatment could protect rats against sensitization and provocation induced by TDI.
We next determined IL-33 levels in NLF and serum. TDI-sensitized AR rats showed significantly elevated IL-33 levels in serum and a trend toward elevation in NLF, while control rats had similar levels of IL-33 in both serum and NAL as control rats. (Figure 3A) (8.68 ± 0.41 in control rats; 10.2 ± 0.44 in AR rats; 8.33 ± 0.39 pg/mL in AR + SST rats). In parallel with the NLF IL-33, serum IL-33 also increased in AR rats, and SST treatment prevented the increase after TDI-sensitization. (Figure 3B; 10.1 ± 0.43 in control rats; 11.9 ± 0.25 in AR rats; 10.4 ± 0.48 pg/mL in AR + SST rats).
Immunohistochemical analysis showed that IL-33 expression in the nucleus of nasal epithelial cells in TDI-sensitized AR rats was dramatically reduced, whereas stable nuclear expression of IL-33 was observed in nasal epithelial cells of control rats (Figure 4A,B). Interestingly, SST treatment significantly restored the decrease in nuclear expression of IL-33 in nasal epithelial cells despite TDI sensitization/provocation (Figure 4A,B).
Next, we examined whether TDI stimulation induces IL-33 release from the nuclei of nasal epithelial cells and whether SST inhibits it. Since TDI is insoluble in the culture medium and cannot be used for cell culture, a TDI-bound HSA (TDI-HSA) was prepared. After 1 h of TDI-HSA treatment, the percentage of cells expressing IL-33 in the nucleus decreased (Figure 5A,B). Pretreatment with SST significantly inhibited the release of IL-33 from the nuclei of epithelial cells upon TDI-HSA stimulation (Figure 5A,B).
This study showed that SST, a Kampo medicine, suppressed symptoms associated with TDI-induced AR at least partially via inhibiting IL-33 release from the nuclei of nasal epithelial cells both in vivo and in vitro experiments. The inflammatory response of the nasal mucosa consists of the following phases: IgE-mediated immediate mast cell response. Late-phase reaction and persistent allergic inflammation through mobilization of T cells expressing TH2 cytokines such as IL-4/5, which are growth factors for eosinophils, basophils, and eosinophils. Recent studies have revealed an additional important step: the expression and release in epithelial cells of cytokines such as IL-33, which regulates the TH2 cytokine response [17]. Critical roles of IL-33 in the AR development shown in human and experimental animals are functions to stimulate mast cells [2]. IL-33 protein is constantly localized in the nucleus of nasal epithelial cells in the normal condition and is released into NLF in response to allergen challenges, such as ragweed pollen [2]. Importantly, IL-33 knockout mice showed little AR response after sensitization with the allergen [2]. Elevated serum IL-33 levels have been reported in patients with seasonal AR, and a significant association between AR susceptibility and IL-33 polymorphisms has also been found [18]. However, it remains unclear whether IL-33 in the nucleus of nasal epithelial cells would be important for the TDI-induced AR. In this study, we revealed that the provocation with TDI in the TDI-sensitized rats significantly induced IL-33 release from the nucleus of nasal epithelial cells, and pretreatment with SST suppressed TDI-induced AR by inhibiting IL-33 release. TDI is a recognized irritant to the human body and is one of the leading causes of occupational allergic disease in industrialized countries [19]. It has been reported that TDI-sensitized rodents showed similar features of nasal allergic diseases, including increased histamine levels and upregulation of histamine H1 receptors [20,21,22]. On the other hand, SST has been reported to ameliorate AR symptoms in human and animal/cellular models, and few mechanisms of the SST effect have been proposed [11,12,23,24,25,26,27,28]. SST inhibited eosinophilic infiltration of the nasal mucosa and improved symptoms of nasal obstruction in AR patients [12]. SST suppressed histamine release from mast cells in rats without affecting agonist binding to histamine H1 receptors [11]. Tanno et al. showed SST’s effect to inhibit basophil growth and differentiation in vitro and in vivo [23]. SST has also been reported to suppress TH2-type allergic reactions, affecting the differentiation of naive CD4+ T cells to Th1 or TH2 cells. SST decreased the expression of IL-4 mRNA, which is essential for TH2 cell development, and increased IFN-γ expression, which is important for Th1 cell development in mice [24,25]. Tanaka et al. also reported an effect of SST on suppressing the allergen-induced inflammatory process. SST decreased the synthesis of IgE and IL-10 in mononuclear cells of peripheral blood obtained from patients with perennial allergic rhinitis due to Dermatophagoides farinae [12]. We also previously reported that SST suppressed histamine-induced AR symptoms and substance-P/CGRP (calcitonin gene-related peptide) in NLF [26]. One electrophysiological study suggested that SST attenuated the secretion of water and electrolytes from the nasal glands induced by acetylcholine [27]. SST seems to affect the step of the infiltration of mast cells and eosinophils into the nasal mucosa in AR development [28,29]. Nakano et al. showed that SST suppressed phorbol ester-induced increase of IL-33 and histamine H1 receptors in cultured Swiss 3T3 and HeLa Cells [30]. In addition to these reported functions of SST, our study would raise a possible mechanism underlying how SST ameliorates symptoms of AR patients as inhibition of IL-33 release from nasal epithelial cells stimulated by allergens. However, it is still unclear how SST blocks the release of IL-33 from epithelial cells. IL-33 is synthesized as the full-length form and localized in the nucleus in epithelial cells. The full-length IL-33 is cleaved to generate a high-activity form when cells are exposed to environmental aeroallergens. That process has an important function in the rapid and efficient induction of allergic type 2 responses [31]. The step of the cleavage of IL-33 performed by proteases contained in allergens and/or intracellular proteases such as calpain has been reported to be essential for IL-33 release. Allergen-induced secretion of IL-33 from epithelial cells was reported to be mediated by redox-dependent activation of EGFR signaling pathways induced by activation of NADPH oxidase isoform (DUOX1), which in turn activates a protease calpain-2 [32]. Interestingly, extracts and components of herbs consisting of SST have been shown to inhibit EGFR signaling. Extract of Ephedra herb, a component of SST, strongly suppressed EGFR protein expression within several hours [33]. Trans-cinnamaldehyde contained in Cinnamon extract, which is also a component of SST, is reported to inhibit EGFR activation in vitro [34]. 8-gingerol contained in ginger extract had a role as an inhibitor of EGFR [35]. Although the molecular mechanism of the inhibitory effect of SST on IL-33 release from the epithelial cell nucleus needs to be clarified in the future, suppression of EGFR signaling may be one of the mechanisms.
Recent basic research on herbal medicines has revealed many useful actions and their molecular mechanisms. Since IL-33 release from nasal epithelial cells is considered to be a common mechanism of AR exacerbation, the use of SST is highly promising not only for AR caused by TDI but also for AR caused by other allergens. A limitation of this study is that it is not yet clear how SST blocked IL-33 release from epithelial cells. Further studies are needed to clarify its molecular mechanism. It is likely that SST will be used more and more in clinical practice in the future because of the advantages of herbal medicine, which has fewer side effects and has a combined action. | true | true | true |
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PMC9608552 | Qibin Wu,Yachun Su,Yong-Bao Pan,Fu Xu,Wenhui Zou,Beibei Que,Peixia Lin,Tingting Sun,Michael P. Grisham,Liping Xu,Youxiong Que | Genetic identification of SNP markers and candidate genes associated with sugarcane smut resistance using BSR-Seq | 13-10-2022 | sugarcane,smut resistance,BSR-seq,SNPs,key genes,expression pattern,molecular mechanism | Sugarcane smut caused by Sporisorium scitamineum is one of the most severe fungal diseases worldwide. In this study, a cross was made between a smut-resistant variety YT93-159 and a smut-susceptible variety ROC22, and 312 progenies were obtained. Two bulks of progenies were then constructed, one consisted of 27 highly smut resistant progenies and the other 24 smut susceptible progenies. Total RNAs of the progenies of each bulk, were pooled and subject to bulked segregant RNA-sequence analysis (BSR-Seq). A total of 164.44 Gb clean data containing 2,341,449 SNPs and 64,999 genes were obtained, 7,295 of which were differentially expressed genes (DEGs). These DEGs were mainly enriched in stress-related metabolic pathways, including carbon metabolism, phenylalanine metabolism, plant hormone signal transduction, glutathione metabolism, and plant-pathogen interactions. Besides, 45,946 high-quality, credible SNPs, a 1.27 Mb region at Saccharum spontaneum chromosome Chr5B (68,904,827 to 70,172,982), and 129 candidate genes were identified to be associated with smut resistance. Among them, twenty-four genes, either encoding key enzymes involved in signaling pathways or being transcription factors, were found to be very closely associated with stress resistance. RT-qPCR analysis demonstrated that they played a positive role in smut resistance. Finally, a potential molecular mechanism of sugarcane and S. scitamineum interaction is depicted that activations of MAPK cascade signaling, ROS signaling, Ca2+ signaling, and PAL metabolic pathway and initiation of the glyoxalase system jointly promote the resistance to S. scitamineum in sugarcane. This study provides potential SNP markers and candidate gene resources for smut resistance breeding in sugarcane. | Genetic identification of SNP markers and candidate genes associated with sugarcane smut resistance using BSR-Seq
Sugarcane smut caused by Sporisorium scitamineum is one of the most severe fungal diseases worldwide. In this study, a cross was made between a smut-resistant variety YT93-159 and a smut-susceptible variety ROC22, and 312 progenies were obtained. Two bulks of progenies were then constructed, one consisted of 27 highly smut resistant progenies and the other 24 smut susceptible progenies. Total RNAs of the progenies of each bulk, were pooled and subject to bulked segregant RNA-sequence analysis (BSR-Seq). A total of 164.44 Gb clean data containing 2,341,449 SNPs and 64,999 genes were obtained, 7,295 of which were differentially expressed genes (DEGs). These DEGs were mainly enriched in stress-related metabolic pathways, including carbon metabolism, phenylalanine metabolism, plant hormone signal transduction, glutathione metabolism, and plant-pathogen interactions. Besides, 45,946 high-quality, credible SNPs, a 1.27 Mb region at Saccharum spontaneum chromosome Chr5B (68,904,827 to 70,172,982), and 129 candidate genes were identified to be associated with smut resistance. Among them, twenty-four genes, either encoding key enzymes involved in signaling pathways or being transcription factors, were found to be very closely associated with stress resistance. RT-qPCR analysis demonstrated that they played a positive role in smut resistance. Finally, a potential molecular mechanism of sugarcane and S. scitamineum interaction is depicted that activations of MAPK cascade signaling, ROS signaling, Ca2+ signaling, and PAL metabolic pathway and initiation of the glyoxalase system jointly promote the resistance to S. scitamineum in sugarcane. This study provides potential SNP markers and candidate gene resources for smut resistance breeding in sugarcane.
Sugarcane (Saccharum spp. hybrids) is the largest cash sugar crop in the world with a global production of about 1.9 billion tons (Rajput et al., 2021) . It accounts for about 80% of the total sugar in the world and more than 90% in China with an important economic value (Lam et al., 2019; Rajput et al., 2021). Adversities such as drought, low temperature, high salinity, and diseases can seriously affect the yield and quality of sugarcane. Sugarcane smut caused by Sporisorium scitamineum is one of the most severe fungal disease worldwide (Padmanaban et al., 1988; Que et al., 2014a; Lam et al., 2019). The pathogenic mycelium of sugarcane smut invades the apical meristem through buds and produces a “black spike whip” at the tip of the plant, which is the most typical phenotypic trait. When the membrane is broken, numerous thick wall spores became available for disease re-infestation and spread (Nisha et al., 2004; Izadi and Moosawi-Jorf, 2007; Rajput et al., 2021). The severity of sugarcane smut is influenced by pathogenic microspecies, environmental conditions and cultivar characteristics (Akalach and Touil, 1996), and it becomes more severe under favorable conditions and in extreme cases even leading to a complete crop failure (Bachchhav et al., 1979). In addition to direct yield losses, sugarcane smut also causes significant reductions in sucrose content, purity, and other quality indicators (Kumar et al., 1989). The selection of smut-resistant varieties as parental crosses produces a high percentage of smut-resistant individuals in the progeny, so cultivation of smut-resistant varieties is a reliable and practicable measure to prevent and control this disease (Que et al., 2014b; Bhuiyan et al., 2021). However, sugarcane is an allopolyploid crop with a complex genetic background, and resistance to smut is likely to be determined by the cumulative effect of multiple master genes, numerous micro-effect genes and the interaction between sugarcane and S. scitamineum (Wu et al., 2013; Huang et al., 2018; Bhuiyan et al., 2021; Rajput et al., 2021; Ling et al., 2022). Therefore, to establish a rapid and efficient smut resistance breeding technology system, it is necessary to screen and identify as many resistance-linked molecular markers and key genes with potential breeding application value as possible. In 1991, Michelmore et al. (1991) first proposed bulked segregant analysis (BSA) and successfully used it to screen for markers linked to target genes in Lactuca sativa. It is an efficient method for identifying markers closely linked to phenotypically related genes (Giovannoni et al., 1991). BSA combined with whole genome sequencing (BSA-Seq) was first used in model plants with small genomes such as Arabidopsis thaliana (Austin et al., 2011; Greenberg et al., 2011; Schneeberger and Weigel, 2011; Lindner et al., 2012;) and Oryza sativa (Abe et al., 2012; Takagi et al., 2013). In sugarcane, Wang et al. (2021) established a poly BSA-Seq approach using single-dose polymorphic markers, based on optimizing the traditional BSA-Seq, and further using this method, four molecular markers tightly linked to leaf blight (Stagonospora tainanensis) resistance were obtained, and 12 differentially expressed genes (DEGs) associated with leaf blight resistance were screened within a 1.0 Mb region of these molecular markers. However, BSA-Seq was costly for a complex large genomic species, especially the allopolyploid sugarcane. In 2012, Liu et al. (2012) reported a bulked segregation RNA-Seq (BSR-Seq) method, and successfully localized and cloned an epidermal wax synthesis gene glossy3 in Zea mays. Currently, BSR-Seq has been widely used in the localization and cloning of genes in plants. Li et al. (2013) identified another epidermal wax synthesis gene glossy13 via BSR-Seq along with Seq-Walking. In Triticum aestivum, Zhu et al. (2020) constructed a mixed bulk of wheat resistant/susceptible powdery mildew (Blumeria graminis f. sp. tritici, Bgt) and successfully screened 3,816 differential single nucleotide polymorphisms (SNPs) and 3,803 DEGs by BSR-Seq, among which, 14 genes were up-regulated in the plant-pathogen interaction pathway. Ramirez-Gonzalez et al. (2015) constructed a mixed bulk with resistance/susceptibility to wheat yellow rust (Puccinia striiformis f. sp. tritici, PST) in polyploid wheat F2 generation and mapped the rust resistance gene Yr15 to a 0.77-cM interval. In addition, the wheat stripe rust resistance genes YrZH22 (Wang et al., 2017), YrZM103 (Zhang et al., 2017), Yr26 (Wu et al., 2018b), Yr041133 (Li et al., 2022) were successfully localized and identified by RNA-Seq. In sugarcane, only Gao et al. (2022) constructed a genetic map of sugarcane-S. scitamineum interactions with an average distance of 1.96 cM using specific locus amplified fragment sequencing (SLAF-Seq) and BSR-Seq. The map contained 21 major QTLs with phenotypic variance explanation (PVE) of more than 8.0%, among which 10 QTLs were stable (repeatable) with PVEs ranging from 8.0 to 81.7%, and 77 SNPs from major QTLs were then converted to kompetitive allele specific PCR (KASP) markers, of which five were highly significantly linked to smut resistance in sugarcane. In the present study, 312 F1 progenies were firstly created by crossing YT93-159 (smut-resistant, female) with ROC22 (smut-susceptible, male). After disease evaluation in field, 27 highly resistant and 24 susceptible progenies (5 highly susceptible and 19 susceptible) were selected to construct a resistant bulk and a susceptible bulk, respectively. Secondly, BSR-Seq analysis was used to obtain SNPs and DEGs. Thirdly, two algorithms, ΔSNP-index association analysis and Euclidean distance (ED), were used to identify and locate SNPs and smut resistance-related candidate genes. Fourthly, the expression patterns of candidate genes were analyzed to identify key genes associated with smut resistance by exploring the BSR-Seq data, the RNA-Seq data from different tissues of ROC22 (unpublished), the RNA-Seq data from YT93-159 and ROC22 infected with S. scitamineum for WGCNA analysis (Wu et al., 2022), and the real-time quantitative PCR (RT-qPCR) data. Finally, the BSR-Seq and WGCNA data were combined to depict the potential molecular mechanism of sugarcane and S. scitamineum interaction. This study is expected to set up a theoretical basis for exploring the molecular mechanism of sugarcane resistance to S. scitamineum, and to provide potential marker/gene resources for the molecular breeding of smut resistance in sugarcane.
Mixed spores of sugarcane smut were collected from the Sugarcane Base in Baise, Guangxi (longitude 106°53’-107°26’E, latitude 23°16’-24°01’N) and the Experiment Station of Fujian Agriculture and Forestry University (FAFU), Fuzhou, Fujian (longitude 119°23’E, latitude 26°11’N), then stored in a 4°C refrigerator after drying. A population of 312 F1 progenies was made from a cross between YT93-159 (smut resistant) and ROC22 (smut susceptible) in 2014. In February 2019, 60 double-bud stems of each progeny were packed in net bags, immersed in a smut spore suspension at a concentration of 5×106 spores mL-1 for 15 min, removed and moistened at 25 to 28°C for 24 h. Then, 20 stems of each progeny were planted in a single row (3.0 m row length and 1.0 m row spacing) field plot in Baise and Fuzhou with three replications (Cang et al., 2021). During the growing season of 2019, the number of total and diseased plants within each plot were counted during field surveys and disease incidence rate was calculated ( Supplementary Table S2 ). Level of resistance to sugarcane smut was classified according to percent of diseased plants (Que et al., 2006): highly resistant (0 to 3.0%), resistant 1 (3.1 to 6.0%), resistant 2 (6.1 to 9.0%), moderately resistant (9.1 to 12.0%), moderately susceptible (12.1 to 25.0%), susceptible 1 (25.1 to 35.0%), susceptible 2 (35.1 to 50.0%), highly susceptible 1 (50.1 to 75.0%), and highly susceptible 2 (75.1 to 100%).
In December 2019, mature stems of 27 highly resistant progenies, 24 susceptible progenies (6 highly susceptible and 18 susceptible), and the two parental varieties were cut ( Figure 1 and Supplementary Table S2 ). In reference to Su et al. (2014), the stems were cut into single-bud setts, soaked in water for 24 h, and then germinated for 2-3 d in a greenhouse at 32°C/65% relative humidity (RH). When the shoots grew to 1-2 cm, they were syringe-inoculated with a 0.5 μL S. scitamineum spore suspension containing 5×106 mL-1 in 0.01% (v/v) Tween-20. The inoculated shoots were cultured at 28°C/65% RH under alternative 12 h light/12 h darkness. At least six shoots were excised with three replications at 48 h post-infection, quickly frozen in liquid nitrogen, and stored at -80°C until total RNA extraction. Total RNA was extracted from 53 samples (two parents, 27 highly resistant progenies and 24 susceptible progenies) using Trizol reagent (Invitrogen, CA, USA) following the manufacturer’s protocol. The total RNA of YT93-159 was designated as T01. The total RNA of ROC22 was designated as T02. Equal amounts of total RNA from the 27 highly resistant progenies were mixed to form the resistant bulk as T03. Equal amounts of total RNA from the 24 susceptible progenies were mixed to form the susceptible bulk as T04. After validation by agarose gel electrophoresis and NanoDrop One (Thermo Fisher Scientific, Waltham, MA, USA), the four total RNA samples were sent to Baimaike Biotechnology Co., Ltd. (Beijing, China) for cDNA library construction.
RNA concentration and integrity number (RIN) of the four total RNA samples were measured with an Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA, USA). The requirements for cDNA library construction and subsequent sequencing were met when the RIN was ≥7. mRNA was enriched with magnetic beads with Oligo (dT) and then randomly interrupted by the addition of fragmentation buffer. Double-stranded cDNAs were synthesized using TruSeq Stranded mRNA LT Sample Prep Kit (Illumina, CA, USA), purified, end-repaired, A-tailed, sequenced, fragment size selected, and finally enriched by PCR to obtain cDNA libraries (Bolger et al., 2014). Libraries were analyzed by Agilent Bioanalyzer 2100 and then sequenced by Illumina HiSeq™ (Beijing Baimaike Biotechnology Co., Ltd., China). Sequencing depth was set at an average of 15 GB of clean data per parent and 60 GB of clean data per bulk. Quality of sequencing data was assessed using FastQC and Trimmomatic software (Kim et al., 2015). Raw data were filtered to remove adapter reads, low quality reads, joint sequences, and ribosomal RNAs to obtain high quality clean reads (Bolger et al., 2014). The whole genome sequence of Saccharum spontaneum (sspon_v201901030) (Zhang et al., 2018) was used as the reference genome. The STAR (v2.3.0e) software (Dobin et al., 2013) was used to align the clean reads with the reference genome to obtain mapped reads for subsequent analysis.
Due to data and software limitations, the annotation of reference genomes is often not sufficiently accurate, and the original annotated gene structure needs to be optimized. Based on the annotation information of the S. spontaneum genome, the untranslated region (UTR) was extended upstream and downstream to correct the gene boundary if the region outside the original gene boundary was supported by continuous mapped reads. The Cufflinks v2.2.1 software (Florea et al., 2013) was used to splicing the alignment results, and compared with the initial annotation results to discover new transcripts or new genes that were not originally annotated. Then, the ASprofile v1.0.4 software (Trapnell et al., 2010) was used to obtain types and corresponding numbers of alternative splices. The BLAST software (Ye et al., 2006) was also applied to annotate the new transcripts or new genes with functions from several databases such as NR (RefSeq non-redundant proteins) (Deng et al., 2006), SWISS-PROT (http://www.expasy.org/sprot/ and http://www.ebi.ac.uk/swissprot/) (Boeckmann et al., 2003), GO (Gene Ontology) (Ashburner et al., 2000), KEGG (Kyoto Encyclopedia of Genes and Genomes) (Kanehisa et al., 2004), COG (Clusters of Orthologous Groups) (Tatusov et al., 2000), KOG (Clusters of orthologous groups for eukaryotic complete genomes) (Tatusov et al., 2000), eggNOG (evolutionary genealogy of genes: Non-supervised Orthologous Groups) (Jensen et al., 2007) and Pfam 35.0 (http://pfam.xfam.org/) (Mistry et al., 2021).
FPKM (fragments per KB of transcript per million fragments mapped) was used as a measure of transcription or gene expression level (Garber et al., 2011) after the clean reads were aligned to S. spontaneum genome sequence to obtain the corresponding positional information. EBSeq v1.6.0 (Leng et al., 2013) was used to obtain DEGs between the two samples when fold change was > 2 and false discovery rate (FDR) was< 0.01. Statistical and clustering analyses were performed on DEGs among the parents and the two bulks to present genome-wide expression patterns including candidate regions (Zhu et al., 2020), and functional DEGs annotation.
SNPs were detected following the reference flowchart of GATK v3.2-2 software (McKenna et al., 2010). Briefly, based on the localization of clean reads along with the S. spontaneum genome, GATK was used to perform local realignments and base recalibrations to ensure the accuracy of the SNPs. Then, SNPs with multiple genotypes,< four support degree, consistency between the resistant and the susceptible bulks, and inconsistency between the parents and corresponding bulks were filtered to obtain high quality and credible SNPs (Reumers et al., 2012). SNP-index is a method used mainly to find significant genotype frequency differences between mixed bulks (Trapnell et al., 2010). The difference is counted by the ΔSNP-index. The closer the ΔSNP-index is to 1, the stronger the association of the SNP marker with the trait. To eliminate false positive loci, the ΔSNP-index was fitted using the SNPNUM method (Altschul et al., 1997) based on the SNPs’ positions on the reference genome. Then, the ΔSNP-index between resistant and susceptible parents and the bulks was calculated for each SNP using the following formula: Referring to the method of Takagi et al. (2013), the threshold for SNP detection was set as a test of 100,000 permutations in coupling with a 99% confidence. SNPs with larger than the threshold ΔSNP-index values (set as 0.05 in this study) in candidate regions were selected as candidate loci that were associated with smut resistance in sugarcane. The Euclidean distance (ED) is a method that uses RNA-Seq data to find markers significantly different between mixed bulks and to assess regions of association with traits (Trapnell et al., 2010). To eliminate background noise, the original ED value was processed to (ED)2 (Altschul et al., 1997), which was taken as the correlation value. The median +3 × standard deviation (set as 0.04 in this study) of the fitted values for all loci was taken as the correlation threshold for analysis (Trapnell et al., 2010). In this study, ΔSNP-index and ED analysis were used to screen for genes and candidate regions associated with sugarcane smut resistance. The common non-synonymous mutant genes obtained by both methods were removed, and all the remaining non-synonymous mutant genes were identified as candidate genes.
The conserved structural domains of the candidate genes from the candidate regions were analyzed using the NCBI database (Marchler-Bauer et al., 2017). The functions of the candidate genes were annotated according to Arabidopsis homologs from the TAIR database (https://www.arabidopsis.org/). To identify the key genes, the expression patterns of the candidate genes were analyzed by the BSR-Seq data, RNA-Seq data of five different tissues (root, epidermis, pith, leaf, and bud) of ROC22 (unpublished), and the WGCNA data of YT93-159 and ROC22 infected by S. scitamineum (Wu et al., 2022), and the expression heat map of the key genes was plotted using TBtools (Chen et al., 2020).
On 0 d, 1 d, 2 d, and 5 d post S. scitamineum inoculation, the shoots of YT93-159 and ROC22 were sampled as described earlier for RT-qPCR analysis. Twenty primer pairs were designed for the key genes by Beacon Designer 8.0 ( Supplementary Table S1 ). GAPDH was used as the internal reference gene (Iskandar et al., 2004). At each time point, three independent biological replicates were taken. RT-qPCR reactions was performed on ABI QuantStudio™ 3 system (Thermo Fisher Scientific, Waltham, MA, USA). Total reaction volume was 25 µL, containing 12.5 µL FastStart Universal SYBR Green PCR Master (Roche, Shanghai, China), 0.5 µL of each primer (10 µM), 1.0 µL template (10×cDNA diluted liquid), and 10.5 µL ddH2O. The thermal cycling program was: 50°C for 2 min; 95°C for 10 min; and 40 cycles of (95°C for 15 s; 60°C for 1 min). Expression levels of the key genes were calculated using the 2−ΔΔCT method (Livak and Schmittgen, 2001). Histograms were graphed by GraphPad Prism 6. Significance (p < 0.05) and standard error (SE) were determined by the Duncan’s new multiple range test.
Smut disease survey data were shown in Supplementary Table S2 . The disease grade of each F1 progeny was assigned according to Que et al. (2006). Based on the grades, 27 progenies were selected to constitute the resistant bulk and 24 progenies (6 highly susceptible and 18 susceptible) were selected to form the susceptible bulk ( Figure 1 and Supplementary Table S2 ).
A total of 164.44 GB clean data were obtained from the four samples by BSR-seq analysis, including 17.46 GB from YT93-159 (T01), 18.49 GB from ROC22 (T02), 60.19 GB from the resistant bulk (T03), and 68.28 GB from the susceptible bulk (T04). The GC content ranged from 52.20% to 54.02% and the Q30 base percentage was above 93.27% ( Table 1 ). The efficiency of sequence alignment to the S. spontaneum reference genome was 74.34% for T01, 74.05% for T02, 73.57% for T03, and 71.40% for T04, respectively ( Table 1 and Supplementary Figure S1 ). These results indicated that the quality of the sequencing data was high and met with the requirements of subsequent analysis.
Gene locations and size comparisons were conducted in reference to the S. spontaneum reference genome. A total of 17,477 genes were optimized at the 5’ and 3’ untranslated regions (UTRs) ( Supplementary Table S3 ). Alternative splicing types were divided into seven categories by the ASprofile software (Trapnell et al., 2010), namely, transcription start site (TSS), transcription terminal site (TTS), single exon skipping (SES), multi exon skipping (MES), single intron retention (SIR), multi-intron retention (MIR), and alternative exon ends (AE). As shown in Table 2 , 170,932, 170,452, 176,098, and 176,722 alternative splicing transcripts were obtained for YT93-159 (T01), ROC22 (T02), the resistant bulk (T03), and the susceptible bulk (T04), respectively. TSS accounted for the most, followed by TTS. TSS and TTS together accounted for about 90% of all alternative splicing. A total of 17,066 new genes were identified after filtering out sequences encoding peptide chains that were too short (less than 50 amino acid) or containing only one exon. Finally, 12,138 new genes were annotated upon blasting with various databases ( Figure 2 ).
A total of 64,999 genes were identified by BSR-Seq sequencing, of which 7,295 were DEGs ( Figure 3 ). The number of DEGs between the two parents (T01 vs. T02) was 5,166, of which 2,350 were up-regulated and 2,816 were down-regulated ( Figure 3B ). The number of DEGs between the resistant and the susceptible bulks (T03 vs. T04) was 2,636, of which 1,816 were up-regulated and 820 were down-regulated ( Figure 3B ). GO enrichment analysis showed that only 1,018 DEGs could be significantly enriched ( Figure 4A ). The 771 DEGs between the two parents (T01 vs. T02) ( Supplementary Table S4 ) and the 247 DEGs between the resistant bulk and the susceptible bulk (T03 vs. T04) ( Supplementary Table S5 ) were mainly enriched in metabolic process, cellular process, single-organism process of the biological process (BP) category, cell, cell part, organelle of the cellular component (CC) category, binding, catalytic activity, and transporter activity of the molecular function (MF) category ( Figure 4A ). KEGG enrichment analysis showed that DEGs between the two parents (T01 vs. T02) and between the resistant bulk and the susceptible bulk (T03 vs. T04) were enriched in 90 and 40 metabolic pathways, respectively ( Figure 4B ). The DEGs between the two parents (T01 vs. T02) were mainly enriched in ribosome, plant-pathogen interaction, phenylpropanoid biosynthesis, phenylalanine metabolism, homologous recombination, glutathione metabolism, DNA replication, and carbon metabolism ( Figure 4B and Supplementary Table S6 ). The DEGs between the resistant bulk and the susceptible bulk (T03 vs. T04) were mainly enriched in spliceosome, protein processing in endoplasmic reticulum, proteasome, plant-pathogen interaction, plant hormone signal transduction, glutathione metabolism, and aminoacyl-tRNA biosynthesis ( Figure 4B and Supplementary Table S7 ). Overall, the DEGs were mainly enriched in stress-related metabolic pathways, such as carbon metabolism, phenylpropanoid biosynthesis, phenylalanine metabolism, plant hormone signal transduction, glutathione metabolism, and plant-pathogen interactions.
Mining SNPs from the BSR-Seq data resulted in a total of 2,341,449 SNPs, including 501,220 from YT93-159 (T01), 472,956 from ROC22 (T02), 674,681 from the resistant bulk (T03), and 692,232 from the susceptible bulk (T04) ( Figure 5 ). After filtering out the SNPs common to all four samples, a total of 1,069,497 SNPs was remained. Then SNPs with multiple genotypes (2,689), with support degree< 4 (505,477), with consistent genotypes between the resistant bulk and the susceptible bulks (158,595), with inconsistency between the two parents or between the two corresponding bulks (356,790) were filtered out, leaving 45,946 high quality, credible SNPs for ΔSNP-index and ED analyses ( Figure 5 ). A total of 32 candidate regions associated with smut resistance were identified by the ΔSNP-index method. The total length of these regions was 36.37 Mb containing 889 genes and 103 non-synonymous mutant genes ( Figure 6A and Supplementary Table S8 ). On the other hand, the ED algorithm analysis identified 15 candidate regions associated with smut resistance with a total length of 10.40 Mb, 237 genes, and 30 non-synonymous mutant genes ( Figure 6B and Supplementary Table S9 ). Combining the results from both ΔSNP-index and ED analyses resulted in a 1.27 Mb region that was localized between 68,904,827 and 70,172,982 on S. spontaneum chromosome Chr5B. This region contained 21 genes and 4 non-synonymous mutant genes. After removing the common non-synonymous mutant genes obtained by both ΔSNP-index and ED methods, all the remaining non-synonymous mutant genes were regarded as candidate genes. Ultimately, a total of 129 candidate genes associated with smut resistance were identified.
To obtain functional information, the conserved structural domains of 129 candidate genes were analyzed by Conserved Domain Database in NCBI (Marchler-Bauer et al., 2017) and the homologous functions of these genes in Arabidopsis were annotated with TAIR database ( Supplementary Table S10 ). The results showed that among the 129 candidate genes, 24 either code key enzymes involved in signaling pathways or are transcription factors that closely relate to plant stress resistance. The examples include the PHD-type (PHD-ZFP) and BED-type zinc finger proteins (BED-ZFP), ethylene response factor (ERF), eukaryotic translation initiation factor 2b (eIF2b), WRKY53, and MYB transcription factors; MAPK, MEK, and Raf-like genes in mitogen-activated protease kinase signaling pathway; calmodulin 42 (CML42) and calcineurin B-like interacting protein kinase (CIPK) genes in calcium (Ca2+) signaling pathway; the glyoxalase I 10 (GLYI10) and glyoxalase II 21 (GLYII21) genes in the glyoxalase system; the peroxidase (POD) and catalase 1 (CAT1) genes in reactive oxygen species (ROS) signaling pathway; and other protease genes such as leucine-rich repeat receptor-like protein kinase (LRR-RLK), cyclin-dependent kinase (CDK), lipoxygenase (LOX), 4-coumarate:CoA ligase (4CL), serine protease inhibitor (SPI), purple acid phosphatase (PAP), glucose transporter (GLUT), potassium transporter (KUP), and ankyrin-like (ANK).
As shown in Figure 7A , 24 key genes were expressed in all four samples, namely, YT93-159 (T01), ROC22 (T02), the resistant bulk (T03) and the susceptible bulk (T04). The expression levels of ERF, MYB, eIF2b transcription factors, and protease genes (ANK, CIPK, PAP, SPI, and GLYI10) were high, but low for CDK, Raf-like, GULT, CAT1, and KUP ( Figure 7A and Supplementary Table S11 ). Twenty-one key genes were expressed in all the five tissues of ROC22, among which WRKY53, ANK, CML42, CIPK, eIF2b, and GLYI10 expressed at a high level, while BED-ZFP, CDK, PHD-ZFP, GLUT, MEK, and GLYII21 expressed at very low level ( Figure 7B ). LOX only expressed in buds. MYB mostly expressed in buds. Raf-like and CAT1 mostly expressed in leaves. 4CL, LRR-RLK, and KUP mostly expressed in roots ( Figure 7B ). The WGCNA data (Wu et al., 2022) collected at six time points post S. scitamineum-inoculation of YT93-159 and ROC22 showed that GLYI10, CIPK, PAP, MYB, eIF2b, and ANK expressed at high levels, followed by CML42, SPI, 4CL, ERF, MAPK, POD, WRKY53, and LRR-RLK, while GLYII21, LOX, BED-ZFP, and CDK expressed at low levels ( Figure 7C ). Both CIPK and WRKY53 expressed at higher levels in the smut-resistant variety than in the susceptible variety, while the opposite was true for eIF2b and CML42. In addition, the expression level of GLYI10 increased gradually post S. scitamineum-inoculation and peaked at 5 d ( Figure 7C ).
Only 20 key genes were successfully amplified by RT-qPCR. All the 20 key genes could be induced by S. scitamineum infection. The expression levels of 15 genes were remarkably higher in YT93-159 than ROC22 ( Figure 8 and Supplementary Table S12 ). In YT93-159, the expression level of LRR-RLK, MEK, CML42, MYB, and KUP increased gradually, and peaked on 5 d at a rate of 6.01-, 3.80-, 3.82-, 4.63-, and 3.21-fold higher than the control (0 d), respectively. However, their expression levels were largely unchanged or slightly up-regulated in ROC22 ( Figures 8A, C, I, N, Q ). Similarly, the expression level of GLYI10 and GLYII21 was higher in YT93-159 than in ROC22, gradually increased from 0 d to 5 d post S. scitamineum-inoculation and reached the peak at 5 d ( Figures 8G, H ). The expressions of Raf-like, CAT1, BED-ZFP, and SPI were up-regulated upon S. scitamineum inoculation and reached the peak at 1 d post inoculation ( Figures 8D, E, L, S ), while the expressions of PHD-ZFP, eIF2b, WRKY53, and ANK reached the peak at 2 d post inoculation ( Figures 8K, M, O, R ). In addition, genes MAPK, POD, CIPK, LOX, and PAP were also induced to express upon S. scitamineum inoculation at similar levels among different samples ( Figures 8B, F, J, P, T ). These results indicated that the expression levels of all 20 key genes were up-regulated by varying degrees upon S. scitamineum inoculation and that the expression patterns were certainly different between sugarcane varieties either resistant or susceptible to the smut disease.
Sugarcane is an allopolyploid plant with a complex genetic background and an extremely low recombination rate of excellent genes. The resistance to sugarcane smut is likely to be determined by the cumulative effect of multiple master genes, numerous micro-effect genes, and the interaction between sugarcane and the smut pathogen S. scitamineum (Wu et al., 2013; Huang et al., 2018; Bhuiyan et al., 2021; Rajput et al., 2021; Ling et al., 2021). It is thus of great significance to mine and identify molecular markers and key genes in sugarcane genome that associate with smut resistance. At present, BSR-Seq has been widely used in localizing and cloning of genes in plants (Liu et al., 2012; Li et al., 2013; Zhu et al., 2020). We applied this technique in sugarcane and found 45,946 high quality and credible SNP markers, a 1.27 Mb chromosome region Chr5B, and 129 candidate key genes that associated with smut resistance ( Supplementary Table S10 ). We also explored the NCBI and TAIR databases to functionally annotated 24 key genes, of which 20 were validated by RT-qPCR ( Figure 8 ). These key genes play an important role in sugarcane’s response to S. scitamineum infection through the glyoxalase system, MAPK cascade signaling, ROS signaling, Ca2+ signaling, and other resistance-associated metabolic pathways. The glyoxalase enzyme system, including GLYI and GLYII, is the most efficient way to remove excess toxic MG and is important to cope with various abiotic stresses and pathogen infections in plants (Tardieu and Tuberosa, 2010). Ghosh and Islam (2016) reported that the expression of GmGLYI-6, GmGLYI-9 GmGLYI-20, GmGLYII-6, and GmGLYII-10 were up-regulated when soybean plants were infected by various pathogens. Similarly, Yan et al. (2018) showed that under Plasmodiophora brassicae infestation, BrGLYI1, BrGLYI2, BrGLYI6, BrGLYI11, BrGLYI16, BrGLYII8, and BrGLYII10 genes of Brassica rapa were induced to express at an up-regulated level. Álvarez Viveros et al. (2013) found that simultaneous transfer of overexpressed GLYI and GLYII genes into Solanum lycopersicum Mill. reduced its oxidative stress response and thus improved its tolerance to salt stress. Singla-Pareek et al.(2003); Singla-Pareek et al.(2006) and Singla-Pareek et al.(2008) reported that simultaneous transfer of overexpressed GLYI and GLYII genes into Nicotiana tabacum helped improve its tolerance to heavy metals and salt stress. Our previous study also indicated that the sugarcane SoGloI gene played a role in sugarcane’s response to various biotic and abiotic stresses (Wu et al., 2018a). In this study, during 0 d to 5 d post S. scitamineum-infection in both varieties YT93-159 and ROC22, the expression level of GLYI10 and GLYII21 genes was continuously up-regulated by approximately 4.38-fold and 2.84-fold comparing to the control at 5 d, respectively. The expression level of GLYI10 and GLYII21 was also higher in resistant variety YT93-159 than susceptible variety ROC22 ( Figures 8G, H ), confirming that the GLYI10 and GLYII21 genes are responding to S. scitamineum infection by scavenging excess toxic MG. The MAPK cascade signaling pathway is prevalent and highly conserved in plants. It involves three protein kinases, MAPKKK, MAPKK, and MAPK, in functional tandem (Hwa and Yang, 2008). When plants were infected with various pathogens, PAMPs bind to RLKs to activate MPAKK, MAPKK, and MAPK in turn, and then send signals to activate specific transcription factors in the nucleus for functional gene expression (Johnson and Lapadat, 2002). LRR-RLK is the largest class of the plant RLKs family and plays critical roles in plant growth and development, hormone signaling, abiotic stresses, and pathogen defense (Shiu and Bleecker, 2001). In Arabidopsis, BRI1-associated kinase 1 (BAK1) is a member of the LRR-RLK family that can interact with brassinosteroids insensitive 1 (BRI1). BRI1 and BAK1 are receptors and co-receptors of brassinosteroids (BRs), respectively, and participate in the plant BRs signaling pathway (Li et al., 2002; Nam and Li, 2002). BAK1 binds to the bacterial flagellum protein receptor kinase (FLS2) to form a heterodimer, which can induce disease resistance response in plants (Chinchilla et al., 2007). The rice Xa21 gene encodes LRR-RLK, the LRR motif in the extracellular region recognizes and binds the toxic compounds produced by the pathogen, thereby enhancing the resistance to Xanthomonas oryzae pv. oryzae in rice (Song et al., 1995). In this study, the expression level of LRR-RLK gene was unchanged in the susceptible variety ROC22 but was gradually increased in the resistant variety YT93-159 by about 6.01-fold of the control at 5 d post S. scitamineum-inoculation ( Figure 8A ), suggesting that LRR-RLK play a role in sugarcane and S. scitamineum interaction. It is interesting that, when Asai et al. (2002) treated Arabidopsis protoplasts with FLS2, the expression of MEKK1, MKK4/MKK5, MPK3/MPK6, WRKY22, and WRKY29 was sequentially activated, and the activation events ultimately induced the expression of defense genes. This is the first complete MAPK signaling module identified in plants to fight against pathogen attacks. Wan et al. (2004) found that chitin elicitors induced AtMAPK expression and increased enzymatic activity of AtMPK3 and AtMPK6 with increased levels of WRKY22/29/33/53 transcripts. A rice D-subclass MAPK gene, BWMK1, was induced to express at 4 h after infection upon Magnaporthe grisea. Overexpressed BWMK1 induced constitutive expression of pathogenesis-related (PR) genes and enhanced resistance to rice blast disease (He et al., 1999). The rice MPKK10.2 gene acted in the cross-point of two MAPK cascades leading to X. oryzae pv. oryzicola resistance and drought tolerance (Ma et al., 2017). A cotton Raf-like MAP3K gene, GhMAP3K65, enhanced the sensitivity to pathogen infection and heat stress by negatively modulating growth and development in transgenic Nicotiana benthamiana (Zhai et al., 2017). Ali et al. (2021) identified 15 ShMAPKs, 6 ShMAPKKs, and 16 ShMAPKKKs genes from the genome of cultivar R570, of which ShMAPK07 and ShMAPKKK02 were defense-responsive genes when sugarcane plants were challenged by both Acidovorax avenae subsp. Avenae and Xanthomonas albilineans. In this study, the expression of MAPK, MEK, and Raf-like genes were up-regulated upon S. scitamineum infection ( Figures 8B–D ). In particular, the expression level of MEK gene was gradually increased to about 3.8-fold of the control at the peak (5 d) in YT93-159, while a slightly up-regulated expression was observed in ROC22 ( Figure 8C ). The results indicated that these three genes may synergistically activate the MAPK cascade signaling pathway to enhance sugarcane’s resistance to smut. ROS, including superoxide anion (O2 -), singlet oxygen (1O2), hydrogen peroxide (H2O2), and hydroxyl radical (-OH) is produced in plants in response to abiotic stresses and various bacterial and fungal diseases (Choudhury et al., 2017). ROS acts as a signaling molecule in plant growth and development. Too low a level of ROS inhibits cell growth, while too high a level of ROS is cytotoxic. It is essential to keep a reasonable level of intracellular ROS and maintain a dynamic balance between ROS production and cleavage (Wang et al., 2016). Main enzymes that play a role in ROS scavenging in plants include superoxide dismutases (SODs), ascorbate-glutathione, glutathione peroxidases (GPXs), and CATs (Mittler, 2002). CATs are a class of highly active enzymes essential for ROS detoxification (Mhamdi et al., 2010). CAT2 overexpression led to a higher CAT enzyme activity and enhanced resistance to oxidative stress and pathogen infection in transgenic N. benthamiana plants (Polidoros et al., 2001). Two sugarcane catalase genes, ScCAT1 and ScCAT2, played a positive role in immune responses in sugarcane-S. scitamineum interactions, as well as in various abiotic stresses (Su et al., 2014; Sun et al., 2018). In this study, the expression of CAT1 was also up-regulated and reached the peak of 13.06-fold increase at 1 d post S. scitamineum-inoculation in YT93-159 ( Figure 8E ). POD is another oxidoreductase that scavenges ROS in plants and plays an important role in plant response to hormones, drought, oxidative stress, and pathogen attack (Valério et al., 2004). Hu et al. (2015a); Hu et al. (2015b) identified four III-class peroxidases gene (KJ001797, KJ001798, SsPOD-1, and KJ001799) from S. officinarum, S. spontaneum, and S. arundinaceum, respectively, with highly conserved functional regions. Su et al. (2017) identified the ScPOD02 gene from a smut-resistant genotype Yacheng 05-179 two days post S. scitamineum-inoculation. The transcripts of ScPOD02 were up-regulated in smut-resistant varieties but remained unchanged or slightly reduced in susceptible varieties. In this study, POD gene expression was up-regulated upon S. scitamineum infection. The expression level of POD was slightly higher in YT93-159 than ROC22 ( Figure 8F ). Thus, it is speculated that both CAT1 and POD genes are involved in the ROS signaling pathway by scavenging excess ROS to effectively improve the resistance to sugarcane smut. Ca2+ plays an important role as a second messenger when plants are subjected to abiotic or pathogen attack (Boudsocq and Sheen, 2013). Ca2+-sensor proteins include calmodulin (CAM), calcineurin B-like protein (CBL), CML, and CDPK. Among them, CML is a family of plant-specific Ca2+ sensor proteins that are widely involved in various processes of plant growth and development (Boudsocq and Sheen, 2013). Heyer et al. (2022) reported that Ca2+ sensor proteins CML37 and CML42 antagonistically regulated plants’ defense against insect infestation by Spodoptera littoralis and drought. Vadassery et al. (2012) found that Ca2+ and phytohormone were induced along with CML42 gene expression when stimulated by S. littoralis in A. thaliana. CML42 acted as a negative regulator in plant defense by decreasing COI1-mediated JA sensitivity and JA-responsive gene expression. In the present study, the expression of CML42 was up-regulated upon S. scitamineum infection. The expression level of CML42 was gradually increased in YT93-159 to about 3.82-fold of the control at 5 d but remained unchanged in ROC22 ( Figure 8I ). CBL and CIPK form an important signaling regulatory network in response to abiotic stress (Mao et al., 2016). The interaction between CIPK24/SOS2 (salt-overly-sensitive) and CBL4/SOS3 may directly regulate the downstream component SOS1, a putative Na+/H+ antiporter, thereby enhancing the salt detoxification process in Arabidopsis (Shi et al., 2000; Chinnusamy et al., 2004). Su et al. (2020) identified 48 SsCIPKs from S. spontaneum and cloned 10 ScCIPK genes from the sugarcane cultivar ROC22. Six ScCIPK genes (1, 2, 15, 20, 21, and 28) were up-regulated under polyethylene glycol (PEG) stress. Three ScCIPK genes (1, 2, and 28) were up-regulated upon NaCl stress. Transient overexpression of ScCIPKs in N. benthamiana plants demonstrated that the ScCIPK genes responded to various abiotic stresses and bacterial infections by participating in ethylene synthesis pathway. In this study, CIPK gene was induced to express at a similar level in different samples upon S. scitamineum infection ( Figure 8J ). It is thus suggested that Ca2+ sensor proteins CML42 and CIPK are involved in the Ca2+ signaling pathway and enhance the resistance to S. scitamineum in sugarcane. A potential molecular mechanism of sugarcane and S. scitamineum interaction was depicted by combining the results of BSR-Seq with WGCNA data (Wu et al., 2022) ( Figure 9 ). When sugarcane is infected with S. scitamineum, pathogen-associated molecular proteins (PAMPs) bind to receptor-like proteins (RLKs) to activate MAPKKK, MAPKK, and MAPK in sequence (Johnson and Lapadat, 2002). MAPK is activated and enters the nucleus, and thus promotes gene expression by activating transcription factors ZFP, MYB and WRKY53. The transcription factors regulate abscisic acid (ABA), salicylic acid (SA), jasmonic acid (JA), gibberellin (GA), ethephon (ET), and other hormone metabolic pathways, thereby enhancing the resistance to sugarcane smut disease. Upon sugarcane and S. scitamineum interaction, reactive oxygen species (ROS) are generated, which cause a hypersensitive response (HR) to a certain extent, thereby inducing plant resistance. However, when ROS accumulates in excess, they become toxic and cause damage to plant growth. Protein kinases such as CAT, POD, and glutathione S-transferases (GST) are effective in scavenging excess ROS to maintain a dynamic balance of ROS in plants. Moreover, the binding of PAMPs to RLKs also causes a [Ca2+] burst that activates the production of calcium sensor proteins, such as CML and calcium-dependent protein kinase (CDPK), which in turn activate the nitric-oxide synthesis (NOS) and phenylpropanoid (PAL) metabolic pathway to produce more lignin and flavonoids, thereby enhancing the resistance to sugarcane smut. Furthermore, under the stress of smut disease, a large amount of toxic methylglyoxal (MG) is produced in sugarcane, which activates the glyoxalase system to remove MG to alleviate or relieve the effect of excess MG on sugarcane growth and development. In summary, the activation of MAPK cascade signaling, ROS signaling, Ca2+ signaling, and PAL metabolic pathway and the initiation of the glyoxalase system jointly promote the resistance to sugarcane smut disease.
In this study, a field disease survey was conducted on 312 F1 progenies of a crossing between smut-resistant variety YT93-159 and smut-susceptible variety ROC22. Based on the disease data, one smut-resistant bulk (27 progenies) and one smut-susceptible bulk (24 progenies) were constructed. BSR-Seq technology was then used to sequence YT93-159, ROC22, and the two bulks to yield 164.44 GB clean data. A total of 17,477 genes were optimized, 12,138 new genes were annotated, and 7,295 DEGs were identified using the STAR (v2.3.0e) software and a S. spontaneum reference genome. GO and KEGG enrichment analyses revealed that the DEGs were mainly enriched in stress-related metabolic pathways (carbon metabolism, phenylalanine metabolism, plant hormone signal transduction, and glutathione metabolism) and plant-pathogen interactions. In addition, 45,946 high quality SNPs were identified. A 1.27 Mb chromosome region associated smut resistance was localized to S. spontaneum Chr5B (68,904,827 to 70,172,982). One hundred and twenty-nine candidate genes were identified based on both ΔSNP-index and ED methods. Furthermore, 24 key genes encoding enzymes in signaling pathways or transcription factors were found, which were closely related to stress resistance. RT-qPCR analysis confirmed that 20 key genes were induced to express upon S. scitamineum infection, and the expression levels were significantly higher in YT93-159 than ROC22. Combining the results of BSR-Seq with our previous WGCNA study (Wu et al., 2022), a potential molecular mechanism of sugarcane and S. scitamineum interaction is drawn in Figure 9 , which indicates that the activation of MAPK cascade signaling, ROS signaling, Ca2+ signaling, and PAL metabolic pathway and the initiation of the glyoxalase system may jointly promote the resistance to S. scitamineum in sugarcane. The results should benefit further understanding of the molecular mechanisms of smut resistance and provide many SNPs and gene resources for future smut resistance breeding in sugarcane.
The raw sequencing data were deposited in National Genomics Data Center (NGDC), Beijing Institute of Genomics, Chinese Academy of Sciences, under Project PRJCA012242 with Genome Sequence Archive (GSA) number CRA008356 (https://bigd.big.ac.cn/gsa/browse/CRA008356).
QW, Y-BP, GPM, LX and YQ conceived and designed the experiments, QW, YS, WZ, PL, and TS performed the experiments, QW, FX, BQ and YS analyzed the data, QW wrote the original manuscript, Y-BP, GPM, YS, LX and YQ revised the manuscript. All authors contributed to the article and approved the submitted version.
This research was funded by the National Key R&D Program of China (2019YFD1000500), National Natural Science Foundation of China (31971992 and 31781688), Natural Science Foundation of Fujian Province, China (2020J01591 and 2015J06006), China Agriculture Research System of MOF and MARA (CARS-17), and a Non-Funded Cooperative Agreement between the USDA-ARS and NRDCSIT on Sugarcane Breeding, Varietal Development, and Disease Diagnosis, China (Accession Number: 428234).
The authors are thankful to Zhongqi He and Perng-Kuang Chang for reviewing the manuscript with excellent comments. USDA is an equal opportunity provider and employer. And, we are grateful to the reviewers for their helpful comments on the original manuscript. We would like to thank editors for their efficient works.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. | true | true | true |
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PMC9609286 | Meile Mo,Xiaoyun Ma,Yihuan Luo,Chao Tan,Bihu Liu,Peng Tang,Qian Liao,Shun Liu,Hongping Yu,Dongping Huang,Xiaoyun Zeng,Xiaoqiang Qiu | Liver-specific lncRNA FAM99A may be a tumor suppressor and promising prognostic biomarker in hepatocellular carcinoma | 26-10-2022 | Hepatocellular carcinoma,Liver-specific lncRNA,FAM99A,Prognosis,Tumor growth | Background Increasing evidence shows that liver-specific long non-coding RNAs (lncRNAs) play important roles in the development of hepatocellular carcinoma (HCC). We identified a novel liver-specific lncRNA, FAM99A, and examined its clinical significance and biological functions in HCC. Methods The expression level and clinical value of FAM99A in HCC were examined using The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases, and were further verified using quantitative real-time polymerase chain reaction (qRT–PCR) in our HCC cohort. Univariate and multivariate Cox proportional hazards regression models were also applied to identify independent prognostic indicators for HCC patients. Cell counting kit-8, colony formation, and Transwell assays were performed to evaluate the effects of FAM99A on the proliferation, migration, and invasion abilities of HCC cells in vitro. A subcutaneous xenograft tumor model was implemented to determine the effect of FAM99A on the tumor growth of HCC cells in vivo. RNA pull-down and mass spectrometry assays were performed to reveal the potential molecular mechanisms of FAM99A in HCC. Results The three public online databases and qRT–PCR data showed that FAM99A was frequently downregulated in HCC tissues and inversely correlated with microvascular invasion and advanced histological grade of HCC patients. Kaplan–Meier survival analysis indicated that decreased FAM99A was significantly associated with poor overall survival of HCC patients based on TCGA database (P = 0.040), ICGC data portal (P < 0.001), and our HCC cohort (P = 0.010). A multivariate Cox proportional hazards regression model based on our HCC cohort suggested that FAM99A was an independent prognostic factor of overall survival for HCC patients (hazard ratio: 0.425, P = 0.039). Upregulation of FAM99A suppressed the proliferation, colony formation, migration, and invasion capacities of HCC cells in vitro, and knockdown of FAM99A had the opposite effects. A subcutaneous xenograft tumor model demonstrated that overexpression of FAM99A significantly inhibited the tumor growth of HCC cells in vivo. Seven tumor-related proteins (PCBP1, SRSF5, SRSF6, YBX1, IGF2BP2, HNRNPK, and HNRNPL) were recognized as possible FAM99A-binding proteins by the RNA pull-down and mass spectrometry assays. Conclusion Our results suggest that FAM99A exerts cancer-inhibiting effects on HCC progression, and it may be a promising prognostic indicator for HCC patients. Supplementary Information The online version contains supplementary material available at 10.1186/s12885-022-10186-2. | Liver-specific lncRNA FAM99A may be a tumor suppressor and promising prognostic biomarker in hepatocellular carcinoma
Increasing evidence shows that liver-specific long non-coding RNAs (lncRNAs) play important roles in the development of hepatocellular carcinoma (HCC). We identified a novel liver-specific lncRNA, FAM99A, and examined its clinical significance and biological functions in HCC.
The expression level and clinical value of FAM99A in HCC were examined using The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and Gene Expression Omnibus (GEO) databases, and were further verified using quantitative real-time polymerase chain reaction (qRT–PCR) in our HCC cohort. Univariate and multivariate Cox proportional hazards regression models were also applied to identify independent prognostic indicators for HCC patients. Cell counting kit-8, colony formation, and Transwell assays were performed to evaluate the effects of FAM99A on the proliferation, migration, and invasion abilities of HCC cells in vitro. A subcutaneous xenograft tumor model was implemented to determine the effect of FAM99A on the tumor growth of HCC cells in vivo. RNA pull-down and mass spectrometry assays were performed to reveal the potential molecular mechanisms of FAM99A in HCC.
The three public online databases and qRT–PCR data showed that FAM99A was frequently downregulated in HCC tissues and inversely correlated with microvascular invasion and advanced histological grade of HCC patients. Kaplan–Meier survival analysis indicated that decreased FAM99A was significantly associated with poor overall survival of HCC patients based on TCGA database (P = 0.040), ICGC data portal (P < 0.001), and our HCC cohort (P = 0.010). A multivariate Cox proportional hazards regression model based on our HCC cohort suggested that FAM99A was an independent prognostic factor of overall survival for HCC patients (hazard ratio: 0.425, P = 0.039). Upregulation of FAM99A suppressed the proliferation, colony formation, migration, and invasion capacities of HCC cells in vitro, and knockdown of FAM99A had the opposite effects. A subcutaneous xenograft tumor model demonstrated that overexpression of FAM99A significantly inhibited the tumor growth of HCC cells in vivo. Seven tumor-related proteins (PCBP1, SRSF5, SRSF6, YBX1, IGF2BP2, HNRNPK, and HNRNPL) were recognized as possible FAM99A-binding proteins by the RNA pull-down and mass spectrometry assays.
Our results suggest that FAM99A exerts cancer-inhibiting effects on HCC progression, and it may be a promising prognostic indicator for HCC patients.
The online version contains supplementary material available at 10.1186/s12885-022-10186-2.
Liver cancer is a common solid malignancy. It was the sixth most common tumor and the third leading cause of cancer-related mortality worldwide according to the World Health Organization’s statistics in 2020 [1]. China has a high incidence of liver cancer, which account for more than half of all cases worldwide [2]. For Chinese males, liver cancer ranks second only to lung cancer in cancer-related deaths [3]. Of all types of liver cancer, hepatocellular carcinoma (HCC) is the most common type, and it is responsible for approximately 75-80% of all liver cancers. Hepatic resection and liver transplantation remain the first choice for patients with early- and mid-stage HCC. However, the 5-year recurrence rate of HCC is approximately 80% after radical treatment [4]. Most patients are diagnosed with late-stage HCC, who have limited therapeutic options and generally have expected median survival times of 6-8 months [5]. Therefore, more research is urgently needed to comprehensively understand the molecular mechanisms of HCC and identify new biomarkers and therapeutics for HCC patients. Long non-coding RNAs (lncRNAs) are a class of endogenous non-coding RNAs that are longer than 200 nucleotides and exert pivotal effects in the pathogenesis and progression of cancers [6–8]. Accumulating studies have shown that lncRNAs play important roles in regulating various biological processes, including chromatin and genome dynamics, gene expression, development, and cell differentiation [9–11]. Many dysregulated lncRNAs are involved in the tumorigenesis and development of HCC. For example, lncRNA PCNAP1 accelerated hepatitis B virus (HBV) replication and hepatocarcinogenesis by modulating the miR-154/PCNA/HBV cccDNA signaling pathway [12]. LINC00662 was upregulated in HCC and promoted HCC progression by activating the Wnt/β-catenin signaling pathway and M2 macrophage polarization [13]. Another lncRNA, p53-stabilizing and activating RNA (PSTAR), was downregulated in HCC and suppressed HCC cell proliferation and tumorigenicity by inducing p53-mediated cell cycle arrest [14]. However, the mechanisms of HCC are not clear, and many dysregulated lncRNAs must be explored. Compared to protein-coding genes, lncRNAs have more tissue-specific expression characteristics [6]. Many lncRNAs with liver-specific expression patterns were identified as significantly related to the occurrence and progression of HCC. For example, lncRNA HULC (highly upregulated in liver cancer) accelerates HCC progression and attenuates the sensitivity of HCC cells to chemotherapeutic agents [15–18]. LINC01093 is downregulated in HCC tissues and suppresses HCC growth and metastasis [19]. Our previous studies also identified three liver-specific lncRNAs, FAM99B [20], LINC02499 [21], and LINC01146 [22], that were all downregulated in HCC and exerted similar inhibitory effects on the proliferation, migration, and invasion of HCC cells. The current study identified a novel liver-specific lncRNA, FAM99A (family with sequence similarity 99 member A), which was specifically expressed in normal liver tissues based on the RNA-sequencing data from the Genotype-Tissue Expression (GTEx) project (https://gtexportal.org/home/) (Additional file 1: Figure S1). Given the great importance of liver-specific lncRNAs in the development of HCC, we comprehensively examined the expression level of FAM99A in HCC tissues based on public online databases and our HCC cohort. The clinical significance and prognostic value of FAM99A in HCC patients were also investigated. The biological function of FAM99A on HCC cell proliferation, migration, invasion, and tumor growth was evaluated in vitro and in vivo. RNA pull-down assay and mass spectrometry analysis were also performed to investigate the potential molecular mechanisms of FAM99A in impeding the progression of HCC.
The Ethics Committee of Guangxi Medical University approved the study. HCC tissues and corresponding paracancerous tissues were collected from 62 HCC patients who underwent radical surgical resection at the Affiliated Cancer Hospital of Guangxi Medical University between February 2016 and December 2019. The diagnosis of HCC was confirmed by pathological examination. The tissue samples were snap frozen in liquid nitrogen and stored in liquid nitrogen until use. Patients with a history of preoperative chemotherapy or radiotherapy were excluded. Informed consent was obtained from all included patients, and patients were followed up until December 2021.
The RNA sequencing (RNA-seq) data (371 HCC tissues and 50 adjacent normal tissues) of HCC patients (level 3) were extracted from The Cancer Genome Atlas (TCGA) database (https://cancergenome.nih.gov/) (up to January 14, 2019). Transcript expression data were calculated as transcripts per million (TPM) and normalized by converting to log2 (TPM + 1). The clinical parameters and follow-up information were also downloaded to assess the clinical significance of FAM99A. For overall survival (OS) analysis, 370 cases were included after eliminating one patient without follow-up OS data. The RNA-seq and clinical data of another HCC cohort were obtained from the International Cancer Genome Consortium (ICGC) data portal (https://dcc.icgc.org/) (Data Release 28) on June 2, 2020. Due to a lack of paracancerous tissue expression data, the Liver Cancer, France [LICA-FR] cohort was excluded from our study. For the Liver Cancer, RIKEN, Japan [LIRI-JP] cohort, a total of 221 HCC tissues and 200 adjacent normal tissues were enrolled in the research after eliminating two metastatic tumor cases. The normalized read counts in this cohort were used for analysis, and OS analysis was performed to evaluate the prognostic significance of FAM99A. To draw a comprehensive result, we also extracted microarray datasets containing FAM99A expression data from the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/) (up to October 4, 2020). The retrieved keywords are listed below: (long non-coding RNAs OR lncRNAs OR non-coding RNAs) AND (hepatocellular carcinoma OR HCC OR liver) AND (cancer OR tumor OR carcinoma OR neoplasm* OR malignant*). The “Top Organisms” was restricted to “Homo sapiens”. Studies that simultaneously met the following two inclusion criteria were included: 1) HCC tissues and peritumoral liver specimens were included in the study (≥ 5 samples each group); and 2) the RNA profiling included the expression data of FAM99A. The basic information and FAM99A expression were carefully extracted from eligible datasets. A meta-analysis based on GEO datasets was subsequently performed.
The human HCC cell lines (Huh-7, Hep 3B, HepG2, HCCLM3, MHCC97L and MHCC97H) were maintained in our laboratory, and their background information has been previously described in detail [20]. Cells were cultured in Dulbecco’s modified Eagle’s medium (DMEM, Gibco, USA) or Minimum Essential Medium (MEM, Gibco, USA) supplemented with 10% fetal bovine serum (FBS, Gibco, Australia) at 37 °C in a humidified incubator containing 5% CO2. To obtain FAM99A overexpression cell lines, full-length FAM99A (1430 bp) was ligated into the LV5 (EF-1aF/GFP&Puro) vector. A nonsense oligonucleotide was used as a negative control (Lv-NC). The lentiviruses were synthesized by GenePharma Co., Ltd. (Shanghai, China). For FAM99A knockdown, short hairpin RNA (shRNA) targeting FAM99A was inserted into the GV493 (hU6-MCS-CBh-gcGFP-IRES-puromycin) vector. A non-silencing shRNA was used as a negative control (sh-NC). The lentiviruses were synthesized by Genechem Co., Ltd. (Shanghai, China). The following shRNA sequences were used: sh-NC, 5’-TTCTCCGAACGTGTCACGT-3’; sh-FAM99A, 5’-AATAAAAGTCACAGGACAA-3’. Cells (8 × 104 cells/well) were seeded into 6-well plates, incubated for 24 hours, then infected with lentiviruses according to the manufacturer’s instructions. Seventy-two hours after infection, the cells were exposed to puromycin (3.5 μg/ml) for two weeks to obtain stably transfected cell lines. The overexpression and knockdown efficiency of FAM99A in HCC cell lines were verified using qRT-PCR.
Total RNA of liver cancer cells and HCC samples was extracted using TRIzol reagent (Invitrogen, USA), and 900 ng of total RNA was reverse transcribed to complementary DNA using the PrimeScript™ RT reagent Kit with gDNA Eraser (Takara, Japan). The relative RNA expression level was evaluated using the TB GreenTM Premix Ex TaqTM II Kit (Takara, Japan) in a real-time PCR system (Applied Biosystems StepOnePlus, USA). GAPDH was used as an internal control. The transcript level was calculated using the 2−△△Ct method, and the primers for GAPDH and FAM99A are listed below: GAPDH-forward: 5’-AGCCACATCGCTCAGACAC-3’, GAPDH-reverse: 5’-GCCCAATACGACCAAATCC-3’; FAM99A-forward: 5’-CTCTTGTCCAGGTCAGCATCTC-3’, FAM99A-reverse: 5’-ACGCATCACAAAACAGCCAC-3’.
Isolation of cytoplasmic and nuclear RNAs in Hep 3B cells was performed using the PARIS Kit (Invitrogen, USA) according to the manufacturer’s instructions. The relative expression of FAM99A in cytoplasmic and nuclear fractions was determined using qRT-PCR. GAPDH and U6 were used as cytoplasmic and nuclear controls, respectively. The following primers were used for U6: U6-forward: 5’-CGCTTCGGCAGCACATATA-3’; U6-reverse: 5’-TTCACGAATTTGCGTGTCAT-3’.
For the Cell Counting Kit-8 (CCK-8, Dojindo, Japan) cell proliferation viability assay, stably transfected Huh-7 (3000 cells/well) and Hep 3B (2000 cells/well) cells were seeded into 96-well plates. A total of 10 μl of CCK-8 reagent was added to each well after 1, 2, 3, 4, and 5 days of incubation, and the absorbance was measured at 450 nm after incubation at 37 °C for another 2 hours. For the plate clone formation assay, stably transfected Huh-7 (1500 cells/well) and Hep 3B (500 cells/well) cells were seeded into 6-well plates and maintained in medium supplementing with 10% FBS for 14 days (Huh-7) or 10 days (Hep 3B). Colonies were fixed with methanol for 15 minutes, and stained with 0.1% crystal violet for another 15 minutes.
Transwell inserts (Costar, Corning, USA) with 8-μm polycarbonate membranes were used to assess the migration ability of cancer cells. The upper chambers coated with Matrigel (Corning, USA) were used for the invasion assay in vitro. Briefly, 200 μl serum-free medium containing 1×105 cells was added to the upper chambers. The bottom chambers received 600 μl culture medium containing 10% FBS. After incubating for 1 day (Huh-7) or 2 days (Hep 3B), the cells remaining in the upper chambers were gently wiped with wet cotton swabs. The migrated or invaded cells were fixed with methanol and stained with 0.1% crystal violet for 15 minutes. Five fields were randomly selected for imaging using the microscope EVOS FL Auto Cell Imaging System (EVOS FL, Thermo Fisher Scientific, USA).
BALB/c nude mice (4 weeks, male) were provided by the Experimental Animal Center of Guangxi Medical University and raised under specific pathogen free (SPF) conditions. Nude mice were randomly divided into 2 groups (n = 8 per group). A total of 5×106 Huh-7 cells stably transfected with FAM99A overexpression (Lv-FAM99A) or negative control (Lv-NC) were resuspended in a mixture of 50 μl PBS and 50 μl Matrigel (Corning, USA) and subcutaneously injected into the right flank of mice. The tumor size was measured every 3 days using a Vernier Caliper, and the tumor volume was calculated by the formula: tumor volume (mm3) = 0.5×L×W2 (L, longest diameter; W: shortest diameter). Four weeks later, the nude mice were sacrificed, and their subcutaneous tumors were isolated and weighed. The tumors were divided into two parts. One part was frozen in liquid nitrogen for RNA extraction, and the other part was fixed using 4% paraformaldehyde for hematoxylin-eosin (HE) and immunohistochemical (IHC) staining. All animal experiments were performed in accordance with the Guiding Principles for Care and Use of Experimental Animals, and the Animal Care & Welfare Committee of Guangxi Medical University approved this study (approval number: 202012016).
Paraformaldehyde-fixed tissues were trimmed, dehydrated in gradient alcohol and embedded in paraffin. After deparaffinization with xylene and rehydration in an ethanol gradient, paraffin sections were stained with hematoxylin for 5~10 minutes followed by several dips in 1% hydrochloric acid alcohol. After rinsing with distilled water, the sections were stained with a 1% eosin aqueous solution for 3 minutes, dehydrated in gradient alcohol and cleared with xylene. For IHC staining, citrate buffer was used for antigen retrieval, and 3% hydrogen peroxide was used to block the activity of endogenous peroxidase. The paraffin sections were washed with PBS and incubated with the diluted primary antibody anti-Ki67 (1:200, Abcam, ab16667) for 1 hour at 37 °C. After incubation with horseradish peroxidase (HRP)-labeled secondary antibody for 30 minutes, sections were stained with diaminobenzidine (DAB) chromogen and counterstained with hematoxylin. The sections were scanned using the TissueFAXS PLUS system (TissueGnostic, Austria), and the sum of integrated optical density (IOD) was calculated using Image-Pro Plus 6.0 software.
Cy3-labeled oligonucleotide probes specifically targeting FAM99A were designed and synthesized by Genechem (Shanghai, China). Human 18S FISH probe mix and U6 FISH probe mix were synthesized by RiboBio (Guangzhou, China). RNA-FISH was accomplished using a RiboTM Fluorescent in Situ Hybridization Kit (RiboBio, Guangzhou, China) according to the manufacturer’s protocol. Briefly, Hep 3B and Huh-7 cells were cultured on cell climbing slices, washed with PBS, and fixed with 4% paraformaldehyde for 10 minutes. After washing with PBS three times, the cells were permeabilized with 0.5% Triton X-100 for 5 minutes at 4 °C. The permeabilized cells were incubated with pre-hybridization buffer for 30 minutes and incubated with hybridization buffer containing probes overnight at 37 °C. Nuclei were counterstained with 4′-6-diamidino-2-phenylindole (DAPI) for 10 minutes, and cells were washed with PBS three times. Cells were visualized, and images were captured using a confocal microscope (LSM 800, Zeiss, Germany).
RNA pull-down was performed using the Pierce Magnetic RNA-Protein Pull-Down Kit (Thermo Fisher Scientific, USA). The sense and antisense chains of FAM99A were transcribed in vitro using T7 RNA Polymerase (Roche, USA), and labeled with Biotin RNA Labeling Mix (Roche, USA) according to the manufacturer’s instructions. The biotin-labeled RNAs were incubated with streptavidin magnetic beads (Invitrogen, USA) at 4 °C overnight to obtain the bead-RNA complex. Cell lysates (approximately 1 mg protein) were added to the bead-RNA complexes and incubated at room temperature for 1 hour. The precipitated complexes were washed with washing buffer three times and boiled in SDS buffer. The retrieved RNA-binding proteins were separated using electrophoresis and visualized by silver staining. The eluted proteins were also collected to performed mass spectrometry analysis using a Q Exactive System (Thermo Fisher Scientific, USA).
Numerical data are presented as the means ± standard deviation (SD). Paired-samples and independent-samples t tests were implemented to compare differences between two groups. The expression level of FAM99A was divided into high expression and low expression groups according to their median values. The chi-squared test was performed to explore the clinicopathological significance of FAM99A. The OS between the high and low FAM99A expression groups was compared using Kaplan–Meier curves with the log-rank test (two sides). Univariate and multivariate Cox proportional hazards regression models were also applied to determine the independent prognostic factors of HCC patients. All of the above analyses were performed using SPSS 20.0 software, and the results with P < 0.05 were considered statistically significant. The graphs were drawn using GraphPad Prism 8.0 software. To integrate the microarray results of GEO datasets, meta-analysis was implemented using STATA 15.0 software. The chi-squared and I-squared tests were used to examine the heterogeneity between the included datasets. When I2 > 50% or P < 0.05, significant heterogeneity existed among datasets, and the random-effect model was selected. Otherwise, the fixed-effect model was used for meta-analysis. A forest plot was drawn to obtain the standardized mean difference (SMD) and 95% confidence interval (CI). When P < 0.05, a pooled SMD < 0 indicated that the expression level of FAM99A was significantly downregulated in HCC tissues compared to adjacent normal tissues. In contrast, FAM99A was considered upregulated in HCC tissues when P < 0.005 and the combined SMD > 0.
To investigate the role of FAM99A in HCC development, we extracted the RNA-seq data of FAM99A from TCGA database. The results indicated that the expression level of FAM99A in HCC tissues was significantly lower than noncancerous tissues (2.39 ± 2.14 vs. 4.95 ± 0.73; P < 0.001; Fig. 1A). The patients were divided into high (> 1.619) and low expression (≤ 1.619) groups according to the median value of FAM99A. The clinical significance of FAM99A in HCC patients was also examined. The results revealed that low FAM99A expression significantly correlated with vascular invasion (P < 0.001) and advanced histological grade (P = 0.004; Table 1). The Kaplan–Meier survival curve showed that HCC patients with lower FAM99A expression tended to have poor OS (χ2 = 4.199, P = 0.040; Fig. 1B). We also examined FAM99A expression in the LIRI-JP cohort based on the ICGC database. The results showed that FAM99A was downregulated in HCC tissues versus adjacent normal tissues (8.93 ± 16.21 vs. 19.83 ± 13.65; P < 0.001; Fig. 1C). To examine the prognostic significance of FAM99A, 221 patients were divided into high and low expression groups based on the median (2.00) of FAM99A. Kaplan–Meier survival analysis demonstrated that patients in the low FAM99A expression group had a shorter OS time than patients in the high FAM99A expression group (χ2 = 13.495, P < 0.001; Fig. 1D).
To support a comprehensive conclusion, we also retrieved microarray chip data containing FAM99A expression from the GEO database. Based on our inclusion criteria, 18 eligible GEO datasets were enrolled, and detailed information of these GEO datasets is listed in Table 2. As shown in Fig. 2, there was significant heterogeneity among these 18 GEO datasets (I2 = 87.7%; P < 0.001). Therefore, a random-effect model was applied for the meta-analysis. The pooled SMD suggested that the expression of FAM99A was decreased in HCC tissues compared to noncancerous tissues (SMD = -1.162, 95% CI (-1.541, -0.783); P < 0.001; Fig. 2).
To further confirm the expression level of FAM99A in HCC, qRT-PCR was performed to compare FAM99A expression between 62 pairs of HCC and corresponding peritumoral liver specimens. We found that FAM99A was remarkably downregulated in 98.39% (61/62) of HCC samples (1.84 ± 3.62 vs. 22.27 ± 14.27; P < 0.001; Fig. 3A, B). To investigate the clinical significance of FAM99A in HCC, patients were also divided into a high expression group (> 0.417) and a low expression group (≤ 0.417) on the basis of the median expression of FAM99A. Consistent with the TCGA results, low expression of FAM99A significantly correlated with microvascular invasion (P = 0.041; Table 3). Kaplan–Meier survival curve was also performed, and the results showed that decreased FAM99A expression was remarkably associated with reduced OS of patients in our cohort (χ2 = 6.658, P = 0.010; Fig. 3C). The results of univariate Cox regression analysis found that tumor size, BCLC stage, Child-Pugh classification grade, microvascular invasion, Edmondson-Steiner grade, and FAM99A expression level correlated with the OS of HCC patients (P < 0.1; Table 4). Further multivariate Cox regression analysis demonstrated that the expression level of FAM99A was an independent prognostic factor for HCC patients (hazard ratio: 0.425, 95% CI: 0.189–0.958, P = 0.039; Table 4).
To determine the function of FAM99A in HCC cell lines, qRT-PCR was performed to examine the expression level of FAM99A in six HCC cell lines, and the results are shown in Fig. 4A. Hep 3B and Huh-7 cells with relatively high expression of FAM99A were selected for gain-of-function and loss-of-function analyses. The qRT-PCR results showed that FAM99A was successfully overexpressed in the Lv-FAM99A group in Hep 3B (P < 0.001) and Huh-7 cells (P < 0.01; Fig. 4B) but was downregulated in the sh-FAM99A group in Hep 3B (P < 0.001) and Huh-7 cells (P < 0.01; Fig. 4C) compared to the corresponding negative control group. The CCK-8 assay showed that the overexpression of FAM99A significantly inhibited the viability of Hep 3B and Huh-7 cells (both Ps < 0.05; Fig. 4D). In contrast, FAM99A knockdown evidently facilitated the viability of Hep 3B and Huh-7 cells (both Ps < 0.01; Fig. 4E). The colony formation assay also showed that overexpression of FAM99A remarkably impeded the clonogenicity of Hep 3B (P < 0.05) and Huh-7 cells (P < 0.001; Fig. 4F), but the colonies were significantly increased when FAM99A was knocked down in Hep 3B (P < 0.01) and Huh-7 cells (P < 0.05; Fig. 4G). These results indicated that FAM99A suppressed the proliferation of HCC cells.
The effects of FAM99A on the migration and invasion of HCC cells were further investigated using Transwell migration and invasion assays. Upregulation of FAM99A significantly restrained the cell migration and invasion abilities of Hep 3B and Huh-7 cells (all Ps < 0.001; Fig. 5A, B). In contrast, silencing FAM99A remarkably enhanced the cell migration and invasion activities of Hep 3B and Huh-7 cells (all Ps < 0.001; Fig. 5C, D). These findings suggested that FAM99A inhibited the migration and invasion behavior of HCC cells.
To test whether FAM99A suppressed tumor growth in vivo, a subcutaneous xenograft tumor model was constructed in nude mice (Fig. 6A, B). As shown in Fig. 6C and D, the tumor volume and tumor weight of the Lv-FAM99A group were lower than the control group, which demonstrated that FAM99A markedly impeded the tumor growth of Huh-7 cells in vivo (both Ps < 0.05). The FAM99A expression level in solid tumors was confirmed using qRT-PCR, and the results revealed that FAM99A expression was significantly higher in the Lv-FAM99A group than the control group (P < 0.001; Fig. 6E). HE staining suggested that upregulation of FAM99A decreased cell necrosis and infiltration of tumor tissues compared to the negative control group (Fig. 6F). The staining intensity of the proliferation marker Ki-67 in the Lv-FAM99A group was evidently weakened compared to the control group (P < 0.001; Fig. 6G). These results confirmed that FAM99A suppressed HCC cell growth in vivo.
The subcellular localization of lncRNAs determines the dominant mechanism of its molecular functions. We examined the subcellular localization of FAM99A using the lncATLAS website, which is a comprehensive resource of lncRNA localization in human cells based on RNA-seq datasets [23] (https://lncatlas.crg.eu/). The results suggested that FAM99A was primarily located in the nuclei of HepG2 cells and showed a characteristic liver-specific expression pattern (Fig. 7A). Subcellular fractionation and FISH assays were performed to confirm the results from the online database. Subcellular fractionation analysis indicated that FAM99A was mostly located in the nucleus of Hep 3B cells (93.16% in the nucleus vs. 6.84% in the cytoplasm; Fig. 7B). As shown in Fig. 7C, the FISH assay also suggested that FAM99A was primarily localized in the nuclei of Hep 3B and Huh-7 cells. An RNA pull-down assay was performed to identify proteins that may combine with FAM99A, and mass spectrometry analysis identified 266 differential proteins of FAM99A compared with its antisense strand (Additional file 2: Table S1; Fig. 8A, B). These 266 differential proteins were imported into DAVID 6.8 [24] (https://david.ncifcrf.gov/), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment [25–27] was performed. The results suggested that these proteins were primarily enriched in the “Biosynthesis of antibiotics”, “Carbon metabolism”, “Ribosome”, “Complement and coagulation cascades”, “Biosynthesis of amino acids”, “Pentose phosphate pathway”, “Glycolysis/Gluconeogenesis”, and “Focal adhesion” pathways (Benjamin adjust P < 0.05; Table 5). We also predicted the RNA binding proteins of FAM99A using the RBPmap website [28] (http://rbpmap.technion.ac.il/). We found seven proteins that were recognized in the RNA pull-down and RBPmap results, including PCBP1 (Poly (rC) binding protein 1), SRSF5 (serine/arginine-rich splicing factor 5), SRSF6 (serine/arginine-rich splicing factor 6), YBX1 (Y-box-binding protein 1), IGF2BP2 (insulin-like growth factor 2 mRNA-binding protein 2), HNRNPK (heterogeneous nuclear ribonucleoprotein K), and HNRNPL (heterogeneous nuclear ribonucleoprotein L) (Fig. 8C).
Liver-specific lncRNAs are emerging as pivotal regulators in the tumorigenesis and progression of HCC. We identified for the first time that FAM99A exhibited a highly liver-specific expression pattern, which suggests that FAM99A plays important roles in HCC. FAM99A was steadily downregulated in HCC tissues and negatively correlated with vascular invasion and advanced histological grade of HCC patients. The Kaplan-Meier curve analysis also revealed that patients with low expression of FAM99A tended to have poor OS based on the data from the TCGA database, ICGC database, and our HCC cohort. A multivariate Cox regression model demonstrated that FAM99A was an independent factor for the OS of HCC patients. In vitro experiments indicated that overexpression of FAM99A inhibited the cell proliferation, migration, and invasion abilities of HCC cell lines, and knocking down FAM99A produced the opposite effects. The subcutaneous tumor formation model suggested that FAM99A suppressed tumor growth of HCC cells in vivo. Our findings suggested that FAM99A exerted a cancer-inhibiting effect in HCC progression. Patients with malignant clinical features tend to have a poor prognosis in HCC. Our study found that patients with reduced expression of FAM99A were more likely to develop microvascular invasion and advanced histological grade. Another study also revealed that the downregulation of FAM99A was significantly associated with incomplete tumor capsule, tumor differentiation, recurrence, and poor prognosis of HCC patients [29]. These two studies revealed that FAM99A was an independent prognostic indicator for the OS of HCC patients. These results suggested that decreased expression of FAM99A contributed to the progression of HCC and may be a promising prognostic indicator for HCC patients. To the best of our knowledge, we are the first to identify FAM99A as a liver-specific lncRNA based on the GTEx project. A series of gain- and loss-of-function studies suggested that FAM99A suppressed the proliferation, migration, and invasion of HCC cells. Recent studies also reported the antitumorigenic functions of FAM99A in HCC. BX Zhao et al. found that FAM99A inhibited HCC metastasis and epithelial-mesenchymal transition by sponging miR-92a [29]. Another study demonstrated that FAM99A suppressed HCC cell viability and GLUT1-mediated glycolysis, which inhibited HCC progression [30]. Many abnormally expressed tissue-specific lncRNAs are involved in the tumorigenesis and progression of cancers. LINC00993 is a breast-specific lncRNA that is downregulated in triple-negative breast cancer (TNBC), and it suppressed the tumor growth of TBNC [31]. Testis developmental related 1 (TDRG1), also known as LINC00532, is expressed exclusively in the testis. TDRG1 is upregulated in testicular seminoma tissues and promotes the development, migration, and chemotherapy resistance of seminoma cells [32–34]. Prostate enriched lncRNA (PSLNR) is a prostate-specific lncRNA that inhibits prostate cancer progression via the p53-dependent pathway [35]. In contrast, another two prostate-specific lncRNAs (PCGEM1 and PCA3) are overexpressed in prostate cancer and promote the cell proliferation ability and inhibit the cell apoptosis ability of prostate cancer cells [36–39]. Notably, PCA3 is promising as a more efficiency diagnostic biomarker for prostate cancer than the currently used prostate-specific antigen [40, 41]. Given the important roles of tissue-specific lncRNAs in cancer, more studies are needed to further explore FAM99A as a diagnostic biomarker or therapeutic target for HCC patients. The molecular mechanisms of lncRNAs primarily depend on its localization in cells. We revealed for the first time that FAM99A was primarily located in the nucleus using subcellular fractionation and RNA-FISH assays, which indicated that FAM99A may play key regulatory roles in pivotal nuclear processes, such as chromatin organization, transcriptional and post-transcriptional programs, subcellular structures, and nuclear structure organization. These nuclear processes required the interaction of lncRNAs with RNA binding proteins (RBPs) almost universally [42, 43]. Therefore, we performed an RNA pull-down assay and identified that FAM99A pulled down 266 proteins using mass spectrometry analysis. To reveal the underlying mechanisms of FAM99A in HCC, these 266 proteins were used in KEGG pathway enrichment analysis. The results demonstrated that these proteins were primarily involved in several important cancer-related pathways, including complement and coagulation cascades [44], the pentose phosphate pathway [45–47], glycolysis/gluconeogenesis [48], and focal adhesion [49, 50]. We also identified seven proteins (PCBP1, SRSF5, SRSF6, YBX1, IGF2BP2, HNRNPK, and HNRNPL) that may interact with FAM99A. These seven proteins play crucial roles in tumor occurrence and development. PCBP1 is a multifunctional RBP that regulates the alternative splicing, translation, and RNA stability of many cancer-related genes to exert its cancer-inhibiting effect [51–53]. SRSF5 and SRSF6 belong to the serine/arginine-rich (SR) protein family, which is an important class of splicing regulators. SRSF5 and SRSF6 play important roles in the development and progression of cancers [54–57]. YBX1, also known as YB-1, binds DNA and RNA, and it is closely related to various malignant phenotypes of cancer cells, including tumor cell proliferation, metastasis, angiogenesis, and drug resistance [58, 59]. IGF2BP2 is a member of the conserved oncofetal RNA-binding protein family that acts as a N6-methyladenosine (m6A) reader, and it is involved in the development and progression of various cancer types [60–62]. HNRNPK and HNRNPL belong to the heterogeneous nuclear ribonucleoprotein family and interact with tumor-associated lncRNAs to regulate the tumorigenesis and progression of various cancers, including HCC [63–65]. Given the great importance of these seven proteins, further validation and functional experiments are needed to reveal the relationships between FAM99A and these proteins. To the best of our knowledge, the current study is the first study to fully examine the expression level and clinical and prognostic significance of FAM99A in HCC based on three public online databases and our own HCC cohort. The current study used meta-analysis to pool FAM99A expression in HCC based on 18 GEO datasets for the first time. The ICGC database was used for the first time to examine the expression level and survival significance of FAM99A in HCC. We also determined the subcellular localization of FAM99A in HCC cells for the first time and performed an RNA pull-down assay to enrich the mechanistic research of FAM99A in HCC development. Although we identified seven key RBPs that may interact with FAM99A, we were unable to verify their binding modes and mechanisms due to limitations of the experimental conditions and experimental levels. Therefore, further in-depth studies are needed to elucidate the comprehensive mechanisms of FAM99A in HCC development.
FAM99A is a liver-specific lncRNA that is downregulated in HCC and negatively associated with poor prognosis in HCC patients. It may exert its tumor-suppressing function via binding with some critical RBPs. Our study enriches the understanding of the effects of FAM99A in HCC and suggests that it may serve as a promising prognostic biomarker for HCC patients.
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PMC9609348 | 36287119 | Esmaeel Babaeenezhad,Fakhraddin Naghibalhossaini,Masoumeh Rajabibazl,Zohreh Jangravi,Forouzan Hadipour Moradi,Mohammad Davood Fattahi,Jörg D. Hoheisel,Mostafa Moradi Sarabi,Soroosh Shahryarhesami | The Roles of microRNA miR-185 in Digestive Tract Cancers | 08-10-2022 | miR-185,tumor suppressor,oncomiR,oral cancer,gastrointestinal cancer,epigenetics,long non-coding RNA | Digestive tract cancers represent a serious public health issue. In recent years, evidence has accumulated that microRNA miR-185 is implicated in the pathogenesis of this group of highly malignant tumors. Its expression variations correlate with clinical features, such as tumor size, lymph node metastasis, tumor node metastatic stage, survival, recurrence and response to adjuvant therapy, and have diagnostic and prognostic potential. In this review, we compile, evaluate and discuss the current knowledge about the roles of miR-185 in digestive tract cancers. Interestingly, miR-185 is apparently involved in regulating both tumor suppressive and oncogenic processes. We look at downstream effects as well as upstream regulation. In addition, we discuss the utility of miR-185 for diagnosis and its potential concerning novel therapeutic approaches. | The Roles of microRNA miR-185 in Digestive Tract Cancers
Digestive tract cancers represent a serious public health issue. In recent years, evidence has accumulated that microRNA miR-185 is implicated in the pathogenesis of this group of highly malignant tumors. Its expression variations correlate with clinical features, such as tumor size, lymph node metastasis, tumor node metastatic stage, survival, recurrence and response to adjuvant therapy, and have diagnostic and prognostic potential. In this review, we compile, evaluate and discuss the current knowledge about the roles of miR-185 in digestive tract cancers. Interestingly, miR-185 is apparently involved in regulating both tumor suppressive and oncogenic processes. We look at downstream effects as well as upstream regulation. In addition, we discuss the utility of miR-185 for diagnosis and its potential concerning novel therapeutic approaches.
Digestive tract cancers are considered a major global health problem. They account for about one-third of global cancer cases and cancer-related mortality [1,2]. Overall, they are responsible for about 4.1 million new cases worldwide and lead to about 3 million deaths each year. Despite advances in treatment strategies, many patients still face poor outcomes; the five-year survival rate of gastric cancer (GC) patients, for example, is less than 10%; the mortality of colorectal cancer (CRC) ranks second among malignant tumors [3] but will be replaced by pancreatic cancer at around 2030 [4]. Digestive tract cancer carcinogenesis is associated with genetic and epigenetic changes [5]. Chromosomal instability—originating from chromosome segregation dysfunction, defective DNA damage response, or induced by Helicobacter pylori—as well as microsatellite instability resulting from mismatch repair deficiency are among the most common genetic alterations observed. With respect to epigenetic alterations, aberrant DNA methylation, histone modifications, and altered microRNA (miR) expression are the most important factors involved in pathogenesis [6,7]. Over the years, we have studied the miR-content in cancer tissues and blood samples for both understanding tumor biology and determining the diagnostic potential [8,9,10,11]. miRs are non-protein-encoding RNA molecules that play a critical role in the post-transcriptional regulation of gene expression. The biogenesis of miRs is a multistep process that takes place in the cells’ nucleus and cytoplasm. They are transcribed from intergenic or intronic regions by RNA polymerase II as a primary miRNA and then processed by RNase III enzyme Drosha into stem and loop structures called pre-miRNAs. They are transferred to the cytoplasm and converted to mature double-stranded molecules by the RNase III enzyme Dicer [12,13]. Together with Argonaute proteins, miRs participate in the formation of an RNA-induced silencing complex (RISC), which leads to the degradation or translational suppression of specific target mRNAs. Alteration of miR expression contributes to the development of many diseases. Not surprisingly, miRs are involved in regulating fundamental functions related to cancer, such as cellular proliferation and apoptosis, cell cycle and metabolism, and differentiation [14]. Depending on the physiological environment and pathological condition, miRs may act as oncogenes that induce tumorigenesis or tumor suppressors that restrain tumor development. They also allow a prognosis for different cancer types and have a significant diagnostic and therapeutic potential [15]. MicroRNA miR-185 has been found frequently to vary in abundance in samples from cancer patients compared to samples from healthy donors. The gene is located at chromosome 22q11.21. The miR precursor consists of 82 nucleotides and is the source of the two mature molecules miR-185-5p and miR-185-3p; according to available data, miR-185-5p is the predominantly produced molecule (Figure 1). Dysregulation of miR-185 has been found in various pathological states, and there is much evidence for the misregulation of miR-185 in human cancers. The majority of experimental data indicates that miR-185 exhibits tumor-suppressive activities by affecting many critical biological processes such as cell cycle, epithelial-to-mesenchymal transition (EMT), apoptosis, autophagy, invasion and metastasis. However, fewer but nevertheless several studies have reported that miR-185 acts as an oncogene. Because of its apparently central role in tumor-related, miR-based regulation and the lack of a compilation and review of the very many different activities related to miR-185 regulation, we set out to summarize the current knowledge about the roles of miR-185 in digestive tract cancers and the molecular mechanisms that are affected. We look at downstream genes regulated by miR-185 and upstream molecules that are involved in controlling miR-185 expression. In addition, we discuss its utility for diagnosis and the potential for therapy. For simplicity, we ordered the review according to the location of the respective tissue in the body, starting with oral cancer and finishing with colorectal tumors. For an overview, a graphical synopsis of the various miR-185 dependent functional consequences is shown (Figure 2). More detailed information is provided as Supplementary Material for particular tumor entities, such as the sample types (tissue, plasma, cell lines, animal tissue) and numbers that were analyzed in the respective study as well as the observed molecular effects. In addition, information is given in the text paragraphs below about which target gene or pathway is known to be affected by miR-185-5p or miR-185-3p, respectively.
There is a growing body of research about the roles of miR-185 in the pathogenesis of oral and pharyngeal cancers (Supplementary Tables S1 and S2). Generally, miR-185 is downregulated in these tumors and acts as a tumor suppressor. Low expression of miR-185-3p in nasopharyngeal carcinoma patients is correlated with poor overall and recurrence-free survival as well as a weak response to radiotherapy by suppressing SMAD7 and WNT2B expression [16,17]. miR-185-3p also inhibits cellular growth and metastasis and promotes apoptosis in nasopharyngeal carcinoma cell lines [17,18]. SMAD7 functions as an antagonist of transforming growth factor β (TGF-β) type I receptor (TGF-βR1) and contributes to the development of various cancers [19]. High miR-185 expression induces apoptosis and autophagy via Homeobox C6 (HOXC6) transcription factor, repressing the oncogenic TGF-β1/mTOR pathway [20]. Low miR-185 expression and high levels of HOXC6 were found to be associated with lymph node metastasis, higher stages and lower survival. Variations of HOXC6, SMAD7, YWHAZ, FOXD3, RAB14, and ZNF703 exhibited specificity for oral cancers. However, there are no detailed studies about the diagnostic or prognostic value of miR-185 variations. Enforced expression of miR-185 has reduced dysplasia, cell proliferation and angiogenesis in oral potentially malignant disorders (OPMDs) [21]. There was no significantly different miR-185-5p expression in tonsillar squamous cell carcinoma (TSCC), base of tongue squamous cell carcinoma (BOTSCC) and normal tonsillar tissue. However, high miR-185-5p expression was correlated to human papillomavirus (HPV)-negativity and decreased survival in TSCC/BOTSCC [22]. This implies that miR-185-5p may be more significant for tumor progression than carcinogenesis. In contrast to the above reports, miR-185 up- rather than downregulation in oral squamous cell carcinoma (OSCC) samples was reported by Ramdas et al. [23]. However, this contradictory data is based only on the very low number of just five investigated samples and needs to be considered with much caution. Besides its involvement in tumorigenesis, the role of miR-185-5p in chemotherapy resistance was investigated. Its upregulation reduced cell viability in cisplatin-resistant tongue and larynx squamous cell carcinoma cell lines by downregulating the genes for aquaporin-3 (AQP3), caspase-14 (CASP-14) and arachidonate 12R-lipoxygenase (ALOX12B) [24]. More recently, miR-185 has been reported to considerably weaken OPMDs by reducing inflammation and inducing apoptosis through Akt/NF-kB and Akt/CASP-9 related pathways. In addition, while no significant difference in the miR-185-5p serum levels was found in oropharyngeal cancer patients and healthy subjects, serum levels were increased following radiotherapy and allowed an accurate prediction of severe radiation-induced xerostomia [25].
Long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) are major upstream regulators of miR-185 (Figure 3). Higher levels of lncRNA Forkhead box D3 antisense RNA1 (FOXD3-AS1) led to increased expression of FOXD3 and the development of nasopharyngeal carcinoma through absorption of miR-185-3p [26]. Conversely, overexpression of miR-185-3p in nasopharyngeal carcinoma cells reversed oncogenic activities of FOXD3-AS1. circRNA circ0058106 suppressed miR-185-3p in hypopharyngeal squamous cell carcinoma thereby increasing proliferation, metastasis and EMT by activating the Wnt2b/β-catenin/c-Myc pathway [27]. Third, lncRNA LINC00958 promoted OSCC through sponging miR-185-5p [28]. Its overexpression directly suppressed miR-185-5p and indirectly enhanced YWHAZ expression, which is known to function as an oncogene [29]. Forced expression of miR-185-5p, however, counteracted this effect and suppressed YWHAZ both at mRNA and protein levels. lncRNA PDIA3P promoted OSCC progression through sponging miR-185-5p, thereafter activating CCND2 (cyclin D2). Interestingly, the CCND2 mRNA level was significantly higher in OSCC tissues, but its protein level was downregulated by miR-185-5p overexpression [30]. As a fifth molecules, lncRNA LSINCT5 was aberrantly upregulated in OSCC and drove tumor progression by decreasing miR-185-5p and increasing ZNF703 levels [31]; ZNF703 has been characterized to be an oncogene in OSCC and a target of miR-185-5p. Finally, lncRNA KCNQ1OT1 accelerated OSCC tumorigenesis and reduced apoptosis by direct inhibition of miR-185-5p [32].
There are inconsistent reports about the function of miR-185 in esophageal cancers (Supplementary Table S3). Some evidence shows that miR-185 is upregulated in esophageal squamous cell carcinoma (ESCC) patients and associated with tumor node metastasis (TNM) stages [33,34,35]; this may have diagnostic potential. On the other hand, high expression of miR-185 led to a favorable prognosis in esophageal carcinoma patients and suppressed the malignancy and growth of esophageal carcinoma cells in vivo [36]. Also, upregulation of miR-185 in ESCC cells reduced proliferation, invasion, migration and metastasis through downregulation of the genes of the receptor for advanced-glycation end products (RAGE) and transcription factor Six1 [37,38]. RAGE activates several signaling pathways in various cancers and is involved in processes, such as cell proliferation, autophagy and apoptosis [39]. The homeobox protein Six1 plays an oncogenic role in various cancers [40]. miR-185 has also been implicated in the radioresistance of esophageal cancer. In two studies, Su et al. [41] and Zheng et al. [42] produced miR profiles of radioresistant esophageal cancer cells and reported downregulation of miR-185 expression compared to the radiosensitive parental cell line. High plasma levels of miR-185 were also observed in ESCC patients who responded to radiotherapy [43].
lncRNA that is highly expressed in hepatocellular carcinoma exhibits oncogenic activities in esophageal carcinoma by upregulating the kallikrein-related peptidase 5 (KLK5) gene through sponging miR-185 [36]. Liu and colleagues [44] observed that the lncRNA KLF3-AS1, which exhibits a low expression in tumor tissues, functions as a competing endogenous RNA (ceRNA) for miR-185-5p in ESCC. The Krüppel-like factor 3 (KLF3) gene exhibits a variety of biological effects, such as regulation of differentiation and apoptosis. The results suggest that KLF3 acts as a tumor suppressor in ESCC by inhibiting migration and invasion of tumor cells. Loss of KLF3-AS1 results in high levels of miR-185-5p, which in turn lead to KLF3 downregulation (Figure 3).
Generally, miR-185-5p is downregulated in gastric cancer (GC) promoting cell proliferation and invasion while simultaneously inhibiting apoptosis and necrosis [45] (Supplementary Table S4). Several studies suggest a miR-185-dependent mechanism for regulating the antitumor effects of GKN1. Gastrokine 1 (GKN1) is a protein expressed in gastric mucosa and exhibits gastroprotective effects [46,47]. The expression levels of GNK1 and miR-185 are low in GC patients and negatively correlated with the methylation status of the CpG island methylator phenotype and high transcript levels of both the DNA methyltransferase 1 (DNMT1) and the enhancer of zeste homolog 2 (EZH2) gene [48]. miR-185 is induced by GKN1 and required for GKN1’s tumor-suppressive activities [49]. Interestingly, GKN1 increased the expression of miR-185 in GC cell lines by suppressing c-Myc, which was recognized as a suppressor of miR-185 by binding to its promoter. Increased miR-185 expression in GC cell lines led to cell cycle arrest and reduced proliferation by inactivating DNMT1 and EZH2 and increasing the expression of the cyclin dependent kinase inhibitor 2A (CDKN2A) and E-cadherin (CDH1) genes. The increased expression was the result of GKN1/miR-185/DNMT1/EZH2–mediated hypomethylation of the promoter regions. The N-terminal hydrophobic region and BRICHOS domain of GKN1 were sufficient for its tumor-suppressive functions [50]. Downregulated GKN1 and miR-185 have also been shown to be negatively correlated with the high expression of ras homolog gene family member A (RHOA) [51]. GKN1 promotes the miR-185 expression level in GC cell lines and significantly inhibits cell migration and invasion by repressing RHOA in a miR-185 dependent manner. RhoA is a member of the Rho subgroup of the Ras superfamily and exhibits oncogenic activities [52]. Some studies have reported that miR-185 is involved in regulating the chemosensitivity of GC. miR-185 is downregulated in drug-resistant GC cells. miR-185 inhibition increased resistance to chemotherapeutic drugs by upregulating multidrug resistance (MDR) genes [53]. Inversely, enhanced expression led to more chemosensitivity of GC cells by reducing the expression of apoptosis repressor with caspase recruitment domain (ARC) both in in vitro and in vivo [54]. An upregulation of miR-185 upon cisplatin or doxorubicin therapy was the result of direct binding of Runt-related transcription factor 3 (RUNX3) to the binding site 3 (BS3) region of the miR-185 promoter, which finally triggered the promoter activity of miR-185. Tan et al. [55] reported the involvement of miR-185 in zinc finger protein 139 (ZNF139) induced enhancement of MDR in GC cells. ZNF139 was confirmed to be an upstream regulator of miR-185 that diminishes miR-185 transcription. This increased MDR characteristics in GC cells through the induction of the MDR-associated genes MDR1/P-gp, MRP and BCL-2. In another study, a significantly lower level of serum miR-185 was observed in patients with advanced GC, but it increased after neoadjuvant chemotherapy with the SOX regimen [56]. This could be used as a biomarker to predict the outcomes of neoadjuvant chemotherapy. miR-185 levels correlate with advanced clinical stage, lymph node metastasis and poor clinical outcomes [57]. Additionally, miR-185-5p expression was associated with clinicopathological characteristics, including tumor size, differentiation, and lymphatic metastasis [58]. Enforced expression suppressed the genes DNMT1 and tripartite motif-containing 29 (TRIM29) and resulted in a rescue of GC proliferation and metastasis, as well as an induction of apoptosis and cell cycle arrest [59]. TRIM29 is a transcription factor that has shown oncogenic or tumor-suppressive activities dependent on the tumor type. One of the mechanisms of the tumor suppressive activity of miR-185 is related to its ability to directly suppress the TRIM29-Wnt/β-catenin signaling axis. Interestingly, miR-185-5p mimics significantly increased apoptosis of GC cells by decreasing expression of BCL-2 and the gene of the X-linked inhibitor of apoptosis protein (XIAP), simultaneously increasing CASP-8 and CASP-3 expression and activities [58]. Enforced overexpression of miR-185-3p in GC cells led to a strong inhibition of EMT and migration and increased apoptosis through inactivation of the PI3K/Akt axis by downregulation of the cathepsin D gene (CTSD) [60]. Conversely to the data above, some studies reported high miR-185 levels in GC (Supplementary Table S4). Yao et al. [61], Treece et al. [62], and Zhang et al. [63] found considerable upregulation of miR-185 in GC patients, for example. Looking for biomarkers for GC detection, Zhou et al. [64] investigated the levels of miRs in plasma, GC tissues and plasma exosomes derived from GC patients. They observed an increased level of miR-185 in all three sample types. In addition, a higher level was detected in the plasma of patients with high- as compared to low-stage GC. In a similar study, miR-185-5p expression was found to be high in serum, tissue, and serum exosomes and, along with other miRs, showed diagnostic value for GC detection [65]. High miR-185 levels have also been associated with chemoresistance [66].
In addition to lncRNAs, the genes GNK1, c-Myc, ZNF139, and RUNX3 encode specific upstream regulators of miR-185 in GC. Interestingly, other lncRNAs were identified to regulate the expression of miR-185 in GC than in oral, pharyngeal or esophageal cancers (Figure 3). lncRNA XIST has been shown to regulate miR-185 expression [67] and functioned as a ceRNA for miR-185 to decrease miR-185-induced TGF-β1 suppression and accelerate GC progression. Wu and colleagues [68] reported that lncRNA FOXD2-AS1 was able to increase apatinib resistance by direct suppression of miR-185-5p and decreased the inhibitory effects of miR-185-5p on CCND2. By contrast, ectopic expression of miR-185-5p could suppress the FOXD2-AS1 effect on apatinib resistance and sensitize GC cells to apatinib through CCND2 suppression [68].
There is growing evidence that miR-185 expression in the liver has implications through regulating several oncogenes in hepatocellular carcinoma (HCC) (Supplementary Table S5). This includes the gene cell division cycle 42 (CDC42) which belongs to the Rho GTPase family; its oncogenic activities are well documented in various cancers [69]. The Rho-associated coiled-coil containing protein kinase 2 gene (ROCK2) encodes for a serine/tyrosine kinase that is involved in controlling HCC proliferation, metastasis and chemoresistance [70]. CDC42 and ROCK2 are direct targets of miR-185 and overexpressed in HCC cells due to miR-185 deficiency [71,72]. When miR-185-5p expression was restored, cell migration and invasion capacity were dramatically reduced. A low level of miR-185-5p expression in HCC tissues was also positively correlated with aggressive clinicopathological features, such as high TNM stage and lymph node metastasis. Integrin-β5 (ITGB5) is involved in the cell interaction with the extracellular matrix (ECM) and promotes HCC carcinogenesis by activation of the WNT/β-catenin pathway; homeobox gene SIX2 is a known oncogene. Both are targeted by miR-185 [73,74]. Through the inactivation of the WNT/β-catenin pathway in an ITGB5-dependent manner and the suppression of SIX2, miR-185 exerts anti-growth and anti-metastatic effects on HCC cells and reverses EMT. Qadir et al. [75] observed a low miR-185 expression in HCC tissues infected with hepatitis B (HBV) and hepatitis C viruses (HCV) as well as three different HCC cell lines. This study indicated that restoration of miR-185 expression suppressed DNMT1 expression and PTEN promoter methylation, increased PTEN expression and consequently PTEN-induced Akt inhibition and thus prevented HCC growth. Epigenetic control of PTEN by miR-185 has also been shown in cholangiocarcinoma [76]. Additionally, miR-185 was found to inhibit proliferation and cell cycle progression and induce apoptosis and autophagy in HCC cells by targeting different genes in the Akt signaling pathway, including AKT1, RICTOR and RHEB [77]. Downregulation of miR-185 is considered a prognostic marker of HCC. Zhi and colleagues [78] reported accurate prognosis of survival and tumor recurrence in patients with early-stage HCC, a finding that was confirmed by others [79]. Furthermore, miR-185 mimics caused a considerable suppression of cell growth and invasion in HCC cell lines. Serum miR profiling in HCV-positive HCC patients recognized six dysregulated miRs, which showed a high diagnostic accuracy for discriminating HCV-positive HCC from non-HCC individuals [80]. miR-185-5p was one of them, exhibiting lower abundance in cancer patients. As opposed to the above-mentioned publications, a few studies have reported upregulation of miR-185 and an oncogenic activity in HCC (Supplementary Table S5). Wen and colleagues [81] evaluated the miR profiles of plasma derived from HBV-positive HCC patients. They highlighted eight overexpressed miRs, including miR-185-5p, with diagnostic potential. Likewise, Tang and colleagues [82] determined miR profiles in HCC clinical samples and found 20 miRs that were related to venous metastasis, again including miR-185. Placenta-specific 8 (PLAC8) or Onzin is a highly conserved cysteine-rich protein that is downregulated in HCC and acts as a tumor suppressor. High expression of miR-185-5p actually downregulates PLAC8 expression [83].
Some lncRNAs have been shown to be involved in miR-185 regulation (Figure 3). One molecule is lncRNA FOXD2-AS1. Its knockdown effectively increased miR-185 expression, thus suppressing AKT and subsequently reducing proliferation, invasion and migration [84]. Linc00176 also seems to play an oncogenic role. Upon its knockdown, there is a release of miR-185-5p and an induction of cell cycle arrest and necroptosis [85]. LncRNA MEG3 is downregulated in HCC due to methylation of its promoter region. It exerts tumor-suppressive activities via regulation of p53 expression [86]. Zamani et al. [87] reported that dendrosomal curcumin increases miR-185 expression in HCC cells and thereby induces promoter DNA hypomethylation and upregulation of MEG3 through DNMT1 targeting. Besides lncRNA molecules, miR-185 is regulated by a different mechanism via the receptor for activated protein kinase C (RACK1) [88]. RACK1 is downregulated in HCC tissues. While it is not needed for maturation, it is required for full miR-185 functioning. RACK1 interacts with a part of the Dicer complex called KH-type splicing regulatory protein (KSRP) and is necessary for the recruitment of mature miR-185 to RISC. Knockdown of RACK1 caused a weaker miR-185-mediated inhibition of the expression of target genes in HCC cells. Another process involves ELK1. It is a transcription factor that belongs to the ternary complex factor subfamily of the ETS family and is involved in viral life cycles and virus-associated diseases. Fan et al. [89] found that miR-185-5p inhibits HBV replication and gene expression in HCC cell lines in an ELK1-dependent manner and also reduced HBV preS1 promoter activity by inhibiting ELK1. A rescued ELK1 expression reversed the suppressive effects of miR-185-5p.
Pancreatic cancer (PDAC) mortality is close to incidence and will become the second most frequent cause of cancer-related death by about 2030. Concerning miR-185 (Supplementary Table S6), an inverse association between lncRNA XIST and miR-185-5p has been described. Enforced expression of miR-185-5p or knockdown of XIST in PDAC cell lines reduced cell proliferation and triggered cell cycle arrest and apoptosis. XIST also suppressed miR-185-5p through direct interaction, which in turn inhibited oncogene CCND2 [90]. lncRNA PCAT6 promotes progression and tumorigenesis of PDAC through sponging of miR-185-5p and a resulting upregulation of the chromobox 2 (CBX2) gene [91]. In contrast, miR-185-5p mimics interfered with oncogenic activities of PCAT6. Overexpression and oncogenic activities of CBX2 have also been reported in several other cancers. In another study, increased abundance of transcriptional coactivator with PDZ-binding motif (TAZ) has been shown in tissues, serum and pancreatic fluid of PDAC patients and is negatively correlated with miR-185 expression. TAZ is a 14-3-3 binding protein with confirmed oncogenic activities in PDAC [92]. miR-185 inhibited the proliferation of PDAC cells by downregulating the TAZ-encoding gene TAFAZZIN through the direct targeting of the 3′-UTR of its mRNA [93]. Rather than suppressing tumor-related activities, Gao et al. [94] reported that upregulation of miR-185 contributes to PDAC development by downregulation of NTRK3 and CORO2B. The analysis was based on data obtained from only few tissues, however. NTRK3 belongs to the neurotrophin receptor family and modulates cell survival, while CORO2B is an actin-binding protein. Diagnostically, higher levels of miR-185 and six other miRs (miR-20a, miR-21, miR-24, miR-25, miR-99a, and miR-191) were found in a comparison of PDAC patients to healthy donors and patients with chronic pancreatitis [95]. The panel could discriminate different tumor stages with high sensitivity and specificity. Overall, however, miR-185 has not been found to contribute significantly to blood-based diagnosis of PDAC [96].
Colorectal cancer (CRC) is the third most common cancer in the world, with a high mortality rate [97]. The role of miR-185 in CRC has been widely evaluated. Most studies confirm that miR-185 is downregulated and exhibits tumor suppressing activity via several cellular processes (Supplementary Table S7). The Wnt/β-catenin pathway is one of the targets implicated in CRC development. miR-185 upregulation reduced aggressive features of CRC by directly targeting WNT1 as well as MYC and CCND1 further downstream [98,99]. Urothelial carcinoma-associated 1 (UCA1) is an lncRNA that is significantly upregulated in CRC and involved in its progression [100]. UCA1 functions as a sponge for miR-185-5p, thereby activating the WNT1-inducible signaling pathway protein 2 (WISP2)/β-catenin pathway and promoting CRC. Other studies have shown that miR-185-5p and miR-185-3p considerably reduced CRC growth and its metastatic and angiogenic potential by directly suppressing RHOA [101], CDC42 [101], c-MYC [102], and the gene of aquaporin 5 (AQP5) [103]. The stromal interaction molecule 1 (STIM1) gene is also directly affected [104]; it is an endoplasmic reticulum calcium sensor that activates the store-operated calcium influx when ER discharges calcium. Further, insulin-like growth factor 1 receptor (IGF1R) and insulin-like growth factor 2 (IGF2) are targeted by miR-185 [105]. IGF1R is a tyrosine kinase receptor that plays a pivotal role in angiogenesis, growth, metastasis and resistance to apoptosis in CRC. IGF2 triggers the signaling pathway associated with proliferation and survival of CRC through IGF1R. Hypoxia-inducible factor-2α (HIF-2α) is another verified target of miR-185 [106]. It increases hypoxic tumor cell proliferation through c-Myc activation and leads to CRC progression by deregulating iron homeostasis. Additionally, suppression of these genes by miR-185 noticeably inhibited EMT, increased apoptosis and reduced CRC resistance to chemotherapy and radiotherapy [103,104,105]. Yuan and colleagues [107] reported that miR-185 blocks MMP-9 and VEGF expression as well as the metastatic ability of CRC cells by repressing DC-SIGN. DC-SIGN is a C-type lectins domain family 4 member and abundantly expressed in immature dendritic cells. The miR-185 repression of DC-SIGN prevents β-catenin translocation to the nucleus of CRC cells by inactivation of the PI3K/Akt/GSK-3β pathway and the DC-SIGN/TCF1/LEF1 pathway could directly downregulate miR-185 in CRC. Li et al. [108] found a low-frequency 3′-UTR variant rs12915554 in GREM1—a member of the TGF-β superfamily—associated with enhanced CRC susceptibility. Interestingly, this variation stabilized the GREM1 transcript by disturbing the binding of miR-185-3p in CRC cells. In another paper, it was reported that miR-185-3p is downregulated in CRC due to the impairment of argonaute 2 (AGO2), a key regulator in miR processing [109]. Treatment with a miR-185-3p mimic could reduce metastasis by downregulating NRP1. NRP1 has been shown to interact with different ligands and receptors to increase tumorigenesis and EMT [110]. miR-185 expression has shown potential in diagnosis and prognosis of CRC. Significantly lower miR-185 expression was found in cancerous than non-cancerous tissues [111,112]. In addition, this was associated with advanced clinical stage and metastasis. Downregulation was also reported in CRC patients with subsequent relapse. miR-185 had a prognostic value for metastasis-free survival in combination with four other miRs [113]. Similarly, the miR-185 abundance in exosomes derived from serum was reduced in samples from patients with recurrence of liver metastasis [114]. The expression of miR-185 in circulating tumor cells derived from metastatic CRC patients during a chemotherapy course was transient and fluctuating [115]. Low miR-185-5p expression in CRC tissue samples is also associated with liver metastasis [116], while higher plasma levels were predictive for chemotherapy response. Expressions of miR-185 and that of affected genes, such as glutathione peroxidases 2 (GPX2) and selenophosphate synthetase 2 (SEPHS2), decreased considerably in tumor cells grown in selenium-deficient medium [117]. In contrast to the majority of findings, there are few studies that reported miR-185 upregulation in CRC, actually promoting carcinogenesis (Supplementary Table S7). Akcakaya et al. [118] showed correlation with poor survival and development of metastatic disease in CRC patients, implying that miR-185 might have a negative prognostic role in CRC. Recently, another study identified that miR-185-5p-induced suppression of AT-rich interaction domain 1A (ARID1A), a tumor suppressor gene, was associated with poor prognosis and adverse outcomes [119]. Zhang et al. [120], finally, reported that miR-185 was expressed at a high level in colon cancer stem cells and seems to be important in maintaining stemness. Moreover, its expression was significantly increased during the transformation of normal intestine treated with a carcinogenic agent [121].
As for other types of digestive tract cancers, lncRNAs have been identified to trigger CRC progression via miR-185 regulation (Figure 3). High expression of lncRNA UCA1 in CRC cells led to a notable increase in the migration, invasion and EMT by binding to miR-185-5p, which directly activated the MAPK14/MAPKAPK2/Hsp27 axis [122] and repressed NOTCH3 [123]. In another study, overexpressed LINC00152 promoted CRC growth by sponging miR-185-3p and upregulating its direct target fascin actin-bundling protein 1 (FSCN1). However, miR-185-3p activation partially reversed this effect [124]. NEAT1 increased CRC metastasis through miR-185-5p absorption and IGF2 induction. By contrast, miR-185-5p mimics decreased NEAT1-induced CRC metastasis [125]. As shown also in HCC, FOXD2-AS1 downregulated miR-185-5p and thus upregulated its target CDC42. This resulted in an increased proliferation, migration and invasion capacity of CRC cells [126]. In turn, miR-185-5p overexpression considerably reversed the aggressive features. HAGLR is a recently discovered lncRNA that is aberrantly expressed in many malignancies. Recently, it was reported that HAGLR is upregulated in CRC and promotes cancer in a xenograft model by targeting miR-185-5p, leading to upregulated CDK4 and CDK6 [127]. This effect could be reversed by overexpressing miR-185-5p. Another study showed that high expression of circRNA ArfGAP with FG repeats 1 (circAGFG1) increased the expression of transcription factor YY1, induced CTNNB1 (β-catenin) and triggered metastasis and stemness by adsorbing miR-185-5p [128]. LncRNA differentiation antagonizing non-coding RNA (DANCR) has been shown to be upregulated in CRC [129]. Overexpression increases high mobility group A2 (HMGA2) expression by repressing miR-185-5p and promotes CRC progression. ASB16 antisense RNA 1 (ASB16-AS1) has also been shown to drive CRC progression by downregulating miR-185-5p and upregulating TEA domain transcription factor1 (TEAD1) as the main effector of Hippo signaling [130].
Dysregulation of miR-185 is strongly implicated in the pathogenesis of digestive tract cancers and associated with clinical outcome. The fact that the molecule is relevant to all cancers indicates that it has a central role in regulating tumor biology. Both miR-185-5p and miR-185-3p are functionally active in the various tumors and numerous target genes or pathways have been reported (Table 1). On the basis of the currently available data, it seems as if more genes are affected by miR-185-5p than miR-185-3p. However, this could be accidental or due to a bias in the studies performed. In addition, quite a few analyses did not specify the variant and only reported a role of miR-185 globally. It is reassuring, however, that no gene was found to be regulated by both miR-185-5p and miR-185-3p in different tumor entities. For three genes—CDC42, PLAC8, and RHOA—for which no information about the variant is available in some tumor types, comparison between different datasets or tumor entities may suggest which miR-185 version is responsible for their regulation. For these and all others, however, comparison to the respective target mRNA would provide the relevant information. The fact that most regulation seemingly occurs via miR-185-5p and less via miR-185-3p suggests that oncogenic and tumor suppressive functions could be linked to one of the two mature miRs, respectively. Alternatively, the use of either miR-185 variant could be organ-related. However, no such correlation was apparent from the available data. While many functional aspects of miR-185 variation have been demonstrated, the reported analyses have been focusing on particular gene functions, providing only many fragmented views while lacking a more comprehensive understanding of parallel or synergistic regulative processes and their interaction. A good example for this is the reports about the regulation of miR-185 in oral cancer and its functional consequences (Table 2). Each study looked at only one regulator of miR-185 and the effects that the reduced miR-185 level had for one or a few genes. While the same process—reduction of miR-185—was described in all cases, there was basically no apparent overlap between the results of the six studies. About three quarters of the publications discussed in this review have reported that miR-185 predominantly exhibits tumor-suppressive functions. Conversely, however, some studies suggest that it functions as an oncomiR. Such contradictory results were reported for nearly all mentioned tumor forms. From the publications and data looked at as part of this review, there was no factor with apparent responsibility for this striking difference. However, the number of publications indicating an oncomiR function is too large to merely assume erroneous studies. In several cases, the relevant target genes responsible for the actual oncogenic activity have not been described. Further analyses are required to elucidate the reasons for the differences. To some extent, the contradictory results might be associated with differences in methodologies and analyzed material. For example, Cao et al. [122] found that miR-185 was downregulated in CRC cell lines, SW480, SW620, and HT-29, compared to a non-cancerous colon cell line and miR-185 mimics inhibited cell proliferation and migration. Baldi et al. [119] reported instead overexpression of miR-185 in CRC cell lines HCT116 and LoVo and thereby oncogenic activities. Analyzing miR-185 expression in only relatively few samples may also result in contradictory results. Interestingly, however, there are results that were produced with the same methodology and on the same cell lines but nevertheless led to opposing results. Zou et al. [83] found that overexpression of miR-185 triggered oncogenic activities and enhanced cell viability in HCC cell lines Huh-7 and HepG2. By contrast, Tran et al. [85] showed that miR-185 inhibition rescued the same two HCC cell lines from cell death. One cannot exclude entirely that an undetected heterogeneity between the actual cell lines used in the different laboratories may be a factor, which would be difficult to unravel retrospectively, though. In terms of biology, the contradictory roles of miR-185 may be dependent on its expression level and cellular abundance in relation to the expression of its targets (dose-dependent effects), as has been reported for other miRs [131]. Another explanation could be genomic heterogeneity. This is a known characteristic of human cancers and has been observed in tumors derived from different patients and even within a tumor from an individual patient [132]. Due to the context-dependent function of miRs [133], contextualization of miR-185 in specific genetic backgrounds may be an explanation for the functional discrepancies. Several lncRNAs and circRNAs as well as AGO2, c-Myc, GNK1, RUNX3, RACK1, ZNF139, and the DC-SIGN/TCF1/LEF1 pathway have been reported as upstream regulators implicated in controlling miR-185 abundance and function in digestive tract cancers. Of these, lncRNA FOXD2-AS1 is an lncRNA that commonly regulates miR-185 abundance in colorectal, gastric, and liver cancers. Additionally, lncRNA XIST regulates miR-185 in both gastric and pancreatic cancers. However, identification of these overlapping processes in regulation is rather coincidental. Still missing to date are comprehensive studies of the mechanisms involved in the control and regulative processes of miR-185; synergies or complementation may have an effect that can only be revealed by such analyses. Moreover, epigenetic mechanisms, such as histone modifications and promoter DNA methylation, have not been investigated much. In terms of functioning as a biomarker, several studies looked at the diagnostic and prognostic potential of miR-185 in tissue, plasma/serum or exosomes derived from patient blood. miR-185 is likely to be informative in combination with other features, acting more as a pan-cancer marker. However, there is still too little knowledge about the rules governing the changes and the processes that are possibly involved, such as an exosomal transfer of miR-185. Furthermore, the association between miR-185 expression and tumor microenvironment and immune cell infiltrations has not been examined yet. Particularly for CRC, miR-185 could even hold some promise as a therapeutic target, but much more data and detailed information will be required to assess its real potential. | true | true | true |
PMC9609424 | Wen-Jing Zeng,Lei Zhang,Hui Cao,Dongjie Li,Hao Zhang,Zhiwei Xia,Renjun Peng | A novel inflammation-related lncRNAs prognostic signature identifies LINC00346 in promoting proliferation, migration, and immune infiltration of glioma | 13-10-2022 | glioma,lncRNA,inflammation,prognostic,target | In this study, a total of 13 inflammation-related lncRNAs with a high prognostic value were identified with univariate, multivariate Cox regression analysis, and LASSO analysis. LINC00346, which is one of the 13 lncRNAs identified, was positively associated with type 2 macrophage activation and the malignant degree of glioma. Fluorescence in situ hybridization (FISH) and immunohistochemical staining showed that LINC00346 was highly expressed in high-grade glioma, while type 2 macrophages key transcription factor STAT3 and surface marker CD204 were also highly expressed simultaneously. LINC00346 high-expression gliomas were more sensitive to the anti–PD-1 and anti-CTLA-4 therapy. LINC00346 was also associated with tumor proliferation and tumor migration validated by EdU, cell colony, formation CCK8, and transwell assays. These findings reveal novel biomarkers for predicting glioma prognosis and outline relationships between lncRNAs inflammation, and glioma, as well as possible immune checkpoint targets for glioma. | A novel inflammation-related lncRNAs prognostic signature identifies LINC00346 in promoting proliferation, migration, and immune infiltration of glioma
In this study, a total of 13 inflammation-related lncRNAs with a high prognostic value were identified with univariate, multivariate Cox regression analysis, and LASSO analysis. LINC00346, which is one of the 13 lncRNAs identified, was positively associated with type 2 macrophage activation and the malignant degree of glioma. Fluorescence in situ hybridization (FISH) and immunohistochemical staining showed that LINC00346 was highly expressed in high-grade glioma, while type 2 macrophages key transcription factor STAT3 and surface marker CD204 were also highly expressed simultaneously. LINC00346 high-expression gliomas were more sensitive to the anti–PD-1 and anti-CTLA-4 therapy. LINC00346 was also associated with tumor proliferation and tumor migration validated by EdU, cell colony, formation CCK8, and transwell assays. These findings reveal novel biomarkers for predicting glioma prognosis and outline relationships between lncRNAs inflammation, and glioma, as well as possible immune checkpoint targets for glioma.
Glioma is the most frequent adult malignant brain tumor in the central nervous system. Gliomas are classified into grades I–IV based on the degree of malignancy; grades I and II tumors are regarded low-grade gliomas, while grades III along with IV tumors as high-grade gliomas, with glioblastoma (GBM) being the most aggressive malignant tumor type (1). Despite tremendous improvements in surgical resection approaches, radiotherapy, along with chemotherapy, the median survival time in individuals with GBM receiving these forms of treatment is 15 to 18 months (2, 3). So, it is pivotal to specify novel biomarkers and strategies for the diagnosis and effective treatment of malignant gliomas. Epidemiological studies showed that a quarter of tumors are remarkably linked to inflammation (4). Inflammation enhances the onset and progress of cancer through complicated physiological along with biochemical processes (5, 6). Recent investigations regarded glioma, especially GBM, as a type of cancer strongly associated with inflammation status and immune responses (5). Regarding high-grade gliomas, the brain tissue is infiltrated with numerous immune cells, consisting of macrophages, microglia, neutrophils, and eosinophils (4). Stimulation of the immune cells by various signaling factors leads to intracellular oxidative stress and cytokine-mediated cascades of inflammation, causing DNA damage along with diminished DNA repair. This then drives subsequent mutations and contributes to additional mutations as well as epigenetic alterations as glioma cells progress (7, 8). In addition, chemokines, cytokines, and growth factors secreted within the tumor microenvironment polarize M1 to the immunosuppressive M2 phenotype, contributing to the glioma’s progression (9). Compared with glioma patients with higher levels of M2 macrophage, patients with elevated levels of M1 macrophage had better survival (10). Even though some research progress has been made in exploring the relationship between inflammation and gliomas (3, 11) further studies are needed to provide insights on how inflammation associates with gliomas and specify the novel biomarkers and therapeutic targets for gliomas. Whole-genome sequencing reveals that more than 90% of the human genome is transcribed, but only approximately 2% of transcribed RNA is translated into protein (12). The remaining portion of genome mainly encodes non-coding RNA, particularly lncRNAs (13, 14). LncRNAs constitute nonprotein coding transcripts consisting of more than 200 nucleotides. Previously, lncRNAs were often considered as transcriptional “noise” (15). However, recent studies reported that several lncRNAs have other critical functions. They function through the competitive binding of molecules such as miRNAs (16). Studies reported that aberrant lncRNA expression in a surgical glioma section is implicated in glioma development by regulating cell proliferation, apoptosis, GSC self-renewal, differentiation, and inflammation (17–19). Bioinformatics analysis found that lncRNA expression patterns in clinical glioma specimens correlated with histological differentiation and malignancy grade, which may have remarkable clinical impacts for glioma sub-classification, diagnosis, and prognostication (20). Other studies found that RP11-732M18.3 was highly overexpressed in glioma cells, which not only promote glioma angiogenesis by accelerating the transcription and secretion of VEGFA but also facilitate glioma growth through accelerating p21 degradation (21, 22). More recently, a risk model of constructed from eleven inflammation-related lncRNAs was reported as a potential prognostic biomarker for patients with lower-grade gliomas (23). However, systematic studies on the relationship between inflammation-related lncRNAs and glioma prognosis remain unclear. Herein, inflammation-related lncRNAs with prognostic significance were screened to estimate the survival of individuals with gliomas. Gene expression data coupled with the matching clinical data of individuals with gliomas were abstracted from two data resources: The Cancer Genome Atlas (TCGA), as well as the Chinese Glioma Genome Atlas (CGGA). A total of 13 prognostic lncRNAs related to inflammation were identified utilizing univariate and multivariate analysis and lasso regression analysis, and a risk scoring model was subsequently established. Gene Ontology (GO) explorations were employed to clarify the biological functions and the mechanisms relevant to lncRNAs. Gene set enrichment analysis (GSEA) was carried out to determine remarkably enriched cascades as per the risk scores. FISH, immunohistochemical staining, and FASC were performed to explore the relationship between LINC00346 and macrophages. CCK8, 5-ethynyl-2’-deoxyuridine (EdU), colony formation, and transwell assays were utilized to explore the tumor-promoting role of LINC00346. These data will greatly help to the precise diagnosis and individualized treatment for gliomas patients.
Gene expression profile and the matched clinical data were abstracted from the TCGA data source (https://xena.ucsc.edu) and the CGGA data resource (http://www.cgga.org.cn). The TCGA cohort served as the training set, while the CGGA dataset served as the validation set. A total of 645 samples were obtained from the TCGA (LGG samples 508 and GBM samples 137) and 306 samples from CGGA (LGG samples 169 and GBM samples 137). The gene sets related to inflammation were obtained using the Molecular Signatures Database (MSIGDB) (https://www.gsea-msigdb.org/gsea/msigdb). Somatic mutation and copy number variation (CNV) data of individuals with gliomas were obtained from the TCGA database.
Firstly, inflammatory-related lncRNAs were obtained through correlation analysis using GO (biological process) terms of inflammatory-related gene sets. Univariate Cox regression was used to specify lncRNAs linked to the survival of glioma patients. Subsequently, multivariate Cox regression analysis was carried out to determine lncRNAs with independent prognostic significance. LASSO regression analysis was then performed to identify the signature lncRNAs. A set of predictive lncRNAs along with their regression coefficients were determined (β).
To explore the role of the 13 identified lncRNAs in gliomas, glioma patients were clustered into different groups using the R “ConsensusClusterPlus” package. Survival analyses were performed with the R “survival” package.
To assess the connection between the risk score and the genomic features in gliomas, CNV coupled with somatic mutation assessments were conducted on the basis of the TCGA dataset. GSITIC assessment was carried out to elucidate genomic event enrichment.
We conducted univariate Cox regression on the risk score and clinical features with P < 0.0.5 as the threshold. Multivariate Cox analyses of the chosen characteristics were conducted, and a nomogram was created by the regplot package. Afterward, assessment of the risk nomogram was done with a calibration curve along with the AUC.
The GSVA package was adopted to calculate the enrichment status of GO terms in TCGA and CGGA datasets. Correlation assessment was conducted between the risk score and GO terms, and items exhibiting P < 0.05 coupled with a high correlation coefficient were chosen (12). Correlation assessment between the risk score, and inflammation/immune cell type was conducted via the gene expression patterns from the TCGA datasets in R.
Glioma sections were obtained from the surgically resected glioma tissues of patients in Xiangya Hospital. Informed consent were obtained from all patients. All procedures were approved by the Ethics Committee of Xiangya Hospital, Central South University. Dewaxing of 5-µm-thick paraffin sections was done in xylene, followed by rehydration using different alcohol grades. Blocking of the activity of endogenous peroxidase was done via inoculation with H2O2 (0.3%). Afterward, we inoculated the sections with citrate buffer (0.1M; pH 6.0) followed by autoclaving for 3 min at 121˚C to enhance the accessibility of the antigen. Next, the sections were cooled and inoculated with H2O2 (0.3%) for 20 min to dampen the activity of endogenous peroxidase. Thereafter, rinsing of the sections in PBS (pH 7.2) was done and subsequently inoculated with antibodies (STAT3 and CD204; cat. no. sc-100627; Santa Cruz Biotechnology, Inc., United States). After that, the slides were inoculated with the secondary antibody (IgG) and rinsed in PBS, and the visualization of the peroxidase reaction was done via inoculation of the slides with DAB (0.02%), PBS (0.1%), and H2O2 (0.3%). Finally, hematoxylin counterstaining was done, and subsequent dehydration in graded alcohol was performed and was then mounted in resin mount. Immunostaining results were evaluated separately by two independent pathologists.
U87-MG and U251 cells, were supplied by Procell Life Science & Technology Co., Ltd (Hubei, China) and inoculated in DMEM (Sigma, USA) with 10% FBS (Gibco, USA) as well as 1% penicillin–streptomycin. The cells were grouped as follows: control group, siRNA-NC group, and LINC00346-siRNA group. Si-RNA was purchased from HonorGene (Changsha, China). About 5 µl siRNA and 5 µl lipofectamine 2000 (Invitrogen, USA) were diluted with a 95 µl serum-free medium. Then siRNA and lipofectamine were mixed and incubated at room temperature for 20 min. Finally, the mixed solution is introduced to each well of the culture plate containing the cells and the culture medium. The cells were transfected for 48 h and then harvested for subsequent experiments.
Cell viability was detected by a CCK8 assay. The transfected cells were cultured in a 96-well plate for 24, 48, 72, and 96 h. Then, 100 μl of a medium containing CCK8 (Dojindo, Japan) was added to each well, and the optical density (OD) value at 450 nm was detected with a microplate reader (BioTek) after 4 h of incubation.
The cells were digested into single cells with trypsin and suspended in a serum-free medium, and then 100 µl of the cell suspension was introduced to the upper compartment of the transwell. About 500 µl of complete medium with 10% FBS was added to the lower chamber and the cells were incubated for 48 hours. Then, washed the upper chamber with PBS and wiped off the cells on the upper chamber. After fixing, the cells were stained with crystal violet. It was then washed with PBS, the membrane was placed on a glass slide, and observed under an inverted microscope. After decolorization with 10% acetic acid, the OD value at 550 nm was detected.
The cells were resuspended and planted in six-well plates. After 2–3 weeks of incubation, the culture medium was discarded, and fixed the cells with 4% paraformaldehyde. Then the cell clones were dyed with crystal viole for 30 minutes at room temperaturet. The staining solution was washed off, and a picture of the colony was taken. After decolorization, the OD value at 550 nm was measured.
The EdU (5-ethynyl-2’-deoxyuridine) assay was used to evaluate cell proliferation (RiboBio, China). The cells were incubated overnight with 100 µl 50 μM EdU medium and then fixed with 4% paraformaldehyde. Then 100 µl 1×Apollo® staining reaction solution was added and incubated for 30 min. After washing with 0.5% TritonX-100, the cells were incubated with 100 µl 1 × Hoechst 33342 reaction solution for 30 min. Finally, the cells were observed with a confocal microscope and pictures were taken. The cell proliferation rate was then calculated.
R software was used for all statistical analyses. Remarkable differences between and among groups of normal distributed variables were measured using t-test or one-way ANOVA, respectively. Significant differences between and among groups of abnormal distributed variables were measured by Wilcoxon test or Kruskal-Wallis test, respectively. The chi-square test was application to the categorical data. The overall survival analysis was conducted with the Kaplan–Meier approach, and Cox regression was carried out using the R survival package. The gene set variation analysis (GSVA) package was adopted to compute the enrichment status in GO (Biological Process) (12). The R survival ROC package was adopted to create and visualize receiver operating characteristic (ROC) curves and compute the area under the curve (AUC) (13). All the statistical analyses were implemented in R. Somatic mutations and somatic copy number alternations (CNAs) data were abstracted from the TCGA data resource. P< 0.05 means statistically significant.
As shown in Figure 1 , 13,895 lncRNAs were obtained by intersecting the lncRNAs in TCGA and CCGA datasets. Univariate and multivariate Cox regression analyses were performed to explore the relationship of the patients’ disease-specific survival (DSS) with different lncRNAs expression levels. Univariate Cox analysis recognised 286 lncRNAs and multivariate Cox analysis identified 184 lncRNAs, which were deemed to have remarkable prognostic capacity at P-value < 0.05. Exploring interpretable estimation rules in high-dimensional data using lasso regression. Finally, 13 inflammation-related lncRNAs (ADD3-AS1, AL356019.2, LEF1-AS1, LINC00346, WDR11-AS1, TMEM72-AS1, AC007744.1, AC073896.2, AL392083.1, AC062021.1, AC093726.1, AC093895.1, and NR2F2-AS1) with prognostic values were identified using LASSO regression in TCGA dataset ( Figures 2A, B ). Figures 2C, F exhibited the expression of inflammation-related lncRNAs in the TCGA and CCGA datasets, respectively. To verify the sensitivity and specificity of the prognostic model based on the 13 inflammation-related lncRNAs, the AUC and ROC were determined. AUC values for 3- and 5-year in the TCGA dataset were 0.902 and 0.836, respectively, and the 3- and 5-year in the CGGA dataset were 0.830 and 0.841, respectively ( Figures 2D, E ), indicating that the risk score model showed good accuracy. In the TCGA cohort, there were significant differences in the risk score between the patients separately stratified by 1p/19q status, grade, IDH mutation status, MGMT promoter status, and molecular subtypes, but not by gender. Glioma patients with the classical and mesenchymal molecular subtype, IDH-wild type, 1p/19q non-co-deletion status, and MGMT promotor unmethylation showed higher risk scores ( Figure S1 ). In addition, the risk score escalated with the increasing of WHO grade in gliomas. These results indicated that risk score was important in predicting the clinical–pathological characteristics of glioma. The overall survival (OS) of the total, LGG, and GBM patients from the TCGA dataset are shown in Figures 2G–I . Patients were stratified into high- and low-risk groups based on the median risk score. There was a statistically remarkable difference in OS between high and low-risk groups and similar results were found in the CGGA cohort ( Figures 2J–L ). The disease-specific survival (DSS) and the progression-free interval (PFI) of patients in the high- and low-risk groups were also analyzed, and the results showed significant differences ( Figures S2A–F ).
On the basis of the expression similarity, the clustering stability of the TCGA dataset rising from k=2 to 10 was shown ( Figures S3A, S4A, B ). When k=2, the relative change in the area under the CDF indicated a flat middle segment ( Figure S4A ). Therefore, k=2 was selected as an adequate choice. Heatmap of the consensus matrix for k = 2 in the TCGA dataset is exhibited in Figure S4C . The predictive genes classified two clusters of glioma patients in the TCGA dataset with distinct clinical outcomes and clinicopathological characteristics by consensus clustering. Between groups, survival probability and PCA distribution were clearly separated ( Figures S4D, S5A–C ), and similar results were found in the CCGA dataset ( Figures S3B, S4E, F, S5D–F ).
Somatic mutation and CNV analysis demonstrated remarkable differences between the high- and low-risk groups. Considering the somatic copy number alternations’ functions in oncogenesis, the CNV between low- and high-risk samples was explored. In gliomas, the incidence of Chr 7 amplification and Chr 10 deletion escalated as the risk score increased, while the incidence of 1p/19q codeletion reduced with the risk score ( Figure 3A ). The GISTIC 2.0 assessment uncovered numerous remarkable amplified regions containing multiple oncogenes in glioma patients with higher risk scores, consisting of 1q32.1 (PIK3C2B), 12q14·1(CDK4), 7p11·2 (EGFR), and 4q12 (PDGFRA). Focal deletion peaks were detected in the high-risk group, for instance, 9p21·3 (CDKN2A), 10q23·31 (PTEN, KLLN), and 10q26·3 (DUX4). The focal amplification and deletion peaks also presented in the patients with low risk scores, their G values were significantly lower. Moreover, there were remarkable amplification regions (19p13.3, 12q14.1, 11q24.1, and 4q12) and deletion (9p21.3, 11p15.5, 4q34.1, and 10q26.3) reported gliomas with low-risk score groups ( Figures 3B, C ). Somatic mutations presented in 285 (89.91%) and 313 (99.05%) of samples in the high- and low-risk groups, respectively. The mutation frequencies of IDH1 and ATRX in gliomas with a low-risk score were remarkably higher than in gliomas with a high-risk score (IDH1, 92% vs. 31%; ATRX, 33% vs. 21%), while TTN and MUC16 had lower mutation frequencies (TTN, 8% vs. 21%; MUC16, 7% vs. 10%). EGFR (21%), PTEN (17%), and NF1 (12%) mutations in the high-glioma risk score group and CIC (28%), FUBP1 (11%), and NOTCH1 (10%) mutations in the low-risk score group were identified ( Figures 3D, E ), and the mutation frequency of these genes was greater than 10%.
To further assess the function of the 13 prognostic inflammatory-related lncRNAs signature, GO and GSVA analyses were performed with TCGA and CGGA datasets. The top enriched lncRNAs functions included negative modulation of regulatory T-cell differentiation, the immune response to the tumor cell, positive modulation of tolerance induction, inflammatory cell apoptotic process, chronic inflammatory response, negative regulation of response to cytokine stimulus, macrophage activation, and regulation of macrophage differentiation ( Figures 4A, D ). Even though the orders of each enriched lncRNAs function in the TCGA and CCGA datasets were different, their overall correlation with immune function was similar. Further analyses of inflammation genes correlated with the prognostic inflammatory-related lncRNAs signature in the TCGA and CCGA datasets showed that the genes IgG, interferon, MHC-II, HCK, LCK, MHC-I, and STAT1 were the most related to the 13 prognostic inflammatory-related lncRNAs signature ( Figures 4B, E ). To further detect whether the 13 prognostic inflammatory-related lncRNAs signature affected the immune cells, the types of immune cells involved in gliomas were analyzed, and it was found that the T helper cells, NK cells, DC, macrophages, etc., are all involved in gliomas ( Figures 4C , F ). Immunity correlation analysis showed that macrophages, neutrophils, eosinophils, and Th2 cells had the highest correlation with the selected lncRNAs in the TCGA and CCGA datasets ( Figure S6 ). This indicated that the 13 prognostic inflammatory-related lncRNAs signature may participate in immune responses in glioma progression. We created a ceRNA regulatory network through the 13 lncRNAs ( Figure S7 ).
To determine if this prognostic classifier could perform as an independent indicator in gliomas, a nomogram that integrated lncRNAs classifiers and clinicopathological characteristics, including age, IDH mutation, 1p/19q, and the WHO grade, was constructed to estimate the 3- and 5-year survival rate in glioma patients ( Figure 5A ). The calibration curve exhibited that the estimated 3- and 5-year survival rates were remarkably correlated with the observed ratio in the TCGA and CCGA datasets ( Figures 5B, C ). These results demonstrated that lncRNA signatures' stability and predictive performance are superior to multiple clinical features. As shown in Figure 5D , OS differences between the high- and low-risk groups were statistically significant (P < 0.05). Similar results were found in the CGGA dataset ( Figure 5E ). Then, the AUC and the ROC were determined and the 3- and 5-year survival rates were compared. The risk scoring model was found to exhibit good accuracy ( Figures 5F, G ). Decision curve analysis (DCA) was employed to assess the risk score model. DCA results for 5-year survival predictions showed that the multifactor model prognostic estimation based on the lncRNAs added more net benefit than the “IDH-only” or “grade-only” strategies in the TCGA datasets ( Figure S8 ). To assess whether immune therapy deserved to be tried, the TIDE online database was utilized to predict the response of CTLA4 and PD-1 treatment in high- and low-risk gliomas. Significant differences in PD-1 treatment effects were observed between high- and low-risk gliomas ( Figure 5H ), meaning that PD-1 may be a potential immune therapy target for high-risk gliomas. In addition, the expression level of PD-1 ligands CD274 and PDCD1LG2, as well as another immune checkpoint LAG3 were assessed, and the result suggested that LAG3 expression was increased in the high-risk group ( Figures 5I–K ). LINC00346 is associated with the prognosis of gliomas. LINC00346 was selected for further study. In the TCGA dataset, glioma patients were stratified into two groups based on the median expression level of LINC00346. The prognosis of the low LINC00346 expression group was remarkably higher relative to high LINC00346 expression group (P < 0.0001). Furthermore, survival analysis was carried out in sum, LGG, and GBM cohorts. In sum, LGG, and GBM cohorts, the OS, PFI, and DSS of the high LINC00346 expression group were worse than those of the low LINC00346 expression group (P < 0.05) ( Figures 6A–C , S9 ). These findings indicated that LINC00346 is a poor prognostic indicator for gliomas. Based on the possible association between lncRNA and inflammation reported in previous studies (24, 25) and our previous analysis, LINC00346 was suspected to be involved in inflammation based on its association with immune cells. LINC00346 was shown to be highly correlated with immune cells, especially macrophages, neutrophils, and Th2 cells ( Figures 6D, E ). GSEA showed that the high-expression LINC00346 subset was primarily linked to important inflammation-related hallmarks ( Figure S10 ). In addition, the expression levels of CD274 and PDCD1LG2, but not LAG3, were found to be higher in the high LINC00346 expression group ( Figures 6F–H ). To investigate whether the expression of LINC00346 was correlated with immune checkpoint therapy, submap analysis of CTLA4 and PD-1 treatment in LINC00346 was performed in both high- and low-risk groups. As displayed in Figure 6I , LINC00346 high-expression patients may be more sensitive to the anti–PD-1 and anti-CTLA-4 therapy. Meanwhile, to explored whether LINC00346 correlated with the amount of M2 macrophage in gliomas with different grades. FISH experiment was utilized to detect the expression of LINC00346 in different WHO grades of gliomas, and immunohistochemical staining was utilized to investigate the M2 macrophage in gliomas with different WHO grades. M2 surface marker CD204 and key transcription factor STAT3 were used to indicate the M2 macrophage. The results indicated that LINC00346, CD204, and STAT3 were expressed higher in grade IV gliomas than in grade II gliomas ( Figure 6J ), suggesting that the amount of M2 macrophage in different grades of gliomas was positively correlated with the expression. Notably, in GBM and LGG, LINC00364 expression was obviously associated with the expression of ICPs, such as CXCL9, CD40, CD80, and CD28 ( Figure S11 ). Therefore, the amount of M2 was positively related to the glioma grade, which was also consistent with the expression of LINC00346.
To further explore the effect of LINC00346 on gliomas, we used glioma cell lines U251 and U87-MG to conduct further in vitro experiments. The inhibition of LINC00346 expression significantly reduced the proliferation of glioma cells in the EdU assay ( Figures 7A, B ). In addition, the colony formation assay also showed that knockdown of LINC00346 by siRNA significantly inhibited the viability of glioma cells ( Figure 8A ). The CCK8 assay showed that cell viability was inhibited by the silence of LINC00346 ( Figure 8B ). In order to further determine whether LINC00346 affects the metastasis of glioma, transwell migration assay was used to evaluate the migration ability of glioma cells. The results showed that inhibition of LINC00346 with siRNA significantly inhibited the migration of glioma cells ( Figure 8C ). Moreover, in vitro co-culture experiments were utilized to explore the role of LINC00346 in regulating macrophage differentiation and migration. Knockdown of LINC00346 expression in U87 and U251 cells obviously inhibited the migration of HMC3 cells ( Figure S12 ). Above results proposed that the LINC00346 expression may accelerate in glioma progression through regulating glioma cell proliferation and migration ( Figure 9 ).
Glioma is the most common malignant brain tumor in adults. Despite recent therapeutic advances against gliomas, the survival rate of glioma patients remains poor. Over the years, researches have been dedicated to study the link between inflammation and tumor growth. In glioblastoma patients, a more aggressive clinical course has been associated with Foxp3+ T regulatory cells, which mediate immune tolerance and inhibit antitumor immune responses (26). The immune checkpoint ligand programmed death-ligand 1 (PD-L1) functions as an immunoinhibitory molecule expressed in the tumor milieu and promotes glioma infiltration (27, 28). Although there are numerous studies on the relationship between inflammation and gliomas, there is still no proper treatment applied by clinics. Recently, lncRNAs have acquired increased attention, from initiation and development of tumors to inflammatory responses (25, 29). Some studies have reported that lncRNAs such as RP11-284N8.3.1 along with AC104699.1.1 can predict the prognosis and survival of ovarian cancer patients (24). However, studies on inflammation-related lncRNAs in gliomas remain unclear. This study found that 13 lncRNAs (ADD3-AS1, AL356019.2, LEF1-AS1, LINC00346, WDR11-AS1, TMEM72-AS1, AC007744.1, AC073896.2, AL392083.1, AC062021.1, AC093726.1, AC093895.1, and NR2F2-AS1), which were inflammation-related based on functional analysis, were differentially expressed in gliomas and normal brain tissue, and they could independently and accurately predict the prognosis of patients. ADD3-AS1 is another transcript of ADD3 that encodes for Adducin Gamma and is implicated in the spectrin-actin network and present in the extrahepatic biliary epithelium. ADD3-AS1 encodes an lncRNA, which influences the expression of ADD3 (30). However, no studies have reported the involvement of ADD3-AS1 in gliomas. LEF1-AS1 is an lncRNA that acts as an oncogene in glioma. Silencing LEF1-AS1 expression inhibits GBM proliferation and invasion by reducing ERK, as well as Akt/mTOR signaling activities (31). LINC00346 has been documented to be overexpressed in non-small cell lung cancer, bladder cancer, and pancreatic cancer and serves as a positive transcriptional regulator of c-Myc, but its role in glioma has not been reported (32, 33). WDR11-AS1 (csf biomarker) modifies the association between tau positivity and neurodegeneration (34), and it is involved in thyroid cancer (35). TMEM72-AS1 and AC062021.1 are associated with the pathogenesis of major depressive disorder (MDD); however, dysregulated lncRNAs’ contribution to the development of MDD remains elusive (36, 37). AL392083.1 is involved in synergistic neurotoxicity (38). AC093726.1 is associated with breast cancer (39), while AC093895.1 plays a pivotal role in the modulation of cancer-related pathways (40). NR2F2-AS1 is involved in the regulation of cell cycle progression, cell apoptosis, and cell proliferation during cancer (41, 42). Patients harboring high-risk signatures had shortened overall survival, while patients harboring a low-risk signature had prolonged survival. Similar risk scores were obtained from cases in the TCGA and CGGA datasets and were used to confirm the prognostic values of the 13 lncRNAs in gliomas. GSVA was conducted in the 13 prognostic inflammation-related lncRNAs signature to explore associated signaling cascades. GO analysis exhibited that the signature is mostly enriched in inflammation-related reactions. Inflammation has been linked to several tumors. Herein, the 13 lncRNAs primarily participated in the inflammatory response to the tumor and immune-linked molecules. In gliomas, remarkable efforts have been made over the years to establish the molecular signatures that may contribute to the diagnosis or treatment of patients. These studies have uncovered a series of molecular signatures that are linked to the prognosis of gliomas. IDH1 mutation was added into the 2016 WHO glioma classification, and patients with this mutation develop poor prognosis compared with the IDH wild-type patients (1). Moreover, studies showed that genetic aberrations in these genetic markers might lead to remarkable epigenetic changes at the molecular level, including DNA methylation, mRNA expression, and lncRNA expression. This study found that inflammation-related lncRNAs may not only exist in associations with well-recognized genetic biomarkers but also provide new strategy into the prognosis and treatment of gliomas. Among the 13 lncRNAs identified, LINC00346 was found to be the most associated with macrophages. In this study, LINC00346 expression in gliomas was determined by FISH, and type 2 macrophage surface marker CD204 and its key transcription factor STAT3 were determined by IHC staining. Three of the molecules detected were highly correlated with high-grade glioma. Studies have shown that STAT3 is a key transcription factor that supports macrophage differentiating into type 2 macrophage (43) and greatly contributes to tumor progression (9, 44). This study found that the high expression of LINC00346 is linked to the high expression of STAT3 and CD204, and LINC00346 may serve as a positive function in the STAT3 expression, enabling macrophages to differentiate into M2 and supporting the progression of gliomas. This study depicted the relationship between LINC00346 and macrophages. However, more detailed investigations are necessary to focus on finding the exact role of LINC00346 in gliomas, which may be to promote the progress of immunotherapy for gliomas. The targeting of immune checkpoints proved to be an efficient way to treat different tumors (45). In this study, the response of CTLA4 and PD-1 treatment in glioma was evaluated, and difference was found between high- and low-grade glioma groups, hinting that anti-CTLA4 or anti–PD-1 drugs might be a good method for high-grade glioma patients. We selected LINC00346 in the feature lncRNA for further verification and analysis. We established that LINC00346 expression was negatively correlated with the clinical prognosis of gliomas. In vitro experiments, CCK8, and colony formation experiments displayed that silencing the expression of LINC00346 inhibits glioma cell viability. In the EdU test, the inhibition of LINC00346 expression repressed the growth of glioma cells. In addition, inhibition of LINC00346 interferes with glioma cell infiltration. In conclusion, knockdown of LINC00346 inhibited the viability, proliferation, migration, and invasion of glioma cells. This also confirms the result that we found that the inflammation-relative lncRNAs play an essential role in the onset and progress of gliomas. Although our risk model has good performance in predicting the prognosis of glioma patients in the TCGA and CGGA cohorts, there are still many limitations. We should verify the differential expressions of these lncRNAs in glioma tissues and para-cancer tissues, as well as the prognostic value of the risk model with our own samples. The molecular mechanisms of these lncRNAs in gliomas and the efficacy of the risk model in clinical practice remain unclear and further experiments are needed.
In conclusion, this study identified 13 inflammation-related lncRNAs signature that were linked to the survival of glioma patients through bioinformatic analysis. Moreover, the study also described the relationship between LINC00346 and macrophages. These findings may help in the development of efficient biomarkers for use in assessing the appropriateness of immunotherapy and potential implications in the diagnosis and treatment of gliomas.
The datasets used in this study are available in TCGA data source (https://xena.ucsc.edu) and CGGA data portal (http://www.cgga.org.cn).
Conception and design, ZX, W-JZ and LZ; Foundation support, RP and ZX; Acquisition and analysis of data, W-JZ and LZ; Interpretation of data, W-JZ, LZ, HC, DL, and HZ; Drafting the manuscript and revising for submission quality, W-JZ, LZ, HC, DL and RP; Study supervision, RP; All authors contributed to the article and approved the submitted version.
This work was supported by Science Foundation of the AMHT Group (No. 2020YK10) and National Natural Science Foundation of China (No. 81901268).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. | true | true | true |
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PMC9609574 | Karla I. Solis-Andrade,Omar Gonzalez-Ortega,Dania O. Govea-Alonso,Mauricio Comas-Garcia,Sergio Rosales-Mendoza | Production and Purification of LTB-RBD: A Potential Antigen for Mucosal Vaccine Development against SARS-CoV-2 | 20-10-2022 | mucosal adjuvant,humoral response,chimeric antigen,mucosal vaccine,COVID-19 | Most of the current SARS-CoV-2 vaccines are based on parenteral immunization targeting the S protein. Although protective, such vaccines could be optimized by inducing effective immune responses (neutralizing IgA responses) at the mucosal surfaces, allowing them to block the virus at the earliest stage of the infectious cycle. Herein a recombinant chimeric antigen called LTB-RBD is described, which comprises the B subunit of the heat-labile enterotoxin from E. coli and a segment of the RBD from SARS-CoV-2 (aa 439-504, carrying B and T cell epitopes) from the Wuhan sequence and the variant of concern (VOC)—delta. Since LTB is a mucosal adjuvant, targeting the GM1 receptor at the surface and facilitating antigen translocation to the submucosa, this candidate will help in designing mucosal vaccines (i.e., oral or intranasal formulations). LTB-RBD was produced in E. coli and purified to homogeneity by IMAC and IMAC-anionic exchange chromatography. The yields in terms of pure LTB-RBD were 1.2 mg per liter of culture for the Wuhan sequence and 3.5 mg per liter for the delta variant. The E. coli-made LTB-RBD induced seric IgG responses and IgA responses in the mouth and feces of mice when subcutaneously administered and intestinal and mouth IgA responses when administered nasally. The expression and purification protocols developed for LTB-RBD constitute a robust system to produce vaccine candidates against SARS-CoV-2 and its variants, offering a low-cost production system with no tags and with ease of adaptation to new variants. The E. coli-made LTB-RBD will be the basis for developing mucosal vaccine candidates capable of inducing sterilizing immunity against SARS-CoV-2. | Production and Purification of LTB-RBD: A Potential Antigen for Mucosal Vaccine Development against SARS-CoV-2
Most of the current SARS-CoV-2 vaccines are based on parenteral immunization targeting the S protein. Although protective, such vaccines could be optimized by inducing effective immune responses (neutralizing IgA responses) at the mucosal surfaces, allowing them to block the virus at the earliest stage of the infectious cycle. Herein a recombinant chimeric antigen called LTB-RBD is described, which comprises the B subunit of the heat-labile enterotoxin from E. coli and a segment of the RBD from SARS-CoV-2 (aa 439-504, carrying B and T cell epitopes) from the Wuhan sequence and the variant of concern (VOC)—delta. Since LTB is a mucosal adjuvant, targeting the GM1 receptor at the surface and facilitating antigen translocation to the submucosa, this candidate will help in designing mucosal vaccines (i.e., oral or intranasal formulations). LTB-RBD was produced in E. coli and purified to homogeneity by IMAC and IMAC-anionic exchange chromatography. The yields in terms of pure LTB-RBD were 1.2 mg per liter of culture for the Wuhan sequence and 3.5 mg per liter for the delta variant. The E. coli-made LTB-RBD induced seric IgG responses and IgA responses in the mouth and feces of mice when subcutaneously administered and intestinal and mouth IgA responses when administered nasally. The expression and purification protocols developed for LTB-RBD constitute a robust system to produce vaccine candidates against SARS-CoV-2 and its variants, offering a low-cost production system with no tags and with ease of adaptation to new variants. The E. coli-made LTB-RBD will be the basis for developing mucosal vaccine candidates capable of inducing sterilizing immunity against SARS-CoV-2.
Coronaviruses are a group of positive-sense single-stranded RNA viruses with the largest and most stable known RNA genome. These viruses belong to the Coronaviridae family and are grouped into four genera: alpha-, beta-, gamma-, and delta-coronavirus. Of these four genera, only alpha- and beta-coronaviruses can infect humans. Most of the viruses that belong to these two genera can cause the “common cold” (e.g., CoV-NL63 and CoV-HKU1). However, three species can cause severe infections, leading to pneumonia and even resulting in death (i.e., SARS-CoV, SARS-CoV-2, and MERS-CoV). The appearance of SARS-CoV and MERS-CoV, which have a significantly higher mortality rate than SARS-CoV-2, led to the development of some vaccines. However, most of these efforts did not result in licensed vaccines, perhaps due to the limited human-to-human transmission. Furthermore, some SARS-CoV vaccines produced an antibody-dependent enhancement (ADE) infection in vaccinated individuals [1,2,3]. In the case of SARS-CoV, this phenomenon has been associated with vaccines displaying the full-length spike (S) protein. Most of the vaccines in clinical trials were either inactivated or live-attenuated viruses. Unfortunately, the high levels of human-to-human transmission for SARS-CoV-2 have resulted in a completely different scenario from the SARS-CoV and MERS-CoV epidemics. The scale and duration of the SARS-CoV-2 pandemic resulted in a worldwide halt of most activities. In some cases, these restrictions have been in place for a year, causing devastating and long-lasting effects that will take decades to overcome. The COVID-19 pandemic has resulted in an extraordinary worldwide effort to develop novel vaccines quickly and safely against SARS-CoV-2 [4]. Interestingly, none of the approved vaccines has been based on the technologies used for SARS-CoV [5]. Instead, most of these vaccines are based on technologies that have been in the pipeline for quite a while [6]. The vaccines from Moderna/NIH and Pfizer/BioNTech rely on mRNAs encapsulated in lipids and polymers that code for the prefusion form of the spike (S) protein. In contrast, most of the remaining approved vaccines (i.e., AstraZeneca/Oxford, CanSino, Johnson & Johnson, and Gamaleya Institute) are based on adenoviral vectors [7,8,9]. The vaccine from Sinovac is formulated with the inactivated virus [10]. The efficacy of these vaccines varies between 60% and 98% [11]; however, the emergence of variants with mutations in the S protein (e.g., B.1.1.7, B.1.315, and P.1) could decrease the efficacy of these vaccines. Furthermore, the use of adenovirus-based vaccines (AdV5 and AdV26) has the drawback that countries with a high prevalence of these viruses can result in populations with neutralizing antibodies against them, further decreasing the efficacy of the vaccine [12,13]. Finally, one subunit-based vaccine (Novavax) is close to being approved in North America and Europe [14]. The production of biopharmaceuticals in E. coli offers a robust and well-established platform that can be easily transferred from an academic laboratory to a manufacturing facility [15]. This expression system can be easily scaled up, and the regulatory aspects of the production have been in place for a long time such that they are considered standard. Recombinant subunit vaccines have the advantages of not containing a pathogenic organism (viral or bacterial), their composition is exactly known for each batch, they can be produced using different platforms and fermentation processes, large-scale production is relatively simple and cost-effective, and both the expression system and the antigen can be easily modified by genetic engineering [16,17,18]. It is important to point out that contrary to live-attenuated, inactivated, and viral vector-based vaccines, the level of biosafety required for the expression of recombinant subunit vaccines is lower; therefore, their overall cost can be significantly lower. Furthermore, there is no risk of the vaccine resulting in a viral infection due to defective manufacturing procedures. Subunit vaccines still must overcome some challenges. In general, multiple doses are required due to their low immunogenicity compared to the use of the whole pathogen. One approach to enhance the immunogenicity consists in fusing (genetic or chemically) the antigen of interest with a highly immunogenic protein subunit from bacteria (e.g., the E. coli heat-labile enterotoxin B subunit toxin [LTB] [19,20] or cholera toxin B subunit [CTB]). A major challenge when expressing recombinant proteins in bacteria is to obtain the antigen in the properly folded state, i.e., to avoid the expressed protein from generating inclusion bodies, which requires protein refolding to its native and functional state. This problem can be solved by fusing the protein with another protein that increases antigen solubility inside the cell (e.g., SUMO) [21], although this protein must be cleaved from the antigen once it is in a stable form and purified. Nonetheless, a rational protein design that takes into consideration the fermentation and purification protocols, the secondary and tertiary structure of the target antigen, the amount and distribution of hydrophobic amino acids, and the sequences that can be recognized by the bacterium can greatly increase the solubility of the antigen in the cell [22,23,24]. Considering that the current COVID-19 vaccines are intramuscularly administered and that the lack of prevention of virus infection is associated with a poor induction of mucosal IgA responses, there is an urgent need to explore new vaccination approaches focused on the induction of effective mucosal responses. One straightforward approach to achieving this goal is the development of nasal or oral vaccines, which effectively boost the mucosal immune system in the compartment used for vaccine delivery and even in distant compartments. It is well known that oral immunization results in GALT-mediated antigen processing with subsequent homing to pulmonary tissues in which IgA production is induced [25]. In the current scenario, such mucosal vaccines may serve as boosters of the immunity induced by parenterally-administered vaccines. Since the B subunit of E. coli enterotoxin is recognized as a potent mucosal adjuvant, in this study a chimeric protein (LTB-RBD) based on this carrier and a segment of RBD targeting T and B cell epitopes is reported. The production in E. coli of LTB-RBD and its purification was performed, and evidence of the immunogenic potential by parenteral and mucosal routes of this chimeric protein was generated.
Two synthetic genes coding for a chimeric protein called LTB-RBD Wuhan strain and VOC delta, respectively, were obtained by GenScript Inc. (Piscataway, NJ, USA), following a codon optimization process according to codon usage in E. coli. The sequence comprises the full-length sequence of LTB fused to aa 439–504 from the S protein. A GPGP linker was placed between the LTB and RBD moieties to facilitate displaying the target antigen. The structure of the recombinant proteins was modeled using the Phyre2 protein fold recognition server [26]. NdeI and XhoI restriction sites were placed at the 5′and 3′ ends, respectively, to facilitate subcloning into pET 21b (+), in which the ORF is fused to a His tag coding sequence at the 3′ end in the case of the Wuhan strain, and without His tag in the case of VOC delta. These procedures were performed following standard molecular cloning protocols. A positive clone carrying the target expression vector was confirmed by restriction profiling and conventional sequencing.
The pET 21b (+)-LTB-RBD construct was transferred to the E. coli Rosetta (DE3) pLysS host. The transformation was performed by heat shock. Afterward, the cells were shaken at 37 °C for 1 h and streaked on LB plates supplemented with ampicillin (100 mg/L) and chloramphenicol (40 mg/L). Cultivation conditions in shake flask cultures were as follows: a single positive colony was inoculated in 500 mL baffled flasks containing 100 mL of LB medium (10 g/L bacto-peptone, 5 g/L yeast extract, and 10 g/L NaCl) supplemented with ampicillin (100 mg/L) and chloramphenicol (40 mg/mL) at 37 °C and 140 rpm. To induce expression of the LTB-RBD fusion protein, cells were grown to an optical density of 0.7–1.0 at 600 nm (OD600 nm), and lactose (15 g/L) or IPTG (0.1 mM) was added. All shake flask cultures were induced at 28 °C. Induction was maintained for 7 h, and samples were withdrawn at 0, 4, and 7 h. Cultivation conditions in batch bioreactor cultures were as follows. A seed culture was prepared in a 1 L flask containing 200 mL of LB medium, incubated at 37 °C, and 140 rpm for 16 h. The cells were harvested by centrifugation and resuspended in 20 mL of fresh LB medium. Upon inoculation of the bioreactor, an OD600 nm of 0.4–0.5 was reached. Batch cultures were grown in a 1.5 L jar fermenter (ez-Control system model 56,156, Applikon Biotechnology, Delft, The Netherlands) containing 1 L of LB medium plus ampicillin (100 mg/L) and chloramphenicol (40 mg/L). pH was maintained at 7.0 ± 0.5 by adding 2 M HCl or 2 M NaOH, and O2 saturation was kept above 40% by culture stirring (400–600 rpm) and aeration (0.5–1.5 L/min). The temperature was held at 37 °C. When culture density reached an OD600 nm of 1.0–1.5, the temperature was decreased to 28 °C, and lactose was added to reach a concentration of 15 g/L and induce expression of LTB-RBD. The batch bioreactor culture was induced for 9–12 h, and 10 mL samples were collected at 3 h intervals. The samples and the endpoint cultures, either from flask or bioreactor fermentations, were centrifuged at 6000 rpm for 10 min at 4 °C, and the pellets were stored at −40 °C. The induced and noninduced samples were analyzed by sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) and/or Western blotting.
The bacterial biomass was collected at 7000 rpm (5 min) and resuspended in a cold solution (100 mM Tris-HCl, 20% (w/v) sucrose, and pH 7.4). 0.2 mL of this solution was used per 20 mg of biomass. The cells were recovered by centrifugation at 7000 rpm for 15 min (4 °C). The pellet was resuspended in injectable water + 0.01 mM PMSF (0.5 mL per 20 mg of biomass). Cells were disrupted while the test tube was kept on ice by applying 6–9 cycles of 30 s on and 30 s off using ultrasonication (GEX130PB device, Twinsburg, OH, USA) at a 70% amplitude. Afterward, the suspension was centrifuged at 7000 rpm for 30 min (4 °C) to recover a pellet composed of inclusion bodies and insoluble cellular components and a supernatant (soluble protein fraction). These fractions were stored at −40 °C until further analysis.
Protein samples were mixed with 5× reducing dye buffer (500 mM DTT, 250 mM Tris-HCl, 10% (w/v) SDS, 50% (v/v) glycerol, 0.1% bromophenol blue, pH 6.8) and boiled for 10 min. Proteins were separated using a 12% denaturing polyacrylamide gel and visualized by Coomassie blue staining. For Western blot analysis, the proteins were transferred from the polyacrylamide gel to a nitrocellulose membrane (Thermo Fisher Scientific, Waltham, MA, USA) for 1 h at 500 mA using an electrophoretic transfer cell (Biorad, Hercules, CA, USA). The membrane was incubated overnight in blocking buffer (5% (w/v) fat-free dry milk dissolved in PBS + 0.05% (v/v) Tween 20). The membrane was subsequently washed three times with PBS + 0.05% (v/v) Tween 20 and incubated with mouse anti-sera (1:1000 dilution) against either the cholera toxin B subunit (CTB), which is an in-house obtained mouse hyperimmune serum using complete Freund’s adjuvant and commercial CTB from Sigma (cat. no. C9903), or a monoclonal mouse antibody (1:3000 dilution) targeting the His tag. The blots were washed and incubated with a goat horseradish peroxidase-conjugated secondary anti-IgG mouse antibody (1:2000 dilution, Sigma, Livonia, MI, USA) for 2 h at room temperature. Antigen detection was performed by incubating blots with the SuperSignal West Pico chemiluminescent substrate (Pierce, Rockford, IL, USA), and the signal was revealed using chemiluminescent-sensitive Kodak film (Kodak, Rochester, NY, USA).
During flask and batch cultivation, cell growth was monitored by measuring OD600 nm. Protein samples were quantified with a protein Bradford assay kit (Ab 102535, Abcam, Cambridge, UK) using bovine serum albumin as standard.
The insoluble fraction obtained upon cell disruption was subjected to a washing procedure (twice with PBS 1× + 1% (v/v) Triton X-100 and twice with PBS 1×) to remove cellular components and solubilize contaminant proteins. The washed pellet was contacted with solubilizing buffer (20 mM phosphate, 500 mM NaCl, 8 M urea, pH 7.1) overnight at 4 °C. Finally, the suspension was centrifuged at 13,000 rpm for 20 min to recover the supernatant with solubilized recombinant protein. This step was repeated twice. This supernatant is the sample used for chromatography. For the LTB-RBD Wuhan sequence construct, which carries a His tag, IMAC was run using a 2 mL column packed with Chelating Sepharose Fast Flow (Pharmacia Biotechnology, Stockholm, Sweden). The adsorbent was charged with Ni2+ ions and equilibrated with binding buffer, and 1 mL of the previously obtained protein extract was injected to the chromatographic system at a flow rate of 0.25 mL/min. After washing the column, protein desorption was accomplished by feeding the desorption buffer (20 mM phosphate, 500 mM NaCl, 500 mM imidazole, 8 M urea, pH 7.1). Fractions containing the protein of interest were collected and subjected to a refolding process using dialysis with a 6–8 kDa MWCO membrane. The buffers used to accomplish refolding, in sequential order, were 20 mM phosphate buffer + 4 M urea at pH 7.1, 50 mM carbonate + 10% (v/v) glycerol + 0.01% (v/v) Tween at pH 9.2, and 10% (w/v) sucrose + 0.01% (v/v) Tween 20. For the LTB-RBD delta variant construct, which lacks tags, the supernatant with solubilized protein was used for IMAC as described above; however, in this case, the fractions containing the protein of interest were collected as unbound protein (several contaminant proteins bound to the immobilized Ni2+ ions). After this purification step, a change in buffer (20 mM Tris-HCl, 10 mM NaCl, 8 M urea, pH 8.6) was applied using dialysis to reduce the concentration of NaCl. A second purification step using anionic exchange chromatography was performed. The fractions obtained containing the recombinant protein were harvested again as unbound protein (the few contaminant proteins found after IMAC were retained by the cationic column) and dialyzed using the following buffers sequence: 20 mM phosphate buffer + 4 M urea at pH 7.1, 50 mM carbonate + 10% (v/v) glycerol + 0.01% (v/v) Tween at pH 9.2, and 10% (w/v) sucrose + 0.01% (v/v) Tween 20.
The purified protein was concentrated using powdered polyethylene glycol (PEG, MW 200,000, Sigma) or by ultrafiltration. The protein solution was transferred to a 6–8 kDa MWCO dialysis membrane, which was covered with PEG powder. After a 20 min incubation at 4 °C, the layer of hydrated PEG over the dialysis bag was removed and replaced with dry PEG. This procedure was repeated 3–5 times. As for ultrafiltration, a Vivaspin 2 column (5 kDa MWCO) was rinsed once by adding a solution composed of 10% (w/v) sucrose + 0.01% (v/v) Tween 20 and centrifuged at 4000 rpm by 10 min. Afterward, the protein solution was placed into the column and concentrated by two cycles of centrifugation of 30 min at 4000 rpm. After these steps, the concentrated protein was quantified.
The immunogenicity of LTB-RBD was assessed in BALB/c mice (n = 4, 12 weeks old), following a protocol approved by the institutional ethics committee (CEID-2020-07R1). The groups received one of the following treatments on days 1 and 14: 5 µg of LTB-RBD plus alum by subcutaneous (s.c.) route, 10 µg of LTB-RBD plus alum by s.c. route, 10 µg of LTB-RBD alone by oral (p.o.) route, 10 µg of LTB-RBD plus 1 µg of cholera toxin, 3 µg of LTB-RBD alone by intranasal (i.n.) route, or 3 µg of LTB-RBD plus 0.3 µg of cholera toxin by i.n. route. Negative control groups were treated with the antigen vehicle alone (10% sucrose, 0.01% Tween 20) by s.c., p.o., or i.n. routes. Dose volumes were the following: 300 µL for s.c., 400 µL for p.o., and 20 µL for i.n. routes. For s.c. formulations, Alum adjuvant was used at a 1:5 ratio (G Biosciences, St. Louis, MO, USA, cat no. 786-1215). Animals were slightly anesthetized with isoflurane right before immunization. All samples were taken from all mice groups on day 27. Blood samples were withdrawn by puncture in the tail. After clot formation, samples were centrifuged at 10,000 rpm for 10 min, and the obtained sera were stored at −20 °C until antibody determination. Feces and mouth wash samples were taken as follows. For feces, 100 mg was collected and resuspended in 500 μL of ice-cold PBS (supplemented with 5% fat-free dry milk and 1 mM of PMSF). Following homogenization using a plastic device, the samples were centrifuged at 7000 rpm and 4 °C for 15 min. Supernatants were transferred to a new tube and kept at 4 °C for immediate analysis by ELISA. Mouth-wash samples were obtained from mice anesthetized with isoflurane, which were subjected to mouth wash with 120 μL of PBS. The obtained washes had 1 mM PMSF added. Feces extracts and mouth-wash samples were immediately plated for antibody determination by ELISA. ELISA was run to measure S protein binding antibodies following previously reported protocols [27]. Polystyrene plates (96 wells) were coated overnight at 4 °C with spike protein (100 ng/well, Sinobiological cat. no. 40589-V08H4) in carbonate buffer (15 mM Na2CO3, 35 mM NaHCO3, pH 9.6). Before all following steps, plates were washed three times with PBS + Tween 0.05% (PBS-T). Plates were blocked with 5% fat-free dry milk in PBS for 2 h at room temperature. Serial dilutions of sera (1:40–1:160) or mouth wash and fecal extracts (1:1 and 1:2) in PBS were added and incubated at 4 °C overnight. Afterward, for all samples, secondary antibodies labeled with goat horseradish peroxidase-conjugated anti-mouse IgG for sera samples or anti-mouse IgA for feces and mouth-wash samples were diluted in PBS (dilution 1:2000) and plated. Finally, an ABTS substrate solution [0.6 mM 2,20-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) + 0.1 M citric acid + 1 mM H2O2, pH 4.35] was added and OD values at 405 nm were measured after 30 min using a MultiskanR FC equipment (Thermo Scientific, Waltham, MA, USA). IgG antibody titers were determined as the reciprocal of the highest dilution of sera with a mean OD value above the OD value from the group treated with the vehicle alone (negative control) plus 2 × SD. Statistical differences were determined using one-way ANOVA employing the GraphPad Prism version 5.01 software (p < 0.05).
The chimeric protein LTB-RBD is based on the LTB carrier and a segment of RBD (aa 439–504 from the S protein). The sequence was chosen because the RBD is considered a crucial antigen targeting T and B cell epitopes. In this study, two constructions were explored based on the RBD of the Wuhan sequence and VOC delta. The main difference between the delta variant and Wuhan sequences is two amino acid changes, arginine (R) instead of leucine (L) and lysine (K) instead of threonine (T). The sequences were modeled using the Phyre2 engine to determine the secondary structure. The in silico analysis did not predict radical changes in the secondary structure between the Wuhan sequence and delta variant due to these amino acid changes (Figure 1 and Figure 2). The expression of LTB-RBD in a Rosetta-pET21b (+)-flask system was assessed using 0.1 mM IPTG at two induction times (4 and 7 h). Analysis of the SDS-PAGE results revealed the presence of a 21 kDa protein in the cultures of both variants induced that was absent in both the preinduction cultures and WT cultures, matching with the theoretical molecular weight for the mature form of LTB-RBD Wuhan sequence (20.5 kDa) since the protein MW comprising the signal peptide is 22.9 kDa (Figure 3). The recombinant LTB-RBD was detected in both the soluble and insoluble fractions with similar abundance in such fractions. As for the LTB-RBD delta construct, similar findings were obtained in terms of expression in inclusion bodies and recombinant protein yields in crude extracts (data not shown). However, in this case, no soluble recombinant protein was observed. Since lactose is a more convenient inducer (lower cost and toxicity) than IPTG, the production of LTB-RBD was assessed using 15 g/L lactose as inducer [28]. SDS-PAGE analysis revealed that the target protein was expressed roughly at similar levels with no major variation in solubility (Figure 3). To further confirm the antigenicity of the E. coli-produced LTB-RBD, a Western blot was performed using either an anti-His antibody or an anti-CTB hyperimmune serum. All these analyses revealed an immunoreactive protein of the same molecular weight that matched the differential protein detected in the SDS-PAGE analysis, confirming the identity and antigenic activity of both LTB and RBD sequences (Figure 4 and Figure 5). The production of LTB-RBD was further scaled up to a 1 L bioreactor using lactose as inducer. The analysis of biomass fractions withdrawn over a period of 9 h in the postinduction phase revealed that the recombinant protein accumulation peaked at 6 h postinduction and remained at a similar level at the end time point (9 h postinduction, Figure 6). In terms of solubility, a shift was observed with respect to flask fermentations, with an increase in the abundance of the protein in the insoluble fraction. The biomass showed a constant increase with a maximum density of 7.5 g/L (OD600nm = 3.0) reached at the endpoint (Figure 6). The purification method based on IMAC allowed obtaining pure LTB-RBD Wuhan with average yields of 1.2 mg per liter of culture (Figure 7). As for the LTB-RBD delta construct, the purification method based on IMAC followed by anionic exchange chromatography allowed reaching yields of 3.5 mg of pure LTB-RBD per liter of culture (1.4 mg of protein per g of fresh biomass, Figure 8). In Figure 9, a summary of the implemented protocols for the two versions of the LTB-RBD produced in this report is presented. The immunogenicity of LTB-RBD was assessed in test mice subjected to two vaccine doses administered by distinct routes. The assessment of anti-spike IgG levels revealed that significant IgG responses were induced in the s.c. immunized group, with a higher response in the high-dose group (average titers of 1600 for the 10 μg LTB-RBD + Al(OH)3 group and 800 for the 5 μg LTB-RBD + Al(OH)3 group). Interestingly, the group orally immunized with 10 μg LTB-RBD + CT had a titer value of 1600, whereas the formulation lacking the CT adjuvant showed very low antibody titer. In contrast, nasal immunization failed to induce relevant IgG responses, regardless of the use of CT as adjuvant (Figure 10A). Regarding the mucosal immune response, IgA measurements in saliva (mouth washes) revealed that significant levels were triggered in the groups immunized by the nasal route, regardless of the use of CT as adjuvant and in the s.c. immunized group receiving the high antigen dose (Figure 10B). Intestinal IgA responses were evaluated by measuring IgA levels in feces, showing that only the nasally immunized groups triggered a significant response, regardless of the use of CT as adjuvant (Figure 10C).
In the present study, LTB-RBD (a chimeric protein comprising a mucosal adjuvant carrier (LTB) and a segment from the RBD of SARS-CoV-2) was produced and purified as an antigen vaccine candidate for the formulation of mucosal vaccines against COVID-19. The LTB-RBD candidate was expressed as inclusion bodies, which is in line with the report by Jegouic et al., who assessed the expression of fragments of the S protein from SARS-CoV-2, including segments covering the RBD sequence; however, they remained in inclusion bodies despite performing the expression at a lower temperature (15 °C) and with different IPTG concentrations [29]. Moreover, recombinant LTB has been previously reported to be mainly produced as inclusion bodies in E. coli [30,31]. The LTB-RBD antigen accumulated as inclusion bodies was subjected to purification and refolding procedures. The presence of urea in the solubilization procedure allowed purifying the candidate using IMAC with Ni2+ ions. In-column refolding was tried, but the results were poor since the protein precipitated inside the column upon urea removal. Once the candidate was desorbed from the Ni2+ ions using imidazole, the eluted fractions containing highly pure protein were subjected to refolding using dialysis. Several buffers were tested to accomplish refolding: the sequence of buffers that successfully produced refolded protein was PBS + 4 M urea at pH 7.1, 50 mM carbonate + 10% (v/v) glycerol 0.01% (v/v) + Tween 20 at pH 9.2, and 10% (w/v) sucrose + 0.01% (v/v) Tween 20 (Table 1). The use of PBS as final buffer for the candidate was forbidden, as it completely precipitated the candidate upon freezing. Studies performed with SARS-CoV sequences revealed that the RBD produced in E. coli is antigenic and immunogenic, although at a lower magnitude than that expressed in mammalian cells, while no yields were reported for the E. coli system [32]. RBD has also been expressed fused to a solubility-enhancing peptide (SEP) tag containing nine arginine residues, resulting in an enhancement in the accumulation of soluble RBD. This RBD version was produced at yields of up to 2 mg/L and recognized the ACE2 receptor and induced antibodies able to interact with a mammalian made S1 protein [33]. Another case is the report by McGuire et al. [34], in which fusion proteins were designed based on the thermophilic family of nine carbohydrate-binding modules (CBM9) as an N-terminal carrier protein and affinity tag and different S protein segments. Among the proteins tested, the one called CBM9-ID-H1 carrying amino acids 540–588 from the S protein was produced at yields up to 122 mg/L of pure protein, which was widely reactive with COVID-19 convalescent sera, suggesting that it retains the antigenic determinants; therefore, it is proposed as a promising immunogen. These studies support the use of E. coli to produce functional SARS-CoV-2 antigens, a system that offers lower cost compared to mammalian cell-based platforms. The functional activity of LTB-RBD was initially assessed in mice subjected to two immunizations by different routes (subcutaneous, oral, and nasal). Since sera from mice s.c. receiving the LTB-RBD antigen (10 µg) plus alum as adjuvant showed significant serum anti-S IgG responses (and modest anti-S IgA responses in the mouth), we believed that this antigen has a promising potential to induce SARS-CoV-2 neutralizing antibodies. Since LTB-RBD was able to induce both systemic IgG responses and modest IgA responses in mucosal compartments when s.c. administered but induced poor seric IgG levels and high IgA responses when i.n. administered, we propose that combining the administration routes in the immunization scheme (i.e., a third boosting i.n. dose in the s.c. immunized group) might lead to optimal immune response comprising robust humoral responses in both the systemic and mucosal levels. These findings justify expanding the characterization of LTB-RBD to assess the cellular response and perform neutralization assays to determine the immunoprotective potential of these vaccine candidates. Currently, seven intranasal anti-SARS-CoV-2 vaccines have reached clinical evaluation. Most of these developments are based on viral vectors or live attenuated viruses [35]. The oral delivery of SARS-CoV-2 vaccines has also been explored. For instance, Jawalagatti et al. [36] reported a Salmonella strain delivering a replicon coding for SARS-CoV-2 RBD, HR, M, and epitopes of nsp13 (RNA helicase), which successfully induced protective immunity in mouse and hamster models of SARS-CoV-2 infection associated to cellular and humoral responses, including the efficient induction of IgA responses in the respiratory mucosa. Immunogenic carriers are essential when the target antigen has low complexity and is therefore not very immunogenic. LTB is a promising carrier as it has immunomodulatory effects and facilitates the translocation of the fused antigen into the submucosa, in addition to increasing the complexity of the antigen. LTB has been used experimentally in numerous studies as carrier of unrelated antigens, with the ability to favor the induction of humoral responses and memory B lymphocytes. LTB enhances the humoral response against unrelated, genetically fused antigens when administered by either i.n. or oral routes [37,38]. In contrast to the LT holotoxin, LTB is not an inherently toxic protein and has been used as adjuvant in a vaccine candidate against ETEC diarrhea that reached clinical trials with positive results [39]. Moreover, LTB has also proven adjuvant activity when parenterally administered [40]; therefore, the design of combined parenteral–mucosal immunization scheme could be achieved with the LTB-RBD antigen to induce proper immune responses in terms of potency and compartmentalization (i.e., parenteral priming and mucosal boosting), offering the potential to prevent virus spread at early stages of infection. Interestingly, sublingual immunization has also been proposed as a convenient route able to induce high immune responses and deserves further exploration for the LTB-RBD antigen [41]. LTB has been used as antigen/adjuvant in an oral vaccine against enterotoxigenic E. coli; it induced antibodies and memory B lymphocytes with no serious adverse effects [42]. Based on this background, LTB was chosen as the carrier agent that will hypothetically increase the induction of immune responses toward SARS-CoV-2 epitopes after administration of the antigen by the nasal or oral route. In this respect, the development of mucosal vaccines not only represents an attractive advantage in terms of friendlier administration (more acceptable by patients) but also the opportunity to achieve the induction of more attractive immunological profiles, considering that immunization by these routes leads to more efficient induction of immune responses in the airway mucosa, which is critical to control or prevent the SARS-CoV-2 infection. The emergence of the delta variant, which became of high epidemiological relevance given its marked pathogenicity and transmissibility and is likely to evade the immunity induced by the Wuhan strain [43], prompted us to design a new version of the LTB-RBD carrying the delta-specific sequence. The expression of this new antigen led to its recovery as an insoluble protein with similar yields to those observed for LTB-RBD Wuhan. Given the regulatory issues associated with using the His tag, this new construct lacks tags, and the purification strategy was then established to account for a tag-free procedure. Anionic exchange chromatography allowed purifying the LTB-RBD-delta antigen to homogeneity. The previously standardized conditions for refolding of the LTB-RBD Wuhan allowed us to successfully refold the LTB-RBD delta antigen with similar yields with respect to the former, suggesting that the methods developed are robust and could be easily applied to newer variants. We are currently assessing this approach to produce an LTB-RBD omicron antigen.
The distinct versions of the LTB-RBD antigen obtained in this study constitute promising candidates for developing vaccines for which detailed expression and purification protocols have been developed. The LTB-RBD production platform used offers low cost, absence of tags, and easy adaptation to new variants, while supporting the development of mucosal vaccines. The obtained LTB-RBD antigens generate the perspective to achieve mucosal anti-COVID-19 vaccines, which promise to induce sterilizing immunity against SARS-CoV-2. | true | true | true |
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PMC9609685 | Hehe Du,Zhenjie Cao,Zhiru Liu,Guotao Wang,Ying Wu,Xiangyu Du,Caoying Wei,Yun Sun,Yongcan Zhou | Cromileptes altivelis microRNA Transcriptome Analysis upon Nervous Necrosis Virus (NNV) Infection and the Effect of cal-miR-155 on Cells Apoptosis and Virus Replication | 03-10-2022 | Cromileptes altivelis,miRNA,nervous necrosis virus (NNV),cal-miR-155,cell apoptosis | MicroRNAs (miRNAs) could regulate various biological processes. Nervous necrosis virus (NNV) is one of the primary germs of the Humpback grouper (Cromileptes altivelis), a commercial fish of great importance for Asian aquaculture. However, there is limited available information on the host-virus interactions of C. altivelis. miRNAs have been shown to play key roles in the host response to infection by a variety of pathogens. To better understand the regulatory mechanism of miRNAs, we constructed miRNA transcriptomes and identified immune-related miRNAs of C. altivelis spleen in response to NNV infection. Reads from the three libraries were mapped onto the Danio rerio reference genome. As a result, a total of 942 mature miRNAs were determined, with 266 known miRNAs and 676 novel miRNAs. Among them, thirty-two differentially expressed miRNAs (DEmiRs) were identified compared to the PBS control. These DEmiRs were targeted on 895 genes, respectively, by using miRanda v3.3a. Then, 14 DEmiRs were validated by qRT-PCR and showed consistency with those obtained from high-throughput sequencing. In order to study the relationship between viral infection and host miRNA, a cell line from C. altivelis brain (CAB) was used to examine the expressions of five known DEmiRs (miR-132-3p, miR-194a, miR-155, miR-203b-5p, and miR-146) during NNV infection. The results showed that one miRNA, cal-miRNA-155, displayed significantly increased expression in response to the virus infection. Subsequently, it was proved that overexpression of cal-miR-155 enhanced cell apoptosis with or without NNV infection and inhibited virus replication in CAB cells. Oppositely, the cal-miRNA-155 inhibitor markedly suppressed apoptosis in CAB cells. The results of the apoptosis-related genes mRNA expression also showed the regulation of cal-miR-155 on the apoptosis process in CAB cells. These findings verify that miR-155 might exert a function as a pro-apoptotic factor in reply to NNV stimulation in CAB cells and help us further study the molecular mechanisms of the pathogenesis of NNV in C. altivelis. | Cromileptes altivelis microRNA Transcriptome Analysis upon Nervous Necrosis Virus (NNV) Infection and the Effect of cal-miR-155 on Cells Apoptosis and Virus Replication
MicroRNAs (miRNAs) could regulate various biological processes. Nervous necrosis virus (NNV) is one of the primary germs of the Humpback grouper (Cromileptes altivelis), a commercial fish of great importance for Asian aquaculture. However, there is limited available information on the host-virus interactions of C. altivelis. miRNAs have been shown to play key roles in the host response to infection by a variety of pathogens. To better understand the regulatory mechanism of miRNAs, we constructed miRNA transcriptomes and identified immune-related miRNAs of C. altivelis spleen in response to NNV infection. Reads from the three libraries were mapped onto the Danio rerio reference genome. As a result, a total of 942 mature miRNAs were determined, with 266 known miRNAs and 676 novel miRNAs. Among them, thirty-two differentially expressed miRNAs (DEmiRs) were identified compared to the PBS control. These DEmiRs were targeted on 895 genes, respectively, by using miRanda v3.3a. Then, 14 DEmiRs were validated by qRT-PCR and showed consistency with those obtained from high-throughput sequencing. In order to study the relationship between viral infection and host miRNA, a cell line from C. altivelis brain (CAB) was used to examine the expressions of five known DEmiRs (miR-132-3p, miR-194a, miR-155, miR-203b-5p, and miR-146) during NNV infection. The results showed that one miRNA, cal-miRNA-155, displayed significantly increased expression in response to the virus infection. Subsequently, it was proved that overexpression of cal-miR-155 enhanced cell apoptosis with or without NNV infection and inhibited virus replication in CAB cells. Oppositely, the cal-miRNA-155 inhibitor markedly suppressed apoptosis in CAB cells. The results of the apoptosis-related genes mRNA expression also showed the regulation of cal-miR-155 on the apoptosis process in CAB cells. These findings verify that miR-155 might exert a function as a pro-apoptotic factor in reply to NNV stimulation in CAB cells and help us further study the molecular mechanisms of the pathogenesis of NNV in C. altivelis.
Nervous necrosis virus (NNV) is the pathogen of viral nervous necrosis (VNN), which was first reported in Australia in the late 1980s and caused the death of many kinds of farmed marine fish species and serious economic loss worldwide since then [1,2]. It was then found that NNV belongs to the Nodaviridae family and is a non-enveloped, icosahedral RNA virus, which consists of two single-stranded positive-sense RNA molecules, RNA1 (3.1 kb) and RNA2 (1.4 kb) [3,4]. VNN is responsible for 100% mortality in most cultured groupers, and the fish seeding industry also loses as a result of VNN attacks in seed stadiums and even in adults. Humpback groupers (Cromileptes altivelis) are an important commodity due to their high economic value. Yet, among numerous kinds of diseases, VNN is one which causes a major loss to fish farmers [5,6]. The mystery of NNV has been gradually revealed, but more is still needed. MicroRNAs (miRNAs), a kind of small non-coding RNA molecule, are usually known to have the function of down-regulating gene expression at the post-transcriptional level. A number of studies have found that miRNAs are crucially important in many biological processes, including but not limited to cell proliferation, cell apoptosis, signal transduction, and tumorigenesis [7,8]. Viral infection alters host miRNA expression, and in turn, host miRNA regulates viral infection [9]. Moreover, miRNAs are also closely related to immunity [10]. Whole miRNA transcriptome profiling analysis by rapid development high-throughput sequencing technologies provides an efficient method for understanding the genetic response of a host to diseases and pathogens. In recent years, researchers have conducted many high-throughput miRNA sequencing experiments of teleost fish or fish cells in response to various viral infections, such as Singapore grouper iridovirus (SGIV), megalocytivirus, viral hemorrhagic septicemia virus (VHSV), and NNV [11,12,13,14,15]. According to these sequencing results, many differentially expressed miRNAs (DEmiRs) were found. Among them, some have been proved to play critical roles in suppressing the inflammatory response and antiviral processes. In miiuy croaker, miR-128, miR-192, miR-145, and miR-375 have been proven to play critical roles in suppressing inflammatory responses by regulating the NF-κB signaling pathway via targeting different genes [16,17,18,19]. However, limited information on the genomics and suitable cell line of C. altivelis has hampered the understanding of the host-virus interactions molecular mechanisms. In this study, high-throughput sequencing was used to identify miRNAs involved in NNV infection progression. Among the DEmiRs we found, cal-miRNA-155 expression significantly increased in response to the virus infection. Furthermore, cal-miR-155 was found to have effects on the regulation of the apoptosis process and the inhibition of the replication of the virus in C. altivelis cells, indicating cal-miR-155 may function as a pro-apoptotic response to NNV stimulation. This research helps us better understand the role of miRNAs in the teleost fish immune system and offers new insight into the virus-cell interaction.
The NNV belonging to the red-spotted grouper nervous necrosis virus (RGNNV) genotype was derived from diseased juvenile C. altivelis, which was ensured to be infected by the NNV. The viral suspension was obtained from diseased fish tissue by homogenization in 10 volumes of PBS, centrifugated at 2000× g for 20 min at 4 °C. The supernatant was filtered through a 0.22 μm sterile filter to eliminate the interference of bacteria. The viral suspension was stored at −80 °C until further use. Inactivated NNV suspension was obtained by exposure under ultraviolet (UV) light for 4 h and shaking every 30 min on a clean bench as previously described with some modifications [20]. The UV-treated inactivation effect on the virus was verified by PCR. The CAB (C. altivelis brain) cell line was established by our lab and cultured in Leibovitz’s L-15 medium (Gibco, Grand Island, NY, USA) supplemented with 15% fetal bovine serum (FBS, Gibco) at 26 °C [21]. The viral stock was titrated in CAB cells by 50% tissue culture infectious dose (TCID50) determined as described previously [22]. In this study, CAB cells were infected with NNV with a multiplicity of infection (MOI) of 10 and incubated at 26 °C in the L-15 medium with 5% FBS.
C. altivelis, with lengths of about 15 cm, were purchased from a fish breeding farm in Danzhou (Hainan Province, China). Before experimentation, fish were cultured for two weeks to adapt to the laboratory environment. The liver, head kidney, and spleen were randomly sampled from 5% of the fish for pathogen examination as reported before [23]. No bacteria or viruses were detected from the examined fish. C. altivelis were challenged by intraperitoneal injection with 0.1 mL of the NNV suspension (1 × 105 TCID50/mL). Meanwhile, an equal volume of PBS was injected as a control. After three- and eight-days post-injection, representing the incubation period and the onset period of the virus in our pre-experiment, the spleen tissues of six fish from each group were sampled and pooled together as one sample for miRNA sequencing. Every group was replicated for three samples. Before tissue collection, fish were euthanized with tricaine methanesulfonate (Sigma, St. Louis, MO, USA). In this study, all experiments involving live fish complied with the regulations for the Administration of Affairs Concerning Experimental Animals promulgated by the Animal Ethics Committee of Hainan University.
The total RNA was isolated from the spleens of the control and infected fish using TRIzol reagent (Life Technologies, Carlsbad, CA, USA) according to the manufacturer’s protocol, and miRNA construction was performed as previously described [13]. To obtain clean read libraries, the miRNA raw data was filtered as follows: removal of contaminated RNA with an adaptor, poly-A, low-quality reads, and length kept between 17 nt to 35 nt. Then, the rRNAs, tRNAs, snRNAs, and snoRNAs were removed. We aligned the clean reads in GenBank and Rfam11. 0, followed by using Bowtie (Version 1.1.1) (Hopkins, Baltimore, MD, USA) to compare each read to the reference genome (mismatch was set to less than or equal to 1), counted the number and percentage of reads, and filtering out the sequences that could not be compared with the reference genome [24,25]. The sequences of known miRNAs were obtained from the miRBase database (http://www.mirbase.org/) (Release 22.1, accessed on 29 October 2020), and the mirDeep2 software (Version 2.0.0.8) (Systems Biology group at the Max Delbrück Center, Berlin, Germany) was used to identify the known miRNAs [26]. miRNA which did not belong to any miRNA family were represented by “unknown” and called novel miRNAs. Novel miRNA secondary structure predictions were made, with hairpin structures predicted using RNAfold WebServer online software (http://rna.tbi.univie.ac.at//cgi-bin/RNAWebSuite/RNAfold.cgi) (accessed on 26 January 2022) based on the reference genome, and the optimal secondary structures with minimum free energy were calculated [27].
For the purpose of studying the origin and evolution of the miRNAs we obtained from C. altivelis, miRNAs data, including their family classification from 25 species, were downloaded from miRBase. Their time tree was built depending on the evolutionary timescale of life by TIMETREE 5 (http://timetree.org/) (accessed on 13 June 2022) [28]. These species included two cephalochordata: Branchiostoma belcheri (bbe) and Branchiostoma floridae (bfl); eight fish: Salmo salar (ssa), Paralichthys olivaceus (pol), Oryzias latipes (ola), Fugu rubripes (fru), Tetraodon nigroviridis (tni), Ictalurus punctatus (ipu), Danio rerio (dre), and Cyprinus carpio (ccr); two amphibians: Xenopus laevis (xla) and Xenopus tropicalis (xtr); four mammals: Monodelphis domestica (mdo), Mus musculus (mus), Homo sapiens (hsa), and Ornithorhynchus anatinus (oan); two reptiles: Anolis carolinensis (aca) and Ophiophagus hannah (oha); two aveses: Gallus gallus (gga) and Taeniopygia guttata (tgu); two uro-chordatas (uch): Oikopleura dioica (odi) and Ciona savignyi (csa); two non-chordatas (nch): Caenorhabditis elegans (cel) and Drosophila melanogaster (dme); one cyclostomata: Petromyzon marinus (pma).
After estimating the expression profiles of miRNA by TPM (transcript per million), the differential expression miRNAs analyses and P value adjustments were performed using the DESeq and Benjamini Hochberg method, respectively [29,30,31]. DEmiRs were screened with p value ≤ 0.05, |log2 (fold change) | ≥1 as the threshold. MiRanda (version 3.3a) (Memorial Sloan-Kettering Cancer Center, New York, NY, USA) was used to predict miRNA target genes [32,33]. After selecting target genes, their GO (Gene Ontology) enrichment and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis were performed. Their relationship is not a simple one-to-one relationship, but a complex many-to-many relationship. The interaction network between miRNA and gene was constructed through association analysis, and the key miRNA and target genes were identified by network analysis.
We selected 14 DEmiRs, including 5 known and 9 novel DEmiRs, and analyzed their relative expression by quantitative real-time PCR (qRT-PCR). The cDNA generation using 1 μg of total RNA, which was leftover post miRNA library construction, was performed using the miRNA 1st Strand cDNA Synthesis kit (by stem-loop) (Vazyme, Nanjing, China) following the instruction from the manufacturer. Briefly, the total RNA was first added to gDNA Wiper Mix to remove the gDNA at 42 °C for 2 min, then stem-loop primers (2 µM), 10 × RT Mix, and HiScript II Enzyme Mix were added to the tubes. The stem-loop primers are shown in supplementary data (Table S3), and designed by the miRNA Design software from Vazyme (Nanjing, China). U6 was selected as the reference gene control [34].
The NNV infection in the CAB cell line was performed as follows. First, the cells were digested and diluted to a cell concentration at 6 × 104 cell/mL, then 2 mL of this suspension was added into a 6-well cell culture plate and incubated for 24 h at 26 °C to approximately 70% confluence. UV-inactivated viral suspension was added simultaneously as a control. After NNV suspension was adsorbed for 2 h (MOI = 10), the medium containing NNV was discarded, and the cells were maintained in low serum (5% FBS) L-15 media at 26 °C. At the time-points 24, 48, and 72 h post-infection (hpi), virus-specific cytopathic effects (CPE) were observed by microscope. Simultaneously, cells were collected at each time-point. The relative expression of the NNV main capsid protein (MCP) gene was assumed to reflect the virus level and β-actin was selected as the reference gene. Primers are listed in Table S4. The mRNA expression of inflammatory factors and apoptosis-related genes, including tumor necrosis factor-alpha (TNF-α), interleukin 1-beta (IL-1β), IL-6, IL-8, interferon-gamma (IFN-γ), FADD, p53, Bcl-2, Bax, Caspase 3, Caspase 6, and Caspase 8, in CAB cells were determined by qRT-PCR. EF-α was selected as the reference gene. For the purpose of examining the miRNAs expression changes in CAB cells post NNV infection, we selected the 5 known DEmiRs (miR-132-3p, miR-194a, miR-155, miR-203b-5p, and miR-146) which were closely related to innate immunity based on previous research reports and examined their expression after CAB cell infection with NNV by qRT-PCR method.
CAB cells were infected with NNV as described above. After 24 h post-infection, the cells were washed with PBS twice, fixed with 4% (v/v) paraformaldehyde (PFA) and permeabilized with Triton X-100 (0.2% in PBS) for immune-fluorescent staining [35]. After washing three times with PBS and blocked with 5% bovine serum albumin (BSA) for 60 min at room temperature, the cells were incubated with anti-MCP polyclonal antibody prepared by us at 1/200 dilution in 0.2% BSA at 4 °C overnight. The cells were washed three times with PBS and incubated with Alexa 488-conjugated secondary antibody at 1/1000 dilution for 1 h at room temperature. After washing three times with PBS, the cells were covered with antifade mounting medium (including DAPI) (Coolaber, Beijing, China) for 10 min and observed under an inverted fluorescence microscope.
We transfected CAB cells with miRNA mimic or inhibitor at different concentrations (50, 100, 200 nM) to determine the best dosage for overexpression or inhibition effect. Cells were transfected with mimic, cal-miR-155 inhibitor, or miRNA controls, using TransIntro® EL Transfection Reagent (TransGen, Beijing, China) according to the manufacturer’s protocol. The sequences for miRNAs are shown in Table S4. After 24 h post-transfection, the cells were infected with NNV as described in Section 2.7.
CAB cells were cultured and infected with NNV as described in Section 2.7. At the end of 24 hpi, apoptosis assays by fluorescence microscope were conducted referring to Sun’s method with some modifications [36]. Briefly, the cells were washed twice with PBS after removing the medium, and then incubated with binding solution, which contained two kinds of staining (Annexin V-FITC and PI), for 15 min at room temperature in the dark. Then, the cells were covered with antifade mounting medium (including DAPI) (Coolaber, Beijing, China) for 10 min and observed using an inverted fluorescence microscope. For flow cytometry assays, the CAB cells were digested and gently washed with PBS twice. Then, the cell suspensions were incubated with 100 µL 1 × binding buffer containing the two stains as above and examined using a Guava easyCyte™ Flow Cytometer (EMD Millipore Corp., Billerica, MA, USA).
The CAB cells were digested, seeded in a Φ10 cm cell-culture dish and incubated at 26 °C. After a 24 h incubation and transfected with the cal-miR-155 mimic or cal-miR-155 inhibitor, cells were infected with NNV as described in Section 2.7. CAB cells from different groups were harvested using a cell lysis buffer (Beyotime, Shanghai, China) containing 1 mM phenylmethylsulfonyl fluoride (PMSF) and was added to 1 × SDS-PAGE loading buffer, boiled for 10 min and separated by 12% SDS-PAGE, followed by transferring to a nitrocellulose membrane (Millipore, Darmstadt, Germany). After blocking with 5% BSA for 2 h, the membranes were incubated with diluted primary polyclonal antibody (anti-MCP, 1/1000 dilution) at 4 °C overnight and secondary antibody (HRP-conjugated goat anti-mouse IgG, 1/2000 dilution) for 1 h at room temperature after washing in TBST for five times of 5 min, respectively. Anti-β-tubulin mouse monoclonal antibody (Bioss, Beijing, China) was used as an internal reference. The results were detected with super sensitive ECL substrate (Biosharp, Anhui, China).
Heat shock proteins (HSPs) are a group of highly conserved proteins that are expressed in every living organism. The HSP70 expression in CAB cells was measured by flow cytometry as previously described with slight modifications [37,38,39]. Briefly, the CAB cells, seeded in a Φ10 cm cell-culture dish, were suspended using 0.25% trypsin (Gibco). After PBS with 0.5% BSA washing, the cells were fixed with 4% PFA at room temperature for 10 min and then permeabilized with 90% ice-cold methanol for 30 min on ice. The cells were incubated with 10% goat serum for 30 min at room temperature to block non-specific protein-protein interaction. Subsequently, 1:100-diluted rabbit anti-HSP70 polyclonal antibody (BIOSS, Beijing, China) was added and incubated at room temperature for 30 min (Rabbit IgG were used as isotype control). After washing, the cells were incubated with the Alexa-488 labeled anti-rabbit antiserum at room temperature for 45 min. After washing twice, the cells were resuspended in PBS and analyzed using flow cytometry (CytoFLEX, Beckman, MIA, CA, USA). As a negative control, a mock treatment group that had no interaction with primary antibodies was incubated with Alexa-488 labeled secondary antibodies.
Statistical analysis was performed using GraphPad Prism (version 8.0.2) (GraphPad Software, San Diego, CA, USA). Statistical significance was evaluated in one way analysis of variance followed by Tukey’s multiple comparison test. Data were expressed as mean ± standard deviation (SD).
In order to explore the miRNAs involved in NNV infection of C. altivelis spleen, the NNV infected C. altivelis spleen miRNA libraries from the third and the eighth day, as well as the PBS control group, were constructed and sequenced, named 3D, 8D, and control, respectively. Before the high-throughput sequencing was conducted, we performed a pre-experiment. The fish were infected by NNV supernatant and PBS (as a control) for 14 days. On the third day post-infection fish showed inappetence in the infected group. Several infected fish showed abnormal swimming behavior and began to die on the eighth day post-infection. Nevertheless, fish in the control group showed intact epithelial surfaces and no death at each time point. The UV treatment inactivated the NNV suspension, and was proved non-toxic to fish (data not shown). After sequencing, 11,437,819, 11,642,611, and 9,526,201 reads of raw data were obtained from control, 3D, and 8D libraries. After filtering, 10,142,275 (88.67% of raw data), 9,592,236 (82.39% of raw data), 7,860,736 (88.67% of raw data) were drawn out as high-grade clean reads form the three sample groups, respectively (Table S1). All of the clean data were with a similar Q30 of over 97%. After merging the same read sequences in clean reads, 332,239, 436,517, and 300,466 unique reads were obtained (Table S2), with the majority of length 22 nt (Figure S1), a common miRNAs size. According to the unique reads count, the maximum number of unique sequence lengths from 17 nt to 35 nt were all found in the 3D library, compared to the other two libraries. In order to check whether the samples were contaminated, Bowtie analysis was used to map the reads to the reference genome of C. altivelis, the reads which did not match were filtered out. At last, the percentage of the clean reads count with the perfect match to the reference were all above 97%, and that for the unique reads was above 76%. Bowtie analysis was also used to align reads with exon and intron sequences of the reference genome, reads only on exons were filtered out. Including the known and the novel, the total number of unique miRNAs that we identified from the control, 3D and 8D libraries were 5,669,967 (73.12%), 7,249,296 (69.82%) and 6,207,487 (73.94%), respectively. Finally, we obtained 30,405, 29,548, and 28,108 miRNAs from the 3D, 8D, and control libraries (Figure S2). The 20 most numerous miRNAs constituted 84.87%, 86.25%, and 83.80% of the total miRNA counts in the control, 3D, and 8D groups, respectively (Table 1). As a result, 676 novel miRNAs could form hairpin structures and representative candidates are listed in Table 2.
The microRNA family is a group of miRNAs from the same ancestor, usually with similar biological functions, but do not necessarily have conserved miRNA primary and secondary structures. Among the clean reads, we detected 6955 miRNAs in the miRBase, 96.38% of them (6703 miRNAs) belonged to 197 miRNA families. Among them, let-7 was the largest miRNA family which has 13 paralogs. The top 10 families included miR-10, miR-17, miR-30, miR-25, miR-8, miR-15, miR-181, miR-29, and miR-130. The 6703 miRNAs mapped to 456 kinds of miRNAs. In order to investigate the conservation of the miRNAs we obtained among different species, the clean reads were mapped onto 42 vertebrate species with miRBase 22.1 using the miRDeep2 software, covering the Mammalia, Aves, Reptilia, Amphibia, Osteichthyes, and Cyclostomata. Finally, we identified 5814 miRNA matures and 1312 were matched to the eight bony fish miRNAs from miRBase (Figure S3A). We analyzed the evolutionary conservation of our miRNA families to the animal kingdom after we sorted the 100 conserved miRNA families into nine groups base on species evolution (Figure S3B).
In total, 32 miRNAs were differentially expressed in two infected groups compared to the control group. In the 3D group, there were 27 miRNAs that were significantly differentially expressed, while only seven in the 8D group. Two miRNAs (cal-novel-441 and cal-novel-607) were shared between the 3D and 8D groups (Figure 1A). Then, we drew volcanic and heat maps between the infection groups and control group to speculate and explore the expression patterns of the DEmiRs during the infection process (Figure 1B,C). As shown in the volcano maps, among the 27 DEmiRs in the 3D group, 12 were up-regulated and 15 were down-regulated, including nine known miRNAs (cal-miR-132-3p, cal-miR-146b, cal-miR-155, cal-miR-184, cal-miR-203a-3p, cal-miR-203b-5p, cal-miR-206-3p, cal-miR-2188-3p, cal-miR-9-5p) and 18 novel miRNAs (cal-novel-287, cal-novel-274, cal-novel-276, cal-novel-110, cal-novel-14-star, cal-novel-144, cal-novel-221, cal-novel-229, cal-novel-23, cal-novel-26, cal-novel-28, cal-novel-30, cal-novel-33, cal-novel-34, cal-novel-41-star, cal-novel-441, cal-novel-607, cal-novel-8-star). Of the seven DEmiRs in the 8D group, five were up-regulated and two down-regulated. Among them, there was one known miRNAs (cal-miR-194a) and six novel miRNAs (cal-novel-401, cal-novel-435, cal-novel-441, cal-novel-46-star, cal-novel-529, cal-novel-607).
To better characterize the function of the C. altivelis miRNAs, miRanda (v3.3a) was used to identify the target genes. The seed sequences, which always begin at the 2-8 nt sequences starting from the 5′ end of miRNA, were selected and predicted with the 3′-UTR of the target mRNAs. Then, the results showed that 1093 miRNAs predicted 16,513 target genes. Fifty-two and one target genes were predicted from 3D and 8D known DEmiRs, and 804 and 38 were from their novel DEmiRs, respectively. As we know, cluster analysis of significantly differentially expressed genes can effectively find the common expression patterns of different genes and infer the similarity of gene function according to the similarity of expression. As showed in Figure 2, after we subjected these target genes to analyze GO and KEGG pathway, we finally clustered 3099 and 306 GO terms for the 3D and 8D groups.
Fourteen randomly selected miRNAs (five known DEmiRs and nine novel DEmiRs) were analyzed by qRT-PCR to confirm the sequencing quality. From the results shown in Figure 3, the expression of these 14 miRNAs were in accordance with those from sequencing. This meant our deep sequencing results were credible.
In order to explore the function of host miRNAs in cell level, the sensitivity to NNV of the CAB cell line was determined. The results show that the CAB cells infected with NNV exhibited obvious CPEs and multiple vacuolations at 24, 48, and 72 hpi compared with the PBS control and UV-inactivated NNV infected cells (Figure 4A). Meanwhile, qRT-PCR of the NNV MCP gene was performed to evaluate the NNV replication in CAB cells. As demonstrated in Figure 4B, the relative expression of the MCP grew in a time-dependent manner in the NNV-infected group. In contrast, no obvious CPEs were observed in UV-inactivated NNV-infected cells, and the MCP mRNA expression was comparable to the cells of the PBS control. These results prove that the tissue grinding fluid was not the cause of the cell cytopathic effect and indicates that NNV replicated well in CAB cells. As shown in Figure 4C, strong fluorescence signals were observed in infected cells, but not in the control group. This indicated that the antibody prepared for NNV mcp reacted specifically with intracellular viral particles, and also substantiated that the NNV virus was proliferating well in the CAB cells. Previous studies have shown that apoptotic signaling is often consistent with observations of high levels of HSPs [40]. Reports have shown that HSP70 is highly conserved and has antiapoptotic activity [38,41,42]. Thus, in the present study, HSP70 was detected as an apoptotic marker by flow cytometry. The fluorescence histogram reveals the percentage of HSP70 expression level in CAB cells. It indicates that the expression of HSP70 was remarkably up-regulated in the NNV-infected cells (Figure 4D). Subsequent results of double staining assays by Annexin V/PI indicate that the CAB cells exhibited both green and red signals after infection with NNV (Figure 4D). These results suggest that NNV infection caused apoptosis in the CAB cells. Then, we further determined the impact of viral stimulation on mRNA expression of inflammatory factor genes, including tumor necrosis factor-alpha (TNF-α), interleukin 1-beta (IL-1β), IL-6, IL-8, interferon-gamma (IFN-γ), and apoptosis-related genes, including FADD, p53, Bcl-2, Bax, Caspase 3, Caspase 6, and Caspase 8, in CAB cells by qRT-PCR. The results indicate that the cells infected with NNV showed significant up-regulation of most of these genes, except Bax and TNF-α, compared with the controls (Figure 4F). Taken together, NNV infection could induce cellular apoptosis and inflammatory responses in CAB cells.
The expression of five known DEmiRs were examined after infection with NNV in the CAB cells. Among them, cal-miR-194a and cal-miR-155 expression at 24 h increased by 2.22-fold, and 5.78-fold, respectively, after NNV infection (Figure 5A). Therefore, we decided to investigate the role of cal-miR-155 in the host response to NNV infection. Subsequently, CAB cells were infected with NNV and the expression of cal-miR-155 at 0, 24, 48, and 72 hpi was examined. From the results in Figure 5B, cal-miR-155 was markedly up-regulated at 24 hpi, but as the time went by, the relative fold expression of cal-miR-155 decreased. These results are illustrative that cal-miR-155 might make an important contribution in host cell response to NNV infection.
It has been previously found that NNV infection can induce cell apoptosis and increase the expression levels of cal-miR-155. Thus, we sought to investigate if there was any relationship between cal-miR-155 expression and cell apoptosis. After transfection, cal-miR-155 mimic at 100 nM and 200 nM greatly increased the expression of miR-155, but the 100 nM showed higher up-regulated effects (Figure 6A). For the cal-miR-155 inhibitor, it showed an obvious suppressive effect in cal-miR-155 expression at 100 nM and 200 nM; but unlike the mimic, it was at 200 nM concentration that showed best inhibition effect (Figure 6B). Whereafter, flow cytometry was used to check apoptosis at 24 h post-transfection. The cells transfected with cal-miR-155 presented a greater apoptotic rate than those transfected with mimic controls (Figure 6C above), while the cells transfected with the cal-miR-155 inhibitor exhibited significantly inhibited apoptosis. Subsequently, the role of cal-miR-155 in NNV-induced apoptosis attracted our attention. The results in Figure 6C (lower) display that cal-miR-155 could obviously enhance the apoptotic effect in CAB cells which already begin apoptosis after infection with NNV. When its expression was suppressed by the cal-miR-155 inhibitor, the apoptosis effect in NNV-infected CAB cells was reduced. Flow cytometry was also used to further analyze HSP70 expression, which is considered to be a reflection of the cells apoptotic level. According to the result in Section 3.6, NNV infection can obviously increase HSP70 expression. After overexpression of cal-miR-155, the level of HSP70 was up-regulated, while its expression was down-regulated after cells were transfected with the cal-miR-155 inhibitor with or without NNV infection (Figure 6E). These results are consistent with the apoptotic trend previously observed. Taken together, the results demonstrate that cal-miR-155 could enhance cell apoptosis whether there is virus present or not.
Then, we investigated the contribution of cal-miR-155 on regulating apoptosis-related genes in CAB cells. We transfected cal-miR-155 mimic and inhibitor into CAB cells. After 24 h, the cells were harvested, and apoptosis-related genes were detected by qRT-PCR. As shown in Figure 7A, the cal-miR-155 mimic significantly up-regulated the expression of FADD, p53, Bax, and Caspase-3/6/8, compared with the mimic control NCs. While the mRNA expression of the gene Bcl-2, an anti-apoptotic gene, was significantly down-regulated. On the contrary, the cal-miR-155 inhibitor significantly increased the expression of Bcl-2 and caspase-3. While, after transfection at 24 h, the CAB cells were infected with NNV. After 24 hpi, the apoptosis-related genes mRNA expressions showed that (Figure 7B), the cal-miR-155 mimic significant up-regulated the expression of FADD and Bax, compared with the mimic control NCs. While Bcl-2 was still significantly down-regulated. On the contrary, the cal-miR-155 inhibitor significantly increased its expression.
Since cal-miR-155 has been proved to have the ability to promote cell apoptosis induced by NNV, we tried to investigate its impact on NNV replication. We transfected CAB cells to overexpress a cal-miR-155 mimic in vitro and examined the relative expression of the MCP gene at 24, 48, and 72 hpi. As shown in Figure 8A, MCP mRNA expression significantly decreased in NNV-infected cells, suggesting that overexpression of cal-miR-155 could inhibit NNV replication. After we inhibited the expression of cal-miR-155 in CAB cells with a cal-miRNA-155 inhibitor, the MCP gene expression in NNV-infected cells was up-regulated at 72 hpi (Figure 8B). We also evaluated the effect of miR-155 on the NNV mcp protein level, and the results were consistent with the qRT-PCR results. The NNV mcp protein level was lower in the miR-155 mimic transfected cells than that of the controls (Figure 8C). On the contrary, the mcp protein level was higher in the miR-155 inhibitor transfected cells than of the control group (Figure 8D). These data clearly show that cal-miR-155 plays an important role in suppressing NNV replication.
In the past few years, high-throughput sequencing of miRNA responding to viral infection in teleost fish or fish cells and our knowledge on the function of these miRNAs has increased rapidly [43]. However, the molecular mechanisms underlying NNV infected C. altivelis is still not clear. In this study, first, the miRNAs caused by NNV in C. altivelis spleen were identified, miRNA libraries of CAB cells infected with NNV at early (3 dpi) and late (8 dpi) stages were constructed, and many DEmiRs were obtained. Secondly and importantly, one of the DEmiRs, cal-miR-155, was screened out to show significant responses to the virus in CAB cells and its overexpression proved to induce apoptosis. As a result, the replication of virus was suppressed. All these results demonstrate that miRNAs may have a critical role in defending against NNV infection. Up to now, people have completed high-throughput miRNA sequencings on teleost fish and cells in response to various viral infections, such as grouper to Singapore grouper iridovirus (SGIV) and NNV, flounder to megalocytivirus and VHSV, grouper fin (GF-1) cells to VHSV, and epithelioma papulosum cyprinid (EPC) cells to VHSV [11,12,13,14,15]. In compliance with studies in other species, for example, P. olivaceus and Cynoglossus semibreves, we also found the majority of miRNA lengths was 22 nt according to the length distribution, a typical length of most animal miRNAs [44,45]. We also classified the 100 miRNA families into nine groups base on the species evolution time, and then analyzed the evolutionary conservation of our miRNA families to the animal kingdom. The results suggest that the miRNA family of C. altivelis is highly conserved among different animal species. According to a lot of previous studies, plenty of DEmiRs have been found during the virus infection process and the expressions of host miRNAs could be affected during this period [12,14,15]. In our study, twenty-seven and seven DEmiRs were found from 3D and 8D libraries, respectively. Obviously, the number of DEmiRs showed a declining trend. GF-1 cells displayed a similar trend with 51 and 16 DEmiRs in the early (3 hpi) and late (24 hpi) stages after infection with the RGNNV, respectively [14]. While it was not all the same, compared to the number at 6 and 12 hpi, the number of DEmiR in VHSV infection olive flounder greatly increased at 24 hpi [13]. This may be owing to the different viral infection systems. In the current study, the DEmiRs exhibited different expression patterns. At early stages, 16 DEmiRs were up-regulated, while at late stages, the number was 14, compared to the control. Most of the DEmiRs that were found in this study were assumed to have important function in the antiviral processes. For example, in mammals, researchers revealed that miR-132-3p overexpression could promote the replication of the influenza A virus (IAV) [46]. High-levels of miR-146a facilitated SGIV replication and reinfection through retarding virus-induced apoptosis in groupers [47]. miR-155 was also exhibited anti-CyHV-3 activity in common carp brain (CCB) cells [48]. Since majority of miRNAs actthrough binding to the target genes to exert their function, the prediction and analysis of miRNA target genes is very important in understanding the function of miRNAs [49]. In this study, ten known DEmiRs and 22 novel DEmiRs targeted 53 and 842 genes of Cromileptes altivelis, respectively. It is commonly known that NNV exerts its effects mainly at the central nervous system [50]. Therefore, fish brain cell lines may become a useful tool for viral study. Some fish brain cell lines have been established for NNV pathogenesis studies, such as Lates calcarifer and Lateolabrax japonicus [51,52]. According to our previous study, the CAB cell line is susceptive to Vibrio harveyi and Edwardsiella tarda [21]. In the current study, we observed typical CPEs after NNV-infection, and the virus replication was also confirmed by qRT-PCR. These indicated that we could use the CAB cell line to study the interactions of host and NNV in vitro. On this basis, we tried to investigate the function of miRNAs in the CAB cell response to NNV infection. Among the five known DEmiRs (miR-132-3p, miR-194a, miR-155, miR-203b-5p, and miR-146), cal-miR-155 showed the highest up-regulation compared to the other four miRNAs, indicating that it might play an essential role in defending against NNV infection in host cell lines. Previous studies have confirmed that miR-155 is related to the immune reaction and has antiviral effects, such as regulating cytokine expression of Cyprinus carpi, and exhibiting anti-CyHV-3 activity in common carp brain (CCB) [48,53,54]. According to the results after cell infection at different time points, the expression of cal-miR-155 markedly decreased after 24 hpi, indicating that the function of cal-miR-155 might act in early-stages of the NNV infection. This is consistent with our findings that cal-miR-155 increased at 3 dpi but decreased at 8 dpi, indicating that it might respond to NNV infection in the early stage. As we mentioned in front, NNV is a non-enveloped virus and needs apoptosis of the host cells to occur for its release. Like most viruses, NNV infection can induce apoptosis [55,56]. Similar findings also appear in the avian reovirus (ARV), another non-enveloped virus [57]. In this study, we confirmed that NNV infection leads to CAB cell apoptosis by Annexin V-FITC/PI double staining and is associated with the increased expression of cal-miR-155. Therefore, we speculated whether cal-miR-155 had any effects on cell apoptosis. To verify this hypothesis, we transfected a mimic or inhibitor to overexpression or suppress cal-miR-155 levels in CAB cells. Results displayed that transfection of the cal-miR-155 mimic significantly increased the apoptosis and, conversely, inhibited cal-miR-155 decreased apoptosis with or without NNV presence. Numerous studies have shown that HSPs are not only heat shock-inducible proteins, but can also be stimulated by other factors, including growth factors, infections and inflammation [58]. During the course of viral infection, the expression of heat shock genes are induced by activation of the cellular stress response and play a key role in regulating apoptosis [40,58]. In this study, NNV infection steeply induced HSP70 expression in CAB cells. This suggested that apoptosis caused by viral infection was accompanied with the HSP70 increase. Moreover, it was also found that transfecting the cal-miR-155 mimic increased HSP70 expression levels significantly with or without the presence of NNV. In the following experiemnts, the opposite result was found after transfection with the inhibitor. It is generally accepted that apoptosis consists of two classic pathways: the intrinsic pathway (mitochondrial pathway) and the extrinsic pathway (receptor-mediated pathway) [59,60]. In our research, the mRNA expression of FADD and TNF-α which belong to extrinsic pathway were not significantly altered after NNV infection, indicating that it was the intrinsic pathway that was activated in CAB cells post-infection. At this time, the cells need to promptly inhibit the occurrence of apoptosis to survive under the pathogen stimulus. This is consistent with our results that the anti-apoptotic factor Bcl-2 was significant increased, while the pro-apoptotic factor Bax was significant decreased. However, the expressions of p53 and caspase 3/6/8 were up-regulated stimulated by the virus, accompanied by the up-regulation of several inflammatory factors. These actions are common to reduce apoptosis induced by viral stimulation. Recently, it was reported that during ISKNV infection in GF-1 cells, Bcl-2/Bcl-xL interacts with Bax/Bak in a dynamic interaction. This is to maintain the mitochondrial function in GF-1 cells [61]. In the current study, transfection of the cal-miR-155 mimic significantly increased apoptosis with or without NNV presence. If the purpose of virus-promoted apoptosis is to release virions and infect more cells, we hypothesize that the mechanism of promoting apoptosis may be a process of suicidal self-protection. According to studies on mammals, miR-155 also has two roles in the regulation of apoptosis. In human THP-1 cells infected with Bacillus Calmette-Guerin (BCG), miR-155 is up-regulated to inhibit cell apoptosis and causes bacilli to escape the host and avoid infection [62]. On the contrary, overexpression of miR-155 enhances apoptosis in a time-dependent manner, suggesting that miR-155 promotes apoptosis, which is consistent with our findings [63]. We have previously discussed that cells activate the intrinsic apoptosis pathway to inhibit their own apoptosis under viral stimulation. Therefore, it perplexes us that apoptosis induced by cal-miR-15 is an enhanced response, or is this mediated through another pathway? For this purpose, FADD, p53, Bcl-2, Bax, Caspase-3/6/8 were detected in both cells infected with and without NNV after transfection with a cal-miRNA-155 mimic or inhibitor for 24 h. Before NNV stimulation to the cell, the expressions were all up-regulated upon cal-miR-155 overexpression, except Bcl-2. FDAA and Caspase-3 were down-regulated, and simultaneously, Bcl-2 was up-regulated, when expression of cal-miR-155 was inhibited. Consistent with this result, AMO-155 (antisense inhibitor) has been reported to reverse H2O2-induced down-regulation of Bcl-2, up-regulation of Bax and cleaved caspase-3 in mice [64]. gga-miR-16-5p has been reported toinhibit the expression of Bcl-2 and enhance FAdV-4 (fowl adenovirus serotype 4)-induced apoptosis [65]. In combination with what was discussed earlier, cal-miR-155 may activate the extrinsic receptor-mediated pathway but this still needs further experiments to prove. Apoptosis induced by viral infection has both positive and negative effects on virus replication [59]. After NNV stimulation, the cells transfected the cal-miR-155 mimic showed the highest degree of apoptosis, this may be the result of the combined action of the two apoptotic pathways. Subsequently, we found that the enhancement of cal-miR-155 had on apoptosis in NNV-infected cells suppressed viral replication. Exactly how miR-155 promotes apoptosis to inhibit the proliferation of the virus and what the target genes are still needs further screening and experimental verification. In conclusion, the current study provided an miRNA transcriptome analysis upon NNV infection of C. altivelis and showed that NNV infection changes the host miRNAs expression in vivo and in vitro. In CAB cells, we found that the expression of cal-miR-155 was up-regulated by NNV infection. cal-miR-155 increased NNV-induced apoptosis and resulted in inhibiting NNV replication. We speculated that NNV induces cell apoptosis through the intrinsic pathway, while cal-miR-155 could increase the apoptotic effect through the extrinsic pathway. From all of these results, cal-miR-155 may contribute in the anti-viral process in host cells and provide new insights in understanding the function of host-virus interaction. | true | true | true |
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PMC9609691 | Jacquelyn C. Serfecz,Yuan Hong,Lauren A. Gay,Ritu Shekhar,Peter C. Turner,Rolf Renne | DExD/H Box Helicases DDX24 and DDX49 Inhibit Reactivation of Kaposi’s Sarcoma Associated Herpesvirus by Interacting with Viral mRNAs | 20-09-2022 | deadbox helicases,KSHV,pattern recognition receptor,innate immunity,type I interferon | Kaposi’s sarcoma-associated herpesvirus (KSHV) is an oncogenic gammaherpesvirus that is the causative agent of primary effusion lymphoma and Kaposi’s sarcoma. In healthy carriers, KSHV remains latent, but a compromised immune system can lead to lytic viral replication that increases the probability of tumorigenesis. RIG-I-like receptors (RLRs) are members of the DExD/H box helicase family of RNA binding proteins that recognize KSHV to stimulate the immune system and prevent reactivation from latency. To determine if other DExD/H box helicases can affect KSHV lytic reactivation, we performed a knock-down screen that revealed DHX29-dependent activities appear to support viral replication but, in contrast, DDX24 and DDX49 have antiviral activity. When DDX24 or DDX49 are overexpressed in BCBL-1 cells, transcription of all lytic viral genes and genome replication were significantly reduced. RNA immunoprecipitation of tagged DDX24 and DDX49 followed by next-generation sequencing revealed that the helicases bind to mostly immediate-early and early KSHV mRNAs. Transfection of expression plasmids of candidate KSHV transcripts, identified from RNA pull-down, demonstrated that KSHV mRNAs stimulate type I interferon (alpha/beta) production and affect the expression of multiple interferon-stimulated genes. Our findings reveal that host DExD/H box helicases DDX24 and DDX49 recognize gammaherpesvirus transcripts and convey an antiviral effect in the context of lytic reactivation. | DExD/H Box Helicases DDX24 and DDX49 Inhibit Reactivation of Kaposi’s Sarcoma Associated Herpesvirus by Interacting with Viral mRNAs
Kaposi’s sarcoma-associated herpesvirus (KSHV) is an oncogenic gammaherpesvirus that is the causative agent of primary effusion lymphoma and Kaposi’s sarcoma. In healthy carriers, KSHV remains latent, but a compromised immune system can lead to lytic viral replication that increases the probability of tumorigenesis. RIG-I-like receptors (RLRs) are members of the DExD/H box helicase family of RNA binding proteins that recognize KSHV to stimulate the immune system and prevent reactivation from latency. To determine if other DExD/H box helicases can affect KSHV lytic reactivation, we performed a knock-down screen that revealed DHX29-dependent activities appear to support viral replication but, in contrast, DDX24 and DDX49 have antiviral activity. When DDX24 or DDX49 are overexpressed in BCBL-1 cells, transcription of all lytic viral genes and genome replication were significantly reduced. RNA immunoprecipitation of tagged DDX24 and DDX49 followed by next-generation sequencing revealed that the helicases bind to mostly immediate-early and early KSHV mRNAs. Transfection of expression plasmids of candidate KSHV transcripts, identified from RNA pull-down, demonstrated that KSHV mRNAs stimulate type I interferon (alpha/beta) production and affect the expression of multiple interferon-stimulated genes. Our findings reveal that host DExD/H box helicases DDX24 and DDX49 recognize gammaherpesvirus transcripts and convey an antiviral effect in the context of lytic reactivation.
Human gammaherpesviruses have been implicated in numerous cancers and autoimmune disorders [1]. Kaposi’s sarcoma-associated herpesvirus (KSHV) is an oncogenic gammaherpesvirus that infects endothelial cells to give rise to Kaposi’s sarcoma (KS), and B cells to give rise to Primary Effusion Lymphoma (PEL) or Multicentric Castleman’s Disease (MCD). The seroprevalence of KSHV infection in the general population varies from <5% in the United States and Asia, to over 50% in sub-Saharan Africa, where it is estimated that 44,000 new cases of KS are diagnosed each year [2,3]. KS causes angiogenesis and dark red lesions on the skin and in the oral cavity, and leads to the transformation of endothelial cells. Although KS is most common in HIV-positive patients, other epidemiological variants occur in immune compromised individuals such as the elderly, children, or organ transplant recipients. Notably, about 1 in 200 transplant patients in the United States develop KS [4]. The disease can be life-threatening if KS reaches the gastrointestinal tract or lungs. Currently, the only treatment is the surgical removal of the lesion or chemotherapy. In contrast, PEL is a very rare and primarily HIV-associated non-Hodgkin’s Lymphoma of B cells [5]. It is a rapidly fatal body cavity-based lymphoma (BCBL) that most likely originates from post-germinal center B-cells [5]. B cells latently infected with KSHV, termed BCBL-1 cells, were able to be cultured from a PEL biopsy and have been used to study latency and reactivation [6,7]. When the immune system is weakened, individuals who are infected with KSHV are more likely to develop KSHV-related malignancies, suggesting a critical role of the immune system in suppressing cancer development [8,9,10]. The innate immune system regulates gammaherpesvirus-induced lytic reactivation in cell culture and in the gammaherpesvirus mouse model [8,11]. RIG-I and RIG-I-like receptors, members of the DExD/H box helicase family, are known to mount an innate immune response against gammaherpesviruses to inhibit viral reactivation [12,13,14]. It is becoming more evident that some of the same innate immune responses that prevent de novo KSHV infection also suppress reactivation from latency occurring within the host cell nucleus [15,16]. In the current model of KSHV infection, Pattern Recognition Receptors (PRRs) including Toll-Like Receptors (TLRs), RIG-I-Like Receptors (RLRs), and Cyclic GMP-AMP Synthase (cGAS) can recognize KSHV and other herpesviruses to stimulate an innate immune response [12,16]. During KSHV reactivation, knockdown of RLRs led to increased lytic reactivation in HEK-293 cells [13]. There are multiple RNA regions in KSHV that could act as potential virus ligands of RIG-I to induce type I interferon responses [17]. However, the mechanism by which RIG-I senses KSHV from within the nucleus during reactivation is unknown. It is uncertain whether RLRs directly recognize viral dsRNA since cGAS, an antiviral DNA-sensing molecule, also recognizes KSHV dsDNA and is involved in crosstalk with RIG-I to stimulate interferon (IFN) genes [18,19]. RNA helicases recognize and bind to segments of double stranded RNA and are involved in many cellular processes including RNA transport, localization, splicing, and translation [20]. The helicases recognize their RNA targets by a combination of secondary structure and by interacting with other RNA binding protein partners [21,22]. When bound, RNA helicases displace RNA:protein complexes to facilitate RNA processing and, in some cases, helicases also have the ability to translocate, and unwind RNA:RNA duplexes [23]. During viral infection, a subset of RNA helicases has the ability to recognize non-self dsRNAs via location in the cell, structural defects such as bulges or loops, and/or nucleotide modifications that all can lead to stimulation of an innate immune response [24,25,26,27]. RIG-I (DDX58) and RIG-I-like receptors are members of the largest superfamily of RNA helicases, termed DExD/H box helicases. This family of helicases are RNA-binding proteins that utilize ATP to unwind short duplex regions of RNA and remodel RNA-protein complexes, thereby altering RNA secondary structure. They are characterized by the DExD/H box helicase domain, a conserved ATP binding motif that contains the amino acid sequence Asp-Glu-x-Asp/His [28,29]. The core homologous DExD/H box domains are centered in the protein and a helicase domain is often at the C-terminal end [29]. Disordered flanking segments at the C-terminal and N-terminal ends are up to hundreds of amino acids in length and are thought to contain linker regions involved in interactions with other proteins or RNAs [30]. There are about 60 DExD/H box helicases in mammalian cells [31]. Based on crystal structures of DExD/H box proteins complexed with RNA, the binding sites in the helicase core contact the RNA backbone exclusively, allowing association with duplex RNA but excluding preferential binding of particular RNA sequence motifs [32]. When a cell is infected with a virus, several DExD/H box helicases that are not part of the RIG-I family have recently been shown to act as viral PRRs or signaling adaptors of innate immune pathways. Some examples are DDX3, DDX41, and DDX24 [30,33,34,35,36,37]. To identify if other DExD/H box helicases can act as PRRs or adaptors of the innate immune system during KSHV viral reactivation, we performed an siRNA knockdown screen of candidate helicases, and identified DDX24 and DDX49 as inhibitors of KSHV lytic reactivation. DDX24 and DDX49 immunoprecipitation followed by RNA enrichment and high-throughput sequencing analysis of helicase-bound RNAs yielded numerous KSHV immediate-early (IE) and early (E) transcripts. Ectopic expression of IE and E KSHV transcripts in the absence of viral infection induced type I interferon production. Moreover, the interaction of DDX24 and DDX49 with viral IE and E transcripts during reactivation stimulated multiple interferon-regulated genes.
Vero rKSHV.219 cells [38], a gracious gift from the Michael Lagunoff lab, were maintained in DMEM with 10% FBS and 10 μg/mL puromycin and induced with 20 ng/mL tetradecanoyl phorbol acetate (TPA) and 2 mM sodium butyrate (NaB). Knockdown BCBL-1 cells were maintained in RPMI 1640 with 10% FBS and 1 μg/mL puromycin, and induced with 20 ng/mL TPA and 2 mM NaB. Electroporated BCBL-1 cells expressing DDK-tagged DExD/H box helicases were maintained in RPMI 1640/10% FBS with 500 μg/mL G418 and induced with 2 mM NaB. TREx BCBL1-Rta cells were engineered by the Jae Jung lab using Flp-mediated site-specific recombination to be inducible for Rta following doxycycline treatment [39]. Lentiviral transduced DExD/H box-Avi overexpressing TREx BCBL1-Rta cells were maintained in RPMI 1640/10% FBS with 1 μg/mL puromycin and induced with 1 μg/mL doxycycline and 0.25 mM phosphonoformic acid (PFA). Cells of THP-1, an acute monocytic leukemia cell line, were incubated in 5% CO2 at 37 °C using RPMI 1640 Medium with 10% fetal bovine serum, 2 nM L-glutamine adjusted to contain 1.5 g/L sodium bicarbonate, 4.5 g/L glucose, 10 mM HEPES and 1.0 mM sodium pyruvate and supplemented with 0.05 mM 2-mercaptoethenol. Cells were maintained at 105 to 106 cells/mL. HEK-blue IFNα/β™ cells were cultured in 5% CO2 at 37 °C using DMEM medium with 10% FBS. Cells are selected with 30 µg/mL blasticidin and 100 µg/mL Zeocin. Cells were subcultured when they reached 70~80% confluency.
Vero rKSHV.219 cells were seeded in individual wells of a 48-well plate at 200 μL of 9.5 × 104 cells/mL in DMEM/10% FBS without antibiotics and grown overnight. A Lipofectamine RNAiMAX transfection (Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA) was performed with a pool of 5 ON-TARGETplus siRNA duplexes that target a particular DExD/H box helicase or scrambled control (Dharmacon, Horizon Discovery, Lafayette, CO, USA) for a final siRNA concentration of 50 nM as per the manufacturer’s instructions. The list of the 22 DExD/H box helicase targets used in the knockdown screen [40] is presented in Table S1. The sequences of siRNAs against specific helicases and controls are in Table S2. After 28 h, the knock-down of control RNAi GFP was confirmed and all siRNA containing media was replaced with 10% FBS media containing 20 ng/mL TPA, and 2 mM NaB to induce lytic reactivation. Fluorescence images were acquired on a Leica DMI 4000 microscope, cell counts were recorded, and DNA was extracted at 48 h post-induction. Three random fields of vision were selected to quantify the adherent Vero cells prior to the 29 h post-induction time point. After the 36 h time point, lytic Vero cells were no longer adherent and could not be quantified.
For RT-qPCR, RNA was extracted from KSHV infected cells throughout the induction time course using RNA Bee (Tel-Test, Friendswood, TX, USA). Total RNA was quantified using Nanodrop 1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), and 0.9 μg Vero RNA or 1.6 μg BCBL-1 RNA was Turbo DNase treated (Invitrogen, Carlsbad, CA, USA), then converted to cDNA with the High-Capacity RNA-to-cDNA Kit (Thermo Fisher Scientific, Waltham, MA, USA). All cDNA analyses were quantified relative to GAPDH. DNA was extracted using a QIAamp DNA Mini kit (QIAGEN, Valencia, CA, USA). KSHV genome copy numbers from 30 ng of total DNA were quantified utilizing LANA N-terminus primers based on a standard curve constructed from known amounts of pcDNA3.1 plasmid DNA. Quantitative, real-time PCR (qPCR) was performed on an ABI StepOnePlus (Applied Biosystems, Foster City, CA, USA) for the Vero intracellular cDNA/DNA and a Roche Light Cycler 96 (Roche, Indianapolis, IN, USA) for the BCBL-1 intracellular cDNA/DNA. DNase-treated RNA was converted to cDNA using RNA-QuantTM cDNA synthesis kit (System Biosciences, Palo Alto, CA, USA) to include RNA classes with strong secondary interactions. Primer sequences are listed in Table S3.
1 × 107 BCBL-1 cells were grown in antibiotic-free RPMI. The next day, they were pelleted and rinsed twice in PBS. The cell suspension was transferred into a 0.4 cm electrode gap cuvette with 10 μg of TrueORF Gold pCMV6-Entry DNA vector (Origene, Rockville, MD, USA) containing the Myc-DDK (FLAG)-tagged helicase of interest (Origene, Rockville, MD, USA) or pmaxGFP (AddGene, Cambridge, MA, USA). Cuvettes were treated 2 times at 250 V, 950 μF with the Gene Pulser Xcell device (Bio-Rad, Hercules, CA, USA). Electroporated BCBL-1 cells were grown for 48 h in non-selective RPMI medium. After the 48 h, media was replaced with RPMI containing 400 μg/mL G418 for 4 weeks.
Monoclonal mouse antibody against DDK (TA50011) was from Origene (Rockville, MD, USA). Monoclonal mouse antibody against Avi was from Genscript (Piscataway, NJ, USA). Goat αDHX29 (sc-107197) and goat αGAPDH (V-18, sc-20357) were from Santa Cruz (Santa Cruz, CA, USA). Mouse monoclonal α-Tubulin (CP06-100UG) was from Oncogene/EMD Millipore (Burlington, MA, USA). IRDye 800-labeled IgG and IRDye 680-labeled IgG Donkey α-goat (925-68074) and α-mouse (925-32212) secondary antibodies were from Li-Cor Biosciences (Lincoln, NE, USA). 8 × 105 cells were pelleted by centrifugation, washed in cold PBS, and resuspended in 80 μL RIPA buffer containing 150 mM NaCl, 50 mM Tris (pH 8), 1% (v/v) NP-40, 0.5% sodium deoxycholate, 2 mM EDTA, and 0.1% SDS with protease inhibitor cocktail (Roche, Indianapolis, IN). Pierce BCA protein assays were performed (Thermo Fisher Scientific, Waltham, MA, USA). Then, 20–40 μg aliquots (25 μg for overexpressing BCBL-1 cells, 20 μg for DHX29 KD, and 40 μg for TREx BCBL1-Rtas) of total protein were separated by 10% SDS-PAGE gels and transferred to a nitrocellulose membrane (Bio-Rad, Hercules, CA, USA). The membranes were blocked with Odyssey blocking buffer (TBS) from Li-Cor Biosciences (Lincoln, NE, USA) at 4 °C overnight and then subsequently incubated with primary antibodies (1:2000 α-DDK, 1:200 α-GAPDH for overexpressing BCBL-1 cells; 1:200 α-DHX29, 1:1000 α-tubulin for DHX29 KD; 1:1000 α-Avi, 1:1000 α-GAPDH for TREx BCBL1-Rtas) for 1 h and secondary antibodies (1:15,000) for 1 h at room temperature. After washing, the membranes were scanned with an Odyssey CLx infrared imaging system (Li-Cor, Lincoln, NE, USA) at wavelengths of 700 and 800 nm to quantify protein bands, and the molecular sizes of the developed proteins were determined by comparison with pre-stained protein markers (Bio-Rad, Hercules, CA, USA).
Hairpin shRNAs in the pLKO.1 lentiviral vector designed by The RNAi Consortium (TRC) targeting DDX24, DDX49, DHX29, and DDX58 DExD/H box helicases were cloned (Dharmacon, Horizon Discovery, Lafayette, CO, USA). Then, 5 μg of each shRNA vector, 2.5 μg psPAX2, and 2.5 μg pMD2.G were co-transfected with TransIT-293 Transfection reagent (Mirus Bio, Madison, WI, USA) in 9 cm plates containing 5 × 106 HEK 293FT cells, as per the manufacturer’s protocol. Eighteen hours after transfection, plasmid-containing media was replaced with fresh DMEM. Seventy-two hours post transfection, the supernatants were collected. All lysates were centrifuged at 3000 rpm for 5 min, 0.45 μm filtered, and stored at −80 °C prior to infection. Then, 5.0 × 106 BCBL-1 cells were resuspended in a 5 mL pool of pure viral supernatant, each containing 1 mL of lentiviral stock targeting a specific DDX or a control lentivirus with empty pLKO.1 construct for 24 h. A pool of 5 different shRNA expressing lentiviruses were used for knock down (Table S4). All lentivirus infected cells were selected in RPMI with 10% FBS and 1 μg/mL puromycin for 13 days and frozen in liquid nitrogen for future analysis.
Lentiviral pLV constructs expressing Avi-FLAG-tagged versions of maxGFP, DHX29, DDX24, DDX49, and DDX58 (designated by NCBI accession number) were obtained from VectorBuilder (Santa Clara, CA, USA). TREx BCBL1-Rta cells were grown under 200 μg/mL hygromycin B selection to a density of 5 × 105 cells/mL. A total of 2 × 106 TREx BCBL1-Rta cells were infected with lentivirus at an MOI of 10 with 8 μg/mL polybrene. Twenty-four hours post infection, the virus containing media was replaced by media containing 0.25 μg/mL puromycin RPMI and cells were grown under selection for 7 days. Then, the puromycin concentration was gradually increased to 1 μg/mL for another 5 days. GFP expressing cells were monitored periodically for successful expression of the transgene. The infection was performed twice independently (two biological replicates).
TREx BCBL1-Rta cells overexpressing maxGFP, DDX24, DDX49, or DDX58 were grown to a density of 5 × 105 cells/mL in 100 mL. One set of flasks was harvested during latency, the other was reactivated in RPMI media containing 1 μg/mL Doxycycline and 0.25 mM PFA for 24 h. When cells were harvested, they were incubated in lysis buffer (20 mM MOPS-KOH pH 7.4, 120 mM KCl, 0.5% Igepal, 2 mM β-Mercaptoethanol supplemented with 200 unit/mL RNasin (Promega) and Complete Protease Inhibitor Cocktail (Roche)) for 20 min on ice. The lysate was cleared by centrifugation and endogenous proteins were immunoprecipitated overnight at 4 °C with 10 μg total of α-Avi tag monoclonal mouse antibody, (2 µg/ul, Genscript, catalog# A01738) composed of a mouse IgG2a heavy chain. Then, 5 mL of the antibody-bound RNA lysate was incubated with 200 μL of protein G MagBeads MX (Genscript, Piscataway, NJ) for 2 h at 4 °C. The beads were washed three times with MOPS buffer and eluted with 0.1 M glycine, pH 2.2 as per the protocol. The RNA was isolated by TRIzol reagent for RNA extraction (Sigma-Aldrich, St. Louis, MO, USA), TURBO Dnase treated (Invitrogen), and subjected to further analysis. Input and IP samples from non-transduced, DDX24-Avi, and DDX49-Avi TREx-BCBL1-Rta cells were collected for sequencing.
Libraries were prepared using the TruSeq Stranded Total RNA kit with Ribo-Zero Gold (Illumina), and were tested on TapeStation (Agilent) to verify the cDNA quality and size for sequencing. Sequencing was performed by the Interdisciplinary Center for Biotechnology Research (ICBR) at the University of Florida on the Illumina HiSeq3000 platform to generate 50 million, 100 base paired-end reads for each sample. The sequencing data were processed and analyzed as follows. First, Illumina adapter sequences were removed from the reads using Trimmomatic. The quality of the reads was then verified with FastQC. After these pre-processing steps, a paired-end alignment was performed against the KSHV BAC16 genome (GQ994935.1) using Bowtie2. The number of aligned reads was normalized using Counts per Million (CPM) for each sample. The CPM values were used to calculate coverage on either strand of KSHV genome. The read coverage of the control samples, which represented non-specific background, was subtracted from those of the experimental DDX24 and DDX49 samples. The normalized coverage tracks of DDX24 and DDX49 were then visualized using Integrative Genomics Viewer tool.
For all experiments, mean ± standard error mean (S.E.M.) were calculated and significance was determined by performing unpaired two-sample Student’s t-tests, using GraphPad Prism software (GraphPad Software, La Jolla, CA, USA). ANOVA was used for grouped analyses.
5 × 105 THP-1 cells were transfected with 5 µg poly(I:C) (InvivoGen), or with 5 µg pcDNA3.2 plasmids containing different KSHV genes, GFP, LANA and empty pcDNA3.1 via Lipofectamine 3000 transfection kit (Thermo Fisher Scientific). The KSHV genes included K2, ORF70, K4, K4.1, K4.2, K5, K9, ORF58, ORF59, ORF65, ORF66, and ORF 67. The mammalian expression vector plasmids expressing KSHV transcripts were gifts from Dr. Denise Whitby in Frederick National Laboratory for Cancer Research (listed in Table S5). The pcDNA3.2 derivatives have the CMV promoter, neo and amp resistance. Supernatants were harvested at 12 h and tested for type I IFN activity using HEK-blue IFNα/β™ cells, which were engineered to monitor JAK-STAT pathway activation by expression of a secreted embryonic alkaline phosphatase (SEAP). SEAP activity produced in response to IFNα/β was measured with a chromogenic substrate. A total of 50,000 HEK-blue IFNα/β™ cells were seeded in 180 µL QUANTI-Blue medium in each well of a 96-well plate. Supernatant from THP-1 cell culture medium (20 µL) was added per well. IFN-α and IFN-β were assessed by measuring SEAP activity using optical density (OD) at 620–655 nm with a microplate reader. Two biological replicates and three technical replicates were performed in each experiment. RNA was extracted from THP-1 cells 12 h post transfection using TRIzol (Invitrogen) and quantified. RNA was converted to cDNA using the high-capacity RNA to cDNA kit (Thermo Fisher Scientific) in a total volume of 20 μL. Real-time PCR was performed using primers targeting human IFN-α and IFN-β, with GAPDH as an endogenous control.
To determine if other DExD/H box helicases in addition to known RLRs affect KSHV lytic reactivation, we performed a transient siRNA knock-down (KD) screen of 22 individual DExD/H box helicases (Table S1) that previously found to have an effect on Myxoma virus infection efficiency [40]. Five siRNAs were used to potently knockdown each DExD/H box helicase to ensure a knockdown as well as patterned modifications for a low off-target effects. The Vero cell line is latently infected with a recombinant virus, rKSHV.219 which expresses the red fluorescent protein (RFP) from the KSHV early lytic PAN promoter, and the green fluorescent protein (GFP) from the EF-1α promoter, and with the gene for puromycin resistance as a selectable marker. Therefore, it facilitates the detection of latent and immediate-early lytic stages of infection via fluorescent markers [38]. Following transfection with siRNA pools targeting individual helicases, the cells were induced at 29 h with tetradecanoyl phorbol acetate (TPA) and sodium butyrate (NaB). Within 24 h, the DHX29 knockdown demonstrated a 90% reduction in reactivation as measured by the percentage of RFP-expressing cell number, and notably, two of the screened helicases, DDX49 and DDX24, showed a 2-fold increase in reactivation (Figure 1A,B). No significant difference was observed for the remaining 19 DExD/H box helicases (Table S1). The sequences of the pooled siRNAs used to knockdown DHX29, DDX24, and DDX49 and for the non-targeting control pool are shown in Table S2. It should be noted that all knockdowns without induction showed negligible background reactivation. Additionally, all cells appeared viable until the chemical inducers were added to the media to activate the KSHV lytic cycle. When RIG-I alone was knocked down in Vero rKSHV.219 cells during the screen, there was no observable change in reactivation when compared to the non-specific control. To confirm that RFP expression truly represented lytic DNA replication, intracellular KSHV genomic DNA was measured by qPCR with primers that amplify the C-terminus of ORF73 encoding LANA. Primer sequences are listed in Table S3. qPCR results estimate the viral copy number at 48 h post-induction representing active KSHV replication. Relative to knockdown with a scrambled siRNA control, DHX29 knockdown had about 2/5 the number of genome copies, DDX49 knockdown demonstrated a 1.5-fold increase, and DDX24 knockdown demonstrated a 3-fold increase in the number of viral genome copies (Figure 1C). Hence, supports that DHX29 acts as a pro-viral sensor, whereas DDX24 and DDX49 act as antiviral sensors, which is in congruence with our observations from fluorescence microscopy. Although Vero rKSHV.219 offered a simple tool for a reactivation screen, Vero cells do not produce type I interferons, one of the two downstream pathways of MAVS signaling. Thus, IFN-competent human BCBL-1 cells were used for further phenotypic studies. To monitor the effects on KSHV reactivation, a DHX29 siRNA expressing lentivirus was prepared via second generation packaging of the pLKO.1 plasmid to achieve a stable knockdown in BCBL-1 cells. qPCR confirmed a 50% decrease in DHX29 mRNA levels (Figure S1). However, we observed no significant KSHV gene expression changes upon viral reactivation in the DHX29 KD cells. One explanation for this is the above-mentioned missing Type I interferon production in Vero cells. Therefore, the remainder of this study focuses on the downstream analysis of the potentially antiviral PRRs, DDX24 and DDX49.
Stable transformants with increased levels of DDX24 and DDX49 were constructed. BCBL-1 cells were transfected via electroporation with plasmid constructs containing DDK-tagged versions of complete DEAD-box open reading frames (Figure 2). Primers targeting the gene body of DDX24 and DDX49 demonstrated an increase in their expression in stably transfected DDX24-DDK and DDX49-DDK cells, respectively, compared to the controls with empty pCMV6 vector, which contained only endogenous DDX24 and DDX49 (Figure 2A). Despite a relatively high abundance of endogenous DDX24 in BCBL-1 cells (based on normalization to GAPDH mRNA), an increase in expression was still observed after ectopic overexpression. Additionally, primers exclusively targeting the DDK tagged 3′ end of the cDNA confirmed the overexpression of exogenous DDX24-DDK and DDX49-DDK in transduced cells (Figure 2B). An immunoblot utilizing an anti-DDK antibody confirmed protein expression of the transgenic helicases, DDX24-DDK and DDX49-DDK (Figure 2C). The survival assay for DDX24-DDK and DDX49-DDK stably transfected BCBL-1 cells during the induction time course (Figure 2D) demonstrated that population cell viability was unaffected in the stably transduced BCBL-1 cells and overexpression of either DDX24 or DDX49 did not determine KSHV viral production in this experiment.
Next, viral latent, immediate-early, and late lytic gene expression were monitored in a time course experiment for up to 72 h post-induction by RT-qPCR (Figure 3). Primer sequences are listed in Table S3. Upon induction, the transcription of the latency antigen, LANA was significantly decreased in the DDX24-DDK and DDX49-DDK overexpressing (OE) cells (Figure 3A,D). The expression of immediate-early gene RTA, the master regulator of lytic KSHV gene expression, was strongly inhibited throughout the 72 h time course upon DDX24 or DDX49 overexpression (Figure 3B,E). Inhibition of RTA suggests that the effects of these host helicases occur very early during reactivation. Late lytic, K8.1, gene expression also remained inhibited throughout the entire induction time course in the DDX49 overexpressing cells (Figure 3C,F). Additionally, to monitor active replication, qPCR was performed on intracellular viral DNA over a time course of 72 h. By 48 h, both DDX24-DDK and DDX49-DDK overexpressing BCBL-1 cells had about 2/3 the number of viral genome copies of that observed in control transfected cells (Figure 4). Hence, in addition to lytic gene expression, KSHV genome replication was also reduced in these stably transfected cells. This further demonstrates that reactivation was suppressed when DDX24 or DDX49 was overexpressed.
Based on the knockdown and overexpression data, there appears to be a functional role of DDX24 and DDX49 in inhibiting KSHV reactivation. We demonstrated a phenotype and wanted to characterize the biological role of these helicases during latent and lytic KSHV infection. It is well established that RIG-I interacts with RNAs during viral infection, and therefore we asked whether DDX24 and DDX49 also directly interact with viral RNAs [41,42]. DDX24 and DDX49-specific RNA immunoprecipitation (RIP) was used to identify the viral RNAs that bind to these helicases. RNA-IP experiments were performed in TREx BCBL1-Rta cells [39] over expressing DDX24 or DDX29. TREx-BCBL1-Rta cells are BCBL-1 cells that contain the lytic Rta transcriptional regulator of KSHV downstream of a tetracycline inducible promoter. In this system we can achieve a more robust induction. Between 60–80% of the total TREx BCBL1-Rta cell population will undergo reactivation, compared to only about 10–20% in induced BCBL-1 cells. To achieve high specificity and expression, we used Avi/FLAG-tagged DexD/H box helicase expressing lentiviruses. The Avi fusion protein contains a 15 amino acid Avi tag (GLNDIFEAQKIEWHE) at the C-terminal end, which allows highly specific IP without contamination of other highly conserved cellular helicases [43]. Three biological groups of TREx-BCBL1-Rta cells were transduced: DDX24, DDX49 and plus GFP, which was used as expression control. Western blot and qPCR were used to assess the expression of the Avi-tagged helicases in TREx BCBL1-Rta cells. The DDX24-Avi and DDX49-Avi recombinant cells were both able to express the helicase of interest (Figure 5).
To identify potential viral RNAs that are bound by tagged DDX24 and DDX49 through next generation sequencing, TREx BCBL1-Rta cells, either transduced by maxGFP or overexpressing DDX24 or DDX49, were grown and harvested either during latency or at different time-points post induction with doxycycline. To verify that the TREx BCBL1-Rta cells were efficiently induced under these conditions, the expression of lytic viral RTA and ORF59 genes was measured by RT-qPCR (Figure S2a). As evidenced by the fold change of RTA and ORF59 after induction, TREx BCBL1-Rta cells were robustly induced. Cell lysates were immunoprecipitated overnight with α-Avi antibody and the antibody-bound lysate was then incubated with beads bound to the heavy chain. RNA was extracted by TRIzol (Sigma-Aldrich), treated with Dnase (Invitrogen), and was then used to prepare libraries for sequencing. To ensure that immunoprecipitation enriched for the Avi-tagged helicases, Western blot assay was performed to detect α-Avi before and after streptavidin bead selection (Figure S2b). After the concentration of the immunoprecipitated lysate, a single distinct band was visible for DDX49-Avi at the correct size, 54 kDa, indicating that Avi-tagged proteins are selectively immunoprecipitated. The reads obtained from sequencing of avi-tag pulled-down RNA were aligned to the KSHV reference genome which showed high coverage at specific KSHV loci especially for immediately early and early gene transcripts (IE, E) (Figure 6), strongly suggesting that DDX24 and DDX49 inhibit KSHV reactivation from within the nucleus by directly interacting with KSHV mRNAs. Since RIP analysis was conducted under non-crosslinking conditions, together with stringent wash steps, only high energy interactions between the helicase and bound RNAs should be recovered. There were almost no reads from maxGFP control wildtype cells compared to the high number of reads resulting from the DDX24 and DDX49 overexpressing cells, which supports the specificity of the RIP experiment. As expected, we observed much lower reads in the latent samples compared to the lytic samples. The DDX24 and DDX49 helicases were observed to bind to several identical KSHV RNAs during lytic infection with varying degrees (Figure 6). We also observed divergent RNA interactions between DDX24 and DDX49, indicating some degree of binding specificity between these two helicases. As previously mentioned, DexD/H box proteins interact with the RNA backbones in a mostly sequence non-specific manner [21]. The unique recognition and downstream enzymatic functions of each of the helicases in vivo are most likely determined by their protein binding cofactors. Three KSHV transcripts, K9, ORF37/38 and ORF 65/66/67 (Figure 6), were found enriched in the DDX49 pulldown but did not associate with DDX24. There were five regions that had the greatest transcript enrichment for both helicases: K2/ORF70/K4/K4.1/K4.2, K5, K7, K8/K8.1 and the ORF58/59 region (Table 1, Figure 6). All these genomic loci include genes that encode for IE and E proteins and many of which are involved in immune modulation, or reactivation. K2 is an IE gene that encodes for vIL-6, which activates JAK/STAT, Mitogen Activated Protein Kinase (MAPK), and Akt signaling pathways to regulate B-cell proliferation, KSHV reactivation and is crucial for B cell survival during PEL [44]. K5 encodes for a ubiquitin ligase that strongly downregulates MHC class I from the cell surface thereby contributing to the immune evasion [45].
It is well-known that during de novo infection, detection of cytosolic KSHV DNA leads to activation of type I interferon production. However, fewer studies have focused on the PRR-dependent recognition of viral RNAs in the context of reactivation of latent viruses from the host cell nucleus. To investigate whether KSHV transcripts generated within the cells during viral reactivation can also affect host innate immune response, THP-1 cells derived from an acute monocytic leukemia were transfected with plasmids encoding candidate IE and E KSHV genes identified from our RIPseq dataset (Figure 6), and type I interferon (IFN) production was assessed. A synthetic dsRNA, Polyinosine-polycytidylic acid (poly(I:C)), was used as a positive control. Poly(I:C) is a molecular pattern associated with viral infection. Poly(I:C) activates the antiviral pattern recognition receptors TLR3, RIG-I/MDA5 and PKR, thereby inducing signaling via multiple inflammatory pathways, including NF-kB and IRFs. A time course was performed to measure type I IFN produced in response to poly(I:C) and 12 h was chosen as the optimal harvest time to examine IFN response to KSHV transcripts (Figure S3). pcDNA3.1 plasmids containing 12 different KSHV genes (Table S5) whose transcripts were identified as being bound by DDX24 and DDX49, were transfected into THP-1 cells. LANA (not enriched during RIPseq), GFP, and empty vector (pcDNA3.1) were used as negative controls. HEK-blue™ IFNα/β cells were utilized to measure secreted Interferon alpha (IFNα) and Interferon beta (IFNβ). HEK-blue™ IFNα/β cells contain a secreted embryonic alkaline phosphatase (SEAP) reporter gene expressed from a type I interferon-stimulated promoter. Poly(I:C) gave a strong signal, and none of the negative controls (empty vector, GFP or LANA) resulted in significant IFN production (Figure 7A). In contrast, all 12 plasmids containing KSHV sequences (K2, ORF70, K4, K4.1, K4.2, K5, K9, ORF58, ORF59, ORF65, ORF66, and ORF67) were able to stimulate secreted type I interferon protein production at levels above the negative controls (Figure 7A). These results are consistent with specific KSHV transcripts triggering IFNα/β production partially through binding to DDX24 and DDX49. To investigate whether the increase in type I IFN production was accompanied by an increase in the steady-state levels of IFN transcripts, RT-qPCR analysis was performed in transfected THP-1 cells. Since IFN-α has a very short half-life, about 2–3 h [52], results for IFN-β transcripts are shown in Figure 7B. IFN-β mRNA expression was highly increased in response to the positive control poly(I:C). In contrast, no significant induction of IFN-β mRNA was observed in cells transfected with the plasmids encoding KSHV IE and E genes that induced type I IFN protein (Figure 7A). Therefore, these results suggest that KSHV IE and E mRNAs stimulate type I interferon production via a post-transcriptional mechanism upon KSHV reactivation.
The biomedical significance of RLRs has been rising rapidly due to their close relatedness to cancer development and progression. They elicit an interferon-mediated response upon recognition of specific nucleic acids derived from viral pathogens. Our study has revealed another two RLRs, DDX24 and DDX49, that function to recognize viral nucleic acids, in addition to well-studied RIG-I and MDA5. The siRNA screen in Vero cells revealed an antiviral activity for DDX24 and DDX49. Previous studies reported cellular helicases that are required for viral life cycles [53], for example, it has been shown that the host DDX3 protein is required for HIV mRNA Rev-RRE export function [54,55]; while the cell also senses abortive HIV-1 transcription products to induce a type I interferon response [55]. DDX24 predominantly localizes in the nucleus [56] and is known to interact with both the innate immune system and KSHV. The DDX24 promoter region has binding sites for several interferon-regulated transcription factors such as STAT1 and IRF7. Additionally, DDX24 interacts with USP7 in the DEAD-box helicase core region (EGPS sequence) and preferentially impedes recruitment of IRF7 by associating with the adaptor proteins FADD and RIP-1 of the caspase pathway [37,57]. Additionally, microRNAs from several oncogenic herpesviruses have recently been reported to target DDX24. quick Crosslinking and Sequencing of Hybrids (qCLASH) datasets demonstrated that KSHV [58], Murine gammaherpesvirus 68 (MHV68) [59] and EBV microRNAs [60] all target DDX24 and negatively regulate DDX24 expression. DDX49 helicase is localized in the nucleus [61] and is required for regulating RNA transcription, stability, and efficient export of non-spliced poly (A)+ RNAs from the nucleus [61]. Interestingly, the majority of KSHV lytic transcripts are not spliced and require nuclear export facilitated by ORF57 during lytic [62]. Moreover, KSHV also encodes ORF10 which inhibits host cellular spliced mRNA export from the nucleus [63]. This further indicates that unlike most of the other PRRs, DDX49-dependent recognition process occurs within the nucleus, where KSHV reactivation initiates. DDX49 may also play a role in oncogenesis since its up-regulation has been reported for multiple malignancies [61]. Vero cells are IFN deficient, suggesting that DDX24 and DDX49 may be acting via an alternate signaling path to suppress reactivation. Based on current literature, the IRF7 and IRF3 pathways may also be involved in DDX24 signaling [37]. To investigate the effects of DDX24 and DDX49 on viral replication, we generated BCBL-1 cells stably overexpressing DDX24-DDK or DDX49-DDK. Over-expression of DDX24 or DDX49 in BCBL-1 cells inhibited KSHV genome replication and expression of immediate-early genes, suggesting that these helicases act during early stages of reactivation. In our study, DDX24 and DDX49 are found to interact with and recognize IE and E transcripts at the beginning of reactivation. Since both helicases are located in the nucleus, where early lytic intron-less KSHV mRNAs should locate for mRNA export, it would be interesting to test whether there is a synergistic effect of both helicases [62]. Multiple studies have shown that RIG-I like receptors can bind to host RNAs during lytic reactivation in PEL cells. Recent studies report that RIG-I like receptors can also bind to viral RNA to inhibit viral reactivation [14]. Formaldehyde RNA immunoprecipitation (fRIP-Seq) for RIG-I showed that KSHV RNAs including PAN, ORF50, ORF52, ORF57, ORF59, and vIL-6 interact with RIG-I to stimulate host innate immunity. To examine whether KSHV RNAs directly bind to DDX24 and DDX49, we performed RIPseq analysis on Rta-inducible TREx BCBL1 cells overexpressing DDX24 or DDX49, which showed enrichment of IE and E lytic KSHV transcripts after overexpression (Figure 6 and Table 1). These data suggest that DDX24 and DDX49 can interact with KSHV transcripts from within the nucleus to negatively regulate reactivation from latency. Some of the highest enriched transcripts that we have observed are K2, ORF70/K4/K4.1/K4.2, K5, and K9 which encode proteins involved in reactivation and immune modulation (Table 1). Of particular importance, K2 or vIL-6 is required for the survival of KSHV-infected B-cells. The K2 transcript has a short but structured 3′-UTR containing hairpins [9,64,65]. In a ribonomics experiment performed by the Damania lab, RIG-I was also found to bind to K2 KSHV RNA fragments in iSLK.219 cells via HITS-CLIP [17]. Additionally, although ORF58 and ORF59, which were enriched in both DDX24 and DDX49 RIP are not directly involved in reactivation, they are critical for viral replication. Several viral transcripts were exclusively enriched in the DDX49 pulldown, K8.1, K9, and ORF 65/66/67. The enrichment of late lytic transcripts ORF65 and K8.1 may explain the prolonged suppression of KSHV reactivation observed during the reactivation time course. To address whether recognition of KSHV transcripts by DDX24 and DDX49 plays a role in the innate immune response, we tested whether type I interferon is induced in response to KSHV IE and E mRNAs, identified by our RIPseq data, in the absence of viral infection. Using HEK-Blue cells as IFN reporter cell lines, we observed that the protein expression level of type I interferon was induced by IE and E KSHV mRNAs, while mRNA level remains unelevated, demonstrating that KSHV IE and E transcripts can induce innate immune responses, probably via a post-transcriptional regulation mechanism. More recently, we analyzed the RNAseq results from the input of our RIPseq analysis in TREx BCBL1 cells overexpressing either DDX24 or DDX49, compared to GFP-transduced cells. We identified a limited number of Interferon-responding genes whose expression levels were altered upon the overexpression of DDX24 and DDX49, including STAT2 and ADAR1, which can facilitate KSHV lytic reactivation [66]. While Ingenuity pathway analysis identified increased type I interferon response in DDX24 and DDX49 expressing cells, the extent of this induction was moderate. This is likely due to the fact that all other IFN inducing mechanisms are also triggered in induced TREx BCBL1 cells. To decipher the direct impact of DDX24 and DDX49 under these conditions will likely require generation of inducible expression systems, which is beyond the scope of this manuscript. The race between host antiviral innate immunity and viral immune evasion strategies is an ongoing topic. Firstly, elucidating the recognition mechanism on DDX24 and DDX49 could help identify more RLRs sensing additional human viruses in the context of reactivation in the future. Studying RNA structure may reveal whether DDX24 and DDX49 have similar or divergent structures compared to RIG-I recognition motifs (PAMP motif). Moreover, the common feature of the sequence or secondary structure of KSHV transcripts could help develop new therapeutic strategies of KSHV infection and its associated disease. For instance, the inability of RIG-I to recognize unpaired exposed 5′ triphosphate (5′ppp) is used to develop novel therapeutic strategies. Secondly, identifying cellular RNA binding partners of DDX24 and DDX49 from the RNA IP dataset may lead to insight into cellular processes regulated by these helicases. snoRNAs are known to be binding partners of DEAD-box helicases, there may also be additional innate immune regulators and/or lncRNAs directly bound by these helicases in the nucleus. DDX24 competitively binds to FADD and RIP, preventing caspase 8 from activating NF-κB, which is necessary for KSHV reactivation and B cell survival [37]. The same apoptotic pathway is also targeted by multiple KSHV gene products [57] Therefore, cells may induce apoptosis in response to DDX24-dependent immune sensing of KSHV IE and E transcripts. Lastly, since DDX24 is targeted by microRNAs in both latently infected B cells, endothelial and fibroblast cells, it will be important to see whether KSHV-induced innate immune responses during reactivation from latency are different in the presence or absence of DDX24-targeting miRNAs.
In summary, based on our screen of human DEAD box helicases and the RIPseq analysis performed in Rta-inducible TREx BCBL1 cells, we identified two novel PRRs that negatively regulate KSHV replication in the context of reactivation from latency. DDX24 and DDX49 are mostly binding IE and E lytic KSHV RNAs, suggesting that DDX24 and DDX49 can recognize KSHV nucleic acids from within the nucleus, thereby inhibiting lytic reactivation. Understanding the molecular mechanisms by which DDX24 and DDX49 inhibit reactivation could lead to therapeutic strategies for EBV and KSHV malignancies whose pathogenesis is driven by both the latent and lytic phases of infection. | true | true | true |
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PMC9609999 | Ning Zhang,Yuanyuan Peng,Linjing Zhao,Peng He,Jiamin Zhu,Yumin Liu,Xijian Liu,Xiaohui Liu,Guoying Deng,Zhong Zhang,Meiqing Feng | Integrated Analysis of Gut Microbiome and Lipid Metabolism in Mice Infected with Carbapenem-Resistant Enterobacteriaceae | 22-09-2022 | carbapenem-resistant Escherichia coli,lipid disturbance,metagenomics,metabolomics,lipidomics,association analysis,Erysipelotrichaceae family | The disturbance in gut microbiota composition and metabolism has been implicated in the process of pathogenic bacteria infection. However, the characteristics of the microbiota and the metabolic interaction of commensals–host during pathogen invasion remain more than vague. In this study, the potential associations of gut microbes with disturbed lipid metabolism in mice upon carbapenem-resistant Escherichia coli (CRE) infection were explored by the biochemical and multi-omics approaches including metagenomics, metabolomics and lipidomics, and then the key metabolites–reaction–enzyme–gene interaction network was constructed. Results showed that intestinal Erysipelotrichaceae family was strongly associated with the hepatic total cholesterol and HDL-cholesterol, as well as a few sera and fecal metabolites involved in lipid metabolism such as 24, 25-dihydrolanosterol. A high-coverage lipidomic analysis further demonstrated that a total of 529 lipid molecules was significantly enriched and 520 were depleted in the liver of mice infected with CRE. Among them, 35 lipid species showed high correlations (|r| > 0.8 and p < 0.05) with the Erysipelotrichaceae family, including phosphatidylglycerol (42:2), phosphatidylglycerol (42:3), phosphatidylglycerol (38:5), phosphatidylcholine (42:4), ceramide (d17:1/16:0), ceramide (d18:1/16:0) and diacylglycerol (20:2), with correlation coefficients higher than 0.9. In conclusion, the systematic multi-omics study improved the understanding of the complicated connection between the microbiota and the host during pathogen invasion, which thereby is expected to lead to the future discovery and establishment of novel control strategies for CRE infection. | Integrated Analysis of Gut Microbiome and Lipid Metabolism in Mice Infected with Carbapenem-Resistant Enterobacteriaceae
The disturbance in gut microbiota composition and metabolism has been implicated in the process of pathogenic bacteria infection. However, the characteristics of the microbiota and the metabolic interaction of commensals–host during pathogen invasion remain more than vague. In this study, the potential associations of gut microbes with disturbed lipid metabolism in mice upon carbapenem-resistant Escherichia coli (CRE) infection were explored by the biochemical and multi-omics approaches including metagenomics, metabolomics and lipidomics, and then the key metabolites–reaction–enzyme–gene interaction network was constructed. Results showed that intestinal Erysipelotrichaceae family was strongly associated with the hepatic total cholesterol and HDL-cholesterol, as well as a few sera and fecal metabolites involved in lipid metabolism such as 24, 25-dihydrolanosterol. A high-coverage lipidomic analysis further demonstrated that a total of 529 lipid molecules was significantly enriched and 520 were depleted in the liver of mice infected with CRE. Among them, 35 lipid species showed high correlations (|r| > 0.8 and p < 0.05) with the Erysipelotrichaceae family, including phosphatidylglycerol (42:2), phosphatidylglycerol (42:3), phosphatidylglycerol (38:5), phosphatidylcholine (42:4), ceramide (d17:1/16:0), ceramide (d18:1/16:0) and diacylglycerol (20:2), with correlation coefficients higher than 0.9. In conclusion, the systematic multi-omics study improved the understanding of the complicated connection between the microbiota and the host during pathogen invasion, which thereby is expected to lead to the future discovery and establishment of novel control strategies for CRE infection.
Carbapenem-resistant Escherichia coli (CRE) represent a severe public health problem [1,2,3]. Infections caused by CRE, such as complicated urinary tract infections, bloodstream infections and pneumonia, are alarming in the clinical setting which are often associated with high mortality [4,5]. Therefore, studies on the pathological mechanism and treatment strategy against CRE infection are extremely urgent. Gut microbiome, which is involved in the regulation of multiple metabolic pathways of the host [6], could assist in the development of new strategies for infectious diseases [7,8]. One of the major functions of the gut microbiome is to prevent the colonization of pathogens and overgrowth of indigenous pathogens [9]. Dysbiosis of gut microbiota could lead to pathologic immune responses, reducing the integrity and function of the intestinal barrier and accelerating the infection process [10]. There has been increasing evidence demonstrating the remarkable impact of gut microbiome on determining the susceptibility to CRE carrying and eventual infection [4,11,12]. A similar finding has been made in our previous study [13]. The basic view of bacterial pathogenesis during infection is the ability of an invader to overcome innate host defenses and the barrier of the resident microbiota [14]. However, the complex interplay between the pathogen, the microbiota and the host, as well as their metabolic characteristics, remains largely unknown so far. Lipidomics is becoming an increasingly powerful tool for systems biology, which greatly expands the fields of traditional repertoire [15,16,17]. The comprehensive study of lipidome based on mass spectrum analysis has been representatively applied in medical microbiology such as infection diagnoses [18]. The metabolically active gut microbial community has a profound effect on the absorption, digestion, metabolism and excretion of lipids [19]. Previous studies have suggested that the gut microbiome plays a particularly important role in the regulation of host cholesterol and sphingolipid homeostasis [20]. The microbiota-modified triglyceride and phosphatidylcholine species in the liver [21] and the excessive production of short-chain fatty acids by intestinal bacteria contributed to the accumulation of lipids in the liver [22]. To the best of our knowledge, there is no publication to investigate the associations of altered gut microbiome and lipid metabolism that occur during CRE infection. In this work, the biochemical and multi-omics technologies, including metagenomics, metabolomics and lipidomics, were used to decipher a key gut microbe which had high association with the lipid disturbance for CRE invasion, and a few targets potentially responsible were identified for future study.
Methanol, chloroform, acetonitrile, isopropyl alcohol and dichloromethane of HPLC grade were purchased from Thermo-Fisher Scientific (Fair Lawn, NJ, USA). Butylhydroxytoluene (BHT) was purchased from ANPEL Laboratory Technologies (Shanghai) Inc. Ultrapure water was freshly prepared by a Milli-Q reference system (Millipore, Bedford, MA, USA). The clinical isolate of CRE (No. 1864) from human rectal swabs was obtained from the Huashan Hospital, Fudan University, China.
The experimental protocol was approved by the animal ethics committee of the school of pharmacy, Fudan University. Six-week-old female ICR mice (16–18 g) were obtained from SLAC Laboratory Animal Co., Ltd. (Shanghai, China). All mice were fed in a barrier system with temperature (24 ± 2 °C), humidity (60 ± 10%) and 12/12 h light/dark cycle, and provided with certified standard rat chow and tap water ad libitum. After one week of acclimatization, mice were randomly assigned into two groups, including the normal control group (NC, n = 8) and the CRE-infected group (CRE, n = 10). Mice in CRE group received a single intraperitoneal injection of CRE in saline (1 × 108 CFU/mL, 200 μL), while mice in NC group were injected intraperitoneally with an equal amount of saline. After 24 h of infection, including the final 12 h of fasting, mice were sacrificed by cervical dislocation, and the samples of feces, liver and abdominal adipose tissue were collected. All samples were promptly frozen in liquid nitrogen, stored at −80 °C, pending for biochemical, metagenomic, metabolomics and lipidomic analyses.
Hepatic lipids were extracted by the previously published method [23]. Briefly, the liver tissues were homogenized with chloroform/methanol (2/1, v/v) to a final volume 20 times that of the tissue sample and followed by a series of dispersion, agitation and centrifugation steps. The hepatic levels of triglyceride (TG), total cholesterol (TC), high-density lipoprotein cholesterol (HDL-c) and low-density lipoprotein cholesterol (LDL-c) were measured by ELISA kits (Huili Biotech, Changchun, China) and a Chemray 240 fully automatic biochemical analyzer (Rayto, Shenzhen, China).
The feces samples were used for intestinal microbiota analysis using 16S rDNA sequencing, as described in detail in our previous paper [13].
The serum and fecal samples were pretreated, and the derivatives were detected and analyzed by Agilent 7890B gas chromatograph system coupled to a Leco Pegasus time-of-flight mass spectrometer (GC-TOFMS). See the previous report for more details [13].
Lipids in live samples were extracted by the method reported by Bligh and Dyer [24]. Briefly, 300 µL mixture of methanol/acetonitrile/water (2/2/1 by volume) along with 0.1% BHT was added in 50 mg of liver tissue and homogenized for 2 min. Then, vortexed for 30 s and centrifuged at 12,000 rpm, 4 °C for 5 min. The supernatant was transferred to a new tube. The extraction was repeated twice. Then, 560 µL of chloroform were added, vortexed for 30 s and centrifuged at 12,000 rpm, 4 °C for 5 min. The under layer organic phase was transferred to auto-sample vials and concentrated to dryness. The extract was reconstituted using the 100 μL of dichloromethane/isopropyl alcohol/methanol solution (1/1/2 by volume). Quality control (QC) sample was prepared by pooling some of the reconstituted solutions of each sample; then analyzed by the same procedure.
An ACQUITY-ultraperformance liquid chromatography (UPLC) system (Waters Corporation, Milford, CT, USA) was used for the separation on a Waters BEH C18 column (100 × 2.1 mm) with 1.7-micrometer particles at 55 °C. The mobile phase consisted of acetonitrile/water (60:40) with 10 mM ammonium formate and 0.1% formic acid (solvent A), and isopropanol/acetonitrile (90:10) with 10 mM ammonium formate and 0.1% formic acid (solvent B), with a flow rate of 0.4 mL/min. The gradient was 95/5~0/100 in 17 min. The injection volumes were 1 μL for ESI (+) mode and 4 μL for ESI (−) mode, respectively. The mass spectrometric data were collected using a Thermo Scientific Q-Exactive Plus mass spectrometry (QE-MS). ESI was used as the ionization source, and the analysis was carried out in both positive mode and negative mode. The Scan mode was set at DDA mode, 1 full scan followed by 6 MS/MS scans. Collision energy was NEC 15, 30, 45 to fragment the ions. Nitrogen (99.999%) was used as collision-induced dissociation gas. The other conditions for the MS were as follows: capillary temperature of 320 °C, the spray voltage of 3.8 kV in positive mode and 3.0 kV in negative mode, S-Lens RF Level of 50 V and the scan range of 150 to 2000 amu.
Data were processed by XCalibur software (Thermo Fisher Scientific, San Jose, CA, USA) for peak picking, alignment and normalization to produce peak intensities for retention time (RT) and m/z data pairs. Compounds were identified based on accurate mass; fragments in MS/MS using LipidSearch software.
The metabolome and lipidome data were imported into the SIMCA 14.1 software (Umetrics, Umeå, Sweden) for supervised orthogonal partial least squares discriminant analysis (OPLS-DA). The discriminatory metabolites in serum and feces, as well as the lipid species in live, were identified by the multivariate and univariate statistical analyses, with the criteria of VIP > 1 in OPLS-DA, p < 0.05 using a two-tailed paired Student’s t-test and fold change >1.2 or <0.8. False discovery rate (FDR) value was obtained to reduce the risk of a false positive by the adjusted p value using the Benjamini and Hochberg method [25]. All the bar plots in this study were generated with GraphPad Prism (version 9.0, GraphPad Software, San Diego, CA, USA). Heatmap was performed using Euclidean Dist algorithm by TBtools software. Cross-omics association study was performed by Spearman correlation analysis and presented by SPSS 26.0 (IBM Corp., Armonk, NY, USA) and R 4.0.5 software until August 2022.
The lipids data were imported into Cytoscape 3.7.1 (https://cytoscape.org/, accessed on 26 August 2022) to visualize the associations of lipids and their co-regulating characteristics. The metabolites–reaction–enzyme–gene network was constructed by Metscape. The genes associated with the significantly changed lipids, which were considered as the potentially responsible targets for CRE infection, were uploaded to STRING (https://cn.string-db.org/, accessed on 26 August 2022) to construct protein–protein interactive network.
The 24 h infection of CRE had no phenotypic effect with the comparable body weight and white adipose tissue weight relative to controls (Figure 1A). Strikingly, the hepatic levels of TG, TC, HDL-c and LDL-c were all increased in CRE-infected mice compared with the control group (Figure 1B). The increases in TC and LDL-c were statistically significant (p < 0.001 or p < 0.05), which suggested that lipid disturbance had occurred in the early stage of infection of CRE.
The 16S rDNA sequencing technology was employed to investigate the CRE-related alterations of gut microbiome in family and genus levels, which were calculated by the summations of all the OTUs of the corresponding family and genus, respectively. A total of five families and nine genera were significantly different in the CRE group relative to controls through LEfSe analysis, as shown in Figure 2A. Three families including Erysipelotrichaceae, Eubacterium_coprostanoligenes and Clostridium methylpentosum were positively correlated with hepatic lipids. Among them, the Erysipelotrichaceae family had the highest relative abundance. Compared with the controls, the relative abundance of Erysipelotrichaceae in the CRE group increased significantly (Figure 2B). The strong correlation of the intestinal Erysipelotrichaceae family was found with the hepatic levels of TC (r = 0.794, p < 0.01) and HDL-c (r = 0.770, p < 0.01) (Figure 2C).
Untargeted metabolomics analysis showed that a total of 74 metabolites in sera and 129 in feces had a significantly different response to CRE infection (Tables S1 and S2). Among them, the six metabolites in sera and twelve in feces were involved in the lipid metabolism. We further investigated the association of the alteration of the intestinal Erysipeltrichaceae family with these metabolites. As shown in Figure 3, the abundance of intestinal Erysipeltrichaceae family had significantly positive correlation with the serum levels of 24,25-dihydrolanosterol (p < 0.01) and capric acid (p < 0.05), and the fecal levels of 2-monopalmitin (p < 0.01), glycerol 1-phosphate (p < 0.01), 1-monopalmitin (p < 0.01), 24,25-dihydrolanosterol (p < 0.01), heptadecanoic acid (p < 0.05), lanosterol (p < 0.05), linoleic acid (p < 0.05) and palmitic acid (p < 0.05). In particular, the changes in 24,25-dihydrolanosterol in both serum and feces were closely related with intestinal Erysipelotrichaceae family, which is a lipid metabolite produced by commensals–host interaction.
To elucidate the lipid characters of CRE infection, we used the ultraperformance liquid chromatography-Q Exactive plus mass spectrometer (UPLC-QEMS) to detect the hepatic lipidome and analyzed the differences. Data quality was assessed by three QC samples. Correlation analysis showed good reproducibility between QCs, with the correlation coefficient value of more than 0.99 (Figure 4), showing a stable analysis system of lipidomics. Totally, 46 and 39 subclasses of lipids in live were detected in electron spray ionization positive and negative modes, i.e., ESI (+) and ESI (−), respectively, including 24 joint subclasses between them (Figure 5A). More details were shown in Figure 5B and Figure 5C, including 2064 lipid species in ESI (+) and 1154 lipid species in ESI (−) mode. Further, the analyses of the differential subclasses of lipids upon CRE infection were performed. As shown in Figure 6A, twelve subclasses in ESI (+) mode including Cer, PC, PG, PIP, AcCa, BiotinylPE, CarE, Hex1Cer, LBPA, LPG, LPS, PG, PIP and PS increased significantly in the CRE group compared with the controls, while six subclasses of Co, PE, LPI, MePC, PEt and PMe significantly decreased. Meanwhile, seven subclasses in ESI (−) mode including Cer, LPA, LPMe, LPS, PEt, PIP2 and PIP3 significantly increased, while six subclasses including Hex3Cer, MGMG, Hex1Cer, Hex2Cer, MGDG and PAF significantly decreased upon CRE treatment (Figure 6B). To characterize the discriminatory lipid species response to CRE, a multivariable model of OPLS-DA was used. The mice in the CRE-infected group and the control group could be well-distinguished, with the model parameters of R2Y = 0.993 and Q2 = 0.871 in ESI (+) mode and R2Y = 0.981 and Q2 = 0.851 in ESI (−) mode, respectively (Figure 7A,B). Permutation test was used to verify the validity of the model and avoid the over-fitting. Figure 7C,D showed that the two OPLS-DA models were robust. Further univariate statistical assessments were performed by Student’s t-test and the calculation of the fold changes. Volcano plots were used to highlight the differentially expressed lipid species between groups (Figure 7E,F). A total of 386 differential lipids were found in the negative ion mode, of which 193 lipids were significantly enriched in the CRE group and 193 lipids were significantly depleted compared with the controls. A total of 664 differential lipids were found in the positive ion mode, of which 336 lipids were significantly enriched and 328 lipids depleted in the CRE group (Figure 8).
The Spearman correlation analyses were performed to further identify the potential associations between the differential lipid species and lipid profiles of TC, TG, HDL-c and LDL-c in the liver. Results showed that a huge panel of lipid species were strongly correlated with the hepatic lipid profile, particularly TC and HDL-c. Exactly, in the positive ion mode of UPLC-QEMS, a total of 278 lipid species were positively and 208 were negatively correlated with TC, respectively. A total of 293 and 243 lipid species had positive and negative correlations with HDL-c. A total of 39 and 62 lipids were positively and negatively correlated with LDL-c. A total of six lipids were positively correlated with TG. Meanwhile, the levels of 203, 20, 212 and 54 lipid species detected in the negative ion mode were significantly correlated with the TC, TG, HDL-c and LDL-c, respectively (Table S3). For the lipid species which had strong correlations with TC, TG, HDL-c and LDL-c, their associations with intestinal Erysipelotrichaceae family were analyzed. As shown in Table 1, the Erysipelotrichaceae family showed high correlation (|r| > 0.8 and p < 0.05) with 35 lipid species. Particularly, the levels of phospholipids including phosphatidylglycerol (42:2), phosphatidylglycerol (42:3), phosphatidylglycerol (38:5) and phosphatidylcholine (42:4), the sphingolipids of ceramide (d17:1/16:0) and ceramide (d18:1/16:0) and the glycolipids of diacylglycerol (20:2) had higher correlation coefficients of 0.90~0.97 with intestinal abundance of the Erysipelotrichaceae family.
The lipid species which had strong correlations with the Erysipelotrichaceae family and lipid profile were mainly involved in four important classes of ceramide, phosphatidylglycerol, phosphatidylcholine and diacylglycerol. These lipids were adopted to construct the lipids–reaction–enzyme–gene interaction network, as shown in Figure 9A. Although it is challenging to understand how lipid composition is translated into function, totally, 58 targets were predicted to potentially associate with the abnormal lipid metabolism in mice infected with CRE. The protein–protein interaction was further analyzed, and the targets of PPAP2C, CHPT1, PPAP2B, PLD2 and PLD1 with higher degrees in the PPI network could be the key targets for preventing and treating the lipid metabolism disorder induced by CRE infection (Figure 9B).
Systematic integration of multiple layers of omics datasets has been indicated to obtain more reliable results and reduce the false-positive risk [26,27], which can provide a basis for generating testable hypotheses and gaining mechanistic insights into the pathophysiology of multiple complex diseases in post-integration analyses [28,29]. In this study, the multi-omics technologies, including metagenomics, metabolomics and lipidomics, comprehensively characterized the alterations of gut microbiome and lipid metabolism occurring during the early infection of CRE in mice. Further integrated analyses revealed that the intestinal Erysipelotrichaceae family had a strong association with hepatic levels of total cholesterol, HDL-cholesterol and a large panel of lipid metabolites in mice with or without CRE exposure, which suggested the potential collusion effect of Erysipelotrichaceae during CRE infection. Lastly, a few targets were identified as potentially responsible for lipometabolic disturbances induced by CRE through network analysis. Carbapenem resistance is more easily transferred horizontally and, therefore, spreads faster worldwide. The main mechanism for carbapenem antibiotics resistance in Enterobacteriaceae is the production of carbapenemase, a diverse family of β-lactamases [30] which worked by binding to the drug, breaking the amide bond of a four-membered azetidinone ring and preventing it from binding to the penicillin-binding protein of the bacterial cell wall [31]. Anyway, Enterobacteriaceae have alternative mechanisms for carbapenem resistance, including the production of other β-lactamases, porin loss and efflux pump overexpression [32], which block the penetration of the antibiotic within the bacterial cell. Cefiderocol, a recently emerging antibiotic with a unique chemical structure [33], exhibits excellent in vitro activity against many clinically relevant Gram-negative pathogens, including multidrug-resistant strains [34]. There is increasing evidence that cefoxiridol is well-suited to help address the growing number of infections caused by carbapenem-resistant and multidrug-resistant Gram-negative bacilli, including broad-spectrum β-lactamases and carbapenemase-producing strains [35]. Diverse roles of the gut microbiota in human health and disease have been recognized [36,37]. The 16S ribosomal RNA gene (16S) sequencing is a culture-free technique to identify the composition of intestinal microbial communities [38], aiming to look for correlations between the microbiota and disease or phenotype, to promote its application in exploring the microbial diversity of functional and pathogenic microorganisms and their interactions in biotechnology processes [39]. These culture-independent and reference-free approaches have proved to be successful strategies for species discovery and characterization [40,41]. Lots of studies have addressed the effect of antibiotic administration on the intestinal microbiota using sequencing technologies [42], revealing the ecological disturbances in the microbiota after antibiotic administration, especially for specific members of the bacterial community that are susceptible or resistant to antibiotics [43]. This post-antibiotic dysbiosis is usually characterized by a loss of diversity, a loss of certain important taxa, shifts in metabolic capacity and reduced colonization resistance against invading pathogens [44]. Infection altered the composition and diversity of gut microbiome, resulting in gut dysbiosis [45]. The family Erysipelotrichaceae has been reportedly linked to the host’s immune [46], which was also identified as a harmful bacterium due to the proinflammatory effect, and associated with elevated serum cholesterol levels [47]. Consistently, we found that the intestinal Erysipelotrichaceae family was strikingly increased upon CRE infection compared with controls and positively correlated with hepatic TC levels. The integrated analysis based on gut and metabolomics further showed that intestinal abundance of Erysipelotrichaceae and serum level of 24,25-dihydrolanosterol had a significantly positive association. The 24,25-dihydrolanosterol is an important cholesterol intermediate and is involved in the biosynthesis of steroids [48,49]. HDL-c mediates reverse cholesterol transport. In this study, hepatic levels of HDL-c unexpectedly increased in mice infected with CRE. Previous studies indicated that HDL-c has potent anti-inflammatory properties that may be critical for protection against infection [50]. The molecular mechanisms of how HDL-c can modulate inflammation is an interesting issue to be explored further. Bacterial pathogens can recruit and use the lipids of a host and can hijack host lipid metabolism that facilitates the persistence of pathogens in the host [51]. For example, the survival of Chlamydia requires lipids from host cells and the absorption of sphingolipids and cholesterol from the host cells [52]. M. tuberculosis can alter the host lipid metabolism to create an environment that allows these intracellular pathogens to survive [53]. The invasion of exogenous pathogens can cause changes in enzymes and lipids that affect specific reactions [54]. Pseudomonas aeruginosa increased the enzymatic activity of the acid sphingomyelinase of macrophages, causing ceramide binding on the raft to activate the organism’s defenses [55]. Viral infection could induce the changes in the expression of cholesterol metabolic enzymes and metabolites in host cells, and the cholesterol metabolism regulated the antiviral response of host cells [56]. The liver, as the central organ in whole-body metabolism such as lipids, is the major source of fatty acid synthesis, as well as the lipoproteins released into the blood [57,58,59]. Our high-coverage lipidomic analysis showed that a huge panel of lipid species were significantly differential upon CRE infection. Further integrated analysis identified several lipid subclasses, belonging to sphingolipids, phospholipids and glycerolipids, that were significantly correlated with hepatic TC, HDL-c and the intestinal Erysipelotrichaceae family. Previous studies have shown that sphingolipid metabolism played a key role in the regulation of inflammatory signaling pathways [60], including bacterial pathogen infection, B cell activation and release of cytokines during infection [61]. Sphingolipids could also affect inflammation-related diseases by inhibiting intestinal lipid absorption [62,63], altering the intestinal microflora [64] and activating anti-inflammatory nuclear receptors [65,66]. Mammalian cell membranes primarily consist of phosphatidylcholine and cholesterol, while bacterial cell membranes are rich in amphoteric phosphatidylethanolamine, anionic phosphatidylglycerol and polyanionic cardiolipin. Pathogens can adapt to their biological sites by changing the composition of the membrane in order to evade the immune mechanisms of the antimicrobial substances and the host [67,68,69]. Triglycerides are located in adipocyte lipid droplets with vesicular transport and cell signal transduction functions, and it is the key to maintaining lipid balance [70]. The identification of the potential targets in this study could lead to a deeper understanding of the lipometabolic disturbance occurring during CRE infection. Phospholipid phosphatase of PPAP2C and PPAP2B participated in the ceramide metabolic process [71]. Cholinephosphotransferase families of CHPT1 and phosphatidylethanolamine N-methyltransferase (PEMT) were found to participate in the phosphatidylcholine biosynthetic process [72]. Phospholipase families of PLD2 and PLD1 were determined as targets for dyslipidemia [73] and can activate MAPK [74]. Cytosolic phospholipase families of PLA2G6, PLA2G1B, PLA2G2F and PLA2G4A played a major role in the remodeling of membrane lipids and the biosynthesis of lipid mediators of the inflammatory response [75]. Sphingomyelin phosphodiesterase families, such as SMPD2 and SMPD4, were associated with the internalization of pathogens, intracellular activation of signaling pathways, induction of apoptosis in infected cells and release of cytokines [76]. Several important metabolic pathways, including arachidonic acid metabolism, glycerophospholipid metabolism, glycosphingolipid metabolism and linoleate metabolism, were found through our metabolites–reaction–enzyme–gene network analysis. Previous studies have shown that the metabolic pathways of linoleic acid and arachidonic acid were up-regulated in the Mycoplasma gallisepticum and Escherichia coli co-infection model [77]. This study had some strengths and weaknesses. To the best of our knowledge, we firstly reported the potential association of the intestinal Erysipelotrichaceae family with hepatic lipid metabolism upon CRE infection. The integration of multi-omics analyses provided a novel insight to reveal the molecular characteristics of CRE infection. However, the mechanisms in which CRE infection affects commensal microbiota and their interplay within the host’s lipid metabolism need to be further studied. Second, adipose tissue plays a central role in systemic metabolic homeostasis. The adipose tissue morphology and the expressions or activities of the vital proteins, such as lipases, were not analyzed in this study, and the weights of brown adipose tissue were not measured. A recent study explored the response of adipocytes to bacterial infection and found that the expression of genes involved in fat metabolism decreased after infection, and the genes related to immune function and cytokine receptor genes were up-regulated, which indicates that the function of adipocytes during infection has changed significantly from lipid metabolism to host defense [78]. Therefore, the metabolomic and lipidomic analyses of adipose tissue upon CRE infection demand exploration in the future.
This pilot study provided a novel insight into CRE infection by a system biology strategy. Hepatic lipid accumulation and the systemic disturbance of gut microbiota were revealed during the early infection of CRE. Metabolomics and lipidomics comprehensively characterized the alterations of circulating metabolites related to lipids metabolism and hepatic lipids compositions response to CRE exposure. The increased intestinal colonization of the Erysipelotrichaceae family was strongly associated with the alterations of TC, HDL-c and a panel of lipid species, particularly those belonging to ceramide, phosphatidylglycerol, phosphatidylcholine and diacylglycerol. The integrated multi-omics study highlighted the interplay of commensals and pathogens for host’s lipid metabolism, which may lead to new therapeutic approaches against infectious diseases in the future. Further studies are needed to explain how host–microbiota–pathogen interactions favorably or negatively influence host survival during CRE infection. | true | true | true |
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PMC9610228 | Fei Zha,Rui Pang,Shixuan Huang,Jumei Zhang,Juan Wang,Moutong Chen,Liang Xue,Qinghua Ye,Shi Wu,Meiyan Yang,Qihui Gu,Yu Ding,Hao Zhang,Qingping Wu | Gene Regulatory Network of the Noncoding RNA Qrr5 Involved in the Cytotoxicity of Vibrio parahaemolyticus during Infection | 21-10-2022 | Qrr5,cytotoxicity,transcriptome,weighted co-expression network analysis,Vibrio parahaemolyticus,virulence factor | Small non-coding RNAs (sRNAs) in bacteria are important regulatory molecules for controlling virulence. In Vibrio spp., Qrr sRNAs are critical for quorum-sensing pathways and regulating the release of some virulence factors. However, the detailed role of Qrr sRNAs in the virulence of Vibrio parahaemolyticus remains poorly understood. In this study, we identified a Vibrio sRNA Qrr5 that positively regulates cytotoxicity and adherence in Caco-2 cells by primarily regulating the T3SS1 gene cluster. A number of 185, 586, 355, and 74 differentially expressed genes (DEGs) detected at 0, 2, 4, and 6 h post-infection, respectively, were mainly associated with ABC transporters and two-component system pathways. The DEGs exhibited a dynamic change in expression at various time points post-infection owing to the deletion of Qrr5. Accordingly, 17 related genes were identified in the co-expression network, and their interaction with Qrr5 was determined based on weighted co-expression network analysis during infection. Taken together, our results provide a comprehensive transcriptome profile of V. parahaemolyticus during infection in Caco-2 cells. | Gene Regulatory Network of the Noncoding RNA Qrr5 Involved in the Cytotoxicity of Vibrio parahaemolyticus during Infection
Small non-coding RNAs (sRNAs) in bacteria are important regulatory molecules for controlling virulence. In Vibrio spp., Qrr sRNAs are critical for quorum-sensing pathways and regulating the release of some virulence factors. However, the detailed role of Qrr sRNAs in the virulence of Vibrio parahaemolyticus remains poorly understood. In this study, we identified a Vibrio sRNA Qrr5 that positively regulates cytotoxicity and adherence in Caco-2 cells by primarily regulating the T3SS1 gene cluster. A number of 185, 586, 355, and 74 differentially expressed genes (DEGs) detected at 0, 2, 4, and 6 h post-infection, respectively, were mainly associated with ABC transporters and two-component system pathways. The DEGs exhibited a dynamic change in expression at various time points post-infection owing to the deletion of Qrr5. Accordingly, 17 related genes were identified in the co-expression network, and their interaction with Qrr5 was determined based on weighted co-expression network analysis during infection. Taken together, our results provide a comprehensive transcriptome profile of V. parahaemolyticus during infection in Caco-2 cells.
Small non-coding RNAs (sRNAs) serve as indispensable regulators of many bacterial signaling pathway cascades and pathogenesis [1,2,3]. A novel sRNA, EsrF, can sense high ammonium concentrations in the colon and facilitate bacterial motility and adhesion to the host cell, thereby promoting the pathogenicity of Escherichia coli O157 [4]. Qrr sRNAs are Hfq-dependent trans-encoded sRNAs involved in the regulation of quorum sensing in Vibrio spp. [5,6,7], which positively regulate the expression of AphA, a low-cell-density core regulator that suppresses the expression of LuxR, a high-cell-density core regulator [8,9,10]. In Vibrio harveyi, the Qrr3 sRNA suppresses LuxR through catalytic degradation, LuxM through degradation, and LuxO through a blockade, as well as activating AphA by exposing the ribosome-combining site, whereas the sRNA itself is decomposed [11,12]. However, five RNAs (Qrr1–5) redundantly inhibited the expression of the master regulator SmcR and modulated the expression of virulence factors in cells lacking iron at low cell density and in Vibrio vulnificus at high cell density [13,14]. Fis is a regulator that negatively regulates the expression of the quorum-sensing master regulator OpaR and inhibits capsule polysaccharide (CPS) and biofilm formation, implying that Fis plays a direct role in the quorum sensing, biofilm formation, and metabolism of V. parahaemolyticus. Interestingly, when Fis binds directly to the regulatory regions of Qrr genes, it acts as a positive regulator [15]. Vibrio parahaemolyticus is a gram-negative bacterium generally isolated from estuarine and marine sources, and its infection leads to gastroenteritis related to seafood consumption in humans worldwide [16,17]. Nevertheless, the pathogenesis of V. parahaemolyticus infection has not been completely and explicitly elucidated. The major virulence factors that have been recently characterized include adhesins, flagella, hemolysin, and two type III secretion systems (T3SSs), namely T3SS1 and T3SS2 [18,19]. Every T3SS produces a distinct group of effectors that contributes to virulence by interacting with various host targets and performing various functions. T3SS1 is responsible for cytotoxicity, whereas T3SS2 is primarily responsible for enterotoxicity [20,21,22,23]. Five Qrrs have been identified in V. parahaemolyticus, but only the functions of Qrr2–Qrr4 have been elucidated. They activate the target gene AphA at a low density and promote the synthesis of the low-density regulator AphA. They can also act on the target gene OpaR and inhibit the production of the high-density regulator OpaR [24,25]. However, the regulation of toxicity by Qrrs in V. parahaemolyticus has not been directly proven. A previous study reported that AphA can positively regulate the expression of T3SS1 to affect virulence, whereas OpaR negatively regulates the expression of T3SS1 [24,26]. T3SS is an injectable needle-like structure consisting of multiple distinct structural proteins that secrete virulence effectors into host cells to cause cellular damage, and T3SS1 mainly causes a cytotoxic effect on the host [17,25,27]. Therefore, it was speculated that the Qrrs of V. parahaemolyticus might regulate the expression of T3SS1 through the quorum sensing pathway to regulate the pathogenicity of the bacteria in the host. Subsequently, in a transcriptome analysis of V. parahaemolyticus-infected human HeLa cells, it was found that the main effectors of T3SS1 were significantly up-regulated at all time points post-infection (2, 3, 4, 6, and 8 h). However, the expression level of AphA did not change significantly upon infection. Similarly, the expression of OpaR did not change significantly during the early period of infection, and a down-regulated expression trend was observed only during the later period of infection [28,29]. Most studies have focused on the regulation of Qrrs associated with quorum-sensing genes [30,31,32]. These studies indicate that Qrrs of V. parahaemolyticus play a vital role in the regulation of virulence genes. However, the role of Qrrs in the virulence of V. parahaemolyticus remains unclear. In this study, we detected the relative mRNA expression levels of Qrrs during the infection of Caco-2 cells with V. parahaemolyticus. One of the Qrrs, Qrr5, showed significant changes in expression during the infection. The Qrr5 mutant strain was utilized to infect Caco-2 cells, and transcriptome analyses were performed during infection. We aimed to unveil the mechanism of Qrr sRNAs in regulating virulence factors in V. parahaemolyticus. The findings of this study are expected to promote the development of effective methods to alleviate the toxicity of this pathogen.
Vibrio parahaemolyticus RIMD2210633 and derivatives were cultured in 3% NaCl (w/v) alkaline peptone water or on chromogenic Vibrio agar (Huankai, Guangzhou, China) at 37 °C. Escherichia coli strain SM17-λ-pir (Stored in our laboratory, Guangzhou, China) was used for plasmid transformation and conjugation. The concentrations of antibiotics: ampicillin (100 μg/mL); chloramphenicol (34 μg/mL) for E. coli; chloramphenicol (5 μg/mL) for V. parahaemolyticus.
The mutant V. parahaemolyticus strain was constructed by deleting the Qrr5 gene, as previously described using SacB-based allelic exchanges with plasmid pDS132 [33]. In brief, for the construction of ΔQrr5, primer sets of Qrr5-U-F/Qrr5-U-R and Qrr5-D-F/Qrr5-D-R were used to amplify the upstream and downstream sections of Qrr5. A 20-bp overlap was added to each PCR fragment amplified by Qrr5-D-R/Qrr5-U-F, allowing the second fragment of approximately 1200-bp, including a 600-bp fragment upstream and about a 600-bp fragment downstream of Qrr5, using the primers Qrr5-U-F and Qrr5-D-R, respectively. The fused fragment was ligated into SphI- and SacI-digested plasmid pDS132 using the In-Fusion HD Cloning kit (Takara, Beijing, China) according to the protocol. The constructed plasmid was transformed into V. parahaemolyticus cells by E. coli S17-λ-pir. Double cross-over mutants were selected on 10% sucrose LB agar plates. The deletion mutants were verified by PCR and sequencing. The complementation of mutants was constructed using plasmid pBAD33, and the Qrr5 fragment with its promoter was ligated into SacI and XbaI-digested pBAD33. Next, pBAD33-Qrr5 was transformed into the mutant strain using E. coli S17-λ-pir. Primers used for plasmid and mutant construction are listed in Table 1 and Table S1.
The secondary structure of RNA was predicted with machine learning using mFold software with machine learning from the UNAFold Web at http://www.unafold.org/ (accessed on 12 October 2021) [34].
To measure growth curves, all strains were inoculated into a 96-well microplate (OD600 = 0.05). Growth curves were obtained by determining the absorbance of each well at 600 nm every 30 min for 24 h at 37 °C utilizing a microplate spectrophotometer (EPOCH2, Bio Tek, Winooski, Vermont, USA), and data were recorded utilizing Gen 5 (EPOCH2).
Caco-2 cells were infected with V. parahaemolyticus as previously described [35]. Briefly, Caco-2 cells at approximately 1 × 105 cells/well were incubated in Dulbecco’s modified Eagle medium (DMEM) (Gibco, ThermoFisher Scientific, Shanghai, China) containing 10% (v/v) fetal cow-like serum and 4.5 g/L D-glucose in 12-well culture dishes (Nunc, Roskilde, Denmark) at 37 °C, under anaerobic conditions with 5% CO2. The cytotoxicity of the wild-type, mutant, and complement strains was detected by estimating the activity of cytoplasmic lactate dehydrogenase (LDH) leaking from Caco-2 cells. Caco-2 cells were infected with wild-type, mutant, and complement strains (1 × 107 CFU/mL) under anaerobic conditions for 2, 4, and 6 h (multiplicity of infection, MOI = 100), and an LDH assay was performed to quantify cytotoxicity, in accordance with the manufacturer’s instructions (Solarbio, Beijing, China). Trypsin-digested Caco-2 cells (105 cells/well) were mixed thoroughly with DMEM- resuspended bacterial solution (107 CFU/mL) at a volume ratio of 1:1 and then co-cultured under anaerobic conditions with 5% CO2 for 1 h. Next, the cells were collected via centrifugation at 1000× g for 10 min and washed twice with phosphate-buffered saline (PBS) (10 mM, pH 7.4) to remove nonadherent bacteria. Then, 500 μL of 0.01% Triton X-100 (Solarbio, Beijing, China) was added to lyse the cells, and finally the cell lysate was gradient-diluted with PBS and counted on plates to obtain the concentration of adherent bacteria. Adherence (%) = (CFUadherence/CFUbefore) × 100%.
Biofilms were established utilizing the method developed by O’Toole and Kolter [36] with minor modifications. In brief, each well of a 96-well polystyrene microliter plate was inoculated with 200 μL of each culture diluted to OD600 = 0.2 in tryptic soy broth supplemented with 3% NaCl. The cells were then incubated at 37 °C without shaking for 48 h. After planktonic cells had been eliminated, biofilms connected to the wall were washed gently with PBS (10 mM, pH 7.4), stained with 230 μL of 0.1% (w/v) crystal violet, and kept in the dark for 20 min. Next, the floating color was eliminated, and the amount of biofilm was evaluated by eluting the crystal violet with 230 μL acetic acid (33%) and estimating the absorbance at 590 nm (OD590).
Caco-2 cells were infected with V. parahaemolyticus under anaerobic conditions for 2, 4, and 6 h (MOI = 100). The culture broth was then centrifuged at 1000× g for 10 min to remove Caco-2 cells, resulting in a supernatant containing V. parahaemolyticus. Next, the Caco-2 cells were washed 4–5 times with PBS by vigorous shaking to wash off the adherent bacteria, which were then collected, and mixed with the bacteria-containing supernatant in a new tube. The bacterial mixture was then centrifuged at 5800× g for 15 min to obtain the total bacterial pellets. The pellets were washed three times with PBS gently (10 mM, pH 7.4) and all experiments were conducted with three biological replicates. Total RNA was extracted using RNAiso Plus (Taraka, Beijing, China), with DNase I treatment for 30 min, followed by purification with ethanol. A RiboCop rRNA depletion kit (Lexogen, Vienna, Austria) was used to remove rRNA from the samples. Successful removal of rRNA was confirmed using an Agilent 2100 bioanalyzer. Fragmented mRNA was used as a template to synthesize the first strand of cDNA, and RNase H was used to degrade the RNA strand for preparation of the second strand of cDNA. The purified cDNA was repaired and connected to the sequencing adapter, and the library was completed after purification. AMPure XP reads were screened at approximately 370–420 bp cDNA.
Read quality was first examined using FastQC [37]. The original read data from RNA-Seq libraries were then mapped to the V. parahaemolyticus RIMD2210633 genome, using Bowtie2 with standard parameterization. The htseq-count tool was used to quantify the reads aligned to the identified genes using gene annotations by Rockhopper. The calculated uniquely mapped read counts were fed into DESeq2 (version 1.14.1) for the quantitation of significant gene expression with standard parameterization. The differentially expressed genes (DEGs) identified by DESeq2 were filtered using a moderate absolute |log2(Foldchange)| >0 and p-value < 0.05. The read counts were transformed into FPKM to evaluate the gene expression levels. The Kyoto Encyclopedia of Genes and Genomes (KEGG) database was used to annotate the lists of significantly expressed genes with pathway details. The Gene ontology (GO) database predicted the GO functions of the genes in V. parahaemolyticus. Heat maps and boxplots were constructed using the R basic package [38].
Temporal analysis was performed using Short Time-series Expression Miner (STEM) software according to the protocol at http://www.cs.cmu.edu/~jernst/stem/ (accessed on 20 December 2021) [39].
A weighted relationship network was developed (with WGCNA) using R package ver.4.1 [40]. After mapping the transcriptome data to the reference transcripts using Kallisto software ver.0.45.0 [41], the transcripts per million (TPM) values of each gene in each sample were calculated. Gene expression analysis was performed for the wild-type and ΔQrr5 groups, and the trend module of the distribution of the associated gene clusters in different groups was established. Given that most related genes should be in the same module, the intersection of the two groups was considered to indicate the related genes. The module to which Qrr5 belongs should be confirmed, and an intersection analysis between the genes of this module and previous genes was conducted. A co-expression network of potential regulatory genes was established considering a co-expression weight greater than 0.5.
qRT-PCR was performed using a two-step qPCR system (LightCycler 96, Roche, Basel, Switzerland) to determine the gene expression level. The extracted RNA was used for reverse transcription to obtain cDNA according to the protocol (Prime Script RT reagent Kit, Takara). TB Green Premix Ex Taq II (Takara) was utilized to carry out the qRT-PCR reaction. The 2−ΔΔCt method was used to assess the fold change in the target genes compared to the housekeeping gene (gyrB). Each qRT-PCR was performed at least three times. Dataset S1 includes the primers used for qRT-PCR.
A one-way ANOVA was used for comparisons among multiple groups. p-value < 0.01 (**); p-value < 0.05 (*) was considered statistically significant and p-value > 0.05 was non-significant (ns). GraphPad Prism ver.8.0 was used for statistical analysis.
It has been found that Qrr1-5 negatively regulate the high cell density quorum-sensing regulator OpaR, which is required for biofilm formation and acts as a repressor of swarming motility [15,33]. However, we found that the cytotoxicity of Caco-2 cells increased with increasing post-infection time, and Qrr5 was significantly upregulated at different post-infection time points compared to Qrr1–4 (Figure 1a,b). A previous study found that only Qrr5 was significantly upregulated when V. parahaemolyticus infested the host, while the expression of Qrr1–Qrr4 showed no change at all time points post-infection, which is completely consistent with our findings [42]. In the functional analysis of Qrr5, we found that Qrr5 was located in chromosome 1 from 1,728,021 to 1,728,129 in V. parahaemolyticus RIMD2210633 (Figure S1). The secondary structure of Qrr5 was portrayed according to its sequence, and the dG was −31.1 (Figure S1).
To examine the function of Qrr5 in V. parahaemolyticus, Qrr5 was deleted from the V. parahaemolyticus RIMD2210633 genome, and the mutants ΔQrr5 and ΔQrr5::Qrr5 were obtained and verified (Figure S2). In order to compare the difference in bacterial growth, we measured the OD600 after culture for 16 h and found no significant difference between the mutant and wild-type strain (Figure 2a). The biofilm formation of ΔQrr5 was significantly decreased at 48 h compared to that of the wild-type strain (Figure 2b). The adhesion rate of mutant ΔQrr5 to Caco-2 cells was also significantly lower than that of the wild-type (Figure 2c). Moreover, the cytotoxicity of Caco-2 cells infected by mutant ΔQrr5 was significantly attenuated compared to that of the wild-type and ΔQrr5::Qrr5 strains at different time points post-infection (Figure 2d). These data demonstrate that Qrr5 is required for biofilm formation, cytotoxicity, and adhesion to Caco-2 cells in V. parahaemolyticus RIMD2210633.
To identify alterations in gene expression of V. parahaemolyticus during infection, which could be triggered by the precise regulation of virulence by Qrr5, we set various time points after infection to Caco-2 cells. Using the Deseq2 program, differentially expressed genes between wildtype and Qrr5 mutant strains (log2FC > 1; log2FC < −1 and p-value < 0.05) were identified during various periods (Figures S3 and S4). A core set of 540 common transcripts was found at various time points, and 185, 586, 355, and 74 transcripts were mainly altered at 0, 2, 4, and 6 h, respectively, in mutant ΔQrr5 compared to those in the wildtype strain (Figure 3a). The numbers of downregulated and upregulated genes among all DEGs between the ΔQrr5 and wild-type strains at various time points post-infection. (Figure 3b).
Bacterial pathogens usually use various strategies to attack mammalian cells, preventing the immune system from responding and causing damage to infected tissue sites [43,44]. Pathogens frequently employ a secretion system to release virulence factors intracellularly into the host cells or extracellularly to interfere with host cell functions and promote bacterial infection [45,46]. Vibrio parahaemolyticus harbors several secretion systems, including T2SS, two T3SSs (T3SS1 and T3SS2), and two T6SSs. All T3SSs are associated with the toxins in mammalian hosts [16]. Reports on the function of T6SSs are limited, but it has been speculated that T6SS2 contributes to bacterial virulence [47,48]. sRNAs are post-transcriptional regulatory molecules that play an indispensable role in adjusting vital processes in bacterial physiology toward host invasion [49,50]. Analysis of the transcripts of V. parahaemolyticus revealed that the deletion of Qrr5 significantly affected the expression of the T3SS1 gene cluster during the infection period, especially at 6 h post-infection (Figure 4). The gene clusters of other secretion systems, such as T3SS2, T6SS, and T2SS, were slightly upregulated during the infection period (Figure 4).
To obtain an outline of the dynamic variations of DEGs during the process of infection with V. parahaemolyticus, we first performed a trend analysis of all genes in the wild-type and Qrr5 knockout strains separately using STEM to determine the expression trend of T3SS1 genes in wild-type. The results revealed that most T3SS1 genes in the wild-type group were distributed in profiles 14 and 15, whereas most T3SS1 genes in the ΔQrr5 group were distributed in profile 10 (Figure 5a,b). Based on the principle that the genes related to T3SS1 regulation should also be classified into the same profile, the genes in profiles 14 and 15 of the wild-type group were intersected with the genes in profile 10 of the ΔQrr5 group, and 138 genes were obtained. The KEGG pathway enrichment results of all genes in profile 14 (the profile where Qrr5 is located) of the wild-type group were mostly concentrated on ABC transporters and two-component systems (Figure 5c). All genes in profile 10 (the profile where most T3SS1 genes are located) of the ΔQrr5 group mainly focused on the biosynthesis of secondary metabolism (Figure 5d). Subsequently, the expression level of Qrr5 was used as a trait for WGCNA. All genes whose expression met the threshold were divided into 11 gene expression modules (Figure 6a). Qrr5 was located in the turquoise module, which was also the gene module most significantly correlated with Qrr5 expression (Figure 6b). The genes in the turquoise module were intersected with the above 138 genes, and a total of 61 intersecting genes with a potential co-expression relationship with Qrr5 were identified, consistent with the expression trend of the T3SS1 genes. KEGG pathway enrichment analysis of the turquoise module, where Qrr5 is located, showed that ABC transporters and two-component systems were the most related pathways (Figure 6c). Considering a co-expression weight greater than 0.5 as a potential direct regulatory relationship, a direct regulatory gene network of Qrr5 was constructed (Figure 6d). The functions of the co-expression network genes are listed in Table S2. The results showed that VP2914, VP0485, VP2492, and VP1665 exhibited strong interactions with Qrr5, which encodes D-2-hydroxyacid dehydrogenase, TIGR01212 family radical SAM protein, ammonium transporter, and T3SS1 chaperone SycN, respectively. A novel sRNA, EsrF, was found to respond to ammonium transporters to sense high ammonium concentrations in the colon and promote E. coli O157:H7 pathogenicity by enhancing bacterial motility and adhesion to host cells. The coexistence of ammonium transporter and channel mechanisms contributed to the virulence of pathogenic fungi [4,51]. Moreover, the T3SS1 chaperone SycN/YscB interacts with the secreted protein YopN via an N-terminal chaperone-binding domain to subvert the defenses of mammalian hosts in Yersinia pestis, and it was found that they interact with both EsaB and EsaM within the bacterial cells, contributing to the pathogenesis of Edwardsiella tarda [52,53,54]. Our results demonstrate that Qrr5 is an important sRNA in the regulation and the pathogenesis of V. parahaemolyticus. Analysis of the expression levels of Qrrs in this transcriptome revealed that Qrr5 was significantly upregulated at all time points post-infection, whereas Qrr1–4 expression showed no significant changes throughout, which was completely consistent with a previous finding [42]. In summary, Qrr5 regulates cytotoxicity mainly by regulating the T3SS1 gene, as well as participating in the ABC transporters and two-component system pathways during infection. The expression of genes involved in the gene regulatory network of Qrr5 was further validated by qRT-PCR. The results showed that the expression patterns of these genes at 0, 2, 4, and 6 h post-infection were generally in accordance with those by RNA-Seq. At each post-infection time point, VP1766, VPA1068, VP0485, VP2914, and VP1665 were up-regulated. Moreover, VP2492, VP0486, and VPA0628 were up-regulated at 2 h and down-regulated at 0, 4, and 6 h post-infection (Figure S5).
This study reveals that Qrr5 is related to cytotoxicity and can significantly positively regulate virulence gene expression levels in V. parahaemolyticus. This study also elucidates the systematic transcriptomic profile of V. parahaemolyticus mutants during infection of Caco-2 cells. A total of 185, 586, 355, and 74 DEGs were obtained at 0, 2, 4, and 6 h post-infection, respectively, which were mainly related to ABC transporters and two-component system pathways. Using WGCNA, we identified 17 key genes involved in the gene regulatory network associated with Qrr5. These findings highlight the importance of core genes that may be regulated by Qrr5 during infection in Caco-2 cells and provide a basis for further research on the mechanisms of V. parahaemolyticus infection. | true | true | true |
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PMC9610555 | 36287866 | Jianli Yin,Ye Ju,Honghao Qian,Jia Wang,Xiaohan Miao,Ying Zhu,Liting Zhou,Lin Ye | Nanoplastics and Microplastics May Be Damaging Our Livers | 04-10-2022 | microplastics,nanoplastics,polystyrene microplastics,oxidative stress,liver injury | Plastics in the environment can be degraded and even broken into pieces under the action of natural factors, and the degraded products with a particle size of less than 5 mm are called microplastics (MPs). MPs exist in a variety of environmental media that come into contact with the human body. It can enter the body through environmental media and food chains. At present, there are many studies investigating the damage of MPs to marine organisms and mammals. The liver is the largest metabolizing organ and plays an important role in the metabolism of MPs in the body. However, there is no available systematic review on the toxic effects of MPs on the liver. This paper summarizes the adverse effects and mechanisms of MPs on the liver, by searching the literature and highlighting the studies that have been published to date, and provides a scenario for the liver toxicity caused by MPs. | Nanoplastics and Microplastics May Be Damaging Our Livers
Plastics in the environment can be degraded and even broken into pieces under the action of natural factors, and the degraded products with a particle size of less than 5 mm are called microplastics (MPs). MPs exist in a variety of environmental media that come into contact with the human body. It can enter the body through environmental media and food chains. At present, there are many studies investigating the damage of MPs to marine organisms and mammals. The liver is the largest metabolizing organ and plays an important role in the metabolism of MPs in the body. However, there is no available systematic review on the toxic effects of MPs on the liver. This paper summarizes the adverse effects and mechanisms of MPs on the liver, by searching the literature and highlighting the studies that have been published to date, and provides a scenario for the liver toxicity caused by MPs.
Global plastic annual production has increased from 1.7 million tons to 360 million tons over the past 70 years [1]. However, due to the production of large quantities of plastic products, low recycling rate, and poor management, plastics are widely present in the ocean, soil, air, and other environmental media with which human beings have close contact [2]. Under the action of physical erosion, biodegradation, or photocatalytic oxidation, plastics entering the environment can be degraded into plastic particles, and those particles with a particle size of less than 5 mm are called microplastics (MPs) [3]. Among them, MPs with a particle size of less than 1000 nm [4] or 100 nm [5] are called nanoplastics (NPs). In this paper, NPs refer to plastics with particle sizes not larger than 1000 nm. There are many kinds of MPs/NPs, such as polyethylene (PE), polypropylene (PP), polyvinyl chloride (PVC), polyamide (PA), polystyrene (PS), polyethylene terephthalate (PET), polymethyl methacrylate (PMMA), micro (nano)plastics [6], etc. Studies have shown that MPs/NPs pollution exists in a variety of environmental media, including terrestrial oceans, rivers, lakes, and polar glaciers [7]. In the global marine environment, the MPs/NPs floating on the sea surface are mainly concentrated in five current circulation belts of the North Atlantic, South Atlantic, North Pacific, South Pacific, and Indian Ocean [7]. The abundance of MPs/NPs in seawater greatly varies in different regions and studies, ranging from 4.8 × 10−6/m3 to 8.6 × 103/m3. In addition, the amount of plastic released into the soil each year is estimated to be 4 to 23 times that of the plastic released into the marine environment [8]. The abundance of MPs/NPs in the soil varies from a few to tens of thousands per kilogram (dry weight). Soils in other countries and regions in the world also face this phenomenon, such as Switzerland [9], Chile [10], Mexico [11], etc. MPs are also contained in the air. Dris et al. detected MPs for the first time in air deposition in Paris, France, with an abundance of 29~280/m2/d [12]. Both field investigations and laboratory studies have uncovered the phenomenon of microplastic transport at the trophic level, and these findings call for attention to the bioaccumulation, biomagnification, and toxic effects of microplastics on the organisms at a high trophic level [13,14]. It is generally believed that MPs/NPs enter the human body mainly through the respiratory tract [15,16] and the digestive tract [17,18]. They can also enter the body through the skin [19,20]. Airborne MPs/NPs can lead to respiratory exposures, while MPs/NPs in food, drinking water, and air deposition can lead to digestive system exposures. As early as 1998, Pauly et al. examined 114 human lung specimens and found fibers of up to 250 μm in length in 99 (87%) of these specimens [21]. This is the first report on the human inhalation of natural fibers and plastic fibers, and it also supports the fact that MPs can enter the human body through respiration. In 2018, Austrian scientists examined the stool of volunteers from 8 countries and found MPs in all the stool samples, confirming the exposure of the human digestive tract to MPs, with an average of 20 plastic particles per 10 grams of stool. There are as many as 9 types of MPs in stool samples, of which PP and PET are the most common [22]. A New York University study found that MPs are 20 times higher in infant feces than in that of adults, likely due to babies’ increased exposure to plastic due to their tendency to crawl on the floor, chew on plastic toys, and use plastic spoons and bottles’ middle. In addition, MPs with a size of 5~10 μm have also been detected in the human placenta [23]; in addition, in plastic-related occupational places, such as PVC production workshops, textile factory workshops, etc., people face higher concentrations of MPs exposure [16]. Many researchers have estimated human MPs intake due to single or multiple exposure routes by summarizing MPs contamination data in food and drinking water. For example, Van Cauwenberghe et al. estimated that Europeans could consume up to 11,000 MPs per person per year through shellfish intake [17]. Cox et al. compiled the data on MPs contamination from salt, seafood, honey, drinking water, and sugar, combined with the dietary habits of Americans, and estimated that the annual intake of microplastics per person was 39,000–52,000 [24]. For the MPs entering the human body from the upper respiratory tract, the human MPs/NPs exposure can be as high as 74,000–121,000 per year. Currently, papers on MPs mainly focus on marine organisms. Studies have found that MPs can accumulate in the digestive tract [25,26] and other parts of marine organisms [27], as well as in rats [28] and mice [29]; for instance, the accumulation of MPs in the testes [30] and kidneys [29] of mice caused intestinal and liver dysfunction and was found to interfere with the immune response of the body and affect the function of the reproductive system. The liver is an organ with a metabolic function in the vertebrate body and plays the role of deoxidation, the storage of glycogen, and detoxification, which render it an important organ in human body. To date, there is no review on the effect of MPs on the liver. In this paper, by searching the published literature on MPs, we describe the damage of MPs to the liver and the relevant mechanisms.
The literature search followed the guidelines of PRISMA. First, we searched the keywords including both “microplastics” or “nanoplastics” and “liver” or “hepatic” on the PubMed or Web of Science until May 2022 (N = 231). Then, we excluded the duplicated references. Reviews and meta-analyses were also excluded (n1 = 17). Finally, we selected the studies for this review by browsing their abstracts (n2 = 214).
Figure 1 shows the co-occurrence analysis of the keywords in eligible papers on the Web of Science. The results of the word frequency table (Table S1) showed that oxidative stress appeared 44 times, ranking third, and the first and second were microplastics and fish. Accumulation and bioaccumulation also occurred more frequently, with 32 and 16 occurrences, respectively. The high frequency of the appearance of such words as metabolism, inflammation, and lipid metabolism indicates that the current papers regarding MPs and NPs are focused on oxidative stress, inflammation, and metabolism.
Internalization of MPs/NPs in soil plants: The discontinuous regions in plant roots, located at the root tips and secondary roots where the endoderm cells are immature, are known pathways of pathogen or bacterial infection; thus, MPs/NPs can directly internalize into the plant body through this cleft entry mode [31]. Internalization of MPs/NPs in phytoplankton and animals: Most of the MPs have a particle size close to the size of algal cells. Even if the particle size is much smaller than that of algal cells, they will hardly be internalized into algal cells due to the difficulty in penetrating the cell wall [32]. However, the latest research has found that some small-sized MPs do have the possibility to enter algal cells [33]. When MPs/NPs come into contact with algal cells, some of the MP/NP particles may be encapsulated by the plasma membrane’s microcapsules on the surface of the algal cells and become embedded in them [34]. Zooplankton can ingest MPs/NPs either actively (because of misjudging MPs/NPs as phytoplankton) or passively (because MPs/NPs are adsorbed on the surface of the phytoplankton or internalized into its cells) [35]. MPs/NPs that enter the organism through the digestive tract and respiratory tract enter the liver through intestinal absorption or epidermal infiltration and can also reach the liver through blood circulation [36]. MPs and larger NPs (greater than 200 nm) are not easily internalized, but 100 nm or smaller particles are easily endocytosed into cells [37]. Endocytosis is a key mechanism [38,39] by which cells take up NPs by wrapping them in vesicles or vacuoles that are pinch-off from their cytoplasmic membranes in an energy-dependent manner. These include clathrin-dependent endocytosis [40] and caveolin-dependent endocytosis [41]. Studies have shown that zebrafish hepatocytes were exposed to 5 mg/L and 50 mg/L of 65 nm PS-NPs, and PS-NPs were efficiently absorbed by ZFL and mainly accumulated in the lysosomes [42]. This indicates that 65 nm PS-NPs are internalized into the liver. In addition to endocytosis, internalization can also be performed in an energy-independent manner through passive diffusion [43].
Current studies have shown that both NPs and MPs (25 nm [28]~90 μm [44]) can accumulate in the livers of marine fish [45] and mammals such as rats [28] and mice [46]. Moreover, whether MPs or NPs can accumulate in the liver and the amount of accumulation are closely related to their particle size. After the exposure of goldfish to 300 mg/L of PS-NPs and PS-MPs with particle sizes of 250 nm and 8 μm for 7 days, the accumulation rate of PS-NPs in the liver of goldfish was higher than that of PS-MPs [47]. The marine medaka was exposed to PS-MPs of 10 μm and 200 μm for 60 days, and PS-MPs of 200 μm were not detected in its liver [48]. Because larger MPs are easily filtered by the gills of marine organisms, smaller plastics, such as those of nanoscale, enter the bloodstream through the gills, initially accumulate in the gut, and then transfer to the liver [11]. Similar results were seen in PS-MPs exposed mice (1.46 × 106 items for 5 μm PS-MPs and 2.27 × 104 items for 20 μm PS-MPs via oral gavage), in which 5 μm PS-MPs accumulated in the kidney and gut more than 20 μm PS-MPs after 28 days of exposure [29]. Meanwhile, in vitro, PS-MPs of 1 μm can hardly enter HL7702 cells, while PS-NPs of 100 nm can enter hepatocytes and cause damage even at low concentrations [46]. Other studies [29,49] have also shown that PS-NPs are more likely to transfer and accumulate in tissues through circulation. Similarly, 65 nm PS-NPs can be absorbed by all zebrafish liver cells after 6 h of incubation, mainly accumulating in the lysosomes. Moreover, the internalization process presents a dose–response mode, that is, the higher the dose, the longer the incubation time, and the more PS-NPs in the cells [42]. However, this previous study did not examine the amount of PS-NPs taken up by zebrafish liver cells. Some studies [37,41] demonstrate that PS-MPs are not easily internalized by cells, while PS-NPs of 100 nm and below are easily taken up through the endocytic machinery. It was confirmed that the hepatic accumulation of MPs or NPs could produce toxic effects on hepatic function [50]. In addition, the toxic effect on hepatic function presented a size- and dose-dependent pattern [47]. The marine medaka was exposed to 10 mg/L PS-MPs of 10 μm and 200 μm for 60 days, and glucose metabolism and amino acid metabolism in the liver were affected; the levels of monosaccharides and amino acids in the 10 μm exposure group were significantly decreased, compared with those in the 200 μm exposure group. The reason for this difference in toxicity is closely related to the particle size of MPs. The particle size of 10 μm PS-MPs is much smaller than 200 μm. Compared with 200 μm PS-MPs, 10 μm PS-MPs are easier to enter the liver; thus, the impact of 10 μm PS-MPs on liver function is more serious than the effect of 200 μm PS-MPs [48]. The parameters and effects of the accumulation of MPs/NPs in the liver are shown in Table 1 and Table 2.
Both MPs and NPs can cause changes in the morphology of the liver to a certain extent, thereby affecting the normal function of the liver. Zebrafish were exposed to PS-MPs/NPs at 70 nm and 5 μm for 3 weeks, and hepatocyte necrosis, infiltration, and lipid droplets were observed in the 2000 μg/L group, suggesting that PS-MPs and PS-NPs can cause liver inflammation and hepatic lipid accumulation [27]. Liyun Yin et al. exposed the marine juvenile jacopever to PS-MPs of 15 μm (1 × 106 particles/L) for 14 days, followed by a 7-day depuration period. In the exposed group, liver congestion was still seen in the fish, which was also confirmed with the analysis of pathological sections. The results also revealed that the damage to the fish liver by PS-MPs is continuous, and the liver damage is not only related to the particle size of MPs but also may be due to the negative charge of PS-MPs [59]. Besides PS-MPs, other kinds of MPs can also cause liver damage, such as PVC-MPs, PE-MPs, etc. [60,61,62]. After the European sea bass was exposed to 100 mg/kg and 500 mg/kg PVC-MPs or PE-MPs with a particle size of 40–150 μm for three weeks, compared with the control group, morphological changes in the hepatocyte and hepatocyte hypertrophy were observed in the exposed group. The void formation was significantly increased, and changes such as sinusoidal and vascular congestion presented among liver cells [62]. Similar results were seen in the tadpoles exposed to PE-MPs. Tadpoles were exposed to PE-MPs with a particle size of 35.46 ± 18.17 μm and a concentration of 60 mg/L for seven days, and in the exposed group, the livers of the tadpoles exhibited greater vasodilation, infiltration, hyperemia, hepatocyte edema-type degeneration, hypertrophy, and hyperplasia than those from the control group. In addition, it is worth noting that the tadpole hepatocyte nuclei exposed to PE-MPs have larger long and short axes, perimeters, areas, and volumes, all of which demonstrate the toxic effects of PE-MPs on the liver [61]. Clarias gariepinus was exposed to PVC-MPs (95.41 ± 4.23 μm) for 45 days with test diets containing 0.5%, 1.5%, and 3.0% PVC, followed by 30-day depuration. After exposure to PVC-MPs, the liver index increased, and glycogen depletion, fat vacuolization and degeneration, and hepatocyte necrosis occurred [62]. Increased liver body index was also observed in the groupers exposed to 20 mg/g of PS-MPs (22.3 μm) for 25 days, and the liver weight was significantly increased, suggesting that MPs could induce liver enlargement [50]. Goldfish were exposed to 10 μg/L, 100 μg/L, and 1000 μg/L of PS-NPs (70 nm) and PS-MPs (5 μm) for 7 days, and the ultrastructure showed increased hepatocyte interstitial space and mitochondrial vacuolation, which suggests that one of the targets of MPs may be mitochondria [63]. In addition to aquatic organisms, changes in liver morphology were also observed in the mice exposed to MPs. Male mice were exposed to 5 μm PS-MPs (20 mg/kg/day via drinking water) for 30 days. Compared with the control group, the mice from the PS-MPs group exhibited severe vacuolar degeneration and chronic inflammatory infiltration in the liver tissue, and hepatocyte edema [64]. In another study, mice were exposed to 100 μg/L and 1000 μg/L of PS-MPs (5 μm) for 6 weeks through drinking water. In the exposed group, the weight of the liver increased, and H&E staining displayed increased hepatic ballooning in the liver [65]. The above studies mainly highlight the effects of MPs on liver morphology, but NPs can also affect liver morphology in marine organisms and mammals. Zacco temminckii was exposed to 60 nm PS-NPs (5 mg/L) for 7 days. In the exposed group, the hepatocytes were destroyed and vacuolated, and the cell nuclei were aggregated and condensed [5]. Zebrafish were exposed to PS-MPs (10 mg/L) with a particle size of 100~120 nm, and after 7 days, hepatic necrosis and nuclear pyknosis were observed. After 35 days, eosinophilic granulomas, necrosis, and cytoplasmic degeneration appeared. Likewise, the fish exposed to 100 mg/L for 7 days exhibited liver histological changes, such as cytoplasmic vacuolation, nuclear pyknosis, and hepatocyte aggregation. On the 35th day, the liver sections displayed major inflammatory changes such as central venous congestion, cytoplasmic vacuolization, and hepatocyte degeneration. Different exposure doses and duration demonstrated that the degree of liver inflammation increased with dose and duration [66]. NPs can still cause pathological damage to the liver through the food chain. PS-NPs (1 mg/L) of 190 nm were transferred from Artemia franciscana to Larimichthys polyactis, and 8 days later, in the liver pathological section of Larimichthys polyactis, decreased liver tissue density and the band necrosis of hepatocytes were identified [67]. After mice were exposed to 100 nm PS-NPs for 60 days, histopathological examination revealed a concentration-dependent increase in PS-NPs-induced hepatocyte injury, including hepatocyte edema, enlarged nuclei, binucleated cells, irregular arrangement of hepatic cords, and portal inflammation [46]. The parameters and the liver morphological changes caused by MPs/NPs are shown in Table 3 and Table 4.
The liver is the site of biotransformation and metabolism of many endogenous and exogenous compounds, and cytochrome P450 oxidase (CYP450) in the liver plays an important role in biotransformation and metabolism. In human hepatocytes, CYP450 is dominated by CYP1, CYP2, and CYP3. These three CYP450 compounds account for 70% of the total CYP450 in the liver and are involved in the metabolism of most drugs and toxicants. Through the action of enzymes, most endogenous compounds are biotransformed into more hydrophilic and polar compounds that can be excreted by the body. CYP450 isoenzymes are mainly involved in phase I reactions of oxidation, reduction, and hydrolysis in vivo, and can be induced and inhibited by exogenous compounds. Current research shows that CYP450 enzymes in the liver of marine organisms are affected by exposure to MPs and NPs. Zebrafish were exposed to 70 nm PS-NPs (0.5 ppm, 1.5 ppm, 5 ppm) for 7 days, and the expression of three CYP enzymes (CYP1A1, CYP11A1, and CYP19A1) was significantly increased in the liver of the zebrafish exposed to 1.5 ppm PS-NPs [51]. Jiannan Ding et al. exposed the red tilapia to 100 nm PS-NPs (1 μg/L, 10 μg/L, 100 μg/L) for 14 days, and the activity of CYP enzymes in the fish liver decreased first and then increased with time [71]. Enzymes such as ALT and AST are mainly present in the cytoplasm of hepatocytes but are released into the blood during liver injury. The activities of ALP, AST, and ALT in the plasma were increased after exposure to PS-NPs [52]. These results were consistent with the findings in the Wistar rat Rattus norvegicus [72]. Wei Cheng et al. differentiated embryonic stem cells into the liver organoids (LOs) and exposed to (0.25 μg/mL, 2.5 μg/mL, 25 μg/mL) 1 μm PS-MPs for 48 hours, and AST and ALT increased in the supernatant of LOs culture medium, meanwhile, the enzymatic activities of AST and ALT within the LOs were inhibited. It was demonstrated that PS-MPs could produce intracellular toxicity. When the LOs were exposed to varying doses of PS-MPs, the mRNA levels of the CYP450 family were upregulated. Among all the increased CYP450 family members, CYP2E1 was upregulated by the PS-MPs most remarkably [73]. Similar results for the upregulation of CYP450 enzymatic activity have been found in many studies [56,74]. Antònia Solomando et al. exposed Sparus aurata Linnaeus to 200–500 μm low-density polyethylene (LDPE-MPs) for 90 days, followed by depuration for 30 days. The activities of GSH-Px and GR and GST in the liver were significantly increased, with some recovery during the depuration [75]. This indicated that long-term sustained exposure is one of the important causes of liver toxicity.
Growing evidence suggests that exposure to MPs/NPs is able to induce oxidative stress and produce oxidative damage in organisms [60,64,76,77] such as crabs, zebrafish, mice, etc. Oxidative damage is mainly manifested as changes in oxidative stress kinase activity, including SOD, CAT, GSH, GSH-Px, GR, GST, etc. The toxicity of MPs/NPs leads to the excessive production of reactive oxygen species (ROS) in the organism [78]. Excessive ROS can damage lipids in cells and lead to lipid peroxidation (LPO) [79]. Once the balance between the production and removal of ROS in the body deteriorates, the body will act through antioxidant enzymes such as SOD, CAT, and GPX to inhibit the development of LPO [80]. The main role of SOD is to catalyze the disproportionation of superoxide anion into oxygen and hydrogen peroxide, which is then catalyzed into H2O by CAT and GSH-Px enzymes [81]. GSH and GST facilitate the combination of glutathione and sulfhydryl transferase to form glutathione peroxidase, which is highly degradable to hydrogen peroxide [82]. In addition, GR exerts its detoxification effect by binding to glutathione-bound heterologous substances and catalyzing the reduction in GSSG to GSH [83]. Malonic dialdehyde (MDA) is one of the most important products of membrane lipid peroxidation, and the content of MDA is an important indicator reflecting the rate and intensity of lipid peroxidation [77]. Compared with MPs, NPs have unique properties that aggregate more easily in living organisms than in the natural environment [84], and their aggregation is further influenced by the engineered function of nanoparticles or incidental coatings (such as fluorescent labels) and water chemistry [85,86]. This aggregation causes damage and induces a body response associated with increased reactive oxygen species, which is accompanied by a stronger oxidative stress response in the liver, further exacerbating the biotoxicity of NPs [87]. A recent study reported that treatment with 50 nm PS-MPs induced stronger oxidative stress and higher levels of antioxidant activation than that with 45 μm PS-MPs in the marine medaka [88] (Figure 2).
There are many studies showing a direct relationship between pollutants and inflammation [89]. Moreover, inflammatory responses also occurred in many organisms exposed to MPs/NPs, mainly manifested as increased expression of inflammatory factors and changes in the activities of enzymes related to inflammatory responses [90]. IL-1β and TNF-α are cytokines that promote inflammatory responses in the body [82], and IFN-γ is an antiviral cytokine that is mainly involved in mediating the immune and inflammatory responses [91]. When the body is exposed to pollutants, these cytokines are secreted from immune cells, mainly macrophages, to regulate the body’s inflammatory response [92,93]. MPO is a ferrous lysosomal enzyme involved in the removal of extracellular foreign matter [94]. The increase in the level of MPO in the body is usually associated with the infiltration of immune cells and the activation of inflammatory responses [95,96]. Nile tilapia were exposed to 350 nm and 9 μm PS-MPs for 28 days. The expression of the IFN-γ gene was upregulated in the fish exposed to 350 nm and 9 μm, and the expressions of IL8, IL-1β, and TNF-α genes were upregulated in the group of fish exposed to 9 μm, compared with the control [97]. Studies have shown that NPs can increase the infiltration of macrophages in the liver, upregulate M1 macrophages, and downregulate M2 macrophages. C57BL/6J mice were gavaged with 500 nm PS-NPs consecutively for 4 weeks (0.5 mg/day), and the percentage of macrophages and M1 macrophages were significantly increased after NPs exposure, while the percentage of M2 macrophages was significantly decreased [98]. Macrophages can be activated and differentiated into two different types of cells, M1 and M2, among which M1 mainly secretes proinflammatory factors and plays an important role in the early stage of inflammation, while M2 expresses the inhibiting inflammatory factors, which play a role in inhibiting the inflammatory response and repairing tissue in the body [99]. After injecting 500 nm PS-NPs in mice for 4 weeks, the expression of the inflammatory factors such as IFN-γ, TNF-α, and IL-1β in the liver was increased, and the levels of P65 and phosphorylated P65 proteins with the NF-κB pathway increased, indicating that NPs may activate the NF-κB signaling pathway in the liver [98] (Figure 3). The parameters and effects of liver inflammation caused by MPs and NPs are shown in Table 5.
MPs/NPs not only cause liver damage through oxidative stress and inflammation but also impair liver function by affecting liver lipid metabolism. Moreover, the effects of MPs on hepatic lipid metabolism were greater than those of NPs. The metabolic results of the zebrafish treated with PS-M/NPs showed that MPs and NPs could induce changes in 21 and 11 metabolites, respectively [27]. Compared with the NPs group, the crude fat in the fish liver of the MPs group was significantly reduced [106]. NPs may directly lead to liver injury and lipid accumulation, while MPs may trigger lipid metabolism disturbances by affecting gut microbial communities and homeostasis [102]. There are also gender differences in MPs/NPs on hepatic lipid metabolism. Liver lipid metabolism in female mice is more likely to be disrupted by MPs/NPs [65]. At present, there are few studies on the mechanism of abnormal liver metabolism after exposure to MPs/NPs, mainly focusing on the level of gene transcription and metabolomics. Studies have shown that MPs/NPs affect the expression of genes involved in lipid metabolism [105,107], such as peroxisome proliferator-activated receptor-alpha (PPARα) [107] and peroxisome proliferator-activated receptor-gamma (PPARγ) [52,102]. PPAR is involved in the regulation of fatty acid signaling as a key regulator of lipid metabolism, and it has three subtypes, namely PPARα, PPARβ/δ, and PPARγ [108]. PPARα regulates gene expression by binding to specific DNA sequences, leading to the transcriptional activation of target genes, such as apolipoprotein, lipoprotein lipase, and acyl-CoA oxidase, which are critical for lipid metabolism. In addition, PPARα has also been shown to regulate glucose metabolism, liver inflammation, and hepatocyte proliferation [109]. PPAR-γ is a ligand-activated nuclear transcription factor that plays a key role in fat absorption, storage, and metabolism [110]. After activation, PPAR-γ participates in lipid metabolism by regulating the expression of related genes. In addition, MPs/NPs can increase the mRNA expression of lipid-synthesis-related genes such as FAS, SREBP1, and PPARγ [102], as well as lipid transport genes such as CD36 and FATP1, and reduce the mRNA expression of lipid catabolism genes such as ATG1 and ACO [52]. Adenosine monophosphate-activated protein kinase (AMPK) plays an important role in regulating the homeostasis of lipid metabolism in the liver [111]. Studies have shown that MPs/NPs may cause lipid deposition in the liver through the inhibition of lipolysis mediated by the AMPK–PPARα signaling pathway [52]. There are also studies showing that gut microbes may also affect lipid metabolism in the liver. Gut bacteria can produce short-chain fatty acids and then participate in lipid metabolism in the liver [112]. MPs/NPs affect the balance of gut microbes, which in turn affects lipid metabolism in the liver [102], which needs to be further explored. The metabolomic results showed that after exposure to MPs/NPs, liver metabolism significantly changed, mainly at the molecular level related to lipid metabolism, such as fatty acids, including monounsaturated fatty acids (MUFA), linoleic acid, FA-αH2, FA-ω-CH3, and fatty acyl chains, as well as choline, cholesterol, and amino acids, all of which are related to lipid metabolism [27]. Choline is an indispensable substance in the process of phospholipid synthesis and transport, which can promote lipid metabolism [113]. Leucine, isoleucine, and valine promote fatty acid metabolism [114], and exposure to MPs/NPs results in a reduction in these fatty acids [29] (Figure 4). The parameters and effects of the abnormal liver lipid metabolism caused by MPs and NPs are shown in Table 6.
The liver is the center of energy metabolism and regulates energy storage through the biosynthesis or oxidation of fatty acids in animals [116,117]. Studies have shown changes in ATP/ADP/AMP metabolites in the liver after exposure to MPs/NPs in the zebrafish, indicating the disruption of energy metabolism in fish [27]. Similar results also suggest that ingestion of MPs depletes the energy reserves of marine worms and copepods [118,119] and affects the feeding activity of fish [120]. The nd5 gene is the core subunit encoding the NADH dehydrogenase (complex I) of the mitochondrial membrane respiratory chain, responsible for electron transfer in oxidative phosphorylation, a necessary process for ATP synthesis, and the mRNA levels of nd5 in fish changed after exposure to NPs, indicating a disturbance in the ability of fish to mobilize energy reserves [107]. It seems that MPs have a greater effect on energy metabolism in fish than NPs. The growth of fish after MPs/NPs exposure gradually decreased with the increase in particle size [106]. One study [106] showed that MPs treatment had a stronger inhibitory effect on the growth of fish than NPs treatment, and the energy reserve in fish after MPs exposure was less than that after of NPs exposure. The levels of most monosaccharides and organic acids were significantly decreased in the liver of the medaka exposed to MPs, indicating that monosaccharide metabolism, tricarboxylic acid cycle, and glycolysis were inhibited in fish [48]. Moreover, the significantly lower levels of 6-phosphate gluconate and ribose in the fish liver indicated that the pentose phosphate pathway was inhibited, and nucleotide synthesis and NADPH production were affected, thereby affecting the energy supply in fish. After mice were exposed to MPs, the concentration of ATP related to energy metabolism in the liver decreased, and the LDH activity increased dramatically [29]. ATP levels and the LDH activity in the liver are related to the amount of energy in the liver [121]. An analysis of differences in serum metabolites between the exposed groups and the control group revealed that the changes in metabolites were related to compounds such as creatine, 2-ketoglutarate, and citric acid, which are vital for energy metabolism [122]. These results suggest that MPs exposure leads to energy deficit in mice. After the body ingests MPs/NPs, MPs/NPs affect normal food intake and damage intestinal function, affect the absorption of nutrients in food, and lead to a decrease in energy in the body [123]. Moreover, the MPs/NPs entering the body affect the normal biological processes of the liver. The transcriptomic analysis of the livers of mice exposed to MPs revealed that multiple biological processes related to energy metabolism, such as glycolysis, glucose transport, fatty acid synthesis, and oxidation, were inhibited [65]. Similar results were found in MPs-exposed fish, MPs exposure also perturbed the metabolomic profile in the fish liver, with alterations in the metabolites mainly involving carbohydrates, fatty acids, amino acids, and nucleic acids. MPs exposure can also cause significant changes in most monosaccharide metabolic pathways, including galactose metabolism, fructose and mannose metabolism, pentose phosphate pathway, pentose and glucuronic acid interconversion, and glycolysis/gluconeogenesis [124]. The functions of the pentose phosphate pathway involve the production of sugar phosphates as biosynthetic intermediates and NADPH as a bioreductant [125], as well as several secondary function-dependent metabolites. Furthermore, glycolysis/gluconeogenesis is the main pathway related to energy metabolism. Thus, MP exposure triggers changes in energy metabolism [126]. The parameters and effects of the abnormal liver energy metabolism caused by MPs and NPs are shown in Table 7.
There are many ways of programmed cell death, including apoptosis, pyroptosis, ferroptosis, etc. Studies have demonstrated that these means of programmed cell death occur in the liver of MPs/NPs-exposed organisms.
Studies have shown that when goldfish and grouper were exposed to PS-MPs, the level of hepatocyte apoptosis was significantly increased [50,128]. Similar results were found in mice. After mice were exposed to PS-MPs, the level of hepatocyte apoptosis was increased, mainly in the early stage [64]. Human SMMC-7721 cells also had elevated levels of apoptosis after exposure to NPs [129]. After exposure to PS-MPs, the Bax/Bcl-2 ratio and the level of caspase, a biomarker for detecting apoptosis in fish [130], were increased in the liver of zebrafish and sea bass [131]. In addition, the ratio of Bax/Bcl2 reflects the activation of procaspase and the occurrence of apoptosis [132]. After exposure to MPs, the expression of Bax and cytochrome C in human hepatocytes was significantly increased, and the expression of Bcl-2 was significantly decreased. After silencing the PERK gene in MPs-exposed human hepatocytes, MPs-induced mitochondrial apoptosis in L02 hepatocytes was attenuated, the expression of Bcl2 was increased, and the expression of Bax and cytochrome C was decreased, indicating that MPs may induce mitochondrial apoptosis through the PERK signaling pathway [133]. Nrf2 signaling is involved in the regulation of many endogenous signals in the body, such as autophagy and protein post-translational modification impairment [134]. As a phase II detoxification enzyme regulated by Nrf2, hepatocyte HO-1 is thought to play a key role in alleviating liver injury by inhibiting oxidative stress and apoptosis [135,136]. Studies have shown that the Nrf2/HO-1 pathway can exert a protective effect on the MPs-induced apoptosis of rat hepatocytes [64]. The effect of NPs on the apoptosis of hepatocytes was greater than that of MPs. Particles of smaller sizes induce higher levels of macrophage apoptosis in the zebrafish liver [137]. Similar results were seen in PE-MPs-exposed fish. The level of apoptosis in the fish liver was elevated after exposure to PE-MPs, and PE-MPs and small particle sizes were found to induce higher levels of apoptosis in the liver [138]. After NP exposure, the p38 MAPK signaling pathway was activated in RAW 264.7 cells and induced apoptosis [139]. In conclusion, MPs/NPs may induce hepatocyte apoptosis by activating PERK and MAPK (Figure 5). The parameters of the liver apoptosis caused by MPs and NPs are shown in Table 8.
The NLRP3 inflammasome is the center of the intracellular regulation of inflammatory responses. NLRP3 is linked to caspase-1 via ASC and induces factor release and caspase-1-dependent pyroptosis [140]. This process causes proinflammatory cells to trigger the proteolytic cleavage of dormant procaspase-1 into active caspase-1, which converts the cytokine precursors pro-IL-1β and pro-IL-18, respectively, to mature and biologically active IL-1β and IL-18 [141]. Current studies have shown that the expression of ASC, caspase-1, and NLRP3 in the mouse liver induced by MPs exposure is significantly increased, and pyroptosis may be the key to MPs-induced damage to the liver tissue [104]. The light-chain subunit solute carrier family 7 member 11 (SLC7A11) plays an important role in ferroptosis, and GPX4 reduces potentially toxic lipid hydroperoxides (L-OOH) to nontoxic lipid alcohols (L-OH), thereby limiting the spread of lipid peroxidation within the membrane and preventing ferroptosis [142]. The expression of SLC7A11 and GPX4 decreased after MPs treatment, confirming that MPs may induce ferroptosis in the liver [104].
In addition to the above-mentioned mechanisms, there are some mechanistic studies on endoplasmic reticulum stress and mitochondrial damage, and autophagy.
The exposure of goldfish to MPs/NPs induced vacuolation in the mitochondria of hepatocytes [63]. After exposure to MPs, the ultrastructure of the mouse liver showed mitochondrial cristae rupture [104] and mitochondrial vacuolization [64]. Mitochondrial DNA damage was also found in the livers of NPs-exposed mice [46]. Changes in mitochondrial morphology are regulated by dynamin-related protein 1 (Drp1) and mitochondrial fusion protein (Mfn2) [143]. After L02 cells were exposed to MPs, Drp1 expression was significantly upregulated, and Mfn2 expression was significantly downregulated [133]. The endoplasmic reticulum stress inhibitor 4PBA prevented the Drp1 upregulation and restored the protein expression of Mfn2 exposed to MPs. These results indicated that alleviating endoplasmic reticulum stress could effectively inhibit MP-induced mitochondrial fission. Similar results were also seen in human liver cell lines. After human LO2 cells were exposed to 80 nm PS-NPs (0.0125, 0.125 mg/mL) for 48 h, the transmission electron microscopy analysis showed that NPs could enter cells and cause mitochondrial damage, resulting in excessive mitochondrial reactive oxygen species production [144]. Furthermore, the mitochondrial membrane potential was altered, and mitochondrial respiration was inhibited. These changes were observed at NP concentrations as low as 0.0125 mg/mL. Untargeted metabolomics confirmed that the most significantly affecting processes were mitochondrial-related. The metabolic functions of L02 cells were more susceptible to NP exposure than human lung epithelial BEAS-2B cells, especially at lower NP concentrations. At present, there are few studies on the damage of MPs/NPs to the liver mitochondria, and there is some evidence that MPs/NPs damage the mitochondria of other organs. Human renal cortical proximal convoluted tubule epithelial cells (HK-2) can increase the levels of mitochondrial ROS and mitochondrial protein Bad after ingesting different concentrations of PS-MPs. MitoTEMPO is a mitochondrial ROS antioxidant that alleviates higher levels of mitochondrial ROS and Bad protein levels [145]. Intracellular mitochondria were damaged in rat basophilic leukemia (RBL-2H3) cells exposed to 50 nm PS-NPs [146].
Microtubule-associated protein light chain 3 (LC3) is a major protein in the autophagy pathway and is the most widely used indicator of autophagosomes [147]. In addition, Sequestosome-1 (SQSTM1), a ubiquitin-binding protein p62, is a protein of the autophagosome cargo that tags other proteins for differentiated autophagy. During autophagy, SQSTM1 is degraded. Both LC3II/I and SQSTM1 ratios are widely used as indicators of autophagy [148,149,150]. Zebrafish and sea bass were exposed to PS-MPs, and the LC3 II/I ratio was increased, and SQSTM1/p62 levels were decreased in the livers of both fish after exposure to PS-MPs, compared with the controls [131]. This result suggests the development of hepatocyte autophagy. Embryonic zebrafish fibroblast cell lines (ZF4) were exposed to PS-NPs at 100 and 1000 nm, and confocal images showed that the NPs of both sizes were deposited in the lysosomes but could escape through the lysosomal rupture. The subsequent deposition of 100-NPs in the cytoplasm leads to the loss of mitochondrial membrane potential and the massive production of reactive oxygen species, which ultimately stimulates the activation of caspases, disrupts mitophagy, and leads to irreversible cell death [151]. In contrast, the toxicity of 1000-NPs to ZF4 cells did not involve the loss of lysosomal permeability and mitochondrial membrane potential. This large-sized nanoplastic lysosomal deposition mainly induces lysosomal acidification, activates autophagy, and disrupts the integrity of cell membranes. Immunohistochemical results [152] showed that the expression of autophagy-associated tubulin (Tub), microtubule-associated protein light chain 3 (LC3), and p62 (Sequestosome 1) increased after the exposure of the marine polychaete Hediste diversicolor to different environmental MPs collected from the southern Mediterranean coast, suggesting that MPs activates the autophagy system of marine hairy organisms. PS-MPs were also found to activate the expression of autophagy-related proteins in chicken cerebellum and avian heart, respectively, in chickens [153] and birds [154].
Endoplasmic reticulum stress can trigger and regulate autophagy [155]. In the PERK pathway, autophagy can be induced through the PERK/eIF2α/ATF4 pathway, or PERK can directly activate autophagy-related gene expression to mediate autophagy [156]. ER stress occurs when GRP78 dissociates from the aforementioned transmembrane proteins and binds with high affinity to accumulated mis/unfolded proteins, while IRE1 and PERK dissociated from GRP78 are activated by trans-autophosphorylation. The dissociated ATF6 is activated by proteolysis, thereby inducing the expression of downstream signaling pathways and the related genes LC3, P62, ATGs, and Beclin1, and finally activating the autophagy pathway [157]. The exposure of mice to MPs induces endoplasmic reticulum stress in the liver. The mRNA levels of the endoplasmic reticulum stress pathway-related markers PERK and CHOP were both increased after MPs exposure, while MPs exposure significantly increased the protein expression levels of p-PERK, p-eIF2α, ATF4, and CHOP in the liver, indicating that MPs can activate the eIF2α-ATF4-CHOP axis in the liver to induce endoplasmic reticulum stress [133]. To sum up, the cell mechanism diagram is as follows (Figure 6).
With the increasing application of plastic products and human exposure, people have gradually begun to pay attention to the adverse effects caused by plastic products. The liver is the body’s largest organ responsible for detoxification and metabolism and undertakes many important activities. The toxic effects of MPs on the liver are receiving more attention from researchers. Currently, the research on the toxic effects of MPs/NPs on the liver mainly focuses on marine fish [69,124]. Since the pollution of MPs in the ocean is not optimistic, and fish are the main marine species that people eat, it is crucial to study the impact of MPs on marine fish. However, MPs are not only present in the ocean [158] but also can be detected in soil [159] and air [160]; thus, it is necessary to study the damage of MPs to mammalian livers. Investigations have been conducted on the effects of MPs on marine fish and mammals; however, more studies are needed to provide scientific theoretical support for plastic control.
People can ingest MPs/NPs from the external environment through diet and breathing. The main way of diet is to consume seafood [161], mainly shellfish such as fish [162,163] and oysters [164]. In addition, MPs/NPs were also detected in many foods, such as sugar, honey [18], salt [165], etc. These foods are closely related to human life and deserve our attention. Studies have shown that MPs/NPs also exist in the air. Although the exposure concentration of MPs/NPs in the air is relatively low, long-term exposure at low concentrations may also cause potential harm to human health. Some occupational groups (such as the synthetic textile industry and plastic industry) are exposed to high concentrations of MPs/NPs every day and are more vulnerable to MPs/NPs than the normal population [16].
The current research on the effects of MPs on liver toxicity still has the following limitations: Toxicity of MPs: In the real environment and process of natural degradation, plastic is subjected to its interactions with physical, chemical, and biological factors [166,167]; thus, the properties of MPs have changed, and the surface can adsorb various persistent organic pollutants [168], heavy metals [169], etc., which will modify the toxicity of MPs, meaning that MPs in the environment are different from the single microplastic prepared by the company that is used in most experiments [170]. At present, there are some studies on MPs combined with other toxicants [171,172], and some MPs are derived from naturally degraded plastics [173] in the environment. Although the experiment is complicated, it is of practical significance. Exposure dose of MPs: The toxicity of MPs depends on many aspects, including the particle size [138], concentration [42], and exposure duration [128] of MPs. Compared with the plastic concentrations in the environment, the doses of MPs used in many studies are excessively large. The highest abundance of MPs/NPs in the ocean can reach 8.6 × 103 particles/m3, and surveys have found that people ingest 39,000–52,000 particles per year on average [24]. Based on this, it is possible to estimate the difference between the doses of MPs/NPs used in the study and the MPs/NPs content in the ocean, and the average annual intake of MPs/NPs by humans. Oryzias melastigmas were exposed to MPs at 1.82 × 1010 particles/m3 [48], which is 7 orders of magnitude higher than MPs/NPs in the ocean. Similar results were found in the experiments of Lu, Y. [27] and Ding, J. et al. [44]. In mammals such as mice, the daily dose used in one study [64] contained 2.27 × 104 MPs, which is almost half of the annual human exposure (that is, if the daily dose of mice is given to humans for one year, the annual total particle intake would be at least two orders of magnitude higher than the estimated actual human annual intake). Additionally, the smaller the particle size of MPs, the higher the number of particles contained. In the same study [64], there were 1.46 × 106 particles in the daily exposure dose of 5 μm MPs. According to the same method, the daily exposure dose w two orders of magnitude higher than the human exposure dose in one year! (i.e., if daily doses in mice were given to humans for a year, the total annual particle intake would be at least orders of magnitude higher than the estimated actual annual intake in humans). Considering that the body is exposed to MPs/NPs in a variety of ways and can accumulate at a high trophic level [174,175,176], many studies have not given the number of particles, which cannot be compared with the content of MPs/NPs in the environment [28,45]. Moreover, in the investigation and research on the abundance of MPs/NPs in water, the investigation methods are not uniform, resulting in inconsistent research units. For example, when trawls are used to sample large-scale water bodies, the MPs/NPs unit are usually expressed as “pieces/km2”, while collecting water samples with buckets, the unit of MPs/NPs are expressed as “pieces/m3” [177]. Similarly, the abundance of MPs/NPs in sediments includes different representations per unit weight (units/kg) and unit area (units/m2) [178], while weight also includes dry and wet weights. In addition, the inconsistency of research methods also affects the reliability of the obtained data and the horizontal comparison of these data; this problem is more prominent in the research on air MPs/NPs that started later [12,179]. Therefore, there is an urgent need to establish a unified standard to quantify microplastics and to compare the doses used in experimental studies with realistic MPs levels. NPs and MPs: To date, many studies focus on the toxic effects of MPs on the liver, and NPs have unique characteristics, which have stronger effects in inducing the production of ROS in the liver and development of oxidative stress and inflammation [87], and more research should be conducted on the effects of NPs. Types of MPs: The current research on MPs mainly focuses on polystyrene MPs [87,180]. Polystyrene plastics are widely used in people’s daily life, but the results of several surveys [181,182,183] show that polyethylene, polyamide, and polyethylene terephthalate MPs are the most abundant in the stomach and liver of marine fish. The shape is mostly fibrous. Subsequent research should be based on real environmental situations, and some other types and shapes of MPs should be studied. Hepatotoxicity of MPs: The research on the toxicity of MPs to the liver mainly focuses on inflammation and oxidative stress, and the possible mechanisms of MPs on liver damage should be further explored to provide scientific theory and foundation for the prevention and control of MPs. This article systematically summarizes the accumulation of MPs/NPs in the liver, and the effects on liver pathology and liver function, and discusses the possible underlying mechanisms to provide clues to the liver injury caused by MPs or NPs. It also provides a scientific basis for future research directions. | true | true | true |
PMC9610657 | Yan Ye,Yao Lin,Zilin Chi,Jiasheng Zhang,Fan Cai,Youzhi Zhu,Dianping Tang,Qingqiang Lin | Rolling circle amplification (RCA) -based biosensor system for the fluorescent detection of miR-129-2-3p miRNA | 24-10-2022 | RCA,Fluorescent detection,Biosensor,miR-129-2-3p | Herein, a versatile fluorescent bioanalysis platform for sensitive and specific screening of target miRNA (miR-129-2-3p) was innovatively designed by applying target-induced rolling circle amplification (RCA) for efficient signal amplification. Specifically, miR-129-2-3p was used as a ligation template to facilitate its ligation with padlock probes, followed by an RCA reaction in the presence of phi29 DNA polymerase. The dsDNA fragments and products were stained by SYBR Green I and then detected by fluorescence spectrophotometry. As a result, miR-129-2-3p concentrations as low as 50 nM could be detected. Furthermore, the expression of miR-129-2-3p in breast cancer patients was about twice that in healthy people. Therefore, the results indicated that the RCA-based biosensor system could be a valuable platform for miRNA detection in clinical diagnosis and biomedical study. | Rolling circle amplification (RCA) -based biosensor system for the fluorescent detection of miR-129-2-3p miRNA
Herein, a versatile fluorescent bioanalysis platform for sensitive and specific screening of target miRNA (miR-129-2-3p) was innovatively designed by applying target-induced rolling circle amplification (RCA) for efficient signal amplification. Specifically, miR-129-2-3p was used as a ligation template to facilitate its ligation with padlock probes, followed by an RCA reaction in the presence of phi29 DNA polymerase. The dsDNA fragments and products were stained by SYBR Green I and then detected by fluorescence spectrophotometry. As a result, miR-129-2-3p concentrations as low as 50 nM could be detected. Furthermore, the expression of miR-129-2-3p in breast cancer patients was about twice that in healthy people. Therefore, the results indicated that the RCA-based biosensor system could be a valuable platform for miRNA detection in clinical diagnosis and biomedical study.
MicroRNAs (miRNAs) are a group of conserved, endogenous non-coding and short RNA (18–25 nucleotides) that regulate gene expression and play essential roles in cells, including proliferation, migration, differentiation, apoptosis and death (Xu et al., 2019; Gregory et al., 2004; Bartel, 2004; Sawyers, 2008; Ambros, 2001; Rossi, 2009). MicroRNAs negatively regulate gene expression via eliciting mRNA degradation or suppressing protein translation by targeting the 3′ or 5′ untranslated region (UTR) of the target gene (Luan et al., 2016; Shazadi et al., 2014, Bartel Wang et al., 2021). Past studies have found that miRNAs, as post-transcriptional regulators of gene expression, are closely associated with a variety of diseases, including cancer (Jiang et al., 2005; Luby & Zheng, 2017; Volinia et al., 2006), and are related to cancer initiation, progression and response to treatments (Brito et al., 2014; Schetter et al., 2008; Asaga et al., 2011). Accordingly, miRNAs extracted from serum or tumor tissue have been regarded as biomarkers for cancer diagnosis (Li et al., 2014; Cao et al., 2011; Krazinski et al., 2019). MiR-129-2-3p is a member of the miR-129 family and is abnormally expressed in some tumors (Xiao et al., 2015; Kang et al., 2013; Lu et al., 2013; Tian et al., 2015; Yang et al., 2015; Tang et al., 2016), which is thought to have an inhibitory effect on various types of tumors (Gao et al., 2016). MiR-129-2-3p plays a pivotal role in gastric cancer by restraining its migration and proliferation in vitro and slowing down gastric cancer growth in vivo via the inhibition of WWP1 (Ma et al., 2019; Yu et al., 2013a; Yu et al., 2013b). Moreover, some researchers also found that the expression of sex-determining region Y-box 4 (SOX4) was negatively correlated with the expression of miR-129-2-3p and miR-129-5p in gastric cancer (Yu et al., 2013a; Yu et al., 2013b). Overexpression of miR-129-2-3p significantly inhibits the proliferation and induces apoptosis of breast cancer cells (Tang et al., 2016). The expression levels of miR-129-2-3p in Ewing sarcoma tumor tissue samples are significantly lower than those in corresponding adjacent normal tissue samples (Tanoglu et al., 2021). The aberrant expression of the miR-129-2-3p is also detected in lung adenocarcinoma (Zhang et al., 2021). In human intrahepatic cholangiocarcinoma tissues and cell lines, the expression is notably decreased and the low expression of miR-129-2-3p is significantly correlated with distant metastasis and clinical stage (Huang et al., 2019). Furthermore, some studies have also reported that miR-129-2-3p is associated with other human diseases. A previous study reported that miR-129-2-3p directly regulates the translation of two genes involved in inflammatory responses and apoptosis (Ccr2 and Casp6), and overexpression of miR-129-2-3p can promote wound healing in type 2 diabetic mice (Umehara et al., 2019). MiR-129-2-3p levels are significantly reduced in patients with ischemic stroke (IS) and are negatively associated with the risk of IS (Chen et al., 2020). The expression of miR-129-2-3p is up-regulated in cortical brain tissue and plasma of refractory temporal lobe epilepsy patients (Sun et al., 2016). In the past few decades, some methods have been used to detect miRNA, including quantitative real-time polymerase chain reaction (qRT-PCR) (Chen et al., 2005), microarray (Thomson et al., 2004), northern blotting (Válóczi et al., 2004) and modified invader assay (Allawi et al., 2004). Some new detection methods have recently been invented, such as representative loop-mediated isothermal amplification (LAMP) (Li et al., 2011) and rolling circle amplification (RCA) (Xu et al., 2018). In this study, an RCA-based biosensor system was used to perform the amplification detection of miRNA (miR-129-2-3p) in vitro. This method achieves signal amplification and biosensing, which has exciting potential in clinical diagnosis.
The DEPC Treated Water (DEPC-H2O) and deoxyribonucleotides mixture (dNTPs) were purchased from Sangon Biotech (Shanghai, China). Phi29 DNA polymerase (10,000 U/mL) and 10 ×phi29 DNA polymerase reaction buffer, SYBR Green I were obtained from Thermo Fisher Scientific (Shanghai, China). T4 DNA ligase and 10 × T4 DNA ligase reaction buffer were provided by TaKaRa Biotechnology Co., Ltd. (Dalian, China). The padlock probe and oligonucleotides in this study were synthesized and PAGE purified by Sangon Biotech (Shanghai, China), the padlock probe was modified with the 5′-phosphate group. The sequences (5′–3′) were shown in Table 1.
The ligation was carried out in 10 µL of the reaction system. 1 µL of 10 × T4 DNA ligase buffer, 2 µL of 10 µM padlock probe, 2 µL of miRNA, and 4 µL of H2O were added to a PCR tube and heated at 90 °C for 3 min for annealing reaction, and then slowly cooled down to room temperature (RT). Subsequently, 350 U/mL T4 DNA ligase (1µL) was added to the reaction solution and incubated at RT for 3 h.
After the ligation reaction, 2 µL of 10 mM dNTP (1 mM), 2 µL of 10 × phi29 DNA buffer, 5.7 µL of H2O, and 0.3 µL of phi29 DNA polymerase (3U) were added to the tube and then kept at 30 °C for 3 h to induce the RCA reaction. Finally, the enzymatic reaction was stopped by maintaining the temperature at 65 °C for 10 min. RCA reaction was carried out in T1 Thermocycler (Biometra, Jena, Germany).
To detect the fluorescence of hybridization events of target/linear padlock probes, 6 µL RCA product and 2 µL of 100 × SYBR Green I were mixed, then incubated at RT for 30 min and diluted to a final volume of 200 µL with DEPC-H2O. The fluorescent spectra were detected by the Hitachi F-7000 fluorescence spectrometer (Hitachi, Ltd., Tokyo, Japan) at RT. The excitation wavelength was set to 480 nm with an emission range of 500 nm–700 nm. A fluorescence peak emission wavelength of 550 nm was recorded to evaluate the capability of our system.
The nucleic acids produced by the RCA reaction were analyzed by PAGE (polyacrylamide gel electrophoresis). Firstly, the dye was pre-mixed with 100 µL DiGelRed, 50 µL loading dye, and 2 µL cyber gold, then a 20 µL aliquot of RCA product solution was mixed with 20 µL mixed dye solution. Subsequently, 5 µL of the resulting solution load was placed into the lane for 30% PAGE. Gel electrophoresis was performed for 30 min at 195 V in a 5 × TBE solution. The ChemiDoc XRS imaging system (BIO-RAD, USA) was used to visualize the gel images.
The serum microarray datasets of breast cancer patients were extracted from Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo/). GSE73002 included 1670 breast cancer patients and 2,682 healthy volunteers. Receiver operating characteristics (ROC) curve analysis was performed to analyze the ability of miR-129-2-3p as a serum biomarker for breast cancer. ROC curve was generated with SPSS software.
The research consisted of 6 breast cancer samples and six healthy volunteer samples. All the patients under went breast resection at the First Affiliated Hospital of Fujian Medical University between January 2020 and June 2021. The inclusion criteria for patients were: (1) histologically confirmed breast cancer; (2) no history of other malignancy; (3) no prior neoadjuvant chemotherapy. The study was performed with the approval of the Ethics Committee of the First Affiliated Hospital of Fujian Medical University. Written informed consent was obtained from the patients, and specimens were stored in the hospital database and used for research.
In order to explore the expression of miR-129-2-3p in breast cancer, GSE73002 datasets were analyzed, revealing that the level of miR-129-2-3p was significantly higher in breast cancer than in healthy volunteers (shown in Fig. 1A). ROC curve analysis showed that miR-129-2-3p expression has potential diagnostic value for breast cancer. Data from the GSE73002 dataset showed that miR-129-2-3p may be an important diagnostic factor for breast cancer (Area Under Curve (AUC) = 0.928; 95% Cl [0.918–0.938]; p < 0.001; Fig. 1B).
The feasibility of the RCA-based biosensor system was verified with fluorescence spectral characteristics. Figure 2A showed the fluorescence emission spectra in the absence (curve b) and presence (curve a) of miR-129-2-3p. Low fluorescence intensity was observed in solutions without miR-129-2-3p. On the contrary, the fluorescence intensity was significantly enhanced after miR-129-2-3p was introduced into the solutions (curve a). These results indicated that miR-129-2-3p could induce ligation reactions, followed by RCA reactions, subsequently leading to the production of a large number of dsDNA fragments. To further explore the feasibility of our strategy, PAGE gel electrophoresis was performed, as shown in Fig. 2B. No bands were observed in the absence of miR-129-2-3p (lane a), but a distinct band appeared in the presence of miR-129-2-3p (lane b). Therefore, the results of fluorescence spectral characteristics and PAGE gel electrophoresis suggested that the RCA-based biosensor system can be used to detect miR-129-2-3p.
The incubation time for ligation and amplification and the concentration of phi29 DNA polymerase and dNTPs play a significant role in these experiments. Therefore, the experimental conditions were optimized to achieve the best performance. As illustrated in Fig. 3A, with the increase of ligation time, the fluorescence changes gradually increased and to stabilized within 3 h. Therefore, a 3 h ligation time was chosen for further experiments. Similarly, the number of RCA reaction products is closely related to the polymerization time, and the measured values are shown in Fig. 3B. Thus, the RCA reactions lasts for 3 h in the proposed biosensor system. Subsequently, the effects of the amount of phi29 DNA polymerase and the concentration of dNTPs on signal intensity were evaluated to further evaluate the biosensor system. All measured data are shown in Figs. 3C and 3D, respectively. Consequently, 2 mM dNTP and 3 U of phi29 DNA polymerase were used in subsequent experiments.
In order to test the capability of the RCA-based biosensor system for miRNA detection, different concentrations of miR-129-2-3p solution were detected. As shown in Fig. 4A, the fluorescence intensity increased with the increase of miR-129-2-3p concentration within 0–150 µM. It indicated that the change in fluorescence intensity reflected the concentration of miR-129-2-3p. As illustrated in Fig. 4B, there was a significant linear relationship between the fluorescence signal and target concentration in the range of 20 nM to 150 µM, and the correlation coefficient R2 was 0.9933. The detection limit (LOD) of the aptasensor was estimated to be 50 nM (S/N = 3). This is the first time that an RCA-based biosensor system has been used to detect miR-129-2-3p, which is expected to provide a sensitive detection method for miR-129-2-3p as a target marker in clinical practice. In the RCA-based biosensor system, the fluorescence signal is caused by SYBR Green I interacting with the dsDNA fragments, which determines the fluorescence signal intensity. Therefore, the padlock probe 2 was designed containing three palindromes to indicate the relationship between the fluorescence signal and the palindromic fragment number. Moreover, two random padlock probes without palindromes were designed to indicate the relationship between the fluorescence signal and the palindromic fragment number. As shown in Fig. 5A, the fluorescence intensity increased about 2 times when the number of palindrome fragments increased to three. As illustrated in Fig. 5B, the fluorescence signal of the two random padlock probes was lower than that of padlock probe1. It seemed that the performance of the RCA-based biosensor system for miR-129-2-3p detection could be further improved by optimizing the number of palindrome fragments.
Apart from the sensitivity of detection, specificity is another key factor for the application of this strategy for miR-129-2-3p analysis. The specificity of detection for miRNA is of great significance due to the short length and similar base sequence of miRNAs. So, five mutations of miR-129-2-3p (3p-A, 3p-B, 3p-C, 3p-D and 3p-E) and miR-129-2-3p were used to assess the detection specificity of the RCA-based biosensor system. As shown in Fig. 6, only target miR-129-2-3p elicited a high fluorescence signal. In contrast, the other five mutated miRNAs only induced slight signal changes: 33.1%, 19.4%, 1.5%, 10.6%, and 21.7%, respectively, compared with perfectly matched miR-129-2-3p. Therefore, the padlock probe1 in this work could specifically hybridize with miR-129-2-3p and promote subsequent reactions. The RCA-based biosensor system can be used to distinguish miR-129-2-3p from other non-target miRNAs.
To study the feasibility of this method in real sample analysis, the fluorescence intensity of miR-129-2-3p was detected in the serum of breast cancer patients and healthy people. As shown in Fig. 7, the fluorescence intensity of miR-129-2-3p in breast cancer patients is about twice that of healthy people.
MiR-129-2-3p is a member of the miR-129 family, and its abnormal expression is frequently detected in tumors; MiR129-2-3p is thought to have an inhibitory effect on various types of tumors. Over the past decades, different methods were used to detect miRNAs, including quantitative real-time polymerase chain reaction (qRT-PCR) (Chen et al., 2005), microarray (Thomson et al., 2004), northern blotting (Válóczi et al., 2004) and modified invader assay (Allawi et al., 2004). However, these methods still have some limitations in clinical diagnosis. For example, PCR may affect gene expression. Northern blotting is a time-consuming process with low sensitivity. Microarray cannot be used due to high cost, lower sensitivity, and poor reproducibility. Herein, a versatile fluorescent bioanalysis platform for sensitive and specific screening of target miRNA (miR-129-2-3p miRNA) was innovatively designed by using target-induced rolling circle amplification (RCA) for efficient signal amplification. The principle of the RCA-based biosensor system for detecting target miRNA is described in Scheme S1. This system consists of padlock probe 1 (including a palindrome sequence) complementary to the sequence of the target miRNA, target miRNA, ligase and polymerase, and SYBR Green I. In the presence of target miRNA, cyclized padlock probe 1 is obtained with the help of T4 DNA ligase. The RCA polymerization reaction is initiated in the presence of phi29 DNA polymerase and dNTPs. As a result, the RCA reaction produced a long single stranded DNA, many copies of dsDNA fragments of the target because of the self-hybridization of the palindromic sequences. Subsequently, the dsDNA binds to SYBR Green I, and the fluorescence signal can be detected by the fluorescence spectrometer. Through this method, as long as target miRNA and padlock probe1 are connected during the reaction, a large number of dsDNA fragments can be produced after RCA. Since the RCA products are long dsDNA, SYBR Green I is an asymmetrical cyanine dye used as a nucleic acid stain to enhance the fluorescence intensity. Therefore, the RCA-based biosensor system is likely to provide good sensitivity for miR-129-2-3p detection. As a result, miR-129-2-3p miRNA concentrations as low as 50 nM can be detected. In the analysis of real samples, the fluorescence intensity of miR-129-2-3p in breast cancer patients is about twice that in healthy people. Therefore, the results indicated that the RCA -based biosensor system has the potential to become a valuable platform for miRNA detection in clinical diagnosis and biomedical study.
In summary, we have developed a specific fluorescent detection method for miR-129-2-3p using a palindromic padlock probe in an RCA-based biosensor system. Target miRNA is used as a polymeric primer to hybridize with the padlock probe. The RCA reactions can easily occur in the presence of polymerases, which produces a large number of dsDNA fragments. SYBR Green I intercalates the dsDNA fragments, and the fluorescence signal is detected. Utilizing this RCA-based biosensor system, miR-129-2-3p can be detected at a concentration as low as 50 nM with a good linear response range, even using only one palindromic padlock probe. In the analysis of real samples, the expression of miR-129-2-3p in breast cancer patients was about twice that of healthy people. This research highlights the potential of this sensing system to detect miR-129-2-3p as a tumor biomarker in cancer diagnosis and prognosis. It also offers a new amplification technique for the biological studies of miR-129-2-3p.
10.7717/peerj.14257/supp-1 Click here for additional data file. 10.7717/peerj.14257/supp-2 Click here for additional data file. 10.7717/peerj.14257/supp-3 Click here for additional data file. 10.7717/peerj.14257/supp-4 Click here for additional data file. 10.7717/peerj.14257/supp-5 Click here for additional data file. 10.7717/peerj.14257/supp-6 Click here for additional data file. 10.7717/peerj.14257/supp-7 Click here for additional data file. 10.7717/peerj.14257/supp-8 Click here for additional data file. | true | true | true |
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PMC9610700 | Bowei Yuan,Congcong Yuan,Lulu Li,Miao Long,Zeliang Chen | Application of the CRISPR/Cas System in Pathogen Detection: A Review | 18-10-2022 | CRISPR/Cas,pathogen detection | Early and rapid diagnosis of pathogens is important for the prevention and control of epidemic disease. The polymerase chain reaction (PCR) technique requires expensive instrument control, a special test site, complex solution treatment steps and professional operation, which can limit its application in practice. The pathogen detection method based on the clustered regularly interspaced short palindromic repeats (CRISPRs) and CRISPR-associated protein (CRISPR/Cas) system is characterized by strong specificity, high sensitivity and convenience for detection, which is more suitable for practical applications. This article first reviews the CRISPR/Cas system, and then introduces the application of the two types of systems represented by Type II (cas9), Type V (cas12a, cas12b, cas14a) and Type VI (cas13a) in pathogen detection. Finally, challenges and prospects are proposed. | Application of the CRISPR/Cas System in Pathogen Detection: A Review
Early and rapid diagnosis of pathogens is important for the prevention and control of epidemic disease. The polymerase chain reaction (PCR) technique requires expensive instrument control, a special test site, complex solution treatment steps and professional operation, which can limit its application in practice. The pathogen detection method based on the clustered regularly interspaced short palindromic repeats (CRISPRs) and CRISPR-associated protein (CRISPR/Cas) system is characterized by strong specificity, high sensitivity and convenience for detection, which is more suitable for practical applications. This article first reviews the CRISPR/Cas system, and then introduces the application of the two types of systems represented by Type II (cas9), Type V (cas12a, cas12b, cas14a) and Type VI (cas13a) in pathogen detection. Finally, challenges and prospects are proposed.
In the natural environment, there are many microorganisms and parasites that can cause diseases as pathogens. An epidemic may threaten the life and health of humans and animals and cause significant economic loss. The occurrence of a serious outbreak often leads to irreversible losses or damage, so an early and rapid diagnosis of the disease is particularly important. The traditional microbial detection techniques are smear microscopy, pathogen isolation, culture and biochemical identification and serological examination. These traditional pathogen detection methods are relatively simple and convenient, can rapidly make preliminary judgments and do not require complex instruments, while still playing an important role at the grassroots level. In molecular biology, polymerase chain reaction (PCR) has been the mainstream choice for molecular diagnostics. This technology uses DNA high-temperature denaturation, low-temperature renaturation and extension at the optimum temperature of polymerase to achieve the amplification of DNA fragments [1]. The PCR instrument manufactured based on this principle is a temperature-control device. Seen as the variable temperature amplification of PCR requires expensive instrument control, special test sites, complex solution processing steps and professional operations, it cannot be satisfactorily utilized in practice; therefore, rapid detection methods that rely on isothermal nucleic acid amplification technology have attracted increasing attention. Common isothermal amplification techniques include loop-mediated isothermal amplification (LAMP) [2], recombinase polymerase amplification (RPA) and the like. LAMP and RPA read amplified signals using colorimetric indicators [3,4], turbidimetry [5], lateral-flow immunoassay [6,7], fluorescence [8,9] and electrochemical methods [10,11]. As the research on the CRISPR/Cas system has intensified, scientists have attempted to combine CRISPR/Cas effectors as biosensors with nucleic acid amplification technology (especially isothermal nucleic acid amplification technology) to develop a simpler, faster and more field-suitable pathogen detection method.
The CRISPR/Cas system is an adaptive immune defense system first discovered in Escherichia coli and used by bacteria and archaea as a defense against virus invasion. CRISPRs refers to clustered regularly interspaced short palindromic repeats, which was first described by Jansen et al. in a study of bacterial and archaeal genome sequences [12]. Its structure is shown in Figure 1. CRISPR sequences consist of repeat sequences and spacer sequences. Repeat sequences are repeating palindromic sequences arranged one after the other, separated by spacer sequences, while the DNA of the spacer sequences is not identical. Research has found that spacer sequences match viral DNA, especially phage DNA [13,14,15]. The upstream gene located at the CRISPR/Cas locus is named CRISPR-associated gene, or Cas gene for short. The Cas gene is closely linked to the CRISPRs site, and the Cas protein expressed thereby plays a key role in the realization of the CRISPR/Cas system function. Cas proteins have helicase and nuclease activities that can cut DNA strands; based on these findings, scientists have proposed the theory that the CRISPR/Cas system defends against virus invasion through three stages of adaptation, expression and interference (Figure 2) [14,16,17,18]. The diversity of the CRISPR/Cas system is ascribed to its abundance of effector proteins, site structures and molecular mechanisms. According to the different types of effector proteins in the interference phase described above, the system is mainly divided into two categories, including 6 types and 33 subtypes. Class 1 systems have complex effectors consisting of multiple Cas proteins that often perform multiple functions when the system is functioning. In contrast, Class 2 systems contain only one crRNA-binding protein. Class 1 comprises Types I, III and IV, and Class 2 includes Types II, V and VI [19,20]; because Class 2 CRISPR/Cas systems contain the properties of a single multidomain effector protein, they are more widely studied and applied than Class 1 systems. Next, the application of two types of systems represented by Type II (Cas9), Type V (Cas12a, Cas12b, Cas14a) and Type VI (Cas13a) in pathogen detection is introduced.
Cas9, as an endonuclease, contains two central HNH and RuvC that exert endonuclease activity. Jinek et al. found that Cas9 has a strong ability to cleave DNA under the mediation of tracrRNA and crRNA [21]. When the CRISPR/Cas system works, tracrRNA forms double-stranded RNA with pre-crRNA through complementary base pairing and assembles into a complex with the protein encoded by cas9. The spacer sequence is then cleaved under the action of RNase III, and finally a crRNA containing a spacer sequence RNA and a partial repeat sequence is formed. This complex binds to the target DNA under the guidance of crRNA, and then the two endonuclease active sites of Cas9 cut the DNA double-strand, in which the HNH site cuts one strand complementary to the crRNA, and RuvC cuts the other strand (Figure 3). Moreover, crRNA and part of the tracrRNA also play the role of guiding Cas9 when they are fused into single guide RNA (sgRNA) [21]. People use artificially designed sgRNA to guide Cas9 gene modification, and have performed various far-reaching applications, including knock-out [22,23], knock-in [24], gene repression or activation [24,25], multiplex editing [24] and functional genomic screens. When it comes to the application of the CRISPR/Cas9 system in pathogen detection, we must mention the work of Pardee et al. [26]. As mentioned above, the Cas9 protein can recognize PAM (protospacer adjacent motif) under the guidance of gRNA, and then exercise its ability to cleave (this is called cis-cleavage). They used the specific cleavage ability of Cas9 on different target sequences, combined with an isothermal RNA amplification technique called nucleic acid sequence-based amplification (NASBA) and the color reaction of filter paper (toehold activation) and developed a low-cost RNA virus detection method (NASBA-CRISPR cleavage, NASBACC). In this method, RNA is reverse-transcribed by NASBA, amplified to obtain dsDNA, and then Cas9 specifically recognizes and cuts dsDNA, and the signal is amplified by the filter-paper color reaction, which means it can accurately detect the Zika virus and use Cas9 to determine single-nucleotide polymorphisms to distinguish different strains with a sensitivity of 1 fM. Wang et al. developed a Cas9 nickase-based amplification reaction (Cas9nAR) method. Cas9nAR uses a sgRNA-Cas9n complex with single-stranded nicking properties, a strand-displacing DNA polymerase and two primers with Cas9n cleavage sequences, through the cyclic process of initiation, extension, nicking and replacement reactions. The target sequence in the sample genomic DNA is amplified at a constant temperature (37 °C). In a sensitivity test for the detection of Salmonella typhimurium, a detection limit similar to that of qPCR was achieved [27]. Although this method has the advantage of amplifying the target sequence without length limitation, compared to NASBACC, its operation is cumbersome, and it is more difficult to apply in practice. In addition, the two active sites of Cas9 were completely inactivated by amino acid mutation, and the resulting deactivated Cas9 (dCas9) still had the ability to bind target DNA under the guidance of sgRNA. Zhang et al. split the luciferase into two halves and then combined it with the dCas9 protein and designed a pair of sgRNAs targeting the upstream and downstream regions of the target DNA. When the dCas9 on both sides recognizes the target DNA and is adjacent, the luciferase activity is activated and emits a highly enhanced fluorescence signal, allowing for the detection of Mycobacterium tuberculosis [28]. Based on this property, several DNA detection methods have been developed (Table 1).
CRISPR/Cas12a is a member of the Class II CRISPR/Cas system [36]. The CRISPR/Cas12a system contains the Cas12a protein and a shorter CRISPR RNA (crRNA). Unlike Cas9, Cas12a does not require the assistance of RNA or other proteins in the process of processing pre-crRNA into mature crRNA, nor does it require RNase III [37]. Therefore, Cas12a can achieve the cleavage of the target without tracrRNA. Cas12a identifies double-stranded DNA (dsDNA) via a single crRNA, thereby inducing staggered DNA breaks on the non-targeting and targeting strands via the RuvC and Nuc endonuclease domains, respectively [38]. Cas12a recognizes the double-stranded DNA of T (thymine)-rich PAM under the guidance of crRNA, and then unzips the double-stranded DNA. After melting, the target strand in the double-stranded DNA is complementary to the crRNA, causing a conformational change, exposing the RuvC site, which cuts the non-target strand (NTS) in the double-stranded DNA, and then cuts the TS; this is referred to as the cis-cleavage activity of Cas12a. After cleavage, the Cas12a-crRNA complex remains bound to the target dsDNA, the NTS is trimmed and the cleavage product is released after cleavage, which leaves the still active RuvC site exposed and able to bind to ssDNA and exert trans-cleavage activity (Figure 4). Therefore, the specificity of Cas12a can be guaranteed by designing crRNA complementary to the target sequence. Then, combined with fluorescent probes or immunochromatography technology, nucleic acid detection can be achieved. Although CRISPR/Cas12a can be directly activated using unamplified target DNA in a sample, amplifying pathogenic nucleic acids to further improve its sensitivity is necessary. Li et al. integrated quenched fluorescent single-stranded DNA reporter probes with PCR amplification to develop a HOLMES detection platform for the rapid detection of target DNA and RNA (Figure 5). Before PCR amplification was added, the minimum detection limit was 0.1 nM, and after combining with PCR, the detection limit could be as low as 10 aM [39]. In addition to the combination of PCR technology, the introduction of isothermal nucleic acid amplification technology eliminates the dependence on high-end temperature control equipment, allowing for the detection method to be used beyond the laboratory and making it possible to implement on-site. Combining Cas12a single-stranded DNase activation with isothermal amplification, Chen et al. developed a method called DNA endonuclease-targeted CRISPR trans reporter (DETECTR); this method can identify HPV16 and 18 types in patient samples within one hour, and the method achieves the attomolar sensitivity of DNA detection [40]. Li et al. applied recombinase-mediated isothermal nucleic acid amplification to this system and established a simple and high-sensitivity detection platform for Listeria monocytogenes, with the detection limit reaching 26 cfu/mL [41]. Zhang et al. also combined recombinase polymerase amplification (RPA) technology with CRISPR/Cas12a to detect SARS-CoV-2 [42]. To make the entire system more convenient and faster, the researchers also added immunochromatographic test strips to visualize the detection results. Wang et al. developed a fast, sensitive, instrument-free ASFV detection method (CRISPR/Cas12a-LFD) based on CRISPR/Cas12a technology and lateral flow detection, wherein ssDNA reporters labeled with quenching fluorescent molecules or digoxigenin and biotin were used for fluorescence and lateral flow detection, respectively. The method is able to complete the entire process from sampling to result reading in one hour, and the sensitivity can reach 20 copies/reaction [43]. Some scholars have also made attempts at signal conversion of the detection system; Liu et al. utilized the signal-amplifying ability of Cas12a and simultaneously used l-methionine stabilized gold nanoclusters as efficient ECL emitters to achieve ECL signal transduction. The method can complete the detection of HPV16 within 70 min, and its detection limit can reach 0.48 pM [44]. Due to its robust sensitivity and operability, Cas12a has also become increasingly active in pathogen detection applications (Table 2). In addition to the above example of Cas12a as a signal sensor, scientists are also working on other easier and more efficient methods. Shen et al. also integrated magnetic nanoparticles with the system to detect Salmonella, which performed well in spiked chicken samples [55].
Like Cas12a, Cas12b, which is also from the Class II, Type V family, has trans-cleavage activity. That is, under the guidance of sgRNA, Cas12b can activate the ability to cleave any ssDNA in the system after it specifically recognizes and binds to the target DNA in the form of complementary base pairing. It was later found that trans-cleavage could be triggered regardless of whether the target is ssDNA or dsDNA. When targeting ssDNA, there is no cleavage site restriction (no PAM required), and the cleavage is faster; however, when dsDNA is used as the target, for Cas12b to perform efficient trans-cleavage, the target dsDNA is required to contain PAM. Based on the trans-cleavage characteristics of Cas12b, Li et al. developed a rapid nucleic acid detection method by combining Cas12b with LAMP isothermal amplification technology and named it HOLMESv2. This method can achieve a detection limit of 10−8 nM for the target, and the specificity reaches a single base [56]. Although they followed up with a one-step HOLMESv2 attempt, the sensitivity was much lower than that of the two-step method, which is also a possible direction for improvement in the future. In recent years, there have also been studies on Cas12b, for example, Sam et al. combined LAMP and Cas12b detection to develop a DNA detection platform for Mycobacterium tuberculosis, named tb-QUICK, with a detection limit of 1.3 copies/μL in two hours [57]. Huang et al. constructed a Cas12b-based detection system for Campylobacter jejuni, and determined the detection limit of the system to be 11 copies/μL by simulating contamination [58]. However, this study took too much time in the early sample processing and nucleic acid extraction stage, which is also a problem that must be considered when attempting rapid nucleic acid detection.
Like other Class II system effector proteins, Cas13a also has a bilobal structure, but it also shows different structural features. It includes a REC flap with Helical-1 domain and a NUC flap with two HEPN domains, as well as two RNase catalytic pockets responsible for cleaving Pre-crRNA and target RNA which are located on Helical-1 and HEPN domains, respectively. When crRNA binds to it to induce significant conformational changes, two conserved HEPN domains form an external catalytic site for cleavage of the target RNA [59]. As early as 2016, Abudayyeh, working in Zhang Feng’s team, proposed, for the first time, that Cas13a (C2c2) has a trans-cleavage activity that does not depend on the nucleic acid sequence [60]. The so-called trans-cleavage activity of Cas13a means that Cas13a forms a ternary complex with the target RNA under the guidance of crRNA, and then realizes the cleavage of any ssRNA in the system. Kellner and others, in the same team, took advantage of this cleavage activity to develop a nucleic acid detection method, named specific high-sensitivity enzymatic reporter unlocking (SHERLOCK) [61,62] (Figure 6). This is also the first time upon which the trans-cleavage activity of the Cas protein has been used to achieve in vitro nucleic acid detection, showing high sensitivity at the aM level and single-base resolution in the detection of the Zika virus and the dengue virus. Recently, some researchers have applied this technology more widely (Table 3). Although the first-generation SHERLOCK platform has been widely studied and applied, it still has some limitations. For example, the first generation of SHERLOCK can qualitatively detect nucleic acids but cannot provide quantitative data, and relies more on fluorescence reading equipment. To improve the detection performance, Feng’s team made improvements to the SHERLOCK system and created the second-generation SHERLOCK system—SHERLOCKv2 [63]. They used four Cas proteins with different cleavage preferences (LwaCas13a, PsmCas13b, CcaCas13b and AsCas12a) and individually designed specific reporters for them to detect the four viruses simultaneously. The researchers also found that the Cas13 cleavage product activates another Cas protein, namely Csm6,which can amplify the detection signal and further enhance the sensitivity of this method. The second single-stranded RNA structure can be cleaved by activated Csm6, which enhances the signal relative to background, improving the kinetics of the SHERLOCKv2 reaction, even achieving results without RPA amplification; compared to the first-generation method, SHERLOCKv2 uses far fewer primers in the pre-amplification step. The modified SHERLOCKv2 is not limited to the output of fluorescent signals, but can also be applied to test strip detection, which makes SHERLOCKv2 very easy to use. Provided that there is a sample, SHERLOCKv2 can play a role in the field. In nucleic acid detection, the sensing method of the signal is also particularly important. Due to the characteristics of low background noise and high signal output efficiency using fluorescence as a signal sensing method, various researchers have also used smartphones [74], microfluidic chips [75] and portable detectors [76] to record fluorescent signals. In addition to advances in fluorescence sensing, Heo et al. utilized reporter RNA (reRNA)-coupled electrochemical sensors as a signal output method. After the crRNA-Cas13a complex activates the activity of trans-cleavage through viral RNA, the cleavage of the reRNA immobilized on the electrode changes the current passing through the electrode, and then reading the current change can quantify the presence of the virus [77]. Liu et al. combined CRISPR/Cas13a with plasmon-enhanced fluorescence. Only reRNAs cleaved by Cas13a without activation could bind to plasmonic fluorescence; therefore, a higher signal intensity indicates that the amount of target RNA present in the original sample is lower. This signal-boosting method shows an almost 100-fold lower limit of detection.
Cas14 is a new DNA-targeting CRISPRs effector protein identified by Doudna et al. in the archaea “DPANN” phylum [78]. Cas14a, like Cas12a, is from the Type V family of the Class 2 system. Cas14a is the smallest Class 2 CRISPRs effector demonstrated to date, containing approximately 400–700 amino acids, half of the Cas9 protein (950–1400 amino acids). Akin to Cas12a, Cas14a also possesses RNA-guided ssDNA-targeting endonuclease activity (Figure 7). The Cas14a protein does not need to recognize the PAM site in the DNA sequence. By combining the non-specific single-stranded DNase activity of the Cas14 protein with isothermal amplification technology, it may be used for high-fidelity DNA single-nucleotide polymorphism genotyping and ssDNA virus detection [79].
We list the CRISPR/Cas proteins in the text and tabulate some of their properties for easy reading (Table 4).
Nucleic acid detection methods based on CRISPR/Cas biosensors have the advantages of strong specificity, high sensitivity and simple operation, and do not require instruments under some conditions. These methods can detect even trace amounts of virus and distinguish between different subtypes or mutations. They can also be integrated with various technologies to suit the needs of different scenarios. However, these techniques still have some problems (described next) that cannot be ignored.
Some CRISPR/Cas effector proteins, such as CRISPR/Cas12a, require PAM sequences to identify target dsDNA. On the one hand, this feature enhances the specificity of target recognition, but on the other hand, it also limits the range of selectable target sequences. Therefore, when detecting shorter sequences, or identifying single-nucleotide polymorphisms, there may be less room for selection, limiting its application. To reduce the dependence on the PAM sequence, the PAM sequence was introduced into the PCR product using primers containing PAM in HOLMES, so that HOLMES can detect dsDNA independently of the PAM sequence [39]. Wang et al. also used the LAMP amplification method to design the core primer containing the PAM site, allowing for the LAMP amplicon to contain a specific PAM site for CRISPR/Cas12a recognition. This method can thus detect any target sequence, even without targets containing PAM sites, as long as the design requirements of LAMP are met [80].
SHERLOCKv2 uses four Cas proteins with different cleavage preferences to cleave individually designed reporters, enabling multiplex detection [62]; however, this has strict requirements on the amount of Cas protein in the system, and different Cas proteins and reporters may cross-cut, thus affecting the results. Therefore, it will be more conducive to multiple detection to find Cas effector proteins with different trans-cutting preferences and avoid Cas effector proteins cutting the same reporter or Cas effector proteins cutting different reporters at the same time. Ackerman et al. also proposed a multiplex virus detection method combining a microwell array with CRISPR/Cas, called ARMEN (arrayed reactions for multiplexed evaluation of nucleic acids); this method can simultaneously distinguish at least 10 related viruses among 169 human diseases with published genome sequences and can identify subtypes of influenza A strains [81]. Welch et al. also introduced microfluidic technology and developed microfluidic combinatorial arrayed reactions for the multiplexed evaluation of nucleic acids, which has been used to detect SARS-CoV-2 [82]. In terms of quantitative detection, the amplification product can easily reach a saturated state due to the high amplification efficiency of the introduced amplification method, and the limitation of reporters makes it difficult to quantify high-concentration targets. Before the introduction of CRISPR/Cas effectors, it is generally necessary to pre-amplify the sample nucleic acid. This step also affects the true concentration of the original sample, making quantitative detection difficult.
How to pre-treat samples rapidly and without contamination is the key to the nucleic acid detection process, especially for complex samples. Most CRISPR/Cas-based pathogen detection methods have pre-processing steps for the original sample. In vitro amplification of nucleic acid may introduce a base mutation of the target sequence due to the fidelity of DNA polymerase, thus interfering with the detection results. Shinoda et al. employed CRISPR/Cas13 and microarray technology to detect non-amplified RNA directly [83]. Although the quantification of nucleic acids is achieved due to the absence of amplification, the sensitivity is reduced compared to pre-amplification followed by detection (fM). In addition, the results of targeted amplification may be false positive due to aerosol pollution. Therefore, in the future, we should look for easier nucleic acid extraction methods to save time, and try to directly detect non-amplifying targets for quantification, find a balance between the two or determine a strong Cas effector protein that is more suitable for complex sample environments without amplification.
When the CRISPR/Cas system is used for nucleic acid detection, a two-step method is generally used. The first step is to conduct nucleic acid pre-amplification, and the second step is to add effector protein complexes and reporters. This may expose the reaction system to RNase in the air during the second step, resulting in contamination and affecting the result. To avoid this, Li et al. integrated the target pre-amplification and biosensing stage in a one-step reaction to achieve the detection of SARS-CoV-2 [84]. Therefore, future research will also explore this aspect, which can not only simplify the operation, but also achieve the purpose of reducing pollution.
Although most of the pathogen detection technologies based on the CRISPR/Cas system are in the research and development stage, they will eventually be applied in practice. How to design this technique to make it more suitable for practical deployment is also a suggested direction for future research. In addition to the efforts in sample handling, avoidance of contamination and quantitative detection, more efficient signal-reading techniques also need to be discovered. Finally, efforts should be made to develop detection methods with strong specificity, high sensitivity, simple operation, controllable costs and simple reading, which are more suitable for practical deployment.
The use of CRISPR/Cas biosensing technology for pathogen detection remains in the research and development stage, and many aspects remain in the exploratory stage, such as the concentration of each component in the reaction system, reaction time, reaction temperature, nucleic acid extraction method and signal output method (each of which will affect the result). For this technique to be used in point-of-care testing, food safety monitoring, etc., it is necessary to customize standards or specifications that conform to practical applications.
Pathogenic microorganisms present in nature seriously threaten public health and the global economy. Therefore, the development of rapid, sensitive, specific, economical and field-applicable pathogen diagnosis methods can help control and prevent disease transmission. The CRISPR/Cas system is not only an excellent gene-editing tool, but also a powerful diagnostic technology. For example, techniques for targeting DNA using the recognition and cleavage capabilities of CRISPR/Cas9 effectors have been developed for molecular diagnostics. Unlike CRISPR/Cas9, CRISPR/Cas12 and CRISPR/Cas13 have been applied in biosensing scenarios due to their trans-cutting ability, creating a new era of molecular diagnosis. Although the pathogen detection technique based on the CRISPR/Cas system has the advantages of rapid detection, low cost and wide applicability, there are still many areas that need to be improved before the actual performance in the complex environments encountered in practice can be accepted, for example: find more efficient methods for original sample processing and simplify nucleic acid extraction steps; to realize multiple detection and quantification of several pathogens; to avoid contamination during the Cas protein effect stage. In conclusion, a pathogen detection technology based on the CRISPR/Cas system provides a new means for pathogen detection molecular methods, and it is now effective and developing rapidly. It is foreseeable that when various disciplines such as materials, communication and AI are applied thereto, the pathogen detection technology based on the CRISPR/Cas system will become a more promising and widely used in vitro pathogen-detection tool. | true | true | true |
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PMC9610715 | Alessandro Maugeri,Caterina Russo,Laura Musumeci,Giovanni Enrico Lombardo,Giovambattista De Sarro,Davide Barreca,Santa Cirmi,Michele Navarra | The Anticancer Effect of a Flavonoid-Rich Extract of Bergamot Juice in THP-1 Cells Engages the SIRT2/AKT/p53 Pathway | 11-10-2022 | Citrus,bergamot,flavonoids,natural products,Citrus bergamia,cancer,acute myeloid leukemia,sirtuins,SIRT2,apoptosis | Novel targets are constantly sought to fight hematologic malignancies. In this regard, high levels of SIRT2 expression are associated with unfavorable prognosis of acute myeloid leukemia. The interest in the plant kingdom has allowed the identification of ever-new anti-leukemic agents. Citrus × bergamia (bergamot) was proved to possess anticancer properties, yet no evidence is available regarding leukemia. For the first time, we studied the potential anti-leukemic effect of a flavonoid-rich extract of bergamot juice (BJe) in THP-1 cells, investigating the underlying mechanisms. Our findings showed that BJe reduced THP-1 cell proliferation, without affecting that of primary PBMCs, blocking the cell cycle in S phase and inducing apoptosis. Triggering of both extrinsic and intrinsic apoptotic pathways was witnessed by cleavage of caspase-8 and -9, which in turn activated caspase-3 and PARP. Interestingly, the increased p53 acetylation in THP-1 cells underlies SIRT2 inhibition by BJe, that was proved also in the isolated enzyme. Moreover, BJe hampered SIRT2 also by lowering its gene expression. Finally, BJe reduced AKT phosphorylation, which we hypothesized being the joining link between SIRT2 and p53, that play a pivotal role in BJe-induced cell cycle arrest and apoptosis in THP-1 cells. Our results suggest BJe as a potential anti-leukemic agent, via targeting of the SIRT2/AKT/p53 pathway. | The Anticancer Effect of a Flavonoid-Rich Extract of Bergamot Juice in THP-1 Cells Engages the SIRT2/AKT/p53 Pathway
Novel targets are constantly sought to fight hematologic malignancies. In this regard, high levels of SIRT2 expression are associated with unfavorable prognosis of acute myeloid leukemia. The interest in the plant kingdom has allowed the identification of ever-new anti-leukemic agents. Citrus × bergamia (bergamot) was proved to possess anticancer properties, yet no evidence is available regarding leukemia. For the first time, we studied the potential anti-leukemic effect of a flavonoid-rich extract of bergamot juice (BJe) in THP-1 cells, investigating the underlying mechanisms. Our findings showed that BJe reduced THP-1 cell proliferation, without affecting that of primary PBMCs, blocking the cell cycle in S phase and inducing apoptosis. Triggering of both extrinsic and intrinsic apoptotic pathways was witnessed by cleavage of caspase-8 and -9, which in turn activated caspase-3 and PARP. Interestingly, the increased p53 acetylation in THP-1 cells underlies SIRT2 inhibition by BJe, that was proved also in the isolated enzyme. Moreover, BJe hampered SIRT2 also by lowering its gene expression. Finally, BJe reduced AKT phosphorylation, which we hypothesized being the joining link between SIRT2 and p53, that play a pivotal role in BJe-induced cell cycle arrest and apoptosis in THP-1 cells. Our results suggest BJe as a potential anti-leukemic agent, via targeting of the SIRT2/AKT/p53 pathway.
Acute myeloid leukemia (AML) is a complex hematological disease characterized by an uncontrolled proliferation of immature myeloid cells (blasts) which, due to their accumulation in the bone marrow, impair normal hematopoiesis. The incidence of AML increases with age and, despite being the most common type of leukemia in adults, it continues to be associated with the lowest survival rate among all the leukemias [1]. Recent advances have shed light on the pathogenesis of AML, revealing its considerable genetic and clinical heterogeneity, which accounts for the constant need to find new treatment strategies for obtaining satisfying therapeutic outcomes and enhanced quality of life of AML patients [2]. Currently, histone deacetylase inhibitors (HDACi) appear to represent a promising therapy for cancer treatment, and an emerging scenario for AML patients unresponsive to conventional chemotherapy [3,4]. Sirtuins, known as silent information regulator proteins (SIRTs), belong to the group of the NAD+-dependent class III histone deacetylases, coming into play in numerous physio–pathological processes, such as the extension of lifespan, age-related disorders, obesity, cardiovascular diseases, neurodegenerative events, and cancer, including leukemias [5]. In this regard, SIRT2, a member of the sirtuin family, has been found to be involved in proliferation and survival of AML cells. This is because mRNA levels of SIRT2 are up-regulated in cells from patients with high-risk AML, where this sirtuin promotes NADPH production by deacetylating the enzyme glucose-6-phosphate dehydrogenase (G6PD) or the phosphorylation of AKT kinase [6,7]. In addition, SIRT2 was also suggested to mediate multidrug resistance of AML cells via ERK1/2 signaling pathways [8]. All this evidence has led SIRT2 to be considered as an unfavorable prognostic biomarker of AML [9] and a new possible target for therapy. In parallel, an increasing interest in the plant kingdom allowed the identification of new chemical entities for the development of anticancer agents, also in the field of leukemia therapy [10]. Among these, flavonoids, plant secondary metabolites found in vegetables and in a variety of Citrus fruits, have gained widespread approval, thanks to their pharmacological properties [11], such as neuroprotective [12,13], antioxidant and anti-inflammatory [14,15], as well as anti-cancer properties [16]. The anti-proliferative, pro-apoptotic and cell cycle-arresting effects of flavonoids contribute to define their anti-leukemic potentiality [17]. In the last decades, Citrus × bergamia Risso et Poiteau (bergamot), a small evergreen tree belonging to the Rutaceae family, was widely appreciated for its fruits, from which bergamot essential oil (BEO) and bergamot juice (BJ) are obtained. BEO is mainly exploited by the perfume industry and was recently studied for its beneficial effects [18,19]. Contrarily, BJ has long been considered a waste product of the essential oil industry, until it was revaluated by the scientific community, thanks to its notable biological properties, which include being anti-infective [20,21], anti-inflammatory [22] and neuroprotective [23]. The flavonoid fraction of BJ (BJe) appears to play a key role in the anticancer effects of bergamot, as documented in both in vitro and in vivo models [24,25]. Nonetheless, no evidence is currently available on the potential role of BJe in AML management. Considering this background, our study was designed to investigate the anti-proliferative effect of BJe in human leukemia monocytic THP-1 cells, focusing on the potential involvement of SIRT2 underlying the mechanism of action.
Bergamot fruits were purchased from the local markets of Reggio Calabria (Italy). After peel removal, fruits were hand-squeezed to remove the primary juice. The residual pulp was processed by a pressing machine to obtain the secondary juice, which was extracted and concentrated, following an SPE extraction by a SupelcleanTM LC-18 SPE cartridge (Supelco Ltd., Bellefonte, PA, USA) according to the procedures described by the seller. The final elution to obtain the flavonoid-rich bergamot fraction was performed with ethanol (utilized as a green solvent) and the eluate was brought to dryness by lyophilization. The obtained powder was stored in the dark at 4 °C prior to use.
Neohesperidin, neoeriocitrin, neodiosmin, vicenin-2, rhoifolin and naringin were supplied by Extrasynthèse (Genay, France) and used as standards. The Iso-Disc P-34, 3 mm diameter PTFE membrane (0.45 μm pore size) was from Supelco (Bellefonte, PA, USA). All the other reagents and chemicals employed in this study were of analytical grade and were purchased from Sigma-Aldrich (Milan, Italy).
A solution of phosphate saline buffer (PBS)/dimethylformamide (0.9:0.1 v/v) was used to solubilize the lyophilized powder, reaching the final concentration of 1.25 mg/mL. The obtained solution was centrifuged for 5 min at 3200 rpm. The supernatant was filtered utilizing an Iso-Disc P-34, 3 mm diameter PTFE membrane and separated by reverse phase high performance liquid chromatography (RP-HPLC).
The separation and identification of compound present in the lyophilized powder were performed using a RP-HPLC with diode array detector (RP-HPLC-DAD) according to Cirmi et al. [13].
The acid hydrolysis has been carried out on BJe based on a previously published work [26].
The human leukemia monocytic THP-1 cell line and human peripheral blood mononuclear cells (PBMCs) were originally obtained from ATCC (Rockville, MD, USA). THP-1 cells were cultured in RPMI 1640 medium with the addition of 10% (v/v) heat-inactivated fetal bovine serum (FBS), penicillin (100 IU/mL) and streptomycin (100 µg/mL), L-glutamine (2 mM), HEPES (10 mM), sodium pyruvate (1 mM), glucose (2.5 g/L), 2-mercaptoethanol (0.05 mM), at 37 °C in a 5% CO2 air humified atmosphere. Each reagent for cell growth was from Gibco (Life Technologies, Monza, Italy). Similarly, PBMCs were cultured in RPMI 1640 supplemented with 10% FBS.
Cell proliferation was determined by the 3-(4,5-dimethylthiazole-2-yl)-2,5-diphenyltetrazolium bromide (MTT) test, as described [27]. Briefly, THP-1 monocytes/PBMCs were seeded in 96-well plates at a density of 5 × 104 cells/well. After 24 h, cells were incubated with fresh medium (for untreated cells) or with medium supplemented with increasing concentrations of BJe (1, 2.5 and 5 mg/mL). After 24, 48 and 72 h, plates were centrifuged to remove supernatants and incubated with phenol red-free fresh medium containing 0.5 mg/mL of MTT (Sigma-Aldrich) at 37 °C for 4 h. Then, the formazan crystals, formed in the wells, were dissolved in 100 µL of a 0.1 N HCl/isopropanol lysis solution. The absorbance of each well was spectrophotometrically measured at a wavelength of 570 nm (reference at 690 nm), using a microplate reader (Bio-Rad Laboratories, Milan, Italy). Results were expressed as percentage of cell viability, compared to untreated cells which were arbitrarily set as 100%. All experiments were performed in eight replicates and repeated three times.
Propidium iodide (PI) exclusion assay was performed to test the cytotoxicity and the membrane integrity after exposure to BJe, as described [28]. Then, THP-1 cells and PBMCs were plated into 24 well-plate (5 × 105 cells/well) and treated with BJe (1, 2.5 and 5 mg/mL) for 24, 48 and 72 h. Afterwards, cells were collected by centrifugation, washed and resuspended in 100 µL of PBS, and then incubated with 10 µL of PI labeling solution (10 µg/mL; Sigma-Aldrich) in darkness at room temperature for 30 min. Dead cells, stained with this DNA intercalating probe, were analyzed by a Novocyte 2000 cytofluorimeter (ACEA Biosciences Inc., San Diego, CA, USA) with FL-2 channel. A minimum of 10,000 events were counted per sample. Percentage of dead cells was calculated versus non-treated cells.
The progression of cells through cell cycle phases was evaluated by PI staining, as described [19]. Briefly, THP-1 cells were seeded in 6-well plates (2 × 105 cells/mL) and, the day after, treated with the BJe (1, 2.5 and 5 mg/mL) for 24, 48 and 72 h. Then, cells were harvested, washed with PBS and fixed with 70% ice-cold ethanol for at least 2 h at 4 °C. Thereafter, cells were washed twice with cold PBS and resuspended in 250 μL of PBS together with 5 μL of RNase A (10 mg/mL; Sigma-Aldrich). After 1 h of incubation at 37 °C, 10 µL of PI (1 mg/mL) were added to samples, which were immediately acquired by Novocyte 2000 cytofluorimeter. Three independent sets of at least 10,000 events were analyzed for each condition.
The involvement of apoptosis was assessed by performing the Annexin V-fluorescein isothiocyanate (FITC)/PI staining, which discriminates from early apoptosis, late apoptosis and necrosis [13]. Briefly, THP-1 cells were seeded in 6-well plates (2 × 105 cells/mL) and, the day after, treated with the BJe (1, 2.5 and 5 mg/mL) for 24, 48 and 72 h. At the end of the treatments, cells were harvested, washed with cold PBS and resuspended in 200 µL of binding buffer 1×, following kit guidelines (BD Biosciences, Milan, Italy). Thereafter, 5 μL of Annexin V-FITC were added to each sample, gently vortexed and incubated in darkness at room temperature for 15 min. After incubation, cells were washed and resuspended in 190 µL of binding buffer plus 10 µL of PI (20 μg/mL). Samples from three independent experiments were analyzed by a Novocyte 2000 flow cytometry, by setting a minimum of 10,000 events for each condition.
An enzyme-linked immunosorbent assay (ELISA) was performed in THP-1 cells treated for 24 h with BJe (1, 2.5 and 5 mg/mL) or with SIRT2 inhibitors, SirReal2 (10 µM; Selleckchem, Houston, TX, USA) and nicotinamide (NAM, 1 mM; Cayman, Ann Arbor, MI, USA), in order to detect the levels of acetylated p53, employing a commercial kit (Biovision, Milpitas, CA, USA). Briefly, protein concentration from cell lysates was determined using Bio-Rad DC Protein Assay (Bio-Rad Laboratory, Hercules, CA, USA) with bovine serum albumin as standard. Equal amounts of proteins for sample were incubated with 100 µL of biotin-conjugated primary antibody for 1 h at 37 °C in provided strips. After washing, 100 µL of streptavidin HRP-conjugated were added to each well and incubated for an additional 30 min at 37 °C. Then, the plate was incubated with 90 μL of TMB substrate at 37 °C in darkness for further 30 min. Finally, 50 µL of stop solution were added to each well to arrest the color formation and absorbance was read with a microplate spectrophotometer (iMark™ microplate reader, Bio-Rad Laboratories) at 450 nm wavelength. Results were calculated as ratio between values detected in untreated and treated cells.
SIRT2 activity assay was performed using a SIRT2 direct fluorescent screening assay kit (Cayman Chemical, Ann Arbor, MI, USA), according to the manufacturer’s protocol. Increasing concentrations of BJe (0.01, 0.1 and 1 mg/mL) were tested, employing the recombinant enzyme provided by the kit. The SIRT2 inhibitor, SirReal2 (140 nM), was used as positive control. Briefly, a substrate solution containing 2 mM nicotinamide adenine dinucleotide (NAD+) and 125 μM peptide was added to the reaction mixture and incubated for 45 min at 37 °C. Following, a stop/developing solution consisting of developer plus nicotinamide was added to each sample to stop the reaction for 30 min. Thereafter, the emitted fluorescence, index of SIRT2 deacetylase activity, was read using a FLUOstar Omega Plate Reader (BMG LABtech, Ortenberg, Germany) at 350–360 nm excitation wavelength and 450–465 nm emission wavelength.
To quantify the gene expression of SIRT2, p53 (TP53), and caspases-8, -9 and -3 (CASP8, CASP9 and CASP3, respectively), THP-1 cells were seeded in 100 mm Petri dishes (1 × 106 cells/dish) and incubated with fresh medium (untreated cells) or BJe (1, 2.5 and 5 mg/mL), for 6, 12 and 24 h (SIRT2) or for 12 h (TP53, CASP8, CASP9 and CASP3) at 37 °C. Afterwards, RNA extraction from untreated and treated cells was performed employing TRIzol reagent (Invitrogen, Carlsbad, CA, USA), according to the manufacturer’s protocol. An equal amount of total RNA (2 µg) for each sample was reverse transcribed into cDNA employing the High-Capacity cDNA Archive Kit (Applied Biosystems, Life Technologies, Foster City, CA, USA), as previously described [29]. Then, quantitative PCR reaction (qPCR) was carried out in a 96 well-plate, using a 7500 qPCR System (Applied Biosystems), in a total volume of 20 µL, including 1x SYBR® Select Master Mix (Applied Biosystems), 0.2 µM of specific primers and 25 ng of RNA converted into cDNA. Primer sequences used for qPCR are listed in Table 1. Data collected were analyzed using the 2−∆∆CT relative quantification method versus β-actin (ACTB), used as endogenous control. The values are presented as n-fold change with respect to untreated cells.
For the evaluation of protein expression, THP-1 cells were grown in 100 mm Petri dishes (1 × 106 cells/dish) and exposed to increasing concentrations of BJe (1, 2.5 and 5 mg/mL) for 24 h. At the end of treatments, cells were harvested, washed with PBS and lysed using RIPA buffer (Sigma-Aldrich), supplemented with 1% cocktail of protease and phosphatase inhibitors (Sigma-Aldrich). The lysed cells were centrifuged at 12,000× g for 15 min at 4 °C and supernatant was collected. For each sample, the protein concentration of supernatant was determined using Bio-Rad DC Protein Assay (Bio-Rad Laboratory) and bovine serum albumin as standard. Equal amounts of proteins (30 μg/lane) were separated by 10% sodium dodecyl sulphate–polyacrylamide gel electrophoresis (SDS-PAGE) and electro-transferred on a polyvinylidene fluoride (PVDF; GVS Life Sciences, ME, USA) or nitrocellulose membranes (Merck Millipore, Darmstadt, Germany), where non-specific binding sites were blocked with 5% (w/v) non-fat dry milk for 1 h at room temperature. Thereafter, membranes were incubated overnight at 4 °C with the following primary antibodies: rabbit monoclonal anti-cleaved caspase-8, anti-caspase-9, anti-caspase-3, anti-poly ADP ribose polymerase (PARP), anti-phospho-AKT and anti-AKT, all diluted 1:1000 in milk or BSA and purchased from Cell Signaling Technology (Danvers, MA, USA). Mouse monoclonal anti-p53 (1:200) was purchased by Thermo-Fisher Scientific (Rockford, IL, USA). Similarly, mouse monoclonal anti-β-actin-peroxidase (1:50,000) was from Sigma-Aldrich. Then, membranes were washed thrice in Tris-buffered saline containing 0.15% of Tween 20 (TBST) and incubated with horseradish peroxidase-conjugated goat anti-mouse or anti-rabbit IgG secondary antibodies (1:5000, Sigma-Aldrich) for 2 h at room temperature. Chemiluminescence of protein bands was obtained using Luminata Forte Western HRP Substrate (Merck Millipore) and visualized by a chemiluminescent detection system C-Digit Blot Scanner (Li-COR Bioscience, Lincoln, NE, USA). Protein bands were quantified using Image Studio software (Li-COR Bioscience). The β-actin was used as housekeeping protein.
Data from three sets of experiments performed in triplicate were expressed as mean ± standard error of the means (SEM). They were statistically evaluated for differences using one-way analysis of variance (ANOVA), followed by the Dunnett’s multiple comparison test (GraphPad Prism Software for Science, San Diego, CA, USA). p-values less than or equal to 0.05 were considered significant.
RP-HPLC-DAD separation was carried out to identify compounds present in the lyophilized powder. The preliminary analysis of chromatographic separation performed at 280 and 325 nm allowed us to discriminate between flavanone and flavone basic skeletons present in the extract. The two chromatograms reported in the Figure 1 showed a prevalence of compounds 2, 4, 6 possessing a flavanone skeleton than compounds 1, 3, 5 belonging to the flavone class. The treatment with aqueous HCl revealed that compounds 2, 4, 6 are not resistant to acid hydrolysis, suggesting the presence of O-linked saccharide moieties in their aglycone structure (data not shown). By comparing the retention time, UV spectra and spiking of these compounds with pure reference compounds, three main peaks of chromatogram were identified as neoeriocitrin (2), naringin (4) and neohesperidin (6). In particular, neohesperidin and naringin represent, by far, the most abundant flavonoids of BJe (0.59 ± 0.037 and 0.44 ± 0.017 mg/mL respectively), since they account for about 83% than whole amount of extract employed to perform the analysis, followed by neoeriocitrin (0.11 ± 0.011 mg/mL). The other three compounds (1, 3, 5) showed UV spectra with maxima of absorption in the 240–280 nm range (called band II) and additionally in the 300–380 nm range (called band I). Compound 1 demonstrated resistance to acid hydrolysis, suggesting the presence of C-linked saccharide moieties in its aglycon structure, while compounds 3 and 5 were not resistant to acid hydrolysis. Therefore, based on the retention time, UV spectra and spiking of the flavones 1, 3, 5 with commercial standards, the remaining three peaks of the chromatogram were identified as vicenin-2 (1), rhoifolin (3) and neodiosmin (5). They were present in an amount well below 0.1 mg/mL. The quantitative determination of flavonoids identified in BJe was depicted in Table 2.
To investigate the effects of BJe on cell proliferation, the MTT test was performed. Figure 2A shows that treatment with BJe was able to hamper THP-1 cell proliferation at the reported times of incubation. After 24 h, only 5 mg/mL of BJe induced a significant decrease of cell proliferation (−48.4 ± 4.6%; p < 0.0001 vs. CTRL). Instead, despite to different extent, both 2.5 and 5 mg/mL of BJe significantly decreased THP-1 cell growth, after 48 h (−22 ± 4%, p < 0.001 and −71.7 ± 5%, p < 0.0001, vs. CTRL, respectively) and 72 h (−39 ± 3%, p < 0.0001 and −82.8 ± 5%, p < 0.0001, vs. CTRL, respectively) of treatment, reaching an IC50 of 2.92 ± 0.32 mg/mL at the latter time point. The concentration of 1 mg/mL did not impair cell growth at any time tested (Figure 2A). Noteworthy, BJe did not significantly alter the proliferation of normal human PBMCs at the same times and concentrations tested in cancer cells (Figure 2B).
At this point, we assessed the potential cytotoxic effect underlying the above documented anti-proliferative activity of BJe, by PI staining. As shown in Figure 3A, the 5 mg/mL concentration induced a cytotoxic effect in THP-1 cells (48.6 ± 2%, p < 0.0001 vs. CTRL) already after 24 h of exposure, whereas the BJe 2.5 mg/mL induced cell death (21.2 ± 3.1%, p < 0.001 vs. CTRL) only after 48 h. Conversely, BJe 1 mg/mL did not exert any cytotoxicity in THP-1 cells (Figure 3A), corroborating the outcome obtained by MTT assay. Again, BJe did not cause any significant increase in PBMCs cell death at any of the timings or concentrations evaluated (Figure 3B).
Following, we investigated whether the anti-proliferative activity of BJe may be linked to its potential capacity to influence the THP-1 cell cycle distribution. Figure 4 shows that the treatment with 2.5 and 5 mg/mL of BJe increased the cell population in S phase (up of 38.3 ± 3% and 31.2 ± 2.8% vs. CTRL after 72 h, respectively), thus decreasing the number of cells in G0/G1 one (down of 29.7± 1.9% and 41.7± 2% vs. CTRL after 72 h, respectively), as well as those in G2/M one (down of 9.4 ± 2% and 7.7 ± 2.2% vs. CTRL after 72 h, respectively). Notably, a modulation of the cell cycle was appreciated already after 24 h of exposure to BJe 5 mg/mL and after 48 h to BJe 2.5 mg/mL, reaching a more evident S phase arrest at longer times. The 1 mg/mL concentration demonstrated not to be able to alter the cell cycle of THP-1 cells at any of the tested timings (Figure 4).
In order to evaluate if apoptosis was implied in the cell death induced by BJe in THP-1 cells, the Annexin V-FITC/PI cytofluorimetric assay was performed. As shown in Figure 5, treatment with 2.5 and 5 mg/mL of BJe for 24 h increased the percentage of cells undergoing apoptosis (both in early and late) up to 11.6 ± 2.1% and 47.8 ± 2.3%, respectively. After 48 h, both 2.5 and 5 mg/mL of BJe induced apoptosis up to 17.5 ± 2.1% and 64.4 ± 2.2%, respectively, and up to 37.3 ± 2.5% and 80 ± 2.5% after 72 h, respectively. Finally, according to cell viability assays, the 1 mg/mL concentration of BJe did not trigger apoptosis at any of the tested timings (Figure 5).
Based on the Annexin V-FITC/PI staining assay results, we focused on the molecular mechanisms underlying the programmed cell death to ascertain whether BJe triggered an extrinsic or intrinsic apoptotic pathway. For this reason, we evaluated the cleavage of caspases, known for their pivotal role in this process, by Western blotting, as well as their gene expression by RT-PCR. Interestingly, as shown in Figure 6A, both 2.5 and 5 mg/mL BJe significantly increased gene expression of CASP8 by 1.38 ± 0.06-fold and 1.5 ± 0.05-fold (p < 0.01 and p < 0.001 vs. CTRL), respectively. BJe also augmented CASP9 mRNA levels by 1.48 ± 0.06-fold and 1.55 ± 0.08 (for both p < 0.001 vs. CTRL), respectively, while CASP3 by 1.4 ± 0.06-fold and 2.06 ± 0.05-fold (p < 0.01 and p < 0.0001 vs. CTRL), respectively (Figure 6A). BJe significantly promoted the cleavage of both caspase-8 and -9 proteins, initiators of extrinsic and intrinsic apoptotic pathway, respectively. In detail, the exposure of THP-1 cells to 2.5 and 5 mg/mL enhanced the levels of cleaved caspase-8 of 13 ± 0.29-fold and 14.8 ± 0.29-fold (for both p < 0.0001 vs. CTRL), as well as cleaved caspase-9 of 1.58 ± 0.08-fold and 1.37 ± 0.07-fold (p < 0.001 and p < 0.01 vs. CTRL). Consequently, caspase-3 cleavage was observed at BJe 2.5 mg/mL and 5 mg/mL concentrations (2.2 ± 0.19-fold, p < 0.01, and 5.05 ± 0.25-fold, p < 0.0001 vs. CTRL, respectively), which in turn activated PARP, downstream effector of caspase-3, with a significant increase of its cleaved form (13.8 ± 0.22-fold for BJe 2.5 mg/mL, p < 0.0001 and 26.8 ± 0.45-fold for BJe 5 mg/mL, p < 0.0001 vs. CTRL; Figure 6B,C). Therefore, the activation of the two caspases proved that BJe induced THP-1 cell death by initiating both extrinsic (caspase-8) and intrinsic (caspase-9) apoptotic pathways.
Recently, SIRT2 was depicted as an unfavorable prognostic marker of AML [9]. For this reason, we investigated the potential modulatory effect of BJe on the SIRT2 enzyme by quantifying the levels of acetylated p53 protein, a well-known SIRT2 substrate. The exposure of THP-1 cells to 1 mg/mL concentration of BJe for 24 h did not alter levels of acetylated p53. Conversely, both 2.5 and 5 mg/mL of BJe significantly increased p53 acetylation (1.55 ± 0.07-fold, p < 0.0001, and 1.84 ± 0.09-fold, p < 0.0001, vs. CTRL, respectively; Figure 7). In addition, to ensure a reliable comparison of these results, we used a specific (SirReal2, 10 µM) and a non-specific (nicotinamide, NAM, 1 mM) SIRT2 inhibitors, as positive controls. Similar to BJe, both inhibitors significantly induced an increase of levels of acetylated p53 with SirReal2 up to 1.27 ± 0.03-fold and NAM up to 1.70 ± 0.05-fold (p < 0.01 and p < 0.0001 vs. CTRL, respectively), suggesting that BJe acted as a SIRT2 inhibitor.
Given the encouraging results obtained in vitro, we wanted to verify whether BJe could directly inhibit the enzymatic activity of SIRT2. To this purpose we assayed its activity employing a cell-free model, consisting of the isolated recombinant enzyme. Interestingly, results of these experiments showed that BJe, at each concentration tested, was able to significantly inhibit the deacetylase activity of SIRT2, confirming the outcomes obtained in vitro (Figure 8). In particular, BJe displayed the strongest inhibitory effect at a concentration of 1 mg/mL, reaching a decrease of 89.9 ± 3% (p < 0.0001, vs. CTRL). Moreover, the 0.1 mg/mL concentration significantly reduced the enzymatic activity of 40 ± 3% (p < 0.001 vs. CTRL), whereas BJe 0.01 mg/mL showed the weakest inhibitory activity against SIRT2 (14.6 ± 2%, p < 0.05 vs. CTRL). Therefore, the calculated IC50 value for BJe was 0.13 ± 0.01 mg/mL. The specific SIRT2 inhibitor SirReal2 was employed at the IC50 concentration (140 nM), as a positive control.
After ascertaining the capability of BJe to inhibit SIRT2 enzymatic activity in both cell-based and cell-free settings, we wondered whether this could be also due to an alteration of SIRT2 gene expression induced by BJe in THP-1 cells (Figure 9). Worthy of note, the results of RT-PCR analysis showed a significant lowering of SIRT2 gene level compared to untreated cells of 1.6 ± 0.06-fold down with 2.5 mg/mL, and of 1.8 ± 0.05-fold down with 5 mg/mL of BJe (for both p < 0.0001 vs. CTRL), already after 6 h of treatment. After 12 h of incubation with BJe, the inhibitory effect on SIRT2 gene expression was almost unchanged with the 2.5 mg/mL (1.49 ± 0.05-fold down, p < 0.0001 vs. CTRL) and was more marked with the 5 mg/mL (3.12 ± 0.06-fold down, p < 0.0001 vs. CTRL). This early modulation of SIRT2 mRNA levels by BJe was attenuated after 24 h, staying significant only for the 5 mg/mL. Again, no significant modulation of SIRT2 levels was observed with 1 mg/mL concentration, at any of the tested timings (Figure 9).
Acknowledging the relevance of the crosstalk between the kinase AKT and p53 in the triggering of apoptosis, as well as the role of SIRT2 in its modulation, we then hypothesized that BJe could alter this pathway to elicit its pro-apoptotic effects (Figure 10). Western blotting data indicated a significant modulation of these factors by BJe (Figure 10A,B). In detail, the phosphorylation levels of AKT significantly decreased in response to BJe after 24 h of incubation, whereas total AKT protein levels kept constant throughout the course of the experiment. This occurred both at 2.5 and 5 mg/mL concentrations, where p-AKT levels lowered of 1.54 ± 0.03-fold and of 2.17 ± 0.03-fold (for both p < 0.0001, vs. CTRL), respectively. Results on p53 demonstrated that BJe was able to modulate the expression of p53 both at gene and at protein level. In the latter case, there was a significant growth in the protein amount with 2.5 mg/mL (of 1.41 ± 0.04-fold) and 5 mg/mL (of 1.50 ± 0.05-fold) concentrations of BJe (for both p < 0.001 vs. CTRL). The results of p53 protein expression reflected and strengthened data obtained from RT-PCR analysis (Figure 10C). Therefore, these outcomes corroborate our assumption that BJe induced apoptosis via inhibiting SIRT2 activity, which in turn decreased the activation of AKT and consequently augmented p53 activity, thus prompting cells to undergo apoptosis (Figure 10D).
The link between nutrition and cancer has garnered an ever-growing interest for the protective effects of plant-derived natural compounds, commonly found in the diet. Among the sources of these valuable molecules, Citrus fruits (CF, i.e., oranges, lemons, limes, bergamots, grapefruits, and tangerines), which are mainly consumed in the Mediterranean diet, stand out among the others [30]. Regarding cancer, the scientific community has long supported their potentiality as anti-tumor agents, hence suggesting Citrus extracts and derivatives as co-adjuvants in cancer therapy [31,32]. This is because CF represent the main dietary source of flavonoids which are shown to interfere with the process of carcinogenesis by hampering multiple signal transduction pathways, by counteracting proliferation, angiogenesis, metastasis or by promoting apoptotic mechanisms [33]. All these properties underlie the antileukemic activity of flavonoids observed both in vitro [34] and in vivo [35]. In the field of AML, continuous efforts are focused on finding new therapeutic approaches with high efficacy and few side effects. Thereby, the role of natural products targeting leukemic cells is being considered a possible help for AML management [36]. On this line, the Citrus flavonoid luteolin was shown to inhibit the growth of MOLM-13 and MV4–11 AML cells by down-regulating eIF4E phosphorylation and arresting the cell cycle in G0/G1 phase [37]. Again, nobiletin was demonstrated to induce antileukemic effects through the down-regulation of c-KIT gene in THP-1 cells [38], while treatment with diosmetin delayed tumor growth in AML mouse xenografts [39]. In recent years, Citrus × bergamia proved to possess anti-cancer properties, among others. We demonstrated that BJ was able to reduce the growth rate of human neuroblastoma SH-SY5Y cells by inducing a cell cycle block in G1 phase and a loss of adhesive capacity [40]. This latter appeared responsible for its anti-migratory effect, leading to the reduction of lung metastasis colonization in a model of spontaneous neuroblastoma metastasis formation in SCID mouse [24]. In parallel, BJ was also shown to inhibit the growth of human hepatocellular carcinoma HepG2 cells by acting on p53, p21 and NF-κB pathways [41]. Afterwards, we focused on the flavonoid-rich extract of BJ, namely BJe, to study its antitumor activity. In vitro, it inhibited the growth of human colorectal carcinoma HT-29 cells by multiple mechanisms, including the increase of reactive oxygen species production, the fall of the mitochondrial membrane potential and oxidative damage to DNA at high concentrations, whereas the inhibition of MAPKs pathways and the modulation of apoptosis- and cell cycle-related proteins occurred at low concentrations [42]. In vivo, BJe was able to prevent spontaneous tumorigenesis in Pirc rats (F344/NTac-Apcam1137), a genetic model of colorectal cancer, through a mechanism linked to its anti-inflammatory and pro-apoptotic properties [25]. Acknowledging the anti-cancer activity of BJe documented in solid tumors and the current evidence on antileukemic potentiality of flavonoids, we investigated, for the first time, the role of BJe in the field of hematological diseases, such as AML. The quali-quantitative determination of flavonoids present in BJe identified neohesperidin and naringin among the most representative components. Although both compounds have been claimed not to be effective in inducing anti-proliferative effects in THP-1 cells at micromolar concentrations [43], this did not occur when we employed the extract. Indeed, the high-water solubility of BJe allowed us to use high extract concentrations that corresponded to testing neohesperidin and naringin in the millimolar range. This appears to explain our strong results, bearing in mind that, in our extract, neohesperidin and naringin were together with other flavonoids that participated in the anti-proliferative effects we observed, probably due to synergistic interaction. Interestingly, the anti-proliferative effects of BJe observed in THP-1 cells did not occur in primary PBMCs, suggesting the safety of our extract. Mechanistically, natural compounds were demonstrated to inhibit proliferation, migration, and tumor progression by inducing cell cycle arrest and apoptosis or autophagy in leukemia cells [36]. On this line, we investigated the mechanism underlying the anti-proliferative effect of BJe by cell cycle and apoptosis studies. In AML, recurrent alterations during S phase cause an accelerated and enhanced replication of genome, that in turn increases cell proliferation and makes cells vulnerable to acquiring mutations, thus limiting the efficacy of chemotherapies [44]. Interestingly, in our work we witnessed an accumulation of cell population in S phase, thus demonstrating the influence of BJe on cell cycle progression of THP-1 cells. At least in part, the block of the cell cycle may be responsible for the pro-apoptotic effect of BJe, that may be also due to the interaction with specific factors linked to apoptosis. This process can be conducted in two distinct yet interconnected signaling pathways, both including the activation of cysteine aspartyl proteases, called caspases. The extrinsic pathway, mediated by caspase-8, describes an apoptotic event triggered by extracellular stimulations, which are recognized and propagated by specific membrane receptors. The intrinsic pathway, originated in mitochondria, can be triggered by multiple intracellular stimulations, through the involvement of caspase-9. Both pathways determine the cleavage of the downstream executioner proteins (i.e., caspase-3), followed by the cleavage of PARP [45]. Interestingly, BJe was able to target both receptor- and mitochondria-mediated apoptosis, as witnessed by the increased cleavage of caspase-8 and -9, respectively. These results reflected both at gene and protein levels. This brought the consequent cleavage of caspase-3 and that of PARP, thus unleashing apoptosis by targeting both extrinsic and intrinsic pathways contemporarily. Notably, this occurs also for the pro-apoptotic effect of BJe in HepG2 cells [41]. In the last decade, SIRTs have been extensively studied as potential molecular targets in several age-related diseases. Of note, small molecules including some flavonoids appeared to be effective SIRT modulators [46]. Previously, we have investigated the role of BJe and of its flavonoids against SIRT1, one of the seven isoforms of the sirtuin family, in an in vitro model of inflammation, observing a modulation of this sirtuin [47]. In tumorigenesis, the SIRT2 enzyme is known to exert a dual role, acting both as a tumor promoter and suppressor [48]. In AML, recent studies have indicated the key role of SIRT2 as a proliferation marker [6,7]. SIRT2 is a histone deacetylase primarily placed in the cytoplasm which transiently migrates in the nucleus during mitosis, by reducing the acetylation level of some substrates (i.e., p53, α-tubulin, FOXOs, NF-κB, PEPCK1 etc.). Consistent with this, very recently, we found that flavanones neohesperidin and naringin, along with their aglycones, inhibited SIRT2 deacetylase activity [49]. Therefore, in our study, we evaluated, for the first time, the possible implication of SIRT2 in the anti-leukemic effect mediated by BJe in THP-1 cells. Since p53 is a target for SIRT2 deacetylation [50], we quantified the level of acetylated p53 in BJe-treated THP-1 cells, experiencing its significant increase. Hence, the high p53 acetylation, meaning its higher activity, suggested an in vitro SIRT2 inhibition mediated by BJe. In addition, the use of SirReal2 and NAM, both SIRT2 inhibitors, further supported the inhibitory effect on SIRT2 by our extract. In particular, BJe showed somewhat higher effect than SirReal2, a SIRT2 specific inhibitor, and comparable to NAM, that is a SIRT2 a-specific inhibitor. This could suggest that these high concentrations of flavonoids present in BJe might simultaneously inhibit more sirtuins causing THP-1 cell death via the increase of p53 acetylation, as suggested by Peck et al. [51]. Our in vitro results were strengthened by the abiotic assay performed with the isolated enzyme, thus suggesting that BJe inhibits SIRT2 activity also by direct targeting this sirtuin. Since the inhibition of enzymes can be either achieved by hampering the gene expression or by the enzymatic activity, we wondered whether, aside from a modulation of the deacetylase activity, the mRNA levels of SIRT2 could also be influenced by our extract. Therefore, we quantified them in THP-1 treated cells, finding an early reduction induced by BJe, already after 6 h of treatment. Notably, the lowering effect on SIRT2 gene expression attenuated after 24 h of incubation with extract. This could be due to the activation of a response by cancer cells, like THP-1, which counteracted SIRT2 down-regulation mediated by BJe, by restoring its mRNA at the level of untreated cells. Moreover, it is known that AKT, a serine/threonine kinase, plays an important role in anti-apoptotic signaling, targeting proteins such as BAD, caspase-9 and p53 [52,53], thus being implicated in several aggressive human cancers, including AML [54]. In addition, some previous data documented a specific interplay between SIRT2 and AKT [6,55]. Indeed, SIRT2 deacetylase activity positively regulates the binding of AKT to inositol 1,4,5-triphosphate (PI3K), leading to the activation of the PI3K/AKT pathway, involved in cell proliferation, survival, and apoptosis. In addition, the overexpression of SIRT2 was significantly associated with an increase of AKT phosphorylation in PADI3-expressing HCT116 colon cancer cells [56]. Interestingly, one of the most abundant flavonoids in Citrus fruits, naringenin, was shown to induce apoptosis in human leukemia THP-1 cells through the down-regulation of AKT and activation of caspase-3 [57]. Accordingly, we showed that treatment of THP-1 cells with BJe led to a significant dephosphorylation, and then inactivation, of AKT kinase, which is hyperactivated in AML [6]. Contextually, we documented that BJe increased both the p53 protein and gene expression, which could result by the action of BJe on AKT, thus suggesting the targeting of the SIRT2/AKT/p53 pathway. Finally, in accordance with the pillars of the circular economy, the interesting pharmacological properties from re-evaluating a Citrus byproduct, demonstrated in this work, support the switch towards a more environmentally sustainable society.
Nature provides a wide range of bioactive compounds, with flavonoids standing out among the others. Citrus fruits are the main dietary source of flavonoids, widely cultivated, processed, and consumed throughout the world. The scenario of hematological diseases, such as AML, means we are constantly looking for innovative drugs and novel approaches, including in the landscape of natural remedies. For the first time, we showed that BJe induces anticancer effects against the human leukemia monocytic THP-1 cell line, causing cell cycle arrest in S phase and triggering the apoptotic machinery, via targeting of the SIRT2/AKT/p53 pathway. Therefore, our results encourage the study of tools in the fight against such a nefarious hematological cancer. | true | true | true |
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PMC9610719 | Md. Akib Ferdous,Sk Injamamul Islam,Nasim Habib,Mazen Almehmadi,Mamdouh Allahyani,Ahad Amer Alsaiari,Alaa Shafie | CRISPR-Cas Genome Editing Technique for Fish Disease Management: Current Study and Future Perspective | 12-10-2022 | CRISPR-Cas,fish,pathogens,phages,RNA | Scientists have discovered many ways to treat bacteria, viruses, and parasites in aquaculture; however, there is still an impossibility in finding a permanent solution for all types of diseases. In that case, the CRISPR-Cas genome-editing technique can be the potential solution to preventing diseases for aquaculture sustainability. CRISPR-Cas is cheaper, easier, and more precise than the other existing genome-editing technologies and can be used as a new disease treatment tool to solve the far-reaching challenges in aquaculture. This technique may now be employed in novel ways, such as modifying a single nucleotide base or tagging a location in the DNA with a fluorescent protein. This review paper provides an informative discussion on adopting CRISPR technology in aquaculture disease management. Starting with the basic knowledge of CRISPR technology and phages, this study highlights the development of RNA-guided immunity to combat the Chilodonella protozoan group and nervous necrosis virus (NNV) in marine finfish. Additionally, we highlight the immunological application of CRISPR-Cas against bacterial diseases in channel catfish and the white spot syndrome virus (WSSV) in shrimp. In addition, the review summarizes a synthesis of bioinformatics tools used for CRISPR-Cas sgRNA design, and acceptable solutions are discussed, considering the limitations. | CRISPR-Cas Genome Editing Technique for Fish Disease Management: Current Study and Future Perspective
Scientists have discovered many ways to treat bacteria, viruses, and parasites in aquaculture; however, there is still an impossibility in finding a permanent solution for all types of diseases. In that case, the CRISPR-Cas genome-editing technique can be the potential solution to preventing diseases for aquaculture sustainability. CRISPR-Cas is cheaper, easier, and more precise than the other existing genome-editing technologies and can be used as a new disease treatment tool to solve the far-reaching challenges in aquaculture. This technique may now be employed in novel ways, such as modifying a single nucleotide base or tagging a location in the DNA with a fluorescent protein. This review paper provides an informative discussion on adopting CRISPR technology in aquaculture disease management. Starting with the basic knowledge of CRISPR technology and phages, this study highlights the development of RNA-guided immunity to combat the Chilodonella protozoan group and nervous necrosis virus (NNV) in marine finfish. Additionally, we highlight the immunological application of CRISPR-Cas against bacterial diseases in channel catfish and the white spot syndrome virus (WSSV) in shrimp. In addition, the review summarizes a synthesis of bioinformatics tools used for CRISPR-Cas sgRNA design, and acceptable solutions are discussed, considering the limitations.
Fish diseases are a serious barrier in the aquaculture sector, affecting more than a billion dollars yearly. Climate change and developing fish farming may influence the balance or imbalance of pathogen, host, and environmental interaction, with new infections being detected or identified annually and more known diseases arising in various global regions and species [1]. Pathogen evolution is thought to be accelerated in intensive farming systems due to the high density of vulnerable hosts, which promotes pathogen transmission and virulence [2]. Because of higher population densities and host–pathogen interactions, this aspect of the farming environment is expected to extend to biological interactions between pathogenic bacteria and their phages, viruses, and parasites. However, many of these diseases or infections have no proven or approved recommended treatments, vaccinations, or control strategies and remain a substantial barrier to the economic sustainability of aquaculture in specific regions and species [1]. Aquaculture enterprises may need new scientific procedures to increase fish production while maintaining trait quality. Several initiatives have been conducted over the last two decades to manage and treat disease in aquaculture species, with varying degrees of success [3]. Many proven aquaculture species, such as tilapia, carp, salmonids, and some marine species (sea bass, sea bream, and grouper), have commercial vaccines for a limited number of diseases and authorized treatments for specific pathogens. However, there is significant variation from country to country and even within a geographic region [1]. Several diseases that have a substantial economic impact in aquaculture are viral infections with no therapies and vaccines, which, if produced, only provide limited protection. Numerous examples of bacterial, parasitic, and fungal diseases can pose significant international economic and welfare concerns to aquaculture. The management and control of parasitic infections are critical not only for the viability of the aquaculture sector but also for preventing horizontal parasite spread to wild fish [4,5]. Chemotherapy has had some promising results and has been proposed as a potential method of treating fish parasites [6]. Such initiatives, however, are incompatible with the United Nations’ Sustainable Development Goals, as well as generally agreed fish welfare and environmental standards. As a result, additional long-term preventative approaches for parasite management must be developed. On the other hand, in aquaculture, increasing production and the frequency of diseases in aquatic animals are driving up antimicrobial use and antimicrobial resistance [7,8] across various farmed aquatic species. Antimicrobial residues in the aquatic environment affect the environmental microbiome, affecting the ecosystem’s capacity for regulation, provisioning, and sustenance [9,10]. As a result, the enrichment of naturally existing pathogens in aquaculture habitats and the usage of antimicrobial treatments provide an appealing opportunity to investigate newer, more robust solutions for aquaculture sustainability. Currently, biotechnology research can address several issues, not just related to aquaculture farming but also environmental issues [3]. Several genome-editing tools have recently been developed, including zinc-finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and more recently clustered regularly interspaced short palindromic repeats (CRISPR) and CRISPR-associated nucleases 9 (Cas9), which have made it possible to edit genes or knock out unwanted parts of them in various animal models [11]. The CRISPR-Cas9 system has been developed as a new elite genome engineering tool, even for organisms where genome editing would be challenging. With this promising new technology, it is possible to overcome several challenges that the aquaculture industry faces. Genomic editing using CRISPR can quickly introduce significant genome changes, making it useful for genetic improvements, disease resistance, and disease control in aquaculture [11,12]. For example, a novel mechanism for RNA-guided immunity against RNA viruses in vertebrates is provided by CRISPR-CasRx for engineering interference against RNA viruses in fish [13]. In addition, about half of all bacteria and almost all archaea possess a CRISPR-Cas system that protects them from foreign genetic elements, such as viruses and plasmids [14]. Thus, the role of the CRISPR-Cas mechanism in the aquaculture field would be crucial for future global food demand. There are several reviews on using CRISPR-based genome editing in aquaculture [15,16]; however, less effort has been made to apply CRISPR-Cas to control diseases in aquaculture. This review focuses on the pathogenic aspects of CRISPR-Cas9 genome editing relevant to aquaculture applications. Furthermore, a workflow for genomic interactions between CRISPR-Cas and phage is presented, primary techniques related to anti-RNA parasite experiments and outcome prediction of disease management in a CRISPR-Cas9 system are described, bioinformatics tools for CRISPR mechanisms are demonstrated, and the future of CRISPR-Cas9 for aquaculture disease management is briefly discussed in this study. We also mention aspects that need to be examined or improved for genome editing to effectively manage microbial diseases in fish farming. Finally, this study is intended to offer an overview of CRISPR genome-editing studies for fish disease management and treatment to encourage more genome editing research and uses in aquaculture.
Bacteria-infecting viruses, generally known as bacteriophages, require a bacterial host to survive. Their numbers in the biosphere make them the most abundant [17]. Bacteriophages are a significant threat to bacteria due to their capacity to infect their bacterial host. When the host’s environment becomes unfavorable, phages may switch to a pseudolysogenic method. Pseudolysogeny is the development of bacteriophages in a host cell without multiplication or replication and occurs with zero degradation of the viral genome. In such situations, prokaryotes, such as bacteria, adopt various defense mechanisms to protect themselves. CRISPR functions as a natural defense mechanism or adaptive immune system of prokaryotes against viral DNA, bacteriophages, and plasmids, which was first reported in the E. coli genome [18]. Adaptive immunity refers to the immunity that an organism acquires after exposure to an antigen, either from a pathogen or vaccine. It may be found in most lysogenic bacteria [19], including two aquaculture-related bacterial species, Flavobacterium psychrophilum [20] and Vibrio anguillarum [21]. The relationship between the phages’ life cycle and CRISPR-Cas is still poorly known [22]. Regarding CRISPR, the repeated sequences of prokaryote DNA nucleotides are described as palindromic repeats because they are the same whether reading forwards or backward. The unique sequences nestled between the palindromic repeats are called spacers. Spacers are the DNA bits originating from the foreign mobile genetic elements (MGEs) that have previously infected the prokaryote and do not belong to the bacterium. Different spacers, potentially originating from different viruses, are sandwiched between the repeated sequences and produce a CRISPR array. In this way, bacteria retain a memory of a past infection [18]. The CRISPR array can undergo transcription to form CRISPR RNA (crRNA) called pre-crRNA. In the next step, the Cas protein becomes involved, which refers to the CRISPR-associate nuclease protein capable of cleaving DNA at specific nucleotide linkages. The presence of tracrRNA with Cas protein was also recorded. Each spacer and palindromic repeat end up with an effector complex consisting of a segment of pre-crRNA, a tracrRNA, and a Cas protein. By cleaving the strand between these complexes, the ribonuclease-3 enzyme helps the cell defend against the invader whose genome produced that crRNA. The whole process neutralizes the virus by preventing viral transcription [18]. The relation of the mechanism of CRISPR-Cas against bacteriophage interference is shown in Figure 1.
Invertebrate parasites may either be free-living or obligatory parasites that depend on their hosts for survival and reproduction. Both obligatory and opportunistic parasites may be found in fish, but obligatory parasites are mainly responsible for causing many parasitic infections in fish. Most fish that seem to be healthy often have small numbers of different parasites on or in their bodies, by which the fish usually suffer little to no danger. However, changes in water temperature or salinity reduce fish immunity causing a significant increase in the number of parasites per fish, and parasitic disease outbreaks frequently happen. In addition, there are connections between parasitic diseases and other infections. It has been noted that cultured fish in captivity are host to a wide range of parasites. Some of these parasites have led to severe disease outbreaks or persistent subclinical effects in farmed fish, costing fish farmers a lot of money. The fish are most vulnerable in the beginning phases of the culture cycle, especially when the fish are tiny and in the hatchery and nursery stages. The three major groups of parasitic organisms that infect farmed fish are protozoa, platyhelminthes, and crustaceans. Many of these parasites can potentially spread disease and result in significant financial losses. Several therapies and preventative measures may be used to deal with parasite assaults, including environmental disinfection, seedling disinfection, health management, nutritional supplements, copper sulfate, potassium permanganate, formalin, zinc sulfate, and ivermectin. However, the use of medications and antibiotics harms the environment and the safety of food. From this perspective, CRISPR-Cas may be a more efficient way to create a species-specific insecticide for defeating parasite infestations by a genetic operation that targets a particular place of the gene for cutting and repair. The most often researched fish parasite species of the Chilodonella protozoan group, Chilodonella piscicola, Cryptocaryon irritans, and Chilodonella uncinata, have all been subjected to CRISPR-Cas anti-parasitic activities [23,24,25]. These hypothermic protozoan parasite species oversee, causing both gill and skin diseases in freshwater fish, which restrict the development of both juvenile and adult fish, especially in spring and fall [25]. Yige Li et al. reduced the survival capacity of C. piscicola by destroying the parasite DNA at a particular location using a combination of Cas9 messenger RNA (mRNA) and single guide RNAs (sgRNA) [25]. In that experiment, the CRISPR-Cas9 system was used to design sgRNA in conjunction with the known sequences of the 18S ribosomal RNA (rRNA), internal transcribed spacer 1 (ITS-1), and 5.8S ribosomal RNA (rRNA) of C. piscicola to destroy the genetic barcode of C. piscicola. As evidence of the efficiency of the chosen sgRNA of 18S rRNA sequence and ITS-1 sequence, the survival rate of the experimental C. piscicola was decreased to 40% compared to the blank control group with zero significant difference. It demonstrated that both sites had the potential to eradicate C. piscicola successfully. According to real-time quantitative PCR (RT-qPCR), Cas9 may either act with a single sgRNA or a combination of two sgRNAs to damage the parasite DNA in a particular area [25]. Majeed et al. also conducted a similar study to control the Aphanomyces invadans pathogen, a causative agent for epizootic ulcerative syndrome (EUS) [26]. In that experiment, scientists applied the CRISPR-Cas9 system for editing the A. invadans genome by targeting the serine protease gene in in vitro and also observed the effect on the virulence and pathogenicity of the A. invadans in vivo. They designed three single guide-RNAs (sgRNA) combined with the Cas9 to form a ribonucleoprotein (RNP) complex and transfected in A. invadans protoplasts and zoospores. Three groups of dwarf gourami (Trichogaster lalius) were taken as test species and experimentally inoculated with (i) non-treated zoospores; (ii) RNP-treated zoospores; and (iii) autoclaved pond water as a negative control to investigate the effect on the virulence in vivo. According to the in vivo results of the study, the CRISPR-Cas9-treated A. invadans zoospores did not express any signs of EUS in the fish [26]. The basic experimental design of developing an RNA anti-parasite using the CRISPR-Cas method is shown in Figure 2.
One of the deadliest viruses that may infect fish is the RNA virus, which is unpredictable and challenging to prevent. They are highly dynamic pathogens because of their short generation times, enormous population numbers, and high mutation rates, among other distinctive traits. Iridoviridae, Adenoviridae, and Herpesvirdae are home to fish viruses with DNA genomes. In contrast, those with RNA genomes are found in the families Picornaviridae, Birnaviridae, Reoviridae, Rhabdoviridae, Orthomyxoviridae, Paramyxoviridae, Caliciviridae, Togaviridae, Nodaviridae, and Retroviridae. RNA-guided immunity against RNA viruses could be developed in the fish body using the antiviral CRISPR-Cas, which had previously been successfully targeted by CRISPR-Cas13 [27,28,29]. On the other hand, CasRx, a small type VI-D effector (Cas13d), can also effectively knock down RNA in RNA viruses [13]. The RNA-targeting CRISPR-CasRx is a programmable system, and Cas13d is a CRISPR effector known as small type VI. The two primary categories of CRISPR-Cas systems are class I, which mediates the interference via multi-effector complexes, and class II, which utilizes a unified, multi-domain effector [30]. In addition, these classes are further divided into six types and thirty-three subtypes based on the genomic architecture of the CRISPR array and its distinct interference effectors. Types II, V, and VI of the CRISPR-Cas systems are within class II. Endonucleases of types II and V are used for the DNA, while those of type VI are only used for RNA [30,31]. To provide prokaryotes with protection against RNA, class II type VI CRISPR-Cas systems use an RNA-guided and RNA-targeting mechanism [32]. The Cas13 effector protein is the same for all type VI CRISPR-Cas systems. Multiple studies have shown further variations of Cas13 proteins belonging to various Cas13 families, which have been categorized into four type VI subtypes (subtypes A–D) [28,30,33]. A novel Cas13 subtype known as CRISPR-Cas13d (CasRx) has also recently been discovered by researchers; it has minimal sequence similarity with earlier Cas13 effectors [33,34]. Compared to other Cas13 effectors, CasRx is more efficient and more robustly activated in cells when RNA-guided RNA cleavage occurs [33,34]. It provides the most effective targeting and has the smallest size, making it perfect for in vivo therapeutic applications [34]. Qing Wang et al. designed synthetic mRNA coding for CasRx and used CRISPR RNAs to guide it to target the nervous necrosis virus (NNV), which is an RNA virus of fish [35,36], and applied the coding both in vitro and in vivo to observe the significance of the dominating RNA virus [13]. Scientists selected the red-spotted grouper (Epinephelus coioides) as a model species for the experiment. The red-spotted grouper NNV (RGNNV) is one of the four classified NNVs; striped jack NNV, tiger puffer NNV, and barfin flounder NNV are the remainder of them [36,37]. NNV is also found in orange-spotted grouper, Sevenband grouper, brown-marbled grouper, turbot, etc. Nervous necrosis viruses (NNV) are icosahedral non-enveloped single-stranded positive-sense RNA viruses (ssRNA+ viruses) classified in the family Nodaviridae which is the pathogen of viral nervous necrosis disease (VNN) that destroys the central nervous system of infected fish [38,39]. The in vitro RGNNV targeting via the CasRx system is shown in Figure 3. Scientists designed the experiment in three basic steps. Firstly, to increase the protein content of CasRx, researchers improved the codons that express it. Secondly, to enhance crRNA expression, the zebrafish U6 promoter was applied. Thirdly, they designed CasRx to eliminate the differences in cellular location. RGNNV is composed of CP and RdRp (Clamping RNA-dependent RNA polymerase). They investigated the possibility that CasRx may target the RGNNV CP and RdRp to induce effective and reliable RNA virus interference [13]. In this research, scientists developed three crRNAs for targeting the sequences of code of CP mRNA and two crRNAs for targeting the sequences of code of RdRp mRNA. To investigate the effect of the CasRx-crRNA complex on the cellular level of RGNNV, grouper spleen (GS) cells were treated with plasmids carrying CasRx and crRNA. The first CasRx expression was observed after 6 h of transfection, and it became more expressive after 12 and 24 h, but it did not show much expression between 24 and 48 h. For each of the five crRNAs, RGNNV infection was carried out after transfecting GS cells with plasmids carrying CasRx-dNLS or CasRx-NLS. All ten combinations reduced the number of viral RNA copies. Furthermore, the virus titer data showed that transfection of cells with CasRx-dNLS or CasRx-NLS and crRNA significantly reduced viral titers and RGNNV pathogenicity. Extensive CPEs were shown when GS cells were treated with RGNNV or CasRx plus nonspecific (ns)-crRNA and RGNNV. Few CPEs were seen when cells were transfected with either CasRx-dNLS or CasRx-NLS and each crRNA. Additionally, the findings of the immunofluorescence experiment showed that GS cells subjected to only RGNNV or CasRx plus ns-crRNA and RGNNV displayed strong fluorescence signals of the RGNNV CP protein. Positive fluorescence signals were markedly reduced when cells were transfected with either CasRx-dNLS or CasRx-NLS and each crRNA. These findings demonstrated that the CasRx system effectively prevented RGNNV infections in vitro [13]. The effectiveness of CRISPR-CasRx has been proven to combat an RNA virus in vertebrates. The degradation of several viral genomic areas led to decreased CP and RdRp mRNA levels, in vitro cell vacuolation, and cumulative mortality in vivo. These results demonstrated the efficacy of using CRISPR-CasRx, a vertebrate RNA virus that may be targeted and interfered with by reducing RGNNV replication and dissemination.
Aquaculture industries worldwide face serious problems such as infectious and parasitic diseases, reduced viability, decreased fertility, poor development, environmental contamination by escapee fish, coastal conflicts, and disagreements over the patenting of research products [15,40]. Among these, disease outbreaks in aquaculture are a major issue that are one of the main reasons for the reduction in fish production. Many reputable shellfish farms report shellfish dying overnight because of viral assaults. In fish aquaculture, reproduction, and development [41,42], growth [43], pigment [44,45], disease resistance [46], trans-GFP usage in research [47], and omega-3 metabolism [48,49] are the qualities that are most often targeted for genetic engineering [50]. However, using molecular biological techniques to resolve diseases has become a core technology. Genomic editing (GE) has created several controls for aquatic diseases, and it will continue to do so in the future in a variety of different ways. Among these GE methods, CRISPR-Cas has been applied to modify several genes for targeting species-specific pathogens as modern technology. CRISPR-Cas has been applied in immunological studies in channel catfish (Ictalurus punctatus) according to several types of research [51,52]. It enhanced the resistance of channel catfish to many diseases by injecting the alligator cathelicidin gene into the fish [53]. Additionally, this technology enhances the fish body’s natural immunity, which works against bacterial diseases or other infectious diseases such as Edwardsiella ictalurid and Flavobacterium columnare [54]. The editing of disease-resistance genes in channel catfish is an additional application of CRISPR-Cas of commercial relevance [51,52,55]. In shrimp and prawns, the eyestalk neuroendocrine complex contains suppressing/inhibiting substances that always prevent breeding and spawning under captivity. These limiting elements also hinder the process of growth. These aquatic organisms’ immune systems have reportedly been weak, making viral and bacterial diseases highly likely to strike them. Certain marine shrimps have already had their gonad-inhibiting hormone (GIH) and molt-inhibiting hormone (MIH) genes evaluated [56,57,58]. Using CRISPR-Cas technology has been able to eliminate the harmful effects of hormones on growth and reproduction, which may open the way to developing a powerful substitute for eyestalk ablation that has a comparable effect. Some researchers have tried to delete the gene using this RNA interference method [59,60]. When working on Penaeus monodon (giant tiger prawn), Treerattrakool et al. used the method of RNA interference to induce maturity in both wild and captive shrimp and reported that shrimps injected with anti-GIH double-stranded (ds) RNA showed enhanced maturation [59]. According to Das et al., RNA interference was used to silence the gonad-inhibiting hormone gene in the eyestalk neuroendocrine complex of the P. monodon (tiger shrimp) [60]. They discovered a three–five times increase in the transcript of the androgenic gland hormone (AGH) in males but no alteration in the expression of vitellogenin in females. Additionally, CRISPR-Cas technology can be utilized to manage bacterial and viral infections, particularly in shrimp and prawns. The CRISPR-Cas process in shrimp and prawns may also function similarly to that of bacteria when viral DNA attacks them. For example, CRISPR-Cas can replicate and insert portions of the white spot syndrome virus (WSSV) DNA into shrimp genomes as “spacers” between the short DNA repeats in CRISPR when WSSV invades them. By providing a template for RNA molecules to rapidly recognize and target the same DNA sequence in the case of future viral infections, these spacers improve the immune response of shrimp. The RNA molecules redirect the CRISPR complex to an incoming sequence of foreign DNA if they recognize it. There, the Plasmid Cas proteins of the shrimp cut the invading gene and render it inactive. The shrimp may be shielded against contagious infections because of this [60]. Culturing commercial species in the aquatic environment, every year, significant losses are attributed to mass mortality, rejection of aquaculture species’ shipments due to a lack of quality standards, the impact of biotic and abiotic stresses on aquaculture species, and the absence of standardized disease control and pollution-impact methods or protocols [3]. However, at this point, we require highly potent technologies to solve some of the significant problems in the aquaculture sector. With the development of CRISPR-Cas technology, it may be possible to solve any biological problems relating to genetic diseases or other problems without significantly changing the genetic makeup of aquaculture species and preventing viral and bacterial infections; CRISPR-Cas technology can prove to be a potent tool [3].
Bioinformatics is a scientific field that generates methodologies and software tools for analyzing biological data. It has been applied in various applications such as in silico studies of biological questions utilizing computational and statistical tools. It is frequently used to find potential genes and single nucleotide polymorphisms (SNPs). Furthermore, a field of study known as proteomics in bioinformatics seeks to comprehend the organizing concepts found in nucleic acid and protein sequences [61]. The main effects of bioinformatics have been the automation of microbial genome sequencing, the creation of integrated databases accessible through the internet, and genome analysis to comprehend gene and genome function. Bioinformatics is now used for a wide variety of other significant tasks in addition to the analysis of gene variation and expression, the analysis and prediction of gene and protein structure, as well as the prediction and detection of gene regulatory networks. It can analyze data more quickly to enhance the accuracy of the findings and explain the causes and phenomena of diseases at the gene/pathway level. The first thing we must understand before relating the CRISPR-Cas system to bioinformatics is that selecting the appropriate CRISPR target gene is an essential step in successfully targeting gene editing. Bioinformatics can be used to locate and insert CRISPR-Cas into the targeted genome [3]. The choice of the target site is constrained by the possibility of off-target editing and variations in editing effectiveness. Numerous computational techniques have been created in recent years to assist researchers in choosing target sites for CRISPR knock-in/out experiments. In developing single-guide RNA (sgRNA) for CRISPR applications, these methods are likely to be helpful in both target site selection and sgRNA creation. The sgRNA design tools are specifically suitable for genetic screening and CRISPR-mediated gene regulation research has also been developed, resulting from the expansion of CRISPR applications. Computational tools have been created to analyze CRISPR genome-edited data produced by Next Generation Sequencing (NGS) systems and aid in sgRNA creation [62]. The CRISPR-Cas9 genome-editing technologies use programmable nucleases to accurately and frequently modify a particular section of the genome which may use RNA-guided nucleases [63,64,65]. CRISPR-Cas has successfully modified specific genomes in significant model species, such as zebrafish [66]. It modifies two RNAs—a transactivating CRISPR RNA (tracrRNA) that base pairs with the crRNA and a CRISPR RNA (crRNA) complementary to the targeted DNA sequence—that recruit Cas9 to the target site. The target sequence should be followed by a protospacer adjacent motif (PAM) sequence for recognition (nGG, where n can be any nucleotide). The crRNA and tracrRNA may be combined to form a single synthetic guide RNA (sgRNA) [66] that works efficiently with Cas9 to cause cleavage of the target site (~20 bp), which must come after the PAM sequence in the genome. Using in vitro transcription promoters such as T7, T3, or SP6 to create sgRNAs restricts the target sequence. Here, the CRISPR-Cas system was used to modify the Xenopus tropicalis (western clawed frog) genome, providing another tool for quick and effective targeted mutagenesis. Cas 9 is a CRISPR-related protein adapted from a naturally occurring genome-editing system and used here as a bacterial immune defense. Most genomic restriction nucleases require substantial and complex PAM sequences that would restrict them due to reduced genome size. Distinct PAMs in the SpCas9 system are used for genome manipulation, including target gene disruption and single base-pair mutations in various organisms and cells. Developing the SpCas9 to identify more PAMs would be an alternative approach to increasing PAM specificity. Although SpCas9 is the most well-known nuclease, Cas9 can also be obtained from many bacterial species. The fundamental difference between them is the PAM sequence required for the cleavage of Cas9 nucleases from different bacteria [67]. The CRISPR-mediated genome editing tools are shown in Table 1, which are used for fish. On the other hand, many genetic disorders and undesirable features are carried on by base-pair changes in the genomic DNA. Base editing, the most recent development of CRISPR-Cas-based technologies, may directly introduce point mutations into cellular DNA without leading to a double-strand DNA break (DSB). The CRISPR-base-edit tools have recently increased by prime editing (PE), which now includes all twelve potential transition and transversion mutations in addition to minor insertion or deletion changes [77]. The base editing resources are shown in Table 2. In addition, to targeting single-point mutations, CRISPR has provided a variety of methods for editing the genome precisely, such as functional gene knockouts and epigenome modifications. Recent research has focused on improving Cas9 selectivity and expanding target coverage; guided evolution has resulted in discovering many Cas9 variants that will significantly expand targeting coverage. Base-editing techniques have also made significant advancements in the investigation of pathogenic variations in animal models; they will speed up the functional verification of potential disease genes in model organisms and the creation of therapeutic tools for the treatment of several disorders [83]. The design of efficient sgRNAs is becoming increasingly difficult since the CRISPR-Cas9 system has swiftly become a ubiquitous gene-editing tool in biological research. To address this critical issue, various bioinformatics techniques have been created. In conclusion, by enhancing experimental planning, data integrity, and computational modeling, researchers created a novel sgRNA design tool that consistently outperformed in various experimental conditions [84].
The field of molecular biology is being revolutionized by the quick advancement of genome-editing technology such as CRISPR-Cas, which allows DNA modification in a broad range of species. It is being considered for several applications, from agriculture to clinical therapeutics [85]. CRISPR-Cas technology has made tremendous strides in recent years and demonstrated significant promise in several areas of life sciences’ study. Despite the impressive CRISPR advancements, a few issues still need to be resolved to develop Cas systems to their full potential. CRISPR technologies have some primary limitations as application measures and genetic perspectives. These technical difficulties can be resolved in the present attempts to address all of those worries. The CRISPR-Cas method is a relatively new gene-editing technology; generally, the methods associated with gene editing are quite expensive. Such an expensive technology in the molecular biological sector, such as CRISPR-Cas, is challenging to adapt in a developing or underdeveloped agriculturally dominant country. As a result, it is tough to quickly implement this technology in aquaculture in these countries, even if it is effective enough to control fish diseases. In addition to being expensive, this technology is also quite complicated. These complexities make it difficult to implement CRISPR-Cas as a commercial method. As a result, institutional education on this method is primarily essential. On the other hand, not all laboratories have sufficient equipment to run this technology except specialized and facilitated laboratories. Due to the problem of inadequate equipment, it is impossible to conduct this technology in all institutional laboratories. Moreover, from a genetic perspective, the insufficient aquatic genomic resource is the major limitation of CRISPR technology. Although scientists have vast genetic information about some worldwide, commercially important model species (e.g., Nile Tilapia, Atlantic Salmon), they are insufficient compared to the total number of aquaculture species, which is over 600 according to the FAO [86]. Furthermore, the identification of trait-related genes is required for the genetic functioning of aquatic species to locate which gene should be targeted [86]. On the other hand, the problem of genomic duplication in fish is more than in other aquatic organisms [87]. In addition, another significant issue with using CRISPR-Cas in treating fish disease is off-target mutations at unwanted sites other than the desired on-target sites [88]. There may have some possible solutions to overcome these limitations. First, future aquatic genome-sequencing will be aided by the reduced cost of sequencing, allowing for establishing the essential genetic base. Second, the steps to start introductory institutional courses on CRISPR-Cas can be helpful in the skill development and enhancement of lab facilities. For the solutions of genetic perspectives, increasing refinements in QTL methods will result in more trait-related genes being identified [89]. On the other hand, potential candidates should be targeted for genes that impart advantageous features across species and lines. A well-designed annealing sgRNA may either avoid or identify off-target mutations by comparing it to current genome assemblies [50].
Although CRISPR-Cas technology is relatively new, it can be undoubtedly said that scientists will rely heavily on it in the future to diagnose and prevent fish diseases. With time, the versatile application of this technology is turning it into a multi-dimensional technology. It is possible to paint an imaginary picture of the future perspective and approaches of CRISPR-Cas. In this step, an attempt can be made to assess this technology’s future status and needs, based on several aspects of fish disease diagnosis and health management. Antibiotics’ massive selection pressures brought on by antibiotic exposure cause commensal and pathogenic microorganisms to develop and propagate antibiotic resistance. This strategy is paradoxical for preventing the fast evolution of new antibiotic-resistant organisms because of the lengthy process of discovering new antibiotics. To deal with diseases brought on by resistant superbugs, alternative strategies including creating nucleic acid-based anti-bacterial therapeutics, anti-bacterial peptides, bacteriocins, anti-virulence chemicals, and bacteriophage therapies should be used. To address antibiotic resistance in this situation, scientists have already begun to use the recently popular CRISPR-Cas system [90]. Antibiotic-resistant superbugs are one of the major concerns today, but CRISPR technology is expected to protect from this problem if used properly; Antibiotics target cellular processes or activities, such as nucleic acid synthesis and cell membrane formation, to impact specialized bacterial mechanisms. These processes cannot destroy specific pathogens in the diverse microbial community—antibiotics damage both the members of the beneficial microbiota and the bacteria that cause infections. There is currently no antibiotic method that targets exclusively pathogenic bacteria. The use of antibiotics nowadays is not species-specific. The CRISPR-Cas9 gene-editing technique and its applications against bacteria will be a crucial strategy to stop the clonal proliferation of dangerous bacteria, offering a novel remedy to the world-wide issue [91]; Among all of the pathogens that cause disease in the fish body, viral diseases can be considered the most dangerous. In particular, fish diseases by RNA viruses cause the most suffering to scientists and fish farmers. From that point of view, since RNA viruses show mutations or Single Nucleotide Polymorphisms (SNP) so frequently, any preventive measures designed to target a particular virus may no longer work after the mutation or SNP. The CRISPR-Cas method can play a vital role in solving this problem in the future. CRISPR-Cas technology has already experimented with the RNA virus targeting method for red-spotted grouper nervous necrosis virus (RGNNV) in fish [13]. Scientists found success in this experiment by using the CasRx-crRNA complex; Apart from these, CRISPR has also been applied for anti-parasitic action. Scientists are also succeeding in this area [25]. Moreover, using this technology, genetically improved or modified species can be created that will be born with high immunity from the beginning of life.
Aquaculture offers microbial populations semi-natural and generally ideal environments. For this reason, pathogenic attacks in aquaculture are always a significant issue that have a direct and adverse effect on production, as the fish are considered an easy host for causative agents. Among all preventive measures, we have focused on the CRISPR-Cas method in this review paper because of the popularity and dependable image created by the vast research interests and practice of CRISPR-Cas in disease management sectors. The CRISPR-Cas system has improved genome-editing technology and shown significant promise in controlling aquaculture diseases. We have reviewed the recent research and projected the uses of CRISPR-Cas in the aquaculture industry. We have discussed the case studies that have previously been conducted on the use of CRISPR-Cas for creating anti-parasitic RNA, as well as the advancement of the CasRx-crRNA complex against fish RNA-virus. Although the use of CRISPR-Cas in aquaculture disease research is still in its early stages compared to its usage in biomedical research, we have reviewed its limits and applications as a new method and attempted to relate it with bioinformatics. | true | true | true |
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PMC9610839 | Tianyuan Song,Minzhi Zhou,Wen Li,Lin Zheng,Jianping Wu,Mouming Zhao | Tripeptide Leu-Ser-Trp Regulates the Vascular Endothelial Cells Phenotype Switching by Mediating the Vascular Smooth Muscle Cells-Derived Small Extracellular Vesicles Packaging of miR-145 | 18-10-2022 | soybean protein-derived peptides,extracellular vesicles,miRNAs,HUVECs,atherosclerosis | Tripeptide LSW, initially identified as a potent ACE inhibitory peptide from soybean protein, was recently reported to exert a protective effect against angiotensin II-induced endothelial dysfunction via extracellular vesicles (EVs). However, the molecular mechanisms, especially in lipid accumulation-induced atherosclerosis, still remain unclear. The study aimed to investigate whether the protective effects of LSW against endothelial dysfunction on vascular endothelial cells (VECs) was via vascular smooth muscle cells (VSMCs)-derived miRNA-145 packaged in EVs. The miRNA-145 was concentrated in EVs from LSW-treated VSMCs (LEVs), internalized into the HVUECs, and targeted the programmed cell death protein 4 (PDCD4) expression of HUVECs. Oxidized low-density lipoprotein (oxLDL) was applied to induce endothelial dysfunction in HUVECs; oxLDL-induced endothelial dysfunction in HUVECs was attenuated by PDCD4 knockout or LEVs incubation. The results of this study suggested a novel function of LSW as a regulator on the functional EVs from vascular cells in the oxLDL-induced atherosclerotic model. | Tripeptide Leu-Ser-Trp Regulates the Vascular Endothelial Cells Phenotype Switching by Mediating the Vascular Smooth Muscle Cells-Derived Small Extracellular Vesicles Packaging of miR-145
Tripeptide LSW, initially identified as a potent ACE inhibitory peptide from soybean protein, was recently reported to exert a protective effect against angiotensin II-induced endothelial dysfunction via extracellular vesicles (EVs). However, the molecular mechanisms, especially in lipid accumulation-induced atherosclerosis, still remain unclear. The study aimed to investigate whether the protective effects of LSW against endothelial dysfunction on vascular endothelial cells (VECs) was via vascular smooth muscle cells (VSMCs)-derived miRNA-145 packaged in EVs. The miRNA-145 was concentrated in EVs from LSW-treated VSMCs (LEVs), internalized into the HVUECs, and targeted the programmed cell death protein 4 (PDCD4) expression of HUVECs. Oxidized low-density lipoprotein (oxLDL) was applied to induce endothelial dysfunction in HUVECs; oxLDL-induced endothelial dysfunction in HUVECs was attenuated by PDCD4 knockout or LEVs incubation. The results of this study suggested a novel function of LSW as a regulator on the functional EVs from vascular cells in the oxLDL-induced atherosclerotic model.
Atherosclerosis (AS) is a vital cause of the cardiovascular disease (CAD) and a serious threat to systemic cardiovascular homeostasis [1,2]. As a vascular disease, its occurrence and development are closely related to vascular aging, lipid accumulation-induced thrombosis, vascular inflammation, and ischemic stroke [3,4]. The vascular endothelial cells (VECs) and smooth muscle cells (VSMCs) could respond to stimulation from the extracellular matrix, the main component of vessels, which was proposed as a crucial mechanism of endothelial dysfunction, sub-endothelial mononuclear cells infiltration, aorta remodeling, lipid deposition, and fibrosis [5]. The accumulation and oxidative modification of low-density lipoprotein (oxLDL) provokes the VECs and induces atherogenesis by combining the specific receptor lectin-type oxidized LDL receptor 1 (LOX-1) [6]. The oxLDL can break through the endothelial barrier and aggregates in the sub-endothelium, where it promotes the differentiation of monocytes into macrophages, which attempt to phagocytose the oxLDL and become lipid-laden foam cells [7]. The lipid-laden foam cells were formed by macrophages endocytosing the oxLDL, which is regarded as an ominous event in the AS [8]. Hence, the oxLDL was used as an induction cytokine for the endothelial dysfunction of AS pathogenesis in the present study. Vascular cells are mediated by various external environmental stimuli (such as fluid shear, chemical reagents, and cytokines) and cause relevant physiological phenotypic switching [9]. In recent years, the nanoscale membrane-bound vesicles named “extracellular vesicles” (EVs) have been identified as an important regulator of intercellular communication, due to their ability to transfer abundant cell-specific cargo that contains proteins, lipids, and nucleic acids, and release them into the extracellular microenvironment [10]. Of particular interest is the single-stranded and non-coding small RNAs (miRNAs) that can be transferred by EVs and regulate specific gene functions in recipient cells [11]. Numerous studies demonstrated that miRNAs are involved in almost all stages of vascular dysfunction in AS. Therefore, miRNAs are commonly served as a classical biomarker to monitor the physiological state of vascular cells [12]. A protein array analysis of small EVs reported that dendritic cells (DC)-derived small EVs could stimulate human umbilical vein endothelial cells (HUVECs) by activating the NF-κB signaling pathway, and tumor necrosis factor-α (TNF-α) on the membrane of small EVs should be mostly responsible for this process [9]. A clinic study found that miRNA-155 was overexpressed in the urinary-EVs from CAD patients and suppressed the anti-inflammatory signaling pathway in macrophages [13]. The VECs-derived EVs enriched with miRNA-126 promoted monocyte adhesion by targeting the vascular cell adhesion molecule 1 (VCAM-1) expression [14]. The miRNA-143 and miRNA-145 have been identified as the key functional molecules in the VSMCs-derived EVs [15]. A research reported that knockout of miRNA-143 and miRNA-145 would disturb vascular homeostasis by inducing VSMCs dysfunction in mice [16]. MiRNA-143 and miRNA-145 were overexpressed in the EVs from KLF2-transduced- and shear-stress-induced HUVECs, and those EVs targeted gene expression of VSMCs after internalizing into VSMCs [17]. However, the mechanism of the association between miRNA-145 and oxLDL-induced endothelial dysfunction is still unclear. Growing evidence indicated that the biopeptides encrypted in natural protein sequences are able to regulate various physiological functions. In recent decades, a variety of bioactive peptides have been identified from different protein sources. Due to their significant health-beneficial activities in many chronic diseases including hypertension, hyperlipidemia, and diabetes, these peptides were considered as the leading compounds in development of nutraceuticals or functional foods [18,19,20,21]. The milk-derived peptides VPP and IPP were found to inhibit the TNF-α-induced endothelial inflammatory by down-regulating pivotal protein expression in the NF-κB signaling pathway [18]. The egg-derived peptides IRW and IQW exhibited antihypertensive effects by promoting vasorelaxant [19]. Peptides HGSEPFGPR, RPRYPWRYT, and RDGPFPWPWYSH isolated from amaranth (Amaranthus hypochondriacus) proteins were reported to reduce the low-density lipoprotein receptor-1 (LOX-1), intercellular cell adhesion molecule-1 (ICAM-1), and matrix metalloproteinase-9 (MMP-9) expression in the THP-1 macrophage, which implied that those peptides have potential to improve AS [20]. Our lab has been dedicated to understanding the relationship between the biopeptides and EVs-regulation, and our recent findings suggested that the soybean protein-derived tripeptide LSW was able to reverse the adverse effects of Ang II-induced VSMCs-secreted EVs on endothelial cells [21]. The transcriptomic analysis demonstrated that the EVs produced by Ang II-induced VSMCs aggravated the endothelial dysfunction through TNF, NF-κB, and NOD-like receptor signaling pathways. However, the mechanisms of miRNAs loading in EVs from LSW-induced VSMCs on endothelial cells were still unclear. Therefore, this study was aimed to investigate the effect of LSW on the expression of miRNA-145 in the VSMCs-derived EVs, and the oxLDL-induced HUVECs were used as an atherosclerosis model to explore the mechanism of tripeptides LSW.
The proliferation and migration of vascular cells are closely responsible for many vascular diseases such as atherosclerosis. Hence, the cck-8 and wound healing assay were performed to test the effects of LSW on the proliferation and migration of VSMCs and HUVECs. Figure 1 showed that the LSW has no effect on proliferation and migration at a gradient concentration from 0.1 to 100 μmol/L. Oxidative stress is the primary adverse reaction of vascular cells responding to external stimuli, which causes a large amount of ROS production and further damages cellular function. Dihydroethidium could be internalized into VSMCs and HUVECs, and intracellular superoxide anions dehydrogenates dihydroethidium to produce ethidium. The ethidium could bind to RNA or DNA to produce red fluorescence. Figure 1B showed no significant change of red fluorescence in the DHE-labeled VSMCs or HUVECs as the concentration of LSW gradually increased. Therefore, the LSW incubation has no effect on the amount of ROS produced within VSMCs and HUVECs. The DHE assay exhibited that LSW was a mild response to oxidative stress in cells (Figure 1B,C). The wound healing assay indicated that the LSW has no specific influence on the migration of HUVECs (Figure 1D,E). Previous studies considered that the LSW as an ACE inhibitor could inhibit the proliferation and migration of Ang II-induced VSMCs, and the data from our lab showed that LSW incubation could revise the anomalous loading of EVs in the Ang II-induced VSMCs [21,22]. Therefore, this result demonstrated that the LSW should be a safe response factor for VSMCs and HUVECs.
The EVs isolated from the medium of VSMCs with or without LSW-incubation were morphologically confirmed using TEM (Figure 2A). The EVs produced by normal VSMCs without LSW incubation were abbreviated as “NEVs”, and the LSW-induced VSMCs-derived EVs were abbreviated as “LEVs”. The EVs extracted through ultracentrifugation displayed a classical “cup-shaped” and double-layer membrane structure under an electron microscope, and the particle diameter was approximately 200 nm (Figure 2A). The results of NTA suggested the diameter and size distribution of two EVs (Figure 2B). In addition, CD9, CD81, and Tsg101 as protein markers of EVs were obviously detected by western blot, which further confirmed the identity of both NEVs and LEVs (Figure 2C). These results indicated that the LSW incubation has not intervened in the process of EV secretion. Remarkably, although the above features conformed to the specifications of “exosomes”, the vesicle sample in the present study was uniformly described as the term “EV” due to the indistinct specific markers of subcellular origin data according to MISEV2018 [23].
The effects of NEVs and LEVs on endothelial dysfunction were explored. The model of endothelial dysfunction was prepared by oxLDL induction, and oxLDL significantly promoted the proliferation of HUVECs based on a cck-8 assay (Figure 3A). The wound healing experiment showed that the speed of scratch healing of oxLDL-induced HUVECs was conspicuously stronger than normal HUVECs, which suggested that oxLDL accelerated the migration capacity. However, LEVs but not NEVs treatment could observably attenuate the oxLDL-induced proliferation and migration of HUVECs (Figure 3B,C). It signifies that the NEVs and LEVs were similar in morphological characteristics but different in physiological functions. In addition, the pure peptide LSW has also performed the same pre-incubation operation, but the single incubation with LSW has no effect on the excessive proliferation and migration of oxLDL-induced HUVECs (Figure 3B,C). The monocytes adhesion assay showed that exposure to oxLDL increased the number of monocytes adhered to endothelial cells, which was reduced by LEVs incubation. Both NEVs and pure peptide LSW treatment had no effect on oxLDL-induced HUVECs (Figure 3D,E). The important adhesion molecules were further investigated, and the results indicated that LEVs could significantly reduce the expression of adhesion molecules VCAM-1, ICAM-1, and E-selectin in the oxLDL-stimulated HUVECs (Figure 3F). The results in Figure 3 demonstrated that the LEVs improved the endothelial dysfunction caused by oxLDL as pro-proliferation, pro-migration, and adhesion molecules up-regulation contrast to NEVs, which encouraged exploring the different molecules packaging between LEVs and NEVs.
Numerous studies reported that miR-145 is responsible for phenotype modulation of VSMCs and can be transferred by EVs [15]. Hence, the miR-145 loading in EVs was quantified by qRT-PCR. As is shown in Figure 4, LSW incubation increased the expression of miR-145 in both VSMCs and VSMCs-derived EVs, but the different degree of miR-145 expression in EVs was much greater than in cells (Figure 4A). This result implied that the LSW could enhance the load of miR-145 into VSMCs-derived EVs. According to the TargetScan database (version: 7.2, http://www.targetscan.org/vert_72/ (accessed on 16 July 2021)), the PDCD4 might be a target of miR-145. The predicted location of binding is in position 590-597 of PDCD4 3ʹ-UTR (Figure 4C). To test this hypothesis, a dual-luciferase reporter assay was performed in HUVECs. Potential binding sites of miRNA-145 in the 3′-UTR of the PDCD4 mRNA were determined by TargetScan. Firefly luciferase activity was significantly reduced when co-transfected with miRNA-145 mimic, indicating that PDCD4 is a target of miRNA-145 (Figure 4C). Remarkably, the miRNA-145 overexpression caused by miRNA-145 mimic transfection down-regulated the PDCD4 mRNA expressions in HUVECs (Figure 4C). Thus, the uptake of EVs by neural stem cell (NSCs) was visualized by labeling EVs with fluorescent lipid dye PKH67. During 12 h incubation of HUVECs with PKH67-labeled EVs, the fluorescent could be well imaged in the cytoplasm location of HUVECs, indicating the HUVECs could efficiently take in EVs derived from VSMCs (Figure 4B). The above results suggested that the LSW could increase the miRNA-145 load in VSMCs-derived EVs, and the LEVs could internalize into HUVECs and target the PDCD4.
The PDCD4 has been defined as a novel programmed cell death factor and is responsible for the malignant proliferation and migration of cells. Hence, the western blot assay indicated that the oxLDL could significantly promote the expression of PDCD4 in the HUVECs (Figure 5A). siRNA was used to knock down the PDCD4 in HUVECs (Figure 5B), and PDCD4 knockdown could inhibit the over-migration of HUVECs with oxLDL-induction (Figure 5C,D). Hence, this result illustrated that the oxLDL might promote migration of HVUECs by up-regulation of PDCD4. The miRNA-145, as a regulator of PDCD4, was loaded into the LEVs. Therefore, we explored the effects of LEVs on the PDCD4 expression in oxLDL-induced HUVECs. In contrast to NEVs, the LEVs could significantly reduce the expression of PDCD4 in oxLDL-induced HUVECs (Figure 5E). Additionally, the qRT-PCR result showed the co-culture with LVEs could decrease the mRNA expression of PDCD4 in the HUVECs, but the not for the NEVs (Figure 5F). In conclusion, the EVs from VSMCs with LSW-treatment could attenuate the oxLDL-induced endothelial pernicious migration.
Previous studies have shown that CVD including hypertension, myocardial infarction, and coronary artery disease are involved in numerous pathophysiological events of dysfunction in the heart and vessels, which has been considered a primary mortality inducement around the world [24]. Atherosclerosis (AS) is a lethal factor to CVD and commonly accompanies an excessive accumulation of lipids in the vascular wall [25]. The endothelium, an important tissue in the vessel, plays a principal role in maintaining the dynamic of vascular tone, angiogenesis, redox reaction, inflammatory, and antithrombotic [26]. Recent studies indicated that endothelial dysfunction, characterized by oxidative stress, chronic inflammation, leukocyte adhesion, and endothelial senescence, should be acknowledged as a vital hallmark of AS [27]. Traditional therapeutic schedule is limited by various side effects, and the edible protein-derived peptides have been explored as a potential nutrient to the cardiovascular system because of its anti-hypertension, ACE inhibition, and vasodilation activities [28]. Our previous study published that the tripeptides LSW from soybean protein hydrolysates could affect the endothelial physiological function through a VSMCs-produced EVs mediating pathway [21]. Further investigation about potential mechanisms of EVs-loading miR-145 on endothelial dysfunction was performed and indicated that the tripeptide LSW could improve oxLDL-induced proliferation, migration, and adhesion by regulating miR-145 packaging in VSMC-derived EVs. Increasing evidence suggests that the communication between vascular endothelial cells and smooth muscle cells plays a crucial role in the AS, and the extracellular vesicles have served as an indispensable mediator of this interaction [29]. On this basis, a series of natural products and biopeptides also respond to the function of EVs regulation. A recent study indicated that the curcumin could improve the migration and lipid accumulation of VSMCs damaged by LPS-induced endothelial EVs, and the differential expression of miRNAs loading in EVs was detected to explain the potential molecule mechanism [30]. The Paeonol from the radix of Cortex Moutan was proved to improve the inflammatory response of endothelial cells by mediating the miRNA-223 packaging in the monocytes-derived EVs [12]. Liu et al. reported that the polysaccharide of Dendrobium officinale could attenuate the inflammatory bowel disease by mediating intestinal EVs with miRNA-433-5p delivering [31]. Consequently, two different EVs from VSMCs with or without peptide LSW incubation were prepared (Figure 2), and the appropriate concentration of LSW was selected not to cause adverse effects on VSMCs and HVUECs (Figure 1). The NTA results showed that the LSW might slightly increase the EVs secretion, but the particle size of LEVs did not be enlarged (Figure 2B). The EV science has found a number of genetic proteins that control EV production, but there is little definitive evidence to link the amount of EV secretion to the phenotypic state of donor cells [32]. The legible mechanism to the pathways of EV secretion will be a long process. Tripeptide LSW was first explored from glycinin A1bB2-784 as an ACE inhibitor with a low IC50 value, which represented a potential anti-hypertensive activity [33]. Subsequently, a Caco-2 transport assay of LSW was established and proved that it was intactly transported across Caco-2 monolayers by the tight junction and peptide transporter 1 (PepT1) mediating paracellular diffusion pathway [34]. Those results might provide a potential that the LSW with a high bioavailability could enter the circulatory system via an oral-absorption pathway. Furthermore, the in vitro vasoactivity of LSW was exhibited by an Ang II-induced VSMCs model, which found that LSW exerted an anti-oxidant and anti-inflammatory activity by reducing the AT1R expression with the Src and ERK1/2 phosphorylation [22]. Hence, the LSW was regarded as a vasoactive peptide. Interestingly, LSW treated alone did not affect oxLDL-induced endothelial proliferation and migration (Figure 3), even though LSW could prompt EVs secretion of VSMCs. However, EVs produced by LSW-induced VSMCs (LEVs) could significantly attenuate oxLDL-induced undesirable migration of VSMCs. Meanwhile, the distinct adhesion ability of LEVs- or NEVs-induced VSMCs has explored a similar result due to a moderate expression of adhesion protein (Figure 3D,F). Although the tripeptide LSW showed some intervening effects on the physiological regulation of EVs in this study, more investigation need to be performed, such as the relationship between biopeptides and EVs-secretion pathways, or the mechanisms of biopeptides intervention miRNA intracellular synthesis, etc. The miRNA-145 has been verified to load into EVs and decrease the atherosclerotic lesion formation in KLF2-expressing VECs [35]. However, the communication between VECs and VSMCs is not monodirectional or simple from VECs to VSMCs, and some changes occurred in VSMCs might interfere the VECs as a downstream form of the conversation in turn [29]. In the present study, we found that LSW not only increased the expression of miRNA-145 in donor VSMCs but also elevated the loading of miRNA-145 in the VSMCs-derived EVs (Figure 4A). Remarkably, although LSW treatment altered the miRNA-145 expression, it did not induce special responses of VSMCs in proliferation and migration (Figure 1). The internalization of EVs into cells was a precondition to deliver miRNAs to recipient cells. The fluorescent staining assay displayed that EVs were traced into HUVECs with the extension of time (Figure 4B). The programmed cell death protein 4 (PDCD4) has been defined as the regulator of myriad cellular events, and a recent study indicated that PDCD4 played a crucial role in the formation of coronary AS plaque and promoted the production of IL-6 and IL-8 in the VSMCs [36]. The miRNA-16 was explored as a target of PDCD4 and participated in suppressing the activation of inflammatory macrophages by the MAPK and NF-κB signaling pathway in AS [37]. However, it is worth exploring whether the antagonistic effect of LEVs on oxLDL-induced endothelial dysfunction stems from PDCD4. Luciferase assay was used to test the interaction between miRNA-145 and PDCD4 in the HUVECs (Figure 4C). The oxLDL elevated the expression of PDCD4 in HUVECs, and PDCD4 knockdown could obviously reduce the migration of oxLDL-induced HUVECs (Figure 5). Those results supported that PDCD4 mediates the damage effects of oxLDL, which is consistent with other studies. Bai et al. suggested that the direct involvement of PDCD4 in oxLDL-induced stress granules through its RNA-binding activity [38]. Another study reported that the miRNA-21 directly targeted the PDCD4 of HUVECs and controlled apoptotic proteins expression [39]. The negative regulatory relationship between miRNA-145 and PDCD4 was further verified in the EVs co-incubation study (Figure 5E,F). Distinct from NEVs, LEVs significantly down-regulated the PDCD4 expression and simultaneously occurred at the level of transcription. Although it may be prudent to suggest that the regulation of EVs on HUVECs is via a miRNA-mRNA sponge, the uniqueness of this relationship need some further work to make it clear.
DMEM cell medium, phosphate-buffered saline (pH 7.4), RPMI-1640 media, and fetal bovine serum (FBS) were obtained from Gibco (Carlsbad, CA, USA). In addition, 0.25% Trypsin-EDTA and penicillin-streptomycin solution were purchased from Sigma-Aldrich (St. Louis, MO, USA). Serum-free media (for exosome culture) was purchased from Umibio (Shanghai, China) Co. Ltd. oxLDL was purchased from Solarbio Life Sciences (Beijing, China). The RIPA buffer, cck-8 kit, DAPI dye, and dihydroethidium (DHE) were purchased from the Beyotime Institute of Biotechnology (Shanghai, China). A protein quantification kit with bicinchoninic acid (BCA) protein was purchased from ThermoFisher Scientific (Waltham, MA, USA). The tripeptides Leu-Ser-Trp (LSW) were purchased from GL Biochem (Shanghai) Ltd. (Shanghai, China). The purity of the peptide was determined by HPLC (99.9% for LSW) according to the manufacturer. After dissolving in 1 × PBS, peptides were aliquoted and stored at −20 °C for cell culture experiments.
Referring to our previous study [21], the VSMCs and HUVECs cell line were a kind gift of Professor Zedong Jiang (Jimei Universtiy, Xiamen, China) and were used in this study between passages 5 and 15. The cells received were maintained in DMEM with 10% FBS containing 100 U/mL penicillin, and 100 g/mL streptomycin at 5% CO2 and 37 °C. The U937 monocytes, a human leukemic monocyte lymphoma cell, were purchased from Nation Collection of Authenticated Cell Cultures (Shanghai, China), and cultured in RPMI-1640 media with 10% FBS. For induction experiments, HUVECs were stimulated with 100 μg/mL oxLDL with or without LSW/EVs (1 μg protein/mL) pre-incubation. The cell proliferation was assessed by a CCK-8 kit following the instructions. Briefly, cells were cultured in 96-well plates with 80% confluency, and then co-incubated with various concentrations (0.1–100 μmol/L) of LSW for another 24 h. The CCK-8 reagent was added to plates at 100 μL/well, and the plate was kept at 37 °C for 2 h. Then, the optical density at 562 nm wavelength was tested by a VARIOSKAN FLASH microplate reader (Thermo Fisher Scientific, Waltham, MA, USA).
After stimulation, the HUVECs with or without LSW/EVs pre-incubation was loaded with 1 μmol/L DHE dye and maintained for 15 min at 37 °C. After that, the medium containing dye was removed and the cells were gently washed with PBS. The intracellular production of ROS was observed using a fluorescent microscope. The wound-healing assay was used to assess the cell migration ability. The cells were inoculated in a 6-well plate with LSW or EVs pre-incubation, and then a scratch was made on a uniform confluent layer using a sterile micropipette tip. The photographs of the scratches were taken immediately at 0 and 24 h, respectively.
The adhesion assay was performed by a co-culture with U937 monocytes and HUVECs as previously showed [40]. Briefly, the U937 monocytes (2 × 104 cells/mL) were labeled with 5 μmol/L calcein-AM for 60 min at 37 °C. Then, the U937 cells were centrifuged to abandon extra stain and added to culture media containing LSW- or EVs-pretreated HUVECs with or without oxLDL induction for 2 h. After that, the free U937 cells were removed by washing the plates two times. Fluorescence photographs were visualized through a fluorescence microscope.
The EVs from the cells culture medium were isolated by an ultra-centrifuge process according to a method described by Théry et al. with small modifications [41]. The VSMCs were incubated with or without 50 μmol/L LSW for 24 h, and the cells were washed three times with PBS. Afterwards, the fresh media for exosome culture (serum-free) were added and continued to culture for 48 h. Then, the culture medium was collected and centrifuged at 300× g for 15 min and then at 3000× g for 30 min. The supernatants were harvested to extract the EVs. The supernatants containing extracellular vesicles were centrifuged at 10,000× g for 60 min at 4 °C to remove cells and debris, and thereafter at 100,000× g for 60 min. The EV particles were concentrated at the bottom of tubes and resuspended with 100 μL PBS. The EVs from normal VSMCs without LSW incubation are named NEVs, and the EVs from LSW-induced VSMCs are named LEVs. The EVs were stored at −80 °C for further research. The EVs were quantified through a BCA kit. The morphology of EVs was observed by TEM testing as described previously [41]. Briefly, the EVs suspension was mixed with an equal volume of 4% paraformaldehyde and deposited on Formvarcarbon-coated EM grids. The photograph was imaged with a transmission electron microscope (Hitachi, Tokyo, Japan). The sizes of EVs were analyzed through a nanoparticle tracking analysis by NanoSight NS3000 instrument (Malvern Panalytical Ltd., Worcestershire, UK) as previously described [42]. The biomarker protein CD9, CD81, and Tsg101 were identified by western blot assay.
To further observe whether the EVs were taken up by HUVECs or not, the EVs were labeled with a green fluorescent dye PKH67, and then co-cultured with HUVECs. In brief, the EVs were resuspended in PBS with 100 μM PKH67 dye for 1 h at 37 °C, and then were co-cultured with HUVECs in a 6-well plate at 37 °C for 6 h. After incubation, the HUVECs were gently washed by PBS and fixed with 4% paraformaldehyde for 15 min at room temperature. The 0.1% Triton X-100xw (Beyotime Institute of Biotechnology, Shanghai, China) was used to permeabilize the HVUECs, and then the cells were stained with DAPI solution for 5 min. The fluorescence images were obtained and used to observe the trace of EVs at different time points.
The cells were transfected with synthesized siPDCD4 packaging in Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA) as previously published [43]. Briefly, the HUVECs were plated onto 6-well plates at a density of 5 × 104 cells/well and allowed to grow overnight. The siPDCD4 or empty vector with 5 μL lipofectamine was co-incubated with cells. After transfection, cells were cultured at 37 °C for 24 h to 72 h, then the cells were harvested and lysed to extract protein or RNAs.
The HUVECs were lysed with RIPA buffer containing proteinase inhibitors. Total proteins were quantified using the BCA assay kit, and the equal protein quantity of lysates was separated by SDS-PAGE electrophoresis and transferred to PVDF membranes for analysis. The membranes were blocked with 6% BSA and then probed overnight with primary antibodies and HRP-conjugated anti-IgG at 4 °C. The enhanced chemiluminescence kit (Beyotime Institute of Biotechnology, Shanghai, China) was used to test the blots. Western blot bands were scanned and saved. The Invitrogen TRIzol reagent was used to extract RNAs from cells according to the manufacturer’s instructions. The cDNA was synthesized by a Transcriptor First-Strand cDNA Synthesis System (Applied Biosystems, Branchburg, NJ, USA). The primer sequences used for the real-time PCR analysis were as follows: miRNA-145 forward: 5′-ACGGTCCAGTTTTCCCAGGAATCCCT-3′; PDCD4 forward: 5′-AGGTTGCTAGATAGGCGGTC-3′; U6 forward: 5′-CTCGCTTCGGCAGCACA-3′; GAPDH forward: 5′-AACGACCCCTTCATTGACCTC-3′ [44]. For miRNA detection, a universal reverse primer from Invitrogen was used. PCR amplification was carried out on a C1000 Touch Fast Real-Time PCR system (Bio-Rad Laboratories, Inc., Hercules, CA, USA) with the primers purchased from Guangzhou RiboBio Company (Guangzhou RiboBio CO. Ltd., Guangdong, China).
For identification of the binding site between miR-145-3p and PDCD4, cells were transfected with a luciferase construct containing PDCD4 with the wild-type or a mutated version of the binding site, co-transfected with miR-145-3p mimic or negative vector. Before transfection, the cells were seeded in a 96-well plate at a density of 1 × 104 cells/well. Then, medium of each well was replaced with fresh liquid. After that, the transfection mixture was prepared and suppled into the plate according to Lipofectamine 3000 instructions. The luciferase activities were measured using a Dual-Luciferase kit (Promega, Madison, WI, USA) according to the manufacturer’s instructions after 48 h of transfection at 37 °C.
Statistical analyses were performed for all results by the SPSS 21.0 software package. All results data were presented as the mean ± SD unless otherwise specified. Differences between means were evaluated by one-way analysis of variance (ANOVA) for multiple comparison tests. p < 0.05 was defined as statistically significant.
In conclusion, this study provided evidence that the antihypertensive peptide LSW protected oxLDL-stimulated endothelial proliferation and migration via VSMCs-derived EVs enriched with miRNA-145. LSW could increase the miRNA-145 expression in VSMCs and loading in VSMCs-produced EVs. The EVs from LSW-stimulated VSMCs attenuated oxLDL-induced endothelial dysfunction by targeting the PDCD4 expression in HVUECs. Based on these results, the peptide LSW is considered a potential agent for mitigating lipid accumulation-induced vascular endothelial dysfunction, and the EVs-mediated transfer of miRNA-145 from VSMCs to VECs may be an effective therapeutic target for endothelial dysfunction. | true | true | true |
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PMC9611982 | Milica Djokic,Tanja Radic,Veljko Santric,Dejan Dragicevic,Sonja Suvakov,Smiljana Mihailovic,Vesna Stankovic,Milica Cekerevac,Tatjana Simic,Marina Nikitovic,Vesna Coric | The Association of Polymorphisms in Genes Encoding Antioxidant Enzymes GPX1 (rs1050450), SOD2 (rs4880) and Transcriptional Factor Nrf2 (rs6721961) with the Risk and Development of Prostate Cancer | 09-10-2022 | reactive oxygen species,oxidative stress,antioxidant enzymes,prostate cancer,SNP | Background and Objectives: Mounting evidence implicates oxidative damage in prostate carcinogenesis, contributing to modifications of macromolecules that drive cellular malignant transformation. Functional single-nucleotide polymorphisms (SNPs) of enzymes involved in redox homeostasis can disrupt pro-oxidant–antioxidant balance, leading to accumulation of reactive oxygen species and oxidative damage. We investigated the potential role of genetic polymorphisms of antioxidant enzymes glutathione peroxidase 1 (GPX1 rs1050450) and superoxide dismutase 2 (SOD2 rs4880) and regulatory antioxidant protein nuclear factor erythroid 2-related factor 2 (Nrf2 rs6721961) in the susceptibility to prostate cancer development (PC) and prognosis. Materials and Methods: We conducted a case–control study consisting of 235 patients with PC and 240 controls. Gene polymorphisms were determined by quantitative polymerase chain reaction (qPCR) and polymerase chain reaction with confronting two-pair primers (PCR-CTTP) methods. Multiple risk models were composed to inspect the separate and mutual effect of multiple genes and in combination with acquired contributory factors on the risk of PC development. Results: Independently, carriers of at least one SOD2*C allele had increased risk of PC development, which was significantly further amplified in advanced statistical models. When tested in combination, individuals with both SOD2*C allele and Nrf2*C/C genotype were also at increased risk of PC development, which was augmented when combined with acquired contributory factors. During the mean 75 ± 25 months of follow-up, investigated gene polymorphisms did not affect overall survival. Conclusion: Our results suggest that these gene polymorphisms could be used as risk biomarkers of PC evolution. | The Association of Polymorphisms in Genes Encoding Antioxidant Enzymes GPX1 (rs1050450), SOD2 (rs4880) and Transcriptional Factor Nrf2 (rs6721961) with the Risk and Development of Prostate Cancer
Background and Objectives: Mounting evidence implicates oxidative damage in prostate carcinogenesis, contributing to modifications of macromolecules that drive cellular malignant transformation. Functional single-nucleotide polymorphisms (SNPs) of enzymes involved in redox homeostasis can disrupt pro-oxidant–antioxidant balance, leading to accumulation of reactive oxygen species and oxidative damage. We investigated the potential role of genetic polymorphisms of antioxidant enzymes glutathione peroxidase 1 (GPX1 rs1050450) and superoxide dismutase 2 (SOD2 rs4880) and regulatory antioxidant protein nuclear factor erythroid 2-related factor 2 (Nrf2 rs6721961) in the susceptibility to prostate cancer development (PC) and prognosis. Materials and Methods: We conducted a case–control study consisting of 235 patients with PC and 240 controls. Gene polymorphisms were determined by quantitative polymerase chain reaction (qPCR) and polymerase chain reaction with confronting two-pair primers (PCR-CTTP) methods. Multiple risk models were composed to inspect the separate and mutual effect of multiple genes and in combination with acquired contributory factors on the risk of PC development. Results: Independently, carriers of at least one SOD2*C allele had increased risk of PC development, which was significantly further amplified in advanced statistical models. When tested in combination, individuals with both SOD2*C allele and Nrf2*C/C genotype were also at increased risk of PC development, which was augmented when combined with acquired contributory factors. During the mean 75 ± 25 months of follow-up, investigated gene polymorphisms did not affect overall survival. Conclusion: Our results suggest that these gene polymorphisms could be used as risk biomarkers of PC evolution.
In developed Western countries, prostate cancer (PC) remains one of the leading cancer-related causes of morbidity and mortality [1]. Excluding age, ethnicity and family history of PC, risk factors contributing to its development are poorly understood. Furthermore, due to PC heterogeneity, differentiation between indolent and aggressive phenotypes poses a challenge, which leads to overdiagnosis, overtreatment and decreased quality of life. In order to help tailor personalized therapeutic strategies for individuals with higher risk of worse outcomes, a variety of studies in recent years have been focused on determining possible biomarkers for risk stratification regarding PC development and survival [2]. Reactive oxygen species (ROS), such as superoxide (O2−) anion, hydrogen peroxide (H2O2) and radical (OH−) anion are common byproducts of aerobic cellular metabolism. They are constantly formed and reduced to less reactive compounds by various enzymatic and non-enzymatic mechanisms. A mounting body of evidence supports the role of ROS in prostate cancer evolution [3,4,5]. It is believed that during chronic inflammation preceding tumorigenesis, the balance of chemokines and cytokines, as well as ROS, is altered [6,7,8]. The accumulation of intracellular ROS leads to modification of macromolecules, including DNA and proteins. Moreover, ROS seem to serve as signaling modifiers by inducing glutathionylation, S-nitrosylation and formation of disulfides of proteins, thus changing their regulatory roles [9]. The aforementioned mechanisms are thought to be implicated both in the onset and progression of complex diseases, such as cancer [10]. The expression of certain regulatory antioxidant proteins, as well as antioxidant enzymes, also seems to be altered, contributing to the transformational potential of the affected cell [11,12]. In addition, genetic polymorphisms found in genes encoding for both regulatory antioxidant proteins, such as Nrf2 (Nuclear factor erythroid 2-related factor 2), and antioxidant enzymes may aid in tumor development and progression [13]. Transcriptional factor Nrf2, a key controller of antioxidant response, is directly regulated by ROS levels [14,15]. Under basal conditions, the level of Nrf2 expression is low. However, in stressed cells, Nrf2 induces the expression of various target genes involved in various cellular processes, such as redox homeostasis, xenobiotic metabolism, DNA repair, energetic metabolism and proteasomal degradation, among others [16]. Recent studies have revealed the dual role of Nrf2 in cancer evolution. The protective role of Nrf2, consisting of quickly inducing target genes and repairing cellular oxidative damage that originates from carcinogens and radiation, has been well established [17,18,19]. It is also proposed that its antitumor effect is achieved by inhibiting NF-kB mediated proinflammatory pathways [20]. However, in already formed cancer cells, Nrf2 activation can further promote cancer proliferation and contribute to radio- and chemoresistance [21,22,23]. Few functional polymorphisms of the Nrf2 gene have been described in the literature, and their impact on various cancers is yet to be fully identified. Among discovered polymorphisms, Nrf2 rs6721961 is one of the more frequently described. It contains a substitution of C to A in the gene’s promoter region at position -617. It has been suggested that this change affects basal expression of the Nrf2 gene and may diminish transcription of its target genes [24]. The cornerstones of the enzymatic defense system for scavenging and neutralizing ROS are the superoxide dismutase (SOD) and glutathione peroxidase (GPX) families of enzymes. O2− is reduced to H2O2 in a reaction catalyzed by SOD, and among the three identified isoenzymes, SOD2 is the most widely studied. In vitro studies have shown that SOD2 overexpression results in a reduced growth rate of androgen-independent prostate cancer cells [25]. Furthermore, it has been reported that SOD2 acts as a mediator, regulating various activities of transcriptional factors [26,27]. Multiple single-nucleotide polymorphisms (SNPs) have been discovered within the SOD2 gene; however, the most extensively researched is substitution of C → T altering alanine to valine at position 16 of the amino acid chain (SOD2 rs4880). This causes a change in the secondary protein structure, which in turn affects import of SOD2 into mitochondria, resulting in reduced activity of the Val variant [28]. Previous population studies have shown an association between this polymorphism and prostate cancer development [29,30,31]. The catalytic role of another main antioxidant enzyme, GPX, is to balance intracellular levels of H2O2 by further reducing it to H2O. Eight isoenzymes have been identified, among the most researched of which is GPX1. Within the GPX1 gene, the most commonly studied polymorphism involves substitution of C → T shifting proline to leucine at position 198 (GPX1 rs4050450). This modification causes conformational change, affecting enzyme activity, which can lead to increased cancer risk [32,33,34]. The aim of our study was to investigate the possible role of functional polymorphisms of antioxidant enzymes SOD2 and GPX1 and regulatory antioxidant protein Nrf2, alone or combined, in susceptibility to prostate cancer development and overall survival in Serbian male patients.
This case–control study enrolled 235 patients with initially localized prostate cancer who were diagnosed and treated at the Urology Clinic of the Clinical Center of Serbia in Belgrade and the Institute of Oncology and Radiology of Serbia from 2013 to 2016. Criteria for enrollment were an age of 18 years or older, histological confirmation of prostate adenocarcinoma by an experienced uropathologist in accordance with the WHO classification [35], physical examination and transrectally guided biopsy and voluntary acceptance to participate in the study prior to the start of the treatment. Information about demographic characteristics, medical history, diagnostics and treatment of prostate cancer was collected from standard questionnaires filled in by patients and from their medical records. The follow-up period for patients with prostate cancer was up to 98 months (from January 2014 to March 2022). The control group consisted of 240 age-matched males with no previously recorded malignancy whose DNA samples are a part of the DNA biobank at the Institute of Medical and Clinical Biochemistry, Faculty of Medicine, University of Belgrade. By selecting control subjects from the same population as the cases, the confounding effect of geographical factors or ethnic background was limited. Informed consent was obtained from all participants. The study was conducted according to the Declaration of Helsinki, the study protocol was approved by the Ethics Committee of the Faculty of Medicine, University of Belgrade (no.1322/XII-15).
Prostate adenocarcinoma was histologically confirmed by an experienced uropathologist in accordance with the WHO classification [35]. The histological assessment comprised the determination of Gleason score by the same dedicated uropathologist. Images indicating the most representative examples of histological findings of Gleason grades, ranging from 1–5, are provided in the Supplementary Materials (Figures S1–S4).
Serum prostate-specific antigen (PSA, ng/mL) concentration in patients diagnosed with prostate cancer was analyzed by CMIA (Cobas, Roche Diagnostics, Rotkreuz, Switzerland) as a part of routine laboratory work in the laboratory of the University Clinical Center of Serbia. Whole blood was used to isolate genomic DNA with a QIAamp DNA mini kit (Qiagen, #51306, Chatsworth, CA, USA) in accordance with the manufacturer’s instructions. GPX1 (rs1050450) and SOD2 (rs4880) gene polymorphisms were determined by quantitative polymerase chain reaction (qPCR) on a Mastercycler ep realplex (Eppendorf, Hamburg, Germany) using applicable Applied Biosystems TaqMan drug metabolism genotyping assays (Life Technologies, Applied Biosystems, Carlsbad, CA, USA). For the SOD2 polymorphism, a C_8709053_10 assay was used, whereas in case of the GPX1 polymorphism, a custom-designed assay was applied with sequences 5′ VIC-ACAGCTGGGCCCTT-MGB-3′ and 5′ FAM-ACAGCTGAGCCCTT-MGB-3′. The Nrf2 (rs6721961) gene polymorphism was detected by polymerase chain reaction with confronting two-pair primers (PCR-CTTP) method, as previously described by Shimoyama et al. [36]. Amplification products were separated on 2% agarose gel by electrophoresis and visualized using a UV ChemiDoc camera (Bio-Rad, Hercules, CA, USA). Lanes consisting of 282 and 113 bp bands were considered to be the C/C genotype, lanes consisting of 282, 205 and 113 bp bands were considered the C/A genotype and lanes consisting of 282 and 205 bp bands were considered to be the A/A genotype.
Statistical analysis was performed using SPSS software version 17.0 (Chicago, IL, USA). Student t-test was used to compare differences between continuous variables, and the χ2 test was used to compare differences between categorical variables. The Hardy–Weinberg equilibrium χ2 test was used to investigate genotype distribution deviation for both groups. Logistic regression was performed to compute the odds ratio (OR) and 95% confidence interval (95% CI) for prostate cancer development relative to the genotype. Multiple risk models were composed in order to inspect the mutual effect of different genes, alone or combined with acquired contributory factors, on risk of prostate cancer evolution. Age, hypertension and body mass index (BMI) were used as acquired contributory factors, as each of these factors was previously linked with an increased risk of cancer development [37,38,39]. The Kaplan–Meier test was utilized to calculate mean survival time and compute survival curves. A log-rank test was used to compare the mean survival time between carriers of different genotypes. p values < 0.05 were considered statistically significant.
The demographic and clinical characteristics of the 475 study participants are shown in Table 1. No significant difference was observed with regard to age and BMI, whereas the presence of hypertension and diabetes mellitus type 2 was higher in the patient group than the control group (p < 0.05). Genotype distributions, as well as the risk of prostate cancer development, are summarized in Table 2. As demonstrated, four risk models were computed: model 1 without any adjustments; model 2 with two other investigated genes as confounding factors; model 3 with age, hypertension and BMI as confounding factors; and model 4 with all combined. Carriers of at least one referent GPX1*C allele had no statistically significant increase in PC risk across all four examined models (OR 1 = 1.12, 95% CI = 0.59–2.13, p = 0.728; OR 2 = 1.06, 95% CI = 0.54–2.08, p = 0.869; OR3 = 1.25, 95% CI = 0.58–2.70, p = 0.570; OR4 = 1.27, 95% CI = 0.55–2.93, p = 0.570). Likewise, individuals with the Nrf2*C/C genotype had slightly increased PC risk, but it did not reach statistical significance. In contrast, individuals with at least one SOD2*C allele had an increased risk of PC development (OR = 1.22, 95% CI = 0.82–1.83, p = 0.326) compared to carriers of the SOD2*T/T genotype. This effect was significantly further amplified by the addition of confounding factors, from a 1.67-fold increase with acquired contributory factors in model 3 (OR = 1.67, 95% CI = 1.01–2.76, p = 0.046) to a 1.82–fold increase with both genetic and acquired factors in risk model 4 (OR = 1.82, 95% CI = 1.09–3.03, p = 0.022). Furthermore, to assess whether genetic variants of GPX1, SOD2 and Nrf2 enzymes can mutually influence the risk for prostate development, the combined effect of gene polymorphisms was tested with and without previously stated confounding factors. Results are summarized in Table 3. Genetic variants that individually contributed to an increment in risk for disease occurrence were considered. The carriers of both SOD2*C/C or SOD2*C/T and Nrf2*C/C genotypes were at 2.5 times greater risk of the development of prostate cancer than individuals with SOD2*T/T and Nrf2*C/A or Nrf2*A/A genotypes, regardless of the presence of the GPX1*C allele (OR = 2.48, 95% CI = 0.82–1.83, p = 0.022; OR = 2.48, 95% CI = 0.87–1.98, p = 0.021; model 1 and model 2, respectively). This effect was enhanced by inclusion of other confounding factors, increasing risk from 3.75-fold in model 3 (OR = 3.75, 95% CI = 1.01–2.76, p = 0.009) to 4-fold in model 4 (OR = 4.07, 95% CI = 1.09–3.03, p = 0.006). The combined effect of the GPX1*C allele with either the SOD2*C allele or the Nrf2*C/C genotype showed no significant influence on PC development across all four investigated models (p > 0.05). Thirteen patients were lost (5%) during the mean follow-up time of 75 ± 25 months (ranging from 2–98 months). Of the remaining 222 patients, 76 died (34%), and 146 (66%) were alive at the end of follow-up period. The overall survival of patients with prostate cancer with respect to GPX1, SOD2 and Nrf2 gene polymorphisms is presented in Figure 1. For GPX1 and SOD2 polymorphisms, survival time did not significantly differ in carriers of at least one referent allele GPX1*C or SOD2*C compared to variant homozygote carriers GPX1*T/T or SOD2*T/T (log rank = 0.135, p = 0.713; log rank = 0.886, p = 0.347; respectively). Similarly, there was no statistically significant difference in overall survival between carriers of at least one variant allele Nrf2*A compared to carriers of referent homozygote Nrf2*C/C (log rank = 0.019, p = 0.891).
Several studies have demonstrated that small yet significant risk for the development and progression of prostate cancer has been associated with deleterious effects of certain polymorphisms found in genes encoding antioxidant enzymes, as well as their regulatory proteins [40,41,42,43]. In this case–control study, we found that a polymorphism found in the gene constituting immediate antioxidant defense, the SOD2*C allele (rs4880), was associated with increased risk of PC development. The risk was further amplified by the addition of both acquired and genetic confounding factors. In addition, when combined with the Nrf2*C/C genotype (rs6721961), the SOD2*C allele enhanced the risk of PC development from 2.48 times when investigated alone to 4.07 times when analyzed in combination with contributory factors. SOD2 gene polymorphisms have been extensively studied in malignant diseases, among which one of the most researched is rs4880. Previous reports demonstrated that individuals with the SOD2*C/C genotype are at increased higher risk of prostate cancer development [30,41,44,45], in accordance with the findings of our study. Moreover, a recent meta-analysis by Zhang et al. identified the SOD2*C allele as a significant contributor to PC risk. Furthermore, they identified an association between the downregulation of SOD2 expression with low levels of SOD2 and shorter disease-free survival [46]. In contrast, in another study that investigated patients with initially non-metastatic disease, SOD2 rs4880 SNP was not reported to contribute to overall survival [47]. During our follow-up period, overall survival curves did not reach statistical significance in relation to the SOD2 gene polymorphism. As previously mentioned, SOD plays a pivotal role in maintaining redox balance and protecting cells from ROS damage [48]. It is postulated that functional polymorphism SOD2 rs4880 contributes to the difference in enzyme activity, with the SOD2*T variant associated with decreased mRNA expression and stability [49,50]. Interestingly, genetic studies that focused on the risk of malignant disease development reported increased risk in individual carriers of the SOD2*C allele, as mentioned above. One proposed explanation for these findings is that superoxide anion production can be mediated by proinflammatory ligands, such as interferon-gamma (IFN-γ), which activates the Janus kinase (JAK) signal transducer and activator of transcription (STAT) pathway and upregulates nicotinamide-adenine dinucleotide phosphate (NADPH) oxidase subunits and advanced glycation end products (AGEs) via the phosphoinositide-3-kinase (PI3)/mitogen-activated protein kinase (MAPK) pathway [51]. In a dismutase reaction, SOD2 produces H2O2, which functions as a secondary messenger. In overexpressed SOD2 cells, the increase in H2O2 can lead to malignant transformation, progression and even therapy resistance [52]. Previous research has established Nrf2 as a key regulatory antioxidant protein that binds to antioxidant response element (ARE) in the promotor region of target antioxidant genes [20,53]. To date, only one study has assessed the polymorphic expression of Nrf2 and identified a non-significant association of Nrf2 rs10506328 polymorphism with prostate cancer [40]. The role of Nrf2 rs6721961 SNP has not yet been studied in relation to susceptibility to prostate cancer development. This particular polymorphism is located in the gene promotor region controlling Nrf2 protein basal expression and self-induction [24,54,55]. In our study, we found no significant association between genetic variants and disease occurrence. However, in breast cancer patients (also a hormone-dependent cancer), the Nrf2*A/A genotype was associated with 4.6 times increased risk of disease development compared to Nrf2*C/C [56]. The same authors also showed that low-extent cytoplasmic Nrf2 protein expression correlated with the Nrf2*T allele among invasive breast cancer patients. This polymorphism has also been investigated in other urinary cancers, such as clear cell renal cell carcinoma (ccRCC), urinary bladder cancer and testicular cancer. In bladder cancer, Nrf2 rs6721961 SNP exerted no influence with respect to risk of disease development [57]. Similar results were obtained in relation to testicular cancer. In particular, although the carriers of the Nrf2*C allele were at a near two-times increased risk of testicular cancer development, the risk was not statistically significant [58]. Recently, in a study conducted in Serbian patients, Nrf2 SNP alone did not influence the risk of ccRCC development; however, the Nrf2*A allele in combination with the SOD2*T allele increased the risk of disease occurrence by almost threefold [13]. In contrast, in our study, the combination of the Nrf2*C/C genotype and the SOD2*C allele amplified the risk of PC development when compared to carriers of both the Nrf2*A allele and the SOD2*T/T genotype. Yamaguchi et al. observed that among ccRCC patients, the Nrf2*A allele was associated with increased protein expression, and in the metastatic setting, it corresponded with worse therapy response and unfavorable outcome [59]. GPX1 counters high H2O2 production by reducing it to H2O [60]. Functional SNP rs1050450 in the GPX1 gene has been previously investigated in association with the risk of prostate cancer development. However, contradictory results have been reported. In particular, the meta-analysis by Men T. et al. demonstrated no significance in PC development among referent and variant allele carriers; they also reported an increase in bladder cancer occurrence in individuals with the GPX1*T allele [61]. In contrast, in a Macedonian population of PC patients, the GPX1*T allele played a protective role in disease development [62]. In this study, GPX1 polymorphism did not contribute to PC risk or overall survival alone or in combination with other investigated gene polymorphisms across all models. The present study is subject to some limitations that should be mentioned. Firstly, because only Caucasian males participated, these results probably cannot be transferred to a population that includes various ethnicities. Secondly, the follow-up period was short, given the long natural evolution of the disease, which could explain the absence of differences in terms of survival. Longer follow-up might help recognize statistically significant differences in overall survival in this study group. Thirdly, the study population was relatively small.
The SOD2 (rs4880) gene polymorphism, alone or in combination with the Nrf2 (rs6721961) gene polymorphism, could serve as a possible biomarker of prostate cancer development. Prostate cancer consists of multiple entities with varying tumor aggressiveness, clinical manifestation and prognosis. Identifying biomarkers that can contribute to detection of the disease at its early stage or recognizing those patients in need of earlier or more aggressive treatment is essential in enabling improved quality of life and longer survival in this population. We hope that this epidemiological study can serve as a basis for further in-depth research comprising various ethnicities with a longer follow-up period and a larger sample of participants. | true | true | true |
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PMC9612563 | Wei Liu,Weihong Liang,Xiao-Peng Xiong,Jian-Liang Li,Rui Zhou | A circular RNA Edis-Relish-castor axis regulates neuronal development in Drosophila | 27-10-2022 | Circular RNAs (circRNAs) are a new group of noncoding/regulatory RNAs that are particularly abundant in the nervous system, however, their physiological functions are underexplored. Here we report that the brain-enriched circular RNA Edis (Ect4-derived immune suppressor) plays an essential role in neuronal development in Drosophila. We show that depletion of Edis in vivo causes defects in axonal projection patterns of mushroom body (MB) neurons in the brain, as well as impaired locomotor activity and shortened lifespan of adult flies. In addition, we find that the castor gene, which encodes a transcription factor involved in neurodevelopment, is upregulated in Edis knockdown neurons. Notably, castor overexpression phenocopies Edis knockdown, and reducing castor levels suppresses the neurodevelopmental phenotypes in Edis-depleted neurons. Furthermore, chromatin immunoprecipitation analysis reveals that the transcription factor Relish, which plays a key role in regulating innate immunity signaling, occupies a pair of sites at the castor promoter, and that both sites are required for optimal castor gene activation by either immune challenge or Edis depletion. Lastly, Relish mutation and/or depletion can rescue both the castor gene hyperactivation phenotype and neuronal defects in Edis knockdown animals. We conclude that the circular RNA Edis acts through Relish and castor to regulate neuronal development. | A circular RNA Edis-Relish-castor axis regulates neuronal development in Drosophila
Circular RNAs (circRNAs) are a new group of noncoding/regulatory RNAs that are particularly abundant in the nervous system, however, their physiological functions are underexplored. Here we report that the brain-enriched circular RNA Edis (Ect4-derived immune suppressor) plays an essential role in neuronal development in Drosophila. We show that depletion of Edis in vivo causes defects in axonal projection patterns of mushroom body (MB) neurons in the brain, as well as impaired locomotor activity and shortened lifespan of adult flies. In addition, we find that the castor gene, which encodes a transcription factor involved in neurodevelopment, is upregulated in Edis knockdown neurons. Notably, castor overexpression phenocopies Edis knockdown, and reducing castor levels suppresses the neurodevelopmental phenotypes in Edis-depleted neurons. Furthermore, chromatin immunoprecipitation analysis reveals that the transcription factor Relish, which plays a key role in regulating innate immunity signaling, occupies a pair of sites at the castor promoter, and that both sites are required for optimal castor gene activation by either immune challenge or Edis depletion. Lastly, Relish mutation and/or depletion can rescue both the castor gene hyperactivation phenotype and neuronal defects in Edis knockdown animals. We conclude that the circular RNA Edis acts through Relish and castor to regulate neuronal development.
Circular RNAs (circRNAs) are the latest addition to the noncoding and regulatory RNA collection. They are characterized as covalently closed RNA loops generated by “head-to-tail” back-splicing events [1,2]. With the development of high-throughput sequencing technologies and bioinformatic approaches, thousands of circRNAs have been identified in a wide variety of eukaryotic organisms including human, mouse, worm, and fruit fly [3–6]. Subsequent functional studies have implicated select circRNAs in various physiological and pathological processes, including testes development [7,8], cell cycle progression [9], cancer-associated cell proliferation [10,11], and neuropsychiatric disorders [12,13]. Circular RNAs can function as regulators of microRNA biogenesis/function, operate as scaffold for the assembly of protein/RNA complexes, or modulate host gene expression. Recently, select circRNAs have been shown to encode functional proteins [14,15]. Since circRNAs generally lack a 5’ cap and poly(A) tail, translation of circRNAs is mediated by cap-independent mechanisms, including internal ribosome entry site (IRES) or N6-methyladenosine (m6A) mediated ribosome recruitment [1,16,17,18]. While circRNAs are present across most cell/tissue types [19–21], they are particularly abundant in the nervous system [3,22–26], suggesting a key role in neurodevelopment. Indeed, a handful of neuronal circRNAs have been functionally characterized. For example, CDR1as binds to microRNA-7 (miR-7) and regulates miR-7 biogenesis, thereby impacting brain development [7,8,12,27]. In addition, circZNF827 functions as a scaffold for a transcription repressive complex containing ZNF827, hnRNP K, and hnRNP L to regulate neuronal differentiation [28]. Furthermore, the psychiatric disease-associated circRNA circHomer1a interacts with the HuD protein and further influences HuD gene expression in the frontal cortex [13]. Despite these well-characterized circRNAs, our knowledge of how circRNAs are involved in neuron development/function is still limited. Thus, it is important to identify and functionally characterize additional neuronal circRNAs in healthy and disease settings, and to elucidate the underlying mechanisms. Innate immunity, the first line of defense, protects hosts against invading microbes and adverse effect of stress signals generated by injured cells. While rapid and robust activation of the innate immune response is crucial for host fitness, aberrant or prolonged activation can cause detrimental consequences. For example, aging and neurodegenerative conditions are often associated with aberrant activation of immunity signaling [29]. In addition, long-term pharmacological suppression of the inflammatory response can lead to a reduction in risk of developing neurodegenerative diseases [30,31]. Thus, the magnitude and duration of innate immunity activation need to be tightly controlled in order to maintain a delicate balance between host defense and nervous system integrity/function. Drosophila melanogaster is a powerful model organism to advance our understanding of the molecular mechanism underlying innate immunity activation. Upon encountering diaminopimelic acid (DAP)-type peptidoglycan (PGN), a cell wall component derived from Gram-negative and certain Gram-positive bacteria, a dedicated IMD (immune deficiency) signaling pathway is activated [32]. The IMD pathway involves membrane-bound receptor PGRP-LC; adaptor molecules IMD and dFADD; ubiquitination enzymes Bendless, dUEV1a and dIAP-2; protein kinase complexes dTAK1/dTAB2 and Ird5/Kenny; and the caspase Dredd, culminating in the proteolytic processing and activation of the NF-κB family transcription factor Relish, nuclear translocation of the N-terminal fragment of Relish, and activation of genes encoding a battery of antibacterial peptides [33–36]. Similar to the observations made in humans, aberrant activation of innate immunity in Drosophila can result in phenotypes indicative of neurodegeneration. For example, depletion of ATM (AT mutated) in glial cells causes elevated expression of innate immune response genes in glial cells as well as neuronal and glial cell death, and a reduction in mobility and longevity [37]. In addition, it has been reported that flies with mutations in dnr1 (defense repressor 1) exhibit shortened lifespan and progressive, age-dependent neuropathology associated with aberrant activation of the IMD pathway and elevated expression of antimicrobial peptide (AMP) genes [38]. Furthermore, ectopic expression of individual AMP genes in the Drosophila brain results in brain damage [38]. These findings highlight the connection between dysregulated innate immunity signaling and neurodegeneration in flies. In a recent study, we describe the identification and functional characterization a circRNA circEct4, also known as Edis (Ect4-derived immune suppressor) [39]. Knockdown of Edis, but not its linear sibling Ect4, specifically in neurons causes hyperactivation of innate immunity and myriad defects in neuronal development. We show that Edis can be translated into a functional protein Edis-p, which binds to, and compromises, the proteolytic processing/activation of the immune transcription factor Relish. In addition, inactivation of Relish in Edis-depleted neurons rescues the innate immunity hyperactivation phenotype, suggesting that Relish acts downstream of Edis to regulate immunity. However, the detailed mechanism underlying the function of Edis in regulating the neurodevelopment is still elusive, and the target/effector gene(s) downstream Edis/Relish remain to be identified and functionally characterized. Here, we report that Edis is critically required for mushroom body (MB) development in Drosophila. We show that the Edis transcript is enriched in neurons, consistent with its role in neuronal development. Notably, loss of Edis leads to axonal misguidance in MB neurons and elevated expression of castor, which encodes a transcription factor critical for neuronal development. Importantly, overexpression of castor phenocopies Edis depletion, and reducing castor levels in Edis-depleted neurons rescues defects in MB morphology, locomotor activity and lifespan. We provide evidence that the immune transcription factor Relish binds to the castor promoter and regulates castor transcription. Thus, our study reveals a crucial function of the circRNA Edis in regulating MB neuronal development in Drosophila, establishes castor as an effector/target gene downstream of Edis, and generates an animal model that can facilitate unraveling of intricate interplay between innate immunity signaling and neuronal development.
To gain a full insight into the function of the circular RNA Edis in vivo, we first analyzed the distribution pattern of Edis in Drosophila melanogaster. Because the tissues/organs of Drosophila larvae can be easily distinguished and separated, different tissues/organs of third instar larvae were collected and levels of Edis were examined. Among the tissues/organs examined, including hemocytes, salivary gland, gut, body wall muscle, and fat body, Edis was most prominently enriched in the brain (Fig 1A). This is consistent with the notion that circRNAs are enriched in the nervous system [22,23,39]. To further visualize the localization pattern of Edis within the brain, RNA fluorescence in situ hybridization assay was performed. Specifically, an RNA probe complementary to sequences at the unique “back-spliced” exon junction was designed to label Edis, which can specifically recognizes Edis (S1A–S1F’ Fig). A control probe was designed against chAT (Choline acetyltransferase) transcript, an enzyme required for the biosynthesis of the neurotransmitter acetylcholine, to label cholinergic neurons [40]. In both chAT-positive and -negative neurons, Edis was predominantly localized in the cytosol (Fig 1B and 1D). Interestingly, chAT and Edis display similar expression patterns (Fig 1B and 1D), suggesting a potential role of Edis in cholinergic neurons in the Drosophila CNS. Mushroom body (MB) neurons are a group of cholinergic neurons in the Drosophila CNS [41] that function in olfactory learning and memory [42]. One MB contains ~2000 Kenyon cells. Each cell body sends out one primary neurite which gives rise to dendrites and then extends axons branching out ventrally and anteriorly through the peduncles. At the end of peduncles, three types of sequentially formed MB axons are segregated into three distinct sets of lobes, namely the γ lobe, α’ and β’ lobes, and α and β lobes (Fig 1E) [43]. The α and β lobes can be recognized for their high levels of FasII expression as in contrast to the much weaker FasII expression in the γ lobe, thus the characteristic morphology of MB can be easily recognized (Fig 1F). Given the high level of Edis expression in the cholinergic neurons, we focused on investigating the role of Edis in MB neuron development in detail. First, we depleted Edis by RNA interference (RNAi) in neurons using a pan-neuronal Gal4 driver, Elav-Gal4, for targeted expression of short hairpin RNA against Edis (shEdis) (Fig 1G). This manipulation resulted in a ~60% reduction of Edis levels in shEdis-expressing neurons, whereas levels of linear sibling Ect4 transcripts were comparable with those in control Elav>shgfp animals (Fig 1H). Strikingly, such Edis knockdown resulted in severe MB phenotypes, including partial or complete absence of α and β lobes, or absence of α lobe accompanied with β lobe fusion (Fig 1F and 1I). In contrast, all the Ect4 knockdown (Elav>dsEct4 and Elav>shEct4) brains showed normal MB morphology (Fig 1F–1I). Ect4 has been previously implicated in neurodevelopment and neuronal cell death upon injury [44], It is possible that the absence of apparent MB morphology defects in Ect4 knockdown animals could be due to a moderate degree of Ect4 knockdown efficiency in both Elav>dsEct4 and Elav>shEct4 flies (Fig 1H). Recently, we show that overexpression of Edis suppressed the IMD innate immunity signaling pathway both in cultured cells and in vivo [39]. In addition, we crossed Elav-Gal4 with UAS-laccase2-Edis flies to drive Edis expression in neurons. We find that restoring Edis expression can rescue the neurodevelopmental phenotypes elicited by Edis knockdown [39], thereby demonstrating the functional relevance of overexpressed Edis. We therefore employed a similar experimental setting to examine whether overexpressed Edis impacts neuronal development. Our analysis reveals that Edis overexpression alone did not impact MB morphology (S1G–S1I Fig). Taken together, these data demonstrate that Edis is enriched in neurons and required for MB neuron development. The MB morphology phenotype in Edis-depleted brains could be due to either axon malformation or defective axon projection to other brain areas. To differentiate these possibilities, we employed the MARCM technique to label individual α/β neurons and their axonal projections [43]. In control samples, mCD8GFP labeled a single MB α/β neuron which branched its axon dorsally along the α lobe and medially along the β lobe (Fig 1J and 1J’). In Edis-depleted brains, the α/β neuron was still projecting along the peduncle and forming two branches. However, instead of the β axon extending along the medial direction, both branches projected to the dorsal direction, forming a thicker α lobe (Fig 1K and 1K’). These results indicate that Edis is required for proper axon projection patterns of the MB neurons.
To further dissect the role of Edis in MB formation, we took advantage of a collection of Gal4 drivers to knock down Edis at various stages of MB neuron development (Fig 2A–2F’). First, worniu-Gal4 (wor-Gal4) was employed to drive Edis depletion in all neuroblasts (MB-NB) of the Drosophila brain [45]. Approximately 44% of the progeny showed MB α lobe missing with β lobe fusion, while the remaining 56% showed α and β lobes missing phenotypes (Fig 2B, 2B’ and 2G). To gain more details, we next tested ok107-Gal4, which is a pan-MB neuronal driver that remains active from the neuroblast (NB) stage to until the differentiated mature neuron stage [46, 47]. Upon ok107-Gal4-driven Edis depletion, progeny flies showed profound missing MB α/β lobe and β lobe fusion phenotypes (Fig 2C, 2C’ and 2G). Next, Edis was depleted by GMR71C09-Gal4, which is active predominantly in the MB-ganglion mother cells (MB-GMCs) and early-born neurons [48]. In this cross, ~57% of the progeny showed missing α and β lobes phenotype, whereas the remaining 43% displayed normal MB morphology (Fig 2D, 2D’ and 2G). Thus, it appears that even though depletion of Edis in the MB-GMCs can lead to MB αβ axonal misprojection, the phenotype was moderate compared with that of flies with Edis knockdown at earlier (MB-NB) developmental stages. In contrast, when the knockdown of Edis was driven by either 201Y-Gal4 or c309-Gal4, which are active only in differentiated mature MB neurons [49], all progeny flies displayed normal MB α/β axon distribution pattern (Fig 2E–2G). Overall, these data indicate that Edis regulates MB formation at developmental stage(s) (NBs and GMCs) prior to the completion of MB neuron differentiation. To confirm these findings, we next employed Gene-switch (GS)-Gal4, a chemical (RU486) dependent inducible UAS/Gal4 system, to precisely control the onset of Edis depletion in the Drosophila CNS at different developmental stages [50]. Specifically, Elav GS-Gal4 was crossed to UAS-shEdis animals. Subsequently, RU486 was added to fly food at various time points, and MB morphology of adult progeny was examined (Fig 2A, and 2H–2K’). When Elav GS-Gal4 was activated at the larval stage (1st instar and 3rd instar), the MB morphology phenotypes were observed (Fig 2I–2J’ and 2L). In contrast, we did not detect any MB defects with the addition of RU486 at the adult stage (Fig 2K, 2K’ and 2L), as MB-NBs are eliminated via apoptosis before eclosion [51]. Based on these orthogonal analyses, we conclude that Edis is required in developing MB neurons, but not in mature, postmitotic MB neurons for αβ axonal guidance. We next examined whether Edis from non-MB neurons is required for MB formation. The mb247-Gal80 transgene was employed to suppress Gal4 activity exclusively in MB neurons [52], whereas Edis is depleted in all other types of neurons by Elav-Gal4. This analysis revealed that depletion of Edis in all non-MB neurons resulted in much milder phenotypes (>90% normal MB morphology) than pan-neuronal Edis depletion (no normal MBs) (compare Figs 1I, 2M, 2M’ and 2O). We conclude that the MB morphology phenotypes were mediated predominantly by Edis depletion in MB neurons. As for the mild MB morphology phenotype (9.7%) observed in mb247-Gal80; Elav>shEdis animals, it could be due to 1) incomplete inhibition of Gal4 by Gal80, and/or 2) minor contribution of Edis from non-MB neurons in the Drosophila CNS that impacts MB formation. Glial cells are an integral component of the nervous system and interact extensively with neurons. Our recent study reveals that Edis is expressed in glia [39]. We therefore depleted Edis in glial cells using Repo-Gal4 [53]. All Repo>shEdis animals showed normal MB morphology (Fig 2N–2O). These results suggest that Edis from the MB neuron precursors, but not that from glia, is crucial for proper MB development.
We found that Edis depletion in the Drosophila CNS leads to activation of the IMD innate immunity signaling pathway, with dramatically elevated expression of several AMP genes that are normally regulated by Relish, a key immune transcription factor (S2 Fig). To investigate the relationship between the neuronal defects and the immunity hyperactivation phenotypes, we examined the impact of Relish mutation on the neuronal phenotypes of Edis knockdown animals. Consistent with recent reports that implicate Relish in neurodevelopment [54,55], we found that about a third of Relish null mutants display β lobe fusion phenotypes (Fig 3A and 3B). Despite of this, in Relish null mutant background, the MB morphology phenotype in Elav>shEdis flies was (at least partially) rescued (Fig 3A and 3B, compare RelE20/E38 with RelE20/+, RelE38/+ or +/+ genetic background). Importantly, a similar observation was made upon depletion of Relish in neurons (Fig 3C and 3D), demonstrating a cell autonomous interaction between Relish and Edis in regulating neurodevelopment. Lastly, the lifespan phenotype was also affected by Relish mutation (Fig 3E). We note that the lifespan differences are complex phenotypes and most likely do not result from Kenyon cell alterations, and that lifespan of Edis knockdown flies was also affected by genetic background (Fig 3E, compare Rel+/+ with RelE20/+ and RelE38/+). Nonetheless, taken together, data from our analysis on MB morphology in various combinations of Edis and Relish mutant/knockdown backgrounds strongly suggest that the neurodevelopmental phenotypes elicited by Edis depletion depend (at least partially) on Relish.
It has been reported that microbial infection or ectopic expression of AMP genes can lead to neurodegeneration [38]. To examine whether forced expression of AMP genes in the brain can lead to defects in MB morphology, we expressed individual AMP genes, including Diptericin A (DptA), Drosocin (Dro), Defensin (Def), and Drosomycin (Drs) in neurons using Elav-Gal4. Interestingly, while a fraction of animals with ectopic expression of any of the four AMP genes displayed defective MB morphology, the phenotype is far milder than that seen in Elav>shEdis animals, as only 10–18% Elav>AMP animals showed MB morphology defects (Fig 4A–4F). In addition, only β lobe fusion, but no missing αβ lobe phenotypes were observed in Elav>AMP animals (Fig 4A–4F). Given that both innate immunity hyperactivation and MB morphology phenotypes elicited by neuronal Edis depletion are suppressed in flies carrying mutations in Relish, our data strongly suggest the presence of additional Edis target/effect gene(s) beside AMPs that act downstream of Relish to regulate MB morphology. To search for these gene(s), we performed RNA-seq analysis using Edis-depleted and control brain tissues, from which we identified a total of 777 transcripts that displayed significant changes in RNA levels upon Edis knockdown (412 upregulated and 365 downregulated) (S1 Table and Fig 4G). Notably, several AMP genes (i.e., CecA1, CecA2, CecB, CecC, DptA, AttC, Mtk, Dro and Drs) were among the group of upregulated genes (Fig 4G), thereby validating our approach. The significantly changed genes could be grouped into 16 functional categories based on their predicted/validated roles using Gene Ontology (GO) (Fig 4H). Among these groups of genes, four are related to immune responses (Fig 4H), in addition to genes implicated in nervous system development. As flies missing neuronal Edis display profound neurodevelopmental phenotypes, we selected 17 neurodevelopment-related genes and performed RT-qPCR to examine their expression level in shEdis brain tissues. We note that not all of the 17 genes have scored in our RNA-seq analysis. Among the genes analyzed, castor was the most significantly activated in Edis knockdown brain (Fig 4I). Castor encodes a transcription factor that is expressed in late stages of embryonic neuroblast lineages, and has been shown to be involved in MB development [56], raising an intriguing possibility that dysregulation of castor expression by Edis depletion might be (at least partially) responsible for the MB morphology phenotypes in the CNS.
Given our findings showing that the expression level of castor was dramatically increased in Edis-depleted CNS (Fig 4I), we next tested whether castor overexpression can lead to the MB morphology phenotypes similar to those observed in Edis knockdown animals. Elav-Gal4 was employed to drive castor-3xHA or mCD8GFP (control) overexpression in the CNS (Fig 5A–5C’), as confirmed by measuring both RNA (Fig 5D) and protein levels of castor-3xHA expression (Fig 5B, 5C and 5E) in the brain tissue. Compared with control samples, overexpression of caster in the CNS indeed resulted in strong MB morphology defects: including β lobe fusion (71%) and missing α lobe accompanied with β lobe fusion (29%) (Fig 5A’–5C’ and 5F). Similarly, when UAS-castor-3xHA was specifically expressed using the MB driver ok107-Gal4, 67% of brains display MB morphology defects (S3A and S3B Fig). These data demonstrate that overexpression of castor compromises MB development. Next, we examined whether castor is important for Edis function by testing whether the MB morphology phenotype in Edis–depleted neurons could be suppressed by down regulation of castor. We employed two independent castor RNAi lines to minimize off-target effects. Both RNAi lines led to a dramatic decrease in levels of castor transcript in Edis knockdown brains (Fig 5H and 5I). Importantly, the MB morphology phenotype in Edis–depleted brains was partially rescued (Fig 5J, >50% normal MB morphology in castor knockdown samples vs. 22% in controls). Similarly, the short lifespan and mobility defects were rescued as well (S3C and S3D Fig). Castor is among the temporal transcription factors expressed in the embryonic neuroblast lineage, and promotes the expression of a downstream factor, grainyhead (grh) [57, 58]. Consistently, grh was also upregulated in Edis knockdown neurons (S4A Fig). Furthermore, overexpression of grh in MB neurons using ok107-Gal4 also resulted in defective MB morphology: β lobe fusion (80%), missing β lobe (13%) and missing α lobe accompanied by β lobe fusion (7%) (S4B Fig). More importantly, the MB morphology phenotype in Edis–depleted brains could also be partially rescued upon knocking down grh expression (Figs S4C and 5K, 69% normal MB morphology in grh knockdown samples vs. 28% in controls). Taken together, these data demonstrate that castor plays an important role downstream of Edis in regulating MB development in the CNS.
Edis encodes a functional protein Edis-p that compromises proteolytic processing/activation of the immune transcription factor Relish, and inactivation of Relish in Edis-depleted neurons suppresses the innate immunity hyperactivation phenotype and rescues the neuronal developmental defects elicited by Edis depletion (Fig 3) [39]. Results of genome-wide Relish ChIP-seq analysis from ModENCODE (https://epic.gs.washington.edu/modERN/faces/index.xhtml;jsessionid=wJaqbmimlEFsQ3UYaWSPTJqytrJLTkEs9oogmBBc.epic) indicated that Relish binds to the castor promoter region (S5A Fig), suggesting that Relish may directly regulate castor transcription in Edis-depleted neurons. To explore this possibility, we introduced Relish mutant alleles into Elav>shEdis animals and measured the impact on the castor RNA levels. Indeed, upregulation of castor expression in Elav>shEdis brains was abrogated in Relish null mutant background (RelE20/RelE38) compared with Relish wildtype and heterozygous backgrounds (RelE20/+ or RelE38/+) (Fig 6A), supporting the notion that Relish is required for the upregulation of castor expression in Edis–depleted neurons. We then searched for candidate Relish binding sites in the caster promoter based on conserved NF-κB-binding sequence [59]. We identified two candidate Relish binding sites upstream of the castor transcriptional start site (Fig 6B). To validate Relish occupancy at these sites, we performed chromatin immunoprecipitation (ChIP)-qPCR using dissected fly brains. Given that overexpression of full-length Relish in neurons driven by Elav-Gal4 causes lethality, and that the antibody against endogenous Relish is not suitable for immunoprecipitation, we chose the UAS-Flag-RelN transgene that encodes the active form of Relish [60]. Interestingly, overexpression of Flag-RelN using the MB neuron-specific ok107-Gal4 driver can lead to defects in MB morphology: only 11% of ok107-Gal4>Flag-RelN brains displayed normal MB morphology, whereas the remaining 89% showed MB morphology defects (S5B and S5C Fig). ChIP analysis of Flag-RelN expressing flies revealed that RelN binds to both sites of the castor promoter. The binding was specific as we detected high affinity binding of RelN to the promoters of two known Relish target genes (Atg1 and DptA) [60]. No obvious binding was detected for the negative control, Diedel (Fig 6C). These data demonstrate that Relish binds to the castor promoter. To further explore the role of Relish in regulating castor expression, we tested the expression of castor gene in response to Edis depletion in cultured cells. Consistent with our recent study [39], we detected an increase in Dpt RNA levels upon Edis depletion in S2 cells. Importantly, levels of castor were also similarly higher in Edis knockdown cells than in control cells (Fig 6D). These results are consistent with elevated castor expression detected in Elav>shEdis brains (Fig 4I). In addition, we also detected an increase in Relish occupancy on castor promoter upon Edis depletion in S2 cells (Fig 6E). To investigate the functional relevance of Relish binding to the castor promoter, we first examined whether castor gene expression can be induced by PGN treatment, a potent activator of Relish. Indeed, levels of castor mRNA were significantly increased in S2 cells treated with PGN, similar to the known Relish target gene Dpt (Fig 6F). Next, we generated reporter constructs in which the luciferase reporter gene was placed downstream of wildtype or mutant castor promoter lacking either or both Relish-binding sites, and then examined whether the reporter gene activity responds to PGN treatment. As a positive control, the Att-luc reporter, which is driven by the AMP gene attacin promoter, is activated by PGN treatment. Importantly, wildtype, but not mutant castor promoter constructs that lack either or both Relish binding sites, responded to PGN treatment (Fig 6G). Furthermore, we conducted similar luciferase reporter assays in Edis-depleted S2 cells. Both att-luc reporter and cas-luc displayed basal expression in the control cell line, but became dramatically activated upon Edis depletion (Fig 6H). Importantly mutant castor promoter constructs failed to respond to Edis depletion in the same assay (Fig 6H). We conclude that Relish binds to the castor promoter and regulates castor transcription, and that Relish binding is critically required for castor activation in response to PGN treatment or Edis depletion.
In a recent study we show that the circRNA Edis compromises innate immunity signaling and regulates neuronal development in Drosophila [39]. However, the detailed molecular mechanism underlying the role of Edis in neuronal development remains unclear. In this study, we report that Edis is primarily enriched in neurons in the brain, and its depletion causes defective axonal projection and abnormal MB morphology. Furthermore, our orthogonal genetic analysis using various developmental stage-specific Gal4 drivers and geneswitch system reveal that depletion of Edis either in neuroblasts or immature neurons (GMCs), but not in fully differentiated neurons, leads to MB developmental defects, therefore uncovering a crucial role of Edis during select stages of neuronal development. We note that while in this study we have been focusing on the mushroom body phenotypes primarily because these defects are easy to detect, it does not imply that the effect is specific to the mushroom body. In fact, we have uncovered additional neurodevelopmental defects in various neural structures in Edis knockdown animals (e.g. giant fiber, ommatidia and neuromuscular junction) [39]. Thus while the mushroom body defects result from cell-autonomous loss of Edis in MB neurons, Edis may be required in many other types of neurons as well. Relish encodes a transcription factor with well-established roles in regulating innate immunity signaling. In addition, our analysis revealed mild MB morphology phenotypes in Relish null mutant animals (Fig 3B), consistent with recent reports that have implicated Relish in neurodevelopment [54,55]. We found that both the innate immunity hyperactivation and defective MB morphology phenotypes of Edis knockdown animals can be suppressed by either mutations or depletion of Relish. Thus our findings are in line with the notion that in Drosophila a small number of transcription factors are often re-used to regulate myriad biological processes. In this case, Relish is involved in the regulation of both innate immunity and neurodevelopment. Our study adds to a growing body of evidence supporting an intimate connection between dysregulation of immunity signaling and neurodevelopment. For example, it has been reported that ectopic expression of individual AMP genes or bacterial infection of the Drosophila brain is sufficient to cause brain damage [38]. Using MB morphology as an in vivo readout in an animal model, we show that forced expression of individual AMP genes in neurons indeed leads to abnormal MB morphology, although the defects are much milder than those elicited by neuronal Edis depletion (Fig 4A–4F). These findings suggest that besides AMPs, there are additional effector/target genes that operate downstream of Edis in regulating neurodevelopment. Indeed, we find that the neuronal transcriptional factor castor is significantly upregulated upon Edis depletion, and further establish castor as an important target/effector gene that acts downstream of Edis to regulate MB neuronal development. Specifically, we show that castor overexpression phenocopies Edis depletion in neurons, and that castor knockdown rescues the neurodevelopmental phenotype in Edis-depleted neurons. Lastly, our analyses reveal that upon Edis depletion, the immune transcription factor Relish binds to the castor promoter and directly upregulates castor transcription both in neurons and in cultured S2 cells. Thus, we propose that a circular RNA Edis-Relish-castor axis regulates neuronal development in Drosophila melanogaster, particularly in MB neurons (Fig 7). We note that knocking down castor only partially rescues the MB neurodevelopmental defects in Edis-depleted animals, as there are still abnormal neurons (Fig 5J). It is possible that levels of castor are still not in the range of “right dosage” in these settings (Fig 5I). Additionally, we cannot exclude the possibility that there are additional factors besides castor that operate downstream of Edis in regulating neuronal development. Castor is among a group of temporal transcription factors in the neuronal lineage, including hunchback (hb), seven-up (svp), Krüppel (Kr), pdm (Flybase: nubbin and pdm2), and grainyhead (grh) [61–63]. Previous studies have implicated some of these factors in MB neurodevelopment. For example, it has been reported that castor and svp are required for generating small Chinmo+ neurons in many different lineages [64], and Chinmo controls the temporal identity of MB neurons [65]. Interestingly, we find that grh is upregulated in Edis knockdown brains (S4A Fig), and that overexpression of grh can also induce MB axonal misguidance (S4B Fig). Importantly, reducing grh expression levels partially rescues the MB morphology defects elicited by Edis depletion (Fig 5K). It is currently unclear whether any additional temporal transcription factors in the neuronal lineage may join castor and grh in mediating the neurodevelopmental defects observed in Edis depleted animals. In light of the sequential/overlapping activities of a series of temporal transcription factors that regulate neuronal development, perhaps Edis and Relish could be part of such regulatory mechanism, which reinforces appropriate timing to stimulate Castor expression. Alternatively, Edis and Relish may be involved in regulating the generation of different types of Kenyon cells, in particular, late-born Kenyon cells (α/β neurons). In addition, circular RNAs are generally more stable than their linear siblings. Since downregulation of Edis leads to Relish activation, is Edis subjected to degradation and/or functional inhibition in order to relieve its inhibitory effects on Relish activation and Castor expression? Future studies are warranted to address these questions. Here we demonstrate that the transcription factor Relish binds to castor promoter and regulates castor expression. We identify twin Relish-binding sites on the castor promoter, and show that both cis-regulatory elements are critically required for castor activation. Interestingly, castor can also be activated by PGN treatment, which potently induces the expression of innate immunity effector genes such as those encoding AMPs (Fig 6F). Thus our data uncover that the neurodevelopmental phenotypes elicited by Edis depletion is due to not only elevated levels of immune effectors such as AMPs, but also dysregulation of essential neuronal genes (e.g. castor and grh). The transcription factor Relish might serve as a crucial link that connects these two processes. In summary, our study shows that the brain-enriched circular RNA Edis plays a crucial role in MB axonal guidance, and generates an animal model to investigate the role of circular RNAs in neuronal development and function. We identify and characterize the neuronal transcription factor castor as an effector/target gene of Edis, demonstrate that castor is transcriptionally regulated by Relish, and establish the function of Edis-Relish-castor axis in regulating neuronal development. Taken together, our study identifies a molecular link between innate immunity and neuronal development, broadens the spectrum of target genes that are transcriptionally regulated by Relish, and suggests a key role of Relish in regulating myriad biological processes including immunity, neurodevelopment and autophagy.
All statistical analyses in this manuscript were performed using biological replicates and the sample number (n) is shown for each dataset in the corresponding legend. Most analyses were performed using the two-tailed unpaired student t-test, except for lifespan experiments, which involved the log-rank test, and MB morphology experiments, which involved Chi-squared test. The data are presented as mean values + standard errors of the mean (SEM). A p value <0.05 was considered statistically significant. * p<0.05; ** p<0.01; *** p<0.001.
To generate the luciferase reporter construct pGL3-cas-luc, the DNA fragment encompassing the castor promoter region (-1~-1924) was amplified from the Drosophila genomic DNA by PCR and inserted into the pGL3 vector using the Kpn I and Hind III restriction sites. Subsequently, based on the pGL3-cas-luc vector, three mutant constructs lacking either (pGL3-casΔ-215-luc and pGL3-casΔ-1429-luc) or both (pGL3-casΔ-215Δ-1429-luc) Relish binding sites were generated using phusion site directed mutagenesis kit (Thermo Fisher, F541). To generate transgenic expression constructs for DptA, Drs, Dro, and Def, DNA fragments encompassing the ORF of these genes were amplified from the Drosophila cDNA by PCR and inserted into the pUAST vector using EcoR I and Xho I restriction sites. All constructs were verified by sequencing. Antibodies used: mouse anti-Fas II (DSHB, 1D4) (IF 1:10); rabbit anti-GFP (Thermo Fisher, A11122) (IF 1:500); rabbit anti-HA (Cell Signaling, C29F4) (IF 1:400, WB 1:1000); M2 monoclonal mouse anti-Flag antibody (Sigma, F-3165) (WB 1:3000) and normal rabbit IgG (Millipore,12–370). Secondary antibodies: Alexa Fluor 488-conjugated anti-mouse IgG (Invitrogen, A21202); Alexa Fluor 488-conjugated anti-rabbit IgG (Invitrogen, A21206); Alexa Fluor 594-conjugated anti-mouse IgG (Invitrogen, A21203); Alexa Fluor 647-conjugated anti-rabbit IgG (Invitrogen, A31573) and goat anti-rabbit IgG antibody HRP conjugate (Millipore, 12–348).
Fly stocks are maintained on a standard fly food (Nutri-Fly, molasses formulation) and kept at 25°C. The genotypes of the fly stocks employed in this study were listed in the supplementary materials and methods.
Drosophila S2 cells were cultured at 25°C in Schneider insect cell culture medium (Sigma-Aldrich, S0146) and supplemented with 10% fetal bovine serum (HyClone, SH30071.03) and 1% penicillin-streptomycin (Gibco, 15140122). For luciferase reporter assay, transfections were performed in a 24-well format by following the calcium phosphate protocol using 2.5M CaCl2 and 2XHEPES buffered saline.
For Drosophila mushroom body neuron fiber staining, adult fly brains were dissected, stained, and imaged as described [66]. Briefly, adult fly heads of 3–5 days were dissected in PTN buffer (0.1 M Sodium Phosphate Buffer, pH 7.2, 0.1% Triton X-100) and fixed with 4% paraformaldehyde (Alfa Aesar, 43368) for 20 minutes at room temperature, followed by rinsing three times in PTN buffer. Samples were blocked with 5% BSA at room temperature for 30 minutes and incubated with primary antibody at 4°C for 48 hours, followed by washing three times with PTN buffer. Samples were then incubated with secondary antibody at 4°C overnight and washed three times as indicated above. Subsequently samples were mounted in 80% glycerol and imaged using a Nikon confocal microscope (Nikon A1, Tokyo, Japan).
To prepare paraffin-embedded sections of Drosophila, dissected adult fly brains were fixed in 4% paraformaldehyde at room temperature for 24 hours, and washed two times with PBS. Samples were then dehydrated by incubating with increasing concentrations of ethanol (40%, 70%, and 100%) at room temperature, followed by incubation in ethanol/xylene solution (1:1) for 10 min. Samples were subsequently incubated in the following solutions at 60–65°C, xylene (30 min), xylene/paraffin (1:1, 30 min), paraffin (4 times, each time for ~1 hour). The sample container was then filled with liquid paraffin and rested at room temperature until the paraffin is solidified. Paraffin embedded samples were sectioned to a thickness of 5 ± 1 μm. For RNA fluorescence in situ hybridization (FISH), two probes were designed by Thermo Fisher: the 20-oligonucleotide Edis probe is complementary to the “back-spliced exon junction” of the circRNA Edis, whereas the control RNA probe is complementary to the Choline acetyltransferase (ChAT) mRNA. and RNA FISH was performed using the ViewRNA ISH Tissue 2-Plex Assay Kit (Affymetrix, QVT0012).
Drosophila heads or S2 cells were collected and total RNA was isolated with TRIzol (Invitrogen, 15596026). RNA samples were subsequently reverse transcribed using Superscript III (Invitrogen, 18080044) and random hexamer primers, and levels of circular and linear RNAs were measured by quantitative PCR. The Real-time RT-PCR analysis was performed using the SYBR Green PCR master mix (BioRad, 1725275). Relative mRNA levels were calculated by normalization against the endogenous the control RpL32 mRNA.
Drosophila heads were collected and homogenized in lysis buffer (25 mM Tris-HCl, pH 7.4, 150 mM NaCl, 1 mM EDTA, 5% glycerol and complete protease inhibitors). Lysates were centrifuged at 2,000 g for 5 min. Then 2X SDS loading buffer was added into the supernatant. Proteins were separated by SDS-PAGE gel and transferred onto a PVDF (Millipore, IPVH00010) membrane. The membrane was blocked with 5% non-fat milk solution and incubated with primary antibody at 4°C overnight, and washed 3 times with TBST buffer (20 mM Tris-HCl, 150 mM NaCl, 0.1% Tween 20). The membrane was then incubated with HRP-conjugated secondary antibody for 1 h at room temperature. The membrane was subsequently washed 3 times with TBST buffer, incubated with ECL (Cyanagen Srl, XLS3-0020) reagents. Images were acquired using ChemiDoc (Bio-Rad).
To perform ChIP assay using Drosophila S2 cells, we followed the protocol reported in our recent study [39]. As for ChIP assay using Drosophila brains, we followed the protocol reported by [67] with minor modifications. Briefly, adult fly brain samples were dissected in ice-cold PBS and fixed in 1 mL cross-linking solution (1.8% formaldehyde, 50 mM HEPES pH 8.0, 1 mM EDTA, 0.5 mM EGTA, 100 mM NaCl) at room temperature. The cross-linking solution was changed 3–4 times during fixation. Cross-linking is terminated by adding 125 mM glycine. Samples were washed in 1 ml buffer A (10 mM HEPES pH 7.6, 10 mM EDTA, 0.5 mM EGTA, 0.25% Triton X-100) for 10 min and subsequently in 1 mL buffer B (10 mM HEPES pH 7.6, 200 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 0.01% Triton X-100) for 10 min. Samples were homogenized in in 0.5 mL RIPA buffer (140 mM NaCl, 10 mM Tris-HCl pH 8.0, 1 mM EDTA, 1% Triton X-100, 0.1% SDS, 0.1% sodium deoxycholate, 1 mM PMSF, 0.5% N-Laurylsarcosine, complete protease inhibitor cocktail), and subsequently sonicated for 22 times for 30 seconds at “High” setting (Bioruptor 300, Diagenode). Lysate was centrifugated for 20 minutes at 4°C at a speed of 16000 g and diluted up to 7.2 mL with RIPA buffer. Ten microliters of diluted chromatin was saved as input control, and 240 μL of sonicated chromatin solution was diluted with 1 mL RIPA buffer for immunoprecipitation process. One hundred microliters of protein A sepharose CL-4B beads (GE healthcare, 17078001) or 40 μl of mouse M2 anti-Flag conjugated agarose beads (Sigma, A2220) were equilibrated in 1 ml RIPA buffer, incubated at 4°C for 1 h, and centrifuged at 4°C for 10 min at 16000 g. Equilibrated M2 anti-Flag conjugated agarose beads were resuspended with the chromatin sample, and protein A sepharose CL-4B beads resuspend with the same amount of chromatin with 2 μl IgG as control. Samples were incubated at 4°C overnight, centrifuged for 1 minute at 2000 g and supernatant was removed. Samples were then washed sequentially with the following buffers: RIPA buffer (5 times), and once with LiCl wash buffer (0.25 M LiCl, 1 mM EDTA, pH8.0, 0.5% NP-40, 0.5% Sodium Deoxycholate, 10 mM Tris-HCl, pH8.0). DNA was eluted with 100 μL elution buffer (1% SDS, 100 mM NaHCO3) by vortexing slowly for 30 minutes at 30°C. Samples were centrifuged for 1 min at 2000 g and the supernatant was transferred into a new tube. Four point eight microliters of 5 M NaCl and 2 μL RNase A (10 mg/mL) were added and samples were incubated at 65°C overnight. Subsequently 2 μL of proteinase K (20 mg/mL) was added and samples were incubated at 65°C for an additional hour. DNA was subsequently purified by phenol/chloroform extraction.
Briefly, ~5×105 of shW, shEdis-A, and shEdis-B stably transfected S2 cells or the parental S2 cells were seeded in 24-well plates the day before transfection. Subsequently, 500 ng empty pGL3 vector, att-luc, cas-luc, casΔ-215-luc, casΔ-1429-luc, or casΔ-215Δ-1429-luc reporter constructs were transfected into these cells together with 20 ng of actin-Renilla luciferase plasmid. Two days post transfection, cells were treated with 250 μM copper for ~3–5 days, to achieve Edis knockdown. Transfected parental S2 cells were further treated with 1 μM 20-hydroxyecdysone (Sigma, H5142) for 24 hours and subsequently left untreated or treated with PGN for 6 hours. Cell suspensions were arrayed in 96-well plates and reporter activity was measured using the Dual-Glo luciferase assay system (Promega, E2920). For data processing, firefly/Renilla ratio was calculated and normalized against control samples.
For lifespan experiments, flies of the indicated genotypes were kept at 25°C (in multiple groups per genotype, 15 flies per group) and survival was monitored daily. To measure locomotor activity, we followed the protocol reported by Liu et al [68] with minor modifications. Briefly, flies in multiple groups of 15 were placed into Falcon culture tubes. Incubated at room temperature for 5 min and tapped to the bottom, and the percentage of flies that can climb over the 2-centimeter mark within 15 seconds was recorded.
RNA-Seq datasets were generated from RNA samples extracted from Elav>shEdis and Elav>shGFP (control) fly heads. Each library was sequenced with paired-end 100 bp reads to a minimum depth of 40 million paired reads on an Illumina HiSeq 4000 sequencer (Illumina). The raw reads were aligned to the Drosophila. melanogaster reference genome by the HISAT2 aligner (v2.0.4) [69] with the default parameters. Ambiguous reads that mapped to more than one region in the genome and aligned reads with MAPQ score less than 10 were removed. The Drosophila. melanogaster reference genome (dm6) and corresponding RefSeq annotation (refFlat.txt.gz 28-May-2017) downloaded from UCSC were used as a reference genome for gene quantification. Gene quantification was performed using the Partek Genomics Suite (version 7.17, Partek), and the raw read counts and normalized read counts (reads per kilobase per million mapped reads [RPKM]) were obtained. Gene with poor read counts in all samples were excluded from further analysis. The differences in gene expression between knockdown and GFP control conditions were assessed using BioConductor edgeR package [70]. The resulting p-values were adjusted using the Benjamini and Hochberg method to control the false discovery rate. Genes with fold-change (FC) over two and p-value less than 0.05 were considered as significantly differentially expressed genes (DEGs). The sequencing data were deposited to Gene Expression Omnibus with the accession number GSE196213.
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PMC9612835 | Xue Ma,Bi-Tao Bu | Anti-SRP immune-mediated necrotizing myopathy: A critical review of current concepts | 13-10-2022 | anti-SRP autoantibodies,immune-mediated necrotizing myopathy,cardiac involvement,ER stress,refractory | Purpose of review This review aims to describe clinical and histological features, treatment, and prognosis in patients with anti-signal recognition particle (SRP) autoantibodies positive immune-mediated necrotizing myopathy (SRP-IMNM) based on previous findings. Previous findings Anti-SRP autoantibodies are specific in IMNM. Humoral autoimmune and inflammatory responses are the main autoimmune characteristics of SRP-IMNM. SRP-IMNM is clinically characterized by acute or subacute, moderately severe, symmetrical proximal weakness. Younger patients with SRP-IMNM tend to have more severe clinical symptoms. Patients with SRP-IMNM may be vulnerable to cardiac involvement, which ought to be regularly monitored and cardiac magnetic resonance imaging is the recommended detection method. The pathological features of SRP-IMNM are patchy or diffuse myonecrosis and myoregeneration accompanied by a paucity of inflammatory infiltrates. Endoplasmic reticulum stress-induced autophagy pathway and necroptosis are activated in skeletal muscle of SRP-IMNM. Treatment of refractory SRP-IMNM encounters resistance and warrants further investigation. Summary Anti-SRP autoantibodies define a unique population of IMNM patients. The immune and non-immune pathophysiological mechanisms are involved in SRP-IMNM. | Anti-SRP immune-mediated necrotizing myopathy: A critical review of current concepts
This review aims to describe clinical and histological features, treatment, and prognosis in patients with anti-signal recognition particle (SRP) autoantibodies positive immune-mediated necrotizing myopathy (SRP-IMNM) based on previous findings.
Anti-SRP autoantibodies are specific in IMNM. Humoral autoimmune and inflammatory responses are the main autoimmune characteristics of SRP-IMNM. SRP-IMNM is clinically characterized by acute or subacute, moderately severe, symmetrical proximal weakness. Younger patients with SRP-IMNM tend to have more severe clinical symptoms. Patients with SRP-IMNM may be vulnerable to cardiac involvement, which ought to be regularly monitored and cardiac magnetic resonance imaging is the recommended detection method. The pathological features of SRP-IMNM are patchy or diffuse myonecrosis and myoregeneration accompanied by a paucity of inflammatory infiltrates. Endoplasmic reticulum stress-induced autophagy pathway and necroptosis are activated in skeletal muscle of SRP-IMNM. Treatment of refractory SRP-IMNM encounters resistance and warrants further investigation.
Anti-SRP autoantibodies define a unique population of IMNM patients. The immune and non-immune pathophysiological mechanisms are involved in SRP-IMNM.
As early as 1975, Bohan and Peter divided inflammatory myopathies into two subtypes, dermatomyositis (DM) and polymyositis (PM) according to the presence of typical skin lesions and common histopathology (1). At present, based on clinical manifestations, serum myositis-specific autoantibodies, and pathological characteristics, idiopathic inflammatory myopathies (IIM) are mainly classified into 4 subgroups: DM, immune-mediated necrotizing myopathy (IMNM), anti-synthetase syndrome, and sporadic inclusion body myositis (sIBM) (2, 3). IMNM has been recently described as a distinct form of IIM. IMNM is characterized by proximal weakness, prominent or scatter myonecrosis, and a paucity of or few lymphocyte infiltrates in muscle biopsy (4). Currently, two principle categories of IMNM, anti-signal recognition particle (SRP) autoantibodies-IMNM (SRP-IMNM) and anti-3-hydroxy-3-methylglutaryl-coa reductase (HMGCR) autoantibodies-IMNM (HMGCR-IMNM) (5, 6), account for the largest proportion of IMNM and are relatively the most described. Other subtypes, including seronegative IMNM, connective tissue disease-related IMNM, statin-related IMNM, cancer-related IMNM, and immune checkpoint inhibitors-induced IMNM are also reported (7–12). The detection of serum anti-SRP autoantibodies in IIM is much earlier than that of anti-HMGCR autoantibodies. Serum anti-SRP autoantibodies in IIM were first detected in 1982 (13, 14) and anti-HMGCR autoantibodies were first detected in 2010-2011 (15, 16). Here, we review manifestations of SRP-IMNM and highlight recent clinical and pathological advances in SRP-IMNM.
SRP, a cytosolic evolutionarily conserved ribonucleotide protein, is located in the endoplasmic reticulum (ER) and consists of six distinct polypeptides, including 9, 14, 19, 54, 68, and 72KDa, binding to a 7S RNA molecule (17, 18). The SRP guides newly synthesized polypeptides to the ER for posttranslational modifications (18). Although it is believed that anti-SRP autoantibodies are associated with polymyositis in previous studies (13, 19), anti-SRP autoantibodies are specific to a unique entity of IIM featuring myofiber necrosis, myofiber regeneration, and minimal inflammation, which is now known as IMNM (20, 21). However, mechanisms of serum anti-SRP autoantibodies production in IMNM remain unclear. HMGCR is also located in ER and is responsible for cholesterol biosynthesis (22, 23). Statins may trigger autoimmune dysregulation to induce the formation of anti-HMGCR autoantibodies in a small percentage of HMGCR-IMNM. The titer of anti-HMGCR autoantibodies is significantly associated with serum creatine kinase (CK) and clinical severity in HMGCR-myopathy with statins exposure but not in patients without a history of statins (24). Statins are likely to have no association with the development of SRP-IMNM. The relationship between serum CK levels and anti-SRP autoantibodies in SRP-IMNM has not yet been elucidated. One study indicates no significant correlations between CK levels and the titer of anti-SRP54 autoantibodies (25). However, another study shows that autoantibody levels are dramatically associated with CK levels in patients with SRP-IMNM receiving therapy (26). In vitro and in vivo experiments reveal that anti-SRP autoantibodies isolated from patients with SRP-IMNM are potentially pathogenic and target skeletal muscle fibers. Anti-SRP autoantibodies can impair myoblast regeneration, result in myofiber atrophy, and increase the production of reactive oxygen species (27). In anti-SRP autoantibodies adoptively transferred mice, skeletal muscle fiber necrosis can be detected and may be mediated by the mechanism of complement-dependent cytotoxicity (28). It remains to be further confirmed whether SRP antigen targets can be ectopically expressed on myofiber sarcolemma and under what conditions this phenomenon occurs.
IIM is a rare disease, with an incidence rate of 9-14/100000 in European countries (29, 30). IMNM comprises 20-38% of IIM (31). SRP-IMNM accounts for 18-39% of IMNM (32). The mean age of SRP-IMNM onset is 40-50 years old and it affects more females than males with a ratio of 1.6-3.6 (25, 33–35). SRP-IMNM is seldom reported in children or juveniles (36–38). Differentiating SRP-IMNM from muscular dystrophy is of critical clinical importance, as SRP-IMNM and limb-girdle muscular dystrophy (LGMD) exhibit similar clinicopathological presentations at times (38–40). These patients can be identified as serum anti-SRP autoantibodies positivity and have a favorable response to immunotherapy. HMGCR-IMNM patients with a family history of cardiomyopathy or myopathy occasionally present a chronic progressive course of weakness, which resembles other acquired myopathy or inherited myopathy and these patients may be misdiagnosed as LGMD (41).
Patients with SRP-IMNM commonly struggle to lift arms and/or squat to stand up, and occasionally find it hard to raise their head (32). SRP-IMNM is clinically characterized by acute or subacute, moderately severe, symmetrical proximal weakness, partially accompanied by myalgia, dyspnea, dysphagia, and muscle atrophy (6, 25, 34, 35, 40, 42). Distal leg, bulbar and axial muscles are incidentally involved in SRP-IMNM (25, 43).Compared to SRP-autoantibodies-negative patients, patients with SRP-IMNM tend to have facial weakness and age at onset is lower (32). The younger patients at onset seem to have more severe clinical symptoms in SRP-IMNM (42, 44). In European countries and Japan, compared to HMGCR-IMNM, the muscle weakness appears to be more severe in SRP-IMNM (6, 42). Neck weakness, dysphagia, respiratory insufficiency, and muscle atrophy occur more frequently in SRP-IMNM than in HMGCR-IMNM (6). Seronegative IMNM patients are more likely to suffer from myalgia in the Chinese population compared to SRP-IMNM and HMGCR-IMNM (7). A highly elevated CK level is prominent in SRP-IMNM, usually more than 1000 IU/L (7, 25, 34, 40, 42). Serum CK levels positively correlate with myofiber necrosis (45). A patient being asymptomatic accompanied by an elevated serum CK is rarely observed in SRP-IMNM (46), but is as well reported in HMGCR-IMNM (47). Human leukocyte antigens DRB1*08:03, B*5001, and DQA1*0104 are more frequently detected in SRP-IMNM and DRB1*11:01 is more prevalent in HMGCR-IMNM (48–51). These antigens derived by DRB1 alleles polymorphism may play key roles in the autoimmunity in IMNM.
There are some conflicting conclusions on cardiac involvement in SRP-IMNM. One case with SRP-IMNM was complicated by cardiomyopathy, gradually developed heart failure, and is ultimately relieved after heart transplantation (52). Some case reports and studies suggest that patients with SRP-IMNM are susceptible to subclinical myocardial damage (32, 52–55). Echocardiogram abnormalities usually appear in SRP-IMNM and account for 61% (25 of 41 patients), most presenting as diastolic dysfunction (32). On the other hand, a low risk of cardiac involvement in SRP-IMNM (2 of 16 patients, 13%) is found (21). Myocardial involvement is an important prognostic indicator for patients. Therefore, further prospective multi-center large-sample studies are required to confirm the degree of vulnerability to cardiac abnormalities in SRP-IMNM. Cardiac magnetic resonance imaging (MRI), as a highly sensitive method, is commonly recommended for screening for myocardial damage in myositis (56–58). Other extramuscular phenotypes are sometimes present in SRP-IMNM. A low proportion of SRP-IMNM is associated with chest pain (8%) (34), arthritis (0-17%) (21, 34, 35, 43), arthralgia (39%) (34), Sicca syndrome (8%) (34), mechanic’s hand (14%) (43), and carpal tunnel syndrome (10-20%) (34, 43). Approximately 20% of patients with SRP-IMNM have ILD (6, 21, 42). Patients with SRP-IMNM seldomly display skin rash (3-6%) (6, 25, 35, 42). A cutaneous lesion occurs more frequently in HMGCR-IMNM (59). Cancer-associated SRP-IMNM is occasionally reported (6, 7, 25). Nevertheless, compared with HMGCR-IMNM and MSA-negative-IMNM, SRP-IMNM have a lower risk of tumor (10, 60). Cancer association is considered a risk factor for the development of HMGCR-IMNM (60). These extramuscular presentations may be key factors affecting the prognosis of SRP-IMNM, especially cancer and ILD.
SRP-IMNM features focal or diffuse muscle edema, atrophy, and fatty infiltration predominantly on a proximal lower extremities muscle MRI scan (61, 62) ( Figures 1A, B ). Compared to DM, fascial edema is less frequently observed in SRP-IMNM ( Figure 1A ) (61). Distal lower extremities on T1-weighted or T2-weighted images are less studied. Compared to DM, PM, and HMGCR-IMNM, muscle abnormalities are more diffuse and common in SRP-IMNM on a thigh muscle MRI scan (61). Of note, the degree of muscle edema is not significantly associated with the disease severity (61). In addition, rapid fat infiltration on an MRI scan may be a risk factor associated with refractory SRP-IMNM (44).
SRP-IMNM is pathologically characterized by patchy or diffuse myonecrosis and myoregeneration ( Figures 2A, B ), accompanied by a paucity of inflammatory infiltrates ( Figures 2C–F ) (40, 64, 65). Myofiber regeneration is considered the physiologic consequence of necrosis and is essential for muscle restoration in skeletal muscle disease (66). The percentage of necrotic myofibers significantly correlates with the percentage of regenerating myofibers in SRP-IMNM (45). To date, the pathological mechanisms of SRP-IMNM are mainly focused on the inflammatory and autoimmunity response. Type 1 helper T cell/classically activated macrophage M1 response derived inflammation is a predominant pathological finding of SRP-IMNM (67). A larger number of endomysial lymphocytic infiltration is rare in specimens from SRP-IMNM (45). SRP-IMNM exhibits diffuse major histocompatibility complex class I (MHC-I) positivity on sarcolemma and scatter membrane attack complex (MAC) deposition on non-necrotic myofibers in muscle specimens (45, 65, 67) A previous study shows that upregulation of MHC-I on the surface of muscle cells induces clinical, histological, and immunological manifestations similar to human myositis in young mice (68). There is a positive correlation between the percentage of MAC deposited fibers and the percentage of necrotic myofibers, suggesting a complement-mediated mechanism in SRP-IMNM (45). Furthermore, dysregulated T cells and the programmed death-1 pathway are described in SRP-IMNM (69). High mobility group box protein 1 (HMGB1), a ubiquitous non-histone nuclear DNA-binding molecule, might play a pro-inflammatory role under disease conditions. The highly sarcoplasmic HMGB1 expression is positively associated with myofiber autophagy, muscle inflammation, myonecrosis, myoregeneration, and muscle weakness in SRP-IMNM (70). Decreased vascular density and enlargement of endomysial capillaries are previously reported in muscle from SRP-IMNM, which may be relevant to ischemia-induced damages (20). These data indicate that autoimmune and inflammatory responses contribute to the pathogenesis of SRP-IMNM to a great extent. Non-immune mechanisms have been explored in IMNM. A previous study indicates that the upregulation of MHC-I expression on myofibers elicits the elevation of ER stress marker, a glucose-regulated protein 78 (GRP78)/immunoglobulin heavy chain binding protein (BiP) in mice (71), suggesting a close relationship between ER stress and the up-regulation of MHC-I in IMNM. Intriguingly, a scattered GRP78/BiP sarcoplasmic expression is detected in muscle specimens from IMNM. Moreover, BiP expression significantly correlates with myofiber autophagy, myonecrosis, myoregeneration, and clinical disease severity in IMNM (63). Another study also implies ER stress is a key pathological mechanism in IMNM (72). The autophagy marker SQSTM1/p62 immunopositivity with large, rimmed vacuoles is considered a pathological feature in sIBM (73), which is different from SQSTM1/p62 fine granular and homogeneous staining in the sarcoplasm of IMNM (74, 75). Other autolysosome markers, including LC3 and LAMP2, also demonstrate a diffuse sarcoplasmic staining pattern in SRP-IMNM (75). Acid phosphatase staining shows randomly distributed lysosomal activation in scattered myofibers (6). Mitophagy may play a role in HMGCR-IMNM (76) and has not been studied in SRP-IMNM. In addition, necroptosis may be involved in myofiber death in SRP-IMNM (77).
Atypical pathological manifestations, including significant mitochondrial abnormality, myofibrillary pathological changes, and granulomatous inflammation, occasionally occur in SRP-IMNM (41). HMGCR-IMNM patients with a disease duration of over three years may resemble LGMD on skeletal muscle pathological presentations (78). Recent studies show unusual pathological changes in damaged HMGCR-IMNM, including perimysium and myofibrillary changes (79) and an increased presence of apoptosis marker B-cell lymphoma 2-positive T-lymphocytes (80), which so far have not been recognized in SRP-IMNM.
Electromyography is an effective examination for distinguishing myogenic damage from a neuromuscular junction and neurogenic damage. SRP-IMNM is characterized by typical myogenic damage, presenting as a positive spike of fibrillation potential in proximal limbs, early recruitment of motor unit potential (20, 32), and prominent spontaneous potential (35). A myotonic potential is sometimes observed in SRP-IMNM (32).
Currently, clinical randomized trials and large sample-sized literature are lacking, making it difficult to reach definite conclusions on the treatment strategies of IMNM. According to the recommendations from the 224th European Neuromuscular Centre (ENMC) International Workshop, initial treatment for SRP-IMNM usually starts with intravenous and/or oral glucocorticoids (4). Depending on the disease severity and response to glucocorticoids monotherapy, the treatment can be supplemented with other immunotherapy at the same time or within one month, such as immunosuppressants, intravenous immunoglobulin (IVIG), and/or rituximab (4). The goal of maintenance treatment is to minimize the symptoms with the lowest dose of glucocorticoids. Generally, steroid monotherapy does not control the disease progression and most patients required additional immunosuppressants to achieve improvement in IMNM (4, 6, 7, 81). A high recurrence risk by decreasing the dose of glucocorticoids is reported (25, 32, 52). Once dyspnea occurs in IMNM, intensive care and augmenting immunotherapy are required, including plasma exchange, cyclophosphamide, and/or cyclosporine (4). Anti-B cell therapies, belimumab (82) and rituximab, seem to be relatively safe and effective medications for the majority of patients with SRP-IMNM (42, 83, 84). The early use of IVIG (85) or co-administration of tacrolimus with corticosteroids (86) dramatically decreases the dose of steroids and improves the symptoms of patients with SRP-IMNM. There are discrepancies among several studies on the prognosis of patients with IMNM. In 224th ENMC International Workshop, SRP-IMNM is regarded as one of the most disabling IIMs, and patients often have poor muscle recovery even with treatment (4). A prior study indicates that 50% of patients with SRP-IMNM achieve satisfactory outcomes with immunotherapy after 4 years, and most patients’ serum CK levels are not restored to normal (42). On the other hand, some studies reveal that most patients with SRP-IMNM obtain satisfactory improvement on formal immunotherapy (7, 21, 86).
Data are limited concerning the treatment of refractory IMNM. The definition of refractory IMNM is not explicit. When the treatment with glucocorticoids with immunosuppressants at a known effective dose is performed for at least three to twelve months, and muscle weakness is still worsening or not better, these patients can be deemed to be refractory (44, 87). There are some risk factors associated with refractory SRP-IMNM, including being male, severe muscle weakness, concurrent ILD, quick development of muscle fatty infiltration, and more B cell activating factor receptor and B lymphocyte infiltration in muscle specimens (44). Rituximab may be an effective treatment strategy against refractory IMNM (83, 84). Some patients with refractory IMNM respond well to tocilizumab (87). High-dose cyclophosphamide is effective for several refractory IMNM patients (88). In addition, a refractory SRP-IMNM patient responds well to myeloid autologous stem cell transplantation (89).
SRP-IMNM is clinically characterized by acute or subacute proximal extremities weakness at the onset. Autoimmune and inflammatory responses play key roles in the pathological mechanism. In addition, ER stress-induced autophagy pathway and necroptosis are involved in the muscular pathogenesis of SRP-IMNM. Most patients with SRP-IMNM at the acute or subacute stage respond well to high-dose steroid therapy. Steroids combined with immunosuppressive agents are recommended to be applied during maintenance therapy. It is required to regularly monitor the disease progression, especially extramuscular manifestations, including cardiac involvement and ILD. The treatment of refractory SRP-IMNM still needs further exploration.
XM contributed to the body, provided the figures, designed the write-up, and made the required changes. B-TB did the critical review and editing. XM and B-TB approved the final manuscript.
This study was supported by the National Natural Science Foundation of China (Grant Number: 81873758).
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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PMC9613565 | 36038677 | O. Y. Burenina,N. L. Lazarevich,I. F. Kustova,T. S. Zatsepin,M. P. Rubtsova,O. A. Dontsova | Expression of CASC8 RNA in Human Pancreatic Cancer Cell Lines | 29-08-2022 | long noncoding RNAs,CASC8,pancreatic cancer | A lot of long non-coding RNAs (lncRNAs) are expressed in human cells in a number of transcripts of different lengths and composition of exons. In case of cancer-associated lncRNAs, an actual task is to determine their specific isoforms, since each transcript can perform its own function in carcinogenesis and might have a unique expression profile in various types of tumors. For the first time, we analyzed the expression of CASC8 lncRNA in human pancreatic ductal adenocarcinoma cell lines and found an abundant isoform that was previously considered as the minor one in this type of cancer. We also revealed extremely high expression levels of all CASC8 transcripts in MIA PaCa-2 cells and, conversely, the lack of this lncRNA in PANC-1. This allows to use them as convenient models for further in vitro studies. | Expression of CASC8 RNA in Human Pancreatic Cancer Cell Lines
A lot of long non-coding RNAs (lncRNAs) are expressed in human cells in a number of transcripts of different lengths and composition of exons. In case of cancer-associated lncRNAs, an actual task is to determine their specific isoforms, since each transcript can perform its own function in carcinogenesis and might have a unique expression profile in various types of tumors. For the first time, we analyzed the expression of CASC8 lncRNA in human pancreatic ductal adenocarcinoma cell lines and found an abundant isoform that was previously considered as the minor one in this type of cancer. We also revealed extremely high expression levels of all CASC8 transcripts in MIA PaCa-2 cells and, conversely, the lack of this lncRNA in PANC-1. This allows to use them as convenient models for further in vitro studies.
Pancreatic cancer (predominantly pancreatic ductal adenocarcinoma, PDAC) is the most lethal among malignant neoplasms. Almost asymptomatic development and late detection of the tumor cause extremely low patient survival, which does not exceed 7% on average over 5 years, and the recurrence rate after surgery or chemotherapy is extremely high [1]. To date, only the CA 19-9 oncomarker, which is determined in blood serum, is used in clinical practice to diagnose and predict PDAC development. However, the scope of its application is extremely limited due to low sensitivity and specificity, since its increase is also observed in other tumors of the digestive system, as well as in non-tumor pathologies [2]. A promising direction is the use of long non-coding RNAs (lncRNAs) as tumor markers, which can be directly measured in biological fluids, biopsy specimens, or postoperative tissues using real-time PCR with reverse transcription (RT-PCR) [3]. To date, dozens of lncRNAs, which expression levels change during PDAC, are known [4, 5]. However, the majority of them, such as MALAT1, ANRIL, H19, UCA1, etc., are “universal” oncogenic lncRNAs. The lncRNAs that are more tissue-specific for the pancreas or for the digestive system in general usually have low expression levels, which limits the possibility of their use in clinical practice and impedes studies on elucidation of their properties and functions. Thus, identification of new lncRNAs which expression is activated in PDAC in comparison to healthy pancreatic tissues is a relevant task. In the course of a preliminary analysis of the available bioinformatic data from TCGA (The Cancer Genome Atlas), we found a miserably studied lncRNA CASC8 (aka “Cancer Susceptibility Candidate 8”). Its expression was significantly activated in cancers of the gastrointestinal tract and lungs (Fig. 1a) with the maximum in the case of PDAC. At the same time in normal pancreatic tissues this lncRNA was almost absent. These differences were statistically significant, which makes possible predicting the diagnostic potential of CASC8 (Fig. 1a). The high mortality rate of patients with elevated levels of CASC8 expression (Fig. 1b) indicates its probable pro-oncogenic role and allows considering this lncRNA as a potential prognostic biomarker. However, most of the published studies on CASC8 were made from analytical point of view and were devoted to establishing the correlation of individual nucleotide polymorphic substitutions (SNP) in its gene with the risks of development of various types of cancer [6]. Attempts to reveal the functional role of the CASC8 lncRNA itself were made only in several experimental studies. It was shown that CASC8 expression in retinoblastoma cells promoted a decrease in the level of tumor suppressor miR-34a and subsequent increase in proliferative activity [7]. In the case of non-small cell lung cancer, decrease of CASC8 expression suppressed the ability of cells to proliferate, invade, migrate, and form colonies and increased their sensitivity to osimertinib [8]. In [9], it was demonstrated that the suppression of CASC8 expression in PDAC leads to the activation of miR-129-5p and the inhibition of TOB1 mRNA; however, the biological significance of this effect, as well as the function of TOB1 in carcinogenesis, has not been elucidated. We found that all studies completely ignored the existence of four different CASC8 isoforms annotated in databases (Ensembl, NCBI, USCS). Moreover, in 2014, the 5'- and 3'-ends of all CASC8 transcripts were determined in colon cancer cells in experiments performed by the RACE (“Rapid Amplification of cDNA Ends”) method [10]. On the basis of the obtained results (Fig. 2a), it can be concluded that CASC8 gene expression products are represented by a long transcript (i1), which contains the first three exons, and two short isoforms: i2 contains exons 5, 6, and 7, whereas i3 contains exons 5 and 7. According to [10], the i1a isoform is not expressed and is probably the result of annotation of the lncRNA precursor. The transcripts i1 and i2/i3 do not have common exons, i.e., they are two completely different lncRNAs. It is noteworthy that, in all previously published studies on CASC8, primers complementary to exons 1–3 (i.e., specific to i1) were used in RT-PCR. However, according to the available information from the UCSC database, this isoform is almost absent in PDAC tissues. Thus, the aim of this study was to identify different CASC8 lncRNA isoforms in PDAC cell cultures and to compare their expression levels. Four lines of pancreatic ductal adenocarcinoma were selected as study objects: MIA PaCa-2, PANC-1, AsPC-1, and Capan-2, which differ in the proliferation rate and/or differentiation grade [11]. PC-3 (prostate adenocarcinoma), VA-13 (lung fibroblasts) and HEK293 (embryonic kidney) cells were also used as controls. The amount of RNA was measured by RT-PCR, and U6 snRNA was used for normalization. The pairs of primers for PCR were selected in the way to be complementary to different CASC8 exons, and in the case of i2 and i3 they were complementary to exon junctions (Fig. 2a); thus, each isoform was amplified independently. All three control lines PC-3, VA-13, and HEK293, as well as Capan-2 (the least aggressive PDAC line), showed extremely low expression levels of the CASC8 i1 transcript, which was barely above the RT-PCR detection level. A slightly more pronounced expression was detected in the case of AsPC-1, whereas the amount of i1 for MIA PaCa-2 was ~20 times higher. In addition, an extremely high level of i2 expression was also detected in these cells, although, according to the distribution of transcripts in other analyzed cell lines, i2 is a minor isoform. Conversely, the i3 isoform apparently is predominant in PDAC cells, and its maximum expression was also detected in MIA PaCa-2. PANC-1 was an exception as far as none of the CASC8 transcripts were detected. Thus, this cell line is a peculiar natural knockdown, which can be used in subsequent experiments. In contrast, MIA PaCa-2 cells express the maximum amount of all CASC8 RNA variants, including the conventionally minor transcript i2. We suggest this difference originates from the different nature of MIA PaCa-2 and PANC-1 [12]. Although both cell lines derive from aggressive poorly differentiated PDAC tumors, they are quite different in morphology, cell population heterogeneity, and expression of various factors. The main controversy is the epithelial–mesenchymal transition (EMT) status of these lines. Although both MIA PaCa-2 and PANC-1 exhibit the so-called mesenchymal phenotype, in most scientific literature they are considered a “quasi-mesenchymal” subtype of PDAC cells [13]. It should be noted that PANC-1 almost does not express E-cadherin and has a more pronounced metastatic potential [12]. Nevertheless, in the AsPC-1 and Capan-2 cell lines, i3 is also the predominant CASC8 isoform. This indicates the limited applicability of the currently published data on the functioning of CASC8 in PDAC [9], because only the i1 isoform was considered in these studies. We suggest that incorrect annotation of CASC8 isoforms may also be the reason why some scientific studies failed to confirm the activation of this lncRNA in the pancreatic tissues of patients with PDAC [14], since primers complementary to i1 were used in RT-PCR. Correct annotation of the major CASC8 transcript in subsequent studies is also necessary for evaluation of its diagnostic potential. Thus, this is the first study that evaluated the expression of various CASC8 isoforms in pancreatic ductal cancer cells and identified the most abundant transcript. We found that the maximum expression of all CASC8 isoforms is observed in the MIA PaCA-2 cell line, whereas in PANC-1 cells this lncRNA is not expressed at all. This difference makes it possible to use PANC-1 as a control line for in vitro experiments and a convenient model object for CASC8 overexpression. Notably, the CASC8 gene is located at the 8q24.21 locus, that encodes a number of important oncogenic lncRNAs (PCAT1, PVT1, CCAT1, CCAT2, CCBC26, etc.) and microRNAs (miR-1204, miR-3686, miR-5194, etc.), which are located in close proximity to the Myc oncogene and affect its expression [15]. Elucidating the functional role of CASC8 (and its individual transcripts) may significantly contribute to understanding the mechanisms of regulation of this locus. | true | true | false |
PMC9613656 | Ying Xu,Luxi Cao,Shuiyu Ji,Wei Shen | LncRNA ANRIL-mediated miR-181b-5p/S1PR1 axis is involved in the progression of uremic cardiomyopathy through activating T cells | 27-10-2022 | Cell biology,Nephrology | This study aimed to explore the regulatory role of lncRNA ANRIL/miR-181b-5p/S1PR1 in UC. UC mouse model was established by 5/6th nephrectomy. We detected body weight, serum levels of renal function and inflammatory factors (biochemical analyzer/ELISA), and cardiac parameters (echocardiography). HE and Masson staining showed the pathological changes and fibrosis in myocardial and nephridial tissues. The expression of ANRIL, miR-181b-5p, and S1PR1 were detected by qRT-PCR or Western blot/immunofluorescence. T cells activation was analyzed by Flow cytometry. ANRIL/S1PR1 were up-regulated and miR-181b-5p was down-regulated in UC mice. ANRIL silencing up-regulated miR-181b-5p and down-regulated S1PR1 (a target of miR-181b-5p). ANRIL silencing increased the body weight, recovered renal function [decreased blood urea nitrogen (BUN) and serum creatinine (Scr)] and cardiac function [decreased left ventricular end-diastolic diameter (LVEDD), LV end-systolic diameter (LVESD), LV systolic anterior wall thickness (LVAWS), LV end-diastolic anterior wall thickness (LVAWD), myocardial performance index (MPI), and isovolumic relaxation time (IVRT); increased LV ejection fraction (LVEF), LVEF/MPI, fractional shortening (FS), and E- and A-waves (E/A)], inhibited the inflammation [decreased interferon (IFN)-γ, interleukin (IL)-2, IL-10, and tumor necrosis factor (TNF)-α], and relieved pathological injuries and fibrosis. ANRIL silencing also recovered the viability and inhibited the inflammation of activated T cells in vitro, and inhibited T cell activation in UC mice in vivo. In addition, miR-181b-5p overexpression exhibited same effects with ANRIL silencing in UC. ANRIL silencing inhibited T cell activation through regulating miR-181b-5p/S1PR1, contributing to the remission of UC. | LncRNA ANRIL-mediated miR-181b-5p/S1PR1 axis is involved in the progression of uremic cardiomyopathy through activating T cells
This study aimed to explore the regulatory role of lncRNA ANRIL/miR-181b-5p/S1PR1 in UC. UC mouse model was established by 5/6th nephrectomy. We detected body weight, serum levels of renal function and inflammatory factors (biochemical analyzer/ELISA), and cardiac parameters (echocardiography). HE and Masson staining showed the pathological changes and fibrosis in myocardial and nephridial tissues. The expression of ANRIL, miR-181b-5p, and S1PR1 were detected by qRT-PCR or Western blot/immunofluorescence. T cells activation was analyzed by Flow cytometry. ANRIL/S1PR1 were up-regulated and miR-181b-5p was down-regulated in UC mice. ANRIL silencing up-regulated miR-181b-5p and down-regulated S1PR1 (a target of miR-181b-5p). ANRIL silencing increased the body weight, recovered renal function [decreased blood urea nitrogen (BUN) and serum creatinine (Scr)] and cardiac function [decreased left ventricular end-diastolic diameter (LVEDD), LV end-systolic diameter (LVESD), LV systolic anterior wall thickness (LVAWS), LV end-diastolic anterior wall thickness (LVAWD), myocardial performance index (MPI), and isovolumic relaxation time (IVRT); increased LV ejection fraction (LVEF), LVEF/MPI, fractional shortening (FS), and E- and A-waves (E/A)], inhibited the inflammation [decreased interferon (IFN)-γ, interleukin (IL)-2, IL-10, and tumor necrosis factor (TNF)-α], and relieved pathological injuries and fibrosis. ANRIL silencing also recovered the viability and inhibited the inflammation of activated T cells in vitro, and inhibited T cell activation in UC mice in vivo. In addition, miR-181b-5p overexpression exhibited same effects with ANRIL silencing in UC. ANRIL silencing inhibited T cell activation through regulating miR-181b-5p/S1PR1, contributing to the remission of UC.
Uremic cardiomyopathy (UC) is a serious complication of chronic kidney disease (CKD) that characterized by left ventricular hypertrophy and interstitial fibrosis. Since UC is associated with the occurrence of arrythmias, cardiac failure and sudden cardiac death, it contributes to the high cardiovascular morbidity and mortality. The pathogenesis of UC is complex, involving a variety of factors, such as anaemia, hypertension, haemodynamic overload, endothelial dysfunction, insulin resistance, mineral metabolism, and circulating uraemic toxins. Notably, T cell immune response also plays an important role in the pathogenesis of UC. The loss of naive T cells and accumulation of memory T cells have been determined to be related with cardiovascular events in the peripheral blood of patients with CKD. Winterberg et al. have found that the increased frequency of T cells is associated with poor diastolic function in children with CKD, and depletion of T cells improves diastolic function and myocardial strain in CKD mice without influencing hypertension and renal dysfunction. Therefore, intervention on T cells may be potential strategy for alleviating myocardial dysfunction in CKD. Long non-coding RNAs (lncRNAs) are a class of RNAs that play critical roles in cardiovascular diseases via regulating diverse physiological processes, such as myocardial fibrosis, cardiomyocyte hypertrophy/apoptosis/autophagy, angiogenesis, mitochondrial homeostasis, and inflammation. LncRNA ANRIL, also known as CDKN2B-AS1 is an important regulator involved in the pathogenesis of cardiovascular disorders. Yang et al. have shown that silencing of ANRIL relieves myocardial cell apoptosis and improves heart function in a mouse model of acute myocardial infarction. Liu et al. have found that ANRIL is up-regulated in patients with acute coronary syndrome, and its silencing relieves the dysfunction of umbilical vein endothelial cells. In addition to that in myocardial tissues, ANRIL silencing simultaneously exerts a protective role in kidney tissues. Thomas et al. have determined that silencing of ANRIL exhibits a protective effect on both the kidney and heart in diabetic mice. Xu et al. have revealed that silencing of ANRIL relieves the damages of nephridial and myocardial tissues in mice with uremic cardiovascular disease. However, the action mechanisms of ANRIL in UC involving T cells are rarely reported. The function of lncRNAs is inseparable from its regulation on the downstream genes of target microRNAs (miRNAs). With emerging knowledge on the crosstalk among lncRNAs, miRNAs, and mRNAs, there diverse downstream miRNA‐mRNA axes of ANRIL have been revealed in different human diseases, such as ANRIL/miR-199a-5p/DDR1 in glioma, ANRIL/miR-424a-5p/HMGA2 in diabetic nephropathy, ANRIL/miR-424a-5p/AKT3 in ovarian endometriosis, ANRIL/miR-320d/STAT3 in thoracic aortic dissection, and ANRIL/miR-126-5p/PTPN7 in coronary atherosclerosis. In addition, a previous study has determined that ANRIL enhances the inflammation in a mouse model of coronary artery disease through down-regulating miR-181b. We suspect that the ANRIL/miR-181b axis may also be involved in the process of UC. In this study, the regulatory role of ANRIL/miR-181b and the downstream S1PR1 in UC was evaluated in a mouse model. The underlying mechanisms of ANRIL/miR-181b/S1PR1 axis involving T cell activation were further analyzed. This study is aimed to reveal T cell-related pathogenesis in UC, and provide potential molecular targets for the prevention and treatment of UC.
A mouse model of UC was established by 5/6th nephrectomy. As shown in Fig. 1A, the body weight was significantly lower in the model mice than that in the controls (P < 0.001). In aspect of renal function, the model mice showed significantly increased serum levels of BUN and Scr compared with the controls (Fig. 1B, P < 0.001). In aspect of cardiac function, the model mice exhibited increased left ventricular end-diastolic diameter (LVEDD), LV end-systolic diameter (LVESD), LV systolic anterior wall thickness (LVAWS), LV end-diastolic anterior wall thickness (LVAWD), myocardial performance index (MPI), and isovolumic relaxation time (IVRT), as well as decreased LV ejection fraction (LVEF), LVEF/MPI, fractional shortening (FS), and E- and A-waves (E/A) compared with the controls (Fig. 1C, P < 0.001). In aspect of inflammation, the serum levels of interferon (IFN)-γ, interleukin (IL)-2, IL-10, and tumor necrosis factor (TNF)-αwere significantly higher in the model mice than those in the controls (Fig. 1D, P < 0.001). In aspect of pathological change, HE staining showed well-arranged myocardial fibers and renal tubules in mice of the control and sham groups. The model mice exhibited disordered myocardial fibers and hypertrophic cardiomyocytes in myocardial tissues, as well as inflammatory cell infiltration and tubular vacuolization in nephridial tissues (Fig. 1E). In addition, Masson staining showed that the model mice presented obvious collagenous fibers in both the myocardial and nephridial tissues (Fig. 1E).
The expression of ANRIL, miR-181b-5p, and S1PR1 were detected in myocardial tissues of the model mice. qRT-PCR revealed that ANRIL and S1PR1 were up-regulated, and miR-181b-5p was down-regulated in the model mice (Fig. 2A–C, P < 0.05). Western blot and immunofluorescence further determined the up-regulation of S1PR1 in the model mice (Fig. 2D,E, P < 0.01). In addition, the activation of T cells was identified in myocardial tissues. As shown in Fig. 2F,G, there were more cells positive for CD3, CD44, and Th17A in the model mice compared with the controls (P < 0.05).
The regulatory effects of ANRIL/miR-181b-5p/S1PR1 involving T cell activation were analyzed in vitro. The transfection of sh-ANRIL significantly down-regulated ANRIL and up-regulated miR-181b-5p in activated T cells (Fig. 3A,B, P < 0.001). MiR-181b-5p was up-regulated by the transfection of miR-181b-5p mimic in activated T cells (Fig. 3B, P < 0.001). The viability of T cells was inhibited following activation, which was recovered by ANRIL silencing or miR-181b-5p overexpression (Fig. 3C, P < 0.001). ANRIL silencing or miR-181b-5p overexpression also weakened the increased IFN-γ, IL-2, IL-10, and TNF-α levels in activated T cells (Fig. 3D, P < 0.01). In addition, S1PR1 was further identified as the downstream target of miR-181b-5p by DLR assay (Fig. 3E, P < 0.001). Then, the qRT-PCR analysis suggested that ANRIL silencing or miR-181b-5p overexpression weakened the upregulation of S1PR1 in activated T cells (Fig. 3F, P < 0.01).
In order to determine the regulatory mechanisms of ANRIL/miR-181b-5p/S1PR1 in UC, ANRIL was silenced and miR-181b-5p was overexpressed in the model mice. As shown in Fig. 4A–E, the intervention of sh-ANRIL significantly down-regulated ANRIL and S1PR1, and up-regulated miR-181b-5p in myocardial tissues of the model mice (P < 0.01). Ago-miR-181b-5p significantly increased miR-181b-5p expression and decreased S1PR1 expression in the model mice (P < 0.05). The following functional assays showed that ANRIL silencing or miR-181b-5p overexpression significantly increased the body weight, recovered renal function (decreased BUN and Scr) and cardiac function (decreased LVEDD, LVESD, LVAWS, LVAWD, MPI, and IVRT; increased LVEF, LVEF/MPI, FS, and E/A), and inhibited the inflammation (decreased IFN-γ, IL-2, IL-10, and TNF-α) in the model mice (Fig. 5, P < 0.05). In addition, the pathological injuries in myocardial and nephridial tissues of the model mice were relieved by ANRIL silencing or miR-181b-5p overexpression (Fig. 6A). The enriched T cells positive for CD3, CD44, and Th17A in the model mice was also significantly decreased by ANRIL silencing or miR-181b-5p overexpression (Fig. 6B,C, P < 0.01).
LncRNAs are non-coding transcripts longer than 200 nucleotides that play important roles in the pathogenesis of cardiovascular diseases. A variety of LncRNAs has emerged as potential biomarkers and therapeutic targets for diverse cardiovascular diseases, such as CHRF, GAS5, MIAT, CARL, MDRL, H19, APF, NRF, and MALAT1. In addition, cardiovascular disorder is one of the leading causes of mortality in patients with CKD. However, there limited lncRNAs are determined in cardiovascular diseases following CKD. Lai et al. have revealed that the plasma level of lncRNA DKFZP434I0714 was increased in uremic patients, presenting an independent predictor of poor cardiovascular outcome. Wang et al. have found that lncRNA ZFAS1 promotes cardiac fibrosis in mice with chronic kidney disease. ANRIL is a specific lncRNA that also involved in the occurrence and development of cardiovascular diseases. Silencing of ANRIL has been reported to benefit for the protection of both myocardial tissues and nephridial tissues. In this study, the regulatory role of ANRIL was evaluated in a mouse model of UC. The results showed that ANRIL was up-regulated in UC mice, and its silencing increased the body weight, recovered renal and cardiac function, and relieved pathological injuries and fibrosis in myocardial and nephridial tissues. These results indicate that ANRIL is a pathogenic gene in UC. Similarly with previous studies mentioned above, silencing of ANRIL may also be a potential target for the remission of UC. LncRNAs can regulate target miRNAs via serving as competing endogenous RNAs. Previous studies have proved that the role of ANRIL in human diseases is realized by targeting specific miRNAs, such as miR-199a-5p, miR-424a-5p, miR-424a-5p, miR-320d, and miR-126-5p. Guo et al. have reported that ANRIL-mediated down-regulation of miR-181b promotes the release of inflammatory factors in mice with coronary artery disease. Consistently, a negative regulatory relationship between ANRIL and miR-181b-5p was revealed in the UC model in this study. MiR-181b is a specific miRNA that plays a crucial role in cardiovascular diseases. For example, the down-regulation of miR-181b is a potential biomarker for heart failure. MiR-181b inhibits the inflammation and myocardial injury in a rat model of sepsis. The down-regulation of miR-181b is associated with plaque formation and vascular endothelial injury in atherosclerosis. In this study, miR-181b-5p was found to be down-regulated in UC mice, and its overexpression recovered cardiac function and relieved pathological injuries in myocardial tissues. These results indicate a protective role of miR-181b-5p in myocardial tissues, which are consistent with previous studies in many other cardiovascular diseases. In addition, miR-181b-5p overexpression also contributes to the recovery of renal function and remission of pathological injuries in nephridial tissues. To combine with the regulatory effect of ANRIL on miR-181b-5p, we suspect that silencing of ANRIL may relieve UC through up-regulating miR-181b-5p. Since lncRNAs and miRNAs are both non-coding RNAs, the downstream mRNAs are crucial for the function of lncRNAs/miRNAs/mRNAs axis. There many targets of miR-181b have been determined in cardiovascular diseases, such as HMGB1, Notch1, MEF2A, HSPA5, and TIMP3. Sphingosine 1 phosphate (S1P) is a signaling lipid along with cardiovascular function in regulating vascular tone, endothelial function, and lymphocyte trafficking. The dysfunction of S1P signal is associated arterial hypertension, atherosclerosis, endothelial dysfunction, and aberrant angiogenesis, contributing to the development of hypertrophic/fibrotic heart disease, myocardial infarction, and heart failure. It is noteworthy that the function of S1P in cardiovascular tissues is reliant on a family of G protein-coupled receptors (S1PR1-5). In this study, S1PR1 was identified as target of miR-181b-5p, which was negatively regulated by miR-181b-5p and positively regulated by ANRIL. Since S1PR1 participates in the regulation of vascular barrier integrity and tone in cardiovascular system, miR-181b-5p-mediated down-regulation of S1PR1 may response for the alleviating role of ANRIL silencing in UC. CKD is an inflammatory condition that associated with abnormal activation of T cells. Via secreting pro-inflammatory cytokines, T cell activation are also critical for the promotion of vascular pathology in cardiovascular diseases. In this study, activated T cells and enhanced inflammation were found in myocardial tissues of UC mice. Our findings are just similar with a previous study that the enriched T cells is a cause of diastolic dysfunction in UC. On the other hand, there is evidence that miR-181b and S1PR1 are involved in the regulation of T cells. Grewers et al. have reviewed that miR-181 family is dynamically regulated during T cell development, dependent on the activation stage of T cells. Pyne et al. have reviewed that S1PRs function in the immune system to regulate T cell subsets and trafficking. In this study, miR-181b-5p overexpression inhibited T cell activation both in vitro and in vivo. Our results indicate that miR-181b-mediated down-regulation of S1PR1 can inhibit the activation of T cells in the UC model. ANRIL silencing also inhibited T cell activation in UC, which may be attributed to its regulation on miR-181b/S1PR1. In addition, a previous study has determined that T cell depletion in CKD mice improves the diastolic function and myocardial strain, but not leads to hypertension and renal dysfunction. We suspect that the blocking of T cell activation that regulated by ANRIL/miR-181b/S1PR1 may contribute to the remission of UC. In conclusion, ANRIL and S1PR1 were up-regulated, and miR-181b-5p was down-regulated in a mouse model of UC. ANRIL silencing-mediated miR-181b-5p/S1PR1 axis recovered renal and cardiac function, inhibited the inflammation, and relieved pathological injuries of myocardial and nephridial tissues in UC mice. In addition, ANRIL silencing inhibited T cell activation through regulating miR-181b-5p/S1PR1, possibly contributing to the remission of UC. However, this study still has some limitations, such as the function of S1PR1 in UC, the relation of T cell activation with UC characteristics, as well as more in-depth action mechanisms of ANRIL in UC. Further researches on these fields are still needed.
Animal experiments were approved by the ethical committee of Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College) in accordance with the Guide for the Care and Use of Laboratory Animals (approval number: 2019-045). Male C57BL/6J mice (10 weeks old) were purchased from HFK Bio (Beijing, China). UC model was established by 5/6th nephrectomy on two poles of the left kidney, followed by complete resection of the left kidney at one week later. Mice underwent laparotomy without nephrectomy were enrolled as the Sham group, and normal mice without treatments were enrolled as the control group (N = 6 in each group). After 5/6th nephrectomy, lentivirus-packaged shRNA-ANRIL (sh-ANRIL, 5′-GGACTAGCTTCAGAAGCTTCT-3′)/shRNA-negative control (sh-NC) and Ago-miR-181b/Ago-NC (RiboBio, Guangzhou, China) were intravenously injected into mice via the tail vein every 3 days.
After modeling, the body weight was measured every 3 days. The blood samples were collected from mice and centrifuged at 1000g for 5 min for separating the serum samples. The serum levels of Blood urea nitrogen (BUN) and Serum creatinine (Scr) were measured on an automatic biochemical analyzer (Cobas c-311, Roche, Basel, Switzerland). The serum levels of TNF-α, IL-2, IL-10, and IFN-γ were measured using commercial Enzyme linked immunosorbent assay (ELISA) kits (Mlbio, Shanghai, China). In addition, the cardiac function was evaluated by echocardiography using a small animal ultrasound imaging system (Visualsonic Vevo 2100, Toronto, Canada). After measurements, mice were anesthetized and sacrificed by cervical dislocation, and the myocardial and nephridial tissues were resected.
HE and Masson staining were performed to evaluate the pathological changes and fibrosis, respectively. The collected myocardial and nephridial tissues were fixed in 10% formaldehyde, embedded in paraffin, and sliced into 5 μm sections. After dewaxed in xylene and rehydrated in graded ethanol, the sections were stained using HE kit (Beyotime, Beijing, China) or MASSON kit (Solarbio, Beijing, China) according to the instructions. The stained sections were subsequently dehydrated with graded ethanol, vitrificated with dimethylbenzene, and observed under a microscope (BX53, Olympus, Japan).
The paraffin-embedded sections of myocardial tissues were dewaxed in xylene, dehydrated with graded ethanol, and microwave irradiated for 15 min at 95 °C for antigen retrieval. The sections were then blocked with 5% bovine serum albumin (BSA) for 1 h and incubated with anti-S1PR1 (1:100; Invitrogen, Carlsbad, CA, USA, MA5-32587) for 12 h at 4 °C. After three times of washing with phosphate buffer saline (PBS), the sections were incubated with Cy3-conjugated secondary antibody (1:1000, Abcam, Cambridge, MA, USA, ab6939) combined with DAPI (Beyotime) for 1 h under darkness. The stained sections were finally captured under a fluorescence microscope (CKX53, Olympus).
Lymphocytes were separated from blood samples by 30 min of centrifugation at 600g. After washed with PBS, the isolated lymphocytes were blocked in 5% BSA for 30 min, and incubated with anti-CD3 (Invitrogen, #11-0032-82), -CD44 (Invitrogen, #12-0441-82), and -Th17A (Invitrogen, #11–7177-81) for 30 min at 4 °C. T cells were analyzed on a flow cytometer (CytoFLEX S, Beckman, Miami, FL, USA). The raw data were collected and exposed to dot plot analysis by applying CellQuest Pro software package.
T cells isolated from blood samples of control mice were cultured in RPMI 1640 Medium supplemented with 10% fetal bovine serum (FBS) and 100 U/mL penicillin/streptomycin at 37 °C with 5% CO2. Anti-CD3 and -CD28 were added to activate T cells. sh-ANRIL/sh-NC and miR-181b-5p mimic (Forward: 5′-AACAUUCAUUGCUGUCGGUGGGU-3′, reverse 5′-CCACCGACAGCAAUGAAUGUUUU-3′)/mimic NC (Forward: 5′-UUCUCCGAACGUGUCACGUTT-3′, reverse 5′-ACGUGACACGUUCGGAGAATT-3′) (RiboBio) were transfected into T cells using Highgene transfection reagent (ABclonal, Wuhan, China).
Cell viability was measured using CCK-8 kit (Beyotime). Simply, cells were seeded into 96-well plates and cultured for 24, 48, and 72 h, respectively. CCK-8 solution at a volume of 10 µL was then added into each well. After 2 h of incubation, the optical density (OD) at 450 nm was detected by a microplate reader (MolecularDevices, CA, USA).
DLR assay was performed to identify the binding relationship between miR-181b-5p and S1PR1. Simply, S1PR1 carrying the binding sequences (S1PR1-WT) and mutant sequences (S1PR1-MUT) were inserted to DLR vector (Beyotime). 293 T cells were co-transfected with S1PR1-WT/MUT and miR-181b-5p mimics/mimics NC for 48 h. Relative fluorescence activity (Fireny/Renilla) was measured using a DLR Kit (Thermo Fisher Scientific, Waltham, MA, USA).
The RNA samples were extracted from myocardial tissues or cells using TRIZOL (Invitrogen), revere-transcribed into cDNAs using PrimeScript RT-PCR kit (Takara, Japan), and then used for qRT-PCR on a PCR instrument (MX3000P, Agilent, Santa Clara, CA, USA). The qRT-PCR program included an initial 95 °C for 3 min, and 40 cycles of 95 °C for 12 s and 62 °C for 40 s. Relative expression level was calculated by the 2−∆∆Ct method. GAPDH was used as an internal control for ANRIL and S1PR1, and U6 was used as an internal control for miR-181b-5p. The primers included ANRIL-F, 5′-ATGAGAAGTCGGACAGTGGC-3′; ANRIL-R, 5′-GCTAAAGCCATTGAGTCGGC-3′; miR-181b-5p-F, 5′-ACACTCCAGCTGGGAACATTCATTGCTGTCGG-3′; miR-181b-5p-R, 5′-TGGTGTCGTGGAGTCG-3′; S1PR1-F, 5′-TCTTCTGCACCACCGTCTTC-3′; S1PR1-R, 5′-CTGCGGCTAAATTCCATGCC-3′; GAPDH-F, 5′-TGTGGGCATCAATGGATTTGG-3′; GAPDH-R, 5′-ACACCATGTATTCCGGGTCAAT-3′; U6-F, 5′-ATGGCGGACGACGTAGATCA-3′; U6-R, 5′-AGCTCTCGGTCATTTCTCATTTT-3′.
Total proteins were lysed from myocardial tissues or cells in RIPA buffer (Beyotime). The protein samples were separated by 10% SDS-PAGE and transferred onto PVDF membranes. The membranes were then blocked with 5% nonfat milk for 1 h, and incubated with anti-S1PR1 (1:1000; Invitrogen) for 12 h at 4 °C. Subsequently, the membranes were washed with TBST for three times and were continues incubated with HRP-conjugated secondary antibody (1:2000, Abcam, ab205718) for 1 h. After visualized using an ECL kit (Thermo Fisher Scientific), the protein bands were quantified by a Gel imaging system (Tanon 1200, Shanghai, China), and band gray was analyzed by ImageJ software. GAPDH (anti-GAPDH, 1:1000; Abcam, ab181602) was used an internal control.
GraphPad Prism 7.0 (GraphPad, San Diego, CA, USA) was used for statistical analysis. The data were presented as mean ± standard deviation. Comparisons among multiple groups were analyzed by one-way analysis of variance (ANOVA) followed by Tukey’s test. Comparisons between two groups were analyzed by t test. P value less than 0.05 was considered as significantly different.
This study was performed in line with the principles of the Declaration of Helsinki. Animal experiments were approved by the ethical committee of Zhejiang Provincial People’s Hospital (Affiliated People’s Hospital, Hangzhou Medical College) and complied with the ARRIVE guidelines.
Supplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3.Supplementary Figure S4. | true | true | true |
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PMC9613660 | 35459909 | Dennis Das Gupta,Christoph Paul,Nadine Samel,Maria Bieringer,Daniel Staudenraus,Federico Marini,Hartmann Raifer,Lisa Menke,Lea Hansal,Bärbel Camara,Edith Roth,Patrick Daum,Michael Wanzel,Marco Mernberger,Andrea Nist,Uta-Maria Bauer,Frederik Helmprobst,Malte Buchholz,Katrin Roth,Lorenz Bastian,Alina M. Hartmann,Claudia Baldus,Koichi Ikuta,Andreas Neubauer,Andreas Burchert,Hans-Martin Jäck,Matthias Klein,Tobias Bopp,Thorsten Stiewe,Axel Pagenstecher,Michael Lohoff | IRF4 deficiency vulnerates B-cell progeny for leukemogenesis via somatically acquired Jak3 mutations conferring IL-7 hypersensitivity | 22-04-2022 | B cells,Cancer models | The processes leading from disturbed B-cell development to adult B-cell progenitor acute lymphoblastic leukemia (BCP-ALL) remain poorly understood. Here, we describe Irf4−/− mice as prone to developing BCP-ALL with age. Irf4−/− preB-I cells exhibited impaired differentiation but enhanced proliferation in response to IL-7, along with reduced retention in the IL-7 providing bone marrow niche due to decreased CXCL12 responsiveness. Thus selected, preB-I cells acquired Jak3 mutations, probably following irregular AID activity, resulting in malignant transformation. We demonstrate heightened IL-7 sensitivity due to Jak3 mutants, devise a model to explain it, and describe structural and functional similarities to Jak2 mutations often occurring in human Ph-like ALL. Finally, targeting JAK signaling with Ruxolitinib in vivo prolonged survival of mice bearing established Irf4−/− leukemia. Intriguingly, organ infiltration including leukemic meningeosis was selectively reduced without affecting blood blast counts. In this work, we present spontaneous leukemogenesis following IRF4 deficiency with potential implications for high-risk BCP-ALL in adult humans. | IRF4 deficiency vulnerates B-cell progeny for leukemogenesis via somatically acquired Jak3 mutations conferring IL-7 hypersensitivity
The processes leading from disturbed B-cell development to adult B-cell progenitor acute lymphoblastic leukemia (BCP-ALL) remain poorly understood. Here, we describe Irf4−/− mice as prone to developing BCP-ALL with age. Irf4−/− preB-I cells exhibited impaired differentiation but enhanced proliferation in response to IL-7, along with reduced retention in the IL-7 providing bone marrow niche due to decreased CXCL12 responsiveness. Thus selected, preB-I cells acquired Jak3 mutations, probably following irregular AID activity, resulting in malignant transformation. We demonstrate heightened IL-7 sensitivity due to Jak3 mutants, devise a model to explain it, and describe structural and functional similarities to Jak2 mutations often occurring in human Ph-like ALL. Finally, targeting JAK signaling with Ruxolitinib in vivo prolonged survival of mice bearing established Irf4−/− leukemia. Intriguingly, organ infiltration including leukemic meningeosis was selectively reduced without affecting blood blast counts. In this work, we present spontaneous leukemogenesis following IRF4 deficiency with potential implications for high-risk BCP-ALL in adult humans.
Two signaling pathways via the Interleukin-7 receptor (IL-7R) and the preB cell receptor (preBCR) ensure an orderly progression of B lymphopoiesis [1–3]. ProB cells adhere to bone marrow (BM) stromal cells (SCs) expressing CXCL12 and VCAM-1 through CXCR4 and VLA-4 respectively, while SC-derived IL-7 induces their proliferation [4]. The formation of the preBCR composed of Igμ protein and the surrogate light chain (ψL), consisting of λ5 and VPREB, marks the entrance to the preB cell stage. Signaling via the preBCR in turn induces the transcription factor (TF) interferon regulatory factor 4 (IRF4) which is also critical during T-cell differentiation [5, 6]. In preB cells, IRF4 halts cycling and facilitates recombination of the light chain locus by RAG1/2 [1]. Despite its importance, Irf4−/− mice still develop, albeit less, surface (s)Igμ+ mature B cells [7], likely due to a partially redundant function of IRF8. Accordingly, Irf4,8−/− B progenitors are completely arrested at the preB cell stage [8]. Disruption of this developmental track can provoke B-cell progenitor acute lymphoblastic leukemia (BCP-ALL). In humans, this disease preferentially affects children (age 0–19), while most deaths however occur in the adult population [9]. Cases affecting adolescents and young adults (AYA) display a different set of driver mutations compared to childhood BCP-ALL [10–13]. Herein, we report that adult Irf4−/− mice spontaneously develop BCP-ALL with age and delineate the steps from disturbed Irf4−/− B lymphopoiesis to overt leukemia.
Following the serendipitous finding, that some aged Irf4−/− mice developed tumors and died, we systematically observed 80 Irf4–/– mice over time. We detected 14 tumors (incidence 17.5%), that spontaneously appeared in lymph node (LN) areas (mean age: 268d, median: 238d, Fig. 1a). Tumors were neither detected in mice younger than 150d nor in C57BL/6 wild-type (wt) mice housed in the same room. All tumors (Fig. 1b shows a representative tumor in situ) were accompanied by lymphadenopathy (arrowheads) and increased spleen size (Fig. 1c). The suspected lymphomatous origin was corroborated microscopically (Fig. 1b, right panels), with infiltration of mononucleated cells into the BM, lung, and liver (Fig. 1d). Due to the known impaired maturation of Irf4−/− preB cells [7], spontaneous eruption of preB-leukemia seemed plausible: In spleen sections (Fig. 1e), infiltrating cells stained positive for both B220 and Igμ (although less than untransformed “follicle” B cells) and Ki67. By flow cytometry, BM samples from tumor mice harbored an expanded pro/preB cell compartment (Hardy fraction (fr.)A-D [14], B220midsIgμ–) (Fig. 1f, g). Fr.A-D cells were detected also in peripheral lymphoid organs and blood of tumor mice (Fig. 1h). Following the Hardy classification (Fig. 1i), we determined tumor cells to be fr.C preB cells (B220midsIgμ–CD43+CD24+BP-1+) (Fig. 1j, k, sFig. 1a). In addition, Igμ, but not Igκ/λ was detected intracellularly (sFig. 1b). Lastly, tumors stained positive for surface λ5, part of the ψL (Fig. 1l). These attributes characterize the disease as preB-I cell BCP-ALL. To prove clonality, we sequenced the VDJ junctions of the IgH region in three tumors (Supplementary Table 1). Almost all sequences per tumor were identical, demonstrating clonality. The tumors (three examples) further displayed copy number variations (CNV) (sFig. 1h), targeting differing genomic regions. Finally, transfer of tumor cells robustly elicited leukemia in wt acceptor mice (sFig. 1d–g) with as little as 500 transferred cells (sFig. 1f, g), indicating bona fide malignancy.
The uniform appearance of BCP-ALL in Irf4−/− mice suggested a defined preleukemic pro/preB cell state vulnerable to immortalization. Dimensional reduction of BM samples stained for B-cell differentiation markers, identified an enlarged fr.C preB cell compartment already in healthy Irf4−/− mice (Fig. 2a–c, sFig. 2a). This disturbed, but productive B-cell maturation confirms and extends previous reports [7]. Expression analysis of IL-7Rα and of CD2 (sFig. 2b, c), which accompanies cytosolic Igμ expression [15] further showed an increased frequency of CD2-/dimIL-7Rα+B220+sIgμ− preB cells in Irf4−/− mice. Purified BM B220+ cells from Irf4−/− and wt mice were cultured with IL-7 (Fig. 2d–g) to compare proliferative capacities. After 6d, Irf4−/− cells had expanded roughly three-fold, whereas wt cell numbers decreased. Phenotypically, Irf4−/− cells accumulated at the fr.C stage (Fig. 2e, f) and expressed surface λ5 (Fig. 2g, h); exactly like Irf4−/− leukemia. In contrast, wt cells differentiated further, losing surface CD43 (making them fr.D) (Fig. 2e, f) with some cells expressing sIgμ (fr.E). Thus, IL-7 unmasked the leukemic potential of the fr.C compartment in Irf4−/− mice with both unchecked proliferation and a reinforced differentiation block. Notably, IL-7 dependent Irf4−/− preB-I cell proliferation was blocked by NIBR3049 and Ruxolitinib, inhibitors of the IL-7R downstream actors JAK3 and JAK1 respectively (sFig. 2d).
As overt leukemia is characterized by systemic presence, we tested whether already preleukemic Irf4−/− B-cell progenitors would leak from the BM. To reduce the complex Hardy classification, we identified early B-cell progenitors, approximately until the preB-I stage, by B220+CD2–/dim expression (sFig. 2b). We detected higher frequencies of splenic B220+CD2–/dim cells in Irf4−/− than in wt mice (Fig. 2h), which accumulated with age. Thus, premature BM evasion adds to the impaired differentiation and hyperproliferation that characterize Irf4−/− preleukemia. Potentially, this finding represented a systemic consequence of reduced vicinity to BM niche cells. We, therefore, analyzed the proximity of Irf4−/− and wt B220+CD2–/dim cells to IL-7+ BMSCs in situ using Il-7eGFP reporter mice (Fig. 2j–m, sFig. 2e, f, Supplementary Movie 1) [16]. In femur cryosections, the B220+CD2–/dim subset (Fig. 2j, arrowheads) but not the whole Irf4−/− B220+ cell compartment was on average located further away from IL-7+ BMSCs, compared to wt control (Fig. 2l, m). We excluded differences in IL-7+ BMSC abundance between genotypes (sFig. 2g). B progenitor retention to BM is secured via the interaction of CXCR4 on pro/preB cells with the IL-7+ BMSC-derived chemokine CXCL12 [17, 18]. Notably, Irf4−/− pro/preB cells expressed markedly lower levels of CXCR4 compared to wt cells (Fig. 2n). Chemokine migration assays with Hardy fr.A-D cells showed that Irf4−/− cells indeed migrated significantly less towards CXCL12 (Fig. 2o). Thus, reduced CXCR4-CXCL12 interaction likely induces the systemic seeding of Irf4−/− B progeny. Inversely, direct cell interactions are likely not responsible, because Irf4−/− and wt fr.A-D cells adhered equally to monolayers of OP-9 cells in vitro (sFig. 2h).
Most likely, a second, acquired genetic alteration was necessary for bona fide leukemia development and arose with low frequency per time, explaining the affected age and relatively low penetrance. Importantly, IL-7 deprivation of BM-evaded pro/preB cells should create strong survival stress and potential selection pressure for bona fide leukemogenesis. To identify somatically acquired mutations, we performed whole-exome sequencing (WES) of three independent tumors (T8, T10, T11) compared to sorted B220+sIgµ− cells from Irf4−/− BM. Comparisons of the single nucleotide variants (SNVs) between the three samples identified nine genes affected in all three tumor samples (Fig. 3a). Out of these, SNVs in four genes (Rrs1, Jak3, AW82073, and Duxf3) showed alternate base frequencies close to 0.5 or 1 (Fig. 3b), suggesting that they could be present on one or both alleles of all leukemic cells. Although we did not exclude the oncogenic potential of the other three genes, we focused on Jak3, because it is associated with IL-7R signaling. We detected Jak3 mutations also in other tumors TD1, TD2, TD3, and T14 by Sanger- and RNA-sequencing (Fig. 3c, Supplementary Table 2). Thus, seven out of seven tested tumors carried Jak3 mutations (“JAK3mut”). All mutations targeted either the active kinase domain or the pseudokinase domain regulating JAK3 activity. Some of these SNVs have been described before [19]. Further, using two different classifiers, no gene fusions could be detected (see methods). Analysis of typical BCP-ALL genes [20] identified some respective mutations at low frequencies, indicative of subclonal events (Fig. 3d). Among these, mutations in Jak1, the partner to JAK3 in IL-7R signaling, were detected in both T8 and T11. To analyze the role of the JAK3mut, we transduced Irf4−/− preB-I cell cultures with retroviruses (RVs) encoding no or wt JAK3 or the JAK3mut R653H and T844M. Culturing transduced cells in the presence of αIL-7 to test for IL-7 independency unexpectedly resulted in cell death after a few days with no benefit for cells expressing JAK3mut (sFig. 3a, b). To test if JAK3mut would confer advantages with limited IL-7, RV-infected Irf4−/− preB-I cell cultures were exposed to decreasing IL-7 concentrations (Fig. 3e–g). At 0.1 and 0.01 ng/ml IL-7, both JAK3mut-, but not JAK3wt-RV led to the outgrowth of transduced over untransduced cells after 6d of culture (Fig. 3f, g). Thus, JAK3mut confer IL-7-hypersensitivity, but not -independency. Accordingly, ex vivo cultured Jak3-mutated T8 and T11 cells also still depended on IL-7 (sFig. 3c, d). However, the tumor cells exhibited increased proliferation (sFig. 3e) and λ5 surface retention (sFig. f, g) in decreased IL-7 concentrations when compared to wt or Irf4−/− preB cell culture.
Because six out of seven Jak3 mutations were C to T base exchanges (Table 2), we suspected a specific mutagenic agent. DNA-editing enzymes including the APOBEC family member AID can deamidate cytosines, e.g., during somatic hypermutation [21, 22]. Repair mechanisms most often ultimately cause C to T conversions [23, 24]. Notably, AID is induced in wt preB cells by IL-7 withdrawal and LPS stimulation and acts as a facilitator of human BCP-ALL [25]. Therefore, we compared Aicda expression in sorted Irf4−/− and wt fr.A-D cells to that of wt mesenteric (m)LN- and CD4+ TH1-cells as controls and to individual leukemia samples. While mLN cells highly expressed Aicda, fr.A-D preB- and leukemia cells, but not TH1-cells, also expressed readily detectable amounts (Fig. 3h). Furthermore, like their wt counterpart [25], in vitro expanded Irf4–/– preB-I cells upregulated Aicda further under LPS treatment and during IL-7 withdrawal (Fig. 3i). This finding can explain how BM evasion and exposure to pathogens might cooperatively initiate mutagenic processes via AID in vulnerable Irf4−/− preB-I cells. To test if T8 exhibited signs of previous AID activity on a global level, we analyzed C:T/G:A-transition frequencies in WES of T8, as well as BM-sorted Irf4−/− and wt fr.A-D cells compared with matched tail-tip samples. Indeed, we found a marked preponderance of C:T/G:A-transitions in T8, when filtering on putative somatic core SNVs (Fig. 3j).
Next, we compared Irf4−/− leukemia to the complex landscape of human BCP-ALL subtypes (reviewed in refs. [11, 26, 27]), using a published human BCP-ALL cohort for which a random forest classifier had been established (Methods for details) [28]. Only mildly (potentially due to the interspecies comparison) elevated prediction scores were generated for Ph+, Ph-like, KMT2a- and DUX4-rearranged human BCP-ALL (sFig. 4a, b). Since all of these except Ph-like are defined by specific gene rearrangements, that we had not detected in Irf4−/− mouse leukemia, we excluded them as comparable candidates. Ph-like ALL harbors recurrent genetic alterations in signaling molecules, especially in CRLF2 and JAK2 [20]. While BCP-ALL overall preferentially affects children, the incidence of the Ph-like subtype increases from 10% in children to above 25% in AYA and adults [20, 29], reminiscent of the older age of Irf4−/− leukemic mice. Furthermore, a published dataset of 154 Ph-like BCP-ALL cases exhibited 10-fold reduced IRF4 transcripts, when compared to other BCP-ALL subtypes [20]. While in human Ph-like ALL, Jak2 is commonly mutated, we report recurrent Jak3 mutations in Irf4−/− mice. As both proteins are part of distinct but similar signaling complexes in B-cell progenitors (Fig. 4a), we investigated structural and functional similarities between the specific Jak3 and Jak2 mutations. Comparisons of amino-acid sequences revealed high protein-wide interspecies and intermolecular similarities for both proteins (Fig. 4b). Mapping the two amino acids R653 and T844 (mutated in Irf4−/− mice) onto JAK3 structure predictions, generated by the alpha-fold algorithm [30], revealed that the two amino acids are in direct contact at an interface of JH1-JH2 domains (Fig. 4c). This interface specifically is highly conserved in JAK2 compared to JAK3 (Fig. 4d, f, sFig. 4c, d). Intriguingly, R683 (corresponding to R653 in JAK3) is by far the most commonly mutated amino acid in JAK2 in Ph-like ALL, while mutations targeting T875 (corresponding to T844) also have been described [31]. These findings suggest that mutations in human JAK2 and mouse JAK3 affect a highly similar functional hotspot. As mentioned above, JAK2 and JAK3 are part of distinct, but similar receptors: JAK3 binds the common γ-chain involved in IL-7 signaling, while JAK2 associates with CRLF2 involved in TSLP signaling. Both signals involve the IL-7Rα chain and the same downstream pathways (STATs, PI3K) [32]. Therefore, the alternative presence of JAK3/JAK2 mutations between mouse and human BCP-ALL might reflect different cytokine preferences. Human proB/preB cells proliferate in response to both TSLP and IL-7 [33]. However, in Irf4−/− BM cells IL-7, but not TSLP induced robust proliferation (Fig. 4g) as well as high frequencies and absolute counts of CD43+ (Fig. 4h) and λ5+ preB cells (Fig. 4i, j).
As Irf4 deletion was a prerequisite for leukemia in our model, we examined the effect of forced IRF4 re-expression, using RVs coding for GFP alone (EV-RV) or plus IRF4 (IRF4-RV). When re-introducing IRF4 into T8 or T11, GFP+ IRF4-expressing-, but not GFP+ control cells gradually disappeared over time (Fig. 5a). AnnexinV/PI stainings confirmed apoptosis (not shown). Further, we noted the loss of surface λ5-expression induced by IRF4-RV (Fig. 5b, c). Comparing the transcriptomes of still viable cells 24 h after transduction revealed strong induction of “apoptotic process” and “innate immune response” gene ontology (GO) gene-sets (gs) (Fig. 5d). Markov clustering of GO gs affected by IRF4 re-expression (Fig. 5e, f) further identified several coregulated B-cell differentiation gs (Fig. 5f), with downregulated ψL components Igll1, Vpreb1, and Vpreb2, but upregulated differentiation genes including Igμ, Igκ, and Blnk (Fig. 5g, h, sFig. 5a, b). Similar results were obtained for T11 (sFig. 5c, d). Therefore, fully transformed leukemia remained targetable by IRF4 re-expression.
Next, we screened a collection of kinase inhibitors for their capacity to kill Irf4−/− leukemia cells in vitro. We included NIBR3049 targeting JAK3, Ruxolitinib, an inhibitor of JAK1/2 (downstream of JAK3), and Dexamethasone, a cornerstone for treating lymphomatous malignancies. Furthermore, we included inhibitors of NFκB (IKK, TAK1), JNK, MEK, ERK, PP2A, GFI1, FAK, and the Bruton tyrosine kinase (BTK) acting downstream of the BCR. A variety of these substances potently killed tumor cells (sFig. 6a), implying the involvement of multiple pathways in leukemia cell survival. The efficacy of Ruxolitinib and NIBR3049 corroborated our results concerning Jak3 driver mutations. Furthermore, inhibitors of GFI1 and PP2A, as well as NFκB and JNK, were potent. In contrast, inhibiting BTK, MEK and ERK had no impact.
Next, we implemented JAK inhibition as in vivo treatment for Irf4−/− leukemia. We began induction therapy with Dexamethasone around day 12 after adoptively transferring 3 × 105 T8.1 cells i.p. into wt mice (Fig. 6a), when overt leukemia was noted in peripheral blood (pB) (Fig. 6b “pre”). After 7d of treatment, leukemic cell numbers in pB were robustly reduced (Fig. 6b “post”), although few cells reproducibly remained detectable (Fig. 6c). Maintenance therapy was continued with Ruxolitinib or vehicle control by oral gavage twice daily for the following 12 days (Fig. 6a, c). Importantly, the half-life of Ruxolitinib in mice is only 0.8 h (“Australian Public Assessment Report for Ruxolitinib”, Australian Government), implying that any observed in vivo effectiveness might be underestimated. Despite maintenance therapy, leukemic cells in pB reappeared, with no significant difference between treatment groups (Fig. 6c). However, treatment with Ruxolitinib resulted in a clear survival benefit (Fig. 6d) and marked improvement of a prominent neurological symptom: in sham-treated animals, temporary limpness of the tail and hind legs occurred seconds after gavage, which we quantified using a newly established scoring system (ranging from 0 to 3, see Methods). Mechanistically, ultrasound imaging revealed an echogenic paravertebral mass (sFig. 7a, b) in score 3, but not score 0 mice. By histology, score 3 correlated with severe infiltration of blasts into the spinal canal (X in Fig. 6f), extending into spinal nerve roots (arrowhead in Fig. 6f). Therefore, paraparesis likely represented a manifestation of mouse leukemic meningeosis, exacerbated by gavage-induced increases in intraabdominal pressure. Paraparesis was reproducibly relieved during Ruxolitinib treatment (Fig. 6e), correlating with the suppression of perimyelon infiltration that ensued in vehicle-treated mice after the end of induction therapy (Fig. 6f, h). In contrast, the severely impaired hematopoiesis in sham-treated mice, indicated by low CAE+ cell frequencies, was not significantly ameliorated by Ruxolitinib (Fig. 6i, j). These findings raised the possibility that Ruxolitinib preferentially targets infiltration of solid organs rather than BM or pB. Accordingly, Ruxolitinib fully blocked the liver infiltration as observed in sham-treated mice (Fig. 6k, l). As tissue infiltration is regulated by homing receptors, we treated T8.1 and T8.2 cells with Ruxolitinib in vitro and recorded the expression of CD29 (integrin β1), which pairs with various integrin alpha chains involved in cell- and tissue adhesion [34, 35]. Notably, on T8.1 and T8.2, Ruxolitinib reduced CD29 expression dose-dependently (sFig. 6c–e) while it even slightly increased expression of MHC I molecules (H2Db, H2Kb), stained as a specificity control.
The herein described spontaneous leukemogenesis in Irf4−/− mouse stresses the particular vulnerability of preB-I cells. Our data provide insights for (a) conditions promoting leukemogenesis, (b) functional consequences of Jak mutations, (c) parallels of mouse and human BCP-ALL and (d) potential in vivo treatment: (a) We provide evidence for a two-hit leukemogenesis model: The first hit (Irf4 loss) resulted in reduced differentiation, IL-7-dependent hyperproliferation, and impaired retention to the BM niche (Fig. 7). A second hit (targeting Jak3 in our model) created a dominant survival signal, probably founding overt preB-I leukemia. The induction of BCP-ALL in Irf4−/− mice are similar to Ikzf1 and Pax5 mutated mouse models [36–38], implying similarities between these TF-alterations. Probably, one shared mechanism is the differentiative impairment. Importantly, for Irf4−/− fr.A-D cells we even detect slightly higher levels of Pax5 compared to wt fr.A-D cells (sFig. 8), ruling out that the findings in Irf4−/− mice merely mirror those of Pax5 deficiency. The reverse remains conceivable; that Ikzf1 and Pax5 mutations converge in lowering IRF4 expression. In addition to mice mutated in Pax5 or Ikzf1, Irf4/Irf8−/−, and Irf4/Spi1−/− mice have been shown to develop leukemia early in life at a high incidence [39, 40]. Contrasting these studies, we report that a single deficiency for IRF4 fully suffices for leukemogenesis. We excluded secondary alterations in Irf8, Spi1 in our model: we found unchanged expression and gene sequence of IRF8 (not shown) and normal amounts of Spi1 transcripts (sFig. 8a) in Irf4−/− fr.A-D cells. The single IRF4 deficiency models potential clonal initiating events better than Irf4/Irf8−/− or Irf4/Spi1−/− mice, because Irf4−/− mice harbor productive B-cell development. We newly describe that a preleukemic alteration can lead to reduced BM retention, presenting a tentative explanation for the induction of mutagenic signals, as deprivation from IL-7 and exposure to bacterial compounds can cooperatively induce the mutagenic agent AID [25]. (b) Why do Jak3 mutations only lead to enhanced sensitivity to, but not complete independence of IL-7? Analysis of JAK3 and JAK2 structure implied that mutations of R683/R653 and T875/T844 might decrease JH1-JH2 interaction strength. This would imply reduced auto-inhibition as the GOF mechanism—in line with findings for the JAK family member TYK2 [41]. This alone cannot explain cytokine independency, owing to the receptor biology: The two preassembled receptor chains keep JAKs intracellularly separated [42]. Ligand binding is needed for a conformational change that brings JAKs into the proximity needed for cross-phosphorylation. To explain our observations, we propose an oncogene model with two equilibria (Fig. 8a, b): the first is determined by cytokine concentration and dictates the probability of receptor conformation change (Fig. 8a). The second, independent equilibrium (Fig. 8b), is determined by the interaction strength at the JH1-JH2 interface and dictates the probability of JH1 and JH2 dissociation. Only the combination of the “bound” and “active” state (Fig. 8c) would result in the elicitation of a signal (Fig. 8c, green frame). In this model, JAK mutations would only affect the second equilibrium (Fig. 8d, red arrows). Sporadic ligand binding would still be needed for elicitation of signaling. The model stringently predicts the better exploitation of low cytokine concentrations for JAKmut that we observed in vitro. Figure 8d depicts theoretical probabilities of receptor states in the presence (top row) or absence (bottom row) of JAK mutations. Our findings that JAK3mut confer heightened cytokine sensitivity, but not -independence, is in contrast to what has been found for JAK2-R683G mutants expressed in the commonly used BaF3 cell line [43]. However, BaF3 cells depend on IL-3 and not IL-7Rα cytokines (i.e., IL-7 or TSLP). Therefore, it remains conceivable, that the IL-3 receptor provides a different physiology, which may deviate from the IL-7R physiology in primary B progenitors. (c) The finding that Irf4−/− preB-I cells respond preferentially to IL-7 over TSLP presents a possible explanation, why mouse models of BCP-ALL acquire Jak3 mutations, and human Ph-like ALL typically harbors Jak2 mutations. Our comparison of JAK structure predictions yielded corresponding mutations likely to elicit similar downstream effects. (d) Lastly, our in vivo experiments reinforce Ruxolitinib as a potential treatment for JAK-driven BCP-ALL. The compound represents an important therapeutic agent in myeloproliferative disease and is already studied for the treatment of Ph-like-ALL [44, 45]. We describe a preferential effect of Ruxolitinib on CNS- and organ infiltration, potentially due to reductions in integrin expression on leukemia cells. These effects are of potential translational importance because current CNS-targeted therapies for ALL remain toxic.
C57Bl/6 mice were purchased from Charles River, Sulzfeld, Germany. Irf4−/− mice [7] and Il-7eGFP mice [46] (provided by Koji Tokoyoda, DRFZ Berlin) were bred on the C57Bl/6 background and housed in the animal facility of the Biomedical Research Center at the University of Marburg, Germany. If not stated otherwise, all mice used in the presented experiments were 8–12 weeks old and sex-matched.
Stable tumor cell lines T8.1, T8.2, and T11 were established from primary Irf4–/– leukemia cells (derived from primary tumor 8, i.e., T8, or tumor 11 (T11)) by culturing them on a monolayer of irradiated (30 Gy) ST2 stromal cells [47] grown to confluency in Opti-MEM medium (31985070, ThermoFisher Scientific) supplied with 1% cell culture supernatant from JIL-7.6 J558 cells [48] (a gift from Fritz Melchers, Berlin) as a source of IL-7. After several passages, T8 and T11 cells grew independently of ST2 cells. For in vitro inhibitor experiments, 2.5 × 105 T8.1 or T8.2 cells (or T11 cells) were cultured in 500 µL RPMI medium in 48 well plates in the presence of the indicated concentrations of inhibitors. To determine the percentage of viable cells, samples were stained using Annexin V and propidium iodide (PI) (see below) after 48 h. Substances used include Defactinib (S7654, Selleckchem), Oxocaenol (O9890, Sigma), GANT61 (Sigma, G9048), SP203580 (EI-286-0001, Enzo), SP600125 (EI-305-0010, Enzo), PD98059, Promega), Ibrutinib (S2680, Selleckchem), BAY11-7082 (ALX-270-219, Alexis), Dexamethasone (PZN 08704491, mibe GmbH) and Ocadaic acid (O4511, Sigma).
Femur and tibia bones from 8 to 12 weeks old mice were explanted and cleaned from adherent tissues. Cells were extracted via centrifugation at 11 × 103 RPM for 10 s. Total BM cells were enriched for B220+ (sIgµ−) B lineage cells using an in-house magnetic-activated cell sorting protocol. Briefly, whole bone marrow cells were stained with a mix of FITC-conjugated antibodies to (Igµ), CD11b, B220, Ter119, CD49b, CD4, and CD8 (all from eBioscience), followed by incubation with an anti FITC/streptavidin/biotin/magnetic bead complex (Miltenyi Biotec) and magnetic sorting using a microcentrifugation tube stand (Miltenyi Biotec) [49]. Sorting efficiency, as confirmed by flow cytometry, routinely exceeded 90%. Cells were seeded at a density of 1 × 105 cells per well in 200 µL RPMI complete (96-well plates, Greiner). Pro/preB cell cultures were propagated with 10 ng/mL rmIL-7 (217-17, Peprotech) in RPMI-1640 medium complete (R8758, Sigma-Aldrich, supplemented with: 10% FCS (Sigma-Aldrich), 2 mM l-glutamine (Biochrom), 50 µM β-mercaptoethanole (Sigma-Aldrich), 0.03/0.05 g per 500 mL Penicillin G/Streptomycin Sulfate, 1% non-essential amino acids (PAA Laboratories)). In some experiments, pro/preB cells (1.25 × 106/mL medium) were treated for 24 h with LPS (Sigma, 1 µg/ml), anti-IL-7 (BioXCell, 10 µg/ml), rmIL-7, or respective combinations, before generating mRNA for qRT-PCR.
For the transwell migration assays, Hardy fr.A-D cells were magnetically sorted from BM of wt and Irf4−/− mice as described above (with addition of FITC-conjugated anti-Igµ antibody), and 2 × 105 cells in RPMI (without additives, FCS-free) containing 10 ng/mL rmIL-7 seeded in 50 µL in the top chamber of 96-well 5 µm pore uncoated 96-well transwell plates (HTS transwell® Corning). The bottom chamber was flooded with 200 µL RPMI containing indicated concentrations of rmCXCL12 (Peprotech). After 16 h, inserts were removed, cells in the bottom chamber were collected, counted, and analyzed for B220 surface expression using flow cytometry. The fraction of migrated cells was calculated as n(migrated) × freqB220(migrated)/n(input) × freqB220(input). Normalization to B220+ cells reduced interexperimental differences due to differences in cell purity after magnetic selection. For OP-9 adhesion assays, 5 × 103 OP-9 cells (a gift from Hyun-Dong Chang, DRZF Berlin) were seeded in 96-well microtiter plates 24 h before the assay. On the day of the assay, fr.A-D cells were purified as above and 2 × 105 fr.A-D cells were seeded on top of OP-9 monolayers in RPMI complete + 10 ng/mL rmIL-7. Plates were centrifuged briefly to accelerate cell descension. After 1 h, suspended cells were collected in the supernatant and by washing OP-9 monolayers two times with PBS.
For surface staining of B lineage markers, cells were harvested, resuspended in PBS/1% FCS and stained with anti-B220 (RA3-6B2, Biolegend), anti-Igµ (II/41, BD Bioscience), anti-CD43 (RM2-5, Biolegend), anti-CD24 (M1/69, invitrogen), anti-BP-1 (BP-1, BD Bioscience), anti-CD2 (RM2-5, Biolegend), anti-CXCR4 (L276F12, Biolegend), anti-CD127 (=IL-7Rα) (A7R34, BD Bioscience), anti-CD179b (=λ5) (LM34, BD Bioscience) as indicated (20 min at room temperature in the dark). All antibodies were employed at a dilution of 1:500. Fluorescence was recorded using either a FACS Aria III (BD) or an Attune NxT (Thermo-Fisher) analyzer. Data analysis was performed using the FlowJo V10 software (BD). For dimensional reduction, we used the t-Distributed Stochastic Neighbor Embedding (tSNE) [50] algorithm built into FlowJo V10. Epitopes on BM cells from Irf4−/− and wt control mice used for dimensional reduction analysis comprised B220, sIgµ, CD43, CD24, BP-1. For RNA and WES analyses, BM cells were surface labeled for B220 and sIgµ expression, and B220+sIgµ− cells were sorted using a FACS Aria III (BD Bioscience). Sorting efficiency was routinely above 95%. To determine cell viability, AnnexinV/PI staining was performed using 5 µL AnnexinV (640905, Biolegend) per 500 µL HBSS. After 20 min of incubation at room temperature in the dark, 1 µL PI (421301, Biolegend) was added, and cells were immediately measured.
CNVs were analyzed in tumor samples 8, 10, and 14 and compared to Irf4−/− normal tail tissue. Whole DNA was extracted from 5 × 106 cells per sample using the Macherey-Nagel NucleoSpin Tissue kit (REF 740952.50) according to the manufacturer’s protocol. Library preparation was performed using the Illumina Nextera DNA kit according to the manufacturer’s instructions. Sequencing was performed on an Illumina-HiSeq-1500 platform in rapid-run mode at the Genomics Core Facility of Philipps-University Marburg. Fastq quality control was performed using custom scripts. Raw sequenced reads were aligned to the Ensembl Mus musculus reference (revision 79) using Bowtie2 (version 2.0.0) [51] with standard parameterization. Analysis of CNVs was performed using the cn.mops (Copy Number estimation by a Mixture Of PoissonS) package (version 1.18.1) [52] with the following parametrization: prior impact = 1, lower threshold −0.9, upper threshold = 0.5 minimum width = 4. Window length was set to 10000 and the algorithm was run in unpaired mode.
Mouse femora from Irf4−/− or wt il-7eGFP reporter mice were explanted, cleaned from soft tissues, and fixated overnight in 4% PFA PBS (Alfa Aesar). Samples were then dehydrated by incubation in 30% sucrose in PBS for 24 h. Dried and dehydrated femora were snap-frozen in cryomolds® (Tissue-Tek) using O.C.T freezing medium (Tissue-Tek) by being placed in a beaker of Hexan, surrounded by a beaker of Acetone and dry ice. Samples were stored at −20 °C until processing. Cryosections of 7 µm were generated with a Leica cryostat (DB80 LX microtome blades, Leica) using Kawamoto tape [53] (Section-lab) as described before [54]. Cryosections were stained with antibodies against B220 (RA3-6B2, Biolegend), CD2 (14-0021-85, eBioscience, conjugated to AF555 using lightning-Link kit, Abcam), GFP (Rockland goat polyclonal anti-GFP, 600-101-215) with secondary rabbit anti-goat F(ab’)2 AF488 (thermo-scientific A21222). Samples were then mounted in DAPI ProLong Gold Antifade (ThermoFisher Scientific). Images were recorded using a Leica confocal (SP8i) microscope. Image analysis was performed in IMARIS (version 9.7.2).
Tissue samples were immediately fixed in 4% PFA PBS solution. Histological analysis was performed on 3 µm thick sections from paraffin-embedded tissue as described previously [55]. Briefly, rehydrated paraffin sections were first blocked with 0.3% H2O2 and goat normal serum. For immunohistochemical (IHC) stainings, rat antibodies against CD45R/B220 (clone RA3-6B2, BD) and KI67 (clone TEC-3, Dako) were then incubated on the tissue slices and the bound antibody was detected with biotinylated goat anti-rat IgG (Southern Biotechnology). Bound antibody was visualized with the Vectastain-kit (Vector Laboratories) according to the manufacturer’s protocol. Hematoxylin-Eosin (HE) stainings were performed according to standard procedures. Cells of the granulocytic lineage were stained on paraffin-embedded tissues with the Naphthol AS-D Chloracetate (Specific Esterase, CAE) Kit (Ref: 91C-1KT, Sigma-Aldrich) according to the manufacturer's protocol. In the in vivo therapeutic experiments, we calculated the narrowing of the spinal cord using the equation At/(Asca–Asp), where Asca is the area of the spinal canal, At that of the tumor, and Asp that of the spinal cord area. Two different cross-sections per animal were examined. The infiltration of the liver was calculated by dividing the tumor area in the liver by the whole area of the liver section. Three whole liver sections were analyzed per animal. All measurements were performed using Fijii [56].
To determine SNV within leukemia samples, genomic (g)DNA was extracted both from primary Irf4−/− tumors as well as FACS-sorted control B220+sIgµ− BM fr.A-D cells using the High Pure PCR Template Preparation kit from Roche (11796828001). The integrity of the resultant gDNA was confirmed in a 2% Agarose gel. Macrogen in Seoul performed SureSelect All Exon V6 library preparation and sequenced exons on a NovaSeq platform producing 2 × 150 bp reads at a coverage of 100× (50× on-target coverage). Fastq quality control was performed using FASTQC (version 0.11.9). Raw sequenced reads were aligned to the Ensembl Mus musculus reference (revision 96) using STAR (version 2.6.1d) using default parametrization. Soft-clipped aligned reads were then subjected to variant calling analysis. Position-wise pile-up files were generated using samtools (version 1.9) with the mpileup option and a pile-up quality threshold of 15, both for single sample and matched variant calling. Subsequently, variant calling was performed for SNP and InDel detection using VarScan2 (version 2.3.9) on single samples with the following parametrization: sampling depth = 100,000, minimum variant frequency = 0.05, minimum coverage = 8, minimum variant reads = 2, minimum average read quality = 15 and a p value threshold was set to 0.05. Only primary alignments were considered, the strand filter was enabled, and duplicates were removed. As a comparison, matched tumor-normal variant calling was performed with VarScan as well using an identical parameter setting with the somatic p value threshold set to 0.05. For Fig. 3n raw sequenced reads were aligned to the Ensembl Mus musculus reference (revision 96) using Burrows-Wheeler Aligner (BWA version 0.7.17) using default parametrization [57]. Prior to variant calling, aligned reads were filtered using a custom filter that excludes reads with more than three mismatches, more than two indels, or a mapping quality below 20 using pysam (version 0.16.0.1). Duplicates were marked and removed using Picard (GATK version 4.1.6.0) [58]. Filtered aligned reads were then subjected to variant calling analysis. Position-wise pile-up files were generated using samtools (version 1.9) with the mpileup option and a minimal base quality threshold of 20. Subsequently, variant calling performed for SNP detection using VarScan2 (version 2.4.4) using matched tumor-normal (somatic) mode with the following parametrization: sampling depth = 100,000, minimum variant frequency = 0.2, minimum coverage = 8, minimum variant supporting reads = 5, minimum average read quality = 20 and a somatic p value threshold was set to 0.05. Only primary alignments were considered, and the strand filter was enabled. SNP calls were filtered to high confidence somatic mutations using VarScan’s somaticFilter method, SNPs with a variant allele frequency above 0 in the matched reference sample were excluded.
SNVs in the JAK3 gene were confirmed by Sanger sequencing of PCR fragments spanning the Jak3 pseudokinase and kinase region (primers used for PCR amplification and Sanger Sequencing: mJAK3 for, mJAK3 rev s. Supplemental Data). Sequencing services were provided by Microsynth Seqlab. To determine the clonality of tumor cells, the VµH region was amplified by PCR. Amplicons were run on an agarose gel and extracted using the QIAquick Gel Extraction Kit (Qiagen). DNA fragments were then cloned into the vector pJet1.2 (Thermo Scientific) and transformed into DH10B E. coli. The indicated numbers of clones (Fig. 1g) for each PCR amplicon were sequenced and aligned with software from IMGT/V-quest [59].
The coding sequence of murine Jak3 was amplified from pCineo-Jak3 (a gift from Olli Silvennoinen from Tampere-university in Finland) and cloned into the pMSCV-Thy1.1 expression plasmid using BamHI and SalI restriction digestion. Site-directed mutagenesis was performed following the manufacturer’s protocol using the Quick-Change II site-directed mutagenesis kit (Agilent Technologies; primers employed are listed in the Supplemental Materials). Viral supernatant from mutated pMSCV-Thy1.1-Jak3 constructs was produced as described previously [49]. For viral transduction, 5 × 105 IL-7 dependent primary Irf4−/− preB-I cell cultures were resuspended in 400 µL RPMI medium (D5030, Sigma-Aldrich) with 600 µL viral supernatant and 1.5 µL polybrene and spun in culture plates at 2700 rpm for 90 min at 37 °C. Cells were then replenished with a conditioned medium and rested for 24 h. Transduction efficiency was measured by flow cytometry using surface staining for Thy1.1 (OX-70, Biolegend). For the IL-7 independency assay (Fig. 3b), transduced cells were split and cultured with either recombinant murine (rm)IL-7 or 10 µg/ml neutralizing anti-IL-7 antibody (BE0048, Bio X Cell).
RNA extraction from primary tumor samples and FACS-sorted B220+ sIgµ− pro/preB cells was performed using Trizol extraction. Quality control was performed using the Bioanalyzer RNA 6000 NanoChip (Agilent Technologies). Library preparation was performed at the Institute for Immunology, University Medical Center of the Johannes Gutenberg-University Mainz using the NEBNext Ultra Library Prep kit (New England Biolabs). For deep sequencing, the Illumina-HiSeq- 4000 platform was used (Beijing Genomic Institute). Quality control on the sequencing data were performed with the FastQC tool (version 0.11.2, https://www.bioinformatics.babraham.ac.uk/projects/fastqc/). RNA-sequencing reads were aligned to the ENSEMBL Mus_musculus.GRCm38 reference genome. The corresponding annotation (ENSEMBL v76) was also retrieved from ENSEMBL FTP website. The STAR aligner (version 2.4.0j) was used to perform mapping to the reference genome. Alignments were processed with the featureCounts function [60] of the Rsubread package, using the annotation file also used for supporting the alignment. Exploratory Data Analysis was performed with the pcaExplorer package [61]. Differential expression analysis was performed with DESeq2 package [62], setting the false discovery rate (FDR) cutoff to 0.1. DESeq2 datasets were analyzed using the GeneTonic [63] and pcaExplorer packages. To assess the possible occurrence of gene fusions, we applied two different methods, Star-Fusion (version 1.10.1) and Arriba (version 2.1.0). For STAR-Fusion, required meta reference files were created from the Ensembl Mus musculus reverence (revision 100) as recommended in the STAR-Fusion manual. In case of Arriba, we used the mm10 + GENCODEM25 assembly. In each case, we used the dockerized versions of the tools. Raw fastq files were used as an input for both tools. Subsequently, raw reads were mapped using the recommended alternative STAR settings recommended in the tools manual to leverage chimeric reads from the alignments. Default filters as recommended by the STAR-Fusion and Arriba manuals were applied to limit the false-positive rate. For the same reason, known blacklisted regions as provided by the Arriba release were excluded from the analysis.
Human genes (GRCh38.p13, v104) with annotated orthologous genes in mice were extracted from ensembl database using the BiomaRt online tool. Gene counts from RNA-sequencing of a previously published human BCP-ALL cohort [28] and of murine tumor samples were subsetted to include only human-mouse orthologous genes. The resulting gene counts were normalized by variant stabilization transformation using the R package DESeq2 version 1.32.0. Allocation of the murine tumor samples to human BCP-ALL molecular subtypes was performed based on gene expression using a random forest machine learning algorithm (R package caret version 6.0-88) trained on the human cohort. Predictions were plotted using R package pheatmap version 1.0.12. Differential gene expression was analyzed in R package DESeq2 and resulting gene lists ranked by log2-fold-change were analyzed in GSEA version 4.1.0.
Mouse JAK2 (AF-Q62120) and JAK3 (AF-Q62137) structure predictions were acquired from the AlphaFold protein structure database [64] and visualized in UCSF ChimeraX (version 1.2.5) [65]. Multiple sequence alignments were performed using the EMBL-EBI Clustal Omega tool.
Total RNA was extracted both from primary Irf4−/− tumors as well as FACS-sorted control B220+sIgµ− BM fr.A-D cells of either Irf4−/− or wt animals using the Gdansk extractme kit (EM09.1) according to the manufacturer’s protocol. cDNA was prepared from whole RNA samples using the RevertAid cDNA kit from Thermo Fisher (K1621). qRT-PCR for Aicda, Spi1, and Pax5 was performed using the SybrGreen MasterMix reagent (4385612, AppliedBiosystems) in a StepOnePlus cycler (AppliedBiosystems). Data presented as percentage of HPRT using the formula x = 1/2(cyclesAicda − cyclesHPRT) × 100.
Mice were injected with 3 × 105 T8.1 cells intraperitoneally and monitored daily for clinical symptoms. When mice began showing signs of general morbidity, leukemia was confirmed by FACS analysis of tail vein blood for B220+ sIgµ− blast cells. When blast cells in pB reached 25 (mean 50)%, therapy was initiated with oral Dexamethasone (Jenapharm) at 6 mg/L supplied ad libitum in the drinking water for seven days. Maintenance therapy comprised either Ruxolitinib-phosphate (S5243, Sellekchem) 1 mg (in 2% DMSO, 30% PEG300 in H2O, as proposed by the manufacturer), Defactinib (S7654, Sellekchem) 1.2 mg (in 5% DMSO, 50% PEG300, 5% Tween 80 in H2O, as proposed by the manufacturer) or vehicle control (5% DMSO, 50% PEG300, 5% Tween 80 in H2O) administered twice daily via oral gavage. During the course of the disease, this treatment led to paraparesis of the hind legs and tail. A clinical scoring system was established according to the extent of paraparesis and mice were scored daily accordingly: Scores 0–3: (0) no paraparesis, (1) paraparesis induced by treatment intervention, resolves within 30 s, (2) paraparesis induced by treatment intervention, does not resolve within 30 s, (3) persistent paraparesis, independent of treatment intervention. Score 3 prompted sacrification of affected mice. High-resolution ultrasound imaging was performed using a Visual Sonics Vevo 2100 System (FUJIFILM VisualSonics, Toronto, Canada) with microscan transducer MS-550-D, 22–55 MHz (FUJIFILM VisualSonics, Toronto, Canada) as described previously [66].
Statistical analysis was performed using the GraphPad 9.0 software. Data are commonly presented as mean ± SD. Prior to significance testing, normal distribution and homogeneity of variances were confirmed by Shapiro–Wilk test and Brown–Forsythe testing. Statistical significance when comparing two normally distributed groups was evaluated using two-tailed unpaired t tests. In case of significant differences in variances between groups, Welch’s correction was applied to account for non-norminal distribution of data. When comparing multiple groups, a one-way or two-way analysis of variance was performed, depending on the number of variables that differed between compared groups. This was followed by a Tukey’s Sidak, or Dunnett’s post hoc test, as indicated in figure legends. An alpha level of P < 0.05 was employed for significance testing. In the in vivo experiment, all animals were included in the analyses.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
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PMC9613700 | Franziska Nadler,Elena Lavdovskaia,Angelique Krempler,Luis Daniel Cruz-Zaragoza,Sven Dennerlein,Ricarda Richter-Dennerlein | Human mtRF1 terminates COX1 translation and its ablation induces mitochondrial ribosome-associated quality control | 27-10-2022 | Translation,Mitochondria,Mitochondrial proteins | Translation termination requires release factors that read a STOP codon in the decoding center and subsequently facilitate the hydrolysis of the nascent peptide chain from the peptidyl tRNA within the ribosome. In human mitochondria eleven open reading frames terminate in the standard UAA or UAG STOP codon, which can be recognized by mtRF1a, the proposed major mitochondrial release factor. However, two transcripts encoding for COX1 and ND6 terminate in the non-conventional AGA or AGG codon, respectively. How translation termination is achieved in these two cases is not known. We address this long-standing open question by showing that the non-canonical release factor mtRF1 is a specialized release factor that triggers COX1 translation termination, while mtRF1a terminates the majority of other mitochondrial translation events including the non-canonical ND6. Loss of mtRF1 leads to isolated COX deficiency and activates the mitochondrial ribosome-associated quality control accompanied by the degradation of COX1 mRNA to prevent an overload of the ribosome rescue system. Taken together, these results establish the role of mtRF1 in mitochondrial translation, which had been a mystery for decades, and lead to a comprehensive picture of translation termination in human mitochondria. | Human mtRF1 terminates COX1 translation and its ablation induces mitochondrial ribosome-associated quality control
Translation termination requires release factors that read a STOP codon in the decoding center and subsequently facilitate the hydrolysis of the nascent peptide chain from the peptidyl tRNA within the ribosome. In human mitochondria eleven open reading frames terminate in the standard UAA or UAG STOP codon, which can be recognized by mtRF1a, the proposed major mitochondrial release factor. However, two transcripts encoding for COX1 and ND6 terminate in the non-conventional AGA or AGG codon, respectively. How translation termination is achieved in these two cases is not known. We address this long-standing open question by showing that the non-canonical release factor mtRF1 is a specialized release factor that triggers COX1 translation termination, while mtRF1a terminates the majority of other mitochondrial translation events including the non-canonical ND6. Loss of mtRF1 leads to isolated COX deficiency and activates the mitochondrial ribosome-associated quality control accompanied by the degradation of COX1 mRNA to prevent an overload of the ribosome rescue system. Taken together, these results establish the role of mtRF1 in mitochondrial translation, which had been a mystery for decades, and lead to a comprehensive picture of translation termination in human mitochondria.
Translation is a multistep high-fidelity process comprising initiation, elongation, termination and ribosome recycling. While tRNA adapter molecules undergo codon-anticodon interaction during initiation and elongation, specific proteinaceous factors, called release factors (RFs), recognize the STOP codon during translation termination in a sequence-dependent manner. RFs mimic tRNA-like structures that allow the interaction with the ribosomal A-site reaching the decoding center and the peptidyl transferase center (PTC). This results in a conformational change within the ribosome that mediates the hydrolysis of the ester bond between the nascent peptide chain and the peptidyl tRNA within the PTC. In bacteria there are two RFs: RF1 and RF2. While RF1 reads UAA and UAG codons, RF2 recognizes UAA and UGA. The codon specificity is defined by the codon-recognition motifs within domain 2, the PxT or SPF motif, respectively, and the tip of the α5 helix. Both factors share the highly conserved GGQ motif within domain 3 that reaches into the PTC during termination and is essential for facilitating peptide hydrolysis. As mitochondria evolved from an α-proteobacterial ancestor, the human mitochondrial translation machinery reveals similarities to its bacterial counterpart, but also significant differences. Human mitochondria use a slightly different genetic code, e.g. UGA encodes for tryptophan instead of being a STOP signal and AGA and AGG are not recognized by a tRNAArg, but terminate the translation of MTCO1 and MTND6 transcripts (mRNA encoding COX1 and ND6). A proposed −1 ribosomal frameshift in both cases would allow the termination at a conventional UAG STOP codon. Consequently, all thirteen mitochondrial DNA(mtDNA)-encoded proteins would terminate either in UAA or UAG. Four members of the release factor family have been identified in human mitochondria: mtRF1a, mtRF1, ICT1 (mL62) and C12ORF65 (mtRF-R). mL62 and C12ORF65 lack domains for codon specificity and are part of the ribosome rescue system in human mitochondria. While mL62 is a homolog of bacterial ArfB (alternative rescue factor B) recognizing stalled mitochondrial ribosomes with an empty A site, C12ORF65 is involved in the mitochondrial ribosome-associated quality control (mtRQC) acting on peptidyl tRNA moieties within split large mitochondrial ribosomal subunits (mtLSU). The overall domain architecture of human mtRF1a and mtRF1 is highly conserved and resembles the one of bacterial RF1. However, mtRF1a reveals the highest sequence and structural similarity to bacterial RF1 (Supplementary Fig. 1) and indeed, terminates translation when recognizing UAA or UAG within the ribosomal A site. The function of the fourth member mtRF1 remains a mystery since it was discovered in 1998. It is an open question why do human mitochondria retain mtRF1, if mtRF1a is able to terminate all thirteen mtDNA-encoded peptides. Compared to canonical RF1 and mtRF1a, mtRF1 shows insertions in the codon-recognition motifs: a PEVGLS hexapeptide instead of the PxT tripeptide motif and an insertion of two amino acids (RT) in the α5 helix (Fig. 1a). No release activity has been measured in vitro so far and also no particles of reconstituted 55 S mitochondrial ribosomes could be solved with bound mtRF1. In this work, we determine the function of mtRF1 in human cells by generating a specific knockout cell line and subsequent investigation of the consequences of loss of function in comparison to mtRF1a-ablated cells. Our results show that mtRF1 is required for mitochondrial function by ensuring proper COX1 translation termination. Although ablation of mtRF1 results in isolated COX deficiency, the activation of mtRQC prevents respiratory incompetence by partially rescuing stalled COX1-translating ribosome complexes.
Although in vitro measurements and high-resolution structures reveal mechanistic insights into the function of mtRF1a, our knowledge regarding the cellular consequences of perturbed translation termination in human mitochondria is limited and only based on short-term siRNA-mediated depletion of mtRF1a. To study the impact of loss of function of translation termination in human mitochondria, we have generated specific knockout cell lines using CRISPR/Cas9 technology. We used guide RNAs targeting exon 1 of mtRF1a and exon 2 of mtRF1, respectively; and isolated clones were confirmed by western blotting and genomic DNA sequencing (Fig. 1b, Supplementary Fig. 2a, b). In both cases premature STOP codons were identified leading to undetectable protein levels. Loss of both mitochondrial RFs affects cellular growth in high-glucose-containing media as the cell number over time is significantly reduced in comparison to the wild-type control, however, mtRF1a ablation is more severe than mtRF1 loss (Fig. 1c). Such growth defect has also been observed in other studies when interfering with mitochondrial translation. In the absence of mtRF1a, cells tend to produce more reactive oxygen species (ROS), which is in agreement with previous observations and suggest mitochondrial dysfunction. ROS production is less pronounced in mtRF1−/−, but still significantly elevated compared to wildtype. Thus, both release factors are critical for mitochondrial function and cellular growth.
In contrast to previous observations, mtRF1a-deficient cells are not able to respire as monitored by real time respirometry in intact cells (Fig. 2a). Therefore, these cells can only survive in the high-glucose containing media, in which they obtain their energy via glycolysis. mtRF1a−/− cells acidify the media relatively fast, indicating the conversion of excess pyruvate into lactate, a typical characteristic of oxidative phosphorylation (OXPHOS)-deficient cells. The oxygen consumption rate is also reduced in mtRF1-ablated cells in comparison to wildtype control, but does not reveal such a profound defect as mtRF1a−/− cells, which is in agreement with the relative growth rates. Nevertheless, these results show that both RFs are required for optimal OXPHOS capacity. To dissect how the loss of mtRF1a or mtRF1, respectively, affects the function of the different respiratory chain complexes, we monitored the amounts and the activity of complex I and IV by in gel activity measurements and in a colorimetric assay (Fig. 2b–d). While mtRF1a ablation results in an almost complete loss of complex I and IV activity, mtRF1 loss does not affect complex I, but reveals a significant reduction in complex IV activity. We further analyzed the individual complexes by Blue-Native PAGE and western blotting, confirming the combined OXPHOS deficiency in mtRF1a−/− with complex II, which is entirely nuclear (nDNA)-encoded, being unaffected (Fig. 2e). No respiratory supercomplexes are formed in the absence of mtRF1a and only the nuclear-encoded F1 part of the ATP synthase is detectable providing the explanation for the drastic respiratory incompetence. In contrast, respiratory supercomplex formation is comparable between wildtype and mtRF1−/− cells. However, the dimeric complex IV at ~400 kDa is strongly reduced in mtRF1-deficient cells as visualized by COX1 antibody on BN PAGE (Fig. 2e, lane 11) and in gel activity indicating an isolated cytochrome c oxidase (COX) deficiency in mtRF1−/−. Next, we investigated the protein steady-state levels of the mtDNA- and nDNA-encoded components of the OXPHOS complexes by western blotting (Fig. 2f, g). In agreement with reduced OXPHOS complexes, the tested mtDNA-encoded proteins are significantly reduced in mtRF1a−/− cells except COX1. In contrast, COX1 is strongly affected in mtRF1−/− while the other tested mtDNA-encoded proteins are unaltered. Similarly, nDNA-encoded structural OXPHOS components are significantly reduced in mtRF1a−/− while only marginally affected in mtRF1−/−. We also investigated the steady-state levels of MITRAC (mitochondrial translation regulation assembly intermediate of cytochrome c oxidase) components, which form an assembly platform that coordinates COX1 synthesis with its subsequent assembly into complex IV. C12ORF62 and MITRAC12 interact with nascent COX1 ribosome complexes and represent essential assembly factors mediating the first steps during COX biogenesis. Mutations in C12ORF62 or MITRAC12 lead to reduced COX1 synthesis and subsequently to isolated complex IV deficiency associated with severe neurological disorders in human patients. While MITRAC12 is not drastically affected in either of the knockouts, C12ORF62 is altered. Whereas mtRF1 loss leads to reduced C12ORF62 levels, mtRF1a deficiency results in elevated amounts (Fig. 2h, i). We further elaborated these findings by 2D PAGE and reveal an accumulation of COX1-containing MITRAC complex at ~200 kDa in mtRF1a−/− while MITRAC is strongly reduced in mtRF1−/− (Fig. 2j). Thus, COX1 is trapped in MITRAC in mtRF1a-deficient cells, as further assembly steps are blocked due to reduced levels of other complex IV constituents such as COX2. In mtRF1−/− cells MITRAC is strongly reduced as indicated by decreased levels in COX1 and C12ORF62 suggesting defects in COX1 synthesis.
We measured the synthesis of mtDNA-encoded proteins by [35S]Methionine de novo labeling and reveal that mtRF1a loss affects the translation of most mtDNA-encoded transcripts including the non-canonically terminated transcript of ND6, but not of COX1 (Fig. 3a). Contrary, the synthesis of COX1 is exclusively affected in mtRF1−/− while others are produced comparable to wild-type control (Fig. 3b). Nevertheless, COX1 is still produced in mtRF1−/− and is detectable in respiratory supercomplexes comparable to wild-type control (Fig. 2e) indicating a higher stability of the reduced newly synthesized COX1 in mtRF1-deficient cells. To prove this hypothesis we monitored the stability of COX1 by [35S]Methionine pulse-chase experiment for 24 h. Indeed, COX1 reveals a higher stability in mtRF1-ablated cells compared to wild-type control (Supplementary Fig. 3a, b). These results suggest that the limiting amounts of COX1 are sequestered by respiratory supercomplexes to enhance its stability and to ensure respiratory competence in mtRF1−/−. These findings are reminiscent to previous observations, where it has been reported that reduced levels of complex IV are preferentially assembled into respiratory supercomplexes also to ensure the assembly and stability of complex I. Thus, both release factors are required for mitochondrial translation and the opposed effects indicate that the factors cannot compensate each other. To ensure that 55 S mitochondrial ribosomes are properly formed, we monitored ribosome particles by sucrose density ultracentrifugation (Fig. 3c). As 28 S small and 39 S large mitoribosomal subunits as well as 55 S ribosomes are detectable, defects in mitochondrial ribosome biogenesis are unlikely. To confirm that the translation defects are specific due to the loss of mtRF1a or mtRF1, respectively, and not caused by an off-target effect due to the CRISPR/Cas9 approach, we ectopically expressed the respective FLAG-tagged open reading frames in the knockout cell lines (Fig. 3d–g). In both cases the translation defect is rescued indicating that the knockouts are specific and the FLAG-tagged variants are functional. Both mitochondrial RFs carry the highly conserved GGQ motif in domain 3 (Fig. 1a), which is essential to facilitate peptide hydrolysis within the ribosome in all kingdoms of life. Mutations within the GGQ motif in bacterial or eukaryotic release factors as well as in human mL62 disable RFs to terminate translation while the proteins are stably expressed and the interaction with the ribosome is maintained. We expressed mitochondrial RF variants in which we mutated the two glycine residues of the GGQ motif into alanine residues (Fig. 3d–g). Mutant variants were expressed to comparable levels as the FLAG-tagged wild-type RFs. However, mitochondrial translation could not be restored indicating that the catalytic activity of both release factors is required for their function and for mtDNA-encoded protein synthesis. Thus, our data indicate that mitochondrial RFs are indeed required for mitochondrial translation termination in vivo and mtRF1 is specifically assigned for COX1 synthesis, while mtRF1a terminates other mitochondrial translation events including ND6.
Defects in mitochondrial translation often lead to an upregulation of mitochondrial transcripts potentially as a compensatory effect as indicated in previous studies. We also investigated the steady-state levels of mitochondrial mRNAs as well as rRNAs by northern blotting in mtRF1−/− and mtRF1a−/− (Fig. 4a, b). The 12 S (MTRNR1) and 16 S rRNA (MTRNR2) remain stable in both knockouts, which is in agreement with the proper formation of mitochondrial ribosomes (Fig. 3c). However, mt-mRNAs are contrarily affected upon loss of mitochondrial RFs. While COX1-encoding mRNA (MTCO1) is significantly reduced in mtRF1-ablated cells, it remains stable in mtRF1a−/−. In contrast, transcripts encoding for COX2 or CYTB are strongly decreased in mtRF1a−/−, but are unaffected upon mtRF1 loss. Mitochondrial transcripts derive from two polycistronic transcripts. With the exception of MTND6 (mRNA encoding for ND6), all of the mt-mRNAs arise from the polycistronic transcript synthesized from the heavy strand. If the loss of mitochondrial RFs would affect mitochondrial transcription, one would expect an overall decrease in all mitochondrial transcripts. However, as we observe a selective decrease in specific transcripts in the individual knockouts, we conclude that it is more likely an issue of RNA stability rather than synthesis. To support this hypothesis, we blocked mitochondrial translation using chloramphenicol and show that MTCO1 levels can be restored in mtRF1−/− (Fig. 4c, d) indicating that the degradation of MTCO1 transcripts is dependent on translation. To ensure that MTCO1 is the only affected transcript in mtRF1−/−, we measured the steady-state level of all mt-mRNAs by NanoString analysis (Fig. 4e). In agreement with our northern blot results, MTCO1 was the only reduced transcript in mtRF1-ablated cells whereas all the other transcripts were comparable to wildtype. Thus, mtRF1 loss induces the degradation of MTCO1, which might be part of a quality control system. Nevertheless, COX1 is not completely diminished and still detectable in mitochondrial supercomplexes in the absence of mtRF1, which ensures respiratory competence in mtRF1−/−. Consequently, an alternative factor must fulfill the task of COX1 translation termination and thus compensate for the loss of mtRF1.
A potential alternative factor responsible for the release of newly synthesized COX1 if mtRF1 is missing might be another member of the mitochondrial release factor family. We measured the steady-state level of mtRF1a, mL62 and C12ORF65 in mtRF1−/− and reveal significant elevated levels of C12ORF65 in mtRF1-ablated cells suggesting that the mitochondrial ribosome-associated quality control machinery is responsible for the rescue of stalled COX1-translating ribosomes (Fig. 5a, b). Levels of mL62, which represents another system to rescue ribosomes stalled on truncated mRNAs, are comparable to wildtype. As mL62 requires an empty A site, it seems to be less likely that mL62 would rescue COX1-translating ribosomes in mtRF1−/−. mtRF1a also appears unaltered in mtRF1−/− and our results actually suggest that mtRF1a and mtRF1 cannot compensate each other. Therefore, C12ORF65, which together with MTRES1 represents the mtRQC, is a promising candidate to facilitate the release of COX1 from the ribosome if mtRF1 is missing. We tested this hypothesis by depleting C12ORF65 in mtRF1−/− and monitored the level of newly synthesized COX1 upon [35S]Methionine metabolic labeling (Fig. 5c, d). C12ORF65 is efficiently downregulated and also leads to a decrease in MTRES1 indicating an interdependence of these factors. In line with our assumption, COX1 synthesis is significantly more decreased upon C12ORF65 depletion in mtRF1−/− than in non-targeting siRNA-treated knockout cells. Thus, loss of mtRF1 induces mtRQC to compensate for deficient termination events during COX1 translation in mtRF1−/−.
The role of mtRF1 during translation termination in human mitochondria was a long standing open question since it was discovered in 1998. Here, we show that mtRF1 is required for COX1 translation termination and thus for cytochrome c oxidase function (Fig. 6). MITRAC is the first assembly platform for newly synthesized COX1, where nascent COX1 interacts with the early MITRAC constituents C12ORF62 and MITRAC12. Both factors are associated with severe mitochondrial diseases with isolated COX deficiency. While patients with mutation in C12ORF62 display fatal neonatal lactic acidosis, mutations in MITRAC12 are associated with neuropathy and exercise intolerance. As the loss of mtRF1 leads specifically to a reduction in COX1 and subsequently to decreased levels of C12ORF62, mtRF1 is a potential candidate when screening patients with isolated COX deficiency. Our data also show the physiological importance of mtRQC during mitochondrial translation termination. It has been recently demonstrated that C12ORF65 is part of the mtRQC and required as a rescue factor for stalled ribosome complexes under conditions of aminoacyl tRNA starvation. This population of stalled ribosomes with intact mRNA are not a preferred substrate for mL62, which requires an empty A site to protrude its C-terminal tail into the mRNA channel, similarly to its bacterial counterpart ArfB. This makes it unlikely that mL62 rescues COX1-translating ribosomes in mtRF1−/− as the A-site would be still occupied with mRNA. The mtRQC would first allow the dissociation of these stalled complexes into the large (mtLSU) and the small mitochondrial ribosomal subunit (mtSSU) followed by the binding of C12ORF65 and MTRES1 to the split mtLSU with the peptidyl tRNA in the P-site (Fig. 6). Finally, C12ORF65 would facilitate the hydrolysis of nascent COX1 from the tRNA. Thus, the activation of mtRQC in mtRF1−/− partially compensate for the abolished COX1 translation termination by mtRF1. This enables mitochondria to produce a certain fraction of COX1, which assembles as part of complex IV into the stable supercomplexes capable of respiration, although to a reduced level compared to wildtype. In agreement with previous studies is that the reduced COX1 tends to be assembled within the supercomplexes and not within the free complex IV, which likely enhances the stability of the reduced newly synthesized COX1 in mtRF1-deficient cells. The reduction of mtRQC by siRNA-mediated depletion of C12ORF65 in mtRF1−/− shows further reduction in COX1 translation indicating the importance of mtRQC for mitochondrial function. This central importance of mtRQC is also demonstrated by a growing group of patients with mutations in C12orf65 developing Leigh syndrome. It is currently unknown which factor acts upstream of C12ORF65-MTRES1, allowing the dissociation of the ribosome into the subunits. Besides the canonical recycling system composed of mtRRF and mtEFG2, the alternative recycling factor GTPBP6 is a potential candidate to be part of the mitochondrial ribosome rescue system. However, both recycling pathways do not prefer ribosomes with a peptidyl tRNA in the P-site. Thus, a so far unidentified factor might be part of the mtRQC, facilitating the dissociation of the ribosome prior to binding of C12ORF65 to the mtLSU. The decrease in MTCO1 suggests a feedback mechanism that allows the specific degradation of this transcript in mtRF1−/−, potentially to prevent an overload of the mtRQC (Fig. 6), which already responds with a higher expression of C12ORF65 to cope with these stalling events. Consequently, mtRQC seems to involve not only the rescue of stalled ribosomes but also the degradation of the respective mRNA to avoid mtRQC stress and to minimize proteotoxic burden. A similar scenario would apply if mtRF1a is missing and e.g. MTCO2 is degraded to avoid too many stalling events of COX2-translating ribosomes. Contrarily to mtRF1−/−, COX1 remains stable in mtRF1a−/−, but becomes stalled in the MITRAC complex as COX2 and COX3 translation are diminished (Fig. 6). Thus, both mitochondrial RFs exhibit substrate specificity and cannot compensate each other. COX1 is a rather unusual case as it terminates in AGA (Supplementary Fig. 4). However, a −1 ribosomal frameshift would allow the termination in the conventional STOP codon UAG. It is still under debate whether a −1 ribosomal frameshift is really occurring during COX1 and also during ND6 translation termination or whether a specialized factor might be responsible in reading AGA and AGG codons. In the past it has been suggested that mtRF1 might be able to recognize these unconventional STOP signals, although experimental evidence is completely missing. Nevertheless, bioinformatic studies using homology modeling and molecular dynamics simulation suggest that the non-canonical mtRF1 with its insertion in the α5 helix and the PEVGLS motif co-evolutionary adapted to the changes within the mitoribosome and that it has a preference for UAA and UAG codons and neither for AGA or AGG or an empty A site. This actually supports the −1 ribosomal frameshift hypothesis. Additionally, COX1 terminates in UAA and not in AGA in other species including mice, rat or bovine, although they also possess mtRF1 and mtRF1a (Supplementary Fig. 4, 5). Thus, if mtRF1 is a specialized release factor for COX1 termination in other species as well and if one considers the bioinformatic preference for UAA and UAG STOP codon by mtRF1, then this would be in favor with the −1 ribosomal frameshift theory in human mitochondria that allows termination in UAG. Along this line, we observed a decrease in ND6 in mtRF1a−/−, but no in mtRF1−/− suggesting that mtRF1a is responsible for ND6 termination (Fig. 3a, b). However, mtRF1a reads specifically UAA and UAG codons, but does not exhibit release activity on AGG codon, which also supports the hypothesis that a −1 ribosomal frameshift allows the conventional termination of ND6 in UAG recognized by mtRF1a. Taken together, we have solved the mystery of the function of mtRF1 in human mitochondria and show that mtRF1 is specifically responsible for the termination of COX1 translation, which likely requires a −1 ribosomal frameshift by the human mitochondrial ribosome.
An extended table of plasmids, oligonucleotides, antibodies and other materials used is provided in Supplementary Table 1 and 2.
HEK293 (Human Embryonic Kidney 293-Flp-In T-Rex, Thermo Fisher Scientific) cells were cultured in DMEM (Dulbecco’s modified Eagle’s medium) supplemented with 10% [v/v] FCS (Fetal Calf Serum), 2 mM L-glutamine, 1 mM sodium pyruvate and 50 µg/ml uridine at 37 °C under 5% CO2 humidified atmosphere. Cells were regularly monitored for the absence of Mycoplasma by GATC Biotech. HEK293 mtRF1 and mtRF1a knockout cell lines were generated using Alt-R CRISPR/Cas9 technology (Integrated DNA Technologies, IDT) according to the manufacturer’s instructions. In brief, cells were co-transfected with crRNA-tracrRNA duplex and Cas9 nuclease. The crRNA was designed to target the first or second exon of either the mtRF1a or mtRF1 gene, respectively. Clones were screened by immunoblotting and verified by TOPO cloning and subsequent sequencing. Usage of the TOPOTM-TA CloningTM Kit (Thermo Fisher Scientific) allows simple analysis of gDNA from respective clones. TOPO cloning is based on ligation of the amplified PCR product of the gDNA sequence targeted by the CRISPR guide RNA into a pCR4-TOPO TA vector. After transformation into OneShotTM competent E.coli cells, clones were selected on ampicilin LB-Agar plates. Picking a statistical relevant number of clones (≥20), their plasmid DNA were sequenced using M13 forward and reverse primers, allowing analysis of occurring INDELs in the genome caused by CRISPR/Cas9 technology. Stable inducible cell lines expressing C-terminal FLAG-tagged versions of mtRF1 or mtRF1a were generated following established protocols. Briefly, HEK293 cell lines were transfected with pOG44 and pcDNA5/FRT/TO plasmids harboring respective FLAG constructs using Lipofectamine3000 (Invitrogen) as transfection reagent according to the manufacturer’s instructions. Cells were selected using 100 µg/ml hygromycin B. Transient siRNA-mediated knockdown was performed by transfecting HEK293 cells with 33 nM siRNA oligonucleotides (Eurogentec) targeting the transcript of interest (see Supplementary Table 1) or non-targeting siRNA as control by using Lipofectamine RNAiMax (Invitrogen) as transfection reagent. Cells were incubated for 72 h at 37 °C under 5% CO2 humidified atmosphere prior to further investigation.
Cells (106) were stained with 5 µM MitoSox Red (Invitrogen) according to the manufacturer’s instructions to detect ROS. For flow cytometry analysis, 10,000 gated events were recorded on a BD FACS Canto II (Becton Dickinson) and analyzed using FACS-Diva software.
Oxygen consumption rates (OCR) were measured using a XF96 Extracellular Flux Analyzer (Seahorse Bioscience). Cells (4 × 104 per well) were directly seeded in assay buffer into a 96-well sample plate, spun down and incubated for 1 h in a non-CO2 incubator at 37 °C before basal respiration was measured. Subsequent automated addition of 3 µM oligomycin, 1.5 µM CCCP and 1 µM antimycin A plus 1 µM rotenone was used to monitor maximal respiration.
Lysis of whole cells was carried out in nonionic lysis buffer (50 mM Tris-HCl pH 7.4, 130 mM NaCl, 2 mM MgCl2, 1% NP-40, 1 mM PMSF and 1x Protease Inhibitor (PI) Cocktail (Roche)). For isolation of mitochondria, cultured cells were harvested and resuspended in homogenization buffer (300 mM trehalose, 10 mM KCl, 10 mM HEPES-KOH pH 7.4) with 1 mM PMSF and 0.2% BSA and homogenized using a Homogenplus Homogenizer Size S (Schuett-Biotec). The crude cell homogenate was separated using differential centrifugation steps: 400 × g, 10 min, 4 °C to remove cell debris, 11,000 × g for 10 min, 4 °C to pellet mitochondria. Mitochondria were resuspended in homogenization buffer and were used immediately or stored at −80 °C. Cell lysates or mitochondria samples were separated on 10–18% Tris-Tricine gels and transferred onto AmershamTM ProtranTM 0.2 µM nitrocellulose membranes (NC, GE Healthcare). For immunodetection, primary antibodies were incubated overnight (4 °C) as indicated (see Supplementary Table 1), secondary antibodies were incubated for 2 h at room temperature and visualized on X-ray films using enhanced chemiluminescence detection kit (GE Healthcare).
Mitoplasts were purified by incubating fresh isolated mitochondria in 0.1% digitonin for 30 min on ice and 0.5 µg Proteinase K per 100 µg mitochondria for 15 min on ice. Proteinase K was blocked by addition of 2 mM PMSF followed by four washing steps. Mitoplasts (500 µg) were lysed (3% sucrose, 100 mM NH4Cl, 15 mM MgCl2, 20 mM Tris-HCl pH 7.5, 1% Digitonin, 1x PI-Mix, 0.08 U/µl RiboLock RNase Inhibitor) and separated by sucrose density gradient centrifugation (5–30% sucrose [w/v] in 100 mM NH4Cl, 15 mM MgCl2, 20 mM Tris-HCl pH 7.5, 1x PI-Mix) at 79,000 × g for 15 h, 4 °C using a SW41Ti rotor (Beckman Coulter). A BioComp fractionator was used to collect fractions 1–16, which were then ethanol precipitated and analyzed via western blotting.
To investigate native protein complexes, mitochondria were solubilized (1% digitonin, 10 mM Tris-HCl pH 7.4, 0.1 mM EDTA, 50 mM NaCl, 10% Glycerol [v/v], 1 mM PMSF) at a concentration of 1 µg/µl for 20 min, 4 °C. Lysates were cleared from insoluble materials by centrifugation for 15 min at 21,000 × g, 4 °C prior to addition of BN Loading Dye (5% Coomassie Brilliant Blue G250 (w/v), 500 mM 6-aminocaproic acid, 100 mM Bis-Tris-HCl pH 7.0). Samples were separated by electrophoresis using 4–14% or 2.5–10% polyacrylamide gradient gels. Proteins were either blotted on PVDF membranes for 1 dimensional analysis via western blotting or further separated into the 2nd dimension via 10–18% Tris-Tricine gels. To monitor in gel activities, the gel was incubated either in complex I (1 mg/ml nitrotetrazoliumbluechlorid and 1 mg/ml NADH in 5 mM Tris-HCl pH 7.4) or complex IV (0.5 mg/ml diaminobenzidine, 20 µg/ml catalase, 1 µg/ml cytochrome c and 75 mg/ml sucrose in 50 mM KPi pH 7.4) solution.
To determine complex I activity, the activity assay kit by abcam was used according to the manufacturer’s instructions. Briefly, 200 µg of cell lysate was loaded per well and oxidation of NADH by complex I was colorimetrically detected as increase in absorbance at OD = 450 nm.
De novo labeling of newly synthesized mitochondrial proteins was performed as followed: Cultured cells were starved in FCS- and methionine-free media, cytosolic translation was inhibited by using 100 µg/ml emetine and incubated in the presence of 200 µCi/ml [35S]Methionine for 1 h in fully supplemented but methionine-free DMEM media. For pulse-chase labeling, cytosolic translation was inhibited using 100 µg/ml anisomycin instead of emetine and after 1 h pulse-labeling, media was changed to normal growth media and cells were harvested at indicated chase-time points. Cell lysates were subjected to SDS-PAGE followed by western blotting. Radioactive labeled mitochondrial translation products were detected using Phosphor screens and Amersham Typhoon imaging system (GE Healthcare).
Total RNA from cultured cells was isolated using TRIzol Reagent (Invitrogen) according to the manufacturer’s instructions. RNA (2 µg) was separated on a denaturing formaldehyde/formamide 1.2% agarose gel and transferred and UV-crosslinked onto Amersham HybondTM-N membrane (GE Healthcare). RNA was visualized using [32P]-radiolabeled probes targeting mitochondrial RNAs as indicated (see Supplementary Table 1).
The experiment was performed following established protocols. Briefly, equal amounts of mitochondria (100 µg) were isolated from the respective cell lines and solubilized into lysis buffer (50 mM Tris-HCl pH 7.4, 150 mM NaCl, 10% Glycerol, 10 mM MgCl2, 1% Digitonin, 1 mM PMSF, 1x PI-Mix (Roche) and 0.08 U/µl RiboLock RNase inhibitor (Thermo Scientific)). RNA isolation from the lysates was performed according to the Ambion/Life Technologies protocol using TRIzol reagent and RNA Clean and Concentrator kit (Zymo Research). Isolated RNA pool was hybridized with TagSet master mix (nCounter ElementsTM XT Reagents, nanoString) and probes targeting individual mitochondrial transcripts or cytosolic 18 S rRNA/5 S rRNA (Supplementary Table 2). Subsequently, the samples were analyzed in a nCounter MAX system (nanoString) following the nanoString Technologies instructions. Collected data were evaluated with nSolver software (nanoString). To assess the abundance of the transcripts of interest, the raw data were normalized to the abundance of cytosolic transcripts (18 S rRNA and 5 S rRNA).
All experiments were carried out at least in biological triplicate and data is presented as means with standard error of the mean (SEM). Protein or RNA signals from western and northern blots were quantified using ImageJ (https://imagej.nih.gov/ij) or ImageQuant TL (GE Healthcare) and data was statistically analyzed by two-sample (equal variances) one-tailed Student’s t-test. Statistical significance was defined by * for p ≤ 0.05, ** for p ≤ 0.01 and *** for p ≤ 0.001. Exact p-values are provided with the source data.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
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PMC9613889 | Jens Bauer,Natalie Köhler,Yacine Maringer,Philip Bucher,Tatjana Bilich,Melissa Zwick,Severin Dicks,Annika Nelde,Marissa Dubbelaar,Jonas Scheid,Marcel Wacker,Jonas S. Heitmann,Sarah Schroeder,Jonas Rieth,Monika Denk,Marion Richter,Reinhild Klein,Irina Bonzheim,Julia Luibrand,Ursula Holzer,Martin Ebinger,Ines B. Brecht,Michael Bitzer,Melanie Boerries,Judith Feucht,Helmut R. Salih,Hans-Georg Rammensee,Stephan Hailfinger,Juliane S. Walz | The oncogenic fusion protein DNAJB1-PRKACA can be specifically targeted by peptide-based immunotherapy in fibrolamellar hepatocellular carcinoma | 27-10-2022 | Cancer immunotherapy,Liver cancer | The DNAJB1-PRKACA fusion transcript is the oncogenic driver in fibrolamellar hepatocellular carcinoma, a lethal disease lacking specific therapies. This study reports on the identification, characterization, and immunotherapeutic application of HLA-presented neoantigens specific for the DNAJB1-PRKACA fusion transcript in fibrolamellar hepatocellular carcinoma. DNAJB1-PRKACA-derived HLA class I and HLA class II ligands induce multifunctional cytotoxic CD8+ and T-helper 1 CD4+ T cells, and their cellular processing and presentation in DNAJB1-PRKACA expressing tumor cells is demonstrated by mass spectrometry-based immunopeptidome analysis. Single-cell RNA sequencing further identifies multiple T cell receptors from DNAJB1-PRKACA-specific T cells. Vaccination of a fibrolamellar hepatocellular carcinoma patient, suffering from recurrent short interval disease relapses, with DNAJB1-PRKACA-derived peptides under continued Poly (ADP-ribose) polymerase inhibitor therapy induces multifunctional CD4+ T cells, with an activated T-helper 1 phenotype and high T cell receptor clonality. Vaccine-induced DNAJB1-PRKACA-specific T cell responses persist over time and, in contrast to various previous treatments, are accompanied by durable relapse free survival of the patient for more than 21 months post vaccination. Our preclinical and clinical findings identify the DNAJB1-PRKACA protein as source for immunogenic neoepitopes and corresponding T cell receptors and provide efficacy in a single-patient study of T cell-based immunotherapy specifically targeting this oncogenic fusion. | The oncogenic fusion protein DNAJB1-PRKACA can be specifically targeted by peptide-based immunotherapy in fibrolamellar hepatocellular carcinoma
The DNAJB1-PRKACA fusion transcript is the oncogenic driver in fibrolamellar hepatocellular carcinoma, a lethal disease lacking specific therapies. This study reports on the identification, characterization, and immunotherapeutic application of HLA-presented neoantigens specific for the DNAJB1-PRKACA fusion transcript in fibrolamellar hepatocellular carcinoma. DNAJB1-PRKACA-derived HLA class I and HLA class II ligands induce multifunctional cytotoxic CD8+ and T-helper 1 CD4+ T cells, and their cellular processing and presentation in DNAJB1-PRKACA expressing tumor cells is demonstrated by mass spectrometry-based immunopeptidome analysis. Single-cell RNA sequencing further identifies multiple T cell receptors from DNAJB1-PRKACA-specific T cells. Vaccination of a fibrolamellar hepatocellular carcinoma patient, suffering from recurrent short interval disease relapses, with DNAJB1-PRKACA-derived peptides under continued Poly (ADP-ribose) polymerase inhibitor therapy induces multifunctional CD4+ T cells, with an activated T-helper 1 phenotype and high T cell receptor clonality. Vaccine-induced DNAJB1-PRKACA-specific T cell responses persist over time and, in contrast to various previous treatments, are accompanied by durable relapse free survival of the patient for more than 21 months post vaccination. Our preclinical and clinical findings identify the DNAJB1-PRKACA protein as source for immunogenic neoepitopes and corresponding T cell receptors and provide efficacy in a single-patient study of T cell-based immunotherapy specifically targeting this oncogenic fusion.
T cell-based immunotherapies, comprising immune checkpoint inhibitors (ICIs), CAR-T cells, and bispecific T cell engager antibodies achieved a breakthrough in the treatment of malignant disease, adoptive T cell transfer and vaccination strategies hold further promise. However, these therapies, which rely on the rejection of cancer cells through recognition of tumor antigens and T cell-mediated cytotoxicity are still only available and effective in small subsets of cancer patients and single tumor entities. One main problem for the development of antigen-specific immunotherapies is the lack of suitable target structures that show natural, highly frequent, and tumor-exclusive presentation on the cell surface of tumor cells and are recognized by the immune system. Tumor antigens are represented by HLA-independent surface molecules or by HLA class I- and HLA class II-presented T cell epitopes, originating from intracellular proteins. In terms of the latter, neoepitopes arising from tumor-specific mutations have been identified in recent years as the main specificity of anti-cancer T cell responses induced by ICIs, and were in turn suggested as prime candidates for T cell-based immunotherapy approaches. In line, response to ICIs correlates with high tumor somatic mutational burden, and neoepitope-based immunotherapies have been applied in individual tumor patients. However, patient/tumor-specificity and intratumoral heterogeneity of somatic mutations, as well as the limited number of somatic mutations that are ultimately translated, processed, and presented as HLA-restricted neoepitopes on the tumor cells restrict the broad applicability of these antigens in particular in low-mutational burden cancer entities. Recently, fusion transcripts, whose products often represent clonal oncogenic drivers, were identified as the source of highly immunogenic neoepitopes, and T cell responses against such fusion protein-derived neoepitopes were detected in patients receiving ICIs and correlated with treatment response. The DNAJB1-PRKACA fusion transcript links exon 1 of the DnaJ homolog subfamily B member 1 gene (DNAJB1) to exon 2–10 of the cAMP-dependent protein kinase catalytic subunit alpha gene (PRKACA). In FL-HCC, the DNAJB1-PRKACA fusion transcript is detectable in 100% of patients and has been identified as the oncogenic driver in tumor pathogenesis indicating expression of the fusion transcript in all tumor cells. FL-HCC is a devastating tumor disease with a 5-year survival of only 45%, which typically affects children and young adults with no history of primary liver disease. The frequency of FL-HCC diagnosis is continuously increasing to ∼5% of all liver cancers today. To date, surgical resection is the only effective therapy if the cancer is diagnosed before the occurrence of metastases, and long-term survival is jeopardized by tumor recurrence calling for the development of specific treatment options for FL-HCC patients. The oncogenic fusion protein DNAJB1-PRKACA represents an attractive target for the development of novel therapies for this devastating tumor disease. Moreover, the recent identification of other cancer entities that express the DNAJB1-PRKACA fusion transcript gives the prospect that targeting the fusion protein might improve treatment options in multiple cancer entities. Here, we show that the DNAJB1-PRKACA fusion transcript is a prime source for broadly applicable neoepitopes and provide the first evidence for their efficacy in immunotherapy approaches in an FL-HCC patient.
To identify potential DNAJB1-PRKACA fusion gene-specific HLA ligands an in silico prediction was conducted based on the DNAJB1-PRKACA protein sequence (NCBI accession 4WB7_A). The in silico prediction workflow using the algorithm NetMHCIIpan identified nine unique binding cores of nine amino acid (AA) lengths for a total of 1290 different HLA class II alleles within the 24 AA fusion region of the DNAJB1-PRKACA protein (Fig. 1a, b). 83.5% of these alleles represent HLA-DP combinations, 11.6% and 5.0% are HLA-DQ or HLA-DR combinations, respectively. Focusing on the population frequencies of the top four alleles covered by the fusion region (HLA-DPA1*02:02-DPB1*05:01, HLA-DPA1*01:03-DPB1*05:01, HLA-DPA1*02:01-DPB1*05:01, and HLA-DPA1*01:03-DPB1*09:01) in a publicly available HLA-DP, -DQ, and -DR allele typed population (Japan pop 17; http://www.allelefrequencies.net), 41.4%, 39.1%, 17.9%, and 12.5% of the donors were calculated to carry the respective allotype, suggesting very broad applicability of this fusion neoepitope based on its’ predicted promiscuous HLA binding. The core sequence RYGEEVKEF, located directly in the middle of the fusion transcript (5 AAs on exon 1 (DNAJB1), 4 AAs on exon 2 (PRKACA)) is predicted to bind to the majority of different HLA class II alleles (60.52% of possible allele/binding core combinations). For HLA class I, 13 DNAJB1-PRKACA-derived HLA ligands were identified for the 20 most frequent HLA class I allotypes of the European population, using a prediction workflow combining the algorithms SYFPEITHI and NetMHCpan (Fig. 1c, Table 1). These 13 HLA class I allotypes within the 22 AA peptide KREIFDRYGEEVKEFLAKAKED (PII-1), spanning the fusion region of the DNAJB1-PRKACA protein, cover 96.6% and 93.8% of the European and world population with at least one HLA allotype, respectively (Fig. 1d, Supplementary Fig. S1a). Of note, the HLA class II-binding core RYGEEVKEF was also predicted as HLA class I ligand binding the alleles HLA-A*24:02, -C*04:01, -C*06:02, and -C*07:02. Cellular presentation of DNAJB1-PRKACA-derived HLA-presented peptides was shown by liquid chromatography–coupled tandem mass spectrometry (LC–MS/MS) of differentiated and maturated monocyte-derived dendritic cells (moDCs; Supplementary Fig. S1b) from healthy volunteers (HV) loaded with the isotope-labeled 22 AA peptide PII-1. The mass spectrometric (MS) identified fragment ion spectra of the experimentally eluted PII-1 were validated using the synthetic peptide (Supplementary Fig. S1c). PII-1 and 12 shorter length variants were identified by MS and predicted to bind to the HLA-DP allele DPA1*01:03-DPB1*05:01 of the respective HV6 with the best NetMHCIIpan binding rank of 0.74 for the binding core RYGEEVKEF (Fig. 1b, Table 2, Supplementary Table 1). De novo priming of CD4+ T cells from HVs with PII-1-loaded mature moDCs induced multifunctional PII-1-specific CD4+ T cells, which showed expression of CD107a and CD154 as well as production of interleukin-2 (IL-2), interferon-γ (IFN-γ), and tumor necrosis factor (TNF) upon PII-1 stimulation (Fig. 1e). Refolding of the DNAJB1-PRKACA protein fusion-derived ligands RYGEEVKEF (PA*24, SYFPEITHI score of 74.19% and NetMHCpan rank of 0.018), and EIFDRYGEEV (PA*68/A*02, A*68:02 NetMHCpan rank of 0.106) was conducted to an HLA-A*24:02-PA*24 monomer and HLA-A*68:02-PA*68/A*02 monomer, respectively. These monomers were used to build artificial antigen-presenting cells (aAPCs) for aAPC-based priming of CD8+ T cells of HVs to validate their immunogenicity. aAPC-based priming induced PA*24-specific and PA*68/A*02-specific CD8+ T cells with frequencies of up to 15.7% (median 4.1%) and 1.1% (median 0.7%) peptide-specific T cells, respectively (Fig. 1f, g, Supplementary Fig. S1d, e, Table 1). Flow cytometry-based functional characterization of PA*24-specific and PA*68/A*02-specific CD8+ T cells showed a polyfunctional phenotype reflected by IFN-γ, TNF, and CD107a production/expression (Fig. 1h, i).
We further conducted an in-depth characterization of PA*24-specific CD8+ T cells using single-cell RNA-sequencing analysis of flow cytometry-based bulk sorted PA*24-specific CD8+ T cells of HV 1 and HV2 which showed a high expression of cytotoxicity markers comprising amongst others GNLY, GZMA, GZMB, GZMK, PRF1, and NKG7, paired with a low expression of the exhaustion marker PDCD1 (Fig. 2a). PA*24-specific CD8+ T cells specifically lysed PA*24-loaded autologous CD8− cells in vitro with up to 82.4% lysis of target cells in comparison to unspecific effector cells at various effector to target ratios (Fig. 2b). Single-cell T cell receptor (TCR) sequencing of the PA*24-specific CD8+ T cell bulks from the two HVs showed high clonality of one dominant TCR clone for HV2 and two clones for HV1 (Supplementary Fig. S2a, b and Supplementary Table 2). There was no overlap of the V(D)J genes of the three TCR clones, but the complementary determining region (CDR) 3-β sequences of the main clone from HV1 and HV2 shared a high sequence identity and similarity of 69.2% and 76.9%, respectively (Fig. 2c, Supplementary Fig. S2c). The CDR3-α sequence of the main TCR clone of HV1 showed, in terms of physiochemical properties of the AA sequences, opposing characteristics regarding chemical groups in comparison to the target peptide PA*24, which could not be observed in the CDR3-α motif cluster of a negative control dataset of published TCR sequences (Supplementary Fig. S2d).
Three HLA class I-expressing hepatocellular carcinoma (HCC) cell lines (HLE, SMMC-7721, and HepG2; Supplementary Table 3) were transduced with an expression construct that allowed the Doxycycline (Dox)-dependent expression of the DNAJB1-PRKACA fusion protein (Fig. 3a, b, Supplementary Fig. S3a). Subsequent MS-based immunopeptidome analysis revealed up to 3688 different HLA class I ligands (mean 2787; Fig. 3c). Twenty unique HLA class I ligands derived from the DNAJB1-PRKACA fusion protein were identified in the three HCC cell lines with two ligands spanning the fusion region (Fig. 3d, Supplementary Fig. S3b). The two cellular processed and presented DNAJB1-PRKACA-derived neoepitopes EIFDRYGEEV (PA*68/A*02) and IFDRYGEEV (PC*04/C*05) identified on SMMC-7721 and HepG2 were predicted to bind to the HLA allotypes HLA-A*68:02 and HLA-C*04:01, respectively, and were validated by comparative spectra analysis using synthetic peptides (Figs. 1c, 3e, Table 1). Of note, the C-terminal AA of PA*68/A*02 and PC*04/C*05 spanning the fusion region leads to altered HLA presentation of the ligands due to a change in the HLA binding motif anchor position. The corresponding wild-type (WT) peptides would display a glycine at the C-terminal end (EIFDRYGEEG (PA*68/A*02-WT) and IFDRYGEEG (PC*04/C*05-WT)), which do not allow HLA class I presentation of the two WT-peptides on the respective as well as on any other HLA allotype according to netMHC-4.0 and SYFPEITHI predictions. To investigate the processing and presentation of DNAJB1-PRKACA-derived HLA class II peptides, mature moDCs of three HVs were incubated with lysate of the HLE cell line after activation of DNAJB1-PRKACA fusion gene expression (Supplementary Fig. S3c). MS-based immunopeptidome analysis of these moDCs revealed up to 8293 different HLA class II peptides (mean 5956; Fig. 3f). Thirteen unique peptides derived from the DNAJB1-PRKACA fusion protein were identified with one peptide EVKEFLAKAKEDFLKK (PII-2) spanning the fusion region (Figs. 1b and 3g). The DNAJB1-PRKACA-derived neoepitope PII-2 was predicted to bind to the HLA allele DRB1*13:02 of the respective HV3 (Fig.1b, Supplementary Table 1). The experimental fragment ion spectrum of the PII-2 peptide was validated with an isotope-labeled synthetic peptide (Fig. 3h). No HLA class I or HLA class II ligands derived either from the two proteins DNAJB1 and PRKACA or the fusion protein were identified in the respective negative controls.
A personalized DNAJB1-PRKACA-derived peptide vaccine was designed for a young patient with histologically confirmed FL-HCC (FL-HCC01), who suffered from multiple tumor relapses after receiving an early liver transplant (LTx), due to unresectable FL-HCC not responsive to chemotherapy (Fig. 4a and Supplementary Fig. S4a). The mTOR inhibitor everolimus was applied for post-transplant immunosuppression. Poly (ADP-ribose) polymerase (PARP) inhibition was initiated based on detectable alterations in the DNA damage response (DDR) pathway (ATM and CHEK2, germline variant, BRCA2 and BAP1 somatic deletion), but without achieving a durable remission. Recurrent tumor manifestations were resected or treated with radiotherapy. Based on the prevalence of the DNAJB1-PRKACA fusion gene (confirmed by Sanger sequencing) a vaccine consisting of three short allotype-matching HLA class I ligands (EIFDRYGEEV (PA*68/A*02), EEVKEFLAKA (PB*44), and IFDRYGEEV (PC*04/C*05)), together with the long peptide KREIFDRYGEEVKEFLAKAKED (PII-1) predicted to bind to the HLA-DP allotype DPA1*01:03-DPB1*06:01 of FL-HCC01 was composed (Supplementary Fig. S4b and Supplementary Tables 1 and 4). The vaccine was applied twice within a 6-week interval and adjuvanted with the toll-like receptor (TLR) 1/2 agonist XS15 (Pam3Cys-GDPKHPKSF) emulsified in MontanideTM ISA51 VG to endorse activation and maturation of antigen-presenting cells and prevent vaccine peptides from immediate degradation, thereby enabling induction of an effective and potent T cell response. The patient mounted a profound T cell response targeting the PII-1 peptide documented 6 weeks after the second vaccination, as analyzed by IFN-γ enzyme-linked immunospot (ELISPOT) assay after in vitro stimulation with the vaccine cocktail peptides (1 mean spot count prior to vaccination versus 872 spot counts post second vaccination; Fig. 4b, c and Supplementary Fig. S4c). In addition, a weak induction of an IFN-γ T cell response was detected after PB*44 stimulation in the ELISPOT assay (0 mean spot count prior to vaccination versus 56 spot counts post second vaccination). Flow cytometry-based characterization of the PB*44- and PII-1-directed T cell responses revealed T helper 1 (Th1) phenotype CD4+ T cells with specific expression of IFN-γ and TNF, whereas no CD8+ T cell-driven response against the PII-1 or the PB*44 was observed, suggesting the PB*44-induced IFN-γ T cell response in the ELISPOT assay as cross-reactivity to the shared binding core of the long PII-1 (Fig. 4d, e). No T cell responses targeting the PA*68/A*02 and PC*04/C*05 were observed. Longitudinal IFN-γ ELISPOT assays showed the persistence of PII-1-specific T cells over time with a constant intensity of response (770 mean spot count) 18 months after the second vaccination (Fig. 4c and Supplementary Fig. S4c). In contrast to the disease prior to vaccination, where the patient regularly suffered from relapses (time to next relapse 2.5–5.0 months (median 3.0 months)), up to now no disease relapse was observed (21 months after the second vaccination) pointing to the clinical efficacy of vaccine-induced DNAJB1-PRKACA-specific T cell responses. In order to identify the vaccine-induced T cell clones expanding in FL-HCC01 post-vaccination, combined single-cell RNA and single-cell TCR sequencing utilizing 10x Genomics single-cell immune profiling was performed. Unsupervised clustering of single-cell RNA sequencing data from vaccine-induced PII-1-reactive CD4+ T cells after in vitro expansion, followed by flow-cytometry-based bulk sort of CD4+ T cells, showed three defined T cell clusters: (I) cytokine and chemokine-expressing activated T cells defined by high expression of IFNG, TNF, GZMB (encoding granzyme B), CCL3, and CCL4 (Fig. 5a–c), (II) T cells exhibiting an exhausted or late effector profile with an expression of PDCD1, LAG3, HAVCR2, and CTLA4 (Fig. 5a, b and Supplementary Fig. S5a), and (III) naïve resting T cells defined by expression of SELL (encoding CD62L), CCR7, and TCF7 (Fig. 5a, b and Supplementary Fig. S5b). In agreement, functional enrichment for the hallmarks of cancer gene sets “TNF signaling via NF-κB” and “inflammatory response” showed an increased gene expression in the cytokine and chemokine expressing activated T cell cluster (Fig. 5d), indicating that this cluster comprised the CD4+ T cells reactive to the PII-1. As expected, VDJ sequencing revealed a high TCR clonality in the activated T cell cluster with 74.2% of cells assigned to large clones (clonality ≥ 4), compared to the naïve resting (1.3%) or exhausted T cell clusters (31.4%) (Fig. 5e and Supplementary Fig. S5c). In total, 10 defined TCR clones were identified, of which eight were predominantly assigned to the activated T cell cluster (Fig. 5f, Supplementary Table 5). The high similarity of physiochemical properties and hydrophilicity of the AA sequences was observed for the CDR3-α/-β sequences of the ten expanded TCR clones, especially for positions four and five of the CDR3-α sequences with opposing characteristics regarding chemical groups in comparison to the target peptide core-binding motif (Fig. 5g, Supplementary Fig. S5d and Supplementary Table 5). By clustering the CDR3-α/-β sequences distinct motif plots were generated for the 10 expanded TCR clones in comparison to the naïve unexpanded TCRs, which showed a specific preference for basic AAs on position five of the CDR3-α sequence which could not be observed in the unexpanded clones (no significant Pearson correlation coefficient to the negative cluster). Specific differences (no significant motif correlation) between the expanded clones and the negative dataset were also observed for position six of the CDR3-α cluster and position six, seven, and eight of the CDR3-β motif (Fig. 5h).
T cell recognition of HLA-presented antigens plays a central role in the immune surveillance of malignant disease. Numerous immunotherapeutic approaches aim to utilize respective tumor antigens to therapeutically induce an anti-tumor T cell response. This study reports on the identification and characterization of immunogenic neoantigens derived from the DNAJB1-PRKACA fusion transcript, which is the oncogenic driver in all patients suffering from FL-HCC. Neoepitopes derived from oncogenic gene fusions have been suggested as a superior category of tumor antigens. This has been attributed to (I) the clonal expression of oncogenic driver gene fusions, (II) the higher degree of sequence alteration compared to somatic point mutations, resulting in increased immunogenicity, and (III) limited down-regulated-based immune escape. In contrast to other fusion transcripts, the DNAJB1-PRKACA fusion generates a defined and unique protein sequence that allows an off-the-shelf application of DNAJB1-PRKACA-derived neoepitopes in cancer immunotherapy. We validated the DNAJB1-PRKACA fusion protein as a source of immunogenic HLA class I and HLA class II-binding antigens inducing both CD8+ and CD4+ T cell responses, which is required for effective anti-cancer immunity. Furthermore, we proved the cellular processing and HLA-restricted presentation of DNAJB1-PRKACA neoepitopes, which is an indispensable prerequisite for therapeutically used tumor antigens in particular regarding the distorted correlation between gene expression and HLA-restricted antigen presentation, with only a small fraction of alterations on DNA level resulting in an HLA-presented neoepitope on the tumor cell surface. However, for the immunogenic neoepitope PA*24 natural processing and presentation could not be validated in the DNAJB1-PRKACA transduced HCC cell lines, within the sensitivity limitation of the current state-of-the-art mass spectrometry-based immunopeptidomics. The sensitivity of shotgun mass spectrometric discovery approaches is, even in the context of immense technical improvements in the last decades, still limited. Therefore, we cannot exclude the low-level presentation of the PA*24. We further report on the clinical application of a DNAJB1-PRKACA neoepitope-based personalized peptide vaccine adjuvanted with the TLR1/2 agonist XS15 and MontanideTM ISA51 VG in a single FL-HCC patient. We observed profound and long-lasting DNAJB1-PRKACA-specific T cell responses showing a clonal expansion of activated CD4+ T cells, despite ongoing mTOR inhibition-based immunosuppression with contradictory reported effects on T cells. Follow-up data until month 18 after vaccination showed the persistence of profound DNAJB1-PRKACA-specific T cell responses. This is in line with the induction of long-term virus-specific T cell responses observed upon XS15-adjuvanted multi-peptide vaccination with our CoVac-1 COVID19 vaccine candidate. Induction of long-lasting T cell responses was mirrored by so far relapse-free survival of the patient, indicating, in line with other tumor vaccines, a potential of DNAJB1-PRKACA-based vaccines to combat residual tumor cells. Peptide vaccination of the FL-HCC patient was applied under continued PARP inhibitor treatment, which started 5 months prior to vaccination and did not prevent the occurrence of relapse applied as a single substance. The combination of PARP inhibition and DNAJB1-PRKACA neoepitope-based peptide vaccine could have positively affected the vaccination response and the prolonged relapse-free survival of the FL-HCC patient based on the beneficial impact of DNA-damaging agents on tumor immunogenicity, which was previously reported for the combination of PARP inhibition and ICIs. We could not detect any vaccine-induced CD8+ T cell responses against the DNAJB1-PRKACA fusion protein. Thus, tumor immune surveillance and relapse-free survival observed in the patient after vaccination might be mediated by DNAJB1-PRKACA-specific CD4+ T cells alone, or accompanied by undetected CD8+ T cells recognizing other tumor antigens that were induced by the CD4+ T cells via epitope spreading. Moreover, the in vitro expansion of patient-derived T cells prior to characterization by flow cytometry and single-cell RNA sequencing might have impacted the phenotypes of these cells. This low side-effect peptide vaccine represents so far the only DNAJB1-PRKACA-targeted therapy and might in the future be applied within combinatorial treatment approaches comprising newly developed small molecules targeting the kinase activity of PRKACA and/or ICIs. Combination with the latter is supported by (I) the high expression of PD-L1 in FL-HCC, (II) the detection and response correlation of fusion protein-specific T cells in patients receiving ICIs, and (III) the first promising clinical results of neoepitope-based vaccines in combination with ICIs. Beyond the design of vaccines, DNAJB1-PRKACA-derived neoepitopes could serve as targets for the development of therapeutic approaches using adoptive T cell transfer and TCR engineering. For this purpose, we identified multiple TCRs from in vitro and in vivo induced DNAJB1-PRKACA-specific T cells displaying a unique basic AA motif of the CDR3-α sequence with opposing characteristics regarding chemical groups in comparison to the target peptide core-binding motif. FL-HCC is a rare tumor disease; however recent reports state that the number of FL-HCC cases might be significantly under-diagnosed, suggesting a growing number of cases in the future. Furthermore, recent advances in genome sequence analysis enable the identification of further cancer entities that express the DNAJB1-PRKACA fusion transcript. This study identifies the DNAJB1-PRKACA fusion transcript as a prime source for broadly applicable neoepitopes and provides evidence for their immunotherapeutic efficacy in a single FL-HCC patient. Open questions remain if future off-the-shelf T cell-based immunotherapies targeting the DNAJB1-PRKACA fusion will be able to overcome the immunosuppressive microenvironment and other escape mechanisms of tumors to natural immune surveillance and which of the multiple predicted HLA allotypes give rise to DNAJB1-PRKACA neoepitopes in vivo and thus which patients will profit from, e.g. a neoepitope-based vaccine. These issues will be addressed in an upcoming clinical trial evaluating the here-defined DNAJB1-PRKACA neoepitopes adjuvanted with the TLR1/2 agonist XS15 emulsified in MontanideTM ISA51 VG in combination with the PD-L1 antibody atezolizumab in a Phase I vaccine study, recruiting patients with various malignant disease expressing the DNAJB1-PRKACA fusion transcript.
The study was performed according to the guidelines of the ethics committee at the medical faculty of the Eberhard-Karls-University and at the University Hospital Tübingen (713/2018B02, 406/2019BO2).
Peripheral blood mononuclear cells (PBMCs) from the FL-HCC patient (n = 1) as well as PBMCs from healthy volunteers (HVs, n = 11) were isolated by density gradient centrifugation and stored at −80 °C until further use for subsequent T cell-based assays. Informed consent was obtained in accordance with the Declaration of Helsinki protocol, and donors were not financially compensated. The study was performed according to the guidelines of the local ethics committees (713/2018B02, 406/2019BO2). HLA typing was carried out by the Department of Hematology and Oncology, Tübingen, Germany (Supplementary Table 1).
The personalized vaccine developed and produced by the Good Manufacturing Practices (GMP) Peptide Laboratory of the Department of Immunology, University Tübingen, Germany, is a peptide-based vaccine containing four DNAJB1-PRKACA-derived peptides (Supplementary Table 4) and the adjuvant lipopeptide synthetic TLR1/2 ligand XS15 (manufactured by Bachem AG, Bubendorf, Switzerland) emulsified in MontanideTM ISA51 VG (manufactured by Seppic, Paris, France). Vaccine peptides (250 µg/peptide) and XS15 (50 µg) are prepared as a water–oil emulsion 1:1 with MontanideTM ISA51 VG to yield an injectable volume of 500 µl. Following disclosure and written consent, the patient received a subcutaneous injection of the personalized vaccine in the lower abdomen. The treatment of the patient was performed within a compassionate use program (expanded access) for personalized peptide vaccination under the project: clinicaltrials.gov NCT05014607. The local Ethics Committee (406/2019BO2) and the regulatory authority (Regierungspräsidium Tübingen) approved the project which was conducted under the German Drug Law §13 paragraph 2b. The patient gave written informed consent for vaccine treatment, sequential blood analysis for immunomonitoring, as well as publishing of the related data.
RNA was extracted from macrodissected 5 µm paraffin sections using the Maxwell® RSC RNA FFPE Kit and the Maxwell® RSC Instrument (Promega, Madison, WI, USA) according to the manufacturer’s instructions. Reverse transcription of RNA and polymerase chain reaction (PCR) of the DNAJB1-PRKACA breakpoint region (forward primer 5′-GTTCAAGGAGATCGCTGAGG-3′, reverse primer 5′- TTCCCGGTCTCCTTGTGTTT-3′) was performed using the QIAGEN OneStep RT-PCR Kit according to the manufacturer’s instructions (Qiagen, Hilden, Germany). To visualize the detection of the DNAJB1-PRKACA fusion, the PCR product was run on an agarose gel. For sequencing, the PCR product was purified (AMPure, Beckman Coulter, Brea, CA, USA) and aliquots were used for the sequencing reaction with 1 μM of the forward or reverse primer and 2 μl of GenomeLab DTCS-Quick Start Master Mix (Beckman Coulter, Brea, CA, USA) in a final volume of 10 μl according to the manufacturer’s protocol. Sequencing reactions were purified (CleanSEQ, Beckman Coulter, Brea, CA, USA) and analyzed in a GenomeLab GeXP Genetic Analysis System, and evaluated by the GenomeLab GeXP software (Beckman Coulter, Brea, CA, USA).
Tissue specimens were obtained during the routine diagnostic procedure, fixed in 4% formalin, and embedded in paraffin (FFPE). 1.5–3.0 µm-thick sections were cut by using a microtome and stained with haematoxylin and eosin (HE) or with Masson’s trichrome as additional routine staining for liver specimens. Immunohistochemistry was performed on an automated immunostainer (VENTANA BenchMark ULTRA, Ventana Medical Systems, Oro Valley, AZ, USA) according to in-house protocols. Slides were stained by using CK7 (1:2000, Clone OV-TL 12/30, Agilent Dako, Santa Clara, CA, USA) and hepatocyte paraffin1 (Hepar1) (1:1000, Clone OCH1E5, Agilent Dako, Santa Clara, CA, USA) as primary antibodies. Slide scans of the hepatectomy specimen were produced by using the Ventana Scanner DP200 (Ventana Medical Systems, Oro Valley, AZ, USA).
HLA class I DNAJB1-PRKACA ligand prediction was performed for all possible 8–12 AA long peptide sequences spanning the fusion region using SYFPEITHI 1.0 and NetMHCpan 4.1 for the 20 most frequent HLA class I allotypes in the European population (tools.iedb.org). HLA class II DNAJB1-PRKACA ligand prediction was performed for all possible 15 AA long peptide sequences spanning the fusion region using NetMHCIIpan 4.0 with all listed allele combinations.
HLA surface expression of HCC cell lines was analyzed using the QIFIKIT bead-based quantitative flow cytometric assay (Dako, K0078) according to the manufacturer’s instructions as described before. In brief, samples were stained with the pan-HLA class I-specific monoclonal antibody (mAb) W6/32 (produced in-house) or IgG isotype control (BioLegend, 400202). Flow cytometric analysis was performed on a FACSCanto II Analyzer (BD).
The HCC cell lines HLE (obtained from the Japan Collection of Research Bioresources (JCRB) Cell Bank), SMMC-7721 (obtained from Woodland Pharmaceuticals), and HepG2 (obtained from the American Type Culture Collection (ATCC)) were cultivated in Gibco Dulbecco’s Modified Eagle Medium supplemented with 10% fetal calf serum (FCS), penicillin, streptomycin (all from Merck) and plasmocin (Invivogen) at 37 °C and 5% CO2 in a humidified atmosphere. The SMMC-7721 cell line is under the list of known misidentified cell lines maintained by the International Cell Line Authentication Committee; however, the cell line was selected due to the specific HLA type and was validated by HLA typing. The DNAJB1-PRKACA coding sequence was synthesized by Thermo Fisher, cloned into the pENTRTM plasmid, and transferred by directional TOPO cloning (pENTRTM/D-TOPOTM cloning kit, Invitrogen) into the pInducer20 (Addgene #44012) destination vector. Lentiviral particles were produced in HEK293T cells (obtained from the DSMZ) by calcium-phosphate transfection of the helper plasmids psPAX2, pMD2.G, and either the empty or the DNAJB1-PRKACA coding pInducer20. The transduced HCC cell lines were selected with G418 (Invivogen) for at least 2 weeks. To induce the expression of DNAJB1-PRKACA, the transduced HCC cell lines were treated with 1 µg/ml Doxycycline (Dox) for 24 h (AppliChem). To validate the DNAJB1-PRKACA expression, cells were lysed in lysis buffer (50 mM Tris–HCl pH 7.4, 150 mM NaCl, 1% Triton X-100, 50 mM NaF, 10 mM Na4P2O7, 10 mM Na4V2O7 and complete protease inhibitor cocktail (Roche)). SDS–PAGE and immunoblotting were performed as described previously. For immunoblotting the primary antibodies anti-PKAα cat (1:1000 dilution, Santa Cruz, clone A-2, Cat# sc-28315, RRID:AB_628136), anti-GAPDH (1:2000 dilution, Cell Signaling, clone D16H11, Cat# 5174, RRID:AB_10622025), and anti-Tubulin (1:2000 dilution, Merck, clone DM1A, Cat# 05-829, RRID:AB_310035) were used. HRP-coupled goat anti-rabbit or goat anti-mouse (both Jackson ImmunoResearch) secondary antibodies were used for visualization. Uncropped and unprocessed scans are supplied in the Source Data file.
HLA class I and HLA class II molecules were isolated by standard immunoaffinity purification using the pan-HLA class I-specific mAb W6/32, the pan-HLA class II-specific mAb Tü-39, and the HLA-DR-specific mAb L243 (all produced in-house) to extract HLA ligands.
Peptide samples were separated by reversed-phase liquid chromatography (nanoUHPLC, UltiMate 3000 RSLCnano, Thermo Fisher, Waltham, MA, USA) and subsequently analyzed in an online coupled Orbitrap Fusion Lumos mass spectrometer (Thermo Fisher). Samples were analyzed in three technical replicates. Sample volumes of 5 µl with shares of 20% were injected onto a 75 µm × 2 cm trapping column (Thermo Fisher) at 4 µl/min for 5.75 min. Peptide separation was subsequently performed at 50 °C and a flow rate of 300 nL/min on a 50 µm × 25 cm separation column (PepMap C18, Thermo Fisher) applying a gradient ranging from 2.4% to 32.0% of ACN over the course of 90 min. Eluting peptides were ionized by nanospray ionization and analyzed in the mass spectrometer implementing a top speed (3 s) HCD (Higher-energy C-trap dissociation) method generating fragment spectra with a resolution of 30,000, a mass range limited to 235–1151m/z, and positive charge states 2–5 selected for fragmentation.
Data processing was performed as described previously. The Proteome Discoverer (v1.4, Thermo Fisher) was used to integrate the search results of the SequestHT search engine (University of Washington) against the human proteome (Swiss-Prot database, 20,279 reviewed protein sequences, September 27, 2013) accompanied by the complete sequence of the DNAJB1-PRKACA fusion protein. Precursor mass tolerance was set to 5 ppm and fragment mass tolerance was set to 0.02 Da. Oxidized methionine was allowed as a dynamic modification. The false discovery rate (FDR, estimated by the Percolator algorithm 2.04) was limited to 5% for HLA class I and 1% for HLA class II. HLA class I annotation was performed using SYFPEITHI 1.0 and NetMHCpan 4.1.
Spectrum validation of the experimentally eluted peptides was performed by computing the similarity of the spectra with corresponding synthetic peptides measured in a complex matrix. The spectral correlation was calculated between the MS/MS spectra of the eluted and the synthetic peptide.
PBMCs were pulsed either with 1 µg/ml or with 5 µg/ml of HLA class I or HLA class II peptide, respectively. Irrelevant peptides with the respective HLA restrictions were used as negative control (YLLPAIVHI for HLA-A*02 (source protein: DDX5_HUMAN), and ETVITVDTKAAGKGK for HLA class II (source protein: FLNA_HUMAN)). Cells were cultured for 12 days adding 20 U/ml IL-2 (Novartis, Basel, Switzerland) on days 2, 5, and 7. Peptide-stimulated PBMCs were analyzed by IFN-γ enzyme-linked immunospot (ELISPOT) assay on day 12, with anti-IFN-γ antibody (clone 1-D1K, 2 µg/mL, MabTech), anti-IFN-γ biotinylated detection antibody (clone 7 B6 1, 0.3 µg/mL, MabTech), ExtrAvidin-Alkaline Phosphatase (1:1000 dilution, Sigma-Aldrich) and BCIP/NBT (5 bromo 4-chloro 3 indolyl-phosphate/nitro-blue tetrazolium chloride, Sigma-Aldrich). Spots were counted using an ImmunoSpot S6 analyzer (CTL, Cleveland, OH, USA) and T cell responses were considered positive if >10 spots/500,000 cells were counted, and the mean spot count was at least three-fold higher than the mean spot count of the negative control.
Biotinylated HLA:peptide complexes were manufactured as described previously and tetramerized using PE-conjugated streptavidin (Invitrogen) at a 4:1 molar ratio.
Priming of peptide-specific cytotoxic T lymphocytes was conducted using aAPCs as described previously. In detail, 800,000 streptavidin-coated microspheres (Bangs Laboratories, Fishers, IN, USA) were loaded with 200 ng biotinylated HLA:peptide monomer and 600 ng biotinylated anti-human CD28 monoclonal antibody (clone 9.3, in-house production). CD8+ T cells were cultured with 4.8 U/µl IL-2 (R&D Systems, Minneapolis, MN, USA) and 1.25 ng/ml IL-7 (PromoKine, Heidelberg, Germany). Weekly stimulation with aAPCs (200,000 aAPCs per 1 × 106 CD8+ T cells) and 5 ng/ml IL-12 (PromoKine) was performed for four cycles.
Functionality of peptide-specific CD4+ and CD8+ T cells was analyzed by surface marker and intracellular cytokine staining (ICS) as described previously. Cells were pulsed with 10 μg/ml of respective peptide and incubated with 10 μg/ml Brefeldin A (Sigma-Aldrich, Saint Louis, MO, USA) and 10 μg/ml GolgiStop (BD, Franklin Lakes, NJ, USA) for 12–16 h. Staining was performed using Cytofix/Cytoperm (BD), Aqua live/dead (1:400 dilution, Invitrogen), APC/Cy7 anti-human CD4 (1:100 dilution, BioLegend, Cat# 300518, RRID: AB_314086), PE/Cy7 anti-human CD8 (1:400 dilution, Beckman Coulter, Cat# 737661, RRID: AB_1575980), Pacific Blue anti-human TNF (1:120 dilution, BioLegend, Cat# 502920, RRID: AB_528965), FITC anti-human CD107a (1:100 dilution, BioLegend, Cat# 328606, RRID: AB_1186036), APC anti-human IL-2 (1:40 dilution, BioLegend, Cat# 500309, RRID: AB_315096), and PE anti-human IFN-γ mAB (1:200 dilution, BioLegend, Cat# 506507, RRID: AB_315440). PMA and ionomycin (Sigma-Aldrich) served as a positive control. Negative control peptides with matching HLA restrictions were used: YLLPAIVHI for HLA-A*02 (source protein: DDX5_HUMAN), KYPENFFLL for HLA-A*24 (source protein: PP1G_HUMAN), EEFGRAFSF for HLA-B*44 (source protein: HLA-DP_HUMAN), and ETVITVDTKAAGKGK for HLA class II (source protein: FLNA_HUMAN). Gating strategies applied for the analyses of flow cytometry-acquired data are provided in Supplementary Figs. S6, S7, and S8. The frequency of peptide-specific CD8+ T cells after aAPC-based priming was determined by Aqua live/dead (1:400 dilution, Invitrogen), PE/Cy7 anti-human CD8 (1:400 dilution, Beckman Coulter, Cat# 737661, RRID: AB_1575980) and HLA:peptide tetramer-PE staining. Cells of the same donor primed with an irrelevant control peptide TYSEKTTLF (source protein: MUC16_HUMAN) and stained with the tetramer containing the test peptide were used as a negative control. The priming was considered successful if the frequency of peptide-specific CD8+ T cells was ≥0.1% of CD8+ T cells within the viable single cell population and at least three-fold higher than the frequency of peptide-specific CD8+ T cells in the negative control. The same evaluation criteria were applied to ICS results. Samples were analyzed on a FACS Canto II cytometer (BD). The gating strategy applied for tetramer staining analysis of flow cytometry-acquired data is provided in Supplementary Fig. S9.
Peptide-specific CD8+ T cells were analyzed for their capacity to induce peptide-specific target cell lysis in the flow cytometry-based VITAL assay. Autologous CD8− target cells were either loaded with the PA*24 peptide or the HLA-matched negative peptide KYPENFFLL (source protein: PP1G_HUMAN) and labeled with CFSE or FarRed (Life Technologies, Carlsbad, CA, USA), respectively. The PA*24-specific effector cells were added in the indicated effector-to-target ratios. Specific lysis of peptide-loaded CD8− target cells was calculated relative to control targets.
For the differentiation of monocyte-derived dendritic cells (moDCs) CD14+ cells were isolated from PBMC using magnetic-activated cell sorting (MACS; Miltenyi, Bergisch Gladbach, Germany), and subsequently cultivated in X-VIVOTM 15 serum-free hematopoietic cell medium supplemented with penicillin, streptomycin, GM-CSF (1000 IU/ml; Miltenyi, Bergisch Gladbach, Germany), and IL-4 (400 IU/ml; Miltenyi, Bergisch Gladbach, Germany) at 37 °C and 5% CO2 in a humidified atmosphere for seven days. Differentiated moDCs were maturated by adding LPS (100 ng/ml; Invivogen, Toulouse, France) to the cell culture medium for 24 h and checked for cell surface marker expression using FITC anti-human CD80 (1:40 dilution, Biolegend, Cat# 305206), BV711 anti-human HLA-DR (1:100 dilution, Biolegend, Cat# 307644), and BV605 anti-human CD86 (1:400 dilution, Biolegend, Cat# 374214). The gating strategy applied for the analysis of flow cytometry-acquired data is provided in Supplementary Fig. S10. Mature moDCs were incubated with the PII-1 peptide for 2 h prior to CD4+ T cell stimulation. CD4+ cells were isolated from PBMC of the same healthy volunteer (HV) using MACS and subsequently cultivated with penicillin, streptomycin, IL-2 (10 U/ml; R&D Systems, Minneapolis, MN, USA), and IL-7 (2.5 ng/ml; PromoKine, Heidelberg, Germany), at 37 °C and 5% CO2 in a humidified atmosphere. Cultured CD4+ cells were stimulated weekly for a total of four weeks with peptide-loaded mature moDCs and IL-12 (5 ng/ml; PromoKine, Heidelberg, Germany). The functionality of peptide-specific CD4+ T cells was analyzed by ICS.
For the generation of tumor lysate, HCC cell lines transduced with the empty or the DNAJB1-PRKACA coding plasmid were treated with IFN-γ and Dox for 24 h. The treated cells were harvested, washed with PBS, subjected to five freeze–thaw cycles, irradiated with 30 Gy, and sonicated for 2 min. The clear supernatant was then added to the cell culture medium of maturated moDCs of HV3, HV4, and HV5 for 24 h, and the antigen-loaded mature moDCs were subsequently harvested for HLA immunoprecipitation.
The HLA allotype distribution and population coverage of the European and world population were calculated with the IEDB population coverage tool (www.iedb.org). All figures and statistical analyses were generated using GraphPad Prism 9.2.0 (GraphPad Software). P values of <0.05 were considered statistically significant. All flow cytometry-acquired data were analyzed with FlowJo 10.0.8 (FlowJo™ Software).
Peptide-specific CD8+ T cells of HVs induced by in vitro aAPC-based priming or in vitro amplified bulk memory CD4+ T cells of the FL-HCC patient were sorted by fluorescence-activated cell sorting (FACS), counted, and washed in 0.04% BSA/PBS according to the 10× Genomics cell preparation protocol. Single cells were partitioned into Gel Beads-in-Emulsion (GEMs) together with 10× barcoded Gel Beads and reverse transcriptase enzymatic reaction using the Chromium Controller instrument (10× Genomics, Pleasanton, CA, USA). Single-cell gene expression libraries and single-cell T-cell receptor (VDJ) libraries were then prepared using the Chromium Next GEM Single Cell 5′Kit v2 (10× Genomics, Pleasanton, CA, USA) and the Chromium Single Cell Human TCR Amplification Kit (10× Genomics, Pleasanton, CA, USA) according to the manufacturer’s instructions. Libraries were pooled and sequenced on a NextSeq 550 (Illumina, San Diego, CA, USA) at 28,806, 138,975, and 3519 mean reads per cell, respectively. Samples were demultiplexed using bcl2fastq version 2.20.0.422 (Illumina, San Diego, CA, USA). Barcode processing, alignment, VDJ annotation, and single-cell 5′gene counting were performed using Cell Ranger Software version 6.0.1 (10× Genomics, Pleasanton, CA, USA). Further data processing, visualization, and analysis were done using scanpy and scirpy for each sample separately. Cells with unique gene counts <200 and without VDJ sequence associated, as well as cells with >10% of mitochondrial genes, were removed from the analysis, keeping 474 cells (FL-HCC01), 115 cells (HV1), and 3338 cells (HV2), respectively. Data was log-normalized to a size factor of 10,000. Only highly variable genes were considered for linear dimensional reduction and were defined by a minimum mean expression of 0.0125, a maximum mean expression of 3, and a minimum dispersion of 0.5. The effect of total counts was regressed out and counts were scaled to unit variance and zero mean for each gene. The dimensionality reduction was done using principal component analysis (PCA). The neighborhood graph and UMAP embedding were computed using the rapids implementation of the UMAP algorithm for 10 neighbors and the first 10 principal components (n_neighbors = 10, n_PC = 10). Unsupervised clustering was performed using the rapids implementation of the Louvain algorithm. Functional enrichment for the hallmarks of cancer gene sets was performed using the decoupler’s run_ora function with default parameters. This analysis was performed on the log normalized counts of all genes present in more than one cell for each sample.
Clustering of variable sequences of CDR3-α and CDR3-β was conducted using GibbsCluster 2.0 with MHC class I configurations and a specified core size of the smallest variable sequence in the positive dataset. The negative dataset of not binding CD3 sequences of CD8+ T cells to the HLA class I target peptide was retrieved from the VDJdb database (https://vdjdb.cdr3.net) containing only human sequences associated with HLA-A*24:02. For the HLA class II target peptide sequences of CD4+ T cells that did not show an in vitro response was defined as a negative dataset. The underlying position-specific scoring matrixes (PSSMs) of the clustering were used to conduct the position-wise Pearson correlation between positive and negative datasets. Correlation significance was assessed using the Pearson correlation test. Similarity and identity of variable CD3 sequences were computed using a pairwise sequence alignment by ClustalW with standard configurations (https://www.ebi.ac.uk/Tools/msa/clustalo/).
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
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PMC9614396 | 36302626 | Yanchao Hu,Chunyan Zhang,Yajie Fan,Yan Zhang,Yiwen Wang,Congxia Wang | Lactate promotes vascular smooth muscle cell switch to a synthetic phenotype by inhibiting miR-23b expression | 01-11-2022 | Lactate,miR-23b,Phenotype switch,SMAD3,Smooth muscle | Recent research indicates that lactate promotes the switching of vascular smooth muscle cells (VSMCs) to a synthetic phenotype, which has been implicated in various vascular diseases. This study aimed to investigate the effects of lactate on the VSMC phenotype switch and the underlying mechanism. The CCK-8 method was used to assess cell viability. The microRNAs and mRNAs levels were evaluated using quantitative PCR. Targets of microRNA were predicted using online tools and confirmed using a luciferase reporter assay. We found that lactate promoted the switch of VSMCs to a synthetic phenotype, as evidenced by an increase in VSMC proliferation, mitochondrial activity, migration, and synthesis but a decrease in VSMC apoptosis. Lactate inhibited miR-23b expression in VSMCs, and miR-23b inhibited VSMC's switch to the synthetic phenotype. Lactate modulated the VSMC phenotype through downregulation of miR-23b expression, suggesting that overexpression of miR-23b using a miR-23b mimic attenuated the effects of lactate on VSMC phenotype modulation. Moreover, we discovered that SMAD family member 3 (SMAD3) was the target of miR-23b in regulating VSMC phenotype. Further findings suggested that lactate promotes VSMC switch to synthetic phenotype by targeting SMAD3 and downregulating miR-23b. These findings suggest that correcting the dysregulation of miR-23b/SMAD3 or lactate metabolism is a potential treatment for vascular diseases. | Lactate promotes vascular smooth muscle cell switch to a synthetic phenotype by inhibiting miR-23b expression
Recent research indicates that lactate promotes the switching of vascular smooth muscle cells (VSMCs) to a synthetic phenotype, which has been implicated in various vascular diseases. This study aimed to investigate the effects of lactate on the VSMC phenotype switch and the underlying mechanism. The CCK-8 method was used to assess cell viability. The microRNAs and mRNAs levels were evaluated using quantitative PCR. Targets of microRNA were predicted using online tools and confirmed using a luciferase reporter assay. We found that lactate promoted the switch of VSMCs to a synthetic phenotype, as evidenced by an increase in VSMC proliferation, mitochondrial activity, migration, and synthesis but a decrease in VSMC apoptosis. Lactate inhibited miR-23b expression in VSMCs, and miR-23b inhibited VSMC's switch to the synthetic phenotype. Lactate modulated the VSMC phenotype through downregulation of miR-23b expression, suggesting that overexpression of miR-23b using a miR-23b mimic attenuated the effects of lactate on VSMC phenotype modulation. Moreover, we discovered that SMAD family member 3 (SMAD3) was the target of miR-23b in regulating VSMC phenotype. Further findings suggested that lactate promotes VSMC switch to synthetic phenotype by targeting SMAD3 and downregulating miR-23b. These findings suggest that correcting the dysregulation of miR-23b/SMAD3 or lactate metabolism is a potential treatment for vascular diseases.
Unlike terminally differentiated muscle cells, including skeletal muscle cells and cardiomyocytes, the contractile state of vascular smooth muscle cells (VSMCs) can switch to a synthetic phenotype in response to various stimuli [1]. The switch of VSMCs to a synthetic phenotype has been implicated in various vascular diseases, including atherosclerosis [2-4]. VSMCs with a synthetic phenotype are less able to contract, migrate to the intima, proliferate, and produce extracellular matrix proteins, leading to vascular dysfunction [5]. Thus, understanding the underlying mechanisms of VSMC phenotypic regulation will lead to developing a novel strategy for preventing and treating vascular diseases. Various factors, including growth factors, mechanical injury, reactive oxygen species, and metabolites [6,7], regulate the VSMC phenotypic switch. Under normal, well-oxygenated conditions, VSMCs exhibit unusually high glycolysis rates, relying heavily on glycolytically generated ATP to sustain various cell functions [8]. Lactate, the end product of glycolysis, has been considered a metabolic waste product for a very long time. However, accumulating evidence suggests that lactate is taken up by numerous cells and functions as a signal in numerous biological processes [9-12]. Additionally, lactate levels rise in response to ischemia, which is linked to various vascular diseases [13]. Recent research has demonstrated that lactate promotes the synthetic phenotype of VSMCs, establishing a link between glycolysis and VSMC phenotypic switch [14]. However, the mechanism underlying lactate's regulation of VSMC phenotype is largely unknown. MicroRNAs (miRNAs) are a cluster of non-coding small RNAs with ~22 nucleotides long mature products [15-17] at play a significant role in cellular processes [18]. Recent research demonstrates that microRNAs play a crucial role in regulating VSMC differentiation and phenotype switch [19]. Tang et al. [20] reported that miR-124 was significantly correlated with the contractile VSMC phenotype and that activation of SP1 could significantly reverse the antiproliferative effect of miR-124. miR-23b is significantly downregulated after vascular injury, and overexpression of miR-23b inhibited the migration markedly by elevating smooth muscle α-actin and smooth muscle myosin-injured arteries; additional analyses revealed that miR-23b modifies the phenotype of VSMCs by targeting SMAD family member 3 and transcription factor forkhead box O4 [21]. In addition, miR-143 and miR-145 stimulate the migration of pulmonary arterial smooth muscle cells by targeting ABCA1 [22]. Despite these, several miRNAs have been identified as regulators of the VSMC phenotype. These include miR-22 [19], miR-100 [23], miR-133 [24], miR-146a [25], miR-221/222 [26], and miR-424 [27]. However, the precise mechanism of VSMC phenotype switching is not yet completely understood. Here, we investigated whether these miRNAs are involved in lactate's regulation of VSMC phenotype and discovered that lactate promotes VSMC's switch to the synthetic phenotype by inhibiting miR-23b expression.
As described previously, primary VSMCs were isolated from the thoracic aortas of standard deviation (SD) rats (170–200 g, male) [28]. Briefly, after anesthetization with the intraperitoneal administration of pentobarbital sodium (60 mg/kg), thoracic aortas were removed and washed three times with phosphate buffer saline. The media layer of the aorta was dissected, cut into pieces, and transplanted into a six-well culture plate. Cells were growing at 37°C in a humidified atmosphere containing 5% CO2 in Dulbecco's modified eagle medium (DMEM) supplemented with 10% fetal bovine serum (FBS), penicillin, and streptomycin for 2 weeks. The experiments used VSMCs between passages 3 and 5. All animal procedures were conducted in accordance with the National Institutes of Health's Guidelines for the Care and Use of Laboratory Animals and with the approval of the Second Affiliated Hospital of Xi'an Jiaotong University's Ethics Committee.
Next, using the CCK-8 Kit, cell viability was determined (Dojindo, Kumamoto, Japan). Briefly, cells were seeded in 96-well plates and 10 μl of CCK-8 (5 mg/ml) was added to the culture medium in each well. The absorbance was measured at 450 nm using an Exl 800 microplate reader (Bio-tek, Winooski, VT, USA). Cell viability (%) = (experimental group OD value − zero group OD value) / (control group OD value − zero group OD value) × 100%.
VSMC apoptosis was determined using the Tunel assay with a Tunel-specific detection kit (Roche, Mannheim, Germany) according to the manufacturer's instructions. Briefly, cells were fixed with 4% paraformaldehyde at room temperature for 10 min, permeabilized with 0.1% Triton X-100, and then fragmented DNA in VSMCs was end-labeled with FITC. In addition, the Tunel-positive cells were examined using a confocal microscope.
Mitochondrial membrane potential was determined using JC-1 staining (KeyGEN biotechnology, Jiangsu, China) according to the manufacturer's instructions, followed by flow cytometry evaluation (BD Bioscience, San Jose, CA, USA). Specifically, Q2 represents the mitochondrial cells that are healthy, whereas Q3 represents the mitochondrial membrane decline cells.
The ability of VSMCs to migrate was evaluated using the Transwell assay. VSMCs were seeded in the upper chamber of the transwell at a concentration of 1.0 × 105 cells/well in 300 μl. The lower chamber was filled with 600 μl of 10% FBS-containing DMEM. The cells in the upper chamber migrated to the lower chamber after 24 h of incubation. The cells on the surface of the lower chambers were then fixed with 20% methanol for 10 min at room temperature and stained with 1% crystal violet (diluted in methanol) for 15 min at room temperature. Under a light microscope, the migrated cells were then quantified.
Total RNA was isolated using RNAiso Plus (Takara, Shiga, Japan) reagent as directed by the manufacturer. cDNA was synthesized from 500 ng of RNA per sample using the Prime Script Master Mix (Takara). Then, quantitative PCR was conducted using a SYBR Green PCR kit (Takara) in a CFX200 (Bio-Rad, Hongkong, China). Each gene's mRNA level was normalized to those of the housekeeping gene GAPDH. The sequences of the primers are listed in Supplementary Table 1.
In addition, miRNA mimics and inhibitors, in addition to their respective negative controls (NC), Empty vector (NC), and SMAD3 overexpression (SMAD3 OE) plasmids, were acquired from RIBOBIO Co. Ltd. (Guangzhou, China). NC mimics (100 nM; #miR1N0000001-1-10); miR-23b mimics (100 nM; 5-UGGGUUCCUGGCAUGCUGAUUU-3); NC inhibitors (200 nM; #miR2N0000001-1-10); miR-222 inhibitors (200 nM; 5´-AGGAUCUACACUGGCUACUGAG-3´), miR-23b inhibitors (200 nM; 5'-AAAUCAGCAUGCCAGGAACCCA-3´), miR-133a inhibitors (200 nM; 5´-CAGCUGGUUGAAGGGGACCAAA-3´), NC (2 μg), or SMAD3 OE (2 μg) were transfected using Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA) according to manufacturer’s instructions. After 48 h of transfection, cells were harvested and used in subsequent experiments.
Wt and Mt SMAD3 3' UTR sequences were cloned into the SpeI and HindIII sites of the pMir-Report Luciferase vector following PCR amplification using template and primers (Applied Biosystems, Foster City, CA, USA). Following the manufacturer's instructions, 5 ng of the resulting construct was transfected into 293T cells with 20 nM control mimics or 20 nM miR-23b mimics using Lipofectamine-2000 (Invitrogen). After 24 h of transfection, luciferase activity in the cells was determined using a Luciferase Assay System (Promega, Madison, WI, USA).
Subsequently, using the RIPA buffer, proteins were extracted from cells for immunoblotting. 15–50 μg of total protein extracts were subjected to sodium dodecyl sulfate-polyacrylamide gel electrophoresis and transferred to a polyvinylidene fluoride membrane. After blocked with 5% skimmed milk, membranes were then probed with anti-Bax (1:2,000 dilution; #ab3203; Abcam, Cambridge, UK), anti-Bcl-2 (1:1,000; #ab32124; Abcam), anti-cleaved-caspase3 (1:500; #ab32042; Abcam), anti-cleaved-caspase9 (1:1,000; #ab2324; Abcam), anti-α-SMA (1:1,000; #ab5694; Abcam), anti-SM22 (1:1,000; #ab14106; Abcam), anti-SM-MHC (1:2,000; #ab133567; Abcam), anti-vimentin (1:2,000; #ab92547; Abcam), anti-collagen I (1:1,000; #ab270993; Abcam), anti-SMAD3 (1:2,000; #ab40854; Abcam), and anti-β-actin (1:5,000; #ab8226; Abcam) at room temperature for 1.5 h. Then, membranes were incubated with the appropriate secondary antibody conjugated to HRP. Then, the BM chemiluminescence blotting system (Thermo Fisher Scientific, Waltham, MA, USA) was used to visualize protein bands, and ImageJ Software (NIH, Bethesda, MD, USA) was used to quantify protein bands.
All values are presented using the mean ± SD format. The data were compared using the unpaired t-test, or one-way ANOVA followed by Tukey's test, as appropriate. The normal distribution of data was analyzed using the Kolmogorov–Smirnov normality test. Using Bonferroni's correction for multiple comparisons. When p < 0.05, differences were considered significant.
VSMCs were treated with lactate (0, 2, 4, or 8 mM) for 3 days. Lactate treatment caused the VSMCs to become less spindle-shaped and to develop the irregular morphology characteristic of synthetic VSMCs (Fig. 1A). The analysis of cell viability revealed that lactate treatment decreased the viability of VSMCs after 24 h of treatment and then significantly increased cell viability in a dose-dependent manner, despite the presence of 6 mM lactate (Fig. 1B). We hypothesized that the cytotoxicity induced by a high lactate concentration (6 mM) may have contributed to this persistent reduction in cell viability. Lactate (4 mM) treatment significantly decreased the apoptotic rate in VSMCs (Fig. 1C), whereas it significantly increased JC-1 signaling in VSMCs (Fig. 1D, E). Furthermore, Western blotting revealed that lactate treatment (4 mM) significantly decreased the levels of Bax, cleaved-caspase-3, and cleaved-caspase-9 in VSMCs, while increasing the level of Bcl-2 (Fig. 1F). These results suggest that lactate treatment may promote the proliferation of VSMCs. In addition, lactate treatment (4 mM) increased VSMC migration as measured by the transwell assay (Fig. 1G). Moreover, lactate treatment (4 mM) for 3 days decreased mRNA levels for markers of the contractile phenotype, including α-SMA, SM22, and SM-MHC, while increasing mRNA levels for markers of the synthetic phenotype, such as vimentin and collagen I (Fig. 1H). These findings suggest that lactate promotes the synthetic phenotype switch of VSMC.
In order to determine whether miRNA is involved in lactate-induced regulation of the VSMC phenotype switch, the reported miRNAs involved in the VSMC phenotype switch were screened. As shown in Fig. 2A, 15 miRNAs were detected, and miR-222, miR-23b, and miR-133a were the top three miRNAs that decreased in lactate-treated VSMCs relative to untreated VSMCs. VSMCs were then transfected with their respective miRNA inhibitors to inhibit miR-222, mi-23b, and miR-133a expression (Fig. 2B). Then, phenotypic markers, including α-SMA, SM22, SM-MHC, vimentin, and collagen I, were then detected in VSMCs transfected with miRNA inhibitors. The qRT-PCR revealed that miR-222, miR-23b, and miR-133a inhibitors inhibited the levels of contractile markers (α-SMA, SM22, and SM-MHC) but increased the levels of synthetic markers (vimentin and collagen I), with the miR-23b inhibitor having the most significant effect (Fig. 2C–E). Consequently, miR-23b was chosen for the subsequent experiments. Overexpression of miR-23b inhibitors significantly increased the viability and migration of VSMCs compared to the NC group, as determined by additional analyses (Fig. 2F, G). These findings suggested that lactate may promote the VSMCs’ switch to a synthetic phenotype via miR-23b downregulation.
A miR-23b mimic was used to determine whether miR-23b contributes to lactate's effects on VSMC phenotype switch. Figure 3A demonstrates that the miR-23b mimic increased miR-23b levels in VSMCs. Thus, miR-23b mimic diminished the effects of lactate (4 mM) on cell viability (Fig. 3B). In addition, miR-23b mimics inhibited lactate-induced apoptosis and JC-1 signaling (Fig. 3C, D). In addition, overexpression of the miR-23b mimic attenuated the lactate-induced migration enhancement (Fig. 3E). In addition, Western blot analysis demonstrated that the overexpression of the miR-23b mimics significantly reversed the effects of lactate on reducing the expression of α-SMA, SM22, and SM-MHC while increasing the expression of vimentin and collagen I (Fig. 3F). These findings suggest that inhibition of miR-23b contributes to the effects of lactate on VSMC synthetic phenotype switch promotion.
miR-23b target genes were predicted by miRDB, TargetScan, ENCORI, and GO 0007050 (cell cycle arrest). Four candidates were screened using a Venn analysis (Fig. 4A). Compared to the NC group, the miR-23b mimic reduced the mRNA levels of WEE1, HMGA2, and SMAD3, with VSMCs showing the greatest reduction (Fig. 4B). Fig. 4C indicates that SMAD3 is a potential target of miR-23b based on the correlation between the protein levels detected by the Western blot and the mRNA levels. Dual-luciferase reporter assay was used to confirm this result. Fig. 4D depicts the complementary sequences between the 3'UTR of SMAD3 and miR-23b. Either wild-type or mutant 3'UTRs containing putative miR-23b binding sites were cloned into a reporter plasmid, and their responsiveness to miR-23b in cells was evaluated. The results demonstrated that miR-23b inhibited luciferase activity in SMAD3 wild-type 3'UTR constructs but had no effect when the miR-23b binding sites were mutated (Fig. 4E, F). Western blot analysis revealed that lactate treatment significantly increased SMAD3 protein expression, whereas miR-23b mimic clearly reversed this upregulation (Fig. 4G). These results indicated that SMAD3 is a miR-23b target.
In order to determine whether it contributes to the effects of lactate on the VSMC phenotype switch, VSMCs were transfected with SMAD3, and miR-23b mimics individually. miR-23b was significantly upregulated in miR-23b mimic overexpressed VSMCs, whereas lactate treatment significantly reversed this upregulation (Fig. 5A). Moreover, SMAD3 expression was significantly increased after transfection with SMAD3 OE plasmid, miR-29b mimic significantly reversed this accumulation, and lactate significantly attenuated miR-29b's effects, thereby increasing SMAD3 expression (Fig. 5A). Lactate treatment reversed the effect of miR-23b to increase the cell viability of VSMCs. In contrast, SMAD overexpression significantly increased the cell viability of VSMCs (Fig. 5B). Meanwhile, SMAD3 decreases the apoptotic rate and promotes JC-1 signaling in VSMCs; however, miR-29b mimic significantly reverses these changes; and lactate treatment clearly reverses the effects of miR-23b to enhance SMAD3 effects on apoptosis and JC-1 signaling in VSMCs (Fig. 5C, D). In addition, miR-23b mimic inhibited the migration enhancement of VSMCs induced by SMAD3 expression, whereas lactate treatment abolished miR-23b's effects on SMAD3-mediated migration enhancement (Fig. 5E). Additionally, the expression of α-SMA, SM22, SM-MHC, vimentin, and collagen I detect the effects of lactate on phenotype switch. The findings revealed that miR-23b significantly inhibited the SMAD3-mediated decrease of α-SMA, SM22, and SM-MHC, as well as the increase of vimentin and collagen I in VSMCs. Conversely, lactate could reverse the effect of miR-23b and enhance the effect of SMAD3 in VSMCs (Fig. 5F). These findings supported the hypothesis that lactate promotes the switch of VSMC to a synthetic phenotype via regulation of the miR-23b/SMAD3 axis.
Lactate is a metabolic byproduct which has recently been shown to function as a signal in various processes, such as wound healing, inflammation, angiogenesis, and cancer development [8-10,29]. Recent research indicates that lactate [14] may play a role in the pathogenesis of vascular diseases, as it promotes the switch of VSMC to the synthetic phenotype. In this study, we discovered that lactate promotes VSMC switch to synthetic phenotype via downregulation of miR-23b, indicating that correcting the dysregulation of the miR-23b/SMAD3 axis may be a potential treatment for vascular diseases. Unlike striated muscle cells, VSMCs exhibit unusually high glycolysis rates even under normal, well-oxygenated conditions, relying heavily on ATP from glycolysis rather than glucose oxidation to maintain their biological activity [30]. It is estimated that only 30% of ATP comes from mitochondrial oxidation, whereas at least 90% of glycolysis flux results in lactate production [31]. Thus, VSMCs produce a considerable amount of lactate. In addition, lactate concentrations rise in response to a variety of stimuli, including ischemia, exercise, cardiac arrest, shock, trauma, and burns [11-13,32]. Myocytes, endothelial cells, and human cytotoxic T lymphocytes take up lactate, inhibiting phosphofructokinase [33], altering gene expression in L6 muscle cells [8], contributing to T-cell migration [29], and promoting tumor growth [9]. As evidenced by the fact that lactate promotes VSMC viability, migration, and expression of synthetic phenotype markers, we discovered that lactate promotes VSMC's switch to the synthetic phenotype. Our findings and those of others link glucose metabolism to VSMC phenotype modulation, suggesting that metabolic disturbance plays a role in VSMC phenotype switching regulation. These findings may explain the role of metabolic dysfunction in inducing vascular dysfunction in vascular diseases. Recent research indicates that microRNAs play an essential role in regulating VSMC differentiation and phenotype switch, and miR-23b is one of the microRNAs that inhibits VSMC switch to synthetic phenotype [21]. Previous research demonstrated that lactate transport was significantly altered in hypoxic muscle and that miR-124 regulated lactate transport by targeting MCT1 [34]. It had also been reported that lactate was significantly upregulated in gastric cancer tumor-infiltrating T cells and was associated with the decreased miR-34a and the increased lactate dehydrogenase A, thus impacting the hypoxic tumor environment, which was tightly correlated with the phenotype control in the development of cancer [35]. Therefore, we hypothesized that lactate may regulate the phenotype switching of cells by targeting multiple miRNAs or by being regulated by miRNAs. We discovered that lactate promotes the switch of VSMC to a synthetic phenotype by downregulating miR-23b. Lactate dose-dependently suppressed the expression of miR-23b in VSMCs. Overexpression of miR-23b promoted the switch of VSMC to a synthetic phenotype and inhibited the effects of lactate on this switch. miR-23b, miR-27b, and miR-24-1 are all expressed from the same primary transcript [36]. It has been demonstrated that miR-23b is associated with cancer development. For instance, miR-23b expressions are reduced in human prostate tumor samples and have an inverse correlation with cell proliferation and migration [37]. In addition, miR-23b inhibits the pathogenesis of multiple autoimmune diseases by targeting cytokine-mediated pro-inflammatory signaling [38]. miR-23b also plays a role in the regulation of VSMC phenotype switching, with miR-23b downregulation promoting VSMC phenotype switching [21]. Several miR-23b targets have been implicated in the regulation of VSMC function, with urokinase-type plasminogen activator (uPA) and SMAD3 being of particular importance. Furthermore, uPA is an indispensable regulator of neointimal growth and vascular remodeling. Studies indicate that increased uPA expression contributes to VSMC proliferation, migration, and neointima formation following injury [39,40]. SMAD3 participates in TGF-β signaling. It was observed to be overexpressed in several vascular diseases [41]. Furthermore, SMAD3 overexpression has been reported to stimulate VSMC proliferation and phenotypic switching [21,42]. Targeting SMAD3, we found that miR-23b regulates VSMC phenotypic switching. The inhibition of SMAD3 diminished the modulatory effects of miR-23b and lactate on the VSMC phenotype. These findings suggested that the miR-23b/SMAD3 axis plays an important role in VSMC phenotypic modulation, and correcting the dysregulation of miR-23b/SMAD3 or lactate metabolism may be an effective treatment for vascular diseases. We discovered that lactate promotes the switch of VSMC to a synthetic phenotype by downregulating miR-23b expression. Additionally, miR-23b promotes the synthetic phenotype of VSMC by targeting SMAD3. The regulation of VSMC phenotype by lactate metabolism may contribute to the maintenance of vascular health and the prevention of vascular diseases, as suggested by these findings.
Supplementary data including one Table can be found with this article online at https://doi.org/10.4196/kjpp.2022.26.6.519. | true | true | true |
PMC9614410 | Aayushi Lohia,Deepak Kumar Sahel,Mohd Salman,Vivek Singh,Indumathi Mariappan,Anupama Mittal,Deepak Chitkara | Delivery strategies for CRISPR/Cas genome editing tool for retinal dystrophies: challenges and opportunities | 13-02-2022 | CRISPR/Cas9,Gene editing,Retinal dystrophies,Non-viral nanocarriers | CRISPR/Cas, an adaptive immune system in bacteria, has been adopted as an efficient and precise tool for site-specific gene editing with potential therapeutic opportunities. It has been explored for a variety of applications, including gene modulation, epigenome editing, diagnosis, mRNA editing, etc. It has found applications in retinal dystrophic conditions including progressive cone and cone-rod dystrophies, congenital stationary night blindness, X-linked juvenile retinoschisis, retinitis pigmentosa, age-related macular degeneration, leber's congenital amaurosis, etc. Most of the therapies for retinal dystrophic conditions work by regressing symptoms instead of reversing the gene mutations. CRISPR/Cas9 through indel could impart beneficial effects in the reversal of gene mutations in dystrophic conditions. Recent research has also consolidated on the approaches of using CRISPR systems for retinal dystrophies but their delivery to the posterior part of the eye is a major concern due to high molecular weight, negative charge, and in vivo stability of CRISPR components. Recently, non-viral vectors have gained interest due to their potential in tissue-specific nucleic acid (miRNA/siRNA/CRISPR) delivery. This review highlights the opportunities of retinal dystrophies management using CRISPR/Cas nanomedicine. | Delivery strategies for CRISPR/Cas genome editing tool for retinal dystrophies: challenges and opportunities
CRISPR/Cas, an adaptive immune system in bacteria, has been adopted as an efficient and precise tool for site-specific gene editing with potential therapeutic opportunities. It has been explored for a variety of applications, including gene modulation, epigenome editing, diagnosis, mRNA editing, etc. It has found applications in retinal dystrophic conditions including progressive cone and cone-rod dystrophies, congenital stationary night blindness, X-linked juvenile retinoschisis, retinitis pigmentosa, age-related macular degeneration, leber's congenital amaurosis, etc. Most of the therapies for retinal dystrophic conditions work by regressing symptoms instead of reversing the gene mutations. CRISPR/Cas9 through indel could impart beneficial effects in the reversal of gene mutations in dystrophic conditions. Recent research has also consolidated on the approaches of using CRISPR systems for retinal dystrophies but their delivery to the posterior part of the eye is a major concern due to high molecular weight, negative charge, and in vivo stability of CRISPR components. Recently, non-viral vectors have gained interest due to their potential in tissue-specific nucleic acid (miRNA/siRNA/CRISPR) delivery. This review highlights the opportunities of retinal dystrophies management using CRISPR/Cas nanomedicine.
Prokaryotes, especially bacteria, dwell in a variety of environments, including many unfavorable conditions. This means they have many methods by which they adapt to survive in the harsh habitat. The defense systems acting in an undefined, natural way include the restriction-modification, abortive infection, and surface exclusion systems [1]. Recent studies have also shown an acquired immune system, such as the clustered regularly interspaced short palindromic repeats (CRISPR), in prokaryotic organisms, both in bacteria and archaea. These are repeat sequence elements with, 21–37 bp in length, separated by spacers of similar size but varying composition. It forms a part of the adaptive immune system developed for protection against the attacking phage. The bacterium cleaves the genome of the invading virus and assimilates short viral genetic segments amongst its CRISPR sequences, which constitutes the pathogen-specific spacer elements. Thus, when the same virus attacks the bacterium subsequently, the CRISPR RNA (crRNA) and trans-activating CRISPR RNA (tracrRNA) guide the organism's CRISPR-associated (Cas) endonuclease to the foreign DNA complementary to its sequence, thereby degrading the invading viral genome [2]. A protospacer adjacent motif (PAM) that is present only in the viral genome and not in the bacterium helps it to differentiate self from non-self, thus cleaving and inactivating the virus [3]. CRISPR/Cas was discovered in 1987 and firstly demonstrated as therapeutic gene editing tool in mammalian cells in 2013 by Zhang and Church [4]. Since then, it has been identified as a potential therapeutic tool for genome editing and has been extensively studied for its application in many genetic and non-genetic diseases, including retinal dystrophies, cancer, hematological disorders, muscular dystrophies, neurodegenerative diseases, etc. The updated classification of CRISPR-Cas systems is based on the sequences of the Cas genes, the order of the repeats within the CRISPR arrays, and the organization of the Cas operons [5]. According to this system, there are three classes of CRISPR-Cas i.e. types I–III. Each type is further divided into subtypes, ranging from I-A to I-F, II-A to II-C, III-A and III-B. The Cas1 and Cas2 genes are present in all CRISPR-Cas types and the presence or absence of specific Cas proteins is the main basis of classification. For example, the Cas3, Cas9 and Cas10 proteins are hallmarks of CRISPR/Cas types I, II and III, respectively. There are systems that do not have the distinct Cas proteins of types I-III, are termed as unclassified (type U) [6]. The mechanism of action involves the formation of a ribonucleoprotein complex (RNP) that consists of the Cas9 protein and a guide RNA (gRNA) that can bind to the location directed by the gRNA on the genomic DNA. Upon binding, Cas9 cleaves the viral DNA, creating a double-stranded break that allows additional DNA modifications on the site [7]. The Cas9 nucleases are designed to lead to a DNA double‐strand break (DSB) at the target site. Repair of the strands takes place through error‐prone non-homologous end joining (NHEJ) or homology‐directed repair (HDR). When a template is absent, NHEJ is activated usually, resulting in insertions and/or deletions (indels) that damage the target genome loci. When a donor template is present with homology to the targeted locus, the HDR pathway follows, enabling precise edits [8]. Recently, the CRISPR-Cas system has progressed as a remarkable engineering tool for carrying out precise and regulated genetic modifications in many microorganisms such as Escherichia coli, Staphylococcus aureus, Lactobacillus reuteri, Clostridium beijerinckii, Streptococcus pneumonia and Saccharomyces cerevisiae [9]. There are many steps involved in the application of CRISPR/Cas for bacterial genome editing. The first one being the selection of the target space in the genome which will also decide the guide RNA to be developed. Until now, various parameters such as sequence setting, gRNA binding stability, chromatin accessibility and PAM sequence have been discussed as important factors. Many software tools have been developed to forecast the on-site and off-site cleavage efficiency of sgRNAs, including CRISPOR, JATAYU and CHOPCHOP, amongst many others. The tool generates a series of sgRNA at different PAM sites within the targeted gene, which are then aligned based on their efficiency in terms of the expected on-target and off-target binding potential, as well as the other variables discussed above [10]. There are also many other parameters, such as specificity and mismatch concerns that must be investigated while creating it as a therapeutic tool. Moving forward, CRISPR/Cas9 system was adopted in three major forms i.e. plasmid, mRNA, and purified active RNP. All these forms have their inherent advantages and limitations (Fig. 1) and are therefore utilized accordingly for the therapeutic purposes. Eye related diseases, especially retinal dystrophies are degenerative conditions marked with clinical and genetic heterogeneity and affect 1 out of every 2000 people all over the globe. More than 238 mutant genes that decide the phenotype are explored till now. The complexity of the neuronal pathways, physiological barrier due to anatomy of the eyes, the structure of each cell, and the diversity of functions of each retinal layer create many challenges in development of therapeutic strategies for these diseases. Most common site where the therapeutic agents need to work is the posterior part of the eyes which is quite inaccessible through conventional routes. Intravitreal route is beneficial in such cases with some risk of eye damage and requires expertise. There are available treatments for dystrophic conditions, such as wet age-related macular degeneration (wAMD), wherein anti-VEGF antibodies are injected through intravitreal route and were found to be beneficial. But the treatment needs multiple dosing over time and can cause eye damage due to multiple intravitreal injections. Therefore, a more relevant system needs to be developed to overcome such hurdles. Gene editing in recent times has grown to treat diseases characterized with gene mutation. CRISPR/Cas9 system could be directed towards a specific gene sequence to correct a mutation. This technique has been explored for retinal diseases, since the unique anatomical position, immune-privileged nature, blood-retinal barrier and identified underlying mutation makes eye, and specifically retina, amenable for therapeutic gene editing [11,12]. Further, it provides immense potential because of its one-time treatment possibilities via gene editing. CRISPR/Cas9 tool, however, is facing several delivery difficulties due to its large molecular weight. Although some viral vectors are available with limitations (Table 1), development of an efficient delivery vehicle for CRISPR/Cas is the need of the hour. Nanotechnology based non-viral carriers such as polymeric nanoparticles, liposomes, micelles, dendrimers etc. are currently being explored for delivery of CRISPR/Cas9. This review highlights the recent scenario of retinal dystrophic conditions and potential CRISPR/Cas based nanomedicines used in the treatment.
After the success of the spCas9 as a gene-editing tool, it has been explored more for several other applications as well. Despite the specificity, the large size of spCas9 makes it difficult to deliver using viral vectors. Therefore, ample variants or orthologs have been discovered till now. Campylobacter jejuni (CjCas9) (984 amino acid) is a smaller Cas9 nuclease discovered in 2017 [15]. Later in 2017, the Zhang group discovered Cas13 as a new orthologue having RNA targeting potential [16]. In 2013, Qi et al. used a dead version of Cas9 (i.e. dCas9) RNPs to suppress the gene expression by interfering with the RNA polymerase binding mechanism [17]. Additionally, the same group reported the application of dCas9 protein fused with transcriptional repressor KRAB (dCas9-KRAB) in gene silencing (CRISPRi) [18]. dCas9 protein fused with transcriptional activators VP64 was explored as gene activator i.e. CRISPRa, and therefore make CRISPR a suitable tool for transcriptional programming [19]. In 2017, the Liu group reported CRISPR as a base editor without introducing DSB [20]. dCas9 fused with epigenome modifier or the fluorescent tag was also used for the epigenome editing [21] and imaging [22] respectively. Recently, CRISPR/Cas system was also utilized as a diagnostic tool for the detection of Covid-19 infection [23]. Collectively, CRISPR/Cas has ample application beyond the DSB mediated gene editing. CRISPR has been potentially recognized as a tool for diagnostics, epigenome editing, gene regulation (CRISPRi/CRISPRa), imaging, etc. (Fig. 2).
The mammalian retina is being widely studied for genetic disorders for several reasons. Multiple phenotypes of the retina can be directly observed, and photographs can be recorded. The effects of psychophysical parameters (acuity, field, color contrast) can be documented and retinal electrophysiology can be used to assess retinal functions [24]. Lastly, the ease of visualization of the retina has led to the development of many animal models that have led to a better understanding of pathways leading to photoreceptor death [25]. Worldwide, 1 in every 2000 people suffers from inherited retinal dystrophies (IRD). Individuals with IRD typically present with progressive vision loss that ultimately results in blindness. Since these diseases are genetic and clinically heterogeneous, hardly any effective treatments are available. Multiple cells, genes and drug-based therapies are in different phases of clinical trials for IRD [26]. IRD are distinguished by continuous degeneration of retinal pigment epithelium (RPE) and the neural retina. These dystrophies are of various types such as cone-dominant dystrophy (cone-rod/cone dystrophy), rod-dominant dystrophy (retinitis pigmentosa/rod-cone dystrophy), pattern dystrophy, macular dystrophy (Best macular dystrophy, Stargardt disease, Sors by fundus dystrophy), photoreceptors and bipolar cells abnormality (congenital stationary night blindness, X-linked retinoschisis), hereditary choroidal diseases, and vitreoretinopathies (Stickler syndrome, Wagner syndrome) [27,28].
Genome therapy using CRISPR-Cas in ophthalmic diseases may be promising, considering the scale of impact on society and various monogenic disorders of the eye [29]. Hopes are high to attenuate inherited retinal disorders due to the multiple clinical trials which have been initiated for specific retinal conditions with advancing gene therapy technology [30]. Table 2 showing various pre-clinical studies related to the use of CRISPR/Cas system for the treatment of retinal dystrophies. Over the last two decades, eye tissue has become a frontline organ for gene therapy. It is achieved either by using viral vectors to transfer correct cDNA copies or through the use of RNA intrusion to knockdown proteins with dominant-negative traits or toxic inclusion of functionalities via gene silencing [31]. Before the arrival of gene therapy, retinal dystrophies were incurable [32]. Indeed, IRDs have been demonstrated as ideal candidates for gene therapy because: (i) they are inherited diseases linked to multiple genes and a subset of them show monogenic inheritance [33], (ii) the cells which are affected (PRs and RPE) can be accessed by various clinical and surgical procedures [34], (iii) the non-invasive diagnosis methods used in the clinics for IR patients could be translated to animal models and (iv) availability of animals models to study the eye conditions. The Phase III data for Spark Therapeutics’ gene therapy product (i.e. SPK-RPE65) for the treatment of patients with visual impairment caused by RPE65 gene defects provides hope for clinical translation opportunities. SPK-RPE65 is an AAV2 gene therapy that delivers the RPE65 gene via subretinal injection to patients with a defective RPE65 gene. Clinical trial outcomes were found beneficial and the therapy, SPK-RPE65, was approved by FDA in 2019 with trade name of LUXTURNA for the treatment of vision loss in the patient [35]. The therapy is based on recombinant adeno-associated virus (AAV) vectors expressing the human RPE65 cDNA using a viral promoter as a control [35]. Although, gene therapy provides immense potential for treatment of various genetic diseases, it poses some disadvantages like off-target effects and risk of mutation in the DNA. These disadvantages limit their application in several cases. Therefore, gene editing tools such as zinc finger nuclease (ZFN) and transcription activator-like effector nucleases (TALEN) have been developed for the treatment of genetic diseases. Moreover, in recent times CRISPR/Cas9 based gene editing tool is being explored for the treatment of genetic disease through its unique site-specific gene editing efficiency. Here, multiple guide RNAs are being used to simultaneously target various sites in the genome, which is a striking feature of the CRISPR/Cas system [36]. A major advantage of deploying CRISPR-Cas is that it is a RNA-based system; thus, custom guide RNAs can be easily designed to target within the genome. Whereas ZFN and TALEN systems, which are protein-DNA interfaces, are protein-dependent making it difficult to engineer for a given target [37]. The potential for multiplexed genome surgery is another interesting feature of the CRISPR-Cas system using several gRNAs for the concomitant editing of multiple sites within the genome [36,38]. Some of the major mutation based retinal dystrophic conditions are discussed below.
Leber's Congenital Amaurosis (LCA) has been known to be the most severe retinal dystrophy as it potentially leads to congenital blindness in less than one year of age. Fourteen mutated genes have been identified by homozygosity mapping, linkage analysis and genome analysis in LCA patients and children with retinal degeneration constituting approximately 70% of the cases [39]. LCA is mostly associated with severe defects which includes roving movements of the eye called nystagmus. Also, slow reactions of the pupil and lack of electroretinographic reactions are some of the symptoms in children [40,41]. Genes involved in LCA encode proteins which are responsible for retinal functions includes photoreceptor morphogenesis (CRB1, CRX), vitamin A cycling (LRAT, RPE65, RDH12), phototransduction (AIPL1, GUCY2D), guanine synthesis (IMPDH1), and outer segment phagocytosis (MERTK) and also intra-photoreceptor ciliary transport processes (CEP290, RPGRIP1, LCA5, TULP1) [39]. The most prominently studied gene for LCA is mutations in the RPE (RPE65) gene, which is responsible for encoding retinoid isomerase [29] whereas, the most frequently occurring mutations are associated with the CEP290 (15%), GUCY2D (12%), and CRB1 (10%). Around 20% of patients in north-western Europe have an intronic CEP290 mutation (p.Cys998X). An AVV-CRISPR system has been developed for in vivo treatment of autosomal dominant retinitis pigmentosa (adRP) and LCA10 in mice. In this study, AAV-SpCas9 vector was delivered via subretinal-injections that targets the rhodopsin (RHO) or CEP290 and Nrl (neural retina leucine zipper transcription factor) gene in mouse models for adRP. The outcomes of the study showed the expression of spCas9 protein in retinal cells of the mice for 9.5 months. While the authors have deployed different AAV serotypes as well as different vector doses, the results proved effective restoration of RP or LCA10 phenotype without off-target effects and adverse toxic reactions [42,43]. Later, the strategy was adopted to resolve the RHO gene mutation in human cells successfully. Collectively, CRISPR/Cas9 technology has shown to be effective at targeting gene/alleles in an efficient and specific manner in this study, demonstrating that it could be used in the treatment of RP and other genetic disorders, including dominant human conditions.
Age-related macular degeneration (AMD) is a multi genetic disorder [29]. Wet AMD, which is the neovascular form of AMD, is marked by abnormal growth of the choroidal vessels in the macula region of the retina, resulting in a loss of central vision. Macula is enriched with cone photoreceptors and is responsible for the bright light activities and color vision [44]. Neovascularization in wAMD occurs due to overproduction of vascular endothelial growth factor (VEGF); hence anti-VEGF agents becomes the therapy of choice [45]. Currently, wet AMD patients are treated with intravitreal injection of anti-VEGF agents such as ranibizumab, aflibercept and bevacizumab [46]. For the treatment of AMD, AAV-CRISPR systems have also been developed based on CjCas9 (Campylobacter jejuni) [47] and LbCpcf1 nucleases (nucleases which are a member of the type-V CRISPR-Cas systems). In the study, authors packaged the CjCas9 gene, its corresponding sgRNA sequence, along with a marker gene into an AAV vector. Being highly specific, CjCas9 can cleave only a restricted number of sites in the human or mouse genome. Hence, when delivered using AAV, CjCas9 lead to targeted mutations in the RPE cells. CjCas9 can be specifically targeted to the Vegfa or Hif1a gene in RPE cells thereby, decreasing the size of laser-induced choroidal neovascularization, making in vivo genome editing with CjCas9 a new advancement in the therapy of AMD. Further, the results indicated an Indel efficiency of 22±3 and 31±2%, for Vegfa and Hifla genes, respectively at 6-weeks post injection of AAV-CjCas9 intravitreally. Moreover, the effect of Indel was seen at protein level as well where significant decrease in VEGF-A protein was observed in RPE cells with respect to control group. In another study, sgRNA/Cas9 expressing plasmid and Cas9 RNPs were delivered using lipofectamine 2000, wherein Cas9 RNPs showed 82%±5% and 57%±3% of indel in NIH3T3 and ARPE-19 cells, respectively. Comparative study indicated that the Cas9 RNPs were more effective w.r.t plasmid at 2nd day of transfection. Further, it was observed that level of VEGF A mRNA and protein reduced to 40%±8% and 52%±9%, respectively in adult retinal pigment epithelial cells (ARPE) after Cas9 RNPs treatment. For in vivo efficacy evaluation, Cy3 labelled RNPs were delivered via intravitreal injection. The results indicated the accumulation of Cy3 dye into RPE cells after 3 days post injection and 25%±3% of indel was also detected in RPE cells at delivery site. Moreover, CNV model was also developed in mice using laser (to mimic wAMD) followed by subretinal injection of Cas9 RNPs. After 3 d, 22%±5% indel was observed in RPE cells for Vegfa gene. Additionally, Cas9 RNPs treatment significantly reduced CNV area by 8%±4%, and VEGF A protein level as well (Fig. 3) [48].
Retinitis pigmentosa (RP), affecting 1 in 4000 people, has become the leading cause of progressive blindness [49]. Classical RP, also known as the rod-cone dystrophy, is identified by a “tunnel vision”, which is a progressive loss of peripheral vision. The first signs include nyctalopia, which is the development of night blindness and difficulties in adapting to the dark that occurred through the loss of rod function in the early years of life [50]. The RPE starts losing its pigment due to loss of photoreceptor that ultimately leads to the accumulation of intraretinal melanin deposits, which look like a “bone spicule” conformation. However, the central vision remains intact until the last stages. It can be inherited through different transmission modes such as autosomal dominant, autosomal recessive or X-linked and is heterogeneously related with mutations in at least 79 genes [49]. RP is mainly of two types, MERTK associated and RPGR X-linked. The RPE apical membrane contains photoreceptors that are light sensitive and their turns over is enabled by MERTK (Mer tyrosine kinase), the receptor involved in phagocytosis of the rods and cones [51]. These photoreceptors must be continuously recycled for efficient working, which is disrupted by mutations in MERTK, leading to degradation and loss of photoreceptors [52]. Meta analyses have revealed that only 3% of MERTK type RP are due to autosomal recessive transmission [53,54] and causes macular atrophy and early-age photoreceptor abnormality [55,56]. Mutations in RP GTPase regulator (RPGR), an X-linked RP (XLRP) is seen in 1 in 3500 people. RPGR, along with the delta subunit of rod cGMP phosphodiesterase, regulates the proteins, and its dysfunction leads to progressive loss of central vision and night blindness [57], [58], [59], [60]. Some in vitro and in vivo studies have been reported where RP has been treated using CRISPR/Cas9 technology. For example, CRISPR/Cas9 tool was used in rat model of adRP by Bakondi et al., in 2016 to ablate mutation in rhodopsin gene (RhoS334). In this study, sgRNA/Cas9 plasmid (targeting exon 1 immediately upstream of a PAM unique to the RhoS334 locus) was administered intravitrealy in S334ter-3 rats. Genome analysis of transfected retinal cells confirmed a cleavage efficiency of 33% and 36% in two different rats. Also, improved visual acuity and extensive preservation of retina was observed via immunohistology following sgRNA/Cas9 plasmid injection [61]. In addition, a CRISPR/Cas based strategy was developed for editing RHO gene mutations. In this study, a plasmid was designed which contained an insert for two sgRNAs targeting RHO gene to cause DSB followed by NHEJ. The outcome of the study dictated successful editing of RHO gene, which further downregulated the expression of RHO protein. Further, Bassuk et al. treated XLRP by correcting RPGR point mutation using CRISPR/Cas9 in iPSCs. In this report CRISPR was used to treat the pathogenic mutation in iPSCs obtained from a patient with photoreceptor degeneration. The authors screened 21 different sgRNAs for editing where g58 was found most effective. Therefore, g58/Cas9 expressing plasmid was designed and transfected into iPSCs along with a RPGR single-stranded oligo deoxy ribonucleotide (ssODN), which acts as a donor during HDR pathway. Further, deep sequencing was performed, and the data revealed the successful correction of mutation in 13% of transfected cells [62]. Moreover, the results showed that TAG (premature stop codon) gets replaced by GAG (wild type codon), which encode glutamate at residue 1024. On the other hand, no changes in the mutation were seen in the untransfected iPSCs. Further it was concluded that the correction rate of 13% was significantly fruitful and can be improved by minimizing error-prone NHEJ by inhibiting DNA ligase IV at the DNA cleavage site. In addition, a CRISPR/Cas-based strategy was developed for editing RHO gene mutations in a mouse model of ADRP. In this study, a plasmid was designed that contained an insert for two sgRNAs, targeting RHO gene (exon 1) having P23H dominant mutation. Firstly, the gene editing was performed in vitro in HeLa cells, where 70%, 76% and 82% of indel frequency was observed with sgRNA1, sgRNA3 and 2sgRNA, respectively, Additional, the RHO expression was also observed using Real time Taqman PCR wherein 35%, 25% and 20% of reduction in expression was seen with cells treated with sgRNA1 sgRNA3, and 2sgRNA, respectively. Later, the electroporation of CRISPR/Cas plasmid containing 2sgRNA along with green fluorescence protein (GFP) expression was performed subretinally in P23 RHO transgenic mice. The GFP expressing section of the retina was isolated and evaluated for the Cas9 expression wherein the Cas9 expression was limited to the cells expressing GFP along with 84 edited sequences [63].
Choroideremia (CHM) is named from the complete loss of the choroid, retina and RPE, exposing the underlying white sclera, which is unique to the disease [64]. CHM is an X-linked recessive degenerative retinal dystrophy, affecting 1 in 50,000 individuals and is only associated with males. Due to mutations in CHM gene, which encodes for Rab escort protein 1 (REP1) and its dysfunction leads to progressive loss of vision and choroid atrophy. It starts with night blindness in the early years of life with a gradual decline in peripheral vision and legal blindness by 50–70 years of age [65]. The CHM disease is characterized by retinal thickening, resulting from Müller cell activation and photoreceptor layer hypertrophy. This further causes RPE depigmentation, degeneration of photoreceptors and retinal remodeling. Hence, retinal remodeling is being considered as a possible strategy for in vivo studies [66].
Stargardt disease is an autosomal recessive genetic disorder majorly caused by mutations in the ABCA4 (ATP-binding cassette, subfamily A, member 4) gene and is the most common form of juvenile-onset macular degeneration. It is characterized by the loss in central vision due to the gradual accumulation of cytotoxic lipofuscin within the RPE [67]. This disease affects at least 1 in 10,000 people, with approximately 31,000 cases in the United States. The disorder consists of a quite fast degeneration of the macula resulting from the deposition of lipid enriched deposits called lipofuscin (comprised mainly of A2E, a vitamin A derivative) in the RPE cell layer. Due to this, the interaction between photoreceptors and RPE is affected, causing the death of photoreceptors by hampering with their ability to uptake nutrients and perform the visual cycle [68].
With a prevalence of 1 in 20 000, usher syndrome is one of the common forms of syndromic IRD. Its unique features include RP and hearing loss [69]. The heterogenous syndrome is classified into three subtypes depending on the progression and severity of the hearing loss and the age of onset of the RP. Usher syndrome type 1 (USH1) is the most critical; usher syndrome type 2 (USH2) presents moderate to severe symptoms and is most frequently observed. Lastly, usher syndrome type 3 (USH3) is characterized by a moderate phenotype, and the onset of the disease and its progression could vary on a case by case basis [70]. USH1 is the most common cause of deaf-blindness in humans, characterized by vestibular dysfunction, profound congenital deafness and RP and is inherited in an autosomal recessive manner. USH1 is caused due to mutations in myosin VIIA, which encodes for an organelle transport protein within the RPE [45]. In 2017, Fuster-Garca et al., proposed the use of CRISPR/Cas9 gene editing to restore c.2299delG mutation in the USH2A gene. Human dermal fibroblasts (HDFs) cells were isolated from an USH2 patient with c.2299delG mutation and used for gene editing. Briefly, using nucleofection, a Cas9 RNPs (comprising Cas9 (15 µg) and sgRNA (20 µg)) was transfected into HDFs of the normal patient, yielding 18% indel frequency. Subsequently, RNPs were co-delivered with ssODN-2299, which yielded HDR efficiency of 5%. Similarly, HDFs of the patient with c.2299delG mutation were transfected with ssODN with a WT sequence and the PAM sequence ablated. As per the results, 6% indel frequency and a 2.5% HDR were detected [71].
Bestrophin, encoded by the BEST1 (VMD2) gene, is a transmembrane protein expressed on the basolateral aspect of the RPE cells and is responsible for the conduction of chlorine across the RPE. Mutations in the BEST1 (VMD2) gene, and hence bestrophin, hampered the fluid transport across the RPE thereby causing debris to build up between the RPE and photoreceptors. Consequently, atrophic macula scar and central visual loss occur in a short span of time, leading to best disease or Vitelliform macular dystrophy. It affects between 1 and 9/100 000 people and is inherited in an autosomal dominant manner. Many other retinal dystrophies can also occur due to BEST1 mutations, including RP and ADVIRC (Autosomal Dominant Vitreo Retino Choroidopathy). Biallelic mutations lead to multifocal small egg yolk deposits leading to Recessive Best Disease. Neovascularization in the choroid, along with hemorrhage and leak into the retina, further aggravates the disease condition and intravitreal anti-VEGF agents can be used for successful therapy [72]. In the year 2020, Sinha et al. demonstrated the effectiveness of gene augmentation in the treatment of the Best disease. Induced pluripotent stem cell-derived RPE (iPSC-RPE) was used as an in vitro Best disease model for this objective. Gene augmentation restored BEST1 gene activity and improve rhodopsin degradation. Meanwhile, some of the mutations did not respond to the gene augmentation, therefore CRISPR/Cas9 was used to investigate the efficiency of site-specific gene editing in iPSCs RPE models. The findings revealed that CRISPR/Cas9 edit the mutant BEST1 gene while leaving the wild-type BEST1 gene intact. Off-target indels were also tested, however, no evidence of off-target gene editing was reported. The study overall revealed the application of CRISPR/Cas based precise and specific gene editing for the management of retinal dystrophies [73].
RS1 is a retinoschisin gene that encodes a protein responsible for the cell adhesion. Mainly observed in males, RS1 mutations cause the development of cystic cavities in the center of the retina that enlarge gradually with age, along with decreased visual acuity. As the dystrophy progresses in the retinal periphery, the condition of the split retina worsens with large atrophic holes; hence, the residual retinal blood vessels left hanging in the vitreous cavity above the retina which may result in vitreous hemorrhage. It has been observed that people with this disease usually have a refractive error of long-sightedness. Worldwide, about 1 in every 5000 to 25,000 suffer from the condition. Children who suffer slowly lose out on central vision; however, most children can complete a fully sighted education, with the help of magnified texts and teachers for visual support. The main therapy for the retinal cysts is the carbonic anhydrase inhibitors, although a significant improvement in symptoms is rare. Huang et al. developed a base editing strategy to cure X-linked juvenile retinoschisis (XLRS) in 2019. Using human induced pluripotent stem cells (hiPSCs) from patients, a 3D retinal organoid model with XLRS characteristics was created in vitro. To evaluate the model, CRISPR/Cas9 targeting the C625T mutation in the RS1 gene was introduced as a plasmid using a viral vector. According to the findings, CRISPR effectively repairs gene mutations while also correcting the phenotypes by up to 50%. The findings also revealed the existence of off-target indel, which is a CRISPR constraint [74].
Congenital stationary night blindness (CSNB), also called nyctalopia, is a non-progressive type of night blindness. Patients suffering from this condition have difficulty observing in low light. The symptoms start early in children along with low amplitude nystagmus, strabismus and reduced visual acuity [75]. Associated with 17 genes, CSNB is a polygenic disease, and diagnosis involves electroretinography to measure photoreceptor function. The ERG results may show lack of rod functioning or incomplete functioning of both cone and rod as well as abnormal fundus upon examination. The state of complete CSNB is caused when bipolar cell signaling is disrupted, leaving a single intact alternate pathway [76]. CSNB is of four subtypes - Schubert Bornstein (branched into complete and incomplete), Riggs, Fundus Albipunctatus and Oguchi Disease, of which the last two types show abnormal fundus. Myopia and photophobia are two of the prominent features observed in patients. Children with ‘incomplete’ CSNB may not be aware of the condition as the symptoms are mild and central vision is reduced from normal [72].
Affecting only 1 in 30 000 to 40 000 people, achromatopsia is a rare autosomal recessive disease. Mutations in six different genes have been identified that are responsible for this disease. 75% cases are due to CNGB3 and CNGA3, while the rest are accounted by GNAT2, PDE6C, PDE6H and ATF6. There is complete color blindness and central vision is diminished. In the early months, patients have to deal with profound photophobia and nystagmus. However, the nystagmus in achromatopsia patients is pendular or horizontal unlike the roving nystagmus of LCA. The diagnosis involves electrophysiology where it is observed that the function of cone photoreceptors is absent, and rods functions normally. Usually, the complete form is seen, and the incomplete form with a milder phenotype tends to be much rare. The symptoms of the disease are mostly constant, and the glare and photophobia can be managed by incorporating red/brown shade glasses.
As the name suggests, cone cell degeneration (COD) or cone followed by rod degeneration (CORD) are progressive retinal dystrophies and are seen from a young age. The major difference between them is that rod involvement increases the severity of the disease and by age 40, these people reach the stage of legal blindness [93]. Examination of the fundus and macula reveals an atrophic appearance or deposits of retinal pigments seen variably in different patients. Mutations in over 30 genes have been found as well as molecular causes identified in around 20% and 74% of autosomal dominant and X-linked COD/CORD respectively, while 23%−25% of autosomal recessive types have been worked out [94]. A number of ongoing clinical trials are living proof that gene therapy has made retinal dystrophies curable [29]. Furthermore, constraints such as multiple intravitreal injections resulting in physical retinal damage and resistance render the current therapy ineffective. Fortunately, the eye, and specifically the retina, is accessible to therapeutic gene editing due to its unique anatomical position, immune-privileged nature, presence of the blood-retinal barrier, and known underlying mutations [12]. As a result, the eye has been extensively studied for gene editing. However, just a few RDs in terms of preclinical evidence related to effective gene editing utilizing CRISPR/Cas have been published, and more research in this field is needed. Interestingly, some preclinical studies have been published wherein wAMD was treated by employing a CRISPR/Cas-based tool to knock out the VEGF A gene in RPE cells. Off-target effects and the deletion of some uncleared functions of the concerned gene are two key pitfalls that could be encountered with CRISPR. On the other hand, several groups are working to integrate data and screen for off-target effects [95], [96], [97]. It will be intriguing to observe if a CRISPR/Cas-based gene editing method can prevent angiogenesis in wAMD in clinical trials as it directly eliminates the fundamental cause of RDs. Further, the CRISPR/Cas-based therapy could also treat RDs with a single dose injection. We have discussed various factors that should be considered while adopting CRISPR/Cas9 for the treatment of RDs (Table 3).
The three main approaches for CRISPR based genome editing include the use of (a) plasmid DNA (pDNA) that expresses the Cas9 protein and sgRNA, (b) mRNA that encodes the Cas protein, and (c) RNP which is a complex of Cas protein and sgRNA. Among reported approaches, the plasmid-based approach is the simplest while the RNP based approach showed minimal off-target effects. Nevertheless, these CRISPR components ought to be delivered to the target cells followed by their translocation to the nucleus. Owing to the nature of the cargo, different challenges including packaging, immunogenicity, mutagenesis, extra- and intra-cellular barrier, etc., needs to be overcome to achieve efficient genome editing (Fig. 4).
The major challenge faced in the delivery of CRISPR components for therapy is their packaging into a single vector system. Through the AAV approach, the maximum possible size for the cargo gene is ∼4.7 kb, whereas that of the SpCas9 gene alone is ∼4.3 kb. Hence, for AAV method, it becomes a hurdle to insert additional CRISPR components like sgRNAs, or extra genes. To solve this issue, various techniques such as using a smaller sized Cas9 (SaCas9) or splitting Cas9 into two vectors, have been propagated, but their feasibility for therapeutic applications have to be evaluated [103]. For the packaging of RNPs, viral vectors cannot be used. The use of non-viral vectors possesses a multitude of problems, be it the high molecular weight of the protein, highly negative charge and/or the stability of the RNPs.
CRISPR/Cas9 is a bacterial immune system, and therefore, being bacteria-derived they could lead to the immune response in the host. Specifically, if the gene-based approach is used, it could lead to the integration of Cas9 protein into the cells of the host. The expression of ectopic Cas9 protein in the individual could cause an MHC class I mediated immune response thereby, eliminating Cas9 expressing cells [104].. A study by Chew et al. showed that the in vivo delivery of AAV vector prompted immunogenicity not against the viral antigens but against the Cas9 protein. According to this result, immunogenicity of AAV-CRISPR-Cas9 has been considered as a key property that destabilizes the host system and will negatively impact its application in vivo [105]. The best results have been found with a protein-based delivery of the CRISPR-Cas system, which has shown the least potential immunogenicity, as the ectopic Cas9 protein is present only transiently in the host cells [103].
A lot of times, the vectors may get inserted in random sites within the genome thereby causing mutagenesis of essential genes. When trials were conducted using a retroviral vector based gene-therapy approach to treat Severe combined immunodeficiency (SCID), it caused leukemic transformation, as the virus got integrated into the host DNA [106] and triggered abnormal expression of the targeted gene. Tumorigenesis can result from vector insertions, when the integration occurs near a protooncogene, thus posing a greater risk for integrating viral vector based CRISPR delivery systems. This issue has been circumvented using non-integrating viral vectors such as AAV-based systems and by protein/RNA based CRISPR delivery systems [103].
CRISPR/Cas system is required to be delivered in cells or tissue of interest without any off-target effects. Therefore, localized delivery route advantageous than systemic delivery of CRISPR/Cas, especially to reduce immunogenicity, avoid off target effects and to improve the target cell edit efficiencies. In the case of retinal dystrophies, localized injections are given via intravitreal or subretinal administrations.
Targeting may be defined as a preferential accumulation of the active agent at a predetermined site which could be a tissue or organ (first order targeting), a specific cell type (second order targeting) or an intracellular site of targeted cells (third order targeting). Targeting is important because the therapeutic product can cause many adverse effects and damage the non-target cells. It also decreases the concentration of drug required to produce desired effect at the site of action. One of the advantages that viral vectors provide is tissue tropism, which will be beneficial for targeted CRISPR/Cas9 delivery [107] . But, if non-viral vectors are employed, specific moieties such as peptides and antibodies will be required for targeting [108]. However, such targeting is quite difficult to achieve due to complications in packaging that may arise due to the insertion of extra biomolecules to a delivery vector along with the CRISPR components.
CRISPR/Cas9 components need to be successfully delivered in sufficient quantity into the target cells by transfection, a prerequisite for efficient genome editing. Transfection methods are of three types, viral, chemical, and physical. Among these, the most used non-viral method is electroporation. But, due to the high electric field strength and accompanying electrochemical reactions, electroporation often causes high post-transfection mortality [109]. The editing efficiency for CRISPR/Cas9 obtained in vivo is much lower than that of those achieved in vitro in cell lines. In another study, when Cas9-RNP was delivered locally into the mouse inner ear, it caused 20% GFP fluorescence loss. This small percentage may work in some diseases such as liver tyrosinemia and muscular dystrophy. The editing efficiency is also linked to delivery efficiency. Recently Cas9-RNP delivery efficiency up to ∼95% in cultured cells has been attained although the in vivo delivery efficiency requires further investigation [110].
One of the biggest setbacks for genome editing technology is the off-target effects. Off-target effects are occurring when the specially engineered sgRNA, apart from targeting the gene of interest, targets the non-specific genes [111,112]. TALENs usually have lesser toxicity and greater specificity than ZFNs. Also, with CRISPRs, different cells may carry different edits even if they get edited by a single gRNA. It has been shown that even a single mismatch between base pairs can decrease binding to a great extent. A mismatch occurring at the 5′ end of the target is much more destructive than the 3′ end. In case of CRISPR therapy as well, off-target effects have been a significant problem. Secondary targets of the sgRNA, which have multiple mismatches with respect to the sgRNA undergo mutations at rate similar to the desired target [113]. The CRISPR/Cas off-target effects are further amplified when viral gene delivery method is employed, possibly due to the long-term constitutive expression of Cas9/sgRNA that leads to continued exposure of Cas9/sgRNA to non-specific genes. Various techniques are being developed to eliminate off-target effects such as designing sgRNA of high specificity [114,115]. In vivo effects for these systems have not been fully developed for the off-target effects. The best technique in this regard remains protein-based delivery of CRISPR since there is only a transient exposure of the host genome to the Cas9/sgRNA, thus decreasing off-target events [116].
Anatomically, anterior and posterior segments of the eye are affected by vision threatening disorders. Most of the currently available ophthalmic preparations are eye drops possessing poor bioavailability through conjunctival route [117]. There are ample of physiological and anatomical barrier which impede the delivery of active pharmaceutical ingredient (API) to affected areas of the eye. Tear film, eye blinking, efflux pump, nasolacrimal drainage, are some of the barrier to drug absorption [118]. Most of the dystrophic conditions require delivery of the therapeutic agent to the posterior portion of the eye and is limited by static barriers including blood-retinal barrier, Bruch's membrane, sclera choroid, and the dynamic barriers i.e. lymph and choroidal blood flow [119]. Intravitreal route is the most common mode of administration of drugs to the posterior chamber of the eye [120]. On the same note there are some limitations such as patient compliance, need expertise, risk of retinal detachment, risk of cataract and hemorrhage [121]. Attempts have been made to overcome existing problems related to the delivery of molecules towards the posterior portion of the eye using nanotechnology-based delivery carriers. Nano size and surface charge of nanoparticle helps target specific retention and conjugation in vivo. Also, nanoparticles with higher zeta potential are supposed to have higher stability. Additionally, cationic nanoparticles are considered more applicable for topical ophthalmic delivery, as conjunctiva and cornea have negative charge on the surface [122]. Therefore, electrostatic interaction helps in the internalization of nanoparticle into eye. For intravitreal injection, anionic nanoparticle diffuses more effectively through vitreous as it is composed of anionic hyaluronic acid which helps anionic particle to reach posterior chamber of the eye without any interaction [123]. On the other hand, cationic nanoparticles interact with anionic hyaluronic species and remain undiffused and are entrapped in the vitreous. Therefore, anionic charge of nanoparticles ease the intravitreal delivery of cargo to posterior part of eye [124]. Till now, several nanocarrier systems have been explored for ocular delivery viz., polymeric micelles, liposome, polymeric nanoparticles, nano-emulsion/suspension, solid lipid nanoparticles etc. These nanocarrier systems provide advantages such as targeted delivery, enhanced bioavailability, sustained release, improving residence time in ocular space etc. [125]. Additionally, for ocular retinal delivery, intraocular route has significant benefits over systemic route. Systemic route poses hurdles such as blood retinal barrier, systemic toxicity of the drug administered, poor target specificity, rapid clearance, off target effects, and only1%−2% drug reaches to the eye via systemic route. Hence intravitreal route can provide distinct advantages and nanomedicines could serve as potential therapeutics for the treatment of retinal dystrophic conditions. On the same note, there are some clinical complications with intravitreal injection such as patient compliance, infectious endophthalmitis, intraocular inflammation, rhegmatogenous retinal detachment, intraocular pressure elevation, ocular hemorrhage, glaucoma, cataract, non-infectious uveitis etc. These complications can be minimized by reviewing patient medical history, appropriate ocular examination, ancillary diagnostic testing, individualized medical decision-making, and proper follow-up by a clinician.
Many genome engineering applications have been developed for CRISPR/Cas systems for in vitro studies in cell lines; however, achieving an efficient in vivo delivery of CRISPR/Cas system is a major challenge as multiple components need to be delivered to the target cell to produce the desired effect. Nucleofection, electroporation and lipid-based deliveries have been tried for plasmid DNA (that encodes for the Cas9-gRNA) through the cell membrane [126]. Electroporation is a technique in which high voltage is applied to create pores in the cell membrane so that, direct transfection can occur into the cells both in vitro and in vivo [127]. Electroporation can be extremely toxic as it disturbs the cell membrane and may even lead to cell death [128]. Microinjection is preferred in rapidly dividing single cells, specifically in larger cells such as fertilized embryos, wherein the CRISPR components are directly injected into the single cell to create varieties of knockout and transgenic animals. However, this is a technically demanding procedure [129]. These mechanical methods are preferred for in vitro editing as they are reproducible, simple and have high levels of gene expression. Further, delivery through these direct methods has been used ex vivo in cells harvested from patients and then reintroduced into their body. Collectively, existing literature revealed the accuracy and efficiency of direct methods, but these methods are limited to in vitro or ex vivo applications. However, efficient in vivo delivery systems need further research and technical advancements. Delivering payload specifically to any organ in the body should have some common factors that need to be considered, such as immune response, hematological toxicity, targeted delivery, distribution, etc. However, there are additional factors to be considered for retinal delivery of the therapeutic agent. The blood-retinal barrier limits the amount of payload that reaches the eye after intravenous injection thus localized injections (such as subretinal, intravitreal, corneal permeation, etc.) are mostly preferred for a retinal delivery route. Fortunately, the anatomical location also makes the eye more feasible to the localized injection. Vitreous fluid is one of the major barriers to retinal delivery. The viscosity and anionic charge of the vitreous fluid must be considered while developing a nano carrier-based delivery system [130]. As reported earlier, the high positive charge nanoparticle gets accumulated within the vitreous due to electrostatic interaction with anionic hyaluronic acid present in the vitreous [131]. Similarly, particle size is also having a considerable impact on vitreous diffusibility [132]; reports say that the particle size below 50 nm showed rapid clearance from the vitreous. Since the volume of the vitreous is very less, the injection volume is limited to a certain amount (25–100 ul). Therefore, the nanocarrier should have sufficient payload capacity so that the desired concentration of the payload could be delivered in that limited injection volume. Being a sense organ, the eye is more sensitive to toxicity, and therefore while selecting a nano-carrier, the toxicity issue should be considered. Although the immune-privileged nature of the eye provides opportunities to use distinct biomaterial used in delivery, despite that, toxicity may lead to the loss of eye integrity and could cause vision loss. Chitosan is the best example, which is known to cause retinal toxicity by inducing an immune response in the eye [133]. An increase in intraocular pressure is also a major challenge in intravitreal delivery and certain measures must be taken to resolve this issue. The intravitreal injection dose must be given by a trained professional because any mistake could lead to a serious eye injury. Additionally, multiple frequent injections need to be avoided in case of retinal delivery.
CRISPR therapy had been developed for the primary purpose of treating inherited genetic diseases. Thus, the carrier package needs to be structured with a high degree of specificity, with no toxicity and rapid elimination after payload delivery [134]. The most widely used method for the efficient delivery of nucleic acid that encodes for the required protein, has been the viral vectors. But, as we have seen in the earlier section, even the viral delivery of CRISPR/Cas components causes undesirable effects such as immunogenicity and insertional mutations, hence restricting their clinical application [135]. Broadly, three types of viral vectors i.e., adenoviral, lentiviral, and AAV have been used to deliver genes that encode Cas9 into cells of interest. While the adenoviral vectors can elicit severe immunogenic reactions against the complex capsid proteins, the lenti and retroviral vectors have the risk of host gene integration and insertional mutations. AAVs are known for their low immunogenicity and target specificity based on their serotypes, and are preferred gene delivery vectors. They also show good transduction efficiencies and long-term transgene expression, without genome integration [136]. Various changes such as removing the endogenous Rep protein and encoding double self-complementary replicase of the viral genome (scAAVs) have led to reduced integration of the vector and improved their transduction efficiency by about 140 times [136,137]. However, the AAV-based viral vectors have limited packaging capacity and this limits the packaging of large gene cargos such as Cas9 and make it difficult to accommodate other regulatory elements such as promoters, polyadenylation signals and selection markers. Splitting the spCas9 into two parts will make the genes fit into the vector, but it reduces the delivery and edit efficiencies [138]. AAVs have been successfully used for in vivo gene delivery and shown long-term therapeutic effects for up to six years in LCA patients administered with AAV2 vector encoding RPE65 [77,139]. AAV and adenovirus delivered RPE65 in the rd12 mouse model of LCA2 have been shown to restore vision significantly [140,141]. The efficacy of AAV gene therapy was convincingly demonstrated in 2008 for the treatment of Leber congenital amaurosis. Many phase I and phase IIa trials for the subretinal delivery of AAV2-RPE65 cDNA have shown no serious adverse effects, along with improved pupillary reflexes, visual acuity and mobility in few of the treated patients [77,142,143]. The first AAV-based gene therapy drug, Glybera, was approved by the European Medicines Agency (EMA) in 2012 for the delivery of LPLD gene, with Luxturna becoming the first AAV gene therapy product to receive US FDA approval five years later, for the delivery of RPE65 gene. Since AAVs pose a significant problem of packaging, many smaller Cas9 orthologs have been isolated from Streptococcus thermophilus (StCas9) [144], Staphylococcus aureus (SaCas9) [145], Campylobacter jejuni (CjCas9) and Neisseria meningitidis (NmCas9) [146]. Kim et al. used a combination of SaCas9 and CjCas9 together along with gRNAs incorporated into a single AAV. Their results showed that the cleaving action was as efficient as SpCas9 in vitro applications. In another study, Lachnospiraceae bacterium (LbCpf1) was used to prepare Cpf1 nuclease which was put together with the crRNA into a single AAV vector [81] showcasing excellent prospects for its use as an in vivo genome editing tool in the therapy of angiogenesis-related disorders. Adenoviral vectors (AVs) are not the most used delivery vectors due to immunological concerns; however, their bigger genomes, episomal nature of intracellular maintenance and efficient transduction are advantages for in vivo delivery systems. The have a high packaging capacity (∼30–40 kb pairs), which can fit all the required elements. Thus, a single virus vector can express the Cas protein and one or many sgRNAs. To facilitate homology-directed repair, large donor DNA sequences can also be co-delivered. Hence, the Cas proteins and sgRNA are expressed proportionately within cells and the episomes may get lost in dividing cells thereby allowing only transient Cas9 expressions and reduced off target risks. AVs have been successfully used for in vivo genome editing in mice, although immune-related toxicities were observed [147]. However, immunogenicity is not a concern for in vitro editing of cell lines and stem cells. One of the first studies on AV was conducted in 1996 by Bennett et al. to study retinal disease in an animal model. They delivered the cDNA encoding phosphodiesterase β subunit into photoreceptors of the rd1 mouse model and shown successful delay of photoreceptor degeneration by six weeks. The disadvantage in AV therapy is their relatively high immunogenicity and the existence of neutralizing antibodies in humans against certain serotypes such as Ad5, renders the vector ineffective in most patients [148]. Studies have shown that subretinal delivery causes a lower T cell-mediated immune response than that of intravitreal injections [149,150]. AVs are being used to inhibit retinoblastoma growth in a mouse model and reduce retinal and choroidal neovascularization in rat and rabbit models [151], [152], [153]. Lentiviral vectors (LVs) are currently one of the most used vectors in the clinical application where long term effects are desired. Lentiviruses belong to the family of viruses known as Retroviridae; they are RNA viruses, which integrate into the host DNA using reverse transcriptase and integrase enzymes. Studies have shown that the LVs are safe and effective for gene delivery into photoreceptor cells of humans [154], [155], [156], [157]. LV packaging capacity is higher than AAVs in the range of approximately 8 to 9 kb. LVs can transduce both non-dividing and dividing cells with very high efficiency and can integrate into the host cell genomes to enable long-term transgene expression. However, long-lasting expression of Cas proteins may increase the risk of unwanted off-target edits [158]. To address this concern, self-inactivating constructs were engineered with two sgRNAs: one against the Cas9 gene and one against the target sequence of interest, thus allowing only transient expression of Cas9 to achieve the desired target site edits. Among the viral delivery methods, AAV vectors are most preferred because of their mild immune response and absence of pathogenicity. AAVs can target non-dividing cells but have a limited packaging size. The development of the shorter dCas9 of 1 kb size has overcome this limitation to some extent [14]. Newer approaches are now being explored to decrease cytotoxicity, and to escape neutralizing antibodies for an overall improvement in vivo transduction efficiencies. As a results, some of the AAV based gene therapy products are already in clinical trials for the treatment of RDs (Table 4).
The problems associated with viral vectors such as immunogenicity and packaging issues have paved way for the development of non-viral systems that are usually better characterized and can be modified chemically to meet the delivery requirements [159]. However, non-viral vectors have concerns related to toxicity, biocompatibility, adverse immunological reactions, risk of release of therapeutic material into non-targeted sites. They also suffer from low in vivo delivery efficiency, although this problem is being solved with the advancements in material sciences. Nanotechnology-based formulations are expected to provide ample benefits over existing approaches [160]. These include (i) sustained release of payload, (ii) improved uptake in retinal cells, (iii) better vitreous penetrability, (iv) could be tailored to achieve cell-specific delivery and (v) reduce vitreal clearance leading to improved exposure time. Improved success has been achieved with newer polymer- and lipid-based complexes that have the required properties for effective transport of their genetic cargo across multiple physiological barriers. As a results, ample of nanobased products are approved by FDA or under investigation (Table 5). For the delivery of CRISPR/Cas components via non-viral vectors, mechanisms of direct conjugation of the active molecule to the excipient has been adopted, such as gRNA or Cas protein conjugation with cell-penetrating peptides (CPPs). Studies by Ramakrishna et al. in HEK293T cells have demonstrated that the conjugation has led to 72% and 62% editing efficiencies with plasmids and RNPs, respectively. But, these CPP systems have not proven efficient to cross all delivery barriers [116]. The following are the current non-viral delivery systems that are being used for CRISPR/Cas9 delivery (Fig. 5).
In 1987, the scientific expression ‘‘lipofection’’ was first used to describe a lipidic system used for gene transfection [161]. It is one of the oldest and widely used techniques for gene transfer. Lipids have been extensively studied for their characteristics as nanocarriers and to electrostatically complex with a negatively charged gene, a positively charged cationic lipid is incorporated in the carrier. Commercially available cationic lipids include N-[1-(2,3-dioleyloxy) propyl]-N,N,N-trimethyl-ammonium chloride (DOTMA), 1,2-dioleoyl-3-trimethylammoniumpropane (DOTAP), 1,2-dimyristyloxypropyl-3-dimethyl-hydroxyethylammonium bromide (DMRIE). and 2,3-dioleyloxy-N-[2(sperminecarboxamido)ethyl]-N,N-dimethyl-1-propanaminium trifluoroacetate (DOSPA) [162]. Wang et al. in their experiments showed that CRISPR/Cas RNP can be administered into the cell using biodegradable cationic lipid nanoparticles, leading to effective knockout of genes [163]. The delivery of supercharged Cre protein and Cas9:sgRNA complexed with bio-reducible lipids into cultured human cells (HeLa-DsRed cells) enabled gene recombination and genome editing with efficiencies greater than 70%. Further, disulfide linkages in the lipid material can be used to trigger release by the degradation of endosomal particles leading to endosomal release. In addition, the authors demonstrated that these lipids are effective for functional protein delivery into mouse brain for gene recombination in vivo [164]. In 2020, Wei et al. used mixture of lipid (5A2-SC8, DOTAP, DMG-PEG, Chol, DOPE) to prepare a lipidic system i.e. 5A2-DOT with different concentrations of DOTAP (10 - 50 mol%). The nanoformulation showed sufficient payload for Cas9 RNPs and showed efficient, precise gene editing (for TdTomato gene) in mice brain, muscle, when administered locally. Further, formulation given intravenously also showed significant gene editing in liver and lungs tissues as well [165].
There are various types of polymer complexes that are being used for CRISPR/Cas delivery. Cationic polymers pose lesser immunogenicity problems as well as are much easier to synthesize on a large scale. Those which have gained attraction include polyethylenimine (PEI), poly(L-lysine) (PLL), poly[2-(dimethylamino) ethyl methacrylate] (PDMAEMA), and polyamidoamine (PAMAM) dendrimers [166]. Among polymeric vectors, polyethylenimine (PEI) has been extensively researched upon. Scientists in 1995 carried out the first successful transfection using PEI, after which it is regarded as a gold standard in the study of polymeric non-viral carriers due to their high transfection efficiency. The characteristics of PEI which make it suitable for gene transfer are its “proton sponge” nature and high charge density [167]. Like cationic lipids, PEI can be complexed with nucleic acids, induce endosomal uptake and release in the cytoplasm of the cell. A study by Zhang et al. shows a formulation made up of PEI-β-cyclodextrin to deliver plasmids coding for sgRNA and Cas9 in HeLa cells, achieving gene knockout [168]. PEI has also been used in a formulation by Sun et al., in which they have used DNA as a nanomaterial for coating of CRISPR/Cas. These particles were encapsulated by PEI to enhance endosomal release. These particles were directly injected in mice which had EGFP tumors, and the resulting phenotypes showed knockout of EGFP [169]. Apart for PEI, the high charge density PLL, a synthetic polypeptide, has been explored for gene delivery. However, due to the absence of buffering capacity (required for endosomal escape), PLL displays lower transfection efficiency than other polyplex systems [170]. Further, the high-molecular-weight PLL leads to a high level of cytotoxicity as it interacts with serum proteins, causing rapid elimination of the complexes from the system. Studies have shown a PLL-PEG copolymer can help solve these issues and facilitates its duration in the system [171]. The success of PLL-PEG copolymer in gene delivery has been proved in both in vitro and in vivo studies. In a study conducted in 2004 on treating cystic fibrosis (CF), PLL-PEG was explored as a gene delivery construct carrying the cystic fibrosis transmembrane regulator encoding gene [172]. PDMAEMA is another non-viral delivery method. A water-soluble cationic polymer, polysaccharide modified PDMAEMAs are gaining significance in CRISPR/Cas delivery [135]. In 2020, Carlos et al., synthesized magnetite/silver-PDMAEMA conjugate and evaluated it for the delivery of CRISPR plasmid. It showed 16.4% loading efficiency with minimum toxicity. Further, colocalization studies were performed in SH-Y5Y cells with lysotracker and data indicated, the Pearson's correlation coefficient approached 0.240 ± 0.024 after 0.5 h and decreased to 0.215 ± 0.029 in a statistically significant manner after 4 h. Collectively, the magnetite/silver-pDMAEMA showed endosomal escape after 4 h of internalization and showed effective delivery potential in vitro [173]. In 2019, Chen et al. developed Cas9 RNPs loaded nanocapsule (NCs) composed of cationic polymer and liposome components along with a glutathione cleavable linker in between. NCs carry 40% of RNPs content with a particle size of 25 nm. The research focused on in vivo gene editing performed in eyes and muscles of transgenic Ai14 mice having three sv40 polyA transcription terminators as stop cassette for TdTomato gene. NCs were decorated with all-trans-retinoic acid (NTRA, binds to inter-photoreceptor retinoid-binding protein) to form NTRA-NCs, and evaluated for gene editing in RPE cells in the eyes of Ai14 mice (Fig. 6). Further, mice were injected with PBS, naked RNPs, NCs and all trans retinoic acid NCs (ATRA-NCs) subretinally and eyes tissue were excised after 12 dpost injection. NCs showed considerable and ATRA-NCs showed significantly higher gene editing in RPE cells in terms of TdTomato fluorescence. Moreover, the NCs were injected intramuscular and NCs showed gene editing and a strong TdTomato fluorescence was observed in muscle tissues. Collectively, the study explored the potential role of non-viral biodegradable NCs in the delivery of high molecular weight Cas9 RNPs for in vivo gene editing application [174].
Polyamidoamine dendrimers (PAMAM) dendrimers are commercially available for gene transfer and one of the most frequently used systems [175]. The structure is made up of a core surrounded by polymeric branches with the surface expressing cationic primary amines that helps them complex to nucleic acids. PAMAM dendrimers are known to be generation-dependent; low-generation (G0–G3) dendriplexes have poor gene transfection efficiencies and are less cytotoxic, but higher generation (G4–G8) PAMAMs have improved gene transfection efficiencies and are more cytotoxic [176]. Yu et al. in their research, prepared a formulation made up of a lipid and dendrimer hybrid. The structure consisted of a long hydrophobic alkyl chain and a low-generation hydrophilic PAMAM dendron. In this way, the system exhibited the pros of both lipid and polymers in delivering siRNA and achieved the efficient gene-silencing effect in vitro and in vivo studies [177]. In another study, l-arginine was used to transform the surface of PAMAM enhancing gene transfection efficiency due to ease of complexation [178]. In 2019, Liu et al., synthesized boric acid rich 5 (G5) amine-terminated PAMAM (P4) for CRISPR/Cas9 RNPs delivery. The P4 dendrimer nanoparticle loaded with RNPs showed particle size of 300 nm. The functionalized dendrimer showed efficient delivery along with 45% of EGFP gene knock out in EGFP-HEK293T cells, analyzed via flow cytometry. Further, another set of experiments were performed to evaluate gene editing efficiency, and CRISPRMax was taken as standard along with P4 dendrimer. CRISPR/Cas9 RNPs were designed for targeting adeno-associated virus integration site 1 (AAVS1) and hemoglobin subunit beta (HBB) and transfection assay was performed followed by T7 endonuclease (T7E) assay, where results indicate indel frequency of 23.1% and 17.5% for AAVS gene with P4 dendrimer/RNPs and CRISPRMax/RNPs, respectively. Likewise, indel frequency of 1.1% and 9.7% was observed for HBB gene with P4 dendrimer/RNPs and CRISPRMax/RNPs, respectively. Collectively, this study explored the potential role of dendrimers as a delivery vehicle for CRISPR/Cas9 [179].
Theoretically, G>A or T>C single-base mutations in any gene related to inherited retinal degeneration could be corrected by using base editing [180]. Nevertheless, all the diseases are not equally responsive to RNA editing. AAV mediated gene replacement provides effective treatment for small gene deliveries. On the other hand, higher rate of mutant allele-specific editing is required for dominant diseases where the mutant alleles have to be efficiently knocked out. Interestingly, larger recessive genes (>4.2 kb) are difficult to correct by gene replacement using AAV and requires in-situ editing strategy either in vitro or in vivo. Stone et al. have listed the major recessive genes such as ABCA4, USH2A, CEP290, MYO7A, EYS, CDH23 along with their relative frequency in the patients with inherited retinal degeneration [181]. The most common single nucleotide mutation is G>A, amenable to currently available base editing techniques. The proportion of G>A or T>C mutation in CEP290 and ABCA4 gene was 9% and 32%, respectively [181]. Further, 6% of the mutations observed in USH2A gene results in the creation of premature stop codon, while missense and donor/acceptor slice mutations were predominantly seen in ABCA4 gene. Collectively, the data suggested the dominancy of G>A mutation (∼75 in number) in both the genes, that leads to premature stop codons. Humans have adenosine deaminase acting on RNA (ADAR) enzymes, which play a key role in adenosine to inosine conversion (A→ I) [182]. Since inosine is read as guanosine by the splicing and translation apparatus, ADARs can also be used to alter RNA splicing to restore normal reading frames and modify amino acid sequences. In 2017, Cox et al. explored a new system called ‘REPAIR’ (RNA Editing for Programmable A to I Replacement), which aimed to cleave RNA at a specific site by the means of a novel protein Cas13a, isolated form the Leptotrichia wadei. Cas13a binds to RNA-RNA hybrid unlike the DNA-RNA hybrid bound by Cas9 and is also a part of the bacterial CRISPR mediated RNA interference system [183]. The Cas13-based RNA editing technique may be advantageous over the conventional DNA editing, as DNA manipulations are permanent in cellular genomes and may have unanticipated long-term side effects. Currently, a synthetic RNA targeting system has been developed with a human protein, with Cas13 like ssRNA recognizing properties. This system termed as CIRTS (CRISPR/Cas inspired RNA targeting system) is an engineered modular RNA-guided RNA-targeting effector system, synthesized totally from human proteins and provide a new tool set to overcome the size and immunogenicity limitations of bacterial CRISPR-Cas systems. This system consists of an engineered gRNA, that carries sequences complementary to the target RNA and also carries a sequence to recruit an engineered hairpin binding protein and a non-specific ssRNA binding protein for complex stabilization and an ADAR2 like effector protein to enable base editing (Fig. 7). CIRTS8 system, when delivered in the form of a plasmid construct, could achieve 47% edit efficiency in HEK293T cells without significant off-target edits [180]. RNA editing thus provides immense potential in repairing pathogenic mutations in many diseases such as Duchenne's muscular dystrophy, cystic fibrosis, hurler's syndrome etc. RNA edits are also reversible and therefore offers improved safety in terms of therapeutic considerations. Inducible RNA editors with automatic switch off systems and external stimuli triggered switch on systems further improves the safety profiles of RNA editing [184].
CRISPR/Cas system has emerged as a rapidly evolving therapeutic tool in genomic engineering. Genome therapy using CRISPR/Cas in ophthalmic diseases is a boon for the society considering the impact it has on the lives of thousands of people. It offers newer hopes of developing promising therapeutics for the treatment of inherited retinal disorders. In the past 20 years, the eye and ocular diseases have caught the limelight of gene therapeutic and cell therapeutic efforts, mainly for the unique anatomical location that enables easy interventions, detailed imaging and documentations; the immune privileged status of the eye; the existence of blood-retinal barrier safety that ensures long-term ocular retention and containment of therapeutics; and finally for the huge societal impact of even marginal improvements in vision to patient beneficiaries. Gene therapeutics either alone or when combined with gene editing strategies can enable delivery of normal gene copies; or in situ mutation editing for normal protein and RNA expression; and targeted disruption of dominant mutant alleles or their transcripts for the reversal of disease phenotypes. Several pre-clinical studies have been initiated using both the viral and non-viral vectors for delivering CRISPR components for therapeutic applications. While it is exciting to watch the fast pace of developments in this field, it is prudent to move forward responsibly, considering both the ethical and societal implications. It is important to exercise well-informed caution and consider both the intended and unintended outcomes, while designing gene editing based therapeutic strategies.
The authors declare no conflict of interest. | true | true | true |
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PMC9614586 | Roberta Angelico,Maria Luisa Framarino-dei-Malatesta,Giuseppe Iaria | COVID-19 in a pregnant kidney transplant recipient - what we need to know: A case report | 18-10-2022 | Kidney transplantation,Pregnancy,SARS-CoV-2 infection,COVID-19 disease,Immunosuppression,Complications,Case report | BACKGROUND In the era of the coronavirus disease 2019 (COVID-19) pandemic, kidney tran splant recipients are more susceptible to severe acute respiratory syndrome co ronavirus (SARS-CoV-2) infection, developing severe morbidity and graft im pairment. Pregnant women are also more likely to develop severe COVID-19 di sease, causing pregnancy complications such as preterm births and acute kidney injury. CASE SUMMARY Herein, we report the case of a pregnant woman with a third kidney tran splantation who developed COVID-19 disease. The reduction of immunosuppressive drugs and strict monitoring of trough blood levels were needed to avoid severe SARS-CoV-2-related complications, and permitted to continue a healthy pregnancy and maintain good graft function. In such a complex scenario, the con comitance of COVID-19-related morbidity, the risk of acute rejection in the hype rimmune recipient, graft dysfunction and pregnancy complications make the management of immunosuppression a very difficult task and clinicians must be aware. CONCLUSION Tailoring the immunosuppressive regimen is a key factor affecting both the graft outcome and pregnancy safety. | COVID-19 in a pregnant kidney transplant recipient - what we need to know: A case report
In the era of the coronavirus disease 2019 (COVID-19) pandemic, kidney tran splant recipients are more susceptible to severe acute respiratory syndrome co ronavirus (SARS-CoV-2) infection, developing severe morbidity and graft im pairment. Pregnant women are also more likely to develop severe COVID-19 di sease, causing pregnancy complications such as preterm births and acute kidney injury.
Herein, we report the case of a pregnant woman with a third kidney tran splantation who developed COVID-19 disease. The reduction of immunosuppressive drugs and strict monitoring of trough blood levels were needed to avoid severe SARS-CoV-2-related complications, and permitted to continue a healthy pregnancy and maintain good graft function. In such a complex scenario, the con comitance of COVID-19-related morbidity, the risk of acute rejection in the hype rimmune recipient, graft dysfunction and pregnancy complications make the management of immunosuppression a very difficult task and clinicians must be aware.
Tailoring the immunosuppressive regimen is a key factor affecting both the graft outcome and pregnancy safety.
Core Tip: Kidney transplant (KT) recipients are susceptible to coronavirus disease 2019 (COVID-19). Pregnant women are more likely to develop severe COVID-19, causing pregnancy complications such as preterm births and acute kidney injury. The management of immunosuppression in pregnant KT recipients with severe acute respiratory syndrome coronavirus infection is crucial for the avoidance of severe morbidity to the patient and the fetus, and to escape renal graft dysfunction.
Kidney transplant (KT) recipients are susceptible to coronavirus disease 2019 (COVID-19), with an associated 18%-39% intensive care admission rate and 13%-39% mortality[1]. Pregnant women are more likely to develop severe COVID-19, causing pregnancy complications such as preterm births and acute kidney injury[2,3].
In October 2020, a 37-year-old woman at 20 wk of gestation, who had received a third KT 2 years ago, presented with fever, cough, and anosmia.
The patient presented with fever, cough, and anosmia.
Her past medical history consisted of end-stage chronic kidney disease due to focal and segmental glomerulosclerosis, requiring three sequential KTs due to chronic rejections with a panel reactive antibody titer of 100%.
The patient’s personal and family histories were unremarkable.
At presentation, the severe acute respiratory syndrome coronavirus (SARS-CoV-2) polymerase chain reaction (PCR) test was positive.
Biochemical tests showed 7.640/μL white blood cells, C-reactive protein of 10.1 mg/L and creatinine of 1.18 mg/dL (baseline at pregnancy: 1.1 mg/dL). The immunosuppression (IS) regimen consisted of steroids (5 mg/d), once-daily tacrolimus (extended-released Envarsus, target level: 7-8 μmol/L) and azathioprine (1 mg/kg/d), the latter started 1 year previously, replacing mycophenolate acid as she declared the intent to become pregnant.
Chest X-ray was negative for pneumonia.
SARS-CoV-2 infection in a KT pregnant lady.
At diagnosis of SARS-CoV-2 infection, azathioprine was suspended, while steroids and tacrolimus were maintained at unchanged doses. During the infection, the patient developed moderate respiratory symptoms and close clinical monitoring was performed, showing persistent stable graft function, steady tacrolimus blood levels and regular fetal growth. One month later, the patient achieved a complete clinical recovery. The SARS-CoV-2 swab became negative after 40 d. At 39 wk of gestation, she had an uneventful delivery of a healthy male infant (weight: 3.2 kg; Apgar score: 9/10) by caesarean section.
At the time of delivery, the placenta and the newborn were not tested for SARS-CoV-2. The patient’s renal graft function remained stable throughout the post-delivery period, and after 17 mo of follow-up the creatinine was 1.09 mg/dL (Table 1). During pregnancy, anti-human leukocyte antigen donor-specific antibody (DSA) screening was performed and these antibodies were not detected. In particular, no evidence of post-COVID-19 DSA was identified. Graft biopsy was not done. At the last follow-up, both the mother and the child were in good clinical condition.
The reduction of the immune response due to both IS drugs and pregnant status render pregnant KT recipients vulnerable to viral infections such as SARS-CoV-2[1,2]. In our case, this was further enhanced by her non-vaccinated status, since at that time the vaccine for SARS-CoV-2 was not available yet. Therefore, the concomitance of COVID-19-related morbidity, the risk of acute rejection in hyperimmune re-KT, graft dysfunction and pregnancy complications make the management of IS a very difficult task. In KT recipients, recommendations suggest the modification of IS drugs according to the severity of COVID-19, ranging from no modification in asymptomatic patients, antimetabolite withdrawal in mild/moderate symptomatic disease, to complete drug discontinuation in severely ill patients requiring mechanical respiratory support[4,5]. In this case, we decided to withdraw azathioprine, which inhibits purine synthesis, aiming to avoid the depletion of T- and B-cells during the SARS-CoV-2 infection. Tacrolimus and steroids at low-doses remained the only IS drugs, without increasing their blood target-levels. The extended-released formula of tacrolimus Envarsus, which provides effective and stable blood concentration with less toxic levels compared to other Tacrolimus formulae[6], permitted the safe control of rejection risk and the avoidance of severe COVID-19. Thus, a recent report suggested that a mammalian target of rapamycin inhibitor may have potential antiviral benefits in SARS-CoV-2 infection[7]. In this case, strict monitoring of DSA was performed before and after COVID-19, since the IS regimen had been reduced. Despite the significant decrease of the IS and the high risk of rejection due to the hyperimmune status of third-KT recipients, our patient did not develop new DSA or rejection episodes. These data confirm a recent report investigating the alloreactive immune response during and after SARS-CoV-2 infection in KT recipients, which showed that the incidence of acute rejection is about 1.3% (all in hospitalized patients) and the occurrence of post-COVID-19 DSA is 4% overall, ranging from 0% to 8% in non-hospitalized and hospitalized patients, respectively[8]. Despite the immunosuppressed status of a third KT pregnant lady, our patient was very lucky because she was in this group of patients who do not develop severe COVID-19 disease. Since the stable kidney function and the pregnant status, we did not perform a graft biopsy in order to avoid possible biopsy-related complications. Additionally, venous thromboembolism prophylaxis was not administrated as no evidence was present, but its utility should be explored in pregnant COVID-19 KT recipients. Pregnancy in KT recipients may be associated with a high-risk of maternal complications and decreased graft function, which could further deteriorate in the presence of COVID-19[9]. In fact, the occurrence of acute kidney injury in infected pregnant KT recipients could be due to the SARS-CoV-2 infection or to other pregnancy-related causes, which need to be differentiated[10]. In immunosuppressed transplant recipients as well as pregnant women, SARS-CoV-2 showed the potently to replicate into the kidney causing renal disfunction[11,12]. Lastly, despite the fact that the risk of acquiring SARS-CoV-2 infection during pregnancy seems to be similar to that of non-pregnant patients, severe maternal COVID-19 is associated with acute kidney injury and preterm birth. The risk of congenital infection with SARS-CoV-2 to the newborn is still unknown[2,13]. In our case, the placenta and the baby were not tested for SARS-CoV-2 PCR, therefore unfortunately we do not have these interesting data. Moreover, despite KT pregnant recipients are more susceptible to chronic infection such as cytomegalovirus (CMV) infection, we didn’t detect any CMV infection during pregnancy. This is the first report focusing on IS management in SARS-CoV-2-positive pregnant KT recipients.
We suggest that all efforts should be made to avoid severe maternal COVID-19 disease through tailored adjustment of the IS regimen and close monitoring of calcineurin inhibitor trough-blood levels, graft function and fetal parameters. Currently, mRNA vaccines against SARS-CoV-2 are recommended both in KT recipients and pregnant women, and may help in preventing severe COVID-19 disease[14,15]. However, KT patients have been shown to frequently be poor responders to the vaccines, thus remaining at high risk of developing severe COVID-19[16], especially in pregnancy. In fact, recent data suggest that only selected KT recipients seem to respond to the third booster dose of SARS-CoV-2 vaccine (assessed by anti-receptor binding domain immunoglobulin G titers and/or positive interferon-gamma-releasing assay)[17]. Moreover, in pregnancy, the boosting effect of a third vaccine dose is suggested to have a potential benefit only in those who completed the two-dose vaccine series in early pregnancy or prior to conception[16]. We feel that, although no data are yet available on the efficacy of the vaccine in preventing COVID-19 disease in pregnant KT recipients, a complete vaccine cycle against SARS-CoV-2 with three doses should preferably be performed before pregnancy. In addition, clinicians should be ready to tailor IS drugs when a member of this rare population is infected by SARS-CoV-2. | true | true | false |
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PMC9614648 | Katerina-Marina Pilala,Maria-Alexandra Papadimitriou,Konstantina Panoutsopoulou,Petros Barbarigos,Panagiotis Levis,Georgios Kotronopoulos,Konstantinos Stravodimos,Andreas Scorilas,Margaritis Avgeris | Epigenetic regulation of MIR145 core promoter controls miR-143/145 cluster in bladder cancer progression and treatment outcome | 07-10-2022 | MT: Non-coding RNAs,DNA methylation,epigenetics,prognosis,tumor progression,miRNA,urothelial bladder carcinoma,bladder tumors,pyrosequencing | Owing to its highly heterogeneous molecular landscape, bladder cancer (BlCa) is still characterized by non-personalized treatment and lifelong surveillance. Motivated by our previous findings on miR-143/145 value in disease prognosis, we have studied the underlying epigenetic regulation of the miR-143/145 cluster in BlCa. Expression and DNA methylation of miR-143/145 cluster were analyzed in our screening (n = 162) and The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA; n = 412) cohorts. Survival analysis was performed using tumor relapse and progression as clinical endpoints for non-muscle-invasive bladder cancer (NMIBC; TaT1), while disease progression and patients’ death were used for muscle-invasive bladder cancer (MIBC; T2-T4). TCGA-BLCA served as validation cohort. Bootstrap analysis was carried out for internal validation, while decision curve analysis was used to evaluate clinical benefit. TCGA-BLCA and screening cohorts highlighted MIR145 core promoter as the pivotal, epigenetic regulatory region on cluster’s expression. Lower methylation of MIR145 core promoter was associated with aggressive disease phenotype, higher risk for NMIBC short-term progression, and poor MIBC survival. MIR145 methylation-fitted multivariate models with established disease markers clearly enhanced patients’ risk stratification and prediction of treatment outcome. MIR145 core promoter methylation was identified as a potent epigenetic regulator of miR-143/145 cluster, supporting modern personalized risk stratification and management in BlCa. | Epigenetic regulation of MIR145 core promoter controls miR-143/145 cluster in bladder cancer progression and treatment outcome
Owing to its highly heterogeneous molecular landscape, bladder cancer (BlCa) is still characterized by non-personalized treatment and lifelong surveillance. Motivated by our previous findings on miR-143/145 value in disease prognosis, we have studied the underlying epigenetic regulation of the miR-143/145 cluster in BlCa. Expression and DNA methylation of miR-143/145 cluster were analyzed in our screening (n = 162) and The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA; n = 412) cohorts. Survival analysis was performed using tumor relapse and progression as clinical endpoints for non-muscle-invasive bladder cancer (NMIBC; TaT1), while disease progression and patients’ death were used for muscle-invasive bladder cancer (MIBC; T2-T4). TCGA-BLCA served as validation cohort. Bootstrap analysis was carried out for internal validation, while decision curve analysis was used to evaluate clinical benefit. TCGA-BLCA and screening cohorts highlighted MIR145 core promoter as the pivotal, epigenetic regulatory region on cluster’s expression. Lower methylation of MIR145 core promoter was associated with aggressive disease phenotype, higher risk for NMIBC short-term progression, and poor MIBC survival. MIR145 methylation-fitted multivariate models with established disease markers clearly enhanced patients’ risk stratification and prediction of treatment outcome. MIR145 core promoter methylation was identified as a potent epigenetic regulator of miR-143/145 cluster, supporting modern personalized risk stratification and management in BlCa.
Bladder cancer (BlCa) represents the second most common malignancy of the male genitourinary tract, succeeding prostate cancer, and the sixth most frequently diagnosed malignancy among men, worldwide., The vast majority of bladder tumors originate from the urothelium of the bladder wall, and urothelial bladder carcinomas (UBC; >90%) are further subclassified into non-muscle-invasive bladder cancer (NMIBC; Tis, Ta, T1) and muscle-invasive bladder cancer (MIBC; T2-T4), based on the invasion of the bladder’s detrusor muscle., Patients with newly diagnosed NMIBC (∼75% of primary UBC) are characterized by frequent relapses (∼50%–70%) and progression to muscle-invasive disease (∼15%),, while primary MIBC (∼25% of UBC) is considered life threatening, displaying strong metastatic potential. Despite the marked reduction in disease-specific mortality over the last decades, owing to significant advances in disease diagnosis and therapy, there are yet to develop improvements regarding prognosis of treatment responses and personalized post-treatment management. Current disease prognosis relies on patients’ clinicopathological traits, mainly on pathological/clinical staging, tumor grade and multifocality, as well as the presence of carcinoma in situ (CIS). However, tumor heterogeneity—at the molecular and cellular levels—results in significantly varied disease course, even for the same risk-group patients.9, 10, 11, 12 As a result, BlCa management demands lifelong surveillance strategies, with invasive and frequent cystoscopies, affecting both patients’ quality of life and healthcare system financial costs. In this regard, the identification of novel molecular markers could ameliorate patients’ personalized prognosis and risk stratification and minimize unnecessary interventions, in correspondence with modern precision medicine. MicroRNAs (miRNAs) constitute an ever growing family of endogenous small (∼22 nt) non-coding RNAs (sncRNAs), representing the most powerful post-transcriptional regulators of gene expression. In this regard, miRNAs finely tune numerous biological processes, including cell proliferation, apoptosis, and migration, displaying tumor-suppressive or oncogenic roles according to their effects on cellular transformation and homeostasis. miR-143 and miR-145 (miR-143/145) are transcribed to as bicistronic primary transcript, from the MIR143/MIR145 gene cluster (miR-143/145 cluster) on the 5q32 chromosomic region and are considered potent tumor suppressors via directly targeting known oncogenes, including KRAS, MYC, AKT, IGF1R, and IRS1/2. The expression of miR-143/145 is commonly deregulated in numerous malignancies, such as breast, prostate, clear-cell renal cell, colorectal, and head and neck squamous cell carcinomas, being implicated both in tumorigenesis and disease progression, as well as in supporting patient prognostication. Focusing on BlCa, our previously published findings revealed that the miR-143/145 cluster is significantly downregulated in bladder tumors compared with healthy bladder specimens, while elevated miR-143/145 levels are associated with disease aggressiveness, predicting progression of superficial tumors and high morbidity of muscle-invasive patients. Herein, in order to study the epigenetic regulation of miR-143/145 cluster in bladder tumors, we have analyzed DNA methylation levels of proximal and core promoter regions and evaluated their impact on miR-143/145 expression, as well as their clinical value in improving patients’ risk stratification and prediction of post-treatment disease course.
To identify the genomic regions of MIR143/145 cluster with epigenetic impact on miR-143/145 regulation, in silico analysis by UCSC Genome Browser (https://genome.ucsc.edu/) was performed to assign CpG sites with known Illumina CpG loci IDs (cg#) across genome. The analysis resulted in the identification of three CpG-rich regions: (1) the CpG island upstream to gene cluster (chr5:148.737.347–148.737.764), and the promoters—distal regulatory elements, proximal, and core promoter regions—of (2) MIR143 (chr5:148.808.481–148.808.586) and (3) MIR145 (chr5:148.810.209–148.810.296) genes (Figure 1A). The expression analysis of the cluster highlighted the strong correlation between miR-143-3p (miR-143) and miR-145-5p (miR-145) guide strands in bladder tumors of both our screening (Spearman rs = 0.934, p < 0.001) and The Cancer Genome Atlas Urothelial Bladder Carcinoma (TCGA-BLCA; Spearman rs = 0.646, p < 0.001) cohorts (Figure 1B), as well as the significantly reduced miR-143/145 expression compared with normal urothelium (Figure 1C). This potent co-regulation of miR-143/145 discloses a common and dominant regulatory mechanism in bladder tumor cells and prompted us to analyze the methylation imprinting of the identified CpG-rich regions of MIR143/145 locus using the Infinium Methylation450k data of TCGA-BLCA cohort. The heatmap of MIR143/145 locus CpG-rich regions methylation of TCGA-BLCA cohort is depicted in Figure 1D. Within the CpG island, all CpG sites were revealed to be hypomethylated (Δβ<0.2), while their average methylation had only weak correlation with miR-143/145 levels (Figure 1E). On the contrary, CpG loci in MIR143 and MIR145 promoters displayed significantly higher imprinting, compared with CpG island (Figure 1D), while Spearman analysis highlighted the significantly stronger correlation of MIR145 promoter methylation with miR-143/145 levels compared with MIR143 promoter (Figures 1F and 1G). Accordingly, the analysis of miR-143/145 passenger strands (miR-143-5p; miR-143∗ and miR-145-3p; miR-145∗) in TCGA-BLCA cohort confirmed the higher impact of MIR145 promoter imprinting (Figure S1). Moreover, in line with the loss of miR-143/145 in bladder tumors, compared with normal urothelium, the methylation of MIR145 proximal and core promoter regions was significantly elevated in bladder tumors, which was not observed in the case of the CpGs of MIR143 promoter (Figures 1H and 1I). In the light of those findings, we decided to further study the role of MIR145 promoter imprinting on the epigenetic regulation of the miR-143/145 cluster and the clinical/treatment outcome of the BlCa patients by targeting the CpG sites −112, −109, −106 nt (proximal promoter) and −32, −29, −20 nt (core promoter) upstream of the MIR145 transcription start site.
Spearman correlation analysis verified the strong negative association of miR-143/145 levels and MIR145 promoter methylation in bladder tumors (Figures 2A–2C). MIR145 core promoter region was revealed to have a superior impact on the regulation of the cluster (Figure 2A), as its hypermethylation resulted in a more robust downregulation of miR-143/145 levels (miR-143: rs = −0.401; p < 0.001; miR-145: rs = −0.425; p < 0.001) compared with the proximal promoter (miR-143: rs = −0.267; p = 0.023; miR-145: rs = −0.273; p = 0.020). Indeed, the methylation imprinting of each CpG locus analyzed was inversely correlated with miR-143 (Figure 2B) and miR-145 (Figure 2C) levels, whereas CpG (−29) and CpG (−20) of the MIR145 core promoter region revealed to hold the greatest impact on cluster’s regulation. Descriptive statistics of percent methylation levels of CpG sites (Table S1) highlighted the significantly increased methylation tendency from proximal (median percent methylation: 33.4% [−112], 32.4% [−109], 23.7% [−106]) to core (median percent methylation: 60.2% [−32], 80.8% [−29], 80.5% [−20]) promoter regions in bladder tumors, and thus the higher impact of MIR145 core promoter on cluster’s expression. Moreover, significantly elevated methylation was highlighted for all studied CpG sites in bladder tumors compared with the matched adjacent normal urothelium (Figure 2D), in line with the strong downregulation of miR-143/145 in bladder carcinoma. Representative pyrosequencing pyrograms are shown in Figure S2. Despite the tumor-suppressive functions of miR-143/145 and their loss in bladder tumors compared with the normal urothelium, our previous study revealed the significant correlation of the within-tumors miR-143/145 levels with unfavorable clinicopathological features and poor patient prognosis. In agreement with our previous findings and the working hypothesis of the present study, the analysis of the screening cohort highlighted the association of reduced MIR145 promoter methylation with aggressive disease phenotype (Figures 2E–2G), in terms of muscle-invasive tumors (Figure 2E), advanced tumor stage (Figure 2F), and high grade (Figure 2G). Similar to the overall methylation profile of MIR145 promoter, the core promoter CpG sites presented significantly higher methylation imprinting compared with proximal promoter, independent of the examined variable. Motivated by these observations, we decided to perform a comprehensive clinical evaluation of MIR145 core promoter methylation in BlCa patients.
Due to the different course of the disease, survival analysis was performed separately in the NMIBC and MIBC cohorts, using tumor relapse and progression (recurrence of higher/invasive stage), as well as disease progression (recurrence/metastasis or death; whichever came first) and patient’s death as clinical endpoint events, respectively. In this regard, 153 patients (NMIBC: 87; MIBC: 66) were adequately followed-up and nine patients were excluded due to insufficient monitoring data. During the median follow-up time (reverse Kaplan-Meier method) of 38.0 months (95% CI: 34.03–41.97), disease recurrence and progression were detected in 37 (42.5%) and 18 (20.7%) NMIBC patients, respectively. With respect to MIBC (T2-T4), 37 (56.1%) patients progressed, and 34 (51.5%) patients died. The mean disease-free survival (DFS) and progression-free survival (PFS) of the NMIBC patients were 45.91 months (95% CI: 38.81–53.00) and 60.80 months (95% CI: 54.98–66.62), respectively, while the overall survival (OS) of the MIBC patients was 65.81 months (95% CI: 48.92–82.69). Figure 3 presents the design and the REMARK diagram of the study. For the followed-up cohort (NMIBC: 87; MIBC: 66), core/proximal promoter methylation levels were available for 85/85 NMIBC and 63/65 MIBC patients, respectively. Kaplan-Meier survival curves are presented in Figure 4, while Cox proportional regression analysis is summarized in Figure 5 and Tables S2 and S3. Kaplan-Meier survival curves clearly highlighted the markedly shorter PFS (p = 0.023; Figure 4A) interval of TaT1 patients with lower methylation of MIR145 core promoter compared with those presenting hypermethylation. Additionally, univariate Cox proportional regression corroborated the significantly higher risk for short-term disease progression (hazard ratio [HR]: 2.803; 95% confidence interval [CI]: 1.103–7.124; bootstrap p = 0.009; Figure 5A) of NMIBC patients with reduced MIR145 core promoter methylation. More importantly, multivariate Cox models strongly verified the clinical value of MIR145 core promoter hypomethylation for NMIBC progression to invasive disease stages (HR: 2.777; 95% CI: 1.071–7.198; bootstrap p = 0.029; Figure 5B) independently of tumor stage, grade, gender, and age. Regarding NMIBC relapse, both Kaplan-Meier (Figure 4B) and Cox regression (Table S2) analyses showed worse DFS of the patients with decreased methylation, although not in a statistically significant manner. Focusing on MIBC patient’s outcome following radical cystectomy, Kaplan-Meier survival analysis unveiled the stronger risk for disease progression (p = 0.029; Figure 4C) and poor OS (p = 0.049; Figure 4D) of the T2-T4 patients with decreased methylation imprinting. Additionally, univariate Cox analysis verified the unfavorable PFS (HR: 2.060; 95% CI: 1.048–4.047; bootstrap p = 0.023; Table S3) and increased morbidity (HR: 1.983; 95% CI: 0.983–4.000; bootstrap p = 0.043; Figure 5C) of MIBC patients with hypomethylated MIR145 core promoter. Strikingly, multivariate Cox regression models, adjusted for patients’ stage, gender, and age, demonstrated the reduced methylation levels of MIR145 core promoter as an independent predictor of MIBC progression (HR: 2.962; 95% CI: 1.382–6.349; bootstrap p = 0.005; Table S3) and poor survival (HR: 2.729; 95% CI: 1.247–5.970; bootstrap p = 0.010; Figure 5D). Consistent with our findings, the survival analysis of the TCGA-BLCA validation cohort clearly validated the inferior PFS and OS of patients with decreased methylation of the MIR145 core promoter. More precisely, Kaplan-Meier curves presented the significantly shorter PFS (p = 0.034; Figure 4E) and OS (p = 0.005; Figure 4F) of the patients with lower methylation of MIR145 core promoter CpGs, which was also confirmed by univariate Cox analysis for both PFS (HR: 1.380; 95% CI: 1.024–1.860; bootstrap p = 0.034) and OS (HR: 1.539; 95% CI: 1.140–2.077; bootstrap p = 0.003). Ultimately, the survival assessment of MIR145 proximal promoter methylation profile (Figure S3) revealed a weaker association with patients’ outcome compared with the core promoter, both in the screening and TCGA-BLCA validation cohorts, supporting the superior clinical value of core promoter CpGs in disease prognostication.
Prompted by the independent prognostic value of MIR145 core promoter methylation, we thereafter analyzed its ability to improve the performance of the established disease prognostic markers. In this regard, the integration of MIR145 methylation with the European Organisation for Research and Treatment of Cancer (EORTC) risk score—a widely used clinical predictor of NMIBC progression—was shown to significantly ameliorate the risk stratification of low-risk (LR) and intermediate-risk (IR) patients for disease progression (p = 0.038; Figure 6A). Similarly, the incorporation of MIR145 methylation with tumors’ stage offered superior risk stratification of MIBC patients, enhancing their post-treatment outcome prognosis (p = 0.001; Figure 6B). Kaplan-Meier survival curves for EORTC risk score and tumor stage of the same NMIBC and MIBC cohorts, respectively, are included in Figure S4. In the light of those findings, decision curve analysis (DCA) was conducted according to Vickers et al. to evaluate the clinical net benefit of MIR145 methylation evaluation in disease and treatment prognostication. The DCA control model consisted of the established and clinically used markers including tumor stage, grade, and EORTC risk group for NMIBC or tumor stage for MIBC patients. Decision curves clearly highlighted the superior clinical benefit of the MIR145 core methylation-fitted multivariate model for both the PFS and OS post-treatment outcome of NMIBC (Figure 6C) and MIBC (Figure 6D), respectively, compared with the control model.
Despite the remarkable alleviation of BlCa-specific mortality due to recent advances in clinical treatment (evolution of imaging, improvements in surgical techniques, and new diagnostic modalities), the lack of modern precision medicine and personalized management entails inadequate prognostication and prediction of disease course. Meanwhile, the lifelong patient surveillance strategies, due to the high propensity for multiple recurrences and/or disease progression, classify BlCa as the most expensive per-patient-to-treat neoplastic disease, with an important financial burden for healthcare systems., In this regard, the identification of novel molecular predictors could ameliorate patients’ risk stratification and provide tailored treatment decisions, improving thus patients’ quality of life and disease management. As highlighted by the ENCODE project, ∼70% of the human genome encodes ncRNAs, whereas miRNAs have emerged as the most powerful modulators of gene expression, acting at post-transcriptional and epigenetic levels. miRNAs are actively transcribed and orchestrate almost all aspects of biological processes, ensuring cellular homeostasis and normal physiology, while their aberrant regulation constitutes a hallmark of cancer onset and progression in numerous malignancies, including BlCa., Prompted by our previous study, which disclosed the potent clinical significance of miR-143/145 cluster in BlCa progression, we decided to further investigate the underlying epigenetic control of cluster’s expression in bladder tumors. DNA methylation constitutes a fundamental regulatory mechanism of gene expression, predominantly resulting in gene silencing. Nearly half of all known human miRNA genes are associated with CpG islands, while methylation imprinting has been found to downregulate the expression of potent onco-suppressor miRNAs, including miR-34, miR-124a, and miR-127 in different cancers.35, 36, 37 In this regard, we have analyzed the DNA methylation imprinting of the miR-143/145 gene cluster in bladder urothelium to identify its impact on the epigenetic regulation of the cluster and to assess its clinical utility in improving patients’ risk stratification and personalized prognosis. Using in silico analysis of TCGA-BLCA cohort, the CpG island of MIR143/145 cluster was found non-methylated (“cold”), while the MIR143 promoter imprinting appeared inconsistent with miR-143/145 expression profile in malignant and normal urothelium, indicating a weak impact on cluster’s regulation. On the contrary, MIR145 promoter methylation demonstrated the most robust correlation with the miR-143/145 profile in tumors and the normal urothelium. The methylation analysis of our screening cohort confirmed the hypermethylation of the MIR145 promoter in bladder tumors compared with the matched normal urothelium, in agreement with miR-143/145 loss. Consistent with our findings, miR-143/145 cluster epigenetic silencing by MIR145 promoter hypermethylation has also been documented in prostate, lung, esophageal, and laryngeal carcinomas, resulting in apoptosis inhibition and cell proliferation enhancement.38, 39, 40, 41, 42 Strikingly, the analysis revealed a significantly increased methylation tendency from the proximal to the core MIR145 promoter, and the robust negative correlation of miR-143/145 levels with MIR145 core promoter methylation. Focusing on miR-143/145 cluster’s clinical value for the patients, our group has previously reported the association of elevated miR-143/145 tumor levels with aggressive disease phenotype and unfavorable patient prognosis. In agreement with our previous findings, reduced MIR145 promoter methylation was significantly correlated with muscle-invasive disease and advanced tumor stage and grade. Moreover, the survival analysis highlighted the association of MIR145 core promoter hypomethylation with poor post-treatment disease outcome. In particular, the reduced MIR145 core promoter methylation resulted in significantly higher risk for the short-term progression of NMIBC (TaT1) patients to invasive disease stages following transurethral resection of bladder tumors (TURBT), as well as in worse survival outcome of MIBC (T2-T4) patients following radical cystectomy (RC), independently of patients’ clinicopathological data. Interestingly, the analysis of TCGA-BLCA validation cohort clearly confirmed the correlation of MIR145 core promoter hypomethylation with poor OS and PFS intervals. Notably, the methylation status of the MIR145 core promoter emerged as an independent and vigorous prognostic indicator, enhancing the clinical net benefit of widely used disease markers and ameliorating risk stratification of BlCa patients. The superior risk stratification of both NMIBC and MIBC patients could be translated in the clinical practice and affect clinical decision-making either on disease treatment or post-treatment monitoring. More precisely, LR/IR NMIBC patients with significantly increased risk for progression to invasive disease stages could be candidates for High Risk-like treatment/management, including intravesical bacillus Calmette-Guérin (BCG) administration (1–3 years), more intensive monitoring, and also focusing on patients’ awareness. Those patients, in case of BCG-unresponsive tumors/short-term disease relapsing tumors, should be considered as candidates for RC. Moreover, the identification of T3/T4 patients with significantly short post-treatment survival expectancy could be considered, along patients’ age, comorbidity, and frailty, as candidates for palliative therapy to avoid unnecessary cystectomy (radical or palliative) and chemotherapy and to minimize side effects and healthcare system costs, in a shared decision-making. Definitely, future prospective studies of MIR145 promoter methylation in BlCa are of high clinical interest both to confirm our findings on disease prognosis and more importantly to highlight the clinical benefit in real-time treatment and/or monitoring decision for the patients. The association of MIR145 promoter hypomethylation (observed in the present study) and miR-143/145 overexpression with poor disease prognosis seems to contradict the well-documented tumor suppressor role of the cluster. However, recent findings have challenged this one-way scenario of tumor suppressor function, emerging a pluripotent role of miR-143/145 in stromal and epithelial cells of epithelial origin carcinomas. Indeed, Dimitrova et al. have documented the pro-tumorigenic contribution of miR-143/145 in lung adenocarcinoma in vivo, where tumor-specific deletion of miR-143/145 did not affect tumorigenesis. However, stromal miR-143/145 overexpression mediated silencing of CAMK1D, an inhibitory kinase that abrogates mitotic entry, and stimulated endothelial cells proliferation and neoangiogenesis. This tumor-promoting role of stromal miR-143/145 is in line with the well-documented role of the cluster in facilitating the differentiation of multipotent stem cells and adult fibroblasts to vascular smooth muscle cells,, as well as in maintaining the normal paracrine IGF signaling, through negative regulation of IGFBP5 by smooth muscle and myofibroblasts. Furthermore, miR-145 has been reported to facilitate metastasis in colorectal cancer via downstream attenuation of G1/S cell cycle checkpoint and neuregulin pathways. Moreover, elevated levels of miR-143/145 have been demonstrated to enhance cell invasion and epithelial-to-mesenchymal transition in breast tumors, via repressing transcriptional activators of tight junction proteins, such as CREB1, and triggering TGF-β axis by targeting TGIF, a well-known transcriptional co-repression of SMAD. Finally, miR-143-mediated targeting of FNDC3B documented to promote cell invasion and metastatic potential in prostate and hepatocellular cancers., Overall, these previous studies and our findings argue against a universal and cell-independent tumor-suppressor role of miR-143/145 in epithelial cancers, indicating the deregulation of the cluster’s epigenetic/transcriptional control in the tumor microenvironment as a potential tumor-promoting mode of action in human malignancies and supporting future functional studies toward this direction. In conclusion, we have studied MIR143/145 gene cluster methylation in BlCa, highlighting that miR-143/145 cluster is epigenetically regulated in bladder tumors, while MIR145 core promoter represents the key regulatory region of cluster modulation. Reduced methylation levels of MIR145 core promoter were strongly associated with more aggressive phenotype of BlCa and higher risk for disease progression and poor treatment outcome of the patients. Notably, multivariate prognostic models including MIR145 methylation imprinting resulted in a superior risk stratification of the patients, toward personalized treatment and monitoring decisions.
The screening cohort of the study consisted of 162 patients diagnosed with primary UBC. Fresh-frozen bladder tumors were obtained via either TURBT for NMIBC patients (TaT1) or RC for MIBC patients (T2-T4) at “Laiko” General Hospital, Athens, Greece. Patients’ clinicopathological characteristics are summarized in Table S4. Adjacent normal bladder tissue specimens were also acquired by 96 patients of the cohort, according to pathologist’s evaluation for the absence of dysplasia and CIS. The patients received adjuvant therapy in agreement with European Association of Urology (EAU) guidelines, while none of them received any form of neoadjuvant treatment prior to surgery. Bladder tissue specimens were incubated in RNAlater Solution (Ambion), following manufacturer’s instructions, and stored at −80°C until further processing. NMIBC patients’ risk stratification was performed according to the EORTC guidelines and post-treatment monitoring included cystoscopy and urinary cytology (for high-grade tumors) according to EAU guidelines. MIBC patients’ (T2-T4) were followed-up by renal ultrasound at 3 months and thoracoabdominal computed tomography (CT)/magnetic resonance imaging (MRI) every 6 months, while additional kidney ultrasound, thoracoabdominal CT/MRI, bone scan, and brain MRI were performed following symptoms. NMIBC patients’ disease recurrence (same or lower pathologic tumor stage) and progression (recurrence of higher/invasive stage) were confirmed by histology findings of a TURBT, which was performed after a positive follow-up cystoscopy, while MIBC patients’ recurrence was detected by a follow-up CT. The present study was approved by the Ethics Committee of “Laiko” General Hospital, Athens, Greece, and conducted in consonance with 1975 Declaration of Helsinki ethical standards, as revised in 2008. Informed consent was obtained by all participating patients.
The TCGA-BLCA cohort was utilized as validation cohort of the study. TCGA-BLCA consists of 412 patients diagnosed with UBC (n = 409), papillary adenocarcinomas (n = 1), epithelial carcinomas (n = 1), and squamous cell carcinomas (n = 1), including mainly muscle-invasive tumors (T2-T4; n = 406, 98.5%), as well as of 23 matched normal tissues. DNA methylation (available for n = 412 tumors; n = 21 normal specimens) and mRNA (available for n = 409 tumors; n = 19 normal specimens) expression profiles were generated by Illumina Infinium HumanMethylation450 platform and Illumina HiSeq 2000 RNA Sequencing platform, respectively, and their data along with patients’ clinicopathological characteristics of the TCGA-BLCA project can be retrieved by public UCSC XENA Browser (https://xenabrowser.net/datapages/).
The NCBI database (https://www.ncbi.nlm.nih.gov/gene) and the UCSC Genome Browser gateway (https://genome.ucsc.edu/) were used to analyze the genome structure of the miR-143/145 cluster, exploiting GRCh37/hg19 assembly. Expression levels of miR-143/145 cluster, as well as the distribution and the methylation levels of Illumina CpG loci IDs (cg#) in MIR143/145 regulatory regions within TCGA-BLCA cohort were visualized by XENA Browser Visualization Tool (https://xenabrowser.net/).
Following pulverization of 40–100 mg of fresh-frozen tissue specimen, total RNA and genomic DNA (gDNA) were extracted using TRI-Reagent (Molecular Research Center, Cincinnati, OH, USA) according to the manufacturer’s instructions. Total RNA was dissolved in RNA Storage Solution (Invitrogen, Carlsbad, CA, USA), and genomic DNA in 8 mM NaOH, pH-adjusted by addition of 0.1 M HEPES buffer. Both RNA and DNA samples were stored at −80°C until analysis. DNA/RNA concentration and purity were determined spectrophotometrically at 260 and 280 nm, while agarose gel electrophoresis was performed to evaluate RNA integrity.
Polyadenylation of 1 μg of total RNA at the 3′ end was carried out in a 10-μL reaction, containing 800 μM ATP and 1 U of E. coli poly (A) polymerase (New England Biolabs, Ipswich, MA, USA), at 37°C for 60 min. Polymerase heat inactivation was performed at 65°C for 10 min. The polyadenylated total RNA was reverse transcribed, using the oligo-dT adapter primer (Table S5) in a final reaction volume of 20 μL. The reaction mixture consisted of 50 U M-MLV Reverse Transcriptase (Invitrogen), 40 U RNaseOUT Recombinant Ribonuclease Inhibitor (Invitrogen), 500 μM dNTPs mix, and 0.25 μM oligo-dT adapter, at 37°C for 60 min. Reverse transcriptase was inactivated by heating at 70°C for 15 min.
SYBR-green fluorescent-based quantitative real-time PCR (qPCR) assays were used in order to quantify miR-143-3p and miR-145-5p levels. Specific forward primers for miR-143-3p, miR-145-5p, and the small nucleolar RNA, C/D box 48 (SNORD48), also known as RNU48, were designed based on their published sequences (NCBI Reference Sequence: NR_029684.1, NR_029686.1 and NR_002745.1, respectively) and in silico analysis. Each specific forward primer is combined with a universal reverse primer (Table S5), which is complementary to the oligo-dT adapter sequence, giving rise to 65 bp amplicons for miR-143-3p and miR-145-5p, and a 105 bp amplicon for RNU48. The qPCR reactions were performed in the 7500 Real-Time PCR System (Applied Biosystems, Carlsbad, CA, USA), and the 10-μL reaction mixture consisted of Kapa SYBR Fast Universal 2X qPCR Master Mix (Kapa Biosystems, Woburn, MA), 200 nM of each PCR primer, and 0.2 ng of cDNA template. The thermal protocol included an initial 3-min step at 95°C for polymerase activation, followed by 40 cycles of denaturation step at 95°C for 15 s and primer annealing and extension step at 60°C for 1 min. Thereafter, dissociation curves and agarose gel electrophoresis were performed to discriminate specific amplicons from the non-specific products and/or primer dimers. The expression analysis of miR-143 and miR-145 was carried out using the 2−ΔΔCT relative quantification method. All reactions were performed in duplicates, and the average Ct was used for the quantification analysis. RNU48 was utilized as endogenous reference control for normalization purposes.
Conversion of the unmethylated cytosine (C) residues to uracils (U) was performed with EpiMark Bisulfite Conversion Kit (New England Biolabs). Particularly, 1.5 μg of genomic DNA was incubated with sodium bisulfite mix under alternative cycles of thermal denaturation with incubation reactions: 95°C for 5 min, 65°C for 30 min, 95°C for 5 min, 65°C for 60 min, 95°C for 5 min, and 65°C for 90 min. Following completion of bisulfite conversion, desulfonation, sample clean up, and elution were performed via EpiMark spin columns according to manufacturer’s instructions. Bisulfite-treated DNA was stored at −80°C until analysis.
Two distinctive PCR assays were developed and validated for the proximal (proximal assay) and core (core assay) promoter regions of MIR145, in which bisulfite-treated gDNA was used as template for the amplification of 127 bp and 141 bp sequencing products, respectively. Specific PCR and sequencing primers for bisulfite-treated gDNA were designed using PyroMark Assay Design 2.0 Software (Qiagen, Manchester, UK) and the published sequences (NCBI Reference Sequence: NC_000005.9) (Table S5). Each PCR reaction was conducted in a final volume of 25 μL, containing 1.5 μL bisulfite-treated DNA, 200 μM dNTPs mix, 400 nM of forward and reverse primers, and 1 U of EpiMark Hot Start Taq DNA Polymerase (New England Biolabs). PCR cycling conditions were 95°C for 30 s, followed by 40 cycles of 95°C for 15 s, 55°C (proximal assay) or 56°C (core assay) for 30 s, and 68°C for 1 min, with a final extension at 68°C for 5 min. Non-template controls were included in each PCR reaction. Agarose gel electrophoresis was performed to evaluate the amplification of specific PCR products, as well as absence of non-specific products and/or primer dimers.
The biotinylated PCR products—in a total volume of 18 μL—were mixed with 20 μL Binding Buffer (Qiagen) and 2 μL streptavidin-sepharose high-performance beads (GE Healthcare, Chicago, USA), followed by shaking at 1,200 rpm for 20 min, in order to facilitate the immobilization. Thereafter, the immobilized PCR products were purified to single-stranded amplicons, using the PyroMark Q24 vacuum workstation (Qiagen) according to manufactures guidelines. The biotinylated ssDNA amplicons were mixed with 0.3 μM of sequencing primers (Table S5) in annealing buffer (Qiagen) and then heated for 2 min at 80°C and cooled at room temperature for 7 min for primer hybridization. Methylation analysis was carried out by pyrosequencing using the PyroMark Gold Q24 Reagents (Qiagen) in PyroMark Q24 Pyrosequencer (Qiagen), according to the manufacturer’s instructions. Quantification of percent methylation of the targeted CpGs was performed PyroMark Q24 Software 2.0 (Qiagen). Efficiency of the bisulfite conversion process was assessed by the conversion of non-CpG cytosine residues within the sequence to analyze.
Statistical analysis was performed by IBM SPSS Statistics 20 software (IBM, Armonk, NY, USA). The normal distribution of the data was evaluated by Sapiro-Wilk and Kolmogorov-Smirnov tests. Due to absence of normal distribution, the non-parametric Wilcoxon singed rank test was used to analyze miR-143/145 gene cluster expression and methylation levels between bladder tumors and normal adjacent urothelium, while Mann-Whitney U and Kruskal-Wallis tests were applied accordingly to evaluate the association of cluster’s expression and methylation with patients’ clinicopathological data. Survival analysis was carried out by Kaplan-Meier curves, using log rank test, as well as univariate and multivariate Cox proportional regression analysis. The promoter methylation optimal cutoff values were adopted by X-tile algorithm. Internal validation was accomplished by bootstrap Cox proportional regression analysis based on 1,000 bootstrap samples. Ultimately, DCA was applied in order to evaluate MIR145 promoter methylation clinical benefit in disease prognosis and patients’ clinical outcome, in accordance with Vickers et al., using STATA 13 software (StataCorp, College Station, TX, USA). | true | true | true |
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PMC9615154 | Zhengquan Wu,Ke Lei,Huaizhi Li,Jiali He,Enxian Shi | Transcriptome-based network analysis related to M2-like tumor-associated macrophage infiltration identified VARS1 as a potential target for improving melanoma immunotherapy efficacy | 27-10-2022 | Tumor associated macrophages,Melanoma,Macrophage polarization,Immunotherapy,Prognostic model,VARS1 | Rationale The M2-like tumor-associated macrophages (TAMs) are independent prognostic factors in melanoma. Methods We performed weighted gene co-expression network analysis (WGCNA) to identify the module most correlated with M2-like TAMs. The Cancer Genome Atlas (TCGA) patients were classified into two clusters that differed based on prognosis and biological function, with consensus clustering. A prognostic model was established based on the differentially expressed genes (DEGs) of the two clusters. We investigated the difference in immune cell infiltration and immune response-related gene expression between the high and low risk score groups. Results The risk score was defined as an independent prognostic value in melanoma. VARS1 was a hub gene in the M2-like macrophage-associated WGCNA module that the DepMap portal demonstrated was necessary for melanoma growth. Overexpressing VARS1 in vitro increased melanoma cell migration and invasion, while downregulating VARS1 had the opposite result. VARS1 overexpression promoted M2 macrophage polarization and increased TGF-β1 concentrations in tumor cell supernatant in vitro. VARS1 expression was inversely correlated with immune-related signaling pathways and the expression of several immune checkpoint genes. In addition, the VARS1 expression level helped predict the response to anti-PD-1 immunotherapy. Pan-cancer analysis demonstrated that VARS1 expression negatively correlated with CD8 T cell infiltration and the immune response-related pathways in most cancers. Conclusion We established an M2-like TAM-related prognostic model for melanoma and explored the role of VARS1 in melanoma progression, M2 macrophage polarization, and the development of immunotherapy resistance. Supplementary information The online version contains supplementary material available at 10.1186/s12967-022-03686-z. | Transcriptome-based network analysis related to M2-like tumor-associated macrophage infiltration identified VARS1 as a potential target for improving melanoma immunotherapy efficacy
The M2-like tumor-associated macrophages (TAMs) are independent prognostic factors in melanoma.
We performed weighted gene co-expression network analysis (WGCNA) to identify the module most correlated with M2-like TAMs. The Cancer Genome Atlas (TCGA) patients were classified into two clusters that differed based on prognosis and biological function, with consensus clustering. A prognostic model was established based on the differentially expressed genes (DEGs) of the two clusters. We investigated the difference in immune cell infiltration and immune response-related gene expression between the high and low risk score groups.
The risk score was defined as an independent prognostic value in melanoma. VARS1 was a hub gene in the M2-like macrophage-associated WGCNA module that the DepMap portal demonstrated was necessary for melanoma growth. Overexpressing VARS1 in vitro increased melanoma cell migration and invasion, while downregulating VARS1 had the opposite result. VARS1 overexpression promoted M2 macrophage polarization and increased TGF-β1 concentrations in tumor cell supernatant in vitro. VARS1 expression was inversely correlated with immune-related signaling pathways and the expression of several immune checkpoint genes. In addition, the VARS1 expression level helped predict the response to anti-PD-1 immunotherapy. Pan-cancer analysis demonstrated that VARS1 expression negatively correlated with CD8 T cell infiltration and the immune response-related pathways in most cancers.
We established an M2-like TAM-related prognostic model for melanoma and explored the role of VARS1 in melanoma progression, M2 macrophage polarization, and the development of immunotherapy resistance.
The online version contains supplementary material available at 10.1186/s12967-022-03686-z.
Melanoma is a highly aggressive skin cancer with early metastases and have the highest mortality rate in skin cancer [43]. Its incidence has increased in recent years and it has become one of the fastest growing tumors. Diagnosis rates are also increasing among young people [44]. Despite the recent advances in neoadjuvant immunotherapy, chemotherapy, and targeted therapy improving patient prognosis, many patients only achieve temporary remission and eventually develop therapy resistance. Therefore, the mortality rates continue to be unacceptably high [2, 24]. Bone marrow-derived cells penetrate the tumor and differentiate into macrophages termed tumor-associated macrophages (TAMs), which are the main component of tumor-infiltrating leukocytes [49]. Most TAMs not only lose the ability to combat tumor progression but also support tumor cell growth and metastasis [3, 40]. TAMs help to build an immune dysfunctional microenvironment in tumors by secreting many immunosuppressive cytokines [5, 26]. Furthermore, as a major source of PD-L1, TAMs inhibit cytotoxic T cell infiltration and function, which drives undesirable resistance to neoadjuvant immunotherapy [33]. In tumors, TAMs predominantly polarize into the pro-tumoral M2 phenotype [32, 48] and a high M2/M1 ratio is an independent prognostic factor in many cancers, especially melanoma [12, 34, 48]. Therefore, it is necessary to describe molecular characteristics combining patients’ M2-like TAMs infiltration and to determine the key regulatory factors of M2-like TAM polarization. To provide new insights into the molecular features of M2-like TAM infiltration in patients with melanoma, we identified two distinct clusters (Cluster 1 and Cluster 2) based on the gene module most positively correlated with M2-like TAM infiltration in The Cancer Genome Atlas skin cutaneous melanoma (TCGA-SKCM) dataset. Then, we investigated the differences in prognosis, multi-omics, and functional enrichment between the two clusters. Next, we constructed a prognostic model according to the differentially expressed genes (DEGs) of the two clusters and compared the prognosis, immune cell infiltration, immune-related gene profile, and immunotherapy response in the high- and low-risk groups. Subsequently, VARS1 was characterized as the hub gene of the module most associated with M2-like TAM infiltration, which suggested that VARS1 is linked to TAM polarization and could be defined as a new potential target in melanoma progression. VARS1 is a member of the aminoacyl-tRNA synthetases (ARSs) and its primary function is to link valines to their corresponding tRNAs in protein synthesis [28]. VARS1 mainly plays an important role in progressive brain disease [14]. Walbrecq et al. proved that hypoxia induced VARS1-bearing extracellular vesicle secretion by melanoma, which correlated with worse melanoma outcomes [60]. Nevertheless, the role of VARS1 in melanoma remains unclear. Our study demonstrates that VARS1 expression was negatively correlated with the immune-related signaling pathways and the infiltration of antitumor cells such as CD8 T cells but was positively correlated with the accumulation of M2-like TAMs. VARS1 overexpression promoted M2-like macrophage polarization and melanoma cell migration and invasion in vitro, while knockdown of VARS1 decreased melanoma cell migration and invasion. VARS1 was inversely correlated with several immune checkpoint genes and could be a predictive biomarker of anti-PD-1 immunotherapy response. Furthermore, pan-cancer analysis revealed that VARS1 correlated negatively with CD8 T cell infiltration in most cancers and demonstrated unfavorable prognostic value in several cancers.
The analyses involved patients from four SKCM cohorts (GSE65904, GSE98394, GSE78220, GSE91061) and TCGA-SKCM. Patients without survival information and RNA sequencing (RNA-seq) data were excluded from the analysis. For the Gene Expression Omnibus (GEO) dataset, related clinical data and transcriptome expression data were downloaded using the R GEOquery package [8] and the related GEO datasets were merged using the ComBat algorithm [31]. Transcriptome FPKM (fragments per kilobase transcript per million fragments) value and clinical data were downloaded from the Genomic Data Commons (GDC, https://portal.gdc.cancer.gov/) using the R TCGAbiolinks package [7]. The FPKM values were transformed to TPM (transcripts per million) values for subsequent analyses.
We constructed mRNA co-expression networks in TCGA-SKCM dataset using the R WGCNA package [29]. First, the Pearson correlation coefficient between each pair of genes was calculated to obtain a similarity matrix. WGCNA converted the similarity matrix to an adjacency matrix using a power function. Among all soft thresholds (β) with R2 > 0.9, we chose the automatic value β (β = 5) returned by the WGCNA pickSoftThreshold function. As recommended by the WGCNA guidelines, 0.25 was chosen as the network merge height. We used default settings for other WGCNA parameters.
We selected the module associated with the infiltration of M2-like TAMs and CD8 T cells and the genes in this module underwent univariate Cox regression analysis. Then, the 125 genes associated with survival in univariate analysis (p < 0.05) were entered into the R ConsensusClusterPlus package [62] to perform consensus clustering for TCGA-SKCM patients. The optimal K value was identified as 2 based on the result of the cluster consensus value and cumulative distribution function.
The DEGs of two clusters with a false discovery rate (FDR) < 0.05 were identified by the R DESeq2 package [36]. Then, the 10,269 DEGs underwent univariate Cox analysis in TCGA dataset and yielded 3390 progression-associated genes (p < 0.05). Further reduction of candidate genes using lasso (least absolute shrinkage and selection operator) logistic regression with 10-fold cross-validation was performed via the R glmnet package [13]. Then, the genes were filtered further using a multivariate proportional hazard regression model (using both stepwise regression). The risk score was calculated as follows: 0.323×ATP13A5 + 0.465×C1orf105 + 0.195×TM6SF2 + 0.151×HEYL + 0.146×PTK6 + 0.065×KIT + 0.049×ENTHD1–0.209×SLC18A1–0.201×ZMAT1–0.158×CD14. The TCGA and validation cohort risk scores used the same model score threshold. Patients were stratified into low- and high-risk groups based on the median risk score cut-off and the differences in overall survival (OS) were compared using the R survival package [56]. The area under the curve (AUC) was calculated with the R timeROC package [35] to evaluate the accuracy of the prognostic model.
Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were performed with the gsva [20] and clusterProfiler [65] packages in R, respectively. The gene sets for GSVA and GSEA were downloaded from the Molecular Signatures Database (MSigDB) v7.4 database. Immune cell infiltration was quantified using the CIBERSORT algorithm [47] based on the TPM value of TCGA-SKCM patients.
Somatic mutations and somatic copy number alterations (CNAs) were downloaded from GDC using the R TCGAbiolinks package. The somatic mutations and CNAs (GISTIC output) data were visualized using the R maftools package [41]. The significant CNA amplifications and deletions were identified by GISTIC 2.0 [42]. The methylation data of TCGA patients were downloaded from the GDC portal. Differentially methylated CpGs between Cluster 1 and Cluster 2 were examined with the t-test. CpGs in chromosomes X and Y were excluded from the analysis. CpGs with FDR < 0.05 were characterized as differentially methylated CpGs.
The STRING database (v.11.5) was used to establish PPIs between genes in the WGCNA module with a confidence level of 0.4, and the interaction network was visualized using Cytoscape. The hub genes of the WGCNA module were screened with the Closeness, Stress, and Radiality algorithms of the cytoHubba plugin [6] in Cytoscape.
We used SK-MEL-28 (ATCC, Cellcook Biotechnology, Guangzhou, China), A375 (ATCC, Cellcook Biotechnology, Guangzhou, China), and THP1 cells (ATCC, Cellcook Biotechnology, Guangzhou, China) for in vitro experiments. A375 and SK-MEL-28 cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS) and 1% penicillin-streptomycin (all from Gibco, Carlsbad, CA, USA). The THP1 cells were cultured in RPMI 1640 medium containing 10% FBS, 1% penicillin-streptomycin, 2 mM glutamine, 10 mM HEPES, and 1× non-essential amino acids (all from Gibco). The VARS1 overexpression (pCR4-TOPO-VARS1) and control vector plasmids were purchased from Miaoling Company (Miaoling, Wuhan, China) and the small interfering RNAs (siRNAs) targeting VARS1 and the siRNA control were purchased from RiboBio (Guangzhou, China). The sequences of the VARS1-targeting siRNAs were as follows: GGAAACGCTCCCTGTCACAAA (VARS1 siRNA1) and GCCGGATCTGGAATAATGTGA (VARS1 siRNA2). For transient transfection, A375 and SK-MEL-28 cells were transfected with overexpression plasmid or siRNAs, respectively, using transfection reagents (Lipofectamine 3000, Invitrogen, CA, USA) for 48 h, followed by further functional assays.
Total RNA extraction and qRT-PCR were conducted as previously described [64]. The qRT-PCR forward and reverse primer sequences were as follows: (1) β-actin, CTCGCCTTTGCCGATCC and TTCTCCATGTCGTCCCAGTT; and (2) VARS1, CCGTGCTAGGAGAAGTGGTT and TCTCTGGTTTTGGTTTCTTCTCCC, respectively. The western blotting was performed as previously described (36) with primary antibodies against VARS1 (WH0007407M1, Sigma, Germany) and α-tubulin (A11126, Invitrogen, CA, USA).
The migration and invasion assays were performed as previously described [64]. After cleaning the cells on the top of the insert, cells growing through the porous membrane were photographed with an inverted light microscope (×100). The relative numbers of migrating and invasive cells were calculated using ImageJ (ImageJ National Institutes of Health, USA).
THP1 cells were treated with 320 nM phorbol-12-myristate-13-acetate (PMA) for 6 h and differentiated into macrophages, then maintained in the medium with PMA for 16 h to generate M0 cells as described before [17, 37, 52, 63]. To analyze the influence of VARS1 on macrophage polarization, we collected the culture supernatants of VARS1-overexpressing A375 cells at 24 h. For the CM collection method, we first seeded equal numbers (1 million cells) of VARS1-overexpressed and control cells separately in 100 mm tissue culture dishes with complete medium. When cells have grown to 70–80% confluency, replace the medium with fresh serum-free medium. After 24 h of cell culture, CM was collected and passed through a 0.22 μm filter (Millipore). Then we added the supernatant to THP-1 cell culture medium and continue to culture M0 THP1 cells. After 4 days, the THP1 cells were harvested and stained with CD86 (#374,202, BioLegend) and CD206 (#321,102, BioLegend). After 45-min incubation on ice, the cells were washed three times with phosphate-buffered saline (PBS) buffer and resuspended in fluorescence-activated cell sorting (FACS) buffer (2% FBS in PBS buffer) for flow cytometric analysis.
We integrated two datasets of patients with melanoma treated with anti-PD-1 (GSE78220 and GSE91061). Further analyses were performed only on treatment-naïve patients. Then, the immunotherapy response was predicted using the SubMap online tool [30].
Survival differences between groups were assessed using Kaplan-Meier curves and log-rank tests. Prognostic factors were determined with univariate and multivariate Cox regression analyses. Correlation coefficients were calculated by Pearson and Spearman correlation analyses. Normal and non-normal variables were compared using the unpaired Student t-test and the Mann-Whitney U test, respectively. One-way analysis of variance and the Kruskal-Wallis test were used as parametric and nonparametric methods, respectively, for comparing > 2 groups. Genes with differential mutations and differential copy number losses and gains were examined with chi-square and Fisher’s exact texts. The statistical analysis was performed using R software and values represent the mean ± standard deviation. P < 0.05 was considered statistically significant.
First, we used the CIBERSORT algorithm to assess the fraction of immune cell infiltration in patients. In TCGA and GSE98394 datasets, patients with a higher proportion of M2 macrophage infiltration had worse prognosis (Fig. 1 A and Figure S1A). Considering that more M2 macrophages appeared to be associated with poorer prognosis and CD8 T cell infiltration, we performed WGCNA to detect the module related to CD8 T cell and M2 macrophage infiltration (Figure S1D). We select the soft threshold power β = 5 (scale-free R2 = 0.90) to construct a scale-free network (Figures S1B, S1C). The heatmap demonstrates that the yellow module was negatively and positively correlated with the infiltration of CD8 + T cells and M2 macrophages, respectively, in TCGA-SKCM (Fig. 1B). We used the genes in the yellow module and survival data in TCGA-SKCM dataset to perform univariate Cox regression analysis, and 125 genes were associated with OS in TCGA-SKCM. We used the R ConsensusClusterPlus package for consistent clustering in TCGA-SKCM dataset based on the 125 prognostic genes and identified two clusters: Cluster 1 (319 cases) and Cluster 2 (148 cases) (Fig. 1 C and Figure S1E, S1F). Principal component analysis also suggested that these two populations were distinct groups (Figure S1G). Cluster 1 had worse OS outcomes than Cluster 2 (log-rank p = 0.0071, Fig. 1D).
To demonstrate signaling pathway activation in each cluster, we calculated the GSVA enrichment scores using Kyoto Encyclopedia of Genes and Genomes (KEGG) signaling pathway gene sets in MSigDB v7.4. Figure 2 A depicts the top 20 enriched pathways in each cluster. In comparison with Cluster 2, Cluster 1 was characterized by the lack of immune-related pathways, such as T cell receptor signaling pathways. A previous study divided TCGA-SKCM tumors into three subtypes [1]: (1) immune, (2) keratin, and (3) MITF-low. We found that Cluster 1 contained a higher proportion of the keratin subtype (57% vs. 13%) and a lower proportion of the immune subtype (34.7% vs. 56.2%) than Cluster 2 (Fig. 2B). GSEA indicated that the M2 macrophage pathway was enriched in Cluster 1 (Fig. 2 C). Examination of the differential expression of immune checkpoint genes revealed that Cluster 2 demonstrated higher immune checkpoint-related gene expression compared with Cluster 1 (Fig. 2D). To investigate mutations in each cluster, we highlighted the top 20 significantly mutated genes (SMGs) in the two clusters with a waterfall plot (Fig. 3 A, 3B). The two clusters shared most of the SMGs. However, Cluster 1 contained unique SMGs, including XIRP2 (31%), FAT4 (31%), USH2A (30%), and ANK3 (29%) while Cluster 2 contained unique SMGs that included FLG (40%), APOB (40%), and CSMD2 (37%). A recent prospective study found that higher tumor mutation burden (TMB) is associated with better immunotherapy response [4]. Cluster 2 samples demonstrated higher TMB severity than Cluster 1 samples (Figure S2A). We used GISTIC 2.0 to analyze the somatic copy number variation (SCNV) and summarized the amplified and deleted areas of Cluster 1 and Cluster 2. Cluster 1 contained a total of 56 focal deletion peaks and 69 focal amplification peaks, while Cluster 2 contained 37 focal deletion peaks and 28 focal amplification peaks (Fig. 4 A, 4B). Examination of the frequency of immune checkpoint gene amplification or deletion in each subtype revealed that Cluster 2 contained more amplification of immune checkpoint (VTCN1, TNFRSF family) and effector T cell function genes (GZMK, GZMA, IFNG) while Cluster 1 had more deletions (VTCN1, ADORA2A, TJP1, IDO1, HAVCR2) (Fig. 4 A, 4B). We used the R ChAMP package [57] with FDR < 0.05 to analyze the methylation differences in the two clusters and obtained 28,870 differentially methylated probes (DMPs) between Cluster 1 and Cluster 2. Interestingly, CD8A and HAVCR2 of Cluster 1 had increased methylation levels than that in Cluster 2 (Fig. 4 C).
We explored the DEGs between the two clusters to construct a prognostic model (Fig. 5 A). First, we performed univariate Cox analysis on the DEGs and obtained 3390 genes with prognostic significance. Then, we performed lasso regression and multivariate Cox analysis based on the 3390 genes to construct a prognostic model in TCGA-SKCM dataset (Figure S2B, S2C). The risk score was calculated as follows: 0.323×ATP13A5 + 0.465×C1orf105 + 0.195×TM6SF2 + 0.151×HEYL + 0.146×PTK6 + 0.065×KIT + 0.049×ENTHD1–0.209×SLC18A1–0.201×ZMAT1–0.158×CD14. Then, TCGA-SKCM patients were divided into high- and low-risk groups based on their risk scores. Patients with higher risk scores had worse OS prognosis, and Cluster 1 patients had higher risk scores (Fig. 5B C). Time-dependent AUC and the AUCs at 1 (0.70), 2 (0.74), 3 (0.72), and 5 (0.74) years suggested that the M2 macrophage cluster-related risk score had potential value for predicting the OS of patients with melanoma in TCGA datasets (Fig. 5D and Figure S2D). To verify the prognostic significance of the model, we used the same model score threshold to calculate the risk score in a validation cohort (GSE65904), which yielded a similar result, where patients with higher risk scores had worse OS, and the risk score had prognostic value (Fig. 5E F and Figure S2E). The risk score was identified as an independent prognostic factor in both TCGA and GSE65904 datasets (Table S1).
The risk score played an important role in melanoma progression. To assess the influence of the M2 macrophage cluster-related risk score on the tumor microenvironment (TME), we compared the immune cell infiltration between the high and low score groups. Patients with high risk scores had increased M2 macrophage infiltration and decreased CD8 T cell infiltration compared to patients with low risk scores (Fig. 5G). We also explored differences in the expression of HLA family genes and immune checkpoint markers in the high and low risk score groups in TCGA and GEO datasets. The high risk score group had significantly increased expression of the antigen-presentation and immune checkpoint-related genes in comparison to the low risk score group of TCGA datasets (Fig. 6 A–C). Consistent with these results, analysis of GSE65904 sample data yielded similar results (Figure S3A–C). Furthermore, we applied our M2 macrophage cluster-related model to the merged datasets (GSE78220 and GSE91061) with available immunotherapy outcomes and examined the risk score of melanoma patients. To further observe the different response to immunotherapy in high risk score and low risk score groups, we found that patients with high risk score had higher proportion of non-responders to immunotherapy compared to patients with low risk score (64% vs. 28%). (Fig. 6D)
We explored the hub genes in the yellow module. We used the 275 genes in the yellow module to construct a PPI network based on the STRING database results. Then, the top hub genes were determined via the Closeness, Stress, and Radiality algorithms in the Cytoscape cytoHubba plugin (Figure S4). The hub gene essential for melanoma cell growth was determined with DepMap (https://depmap.org/portal/download/), a CRISPR-based database for genome-wide loss-of-function screening. Only VARS1 was identified by intersecting the gene sets obtained from these four methods (Fig. 7 A). In TCGA dataset, high VARS1 expression correlated with shorter OS (Fig. 7B). Furthermore, we explored which cell type mainly expressed VARS1 in melanoma. The result of single-cell RNA-seq of the GSE115978 dataset demonstrated that VARS1 was expressed predominantly in tumor cells but not in stromal and immune cells (Fig. 7 C). Additionally, high risk score patients had higher VARS1 expression levels than low risk score patients (Figure S5A). We also examined whether VARS1 played an important role in melanoma progression and constructed VARS1-overexpressing and VARS1 knockdown A375 and SK-MEL-28 cell lines (Figure S5B). VARS1 overexpression promoted the migration and invasive ability of the cells while VARS1 suppression significantly decreased it (Fig. 7D–F). GSEA indicated that high VARS1 levels positively correlated with the metastasis-related pathway in TCGA-SKCM dataset (Fig. 7G). Furthermore, a search of the Human Protein Atlas (HPA) database [58, 59] showed that VARS1 expression was increased in primary melanoma compared to normal skin tissue, and further increased in metastatic melanoma (Figure S5D).
To investigate the VARS1-related pathways, we divided TCGA-SKCM dataset patients into two groups based on the median VARS1 gene expression. GSVA of the KEGG pathways revealed that the immune-related pathways, such as the T cell receptor pathway, were enriched in patients with low VARS1 expression, while tumor growth pathways such as the cell cycle pathway and the mTOR pathway were enriched in patients with high VARS1 expression (Fig. 8 A). We examined the correlation between VARS1 expression and the CIBERSORT immune cell infiltration score. VARS1 expression positively correlated with intratumoral M2 macrophage infiltration and negatively correlated with M1 macrophage and CD8 T cell infiltration (Fig. 8B C). To elucidate the role of VARS1 in M2 macrophage polarization, THP1 cells were treated with the supernatant of A375 cells line overexpressing VARS1 (VARS1-A375) and A375 vector (vector-A375) cell lines and detected the M1 and M2 macrophage markers. Flow cytometry revealed a 3-fold increase in the expression of the M2 macrophage marker CD206 in THP1 cells treated with VARS1-A375 supernatants compared with those treated with vector-A375-supernatants, while the expression of CD86, an M1 macrophage marker, decreased by 15.2% (Fig. 8D). Taken together, these results indicate that VARS1 may play important roles in M2 macrophage infiltration and polarization.
High VARS1 expression correlated negatively with CD8 T cell infiltration in TCGA-SKCM dataset (Fig. 9 A). The expression of many immune checkpoint genes was negatively associated with VARS1 expression in both TCGA and GSE65904 datasets (Fig. 9B and Figure S6A). Previous studies have shown that TGF-β1 is involved in PD-1 immunotherapy resistance and M2 macrophage polarization [11, 66]. Here, the enzyme-linked immunosorbent assay demonstrated that the supernatant of VARS1-overexpressing cells had significantly increased TGF-β1 concentrations compared to that of vector cells (Figure S5C). We performed SubMap analysis to assess the anti-PD-1 immunotherapy response in high- and low-VARS1 expression patients with melanoma. The results demonstrated that low VARS1 expression predicted partial response (PR) to anti-PD-1 immunotherapy whereas high VARS1 expression predicted resistance (SD) to anti-PD-1 immunotherapy (Fig. 9 C). To explore the suppressive role of VARS1 in immune regulation, we used different algorithms to investigate the correlation between VARS1 gene expression and CD8 T cell infiltration in Pan-TCGA datasets. The heatmap showed that VARS1 gene expression and CD8 T cell infiltration were inversely correlated in most cancers (Fig. 9D). GSEA indicated that many immune-related pathways, such as the T cell-mediated cytotoxicity pathway, were enriched in the patients with high VARS1 expression in 70% of cancer types (Fig. 9E). Finally, we evaluated the association between VARS1 and OS across 33 cancer types. High VARS1 expression was correlated with poorer survival in six cancer types (Figure S6B), including KICH (hazard ratio [HR] = 2.80), MESO (HR = 1.74), SKCM (HR = 1.32), SARC (HR = 2.25), LAML (HR = 1.69), and CESC (HR = 1.49) and with better survival in READ (HR = 0.47). These results suggest that VARS1 may have predictive value for patient prognosis and PD-1 immunotherapy efficacy.
Melanoma has been recognized as the most aggressive type of skin cancer and is particularly responsive to immunotherapy such as immune checkpoint blockade with CTLA4 and PD-1 antagonists [38]. Immunotherapy can improve patient outcomes obviously, especially for patients with stage IV melanoma, but the mortality rates would become quite high once patients develop immunotherapy resistance [2, 53, 54]. Nevertheless, the goal of addressing and predicting immunotherapy response in melanoma has been reached. Considering that numerous studies have demonstrated the importance of TAMs in clinical outcome and immunotherapy resistance in melanoma, we applied WGCNA to identify a M2-like TAM module in melanoma for the first time and examine the reliability of M2-like TAMs as a prognostic marker in melanoma and in predicting immunotherapy response. Recent studies have demonstrated the prognostic importance of TAMs in various cancers. The presence of TAMs, mainly M2-like TAMs, is not only correlated with poor outcome in various tumors, but is also associated with the generation of an immunosuppressive TME [16, 22, 46]. As an important source of inflammatory cytokines and growth factors, M2-like TAMs support angiogenesis, which results in the promotion of tumor cell proliferation and survival [9, 21, 51]. A previous study reported that TAM-derived VEGFA enhanced vascular permeability, thereby facilitating cancer cell intravasation and metastasis [19]. Moreover, M2-like TAMs express PD-L1, a major negative regulatory ligand suppressing cytotoxic T lymphocyte (CTL) activation in the TME. In some cancers, M2-like TAM-derived PD-L1 is more effective than cancer cell-derived PD-L1 for suppressing CTL function [27, 50]. Recent studies have demonstrated that M2-like TAM-derived factors, such as interleukin (IL)-6, IL-10, and milk fat globule-epidermal growth factor VIII (MFG-E8), can suppress naïve T cell proliferation, promote carboplatin resistance, and enhance tumor growth [23, 39, 61]. Furthermore, depleting or downregulating M2-TAMs suppressed tumor growth by inactivating CCL2 and/or CCR2 signaling [55]. However, a M2-like TAM-related prognostic model in melanoma has not been explored. Based on the importance of M2-like TAMs to clinical outcome and the immunosuppressive TME, we inferred that a gene module associated with M2-like TAMs in melanoma could be applied to establish a prognostic model that could provide predictive value in clinical outcome and immunotherapy response in melanoma. We first validated that the high score of M2-like macrophages is significantly associated with poorer survival in TCGA and GSE98394 datasets. To examine the reliability of M2-like TAMs as a prognostic marker in melanoma, two clusters were grouped by genes in a M2-like TAM-related module and demonstrated different OS and clinical features. With poorer OS, Cluster 1 was characterized by enrichment of the M2 macrophage pathway and the lack of immune response pathways, such as the T cell receptor signaling pathway, complement and coagulation cascades, and leukocyte transendothelial migration. The activation of these immune response pathways is associated with good immunotherapy response and good clinical outcome [10, 15, 18, 54], indicating that the lack of immune response pathways was one of the major leading causes of the poorer outcome in Cluster 1 as compared with Cluster 2. Furthermore, the transcriptomic classification of melanoma includes the immune, keratin, and MITF-low subtypes. Compared with Cluster 2, Cluster 1 had a lower proportion of immune-subtype melanoma, which is associated with overexpression of the immune-related genes and more favorable post-accession survival. Moreover, Cluster 1 also contained a higher proportion of the keratin subtype, which exhibits worse outcome when compared with the immune and MITF-low subtypes. As an emerging predictive biomarker of cancer immunotherapy, elevated TMB can be associated with increased clinical benefit from immune checkpoint blockade therapies [4]. Interestingly, Cluster 2 had higher TMB severity than Cluster (1) Recent studies have also shown that checkpoint blockade immunotherapy response is correlated with the immune checkpoint gene and ligand receptor expression level [45]. Cluster 2 had more amplifications of the immune checkpoint and effector T cell function genes, while Cluster 1 had more deletions of the genes. This indicated that Cluster 1 had more decreased benefit from immunotherapy compared to Cluster (2) Our results suggest that the identified M2-like TAM module is reliable for providing meaningful prognostic value in the clinical outcome and immunotherapy response in melanoma. We further identified a M2 macrophage cluster-related prognostic model and generated a prognostic risk score based on the DEGs between the M2-like TAM-related clusters. In TCGA cohort, Cluster 1 had a significantly higher risk score than Cluster 2, and OS was significantly decreased in the high risk score group compared to the low risk score group. Moreover, a higher risk score was associated with a series of tumor immunogenic factors. In our study, the high risk score group demonstrated less CD8 + T cell infiltration and more M2 macrophage infiltration compared to the low risk score group. Previous studies have proven that inhibiting antigen presentation is associated with immune evasion. The antitumor immune response is mainly centered on antigen presentation. Our result demonstrated that the high risk score group had significantly suppressed antigen presentation compared to the low risk score group, indicating that a higher risk score was associated with lower immunotherapy response. Furthermore, our findings also demonstrate that compared with the low risk score group, the high risk score group had decreased expression of the immune checkpoint genes and the majority of ligand receptors, including CCL5, CXCL9, and IFNG. This observation prompted us to examine the prognostic value of this risk score in immunotherapy outcomes: there was a higher percentage of SD/progressive disease in high-risk patients than in low-risk patients. Hence, the risk score based on the M2-like TAM-related prognostic model represented an independent prognosticator of OS and immunotherapy response in melanoma. With the aim of identifying a potential biomarker for predicting OS and immunotherapy response in melanoma, we identified the top hub genes in the specific M2-like TAM module via three different algorithms. Interestingly, only VARS1 was identified after intersection between these hub genes and the melanoma cell growth-related genes in the DepMap database, indicating that VARS1 was associated with M2-like TAM polarization and melanoma tumor cell growth. Moreover, our results showed that VARS1 was mainly expressed by tumor cells and that high VARS1 expression was significantly associated with poor OS and the metastasis-related pathway in TCGA-SKCM dataset. As an ARS member, VARS1 plays an important role in protein synthesis. Recent studies have shown that ARSs are involved in various physiological and pathological processes, especially tumorigenesis, and could be potential biomarkers and therapeutic targets in cancer treatment [25]. However, only one study reported that VARS1-bearing extracellular vesicles were associated with worse clinical outcome in melanoma [60]. The role of VARS1 in melanoma remains unclear, which prompted our exploration of the function of VARS1 as a potential prognostic biomarker in melanoma. Our in vitro experiments demonstrated that A375 and SK-MEL-28 cell migration and invasive ability was significantly increased after VARS1 was overexpressed, while VARS1 knockdown decreased it. Moreover, high VARS1 expression was associated with low immune-related signaling pathway enrichment, low immune checkpoint expression, and low CD8 T cell infiltration and predicted anti-PD-1 immunotherapy resistance, which indicated that the upregulation of VARS1 can be associated with low immunotherapy response and poor clinical outcome in melanoma. Previous studies have also shown that the tumor-suppressing effect of the TGF-β1 signaling pathway has an essential function in poor immunotherapy response [11]. Our in vitro experiments demonstrated that VARS1 upregulated TGF-β1 expression in tumor cells and the M2 macrophage marker CD206. In addition, our analysis of the Pan-TCGA datasets supported the idea that high VARS1 expression was correlated with poor CD8 T cell infiltration in most cancers. Taken together, our results suggest that, as the hub gene related to the M2-like macrophage module, VARS1 exerts an immunosuppressive effect on melanoma progression and is a potential predictive biomarker of clinical outcome and immunotherapy response in melanoma, which requires further investigation in prospective studies and larger populations. Our study has potential weaknesses. It is a retrospective study and requires a multi-center cohort study to validate the predictive value of this M2-like TAM-related prognostic model and VARS1 as a predictive biomarker of anti-PD-1 immunotherapy response in melanoma. In addition, further animal experiments are necessary for exploring the functional role of VARS1 in melanoma, which can help provide more robust clues to guide clinical application.
Our studies identified a M2-like TAM-related prognostic model for predicting OS and immunotherapy resistance in melanoma and explored the potential predictive value of VARS1 in melanoma immunotherapy. We hope that our research widens the current understanding of the role of M2-like TAMs in the biology of melanoma and prognosis prediction and that VARS1 can be a novel predictive biomarker of clinical outcome and immunotherapy response in melanoma. Figures and legends.
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PMC9615160 | Zhilei Zhang,Ye Zhou,Yuming Jia,Chao Wang,Meng Zhang,Zhuo Xu | PRR34-AS1 promotes exosome secretion of VEGF and TGF-β via recruiting DDX3X to stabilize Rab27a mRNA in hepatocellular carcinoma | 27-10-2022 | Hepatocellular carcinoma,PRR34-AS1,Exosome,Rab27a | Background Exosomes are deemed to be an important tool of intercellular communicators in cancer cells. Our study investigated the role of PRR34 long non-coding RNA antisense RNA 1 (PRR34-AS1) in regulating exosome secretion in hepatocellular carcinoma (HCC) cells. Methods Quantitative real-time polymerase chain reaction (RT-qPCR) analyzed the expression of PRR34-AS1. We assessed the function of PRR34-AS1 on the biological changes of THLE-3 cells and HCC cells. The downstream interaction between RNAS was assessed by mechanistic experiments. Results PRR34-AS1 expression was upregulated in HCC cells in comparison to THLE-3 cells. PRR34-AS1 depletion repressed HCC cell proliferation, migration and invasion as well as EMT phenotype, while PRR34-AS1 up-regulation accelerated the malignant phenotypes of THLE-3 cells. PRR34-AS1 recruited DDX3X to stabilize the mRNA level of exosomal protein Rab27a. Moreover, PRR34-AS1 facilitated the malignant phenotypes of THLE-3 cells by elevating Rab27a expression to promote the exosome secretion of VEGF and TGF-β in HCC cells. Conclusions The current study revealed a novel function of PRR34-AS1 in accelerating exosome secretion in HCC cells and offered an insight into lncRNA function in the regulation of tumor cell biology. Graphical Abstract Supplementary Information The online version contains supplementary material available at 10.1186/s12967-022-03628-9. | PRR34-AS1 promotes exosome secretion of VEGF and TGF-β via recruiting DDX3X to stabilize Rab27a mRNA in hepatocellular carcinoma
Exosomes are deemed to be an important tool of intercellular communicators in cancer cells. Our study investigated the role of PRR34 long non-coding RNA antisense RNA 1 (PRR34-AS1) in regulating exosome secretion in hepatocellular carcinoma (HCC) cells.
Quantitative real-time polymerase chain reaction (RT-qPCR) analyzed the expression of PRR34-AS1. We assessed the function of PRR34-AS1 on the biological changes of THLE-3 cells and HCC cells. The downstream interaction between RNAS was assessed by mechanistic experiments.
PRR34-AS1 expression was upregulated in HCC cells in comparison to THLE-3 cells. PRR34-AS1 depletion repressed HCC cell proliferation, migration and invasion as well as EMT phenotype, while PRR34-AS1 up-regulation accelerated the malignant phenotypes of THLE-3 cells. PRR34-AS1 recruited DDX3X to stabilize the mRNA level of exosomal protein Rab27a. Moreover, PRR34-AS1 facilitated the malignant phenotypes of THLE-3 cells by elevating Rab27a expression to promote the exosome secretion of VEGF and TGF-β in HCC cells.
The current study revealed a novel function of PRR34-AS1 in accelerating exosome secretion in HCC cells and offered an insight into lncRNA function in the regulation of tumor cell biology.
The online version contains supplementary material available at 10.1186/s12967-022-03628-9.
Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide, with a high morbidity and mortality rate [1, 2]. Surgical resection is identified as the most effective therapy for HCC treatment, but high recurrence and distant metastasis after surgery result in poor prognosis of HCC [3, 4]. Exosomes play important roles in multiple aspects of HCC, which include angiogenesis, chemoresistance and metastasis [5]. Moreover, some exosome biomarkers are used for the early diagnosis and prognosis of HCC patients [6, 7]. Cancer cell-secreted exosomes were considered as important messengers in intercellular communication, thus many studies have unveiled the function of exosome in tumor microenvironment [8]. Nevertheless, few studies have addressed the underlying molecular mechanisms of tumor cell secretion of exosomes. Exosomes are 30–150 nm nano-sized vesicles containing various types of nucleic acids, such as proteins, RNAs and lipids [9]. Exosome biogenesis is formed by the budding of multivesicular body (MVB) membranes inwards to shape intra-luminal vesicles, ultimately maturing and contained within MVBs [10]. Multiple effectors and molecular mechanisms are involved in regulating the intracellular trafficking steps such as MVB movement, docking, and integration of exosomes with the plasma membrane before release [11]. Rab GTPases are responsible for regulating MVB motility and plasma membrane docking [12]. The Rab family contains many subtypes, and some Rab GTPases are crucial mediators in modulating exosome secretion, such as Rab27A and Rab27B. Rab27A and Rab27B have been reported to be the primary components of vesicle trafficking in exosome secretion and play critical roles in tumor progression and metastasis [13]. Nevertheless, the mechanisms by which Rab GTPases control the secretion of exosome in HCC cells remain to be further investigated. According to global gene expression data in mammalian species, the majority of the genome is transcribed into non-coding RNAs (ncRNAs) [14]. Long non-coding RNA (lncRNA) is a class of ncRNAs greater than 200 nucleotides in length, and has been emerged as pivotal regulators in cancer progression [15]. LncRNAs can regulate gene expression through transcriptional regulation, post-transcriptional regulation, chromatin modification as well as genomic imprinting [16]. Aberrantly expressed lncRNAs can participate in HCC progression as oncogenes or tumor suppressors [17]. MCM3AP-AS1 exerts an oncogenic role in HCC progression via interacting with miR-194-5p to elevate FOXA1 expression [18]. MAGI2-AS3 impedes cell proliferation and migration in HCC via the miR-374b-5p/SMG1 axis [19]. LncRNA PRR34 long non-coding RNA antisense RNA 1 (PRR34-AS1) regulates HCC cells malignant phenotypes through miR-296-5p/SOX12/E2F2 axis [20] or miR-498/TOMM20/ITGA6 axis [21]. In this study, to further reveal the effect of PRR34-AS1 in HHC, we probed into the role of PRR34-AS1 in HCC, and investigated the mechanism between HCC cells and THLE-3 cell by exosome secretion. Our study might offer a novel sight for HCC treatment.
HCC cells including HLF, Huh-7, SNU-449, HepG2 and LM3 were obtained from COBIOER (Nanjing, China). HLF, Huh-7 and LM3 cells were grown in DMEM. SNU-449 cells were grown in RPMI-1640 medium. HepG2 cells were grown in MEM. All mediums were obtained from Gibco (Grand Island, USA). Human liver epithelial cell (THLE-3) was obtained from ATCC (Manassas, VA, USA) and kept in BEGM (Lonza/Clonetics Corporation, Walkersville). Cells were left to grow at 37 °C under a humid environment with 5% CO2.
Total RNAs were extracted with the application of Trizol reagent (Invitrogen, USA). For the evaluation of gene expression, cDNA was synthesized with the application of PrimeScript RT master mix (Takara, Japan). Next, SYBR Green PCR Master Mix (Applied biosystems) was utilized to conduct qPCR with 2−△△CT calculation. GAPDH served as control.
Specific shRNAs targeting PRR34-AS1 (sh/PRR34-AS1), as well as DDX3X (sh/DDX3X), Rab27a (sh/Rab27a) and negative control (sh/NC) were synthesized by Genechem (Shanghai, China). Besides, NC or PRR34-AS1 or Rab27a were obtained from RiboBio (Guangzhou, China). Cells were collected for further experiments after 48 h of transfection.
Cell proliferation was assessed with the application of EdU kit (RiboBio, Guangzhou, China). Cells in 96-well plates were incubated to 90% confluence, and then cultured with 100 μL of 50 μM EdU diluent for 2 h. After fixation with 4% paraformaldehyde and 0.5% Triton X-100 treatment, cells were incubated in the dark with 100 μL of 1× Apollo® staining reaction. DAPI was applied for nuclear redyeing. Images were acquired by a laser confocal microscope.
Cells were planted into 6-well plates (600 cells/well) for 2 weeks, followed by the treatment of 4% paraformaldehyde and 0.5% crystal violet. Finally, the number of colonies (> 50 cells) was counted.
Cells were planted into 6-well plates until completely adhered to the wall, and then cells were scratched for cell culture. Images of wound healing were obtained at 0 h and 24 h after scratching, and the relative wound width was calculated.
Transwell chambers (8-μm pores, Corning, USA) pre-coated with Matrigel (BD, USA) were used for invasion assay. Cells were planted into the upper chamber, and the bottom chamber was filled with medium containing 10% FBS. After 24 h of incubation, cells were removed from the upper surface, and the invaded cells were subjected to the fixation with 4% paraformaldehyde for 10 min at room temperature, and stained in 0.5% crystal violet. The average number of invaded cells per field was assessed using a microscope.
Cells were cultured in RIPA buffer (Beyotime) for the extraction of proteins. BCA protein assay kit II (Beyotime) was performed for determining protein concentrations. The extracted proteins were subjected to the separation on 10% SDS-PAGE and then transferred onto PVDF membrane (Invitrogen), followed by being blocked with 3% BSA. Blots were incubated overnight at 4 °C with antibodies against E-cadherin (1/1000), N-cadherin (1/1000), Vimentin (1/1000), Slug (1/1000), Twist (1/1000), GAPDH (1/1000), CD9 (1/2000), CD63 (1/2000), HSP70 (1/1000), TSG101 (1/1000), DDX3X (1/1000), VEGF (1/1000), TGF-β (1/1000) and Rab27a (1/1000) and all these antibodies were procured from Abcam. Next, blots were incubated with horseradish peroxidase (HRP)-labeled secondary antibodies. The blot signals were measured through enhanced chemiluminescence reagents.
Cells were treated with 4% paraformaldehyde and 0.5% Triton X-100 for fixation and permeabilization, and then blocked in 3% bovine serum albumin. Subsequently, cells were cultured overnight at 4 °C with primary antibodies and cultured for 1 h at 37 °C with specific secondary antibodies. After washing, DAPI was utilized for nuclear redyeing. The images of Immunofluorescence staining were obtained under a confocal microscope.
Exosomes were separated from cells using Ribo exosome isolation reagent (RiboBio, Guangzhou, China) based on the user manual. Cells were centrifuged at 2000×g for 30 min, and mixed with Ribo exosome solution reagent. The mixtures were refrigerated at 4 °C overnight, followed by centrifugation at 1500×g for 30 min. Exosomes were obtained after removing the supernatants.
PKH67 (1 μM, Sigma-Aldrich) was commercially obtained to label the exosomes according to the supplier’s protocol. Cell nuclear was double-stained by DAPI. Images were observed under a laser scanning microscope.
The concentration and distribution of exosome size was measured using ZetaView PMX 10 (Particle Metrix, Germany). Exosomes were re-suspended in granular PBS and added to the instrument. Each sample was recorded in 5 videos to confirm the numbers and size distribution of exosomes.
Cells were centrifuged and treated with 2.5% glutaraldehyde and then treated by osmium tetroxide. Next, cells were embedded with epoxy resin and cut into sections at a thickness of 100 nm, followed by the treatment of uranyl acetate and lead citrate. The images of microscopic samples were captured using a transmission electron microscope.
Cy3-labeled PRR34-AS1 probe was synthesized (RiboBio, Guangzhou, China). A FISH Kit (RiboBio) was applied for assessing the subcellular localization of PRR34-AS1 base on the manufacturer’s instructions.
Subcellular isolation of RNAs was performed with the application of Cytoplasmic and Nuclear RNA Purification Kit which was procured from Norgen (Thorold, ON, Canada). Nuclear and cytoplasmic fractions were measured via RT-qPCR.
For Rab27a promoter analyses, the sequence of Rab27a promoter was firstly sub-cloned into pGL3-basic reporter vectors (Promega, Madison, WI, USA). After the co-transfection of downregulated PRR34-AS1 and plasmids, the activity of Rab27a promoter was evaluated using a luciferase assay kit (Promega).
Magna RNA-binding protein immunoprecipitation kit (Millipore, USA) was utilized to assess the binding between RNAs. Cell lysates were cultured with RIP buffer and antibodies (anti-Ago2, anti-SNRNP70 and anti-DDX3X) conjugated with magnetic beads for 1 h. The enrichment of RNAs was evaluated by RT-qPCR.
Pierce Magnetic RNA–Protein Pull-down kit (Thermo fisher scientific) was used in this assay in the light of supplier’s instructions. PRR34-AS1 sense and PRR34-AS1 antisense were in vitro transcripted and labeled. Biotin-labeled PRR34-AS1 (50 pmol) was cultured with streptavidin magnetic beads (50 μL) with rotation, and then hatched in cell lysates. The eluted proteins were measured by western blot after mass spectrometry.
After reaching 80% confluence, cells were treated with actinomycin D (4 µg/mL, Sigma-Aldrich, USA) to inhibit transcription. The relative mRNA levels were analyzed by RT-qPCR at the indicated time points.
Data were processed by GraphPad Prism 6.0 Software and displayed as means ± standard deviation of three replicates. The Student’s t-test and two-way ANOVA were used to analyze differences between two groups or more than two groups. Data were defined as statistical significance if P < 0.05.
First, GEPIA 2 (http://gepia2.cancer-pku.cn) database was applied to assess PRR34-AS1 expression in liver hepatocellular carcinoma (LIHC) tissues. PRR34-AS1 exhibited a markedly high expression level in LIHC tissues (Additional file 1: Fig. S1A). Consistently, RT-qPCR analysis indicated that PRR34-AS1 was highly expressed in HCC cells including HLF, Huh-7, SNU-449, HepG2 and LM3 in comparison to THLE-3 cells (Fig. 1A). Since HepG2 and LM3 cells presented the highest PRR34-AS1 expression among the selected cells, both HepG2 and LM3 cells were selected for subsequent assays. We stably silenced PRR34-AS1 expression via transfecting two shRNAs targeting PRR34-AS1 into HepG2 and LM3 cells (Additional file 1: Fig. S1B). As shown in EdU and colony formation assays, PRR34-AS1 depletion reduced the percent of EdU positive cells and the number of colonies (Additional file 1: Fig. S1C, D). Meanwhile, the relative wound width was obviously increased when PRR34-AS1 was knocked down, suggesting that PRR34-AS1 silencing inhibited cell migratory ability in HCC (Additional file 1: Fig. S1E). Additionally, the number of invaded cells was also decreased in PRR34-AS1-silenced HepG2 and LM3 cells (Additional file 1: Fig. S1F). Moreover, we also used western blot to evaluate the role of PRR34-AS1 depletion on EMT in HCC cells. It turned out that PRR34-AS1 absence caused an upregulation in the level of epithelium marker (E-cadherin) whereas diminished the levels of mesenchymal markers (N-cadherin and Vimentin) and EMT transcription factors (Slug and Twist), implying that PRR34-AS1 silencing suppressed the EMT process in HCC cells (Additional file 1: Fig. S1G). Collectively, PRR34-AS1 enhances cell proliferative, migratory, invasive and EMT phenotypes in HCC. We next up-regulated PRR34-AS1 expression in THLE-3 cells (Fig. 1B) and found that the EdU positive stained cells were sharply increased in THLE-3 cells after PRR34-AS1 overexpression (Fig. 1C). Likewise, PRR34-AS1 overexpression led to an augment on the number of colonies (Fig. 1D), suggesting that PRR34-AS1 up-regulation facilitated the proliferation ability of THLE-3 cells. Besides, we found that the relative wound width was shorter in PRR34-AS1 groups compared to NC group, implying that up-regulated PRR34-AS1 promoted the migratory capacity of THLE-3 cells (Fig. 1E). Simultaneously, transwell assays disclosed that PRR34-AS1 overexpression enhanced the invasive capacity of THLE-3 cells (Fig. 1F). Additionally, we also assessed the influence of PRR34-AS1 up-regulation on EMT in THLE-3 cells. E-cadherin level was obviously reduced whereas the levels of N-cadherin, Vimentin, Slug and Twist were enhanced in PRR34-AS1-up-regulated THLE-3 cells (Fig. 1G). Similarly, the intensity of E-cadherin was weakened whereas N-cadherin intensity was strengthened in THLE-3 cells when PRR34-AS1 was overexpressed, implying that PRR34-AS1 overexpression accelerated EMT in THLE-3 cells (Fig. 1H). Taken together, PRR34-AS1 up-regulation facilitated the malignant capacities of THLE-3 cells.
Then, we wondered whether HCC cells could secrete exosomal PRR34-AS1 and transmit them to THLE-3 cells, leading to the promotion of cell proliferation, migration, invasion and EMT in THLE-3 cells. After the co-culture of HepG2 or LM3 cells with THLE-3 cells, we performed functional assays in co-cultured cells. Cell proliferation ability was enhanced in co-cultured cells relative to THLE-3 cells (Fig. 2A, B). Consistently, the potential of cell migration and invasion was markedly promoted in co-cultured cells (Fig. 2C, D). Moreover, we found that E-cadherin level was reduced while the level of N-cadherin, Vimentin, Slug and Twist were elevated in co-cultured cells (Fig. 2E). Meanwhile, the data from immunofluorescence staining also uncovered that the co-cultured cells had a stronger EMT capacity than THLE-3 cells (Fig. 2F). However, PRR34-AS1 expression had no marked change in co-cultured cells (Fig. 2G), which excluded the hypothesis of exosomal PRR34-AS1 in HCC cells. Collectively, the increased cell malignant phenotypes were observed in co-cultured HCC and THLE-3 cells.
Then, we speculated the existences of other exosome transmitted from HCC cells to THLE-3 cells. Exosomes from HCC cells were isolated and labeled with PKH67 dye (green). The result showed that PKH67-labeled exosomes dispersed in HCC cell cytoplasm (Fig. 3A). Besides, we found that exosomes ranged from 50 to 150 nm in diameter (Fig. 3B). Moreover, we found that the exosomes under TEM had the typically round or cup-shaped morphology (Fig. 3C). Additionally, western blot analysis further confirmed that HCC cells had abundant exosomal proteins (CD9, CD63, HSP70 and TSG101) and extracellular vesicle proteins (CD81, ERBB2 and HLA-A) (Fig. 3D). All above results validated the existence of exosomes in HCC cells. Subsequently, we treated HCC cell-secreted exosomes into THLE-3 cells to observe the changes of their cellular phenotypes. As shown in Fig. 3E, F, the proliferation ability was obviously increased in THLE-3 cells (Fig. 3E, F). Meanwhile, the capacities of migration and invasion were apparently enhanced in THLE-3 cells treated with HCC cell-secreted exosomes (Fig. 3G, H). Moreover, we found that the EMT process was also accelerated in THLE-3 cells treated with HCC cell-secreted exosomes (Fig. 3I). Expectedly, the expression of PRR34-AS1 had no significance change in THLE-3 cells treated with HCC cell-secreted exosomes (Fig. 3J). Collectively, HCC cell-secreted exosomes enhanced cell proliferation, migration, invasion and EMT.
We extracted the exosomes secreted from HCC cells and found that the level of total exosomal protein, exosome markers (CD9, CD63, HSP70 and TSG101) and extracellular vesicle proteins (CD81, ERBB2 and HLA-A) was obviously decreased when PRR34-AS1 was downregulated (Fig. 4A–D). Therefore, we speculated that the level of HCC cell-secreted exosomal proteins might be regulated by PRR34-AS1. We then analyzed the level of exosomal proteins in HCC cells treated with downregulated PRR34-AS1. The level of Rab27a was substantially inhibited in HCC cells after PRR34-AS1 silencing and other exosomal proteins had no marked changes (Fig. 4E). Taken together, PRR34-AS1 regulated exosomal protein Rab27a in HCC cells.
To uncover the regulatory mechanism of PRR34-AS1 in HCC cells, we firstly assessed the cellular distribution of PRR34-AS1 in HCC cells. As shown in Fig. 5A and Additional file 2: Fig. S2A, PRR34-AS1 was primarily distributed in cell cytoplasm, indicating that PRR34-AS1 might post-transcriptionally regulate Rab27a in HCC cells. Meanwhile, luciferase reporter assays further confirmed this point, as indicated that PRR34-AS1 could not affect the luciferase activity of Rab27a promoter (Fig. 5B). It is well-acknowledged that lncRNA acts as a ceRNA to regulate cancer progression at post-transcriptional levels. We wondered whether PRR34-AS1 existed in the RNA-induced silencing complex (RISC) to bind to miRNAs and thereby regulated Rab27a expression. However, the outcome from RIP assays dispelled this hypothesis, as indicated that the enrichment of PRR34-AS1 had no significant change in Anti-Ago2 groups (Fig. 5C). Thus we further treated protein synthesis inhibitor (actinomycin D) into HCC cells to assess Rab27a mRNA level. The half-life of Rab27a mRNA was obviously declined after PRR34-AS1 silencing, indicating the regulatory effect of PRR34-AS1 on the stability of Rab27a mRNA (Fig. 5D). Then, the electrophoretic gel indicated that one specific band appeared at approximately 73 kDa and we finally identified it as DDX3X protein (Fig. 5E). Furthermore, the combination between PRR34-AS1 and DDX3X was confirmed by western blot and RIP analyses (Fig. 5F, G). Intriguingly, we also found the combination between Rab27a and DDX3X in HCC cells via RNA pull down assays (Fig. 5H). Moreover, we found that the combination of Rab27a and DDX3X was lessened in HCC cells after the downregulation of PRR34-AS1 (Fig. 5I). Thus we conjectured that PRR34-AS1 might interact with DDX3X to regulate Rab27a mRNA stability. We knocked down the levels of DDX3X and revealed that the levels of Rab27a were markedly decreased in HCC cells after DDX3X depletion (Additional file 2: Fig. S2B, C). Meanwhile, the stability of Rab27a mRNA was also reduced when DDX3X was silenced in HCC cells treated with actinomycin D (Fig. 5J). Collectively, PRR34-AS1 interacted with DDX3X to mediate Rab27a mRNA stability.
Next, we investigated the role of Rab27a in THLE-3 cells. We overexpressed Rab27a expression in THLE-3 cells and observed that the proliferation ability was obviously enhanced when Rab27a was up-regulated in THLE-3 cells (Fig. 6A–C). In parallel, the capacities of migration and invasion were promoted in THLE-3 cells after Rab27a up-regulation (Fig. 6D, E). Moreover, we found that Rab27a overexpression elevated the EMT process in THLE-3 cells (Fig. 6F, G). It has been reported that Rab27a promotes tumor growth via increasing VEGF and TGF-β secretion in vitro [22]. Herein, we also found that the protein concentration of VEGF and TGF-β in THLE-3 cells as well as their protein levels in HCC cell-secreted exosomes was increased after Rab27a up-regulation (Fig. 6H, I). All above results suggested that Rab27a increased VEGF and TGF-β secretion in HCC cells and transmitted them into THLE-3 cells to elevate cell proliferative, migratory and invasive phenotypes as well as EMT. To explore the effect of PRR34-AS1 and Rab27a on the malignant phenotypes of THLE-3 cells, rescue experiments were arranged after the verification of the knockdown efficiency of Rab27a (Additional file 3: Fig. S3A). As indicated in Additional file 4: Fig. S4A, B, Rab27a silencing reversed the enhanced proliferation ability in THLE-3 cells after PRR34-AS1 overexpression. Besides, it was found that the enhanced migration and invasion capacities induced by PRR34-AS1 up-regulation were abolished by co-transfection of sh/Rab27a#a (Additional file 4: Fig. S4C, D). Moreover, the decreased protein level of E-cadherin and the enhanced levels of N-cadherin, Vimentin, Slug and Twist in THLE-3 cells transfected with PRR34-AS1 overexpression was overturned by co-transfection of sh/Rab27a#a (Additional file 4: Fig. S4E). Meanwhile, the data of immunofluorescence assays further confirmed that Rab27a depletion abrogated the increased EMT process in THLE-3 cells after PRR34-AS1 augment (Additional file 4: Fig. S4F). Taken together, PRR34-AS1 facilitated THLE-3 cells proliferation, migration, invasion and EMT via elevating Rab27a expression.
In this research, we verified that lncRNA PRR34-AS1 recruited DDX3X to stabilize Rab27a mRNA and thereby promoted cell proliferative, migratory, invasive and EMT phenotypes in HCC. Meanwhile, PRR34-AS1 up-regulated Rab27a expression to increase the exosome secretion of VEGF and TGF-β in HCC cells and transmitted them into THLE-3 cells to accelerate the malignant phenotypes of THLE-3 cells. Our study revealed a novel function of PRR34-AS1 in regulating exosome secretion from HCC cells to THLE-3 cells, which provides a promising method for HCC treatment. In recent years, lncRNAs have emerged as a pivot in the field of cancer research. LncRNA-D16366 is lowly expressed in HCC and might be an independent diagnostic and prognostic indicator for HCC [23]. RGMB-AS1 plays as a tumor suppressing role in HCC and is an independent favorable prognostic factor for patients with this disease [24]. All above studies demonstrated the anti-cancer effects of lncRNAs in HCC. In our research, we proved the oncogenic effect of lncRNA PRR34-AS1 in HCC. Furthermore, PRR34-AS1 exhibited a high expression level in HCC. PRR34-AS1 knockdown inhibited HCC cell proliferative, migratory, invasive and EMT abilities. Taken together, lncRNAs play an important role in the regulation of HCC progression. Our study also found that PRR34-AS1 overexpression facilitated THLE-3 cell malignant phenotypes, suggesting the possibility of exosomes in HCC cells. Exosome-mediated communication in the tumor microenvironment contributes to HCC progression [5]. Exosome-transmitted lncRNA SENP3-EIF4A1 inhibits cell migration and invasion in HCC [25]. High expression of exosomal H19 enhances the proliferation and motility of HCC cells [26]. All these evidence suggested the regulation of exosomal lncRNA in HCC progression. In our study, we confirmed that the capacities of proliferation, migration, invasion and EMT were increased in THLE-3 cells co-cultured with HCC cells as well as in THLE-3 cells treated with HCC-secreted exosomes. However, PRR34-AS1 expression was not affected in THLE-3 cells co-cultured with HCC cells as well as in THLE-3 cells treated with HCC-secreted exosomes, which ruled out the existence of exosomal PRR34-AS1 in HCC. As we all know, exosomes are a type of secretory membrane vesicle with the structural and biochemical characteristics of multivesicular endosomes [27]. Several Rab GTPases, including Rab27a, Rad27b, and Rab35, were previously found to play a crucial role in regulating exosome secretion and influencing cellular process [28]. It has been reported that lncRNA HOTAIR motivates exosome secretion via mediating RAB35 and SNAP23 in HCC [29]. In our study, we demonstrated that PRR34-AS1 silencing obviously reduced the expression of Rab27a. It has been reported that KIBRA modulates exosome secretion via repressing the proteasomal degradation of Rab27a [30]. All above results uncovered that PRR34-AS1 regulated Rab27a to affect exosome secretion in HCC cells. Moreover, our study found that PRR34-AS1 interacted with DDX3X to regulate the stability of Rab27a mRNA. In line with our study, HHIP-AS1 inhibits HCC progression through recruiting HUR to stabilize HHIP mRNA [31]. FAM83A-AS1 expedites HCC progression by interacting with NOP58 to increase the mRNA stability of FAM83A [32]. After confirming the regulatory mechanism between PRR34-AS1 and Rab27a, we revealed that Rab27a accelerated cell malignant phenotypes in THLE-3 cells. More importantly, Rab27a up-regulation also promoted exosome secretion of VEGF and TGF-β, suggesting that PRR34-AS1 regulated Rab27a to promote the exosome secretion of VEGF and TGF-β and thereby transmitted them into THLE-3 cells to accelerate cell malignant phenotypes. However, our study had some limitations. We didn’t unravel the detailed mechanism of Rab27a on regulating exosome secretion of VEGF and TGF-β, which will be addressed in the future.
Additional file 1: Figure S1. PRR34-AS1 silencing inhibited HCC cell malignant phenotypes. A GEPIA 2 database analyzed PRR34-AS1 expression in LIHC tissues and normal tissues. B PRR34-AS1 expression in HCC cells was analyzed via RT-qPCR after PRR34-AS1 silencing. N = 3. Data were analyzed via DGP PCR quantification. C, D HCC cell proliferation was assessed by proliferation assays after PRR34-AS1 silencing. N = 3. E, F HCC cell migratory and invasive abilities was measured after PRR34-AS1 silencing. N = 3. G The EMT process of HCC cells was evaluated after PRR34-AS1 silencing. N = 3. *P < 0.05, **P < 0.01.Additional file 2: Figure S2. DDX3X positively regulates Rab27a expression in HCC cells. A The cellular distribution of PRR34-AS1 in HCC cells was confirmed by subcellular fractionation assay. N = 3. B, C The mRNA and protein levels of DDX3X and Rab27a were analyzed in HCC cells transfected with sh/DDX3X#a/b. N = 3. **P < 0.01. Data were analyzed via DGP PCR quantification.Additional file 3: Figure S3. Transfection efficiency of Rab27a in HCC cells. A Rab27a levels in HCC cells was detected by RT-qPCR and western bot analyses after Rab27a silencing. N = 3. **P < 0.01.Additional file 4: Figure S4. PRR34-AS1 enhances THLE-3 cell malignant phenotypes via elevating the expression of Rab27a. A–F Rescue experiments were carried on in THLE-3 cells transfected with NC, PRR34-AS1, PRR34-AS1 + sh/Rab27a#a to assess cell proliferative, migratory and invasive as well as EMT phenotypes. N = 3. Scale bar = 50 µm. **P < 0.01. | true | true | true |
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PMC9615555 | Baodong Wang,Na Xu,Li Cao,Xiaojun Yu,Shanxi Wang,Qikun Liu,Yinguang Wang,Haoran Xu,Yang Cao | miR-31 from Mesenchymal Stem Cell-Derived Extracellular Vesicles Alleviates Intervertebral Disc Degeneration by Inhibiting NFAT5 and Upregulating the Wnt/β-Catenin Pathway | 20-10-2022 | In this study, we explored the regulatory mechanism of intervertebral disc degeneration (IDD) that involves miR-31 shuttled by bone marrow mesenchymal stem cell-derived extracellular vesicles (BMSC-EVs) and its downstream signaling molecules. Nucleus pulposus cells (NPCs) were isolated and treated with TNF-α to simulate IDD in vitro. The TNF-α-exposed NPCs were then cocultured with hBMSCs or hBMSC-EVs in vitro to detect the effects of hBMSC-EVs on NPC viability, apoptosis, and ECM degradation. Binding between miR-31 and NFAT5 was determined. A mouse model of IDD was prepared by vertebral disc puncture and injected with EVs from hBMSCs with miR-31 knockdown to discern the function of miR-31 in vivo. The results demonstrated that hBMSC-EVs delivered miR-31 into NPCs. hBMSC-EVs enhanced NPC proliferation and suppressed cell apoptosis and ECM degradation, which was associated with the transfer of miR-31 into NPCs. In NPCs, miR-31 bound to the 3′UTR of NFAT5 and inhibited NFAT5 expression, leading to activation of the Wnt/β-catenin pathway and thus promoting NPC proliferation and reducing cell apoptosis and ECM degradation. In addition, miR-31 in hBMSC-EVs alleviated the IDD in mouse models. Taken together, miR-31 in hBMSC-EVs can alleviate IDD by targeting NFAT5 and activating the Wnt/β-catenin pathway. | miR-31 from Mesenchymal Stem Cell-Derived Extracellular Vesicles Alleviates Intervertebral Disc Degeneration by Inhibiting NFAT5 and Upregulating the Wnt/β-Catenin Pathway
In this study, we explored the regulatory mechanism of intervertebral disc degeneration (IDD) that involves miR-31 shuttled by bone marrow mesenchymal stem cell-derived extracellular vesicles (BMSC-EVs) and its downstream signaling molecules. Nucleus pulposus cells (NPCs) were isolated and treated with TNF-α to simulate IDD in vitro. The TNF-α-exposed NPCs were then cocultured with hBMSCs or hBMSC-EVs in vitro to detect the effects of hBMSC-EVs on NPC viability, apoptosis, and ECM degradation. Binding between miR-31 and NFAT5 was determined. A mouse model of IDD was prepared by vertebral disc puncture and injected with EVs from hBMSCs with miR-31 knockdown to discern the function of miR-31 in vivo. The results demonstrated that hBMSC-EVs delivered miR-31 into NPCs. hBMSC-EVs enhanced NPC proliferation and suppressed cell apoptosis and ECM degradation, which was associated with the transfer of miR-31 into NPCs. In NPCs, miR-31 bound to the 3′UTR of NFAT5 and inhibited NFAT5 expression, leading to activation of the Wnt/β-catenin pathway and thus promoting NPC proliferation and reducing cell apoptosis and ECM degradation. In addition, miR-31 in hBMSC-EVs alleviated the IDD in mouse models. Taken together, miR-31 in hBMSC-EVs can alleviate IDD by targeting NFAT5 and activating the Wnt/β-catenin pathway.
Low back pain is common and associated with intervertebral disc degeneration (IDD) [1, 2]. Back pain bothers most people, reportedly to be around 80% of all adults worldwide [3]. The incidence of lower back pain increases with age, and back pain is the number one cause of disability in the elderly [4]. In addition to the elderly, back pain also significantly limits the activity of younger adults [3]. Additionally, low back pain is characterized as the number one cause of disability globally back, showing increasing health and financial burden [5]. Therefore, it is essential to discern the mechanism of IDD that leads to back pain and find better treatments. The pathogenesis of IDD remains to be completely understood. It is believed that increased degradative enzymes and proinflammatory cytokines as well as reductions in matrix proteins are major mechanisms for the progression of IDD [6]. There are two major components in intervertebral disc (IVD): annulus fibrosus and nucleus pulposus (NP), and the latter is centrally located in the IVD and therefore important in the function and structure of IVD [2]. Importantly, NP cell (NPC) apoptosis has been shown to be involved in IDD [7]. Here, we focused on studying apoptosis, inflammatory response, and the expression of extracellular matrix- (ECM-) related genes in NPCs. Bone marrow mesenchymal stem cell-derived extracellular vesicles (BMSC-EVs) have been documented to inhibit IDD [8]. EVs carry many molecules, including proteins, lipids, and RNAs involved in cell communications [9]. MicroRNA- (miR-) 31 has been reported to be carried by MSC-EVs [10]. miR-31 is well known for regulating cell functions, including cell apoptosis and proliferation [11, 12]. A recent study has identified miR-31 as a key regulator of IDD since its overexpression facilitates NPC proliferation and ECM formation, inhibits NPC apoptosis, and reduces the level of matrix-degrading enzymes in NPCs [13]. Importantly, miR-31 has been identified to target NFAT5 and regulate its expression in Ewing Sarcoma [14]. NFAT5, one of the members of the NFAT family, is a transcription factor that regulates cell functions, including cell invasion and apoptosis [15]. NFAT5 bears great responsibility in the postnatal homeostasis of the spine and controls a variety of functions, including cellular osmoadaptation and axial skeleton embryogenesis [16]. Importantly, NFAT5 is involved in the inflammatory response and osmotic loading in NPCs [17]. Moreover, previous data have shown that NFAT5 inhibits the activation of the Wnt pathway [18]. Wnt/β-catenin is highly conserved during evolution and closely associated with inflammation [19]. In addition, activation of the Wnt/β-catenin pathway possesses potentials to modulate cell behavior, cell fate, cell proliferation, and survival in both embryos and adults [20]. Importantly, the Wnt pathway has been shown to be involved in the progression of IDD through inflammation-related mechanisms [21]. Notably, previous research has shown that miR-31 can regulate tumorigenesis through activation of the Wnt signaling pathway [22]. Therefore, in this study, we aimed to investigate whether bone marrow mesenchymal stem cell-derived EVs (BMSC-EVs) carried miR-31 to be involved in IDD through regulation of the NFAT5/Wnt/β-catenin axis.
Isolation experiments of NPCs were approved by the Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology. The study involving humans was ratified by the Medical Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, and was consistent with the Declaration of Helsinki. An informed consent form was signed by each participant. Animal experiments were completed under the ratification of the Animal Ethics Committee of Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology.
Downstream genes of miR-31 were predicted utilizing RAID (score > 0.95), TargetScan (cumulative weighted context + +score≦−0.5), mirDIP (integrated score > 0.7), and miRWalk databases (energy < −20, accessibility < 0.01, au > 0.55). Then, the Venn diagram was plotted to obtain the key genes using the jvenn tool [23]. A PPI network of the downstream genes and intersected key genes was constructed using GeneMANIA [24]. According to the map plotted using Cytoscape [25], the core degree was determined, and the genes with the highest core degree were selected to further predict the related genes through GeneMANIA. With the online tool KOBAS, KEGG pathway enrichment analysis on the downstream genes and their related genes was implemented, followed by the determination of related downstream pathways by looking up relevant literature.
Human normal NPCs were isolated by 0.2% collagenase digestion and subcultured utilizing monolayer adherence. Five cases of intraoperative IVDs of patients with fresh cervical vertebral fractures (no more than 3 days) were collected, including 2 C4-5 discs and 3 C5-6 discs. The samples were washed with D-Hank's solution three times in 0.5 h to remove blood stains. The annulus fibrosus, cartilage plate, and junction tissue were cut off, and the remaining jelly-like nucleus pulposus tissues were cut into 1 × 3 mm pieces with scissors and then digested with 0.2% type II collagenase for 4 h at 37°C. Digested tissues were centrifuged at 1000 × g for 5 min, with supernatant discarded. Cells in the lower layer were resuspended in DMEM-F12 medium containing 20% fetal bovine serum (FBS; 30067334, Thermo Fisher Scientific (China) Co., Ltd., Shanghai, China), seeded in 6 cm culture dish, and incubated at 37°C with 5% CO2. The medium was refreshed every 48 h with DMEM-F12 medium replenishing 10% FBS. Cell morphology was identified under a high magnification inverted microscope (EVOS XL Core, Thermo Fisher Scientific). After reaching 90% confluence in about 15 days, the cells were passaged at a ratio of 1 : 4 after trypsinization. Cells in passage 3 were used in this study. Immunofluorescence was used to detect the expression of type II collagen (Col II), KRT-19, HIF-1α, SOX-9, and aggrecan (ACAN) in NPCs. In brief, cell slides were fixed in 4% paraformaldehyde at ambient temperature for 15 min and permeabilized with 0.5% Triton X-100 for 15 min. Next, the slide was blocked with 5% goat serum blocking solution for 30 min and probed with primary antibodies to HIF-α (ab51608, 1 : 500, Abcam, Cambridge, UK), KRT-19 (ab7754, 1 : 300, Abcam), SOX-9 (ab185966, 1 : 200, Abcam), Col II (ab34712, 1 : 200, Abcam), and ACAN (MA3-16888, 1 : 500, Invitrogen) at 4°C overnight. The next day, the cells were reprobed with fluorescently labeled secondary antibody IgG (mouse, ab150113, 1 : 200) or IgG (rabbit, ab150077, 1 : 200) at ambient temperature for 1 h and incubated with 4′,6-diamino-2-phenylindole (DAPI) (Sigma-Aldrich, St. Louis, MO, 1 : 2000) in the dark for 5 min, followed by observation under a microscope.
Logarithmically growing NPCs at passage 2 were trypsinized, seeded in 6-well plates (6 × 105 cells/well), cultured at 37°C with 5% CO2, and transfected employing the Lipofectamine 3000 reagent (Gibco, Waltham, MA) with miR-31 mimic (B01001), mimic-NC (B04001), miR-31 inhibitor (B03001), and inhibitor-NC (B04003) (GenePharma, Shanghai, China). The NFAT5 coding sequence was amplified by EcoRI and XhoI (Thermo Fisher Scientific) double digestion and cloned into pcDNA3.1 (+) vector (Invitrogen, Carlsbad, CA). The cells were continuously cultured for 48 h and then used for subsequent experimentations. For simulating IDD in vitro, we used TNF-α to induce NPC apoptosis [26]. Cells were incubated for 48 h and then treated with TNF-α (5 ng/mL) for 12 h.
Bone marrow specimens were provided by 3 hospitalized patients (2 males and 1 female, aged 26-52 years) with femur necrosis following MRI-based diagnosis at Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology. Patients with no loss of femoral head height were included while those with trauma, cardiovascular diseases, or tumor infiltration were excluded from this study. Human BMSCs were isolated from the bone marrow specimens of 3 donors as previously described [27] and cultured in DMEM-F12 medium (Hyclone, Logan, UT) with 10% fetal bovine serum (FBS, 10099141, Gibco) and 0.2% penicillin-streptomycin solution (Hyclone). hBMSCs were passaged every 3 days, and the cells at passage 3 were used for subsequent experimentations. Next, the isolated hBMSCs were cultured in the OriCell™ MSC osteogenic, adipogenic, or chondrogenic differentiation medium (Cyagen, Guangzhou, China) for identification utilizing alizarin red staining, oil red O staining, and Alcian blue staining. hBMSCs were trypsinized, centrifuged, and incubated with mouse monoclonal antibodies against CD105 (1 : 100, ab11414, Abcam), CD73 (1 : 50, ab239246, Abcam), CD90 (1 : 1000, ab23894, Abcam), CD45 (1 : 50, ab27287, Abcam), CD34 (1 : 50, ab131589, Abcam), CD14 (1 : 200, ab28061, Abcam), CD19 (1 : 50, ab134114, Abcam), murine monoclonal antibody HLA-DR (1 : 50, ab1182, Abcam), and FITC-conjugated goat anti-mouse IgG isotopic antibody (1 : 1000, BD Biosciences, San Jose, CA; serving as a negative control (NC)). Samples were quantified utilizing the FACSVerse system (BD Bioscience), and the results were analyzed by FlowJo software (Tree Star, Ashland, OR) to quantify the expression of surface antigens and nonsurface antigens of hBMSCs.
FBS was ultracentrifuged at 100,000 × g for 16 h at 4°C to remove EVs from the FBS. The obtained EV-depleted FBS was applied for this assay. After cell incubation for 72 h, the medium was collected and the EVs were separated by centrifugation (at 300 × g for 10 min, 2000 × g for 15 min, and 12000 × g for 30 min). Then, cells were passed through a 0.22 μM filter. The supernatant was further ultracentrifuged at 1,000,000 × g for 2 h at 4°C, washed in PBS, and ultracentrifuged under the same conditions. Finally, EVs were resuspended in approximately 100 μL PBS. EVs were isolated from the hBMSCs from three patients, mixed evenly, and stored at -80°C for standby or immediate use. Particle size analysis was performed by NanoSight instrument (Salisbury, UK). The motion trajectory of each EV was analyzed and automatically converted into the diameter and concentration of EVs according to the principle of Brownian motion, which can be converted into the original concentration according to the dilution ratio [28]. A Hitachi H7650 transmission electron microscope (Tokyo, Japan) was adopted to study the characterization of EVs [29].
The isolated BMSC-EVs were incubated with proteinase K (0.05 μg/μL; Sigma Aldrich) for 10 min at 37°C and then with 5 mM phenylmethylsulfonyl fluoride (PMSF; Sigma Aldrich) for 10 min at ambient temperature to limit proteinase K activity, followed by complete inactivation of proteinase K by heating at 90°C for 5 min. Following this, samples were incubated with RNase A at a final concentration of 0.5 μg/μL (Thermo Fisher Scientific) for 20 min at 37°C to digest the exposed RNA. In the control group, proteinase K, PMSF, or RNase A was substituted by the same amount of PBS. RNA was finally extracted and used for subsequent analysis.
Purified EVs were labeled with a PKH67 green fluorescence kit (Sigma-Aldrich). EVs were resuspended in 1 mL of diluent C solution. PKH-67 ethanol dye solution (4 μL) was added to 1 mL of diluent C to prepare a 4 × 10−6 M dye solution. EV suspension was mixed with the dye solution for 5 min. BSA (1%, 2 mL) was added for 1 min to stop staining. The labeled EVs were ultracentrifuged at 100,000 × g for 70 min, washed with PBS, ultracentrifuged again, and resuspended in 50 μL PBS. EVs were cocultured with NPCs for 12 h at 37°C, and the NPCs were fixed with 4% paraformaldehyde after which nuclei were stained with DAPI. The uptake of labeled EVs by NPCs was measured with a fluorescence microscope (Zeiss, Oberkochen, Germany).
BMSCs were seeded in 6-well plates at 6.0 × 105 cells/well and transduced with lentivirus carrying miR-31-knockdown (anti-miR-31; GL–02; GeneCopoeia, Rockville, MD) and negative control (anti-NC; GL–02; GeneCopoeia, Rockville, MD) using a Lentiviral Transduction Kit (MOI = 50), followed by culture at 37°C in 5% CO2 for 72 h. The uptake of EVs from BMSCs carrying Cy3-miR-31 by recipient cells (NPCs) was conducted. Briefly, hBMSCs were transfected with Cy3-miR-31 mimic (GenePharma) employing the Lipofectamine 3000 reagent (Invitrogen, L3000001). Six h later, the serum-free medium was changed with the 10% EV- and serum-free medium, and cells were cultured for 48 h. Cell supernatant was collected, resuspended in PBS, and added to NPCs. With the same method, cells were fixed with 4% paraformaldehyde, and the cytoskeleton was labeled with Phalloidin-iFLuor 488 (1 : 1000, #ab176753, Abcam, green fluorescence) at ambient temperature for 30 min. Nuclei were stained with DAPI (D9542, Sigma-Aldrich). NPCs (green) under a microscope or a confocal microscope (LSM 710, Zeiss) were observed to internalize the EVs (red) from BMSCs carrying Cy3-miR-31 [30, 31].
The transwell system (6-well plate with the diameter of a single well of 12 mm) was used to coculture NPCs and hBMSCs indirectly. hBMSCs (2 × 105 cells/well) were settled in the upper chamber while NPCs (2 × 105 cells/well) were in the lower chamber for 14 days of coculture (about 60% confluence). During the coculture of NPCs with BMSC-EVs, BMSC-EVs (final concentration of 50 μg/mL) were directly added to the NPC medium, and PBS was used as a control for BMSC-EVs. NPCs were mainly grouped into PBS, TNF-α (NPCs were exposed to 5 ng/mL TNF-α), hBMSCs (NPCs were cocultured with hBMSCs), GW4869 (NPCs were treated with 5 μM EV inhibitor GW4869), hBMSCs-EVs (NPCs were cocultured with hBMSC-EVs), hBMSCs-EVs NC inhibitor (NPCs were cocultured with EVs from hBMSCs transduced with lentivirus carrying NC inhibitor), and hBMSCs-EVs anti-miR-31 (NPCs were cocultured with EVs from hBMSCs transduced with lentivirus carrying miR-31-knockdown).
HEK293T cells (ATCC) were cultured in 48-well plates for 24 h. pRL-TK luciferase reporter plasmid (Promega, Madison, WI) was used to construct NFAT5 wild type (WT) 3′UTR or mutant (Mut) plasmids, which were then cotransfected with 50 nmol/L miR-31 mimic or mimic-NC into HEK293T cells for 48 h. The Dual Luciferase Reporter Assay System (Promega) was employed to quantify the relative luciferase activity, with Renilla luciferase used as an internal reference.
Cells were seeded into 96-well plates, cultured for 24 h, and transfected with plasmids, TOPFlash or FOPFlash, and internal reference pRL-TK plasmids (Promega) by referring to Lipofectamine 2000 reagent instructions (11668019, Thermo Fisher Scientific). The plasmids were then mixed with 100 μL L-DMEM and left to stand at ambient temperature for 5 min. Next, 0.5 μL Lipofectamine 2000 was mixed with 100 μL L-DMEM and left to stand at ambient temperature for 5 min. Next, the culture medium was washed off, and cells were washed with L-DMEM and transfected with the mixed transfection medium for 6 h, followed by culture in the renewed complete culture medium. After 24-48 h, the culture medium was discarded, and the Dual Luciferase Reporter Assay System (E1910, Promega) was adopted to quantify the activities of Renilla and Firefly luciferase in each well. The ratio of the two reflects the activation level of transcription factors in the intracellular Wnt/β-catenin pathway [32].
The CCK-8 kit (C0037, Beyotime, Shanghai, China) was adopted to test the effects of hBMSC-EVs on the proliferation of NPCs. Cells were seeded at 2 × 103 cells/well in 96-well plates. After 24 h, 10 μL of the CCK-8 reagent was added to 100 μL of complete culture medium at different time points (0, 24, 36, 48, and 72 h) and continued to incubate for 4 h. The OD value of each well was quantified at 450 nm utilizing a Multiskan FC microplate reader (51119100, Thermo Fisher Scientific).
Apoptosis was checked with the help of the Annexin V-FITC/PI staining kit (BD Biosciences). NPCs were seeded in a 6-well plate and treated with PBS or hBMSC-EVs for 24 h upon reaching 70% confluence. Next, the cells were digested with trypsin without EDTA and stained with Annexin V-FITC/PI. After 15 min, the cells were detected utilizing a flow cytometer (BD FACSVerse™; BD Bioscience). Fluorescence was initiated by excitation at 488 nm (FITC) and 535 nm (PI) and was evaluated utilizing emission filters at 525 nm (FITC) and 615 nm (PI).
C57BL/6J mice (n = 60, 2-3 months old, 18-20 g; Beijing Vital River Laboratory Animal Technology Co., Ltd., Beijing, China) were equilibrated for 1 week, followed by tail needle puncture to mimic IDD in vivo [7, 33]. Needle puncture was performed under general anesthesia (2% isoflurane oxygen) and sterile conditions. Caudal IVDs (cc4-5 and cc6-7) were exposed through palpation and a 24 mm dorsal lateral incision. After determining the location of IVDs microscopically (M60, Leica Microsystems, IL), a 26 G needle was used to puncture 50% of the width of the back. The wound was closed by sutures (Prolene 8-0 sutures). Mice were intraperitoneally injected with penicillin sodium 200,000 U/kg (North China Pharmaceutical, Shijiazhuang, Hebei, China) once a day for 3 days to prevent infection. Mice were housed at 23-25°C separately and fed with standard chow. The success rate was 96% (48/50). One week after the operation, mice were randomized into (10 mice in each group) control (mouse skin was cut and then immediately sutured), IDD (mice following IDD model construction), IDD+saline (IDD mice injected with equal volume of sterile saline), IDD+hBMSCs-EVs (IDD mice injected with hBMSC-EVs), IDD+hBMSCs-EVs anti-NC (IDD mice injected with hBMSCs-EVs anti-NC), and IDD+hBMSCs-EVs anti-miR-31 (IDD mice injected with hBMSCs-EVs anti-miR-31). IDD mice were anesthetized, and a small incision was prepared to expose the previously punctured IVDs. A total of 50 μL of sterile saline containing different purified EVs (approximately 1.5 × 106) was injected slowly into the punctured disc site using a 0.38 mm × 8 mm syringe. The injection was repeated 4 weeks later. After 9 weeks, MRI was used to detect the puncture IVD of mice after the operation, and then, all mice were euthanized with CO2. IVD tissues were harvested and stored for subsequent experiments. To track the distribution of EVs in the mouse, EVs were prelabeled with PKH26.
IVD tissues were fixed in 4% paraformaldehyde for 48 h, decalcified in 25% formic acid and 10% sodium citrate for 2 d, embedded in paraffin, and cut into 5 μm sections. Sections were subjected to hematoxylin-eosin (HE) or TUNEL staining. Histological image analysis was completed under an optical microscope (DMM-300D, Caikon, Shanghai, China). The grading score for HE staining was performed by referring to the criteria established previously [34]. Apoptotic activity was determined by an in situ luciferin cell death detection kit (Roche, Mannheim, Germany). TUNEL-positive cells were analyzed by Image-Pro Plus software (Media Cybernetics, Silver Spring, MA). Data collection and analysis were completed by two independent investigators blinded to experimental groups.
Serum of mice was collected, and the levels of TNF-α and IL-1β were detected by the ELISA Kit (DTA00D, DLB50, R&D, Minneapolis, MN).
Total RNA from cells or tissues was extracted with the TRIzol reagent (Invitrogen) and then reversely transcribed into cDNA using the PrimeScript RT kit (Takara, Kusatsu, Japan). For miRNA detection, the RNA was reverse transcribed into cDNA using the miRcute Plus miRNA first-strand cDNA synthesis kit (TianGen, Beijing, China). miRNA in the medium (350 μL) and EVs (100 μg) was extracted with the help of the mirVana PARIS Kit (Ambion, Austin, TX). An exogenous reference cel-miR-39 (1 pmol/sample; TianGen) was added. RT-qPCR for mRNA was performed using the SYBR Premix Ex Taq Reagent Kit (Takara) and ABI StepOne real-time PCR system (Applied Biosystems, Foster City, CA). RT-qPCR for miRNA was processed utilizing the miRcute Plus miRNA qPCR Detection Kit (TianGen). β-Actin was used as a normalizer for mRNA while U6 for miRNA. In addition, miRNA expression in culture media and EVs was normalized to the exogenous reference cel-miR-39. The relative expression of target genes was calculated by the 2ΔΔCT method. Primer sequences are described in Supplementary Table 1.
EV markers were determined by resuspending hBMSC-EVs in precooled protease inhibitor (Roche)-containing lysis buffer. The lysate was dissolved in 3× Laemmli's sample buffer, heated for 5 min, separated with 12% SDS-PAGE, and transferred to a nitrocellulose membrane. The membrane was blocked with 5% nonfat milk in pH 7.4 phosphate buffer (137 mmol/L NaCl, 2.7 mmol/L KCl, 10 mmol/L Na2HPO4, and 2 mmol/L KH2PO4) and 0.05% Tween. Next, the membrane was incubated with primary rabbit antibodies against tumor susceptibility gene 101 (TSG101) (1 : 1000, ab30871, Abcam), CD63 (1 : 1000, ab216130, Abcam), ALIX (1 : 1000, ab88743, Abcam), and GRP94 (1 : 1000, ab13509, Abcam) and then with horseradish peroxidase-conjugated secondary antibody (1 : 5000, ab6721, Abcam). The membrane was visualized employing the enhanced chemiluminescence reagent (170-8280, Bio-Rad Laboratories, Hercules, CA). Ponceau red was used as an internal reference. Data analysis was completed utilizing ImageJ software (National Institutes of Health, Bethesda, MD). Protein expression was also determined in cell and tissue lysate. Protein concentration was determined employing a Bradford assay (Bio-Rad). Protein was then separated with 10% SDS-PAGE and transferred to a nitrocellulose membrane. Next, the membrane was incubated with primary antibodies against NFAT5 (ab3446, Abcam), β-catenin (ab6302, Abcam), cleaved caspase-3 (ab2302, Abcam), Bcl-2 (ab59348, Abcam), Bax (ab32503, Abcam), ACAN (ab3778, Abcam), Col II (ab34712, Abcam), SOX-9 (ab26414, Abcam), MMP-3 (ab52915, Abcam), TIMP-1 (ab61224, Abcam), and Nanog (1 : 1000, ab109250, Abcam). GAPDH (1 : 5000, 5174, Cell Signaling Technology) was used as an internal reference.
Statistical analysis was completed with the help of SPSS 21.0. Data were expressed as the mean ± standard deviation. Data obeying normal distribution and homogeneity of variance between two groups were compared by the unpaired t-test. Data comparison among multiple groups was started by one-way ANOVA and Tukey's post hoc test. Data comparison between groups at different time points was processed by repeated measures ANOVA and Bonferroni's post hoc test. In the samples with skewed distribution or defect variances, the rank-sum test was used. Differences were deemed significant when p < 0.05.
Flow cytometric data revealed that the expression of BMSC surface antigens CD73, CD90, and CD105 was higher than 95% of all cells, while that of non-BMSC surface antigens CD34, CD45, CD14, CD16, and HLA-DR was less than 2% (Supplementary Figure 1A). These results suggested that the isolated hBMSCs were of high purity. In addition, under an optical microscope, hBMSCs were spindle-shaped and grow in colonies. After differentiation induction under different conditions, red calcium nodules, red lipid-like cells, and blue collagen staining were observed following alizarin red staining, oil red O staining, and Alcian blue staining, respectively, suggesting that hBMSCs had osteogenic, adipogenic, and chondrogenic differentiation potentials (Supplementary Figure 1B). Furthermore, the expression of BMSC stemness marker protein Nanog in hBMSCs at passage 3 was basically the same as that in the hBMSCs at passage 1 (Supplementary Figure 1C), which indicated that the BMSCs at passage 3 still retain the stemness of BMSCs when we used the BMSCs at passage 3 for experiments. Under an electron microscope, the EVs had saucer-like structures with clear membranes, about 200 nm (Figure 1(a)). EVs derived from hBMSCs had diameters ranging from 100 to 400 nm, with the highest concentration of 1.8 particles/mL (Figure 1(b)). Furthermore, the results of Western blot showed the presence of expression of EV marker proteins ALIX, CD63, and TSG101 in the isolated BMSC-EVs, but no endoplasmic reticulum-associated protein GRP94 (Figure 1(c)). After TNF-α exposure, miR-31 expression was increased in the supernatant of BMSC-EVs (Figure 2(a)). Moreover, the combined use of proteinase K and RNase did not reduce the level of miR-31 in EVs; only when the EV membrane was damaged by Triton and subjected to the action of RNase was the expression of miR-31 reduced (Figure 2(b)). These results indicated that BMSCs could secrete EVs encapsulating miR-31.
In order to explore the potential function of miR-31 delivered by hBMSC-EVs in NPCs, we firstly isolated NPCs and observed the morphology of NPCs using a microscope. The results showed that the shape of NPCs was fusiform or multiangle, and the process of cytoplasm was long (Figure 3(a)). The purified NPCs were identified by immunofluorescence. The results showed that Col II, KRT-19, HIF-1α, SOX-9, and ACAN expressions were positive in the NPCs at passage 2 (Figure 3(b)), demonstrating the successful isolation of NPCs. PKH67-labeled hBMSC-EVs (green) were cocultured with hBMSCs transfected with the Cy3-labeled miR-31 (red; Cy3-miR-31-BMSCs) (Figure 3(c)). NPCs showed green fluorescence following coculture with EVs (Figure 3(d)) and red fluorescence after coculture with Cy3-miR-31-BMSCs (Figure 3(e)), suggesting that hBMSC-EVs could carry miR-31 and deliver it into NPCs. Moreover, increased miR-31 expression was found in NPCs cocultured with hBMSC-EVs, and the addition of TNF-α further increased miR-31 expression in NPCs cocultured with hBMSC-EVs (Figure 3(f)). Thus, these data revealed that hBMSC-EVs could transfer miR-31 to NPCs.
The effect of BMSC-EVs delivering miR-31 on NPCs was subsequently evaluated. TNF-α treatment reduced miR-31 expression in NPCs (Figure 4(a)), cell proliferation (Figure 4(b)), and increased apoptosis (Figure 4(c)). TNF-α treatment also increased cleaved caspase-3 and Bax expression while decreasing Bcl-2 expression in NPCs (Figure 4(d)). In addition, TNF-α treatment in NPCs reduced the expression of ECM synthesis-related genes (ACAN, Col II, and SOX-9), while increasing that of ECM degradation-related genes MMP-3 and TIMP-1 (Figure 4(e)). However, the addition of BMSCs reversed the above effects of TNF-α, but further treatment with GW4869 caused similar results to those of TNF-α (Figures 4(a)–4(e)). These results showed that hBMSC-EVs may promote NPC proliferation and inhibit cell apoptosis and ECM degradation.
Next, we sought to determine whether BMSC-EVs stimulated NPC proliferation and repressed cell apoptosis by delivering miR-31 to NPCs. Coculture with hBMSC-EVs in TNF-α-treated NPCs increased miR-31 expression (Figure 5(a)) and cell proliferation (Figure 5(b)), but repressed apoptosis (Figure 5(c)), decreased cleaved caspase-3 and Bax expression, and increased Bcl-2 expression (Figure 5(d)) when compared to TNF-α treatment alone. In addition, hBMSC-EVs also increased the expression of ACAN, Col II, and SOX-9, while decreasing that of MMP-3 and TIMP-1 (Figure 5(e)). In contrast, BMSCs-EVs+anti-miR-31 in TNF-α-treated NPCs reversed the effects of hBMSC-EVs (Figures 5(a)–5(e)). Altogether, the above results indicated that hBMSCs transferred miR-31 to NPCs where miR-31 induced NPC proliferation and inhibited cell apoptosis and ECM degradation.
The Venn diagram of the downstream genes of miR-31 predicted by the RAID, TargetScan, mirDIP, and miRWalk databases suggested that SATB2 and NFAT5 were at the intersection (Supplementary Figure 2A). GeneMANIA was used to predict the genes related to the key downstream genes, followed by the construction of a PPI network. Then, Cytoscape was adopted to calculate the core degree with the results presenting that the core degree of NFAT5 was 32 (Figure 6(a)). Meanwhile, evidence has indicated that NFAT5 can promote the occurrence of IDD [35]. Thus, NFAT5 was selected for subsequent experiments. The TargetScan database predicted the binding sites of miR-31 in the NFAT5 3′UTR in humans, mice, and rats (Supplementary Figure 2B). The dual luciferase reporter assay further verified that the luciferase activity of NFAT5 WT 3′UTR was decreased in response to transfection with miR-31 mimic, but that of NFAT5 MUT 3′UTR was unaffected (Figure 6(b)). Moreover, miR-31 mimic elevated miR-31 expression but reduced NFAT5 expression, while miR-31 inhibitor exerted opposite results (Figures 6(c) and 6(d)). These results suggested that miR-31 targeted NFAT5 3′UTR and limited its expression. GeneMANIA predicted 20 genes related to NFAT5 (Supplementary Figure 3A). Enrichment analysis of NFAT5 and its related genes using KOBAS showed that related genes were mainly enriched in the Wnt pathway (Supplementary Figure 3B). Previous studies have shown that NFAT5 inhibits activation of the Wnt pathway [18], and the Wnt pathway can inhibit the occurrence of IDD [21]. Therefore, we speculated that miR-31 targeted NFAT5 and mediated the Wnt pathway to regulate IDD. To test this conjecture, Western blot was first conducted to detect the expression of the Wnt/β-catenin pathway-related proteins in the presence of miR-31 mimic or inhibitor. The results described that miR-31 mimic decreased NFAT5 expression and increased β-catenin expression (Figure 6(e)). In contrast, miR-31 inhibitor led to opposite results (Figure 6(e)). In addition, TOPFlash results showed that the transcription activity of TCF/LEF was increased by miR-31 mimic, but decreased by miR-31 inhibitor (Figure 6(f)). Furthermore, TNF-α treatment increased NFAT5 protein expression, decreased β-catenin expression, and reduced transcription activity of TCF/LEF in NPCs (Figures 6(g) and 6(h)), while BMSCs-EVs had opposite effects (Figures 6(i) and 6(j)). Furthermore, compared with BMSCs-EVs+anti-NC, BMSCs-EVs+anti-miR-31 treatment increased NFAT5 protein expression, decreased β-catenin expression, and reduced transcription activity of TCF/LEF in NPCs (Figures 6(i) and 6(j)). The aforementioned results indicated that miR-31 targeted NFAT5, upregulated the expression of β-catenin protein, and activated the Wnt/β-catenin pathway.
The aforementioned results allowed us to speculate that the effect of miR-31 in hBMSC-EVs on the NPC biological function was associated with NFAT5. Treatment with oe-NFAT5 in TNF-α-treated NPCs increased NFAT5 protein expression, decreased β-catenin protein expression (Figure 7(a)), reduced the transcription activity of TCF/LEF (Figure 7(b)), decreased cell proliferation (Figure 7(c)), and increased cell apoptosis (Figure 7(d)). In addition, NFAT5 overexpression also increased cleaved caspase-3 and Bax protein expression but diminished Bcl-2 protein expression (Figure 7(e)). NFAT5 overexpression reduced the protein expression of ACAN, Col II, and SOX-9 while increasing that of MMP-3 and TIMP-1 (Figure 7(f)). Conversely, the addition of hBMSC-EVs reversed the effects of NFAT5 overexpression (Figures 7(a)–7(f)). These lines of evidence demonstrated that miR-31 in hBMSC-EVs inhibited the expression of NFAT5 in NPCs, thereby accelerating NPC proliferation and inhibiting NPC apoptosis and ECM degradation.
We finally aimed to characterize the effect of miR-31 in hBMSC-EVs on IDD in vivo. PKH26-labeled hBMSC-EVs were distributed in NP tissues (Figure 8(a)). miR-31 expression was reduced in the IVD tissues of IDD mice (Figure 8(b)). Serum levels of TNF-α and IL-1β were found to be increased in IDD mice (Figure 8(c)). In addition, the protein expression of NFAT5, cleaved caspase-3, Bax, MMP-3, and TIMP-1 was elevated while that of β-catenin, Bcl-2, ACAN, Col II, and SOX-9 was decreased in the IVD tissues of IDD mice (Figures 8(d)–8(f)). Additionally, an increase was noted in the cell apoptosis (Figure 8(g)) and histological score (Figure 8(h)) in the IVD tissues of IDD mice. The addition of hBMSC-EVs reversed the above changes in IDD mice (Figures 8(a)–8(h)). However, anti-miR-31 abrogated the effect of hBMSCs-EVs (Figures 8(a)–8(h)). These results indicated that miR-31 in BMSC-EVs alleviated IDD in mice.
There are a number of important findings in this study. First, we found that hBMSC-EVs carried and delivered miR-31 into NPCs. TNF-α treatment significantly decreased the expression of miR-31 in NPCs. Second, hBMSC-EVs decreased NPC apoptosis and ECM degradation. Third, we further demonstrated that miR-31 also decreased NPC apoptosis and ECM degradation, which was blocked by miR-31 inhibitor. Fourth, miR-31 bound to and inhibited NFAT5, leading to increased β-catenin and activation of the Wnt/β-catenin pathway. TNF-α treatment also had similar effects that were inhibited by hBMSC-EV treatment. Fifth, NFAT5 overexpression increased NPC apoptosis and ECM degradation, suggesting NFAT5 promoted the progression of IDD. These effects of NFAT5 were inhibited by hBMSC-EV treatment. Lastly, miR-31 expression was decreased in the IVD tissues of IDD mice, together with increased proinflammatory cytokines, ECM degradation, and NPC apoptosis. These effects can be alleviated by hBMSC-EV treatment. Meanwhile, the effect of hBMSC-EV treatment was blocked by miR-31 inhibitor. These results strongly suggested that miR-31 in hBMSC-EVs inhibited NFAT5, leading to activation of the Wnt/β-catenin pathway and alleviating IDD. Accumulating evidence has documented the increased degradative enzymes and proinflammatory cytokines in IDD [6, 36, 37]. Notably, apoptosis of NPCs is a major cause of IDD [7]. One of the most important findings in this study was that miR-31 was delivered by hBMSC-EVs into NPCs. EVs are well known for cell-to-cell communications through proteins, lipids, or RNAs [9]. miR-31 has been confirmed to be encapsulated in synovial MSC-EVs and can be transferred into the target chondrocytes [10]. Besides, miR-31-5p is highly expressed in MSC-exosomes, which can deliver miR-31-5p into endplate chondrocytes [38]. Moreover, we found that miR-31 inhibited the apoptosis of NPCs. This result was in line with a previous study showing that miR-31 regulates cell apoptosis, possibly through the phosphatidylinositol-3 kinase/protein kinase B pathway [11]. Subsequent results of this study revealed that miR-31 bound to NFAT5 3′UTR and inhibited the expression of NFAT5. Consistently, a previous study has underpinned that NFAT5 is a target gene of miR-31 in glioma cells [39]. NFAT5 is known to regulate cell apoptosis [15], which is comparable to our results that NFAT5 overexpression increased apoptosis in NPCs. Increased proinflammatory cytokines were also found in IDD, which is also consistent with a previous study showing that NFAT5 is involved in the inflammatory response [17]. In addition, we found that NFAT5 overexpression decreased ECM synthesis. NFAT5 has been shown to regulate the formation of ECM by controlling the acquisition of collagen through the sonic hedgehog pathway [40]. Another study demonstrated that NFAT5 regulated ECM turnover by regulating the expression of ACAN [41]. These results showed the critical role of NFAT5 in the regulation of ECM. Downstream to NFAT5, we found that reduced NFAT5 expression led to increased expression of β-catenin. This result is in line with a previous study showing that NFAT5 inhibited activation of the Wnt pathway [18]. Wnt/β-catenin is important in IDD due to their inflammatory functions and roles in regulating cell apoptosis [19, 20]. There are a few limitations to this study. First, the conclusion of this work should be validated in large cohorts due to the relatively small case of patients. Second, although the Wnt/β-catenin was implicated in this study, a causal relationship was not established. Further studies should include Wnt/β-catenin overexpression or knockdown experiments to study its impact on IDD. Third, we only discerned the interaction between miR-31 and Wnt/β-catenin, which requests further research about the specific mechanism between NFAT5 and Wnt/β-catenin for validation of the reported signaling axis. Fourth, miR-31 has been documented to target several genes in degenerative NPCs, such as MMP3, SDF-1, CXCR7, and ATF6, thus participating in the IDD progression [13, 38, 42], and we thereby cannot exclude the involvement of these targets in the alleviation of miR-31 in BMSC-EVs in the IDD progression due to the complex microenvironment.
In conclusion, miR-31 from BMSC-EVs can alleviate IDD through inhibition of NFAT5 and activation of the Wnt/β-catenin pathway (Figure 9). Therefore, miR-31 and its downstream pathway molecules may be novel therapeutic modalities for IDD treatment that deserves further investigation. | true | true | true |
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PMC9615941 | Ming Yang,Xudong Zhu,Yang Shen,Qi He,Yuan Qin,Yiqun Shao,Lin Yuan,Hesong Ye | GPX2 predicts recurrence-free survival and triggers the Wnt/β-catenin/EMT pathway in prostate cancer | 25-10-2022 | Prostate cancer,Recurrence-Free Survival,EMT,Wnt/β-catenin pathway,GPX2 | Objective This study aimed to establish a prognostic model related to prostate cancer (PCa) recurrence-free survival (RFS) and identify biomarkers. Methods The RFS prognostic model and key genes associated with PCa were established using Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression from the Cancer Genome Atlas (TCGA)-PRAD and the Gene Expression Omnibus (GEO) GSE46602 datasets. The weighted gene co-expression network (WGCNA) was used to analyze the obtained key modules and genes, and gene set enrichment analysis (GSEA) was performed. The phenotype and mechanism were verified in vitro. Results A total of 18 genes were obtained by LASSO regression, and an RFS model was established and verified (TCGA, AUC: 0.774; GSE70768 , AUC: 0.759). Three key genes were obtained using multivariate Cox regression. WGCNA analysis obtained the blue module closely related to the Gleason score (cor = –0.22, P = 3.3e − 05) and the unique gene glutathione peroxidase 2 (GPX2). Immunohistochemical analysis showed that the expression of GPX2 was significantly higher in patients with PCa than in patients with benign prostatic hyperplasia (P < 0.05), but there was no significant correlation with the Gleason score ( GSE46602 and GSE6919 verified), which was also verified in the GSE46602 and GSE6919 datasets. The GSEA results showed that GPX2 expression was mainly related to the epithelial–mesenchymal transition (EMT) and Wnt pathways. Additionally, GPX2 expression significantly correlated with eight kinds of immune cells. In human PCa cell lines LNCaP and 22RV1, si-GPX2 inhibited proliferation and invasion, and induced apoptosis when compared with si-NC. The protein expression of Wnt3a, glycogen synthase kinase 3β (GSK3β), phosphorylated (p)-GSK3β, β-catenin, p-β-catenin, c-myc, cyclin D1, and vimentin decreased; the expression of E-cadherin increased; and the results for over-GPX2 were opposite to those for over-NC. The protein expression of GPX2 decreased, and β-catenin was unchanged in the si-GPX2+ SKL2001 group compared with the si-NC group. Conclusion We successfully constructed the PCa RFS prognostic model, obtained RFS-related biomarker GPX2, and found that GPX2 regulated PCa progression and triggered Wnt/β-catenin/EMT pathway molecular changes. | GPX2 predicts recurrence-free survival and triggers the Wnt/β-catenin/EMT pathway in prostate cancer
This study aimed to establish a prognostic model related to prostate cancer (PCa) recurrence-free survival (RFS) and identify biomarkers.
The RFS prognostic model and key genes associated with PCa were established using Least Absolute Shrinkage and Selection Operator (LASSO) and Cox regression from the Cancer Genome Atlas (TCGA)-PRAD and the Gene Expression Omnibus (GEO) GSE46602 datasets. The weighted gene co-expression network (WGCNA) was used to analyze the obtained key modules and genes, and gene set enrichment analysis (GSEA) was performed. The phenotype and mechanism were verified in vitro.
A total of 18 genes were obtained by LASSO regression, and an RFS model was established and verified (TCGA, AUC: 0.774; GSE70768, AUC: 0.759). Three key genes were obtained using multivariate Cox regression. WGCNA analysis obtained the blue module closely related to the Gleason score (cor = –0.22, P = 3.3e − 05) and the unique gene glutathione peroxidase 2 (GPX2). Immunohistochemical analysis showed that the expression of GPX2 was significantly higher in patients with PCa than in patients with benign prostatic hyperplasia (P < 0.05), but there was no significant correlation with the Gleason score (GSE46602 and GSE6919 verified), which was also verified in the GSE46602 and GSE6919 datasets. The GSEA results showed that GPX2 expression was mainly related to the epithelial–mesenchymal transition (EMT) and Wnt pathways. Additionally, GPX2 expression significantly correlated with eight kinds of immune cells. In human PCa cell lines LNCaP and 22RV1, si-GPX2 inhibited proliferation and invasion, and induced apoptosis when compared with si-NC. The protein expression of Wnt3a, glycogen synthase kinase 3β (GSK3β), phosphorylated (p)-GSK3β, β-catenin, p-β-catenin, c-myc, cyclin D1, and vimentin decreased; the expression of E-cadherin increased; and the results for over-GPX2 were opposite to those for over-NC. The protein expression of GPX2 decreased, and β-catenin was unchanged in the si-GPX2+ SKL2001 group compared with the si-NC group.
We successfully constructed the PCa RFS prognostic model, obtained RFS-related biomarker GPX2, and found that GPX2 regulated PCa progression and triggered Wnt/β-catenin/EMT pathway molecular changes.
Prostate cancer (PCa) is one of the most common male malignant tumors in the United States and the second leading cause of cancer-related deaths in men (Kang et al., 2020). More than 80% of PCa cases are diagnosed as local diseases and usually treated by radical prostatectomy. However, about 15% of patients have a biochemical recurrence within 5 years after surgery, and the recurrence rate has been reported to be as high as 40% within 10 years. Local PCa that relapses after treatment can progress to fatal castration-resistant prostate cancer (CRPC) (Li et al., 2017). The causes of PCa recurrence are complex and diverse, and the specific mechanism has not yet been clarified (Siegel, Miller & Jemal, 2019). Therefore, research on the mechanism of PCa recurrence and the application of prognostic biomarkers may be of great significance in improving the survival rate of patients with PCa. Many studies have shown that the epithelial–mesenchymal transition (EMT) and Wnt/β-catenin signaling pathways play an essential role in the occurrence and development of PCa (Montanari et al., 2017). EMT is necessary for PCa occurrence and distant metastasis, and plays a critical role in PCa metastasis to other organs (He et al., 2020). Epithelial cells attain the biological characteristics of stromal cells (Chaves et al., 2021). Studies have shown that the EMT and Wnt/β-catenin signaling pathways are closely related. Wnt binding to its receptor frizzled protein results in protein phosphorylation, which inhibits GSK-3 β activity. Consequently, β-catenin degradation is blocked and β-catenin accumulates in the cytoplasm, enters the nucleus, interacts with cytokines, activates the transcription of downstream target genes, induces EMT in cells, and promotes tumor growth and metastasis (Hseu et al., 2019; Sun et al., 2020). Bioinformatics analysis is one of the crucial methods used for gene molecular research based on Big Data (Hutter & Zenklusen, 2018; Botía et al., 2017). In this study, PCa RFS–related differentially expressed genes (DEGs) were screened by analyzing the data of PCa-related gene expression and clinicopathological characteristics in the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We analyzed the protein–protein interaction (PPI) based on DEGs. Survival, Cox regression, and LASSO regression analyses were used to establish and verify the prognostic model. The DEGs between different Gleason scores of PCa tissues and the key gene glutathione peroxidase 2 (GPX2) were obtained by weighted gene co-expression network analysis (WGCNA). The GSEA of GPX2 and its significance in prognosis and immunity were analyzed. Finally, a series of in vitro experiments were conducted to explore the potential role of GPX2 in PCa, so as to provide new clues for diagnosing and treating PCa.
The data of gene expression profiles from the TCGA-PRAD dataset were downloaded from the TCGA database and standardized. At the same time, the clinical information of patients, including age, gender, TNM stage, pathological stage, and prognosis, was downloaded. The samples with incomplete clinical information and survival data were excluded. A total of 481 PCa samples and 51 adjacent tissue samples were included in the study. Three datasets of gene expression and clinical profiles of Pca were downloaded from the GEO database (GSE70768, GSE46602, and GSE6919).
The data were analyzed using the R software DESeq and Limma package. The volcano and heat maps were drawn using the P value <0.05 and —logfc—>2 as the screening conditions. After taking the intersection, Pca DEGs were obtained. The PPI network of DEGs was constructed using the STRING database, and the setting was adjusted to the interactive score of 0.7. Cytohubba and MCODE modules were used to screen Top30 and topology-related genes from Cytoscape 3.7.2. The prognostic model was established using univariate Cox, LASSO, and multivariate Cox regression analyses. Finally, the model was validated in the TCGA-PRAD and GSE70768 datasets.
A total of 2,191 TCGA DEGs were analyzed using WGCNA. The Pearson correlation coefficient between genes was calculated. The scale-free network was constructed and the appropriate threshold was selected for network construction. Using two-step construction, the adjacency matrix was transformed into a topological overlap matrix, the clustering tree was generated through hierarchical clustering, and clustering was combined through a dynamic cut. The significance of gene and module was estimated, and the clinical sample grouping information was obtained. The identity of each gene module was calculated to measure the importance of genes in each module. Setting parameters —gene module—>0.8 and —gene significance—>0.2 as criteria, we screened the hub genes of modules closely related to clinical traits. The blue module was found to be significantly related to the Gleason score. The key genes and blue modules were crossed, and the single gene GPX2 was obtained. The TCGA-PRAD dataset verified the GPX2-predicted PCa RFS.
GSEA 4.0.1 was used to compare and analyze the DEGs between high- and low-expression groups of GPX2 in the PCa tumor samples of the TCGA-PRAD dataset. Gene set database selection KEGG v7. 4 was used to set the replacement times to 1,000; P < 0.05 and false discovery rate (FDR) <0.25 indicated significantly enriched genes. Between May 2021 and January 2022, the Second Affiliated Hospital of Nanjing University of Chinese Medicine collected tissues from 20 patients with PCa (10 patients with a high Gleason score ≥8 and 10 patients with a low Gleason score ≤7) and 10 patients with benign prostatic hyperplasia. Detailed information is shown in Table S1. All patients signed an informed consent form. This study was approved by the ethics committee of the Second Affiliated Hospital of the Nanjing University of Chinese Medicine (2021SEZ-030-01). The biopsy samples were collected, fixed with 10% formaldehyde, and embedded in paraffin after routine treatment. The prepared wax block was cut into sections with a thickness of 2 µm. Immunohistochemical staining was performed on the treated sections. The processed sections were stained with GPX2 (ab140130; Abcam, Cambridge, UK). Two pathologists used the double-blind method to judge each slice. The sections were observed using a low-power mirror under a microscope to select the best field of vision, and then a high-power lens was used in this range 10 × 40. Five visual fields were randomly observed, and the IHC score was defined as the product of the frequency of positive cells and the intensity of staining.
In this study, the CIBERSORT algorithm was used to calculate the infiltration proportion of 22 kinds of immune cells in PCa tissue, and 481 PCa samples were analyzed using the R software. The samples with a P value <0.05 were included in the follow-up analysis. Taking the median expression level of GPX2 mRNA as the boundary, the samples were divided into high- and low-expression groups.
Human PCa cell lines (PC-3, DU145, LNCaP, and 22RV1) were purchased from Procell (Wuhan, China). The cells were cultured in RPMI-1640 (bl303a; Biosharp, Anhui, China) at 37 °C and in the presence of 5% CO2. The cells adhered to the wall and were passaged every 3 days. The cells in the logarithmic growth phase were used for the experiment. TRIzol reagent (bs259a, Biosharp, China) was used to extract the total RNA of PC-3, LNCaP, 22Rv1, and DU145 cells. A reverse transcription kit (11119es60; Yeasen, Shanghai, China) was used to reverse transcribe RNA into cDNA, and a SYBR Green Kit (11201es50; Yeasen, China) was used for qRT-PCR amplification, with β -actin as an internal control. The 2- Δ ΔCT method was used for calculation. The primers were as follows: GPX2: 5′-GCCTCCTTAAAGTTGCCATA-3′ and 5′-GCCCAGAGCTTACCCA-3′; β -actin: 5′-GAAGAGA-GAGACCCTCACGCTG-3′, and 5′-ACTGTGAGGAGGGGAGATTCAGT-3′. The experiment was repeated three times.
LNCaP and 22RV1 cells in the logarithmic growth phase (n = 200,000) were inoculated into the cell culture plate and transfected according to the Lipofectamine 2000 (11668-027, Invitrogen, Waltham, MA, USA) instructions. They were divided into GPX2 low expression (si-GPX2) and negative control (si-NC) groups, GPX2 overexpression (over-GPX2) and negative control (over-NC) groups, and SKL2001(HY-101085, MCE, USA) +si-GPX2 groups. The transfection effect was verified using qRT-PCR and Western blotting. The experiment was repeated three times.
LNCaP and 22RV1 cells in the logarithmic growth phase (n = 2,000) were inoculated into the cell culture plate. After undergoing corresponding treatment according to experimental grouping, 10 µL of cells were added to each well containing CCK-8 solution (PR645; Dojindo, Kumamoto, Japan). The culture plate was incubated and the absorbance value was determined to be 450 nm using a microplate reader (SpectraMax i3; Molecular Devices, San Jose, CA, USA). Cell proliferation inhibition rate = (control group absorbance value–experimental group absorbance value)/control group absorbance value ×100%. The experiment was repeated three times.
The transfected cells were collected and digested with trypsin without EDTA. The adherent cells were collected and centrifuged. The supernatant was discarded and the cell precipitate was washed twice with phosphate-buffered saline (PBS). Annexin V–FITC/PI (556547, BD; Franklin Lakes, NJ, USA) was added. After incubation in the dark at room temperature for 5 min, we detected the apoptosis rate of LNCaP and 22RV1 cells using a flow cytometer (LSRII instrument; BD, Franklin Lakes, NJ, USA). The experiment was repeated three times.
The transfected cells were collected and the cell concentration was adjusted to 3 × 105/mL. The cells were inoculated into the upper layer of the Transwell chamber (3422; Corning, Corning, NY, USA) which contained a serum-free medium, and 100 µL/well of the cell suspension was added. Additionally, 600 µL of the fresh culture medium was added to the lower layer of the chamber. The liquid in the upper chamber was discarded after culturing for 24 h, and the cells were wiped off the upper-chamber membrane with a wet cotton swab. The cells on the lower-chamber membrane were fixed with methanol for 20 min, dyed with crystal violet, rinsed with PBS until the background was clean, dried, and imaged after sealing. ImageJ software was used to count the number of transmembrane cells. The experiment was repeated three times.
The total protein was extracted from human PCa cells following the instructions of the total protein extraction kit (bl521a; Biosharp, Shandong, China); subsequently, the protein concentration was detected using the diquinoline formic acid method. The denatured protein samples were separated by electrophoresis according to which membrane was transferred using the semi-dry method. After sealing the membrane with skimmed milk powder for 2 h, we added β-actin (gb12001; Servicebio, Wuhan, China), Wnt3a (2721; Cell Signaling Technology, Danvers, MA, USA), GSK3 β (ab2602; Abcam, UK), phosphorylated (p)-GSK3 β Ser9 (ab131097; Abcam, UK), β-catenin (ab32572; Abcam, UK), p- β-catenin (ab27798; Abcam, UK), C-myc (ab32072; Abcam, UK), Cyclin D1 (2978; Cell Signaling Technology, USA), vimentin (3195; Cell Signaling Technology, USA), E-cadherin (60330-I-Ig, Proteintech, USA), and GPX2 (ab140130; Abcam, UK) antibodies. Subsequently, the membrane was incubated overnight at 4 °C, then incubated with primary and secondary antibodies at room temperature for 2 h, exposed, and developed using the ECL film. The protein expression was analyzed, and the experiment was repeated three times.
The Student t-test was used for continuous variables, while the classification variables were analyzed using the χ2 test. Cox and LASSO regression models were used to analyze the predictors of RFS. The data were expressed as mean ± standard deviation. All data were analyzed with R version 4.1.2, SPSS 24.0 and GraphPad Prism 8.0. A P value <0.05 indicated a significant difference. All tests were repeated three times.
The GSE46602 dataset had 211 upregulated genes and 409 downregulated genes. The TCGA-PRAD dataset was comprised of 898 upregulated genes and 1,293 downregulated genes. The volcano and heat maps showed DEGs (Figs. 1A and 1B). After further taking the intersection of the aforementioned datasets, we obtained the common 262 DEGs (94 upregulated genes and 168 downregulated genes) (Table 1 and Fig. 1C). STRING and Cytoscape were used to construct the PPI network of DEGs (Fig. 1D). Cytohubba and MCODE modules were used to screen Top30 and topology-related DEGs (Figs. 1E and 1F).
A total of 32 genes related to the prognosis of PCa RFS were analyzed using univariate Cox analysis, and a prognostic model based on 18 genes of LASSO regression was constructed: EZH2*0.46 + ELL3*−0.18 + APOC1*−0.04 + NME1*−0.22 + FAM222A*−0.60 + SLC43A1*−0.71 + GCNT1*−0.04 + FOXD1*0.17 + COL2A1*0.028 + GPX2*−0.11 + FOXQ1*0.08 + ID4*−0.32 + IER3*−0.17 + SGCE*−0.13 + ANO5*−0.25 + FBXO17*−0.03 + PNMA8A*0.49 + EDN3*−0.03 (Figs. 2A and 2B). According to the median risk score of the prognostic model, we divided patients with PCa into high-risk and low-risk groups, and the RFS-related scatter plot and the heat map of the prognostic model were constructed (Figs. 2C–2E). The ROC curve of the risk score was also generated, with AUC = 0.774 (Fig. 2F). The GSE70768 dataset was used to verify the prognostic model, and the RFS-related scatter plot and the heat map of the prognostic model were constructed (Figs. 2H–2J); the ROC curve of the risk score had AUC = 0.759 (Fig. 2K). Three key genes obtained by the intersection of the prognostic model and Top30 were GPX2, EZH2, and COL2A1 (Fig. 2L). The multivariate Cox regression showed that the three genes significantly correlated with patient RFS (P = 0.001, 0.023,0.008, Fig. 2M).
According to WGCNA analysis and taking the correlation coefficient of 0.85 as the standard, the pickSoft threshold function was used to select the weight parameter of the adjacency matrix (soft threshold); β = 2 was the standard gene module (Figs. 3A–3C). Using the two-step method, the minimum number of genes in each gene module was set to 30, and the height of cutting branches and merging modules was set to 0.25. Finally, nine modules were obtained (Fig. 3D). Of these, we selected the blue module for this study, which was comprised of 450 genes with the correlation (r = -−0.22, P = 3.3e−05) (Fig. 3E). The intersection of blue module and key genes yielded only one gene GPX2 (Fig. 3F), which was used as in follow-up research.
To further evaluate the prognostic value of GPX2 for PCa, the prognostic nomogram was constructed by integrating clinical factors and gene expression (Fig. 4D), and the correction curve was drawn to evaluate the predictive ability of the nomogram. The correction curve showed that the risks predicted by the nomogram were highly consistent with the observed RFS for 1, 3, and 5 years (Figs. 4A–4C).
The GSEA analysis showed that GPX2 expression was mainly related to EMT and infectious disease biology, including the EMT and Wnt signaling pathways (Figs. 4E and 4F). The high expression of GPX2 could inhibit the activity of the aforementioned pathways, thus inhibiting the occurrence and development of PCa.
A significant difference was found in the degree of immune cell infiltration in 61 samples (20 in the GPX2 low-expression group and 41 in the GPX2 high-expression group) (Figs. 5A and 5B). Eight kinds of immune cells (activated dendritic cells, resting dendritic cells, M0 macrophages, M2 macrophages, monocytes, neutrophils, resting memory CD4 T cells, and CD8 T cells) showed GPX2 expression with significant differences (Fig. 5C).
The immunohistochemical analysis showed that a GPX2-positive immune reaction was located in the cytoplasm and brownish yellow particles existed in the cytoplasm (Fig. 6A). GPX2 expression in benign prostatic hyperplasia tissue was significantly higher than in PCa tissue, with no significant difference in Gleason score (Fig. 6B). GSE66602 and GSE6919 datasets also confirmed the aforementioned results (Fig. 6C).
qRT-PCR and Western blotting were used to detect the highest mRNA level of GPX2 in LNCaP and 22RV1 cells, and GPX2 was used for subsequent experiments (Fig. 6D). Western blotting showed that the expression of GPX2 in the si-GPX2 group was significantly lower than that in the si-NC group. Also, the expression of GPX2 in the over-GPX2 group was significantly higher than that in the over-NC group, indicating that silencing and overexpression were successful and could be used in subsequent experiments (Figs. 6E and 6F).
Compared with the si-NC group, the si-GPX2 group showed the inhibition of cell proliferation (Figs. 7A and 7D) and invasion (Figs. 7C and 7F), and the promotion of cell apoptosis (Figs. 7B and 7E). Compared with the over-NC group, the over-GPX2 group showed the promotion of cell proliferation (Figs. 7A and 7D) and invasion (Figs. 7C and 7F), and inhibition of apoptosis (Figs. 7B and 7E).
The protein expression of Wnt3a, GSK3 β, p-GSK3 β, β-catenin, p- β-catenin, c-myc, cyclin D1, and vimentin decreased and that of E-cadherin increased in the si-GPX2 group compared with the si-NC group. The results for over-GPX2 were opposite to those for over-NC (Figs. 8A–8B and 8E– 8F). Additionally, the protein expression of β-catenin increased and that of GPX2 decreased in the si-GPX2 + SKL2001 group compared with the si-NC group (Figs. 8C–8D and 8G–8H).
Although the incidence rate of PCa in Asia is far lower than that of Europe and North America, the incidence and mortality rate of PCa in China has rapidly increased in recent years (Bray et al., 2018; Gu et al., 2018). The routine clinical application of prostate-specific antigen has produced good results in helping the early diagnosis of PCa (Perera et al., 2021). However, PCa is a clinically heterogeneous cancer with large individual differences, particularly involving the diagnosis and treatment of early tumor diagnosis and later tumor progression, tumor metastasis, and hormone resistance (Ji et al., 2019). Identifying the genes related to the occurrence and development of PCa and clarifying the pathogenesis of cancer can provide a theoretical basis for preventing and treating PCa (Giri et al., 2018; Velho et al., 2018). Therefore, in-depth clinical and basic research involving a larger sample size is necessary in order to explore reliable diagnosis and treatment methods. PCa is a complex disease affected by multiple genes. In this study, we developed a risk score based on 18 genes that was verified using GSE70768 and TCGA-PRAD datasets, with a good prediction performance. WGCNA was used to analyze the TCGA-PRAD dataset, and nine modules associated with the pathological grade, Gleason scores, TNM stage, and clinical characteristics of PCa were obtained. We selected the Gleason score and blue module for analysis and found a significant correlation (Cor = -−0.22, P = 3.3e−05). Then, we predicted the intersection of three parts of the model genes using the blue module, Top30, and key genes. Finally, only the core gene GPX2 was obtained. A nomogram was constructed to predict the recurrence of PCa. The nomogram could predict the possibility of recurrence in PCa patients and was more accurate than clinical indicators. GPX2, also known as gastrointestinal-specific glutathione peroxidase, is a selenium-containing protein. It is mainly expressed in the gastrointestinal system and exerts anti-inflammatory and antioxidant effects (Lennicke et al., 2017). In recent years, GPX2 has been found to be highly expressed in a variety of tumors, especially inflammation-induced tumors, and may promote cell proliferation and inhibit apoptosis (Minato et al., 2021; Ji et al., 2021; Tian et al., 2021). GPX2 is also overexpressed in human and mouse CRPC cells and promotes the malignant proliferation of PCa cells. Inhibition of GPX2 expression significantly inhibited the proliferation of PCa cells and made them stagnate in the G2/M phase (Naiki et al., 2014). Inhibiting the expression of GPX2 can also improve the level of reactive oxygen species (Wu et al., 2021). These results showed that GPX2 had a certain correlation with tumor immunity. In this study, the high and low expression of GPX2 could influence eight kinds of immune cells to participate in the immune response of PCa. However, there have been few studies on the relationship between the expression of GPX2 and PCa prognosis and mechanism. The Gleason score system is a PCa pathological grading system that was introduced in 1974 (Gleason & Mellinger, 1974). It has become the most powerful tool used to predict the prognosis of patients with PCa (Nagpal et al., 2020). It is closely related to the differentiation and invasion of PCa, which is of great significance for clinicians choosing and making treatment plans (Thomsen et al., 2020). IHC staining showed that the expression of GPX2 in PCa tissues had no significant correlation with the Gleason score; two datasets (GSE66602 and GSE6919) were used to verify the same results. Therefore, we speculated that GPX2 played an important role in PCa with no correlation to the Gleason score. We concluded that data mining must be combined with experimental verification. Furthermore, the survival prognosis of patients with high and low GPX2 expression was analyzed according to the public datasets GSE70768 and TCGA-PRAD. The results showed that the RFS time of patients in the GPX2 high-expression group was shorter than that of patients in the GPX2 low-expression group. In addition, this study used TCGA-PRAD data to construct a nomogram to predict the prognosis of patients with PCa, which helped us more intuitively understand the importance of the GPX2 expression levels in predicting PCa prognosis. We used lentivirus transfection technology to promote the over-expression and low expression of GPX2 in LNCaP and 22RV1 cells in order to determine the role of GPX2 in the occurrence and development of PCa. The corresponding in vitro cell experiment results showed that inhibiting the expression of GPX2 could inhibit the proliferation and invasion of LNCaP and 22RV1 cells and induce apoptosis. Also, promoting the expression of GPX2 could promote proliferation and invasion and prevent the apoptosis of LNCaP and 22RV1 cells. The Wnt/β- catenin and EMT pathways were closely related to the occurrence and development of PCa (Kaplan et al., 2021; Chaves et al., 2021). Nath et al. (2019) found that Abi1 loss promoted the progression of PCa by modulating the Wnt signal and inducing EMT. Zhang & Li (2020) found that long noncoding RNA NORAD contributed to the metastasis of PCa via the Wnt/β-catenin/EMT pathway. However, the modulation of GPX2 on the Wnt/β-catenin/EMT pathway has not been reported. This study was novel in reporting that when the expression level of GPX2 changed, the proteins related to the Wnt/β-catenin/EMT pathways also changed. Therefore, we concluded that the mechanism of GPX2 in influencing the occurrence and prognosis of PCa was related to the Wnt/β -catenin/EMT signaling pathway. However, despite the clinical significance of our findings, this study had some limitations. First, although the performance and AUC values of the calibration curve were excellent in the validation group, multicenter clinical application is still needed to further evaluate the external utility of the prognostic model. Only 262 genes were defined as genes related to the recurrence of PCa, and the construction of the prognostic model was evaluated. Some important genes might have been excluded before establishing the prognostic model. Second, GPX2 was highly expressed in benign prostatic hyperplasia compared with PCa. However, over-GPX2 promoted PCa cell proliferation and invasion and inhibited apoptosis, indicating that over-GPX2 promoted tumor progression to a certain extent. The underlying mechanism needs to be further examined. In conclusion, the expression of GPX2 in PCa can be used as a new prognostic biomarker of RFS of PCa. GPX2 might regulate PCa progression via the Wnt/β-catenin/EMT pathway, and is expected to become a potential target for treating PCa.
10.7717/peerj.14263/supp-1 Click here for additional data file. 10.7717/peerj.14263/supp-2 Click here for additional data file. 10.7717/peerj.14263/supp-3 Click here for additional data file. 10.7717/peerj.14263/supp-4 Click here for additional data file. 10.7717/peerj.14263/supp-5 Click here for additional data file. 10.7717/peerj.14263/supp-6 Click here for additional data file. | true | true | true |
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PMC9616079 | Yunfeng Niu,Gaoyan Wang,Yan Li,Wei Guo,Yanli Guo,Zhiming Dong | Corrigendum: LncRNA FOXP4-AS1 promotes the progression of esophageal squamous cell carcinoma by interacting with MLL2/H3K4me3 to upregulate FOXP4 | 14-10-2022 | ESCC,FOXP4-AS1,long noncoding RNA,FOXP4,MLL2 | Corrigendum: LncRNA FOXP4-AS1 promotes the progression of esophageal squamous cell carcinoma by interacting with MLL2/H3K4me3 to upregulate FOXP4
In the original article, there was a mistake in Figure 1 as published. In Figure 1B , “GSE151633” should be “GSE161533”. The corrected Figure 1 appears below. In addition, there was a mistake in Figure 2 as published. In Figure 2F , the original image of the clone formation experiment has been reworked. The corrected Figure 2 appears below. In addition, there was a mistake in Figure 3 as published. In Figure 3F , “Relative expression of FOXP4-AS1” has been changed to “Relative Expression of FOXP4-AS1”. #In Figure 3A, the title of the Y axis has changed from "Expression of FOXP4 (2-△△CT)" to "Relative Expression of FOXP4". The corrected Figure 3 appears below. In addition, there was a mistake in Figure 4 as published. In Figure 4D , “Relative expression of FOXP4-AS1” has been changed to “Relative Expression of FOXP4-AS1”. In Figure 4C, the title of the Y axis has changed from “Expression of MLL2 (2-△△CT)" to “Relative Expression of MLL2”. The corrected Figure 4 appears below. In addition, there was a mistake in Figure 5 as published. In Figure 5I , the original image of the clone formation experiment has been reworked. The corrected Figure 5 appears below. In addition, there was a mistake in Figure 6 as published. In Figure 6I , the luciferase reporter gene experiment was reworked. The corrected Figure 6 appears below. In addition, there was a mistake in Figure 7 as published. In Figure 7 , the overall experiment was divided into four groups. The overexpression of FOXP4-AS1 promoted metastasis and invasion of esophageal squamous carcinoma cells, however transfection of pcDNA3.1-FOXP4-AS1 and sh-FOXP4 attenuated the ability of transfection of pcDNA3.1-FOXP4-AS1 alone to metastasize and invade esophageal squamous carcinoma cells. The corrected Figure 7 appears below. In addition, there was a mistake in Figure S1 as published. In Figure S1I, “groups” should be “We divided the group into four groups”. The updated file may be viewed via the original article. The authors apologize for these errors and state that they do not change the scientific conclusions of the article in any way. The original article has been updated.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. | true | true | true |
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PMC9616121 | Tainara C. Michelotti,Brent R. Kisby,Lauryn S. Flores,Alexandra P. Tegeler,Mohamed Fokar,Chiquito Crasto,Bruno C. Menarim,Shavahn C. Loux,Clarissa Strieder-Barboza | Single-nuclei analysis reveals depot-specific transcriptional heterogeneity and depot-specific cell types in adipose tissue of dairy cows 10.3389/fcell.2022.1025240 | 14-10-2022 | single-nuclei analysis,dairy cow,adipose tissue metabolism,depot differences,progenitor cell | Adipose tissue (AT) is an endocrine organ with a central role on whole-body energy metabolism and development of metabolic diseases. Single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq, respectively) analyses in mice and human AT have revealed vast cell heterogeneity and functionally distinct subtypes that are potential therapeutic targets to metabolic disease. In periparturient dairy cows, AT goes through intensive remodeling and its dysfunction is associated with metabolic disease pathogenesis and decreased productive performance. The contributions of depot-specific cells and subtypes to the development of diseases in dairy cows remain to be studied. Our objective was to elucidate differences in cellular diversity of visceral (VAT) and subcutaneous (SAT) AT in dairy cows at the single-nuclei level. We collected matched SAT and VAT samples from three dairy cows and performed snRNA-seq analysis. We identified distinct cell types including four major mature adipocytes (AD) and three stem and progenitor cells (ASPC) subtypes, along with endothelial cells (EC), mesothelial cells (ME), immune cells, and pericytes and smooth muscle cells. All major cell types were present in both SAT and VAT, although a strong VAT-specificity was observed for ME, which were basically absent in SAT. One ASPC subtype was defined as adipogenic (PPARG+) while the other two had a fibro-adipogenic profile (PDGFRA+). We identified vascular and lymphatic EC subtypes, and different immune cell types and subtypes in both SAT and VAT, i.e., macrophages, monocytes, T cells, and natural killer cells. Not only did VAT show a greater proportion of immune cells, but these visceral immune cells had greater activation of pathways related to immune and inflammatory response, and complement cascade in comparison with SAT. There was a substantial contrast between depots for gene expression of complement cascade, which were greatly expressed by VAT cell subtypes compared to SAT, indicating a pro-inflammatory profile in VAT. Unprecedently, our study demonstrated cell-type and depot-specific heterogeneity in VAT and SAT of dairy cows. A better understanding of depot-specific molecular and cellular features of SAT and VAT will aid in the development of AT-targeted strategies to prevent and treat metabolic disease in dairy cows, especially during the periparturient period. | Single-nuclei analysis reveals depot-specific transcriptional heterogeneity and depot-specific cell types in adipose tissue of dairy cows 10.3389/fcell.2022.1025240
Adipose tissue (AT) is an endocrine organ with a central role on whole-body energy metabolism and development of metabolic diseases. Single-cell and single-nuclei RNA sequencing (scRNA-seq and snRNA-seq, respectively) analyses in mice and human AT have revealed vast cell heterogeneity and functionally distinct subtypes that are potential therapeutic targets to metabolic disease. In periparturient dairy cows, AT goes through intensive remodeling and its dysfunction is associated with metabolic disease pathogenesis and decreased productive performance. The contributions of depot-specific cells and subtypes to the development of diseases in dairy cows remain to be studied. Our objective was to elucidate differences in cellular diversity of visceral (VAT) and subcutaneous (SAT) AT in dairy cows at the single-nuclei level. We collected matched SAT and VAT samples from three dairy cows and performed snRNA-seq analysis. We identified distinct cell types including four major mature adipocytes (AD) and three stem and progenitor cells (ASPC) subtypes, along with endothelial cells (EC), mesothelial cells (ME), immune cells, and pericytes and smooth muscle cells. All major cell types were present in both SAT and VAT, although a strong VAT-specificity was observed for ME, which were basically absent in SAT. One ASPC subtype was defined as adipogenic (PPARG+) while the other two had a fibro-adipogenic profile (PDGFRA+). We identified vascular and lymphatic EC subtypes, and different immune cell types and subtypes in both SAT and VAT, i.e., macrophages, monocytes, T cells, and natural killer cells. Not only did VAT show a greater proportion of immune cells, but these visceral immune cells had greater activation of pathways related to immune and inflammatory response, and complement cascade in comparison with SAT. There was a substantial contrast between depots for gene expression of complement cascade, which were greatly expressed by VAT cell subtypes compared to SAT, indicating a pro-inflammatory profile in VAT. Unprecedently, our study demonstrated cell-type and depot-specific heterogeneity in VAT and SAT of dairy cows. A better understanding of depot-specific molecular and cellular features of SAT and VAT will aid in the development of AT-targeted strategies to prevent and treat metabolic disease in dairy cows, especially during the periparturient period.
Adipose tissue (AT) is a central metabolic organ that regulates whole-body energy homeostasis. In dairy cows, abnormal AT responses to changes in the endocrine status and energy balance are associated with the development of metabolic disease (Contreras and Sordillo, 2011; De Koster and Opsomer, 2013; Contreras et al., 2017; Contreras et al., 2018). Features of AT dysfunction in dairy cows include dysregulated inflammation with increased infiltration of macrophages, excessive adipocyte lipolysis, and persistent insulin resistance (De Koster et al., 2017; Contreras et al., 2018). Decreased adipogenic capacity and changes in AT extracellular matrix (ECM) function and deposition have also been implicated as important features of AT dysfunction in humans and mice with metabolic disease (Muir et al., 2016; Baker et al., 2017; Strieder-Barboza et al., 2020), but scarcely reported in dairy cows. Adipose tissue surrounding abdominal viscera in the mesentery and omentum, also known as visceral AT (VAT), is structurally and functionally different from that present in subcutaneous areas (subcutaneous adipose tissue - SAT). In dairy cows, VAT exhibits decreased adipocyte size and adipogenic capacity (lower number of adipocyte progenitor cells) and has an increased pro-inflammatory reaction in response to metabolic disease compared with SAT, which has larger adipocytes and more robust lipolytic responses (Contreras et al., 2015; Depreester et al., 2018; Strieder-Barboza et al., 2019). These depot-specific structural and functional responses to metabolic disease suggest differential regulation of systemic metabolic function by the VAT and SAT depots in dairy cows. This may have important implications for understanding the pathogenesis of disease in dairy cows and developing novel cell-targeted interventions to prevent and treat metabolic disease. Recent studies using single-cell and single-nuclei RNA sequencing analysis (scRNA-seq and snRNA-seq, respectively) of AT in humans and mice revealed significant cell heterogeneity and functionally distinct subpopulations of adipocyte progenitor cells, endothelial cells, mature adipocytes, and among other cell types (Vijay et al., 2020; Emont et al., 2022; Strieder-Barboza et al., 2022). Furthermore, AT presents depot- and disease-specific molecularly and functionally distinct subpopulations, with important implications in the development of metabolic diseases. For example, different subpopulations of stromal cells have been shown to inhibit or enhance inflammation, lipolysis, and adipogenesis in the AT (Burl et al., 2018; Hepler et al., 2018; Schwalie et al., 2018; Merrick et al., 2019; Vijay et al., 2020; Sárvári et al., 2021). These findings point to a complex network of cell subpopulations that regulate AT metabolic function in a depot-specific manner. Elucidating the molecular and cellular features of SAT and VAT that generate differential metabolic effects could help in understanding how specific AT depots contribute to disease development in dairy cows, especially during the periparturient period. Our objective for this study was to elucidate differences in cellular diversity of VAT and SAT in dairy cows at the single-nuclei level. We revealed that VAT and SAT from dairy cows are highly heterogeneous and contain depot-specific cell subtypes. These findings highlight the uniqueness of AT as a target organ for modulating systemic metabolism and preventing metabolic diseases in dairy cows.
Visceral adipose tissue was collected from greater omentum and SAT from the right flank of the same three Holstein dairy cows in a local abattoir (Supplementary Figure S1A). Animals were randomly selected from a group of adult Holstein cows brought to the harvest plant. We did not have access to any data about these animals. The samples were collected within 15 min after exsanguination and snap frozen in liquid nitrogen, transported to the laboratory, and stored at −80°C until further processing. Nuclei-isolation from bovine VAT and SAT was adapted from a previous protocol with brain tissue (Krishnaswami et al., 2016). Briefly, 500 mg of each cryopreserved VAT or SAT were washed with sterile RNase free cold 1X PBS (10XPBS buffer, pH 7.4, Invitrogen, Cat. No. AM9625 diluted 1:10 with Nuclease-free water, Invitrogen, Cat. No. AM9932), then minced with a scalpel using a petri dish on ice (Supplementary Figures S1B–C). Samples were homogenized in a precooled 15 ml glass dounce homogenizer (10 strokes with pestle A followed by 15 strokes with pestle B) with 1.5 ml of homogenization buffer composed by 5 mM MgCl2 (Invitrogen, Cat. No. AM9530G), 10 mM Tris Buffer (pH 8.0) (Invitrogen, Cat. No. AM9855G), 25 mM KCl (Invitrogen, Cat. No. AM9640G), 250 mM sucrose (Sigma-Aldrich, Cat. No. S0389-500 g), 1X protease inhibitor (Roche, Cat. No. 11836170001), 1 µM DL-Dithiothreitol solution (DTT, Sigma-Aldrich, Cat. No. 646563-10X), 0.4 U/µl Ribolock RNase Inhibitor (40 U/μl) (ThermoScientific, Cat. No. EO0381), 0.2 U/µl Superasin (20 U/µl) (Invitrogen, Cat. No. AM2696), and 0.1% Triton X-100 (Sigma-Aldrich, Cat. No. T8787-100ML). Samples were then strained with pre-wet 100 µm and 40 µm filters into a 50 ml conical tube. Next, each sample was transferred to two 1.5 ml pre-chilled microcentrifuge RNase free tubes and centrifuged at 500 × g, 4°C for 5 min. Supernatant was pipetted off leaving approximately 50 µL containing the nuclei pellet, which was resuspended in 500 µl of 1% BSA-PBS (Bovine serum albumin, Sigma-Aldrich, Cat. No. A7030-10G) including 0.2 U/μl of Ribolock RNase Inhibitors. Before proceeding with fluorescence activated cell sorting (FACS), a subsample of nuclei was used to assess the overall quality of the nuclei by staining with trypan blue and visualized by phase-contrast light microscopy (Supplementary Figure S1C). To enable the sorting of the nuclei, we immune-stained the nuclei samples with propidium iodide (PI) (Invitrogen, Cat. No. V13241, 10 µg/ml) in a 1:100 dilution, leaving approximately 20–30 µl of sample to be used as unstained control. Each sample was transferred into a pre-coated 5 ml polystyrene flow cytometry tube and sorted using a 100 µM nozzle in a BD FACSAria II Cell Sorter (BD Biosciences, Franklin Lakes, NJ, United States). Sorting strategy included doublet discrimination and selection of intact nuclei by sub-gating on PI stanning (Supplementary Figure S1D). PI+ nuclei was sorted directly into a 1.5 ml microcentrifuge tube containing 20 µl of 1% BSA-PBS and 0.2 U/μl of Ribolock RNase Inhibitors. After nuclei-isolation, samples were centrifuged at 100 × g, 4°C for 6 min, and the supernatant was pipetted off leaving approximately 50 µl of resuspended nuclei sample. Single nuclei suspensions were subjected to final nuclei counting on an automated cell counter (Countess 2, Life Technologies Inc., Carlsbad, CA, United States) and diluted to a concentration of 700–1,000 nuclei/µl. 3′ single nuclei libraries were generated following manufacturer’s user guide: 10X Genomics Chromium Next GEM Single Cell 3′ Reagent Kits v3.1 (Dual Index). Final library quality was resolved using DS 5000 HS assay kit using Tape Station 4200 (Agilent Technologies, Santa Clara, CA, United States). The libraries were quantified using Qubit dsDNA HS assay kit on Qubit Fluorometer version 2.0 (Life Technologies Inc., Carlsbad, CA, United States) (Supplementary Figure S1E).
The pooled nuclei libraries were subjected to 150 bp paired-end sequencing according to the manufacturer’s protocol (Illumina NovaSeq 6000) (Supplementary Figure S1F). Bcl2fastq2 Conversion Software (Illumina) was used to generate de-multiplexed Fastq files, and the CellRanger Pipeline (10X Genomics) was used to align reads and generate count matrices. Data analysis was performed using the scRNA-Seq package Seurat v3.1.4 (Stuart et al., 2019) in the R environment (version 4.1.3), following recommended practice for scRNA-Seq analysis, including quality control, normalization, and scaling of data, feature selection, dimensionality reduction, clustering, and visualization of data (Supplementary Figure S1G). Data quality control was performed by: 1) removing genes observed in fewer than 3 cells to avoid random noise; 2) filtering nuclei with a minimum gene count of 300 and a maximum of 6,000 genes; and 3) setting a threshold of <15% for mitochondrial gene expression. In Seurat, data were normalized using the “NormalizeData” function while “CellCycleScoring” was used to classify nuclei regarding cell cycle stage and assign respective scores to each nucleus. Variability was regressed out by the difference between G2M and S phase score based on the gene expression of cell cycle genes by scaling and centering the residuals as implemented in the function “ScaleData”. Using the “FindVariableFeatures” function in Seurat, we identified 8000 highly variable genes. The scaled and normalized expression data of respective genes served as input for a principal component analysis (PCA), and the top 30 dimensions were used to plot the variability between cells in a two-dimensional diagram by means of the Uniform Manifold Approximation and Projection (UMAP) procedure to reduce the dimensionality of the data. Cells were clustered into subpopulations according to the same dimensions using the “FindClusters” function with a 0.6 resolution, which is a graph-based clustering approach. The top genes in each cluster were identified in comparison with all the other clusters using the “FindMarkers” function while keeping a cutoff of Log2FoldChange > 0.5 and Adjusted p value < 0.05, calculated based on Bonferroni correction using all genes in the dataset. Genes expressed in a minimum of 10% of nuclei in the test population were considered for analysis. Cell types were assigned manually to each cluster based on known expressions of signature genes. Clusters with nuclei that expressed marker genes for a specific cell type at the highest levels were assigned the corresponding cell type label (Supplementary Table S1). The subclustering of major cell subtypes was created from the “Subset” function of Seurat. Dimensional reduction, UMAP, and clustering were performed as described above, except for resolution, which was increased to 0.8. Finally, using the “Subset” and “FindMarkers” function, we compared each cluster between SAT and VAT and identified the top genes following the same specifications as described above. Overall, the cell type-specific contrasts between depots were analyzed for Gene ontology (GO) enrichment pathways. Analyses were performed on the entire gene list using clusterProfiler (version 4.0.0) in the R environment (version 4.1.3). Genes were evaluated for enrichment in GO Biological Process (BP), Molecular Function (MF), and Cellular Component (CC) using Adjusted p value < 0.05 after Benjamini–Hochberg correction. All pathways and Adjusted p values can be found in Supplementary Table S2. Contrast between different adipose stem and progenitor cells and mature adipocytes populations were also analyzed for GO enrichment pathways following the same specifications described above, and results can be found in Supplementary Tables S3, S4, respectively. Using available datasets, we compared bovine AT snRNA-seq with human AT scRNA-seq. Human AT scRNA-seq was handled as described above, using the same parameters of quality control, normalization, scaling of data, and feature selection. To compare the different datasets, we identified anchors using the “FindIntegrationAnchors” function, then performed the integration analysis using the “IntegrateData” function of Seurat. Dimensionality reduction, clustering, and visualization of data followed the parameters mentioned above. UMAPs were plotted using the group. by = “orig.ident” and split. by = “orig.ident” statements in order to highlight contrast between studies.
Adipose tissue samples were collected from randomly selected Holstein cows in a local abattoir and stromal vascular fraction (SVF) isolated as previously reported (Strieder-Barboza et al., 2018; Strieder-Barboza and Contreras, 2019). Briefly, abdominal SAT and omental VAT were collected in Krebs-Ringer modified buffer (KRB) at 37°C supplemented with HEPES 10 M (pH = 7.3, ThermoScientific, Cat. No. J16924. K2) and gentamicin (50 mg/ml, Sigma-Aldrich, Cat. No. G1397-10 ml). Approximately 5 g of each sample was digested in 15 ml of collagenase type II solution (2 mg/ml; Gibco, Cat. No. 17101-015) for 1 h at 37°C in a shaker (490 rpm). Tissue digesta was sequentially filtered through 100- and 40-µm cell strainers (FisherScientific, Cat. No. 22-363-549 and 22-363-547) using 5 ml of KRB with 4% BSA (FisherScientific, Cat. No. BP1600-100), and filtrate centrifuged (800 × g, 10 min, room temperature-RT). After incubation with ×1 red blood cells lysis buffer (6 min; Biolegend, Cat. No. 420301), cells were resuspended in 5 ml of cold 1X PBS and centrifuged (800 × g, 5 min, RT). The resultant cell pellet was resuspended and plated in T25 flasks (Falcon, Cat. No. 353109) using basal preadipocyte medium containing Dulbecco’s modified Eagle’s medium F12 50:50 (Gibco, Cat. No. 11320-033), 10% fetal bovine serum (Gibco, Cat. No. 16140071), 1% (v/v) antibiotic-antimycotic (Gibco, Cat. No. 15240-062), 100 μmol/L of ascorbic acid (Sigma-Aldrich, Cat. No. A4544-25G), 33 μmol/L of biotin (Sigma-Aldrich, Cat. No. B4639-500 mg), and 20 mmol/L of HEPES 10M (pH = 7.3, ThermoScientific, Cat. No. J16924.K2) and incubated at 37°C in a humidified atmosphere with 5% CO2 with media replacement every 48 h. Preadipocytes were obtained by outgrowth of plastic adherent cells from the SVF cells after two serial passages in culture flasks. If performing flow cytometry in the whole SVF, resultant cells were counted using 0.4% trypan blue stain (Gibco, Cat. No. 15250-061) and an automated cell counter (Countess 3, Life Technologies Inc., Carlsbad, CA, United States), then resuspended at 1 × 106 cells/ml in Fluorescence-Activated Cell Sorting (FACS) buffer containing ×1 PBS with 0.1% sodium azide (NaN3; Sigma-Aldrich, Cat. No. S2002-25G) and 2% fetal bovine serum (FBS, Gibco, Cat. No. 16140071).
In vitro cultured SAT and VAT preadipocytes were plated in distinct cell culture vessels (6-, 8-, 12-, or 24-well plates) and allowed to proliferate to confluency in basal preadipocyte medium. Adipogenic induction was performed using basal medium supplemented with 5 μmol/L of troglitazone (AdipoGen Life Sciences, Cat. No. AG-CR1-3565-M005), 0.5 mmol/L of 2 isobutyl-1-methylaxanthine (IBMX; AdipoGen Life Sciences, Cat. No. AG-CR1-3512-G001) and the following reagents from Sigma-Aldrich: 5 μg/ml of insulin (Cat. No. 10516-5ML), 10 mM acetate (Cat. No. 3863-50ML), and 1 μmol/L of dexamethasone (Cat. No. D2915-100MG). After the first 48 h, IBMX and dexamethasone were removed from the medium and cells allowed to differentiate for additional 8 days (10 days total).
Total RNA from adipose tissue was extracted using the RNeasy Lipid Tissue Mini kit (Qiagen, Cat. No. 74804) and RNA quality was determined using RNA Screen Tape (Agilent). Samples with RNA integrity number (RIN) greater than 7 were used in the subsequent steps. Messenger RNA purification, RNA fragmentation, double stranded cDNA, and adaptor ligation were generated using llumina Stranded mRNA Prep kit according to the manufacture’s protocol (Illumina, Cat. No. 20040534). PCR enriched libraries were quantified using the Quant-iT PicoGreen™ dsDNA Assay Kit (Invitrogen, Cat. No. P11496) and equimolar indexed libraries were pooled. Pooled libraries were checked using the Agilent Tapestation 2200 and quantified by qPCR. The libraries were then diluted to 250 p.m. and spiked with 1% phiX libraries (Illumina control). The transcriptome sequencing was performed on the barcoded stranded RNA-Seq libraries using Illumina NovaSeq 6000 flow cell, paired-end reads (2 × 50 bp) targeting at least 30 million reads per sample. FASTQ reads were trimmed for quality and adapters with TrimGalore 0.4.3 and mapped to bovine genome ARS-UCD1.2 with STAR-2.7.2a (Dobin et al., 2012) in the two-pass mode, quantMode GeneCounts including the following specifications (--outSAMstrandField intronMotif --outFilterIntronMotifs RemoveNoncanonicalUnannotated --alignEndsType Local --chimOutType WithinBAM --twopassMode Basic --twopass1readsN -1). Annotation was performed using Ensembl v106. Normalization of expression values was performed using gene length corrected trimmed mean of M-values (GeTMM) (Smid et al., 2018). Depot-specific genes (as shown in Figure 2F) were selected based on the expression in SAT or VAT samples exclusively. Differentially expressed genes were identified by One between VAT and SAT samples based upon a robust Benjamini–Hochberg corrected false discovery rate (FDR) p value < 0.05 (JMP 14 Pro). We used Ingenuity Pathway Analysis software (IPA, 2018) (Krämer et al., 2014) to identify activated and inhibited signaling pathways comparing VAT versus SAT using DEGs. The analysis output provided a–log p value, Z-scores, and molecules/genes for each pathway. Z-scores were considered significant if they had p value < 0.05 and activation Z-score >2 (activated) or < −2 (inhibited) (Menarim et al., 2021).
We characterized the frequency of major types of cells present in SAT and VAT samples observed in the snRNA-seq analysis using flow cytometry (Attune NxT Flow Cytometer; Invitrogen, Waltham, MA, United States). Briefly, we collected VAT and SAT from an independent cohort of 10 Holstein dairy cows randomly selected at a local abattoir, and tissue was collected and processed as described above. After the SAT and VAT dissociation, cells were collected in FACS buffer followed by immunostaining with the following antibodies at 4°C for 30 min: 1.5 µg of FITC anti-bovine CD31 (ThermoScientific, Cat. No. MA1-80360), 10 µL of PE anti-bovine CD45 (ThermoScientific, Cat. No. MA1-81458), and 2 µg of unconjugated anti-human mesothelin antibody (ThermoScientific, Cat. No. PA5-79697). Primary antibodies were added to one million cells in 100 µL staining volume. For mesothelin (MSLN; ME marker) staining, 5 µg of goat anti-rabbit IgG (H + L) Alexa Fluor™ 647 (ThermoScientific, Cat. No. A-21244) was used as secondary antibody and incubated at 4°C for 15 min. Adipose stem and progenitor cells (ASPC) were defined as CD31−CD45−; EC as CD31+CD45− and general immune cells as CD31−CD45+. Percentage of mesothelin positive cells were calculated based on total VAT or SAT SVF cells, ASPCs (CD31−CD45−), and CD31 positive or negative cells (as shown in Figure 4G) due to the uncertainty whether these cells are adipocyte progenitors or an independent pool of cells. (Chau et al., 2014; Gupta and Gupta, 2015; Westcott et al., 2021). Remaining cell populations were quantified as percentage of total cells. Controls included unstained cells and cells with single stains for each antibody. Statistical analysis was performed using the PROC MIXED of SAS (version 9.4; SAS Institute Inc., Cary, NC) with depot as fixed effect, and cow within depot as a random effect. Main effects terms were considered significant when p value ≤ 0.05 and tendencies when p value < 0.10.
In vitro-cultured VAT and SAT preadipocytes and adipocytes (at 0 and 10 days of differentiation, respectively) were collected in RNA lysis buffer (RLT buffer, Qiagen, Cat. No. 1015762) after Two subsequent washes with cold 1X PBS. RNA was isolated with RNeasy Mini Kit (Qiagen, Cat. No. 74104) and its concentration and integrity were evaluated in a Take3 plate (Cytation5 multi-mode reader, Biotek, Santa Clara, CA, United States). cDNA synthesis was performed with SuperScript IV VILO Master Mix (Invitrogen, Cat. No. 11756050) in a MiniAmpPlus thermal cycler (Applied Biosystems, Waltham, MA, United States; Cat. No. A37835). qRT-PCR was conducted with TaqMan® gene expression assays and reagents (Life Technologies Inc., Carlsbad, CA, United States) in a QuantStudio 6 Pro (Applied Biosystems, Waltham, MA, United States; Cat. No. A43180). TaqMan assays used were PPARG (Bt03217547_m1), MSLN (Bt03263572_m1), ADIPOQ (Bt03292341_m1), UPK3B (Bt03218076_m1), EIF3K (Bt03226565_m1), LUM (Bt03211921_m1), B2M (Bt03251628_m1), and WT1 (custom preparation). Data are presented as fold changes in mRNA expression calculated from least squares means differences according to the formula 2−∆∆Ct (CT = cycle threshold), where and . Housekeeping genes were selected based on previous dairy cows adipose tissue studies (Strieder-Barboza et al., 2017; Strieder-Barboza and Contreras, 2019). Preadipocytes and SAT were used as calibrator samples for the differentiation and depot effects, respectively. For reporting, expression data were normalized to the arithmetic mean of the two housekeeping genes (EIF3K and B2M). Statistical analysis was performed on the ∆Ct values as described previously (Steibel et al., 2009), using the PROC MIXED of SAS (version 9.4; SAS Institute Inc., Cary, NC) with day of differentiation and depot as fixed effects, and cow within depot as a random effect. Main effects terms were considered significant when p value ≤ 0.05 and tendencies when p value < 0.10.
Immunofluorescence was performed with whole AT tissue and cultured preadipocytes and adipocytes. VAT and SAT preadipocytes and adipocytes (at 0 and 10 days of differentiation, respectively) cultured in glass bottom chamber-slides (Thermo Scientific, Nunc™ Lab-Tek™ II Chambered Coverglass, Cat. No. 155409) were fixed in 4% paraformaldehyde (PFA, Alfa Aesar, Cat. No. 43368) for 10 min (room temperature-RT, in the dark), rinsed three times with 1X PBS and permeabilized with 0.25% Triton X-100 (Sigma-Aldrich, Cat. No. T8787-100ML) for 15 min at RT. After rinsing twice with 1X PBS, cells were blocked for 1 h with 2% BSA-PBS at RT, followed by overnight incubation (4°C, gentle shaking) with primary antibodies. For preadipocytes, cells were stained for lumican (LUM; Invitrogen, Cat. No. MA5-34828; 1:200 antibody: 0.2% BSA-PBS) and Wilms tumor protein (WT1, Invitrogen; Cat. No. MA5-38660; 1:500 antibody: 0.2% BSA-PBS). Adipocytes were stained for adiponectin (ADIPOQ; Invitrogen, Cat. No. MA1-054; 2 μg/ml) and leptin (LEP, Bioss, Cat. No. BS-0409R; 1:200 antibody: 0.2% BSA-PBS). Following three consecutive washes with ×1 PBS, cells were co-incubated with host-specific secondary antibodies [Invitrogen, Alexa Fluor™ 488 donkey anti-rabbit IgG (Cat. No. A-21206) and Alexa Fluor™ 568 goat anti-mouse IgG (Cat. No. A-11031)] at 2 μg/ml in 0.2% BSA-PBS for 1 h at RT. Cells were subsequentially washed three times with 1X PBS and stained with DAPI (1 μg/ml, ThermoScientific, Cat. No. 62248) for 10 min at RT. Finally, cells were washed twice with 1X PBS and imaged in a Cytation5 multi-mode reader (Biotek, Santa Clara, CA, United States). For whole AT staining, samples were fixed for 20 min in 4% PFA (RT, dark room) and blocked in 1X PBS with 5% BSA + 0.1% Triton X-100 60 min RT. After Three consecutive washes with 1XPBS, antibody incubations were performed, and samples were imaged as described above. Negative controls included samples stained with only the secondary antibody. The same imaging settings were used to image controls and samples.
We generated snRNA-seq data from abdominal SAT and omental VAT samples derived from Holstein dairy cows (Supplementary Figures S1A–G). Across six matched SAT and VAT samples, 13 clusters (Figure 1A) were identified among 11,271 nuclei, in which 7,018 pertained to SAT and 4,253 to VAT. Manual annotation via expression of signature genes defined cell types (Supplementary Table S1), which consisted of mature adipocytes (AD; ADIPOQ, LEP), adipose stem and progenitor cells (ASPC; PDGFRA, PPARG), endothelial cells (EC; VWF, PECAM1), macrophages/monocytes (MAC; CD163, MRC1, CD14), natural killer and T-cells (NKT; CD52, CD3E), mesothelial cells (ME; WT1, MSLN), and pericytes/smooth muscle cells (PE/SMC; NOTCH3, MYL9) (Figures 1B,C). Across SAT and VAT, AD was the most abundant cell type, followed by ASPC, EC, MAC, ME, PE/SMC, and NKT (Figure 1D). We compared our dataset from dairy cow AT with recently published scRNA-seq datasets from human SAT and VAT (Figures 1E,F) (Merrick et al., 2019; Vijay et al., 2020). We observed similarities in the identification of cell types, such as ASPC, EC, ME, PE/SMC, and immune cells with both datasets. As expected from scRNA-seq performed with AT SVF, mature adipocytes were practically absent. Human SAT from the Merrick et al. (2019) dataset revealed a strong and diverse presence of ASPC populations, while immune cells, EC, and PE/SMC were proportionally less abundant in comparison to our dairy cow SAT data (Figure 1E). Across SAT and VAT, human AT from Vijay et al. (2020) dataset presented greater abundance of ME and immune cells than our results from dairy cows (Figure 1F) and may be related to AT species-specific characteristics or distinct methods to dissociate cells vs. nuclei from AT samples.
Single-nuclei RNA sequencing analysis of SAT and VAT revealed more than 300 differentially expressed genes (DEGs) between the depots (Figures 2A,B). There was a marked increase in the expression of components of the complement system on VAT, especially C3 – the gene with the highest contrast between VAT and SAT (Log2FoldChange = 4.26) (Figures 2A,B). In contrast, the gene expression profile of SAT revealed an increased expression of EGR1, genes of the FOS family (FOS and FOSB), fatty acid synthesis (FASN, ACLY, and SCD), and Hox genes (HOXA9, HOXD4, HOXC6, and HOXA6) (Supplementary Table S5). All main cell types, including AD, ASPC, EC, PE/SMC, MAC, and NKT were present in both depots, except for ME, which was a VAT-specific cell subtype, with little to no expression in SAT (Figures 2C,D). When comparing average gene expression of cell type signature genes between VAT and SAT (Figure 2E), we identified several DEGs that were depot-specific, such as the increased expression of FASN in SAT AD subpopulations compared with VAT, the increased expression of ME-markers WT1, UPK3B, and MSLN in VAT compared with SAT, and the decreased expression of CD68 and S100A12 in SAT MAC compared with VAT. Taken together, these data suggest depot-specific differences in AT cell types, which may reflect distinct metabolic and immune functions for VAT and SAT. Evaluation of the top SAT and VAT DEG from our bulk RNA-Seq analysis support cell type-specific gene expression (Figure 2F). SAT-specific HOXA10, HOXC10, HOXA11 and TMEM210 are specially enriched in SAT AD and ASPC. GRB14 and GOT1L1 are unique to VAT ADs and ASPCs, while KRT17 and DLK1 are expressed only in VAT AD and ASPC, respectively. These findings validate our snRNA-seq methodology in identifying depot-specific cell subtypes in which AT bulk RNA-seq DEGs are expressed. Mature adipocytes (AD), defined as nuclei expressing ADIPOQ and/or LEP, were the most frequent cell type detected in both VAT and SAT, corresponding to approximately 40% of total nuclei (Figure 2G). Abundance of EC and ASPC was increased in SAT compared to VAT by approximately 2-fold, suggesting increased angiogenic and adipogenic capacity, respectively, in SAT compared with VAT in dairy cows. Notably, VAT has higher proportions of both MAC and NKT compared with SAT (Figure 2G), which agrees with the enrichment in pathways of complement activation and immune responses in VAT compared with SAT (Figure 2H). Enrichment analysis also implied a decreased capability of VAT to regulate cellular ketone metabolic process, thus indicating the involvement of VAT dysfunction in ketosis pathogenesis in dairy cows. We validated the snRNA-Seq results using flow cytometry analysis to quantify proportions of bulk ASPC and immune cells in VAT and SAT samples obtained from an independent cohort of dairy cows (Figure 2I).
We identified three distinct subtypes of adipose stem and progenitor cells (ASPC1-3, Figure 3A; Supplementary Table S6). ASPC1 showed greater expression of PPARG (Figures 3B,C), a master regulator of lipid biosynthesis and adipocyte differentiation (Schadinger et al., 2005; Guo et al., 2019), as well as SLC1A3, LIPE, GPAM and LMO4, suggesting that these cells are committed adipocyte precursors (Hepler et al., 2018). In contrast, ASPC2 and ASPC3 showed increased expression of PDGFRA (Figures 3B,C), a known marker of fibro-adipogenic progenitor cells (FAP), which have the capability to differentiate into adipocytes or activated fibroblasts (Contreras et al., 2021; Dohmen et al., 2022). Compared with ASPC1, the PDGFRA + ASPC2 and ASPC3 FAPs had greater expression of extracellular matrix (ECM) genes, such as FBN1, FN1, LAMA2, COL14A1, and MFAP5 (Figure 3C). Interestingly, ASPC3 was uniquely enriched for COL1A1, COL6A1, FN1, LOX, and LUM, which are fibrosis markers recently associated with a specific subset of PDGFRA + progenitor cells in murine model of obesity (Marcelin et al., 2017). Enrichment analysis of adipogenic ASPC1 vs. ASPC2 and ASPC3 FAP subtypes (Figure 3D; Supplementary Table S3) revealed the activation of pathways related to lipid metabolism (e.g., lipid metabolic process, glycerolipid metabolic process, acylglycerol metabolic process, and cholesterol homeostasis) in ASPC1, while pathways associated to ECM, collagen-containing ECM, complement activation, and immune response were enriched in FAP ASPC2 and ASPC3 (Figure 3D). Next, we investigated differences between the two FAP ASPC subtypes detected in both SAT and VAT of dairy cows. Analysis of ASPC2 DEG revealed a greater expression of “classic” FAP markers, such as components of ADAM (metalloprotease—disintegrin) family, BMP1, EBF1, FGFR1, and TGFB receptors (that may participate into FAP fibrogenic differentiation). In contrast, ASPC3 had COL1A1, COL1A2, COL6A1, FN1, DCN, FBLN1, MMP2, LUM, OGN, SPARC, and LOX, known markers of tissue fibrosis (Sun et al., 2013), among the most upregulated genes. Several of these markers have been consistently associated with AT fibrosis, decreased adipogenic capacity, and dysfunction in human and mice models. Additionally, ASPC3 had increased expression of pro-inflammatory markers, e.g., CCL2, CXCL3, C3, C1S, and PTGS2, similar to the previously reported fibro-inflammatory progenitors (‘FIPs’) in mice AT, which exhibited a pro-fibrogenic/pro-inflammatory profile (Hepler et al., 2018). Consistently, enrichment analysis comparing “classic-FAP” ASPC2 vs. “fibrogenic FAP” ASPC3 revealed an upregulation of pathways associated with ECM organization, immune system process, cell differentiation, and programmed cell death (Figure 3E; Supplementary Table S3). Overall, these findings suggest that ASPC3 has a pro-inflammatory and profibrogenic profile that may drive AT fibrosis and negatively affect adipogenesis. There was a greater proportion of ASPC in SAT than in VAT (Figure 3F), which was confirmed by flow cytometry analysis of SAT and VAT SVF (Figure 1I). Notably, the proportion of adipogenic ASPC1 was decreased by 10% in VAT compared with SAT, implying a decreased adipogenic capacity of VAT compared to SAT. Enrichment analysis of VAT ASPCs vs. SAT ASPCs (Supplementary Table S2) revealed an activation of immune response, complement activation, and ECM pathways, and a suppression of insulin-like growth factor receptor signaling pathway (Figure 3G), suggesting a pro-inflammatory potential of VAT ASPCs. Moreover, overall gene expression comparison between depots (Supplementary Table S7) revealed an upregulation of many complement system genes, such as C3, C4a, C1QC, C1S, C1R, and SERPING1, involved in the regulation of the complement cascade (Gorelik et al., 2017; Lubbers et al., 2020), as well as pro-fibrotic markers, LUM, FN1, and FBLN1 in VAT ASPCs. In contrast, FASN, SCD, IGFBP5, and IGFBP3 were upregulated in SAT ASPCs. We observed no differences on PPARG and LUM gene expression between SAT and VAT in in vitro cultivated using qRT-PCR analysis (Figure 3H; n = 4).
Mesothelial cells were annotated based on the expression of the signature marker genes MSLN, KRT19, WT1, and UPK3B. Subclustering of ME resolved three major subtypes (Figure 4A). When contrasting the distinct ME subpopulations, we observed a unique pattern of WT1 and UPK3B expression. While MSLN and KRT19 were expressed in all ME subtypes, ME1 was WT1 + UPK3B − , ME2 was WT1 + UPK3B +, and ME3 WT1 - UPK3B − (Figure 4B). Interestingly, ME2 was enriched for genes associated with inflammation, such as C3, CFB, C1S, and CD99, as well as genes related to adipogenesis (CD34, IGF2) and fibrosis (CD9, SPARC, COL8A1) (Marcelin et al., 2017). The potential roles of ME on AT inflammation, adipogenesis, and fibrosis and whether their distinct transcriptional profiles translate into functional differences among ME subtypes remain to be established. We observed strong depot differences: ME were practically absent in SAT (0.3% of total nuclei), while ME represented around one sixth of total population of VAT cells (15.6%; Figure 4C). This was also confirmed by the marked upregulation of MSLN and KRT19, and to a lesser extent WT1 and UPK3B in VAT vs. SAT in an independent cohort of dairy cows (Figure 4D). These results agree with previous snRNA-seq and scRNA-seq studies in mouse and human models, which indicate a specific expression of MSLN and other mesothelial markers in VAT (Vijay et al., 2020; Emont et al., 2022). We validated our snRNA-seq results by qRT-PCR, flow cytometry, and immunofluorescence: qRT-PCR showed greater mRNA expression of the ME markers MSLN, WT1 and UPKB3 in VAT compared to SAT (Figure 4E; n = 4). Accordingly, whole tissue immune-stained with WT1 confirmed the absence of WT1 expression in SAT, but abundant expression in VAT (Figure 4F). Using flow cytometry, we confirmed a greater proportion of MSLN positive cells in SVF from VAT compared to SAT (Figure 4G). Finally, immunofluorescence imaging of SVF revealed few cells expressing WT1 in VAT, but not in SAT (Figure 4F); additionally, we observed that a few VAT cells may co-express WT1 and LUM, which was upregulated in FAP ASPCs in our snRNA-seq analysis.
Our snRNA-seq approach allowed us to recollect data about distinct subtypes of mature adipocytes, which are usually excluded from scRNA-seq due to the incompatibility of adipocyte size with microfluidics and/or the absence of mature adipocytes in studies using AT SVF cells. Our analysis resolved four different populations of mature adipocytes (AD1-4, Figure 5A) annotated based on the expression of ADIPOQ and/or LEP. While AD1 and AD3 were characterized by the increased expression of both ADIPOQ and LEP (ADIPOQ + LEP + ), AD2 and AD4 were characterized by the selective expression of ADIPOQ (ADIPOQ + ) or LEP (LEP + ), respectively (Figure 5B). While AD4 had the lowest average expression of LEP and downregulation of ADIPOQ, LPIN1 was one of the most upregulated genes in AD4. LPIN1 is a reciprocal regulator of triglyceride synthesis and hydrolysis in adipocytes (Mitra et al., 2013), therefore, a marker of adipocytes (Supplementary Table S8). Immunofluorescence imaging of adipocytes stained with ADIPOQ and LEP corroborate with our snRNA-seq findings, in which we observed that adipocytes have a selective expression of ADIPOQ or LEP, while other cells seem to express both proteins (Figure 5F). Based on DEG analysis between AD subtypes (Supplementary Table S8), we examined for evidence of ADs with profiles similar to adipogenic or fibro-adipogenic ASPC, or ME cells. We observed that AD1 has an adipogenic profile (Figure 5B), like ASPC1, with high expression of classical lipid synthesis regulators. In contrast to AD1, AD2 and AD3 have a FAP ASPC-like gene profile. For example, PDGFRA and ECM genes, including DCN, FBN1, LAMA2, and COL1A1/A2 are upregulated in AD2, similar to our FAP ASPC3. AD3 has a FAP-like profile due to the upregulation of genes involved in adipogenesis and lipid metabolism (ADIRF, THRSP, SCD, ACLY, FABP4, AGPAT2, APOE, and LPL), as well as ECM genes, such as CLU, VIM, and SPARC. Interestingly, AD3 also had a greater expression of mitochondrial-related genes, including ND4, ND1, COX1, COX2, and COX3, when compared to the other AD subtypes, which might indicate a brown adipose tissue-like profile, or be correlated with greater adipocyte lipogenesis and mitochondrial oxidative capacity in this specific subpopulation (Kaaman et al., 2007; Lu et al., 2018). AD4 had a unique profile with high LPIN1, CSF1, and collagens (COL18A1, COL5A1, and COL4A2) expression, implying a FAP-like profile that does not overlap with AD2 and AD3. We did not find significant similarities between AD4 and ASPCs or ME cells. ME gene markers, such as MSLN, KRT19, WT1, and UPK3B were not differentially expressed in AD subtypes. However, AD1, AD2, and AD3 subtypes expressed at least one of these markers (Figure 5B). Since heatmaps are based on average expression, there is a possibility that only a few cells within AD1-3 subtypes expressed high ME markers, while others did not express any of them at all. Enrichment analysis revealed activation of pathways related to protein synthesis and mitochondria respiratory chain complex on AD3 when compared to other AD populations, while AD2 showed an enrichment in cell differentiation and development pathways (Supplementary Table S4). No pathways were significantly activated in AD1 or AD4. All AD subpopulations were present in both VAT and SAT (Figure 5C) and had similar proportions within each depot. Overall, VAT ADs had increased expression of complement system genes (C3, CFB, C1QA, C1QB, and C1QC), as well as SERPING1 (Supplementary Table S9). Moreover, we observed an increased expression of PPARG, LPIN1, PNPLA2, and LIPE in VAT adipocytes when compared to SAT (Figure 5D). In contrast, SAT adipocytes had greater expression of EGR1 and de novo fatty acid synthesis genes (FASN, ACLY, and SCD) (Figure 5D). Interestingly, seven heat shock protein genes (e.g., HSPA8 and HSPB1) were upregulated in SAT, but not in VAT (Supplementary Table S9). No depot-differences were observed in ADIPOQ expression, which was confirmed in vitro through ADIPOQ mRNA quantification in adipocytes after 10 days of differentiation (p value > 0.10). Enrichment analysis comparing VAT vs. SAT ADs revealed a suppression of glucose and lipid metabolic process, steroid biosynthetic process, and oxidoreductase activity pathways, and an activation of pathways related to protein synthesis and humoral immune response in VAT (Figure 5E, Supplementary Table S2).
We identified two major clusters of immune cells (MAC and NKT; Figures 6A,B), which represented 9.8% of total nuclei across SAT and VAT (Figure 2D). MAC expressed markers of both macrophages (MRC1, MSR1, CD68, and CD163) and monocytes (S100A12 and CD14), while NKT had increased expression of T-cell markers, including CCL5, CD3E, CD2, CD247, and CD52, and natural killer cells, such as NKG7 and CTSW (Figure 2B). Across depots, mononuclear phagocytes (macrophages and monocytes) were the most abundant immune cell type (80% of immune cells nuclei), while lymphocytes (T-cells and NK cells) represented the remaining 20%. Comparison between AT depots showed that VAT has greater proportion of both NKT (3.0% vs. 1.3% of total nuclei) and MAC (10.8% vs. 6.1%) when compared to SAT (Figure 1G). NKT had an increased expression of CCL5, a chemokine involved in the chemotaxis of activated T cells (Murooka et al., 2008; Chan et al., 2012), with approximately 35% of NKT cells expressing CCL5 (Supplementary Table S1), suggesting a considerable level of immune cell activation. Sub-analysis of MAC identified five main macrophage subtypes (MAC1–5, Supplementary Table S10). All MAC subclusters were present across both VAT and SAT depots, except MAC3, present exclusively on SAT (Figure 6C). We also identified high expression of S100A12 and S100A8 in MAC1, suggesting these cells are monocytes or differentiating macrophages (Jaitin et al., 2019; Vijay et al., 2020; Strieder-Barboza et al., 2022). There was a significant upregulation of several complement and complement receptor genes in MAC1, such as C3, CFI, CFB, CD55, CFH, CR2, C1QC, C1QB, and C1QA among others, suggesting MAC1 cells involvement on complement activation during inflammation in AT. MAC2 were enriched for CD163, a marker for perivascular macrophage, TGFBI, MRC1/CD206, and F13A1. Both, CD206+ and F13A1+ macrophages, have been associated with AT dysfunction, pro-inflammatory responses, and AT remodeling in human obesity (Kaartinen et al., 2021; Muir et al., 2022). SAT-specific MAC3 was majorly characterized by ABL1, which’s expression regulates macrophage podosome formation, SPTBN1, ZBTB16, and ADAMTSL3. In agreement with previous studies in human AT (Jaitin et al., 2019; Vijay et al., 2020; Strieder-Barboza et al., 2022), MAC4 had a lipid associated macrophage (LAM) profile with high expression of FABP4, LPL, CD36, FASN, CD9, among other lipid related genes (Figure 6D, Supplementary Table S10). Abundance of MAC4 nuclei was increased by approximately 2-fold in VAT compared to SAT (20.3% vs. 10.6%) and SAT MAC4 had increased expression of lipogenic genes (FASN, SCD, and ACLY) when compared to VAT MAC4 (Supplementary Table S12). Finally, MAC5 showed an increased expression of genes related to lipid metabolism other than those associated with LAM, e.g., LIPE, GPAM, ADM, and LPIN1. Notably, LPIN1, has been reported as a mediator of macrophage pro-inflammatory activation and a link between lipid biosynthesis and systemic inflammatory responses (Meana et al., 2014). A general comparison of immune cell gene expression between depots (Supplementary Table S11) revealed that VAT immune cells had greater expression of complement system genes (C3, CFB, C1QA, C1QB, and C1QC), bovine major histocompatibility complex genes (BOLA-DRA and BOLA-DMR), S100 protein family genes (S100A9, S100A10, S100A11, and S100A12), and phagocytes oxidase system genes (CYBA and CYBB) compared with SAT immune cells. We observed the activation of GO pathways related to immune and inflammatory response, complement activation, phagocytosis, and leukocyte migration and mediated immunity in VAT immune cells (Figure 6E, Supplementary Table S2). In addition, bulk RNA-seq analysis revealed a greater expression of key immune response and inflammation-related DEG in VAT compared to SAT (Figure 6F; Supplementary Table S16), including CD4, CXCR6, CXCR4, IL7R, REL, RALB, and IL33. Accordingly, we observed the activation of numerous immune response and inflammation-related pathways in VAT compared to SAT (Table 1). Interestingly, several classical inflammatory genes were not differentially expressed (FDR>0.05) but were present in the transcriptome of VAT and SAT, including IL4, CSF1, CD68, CD86, CD83, CD80, IL1B, CSF1R, STAT1, RXRG, REL, IL1A, GPR85, IL6, RXRA, FOXP3, NFKB2, RELA, IL4R, RXRB, and STAT6 (Supplementary Table S15). Taken together, these results highlight a greater abundance of immune cells in VAT, which may be associated with an increased inflammatory response as these cells seem to be more immunologically activated when compared to SAT immune cells.
We identified two subtypes of endothelial cells (EC), both expressing the EC signature gene PECAM1 (Figure 6G). EC1 subtype had a pronounced expression of classic endothelial cell markers, such as VWF, CD300LG, ADAMTS9, TM4SF1, ACKR1, and TFPI (Figure 6G). Expression of FABP4 and CD36, which are expressed in AT microvascular endothelial cells and involved in endothelial fatty acid handling machinery (Briot et al., 2018), was also abundant in EC1 (Supplementary Table S1). SAT had twice as many vascular EC1 than VAT (12.5% vs. 6.0%) (Figure 1G), and an increased expression of the FOS family genes (FOSB and FOS), EGR1, ACKR, a chemokine receptor, and CD74, a cell-surface receptor for the cytokine macrophage migration inhibitory factor, which may be observed in activated endothelial cells (Naeim, 2008). In contrast, VAT EC1 had increased expression of complement genes (C3, C1QA, and CFB) and bovine major histocompatibility complex (BOLA) (Supplementary Table S13). An activation of immune response and complement activation pathways was observed in VAT EC1 when compared to SAT EC1 (Supplementary Table S13). In contrast to EC1, EC2 cells had increased expression of MMRN1, LYVE1, CCL21, and PROX1, markers of lymphatic endothelial cells. These results indicate the presence of lymphatic vasculature in AT of dairy cows, although considerably less pronounced when compared to vascular endothelial cells (0.3% vs. 10.1% of total nuclei). Lymphatic endothelial cells (EC2) expression profile was similar between depots (Supplementary Table S13). Pericytes and smooth muscle cells (PE/SMC, Figure 2A) were identified based on the expression of pericytes (NOTCH3 and PDGFRB) and smooth muscle cells marker genes (ACTA2 and MYL9) (Figures 2B,C). Across depots, PE/SMC cells were not greatly abundant in dairy cows AT (2.75% of total nuclei; Figure 2D). When contrasting depots, we observed that SAT had a greater proportion of PE/SMC than VAT (3.2% vs. 2.0% of total nuclei; Figure 1G). An overall comparison on the gene expression between VAT and SAT PE/SMC (Supplementary Table S14) showed an increased expression of C3, C1Qa, C1Qc, CFB, and LPL, while a decreased expression of FOS, FOSB, and EGR1 of VAT PE/SMC when compared to SAT PE/SMC.
In this study, we used both snRNA-seq and bulk RNA-seq to better understand the heterogeneity and depot-specific characteristics in AT of dairy cows. We have summarized the gene profile of each cell type and subtype in VAT and SAT of dairy cows revealed by our study in Graphical abstract. Part of the different cell types and depot-specificities characterized in the present study have been previously reported in mice and human models (e.g., Vijay et al., 2020; Emont et al., 2022; Strieder-Barboza et al., 2022). Compared with human AT single-cell databases, we observed that AT from dairy cows is as diverse as human SAT and VAT with similar cell types and subtypes, including numerous ASPCs, EC, and immune cells, and VAT-specific ME. These similarities open opportunities for using dairy cows as a model to study comparative human diseases that are associated with AT dysfunction. However, how the distinct cell subtypes contribute to the pathogenesis of diseases in dairy cows are yet to be studied. Both snRNA-seq and bulk RNA-seq results showed consistent upregulation of HOX genes in SAT of dairy cows. HOX genes are known as a subset of the homeobox family transcription factors that play a key role during the differentiation of a variety of mammalian tissues. In humans, HOX genes regulate in vivo and in vitro adipogenesis (Cowherd et al., 1997; Cantile et al., 2003), and have depot-specificities (Yamamoto et al., 2010; Ahn et al., 2019). PPARG modulates different HOX genes in the AT, such as HOXD4 (Kumar et al., 2021), which in our study was greatly expressed in SAT when compared to VAT (Supplementary Table S5). In ruminants, HOX and HOX-related genes play potential roles in regulating regional fat distribution in fat-tailed sheep (Kang et al., 2017). Different to our results, in which HOXA9 was greatly expressed in abdominal SAT, in fat-tailed sheep, there was a downregulation of HOXA9 expression in thoracic SAT when compared to perirenal VAT and tailhead SAT. Interestingly, while our bulk RNA-seq revealed that HOXA10 and HOXC10 expression was unique to SAT in dairy cows, in fat-tailed sheep, these two genes were expressed in all evaluated depots, but their relative expression level was greater in tailhead SAT than thoracic SAT and perirenal VAT (Kang et al., 2017). Overall, only scarce studies report potential roles of HOX genes, thus highlighting the need for further studies elucidating depot-specificities and role of HOX genes in AT function and metabolism. One of the most evident depot-differences observed in our study was the greater expression of C3 and other complement genes in VAT relative to SAT. Among the 13 clusters identified in our data, 11 had a greater expression of C3 in VAT compared to SAT. C3 was among the 5 most DEG (>Log2FC and Adj p value < 0.05) in most of these clusters. Studies reporting the roles of complement system in dairy cows’ AT are scarce (Zachut and Contreras, 2022) and C3 expression data are only available in subcutaneous AT samples (Zachut et al., 2018; Takiya et al., 2019; Salcedo-Tacuma et al., 2020). For example, Salcedo-Tacuma et al. (2020) reported upregulation of genes encoding the complement proteins C3 in postpartum dairy cows, while Zachut et al. (2018) showed an enrichment of complement pathway in AT of dairy cows that lost body weight intensively postpartum compared with cows that lost weight less intensively. These findings highlight a potential relationship between the expression of complement system genes and proteins with a pro-inflammatory, pro-lipolytic and pro-oxidative status in periparturient dairy cows. In humans, the increased expression of complement system genes, especially C3, has been correlated with metabolic dysfunction, including insulin resistance, inflammation, obesity, and diabetes (Engström et al., 2005; Barbu et al., 2015; Moreno-Navarrete and Fernández-Real, 2019). Gabrielsson et al. (2003) report a 4-fold increase in C3 mRNA levels in omental VAT compared with abdominal SAT in obese male subjects, and suggest that C3 produced by the AT, especially omental VAT, as a key contributor to the plasma pool of C3, further corroborating the role of AT in systemic metabolism and inflammatory response. During adipogenesis, adipose stem and progenitor cells (ASPC) proliferate (hyperplasia) and then accumulate lipids, increasing in size (hypertrophy). Both the increased number of adipocyte progenitors and the increase in intracellular lipids enhance AT adipogenic capacity. Hyperplastic growth is critical for a proper AT function and overall metabolic health (Ghaben and Scherer, 2019; Merrick et al., 2019). In dairy cows, a potential inability of AT to buffer excess fatty acids released into the bloodstream during the periparturient period could trigger other metabolic and inflammatory disease (Contreras et al., 2017; Contreras et al., 2018). Although adipogenesis is a central metabolic process in the AT, the definition of ASPC and its markers genes are not unanimous across literature even among research studying the same species. Moreover, the cellular hierarchy and biological mechanisms dictating ASPC differentiation are not yet completely understood (Merrick et al., 2019). The gene expression of commonly used ASPC markers by previous studies in human and mice models, e.g., DPP4, CD142, CDF, TM4SF1, and CD34, was negligible or absent in our dairy cow snRNA-seq database. In our study, we primarily identified ASPC subtypes based on the expression of PPARG, gene expressed during early ASPC differentiation, and PDGFRA, an ASPC marker used by different murine and human experiments (Merrick et al., 2019). In our study, we observed an increased abundance in overall ASPCs in SAT vs. VAT, as well as in the adipogenic-ASPC subtype, which greatly expressed genes that are upregulated during adipocyte differentiation, such as PPARG and FABP4. This profile is typical of ASPCs that are “committed preadipocytes” (e.g., Sun et al., 2020; Emont et al., 2022), meaning these cells are poised to differentiate into mature adipocytes. This contrasts with recent snRNA-seq data in human subjects with obesity (Strieder-Barboza et al., 2022), and may reflect the limited hyperplastic capacity of obese AT, which is more likely to expand through adipocyte hypertrophy (Ghaben and Scherer, 2019). Increased SAT adipogenic capacity in dairy cows might be fundamental in offsetting negative metabolic consequences of excessive concentrations of circulating free fatty acids during early postpartum of high producing dairy cows (Yung and Tak Mao, 2007; Gupta and Gupta, 2015). In contrast, defects in AT adipogenic capacity are associated to fibrosis and inflammation in human dysfunctional AT (Gyllenhammer et al., 2016). Our snRNA-seq data identified two subtypes of FAP ASPCs, which have the capability to differentiate into adipocytes or activated fibroblasts increasing ECM deposition (Contreras et al., 2021; Dohmen et al., 2022). Adipogenesis is inseparable from fibrogenesis due to closely related developmental origins of adipocytes and fibroblasts. Fibrogenesis refers to the generation of fibroblasts and their synthesis of proteins composing the ECM (Miao et al., 2016). In humans, fibrosis is defined as an excessive accumulation of ECM, such as collagens, which can result from an imbalance between excess synthesis ECM components and an impairment in degradation of these proteins. Thus, increased fibrogenesis can contribute to the development of fibrosis. Our data revealed a pro-fibrogenic/fibrotic potential of ASC3, with upregulation of fibrosis genes (collagens, FN1, DCN, FBLN1, MMP2, LUM, SPARC, and LOX) (Divoux et al., 2010). Additionally, ASPC3 demonstrated a pro-inflammatory potential with upregulation of CCL2, CXCL3, C3, C1S, and PTGS2. This profile overlaps with human VAT ASPCs which are positively correlated with insulin resistance (Vijay et al., 2020) and with mice AT fibro-inflammatory progenitors (FIPs), which also had an anti-adipogenic function (Hepler et al., 2018). Additionally, recent work reported a FAP ASPC subtype in obese human AT with greater adipogenic capacity and lipolytic responses compared with an inflammatory mesothelial-like ASPC subtype (Strieder-Barboza et al., 2022). These findings agree with previous studies identifying increased FAP ASPCs in obesity and reported a close association of FAPs with adipose tissue fibrosis (Marcelin et al., 2017) and type 2 diabetes (Vijay et al., 2020) in human obesity. Overall, these findings suggest that, as in humans and mice (Hepler et al., 2018; Min et al., 2019), bovine AT contains transcriptionally diverse ASPCs which may regulate adipogenesis, inflammation, and fibrosis in a depot-specific manner. Recently, numerous studies using different species aimed at determining the source and function of mesothelial cells (ME) in the AT, with contrasting results. Mesothelial cells derive from mesoderm, express mesenchymal features, and form a monolayer over visceral and parietal surfaces of the peritoneal, pleural, and pericardial cavities (Yung and Tak Mao, 2007; Gupta and Gupta, 2015). Unanimously, literature has demonstrated a striking depot-specific pattern in AT ME, which are VAT-specific (e.g., Chau et al., 2011; Vijay et al., 2020; Emont et al., 2022). In our study, SAT preadipocytes and adipocytes expressed mesothelial markers in vitro, but only 0.3% of SAT nuclei were annotated as ME, thus highlighting a strong depot-specificity of ME in VAT of dairy cows. Current literature is contradictory about whether mesothelial cells are true adipocyte progenitors. In humans, VAT-derived ME-like progenitors exhibited pronounced expression of omentin (ITLN1) and mesothelin (MSLN), while SAT progenitors expressed CFD (Vijay et al., 2020). Chau et al. (2014) observed that Wt1 positive cells can differentiate into adipocytes, muscle cells, and osteoblasts in mice. While Wt1 + ME are considered VAT-specific ASPCs that become adipocytes (Chau et al., 2014), Krt19 + ME did not have adipogenic capacity (Westcott et al., 2021). In our study, all ME expressed MSLN and KRT19. Even though we did not test the adipogenic capacity of ME, we observed an increased expression of CD34 and IGF2, which are known markers of ASPCs and adipocytes in WT1 + UPK3B + ME2. In fact, our flow cytometry data revealed that a proportion of MSLN+ cells correspond to the ASPC population (CD31−CD45−), while others co-express MSLN and the endothelial cell marker, CD31 (Figure 4G). Consistently, Westcott et al. (2021) report that Wt1 expression in mice AT is not exclusive to visceral adipose mesothelium, but also expressed in a population of Pdgfra + preadipocytes, which can originate adipocytes. Our snRNAseq analysis revealed an exclusive expression of WT1 in ME, although around 15% of these cells also expressed PDGFRA. A previous study (Westcott et al., 2021) reports that only WT1 + PDGFRA + cells present in the ME cluster can differentiate into adipocytes, while the WT1 + PDGFRA - fraction cannot. Recent data also describe a pro-inflammatory and low-adipogenic potential of VAT-specific mesothelial cells in human obesity (Strieder-Barboza et al., 2022). To better understand whether ME are adipocyte progenitor cells in dairy cows, ME cells should be isolated from AT and their adipogenic capacity evaluated in different in vitro, conditions, such as the use of basal medium with and without and adipogenic inducers (i.e., insulin, PPARG agonists, or thiazolidinediones). In comparison to scRNA-seq, snRNA-seq allows the sequencing of different cell types regardless of their size. When considering AT analysis, this advantage is substantial due to the large size of mature adipocytes. For that reason, studies revealing mature adipocyte diversity at a single-nuclei level are scarce and have never been previously reported for dairy cows. Adipocytes were generally considered to be monotypic and homogeneous in function (Emont et al., 2022); however, recent evidence shows otherwise (Min et al., 2019; Sárvári et al., 2021; Emont et al., 2022), including the present study. In our study, adipocyte (AD) was the most abundant cell type in both SAT and VAT. We identified four transcriptionally distinct AD subpopulations in both depots, and the analysis of their gene expression suggest similarities with different ASPC subpopulations, consistent with recent studies using snRNA-seq that identified diverse adipocyte subtypes with marked depot-specificities in human AT (Emont et al., 2022; Strieder-Barboza et al., 2022). These findings might translate into distinctive adipocytes origins and metabolic functions in AT of dairy cows. Furthermore, an overall comparison between mature adipocytes in SAT vs. VAT revealed an increased expression of adipogenic and lipogenic genes in SAT and an increased expression of lipolytic genes in VAT. In humans, VAT has been characterized for having greater lipolytic rate than SAT (Arner, 1995; Smith and Zachwieja, 1999), although SAT lipolysis contributes substantially to circulating lipid levels since it’s the body’s largest fat depot (Rydén and Arner, 2017). In dairy cows, few studies have compared SAT and VAT metabolism in different energy balance and lactation stages, possibly due to the difficulty to access VAT in comparison with SAT. However, the available literature corroborates with our findings. For example, in cows with intensive lipolysis, fatty acid profile of plasma non-esterified fatty acids (NEFA) showed remark similarity with fatty acid profile of VAT (Hostens et al., 2012), and AT mass mobilization showed to be greater in VAT than SAT (Ruda et al., 2019). Vascular endothelial cells, mononuclear phagocytes, and lymphocytes were among the different cell populations in which complement system genes (e.g., C3 and CFB) were greatly expressed in VAT than SAT. Different stimuli has been reported to induce the production of complement protein by these cell types. For example in humans, synthesis of C3 by macrophages is increased upon stimulation with acetylated low-density lipoprotein, oxidized low-density lipoprotein, IgA, or IgG immune complexes (Laufer et al., 1995; Mogilenko et al., 2012), while INF-gamma induced synthesis of different proteins of complement system in human endothelial cells (Ripoche et al., 1988). In our study, although the percentage of immune and vascular endothelial cells expressing pro-inflammatory cytokines, such as IL6 and IL1a, were considerable low (<1%), other results indicate a greater inflammatory status of VAT when compared to SAT, which could help us explain the high contrast in the expression of complement system genes between depots. We observed greater proportion of MAC in VAT than SAT. These results corroborate with studies in dairy cows, humans, and mice that revealed greater macrophage infiltration in visceral depots (Weisberg et al., 2003; Harman-Boehm et al., 2007; Akter et al., 2012). Contreras et al. (2015) observed that macrophage infiltration in the AT of dairy cows is associated with metabolic disease (hyperketonemia, increased concentration of blood NEFA, and displaced abomasum) in early postpartum dairy cows; authors reported a significantly higher number of SVF cells expressing macrophage-specific cell surface markers in omental compared with subcutaneous AT (Contreras et al., 2015). We observed leukocyte activation and migration, immune and inflammatory response, phagocytosis, among others biological pathways in VAT immune cells when compared to SAT. Bulk-RNAseq analysis confirmed the upregulation of immune response and pro-inflammatory regulators in VAT vs. SAT and the activation of pathways associated with increased inflammation underlined by T-lymphocytes, NK cells, and macrophages (Table 1), consistent with our snRNAseq results. Our findings are also in line with previous studies in different species reporting enhanced inflammation in VAT. In non-pregnant non-lactating Holstein dairy cows, AT transcript profiles showed that in comparison with SAT, mesenteric VAT had an increased pro-inflammatory response (Moisá et al., 2017). Similarly, in human subjects, VAT has greater pro-inflammatory characteristics, presenting the double proportion of pro-inflammatory macrophages when compared to SAT (Kralova Lesna et al., 2016). Further analysis of MAC revealed five individual subpopulations with different expression profiles and possible different functions in the AT. For example, we identified a MAC subpopulation (MAC4) enriched with lipid metabolism genes such as LIPA, LPL, CD36, FABP4, CD9, LGALS1, and LGALS3 (e.g., Jaitin et al., 2019; Vijay et al., 2020). Jaitin et al. (2019) observed that a population of macrophages expressing lipid-associated genes arises during obesity and are named lipid-associated macrophages (LAMs). Interestingly, LAMs have important function in metabolic homeostasis in a TREM2-dependent manner, buffering the excess lipids accumulated during the development of obesity (Chen et al., 2021). In contrast, loss of TREM2 seems to prevent LAM formation causing adipocyte hypertrophy, weight gain, and insulin resistance (Jaitin et al., 2019; Worthmann and Heeren, 2020). In the present study, however, only around 8% of MAC4 cells expressed TREM2. Based on these previous reports, we speculate that 1) the majority of MAC4 cells in our study do not present the effects on metabolic homeostasis as the ones characterized in human and mice, or 2) factors other than TREM2 may affect the function of lipid-associated macrophages in dairy cows, that greatly expressed the genes necessary to excessive lipids handling regardless of TREM2 expression level. Higher presence of MAC4 in VAT of dairy cows might also indicate a greater necessity of fatty acid buffering in the visceral AT. Overall, we emphasize that further studies are necessary to elucidate the specifics of these immune cell subpopulations, especially in determining their pro or anti-inflammatory phenotypes and their role in AT dysfunction. In addition to MAC, the most prevalent immune cells observed in our study, we also characterized a less abundant immune population of NKT cells. In agreement with our experiment, other studies using snRNA-seq have reported the presence of natural killer and T cells in the AT of humans and mice in lower proportions when compared to MAC (Emont et al., 2022; Strieder-Barboza et al., 2022) Although these natural killer and T cells represented the rarest cell type in the AT of dairy cows and their characterization in subtypes was not feasible in our database, their presence in the tissue is particular of notice. NK and T cells are lymphoid cells, and recently have been gaining notoriety due to their important regulatory role in the AT. Through the production of cytokines and influencing macrophages polarization, distinct populations of these immune cells can either improve metabolic homeostasis or contribute with metabolic disorders (Vivier et al., 2008; Lee et al., 2016; Wang and Wu, 2018; Ferno et al., 2020). Endothelial cells represented around 10% of all cells in the AT of dairy cows. Further analysis revealed distinct gene expression and the presence of two different populations: vascular and lymphatic EC. Vascular EC was predominant in abundance compared to lymphatic EC. Interestingly, vascular EC expressed genes involved in lipid metabolism (e.g., FABP4 and CD36), which might corroborate with studies suggesting vascular endothelium can originate mature adipocytes (Tran et al., 2012; Min et al., 2016). However, further studies are necessary to demonstrate this hypothesis in dairy cows’ AT. Studies also suggest that ECs play essential role in the maintenance of fatty acid fluxes and inflammatory response in the AT (Briot et al., 2018). Lymphatic ECs have been recently shown as an important link between lymphatic vessels and AT, with a bilateral relationship between lymphatic dysfunction and occurrence of obesity and fat accumulation (Escobedo and Oliver, 2017). In our study, lymphatic EC represented only 0.3% of total nuclei and differences between depots were not observed. However, it is interesting to highlight that lymphatic EC were present in both SAT and VAT, in contrast to a study by Vijay et al. (2020), in which authors revealed a strong visceral depot-specificity. Limitations of our study include limited batch size, unknown health status of sampled animals, and the relative low number of total nuclei sequenced compared with previous studies in mice and human models. Moreover, absence of similar studies in bovine or other ruminant species makes it particularly challenging to correlate some of our findings. Adipose tissue analysis at a single-cell and single-nuclei level allows targeted gene expression changes within specific cell populations, lineage dynamics, and mechanisms governing the development and function of adipocytes in a depot-dependent manner. In summary, for the first time, we demonstrated depot-specific heterogeneity at a single-nuclei level in VAT and SAT of dairy cows. Our data suggest that revealing transcriptionally and functionally distinct depot-specific cell types is a promising step towards elucidating mechanisms linking AT dysfunction and the occurrence of metabolic diseases in dairy cows, which could then guide us to define targeted approaches to prevent their occurrence at a farm level. | true | true | true |
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PMC9616528 | 35796629 | Dinum Herath,Charlotte Voogd,Matthew Mayo‐Smith,Bo Yang,Andrew C. Allan,Joanna Putterill,Erika Varkonyi‐Gasic | CRISPR‐Cas9 ‐mediated mutagenesis of kiwifruit BFT genes results in an evergrowing but not early flowering phenotype Dinum Herath et al. | 26-07-2022 | Actinidia chinensis,kiwifruit,CRISPR‐Cas9,BFT,dormancy,flowering | Summary Phosphatidylethanolamine‐binding protein (PEBP) genes regulate flowering and architecture in many plant species. Here, we study kiwifruit (Actinidia chinensis, Ac) PEBP genes with homology to BROTHER OF FT AND TFL1 (BFT). CRISPR‐Cas9 was used to target AcBFT genes in wild‐type and fast‐flowering kiwifruit backgrounds. The editing construct was designed to preferentially target AcBFT2, whose expression is elevated in dormant buds. Acbft lines displayed an evergrowing phenotype and increased branching, while control plants established winter dormancy. The evergrowing phenotype, encompassing delayed budset and advanced budbreak after defoliation, was identified in multiple independent lines with edits in both alleles of AcBFT2. RNA‐seq analyses conducted using buds from gene‐edited and control lines indicated that Acbft evergrowing plants had a transcriptome similar to that of actively growing wild‐type plants, rather than dormant controls. Mutations in both alleles of AcBFT2 did not promote flowering in wild‐type or affect flowering time, morphology and fertility in fast‐flowering transgenic kiwifruit. In summary, editing of AcBFT2 has the potential to reduce plant dormancy with no adverse effect on flowering, giving rise to cultivars better suited for a changing climate. | CRISPR‐Cas9 ‐mediated mutagenesis of kiwifruit BFT genes results in an evergrowing but not early flowering phenotype Dinum Herath et al.
Phosphatidylethanolamine‐binding protein (PEBP) genes regulate flowering and architecture in many plant species. Here, we study kiwifruit (Actinidia chinensis, Ac) PEBP genes with homology to BROTHER OF FT AND TFL1 (BFT). CRISPR‐Cas9 was used to target AcBFT genes in wild‐type and fast‐flowering kiwifruit backgrounds. The editing construct was designed to preferentially target AcBFT2, whose expression is elevated in dormant buds. Acbft lines displayed an evergrowing phenotype and increased branching, while control plants established winter dormancy. The evergrowing phenotype, encompassing delayed budset and advanced budbreak after defoliation, was identified in multiple independent lines with edits in both alleles of AcBFT2. RNA‐seq analyses conducted using buds from gene‐edited and control lines indicated that Acbft evergrowing plants had a transcriptome similar to that of actively growing wild‐type plants, rather than dormant controls. Mutations in both alleles of AcBFT2 did not promote flowering in wild‐type or affect flowering time, morphology and fertility in fast‐flowering transgenic kiwifruit. In summary, editing of AcBFT2 has the potential to reduce plant dormancy with no adverse effect on flowering, giving rise to cultivars better suited for a changing climate.
Abundant and synchronous budbreak and flowering in spring are critical for the productivity of many important temperate fruit crops including the woody perennial vine, Actinidia chinensis (kiwifruit). Temperate trees and vines typically undergo short day‐induced growth cessation and leaf drop in autumn. This is followed by dormancy, a bud‐protective period during winter where no visible growth occurs until a prolonged exposure to cold reactivates the ability to grow. These processes are accompanied by major metabolic, physiological and molecular changes (reviewed in Beauvieux et al., 2018; Brunner et al., 2017; Ding and Nilsson, 2016; Liu and Sherif, 2019; Maurya and Bhalerao, 2017; Singh et al., 2017; Yang et al., 2021). Having a sufficient period of winter chilling to break dormancy is a key environmental determinant of the degree of budbreak, flowering and fruit set in spring. Thus, temperate fruit productivity may be impeded in growing areas with warm winters and by ongoing global warming, owing to insufficient winter chilling (Atkinson et al., 2013; Luedeling, 2012). Kiwifruit growers have identified warmer temperatures during the winter period as their most pressing climatic risk (Cradock‐Henry, 2017), which is currently overcome by application of undesirable dormancy‐breaking chemicals (Ziosi et al., 2015). One of the solutions is the development, selection and breeding of cultivars with improved performance, including low‐chill‐requiring varieties. Thus, our research has focused on investigating the molecular‐genetic regulation of kiwifruit phenology with a particular focus on seasonally controlled cycles of growth and dormancy. Research in this global crop is supported by powerful genomic tools and the ability to stably transform kiwifruit, combined with mutagenesis using CRISPR‐Cas9 gene editing (Brian et al., 2021; Pilkington et al., 2018; Varkonyi‐Gasic et al., 2019). Using transgenic overexpression and more recently gene editing, we were able to show that kiwifruit homologues of MADS‐box genes SHORT VEGETATIVE PHASE (SVP), a SUPPRESSOR OF OVEREXPRESSION OF CONSTANS1 (SOC1) gene AcSOC1i and a FLOWERING LOCUS C‐LIKE (AcFLCL) all have roles in different aspects of kiwifruit seasonal phenology (Voogd et al., 2015, 2021; Wu et al., 2012, 2017, 2018, 2019). Members of the phosphatidylethanolamine‐binding proteins (PEBP) gene family are present in all eukaryotes and are central to plant development and physiology. The PEBP proteins include homologues of the well‐described Arabidopsis thaliana (Arabidopsis) FLOWERING LOCUS T (FT), encoding FT that functions as a florigen and TERMINAL FLOWER1 (TFL1) and CENTRORADIALIS (CEN), acting to repress flowering and maintain meristem indeterminacy (reviewed in Jin et al., 2021; Périlleux et al., 2019; Putterill and Varkonyi‐Gasic, 2016). Previously, we have reported key roles for kiwifruit FT and CEN genes in controlling reproductive maturity and determinacy (Moss et al., 2018; Varkonyi‐Gasic et al., 2013, 2019; Voogd et al., 2017). These are members of the 13 gene kiwifruit PEBP gene family, of which three are FT genes (AcFT, AcFT1 and AcFT2). Overexpression of any of the kiwifruit FT genes resulted in extremely early flowering of the regenerating tissue in vitro, indicating that all have the biochemical ability to promote flowering. Of the three, AcFT transcript abundance positively correlates with winter chilling and expression in spring, indicating a likely link of AcFT with budbreak and flowering. Two CEN genes, AcCEN and AcCEN4, were the most highly expressed of the five CEN genes in axillary buds during active growth. Mutation of these two genes by CRISPR‐Cas9 gene editing led to very early reproductive maturation (Varkonyi‐Gasic et al., 2019), increased determinacy and continuous flowering in kiwifruit, indicating that these CEN genes probably promote vegetative development and indeterminacy and repress flowering, as observed in other plants. Compact, rapid‐cycling plants produced by CRISPR‐Cas9 gene editing of one or both of these genes has allowed generation of model kiwifruit plants, useful for the analysis of gene function (Akagi et al., 2019; Varkonyi‐Gasic et al., 2021). The kiwifruit PEBP family also contains three BROTHER OF FT AND TFL1 (BFT) genes whose predicted proteins form a separate subclade within the CEN/TFL1 lineage (Voogd et al., 2017). However, the specific functional roles of BFT genes are much less well understood in kiwifruit and in other plants. In Arabidopsis, BFT shows a similar floral repressive activity to TFL1/CEN‐like genes when constitutively expressed, delaying terminal flower formation and repressing axillary inflorescence development when overexpressed, but without the ability to complement the terminal flower phenotype of tfl1 mutants (Chung et al., 2010; Yoo et al., 2010). The absence of BFT did not affect flowering time; however, increased numbers of secondary inflorescences when bft mutation was combined with tfl1 suggested a role in inflorescence meristem development (Yoo et al., 2010). Furthermore, a triple tfl1 atc bft mutant showed the latest flowering amongst a comprehensive set of mutants of the PEBP gene family in Arabidopsis (Kim et al., 2013). BFT was induced by stress while flowering of the bft mutant was insensitive to stress such as high salinity, suggesting that BFT delayed flowering and axillary inflorescence development under stress conditions (Chung et al., 2010; Ryu et al., 2011, 2014) by competing with FT for binding to the FD transcription factor (Ryu et al., 2014). BFT homologues in woody perennial plants have also been associated, via their expression patterns, with repression of axillary bud or shoot growth (Carmona et al., 2007; Foster et al., 2013; Voogd et al., 2017), suggesting a conserved role in regulation of axillary meristem activity. In kiwifruit, amongst the three AcBFT genes, qRT‐PCR analysis indicated that AcBFT2 had a striking expression pattern with elevated transcript accumulation during winter in an axillary bud time‐course, peaking at the time of leaf drop (Voogd et al., 2017). This pattern of expression suggested that it may represent a candidate for altering kiwifruit dormancy and chilling requirement. Overexpression of the AcBFT genes, particularly AcBFT2, in Arabidopsis led to delayed flowering and reduced determinacy, suggesting a role in regulation of flowering (Voogd et al., 2017). Here, we present the results of CRISPR‐Cas9 gene editing of BFT genes in kiwifruit. These experiments produced plants displaying an evergrowing phenotype because of delayed onset of growth cessation and leaf drop and earlier budbreak compared with the controls. The plants also produced more branches. These phenotypes were associated with bi‐allelic mutations in AcBFT2. Major changes to the global transcriptome of axillary buds at both the onset of cessation of growth in autumn and at the time of budbreak in the Acbft mutants were observed compared with control buds. However, unlike mutations in the related AcCEN genes, reproductive maturity was not affected in the transgenic plants because they did not flower over 3 years and the plants were not more compact than controls. Furthermore, generating a double Acbft cen4 mutant by gene editing in the rapid flowering Accen4 mutant background indicated that the Acbft mutation did not appear to interfere with the timing of flowering or flower and fruit production in the cen4 background. An implication of this work is that kiwifruit BFT regulates bud dormancy. Thus, we provide tools for increasing understanding of the fundamentals of dormancy control and perhaps ultimately increasing productivity in warm climates with low winter chilling.
The kiwifruit PEBP gene family consists of 13 genes, encoding predicted proteins that separate into four lineages (Voogd et al., 2017) (Figures 1a, S1). Among the three AcBFT proteins, AcBFT2 and AcBFT3 are the most similar (89.6% identical), while they are 87.3% and 81.5% identical to AcBFT1. The three proteins (AcBFT1, AcBFT2 and AcBFT3) are in turn 71.3%, 68.4% and 67.2% identical to Arabidopsis BFT. In our previously reported RNA‐seq datasets (Brian et al., 2021; Voogd et al., 2021), AcBFT1 showed very low levels of expression in all tissues, including axillary buds collected each month from field‐grown vines over a yearly time course (Figure 1b). In contrast, AcBFT2 transcripts accumulated in canes and shoots, and showed the highest levels in axillary buds (Figure 1b). AcBFT2 transcript levels in axillary buds increased with growth cessation, peaked at the time of leaf drop and establishment of bud dormancy, and then declined with resumption of growth, as previously observed (Voogd et al., 2017). AcBFT2 expression rapidly increased in axillary buds on excised canes exposed to an extended period of cold (Figure 1b). While AcBFT3 shared a similar pattern of expression to AcBFT2 in the axillary bud time course and in excised canes exposed to cold, transcript levels were at considerably lower abundance (transcript accumulated to ~200 fold lower levels than those of AcBFT2). Amongst the different kiwifruit tissues tested, AcBFT3 transcripts showed the greatest abundance in mature (source) leaves (Figure 1b). To further investigate AcBFT2 tissue‐specific expression, a transcriptional fusion of the 2.5‐kb fragment upstream from the AcBFT2 translation start site with the reporter gene uidA (GUS) was introduced into Arabidopsis. GUS expression was detected in the vascular tissue of cotyledons and rosette leaves, but no staining was seen in cauline leaves, inflorescence stem or flowers. GUS staining was also detected in the stem vasculature and axillary buds of mature transgenic Arabidopsis (Figure 1c), demonstrating expression was confined to vascular tissue and buds, as previously shown for kiwifruit FT genes (Moss et al., 2018) and partly resembling the profile described for Arabidopsis BFT promoter fusion with GUS (Ryu et al., 2011). Like AcFT, AcBFT2 protein had the ability to interact with the kiwifruit FD protein, as detected by yeast two‐hybrid assays (Figure S2).
The AcBFT2 transcript showed high expression with a peak abundance in axillary buds that correlated with growth cessation and winter dormancy. To further study the role of AcBFT genes, particularly AcBFT2, CRISPR‐Cas9‐mediated gene editing was performed. We aimed to induce bi‐allelic editing of AcBFT2, while also potentially targeting AcBFT3 (Figure 2a). Because of its nearly undetectable expression, we predicted that AcBFT1 was unlikely to be functional, so it was not specifically targeted in this study (Figure 2b). Four guide sequences were designed based on the available published A. chinensis BFT sequences genome (Pilkington et al., 2018) (Figure 2b). SgRNA1 and sgRNA2 targeted exon 1 of AcBFT2 specifically, while sgRNA3 and sgRNA4 targeted exons 2 and 4, respectively, in both AcBFT2 and AcBFT3 (Figure 2b). These were used to generate a polycistronic tRNA‐sgRNA construct, which was introduced into ‘Hort16A’ kiwifruit. A total of 88 independent T0 transgenic lines carrying the AcBFT editing construct were produced and transferred to the glasshouse, in parallel with 10 control lines. Genotyping with AcBFT2 allele‐specific primers confirmed bi‐allelic edits in AcBFT2 in 23 lines and mutations affecting one of the AcBFT2 alleles in 13 lines (Table 1). Only wild‐type AcBFT2 sequences were identified in the remaining 52 lines, and these were maintained as non‐edited controls. Most of the mutations corresponded to sgRNA4 targeting exon 4, followed by sgRNA3 and sgRNA2. No mutations were identified in the sequence targeted by sgRNA1 in any of the lines. The mutations ranged from single nucleotide deletions to larger (>50 bp) deletions, or single nucleotide insertions occurring at different frequencies (Figure S3). Thirteen lines were subjected to further genotyping for edits in AcBFT3. Additional edits of one or both alleles of AcBFT3 were identified in lines with bi‐allelic AcBFT2 mutations. Two analysed lines had edits in a single AcBFT2 and both AcBFT3 alleles. However, lines with wild‐type AcBFT2 sequences also had no edits in AcBFT3 alleles, except one line with edits in only one of the AcBFT3 alleles (Table 1). Therefore, as none of the transgenic lines we analysed carried bi‐allelic mutations specific to AcBFT2 or AcBFT3 only, we refer to them as Acbft mutants.
Gene‐edited Acbft and control lines grown in the glasshouse were monitored over 3 years (2019, 2020 and 2021). Each year, autumn leaf senescence was followed by substantial natural abscission and budset in control lines, with most leaves dropping and all buds displaying a dormant appearance in winter (Figure 3a). Strikingly, much less growth cessation and senescence were seen in the Acbft lines with bi‐allelic edits in AcBFT2. While some buds appeared dormant, shoots with mostly green leaves, lack of budset and sporadic shoot emergence from distal buds were observed, producing an evergrowing phenotype (Figure 3b–d). Phenotypes of lines with edits in only one AcBFT2 allele were comparable to controls (Table 1). Therefore, the loss of AcBFT2 function caused by bi‐allelic edits probably underpins the delay in growth cessation, onset of leaf senescence and budset in these Acbft lines. To further evaluate the role of AcBFT genes in regulation of budbreak in kiwifruit, Acbft, control and non‐edited plants were pruned each year in June to a similar size and defoliated. After defoliation, buds on all plants appeared dormant, but visible growth quickly resumed in the Acbft lines with bi‐allelic AcBFT2 edits (Figures 3e–g, S4, Table 1). Within a period of 4 weeks from defoliation (in July), scoring of distal buds across edited and control lines identified shoot outgrowth and very few dormant buds in edited lines (Figure 3e,f). In contrast, the appearance of controls and non‐edited lines was mostly dormant, with little visible budbreak (Figure 3e,f) and no or little growth observed after an additional period of 4 weeks, in August (Figures 3g, S4). Budbreak comparable to that in controls was also found in lines with mutations in a single AcBFT2 allele, in spite of additional edits in AcBFT3 (Figure S4, Table 1).
The general appearance of Acbft gene‐edited lines also suggested an increase in branching, with more axillary shoots observed than in controls. Upon transfer to the soil, the control lines displayed the usual kiwifruit growth pattern, with a rapidly growing single main shoot. In edited lines, concomitant development of two or three shoots was seen (Figure 4a,b) and unpruned plants commonly had multiple branches by the first year's winter (Figure 4c–f). However, Acbft plants were no more compact than controls and none of the Acbft lines flowered over three seasons (2019–2022), indicating that Acbft mutations do not accelerate reproductive maturity, unlike mutations in AcCEN/AcCEN4 (Varkonyi‐Gasic et al., 2019). To further investigate any effects on flowering, editing of AcBFT2 was also carried out in the fast‐flowering kiwifruit cen4 mutant (U6‐CEN4#7). Six cen4 plants were identified that had bi‐allelic edits in the AcBFT2 gene and four with mono‐allelic mutations (Table 2). Additional branching was again observed in the double mutant Acbft cen4 lines relative to the cen4 mutant (Figure 4g,h). However, mutation of Acbft2 in the early‐flowering cen4 background did not have an effect on flowering time. A terminal flower was produced after 11–12 leaves in the cen4 lines and in the double Acbft cen4 mutant, indicating no additive effects of the Acbft2 mutation (Figure 4i). Initially, a small reduction in the number of axillary flowers on the main shoot was seen in the double mutant lines (Figure 4j); however, all shoots were floral (Figure 4k,l, Table 2). The morphology of the flowers was not affected and pollination resulted in the development of fertile fruit (Figure S5).
To investigate the molecular mechanisms of AcBFT action, RNA‐seq transcriptome analysis of buds collected from Acbft and control lines at two time points (May and July, 2020) was performed (Table S1). Three Acbft lines with bi‐allelic AcBFT2 edits were chosen and compared with controls, using buds with similar visible morphology (Figure 5a). Analysis of bud transcriptomes collected in autumn (May), identified 894 differentially expressed genes (DEGs) with the minimum of twofold change in expression (DESeq, adjusted P‐value < 0.05, FPKM >1) between Acbft and control lines (Table S2). Of those, 628 and 266 were up‐ and down‐regulated, respectively, in Acbft lines in May (Figure 5b). A subset of these DEGs remained up‐ and down‐regulated in July (304 and 85 respectively), with a further 2599 genes showing increased and 494 genes showing decreased expression in July, with at least twofold change in expression (Figure 5b, Table S3). Hierarchical clustering demonstrated a clear separation between the Acbft and control lines (Figure 5c,d). Comparison with transcriptomes of buds across phenological stages collected from field‐grown plants (Brian et al., 2021; Voogd et al., 2021) grouped the Acbft bud DEGs in May with wild‐type samples from earlier in the year (April), while as expected a correlation with winter dormant buds (May–June) was seen for the controls (Figure 5c). The apparent difference in the dormancy status between the buds was further supported by the predicted function of DEGs. GO‐term categories related to abscisic acid (ABA) and gibberellic acid (GA) metabolism and signalling, auxin polar transport, growth and morphogenesis and vascular transport were enriched amongst up genes in Acbft buds in May (Figure S6a). Genes down in Acbft displayed enrichment in stress‐related categories (Figure S6a). For the July‐sample DEGs, the control was placed within the group with little active growth, while a large proportion of Acbft DEGs were highly up‐regulated compared with all other samples (Figure 5d). Enrichment in DNA replication, cell cycle and cell division‐related categories corresponding to resumption of active growth was seen in DEGs up‐regulated in Acbft buds in July (Figure S6b). In contrast, temperature, osmotic and other stress response categories were enriched amongst the 579 genes down‐regulated in Acbft relative to controls (Figure S6b). The subsets of genes consistently up‐ or down‐regulated in Acbft mutants, regardless of sampling date (Figure 5b, Table S4) were subjected to further analysis. They represent genes likely to be regulated by the presence of AcBFT in buds. There were 304 and 85 genes that were always up‐ or down‐regulated, respectively, in Acbft lines compared with controls (Figure 5b). As expected, AcBFT2 itself was one of these down‐regulated genes in Acbft mutants, but not AcBFT1 and AcBFT3. The set of 85 genes with low expression in the absence of AcBFT contained genes encoding for temperature, oxygen and other stress‐response proteins such as catalases, peroxidases, heat shock proteins and enzymes producing osmoprotectants and transporters (Table S4). Genes encoding transcription factors and hormone response genes were also identified, including a MADS‐box gene with homology to AGL16, AP2/ERF family transcription factors, and two bZIP HY5 homologues (Table S4). Examination of these down‐regulated transcription factors in Acbft in yearly expression profiles of field‐grown wild‐type buds across phenological stages (Brian et al., 2021; Voogd et al., 2021) indicated that some of them showed a pattern similar to the expression to AcBFT2, consistent with them being able to mediate AcBFT function, by interacting with AcBFT, or acting upstream or downstream of AcBFT itself (Figure 5e). Furthermore, DEGs whose expression increased in the absence of AcBFT2 were considered as possible targets of AcBFT‐mediated repression. Amongst the 304 genes that were always up‐regulated in Acbft mutants regardless of the sampling date (Figure 5b), there were multiple CYCLIN and EXPANSIN homologues, possibly marking cell divisions and expansion, and genes encoding transcription factors implicated in different aspects of differentiation and development, for example WOX and TCP, and with growth or flowering, such as SPL, CYCLING DOF FACTORS and FRUITFUL/AGL8 homologues (Table S4), possibly contributing to the evergrowing phenotype. Many of these genes showed a pattern opposite to AcBFT2 in wild‐type buds sampled across phenological stages, with no or low expression during the dormancy period (Figure 5e). None of the three kiwifruit FT genes was identified as differentially expressed, and very low expression (<1 FPKMs) was detected in all the samples (Table S1). Similarly, no or very low expression (FPKM <5) was detected for kiwifruit PEBP genes other than AcBFT2 (FPKM >40). AcCEN was identified in Acbft buds in July, while AcBFT1 and AcCEN2 could be detected in May in Acbft and control buds respectively (Tables S2‐S4). Elevated AcFD (Acc05237) in May compared with July was detected in both edited and control lines, while AcFD expression declined in kiwifruit buds during dormancy and cold treatment (Figure S7).
Members of the PEBP gene family are present in all eukaryotes and are central to plant development and physiology. Yet, in contrast to the pivotal roles FT and TFL1 genes play in Arabidopsis architecture and flowering, BFT appears to have a relatively minor role in contributing to flowering time control and to regulation of Arabidopsis axillary inflorescence development under stress conditions (Chung et al., 2010; Ryu et al., 2011; Yoo et al., 2010), through competition with FT for binding to FD (Ryu et al., 2014). The Actinidia PEBP family contains multiple members, arising from recent whole‐genome duplication events (Huang et al., 2013), with the BFT lineage consisting of three genes. AcBFT2 and AcBFT3 have high similarity in sequence, but differential expression in stem and leaf tissues and distinctively high accumulation of AcBFT2 transcript in dormant buds. In contrast, AcBFT1 is more divergent and is barely expressed. The three AcBFT proteins are predicted to have one or both the conserved His and Asp residues responsible for floral repressor activity of TFL1 (Ahn et al., 2006; Hanzawa et al., 2005). Indeed, a delay in bolting combined with changed inflorescence architecture was demonstrated upon expression of AcBFT genes in Arabidopsis, implicating the AcBFT family in repression of flowering in a similar manner to AcCEN and AcCEN4 (Voogd et al., 2017). In particular, a compact, rapid‐cycling kiwifruit produced by gene editing of the CEN genes (AcCEN and AcCEN4) demonstrated the role of these genes as central repressors of the onset of the reproductive phase. However, we show here that AcBFT genes are not likely to regulate flowering in kiwifruit. In contrast to the short juvenility, compact size and continuous flowering of cen or cen4 mutants (Varkonyi‐Gasic et al., 2019), the loss of AcBFT did not affect reproductive maturity and plant size in gene‐edited kiwifruit plants. Furthermore, editing of AcBFT in the rapid flowering cen4 background did not impact flowering time, similarly to the bft tfl1 double mutant, but unlike the tfl atc bft triple mutant in Arabidopsis (Kim et al., 2013; Yoo et al., 2010), or the faster flowering double cen4 cen kiwifruit mutant (Varkonyi‐Gasic et al., 2019). A minor reduction in flower numbers was observed on the main shoot, which could be interpreted as a competition for resources in flower vs axillary shoot development. This finding was comparable to the accelerated termination of primary inflorescence (manifested through reduction in solitary flower numbers) and precocious development of axillary inflorescences in the absence of BFT function in Arabidopsis (Yoo et al., 2010). However, all shoots of double mutants were floral and there were no adverse effects on fruit development or fertility.
In temperate perennial trees and vines such as kiwifruit, the timing of dormancy onset and release is highly regulated to enable survival of unfavourable conditions and maximize production in the following growing season. Autumn conditions initiate growth cessation, preparing plants for the harsh winter conditions. In turn, winter chilling accumulation is required to break dormancy and initiate flowering (Lionakis and Schwabe, 1984a,b). Warming winter temperatures pose a large risk to kiwifruit productivity. Understanding the molecular regulation of kiwifruit phenology, with a particular focus on seasonally controlled cycles of growth and dormancy, is essential for development and selection of low‐chill varieties. Here, editing of AcBFT demonstrated an important role for these genes in promoting the onset of dormancy. This is consistent with recent findings suggesting that PEBP proteins integrate various environmental conditions with internal sink and source transitions, to initiate flowering (Andrés et al., 2020) or outgrowth of different organs, for example, underground storage organs (Abelenda et al., 2019) that enable survival and ultimately, reproduction. AcBFT performs a similar pivotal role as a regulator of kiwifruit bud phenology, thus controlling its temperate perennial life history and reproduction strategy. Because of the high sequence similarity between AcBFT genes and alleles, we were unable to design gene‐specific editing guides. Lines with edits in both alleles of AcBFT2 demonstrated an evergrowing phenotype, suggesting a role for AcBFT2 in seasonal growth cessation and establishment of winter dormancy, consistent with AcBFT2 accumulation in dormant buds (Voogd et al., 2017). However, lines with bi‐allelic mutations in AcBFT2 locus contained additional edits in AcBFT3. Therefore, at this stage, it remains unclear if bi‐allelic editing of AcBFT2 only is sufficient for the evergrowing phenotype. We were also unable to fully exclude possible edits in AcBFT1. Despite the very low expression in various tissues, a possibility exists that AcBFT1 is regulated in a highly specific temporal manner and accumulates in very specific cells, contributing to the regulation of growth and dormancy cycles. Therefore, edits in both alleles in AcBFT2, with the possible contribution of mutations in other AcBFT loci, cause continual growth in autumn and promote budbreak upon defoliation in winter. Nevertheless, evidence from two lines demonstrated that bi‐allelic mutations in AcBFT3, even in a combination with a mutation in one of the AcBFT2 alleles, are insufficient for continual shoot growth in winter. Taken together and combined with elevated AcBFT2 but not AcBFT1 and AcBFT3 in dormant buds, we conclude that AcBFT2 acts to repress growth in dormancy‐inducing conditions. This is comparable to the BFT role in axillary inflorescence development under stress conditions in Arabidopsis (Chung et al., 2010; Ryu et al., 2011, 2014) and consistent with findings that PEBP genes regulate seasonal growth in woody perennials (Böhlenius et al., 2006; Hsu et al., 2011; Miskolczi et al., 2019; Mohamed et al., 2010; Rinne et al., 2011). Some BFT‐like genes from woody perennial plants have also been clearly associated with expression in buds and stems, resembling AcBFT2. For example, the grape BFT‐like VvTFL1c transcript accumulates during latent bud development (Carmona et al., 2007). Expression in stems was also reported in apple, with two BFT transcripts highly enriched in dwarfing rootstocks (Foster et al., 2013), implicating BFT in reduced scion vigour accompanied with precocity. Recently, expression of BFT was detected in Arabidopsis leaf, correlating with bolting and onset of senescence (Hinckley and Brusslan, 2020), which may be similar to our observation of elevated AcBFT3 in mature, source leaves, as well as GUS profiles obtained with AcBFT2 promoter in Arabidopsis. Accumulation in stem and leaf combined with expression in vascular tissue brings about the possibility that BFT genes may exert their function outside the domains of their expression, either through activation of a mobile signal, inactivation of another mobile signal, or translocation of their own protein. Non‐cell autonomous activity over long distances has been reported for FT florigen (Corbesier et al., 2007; Lifschitz et al., 2006; Lin et al., 2007) and more recently in regulation of vegetative phenology in poplar (Miskolczi et al., 2019), while TFL1 moves within cell layers of the shoot apical meristem (Conti and Bradley, 2007; Goretti et al., 2020), but future work will be needed to address if either of these scenarios applies to AcBFT. The mechanism of AcBFT action may include an interaction with an FD transcription factor AcFD; however, it remains unclear if kiwifruit BFT proteins exert their function through competing with other PEBP proteins. For example none of the FT genes was expressed in the buds, and FT expression in juvenile plants is very low (Varkonyi‐Gasic et al., 2013), but this may not reflect FT protein activity. Similarly, AcFD is expressed during active growth and down‐regulated in dormant buds when AcBFT2 accumulation peaks. It is possible that the accumulation and removal of BFT activity may be sufficient to regulate bud phenology in kiwifruit independently of FT. This may be similar to kiwifruit CEN genes, whose removal by CRISPR‐Cas9 editing was sufficient to induce precocious flowering (Varkonyi‐Gasic et al., 2019). Loss of AcBFT and the resulting evergrowing phenotype were accompanied by down‐regulation of homologues of transcription factors such as AP2/ERFs, acting as key participants in stress and hormone responses (reviewed in Xie et al., 2019), HY5 with many roles in plant growth and development (reviewed in Gangappa and Botto, 2016) and AGL16, which is a negative regulator of salt stress response and a flowering repressor, interacting with SVP and FLC to down‐regulate FT (Szaker et al., 2019; Zhao et al., 2021). In contrast, expression opposite to that of AcBFT2 was shown for multiple regulatory genes and transcription factors implicated in growth. The predicted function of these genes, combined with an expression similar or opposite to that of AcBFT2, is consistent with roles in the dormancy regulatory networks in which AcBFT plays a central role, as demonstrated by mutagenesis in this study. In summary, editing of kiwifruit BFT results in an evergrowing habit and considerably reduces the chilling requirement for spring budbreak, providing the opportunity to grow kiwifruit in regions with insufficient chilling without the application of dormancy‐breaking chemicals. The generated lines provide a tool to understand the molecular regulation of seasonal growth and dormancy cycles in kiwifruit. Furthermore, gene editing of BFT genes could accelerate the development of new low‐chill varieties across the Actinidia genus.
Stock plants of Actinidia chinensis (kiwifruit) Planch. var. chinensis ‘Hort16A’ and a fast‐flowering cen4‐edited ‘Hort16A’ line, U6‐CEN4#7 (Varkonyi‐Gasic et al., 2019) used in transformation experiments, were obtained from in vitro collections held at Plant & Food Research, Auckland, New Zealand. For kiwifruit gene expression graphs, the samples and RNA‐seq analysis have been described previously (Brian et al., 2021; Voogd et al., 2021). The RNA‐seq of Acbft mutants and control plants was performed with bud material collected from 2‐year‐old transgenic plants in winter (May and July 2020). Arabidopsis Col‐0 was used for transformation of the promoter fusion construct.
The construct for CRISPR‐Cas9‐mediated mutagenesis was designed to contain a polycistronic tRNA‐sgRNA cassette with four sgRNA sequences, placed under the control of the Arabidopsis U6‐26 promoter. Suitable targets within the AcBFT2 locus were identified using the Geneious 10.0.9 (https://www.geneious.com) CRISPR selection tool, applying the described quality scoring (Doench et al., 2014). The entry clone pENTR‐pAtU6.26‐BFT‐g1.g2.g3.g4 was constructed using assembly of Golden Gate parts amplified by PCR as described by Voogd et al. (2021). This entry clone was subsequently cloned into pDE‐KRS (Varkonyi‐Gasic et al., 2019) and pDE‐KRS‐HYG (Akagi et al., 2019) by Gateway cloning to produce pDE‐KRS‐pAtU6.26‐BFT‐g1.g2.g3.g4 (kanamycin resistance) and pDE‐KRS‐HYG‐pAtU6.26‐BFT‐g1.g2.g3.g4 (hygromycin resistance). To generate control transgenic lines, the pDE‐KRS‐pAtU6.26‐GFP‐g1 vector was used, containing a single sgRNA targeting the green fluorescent protein (GFP) sequence. This plasmid was constructed by exchanging the NheI fragment in pDE‐KRS‐pAtU6.26‐T2_AcCEN4 (described below) by an NheI‐digested PCR fragment produced by overlap PCR using pDE‐KRS‐specific primers and tRNA‐sgRNA cassette‐specific primers flanked by GFP sequence overhangs creating the GFP target. The pDE‐KRS‐pAtU6.26‐T2_AcCEN4 plasmid was constructed similarly to the pDE‐KRS‐pAtU6.26‐BFT‐g1.g2.g3.g4 construct. All oligonucleotide primers are listed in Table S5. For the promoter fusion construct AcBFT2p:GUS, a 2.5‐kb fragment upstream from the AcBFT2 translation start site was amplified from kiwifruit ‘Hort16A’ genomic DNA, re‐amplified to introduce restriction sites and cloned as an AscI/KpnI fragment to generate a transcriptional fusion with the reporter gene uidA (GUS) in the pHEX vector (Moss et al., 2018). All constructs were introduced by electroporation into Agrobacterium tumefaciens strain EHA105 for kiwifruit transformation, and GV3101 for Arabidopsis transformation. For the yeast two‐hybrid assay, AcBFT2 cloned into pDONR221 vector (Voogd et al., 2017) was recombined into pDEST32 (pBDGAL4, bait) and pDEST22 (pADGAL4, prey; Invitrogen) according to the manufacturer's instructions. AcFT and AcFD constructs were as previously described (Varkonyi‐Gasic et al., 2013). Bait and prey constructs were transformed into yeast strains PJ69‐4α and PJ69‐4a respectively (James et al., 1996).
Agrobacterium tumefaciens‐mediated transformation of Arabidopsis was performed by floral dip (Clough and Bent, 1998) and transformation of A. chinensis var. chinensis ‘Hort16A’ was performed as previously described (Wang et al., 2007, 2010), using media supplemented with kanamycin (150 mg L−1) for the selection of transformants and timentin (300 mg L−1) to restrict the bacterial overgrowth. For transformation of fast‐flowering ‘Hort16A’ U6‐CEN4#7 (Varkonyi‐Gasic et al., 2019), the same method was adopted, but hygromycin (10 mg L−1) was used for the selection of transgenic lines. Cultures were maintained in a growth room with temperature at 24 ± 2 °C and 16‐h photoperiod with cool white fluorescent light (35–45 μmol m−2 s−1). Rooted transgenic plants were established in the growth room with controlled temperature and light conditions (temperature 22 °C, 16 h/8 h light/dark), then acclimatized in controlled glasshouse growth rooms (temperature min 18 °C/max 30 °C night/day, 14 h/10 h light/dark). Larger plants (>30 leaves) were transferred to the glasshouse and grown at ambient conditions. To evaluate budbreak frequencies, plants were defoliated in June and monitored for budbreak. Scoring was performed 4 weeks later. Twenty distal buds of each line were evaluated for growth stage and assigned a dormant, budbreak or shoot outgrowth status. Plants were photographed in August to capture a visible difference in appearance of evergrowing and control lines. Observations were carried out over three seasons (2019, 2020 and 2021).
Genomic DNA was extracted from the young leaf tissue using DNeasy plant Mini Kit (Qiagen) following the manufacturer's instruction. PCR amplification was performed using iProof High‐Fidelity DNA Polymerase (Bio‐Rad) with gene or allele‐specific oligonucleotide primers. The PCR conditions were as follows: initial denaturation at 98 °C for 5 min, 35 cycles of denaturation at 98 °C for 15 s, annealing and extension at ≥58 °C for 15 s, final extension at 72 °C for 15–30 s/kb. The annealing temperature for allele‐specific amplification was 63 °C (allele I) and 65 °C (allele II). Amplification products were analysed by agarose gel electrophoresis, purified using the DNA Clean & Concentrate Kit (Zymo Research) and sent for sequence analysis (Macrogen, www.macrogen.com). For subsequent verification, amplification product were cloned into pJET1.2/blunt cloning vector supplied in the CloneJET PCR cloning kit (Thermo Fisher Scientific) and at least four clones of each line were subjected to sequence analysis. Data were analysed using the Geneious ClustalW. Oligonucleotide primer sequences are listed in Table S5.
Total RNA of axillary buds of Acbft mutants and controls (harvested in May and July 2020) was extracted using the Spectrum Plant Total RNA kit (Sigma‐Aldrich, St. Louis, MO). Three biological replicates were used. Library construction, sequencing and bioinformatic analyses were performed at Novogene (www.en.novogene.com) (Illumina NovaSeq 6000, 150 bp paired‐end reads, 30 million reads per sample). Uniquely mapped reads to the A. chinensis Red5 genome (Pilkington et al., 2018) were interrogated for expression, which was presented as average fragments per kilobase of transcript per million reads (FPKM) ± SE of three biological replicates. Differentially expressed genes (DEGs) were identified using DESeq (Anders and Huber, 2010) by Novogene. For functional classification of DEGs, gene annotations were obtained with BLAST v.2.6.0 (Altschul et al., 1997) using A. chinensis Red5 amino acid sequences querying the Arabidopsis TAIR10_pep database, which were then used for Gene ontology (GO) analysis (Botstein et al., 2000) at the Gene Ontology Resource (http://geneontology.org/). Hierarchical clustering was performed using Morpheus (https://software.broadinstitute.org/morpheus/).
Sequence alignment of kiwifruit nucleic acid sequences was performed using Geneious MUSCLE alignment and the phylogenetic tree was built with Geneious tree builder, using the Neighbour‐Joining method, with 1000 bootstrap replicates.
The assay was performed as described previously (Voogd et al., 2017). Briefly, bait and prey constructs were selected on minimal media lacking Leu or Trp (bait or prey respectively), followed by mating on YPAD plates and selection on minimal media lacking both Leu and Trp. The screening was performed on media lacking Trp, Leu and His and supplemented with 1, 3 and 5 mM 3‐amino‐1,2,4‐triazole (3AT). Plates were incubated for 3 days at 30 °C and scored for growth.
The authors declare no conflict of interest.
EV‐G and JP conceived the study, DH, CV and EV‐G designed the experiments, DH, CV and BY conducted the experiments. DH generated and maintained the kiwifruit transgenic lines, MM‐S generated the Arabidopsis transgenic lines. DH, CV, JP and EV‐G analysed the data. DH, EV‐G, JP and CV wrote the manuscript and ACA contributed to the final version of the manuscript. All authors approved the manuscript.
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PMC9616750 | 35922673 | Qian Liu,Yang Shen,Yifang Xiao,Hong Xiang,Ling Chu,Tiansheng Wang,Honghui Liu,Guolin Tan | Increased miR-124-3p alleviates type 2 inflammatory response in allergic rhinitis via IL-4Rα | 03-08-2022 | Allergic rhinitis,Inflammation,miR-124-3p,IL-4Rα,STAT6 | Background and objectives miRNAs play a crucial role in regulating immune responses. However, the effect of miR-124-3p on type 2 inflammation in allergic rhinitis (AR) is unclear. We aimed to study the immune regulation of miR-124-3p in AR and the mechanisms involved. Methods The direct interaction between miR-124-3p and IL-4Rα was confirmed through a dual-luciferase reporter assay. In vitro splenic lymphocytes from mice and peripheral blood mononuclear cells (PBMCs) from healthy individuals were cultured and treated with miR-124-3p mimic/inhibitor. Twenty-four female C57BL/C mice were divided into four groups: control, AR model, miR-124-3p agomir, and miR-124-3p antagomir groups (n = 6 per group). The allergic responses were evaluated based on the number of sneezing and nasal scratching, the serum HDM-specific IgE (sIgE) levels, and the degree of nasal mucosa eosinophil infiltration. The expression of IL-4Rα, p-STAT6, and type 2 inflammatory cytokines (IL-4, IL-5 and IL-13) in lymphocytes or nasal mucosa was determined by qPCR, western blotting, flow cytometry, immunohistochemistry and immunofluorescence. Results miR-124-3p directly targets the 3'UTR of IL-4Rα. The miR-124-3p mimic lowered the IL-4Rα, p-STAT6, IL-4, IL-5, and IL-13 expression levels in both mouse splenic lymphocytes and human PBMCs in vitro, and the miR-124-3p inhibitor rescued these changes. Furthermore, the miR-124-3p agomir decreased the levels of IL-4Rα and IL-4 in nasal mucosa, Th2 differentiation in spleen, and allergic response in AR mice. Moreover, the miR-124-3p antagonist increased the IL-4Rα and IL-4 levels and further aggravated the allergic responses. Conclusions miR-124-3p might attenuate type 2 inflammation in AR by regulating IL-4Rα signaling, and miR-124-3p may be a promising new target in AR treatment. Supplementary Information The online version contains supplementary material available at 10.1007/s00011-022-01614-x. | Increased miR-124-3p alleviates type 2 inflammatory response in allergic rhinitis via IL-4Rα
miRNAs play a crucial role in regulating immune responses. However, the effect of miR-124-3p on type 2 inflammation in allergic rhinitis (AR) is unclear. We aimed to study the immune regulation of miR-124-3p in AR and the mechanisms involved.
The direct interaction between miR-124-3p and IL-4Rα was confirmed through a dual-luciferase reporter assay. In vitro splenic lymphocytes from mice and peripheral blood mononuclear cells (PBMCs) from healthy individuals were cultured and treated with miR-124-3p mimic/inhibitor. Twenty-four female C57BL/C mice were divided into four groups: control, AR model, miR-124-3p agomir, and miR-124-3p antagomir groups (n = 6 per group). The allergic responses were evaluated based on the number of sneezing and nasal scratching, the serum HDM-specific IgE (sIgE) levels, and the degree of nasal mucosa eosinophil infiltration. The expression of IL-4Rα, p-STAT6, and type 2 inflammatory cytokines (IL-4, IL-5 and IL-13) in lymphocytes or nasal mucosa was determined by qPCR, western blotting, flow cytometry, immunohistochemistry and immunofluorescence.
miR-124-3p directly targets the 3'UTR of IL-4Rα. The miR-124-3p mimic lowered the IL-4Rα, p-STAT6, IL-4, IL-5, and IL-13 expression levels in both mouse splenic lymphocytes and human PBMCs in vitro, and the miR-124-3p inhibitor rescued these changes. Furthermore, the miR-124-3p agomir decreased the levels of IL-4Rα and IL-4 in nasal mucosa, Th2 differentiation in spleen, and allergic response in AR mice. Moreover, the miR-124-3p antagonist increased the IL-4Rα and IL-4 levels and further aggravated the allergic responses.
miR-124-3p might attenuate type 2 inflammation in AR by regulating IL-4Rα signaling, and miR-124-3p may be a promising new target in AR treatment.
The online version contains supplementary material available at 10.1007/s00011-022-01614-x.
Allergic rhinitis (AR) is an IgE mediated inflammatory disease with increasing prevalence [1, 2]. Patients with AR often experience symptoms of watery rhinorrhea, sneezing, itching and nasal congestion. Increasing evidence shows that type 2 inflammation is the main pathophysiology mechanism of AR [1, 3]. Although the ARIA (Allergic Rhinitis and its Impact on Asthma) guideline provides a global, evidence-based, pragmatic, stepwise approach to the treatment of AR [4], several patients are unsatisfied with the current treatments. Therefore, the identification of new therapeutic targets in AR is necessary. MicroRNAs (miRNAs) are small noncoding RNAs of approximately 22 nucleotides that regulate most cellular processes via posttranscriptional control [5]. One miRNA can target multiple mRNAs, often within the same signaling pathway. Dysregulated miRNA expression may alter particular cellular functions and contribute to the development of various diseases [6]. In recent years, miRNAs have attracted extensive scientific attention due to their importance in the pathophysiology of allergic diseases. For example, the profiles of miRNA expression in allergic skin conditions and asthma were recently reported, and the results uncovered some allergic-related miRNA signatures, such as miR-21, miR-124, miR-151a, and miR-155 [7]. A subset of circulating miRNAs in plasma, miR-206, miR-338-3p, miR-329, and miR-26a, were found to be differentially expressed in patients with AR compared with healthy individuals or those with asthma [8]. Our previous study found that miR-124-3p was significantly decreased in the nasal mucosa of AR mice, and the expression was increased after anti-inflammatory treatment [9], demonstrating that miR-124-3p might be a target in AR. It has been reported that miR-124-3p alleviates nasal inflammation by inhibiting dipeptidyl peptidase-4 in allergic mice [10], but the effect of miR-124-3p on type 2 inflammation in AR is unclear. In this study, we investigated the immune regulation of miR-124-3p in type 2 inflammation and the possible mechanism in AR.
Six-to-eight-week-old wild-type female C57Bl/6J mice free of murine-specific pathogens were obtained from the Medical Experimental Animal Center of Central South University (Changsha, China). The mice were housed in the pathogen-free facility with a 12-h light/12-h dark cycle and free access to food and water. All procedures were approved by the Central South University Animal Ethics Committee.
This study was approved by the ethics committee of the Third Xiangya Hospital of Central South University (No: 2022-S132). All volunteers signed informed consent forms. Blood samples were collected from 12 healthy volunteers, including six men and six women without a diagnosis of AR and who had a negative skin prick test (SPT) or specific serum IgE (sIgE) measurement. Human PBMCs were prepared from venous blood using the Ficoll–Hypaque method (TBD, China). The isolated human PBMCs were used for in vitro culture.
The flowchart of mouse allocation and treatments is shown in Fig. 1. Briefly, the mice were sensitized using house–dust–mite (HDM) antigen as follows: 40 µg of HDM extract (D. pteronyssinus, Greer Labs) diluted in 200 µL of sterile normal saline was administered to the mice by four intraperitoneal injections on days 1, 5, 10 and 14. Intranasal challenge was performed using 20 µg of HDM diluted in 20 µL of normal saline (NS) once a day from days 15 to 21. The allergic symptom score was calculated 15 min after the last challenge on day 21. In detail, 24 mice were randomly allocated into four groups: NS (n = 6), AR (n = 6), agomir (n = 6), and antagomir (n = 6). The mice in the NS group were sensitized and intranasally challenged with NS only, and those in the AR group were sensitized by intraperitoneal injections and intranasally challenged with HDM allergen. The sensitized mice in the agomir group and antagomir groups received the intranasal HDM challenge and then a nasal administration of 20 µL of 1.4 nmol miR-124-3p agomir or 20 µL of 2.8 nmol antagomir (GenePharma, Shanghai) once a day from days 15 to 21. The mice were euthanized 24 h after the final challenge, peripheral blood was obtained by extracting the eyeball, and the nasal mucosa and spleen were isolated by precise dissection.
Serum from mice was prepared by centrifugation of blood and then cryopreserved at − 80 °C. The serum level of HDM-specific IgE was measured using an enzyme-linked immunosorbent assay (ELISA) kit (Chondrex, Redmond, WA, USA) according to the manufacturers’ protocol.
Fresh murine nasal mucosa samples were fixed with 4% paraformaldehyde overnight, decalcified in EDTA, embedded in paraffin, dewaxed, rehydrated, and used for hematoxylin and eosin (H&E) staining, immunohistochemistry and immunofluorescence analyses. The staining was performed using standard procedures.
The nasal mucosa tissue sections were blocked in blocking buffer for 1 h and then incubated with goat anti-rabbit IL-4 overnight at 4 °C. The sections were washed with PBS supplemented with 0.1% Tween-20 (PBS-T) and incubated with HRP-conjugated donkey anti-goat IgG antibody for 1 h at room temperature. After staining, all the samples were washed with PBS-T and then mounted for imaging. Images were captured using a microscope (Olympus, Japan).
Freshly dissected murine nasal mucosa specimens were collected and processed as described above. Sections were deparaffinized, subjected to antigen repair, blocked with BSA blocking buffer for 30 min and then stained with rabbit anti-IL-4Rα (Thermo Fisher) overnight. Donkey–anti-rabbit FITC (Proteintech, China) was used as the secondary antibody. DAPI (Proteintech, China) was used for counterstaining. The slides were sealed with anti-fluorescence quencher. All images were captured with a confocal microscope (Leica, Germany).
Splenic lymphocytes were isolated by Ficoll–Hypaque density centrifugation. Briefly, the spleens were mechanically minced into a homogenous paste with a scalpel on a dish plate and washed with PBS containing 2% FBS. After incubation in a 24-well plate for 30 min at 37 °C in a humidified incubator, cell suspensions were passed through a 70 µm Falcon nylon cell strainer, and lymphocytes were isolated from single cell suspensions of the spleen using lymphocyte separation medium (TBD, China). The isolated lymphocytes were used for further experiments, such as flow cytometry.
Splenic lymphocytes from mice or human PBMCs were cultured in RPMI 1640 medium supplemented with 10% FBS, 1 µg/mL anti-CD3 (BioLegend, USA), 1 µg/mL anti-CD28 (BioLegend, USA) and 100 U/mL mouse IL-2 (PeproTech, USA) in a humidified incubator containing 5% CO2 at 37 °C. They were transferred to a 24-well plate at 1 × 106 cells/well in medium for 16 h and then transiently transfected with 2.5 µg of miR-124-3p mimic or mimic negative control or 5 µg of inhibitor or inhibitor negative control using Lipofectamine 3000 according to the manufacturer’s protocol for suspension cells. Each transfection was performed in triplicate in 24-well plates. Twenty-three hours after transfection, 2.5 µg of HDM antigen was added to the medium and incubated for 1 h. The cells in each well were then collected for extraction of RNA and protein.
Total RNA from murine nasal mucosa, splenic lymphocytes and human PBMCs was extracted using TRIzol reagent (Invitrogen, USA). RNA was reverse transcribed using ReverTra Ace® qPCR RT Master Mix with gDNA Remover (Toyobo, Japan). Real-time PCR was performed using KOD SYBR qPCR Mix (Toyobo, Japan), and detection was achieved using the LightCycler 480 System (Roche, Switzerland) according to the manufacturer’s instructions. All data were normalized to the β-actin levels and expressed relative to the control, and relative expression was calculated using the equation 2−ΔΔCt. The primer sequences are presented in Supplementary Table 1.
Protein lysates from splenic lymphocytes and human PBMCs were processed in RIPA buffer (Kaiji Biotech, China) in the presence of phosphatase inhibitor and protease inhibitor cocktails. The total protein concentrations were measured by BCA assay (Kaiji Biotech, China). The samples were separated on 8–12% SDS–PAGE gels and then transferred to PVDF membranes (Millipore, USA). The membrane was incubated with primary antibody overnight at 4 °C and then with IRDye secondary antibodies (LI-COR Biosciences, USA) for 1 h at room temperature. The membrane was subsequently subjected to three 10-min washes with TBST buffer. The bands were scanned and quantified using a LI-COR Odyssey CLx scanner (LI-COR Biosciences, USA). β-Actin or Gapdh was used as an internal reference.
Flow cytometric analyses for the T cell phenotyping of splenic lymphocytes were performed as follows. Prior to cell surface staining, cells (1 × 106) were seeded into a 24-well plate and stimulated with PMA/Ionomycin mixture and BFA/monensin mixture for 4 h. For surface marker staining, cells (1 × 106) were stained with anti-CD4 conjugated with APC-Cyanine7 (eBioscience, USA) for 30 min on ice and then washed in PBS containing 5% FBS. For intracellular cytokine staining, cells (1 × 106) were fixed with the FoxP3/Transcription Factor Staining Buffer Kit (eBioscience, USA) and then incubated with anti-IL4 conjugated with PE-Cyanine7 and anti-IFN-γ conjugated with PerCP-Cyanine5.5 (eBioscience, USA) for 30 min. Cytokine level in CD4+ lymphocytes was analyzed using FlowJoV10 (Tree Star, Ashland, OR).
IL-4Rα was predicted to be the underlying target of miR-124p by an online bioinformatics analysis (PicTar, TargetScan and miRBase). An interaction diagram of mmu-miR-124 and wild-type IL-4Rα-UTR is shown in Fig. 2a. Dual-luciferase assays were implemented using the luciferase reporter assay system in accordance with the manufacturer’s instructions (Promega). The mutated (Mut) or wild-type (WT) IL-4Rα-3′-UTR sequence including the mmu-miR-124 targeting site was inserted into the XhoI/BamHI sites of the pLUC vector to construct pLUC-luc-IL-4Rα. All constructs were verified by sequence analysis (Supplementary Fig. 1). HEK-293T cells were prepared, seeded in 96-well plates and then transfected with pLUC-luc-IL-4Rα (0.2 µg), mmu-miR-124 negative or mimic control (0.45 µg) or Renilla luciferase (0.15 µg) by adopting FuGENE® HD (Roche, Switzerland). The transfections were performed in duplicate, and each experiment was repeated in triplicate. Luciferase activity was detected after 48 h using the Luciferase Reporter Assay System (Promega). IL-4Rα-3′-UTR activity is expressed as a ratio of firefly luciferase activity to Renilla luciferase activity.
All quantification results are shown as the means (± SEMs) of at least three independent experiments. Statistical comparisons between two groups were conducted by unpaired two-tailed Student’s t tests. One-way analysis of variance (ANOVA) was used for comparisons of multiple groups. The statistical analyses were performed using GraphPad Prism 8 software (GraphPad Software Inc.). A p value < 0.05 was considered to indicate statistical significance.
Based on a previous bioinformatics analysis, IL-4Rα was identified as a novel target of miR-124-3p. To evaluate whether miR-124-3p directly targets IL-4Rα mRNA, we first searched the 3’-UTR sequence of IL-4Rα as the potential binding site of miR-124-3p (Fig. 2a). We then performed a luciferase reporter assay to predict the interaction between miR-124-3p and IL-4Rα (Fig. 2b). We found that miR-124-3p significantly reduced the luciferase activity in the IL-4Rα WT group, but no change in luciferase activity was detected in the IL-4Rα Mut group with mutation in the predicted miR-124-3p targeting sequence in the reporter vector (pLUC-IL4RαMut-3′-UTR). This result indicates that miR-124-3p may directly target IL-4Rα by binding to 3′-UTR sites.
To investigate whether miR-124-3p directly downregulates IL-4Rα and further reduces the levels of type 2 cytokines in vitro, splenic lymphocytes from mice were cultured and transfected with miR-124-3p mimic and miR-124-3p inhibitor. First, we confirmed that the miR-124-3p mimic decreased both the mRNA and protein expression of IL-4Rα in splenic lymphocytes (Fig. 3a, e, f). Similarly, we found that the transcription factor p-STAT6, which is crucial for the induction of type 2 immune responses, was decreased after miR-124-3p mimic treatment (Fig. 3e, g). Furthermore, both the mRNA and protein levels of type 2 cytokines (IL-4, IL-5 and IL-13) were decreased in splenic lymphocytes after treatment with the miR-124-3p mimic (Fig. 3b–d, k–n). In contrast, the miR-124-3p inhibitor significantly increased the mRNA level of both IL-4Rα and type 2 cytokines (Fig. 3a–d). However, the protein levels of IL-4Rα, p-STAT6 and IL-5 were increased, albeit not significantly, after miR-124-3p inhibitor treatment (Fig. 3h–j, o, q). Interestingly, the protein levels of IL-4 and IL-13 were further increased after miR-124-3p inhibitor treatment (Fig. 3o, p, r).
To more comprehensively validate the role of miR-124-3p in the type 2 inflammatory response, we also transfected human PBMCs with miR-124-3p mimic and miR-124-3p inhibitor. The results were similar to those found in mouse splenic lymphocytes. The miR-124-3p mimic decreased both the mRNA and protein expression levels of IL-4Rα and protein level of p-STAT6 in human PBMCs (Fig. 4a, e, f, g). Furthermore, both the mRNA and protein levels of type 2 cytokines (IL-4, IL-5 and IL-13) were decreased in human PBMCs after treatment with the miR-124-3p mimic (Fig. 4b–d, k–n). In contrast to the effect of the mimic, the miR-124-3p inhibitor markedly elevated the mRNA levels of both IL-4Rα and type 2 cytokines (Fig. 4a–d, o–r). However, the protein level of IL-4Rα was unchanged after miR-124-3p inhibitor treatment (Fig. 4h, i). However, the protein levels of p-STAT6 and type 2 cytokines were further increased after miR-124-3p inhibitor treatment (Fig. 4h, j, o–r).
To further validate the function of miR-124-3p in vivo, we established the HDM-induced AR mouse model and treated the mice with the miR-124-3p agomir and antagomir. The detailed protocol is shown in Fig. 1. The expression of IL-4Rα in the nasal mucosa was determined by immunofluorescence. As shown in Fig. 5a, b, compared with the control group, the percentage of IL-4Rα positive cells in the nasal mucosa was significantly increased in the AR group. Treatment with the miR-124-3p agomir and antagomir markedly reduced and elevated the expression of IL-4Rα in the nasal mucosa of AR mice, respectively. Furthermore, the IL-4Rα mRNA level in the nasal mucosa of AR mice was decreased and increased after miR-124-3p agomir and miR-124-3p antagomir treatment, respectively (Fig. 5c). In addition, we examined the expression of IL-4 in the nasal mucosa by immunohistochemistry (Fig. 5d). Similarly, the number of IL-4 positive cells in the nasal mucosa of AR mice was significantly decreased and increased by miR-124-3p agomir and miR-124-3p antagomir treatment, respectively (Fig. 5e).
To verify whether miR-124-3p affects Th2 cell differentiation in vivo, we examined the proportions of Th1/Th2/Th17/Tregs in the splenocytes of mice by flow cytometry. The results showed high increases in the Th2 and Th1 percentages and Th2/Th1 ratio in the AR group compared with the control group, which indicated the development of a Th2 inflammatory response in HDM-sensitized AR mice (Fig. 6). Subsequently, the Th2 inflammatory response was attenuated in AR mice treated with the miR-124-3p agomir (Fig. 6c, d). In addition, the Th2 percentage and Th2/Th1 ratio of the antagomir group were higher than those of the agomir group, but the difference was not significant (Fig. 6d, f). On the other hand, the proportions of Th17 cells and Tregs were not significantly different among these groups (Supplementary Fig. 2).
To evaluate the effect of miR-124-3p on the nasal allergic response in AR mice, we examined the nasal symptoms, serum HDM-specific IgE levels, and eosinophil infiltration in the nasal mucosa. As shown in Fig. 7a, b, the numbers of sneezing and nasal scratching were markedly higher in the AR group than in the control group but were decreased significantly in the miR-124-3p agomir group compared with the AR group. The antagomir group exhibited a higher number of sneezing and nasal scratching than the agomir group, but the differences were not significant compared the numbers found for the AR group. Furthermore, the high level of serum HDM-specific IgE in the AR group was significantly decreased and increased after treatment with the miR-124-3p agomir and antagomir, respectively (Fig. 7c). Moreover, the number of infiltrated eosinophils in the lamina propria of the nasal mucosa was observed by H&E staining. As shown in Fig. 7d, e, the eosinophil count in the lamina propria of AR group was higher than that of the control group. Compared with the AR group, the eosinophil count was significantly decreased in the agomir group but not changed significantly in the antagomir group; however, the antagomir group had higher eosinophil counts than the agomir group.
AR is a global disease with increasing prevalence that places a great burden on the quality of life of patients [1–3]. Type 2 inflammation was recently reported as the main pathophysiological mechanism of AR [11]. Finding a therapeutic target for reducing type 2 inflammation is important for AR treatment. Herein, we found that miR-124-3p may be a potential target in AR treatment. Increased miR-124-3p downregulates IL-4Rα expression and further attenuates type 2 inflammation in AR mice, as demonstrated by decreases in the IL-4, IL-5, and IL-13 cytokine levels, Th2 differentiation, and p-STAT6 expression in nasal mucosa and lymphocytes. It has been reported that miRNAs play a critical role in allergic diseases, including asthma and AR [8]. Our previous study screened some differential genes in the nasal mucosa and found that the miR-124-3p levels were decreased in AR mice and increased in anti-inflammatory mice treated with ipratropium bromide [9]. To investigate whether miR-124-3p might be a therapeutic target in AR, we first predicted the possible functional genes that bind to miR-124-3p via bioinformatic analysis. The results showed that IL-4Rα is a putative target gene of miR-124-3p. The dual-luciferase reporter assay further confirmed that miR-124-3p directly binds to the 3’UTR of IL-4Rα. These results indicated that miR-124-3p might downregulate AR via IL-4Rα signaling, which plays a role in promoting type 2 inflammation. MiR-124 is reportedly expressed in various tissues and is particularly highly expressed in immune cells and organs, including peripheral blood mononuclear cells, bone marrow, lymph nodes, and thymus [12–15]. A previous study showed that miR-124 could suppress CD4+ T cell immunoactivity by targeting interferon regulatory factor 1 (IRF1) [16]. Moreover, miR-124 exerts a broad antiproliferative effect, and robustly inhibits transactivation of nuclear factor of activated T cells (NFAT) activity [17]. Thus, miR-124 expression may function as a negative regulator to control inflammation. Similar to our previous study, miR-124-3p is reportedly decreased in the nasal mucosa of AR mice and patients with AR [10]. Furthermore, recent studies showed that the overexpression of miR-124 could dramatically inhibit the activation of NF-kB and the expression of inflammatory factors to regulate innate immunity [18, 19]. However, the effect of miR-124-3p on immune response, particularly type 2 inflammation, in AR is unclear. To clarify the downregulation of miR-124-3p in type 2 inflammation in AR, we examined the expression of IL-4Rα and type 2 cytokines (IL-4, IL-5 and IL-13) in vitro. The expression of IL-4Rα, p-STAT6, IL-4, IL-5 and IL-13 was significantly reduced in splenic lymphocytes after miR-124-3p mimic treatment, and the miR-124-3p inhibitor rescued these changes. IL-4Rα, a common receptor shared by IL-4 and IL-13, signals via STAT6 and plays an important role in type 2 allergic immunity [20–22]. In addition, in this study, we isolated human PBMCs from healthy individuals for in vitro culture exposed to miR-124-3p. Similar to the results found for mouse splenocytes, miR-124-3p also suppressed the type 2 inflammatory response in human PBMCs. Therefore, the inhibitory effects of miR-124-3p on type 2 inflammation in AR may be mediated by the IL-4Rα/STAT6 signaling pathway. To verify the suppressive immune regulation of miR-124-3p in AR, HDM-induced AR mice were established. We found that the miR-124-3p agomir reduced IL-4Rα and IL-4 expression in the nasal mucosa, Th2 differentiation in splenic lymphocytes and the allergic response in AR mice. Furthermore, the miR-124-3p antagomir significantly increased the IL-4Rα and IL-4 levels and aggravated the allergic response, including nasal symptoms, serum sIgE levels and eosinophil infiltration in the nasal mucosa. These in vivo experiments demonstrated that miR-124-3p may alleviate type 2 inflammation and further attenuate the allergic response in AR. miR-124-3p plays a critical role in anti-inflammation and can reduce the production of proinflammatory cytokines, including IL-6 and TNF-α [23–25]. Similarly, a study on asthma indicated that miR-124 may be involved in eosinophilic inflammation and has good diagnostic value for asthma [26]. Moreover, it has been reported that the upregulation of miR-124-3p improves eosinophil infiltration, inhibits apoptosis of the nasal mucosa in AR mice and alleviates allergic nasal symptoms [10]. However, there is no report on the regulation of type 2 inflammation by miR-124-3p in AR. We investigated the immune regulation mediated by miR-124-3p in AR mice and the possible mechanism involved using both in vitro and in vivo methods, and the results will provide novel therapeutic evidence for AR. In conclusion, miR-124-3p might attenuate type 2 inflammation in AR by downregulating IL-4Rα signaling. Our findings may be beneficial for advancing the clinical application of miRNAs in AR treatment, and miR-124-3p may be a novel therapeutic target in AR.
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PMC9616853 | Yongjia Wang,Yuqin Zhang,Zixuan Wang,Lu Yu,Keli Chen,Yuwen Xie,Yang Liu,Weijie Liang,Yilin Zheng,Yizhi Zhan,Yi Ding | The interplay of transcriptional coregulator NUPR1 with SREBP1 promotes hepatocellular carcinoma progression via upregulation of lipogenesis | 28-10-2022 | Oncogenes,Cancer metabolism,Gastrointestinal cancer | Nuclear protein 1 (NUPR1) is a transcriptional coregulator that has been implicated in the development of various cancer types. In addition, de novo fatty acid synthesis plays a pivotal role in hepatocellular carcinoma (HCC) development. However, little is currently known on the role of NUPR1 in hepatocellular carcinoma. In this study, bioinformatics analysis was conducted to analyze the expression level, prognosis value and enriched pathways of NUPR1 in Liver Hepatocellular Carcinoma (LIHC). We found that NUPR1 was significantly upregulated in human hepatocellular carcinoma cells compared with normal hepatocytes from LIHC patients in TCGA cohorts and our patients. Kaplan–Meier analysis and COX proportional hazard progression model showed that high expression of NUPR1 was correlated with a poor prognosis of LIHC patients. CCK-8, EdU and colony formation assays were performed to explore the effect of NUPR1 on the proliferation of HCC cells, then wound healing and transwell migration assays were performed to evaluate the effects of NUPR1 on cell migration. Furthermore, subcutaneous xenograft models were established to study tumor growth. Results showed that NUPR1 overexpression correlated with a highly proliferative and aggressive phenotype. In addition, NUPR1 knockdown significantly inhibited hepatocellular carcinoma cell proliferation and migration in vitro and hindered tumorigenesis in vivo. Mechanistically, endogenous NUPR1 could interact with sterol regulatory element binding protein 1 (SREBP1) and upregulated lipogenic gene expression of fatty acid synthase (FASN), resulting in the accumulation of lipid content. Moreover, pharmacological or genetic blockade of the NUPR1-SREBP1/FASN pathway enhanced anticancer activity in vitro and in vivo. Overall, we identified a novel function of NUPR1 in regulating hepatocellular carcinoma progression via modulation of SREBP1-mediated de novo lipogenesis. Targeting NUPR1-SREBP1/FASN pathway may be a therapeutic alternative for hepatocellular carcinoma. | The interplay of transcriptional coregulator NUPR1 with SREBP1 promotes hepatocellular carcinoma progression via upregulation of lipogenesis
Nuclear protein 1 (NUPR1) is a transcriptional coregulator that has been implicated in the development of various cancer types. In addition, de novo fatty acid synthesis plays a pivotal role in hepatocellular carcinoma (HCC) development. However, little is currently known on the role of NUPR1 in hepatocellular carcinoma. In this study, bioinformatics analysis was conducted to analyze the expression level, prognosis value and enriched pathways of NUPR1 in Liver Hepatocellular Carcinoma (LIHC). We found that NUPR1 was significantly upregulated in human hepatocellular carcinoma cells compared with normal hepatocytes from LIHC patients in TCGA cohorts and our patients. Kaplan–Meier analysis and COX proportional hazard progression model showed that high expression of NUPR1 was correlated with a poor prognosis of LIHC patients. CCK-8, EdU and colony formation assays were performed to explore the effect of NUPR1 on the proliferation of HCC cells, then wound healing and transwell migration assays were performed to evaluate the effects of NUPR1 on cell migration. Furthermore, subcutaneous xenograft models were established to study tumor growth. Results showed that NUPR1 overexpression correlated with a highly proliferative and aggressive phenotype. In addition, NUPR1 knockdown significantly inhibited hepatocellular carcinoma cell proliferation and migration in vitro and hindered tumorigenesis in vivo. Mechanistically, endogenous NUPR1 could interact with sterol regulatory element binding protein 1 (SREBP1) and upregulated lipogenic gene expression of fatty acid synthase (FASN), resulting in the accumulation of lipid content. Moreover, pharmacological or genetic blockade of the NUPR1-SREBP1/FASN pathway enhanced anticancer activity in vitro and in vivo. Overall, we identified a novel function of NUPR1 in regulating hepatocellular carcinoma progression via modulation of SREBP1-mediated de novo lipogenesis. Targeting NUPR1-SREBP1/FASN pathway may be a therapeutic alternative for hepatocellular carcinoma.
Hepatocellular carcinoma (HCC) is the sixth most prevalent cancer and the third-leading cause of cancer-related death worldwide, with increasing incidence and few effective treatment options available [1]. The majority of HCC occurs in the setting of chronic liver diseases, such as infection with hepatitis B or hepatitis C and non-alcoholic steatohepatitis [2]. The liver is a central metabolic organ within the human body that has been established to play a central role in the synthesis, storage and degradation of lipids. Accumulating evidence shows that lipid metabolism is altered in human liver tumors, which upregulates de novo lipogenesis to ensure that proliferating cells have access to extra lipids for membrane biogenesis, energy source, signaling lipid molecules and post-translational modifications [3, 4]. In addition, aberrant expression and activity of key enzymes involved in de novo fatty acid synthesis, such as fatty acid synthase (FASN) and stearoyl-CoA desaturase 1 (SCD1), have been identified to contribute to HCC development [5]. Sterol response element-binding protein 1 (SREBP1), a master transcription factor of de novo lipogenesis and lipid homeostasis, has been reported to induce lipogenic reprogramming of tumor cells and provide a critical link between oncogenic signaling and tumor metabolism [6]. The cleavage of precursor SREBP1 yields the mature SREBP1 (mSREBP1) product. Once mature, active SREBP1 translocates to the nucleus and transactivates the expression of its target genes, such as FASN and SCD1 [7]. Nevertheless, SREBP1 requires additional co-regulatory transcription factors to regulate promoters properly [8]. Nuclear protein 1 (NUPR1/p8/COM1) is a transcriptional coregulator and a multifunctional stress-associated protein that has recently elicited great attention for its role in several protumorigenic processes in various cancer types, including cell growth, migration, invasion, drug resistance, ferroptosis, angiogenesis and mitochondrial respiratory [9–11]. Elevated expression of NUPR1 has been associated with a high-fat diet, and it has been suggested that NUPR1 can protect tissues from cell injury in the context of obesity and a high-fat diet [12, 13]. Given that the liver is the most important organ involved in lipid synthesis and metabolism and NUPR1 is related to lipid metabolism, we sought to examine whether NUPR1 could affect HCC progression by altering lipid metabolism. In the present study, we found that NUPR1 could interact with mSREBP1, indicating that NUPR1 acts as a co-regulatory transcription factor of SREBP1. Moreover, we found that NUPR1 could promote the malignant potential of liver cancer cells by facilitating nuclear translocation of the transcriptionally active form of SREBP1 and transactivating target genes FASN and SCD1, resulting in lipid accumulation. In addition, pharmacological or genetic blockade of the NUPR1-SREBP1/FASN pathway enhanced anticancer activity in vivo and in vitro.
To preliminarily investigate the transcriptional expression level of NUPR1 in cancers, we analyzed a TCGA pan-cancer cohort. We found that NUPR1 was upregulated in multiple cancer types, including LIHC (liver hepatocellular carcinoma (HCC)), BRCA (breast invasive carcinoma), GBM (glioblastoma multiforme), KICH (kidney chromophobe), KIRC (kidney renal clear cell carcinoma), KIRP (kidney renal papillary cell carcinoma) and PRAD (prostate adenocarcinoma) (Fig. 1A). After stratifying by clinical stage, Cox proportional hazard model was used to evaluate the significance of NUPR1 on patient outcomes. We found that NUPR1 was a risk factor for LIHC and THYM (Thymoma) (Fig. 1B). In addition, the Kaplan–Meier Curve showed that NUPR1 expression was associated with LIHC patient survival, as the low-expression group had significantly better 10-year overall survival than the high-expression group (p = 0.033 Fig. 1C). To further substantiate the clinical relevance of our findings, we analyzed the NUPR1 expression in tissues from LIHC patients, which showed that the expression of NUPR1 was significantly increased in tumor tissues compared to normal tissues (Fig. 1D, E). These results provide compelling evidence that NUPR1 may be related to LIHC progression.
To demonstrate the role of NUPR1 in the growth and migration of liver cancer cells, we overexpressed NUPR1 in MHCC-97H and SK-Hep1 cell lines and stably knocked down NUPR1 in Huh7 and SMMC-7721 cell lines based on their expression of endogenous NUPR1(Supplementary Fig. 1A) [14]. The efficiency of these NUPR1 overexpression vectors (LV-NUPR1) and shRNAs was first assessed by qRT-PCR (Fig. 2A). Then western blot was performed to confirm the stable overexpression of NUPR1 in MHCC-97H and SK-Hep1 cells and downregulation in Huh7 and SMMC-7721 cells, respectively (Fig. 2B and Supplementary Fig. 1B,C). Subsequently, CCK-8, EdU and colony formation assays were performed to explore the effect of NUPR1 on HCC cell proliferation. As shown in Fig. 2C, NUPR1 overexpressing HCC cells exhibited a significantly enhanced proliferation rate. Similarly, in the EdU assay, NUPR1 upregulation increased the percentage of EdU‐positive cells (Fig. 2D). Moreover, downregulating NUPR1 expression significantly inhibited the proliferation of Huh7 and SMMC-7721 cells leading to fewer colonies (Fig. 2C, G). MHCC-97H cells with LV‐NC/LV-NUPR1 and SMMC-7721 cells with blank/shRNA1 were injected subcutaneously into BALB/c naked mice. NUPR1 overexpression significantly increased tumor volume and weight in the xenograft mouse model (Fig. 2E), while NUPR1 knockdown suppressed tumor growth (Fig. 2H). NUPR1 overexpression in xenograft tumor samples was further demonstrated by IHC (Fig. 2F). Overall, these findings suggest that NUPR1 can promote the proliferation of HCC cells and tumor growth in vitro and in vivo.
The wound healing and transwell migration assays were performed to evaluate the effects of NUPR1 on cell migration. The cell scratch test revealed that NUPR1 overexpression significantly enhanced the wound gap closure in MHCC-97H and SK-Hep1 cells compared to the control group (Fig. 3A). Knockdown of NUPR1 significantly suppressed these changes in Huh7 and SMMC-7721 cells (Fig. 3B). Meanwhile, in the transwell migration assay, NUPR1 overexpressing tumor cells (MHCC-97HLV-NUPR1, 57.00 ± 4.359, and SK-Hep1LV-NUPR1, 153.3 ± 7.126) exhibited a significantly more invasive phenotype than control cells (MHCC-97HLV-NC, 29.33 ± 2.186, and SK-Hep1LV-NC, 85.00 ± 1.528) (p < 0.01) (Fig. 3C). However, NUPR1 knockdown suppressed the migration ability of Huh7 cells (Fig. 3D). These findings suggest that NUPR1 has a stimulatory effect on cell migration in HCC cells.
The above experimental results suggested that NUPR1 was associated with a proliferative and aggressive phenotype in HCC cells. To further explore the underlying molecular mechanisms, we collected human RNA-seq data from TCGA database LIHC project. Differentially expressed upregulated genes between the high NUPR1 expression and low expression groups were subjected to KEGG pathway enrichment analysis. We found a strong association between NUPR1 and non-alcoholic fatty liver disease (hsa04932) as well as cholesterol metabolism (hsa04979) (Supplementary Table 1) [15]. It has been established that transcriptional coregulators can physically interact with transcription factors and regulate a common subset of target genes of these transcription factors [16]. Accordingly, we sought to identify direct binding partners of NUPR1 in MHCC-97H and SK-Hep1 cells transfected with Flag-tagged NUPR1 by co-immunoprecipitation. Importantly, we identified mSREBP1 as a NUPR1-interacting protein. Besides, additional co-regulatory transcription factors are required in all SREBP-regulated promoters studied to date. As shown in Fig. 4A, we performed anti-Flag immunoprecipitation followed by immunoblotting with anti-SREBP1 and detected a band of the active mature form of SREBP1 (mSREBP1). Immunoprecipitation of endogenous SREBP1 followed by immunoblotting for NUPR1 was conducted (Fig. 4B). The above immunoprecipitation assays demonstrated the interaction between NUPR1 and mSREBP1 in MHCC-97H and SK-Hep1 cells. Meanwhile, we analyzed the effect of NUPR1 on the expression of SREBP1 and its subcellular localization. The qRT-PCR and western blotting results showed that overexpression of NUPR1 significantly upregulated SREBP1 expression in MHCC-97H and SK-Hep1 cells compared with the control group (Fig. 4C, E), whereas downregulating NUPR1 decreased SREBP1 expression (Fig. 4D, F). Furthermore, cell fractionation and western blotting demonstrated that overexpression of NUPR1 increased the nuclear expression of SREBP1 in MHCC-97H and SK-Hep1 cells (Fig. 4G), while knockdown of NUPR1 reduced the nuclear localization of mSREBP1 (Fig. 4H). In addition, we also observed a connection between NUPR1 and SREBP1 in HCC patient samples by immunohistochemistry (IHC)(cor = 0.4506, p = 0.0233, Fig. 5E). Taken together, these experiments demonstrated that NUPR1 increased SREBP1 expression and nuclear localization and could bind to mSREBP1, suggesting that NUPR1 is a transcriptional coregulator of SREBP1.
Increased nuclear SREBP1 protein has been associated with elevated mRNA levels of known SREBP1 target genes involved in fatty acid biosynthesis. Therefore, we assessed the expression of fatty acid-metabolizing enzymes in our cell lines. As shown in Fig. 5A, the mRNA expression levels of FASN, SCD1 and fatty acid desaturase 2 (FADS2) were significantly increased in NUPR1 overexpressing MHCC-97H cells, and the transcriptional levels of FASN, SCD1 and fatty acid desaturase 1 (FADS1) were upregulated in NUPR1 overexpressing SK-Hep1 cells, which were subsequently confirmed by western blot (Fig. 5B). Moreover, NUPR1 knockdown led to significant downregulation of mRNA and protein levels of FASN, SCD1, FADS1 and FADS2, compared to non-targeting shRNA control in Huh7 and SMMC-7721 cells (Fig. 5C, D). Furthermore, results from IHC of HCC patient samples showed a positive and significant correlation between NUPR1 and FASN (cor = 0.5047, p = 0.0012, Fig. 5F). Besides upregulation of SREBP1 and its target genes, NUPR1 overexpression also induced the accumulation of lipid droplets in HCC cells (Fig. 5G). These results indicate that NUPR1 might act as a transcriptional coregulator of SREBP1, thus elevating the expression of related target genes, leading to the accumulation of intracellular lipid droplets.
Fatostatin, a specific inhibitor of SREBP activation, has been reported to possess antitumor activity in multiple cancer types [17]. As expected, NUPR1 overexpression‐induced lipogenic enzymes expression could be suppressed by co-incubation with 20 μM Fatostatin in MHCC-97H and SK-Hep1 cells (Fig. 6A). Moreover, the fast cell proliferation rate induced by NUPR1 overexpression could also be reversed by pharmacological inhibition of SREBP1 (Fig. 6B, C). The migration ability of NUPR1-overexpressing MHCC-97H cells treated with Fatostatin was also reduced compared with the control group (Fig. 6D). In addition, HCC cells treated with Fatostatin exhibited decreased lipid droplet accumulation (Fig. 6E). Overall, these results confirmed that NUPR1 overexpression-induced cell proliferation, migration and lipid droplet accumulation could be reversed by SREBP1 inhibition in vitro. The above experiments showed the effects of NUPR1 on modulating the malignant phenotype in HCC cells were dependent on SREBP1 expression. Since FASN is a key target gene of SREBP1, we hypothesized that inhibition of FASN could also reverse NUPR1-induced changes. C75, an irreversible inhibitor of FASN, was used to further examine the role of FASN in NUPR1-mediated malignant phenotype in HCC cells followed by CCK8 and transwell migration assays. As expected, NUPR1 overexpression-induced cellular proliferation could be reversed by administration of 50 μM C75 (Fig. 7A, B). Similarly, migration experiments revealed a reduced capacity for migration upon C75 treatment (Fig. 7D). As shown in Fig. 7C, lipid droplet accumulation was also reduced in HCC cells after inhibition with FASN. These observations implied that NUPR1 promoted the proliferation and migration of HCC cells by upregulating the transcriptionally active form of SREBP1, co-regulating FASN expression and promoting lipid accumulation.
NUPR1 is a stress-inducible nuclear protein that responds to cellular stress and features cancer initiation and development properties. The present study demonstrated that NUPR1 promoted HCC cells growth and migration contributing to hepatocellular cancer progression, consistent with the published literature. Elevated expression of NUPR1 in the context of a high-fat diet has previously been reported. Recent studies suggest NUPR1 protects tissues from cell injury in the context of obesity and high-fat diet [13]. Taken together, these findings strongly support a relationship between NUPR1 and lipid metabolism. Given that NUPR1 is highly expressed following a high-fat diet and participates in HCC pathogenesis, we sought to shed light on a possible relationship between NUPR1 and lipid metabolism in HCC. In the present study, a novel role of NUPR1 was uncovered in LIHC progression. NUPR1 was shown overexpressed in liver tumor tissues and correlated with a poor prognosis in LIHC patients. Interestingly, we found an interplay between NUPR1 and SREBP1. Given that NUPR1 belongs to the HMG family of chromatin remodelers with transcriptional cofactor activity, it may play an important role as a transcriptional coregulator. In subsequent experiments, we found that NUPR1 promoted HCC cell proliferation and migration by facilitating nuclear translocation of the transcriptionally active form of SREBP1 and transactivating target genes such as FASN to promote lipogenesis. Pharmacological or genetic blockade of the NUPR1-SREBP1/FASN pathway enhanced anticancer activity in vitro and in vivo. These results indicate that NUPR1 plays a cancer-promoting role by enhancing SREBP1-mediated expression of FASN and de novo lipogenesis in HCC cells. While NUPR1 has been reported in the initiation and progression of tumors, little is known on the mechanism(s) underlying the regulation of lipid metabolism of cancer cells by NUPR1. It is widely acknowledged that actively proliferating cells, especially tumor cells, have increased demands for lipids and are dependent on de novo synthesis. An enhanced de novo lipogenesis in cancer cells has long been recognized as an important characteristic of malignant tumors [18]. In this regard, elevated expression of SREBP1 has been observed in numerous types of cancer and linked to aggressive and malignant phenotypes [19, 20]. What’s more, additional co-regulatory transcription factors are required for SREBP-regulated promoters. An increasing body of evidence suggests that the maturation and nuclear translocation of SREBP1 are regulated by a variety of proteins, protein-protein interactions and epigenetic modification, which elegantly link the extracellular signals, such as insulin, or intracellular signals, such as oxidative stress, to lipid biosynthesis by modulating the transcriptional activity of SREBP1 [21, 22]. For highly proliferating cells, tumor-associated FASN is necessary for membrane lipid production and lipid droplet formation to support the increased proliferation and metabolism [23]. Unsaturated fat is a type of fatty acid with at least one double bond within the fatty acid chain that can be catalyzed by fatty acid desaturases. Unsaturated fatty acids are a key component of the phospholipids in cell membranes and help maintain membrane fluidity [24]. There is ample evidence to suggest that increased lipid unsaturation is a metabolic marker for cancer stem cells, and unsaturated fatty acids are involved in tumor progression [25, 26]. Borrello et al. pointed out that NUPR1 protected the liver from lipotoxicity by regulating fatty acid metabolism [27]. In our study, the role of NUPR1 as a regulator of fatty acid metabolism was substantiated. Although considerable efforts have been made to determine the mechanisms of lipogenic enzymes regulation, it is still unclear how dysregulation of fatty acids affects cell fate in cancer. Previous studies have demonstrated that inhibition of NUPR1 by ZZW-115, a strong NUPR1 inhibitor, yielded a powerful anticancer effect in HCC in vitro and in vivo [28]. More importantly, we demonstrated that high NUPR1-expressing hepatocellular cancer cells showed a strong proliferative ability in vitro and in vivo, while NUPR1 knockdown inhibited tumor growth in the subcutaneous xenograft model. We also found that hepatocellular cancer cells with higher NUPR1 expression levels exhibited a more invasive phenotype. Collectively, NUPR1 is a promising therapeutic target of HCC and ZZW-115 has huge prospects for clinical application in treating liver cancers. However, this study had several limitations. First, the underlying mechanism of NUPR1 in the lipid metabolism of HCC cells was not clarified in the present study. Moreover, it should be borne in mind that several fatty acid desaturases may be regulated by NUPR1, while the specific role of unsaturated fatty acids was not investigated. Besides, functional enrichment analysis showed NUPR1 was associated with non-alcoholic fatty liver disease, a major risk factor for HCC. However, the functional significance of NUPR1 in non-alcoholic fatty liver disease was not addressed. Another limitation in this study was that NUPR1 was expressed mainly in the areas surrounding the inflammatory infiltrates or fibroblast infiltration based on our IHC staining results. In addition, NUPR1 has been reported to play a crucial role in the fibrosis of the liver, pancreas and kidney [15, 29, 30]. There may be a close relationship between NUPR1 and the microenvironment in chronic hepatitis and liver cancer progression. Further studies are warranted to investigate the specific underlying mechanisms.
In summary, our findings provide novel insights on NUPR1 function and describe a putative mechanism of increased HCC cell proliferation and migration via regulation of lipid synthesis. Importantly, NUPR1 is a highly promising therapeutic target for HCC patients.
First, we compared NUPR1 expression between tumor and normal tissues across diverse cancer types using publicly available online Tumor Immune Estimation Resource 2.0 (TIMER 2.0) database, a comprehensive resource from The Cancer Genome Atlas (TCGA). Subsequently, we explored the association between NUPR1 expression and the clinical outcome of LIHC patients using the Cox proportional hazard model, adjusted by clinical stage using TIMER 2.0. In addition, we divided the LIHC samples into three groups based on their degree of NUPR1 expression (upper quartile; interquartile range; lower quartile) and compared the 10-year overall survival between the upper quartile group (High Group) and lower quartile group (Low Group). As for enrichment analysis, we collected human LIHC RNA-seq data from TCGA database LIHC project and divided the tumor samples into three groups based on their degree of NUPR1 expression (upper quartile, High Group; interquartile range; lower quartile, Low Group). The differentially expressed upregulated genes between the High NUPR1 expression and Low expression groups were subjected to Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using the R package clusterProfiler.
Formalin-fixed, paraffin-embedded specimens, including primary carcinoma specimens (n = 50) and corresponding non-tumor normal tissues specimens used for IHC were collected from HCC patients. All clinical specimens were derived from surgeries performed at Nanfang Hospital and were confirmed as HCC by a pathologist. All experiments involving human tissues were in accordance with the principles of the Declaration of Helsinki and approved by the Institutional Review Board of Nanfang Hospital (Table 1).
Tissues were fixed with 10% formalin solution and embedded in paraffin. 5 µm-thick sections were cut and baked for 60 min at 60 °C. Then, the tissue sections were deparaffinized with xylenes, rehydrated in graded ethanol, and then brought to distilled water. For antigen retrieval, sections were submerged in citrate buffer (pH 6.0) in a pressure cooker at high pressure for 5 min. Endogenous peroxide was blocked with 3% hydrogen peroxide, and 5% bovine serum albumin in PBS solution was added for 30 min to block nonspecific binding at room temperature, followed by primary antibody NUPR1 (1:150 dilution, Proteintech, Beijing, China; 15056-1-AP) incubation overnight at 4 °C. The following day, tissue sections were washed with PBS three times, incubated with secondary antibody for 60 min and washed again. After immunostaining with a DAB kit (MXB biotechnologies), sections were counterstained with hematoxylin, dehydrated and sealed.
The MHCC-97H, SK-Hep-1, Huh7 and SMMC-7721 cell lines were obtained from ATCC and cell authentication was performed via STR profiling and species authentication. Cells were cultured in Dulbecco’s modified eagle medium (DMEM) containing 10% fetal bovine serum (FBS) at 37 °C in 5% CO2. Cells were passaged before in vivo implantation, and adherent cells were harvested with 0.25% trypsin. Lentiviruses for overexpression of NUPR1 (LV-NUPR1) and NUPR1 shRNAs were designed and synthesized by wzbio (Shandong, China). For lentiviral transduction, the cell lines were cultured in 24-well plates containing 0.5 ml of DMEM medium supplemented with 5 mg/ml polybrene (Sigma, Shanghai, China) and 20 µl of viral concentrates. After 12 h infection, cells were washed and allowed to recover for 24 h before any further procedure.
Total protein extracts were obtained by lysing tumor cells in RIPA lysis buffer (Beyotime, P0013B) containing phosphatase and protease inhibitors (Kangwei, CW2200S and CW2383S). Protein concentration was analyzed by the Bradford assay (Sigma-Aldrich). Samples were size-fractionated by SDS-PAGE and then transferred to polyvinyl difluoride membranes. Blots were incubated overnight at 4 °C with primary antibody, followed by exposure to the secondary antibody goat anti-rabbit or anti-mouse IgG. Nuclear and cytoplasmic fractions were separated using a Nuclear and Cytoplasmic Protein Extraction Kit (Beyotime, P0028) according to the manufacturer’s instructions.
For protein immunoprecipitation, cells (1 × 107) overexpressed Flag-tagged NUPR1 were harvested, and lysate samples containing 2000 μg total protein were immunoprecipitated with 4 μl M2-Flag (Sigma) or SREBP1 (Santa Cruz Biotechnology) respectively at 4 °C overnight, followed by incubation with 40 μl Protein A/G plus agarose beads (Santa Cruz Biotechnology) at 4 °C for 4 h. Beads were washed and boiled in 40 µl of loading buffer, and then western blotting was conducted using SREBP1 (Proteintech, 14088-1-AP) or anti-NUPR1 (Proteintech, 15056-1-AP). The protein of input used in Fig. 4-A was about 280 µg and 8 μl used for IP.
Total RNA of cultured cells was isolated using TRIzol reagent (Accurate Biotechnology, AG21102) according to the manufacturer’s protocol. Then total RNA was reverse transcribed into complementary DNA using the Evo M-MLV RT premix for qRT-PCR (Accurate Biotechnology, AG11706). For data analysis, gene expression was normalized with beta-actin and expressed as the relative expression, which was determined by the threshold cycle (CT) as fold change = 2–Δ(ΔCT), where ΔCT = CT NUPR1 − CT β-actin and Δ(ΔCT) = ΔCT tumor–ΔCT normal. Primer sequences are provided in Supplemental Table S2.
For cell viability detection, the Cell Counting Kit-8 (CCK8) and 5-ethynyl-2′-deoxyuridine (EdU) assays were performed according to the manufacturer’s protocol. The cells were seeded in a 96-wells plate at a density of 1 ×103 cells/well and cultured in a 10% FBS DMEM medium. CCK8 reagent (Dojindo, Tokyo, Japan) was added and incubated for 2 h once a day for 6 or 7 consecutive days. EdU staining was performed using the EdU labelling kit (Ribobio, C10310-1). Cell colony formation ability was measured by plate colony formation assay. Cells (1000 or 500 cells/per well) were plated into 6-well plates and cultured for 2 weeks. Colonies were fixed using 4% paraformaldehyde, stained using 0.1% crystal violet solution and counted by Image J software. Cell viability after drug treatment was assessed in the following HCC cancer cell lines: MHCC-97H and SK-Hep1. Cells were seeded in 96-well plates (4000 cells/well) on the first day. After culture overnight, the cell medium was replaced with Fatostatin (20 μM, MCE, HY-14452) or C75 (50 μM, MCE, HY-12364) on the second day. After 48 h drug treatment, CCK8 or EdU was used to measure the cell viability, and cell viability rates were compared with the untreated group.
The cells were digested with trypsin, centrifuged, and washed twice with PBS. The concentration of cells was adjusted to 5 × 105cells/ml in a serum-free medium. The transwell chamber with 8.0 μm (for SK-Hep1, Huh7 and SMMC-7721) or 12.0 μm (for MHCC-97H) pores was inserted into a 24-well plates, 200 μl cell suspension was added in the transwell chamber. 700 μl medium containing 20% FBS was added in the lower chamber. SK-Hep1, Huh7 and SMMC-7721 cells were cultured for 10 h and MHCC-97H cells were cultured for 72 h at 37 °C in 5% CO2. After incubation, the chambers were washed twice with PBS, then fixed with 4% paraformaldehyde for 30 min, stained with 0.1% crystal violet for 20 min, and washed twice with PBS again. Transwell chamber results were photographed using an inverted microscope.
Cells were seeded in the 6-well plate with the 10% FBS medium. When the cells were grown to 90% confluence, a scratch was made across the cell monolayer with a 10 µl pipette tip to create a uniform cell-free wound area. Debris was removed by gently washing with PBS and cells were cultured in serum-free medium. The length of the cell-free area was monitored and photographed at 0, 48, and 96 h using light microscopy. Data are expressed as a percentage of the initial length at time zero.
Most hepatic lipid is stored in the hepatocyte as cytosolic lipid droplets, neutral lipid depots composed of triacylglycerol and cholesteryl esters. Therefore, BODIPY 493/503 was used to detect the neutral lipid depots in MHCC-97H and SK-Hep1 cells [31]. Cells were grown in a confocal Petri dish (NEST) and incubated for 72 h. Then the cells were washed with PBS and fixed with 4% paraformaldehyde. Subsequently, the cells were incubated with 2 µM BODIPY (Glpbio) staining solution, and then washed with PBS for three times. The nuclei were stained with DAPI and the cells were observed under a confocal laser microscope.
We created a mouse modle of cancer induced by subcutaneous injection of MHCC-97H (LV-NC and LV-NUPR1) cells or SMMC-7721 (SMMC-7721 and SMMC-7721 shNUPR1-1) cells. Five-week-old male BALB/c nude mice were purchased form the Guangdong Medical Laboratory Animal Center, Guangdong, China. The mice were randomly assigned to two groups. A total of 5 × 106 cells were suspended in 100 µl of PBS and subcutaneously injected into the right flank of BALB/c nude mice. The tumorigenesis of the nude mice was observed every 3 days. Mice were sacrificed 28 days after injection, and xenografts were dissected, photographed, weighted, fixed in neutral buffered formalin and subsequently analyzed by IHC. Animal procedures were approved by the Institutional Review Board of Nanfang Hospital.
The statistical software GraphPad Prism was used to evaluate the data in this study. An unpaired two-tailed Student’s t test was used to compare two groups, and comparisons between multiple groups were analyzed by one-way ANOVA with Dunnett post hoc test. Correlation analysis was assessed using the Pearson correlation coefficient. A p value < 0.05 was statistically significant. Error bars in all figures represent the mean ± SD. *p < 0.05; **p < 0.01; ***p < 0.001; “ns” not significant.
Supplementary figure legneds Supplementary tables Supplementary Figure 1 Full and uncropped western blots | true | true | true |
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PMC9616979 | Yong-qin Pan,Kun-song Huang,Tsz-Hong Chong,Jin-yi Li | LINC01089 blocks malignant progression of thyroid cancer by binding miR-27b-3p to enhance the FBLN5 protein level | 28-10-2022 | lncRNA,Metastasis,microRNA,Thyroid cancer | LINC01089 suppresses the malignant progression of breast, colorectal, and non-small cell lung cancers. However, the function of LINC01089 in thyroid cancer has not yet been elucidated. Here, The Cancer Genome Atlas (TCGA) database showed that LINC01089 expression is remarkably reduced in thyroid cancer tissues. Lower LINC01089 expression was correlated with higher tumor stage and regional lymph node metastasis. Furthermore, LINC01089 overexpression effectively blocked thyroid cancer cell proliferation, migration, and invasion. LINC01089 acted as a competing endogenous RNA for miR-27b-3p, thus inhibiting miR-27b-3p expression. miR-27b-3p overexpression promoted the proliferation, migration, and invasion of thyroid cancer, reversing the effect of LINC01089 overexpression on thyroid cancer. Fibulin-5 (FBLN5) was discovered as a target of miR-27b-3p in thyroid cancer. FBLN5 expression was found to be underexpressed in thyroid cancer and was enhanced and reduced by LINC00987 overexpression and miR-27b-3p overexpression, respectively. Furthermore, FBLN5 knockdown promoted the malignant progression of thyroid cancer cells by counteracting the effect of LINC00987. In conclusion, LINC01089 plays a tumor-suppressive role by binding miR-27b-3p to increase FBLN5 expression, confirming that LINC01089 has tremendous potential to become a therapeutic target for thyroid cancer treatment. | LINC01089 blocks malignant progression of thyroid cancer by binding miR-27b-3p to enhance the FBLN5 protein level
LINC01089 suppresses the malignant progression of breast, colorectal, and non-small cell lung cancers. However, the function of LINC01089 in thyroid cancer has not yet been elucidated. Here, The Cancer Genome Atlas (TCGA) database showed that LINC01089 expression is remarkably reduced in thyroid cancer tissues. Lower LINC01089 expression was correlated with higher tumor stage and regional lymph node metastasis. Furthermore, LINC01089 overexpression effectively blocked thyroid cancer cell proliferation, migration, and invasion. LINC01089 acted as a competing endogenous RNA for miR-27b-3p, thus inhibiting miR-27b-3p expression. miR-27b-3p overexpression promoted the proliferation, migration, and invasion of thyroid cancer, reversing the effect of LINC01089 overexpression on thyroid cancer. Fibulin-5 (FBLN5) was discovered as a target of miR-27b-3p in thyroid cancer. FBLN5 expression was found to be underexpressed in thyroid cancer and was enhanced and reduced by LINC00987 overexpression and miR-27b-3p overexpression, respectively. Furthermore, FBLN5 knockdown promoted the malignant progression of thyroid cancer cells by counteracting the effect of LINC00987. In conclusion, LINC01089 plays a tumor-suppressive role by binding miR-27b-3p to increase FBLN5 expression, confirming that LINC01089 has tremendous potential to become a therapeutic target for thyroid cancer treatment.
Thyroid cancer, a notable endocrine cancer, is the fourth most prevalent cancer worldwide, and its morbidity rate has rapidly increased over the past few decades [1]. Patients with thyroid cancer have a satisfactory prognosis after treatment, with a 5-year survival rate of over 98% [2]. Nevertheless, thyroid cancer has a strong tendency to metastasize to neck lymph nodes (20–50%) and exhibit distant metastasis (3–15%), which promotes tumor recurrence and results in a 5-year survival rate of metastatic patients as low as ~ 50% [3–6]. Therefore, identifying novel therapeutic targets for use in the treatment of metastatic thyroid cancer is critical. Long noncoding RNAs (lncRNAs) play an important role in regulating thyroid cancer initiation and progression. MALAT1 and OIP5-AS1, which contribute to thyroid cancer growth and progression, play an oncogenic role [7, 8]. In contrast, SLC26A4-AS1 and PTCSC3, which alleviate thyroid cancer initiation and progression, have tumor-suppressive effects [9, 10]. Therefore, further understanding of the role of lncRNAs in thyroid cancer progression is crucial. As reported, LINC01089 suppresses the malignant progression of non-small cell lung cancer (NSCLC), breast cancer, and colorectal cancers [11–13]. Whereas, LINC01089 knockdown enhanced chemosensitivity for sorafenib in hepatocellular carcinoma cells [14]. However, the function of LINC01089 in thyroid cancer has not yet been elucidated. Notably, many lncRNAs that ‘sponge’ specific miRNAs via ceRNAs function to control tumor development. LINC01089 can sponge miR-27a/27b/3187-3p in breast, gastric, and NSCLC cancers, respectively [12, 13, 15]. miRNAs play an important role in the regulation of thyroid cancer. miRNA such as miR-144-3p reduced the malignant progression of thyroid cancer while let-7e-5p promoted the malignant progression of thyroid cancer [16, 17]. Therefore, we hypothesized that miRNAs might be sponged by LINC01089, which prevents the progression of thyroid cancer. Fibrin enhances cell-to-cell adhesion in the cellular microenvironment and also affects cell behavior [18]. Fibulin-5 (FBLN5), a fibrin, has been shown to enhance endothelial cell adhesion [19]. FBLN5 reduced the invasion, mammospheres formation, and proliferation capacity of cancer cells via silencing β-catenin phosphorylation in breast cancer and NSCLC, acting as a cancer suppressor gene [20–22]. Whether FBLN5 acts downstream of LINC01089 regulation in thyroid cancer is unclear. Here, the key functions and mechanisms of LINC01089 in the malignant progression of thyroid cancer were studied. The results demonstrate the potential of LINC01089 for use as a therapeutic target in the treatment of thyroid cancer.
LINC01089 expression and differential low expression of mRNA in thyroid cancer and normal thyroid samples were analyzed using GEPIA2.0. The possible adsorbed miRNA of LINC01089 was analyzed via starBase 3.0 [23] and LncBase Predicted v.2. Differentially expressed miRNAs in thyroid cancer were analyzed using OncomiR [24]. MiR-27b-3p and miR-129-5p expression were found in thyroid cancer using starBase 3.0 [23]. The subcellular localization of LINC01089 expression was determined using the lncLocator (http://www.csbio.sjtu.edu.cn/bioinf/lncLocator/).
Cells from a normal thyroid follicular epithelial cell line (Nthy-ori 3-1) and two thyroid cancer cell lines (TPC-1 and CAL-62) (ZQXZ, Shanghai, China) were cultured according to the recommendations. The LINC01089 sequence (NR_152740.1) was compounded and cloned into pcDNA3.1 (Promega, Madison, WI, USA) and named ovLINC01089. Empty pcDNA3.1 acted as a negative control (ovNC). Additionally, NC mimic, miR-27b-3p mimic (miR mimic), siRNA for FBLN5 (siFBLN5), and NC siRNA (siNC) were procured from RiboBio (Guangzhou, China). Lipofectamine 3000 (Invitrogen, Carlsbad, CA, USA) was used for transfecting experiments.
Total RNA was extracted from Nthy-ori 3-1, TPC-1, and CAL-62 cells using TRIzol reagent (Invitrogen). The quality and concentration of RNA were detected using an Eppendorf BioPhotometer Plus (Munchen, Germany). RNA was reverse-transcribed using the RT Master Mix Kit (Promega, Madison, WI, USA), and qPCR was performed using SYBR Green qPCR SuperMix (Invitrogen) with the ABI PRISM® 7500 Sequence Detection System (Invitrogen). The primer sequences were as follows: LINC01089: 5′-CAGCGCTCAGCCTTCAGTAA-3′ (forward) and 5′-CGTTTATTGAGAGGCAGTTGTG-3′ (reverse); β-actin: 5′-GCATGGGTCAGAAGGATTCCT-3′ (forward) and 5′-TCGTCCCAGTTGGTGACGAT-3′ (reverse); miR-27b-3p: 5′-ACACTCCAGCTGGGTTCACAGTGGCTAAGTT-3′ (forward) and 5′-CTCAACTGGTGTCGTGGA-3′ (reverse); and U6: 5′-CTCGCTTCGGCAGCACA-3′ (forward) and 5′-AACGCTTCACGAATTTGCGT-3′ (reverse). LINC01089 and miR-27b-3p expression were normalized to GAPDH and U6, respectively.
The proliferation of TPC-1 and CAL-62 cells was assessed at 24, 48, and 72 h using the Cell Counting Kit-8 assay (CCK8; Dojindo, Kumamoto, Japan) at 450 nm. Migration and invasion of TPC-1 and CAL-62 cells were analyzed using the Transwell assay with or without Matrigel (BD Biosciences), based on a previous study [13].
The wild-type and mutational-type sequences of LINC01089 and FBLN5 3′-UTR sequences were compounded and cloned into the pmirGLO luciferase reporter vector (Promega). Luciferase reporter vectors and miR-27b-3p mimics were co-transfected into HEK-293T cells. After transfection for 24 h, luciferase activity was analyzed using the Dual-Luciferase kit (Promega) and was normalized to Renilla luciferase activity.
FBLN5 was analyzed by western blotting based on a previous study [13]. Briefly, the total protein (30 μg per lane) was isolated from Nthy-ori 3-1, TPC-1, and CAL-62 cells using 10% SDS-PAGE, and then the proteins were transferred onto methanol-pretreated polyvinylidene fluoride membranes. Next, membrane blocking and primary rabbit monoclonal antibody treatment were performed using the following antibodies: anti-fibulin 5 (1:3000, ab109428, Abcam) and anti-GAPDH (1:10,000, ab181602, Abcam), respectively. A dilution of Goat Anti-Rabbit IgG H&L (HRP) (1:10,000, ab205718, Abcam) was added. Enhanced chemiluminescent reagent (Thermo Scientific Pierce, Rockford, IL, USA) was used to visualize the protein abundance.
The AGO2-RIP assay was performed using the RIP Kit (Boxin, Guangzhou, China) following the manufacturer’s instructions. Then, miR-27b-3p and LINC01089 expressions were analyzed by RT-qPCR.
The statistical analyses were performed using SPSS 21.0 (IBM SPSS Statistics). Experimental data that had consistent normal distributions are shown as the mean ± standard deviation (SD). One-way analysis of variance (ANOVA) was used to study the differences between three groups, followed by Tukey’s post hoc test to study the differences between two groups. Differences between the two groups were assessed using the unpaired t-test. p < 0.05 was considered statistically significant.
First, the LINC01089 expression was analyzed using GEPIA. Figure 1A shows that LINC01089 expression was lower in thyroid cancer samples compared with that in normal samples. Moreover, LINC01089 expression was lower in thyroid cancer cells (TPC-1 and CAL-62) compared with that in normal Nthy-ori 3-1 cells (Fig. 1B). Additionally, lower LINC01089 expression was related to female patients, greater tumor size, and the metastasis of regional lymph nodes (Table 1). LINC01089 was not significantly associated with age, distant metastasis stage, pathologic stage, progression-free interval, and overall survival in patients with thyroid cancer (Table 1). The results suggested that low expression of LINC01089 may be associated with tumor proliferation and migration.
To validate the effect of LINC01089, we enhanced LINC01089 expression by transfecting pcDNA3.1/LINC01089. LINC01089 expression was higher in the ovLINC01089 group compared with that in the ovNC group in both TPC-1 and CAL-62 cells (Fig. 2A). Furthermore, the proliferation, migration, and invasion capacity of TPC-1 and CAL-62 cells were decreased in the ovLINC01089 group compared with that in the ovNC group (Fig. 2B and C).
LINC01089 expression mainly occurs in the cytosol according to lncLocator analysis, suggesting that LINC01089 may play a regulatory role by adsorbing miRNA. The predicted targets of LINC01089 were analyzed using starBase 3.0 and LncBase Predicted v.2. In addition, differentially expressed miRNAs in thyroid cancer were analyzed using OncomiR. The overlapping part of the three sets of data revealed two potential target miRNAs (Fig. 3A). Among the two miRNAs, miR-27b-3p expression was markedly overexpressed, while miR-129-5p expression was markedly reduced in the tumor samples from TCGA data (Fig. 3B). LINC01089 had binding sites with miR-27b-3p (Fig. 3C). The luciferase activity was markedly lowered in the miR-27b-3p mimic + wild-type-LINC01089 group, while there was no marked effect in the miR-27b-3p mimic + mutant-LINC01089 group, compared with the corresponding NC mimic + group (Fig. 3C). The AGO2-RIP analysis results showed that LINC01089 and miR-27b-3p expression in the AGO2 group were markedly higher than those in the IgG group (Fig. 3D). AGO2-RIP and luciferase activity analysis indicated that LINC01089 could sponge to miR-27b-3p. Moreover, miR-27b-3p expression in TPC-1 and CAL-62 cells was significantly higher than that in Nthy-ori 3-1 cells (Fig. 3E). Intriguingly, RT-qPCR analysis showed a decline of miR-27b-3p expression in TPC-1 and CAL-62 cells transfected with ovLINC01089 (Fig. 3F). These findings suggest that LINC01089 can sponge and inhibit miR-27b-3p expression in TPC-1 and CAL-62 cells.
Next, we investigated whether miR-27b-3p participates in the effect of LINC01089 on the biological function of thyroid cancer. First, to assess whether LINC01089 would saturate with the increase of miR-27b-3p, we performed rescue experiments and co-transfected ovLINC01089 and the different concentrations of miR-27b-3p-mimic into TPC-1 and CAL-62 cells. Compared with that in the ovLINC01089 + NC mimic group, miR-27b-3p expression in the ovLINC01089 + 10/20/50/100/200 nM miR mimic groups was high in both TPC-1 and CAL-62 cells (Fig. 4A). Whereas, miR-27b-3p expression exhibited no change between ovLINC01089 + NC mimic and ovLINC01089 + 1 nM miR mimic groups (Fig. 4A). In addition, LINC01089 expression exhibited no change between ovLINC01089 + NC mimic and ovLINC01089 + miR mimic (200 nM) groups, suggesting that miR-27b-3p overexpression did not affect LINC01089 expression (Fig. 4A). Meanwhile, compared with that in the ovLINC01089 + NC mimic group, the proliferation capacity of thyroid cancer cells was significantly increased in the ovLINC01089 + 100/200 nM miR mimic group in TPC-1 cell and in the ovLINC01089 + 20/50/100/200 nM miR mimic group in CAL-62 cell, while other ovLINC01089 + miR mimic groups had no significant change (Fig. 4B). According to the expression results of miR-27b-5p and proliferation results in TPC-1 and CAL-62 cells (Fig. 4A and B), it showed that only when the expression of miR-27b-5p reached a certain level, the effect of LINC01089 on cell proliferation could be significantly reversed. This result shows that LINC01089 will saturate with the increase of miR-27b-3p, causing miR-27b-3p to reverse LINC01089 effect. Compared with that in the ovLINC01089 + NC mimic group, the migration and invasion capacity of thyroid cancer cells were significantly increased in the ovLINC01089 + miR mimic (200 nM) group (Fig. 4C). This result suggested that miR-27b-3p overexpression promotes the malignant progression potential of thyroid cancer cells and weakens the LINC01089 effect.
First, significantly decreased genes in thyroid cancer were analyzed by GEPIA. Then, the possible target of miR-27b-3p was analyzed via Starbase 3.0, miRDB, and targetscan 8.0. The intersection of the above two sets of results indicated 14 potential target genes (Fig. 5A). Among the 14 potential target genes, FBLN5 mRNA expression was significantly decreased in thyroid cancer, as determined by GEPIA analysis (Fig. 5B). The luciferase assay showed that after co-transfection with wild-type FBLN5 3ʹ-UTR, the relative fluorescence value in the miR-27b-3p mimic group was significantly lower than that of the NC group, while the relative fluorescence value showed no significant change between the miR-27b-3p-mimic + mutant FBLN5 3ʹ-UTR and NC-mimic + mutant FBLN5 3ʹ-UTR groups (Fig. 5C), suggested that FBLN5 3ʹ-UTR is bonded with miR-27b-3p. In addition, the FBLN5 protein level was significantly decreased in both thyroid cancer cells. The FBLN5 protein level was increased after LINC01089 overexpression, while it was reduced in TPC-1 and CAL-62 cells after ovLINC01089 and miR-27b-3p mimic co-overexpression (Fig. 5D).
Next, we investigated whether FBLN5 is involved in the influence of LINC01089 on the biological function of thyroid cancer. We performed rescue experiments and co-transfected si FBLN5 (siNC) and ovLINC01089 into TPC-1 and CAL-62 cells. Compared with that in the ovLINC01089 + siNC group, the FBLN5 protein level in the ovLINC01089 + siFBLN5 group was significantly down-regulated in both TPC-1 and CAL-62 cells (Fig. 6A). Meanwhile, the proliferation, migration, and invasion capacity of thyroid cancer cells were increased in the ovLINC01089 + siFBLN5 group (Fig. 6B and C). This result demonstrated that FBLN5 knockdown promotes the progression potential of thyroid cancer and weakens the LINC01089 effect.
Neck lymph nodes and distant metastasis in thyroid cancer promote tumor recurrence and a low survival rate [3–6]. In this study, LINC01089 expression in thyroid cancer samples and cells was markedly reduced. Lower LINC01089 expression was significantly associated with higher T/N stages. These results verified that LINC01089 might be involved in the development of thyroid cancer. LINC01089 acts as a tumor suppressor that inhibits malignant progression capacity in breast cancer, NSCLC, and colorectal cancers [11–13]. Similarly, our study found that LINC01089 overexpression blocked the malignant progression capacity of thyroid cancer, verifying that LINC01089 also acts as a tumor suppressor. Additionally, we found that miR-27b-3p expression was enhanced in thyroid cancer tissues and cells. It is controversial whether abnormally expressed miR-27b-3p acts as an oncogene or an anti-oncogene in cancer. miR-27b-3p inhibited malignant progression capacity by targeting RUNX1, Nrf2, and MARCH7 in gastric cancer, esophageal squamous cell carcinoma, and endometrial cancer, respectively [25–27]. In contrast, miR-27b-3p accelerated tumor progression via PPARG and HOXA10 in triple-negative breast cancer and colorectal cancer, respectively [28, 29]. In addition, the knockdown of miR-27b-3p reduced the chemoresistance in doxorubicin-resistant thyroid cancer cells [30], showing that miR-27b-3p plays an oncogene role in thyroid cancer. Consistently, we found that miR-27b-3p plays an oncogene role in thyroid cancer. Additionally, LINC01089 can bind with miR-27b-3p and inhibit its expression. miR-27b-3p overexpression successfully weakened the LINC01089 effect in thyroid cancer. These results suggest that LINC01089 blocks the malignant progression of thyroid cancer cells by sponging and inhibiting miR-27b-3p. Subsequently, miRNAs bind with the 3ʹ-UTR of the target gene to reduce its protein level, thereby exerting a regulatory function. FBLN5 was discovered in this study to be the targeted gene of miR-27b-3p in thyroid cancer. In addition, FBLN5 expression was reduced by miR-27b-3p overexpression, suggesting that FBLN5 is the downstream target gene of LINC00987 in thyroid cancer. FBLN5 expression was decreased in lung adenocarcinoma, cervical cancer, and prostate cancer [21, 31, 32]. FBLN5 overexpression reduces tumor angiogenesis, suppresses lung adenocarcinoma and ovarian cancer progression, and has anti-tumor function [21, 33, 34]. In addition, FBLN5 acted as a target gene that can be regulated by miRNAs, such as miR-552/370/27a-3pp, thereby exerting the function of an anti-oncogene in NSCLC, breast cancer, and ovarian carcinoma [22, 35, 36]. Here, FBLN5 was found to be underexpressed in thyroid cancer, and its expression was enhanced by LINC00987 overexpression. Furthermore, the knockdown of FBLN5 promoted the malignant progression of thyroid cancer cells, reversing the effect of LINC00987. These results suggested that FBLN5 acts as a tumor suppressor and was the downstream target gene of LINC00987 in thyroid cancer. LINC01089 exerts a tumor suppressor effect by up-regulating the level of FBLN5. Combined with the regulatory relationship between miR-27b-3 and FBLN5 and the adsorption relationship between LINC01089 and miR-27b-3p, these results suggested that LINC01089 upregulates the expression of FBLN5 by adsorbing miR-27b-3p and exerts a tumor suppressor function in thyroid cancer. However, this study had some limitations. First, the other target genes of miR-27b-3p need to be further explored. In addition, the function of LINC01089 in thyroid cancer needs to be further verified at the animal level. Finally, because of the small number of thyroid cancer patients that experience metastases and death, the relationship between LINC01089 expression and recurrence and death needs to be further investigated. In conclusion, LINC01089 plays a tumor-suppressive role in malignant progression by inhibiting miR-27b-3p and increasing FBLN5 protein, confirming that LINC01089 has tremendous potential for use in the treatment of thyroid cancer. | true | true | true |
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PMC9617024 | 36259475 | Yaser Masoumi-Ardakani,Hamid Najafipour,Hamid Reza Nasri,Soheil Aminizadeh,Shirin Jafari Jafari,Zohreh Safi | Moderate Endurance Training and MitoQ Improve Cardiovascular Function, Oxidative Stress, and Inflammation in Hypertensive Individuals: The Role of miR-21 and miR-222: A Randomized, Double-Blind, Clinical Trial | 09-10-2022 | Endurance Training,Hypertension,miR-21,miR-222,MitoQ | Objective Hypertension (HTN) is among the leading causes of myocardial infarction, stroke, and kidney disease. The MitoQ supplement is a mitochondrial-targeted antioxidant that attenuates the generation of reactive oxygen species (ROS). miRNAs play an essential role in the pathophysiology of HTN. Regular aerobic exercise is recommended to decrease the risk of cardiovascular disease. We aimed to evaluate the effects of MitoQ supplementation and moderate endurance training (ET), alone and in combination, on cardiac function, blood pressure, the circulatory levels of miRNA-21 and miRNA-222, and oxidative status in individuals with HTN. Materials and Methods In a double-blind, randomized clinical trial (except for ET group), 52 male hypertensive subjects (40-55 years old) were randomly divided into four groups (n=13): Placebo, MitoQ (20 mg/day, oral), ET (Cycle ergometer, moderate intensity, 40-60% VO2peak, three sessions/week for six weeks), and MitoQ+ET. Cardiac echocardiography indices, serum oxidative and inflammation status, and miRNAs 21 and 222 were assessed before and after interventions. Results Left ventricular mass [effect size (ES): -6.3, 95% confidence interval (CI): -11.2 to -1.4] and end-systolic/ diastolic diameters significantly improved in the intervention groups (ES: -0.05, 95% CI: -0.11 to 0.00 and -0.09, 95% CI: -0.16 to -0.02). Total serum antioxidant capacity (TAC) increased (ES: 36.0, 95% CI: 26.1 to 45.8), and malondialdehyde (MDA) (ES: -0.43, 95% CI: -0.53 to -0.32), IL-6 (ES: -1.6, 95% CI: -1.98 to -1.25), miR-21 (ES: -0.48, 95% CI: -0.61 to -0.35), and miR-222 (ES: -0.31, 95% CI: -0.44 to -0.18) significantly decreased in response to ET, MitoQ, and their combination. Conclusion MitoQ and ET, individually and more pronouncedly in combination, can improve cardiovascular health in people with high blood pressure (BP) by reducing inflammation and increasing antioxidant defense, in association with reduction in circulatory miR-21 and miR-222 levels (registration number: IRCT20190228042870N1). | Moderate Endurance Training and MitoQ Improve Cardiovascular Function, Oxidative Stress, and Inflammation in Hypertensive Individuals: The Role of miR-21 and miR-222: A Randomized, Double-Blind, Clinical Trial
Hypertension (HTN) is among the leading causes of myocardial infarction, stroke, and kidney disease. The MitoQ supplement is a mitochondrial-targeted antioxidant that attenuates the generation of reactive oxygen species (ROS). miRNAs play an essential role in the pathophysiology of HTN. Regular aerobic exercise is recommended to decrease the risk of cardiovascular disease. We aimed to evaluate the effects of MitoQ supplementation and moderate endurance training (ET), alone and in combination, on cardiac function, blood pressure, the circulatory levels of miRNA-21 and miRNA-222, and oxidative status in individuals with HTN.
In a double-blind, randomized clinical trial (except for ET group), 52 male hypertensive subjects (40-55 years old) were randomly divided into four groups (n=13): Placebo, MitoQ (20 mg/day, oral), ET (Cycle ergometer, moderate intensity, 40-60% VO2peak, three sessions/week for six weeks), and MitoQ+ET. Cardiac echocardiography indices, serum oxidative and inflammation status, and miRNAs 21 and 222 were assessed before and after interventions.
Left ventricular mass [effect size (ES): -6.3, 95% confidence interval (CI): -11.2 to -1.4] and end-systolic/ diastolic diameters significantly improved in the intervention groups (ES: -0.05, 95% CI: -0.11 to 0.00 and -0.09, 95% CI: -0.16 to -0.02). Total serum antioxidant capacity (TAC) increased (ES: 36.0, 95% CI: 26.1 to 45.8), and malondialdehyde (MDA) (ES: -0.43, 95% CI: -0.53 to -0.32), IL-6 (ES: -1.6, 95% CI: -1.98 to -1.25), miR-21 (ES: -0.48, 95% CI: -0.61 to -0.35), and miR-222 (ES: -0.31, 95% CI: -0.44 to -0.18) significantly decreased in response to ET, MitoQ, and their combination.
MitoQ and ET, individually and more pronouncedly in combination, can improve cardiovascular health in people with high blood pressure (BP) by reducing inflammation and increasing antioxidant defense, in association with reduction in circulatory miR-21 and miR-222 levels (registration number: IRCT20190228042870N1).
Hypertension (HTN) is a risk factor for other notable cardiovascular diseases (CVDs) such as myocardial infarction and stroke (1). HTN causes 9.4 million deaths annually worldwide and is estimated to affect about 1.56 billion people by 2025 (2). Primarily because of the unhealthy and sedentary lifestyle in the new generation, the prevalence and incidence of HTN are still increasing in many countries despite the numerous antihypertensive drugs available. Therefore, having physical activity is being increasingly advised as a non-pharmaceutical treatment strategy to prevent and treat HTN. Regular aerobic exercise reduces blood pressure (BP) and attenuates the risk of CVDs (3). Oxidative stress and inflammation have also been introduced as mediators for the initiation and progression of HTN (4). Exercise training has been shown to modify the production of cytokines involved in inflammatory processes, such as interlukin-1beta (IL-1β), IL-6 and tumor necrosis factor-alpha (TNF-α) (5), and long-term endurance training (ET) has been demonstrated to reduce IL-6 and hs-CRP levels (6). Gaeini et al. (7) showed that the oxidant factor malondialdehyde (MDA) decreases even after one session of ET. Other investigators have also reported that strength training decreases MDA and increases total antioxidant capacity (TAC) and glutathione peroxidase (GPx) activity in older men and women (8, 9). Mitochondria are the sites of energy production in cells but are also involved in the production of free radicals (10), and their dysfunction plays an essential role in the pathophysiology of hypertensive injuries. In this regard, MitoQ, a mitochondrial-targeted antioxidant, has been reported to reduce free radicals, improve heart function, and attenuate BP (11-13). MicroRNAs (miRNAs) are short single-stranded endogenous RNAs that target mRNAs and are involved in the development of HTN (4, 14). MiR-21 is expressed in the cardiovascular system and is associated with CVD and different types of HTN (15). MiR-222 suppresses mitochondrial and endothelial cell function by reducing PGC-1α (16). This micro-RNA plays an essential role in cardiac physiology and pathophysiology and has been introduced as a cardiac function biomarker (17). Due to the role of oxidative stress in HTN, the role of mitochondria in ROS production, the protective effect of MitoQ on mitochondrial free radical production, and the role of exercise training on reducing oxidative stress and amelioration of HTN, this study was aimed to evaluate the effects of MitoQ supplementation and ET, alone and in combination, on cardiac function and oxidative and inflammatory status in hypertensive individuals. In this regard, the effect of the described interventions was assessed on the circulating levels of miR-21 and miR-222 as probable mediators in the interaction between ET and MitoQ supplementation in hypertensive patients.
In this double-blind randomized clinical trial study, the materials used and their sources were MitoQ (MitoQ Ltd., New Zealand), TAC, MDA, IL-6 kit (Thermo Fisher Scientific, USA), RNA isolation kit (Norgen Biotek, Cat. No. 17200, Canada), cDNA synthesis kit (Norgen Biotek, Cat. No. 54410, Canada), cel-miR-39 (Norgen Biotek, Cat. No. 59000, Canada), SYBR green (Ampliqon, Cat. No. A325402, Denmark), universal primer (reverse) (Norgen Biotek, Cat. No. 59000, Canada), and forward primers (Metabion, Germany). MitoQ capsules contained MitoQuinol (as MitoQuinol Mesylate) 20 mg as active ingredient, and microcrystalline cellulose (MCC), tapioca, and silicon dioxide (SiO2) as excipients. The role of these excipients compounds in capsule formulation are: SiO2 as an anti-caking agent, adsorbent, or glidant to allow powder to flow freely; MCC as a segregation inhibitor to improve drug content uniformity; and tapioca starch, as diluent due to its good flow ability. These compounds are generally inactive ingredients especially when used in low amounts, and we did not find any evidence for them to have side effects on the cardiovascular system.
We used G*Power:3.1.9.4 version software to calculate the study sample size. Based on the nature of the study; repeated measure ANOVA, within-between interactions (see below: statical analysis), number of groups=4, given the asumptions of α=0.05, 1-β=0.8, effect size (ES)=0.24, the total sample size was computed to be 52. In this double-blind, randomized clinical trial, middle-aged patients (40-55 years old) with moderately high BP (between SBP/DBP 140/90 to 150/100 mmHg) were entered into the study. Blinding was complete for the laboratory to measure inflammatory and oxidant factors; and for the cardiologist assessing cardiac hypertrophy and functional variables. It was not possible to make the protocol blinded in all aspects for those that exercise (ET group). Participants were selected mostly from participant in KERCADRS (Kerman Coronary Artery Disease Risk Factor Study with sample size of 10,000, aged 15- 80 years old) (45 subjects) and some from patients referred to the cardiovascular clinic of Shafa Hospital in Kerman, Iran (7 subjects) from May 2019 to March 2020. All protocols, goals, and potential benefits and risks of the study were explained to the participants, and they signed an informed consent form. All procedures followed the standards set by the latest revision of the Declaration of Helsinki and were reviewed and approved by the Ethics Committee of Kerman University of Medical Sciences (IR.KMU.REC.1397.595) and by the national RCT registry (IRCT20190228042870N1). Figure 1 shows the study flowchart. HTN was defined according to the criteria from the European Heart Association as systolic BP (SBP) ≥ 140 mm Hg or diastolic BP (DBP) ≥ 90 mm Hg. Participants with kidney, liver, and lung diseases, diabetes, cancer, known CVDs other than HTN (e.g., valvular heart disease, coronary disease and heart failure), antihypertensive and diuretic medications, SBP>150 mmHg and/or DBP>100 mmHg, high body mass index (BMI≥30 kg/m2), and orthopedic disabilities were excluded from the study. The participants’ demographic information (age, sex, history of HTN, level of physical activity, alcohol consumption, and medications) was collected by faceto-face interviews using a validated questionnaire. In the KERCADR study the baseline level of physical activity is determined using the global physical activity questionnaire (GPAQ), which includes all kind of activities, working, playing, training, housekeeping, and recreational activities (18). Fifty-two subjects were randomly divided on the basis of throwing dice into four groups of Placebo, MitoQ (20 mg/day, oral) (13), ET, and MitoQ+ET, with 13 individuals in each group (Fig .1). Moderate-intensity ET [40 to 60 % VO2 peak, heart rate (HR) 120-140 b/ minutes, duration 45 minutes, three sessions/week], was performed for six weeks. The blood samples were taken at baseline and at the end of the study (day 43). The clotted blood sample was centrifuged (3000 g for 10 minutes), and the serum was stored at -80° C for determining miRNAs 21 and 222, TAC, MDA, and IL-6.
BP (SBP and DBP) was measured with an automated device (Omron, M6 Comfort, Japan) to avoid the possibility of investigator bias in measurement. Measurement was performed twice after at least 10 minutes at rest (30 minutes apart) in a sitting position, and the values were averaged. The participants were asked to avoid consuming coffee, tea, soft drinks, supplements, and alcohol at least two hours before BP recording.
Bodyweight was measured by a medical beam balance (Allegro Medical, USA), and BMI was calculated [weight (in Kg)/height (in meter)2 ] and classified as normal (BMI<25), overweight (BMI between 25 and 29.9) and obese (BMI≥30). For assessing body fat, we used a caliper (Saehan skinfold caliper, South Korea) to measure skinfold thickness (at seven points). The Jackson and Pollock formula was used for calculating the percentage of body fat as follows (19): Body fat (%)=495/((1.112-(0.00043499 s)+(0.00000055 s s)-(0.00028826 a))-450 s: Sum of skinfolds at seven points, a: Age in years.
This test is a kind of ergometer test that is used for measuring VO2 max, as a factor for determining aerobic capacity and physical fitness. The subjects were asked to avoid drinking alcohol or caffeine-containing products, smoking, and doing strenuous activity for at least 12 hours before the test. In groups with ET, a cardiopulmonary exercise test (CPET) was performed for estimating the peak power and VO2 peak by the ergometer. The Astrand test consisted of having subjects pedal for six minutes against a constant load. This was conducted on a cycle ergometer (Monark, Ergomedic 839 E, Sweden) coupled with a gas analyzer (Cortex, Metalyzer 3B, Germany) while participants cycled in an upright position. The test consisted of a steady-state resting period, 2 minutes of warm-up without load, followed by a constant protocol in which participants pedaled at a rate of 50 ± 5 rpm for six minutes while maintaining the heart rate (HR) between 120 and 140 bpm (20) (the HR range was required to predict VO2 peak from the nomogram, Fig .2). Oxygen saturation (SpO2) (pulse oximeter, Beurer, Germany), HR, oxygen uptake (VO2), and respiratory exchange ratio (RER) were determined. Mean HR and output wattage was used to calculate the maximum oxygen consumption, and finally, the age coefficient was added to the values (21). Successful tests were defined as the completion of the 6-min test at a workload to induce HRs within the range of 120-140 bpm.
Moderate intensity ET was performed on a cycle ergometer for six weeks, three sessions a week, in the Faculty of Sport Sciences of Shahid Bahonar University of Kerman under the guidance of an expert tutor. Based on the output wattage and the amount of oxygen consumption in the Astrand test, the first training session was performed for 15 minutes with 40% to 60% of the maximum output wattage. In subsequent sessions, an average of 2 minutes was added to the training time until the duration reached ∼45 minutes. The ET duration and intensity were maintained constant at these levels in the last two weeks. Before and during training (at exercise peak), SBP, DBP, SpO2 and HR were measured (22).
Cardiac function and hypertrophy parameters were assessed by a cardiologist. Parameters including left ventricle ejection fraction (EF), left ventricular endsystolic diameter (LVESD), left ventricular enddiastolic diameter (LVEDD), relative wall thickness (RWT), left ventricular mass (LV mass), LV mass index (LV mass per body surface area in m2), and LV filling as measured by the early-to-late trans-mitral valve flow velocity ratio (E/A ratio) were obtained by a two-dimensional mode ultrasound machine (Philips, EPIQ, USA). Guided M-mode frames were scanned with simultaneous ECG for determination of HR. These parameters were assessed at baseline and the day after the end of the study.
A 5 mm fasting venous blood sample was taken from the participants at baseline and at the end of the study (day 43). The samples were centrifuged (3000 g for 10 minutes) after 20 minutes clotting time at room temperature. The ferric reducing ability of plasma (FRAP) method suggested by Benzie and Strain (1996) was used to quantify the serum TAC (23). At low pH, antioxidants present in the sample are able to reduce ferric (Fe III) tripyridyltriazine complex to an intense blue-colored ferrous (Fe II) form. This complex has a maximum absorbance at 593 nm and the blue color intensity is proportional to the antioxidant capacity of the sample. In brief, 5 μL of serum sample and 70 μL of FRAP reagent were incubated at 37˚C for 5 minutes. Then the absorbance at 593 nm was measured. For providing standard curve, known concentrations of ferrous iron were incubated with FRAP reagent and their optimal density (OD) was recorded at 593 nm to provide a concentration-response curve. Then the sample ODs were fitted on the curve to find out each serum TAC value.
MDA is an organic compound considered an index of cell membrane lipid peroxidation. The thiobarbituric acid (TBA) assay method was used (24). In a mixture of trichloroacetic acid (TCA) and TBA-hydrochloric acid, MDA reacts with TBA and develops a pink color with maximum absorbance at 535 nm. We used 20 ul of serum sample with the mixture mentioned above to determine MDA concentrations.
Serum IL-6 concentration was measured by a specific human IL-6 ELISA kit (EH2IL6, Thermo Fisher Scientific, USA). In this method, 50 µl of serum is loaded into the wells containing IL-6 antibody. Then a washing step was performed to wash the other analytes. In the next step, a substrate was added, resulting in a blue color development proportional to the amount of IL-6 in the serum. Finally, the reaction was stopped by adding the stop solution, and the amount of yellow color was assessed at a wavelength of 450 nm by an ELISA reader (DRG instrument, Cat. No. ELM-2000, Germany).
miRs were measured by reverse transcription quantitative polymerase chain reaction (RT-qPCR) method. The total serum RNA was extracted using a total RNA extraction kit. Briefly, 150 μl of serum was incubated with RL buffer and then loaded into the column that specifically captured the RNA. Finally, these captured RNAs were washed from the column by RNAse-free water. The extracted RNA concentration and purity were determined by NanoDrop ND-2100 (Thermo Fisher Scientific, USA). To reduce sampling errors and normalize samples 3.5 μl Canorhabditis elegans miR-39 (cel-miR-39) was added to each sample as external control. Then, cDNA was synthesized from 5 μl extracted RNA using the microScript microRNA cDNA synthesis kit. To perform real-time PCR, we used synthesized cDNA, specific primers (for miR21 and miR-222), and high ROX RealQ Plus Master Mix Green, and the mixture was amplified in the StepOnePlus instrument (Applied Biosystems, USA). The relative expressions of miR-21 and miR-222 were normalized to cel-miR-39 as external control. The expression was calculated as fold change according to the formula, Fold change=2–ΔΔCT, where ΔΔCT=[(CT gene-CT cel-miR-39)treatment-(CT gene-CT cel-miR-39)]CTL (25). The forward primer sequences of miRs were: miR-21: 5′-TAGCTTATCAGACTGATGTTGA-3′ miR-222; 5′-AGCTACATCTGGCTACTGGGT-3′ cel-miR-39: 5′-UCACCGGGUGUAAAUCAGCUUG-3′ We used a universal primer that the company supplied as a reverse primer in the reactions.
Data analysis was performed by SPSS software (SPSS version 26, SPSS Inc., Chicago, IL, USA). The data distribution was determined by the KolmogorovSmirnov test, and if it was normal, two-way repeated measure ANOVA was used to assess the differences among the study groups, followed by Bonferroni post hoc test for pairwise comparisons. Nonparametric equivalent tests were used when the distribution of the data was not normal. Comparison of variables in each group between the baseline and its own follow up value (e.g., for HR, BMI, VO2 peak values) was performed by the paired t test. The Chi-Square test was used for descriptive statistics (history of HTN, smoking, and the level of physical activity). P≤0.05 was considered as the significance level.
The study groups were similar in demographic and general characteristics at baseline, and no significant differences were observed among them in these aspects (Table 1). The baseline parameters including: age (P=0.16), history of HTN (P=0.39), BMI (P=0.18), the basal level of physical activity (P=0.15), SBP (P=0.29), and DBP (P=0.09) were not significantly different among the groups. Body weight and BMI in the groups that performed ET significantly decreased (P<0.05) compared to the baseline values. Resting heart rates (HR) were 76 ± 1.8, 76 ± 1.9, 72 ± 1.8 and 74 ± 2.0 beats/minutes in placebo, MitoQ, ET and MitoQ+ET groups, respectively (P>0.05). Peak HR and baseline VO2 in the ET group were 127 ± 2.5 beats/minutes and 3.1 ± 0.13 L/minutes, respectively; in MitoQ+ET group these values were 132 ± 2.5 beats/ minutes and 3.0 ± 0.13 L/minutes, respectively. Both of these variables were in the range anticipated by executing moderate ET protocol.
Exercise and MitoQ alone and in combination significantly decreased SBP compared to the baseline (P<0.001, Table 1), DBP decreased only in the combined (MitoQ+ET) group compared to the baseline (P<0.01).
Body fat percentage showed a significant decrease in the ET and the ET+MitoQ groups (P<0.05) compared to their baseline (Table 1). Also resting SpO2 s were 95 ± 0.1%, 93 ± 0.2%, 95 ± 0.2% and 95 ± 0.2% in the placebo, MitoQ, ET and MitoQ+ET groups, respectively (P>0.05). Peak SpO2 in ET was 94 ± 0.2% and in MitoQ+ET was 93 ± 0.2%, which were both in the normal range.
Table 2 shows that LV mass (normal range: 96-200 g) and LVESD (normal range: 2.5-4.0 cm) significantly decreased in the combined group compared to their pre-intervention values (P<0.01). However, EF (normal range: 52-72%), LV mass index (normal range: 50-102 g/ m2), LVEDD (normal range: 4.2-5.8 cm), RWT (normal range: 0.24-0.42 cm), and E/A ratio (normal value: 1.35 ± 0.5) were not changed significantly by the interventions (Table 2).
After six weeks of ET and MitoQ intake, the serum TAC level significantly increased, and MDA and IL-6 significantly decreased in all intervention groups compared to their baseline values (P<0.001, Fig .3). The effect of combined therapy on TAC and MDA was more than the effect of ET or MitoQ alone.
MitoQ, ET, and MitoQ+ET interventions caused a significant reduction in serum miR-21. ET and MitoQ+ET reduced serum miR-222 significantly as well (Fig .4).
The main results of the present study were that the combination of MitoQ and ET significantly decreased SBP and improved cardiac function indices, including LV mass and LVESD in hypertensive patients. Also, MitoQ, ET, and their combination reduced IL-6 levels and increased antioxidant defense capacity. Serum miR21 levels decreased in all intervention groups, while ET and MitoQ+ET interventions reduced miR-222. MiR-21 is related to vascular remodeling, ROS production, level of C-reactive protein (CRP), and arterial stiffness (15, 26, 27). MiR-21 was also related to left ventricular (LV) mass index in patients with HTN. Our results showed that MitoQ, ET, and their combination significantly lowered circulating miR-21 levels, which was associated with reducing LV mass and LVESD. This confirms the improving effect of ET, MitoQ, and their combined therapy on cardiac hypertrophy and on improving cardiac systolic function. It has been well documented that oxidative stress, some miRNAs, and inflammation are involved in HTN development (4). Here, MitoQ and ET alone and in combination could reduce the expression of the two HTNassociated miRNAs, miR-21 and miR-222, caused a reduction in the inflammatory (IL-6) and oxidative (MDA) factors, and increased the antioxidant capacity (TAC) in our hypertensive participants. MitoQ and ET also reduced SBP more efficiently when they were combined compared to their individual effects. The results are in line with our hypothesis that MitoQ, as a mitochondria-targeted antioxidant, may cause improvement in body redox state in association with reduction of miR-21 and miR-222. The more efficient effect of combination therapy in reducing HTN may be related to a more efficient reduction in MDA and a greater decrease in miR-222 expression. On the other hand, it has been reported that exercise attenuates pro-inflammatory cytokines (IL-1, IL-6, and TNF-α) levels and increases anti-inflammatory cytokines (IL-10) (28). In this study, MitoQ and moderate ET, alone and in combination, reduced IL-6 and increased TAC serum levels in hypertensive individuals associated with lowering of miR-21 and miR-222 levels (especially miR21). However, there are some studies whose findings are inconsistent with our results. For instance, in one study, two weeks of continuous ET (3 days/week) did not change CRP, IL-6, and IL-10 levels (29). In another study, after 16 weeks of ET, there was no change in IL-6 levels (30). Also, combined aerobic and resistance training (12 weeks) had no effects on IL-6 level (31). The discrepancies may be related to the differences in the intensity of training, which has been reported to be a determining factor affecting IL-6 release into the circulation (32). Also, the different periods and types of training may be factors that can affect the IL-6 production or release. In a study in young healthy men with MitoQ supplementation for 3 weeks during ET, it was observed that MitoQ did not affect skeletal muscle or whole-body aerobic adaptations to exercise training (33). These results verify that the responses to MitoQ may be dependent on healthy/diseased conditions, and/ or the duration of supplementation. Meanwhile this study suggests that although MitoQ may not modulate the ET adaptations, it still potentiates the beneficial effects of exercise on HTN, being found in the present study. Previous studies have proved that miR-21 exacerbates HTN by increasing IL-6 and TNF-α levels (15, 26). However, some studies have shown that miR-21 was not altered by exercise training (34). These discrepancies over miR-21 levels following exercise may be due to the different types of exercise or the sample used to measure miR-21 levels (blood or tissues). Similarly, there has been inconsistent data regarding miR-222 actions, including both its beneficial and detrimental effects. In one study on aortic endothelial cells, miR-222 caused mitochondrial dysfunction and ROS generation (16), and its inhibition reduced inflammation-induced ROS generation (35). Conversely, circulating miR-222 has been shown to be an athero-protective agent by affecting eNOS activity, thereby dilating blood vessels and lowering BP (36). It also protects cardiac tissue against ischemic injury (17). However, there is no data about the effect of MitoQ on miR-222 in human. This study showed that oral MitoQ supplementation did not change circulating miR-222 levels in hypertensive patients in spite of reducing their BP. At the same time, ET and the combination of MitoQ and ET significantly reduced miR-222 and BP levels. It seems that, unlike ET, the mechanism by which MitoQ reduces BP is independent of changes in miR-222 levels. That is why the BP lowering effect of MitoQ is added to the BP lowering effect of ET without more reduction in the level of miR-222 when they are combined. Regarding the effect of exercise on miR-222, a study showed that circulating miR-222 increased after acute exhaustive cycling exercise (37). In the present study, in which the participants performed moderate ET, a reduction in the circulating miR-222 was observed. It seems that the expression of this miRNA and its beneficial/detrimental effects depend on the type, intensity, and duration of exercise (38). We acknowledge the limitations of our study. Due to ethical considerations, we could not include patients with more severe HTN in the study as otherwise we had to discontinue their medications and put them at a high risk when performing ET. MitoQ may be found more effective in lowering BP in patients with more severe HTN, especially when combined with ET. Also, in a study with a higher sample size the effect of MitoQ may be more pronounced and the results may strengthen. Moreover, higher doses of MitoQ and more extended periods of treatment may show better outcomes. These modalities need further investigation.
Overall, the data from this study showed that concurrent moderate ET and MitoQ significantly reduced BP, MDA, and IL-6 serum levels in HTN subjects. It also increased TAC and either improved the antioxidant status or reduced free radicals. Other outcomes were reduction in serum miR-21 and miR-222 levels in hypertensive subjects, which was associated with improvement in cardiac LV mass index and systolic function. More studies with higher doses of MitoQ and more extended periods of treatment, alone and in combination with ET, are needed to further clarify the effects of these interventions on BP of patients with different levels of HTN. | true | true | true |
PMC9617025 | 36259474 | Fatemeh Mirzadeh Azad,Elham Taheri Bajgan,Parisa Naeli,Alexander Rudov,Mahrokh Bagheri Moghadam,Mozhgan Sadat Akhtar,Akram Gholipour,Seyed Javad Mowla,Mahshid Malakootian | Differential Expression Pattern of linc-ROR Spliced Variants in Pluripotent and Non-Pluripotent Cell Lines | 09-10-2022 | linc-ROR,Pluripotency,Spliced Variants,Stem Cell | Objective The human large intergenic non-coding RNA-regulator of reprogramming program (linc-ROR) is known as a stem cell specific linc-RNA. linc-ROR counteracts differentiation via sequestering microRNA-145 (miR-145) that targets OCT4 transcript. Despite the research on the expression and function, the exact structure of Linc-ROR transcripts is not clear. Considering the contribution of alternative splicing in transcripts structures and function, identifying different spliced variants of linc-ROR is necessary for further functional analyses. We aimed to find the alternatively spliced transcripts of linc-ROR and investigate their expression pattern in stem and cancer cell lines and during neural differentiation of NT2 cells as a model for understanding linc-ROR role in stem cell and differentiation. Materials and Methods In this experimental study, linc-ROR locus was scanned for identifying novel exons. Different primer sets were used to detect new spliced variants by reverse transcription polymerase chain reaction (RT-PCR) and direct sequencing. Quantitative PCR (qPCR) and RT-PCR were employed to profile expression of linc-ROR transcripts in different cell lines and during neural differentiation of stem cells. Results We could discover 13 novel spliced variants of linc-ROR harboring unique array of exons. Our work uncovered six novel exons, some of which were the product of exonized transposable elements. Monitoring expression profile of the linc-ROR spliced variants in a panel of pluripotent and non-pluripotent cells exhibited that all transcripts were primarily expressed in pluripotent cells. Moreover, the examined linc-ROR spliced variants showed a similar down- regulation during neural differentiation of NT2 cells. Conclusion Altogether, our data showed despite the difference in the structure and composition of exons, various spliced variants of linc-ROR showed similar expression pattern in stem cells and through differentiation. | Differential Expression Pattern of linc-ROR Spliced Variants in Pluripotent and Non-Pluripotent Cell Lines
The human large intergenic non-coding RNA-regulator of reprogramming program (linc-ROR) is known as a stem cell specific linc-RNA. linc-ROR counteracts differentiation via sequestering microRNA-145 (miR-145) that targets OCT4 transcript. Despite the research on the expression and function, the exact structure of Linc-ROR transcripts is not clear. Considering the contribution of alternative splicing in transcripts structures and function, identifying different spliced variants of linc-ROR is necessary for further functional analyses. We aimed to find the alternatively spliced transcripts of linc-ROR and investigate their expression pattern in stem and cancer cell lines and during neural differentiation of NT2 cells as a model for understanding linc-ROR role in stem cell and differentiation.
In this experimental study, linc-ROR locus was scanned for identifying novel exons. Different primer sets were used to detect new spliced variants by reverse transcription polymerase chain reaction (RT-PCR) and direct sequencing. Quantitative PCR (qPCR) and RT-PCR were employed to profile expression of linc-ROR transcripts in different cell lines and during neural differentiation of stem cells.
We could discover 13 novel spliced variants of linc-ROR harboring unique array of exons. Our work uncovered six novel exons, some of which were the product of exonized transposable elements. Monitoring expression profile of the linc-ROR spliced variants in a panel of pluripotent and non-pluripotent cells exhibited that all transcripts were primarily expressed in pluripotent cells. Moreover, the examined linc-ROR spliced variants showed a similar down- regulation during neural differentiation of NT2 cells.
Altogether, our data showed despite the difference in the structure and composition of exons, various spliced variants of linc-ROR showed similar expression pattern in stem cells and through differentiation.
One of the main achievements of genomic era was the discovery of myriads of long non coding RNA transcripts (lncRNAs) that show state specific expression in different samples and biological processes. This finding suggested lncRNAs as new regulators of diseases and biological events (1, 2). lncRNAs could be detected in the nucleus and cytoplasm and they could exert their regulatory functions through a broad range of mechanisms entailing hybridization to RNA (3) or DNA sequences (4), interaction with transcription factors (5), epigenetic regulators (6). One functional manifestation that showcased the dependency of lncRNAs on interactions with different molecules, was discovery of lncRNAs that operate as competitive endogenous RNA (ceRNA) to compete with mRNAs for binding to regulatory miRNAs (7). However, there are still incomplete experimental evidences to validate the exact mechanisms of lncRNA-disease associations (8). Previous studies demonstrated that some long noncoding RNAs such as metastasis associated lung adenocarcinoma transcript 1 (MALAT1) and psoriasis susceptibility 1 candidate 3 (PSORS1C3) were able to exert their various regulatory roles at transcriptional and post-transcriptional levels through production of different transcript spliced variants (9-11). Among lncRNAs, long intergenic non coding RNAs (linc-RNAs) are located between two protein coding genes and usually display high expression levels. lincRNAs exhibit specific expression patterns in different cell types and tissues. They are involved in cellular processes like stemness maintenance, cell cycle regulation and differentiation, however, their exact mechanism of function is still unresolved (12, 13). The large intergenic non-coding RNA-regulator of reprogramming (linc-ROR, lincRNA-ROR), was firstly introduced in 2010 by Loewer et al. (14) as a 2.6 kb long transcript. This linc-RNA modulates reprogramming of the human induced pluripotent stem cells by sequestering miR-145 (15). Deregulation of linc-ROR expression is associated with tumorigenesis in various malignancies such as esophageal (16), pancreatic (17), gastric (18), colon (19), ovarian (20) and breast cancers (21). However, complete transcript repertoire of this lncRNA has not been clarified yet. In this study, we experimentally validated novel transcript variants for linc-ROR (Fig .1A, B). We also monitored expression pattern of some of these novel spliced variants in different cells and during the neural differentiation of NTera-2 cells to see if they behaved differently.
The study was approved by Research Ethics Committee of Rajaie Cardiovascular Medical and Research Center (IR.RHC.REC.1397.016).
Using the UCSC genome browser, linc-ROR genomic locus was scanned for the conserved regions and existence of TE elements specifically long interspersed nuclear elements (LINE) and short interspersed nuclear elements (SINE) repeats.
Dr. Peter Andrews, University of Sheffield was generously provided the human embryonic stem-like cell line NTERA2cl.D1 (NT2). The human embryonic stem cell lines, including hESC-RH5, hESC-RH6 (22), human induced pluripotent stem cell line 1 and human induced pluripotent stem cell lines 4 (hiPSC1 and hiPSC4 respectively) were obtained from Royan institute (Tehran, Iran) and cultured as described previously (23). Human cell lines emanated from bladder carcinoma (5637), breast adenocarcinoma (MCF-7), hepatocellular carcinoma (HepG2), prostate cancer (PC3), prostatic adenocarcinoma (LNCAP), colorectal adenocarcinoma (HT-29), malignant glioma cell lines (U-87MG, A172), brain astrocytoma (1321N1), medulloblastoma (DAOY), cervix adenocarcinoma (Hela), hepatoblastoma (Huh-7), colon adenocarcinoma (SW480), esophageal squamous cell carcinoma (KYSE-30) and gastric carcinoma (AGS), were obtained from Pasture Institute of Iran (Tehran, Iran). The human embryonic kidney 293 (HEK293T), human lung adenocarcinoma (A549), human USSC (unrestricted somatic stem cells) and fibroblast cells were purchased from the Stem Cell Technology Company (Tehran, Iran). The cells were cultivated to reach 70% confluency before collection at 37◦ C with 5% humidified CO2 in RPMI 1640 (for U-87MG, A172, 1321N1, DAOY) or high glucose Dulbecco’s Modified Eagle Medium (DMEM, Invitrogen, USA) supplemented with 10% FBS (Invitrogen), 100 U/ Ml penicillin, 100 mg/ml streptomycin and 25 ng/ml amphotericin B.
Total RNA was extracted from cell lines using TRIzol reagent (Invitrogen, USA), according to the manufacturer’s instructions. RNase free DNaseI (TaKaRa, Japan) treatment was applied to remove any possible traces of DNA contamination. Reverse transcription of RNA was primed using an oligo (dT) primer and random hexamer by applying the PrimeScript TM Reagent kit (TaKaRa, Japan). Each sample had a no-reverse transcription (NoRT) control in parallel with the DNase-treated RNA to detect any potential non-specific amplification of genomic DNA.
The GeneRunner (version 3.02, Hastings Software Inc., USA), PerlPrimer v1.1.16 and Oligo v 6.54 softwares were utilized to design the specific amplifying primers for qualitative and quantitative reverse-transcription PCR (RT-PCR) of both linc-ROR (GenBank accession numbers NR_048536 (HQ315778), AB844430, AB844431, AB844432, AB844433, AB908956 and AB932951) and β2 microglobulin (β2M, as an internal control; GenBank accession number: NM_004048.2), human glyceraldehyde 3-phosphate dehydrogenase (GAPDH, as an internal control; GenBank accession number: NM_002046). RT-PCR reactions were carried out using 2 µl of the synthesized cDNA, 0.5 mM of each primer and 10 µl of Taq DNA polymerase master mix RED (Ampliqon, Denmark). PCR cycling parameters were comprised of initial cDNA denaturation of 5 minutes at 94ºC, followed by 45 seconds at 94ºC, 45 seconds at annealing temperature 60ºC and DNA extension for 1 minute at 72ºC for 35 and 26 cycles (for linc-ROR and β2M amplification, respectively). A final extension step was performed at 72ºC for 10 minutes. All PCR reactions incorporated no template controls or no RT reaction. Sequences of the designed oligos were listed in Table 1. PCR products were electrophoresed on 1% or 1.5% agarose gel electrophoresis, followed by staining with ethidium bromide and visualization through UV light exposure. Quantitative RT-PCR (qRT-PCR) was carried out applying 2 µl of the synthesized cDNA, 10 µl of SYBRGreen ready mix (TaKaRa, Japan), 0.1 µl of Rox and 0.5 µM of each specific primer. β2M gene was utilized as an internal control, and expression of the other genes was normalized to its expression level using the 2-∆Ct method. ABI 7500 real-time PCR system (Applied Biosystems, USA) was employed to execute the PCR reactions using the following cycling conditions: initiation at 95o C for 15 minutes, amplification for 40 cycles with denaturation at 95o C for 15 seconds, annealing at 62.5o C for 30 seconds and extension at 72o C for 30 seconds. Melt curves were analyzed to validate the PCR products and amplified products were sequenced.
All linc-ROR PCR products were purified from agarose gel with ExpinTM combo kit (GeneAll, South Korea). Then, PTG19-T vector (Vivantis, Malaysia) was applied to clone the purified products using T4 DNA ligase (Fermentas, USA) and transformed to DH5α competent cells (TaKaRa, Japan). Recombinant colonies with resistance to ampicillin (Sigma-Aldrich, USA) were selected as positive ones. Universal M13 primers were employed to select different variants of the linc-ROR gene via colony check PCR. Sanger sequencing (Microgen, South Korea) was utilized to validate identity and validity of the PCR products.
Peter Andrews protocol, which was described before (24), was applied to induced neural differentiation of NT2 cells. Concisely, NT2 cells were treated with 10-5 M all-transretinoic acid (RA, Sigma-Aldrich, USA) for up to 21 days. Then, the cells were passaged and re-cultured without retinoic acid for additional two weeks. NT2 cells were also treated with 1% DMSO (RA solvent) as a control group. The cells were then collected at different time points (3rd, 7th, 14th, 21st, 25th and 32nd days) for further analyses.
All experiments were replicated at least three times. GraphPadPrism 8 software (GraphPad Software,USA) was employed to perform Student T test. All values were presented as means ± standard error of mean (means ± SEM) and P<0.05 were considered statistically significant.
To profile expression of linc-ROR in different stem and cancer cell lines, we firstly designed a primer set on the lncRNA terminal exon (F4R4 primers, Fig .1A) with the idea to detect all possible spliced variants (Fig .1B). Our initial investigation on the expression of linc-ROR transcripts by qRT-PCR revealed that NT2 cells had the highest expression levels for linc-ROR (Fig .2A). Among the cancer cells, Huh-7, HEK293T and DAOY showed a relatively higher expression level for linc-ROR. Expression of linc-ROR was undetectable in fibroblast cells representing somatic normal cells. Our bioinformatics analysis on the linc-ROR locus revealed that this lncRNA was overlapped with different transposable elements (TEs, Fig .1A). Based on the previous researches, TEs could contribute to create new alternatively spliced variants since they provide splice site acceptors and donors and they can be exonized into the transcript (25). To find out whether linc-ROR transcripts contained any TE driven exons, we specifically designed different sets of oligos mapping on different TEs to capture those potential transposon containing exons (Fig .1A, B). Primers, designed on the junction of linc-ROR first and second exons (F3) and reverse primers on exon 3 (R1), were used to capture transcripts with possible TEs as exon. These primers were used in RT-PCR on NT2 cells since they showed the highest expression level of linc-ROR. In this experiment, we could detect several amplicons with different sizes that differ from what we expected to see from the RefSeq sequence (320 bp, Fig .2B). To uncover the identity and structure of these bands, they were isolated from the gel, cloned, sequenced and aligned against the human genome (hg19) and transcript sequences. The results demonstrated existence of five novel linc-ROR spliced variants retaining different parts of the intron 2 sequence, as novel exons, some overlapped with TE elements. To examine whether we could also detect transcripts with extra exons in different cancer cells, we carried out RTPCR analysis with the same set of primers in a panel of different cells. The result revealed a diverse pattern of expression for different variants in those cell lines (Fig .2C). The HT-29 and 5637 cells displayed almost the same expression pattern as NT2 cells. Furthermore, our bioinformatic analysis displayed that there are three highly conserved regions approximately in the middle of the second intron of linc-ROR and we hypothesized that maybe these parts also could take part in creating new variants as well. Our RT-PCR outcomes using F1R3 primers in NT2 cells exhibited a novel spliced variant of linc-ROR (Fig .2D) with the AB908956 accession number (variant 6) that contained exon 1, 2 and a part of the conserved sequence in the second intron (Fig .1A, B). To find whether we could detect the transcripts containing novel exons in full length, we designed two sets of primers, located on the beginning (Fex) of exon 1 and the end of exon 4 (Rex), followed by performing RT-PCR on human pluripotent cells (hiPSC1, hiPSC4, hESC-RH5 and hESC-RH6). The expected band for the transcript containing all exons was about 2.6 kb. Our results exhibited that there were additional bands with alternative sizes (Fig .2E). To confirm identity of the bands and presence of novel exons, all bands were extracted from the gel, cloned, sequenced and aligned to the human genome and transcript sequences (UCSC genome browser, hg19). Aside from validating the spliced variants that we first detected in NT2 cells, we identified eight novel variants for linc-ROR. Blat analysis exhibited two of the newly identified variants, retained some parts of LINE and SINE sequences which are located in intron 2 of the linc-ROR gene (Fig .1A, B).
To scrutinize the correlation between the expression of Linc-ROR spliced variants and the undifferentiated state of human embryonic stem and embryonic carcinoma cells, NT2 human cells were treated with all-trans-retinoic acid (24). qRT-PCR results (using primers which could detect all transcripts) demonstrated that expression of linc-ROR was downregulated in the course of neural differentiation. Surprisingly, decline in linc-ROR expression was detected before downregulation of master regulators of pluripotency OCT4A, SOX2, NANOG and miR-302b. Adversely, expression of miR-145 was upregulated gradually during differentiation (Fig .3A). To see if the observed downregulation for linc-ROR was a trend affecting different variants, we used semiqRT-PCR. Our results revealed that all detected linc-ROR spliced variants were downregulated the same way in the course of neural differentiation (Fig .3B, C), a pattern that was not seen in tumor samples, where some isoforms showed upregulation and some exhibited downregulation [unpublished data, (16)].
linc-ROR plays a pivotal role in regulating selfrenewal and reprogramming of pluripotent stem cells (12, 15). linc-ROR, some of the major regulators of pluripotency and self-renewal such as OCT-4 share common expression signatures in some tumors and cancer cell lines (26) which advocate the hypotheses of the involvement of cancer stem cells and potential association of these factors in tumorigenesis. By scanning the genomic location of Linc-ROR which harbored transposon elements, we predicted there must be some transcript spliced variants of Linc-ROR. Our study has introduced and confirmed existence of the 14 different spliced variants of lincROR expressing in different stem and non-stem cells and in the course of differentiation. Our group previously reported differential expression of the three spliced variants of linc-ROR in human esophageal squamous cell carcinoma (16). In this study, we introduced 13 novel spliced variants for linc-ROR. Our investigations showed these variants were highly expressed in stemlike cells and differentially detectable in different cancer cell lines. For instance, linc-ROR expression level was not similar in different glioblastoma cell lines that we examined in this research. This could be due to differences in cancer stem cell pool, represented grade and stage and intra population heterogeneity of these cell lines. The evolutionary role of alternative splicing in finetuning gene function and transcriptome dynamics in eukaryotes has been established previously (27) and it is known for many protein coding (28) and noncoding genes (10) with different transcript spliced variants. For instance, OCT4, a master regulator of pluripotency, has various isoforms with different expression and distinct functional roles (10, 26, 29). Our team also previously reported this phenomenon for long noncoding RNA PSORS1C3, which has 24 spliced variants with different expression pattern and function in pluripotent and non-pluripotent cells (10, 11, 30). Another example is MALAT1, one of the well-studied lncRNAs with various spliced variants that differ greatly in their expression patterns and functions in different cells and tissues (9, 31). There is an increasing number of publications on the function of linc-ROR and its expression. However, differential expression of its spliced variants in different cell types and tissues needs to be elucidated. Here we detected new variants of linc-ROR transcripts in pluripotent stem cells and cancer cells, showing their expression alteration during neural differentiation. Our data indicated that many of linc-ROR spliced variants behaved similarly and their abrupt downregulation upon differentiation fit with their suggested role as guardians of stemness circuits. Furthermore, the genomic location of linc-ROR contained transposon elements (TE), which are considered as selfish genomic parasites (32). Several studies argued that TE were involved in different transcriptional regulatory networks (32-34), as well as harboring splicing signals, leading to being spliced as new exons (25, 35). Our result exhibited that these elements could contribute to linc-ROR transcripts as new exons. TE derived sequences are considered as functional domains of lncRNAs which enable them to interact with and regulate RNA species and proteins (36). It is possible that linc-ROR exonized TEs are also involved in different functional abilities of their harboring transcripts. To validate this, further investigations are required. Here, we showed that linc-ROR had higher expression level in NT2 cells, as a pluripotent human embryonal carcinoma cell line. This data is in line with the published association of linc-ROR with stemness state (14). Our data also pinpointed the significant decrease in linc-ROR and its transcript spliced variants expression throughout neural differentiation in both quantitative and semi-quantitative approaches which is in line with previous findings on the contribution of linc-ROR in self renewal and pluripotency through regulating core stemness factors (15, 37-39). Mis-regulation of mRNA splicing can affect signaling pathways and contribute to diseases like cancer (40). The alternative splicing of lncRNAs might also impact various cellular processes, however, understanding the exact molecular effect of alternatively spliced exons needs to be further investigated.
The detailed picture of linc-ROR regulatory network is still missing. We identified 13 novel splice variants for linc-ROR that were expressed in pluripotent and some cancer cells. Our results, together with those from previous studies, depicted new insights into investigations of the molecular repertoire of lncRNAs, suggested a new angle for the scrutinizing lncRNA genes and showed the missing pieces that are needed for lncRNAs expression and function investigations. | true | true | true |
PMC9617129 | Mengmeng Li,Lei Jiao,Yingchun Shao,Haodong Li,Lihua Sun,Qi Yu,Manyu Gong,Dongping Liu,Yanying Wang,Lina Xuan,Xuewen Yang,Yunmeng Qu,Yaqi Wang,Lintong Jiang,Jingjing Han,Ying Zhang,Yong Zhang | LncRNA-ZFAS1 Promotes Myocardial Ischemia-Reperfusion Injury Through DNA Methylation-Mediated Notch1 Down-Regulation in Mice | 26-09-2022 | DNA methylation,long noncoding RNA,myocardial ischemia-reperfusion injury,Notch1,ZFAS1,AAV, adeno-associated virus,DNMT, DNA methyltransferase,HR, hypoxia/reoxygenation,lncRNA, long noncoding RNA,MI, myocardial infarction,MIRI, myocardial ischemia-reperfusion injury,NICD, Notch intracellular domain,NMCM, neonatal mouse cardiac myocytes,NMN, nicotinamide mononucleotide,ROS, reactive oxygen species,shZFAS1, short hairpin RNA ZFAS1,siZFAS1, small interfering RNA ZFAS1,TG, transgenic,WT, wild-type,ZFAS1, zinc finger antisense 1 | Visual Abstract | LncRNA-ZFAS1 Promotes Myocardial Ischemia-Reperfusion Injury Through DNA Methylation-Mediated Notch1 Down-Regulation in Mice
Coronary artery disease is one of the most common causes of mortality in the world. The standard treatment for repairing blood supply to the ischemic myocardium is recovery of reperfusion. However, reperfusion also induces major cardiac damage, which is commonly known as myocardial ischemia-reperfusion injury (MIRI). MIRI has been correlated with oxidative stress, autophagy, endoplasmic reticulum stress, apoptosis, calcium overload, and epigenetic alterations. Oxidative stress or reactive oxygen species (ROS) generation is the important initiating mechanism of MIRI, the occurrence of which is the key to distinguishing myocardial infarction (MI) from MIRI. MIRI is a complex pathologic condition involving various signaling pathways. The Notch signaling pathway is involved in a variety of heart functions5, 6, 7 and is composed of 4 Notch proteins (Notch 1-4) and 5 ligands, namely, Jagged 1, Jagged 2, Delta-like 1, Delta-like 3, and Delta-like 4. The Notch protein is cleaved into the Notch intracellular domain (NICD) and released into the cytoplasm when the ligand on the neighboring cell surface interacts with the Notch receptor. After being cleaved, the NICD enters the nucleus and binds with the DNA-binding protein CSL (CBF1)/Su (H)/Lag-1, which triggers downstream gene transcription (such as Hes1 and Hey1). Inhibiting the Notch signaling has been shown to alter the energy supply of cardiomyocytes, leading to cardiac dysfunction. Notch1 gene inhibition can reduce the expression of Hey1 while enhancing Runx2 expression, resulting in aortic valve calcification. High glucose can increase the sensitivity to myocardial ischemia by inhibiting the Notch signaling pathway in mice. Importantly, Notch1, as a cardioprotective factor, can inhibit cardiomyocyte apoptosis and oxidative stress caused by MIRI, yet its upstream regulatory mechanism is still unclear. DNA methylation is an epigenetic alteration correlated with changes in transcription. In cell proliferation, cell differentiation, apoptosis, and autoimmunity, DNA methylation is critical. The inhibition of Apaf1 expression is enhanced by DNA methylation of its promoter. Long noncoding (lnc) RNA, cardiomyocyte proliferation regulator, also participates in cardiomyocyte proliferation and cardiac repair by regulating the DNA methylation of MCM3. Transcriptome analysis in the human failing heart also changes the level of DNA methylation and is related to myocardial dysfunction., However, the function of DNA methylation in MIRI regulation is mostly unclear. LncRNAs are differentially expressed in cardiac diseases,, and our previous studies unraveled that zinc finger antisense 1 (ZFAS1) is deregulated in MI. Specifically, circulating ZFAS1 was proposed as a potential independent predictor of MI. ZFAS1 can act as an endogenous SERCA2a inhibitor to regulate contractile function and further induce mitochondrial-mediated apoptosis. However, the role of ZFAS1 in MIRI is currently unclear. Nicotinamide mononucleotide (NMN) is an essential part in NAD+ synthesis, which has multiple pharmacologic effects on heart disease.27, 28, 29 NMN exerts acute myocardial protection by targeting SIRT1 to stimulate glycolysis. In aging mice, NMN and melatonin can protect against MIRI by activating SIRT3/FOXO1 and decreasing apoptosis. Importantly, NMN protects the MIRI by increasing the level of NAD+ in the heart. However, whether NMN is related to DNA methylation in MIRI remains unknown. The general purpose of the present study was to determine the role and mechanism of ZFAS1 in MIRI regulation, as well as whether DNA methylation and Notch signaling pathways are key controllers in this process.
Detailed descriptions on the materials and methods used in this study are provided in the Supplemental Methods section.
As previously described, the MIRI model was used. Mice were intubated and anesthetized under direct vision, and their chests were opened to reveal the heart. The left anterior descending coronary artery was ligated with 7-0 line for 30 minutes to cause myocardial ischemia. The line was released for 48 hours to allow for reperfusion. NMN (500 mg/kg/d intraperitoneally) was administered to MIRI mice for 2 days. Use of the animals was approved by the Ethics Committee of Harbin Medical University and conformed to the Guide for the Care and Use of Laboratory Animals published by the U.S. National Institutes of Health.
As previously described, cardiac-specific ZFAS1 knock-in (transgenic [TG]) mice were created.
Data are presented as mean ± SEM of at least 3 independent experiments. Unpaired Student's t-test was used for comparisons between 2 groups. One-way and 2-way analysis of variance tests were used to compare parameters among 3 or more independent groups, whereas the t-test was used to compare 2 groups. Pairwise comparisons between groups were made with the Bonferroni multiple comparisons test when analysis of variance yielded significant differences. A 2-tailed P value <0.05 was considered to indicate statistical significance. Pearson correlation test was used to analyze the correlation of the parameters in the 2 groups in Figure3B. The statistical analyses were performed with the use of Prism version 8.0 software (GraphPad Software).
We constructed a mouse MIRI model to investigate the function of ZFAS1 in MIRI, and confirmed that cardiac function was clearly reduced in this condition (Figures 1A and 1B). MIRI mice had a larger proportion of ZFAS1 in their hearts (Figure 1C). In cardiomyocytes exposed to hypoxia/reoxygenation (HR) (Figure 1D), cell viability was severely reduced, and ZFAS1 expression was dramatically elevated (Figure 1E). Next, we inhibited with adeno-associated virus (AAV) vectors to examine ZFAS1 function (Figure 1F). Quantitative reverse-transcription polymerase chain reaction was used to confirm the efficiency of short hairpin RNA (sh) ZFAS1-V in knocking down endogenous ZFAS1 in MIRI mice (Supplemental Figure 1). ZFAS1 knockdown significantly improved MIRI-induced impairment of cardiac function (Figure 1G). Furthermore, shZFAS1-V reduced the infarct size of MIRI hearts (Figure 1H). Transfection of small interfering RNA (si) ZFAS1 mitigated the HR-induced reduction of cell viability (Figure 1I) and Live/Dead Viability/Cytotoxicity assay showed similar results (Figure 1J). Terminal deoxynucleotide transferase–mediated dUTP nick-end labeling (TUNEL) staining indicates that apoptosis was restored by siZFAS1 (Figure 1K). The enhanced ROS generation induced by HR was reduced by siZFAS1 (Figure 1L). Intracellular Ca2+ overload occurs during both MI and MIRI, and our previous study verified that ZFAS1 could cause intracellular Ca2+ overload. Therefore, we conducted additional experiments to determine whether ZFAS1 promotes ROS levels after excluding the effect of ZFAS1 on calcium overload. BAPTA, a calcium chelator, was administrated to both the HR-treated cardiomyocytes and the ZFAS1-overexpressed cardiomyocytes. As shown in Supplemental Figure 2, the viability of cardiomyocytes was significantly increased after BAPTA administration, which was further increased after ROS was scavenged by N-acetyl-l-cysteine (NAC). As shown in Supplemental Figures 2E and 2F, the level of ROS did not change significantly after administration of BAPTA, whereas it was significantly decreased after administration of NAC. These data indicated that ZFAS1 still exerts an effect on ROS levels, excluding the regulation of calcium overload, and this further suggests that ZFAS1 has other underlying mechanisms in the regulation of MIRI.
To understand the molecular processes supporting MIRI by ZFAS1 (Figure 2A), we constructed cardiac specific ZFAS1 knock-in TG mice (Figure 2B). ZFAS1 was upregulated in TG mice (Supplemental Figure 3). We compared the transcriptomes between TG and wild-type (WT) hearts by gene expression profiles (Figure 2C). The experiment revealed 30 down-regulated genes and 19 up-regulated genes in TG mice (Figure 2D). A Gene Ontology (GO) functional analysis of these 49 differentially expressed genes was then conducted with the cluster profile bioinformatics tool (Figure 2E). Next, we predicted and screened the direct target of ZFAS1 from 4 pathways, including the activation of cardiovascular phylogeny, stress response, programmed cell death cardiac development, and apoptosis process (Figure 2F), and identified that Notch1 and NTRK3 might be potential targets of ZFAS1 (Figure 2G). Notch signaling pathway is one of the top 30 rich Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, according to a KEGG pathway enrichment analysis of differentially expressed (Figure 2H). Therefore, our follow-up research mainly discusses the role of Notch1 in MIRI.
Notch1 expression was identified in TG mice to examine the influence of ZFAS1 on Notch1. Notch1 mRNA levels were significantly lower in TG mice, which is negatively connected to ZFAS1 expression (Figures 3A and 3B). Similarly, NICD in TG mice was considerably lower than in WT mice (Figure 3C), which was verified by the immunofluorescence (Figure 3D). In TG mice, the mRNA and protein levels of Hes1 and Hey1, downstream regulators of the Notch signaling pathway, were both reduced compared with WT mice (Figures 3D to 3H).
Next, we characterized the regulatory effects of ZFAS1 on the Notch signaling pathway in MIRI and found that the mRNA level of Notch1 was reduced during HR, which was reversed by siZFAS1 (Figure 4A). Figures 4B and 4C showed the results that inhibiting the expression of ZFAS1 remarkably increased NICD protein levels. In neonatal mouse cardiac myocytes (NMCM) exposed to HR damage, similar expression modifications of Hey1 and Hes1 were consistently found among constructs (Figures 4D and 4E). The cardiac expression of Notch1, Hes1, and Hey1 was prominently decreased in MIRI mice, and shZFAS1-V normalized these anomalies (Figures 4F to 4H). The Notch signaling pathway appears to be involved in the regulation of MI and MIRI. We conducted additional experiments to verify the regulation differences of the Notch signaling pathway between MI and MIRI mice, and the results showed that the expressions of Notch1, Hes1, and Hey1 were dramatically reduced in both the MI and the MIRI groups, and the decrease was more severe in the MIRI group. These data indicate that the Notch signaling pathway is a key target for MIRI regulation (Supplemental Figure 4).
To elucidate whether ZFAS1 induces MIRI by targeting the Notch signaling pathway, we explored with the use of the γ-secretase inhibitor DAPT, an inhibitor of the Notch signaling pathway. DAPT could abolish the protective effect of siZFAS1 in HR-treated cardiomyocytes, as determined by Live/Dead Viability/Cytotoxicity assay and ROS staining (Figures 5A to 5D). TUNEL staining also showed that DAPT could reverse the protective effect of siZFAS1 on HR-treated cardiomyocytes (Figures 5E and 5F). It is further confirmed that the ZFAS1-Notch signal axis plays a key role in MIRI. Our previous study identified that SERCA2a was an important target of ZFAS1 in regulating MI, so does it have the same role in MIRI? The expression of SERCA2a was significantly decreased in HR-treated cardiomyocytes, and siZFAS1 normalized these anomalies (Supplemental Figures 5A and 5B). HR-treated cardiomyocytes experienced calcium overload, which was likewise alleviated by siZFAS1 (Supplemental Figure 5C). These data indicated that the ZFAS1-SERCA2a axis participated in the regulation of MIRI. To distinguish the main regulation mechanism of ZFAS1 in MIRI, cyclopiazonic acid (CPA), the SERCA2a inhibitor, and DAPT, the Notch signaling pathway inhibitor, were used. After ZFAS1 knockdown, CPA dramatically affected the viability of HR-treated cardiomyocytes. And administration of DAPT more dramatically reduced the viability of cardiomyocytes (Supplemental Figure 5D). Moreover, the SERCA2a inhibitor CPA significantly inhibited the protective effect of siZFAS1 on Hypoxia/reoxygenation-treated cardiomyocytes, whereas DAPT administration did not further inhibit this effect (Supplemental Figure 5E). These data indicated that the ZFAS1-Notch1 axis played a major role in MIRI rather than MI.
A question we asked was how ZFAS1 regulates the transcription of Notch1. ZFAS1 is localized within the nucleus of cardiomyocytes. These facts prompted us to conjecture that ZFAS1 might regulate Notch1 transcription through an epigenetic mechanism, such as DNA methylation. We found a substantial CpG island in the promoter region of Notch1, using the UCSC Genome Browser and Methprimer to predict the probable domain of Notch1 for DNA methylation (Figure 6A). The methylation inhibitor 5-aza-2′-deoxycytidine (5-aza) dramatically enhanced the transcription level of Notch1 in cardiomyocytes (Figure 6B). Next, In TG mice, bisulfite sequencing revealed a large increase in methylation at the Notch1 promoter region (Figures 6C and 6D). So how does ZFAS1 affect the methylation of Notch1 promoter region? GO analysis found that ZFAS1 has the potential to affect the DNA-binding and protein-binding pathways (Figure 2F). LncRNAs can limit gene transcription by binding to DNA and building RNA:DNA triplexes, which recruit DNA methyltransferases (DNMTs) and increase DNA methylation modification in target areas., Using Freiburg RNA Tools, we identified a binding site for ZFAS1 within the proximal promoter region of Notch1 (Figure 6E). The direct functional interaction between ZFAS1 and Notch1 promoter region was experimentally verified by means of luciferase reporter assay (Figure 6F). Subsequently, the direct physical interaction between ZFAS1 and the Notch1 promoter region was also verified by means of the ChIRP technique with ZFAS1 antisense as a negative control (Figure 6G). Furthermore, prediction with the use of RNA-Protein Interaction Prediction (RPISeq) database suggests that ZFAS1 has the potential to bind methylase DMNT3b (Supplemental Figure 6). The immunoprecipitation of DNMT3b carried a significant quantity of ZFAS1, and a CHIP experiment revealed that DNMT3b binding to Notch1 promoter CpG islands was considerable (Figure 6I). According to these findings, ZFAS1 binds to the Notch1 promoter area and recruits DNMT3b to enhance Notch1 DNA methylation.
Then, using nucleotide replacement, we engineered a mutation in the ZFAS1 sequence to disrupt its Notch1 binding site (mut-ZFAS1) (Figure 7A) and investigated the impact of mut-ZFAS1 on Notch1 function. As anticipated, ZFAS1 overexpression in NMCMs transfected with ZFAS1-carrying plasmid considerably decreased the transcript levels of Notch1, Hey1, and Hes1 (Figures 7B, 7D, and 7F), and mut-ZFAS1 failed to elicit any effects. Similar patterns of expression alterations were seen of NICD and Hey1 (Figures 7C and 7E) protein levels. However, NMCMs transfected with mut-ZFAS1–carrying plasmid lost this function. Furthermore, as shown in Figures 7G to 7L, the capacity of mut-ZFAS1 to modify cell viability, cell apoptosis, and ROS levels was likewise lost. The results indicate that the binding of ZFAS1 to the Notch1 promoter region is a critical step in its impact on MIRI.
With the mechanism of DNA methylation in MIRI clarified, is there a drug that can interfere with the DNA methylation process of Notch1 to improve MIRI? We know that DNMT3b plays an important role in bridging ZFAS1 to DNA methylation of Notch1. Therefore, we searched for a known drug that can target DNMT3b. Molecular docking revealed that the amino acid residues G272 and K315 of DNMT3b can form hydrogen bond interactions with NMN, and amino acid residues H311, D273, G274, W270, F271, Q269, and M249 can form hydrophobic interactions with NMN (Figure 8A). Meanwhile, we discovered that NMN treatment strongly shifted the DNMT3b melting curve compared with dimethylsulfoxide in cells (Figures 8B and 8C). Surprisingly, NMN promotes the expression of Notch1 under HR conditions (Figures 8D and 8E). In addition, NMN improved the viability of cardiomyocytes under HR conditions (Figures 8F and 8G) and mitigated the apoptosis of cardiomyocytes induced by HR (Figure 8H). Furthermore, NMN reduces the infarct size of MIRI hearts and improves impaired heart function (Figures 8L to 8N). The above results indicated that NMN can relieve MIRI, likely by inhibiting the DNA methylation of the Notch1 promoter. As a downstream effector of NMN, Sirt1 has been identified as a key regulator for MIRI. So additional experiments were done to determine whether Sirt1 is involved in the NMN-Notch1 regulation pathway in MIRI (Supplemental Figure 7). The Sirt1 inhibitor selisistat had no effect on Notch1 expression in HR-treated cardiomyocytes after NMN injection. Moreover, Sirt1 agonist SRT-2104 showed no effects on Notch1 in the normal cardiomyocytes. Therefore, we think that Sirt1 is not involved in the NMN-Notch1 regulation pathway in MIRI.
ZFAS1 is closely associated with cardiovascular diseases. ZFAS1 was shown to be a possible biomarker for MI based on current evidence. Our research group discovered that as a SERCA2a inhibitor, ZFAS1 can cause intracellular Ca2+ excess and contractile failure. Furthermore, ZFAS1 produced intracellular Ca2+ excess, resulting in cardiomyocyte death, according to our findings. Wu et al also demonstrated that ZFAS1 promotes the functional availability of miR-150 by acting as a competing endogenous RNA to promote apoptosis. In the present investigation, we discovered that ZFAS1 was elevated during MIRI and that it played a role in myocardial injury by increasing oxidative stress and apoptosis.
In cardiovascular diseases, Notch signaling is crucial. Notch gene re-expression after the myocardial injury is an adaptive response secondary to myocardial injury. Olmesartan can improve ventricular remodeling in chronic pressure overload mice by activating the Dll4-Notch1 signaling pathway. Also, Notch signaling protects against MIRI in part caused by antioxidative and antinitrative actions mediated by PTEN/Akt. Our present work conducted gene expression profiling experiments on ZFAS1 knock-in mice and determined that ZFAS1 can target Notch1 and regulate the Notch signaling pathway. Further verification also confirmed that ZFAS1 can inhibit the Notch signaling pathway by regulating Notch1. The inhibitory effect of MIRI on the Notch signaling pathway can be restored by knocking down ZFAS1; however DAPT, a Notch signaling pathway inhibitor, can reverse this effect even more. According to our findings, ZFAS1 functions as an upstream factor in the Notch signaling pathway and is essential in MIRI.
Based on our previous work, we know that ZFAS1 is distributed in the nucleus and cytoplasm, and previous research mainly explored its role in the cytoplasm. The methylation of genomic loci controlled by nuclear lncRNA is a newly recognized defining attribute of lncRNAs, according to a vast amount of experimental evidence. Our present study confirmed that Notch1 is hypermethylated in the heart tissue of ZFAS1 TG mice. By binding to DNA and generating RNA:DNA triplexes that can recruit DNMTs and promote DNA methylation of particular areas, lncRNAs can suppress gene transcription., Intriguingly, our observations found by means of ChiRP assay that ZFAS1 can bind to the Notch1 promoter, and by means of ChIP and RIP assays, we determined that ZFAS1 recruited DNMT3b in contrast to DNMT1 and DNMT3a. We specifically mutated the region where ZFAS1 binds to Notch1 and found that its recruitment of DNMT3b mainly relies on its binding to the Notch1 promoter region. Despite this, our findings suggest that the interaction between lncRNA and DNA promoters can recruit DNMTs and contribute to DNA methylation and subsequent molecular processes.
It has been reported that NMN can cooperate with melatonin to protect MIRI, and it can protect MIRI by regulating mitochondrial ROS and redox. We found that NMN can significantly attenuate the apoptosis of HR-induced cardiomyocytes and improve the cardiac function of MIRI mice. Unlike in previous studies, we think that this effect is related to the molecular conformation of NMN and DNMT3b and the regulation of the methylation process.
Researchers have gradually recognized the regulatory role of ZFAS1 in heart disease. For MI, because of the absence of oxygen, cellular metabolism shifts to anaerobic respiration, producing lactate and causing a drop in intracellular pH, which activates the Na+-Ca2+ exchanger and causes intracellular Ca2+ overload. Thus, impairment of intracellular Ca2+ homeostasis is a key process in causing the dysfunction of MI. Our previous research confirmed that unusually high expression of ZFAS1 can lead to intracellular Ca2+ overload and, as a result, myocardial dysfunction in MI. MIRI, a completely different disease from MI, occurred caused by the production of a burst of detrimental oxidative stress and the accumulation of ROS, which mediates myocardial injury and cardiomyocyte death contributing to intracellular Ca2+ overload and damaged cell membrane. Thus, oxidative stress or ROS generation is the important initiating mechanism of MIRI, the occurrence of which is the key to distinguishing MI from MIRI. And our data demonstrated that ZFAS1 has a regulatory effect on MIRI and indicated that ZFAS1 exerts an effect on ROS levels, excluding the regulation of calcium overload. Moreover, although our data demonstrated that the ZFAS1-SERCA2a axis participated in the regulation of MIRI, we clarified that targeting the Notch signaling pathway is the main regulation mechanism of ZFAS1 in MIRI. Furthermore, ZFAS1 is reported to inhibit in lipopolysaccharide (LPS)–treated cardiomyocytes, and overexpression of ZFAS1 prevents LPS-induced apoptosis. However, both our previous studies and the findings by Huang et al and Wu et al have verified that knockdown of ZFAS1 could protect the cardiomyocytes from apoptosis. Here, we propose that LPS-induced cellular damage differs mechanistically from that of MIRI. The beneficial effects of inhibition of ZFAS1 and activation of Notch signaling on cardiomyocyte apoptosis are well established.
We are aware that the preapplication of AAV9-shZFAS1 may be a limitation of this study. This 3-week advance delivery of AAV9-shZFAS1 has limitations for clinical treatment of MIRI, and the protective impact of knockdown on ZFAS1 on MIRI was established only at the mechanistic level in our investigation, but it opens up the possibility of rectifying the functional impairment of the heart induced by ischemia-reoxygenation. In a follow-up study, we would consider establishing nanoparticles or exosome-encapsulated shZFAS1 or mut-ZFAS1 to provide the possibility for clinical treatment of heart diseases.
As a basis of these observations, we suggest the following paradigm for ZFAS1 to regulate apoptosis in cardiac ischemia-reperfusion injury: MIRI → ZFAS1↑ → Notch1 DNA methylation ↑ → Notch signaling pathway ↓ → cell apoptosis ↑ → heart function ↓. As shown in the Visual Abstract, up-regulated ZFAS1 facilitates DNMT-Notch1 interaction and promotes DNA methylation–mediated Notch1 down-regulation, according to our findings, which explains how up-regulated ZFAS1 underpins the genesis of MIRI. By interfering with this epigenetic process, NMN could be used to treat MIRI as a potential therapeutic therapy.PerspectivesCOMPETENCY IN MEDICAL KNOWLEDGE: The present study identified the ZFAS1-Notch1 axis as a molecular integrator and therapeutic target of oxidative stress–induced cardiac dysfunction in MIRI. Indeed, genetic inhibition of ZFAS1 or activation of Notch1 significantly attenuated the typical hallmarks of maladaptive remodeling and directly improved cardiac function in a relevant preclinical animal model. NMN can activate Notch1 through epigenetic regulation and improve cardiac function after MIRI.TRANSLATIONAL OUTLOOK: ZFAS1 could be considered as a novel therapeutic target for maintaining cardiac function in MIRI. NMN or other forms of ZFAS1 inhibitor could be developed into a novel therapeutic agent for ameliorating cardiac dysfunction after MIRI.
The National Key R & D Program of China (2017YFC1702003) and the National Natural Science Foundation of China (81970320, 81773735, 81961138018, 91949130, and 82003749) both contributed to this research. The authors have reported that they have no relationships relevant to the contents of this paper to disclose. | true | true | true |
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PMC9617132 | Long Chen,Hanning Liu,Cheng Sun,Jianqiu Pei,Jun Li,Yue Li,Ke Wei,Xiaoyi Wang,Peng Wang,Fangzhou Li,Shujie Gai,Yan Zhao,Zhe Zheng | A Novel LncRNA SNHG3 Promotes Osteoblast Differentiation Through BMP2 Upregulation in Aortic Valve Calcification | 26-09-2022 | BMP2 pathway,calcific aortic valve disease,long noncoding RNA,osteoblast differentiation,therapeutic target,ADV, adenovirus,ALP, alkaline phosphatase,ApoE−/−, apolipoprotein E-deficient,ASO, antisense oligonucleotide,BMP, bone morphogenic proteins,CAVD, calcific aortic valve disease,EZH2, enhancer of zeste 2,FISH, fluorescence in situ hybridization,HCD, high-cholesterol diet,hVICs, human aortic valve interstitial cells,lncRNA, long noncoding RNA,ND, normal diet,PRC2, polycomb repressive complex 2,RT-qPCR, reverse-transcriptase-quantitative polymerase chain reaction,RUNX2, Runt-related transcription factor 2,SNHG3, small nucleolar RNA host gene 3,TGF, transforming growth factor | Visual Abstract | A Novel LncRNA SNHG3 Promotes Osteoblast Differentiation Through BMP2 Upregulation in Aortic Valve Calcification
Calcific aortic valve disease (CAVD) is the most common heart valve disease and the most common cause of aortic valve replacement, and there are no effective treatments to halt or slow down the disease progression. However, the causes and pathogenesis of CAVD remain to be unexplored. CAVD is an active and progressive process associated with endothelial dysfunction, immune cell infiltration, calcium deposition, and extracellular matrix remodeling, which eventually result in human valve interstitial cells (hVICs) undergoing a phenotype transition to become osteoblast-like cells. Long noncoding RNAs (lncRNAs), a class of transcripts longer than 200 nucleotides, have recently emerged to play important roles in diverse cellular processes and the development of various diseases., LncRNAs can regulate gene expression at multiple levels by interacting with RNA, DNA, and proteins. The effects of lncRNAs in CAVD have been preliminarily explored. LncRNA H19 downregulates Notch1 to promote mineralization in vitro and has not been further verified in CAVD models. However, the role of lncRNAs in CAVD remains unelucidated. We identified an lncRNA named small nucleolar RNA host gene 3 (SNHG3, NR_036473.1, NONHSAT001953) that was upregulated in calcific aortic valves. SNHG3 is located at chromosome 1p35 with approximately 2.3 kb nucleotides, which contributes to the development of Alzheimer disease and various malignant tumors.8, 9, 10 SNHG3 has not been previously reported to be associated with cardiovascular disease. Nevertheless, some studies have reported that SNHG3 can mediate molecular markers related to CAVD, such as Notch1 and Runt-related transcription factor 2 (RUNX2) in some tumors., Therefore, SNHG3 may be a new therapeutic target for CAVD. Bone morphogenic proteins (BMPs), which belong to the transforming growth factor(TGF)-β superfamily, are recognized as potent pro-osteogenic factors., BMP2 was considered to be highly expressed and play a vital role in CAVD, and phosphorylated Smad1 and 5 (pSmad1/5), as the main signal transducer of BMP2 signaling pathway also increased significantly in calcific aortic valves. Nevertheless, the mechanism of significant BMP2 upregulation during CAVD remains unclear. In this study, we aimed to explain the role of lncRNAs in regulating the mechanism of CAVD. These findings provide new insights on SNHG3, which may promote CAVD progression and be considered a therapeutic target.
Detailed materials and methods are found in the Supplemental Appendix.
This study was performed with the approval of the institutional ethics committees of the Fuwai Hospital, Chinese Academy of Medical Sciences (No. 2012-404), and complied with the Declaration of Helsinki. All the patients signed written informed consent before participating in the study. Human calcific aortic valve leaflets were obtained from patients with CAVD during aortic valve replacement. Control nonmineralized aortic valves were collected from the explanted hearts of patients who underwent heart transplantation procedures. The clinical characteristic patients for lncRNA Sequencing are shown in Supplemental Table 1 and for RT-qPCR analysis are shown in Supplemental Table 2. The clinical characteristics of patients for cell cultures are shown in Supplemental Table 3.
All animal studies were carried out in accordance with protocols approved by the Ethics Committee of Fuwai Hospital, Chinese Academy of Medical Sciences for the Use and Care of Laboratory Animals (No. FW-2020-0022), and all the procedures complied with US National Institutes of Health (NIH Publication No.85-23, revised 1996) on the protection of animals used for scientific purposes. Adult apolipoprotein E-deficient (ApoE−/−) (C57BL/6 background) mice were purchased from Beijing Vital River Laboratory Animal Technology Co, Ltd, and housed in a pathogen-free, temperature-controlled environment under a 12:12 hour light-dark cycle.
Continuous data are presented as the mean ± SEM or mean ± SD for normally distributed data. The normality of the distribution of continuous data was confirmed by Shapiro-Wilk test and was visualized by a Q-Q plot. The Levene test was used to confirm the homogeneity of variance of continuous data. For normally distributed data, comparisons between the 2 groups were evaluated for significance using the unpaired Student’s t-test or Welch’s t-test, whereas comparisons among 3 or more groups were evaluated for significance using analysis of variance followed by least significance difference, Holm-Sidak, Dunnett’s, and Bonferroni multiple comparison post hoc test using the SPSS software. The data that are not normality distributed were compared using the Mann-Whitney U test (2 groups) or Kruskal-Wallis test (>2 groups). The counts of category data were compared using chi-square analysis between 2 independent groups. The association between the 2 continuous variables was evaluated using a 2-tailed Pearson’s correlation analysis. Statistical significance was set at P < 0.05. Differential expression analysis of lncRNAs in CAVD and the nonmineralized control group using the limma R package and the log2 fold change was computed as log2 (calcified aortic valve) minus log2 (nonmineralized aortic valve).
To identify important lncRNAs related to the development of CAVD, we performed transcriptomic sequencing on 10 patients with CAVD and 12 controls (nonmineralized aortic valves). The calcification staining for human aortic valve tissues are shown in Supplemental Figure 1. The volcano and plot of lncRNAs differentially expressed is presented in Figure 1A and the hierarchical cluster analysis and heat mapping is showed in Figure 1B. Compared with nonmineralized aortic valves, 33 lncRNAs were upregulated and 39 were significantly downregulated in CAVD (filtered by |fold change| >2 and P < 0.05) (Supplemental Table 4). We verified the top 10 highly expressed lncRNAs in 50 samples (30 cases of CAVD and 20 controls) by reverse-transcriptase quantitative polymerase chain reaction (RT-qPCR, the primers and probes for RT-qPCR are shown in Supplemental Table 5) and found that 7 of 10 of these lncRNAs were upregulated in patients with CAVD (Supplemental Figures 2A to 2J). Studies by Mathieu found H19 was upregulated in CAVD and promotes mineralization of hVICs; we also found H19 was highly expressed in calcific aortic valve tissues through transcriptomic sequencing and validated in RT-qPCR assays in expanded samples of CAVD. Therefore, we used H19 as a positive control. Next, we isolated hVICs from nonmineralized aortic valves, which were confirmed by immunofluorescence staining with anti-alpha smooth muscle actin and anti-vimentin (Supplemental Figure 3). Functional antisense oligonucleotide (ASO) screening to identify calcification-related lncRNAs showed silencing of H19, SNHG3, and SFTA1P downregulated 3 osteogenic markers (alkaline phosphatase [ALP], RUNX2, and OSTEOPONTIN) in osteogenic medium-induced hVICs; the effect of SNHG3 knockdown was very prominent (Supplemental Figures 2K to 2M). Hence, in our subsequent experiments we focused on SNHG3. SNHG3 (NR_036473.1, NONHSAT001953) is located on human chromosome 1 with 4 exons and low coding potential. RNA fluorescence in situ hybridization (RNA-FISH) showed that SNHG3 was mainly distributed in the cell nucleus (Figure 1D). We detected expression of SNHG3 and osteoblastic differentiation markers in osteogenic medium-induced hVICs. As shown in Figure 1E, SNHG3 expression was induced on stimulation of hVICs with the osteogenic medium. In addition, the level of SNHG3 was strongly positively correlated with 3 known osteoblastic differentiation markers (ALP, osteopontin, and osteocalcin) at 0, 1, 3, 5, and 7 days after osteogenic induction of hVICs (Figure 1F to 1H). These data indicated that SNHG3 may participate in CAVD progression through its association with osteoblast differentiation of hVICs.
We built an animal model of CAVD by administering ApoE−/− mice with a high-cholesterol diet for 24 weeks (HCD group) and another group of ApoE−/− mice received a normal diet as a negative control group (ND group). From the 12th week of the high-cholesterol diet to the 24th week every 4 weeks, echocardiography was used to evaluate the degree of aortic valve stenosis in the mice of the 2 groups (Figure 2A), and the mice in the HCD group had a significant increase in transvalvular peak jet velocity and a decrease in aortic valve area, which were in a time-dependent manner (Supplemental Figure 4). We then collected the aortic valve and evaluated the morphology of the valve leaflet thickness and degree of calcification. Hematoxylin-eosin staining showed that aortic valve leaflet thickness was consistently increased in the HCD group compared with that in the ND group at the 24th week (Figure 2B). This is consistent with the increased calcium deposits in the aortic valve leaflets of the animals, as assessed by Alizarin Red S (Figure 2C) and Von Kossa staining (Figure 2D). Moreover, we also found that SNHG3 expression was significantly higher in the HCD group than in the ND group in situ hybridization with RNA scope (Figure 2E). In addition, we then collected aortic valves and extracted RNA from both groups to evaluate the level of SNHG3 and osteogenic differentiation markers at 12, 16, 20, and 24 weeks. RT-qPCR results showed that SNHG3 in the HCD group was significantly upregulated compared with that in the ND group and gradually increased from the 12th to 24th week of the high-cholesterol diet (Figure 2F). As expected, the levels of osteogenic differentiation markers showed an increasing trend similar to that of SNHG3 (Figures 2G-2I), which indicated that SNHG3 expression was strongly positively correlated with CAVD progression in vivo.
Considering that hVICs express SNHG3 and that its level is increased and correlated with osteoblastic differentiation markers during CAVD, we hypothesized that SNHG3 might contribute to reprogramming hVICs toward an osteogenic phenotype. Human VIC cultures were treated with a mineralizing medium for 7 days, and osteogenic genes were measured after silencing SNHG3 with an ASO. SNHG3 was downregulated approximately 75% by ASO (Figure 3A). HVICs were collected to detect ALP activity, calcified nodule formation, calcium ion concentration in cultures, and protein levels of osteoblastic differentiation markers. The results showed that ASO-mediated SNHG3 silencing in hVICs negated the osteoblastic differentiation medium-induced increase in ALP activity (Figure 3B), calcium ion concentration (Figure 3C), calcified nodule formation (Figure 3D), and protein levels of osteoblastic differentiation markers (Figure 3E) compared with the control cells. In contrast, the overexpression of SNHG3 through an adenovirus (ADV) vector (Figure 3F) resulted in further increases in ALP activity (Figure 3G), calcium ion concentration (Figure 3H), calcified nodule formation (Figure 3I), and protein levels of osteoblastic differentiation markers in hVICs (Figure 3J). These results indicate that SNHG3 plays a positive role in the osteoblast differentiation of hVICs.
We tested the therapeutic potential of SNHG3 inhibition in an animal model of aortic valve calcification. ApoE−/− mice were fed a high-cholesterol diet for 24 weeks to develop aortic valve calcification and were randomly treated for 12 weeks with twice-a-week injection of ASO against a scrambled sequence (ASO-NC group) or mus-SNHG3 (ASO-SNHG3 group) at the 12th week of a high-cholesterol diet (Figure 4A). Echocardiographic assessment of heart aortic valve stenosis in mice treated with ASO-SNHG3 showed a significant decrease in transvalvular peak jet velocity and a significant increase in aortic valve area at the time of euthanization (Figures 4B to 4D, Supplemental Table 6), which means that SNHG3 silencing can ameliorate the aortic valve calcification in a mice model. In vivo repression of SNHG3 consistently decreased aortic valve leaflet thickness compared with that in mice treated with ASO-NC, as assessed by hematoxylin-eosin staining (Figure 4E). This is in line with the decreased calcium deposits in the aortic valve leaflets of mice treated with ASO-SNHG3 when compared with ASO-NC, as assessed by Alizarin Red S (Figure 4F) and von Kossa staining (Figure 4G). As expected, ASO-mediated silencing of SNHG3 assessed by in situ hybridization with RNA scope (Figure 4H) and RT-qPCR (Figure 4I) led to a significant decrease in osteogenic differentiation markers (Figures 4J to 4L) in aortic valve leaflets of mice. However, SNHG3 silencing failed to modulate the levels of glucose, total cholesterol, low-density lipoprotein, and triglycerides (Supplemental Table 7), which suggests that SNHG3 silencing ameliorates aortic valve calcification in ApoE−/− mice independently of metabolic regulation. These results further verify the role of SNHG3 in osteogenic differentiation and provide potent evidence for a therapeutic strategy targeting SNHG3 in CAVD treatment.
To identify the molecular mechanism by which lncRNA SNHG3 promotes aortic valve calcification, we examined the mRNA expression profiles of hVICs after knockdown of SNHG3 with ASO and overexpression of SNHG3 with ADV. A total of 126 upregulated and 296 downregulated genes were identified in the SNHG3-silenced hVICs compared with the control cells, and 197 upregulated genes and 252 downregulated genes were identified in the SNHG3-overexpressed hVICs (filtered by |fold change| >2 and P < 0.05) (Figure 5A). To identify coordinated changes in the expression of functionally related genes, we performed Kyoto Encyclopedia of Genes and Genomes enrichment analysis and gene set enrichment analysis., We found several pathways associated with aortic valve calcification, such as the calcium, MAPK, and TGF-β signaling pathways (Figure 5B, Supplemental Figures 5A and 5B). In addition, we found that the gene set of aortic valve stenosis and increased bone mineral density were upregulated when SNHG3 was overexpressed in hVICs (Supplemental Figures 5C and 5D). Among them, the TGF-β signaling pathway was significantly downregulated in the case of SNHG3 knockdown (Supplemental Figure 6A) and was significantly upregulated under conditions of SNHG3 overexpression (Supplemental Figure 6B). We also screened the differentially expressed genes between 2 RNA sequences, and BMP2 was also significantly downregulated as a result of SNHG3 knockdown and was significantly upregulated in the SNHG3-overexpression group (Figure 5A). BMP2 is a member of the TGF-β superfamily that plays essential roles in aortic valve calcification, and the BMP2 signaling pathway is also a part of the TGF-β signaling pathway involved in osteogenic differentiation of hVICs., We hypothesized that SNHG3 might activate the BMP2 signaling pathway to promote osteoblast differentiation of hVICs. Next, we confirmed the link between SNHG3 and BMP2 by RT-qPCR (Figures 5C and 5D) and western blot and found the level of BMP2 expression and its downstream effector smad1 phosphorylation (Figures 5E and 5F) dependent on SNHG3. Furthermore, we explored the relationships among SNHG3, the BMP2 signaling pathway, and osteoblastic differentiation using 2 rescue experiments. The down-regulation of protein levels of osteoblastic differentiation markers induced by ASO-SNHG3 in hVICs was efficiently reversed by BMP2 stimulation (Figure 5G). In addition, the upregulation of protein levels of osteoblastic differentiation markers caused by overexpression of SNHG3 with ADV was partially reversed by the BMP2 signaling pathway inhibitor LDN-193189 (Figure 5H). To further confirm the in vitro results, we examined the levels of p-Smad1/5 in an HCD-induced aortic valve calcification model treated with ASO-SNHG3. We found p-Smad1/5 was decreased more in the ASO-SNHG3 group than that in the ASO-NC group (Figure 5I). These analyses show that SNHG3 activates the BMP2 signaling pathway to promote osteoblast differentiation of hVICs.
We performed subcellular fractionation of hVICs to understand the molecular role of SNHG3 in the upregulation of BMP2. SNHG3 was localized in the cytoplasm and the nuclear fraction, with most nuclear SNHG3 being bound to chromatin (Figure 6A), in accordance with SNHG3 FISH (Supplemental Figure 7), which suggests a potential involvement of SNHG3 in the epigenetic regulation of BMP2 expression. The cytoplasmic/nuclear distribution was not affected by the pro-calcification stimulation (Supplemental Figure 8). A common function of lncRNAs is their association with regulatory proteins (transcription factors and chromatin remodelers) to tether them as ribonucleoprotein complexes to their target sites. In this context, SNHG3 was previously shown to interact with enhancer of zeste 2 (EZH2) in cancer cells, an enzymatic catalytic subunit of the histone methyltransferase polycomb repressive complex 2 (PRC2) that primarily tri-methylates lysine residue 27 on histone H3 (H3K27me3), establishing suppressive histone marks. Our in silico analysis revealed a high propensity for interaction between the central region of SNHG3 and EZH2 (Figure 6B). Sequence-based prediction of SNHG3-EZH2 interaction probabilities was 0.85 with random forest and 0.96 with support vehicle machine (Supplemental Figure 9A). The CatRAPID fragment tool revealed that the EZH2 region (amino-acid residues 326-377) and SNHG3 region (1932-2025 base pairs [bp]) had the highest interaction propensity, discriminative power, and normalized score, which means that these 2 parts have the greatest possibility of interaction with each other (Supplemental Figure 9B). We identified a common motif with 2 paired 4-nt loop secondary structures in the SNHG3 region (1932-2025 bp) that is a typical feature responsible for the interaction with EZH2 (Supplemental Figure 9C)., The EZH2 region (amino-acid residues 326-377) included a Thr-345, a conservative cyclin-dependent kinase phosphorylation site, which can be phosphorylated by AKT1 and promote maintenance of H3K27m3 levels at EZH2-target loci, thus leading to epigenetic gene silencing (Supplemental Figure 9D). RNA immunoprecipitation with specific antibodies against EZH2 confirmed this interaction in vitro, as indicated by ∼240-fold enrichment of SNHG3 over immunoglobulin G control (Figure 6C). SNHG3 silencing abolished the enrichment, suggesting that the interaction between SNHG3 and EZH2 is specific. In addition, biotinylated SNHG3 probes were used to pull down EZH2 (Figure 6D). SNHG3 silencing specifically increased trimethylation at H3K27 sites (Figure 6E), whereas SNHG3 overexpression reduced H3K27 trimethylation globally (Figure 6F). To clarify whether the BMP2 H3K27 trimethylation or mRNA expression is dependent on SNHG3, chromatin immunoprecipitation-qPCR results confirmed that ASO-SNHG3 treatment increased H3K27 trimethylation at the BMP2 locus and decreased BMP2 mRNA levels, which were partially rescued by the knockdown of EZH2 (Figures 6G and 5C). Overexpression of SNHG3 decreased BMP2 H3K27 trimethylation and strengthened its expression (Figures 6H and 5D). In addition, the methylation status of the BMP2 promoter was remarkably decreased by SNHG3-overexpression or methyltransferase inhibitor, 5-Aza-CdR in hVICs (Figure 6I). In contrast, the ectopic SNHG3 silencing increased the methylation level of the BMP2 promoter region, which was also alleviated by EZH2 knockdown (Figure 6J). Our data suggest that SNHG3 functions as a key activator of BMP signaling by preventing PRC2-mediated epigenetic repression of the BMP2 locus. In summary, we demonstrate that lncRNA SNHG3 acts as a decoy lncRNA and physically interacts with EZH2, a core component of PRC2, to suppress the trimethylation of the BMP2 promoter, resulting in the upregulation of the BMP2 signaling pathway, thereby promoting osteoblast differentiation of hVICs in CAVD progression (Figure 7).
At present, there is an unmet medical requirement to discover novel potential therapeutic targets for treating CAVD. In this study, our data generated the following novel findings: 1) SNHG3 expression is significantly higher in human calcific aortic valves than that in nonmineralized aortic valves; 2) SNHG3 can promote CAVD with evidence from in vitro and in vivo models; and 3) the first evidence that SNHG3 promotes aortic valve calcification through its epigenetic reprogramming of BMP2 gene. SNHG3 interacts with EZH2 to suppress the trimethylation of the BMP2 promoter, resulting in the upregulation of the BMP2 pathway during aortic valve calcification progression. Taken together, our results confirm that SNHG3 might be a novel therapeutic target for the treatment of aortic valve calcification. LncRNAs are located in the nucleus mainly and subcellular localization patterns of lncRNAs reveal fundamental insights into their biology and foster hypotheses for potential molecular roles. SNHG3 is mainly localized in the nucleus rather than the cytoplasm in gastric cells, and we found a similar result when we performed an RNA-FISH assay and cytoplasmic and nuclear RNA qPCR assays in hVICs, indicating that SNHG3 might regulate gene expression at the transcriptional level. Recently, a number of lncRNAs, termed epi-lncRNAs, have been involved in epigenetic regulation by directly interacting with epigenetic modifiers in the nucleus., Mechanistically, different lncRNAs interact with the epigenetic modifier PRC2 to repress or promote the binding and methylation of chromatin at specific subsets of genes., The SNHG3 interaction with epigenetic modifiers to regulate gene expression is supported by the evidence generated in our study. SNHG3 is associated with PRC2 enzymatic catalytic subunit EZH2 in cancer cells. It was unknown whether this link exists in the hVICs, and we performed in silico and in vitro binding assays that demonstrated that SNHG3 physically interacts with the PRC2 core subunit EZH2. The predicted secondary SNHG3 structure (region: 1932-2025 bp) with 2 paired 4-nt motifs is responsible for its interaction with EZH2. The binding region of EZH2 with SNH3 contains Thr-345, which is essential for maintaining the H3K27m3 level at EZH2-target loci. Therefore, SNHG3 binds with EZH2 in the nucleus and occupies the Thr-345 phosphorylation site of CDK1 to decrease H3K27m3 levels at EZH2-target loci, ultimately leading to epigenetic gene upregulation. Moreover, SNHG3 binds EZH2 to suppress promoter-specific H3K27me3 levels. This negative regulation of PRC2 function is consistent with previous studies showing that other lncRNAs interact with EZH2., Here, we performed chromatin immunoprecipitation-qPCR and bisulfite sequencing PCR analyses, which demonstrated that SNHG3 can specifically alter the BMP2 promoter region trimethylation level. To the best of our knowledge, the present study revealed that SNHG3 interacts with the epigenetic modifier PRC2 to alter the methylation level of the target gene. The master osteoblast protein BMP2, a secreted ligand of the TGF-β superfamily, closely binds to BMPRI/II to promote osteoblast differentiation of hVICs in CAVD., Previous studies reported that lipid accumulation, oxidative stress, and inflammation can mediate valvular calcification via BMP2. BMP2 can also upregulate a chondro-osteogenic pathway involving the transcription factor RUNX2, which promotes CAVD progression. Here, we performed gain and loss of function assays, transcriptional sequencing bioinformatics analysis, and rescue experiments to verify that SNHG3 can regulate the BMP2 pathway to induce an osteogenic program in the aortic valve. CAVD is the most prevalent form of aortic valve disease worldwide because of the lack of effective pharmacotherapy. It is necessary to understand the mechanism of its progression to develop new pharmacotherapies. The complex and multifaceted pathobiology process involving fibro-calcific remodeling, lipid accumulation and oxidation, chronic inflammation, and osteogenic differentiation of hVICs have been found to contribute to CAVD, suggesting that targeting these biological processes may result in the development of novel therapeutic interventions to halt disease progression or treat CAVD. lncRNAs play a prominent role in the regulation of osteoblast differentiation in many cell lines. For instance, lncRNA TUG1 can promote osteoblast differentiation by sponging miR-204-5p to upregulate RUNX2 in human hVICs, whereas silencing of lncRNA NONHSAT009968 reduces staphylococcal protein A-inhibited osteogenic differentiation in human bone mesenchymal stem cells. To the best of our knowledge, this is the first study to report that the silencing of SNHG3 significantly inhibited osteogenic differentiation by epigenetic regulation of the BMP2 pathway in vitro and in vivo. Therefore, ASO-delivery of SNHG3 may represent a novel RNA-based therapy for CAVD.
First, we only explored the epigenetic regulatory function of SNHG3 in the nucleus. It should be noted that apart from the cell nucleus, SNHG3 is also expressed in the cytoplasm of hVICs. Further studies are necessary to investigate the other roles of SNHG3 in hVICs. Second, we only identified that SNHG3 could interact with EZH2 to suppress trimethylation of the BMP2 promoter, resulting in the upregulation of BMP2. Whether SNHG3 could function as an alternative way to regulate the other key regulators in CAVD progression requires further investigation. In addition, to expand the scope of screening candidate genes, we analyzed the differentially expressed genes without adjusting for type I error using a false discovery rate, which did not affect SNHG3 was screened out as the key lncRNA in our research.
In the present study, we identified SNHG3 as a novel positive regulator of osteogenic differentiation in CAVD pathogenesis. Moreover, SNHG3 physically interacts with the PRC2 core subunit EZH2 in the nucleus of hVICs to suppress the trimethylation of the BMP2 promoter, resulting in the upregulation of the BMP2 signaling pathway during CAVD. SNHG3 silencing significantly alleviates aortic valve calcification in experimental animals, providing a novel therapeutic target for CAVD.PerspectivesCOMPETENCY IN MEDICAL KNOWLEDGE: Long noncoding RNA SNHG3 is significantly upregulated in patients with calcific aortic valve. SNHG3 interacts with PRC2 to decrease the trimethylation levels of BMP2 promoter region, resulting in upregulating the BMP2 expression at the transcriptional level, and thereby promotes osteogenic differentiation of human aortic valve interstitial cells in progression of aortic valve calcification.TRANSLATIONAL OUTLOOK: SNHG3 silencing significantly alleviates aortic valve calcification in experimental animals, providing a novel therapeutic target for CAVD.
This work was supported by research grants from the National Nature Science Foundation of China (81900352 to Dr Liu, 81770233 to Dr Zheng, and 81830072 to Dr Zheng). All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. | true | true | true |
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PMC9617450 | Guanghui Zhu,Yu Xia,Ziyue Zhao,Aoyu Li,Hui Li,Tao Xiao | LncRNA XIST from the bone marrow mesenchymal stem cell derived exosome promotes osteosarcoma growth and metastasis through miR-655/ACLY signal | 29-10-2022 | Bone marrow mesenchymal stem cells,Osteosarcoma,Exosome,LncRNA XIST,miR-655 | Background Long non-coding RNA X-inactive specific transcript (XIST) regulates the progression of a variety of tumors, including osteosarcoma. Bone marrow mesenchymal stem cells (BMSCs) can be recruited into osteosarcoma tissue and affect the progression by secreting exosomes. However, whether BMSCs derived exosomes transmit XIST to regulate the growth and metastasis of osteosarcoma and the related mechanism are still unclear. Method In this study, BMSCs derived exosomes were used to treat human osteosarcoma cells MG63 and 143B, and the level of XIST in BMSCs was intervened by siRNA. CCK-8, EdU, transwell assays were used to analyze the changes of cell proliferation, migration and invasion. Bioinformatics analysis, RNA pulldown and dual-luciferase reporter gene assays validated the targeted relationship of XIST with miR-655 and the interaction between miR-655 and ACLY 3’-UTR. 143B/LUC cell line was used to establish an animal model of in situ osteosarcoma to verify the found effects of XIST on osteosarcoma. Oil Red O staining, Western blot and so on were used to detect the changes of lipid deposition and protein expression. Results It was found that BMSCs derived exosomes promoted the proliferation, migration and invasion of osteosarcoma cells, and the down-regulation of XIST inhibited this effect. miR-655 mediated the role of BMSCs derived exosomal XIST in promoting the progression of osteosarcoma and down-regulation of miR-655 could reverse the effects of inhibiting XIST on the proliferation, migration and invasion of osteosarcoma cells. Meanwhile, animal level results confirmed that BMSCs derived exosomal XIST could promote osteosarcoma growth and lung metastasis by combining with miR-655. In-depth mechanism study showed that BMSCs derived exosomal XIST combined with miR-655 to increase the protein level of ACLY, which led to lipid deposition and activate β-catenin signal to promote the proliferation, migration and invasion of osteosarcoma cells. Conclusion This study showed that BMSCs derived exosomal XIST could enter osteosarcoma cells, bind and down-regulates the level of miR-655, resulting in an increase in the level of ACLY, thus increasing the lipid deposition and the activity of β-catenin signal to promote the growth and metastasis of osteosarcoma. Supplementary Information The online version contains supplementary material available at 10.1186/s12935-022-02746-0. | LncRNA XIST from the bone marrow mesenchymal stem cell derived exosome promotes osteosarcoma growth and metastasis through miR-655/ACLY signal
Long non-coding RNA X-inactive specific transcript (XIST) regulates the progression of a variety of tumors, including osteosarcoma. Bone marrow mesenchymal stem cells (BMSCs) can be recruited into osteosarcoma tissue and affect the progression by secreting exosomes. However, whether BMSCs derived exosomes transmit XIST to regulate the growth and metastasis of osteosarcoma and the related mechanism are still unclear.
In this study, BMSCs derived exosomes were used to treat human osteosarcoma cells MG63 and 143B, and the level of XIST in BMSCs was intervened by siRNA. CCK-8, EdU, transwell assays were used to analyze the changes of cell proliferation, migration and invasion. Bioinformatics analysis, RNA pulldown and dual-luciferase reporter gene assays validated the targeted relationship of XIST with miR-655 and the interaction between miR-655 and ACLY 3’-UTR. 143B/LUC cell line was used to establish an animal model of in situ osteosarcoma to verify the found effects of XIST on osteosarcoma. Oil Red O staining, Western blot and so on were used to detect the changes of lipid deposition and protein expression.
It was found that BMSCs derived exosomes promoted the proliferation, migration and invasion of osteosarcoma cells, and the down-regulation of XIST inhibited this effect. miR-655 mediated the role of BMSCs derived exosomal XIST in promoting the progression of osteosarcoma and down-regulation of miR-655 could reverse the effects of inhibiting XIST on the proliferation, migration and invasion of osteosarcoma cells. Meanwhile, animal level results confirmed that BMSCs derived exosomal XIST could promote osteosarcoma growth and lung metastasis by combining with miR-655. In-depth mechanism study showed that BMSCs derived exosomal XIST combined with miR-655 to increase the protein level of ACLY, which led to lipid deposition and activate β-catenin signal to promote the proliferation, migration and invasion of osteosarcoma cells.
This study showed that BMSCs derived exosomal XIST could enter osteosarcoma cells, bind and down-regulates the level of miR-655, resulting in an increase in the level of ACLY, thus increasing the lipid deposition and the activity of β-catenin signal to promote the growth and metastasis of osteosarcoma.
The online version contains supplementary material available at 10.1186/s12935-022-02746-0.
Osteosarcoma is a common malignant bone tumor in children, adults and young adults [1]. Surgical resection and neoadjuvant chemotherapy are the main treatment methods for newly diagnosed osteosarcoma patients [2]. Although the 5-year survival rate is 60–70%, many patients still die of the rapid progress and strong invasiveness of the disease [3, 4]. New therapeutic methods are urgent to be developed, and its pathological mechanism is the basis for the development of new therapeutic strategies. Bone marrow mesenchymal stem cells (BMSCs), derived from bone marrow stroma, as a kind of pluripotent stem cells, can differentiate into a variety of terminal effector cells, such as osteoblasts and adipocytes [5]. Studies have shown that BMSCs can target and migrate to tumor sites. As one of the components of tumor microenvironment, BMSCs regulate multiple processes of tumor progression [6]. In osteosarcoma, BMSCs can be recruited to tumor sites, which plays a critical role in osteosarcoma malignant [7]. Therefore, it is particularly necessary to deeply unveil the mechanism of BMSCs promoting the progression of osteosarcoma. Exosomes are extracellular vesicles with a diameter of 40-100 nm secreted by cells and mediate cell–cell interaction [8]. Importantly, current studies have confirmed that exosomes from BMSCs promote the progression of osteosarcoma [9, 10]. The function of exosomes is related to their cell surface molecules and cellular contents. Long non coding RNA (LncRNA) is one of the contents [11]. Exosomal lncRNA participates in the regulation of the process of a variety of diseases by mediating cell–cell interactions, including tumors [12]. For example, macrophage derived exosomes can enter osteosarcoma cells by transferring LIFR-AS1, and combine with miR-29a to promote the expression of NFIA, promote cell proliferation, invasion and inhibit apoptosis [13]. The key is that BMSCs exosomes can enter osteosarcoma cells with transferring lncRNA PVT1. On one hand, PVT1 can combine with miR-183 to promote ERG expression, on the other hand, PVT1 can directly combine with ERG to inhibit its ubiquitination degradation, and finally enhance the growth and metastasis of osteosarcoma [14]. This study preliminarily showed that lncRNA mediates the role of BMSCs derived exosomes in regulating the progression of osteosarcoma. However, little details is known about this, so more and more in-depth studies are needed to unveil other lncRNAs that mediate exosomes from BMSCs to regulate the progression of osteosarcoma, and their mechanisms. Long non coding RNA X-inactive specific transcription (XIST) is one of the earliest lncRNAs ever found to play a key role in X chromosome inactivation, which affects the progression of a variety of tumors [15]. In osteosarcoma, most studies have shown that XIST plays a role in promoting cancer. For example, in osteosarcoma, the expression of XIST is up-regulated, which is closely related to tumor size, clinical stage and distant metastasis [16, 17]. In vitro and in vivo studies have found that XIST promotes the progression of osteosarcoma, and its mechanisms include promoting Rab16 expression by binding miR-758, promoting SNAI1 expression by binding miR-153, and inhibiting NF-κB/PUMA signal can further antagonize apoptosis and mediate the regulation of AGO2 expression by HuR [18–21]. Importantly, XIST can exist in exosomes, and the level of XIST in serum exosomes of patients with recurrent triple negative breast cancer (TNBC) is higher than that of patients without recurrence [22]. However, it is unclear whether BMSCs derived exosomes affect osteosarcoma progression through XIST. In this study, we intended to isolate BMSCs derived exosomes, treat osteosarcoma cells with these exosomes, observe the changes of XIST level in osteosarcoma cells, analyze the effects of BMSCs derived exosomes on osteosarcoma growth and metastasis through XIST, and then study the mechanism of BMSCs derived exosome XIST regulating osteosarcoma progression, unveil the downstream molecules and signal pathways.
Human BMSCs(Procell, CP-H166) were cultured in DMEM/F12 (Procell, PM150310) plus 10% FBS (Procell, 164210-500). The cell lines MG63 and 143B were obtained from American type culture collection (Manassas, VA), the cell line 143B/LUC was purchased from Ming Zhou Bio., all cell lines were cultured in MEM (Procell, PM150410) plus 10% FBS.
BMSCs were characterized by detecting cell surface antigen using flow cytometry (BD, C6). Briefly, BMSCs at passage 3 reaching 90% confluence were washed twice with phosphate buffered saline (PBS), digested with 0.25% trypsin, collected after digestion, centrifuged at 1500 rpm for 5 min to remove the supernatant, and then washed twice with PBS and centrifuged again. After cell counting, the cell concentration was adjusted to 1 × 105 cells/mL. Then the primary antibodies (CD29, Invitrogen, 11-0299-42; CD45, Invitrogen, 11-0459-42; CD90, Invitrogen, 11-0909-42) were added and the cells were incubated in the dark for 30 min at 4 °C. After that, the cells were centrifuged at 1500 rpm for 5 min to remove the supernatant, and PBS was added to resuspend for cleaning and the cells were centrifuged again to remove the supernatant. 200 μL PBS was added to resuspend the cells and the samples were detected by flow cytometry to analyze the proportion of CD29, CD45 and CD90 positive cells.
BMSCs at passage 4 were seeded in six-well plates at the concentration of 3 × 104 cells/mL and treated with osteogenic induction medium (Procell, PD-007) for 14 days. The cells were collected and tested according to the instructions of the alkaline phosphatase (ALP) kit (Changchun Huili, C003-b). The absorbance (OD) at 450 nm was measured with a microplate reader (MD, Flexstation 3) to evaluate the ALP level. The osteogenic induction medium treated the cells for 21 days. The cells were fixed with 4% paraformaldehyde (Sinopharm group, 80096618), stained with 1% alizarin red (Sigma-Aldrich, St. Louis, Mo) for 5 min to show mineralized nodules. The staining results were observed under an inverted microscope and photographed.
BMSCs at passage 5 were seeded 6-well-plates at the concentration of 2 × 104 cells/mL and maintained in adipogenic differentiation basic medium A (Procell, PD-019) for 3 days, followed by 1 day in adipogenic differentiation basic medium B (Procell, PD-019). Both steps were repeated up to 14 days (indicated as the 3rd cycle). Fixed cells with 4% paraformaldehyde and stained with Oil Red O (Servicebio, G1015) for 30 min. The staining results were observed under an inverted microscope and photographed. For tumor tissue samples, the tissue was fixed with 4% paraformaldehyde for 24 h, embedded in paraffin, sliced to 4 μm sections, stained with Oil Red O for 30 min, observed under an inverted microscope and photographed.
BMSCs at passage 3–5 were cultured in DMEM/F12 with 10% FBS (SBI, EXO-FBS-50A-1) without exosomes. After 48 h, the supernatant of culture medium was collected, centrifuged at 300 g at 4 °C for 10 min to remove possible cell components, then centrifuged again at 2000 g for 10 min to remove cell debris, and then centrifuged under 10000 g at 4 °C for 30 min to remove large membrane vesicles. The obtained supernatant was then centrifuged at 120000 g for 70 min at 4 °C. After removing the supernatant, the bottom sediment was resuspended in PBS, and then the sample was ultracentrifuged at 140000 g for 90 min at 4 °C. The bottom sediment was collected as the exosome sample. The isolated BMSCs exosomes were incubated overnight with 2.5% glutaraldehyde solution at 4 °C, then fixed with 1% osmium tetroxide for 1.5 h, stained with 1% uranyl acetate (pH4.0), observed under transmission electron microscope (JEOL, JEM-2100 plus) for morphology and size, and photographed at 200 kV. The levels of exosome specific markers CD9, CD63 and CD81 were detected by Western blot, and the endoplasmic reticulum marker Calnexin was used as the negative control.
PKH26 Red Fluorescent Cell Linker Minin Kit (Sigma, MINI26) was used to exosomes label: 500 μL diluent C solution was added to 200 μL exosome suspension, a 1.5 mL Eppendorf (EP) tube was prepared, 4 μL PKH26 dye and 500 μL diluent C solution was added to it. The above two liquids were mixed well in the dark. After 5 min, 1 mL of 10% bovine serum albumin was added to the mixture to stop dyeing. A 0.22 μm cell membrane was put on a small ultracentrifuge tube, and 6 mL PBS was added to it, and the sample was centrifuged at 100000 g for 90 min, and the sediment was collected and resuspended with PBS, and the sample solution was stored. The steps of the uptake of exosomes by osteosarcoma cells were as follows: MG63 and 143B cells were allowed to proliferate to 70% confluence in 12-well-plates, and the medium was replaced with the fresh culture medium supplemented with PKH26 labeled exosomes. After incubated for 12 h, the cells were washed twice with PBS, then fixed with 4% paraformaldehyde for 20 min, the nuclei were stained with 4’,6-diamidino-2-phenylindole (DAPI). Finally, the cells were observed and photographed under the laser confocal fluorescence microscope (Olympus,BX53).
Total RNA was extracted from cell or tissue samples by Trizol (Invitrogen™). Exosomal RNA was extracted with a kit (Norgen BioTek, NGB-58000). The first strand cDNA synthesis reaction system was prepared in RNase free PCR tube, and theEasyScript first strand cDNA synthesis Supermix k it is used to reverse transcribe RNA into cDNA. SYBR Green qPCR Supermix and Applied Biosystems® 7500 sequence detection system was used to detect the RNA level. GAPDH was used as the internal reference of XIST, and U6 was used as the internal reference of miRNA. PCR primers were purchased from genscript Biotechnology Co., Ltd. The primer sequences were: XIST forward 5’-ACGCTGCATGTGTCCTTAG-3’ and reverse 5’-GAGCCTCTTATAGCTGTTTG-3’; GAPDH forward 5’-TCAAGAAGGTGGTGAAGCAGG-3’ and reverse 5’-TCAAAGGTGGAGGAGTGGGT-3’; miR-655 forward 5’-TGCGCATAATACATGGTTAACC-3’; miR-374c forward 5’-TGCGCATAATACAACCTGCTAA-3’; miR-5590 forward 5’-TGCGCAATAAAGTTCATGTAT-3’; miR-129-1 forward 5’-TGCGCAAGCCCTTACCCCAAAA-3’; miR-129-2 forward 5’-TGCGCAAGCCCTTACCCCAAA-3’; reverse 5’-CCAGTGCAGGGTCCGAGGTATT-3’;U6 forward 5’-CGCTTCGGCAGCACATATAC-3’ and reverse 5’-AAATATGGAACGCTTCACGA-3’. Relative expression was calculated using the 2−△△CT method.
The total proteins of exosomes, cells and tissues were extracted with RIPA lysate (Beyotime, P0027), and the cytoplasm and nuclear proteins were extracted with NE-PER™ nuclear and cytoplasmic Extraction Reagent (Thermo Scientific™, 78833). Protein concentration was measured with the BCA protein assay kit (Beyotime, P0012S). Protein was separated by 10% sodium dodecylsulfate-polyacrylamide gel electrophoresis (SDS-PAGE). Then, the protein was transferred to PVDF membrane (Immobilon-P, Millipore, Billerica, MA, United States) with the electrical transferring apparatus. The PVDF membrane was incubated with primary antibodies diluted with blocking solution overnight at 4℃ respectively (GAPDH, Abcam, ab8245; Lamin B1, Abcam, ab16048; β-catenin, Abcam, ab32572; CD9, Abcam, ab263019; CD63, Abcam, ab134045; CD81, Abcam, ab79559; calnexin, CST, 2679; ARPP19, Affinity, DF9325; TOB1, ProteintechGroup, Inc, 14915-1-AP; ACLY, Abcam, ab40793. the dilution ratio was 1:1000), and the corresponding HRP labeled secondary antibody (HRP labeled Sheep anti-mouse secondary antibody, Wuhan Boster Biological Technology., Ltd., BA1051; HRP labeled Sheep anti-rabbit secondary antibody, Wuhan Boster Biological Technology., Ltd., BA1054; the dilution ratio was 1:5000) was diluted with the blocking solution and the PVDF membrane was incubated at room temperature. The enhancer in ECL reagent was mixed with stable peroxidase solution at the ratio of 1:1, and the mixture was dropped onto the PVDF membrane. After the X-ray film was pressed, the film was successively put into the developing solution for development, fixer for fixation, and the film is developed. Western blot results were analyzed using Image J software (National Institutes of Health, USA).
Cy3-labeled oligonucleotide probes for XSIT was applied for RNA FISH, the probes were designed and synthesized by Genepharma (Shanghai, China). Cells were seeded in a glass-bottom dish. Then the cells were incubated with prehybridization solution at 37 °C for 30 min and the probes were added to dish and the hybridization was performed overnight. Then the cells were washed with buffer I (4 × SSC, 0.1% Tween-20) for 3 times, wash with buffer II (2 × SSC) once, and wash with buffer III (1 × SSC) once. After being washed with phosphate-buffered saline, the cells were incubated with DAPI to stain cell nuclear. The images were acquired on confocal microscope.
The transfection was performed after the confluence of cells reached 70%. XIST specific interfering siRNA, miR-655 agomir and antagomir were all purchased from Genepharma (Shanghai, China), and the vectors were constructed in this laboratory. Lipofectamine™ 3000 (Invitrogen™) was used for transfection, and the follow-up experiment was carried out 72 h later. XIST siRNA sequence: siNC: 5'-UUCUCCGAACGUGUCACGUdTdT-3'; siXIST#1: 5'-GUAUCCUAUUUGCACGCUAdTdT-3'; siXIST#2: 5'-GCCCUUCUCUUCGAACUGUdTdT-3'; siXIST#3: 5'-GUAUCCUAUUUGCACGCUAdTdT-3'. miR-655, sense 5’-AUAAUACAUGGUUAACCUCUUU-3’ and antisense 5’-AGAGGUUAACCAUGUAUUAUUU-3’; NC/Anti-NC, sense 5’-UUCUCCGAACGUGUCACGUTT-3’ and antisense 5’-ACGUGACACGUUCGGAGAATT-3’; Anti-miR-655, 5’-AAAGAGGUUAACCAUGUAUUAU-3’.
Cell viability was accessed by using a CCK-8 (MCE, HY-K0301). After the cells were digested with trypsin, the cell concentration was adjusted to 5 × 104 cells/mL. The cell was inoculated in 96-well-plates at 100 μL per well. The cell suspension and blank group were set at the same time. 10 μL CCK-8 reagent was added to each well and the plate was incubated for 2 h in the incubator. Finally, the absorbance at 450 nm of each well was measured with the microplate reader.
The cells were inoculated in 96-well-plates (5000 cells per well) and incubated for 24 h. The cells were then incubated in 10 μM EdU reagent for 4 h, fixed with 4% paraformaldehyde, permeated by 0.5% Triton X-100, and stained with 1 × Apolloreagent for 30 min. Nuclei were stained with 1 × DAPI, and the cells were visualized under a fluorescence microscope.
The cells were cultured in serum-free medium for 6 h, starved, digested with trypsin, and prepared to single cell suspension with serum-free medium. The cell concentration was adjusted to 5 × 105 cells/mL, and 200 μL cell suspension was inoculated in the upper chamber (for the invasion assay, chambers were coated with Matrigel). 600 μL medium containing 5% FBS was set in the lower chamber. After 24 h of culture, the Transwell chamber was taken out, the medium in the chamber was sucked out, and the residual cells in the upper chamber were gently wiped off with a cotton swab. The Transwell chamber was fixed with 4% paraformaldehyde for 30 min, stained with 0.1% crystal violet dyeing solution for 1 h, and observed and photographed under inverted fluorescence microscope.
RNA pulldown assay was performed according to the instructions of the RNA Pull-Down Kit (Thermo Fisher Scientific, Inc.). Briefly, full length of XIST sense/antisense was transcribed in vitro using Large Scale RNA Production Systems (Promega, USA) and labeled with Biotin using Biotin RNA Labeling Mix (Roche, Switzerland). Then 1 mg cell lysates extracted from osteosarcoma cells was incubated with 3 μg purified biotinylated transcripts for 1 h at 4 °C with rotation. Then the streptavidin agarose beads were added into cell protein lysate to precipitate the RNA-RNA complex. Elution buffers were used to elute the complex. The elute was collected, RNA was extracted with Trizol and stored at − 80 °C, and the amount of enriched miRNA was detected.
The psiCHECK-2 plasmid containing wild-type XIST(XISTWT) or mutated at the putative miR-655 binding sites (XISTMT) were designed, meanwhile the psiCHECK-2 plasmid containing wild-type ACLY 3’-UTR (ACLY 3’-UTRWT) or mutated at the putative miR-655 binding sites (ACLY 3’-UTRMT) were designed. When the cells reach 70% confluence, Lipofectamine™ 3000 was used to transfect cells with 2 μg plasmid and miR-655 agomir or NC. Dual luciferase Reporter Assay System (Promega, E1910) was used to detect the activity of firefly luciferase and Renillia luciferase: Add lysis buffer to lysed cells, Luciferase Assay Reagent II or Stop&Glo buffer were successively added,and detected by multi-functional enzyme marker.
The online database Starbase 3.0 (https://starbase.sysu.edu.cn) was used to predict the target genes of miR-655. In brief, the following steps were followed: (1) select miRNA-mRNA from miRNA-Target; (2) select miR-655-3p from microRNA; (3) select high stringency (≥ 3) from CLIP Data, select medium stringency (≥2) from Degrdome Data, select 4 programs from Program Number; (4) the obtained top three potential target genes ARPP19, TOB1, and ACLY;(5) then confirm miR-655 target genes through Western blot and dual luciferase reporter assay.
RIPA lysate lysed cells or tissue samples for 40 min. Next, the cell homogenate was prepared for lipids extraction using chloroform/methanol (2:1). Intracellular triglyceride and total cholesterol were measured by using the triglyceride assay kit (Biovision, USA) and cholesterol assay kit (Biovision, USA) according to the manufacturers’ protocol, respectively.
The animal experiments were approved by the animal research committee of the Hunan Children's Hospital and were performed in accordance with established guidelines (approval No.: HCHDWLL-2022-01). Female BALB/c nude mice aged 4–6 weeks (purchased from Changzhou Cavens Experimental Animal Co., Ltd.) were fed under standard experimental animal feeding conditions: 21 °C, 12 h light–dark-cycle, with sufficient water and food supply. 143B cells stably expressing luciferase (143B/LUC) cells in logarithmic growth phase was used and the cell concentration was adjusted to 2 × 107/mL, and 100 μL cell solution was injected into the tibial bone marrow cavity of BALB/c nude mice to establish an in-situ osteosarcoma model. One week after the cell injection, the mice were randomly divided into five group (n = 5): Ctrl, ExosiNC, ExosiXIST, ExosiXIST + Anti-NC, ExosiXIST + Anti-miR-655. The amount of exosome injection was 1 mg/kg, once every 3 days, a total of 6 times; the dosage of anti-NC or anti-miR-655 was 80 mg/kg, which was injected continuously for 3 days. The mice were anesthetized with isoflurane on the 0d, 14d and 28d after the cell inoculation. 150 mg/kg d-fluorescein was injected intraperitoneally, after 15 min later, the tumor growth and lung metastasis were detected by IVIS Lumina Imaging System (Xenogen). The mice were subsequently sacrificed, and the posterior limbs with tumors and the lungs were finely excised for further study. The tumor tissues were weighed, fixed for immunohistochemical and hematoxylin–eosin staining analysis, and lysed for western blotting and the numbers of metastatic lung nodules were counted using a dissecting microscope.
The tumor tissue was fixed with 4% paraformaldehyde for 24 h, embedded in paraffin, and sliced into 4 μm slices, dewaxed in xylene, rehydrated with graded alcohol, and incubated with 3% H2O2 to block endogenous peroxidase activity. 10 mM sodium citrate (pH6.0) was boiled for 30 min for antigen repair, 5% BSA was used to block the slice for 30 min, and Anti-Ki67 antibody (Abcam, ab15580, 1:200) was added to incubate overnight at 4 °C. PBS was used to wash the slice for 3 times and the slice was incubated with secondary antibody at room temperature for 30 min. Images were captured using a microscope (Leica).
The lung tissue was fixed in 4% paraformaldehyde for 24 h and embedded in paraffin and 4 μm slices were prepared. Next, the sections were stained with hematoxylin solution for 5 min, incubated in 5% acetic acid, and then rinsed with tap water. Next, the sections were stained with eosin solution for 3 min and followed by dehydration with graded alcohol and clearing in xylene. At last, the sections were sealed, and the morphology of lung tissue was observed under a microscope.
All statistical analysis were performed using graphpad prism 9.0. The quantitative data were shown as mean ± standard deviation (SD). The significance between the two groups were analyzed by Student's t-test. The significance between multiple groups were determined by one-way variance (ANOVA) analysis and LSD post hoc multiple comparison test. Statistical significance is indicated by *p < 0.05 and **p < 0.01.
The cultured BMSCs were observed under light microscope, and the cells were spindle-shaped and wall-attached (Additional file 1: Fig. S1A). Then, MSC-specific surface markers (CD29 and CD90) and a hematopoietic marker CD45 were determined using flow cytometry, and the results showed that the positive rates of CD29 and CD90 were 99.72% ± 0.052% and 99.76% ± 0.035%, respectively, while the positive rates of CD45 was only 0.09% ± 0.017% (Additional file 1: Fig. S1B, C). Subsequently, ALP level analysis, Alizarin Red standing and Oil Red O standing confirmed that the cells had osteogenic and adipogenic differentiation potential (Additional file 1: Fig. S1D–F). The exosomes from BMSCs were further isolated. Transmission electron microscopy (TEM) analysis showed that the exosomes from BMSCs were round membrane-bound vesicles with a size ranging from 40 to 100 nm in diameter (Fig. 1A). Western blot analysis showed that the exosome marker molecules CD9, CD63 and CD81 were highly expressed, and the endoplasmic reticulum marker molecule Calnexin was lowly expressed (Fig. 1B). BMSCs derived exosomes were labeled with PKH26 and then incubated with osteosarcoma cell lines MG63 and 143B, respectively. It was found that osteosarcoma cell lines MG63 and 143B could ingest BMSCs derived exosomes (Fig. 1C). Osteosarcoma cell lines MG63 and 143B were treated with BMSCs derived exosomes of different concentrations, and the level of XIST in cells was significantly increased (Fig. 1D). Fluorescence in situ hybridization (FISH) experiment also confirmed that the treatment with BMSCs derived exosomes on osteosarcoma cell lines MG63 and 143B could increase the level of XIST in these cells, and XIST was mainly localized in the cytoplasm (Fig. 1E). This indicated that osteosarcoma cells could uptake BMSCs derived exosomes, resulting in an increase in the level of XIST in the cells.
Studies have shown that XIST can regulate the progression of osteosarcoma [23, 24], and BMSCs derived exosomes can increase the level of XIST in osteosarcoma cells. We speculated that BMSCs derived exosomes can affect osteosarcoma cell proliferation, migration and invasion through XIST. To verify this idea, XIST siRNA were synthesized and screened (Additional file 1: Fig. S2A). By extracting the exosomes derived from BMSCs after transfect siNC or siXIST, respectively, it was found that downregulated the level of XIST in BMSCs could significantly reduce XIST level in the exosomes (Additional file 1: Fig. S2B), after treating osteosarcoma cell lines MG63 and 143B, the level of XIST in osteosarcoma cells also decreased (Fig. 2A). Further functional analysis showed that BMSCs derived exosomes increased the osteosarcoma cell viability, DNA replication, migration and invasion, while down-regulating the level of XIST could significantly inhibit those effects (Fig. 2B–E). The results indicated that BMSCs derived exosomes could enhance osteosarcoma cell proliferation, migration and invasion through XIST.
The mechanism of lncRNA is closely related to its localization in cells. Based on previous studies, it was found that BMSCs derived exosomes XIST were mainly localized in the cytoplasm after entering osteosarcoma cells. We intended to explore the mechanism of BMSCs derived exosomal XIST promoting osteosarcoma progression through miRNA. With the online prediction of the miRNAs interacting with XIST through Starbase 3.0, we selected strict stringency (≥ 5) based on CLIP data and high stringency (≥ 3) based on Degradome data to obtain 12 miRNAs potentially binding to XIST. Then we analyzed the novelty and research status of those miRNAs in the field of cancer. We focused on miR-374c, miR-655, miR-5590, miR-129-1 and miR-129-2. In RNA pulldown experiment, it was found that XIST could bind to miR-655 (Fig. 3A). Further, we synthesized miR-655 agomir and antagomir, and verified their effects in osteosarcoma cell line MG63 (Additional file 1: Fig. S3). Dual luciferase reporter gene assay further confirmed the targeted relationship of XIST and miR-655 (Fig. 3B, C). Quantitative analysis showed that BMSCs derived exosomes could reduce the level of miR-655 in osteosarcoma cells. After inhibiting BMSCs XIST, the level of miR-655 in osteosarcoma cells was restored (Fig. 3D). These results indicated that the BMSCs derived exosomal XIST enter into osteosarcoma cells could bind to miR-655 and reduce the level of miR-655.
Previous results showed that BMSCs derived exosomes XIST can bind and down-regulate the level of miR-655 in osteosarcoma cells. It is interesting to investigate whether miR-655 regulate the level of XIST. We found that inhibition of miR-655 could increase the level of XIST. In addition, inhibition of miR-655 could effectively antagonize the effect of BMSCs derived exosomes XIST on the level of XIST in osteosarcoma cells (Fig. 4A, B), suggesting that XIST and miR-655 in osteosarcoma cells could bind and regulate the level of each other, that is, there was a ceRNA mechanism. Studies have shown that miR-655 plays a role in inhibiting tumor progression in a variety of tumors, including hepatocellular carcinoma, esophageal squamous cell carcinoma and prostate cancer [25–27]. The decrease of the level of miR-655 In osteosarcoma tissues, the miR-655 level decreased, and inhibits osteosarcoma progression [28]. So the next question was whether the BMSCs derived exosomal XIST affect the biological function of osteosarcoma cells by binding miR-655. We found that inhibiting the expression of miR-655 increased the cell viability, the positive rates of EdU cells, the number of migrating and invasive cells, which again showed that miR-655 played a role in inhibiting osteosarcoma progression. On the basis of inhibiting XIST, downregulated miR-655 level restored the cell viability, DNA replication level, migration and invasion ability (Fig. 4C–E). These results suggested that BMSCs derived exosomal XIST can promote the osteosarcoma cell proliferation, migration and invasion by binding miR-655.
In order to further clarify the effect of BMSCs derived exosomal XIST combined with miR-655 on osteosarcoma, we established an osteosarcoma in situ model by injecting 143B/LUC cells into the tibial bone marrow cavity of BALB/c nude mice. The results showed that with the passage of time, the tumor volume and weight increased significantly, and obvious lung metastasis occurred at 28 days, while only a small number of lung metastases occurred at 14 days, and the metastatic tumors were small. Treated with BMSCs derived exosomes increased the tumor volume and weight, Ki67 positive staining was also significantly enhanced, the tumor metastasis was accelerated, and the number of lung metastases was significantly increased. However, down-regulation of XIST reduced tumor volume and weight, decreased Ki67 positive staining intensity, and inhibited lung metastasis. On this basis, downregulated miR-655 level, tumor volume and weight were significantly restored, Ki67 positive staining intensity was increased, and lung metastasis was also restored (Additional file 1: Fig.S4, Fig. 5A–G). These in vivo experimental results confirmed that BMSCs derived exosomes XIST can promote tumor growth and metastasis by binding miR-655.
In order to further clarify the mechanism of promoting osteosarcoma progression by BMSCs derived exosomal XIST combined with miR-655, we first analyzed the target gene of miR-655 through Starbase 3.0. We set clip data as high stringency (≥3), Degrdome data as medium stringency (≥2), and Program number as 4 programs. The top 3 genes were ARPP19, TOB1, and ACLY, respectively. It was found that BMSCs derived exosomes could promote the expression of ACLY in osteosarcoma cells, inhibition of XIST expression reduces ACLY level. On this basis, downregulated miR-655 level, the expression of ACLY was restored. However, the protein levels of ARPP19 and TOB1 did not change among the treatment groups (Fig. 6A). At the in vivo level, we also found a similar phenomenon (Fig. 6B). Further, we confirmed by dual luciferase reporter gene assay that miR-655 bound to the ACLY 3 '-UTR. After mutated at the putative miR-655 binding sites, the relative luciferase activity was restored (Fig. 6C, D), which indicated that BMSCs derived exosome XIST can promote the expression of ACLY in osteosarcoma cells by binding to miR-655.
Prior studies have shown that ACLY is promotes lipid synthesis and enhancing β-catenin signaling [29, 30], while the elevation of lipid synthesis and β-catenin signaling activity can accelerate tumor progression [31, 32]. While BMSCs derived exosomal XIST was found able to promote ACLY expression by binding miR-655, it was interesting to study how the above effect promotes lipid synthesis and enhances β-catenin signal activity, which is currently unknown. We found that BMSCs derived exosomes promoted lipid accumulation in osteosarcoma cells, significantly increased triglyceride (TG) and total cholesterol (TC) levels, decreased lipid deposition levels in cells after inhibiting XIST, on this basis, downregulated miR-655 level, the lipid levels, TG and TC contents were also restored, Inhibit the expression of ACLY, the level of TG and TC decreased accordingly (Fig. 7A–D). Analysis of β-catenin expression and its protein content in cytoplasm and nucleus, we found that BMSCs derived exosomes increased the protein level of β-catenin in total and nuclear protein, after inhibiting XIST, the content of β-catenin in total and nuclear protein decreased, on this basis, after the expression of miR-655 was down-regulated, the content of β-catenin in total and nuclear protein restored; after the expression of ACLY was inhibited, the content of β-catenin in total protein and nuclear protein decreased accordingly (Fig. 7E). These results suggested that BMSCs derived exosomal XIST can promote ACLY expression by binding miR-655, thus promoting lipid accumulation and increasing β-catenin on total and on the nuclear level. We further verified the results at the animal level. It was found that BMSCs derived exosomes promoted lipid deposition and increased TG and TC levels in osteosarcoma tissues. After inhibiting XIST, lipid deposition and TG and TC levels decreased. On this basis, after downregulated miR-655 level, lipid deposition, TG and TC levels were restored (Fig. 8A–C). We also analyzed the level of β-catenin, and it was found that the exosomes from BMSCs increased the content of β-catenin in total and nuclear protein, after inhibiting XIST, the content of β-catenin in total and nuclear protein decreased. On this basis, after downregulated miR-655 level, the content of β-catenin in total and nuclear protein levels was restored (Fig. 8D). These results were consistent with the cell level. It was confirmed that BMSCs derived exosomal XIST can promote ACLY expression by binding miR-655, thus promoting lipid accumulation and enhancing β-catenin signal activity.
Finally, we analyzed the effect of BMSCs derived exosomal XIST on the biological function of osteosarcoma by enhancing ACLY expression in combination with miR-655. The results showed that BMSCs derived exosomal XIST increased the cell viability and increased the proportion of EdU positive cells by binding miR-655. On this basis, inhibition of ACLY expression or treatment with ACLY inhibitor SB204990 decreased the cell viability and the proportion of EdU positive cells (Fig. 9A, B). There results showed that BMSCs derived exosomal XIST could increase the ACLY level by binding miR-655, thereby promoting the proliferation of osteosarcoma cells. Similarly, through migration and invasion assays, it was found that BMSCs derived exosomal XIST could promote the migration and invasion by combining miR-655, on this basis, inhibiting the expression of ACLY or treating with SB204990 would reduce the migration and invasion ability of osteosarcoma cells (Fig. 9C). These results indicated that BMSCs derived exosomal XIST can increase ACLY expression by binding to miR-655, thereby enhancing the migration and invasion of osteosarcoma.
Osteosarcoma mostly occurs in the epiphysis of long bone marrow, such as femur, tibia and humerus. It is characterized by the formation of immature bone or osteoid tissue by tumor cells [33]. BMSCs derived exosomes can promote the malignant progression of osteosarcoma, but the mechanism is still insufficient, especially the role and mechanism based on lncRNA still need to be revealed. In this study, we found that BMSCs derived exosomes can deliver XIST into osteosarcoma cells, bind and down-regulate the level of miR-655 through ceRNA mechanism, thus leading to increase of ACLY expression, and promoting lipid synthesis, enhanced β-catenin signal activity, accelerates the growth and metastasis of osteosarcoma. As the progenitor cells derived from bone marrow, BMSCs have the potential of self-renewal and multi differentiation [34]. In different tumors, BMSCs play different roles. For example, BMSCs promote the proliferation and angiogenesis of breast cancer and prostate cancer [35], but inhibit the progression of glioma and hepatocellular carcinoma, this effect should be related to their derived exosomes [36, 37]. There are similar phenomena in osteosarcoma. Most studies have shown that BMSCs promote the progression of osteosarcoma, and its mechanism includes the role of transferring exosome [38]. Interestingly, Zhou et al. found that BMSCs can transfer miR-1913 into osteosarcoma cells through exosomes, reduce the expression level of NRSN2, and play a role in inhibiting the proliferation, migration and invasion of osteosarcoma [39]. Our study confirmed that BMSCs derived exosomes can promote the osteosarcoma cell proliferation, migration and invasion in vitro and the growth and lung metastasis in vivo, which is consistent with the conclusions of most studies. XIST is the first found lncRNA to play a key role in X chromosome inactivation [40]. Subsequent studies have shown that XIST is involved in tumor progression. However, whether it is an oncogene or a tumor suppressor gene is not clear, and both sides has supporting reports. Even in the same tumor, there are inconsistent research results, including hepatocellular carcinoma, breast cancer, ovarian cancer and renal cell carcinoma [15]. This phenomenon also exists for osteosarcoma. Most studies support that XIST plays a role as an oncogene. In addition to the mechanisms described above, it also includes promoting RSF1 expression by combining miR-193a-3p, promoting RAP2B expression by combining miR-320b, and recruiting methyltransferase EZH2 to promote the modification of P21 promoter H3K27m3, thereby inhibiting P21 expression [23, 24, 41]. However, Zhang et al. found that the level of XIST in osteosarcoma decreased, and its level is negatively correlated with the overall survival of patients, its anti-cancer effect is related to the combination of miR-21-5p and then promotes PDCD4 expression [42]. We found for the first time that BMSCs derived exosomes can transmit XIST to promote osteosarcoma progression. After down-regulating the expression of XIST, the cell proliferation, migration and invasion in vitro, tumor growth and lung metastasis in vivo were inhibited, indicating that BMSCs derived exosomal XIST play a role in promoting cancer, which is consistent with most research reports. The mechanism of the function of XIST mainly includes binding chromatin modifying molecules and binding miRNAs as molecular sponges to regulate the expression of target molecules [15]. We found that XIST was mainly localized in the cytoplasm of osteosarcoma cells. When treated with BMSCs derived exosomes, the staining intensity of XIST in the cytoplasm was significantly increased. Ii is considered that the place where XIST binds to chromatin modifying molecules is in the nucleus, and the place where XIST acts as a molecular sponge to bind miRNA is in the cytoplasm, we intended to preliminarily explore the mechanism of the BMSCs derived exosomal XIST from the perspective of miRNA. The results showed that BMSCs derived exosomal XIST could bind miR-655 based on ceRNA mechanism after it enter into osteosarcoma cells. miR-655 is considered as a regulatory molecule with therapeutic potential, and has attracted more and more attention in recent years [43]. The related research reports mainly focus on the field of tumor, as a tumor suppressor gene to regulate cell proliferation, apoptosis, migration, invasion, angiogenesis, EMT and other processes, and the tumor kinds includes oesophageal square cell carcinoma, prostate cancer and triple negative blast cancer [25, 26, 44]. In osteosarcoma, the level of miR-655 decreased, LINC00689 can increase the expression level of SOX18 by combining with miR-655, thus promoting the osteosarcoma cell proliferation, migration and invasion [28]. It is preliminarily suggested that miR-655 plays a role in inhibiting the progression of osteosarcoma. In this study, we found that inhibiting the expression of miR-655 alone significantly enhanced the osteosarcoma cell proliferation, migration and invasion, which once again confirmed the role of miR-655 in inhibiting osteosarcoma progression. On the basis of down-regulation of XIST, after the expression of miR-655 in osteosarcoma cells was inhibited, the cell proliferation, migration and invasion in vitro, tumor growth and metastasis in vivo were significantly restored. This indicated that BMSCs derived exosomal XIST can promote osteosarcoma progression by binding miR-655. miRNA plays a role in a variety of mechanisms [45], of which the most widely studied one is to regulate the gene expression by targeting and binding the 3'-UTR of the target molecule. Through bioinformatics and experimental studies, we confirmed that ATP citrate lyase (ACLY) is a target gene of miR-655. ACLY plays a role in promoting cancer in a variety of tumors, including osteosarcoma [45], and targeting ACLY is considered to be one of the important strategies for tumor prevention and treatment [46]. We found that BMSCs derived exosomal XIST combined with miR-655 could increase the expression level of ACLY and promote the proliferation, migration and invasion of osteosarcoma cells. At present, studies have shown that ACLY can catalyze citric acid to produce Acetyl CoA (AcCoA) in the cytoplasm, which is the raw material for the synthesis of fatty acids and mevalonate, further to generate lipids. Therefore, it is widely reported that ACLY can promote lipid synthesis [30]. There are many kinds of lipids in organisms, their functions include energy storage, oxidation and energy supply, forming lipid bilayer of biofilm, and participating in cell recognition and signal transmission. The rapid proliferation and metastasis of tumor cells require a lot of material and energy, and lipids can support this demand [32, 47]. In this study, we failed to fully reveal the effect of BMSCs exosomal XIST on the lipid expression profile in osteosarcoma cells by mass spectrometry, but by analyzing the common and key lipid levels of TG and TC, we found that BMSCs derived exosomes increased the TG and TC levels in cells and tissues. After inhibiting XIST, the TG and TC levels decreased, and this effect was related to the combination of miR-655 and the promotion of ACLY expression. Similarly, we also found a similar trend through Oil Red O staining experiment, which preliminarily suggests that BMSCs derived exosomal XIST combined with miR-655 promotes ACLY expression, and then regulates the lipid level in cells, which is one of the mechanisms of accelerating the growth and metastasis of osteosarcoma. In addition, ACLY can be combined with β-catenin and enhance its stability, promote its transfer from cytoplasm to nucleus, and enhance its transcriptional regulation activity, while β-catenin has been recognized to function as a promoting role in tumor progression, including regulating the expression of cyclin and EMT process related molecules, thus promoting the growth and metastasis of tumor cells [29]. At the same time, it is also reported that XIST can enhance the signaling activity of β-catenin, whose mechanism includes binding miR-1264 to promote WNT5A expression, binding miR-34a to promote WNT1 expression, binding miR-139 to promote WNT1 and β-catenin expression and binding miR-744/RING1 to enhance the signal activity of Wnt/β-catenin [48–51]. In this study, we confirmed that the BMSCs derived exosomal XISTcould increase the level of β-catenin and promote its entry into nucleus, and the promotion of ACLY expression by binding miR-655 is the mechanism of its effect. BMSCs derived exosomal XIST binds miR-655 to promote ACLY expression, and then enhances β-catenin signal activity, which is another mechanism of XIST to accelerate the growth and metastasis of osteosarcoma. Therefore, this study confirmed that BMSCs derived exosomes can transfer XIST into osteosarcoma cells, promote the growth and metastasis of osteosarcoma, combine with miR-655 and down-regulate its level to promote ACLY expression, resulting in increased lipid synthesis and the increase of signal activity of β-catenin, which is the mechanism of the action of XIST.Although the collection of large clinical samples is a time-consuming and demanding work, the preclinical research results gave us confidence. We plan to collect peripheral blood serum samples of osteosarcoma patients at different stages, isolate and detect the XIST level in serum exosomes, so as to clarify the difference between osteosarcoma patients and normal people, as well as the difference between the XIST level in serum exosomes of osteosarcoma patients at different stages. We plan to analyze the value of BMSCs derived exosomal XIST as a disease biomarker and for postoperative monitoring, as well as preclinical research and clinical research results, which will provides a theoretical basis for the development of new treatment strategies, especially for osteosarcoma patients with lung metastatic and with poor prognosis.
Additional file 1: Figure S1. Characterization of BMSCs. A Morphology of BMSCs under light microscope; B, C Flow cytometry and statistical analysis of the positive rates of MSC-specific surface markers (CD29 and CD90) and a hematopoietic marker CD45 (n = 3); D The analysis of ALP level after osteogenesis induction 14d(n = 3); E The level of mineralized nodules analyzed with Alizarin red staining after osteogenesis induction 21d (n = 3); F The level of lipid deposition analyzed with Oil Red O staining after lipogenesis induction (n = 3). **represents p < 0.01. Figure S2. XIST siRNA reduced XIST levels in BMSCs and their secreted exosomes. A qRT-PCR results to screen siRNA that specifically down regulated the level of XIST in BMSCs(n = 3); B qRT-PCR analysis of the effect of down-regulation of the level of XIST in BMSCs on the content of XIST in their secreted exosomes (n = 3). **represents p < 0.01. Figure S3. MiR-655 agomir and antagomir were transfected into MG63 cells, after 48 h, the miR-655 level was detected by qRT-PCR (n = 3). **represents p < 0.01. Figure S4. qRT-PCR analysis the levels of XIST and miR-655 in osteosarcoma tissue (n = 3). **represents p < 0.01. Figure S5. ACLY specific siRNA screening. SiNC and siACLY were transfected into MG63 cells, after 72 h, the ACLY level was detected by qRT-PCR(n = 3). **represents p < 0.01. | true | true | true |
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PMC9617565 | 36198264 | Jun Shirakawa,Yu Togashi,Giorgio Basile,Tomoko Okuyama,Ryota Inoue,Megan Fernandez,Mayu Kyohara,Dario F. De Jesus,Nozomi Goto,Wei Zhang,Takahiro Tsuno,Tatsuya Kin,Hui Pan,Jonathan M. Dreyfuss,A.M. James Shapiro,Peng Yi,Yasuo Terauchi,Rohit N. Kulkarni | E2F1 transcription factor mediates a link between fat and islets to promote β cell proliferation in response to acute insulin resistance | 04-10-2022 | SUMMARY Prevention or amelioration of declining β cell mass is a potential strategy to cure diabetes. Here, we report the pathways utilized by β cells to robustly replicate in response to acute insulin resistance induced by S961, a pharmacological insulin receptor antagonist. Interestingly, pathways that include CENP-A and the transcription factor E2F1 that are independent of insulin signaling and its substrates appeared to mediate S961-induced β cell multiplication. Consistently, pharmacological inhibition of E2F1 blocks β-cell proliferation in S961-injected mice. Serum from S961-treated mice recapitulates replication of β cells in mouse and human islets in an E2F1-dependent manner. Co-culture of islets with adipocytes isolated from S961-treated mice enables β cells to duplicate, while E2F1 inhibition limits their growth even in the presence of adipocytes. These data suggest insulin resistance-induced proliferative signals from adipocytes activate E2F1, a potential therapeutic target, to promote β cell compensation. | E2F1 transcription factor mediates a link between fat and islets to promote β cell proliferation in response to acute insulin resistance
Prevention or amelioration of declining β cell mass is a potential strategy to cure diabetes. Here, we report the pathways utilized by β cells to robustly replicate in response to acute insulin resistance induced by S961, a pharmacological insulin receptor antagonist. Interestingly, pathways that include CENP-A and the transcription factor E2F1 that are independent of insulin signaling and its substrates appeared to mediate S961-induced β cell multiplication. Consistently, pharmacological inhibition of E2F1 blocks β-cell proliferation in S961-injected mice. Serum from S961-treated mice recapitulates replication of β cells in mouse and human islets in an E2F1-dependent manner. Co-culture of islets with adipocytes isolated from S961-treated mice enables β cells to duplicate, while E2F1 inhibition limits their growth even in the presence of adipocytes. These data suggest insulin resistance-induced proliferative signals from adipocytes activate E2F1, a potential therapeutic target, to promote β cell compensation.
β cell dysfunction in patients with type 2 diabetes potentially occurs secondary to inappropriate insulin secretory response to insulin resistance and a relative insufficiency in β cell volume. Therefore, enhancing β cell proliferation to increase functional β cell mass to a similar level as in healthy people is a strategy to slow progression and potentially reverse the course of the disease. The high-fat diet-induced obese (DIO) mouse model is commonly used to dissect the mechanism(s) underlying adaptive β cell proliferation in response to chronic insulin resistance (Golson et al., 2010; Shirakawa and Kulkarni, 2016). In DIO mice, the insulin/insulin receptor substrate (IRS) signaling proteins that activate the downstream pathways involving CENP-A have been reported to be important for β cell replication (Kubota et al., 2004; Shirakawa et al., 2017b; Terauchi et al., 2007). Downstream of insulin signaling via IR/IRS-2, the nuclear exclusion of forkhead box O1 (FoxO1) contributes to the β cell proliferative response to insulin resistance in DIO mice (Takamoto et al., 2008). However, in human type 2 diabetes, the expression of insulin receptor (IR) and its downstream factors, including IRS-2, are attenuated in islets/β cells. Hence, exploring alternative pathway(s) that can drive β cell proliferation independent of the IR or IRS-2 pathway are desirable to identify candidate targets for the therapeutic restoration of β cell volume in patients with type 2 diabetes. Pharmacological inhibition of IR can induce acute insulin resistance with marked hyperglycemia and hyperinsulinemia (Shirakawa et al., 2014). Previous studies demonstrated that injection for 7 days of S961, an IR antagonist (Schaffer et al., 2008), or OSI-906, a dual inhibitor for IR and insulin-like growth factor receptor (IGF1R) (Mulvihill et al., 2009) can independently facilitate potent β cell proliferation in mice (Tajima et al., 2017). Since S961 and OSI-906 each exert their inhibitory effects systemically, which includes the endocrine pancreas, pathways that are independent of IR signaling in β cells are likely to contribute to the β cell replication in response to the compounds. Thus, these acute insulin resistance models would be useful to identify mechanisms that can increase β cell mass even if the IR-mediated signal is attenuated. To directly evaluate the pathways that are independent of IR/IRS signaling in the regulation of β cell proliferation induced by acute insulin resistance, we treated β cell-specific IR knockout (βIRKO) or IRS-2-deficient mice either with S961 or OSI-906. Global gene expression analysis of islets from S961-injected mice revealed a transcription factor, E2F transcription factor 1 (E2F1), that mediates β cell proliferation independent of IR/IRS both in vivo and in vitro. Co-culture of islets with adipocytes suggested that a fat-derived factor contributed to the S961-induced β cell proliferation acting via E2F1. Our data point to a fat-pancreas axis acting via E2F1 in the β cell compensation in response to acute insulin resistance.
We used a subcutaneous osmotic pump to inject S961, an IR antagonist, into control (IR-floxed) and βIRKO mice for 9 days, followed by evaluation of glucose homeostasis and analyses of β cell proliferation. No effects on body weight were evident between groups over the duration of the injection (Figure 1A). As expected, S961 induced hyperglycemia within a few days after the injection, equally in both groups, and the glucose levels remained elevated through the 9-day period with no significant differences at any time points (Figure 1B). Evaluation of insulin sensitivity by intraperitoneal injection of insulin on day 9 showed severe resistance that was similar between control and βIRKO mice (Figure 1C). Thus, treatment with S961 resulted in marked hyperinsulinemia, and there was no difference between the two genotypes (Figure 1D). We next evaluated the compensatory response by measuring β cell proliferation and mass and observed that both parameters were significantly and equally increased by S961 in both groups (Figures 1E and 1F). These results suggested that the β cell is capable of proliferating and increasing its mass in response to systemic acute insulin resistance by a pathway that is independent of a functional IR. The close homology between the IGF-1 and IRs and the ability of the former to compensate for signaling in the absence of the latter prompted us to treat βIRKO mice with OSI-906, a dual inhibitor for IR and IGF-1 receptors (IGF1Rs) for 8 days. While there were no differences in body weight changes between the two groups treated with OSI-906, we observed a similar level of hyperglycemia and hyperinsulinemia as that observed with S961 in both groups (Figures 1G–1I). Thus, OSI-906 increased β cell mass and proliferation in βIRKO mice to a similar extent as in control mice (Figures 1J and 1K), indicating that IGF1 receptors are unlikely to mediate the proliferation signals. To examine whether this observation can be generalized to other models lacking proteins in the insulin/IGF-1 signaling pathway, we undertook similar studies by treating IRS-2 KO mice with OSI-906. A similar series of observations on hyperglycemia and increases in β cell mass (1.6- versus 1.5-fold) and proliferation (1.9- versus 2.2-fold) in both wild-type and IRS2KO mice (Figures S1A–S1G) pointed to induction of β cell proliferation by OSI-906 that is independent of insulin/IGF1R/IRS-2 signaling.
To begin to examine the pathways activated by acute insulin resistance, we compared the gene expression profiles of freshly isolated islets obtained from mice treated with S961 versus vehicle and focused on the genes that showed a change in expression in response to IR inhibition in vivo (Table S1). For example, we observed that mitosis-related genes, such as centromere protein A (Cenpa), PDZ-binding kinase (Pbk), protein regulator of cytokinesis 1 (Prc1), minichromosome maintenance complex component 5 (Mcm5), cell division cycle-associated 3 (Cdca3), cell division cycle 20 (Cdc20), Polo-like kinase 1 (Plk1), cyclin B1 (Ccnb1), or baculoviral IAP repeat containing 5 (Birc5) were all significantly increased in islets from mice treated with S961, while conversely, IR-mediated signaling genes such as cyclin D1 (Ccnd2) and Irs2 were decreased in these islets (Figure 2A; Table 1). Notably, CENP-A showed the lowest false discovery rate (FDR) among all detected genes (FDR Q = 3.98e–6, p = 4.54e–10). Comprehensive pathway analysis of upregulated and downregulated genes suggested that the mitotic G2/M cell-cycle-related pathways were involved in S961-mediated β cell proliferation (Figure 2B). Previously, we have reported that β cell-specific CENP-A KO mice failed to increase β cell proliferation in response to S961 administration (Shirakawa et al., 2017b). The transcription factor forkhead box M1 (FoxM1) regulates CENP-A expression and its deposition to the centromere through PLK1 that is downstream of IR signaling (Shirakawa et al., 2017b). These results prompted us to examine whether S961 is able to engage the FoxM1/PLK1/CENP-A signaling pathway to promote β cell replication even in the absence of functional IR signaling. Nuclear export of the transcription factor FoxO1 by insulin signaling is evident during adaptive β cell proliferation in DIO mice (Mezza et al., 2016; Terauchi et al., 2007). In contrast, in unstressed wild-type mice, FoxO1 was mainly localized to the cytosol in β cells (Figure S2). The predominant nuclear localization of FoxO1 in β cells observed in vehicle-treated control and βIRKO mice (Figure 2C) was further enhanced by S961 treatment compared with vehicle in both groups (Figure 2C). However, the fluorescence intensity of CENP-A detected by immunostaining was markedly increased in proliferating β cells in both S961-treated control and βIRKO mice (Figure 2D). The expression of Cenpa, Plk1, or Birk5 genes was significantly upregulated in the islets from S961-treated control and βIRKO mice, while the increase in expression of Foxm1 and cyclin-dependent kinase 1 (Cdk1) genes did not reach statistical significance in the latter group (Figure 2E). Expression of Insr, Igf1r, Irs2, or Ccnd2 genes showed no increment in islets of both mice (Figure 2E). These data argue that S961 engages the CENP-A-mediated pathway to induce β cell replication independent of signaling via the IR.
To identify the potential transcription factors that mediate S961-induced IR-independent β cell proliferation based on binding sites, we analyzed the Molecular Signatures Database (MsigDB) transcription factor target (TFT) gene sets (Liberzon et al., 2015). Remarkably, 18 of the top 25 TFT gene sets among the upregulated genes in response to treatment with S961 were related to the E2 factor (E2F) family of transcription factors (Figure 3A). The microarray analysis indicated that the expression of E2F1 and E2F2 were increased in islets following the treatment with S961 (Table 2). Validation studies revealed that the gene expression of E2F1, but not E2F2, E2F3, or E2F4, was significantly upregulated in islets from both S961-treated control and βIRKO mice (Figure 3B). In previous studies, E2F1 deficiency has been reported to reduce β cell mass, and conversely, forced expression of E2F1 has been shown to facilitate β cell proliferation (Fajas et al., 2004; Grouwels et al., 2010). To examine the specificity of the role of E2F1, we concomitantly administered either 2 mg HLM006474 (Ma et al., 2008), an inhibitor for E2F family including E2F1, or vehicle to S961-treated wild-type animals intraperitoneally once a day for 7 days, followed by evaluation of β cell proliferation and mass. Body weights (Figure S3A) and blood glucose levels (Figure S3B) showed no significant differences between vehicle- and HLM006474-treated groups. However, importantly, the enlarged β cell mass secondary to the enhanced β cell proliferation induced by S961 were blunted in the HLM006474-treated group (Figures 3C and 3D), likely due to the inhibition of the E2F family. Furthermore, the expression of Foxm1, Cenpa, Plk1, Cdk1, and Birk5 genes was also attenuated in the islets from mice co-treated with S961 and HLM006474 compared with mice co-treated with S961 and vehicle (Figure 3E).
To explore the source of the factor that promotes an increase in β cell mass in acute insulin resistance, we turned to in vitro studies using β cell lines and used the MTT assay to assess cell viability. Treatment of control, IRS1KO, IRS2KO, or βIRKO β cell lines (Assmann et al., 2009; Kulkarni et al., 1999) with 20% serum obtained from mice treated with the S961 compound increased cell viability compared with cells treated with 20% serum from vehicle-treated animals (Figure 4A). These data suggest that some component in the circulation mediates S961-induced β cell proliferation that is independent of signaling via IRs and its major substrate proteins IRS1 and 2. Because 10% serum had no effects on cell viability and 30% serum demonstrated similar results to those of 20% serum in MTT assay (data not shown), we chose the latter for subsequent experiments. We also confirmed that 20%or 30% mouse serum did not prevent phosphorylation of Akt or ERK, which mediate growth factor signaling, compared with FBS, in mouse islets (Figure S4A). Knockdown of CENP-A (Shirakawa et al., 2017b) also attenuated the increase in β cell viability induced by S961-treated serum (Figure S4B), while treatment with the E2F inhibitor (HLM006474) reduced the S961 serum-induced β cell survival rate (Figure S4C). Finally, as an alternative approach, knocking down E2F1 in β cells by short hairpin RNA (shRNA)-expressing lentivirus (Figure 4B) blunted the serum-mediated S961 induction of β cell viability and proliferation (Figures 4C and 4D). To examine the physiological relevance of the data in the cell lines, we repeated the studies using freshly isolated islets. Indeed, treatment with 20% serum from S961-treated mice augmented β cell proliferation in both mouse (Figure 4E) and human islets (Figure 4F). Furthermore, E2F inhibition blunted the increase in β cell proliferation induced by serum from S961-treated animals in both mouse and human islets (Figures 4E and 4F). Taken together with the data from the cell lines, these results suggested that circulating factors induced by the systemic effects of S961 enhance the proliferative capacity of both mouse and human β cells that require E2F1.
Among β cell growth factors, serpin family B member 1 (Serpinb1) was identified in the liver-specific IR KO (LIRKO) mouse, a model of chronic insulin resistance (El Ouaamari et al., 2016), while insulin-like growth factor binding protein 1 (Igfbp1) was identified using a genetic screen of zebrafish islets to potentiate trans-differentiation of α into β cells (Lu et al., 2016). In the current study, hepatic gene expression of Serpinb1 and Igfbp1 were both increased in response to S961 treatment, suggesting that these two circulating factors could contribute to the increase in β cell mass (Figure S5). However, to specifically examine the contribution of factors from specific metabolic tissues during acute insulin resistance, we examined the liver and adipose tissue, which are known to be associated with enhanced β cell proliferation during states of chronic systemic insulin resistance (Bluher et al., 2002; El Ouaamari et al., 2013; Michael et al., 2000). To this end, we independently co-cultured freshly isolated islets from vehicle-treated mice with either primary hepatocytes or primary adipocytes harvested from S961-treated mice (Figure 5A). Co-culture with hepatocytes increased β cell replication in islets equally between S961-treated mice and vehicle-treated mice (Figure 5B). Inhibition of E2F attenuated the increase in EdU incorporation induced by hepatocyte co-cultivation (Figure 5B). On the other hand, and interestingly, co-culture with adipocytes from S961-treated mice enhanced β cell proliferation even further compared with adipocytes from normal saline (Ns)-treated mice (Figure 5C), and the proliferative effects were attenuated in the presence of the E2F inhibitor HLM006474 (Figure 5C). These results argue for the existence of a humoral factor(s) derived from adipocytes that is able to enhance β cell growth. Since a recent report implicated the Fabkin complex from fat to reduce β cell mass (Prentice et al., 2021), we explored its expression in adipose tissue. The expression of fatty acid-binding protein 4 (FABP1) and nucleoside diphosphate kinase-a (NDPK-a), but not adenosine kinase (ADK), were increased in adipose tissue from S961-treated mice compared with that from saline-treated mice (Figure S6). Thus, it is unlikely that the Fabkin complex is involved in the increased β cell replication by S961-treated adipose tissue.
Pancreatic β cells exhibit striking plasticity in vivo in response to both acute and chronic states of insulin resistance. When these compensatory responses are dysregulated, β cell failure ensues and triggers the development of diabetes. It is possible that an increase in functional β cell volume could be achieved by harnessing these adaptive properties of the insulin secreting cells as one potential therapeutic approach to treat diabetes. Pharmacological induction of acute insulin resistance with S961 or OSI-906 has been reported to lead to hyperinsulinemia that is accompanied by a marked increase in β cell numbers (Shirakawa et al., 2014, 2017b), suggesting the feasibility of augmenting functional insulin-secreting β cell mass. Here, we report that a sensing of the reduced insulin signaling in adipocytes acts as a potential trigger to enhance β cell multiplication via humoral factor(s) that requires the transcription factor E2F1. Our studies suggest pathways that are independent of insulin and glucose, each of which have been reported to promote β cell growth. First, although IRs are involved in the adaptive β cell expansion in mice that are chronically insulin resistant in peripheral issues (Okada et al., 2007), the observation that βIRKO mice have the ability to show a significant increase in β cell mass and proliferation in response to antagonists of the insulin/IGF1Rs (e.g., S961 or OSI-906) in an acute setting suggests activation of reserve pathways that are independent of IR/IGF-1R signaling. This notion is further supported by the comparable β cell growth in OSI-906-treated IRS-2-deficient mice and by accumulation of FoxO1 in the nucleus after treatment with S961 in both control and βIRKO animals and the absence of increase in expression of insulin signaling-related genes including IR, IRS-2, and cyclin D2 in islets obtained from S961-treated mice. Thus, acute insulin resistance facilitated β cell replication in an IR/IGF1R-independent fashion. Second, glucose is known to activate proliferation pathways in β cells (Stamateris et al., 2016). However, some studies argue for effects independent of glucose (Okada et al., 2007; Togashi et al., 2014). For example, we previously demonstrated that β cell proliferation persisted in OSI-906-treated mice despite normalization of blood glucose levels by the sodium glucose co-transporter (SGLT)-2 inhibitor (Shirakawa et al., 2020). In our current study, the downregulation of insulin signaling and nuclear export of FoxO1, which is the opposite of the effects observed in glucose-induced proliferation (Terauchi et al., 2007), indicates that it is unlikely that hyperglycemia principally triggers β cell proliferation. The CENP-A pathway in conjunction with FoxM1 and PLK1 is essential for the adaptive β cell proliferation downstream of the insulin signaling network (Shirakawa et al., 2017b). We previously reported that β cell-specific CENP-A KO mice exhibit impaired β cell proliferation after injection with S961 (Shirakawa et al., 2017b). However, the upregulation of CENP-A signaling and G2/M phase-related cell-cycle genes in βIRKO mice treated with S961 reported in the current study suggests IR-independent mechanism(s) can also activate CENP-A. It is notable that the FoxM1/PLK1/CENP-A signaling is known to be activated during β cell proliferation induced by parasympathetic nerve activation (Yamamoto et al., 2017). Whether a similar neuronal relay signaling pathway is activated in β cells in response to S961 treatment requires additional investigation. The identification of E2F1 in the MsigDB TFT gene datasets as a transcriptional regulator of β cell proliferation was validated by using an inhibitor that abolished further replication induced by S961. Although previous studies have shown that E2F1 plays a crucial role in β cell proliferation (Fajas et al., 2004; Grouwels et al., 2010) and modulates insulin secretion by the transcriptional regulation of Kir 6.2, a KATP channel component, via the insulin signaling pathway (Annicotte et al., 2009), little is known about the relevance of E2F1 in the β cell growth response to insulin resistance. The ability of S961 to induce β cell proliferation in our model suggests that the induction of E2F1 occurs by an insulin signaling-independent mechanism. Since the E2F inhibitor attenuated the expression of FoxM1, PLK1, and CENP-A in islets from S961-treated mice, E2F1 could be acting upstream of FoxM1 to modulate β cell proliferation. p53 and ataxia telangiectasia mutated (ATM) reportedly increased FoxM1 expression through E2F1 in epirubicin-resistant MCF-7 breast carcinoma cells (Millour et al., 2011). The p38 and mitogen-activated protein kinase (MAPK)-activated protein kinase 2 (MAPKAPK2) pathway also enhanced E2F1-induced FoxM1 expression in MCF-7 cells (de Olano et al., 2012). In soft tissue sarcoma tumorigenesis, MAPK-interacting serine/threonine-protein kinase 1 and 2 (MNK1/2) upregulated E2F1, FOXM1, and WEE1 expression (Ke et al., 2021). Considering their roles in regulating cell proliferation and survival, it is possible that p53 or MAPK signaling pathways contribute to the S961-induced E2F1 recruitment to replicate β cells. We and others have reported the identification of circulating factors contributing to β cell proliferation in diverse models (Dirice et al., 2014; El Ouaamari et al., 2013; Fernandez-Ruiz et al., 2020; Flier et al., 2001; Kondegowda et al., 2015; Shirakawa et al., 2017a, 2020). Furthermore, a combination of different agents, GLP-1 receptor agonists, and DRYK1A inhibitors are able to promote effective β cell replication in human islets (Ackeifi et al., 2020). In the current study, although the expression of SerpinB1 in the liver was increased after S961 injection, the enhanced proliferation observed with co-culture of islets with adipocytes suggests that factors in addition to SerpinB1 likely promote β cell replication in response to the S961-induced acute insulin resistance. It is important to recognize that numerous humoral factors including apolipoproteins, signaling lipids, inflammatory cytokines, or microRNAs (miRNAs) in exosomes that are secreted from adipocytes all have the potential to regulate diverse aspects of β cell biology (Basile et al., 2019; Shirakawa and Kulkarni, 2016). While the adipokine adipsin has been reported to enhance insulin secretion and protect β cells from apoptosis or dedifferentiation, it did not alter β cell proliferation (Gomez-Banoy et al., 2019; Lo et al., 2014). Among other fat-associated proteins, Fabkin, the FABP4-ADK-NDPK complex in adipocytes, has been reported to decrease β cell mass, possibly by decreasing proliferation or increasing apoptosis (Prentice et al., 2021). However, in our study, the expression of genes that constitute the Fabkin complex were increased in fat tissue of S961-treated mice despite the increase in β cell mass and proliferation and thus ruling out its direct involvement. Thus, while our studies point to adipokines as part of a cocktail of factors that can promote β cell growth, detailed analyses using independent tissue-specific KOs of SerpinB1, IGFBP1, Fabkin, or their combinations are required to explore their specific contributions. In summary, we propose a link between fat and islets in the β cell proliferation evident in states of acute insulin resistance. Systemic inhibition of IR signaling by S961 facilitates the secretion of humoral factors from adipose tissue, leading to enhanced E2F1 expression in β cells via an IR/insulin-independent signaling pathway. These observations point to adipocytes and E2F1 signaling in β cells as potential targets to compensate for the β cell loss in patients with diabetes.
There are limitations to this study. Since HLM006474 is not a specific inhibitor for E2F1, it is possible that multiple E2F members are involved in the regulation of β cell replication even though a requirement for E2F1 is supported by knockdown experiments. Visceral adipose tissue, used for co-culture in this study, has no direct drainage into the pancreas, and a putative fat-derived factor should engage systemic circulation to reach β cells. Studies comparing the effects of visceral versus subcutaneous adipose tissue are warranted. Furthermore, since most humoral factors in fat tissue are derived from the stromal vascular fraction (SVF), experiments using a co-culture system with adipose tissue explants or SVF using an integrated approach such as lipidomics and/or metabolomics coupled with exosome analyses would be informative. We used the βIRKO model to allow comparing with our previous results of impaired β cell proliferation in response to chronic insulin resistance (Kulkarni et al., 1999; Okada et al., 2007). However, given that RIP-Cre mice reportedly express a human growth hormone (hGH) minigene in islets with confounding effects on β cell replication (Baan et al., 2015; Brouwers et al., 2014), the use of the Ins1-Cre knockin mouse model will allow clarifying these issues (Thorens et al., 2015). We have used IR-floxed, but not RIP-Cre, mice as controls. Considering potential effects of ectopic Cre expression in the RIP-Cre mouse (Wicksteed et al., 2010), future studies should compare with Cre-only mice to validate our findings. Although we employed in vitro co-culture experiments with serum, hepatocytes, or adipocytes to model interactions via humoral factors, one has to also consider non-secretory components in media or islet-derived factors that can impact β cell replication. Parabiosis coupled with transplantation studies might reveal direct effects of circulating factors.
Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Rohit N. Kulkarni MD PhD. ([email protected]).
The cell lines generated in this study are available from the lead contact upon request without restriction.
Microarray data have been shown in Table S1. Original western blot images are available in Data S1. This paper does not report original code. Any additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.
The βIRKO mice and littermate control insulin-receptor-floxed mice on a C57B6 background (8–11-week-old, male) were obtained as described (Kulkarni et al., 1999) and housed in pathogen-free facilities on a 12 h light–dark cycle at the Animal Care Facility of Joslin Diabetes Center, Boston, MA, USA. The IRS-2 KO mice and littermate control wild type mice on a C57B6 background (8-week-old, male) were obtained as described (Kubota et al., 2000) and housed in pathogen-free facilities on a 12 h light–dark cycle at the Animal Facility of Yokohama City University, Yokohama, Japan. Mice were injected with BrdU intraperitoneally (100 mg/kg body weight) 6 hours prior to animal sacrifice for immunostaining of the pancreas. All protocols were approved by the Institutional Animal Care and Use Committee of the Joslin Diabetes Center and the Yokohama City University Institutional Animal Care and Use Committee (IACUC) (Permit Number: F-A-14–041). This study was conducted in accordance with National Institutes of Health guidelines and the guidelines of the Animal Care Committee of Yokohama City University. All animals were housed in the Association for Assessment and Accreditation of Laboratory Animal Care International (AAALAC) accredited Animal Facility at Joslin Diabetes Center and Yokohama City University.
β-cell lines from control, IRS1KO, IRS2KO, or βIRKO mice were generated in our laboratory as described previously (Assmann et al., 2009; Kulkarni et al., 1999). CENP-A knockdown and control scramble shRNA transduced cells were generated in a previous study of our laboratory (Shirakawa et al., 2017b). All β-cell lines were from male mice. The control cells were used between passages 14 to 26, IRS1KO and IRS2KO cells between passages 11 to 22 and βIRKO cells between passages 9 to 21. Cells were maintained in DMEM media containing 25 mM glucose, supplemented with 10% FBS. Experiments were performed using 80–90% confluent cells.
Human islets (3 males and 4 females, 22–52 years old) were obtained from the Clinical Islet Laboratory and Clinical Islet Transplant Program of University of Alberta or the Alberta Diabetes Institute IsletCore of the University of Alberta. Ethics approval and informed consent from donors or families were obtained in each institute. Details of human islets are described in Table S2. All studies and protocols used were approved by Yokohama City University Ethics Board (approval B171100025) and the Joslin Diabetes Center’s Committee on Human Studies (approval CHS#5–05). Upon receipt, islets were cultured overnight in Miami Media #1A (Cellgro). The islets were embedded in agarose and used for immunostaining studies.
S961 was received as a gift from Dr. Lauge Schä ffer from Novo Nordisk (Schaffer et al., 2008). For S961 studies, 8–11-week-old mice (n = 5–6 per group) were anesthetized by intraperitoneal injection 0.3 mg/kg of medetomidine hydrochloride (Kyoritsu Seiyaku Co., Japan), 4.0 mg/kg of midazolam (Maruishi Pharmaceutical Co., Japan), and 5.0 mg/kg of butorphanol tartrate (Meiji Seika Pharma Co., Japan) (5 μL/g body weight) and infused with normal saline (Ns)/PBS alone or Ns/PBS with the insulin receptor antagonist S961 at the dose of 10 nmoles/week (1.43 nmoles/day) for 7–9 days. Infusion was carried out using osmotic pumps (ALZET 2001) implanted subcutaneously. HLM006474 (10 mg/mL) or vehicle (2.5% vol/vol dimethylsulphoxide, 28% wt/vol 2-hydroxypropyl-β-cyclodextrin, 10% vol/vol PEG400 in distilled water) were injected intraperitoneally (5 μL/g body weight) once a day between 08:00 and 09:00 hours. For OSI-906 studies in βIRKO mice, 8–10-week-old bIRKO mice and littermate floxed mice were infused with vehicle (30% PEG400, 0.5%Tween 80, 5% propylene glycol) alone or vehicle with the insulin recptor and IGF1 receptor dual inhibitor OSI-906 at the dose of 10.5 mg/week (1.5 mg/day) using osmotic pumps (ALZET 2001) for 8 days. For OSI-906 studies in IRS2 KO mice, 8-week-old IRS-2 KO mice and littermate wild type mice were given 10 μL/g weight of either the vehicle (30% [wt/vol.] Solutol HS-15; BASF, Ludwigshafen am Rhein, Germany) or OSI-906 (45 mg/kg BW/day or 15 mg/kg BW/day) by gavage for 7 days, as previously described (Shirakawa et al., 2020), between 08:00pm and 09:00pm. Solutol HS-15 was dissolved in water at 30% w/v. The powder of OSI-906 was dissolved in 30% Solutol at a concentration of 4.5 mg/mL. We confirmed that above concentrations of S961 and OSI-906 were appropriate for the assessment of β-cell proliferation without reduction in body weight and enough to suppress IR signaling in the liver, adipose tissue, or skeletal muscle, in 10-week-old C57Bl6 mice. The blood glucose levels were determined using a Contour blood glucose meter (Bayer Health Care) or a Glutest Neo Super (Sanwa Chemical Co., Tokyo, Japan). The plasma insulin levels were measured with an insulin ELISA kit (Crystal Chem Inc. or Morinaga). An insulin tolerance test was performed by intraperitoneal injection with human insulin (0.75 mU/g body weight).
More than five pancreatic tissue sections from each animal were analyzed after fixation and paraffin embedding. The sections were immunostained with antibodies to insulin (Abcam, ab7842), biotinylated secondary antibody with a VECTASTAIN Elite ABC Kit (Vector Laboratories), and a DAB Substrate Kit (Vector Laboratories) to examine β-cell mass using bright-field microscopy. The proportion of the area of pancreatic tissue occupied by the β-cells was calculated using Image J software. β-cell mass was estimated for each animal by determining the proportion of the β-cell area per animal multiplied by the pancreatic weight. Mouse pancreases were analyzed by immunostaining using anti-insulin (Abcam, ab7842), anti-BrdU (Dako, m0744), anti-mouse-CENP-A (Cell Signaling #2048), or anti-FoxO1 (Cell Signaling #2880) antibodies for immunofluorescence. Cell counting was manually performed in a blinded fashion by a single observer. BrdU+ β-cells were assessed by confocal microscopy (LSM-7 DUO, Carl Zeiss, or Fluo View FV1000-D, Olympus). Insulin+ cells showing nuclear DAPI staining were considered as β-cells. Insulin+ cells showing nuclear colocalized staining for DAPI+ and BrdU+ were counted as proliferating β-cells. At least 1000 β-cells per mouse were analyzed. The fluorescence levels of FoxO1 and CENP-A were determined using Image J software. All images, which were acquired under the same condition, were converted to gray scale. Then, we randomly selected 10 regions of nuclei or cytoplasm of separate islets in each group and measured fluorescence levels. The fluorescent intensity was normalized by the mean background fluorescence levels.
Total RNA was extracted using RNeasy Mini Kit (QIAGEN). One μg RNA was reverse-transcribed using a High-Capacity cDNA Reverse Transcription Kit (Applied Biosystems). Quantitative PCR was performed in an ABI 7900HT system, using SYBR Green Supermix (Bio-Rad). GAPDH was used as an internal control. Primers described in Table S2 were used for amplification.
10 nM S961 in PBS or PBS was loaded into Alzet osmotic pump 2001 and implanted subcutaneously at the back C57BL/6J male mice (average body weight of 25 gram). All mice were treated with PBS or S961 for 7 days and pancreatic islets were isolated using standard procedure (Shirakawa et al., 2013). Total RNA from pancreatic islets were extracted using TRIzol reagent (Invitrogen) and the contaminating genome DNA was removed using Qiagen RNeasy mini kit. For microarray analysis, the total RNA was amplified and biotin labeled using Illumina TotalPrep RNA Amplication kit (Ambion). The cRNA was analyzed by in house Illumina BeadArray Reader and quantified using Illumina BeadStudio.
Islets were isolated from 8–12 weeks old wild type C57BL/6 male mice using intraductal collagenase technique (Shirakawa et al., 2013). Islets were handpicked and cultured overnight in RPMI 1640 media containing 5mM glucose and 10% fetal bovine serum (FBS).
For a modified MTT assay, cells were plated in 96-well plates @ 104 cells in each well. The cell viability was determined using the CellTiter 96 Non-Radioactive Cell Proliferation Assay (Promega, G4001) according to the manufacturer’s instructions. For EdU incorporation assay, cells or islets were treated with 10 μm EdU (4h for cells, 24hr for mouse islets, and 48hr for human islets) and stained with Click-iT Plus EdU Alexa Fluor 488 or 594 Imaging Kit (Thermo Fisher, C10637, C10639). Insulin+ cells showing nuclear colocalized staining for DAPI+ and ErdU+ were counted as proliferating β-cells. The proliferating β-cells were measured for 1,000 or more insulin-positive islet cells per mouse or sample of β-cell lines, or for 12,000 or more β-cells in human islets per donor in each of the groups (1,000–1,200 cells in Figure 4D, 1,200–1,500 β-cells in Figures 4E, 5B, 5C, and 12,000–21,000 β-cells in Figure 4F). Proliferation was determined to be 0% when no EdU-positive β-cells were found in both >100 human islets and >12000 insulin-positive β-cells in >5 independent sections.
Lentiviral particles for murine E2F1 short hairpin RNA (shRNA) (sc-29297-V) and control scramble shRNA (sc-108080) were purchased from Santa Cruz. Cells were infected by adding the lentiviral particles to the culture with polybrene (sc-134220). For generating stable cell lines, cells were treated with 4 mg/mL of puromycin 48 hours after the transduction and were maintained in selection media for more than 14 days. We generated two separate stable cell lines in each group.
Cells were solubilized in M-PER lysis buffer (Thermo Scientific #78501) with protease inhibitors and phosphatase inhibitors (Sigma P8340, P5726, P0044), and protein concentration was measured using a BCA protein assay kit (Pierce). The extracts were subjected to western blotting with primary antibodies overnight at 4°C. Mouse E2F1 (ab179445) and α-tubulin (ab7291) are from Abcam. Densitometry was performed using Image J software.
The cells or islets were treated with serum from vehicle-, S961-treated mice on day 7 (20% v/v). HLM006474 were added to culture media at the concentration of 10 μM when culture or coculture were started. For hepatocytes isolation, mice were anesthetized with 0.3 mg/kg medetomidine, 4.0 mg/kg midazolam and 5.0 mg/kg butorphanol; the portal vein was cannulated, and the liver was perfused with Liver Perfusion Medium (1X) (Thermo Fisher Scientific, 17703038) and digested with Liver Digest Medium (Thermo Fisher Scientific, 17703034) according to manufacturer instructions. Hepatocytes were washed twice in Hepatocyte Wash Medium (Invitrogen, catalog #17704024). The hepatocytes were seeded in collagen-coated 12-well plates (BD BioCoat) at a density of 3 × 3 105 cells/well in 25 mM glucose-containing DMEM and 10% FBS (v/v). Sixteen hours later, hepatocytes were cultured with 100 islets plated in a Netwell insert with a 74-μm mesh size polyester membrane (Corning, Inc.) for 24 hours in 50% RPMI1640 and 50% DMEM (v/v) medium containing 5.5 mM glucose and 10% FBS. Adipocytes were prepared by collagenase digestion (collagenase type I, Thermo Fisher Scientific, 17100017) of epididymal fat tissue, as described previously (Shirakawa et al., 2011). Epididymal fat tissue from each mouse are washed, minced with dissecting scissors to very fine pieces, and digested with collagenase at 37°C for 60 min. After centrifugation, adipocytes fraction was isolated by flotation from stromal vascular fraction (SVF) pellet fraction. Adipocytes (from 25 mg of epididymal fat) were co-cultured with 100 islets above the co-culture Netwell insert for 24 hours in 50% RPMI1640 and 50% DMEM (v/v) medium containing 5.5 mM glucose and 10% FBS.
No inclusion and exclusion criteria were applied to the data collection or the subject selection in this study.
All experiments were independently repeated at least three times. Results are shown as means ± SE. Statistical analyses were conducted using Prism 7 software (GraphPad Software). Gaussian distribution was determined by using a D’Agostino-Pearson test. Statistical comparisons between groups were analyzed for significance by an unpaired two-tailed Student’s t-test and a one-way analysis of variance (ANOVA) with post-hoc Tukey tests for a parametric test, or a Mann-Whitney U test for a nonparametric test. Differences are considered significant at p < 0.05. The exact values of n (depending on the experiment referring to number of animals, donors, or number of independent measurements), statistical measures (mean ± SE) and statistical significance are reported in the figures and in the figure legends. | true | true | true |
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PMC9617605 | Zhichen Xu,Dongjuan Chen,Tao Li,Jiayu Yan,Jiang Zhu,Ting He,Rui Hu,Ying Li,Yunhuang Yang,Maili Liu | Microfluidic space coding for multiplexed nucleic acid detection via CRISPR-Cas12a and recombinase polymerase amplification | 29-10-2022 | Lab-on-a-chip,Viral infection,Nucleic acids,Infectious-disease diagnostics | Fast, inexpensive, and multiplexed detection of multiple nucleic acids is of great importance to human health, yet it still represents a significant challenge. Herein, we propose a nucleic acid testing platform, named MiCaR, which couples a mi crofluidic device with CRISPR- C as12a a nd multiplex r ecombinase polymerase amplification. With only one fluorescence probe, MiCaR can simultaneously test up to 30 nucleic acid targets through microfluidic space coding. The detection limit achieves 0.26 attomole, and the multiplexed assay takes only 40 min. We demonstrate the utility of MiCaR by efficiently detecting the nine HPV subtypes targeted by the 9-valent HPV vaccine, showing a sensitivity of 97.8% and specificity of 98.1% in the testing of 100 patient samples at risk for HPV infection. Additionally, we also show the generalizability of our approach by successfully testing eight of the most clinically relevant respiratory viruses. We anticipate this effective, undecorated and versatile platform to be widely used in multiplexed nucleic acid detection. | Microfluidic space coding for multiplexed nucleic acid detection via CRISPR-Cas12a and recombinase polymerase amplification
Fast, inexpensive, and multiplexed detection of multiple nucleic acids is of great importance to human health, yet it still represents a significant challenge. Herein, we propose a nucleic acid testing platform, named MiCaR, which couples a microfluidic device with CRISPR-Cas12a and multiplex recombinase polymerase amplification. With only one fluorescence probe, MiCaR can simultaneously test up to 30 nucleic acid targets through microfluidic space coding. The detection limit achieves 0.26 attomole, and the multiplexed assay takes only 40 min. We demonstrate the utility of MiCaR by efficiently detecting the nine HPV subtypes targeted by the 9-valent HPV vaccine, showing a sensitivity of 97.8% and specificity of 98.1% in the testing of 100 patient samples at risk for HPV infection. Additionally, we also show the generalizability of our approach by successfully testing eight of the most clinically relevant respiratory viruses. We anticipate this effective, undecorated and versatile platform to be widely used in multiplexed nucleic acid detection.
Fast, inexpensive, and multiplexed detection of pathogens is of great value to human health and global security. Several diagnostic applications require the rapid detection of diverse nucleic acids. These include the identification of different types of pathogens (e.g., the quick differentiation of SAR-CoV-2 from the influenza virus or other respiratory viruses during the COVID-19 pandemic) and the discrimination of viral variants (e.g., subtyping of human papillomavirus [HPV], which has >100 variants). Next-generation sequencing (NGS) can provide detailed information and allow the identification of a wide range of pathogens. However, the turnaround time and cost are both high. Although various nucleic acid amplification methods can be used to detect multiple targets at a lower cost, some limitations and challenges remain. An ideal multiplexed detection platform would have the capability to address these above-mentioned problems. Such a platform should be able to: (i) correctly and efficiently amplify multiple (e.g., ≥4) nucleic acid targets; (ii) amplify and identify the targets at a suitable temperature; (iii) precisely recognize the targets without any interference; and (iv) accurately distinguish between signals and correlate them with each individual target. Polymerase chain reaction (PCR) has long been utilized for multiplexed amplification. Real-time PCR is commonly used as the gold standard method for pathogen identification. To examine multiple targets, real-time PCR often relies on the detection of different fluorophores during amplification through nucleic acid hybridization. However, this detection approach has two primary limitations. First, the emission spectra of fluorescent probes often overlap, and therefore, the number of targets that can be tested is limited. Additionally, off-target amplification can confound target identification owing to the unavailability of rectification mechanisms, and this problem worsens during multiplexed detection. Thus, although real-time PCR can address the first two challenges, it does not solve the last two. Recently, CRISPR-based diagnostic (CRISPR-Dx) methods have been widely implemented for diagnosing infections due to their high specificity and sensitivity. These platforms harness the collateral cleavage activity of various Cas proteins (e.g., Cas12a, Cas13a, Cas14) to cut the designed reporters after activation. To further increase detection sensitivity, CRISPR-powered detection strategies are typically combined with isothermal amplification methods such as recombinase polymerase amplification (RPA) and loop-mediated isothermal amplification (LAMP). RPA, a fast and high-fidelity amplification method, is used more commonly because the reaction requires only a single temperature (37–42 °C) that can be easily achieved with simple heaters and is also compatible with the optimum temperature of CRISPR-Cas systems. Moreover, the detection specificity is guaranteed due to the sequence specificity between CRISPR RNA (crRNA) and the amplicons. The first CRISPR-Dx platform called SHERLOCK was developed using Cas13a and reverse-transcription RPA (RT-RPA) for the detection of Zika and Dengue viruses. This platform had attomole (aM) sensitivity and single-base mismatch specificity. Subsequently, the multiplexed platform SHERLOCKv2 was developed by combining four CRISPR enzymes (LwaCas13a, PsmCas13b, CcaCas13b, and AsCas12a) with specific cleavage preferences and four types of fluorescent probes (FAM, TEX, Cy5, and HEX) to detect four targets. This platform had high potential as a multiplexable and sensitive platform for nucleic acid testing (NAT). However, it was challenging to increase the number of targets because of the difficulties in choosing additional appropriate CRISPR enzymes and probes. Therefore, in order to identify more pathogen targets, another Cas13-based platform called CARMEN was developed recently. This system allows the simultaneous detection of up to 169 human-associated viruses using microwell array technology to pair CRISPR reagents and amplified samples. The readouts are obtained by identifying a pool of color codes that result from different ratios of four Alexa Fluor dyes. Though CARMEN represents a milestone for scalable and multiplexed pathogen detection, its widespread use may be compromised due to the multi-step color coding/decoding and the complicated assay protocol. Additionally, owing to the four-fluorophore-based color-coding strategy, concerns regarding signal overlap due to the wide emission spectra of the fluorophores remain. Therefore, despite advancements in CRISPR-Dx technologies for multiplexed pathogen detection, the fourth challenge (generating distinguishable signal codes for multiple targets) remains unmet. Indeed, a recent work developed an improved CARMEN system (named microfluidic CARMEN, mCARMEN), which relies on commercially available Fluidigm microfluidics and instrumentation. It could be a promising strategy for the detection of multiple viruses, but the instrumentation setup and the device are relatively expensive and these might compromise its wide application in resource-restricted areas. Therefore, an “ideal” suitable and versatile platform that fulfills all the four requirements remains elusive. Herein, we report the development of a unique platform that couples a microfluidic device with CRISPR-Cas12a and multiplex RPA (MiCaR) to address all the challenges associated with multiplexed NAT. In addition to deploying the promising features of RPA and the CRISPR system, our strategy utilizes the unique properties of microfluidics to allow precise fluid control and spatial reagent arrangement. Instead of using multiple fluorophores or color coding to indicate different targets, MiCaR distinguishes between target signals based on spatial positions or space coding using only a single fluorophore. By preloading various crRNAs into the 30 designated wells, the system simultaneously initiates up to 30 CRISPR-based cleavage assays after the sample is pipetted onto the center of the Starburst-Shaped microchip (SS-Chip). In this study, we evaluate the performance of the SS-Chip to ensure homogenous liquid division and mixing. We select the nine HPV subtypes targeted by 9-valent HPV vaccine (9vHPV). Subsequently, we carefully optimize and demonstrate the efficiency and specificity of the multiplex RPA assay. Additionally, we conduct a test containing 9 × 9 crRNAs against HPV subtypes to determine the optimal crRNA combination. Moreover, extensive testing demonstrates that MiCaR can achieve a detection sensitivity of 0.1 nM and 0.26 aM for unamplified and amplified targets, respectively. Furthermore, we apply MiCaR to test samples from patients at risk of HPV infection. In total, we test 100 samples, and 3000 assays were performed. MiCaR shows great stability, and the on-chip HPV subtyping results are highly consistent with clinical results, achieving a sensitivity of 97.8% and specificity of 98.1%. Finally, we apply MiCaR to successfully test eight common respiratory viruses to confirm the generalizability of our approach. Therefore, the findings show that MiCaR allows rapid, low-cost, and multiplexed NAT with high sensitivity, specificity, and reliability. This approach could be widely applied for the diagnosis of specific infections and provide significant health-related and clinical benefits.
To address all the four challenges mentioned earlier, we developed the MiCaR system (Fig. 1) as a universal platform for multiplexed NAT. Herein, we demonstrate its use in HPV subtyping. After sampling, cervical cells were heated to induce the release of HPV DNA. Then, direct multiplex RPA was performed without DNA extraction (Fig. 1a). The amplified products were loaded onto the microfluidic device and tested using the CRISPR-based cleavage assay. The readout was obtained via an automatic fluorescence imaging system. As shown in Fig. 1b, the detection by MiCaR relies on a Starburst-Shaped Chip (SS-Chip), which is designed as a hub-and-spoke network. The sample is loaded into the hub well at the center, and the spoke microchannels homogeneously distribute the sample into 30 labeled wells pre-loaded with a specific Cas12a-detection mix containing Cas12a, crRNA, and a fluorescence reporter. The reporter used is a fluorophore quencher (FQ)-labeled oligonucleotide named TBA11 (GGTTGGTGTGG), a truncated form of thrombin binding aptamer (TBA), that has been proven to have higher sensitivity than a normal ssDNA reporter. If the HPV DNA matches the crRNA, the relevant wells (three wells used as a group for testing the same HPV subtype) will show a bright fluorescence signal. However, a low background signal will be obtained when there is a mismatch between the HPV DNA and the crRNA. A blank control (with reporter and Cas12a in the absence of crRNA) was tested on the device to obtain the basic background. This space-coding based strategy enabled the efficient identification of multiple targets by employing the “amplify together and detect individually” principle. The use of only one fluorescent probe eases system setup and data analysis, and the triplicate on-chip test (for the same target) also ensures detection accuracy and reliability.
There are >100 HPV subtypes, including high-risk and low-risk types, which can cause cervical cancer and condyloma acuminatum, respectively. As the risk associated with different HPV subtypes varies, it is necessary to screen and identify infections and prevent subsequent complications, such as cervical cancer. Vaccines can prevent cancer and diseases caused by some HPV infections. Currently, 9-valent (HPV−6, −11, −16, −18, −31, −33, −45, −52, −58) HPV vaccines are considered the most powerful for preventing HPV infection. In this study, we selected the nine HPV subtypes targeted by the 9vHPV to develop the MiCaR-based detection system. We first designed the relevant RPA primers for the nine targets. RPA is a highly sensitive isothermal amplification method that requires minimal sample preparation (Fig. 2a, left). Since the HPV subtype is determined by the L1 gene sequence, we designed a series of primers against this region in the nine targets (Fig. 2a, right). The L1 gene sequences of these nine HPV subtypes show high similarity (Supplementary Data 1, Supplementary Table 1). To select the applicable primers, two rounds of designing and testing were performed. In round one, 10 pairs of primers were designed (Supplementary Table 2, one for each HPV subtype, with an additional one for HPV-11 owing to its high similarity with HPV-6; Supplementary Table 1). The performance of these primers was determined using agarose gel electrophoresis (Supplementary Fig. S1a). Most of the primers worked well and displayed clear and dense bands. However, primers for HPV-6 and HPV-45 did not generate distinct bands. Therefore, three additional pairs of primers were designed for these two subtypes based on the general principles of primer design (Supplementary Table 3). It was observed that the Primer HPV-6a, -6b, -45a, -45b, and -45c amplified the relevant targets successfully (Supplementary Fig. 1b). To choose the preferable primers for HPV-6, -11 and 45, we comprehensively checked the sequence similarity between the amplification regions of the primer candidates (HPV-6a, -6b, -11a, -11b, -45a, -45b and -45c) and the L1 gene of the other eight subtypes. As shown in Supplementary Fig. 2a and Supplementary Data 2, the amplification region of Primer 6b showed a relatively greater difference than Primer 6a, which should contribute to reducing potential cross-reactivity in the subsequent CRISPR-based assay. Similarly, Primer 11b and 45a were theoretically superior to their counterparts (Supplementary Data 2, Supplementary Table 4). Therefore, Primer HPV-6b, -11b, -16, -18, -31, -33, -45a, -52, -58 were selected for the final panel (Supplementary Table 5). The gel results demonstrated the successful amplification of all nine targets (Fig. 2b). The L1 gene sequences and amplification regions of the nine HPV subtypes are listed in Supplementary Data 3. In addition to an appropriate amplification region, optimal primer constitutions and concentrations are important for successful multiplex RPA assays. The Basic Kit Quick Guide for single RPA recommends a primer concentration of 1 μM. Thus, we initially tested RPA performance with the nine targets and nine pairs of primers (1 μM for each pair, and 9 μM in total). As shown in Supplementary Fig. 3a, severe primer dimer formation was observed, and no amplicons were obtained. Hence, RPA assays were conducted using only one target (HPV-16) with lower primer concentrations (0.1, 1, and 5 μM in total) to identify the best primer pool. As indicated in Supplementary Fig. 3b, the pool with a total concentration of 1 μM (~0.11 μM for each pair) appeared optimal for efficiently amplifying the target HPV-16 without causing significant primer dimer formation. Hence, this primer constitution was used for subsequent assays. Next, we evaluated the performance of the primer pool in multiplex RPA. The products of the 9-plexed PRA were identified through next-generation sequencing (NGS) (Supplementary Fig. 4). The results indicated that a variety of amplicons were produced for each of the nine targets (Fig. 2c). This was expected, as amplicons of different lengths can be generated within a reaction for a single target in RPA. The top 5 sequences of the products showed high-fidelity to the original sequences (Supplementary Data 4). The relatively lower amplicon fidelity of HPV-11 and HPV-18 was resulting from one or two amplicons with shorter lengths comparing to the original templates. Anyway, all these amplicons showed a 100% fidelity in the crRNA binding regions. These results demonstrated the effectiveness of the primer pool. Subsequently, we tried to optimize the crRNA pool. Initially, the crRNA pool was generated by only comparing the crRNA of a certain target to the amplification regions of non-targets (Supplementary Table 6). However, this led to cross-activity between the initial crRNA of HPV-16 (HPV-6 crRNA-I) and HPV-11 (Supplementary Fig. 5). To efficiently avoid cross-reactivity among the targets, a refined approach was used to design the optimal crRNA pool. HPV-16 was selected to generate proof of concept. First, all four potential crRNAs were designed within the RPA region according to the basic principles of crRNA design (Fig. 2d). Then, each crRNA was compared with the full L1 gene (and not just the amplification region) of the other eight HPV subtypes (Supplementary Fig. 6). Given that HPV is a double-stranded DNA virus, the sequence comparison could be performed directly using the crRNA and gene sequence. Comparison results showed that crRNA-2, -3, -4, and -5 were highly similar (difference of only two nucleotides) to certain regions of the full L1 gene in the other eight HPV subtypes (Fig. 2d, Supplementary Fig. 6b–d). Accordingly, crRNA-1 that showed the most significant differences was selected for HPV-16 (Supplementary Fig. 6a). The crRNAs for the other subtypes were designed in a similar manner. All the crRNA sequences are listed in Supplementary Table 7.
Using the comprehensive theoretical analysis described above, optimal crRNAs were designed for all nine HPV targets. Then, a 9 × 9 matrix test (9 crRNAs × 9 HPV L1 gene plasmids; Fig. 3a, left panel) was designed to verify crRNA performance (i.e., recognition specificity). Additionally, a control group without any target gene was set up. After testing, widespread cross-activity was not observed (Fig. 3a, right panel). Similar trends were observed when the crRNAs were analyzed separately (Fig. 3b). Of note, the crRNA for HPV-58 showed a relatively high background for HPV-33. However, this was negligible compared to the signal for the actual matched sample (crRNA [HPV-58] against HPV-58). These crRNAs with high specificity were then used for subsequent multiplexed NAT. We next applied the CRISPR/Cas12a system to detect the products obtained through the 4-plexed and 9-plexed RPA assay. The trans-cleavage capability of Cas12a is activated when the crRNA recognizes its HPV target in the RPA product. Accordingly, the TBA11 reporter is cut into smaller elements. The detection results were first analyzed using denaturing PAGE. Figure 3c shows that the reporters in lanes 2–5 had been cleaved. In the other lanes, the reporter was intact, indicating that the Cas12a system was not activated. Next, the 9-plexed RPA products were evaluated. As shown in Fig. 3d, the results demonstrate that all nine lanes (lanes 2–10) had small reporter segments, suggesting that the nine HPV subtypes were efficiently amplified and successfully activated the Cas12a system. Some bands corresponding to cleaved reporters had different intensities (Fig. 3c, d), which could be due to variations in the cleavage efficiencies of the Cas12a system activated by different targets. The control (no HPV template) did not activate Cas12a in either the 4-plexed or 9-plexed assay. Next, we examined the cleavage products of the 4-plexed and 9-plexed assays based on the fluorescence readout that would be used in subsequent chip-based experiments. Consistent with the results of denaturing PAGE, four and nine bright fluorescence signals were observed in the 4-plexed and 9-plexed assays, respectively (Fig. 3e). These results confirmed the excellent performance of the multiplex RPA and CRISPR/Cas system, and laid a solid foundation for the subsequent on-chip HPV subtyping analysis. Based on these results, we also proposed a procedure for designing RPA primers and crRNAs for multiplexed NAT (Fig. 3f). Furthermore, we investigated the detection sensitivity of the multiplex RPA. A series of plasmid samples were prepared for the nine HPV targets with different concentrations (10−12, 10−13, 10−14, 10−15, 10−16, 10−17, 10−18 and 0 M). These samples were amplified and measured via the Cas12a-based assay (Fig. 4a). The detection results were shown in Fig. 4b. The 9-plexed RPA assay (1× primer for each target) achieved a sensitivity of 10−17–10−18 M for all the targets except HPV-18. The lower sensitivity for HPV18 could result from a relatively lower primer efficiency during the amplification for low-concentration templates. This was improved by using 3× HPV18 primer in the subsequent 9-plexed RPA assay, and finally it also approached to 10−18 M.
We conducted a series of experiments to evaluate the performance of the SS-Chip. Micrographs and photographs of the SS-Chip, which has a microwell radius of 750 µm and a microchannel width of 100 µm, are shown in Fig. 5a and Supplementary Fig. 7. The SS-Chip could deliver the fluid from the central well to the surrounding 30 outlet wells through its microchannels. To assess the aliquoting ability of the SS-Chip, 240 µL of food dye was injected into the central well. The volume of dye in each surrounding well was assessed using a precision Hamilton syringe. As shown in Fig. 5b, the solution could be divided equally within the 30 designated wells. The operation of the SS-Chip is shown in Supplementary Movie 1. To mimic the solution mixing occurring during the cleavage reaction, a fluorescein solution was pre-loaded into the 30 designated wells. Then, sulforhodamine B solution was injected into the central well. After 5 min, fluorescence images of the green and red channel were captured for each well (Fig. 5c). The merged image showed a homogeneous yellow color in each well, demonstrating that the solutions were completely mixed. Then, a Cas12a cleavage assay was conducted using the device to verify whether the CRISPR/Cas12a detection assay could be performed on the SS-Chip. The results (Fig. 5d) revealed similar fluorescence intensities in the 30 wells, further demonstrating the unbiased solution distribution and great mixing achieved by the device. These results laid a firm foundation for subsequent on-chip CRISPR/Cas detection assays. To determine the optimum assay duration for the on-chip assay, the fluorescence intensities were collected at different time points. Positive signals (test sample) increased in a time-dependent manner, while those of the Control did not change significantly (Fig. 5e). In consideration of both the assay duration and signal intensity, t = 15 min was chosen as the readout time for the on-chip assay. To evaluate the detection sensitivity of MiCaR, a series of unamplified and amplified HPV-16 plasmids were tested. A significant difference was detected for 0.1 nM of the unamplified plasmid versus the buffer (Fig. 5f). For the RPA-amplified HPV-16 plasmid, MiCaR could differentiate the blank sample from a 1 × 10−18 M plasmid sample (Fig. 5g). As the fluorescence signals increased proportionally with the logarithm of plasmid concentrations ranging from 0 to 1 × 10−16 M, the limit of detection (LOD) was calculated to be 2.67 × 10−19 M (0.26 aM) based on the 3σ/slope, where σ represents the standard deviation of readouts for three blank samples.
We next explored whether MiCaR can be used to analyze patient samples for HPV infection. To this end, 9-plexed HPV subtyping was performed using 100 human cervical swabs that were previously analyzed in a clinical laboratory (Supplementary Table 8). In the previous analysis, a color-coding-based PCR method was used for HPV subtyping. In this method, multiple color beads were used to differentiate between HPV subtypes, and the time taken was 3 h. In contrast, MiCaR used the space-coding approach to accurately identify multiple HPV subtypes with only one fluorophore (Fig. 6a). The Cas12a-crRNA mix was pre-loaded into the 30 designated wells, and the corresponding subtypes in the sample were recognized after RPA. The assay time required for MiCaR was about 40 min, including RPA, the on-chip reaction, and readout. Figure 6b shows the on-chip readout for a patient sample (#53). The wells (three as a group) designated for HPV-6, -16, and -45 showed significantly higher fluorescence than the other wells, suggesting that the sample was positive for these subtypes. The MiCaR-based quantitative results were compared to the previous clinical results for sample #53, demonstrating the high consistency between the two detection methods (Fig. 6c). The actual values of fluorescence intensity differed because the two approaches used different primers and detection mechanisms. The on-chip imaging readouts for several other samples are shown in Supplementary Fig. 8a. The 9-plexed HPV subtyping results of all 100 samples are shown in Fig. 6d, along with the clinical heatmap results (47 positive, 53 negative) (top) and the original on-chip imaging results (bottom). This comparison demonstrated the high concordance between the results of the two methods. The only exceptions were Samples #38 and #77. For Sample #38, the clinical results revealed the presence of HPV-16 and -52. However, according to MiCaR, the sample was positive for HPV-11 and -52. This difference is interesting, especially because one of the two recognized subtypes was common to the two assays. Nevertheless, we counted this as a false negative result based on HPV-16 discrimination. Moreover, MiCaR showed that Sample #77 was HPV-16-positive, while the clinical assay showed that this sample was negative for HPV. This false-positive could have resulted from contamination during sample transfer or detection. To systematically analyze the performance of the MiCaR detection platform, a statistical analysis was conducted by comparing the on-chip results with those of the clinical assay. As shown in Supplementary Fig. 8b, the positive and negative results of all the nine subtypes were summed up in the pool of 100 samples, showing that their frequencies differed. Overall, MiCaR demonstrated a good performance in testing these patient samples, with a 97.8% positive predictive agreement and 98.1% negative predictive agreement (Fig. 6e). The sensitivity and specificity were calculated to be 97.8% and 98.1%, respectively. After testing clinical samples under laboratory conditions, we examined whether MiCaR could be used in the field, where access to proper heating incubators is limited. A steam eye mask, which is available even at home, was used to heat the device with a proper temperature (Supplementary Fig. 9a, b). The device was loaded with the CRISPR/Cas Reaction Mixes and a patient sample (#53). After 15 min of incubation, fluorescent images of the 30 outlet wells were obtained and analyzed. As shown in Supplementary Fig. 9c, the results obtained through this field-based assay with an alternative heat source were highly consistent with those produced using a normal heater (Fig. 6b, c). These findings revealed that MiCaR holds great potential in the point-of-care testing of multiple nucleic acids.
Lastly, we applied the MiCaR-based approach to the detection of eight most clinically relevant respiratory viruses. This RVP includes influenza B virus (FLUBV), human coronavirus NL63 (HCoV-NL63), human coronavirus OC43 (HCoV-OC43), human respiratory syncytial virus (HRSV), human Coronavirus HKU1 (HCoV-HKU1), SARS-CoV-2, human parainfluenza virus serotype 3 (HPIV-3) and human metapneumovirus (HMPV). We first designed multiple pairs of RPA primers for each of the eight viruses (as shown in Supplementary Table 9), and the optimum primer was selected out based on the agarose gel electrophoresis (Supplementary Fig. 10, as indicated by the red numbers). Next, the crRNAs for recognizing the eight respiratory viruses were designed against the amplification region determined by the optimum RPA primers (Supplementary Table 10). The optimum crRNA with the proper secondary structure was selected out based on the software predication results. Then the performance of these crRNAs was evaluated by the 8 × 8 matrix-based activity test. Figure 7a shows that these crRNAs have great specificity against the relevant target. Subsequently, an 8-plexed RPA was performed and the products were verified by using the Cas12a assay. The results in Fig. 7b demonstrates that all the target viruses were successfully amplified and recognized. Furthermore, MiCaR was used to verify the 8-plexed RPA amplicons. The original detection images and bar plots are shown in Fig. 7c. All these results further proved that our approach could be used as a versatile strategy for multiplexed NAT.
The quick and cost-effective detection of multiple nucleic acids is essential for the efficient diagnosis of infectious diseases. The current PCR-based gold-standard tests can only detect few targets. Moreover, they require a long turnaround time and complex instruments, which are usually available only in central laboratories. Therefore, this method is not ideal for rapid and inexpensive NAT. Here, we report a unique assay called MiCaR that couples multiplex RPA with the CRISPR-Cas12a system and a 30-plexed SS-Chip to enable the rapid and low-cost identification of dozens of nucleic acid targets. RPA can amplify nucleic acids efficiently at isothermal temperatures, greatly simplifying testing and reducing the requirement for expensive equipment. Multiplex RPA has been widely used for NAT in previous study. However, to our knowledge, the number of targets analyzed simultaneously in a single assay has been limited to a maximum of 4. This limitation of RPA-based NAT could be due to two factors: primer design and target discrimination. According to the TwistDx RPA assay manual, there are no designing rules that guarantee the good amplification performance of RPA primers (unlike PCR primers, which typically work well). The recommendations suggest that a series of candidate primer pairs should be designed and screened, and the optimal one should be selected. Additionally, it is possible that primers optimized for single assays will not perform well in multiplexed assays. Therefore, significant efforts are required to design and optimize RPA primers, especially when there are multiple targets. For example, a recent study aimed to develop an RPA primer pool for 13 HPV subtypes. However, only three of the 13 primers produced strong signals, and the others provided very weak amplification. Ultimately, the researchers chose to modify commonly used PCR primer sets to perform the RPA assay and finally amplified and detected each 13 HPV subtype individually. In our study, to determine the optimal RPA primer combination for the nine HPV subtypes targeted by 9vHPV, several rounds of experiments, including primer design, sequence alignment, primer evaluation, and combination testing with the CRISPR/Cas12a assay, were performed (Fig. 2, Supplementary Figs. 1–3, Supplementary Data 1–2). The final primers showed great performance in single-, 4-, and 9-plexed RPA, with great efficiency and specificity, as indicated by the results of agarose gel electrophoresis, denaturing PAGE, NGS, and fluorescence measurement (Figs. 2, 3). The other issue that needs to be addressed for multiplex RPA-based NAT is the accurate and simultaneous identification of signals for each target. The amplified targets can be identified individually, but this prolongs the assay duration and increases detection costs. Most multiplexed amplification assays discriminate among targets using different fluorescent probes that contain regions complementary to the target amplicons. Although a few fluorophores are available for NAT, the inherent spectrum overlap between them poses a barrier to sufficient multiplexed detection. Moreover, target identification based only on sequence paring can compromise detection accuracy owing to off-target amplification. Herein, we coupled the CRISPR-Cas12a system with microfluidics technology to conquer these challenges. High specificity was achieved through Cas12a–crRNA binding and recognition. We screened multiple available crRNAs across the L1 gene of all nine HPV subtypes according to basic designing principles. During the first round of crRNA testing, cross reactivity between HPV-6 crRNA-I and the HPV-11 plasmid was detected (Supplementary Fig. 5, Supplementary Table 6). Subsequently, comprehensive comparisons were performed using these potential crRNA sequences and other non-target L1 genes to ensure a difference of at least three nucleotides, which could prevent the crosstalk between a crRNA and the non-target subtypes. After this round of checks, the best crRNAs against the nine targets were selected. Finally, we tested and confirmed the specificity of each crRNA by designing a matrix test of 9 crRNAs × 9 targets and performed 4- and 9-plexed assays (Fig. 3a–e). The multiplexed assays also demonstrated a great sensitivity of ~attomole for the nine HPV subtypes (Fig. 4). Given the significant effort to obtain an optimal RPA primer pool and coupled Cas12-crRNA panel for detecting multiple targets, as mentioned previously, we proposed a procedure for primer designing in such cases. The steps are summarized in Fig. 3f and could be used in a variety of such studies. For example, we also successfully applied this strategy to testing the eight common respiratory viruses (Fig. 7a, b). Microfluidics technology has been extensively used for molecular detection. However, common microfluidic platforms have complicated fluidic networks, and often require valves, accessory pumps or other complex controlling systems. Thus, they are not ideal for the quick and on-site detection of multiple targets. For example, some droplet- and electrochemistry-based microfluidic biosensors were proposed for nucleic acid detection, but the targets that can be analyzed simultaneously are limited. Though CARMEN and mCARMEN enables the testing of over one hundred nucleic acid targets, the relatively complex setup or expensive instrumentation hinders their wide use. To aid the identification of multiple targets, we developed a simple 30-plexed microfluidic SS-Chip. This chip has an undecorated hub-spoke network that can efficiently divide the sample into smaller, equal volumes after simple pipetting. The amplified sample is allowed to react with a pre-loaded Cas12a/crRNA Mix inside space-coded wells, allowing the precise identification of multiple targets with only one fluorescent probe. The SS-Chip can simultaneously provide three readouts (as a group) for each of the nine targets, thus having a significant advantage with respect to assessing detection performance. The on-chip assay takes only 40 min and its LOD reaches 2.67 × 10−19 M, which are significantly superior to RT-PCR (>2 h, LOD ~ 1.6 × 10−18 M). In this study, we tested 100 patient samples, and the on-chip results were highly consistent with clinic results, showing a specificity of 97.8% and sensitivity of 98.1%. The differences in fluorescence for three independent measurements in the same group were minimal, indicating the accuracy and stability of MiCaR-based detection (Figs. 6, 7c). Further, we demonstrated the potential of MiCaR as a point-of-care tool for applications in the field, wherein access to sophisticated instruments is limited. The global outbreak of COVID-19 highlights the critical need for the fast and reliable detection of multiple pathogens (e.g., to discriminate between respiratory viruses and mutated strains) in the laboratory and clinic and even at home. Therefore, the efficient identification of target pathogens can facilitate the management and containment of relevant infectious diseases. Though we only demonstrated the proof-of-concept by testing the nine HPV subtypes and eight respiratory viruses, the 30-plexed SS-Chip can detect 30 targets simultaneously. The space-coding based strategy is straightforward and displays results using only one fluorescent probe. It is also very convenient to expand the multiplexing capability of this chip by directly adding more microchannels and wells. Another direction to further improve the MiCaR system is to optimize the amplification and detection conditions and generate a one-pot assay, which should further ease the operation and reduce the duration time. Moreover, automatic operation could be made possible by integrating an undecorated microelectromechanical setup with the MiCaR system, as the isothermal amplification and single-probe based systems can simply be mounted. In summary, we developed a multiplexed NAT system, named MiCaR, by leveraging the great performance of multiplex RPA for amplification, the high specificity of CRISPR/Cas12a system in target recognition, and the appreciable utility of a microfluidic device for liquid handling. Using a simple procedure and short assay duration, MiCaR can detect multiple targets. MiCaR also showed reliable and accurate detection results when compared with clinical reports. Hence, we anticipate that MiCaR will be used widely for rapid and sensitive NAT across a range of biotechnology and healthcare-related applications.
The study was approved by the Committee on Human Research of Maternal and Child Health Hospital of Hubei Province (2020IECXM045) and China’s Ministry of Science and Technology. All research was performed in accordance with relevant guidelines and regulations. Informed consent was obtained from all participants.
A TwistAmp Basic kit containing RPA freeze-dried pellets was purchased from TwistDx (Cambridge, UK). Lachnospiraceae bacterium ND2006 Cas12a (LbCas12a) was purchased from Meige company (Guangzhou, China). Plasmids containing nine types of the HPV L1 gene, relevant gene sequences of the eight respiratory viruses and TBA11-FQ were obtained from Tsingke Company (Beijing, China). The primers and crRNAs used were synthesized by Sangon Biotech Company (Shanghai, China). DNA agarose and nucleic acid gel stains were purchased from Yeasen Company (Shanghai, China). Fluorescein and sulforhodamine B were purchased from Shanghai Aladdin Biochemical Technology Co. Ltd. (Shanghai, China). The steam eye mask was purchased from Kao Commercial (Shanghai, China).
Oligonucleotides were designed, and structural prediction and ΔG calculation were performed using the mFold webserver (http://www.unafold.org/mfold/software/download-mfold.php) and OligoAnalyser (https://eu.idtdna.com/calc/analyzer). The RPA primers for the nine subtypes of HPV were designed following the instructions of the TwistAmp Assay Design Manual (https://www.twistdx.co.uk/wp-content/uploads/2021/04/twistamp-assay-design-manual-v2-5.pdf) using the DNAMAN 9 (https://www.lynnon.com/) and CE Design 1.03 (http://www.ce-mark.com/ce-design.htm) tools. The crRNA candidates for the nine types of HPV were designed using the common principles of Cas12a crRNA design (https://international.neb.com/faqs/2018/05/03/how-do-i-design-a-guide-rna-for-use-with-engen-lba-cas12a). These HPV L1 genes were checked and found to be conserved using NCBI data through multiple sequence alignments analysis.
RPA reactions were carried out using a commercially available kit (https://www.twistdx.co.uk/wp-content/uploads/2021/04/INTABAS-v3.0-TwistAmp-Basic-Kit-Quick-Guide-INTABAS-1-1.pdf) according to the manufacturer’s instructions. One freeze-dried RPA pellet provided the RPA mix required for one reaction. First, the rehydration solution was prepared as follows: forward primer (2.4 µL, 10 µM), reverse primer (2.4 µL, 10 µM), rehydration buffer (29.5 µl), DNA template (5 µL, the concentration is changed for different purposes), and ddH2O (8.2 µL). This mixture was vortexed and then spun briefly. The rehydration solution was transferred to the reaction pellet and mixed using a pipette until the entire pellet had been resuspended. Then, magnesium acetate (2.5 µL, 280 µM) was added, and the solution was mixed well. The amplification reaction was performed at 39 °C for 20 min. For the negative control, the reaction was set up using ddH2O instead of the DNA template. Thereafter, the RPA products were characterized using different methods or stored at −20 °C for subsequent Cas12a-based cleavage assays.
Both the forward and reverse primers used were 2.4 µL (10 µM), resulting in a total primer concentration of ~1.0 µM in the 50-µL single-plex RPA assay. The instructions of the RPA kit mentioned that the primer constitution must be optimized during multiplexed amplification to avoid primer aggregation and obtain sufficient amplicons for each target. The primer pool was prepared by adding equimolar quantities of the forward and reverse primers for all nine HPV subtypes. First, the forward and reverse primer pool for each subtype was prepared (1 µM each), resulting in a total primer concentration of 9 µM in the assay. The results showed that the RPA assay for the nine targets did not work due to the formation of primer dimers. Hence, the primer concentration needed to be reduced. Subsequently, forward and reverse primer pools with three different total concentrations of 0.1, 1.0, and 5 µM were prepared for testing amplification performance (using the HPV-16 plasmid as the target to find the optimal pool). The primer pool, which contained all nine types of forward and reverse primers (2.4 µL; 1, 10, or 50 µM), rehydration buffer (29.5 × 9 µl), and ddH2O (8.2 × 9 µL), was prepared once for nine reactions in one centrifuge tube. Then, it was divided into nine equal parts for later use.
First, all the nine types of forward and reverse primers (2.4 µL; 10 µM), rehydration buffer (29.5 × 9 µl), and ddH2O (8.2 × 9 µL) were mixed. The solution was divided into nine equal parts. Each part was then vortexed and spun briefly. The rehydration solution was transferred to the reaction pellet and mixed using a pipette until the entire pellet had been resuspended. For the 4-plexed RPA, the DNA templates of HPV−6, HPV-11, HPV-16, and HPV-18 were added. The concentration of each subtype was 1.0 × 10−11M. For the 9-plexed RPA, the DNA templates of all nine HPV subtypes were added, with the concentration of each being 1.0 × 10−11M. Then, magnesium acetate (2.5 µL, 280 µM) was added and the solution was mixed well. The amplification reaction was performed at 39 °C for 20 min.
The cleavage assay was performed based on previous studies. The assay buffer contained 10 mM Tris, 70 mM KCl, and 10 mM MgCl2 and had a pH of 7.9. Before the assay, the TBA11-FQ reporter oligonucleotides were heated at 95 °C for 10 min and cooled down before use. The assay procedure was as follows. First, LbCas12a (2 µL, 2 µM), crRNA (4 µL, 1 µM), and the cleavage buffer (29 µL) were preincubated at 37 °C for 10 min to generate the LbCas12a/crRNA complex. Then, the target DNA (5 µL, 10 nM plasmid or RPA product) or cleavage buffer (5 μL, as negative control) and the TBA11 reporter (10 µL, 1 µM) were added to the previous solution to generate a 50-μL reaction system. The cleavage reaction was performed at 37 °C for 15 min. Subsequently, the cleavage solution was heated at 65 °C for 10 min to stop the reaction.
The cleavage results were characterized on a microplate reader (SpectraMax i3x, Molecular Devices, CA, USA). The excitation wavelength was set to 488 nm, and fluorescence emission was collected at 518 nm. For each HPV subtype, multiple crRNA candidates could be designed based on the L1-gene according to the basic designing principle of Cas12a-crRNA. However, the designed crRNA for a specific subtype could recognize other subtypes due to the high homology among different HPV subtype sequences. Therefore, experiments were conducted to screen for crRNA cross-reactivity. A careful comparison of the crRNA with the sequences of non-target HPV subtypes was performed to identify crRNAs that could differentiate between the target and non-targets. The cross-reactivity of the nine crRNAs was tested against the L1 gene plasmid of the nine HPV subtypes using a 9×10 cleavage assay. This assay also included a group of controls without any plasmid. The cleavage assay solutions were prepared in a 96-well plate following the protocol described above. The fluorescence obtained was measured using a microplate reader.
Plasmid samples with different concentrations (10−12, 10−13, 10−14, 10−15, 10−16, 10−17, 10−18 and 0 M) were prepared as the templates (each sample was a mixture of the nine HPV subtypes with the relevant concentration). Then a multiplex RPA assay (forward and reverse primer: 0.11 µM for each target, 2.4 µL of total volume) were carried out for each sample. The RPA products were evaluated with the Cas12a assay. For HPV-18, we also increased its primer to 3 folds of the primary concentration and performed the 9-plexed RPA assay.
LbCas12a (2 μL, 2 μM) and crRNA (4 μL, 1 μM) were preincubated in a total of 20 μL buffer (70 mM K+) at 37 °C for 10 min. Then FAM-labeled TBA11 (25 µL, 10 µM) and the product of the 4- and 9-plexed RPA (5 µL, 50 nM) were added into the reaction solution and the mixtures were incubated at 37 °C for 30 min. The system was heated at 65 °C for 10 min to stop the cleavage reaction. Then the solution was mixed with the loading buffer and loaded into a 20% denaturing PAGE gel containing 8 M urea. Electrophoresis was carried out at 120 V (about 40 V/cm) for 90 min (Mini-PROTEAN Tetra Cell system, Bio-Rad) in the 1×TBE buffer. The gel was scanned using Bio-rad ChemiDoc MP (170–8280) (BioRad Company, Shanghai, China) to obtain the readout.
Agarose gel electrophoresis was performed to analyze the products of RPA. The RPA products were separated with agarose gel (3.5%, w/v, pre-stained with GelRed) at 150 V/180 mA for 1 h. Finally, the DNA agarose gel was exposed to UV (BioRad Company, Shanghai, China) to take the gel graph.
Though agarose gel can roughly identify the size of the RPA products, it cannot provide detailed information about the sequence of the multiplex RPA products. To characterize the sequence of the amplicons of the nine HPV subtypes, the products of the multiplex RPA were sent to Sangon Biotech company and identified by next-generation sequencing (NGS).
The SS-Chip was fabricated using standard soft-lithography technique. First, negative photoresist SU-8 3025 was spin coated on a silicon wafer (800 rpm, 40 s; 3000 rpm, 60 s; corresponding to a height of ~25 µm), followed by a prebake (65 °C, 10 min; 95 °C, 25 min). After the UV-exposure (8 s, 5 mJ/cm2), the photoresist was post-baked (95 °C, 5 min) and developed. A hard bake (135 °C, 60 min) was processed to obtain the final mold. A mixture consisting of PDMS and curing agent (10:1) was poured onto the mold. After a heating process (65 °C, 2 h), the PDMS slab was cut and punched to get the inlet and outlets. Then a glass slide (75 mm × 50 mm) was treated with Aquapel (Aquapel Glass Treatment, PA, USA) to make it hydrophobic (see the following for more details), and served as the substrate. The prepared PDMS was attached (reversible bonding) onto the hydrophobic glass slide without plasma treatment. The PDMS could be detached and reused for >20 times after thorough alcohol wash and nitrogen blow drying.
To perform the on-chip assay, we first load different Detection Master Mixes (DMMs, including Cas12a, different crRNAs, and the reporter) in the relevant outlet wells, and then inject the sample from the central inlet to disperse it into each outlet. To avoid the DMM solution to flow back from the outlet into the inlet and cause unwanted cross contamination during the testing, Aquapel was used to make the microchannels hydrophobic. This ensures the DMM solution to stay in the designated outlet and facilitates the mixing of DMM and the sample. It is simple to carry out the Aquapel treatment. The treatment procedure is: use a pipette to drop ~1 mL Aquapel onto the surface of the glass slide, spread the Aquapel evenly with the pipette tip, wipe out the extra Aquapel on the glass slide after 10-min drying.
To evaluate the SS-Chip’s ability of solution aliquoting, green food dye (240 µL) was injected from the central inlet. The solution flowing into the 30 outlets was withdrawn, and the solution volume from each outlet was measured via a 10-µL Hamilton syringe. To mimic solution mixing during the cleavage reaction, fluorescein solution (10 µM, 4 µL) was pre-loaded into each well. Subsequently, sulforhodamine B solution (3 µM, 120 µL) was injected from the inlet. After 5 min, the fluorescence images from the green and red channel were captured for each outlet.
First, Cas12a (100 nM) and crRNA (100 nM) were pre-incubated at 37 °C for 10 min. Then, the TBA11-FQ reporter (2.5 µM) was added to prepare the Detection Master Mix (DMM). The total volume of the DMM depended on how many wells were to be used for testing. Typically, for each well, 5 µL DMM was added separately. To test in triplex, the mix was added to three adjacent wells. Then, 150 µL solution containing one or more HPV subtype plasmids (5 nM for each) was injected from the central inlet and divided within the 30 outlets and allowed to react with the pre-loaded DMM. Each well had 5 µL of the DMM and 5 µL of the target. The SS-Chip was incubated at 37 °C for a certain period (depending on the goal of the test; typically, the duration was 15 min). Subsequently, fluorescence images were obtained for each well.
The detection sensitivity of the SS-Chip for plasmids without pre-amplification was first characterized. A series of HPV-16 plasmid solutions with concentration from 0 to 10 nM were tested. Five microliters of these samples were added to the outlet wells (each sample in triplicate). Then, 150 µL DMM for HPV-16 was injected from the central inlet, and the device was incubated at 37 °C for 15 min. Subsequently, imaging was performed. Next, the LOD for testing plasmids after pre-amplification was examined. The template HPV-16 plasmid was diluted to 10−13 M, 10−14 M, 10−15 M, 10−16 M, 10−17 M and 10−18 M and amplified via RPA as described above. Five microliters of the RPA product of each sample were added to the outlet wells (each sample in triplicate). For the negative control, 5 µL ddH2O was added instead of the RPA product. Then, 150 µL DMM for HPV-16 was injected from the central inlet, and the device was incubated at 37 °C for 15 min. Subsequently, imaging was performed.
Human cervical cell specimens (n = 100) used in this study were collected in multiple times from Maternal and Child Health Hospital (MCHH) of Hubei Province, Huazhong University of Science and Technology. The study was approved by the MCHH Committee on Human Research (2020IECXM045). These samples were screened in the clinical laboratory for HPV infection by PCR assays (Tellgen Corporation, Shanghai, China) prior to our assay. The collected samples were anonymized and there is no personal identification data for the individuals. The collected samples were heated at 95 °C for 10 min to release the HPV DNA and stored at −20 °C before use.
The samples were screened in the clinical laboratory for HPV infection by multiplex PCR assays (Tellgen Corporation, Shanghai, China) prior to our assay. Briefly, the HPV DNA released from the sample was amplified by multiplex PCR with biotin-labeled primers. Then the amplicons were hybridized to color-coded microspheres coated with HPV subtype-specific probes. Next, the microspheres were incubated with phycoerythrin (PE)-conjugated streptavidin (SA-PE). After through wash, the microspheres were read on a Luminex 200 system (Luminex Corporation, Texas, USA). The HPV subtypes were determined based on the fluorescent dye signature carried by the microspheres.
First, the samples were amplified via multiplex RPA. The procedure was similar to that described above. One freeze-dried RPA pellet provided the RPA mix required for one sample. First, the rehydration solution was prepared: equimolar quantities of the nine forward primers for the nine subtypes (0.11 µM for each, 2.4 µL of total volume) were mixed with equimolar quantities of the corresponding nine reverse primers (0.11 µM for each, 2.4 µL of total volume), rehydration buffer (29.5 µl), sample (5 µL), and ddH2O (8.2 µL). After through mixing, the rehydration solution was added to resuspend the reaction pellet. Following the addition of magnesium acetate (2.5 µL, 280 µM), the amplification reaction was performed at 39 °C for 20 min. For the negative control, the reaction was set up using ddH2O instead of the sample. Then, the amplified sample products were tested on the SS-Chip. DMMs for the nine HPV subtypes were prepared as described above in a 1.5 mL centrifuge tube. For the negative control, crRNA was replaced with buffer. The tubes with the DMMs were incubated at 37 °C for 10 min and then transferred to an ice box. Then, 5 µL of the DMMs were added to the 30 reaction wells, with HPV-6, HPV-11, HPV-16, HPV-18, HPV-31, HPV-33, HPV−45, HPV-52, HPV-58, and Negative Control loaded into wells #1–#3, #4–#6, #7–#9, #10–#12, #13–#15, #16–#18, #19–#21, #22–#24, #25–#27, and #28–#30, respectively. The multiplex RPA product of each sample (50 µL) was diluted to 150 µL and injected from the central inlet and mixed with the DMM at each outlet. Then, the SS-Chip was incubated at 37 °C for 15 min and imaged to obtain the readout.
EVOSTM M5000 imaging system was used to take the fluorescence images. Each reaction well was imaged under the 10× microscope and a total of 30 pictures were obtained. The acquired pictures were further analyzed using Image J 1.8.0 and Origin 9. To quantitatively measure the fluorescence value of each reaction well, the image was loaded into the Image J software. Then, a few steps were processed as: “Image”, “Color”, “Split Channels”, “Choosing Green channel” and “Analysis of the value of green”.
Eight of the most clinically relevant viruses, including influenza B virus (FLUBV), human coronavirus NL63 (HCoV-NL63), human coronavirus OC43 (HCoV-OC43), human respiratory syncytial virus (HRSV), human Coronavirus HKU1 (HCoV-HKU1), SARS-CoV-2, human parainfluenza virus serotype 3 (HPIV-3) and human metapneumovirus (HMPV), were selected. The RPA primers for the RVP were designed following the instructions of the TwistAmp Assay Design Manual using the DNAMAN 9 and CE Design 1.03 tools, similar to the HPV panel. Multiple pairs of primers were designed to target specific gene of each virus (FLUBV, PB1 gene; HCoV-NL63, Orf1ab gene; HCoV-OC43, Orf1ab gene; HRSV, M gene; HCoV-HKU1, Orf1ab gene; SARS-CoV-2, Orf1ab gene; HPIV-3, M gene; and HMPV, F gene; the gene sequences are listed in Supplementary Data 5). Then, single-plex RPA assay was carried out for each pair of primers with a plasmid template (10−10 M, 5 µL). The RPA products were characterized using agarose gel electrophoresis. The optimum primer that produced a relatively denser band was selected out. Then the crRNA candidates for recognizing the eight respiratory viruses were designed against the amplification region determined by the optimum RPA primers. Next, structural prediction and ΔG calculation were performed using the mFold webserver and OligoAnalyser. The optimum crRNA with the proper secondary structure was selected out based on the predication results. Then the crRNAs were synthesized and evaluated by the 8 × 8 matrix-based activity test. Every crRNA was tested by using the eight respiratory viruses, and (−) was used as Control, in which no plasmid was added. Next, the 8-plexed RPA assays were performed to amplify the plasmids with a concentration of 10−12 M for each respiratory virus. The amplicons were first tested by the Cas12a-based assay, with the results measured by using a microplate reader. (−) was used as Control, in which no plasmid was added during the RPA assay. Furthermore, MiCaR was applied to measure the products of the 8-plexed RPA assay. To test in triplex, the DMM was added to three adjacent wells. (−) was used as Control, in which no crRNA was added. Then, 150 µL solution containing the 8-plexed RPA products was injected from the central inlet and divided into the 30 outlets to react with the pre-loaded DMM. The SS-Chip was incubated at 37 °C for 15 min and the fluorescence images were captured for each well.
Unless specified otherwise, statistical analysis was performed using Origin 9. Standard deviations and mean values were calculated using data from at least three identical assays. The unpaired two-tailed t-test was performed in Excel (Microsoft Office 2019) to compare fluorescence signals of two cohorts. A P-value < 0.05 was considered to be statistically significant. Positive predictive agreement (PPA), negative predictive agreement (NPA), sensitivity, and specificity were calculated using MedCalc (https://www.medcalc.org/calc/diagnostic_test.php). No statistical method was used to predetermine sample size. No data were excluded from the analyses. The experiments were not randomized. The investigators were not blinded to allocation during experiments and outcome assessment.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Supplementary Information Peer Review File Description of additional supplementary files Supplementary Data 2 Supplementary Data 3 Supplementary Data 4 Supplementary Data 5 Supplementary Movie 1 Reporting summary | true | true | true |
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PMC9617619 | 36109165 | Michal Lipinski,Sergio Niñerola,Miguel Fuentes-Ramos,Luis M. Valor,Beatriz del Blanco,Jose P. López-Atalaya,Angel Barco | CBP Is Required for Establishing Adaptive Gene Programs in the Adult Mouse Brain | 19-10-2022 | activity-driven transcription,CBP,intellectual disability,lysine acetylation,neuroepigenetics,p300 | Environmental factors and life experiences impinge on brain circuits triggering adaptive changes. Epigenetic regulators contribute to this neuroadaptation by enhancing or suppressing specific gene programs. The paralogous transcriptional coactivators and lysine acetyltransferases CREB binding protein (CBP) and p300 are involved in brain plasticity and stimulus-dependent transcription, but their specific roles in neuroadaptation are not fully understood. Here we investigated the impact of eliminating either CBP or p300 in excitatory neurons of the adult forebrain of mice from both sexes using inducible and cell type-restricted knock-out strains. The elimination of CBP, but not p300, reduced the expression and chromatin acetylation of plasticity genes, dampened activity-driven transcription, and caused memory deficits. The defects became more prominent in elderly mice and in paradigms that involved enduring changes in transcription, such as kindling and environmental enrichment, in which CBP loss interfered with the establishment of activity-induced transcriptional and epigenetic changes in response to stimulus or experience. These findings further strengthen the link between CBP deficiency in excitatory neurons and etiopathology in the nervous system. SIGNIFICANCE STATEMENT How environmental conditions and life experiences impinge on mature brain circuits to elicit adaptive responses that favor the survival of the organism remains an outstanding question in neurosciences. Epigenetic regulators are thought to contribute to neuroadaptation by initiating or enhancing adaptive gene programs. In this article, we examined the role of CREB binding protein (CBP) and p300, two paralogous transcriptional coactivators and histone acetyltransferases involved in cognitive processes and intellectual disability, in neuroadaptation in adult hippocampal circuits. Our experiments demonstrate that CBP, but not its paralog p300, plays a highly specific role in the epigenetic regulation of neuronal plasticity gene programs in response to stimulus and provide unprecedented insight into the molecular mechanisms underlying neuroadaptation. | CBP Is Required for Establishing Adaptive Gene Programs in the Adult Mouse Brain
Environmental factors and life experiences impinge on brain circuits triggering adaptive changes. Epigenetic regulators contribute to this neuroadaptation by enhancing or suppressing specific gene programs. The paralogous transcriptional coactivators and lysine acetyltransferases CREB binding protein (CBP) and p300 are involved in brain plasticity and stimulus-dependent transcription, but their specific roles in neuroadaptation are not fully understood. Here we investigated the impact of eliminating either CBP or p300 in excitatory neurons of the adult forebrain of mice from both sexes using inducible and cell type-restricted knock-out strains. The elimination of CBP, but not p300, reduced the expression and chromatin acetylation of plasticity genes, dampened activity-driven transcription, and caused memory deficits. The defects became more prominent in elderly mice and in paradigms that involved enduring changes in transcription, such as kindling and environmental enrichment, in which CBP loss interfered with the establishment of activity-induced transcriptional and epigenetic changes in response to stimulus or experience. These findings further strengthen the link between CBP deficiency in excitatory neurons and etiopathology in the nervous system. SIGNIFICANCE STATEMENT How environmental conditions and life experiences impinge on mature brain circuits to elicit adaptive responses that favor the survival of the organism remains an outstanding question in neurosciences. Epigenetic regulators are thought to contribute to neuroadaptation by initiating or enhancing adaptive gene programs. In this article, we examined the role of CREB binding protein (CBP) and p300, two paralogous transcriptional coactivators and histone acetyltransferases involved in cognitive processes and intellectual disability, in neuroadaptation in adult hippocampal circuits. Our experiments demonstrate that CBP, but not its paralog p300, plays a highly specific role in the epigenetic regulation of neuronal plasticity gene programs in response to stimulus and provide unprecedented insight into the molecular mechanisms underlying neuroadaptation.
Activity-driven transcriptional and epigenetic mechanisms serve many processes in the adult brain, including the modulation of synaptic plasticity and memory, and the adaptation to environmental conditions (Gräff and Tsai, 2013; Lopez-Atalaya and Barco, 2014; Yap and Greenberg, 2018; Fuentes-Ramos et al., 2021). These mechanisms can provide a molecular substrate for enduring changes in the behavior of an animal by fine-tuning gene expression according to the neuronal activation history (Fernandez-Albert et al., 2019; Marco et al., 2020). The ubiquitous detection of transcriptional dysregulation and chromatin alterations in disorders associated with cognitive impairments and maladaptive behaviors further highlights the relevance of these mechanisms in cognition (Bjornsson, 2015; Nestler et al., 2016). Studies in humans and rodents have strongly linked the activity of the transcriptional coactivators CREB binding protein (CBP) and p300 with cognitive processes (Lopez-Atalaya et al., 2014). Both paralogous proteins have intrinsic lysine acetyltransferase (KAT) activity and are also known as KAT3A and KAT3B, respectively. Whereas the germinal loss of either of these proteins leads to early embryonic death (Yao et al., 1998), hemizygous mutations in the genes encoding for CBP and p300 are linked in humans to a rare intellectual disability disorder known as Rubinstein–Taybi syndrome (RSTS; Petrij et al., 1995; Roelfsema et al., 2005), and cause cognitive impairments and other syndromic features in mice (Alarcón et al., 2004; Viosca et al., 2010). Pharmacological experiments and genetic studies in which CBP is specifically eliminated during development or in the early postnatal brain have later shown that cognitive impairments are not exclusively derived from developmental defects and pointed to a role for these proteins in neuronal plasticity in the adult brain (Chen et al., 2010; Barrett et al., 2011; Valor et al., 2011). However, the gene ablation strategies used in those studies produced mosaic recombination or triggered gene ablation in young neurons that are part of immature circuits still undergoing pruning and refinement (Semple et al., 2013), two processes in which CBP plays a critical role (del Blanco et al., 2019). These confounding factors complicate the interpretation of adult-specific functions. Compared with CBP, the impact of selectively eliminating p300 in forebrain neurons has been little investigated (Oliveira et al., 2011). We have recently shown, using an efficient, inducible gene ablation approach, that CBP and p300 are jointly responsible for maintaining the active status of neuronal identity genes throughout life (Lipinski et al., 2020). Here, we used the same inducible gene ablation approach to investigate whether the individual elimination of CBP or p300 in forebrain neurons specifically compromised plasticity and adaptive processes in the adult brain. The combination of RNA-sequencing (seq) and ChIP-seq screens in the hippocampi of mice lacking CBP in excitatory neurons revealed deficits that reduced the transcriptional competence of genes linked to learning and memory. These transcriptional and epigenetic alterations correlated with impaired performance in several memory tasks. Furthermore, CBP-dependent gene dysregulation and behavioral deficits became more prominent in aged mice and in paradigms that involve a chronic or recurrent change in transcription induced by experience or environmental change. Comparative analysis with conditional knock-out mice for the CBP paralog p300 indicated that these functions are specific to CBP.
The generation of Crebbpf/f (Zhang et al., 2004), Ep300f/f (Kasper et al., 2006), Crebbp+/− (Tanaka et al., 1997), and CaMKIIα-creERT2 (Erdmann et al., 2007) mice have been previously described. We generated tamoxifen (TMX)-regulated, forebrain-restricted KO (referred to as ifKO) by crossing Camk2α-creERT2 and the corresponding floxed strains (Fig. 1a). CBP-ifKO mice and p300-ifKO mice express truncated proteins (CBPstop523 and p300stop587, respectively) that lack KAT activity and other critical domains involved in protein–protein interactions (Lipinski et al., 2020). The genetic background of all mice was C57BL/6J. Analyses were conducted in young adult (3- to 6-month-old) mice, except for those conducted in aged (15- to 20-month-old) mice (seen Fig. 3). Recombination of floxed alleles was induced by five intragastrical administrations of TMX (20 mg/ml dissolved in corn oil; Sigma-Aldrich) on alternate days (Fiorenza et al., 2016). In all our experiments with ifKOs, we used CaMKIIα-creERT2– littermates treated with tamoxifen as controls. To induce status epilepticus (SE) by kainic acid (KA), we used two different protocols, as indicated: a single intraperitoneal injection of 25 mg/kg or progressive administration starting with 5 mg/kg and injecting 2.5 mg/kg with 20 min intervals until the animal reached twice the level 4 in the Racine scale or level 5 once (Umpierre et al., 2016). In the kindling experiment, we injected the mice with 45 mg/kg pentylenetetrazol (PTZ) in alternate days and evaluated their behavior during 15 min by using a modified Racine scale. In the enriched environment (EE) experiments, standard housing consisted of 30 × 15 × 11 cm clear cages occupied by up to five mice, while EE boxes were large white Plexiglas boxes (70 × 150 × 30 cm) occupied by up to 15 mice. We used natural materials, plastic tubing, running wheels, and toys to create an EE whose configuration was modified every 48 h shortly before the start of the dark cycle. Mice were maintained and bred under standard conditions, consistent with Spanish and European regulations and approved by the Institutional Animal Care and Use Committee.
For behavioral testing, we used adult mutant and control littermates of both sexes, except in the case of the EE experiments that were conducted exclusively with females to prevent the frequent fighting between males housed in an enriched environment. The open field (OF), elevated plus maze (EPM), and fear-conditioning tasks were performed as previously described (Viosca et al., 2009, 2010). The Porsolt and marble-burying (MB) tasks were also performed as described previously (Scandaglia et al., 2017). To study motor coordination and learning, mice were trained on a RotaRod (Ugo Basile) at a constant speed (4 rpm) four times per day during 2 d. In the novel object recognition (NOR) memory task, the procedure was similar to that described by Puighermanal et al. (2009): mice were habituated for 10 min to a white acrylic box (48 × 48 × 30 cm) 1 d before the training session. The next day, mice were exposed to a 10 min training session, and 24 h later to a 10 min test session. A three-chambered social test was performed as previously described (Ito et al., 2014). Mice were placed in the center chamber with access to both side chambers containing one empty cylinder and were allowed to freely explore the arena for 5 min (Hab). Then, a mouse with the same sex and age than the tested mouse (cage mate) was placed in one of the two empty cylinders (we systematically alternated left and right cylinders for the locations of the empty cylinder and the cage mate), and we allowed another 10 min of exploration. The interaction with the empty or the occupied cylinder was scored (sociability test). Subsequently, the tested mouse was returned to its home cage and an unfamiliar mouse was introduced into the empty cylinder to assess social recognition. Ten minutes later, the tested mouse was reintroduced in the same context, and we scored the time that the tested mice spent interacting with the familiar and novel mouse during 10 min (social recognition test). Spatial learning in the Morris water maze (MWM) was assessed in a circular tank (diameter, 170 cm) filled with opaque white water as previously described (Viosca et al., 2009). A platform of 10 cm diameter was submerged below the water surface in the center of target quadrant. Mice were monitored with the video-tracking software SMART (Panlab S.L.) during visual sessions (V; 3 d: V1 to V3), when the platform was visible, and during the hidden sessions (H; 9 d: H1 to H9). Each test had a maximum duration of 120 s; if the mouse did not reach the platform it was guided to it. Memory retention probe trials (PTs) of 60 s were performed at the beginning of day H5 (PT1) and 24 h after the last day of the MWM (H10, PT2). The MWM results (see Figs. 2, 8) correspond to experiments in which cohorts of ifKO mice and control littermates housed in a standard cage (SC) or an EE box were evaluated in parallel; the graphs seen in Figure 2 facilitate the comparison between genotypes, whereas those seen in Figure 8 highlight the housing effect on the different genotypes. Regarding statistical analyses, a nonparametric Mann–Whitney U test/Wilcoxon rank-sum test was used for pairwise comparison. Training curves were analyzed using repeated-measures ANOVAs including session as the within-subject factor and genotype as the between-subjects factor. Mean ± SEM values are presented in the figures. Additional statistical information for the behavioral testing is provided in Extended Data Figures 2-1, 3-1, and 8-1.
Perfused mouse heads in agarose were examined in a horizontal 7 T scanner with a 30-cm-diameter bore (model Biospec 70/30 v, Bruker Medical), as previously described (Scandaglia et al., 2017). Nissl staining was conducted as previously described (Lopez de Armentia et al., 2007). In both techniques, the hippocampal area per slice was measured using ImageJ.
Western blot (WB) and immunohistochemistry (IHC) analyses were conducted as previously described (Sanchis-Segura et al., 2009). The following primary antibodies were used in this study: anti-CBP [catalog #sc-583, Santa Cruz Biotechnology (chromatin immunoprecipitation [ChIP], 10 μg)]; anti-CBP [catalog #sc-369, Santa Cruz Biotechnology (WB, 1:500)]; anti-CBP [catalog #sc-7300, Santa Cruz Biotechnology (IHC, 1:100; WB, 1:500)]; anti-p300 [catalog sc-585, Santa Cruz Biotechnology (IHC, 1:100; ChIP, 10 μg)]; acetyl-histone antibodies specific for the pan-acetylated forms of H2A (K5, K9), H2B (K5, K12, K15, K20), H3 (K9, K14), and H4 (K5, K8, K12, K16) produced in our laboratory (WB, 1:500; Sanchis-Segura et al., 2009); commercially available antibodies against H3K9,14ac (06–599), H2B (07–371), H3 (05-499), H2BK15ac (07–343), and H2BK20ac (07–347), from Millipore; H2BK5me2 (catalog #ab17351, Abcam); H2BS14p (07–191), from Millipore; H3K27ac [catalog #ab4729, Abcam (IHC, 1:1000; WB, 1:1000; ChIP, 5 μg)]; anti-NeuN [catalog #MAB377, Millipore (IHC, 1:500)]; anti-GFAP Sigma G9269 (IHC: 1:200; ICC: 1:100); anti-Cre recombinase (Kellendonk et al., 1999; IHC: 1:500); anti-β-actin (F5441; WB, 1:1000). Biotinylated anti-mouse (1:500; catalog #B0529, Sigma-Aldrich) and anti-rabbit (1:3000; B8895, Sigma-Aldrich) antibodies were used in the DAB staining (catalog #11718096001, Sigma-Aldrich). Fluorophore-coupled secondary antibodies were acquired from Thermo Fisher Scientific and used in a dilution 1:400. Cell counting and length measurements were performed using ImageJ software.
Quantitative real-time PCRs (qRT-PCRs) were performed as detailed in the study by Valor et al. (2011). All primer sequences are available on request. Each independent sample was assayed in duplicate and normalized using GAPDH levels. RNA-seq was performed as detailed in the study by Lipinski et al. (2020) with minimal modifications (i.e., single-end libraries; length, 50 bp; HiSeq 2500 Sequencer, Illumina). Information, for example, about the number of replicates and millions of reads per sample is shown in Extended Data Figure 1-1 (see Figs. 1, 7, 9, experiments). Alignment was performed with STAR (version 2.6.1) to mm10 (GRCm38) genome and only reads with mapq > 10 were considered for further analysis. Counts were quantified to exons using Rsubread and Mus_musculus.GRCm38.99.gtf from Ensembl. Differential expression analysis was performed with Deseq2 (version 1.30.1). For differentially expressed genes (DEGs) in p300-ifKO mice, each sample corresponded to total RNA from the hippocampus of individual p300-ifKO mice or control littermates (n = 3- to ∼4-month-old male mice). For DEGs in CBP-ifKO mice, we conducted the following three independent experiments: (1) each sample corresponded to total RNA from pooled hippocampi of three female mice housed in SCs (n = 3- to ∼4-month-old CBP-ifKO mice or control littermates); (2) samples corresponding to total RNA from hippocampus of individual saline-treated male mice in the kindling experiment (see Fig. 7; n = 3- to ∼4-month-old CBP-ifKOs or control littermates); and (3) samples correspond to total RNA from hippocampi of individual SC-housed female mice in the EE experiment (see Fig. 9; n = 3- to ∼4-month-old CBP-ifKOs or control littermates). For statistical analysis, we used the Wald test in Deseq2 to retrieve DEGs comparing control and CBP-ifKO mice. We identified as bona fide DEGs in CBP-ifKOs those genes retrieved in the three RNA-seq screens. In the RNA-seq screen for kindling-regulated transcripts (see Fig. 7), we also used the Wald in Deseq2 to retrieve DEGs comparing the PTZ45 versus saline conditions in both genotypes. The DEGs in the control group were used to elaborate on the heatmap seen in Figure 7f. In the RNA-seq screen for EE-regulated transcripts (see Fig. 9a), a likelihood rate test was performed with all the conditions in Deseq2. The design was Genotype + Environment + Genotype:Environment. We selected those genes presenting the main effect for environment and for genotype–environment interaction; outlayer genes were removed by increasing the cooksCutoff to 0.999, and genes with fold > |0.5| were selected.
ChIP-seq and ChIP-qPCR assays were performed as detailed in the studies by Lopez-Atalaya et al. (2013); Valor et al. (2013b) and Lipinski et al. (2020) using the following antibodies: H2B-ac (K5, K12, K15, K20; Sanchis-Segura et al., 2009), AcH27ac (catalog #ab4729, Abcam), H3K9,14ac (catalog #06-599, Millipore), RNAPII (catalog #sc-9001, Santa Cruz Biotechnology), and TFIIB acetylated at K238 (catalog #ab5210, Abcam). All primer sequences are available on request. ChIP-seq libraries were single-end, 50 bp in length, and sequenced in a HiSeq 2500 apparatus (Illumina). Information about, for example, the number of replicates and the millions of reads per sample is shown in Extended Data Figure 4-1 (Figs. 4, 5, 9, experiments). In the H3K27ac ChIP-seq screen (see Fig. 5), we used datasets from two different experiments, as follows: (1) comparison of samples corresponding to bulk chromatin from the hippocampus of individual (∼4-month-old) CBP-ifKO and control female mice housed in SC or EE (n = 2/group; i.e., 4 vs 4 in the genotype-based screen); and (2) samples corresponding to pooled samples from the hippocampi of three CBP-ifKO or control male mice (∼4 months old) housed in SCs (n = 2/group). We used the first experiment (with larger sample sizes) to retrieve the differentially acetylated regions (DARs; see Fig. 5a,b). The H3K27ac-enriched regions retrieved in that screen were used to normalize the second experiment. Both experiments retrieved similar results after normalization. The profiles seen in Figure 5, a, d, e, and g, correspond to the second experiment. For the H2Bac ChIP-seq screens, we used bulk chromatin from pooled hippocampi of three CBP-ifKO mice or three control male mice (n = 1/group); we also compared samples from Crebbp+/− mice and their control littermates (bulk chromatin from pooled hippocampi of three male mice; n = 1/group). For the H3K9,14ac ChIP-seq screen, we used bulk chromatin from pooled hippocampi of three CBP-ifKO or three control male mice (n = 1/group). For the RNAPII, the ChIP-seq screen used bulk chromatin from the hippocampus of 1 CBP-ifKO or 1 control male mouse (n = 1/group). For the TFIIBac ChIP-seq screen, we used bulk chromatin from the hippocampus of 1 CBP-ifKO or 1 control male mouse (n = 1/group). For the H3K27ac ChIP-seq screen using sorted neuronal nuclei, the nuclei were isolated from the hippocampus of 1 control female mouse housed in SC or EE box (n = 1/group). In addition, we used the following datasets generated in previous studies: ChIP-seq for H3K4me3 (Scandaglia et al., 2017), CBP binding (Lipinski et al., 2020), and assay for transposase-accessible chromatin (ATAC)-seq (Fernandez-Albert et al., 2019) data in wild-type mice were downloaded from Gene Expression Omnibus (GEO; GSE85873, GSE133018, and GSE125068, respectively), and presented in the heatmaps (see Fig. 5d,e) and the IGV profiles (see Fig. 9f). Alignment was done with bowtie2 (version 2.3.4.3). Blacklist regions were removed, and only reads with mapq > 30 and aligned to nuclear chromosomes were used for further analysis. For peakcalling, macs2 was used, and counts were extracted with diffbind (version 3.0.15). Deseq2 was used for the DARs analysis (Fig. 5), using the likelihood rate test and removing the environment effect (EE vs SC) effect. Similar to the description of RNA-seq screen (see Fig. 9), we used Deseq2 and the design Genotype + Environment + Genotype:Environment in selecting regions that present the main effect for environment and for the interaction by selecting those with fold change > |0.25|. To generate normalized coverage tracks, scaling factors for each sample were obtained using Deseq2 sizeFactors. In those cases where samples libraries differences were >5%, we scaled coverages using deeptools and 1/Deseq2 value in the scaleFactor argument. The genomic tracks corresponding to dKAT3-ifKOs ATAC-seq were downloaded from GSE133018 (Lipinski et al., 2020). In all other cases, Reads per Genomic Content was used for normalization. ChipPeakAnno (v 3.20.1) package was used for annotation of ChIP peaks to genomic features. Homer (version 4.10) was used to obtain the number of normalized reads (see Fig. 5i, boxplot); these values were plotted using GraphPad, and the Wilcoxon rank-sum test (Wilcox.test one tail with alternative = “greater”) was used for statistical analysis. The selection of 200 random genes expressed in neurons was obtained from the set of genes with more than five transcripts per million in the nuRNA-seq dataset in the study by Fernandez-Albert et al. (2019).
In both RNA-seq and ChIP-seq experiments, Fastqc (version 0.11.8) was used for quality control of next-generation sequencing (NGS) data, and adapter trimming was performed with trim_galore (version 0.6.4_dev). Reads were aligned to mm10 (GRCm38) as indicated above. Aligned files were processed using samtools (version 1.7) and deeptools (version 3.5.0). IGV (version 2.6.3) was used for visualization and interactive exploration of genomic data. Statistical analyses of RNA-seq and ChIP-seq data were performed in R (version 3.6.1). Gene ontology enrichment analyses were performed with WebGestalt; in the H3K27ac analysis, the weighted set cover option was selected to retrieve the gene ontology (GO) terms.
The experimenters were blind to genotypes, and the result of the PCR-based genotyping was provided as a factor for statistical analysis of the behavioral data. All statistical analyses were two tailed except when indicated otherwise. p Values or p-adjusted values were considered significant at p < 0.05 except when indicated otherwise. Mean ± SEM values and percentages are presented in bar graphs. Additional information concerning the experimental design of individual experiments, including the precise statistical tests, and the statistical software used to perform analyses is provided in previous Materials and Methods subsections, an extended data table, and the corresponding figure legends.
The accession number at GEO for the RNA-seq and ChIP-seq datasets reported in this article is GSE200594. In addition, we used the datasets GSE43439, GSE85873, GSE133018, and GSE125068 in specific comparisons.
TMX-treated CBP-ifKO mice showed widespread loss of CBP immunoreactivity in pyramidal and granular neurons in the hippocampus a few weeks after TMX treatment (Fig. 1a–c). Other brain regions that also express the Camk2a-creERT2 driver and are known to play a role in learning and memory processes, such as the amygdala and cortex, also lost CBP immunoreactivity, whereas areas in which the driver is not active, such as the cerebellum and the basal ganglia, were spared. Similar results were observed in p300-ifKOs when antibodies against p300 were used (Fig. 1a; Lipinski et al., 2020, their Supplementary Fig. S1A,B). 10.1523/JNEUROSCI.0970-22.2022.f1-1 10.1523/JNEUROSCI.0970-22.2022.f1-2 Although neither CBP-ifKOs nor p300-ifKOs displayed overt neurologic abnormalities after gene ablation, previous studies on conditional CBP KOs have shown that specific forms of neuronal plasticity were compromised by the loss of CBP (Chen et al., 2010; Barrett et al., 2011; Valor et al., 2011; Lipinski et al., 2019). We conducted RNA-seq screens in the hippocampus of CBP-ifKOs, p300-ifKOs, and their control littermates 1 month after inducible gene ablation in adult excitatory neurons to explore the specific impact of eliminating either one of these coactivators at the whole-transcriptome level (Fig. 1a, bottom scheme, Extended Data Fig. 1-1). The screen for bona fide CBP-regulated genes (based on three independent RNA-seq experiments) retrieved >200 genes deregulated in the hippocampus of CBP-ifKOs (Fig. 1d, top; adjusted p-value < 0.05). In contrast, the loss of p300 in mature hippocampal neurons did not cause significant changes in gene expression (Fig. 1d, bottom; adjusted p-value < 0.05) except for the upregulation of the genes Arsi and Esr1 (also detected in CBP-ifKOs), which is a feature of Camk2a-creERT2 transgenics (Arsi is adjacent to the Camk2a promoter and is part of the BAC used to create this Cre-driver strain; the upregulation of Esr1 is limited to the exons that are part of the creERT2 chimeric construct). GO analysis of the 192 downregulated genes in CBP-ifKOs retrieved a strong enrichment for terms related to neuronal growth, learning and memory, and synaptic transmission (Fig. 1e). For instance, several activity-regulated genes encoding important plasticity-related factors (Fernandez-Albert et al., 2019), such as the brain-derived neurotrophic factor (BDNF), neuronal pentraxin (Nptx2), and the neuropeptides enkephalin and dynorphin, were consistently downregulated (Fig. 1f, Extended Data Fig. 1-2). Many of the affected genes are included in the group of so-called late response genes (LRGs). The transcripts encoding postsynaptic proteins, such as C1ql2, calmodulin kinases, and serotonin and somatostatin receptors, were also reduced, suggesting that synaptic plasticity is altered in CBP-ifKOs. In contrast, the set of upregulated genes (only 19 genes) was not enriched in any biological process or molecular function. We confirmed some of these changes in independent qRT-PCR assays (Fig. 1g). Note that although the transcriptional dysregulation in CBP-ifKOs affected genes of great relevance in plasticity, the changes were relatively weak when compared with those observed in double ifKOs for CBP and p300 (called dKAT3-ifKOs) in which thousands of genes were severely dysregulated and many changes were greater than twofold (Lipinski et al., 2020; Fig. 1h). This comparison indicates that the expression of p300 compensated for the loss of CBP in >95% of the genes (Fig. 1i). Furthermore, comparison with a differential expression screen in Crebbp+/− mice (Lopez-Atalaya et al., 2011) revealed that the set of genes downregulated in CBP-ifKOs also showed a global trend toward downregulation in hemizygous mice (Fig. 1j), thereby underscoring the potential relevance of these transcriptional changes in RSTS etiopathology. Overall, these analyses demonstrate that the expression of a subset of plasticity genes, including genes as relevant as Bdnf, Penk, and Nptx2, is highly dependent on CBP levels.
We next conducted a detailed behavioral characterization of CBP and p300-ifKOs to assess how the regulated ablation of these genes in the adult brain affected multiple behavioral traits (Fig. 2a, Extended Data Fig. 2-1). CBP-ifKOs did not show any difference in locomotion or anxiety behavior in an OF (Fig. 2b). Anxiety in the EPM task (Fig. 2c), motor coordination in the RotaRod (Fig. 2d), and depressive-like behaviors in the Porsolt forced swim test (Fig. 2e) were also unaffected. However, CBP-ifKOs displayed significantly less MB behavior than their control littermates (Fig. 2f). Loss of CBP in the excitatory forebrain neurons also caused impairments in cognitive tasks, such as NOR memory (Fig. 2g) and social recognition memory (Fig. 2h), while spatial navigation in the MWM (Fig. 2i) and contextual and cued fear conditioning (FC) memory were unaffected (Fig. 2j). In contrast, the p300-ifKOs did not show any significant difference in locomotion, anxiety, depression, marble-burying behavior, or memory tasks (Fig. 2k–s). 10.1523/JNEUROSCI.0970-22.2022.f2-1 Overall, these experiments demonstrate that CBP, but not p300, is required in adult excitatory neurons for optimal performance in different cognitive tasks that rely on spontaneous exploratory behavior. These results extend previous observations in noninducible CBP forebrain-specific KOs (Chen et al., 2010; Valor et al., 2011) and provide conclusive evidence for a pivotal contribution of CBP to adult cognitive function independent of its roles during brain development and maturation.
We extended the neurologic characterization of CBP-ifKOs to older mice. More than 1 year after gene ablation, the reduction in body weight observed in young CBP-ifKO males when compared with control littermates became highly significant and was detected also in females (Fig. 3a). Aged CBP-ifKOs, like the younger ones, displayed significant deficits in some LRGs, such as Bdnf and Nptx2, but not in immediate early genes (IEGs), such as Arc, Fos, and Npas4 (Fig. 3b,c). The chronic downregulation of plasticity genes triggered additional deficits. For instance, the brain of aged CBP-ifKOs showed a moderate atrophy of the ventral hippocampus area (Fig. 3d), although we did not detect any apparent neuronal loss (NeuN+ cells in CA1 field: Mann–Whitney U test = 7, p > 0.05 two tailed) or gliosis (Fig. 3e,f), which suggests a reduction of the hippocampal neuropil that is consistent with our GO analysis (Fig. 1c). Moreover, aged CBP-ifKOs displayed behavioral deficits not observed in younger mice, such as gait abnormalities (Fig. 3g), spatial learning deficits in the MWM (Fig. 3h) and deficient fear memory extinction (Fig. 3i,j, Extended Data Fig. 3-1). In conclusion, the analyses of elderly mice revealed a progressive deterioration with age, which is consistent with observations in RSTS patients (Stevens et al., 2011), and point to a role for deficient CBP activity in age-related brain pathologies. 10.1523/JNEUROSCI.0970-22.2022.f3-1
Previous Western blot analyses in Crebbp+/− mice (Alarcón et al., 2004; Lopez-Atalaya et al., 2011) and CKIIcre/CBPf/f mice (Valor et al., 2011) have shown that the loss of CBP causes a reduction of neuronal lysine acetylation that differentially affected specific residues in the nucleosome histone tails. We extended these analyses to the brains of CBP-ifKOs and found lysine acetylation deficits that affected histones H2A, H2B, and H3K27 (Fig. 4a). 10.1523/JNEUROSCI.0970-22.2022.f4-1 To link deficient lysine acetylation with transcriptional changes, we extracted hippocampal chromatin from CBP-ifKO and control siblings and performed ChIP-seq against two histone post-translational modifications (hPTMs) that are particularly sensitive to CBP loss: H2Bac and H3K27ac (Alarcón et al., 2004; Valor et al., 2011; Weinert et al., 2018; Lipinski et al., 2020; this study). In parallel, we also examined the biacetylation of histone H3 at K9 and K14, which is in principle not targeted by CBP (Weinert et al., 2018; Extended Data Fig. 4-1). CBP-ifKOs presented a dramatic and global reduction in H2Bac, an hPTM previously linked with learning and memory (Bousiges et al., 2010), along the entire genome (Fig. 4b). This global decrease—manifested in the dramatic reduction of H2Bac levels observed by Western blot (Fig. 4a), immunohistochemistry (Fig. 4c), and ChIP assays (Fig. 4d)—led to a paradoxical increase of H2Bac in promoter regions in ChIP-seq before normalization. This pattern is expected when the changes of a broad-domain histone modification occur at the genome scale (Bonhoure et al., 2014; Orlando et al., 2014; Egan et al., 2016; Guertin et al., 2018). Independent assessment of the four lysine residues in the N-terminal tail of H2B (K5, K12, K15, and K20) by Western blotting confirmed the global hypoacetylation (Fig. 4e). Other post-translational modifications of histone H2B, such as phosphorylation and methylation, were, however, not affected by CBP deficiency (Fig. 4f). Western blots and ChIP-seq experiments revealed similar H2B acetylation deficits in Crebbp+/− mice (Fig. 4g,h). These results prove that the H2B acetylation deficits first reported in hemizygous mice (Alarcón et al., 2004), which narrowly model the RSTS condition, are not the indirect consequence of developmental effects but are the result of the reduced presence of CBP in the adult neurons. In contrast, our previous analyses in Ep300+/− (Viosca et al., 2009, 2010) and p300-ifKO (Lipinski et al., 2020) mice did not reveal H2Bac deficits. The analysis of H3K27ac, which typically decorates active enhancer regions (Tie et al., 2009), did not show the same global effect observed in H2Bac, but revealed significant losses at specific regions. We observed a decrease in H3K27ac in the set of downregulated genes retrieved in the RNA-seq screen (Fig. 5a). Moreover, when we looked for DARs independently of the expression data (Fig. 5b), we found that most of these DARs were located into cis regulatory elements (CREs; Fig. 5c), being the putative enhancers, the region type that displayed the largest decrease in H3K27ac (Fig. 5d). ChIP-seq for CBP in hippocampal chromatin of CBP-ifKOs revealed a pronounced loss of CBP at the affected CREs, indicating that these enhancers are neuronal specific (Fig. 5e). Consistent with this result, GO analysis of the genes associated with these DARs retrieved neuronal-specific terms (Fig. 5f). These results are consistent with our observations in dKAT3-ifKOs (Lipinski et al., 2020), but the changes observed in the p300/CBP double mutants were stronger and much more numerous than in CBP-ifKOs (Fig. 5g). These differences indicate that, although H3K27ac is distinctly dependent on CBP, p300 can maintain functional levels of this hPTM at most loci in the absence of CBP. Contrary to H2Bac and H3K27ac, the profile for H3K9,14ac was very similar in CBP-ifKOs and control littermates. H3K9,14ac levels were unaffected even in downregulated genes (Fig. 5h). These results are consistent with experiments in Drosophila and analysis of the CBP/p300-dependent acetylome (Weinert et al., 2018), indicating that these lysine residues are not preferred substrates of CBP. To shed additional light on defective transcription in CBP-ifKOs, we analyzed the binding of CBP and the RNA polymerase II complex (RNAPII) at downregulated genes using ChIP-seq. RNAPII binding was reduced at the promoter of downregulated genes, concomitantly with CBP binding, H3K27ac levels, and acetylated TFIIB (a subunit of the RNAPII complex regulated by lysine acetylation; Choi et al., 2003; Fig. 5i). In contrast, in a set of 200 random neuronal genes, RNAPII and acetylated TFIIB levels were not reduced. These results indicate that the loss of CBP affects the recruitment of the transcriptional complex at neuronal genes. Overall, these genomic screens and analyses underscore the value of H2B acetylation as a reliable marker for CBP deficiency (including pathologic conditions such as RSTS) and the relevance of H3K27 hypoacetylation in transcriptional dysregulation. They also helped to identify genes particularly sensitive to reduced CBP KAT activity that could be used as biomarkers in pathologies in which this protein has been involved, such as Huntington's disease, Alzheimer's disease and aging-related cognitive decline (Valor et al., 2013a; Achour et al., 2015; Chatterjee et al., 2018; Hervás-Corpión et al., 2018).
Our analyses indicate that plasticity-related genes are more dependent on CBP than other genes. Many of these genes are highly expressed in neurons and show a characteristic temporal expression pattern, in which periods of low activity alternate with strong transcriptional outburst. This pattern may require more KAT activity than other genes to maintain the competence of the locus. We hypothesized that the impact of the absence of CBP might be more notorious on activity-dependent bursting of transcription and concurrent histone replacement, which would cause a depletion of acetylation. To test this hypothesis, we evaluated the transcriptional response triggered during SE resulting from the synchronous activation of hippocampal neurons. This includes a rapid induction of IEGs (e.g., Arc, Fos, Fosb, Npas4) that is followed by a delayed second wave that includes LRGs, such as Bdnf, Nptx2, and Frmpd3. A single dose of the glutamate agonist KA (25 mg/kg) caused SE and triggered a similar induction of IEGs 1 h after KA administration in CBP-ifKO and control littermates. However, all the tested IEGs displayed lower levels in CBP-ifKOs at later time points, indicating that the transcriptional burst is shorter or weaker in the absence of CBP (Fig. 6a). This deficit, like others described in previous figures, was not observed in p300-ifKOs (Fig. 6b). Next, we examined a protocol for seizure induction based on the repeated administration of small doses of KA (2.5 mg/kg every 20 min). In this protocol, control mice needed ∼100 min to develop clonic seizures, whereas CBP-ifKOs showed a delayed (Fig. 6c) and attenuated response manifested both in a lower seizure score (Fig. 6d) and a weaker induction of IEGs (Fig. 6e). qRT-PCR assays confirmed the downregulation of second-wave genes in CBP-ifKOs at the basal state, and revealed no difference in their induction after KA treatment (Fig. 6f,g). These results indicate that although CBP is not essential for IEG induction, it controls the magnitude of the transcriptional burst. The damped induction of IEGs together with the downregulation of other plasticity genes at the basal state could explain the behavioral deficits observed in CBP-ifKOs.
To further test our hypothesis, we challenged CBP-ifKOs in a paradigm that leads to long-lasting changes in response to stimulus: PTZ-induced kindling. In this protocol (commonly used to model epilepsy), an initially subconvulsive dose of PTZ induces progressively more robust seizures after repeated administration (Fig. 7a). This sensitization response is likely to rely on transcriptional changes (Perlin et al., 1993; Liang and Seyfried, 2001). Control mice reacted to the treatment as expected, with an initial latent nonconvulsive period, followed by an incremental appearance of seizures. After 10 d of kindling, a subconvulsive dose of PTZ was sufficient to induce seizures in all individuals. To our surprise, none of the CBP-ifKOs entered the progressive seizure stage within >20 d of the experiment. (Fig. 7b). Other effects of the treatment, such as the halted gain of weight, were observed in both genotypes, demonstrating that the drug was effectively delivered to KO mice (Fig. 7c). We also investigated the transcriptional response associated with kindling in each group. One week after kindling, mice were treated with the same dose of PTZ once again, and 45 min later their hippocampi were extracted for RNA. The RNA-seq analysis revealed the induction of 219 genes in PTZ-treated control mice, whereas CBP-ifKOs showed a much more limited response involving only 62 genes (p-adjusted < 0.05; Fig. 7d–f, Extended Data Fig. 7-1). IEGs, such as Fos and Npas4, were induced in both genotypes, but their induction was weaker in CBP-ifKOs. Intriguingly, PTZ-downregulated genes were less affected than upregulated genes, indicating that the anomalous response was largely restricted to gene activation. These results show that CBP is specifically involved in facilitating the transcriptional response to kindling. 10.1523/JNEUROSCI.0970-22.2022.f7-1
To further confirm the role of CBP in establishing novel gene programs, we evaluated CBP-ifKOs in another paradigm that triggers enduring changes in hippocampal excitatory neurons: EE. This condition is known to improve the cognitive performance of mice and promote neurogenesis and synaptogenesis (van Praag et al., 2000; Kempermann, 2019). These processes rely on changes in the transcriptome of hippocampal neurons (Fischer, 2016; Grégoire et al., 2018; Zhang et al., 2018). Consistent with our hypothesis, experiments in Crebbp+/− mice had revealed an attenuation of the transcriptional program induced by EE (Lopez-Atalaya et al., 2011). However, these deficits could originate from the defective assembly of neuronal circuits because of CBP deficiency during development (del Blanco et al., 2019). To specifically investigate the interaction between CBP loss and EE in the adult brain, we exposed CBP-ifKOs to EE for 1 month and examined their performance in the MWM, a cognitive task highly sensitive to EE (Fig. 8a, Extended Data Fig. 8-1). Mice exposed to EE did not gain weight when compared with animals housed in SCs, likely reflecting the more intense physical activity in EE conditions (Fig. 8b). Differences between the two genotypes emerged when we tested the animals in the MWM. Control mice in EE boxes exhibited better learning of the platform location and performed better in the probe trial than those housed in SCs (Fig. 8c,d). In contrast, CBP-ifKOs exposed to EE did not show significant improvements. To explore the specificity of the CBP function in EE adaptation, we also compared the performance of p300-ifKOs and control littermates housed in SCs or EE boxes and found that in this case both genotypes showed a significant improvement in spatial navigation after EE (Fig. 8e). These results demonstrate that CBP plays a unique role as mediator of EE benefits in the adult brain that is not shared with p300. 10.1523/JNEUROSCI.0970-22.2022.f8-1 Next, we extracted total RNA from the hippocampi of CBP-ifKOs and control littermates maintained in standard housing or exposed to EE. RNA-seq profiling identified 173 genes affected by EE (p-adjusted < 0.05, log2FoldChange > |0.5|, including both genes with a significant EE effect and a housing × genotype interaction; Fig. 9a, Extended Data Fig. 9-1). These transcriptional changes were less prominent in CBP-ifKOs than in their control littermates, particularly for upregulations, which suggests that the resilience of CBP-ifKOs to EE may result from their inability to adjust gene expression. Consistent with this view, ChIP assays at Bdnf (a locus known to respond to EE) showed a significant increase in H3K27ac levels in control mice housed in the EE when compared with CBP-ifKO littermates (Fig. 9b). To further explore this finding, we performed two independent genome-wide screens to detect changes in H3K27ac in response to EE. In the first experiment using bulk chromatin, we observed that EE caused more increases than reductions in H3K27ac (Fig. 9c, Extended Data Fig. 9-1). A second ChIP-seq experiment using sorted nuclei from Camk2a+ hippocampal neurons (Fernandez-Albert et al., 2019), allowed us to specifically examine those changes in H3K27ac that occurred in excitatory neurons. Interestingly, the GO analysis of the DARs that gained H3K27ac on EE retrieved terms related with synaptic signaling and organization, and behavior (Fig. 9d). Furthermore, the comparison of the two screens indicated that most of the regions displaying gains in H3K27ac on EE were located at regulatory regions in neuronal genes (Fig. 9e,f), whereas the regions that displayed losses in H3K27ac mostly came from other cell types (Fig. 9g). Notably, EE-induced changes in H3K27ac were largely suppressed in CBP-ifKOs (p-adjusted < 0.1, log2FoldChange > |0.25|, including both genes with significant EE effect and housing × genotype interaction; Fig. 9f). Among the genes that display an increase in both transcripts and H3K27 acetylation levels in response to EE in control mice but not in CBP-ifKOs, we find Robo3 [a member of the Roundabout (ROBO) gene family that controls neurite outgrowth, growth cone guidance, and axon fasciculation] and Wnt9a (a member of the WNT gene family, which regulates synapse formation and maintenance, axonal remodeling, and dendrite outgrowth; Fig. 9h). Although both genes have been involved in neurodevelopmental processes, their roles in the adult brain remain largely unexplored. 10.1523/JNEUROSCI.0970-22.2022.f9-1
Studies in the last 2 decades have underscored the role of lysine acetylation in neuroplasticity processes, challenging the idea that chromatin regulators are only relevant during development (Gräff and Tsai, 2013; Lopez-Atalaya and Barco, 2014). Here, we refined the gene ablation strategy of previous loss-of-function studies to specifically evaluate the consequences of CBP and p300 ablation in principal neurons in fully mature neuronal circuits. Using this approach, we showed that the ablation of CBP in forebrain excitatory neurons in adult animals compromised their plasticity capabilities, particularly affecting behavioral tasks that rely on spontaneous exploratory behavior, such as novel object recognition and social recognition memory. In addition to these deficits, old CBP-ifKO mice displayed several age-related phenotypes, such as weight loss, impaired gait, spatial navigation, and extinction. In contrast, the elimination of p300 alone did not cause any apparent phenotype. These results are consistent with the weaker phenotype of Ep300+/− mice compared with Crebbp+/− and the milder symptoms and lower prevalence of RSTS2 [catalog #613684, OMIM (Online Mendelian Inheritance in Man); linked to Ep300 mutations] compared with RSTS1 (catalog #180849, OMIM; linked to CREBBP mutations; Lopez-Atalaya et al., 2014). Note, however, that these results do not exclude a role for p300 in cognitive and plasticity processes. On the contrary, our experiments underscore the critical role of p300 in the absence of CBP because a single functional Ep300 allele was sufficient to prevent the dramatic neurologic phenotypes observed after combined elimination of CBP and p300 (Lipinski et al., 2020). Previous pharmacological and genetic experiments have linked CBP with specific forms of memory, particularly NOR (Chen et al., 2010; McQuown et al., 2011; Valor et al., 2011). Why are some forms of memory more sensitive to CBP loss than others? Korzus et al. (2004) suggested that CBP might be particularly important in tasks that recruit an innate behavioral preference for novelty and does not involve an exogenous reinforcers. This view is in agreement with our findings in NOR, social memory, and MB, supporting the idea that stressful reinforcers, such as a swim stress or an electric shock, activate alternative mechanisms that overcome the lack of CBP. Biochemical experiments have demonstrated the eviction of histones during transcription elongation and the faster turnover of nucleosomes in heavily transcribed genes (Ferrari and Strubin, 2015). A direct consequence of the eviction of histones during transcription—particularly histones H2A and H2B (Kireeva et al., 2002), which are among the most sensitive to CBP loss—is that the epigenetic status of the gene would be transiently weakened and more susceptible to disruption. The absence of CBP could deplete the gene from acetylation marks interfering with the self-maintenance of the locus over time. As a result, deficits would emerge on rapid, chronic, or repeated activation. Consistent with this prediction, a stronger phenotype was observed in CBP-ifKOs in paradigms such as SE, kindling, or EE adaptation in which the neuronal transcriptome needs to readjust after the experience (Delorenzo and Morris, 1999; Mirza et al., 2017). In these paradigms, CBP-ifKOs failed to adapt their behavioral and transcriptional response. Previous observations in other organisms are also consistent with this view. For instance in Drosophila, mutants of nejire (CBP ortholog) fail to develop a typical long-lasting alcohol tolerance caused by a single alcohol exposure (Ghezzi et al., 2017). In a different example, dietary restriction increases the expression of CBP or its ortholog gene both in the mouse hypothalamus (Moreno et al., 2016) and in Caenorhabditis elegans (Zhang et al., 2009). In mice, CBP deficiency impaired the response to EE in the central (Lopez-Atalaya et al., 2011) and peripheral (Hutson et al., 2019) nervous systems. In C. elegans, dietary restriction correlated with an increase of the life span and protection from proteotoxicity, and these effects were suppressed by the inhibition of cbp-1 (Zhang et al., 2009). Therefore, although in normal laboratory conditions KATs other than CBP may be sufficient for preserving acetylation levels at most lysine residues, CBP plays a pivotal role in processes in which the neuronal epigenome needs to be edited both during development and in the adult brain. This view and the postulated weakening of acetylation profiles with aging (Peleg et al., 2010; Harman and Martín, 2020) might explain the worsening on the phenotype and the appearance of additional deficits in elderly CBP-ifKOs. In conclusion, our study identifies CBP as the main KAT responsible for refreshing the acetylation status of plasticity-related genes after neuronal activation, preserving the competence and inducibility of these loci over time. This makes CBP a key component of the molecular machinery that enables phenotypical variation of behavioral and cognitive traits in response to experience and environmental changes. Our results also shed new light on neurologic and psychiatric conditions in which a defective or reduced CBP activity has been reported and plasticity mechanisms are compromised, such as RSTS, substance use disorders, several neurodegenerative diseases, and aging-related cognitive impairments. | true | true | true |
PMC9617884 | Matteo Ciciani,Michele Demozzi,Eleonora Pedrazzoli,Elisabetta Visentin,Laura Pezzè,Lorenzo Federico Signorini,Aitor Blanco-Miguez,Moreno Zolfo,Francesco Asnicar,Antonio Casini,Anna Cereseto,Nicola Segata | Automated identification of sequence-tailored Cas9 proteins using massive metagenomic data | 29-10-2022 | Biotechnology,Computational biology and bioinformatics,CRISPR-Cas9 genome editing | The identification of the protospacer adjacent motif (PAM) sequences of Cas9 nucleases is crucial for their exploitation in genome editing. Here we develop a computational pipeline that was used to interrogate a massively expanded dataset of metagenome and virome assemblies for accurate and comprehensive PAM predictions. This procedure allows the identification and isolation of sequence-tailored Cas9 nucleases by using the target sequence as bait. As proof of concept, starting from the disease-causing mutation P23H in the RHO gene, we find, isolate and experimentally validate a Cas9 which uses the mutated sequence as PAM. Our PAM prediction pipeline will be instrumental to generate a Cas9 nuclease repertoire responding to any PAM requirement. | Automated identification of sequence-tailored Cas9 proteins using massive metagenomic data
The identification of the protospacer adjacent motif (PAM) sequences of Cas9 nucleases is crucial for their exploitation in genome editing. Here we develop a computational pipeline that was used to interrogate a massively expanded dataset of metagenome and virome assemblies for accurate and comprehensive PAM predictions. This procedure allows the identification and isolation of sequence-tailored Cas9 nucleases by using the target sequence as bait. As proof of concept, starting from the disease-causing mutation P23H in the RHO gene, we find, isolate and experimentally validate a Cas9 which uses the mutated sequence as PAM. Our PAM prediction pipeline will be instrumental to generate a Cas9 nuclease repertoire responding to any PAM requirement.
Repositioning CRISPR-Cas systems from prokaryotes to mammalian cells boosted genome editing applications in the clinic. Yet, this technology is still limited by major constraints, mainly related to the reduced number of prokaryotic CRISPR-Cas systems active in mammalian cells which hardly respond to the complexity of gene therapy applications. PAM sequences, necessary for nuclease recognition and activity, are key in this context, as they dictate the compatibility of each CRISPR-Cas tool towards specific genomic target sites. Molecular engineering significantly improved Cas9 properties for genome editing including relaxation of PAM requirements, but this approach impacts the activity of the nucleases and still does not respond to all PAM sequences needed to tackle disease-causing mutations. On the other hand, the rich natural PAM-Cas9 diversity remains largely unexplored and its efficient exploitation relies on the availability of comprehensive databases and accurate computational Cas9 discovery and PAM prediction tools. To explore the natural PAM-Cas9 diversity, the pre-determination of the PAM requirements is the crucial step. However, available methods to predict Cas9 PAMs present several limitations (Supplementary Table 1) preventing their effective exploitation: CASPERpam has very low accuracy (10% functional predictions); SPAMALOT does not include any spacer match filtering or consensus processing step and its source code is not publicly available; the method described by Vink et al. associates PAM predictions with clusters of CRISPR repeats, making it not suited to generate predictions for single Cas9 proteins; CRISPRTarget is only available as a Web service and cannot be automatically interrogated for large sequence repositories; Spacer2PAM can be applied to predict PAMs of single Cas9 proteins but its low accuracy (45% functional predictions) still impairs comprehensive analyses. Here we demonstrate that the interrogation of massive metagenomic datasets combined with an improved computational method allows the identification of a vast number of unreported Type II systems and their respective PAM requirements (Fig. 1a).
From a compendium of 825,698 bacterial and archaeal genomes reconstructed via metagenomic assembly of human, host-associated, and non-host associated environmental microbiomes (see Methods section: Catalog of reference and metagenomic-assembled genomes) and 257,670 genomes from microbial isolates retrieved from the NCBI database, we identified the sequence of 92,140 CRISPR-Cas9 loci. Our pipeline then performed Cas9 proteins clustering at multiple sequence identity levels (from 95% to 100%) and applied a PAM-prediction procedure on each cluster. For predicting the PAMs, we first identified sequences adjacent to protospacers by aligning 613,478 unique spacers to phage genomes of the human microbiome: 142,809 viral genomes from the Gut Phage Database, 189,680 from the Metagenomic Gut Viral catalog and 45,872 from de-novo assembled gut phages from highly enriched viromes as profiled via ViromeQC (see Methods section: Viral genomes retrieval from highly enriched viromes). In this procedure we mostly focused on human-associated bacterial and viral genomes as they are largely overrepresented in our dataset, and in particular the human gut has been sufficiently densely sampled to permit multiple reliable host-virus associations via spacer matching (Supplementary Fig. 1). Only full-length, near-perfect matches were retained (at most 4 nucleotide variations), resulting in a total of 39,109,402 putative protospacers. Cas9 clusters with less than 10 mapped spacers were discarded to retain only highly reliable PAM predictions. Upstream and downstream sequences flanking the matches, up to 30 nt, were retrieved. For each Cas9 cluster, sequences flanking the same spacer were realigned and the multiple sequence alignment was collapsed into a single consensus flanking sequence. Nucleotide frequencies in the consensus flanking sequences were computed and represented as sequence logos. A PAM was predicted for a Cas9 cluster if there was at least one conserved position in either the upstream or the downstream flanking sequence (see Methods section: PAM prediction). Repeating the PAM-prediction procedure on the different Cas9 clustering identity thresholds, we concluded that the highest reliability was obtained at 98% identity clustering (see Methods section: PAM comparisons and hierarchical clustering). At this clustering stringency, we obtained PAM predictions for 2546 out of 2779 Cas9 clusters (representing 61,095 Cas9 sequences) with more than 10 mapped spacers (91.6%).
To validate our approach and predictions, we searched our dataset for gene sequences coding for proteins with high sequence identity (>98%) to previously characterized Cas9s: SpCas9, SaCas9, St1Cas9, St3Cas9, and SmCas9. For these Cas9s we obtained sequence predictions corresponding to the described PAMs (Fig. 1b). We further tested our method by cross checking the PAM predictions obtained with our pipeline with the sequences experimentally identified and recently reported and characterized by Gasiunas et al. Of the 79 Cas9s reported, 21 could be used for the evaluation here as they had a close ortholog in our dataset (>98% identity), and for them we confirmed the accuracy of our prediction strategy by obtaining PAM logos with high identity (assessed by Jensen-Shannon distance on nucleotide frequencies, see Methods section: PAM comparisons and hierarchical clustering) with the sequences determined experimentally (Fig. 1c and Supplementary Fig. 2). Overall, 18 out of 21 (85%) PAM predictions generated by our method were correct and the remaining 15% were partial predictions with at least one base correctly identified. Our method exhibited a much higher prediction accuracy compared to Spacer2PAM (Supplementary Fig. 3), which outperforms both CRISPRTarget and CASPERpam and was therefore chosen as the gold standard for the comparison. To further test experimentally the reliability and potential of the PAM prediction pipeline in expanding the Cas9 toolbox, we searched for Cas9 candidates using parameters favoring the identification of functionally active enzymes (with preserved domain structures and located in complete CRISPR-Cas loci) and with reduced molecular size (<1100 amino acids), thus potentially more convenient for genome editing applications. We identified four Cas9s never described before from poorly characterized species (Supplementary Fig. 4) and predicted their PAM logos which were subsequently experimentally validated through an in vitro assay. Results demonstrated a very close identity between in silico and in vitro results as indicated by the small distance (<2 bits for 3 out of 4 Cas9 variants) between predicted and experimentally determined PAMs (Fig. 1d-e), thus further confirming the accuracy and the potential of this PAM prediction pipeline. Overall, our method allowed PAM prediction for the vast majority of Cas9 proteins identified in our repository with 10 or more mapped spacers, across all Cas9 subtypes (93.6% for A, 93.0% for B and 87.9% for C; Fig. 1f).
We then applied our PAM predictor to the metagenomically extended set of 2,546 Cas9 protein families (98% identity clustering) to identify all PAM requirements and explore whether specific PAM groups may exist. Hierarchical clustering on pairwise distance of the predicted PAMs retrieved 32 groups with at least 20 members (see Methods section: PAM comparisons and hierarchical clustering). For each PAM group, a consensus PAM was generated (Fig. 2). Interestingly, the most prevalent PAM sequences represent only a small fraction of all possible PAMs. Therefore, even though the PAM variability is high for type II Cas9, only definite combinations of nucleotides were identified. We further evaluated whether there might be an association between the PAM groups identified in Fig. 2 and specific Cas9 subtypes. After generating a phylogenetic tree of the identified Cas9, we found that almost every PAM group was associated with specific clades of Cas9 proteins (Supplementary Fig. 5), thus suggesting a non-random organization of PAM recognition sequences. For instance, the most abundant PAM group (NGG) was found in a specific branch of type II-A and in almost all type II-B Cas9s.
To prove the potential of this prediction pipeline in genome editing applications we turned to a specific class of mutations that are associated with autosomal dominant genetic diseases due to an induced gain of function to the mutated product. These mutations can be neutralized through CRISPR-Cas knockouts but the lack of allelic discrimination due to various grades of sgRNA mismatch tolerance by Cas9 limits the effective targeting of the mutated allele. Conversely, since PAM sequences are stringent requirements for Cas9 activity, using PAMs matching the mutated bases would allow a specific target separation between the mutated and the wild-type alleles. Consequently, a paramount application of our PAM prediction pipeline is the identification of uncharacterized Cas9s recognizing PAM sequences generated by pathogenic mutations to offer specific targeting options for the mutated allele with a highly secured allelic discrimination. By interrogating the ClinVar database for mutations corresponding to PAMs associated with Cas9s from our metagenomic analysis, we found that a large fraction of pathogenic mutations (98.6% of 89,751 substitutions and small indels with known mode of inheritance) are included in at least one of the identified PAMs, thus having the potential to provide allelic discrimination, with 48.6% of them being autosomal dominant alterations (Fig. 3a). Conversely, we estimated that only 76.1% of pathogenic mutations can be potentially targeted with allelic discrimination by Cas nucleases already used in the genome editing field, thus the PAM diversity identified by our analysis provides a nearly complete coverage of pathogenic mutations that was previously lacking.
As a proof of concept for the potential of our PAM prediction method, we chose a specific dominant-negative mutation, the P23H mutation (c.68 C > A) in the rhodopsin (RHO) gene, which is the most common mutation causing RHO-dependent retinitis pigmentosa. Looking for a Cas9 that could uniquely target the P23H mutation, we identified PrCas9 (Supplementary Table 2), a Cas9 found in an unclassified species from the Proteobacteria phylum, which has a predicted PAM N5T (Fig. 3b), where the T in the PAM is generated by the P23H mutation in RHO (CGAAGT, wild-type sequence CGAAGG, Fig. 3c). We experimentally validated in vitro the PrCas9 PAM preference (PAM NRVNRT, Fig. 3c and Supplementary Fig. 6) and tested its editing activity in mammalian cells with an EGFP disruption assay, generating nearly 50% EGFP disrupted cells (Supplementary Fig. 7). We then assessed its efficiency and specificity towards RHO P23H mutation by co-transfecting cells with PrCas9 and the same sgRNA targeting RHO together with a plasmid over-expressing either the RHO WT or the RHO P23H minigene. We obtained up to 15.8% indels at the RHO P23H locus and the complete absence of indels in the wt sequence, thus demonstrating the efficacy of the selected Cas9 in targeting the RHO specific mutation in mammalian cells (Fig. 3d).
By interrogating an extended metagenomic repository with an improved computational approach, we identified a large variety of previously unreported Cas9 nucleases accompanied by their identified PAM requirements. The PAM prediction pipeline here developed showed overall a high level of reliability and better performance than methods so far developed. As more and more metagenomic data will be available the remaining limited differences between predicted and in vitro determined PAMs can be further reduced: mapping spacers to the wrong viral sequence will be less likely with curated and expanded viral databases; higher numbers of mapped spacers will allow increasing the current minimum number of ten mapped spacers and generate more reliable consensus predictions; finally, more sequences will also minimize the risk to be impacted by mutations in protospacer adjacent sequences introduced in order to evade CRISPR-Cas mediated immunity. The difference between the motif recognized by the spacer acquisition machinery (SAM) and the one recognized by the target interference effector (TIM) that has been observed for some CRISPR-Cas systems is likely not contributing to the discrepancies between predicted and in vitro determined PAMs, since Cas9 is directly involved in spacer acquisition. Our analysis revealed that PAM sequences follow defined nucleotide patterns which are associated with specific Cas9 subtypes and have the potential to overlap with 98.6% of the pathogenic mutations reported in ClinVar. The precise PAM prediction driven by a specific sequence-mutation query allows the identification of tailored Cas9s, such as PrCas9 targeting the P23H RHO mutation. We expect that as reported before several Cas9 variants from the sequence repository here reported will not be active in mammalian cells. Nonetheless, the large variety of orthologs identified in our repository combined with optimized computational pipeline will highly enhance the efficiency of Cas mining protocols. This approach opens to the expansion of the genome editing toolbox with mutation-tailored nucleases and supports the strategy of an application-specific search for suitable natural prokaryotic genome editing tools requiring minimal or no engineering.
The catalog of bacterial and archaeal genomic sequences used in this work was retrieved from: (i) 257,670 publicly available isolated sequences from the NCBI database (available as of January 2021), (ii) 771,529 metagenome-assembled genomes (MAGs) from the Blanco-Miguez et al. study, and (iii) 54,169 additional MAGs obtained with a validated assembly-based pipeline similarly to Pasolli et al. For retrieving these 54,169 additional MAGs, 8487 metagenomic samples (Supplementary Table 3) were assembled using metaSPAdes if paired-end metagenomes were available, and MEGAHIT otherwise. In both cases, default parameters were used. Contigs longer than 1500 nucleotides were binned into MAGs using MetaBAT2.
A total of 45,872 viral genomes were metagenomically assembled from 3044 Human Gut virome datasets as described previously. In brief: the efficacy of viral enrichment in each virome was evaluated with ViromeQC (version 1.0). A total of 255 samples had an enrichment higher than 50X and were retained as highly viral samples. Reads were preprocessed with TrimGalore (version 0.4.4) to remove low quality and short reads (parameters:–stringency 5–length 75–quality 20–max_n 2–trim-n). Reads aligning to the human genome hg19 were also removed with Bowtie2 (version 2.4.1). High quality reads were assembled into contigs with metaSPAdes (version 3.10.1) (k-mer sizes: -k 21,33,55,77,99,127), or MEGAHIT (version 1.1.1). To reduce non-viral contaminants, we removed contigs that mapped to microbial genomes by using the collection of MAGs from Pasolli et al.. Only contigs that were (a) longer than 1500 bp; (b) found within the same microbial species-level genome bin in <30 metagenomes; and (c) found in the unbinned assembled fraction of more than 20 metagenomes, were retained. Contigs from (i) the remaining non-highly enriched viromes, and (ii) from the human gut metagenomes used in Pasolli et al., and that were similar to a potentially highly enriched viral genome, were also mapped against the unbinned contigs of Pasolli et al. with mash (version 2.0). Contigs with a distance lower than 10% (p-value ≤ 0.05) were retained. Finally, we selected 699 complete viral genomes from RefSeq, release 99 by selecting genomes that could be found in at least 20 samples within the unbinned contigs of Pasolli et al.. All mappings were performed with blastn (version 2.6.0) identity >80%, aln. len. >1000 bp). Contigs were clustered at 95% identity with VSEARCH with each cluster needing to contain at least one contig originating from highly enriched viromes.
CRISPRCasTyper (version 1.5.0, default parameters) was used to identify 131,941 CRISPR-Cas loci. This tool identifies CRISPR-Cas loci in MAGs searching for CRISPR arrays and cas operons in close proximity to each other (up to 10,000 bp). Loci containing Cas9 proteins shorter than 950 aa were excluded from the analysis. The resulting 92,140 Cas9 proteins were clustered at 100, 99, 98, 97, 96, and 95% identity using usearch (version 11.0.667) resulting in 27,062, 14,332, 10,475, 8568, 7538, and 6898 clusters respectively. For each Cas9 cluster, spacers in CRISPR arrays adjacent to cas genes were retrieved and oriented according to the orientation of cas1, cas2, and cas9 genes. In total, 613,478 spacers were retrieved from CRISPR arrays and were aligned to 366,233 viral genomes (142,809 from Gut Phage Database, 189,680 from Metagenomic Gut Virus catalog and 45,872 from de-novo assembled gut phages from highly enriched viromes) using blastn (version 2.5.0) to identify putative protospacers. Matches with more than 4 mismatches or gaps were filtered out. For each Cas9 clustering level, clusters with less than 10 mapped spacers were discarded, resulting in 7177 (26.52%), 3908 (27.27%), 2779 (26.53%), 2169 (25.32%), 1814 (24.06%), and 1594 (23.11%) clusters. Since the orientation of CRISPR arrays is unknown, both upstream and downstream flanking sequences, up to 30 nt, were retrieved for each putative protospacer. For each Cas9 cluster, protospacer and their flanking sequences, found using the same spacer, were aligned to each other using MUSCLE (version 3.8.31) and the alignment was collapsed into a single consensus sequence by taking the most frequent base at each position and discarding columns composed mostly (>50%) of gaps. Spacers were aligned exactly to the consensus sequence to define up- and downstream regions, which were then used to compute nucleotide frequencies and generate sequence logos using Logomaker (version 0.8). For each Cas9 cluster, a PAM was considered predicted if there was at least one highly conserved base in only one of the two flanking regions (the PAM can be either upstream or downstream, not both). We defined a highly conserved base as a position in the logo with more information than the maximum between 1 bit and the third quartile plus 1.5 times the interquartile range of the distribution of information in both flanking sequences (i.e. the conserved position is an outlier with at least 1 bit of information). For each clustering level, a PAM was predicted for 6758 (94.16%), 3622 (92.68%), 2546 (91.62%), 1944 (89.63%), 1601 (88.26%), and 1387 (87.01%) clusters with more than 10 mapped spacers.
tracrRNA sequences of the previously uncharacterized Cas9 orthologs were identified computationally, searching for sequences starting with a putative anti-repeat and ending with a Rho-independent transcription terminator (RIT). Putative anti-repeats were identified aligning CRISPR repeats to sequences flanking the CRISPR-Cas locus (up to 1000 nt) using blastn (version 2.5.0) and RITs were predicted using RNIE.
In vitro PAM evaluation of the previously uncharacterized Cas9 orthologs was performed according to the protocol from Karvelis et al.. In brief: for each Cas9 ortholog the human codon optimized version of its coding sequences was ordered as a synthetic construct (Genscript) and cloned into an expression vector for in vitro transcription and translation (IVT) (pT7-N-His-GST- Thermo Fisher Scientific). Reaction was performed according to the manufacturer protocol (1-Step Human High-Yield Mini IVT Kit - Thermo Fisher Scientific). The Cas9-guideRNA RNP complex was assembled by combining 20 μL of the supernatant containing soluble Cas9 protein with 1 μL of RiboLock RNase Inhibitor (Thermo Fisher Scientific) and 2 μg of guide RNA. The Cas9-guideRNA complex obtained was diluted 1:10 as described in Karvelis et al. and used to digest 1 μg of a plasmid (p11-lacY-wtx backbone - Addgene #69056) containing an 8-nucleotide randomized PAM sequence flanking the gRNA target. Digestion reaction was incubated for 1 h at 37 °C. A double-stranded DNA adapter was then ligated to the DNA ends generated by the targeted Cas9 cleavage and the final ligation product was purified using a GeneJet PCR Purification Kit (Thermo Fisher Scientific). One round of a two-step PCR (Phusion HF DNA polymerase - Thermo Fisher Scientific) was performed as described in Karvelis et al. to enrich the sequences that were cut using a set of forward primers annealing on the adapter and a reverse primer designed on the plasmid backbone downstream of the PAM (Supplementary Table 4). A second round of PCR was performed to attach the Illumina indexes and adapters (Nextera XT Index Kit v2 Set A). PCR products were purified using Agencourt AMPure beads in a 1:0.8 ratio. The generated library was analyzed with a 71-bp single read sequencing, using a flow cell v2 micro, on an Illumina MiSeq sequencer. PAM sequences were extracted from Illumina MiSeq reads and used to generate PAM sequence logos. PAM heatmaps were used to display PAM enrichment, computed dividing the frequency of PAM sequences in the cleaved library by the frequency of the same sequences in a control uncleaved library.
Differences between PAM sequences were quantified using the Jensen-Shannon distance (defined as the square root of the Jensen-Shannon divergence). This distance quantifies the difference between predicted and experimentally validated PAMs by comparing the bit-score of each base at every position in both PAM. As shown in Fig. 1e, predictions that closely match experimentally validated PAMs have a distance lower than 2 bits. To find the optimal Cas9 clustering identity level, the Jensen-Shannon distance between predicted and in vitro determined PAMs was computed for 16 Cas9 orthologs characterized by Gasiunas et al. having a prediction at each clustering level. PAM predictions resulting from the 98% identity Cas9 clustering showed the lowest median distance and were therefore chosen for subsequent analyses (Supplementary Table 5). Spacer2PAM predictions were generated using unique spacer sequences derived from the 98% identity Cas9 clustering. Spacer2PAM (version 0.0.0.9000) was run with default parameters, except the e-value threshold which was set to 0.01. A two-sided Welch’s t-test was used to assess the statistical significance (p-value = 1.5e-5) of the difference between the Jensen-Shannon distance of in vitro determined PAMs and predictions generated by Spacer2PAM and by our method. An all-to-all PAM prediction distance matrix was computed and hierarchical clustering was performed to generate PAM groups, using usearch (version 11.0.667, parameters -cluster_aggd -id 0.6 -linkage avg). Consensus PAMs for each cluster were generated using sequences adjacent to protospacers of all cluster members.
Cas9 proteins with a predicted PAM (98% identity clustering) were aligned using mafft (version 7.490, with parameters–maxiterate 10) and a phylogenetic tree was built using FastTree (version 2.1.11, with parameters -spr 4 -mlacc 2 -slownni). Cas9 clades were defined using TreeCluster (version 1.0.3, with parameters -m max_clade) and a range of distance thresholds (0.3–4). Associations between PAM groups and Cas9 clades were assessed using a two-sided Fisher’s exact test using R 4.1.0, computing p-values by Monte Carlo simulation with 100,000 replicates. P-values were adjusted for multiple hypothesis testing using the Benjamini-Hochberg correction and resulted <0.001 for all PAM groups and almost all distance thresholds.
Mutations in the ClinVar database (accessed 6 March 2022) were filtered to select single nucleotide variants and short indels (10 or less nucleotides) annotated as pathogenic or likely pathogenic and associated with pathologies with known mode of inheritance, for a total of 89,751 mutations. Predicted PAMs resulting from the 98% identity Cas9 clustering were converted to consensus sequences by taking at each position bases with more information than half the threshold used previously to define highly conserved bases, to avoid underestimating the number of non-N bases in the consensus sequence. To compute the fraction of mutations that can be targeted by at least a Cas9 in our databank with allelic discrimination, consensus sequences derived by our analysis and PAMs of Cas nucleases already used in the genome editing field were then aligned exactly to wild-type and mutated alleles.
HEK293T/17 obtained from ATCC (CRL-11268) were cultured in DMEM supplemented with 10% fetal bovine serum, 2 mM l-Glutamine, 100 U/ml Penicillin, and 100 μg/ml streptomycin (Life Technologies) and incubated at 37 °C and 5% CO2 in a humidified atmosphere. Cells tested mycoplasma negative (PlasmoTest, Invivogen). For indels analyses, HEK293T/17 cells were seeded in 24-well plate and transfected after 24 h with 1000 ng pX-PrCas9-sgRNA-RHO-P23H, 50 ng pCMV-TO-RHO-P23H or pCMV-TO-RHO-WT and 50 ng pEGFP-IRES-Puro using TransIT-LT1 transfection reagent (Mirus Bio) according to manufacturer’s instructions. 48 hours post-transfection cells were pool-selected with 1 μg/ml Puromycin and collected after 72 hours. Genomic DNA was obtained from cell pellets using the QuickExtract DNA extraction solution (Lucigen) according to the manufacturer’s instructions. The RHO P23 locus was amplified using the HOT FIREPol Multiplex Mix (Solis Biodyne) with primers RHO-TO-F (CAGTGATAGAGATCTCCCTATC) and RHO-int1-R (GAGATAGATGCGGGCTTCCA). PCR amplicons were purified using CleanNGS beads (CleanNA) and Sanger sequenced (Microsynth) using RHO-TO-F primer. Indel levels were evaluated using TIDE.
A pX330-derived plasmid was used to express the Cas9 orthologs in mammalian cells. Briefly, pX330 (Addgene) was modified by substituting SpCas9 and its sgRNA scaffold with the human codon-optimized coding sequence of the variant of interest and its sgRNA scaffold. The Cas9 variants coding sequences, modified, as described before, by the addition of an SV5 tag at the N-terminus and two nuclear localization signals bpNLS (1 at the N-term and 1 at the C-term) and human codon-optimized, as well as the sgRNA scaffolds, were obtained as synthetic fragments from either Genscript or Genewiz. Spacer sequences were cloned into the pX-Cas9 plasmids as annealed DNA oligonucleotides containing a variable 20 or 24-nt spacer sequence using a double BsaI site present in the plasmid. The list of spacers sequences used in the EGFP disruption assay and in the evaluation of editing activity against the RHO P23H mutation is reported in Supplementary Table 6. pCMV-TO-RHO-WT plasmid was obtained by cloning the human rhodopsin (RHO) gene into the pCDNA5/TO plasmid (Addgene). The hRHO gene was PCR-amplified using the primers RHO_gene_F (attaggatccAGAGTCATCCAGCTGGAGCCC) and RHO_gene_R (taatctcgagTGGGGTTTTTCCCATTCCCAGG) from genomic DNA extracted from HEK293T/17 cells using the Phusion high fidelity DNA Polymerase (ThermoFisher Scientific). The P23H mutation was further inserted by site-directed mutagenesis using primers mut-P23H-F (GTGTGGTACGCAGCCaCTTCGAGTACCCACAG) and mut-P23H-R (CTGTGGGTACTCGAAGtGGCTGCGTACCACAC) to generate pCMV-TO-RHO-P23H plasmid. All the oligonucleotides were purchased from Eurofins Genomics.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
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PMC9617889 | Wendong Bai,Hongyan Peng,Jiarui Zhang,Yongmei Zhao,Zhijun Li,Xuelian Feng,Jiang Zhang,Fei Liang,Li Wang,Nan Zhang,Yize Li,Huayu Zhu,Qiuhe Ji | LINC00589-dominated ceRNA networks regulate multiple chemoresistance and cancer stem cell-like properties in HER2+ breast cancer | 29-10-2022 | Breast cancer,Non-coding RNAs,Cancer therapeutic resistance,Prognostic markers | Resistance to human epidermal growth factor receptor 2 (HER2)-targeted therapy (trastuzumab), cancer stem cell (CSC)-like properties and multiple chemoresistance often concur and intersect in breast cancer, but molecular links that may serve as effective therapeutic targets remain largely unknown. Here, we identified the long noncoding RNA, LINC00589 as a key regulatory node for concurrent intervention of these processes in breast cancer cells in vitro and in vivo. We demonstrated that the expression of LINC00589 is clinically valuable as an independent prognostic factor for discriminating trastuzumab responders. Mechanistically, LINC00589 serves as a ceRNA platform that simultaneously sponges miR-100 and miR-452 and relieves their repression of tumor suppressors, including discs large homolog 5 (DLG5) and PR/SET domain 16 (PRDM16, a transcription suppressor of mucin4), thereby exerting multiple cancer inhibitory functions and counteracting drug resistance. Collectively, our results disclose two LINC00589-initiated ceRNA networks, the LINC00589-miR-100-DLG5 and LINC00589-miR-452-PRDM16- mucin4 axes, which regulate trastuzumab resistance, CSC-like properties and multiple chemoresistance of breast cancer, thus providing potential diagnostic and prognostic markers and therapeutic targets for HER2-positive breast cancer. | LINC00589-dominated ceRNA networks regulate multiple chemoresistance and cancer stem cell-like properties in HER2+ breast cancer
Resistance to human epidermal growth factor receptor 2 (HER2)-targeted therapy (trastuzumab), cancer stem cell (CSC)-like properties and multiple chemoresistance often concur and intersect in breast cancer, but molecular links that may serve as effective therapeutic targets remain largely unknown. Here, we identified the long noncoding RNA, LINC00589 as a key regulatory node for concurrent intervention of these processes in breast cancer cells in vitro and in vivo. We demonstrated that the expression of LINC00589 is clinically valuable as an independent prognostic factor for discriminating trastuzumab responders. Mechanistically, LINC00589 serves as a ceRNA platform that simultaneously sponges miR-100 and miR-452 and relieves their repression of tumor suppressors, including discs large homolog 5 (DLG5) and PR/SET domain 16 (PRDM16, a transcription suppressor of mucin4), thereby exerting multiple cancer inhibitory functions and counteracting drug resistance. Collectively, our results disclose two LINC00589-initiated ceRNA networks, the LINC00589-miR-100-DLG5 and LINC00589-miR-452-PRDM16- mucin4 axes, which regulate trastuzumab resistance, CSC-like properties and multiple chemoresistance of breast cancer, thus providing potential diagnostic and prognostic markers and therapeutic targets for HER2-positive breast cancer.
Breast cancer is the most prevalent malignancy, with high worldwide mortality in women. Overexpression of human epidermal growth factor receptor 2 (HER2) occurs in 25–30% of breast cancers and is associated with poor prognosis. Though HER2-targeted therapy, such as trastuzumab, improves survival dramatically and is the most highly recommended treatment for HER2-positive patients with early-stage or metastatic breast cancer, high rates of inherent or acquired trastuzumab resistance pose a major obstacle. In addition, emerging evidence suggest that trastuzumab resistance is closely associated with epithelial–mesenchymal transition (EMT), multiple drug resistance (MDR), and cancer stem cell (CSC)-like properties, which make it more complex to treat trastuzumab-resistant breast cancers. However, key regulatory nodes that are concurrently involved in trastuzumab resistance, MDR, and CSC properties have yet to be uncovered. It is urgent to identify molecular links that concurrently regulate these processes as an approach to developing more effective therapeutic targets. Noncoding RNAs (ncRNAs) compose the large majority (~98%) of human transcriptome and participate as key players in diverse biological processes. Long noncoding RNAs (lncRNAs) are a class of ncRNAs that are longer than 200 nucleotides and have limited or no protein-coding capacity. LncRNAs, in conjunction with microRNAs or other signaling partners, regulate complex networks, and recent evidence is emerging for key roles of lncRNAs as regulators and potential targets in breast cancer. For example, LncRNA CARMN, acting via the miR143-3p host gene, counteracts cisplatin resistance in triple-negative breast cancer and is associated with positive prognosis. LncRNA CCAT1 interacts with miR-204/211, miR-148a/152, and Annexin A2, and consequently promotes breast cancer stem cell function by activating WNT/β-catenin signaling. Our previous reports also suggest that ncRNAs, including lncRNA UCA1, miR-200c, miR-221, and miR-375, reverse trastuzumab resistance of HER2-positive breast cancer. Therefore, extensive investigation of lncRNAs is important for the development of novel diagnostic and therapeutic targets in breast cancer. Long intergenic non-protein-coding RNA 589 (LINC00589, NCBI gene ID: 619351), also known as TSLNC8, is located on Chromosome 8p12 and has been validated as a non-protein-coding RNA. In hepatocellular carcinoma, non-small cell lung cancer, and glioma, LINC00589 inhibits proliferation, invasion, and metastasis. On the other hand, in pancreatic cancer, LINC00589 serves as an oncogene by stabilizing CTNNB1. These results imply that LINC00589 is important in cancer progression but that its functions vary among different cancer types. Nevertheless, the role of LINC00589 in breast cancer has not been elucidated. In this study, we evaluate the expression of LINC00589 in trastuzumab-resistant breast cancer tissues and cell lines, and analyze its association with patient prognosis. We also perform gain- and loss-of-function experiments to explore the biological roles of LINC00589 in trastuzumab resistance, MDR, and CSC properties in vitro and in vivo. Furthermore, we investigate the molecular mechanisms whereby LINC00589 exerts its diverse functions in HER2 breast cancer. Our data show that LINC00589 concurrently modulates trastuzumab resistance, MDR and CSC-like properties of HER2 -positive breast cancer. Further mechanistic investigations reveal that LINC00589 serves as a competing endogenous RNA (ceRNA) to regulate DLG5 and PRDM16 expression through binding miR-100 and miR-452. Thus, LINC00589 is a key node for simultaneously controlling trastuzumab resistance, MDR, and CSC-like properties in breast cancer with potential therapeutic value.
The long noncoding RNA LINC00589, is located on Chromosome 8p12 and contains four exons (Supplementary Fig. 1a). Its full-length 1413 bp nucleotides (Supplementary Fig. 1b) and the secondary structure (Supplementary Fig. 1c) were shown. Although LINC00589 has been reported to suppress cell proliferation in hepatocellular carcinoma and non-small cell lung cancer, its biological roles are largely unknown. Especially, its roles in drug resistance to breast cancer have not been investigated prior to this study. Therefore, to uncover the potential functions of LINC00589 in trastuzumab-resistant breast cancer, we obtained biopsies from 71 HER2-positive breast cancer patients who received trastuzumab treatment. Based on the immuno-related response evaluation criteria in solid tumors, the patients were divided into two groups: the responding group (CR + PR, 38 cases) and the non-responding group (SD + PD, 33 cases). qRT-PCR analysis of biopsies revealed a dramatically lower expression of LINC00589 in the trastuzumab non-responding group than in the responding group (Fig. 1A). To investigate the potential predictive value of LINC00589 expression, we established a ROC curve to differentiate the responding patients from the non-responding patients. The area under the curve (AUC), diagnostic sensitivity, and specificity reached 0.808, 78.8% and 80.0%, respectively, with the established cut-offs (2.785) (Fig. 1B). For further verification, we divided the samples into high or low LINC00589 expression groups according to the cutoff value, and the proportion of responding patients was significantly higher in the high LINC00589 expression group (81.08%) than in the low LINC00589 expression group (23.53%) (Fig. 1C). These results suggest that LINC00589 may serve as a diagnostic marker for trastuzumab-responding patients. To further evaluate the prognostic value of LINC00589 expression, we obtained formalin-fixed and parrffin-embedded (FFPE) samples from an independent cohort of 92 trastuzumab-treated HER2-positive breast cancer patients with available clinical data. High and low expression of LINC00589 were determined by ISH as represented in Fig. 1D. The results suggest that there is no obvious correlation between LINC00589 expression and age, menopausal status, histologic grade, lymph node status, ER status or PR status in HER2-positive breast cancer patient tissues; however, LINC00589 expression was significantly correlated with TNM stage (Table 1). Furthermore, Kaplan–Meier analysis indicated that HER2-positive breast cancer patients with high LINC00589 expression had a better overall survival than those with low LINC00589 expression (Fig. 1E). In addition, multivariate Cox regression analysis revealed that LINC00589 expression and lymph node status provided independent prognostic factors for overall survival in the HER2-positive breast cancer patients (Table 2). To provide additional support for the correlation of LINC00589 expression with trastuzumab resistance, we treated SKBR3 breast cancer cells with 5 μg/mL trastuzumab for 6 months, as previously described, and obtained 6 trastuzumab-resistant (TR) clones. The IC50 for 6 TR clones was much upper than WT clone, and IC50 value of 6# TR clones was 24 μg/ml (Supplementary Fig. 2). LINC00589 expression was dramatically lower in all the TR cell clones than in the wild-type cells (WT) cells (Fig. 1F). The 6# TR cell clone that expresses the lowest LINC00589 was selected for further investigation the role of LINC00589 in trastuzumab resistance. Compared with WT cells, the TR cells showed more resistant to trastuzumab treatment, as evidenced by elevated cell viability and IC50 (Supplementary Fig. 3). Altogether, our data indicate that LINC00589 expression is downregulated in trastuzumab-resistant breast cancer and correlates with patient survival, suggesting that LINC00589 may be a valuable diagnostic marker for discriminating trastuzumab responders and a prognostic marker for predicting the survival of HER2-positive breast cancer patients.
To determine the functional role of LINC00589 in trastuzumab resistance of HER2-positive breast cancer cells, we constructed lentiviruses that overexpress or silence LINC00589 (Supplementary Fig. 4a, b). WT and TR SKBR3 and HER2-overexpressing BT474 cells were infected with Lv-NC or Lv-LINC00589 lncRNA expression vector, or sh-NC or sh-LINC00589 lentivirus. CCK-8 assays revealed that overexpression of LINC00589 decreased the cell viability of all the six TR SKBR3 cells (Fig. 2A and Supplementary Fig. 5), while knockdown of LINC00589 increased cell viability in WT SKBR3 and BT474 breast cancer cells, which was verified under increasing doses (Supplementary Fig. 6a–c) or times (Fig. 2B, C) of trastuzumab treatment. In addition, the apoptosis rate of TR cells was increased by LINC00589 overexpression after trastuzumab treatment, while the apoptosis rate of WT cells was decreased by LINC00589 silencing (Fig. 2D, E). We also investigated whether LINC00589 regulates the anchorage-independent growth of HER2-positive breast cancer cells. The data showed that LINC00589 upregulation suppressed the number of soft agar colonies formed in TR cells, while knockdown of LINC00589 increased the number of soft agar colonies formed in WT cells (Fig. 2F, G). Collectively, these findings indicate that LINC00589 re-sensitizes resistant breast cancer cells to trastuzumab.
Based on increasing evidence that trastuzumab-resistant breast cancer cells exhibit CSC-like properties, we sought to determine whether LINC00589 is associated with stemness and multiple chemoresistance in breast cancer cells. The ability to form mammospheres in ultra-low–attaching culture conditions is a common characteristic of CSC-like cells. As shown in Fig. 3, The average number and volumes of the spheres derived from the LINC00589-overexpressed trastuzumab-resistant cells were lower than those derived from control cells (Fig. 3A–C). We also examined the expression status of CD24, CD44, CD133, Nanog, OCT4, and SOX2, which have been extensively used as molecular markers for breast CSCs. When LINC00589 was overexpressed in TR cells, CD24 (a negative marker of CSC) was upregulated, and CD44, CD133, Nanog, OCT4, and SOX2 (positive markers of CSC) were downregulated, which was demonstrated at both the mRNA and protein levels (Fig. 3D, E). These results indicated that LINC00589 was an important regulator of CSC-like properties in breast cancer. Given that CSC-like properties are thought to constitute a leading cause for multiple drug resistance of various cancers, we hypothesized that trastuzumab-resistant breast cancer cells might acquire multiple chemoresistance. Thus, we used several first-line chemotherapeutic drugs for breast cancer, including 5-FU, doxorubicin (Dox), paclitaxel (Pac), cisplatin (Cis), gemcitabine (Gem), and vincristine (VCR), to examine the multiple chemoresistance of TR cells. Compared to WT cells, TR breast cancer cells displayed less sensitivity to each of these drugs (Fig. 3F). However, overexpression of LINC00589 remarkably re-sensitized the TR cells to all of these drugs (Fig. 3G). Consistently, we determined HER2 expression by qRT-PCR and western blot assays (Supplementary Fig. 7a–c), and observed decreased HER2 expression in TR cell lines. Meanwhile, LINC00589 overexpression could not change HER2 expression at both mRNA and protein levels in trastuzumab-resistant breast cancer cells (Supplementary Fig. 7d–f). This suggested that LINC00589 might exerted multiple functions through a HER2-independent mechanism in HER2-positive breast cancer. Consistently with the in vitro experiment, we also observed no correlation between LINC00589 and HER2 expression in patients’ tissues in clinical samples (Supplementary Fig. 7g). Taken together, the above findings suggest that LINC00589 decreases CSC-like properties and reverses the resistance of TR cells to multiple chemotherapeutic agents.
Functional roles of lncRNAs are associated with their cellular localization. To distinguish potential molecular mechanisms whereby LINC00589 exerts its multiple functions in HER2-positive breast cancer, we determined its cellular location. The online lncLocator software predicted that LINC00589 is mainly enriched in the cytoplasm (Fig. 4a). Consistently, subcellular fractionation assays revealed that LINC00589 is mostly distributed in the cytoplasm in both BT474 and SKBR3 cells (Fig. 4b, c). These results raise the possibility that LINC00589 might regulate target protein expression at the post-transcriptional level. As ceRNA mechanism is an important mode for cytoplasmic lncRNA-mediated post-transcriptional regulation, we hypothesized that LINC00589 may competitively sponge miRNAs. To test this hypothesis, we performed an immunoprecipitation assay for Ago2, an important protein component of the RNA-induced silencing complex. The results demonstrate that LINC00589 bind to with Ago2 and was involved in the miRNA-mediated repression of mRNA (Fig. 4d). To further investigate the miRNAs that may be sponged by LINC00589, we used lncBase and obtained 1597 potential binding miRNAs for LINC00589. As we have previously performed a microarray between WT and TR SKBR3 cells (GSE47011), we selected the most highly upregulated miRNAs (fold change >4.0) and evaluated overlap with lncBase-predicted miRNAs, which yielded 9 candidate miRNAs, including miR-100 (miR-100-5p), miR-7 (miR-7-5p), miR-452 (miR-452-5p), miR-224 (miR-224-5p), miR-4288, miR-3926, miR-151a-5p, miR-17-3p, and miR-125b (miR-125b-5p) (Fig. 4e). Among these 9 candidate miRNAs, only miR-100 and miR-452 mimics were found to suppress LINC00589-driven luciferase activity (Fig. 4f). Therefore, we pursued these two miRNAs as candidates for further investigation. To verify that miR-100 and miR-452 can interact with LINC00589, we designed reporter constructs in which the putative miR-100 and miR-452-binding sites in LINC00589 were mutated by site-directed mutagenesis (Fig. 4g). As expected, miR-100 and miR-452 decreased the luciferase activity encoded by the WT LINC00589 vector, whereas, mutations of the binding sites abolished their suppressive effect (Fig. 4h, i). To verify that these miRNAs directly bind LINC00589, we performed MS2 pull-down assays using lysates from WT cells and qRT-PCR confirmation (Supplementary Fig. 8a, b). MS2-LINC00589 precipitated miR-100 and miR-452, but MS2-LINC00589-Mut (with mutation of miR-100 and miR-452 binding sequences) failed to enrich these miRNAs, thus suggesting that LINC00589 directly binds to miR-100 and miR-452 through complementary sequences (Fig. 4j). In addition, LINC00589 overexpression decreased levels of miR-100 and miR-452 in TR cells but LINC00589 knockdown increased levels of miR-100 and miR-452 in WT cells (Fig. 4k, l). Altogether, these data indicate that LINC00589 serves as a platform to sponge miR-100 and miR-452 in breast cancer cells.
Next, we explored the functional roles of miR-100 and miR-452, which are directly sponged by LINC00589 in HER2-positive breast cancer cells. qRT-PCR data suggest that expression of both miR-100 and miR-452 is much higher in TR cells than in WT cells (Fig. 5A). To determine whether miR-100 and miR-452 may also modulate trastuzumab resistance, we constructed lentiviruses expressing shRNAs for miR-100 and miR-452 (Supplementary Fig. 9a, b). After trastuzumab treatment, miR-100 or miR-452 knockdown suppressed the viability of TR cells (Fig. 5B, C) and increased the apoptosis rate of TR cells (Fig. 5D, E). In addition, soft agar colony formation assays revealed that the anchorage-independent growth of TR cells with miR-100 or miR-452 knockdown were dramatically lower than those control groups (Fig. 5F, G). These results suggest that miR-100 and miR-452 are involved in trastuzumab resistance in breast cancer. Further, we investigated the functional roles of miR-100 and miR-452 in CSC-like properties and multiple chemoresistance of HER2-positive breast cancer. The mammosphere-formation data showed that the numbers and volumes of spheres were decreased in TR cells infected with sh-miR-100 and sh-miR-452 lentiviruses as compared to sh-NC lentivirus (Fig. 5H–J). Consistently, sh-miR-100 and sh-miR-452 upregulated the negative stemness marker CD24, and downregulated the positive stemness markers CD44, CD133, Nanog, OCT4, and SOX2, at both the mRNA and protein expression levels (Fig. 5K, L). Finally, the sensitivities of TR cells to 5-Fu, Dox, Paclitaxel, Cisplatin, Gemcitabine and VCR were each increased by miR-100 and miR-452 knockdown (Fig. 5M). Taken together, the above results suggest that miR-100 and miR-452 concurrently regulate trastuzumab resistance, CSC-like properties and multiple chemoresistance in HER2-positive breast cancer cells.
The biological functions of miRNAs rely on their targets that could be bound with and silenced via the RNA-induced silencing complex. Haven demonstrated the ceRNA interactions of LINC00589-miR-100 and LINC00589-miR-452, We sought to identify downstream targets of miR-100 and miR-452 to elucidate the mechanism through which LINC00589 exerted its functions in TR cells. First, we identified potential targets from the human genome using the bioinformatic miRNA target prediction tools, including miRwalk, Starbase, RNA22, PITA, miRDB, and TargetScan. Among the predicted targets, Discs large homolog 5 (DLG5) for miR-100 and PR/SET domain 16 (PRDM16) for miR-452 drawed our attention for the following reasons. DLG5 has been reported to be a suppressor of CSC-like properties in breast cancer cells; and PRDM16 functions as a tumor suppressor by inhibiting the transcription of mucin4 (MUC4), which is associated with trastuzumab resistance and CSC-like properties of cancer cells. To validate these predicted targets for miR-100 and miR-452, we constructed wild-type (WT) and mutant (Mut) dual-luciferase reporter vectors for the DLG5 and PRDM16 3′-UTRs (Fig. 6a). qRT-PCR results confirmed that miR-100 and miR-452 mimic significantly upregulated the endogenous levels of miR-100 and miR-452 in SKBR3 breast cancer cells (Supplementary Fig. 9c, d). Furthermore, dual-luciferase reporter assays showed that miR-100 and miR-452 significantly inhibited WT but not Mut DLG5 and PRDM16 luciferase activity, respectively (Fig. 6b, c). To further investigate the regulatory effects of miR-100 and miR-452 on DLG5 and PRDM16 expression, we performed qRT-PCR and western blotting analyses, which revealed that miR-100 mimic decreases DLG5 mRNA and protein expression (Fig. 6d, e), and miR-452 mimic decreases PRDM16 mRNA and protein expression (Fig. 6f, g). These results suggest that DLG5 and PRDM16 are direct targets for miR-100 and miR-452. Next, we investigated whether LINC00589 regulates DLG5 and PRDM16 via miR-100 and miR-452. Knockdown of LINC00589 decreased DLG5 expression, whereas miR-100-suppression abolished the inhibitory effect of LINC00589 silencing on DLG5 expression in WT cells, as confirmed at both the mRNA (Fig. 6h) and protein levels (Fig. 6i). Furthermore, knockdown of LINC00589 suppressed PRDM16 expression, which was rescued by miR-452 inhibition (Fig. 6j, k). These results suggested that LINC00589 regulated DLG5 and PRDM16 via miR-100 and miR-452, respectively. Further, we constructed PRDM16 overexpression vector to confirm the regulatory effect of PRDM16 on MUC4 levels in breast cancer cells, and the efficiency was tested by qRT-PCR assay (Supplementary Fig. 10a). MiR-452 mimic enhanced MUC4 expression, whereas PRDM16 overexpression abolished the miR-452-induced upregulation of MUC4 in WT cells (Fig. 6l, m). These results provide further evidence for MUC4 as a downstream target in the LINC00589-miR-452-PRMD16 axis. Collectively, our results identified the LINC00589-miR-100-DLG5 and the LINC00589-miR-452-PRDM16-MUC4 axes in breast cancer.
We next investigated whether LINC00589 exertes its biological functions through DLG5 and PRDM16. Overexpression efficacy of pCDNA-DLG5 (Supplementary Fig. 10b), and knockdown efficacy of sh-PRDM16 and sh-DLG5 were determined by qRT-PCR (Supplementary Fig. 10c, d). The CCK-8 assays demonstrated that knockdown of LINC00589 increased the viability of WT SKBR3 and BT474 cells under trastuzumab treatment, but that overexpression of either DLG5 or PRDM16 abolished the increased cell viability induced by sh-LINC00589 (Fig. 7A, B). In contrast, the viability of TR cells was suppressed by LINC00589, while sh-DLG5 or sh-PRDM16 co-transfection abolished the decreased cell viability mediated by LINC00589 (Fig. 7C). Consistently, knockdown of DLG5 and PRDM16 also partially abolished LINC00589-enhanced cell apoptosis after trastuzumab treatment (Fig. 7D, E). Moreover, soft agar colony formation assays revealed that silencing of DLG5 or PRDM16 abrogated LINC00589-induced inhibition of anchorage-independent growth in TR cells (Fig. 7F, G). These data indicate that LINC00589 reverses trastuzumab resistance by regulating DLG5 and PRDM16. To confirm the roles of DLG5 and PRDM16 in LINC00589-regulated CSC-like properties and multiple chemoresistance of breast cancer, we performed mammosphere formation and cell viability assays. The number and volume of spheres were downregulated by LINC00589 in TR cells, while knockdown of either DLG5 or PRDM16 partially abated the suppressive effect of LINC00589 (Fig. 7H–j). Furthermore, qRT-PCR and western blot analyses revealed that knockdown of DLG5 or PRDM16 partially relieved the stimulatory effect of LINC00589 on the expression of the negative stemness marker, CD24, and antagonized the repression of LINC00589 on the expression of the positive stemness markers, CD44, CD133, Nanog, OCT4, and SOX2 (Fig. 7K, L). Finally, cell viability assays demonstrated that silencing of DLG5 or PRDM16 partially abrogated LINC00589-induced sensitization of TR cells to 5-FU, Dox, Pac, Cis, Gem, and VCR. (Fig. 7M). Collectively, these results indicate that LINC00589 regulates trastuzumab resistance, cancer stem cell-like properties and multiple chemoresistance, at least in part, by modulating DLG5 and PRDM16 expression.
To investigate the functional role of the LINC00589-initiated ceRNA networks in trastuzumab resistance in vivo, we established an animal model of nude mice bearing TR breast cancer cell xenografts. TR cells infected with Lv-LINC00589 or Lv-NC were implanted into mammary fat pads of nude mice, which were divided into four groups: group A (Lv-NC + miR-NC), group B (Lv-LINC00589 + miR-NC), group C (Lv-LINC00589 + miR-100) and group D (Lv-LINC00589 + miR-452). When the xenograft volumes reached 50 mm3, miR-100 mimic, miR-452 mimic or control miRNA were injected into the tumors (15 μg /injection, twice a week) before injection of trastuzumab (10 mg/kg). Consistent with our in vitro results, LINC00589 overexpression significantly decreased the tumor volume and weight; however, either miR-100 or miR-452 reversed the repressive effect of LINC00589 on tumor volume and weight in nude mice (Fig. 8A–C). To further validate the effect of LINC00589 on trastuzumab resistance in vivo, we injected TR cells after stable transfection of lentivirus-NC-Luc or lentivirus-LINC00589-Luc. The results demonstrate that LINC00589 inhibited the luciferase activity of tumors, but both miR-100 and miR-452 abated LINC00589-induced suppression of tumor growth (Fig. 8D–F). These in vivo data suggested that LINC00589 reversed trastuzumab resistance via miR-100 and miR-452 in breast cancer in vivo. Next, we explored the downstream activation of LINC00589-initiated ceRNA networks in vivo. Cancer tissues from xenografts were dissected and subjected to RNA isolation and qRT-PCR. Compared to the control vector, LINC00589 decreased the mRNA expression of miR-100, miR-452, and increased the mRNA expression of LINC00589, DLG5 and PRDM16. However, miR-100 abolished the effect of LINC00589 in promoting mRNA expression of DLG5, and miR-452 abated the enhancement of LINC00589 on mRNA expression of PRDM16 (Fig. 8J, K). Moreover, IHC assay data in xenograft tumor tissues revealed that LINC00589 overexpression could upregulate the protein expressions of DLG5 and PRDM16, while miR-100 and miR-452 abrogated the promotion of LINC00589 on the DLG5 and PRDM16, respectively (Fig. 8L). Collectively, these results confirm the LINC00589-initiated ceRNA networks and their downstream targets in vivo.
To further evaluate the relevance of the LINC00589-miR-100-DLG5 and LINC00589-miR-452-PRDM16-MUC4 networks in clinical samples, we evaluated expression levels in HER2-positive breast cancer patients. The mRNA levels of LINC00589 were negatively correlated with mRNA levels of both miR-100 and miR-452, and mRNA levels of miR-100 and miR-452 were conversely correlated with DLG5 and PRDM16, respectively (Fig. 9A–D). Moreover, IHC assay demonstrated low expression of DLG5 and PRDM16 in trastuzumab non-response HER2-positive breast cancer patients but high expression of DLG5 and PRDM16 in trastuzumab-response HER2-positive breast cancer patients (Fig. 9E). These results confirmed the clinical relevance of the LINC00589-initiated ceRNA networks in breast cancer patients in clinic.
Increasing evidence has suggested that drug resistance, EMT and CSC-like properties, which are important causes of cancer progression, may function concordantly. Therefore, the identification of molecular signatures that concurrently regulate these processes holds great significance for cancer characterization, therapy and prognosis evaluation. Here, we provided the evidence that LINC00589 concurrently reverses trastuzumab resistance, MDR and CSC-like properties and serves as an independent prognostic factor in HER2-positive breast cancer. Further mechanistic investigation revealed that LINC00589 exerts its functions via two axes, the miR-100/DLG5 and miR-452/PRDM16 axis, as ceRNA platforms (Fig. 10). These data uncover new signaling networks that underlie the crosstalk between trastuzumab resistance, MDR and CSC-like properties in breast cancer. HER2-targeted therapy is a standard treatment for early or metastatic HER2-positive breast cancer and often improves clinical outcomes; however, primary and acquired resistance occurs in a substantial subset of patients. Many efforts have been made to elucidate the mechanisms of trastuzumab resistance, mainly including but not limited to: (a) HER2 loss, extracellular domain-deficient P95 HER2 expression and MUC4 masking have been demonstrated to block the access of trastuzumab to HER2. (b) High expression of the Delta16 HER2 isoform was shown to mediate optimal efficacy for trastuzumab. (c) Activation of downstream effectors of HER2 signaling (e.g., PTEN) and alternative signaling pathways (e.g., IGF1R signaling) lead to trastuzumab resistance in breast cancer. (d) Breast cancer cells have been demonstrated to escape from antibody-dependent cell-mediated cytotoxicity (ADCC) caused by trastuzumab. These findings imply that trastuzumab resistance may arise from a combination of largely unknown mechanisms. In addition, emerging evidence shows that trastuzumab resistance is not an isolated phenomenon but is often accompanied by MDR and CSC-like properties. However, other potential key regulators that link these processes and their relationships have not been well explored. Noncoding RNAs, most of which have not been functionally annotated, play central roles in various physiological and pathological processes, especially in complicated signaling networks of cancers. Our previous studies and other reports demonstrate that noncoding RNAs also regulate trastuzumab resistance to breast cancer, e.g., miR-200c, miR-221, miR-375, lncRNA TINCR, AGAP2-AS1, and UCA1. LINC00589, also named as TSLNC8, was identified on chromosome 8p12 by Strausberg et al.. Until recently, functional roles of LINC00589 began to get much concern. For example, LINC00589 serves as a tumor suppressor in human glioma and non-small cell lung cancer, and inhibits melanoma resistance to BRAF inhibitor. However, LINC00589 also interacts with HUR and stabilizes CTNNB1 mRNA, thereby promoting cancer progression in pancreatic cancer. Thus, LINC00589 may serve as both a tumor suppressor and a tumor promoter according to different cancer pathological settings. In this study, we determined that LINC00589 is downregulated in trastuzumab-resistant breast cancer and serves as an independent prognostic factor for HER2-positive patients. Furthermore, LINC00589 concurrently reversed trastuzumab resistance, multiple chemoresistance, and CSC-like properties of HER2 breast cancer. Giusti et al. disclosed that HER2 loss also results in both trastuzumab resistance and enhanced stemness of breast cancer, which is consistent with our findings and indicates HER2 loss probably is likely to correlated LINC00589. However, LINC00589 is unlikely to regulate HER2 expression in breast cancer and instead exerts multiple functions in trastuzumab resistance, MDR and CSC-like properties through a HER2-independent mechanism. LncRNAs, miRNAs, and mRNAs have been shown to crosstalk with each other through shared binding sequences within complex signaling networks via ceRNA mechanisms. Emerging evidence indicates that lncRNAs serve as sponges for miRNAs and thereby keep them away from binding sites on target genes in various cancer types. For example, LncRNA DNACR enhances ROCK1-mediated proliferation and metastasis by sponging miR-335-5p and miR-1972 in osteosarcoma. LncRNA LINC01123 sponges miR-199a-5p and triggers proliferation and aerobic glycolysis by regulating c-myc expression in non-small cell lung cancer. LINC00673, which is upregulated by YY1, exerts oncogenic functions in breast cancer by sponging miR-515-5p and subsequently upregulates MARK4 expression, and inhibits the Hippo signaling pathway. In this study, we demonstrate that LINC00589 is mainly localized to the cytoplasm, which is consistent with the possibility that it may function as an endogenous miRNA sponge. Bioinformatics analysis and experimental assays further revealed that miR-100 and miR-452 are direct targets of LINC00589. MiR-100 and miR-452 have been reported as both oncogenes and tumor suppressor genes. For example, miR-100 promotes cetuximab resistance in colorectal cancer but inhibits bladder urothelial carcinogenesis; and miR-452 promotes renal cancer cell invasion and metastasis and colorectal cancer progression but inhibits metastasis of non-small cell lung cancer. In our study, downregulation of both miR-100 and miR-452 suppressed trastuzumab resistance, multiple chemoresistance, and CSC-like properties, thus supporting their oncogenic roles in HER2-positive breast cancer as targets of LINC00589. According to the ceRNA network theory, roles for lncRNAs are dependent on their abilities to regulate miRNA targets that mediate signaling pathways. Therefore, investigating potential targets of miRNAs is important for elucidating the roles and mechanisms of ceRNA networks. Consistently, in this study we identified DLG5 as a target of miR-100 and PRDM16 as a target of miR-452. DLG5 belongs to the membrane-associated guanylate kinase (MAGUK) superfamily and is considered to play multiple roles in various cancers, including an ability to suppress breast cancer stem cell-like characteristics and restore tamoxifen sensitivity by inhibiting TAZ expression and to decrease the formation and function of invadopodia in human hepatocellular carcinoma via Girdin and Tks5. On the other hand, PRDM16, a zinc finger transcription factor hammering the epithelial-to-mesenchymal transition, functions as a suppressor of lung adenocarcinoma metastasis and is associated with patient survival. In kidney cancer, PRDM16 suppresses HIF-targeted gene expression and inhibits tumor growth in vitro and in vivo. In this study, we identified DLG5 and PRDM16 as target genes for miR-100 and miR-452. Thus, our results support the ability of LINC00589 to regulate both miR-100/DLG5 and miR-452/PRDM16 axes, thereby suggesting two crosslinked ceRNA pathways. In support of this possibility, we demonstrated that silencing of either DLG5 or PRDM16 abolished multiple LINC00589-induced effects in HER2-positive breast cancer. In conclusion, we demonstrated that LINC00589 concurrently reverses trastuzumab resistance, multiple chemoresistance and CSC-like properties and acts as an independent prognosis factor for HER2-positive breast cancer. Further, we identified that two ceRNAs networks, LINC00589-miR-100-DLG5 axis and LINC00589-miR-452-PRDM16 axis, that mediate multiple suppressor roles of LINC00589. Our findings suggest that these LINC00589 ceRNA networks could be valuable for predicting trastuzumab efficacy and prognosis, as well as providing promising therapeutic targets for HER2-positive breast cancer in future translational applications.
A total of 71 cases of trastuzumab-treated HER2-positive breast cancer patients were enrolled from General Hospital of Xinjiang Command and Xijing Hospital before chemotherapy was initiated. Cases with complete response (CR) or partial response (PR) were considered as trastuzumab responders, and cases with stable disease (SD) or progressive disease (PD) were defined as trastuzumab non-responders. Clinical tissue samples were obtained during the operation and were immediately frozen at −80 °C until RNA extraction. Another independent cohort of 92 cases of paraffin-embedded samples from HER2-positive breast cancer patients who received trastuzumab were obtained from the General Hospital of Xinjiang Command and Xijing Hospital. Ethical approval was obtained from the Ethics Committee of the General Hospital of Xinjiang Command (number: 201803). All participants provided written informed consent. The detailed clinicopathological characteristics of these paraffin-embedded samples are summarized in Table 1. All patients were pathologically confirmed for diagnosis of HER2-positive breast cancer.
Trastuzumab (Herceptin) was purchased from Roche (Basel, Switzerland) and dissolved in phosphate-buffered saline (PBS). 5-Fluorouracil (5-FU), doxorubicin (Dox), paclitaxel (Pac), cisplatin (Cis), gemcitabine (Gem), and vincristine (VCR) were obtained from Sigma-Aldrich (St Louis, MO, USA).
BT474 human breast cancer cells (HER2-overexpression) were obtained from the American Type Culture Collection (catalog number HTB-20, ATCC) and were cultured in RPMI 1640 supplemented with 10% FBS. Wild-type (WT) SKBR3 human breast cancer cells (catalog number HTB-30, ATCC) were cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS). Trastuzumab-resistant (TR) SKBR3 cells were established by continuous culture of WT SKBR3 cells in the presence of 5 μg/ml trastuzumab for 6 months in a humidified atmosphere of 5% CO2 and 95% air at 37 °C according to our previous reports.
The full-length coding sequences of DLG5 and PRDM16 were amplified and cloned into the pCDNA3.1 overexpression vector (catalog number V79520, Invitrogen, Carlsbad, CA, USA). The control and overexpression vectors were transfected using Lipofectamine 2000 (catalog number 11668019, Invitrogen, Carlsbad, CA, USA) at a 1 μg DNA: 2.5 μl lipofectamine ratio according to the manufacturer’s instructions. miRNA mimics were synthesized by Shanghai Gene Pharma Co, Ltd. The target sequences were as follows: miR-100 mimic: 5’-AACCCGUAGAUCCGAACUUGUG-3’; miR-452 mimic: 5’- AACUGUUUGCAGAGGAAACUGA-3’; miR-7 mimic: 5’ UGGAAGACUAGUGAUUUUGUUGUU-3’; miR-224 mimic: 5’-UCAAGUCACUAGUGGUUCCGUUUAG-3’; miR-4288 mimic: 5’-UUGUCUGCUGAGUUUCC-3’; miR-3926 mimic: 5’-UGGCCAAAAAGCAGGCAGAGA-3’; miR-151a-5p mimic: 5’-UCGAGGAGCUCACAGUCUAGU-3’; miR-17-3p mimic: 5’-ACUGCAGUGAAGGCACUUGUAG-3’; miR-125b mimic: 5’-UCCCUGAGACCCUAACUUGUGA-3’. The working concentrations for miRNA mimics were 30 nM for cell transfection. RNA was transfected into cells using Lipofectamine 2000 according to the manufacturer’s instructions.
Full-length coding sequences of DLG5 and PRDM16 were amplified and cloned into pcDNA3.1 vector. Full-length coding sequences of LINC00589 were amplified and cloned into pLVX-Puro vector (catalog number PT4002-5, Clontech, CA, USA) with or without the luciferase gene. RNA oligos containing siRNA sequences of LINC00589, DLG5, and PRDM16, or containing inhibitor sequences of miR-100 or miR-452 were synthesized and cloned into the shRNA lentiviral vector pLVX-shRNA2 (catalog number PT4052-5, Clontech, CA, USA) with either the puromycin or bleomycin resistance marker gene. The sequences were as follows: siRNA-LINC00589: 5’-GGATGACACCTCCATTCAA-3’; siRNA-DLG5: 5’-GCTCAAGAGCAGCACATCT-3’; siRNA-PRDM16: 5’-CCCACAACTTGCTGGTCAA-3’. miR-100 inhibitor: 5’-CACAAGUUCGGAUCUACGGGUA-3’; miR-452 inhibitor: 5’-UCAGUUUCCUCUGCAAACAGTT-3’. The retrovirus constructs or the empty vector (control vector) were transiently co-transfected with package vectors into 293T cells to produce lentiviruses, which were collected in the viral supernatant 72 h after transfection. Cells infected with the packaged viruses were pre-treated with DEAE dextran (25 μg/ml) for 45 min. 48 h after the infection, cells were screened with puromycin (2 μg/ml) or bleomycin (100 μg/ml), depending on the selection marker of the vector. Cells infected with multiple constructs were selected for infection with each construct.
The sequence of LINC00589 was downloaded from NCBI (gene ID: 619351), from which a 1413–base pair (bp) sequence was extracted. The secondary structure of LINC00589 was predicted by AnnoLnc (http://annolnc.cbi.pku.edu.cn/). The lncRNA subcellular localization was predicted using the online website lncLocator (http://www.csbio.sjtu.edu.cn/bioinf/lncLocator/). The potential sponged miRNAs for LINC00589 and their binding sites were predicted by LincBase tools (http://carolina.imis.athena-innovation.gr/diana_tools). Starbase (http://starbase.sysu.edu.cn/), miRWalk (http://mirwalk.umm.uni-heidelberg.de/), RNA22 (https://cm.jefferson.edu/rna22/), PITA (https://genie.weizmann.ac.il/pubs/mir07/), miRDB (http://mirdb.org/), and TargetScan (http://www.targetscan.org/vert_71/), were used to predict the potential target genes of miR-100 and miR-452.
Total cell RNA was extracted using Trizol (Invitrogen). cDNA was synthesized from 1.0 μg of total RNA, using oligo-dT priming and the Retroscript reverse transcription kit (Ambion, cat no. AM1710). Real-time PCR was performed in triplicate using the Real-Time SYBR Green PCR master mix system (SuperArray Bioscience Corporation, cat no. PA-110) on an Opticon DNA Monitor instrument (Biorad). mRNA levels were normalized to GAPDH, which was used as an internal control. The primer sequences were as follows: forward primer 5’-CACCTCCATTCAACCAATAAGC-3’ and reverse primer 5’-ACCCTGTCCCCAATAACCC -3’ for LINC00589; forward primer 5’-GGTCTCACTCTCTCTTCTGCATCTCT-3’, reverse primer 5’-GGCATCCATCATCTAGTCAAACCTC-3’ for CD24; forward primer 5’-CGACAGCACAGACAGAATCCC-3’, reverse primer 5’- AATCAAAGCCAAGGCCAAGAG-3’ for CD44; forward primer 5’- AGTCGGAAACTGGCAGATAGC-3’, reverse primer 5’-GGTAGTGTTGTACTGGGCCAAT-3’ for CD133; forward primer 5’-AGGCAAACAACCCACTTCTG-3’, reverse primer 5’-TCTGCTGGAGGCTGAGGTAT-3’ for Nanog; forward primer 5’-ATGTGGTCCGAGTGTGGTTC-3’, reverse primer 5’-CAGAGTGGTGACGGAGACAG-3’ for OCT4; forward primer 5’-AACCAGCGCATGGACAGTTA-3’, reverse primer 5’-GACTTGACCACCGAACCCAT-3’ for SOX2; forward primer 5’-CTGCACATCAACCTCAGTGG-3’, reverse primer 5’-CGGCAGCATACACTCCATT-3’ for DLG5; forward primer 5’-AACCAAGCATCAACGCGAAC-3’, reverse primer 5’-AACCCTGGTTCTTAGCCTGC-3’ for PRDM16; forward primer 5’-TGGGACGATGCTGACTTCTC-3’, reverse primer 5’-CCCCGTTGTTTGTCATCTTTC-3’ for MUC4; forward primer 5’-GTTCTCTGCCGTAGGTGTCC-3’, reverse primer 5’-GAACCAGCCAGATGTTCGGC-3’ for HER2; and forward primer 5’- CTCCTCCACCTTTGACGCTG-3’, reverse primer 5’-TCCTCTTGTGCTCTTGCTGG-3’ for GAPDH. For miRNAs, the expression levels were normalized to U6 small nuclear RNA (internal control), and the following universal primers from the QIAGEN kit were used: forward primer 5’-AACCCGTAGATCCGAACTTGTG-3’ for miR-100; forward primer 5’-AACUGUUUGCAGAGGAAACUGA-3’ for miR-452; and forward primer 5’-GTGCTCGCTTCGGCAGCACATAT-3’ for U6. All quantitative reverse transcription polymerase chain reaction (qRT-PCR) analysis was performed in an ABI Prism 7500 (Applied Biosystems). The expression change was calculated using the 2−ΔΔCT method.
The following antibodies were used for western blotting: rabbit monoclonal anti-CD24 (1:500, catalog number ab179821, Abcam, MA, USA), rabbit monoclonal anti-CD44 (1:500, catalog number ab189524, Abcam), rabbit recombinant multiclonal anti-CD133 (1:800, catalog number ab278053, Abcam), mouse monoclonal anti-Nanog (1:1000, catalog number ab173368, Abcam), rabbit monoclonal anti-OCT4 (1:500, catalog number ab200834, Abcam), mouse monoclonal anti-SOX2 (1:1000, catalog number ab79351, Abcam), rabbit polyclonal anti-DLG5 (1:1000, catalog number ab231283, Abcam), rabbit polyclonal anti-PRDM16 (1:1000, catalog number ab106410, Abcam), mouse monoclonal anti-ErbB-2 (1:500, catalog number sc-33684, Santa Cruz Biotechnology, CA, USA), and mouse monoclonal anti-β-actin (1:1000, catalog number A5441, Sigma-Aldrich, MO, USA). Other primary antibodies and secondary antibodies and experimental procedures for immunoblotting are provided in the supplementary experimental procedures.
Four-micrometer sections from breast cancer paraffin-embedded tissue samples were used to perform IHC staining by PV-6000 detection kits, and ZLI-9032 DAB substrate kit (Beijing Zhongshan golden bridge Biotechnology Co. Ltd., Beijing, China) The sections were incubated in anti-DLG5 antibody (1:300, catalog number ab231283, Abcam) or anti-PRDM16 antibody (1:400, catalog number ab106410, Abcam) at 4 °C overnight. The results of IHC staining were evaluated by an experienced pathologist, and quantification of the reaction was performed using the histoscore system as previously described.
The 3’-untranslated region (3’-UTR) of DLG5 and PRDM16 and full-length LINC00589 were amplified from human genomic DNA by PCR and cloned into a modified pGL3 luciferase vector (catalog number E1751, Promega, Madison, WI, USA). Wild-type (WT) and mutant (Mut) binding sites of miR-100 in LINC00589 and the 3’-UTR of DLG5, and binding sites for miR-452 in LINC00589 and the 3’-UTR of PRDM16 were subcloned into the pGL3 basic vector to generate corresponding WT and Mut luciferase reporter vectors. Primers for mutation of binding sites between miR-100 and LINC00589 were: forward-1, 5’-TATGTCAGAGATGCTAGCACTGGCATC-3’; and reverse-1, 5’-AGGTGGCATTCTAGTGGACACTCTTG-3’, forward-2, 5’-CAAGAGTGTCCACTAGAATGCCACCT-3’; and reverse-2, 5’-GGTTTGAAATGAGAGTGTCAACCTTC-3’. Primers for mutation of binding sites between miR-452 and LINC00589 were: forward-1, 5’-GCTGGAAAGTGAGCCTGGATCTCTCT-3’; and reverse-1, 5’-CTGACAAACGTTTTGGGGTTCTCGC-3’, and forward-2, 5’-GCGAGAACCCCAAAACGTTTGTCAG-3’; and reverse-2, 5’-AACCTGAAACTCAGATGGGCAAGATTA-3’. Primers for mutation of binding sites between miR-100 and DLG5 were: forward-1, 5’-GAGAATGCTGTGCTGTGGATGAC-3’; and reverse-1, 5’-TTGGGCACTTAAGTGAAATCAC-3’, forward-2, 5’-GTGATTTCACTTAAGUGCCCAA-3’; and reverse-2, 5’-AGGAGAGGTGCCACCAAGGAGCA-3’. Primers for mutation of binding sites between miR-452 and PRDM16 were: forward-1, 5’-GTTCTTGGCGAGACACAGCTTGAG-3’; and reverse-1, 5’-TTGACAAAAGGAGGAAATAAAA-3’, forward-2, 5’-TTTTATTTCCTCCTTTTGTCAA-3’; and reverse-2, 5’-TCTTCCAAACAATACAAGAAATA-3’.
Breast cancer cells were co-transfected with 150 ng of firefly luciferase reporter plasmid with inserted WT and Mut sequences from LINC00589, DLG5, or PRDM16, together with pRL-SV40 Renilla luciferase vector (catalog number E2231, Promega) and miRNAs (miR-100 mimic, miR-452 mimic or negative control RNA) using Lipofectamine 2000 (Invitrogen). Three independent transfection experiments were performed, each in triplicate. 48 h after transfection, firefly luciferase activity derived from pGL3 plasmids was evaluated and normalized to Renilla luciferase activity using a luciferase assay system (Promega) as reported previously.
Cytotoxicity was analyzed using the cell counting kit-8 (CCK-8) method (MYBiotech, China). Briefly, breast cancer cells were transfected with plasmids and/or miRNA mimics/shRNAs or were infected with lentiviruses. Subsequently, the cells were plated in 96-well plates and exposed to trastuzumab, 5-FU, doxorubicin, paclitaxel, cisplatin, gemcitabine, or vincristine over a time course. After drug treatment, 10 μl CCK-8 solution was added to each well, and the cells were incubated at 37 °C for 4 h. The optical density (OD) was measured at 450 nm (Thermo Scientific, USA), and the half-maximal inhibitory concentration (IC50) of the drug was calculated based on the OD value. The assay was performed at least three times. Cell cytotoxicity was calculated according to the following formula: inhibition ratio (%) = (OD (drug − OD (blank))/(OD (drug control) − OD (blank)) × 100%.
Low melting temperature agarose was mixed with culture medium to obtain 0.6% and 0.35% gel as the “lower” and the “upper” soft agar, respectively. Plates with 6 wells were coated with 1.0 ml lower soft agar. Then 1.0 × 103 cells were resuspended with 2 ml upper soft agar and immediately plated on the lower soft agar. Cells were incubated for 21 days in a 37 °C incubator and were stained with 0.005% Crystal Violet Staining Solution. Colonies were enumerated by microscopy. Experiments were carried out in triplicate and were repeated a minimum of three times.
WT SKBR3 (catalog number HTB-30, ATCC) and TR SKBR3 breast cancer cells were seeded in six-well plates (5 × 105 cells/well) and were transfected with miRNA mimics or plasmids, or they were infected with lentiviruses. The cultures were supplemented with trastuzumab at a final concentration of 5 μg/ ml for WT SKBR3 cells (catalog number HTB-30, ATCC) cells or 25 μg/ml for TR SKBR3 cells. Then, the cells were washed three times with PBS, harvested, stained with annexin V-FITC and propidium iodide (BD Biosciences), and subjected to flow cytometry (BD Biosciences) to detect apoptosis.
Seeded breast cancer cells (2 × 107 cells) were washed with ice-cold PBS and resuspended in the ice-cold cytoplasmic lysis buffer (0.15% NP-40, 10 mM Tris pH 7.5, 150 mM NaCl) for 5 min on ice. The lysates were transferred into ice-cold sucrose buffer and centrifuged at 13,000 × g for 10 min at 4 °C. The supernatant (~700 μL) was collected as the cytoplasmic fraction and the precipitate was collected as the nuclear fraction. The expression of LINC00589 in different subcellular fractionations was analyzed by qRT-PCR.
Breast cancer cells were seeded onto ultra-lowattachment six-well plates (3471; Corning, Corning, NY, USA) at a density of 2000 cells per well. The CSCs were cultured for 14 days using the MammoCult Human Medium Kit (Stemcell Technologies, Vancouver, BC, Canada) according to the manufacturer’s instructions. Mammospheres were digested in trypsin/EDTA and centrifuged at 300 × g for 10 min. Then, the cells were resuspended and cultured for the next round of sphere formation. Cells from the sixth-generation spheres were used to analyze the efficiency of mammosphere formation. Formed spheres were counted manually, and representative images were obtained by microscopy.
To explore the interactions between LINC00589 and miRNAs, we performed a MS2 RNA pull-down assay, in which the MBP-MCP fusion protein recognizes MS2 hairpins. Breast cancer cells were transfected with MS2, LINC00589-MS2 or LINC00589-Mut-MS2 plasmids and harvested 48 h post-transfection. Then, breast cancer cell lysates were incubated with MBP-MCP-coated amylase resin (prepared at 4 °C) for 8 h. Bound LINC00589-MS2 complexes were eluted with 100 μl buffer containing 20 mM maltose after incubation and extensive washing. The eluted complexes were used to identify LINC00589-associated miRNAs. qRT-PCR analysis was performed to identify the miRNAs associated with LINC00589.
TR breast cancer cells were infected with LINC00589 overexpression or control lentivirus and cultured for cell expansion. Female athymic BALB/c nude mice (4–6 weeks, 20 g) were purchased from the Experimental Animal Center, Chinese Academy of Science (Shanghai, China). Mice were housed in a pathogen-free animal facility at 22 ± 2 °C under controlled 12-h light/dark cycles. Mice were given regular chow or special custom diets when indicated and had access to autoclaved water ad libitum. Animals were grouped by simple randomization using a random number table. To form orthotopic mammary fat pad tumors, the surgical area was depilated and swabbed with 70% ethanol before making an incision in the skin of the breast. Next, 4 × 106 cells were subcutaneously injected into the mammary fat pad area in situ. When the volume of xenograft tumors reached 50 mm3, miR-100 mimic, miR-452 mimic or control miRNA mimic complexed with a lipid-based delivery agent (15 μg/injection, twice a week) were injected into the tumors 72 h prior to intravenous injection of trastuzumab (10 mg/kg, twice a week). Tumor volumes were monitored every 3 days for 7 weeks according to the formula: tumor volume (mm3) = length × width2/2. To further investigate the function of LINC00589-initiated ceRNA networks in vivo, another group of nude mice were injected with LINC00589-Luc and control Luc lentivirus-infected TR breast cancer cells and were administered the same treatments described above. Five minutes after administration of 1.5 mg luciferin (Gold Biotech, St Louis, MO, USA), the luciferase activity of tumor xenografts was monitored using an IVIS@ Lumina II system (Caliper Life Sciences, Hopkinton, MA, USA), which was repeated every 3 days. At the end of the experiments, all mice were given euthanasia by amobarbital injection of three times standard doses, and the tumor tissues were isolated and snap-frozen for mRNA expression analysis. The investigators had no bias and special tendency in the processing of animal experiments. All animal experiments were performed according to the guidelines of the Institutional Animal Care and Use Committee of the General Hospital of Xinjiang Command and were approved by the local animal experiments ethical committee.
Expression of LINC00589 in paraffin-embedded breast cancer tissues was determined by in situ hybridization (ISH) experiments. Briefly, after dewaxing and rehydration, the paraffin-embedded breast cancer tissues were digested with 10% trypsin for 40 min at room temperature and then were hybridized with the digoxin-modified LINC00589 probe (5’-TACTGTCTCTCCTCGGAGCAGGATTCCATCTTT-3’, Exiqon, Vedbaek, Denmark) at 55 °C overnight, followed by incubation with antibody against digoxin (Roche) and staining. ISH signals for LINC00589 expression were determined in the form of the mean optical density using the AxioVision Rel.4.6 computerized image analysis system. The staining index (SI) was determined based on both the intensity and proportion of LINC00589. The expression of LINC00589 was evaluated using the SI and scored as 0, 1, 2, 3, 4, 6, or 9. LINC00589 expression was defined as high (SI ≥ 4) or low (SI < 4) based on the distribution of the frequency of SI scores.
Data were analyzed using SPSS 19.0 software for windows. The results are presented as the mean ± SD. Statistical analysis was performed using the Student’s t test or analysis of variance (ANOVA) to compare means of the two groups or multiple groups of in vitro and in vivo data. The χ2 test was used to compare percentages or the association between LINC00589 and clinicopathological parameters. Multivariate Cox regression was used to analyze independent prognostic factors for overall survival in HER2-positive breast cancer patients. The Spearman correlation test was performed to identify the correlation between the mRNA expression of target genes. Receiver Operating Characteristic (ROC) curve analysis was conducted, and the cutoff value was used to discriminate trastuzumab-responding or non-responding HER2-positive breast cancer patients. All data graphs were drawn using the PRISM Software, Version 9 (GraphPad Software, CA, USA). A value of P < 0.05 was considered statistically significant.
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PMC9618055 | 36251989 | Yuxin Song,Xuan-Nhi Nguyen,Anuj Kumar,Claire da Silva,Léa Picard,Lucie Etienne,Andrea Cimarelli | Trim69 is a microtubule regulator that acts as a pantropic viral inhibitor | 17-10-2022 | virus,interferon,innate response,HIV,SARS-CoV2 | Significance The identification and characterization of proteins capable of inhibiting a large spectrum of viruses is a key objective for our comprehension of the infection process and for the development of broad-spectrum antivirals. Searching for interferon-regulated modulators of HIV-1 infection, we identified tripartite motif protein (Trim69) as a protein endowed with broad antiviral properties. Trim69 acts against HIV-1, primate lentiviruses and the negative- and positive-strand RNA viruses vesicular stomatitis virus and severe acute respiratory syndrome coronavirus 2 by commandeering a general program of microtubule stabilization that is commonly observed in interferon-stimulated myeloid cells and that is detrimental to infection. In a panorama in which several viruses use the cytoskeleton for their own advantage, our study identifies Trim69 as a major innate defense factor that remodels microtubules to limit viral spread. | Trim69 is a microtubule regulator that acts as a pantropic viral inhibitor
The identification and characterization of proteins capable of inhibiting a large spectrum of viruses is a key objective for our comprehension of the infection process and for the development of broad-spectrum antivirals. Searching for interferon-regulated modulators of HIV-1 infection, we identified tripartite motif protein (Trim69) as a protein endowed with broad antiviral properties. Trim69 acts against HIV-1, primate lentiviruses and the negative- and positive-strand RNA viruses vesicular stomatitis virus and severe acute respiratory syndrome coronavirus 2 by commandeering a general program of microtubule stabilization that is commonly observed in interferon-stimulated myeloid cells and that is detrimental to infection. In a panorama in which several viruses use the cytoskeleton for their own advantage, our study identifies Trim69 as a major innate defense factor that remodels microtubules to limit viral spread.
In recent years, the search for cellular effectors directed against the HIV-1 retrovirus has drawn particular interest, spurring a number of genetic screens that are defining the complex cellular landscape in which HIV replication occurs. In a search for novel interferon-stimulated genes (ISGs) that could interfere with HIV-1 infection, we have examined the weight of more than 400 ISGs during HIV-1 infection of interferon (IFN)-stimulated macrophage-like cells (THP-1-PMA differentiated) that represent a cellular context particularly restrictive to infection (1). Using a three-layer screen approach that combines functional and evolutionary analyses, we have identified the tripartite motif protein (Trim69) as a regulator of the early phases of the life cycle of HIV-1. Trim69 is a poorly studied member of the large Trim family that includes more than 80 members largely devoted to innate immunity regulation (2). In the past, Trim69 had been controversially linked to apoptosis and p53 signaling (3–6), but evidence of strong positive selection suggested this protein could be potentially involved in a host-pathogen genetic conflict (7). Evidence that this could be the case came only in 2018, when Trim69 was reported to inhibit Dengue virus replication through the degradation of the viral nonstructural protein 3 (NS3) (8). While this finding could not be confirmed by a subsequent study (9), two studies identified Trim69 as an inhibitor of the vesicular stomatitis virus (VSV) (9, 10). The underlying mechanism of viral inhibition and more importantly the spectrum of viruses that can be targeted by this protein remain unclear. In this study, we determine that in myeloid cells, Trim69 is capable of inhibiting not only HIV-1, but also other primate lentiviruses, in addition to the negative-strand RNA virus VSV, and to the positive-strand RNA coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). These viruses are inhibited to varying degrees depending on the combination between virus and cell type, but overall, our results indicate that Trim69 is an antiviral factor endowed with a broad spectrum of inhibition. Trim69 inhibits the early phases of the above-mentioned viruses at entry and early post-entry phases and, in particular, reverse transcription, entry-primary transcription, and RNA replication for HIV-1, VSV, and SARS-CoV-2, respectively. Antiviral inhibition is linked to the ability of Trim69 to induce the accumulation of stable microtubules, and property that we described here is a previously unrecognized common cellular response to IFN stimulation and distinct from the microtubule (MT) stabilization induced by Taxol. Overall, our results uncover Trim69 as a key modulator of MT dynamics during viral infection and further stress the importance that the control of the cytoskeleton network plays during the host-pathogen conflicts that underlie viral infection.
The early phases of HIV-1 infection are inhibited by type 1 interferons and more potently so in cells of the myeloid lineage (1). To identify novel effectors of this antiviral response, we individually silenced >400 ISGs described in the Interferome database by lentiviral-mediated short hairpin RNA (shRNA) transduction in THP-1 cells that were split in two then differentiated into a macrophage-like state with phorbol 12-myristate 13-acetate (PMA). We then examined the susceptibility of these cells to HIV-1 in the presence or absence of IFN-α, using single-round of infection competent viruses coding a Firefly Luciferase reporter. IFN-α modulators were defined as silenced genes that modulated the no IFN-α/IFN-α infectivity ratio (referred to here as the IFN defect) when normalized to the library average value (scheme of Fig. 1A). To exclude modulators of IFN-α signaling rather than of HIV-1 infection per se, a secondary screen was carried out on primary IFN-α hits, by measuring the effects of gene silencing on the production of IP10, a well-described ISG, in THP-1-PMA cells stimulated with IFN-α. A tertiary evolutionary analysis was then performed on remaining hits to prioritize them according to their evolutionary history and their potential roles in host-pathogen evolutionary genetic conflicts, using the DGINN pipeline (detection of genetic innovation) (11). Under these conditions, the primary functional screen yielded 22 genes with significant impact on HIV-1 infection in the presence of IFN-α (2.5-fold changes, Fig. 1B and SI Appendix, Fig. S1) for individual values). Given that the IFN defect ratio can also be influenced by the susceptibility to infection of non-stimulated cells (due for instance to different basal levels of expression of the different genes), candidate genes were re-categorized as anti- or pro-viral factors, according to the susceptibility of silenced THP-1-PMA cells during HIV-1 infection in the absence of IFN (SI Appendix, Fig. S1). Given that silencing of AIM2, TOP1, IGF2BP3, and ABTB2 led to higher HIV-1 infection of unstimulated THP-1-PMA cells, these proteins were re-categorized as antiviral factors. Conversely, silencing of AKAP2 and TNPO3, a well-known HIV cofactor (12) that is not IFN-stimulated but that was introduced in our screen to serve as a sentinel gene, led to lower infection rates in unstimulated cells, so that these proteins were re-categorized as pro-viral factors. The remaining genes did not exhibit significant changes in infectivity in unstimulated cells and were thus not re-categorized. Overall, this analysis led to the identification of five pro- and 17 anti-viral modulators (SI Appendix, Fig. S1) that were then analyzed for their ability to interfere with IP10 secretion upon IFN-α stimulation (Fig. 1C). Under these conditions, silencing of STAT2, IRF9, as well as of the CBP/p300-interacting transactivator with glutamic acid/aspartic acid-rich carboxyl-terminal domain 2 (CITED2) and the lysophosphatidic acid receptor 1 (LPAR1) significantly decreased IP10 secretion (Fig. 1C). While the results obtained with STAT2 and IRF9 were expected, our results highlight a role for CITED2 and LPAR1 in the establishment of an IFN state. For the purpose of this study, the four above-mentioned genes were discarded from subsequent analyses. Most antiviral restriction factors are engaged into molecular evolutionary arms-races with pathogens (13). They therefore present signatures of these conflicts that can be identified by studying the evolution of their orthologs in host sequences. To identify the candidate genes that present such genetic innovations during primate evolution, we screened the functionally retrieved hits with the DGINN pipeline (11), which automatically reconstructs multiple sequence alignments and phylogenies and that identifies events of gene duplication, recombination as well as marks of positive selection based on a coding sequence (Fig. 1D). Automated or manually retrieved sequences of primary hits were used as query for DGINN. We performed analyses in two steps: 1) phylogenetic analyses, and 2) positive selection analyses that combine five methods from PAML Codeml, HYPHY BUSTED, and bpp packages (see Materials and Methods). We found five genes with evidence of strong positive selection in primates, detected by at least four methods (Fig. 1D and SI Appendix, Fig. S2 A–C): absent in melanoma 2 (AIM2), the leukocyte immunoglobulin like receptor LILRA3, the CASP8- and FADD-like apoptosis regulator (CFLAR), the 6-methylated adenosine (m6A) reader IGF2BP3, and Trim69, a poorly studied member of the TRIM family. Five additional genes exhibited some evidence of positive selection, detected by two to three methods: the bromodomain adjacent to zinc finger domain 1A (BAZ1A), the DNA topoisomerase I (TOP1), the A-kinase anchoring protein 2 (AKAP2), the proteasome subunit beta type-8 (PSMB8), and transportin 3 (TNPO3) (Fig. 1D).
Among the top hits, we focused on Trim69, a prototypical member of the C-IV subfamily of Trims (14), which includes Trim5α which is a well-established antiviral factor against primate lentiviruses. Trim69 possesses a RING, B-box, and coiled-coil (CC) domain followed by a PRY-SPRY domain. Trim69 bears 48% and 44% identity with Trim67 and Trim52/Trim47 following a Blast analysis, while its SPRY domain is closer to Trim39 and Trim21 (46% and 45% identity, respectively, SI Appendix, Fig. S3A). Comparison between the in silico modeled SPRY domains of Trim69 and Trim5α indicates that Trim69 lacks the protruding variable loops (V1, V2, and V3) with which Trim5α contacts specifically retroviral capsids, suggesting an altogether different method of action (SI Appendix, Fig. S3B). Despite having being first identified as a spermatids-specific gene (4), Trim69 exhibits a heterogeneous pattern of expression in different cell lines and primary blood cell types tested (SI Appendix, Fig. S4A). In THP-1-PMA cells, Trim69 is expressed already at basal levels and is extremely sensitive to IFN-I (α/β) stimulation, similarly to primary macrophages. To first validate the results obtained in our screen, we generated Trim69 knockout (KO) THP-1 cells by CRISPR-/Cas9-mediated gene deletion (SI Appendix, Fig. S4B for cell viability) and then challenged macrophage-differentiated cells with HIV-1, HIV-2, or SIVMAC lentiviruses in the presence or absence of IFN-α. Under these conditions, removal of Trim69 increased the susceptibility of target cells to the three primate lentiviruses in the presence, but also in the absence of IFN-α (from 2.25 to 3.35 on average), in line with its expression pattern (Fig. 1E). Conversely, and as expected for an antiviral factor, the susceptibility of THP-1 PMA cells stably expressing TRIM69 under the control of doxycycline (overexpressing [OE]; SI Appendix, Fig. S4B for cell viability) was decreased to the same extent during infection with these retroviruses (Fig. 1F). Given that in this set of experiments viruses were pseudotyped with the pantropic envelope VSVg, Trim69 KO and OE cells were also challenged with an HIV-1 virus bearing the R5-tropic HIV-1 envelope JR-FL and similar results were obtained, indicating that the antiviral effects of Trim69 were envelope-independent (Fig. 1G). In agreement with two previous reports (9, 10), Trim69 also potently modulated replication of the negative-strand RNA virus, VSV (Fig. 1H). Next, we examined the ability of Trim69 to interfere with the replication of SARS-CoV-2, a positive-strand RNA coronavirus, in epithelial lung Calu-3 cells. Under these conditions, Calu3 expressing Trim69 were strongly protected from SARS-CoV-2 infection (Fig. 1I), overall indicating that Trim69 is able to interfere with a broad range of RNA viruses that extends from retroviruses to positive- and negative-strand RNA viruses (SI Appendix, Fig. S5 for non-normalized values of infection with the different viruses). Given that SARS-CoV-2 entry in Calu3 cells can occur at both plasma membrane and endo-lysosomes (15), we used Camostat to inhibit TMPRSS2 and to skew virus entry via the latter entry route. Albeit with lower efficiency, Trim69 remained able to inhibit SARS-CoV-2 infection, further strengthening the contention that the virus entry pathway is not a major determinant of susceptibility to Trim69 (SI Appendix, Fig. S6A). Of note, Trim69 overexpression did not inhibit lentivirus infection in HEK293T cells, in agreement with one previous report (9). Inhibition of VSV replication occurred in these cells, albeit to a lower magnitude with respect to what observed in the case of THP-1-PMA cells (SI Appendix, Fig. S6B), suggesting that the extent of the effects of Trim69 may be governed by a combination of both virus and cell-type-specific features.
To identify the step(s) at which Trim69 interfered with virus replication, Trim69-overexpressing THP-1-PMA or Calu3 cells were challenged with the different viruses and the early phases specific to each one were analyzed (schematically resumed in Fig. 2 A–C). In the case of Lentiviruses, THP-1-PMA OE cells were challenged with R5-HIV-1, and virus entry into the cell was measured using the EURT assay [entry/uncoating assay based on core-packaged RNA availability and Translation (16)], which is based on the direct translation of a Luciferase-bearing HIV-1 minigenome incorporated into virion particles (Fig. 2A). Under these conditions, Trim69 did not affect HIV-1 entry. On the contrary, Trim69 impaired the accumulation of all HIV-1 viral DNA intermediates tested (2.7-fold for MSSS to 5.3-fold for 2LTRs), indicating that this protein inhibits reverse transcription rapidly after the entry of viral capsids in target cells. VSV is a negative-strand RNA virus and, as such, it undergoes an obligate round of primary transcription after cell entry that is required for the translation of P, N, and L proteins that in turn ignite viral RNA replication [for a review see (17), Fig. 2B]. Given that translation from primary transcripts is required for viral RNA replication, the translation inhibitor cycloheximide (CHX) can be used to distinguish primary transcription from overall RNA replication. Contrarily to what was observed for HIV-1, TRIM69 exerted a measurable defect in VSV entry into the cell (2.9-fold), and this defect increased during primary transcription (6.5-fold) and overall RNA replication levels (34-fold). Thus, in the case of VSV, Trim69 imparts successive cumulative antiviral effects. SARS-CoV-2 is a positive-strand RNA virus and, as such, its genome can be directly translated in viral proteins that ignite RNA replication [for a review see (18)]. When Calu-3 cells overexpressing Trim69 were challenged with SARS-CoV-2, no major defects were observed at entry, but a defect in RNA replication was clearly observable by 6 h post-infection (Fig. 2C, 3.6-fold, in line with the replication defect observed). Overall, these results indicate that Trim69 inhibits the early steps of viral replication of different viruses at slightly distinct steps: at a post entry step that affects the efficiency of reverse transcription and of viral RNA replication in the case of lentiviruses and of the SARS-CoV-2 coronavirus, and at both entry and post entry events in the case of VSV. In the latter, the efficiency of virus entry in cells expressing Trim69 exhibits a small but detectable defect, together with a more apparent defect in primary transcription.
As a first step to decorticate the functions of Trim69, its intracellular distribution was determined by confocal microscopy. In agreement with a previous report (10), Trim69 adopts a filamentous distribution in the cell cytoplasm which is particularly marked in THP-1-PMA cells (Fig. 3A). Exploratory analyses indicated that Trim69 exhibited little to no colocalization with a series of cellular markers in HEK293T cells (actin, ATG-3, ER, or CLIP170), with the exception of α-tubulin (SI Appendix, Fig. S7). Indeed, Trim69 colocalized strongly in all cell tested with stable microtubules (defined here upon staining with either anti-acetylated or anti-detyrosinated tubulin-specific antibodies) which represent a subset of MTs (Fig. 3B). Interestingly, Trim69-expressing cells exhibited higher levels of stable microtubules, suggesting that this protein could promote their formation. To put this observation in the context of IFN-I responses, we determined whether microtubule stabilization could be observed in response to IFN-I in different primary blood cells (Fig. 3C and SI Appendix, Fig. S8 for separated channels and quantification of stable MTs on a per cell basis). Stimulation of primary blood monocytes as well as of monocyte-derived macrophages and dendritic cells (DCs) led to a pronounced up-regulation of stable microtubules within 24–48 h (monocytes > macrophages > DCs). In contrast, such accumulation was not observed in activated primary lymphocytes. Overall, these results indicate that IFN leads to a program of microtubule stabilization that may be particularly important for antiviral responses in cells of myeloid origins. To determine the role that Trim69 may play in this program, we used THP-1 (cells of myeloid origins more amenable to genetic manipulation). In control cells, stable MTs were present at low levels in the absence of IFN stimulation, but they increased over time following IFN-α stimulation similarly to what was observed in primary macrophages (Fig. 3D and SI Appendix, Fig. S9 for complete pictures and quantification of stable MTs on a per-cell basis). As expected from our previous observations, expression of Trim69 (OE) increased the basal levels of stable microtubules accumulation and this was further stimulated by IFN-α. However, the accumulation of stable microtubules was severely diminished in Trim69 KO cells stimulated with IFN-α, indicating that the accumulation of stable microtubules is an integral part of the antiviral IFN response and that Trim69 plays an instrumental role in this program. Taxol is a compound that also leads to the accumulation of stable MTs in cells. To determine whether the program of MTs stabilization induced by Trim69 was specific or similar to the one induced by Taxol, control or Trim69 overexpressing THP-1-PMA cells were incubated for 24 h with either Taxol or nocodazole (that instead depolymerizes MTs), and then cells were either analyzed by confocal microscopy or challenged with VSV prior to flow cytometry (SI Appendix, Fig. S10). Under these conditions, Taxol exerted a slightly positive effect on VSV infectivity in control cells, while nocodazole led to a specular decrease, in line with previous reports (19, 20). However, neither Taxol nor nocodazole modified the extent of virus inhibition driven by Trim69, strongly supporting the notion that Trim69 drives a specific program of MTs stabilization that is distinct from the one induced by Taxol. Of interest, confocal microscopy analysis revealed differences in the arrangement of stable microtubules that accumulate in the presence of Taxol or Trim69 (SI Appendix, Fig. S10, compare the arrangement of stable microtubules in the zoomed control cell treated with Taxol or in the Trim69 overexpressing one), further corroborating the notion of specificity in the action of Trim69.
To determine whether Trim69 could physically associate to microtubules, a microtubule polymerization/sedimentation assay (21) was performed on THP-1 cell lysates expressing or not expressing Trim69. In this assay, tubulin is induced into polymerization upon incubation with GTP and Taxol, and stabilized microtubules are then purified by ultracentrifugation, along with associated cellular proteins (scheme of Fig. 4A). Accordingly, a large fraction of microtubules sedimented in the pellet fraction upon Taxol stabilization and, when present, Trim69 was also present in this fraction, indicating that Trim69 was indeed physically associated to microtubules (Fig. 4A, fraction P). To determine whether this association was direct, Trim69 was purified from bacteria as a fusion protein with the glutathione-S-transferase protein (GST). Commercially available and pure tubulin was then incubated in the presence of GTP and Taxol before binding to either GST or GST-Trim69 (Fig. 4B). Under these conditions, Trim69 was able to interact with microtubules, indicating that the association between these two components is direct and not bridged by other factors.
Five different isoforms issued from alternative splicing have been recently described for trim69 (from the wild-type A to E) that contain extensive deletion in domains that are normally important for Trim family member’s functions. These isoforms were therefore expressed in THP-1-PMA cells prior to confocal microscopy analysis, or viral challenge with VSV (Fig. 5 A and B and SI Appendix, Fig. S11 for Western blot [WB] analysis and separated immunofluorescence (IF) channels). Under these conditions, isoforms B to E lost their ability to stimulate stable MTs and to protect target cells from viral challenge. Two additional mutations were then introduced in Trim69: mutations in two cysteine residues in the RING domain (C61A/C64A), in addition to the deletion of the PRY-SPRY domain that in certain Trim members represents the domain of interaction with cellular partners (ΔP-SPRY, Fig. 5 A and B and SI Appendix, Fig. S11, as above). Similarly, to what was observed with the Trim69 isoforms, both mutants lost their ability to drive stable MT accumulation and both were unable to prevent viral infection. To gather further insights on the ability of the different mutants not only to stimulate stable microtubules, but also to colocalize with α-tubulin, the Pearson’s coefficients between these two markers were determined (Fig. 5A). Colocalization with α-tubulin was lost for all mutants, suggesting that they have likely lost their ability to associate to microtubules in the first place. Similar results were also obtained in HEK293T cells that do not express Trim69 (SI Appendix, Fig. S12), thus excluding the possibility that the expression of endogenous Trim69 in THP-1 cells interfered with the analysis of the intracellular distribution and behavior of Trim69 mutants through heterodimerization. To assess the ability of Trim69 mutants to directly bind MTs also in vitro, we employed GST-pulldowns and compared the ability of wild-type (WT), C61A/C64A, and ΔP-SPRY Trim69-GST proteins to associate to pure microtubules. Under these conditions, a drastic loss of association was observed for the C61A/C64A mutant in agreement with the loss of MTs association observed by confocal microscopy (SI Appendix, Fig. S13A). However, the ΔP-SPRY Trim69 mutant remained able to associate to MTs, albeit with lower efficiency with respect to WT, somewhat unexpectedly given the distribution of this mutant in the cell. One plausible explanation for this difference could be that association of Trim69 to MTs is more stringent in cells than in vitro (differences in the relative concentration of the two components in the two systems, need to displace MT-bound factors, etc.). Finally, we tested the ability of the two above-mentioned Trim69 mutants to undergo self-ubiquitination, which is often used to measure the weight of the E3-ubiquitin ligase activity in Trim functions (22). To this end, cells were cotransfected with DNAs coding WT, C61A/C64A, and ΔP-SPRY Trim69 along with HA-ubiquitin in the presence of MG132, prior to cell lysis, immunoprecipitation of Trim69 and WB analysis (SI Appendix, Fig. S13B). Under these conditions, WT Trim69 was clearly ubiquitinated, similarly to the two mutants. While inactivation of the E3-ubiquitin ligase activity of Trim69 could have been expected upon inactivation of the two cysteines in position 61 and 64, it is important to remember that the RING domain is composed of seven cysteines and one histidine, the importance of which varies according to the Trim protein examined (22). Overall, these results would thus suggest that the E3-ubiquitin ligase activity of Trim69 is not important in the functions described here given that two loss of function mutants remain competent for their E3-ubiquitin ligase activity. However, given that the Trim69 cellular targets are unknown and that this assay measures only the ability of Trim69 to self-ubiquitinate, it remains possible that these mutants are inactive because they can no longer ubiquitinate their correct targets. Overall, the major conclusion of these results is that that the antiviral effects of Trim69 are intimately linked to its ability to drive the accumulation of stable MTs.
To increase the primate sequences available for phylogenetic analyses (7, 9), we de novo sequenced Trim69 from two additional monkey species, L’Hoest’s monkey (Cercopithecus l’hoesti) and Cotton-headed tamarin (Saguinus oedipus) (Fig. 6A), and we determined the evolutionary history of Trim69 inferred from 26 primate species and 22 simian species (Fig. 6 A and B and SI Appendix, Fig. S14). From these codon alignments, we used multiple methods to detect positive selection at the gene-wide and site-specific levels: BUSTED from HYPHY, Codeml from PAML and Bio++ analyses, all run from the DGINN platform, as well as MEME and FUBAR run from the Datamonkey server (see Materials and Methods for details and references). We confirmed that Trim69 has been under adaptive evolution (SI Appendix, Fig. S14). The site-by-site analyses on the simian alignment allowed us to identify eight sites under positive selection distributed through the protein domains, with three residues (158, 246, and 299) identified by at least two methods (Fig. 6 B and C and SI Appendix, Fig. S14). Because the soTrim69 was one of the most divergent simian sequences from hTrim69 (88.6% identity; Fig. 6B) and differed at seven of eight sites under positive selection, we cloned the soTrim69 gene to determine whether it exhibited distinct, or conserved, functionalities compared to its human counterpart. Stable THP-1-PMA cells expressing each of them were challenged with the indicated viruses prior to analysis by WB, confocal microscopy, and flow cytometry to quantify the extent of infection (Fig. 6D). Under these conditions, soTrim69 was capable of strong induction of stable microtubule formation and exhibited equivalent antiviral properties than hTrim69 upon challenge with VSV or distinct lentiviruses. Overall, these results indicate that the main functional properties are maintained in the soTrim69 ortholog underscoring their importance in the antiviral properties of Trim69. Our data also indicate that lentiviruses are unlikely to have driven positive selection in Trim69.
In this work, we describe Trim69 as the IFN-regulated protein that inhibits a diverse spectrum of viruses by promoting a global program of microtubule stabilization in the cell that is markedly distinct from the one induced by Taxol. By analyzing the response of several primary blood cell types to IFN-α, we show that this program is a previously unrecognized facet of the innate defense system in cells of the myeloid lineage. Furthermore, using THP-1 cells that are more amenable to genetic manipulation, we show that Trim69 plays an instrumental role in this response. Trim69 interferes with model members of the Retroviridae, Rhabdoviridae, and Coronaviridae families that collectively cover a large representation of replication modes among RNA viruses. The magnitude of inhibition depends on the combination between virus and cell type and in the cases of Lentiviruses, the antiviral effect is observed essentially in cells of the myeloid lineage. This is not unprecedented as other cellular factors inhibit HIV-1 in a cell-type-specific manner [e.g., SAMHD1, or APOBEC3A (23, 24)]. Viral inhibition occurs during the early phases of the different viruses’ life cycle, albeit with slight differences. In the case of HIV-1, inhibition occurs at reverse transcription after entry of viral complexes into the cell, and SARS-CoV-2 seems to follow the same inhibitory path, with no measurable defects in virus entry but an early defect in viral RNA replication. Instead, a small but non-negligible defect can be observed at the step of entry in the case of VSV, which is then followed by an additional defect during pioneer transcription, in line with a previous report (10). Although it remains formally possible that Trim69 targets viral components, the very diversity of viruses examined here lends support to the hypothesis that Trim69 modifies the cellular environment in a manner that is preclusive to viral infection. The hypothesis we privilege is that Trim69 interferes with the movements of viral complexes along microtubules. Indeed, HIV-1 viral cores have been visualized as sliding along microtubules with inward rates of 1 μm/s consistent with Dynein-dependent movement (20, 25), and adaptor proteins of this complex have been involved in this association [e.g., the Bicaudal D2 adaptor, BICD2 (26)]. Interestingly, interference with dynein-dependent movement has been associated to an early reverse transcription defect (27) which is consistent with the defects observed here for Trim69 (27). In the case of Rhabdoviruses, the phosphoprotein P, which is part of the viral nucleoprotein complex along with the nucleocapsid protein (N) and the RNA polymerase (L), does interact with the dynein light chain 8 (LC8) (28) and Coronaviruses accumulation in the perinuclear region that evolves in double membrane vesicles (DMVs) is also influenced by dynein (29). It is thus possible that dynein transport is inhibited in the presence of stable microtubules that are decorated with Trim69. Several viruses have been described to induce stable MTs formation: herpesviruses (30, 31), influenza virus (32), hepatitis E virus (33), and adenovirus (34, 35) as well as HIV-1 (36–38). While in some studies the functional importance of stable MT formation remains unclear, and according to our data could even represent a cellular response to viral infection, there are cases in which MT stabilization is associated to a pro-viral functionality. This seems to be the case for HIV-1 in which viral capsids that enter target cells induce prompt MT stabilization and mimic cellular cap-loading proteins to promote their loading onto MTs and their dynein-dependent transport toward the nucleus (36–38). It is therefore counterintuitive that IFN may induce a program of MT stabilization via Trim69, as this particular pool of MTs exhibit higher stability and higher cargo trafficking propensity with respect to the dynamic pool of MTs (39, 40). We believe that this dichotomy is, however, only apparent. First, while it is true that stable MTs can be preferentially used for cargo transport, the initial step of cargo loading is instead highly inefficient when it occurs on microtubules that are already detyrosinated (i.e., stable) (41). As such, it is easy to envision that according to the timing at which microtubules become stabilized with respect to virus entry, MT stabilization can lead to either pro- or anti-viral outcomes. Second, while stable MTs are often considered as a single homogeneous entity, the existence of several posttranslational modifications on MTs, as well as the existence of numerous MT-bound factors, is likely to result in a far more complex functional heterogeneity of microtubules. In line with this contention, our study indicates that as part of an antiviral response, Trim69 starts a program of microtubule stabilization that lead to structures whose functionality is markedly antiviral, while Taxol that also leads to the accumulation of stable MTs exerts slight positive effects on viral infection. Trim69 binds directly to MTs, but contrarily to other members of the Trim family, we could not identify a C-terminal subgroup one signature (COS domain), a 60-amino acid stretch that mediates MT binding in certain Trim family members (42). We then show that intact RING and PRY-SPRY domains are required for the antiviral activities of Trim69, as well as for microtubule stabilization. In light of these results, a most plausible model would therefore be that Trim69 associates to microtubules and contacts relevant substrates via its PRY-SPRY domain, leading to their degradation. However, the importance of the E3-ubiquitin ligase activity in the functions of Trim69 is debated, and the RING domain has also been shown to promote the protein’s multimerization (8, 10). In our hands, the loss-of-function mutants examined here are able to undergo self-ubiquitination, suggesting that the E3-ubiquitin ligase activity of Trim69 may not be involved in the phenotypes described here. We would, however, caution about a quick dismissal of the importance of this property because it remains formally possible that Trim69 mutants have lost their phenotype and because they can no longer target their correct cellular targets, a likely possibility with protein mutants that exhibit drastic changes in localization in the cell, when compared to wild-type. As such, we believe that the relevance of the E3-ubiquitin ligase activity of Trim69 in the phenotypes described here remains still open. Finally, three studies, including the present one, have accumulated evidence of ongoing genetic conflict in primate Trim69 (7, 9), as well as of polymorphism in the human population. Using a very divergent simian Trim69, our results seem to exclude Lentiviruses that have invaded primates as main drivers of this selective pressure. However, Trim69 has been described to physically interact with the P protein of VSV and with the NS3 protein of Dengue virus (8–10), suggesting that viral antagonists of these or of other viral families that remain to be discovered may have exerted a genetic pressure on Trim69. Overall, in a panorama filled with positive cofactors at the level of cytoskeleton, Trim69 represents for the moment the antiviral factor that opposes viral infection by regulating microtubule dynamics. Given the fact that these structures influence several aspects of the cellular physiology, these findings may bear implications that extend beyond viral infection.
Trim69 was identified as relevant hit following an shRNA-based genetic screen that examined the weight of 419 ISGs during HIV-1 infection in macrophage-like THP-1-PMA cells stimulated with interferon. Using stable cells overexpressing or knocked out for Trim69, we have demonstrated that Trim69 acted as broad antiviral factor. Using confocal microscopy and in vitro binding assays, we have then determined that Trim69 binds MTs and it drives a program of MT stabilization in cells required for its antiviral activities. Detailed methods are in SI Appendix. | true | true | true |
PMC9618475 | 36066780 | Mayyas Saleh Jaweesh,Mohamad Eid Hammadeh,Fatina W. Dahadhah,Mohammad A. Al Smadi,Mazhar Salim Al Zoubi,Manal Issam Abu Alarjah,Houda Amor | A lack of a definite correlation between male sub-fertility and single nucleotide polymorphisms in sperm mitochondrial genes MT-CO3, MT-ATP6 and MT-ATP8 | 06-09-2022 | Male sub-fertility,CO3,ATP6 and ATP8 | Background An inability of a man to conceive a potentially fertile woman after a year of unprotected intercourse is defined as male infertility. It is reported that 30–40% of males in their reproductive years have abnormalities in sperm production, either qualitatively or quantitatively, or both. However, genetic factors result in up to 15% of male infertility cases. The present study aimed to analyze the possible correlations between sub-fertility and polymorphisms in sperm mitochondrial CO3, ATP6 and ATP8 genes in sub-fertile men. Methods and results For 67 sub-fertile and 44 fertile male samples, Sanger sequencing of selected mitochondrial DNA genes was done. A total of twelve SNPs in the MT-CO3 gene: rs2248727, rs7520428, rs3134801, rs9743, rs28358272, rs2853824, rs2856985, rs2854139, rs41347846, rs28380140, rs3902407, and 28,411,821, fourteen SNPs in the MT-ATP6: rs2001031, rs2000975, rs2298011, rs7520428, rs9645429, rs112660509, rs6650105, rs6594033, rs6594034, rs6594035, rs3020563, rs28358887, rs2096044, and rs9283154, and ten SNPs in the MT-ATP8: rs9285835, rs9285836, rs9283154, rs8179289, rs121434446, rs1116906, rs2153588, rs1116905, rs1116907, and rs3020563 were detected in the case and control groups at different nucleotide positions. Only the rs7520428 in the MT-CO3 and MT-ATP6 showed a statistically significant difference between sub-fertile and fertile groups in the genotype’s and allele’s frequency test (P < 0.0001 for both). Conclusion The results of our study suggest that male sub-fertility is linked with rs7520428 SNP in MT-CO3 and MT-ATP6. The studied polymorphic variations in the MT-ATP8 gene, on the contrary, did not reveal any significant association with male sub-fertility. Supplementary Information The online version contains supplementary material available at 10.1007/s11033-022-07884-2. | A lack of a definite correlation between male sub-fertility and single nucleotide polymorphisms in sperm mitochondrial genes MT-CO3, MT-ATP6 and MT-ATP8
An inability of a man to conceive a potentially fertile woman after a year of unprotected intercourse is defined as male infertility. It is reported that 30–40% of males in their reproductive years have abnormalities in sperm production, either qualitatively or quantitatively, or both. However, genetic factors result in up to 15% of male infertility cases. The present study aimed to analyze the possible correlations between sub-fertility and polymorphisms in sperm mitochondrial CO3, ATP6 and ATP8 genes in sub-fertile men.
For 67 sub-fertile and 44 fertile male samples, Sanger sequencing of selected mitochondrial DNA genes was done. A total of twelve SNPs in the MT-CO3 gene: rs2248727, rs7520428, rs3134801, rs9743, rs28358272, rs2853824, rs2856985, rs2854139, rs41347846, rs28380140, rs3902407, and 28,411,821, fourteen SNPs in the MT-ATP6: rs2001031, rs2000975, rs2298011, rs7520428, rs9645429, rs112660509, rs6650105, rs6594033, rs6594034, rs6594035, rs3020563, rs28358887, rs2096044, and rs9283154, and ten SNPs in the MT-ATP8: rs9285835, rs9285836, rs9283154, rs8179289, rs121434446, rs1116906, rs2153588, rs1116905, rs1116907, and rs3020563 were detected in the case and control groups at different nucleotide positions. Only the rs7520428 in the MT-CO3 and MT-ATP6 showed a statistically significant difference between sub-fertile and fertile groups in the genotype’s and allele’s frequency test (P < 0.0001 for both).
The results of our study suggest that male sub-fertility is linked with rs7520428 SNP in MT-CO3 and MT-ATP6. The studied polymorphic variations in the MT-ATP8 gene, on the contrary, did not reveal any significant association with male sub-fertility.
The online version contains supplementary material available at 10.1007/s11033-022-07884-2.
Male infertility is related to about 20–70% of infertile couples [1]. It is reported that 30–40% of males in their reproductive years have abnormalities in sperm production, either qualitatively or quantitatively, or both. Oligozoospermia (low sperm count) and asthenozoospermia (low motility) are linked to male infertility in almost half of the cases [2]. Nevertheless, up to 15% of male infertility cases are due to genetic causes [3]. Although some of the related genes are yet unidentified, 40% of idiopathic male infertile cases are attributed to genetic predisposition [4]. Despite the possible role of nuclear genetic variants in male infertility, the sperm mitochondrial DNA genetic alterations are expected to have a significant impact on male infertility or certain subtypes of male infertility such as Asthenozoospermia [5, 6]. The mitochondrial DNA has only exons but no introns, which makes the genetic alteration in the mtDNA more deteriorating which could adversely affect the function of the mitochondrial cellular respiration and eventually a nonfunctional sperm. In Addition, the sperm mtDNA molecule has a very limited and basic repair mechanism because it lacks histones. Collectively, these unique features of the sperm mtDNA make it a vulnerable molecule to any genotoxic agent [7]. Reactive oxygen species (ROS) are by-products of the mitochondrial respiratory chain, the mutation rate generated by ROS is 10–100 times greater than nuclear DNA because the mitochondrial DNA is located in the mitochondrial matrix [8]. The mammalian sperm consists of approximately 80 mitochondria located in the midpiece of the sperm, ensuring proper function of the flagella and normal sperm motility for an efficient fertilization process [9, 10]. Therefore, any genetic alteration of this limited number of mitochondria can be related to the improper function of the sperm [11]. Several studies have reported that mitochondrial dysfunction has a significant impact on sperm structure and motility [12, 13]. In addition, several point mutations and deletions in the mtDNA have been reported in different infertile males [2, 14]. It has been observed that mutations within the mtDNA polymerase gene (POLG) are associated with infertility in men [15]. Moreover, large-scale mtDNA mutations were detected in patient groups with Asthenozoospermia [6, 16]. It was reported that mutations and polymorphisms in the ATP6 and ATP gene sequences are associated with breast cancer especially those of missense type. They cause mitochondrial function disruption [17]. (Grzybowska-Szatkowska, Ślaska, Rzymowska, Brzozowska, & Floriańczyk, 2014). Additionally, Kytövuori and her team in 2016 has reported a novel mutation m.8561C > G in MT-ATP6/8 in an overlapping region and they suggested that this pathogenic mutation is associated with cerebellar ataxia, diabetes mellitus, peripheral neuropathy, and hypogonadotropic hypogonadism and it causes impaired assembly and decreased ATP production of complex V [18]. A novel mutation of the MT-CO3 m.9396 G > A was found to affect the amino acid sequence in the complex IV of the mitochondrial respiratory chain in a patient with mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) [19]. Patients with mutations in the MT-ATP6 gene, which codes for a major part of the Fo proton channel, were identified with diseases caused by ATP synthase failure [20]. A common deletion referred to as class I deletion, flanked by 13-bp direct repeats at the following positions 13,447–13,459 and 8470–8482. This deletion removes genes coding for mitochondrial genes ND3, ND4, ND4L and some parts of ND5 as well as ATP6 and CO3 [16]. Consequently, the current study was conducted to examine a possible association between the MT-CO3, MT-ATP6 and MT-ATP8 genes variants and sperm parameters related to male sub-fertility.
One hundred and eleven individuals were included in this study including fertile and sub-fertile patients visiting the fertility clinic. Participants provided both oral and written permission by signing an informed consent form before sample collection. All volunteers of the case and control group provided an accurate medical history, including age (25–55 years old), smoking habits, chronic medical conditions, prescription medicine use and if there is a history of varicoceles. Semen samples were collected from candidates by masturbation using sterile containers after three days of sexual abstinence. The control group included 44 men with a normal semen analysis (Concentration: 15 × 106 spermatozoa/ml, total motility: ≥ 40%, progressive motility: ≥ 32%, and normal morphology: ≥ 4%) according to WHO guideline 2010, while the sub-fertile group included 67 men with abnormal semen analysis (Concentration < 15 × 106 spermatozoa/ml, motility < 40%, progressive motility < 32%), [Oligozoospermia, n = 10, Asthenozoospermia, n = 23, Teratozoospermia, n = 19, OAT, n = 15]. Patients with a clinical history of chronic diseases such as blood pressure and diabetes, varicoceles, undergoing chemotherapy or radiotherapy, involved in surgery in the reproductive system, or diagnosed with hypogonadotropic hypogonadism (hormonal disorder) and genetic disorders (Klinefelter's syndrome or Y-chromosome microdeletion) were not included in the study. The name of the committee that ethically approved this study is Jordanian Royal Medical Services-Human Research Ethics Committee The approval number is F3/1/ Ethics Committee /9126.
Before DNA extraction all semen samples were washed and purified using a density gradient procedure (45% and 90%) (PureCeption, Cooper surgical, Denmark) as described before [6]. Briefly, Samples were layered over the upper layer of the discontinuous gradient media, then centrifuged at 250 g for 20 min. Next, a sperm washing medium (Global Total HEPES media with HSA) was used to wash the sperm pellet (Cooper surgical, Denmark). A drop of the sperm solution was then examined by microscopic examination to assure the absence of non-sperm cells. Mitochondrial DNA isolation was carried out in two steps. The QIAamp DNA Mini Kit (QIAGEN, Germany) was used first to isolate the whole genomic DNA along with the mitochondrial DNA (The extraction was done according to the instructions recommended by the kit). In the second step, we used the REPLI-g Mitochondrial DNA Kit (QIAGEN, Germany) to isolate and amplify only mitochondrial DNA from the samples (The extraction was done according to the instructions recommended by the kit) [5, 6].
Primers used for the amplification of the MT-CO3, MT-ATP6 and MT-ATP8 genes were designed using the UCSC website and the PRIME 3 software. They were ordered from the Microsynth Seqlab company (Göttingen, Germany) and based on the human mitochondrial sequence; accession number NC_012920, provided by the National Centre of Biotechnology Information (Table 1). A 30 µl PCR reaction mixture was prepared using MyTaqTMHS Red Mix Kit (Bioline, UK) according to the manufacturer’s instructions. Then, the amplification was performed on the thermocycler (C1000TM Thermal cycler, Bio-Rad, United States) using the program listed in Table 1. To verify the presence of the PCR product, 5 µl of the PCR samples were run on gel electrophoresis using 2% and 1.5% agarose (according to the size of the product) along with DNA Ladder (0.1–10.0 kb) (NE Biolabs, USA). They were run at 75 V for 1 h in 1 × TAE buffer then a red-safe stain was used to stain gels, and finally the DNA was imaged by Molecular Imager® Gel Doc™ XR (BIO-RAD, USA).
Samples were purified and sequenced using the Sanger sequencing method by Seqlab (Sequencing Laboratories, Göttingen, GmbH). Mutation surveyor software was used to identify the SNPs of the MT-CO3, MT-ATP6 and MT-ATP8. Thereafter, all resulting SNPs were genotyped using finch TV (Fig. 1).
Comparing genotypes and allele frequencies between the sub-fertile (case) and fertile (control) groups were conducted by using the Chi-square and Fisher's exact tests The genotype frequencies and statistically significant variations from the equilibrium were determined using the Hardy–Weinberg equilibrium test on the detected SNPs. To compare allele frequencies between sub-fertile (case) and fertile (control) groups, odds ratios (ORs) and confidence intervals (95% CIs) were used. If the P-value was less than 0.05, it was considered statistically significant. SPSS Version 22 for Mac was used to conduct statistical analysis.
In the current study, we considered the fertile group as men who had at least one child and presented normal semen analysis parameters [volume: 1.5 ml, sperm count: 15 million spermatozoa/ml; normal forms: 4%; progressive motility: 32%; total motility (progressive + nonprogressive): 40%, according to WHO guideline 2010], and we considered the sub-fertile group as those who failed to have children after minimum 1 year of regular unprotected sexual intercourse and at least had one abnormal sperm parameter under WHO (2010) criteria. Thus, subjects were divided into two groups: a control group (fertile, n = 44) and a case group (sub-fertile, n = 67). The age variance of the sub-fertile and fertile groups did not differ significantly in the study population (P = 0.225). The semen parameters, on the other hand, revealed significant differences between fertile and sub-fertile groups in the mean percentage of sperm concentration, total motility, and morphologically normal spermatozoa (P = 0.0001) (Table 2).
A total of twelve SNPs in the MT-CO3 gene: rs2248727, rs7520428, rs3134801, rs9743, rs28358272, rs2853824, rs2856985, rs2854139, rs41347846, rs28380140, rs3902407, and 28,411,821, fourteen SNPs in the MT-ATP6: rs2001031, rs2000975, rs2298011, rs7520428, rs9645429, rs112660509, rs6650105, rs6594033, rs6594034, rs6594035, rs3020563, rs28358887, rs2096044, and rs9283154, and ten SNPs in the MT-ATP8: rs9285835, rs9285836, rs9283154, rs8179289, rs121434446, rs1116906, rs2153588, rs1116905, rs1116907, and rs3020563 were detected in the case and control groups at different nucleotide positions. (Tables 3, S1, 4, S2, 5, S3). Nine of the SNPs detected in the MT-CO3 gene, 4 of the SNPs in the MT-ATP6 gene and 2 of the SNPs in the MT-ATP8 gene were synonyms variants. (Tables 3, 4, 5), whereas the following SNPs of the MT-ATP6 (rs2001031, rs2000975) and MT-ATP8 (rs1116906, rs1116905) were located to cause a missense variation in the coding protein. To determine whether the variations of MT-CO3, MT-ATP6 and MT-ATP8 were related to sub-fertility, we compared each of the genotypes and allele frequencies between the case and control groups. RS7520428 was found in both MT-CO3 and MT-ATP6 genes and both showed a significant difference in the genotype’s frequency test between sub-fertile and fertile groups (Figs. 1, 2). The rest of the SNPs showed no statistically significant association in frequencies of genotypes and alleles between the present MT-SNPs and male sub-fertility. Furthermore, the Hardy–Weinberg genotype frequency test was performed on all SNPs. Each of these SNPs deviated from HWE in a significant way (P < 0.0001). However, there was no statistically significant difference between sub-fertile males with asthenozoospermia, oligozoospermia, teratozoospermia, asthenoteratozoospermia, oligoasthenoteratozoospermia, and oligoteratozoospermia and those who were fertile (P > 0.05). We evaluated the genotype and allele frequencies of MT-CO3, MT-ATP6, and MT-ATP8 in sub-fertile and fertile groups to see if they were linked to male sub-fertility. Among the 12 MT-CO3, 14 MT-ATP6, and 10 MT-ATP8 detected single nucleotide polymorphisms (SNPs), only the rs7520428 in the MT-CO3 and MT-ATP6 showed a statistically significant difference between sub-fertile and fertile groups in the genotypes and allele’s frequency test (P < 0.0001 for both).
The mitochondrion is the powerhouse of eukaryotic cells because of its capacity to create ATP through oxidative phosphorylation [21]. The mitochondrial function as an energy source is important for sperm function. It was reported that reduced sperm motility is linked to abnormalities in sperm mitochondrial ultrastructure [22]. Any abnormality in mitochondrial respiratory activity will adversely affect the process of spermatogenesis, particularly the advancement of pachytene phases during meiosis and sperm production. Spermatogenic cells will undergo mitochondrial respiratory failure if large amounts of pathogenic mutant mtDNA accumulate in the testicular tissue. Any reduction in the mitochondrion's ability to generate energy during spermatogenesis causes meiotic arrest. Spermatocytes with a lack of oxygen will most likely not complete meiosis and will be killed by apoptosis [23]. Spermatozoa with defective mitochondria produce insufficient ATP and have elevated levels of reactive oxygen species (ROS) or free radicals. In an imbalanced system, the production of ROS and free radicals would cause significant damage to the mitochondria and mtDNA, affecting sperm motility and finally leading to male infertility [24]. More specifically, an inadequate molecular profile of all types of male infertility still remains. Since mitochondrial biogenesis is essential for the proper motility of the sperm, any variations in mtDNA, whether quantitative or qualitative, influence the spermatozoa's cellular functioning. Specific mtDNA deletions in sperm have been linked to poor sperm function. Multiple 7345 and 7599 bp mtDNA deletions have been linked to poor sperm motility [25]. Moreover, studying genetic variations such as SNPs might be a helpful genetic analysis to understand the molecular bases of idiopathic infertility in males. In many studies, certain SNPs have shown an association with certain disorders such as cancer and infertility [24]. This work aimed to investigate whether there was an association between polymorphisms in the mitochondrial genes MT-CO3, MT-ATP6 and MT-ATP8 and male sub-fertility. Accordingly, we performed direct sequencing to check for polymorphisms in the MT-CO3, MT-ATP6, and MT-ATP8 genes in sub-fertile and fertile males, 12 SNPs have been identified in the MT-CO3 gene (rs2248727, rs7520428, rs3134801, rs9743, rs28358272, rs2853824, rs2856985, rs2854139, rs41347846, rs28380140, rs3902407, and 28,411,821), 14 SNPs in the MT-ATP6 (rs2001031, rs2000975, rs2298011, rs7520428, rs9645429, rs112660509, rs6650105, rs6594033, rs6594034, rs6594035, rs3020563, rs28358887, rs2096044, and rs9283154) and 10 SNPs in the MT-ATP8 (rs9285835, rs9285836, rs9283154, rs8179289, rs121434446, rs1116906, rs2153588, rs1116905, rs1116907, and rs3020563). Amongst reported SNPs of the previously mentioned mitochondrial genes, rs7520428 was found in both MT-CO3 and MT-ATP6 genes and both showed a significant difference in the genotype’s frequency test between sub-fertile and fertile groups. In addition, the allele frequency of rs7520428 SNP (A634390G) in MT-CO3 and MT-ATP6 showed a significant association with male subfertility (P < 0.0001). Furthermore, the OR of the rs7520428 SNP was linked to a 40-fold greater risk of subfertility in men compared to fertile ones. This could indicate that expanding the number of wild-type alleles (A) or reducing the number of mutant alleles (G) at A634390G can effectively protect male fertility, whereas increasing the number of G alleles or decreasing A alleles can develop male subfertility. This genetic alteration could be related to male sub-fertility which required further investigation to understand its role in the function of the protein outcome. The current findings showed that nine of the SNPs detected in the MT-CO3 gene, 4 of the SNPs in the MT-ATP6 gene and 2 of the SNPs found in the MT-ATP8 gene were synonyms variants. Synonymous mutations have been hypothesized to have an impact on gene control and the establishment of disorders [26]. It has been found that synonymous variants possibly affect mRNA stability [26]. As a result, functional investigations on these synonymous variants in mtDNA are needed to reveal their potential involvement in sperm function and male sub-fertility. The rs2001031 (A8860G) and rs2000975 (A8701) are missense mutations that alter threonine to alanine and they were significantly associated with subfertility. On the other hand, the following SNPs were detected in the current study and were reported to be pseudogenes: MT-CO3 (rs7520428) MT-ATP6 (rs7520428, rs9645429, rs112660509, rs6650105, rs6594033, rs6594034, rs6594035, rs2096044, rs9283154) MT-ATP8 (rs9285835, rs9285836, rs9283154, rs8179289, rs2153588). Mughal and his group have reported that there was a significant association between the 15 bp deletion (at position 9390 to 9413) of cytochrome C oxidase III and human male infertility (P = 0.033) [27]. In our study, the RS2000975 (A8701C, G) showed no significant association with male sub-fertility in the Genotype frequency (P = 1.0000), on the other hand, a previous study has reported a significant association between A8701G variants and increased risk of fertilization failure [28]. The inconsistent findings are attributed to the genetic variations as well as the subtype of infertility in the study populations. For instance, a recent study reported a significant correlation between polymorphisms of the MT-CYB gene and sub-fertility in men. Particularly, rs527236194, rs28357373 and rs41504845 variants were found significantly related to the sub-fertility group [29]. In conclusion, further research on a larger sample size of different populations is needed to emphasize the role of the reported SNPs in male sub-fertility. In addition, functional studies will be very helpful in understanding the molecular impact of each specific SNP on the function of the protein outcome and the mitochondrial efficiency that may explain their role in sperm function.
Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 28 kb) | true | true | false |
PMC9618475 | 36066780 | Mayyas Saleh Jaweesh,Mohamad Eid Hammadeh,Fatina W. Dahadhah,Mohammad A. Al Smadi,Mazhar Salim Al Zoubi,Manal Issam Abu Alarjah,Houda Amor | A lack of a definite correlation between male sub-fertility and single nucleotide polymorphisms in sperm mitochondrial genes MT-CO3, MT-ATP6 and MT-ATP8 | 06-09-2022 | Male sub-fertility,CO3,ATP6 and ATP8 | Background An inability of a man to conceive a potentially fertile woman after a year of unprotected intercourse is defined as male infertility. It is reported that 30–40% of males in their reproductive years have abnormalities in sperm production, either qualitatively or quantitatively, or both. However, genetic factors result in up to 15% of male infertility cases. The present study aimed to analyze the possible correlations between sub-fertility and polymorphisms in sperm mitochondrial CO3, ATP6 and ATP8 genes in sub-fertile men. Methods and results For 67 sub-fertile and 44 fertile male samples, Sanger sequencing of selected mitochondrial DNA genes was done. A total of twelve SNPs in the MT-CO3 gene: rs2248727, rs7520428, rs3134801, rs9743, rs28358272, rs2853824, rs2856985, rs2854139, rs41347846, rs28380140, rs3902407, and 28,411,821, fourteen SNPs in the MT-ATP6: rs2001031, rs2000975, rs2298011, rs7520428, rs9645429, rs112660509, rs6650105, rs6594033, rs6594034, rs6594035, rs3020563, rs28358887, rs2096044, and rs9283154, and ten SNPs in the MT-ATP8: rs9285835, rs9285836, rs9283154, rs8179289, rs121434446, rs1116906, rs2153588, rs1116905, rs1116907, and rs3020563 were detected in the case and control groups at different nucleotide positions. Only the rs7520428 in the MT-CO3 and MT-ATP6 showed a statistically significant difference between sub-fertile and fertile groups in the genotype’s and allele’s frequency test (P < 0.0001 for both). Conclusion The results of our study suggest that male sub-fertility is linked with rs7520428 SNP in MT-CO3 and MT-ATP6. The studied polymorphic variations in the MT-ATP8 gene, on the contrary, did not reveal any significant association with male sub-fertility. Supplementary Information The online version contains supplementary material available at 10.1007/s11033-022-07884-2. | A lack of a definite correlation between male sub-fertility and single nucleotide polymorphisms in sperm mitochondrial genes MT-CO3, MT-ATP6 and MT-ATP8
An inability of a man to conceive a potentially fertile woman after a year of unprotected intercourse is defined as male infertility. It is reported that 30–40% of males in their reproductive years have abnormalities in sperm production, either qualitatively or quantitatively, or both. However, genetic factors result in up to 15% of male infertility cases. The present study aimed to analyze the possible correlations between sub-fertility and polymorphisms in sperm mitochondrial CO3, ATP6 and ATP8 genes in sub-fertile men.
For 67 sub-fertile and 44 fertile male samples, Sanger sequencing of selected mitochondrial DNA genes was done. A total of twelve SNPs in the MT-CO3 gene: rs2248727, rs7520428, rs3134801, rs9743, rs28358272, rs2853824, rs2856985, rs2854139, rs41347846, rs28380140, rs3902407, and 28,411,821, fourteen SNPs in the MT-ATP6: rs2001031, rs2000975, rs2298011, rs7520428, rs9645429, rs112660509, rs6650105, rs6594033, rs6594034, rs6594035, rs3020563, rs28358887, rs2096044, and rs9283154, and ten SNPs in the MT-ATP8: rs9285835, rs9285836, rs9283154, rs8179289, rs121434446, rs1116906, rs2153588, rs1116905, rs1116907, and rs3020563 were detected in the case and control groups at different nucleotide positions. Only the rs7520428 in the MT-CO3 and MT-ATP6 showed a statistically significant difference between sub-fertile and fertile groups in the genotype’s and allele’s frequency test (P < 0.0001 for both).
The results of our study suggest that male sub-fertility is linked with rs7520428 SNP in MT-CO3 and MT-ATP6. The studied polymorphic variations in the MT-ATP8 gene, on the contrary, did not reveal any significant association with male sub-fertility.
The online version contains supplementary material available at 10.1007/s11033-022-07884-2.
Male infertility is related to about 20–70% of infertile couples [1]. It is reported that 30–40% of males in their reproductive years have abnormalities in sperm production, either qualitatively or quantitatively, or both. Oligozoospermia (low sperm count) and asthenozoospermia (low motility) are linked to male infertility in almost half of the cases [2]. Nevertheless, up to 15% of male infertility cases are due to genetic causes [3]. Although some of the related genes are yet unidentified, 40% of idiopathic male infertile cases are attributed to genetic predisposition [4]. Despite the possible role of nuclear genetic variants in male infertility, the sperm mitochondrial DNA genetic alterations are expected to have a significant impact on male infertility or certain subtypes of male infertility such as Asthenozoospermia [5, 6]. The mitochondrial DNA has only exons but no introns, which makes the genetic alteration in the mtDNA more deteriorating which could adversely affect the function of the mitochondrial cellular respiration and eventually a nonfunctional sperm. In Addition, the sperm mtDNA molecule has a very limited and basic repair mechanism because it lacks histones. Collectively, these unique features of the sperm mtDNA make it a vulnerable molecule to any genotoxic agent [7]. Reactive oxygen species (ROS) are by-products of the mitochondrial respiratory chain, the mutation rate generated by ROS is 10–100 times greater than nuclear DNA because the mitochondrial DNA is located in the mitochondrial matrix [8]. The mammalian sperm consists of approximately 80 mitochondria located in the midpiece of the sperm, ensuring proper function of the flagella and normal sperm motility for an efficient fertilization process [9, 10]. Therefore, any genetic alteration of this limited number of mitochondria can be related to the improper function of the sperm [11]. Several studies have reported that mitochondrial dysfunction has a significant impact on sperm structure and motility [12, 13]. In addition, several point mutations and deletions in the mtDNA have been reported in different infertile males [2, 14]. It has been observed that mutations within the mtDNA polymerase gene (POLG) are associated with infertility in men [15]. Moreover, large-scale mtDNA mutations were detected in patient groups with Asthenozoospermia [6, 16]. It was reported that mutations and polymorphisms in the ATP6 and ATP gene sequences are associated with breast cancer especially those of missense type. They cause mitochondrial function disruption [17]. (Grzybowska-Szatkowska, Ślaska, Rzymowska, Brzozowska, & Floriańczyk, 2014). Additionally, Kytövuori and her team in 2016 has reported a novel mutation m.8561C > G in MT-ATP6/8 in an overlapping region and they suggested that this pathogenic mutation is associated with cerebellar ataxia, diabetes mellitus, peripheral neuropathy, and hypogonadotropic hypogonadism and it causes impaired assembly and decreased ATP production of complex V [18]. A novel mutation of the MT-CO3 m.9396 G > A was found to affect the amino acid sequence in the complex IV of the mitochondrial respiratory chain in a patient with mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) [19]. Patients with mutations in the MT-ATP6 gene, which codes for a major part of the Fo proton channel, were identified with diseases caused by ATP synthase failure [20]. A common deletion referred to as class I deletion, flanked by 13-bp direct repeats at the following positions 13,447–13,459 and 8470–8482. This deletion removes genes coding for mitochondrial genes ND3, ND4, ND4L and some parts of ND5 as well as ATP6 and CO3 [16]. Consequently, the current study was conducted to examine a possible association between the MT-CO3, MT-ATP6 and MT-ATP8 genes variants and sperm parameters related to male sub-fertility.
One hundred and eleven individuals were included in this study including fertile and sub-fertile patients visiting the fertility clinic. Participants provided both oral and written permission by signing an informed consent form before sample collection. All volunteers of the case and control group provided an accurate medical history, including age (25–55 years old), smoking habits, chronic medical conditions, prescription medicine use and if there is a history of varicoceles. Semen samples were collected from candidates by masturbation using sterile containers after three days of sexual abstinence. The control group included 44 men with a normal semen analysis (Concentration: 15 × 106 spermatozoa/ml, total motility: ≥ 40%, progressive motility: ≥ 32%, and normal morphology: ≥ 4%) according to WHO guideline 2010, while the sub-fertile group included 67 men with abnormal semen analysis (Concentration < 15 × 106 spermatozoa/ml, motility < 40%, progressive motility < 32%), [Oligozoospermia, n = 10, Asthenozoospermia, n = 23, Teratozoospermia, n = 19, OAT, n = 15]. Patients with a clinical history of chronic diseases such as blood pressure and diabetes, varicoceles, undergoing chemotherapy or radiotherapy, involved in surgery in the reproductive system, or diagnosed with hypogonadotropic hypogonadism (hormonal disorder) and genetic disorders (Klinefelter's syndrome or Y-chromosome microdeletion) were not included in the study. The name of the committee that ethically approved this study is Jordanian Royal Medical Services-Human Research Ethics Committee The approval number is F3/1/ Ethics Committee /9126.
Before DNA extraction all semen samples were washed and purified using a density gradient procedure (45% and 90%) (PureCeption, Cooper surgical, Denmark) as described before [6]. Briefly, Samples were layered over the upper layer of the discontinuous gradient media, then centrifuged at 250 g for 20 min. Next, a sperm washing medium (Global Total HEPES media with HSA) was used to wash the sperm pellet (Cooper surgical, Denmark). A drop of the sperm solution was then examined by microscopic examination to assure the absence of non-sperm cells. Mitochondrial DNA isolation was carried out in two steps. The QIAamp DNA Mini Kit (QIAGEN, Germany) was used first to isolate the whole genomic DNA along with the mitochondrial DNA (The extraction was done according to the instructions recommended by the kit). In the second step, we used the REPLI-g Mitochondrial DNA Kit (QIAGEN, Germany) to isolate and amplify only mitochondrial DNA from the samples (The extraction was done according to the instructions recommended by the kit) [5, 6].
Primers used for the amplification of the MT-CO3, MT-ATP6 and MT-ATP8 genes were designed using the UCSC website and the PRIME 3 software. They were ordered from the Microsynth Seqlab company (Göttingen, Germany) and based on the human mitochondrial sequence; accession number NC_012920, provided by the National Centre of Biotechnology Information (Table 1). A 30 µl PCR reaction mixture was prepared using MyTaqTMHS Red Mix Kit (Bioline, UK) according to the manufacturer’s instructions. Then, the amplification was performed on the thermocycler (C1000TM Thermal cycler, Bio-Rad, United States) using the program listed in Table 1. To verify the presence of the PCR product, 5 µl of the PCR samples were run on gel electrophoresis using 2% and 1.5% agarose (according to the size of the product) along with DNA Ladder (0.1–10.0 kb) (NE Biolabs, USA). They were run at 75 V for 1 h in 1 × TAE buffer then a red-safe stain was used to stain gels, and finally the DNA was imaged by Molecular Imager® Gel Doc™ XR (BIO-RAD, USA).
Samples were purified and sequenced using the Sanger sequencing method by Seqlab (Sequencing Laboratories, Göttingen, GmbH). Mutation surveyor software was used to identify the SNPs of the MT-CO3, MT-ATP6 and MT-ATP8. Thereafter, all resulting SNPs were genotyped using finch TV (Fig. 1).
Comparing genotypes and allele frequencies between the sub-fertile (case) and fertile (control) groups were conducted by using the Chi-square and Fisher's exact tests The genotype frequencies and statistically significant variations from the equilibrium were determined using the Hardy–Weinberg equilibrium test on the detected SNPs. To compare allele frequencies between sub-fertile (case) and fertile (control) groups, odds ratios (ORs) and confidence intervals (95% CIs) were used. If the P-value was less than 0.05, it was considered statistically significant. SPSS Version 22 for Mac was used to conduct statistical analysis.
In the current study, we considered the fertile group as men who had at least one child and presented normal semen analysis parameters [volume: 1.5 ml, sperm count: 15 million spermatozoa/ml; normal forms: 4%; progressive motility: 32%; total motility (progressive + nonprogressive): 40%, according to WHO guideline 2010], and we considered the sub-fertile group as those who failed to have children after minimum 1 year of regular unprotected sexual intercourse and at least had one abnormal sperm parameter under WHO (2010) criteria. Thus, subjects were divided into two groups: a control group (fertile, n = 44) and a case group (sub-fertile, n = 67). The age variance of the sub-fertile and fertile groups did not differ significantly in the study population (P = 0.225). The semen parameters, on the other hand, revealed significant differences between fertile and sub-fertile groups in the mean percentage of sperm concentration, total motility, and morphologically normal spermatozoa (P = 0.0001) (Table 2).
A total of twelve SNPs in the MT-CO3 gene: rs2248727, rs7520428, rs3134801, rs9743, rs28358272, rs2853824, rs2856985, rs2854139, rs41347846, rs28380140, rs3902407, and 28,411,821, fourteen SNPs in the MT-ATP6: rs2001031, rs2000975, rs2298011, rs7520428, rs9645429, rs112660509, rs6650105, rs6594033, rs6594034, rs6594035, rs3020563, rs28358887, rs2096044, and rs9283154, and ten SNPs in the MT-ATP8: rs9285835, rs9285836, rs9283154, rs8179289, rs121434446, rs1116906, rs2153588, rs1116905, rs1116907, and rs3020563 were detected in the case and control groups at different nucleotide positions. (Tables 3, S1, 4, S2, 5, S3). Nine of the SNPs detected in the MT-CO3 gene, 4 of the SNPs in the MT-ATP6 gene and 2 of the SNPs in the MT-ATP8 gene were synonyms variants. (Tables 3, 4, 5), whereas the following SNPs of the MT-ATP6 (rs2001031, rs2000975) and MT-ATP8 (rs1116906, rs1116905) were located to cause a missense variation in the coding protein. To determine whether the variations of MT-CO3, MT-ATP6 and MT-ATP8 were related to sub-fertility, we compared each of the genotypes and allele frequencies between the case and control groups. RS7520428 was found in both MT-CO3 and MT-ATP6 genes and both showed a significant difference in the genotype’s frequency test between sub-fertile and fertile groups (Figs. 1, 2). The rest of the SNPs showed no statistically significant association in frequencies of genotypes and alleles between the present MT-SNPs and male sub-fertility. Furthermore, the Hardy–Weinberg genotype frequency test was performed on all SNPs. Each of these SNPs deviated from HWE in a significant way (P < 0.0001). However, there was no statistically significant difference between sub-fertile males with asthenozoospermia, oligozoospermia, teratozoospermia, asthenoteratozoospermia, oligoasthenoteratozoospermia, and oligoteratozoospermia and those who were fertile (P > 0.05). We evaluated the genotype and allele frequencies of MT-CO3, MT-ATP6, and MT-ATP8 in sub-fertile and fertile groups to see if they were linked to male sub-fertility. Among the 12 MT-CO3, 14 MT-ATP6, and 10 MT-ATP8 detected single nucleotide polymorphisms (SNPs), only the rs7520428 in the MT-CO3 and MT-ATP6 showed a statistically significant difference between sub-fertile and fertile groups in the genotypes and allele’s frequency test (P < 0.0001 for both).
The mitochondrion is the powerhouse of eukaryotic cells because of its capacity to create ATP through oxidative phosphorylation [21]. The mitochondrial function as an energy source is important for sperm function. It was reported that reduced sperm motility is linked to abnormalities in sperm mitochondrial ultrastructure [22]. Any abnormality in mitochondrial respiratory activity will adversely affect the process of spermatogenesis, particularly the advancement of pachytene phases during meiosis and sperm production. Spermatogenic cells will undergo mitochondrial respiratory failure if large amounts of pathogenic mutant mtDNA accumulate in the testicular tissue. Any reduction in the mitochondrion's ability to generate energy during spermatogenesis causes meiotic arrest. Spermatocytes with a lack of oxygen will most likely not complete meiosis and will be killed by apoptosis [23]. Spermatozoa with defective mitochondria produce insufficient ATP and have elevated levels of reactive oxygen species (ROS) or free radicals. In an imbalanced system, the production of ROS and free radicals would cause significant damage to the mitochondria and mtDNA, affecting sperm motility and finally leading to male infertility [24]. More specifically, an inadequate molecular profile of all types of male infertility still remains. Since mitochondrial biogenesis is essential for the proper motility of the sperm, any variations in mtDNA, whether quantitative or qualitative, influence the spermatozoa's cellular functioning. Specific mtDNA deletions in sperm have been linked to poor sperm function. Multiple 7345 and 7599 bp mtDNA deletions have been linked to poor sperm motility [25]. Moreover, studying genetic variations such as SNPs might be a helpful genetic analysis to understand the molecular bases of idiopathic infertility in males. In many studies, certain SNPs have shown an association with certain disorders such as cancer and infertility [24]. This work aimed to investigate whether there was an association between polymorphisms in the mitochondrial genes MT-CO3, MT-ATP6 and MT-ATP8 and male sub-fertility. Accordingly, we performed direct sequencing to check for polymorphisms in the MT-CO3, MT-ATP6, and MT-ATP8 genes in sub-fertile and fertile males, 12 SNPs have been identified in the MT-CO3 gene (rs2248727, rs7520428, rs3134801, rs9743, rs28358272, rs2853824, rs2856985, rs2854139, rs41347846, rs28380140, rs3902407, and 28,411,821), 14 SNPs in the MT-ATP6 (rs2001031, rs2000975, rs2298011, rs7520428, rs9645429, rs112660509, rs6650105, rs6594033, rs6594034, rs6594035, rs3020563, rs28358887, rs2096044, and rs9283154) and 10 SNPs in the MT-ATP8 (rs9285835, rs9285836, rs9283154, rs8179289, rs121434446, rs1116906, rs2153588, rs1116905, rs1116907, and rs3020563). Amongst reported SNPs of the previously mentioned mitochondrial genes, rs7520428 was found in both MT-CO3 and MT-ATP6 genes and both showed a significant difference in the genotype’s frequency test between sub-fertile and fertile groups. In addition, the allele frequency of rs7520428 SNP (A634390G) in MT-CO3 and MT-ATP6 showed a significant association with male subfertility (P < 0.0001). Furthermore, the OR of the rs7520428 SNP was linked to a 40-fold greater risk of subfertility in men compared to fertile ones. This could indicate that expanding the number of wild-type alleles (A) or reducing the number of mutant alleles (G) at A634390G can effectively protect male fertility, whereas increasing the number of G alleles or decreasing A alleles can develop male subfertility. This genetic alteration could be related to male sub-fertility which required further investigation to understand its role in the function of the protein outcome. The current findings showed that nine of the SNPs detected in the MT-CO3 gene, 4 of the SNPs in the MT-ATP6 gene and 2 of the SNPs found in the MT-ATP8 gene were synonyms variants. Synonymous mutations have been hypothesized to have an impact on gene control and the establishment of disorders [26]. It has been found that synonymous variants possibly affect mRNA stability [26]. As a result, functional investigations on these synonymous variants in mtDNA are needed to reveal their potential involvement in sperm function and male sub-fertility. The rs2001031 (A8860G) and rs2000975 (A8701) are missense mutations that alter threonine to alanine and they were significantly associated with subfertility. On the other hand, the following SNPs were detected in the current study and were reported to be pseudogenes: MT-CO3 (rs7520428) MT-ATP6 (rs7520428, rs9645429, rs112660509, rs6650105, rs6594033, rs6594034, rs6594035, rs2096044, rs9283154) MT-ATP8 (rs9285835, rs9285836, rs9283154, rs8179289, rs2153588). Mughal and his group have reported that there was a significant association between the 15 bp deletion (at position 9390 to 9413) of cytochrome C oxidase III and human male infertility (P = 0.033) [27]. In our study, the RS2000975 (A8701C, G) showed no significant association with male sub-fertility in the Genotype frequency (P = 1.0000), on the other hand, a previous study has reported a significant association between A8701G variants and increased risk of fertilization failure [28]. The inconsistent findings are attributed to the genetic variations as well as the subtype of infertility in the study populations. For instance, a recent study reported a significant correlation between polymorphisms of the MT-CYB gene and sub-fertility in men. Particularly, rs527236194, rs28357373 and rs41504845 variants were found significantly related to the sub-fertility group [29]. In conclusion, further research on a larger sample size of different populations is needed to emphasize the role of the reported SNPs in male sub-fertility. In addition, functional studies will be very helpful in understanding the molecular impact of each specific SNP on the function of the protein outcome and the mitochondrial efficiency that may explain their role in sperm function.
Below is the link to the electronic supplementary material.Supplementary file1 (DOCX 28 kb) | true | true | true |
PMC9618511 | 36129598 | Yanting Zhu,Lixia Dai,Xiangyou Yu,Xintian Chen,Zhenjiang Li,Yan Sun,Yan Liang,Bing Wu,Qiong Wang,Xiaoming Wang | Circulating expression and clinical significance of LncRNA ANRIL in diabetic kidney disease | 21-09-2022 | Diabetic kidney disease,Long-chain non-coding RNA,ANRIL,Gene expression | Background Long noncoding RNA ANRIL has been found to be involved in the pathogenesis of diabetic kidney disease (DKD) and is expected to be a new target for prevention of DKD. However, the circulating expression and clinical significance of ANRIL in DKD patients is uncertain. This study aims to explore this issue. Methods The study consisted of 20 healthy controls, 22 T2DM patients (normalbuminuria) and 66 DKD patients (grouped as follows: microalbuminuria, n = 23; macroalbuminuria, n = 22 and renal dysfunction, n = 21). The expressions of ANRIL in peripheral whole blood of all participants were measured by RT-qPCR. Results The expression of ANRIL was significantly up-regulated in DKD patients (microalbuminuria, macroalbuminuria and renal dysfunction groups) than that in healthy control group. ANRIL was also over-expressed in macroalbuminuria and renal dysfunction groups in comparison with normalbuminuria group. ANRIL expression was positively correlated with Scr, BUN, CysC, urine β2-MG and urine α1-MG; while negatively correlated with eGFR in DKD patients. In addition, ANRIL was the risk factor for DKD with OR value of 1.681. The AUC of ANRIL in identifying DKD was 0.922, and the sensitivity and specificity of DKD diagnosis were 83.3% and 90.5%, respectively. Conclusion Our results indicated that highly expressed ANRIL in peripheral blood is associated with progression of DKD. Circulating ANRIL is an independent risk factor of DKD and has a highly predictive value in identifying DKD. | Circulating expression and clinical significance of LncRNA ANRIL in diabetic kidney disease
Long noncoding RNA ANRIL has been found to be involved in the pathogenesis of diabetic kidney disease (DKD) and is expected to be a new target for prevention of DKD. However, the circulating expression and clinical significance of ANRIL in DKD patients is uncertain. This study aims to explore this issue.
The study consisted of 20 healthy controls, 22 T2DM patients (normalbuminuria) and 66 DKD patients (grouped as follows: microalbuminuria, n = 23; macroalbuminuria, n = 22 and renal dysfunction, n = 21). The expressions of ANRIL in peripheral whole blood of all participants were measured by RT-qPCR.
The expression of ANRIL was significantly up-regulated in DKD patients (microalbuminuria, macroalbuminuria and renal dysfunction groups) than that in healthy control group. ANRIL was also over-expressed in macroalbuminuria and renal dysfunction groups in comparison with normalbuminuria group. ANRIL expression was positively correlated with Scr, BUN, CysC, urine β2-MG and urine α1-MG; while negatively correlated with eGFR in DKD patients. In addition, ANRIL was the risk factor for DKD with OR value of 1.681. The AUC of ANRIL in identifying DKD was 0.922, and the sensitivity and specificity of DKD diagnosis were 83.3% and 90.5%, respectively.
Our results indicated that highly expressed ANRIL in peripheral blood is associated with progression of DKD. Circulating ANRIL is an independent risk factor of DKD and has a highly predictive value in identifying DKD.
Diabetic kidney disease (DKD) is a serious microvascular complication of diabetes mellitus (DM) and a primary etiology for end-stage renal disease (ESRD) worldwide [1–3]. The pathophysiology of DKD is multifactorial in which several common mechanisms involved, such as glomerular and tubulointerstitial inflammation, dysregulated cellular apoptosis and changes in the extracellular matrix (ECM), leading to diabetic glomerular lesions, proteinuria, decreased glomerular filtration rate (GFR) and renal fibrosis [4]. Renal fibrosis is an important pathological feature of DKD, manifested as glomerulosclerosis, tubular interstitial fibrosis and vascular sclerosis, which is majorly caused by epithelial/endothelial-mesenchymal-transition (EMT/EndMT), resulting in abnormal activation of renal myofibroblasts and excessive accumulation of renal ECM [5]. The conventional therapeutics of DKD including RAS blockade and antidiabetic drugs (such as sodium-glucose co-transporter-2 inhibitors, glucagon-like peptide-1 receptor agonists, dipeptidyl peptidase-4 inhibitors) are only able to slow down the rate of progression but not stop or reverse the disease. New drugs targeting DN pathomechanisms have become a major focus for the development of new therapies, such as N-acetyl-seryl-aspartyl-lysyl-proline (AcSDKP), endothelin receptor antagonists, vitamin D receptor activators, pentoxifylline and antioxidants [6, 7]. Currently decreased GFR and albuminuria have long been considered significant manifestations of DKD [8, 9]. However, the effect of GFR and albuminuria on the early diagnosis of DKD is limited, since they cannot identify the DM patients who are at risk of microvascular complications before renal damage actually occurs. Therefore, it is important to explore a new biomarker for the early detection of DKD among DM to increase the time to manage the disease and improve clinical outcome. Long-chain non-coding RNAs (lncRNAs), a novel class of nonprotein-coding functional RNA molecules with a transcript length greater than 200 nucleotides, are recognized as gene expression regulators and involved in a variety of physiological and pathological processes [10, 11]. Increasing evidence suggests that lncRNAs contribute to the pathogenesis of various diseases, including cancers, cardiovascular diseases and kidney diseases [12–15]. Studies have indicated that lncRNAs induce EMT/EndMT and renal fibrosis in DKD [16, 17]. In addition, lncRNAs adopt a secondary structure, which is relatively stable in body fluids, such as blood and urine. Therefore, lncRNAs could potentially serve as biomarkers for predicting the diagnosis and prognosis of diseases or targets for drug treatment. Gene therapy targeting lncRNAs, which is promising for modulating diseases at the genetic level and able to overcome the limitations of incompatible proteins, aims to treat disease by artificially controlling gene expression and is considered to be the “third generation” of therapeutic drugs [15]. However, it is still in the preclinical stage. Antisense RNA to INK4 locus (ANRIL), also known as CDKN2B-AS1, is transcribed from the short arm of human chromosome 9 on P21, which is involved in cell proliferation, migration, apoptosis, inflammation, immune responses and DNA damage [18]. ANRIL has been found to play an significant role in the pathogenesis and development of cardiovascular diseases, type 2 diabetes, atherosclerosis and cancers [19–22]. ANRIL has been also shown to be a molecular marker in the diagnosis of ischemic stroke and an indicator to predict the development of diabetic retinopathy [23, 24]. Study has indicated that ANRIL knock-down suppresses mouse mesangial cell proliferation, fibrosis, inflammation via regulating Wnt/β-catenin and MEK/ERK pathways in DKD [25]. In addition, ANRIL silencing alleviates high glucose-induced inflammation, oxidative stress and apoptosis via upregulation of MME in podocytes [26]. However, the clinical significance of ANRIL in DKD is still unclear. This study aims to examine the expression of ANRIL in peripheral whole blood of DKD patients and to further explore the relationship between ANRIL and DKD, which provided a new theoretical basis for identifying new markers of lncRNAs in DKD patients.
Patients diagnosed with type 2 diabetes mellitus (T2DM) based on the criteria of the American Diabetes Association (ADA) were collected from Shaanxi Provincial People’s Hospital (Xi’an, Shaanxi, China) between September 2020 and December 2021. Patients with type 1 diabetes, secondary diabetes, urinary tract infection, urolithiasis, pregnancy, superimposed systemic diseases and other glomerular diseases were excluded. 88 diabetic patients were enrolled in this study in which 22 were T2DM patients (patients with normalbuminuria, urine albumin creatinine ratio (UACR) < 30 mg/g) and 66 were DKD patients. DKD patients were divided into three groups according to UACR and serum creatinine: (1) Patients with microalbuminuria (UACR 30–300 mg/g), n = 23; (2) Patients with macroalbuminuria (UACR > 300 mg/g), n = 22; and (3) Patients with increased serum creatinine (renal dysfunction) (serum creatinine > 120µmol/L), n = 21. Meanwhile, 20 non-diabetic healthy volunteers were enrolled as control group. The present study was approved by the ethical committee for human investigation of Shaanxi Provincial People’s Hospital and was conducted according to the Declaration of Helsinki. Informed consent was obtained from all participants.
General data were collected as follow: gender, age, body height, body weight, systolic blood pressure (SBP), diastolic blood pressure (DBP) and duration of diabetes on admission. Body mass index (BMI) was calculated using the formula: BMI = body weight/body height2 (kg/m2). The following laboratory parameters were obtained from each patient: glycated hemoglobin A1c (HbA1c), fasting serum glucose (FSG), UACR, urine β2-microglobulin (β2-MG), urine α1-microglobulin (α1-MG), serum creatinine (Scr), blood urea nitrogen (BUN), Cystatin C (CysC), neutrophil gelatinase-associated lipocalin(NGAL), uric acid (UA), white blood cell (WBC), hemoglobin (HGB), albumin (ALB), triglyceride (TG), total cholesterol (TC), high density lipoprotein (HDL) and low density lipoprotein (LDL) at the time of enrollment. UACR was calculated using the formula: UACR = urine albumin/creatinine. The eGFR was calculated according to the modified MDRD formula: eGFR = 186 × Scr− 1.154 × Age− 0.203 × gender (1 if male, 0.742 if female).
Peripheral whole blood was collected by venipuncture with an ethylenediaminetetraacetic acid (EDTA) anticoagulant vacutainer from all patients and stored at − 80 °C until analysis. Total RNA was extracted as soon as possible.
1mL peripheral whole blood was centrifuged at 3000rpm for 5min and the supernatants were removed. Then 3mL red cell lysis buffer added and mixed before being centrifuged at 3000rpm for 5min. The supernatant was then discarded and the extracted leukocytes were collected. The total RNA of leukocytes was extracted using Trizol reagent (Servicebio, Wuhan, China), and then dissolved in RNase-free water. The concentration of RNA was determined using NanoDrop 2000 (Thermo scientific, Waltham, MA, USA). Extracted RNA was reversibly transcribed into complementary DNA (cDNA) using Servicebio®RT First Strand cDNA Synthesis Kit (Servicebio, Wuhan, China). Quantitative real-time polymerase chain reaction was performed using SYBR Green qPCR Master Mix (Servicebio, Wuhan, China) on a CFX RT-PCR system (Bio-Rad). PCR reaction system was as follows: pre-degenerated at 95°C for 10min, followed by 40 cycles of 95°C for 15s and 60°C for 30s. The expression level of lncRNA ANRIL was normalized to the expression level of GAPDH as a housekeeping gene. The relative quantitative value was expressed by the 2−ΔΔCt method. ANRIL primer sequences were shown as follows: upstream: 5’-AGGGTTCAAGCATCACTGTTAGG-3’; downstream: 5’-GAAACCCCGTCTCTACTGTTACCT-3’.
SPSS software, version 18.0 (SPSS, Inc., Chicago, IL, USA) was used for statistical analysis. Normally distributed data were presented as mean ± S.D. and non-normally distributed data were expressed as median range. Differences between two groups for quantitative data and qualitative data were compared using a t test and chi-square test, respectively. Comparisons of normally distributed data in 3 or more groups were analyzed using one-way ANOVA, while non-normally distributed data were analyzed using nonparametric counterpart Kruskal-Wallis test. Correlations were examined using Pearson’s correlation analysis. Binary regression analysis was used to determine the influence factors of the presence of DKD. The diagnostic value of ANRIL was evaluated by ROC curve analysis. Area under the ROC curve (AUC) was calculated. When AUC = 0.5, diagnostic value was denied. The cut-off value and corresponding sensitivity and specificity were determined according to ROC curve analysis. P < 0.05 was considered to represent significant differences between groups.
The basic characteristics of individuals involved in this study and results of biochemical analyses within the study groups were displayed in Table 1. Age, sex and BMI were matched for each group. There was no statistical differences in TG, TC, HDL and LDL among the all groups (P > 0.05). SBP and DBP were extremely higher in renal dysfunction group than in other groups (P < 0.01). Scr, BUN, CysC and NGAL levels in renal dysfunction group were also higher than those in other diabetic patients and healthy controls (P < 0.05). HGB and ALB were significantly decreased in renal dysfunction group compared to other diabetic patients and healthy controls (P < 0.05). EGFR was also significantly lower in renal dysfunction patients than other groups (P < 0.01). Slight significance was also observed in parameters such as UA and WBC between renal dysfunction patients and other four groups (P < 0.05). In addition, the disease duration was increased in DKD patients (microalbuminuria, macroalbuminuria and renal dysfunction groups) in comparison with DM patients (P < 0.05). UACR, urine β2-MG and urine α1-MG values of the macroalbuminuria and renal dysfunction groups were obviously higher than those of the normalbuminuria group (P < 0.01). Increased HbA1c was evident in renal dysfunction groups in comparison with normalbuminuria and microalbuminuria groups (P < 0.05). FSG were significantly higher in DM and all DKD patient than that in healthy control group (P < 0.01).
The differential expression of ANRIL in normalbuminuria, microalbuminuria, macroalbuminuria, renal dysfunction groups and healthy control group are shown in Fig. 1. The result indicates that the expression of ANRIL was significantly up-regulated in DKD patients (microalbuminuria, macroalbuminuria and renal dysfunction groups) than that in healthy control group (P < 0.01). In addition, ANRIL was over-expressed in macroalbuminuria and renal dysfunction groups in comparison with normalbuminuria group (P < 0.01). Whereas, there was no significant difference in ANRIL expression between normalbuminuria group and healthy controls (P = 0.199).
The correlations between ANRIL expression and clinical parameters in DKD patients were analyzed using Pearson’s linear correlation. Fig. 2 reveals that ANRIL expression was positively correlated with Scr, BUN, CysC, urine β2-MG and urine α1-MG in DKD patients (all P < 0.05). In addition, a negative correlation between ANRIL expression and eGFR was observed (P = 0.01) (Fig. 2). There were no correlation between ANRIL expression and BMI, disease duration, SBP, DBP, HbA1c, FSG, UACR, NGAL, UA, WBC, HGB, ALB, TG, TC, HDL, LDL.
Binary regression analysis showed that ANRIL, SBP, α1-MG and disease duration were the risk factors of DKD, with OR value of 1.681, 1.248, 1.142 and 1.599 (P < 0.05); while, HGB was found to be the protective factor of DKD, with OR value of 0.838 (P < 0.05) (Table 2).
To confirm the predictive value of circulating ANRIL as the biomarker for the early diagnosis of DKD, the diagnostic value of ANRIL was evaluated by ROC curve analysis. As depicted in Fig. 3, the AUC of ANRIL was 0.922. The sensitivity of ANRIL for predicting DKD was 83.3%. The specificity was estimated at 90.5%. The diagnostic cutoff point was 3.059.
DKD is a common DM complication characterized by a progressive damage of kidney structure and deterioration of renal function, which has become the primary cause of ESRD worldwide [1–3, 8]. Renal fibrosis, a common pathological feature of DKD, characterized by abnormal activation of renal myofibroblasts and excessive accumulation of renal ECM, is majorly caused by EMT/EndMT [5]. DKD remains a main challenging clinical problem in spite of continual progress in treatment and management. Increasing evidences demonstrated that lncRNA plays critical role in the pathogenesis of DKD [14]. LncRNAs are involved in the progression of kidney disease through regulation of many important factors, such as pathologic processes in mesangial cells/podocytes, reactive oxidative species, EMT/EndMT via regulation of diverse targets or functioning as sponges for regulatory microRNAs [14]. Studies have indicated that several lncRNAs induce EMT and renal fibrosis in kidney tissue of DKD animal model and high glucose-stimulated HK2 cells/podocyte via binding to its targeting miRNAs [16, 27, 28]. Furthermore, it has been shown that lncRNA H19 promotes EndMT in TGF-β2-induced fibrosis in human dermal microvascular endothelial cells and in the kidney of streptozotocin-induced diabetic CD-1 mice via regulation of its target miR-29a [17]. Recently there has been more attention about lncRNA ANRIL and its impact on the development of DKD. ANRIL contains 19 exons, spans a region of 126 kb in the antisense orientation of the p15/CDKN2B-p16/CDKN2Ap14/ARF gene cluster [29]. Studies have showed that ANRIL mediates EMT by regulating downstream gene/protein in cancers, such as pancreatic cancer, laryngeal squamous cell carcinoma and renal cell carcinoma [30–32]. ANRIL has also been found to regulate functional and structural alterations in the kidneys in diabetes through controlling the expressions of ECM proteins and VEGF [20]. Another study has suggested that ANRIL silencing alleviates high glucose-induced inflammation, oxidative stress and apoptosis via upregulation of MME in podocytes [26]. In addition, it has been shown that ANRIL promotes pyroptosis and kidney injury in DKD acting as miR-497 sponge [33]. ANRIL in peripheral whole blood, mainly expression in leukocytes, is relatively stable. This present study examines the expression of ANRIL in peripheral whole blood and shows that the expression of ANRIL in peripheral whole blood was significantly upregulated in DKD patients than those in healthy controls and T2DM patients. ANRIL expression showed a positive correlation with Scr, BUN, CysC, urine β2-MG and urine α1-MG, while negatively correlated with eGFR in DKD patients. These above clinical parameters were used to evaluate kidney function status of DKD. Binary regression analysis showed that ANRIL was the risk factor of DKD. The results indicated that ANRIL might be involved in the kidney impairment of DKD; might be play a key role in the pathogenesis of DKD and might be an efficient target for DKD prevention and treatment. In the early stage, DKD begin from glomerular hyperfiltration, without any clinical symptoms, followed by the development of microalbuminuria. Along with gradual progression, DKD manifests as a clinical syndrome including persistent albuminuria, increased blood pressure, sustained reduction in GFR and increased cardiovascular events [4, 9, 34]. Albuminuria is one of the most characteristic clinical signs in DKD, and is used as an important index for laboratory diagnosis of early DKD and evaluating active and deteriorating condition in DKD [4, 8]. In our study, ANRIL was over-expressed in macroalbuminuria and renal dysfunction groups in comparison with normalbuminuria group. Furthermore, the sensitivity and specificity of ANRIL for predicting DKD were 83.3% and 90.5%, respectively. These results implied that ANRIL can be used as an early diagnostic biomarker for the occurrence of DKD and a predictor for the progression and outcome of DKD in patients. Nonetheless, the influential factors of DKD are diverse. The specific pathogenesis, diagnosis and outcome assessment should be further elucidated. This is the first study that revealed an independent relationship between ANRIL expression in peripheral whole blood and DKD patients. While, there is still a limitation in this study. Sample size in this study was small, which might result in bias of the results evaluating the association of ANRIL expression and DKD. In conclusion, our findings provided new evidence that the presence and progression of DKD is associated with an over-expressed ANRIL in peripheral whole blood. Circulating ANRIL is an independent risk factor of DKD and has a highly predictive value in identifying DKD. | true | true | true |
PMC9618630 | Eleonora Loi,Loredana Moi,Paola Cabras,Giulia Arduino,Giulia Costanzo,Stefano Del Giacco,Henry A. Erlich,Davide Firinu,Aldo Caddori,Patrizia Zavattari | HLA-C dysregulation as a possible mechanism of immune evasion in SARS-CoV-2 and other RNA-virus infections | 17-10-2022 | COVID-19,SARS-CoV-2,DNA methylation,HLA-C,gene expression,enhancer transcriptional regulation,symptomatic and asymptomatic COVID-19,upper airways cells | One of the mechanisms by which viruses can evade the host’s immune system is to modify the host’s DNA methylation pattern. This work aims to investigate the DNA methylation and gene expression profile of COVID-19 patients, divided into symptomatic and asymptomatic, and healthy controls, focusing on genes involved in the immune response. In this study, changes in the methylome of COVID-19 patients’ upper airways cells, the first barrier against respiratory infections and the first cells presenting viral antigens, are shown for the first time. Our results showed alterations in the methylation pattern of genes encoding proteins implicated in the response against pathogens, in particular the HLA-C gene, also important for the T-cell mediated memory response. HLA-C expression significantly decreases in COVID-19 patients, especially in those with a more severe prognosis and without other possibly confounding co-morbidities. Moreover, our bionformatic analysis revealed that the identified methylation alteration overlaps with enhancers regulating HLA-C expression, suggesting an additional mechanism exploited by SARS-CoV-2 to inhibit this fundamental player in the host’s immune response. HLA-C could therefore represent both a prognostic marker and an excellent therapeutic target, also suggesting a preventive intervention that conjugate a virus-specific antigenic stimulation with an adjuvant increasing the T-cell mediated memory response. | HLA-C dysregulation as a possible mechanism of immune evasion in SARS-CoV-2 and other RNA-virus infections
One of the mechanisms by which viruses can evade the host’s immune system is to modify the host’s DNA methylation pattern. This work aims to investigate the DNA methylation and gene expression profile of COVID-19 patients, divided into symptomatic and asymptomatic, and healthy controls, focusing on genes involved in the immune response. In this study, changes in the methylome of COVID-19 patients’ upper airways cells, the first barrier against respiratory infections and the first cells presenting viral antigens, are shown for the first time. Our results showed alterations in the methylation pattern of genes encoding proteins implicated in the response against pathogens, in particular the HLA-C gene, also important for the T-cell mediated memory response. HLA-C expression significantly decreases in COVID-19 patients, especially in those with a more severe prognosis and without other possibly confounding co-morbidities. Moreover, our bionformatic analysis revealed that the identified methylation alteration overlaps with enhancers regulating HLA-C expression, suggesting an additional mechanism exploited by SARS-CoV-2 to inhibit this fundamental player in the host’s immune response. HLA-C could therefore represent both a prognostic marker and an excellent therapeutic target, also suggesting a preventive intervention that conjugate a virus-specific antigenic stimulation with an adjuvant increasing the T-cell mediated memory response.
The present study is part of a project that aimed to compare the genomic DNA methylation profile of cells collected by nasopharyngeal swabs from symptomatic patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, responsible for the coronavirus disease 2019 (COVID-19), with that of comparable samples from subjects positive for SARS-CoV-2 but asymptomatic, from subjects negative for SARS-CoV-2 and from individuals with a previous SARS-CoV-2 infection. The epithelial respiratory cells represent the first barrier between the organism and the surrounding environment. They are known to act as antigen-presenting cells (APCs) during respiratory viral infections (1, 2). This study is based on previous knowledge obtained on cells infected with another pathogenic coronavirus, such as MERS-CoV, of alterations in the methylome of the infected host cell. In particular, previous studies have shown that MERS-CoV targets regions of the host cell genome that are essential to trigger an immune response against pathogens such as bacteria and viruses, e.g. HLA (Human Leukocyte Antigens) genes, inhibiting them in this role and thus rendering the infected individual incapable, or much less efficient in triggering an effective immune response against infection (3). HLA genes encode proteins essential in immune function (the Major Histocompatibility Antigens), involved in the presentation of the antigen to the immune system; they belong to two different types: HLA class I and class II antigens. HLA/MHC class I molecules (A, B, and C) present intracellular antigens, such as viral or tumor antigens, to CD8 positive T cells (cytotoxic T lymphocytes, CTLs), stimulating a cytotoxic immune response and to natural killer (NK) cells. For example, if the cell becomes infected with a virus, the HLA system carries protein fragments of the virus to the surface of the cell so that it can be destroyed by the immune system. These peptides, usually small polymers of 9 amino acids, are produced from proteins digested in proteasomes. CTLs can recognize HLA-peptide complex through their T cell receptor. HLA/MHC class II (DP, DM, DOA, DOB, DQ and DR) present extracellular antigens, to CD4 lymphocytes, inducing a helper T cell response which consists of supporting the activation of CD8 lymphocytes and establishing long-term memory. In addition, helper T lymphocytes support the production by B lymphocytes of neutralizing antibodies against the specific antigen. Viruses are intracellular antigens, and they can be subjected to proteolytic digestion in the proteasome. In the endoplasmic reticulum, these antigenic peptides bind HLA class I molecules. However, both class I and II HLA molecules can process intracellular and extracellular antigens. The HLA-peptide complexes are then translocated to the cell membrane, where class I are ubiquitously expressed, while class II are expressed by cells specialized for antigen presentation, such as dendritic cells, monocytes, macrophages and B lymphocytes. As noted above, one of the mechanisms by which viruses modify the expression of immune-related genes in the host cell, including HLA genes, is to induce changes in the methylation profile of genomic DNA. DNA methylation, at cytidines adjacent to guanosines (CpG loci), is an epigenetic mechanism normally used in cells contributing to regulate gene expression. Typically, a methylation status of a gene’s CpGs in the regulatory sequences is associated with its shutdown, while the absence of methyl groups in those regions is typically associated with active transcription of that gene. Dysregulation of this crucial mechanism is involved in the development and progression of many human diseases (4), including cancer as the most noteworthy example in which the identification of DNA methylation alterations has also provide the definition of clinically-relevant biomarkers (5–11). In the context of infectious diseases, a growing body of evidence underlines its role in their pathogenesis and in the development of chronic diseases triggered by the modifications induced by the pathogens (12). Since SARS-CoV-2 belongs to coronaviruses such as SARS-CoV-1 and MERS-CoV, we hypothesized that it may act similarly by influencing the expression of HLA genes by modulating their methylation profile. Recently, the research group of Prof. Esteller conducted an epigenome-wide association study (EWAS) to identify candidate loci regulated by DNA methylation, potentially involved in the onset of COVID-19 in patients without comorbidities. Among the main findings of this study, whole blood DNA methylation alteration was observed in genes, including HLA class I C (HLA-C), mainly involved in the response of interferon to viral infection (13). It is also important to consider that HLA-C, the most recently evolved class I locus (only present in humans and great apes (14)), although expressed on the cell surface about ten times lower than HLA-A and B, represents a potentially particular target for the mechanisms put in place by viral infections, acting as a ligand for both T cell receptors and NK cell receptors (15, 16). More recently, Balnis and colleagues published the results of a study in which they compared differentially methylated regions of circulating blood DNA from hospitalized COVID-19-positive and COVID-19-negative patients and previously reported data from healthy individuals collected before the pandemic. The authors also conducted an mRNA expression analysis of immuno-related genes, showing that DNA methylation alterations were inversely correlated with gene expression levels, confirming a prevalence of promoter hypermethylated profile in severe COVID-19 patients (17). Our study investigated the levels of HLA-C expression in upper respiratory tract cells, showing that symptomatic patients show significantly lower levels than asymptomatic patients and SARS-CoV-2 negative people. These results are consistent with previous observations and contribute to our understanding of the role that these DNA methylation alterations may play in the pathological course of COVID-19. Figure 1 shows an overview of possible mechanism used by SARS-CoV-2 to evade the host’s immune response.
Enrolment of the participants took place between May 2020 and April 2021. The study workflow is summarized in Figure 2 .
The genome-wide methylation study was performed on 13 COVID-19 patients and three healthy controls. COVID-19 patients were recruited at “Santissima Trinità” hospital (Cagliari, Italy). Eligibility criteria for the COVID-19 group included: at least 18 years of age, positivity to nasopharyngeal swab for SARS-CoV-2 by PCR. Patients were classified as symptomatic (n=8), who were hospitalized for severe COVID-19 pneumonia (n=7) and/or showed other COVID-19 related symptoms, and as asymptomatic (n=5). Healthy controls were healthcare workers from the University Hospital “Policlinico Duilio Casula” (Monserrato, Italy) resulted negative to screening for SARS-CoV-2. Clinicopathological characteristics of the COVID-19 patients and relevant data of healthy controls are summarized in Supplementary Table 1 .
HLA-C gene expression was tested in 61 COVID-19 patients (including the 13 patients subjected to the global DNA methylation profiling and 48 additional patients), eight healthy controls and eight subjects with a previous SARS-CoV-2 infection. COVID-19 patients were enrolled at “Santissima Trinità” (Cagliari, Italy) and “Policlinico Duilio Casula” (Monserrato, Italy) hospitals, post-COVID-19 participants were recruited from “Policlinico Duilio Casula”. COVID-19 patients (>18 years old and positive for SARS-CoV-2 infection) were symptomatic (n=45) and asymptomatic (n=8). Clinicopathological information were not available for eight samples. Participants were eligible as post-COVID-19 subjects if they had a previous SARS-CoV-2 infection (5 symptomatic and 3 asymptomatic) and resulted negative to two consecutive (in a range of 2-3 days) SARS-CoV-2 screening tests by PCR. Of note, one subject was analysed before SARS-CoV-2 infection (T0) and at two time points post asymptomatic COVID-19: T1, coincident with the date of the first negative swab (last day of treatment with hydroxychloroquine) and T2, 18 days after. SARS-CoV-2 test negative participants were healthcare workers from “Policlinico Duilio Casula”. Supplementary Table 1 reports clinicopathological characteristics of the COVID-19 patients and relevant data of post-COVID-19 and healthy subjects. A further analysis was conducted by applying the same criteria applied in Castro de Moura et al. work (13) to exclude patients with age > 61 years and comorbidities (including obesity, diabetes, hypertension, autoimmune disorders, and chronic cardiovascular or lung diseases).
Nasopharyngeal swabs were collected from the participants and immediately stored in a tube with TRIzol reagent (Thermo Fisher Scientific, Waltham, Massachusetts, USA).
After removing the swab, taking care to carefully squeeze all the mucus soaked in it, chloroform is added to TRIzol. After homogenization, different phases are separated: a clear upper aqueous layer (containing RNA), an interphase, and a lower organic layer (containing the DNA and proteins). The addition of isopropanol to the aqueous phase allows for the isolation of RNA by precipitation. Adding ethanol to the interphase/organic layer allows DNA to precipitate. The addition of isopropanol to the phenol-ethanol supernatant allows the proteins to precipitate. After washing to remove any impurities, DNA, RNA and proteins are resuspended in aqueous solutions and used for molecular investigations. DNA and RNA concentration was quantified by UV spectrophotometry (NanoPhotometer™ Pearl, Implen) and by fluorometric reading (Quant-iT™ PicoGreen® dsDNA Assay Kit; Quant-iT RiboGreen RNA Kit).
DNA samples were treated with sodium bisulfite using EZ DNA Methylation Gold Kit (Zymo Research).
Bisulfite converted samples were shipped to the “Italian Institute for Genomic Medicine” (Candiolo, Italy) and subjected to DNA methylation analysis using the Illumina Infinium MethylationEPIC BeadChips, which interrogate >850,000 loci. Following a rigorous quality control of the post-analysis data, the company communicated that all the samples analysed passed this control. Raw data were transmitted to the research group by Wetransfer.
Reverse transcription of 1 μg (with the exception of few cases with limited RNA amount) of RNA to cDNA was performed using the High-Capacity Kit (Applied Biosystems, Carlsbad, CA, USA). Gene expression analysis of HLA-C (Hs03044135_m1) and GAPDH (Hs03929097_g1), used as endogenous gene, was performed by TaqMan Gene Expression Assays (FAM-MGB) (Thermo Fisher Scientific, Waltham, Massachusetts, USA). The assays were conducted in triplicate and the experiment was conducted on a DNA Engine Opticon 2 Real-Time Cycler (Bio-Rad, Hercules, CA, USA) using the following PCR conditions: initial activation 95°C for 10 minutes, 50 cycles of denaturation at 95°C for 15 seconds and annealing/extension at 60°C for 1 minute.
Raw DNA methylation data (idat files) were analysed using RnBeads (18, 19) installed in R environment. RnBeads workflow consists in: quality control, filtering and normalization, export of processed data, exploratory analysis and differential DNA methylation analysis. The background was subtracted using the methylumi package (method “enmix.oob”) (20). The methylation β values were normalized using the BMIQ normalization method (21). The differential methylation analysis was performed based on two comparisons: COVID-19 vs controls and asymptomatic vs symptomatic COVID-19 patients. CGIs were annotated to the nearest genes and transcripts using R annotation package FDb.InfiniumMethylation.hg19 (22). We focused our attention on differentially methylated CpG islands (CGI) (comb.pval < 0.05 and/or Δβ > 0.05 indicating hypermethylated CGI or Δβ < -0.05, indicating hypomethylated CGI) associated with immunologically relevant genes. The list of genes curated with functions and Gene Ontology terms was retrieved from Immport.org. The categories included: Antigen Processing and Presentation (148 genes), Antimicrobials (535 genes), BCR Signaling Pathway (272 genes), Chemokine Receptors (53 genes), Chemokines (102 genes), Cytokine Receptors (307 genes), Cytokines (456 genes), Interferon Receptor (3 genes), Interferons (17 genes), Interleukins (47 genes), Interleukins Receptor (42 genes), Natural Killer Cell Cytotoxicity (134 genes), TCR signaling Pathway (291 genes), TGFb Family Member (33 genes), TGFb Family Member Receptor (12 genes), TNF Family Members (12 genes), TNF Family Members Receptors (19 genes). For HLA-C gene, the analysis was also conducted at the CpG site level.
Processed Illumina EPIC methylation data of bisulfite converted DNA from whole blood of 102 COVID-19 patients and 26 non-COVID-19 patients (17) were retrieved from the NCBI Gene Expression Omnibus (GEO) Portal under the accession number GSE174818. Data were downloaded using the Bioconductor package “GEOquery” (23).
We consulted HACER database (http://bioinfo.vanderbilt.edu/AE/HACER/index.html) to investigate the potential presence of integrated enhancers associated with HLA-C gene.
As reported above, DNA methylation data were analysed using RnBeads (18, 19). This tool performs the differential methylation analysis with hierarchical linear models as implemented in the limma package (24). Gender and age data were used as covariate for adjusting p-values in the limma differential methylation analysis. RnBeads computes p-values for all covered CpG sites. The uncorrected CpG-level p-values are then combined at the level of predefined genomic regions using a generalization of Fisher’s method (25). Aggregate p-values are subjected to multiple-testing correction using Bonferroni-Benjamini false discovery rate (FDR).
Gene expression data were analysed by the ΔΔCt method (26). Statistics was calculated using Welch’s t-test considering the average ΔCt for each tested group.
The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee of “ATS Sardegna” (224/2020/CE). All the analysed biological samples were obtained with written informed consent from participants prior to inclusion in the study.
We performed a whole genome methylation profiling of 13 COVID-19 patients, divided into asymptomatic and symptomatic patients, and three healthy controls.
Based on the knowledge that the infection of other coronaviruses is associated with DNA methylation alteration of genes involved in the generation of immune responses against viruses and bacteria, we focused our attention on CGIs associated with immunologically relevant genes belonging to the categories reported in Table 1 . The table shows the number of differentially methylated CGIs according to different criteria. The comparison between COVID-19 and control samples did not reveal alterations in these categories: Interferon Receptor, Interferons, TGFb Family Member Receptor, TNF Family Members and TNF Family Members Receptors. The most affected categories resulted: Cytokine Receptors (20%), Antimicrobials (19%), and Cytokines (16%) ( Figure 3A ). Of note, most differentially methylated CGIs were hypomethylated. In the differential methylation analysis between asymptomatic and symptomatic patients, no alterations were detected in Interferon Receptor, Interferons, Interleukins Receptor, Interleukins, TGFb Family Member, TNF Family Member. The most affected categories were: Antimicrobials (24%), Cytokine Receptors (23%) and Cytokines (17%) ( Figure 3B ). The majority of DNA methylation alterations were hypermethylation events.
It has been previously hypothesized that other coronaviruses such as SARS-CoV-1 and MERS-CoV affect the methylation pattern of HLA genes (3). Recently, a large epigenome-wide study of 407 COVID-19 patients has shown that methylation alteration of two CpG loci (cg08309069 and cg05030953) was negatively associated with the clinical severity of the disease. The DNA methylation analysis of immune-related genes has pointed out a CGI (chr6:31276242-31276526) associated with HLA-C that was hypermethylated (Δβ=0.05) in COVID-19 patients compared to controls ( Table 2 , Figure 4A ). We carried out a comprehensive analysis of all CpG sites associated with HLA-C gene ( Table 2 , Figures 4A, B ). It should be noted that from both the case-control and symptomatic-asymptomatic differential methylation analyses, no CGI associated with the transcription start site of the HLA-A and HLA-B genes was significantly altered ( Supplementary Figures 1A, B ). The results confirmed hypomethylation of the two CpG sites reported by Castro de Moura et al. (13) in symptomatic patients compared to asymptomatic ones but also in COVID-19 patients compared to controls ( Table 2 and Figure 4B ). Hypomethylation was also extended to adjacent CpG sites in the S-shore of CGI located at chr6:31238852-31240120 (average Δβ= |0.14| and average Δβ= |0.10|, respectively in the differential methylation analyses between COVID-19 patients vs controls and asymptomatic vs symptomatic patients) ( Table 2 and Figures 4A, B ). COVID-19 patients also displayed hypomethylation of the N-shore region of the same CGI (average Δβ= |0.09|), while the CGI itself was not differentially methylated ( Table 2 and Figures 4A, B ). Of note cg13273236 was not differentially methylated between COVID-19 patients and controls but hypermethylated in symptomatic patients ( Table 2 and Figure 4A, B ) as well as the region between the S-shore of CGI at chr6:31238852-31240120 and the altered CGI at chr6:31276241-31276526.
In order to validate our results, we analysed GSE174818 dataset including methylation data from whole blood samples of 102 COVID-19 patients and 26 non-COVID-19 subjects (17). As evident from Figure 4C , we observed a similar methylation pattern throughout HLA-C gene although with less pronounced alterations.
Transcript expression of HLA-C was evaluated in 61 COVID-19 patients, eight post-COVID-19 subjects and eight controls with no previous infection of SARS-CoV-2. A statistically significant downregulation (p-value < 0.0001) was observed in COVID-19 patients compared to controls. Of note, post-COVID-19 subjects showed intermediate transcript levels between COVID-19 patients (p-value < 0.0001) and controls (p-value < 0.0001) ( Figure 5A ). In order to eliminate potential confounding factors that can affect HLA-C gene expression, we applied the same criteria of Castro de Moura et al. (13) as described in Materials and Methods. The reduction of HLA-C transcript levels was even more pronounced (p-value < 0.0001) in this restricted sample group (n=18) ( Figure 5B ). We also investigated whether HLA-C expression could be correlated with COVID-19 severity. For this analysis, samples with missing clinical information were excluded. Indeed, the symptomatic group of patients (n=45) displayed statistically significant lower expression levels (p-value < 0.0001) than the asymptomatic (n=8) group ( Figure 5C ), that displayed a similar expression to post-COVID-19 subjects. Overall, both symptomatic and asymptomatic COVID-19 patients showed lower HLA-C expression than controls (p-value < 0.0001). Notably, among the symptomatic group, one patient, also analysed in the methylation study, was paucisymptomatic and actually displayed methylation and expression patterns more similar to asymptomatic patients and for this reason was finally considered in this group in the expression analysis. Finally, we explored HLA-C expression in one subject at three different time points ( Figure 5D ): T0 before SARS-CoV-2 infection and T1 and T2 after COVID-19 recovery (both at a clinical point of view and negative to SARS-CoV-2 test), the last day of hydroxychloroquine treatment (T1) and 18 days after treatment (T2). A statistically significant decrease of HLA-C levels (p-value=0.027) was observed at T1 compared to T0, followed by statistically significant increase at T2 to levels even higher than T0 (p-value= 0.011).
By consulting the HACER database, we found several integrated enhancers associated with this gene ( Figure 6A ). Interestingly, CGI chr6:31276241-31276526 (Δβ=0.05 in COVID-19 patients vs controls) overlaps with an integrated enhancer (chr6:31260493-31279454) ( Figure 6B ). Moreover, the analysis showed that the enhancers (AE_hg19_GM12878-ENCODE_504492 and AE_hg19_GM12878_21928) located at chr6:31275830-31276119 and chr6:31274921-31278342 (sub-regions of the integrated enhancer at chr6:31260493-31279454) in GM12878 (B-lymphocyte) cell line are bound by NFYB (among other transcription factors: EBF1, PBX3 and SP1) and regulates HLA-C, among others but the only HLA-class I gene targeted. Moreover, part of the S-shore region of CGI chr6:31238852-31240120 altered in our work and in Castro de Moura et al. (13), belongs to an associated enhancer chr6:31240851-31241006, proximal to HLA-C gene ( Figure 6C ). Figure 6D describes a hypothetic mechanism of HLA-C regulation by an enhancer.
This study takes start from an EWAS analysis conducted on nasopharyngeal swab samples of a small group of 13 COVID-19 patients and three controls. As far as we know, this is the only methylome study performed to date on upper airway cells of COVID-19 patients, the first cells interested in the infection acting as APCs during respiratory diseases, since most of the previous research has been conducted on whole blood samples (13, 17, 27–29). It is important to take into account that a mechanism observed in epithelial cells might be not evident in blood cells. In fact, Bortolotti and colleagues demonstrated that by setting up co-cultures of lung epithelial cells transfected with spike proteins and NK cells, intracellular expression of S1 SARS-CoV-2 protein in the epithelial cells reduces the activation of NK cells but this does not happen using lymphoblastoid cultures (30). The authors conclude that this phenomenon could explain the observation of a break in the interplay of lung epithelial cells and immune cells in SARS coronavirus patients, leading to an exhausted immune response (31). The present research is based on the findings of a study conducted on epithelial cell cultures infected with various pathogenic viruses that has shown that MERS-CoV inhibits the antigen presentation by altering the epigenetic landscape of the host cell. In particular, the results suggested that DNA methylation, rather than histone modifications, plays a crucial role in MERS-CoV-mediated antagonism of antigen-presentation gene expression. Indeed, the authors observed, after infection, hypermethylation, and down-regulation, of genes associated with antigen presentation (3). Despite the immense amount of scientific publications on SARS-CoV-2 infection, it is surprising that there has not been much attention on the expression levels of the genes of the HLA/MHC system, whose role in the immune response makes them very strong candidate genes. Nevertheless, the groups that have analyzed this aspect have generally found a reduced expression of HLA class I and II genes in agreement with our results (30, 32–35). In fact, class I genes characterize the cell-mediated adaptive response, but MHC is also highly upregulated during the initial innate immune response. Our work confirmed the presence of significant alterations in DNA methylation profiles between patients and controls and between symptomatic and asymptomatic patients. As in other published studies, although performed on different biological matrices (upper airway cells in the present study, blood in previous studies (13, 17)), the analysis of DNA methylation profiles reveals that most of the alterations are hypomethylation events and map at CGI associated with immune-related genes (17), in particular with those belonging to antimicrobials, cytokines and cytokines receptors categories. Interestingly, in agreement with the results of Menachery et al. (3), we found a differentially methylated CGI (chr6:31276241-31276526) associated with HLA-C, a gene belonging to antigen processing and presentation category. Interestingly, Menachery et al. did not confirm HLA-C alteration neither at methylation nor gene expression level in cell lines infected by H1N1 influenza virus (3), as can also be observed in gene expression results obtained from a patient we examined, infected by H1N1 (data not shown). By analysing the methylation status of the entire HLA-C region, we focused our attention on an interesting overlap with the results obtained by Castro de Moura et al. (13) and also validated in Balnis et al. data (17), regarding two hypomethylated CpG loci, associated with the clinical severity of COVID-19, in an S-shore region of a CGI encompassing the first exons of HLA-C gene. Notably, our results showed that this association with the clinical severity is even accentuated and the alteration is also extended to other CpG sites in the S-shore and N-shore region of the same CGI (chr6:31238852-31240120), per se not altered. This observation may be due to the different type of cells analysed suggesting a more pronounced effect on respiratory cells, the first barrier of defense against respiratory infections. Of note, the region between the S-shore of this CGI and the next CGI displayed an extended hypermethylation in symptomatic COVID-19 patients compared to asymptomatic ones. Interestingly, as noted above, this last-mentioned CGI (chr6:31276241-31276526), upstream HLA-C, was hypermethylated in COVID-19 patients compared to controls. This observation is replicated by analyzing the data made available by Balnis and colleagues (17), obtained from a much larger cohort of patients. The identification of a differential methylation pattern among patients with different prognosis, led us to investigate the expression levels of the gene. The consequent analysis of HLA-C expression by q-PCR actually showed a very statistically significant down-regulation of the gene in patients compared to controls, even more pronounced in the symptomatic ones, especially in those without other comorbidities, that could affect HLA-C expression. The levels seem to re-normalize after the viral clearance and disappearance of symptoms (post-COVID-19). Of note, it has been shown that ciliated cells from severe COVID-19 patients display a reduced overexpression of HLA-C, among other genes, compared with those from patients with moderate symptoms (36). The association between the methylation status of the HLA-C-associated distal CGI and the region upstream HLA-C (S-shore region of CGI chr6:31238852-31240120) and the expression levels of the gene seems to fit perfectly with the hypothesis that this region represents enhancers for the HLA-C gene, as highlighted by the bioinformatic analysis and by the evidence that these regions are sensitive to DNase I and coincident with peaks of H3K27Ac, associated with the higher activation of transcription and therefore defined as an active enhancer mark. Importantly, it has been shown that DNA methylation may regulate the transcription of HLA-A locus (37), while it has been supposed that HLA-B and HLA-C expression is not regulated by DNA methylation since these alleles have been shown to be unmethylated (37). However, we found an altered DNA methylation pattern of this region in COVID-19 patients. Interestingly, the distal regulatory region is bound by NFYB, a transcription factor, part of the enhanceosome known to regulate HLA genes (38), and resulting to regulate HLA-C from the bioinformatic analysis. In fact, as known, while HLA class II molecules are expressed in specialized APCs, HLA class I molecules are ubiquitously expressed and different regulators are involved in their expression. NLRC5/CITA (NOD-like receptor family CARD domain containing 5/Class I TransActivator) is the MHC class I regulator in selected cell subset (38). However, this factor lacks a DNA binding domain and thus requires other factors, that collectively form the enhanceosome, to contact the MHC class I promoter region at the level of an SXY-module containing a S, X1, X2 and Y box (38, 39). Y box is bound by an NFY-complex consisting of NFYA, NFYB and NFYC subunits (40). Moreover, HLA class I genes are additionally regulated by distal enhancers other than core promoter elements. Interestingly, it has been shown that the mechanism by which SARS-CoV-2 can inhibit MHC class I pathway is the suppression both at transcriptional and functional level of NLRC5 in the lung and airway epithelial cells during infection, consequently interfering with the CD8 T cell action and leading to higher risk of exacerbation of viral loads and prolonged infection (34). However, as the authors explained the inhibitory effect of SARS-CoV-2-ORF6 on MHC class I suppression can be observed only under IFNγ treatment and thus cannot explain the downregulation observed in COVID-19 patients (34). Moreover, it is important to consider that HLA-C, in contrast to HLA-A and HLA-B do not present NF-kB binding sites and indeed its expression is weakly induced by inflammatory cytokines such as IFNγ (41). Therefore, it is plausible that DNA methylation of HLA-C regulatory region bound by the enhanceosome complex and the distal enhancer may be an additional mechanism contributing to HLA class I downregulation directly or by non-coding RNAs mapping on HLA-C regulatory region, as already suggested (42). In fact, it is known that DNA methylation of enhancers is associated with gene expression dysregulation (43). Moreover, also NLRC5 expression may be dysregulated by other mechanisms such as promoter methylation, copy number alterations and genetic mutations and, as suggested, its expression levels may be associated with COVID-19 severity and mortality (34). For instance, HIV has been shown to alter the expression of NLRC5 by regulating its DNA methylation pattern (44). Interestingly, we found that COVID-19 patients displayed altered methylation in NLRC5. A further confirmation of the association between HLA-C expression levels and disease severity, is the case of a paucysymptomatic patient, although not statistically representative. The patient was initially classified in the symptomatic group, although presenting modest clinical signs. From the analysis of the methylation profile, the beta values of the loci examined were more similar to those found in non-symptomatic patients. HLA-C expression levels confirmed a phenotype more similar to asymptomatic patients than to symptomatic ones. Another interesting observation has emerged from the study of a subject followed since before the infection and at two different time points after the viral clearance. This subject showed a reduction of HLA-C expression levels at T1 and a recovery to the initial situation with even higher levels of expression. The hypothesis above described, that DNA methylation, normally not used in cells to regulate HLA-C expression, could be instead exploited as a mechanism induced by SARS-CoV-2 to downregulate this locus, would be absolutely plausible considering the observations made in infections due to other viruses, such as HIV, particularly persistent and capable of evading the host’s immune response. Many pathogens evade CTLs by downregulating HLA molecules on infected cells. The strategy of a virus to induce HLA molecules downregulation, in particular HLA-C, is well known for retroviruses. For example, most primary HIV-1 clones downregulate HLA-C, reducing the ability of HLA-C restricted CTLs to suppress viral replication in CD4 + cells (15). The down-modulation of HLA-C can be also associated with its reduced binding to the respective inhibitory receptors (KIR) present on the surface of NK cells, dependent on both host genetics and the extent of virus-mediated HLA-C downregulation (45). Therefore, also host genetics can contribute to a different predisposition to viral infections and to a different scenario of responses to the pathogen (45). It should be pointed out that, although HLA-C is expressed at lower level at cell surface than the other HLA-class I molecules and therefore its role in the adaptive immune responses has been considered as marginal, it acts as a natural ligand for KIR that are able to recognize virtually all HLA-C allotypes (46). Therefore, as mentioned in the introduction, HLA-C represents a ligand for both T cell receptors and NK cell receptors (15, 16). These evolutionary characteristics conferring to HLA-C locus particular efficacy in exerting immuno pressure on viral infection, have probably made it a preferential target by viral mechanisms (15). It is therefore natural to consider the down-regulation of HLA-C as a mechanism to evade both CTL and NK mediated immune responses. Once again, it is emblematic in this regard to observe HIV-1 infection, in which it has been shown that higher levels of HLA-C expression, regardless of specific allotypes, and specific peptides, are associated with better prognosis. This mechanism would be due at least in part to the consequent increase in the CTL-mediated response, thus exerting a higher immune pressure on the virus (47). Differences in expression even only twofold greater would improve CTL-mediated responses in vivo (48). Furthermore, HLA-C expression levels correlate inversely with viral load in patients not treated with anti-retroviral therapy (47). HIV-1 modulates the HLA-C expression through the accessory protein Vpu, with different intensities by the various viral strains and adapting the down-modulation to the HLA-C genotype of the host (15, 49). As mentioned, contrary to HLA-A and HLA-B, virtually all HLA-C allotypes are recognized by a number of inhibitory and activating KIRs, making HLA-C a dominant ligand for the regulation of NK cell activity (50, 51). It has also been shown that KIR+ NK cells can recognize HIV-1-Vpu-mediated alterations of HLA-C expression (16, 45). Consequently, it is not difficult to hypothesize that more “evolved” viruses aim at down-regulating HLA-C, which in turn is evolutionarily more diversified, therefore more capable of responding to the most varied types of infections, although lower expressed among HLA class I loci. It could be hypothesized that SARS-CoV-2 modulates the expression of HLA-C by means of an accessory protein similar to Vpu. In fact, among others, SARS-CoV-2 encodes a small transmembrane protein, called envelope (E), whose functions are not yet fully elucidated but which forms an ion channel that resembles, although different, viroporins such as Vpu (HIV) or M2 (influenza virus) (52). However, as shown and discussed above, the results of our study also strongly suggest an epigenetic mechanism, i.e. inducing host DNA methylation alteration, by which SARS-CoV-2 could down-modulate HLA-C expression, as hypothesized for example for MERS-CoV (3). A role for reduced KIR/HLA-C combination as risk factor for severe or fatal SARS-CoV-2 evolution has been demonstrated (in a cohort of patients coming from the same geographic area of this study); so a reduction of HLA-C expression such that we have found may reduce the activity of NK cells (in particular the memory-like NK) against the virus and thereby contribute to impaired viral clearance at early stages of infection (53). From this evidence, it is clear that manipulation of the HLA class I presentation pathway through various mechanisms limiting their cell surface expression, which is shared by some other viruses (54) and also human coronaviruses (3), may represent a mechanism to escape/delay the early innate and adaptive immune response. This can reduce the efficacy of CD8+ T cells to recognize viral peptides presented by HLA class I molecules and thereby delay viral clearance, also not allowing a long memory of the infection to develop. By acting in this way, the virus would have shown an adaptation that makes it capable of maintaining its stay in the host population longer. In conclusion, our results pointed out the reduction of HLA-C expression in COVID-19 patients, more pronounced in the severe cases, suggesting this molecule involved in antigen presentation as a potential prognostic marker and therapeutic target in RNA virus infections. Moreover, this discovery opens the possibility to design a vaccine conjugating SARS-CoV-2-specific antigen with an adjuvant that can stimulate the activation of T cells responsible for the immunological memory against the infection.
The data presented in the study are deposited in the MoBGE lab repository (Department of Biomedical Sciences, University of Cagliari), available here: https://drive.google.com/drive/folders/1C4EzXOhBqZW9XuoZep4nsUy1JfB8mPYu?usp=sharing.
The studies involving human participants were reviewed and approved by Ethics Committee of “ATS Sardegna” (224/2020/CE). The patients/participants provided their written informed consent to participate in this study.
PZ and DF conceived the project. AC oversaw the management of COVID-19 patients. PZ coordinated the entire research project. PC, GA, GC, SD-G, DF and AC collected the samples and clinical information. PZ and LM purified and processed all DNA, RNA, cDNA samples for the whole-genome methylation assay and gene expression assay. EL performed the bioinformatics analyses of methylome data and functional annotation, performed gene expression assay. EL and PZ analysed gene expression data and interpreted results of all experimental procedures, wrote the original draft and final version of the manuscript. DF and HE made critical revision of the manuscript for important intellectual content. All authors critically reviewed and commented the manuscript. All authors contributed to the article and approved the submitted version.
This work was supported by grants from Fondo per la Ricerca Locale (ex 60%), Università di Cagliari, to PZ and from Fondazione di Sardegna (CUP:F73C22001270007), to DF and PZ.
We thank all COVID-19 patients and healthy participants for making this research possible. We wish to thank the healthcare workers from the sample collection centers for their kind collaboration and Dr. Sandra Orrù for advice and continuous support in this research.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. | true | true | true |
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PMC9618941 | Josef Horak,Alexandra Dolnikova,Ozge Cumaogullari,Andrea Cumova,Nazila Navvabi,Ludmila Vodickova,Miroslav Levy,Michaela Schneiderova,Vaclav Liska,Ladislav Andera,Pavel Vodicka,Alena Opattova | MiR-140 leads to MRE11 downregulation and ameliorates oxaliplatin treatment and therapy response in colorectal cancer patients | 17-10-2022 | miR-140,colorecal cancer,MRE11,oxaliplatin,therapy response,DNA damage,DNA repair,miRNA | Cancer therapy failure is a fundamental challenge in cancer treatment. One of the most common reasons for therapy failure is the development of acquired resistance of cancer cells. DNA-damaging agents are frequently used in first-line chemotherapy regimens and DNA damage response, and DNA repair pathways are significantly involved in the mechanisms of chemoresistance. MRE11, a part of the MRN complex involved in double-strand break (DSB) repair, is connected to colorectal cancer (CRC) patients’ prognosis. Our previous results showed that single-nucleotide polymorphisms (SNPs) in the 3′ untranslated region (3′UTR) microRNA (miRNA) binding sites of MRE11 gene are associated with decreased cancer risk but with shorter survival of CRC patients, which implies the role of miRNA regulation in CRC. The therapy of colorectal cancer utilizes oxaliplatin (oxalato(trans-l-1,2-diaminocyclohexane)platinum), which is often compromised by chemoresistance development. There is, therefore, a crucial clinical need to understand the cellular processes associated with drug resistance and improve treatment responses by applying efficient combination therapies. The main aim of this study was to investigate the effect of miRNAs on the oxaliplatin therapy response of CRC patients. By the in silico analysis, miR-140 was predicted to target MRE11 and modulate CRC prognosis. The lower expression of miR-140 was associated with the metastatic phenotype (p < 0.05) and poor progression-free survival (odds ratio (OR) = 0.4, p < 0.05). In the in vitro analysis, we used miRNA mimics to increase the level of miR-140 in the CRC cell line. This resulted in decreased proliferation of CRC cells (p < 0.05). Increased levels of miR-140 also led to increased sensitivity of cancer cells to oxaliplatin (p < 0.05) and to the accumulation of DNA damage. Our results, both in vitro and in vivo, suggest that miR-140 may act as a tumor suppressor and plays an important role in DSB DNA repair and, consequently, CRC therapy response. | MiR-140 leads to MRE11 downregulation and ameliorates oxaliplatin treatment and therapy response in colorectal cancer patients
Cancer therapy failure is a fundamental challenge in cancer treatment. One of the most common reasons for therapy failure is the development of acquired resistance of cancer cells. DNA-damaging agents are frequently used in first-line chemotherapy regimens and DNA damage response, and DNA repair pathways are significantly involved in the mechanisms of chemoresistance. MRE11, a part of the MRN complex involved in double-strand break (DSB) repair, is connected to colorectal cancer (CRC) patients’ prognosis. Our previous results showed that single-nucleotide polymorphisms (SNPs) in the 3′ untranslated region (3′UTR) microRNA (miRNA) binding sites of MRE11 gene are associated with decreased cancer risk but with shorter survival of CRC patients, which implies the role of miRNA regulation in CRC. The therapy of colorectal cancer utilizes oxaliplatin (oxalato(trans-l-1,2-diaminocyclohexane)platinum), which is often compromised by chemoresistance development. There is, therefore, a crucial clinical need to understand the cellular processes associated with drug resistance and improve treatment responses by applying efficient combination therapies. The main aim of this study was to investigate the effect of miRNAs on the oxaliplatin therapy response of CRC patients. By the in silico analysis, miR-140 was predicted to target MRE11 and modulate CRC prognosis. The lower expression of miR-140 was associated with the metastatic phenotype (p < 0.05) and poor progression-free survival (odds ratio (OR) = 0.4, p < 0.05). In the in vitro analysis, we used miRNA mimics to increase the level of miR-140 in the CRC cell line. This resulted in decreased proliferation of CRC cells (p < 0.05). Increased levels of miR-140 also led to increased sensitivity of cancer cells to oxaliplatin (p < 0.05) and to the accumulation of DNA damage. Our results, both in vitro and in vivo, suggest that miR-140 may act as a tumor suppressor and plays an important role in DSB DNA repair and, consequently, CRC therapy response.
Treatment failure of colorectal cancer (CRC) therapy, represented by the development of drug resistance or outgrowth of metastasis, is a major complication for CRC patients. There is a crucial clinical need for predictive biomarkers that indicate the success or failure of cancer treatment. A better understanding of the cellular processes associated with drug resistance will eventually lead to improved treatment response by applying more effective combination therapies (1). Cancer cells react toward chemotherapeutics in different modes, such as by modifying DNA repair pathways. DNA repair plays a major role in the cancer therapy response, as chemotherapeutics usually induce various types of DNA damage in cancer cells (2). The overexpression of DNA repair genes in the tumor may confer more efficient repair of induced damage and thus contribute to chemoresistance and impaired therapy response (3). However, downregulation of the DNA repair genes may confer a better therapy response but may also give a basis for the appearance of new mutations and cancer progression (4). Oxaliplatin (oxalato(trans-l-1,2-diaminocyclohexane)platinum; OX) belongs to the most used chemotherapeutics in CRC treatment. OX is a genotoxic drug that induces the formation of DNA crosslinks, thus directly impairing the structure of DNA, inhibiting DNA replication and RNA synthesis, and inducing apoptosis (5). One of the most crucial repair pathways to deal with DNA crosslinks is homologous recombination (HR), a constituent of double-strand break (DSB) repair (6). MRN complex, a protein complex consisting of MRE11-RAD50-NBS1, plays an important role in the initial processing of DSB repair. The impaired function of the MRN complex leads to gene instability and DNA damage accumulation, a prerequisite of malignant transformation (7). Mutations in MRE11 predispose to CRC and are frequent in primary CRC with mismatch repair deficiency (8). Patients with the decreased expression of MRE11 were more sensitive to OX treatment, with more significant tumor mass reduction and more prolonged progression-free survival (9). Moreover, single-nucleotide polymorphisms (SNPs) in the 3′ untranslated region (3′UTR) of MRE11 gene are associated with decreased cancer risk but with shorter survival in CRC patients, which implies the role of microRNA (miRNA) regulation in CRC (10). MiRNAs are signaling molecules in various cell processes functioning mainly as the suppressors of gene expression through interaction with 3′UTRs of target mRNAs. However, miRNAs have also been shown to interact with other regions of mRNA and can even activate gene expression under certain conditions (11). There are several mechanisms by which the deregulation of miRNAs can influence malignant transformation (for review, see (12)). Regardless of the mechanism, miRNA dysregulation can potentiate CRC development by acquiring one or more hallmarks of cancer (13). Despite some evidence of miRNAs influencing the CRC sensitivity to the therapy, there is a scarcity of miRNAs associated with OX therapy response (14). The main aim of this study was to investigate the effect of miRNAs on the OX therapy response of CRC patients. Based on our previous published study, where we observed an association of SNPs in the 3′UTR of the MRE11 gene with decreased CRC risk (10), we performed in silico analysis of miRNAs associated with MRE11 and found 187 miRNAs with MRE11 as a predicted target. By additional analysis using The Cancer Genome Atlas (TCGA) database, we have identified miR-140 as the best candidate for further investigation. Our results suggest that the miR-140/MRE11 axis is associated with improved therapeutic response in oxaliplatin-treated CRC patients.
Paired tumor and non-malignant adjacent mucosa samples were obtained from 50 patients who underwent surgery between the years 2011 and 2015 and in whom all information was followed and updated in 2021 (patients’ characteristics in Table 1 and Supplementary Table 1 ). All the patients provided signed consent for participation and their medical documentation for research. The design of the study was approved by the Ethical Committee of the Institute of Experimental Medicine, Prague, Czech Republic. RNA was isolated from tissues by miRNeasy® Mini Kit (50) (Qiagen, Hilden, Germany).
Data from TargetScan (15) were extracted by multiMiR R package (16). All miRNA-Seq transcriptional profiles and detailed clinical information were downloaded from TCGA (https://portal.gdc.cancer.gov) using the TCGAbiolinks R package (17). For the present study, data from the project TCGA-READ (rectal adenocarcinoma, n = 155) and TCGA-COAD (colon adenocarcinoma, n = 476) for every miRNA were separately analyzed and filtered according to the following criteria: 1) analyses were performed on CRC patients who had miRNA expression level data available, and 2) clinical data including survival data were also available. Finally, for miR-140, a total of 570 patients presented expression levels.
Human colorectal cancer cell lines HCT116, DLD1, and HT29 were obtained from Merck (Darmstadt, Germany). Cell lines were cultured in Dulbecco’s modified Eagle’s medium (DMEM) (Merck, Germany) with 10% fetal bovine serum (Merck, Germany), 1 mM of l-glutamine (Biosera, Nuaille, France), 1 mM of sodium pyruvate (Biosera, Nuaille, France), and 1 mM of penicillin/streptomycin (Biosera, Nuaille, France). All cells were cultured in a humidified incubator at 37°C, with 5% CO2.
Cells were transfected in 6-well plates at 60%–80% confluency with 2.5 pmol of MISSION miRNA hsa-miR-140-3p miRNA Mimics (Ambion, Austin, TX, USA) or with Negative Control miRNA Mimics (Ambion, USA) with no homology to the human genome using Lipofectamine® RNAiMAX 2000 (Invitrogen™) according to the manufacturer’s protocol. All the experiments in cell lines were performed in three independent repeats. The efficiency of transfection was analyzed by qPCR measuring expression levels of transfected miRNAs as compared to negative controls.
Forty-eight hours after transfection, total RNA (including miRNAs) was extracted from cells using Qiagen miReasy Mini Kit (Qiagen, Germany) according to the manufacturer’s protocol. The concentration of the total RNA was measured by Nanodrop™ 8000 Spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), and the integrity of mRNA (RNA integrity number (RIN)) of each sample was determined by Agilent RNA 6000 Nano Kit by Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA). Reverse transcription was performed using the High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific, USA), according to the manufacturer’s protocol.
Expression levels of miR-140 were measured using TaqMan MicroRNA Assays at 7500 Real Time PCR System (Thermo Fisher Scientific, USA). The reaction contained 2 μl of a sample with 40 ng of cDNA, 10 μl of TaqMan™ Universal PCR Master Mix, 1 μl of the assay, and 7 μl of RNAse-free water. The thermal protocol was as follows: 50°C for 2 min, 95°C for 10 min, 40 cycles of 95°C for 15 s, and 60°C for 60 s plus melting curve analysis. MiRNA expression was normalized to RNU6B, and all data were subsequently analyzed by the 2−ΔΔCt method.
Oxaliplatin, obtained from Merck (Germany), was dissolved in dimethyl sulfoxide (DMSO; Merck, Germany) at the concentration of 100 mM and stored at 4°C. To assess the chemosensitivity of CRC cells with overexpressed miR-140 and control cells, both cells were treated with a 6 μM concentration of oxaliplatin 24 h after miRNA mimics transfection and analyzed for cell viability.
For clonogenicity formation assay (CFA), 48 h after cell transfection with miRNA mimics, 500 cells per well were plated for colony formation assay onto 6-well plates and cultured in DMEM. Twelve days later, colonies were fixed with 3% formaldehyde, stained with 1% crystal violet, and counted. For proliferation assay, cells were plated onto 96-well plates at a density of 3 × 104 cells per well. The metabolic activity of the cells was measured 24 h after plating by adding WST-1 solution into the media as recommended by the manufacturer (Merck, Germany). Absorbance at 450 and 690 nm was measured on BioTek ELx808 absorbance microplate reader (BioTek, Winooski, VT, USA).
Cells were seeded on 12 well plates (5 × 105 cells/ml), harvested, washed with PBS, and centrifuged at 1,000 rpm for 10 min. Then, 1 ml of propidium iodide (PI) staining solution (0.02 µg/µl of PI, 0.02 mg/ml of RNase, and 0.05% Triton X-100) was added to the cell pellet, and cells were incubated for 30 min at 37°C in the dark. After incubation, samples were analyzed using a flow cytometer (Apogee A-50 micro, Apogee, Hertfordshire, UK). Measured data were evaluated with FlowLogic software (Inivai Technologies, Mentone, VIC, Australia).
Proteins (20 μg) were loaded and separated in 10% sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE) gels at 15 mA for 60 min. Then, the separated proteins were transferred to 0.45 µm Amersham Protran Nitrocellulose Blotting Membrane (GE Healthcare, Life Sciences, Marlborough, MA, USA) in methanol transfer buffer using Mini Trans-Blot Cell (Bio-Rad Laboratories, Hercules, CA, USA). The membranes were blocked with 5% bovine serum albumin (BSA) in Tris-buffered saline containing Tween 20 (TBST; 20 mM of Tris–HCl, pH 7.4, 0.15 M of NaCl, and 0.1% Tween 20) for 1 h and incubated with anti-MRE11, anti-γH2AX, anti-RAD51 (Cell Signaling, Leiden, the Netherlands) and anti-GAPDH antibodies (Abcam, Cambridge, UK) at 4°C overnight, followed by incubation with goat anti-rabbit secondary antibody conjugated with horseradish peroxidase (Abcam, Cambridge, UK). The membranes were then incubated with Immobilon Western Chemiluminescent HRP Substrate (EMD Millipore Corporation, Billerica, MA, USA) and visualized by Azure c600 (Azure Biosystems, Dublin, CA, USA).
For the preparation of recombinant lentiviruses expressing MRE11 shRNAs, HEK293FT cells (Thermo Fisher, Waltham, MA, USA) seeded in 6-well plates were co-transfected with pLKO1 mission MRE11 shRNA plasmids and helper plasmids psPax2 and pMD2.g (Addgene, Cambridge, MA, USA) using Lipofectamine 3000 (Thermo Fisher, Massachusetts, USA). Six hours later, the medium was replaced with fresh DMEM without antibiotics. After 48 h, the recombinant lentivirus-containing culture medium was harvested and centrifuged at 15 min, 3,000 rpm, and 4°C to remove any floating cells and cell debris. The cleared media containing lentiviruses were at 1:3 and 1:10 v/v ratios, added to HCT116 cells and plated in a 12-well plate, and after 24 h; the media were replaced with the fresh cultivation medium; cell cultures containing integrated lentiviruses were selected by using 2 μg/ml of puromycin for 4–5 days. Transfected cells were then tested using genomic PCR and Western blotting analysis for the genetic elimination/loss of expression of the MRE11 gene.
Statistical analyses were performed using pairwise comparison by Student’s t-test and two-way ANOVA (GraphPad Prism8, GraphPad Software, La Jolla, CA, USA; www.graphpad.com). The results represent the mean value of three independent experiments ± SD; the significance level was set at p ≤ 0.05. Statistical analysis for TCGA data was performed using the R environment using the dplyr and survival, survminer, and ggplot2 packages. The survival significance was measured by a log-rank test.
Using TargetScan (15), we found 187 miRNAs with MRE11 as a predicted target ( Supplementary Table 2 ) with 111 miRNAs with data sufficient for progression-free survival (PFS) calculation in the TCGA database. Out of these 111 miRNAs, eight had a statistically significant impact on PFS (p < 0.05, Supplementary Table 3 ). We identified miR-140 as the candidate for further investigation, as it displayed the strongest statistically significant association with PFS ( Figure 1 , p < 0.01) in the group of analyzed miRNAs supported by data from more than 500 patients.
We investigated the expression levels of MRE11 and miR-140 in 50 CRC tumor tissues and adjacent non-malignant mucosa samples ( Table 1 and Supplementary Table 1 ). The levels of miR-140 were significantly lower in tumor tissue ( Figure 2A , p < 0.01) compared to adjacent mucosa. MRE11 levels were moderately, but not significantly, higher in tumor tissues ( Figure 2B , p = 0.11). A significant decrease in miR-140 in patients’ CRC samples led only to a moderate non-significant increase in MRE11, which might be due to broader regulation, mixed phenotype, or complex treatment. The Kaplan–Meier analysis showed, in concordance with TCGA results, that lower expression of miR-140 in tumor tissue is associated with poor PFS ( Figure 2C , p < 0.05). Because metastatic CRC has a higher mortality rate and treatment is much more challenging, we have also investigated the association between miR-140 and metastatic formation. Our data showed that decreased expression of miR-140 is associated with the metastatic phenotype of CRC ( Figure 2D , p < 0.05).
To select the appropriate colorectal cell line for transient transfection, we measured the expression levels of miR-140 in different CRC cell lines ( Supplementary Figure 1A ), and we decided on DLD1 by transient transfection of miR-140 by miRNA mimics. We have reached a significant increase in miR-140 levels stable up to 72 h ( Supplementary Figure 1B ). Our data showed that overexpression of miR-140 using miRNA mimics decreased the protein levels of MRE11 ( Figure 3A ) as well as mRNA levels of MRE11 ( Figure 3B ).
MRE11 is a crucial component of the MRN complex associated with DSB repair (18). Therefore, we evaluated the effect of miRNA mimic-induced miR-140 overexpression on one of the markers of DSB DNA damage and γH2AX protein accumulation (19). Western blotting analysis showed higher levels of γH2AX after miR-140 miRNA mimics in the CRC cell line ( Figure 4 ).
The effect of miR-140 overexpression induced by miRNA mimics on CRC cell proliferation was measured using the WST-1 assay. Figure 5A shows that overexpression of miR-140 leads to decreased cell proliferation, pronounced 24 h after transfection (p = 0.05). However, miR-140 overexpression does not affect clonogenic potential ( Figure 5B ). In addition, flow cytometry analysis of the cell cycle showed that overexpression of miR-140 leads to moderate accumulation of cells in the G1 phase ( Figure 5C ).
Oxaliplatin is a third-generation platinum compound with an important role in CRC treatment. Therefore, we have investigated miR-140 in relation to the oxaliplatin sensitivity of CRC cells. Cell proliferation after oxaliplatin treatment in DLD1 cells overexpressing miR-140 significantly decreased after 48 and 72 h ( Figure 6A , p < 0.05). The clonogenic potential of the cells (CFA) revealed a significant decrease in colony numbers ( Figure 6B , p < 0.05). Cell cycle analysis of oxaliplatin-treated cells showed that overexpression of miR-140 leads to an increase in cells in the G1 phase and a decrease in those in the S phase ( Figure 6C ).
Our in silico analysis proposed a potential connection between miR-140 and MRE11. To further analyze the effect of miR-140 on oxaliplatin sensitivity through MRE11, we used recombinant lentiviruses expressing MRE11 shRNAs and established CRC cell lines with suppressed levels of MRE11 ( Figure 7A ). Cellular growth after miR-140 overexpression was not changed in parental and shMRE11 cell lines ( Figures 7B, C ). The measurement of cellular growth of HCT116 with overexpression of miR-140 and oxaliplatin treatment showed decreased cellular growth (p = 0.05) ( Figure 7D ). However, the analysis of cell growth did not show increased oxaliplatin sensitivity of shMRE11 cells with overexpressed miR-140 ( Figure 7E ).
Poor therapy response and chemoresistance pose significant complications in CRC treatment, leading to ineffective therapy, tumor progression, metastasis, relapse of disease, and impaired patient survival. Based on our previous evidence that miRSNPs in the MRE11 gene influence CRC risks and survival (10), in the present study, we investigated the effect of the miRNA/MRE11 axis on the oxaliplatin therapy response of CRC patients. Despite the multidisciplinary approach and chemotherapy improvement, there is a considerable percentage of patients with inadequate response to treatments and a poor prognosis. Currently, there is a lack of properly validated predictive factors for CRC treatment response, and the emergence of resistant clones is a non-negligible reason for therapeutic failure and potential metastasis development (20). In our study, we defined the association of miR-140 expression with PFS, where lower miR-140 expression is associated with poor survival. Furthermore, our results showed lower levels of miR-140 in tumor tissue. MiR-140 expression has been previously studied mainly in association with cancer development and recurrence. Zheng et al. performed a meta-analysis and found a strong correlation between high expression of miR-140 and better overall survival (OS) in several cancers. Conversely, low expression is associated with advanced stages, worse histologic type, and lymph node metastasis (21). MiR-140 could also remarkably reduce the tumor size in gastric cancer xenograft mice (22). Yuan et al. found that miR-140 is significantly downregulated in non-small lung carcinoma (NSCLC) tissues and cell lines (23). In recent years, there has been increasing evidence of a miR-140 role in the response to platinum derivative treatment in different cancers. Meng et al. described that miR-140 promoted autophagy mediated by HMGN5 and sensitized osteosarcoma cells to chemotherapy (24). Furthermore, miR-140 acts as a tumor suppressor in breast cancer by inhibiting FEN1 from repressing DNA damage repair. The authors of the published work reveal miR-140 to be a new anti-tumorigenesis factor for adjuvant breast cancer therapy (25). These results suggest a therapeutic potential of miR-140 in cancer treatment. Lui et al. demonstrated that plasma exosomal miR-140 in CRC patients was lower than in healthy controls, and their work supports our findings that miR-140 exerts a tumor suppressor ability (26). Moreover, we found that decreased expression of miR-140 was associated with metastatic CRC phenotype. Our findings are consistent with a study by Shahabi et al. (2020). The authors showed that low expression of miR-140 is associated with lymph node metastasis in breast cancer (27). Our in vitro analysis revealed an association of miR-140 overexpression with decreased CRC cell survival and accumulation of DNA damage. Moreover, overexpression of miR-140 enhances the sensitivity of colorectal cells to oxaliplatin. The important role of miRNA in oxaliplatin resistance in CRC was also proven by Wang et al. (28). They published evidence that overexpression of miR-29b re-sensitized OR-SW480 cells to oxaliplatin treatment. MiR-140 also re-sensitizes cisplatin-resistant NSCLC cells to cisplatin treatment through the SIRT1/ROS/JNK pathway (29). Direct or indirect induction of DNA damage is the main goal of most cancer treatment regimens. Therefore, the process of DNA damage repair plays an important role in therapy response and chemotherapy resistance. Unfortunately, cancer cells can initiate DNA repair, which plays a role in therapy response (3) and chemotherapy resistance (2). The clinical importance of HR for cancer therapy, mainly of MRE11, RAD50, and, NBS, has already been reported (30). According to Pavelitz et al., deficient MRE11 protein is a marker of better prognosis for CRC patients irrespective of treatment in the long term (31). We previously described the significant influence of miRNA binding sites (miRSNPs) in the MRE11 gene on CRC risks and survival (10). The importance of SNPs in miRSNPs of DNA repair genes has been also described in other types of cancer (32). MiR-140 was predicted as a potential interacting partner for MRE11 by TargetScan (15). In vitro overexpression of miR-140 causes the decrease of MRE11 protein levels. We did not observe any effect of miR-140 on cell proliferation and oxaliplatin sensitivity in the cells with inhibited MRE11 (shMRE11). Based on this data, we hypothesize that miR-140 affects oxaliplatin sensitivity in CRC cells via MRE11, or miR-140 may cooperate with MRE11 and may affect oxaliplatin sensitivity in tested cells. MRE11 downregulation may lead to impairment of MRN complex and thus to inefficient HR and subsequent damage accumulation (33). That is in accordance with our results, as we observed the accumulation of γH2AX, a marker of DNA damage, following overexpression of miR-140. Despite intensive research, the efficiency of CRC therapy remains low. Searching for novel prognostic and predictive biomarkers may lead to better therapy responses. The presence of miRNAs in blood plasma gives miRNAs a solid potential to be easily accessible biomarkers. However, their use may be compromised by the interindividual variability of cancer patients and large intratumor heterogeneity. Our results indicate miR-140 as a tumor suppressor and potential predictive biomarker for oxaliplatin treatment. We believe that identifying and validating novel biomarkers will ultimately lead to more personalized cancer therapy and improve the quality of a CRC patient’s life.
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
This study was reviewed and approved by the Ethical Committee of the Institute of Experimental Medicine, Prague, Czech Republic. The patients/participants provided written informed consent to participate in this study.
JH, AD, AC, OC, and AO performed the experiments. LA coordinated the cell line establishment. ML, LV, and MS were responsible for the collection of patients’ samples. PV reviewed the manuscript and discussed the results. AO coordinated the study and wrote a manuscript, JH wrote the manuscript. All authors contributed to the article and approved the submitted version.
The study was supported by the Grant Agency of Charles University (GAUK 784120), the Czech Science Foundation (20-03997S, 21-27902S, and 21-04607X), the Czech Health Research Council (grants AZV NV18/03/00199), Charles University grant Unce/Med/006, the Charles University Research Fund (Cooperation No. 43—Surgical Disciplines and the Cooperation Program, research area Oncology and Haematology), EFRR [project No. CZ.02.1.01/0.0/0.0/16_019/0000787 “Fighting INfectious Diseases”, awarded by the MEYS CR], and the National Operation Programme: National Institute for Cancer Research LX22NPO05102.
The results shown in the section “In silico analysis of miRNAs targeting MRE11” are in part based upon data generated by TCGA Research Network: https://www.cancer.gov/tcga.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. | true | true | true |
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PMC9619056 | Najmeh Alsadat Abtahi,Seyed Morteza Naghib,Fateme Haghiralsadat,Mohammadmahdi Akbari Edgahi,Esfandyar Askari | A comparative study on biopharmaceutical function of curcumin and miR-34a by multistimuli-responsive nanoniosome carrier: In-vitro and in-vivo 10.3389/fmolb.2022.1043277 | 17-10-2022 | curcumin,gene expression,coadminstration,nanopartcicles,chemotherapy,niosome | This research conducted a comparative study on nanoscaled niosomal structures consisting of Tween-80, Tween-60, cholesterol, and dioleoyl-3-trimethylammonium propane (DOTAP). Thin-film hydration technique was used for the preparation and entrapment of curcumin and miRNA in niosomal formulations for enhancing the stability and delivery rate of the agents. Herein, the influence of Tween-80, Tween-60, cholesterol, and DOTAP on the entrapment efficiency (EE%) of curcumin and the physicochemical properties of the carrier are fully discussed. The optimum engineered formulation resulted in a positive charge of +11.23 mV, high EE (100%), smooth surface, spherical shape, small diameter (90 nm), and good stability in physiological buffers. Also, an accelerated cellular uptake, as well as drug release in PBS (pH 7.4, 37°C) after 72 h, were observed. The cytotoxic activity of curcumin (Cur)/miR-34a-loaded nanoparticles was determined by the MTT assay. The results displayed an improved cytotoxic activity of Cur-niosome towards cancer cells compared to free-dispersed Cur. The uptake of Cur-loaded niosome by A280s and A280cp-1 cancer cell lines faced 2.5 folds drop in the concentration compared to its free form. Generally, Cur-niosome exhibits a significant accumulation of superior anti-cancer properties. Likewise, the cytotoxicity of miR-34a-niosome against tumor cells was higher in comparison with its free form. The anti-cancer effects of the gene/drug delivery were investigated in the 4T1 xenografted Balb/C mouse tumor model. According to the in vitro and in vivo results, gene delivery from the modified niosome nanoparticles was distinctly greater than Cur delivery. Therefore, it was concluded that encapsulation of genes in the nano-niosomal delivery system is a promising procedure for the treatment of cancer cells. | A comparative study on biopharmaceutical function of curcumin and miR-34a by multistimuli-responsive nanoniosome carrier: In-vitro and in-vivo 10.3389/fmolb.2022.1043277
This research conducted a comparative study on nanoscaled niosomal structures consisting of Tween-80, Tween-60, cholesterol, and dioleoyl-3-trimethylammonium propane (DOTAP). Thin-film hydration technique was used for the preparation and entrapment of curcumin and miRNA in niosomal formulations for enhancing the stability and delivery rate of the agents. Herein, the influence of Tween-80, Tween-60, cholesterol, and DOTAP on the entrapment efficiency (EE%) of curcumin and the physicochemical properties of the carrier are fully discussed. The optimum engineered formulation resulted in a positive charge of +11.23 mV, high EE (100%), smooth surface, spherical shape, small diameter (90 nm), and good stability in physiological buffers. Also, an accelerated cellular uptake, as well as drug release in PBS (pH 7.4, 37°C) after 72 h, were observed. The cytotoxic activity of curcumin (Cur)/miR-34a-loaded nanoparticles was determined by the MTT assay. The results displayed an improved cytotoxic activity of Cur-niosome towards cancer cells compared to free-dispersed Cur. The uptake of Cur-loaded niosome by A280s and A280cp-1 cancer cell lines faced 2.5 folds drop in the concentration compared to its free form. Generally, Cur-niosome exhibits a significant accumulation of superior anti-cancer properties. Likewise, the cytotoxicity of miR-34a-niosome against tumor cells was higher in comparison with its free form. The anti-cancer effects of the gene/drug delivery were investigated in the 4T1 xenografted Balb/C mouse tumor model. According to the in vitro and in vivo results, gene delivery from the modified niosome nanoparticles was distinctly greater than Cur delivery. Therefore, it was concluded that encapsulation of genes in the nano-niosomal delivery system is a promising procedure for the treatment of cancer cells.
While an alternation occurs in biological systems, there is a tendency for cells to lose their normal function and begin to do abnormal activities. Herein, p53 is a tumor suppressor protein that is highly critical in cell response to stresses such as DNA injury and oncogenes activation. Many tumor malignancies arise when their functionality is faced with problems (Boutelle and Attardi, 2021). P53 can modify specific gene expression captured via apoptosis, improve DNA repair, and prevent angiogenesis (Merlin et al., 2021). As stated by many studies, the most frequent miRNA which is induced via this tumor suppressor protein is the miR-34 group (Sargolzaei et al., 2020). The anti-cancer mechanism of this category of miRNAs is based on the suppression of tumors which approves their potential for anti-tumoral purposes (Najminejad et al., 2019). Generally, microRNAs or miRNAs are small-sized RNAs with non-coding ability that strongly influence tumorigenesis, apoptosis, cell proliferation, and differentiation. They can alter gene expression in the post-transcriptional stage (Tricoli and Jacobson, 2007). Several pieces of research displayed a significant reduction in the level of specific miRNAs which operate as tumor suppressors within cancerous areas. From experimental findings, miRNAs with such properties are categorized in 15a, 16-1, 143, let-7, 145, and 34 (Pidíkova et al., 2020). The miR-34 family involves important mediators with tumor-suppressing ability. Various documents indicate that through ectopic expression of miR-34s, many factors, including proliferation, invasion, metastasis, and epithelial-to-mesenchymal transition, are eliminated. Besides, an initial viral vector in miR-34 delivery is critical (Flood et al., 2019). Two commonly used materials are niosomes and liposomes to deliver miRNA to the cancer site. In recent years, the use of these vesicular carriers in systemic delivery applications has received a lot of interest because of their acceptable entrapment efficacy, reduced side effects, increased drug solubility, long-term blood circulation, and capability to target a specific spot (Abtahi et al., 2022). Curcumin (Cur) has also attracted scientific research in terms of delivery applications. Cur is a yellowish natural diphenolic compound derived from the rhizome of Curcuma Longa, turmeric, which has been widely used as a spice or as a medicine in Asian countries (India, China, and Indonesia) for the treatment of a variety of illnesses, including stomach disorders, skin diseases, bile formation problems, anorexia, rhinitis, sinusitis, cough, diabetic lesions, liver function problems, and rheumatism. Polyphenol was revealed to have numerous pharmacological effects in 1970, including antibacterial, anti-inflammatory, antioxidant, and anti-tumor capabilities (Chauhan et al., 2019; He et al., 2021; Hassan et al., 2022; Moghadam et al., 2022). Cur’s anti-cancer mechanism on malignant cells is based on complicated molecular signaling pathways, including estrogen receptor, proliferative pathways, and human epidermal growth factor 2 receptor, and eventually, induces apoptosis, according to recent reviews in this field (Wang Y et al., 2016; Kunnumakkara et al., 2017; Talib et al., 2018; Farhadihosseinabadi et al., 2019; Song et al., 2019). Moreover, Cur is a regulator of the p53 protein in breast cancer (Talib et al., 2018). Cur has been shown to regulate several cell signaling pathways, including cell survival (c-IAP1, cFLIP, Bcl-xL, Bcl-2, XIAP), cell multiplication (cyclin D1, c-myc), caspase stimulation (caspase-3, 8, 9), protein kinase (JNK, Akt, and AMPK), mitochondrial, death receptor (DR4, DR5), and tumor inhibitor (Yen et al., 2019). Despite its positive characteristics, Cur free use in clinical cancer therapy is limited because of its low water solubility, poor oral absorption, and rapid metabolism. As a result, scientists have been looking for novel ways to address the aforementioned shortcomings (Chendil et al., 2004; Mozafari et al., 2015; Wong et al., 2019). Nanotechnology helps to solve these issues by establishing cancer diagnostics and treatment (Meng et al., 2020; Khan et al., 2021; Yi et al., 2021). The creation of drug delivery systems (DDSs) aids in the distribution of low-affinity medications to tissues and cells, improving treatment effectiveness and reducing systemic adverse effects at the target location (Meng et al., 2020; Eftekhari et al., 2021). The excellent efficacy of encapsulating, managing drug release, improving drug solvability, conveying hydrophilic and hydrophobic pharmaceuticals, and longer blood circulation given by these carriers have made vesicular medication administration popular in recent decades (Wu et al., 2021; Almansob et al., 2022). Due to its ability to capture both hydrophobic and hydrophilic medicines within its bilayer via non-polar and core cavity regions, niosomes have attracted increasing scientific interest as valuable DDSs in recent years. Niosome vesicles are non-ionic multilamellar or unilamellar surfactants ranging from 10 to 1,000 nm and are non-immunogenic, biodegradable, and biocompatible. Since the non-ionic surfactants used for the preparation of niosome are cheaper than phospholipids, they also have taken a larger part in nanotechnology. Niosomes are being studied for the delivery of drugs like Roxithromycin, Bovine Serum Albumin, and Doxorubicin, as well as siRNA/miRNA delivery. As a result, various nanocarrier formulations for Cur have been introduced, including liposomes and niosomes. Similarly, manifold documents on the nano-potential carriers for transporting other therapeutic medicines, such as miRNA, have been published. Curcumin and miR-34a were therefore incorporated into novel niosomal nanoformulations in the current work to boost cancer cell targeting while lowering harmful effects on healthy cells. To do this, we created and improved multiple nano-drug formulations, described them chemically and physically, and then tested their biological effects on normal and cancer cell lines. Co-delivery refers to the use of curcumin and miR-34a in the same carrier for medication delivery. Curcumin and miR-34a packaged in niosomes were delivered together, and the impact was studied in vitro and in vivo (Nikniaz et al., 2021).
Herein, Tween-80 (T-80) and Tween-60 (T-60) (DaeJung Chemicals & Metals, Seoul, South Korea), DMSO (3-[4,5-dimethylthiazol-2-yl]-2), curcumin, cholesterol, and MTT (3-[4,5-dimethylthiazol-2-yl]-2,5 diphenyl tetrazolium bromide) (Sigma-Aldrich, Missouri, United States), and DOTAP (1,2-dioleoyl-3-trimethylammonium-propane), were acquired for this study. Human ovary cancer cells (Pasteur Institute, Tehran, Iran), FBS, antibiotics, PBS, cell culture medium (RPMI 1640), trypsin, and sodium pyruvate were also provided for the experiment (Gibco, New York, United States). All the chemicals were of analytical grade and no extra purification process was applied for salts, chemicals, and solvents, except in particular cases. The order synthesis of miR-34a primer was forward, CTTGAACTCCTGGGGCCTGAAG; reverse, GCCAAAGAAACACTCACAGCT, and Eurofins Genomics Ebersberg were utilized for this reason.
In the preparation of niosomes, thin-film hydration technique was employed. Cholesterol and T-80 were first computed precisely and then dissolved in 100 ml of chloroform. Then, a rotating flash evaporator (Ultrasonics GmbH, Heidolph, Germany) was conducted to form a thin lipid at low pressures. Afterward, the obtained film underwent 3 ml of PBS for the hydration process at pH 7.4 and 60°C. To decrease the mean size of vesicles, a microtip probe sonicator was utilized to operate the sonication process of hydrated lipids for 30 min. Then, the niosome formula was monitored to detect the physical properties. In the next step, for achieving enhanced stability of niosome formula cationic lipid, DOTAP, was added. Besides, forming a thin layer on a rotating evaporator resulted in eliminating organic solvent at 45°C. For obtaining niosome suspension, the hydration of the thin layers was achieved by adding PBS at pH 7.4 and 60°C for 45 min. Finally, the niosomal solution was sonicated using (Ultrasonics GmbH, Hielscher, Germany) for 15 min to reduce the average particle size.
To assess the overall characteristics of niosomes, including particle size, zeta potential, and poly-dispersity index (PDI), dynamic light scattering (DLS) analysis was conducted by Brookhaven Corp Instrument (Brookhaven Instruments; Holtsville; NY: United States). Likewise, Atomic force microscopy (AFM), Scanning electron microscopy (SEM), and Transmission electron microscopy TEM (AFM5100N, HITACHI) were conducted to assess the morphology of the gene and drug-loaded niosome. The resulting data and mean values were used for triplicate measurement.
To measure the encapsulation efficiency, first, the unencapsulated drugs were subtracted from the loaded niosomes by immersing them into the dialysis cellulose membrane tubing (12 kDa MWCO) against 4°C PBS solution. Afterward, the niosomes were lysed using isopropanol and a UV-Vis spectrophotometer, (T80+, PG Instruments, United Kingdom) 429 nm wavelength, was utilized to determine the encapsulation efficiency according to the following equation:
To assess the released quantity of Cur from niosomes, a 12 kDa MWCO dialysis tube was employed (Shaker et al., 2015). For this purpose, 10 mM PBS was used as the buffer solution, and samples were held at 25, 37, and 42°C for 72 h, at pH values of 5.5, 6.5, and 7.4. Afterward, all samples were individually suspended into the dialysis bag and stirred constantly. At each interval, aliquots of PBS were taken and the same volume of PBS was inserted. Ultimately, UV-Vis spectrophotometry was employed to determine the release rate (Shaker et al., 2015).
To assess the effect of long-lasting storage on the leakage behavior of miRNA from nanocomplexes, samples were held at 4°C for 2 months. Afterward, nanometer the stability of all samples was recorded using spectrophotometric at 260 nm.
The physical characteristic of Cur-loaded niosome, parameters such as the size of particles, PDI, residual quantity, and zeta potential were obtained using DLS analysis through Zeta PALS zeta potential and particle size analyzer (Br particle size distribution, Brookhaven Instruments, New York, United States) at intervals of 15, 30, and 60 days.
The morphology of nanoparticles was examined by subjecting a drop of the nanoparticle solution to negative staining with 1% (w/v) phosphor tungstic sodium solution and AFM, SEM, and TEM were performed.
To culture cells, first, human ovarian cancer A2780s and A2780cp-1 cells (Iranian Biological Resource Center, Tehran, Iran) were preserved as monolayer cultures in a mixture of Ham and RPMI-1640 medium (Ino Clon, Tehran, Iran) augmented with 2 mM of GlutaMAX™-I (100X), 10% FBS, and 1 mg/ml penicillin/streptomycin (Gibco, Massachusetts, United States). The MCF-10A cell line (Iranian Biological Resource Center, Tehran, Iran), a non-tumorigenic human breast epithelial cell line, was cultured in DMEM/F12 Ham’s mixture enriched with 1 mg/ml of penicillin/streptomycin, 5% horse serum, 2 mM of Gluta MAX™-I (Gibco, Massachusetts, US), 100 ng/ml cholera toxin, 10 μg/ml insulin, EGF 20 ng/ml, and hydrocortisone 0.5 μg/ml (Sigma-Aldrich, Missouri, United States). The MCF-10A cells were utilized as the control group in all tests.
The cytotoxic activity of Cur-loaded nanoparticles was clarified by MTT assay (Sigma-Aldrich, Missouri, US) (Wang J et al., 2016; Xu et al., 2016). Briefly, cells were seeded in 96-well plates at 10,000 cells per well. After a day, 200 μL of a new media containing sequential dilutions of several formulations, including niosomal Cur, free Cur solution, and free niosome, was inserted into the cells. Then, all of the prepared samples were incubated for 1, 2, and 3 days (s). Next, in each 96-well plate, 20 ml of MTT (5 mg/ml in PBS) was added and followed by incubation at 37°C for 3 h for further verification. Next, the medium was carefully separated, instead, to suspend the produced formazan crystals, each well received 180 ml of dimethyl sulfoxide. Records of optical density in each well were conducted using EPOCH Microplate Spectrophotometer (synergy HTX, BioTek, Vermont, US) at the wavelengths of 570 and 630 nm. The cytotoxic activity of samples was shown as the value of the Inhibitory Concentration (IC50), which represents the least concentration of drug to inhibit 50% of cell growth compared to the control. The Cur IC50 levels as single drugs were estimated by Graph Pad Prism 6. The test was performed in triplicate.
A2780s and A2780cp-1 cells were seeded in 6-well plates at a concentration of 50,000 cells/well and kept in an incubator for 1 day to enable cell attachment. Next, different concentrations of Cur were added to wells. Then, cells were incubated for another 3 h followed by washing thrice with cold PBS and fixed with a mixed solution of methanol and citric acid (3:1) (Sigma-Aldrich, Missouri, United States). After staining with 4′,6-diamidino-2-phenylindole (DAPI, 0.125 μg/ml, Thermo Fisher Scientific, Massachusetts, US), cell images were obtained using fluorescent microscopy (BX61, Olympus, Japan) (Balakrishnan et al., 2009). The test was done only once.
In apoptosis of cells, they were subjected to different samples as free Cur, free miRNA, niosomal Cur, and niosomal miRNA at IC50 concentration for all of them, and annexin V-FITC/PI double staining (Sigma-Aldrich, Missouri, United States) was performed to determine the dead cells. First, MCF-7 cells were seeded into 6-well plates at a concentration of 100,000 cells/well and incubated for 24 h. Afterward, for detaching the cells, 0.25% trypsin/EDTA was added to the wells and then they were centrifuged at 1,500 rpm for 3 min. Next, the pellet was dissolved in the ice-cold PBS at pH 7.4 and then 3 μl of Annexin V-FITC was inserted into the suspension. Ultimately, 3 μl of propidium iodide (PI) stock solution was inserted into the cells, followed by incubating for 30 min on ice. BD FACSCalibur instrument was used to analyze the flow cytometry and detect the stained cells.
To perform in-vivo examination according to NIH and IACUC guidelines, 35 six- to 8-week female BALB/c mice (Pasteur Institute, Tehran, Iran) with 20–25 g bodyweight were purchased and kept in a sterilized condition. First, each mouse was treated with 5 × 106 4T1 cells subcutaneously into the right flank. Next, they were randomly divided into 4 groups of 5 mice when the tumor reaches 100 mm3. Free Cur and miRNA with 2.5 mg/kg and niosomal Cur and miRNA with 10 μg per mouse were injected within the tail vein of each group of mice. For the control group, normal saline was injected. The injections were repeated every 3 days up until 12 days and at each interval, the tumor size and bodyweight of each mouse were measured. All mice were sacrificed on the 21st day. The tumor size was measured according to the equation (Abtahi et al., 2022):Where V represents the volume of the tumor, L and W are the lengths of the tumors, respectively. Also, the equation below was utilized to measure the inhibition rate (Abtahi et al., 2022):Where Wc and Wt represent the average weight of tumors in the control and treated groups, respectively.
Data were analyzed statistically using the GraphPad Prism6 software and reported as mean ± standard deviation. Student t-test was utilized to compare 2 independent groups, and multiple samples were compared by the ANOVA test. p values with less than 0.05, were considered as significant.
Because of the widespread use of chemical pharmaceuticals and their negative side effects, interest in alternative therapies such as herbal medicines and gene therapy has grown. Herbal anticancer therapies have been pushed, including the isolation and identification of active plant components. New treatment tactics have recently switched to hybrid active agents, which are thought to be far more successful than separate forms. As a result of the reduced doses for each medicine, the treatment’s efficacy improves while adverse effects diminish. Curcumin, a yellow pigment extracted from the rhizome of the turmeric plant, has a wide range of medicinal benefits. Curcumin has also been demonstrated to have anticancer properties. It was recently incorporated as a chemical prophylactic factor in the first phase of clinical research. Curcumin may make cancer cells more responsive to several therapies, including chemotherapy and gene therapy, as a new anticancer agent. Curcumin’s therapeutic effectiveness, on the other hand, is hampered by its poor water solubility and, as a result, reduced therapeutic index. Nanotechnology is one of the powerful tools for increasing stability in aqueous solutions. The therapeutic application of miRNAs has been documented in numerous research. However, their effectiveness is contingent on the carrier’s ability to transport them to cancer cells. MiRNA should be protected from nuclease degradation in an optimal transport carrier under systematic circulation. All requirements are endosomal escape, biocompatibility, renal and hepatic clearance resistance, and reversible physical binding to miRNAs. As previously said, niosomes are better than phospholipid vesicles because of their high drug loading efficiency, biocompatibility, biodegradability, low cost of manufacture, lack of organic solvents, easy storage, and superior stability. Herein, curcumin and miR-34a were separately loaded in DOTAP-containing cationic niosomal formulations. Tween-80, a safe and healthful surfactant, and Tween-60, a commonly used surfactant in niosome synthesis, were used to make the niosome vesicles. The curcumin and gene entrapment efficiency, particle charge, and size of each formulation were all evaluated. Release profiles were obtained by releasing the curcumin in different buffers. The optimal formulation was tested in cytotoxicity, cellular uptake, gene expression, apoptosis, and in vivo tests to assess their significance in cancer therapy.
Curcumin delivery systems are optimized based on several parameters, including the nanocarrier size, zeta potential, and entrapment efficiency (EE%, Table 1). Cholesterol is a key element of niosomes that impact their physicochemical properties and stability. The EE of nanocarriers increased from 35.78 to 48.45% when cholesterol content was increased from 10 to 20% (F1 and F2), the zeta potential was modified from - 24.88 to -22.43 mv, and the size of nanocarriers dropped from 134.12 to 129.34 nm. The type of nonionic surfactant appears to be a critical factor in the influence of cholesterol percentage on niosome size. It has been reported that the rate of average size reduction by adding cholesterol depends on the surfactant type (Alemi et al., 2018).
For investigating the effect of T-60 on the EE%, zeta potential, and size of nanocarrier, the cholesterol concentration was kept constant, and T-60 was (30%) added to the formulation (F3). The entrapment of curcumin within the niosomes raised to 79% and reduced the nanocarrier average diameter. Interestingly, EE% of curcumin exceeded 90% when DOTAP was introduced into the formulation (F4 and 5). DOTAP also enhanced the zeta potential, positively charged, and reduced the size of the particles. Therefore, it was concluded that the Cur-niosome preparations comprising Tween-80:Tween-60:cholesterol:DOTAP with the molar ratio of 63:20:10:7 (F5) owned the desirable properties based on the particle size and EE%. The DLS assay indicated that the average diameter of F5 was 87.23 ± 234 nm at a homogenized pressure of 800 bar. Moreover, including DOTAP in the formulation made the size reduction and helped the vesicles to maintain the drugs and displayed no remarkable alterations in 2 months of storing compared to other formulations with respect to vesicle size zeta potential and EE%.
For preparing the miR-34a-niosome, almost the same material ratio used in F4, and F5 formulations of Cur-niosome, were employed. Because of their positive surface charge, positively charged lipids are frequently used to carry nucleic acids since these materials allow the spontaneous interconnection of negatively charged nucleic acids. The result is known as lipoplex. It has been shown that cationic lipids, such as 1,2-dioleoyl-3-trimethylammonium-propane (DOTAP), enhance positive loading on vesicles, making vesicles easier to connect with negative cell membranes in vitro. Due to their versatility, they are the most suitable gene carriers. In general, DOTAP-containing lipid vesicles have lower toxicities compared to those containing other kinds of lipids due to the inclusion of two ester linkages within the structure, says Balazs et al. (Balazs and Godbey, 2011). Likewise, with the addition of 10 and 15% DOTAP, particle size, charge, and zeta potential have changed. Therefore, it was concluded that the miRNA-niosome preparations comprising Tween-80:Tween-60:cholesterol:DOTAP with the molar ratio of 58:17:10:15 (F5) had the desirable property based on the zeta potential and particle size (Table 2).
The inner structure of drug-loaded niosomes was assessed by the AFM and shown in Figures 1A–F. The images were obtained from the optimal formula of niosomes in a sphere shape. SEM and TEM images were obtained in a similar way and shown in Figures 2A,B. In the images, niosome vesicles seemed to be spherical with smooth surfaces. The smooth surface provides the desired property for interaction with cells.
The niosomes’ in vitro drug release patterns in plasma and PBS were studied at 25°C, 37°C, and 42°C at pH values of 5.5, 7.4, and 9 (Figures 2C–H). Plasma was utilized to explore the release experiment in a nearly human-like environment. By modifying the solubility of the delivery system or cleaving pH-sensitive bonds upon a pH gradient, pH-sensitive delivery systems can lead to a site-specific release of medicinal payloads. This pH gradient, which may be found in a variety of bodily locations (including the tumor environment, the gastrointestinal tract, the vaginal area, and blood arteries), can be exploited as an endogenous stimulus for developing smart delivery systems and regulating medication release. The extracellular pH of cancer cells is lower than that of normal cells, whereas the intracellular pH of cancer cells is greater. As a result, the drug’s release profile was studied under neutral, acidic, and alkaline pH environments. Every release profile is biphasic, with varying gradients. Cancer cells/tissues have a greater temperature than normal cells, which might be exploited to administer stimuli-responsive tumor-targeted drugs. At all of the pHs studied, the temperature influenced the release of curcumin from F5. For example, at pH 7.4, the release was 15.87, 19.84, and 23.76% at 25, 37, and 42°C in plasma, respectively. Furthermore, the data demonstrate that medication release is greater at acidic pH levels, which are typical of the tumor microenvironment than in neutral settings. Curcumin release from niosomal nanoformulation follows a two-phase pattern, according to in vitro release experiments. In the first phase, the medication is released quickly, followed by a slower release in the second phase. The dialysis bag or the medication size has no effect on the release. The initial rapid release is likely controlled by a diffusion mechanism caused by a curcumin gradient of concentration between the niosome and the buffer surrounding the dialysis bag, whereas the slow release in the second phase is caused by controlled release from the bilayer membrane of the niosomal formulation.
Curcumin is a flammable, water-insoluble chemical. Curcumin was integrated into cationic niosomes to improve the drug’s stability and absorption. The drug-loaded nanocarrier’s great stability after protracted storage is critical for the drug’s effectiveness. The influence of particle size and zeta potential on vesicular system stability is well understood. As a result, barriers between vesicular particles are built to avoid accumulation. A charged molecule, for example, can be put on the system’s surface. The zeta potential is a charge indicator, and if the particles have the right zeta potential, they repel each other and do not combine. NCur (F5) was therefore kept at 4°C for 15, 30, 45, and 60 days. The zeta potential, vesicle size, PDI, and EE% were measured after each time interval. From the results in Figure 3, the size/PDI of the vesicles and the EE% of the optimal niosomal formulation (F5) showed no significant changes after 60 days of storage when compared to pristinely synthesized samples (p-value 0.05), indicating that NCur is physically stable for at least 60 days. Therefore, the inclusion of DOTAP in the niosomal formulations only affected the zeta potential and either vesicle with a positively or negatively charged surface (with or without DOTAP), did not show alternation in their stability after 15, 30, 45 and 60 days in storage.
To confirm that cationic niosomes and miR-34a formed a compound, a gel retardation experiment was employed. Cationic niosomes bind miR-34a in a dosage-dependent manner, as seen in Figure 3B. As can be observed, the best condition was a ratio of 20:0.15 (cationic niosomes:mi-RNA34a). The loading of mi-RNAs onto cationic niosomes was ensured by zeta potential examination of the cationic niosomes before and after loading with miR-34a, as shown in Table 2. Furthermore, the size of the two parties grows due to electrostatic bonding and complex development. By forming a spatial barrier, the long chains of miR-34a that connect to the surface of niosomes directly enhance the hydrodynamic diameter. The final formulation (F5) has a size of 100 nm, which is satisfactory. The niosomes were stored at 4°C for 4 months to investigate the effect of long-term storage on miR-34a leaking from the surface. The complexes were stable with the lowest leakage after 4 months, according to the data (Figures 3C,D). The formulation is stable, according to the results of the stability test (electrophoresis), and the mi-RNA loaded in the niosome with a volume ratio of 20:0.15 (Niosome:mi-RNA) forms a complex.
Initially, we looked at the effects of free and niosomal curcumin on the viability of normal (MCF10-a) and cancer cell lines (A2780S, A2780CP1). We did this by calculating the IC50 values after 24, 48, and 72 h. According to the findings, normal cells required higher concentrations of either fCur or NCur to achieve IC50 than malignant cells. Furthermore, the drug encapsulated in niosomes was observed to improve cytotoxicity as compared to fCur. In comparison with free Cur, the IC50 values for A2780CP-1 and A2780S cells treated with NCur decreased by 45.6, 34.8, and 34.1%, respectively when incubated for 24 h. After 72 h, the disparity in the IC50 values of cancer cells cultured with free and encapsulated medications grew significantly. To evaluate the cytotoxic activities of free and niosomal Cur and miRNA on the human ovarian cancer A2780s, A2780cp, and MCF10-A cell lines were measured using the MTT test after incubation of cells for 24, 48, and 72 h. First, the inhibitory effects of free and niosomal Cur on A2780s, A2780cp, and MCF10-A cells were determined by dose-response tests. Next, the IC50 levels of these substances were evaluated (Figures 4A,B). Results displayed higher concentrations of drugs for MCF-10A than A2780s and A2780cp cells to achieve IC50 value. In comparison with the free state, the utilization of niosomes demonstrated remarkably lower IC50 values for all the cell lines. The observations showed that free Cur and Cur-niosome had less effect on the MCF10-A cell line. At an equal dosage, drugs with niosomal carriers showed higher cytotoxicity than free drugs (Figures 4C,D). A total comparison in the average of the mean values of all cell lines after 24, 48, and 72 h showed that there are significant differences in the first day between the free and niosomal forms. Although the differences reduce after 72 h, still the niosomal forms demonstrated higher toxicity than their free forms (Figure 4E).
In the cellular uptake tests, cancer cell models A2780S and A270cp-1 were utilized (Figures 5, 6, 7, 8). The cellular uptake behavior of two niosomal formulations was examined. Fluorescence microscopy was used to capture pictures of cancer cell lines treated with unloaded niosome, fCur, fmiR, NCur, and NmiR (Figures 5, 6, 7, 8). Cells treated with fCur or NCur glow red, whereas cells treated with fmiR or NmiR fluoresce green. The fluorescence intensity of cells incubated with niosomal nanoformulations was higher than that of cells treated with free versions of the medication or gene. Furthermore, although miR-34a accumulated in the cytoplasm, curcumin accumulated in both the nucleus area and the cytoplasm. Cancer cells are found to effectively absorb cationic niosomes. Endocytosis plays a significant role in the penetration of drug-loaded niosomes into cells as well as the transport of curcumin and miRNA into them as compared to free medicines transferred through the cell membranes via a diffusion process. Niosomes enhanced the solubility of curcumin, which has been considered as a hydrophobic medicine.
To measure the gene silencing efficiency of Cur and miR-34a, the expression of Nf-ĸB and P53 was evaluated to confirm their downstream impact. Generally, the anti-cancer mechanism of Nf-ĸB is based on the mitochondrial apoptosis pathways. Herein, qRT-PCR techniques were employed to detect the expression of Nf-ĸB and P53. For this purpose, first, cells were subjected to free Cur and miRNA, as well as niosomal Cur and miRNA, then, the expression of the genes was measured. The results showed a minor reduction in NF-κB and major improvement in P53 gene expression compared to the control group. Moreover, using the niosomal carrier remarkably enhanced the expression compared to their free forms. Specifically, it was observed that the gene expression using miRNA was higher than Cur. Ultimately, the gene expression of P53 and Nf-ĸB can be effectively improved by the utilization of nanocarriers by facilitating the intracellular delivery of medicines. The same results were achieved by both cytotoxicity and apoptosis assays.
To perform the apoptosis test on the cells, Annexin V-FITC/PI double staining assay was utilized on free Cur, free miRNA, niosomal Cur, and niosomal miRNA for 24 h and the result was obtained using a flow cytometer. Generally, in this assay, the phospholipid phosphatidylserine (PS) of the cell membrane of apoptotic cells translocates from the inward to the outward surface of the plasma membrane and subjects the PS to the exterior cellular environment. Annexin V is a Ca2+-dependent phospholipid-binding protein with 35–36 kDa which has a high affinity to bind with PS of apoptotic cells. To improve the detection while maintaining the high affinity to PS, it is possible to conjugate annexin V to the fluorescent molecule like fluorescein isothiocyanate (FITC) and synthesis of Annexin V-FITC. Thus, to conduct the flow cytometric analyses, Annexin V-FITC was utilized which is a highly reactive probe for measuring the cells apoptosis. Moreover, to stain DNA, propidium iodide can be utilized not only is it a fluorescent DNA intercalating agent, but it also cannot penetrate inside the live or early apoptotic cells. Ultimately, the results were recorded after treatment for 48 h and displayed in Figure 9. It was observed that the utilization of niosomal carriers could effectively increase the apoptosis rate compared to their free forms. Moreover, the impact of miR-34a on cells in both forms was relatively higher than Cur.
To assess the antitumor activity of the formulations, 4T1 xenografted Balb/C mouse tumor models were conducted. The mice from each group were subjected to one medicine including free Cur, free miRNA, niosomal Cur, and niosomal miRNA, as well as normal saline, every 3 days until 12 days. Medicins were injected into the tail vein of the mice. After 21 days, to evaluate the tumor volumes after the sacrifice and removal, a digital vernier caliper was employed. The results demonstrated that free Cur and miRNA groups displayed a reduction in the tumor size compared to the saline group. Likewise, niosomal Cur and miRNA groups had even smaller tumors than the previous groups (Figure 10E). It can be concluded that the decrease in cell growth was the result of the constant inhibition by the samples. The data concluded that niosomal miRNA could enhance the tumor prohibition activities and also, it presented an augmented therapeutic impact on cells more than that of free miRNA or free forms of Cur and miR-34a (Figures 10C,D). The cytotoxicity of all medicine was assessed by weighing the mice at each interval (Figures 10A,B; Table 3). The body weight of treated mice encountered a minor reduction after 21 days, whereas the saline group gained weight. Meanwhile, on the 6th day, Cur groups faced the highest and the niosomal miRNA the lowest reduction in the bodyweight compared to the other groups.
Chemotherapy is rapidly becoming the primary mode of treatment for a wide range of malignancies. The key issues in this context are the level of safety and the clinical efficacy of drugs. However, the use of chemical compounds has side effects such as toxicity, weight loss, and limited therapeutic efficiency. Further research on herbal medication, either alone or in combination with chemotherapies, has recently shown its promise for cancer therapy. Curcumin, a yellowish spice obtained naturally from the rhizome of Curcuma longa, has been utilized for millennia in Asian nations and has been shown to have a wide variety of pharmacologic effects. This powder has antitumoral properties by inhibiting cell cycle signaling pathways, resulting in cell death. Cur has been shown in several studies to be effective against various cancer cell lines, including cervical, oral epithelial, brain, breast, hepatic, leukemia, colon, ovarian, pancreatic, melanoma, gastric, and prostate (Syng-Ai et al., 2004; Yallapu et al., 2013; Ag Seleci et al., 2016). Despite its impressive footprints, Cur’s wide therapeutic benefits are restricted due to its weak water solubility and fast metabolism. To address this issue, nanotechnology developed nano DDSs including vesicular nanocarriers and niosomes. Surfactant and cholesterol are combined to generate unilamellar or multilamellar vesicles that contain both hydrophobic and hydrophilic drugs. As a result, the use of DDSs boosted medication efficacy, chemical stability, lowered economic preparation costs, and simplified long-term storage (Feng, 2004; Sezgin-Bayindir and Yuksel, 2012; Yallapu et al., 2012). To encapsulate Cur, a new cationic niosomal preparation was designed in the present research. The vesicular system was made using T-80 and T-60 as the most frequently used surfactants in the new formulation of niosomes and industrial surfactants, respectively. The introduced formulations were analyzed by their entrapment effectiveness, zeta, drug release, and vesicle size. The size of nanoparticles has significant effects on the physical stability, cellular intake, and drug release from nanoparticles (Mukerjee and Vishwanatha, 2009). The sizes of nanoparticles in this study were between 87 and 135 nm compared to the other study which they were ranged from 50 to 500 nm, with different Cur formulations indeed (Ravindran et al., 2009). The zeta potential of the Cur preparation was +11.23 mV, which guaranteed physical stability and prevented aggregation during long-term storage (Garces et al., 2015). The investigation of the nano-vesicle morphology images (AFM and SEM) displayed spherical and circular particles with flat faces. After prolonged storage of 60 days, the result did not show any significant changes in the physiochemical features of the encapsulated drug inside the nanoparticles compared to the original samples. In-vitro treatment with Cur in both niosomal and free forms was exerted on A2780s and A2780cp cell lines, as the cancer cells and MCF10A cells, as control cells by a standard MTT assay. The results showed that Cur-contained nanoparticles had lower IC50 values on cancer cells than free Cur. Furthermore, cancer cells have reduced vitality than normal cells when treated with niosomal Cur. By activating growth inhibitory pathways in tumor cells, Cur-loaded nanoparticles might downregulate growth factors and their receptors, including NF-B activity, signaling channels for PI3K-AKT activity, and therefore block cancer cell development (Wang J et al., 2016). As a consequence of the gene expression data, free Cur had a similar impact on A2780s and A2780cp cells as cancer cells, but its adverse effects on normal cells were significantly lower. Furthermore, fluorescence microscopy on intracellular accumulation was explored, and it revealed that cancer cell absorption of Cur was simpler than free Cur. Finally, it was determined that the absorption of niosomal Cur by cancer cells in vitro may boost therapeutic optimism as a potential cancer therapy approach (Ravindran et al., 2009). MicroRNA-34a (miR-34a) is a miRNA that is transcriptionally controlled by the p53 network and has been found to be significantly downregulated in a number of malignancies. In triple-negative and mesenchymal-type breast cancer cell lines, miR-34a expression has been found to be lower. Through targeting Bcl-2, CD44, and SIRT1 (silent information regulator 1), Rac1, Fra-1, Notch-1, and different cyclins, exogenous expression of miR-34a in breast cancer cells caused cell death and decreased cell proliferation and migration. For miRNA delivery, a number of viral carriers have been developed, with good transfection effectiveness across a wide range of cell types. However, safety problems are now seen as a barrier to viral vector-based therapy’s practical implementation. Many kinds of nanoparticles have been utilized in gene delivery and transfection because of their small size, surface charge, and high surface area. In this study, DOTAP prepared the positive surface charge to link the miR-34a gene electrostatically on the surface of nanoformulation. Utilizing electrostatic interactions for loading the genes are the facile and conventional methods in nanoformulation when the controlled release is matter. Deng et al. fabricated a miR-34a containing nanoformulation using chitosan, hyaluronic acid, and doxorubicin for codelivery of doxorubicin and miR-34a against triple-negative breast cancer (Deng et al., 2014). They found that molecular weight and electrostatic interaction density of miR-34a strongly influenced the release profile of genes and drugs. The employment of suitable materials such as DOTAP, Tween-60, Tween-80, and cholesterol a, which have already been used in drug delivery applications, is a point of strength in our inquiry regarding the production of nanomaterials for the effective distribution of miR-34a. These nanomaterials produced an efficient anticancer impact against ovarian carcinoma in vitro and 4T1 breast cancer model in vivo by way of intracellular localization of miR-34a, as established in vitro. The finding of the present study was in line with our previous reports and other research on niosome-based systemic drug delivery.
Ovary cancer is a primary cause of mortality in gynecological malignancies, which can be treated with surgery, chemotherapy, and radiation therapy. The majority of ovary cancers have acceptable initial responses to the existing therapeutics. One of the suitable approaches for the treatment of present ovary cancer is the use of chemo/radio-sensitizer accompanied by chemo/radiation methods. Hence, curcumin has earned the attention of researchers in the treatment of malignancies with unique properties such as anti-apoptosis, anti-inflammation, anti-angiogenesis, and chemo-sensitivity. Nevertheless, the weak aqueous solubility and fast metabolism of curcumin significantly restricted its clinical application. Using a vesicular nanocarrier, e.g., niosomes, which are a substitute for phospholipid vesicles for encapsulating hydrophobic drugs, helped to treat cancers due to the provision of high encapsulation efficiency, biocompatibility, biodegradability, low formulation costs, and adequate stability. Also, such nanocarriers are devoid of organic solvents and can be stored easily. Herein, we synthesized a curcumin-contained niosomal system, specifically designed for the effective delivery of curcumin to treat ovary cancer. According to the results, niosomal protection improved biochemical stability and elevated the entrapment efficiency of curcumin. Besides, it demonstrated improved cellular intake and cytotoxicity against the ovarian cancer cells, A280s, and A280cp-1. Altogether, curcumin-niosomes have the potential to be a suitable delivery system for curcumin to combat ovary cancer. The cellular toxicity of miR-34a-niosome was enhanced in cancer cells, compared to free miR-34a. Likewise, the antitumor influence of gene delivery was studied in vivo. According to the in vitro and in vivo results, gene delivery from the developed nanoscaled niosomes was more successful than curcumin delivery. Therefore, gene therapy using niosomal particles was a favorable strategy for advanced cancer therapy. | true | true | true |
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PMC9619287 | Xuguang Yuan,Yanan Jing,Mengkai Guang,Junfei Zhu,Ji Wang,Yang Wang,Ye Zhang | GAS5 alleviates cisplatin drug resistance in oral squamous cell carcinoma by sponging miR-196a | 29-10-2022 | Cisplatin,growth-arrest-specific transcript 5,oral squamous cell carcinoma,miR-196a,long non-coding RNA,chemotherapy resistance | Objective The long non-coding RNA Growth-arrest-specific transcript 5 (GAS5) has been extensively linked with the ability of cancer cells to resist chemotherapeutic interventions. This prospective study aimed to investigate the role of GAS5 in oral squamous cell carcinoma (OSCC), which has been poorly characterized to date. Methods GAS5 and miR-196a expression levels were detected by quantitative real-time PCR analysis. Cisplatin (DDP) sensitivity and apoptosis levels were determined using Cell Counting Kit 8 and flow cytometry, respectively. Luciferase reporter and RNA immunoprecipitation assays were performed to confirm target miRNAs of GAS5. Results We found that GAS5 was expressed at low levels in DDP-resistant OSCC cell lines and tissues, and that GAS5 levels were intricately linked to the survival rates of OSCC patients. GAS5 overexpression led to the recovery of DDP sensitivity in CAL27/DDP cells. Additionally, in both DDP-resistant and -sensitive lines, GAS5 showed a cytoplasmic distribution and downregulated miR-196a in OSCC tissues. Exogenous transfection of miR-196a alleviated the effects of GAS5 on DDP sensitivity, confirming this as the mechanism of chemoresistance. Conclusions These findings may provide new targets for the treatment of chemotherapy-resistant OSCC. | GAS5 alleviates cisplatin drug resistance in oral squamous cell carcinoma by sponging miR-196a
The long non-coding RNA Growth-arrest-specific transcript 5 (GAS5) has been extensively linked with the ability of cancer cells to resist chemotherapeutic interventions. This prospective study aimed to investigate the role of GAS5 in oral squamous cell carcinoma (OSCC), which has been poorly characterized to date.
GAS5 and miR-196a expression levels were detected by quantitative real-time PCR analysis. Cisplatin (DDP) sensitivity and apoptosis levels were determined using Cell Counting Kit 8 and flow cytometry, respectively. Luciferase reporter and RNA immunoprecipitation assays were performed to confirm target miRNAs of GAS5.
We found that GAS5 was expressed at low levels in DDP-resistant OSCC cell lines and tissues, and that GAS5 levels were intricately linked to the survival rates of OSCC patients. GAS5 overexpression led to the recovery of DDP sensitivity in CAL27/DDP cells. Additionally, in both DDP-resistant and -sensitive lines, GAS5 showed a cytoplasmic distribution and downregulated miR-196a in OSCC tissues. Exogenous transfection of miR-196a alleviated the effects of GAS5 on DDP sensitivity, confirming this as the mechanism of chemoresistance.
These findings may provide new targets for the treatment of chemotherapy-resistant OSCC.
Oral squamous cell carcinoma (OSCC) is an aggressive cancer of the digestive system that exhibits a high prevalence and morbidity. Despite the advancements in OSCC treatments, only modest improvements in patient outcomes have been made; thus new treatments for this malignancy are urgently required. The current arsenal for OSCC of drugs has suffered from chemoresistance and tumor relapse, both of which lead to a loss of treatment efficacy. The molecular mechanisms and prognostic factors that govern chemoresistance in OSCC remain poorly characterized, and further studies are required in this area. RNAs that lack coding function (non-coding RNAs) regulate gene expression in a range of cell types. Such RNAs that exceed 200 nucleotides in length are termed long non-coding (lncRNAs). Dysregulation of lncRNAs in cancer cells has been implicated in tumor growth and formation. Depending on the tumor-type, lncRNAs can promote cancer formation or can act as inhibitors of metastatic processes. Many lncRNAs contribute to tumor prognosis, cancer diagnosis, and drug resistance. However, the contribution of lncRNAs to OSCC formation has remained relatively poorly defined. One such exemplar of a lncRNA that is linked to cancer formation is Growth-arrest-specific transcript 5 (GAS5), which is a lncRNA that is suppressed in several cancer types in which it drives tumorigenesis including lung, gastric, and ovarian cancers. Low levels of GAS5 expression also occur in chemotherapy-resistant cancer cells, but the underlying mechanism of GAS5 in chemoresistance remains poorly characterized. OSCC tends to develop chemoresistance to the current front-line chemotherapeutic agent, cisplatin (DDP). Knowledge of this resistance at the molecular level could improve OSCC therapy and contribute to improving patient survival and prognosis. Herein, we aimed to demonstrate the functional role of GAS5 in DDP resistance and uncover its underlying mechanism of action in OSCC.
OSCC tumor and matched normal tissue samples were obtained from OSCC patients treated at China-Japan Friendship Hospital between July 2017 and May 2019. For all patients, consent to participate was provided in written form. All patient details have been de-identified. This study was approved by the ethics committee of China-Japan Friendship Hospital (2017-28). The reporting of this prospective study conforms to STROBE guidelines. CAL27 (OSCC) and normal NOK cells were purchased from the Shanghai Model Cell Bank (Shanghai, China) and cultured in RPMI medium under standard culture conditions. DDP-resistant OSCC cells (CAL27/DDP) were established by exposing CAL27 cells to increasing doses of DDP in a stepwise manner.
SiRNAs were designed against GAS5 (si-GAS5), scrambled controls (si-con), miR-196a, and anti-miR-196a by GenePharma (Suzhou, China). GAS5 was sub-cloned into pcDNA-3.1 (oe-GAS5) for overexpression studies. CAL27 and CAL27/DDP cells were transfected for 48 hours using Lipofectamine2000 (Thermo Fisher Scientific, Waltham, MA, USA) according to the manufacture’s protocol, and then subjected to functional experiments. Quantitative real-time PCR (qPCR) was used to assess transfection efficiency.
Cells and tissues were lysed in TRIzol (Invitrogen, Waltham, MA, USA) for RNA extraction. cDNA was synthesized using M-MLV Reverse Transcriptase, and qPCR was performed on an ABI Prism 7900 using VeriQuest Fast SYBR Green qPCR Master Mix (Thermo Fisher Scientific). Primer sequences were as follows: GAS5 forward, 5′-AGCTGGAAGTTGAAATGG-3′, GAS5 reverse, 5′-CAAGCCGACTCTCCATACC-3′ and miR-196a forward, 5′‑ACCTGCGTAGGTAGTTTCATGT‑3′, miR-196a reverse, 5′‑CGTCAGAAGGAATGATGCACAG‑3′. The 2−ΔΔCt method was used for relative gene expression analysis. Values were normalized to U6 or GAPDH.
To assess DDP sensitivity, cells in 96-well plates were treated with 0.5, 1, 2, 4, 8, 16, 32 μM DDP for 48 hours. Cells were then assessed for viability throughout treatment using Cell Counting Kit 8 (CCK-8) reagent. Absorbance values at 450 nm were detected using a microplate reader and normalized to no-drug controls.
To assess DDP-induced cell death, cells were treated with DDP for 48 hours and stained with a commercial propidium iodide and Annexin V staining kit (KeyGEN Biotech, Nanjing, China). Apoptosis rates were assessed by flow cytometry.
Subcellular fractionations were separated using PARIS Kits (Life Technologies, Carlsbad, CA, USA). GAS5 levels in each fraction were assessed by qPCR.
Wild type (with predicted miR-196a binding sites) and mutant GAS5 luciferase reporter vectors (GAS5-WT and GAS5-MUT, respectively) were obtained from GenePharm and transfected into cells together with miR-con or miR-196a. After transfection for 48 hours, dual luciferase assays were performed using commercially available kits (Promega, Madison ,WI, USA).
The Magna RIP RNA-Binding IP Kit (Millipore, Burlington, MA, USA) was employed for binding assays. Cells were lysed with RIPA buffer and treated with magnetic beads bound to anti-Ago2 or anti-IgG antibodies (Millipore). Samples were treated with proteinase K, RNA was isolated, and miR-196a and GAS5 expression were assessed by qPCR.
For statistical comparisons, data were analyzed using SPSS software (IBM, Armonk, NY, USA). Data are presented as the mean ± SD. The Student’s t test or a one-way analysis of variance were used for data comparisons. P < 0.05 was deemed statistically significant.
To assess the role of GAS5 in OSCC, we first detected GAS5 expression in tumor tissues of OSCC patients (n = 32) and compared its expression with normal tissues (n = 32). We observed a significant downregulation of GAS5 in OSCC tumor tissues (Figure 1a). GAS5 was most notably downregulated in the OSCC tissues of DDP-resistant patients compared with the OSCC tissues of DDP-sensitive patients (Figure 1b). Similarly, the same trends in GAS5 levels were discovered in DDP-resistant OSCC CAL27/DDP cells (Figure 1c). Additionally, Kaplan–Meier survival analysis indicated that low GAS5 expression was linked with shorter survival in OSCC patients (Figure 1d). Together, these data demonstrate that low GAS5 expression is strongly associated with DDP resistance and implicate GAS5 in the cellular processes that drive tumor formation in OSCC.
We next compared the IC50 of DDP using CCK-8 assays in CAL27 and CAL27/DDP cells. CAL27/DDP cells were largely insensitive to DDP (Figure 2a). The exogenous expression of si-GAS5 in CAL27 cells reduced GAS5 expression, while oe-GAS5 increased GAS5 levels in CAL27/DDP cells (Figure 2b and 2c). Silencing GAS5 decreased the potency of DDP in CAL27 cells (Figure 2d). In contrast, the exogenous expression of GAS5 enhanced the sensitivity of CAL27/DDP cells to DDP (Figure 2e). Silencing GAS5 also reduced apoptosis rates in response to DDP (Figure 2f). GAS5 overexpression enhanced the DDP-induced apoptosis rate of CAL27/DDP cells (Figure 2g), thus overcoming DDP resistance. These data strongly implicate GAS5 in DDP resistance in OSCC tumors.
To study the molecular mechanisms of GAS5 related its role in DDP resistance, we predicted the downstream miRNAs of GAS5 using StarBase (http://starbase.sysu.edu.cn/) and miRanda (www.microrna.org) software. Figure 3a shows that GAS5 harbors miR-196a-binding sites. As determined by subcellular location assays, GAS5 was found to be expressed in the cytoplasm of CAL27 and CAL27/DDP cells (Figure 3b and 3c). RIP and dual luciferase reporter assays were used to assess the interaction between GAS5 and miR-196a. In both CAL27 and CAL27/DDP cells, upregulation of miR-196a dramatically reduced the activity of GAS5-WT but did not influence GAS5-MUT vectors (Figure 3d and 3e). RIP assays revealed that GAS5 and miR-196a were abundant in the anti-Ago2 group compared with the anti-IgG group (Figure 3f and 3g). Upregulating GAS5 also reduced miR-196a expression in CAL27 and CAL27/DDP cells. miR-196a expression was unaffected by overexpressing GAS5-MUT (Figure 3h and 3i) and was at high levels in drug-resistant OSCC tissues compared with tissues from drug-sensitive patients (Figure 3j). Moreover, GAS5 and miR-196a expression levels showed a negative correlation in OSCC tissues (Figure 3k). Together, these data show that GAS5 functions directly as a miR-196a sponge.
We next transfected cells with miR-196a or anti-miR-196a and confirmed their expected effects on miR-196a expression through qPCR (Figure 4a and 4b). In CAL27 cells, miR-196a overexpression enhanced DDP resistance, while its inhibition imparted enhanced DDP sensitivity in CAL27/DDP cells (Figure 4c and 4d). Upregulating miR-196a also led to a loss of DDP-induced apoptosis in CAL27 cells (Figure 4e). In contrast, inhibiting miR-196a led to higher levels of DDP-induced apoptosis in CAL27/DDP cells (Figure 4f). These data implicate miR-196a as a key mediator of DDP resistance during OSCC progression.
We next investigated the molecular mechanism(s) of GAS5 by expressing si-GAS5 ± anti-miR-196a in CAL27 cells and exogenously expressing oe-GAS5 ± miR-196a in CAL27/DDP cells. The effects of silencing GAS5 were alleviated by anti-miR-196a in CAL27 cells (Figure 5a). Consistent with these findings, miR-196a mimics overcame the effects of GAS5 in CAL27/DDP cells (Figure 5b). CCK-8 viability assays showed that the reduced DDP sensitivity of CAL27 cells following GAS5 silencing could be recovered by expressing miR-196a (Figure 5c). DDP sensitivity was also enhanced by miR-196a in CAL27/DDP cells overexpressing GAS5 (Figure 5d). Anti-miR-196a expression in CAL27 cells also prevented the apoptotic effects of silencing GAS5 (Figure 5e), while miR-196a prevented the pro-apoptotic effects of GAS5 in CAL27/DDP cells (Figure 5f). Thus, GAS5 overexpression enhanced DDP sensitivity in OSCC cells through its targeting of miR-196a.
LncRNAs have been strongly linked to chemoresistance. OSCC is treated with chemotherapeutic agents, but resistance to these drugs remains a major clinical challenge. Herein, we investigated the role of GAS5 in OSCC, which had remained largely uncharacterized. In this study, we found that GAS5 is expressed to low levels in DDP-resistant OSCC cells and tissues, and its expression contributes to the survival of OSCC patients. Overexpressing GAS5 restored DDP sensitivity in CAL27/DDP cells and downregulated miR-196a in OSCC tissues. The transfection of exogenous miR-196a alleviated the effects of GAS5 on DDP sensitivity, confirming this to be a key contributor of chemoresistance. Conversely, silencing GAS5 enhanced the drug-sensitivity of DDP-resistant OSCC through its targeting of miR-196a, as evidenced by a decline in cell viability and an increase in cell apoptosis. These data provide valuable information for the development of more effective therapeutics, which is indicated that the promise of targeting GAS5 to restore chemosensitivity in OSCC. This is particularly important given the emergence of lncRNAs as powerhouses in tumor therapy and chemoresistance. New and more effective therapeutic strategies are required to circumvent chemoresistance. Here, we showed that the suppression of GAS5 in drug-resistant OSCC can predict survival. GAS5 overexpression could promote the recovery of DDP resistance in OSCC cells, highlighting this as a novel therapeutic angle. This aligns with other studies that have shown an association between GAS5 and DDP resistance, a notable example being resistant breast cancers. Restoring GAS5 expression could enhance DDP sensitivity through its ability to target miR-221-3p to regulate dickkopf 2 (DKK2) expression and activity. In hepatocellular carcinoma (HCC), GAS5 sponges miR-21 and increases PTEN expression, eventually overcoming HCC resistance to DDP. GAS5 enhancement also reduces resistance to doxorubicin in DDP-resistant bladder transitional cell carcinoma T24/DOX cells by enhancing DDP-induced apoptosis and inhibiting the anti-apoptotic protein Bcl-2. Although there is a link between lncRNAs and chemoresistance, relatively few lncRNAs have been mechanistically characterized. Recently, lncRNAs have been documented to take part in tumor progression by acting as competing endogenous RNAs. Remarkably, it was found that the GAS5 sequence contains miR-196a-binding sites as predicted by StarBase and miRanda. More significantly, miR-196a mediates chemo resistance. MiR-196a-5p overexpression, which is induced by UCA1, activates the transcription factor CREB by binding to its promoter. This contributes to chemoresistance to cisplatin/gemcitabine via increasing cell proliferation and inhibiting apoptosis in bladder cancer cells. Suppressing miR-196a could alleviate cisplatin resistance in A549/DDP cells by reducing the expression of drug resistance proteins including multidrug resistance 1 (MDR1) and multidrug resistance protein 1 (MRP1). Additionally, miR-196a has been shown to be overexpressed in DDP-resistant breast cancer cells. However, the mechanisms through which miR-196a participates in DDP resistance in OSCC remain undefined. Herein, we report the upregulation of miR-196a in OSCC tissues as well as in DDP-resistant OSCC tissues and cells. MiR-196a could confer DDP resistance in OSCC cells. Additionally, overexpressing miR-196a reversed the effect of GAS5 upregulation on the sensitivity of OSCC cells to DDP, fitting the established notion of miR-196a as a principal regulator in GAS5-mediated DDP sensitivity in OSCC. However, there are some limitations to this study. First, further study is required to investigate the downstream target genes and signaling pathways regulated by the GAS5/miR-196a axis in OSCC. Additionally, in vivo animal experiments should be performed to confirm our in vitro results.
We showed that GAS5 has low expression in DDP-resistant OSCC cell lines and tissues and is intricately linked to the survival rates of OSCC patients. Overexpressing GAS5 could restore DDP sensitivity in CAL27/DDP cells and downregulated miR-196a in OSCC tissues. Transfecting exogenous miR-196a alleviated the effects of GAS5 on DDP sensitivity, confirming its importance in chemoresistance. These findings highlight GAS5 to be a promising therapeutic target for future DDP-related interventions. | true | true | true |
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PMC9619369 | Jee-Young Mock,Aaron Winters,Timothy P. Riley,Richele Bruno,Martin S. Naradikian,Shruti Sharma,Claudia A. Jette,Ryan Elshimali,Casey Gahrs,Dora Toledo-Warshaviak,Anthony P. West,Alexander Kamb,Agnes E. Hamburger | HLA-A∗02-gated safety switch for cancer therapy has exquisite specificity for its allelic target antigen | 04-10-2022 | Tmod,logic gate,selectivity,CAR,cell therapy,structure,PA2.1 antibody,blocker | Innovative cell-based therapies are important new weapons in the fight against difficult-to-treat cancers. One promising strategy involves cell therapies equipped with multiple receptors to integrate signals from more than one antigen. We developed a specific embodiment of this approach called Tmod, a two-receptor system that combines activating and inhibitory inputs to distinguish between tumor and normal cells. The selectivity of Tmod is enforced by the inhibitory receptor (blocker) that recognizes an antigen, such as an HLA allele, whose expression is absent from tumors because of loss of heterozygosity. Although unwanted cross-reactivity of the blocker likely reduces efficacy rather than safety, it is important to verify the blocker’s specificity. We have tested an A∗02-directed blocker derived from the PA2.1 mouse antibody as a safety mechanism paired with a mesothelin-specific activating CAR in our Tmod construct. We solved the crystal structure of humanized PA2.1 Fab in complex with HLA-A∗02 to determine its binding epitope, which was used to bioinformatically select specific class I HLA alleles to test the blocker’s functional specificity in vitro. We found that this A∗02-directed blocker is highly specific for its cognate antigen, with only one cross-reactive allele (A∗69) capable of triggering comparable function. | HLA-A∗02-gated safety switch for cancer therapy has exquisite specificity for its allelic target antigen
Innovative cell-based therapies are important new weapons in the fight against difficult-to-treat cancers. One promising strategy involves cell therapies equipped with multiple receptors to integrate signals from more than one antigen. We developed a specific embodiment of this approach called Tmod, a two-receptor system that combines activating and inhibitory inputs to distinguish between tumor and normal cells. The selectivity of Tmod is enforced by the inhibitory receptor (blocker) that recognizes an antigen, such as an HLA allele, whose expression is absent from tumors because of loss of heterozygosity. Although unwanted cross-reactivity of the blocker likely reduces efficacy rather than safety, it is important to verify the blocker’s specificity. We have tested an A∗02-directed blocker derived from the PA2.1 mouse antibody as a safety mechanism paired with a mesothelin-specific activating CAR in our Tmod construct. We solved the crystal structure of humanized PA2.1 Fab in complex with HLA-A∗02 to determine its binding epitope, which was used to bioinformatically select specific class I HLA alleles to test the blocker’s functional specificity in vitro. We found that this A∗02-directed blocker is highly specific for its cognate antigen, with only one cross-reactive allele (A∗69) capable of triggering comparable function.
Engineered immune cells have emerged over the past decade as a promising therapeutic modality for cancer and other diseases.1, 2, 3 However, cell therapy must still overcome a key obstacle in oncology: the scarcity of molecules that unequivocally differentiate tumor from normal tissues. Cell therapies have the potential to directly tackle this challenge because they can integrate multiple signals in complex cellular environments. This capability enables more sophisticated mechanisms to detect tumor cells. In principle, nuanced responses to distinct cellular antigen profiles may allow engineered cells to deliver selective cytotoxic blows, perhaps approaching the extraordinary precision of the adaptive immune system. One recently described synthetic circuit designed to integrate multiple signals is Tmod (A2 Biotherapeutics), a dual-receptor system that incorporates an activating chimeric antigen receptor (CAR) or T cell receptor (TCR) and an inhibitory component.5, 6, 7 In one format, Tmod cells express an HLA-I-gated blocker that protects normal tissues while releasing the brake on cytotoxicity against cells that lack expression of HLA-A∗02 (referred to as A∗02 from this point on). Because a high percentage of tumors (∼15%; https://www.cancer.gov/tcga), retain only one HLA allele because of loss of heterozygosity (LOH), heterozygous A∗02 patients with specific LOH in their tumors can be identified for treatment., Such LOH irreversibly eliminates an allele such as A∗02 that would otherwise protect the neoplastic cells from attack by Tmod cells. The Tmod system is flexible and modular, allowing a variety of activator receptors to be paired with the A∗02 blocker. Furthermore, other HLA-I alleles can serve as the blocker antigen, readily extending the platform to patients beyond those with germline A∗02. HLA-I is a surface protein expressed throughout the body, providing a broadly active safety input. HLA-I proteins also display sequence variation on the surface of their extracellular domains, allowing discrimination of one allelic product vs. another by a specific blocker, thus marking specific tumor cells for destruction. However, the extensive polymorphism of HLA alleles engenders a risk that binders may cross-react with related allelic products. Indeed, HLA is one of the most polymorphic loci known, with thousands of closely related alleles in the human population. Therefore, it is important that a Tmod blocker component intended for the clinic be tested thoroughly to understand the limits of its specificity. Such data help inform decisions about which patients should be included and excluded from treatment, maximizing the chances for benefit in the population. Specifically, detailed information about blocker selectivity can identify A∗02(+) patients who may not respond to the Tmod therapy because they express an HLA-I allele that cross-reacts with the A∗02 blocker, impeding efficacy even in tumors that have been selected for A∗02 loss. Here we describe detailed analysis of an A∗02-directed blocker module that is part of several therapeutics under consideration for clinical development., This blocker has a ligand-binding domain (LBD) based on the PA2.1 monoclonal antibody (mAb) discovered more than 40 years ago and characterized by Parham and colleagues in subsequent years., We solved the crystal structure of the PA2.1 Fab in complex with A∗02:01 to identify the binding epitope of PA2.1 that was previously defined by a variety of biochemical, structural modeling, and comparative sequence studies. We determined PA2.1 recognition is driven by a 13-residue epitope, with 2 key residues sufficiently differentiating HLA-A∗02 from other alleles. A thorough functional analysis showed that, apart from the previously identified strongly cross-reacting A∗69 allele, the A∗02-directed blocker displays very high specificity for its cognate A∗02 antigen in both primary T and Jurkat cells, with only weak cross-reactivity to two rare A∗24 alleles. These findings support the choice of this blocker to explore the behavior of the Tmod system in clinical trials.
We first set out to confirm the published binding behavior of the PA2.1 mouse IgG (muIgG) LBD. Because we were ultimately interested in testing the sequence in a single-chain variable fragment (scFv) format, we converted the mouse mAb to its murine and humanized scFv equivalents (muscFv, huscFv; see materials and methods). The scFv was expressed with either a LIR-1 hinge or mutant monomeric CD8 hinge lacking cysteine residues and fused via its C terminus to a stabilized, monomeric human Fc (monoFc) and purified from Expi-CHO-S cells (Figure S1A). These scFv-hinge-Fcs were used to confirm the binding profile of the LBD across 72 HLA-I antigens displayed in the FlowPRA Single Antigen kit (One Lambda) (Figure S1B). FlowPRA is a binding detection method based on HLA-I single antigen beads (SABs) analyzed using flow cytometry (Figure S1C). Similar bead-based kits were previously used to assess specificity of the PA2.1 mAb in the IgG format. A positive pan-HLA-I mAb (W6/32) served as positive control and comparator; an irrelevant mesothelin (MSLN) directed scFv hinge-Fc served as negative control. At 5 μg/mL concentration, the muIgG, the muscFv, and the huscFv all showed selective binding to A∗02 and A∗69 alleles (Figure S1D), consistent with previous studies. Weak, concentration-dependent binding was detected to other HLA-I alleles in both the IgG and scFv formats, also as previously seen.
To fully define the molecular basis of PA2.1 specificity, we determined the co-crystal structure of the PA2.1 Fab with HLA-A∗02 (Figure 1A; see also Table S1). The PA2.1 complementarity-determining regions (CDRs) were grafted onto a humanized Fab framework and purified as a complex with soluble A∗02:01 bound to a modified NY-ESO-1-derived peptide MHC (A∗02:01-NY-ESO-1[V] pMHC). The Fab-pMHC complex produced well-formed crystals and a complete dataset was collected and refined at 2.9 Å. Using the refined structures, atomic interactions were calculated within the Rosetta Protein Design Suite and mapped onto the structure. As predicted by previous studies, the Fab primarily interacts with residues on the α2 domain of the HLA molecule (Figure 1A). Of the 7 residues initially predicted, 5 (W107, G162, E161, E166, and R169) were confirmed to make direct interactions with PA2.1 (Figure 1A, residues highlighted in magenta). The structure revealed that the other 2 residues (W167 and Y171) previously predicted to be critical for PA2.1-A∗02 interaction are not located at the binding interface and instead reside within the peptide-binding groove (Figure 1A). In addition to the amino acids previously identified, the PA2.1 Fab forms additional van der Waals interactions with HLA-A∗02 residues D106, R108, F109, L110, V165, E173, K176, and Q180 (Figure 1B). Most of the contacts are mediated by CDRs 1, 2, and 3 of the heavy chain, with some contributions from CDR1 and 3 of the light chain (Figure S2).
Using the expanded epitope identified above, we next undertook a bioinformatics search to identify the key determinants of PA2.1 specificity. From the ∼21,000 total HLA class I alleles in the international ImMunoGeneTics information system (IMGT), we identified ∼10,000 sequences with nonsynonymous substitutions. Excluding sequences without a predicted transmembrane domain (TM) left ∼4,000 HLA-I homologs for analysis (Figure 2A). Sequence alignment revealed that the region comprising the 13-residue PA2.1 epitope is highly conserved, with most alleles sharing more than 90% epitope identity with HLA-A∗02:01 (Figure 2B). Indeed, the sequence of HLA-A∗02:01 differed significantly from the consensus at only two positions: W107, previously identified as a key contributor to PA2.1 recognition, and F109, implicated only through the structure described in this study (Figure 2B, highlighted in arrows). Further analysis of the major alleles revealed 96.1% of investigated class I alleles clustered into 7 epitope clades, with only one clade (encompassing HLA-A∗02:01) sharing both W107 and F109 (Figure 2C). Structural models of the PA2.1 interaction with each of these seven clades revealed that computationally generated changes to either W107 or F109 resulted in substantial increases in predicted free energy of the complex (Table S2). Although Rosetta energy units are arbitrary and relative, higher scores often translate to weaker affinities and thus provide a structural rationale for the PA2.1 specificity observed. Indeed, only the clade containing the unmodified 13-residue HLA-A∗02:01 epitope did not negatively affect predicted binding affinity (Table S2). This clade consisted of 253 unique HLA alleles (Figure 2A), which mostly encompassed HLA-A∗02 alleles, except for three HLA-A∗69 variants. Next, we individually examined the sequences of the remaining 3.9% (149 total) unclustered alleles and identified 20 additional alleles that differed from the HLA-A∗02:01 epitope sequence by a single residue. Thirteen of these alleles shared both W107 and F109, with 11 comprising HLA-A∗02 alleles and two rare HLA-A∗24 alleles (A∗24:14 and A∗24:410; population frequency < 1e-5). Three alleles did not contain W107 (A∗02:784, A∗11:206, and A∗26:39) and the remaining four alleles (A∗02:449, A∗02:661, C∗04:332, and C∗08:32) did not contain F109 but were otherwise identical to the HLA-A∗02:01 epitope sequence.
To investigate the significance of the individual epitope clusters, we mapped the epitopes of the most frequent alleles in the human population. As expected, the top 25 most frequent HLA-A, B, and C alleles represented all 7 of the major epitope clades, with 5 common HLA-A∗02 alleles sharing the full HLA-A∗02:01 epitope sequence (Figure 2D). Coincidentally, this analysis included many of the common alleles evaluated in the binding studies above (Figure S1), which further validated the selectivity of PA2.1 for the HLA-A∗02:01 epitope clade. However, as binding does not fully predict function for TCRs, CARs, and their inhibitory receptor counterparts (see below,), we selected a set of 15 alleles for functional analysis. We prioritized alleles on the basis of number of mismatched epitope residues as well as frequency of occurrence. We also considered alleles of lower population frequency with ≤1 mismatched residue to capture alleles with high sequence similarity to HLA-A∗02. The selected 15 alleles encompassed high-frequency alleles from 5 of the 7 epitope clades with high similarity to HLA-A∗02:01, in addition to a lower frequency A∗69 allele that shares the full epitope and two rare HLA-C alleles that differ from HLA-A∗02:01 only at the newly identified F109 position (Figure 2D, highlighted in blue italics). To create constructs whose expression and function in cells could be monitored easily, we designed and synthesized mRNA encoding N-terminally FLAG-tagged HLA-I antigens of interest. Surface expression of one of these representative HLA molecules, A∗02:01, could be detected using both anti-A∗02 and anti-FLAG antibodies (Figure 3A). Importantly, mRNA transfected HeLa cells expressed A∗02 surface protein comparably to normal human tissue as previously reported. To characterize the effect of the N-terminal FLAG-tag on surface expression and recognition by PA2.1-derived receptors, we titrated synthetic mRNA encoding the HLA-I heavy chain with or without N-terminal FLAG-tag in A∗02(−) HeLa cells. When these HeLa cells were mixed with Jurkat effector cells that expressed an MSLN CAR ± the A∗02 blocker, dose-response curves were generated consistent with the proper folding and function of the untagged (Figure 3B) and FLAG-tagged A∗02:01 molecules (Figure 3C). With these reagents in hand, we proceeded to test A∗02 blocker selectivity in primary T cells. Of the 15 alleles selected for investigation, all but one of the rare C alleles expressed well (Figure 4A). MSLN Tmod cells generated from three donors (Figure 4B) were co-cultured with MSLN(+) GFP and Renilla luciferase-expressing HeLa cells transfected with different FLAG-tagged HLA-I heavy chain mRNAs (Figure 4A). Only clade I members A∗02 and A∗69, which contain the full 13-residue epitope, including both critical aromatic residues W107 and F109, triggered blocking function when the target cells expressed the HLA-I antigen, revealed by the presence of viable transfected HeLa cells in the co-culture 48 h after incubation (Figure 4C).
Because primary T cell cytotoxicity is the most direct therapeutically relevant readout, these data strongly support the functional specificity of the A∗02-directed blocker. However, given the complexity of primary T cell assays, further exploration of the specificity in the context of other conventional assays was deemed worthwhile. We thus tested additional functions of the A∗02 blocker in Jurkat cell assays. In the first case, we explored the ability of these alleles to block MSLN CAR function in Jurkat cells that co-expressed the blocker. These experiments were conducted in a similar fashion as the primary T cell assays except that NFAT-reporter-engineered Jurkat cells were used as effector cells. Comparisons of MSLN(+) HeLa target cells with or without expression of the HLA-I allele demonstrated that A∗02 and A∗69 blocked activation robustly (Figure 5A; see also Figures S3A and S3B). Additionally, the rare A∗24 alleles containing 12 of 13 epitope residues weakly cross-reacted with PA2.1, confirming that although W107 and F109 are most critical for the interaction, all 13 residues contribute to the functional response of the receptor. Encouraged by these data that extended the findings in primary T cells to another cell type (Jurkat), we tested an even simpler format for detection of off-target functional activity of PA2.1 scFvs. In this case, we constructed a third-generation activating CAR (CD28-41BB-CD3ζ) that incorporated the PA2.1 scFv as the LBD. As expected, only the alleles containing both critical epitope residues, W107 and F109, produced activation signal. Furthermore, only the two alleles with all 13 epitope residues, A∗02 and A∗69, elicited substantial activation of Jurkat cells, suggesting that the specificity of the LBD observed in the blocker format is retained in the activator format (Figure 5B; see also Figures S3C and S3D). As a final step in the characterization of the PA2.1 scFv, we used the Jurkat assays to confirm that the scFv has pan-A∗02 specificity. We tested the next 5 most frequent A∗02 alleles in clade I (Figure 2D, highlighted in pink italics), A∗02:02, A∗02:03, A∗02:05, A∗02:06, and A∗02:07, and showed the expected function in both the blocker (Figure 5C) and activator (Figure 5D) formats. These results confirm that the epitope revealed by the crystal structure is predictive of function of PA2.1-derived chimeric immune receptors.
The Tmod system combines two receptors to enforce specificity of response: an activator and a blocker. Tmod constructs and their individual components can be tested for off-target behavior in highly sensitive functional assays. Such assays are preferable to simple binding studies because of the well-known disconnect between binding and function for TCRs and CARs.,, Tmod cells are expected to respond specifically to target antigens because their activation is initiated by receptors that are often highly optimized TCRs or CARs derived from mAbs. Adding to this level of specificity, the Tmod constructs studied here include an inhibitory receptor that targets a blocker ligand (A∗02) ubiquitously expressed on nucleated cells that should tamp down any CAR or TCR activation stimulus. Indeed, Tmod constructs have been shown to exhibit minimal activation in the presence of activating stimuli derived from either CARs or TCRs, so long as the blocker antigen is present on the target cells.5, 6, 7, Unwanted inhibition caused by expression of closely related HLA-I alleles is the focus of this paper. We have systematically surveyed a large number of HLA-I alleles and concentrated on those most likely to cross-react with the A∗02-directed blocker module of interest. Such cross-reactivity, if unaccounted for, would be expected to blunt the efficacy of Tmod treatment in patients who carry the cross-reacting alleles. If cross-reactivities were known, patients with corresponding haplotypes could be excluded from treatment by an appropriate diagnostic test. However, such exclusions limit the breadth of application of the therapy and would be especially problematic if the excluded alleles were frequent thereby decreasing the eligible patient population. Here we focused on the frequent HLA-I alleles because they are most likely to be encountered in a clinical context. To extend these results even more broadly, it will ultimately be useful to develop predictive models based solely on primary sequence. Structure-function studies of CARs have shown that the specificity and sensitivity tracks mainly with the LBD., Our results with the A∗02 blocker suggest that this feature also applies to inhibitory receptors. As the Tmod approach is extended beyond the A∗02-directed blocker to blockers that target other HLA alleles, it will be critical to develop evidence similar to that reported here which support specificity of function in the context of the highly variable HLA genotypes that exist in the patient population. On the basis of the overall consistency of the results presented here, we believe it may ultimately be possible to test cross-reactivity of other blockers (i.e., beyond the PA2.1-based blocker) using the simpler format of Jurkat cell assays. Alleles that display cross-reactivity above a certain threshold can then be excluded from treatment because of the possibility that they may limit efficacy. To investigate potential cross-reactivity, we have taken advantage of the enormous datasets collected over decades of HLA study. The central importance of HLA genes to immune function, and graft rejection in particular, has triggered massive investment in the collection of detailed population-based information. In addition, HLA allelic products were among the first targets studied with mAbs in the 1970s. The utility and specificity of these mAbs reflect the diligence of early investigators who discovered and characterized them. The sensitive binding and functional assays used here confirmed the previously known reactivity of the PA2.1 LBD to both A∗02 and A∗69, and uncovered weak cross-reactivity to A∗24:14 and A∗24:410. Although it is tempting to conclude from this experience that binding studies may be sufficient, collective experience in this field suggests that functional assays combined with sequence-based analysis are vital to ensure the most potent, individualized treatment for patients.,
PA2.1 humanization was carried out by grafting mouse CDRs onto frameworks of human antibody sequences with close sequence identity to the mouse framework. scFvs were then designed using flexible (G4S)3 linker to connect the VH and VL domains. All soluble scFvs were fused to either CD8α hinge (residues 138–182) with disulfide cysteines mutated to serine, or LILRB1 (LIR-1) hinge (residues 398–461), followed by previously described C-terminal monomeric Fc. All third-generation activator CAR constructs contained CD8 hinge fused to CD28 TM, as well as CD28, 4-1BB, and CD3z intracellular domains (ICDs). All blocker receptor constructs contained LILRB1 hinge, TM, and ICD. Template used for N-terminally FLAG-tagged HLA mRNA synthesis contained 5′ T7 promoter followed by the V kappa 1 signal peptide, 1× FLAG peptide (DYKDDDDK), and G4S linker. All DNA constructs were assembled using Golden Gate Assembly. DNA templates were either amplified by PCR or linearized by restriction enzyme digest, then mRNA was synthesized using the HiScribe T7 ARCA mRNA kit (New England Biolabs). The in vitro synthesized mRNA was purified using the Monarch RNA Cleanup Kit (New England Biolabs), eluted in 1 mM sodium acetate, and stored at −80°C.
The muPA2.1 IgG was generated via hybridoma culture (American Type Culture Collection [ATCC] HB-117) followed by protein G immunoaffinity purification. DNA plasmid encoding the PA2.1-derived scFv-Fc was transfected into Expi-CHO-S cells (Thermo Fisher Scientific) using the ExpiFectamine CHO Transfection kit per manufacturer’s instructions (Thermo Fisher Scientific). During the 7–10 days of expression, cell viability was monitored, and conditioned medium was harvested at 10 days or if cell viability dipped below 80%. The conditioned medium was diluted 1:1 with Pierce Protein A/G IgG Binding Buffer (Thermo Fisher Scientific). Pierce Protein A/G Plus Agarose resin (1 mL) was carefully loaded onto gravity purification column and equilibrated with Pierce Protein A/G IgG Binding Buffer. The 1:1 diluted conditioned medium was loaded onto equilibrated resin. The resin was washed 1× with 10 mL of Pierce Protein A/G IgG Binding Buffer. Bound and washed scFv-Fc fusion molecules were eluted with 9 mL Pierce IgG Elution Buffer (Thermo Fisher Scientific) supplemented with 1 mL of 1 M Tris-HCl (pH 8.0) for pH neutralization. The resulting proteins were concentrated and loaded onto Superdex Increase 200 chromatography column (Cytiva) using gel filtration buffer, 20 mM Tris (pH 8.0), and 150 mM NaCl. Fractions were pooled, concentrated, and flash frozen in liquid nitrogen until use.
FlowPRA Single Antigen beads (One Lambda) displaying 72 HLA class 1 alleles were washed and arrayed into 96-well microtiter plates. Twenty-five and 5 μg/mL concentrations of either mouse IgG or scFv-Fc fusion proteins were prepared via dilution into 1× FlowPRA dilution buffer (One Lambda). The mouse IgG and fusion proteins were incubated with each group of FlowPRA beads for 1 h, followed by 3 complete washes with FlowPRA dilution buffer (One Lambda). The bead + scFv-Fc complexes were incubated with a goat anti-human IgG, Fc-specific APC secondary antibody (The Jackson Laboratory), while mouse IgG bead complexes were incubated with a goat anti-mouse IgG, Fc APC secondary antibody (The Jackson Laboratory). After 30 min, the bead sets were again washed 3 times and resuspended in wash buffer, and data were acquired on a BD FACS Canto flow cytometer running Diva software. FCS files were analyzed using FlowJo software to quantify allele-specific MFI values.
For Fab production, Expi-CHO-S cells were transfected and conditioned medium harvested as described above. Harvested conditioned medium was loaded directly onto 5 mL CaptureSelect CH1-XL pre-packed column (Thermo Fisher Scientific) using loading buffer 10 mM Tris-HCl (pH 8.0), 100 mM NaCl. Column was washed with 10 column volumes of loading buffer. Fab was eluted using 5 column volumes of 50 mM acetate (pH 5.0) buffer directly into tubes containing 1 M Tris-HCl (pH 8.0), bringing the final concentration of Tris-HCl (pH 8.0) in the fractions to 100 mM. Fractions containing Fab were pooled, concentrated, and loaded onto Superdex Increase 200 column using crystallization gel filtration buffer (20 mM Tris [pH 8.0], 50 mM NaCl). Correctly folded fraction was pooled and concentrated. HLA-A∗02-NY-ESO-1(V) peptide MHC complex was expressed and purified as described previously with modifications. Briefly, HLA-A∗02:01 and human β-2 microglobulin were expressed in One Shot BL21(DE3)pLysS (Invitrogen) cells. Cells were lysed and inclusion body was isolated and washed. Inclusion body was solubilized in 25 mM MES (pH 6.0), 8 M urea, and 10 mM EDTA. Insoluble fraction was removed by centrifuging at 10,000 × g for 20 min at 4°C. Urea-solubilized protein was flash frozen in liquid nitrogen until use. HLA-A∗02:01, human β-2 microglobulin, and a modified NY-ESO-1(V) peptide (SLLMWITQV; with a valine [V] substituted for the C-terminal cysteine) was refolded in 100 mM Tris-HCl (pH 8.0), 500 mM L-arginine, 2 mM EDTA, 5 mM reduced glutathione, 0.5 mM oxidized glutathione, and 1× protease inhibitor cocktail (Thermo Fisher Scientific) at 10°C while stirring. After 48–72 h, resultant refolding reaction was filtered using 0.4 μM vacuum filtration device and concentrated using Amicon centrifugal concentrators (Millipore). The protein sample was desalted using the PD-10 Desalting Column (Cytiva) to 20 mM Tris-HCl (pH 8.0). The buffer exchanged sample was loaded onto HiTrap CaptoQ ImpRes anion-exchange chromatography column (Cytiva), with salt gradient from 0 to 150 mM NaCl (20 mM Tris-HCl [pH 8.0]). Fractions containing pMHC probe was confirmed by SDS-PAGE Coomassie staining. Fractions were pooled, concentrated, and loaded onto Superdex Increase 200 column (Cytiva) with final gel filtration buffer 20 mM Tris-HCl (pH 8.0), 50 mM NaCl.
To generate crystallization-grade huPA2.1 Fab/A∗02-NY-ESO-1(V) complex, Fab and A∗02-NY-ESO-1(V) pMHC purified above were co-incubated at a 1:1 or 1:10 ratio. The resulting complex was loaded onto Superdex Increase 200 column (Cytiva), and fractions running at higher molecular weight was confirmed to be Fab-A∗02-NY-ESO-1(V) complex by SDS-PAGE. The complex fraction was pooled and concentrated to ∼8 mg/mL and left at 4°C for ∼4 days. Prior to crystallization trial, the concentrated protein sample was centrifuged at ∼18,000 × g at 4°C for 10 min to remove any precipitants. Crystallization trials with commercial screens (Hampton Research) were performed at room temperature using the sitting drop vapor diffusion method by mixing equal volumes of Fab-pMHC complex and reservoir using a TTP LabTech Mosquito robot. Initial crystal hits were optimized manually in large-format grease trays. Fab-pMHC crystals obtained in 0.2 M L-Proline, 0.1 M HEPES (pH 7.5), 13% PEG3350 were cryoprotected in a mixture of well solution with 20% glycerol and then frozen in liquid nitrogen. X-ray diffraction data were collected for Fab-pMHC crystals at the Stanford Synchrotron Radiation Lightsource (SSRL) beamline 12-2 on a Pilatus 16 M pixel detector (Dectris) at a wavelength of 0.98 Å. Data from a single crystal in the P212121 space group were indexed, integrated, and scaled using XDS and merged using AIMLESS version 0.7.4 in CCP4 version 7.0.6 (Table S1). The structure was determined by molecular replacement with PHASER using the coordinates for HLA-A2 with NY-ESO-1 peptide analog (PDB: 1S9X) and BHA10 Fab (PDB: 3HC0) after trimming heavy and light chain variable domains using Sculptor as search models. Refinement of coordinates was performed using Phenix version 1.19.1 and cycles of manual building in Coot (Table S1). The atomic model generated from the X-ray crystallographic study of the PA2.1 Fab-HLA-A∗02:01-NY-ESO-1(V) structure has been deposited at the Protein Data Bank (PDB) under accession code 8EB2.
Structural modeling and analysis of the PA2.1/pMHC interaction was performed using PyRosetta and the ref2015 score function. Significant interactions between PA2.1 and HLA-A∗02:01 were ranked according to scored energies and used to assemble the complete PA2.1 epitope. For modeling individual clades, the initial atomic coordinates were brought to a local energy minimum through five cycles of backbone minimization and rotamer optimization using the fastrelax protocol. For each epitope clade, mutations were computationally introduced onto the HLA-A∗02 scaffold. This was followed by 50 Monte Carlo-based simulated annealing steps for the polypeptide backbone and surrounding residues. The final models were ranked relative to the relaxed starting model using the ref2015 score function.
Wild-type (WT) HeLa cells were cultured in fetal bovine serum (FBS) containing MEM media (Gibco) to approximately 80% confluency in T-225 flasks. On the day of transfection, the cells were lifted from the flasks using TryplyE express (Gibco), counted, and resuspended to 1.1e7 vc/mL. Select FLAG-tagged HLA mRNA was serially diluted 2-fold across 15 points in SE transfection buffer (Lonza) in 96-well v-bottom plates. WT HeLa cells were added to each well containing the mRNA at a concentration of 1.33e7 vc/mL. The mRNA/cell mixture was transferred to a 16-well Lonza 4D cuvette and electroporated according to the manufacturer’s protocol established for HeLa cells. Post-transfection, the cells were immediately placed into serum containing MEM growth media and seeded into rows of 384-well culture plates at a density of 2,500–5,000 cells/well, depending on experiment. Remaining transfected HeLa cells were seeded into separate 96-well plates specifically for fluorescence-activated cell sorting (FACS) expression testing. Plates were cultured for 18 h at 37°C and 5% CO2.
Primary human T cells (Hemacare) from three HLA-A∗02:01(−) donors depleted of CD56(+) cells and enriched on CD4 and CD8 (Miltenyi) were transduced with lentivirus (Lentigen) encoding the MSLN Gen3 CAR and PA2.1 blocker from a bicistronic vector at an MOI of 40. Cells were grown in GREX (Wilson Wolf) according to the manufacturer’s instructions in X-VIVO 15 (Lonza, no phenol red) supplemented with 1% human serum (CELLect) and 300 IU/mL IL-2 (StemCell). Cytotoxicity assay was conducted as previously described with the following modifications: Renilla luciferase(+) GFP(+) (rLuc GFP, Biosettia) HeLa target cells were used to enable Incucyte visualization and terminal luminescence measurements. RLuc GFP HeLa cells were transfected with 500 ng of various FLAG-HLA allele mRNA as described above, and 2,500 transfected target cells were allowed to settle overnight in 384-well plates. RLuc GFP HeLa targets were co-cultured with 1,250 Tmod or untransduced T cells for an effective E:T of 1:2 the following day for 48 h. All conditions were performed in triplicate in X-VIVO 15 media supplemented with 1% human serum without phenol or cytokine. Using Renilla luciferase substrate (Promega), relative luminescence values were captured (Tecan), and percentage specific blocking was calculated using the following formula:where L is raw luminescence value, UTD is untransduced, and TD is Tmod transduced T cell conditions. Incucyte software was used to calculate GFP(+) surface area between conditions. Percentage specific blocking was calculated using total GFP(+) surface area at 48 h with the following formula:where A is GFP+ surface area.
Jurkat-NFAT luciferase cells were cultured in FBS containing RPMI growth media to 1.5e6 vc/mL. Lentiviral constructs (Lentigen) were used at MOI of 5–10 to transduce Jurkat-NFAT luciferase cells with appropriate activator and blocker CARs and generate stable expressing activator and/or blocker expressing effector cells. Alternatively, Jurkat-NFAT luciferase cells were counted and 2e6 viable cells were resuspended in 120 μl of R2 buffer (Thermo Fisher Scientific) containing 2 μg DNA encoding the appropriate activator or blocker receptor construct. Immediately after transfection, cells were cultured overnight at 37°C 5% CO2 in RPMI containing 20% FBS for co-culture assays the following day. Five thousand activating and/or blocker receptor expressing Jurkat-NFAT luciferase cells were combined with 5,000 HeLa HLA-I transfected target cells as described above in triplicate wells of a 96-well plate. The co-culture plates were incubated at 37°C, 5% CO2 for 6 h. Fifteen microliters per well of luciferase substrate (BPS Biosciences) was added to each well of the plate. The plate was then incubated at room temp for 15 min and read on a Tecan M1000 luminescent plate reader with 100 ms integration time/well.
Raw data will be made available upon reasonable request. | true | true | true |
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PMC9619378 | Junyao Jiang,Pin Lyu,Jinlian Li,Sunan Huang,Jiawang Tao,Seth Blackshaw,Jiang Qian,Jie Wang | IReNA: Integrated regulatory network analysis of single-cell transcriptomes and chromatin accessibility profiles | 14-10-2022 | Biochemistry,molecular network,transcriptomics | Summary Recently, single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) have been developed to separately measure transcriptomes and chromatin accessibility profiles at the single-cell resolution. However, few methods can reliably integrate these data to perform regulatory network analysis. Here, we developed integrated regulatory network analysis (IReNA) for network inference through the integrated analysis of scRNA-seq and scATAC-seq data, network modularization, transcription factor enrichment, and construction of simplified intermodular regulatory networks. Using public datasets, we showed that integrated network analysis of scRNA-seq data with scATAC-seq data is more precise to identify known regulators than scRNA-seq data analysis alone. Moreover, IReNA outperformed currently available methods in identifying known regulators. IReNA facilitates the systems-level understanding of biological regulatory mechanisms and is available at https://github.com/jiang-junyao/IReNA. | IReNA: Integrated regulatory network analysis of single-cell transcriptomes and chromatin accessibility profiles
Recently, single-cell RNA sequencing (scRNA-seq) and single-cell assay for transposase-accessible chromatin using sequencing (scATAC-seq) have been developed to separately measure transcriptomes and chromatin accessibility profiles at the single-cell resolution. However, few methods can reliably integrate these data to perform regulatory network analysis. Here, we developed integrated regulatory network analysis (IReNA) for network inference through the integrated analysis of scRNA-seq and scATAC-seq data, network modularization, transcription factor enrichment, and construction of simplified intermodular regulatory networks. Using public datasets, we showed that integrated network analysis of scRNA-seq data with scATAC-seq data is more precise to identify known regulators than scRNA-seq data analysis alone. Moreover, IReNA outperformed currently available methods in identifying known regulators. IReNA facilitates the systems-level understanding of biological regulatory mechanisms and is available at https://github.com/jiang-junyao/IReNA.
Dynamic changes of trans-regulators (e.g., transcription factors) and cis-regulatory elements (e.g., promoters) control gene expression in biological systems (Thompson et al., 2015). This fact makes it possible to infer gene regulatory networks using transcriptomic and epigenomic profiles. Recent advances in single-cell sequencing technologies provide new opportunities to reconstruct cell-type-specific regulatory networks (Macosko et al., 2015). Single-cell transcriptomes have been widely detected through single-cell RNA sequencing (scRNA-seq). Currently, dozens of methods have used scRNA-seq data to infer regulatory networks, including top-performing methods GENIE3 and PIDC (Pratapa et al., 2020; Huynh-Thu et al., 2010; Chan et al., 2017). Software SCODE was also developed to infer regulatory networks from scRNA-seq data based on ordinary differential equations (Matsumoto et al., 2017). Complementary to scRNA-seq, epigenomic profiling technique of assay for transposase-accessible chromatin using sequencing (ATAC-seq), including the latest single-cell ATAC-seq (scATAC-seq), measures accessible states of cis-regulatory elements to trans-regulators facilitating regulatory network inference (Buenrostro et al., 2013). Using scRNA-seq and bulk ATAC-seq data, we have developed a method to infer regulatory networks controlling retinal regeneration (Hoang et al., 2020). Recently, several methods have been developed to integrate scATAC-seq and scRNA-seq data for regulatory network inference, e.g., SOMatic and DIRECT-NET (Jansen et al., 2019; Zhang et al., 2022). However, few methods comparatively assess the performance of network analysis by integrating scRNA-seq and scATAC-seq data relative to using scRNA-seq data alone. Besides network inference, dissecting regulatory networks to detect modules and to identify key regulators is another major challenge in network biology. Clustering and decomposition are two frequently used methods for module detection (Saelens et al., 2018). Weighted correlation network analysis (WGCNA) performed hierarchical clustering of expression profiles to detect gene modules after correlation-based network inference (Langfelder and Horvath, 2008). Using single-cell transcriptomes, the SCENIC software combined GENIE3 and Rcistarget separately for network inference and identification of key transcription factors (Aibar et al., 2017). Although current methods can infer regulatory networks to identify gene modules and key individual regulatory genes, they do not construct a simple and statistically robust regulatory network among modules to provide biological insights. Here, we developed IReNA to perform regulatory network analysis by integrating scRNA-seq and scATAC-seq data. As only scRNA-seq data are available for many biological samples, IReNA can also provide network analysis using only scRNA-seq data. Using IReNA, we analyzed published single-cell RNA-seq and ATAC-seq profiles, reconstructed regulatory networks, identified key regulators, and revealed simplified regulatory networks among modules. Integrated analysis of scRNA-seq and scATAC-seq data showed obviously improved performance on regulatory network inference. In comparison with Rcistarget from SCENIC software, one of the most frequently used existing methods, IReNA had a better overall performance at identifying known regulators.
To perform regulatory network analysis using single-cell RNA-seq data or integrating with ATAC-seq data, we developed IReNA which consists of two components: network inference and network decoding (Figures 1 and S1). Two pipelines of network inference were developed separately for analyzing scRNA-seq data alone or integrating scRNA-seq data with bulk or single-cell ATAC-seq data. We compared top-performing methods and chose the tree-based ensemble method GENIE3 as the default method for network inference using scRNA-seq data in IReNA (Figure S2A). After potential regulatory relationships were inferred by GENIE3, transcription factor binding motifs were used to refine regulatory relationships. If bulk or single-cell ATAC-seq data are available, both transcription factor binding motifs and footprints are used to refine regulatory relationships. For scATAC-seq data, peaks were linked to genes and used to identify transcription factor binding motifs and footprints. Network decoding in IReNA included network modularization, identification of enriched transcription factors, and a unique function for the construction of simplified regulatory networks among modules. Network modularization was based on K-means clustering of gene expression. Two statistical tests were applied to the modularized regulatory networks to separately identify enriched transcription factors and significant regulatory relationships among modules. To illustrate the features and application of IReNA, we used public scRNA-seq and scATAC-seq data of hepatocytes from a mouse model of hepatectomy in the study of liver regeneration (Seidman et al., 2020). Although scRNA-seq and scATAC-seq data were obtained, the original study didn’t perform regulatory network analysis.
Here, IReNA was applied to infer regulatory networks using scRNA-seq data alone. We firstly analyzed scRNA-seq data to identify genes used for network inference. 2,815 hepatocytes from the control and 48 h liver tissues after hepatectomy were used to construct the trajectory based on their single-cell expression profiles (Figure 2A). In the trajectory, we observed three branches, including the rest, activation, and proliferation. Based on the trajectory, we calculated the pseudotime and identified 4,014 differentially expressed genes (DEGs) including 165 transcription factors changed during the pseudotime. Meanwhile, we identified 45 transcription factors that are expressed in more than 5% hepatocytes but not statistically differential during the pseudotime. Next, all 4,059 DEGs and expressed transcription factors were divided into five modules through K-means clustering of the smoothed expression profiles (Figure 2B). Genes in each module showed specific expression profiles. For instance, genes in the first and fifth modules were specifically expressed in the rest and proliferating hepatocytes, respectively. Function enrichment analysis revealed relevant biological functions enriched in each module of genes, including fatty acid metabolism, organic acid catabolism, cytoplasmic translation, autophagy, and cell cycle (Figure 2C). We then applied GENIE3 to infer regulatory relationships of all DEGs and expressed transcription factors in hepatocytes. We identified 434,218 potential regulatory relationships, each of which has >0.0001 wt and contains at least one transcription factor. For each regulatory relationship, Pearson’s correlation was calculated to determine the positive or negative regulation. To refine 434,218 potential regulatory relationships, we further analyzed transcription factor binding motifs in the promoter regions of genes. Totally, 180,322 regulatory relationships with binding motifs were used to reconstruct regulatory networks (Figure 2D). Meanwhile, regulatory networks were modularized according to five modules of genes identified through K-means clustering in Figure 2B. Statistically analyzing modular regulatory networks, we identified 115 transcription factors that significantly regulated each module of genes. We reconstructed regulatory networks of these 115 enriched transcription factors, which were also divided into five modules (Figure 2E). To obtain a simple regulatory network among modules for providing biological regulation insights, we performed the hypergeometric test to analyze modular regulatory networks of enriched transcription factors. We separated positive regulations and negative regulations to identify significant activations and repressions among modules. Fifteen significant regulatory relationships among modules were identified and used to establish simplified regulatory networks among modules (FDR < 0.005, Figure 2F). We showed representative biological function and transcription factor in each module. Simplified regulatory networks indicate that transcription factors related to fatty acid metabolism, organic acid catabolism, and autophagy significantly activated each other. In return, these factors significantly repressed transcription factors controlling cell cycle regulation. These suggest that the inhibition of transcription factors related to fatty acid metabolism, organic acid catabolism, and autophagy may activate cell cycle progression. These predictions obtained from simplified regulatory network analysis were supported by previous studies. Hepatic nuclear factor 4 alpha (Hnf4a) as a well-known marker gene of hepatocytes, coordinated organic acid metabolism and activated the transcription of autophagy-related gene Ulk1 in the liver (Martinez-Jimenez et al., 2010; Lee et al., 2021). According to gene ontology, Hnf4a negatively regulates the mitotic cell cycle. In simplified regulatory networks, we also observed that transcription factors regulating the ribosome may repress transcription factors of fatty acid metabolism. This is consistent with that hepatic rRNA transcriptional repression is essential for energy storage and lipid metabolism (Oie et al., 2014).
Next, IReNA was used to infer regulatory networks by integrating scRNA-seq and scATAC-seq data from the study of liver regeneration. We analyzed scATAC-seq data of 7,004 hepatocytes after hepatectomy using ArchR (Granja et al., 2021). We identified 94,595 significant peak-to-gene links, each of which had a high peak-to-gene correlation, e.g., Rora and Mlx (Figure 3A). We further uncovered the binding sites of transcription factors to peaks and identified 386,597 regulatory relationships of transcription factors to genes. Among 386,597 regulatory relationships, 154,601 regulatory relationships had high footprint occupancy scores. Overlapping 154,601 regulatory relationships with 434,218 potential regulatory relationships inferred by GENIE3, we refined 47,721 regulatory relationships to reconstruct regulatory networks which consisted of 3,185 genes. Analyzing modular regulatory networks of 3,185 genes, we identified 47 transcription factors that significantly regulate genes in each module. We performed functional enrichment analysis on target genes of 47 enriched transcription factors and observed a significant enrichment of cell type-specific functions, such as hepatocyte proliferation (q value = 6.27 × 10−3). We then reconstructed modular regulatory networks of 47 enriched transcription factors (Figure 3B). Six significant regulation relationships among modules were identified to reconstruct simplified regulatory networks among modules (FDR < 0.05, Figure 3C). We found that intermodular regulatory networks from the integrated analysis of scRNA-seq and scATAC-seq data were consistent with intermodular regulatory networks obtained from analyzing scRNA-seq data alone. We also observed that the module of transcription factors related to organic acid catabolism of hepatocytes, e.g., Hnf4a, significantly repressed the cell cycle in liver regeneration. This suggests that the inhibition of hepatocyte metabolism may promote liver regeneration.
To directly compare IReNA with other methods of network analysis, we analyzed scRNA-seq data to reconstruct regulatory networks and identified key transcription factors using GENIE3 and Rcistarget package from the SCENIC software (Aibar et al., 2017). Using Rcistarget, we refined 39,192 regulatory relationships from 434,218 potential regulatory relationships inferred by GENIE3. Next, we identified 108 transcription factors whose binding motifs were overrepresented in the promoter regions of 4,059 DEGs and expressed transcription factors identified by scRNA-seq data analysis. We obtained 1,761 significant regulatory relationships for 108 enriched transcription factors and then reconstructed modular regulatory networks of enriched transcription factors (Figure 3D). Using chromatin immunoprecipitation followed by sequencing (ChIP-seq) data and genetic perturbation data of transcription factors from liver samples obtained from public databases, we assessed the performance of IReNA and Rcistarget on regulatory network inference. Regulatory relationships identified by GENIE3 analysis of scRNA-seq data were refined separately by integrating scATAC-seq data in IReNA and overlapping DNA motifs by Rcistarget. Using ChIP-seq data alone, we observed that integrative network analysis of scRNA-seq and scATAC-seq data using IReNA showed higher precision and recall of regulatory relationships than Rcistarget analysis (Figure 3E). Similar results were observed when both ChIP-seq data and genetic perturbation data were used (Figure S2B). These results indicated that the integrated analysis of scRNA-seq and scATAC-seq data in IReNA had overall better performance on regulatory network inference than did Rcistarget analysis. We then compared regulatory networks of enriched transcription factors inferred through three different approaches described above. Among 47 transcription factors identified by IReNA using the integrated analysis of scRNA-seq and scATAC-seq data, 36 (76.60%) transcription factors were also present in regulatory networks inferred using IReNA analysis of scRNA-seq data alone (Figure S2C). By comparing gene regulatory networks obtained by analyzing only scRNA-seq data separately using IReNA and Rcistarget, we observed an overlap of 66.96% (77 in 115) of all enriched transcription factors. These results indicated that a large fraction of transcription factors was identified by two methods of network analysis. To assess the significance of transcription factors identified using IReNA or Rcistarget, we manually examined whether these factors had been previously reported in the literature related to liver regeneration. To compare different methods, we ranked the enriched transcription factors according to the significance in statistics (FDR for IReNA or normalized enrichment score for Rcistarget). We found that regulatory networks from the integrated analysis of scRNA-seq and ATAC-seq data had the best performance, 70.0% of transcription factors in the top 20, 60.0% in the top 30 and 61.7% of all enriched transcription factors were previously reported in liver regeneration-related literature (Figure 3F and Table S1). For regulatory networks inferred from analyzing scRNA-seq data alone, 65.0% of transcription factors in the top 20, 60.0% in the top 30 and 39.1% of all transcription factors were reported in liver regeneration-related literature. For regulatory networks inferred by Rcistarget from SCENIC software, 30.0% of transcription factors were in the top 20, 36.7% in the top 30 and 35.3% of all transcription factors were reported in liver regeneration-related literature. Among three types of regulatory networks, the highest fraction of transcription factors was reported for regulatory networks from the integrated analysis of scRNA-seq and scATAC-seq data. We also used the software CoCiter to assess the co-citation significance of transcription factors with liver regeneration-related terms in the literature. Similarly, we observed that scRNA-seq and scATAC-seq data analysis using IReNA identified the highest fraction of transcription factors (Figure S2D). These results indicate that the integrated network analysis of scRNA-seq and scATAC-seq data using IReNA improved the precision of identifying known transcription factors. Moreover, IReNA shows a better performance at identifying known regulators than the Rcistarget method from SCENIC software. To further demonstrate the performance of IReNA, we conducted regulatory network analysis on another two datasets from heart regeneration and NASH (Cui et al., 2020; Seidman et al., 2020). Prior to inferring gene regulatory networks controlling heart regeneration, we reconstructed the trajectory of 4,884 cardiomyocytes from neonatal heart tissues (Figure S3A). We found that cardiomyocytes formed two distinct branches (named the activation branch and the proliferation branch) following myocardial infarction. We further identified and divided 4,340 DEGs and expressed transcription factors into 4 modules (Figure S3B). Genes expressed in the activation branch (module 2 and 3) were related to oxidative phosphorylation and muscle cell differentiation, whereas genes specifically expressed in the proliferation branch (module 4) are enriched for the cell cycle (Figure S3C). Then, regulatory networks were inferred using the same three methods described above (Figures 4A, 4B, S3D, and S3E). In comparison with regulatory networks inferred by Rcistarget, regulatory networks reconstructed by IReNA contained a higher fraction of transcription factors previously reported in the literature related to heart regeneration (Figure 4C and Table S1). The precision of identifying known regulators among the top 20 transcription factors in heart regeneration were 45.0%, 30.0 and 20.0% respectively for scRNA-seq + scATAC-seq + IReNA, scRNA-seq + IReNA and scRNA-seq + Rcistarget. We also observed that scRNA-seq + scATAC-seq + IReNA identified the highest fraction of known regulators for both the top 30 transcription factors and all enriched transcription factors. For the study of NASH, we used 2,748 Kupffer cells to construct the trajectory (Figure S4A). 2,742 DEGs and expressed transcription factors were identified and divided into three modules, which were separately enriched for myeloid cell differentiation, ribosome, and oxidative phosphorylation (Figures S4B and S4C). In NASH, only bulk ATAC-seq data were available and used to refine regulatory relationships inferred from scRNA-seq data analysis (Figure S4D). We reconstructed three types of regulatory networks and a simplified regulatory network among modules (Figures 4D, 4E, S4E, and S4F). Regulatory network comparison of NASH study has a similar trend with studies in liver regeneration and heart regeneration. IReNA analysis using scRNA-seq and bulk ATAC-seq data identified the most transcription factors (85.0% of transcription factors in the top 20, 66.7% in the top 30 and 74.1% of all enriched transcription factors) which were reported to associate with NASH, followed by IReNA analysis using scRNA-seq data alone (75.0% in the top 20, 66.7% in the top 30 and 66.7% of all enriched transcription factors), and finally Rcistarget analysis using scRNA-seq data alone (50.0% in the top 20, 53.3% in the top 30 and 48.7% of all enriched transcription factors) (Figure 4F and Table S1). The integrated analysis of single-cell or bulk ATAC-seq data with scRNA-seq data overall substantially improved the reconstruction of gene regulatory networks, and had higher precision of identifying known transcription factors. In addition, transcription factors enriched through analyzing scRNA-seq data alone using IReNA showed improved accuracy relative to those identified using Rcistarget from SCENIC software.
In the study, we developed IReNA to perform regulatory network analysis, including network inference and network decoding. In IReNA, gene regulatory networks are inferred by analyzing either scRNA-seq data alone or by integrating scRNA-seq and scATAC-seq data. The regulatory relationships between transcription factors and target genes are firstly inferred according to the weights calculated by GENIE3 using scRNA-seq data. Then, transcription factor binding motifs are identified to refine regulatory relationships if only scRNA-seq data are available. When both scRNA-seq and ATAC-seq data are used, transcription factor binding motifs and footprints are applied to further refine transcriptional regulatory relationships used for network inference. IReNA also provides functions to decode inferred regulatory networks, including the modularization of regulatory networks, the enrichment of transcription factors, and the construction of simplified regulatory networks among modules. In IReNA, we developed several specific functions for network analysis. First, unlike the methods using scRNA-seq data for network analysis, IReNA integrates scRNA-seq data with single-cell or bulk ATAC-seq data to reconstruct regulatory networks. Analysis of three independent datasets consistently indicates that the integrated analysis of scRNA-seq and ATAC-seq data could more precisely identify known regulators than network analysis using scRNA-seq data alone. Second, IReNA could provide cell state-specific regulatory networks through modularizing regulatory networks. Regulatory networks are modularized according to the clustering results of gene expression profiles which represent different cell states and specific biological functions. Third, IReNA statistically analyzes modular regulatory networks and identifies reliable transcription factors including known regulators. Applied to multiple datasets, the method used in IReNA showed a consistently better performance on the identification of known regulators than Rcistarget, which conducts transcription factor enrichment analysis based on the rank of all genes for each motif (Imrichová et al., 2015). Fourth, we created a unique function in IReNA to construct the simplified regulatory networks among modules that reveal key regulatory modules and factors, facilitating the interpretation of dynamic biological regulations. Consistent results from IReNA analysis were obtained in three independent datasets, suggesting IReNA could provide robust network analysis by combining modularization analysis and statistical tests. In the light of the sparsity of scRNA-seq data, we smoothed gene expressions to calculate correlations used for the signs of regulatory relationships in IReNA. IReNA could directly calculate correlations using original expression data independent of the pseudotime. However, the smoothed gene expressions improved regulatory network inference relative to original gene expressions (Figure S2A). Gene expressions were smoothed according to the pseudotime which was calculated by Monocle in IReNA. We compared other methods for calculating the pseudotime and found that Monocle-based pseudotime showed a better performance than Slingshot-based pseudotime for network inference (Figure S2A). We used IReNA to infer regulatory networks for the same cell type, or for several cell types which are related by the lineage during the development or disease progression. Correspondingly, cell type-specific or lineage-specific transcriptomes and epigenomes should be extracted prior to network inference. Transcriptomes could be measured by bulk RNA-seq or scRNA-seq, whereas epigenomes may be detected using either bulk ATAC-seq or scATAC-seq. Transcriptomes and epigenomes used in IReNA should be matched for the same cell type or the same condition. Currently, IReNA has used scRNA-seq and scATAC-seq data of unpaired cells from the same condition to perform network analysis. However, parallel scRNA-seq and scATAC-seq profiles from the same cells are emerging, and updates to IReNA will accommodate these new data. In previous studies, we demonstrated that IReNA can be used to integrate scRNA-seq and bulk ATAC-seq to reconstruct modular gene regulatory networks controlling retinal regeneration and retinal development (Hoang et al., 2020; Lyu et al., 2021). We also identified key transcription factors and constructed the simplified regulatory networks regulating different cell states in retinal Müller glia in zebrafish and mice. In zebrafish, we predicted that reactivity-related transcription factors such as hmga1 and yap1 promoted the proliferation of Müller glia. In mice, network analysis indicated that nuclear factors Nfia/b/x, which were found in regulatory modules that maintain or restore the resting glial state, repressed the reactivity of Müller glia. Using the genetic loss of function analysis, we confirmed that several transcription factors identified by IReNA were critical for retinal regeneration, including hmga1a and yap1 in zebrafish and Nfia/b/x in mice (Hoang et al., 2020). These results indicate that IReNA analysis can provide valuable regulatory network insights and reveal key regulators in distinct biological processes including retinal regeneration. Using public scRNA-seq and ATAC-seq data from three studies, we further performed regulatory network analysis through IReNA, which provides meaningful biological insights. According to simplified intermodular regulatory networks in liver regeneration, organic acid catabolism-related modules and transcription factors are needed to be repressed to activate cell cycle progression in hepatocytes, e.g., hepatocyte nuclear factor 4 alpha (Hnf4a) (Figures 2F and 3C). The study in mouse liver regeneration demonstrated that the deletion of Hnf4a leads to sustained proliferation (Huck et al., 2019). In heart regeneration, simplified regulatory networks among modules imply that regulatory modules and transcription factors controlling the heart process promote the cell cycle of cardiomyocytes (Figure 4B). It has been reported that one of such factors Gata4 could activate heart regeneration in zebrafish and mice (Kikuchi et al., 2010; Malek Mohammadi et al., 2017). In regulatory networks of NASH, we identified IRF5 which was reported to promote hepatic fibrosis in Kupffer cells in nonalcoholic fatty liver disease (Alzaid et al., 2016). These results indicate that IReNA could be applied to identify key modules and transcription factors that regulate a range of different biological processes, although further functional validation of putative key regulators is required. In summary, we have developed IReNA to perform regulatory network analysis, including network inference, network modularization, transcription factor enrichment, and the construction of simplified regulatory networks among modules. IReNA showed a consistently better performance at identifying known regulators when integrating scRNA-seq data with scATAC-seq data to reconstruct gene regulatory networks. IReNA also outperformed the existing method Rcistarget from SCENIC software in identifying known regulators. Key transcription factors and regulatory relationships identified by IReNA are potential targets controlling tissue regeneration, diseases, and other dynamic biological processes. Through the construction of modular regulatory networks and simplified regulatory networks among modules, IReNA facilitates the understanding of regulatory mechanisms and provides meaningful biological insights.
IReNA depends on the network inference method GENIE3 and K-means clustering method for gene modularization which require application-specific analysis of single-cell sequencing data.
Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact Jie Wang ([email protected]) upon request.
This study did not generate new unique reagents.
To demonstrate analysis flow of IReNA, we used public scRNA-seq and ATAC-seq data from three studies, which analyzed datasets obtained from models of liver regeneration, heart regeneration, and nonalcoholic steatohepatitis (NASH), respectively (Seidman et al., 2020; Cui et al., 2020; Chen et al., 2020). For liver regeneration, partial hepatectomy (PHx) was performed in adult mice (Chen et al., 2020). The study conducted both scRNA-seq and scATAC-seq on liver tissues at 0 and 48 hours after PHx (accession number GSE158866 and GSE158873, available at gene expression omnibus database https://www.ncbi.nlm.nih.gov/geo/). According to the original annotation of cell types in the study, there are 2,815 and 7,004 hepatocytes measured separately by scRNA-seq and scATAC-seq. In the study of heart regeneration, scRNA-seq profiles were measured on 4,884 cardiomyocytes at 1 day, 3 days after myocardial infarction, and 1 day after sham surgery in neonatal mice (accession number GSE130699) (Cui et al., 2020). Meanwhile, scATAC-seq was performed on 755 cardiomyocytes at 3 days after myocardial infarction on postnatal day one (accession number GSE142365). The scRNA-seq and bulk ATAC-seq data from the study of NASH are available through the accession numbers GSE128334 and GSE128335 (Seidman et al., 2020). In this study, scRNA-seq profiles were measured on 6,184 non-parenchymal cells, including 2,748 Kupffer cells, from liver tissues of healthy and NASH mice. Bulk ATAC-seq was conducted on Kupffer cells from two healthy and two NASH samples.
If no specific parameters were reported in IReNA, default parameters were used for existing software. Prior to network inference, we identified differentially expressed genes (DEGs) and the expressed transcription factors as the potential genes in regulatory networks. We compared Monocle (version 2.1.8) and Slingshot (version 2.5.2), which are two frequently used methods for calculating the pseudotime of single cells (Qiu et al., 2017; Street et al., 2018). In IReNA, Monocle was used as the default method to construct the trajectory and to infer the pseudotime of individual cells from scRNA-seq data. The smoothed expression profiles were calculated according to the pseudotime and branches on the trajectory. If there is a branch in the trajectory, pseudotime in each branch is divided into 20 equal intervals. Otherwise, pseudotime is divided into 50 equal intervals. Then, we calculated the average expression profile of single cells in each interval and obtained the smoothed expression profiles. DEGs were further identified according to the pseudotime of individual cells. To obtain reliable DEGs in pseudotime analysis, we used rigorous statistical thresholds, including q-value < 0.005, fraction of expressed cells >10% and single-cell expression difference >0.1. Single-cell expression difference was defined as previously described (Hoang et al., 2020). The formula for single-cell expression difference was as follows: single-cell expression difference = Q95-expression - Q5-expression, where Q95-expression and Q5-expression represent 95% quantile and 5% quantile of expression values across all intervals of the pseudotime, respectively. Given that some key transcription factors may not be DEGs, we also included transcription factors which expressed in >5% cells for network analysis. According to the previous evaluation of the methods for network inference, we compared two top performing methods GENIE3 (version 1.16) and PIDC (version 0.1.1) (Chan et al., 2017; Huynh-Thu et al., 2010). GENIE3 was selected as the default method for network inference in IReNA. GENIE3 infers regulatory relationships of transcription factors to target genes based on random forest regression (Huynh-Thu et al., 2010). We used GENIE3 to calculate the weight of the regulation for each gene pair based on scRNA-seq data. Several cutoffs for the weight of the regulation were assessed according to the number of regulations and degree distribution of genes after pruning. The default weight >0.0001 was used in liver regeneration, whereas the weight >0.0003 was used in heart regeneration and NASH. Meanwhile, only gene pairs which contain at least one transcription factor from the TRANSFAC database (version 2018.3) were chosen as potential regulatory relationships. To determine activating and repressive regulatory relationships, we calculated Pearson’s correlations of all gene pairs using the smoothed expression profiles. The regulation types of gene pairs were defined as activation and repression separately for positive correlations and negative correlations. Original expression profiles were also used to calculate gene correlations which were compared with the correlations from analyzing the smoothed expression profiles. To compare different methods for network inference, we used the relevant evaluation framework BEELINE (Pratapa et al., 2020).
If bulk or single-cell ATAC-seq data is not available, gene regulatory relationships inferred from scRNA-seq data analysis were further refined according to transcription factor binding motifs present in the promoter regions of genes. Fimo (version 5.4.1, parse-genomic-coord, max-stored-scores = 2,000,000) was used to identify transcription factor binding motifs in the promoter regions (ranging from 1,000 bp upstream to 500 bp downstream of the transcription start sites) of the genes (Bailey et al., 2015). Uniform background model was used and contained in the motif file. Position weight matrices of binding motifs were from TRANSFAC database (version 2018.3). Regulatory relationships are selected for further network analysis if the binding motif of transcription factor occurs in the promoter region of the target gene.
If bulk ATAC-seq data is available, we use the following six steps to preprocess raw data in fastq format. (I) Remove adaptors of pair-end raw reads using fastp software (version 0.21.0) (Chen et al., 2018). (II) Align reads the GRCm38/mm10 genome using bowtie2 (version 2.4.1) with default parameters (Langmead and Salzberg, 2012). To precisely locate the center on the Tn5 cut site, we shifted reads on the forward strand by +4 bp and reads on the reverse strand by −5 bp from the bam-files using the function shiftGAlignmentsList from R package ATACseqQC (Ou et al., 2018). (III) Filter low-mapping-quality reads (MAPQ < 10) and exclude duplicated reads separately using Samtools (version 1.3.1) and Picard (http://broadinstitute.github.io/picard/) (Li et al., 2009). (IV) Call peaks through MACS2 (version 2.1.0) with the parameter extsize = 200 and shift = 100 (Zhang et al., 2008). (V) Use HTseq (version 0.12.4) to calculate the count number of each peak (Anders et al., 2015). (VI) Combine the peaks across all samples to obtain the union peaks and identify differentially accessible peaks using EdgeR (version 3.32.0) (Robinson et al., 2010). Different from bulk ATAC-seq data, scATAC-seq data was preprocessed through following steps. (I) Map raw sequencing data in fastq format to the reference genome (GRCm38/mm10) with cellranger (version 2.0.0) (Zheng et al., 2017). Reads were shifted on the forward strand by +4 bp and on the reverse strand by −5 bp. (II) Use ArchR (version 1.0.1, minTSS = 4, minFrags = 1,000, dimsToUse = 2:30, knnIteration = 1,500) to integrate scATAC-seq and scRNA-seq data with unconstrained integration methods (Granja et al., 2021). We identified peak-to-gene links by calculating the correlation between peak accessibility and gene expression across individual cells, and retained peak-to-gene links with absolute value of correlation >0.2, FDR < 1E-6, varCutOffATAC (variance of peak accessibility) > 0.7 and varCutOffRNA (variance of gene expression) > 0.3. Here, strict parameters were set to control the number of significant peak-to-gene links. After processing bulk or single-cell ATAC-seq data, the following steps were used to identify footprints and to refine regulatory relationships. (I) Identify the footprints of peaks through HINT (version 0.13.2) and select high-quality footprints (tag-count score >80th percentile) for downstream analysis (Li et al., 2019). (II) Select footprints which are covered by differentially accessible peaks. (III) Run Fimo to find binding motifs in the footprints according to the position weight matrices of motifs from TRANSFAC database (Bailey et al., 2015). (IV) Identify footprint-related genes. For bulk ATAC-seq data, ChIPseeker (version 1.26.2, tssRegion = from upstream 3,000 to downstream 3,000) was used to annotate footprint regions. Genes related to footprint regions were considered as footprint-related genes (Yu et al., 2015). For scATAC-seq data, genes linked to peaks by ArchR were considered as footprint-related genes. (V) Use Rsamtools (version 2.6.0) to obtain the sequencing depth of the mapped reads which was used to calculate the number of insertions at each position of footprints (https://bioconductor.org/packages/Rsamtools). (VI) Use the number of insertions to calculate footprint occupancy score (FOS), and then select regulatory relationships which have high FOS (FOS > 0.1) to reconstruct regulatory networks. FOS was calculated using the formula defined as previously described (Wang et al., 2018).where NL, NC and NR are numbers of insertions separately in the left, center and right regions of the motif.
Given the sparsity of scRNA-seq data, we used the smoothed expression profiles to perform gene co-expression analysis. DEGs and the expressed transcription factors were divided into different modules using the K-means clustering of the smoothed expression profiles. The optimal number of modules was determined by the silhouette coefficient calculated by R package ‘cluster’. Based on the modules of genes, the inferred regulatory networks were modularized. For each module, we used ClusterProfile (version 3.18.1) to perform functional enrichment analysis which is based on gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) databases (Yu et al., 2012).
Refined regulatory relationships were used to reconstruct modular regulatory networks of DEGs and the expressed transcription factors. Cytoscape (version 3.8.2) was used to display regulatory networks (Shannon et al., 2003). We performed the hypergeometric test to calculate the probability P that an individual transcription factor regulates a module, and then adjusted p values to false discovery rate (FDR) values. Enriched transcription factors which significantly regulate each module of genes were used to reconstruct regulatory networks. For the hypergeometric test, the probability was calculated as follows.Here, is a binomial coefficient. For identifying the significant transcription factor regulating module A, N and n represent numbers of all regulations and regulations targeting module A, respectively. K and k separately indicate the number of regulations from transcription factor and the number of regulations targeting module A from transcription factor.
Based on modular regulatory networks of enriched transcription factors, we carried out another hypergeometric test to determine significant regulatory relationships among modules and reconstructed simplified regulatory networks among modules. For positive and negative regulations, we separately performed hypergeometric tests to identify significant regulatory relationships among modules. If positive (or negative) regulations among modules are statistically significant, activating (or repressive) connections are present among modules in the simplified regulatory networks. The same formula as (Equation 2) was used to calculate the probability of the regulation of module A to module B. In the formula, N and n represent numbers of all regulations and regulations from module A, respectively. K and k separately indicate the number of regulations from module B and the number of regulations from module A to module B. The p values of regulations among modules were adjusted to FDR values. Regulatory relationships with FDR < 0.05 were regarded as significant regulations, and used to construct the simplified regulatory networks among modules. The FDR < 0.05 was used for the simplified regulatory network in heart regeneration and NASH. In liver regeneration, FDR < 0.005 was used to obtain more reliable regulatory relationships between modules. Given that biological functions are enriched for genes in each module, simplified regulatory networks suggest that enriched biological functions in one module may regulate biological functions associated with other modules.
To compare IReNA with existing methods for network inference and transcription factor enrichment, we used Rcistarget from SCENIC software to identify key regulators analyzing the same scRNA-seq data (Aibar et al., 2017). In network inference, we used candidate regulatory regions identified by i-cisTarget to refine regulatory relationships inferred from scRNA-seq data analysis (Imrichová et al., 2015). To measure the enrichment of transcription factor binding motifs in the promoter regions of DEGs, Rcistarget calculates the normalized enrichment score (NES). We reconstructed regulatory networks for transcription factors which have >3 NES. For the comparison of Rcistarget, we ranked transcription factors according to NES. To assessed the accuracy of regulatory relationships inferred by IReNA and Rcistarget, we used ChIP-seq data and genetic perturbation data of transcription factors separately from ChIP-Atlas (https://chip-atlas.org/) and KnockTF (http://www.licpathway.net/KnockTF/index.php) databases (Zou et al., 2022; Feng et al., 2020). We used regulatory relationships of transcription factors reported in literature of liver regeneration and their gene targets to assess the performance of IReNA and Rcistarget on network inference. Liver-specific ChIP-seq and genetic perturbation data were used to assess regulatory relationships inferred in liver regeneration. We obtained 368,599 regulatory relationships from liver ChIP-seq data which are obviously a larger number than 2,500 regulatory relationships obtained from genetic perturbation data in liver samples. Given this, ChIP-seq data alone, and the combination of ChIP-seq and genetic perturbation data were separately used as the ground truth to evaluate the inferred regulatory networks. To compare regulatory networks inferred from the integrated analysis of both scRNA-seq and ATAC-seq data, and from scRNA-seq data alone, we examined whether top and all enriched transcription factors in network analysis had been previously reported in literature. Enriched transcription factors were ranked by FDR or NES. For the study of nonalcoholic steatohepatitis, we used biological terms ‘nonalcoholic steatohepatitis’, ‘steatosis’ and ‘fatty liver disease’ to search the Google Scholar and PubMed databases. We searched biological terms ‘liver regeneration’, ‘hepatic regeneration’ and ‘hepatocyte regeneration’ for liver regeneration. The terms ‘heart regeneration’, ‘cardiac regeneration’ and ‘myocardial regeneration’ were used for heart regeneration. We confirmed if the gene symbol and/or common gene name for individual transcription factors were present in literature. We also searched for gene aliases according to the NCBI database (https://www.ncbi.nlm.nih.gov/gene/). If the gene symbol/gene name and biological term are both present in the title or the same sentence in the abstract, the biological function of the enriched transcription factor is regarded to have been reported in literature. Two researchers independently searched literature and checked the results. We also calculated the significance of the co-citation between each transcription factor and all specific terms using CoCiter (version 2.1) which is based on literature from the PubMed database (Qiao et al., 2013). p values from CoCiter were used to assess the association of transcription factors with specific terms. | true | true | true |
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PMC9620422 | Qinghua Fang,Jing Wang,Jiangjun Wei,Xianglin Long,Yao Wang,Jiacheng He,Xin Yuan,Jianlin Du | Transcriptomic profile analysis of the left atrium in spontaneously hypertensive rats in the early stage 10.3389/fphar.2022.989636 | 17-10-2022 | hypertension,left atrium,transcriptome,atrial fibrosis,cardiac metabolism changes | Left atrial remodeling, characterized by enlargement and hypertrophy of the left atrium and increased fibrosis, was accompanied by an increased incidence of atrial fibrillation. While before morphological changes at the early stage of hypertension, how overloaded hypertension influences the transcriptomic profile of the left atrium remains unclear. Therefore, RNA-sequencing was performed to define the RNA expressing profiles of left atrium in spontaneously hypertensive rats (SHRs) and normotensive Wistar-Kyoto (WKY) rats as a control group. We also compared the changes in the RNA expression profiles in SHRs treated with an angiotensin receptor blocker (ARB) and angiotensin receptor-neprilysin inhibitor (ARNI) to assess the distinct effects on the left atrium. In total, 1,558 differentially expressed genes were found in the left atrium between WKY rats and SHRs. Bioinformatics analysis showed that these mRNAs could regulate upstream pathways in atrial remodeling through atrial fibrosis, inflammation, electrical remodeling, and cardiac metabolism. The regulated transcripts detected in the left atrial tissue in both the ARB-treated and ARNI-treated groups were related to metabolism. In contrast to the ARB-treated rates, the transcripts in ARNI-treated rats were mapped to the cyclic guanosine monophosphate-protein kinase G signaling pathway. | Transcriptomic profile analysis of the left atrium in spontaneously hypertensive rats in the early stage 10.3389/fphar.2022.989636
Left atrial remodeling, characterized by enlargement and hypertrophy of the left atrium and increased fibrosis, was accompanied by an increased incidence of atrial fibrillation. While before morphological changes at the early stage of hypertension, how overloaded hypertension influences the transcriptomic profile of the left atrium remains unclear. Therefore, RNA-sequencing was performed to define the RNA expressing profiles of left atrium in spontaneously hypertensive rats (SHRs) and normotensive Wistar-Kyoto (WKY) rats as a control group. We also compared the changes in the RNA expression profiles in SHRs treated with an angiotensin receptor blocker (ARB) and angiotensin receptor-neprilysin inhibitor (ARNI) to assess the distinct effects on the left atrium. In total, 1,558 differentially expressed genes were found in the left atrium between WKY rats and SHRs. Bioinformatics analysis showed that these mRNAs could regulate upstream pathways in atrial remodeling through atrial fibrosis, inflammation, electrical remodeling, and cardiac metabolism. The regulated transcripts detected in the left atrial tissue in both the ARB-treated and ARNI-treated groups were related to metabolism. In contrast to the ARB-treated rates, the transcripts in ARNI-treated rats were mapped to the cyclic guanosine monophosphate-protein kinase G signaling pathway.
Hypertension is the most important controllable risk factor in cardiovascular disease (Mills et al., 2020). Persistent blood pressure overload in hypertensive patients may induce left ventricle hypertrophy, heart failure, enlargement of the left atrium, arrhythmia (especially atrial fibrillation, AF), and cardiovascular death (Kamioka et al., 2018; Parker et al., 2020; Kario and Williams, 2021). Hypertension can rapidly induce atrial remodeling, including left atrial hypertrophy, fibrosis, and an inflammatory response (Gumprecht et al., 2019; Kim et al., 2019; Wu et al., 2021). Short-term and moderate stress overload pressure results in ultrastructural changes in left atrial cells before structural remodeling of the left ventricle (Aguas et al., 1981). Hypertension is the most significant population-attributable risk factor for AF that is independent and potentially controllable (Rahman et al., 2016). However, the mechanism that allows hypertension to lead to AF remains unclear. Therefore, identifying the transcriptional characteristics of the left atrium in the early stage of hypertension may help to reveal the atrial arrhythmia substrate induced by overloaded pressure. Single-cell RNA-seq and bulk RNA-seq were used to delineate the transcriptomic profiles of heart and aorta in hypertensive animal models, systematically revealing the mechanisms of cardiac vascular remodeling, including activation of fibroblasts and vascular smooth muscle cells, dysregulation of interactions between macrophages and T cells, which were linked to multiple signaling pathways, such as TGF-β signaling pathway, cytokine, MAPK Signaling pathway (Costa Ade and Franco, 2015; Li et al., 2016; Xu et al., 2018; Cheng et al., 2021). Heart failure model, the most typical cardiac remodeling model, revealed multiple mechanisms involved in cardiac remodeling by transcriptome sequencing, including activation of myofibroblast (Chothani et al., 2019; Ramanujam et al., 2021) and immune cells (Martini et al., 2019; Abplanalp et al., 2021), mitochondrial dysfunction (Sweet et al., 2018; Zhuang et al., 2022), proinflammatory signaling (Costa Ade and Franco, 2015; Hahn et al., 2021) and TGF-β signaling pathway (Stratton et al., 2019). Although there have been many studies on RNA-seq in exploring the mechanism in target organ remodeling in hypertension, the transcriptomic characteristics of hypertension-induced atrial remodeling are still lacking. Given the close link between hypertension and AF, antihypertensive drugs may potentially reduce the risk of AF, especially the renin–angiotensin–aldosterone system inhibitor because of its anti-myocardial remodeling effect (Rahman et al., 2016; Seccia et al., 2017). Both angiotensin II type 1 receptor antagonists and sacubitril/valsartan were demonstrated to attenuate adverse cardiac remodeling by reversing cardiac fibroblasts and hypertrophy (Kusaka et al., 2015; Garvin et al., 2021). Sacubitril/valsartan was proven to be superior in reducing left ventricular hypertrophy because it targets both the renin–angiotensin system and neprilysin, and thus this therapy has an advantageous cardiovascular prognosis in patients with hypertension compared with unitary treatment using olmesartan (Schmieder et al., 2017). However, the specific mechanisms associated with reverse cardiac remodeling under angiotensin receptor blocker (ARB) or sacubitril/valsartan treatment remain unclear. In the present study, we conducted RNA-seq to compare the transcriptional differences in the left atrium in spontaneously hypertensive rats (SHRs) and Wistar-Kyoto (WKY) rats (Figure 1). Furthermore, we characterized the biological functions of these differentially expressed genes (DEGs) by bioinformatics analysis to further understand the effects of hypertension on the left atrium. In addition, transcriptome analysis was performed for the left atrium tissues of SHRs fed saline, ARB, and sacubitril/valsartan. Bioinformatics analysis was also performed to demonstrate the changes in gene expression associated with the different treatments in order to elucidate the mechanisms responsible for atrial remodeling under treatment with ARB and sacubitril/valsartan. Furthermore, we compared the transcriptional differences in rats under different treatments to identify their distinct effects on the left atrium of ARB and sacubitril/valsartan. This transcriptomic profile of left atrium enables a more furtherly understand its mechanism of development of left atrial transcriptional remodeling in early hypertension.
Fourteen-week-old male SHRs (N = 9) and WKY rats (N = 3) were purchased from Vital River Laboratory Animal Technology Co. Ltd. (Beijing, China). The first group comprising the normotensive control (WKY, N = 3), was fed with saline (7.5 ml/kg/day) routinely and independently for 4 weeks. The SHRs were randomly divided into three groups: which were fed with saline (7.5 ml/kg/day, N = 3), valsartan (30 mg/kg/day, N = 3) and sacubitril/valsartan (60 mg/kg/day, N = 3) for 4 weeks. All animal protocols were approved by the Animal Research Ethics Committee of Chongqing Medical University.
Immediately after anesthetizing the rats by intraperitoneal injection of 20% ethyl carbamate, the left atrium was ablated through thoracotomy and a portion was immersed in ice-cold isolation buffer, which was rapidly frozen at −80°C to prepare for RNA-seq. Other parts of the atrial tissue were fixed in 8% neutral formaldehyde and embedded in paraffin. After dewaxing, the paraffin sections were stained with hematoxylin and eosin (H&E) and Masson’s trichrome. The sections were observed under a microscope at 200× lens (Zhu et al., 2018). H&E staining was conducted for histological determination of myocardial injury by quantifying the ratio of the inflammatory cell infiltration and necrosis area relative to the entire field as described in previous studies (Rezkalla et al., 1988), as follows: score 0 = 0 (no myocardial damage observed); score 1 = 0%–25%; score 2 = 25%–50%; score 3 = 50%–75%; score 4 = 75%–100% (Supplementary Table S1). Similarly, the extent of myocardial fibrosis was quantified by Media Cybernetics (United States) using the Masson’s trichrome. The ratio of the collagen fiber area was calculated as the area with positive staining for collagen relative to the entire visual field of the section (Takemoto et al., 1997). The area density was defined as the integral optical density (IOD) divided by the pixel area. One section was randomly selected from each rat in the four groups. Three different fields in each section were selected for scoring according to the criteria above.
Total RNA was extracted from the left atrium using TRIzol (Invitrogen, Carlsbad, CA, United States) according to the manufacturer’s instructions, and then quality controlled and quantified using a NanoDrop and Agilent 2100 bioanalyzer (Thermo Fisher Scientific, MA, United States), respectively. RNA-seq was conducted by a commercially available service (service ID: F21FTSCCWLJ1374_MOUmpqzN, BGI-Shenzhen, China). Briefly, after breaking the total RNA into short fragments, mRNA was enriched using oligo (dT) magnetic beads, followed by cDNA synthesis. Double-stranded cDNA was purified and enriched by PCR amplification, after which the library products were sequenced using a BGIseq-500. The sequencing data were filtered with SOAPnuke (v1.5.2). The clean reads were mapped to the reference genome using HISAT2 (v2.0.4). Bowtie2 (v2.2.5) was applied to align the clean reads to the reference coding gene set, and the expression levels of genes were then calculated using RSEM (v1.2.12). All RNA-seq data has been uploaded to the GEO database and can be queried through GSE207283.
The raw counts were used to calculate the expression level of each gene, and DESeq2 (v1.4.5) was employed to compare the expression levels of genes between different samples. DEGs were filtered using the following criteria: log2FC ≥ 1 and Q value ≤ 0.05. The DAVID online analysis tool was used to perform functional cluster analysis for the DEGs between the WKY and SHR groups. The biological functions of DEGs were determined according to the significantly enriched Gene Ontology (GO) terms (http://www.geneontology.org/). Fisher’s exact and multiple comparison tests were used to calculate the significance level (p-value) and false positive rate (FDR) for each function, and the significant functions of DEGs were screened using the threshold of p < 0.05. Pathway analysis was conducted based on the Kyoto Encyclopedia of Genes and Genomes (KEGG; http://www.genome.jp/kegg/) to explore the significant pathways. Pathways with FDR ≤ 0.5 were defined as significantly enriched. Gene set enrichment analysis (GSEA) was performed using software (Subramanian et al., 2005) to quantify the normalized enrichment score and FDR. Principal component analysis, volcano plot and protein–protein interaction network analysis were performed in BGI online system (Dr.Tom). Key driver gene analysis (KDA) was carried out using BGI online system (Dr.Tom). Specifically, KDA analysis takes as input a set of genes (G) and a directed gene network (N), aiming at identifying the key regulators of the gene set associated with a given network (Rual et al., 2005; Tran et al., 2011). The size of h-layer neighborhood (HLN) for each node was calculated. The value of HLN is equal to the number of downstream nodes in the range h away from the specific nodes. The nodes are selected as candidate drivers if their HLN values are greater than + σ (μ), where μ is defined as the composite set of HLNS of all nodes, is the mean value of μ, and σ(μ) is the standard deviation of μ. The candidate drivers without any root node are global drivers, which is defined as key driver genes, while the rest were local drivers. Nodes with out-degree above +2σ (d) are global driver genes, where d is defined as the set of out-degrees of all nodes, is the mean of d, and σ (d) as the standard deviation of d.
Immunohistochemistry techniques were used to study the expression of transforming growth factor-β (TGF-β). Specimens were incubated overnight with primary antibodies at 4°C and then incubated with horseradish peroxidase-labeled secondary antibodies at room temperature for 1 h. DAB color developing solution was used for the chromogenic reaction. The antibodies comprised the primary antibody anti-TGF-β1 rabbit antiserum (Servicebio, China) and secondary antibody horseradish peroxidase-conjugated goat anti-rabbit immunoglobulin G (Servicebio, China). Sections were then processed by microscopy (Nikon) and analyzed with Aipathwell digital pathology image analysis software. The mean density was defined as the integrated optical density divided by the quantity of positive cells. Six different fields were selected for quantitative analysis in each group.
PCA showed a closer distance on the scatter plot among groups than between groups, thereby indicating that there were significant differences between the atrial tissues from normotensive and spontaneous hypertension rats (Figure 2A). The transcriptional differences in the four groups are shown in Figure 2B and Supplementary Figure S1.
H&E was used to evaluate histological cardiac damage and inflammation (Supplementary Table S2). The results of H and E results indicated no apparent necrosis or inflammatory infiltration in the left atrial tissue under light microscopy in any group. Further quantitative analysis detected no significant differences among the four groups (Figure 3A). Masson’s trichrome was used to estimate the degree of myocardial fibrosis (Supplementary Tables S3, S4). The mean area density values did not differ significantly in the SHR, ARB-treated, and ARNI-treated groups compared with the WKY group (Figure 3B). The area ratio of collagen fibers was slightly elevated in SHRs compared with WKY rats (Figure 3C). These results suggest that the hypertension overloading pressure did not lead to significant histological changes manifested as inflammation and fibrosis in the left atrium in the early development stage of hypertension development. Early administration of inhibitors of the renin–angiotensin system did not influence the histology of the left atrium.
The gene expression profiles for the left atrium tissue were compared in the normotensive and spontaneously hypertensive groups to characterize the effects of hypertension overload pressure on the transcription levels (Figures 4A–C). In total, 1,558 DEGs were observed in the SHR group compared with the WKY group, where 873 genes were upregulated and 685 were downregulated. GO assignments were used to classify the genes associated with the transformation of the left atrium in the SHRs. As shown in Figures 4D,E, compared with the WKY rats, the upregulated transcripts in SHRs were enriched in 1) biological process (BP): calcium ion transmembrane transport, noncanonical Wnt signaling pathway, cell adhesion and SMAD protein signal transduction; 2) molecular function (MF): calcium channel activity, Wnt-protein binding, voltage-gated ion channel activity, and I-SMAD binding; and 3) cellular component (CC): voltage-gated calcium channel complex, cell junction, and collagen-containing extracellular matrix. The downregulated transcripts were enriched in: 1) BP: fatty acid metabolic process, lipid metabolic process, oxidation–reduction process, and intracellular distribution of mitochondria; 2) MF: fatty-acyl-CoA binding, calmodulin binding, and extracellular matrix structural constituent; and 3) CC: mitochondrion, extracellular matrix, cell junction, and mitochondrial membrane. We performed pathway enrichment analysis with KEGG to further characterize the DEGs. As shown in Figures 4F,G, in the left atrial tissues of these hypertensive rats, compared with the WKY group, the upregulated transcripts were related to the TGF-β signaling pathway, calcium signaling pathway, MAPK signaling pathway, and NF-kappa B signaling pathway, whereas the downregulated transcript were related to the PPAR signaling pathway, fatty acid metabolism, carbon metabolism, PI3K-Akt signaling pathway, and apelin signaling pathway.
Based on the protein–protein interaction network analysis of all the dysregulated genes (1,558 genes) in SHRs and the WKY rats, we selected 15 key driver genes in the prominent regulatory position (Figures 5A,B). We performed KEGG pathway analysis for further characterize the key genes. The interaction network obtained between the significantly enriched KEGG pathways and the genes determined by KDA is shown in Figure 5C. The key genes were enriched in pathways including fatty acid metabolism, and PPAR signaling pathway. The CytoHubba app in Cytoscape 3.8.2 software was used to select the hub genes among the 1558 dysregulated genes in the left atrium in hypertensive rats. Twenty hub genes were selected, including Hadha, Hadhb, Eci2, and Acadl (Figure 5D). As shown in Figure 5E, GO analyses were conducted and they identified, GO-BP terms: fatty acid metabolic process, fatty acid beta-oxidation and lipid metabolic process; GO-MF terms: fatty-acyl-CoA binding and enoyl-CoA hydratase activity; GO-CC terms: mitochondrion, peroxisome and mitochondrial matrix. In addition, the top 20 GO and KEGG pathways of the 30 hub genes were identified by Metascape (Figure 5F) included the fatty acid catabolic process, fatty acid degradation and PPAR signaling pathway.
Gene set enrichment analysis (GSEA) was performed to assess the concentration of genes regulated by sacubitril/valsartan and ARB in different gene sets in the KEGG pathways. As shown in Figure 6A, after the 4 weeks of treatment with ARB, the atrial tissues were mainly regulated in ribosome, proteasome, oxidative phosphorylation, and biosynthesis of amino acids. In the sacubitril/valsartan-treated group, the changes were mainly in the citrate cycle, AMPK signaling pathway, fatty acid elongation, propanoate metabolism, carbon metabolism, and PPAR signaling pathway (Figure 6B). We note that the pathways enriched in the ARB and ARNI groups were both involved in cardiometabolic pathways, thereby suggesting that the two drugs may contribute to repairing damage to the left atrium through this common mechanism. Furthermore, GSEA was performed to compare the different mRNAs between the ARB- and sacubitril/valsartan-treated groups to identify the differences in atrial remodeling reversal mechanisms. As shown in Figure 6C, compared with ARB, mRNAs regulated in the sacubitril/valsartan-treated group were mainly enriched in the PPAR signaling pathway, ECM-receptor interaction, cGMP-PKG signaling pathway, and MAPK signaling pathway.
The TGF-β-positive cells manifested as brown and the nucleus was stained blue by immunohistochemical staining (Supplementary Table S5). As shown in Figure 7, the expression level of TGF-β was higher in the left atrium of SHRs compared with normotensive rats, which was consistent with the changes in the gene expression levels.
Complex changes in the atrium increase the susceptibility and progression to AF, and stimulate AF-associated diseases, and thus they are defined as “atrial cardiomyopathy” according to a recent consensus study (January et al., 2019). The multidirectional association between elevated blood pressure and AF has not been elucidated, and the main theories currently focus on complex associations such as structural remodeling, electrophysiology, neuroendocrine, inflammation, and autonomic mechanisms (Dzeshka et al., 2017). Transcriptome and proteome analyses were used to comprehensively understand the changes caused by hypertension and to further study the AF substrate in hypertension (Alvarez-Franco et al., 2021). In a previous study, Julio et al. observed 15 altered proteins in the early stage of left ventricular hypertrophy in SHRs compared with normotensive rats by proteomic analysis, and they mediated hypertension-induced cardiac hypertrophy (Gallego-Delgado et al., 2006). In this study, we first determined the transcriptomic features of the left atrium in SHRs. GO, GSEA, and KEGG pathway analysis suggested that the regulated transcripts were attributed to multiple functions, such as TGF-beta signaling pathway, SMAD signaling pathway, fatty acid metabolism, oxidative phosphorylation, the citrate cycle, propanoate metabolism, NF-κB pathway, MAPK, and calcium signaling pathway, which could be associated with atrial fibrosis, inflammation, electrical remodeling, and metabolic changes. According to the KDA analysis of DEGs and further relation network with KEGG pathway analysis, most key driver genes were involved in cardiac metabolism, such as fatty acid metabolism, carbon metabolism, propanoate metabolism and PPAR signaling pathway, suggesting these pathways may play pivotal roles in the pathophysiology of atrial fibrillation in hypertension. Meanwhile, we noticed that Hadha, Hadhb, and Eci2, the top three of the hub genes, were both related to fatty acid metabolism. Cardiac remodeling is characterized by metabolic remodeling, especially down-regulation of fatty acid oxidation, which can further aggravate pathological remodeling (Kolwicz et al., 2013; Mouton et al., 2020). Hadha and Hadhb play a key role in fatty acid oxidation and cardiolipin remodeling, and are involved in cardiac remodeling and systolic dysfunction in heart failure (Le et al., 2014; Miklas et al., 2019; Dagher et al., 2021). The expression of PPAR and medium chain Acyl CoA dehydrogenase was decreased in 4-month-old SHRs (Purushothaman et al., 2011). PPAR activation and increased fatty acid metabolism were observed in SHRs after 4 months treatment of medium-chain triglycerides, accompanied by reduction of oxidative stress and improvement of myocardial hypertrophy (Saifudeen et al., 2017). Our results proved that genes involved in fatty acid metabolism were significantly dysregulated before the onset of heart failure, even before cardiac structural changes, suggesting that fatty acid metabolim may be involved in the structural remodeling of left atrium at the early stage of hypertension. The following contents will describe the transcriptional characteristics of hypertensive left atrial in terms of atrial fibrosis, cardiac metabolism, cardiac inflammation, and electrical remodeling.
In the present study, genes involved in the TGF-β signaling pathway were significantly dysregulated. The TGF-β1 pathway is involved in the development and propagation of AF. The TGF-β1 pathway is linked to atrial fibrosis, and the most common mechanisms involved include the SMAD signaling pathway, the endothelial to mesenchymal transition, and the CD44 signaling pathway (Babapoor-Farrokhran et al., 2021). A recent study showed that serum levels of TGF-β1 gradually increased in the following four groups: control group, hypertensive patients, paroxysmal AF secondary to hypertension, and chronic AF secondary to hypertension, thereby demonstrating that TGF-β1 may contribute to the initiation and sustainment of AF in hypertensive patients via atrial remodeling and fibrosis (Lin et al., 2015). The upregulated expression of TGF-β in the atrium results in increased collagen I and III fibrosis, and pirfenidone significantly reduces arrhythmogenic atrial remodeling by suppressing TGF-β1 expression (Lee et al., 2006; Kong et al., 2014). In summary, hypertension may lead to left atrial fibrosis and structural remodeling, and further increase the susceptibility to AF by upregulating TGF-β1.
The abnormal metabolic milieu is considered a critical amplifier in cardiac injury during hypertension and it plays an essential role in AF (Pfeffer et al., 2019). In a previous study of the early stage of hypertension development, profound changes in metabolites were observed before the impairment of cardiac function, which comprised increased glucose uptake and oxidation, an increased substrate supply, and elevated pyruvate and fatty acyl groups (Li et al., 2019). Abnormal myocardial fatty acid metabolism was shown to induce the incidence and persistence of AF (Shingu et al., 2020). Changes in fatty acid metabolism, oxidative phosphorylation, and the citrate cycle were also observed in our study. Mitochondrial dysfunction is a significant feature of the heart in hypertensive patients and it leads to the transformation of metabolism to glycolysis (Zhang et al., 2015). Nevertheless, insulin resistance reduces the utilization of glucose, which further aggravates myocardial injury (Mouton et al., 2020). Similarly, in our study, we observed significant changes in genes associated with mitochondria and insulin resistance.
Both NF-κB and MAPK can be activated by toll-like receptors to increase the expression of cytokines such as IL-6 and TNF, which induce inflammation, which participates in atrial remodeling (Kawano et al., 2005; Kawai and Akira, 2010). NF-κB may be involved in the oxidative stress process through the phosphatidylinositol 3-kinase/protein kinase B pathway, which is a common signal that cross-links with nuclear factor E2-related factor 2 (Nrf2) (Jayasooriya et al., 2014). Inhibition of NF-κB has been shown to activate Nrf2, which protects the cardiovascular system from pathological cardiac remodeling by reducing oxidative stress responses (Zhou et al., 2014). We found that genes associated with the NF-κB pathway were significantly dysregulated in the left atrium of SHRs, thereby suggesting that overloaded hypertension may induce atrial remodeling through NF-κB and further increase the incidence of AF. Hypertension-induced atrial remodeling activates hypoxia-inducible factor-1(HIF-1), which further activates monocyte libraries and proinflammatory cytokines (Rius et al., 2008; Fujisaka et al., 2013).
High hydrostatic pressure has been shown to affect the expression of potassium and calcium channels in the left auricle in SHRs and lead to electrical remodeling of the left atrium (Li et al., 2020). Similarly, we found that genes related to ion channels were significantly dysregulated in the left atrium of SHRs. The mechanisms associated with AF include triggers that generate ectopic activity or modifiers of substrate promoted re-entry (Thomas and Abhayaratna, 2017). Electrical remodeling plays a crucial role in AF and its molecular mechanism is based on ion channel expression and/or phosphorylation (Schotten et al., 2011). In particular, electrical reconstruction promoted ion channel (decreased L-type Ca2+ current, rectifier background K+ current) changes to result in a shortened atrial effective refractory period, prolonged excitability interval, and facilitated re-entry (Wiedmann et al., 2018; Dridi et al., 2020).
In the present study, we observed a common metabolism-related gene change in the ARB-treated and ARNI-treated groups. Renin–angiotensin system blockers can potentially improve cardiometabolic parameters, such as insulin resistance, glucose metabolism, and adipose tissue dysfunction (Jahandideh and Wu, 2020). Interestingly, these regulated pathways involved in cardiac metabolism, especially regulated by ARNI, such as fatty acid elongation and propanoate metabolism, were also dysregulated in the SHR. Therefore, we speculate that ARNI and ARB may reverse atrial remodeling by uniformly alleviating cardiometabolic dysfunction. Previous studies have shown that ARNI can improve cardiac function in patients with heart failure by improving ventricular fibrosis, reducing cardiac hypertrophy and cardiac inflammation (Lara et al., 2012; Pascual-Figal et al., 2021), while cardiac metabolism was rarely mentioned. The main source of energy consumed by healthy myocardium is fatty acid oxidation, whereas a shift from free fatty acid to glucose utilization is observed in failing heart (Li et al., 2019). Our results provide a new insight for the application of ARNI in the early prevention of heart failure caused by overloaded pressure. Given the superior prognosis when treating cardiovascular disease with sacubitril/valsartan compared with ARB, we compared the differences in the left atrium under treatment with these drugs. The results showed that the regulated mRNAs were enriched in ECM-receptor interactions and the cGMP-PKG signaling pathway. Notably, genes involved in cGMP-PKG signaling pathway were up-regulated in SHR, but the expression changes of these genes were reversed in the sacubitril/valsartan treated rats. As an inhibitor of endopeptidase enzyme neprilysin, sacubitril/valsartan reduces natriuretic peptides (NPs) degradation and lead to enhanced NP action (Ishii et al., 2017). NPs act as key negative regulators during cardiac hypertrophy and remodeling by activating cGMP-dependent PKG (Takimoto, 2012; Kong and Blanton, 2013). A recent study has shown that sacubitril/valsartan can significantly improve stress-induced myocardial fibrosis by regulating atrial natriuretic peptide-induced PKG signaling in cardiac fibroblasts and inhibiting the expression of fibroblast transformation-related processes, which are not generated by treatment with the molar equivalent of valsartan (Burke et al., 2019), and our results are consistent with these changes. Besides, sacubitril valsartan was shown to significantly increase circulating cGMP levels in beagles compared with valsartan (Mochel et al., 2019). Therefore, we hypothesized that sacubitril/valsartan may reverse hypertension-induced left atrial remodeling through cGMP-PKG signaling pathway, which need further in vivo and in vitro experiments to confirm.
In this study, we employed transcriptomic analysis using RNA-seq to determine the changes in the gene expression levels in the left atrium in SHRs compared with WKY rats, and SHRs under treatment with anti-hypertension drugs. Intensive bioinformatics analysis identified atrial fibrosis, inflammation, electrical remodeling, and metabolism changes as critical BPs, and essential pathways were also identified under sacubitril/valsartan and ARB interventions. Meanwhile, we emphasize the importance of cardiac metabolic remodeling and Rac1 in inducing and reversing left atrial remodeling at the early stage of hypertension. Overall, the results obtained in this study might provide insights into the underlying mechanisms associated with the AF substrate in spontaneous hypertension and potential treatment targets for preventing the incidence of AF in hypertension. | true | true | true |
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PMC9620621 | Hongyan Peng,Shuting Wu,Shanshan Wang,Qinglan Yang,Lili Wang,Shuju Zhang,Minghui Huang,Yana Li,Peiwen Xiong,Zhaohui Zhang,Yue Cai,Liping Li,Youcai Deng,Yafei Deng | Sex differences exist in adult heart group 2 innate lymphoid cells | 31-10-2022 | Group 2 innate lymphoid cells (ILC2s),Heart,Sex difference,Klrg1,IL-33 | Background Group 2 innate lymphoid cells (ILC2s) are the most dominant ILCs in heart tissue, and sex-related differences exist in mouse lung ILC2 phenotypes and functions; however, it is still unclear whether there are sex differences in heart ILC2s. Results Compared with age-matched wild-type (WT) male mice, 8-week-old but not 3-week-old WT female mice harbored an obviously greater percentage and number of heart ILC2s in homeostasis. However, the percentage of killer-cell lectin-like receptor G1 (Klrg1)− ILC2s was higher, but the Klrg1+ ILC2s were lower in female mice than in male mice in both heart tissues of 3- and 8-week-old mice. Eight-week-old Rag2−/− mice also showed sex differences similar to those of age-matched WT mice. Regarding surface marker expression, compared to age-matched male mice, WT female mice showed higher expression of CD90.2 and Ki67 and lower expression of Klrg1 and Sca-1 in heart total ILC2s. There was no sex difference in IL-4 and IL-5 secretion by male and female mouse heart ILC2s. Increased IL-33 mRNA levels within the heart tissues were also found in female mice compared with male mice. By reanalyzing published single-cell RNA sequencing data, we found 2 differentially expressed genes between female and male mouse heart ILC2s. Gene set variation analysis revealed that the glycine, serine and threonine metabolism pathway was upregulated in female heart ILC2s. Subcluster analysis revealed that one cluster of heart ILC2s with relatively lower expression of Semaphorin 4a and thioredoxin interacting protein but higher expression of hypoxia-inducible lipid droplet-associated. Conclusions These results revealed greater numbers of ILC2s, higher expression of CD90.2, reduced Klrg1 and Sca-1 expression in the hearts of female mice than in male mice and no sex difference in IL-4 and IL-5 production in male and female mouse heart ILC2s. These sex differences in heart ILC2s might be due to the heterogeneity of IL-33 within the heart tissue. Supplementary Information The online version contains supplementary material available at 10.1186/s12865-022-00525-0. | Sex differences exist in adult heart group 2 innate lymphoid cells
Group 2 innate lymphoid cells (ILC2s) are the most dominant ILCs in heart tissue, and sex-related differences exist in mouse lung ILC2 phenotypes and functions; however, it is still unclear whether there are sex differences in heart ILC2s.
Compared with age-matched wild-type (WT) male mice, 8-week-old but not 3-week-old WT female mice harbored an obviously greater percentage and number of heart ILC2s in homeostasis. However, the percentage of killer-cell lectin-like receptor G1 (Klrg1)− ILC2s was higher, but the Klrg1+ ILC2s were lower in female mice than in male mice in both heart tissues of 3- and 8-week-old mice. Eight-week-old Rag2−/− mice also showed sex differences similar to those of age-matched WT mice. Regarding surface marker expression, compared to age-matched male mice, WT female mice showed higher expression of CD90.2 and Ki67 and lower expression of Klrg1 and Sca-1 in heart total ILC2s. There was no sex difference in IL-4 and IL-5 secretion by male and female mouse heart ILC2s. Increased IL-33 mRNA levels within the heart tissues were also found in female mice compared with male mice. By reanalyzing published single-cell RNA sequencing data, we found 2 differentially expressed genes between female and male mouse heart ILC2s. Gene set variation analysis revealed that the glycine, serine and threonine metabolism pathway was upregulated in female heart ILC2s. Subcluster analysis revealed that one cluster of heart ILC2s with relatively lower expression of Semaphorin 4a and thioredoxin interacting protein but higher expression of hypoxia-inducible lipid droplet-associated.
These results revealed greater numbers of ILC2s, higher expression of CD90.2, reduced Klrg1 and Sca-1 expression in the hearts of female mice than in male mice and no sex difference in IL-4 and IL-5 production in male and female mouse heart ILC2s. These sex differences in heart ILC2s might be due to the heterogeneity of IL-33 within the heart tissue.
The online version contains supplementary material available at 10.1186/s12865-022-00525-0.
Group 2 innate lymphoid cells (ILC2s) are rare but potent ILCs that are involved in allergies and infections by mediating a type 2 immune response [1, 2]. ILC2s are characterized by the expression of the transcription factor Gata3 and the surface markers CD127 (IL-7R), CD90.2 (Thy1.2), and ST2 (IL-33R) (Lin−CD127+CD90.2+ST2+). They can produce type 2 cytokines, including interleukin (IL)-4, IL-5, and IL-13 [1, 2]. Our previous study and others have reported that ILC2s are the most dominant population of ILCs in the heart; this population is identified as CD45+Lin−CD127+CD90.2+ST2+ cells [3, 4]. We found that heart-resident ILC2s have a unique phenotype characterized by lower expression of Icos, CD25 (IL-2Rα), and Ki-67 but higher expression of stem cell antigen 1 (Sca-1) and Gata3 and a stronger ability to produce interleukin (IL)-4 and IL-13 than lung ILC2s [3]. Growing evidence shows that sex differences exist in innate and adaptive immune cells; for instance, male mice have higher NK-cell frequencies in peripheral blood [5], and female mice have higher levels of CD8+ T cells and lower levels of regulatory T cells (Tregs) in adipose tissue [6]. Moreover, recent studies have revealed sex-related differences in mouse lung ILC2 phenotypes and functions, which also show strain differences [7]. Heart ILC2s, as a dominant population of ILCs in the heart, have been reported to play a protective role in a mouse model of atherosclerosis [8] and contribute to IL-33-mediated protection of cardiac fibrosis in a mouse model of catecholamine-induced cardiac fibrosis [9]. However, it is still unclear whether there are sex differences in heart ILC2s. To understand the sex differences in heart ILC2s, we investigated the number, phenotypes, and functions of heart ILC2s in both male and female mice at homeostasis. Here, we showed that male and female mice showed significant differences in the percentage and number of total ILC2s, as well as the Klrg1+ and Klrg1− ILC2 subsets, in the heart at 8 weeks old. Compared with male mice, female mouse heart ILC2s exhibited higher expression of CD90.2 and Ki67 and lower expression of Klrg1 and Sca-1; however, there was no difference in IL-4 and IL-5 production in male and female mouse heart ILC2s. As ILC2s are thought to be regulated by regulatory T (Treg) cells that also express IL-33R [10, 11] and sex hormones are known to affect Treg cells, we also used T and B-cell-deficient mice, [6] namely Rag2-deficient (Rag2−/−) mice, to verify the sex difference in heart ILC2s. Eight-week-old Rag2-deficient (Rag2−/−) mouse heart ILC2s and the Klrg1+ and Klrg1− ILC2 subsets exhibited trends similar to those in WT mice. We also found that increased IL-33 mRNA levels existed within the heart tissues of female mice compared with male mice, which might be responsible for the sex difference in heart ILC2 frequencies and Klrg1 expression. Then, we reanalyzed the single-cell RNA sequencing (scRNA-seq) data and showed 2 differentially expressed genes and differential signaling pathways between male and female mouse heart ILC2s. Heart ILC2s can be divided into 2 clusters, and one cluster with relatively lower expression of Semaphorin 4a (Sema4a) and thioredoxin interacting protein (Txnip) but higher expression of hypoxia-inducible lipid droplet-associated (Hilpda).
To investigate the differences in heart ILC2s between male and female mice, we collected Percoll-enriched heart lymphocytes from 3- and 8-week-old male and female mouse hearts. The gating strategy for heart ILC2s is shown in Fig. 1A. At 3 weeks of age, the percentage and number of heart ILC2s were indistinguishable between male and female mice (Fig. 1B). At 8 weeks of age, both the percentage and the total number of heart ILC2s were higher in female mice than in age-matched male mouse hearts (Fig. 1C). Based on Klrg1 expression, a recent study reported that the numbers of Klrg1− ILC2s in the bone marrow and lungs are downregulated by androgen [12]. Therefore, we further investigated the sex differences in Klrg1− and Klrg1+ ILC2s. The percentages and numbers of heart Klrg1− ILC2s were higher in female mice than in male mice at the age of both 3 and 8 weeks when CD45+Lin−CD127+CD90.2+ST2+ ILC2s were assessed. For heart Klrg1+ ILC2s, the percentage was decreased, but the numbers were increased in 8-week-old mice, but a decreased percentage and a decreasing trend in the number of heart Klrg1+ ILC2s were found at 3 weeks of age (Fig. 1D–E). In addition, we also measured the protein levels of Gata3 in heart ILC2s and found that CD45+Lin−CD127+CD90.2+ST2+ cells showed over 90% Gata3 expression, and the percentage and numbers of heart CD45+Lin−CD127+CD90.2+ST2+Gata3+ cells [9] were also higher than those in male mice (Additional file 1: Fig. S1A). To further illustrate heart ILC2s exist sex difference, we also gated CD45+Lin−CD90.2+CD127+Gata3+ for ILC2 as done in another published article [13], and the data showed that the percentage of ILC2s among CD45+ cells were higher in female mice than male mice (Additional file 1: Fig. S1B). The above data suggest that sex hormones may play an extrinsic role in determining the number of heart ILC2s, especially the numbers of Klrg1− ILC2s and Klrg1+ ILC2s in the heart.
We next investigated the sex differences in murine heart ILC2 phenotypes, including surface markers, transcription factors, and proliferation. In total heart ILC2s, the geometric mean fluorescence intensity (gMFI) of Klrg1 and Sca-1 was lower, while CD90.2 (Thy1.2) was higher in 8-week-old female mice than in age-matched male mice (Fig. 2A). There were no sex differences in the gMFIs of other surface markers, including Icos, CD25 (IL-2Rα), CD127 (IL-7R) and ST2 (IL-33R), or in that of the transcription factor Gata3 (Fig. 2A). In addition, female mouse heart ILC2s had a stronger proliferation ability than male mice, which was reflected by increased Ki67+ cells (Fig. 2B). These results demonstrated that mouse heart total ILC2s had distinct sex differences in terms of the surface expression of Klrg1, Sca-1, CD90.2 and proliferation ability. Because our results showed that the numbers of Klrg1− ILC2s and Klrg1+ ILC2s in the heart exhibited sex differences, we next determined the sex differences in heart Klrg1− or Klrg1+ ILC2 phenotypes. Our results showed that the gMFI of CD90.2 was increased, and the gMFI of Sca-1 was decreased in both Klrg1− and Klrg1+ ILC2s in the heart of female mice, but the gMFI of Klrg1 was also decreased in Klrg1+ ILC2s in female mouse hearts (Fig. 2A). The number of Ki67-positive cells was higher in both Klrg1− ILC2s and Klrg1+ ILC2s in female mouse hearts (Fig. 2B). There were no sex differences in CD25, Icos or Gata3 among heart Klrg1− ILC2s and Klrg1+ ILC2s (Fig. 2A).
ILC2s are known to produce the main type 2 cytokines, IL-4 and IL-5, after stimulation with IL-25, IL-33 and TSLP. To investigate the expression of these cytokines in male and female mouse heart ILC2s, we stimulated heart lymphocytes with 50 ng/ml IL-33 for 4 h and then determined the production of IL-4 and IL-5 by the ILC2s through flow cytometry. Our results showed that heart ILC2s showed a lower percentage of IL-4 in female mice than in age-matched male mice (Fig. 3A), but the total number of IL-4+ ILC2s and the gMFI of IL-4 exhibited no significant difference between male and female mouse heart ILC2s (Fig. 3A). There was no significant difference in IL-5 production, regarding the percentage, total number and gMFI, in heart ILC2s between male and female mice (Fig. 3B). The above data suggest that IL-4 and IL-5 secreted by heart ILC2s are not affected by sex hormones.
ILC2s are thought to be regulated by Treg cells that also express ST2 [10, 11], and sex hormones are known to affect Treg cells [6]. As such, we also determined the percentage and number of heart ILC2s in male and female Rag2−/− mice, which lack functional T and B cells, at the age of 8 weeks old. The data showed a higher percentage and number of total heart ILC2s in female mice than in male mice (Fig. 4A). The percentages and numbers of heart Klrg1− and Klrg1+ ILC2s between male and female Rag2−/− mice showed trends similar to those observed for WT mice (Fig. 4B). Interestingly, the number of total heart ILC2s in Rag2−/− mice was almost threefold higher than that in WT mice (male mice: 258.89 ± 157.84 vs. 1000.6 ± 220.18; female mice: 570.11 ± 310.08 vs. 1612.00 ± 321.78). The increased number of heart ILC2s in Rag2−/− mice was mainly contributed by Klrg1− ILC2s. This difference might be attributed to Treg cells, partly because Treg cells repress ILC2 functions directly or compete for IL-33 in heart tissue [10]. Because our results showed that the phenotypes of heart ILC2s and Klrg1− ILC2s and Klrg1+ ILC2s, including Klrg1 and CD90.2, were different in male and female WT mice, we also determined the sex difference of heart Klrg1− or Klrg1+ ILC2s in phenotypes. Our results showed that the gMFI levels of Klrg1 were lower but CD90.2 was higher in female Rag2−/− mice (Fig. 4C), which was similar in WT mice. Moreover, the gMFI level of CD90.2 were increased in both subsets of female mice heart, but the gMFI level of Klrg1 were decreased in female mouse heart Klrg1+ILC2s, and the gMFI level of CD127 was higher in only female mouse heart Klrg1− ILC2s (Fig. 4C). There was no sex difference in ST2 among heart Klrg1− ILC2s and Klrg1+ ILC2s (Fig. 4C).
We further explored whether the sex differences in heart ILC2s are dependent on cell intrinsic sex hormone receptor expression levels or extrinsic cytokines from local heart tissues. Previous studies have found that lung ILC2s showed higher expression levels of androgen receptor (Ar), lower expression of estrogen receptor 1 (Esr1) and no expression of Esr2 [14], and sex hormones, such as androgen, play an extrinsic role in determining the numbers of lung ILC2s and ILC2 progenitors [15, 16]. To this end, we measured the mRNA expression of Ar, Esr1 and Esr2 in both male and female mouse heart ILC2s and found that the mRNA levels of Ar, Esr1 and Esr2 were undetectable in both male and female mouse heart ILC2s (Fig. 5A). Consistently, the percentages and surface marker expression of heart ILC2s from both male and female mice were not changed after stimulation with different concentrations of 17β-E2 (estrogen) or testosterone (androgen) for 12 h (Additional file 1: Fig. S2A-S2D). Interestingly, there was no significant difference between Klrg1, CD90.2 and Sca-1 expression levels in heart ILC2s between female and male mice after in vitro coculture. This suggests a cell-extrinsic factor existed in the heart tissue may contribute to the sex differences of heart ILC2s. To further explore the extrinsic factors, such as cytokines, that respond to sex differences in heart ILC2s, we determined the mRNA levels of IL-33, which is reported to maintain ILC2 homeostasis and expansion in the heart tissues of both male and female mice in a homeostatic state [9, 17, 18]. As IL-33 is mainly expressed by nonhematopoietic cells, such as fibroblasts, epithelial cells, and endothelial cells [19], we used whole heart tissue for RNA preparation. The data revealed relatively higher mRNA levels of IL-33 in female mouse heart tissue than in male mouse heart tissue (Fig. 5B). Collectively, these data suggested that the sex difference in heart ILC2s is not dependent on sex hormone receptor expression but might be associated with the higher levels of IL-33 within the whole heart tissue of female mice.
To further explore the sex difference of heart ILC2s at the single-cell level, we reanalyzed the single-cell RNA sequencing (scRNA-seq) data by using two published datasets together, one containing 2 [20] and one containing 4 cardiac nonmyocytes samples [21], both of which were pooled from one paired WT female and male heart tissue, for scRNA-seq. After initial quality control checks, a total of 5793 CD45+ cells were acquired and subdivided into 8 main clusters based on marker gene expression across all cells by uniform manifold approximation and projection (UMAP) analysis (Additional file 1: Fig. S3A). Cell plots with red circles were attributed to ILC2s, which was confirmed by the high expression of Gata3 and Il7r (Additional file 1: Fig. S3A-S3B). Because these scRNA-seq samples were mixtures of cells derived from both male and female mice, we isolated male and female CD45+ cells based on the expression of the female-specific gene Xist (X-inactive specific transcript) [21] (Additional file 1: Fig. S3C). The data showed relatively higher percentages of ILC2s among CD45+ cells in female mice than in male mice (Fig. 6A), which is consistent with our flow cytometric results. Due to the limited cell numbers of heart ILC2s acquired from the published data, we only identified Xist and Tyrosylprotein sulfotransferase 2 (Tpst2) as differentially expressed genes (DEGs) in heart ILC2s from male and female mice (adjust P < 0.05) (Fig. 6B and Additional file 2: Table S1). Although without statistical significance, DEAD-Box helicase 3 Y-linked (Ddx3y) (30.8% of total male ILC2), Killer cell lectin like receptor K1 (Klrk1) (25.6% of total male ILC2s), and Il18 receptor 1 (Il18r1) (25.6% of total male ILC2s) were only detected in male ILC2s, while Fragile X mental retardation autosomal homolog 1 (Fxr1) (27.4% of total female ILC2s) and ADP ribosylation factor like GTPase 5b (Arl5b) (33.9% of total female ILC2s) were mainly detected in female ILC2s (Additional file 2: Table S1). Gene set variation analysis (GSVA) revealed that the glycine, serine and threonine metabolism pathway was upregulated, while other signaling pathways, such as the B-cell receptor signaling pathway and alpha-linolenic acid metabolism, were downregulated in female heart ILC2s compared with male heart ILC2s (Fig. 6C). To explore the heterogeneity of heart ILC2s in both female and male mice, heart ILC2s were further subdivided into 2 clusters, Cluster 0 and 1 (Fig. 6D). Due to the limited cell numbers of heart ILC2s acquired from the published data, only 3 DEGs were found between the two clusters after adjusting P values, including Semaphorin 4a (Sema4a), Thioredoxin interacting protein (Txnip), and Hypoxia-inducible lipid droplet-associated (Hilpda) (adjust P < 0.05) (Additional file 2: Table S2). Cluster 1 showed relatively lower expression of Sema4a and Txnip and higher expression of Hilpda than Cluster 0 (Fig. 6E). In addition, Cluster 1 also showed relatively higher expression of Gata3, Areg, Icos, and Kit but lower expression of Klrg1 than Cluster 0, although without statistical significance (Fig. 6E). The percentages of Clusters 0 and 1 were 35.71% and 64.29%, respectively, in male heart ILC2s and 55.56% and 44.44%, respectively, in female mouse heart ILC2s (Fig. 6F left panel). After calculating the percentage of Cluster 0 and 1 heart ILC2s among CD45+ cells, we found that the percentage of Cluster 0 heart ILC2s among CD45+ cells showed no difference between female and male mice, whereas the percentage of Cluster 1 heart ILC2s among CD45+ cells was obviously higher in female mice than in male mice (Fig. 6F right panel). Due to limited cell numbers of heart ILC2s in this dataset, we did not find any DEGs after adjusting P values between male and female mice in cluster 0 and 1 except Xist (Additional file 2: Table S3-S4). Although without statistical significance, 149 genes were mainly detected in cluster 0 of female, including C–C motif chemokine ligand 3 (Ccl3) (25% of female in cluster 0) (Additional file 2: Table S3) that has been reported to have sex difference in the hippocampus of rats [22]. And 26 genes were only detected in cluster 1 of female, such as Peroxisome proliferator activated receptor gamma (Pparg) (22% of female in cluster 1) (Additional file 2: Table S4), which has been found that Pparg-regulated genes were upregulated in female adipose tissue compared with male adipose [23]. These findings suggest that the higher percentage and greater numbers of ILC2s in female heart tissue are mainly due to more Cluster 1 cells.
Recent studies have highly demonstrated tissue- and even strain-specific sex differences in resident ILC2s [12, 15, 24]. The frequency and numbers of ILC2s were found to be higher in the visceral adipose tissue, mesenteric lymph nodes and lungs of adult female mice than in those of male mice under steady-state conditions [12, 15]. We and other groups have reported that ILC2s are the most dominant population of ILCs in the heart [3, 4]. In the current study, we found that the percentage and number of total ILC2s in the heart were significantly higher in female mice than in male mice in adulthood but not at 3 weeks old in the steady state. In addition, female and male Rag2−/− mice showed similar trends for the percentage and numbers of total ILC2s and Klrg1 expression. In female C57BL/6 mice, lung ILC2s exhibited increased expression of CD90.2 and decreased expression of Klrg1 [7, 12, 15]. Similarly, our current study showed that female mouse heart ILC2s exhibited higher expression levels of CD90.2 but lower expression of Klrg1 and Sca-1 at 8 weeks of age. Klrg1 is expressed on ILC2s, natural killer (NK) cells and CD8+ T cells and is an inhibitory receptor that contains an immunoreceptor tyrosine-based inhibitory motif (ITIM) [25–27]. Recent reports have revealed that Klrg1 interacts with E-cadherin in epithelial tissue to blunt mouse lung ILC2 proliferation [28, 29]. Consistent with this scenario, our data showed that female heart Klrg1+ ILC2s showed a reduced MFI for Klrg1 along with increased Ki67 expression. Consistently, our scRNA-seq analysis showed that female mouse heart ILC2s had relative higher expression of Fxr1 and Arl5b, though without statistical significance. Fxr1 is RNA-binding protein, which promotes cancer cell proliferation, including prostate cancer cells [30] and ovarian cancer cells [31], whereas male mouse heart ILC2s showed upregulation of α-linolenic acid metabolism, which decreases T-cell proliferation and differentiation [32]. Previous studies have demonstrated that sex differences in ILC2s are regulated by both sex hormone receptor-dependent and -independent pathways [14]. In our study, murine heart ILC2s expressed almost undetectable sex hormone receptors, and heart ILC2s showed no sex differences in response to sex hormone stimulation, which suggests that the sex difference in murine heart ILC2s is not dependent on sex hormones. However, our results revealed a relatively higher mRNA level of IL-33 in the heart tissue of female mice than in male mice at a steady state. Previous studies have demonstrated that IL-33 enhances ILC2 percentages at steady state [17]. All this evidence suggests that the heterogeneity of IL-33 levels in the heart tissue may contribute to the increased percentage of heart ILC2s in female mice at a steady state. Previous studies have shown that ILC2s are a heterogeneous population based on their differential responses to the microenvironment in the lung [33], skin [34] and small intestine [35]. It can be subdivided into inflammatory ILC2s (iILC2s) responding to IL-25 or helminthic infection and natural ILC2s (nILC2s) responding to IL-33 [33, 36]. Although there were only 101 ILC2 cells, we could still subdivide heart ILC2s into 2 clusters, Clusters 0 and 1. The percentage of Cluster 1, but not Cluster 0, heart ILC2s among CD45+ cells in each heart were obviously higher in female mice than in male mice, which was responsible for the higher percentage and greater numbers of ILC2s in female heart tissue. Cluster 1 showed relatively lower expression of Sema4a and Txnip and higher expression of Hilpda than Cluster 0. Additionally, Cluster 1 showed lower expression of Klrg1 but higher expression of GATA3, although without statistical significance, which might be due to the limited cell numbers we acquired from the published data. Sema4a is a Semaphorin that functions in angiogenesis, tumor and immune responses [37] and plays an important role in T-cell activation and the balance between Th1 and Th2 cell responses [38, 39]. Txnip, a thioredoxin (TRX)-binding protein, is involved in the production of IFN-γ in natural killer cells [40] and is associated with nod-like receptor protein 3 (NLRP3) inflammasome activation [41]. Hilpda, an oncogenic gene, is widely expressed in various tumors and is related to macrophage infiltration in the tumor microenvironment [42]. However, we did not find any differentially expressed genes between male and female mice in cluster 0 and 1 due to the limited cell numbers of heart ILC2s we used. Therefore, the detailed surface marker and functional and sex difference between Clusters 0 and 1 should be further discussed, which warrants another separate study.
In summary, the results of the present study present sex differences in adult murine heart ILC2s, characterized by female mice harboring obviously greater numbers of heart ILC2s in homeostasis due to a major subset of Klrg1− ILC2s and no sex difference in IL-4 and IL-5 production, which might be because of the heterogeneity of IL-33 levels within the heart tissue. By reanalyzing the published scRNA-seq data, heart ILC2s can be subdivided into 2 clusters. The percentage of Cluster 1 heart ILC2s among CD45+ cells in each heart was obviously higher in female mice, which is a response to the higher percentage and greater numbers of ILC2s in female heart tissue.
Male and female wild-type (WT) C57BL/6 mice were maintained in the Hunan Children’s Research Institute pathogen-free animal facility of Hunan Children’s Hospital (Changsha, Hunan, China). Mice were housed in cages (4–5 mice maximum per cage) at 22–25 °C and 50 ± 10% relative humidity with a 12-h light/dark cycle, periodic air changes, and free access to water and food. Congenic Rag2−/− mice on the C57BL/6 background were obtained from Changzhou Cavens Laboratory Animal Co., Ltd. (Changzhou, Jiangsu, China). All animal procedures and protocols were approved by the Animal Ethics Committee of Hunan Children’s Hospital and followed the guidelines of the Institutional Animal Care and Use Committees of Hunan Children’s Hospital (Changsha, Hunan, China).
A single-cell suspension was prepared as described previously [3]. Mice were anesthetized with 2% pentobarbital sodium, and the heart was slowly perfused with cold phosphate-buffered saline (PBS) administered via the left ventricle with a 5-ml syringe to remove peripheral blood cells. Then, heart tissues were cut into approximately 1-cm2 pieces and digested for 45 min at 37 °C in Hank’s solution containing 10% fetal bovine serum (FBS) (Biological Industry, Kibbutz Beit Hemek, Israel), 0.5 mg/ml collagenase I (Sigma‒Aldrich, St. Louis, MO, United States), and 0.5 mg/ml collagenase II (Gibco, Waltham, MA, United States). After digestion, the cells were resuspended in 20% Percoll (GE Healthcare, Pittsburgh, PA, United States) in RPMI 1640 medium (Biological Industry, Kibbutz Beit Haemek, Israel) containing 5% FBS, and a single mononuclear cell suspension was collected after centrifugation (2000 rpm, room temperature, 5 min).
The antibodies used for flow cytometry were commercially purchased and are listed in Table 1. For surface markers, single-cell suspensions derived from heart tissues were stained by incubating the cells with antibodies in staining buffer (PBS containing 2% mouse serum, 2% horse serum and anti-CD16/CD32 blocking antibodies (eBioscience, San Diego, CA, United States)) for 15 min at room temperature in the dark. For live-dead staining, cells were incubated with 7-AAD in apoptosis staining buffer (BioLegend, San Diego, San Diego, CA, United States) for 15 min at 4 °C after surface marker staining. Gata3 and Ki67 were stained as recommended by the manufacturer using the Foxp3/Transcription Factor Staining Buffer Set Kit (eBioscience, San Diego, CA, United States). The lineage (Lin) markers included CD3ε and CD19. Isotype-matched control antibodies were used at the same concentration as the corresponding test antibody. All flow cytometry experiments were carried out on a BD LSRFortessa (BD Biosciences, San Diego, CA, United States). Data were analyzed with FlowJo software (version 10.0; FlowJo LLC, Ashland, OR United States).
For analysis of the sex hormone receptor of heart ILC2s, single mononuclear cell suspensions were isolated from male and female mouse heart tissues according to the above description, and heart ILC2s were sorted by fluorescence-activated cell sorting (FACS) using a FACSAria III cell sorter (BD Biosciences, San Jose, CA, USA) after gating on CD45+Lin−CD127+CD90.2+ST2+ cells. The purity of heart ILC2s was measured with the gating strategy (Additional file 1: Fig. S4). Then, the sorted heart ILC2s with purity > 90% were used for RNA extraction with an RNAprep Pure Micro Kit (Tiangen Biotech, Beijing, China). To analyze the expression of IL-33 in heart tissue, total RNA was extracted from whole heart tissue from male and female mice using TRIzol (Invitrogen, Waltham, MA, United States), as described previously [3]. Total RNA was then reverse transcribed into cDNA using Evo M-MLV RT Premix (AG Biotechnology (Hunan), Changsha, China). Real-time qPCR was performed using a SYBR® Green Premix Pro Tag HS qPCR Kit (AG Biotechnology (Hunan), Changsha, China) with a Roche LightCycler® 480 II. Primer sequences used for qRT‒PCR were obtained from reported literature or designed by Pubmed Primer-BLAST. Primer sequences used for qRT‒PCR were designed by PubMed Primer-BLAST, including Ar forward, 5′-CAGGAGGTAATCTCCGAAGGC-3′; Ar reverse, 3′-ACAGACACTGCTTTACACAACTC-5′; Esr1 forward, 5′-CCCGCCTTCTACAGGTCTAAT-3′; Esr1 reverse, 3′-CTTTCTCGTTACTGCTGGACAG-5′; Esr2 forward, 5′-CTGTGATGAACTACAGTGTTCCC-3′; Esr2 reverse, 3′-CACATTTGGGCTTGCAGTCTG -5′. Primer sequences used for qRT‒PCR were obtained from the reported literature, including IL-33 forward, 5′-CCCTGGTCCCGCCTTGCAAAA-3'; IL-33 reverse, 3′-AGTTCTCTTCATGCTTGGTACCCGA-5′ [3]; GAPDH forward, 5′-AGGTCGGTGTGAACGGATTTG-3′; GAPDH reverse, 3’ TGTAGACCATGTAGTTGAGGTCA-5′.
For the analysis of the heart ILC2 response to sex hormones, single mononuclear cell suspensions isolated from heart tissues were stimulated with 0, 0.1, 1 and 10 nM 17β-estradiol (17β-E2) and testosterone (Medchem Express, Monmouth Junction, NJ, United States) supplemented with 10 ng/mL IL-2 and IL-7 (BioLegend, San Diego, San Diego, CA, United States) to survival for 12 h [9] and then stained for surface markers. To rule out sex hormone effects from the culture medium and FBS, we used charcoal dextran stripped FBS (Serana Europe GmbH, Brandenburg, Germany) and phenol red free culture medium (Boster Biological Technology, Wuhan, China) in this experiment. All flow cytometry experiments were carried out on a BD LSRFortessa. Data were analyzed with FlowJo software.
For intracellular IL-5 and IL-4 staining, single-cell suspensions isolated from heart tissues were stimulated with 50 ng/ml IL-33 (BioLegend, San Diego, San Diego, CA, United States) plus BD Golgi Plug protein transport inhibitor (BD Biosciences, San Diego, CA, United States) for 4 h and then stained for surface markers. After washing, the cells were fixed with the Fixation/Permeabilization Solution Kit (BD Biosciences, San Diego, CA, United States) following the manufacturer’s instructions and stained with anti-IL-4 or anti-IL-5 antibodies. Isotype-matched control antibodies were used at the same concentration as the corresponding test antibody. All flow cytometry experiments were carried out on a BD LSRFortessa. Data were analyzed with FlowJo software.
A total of 6 cardiac nonmyocyte samples pooled from one paired WT female and male mouse heart tissue were downloaded from the ArraryExpress database [20, 21] for downstream analysis. Cell Ranger version (version 6.0.1) was used to process raw sequencing data. The Seurat R package (version 4.0.5) was applied in our study to convert the scRNA-seq data as a Seurat object [43]. Cells that expressed fewer than 300 genes or more than 5000 genes or more than 20% mitochondrial genes were removed at the quality control step. Data were then normalized by the SCT transform R package (version 0.3.2) [44]. Next, we used the “RunPCA” function to reduce the dimensionality of the scRNA-seq data. Subsequently, we used the “RunPCA” function to conduct UMAP analysis. We also used the “Find Clusters” and “Find All Markers” functions to conduct cell clustering analysis and detect gene expression markers. Afterward, we used the Single R package and Cell Marker dataset to annotate the cell types in our study [45]. The “subset” function was also applied to extract the subcluster for downstream analysis and then annotated and analyzed as described above. Cells with Xist gene expression ≥ 0.1 were identified from female mice. To analyze the DEGs between female and male ILC2s, the value of the logFC threshold was set to 0.25 to filter the DEGs (Adjust p value < 0.05), and the heatmap was produced by the Complex Heatmap R package (version 2.11.1). A total of 186 KEGG pathway were download from the Molecular Signatures Database (MSigDB, http://www.gsea-msigdb.org/gsea/msigdb/index.jsp) by the package “msigdbr”, with the permission from Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.kegg.jp/) website [46–49]. GSVA analysis was performed to assess these KEGG pathway activation in the GSVA R package [50]. The parameters of msigdbr were set as follows: species = “Mus musculus”, category = “C2”, subcategory = “KEGG”.
All data are expressed as the mean ± SD, and statistical analyses were performed with SPSS software for Windows (Version 23, SPSS Inc., Chicago, IL, United States). Statistical analysis was performed with an unpaired Student’s t test for comparisons of two independent experimental groups. If the litter effect was very obvious among independent experiments, two-way ANOVA (sex and litter) followed by Dunnett’s test was used [51]. In each analysis, there were n = 5–12 replicates per group, and the results are representative of at least two independent experiments. Statistical significance was defined as p < 0.05. The number of mice used in each group is indicated in the figure legends. All graphs were produced by GraphPad Prism 8.0 for Windows software (GraphPad Software Inc., La Jolla, CA, United States).
Additional file 1. Figure S1: The percentage and numbers of heart Gata3+ ILC2s in 8-week-old wild-type C57BL/6 mice. A Cumulative frequencies and enumeration of heart Gata3+ ILC2s among ILC2s (identified as CD45+Lin-CD127+CD90.2+ST2+ cells, left) or CD45+ lymphocytes (middle) in 8-week-old wild-type mice by flow cytometric analysis. B Cumulative frequencies and enumeration of heart GATA3+CD127+ ILC2s among CD45+ lymphocyte in 8-week-old wild type mice by flow cytometric analysis. Each dot represents one mouse; different colors represent different litters; error bars represent the mean ± SD; *p < 0.05, **p < 0.01. Unpaired two-tailed Student’s t test (A-B). Figure S2: The responsiveness of heart ILC2s to sex hormones. A-B The cumulative frequencies of heart ILC2s among CD45+ cells treated with the indicated concentrations of 17β-E2 (A) and testosterone (B) for 12 hours. C-D The gMFIs of the indicated surface markers on heart ILC2s for both male and female mice after stimulation with the indicated concentrations of 17β-E2 (C) and testosterone (D) for 12 hours. Each dot represents one mouse; different colors represent different litters; error bars represent the mean ± SD; two-way ANOVA followed by Dunnett’s test (A-D). Figure S3: scRNA-Seq analysis of heart lymphocytes from both male and female mice related to Fig. 6. A UMAP reduction and data visualization of major heart CD45+ cells with high expression of CD45. After unsupervised clustering, different types of lymphocytes were identified by corresponding markers. In the total heart, ILC2s are highlighted by red circles. B Violin plots showing the expression of ILC2 marker genes (Gata3, Il7r) in heart CD45+ cells. C UMAP plot showing the heart lymphocyte cell types in male and female mice. Figure S4: Proportion of ILC2s in mouse heart tissues before or after FASC sorting. Evaluation of the purity of sorted heart ILC2s from one representative samples. Gating strategy of heart ILC2s sorting and the percentage of each gate are shown (Top: Pre-sort; Bottom: Post-sort). The purity of heart ILC2s after FASC sorting was 90.6%.Additional file 2. DEGs of ILC2 and its clusters between female and male mice. | true | true | true |
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PMC9622316 | 35702821 | Shan Gao,Tingting Guo,Shuyu Luo,Yan Zhang,Zehao Ren,Xiaona Lang,Gaoyong Hu,Duo Zuo,Wenqing Jia,Dexin Kong,Haiyang Yu,Yuling Qiu | Growth Inhibitory and Pro-Apoptotic Effects of Hirsuteine in Chronic Myeloid Leukemia Cells through Targeting Sphingosine Kinase 1 | 15-06-2022 | Chronic myeloid leukemia,SPHK1,Hirsuteine,Sphingolipid rheostat,SPHK1/S1P/S1PR1,BCR-ABL/PI3K/Akt | Chronic myeloid leukemia (CML) is a slowly progressing hematopoietic cell disorder. Sphingosine kinase 1 (SPHK1) plays established roles in tumor initiation, progression, and chemotherapy resistance in a wide range of cancers, including leukemia. However, small-molecule inhibitors targeting SPHK1 in CML still need to be developed. This study revealed the role of SPHK1 in CML and investigated the potential anti-leukemic activity of hirsuteine (HST), an indole alkaloid obtained from the oriental plant Uncaria rhynchophylla, in CML cells. These results suggest that SPHK1 is highly expressed in CML cells and that overexpression of SPHK1 represents poor clinical outcomes in CML patients. HST exposure led to G2/M phase arrest, cellular apoptosis, and downregulation of Cyclin B1 and CDC2 and cleavage of Caspase 3 and PARP in CML cells. HST shifted sphingolipid rheostat from sphingosine 1-phosphate (S1P) towards the ceramide coupled with a marked inhibition of SPHK1. Mechanistically, HST significantly blocked SPHK1/S1P/S1PR1 and BCR-ABL/PI3K/Akt pathways. In addition, HST can be docked with residues of SPHK1 and shifts the SPHK1 melting curve, indicating the potential protein-ligand interactions between SPHK1 and HST in both CML cells. SPHK1 overexpression impaired apoptosis and proliferation of CML cells induced by HST alone. These results suggest that HST, which may serve as a novel and specific SPHK1 inhibitor, exerts anti-leukemic activity by inhibiting the SPHK1/S1P/S1PR1 and BCR-ABL/PI3K/Akt pathways in CML cells, thus conferring HST as a promising anti-leukemic drug for CML therapy in the future. | Growth Inhibitory and Pro-Apoptotic Effects of Hirsuteine in Chronic Myeloid Leukemia Cells through Targeting Sphingosine Kinase 1
Chronic myeloid leukemia (CML) is a slowly progressing hematopoietic cell disorder. Sphingosine kinase 1 (SPHK1) plays established roles in tumor initiation, progression, and chemotherapy resistance in a wide range of cancers, including leukemia. However, small-molecule inhibitors targeting SPHK1 in CML still need to be developed. This study revealed the role of SPHK1 in CML and investigated the potential anti-leukemic activity of hirsuteine (HST), an indole alkaloid obtained from the oriental plant Uncaria rhynchophylla, in CML cells. These results suggest that SPHK1 is highly expressed in CML cells and that overexpression of SPHK1 represents poor clinical outcomes in CML patients. HST exposure led to G2/M phase arrest, cellular apoptosis, and downregulation of Cyclin B1 and CDC2 and cleavage of Caspase 3 and PARP in CML cells. HST shifted sphingolipid rheostat from sphingosine 1-phosphate (S1P) towards the ceramide coupled with a marked inhibition of SPHK1. Mechanistically, HST significantly blocked SPHK1/S1P/S1PR1 and BCR-ABL/PI3K/Akt pathways. In addition, HST can be docked with residues of SPHK1 and shifts the SPHK1 melting curve, indicating the potential protein-ligand interactions between SPHK1 and HST in both CML cells. SPHK1 overexpression impaired apoptosis and proliferation of CML cells induced by HST alone. These results suggest that HST, which may serve as a novel and specific SPHK1 inhibitor, exerts anti-leukemic activity by inhibiting the SPHK1/S1P/S1PR1 and BCR-ABL/PI3K/Akt pathways in CML cells, thus conferring HST as a promising anti-leukemic drug for CML therapy in the future.
Chronic myeloid leukemia (CML) is a slowly progressing hematological malignancy characterized by the accumulation of transformed hematopoietic progenitor cells in bone marrow and peripheral blood. The Philadelphia chromosome (Ph), which results from [t(9;22)(q34;q11.2)] reciprocal translocation, and its gene product bcr-abl are present in 95% of CML patients (Lion, 2011). Mechanistically, bcr-abl encodes BCR-ABL. BCR-ABL is an oncoprotein with consistent tyrosine kinase activity, which leads to cell transformation and uncontrolled cell growth (Osman and Deininger, 2021). As a result, Ph, bcr-abl and BCR-ABL proteins with a molecular weight of 210-kDa, serve as the basis of pathology, diagnosis, and monitoring in CML. Therefore, during the past decades, the abnormal fusion protein BCR-ABL, which has tyrosine kinase activity, represents a logical target for CML therapy (Mojtahedi et al., 2021). Tyrosine kinase inhibitors (TKIs) that selectively target BCR-ABL, such as Imatinib, nilotinib, and dasatinib, have been developed. TKIs have led to extended lifespans for many patients with CML and are considered to be the most spectacular success in the field of targeted cancer therapy (Amir and Javed, 2021). However, a considerable number of patients cannot benefit from TKIs because of their toxicity, resistance, and intolerance (Cortes and Lang, 2021). Therefore, novel treatment options are urgently required. Sphingolipids, ubiquitous cell membrane constituents, are involved in various diseases, including cancer (Codini et al., 2021). Metabolites of sphingolipids, such as ceramide (Cer), sphingosine (Sph), and sphingosine 1-phosphate (S1P), are viewed as important signaling effectors that regulate many cellular processes, ranging from cell growth to cell apoptosis and cellular responses to stress (Velazquez et al., 2021). Cer can be deacylated to form Sph, which can be further phosphorylated to produce S1P. Evidence has identified that Cer/Sph induces cell apoptosis, whereas S1P promotes cell survival and migration. These oppositely acting molecules are interconvertible within cells, and their balance has been regarded as cellular “sphingolipid rheostat,” which determines cell fate (Green et al., 2021). Sphingosine kinases (SPHKs) catalyze the conversion of Sph to S1P and are considered to be the particular enzymes regulating the sphingolipid rheostat. SPHKs are evolutionarily conserved lipid kinases. There are two isoforms in mammalian cells, SPHK1 and SPHK2, of which SPHK1 is widely reported to have established roles in cancer initiation, progression, and drug resistance in numerous cancers, including leukemia (Green et al., 2021; Velazquez et al., 2021). Moreover, elevated SPHK1 levels have also been found in several cancers, such as breast, lung, colorectal, kidney and brain cancers (Lupino et al., 2019). Thus, SPHK1 is considered a druggable target for cancer therapy. However, targeting SPHK1 with small-molecule inhibitors in CML has rarely been reported. Bioactive natural products are important sources of many current therapeutic agents, either in their original form or as derivatives. Due to their diverse mechanisms of action, various natural compounds derived from medicinal plants have been extensively studied to treat cancer (Wilson et al., 2020). Hirsuteine (HST) is an indole alkaloid obtained primarily from the hooks or hook-bearing branches of the oriental plant Uncaria rhynchophylla and is used in the treatment of cerebrovascular disorders and neurodegeneration (Mohd Sairazi and Sirajudeen, 2020). Qi et al. (2014) reported that HST protected normal neuronal cells from glutamate-induced cell death, thus exhibiting neuroprotective efficacy. Earlier pharmacological studies with HST have demonstrated that it possesses antihypertensive, anti-inflammatory, neuromodulatory, and protective effects, and exerts cytotoxicity and MDR-reversal activity in human hepatocellular carcinoma cells (Huang et al., 2017; Kushida et al., 2021). However, the anti-leukemic activity of HST against CML and its underlying mechanisms are not well understood. This study aims to explore the potential anti-leukemic efficacy of HST in CML cells and the underlined mechanism.
Hirsuteine was purchased from Abphyto biotech (Chengdu, Sichuan, China). 3-(4, 5-Dimethyl-2-thiazolyl)-2, 5-diphenyl-2Htetrazolium bromide (MTT) reagent was from Amresco (Solon, OH, USA). Propidium iodide (PI) was from Sigma-Aldrich (St. Louis, MO, USA). The Annexin V-FITC/PI Apoptosis Detection Kit was purchased from Dalian Meilun Biological Product Factory (Dalian, Liaoning, China). TRIzol reagent was purchased from Absin (Shanghai, China). Human S1P ELISA kit (ELS11663), Human Cer ELISA kit (ELS11665) and Human SPHK1 ELISA Kit (ELS11669) were bought from Tianjin Dingguo Biotechnology (Tianjin, China). Fetal bovine serum (FBS) was from Biological Industries (Kibbutz Beit-Haemek, Israel). Antibodies against Cyclin B1 (1:1,000, #4135), CDC2 (1:1,000, #9116), Cyclin D1 (1:1,000, #55506), Caspase 3 (1:1,000, #9662), PARP (1:1,000, #9532), Caspase-8 (1:1,000, #9746), Caspase-9 (1:1,000, #9502), Cytochrome c (1:1,000, #4272), BCR-ABL (1:1,000, #2862), p-BCR-ABL (Y412) (1:1,000, #2865), PI3K-p110α (1:1,000, #4249), p-Akt (Ser473) (1:1,000, #3787), Akt (1:1,000, #9272), β-actin (1:1,000, #8457), anti-rabbit and anti-mouse HRP-conjugated secondary antibodies (1:2,000) were purchased from Cell Signaling Technology (Danvers, MA, USA). Antibody against Bcl-2 (1:1,000, #SC-7382) was from Santa Cruz Biotechnology (Santa Cruz, CA, USA). Antibody against p-SPHK1 (Ser225) (1:1,000, #19561-1-AP) was from Proteintech (Wuhan, Hubei, China). Antibody against SPHK1 (1:1,000, #A0139) was from ABclonal (Wuhan, Hubei, China), Human SPHK1 overexpression plasmid was purchased from Genechem (Shanghai, China).
Human CML cell line K562 was obtained from the National Collection of Authenticated Cell Cultures (Shanghai, China), K562/G01, an Imatinib-selected multidrug resistance (MDR) cell subline, was obtained from the Institute of Hematology, Chinese Academy of Medical Sciences (Tianjin, China). Human peripheral blood mononuclear cells (PBMCs) were isolated from the blood samples of healthy volunteers with the approval of the Ethics Committee of Tianjin Medical University Cancer Institute and Hospital using Dakewei Human Lymphocyte Separation Medium (Beijing, China). All cells were cultured in RPMI-1640 containing 10% FBS, 10 µg/mL streptomycin, and 100 U/mL penicillin, in a humidified incubator at 37°C with 5% CO2. Cells were authenticated by short tandem repeat (STR) analysis. For K562/G01 cell culture, 1 μg/mL of Imatinib was added to the culture medium to maintain the MDR characteristics. K562/G01 was further grown in a drug-free culture medium for 10 days before assay. Cells were seeded onto twelve-well plates and transfected with 1 µg of SPHK1 or empty vector control plasmid using Lipofectamine® 2000 (Invitrogen Corp., Carlsbad, CA, USA) in a condition of 37°C, 5% CO2 for 6 h. The culture medium was then replaced by fresh RPMI-1640 medium containing antibiotics and FBS before subsequent experiments.
MTT assay was performed as we previously reported (Yin et al., 2019). Cells were harvested and seeded at a density of 4×104 cells/well onto a 96-well plate, then treated with HST at various concentrations for 48 h or at different time for 32 µM. After further incubation with 5 mg/mL MTT at 37°C for 4 h, the medium was discarded, and the produced formazan blue was dissolved with 100 µL DMSO. The absorbance value at 490 nm was measured by using a microplate reader iMark (BIO-RAD, Hercules, CA, USA).
Cell cycle analysis and cell apoptosis were detected by flow cytometry as we reported before (Zhang et al., 2019). In brief, for cell cycle analysis, cells under detection were harvested and fixed in 75% ethanol overnight, washed with PBS, and stained with propidium iodide (PI) staining (50 μg/mL PI, 100 μg/mL RNase A and 0.5% Triton X-100) at 4°C for 30 min in the dark. For cell apoptosis, cells under detection were harvested and stained with Annexin V-FITC/PI double staining. The samples were immediately analyzed by BD Accuri C6 flow cytometer (BD Biosciences, San Jose, CA, USA).
qRT-PCR was conducted as previously described (Zhou et al., 2016). Total RNA was extracted using TRIzol reagent (Life Technologies, Carlsbad, CA, USA). 500 ng of total RNA was reverse transcribed into cDNA using PrimeScript RT Master Mix Reagent Kit (TaKaRa, Tokyo, Japan). qPCR was conducted using TB Green Premix Ex Taq Reagent Kit (TaKaRa) according to the manufacturer’s instructions, and detected by a CFX96TM Real-Time PCR Detection System (BIO-RAD). The sequences of primers were listed in Supplementary Table 1. The relative gene expression was determined by the comparative Ct (ΔΔCt) method. GAPDH was used as the reference gene for normalization.
Western blotting was conducted as previously described (Zhou et al., 2016; Yu et al., 2018). Briefly, cells were lysed by RIPA lysis buffer, quantified, and resolved by SDS-PAGE followed by immunoblotting using specified primary antibodies and HRP-conjugated secondary antibodies, respectively. The signals were visualized using ECL reagents and quantified by Image J (Rawak Software Inc., Stuttgart, Germany). β-actin was the internal reference.
SPHK1 activity, S1P concentration, and Cer concentration were determined by ELISA. Cells were seeded at a density of 4×105 cells/well in 6-well plates. After stepwise increasing concentrations of HST treatment for 48 h, the cells were harvested. The supernatant was taken for examination of S1P concentration using ELISA kit according to the manufacturer’s instructions. The cell precipitation was repeatedly freeze-thaw lysed and cell lysates were used to determine SPHK1 activity and Cer concentration by using Human SPHK1 ELISA Kit and Human Cer ELISA Kit, respectively, according to the manufacturer’s instructions.
Target engagement assay of SPHK1 was performed by CETSAs, as reported previously (Liu et al., 2020; Yin et al., 2021). Briefly, cells were treated with DMSO or 32 μM HST for 4 h. Cells were then collected, washed with PBS, and resuspended in 600 μL PBS containing protease inhibitor cocktail. Each cell suspension was divided and 100 μL aliquots were transferred into PCR tubes, heated at different temperatures for 3 min followed by cooling for 3 min at room temperature. The cells were then repeatedly freeze-thawed using liquid nitrogen. Subsequently, after centrifugation, cell lysates were separated and quantification of the remaining soluble protein was achieved by western blot analysis.
Docking simulation was operated using the Discovery-Studio 2017 R2 molecular modeling software (Dassault Systèmes Information Technology Co., Ltd., Shanghai, China). The three-dimensional (3D) structures of HST were generated with ChemDraw (PerkinElmer Inc., MA, USA) and were energy minimized with CHARMm force field. The initial 3D geometric coordinates of SPHK1 (PDB code: 3vzb) were obtained from the Protein Databank (PDB) (https://www.rcsb.org/structure/3VZB/). Then, the protein structure was prepared by removing water molecules and adding hydrogen. CDOCKER protocols were employed as docking approaches and calculated the predicted binding energy (kcal/mol). The complex structure with the most favorable binding-free energies was selected as the optimal docked conformation for late experimental verification.
Clinical data can be obtained via GEO (https://www.ncbi.nlm.nih.gov/geo/) with the publically available dataset (GSE71014). The SPHK1 expression in CML patients was analyzed by the Kaplan-Meier estimate.
All experiments were repeated at least in triplicate. Results are presented as mean ± SD. Statistical analyses were determined by Student’s t-test and one-way ANOVA using GraphPad software. Relapse-free survival (RFS) rates were plotted using Kaplan-Meier analysis and compared with the long-rank test. Differences at p-value<0.05 were considered statistically significant.
First, to evaluate the clinical correlation of SPHK1 in CML patients, a Kaplan-Meier estimate was conducted. Fig. 1A showed that CML patients with higher SPHK1 expression usually displayed poorer relapse-free survival (RFS) rates than those with lower SPHK1 expressions (GSE71014, p=0.0263). The expression of SPHK1 in human PBMCs and a panel of human cancer cell lines representing various cancers including colon, lung, breast, and brain cancers, and leukemia, was analyzed by western blotting. As shown in Fig. 1B, extremely high levels of SPHK1 protein were found in various human cancer cell lines, and SPHK1 expression in K562 and K562/G01 cells was significantly higher than that in PBMCs. These results demonstrate that SPHK1 is upregulated in CML cells and that SPHK1 overexpression correlates with poor clinical outcomes in CML patients.
The structure of HST is shown in Fig. 2A. The MDR characteristics of K562/G01 were first determined by the MTT assay. K562/G01 cells showed obvious resistance to Imatinib (Supplementary Fig. 1). The cell growth inhibitory effect of HST on K562 and K562/G01 was assessed by the MTT assay. As shown in Fig 2B and 2C, HST potently reduced cell growth in a time-dependent (32 μM, 12-48 h) and dose-dependent (4-100 μM, 48 h) manner in both cell lines. The IC50 values of HST in K562 and K562/G01 cells were 12.33 μM and 12.77 μM, respectively. To explore the mechanism of growth inhibition of CML cells by HST, cell cycle distribution and cell apoptosis were further assessed by flow cytometry analysis after HST exposure. As shown in Fig. 2D and 2E, HST (32 μM, 48 h) increased the G2/M phase cell population to 45.9% in K562 cells and 41.6% in K562/G01 cells compared with the cells treated with vehicle, which was 27.8% in K562 cells and 17.8% in K562/G01 cells, suggesting that HST arrested the G2/M cell cycle in both CML cell lines. The results of apoptosis indicated that HST (32 μM, 48 h) exposure led to a potent increase in the apoptotic population in both K562 and K562/G01 cells (Fig. 2F, 2G, Supplementary Fig. 3), suggesting that HST promoted CML cell apoptosis. Consistently, similar results were obtained using western blotting. As shown in Fig. 2H and Supplementary Fig. 2A, HST led to the downregulation of Cyclin B1 and CDC2, and upregulation of Cyclin D1 in both cell lines. These apoptosis results were further reinforced by western blotting, in which HST induced the cleavage of PARP, Caspase 3, Caspase 8, and Caspase 9. Moreover, HST treatment led to an increase in Cytochrome c release and a decrease of Bcl-2 (Fig. 2I, Supplementary Fig. 2B). These results indicated that HST inhibits proliferation and induces apoptosis in CML cells.
It has been known that sphingolipid rheostat play key roles in regulating many cellular processes in cancer (Zheng et al., 2019). To characterize the effects of HST on sphingolipid rheostat, K562 and K562/G01 cells were treated with HST or vehicle, and the level of S1P and Cer was determined by ELISA. As shown in Fig. 3A and 3B, in K562 and K562/G01 cells, Cer were significantly increased by HST treatment, whereas S1P were decreased, suggesting that HST shifts the sphingolipid rheostat from S1P towards Cer in CML cells. As SPHK1 is the rate-limiting enzyme governing sphingolipid rheostat activity, we hypothesized that HST might inhibit SPHK1. To test this possibility, cellular SPHK1 activity was detected by ELISA. The result in Fig. 3C showed that cellular SPHK1 activity was markedly reduced by HST in K562 and K562/G01 cells. Furthermore, it has been demonstrated that S1P regulates diverse cellular functions through extracellular ligation to S1P receptors (S1PRs). Therefore, S1PR1 expression in response to HST was examined by qRT-PCR. The results in Fig. 3D show that HST sharply repressed S1PR1 expression in both CML cell lines. As shown in Fig. 3E, HST repressed p-SPHK1 (Ser225), SPHK1, p-BCR-ABL (Tyr412), BCR-ABL, PI3K-p110α, and p-Akt (Ser473) in K562 and K562/G01 cells. In summary, the above results suggest that HST modulates sphingolipid rheostat and represses the SPHK1/S1P/S1PR1 and BCR-ABL/PI3K/Akt signaling pathways in CML cells.
To examine whether HST potentially targets SPHK1 and to gain insights into the detailed interactions of HST with SPHK1, a molecular docking simulation was first performed. The CDOCKER docking results in Fig. 4A and 4B show that HST can be docked with the Arg57, Glu343, Glu86, Leu83 and Arg191 residues of SPHK1 (binding energy: –6.6 kcal/mol). Based on the interactions of HST with SPHK1 at the Arg191 residue, which is an ATP-binding site residue of the enzyme (Jairajpuri et al., 2020), we deduced that HST may serve as an ATP-competitive inhibitor of SPHK1. CETSAs have been widely used to investigate interactions between proteins and their ligands. To confirm these results, CETSAs were further performed. As shown in Fig. 4C and 4D, HST (32 μM) induced SPHK1 thermal stability at the indicated temperatures compared to the vehicle in K562 and K562/G01 cells, indicating potential protein-ligand interactions between SPHK1 and HST in CML cells.
To explore the involvement of SPHK1 in the anti-leukemic properties of HST in CML cells, cell growth, apoptosis, sphingolipid rheostat, and related signaling cascades in K562 and K562/G01 cells in response to HST were examined after SPHK1 overexpression. Fig. 5A shows that SPHK1 was overexpressed in K562 and K562/G01 cells when the cells were transfected with SPHK1 vector or empty vector for 6 h. MTT assays showed that SPHK1 overexpression significantly prevented the proliferation inhibition induced by HST in K562 and K562/G01 cells (Fig. 5B, 5C). The results of apoptosis showed that HST promoted apoptosis in K562 and K562/G01 cells, whereas these effects were attenuated by SPHK1 overexpression in both cell lines (Fig. 5D-5F). Similar results were also found after treatment with PF543, a known SPHK1 inhibitor. ELISA experiments examining Cer and S1P concentrations showed that SPHK1 overexpression antagonized the effect of HST on sphingolipid rheostat in both cell lines (Fig. 5G, 5H). Protein expression indicated that p-SPHK1 (Ser225), SPHK1, p-BCR-ABL (Tyr412), BCR-ABL, PI3K-p110α, and p-Akt (Ser473) were downregulated by HST, whereas this inhibitory effect of HST was reversed by SPHK1 overexpression in both cell lines (Fig. 5I). qRT-PCR results showed that the downregulation of S1PR1 caused by HST treatment was also counteracted by SPHK1 overexpression (Fig. 5J). In summary, these data indicate that overexpression of SPHK1 attenuates the growth inhibitory and pro-apoptotic activities of HST in K562 and K562/G01 cells, suggesting that HST exerts anti-leukemic effects on CML cells probably through SPHK1 suppression.
Although studies have already demonstrated the anticancer potential of HST, the anti-leukemic effect of HST and its underlying mechanisms remain to be explored. This study showed that HST inhibited cell proliferation, promoted cell apoptosis, and arrested the cell cycle at the G2/M phase, thus exerting potential anti-leukemic efficacy in K562 and K562/G01 cells. Recent evidence has demonstrated the oncogenic characterization of SPHK1 in various types of cancer because of its association with many cellular activities important for cancer including growth, transformation, metastasis, and chemotherapy resistance (Pitman and Pitson, 2010; Zheng et al., 2019; Velazquez et al., 2021). Researchers have also reported overexpression of SPHK1 in a diverse array of cancers and its relationship with poor prognosis (Pitman and Pitson, 2010; Lupino et al., 2019; Velazquez et al., 2021). Therefore, SPHK1 has gained increasing attention over the past few decades as a potential drug target against cancer, including leukemia. This study showed that SPHK1 is upregulated in K562 and K562/G01 cells and overexpression of SPHK1 is closely associated with unfavorable prognosis in CML patients, consistent with the important role of SPHK1 as a novel molecular target in CML therapy. Owing to the high level of interest in SPHK1 signaling, several SPHK1 inhibitors are currently undergoing preclinical research, and only a few agents have entered clinical trials as chemotherapeutics against cancers (Pitman and Pitson, 2010; Companioni et al., 2021). Potent and selective SPHK1 inhibitors with low toxicity obtained from natural sources are needed. Therefore, we propose that HST may be an ATP-competitive inhibitor of SPHK1. It has been reported that SPHK inhibitors mainly target three binding sites of SPHKs, namely, the Sph binding pocket, ATP binding pocket, and dimerization site (Ding et al., 2021). In our study, HST formed a close association with Arg57, Glu343, Glu86, Leu83, and Arg191 residues, in which Arg191 is an ATP-binding site residue of SPHK1 and the others are key residues for enzyme activity of SPHK1 (Jairajpuri et al., 2020; Ding et al., 2021), suggesting that HST targets the ATP-binding pocket of SPHK1 and inhibits SPHK1. Further studies may involve an experiment to show whether mutation on the residues of SPHK1 could change the current results or not, which may act as strong evidence to support the in silico study. S1P and Cer are bioactive lipid mediators involved in various pathophysiological processes (Green et al., 2021). Our results showed that in both K562 and K562/G01 cells, HST shifted the rheostat from S1P towards Cer, manifesting pro-apoptotic functions, and finally exhibiting anti-leukemic efficacy in CML cells. SPHK1 is the rate-limiting enzyme in sphingolipid rheostat. Stimulation of SPHK1 in response to diverse growth factors or other stimuli mediates the intracellular conversion of Cer and Sph to S1P (Sukocheva et al., 2020; Green et al., 2021). S1P, in turn, acts either as an intracellular secondary messenger or as an extracellular ligand that activates S1PRs in an autocrine and/or paracrine manner. Extracellular S1P transported by several ATP-binding cassette (ABC) transporters accesses and activates S1PRs, and the latter, through binding with different G-proteins, such as Go, Gi, G12/13 and Rho, initiates intracellular signaling cascades such as PI3K, Akt, and MAPK, thereby participating in diverse cellular functions (Hannun and Obeid, 2018). The SPHK1/S1P/S1PR1 pathway has become a point of interest in cancer therapy, as it plays a key role in maintaining cancer survival signals and leading to cancer progression, including leukemia. S1PRs are a family of S1P-specific G protein-coupled receptors. To date, five subtypes of S1PRs (S1PR1-5) have been reported. S1PR1-3 are ubiquitously expressed in most cells, whereas S1PR4 and S1PR5 are less abundant and are restricted to distinct cells (Blaho and Hla, 2014). In the present study, we found that, in K562 and K562/G01 cells, S1PR1-3 were commonly expressed subtypes, while S1PR4 expression was relatively low and S1PR5 was almost undetectable (Supplementary Fig. 4). Because S1PR1 was highly expressed in K562 and K562/G01 cells as well as the involvement of S1PR1 in cancer progression (Patmanathan et al., 2017), S1PR1 was selected for subsequent experiments. Previous findings have demonstrated that SPHK1 has intrinsic catalytic activity, and phosphorylation of SPHK1 at Ser-225 increases its catalytic activity and induces its translocation to the cell membrane, which is important for the oncogenic signaling of SPHK1 (Pitson et al., 2003). In this study, HST inhibited cellular SPHK1 activity, decreased S1P and S1PR1, and downregulated p-SPHK1 (Ser225), p-BCR-ABL (Tyr412), PI3K-p110α, and p-Akt (Ser473), thereby blocking SPHK1/S1P/S1PR1 and BCR-ABL/PI3K/Akt signaling in K562 and K562/G01 cells (Fig. 6). However, SPHK1 overexpression reversed the inhibitory effect of HST on these signaling cascades in both cell lines. Notably, a previous study has reported that BCR-ABL can increase the expression and cellular activity of SPHK1 in CML cells (Li et al., 2007). There may be controversial issues regarding the relationship between SPHK1 and BCR-ABL in the cell-signaling cascade. Further studies, including whether SPHK1 is located upstream or downstream of BCR-ABL, and whether SPHK1 signals directly to BCR-ABL through S1PR1, are required to address these questions. However, our present results are consistent with the emerging oncogenic role of SPHK1 in CML and demonstrate the potential of SPHK1 inhibitors in CML therapeutics. Furthermore, we also found that the protein expression of SPHK1 and BCR-ABL was decreased by HST in both cell lines. It seems difficult to explain the mechanism based on our present results. However, it is known that Cer can bind to and activate lysosomal cathepsin D, which can degrade various proteins and enzymes (Ren et al., 2010). Furthermore, it has been reported that many apoptotic stimuli can increase lysosomal permeability and induce the release of cathepsins, resulting in the degradation of various cytosolic proteins (Ren et al., 2010). As a result, it may be speculated the downregulation of SPHK1 and BCR-ABL protein expression by HST might be mediated by Cer accumulation, which could activate cathepsins and induce protein degradation. Further studies are required to address the details. This study shows that, in K562 and K562/G01 cells, HST inhibits cell proliferation, arrests the cell cycle, and promotes cell apoptosis, possibly resulting from inhibition of SPHK1, thereby conferring HST as a potential anti-leukemic drug candidate against CML. | true | true | true |
PMC9622368 | 36250437 | Yuejiu Pang,Dingzhen Luo,Shuhua Wang | miR-128-3p inhibits the inflammation by targeting MAPK6 in penicillin-induced astrocytes | 17-10-2022 | epilepsy,inflammation,miR-128-3p,MAPK6 | Objective Epilepsy causes physical and mental damage to patients. As well known, microRNAs (miRNAs) provide therapeutic target potentials for patients with epilepsy. miR-128-3p was previously reported to be downregulated in temporal lobe epilepsy (TLE) patients, however, its detailed function in epilepsy is unknown. Methods Astrocytes function in epilepsy, penicillin-induced astrocytes can be used as a model for seizures in vitro. Currently, the expression levels of mitogen-activated protein kinase 6 (MAPK6), interleukin-1 beta (IL-1β) and tumor necrosis factor-alpha (TNF-α) were determined by western blot and reverse transcription-quantitative PCR analyses (RT-qPCR). The expression level of miR-128-3p was evaluated by RT-qPCR. TargetScan 7.1 and dual luciferase reporter assay were used for prediction and verification of interaction between miR-128-3p and MAPK6 3′ untranslated region (UTR). Cell viability was detected by 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay. Results We found that penicillin-induced decrease in cell viability, and increase of TNF-α/IL-1β in primary astrocytes. There were lower miR-128-3p and higher MAPK6 in penicillin-treated primary astrocytes. miR-128-3p overexpression rescued penicillin-induced reduction of cell viability, and upregulation of TNF-α/IL-1β, which was partially abolished by MAPK6 overexpression. Conclusion Altogether, miR-128-3p attenuates penicillin-induced cell injury and inflammation in astrocytes by targeting MAPK6, thus providing a protective role in epilepsy. | miR-128-3p inhibits the inflammation by targeting MAPK6 in penicillin-induced astrocytes
Epilepsy causes physical and mental damage to patients. As well known, microRNAs (miRNAs) provide therapeutic target potentials for patients with epilepsy. miR-128-3p was previously reported to be downregulated in temporal lobe epilepsy (TLE) patients, however, its detailed function in epilepsy is unknown.
Astrocytes function in epilepsy, penicillin-induced astrocytes can be used as a model for seizures in vitro. Currently, the expression levels of mitogen-activated protein kinase 6 (MAPK6), interleukin-1 beta (IL-1β) and tumor necrosis factor-alpha (TNF-α) were determined by western blot and reverse transcription-quantitative PCR analyses (RT-qPCR). The expression level of miR-128-3p was evaluated by RT-qPCR. TargetScan 7.1 and dual luciferase reporter assay were used for prediction and verification of interaction between miR-128-3p and MAPK6 3′ untranslated region (UTR). Cell viability was detected by 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide (MTT) assay.
We found that penicillin-induced decrease in cell viability, and increase of TNF-α/IL-1β in primary astrocytes. There were lower miR-128-3p and higher MAPK6 in penicillin-treated primary astrocytes. miR-128-3p overexpression rescued penicillin-induced reduction of cell viability, and upregulation of TNF-α/IL-1β, which was partially abolished by MAPK6 overexpression.
Altogether, miR-128-3p attenuates penicillin-induced cell injury and inflammation in astrocytes by targeting MAPK6, thus providing a protective role in epilepsy.
Epilepsy is a chronic neurological disorder caused by abnormal neuronal activity in the brain, which predisposes the patients to epileptic seizures throughout the lifetime and leads to social discrimination [1–3]. Epileptic seizure causes severe physical and mental damage to patients, with the potential pathogenesis of structural and functional injuries to the hippocampus and the limbic system, which are resulted from pathological changes including mossy fiber sprouting, neuronal apoptosis and synaptic plasticity [4–6]. The lifetime prevalence of epilepsy is approximately 1% globally [1]. Brain inflammation is conducive to the determination of the seizure threshold from the susceptible regions in the brain, and functions in the precipitation and recurrence of seizures [7,8]. Unfortunately, the current anti-epileptic drugs are effective in only <35% of patients [9]. Up to now, there have not been specific anti-epileptic drugs for patients with epilepsy. As acknowledged, microRNAs (miRNAs) belong to a family of non-coding RNAs which reduce protein expression by inhibiting mRNA stability and translation, thus providing essential biomarkers and therapeutic target potentials for patients with epilepsy [10,11]. Downregulation of miR-128 in epilepsy has been reported in several studies, for instance, miR-128-2 is decreased in mice postnatal neurons, which upregulates motor activity and fatal epilepsy in mice [12], and miR-128-3p is downregulated in temporal lobe epilepsy (TLE) patients and all phases of TLE development in rats [13], 2R, 4R-APDC prevents hippocampal cells from seizure-induced apoptosis by upregulating miR-128-3p [14]. However, the detailed function of miR-128-3p in epilepsy has not been clarified. The genes which are involved in inflammation and can be targeted by miR-128-3p attract our attention. By searching articles, the mitogen-activated protein kinase (MAPK) signaling pathway that takes part in numerous cellular processes including inflammation [15], comes into our sight. Among these, MAPK6 is dysregulated in numerous diseases, and can be targeted by various miRNAs. For instance, let7f-5p inhibits inflammation by targeting MAPK6 in pneumonia [16]; MAPK6 promotes the production of IL-8 and chemotaxis in breast cancer [17]; miR-26a-5p negatively regulates neuropathic pain by targeting MAPK6 [18]. However, the role of MAPK6 in epilepsy as well as the relationship between miR-128-3p and MAPK6 in epilepsy has not been investigated yet. The current study aimed to explore whether the function of miR-128-3p in epilepsy can be realized by regulation of MAPK6.
Briefly, the neonatal (1–5 day postnatal) Sprague–Dawley male rats (n = 8) were subjected to euthanasia with cervical dislocation by pinching and disrupting the spinal cord in the high cervical region. Primary astrocytes were extracted from the cerebral cortices of the rats, then seeded into flasks with the culture medium (DMEM/F12, 15% FBS, L-glutamine and 500 ng/ml insulin), and incubated till confluence, as a previous study described [19]. The current study was approved by the Ethics Committee of Shandong Provincial Hospital Affiliated with Shandong First Medical University. Penicillin was commonly used for the establishment of experimental epilepsy in animals [20,21]. The amount of penicillin is crucial in epilepsy models, eg, in the intracortical penicillin rat model, the higher the penicillin dose, the higher the number of hippocampal pyramidal neuronal loss [22]. The same was in neuronal cell culture, 100–5 000 μM penicillin is required to block GABA [23], while GABAergic action is pivotal in astrocyte cell death [24]. In the current study, primary astrocytes were stimulated by penicillin 2 500 µM (500 IU) for 12 hours for the establishment of an in vitro model of seizure (astrocyte death) as previously described [25]. Above all, the results are related to epilepsy.
miR-128-3p mimic, miR-negative control (NC) mimic, pcDNA3.1 and pcDNA3.1-MAPK6 were obtained from GenePharma (Shanghai, China). Cell transfection of miR-128-3p mimic (50 nM) and pcDNA3.1-MAPK6 (100 ng) was performed by Lipofectamine 2000 (Invitrogen). At 48 hours after cell transfection, cells were harvested for the subsequent experiments.
Total RNA was isolated from cells by TRIzol (Invitrogen). cDNA was synthesized from the above products by PrimeScript RT kit (Takara Bio, Inc.). After that, RT-qPCR was carried out with SYBR Premix Ex Taq (Takara Bio, Inc.) by the Bio-Rad CFX96 Real-Time PCR system (Bio-Rad Laboratories, Inc.). Expression of interleukin-1 beta (IL-1β), tumor necrosis factor-alpha (TNF-α), MAPK6 and miR-128-3p were referred to as GAPDH and U6, respectively. The primer sequences were as listed: IL-1β, forward 5′-TGAAATGCCACCTTTTGACAG-3′ and reverse 5′-CCACAGCCACAATGAGTGATAC-3′; TNF-α, forward 5′-CCTGTCTCTTCCT ACCCAACC-3′ and reverse 5′-GCAGGAGTGTCCGTGTCTTC-3′; MAPK6 forward 5′- TAAAGCCATTGACATGTGGG-3′ and reverse 5′- TCGTGCACAACAGGGAT AGA -3′; GAPDH, forward 5′-CTTTGGTATCGTGGAAGGACTC-3′ and reverse 5′-GTAGAGGCAGGGATGATGTTCT-3′. miR-128-3p forward 5′-TCACAGTGAACCGGTCTCTTT-3′ and reverse 5′-AAAGAGACCGGTTCACTGTGA-3′; U6 forward 5′-GCTTGCTTCGGCAGCACATATAC-3′ and reverse 5′- TGCATGTCATCCTTGCTCAGGG-3′. The cycling conditions were as listed: 95°C for 5 minutes, followed by 40 cycles of 95°C for 10 seconds, 60°C for 30 seconds and 72°C for 1 second.
Total protein was isolated from cells by RIPA (Sigma-Aldrich) on ice. The supernatants were centrifuged at 12 000g for 10 minutes at 4°C before the collection of the supernatant proteins. The concentration of protein in each sample was measured by the BCA kit (Sigma-Aldrich). Each protein sample (15 µg) was separated by the SDS-PAGE and transferred onto a PVDF membrane. Then the PVDF membrane was blocked by 5% fat-free milk at 37°C for 1 hours, incubated with primary antibodies against IL-1β (ab200478; RRID:AB_2888939; 1:1,000; Abcam), TNF-α (ab205587; RRID:AB_2889389; 1:1,000; Abcam), MAPK6 (ab53277; RRID:AB_2140288; 1:1,000; Abcam) and GAPDH (ab181602; RRID:AB_2630358; 1:1,000; Abcam) at 4°C overnight; and HRP-conjugated goat anti-rabbit secondary antibody (ab7097; RRID:AB_955411; 1:3,000; Abcam) at 37°C for 1 hours, successively. At last, the protein bands were visualized by ECL Kit (Pierce). Immunodetection was analyzed by Image Lab version 3.0 (Bio-Rad Laboratories, Inc.). IL-1β, TNF-α, and MAPK6 were referred to as GAPDH.
Cell viability was measured by MTT assay. Cells (5 × 103 cells/well) were seeded in 96-well plates. After incubation for 24 hours at 37°C, each well was added with MTT (20 µl of 5 mg/ml) and incubated for another 4 hours at 37°C. Thereafter, each well was added with DMSO (150 µl). To dissolve the dye thoroughly, the microtitre plate was shaken by a shaker. Finally, the absorbance at 570 nm was recorded by a Bio-Rad iMark plate reader (Bio-Rad Laboratories, Inc.).
The interaction between miR-128-3p and MAPK6 was predicted by the online tool TargetScan 7.1 (http://www.targetscan.org/vert_71/). Wild type (WT, 5′-GUUAAGUAAAGUGCUCACUGUGU-3′) and mutant (Mut, 5′-GUUAAGUAAAGUGCUCAACGUGU-3′) MAPK6 3′UTR contained pGL3‑luciferase reporter plasmid (Promega Corporation, Madison, WI, USA) were cloned by GenePharma (Shanghai, China). miR-128-3p mimic or miR-NC mimic was co-transfected with WT MAPK6 3′UTR or Mut MAPK6 3′UTR into primary astrocytes by Lipofectamine 2000 (Invitrogen). At 48 hours after cell transfection, the luciferase reporter activity was determined by Dual Luciferase Assay System (Promega Corporation), which was referred to as Renilla luciferase activity.
Each experiment was repeated three independent times. Data were analyzed by Graphpad Prism 6 and expressed as mean ± SD. Two groups were compared by unpaired Student’s t test, and three or more groups were compared by one-way ANOVA followed with Newman Keuls analysis. P value < 0.05 indicated statistically significant.
As presented in Fig. 1a, there was decreased cell viability in the epilepsy group when compared to the control group. In addition, there was increased mRNA (Fig. 1b) and protein level (Fig. 1c,d) of TNF-α and IL-1β in the epilepsy group compared with the control group. The results indicated the successful induction of injury and inflammation by penicillin in primary astrocytes.
Thereafter, we aimed to examine the expression changes of miR-128-3p and MAPK6 in penicillin-treated primary astrocytes. As presented in Fig. 2a, there was downregulated miR-128-3p expression in the epilepsy group in contrast to the control group. However, there was upregulated mRNA (Fig. 2b) and protein level (Fig. 2c,d) of MAPK6 in the epilepsy group in comparison with the control group. These findings suggested the potential involvement of miR-128-3p and MAPK6 in the penicillin-induced primary astrocytes. We were eager to identify the detailed functions of miR-128-3p in penicillin-induced primary astrocytes.
As shown in Fig. 3a, the miR-128-3p level was increased by the miR-128-3p mimic in comparison with the miR-NC mimic. Overexpression of miR-128-3p reversed the upregulation of mRNA (Fig. 3a) and protein level (Fig. 3b,c) of MAPK6 induced by epilepsy, which was partially abolished by the overexpression of MAPK6. These findings implied the potential regulatory relation between miR-128-3p and MAPK6 in penicillin-treated primary astrocytes.
The results of the MTT assay exhibited that, overexpression of miR-128-3p reversed the inhibitory effects of penicillin on cell viability in primary astrocytes, which was partially abolished by the overexpression of MAPK6 (Fig. 4). The results of RT‑qPCR and western blot showed that overexpression of miR-128-3p reversed the upregulation of mRNA (Fig. 5a) and protein level (Fig. 5b,c) of TNF-α and IL-1β induced by epilepsy, which was partially abolished by the overexpression of MAPK6. The above findings exerted that in primary astrocytes, miR-128-3p rescued penicillin-induced cell injury and inflammation by regulating MAPK6.
At last, we were eager to clarify the relationship between miR-128-3p and MAPK6 in primary astrocytes. As shown in Fig. 6a, MAPK6 3′UTR was predicted to be complementary to miR-128-3p. In addition, compared to the miR-NC mimic, the miR-128-3p mimic significantly decreased the luciferase activity of primary astrocytes transfected with WT MAPK6; however, the miR-128-3p mimic failed to decrease the luciferase activity of primary astrocytes transfected with Mut MAPK6 (Fig. 6b). The results proposed that in primary astrocytes, miR-128-3p indeed targeted MAPK6 3′UTR.
Epilepsy is one of the most frequent neurological disorders [26], which is characterized by epileptic seizures [3]. However, there is a lack of effective treatment strategies for epilepsy. In the brain, astrocytes provide structural and functional support for neurons, also, they take part in ionic homeostasis and energy metabolism [27], synaptic network formation [28] and synaptic transmission [29], etc. Additionally, astrocytes which arouse and magnify immune-related mechanisms, are correlated with numerous human diseases, including epilepsy [30]. Mounting evidence have suggested the potential participation of inflammation in the injured brain during the progression of epilepsy [8,31]. For instance, an in vitro study has verified the potential of active astrocytes in producing inflammatory cytokines, evidenced by the upregulation of IL-1β and TNF-α in the tissues from the experimental and human epileptogenic brain [32]. Currently, we first demonstrated the reduced cell viability and elevated TNF-α and IL-1β levels in the penicillin-treated primary astrocytes. Afterward, penicillin-treated primary astrocytes were used as an experimental model of epilepsy for the subsequent experimentations. miR-128-3p plays different roles (protective or deleterious) in different diseases, for instance, miR-128-3p prevents dopamine neurons from apoptosis by targeting AXIN1 in Parkinson’s disease (PD) [33], miR-128-3p accelerates the progression of Alzheimer’s disease (AD) by targeting PPARγ [34], and miR-128-3p worsens doxorubicin-induced liver injury by targeting Sirtuin-1 [35]. Regarding the role of miR-128-3p in epilepsy, it remains controversial. For example, miR-128-3p exerts deleterious function on neuron migration and outgrowth by targeting Phf6 [36]; however, 2R and 4R-APDC prevent hippocampal cells from apoptosis after seizure by increasing the expression of miR-128-3p [4]). Herein, we found decreased miR-128-3p level in penicillin-treated primary astrocytes, additionally, overexpression of miR-128-3p rescued penicillin-induced downregulation of cell viability, and upregulation of IL-1β and TNF-α in primary astrocytes, which suggested the protective role of miR-128-3p in epilepsy, and were consistent with the findings of Feng et al. [4]. As miRNAs can inhibit protein expression by inhibiting mRNA stability and translation [10,11]. The genes which are involved in inflammation and can be targeted by miR-128-3p attracted our attention. As well-known, the MAPK signaling pathway plays a role in numerous cellular processes including inflammation [15]. Among these, MAPK6 is also found to be dysregulated in inflammation, and can be targeted by various miRNAs. For instance, let7f-5p inhibits inflammation by targeting MAPK6 in pneumonia [16]; MAPK6 regulates the production of IL-8 and chemotaxis in breast cancer [17]. However, the role of MAPK6 as well as the relation between miR-128-3p and MAPK6 in epilepsy has not been reported yet. Herein, we found increased MAPK6 levels in penicillin-treated primary astrocytes, and overexpression of MAPK6 partially reversed the protective role of miR-128-3p on penicillin-induced primary astrocytes. By searching the online tool TargetScan 7.1 (http://www.targetscan.org/vert_71/), the interaction between miR-128-3p and MAPK6 exhibited a relatively higher context++ score percentile, ie, 90. Moreover, miR-128-3p was identified to target MAPK6 3’UTR, indicating the involvement of MAPK6 in the progression of epilepsy. Taken together, miR-128-3p reverses penicillin-induced cell injury and inflammation in astrocytes by targeting MAPK6, providing a protective role in epilepsy. There are two limitations in the current study, (1) only a few measures of cell damage and protection were presented, no functional studies were conducted, which will be investigated in the future study; (2) The penicillin model was used to replicate aspects of astrocytic damage in epilepsy, but it is vetted over 30 years ago, which will be improved in the future study.
There are no conflicts of interest. | true | true | true |
PMC9622406 | Ying Zhao,Shujun Wang,Shufang Liu,Qingfeng Yan,Yueping Li,Yunzhong Liu | CircHSPG2 absence weakens hypoxia-induced dysfunction in cardiomyocytes by targeting the miR-25-3p/PAWR axis | 01-10-2022 | Myocardial infarction (MI),hypoxia,circHSPG2,miR-25-3p,pro-apoptotic WT1 regulator (PAWR) | Background Circular RNAs (circRNAs) are important regulators in human cardiovascular diseases. Here, we investigated the role of circRNA heparan sulfate proteoglycan 2 (circHSPG2) in hypoxia-induced myocardial infarction (MI) and its associated mechanism. Methods Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and Western blot assay were conducted to examine RNA and protein expression. Cell viability was analyzed by 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide (MTT) assay. Cell proliferation was assessed by 5-Ethynyl-2'-deoxyuridine (EdU) assay and colony formation assay. Flow cytometry (FCM) analysis was carried out to analyze the apoptosis of AC-16 cells. Lactate dehydrogenase (LDH) assay was implemented to assess cell death. Dual-luciferase reporter assay and RNA immunoprecipitation (RIP) assay were performed to verify the target relationship between microRNA-25-3p (miR-25-3p) and circHSPG2 or pro-apoptotic WT1 regulator (PAWR). Results Hypoxia treatment up-regulated the expression of circHSPG2 in AC-16 cells. Hypoxia exposure reduced the viability, suppressed the proliferation and induced the apoptosis of AC-16 cells, and these effects were diminished by the silence of circHSPG2. CircHSPG2 acted as a molecular sponge for miR-25-3p. CircHSPG2 absence-mediated effects on hypoxia-induced AC-16 cells were largely reversed by anti-miR-25-3p. miR-25-3p bound to the 3' untranslated region (3'UTR) of PAWR. PAWR overexpression largely counteracted miR-25-3p-mediated effects on hypoxia-induced AC-16 cells. CircHSPG2 positively regulated the expression of PAWR by acting as miR-25-3p sponge in AC-16 cells. Conclusions CircHSPG2 silencing protected AC-16 cells against hypoxia-induced dysfunction by targeting miR-25-3p/PAWR axis. | CircHSPG2 absence weakens hypoxia-induced dysfunction in cardiomyocytes by targeting the miR-25-3p/PAWR axis
Circular RNAs (circRNAs) are important regulators in human cardiovascular diseases. Here, we investigated the role of circRNA heparan sulfate proteoglycan 2 (circHSPG2) in hypoxia-induced myocardial infarction (MI) and its associated mechanism.
Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and Western blot assay were conducted to examine RNA and protein expression. Cell viability was analyzed by 3-(4,5-Dimethylthiazol-2-yl)-2,5-Diphenyltetrazolium Bromide (MTT) assay. Cell proliferation was assessed by 5-Ethynyl-2'-deoxyuridine (EdU) assay and colony formation assay. Flow cytometry (FCM) analysis was carried out to analyze the apoptosis of AC-16 cells. Lactate dehydrogenase (LDH) assay was implemented to assess cell death. Dual-luciferase reporter assay and RNA immunoprecipitation (RIP) assay were performed to verify the target relationship between microRNA-25-3p (miR-25-3p) and circHSPG2 or pro-apoptotic WT1 regulator (PAWR).
Hypoxia treatment up-regulated the expression of circHSPG2 in AC-16 cells. Hypoxia exposure reduced the viability, suppressed the proliferation and induced the apoptosis of AC-16 cells, and these effects were diminished by the silence of circHSPG2. CircHSPG2 acted as a molecular sponge for miR-25-3p. CircHSPG2 absence-mediated effects on hypoxia-induced AC-16 cells were largely reversed by anti-miR-25-3p. miR-25-3p bound to the 3' untranslated region (3'UTR) of PAWR. PAWR overexpression largely counteracted miR-25-3p-mediated effects on hypoxia-induced AC-16 cells. CircHSPG2 positively regulated the expression of PAWR by acting as miR-25-3p sponge in AC-16 cells.
CircHSPG2 silencing protected AC-16 cells against hypoxia-induced dysfunction by targeting miR-25-3p/PAWR axis.
Myocardial infarction (MI) is one of the leading pathological causes of disability and mortality in cardiovascular diseases (1). The apoptosis, hypertrophy, and inflammatory response of cardiomyocytes are related to MI (2,3), which eventually results in heart failure (4,5). Cardiomyocytes are terminally differentiated cells without regenerative potential. Therefore, investigating the molecular mechanism behind hypoxia-induced dysfunction of cardiomyocytes is essential to improve the clinical treatment strategies for MI. Circular RNAs (circRNAs) are implicated in the regulation of almost all cellular processes, and previous articles have pointed out that circRNAs play pivotal functions in the pathogenesis of multiple heart diseases, including MI, heart failure, and hypertrophy (6,7). CircRNA heparan sulfate proteoglycan 2 (circHSPG2; hsa_circ_0010729) is a newly discovered circRNA, which is related to hypoxia-induced growth of vascular endothelial cells (8). Lei et al. found that circHSPG2 silencing attenuates hypoxia-induced cardiomyocyte injuries by releasing miR-27a-3p and repressing TRAF5 expression (9). Moreover, Zhang et al. demonstrated that hypoxia exposure enhances circHSPG2 expression in cardiomyocytes, and circHSPG2 silencing suppresses hypoxia-evoked injury in cardiomyocytes through mediating miR-370-3p/TRAF6 signaling cascade (10). The potential regulatory mechanism behind the protective role of circHSPG2 in hypoxia-induced cardiomyocytes was further explored. Accumulating evidence has demonstrated that circRNAs can regulate cell biological behaviors by acting as microRNA (miRNA) sponges (11). miRNAs play pivotal regulatory roles in cardiovascular diseases, including dilated cardiomyopathy (12), cardiac remodeling (13), and heart failure (14). Through bioinformatics analysis via Starbase database, it was found that miR-25-3p harbored the complementary sites with circHSPG2. Pan et al. demonstrated that miR-25 overexpression protects cardiomyocytes from oxidation-mediated injury by regulating mitochondrial calcium uniporter expression (15). Yao et al. found that miR-25 attenuates sepsis-evoked apoptosis of cardiomyocytes by targeting PTEN (16). Qin et al. found that miR-25 abundance is reduced in hypoxia-induced cardiomyocytes, and it facilitates cell proliferation and migration abilities and hampers cell apoptosis in cardiomyocytes by modulating Bim (17). Here, the function of miR-25-3p and its related mechanism in hypoxia-induced myocardial damage were investigated in this study. Through bioinformatics analysis using Starbase database, it was found that pro-apoptotic WT1 regulator (PAWR) possessed the complementary sites with miR-25-3p. Chai et al. found that PAWR expression is up-regulated in hypoxia-induced cardiomyocytes, and circ_0068655 facilitates the apoptosis of cardiomyocytes by up-regulating PAWR via sponging miR-498 (18). The associated relationship of miR-25-3p and PAWR was tested. In the current study, we first analyzed the role of circHSPG2 in MI using hypoxia-induced AC-16 cell model. Then, bioinformatics analysis was conducted to establish circHSPG2/miRNA/mRNA axis, and rescue experiments were conducted to verify the working mechanism of circHSPG2 in hypoxia-induced AC-16 cells. We aimed to identify novel targets for MI intervention and treatment.
Human ventricular cardiomyocytes (AC-16) (19) were purchased from Beijing Institute for Cancer Research Collection (Beijing, China). Dulbecco’s modified Eagle’s medium/F12 (#11320033; DMEM/F12; Invitrogen, Waltham, MA, USA) with the supplement of 10% fetal bovine serum (#10099141C; FBS, Gibco, Carlsbad, CA, USA) and 1% penicillin/streptomycin solution (#15070063; Gibco) was utilized for the cultivation of AC-16 cells. AC-16 cells were cultured under the standard condition (5% CO2, 37 ℃). The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).
AC-16 cells were cultured under the condition of 1% O2, 94% N2, and 5% CO2 for 24 h to induce hypoxia, and cells growing under normoxic condition were regarded as the control.
RNA samples were isolated from AC-16 cells using TRIzol reagent (#12183555; Invitrogen). The concentration and purity of RNA samples were analyzed using NanoDrop 2000 (NanoDrop Technologies, Wilmington, DE, USA). For circRNAs and mRNAs, reverse transcription was implemented using a commercial Prime-script RT reagent Kit (#RR037A; Takara, Dalian, China), and qPCR was carried out using a commercial Premix Ex Taq Kit (#RR047A; Takara). For miRNAs, complementary DNA (cDNA) was synthesized using a commercial Taqman MicroRNA Reverse Transcription Kit (#4366596; Applied Biosystems, Foster City, CA, USA), and thermal cycling reaction was carried out using a commercial Taqman Universal Master Mix II (#4440043; Applied Biosystems). qPCR reaction was conducted on 7500 Fast Real-Time PCR system (Thermo Fisher Scientific, Waltham, MA, USA). The thermo cycling condition was listed as below: 95 ℃ for 3 min and 38 cycles of 95 ℃ for 20 sec, 60 ℃ for 30 sec, and 72 ℃ for 30 sec. All primers used in RT-qPCR were shown in Table 1. The relative fold changes were analyzed by the 2−ΔΔCt method. RT-qPCR was repeated three times with three technical repetitions each time.
The circular structure of circHSPG2 was tested with RNase R (Applied Biological Materials, Vancouver, Canada). RNA samples were treated with RNase R (100 µg/mL) at 37 ℃ for 20 min. RT-qPCR was implemented to analyze circHSPG2 and linear HSPG2 mRNA abundance. RT-qPCR was repeated three times with three technical repetitions each time. The cytoplasmic and nuclear fractions of cicHSPG2 were isolated using the commercial PARISTM Kit (#AM1921; Thermo Fisher Scientific). This experiment was repeated three times.
Small interfering (si)RNA against circHSPG2 (si-circHSPG2), negative control of siRNA (si-NC), overexpression plasmid of circHSPG2 (oe-circHSPG2), pLO-ciR vector (vector), mimics of miR-25-3p (miR-25-3p), miR-NC, inhibitor of miR-25-3p (anti-miR-25-3p), anti-miR-NC, PAWR overexpression plasmid (PAWR), and pcDNA3.1(+) were synthesized or constructed by Geneseed (Guangzhou, China) and GeneChem (Shanghai, China). Plasmid (1 µg), miRNA mimics (50 nM), or miRNA inhibitor (20 nM) was transfected into AC-16 cells using Lipofectamine 3000 (Invitrogen). For hypoxia and transfection co-treatment, AC-16 cells were induced by hypoxia for 24 h after transfection for 24 h.
A total of 5×103 AC-16 cells were seeded onto 96-well plates. The next day, AC-16 cells were incubated with 20 µL of MTT reagent (#11465007001; 5 mg/mL; Sigma, St. Louis, MO, USA) for 4 h at 37 ℃. Then, 100 µL of dimethyl sulfoxide (#11465007001; DMSO, Sigma) was pipetted to the wells to dissolve the formazan products, and the plates were gently shaken for 10 min. The optical density was examined at the wavelength of 570 nm under a microplate reader (Bio-Rad). MTT assay was repeated three times with six technical repetitions each time.
AC-16 cells in 96-well plates were incubated with 50 µM EdU (#C10310-1; RiboBio, Guangzhou, China) for 1 h followed by immobilization with 100 µL of 4% paraformaldehyde (Sangon Biotech, Shanghai, China) at 25 ℃ for 30 min. Cell nucleus was marked with 100 ng/mL 4', 6-diamidino-2-phenylindole (#MBD0015; DAPI; Sigma) for 10 min. Five random fields were captured under a Nikon fluorescence microscope (Eclipse Ti2‑U; Nikon) and the positive rate was analyzed. EdU assay was repeated three times with three technical repetitions each time.
AC-16 cells were seeded onto 12-well plates at 200 cells/well and were continued to culture for 12 d. The culture supernatant was replenished every 4 d. The colonies were immobilized with 4% paraformaldehyde (#E672002-0500; Sangon Biotech) for 15 min at room temperature, and were then stained with 0.5% crystal violet for 10 min at room temperature. Colony number (with more than 50 cells) was analyzed under an optical microscope (Olympus, Tokyo, Japan). Colony formation assay was repeated three times with three technical repetitions each time.
Cell apoptosis was analyzed by fluorescein isothiocyanate (FITC) and propidium iodide (PI) double-staining using the commercial Apoptosis Detection Kit (#40302ES20; Qcbio Science & Technologies, Shanghai, China). AC-16 cells were dispersed in 100 µL of binding buffer, and then cells were incubated with 10 µL of Annexin V-FITC and 10 µL of PI for 15 min at room temperature in the dark. Cells were loaded onto the flow cytometer (Beckman Coulter, Fullerton, CA, USA) for the analysis of apoptosis rate. FCM analysis was repeated three times with three technical repetitions each time.
Proteins in AC-16 cells were isolated with the radio-immunoprecipitation assay (RIPA) lysis buffer (Sigma). Protein samples (35 µg) were loaded onto 10% separating gel and blotted onto a polyvinylidene difluoride membrane (Millipore, Billerica, MA, USA). After blocking with 5 mL 5% skimmed milk for 1 h at room temperature, the primary antibodies were incubated with the membrane overnight at 4 ℃. The next day, the membrane was incubated with the secondary antibody for 2 h at room temperature. Protein blots were analyzed using the enhanced chemiluminescence (ECL) kit (Pierce, Waltham, MA, USA). The intensities of protein bands were quantified using the Image Lab analysis software (National Institutes of Health, Bethesda, MD, USA). The primary antibodies were all purchased from Abcam (Cambridge, MA, USA), including anti-B cell leukemia/lymphoma 2 (anti-Bcl-2; 1/1000; ab32124), anti-Bcl-2 associated X, apoptosis regulator (anti-Bax; 1/1000; ab32503), anti-PAWR (ab92590; 1/10000), and anti-GAPDH (ab8245; 1/5000). Western blot assay was repeated three times.
Cell death was assessed by quantifying the level of LDH. The release of LDH in the culture supernatant was analyzed using the LDH cytotoxicity assay kit (#C0017; Beyotime, Haimen, China) according to the manufacturer’s instructions. In brief, a total of 5×103 AC-16 cells were seeded onto 96-well plates. Then, 120 µL of culture supernatant was incubated with 60 µL of LDH test working reagent for 30 min in the dark. The absorbance was detected at the wavelength of 490 nm under a microplate reader (Bio-Rad). LDH assay was repeated three times with six technical repetitions each time.
The circHSPG2-miRNA interactions and miR-25-3p-mRNA interactions was established by Starbase database (http://starbase.sysu.edu.cn).
The fragment of circHSPG2 or PAWR 3’ untranslated region (3’UTR), including the wild-type or mutant binding sites with miR-25-3p, was inserted into pmirGLO plasmid (Promega, Madison, WI, USA) to generate WT/MUT-circHSPG2 and WT/MUT-PAWR 3’UTR. AC-16 cells were seeded onto 24-well plates at 4×104 cells/well. The luciferase plasmids (100 ng) were transfected into AC-16 cells with miR-25-3p (50 nM) or miR-NC (50 nM) using Lipofectamine 3000 (Invitrogen). After transfection for 48 h, the luciferase intensities were examined using the commercial dual-luciferase reporter assay system kit (Promega). The intensities of fluorescence were determined using a GloMax 20/20 luminometer (Promega). Firefly luciferase intensity was normalized to Renilla luciferase activity. Dual-luciferase reporter assay was repeated three times.
AC-16 cells were plated onto the 6-well plates at 2×105 cells/well. AC-16 cells transfected with miR-NC (50 nM) or miR-25-3p (50 nM) were disrupted with the RIP buffer for 5 min on the ice, and then cell lysates were mixed with magnetic beads conjugated with Argonaute 2 (Ago2) antibody (ab186733; 1: 50; Abcam) or immunoglobulin G (IgG) antibody (ab172730; 1: 100; Abcam) for 3 h at 4 ℃. The beads were washed with 500 µL RIP wash buffer twice and were incubated with RIP Immunoprecipitation buffer. The mixture was then centrifuged at 14,000 rpm for 10 min at 4 ℃. RNA was extracted with TRIzol reagent (Invitrogen), and the immune-precipitated RNAs were examined by RT-qPCR. RIP assay was repeated three times.
Data were processed by GraphPad Prism 7 software (GraphPad) and were represented as mean ± standard deviation (SD). The differences were assessed by Student’s t-test (two groups) or one-way analysis of variance (ANOVA) followed by Tukey’s test (three or more groups). P<0.05 was considered statistically significant.
The chromosomal localization of circHSPG2 (3475 nt) was shown in Figure 1A. We found that circHSPG2 was significantly up-regulated after hypoxia exposure for 24 h in AC-16 cells (Figure 1B). CircHSPG2 was resistant to the degradation of RNase R, while RNase R treatment markedly reduced the level of its linear counterpart HSPG2 (Figure 1C), suggesting that circHSPG2 was indeed a circular transcript. Before exploring the biological function of circHSPG2, we first analyzed the subcellular localization of circHSPG2. With GAPDH or U6 as cytoplasmic or nuclear indicator, it was found that circHSPG2 was majorly distributed in the cytoplasmic fraction of AC-16 cells (Figure 1D), suggesting that circHSPG2 might function in post-transcriptional level.
To explore the biological significance behind the abnormal up-regulation of circHSPG2 in hypoxia-induced AC-16 cells, we performed loss-of-function experiments. Hypoxia-induced up-regulation of circHSPG2 in AC-16 cells was neutralized by si-circHSPG2 (Figure 2A). It was observed that hypoxia exposure reduced the viability of AC-16 cells, and this suppressive effect was partly diminished by si-circHSPG2 (Figure 2B). Hypoxia treatment suppressed the proliferation of AC-16 cells, evidenced by the reduced percentage of EdU+ cells and number of colonies (Figure 2C,2D). The addition of si-circHSPG2 partly restored the proliferation ability in hypoxia-induced AC-16 cells (Figure 2C,2D). Hypoxia-induced the apoptosis of AC-16 cells, which was alleviated by the introduction of si-circHSPG2 (Figure 2E). Two apoptosis-associated proteins, including pro-apoptotic protein Bax and anti-apoptotic protein Bcl-2, were measured by Western blot assay. Hypoxia-induced up-regulation of Bax and down-regulation of Bcl-2 were largely reversed by si-circHSPG2 (Figure 2F). Combined with the results in Figure 2E, these data demonstrated that hypoxia-induced apoptosis in AC-16 cells was partly dependent on the up-regulation of circHSPG2. LDH assay demonstrated that hypoxia-induced cell death was largely attenuated by the silence of circHSPG2 in AC-16 cells (Figure 2G). Taken together, hypoxia-induced dysfunction of AC-16 cells was partly based on the up-regulation of circHSPG2.
Bioinformatics software Starbase was utilized to predict the miRNA targets of circHSPG2, and miR-25-3p was predicted as one of the targets of circHSPG2. The putative binding sequence between circHSPG2 and miR-25-3p was shown in Figure 3A. RT-qPCR assay confirmed that miR-25-3p mimic (miR-25-3p) was effective in up-regulating miR-25-3p level in AC-16 cells (Figure 3B). Dual-luciferase reporter assay and RIP assay were conducted to verify whether circHSPG2 can bind to miR-25-3p. The luciferase activity was significantly reduced in WT-circHSPG2 group with the overexpression of miR-25-3p, while the luciferase activity was unaffected in MUT-circHSPG2 group with the transfection of miR-NC or miR-25-3p (Figure 3C), demonstrating that circHSPG2 directly interacted with miR-25-3p. RIP assay revealed that circHSPG2 can bind to exogenous miR-25-3p in RNA-induced silencing complex (RISC) when using Ago2 antibody (Figure 3D). Hypoxia treatment reduced the expression of miR-25-3p in AC-16 cells (Figure 3E). Then, we analyzed whether circHSPG2 can regulate the expression of miR-25-3p in AC-16 cells. RT-qPCR combined with divergent primers verified the transfection efficiency of circHSPG2 overexpression plasmid (oe-circHSPG2) and si-circHSPG2 in AC-16 cells (Figure 3F). CircHSPG2 overexpression notably reduced the expression of miR-25-3p, and circHSPG2 absence markedly up-regulated miR-25-3p level in AC-16 cells (Figure 3G). These results together demonstrated that miR-25-3p was a target of circHSPG2, and it was negatively regulated by circHSPG2 in AC-16 cells.
Considering that circHSPG2 absence up-regulated the expression of miR-25-3p, we co-transfected hypoxia-induced AC-16 cells with si-circHSPG2 and anti-miR-25-3p to explore whether circHSPG2 silencing-induced effects were partly dependent on the up-regulation of miR-25-3p. The addition of anti-miR-25-3p reduced the expression of miR-25-3p in AC-16 cells (Figure 4A). Cell viability was decreased by the addition of anti-miR-25-3p in AC-16 cells (Figure 4B). The introduction of anti-miR-25-3p suppressed the proliferation of AC-16 cells (Figure 4C,4D). The addition of anti-miR-25-3p triggered the apoptosis of AC-16 cells (Figure 4E). The protein expression of Bax was up-regulated while the protein level of Bcl-2 was reduced by silencing miR-25-3p (Figure 4F,4G). The addition of anti-miR-25-3p induced cell death (Figure 4H). Overall, circHSPG2 silencing-mediated protective effects in hypoxia-induced AC-16 cells were largely based on the up-regulation of miR-25-3p.
The possible mRNA targets of miR-25-3p were predicted by Starbase database, and the putative binding sequence between miR-25-3p and PAWR was shown in Figure 5A. The overexpression of miR-25-3p significantly reduced the luciferase activity of WT-PAWR 3'UTR rather than MUT-PAWR 3'UTR (Figure 5B), suggesting that miR-25-3p directly interacted with the 3'UTR of PAWR. RIP assay revealed that there was spatial interaction between miR-25-3p and PAWR in RISC (Figure 5C). The expression of PAWR protein was markedly up-regulated in AC-16 cells upon hypoxia treatment (Figure 5D) and si-PAWR introduction repressed PAWR protein levels in AC-16 cells with hypoxia treatment (Figure S1A). Also, PAWR silencing weakened hypoxia-mediated effects on AC-16 cell proliferation, apoptosis, and cytotoxicity (Figure S1B-S1G). High transfection efficiency of anti-miR-25-3p was verified by RT-qPCR in AC-16 cells (Figure 5E). miR-25-3p overexpression reduced the protein expression of PAWR, and miR-25-3p knockdown up-regulated the level of PAWR protein in AC-16 cells (Figure 5F,5G). Taken together, miR-25-3p negatively regulated PAWR expression by interacting with its 3'UTR in AC-16 cells.
Rescue experiments were conducted to analyze whether miR-25-3p regulated the biological phenotypes of AC-16 cells by targeting PAWR. miR-25-3p overexpression reduced the protein expression of PAWR, and the protein level of PAWR was largely recovered by the addition of PAWR plasmid (Figure 6A). miR-25-3p overexpression protected AC-16 cells from hypoxia-induced dysfunction in AC-16 cells (Figure 6B-6H). Furthermore, the addition of PAWR plasmid reduced the viability and suppressed the proliferation of AC-16 cells (Figure 6B-6D). PAWR overexpression also induced the apoptosis of AC-16 cells (Figure 6E). The protein level of Bax was up-regulated while the protein level of Bcl-2 was decreased by the introduction of PAWR plasmid (Figure 6F,6G). LDH assay displayed that cell death was induced by the addition of PAWR plasmid (Figure 6H). Collectively, miR-25-3p exerted a protective role in hypoxia-induced AC-16 cells partly by down-regulating PAWR.
CircHSPG2 absence reduced the protein expression of PAWR, and the protein level of PAWR was largely rescued by the addition of anti-miR-25-3p in AC-16 cells (Figure 7A). CircHSPG2 overexpression increased the protein expression of PAWR, and addition of miR-25-3p decreased the protein level of PAWR in AC-16 cells (Figure 7B). These results suggested that circHSPG2 positively regulated PAWR expression by sponging miR-25-3p in AC-16 cells.
Due to the high stability, abundant expression, and tissue-specific expression pattern, circRNAs are considered as promising bio-markers for human diseases (20). Nevertheless, the biological roles of circRNAs in cardiac pathophysiology remain largely unknown. Several articles reported that circRNAs are dysregulated in multiple cardiovascular diseases. For instance, circ_000203 is reported to be up-regulated in mouse model and cell model of cardiac hypertrophy, and circ_000203 facilitates cardiac hypertrophy progression by targeting miR-26b-5p/miR-140-3p-Gata4 axis (21). CircTLK1 is reported to be up-regulated in myocardial ischemia/reperfusion injury mouse model, and it aggravates ischemia/reperfusion injury through mediating miR-214/RIPK1 signaling (22). As for circHSPG2, previous studies found that circHSPG2 absence exerts a protective role in cardiomyocytes upon the stimulation of hypoxia or oxygen-glucose deprivation (9,23,24). We found that hypoxia exposure enhanced the expression of circHSPG2 in cardiomyocytes. Hypoxia treatment inhibited the viability and proliferation and induced the apoptosis of cardiomyocytes, and circHSPG2 silencing using si-circHSPG2 attenuated hypoxia-induced dysfunction in cardiomyocytes, suggesting that the up-regulation of circHSPG2 was important for hypoxia-induced dysfunction in cardiomyocytes. A previous report showed that hyperoxia gradually increases cellular inflammation and cytotoxicity (25), and whether hyperoxia affects the level of circHSPG2 in cardiomyocytes can be further explored in the future. Accumulating evidence have verified that circRNAs can sequester miRNAs to regulate the expression and biological roles of miRNAs (11,26). Through bioinformatics analysis using Starbase software and experimental verification, the associated relationship between circHSPG2 and miR-25-3p was validated. Hypoxia stimulation reduced the level of miR-25-3p, and miR-25-3p was reversely modulated by circHSPG2 in AC-16 cells. miR-25-3p is identified to be a pro-tumor factor in multiple cancers (27-30). As for hypoxia-induced myocardial damage, Qin et al. found that miR-25 abundance is reduced in hypoxia-induced cardiomyocytes, and it facilitates the proliferation and migration capacities and reduces the apoptotic rate of cardiomyocytes (17). Two previous studies reported that miR-25 protects cardiomyocytes from oxidation or sepsis-induced injury by targeting mitochondrial calcium uniporter (15) or PTEN (16), respectively. Consistently, we found that miR-25-3p overexpression largely restored the viability and proliferation and suppressed the apoptosis of hypoxia-treated AC-16 cells. To investigate whether circHSPG2 functioned by targeting miR-25-3p in hypoxia-induced AC-16 cells, rescue experiments were implemented. The results presented that circHSPG2 absence protected AC-16 cells from hypoxia-induced dysfunction partly by up-regulating miR-25-3p, which further demonstrated the protective role of miR-25-3p in AC-16 cells upon hypoxia stimulation. The interaction between miR-25-3p and PAWR was identified in AC-16 cells. It was observed that hypoxia stimulation up-regulated the protein expression of PAWR in AC-16 cells. PAWR was reversely modulated by miR-25-3p in AC-16 cells. Previous articles reported that PAWR acts as an anti-tumor factor in multiple cancers by inhibiting cell proliferation and inducing cell apoptosis (31-33). Moreover, it is reported that circ_0068655 facilitates the apoptosis of cardiomyocytes by binding to miR-498 to up-regulate the expression of PAWR (18). We found that miR-25-3p overexpression-mediated protective effects in hypoxia-induced AC-16 cells were largely reversed by the addition of PAWR plasmid, indicating that miR-25-3p protected AC-16 cells from hypoxia-induced dysfunction partly by down-regulating PAWR. Finally, it was found that circHSPG2 acted as a positive regulator of PAWR by acting as miR-25-3p sponge in AC-16 cells. FBXWJ is reported as another target of miR-25 (34), and whether FBXWJ is regulated by the circHSPG2/miR-25-3p axis can be explored in the future. In summary, the data in this study together presented that circHSPG2 absence protected AC-16 cells from hypoxia-induced injury through mediating miR-25-3p/PAWR axis (Figure 8). Our results suggested that circHSPG2 knockdown might be a promising therapeutic strategy to alleviate hypoxia-induced myocardial damage. In future, animal experiment is needed to analyze the effect, side effect, and specificity of circHSPG2 knockdown therapy in myocardial infarction treatment.
The article’s supplementary files as 10.21037/cdt-22-197 10.21037/cdt-22-197 10.21037/cdt-22-197 10.21037/cdt-22-197 | true | true | true |
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PMC9622441 | Yan Deng,Yong Li,Jia-Long Wu,Ting Zhou,Meng-Yue Tang,Yong Chen,Hou-Dong Zuo,Wei Tang,Tian-Wu Chen,Xiao-Ming Zhang | Radiomics models based on multi-sequence MRI for preoperative evaluation of MUC4 status in pancreatic ductal adenocarcinoma: a preliminary study | 01-11-2022 | Radiomics,magnetic resonance imaging (MRI),pancreatic ductal adenocarcinoma (PDAC),Mucin 4 (MUC4) | Background Mucin 4 (MUC4) overexpression promotes tumorigenesis and increases the aggressiveness of pancreatic ductal adenocarcinoma (PDAC). To date, no study has reported the association between radiomics and MUC4 expression in PDAC. Thus, we aimed to explore the utility of radiomics based on multi-sequence magnetic resonance imaging (MRI) to predict the status of MUC4 expression in PDAC preoperatively. Methods This retrospective study included 52 patients with PDAC who underwent MRI. The patients were divided into two groups based on MUC4 expression status. Two feature sets were extracted from the arterial and portal phases (PPs) of dynamic contrast-enhanced MRI (DCE-MRI). Univariate analysis, minimum redundancy maximum relevance (MRMR), and principal component analysis (PCA) were performed for the feature selection of each dataset, and features with a cumulative variance of 90% were selected to develop radiomics models. Clinical characteristics were gathered to develop a clinical model. The selected radiomics features and clinical characteristics were modeled by multivariable logistic regression. The combined model integrated radiomics features from different selected data sets and clinical characteristics. The classification metrics were applied to assess the discriminatory power of the models. Results There were 22 PDACs with a high expression of MUC4 and 30 PDACs with a low expression of MUC4. The area under the receiver operating characteristic (ROC) curve (AUC) values of the arterial phase (AP) model, the PP model, and the combined model were 0.732 (0.591–0.872), 0.709 (0.569–0.849), and 0.861 (0.760–0.961), respectively. The AUC of the clinical model was 0.666 (0.600–0.682). The combined model that was constructed outperformed the AP, the PP, and the clinical models (P<0.05, although no statistical significance was observed in the combined model vs. AP model). Conclusions Radiomics models based on multi-sequence MRI have the potential to predict MUC4 expression levels in PDAC. | Radiomics models based on multi-sequence MRI for preoperative evaluation of MUC4 status in pancreatic ductal adenocarcinoma: a preliminary study
Mucin 4 (MUC4) overexpression promotes tumorigenesis and increases the aggressiveness of pancreatic ductal adenocarcinoma (PDAC). To date, no study has reported the association between radiomics and MUC4 expression in PDAC. Thus, we aimed to explore the utility of radiomics based on multi-sequence magnetic resonance imaging (MRI) to predict the status of MUC4 expression in PDAC preoperatively.
This retrospective study included 52 patients with PDAC who underwent MRI. The patients were divided into two groups based on MUC4 expression status. Two feature sets were extracted from the arterial and portal phases (PPs) of dynamic contrast-enhanced MRI (DCE-MRI). Univariate analysis, minimum redundancy maximum relevance (MRMR), and principal component analysis (PCA) were performed for the feature selection of each dataset, and features with a cumulative variance of 90% were selected to develop radiomics models. Clinical characteristics were gathered to develop a clinical model. The selected radiomics features and clinical characteristics were modeled by multivariable logistic regression. The combined model integrated radiomics features from different selected data sets and clinical characteristics. The classification metrics were applied to assess the discriminatory power of the models.
There were 22 PDACs with a high expression of MUC4 and 30 PDACs with a low expression of MUC4. The area under the receiver operating characteristic (ROC) curve (AUC) values of the arterial phase (AP) model, the PP model, and the combined model were 0.732 (0.591–0.872), 0.709 (0.569–0.849), and 0.861 (0.760–0.961), respectively. The AUC of the clinical model was 0.666 (0.600–0.682). The combined model that was constructed outperformed the AP, the PP, and the clinical models (P<0.05, although no statistical significance was observed in the combined model vs. AP model).
Radiomics models based on multi-sequence MRI have the potential to predict MUC4 expression levels in PDAC.
Pancreatic ductal adenocarcinoma (PDAC) is a malignant digestive system tumor and is the third leading cause of death among cancers (1). Although radical resection is the only possible curative treatment, the addition of neoadjuvant chemotherapy can improve the survival rate of patients with PDAC (2). Due to the heterogeneity of tumors, sensitivities toward chemotherapeutic drugs are diverse among different people. Even though progress has been made in diagnosing and treating pancreatic cancer, the prognosis of PDAC is still poor, with a reported 5-year survival rate of 7.2% (3). Therefore, it is essential to discover molecular markers that can be targeted by chemotherapy and used to predict patient prognosis in PADC patients. Recently, individualized cancer treatment has aroused great interest and is being widely studied. The use of molecular biomarkers helps the optimization of appropriate treatment and evaluation of prognosis in patients with cancer (4). Mucin 4 (MUC4) is a highly glycosylated, membrane-bound protein. The overexpression of MUC4 promotes tumorigenesis and increases the aggressiveness of PDAC (5). The high expression of MUC4 promotes tumor cell metastasis, regulates the interaction between tumor cells and microenvironmental components, and promotes tumor cell resistance to chemotherapy (6,7). Studies have confirmed that the PDAC group with a high expression of MUC4 is less sensitive to gemcitabine (8,9). The high expression of MUC4 is closely related to the poor prognosis of patients with PDAC (10). Therefore, it is essential to detect the status of MUC4 for optimizing individualized treatment and evaluating the prognosis of patients with PDAC. Immunohistochemistry (IHC) is a conventional, albeit invasive method used for detecting MUC4 expression status. Magnetic resonance imaging (MRI) is capable of noninvasively detecting pancreatic malignancy. However, MRI is generally not used to describe features other than tumor location, size, and general appearance. Radiomics can obtain a large quantity of potential feature data that is not seen by the naked eye from traditional images in a noninvasive and high throughput way and quantitatively analyze the obtained feature data to provide a basis for treatment decision-making (11). It relies on objective computer measurements rather than a radiologist’s subjective assessment (12). Radiogenomics is an emerging field in which the relationship between radiological features and genetic characteristics are studied. Routine image data are transformed into mineable quantitative data where quantitative features are extracted and linked to specific genomic profiles and outcomes (13). Some progress has been made in various tumors using radiogenomics applications, such as rectal cancer, breast cancer, and brain glioblastoma (14-16). There has been no report, however, about the association between radiomics and MUC4 expression status in PDAC patients. This study aimed to identify the correlation between radiomics and MUC4 in PDAC. The radiomics features were extracted from multi-sequence MRI, and radiomics models were developed to explore their utility in preoperatively predicting MUC4 expression levels in PDAC. We present the following article in accordance with the TRIPOD reporting checklist (available at https://qims.amegroups.com/article/view/10.21037/qims-22-112/rc).
The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). This retrospective study received ethical approval from the Institutional Review Board (IRB), and the requirement for informed consent was waived. Consecutive patients with pathologically confirmed PDAC and preoperative MRI admitted at our institution from March 2016 to September 2019 were identified. The paraffin sections of each patient were collected for IHC detection of the status of MUC4 expression in PDAC. Our study recruited patients who met the following criteria: (I) total tumor resection and a pathological diagnosis of PDAC; (II) available IHC detection of MUC4 expression status; (III) available preoperative multi-sequence MR scan; and (IV) available complete clinical data sets. Patients were excluded according to the following criteria: (I) absence of paraffin-embedded sections or inability to evaluate MUC4 expression; (II) previous administration of preoperative therapy, such as chemoradiotherapy; (III) incomplete imaging data or poor image quality; or (IV) >30-day interval between surgery and MR examination. The flowchart of patient recruitment is shown in Figure 1. Finally, 52 patients (mean age, 64 years; males =30) with PDAC were included in our study. Clinical data, including age, gender, tumor location, tumor size, tumor differentiation, lymph node status, and serological carbohydrate antigen 19-9 (CA19-9) levels, were recorded from the admission notes. Tumor size was based on the product of the lesion’s length, width, and height measured on surgical specimens. According to the pathological results, lymph node status was divided into positive and negative status. In addition, we followed up on the overall survival (OS) rate of patients for 1 year. The OS was defined as the time interval between the date of operation and the date of death or the last known date of life. Taking the follow-up for 1 year as the boundary, more than 1 year of survival was deemed survival, and less than 1 year of survival was deemed non-survival or dead. We compared the correlation between OS and MUC4 expression levels.
The MUC4 expression level was identified using standard IHC methods. Paraffin sections of PDAC were collected from the Pathology Department of our unit. The department made 5 µm-thick sections and used MUC4-specific antibodies (Abcam, Cambridge, MA, USA) for IHC staining. Two experienced pathologists (with 5 and 8 years of experience, respectively) performed the IHC scoring independently and were blinded to the clinical data of these patients; they discussed the results when their scores were inconsistent. The score was based on the degree of staining and the proportion of positive cells (H-score). The evaluation criteria for the degree of staining were as follows: no color, 0 score; pale yellow, 1 score; claybank, 2 scores; and brown, 3 scores. The evaluation criteria for the proportion of positive cells were as follows: no more than 5%, 0 score; 5–25%, 25 scores; 25–50%, 50 scores; 50–75%, 75 scores; and over 75%, 100 scores. Positive staining over 5% was defined as positive expression. High and low expressions of MUC4 were defined as H-score >100 and H-score ≤100, respectively (8). The IHC and corresponding histopathological results of high and low MUC4 expression in different patients are shown in Figure 2.
All patients were scanned with a 3.0-T MR machine (MR 750; GE Medical Systems, Waukesha, WI, USA; Achieva, Philips, the Netherlands) with a 32-channel body phased-array coil. The scan sequence included T2-weighted imaging (T2WI), pre-contrast T1-weighted imaging (T1WI), and the arterial, portal-venous, and delayed phases of dynamic contrast-enhanced MRI (DCE-MRI). The contrast agent for dynamic enhancement was gadopentetate dimeglumine (Magnevist, Bayer Schering, Guangzhou, China); the dose was 0.2 mmol/kg (approximately 20 mL), which was injected intravenously with a pressure syringe (Spectris MR Injection System; MEDRAD, Inc., Pittsburgh, PA, USA) at 2–3 mL/s, followed by flushing with 20 mL of saline. Scanning was performed at 30, 60, and 120 s after the injection of contrast agent to obtain images of the arterial phase, portal vein phase, and delayed phase, respectively. Details of the parameters are shown in Table 1.
Two radiologists with experience in abdominal diagnosis (Reader 1 with 4 years and Reader 2 with 7 years, respectively) manually outlined the region of interest (ROI) based on arterial phase and portal phase around the tumor edge, layer by layer, without knowing the clinical and pathological data of the patient. Two corresponding independent feature sets (arterial phase, portal phase) were produced, and radiomics models were built. To reduce the influence of the volume effect on the peripancreatic fat space or normal pancreas, the outlined ROI was slightly smaller than the area of the actual lesion (17). The process was implemented using an open-source software package, Imaging Biomarker Explorer (IBEX, β1.0; University of Texas MD Anderson Cancer Center, Houston, TX, USA; http://bit.ly//), which runs on MATLAB 2016b (The MathWorks Inc., Natick, MA, USA). The radiomics workflow is shown in Figure 3. Four sets of features from IBEX were opted for, including the intensity histogram, the gray-level co-occurrence matrix (GLCM), the gray-level run-length matrix (GLRLM), and the shape. We identified 350 radiomics features in arterial phase and portal phase, each of the independent feature sets, respectively (see Appendix 1, which elucidates the features of information). All patients included in this study had identical MR scan sequences and parameters. Thus, no image preprocessing methods were applied. To eliminate the effect of different dimensions of features and to make the results more reliable, we performed z-score standardization on all data sets (see Appendix 2, which explains the data preprocessing method).
We selected all patients to evaluate the repeatability of radiomics of different feature sets. We drew the ROI contours of the arterial phase and the portal phase from multi-sequenced MRI, thus generating corresponding feature subsets. Intraobserver consistency was checked in the results illustrated by Reader 1, who drew the ROI twice, and the interval time between the two outlines was greater than one week. Inter-observer consistency was checked by examining the outcomes reported by Reader 2, who independently drew the ROI, and comparing them with the consequences of the first outcomes reported by Reader 1. The interclass correlation coefficient (ICC) was used as a measurement index in the intra- and inter-observer consistency evaluation. Intra- and inter-observer consistency were checked for all radiomics features generated from the ROIs extracted by Reader 1 and Reader 2. When the ICC score exceeded 0.75, consistency was considered good (18). Due to the characteristics of voxel size and gray-scale dependence, not all radiomic features reached a satisfactory consistency (19).
A large number of redundant radiomics features were used in the operation of the classifier, which would cause over-fitting and lead to dimensional disasters. To avoid this, it was necessary to select suitable features through dimensionality reduction. First, univariate analysis, including the independent samples t-test or the Mann-Whiney U test, was applied to identify the features in each data set which exhibited a statistically significant difference between high and low MUC4 expression levels. A false discovery rate (FDR) was used to revise the P-value to decrease a risk type I error. Then, the minimum redundancy maximum relevance (MRMR) algorithm was applied to each data set to select a non-redundant and highly informative set of features. The top 15 features were picked out using MRMR. Then, principal component analysis (PCA) was applied for dimensionality reduction and feature selection. The main component features with the strongest discriminative power and cumulative variance of 90% were selected. The selected features of each dataset were modeled by multivariable logistic regression (see Appendix 3, which explains the process of feature selection and modeling). In addition, the most discriminative features from each dataset and clinical characteristics were combined to generate a joint radiomics model (combined model), and multivariable logistic regression was applied for modeling. A 5-fold, cross-validation method was used to verify the performance of the combined model. Several clinical characteristics, the size, and the differentiation degree of the tumor were selected via univariate analysis for the continuous variables and the Pearson chi-square test for the categorical variables to construct the clinical model. The area under the receiver operating characteristic (ROC) curve (AUC) and other evaluation metrics, such as accuracy, sensitivity, and specificity, were applied to evaluate the performance of these radiomics and clinical models. The DeLong test was used to compare the AUC among the four radiomics and clinical models.
Regarding the clinical data, continuous variables were assessed by univariate analysis. Pearson’s chi-square test or Fisher’s exact test were used to assess categorical variables and the data were analyzed using SPSS 23.0 (IBM Corp., Armonk, NY, USA). The dimensionality reduction and model-building processes of the radiomics features, including MRMR, PCA, multivariable logistic regression model, and ROC curve analyses, were implemented in R 3.5.2 (https://www.r-project.org/). Kfoldclass was implemented in Stata 15.0 (Stata Corp., College Station, TX, USA) for the 5-fold cross-validation. P value less than 0.05 was considered statistically significant.
A total of 52 patients with PDAC were included in this retrospective study. There were 22 cases with a high expression and 30 cases with a low expression of MUC4. Among the clinical characteristics, only tumor size and tumor differentiation were significantly different between the high- and low-MUC4 expression groups (P<0.05). We compared the OS between high- and low-MUC4 expression groups and demonstrated that the higher the expression level of MUC4, the lower the 1-year survival rate of PDAC patients. The baseline characteristics were recorded in Table 2. The preoperative MRI of the MUC4 expression status of patients with PDAC is shown in Figure 4.
The mean values of the interobserver agreement of radiomics features were 0.951 and 0.958 for the arterial phase model and portal phase model feature sets, respectively. For the intraobserver agreement, the mean values were 0.952 and 0.970 for the arterial phase and portal phase feature sets, respectively. Ultimately, through the ICC reliability test, the remaining features of the arterial phase and portal phase datasets were 336 and 334 for the following analysis, respectively.
After single factor analysis, the selected features with a significant difference were 324 and 307 in the arterial phase and portal phase feature sets. We used the top 15 radiomics features after the MRMR algorithm for PCA analysis. For PCA dimensionality reduction, five principal component features of the arterial phase feature set and four principal component features of the portal phase feature set closely related to the MUC4 expression status of PDAC were selected. Combining these selected features of each feature set and clinical characteristics generated a combined radiomics model, including 11 features. Multivariable logistic regression was performed for the classifier. The AUCs of the arterial phase model, portal phase model, and combined model were 0.732 (0.591–0.872), 0.709 (0.569–0.849), and 0.861 (0.760–0.961), respectively. The sensitivity/specificity of the arterial phase model, portal phase model, and combined model were 0.727/0.700, 0.773/0.600, and 0.909/0.682, respectively. The AUC of the clinical model was 0.666 (0.600–0.682). The sensitivity and specificity of the clinical model were 0.682 and 0.600, respectively (Table 3). The combined model achieved the best performance among these radiomics and clinical models (comparing the AUC of all models: P<0.05), although no statistical significance was observed in the combined model vs. Arterial phase model. These results are shown in Figure 5. We used a 5-fold cross-validation to test the effectiveness of the combined model, and the results are shown in Figure 6.
In this study, we presented a noninvasive imaging biomarker to preoperatively evaluate the MUC4 expression status of patients with PDAC. Our results suggested that radiomics models based on multi-sequence MRI features have the potential to distinguish between high and low status of MUC4 expression in PDAC. The microenvironment of the tumor is related to tumorigenesis and its progression. Previous studies (5,10,20) have confirmed that MUC4 increases the aggressiveness and malignancy of PDAC, making the prognosis of patients with PDAC poor. Our study compared the correlation between OS and MUC4 expression levels, and the results are in line with the previous survey. The expression of MUC4 is negatively correlated with the survival rate of patients with PDAC. A series of studies have successfully linked tumor radiomics and biological behavior. For instance, Li et al. (16) used 41 texture features in a logistic regression model that could successfully predict the epidermal growth factor receptor (EGFR) expression status of low-grade glioblastoma. Wang et al. (21) used a radiomics model based on MRI to predict EGFR mutation in patients with lung adenocarcinoma, and achieved good results. However, few studies have established links between radiomics and pancreatic cancer. Permuth et al. (22) demonstrated that radiomics combined with messenger RNA (mRNA) expression could more accurately predict the pathology of intraductal papillary mucinous neoplasms. No study has reported on the application of radiomics in predicting the MUC4 expression status in patients with PDAC. Our study developed a non-invasive, quantitative, pre-operative radiomics model to predict the level of MUC4 expression in patients with PDAC. Five principal component features of the arterial phase feature set and four principal component features of the portal phase feature set were generated by applying a logistic regression method to each model. The prediction performance of the single sequence radiomics models and the clinical model were poor; the AUC values of the arterial phase model and portal phase model were 0.732 and 0.709, respectively. The AUC values among the arterial phase model, portal phase model, and clinical model showed no statistical significance. This may be associated with the fact that less information was extracted from a single sequence, such that the established model did not work well. In addition, the clinical model established by the size and differentiation degree of a tumor was inherently related to the prognosis of pancreatic cancer, and these clinical indicators were related to the expression of MUC4 in pancreatic cancer. The combined model achieved the best performance with a significantly higher AUC of 0.861 among these models. The same trend was seen in the sensitivity and accuracy; however, specificity was an exception, with that of the specificity of the arterial phase model being higher. These results suggested that combining the arterial phase model, the portal phase model, and the clinical model together would increase the predictive value of the status of MUC4 expression. These results, associated with combining a multi-sequence, could provide more comprehensive information than any individual sequence. Our results were consistent with a previous study (23) which used a multi-sequence radiomics model to evaluate the pathological grade in bladder cancer and demonstrated that the joint model achieved the best performance. In addition, the combined model combined several of the most discriminative principal components selected from every single sequence, giving this model a particular advantage. Our study also evaluated the relationship between MUC4 expression level and gender, age, tumor location, tumor size, tumor differentiation, lymph node status, and CA19-9 level. Only the degree of tumor differentiation and tumor size significantly differed between the two groups. This may have been caused by the degree of tumor differentiation reflecting the tumor’s biological behavior; the lower the degree of differentiation, the more malignant the tumor. The tumor size was related to the clinical stage and reflected the clinical progress of the tumor (24). In general, the later the tumor stage, the higher the degree of malignancy. A meta-analysis conducted by Huang et al. (25) confirmed that MUC4 expression was related to tumor stage, malignancy, and lymph node status. However, there was no significant difference in the lymph nodes between the high- and low-MUC4 expression groups; this may have been related to their small sample size. In addition, several studies (26,27) have reported that the positive lymph node rate is positively correlated with the number of lymph nodes detected. Thus, the relatively few lymph node examinations conducted by our institution potentially led to a lower detection rate, which may have skewed the result. The clinical model was built by combining tumor size and tumor stage. Compared with the combined model in terms of performance, the combined model performed better. The expression of MUC4 reflected the microenvironment of pancreatic cancer. Radiomics can capture much information which is invisible to the naked eye and can thus reveal the tumor microenvironment. This study had some limitations. Our research was conducted retrospectively. Prospective studies are needed to assess MUC4 expression status. Furthermore, the small sample size does not support external validation, which is the most critical validation strategy (12). Thus, we used a five-fold cross-validation, which is the most frequently used internal validation approach (28). Multicenter and large-scale research needs to be performed to support our findings. Focusing on the arterial phase and portal phase sequences was another limitation of the study; however, a previous study (29) suggested that pancreatic cancer is better visualized in the arterial and portal phases.
Our study confirmed that the radiomics model based on a multi-sequence MRI has the potentiality to predict MUC4 expression levels in PDAC. Our results provide the foundation for developing a non-invasive diagnostic method and better management of patients with PDAC.
The article’s supplementary files as 10.21037/qims-22-112 10.21037/qims-22-112 10.21037/qims-22-112 | true | true | true |
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PMC9622505 | Siping Chen,Man Yang,Haikun Yang,Qiaofei Tang,Chunming Gu,Weifeng Wei | Identification and validation of a 9-gene signature for the prognosis of ovarian cancer by integrated bioinformatical analysis | 01-10-2022 | Ovarian cancer (OC),biomarker,prognosis,tumor microenvironment,diagnosis | Background Ovarian cancer (OC) is the most lethal malignancy among gynecological cancers worldwide. It is urgent to identify effective biomarkers for the prognosis and diagnosis of OC. Methods We analyzed 4 OC Gene Expression Omnibus (GEO) data sets to detect differentially expressed genes (DEGs). To explore potential correlations between the gene sets and clinical features, we conducted weighted gene co-expression network analysis (WGCNA). Hub genes were identified from the key modules by univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses and risk scores were calculated based on the expressions of the hub genes. Univariate and multivariate Cox regression analyses were conducted to determine the values of the diagnoses for OC patients. We also determined the predictive value of the long non-coding RNA (lncRNA) score in response to immunotherapy and chemotherapeutic drugs. Results DEGs were analyzed between the OC and normal ovarian tissues and prognostic modules were identified by a WGCNA. Nine hub genes chose from the prognostic modules were determined the prognostic values in OC. The risk scores were calculated based on the expression of hub genes, and patients with high-risk scores had poor survival. Univariate and multivariate Cox regression analyses showed that the risk score was an independent prognostic factor for OC. Additionally, the levels of hub genes were also found to be related to immune cell infiltration in OC microenvironments. An immunotherapy cohort showed that high-risk scores enhanced the response to anti-programmed death-ligand 1 (PD-L1) immunotherapy and was remarkably correlated with the inflamed immune phenotype, and had significant therapeutic advantages and clinical benefits. Further, patients with high-risk scores were more sensitive to midostaurin. Conclusions We identified the risk score including protein phosphatase, Mg2+/Mn2+ dependent 1K (PPM1K), protein phosphatase 1 catalytic subunit alpha (PPP1CA), exostosin glycosyltransferase 1 (EXT1), RAB GTPase activating protein 1 like (RABGAP1L), mitotic arrest deficient 2 like 1 (MAD2L1), xeroderma pigmentosum complementation group C (XPC), Egl-9 family hypoxia inducible factor 3 (EGLN3), cyclin D1 binding protein 1 (CCNDBP1), and zinc finger protein 25 (ZNF25), and validated their prognostic and predicted values for OC. | Identification and validation of a 9-gene signature for the prognosis of ovarian cancer by integrated bioinformatical analysis
Ovarian cancer (OC) is the most lethal malignancy among gynecological cancers worldwide. It is urgent to identify effective biomarkers for the prognosis and diagnosis of OC.
We analyzed 4 OC Gene Expression Omnibus (GEO) data sets to detect differentially expressed genes (DEGs). To explore potential correlations between the gene sets and clinical features, we conducted weighted gene co-expression network analysis (WGCNA). Hub genes were identified from the key modules by univariate Cox regression, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses and risk scores were calculated based on the expressions of the hub genes. Univariate and multivariate Cox regression analyses were conducted to determine the values of the diagnoses for OC patients. We also determined the predictive value of the long non-coding RNA (lncRNA) score in response to immunotherapy and chemotherapeutic drugs.
DEGs were analyzed between the OC and normal ovarian tissues and prognostic modules were identified by a WGCNA. Nine hub genes chose from the prognostic modules were determined the prognostic values in OC. The risk scores were calculated based on the expression of hub genes, and patients with high-risk scores had poor survival. Univariate and multivariate Cox regression analyses showed that the risk score was an independent prognostic factor for OC. Additionally, the levels of hub genes were also found to be related to immune cell infiltration in OC microenvironments. An immunotherapy cohort showed that high-risk scores enhanced the response to anti-programmed death-ligand 1 (PD-L1) immunotherapy and was remarkably correlated with the inflamed immune phenotype, and had significant therapeutic advantages and clinical benefits. Further, patients with high-risk scores were more sensitive to midostaurin.
We identified the risk score including protein phosphatase, Mg2+/Mn2+ dependent 1K (PPM1K), protein phosphatase 1 catalytic subunit alpha (PPP1CA), exostosin glycosyltransferase 1 (EXT1), RAB GTPase activating protein 1 like (RABGAP1L), mitotic arrest deficient 2 like 1 (MAD2L1), xeroderma pigmentosum complementation group C (XPC), Egl-9 family hypoxia inducible factor 3 (EGLN3), cyclin D1 binding protein 1 (CCNDBP1), and zinc finger protein 25 (ZNF25), and validated their prognostic and predicted values for OC.
Ovarian cancer (OC) is by far the most aggressive malignancy among female reproductive carcinomas, and it has been reported that there are approximately 300,000 new cases per year globally (1). Due to its uncertain etiology and a lack of efficient early detection biomarkers, approximately 70% of OC individuals are already at an advanced stage with widespread metastases at the time of diagnosis (2). Even though some genetic risk factors like breast cancer 1 (BRCA1) and breast cancer 2 (BRCA2) are identified in OC (3), the development of new predictive biomarkers is important for the understanding of OC. Despite some advances in the past few decades, the long-term survival rate of individuals with OC remains dismal (4). Thus, it is vital to identify novel biomarkers for the prognosis and early diagnosis to elevate the survival rates of OC patients. Recently, various biomarkers have been identified as having a significant effect on oncogenicity and as having the potential to be used to treat OC. In-vitro and in-vivo research has shown that the silencing of mitochondrial elongation factor 2 greatly inhibits OC cell growth and metastasis by blocking epithelial-to-mesenchymal transition, and causing cell death, and thus is considered a viable prognostic indicator of OC (5). Carbohydrate antigen 25 (CA125) was detected as a biomarker for OC with the specificity of 78% (95% CI: 76–80%) (6). Moreover, circulating tumor DNA is a promising biomarker with an estimated sensitivity of 70% and specificity of 90% for quantitative analysis in OC (7). Lung adenocarcinoma (LUAD) has been shown to be associated with a 9-gene signature that is independently linked to the disease and substantially correlated with immunological infiltration (8). This signature might provide direction for LUAD patients’ prognoses and molecular-targeted treatment (8). Due to the unsatisfied clinical utility, it is urgent to identify more effective biomarkers to improve the survival of OC. We built a prognostic risk model comprising protein phosphatase, Mg2+/Mn2+ dependent 1K (PPM1K), protein phosphatase 1 catalytic subunit alpha (PPP1CA), exostosin glycosyltransferase 1 (EXT1), RAB GTPase activating protein 1 like (RABGAP1L), mitotic arrest deficient 2 like 1 (MAD2L1), xeroderma pigmentosum complementation group C (XPC), Egl-9 family hypoxia inducible factor 3 (EGLN3), cyclin D1 binding protein 1 (CCNDBP1), and zinc finger protein 25 (ZNF25) functions as innovative biomarkers, and validated the prognostic and predictive significance of the model in providing molecular evidence of OC. We present the following article in accordance with the TRIPOD reporting checklist (available at https://atm.amegroups.com/article/view/10.21037/atm-22-3752/rc).
The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/repository) provided us with the ribonucleic acid sequencing (RNA-seq) data of 379 OC patients and the relevant clinical characteristics. We also acquired 4 microarray data sets GSE105437 (9), GSE14407 (10), GSE54388 (11), GSE69428 (12), and a immunotherapy cohort (IMvigor210) (13) from the Gene Expression Omnibus (GEO) database. GSE26193 was used as a training cohort while GSE18520 was treated as a validation cohort and TCGA as an external validation cohort for clinicopathological features. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013).
When comparing the expression profiling data between the OC and adjacent normal samples, the “limma” R program was employed as a screening tool. A |log 2-fold change (FC)| >1 and an adjusted P value <0.05 were considered significant.
A WGCNA was conducted to identify the co-expressed gene modules and examine the links between the gene networks and the clinical characteristics (14). The WGCNA was conducted using the “WGCNA” function in the R platform to develop a matrix establishing the module-trait correlations between the prognostic genes and the grade based on the β value, and the Pearson correlation test was used (soft-thresholding value).
To determine the biological roles of the key genes in the modules, we used the “clusterProfiler” R program to conduct Gene Ontology (GO) functional annotations (15) and a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis using the key genes in the modules (16). The cutoff value for the adjusted P value in relation to the false discovery rate was set at 0.05.
To select the prognostic genes from the DEGs, we performed multivariate and univariate Cox regression analyses. A LASSO regression analysis was employed to examine the genes that had been chosen for further investigation. To choose the prognostic genes, the LASSO Cox regression technique was employed in conjunction with the “glmnet” program to determine the variable coefficients. To determine each sample’s risk score, we used the following equation: risk score = (coefficient mRNA1 × expression of mRNA1) + (coefficient mRNA2 × expression of mRNA2) + … + (coefficient mRNAn × expression mRNAn). The threshold level was determined by taking the median risk score for all the specimens. The specimens in the training and validation cohorts were classified into high- and low-risk groups based on the threshold value.
To discover the independent predicting variables, the “rms” tool in the R program was used to create the nomogram and calibrate curves. With the help of a receiver operating characteristic curve analysis, we successfully verified the specificity and sensitivity of the nomogram in terms of overall survival (OS) prediction.
The Spearman’s correlation test was used to examine the possible link between the hub genes’ expression levels and the levels of the 6 tumor-infiltrating immune cells (TIICs). The data were acquired from the Tumor Immune Estimation Resource (TIMER; https://cistrome.shinyapps.io/timer/). A P value <0.05, when corrected for tumor purity, was considered significant.
To evaluate the different sensitivities to chemotherapeutic agents for the high and low long non-coding ribonucleic acid (lncRNA) score subgroups, the pRRophetic algorithm was used to predict the 50% inhibiting concentration (IC50) value of the 138 drugs based on the Cancer Cell Line Encyclopedia (17).
All the computational and statistical studies were undertaken using the R program (version 4.0.3). The Kaplan-Meier technique was employed to conduct a survival analysis of the data. Statistically significant differences were set at two-sided P<0.05.
Following the examination of the GEO database, we identified 4 potentially useful microarray data sets (i.e., GSE105437, GSE14407, GSE54388, and GSE69428), which were included in our research. Table 1 summarizes the most essential features of the GEO data sets that were included. A total of 46 OC and 33 normal tissues were included in our study. Based on the findings recorded from the Robust Rank Analysis (RRA) analysis with an adjusted P value <0.05 and a |log 2-FC| >1, we identified 499 DEGs that were significant, of which 196 were upregulated and 303 were downregulated (see Figure 1A). S100 Calcium Binding Protein 2 (S100A2) was the top-ranking gene among all those that were upregulated (P=9.50E-10, adjusted P=2.06E-05), while OGN (P=8.07E-10, adjusted P=1.75E-05) was the most considerably downregulated gene in the OC samples. Further, the top 20 upregulated and downregulated DEGs were shown on a heatmap (see Figure 1B). To illustrate the expression profiles of these genes and their corresponding chromosome sites, the 50 most upregulated and downregulated genes were selected (see Figure 1C). The top 5 upmodulated genes (i.e., S100A2, RRM2, PTH2R, KLK6, and MELK) were distributed in chromosomes 1, 2, 2, 19, and 9. While, the top 5 downregulated genes (i.e., OGN, TCEAL2, ALDH1A1, NDNF, and BCHE) were distributed in chromosomes 9, X, 9, 4, and 3.
The biological roles of DEGs were investigated using GO annotations. Mitotic nuclear division, mitotic sister chromatid segregation, and sister chromatid segregation were the biological processes (BPs) that were the most enriched (see Figure 2A). In relation to the cellular components (CCs), the spindle, chromosome, centromeric region, and midbody were the most enriched (see Figure 2B). In relation to the molecular functions (MFs), the DEGs enrichment was predominantly found in microtubule binding, tubulin binding, and heparin-binding (see Figure 2C). Additionally, the enriched KEGG pathways included the cell cycle, retinol metabolism, and protein 53 signaling pathways (see Figure 2D).
To identify which major modules were strongly linked to the clinical characteristics of OC, a WGCNA was conducted of the GSE26193 data set, which included the DEGs (see Figure 3A). Following the establishment of the soft-threshold power as 4 (scale-free R2=0.95 slope =−2.36; see Figure 3B,3C), 30 modules were obtained from the co-expression network after integrating similar modules based on a cutoff height of 0.25 (see Figure 3D). The green, green-yellow, and turquoise modules were found to be substantially linked to the clinical features, as shown by a heatmap of the module-trait associations (see Figure 3E). The module relevance of these 3 modules was greater than that of other modules, indicating that there was a significant link to of the modules the grade (see Figure 3F).
To detect the hub genes from the modules, we analyzed the members from the 3 modules. Figure 4A shows the P values and the relationship between the gene significance values and module membership. The univariable and multivariable Cox regression analyses revealed that PPM1K, PPP1CA, EXT1, RABGAP1L, MAD2L1, XPC, EGLN3, CCNDBP1, and ZNF25 might have prognostic value (see Figure 4B,4C). The expression levels of CCNDBP1, PPM1K, RABGAP1L, and ZNF25 were considerably lower in the OC samples than the normal tissue samples, while EGLN3, MAD2L1, PPP1CA were much lower in the normal tissue samples than the OC samples (see Figure 4D). Further, immunohistochemical staining data acquired from the Human Protein Atlas (HPA) database confirmed the consistent protein expression levels of the 7 hub genes, excluding EGLN3 and EXT1, which were not stained in the HPA data (see Figure 4E).
A regression analysis using the LASSO was used to predict the therapeutic outcomes of the 9 hub genes in OC patients with high accuracy. The study was conducted using the expression patterns of the 9 hub genes from the GEO training cohort. The coefficients produced by the LASSO technique were used to compute the risk scores of the training (GSE26193) and validation (GSE18520) cohorts, and in this case, the following formula was used: (risk score = 0.54302496 × PPM1K − 0.4778786 × PPP1CA + 0.08080111 × EXT1 + 0.6944571 × RABGAP1L − 0.0278971 × MAD2L1 + 0.43948467 × XPC − 0.2467983 × EGLN3 − 0.5788344 × CCNDBP1 + 0.75443752 × ZNF25). Based on the median risk score, patients were categorized into the following 2 groups: the low- and high-risk groups. Figure 5A,5B show the distribution of the risk scores, OS, and the expression patterns of the 9 hub genes across the training and validation groups. An examination of the Kaplan-Meier survival data derived from this model demonstrated that patients belonging to the low-risk group had significantly longer survival times than those belonging to the high-risk group (see Figure 5C). This was consistent with TCGA results (see Figure S1).
We conducted univariate and multivariate Cox regression analyses of the grade, stage, and risk score to examine whether the newly developed immune risk score model was independent of other clinicopathologic factors (univariate: HR =1.853, P<0.001; multivariate: HR =1.698, P=0.003, see Figure 6A,6B). The findings from the 2 analyses demonstrated that the risk score was an independent prognostic predictor for OC. A nomogram was created to help us illustrate our model, and we discovered that the grade, risk score, and stage of OC patients may all be used to predict their survival (see Figure 6C).
The TIMER analysis indicated that the expression of PPM1K was linked to dendritic cells, cluster of differentiation (CD)8+ T cells, macrophages, and neutrophil cells. PPP1CA was shown to be closely linked to dendritic cells, B cells, macrophages, and neutrophils. MAD2L1 expression was linked to dendritic cells, macrophages, and neutrophils. XPC expression was linked to dendritic cells and B cells. EGLN3 expression was linked to macrophages, neutrophils, and dendritic cells. There was a substantial association between CCNDBP1 expression and dendritic cells, B cells, CD8+ T cells, and neutrophils. ZNF25 was only correlated with macrophages. No significant link was found between EXT1 and RABGAP1L expression (see Figure 7).
To examine the possible mechanisms of the lncRNA score in OC, we determined the enriched pathways between the high- and low-risk score groups using KEGG. In the low-risk score group, immune-related pathways were enriched, including antigen processing and the presentation and intestinal immune network for immunoglobulin A (IgA) production (see Figure 8A). Moreover, homologous recombination, the cell cycle, and mismatch repair were also highly enriched in the low-risk score group. Additionally, the risk score was strongly associated with repair like mismatch repair, nucleotide excision repair, and deoxyribonucleic acid (DNA) damage repair (see Figure 8B). We explored the predictive significance of the risk score in relation to responsiveness to immune checkpoint blockade (ICB) treatment in 2 immunotherapy groups. The expression of programmed death-ligand 1 (PD-L1) was high in the high-risk score patients (see Figure 9A). Patients receiving anti-PD-L1 treatment with high-risk scores had a more favorable prognosis than in the low-risk score group (IMvigor210; see Figure 9B). Patients with high-risk scores had remarkable therapeutic benefits and showed enhanced immune responsiveness to the PD-L1 blockade (see Figure 9C,9D). Further, patients who had a combined high-risk score and high neoantigen load benefited significantly in terms of survival (see Figure 9E). In IMvigor210, the high-risk scores were significantly associated with the inflamed immune phenotype, and the checkpoint inhibitors exerted an anti-tumor effect in this phenotype (see Figure 9F). Thus, the risk score was shown to be significantly correlated with tumor immune phenotypes and was useful in predicting the responses of patients to anti-PD-L1 immunotherapy.
To evaluate the value of the risk score in predicting patients’ responses to drugs, the IC50 values of 138 drugs were calculated (see Figure 10A). We found that the high-risk score patients had a greater sensitivity to midostaurin (see Figure 10B), Nutlin.3a (see Figure 10C), PD.173074 (see Figure 10D), and NVP.BEZ235 (see Figure 10E). Thus, the risk score appeared to be a predictive biological marker for medications against OC.
We discovered 499 DEGs between the OC and neighboring normal tissues in our investigation. Further, we performed a WGCNA, and 9 hub genes were chosen by multivariate and univariate Cox regression analyses and LASSO. A prognostic risk model, which was considered to be an independent indicator, was also strongly linked to the infiltration of immune cells. Thus, a prognostic risk model comprising PPM1K, PPP1CA, EXT1, RABGAP1L, MAD2L1, XPC, EGLN3, CCNDBP1, and ZNF25 may be used as an innovative biological marker with prognostic and predictive significance for OC. To identify the OC-related hub genes, we aggregated the expression patterns from 4 GEO data sets. S100A2 was the most highly expressed gene in the OC tissues, which is consistent with a similar finding that S100A2 overexpression increases glucose metabolism and proliferation in colorectal cancer (18). Additionally, OGN, which was the most lowly expressed gene in OC, has been shown to be a tumor inhibitor in bladder cancer (19). We discovered that DEGs are linked to cell proliferation after examining their expression levels in the GO and KEGG pathways. The WGCNA was used to identify the co-expression modules linked to the clinical characteristics. Based on the univariate, multivariate Cox regression, and LASSO analyses, 9 key genes (i.e., PPM1K, PPP1CA, EXT1, RABGAP1L, MAD2L1, XPC, EGLN3, CCNDBP1, and ZNF25) were found to be correlated with the prognosis of OC. PPM1K has not been closely studied in OC. Research has shown that a PPM1K deficiency results in a significant reduction in MEIS1/p21 signaling, which reduces hematopoietic stem cells’ glycolysis and quiescence, and the deletion of PPM1K greatly prolongs survival in a mouse leukemia model (20). Congruent with our findings, research has shown that PPP1CA is expressed at a higher level in esophageal squamous cell carcinoma samples than adjoining normal samples (21). Previous research has shown that EXT1 is downregulated in acute lymphoblastic leukemia (22), but the levels of EXT1 did not differ in our study between the OC and normal tissues. In breast cancer cells and mouse fibroblasts, RABGAP1L has been shown to be an important aspect for conformational specific integrin trafficking and to delineate the function of Rabgap1 in β1-integrin-induced cell migration (23). The overexpression of MAD2L1 has been discovered in diverse malignancies, such as lung (24), breast (25), and gastric cancers (26). XPC has been identified as an essential protein that recognizes DNA damage and performs an integral function in the repair of nucleotide excision and the modulation of cell growth and viability in non-small cell lung cancer (27). EGLN3 regulates tumor cell apoptosis and proliferation in glioma (28). When tested on dedifferentiated liposarcoma cells, CCNDBP1 was shown to greatly reduce cancer cell clone creation, and the proliferative, migratory, and invasive capacities of cells (29). ZNF25 performs a critical function in the differentiation of human bone marrow stromal or mesenchymal stem cells (hMSCs) to osteoblasts (30), but it is not well studied in cancers. Thus, the 9 hub genes identified perform important roles in the modulation of biological activities in cells. Tumor microenvironments and immune cell infiltration are becoming more popular topics of research (31-34). The 9 genes identified in the present research are linked to neutrophils, B cells, CD8+ T cells, macrophages, and dendritic cells, and these genes may have promising applications in immunotherapies. Notably, in lung cancer, TIICs are thought to be major drivers of both patients’ prognoses and their responsiveness to immunotherapeutic treatments. However, the possible mechanisms of the biomarkers and immune cells remain to be explored. The KEGG analysis indicated that the immune-related pathways were enriched. Notably, antigen processing and presentation and the intestinal immune network for IgA production were enriched in the low-risk score group. We also explored the predictive value of the risk score in relation to patients’ responses to immunotherapy. The PD-L1 blockade proved to have more therapeutic advantages and produce more immune responses in patients with high-risk scores. Further, the combination of a high-risk score and a high neoantigen burden served as a significant predictor of survival. Notably, higher-risk scores were closely associated with an inflamed immune phenotype, which provided evidence that high-risk scores were beneficial for immunotherapy. A combination of the results from the immunotherapy cohorts strongly supported the supposition that the risk score is a predictor of the immunotherapeutic response in OC patients. Additionally, potential chemotherapeutic drugs were predicted based on the risk score, indicating that the risk score is a meaningful tool for evaluating the drug sensitivity of OC patients.
In summary, we established a Riskscore system to identify the OC patients who are eligible for immunotherapy and predict their sensitivity to chemotherapeutic drugs. However, it should be noted that further investigations on the role and mechanism of this 9-gene signature in the progression of OC need to be conducted.
The article’s supplementary files as 10.21037/atm-22-3752 10.21037/atm-22-3752 10.21037/atm-22-3752 | true | true | true |
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PMC9622866 | Fatemeh Nourmohammadi,Mohammad Mahdi Forghanifard,Mohammad Reza Abbaszadegan,Vajiheh Zarrinpour | EZH2 regulates oncomiR-200c and EMT markers in esophageal squamous cell carcinomas | 31-10-2022 | Cancer,Cell biology,Genetics,Molecular biology | EZH2, as a histone methyltransferase, has been associated with cancer development and metastasis possibly through the regulation of microRNAs and cellular pathways such as EMT. In this study, the effect of EZH2 expression on miR-200c and important genes of the EMT pathway was investigated in esophageal squamous cell carcinoma (ESCC). Comparative qRT-PCR was used to examine EZH2 expression in ESCC lines (YM-1 and KYSE‐30) following the separately transfected silencing and ectopic expressional EZH2 vectors in ESCC. Subsequently, expression of miR-200c and EMT markers was also assessed using qRT-PCR, western blotting and immunocytochemistry. Underexpression of Mir200c was detected in YM-1 and KYSE-30 cells after EZH2 silencing, while its overexpression was observed after EZH2 induced expression. Following EZH2 silencing, downregulation of mesenchymal markers and upregulation of epithelial markers were detected in the ESCCs. Our results demonstrate that EZH2 regulates the expression of miR-200c and critical EMT genes, implying that overexpression of Zeb2, Fibronectin, N-cadherin, and Vimentin lead to a mesenchymal phenotype and morphology while underexpression of epithelial genes, enhance cell migration after enforced expression of EZH2 in ESCCs. EZH2 gene can be a beneficial treatment marker for patients with esophageal cancer through decrease invasiveness of the disease and efficient response to neoadjuvant therapy. | EZH2 regulates oncomiR-200c and EMT markers in esophageal squamous cell carcinomas
EZH2, as a histone methyltransferase, has been associated with cancer development and metastasis possibly through the regulation of microRNAs and cellular pathways such as EMT. In this study, the effect of EZH2 expression on miR-200c and important genes of the EMT pathway was investigated in esophageal squamous cell carcinoma (ESCC). Comparative qRT-PCR was used to examine EZH2 expression in ESCC lines (YM-1 and KYSE‐30) following the separately transfected silencing and ectopic expressional EZH2 vectors in ESCC. Subsequently, expression of miR-200c and EMT markers was also assessed using qRT-PCR, western blotting and immunocytochemistry. Underexpression of Mir200c was detected in YM-1 and KYSE-30 cells after EZH2 silencing, while its overexpression was observed after EZH2 induced expression. Following EZH2 silencing, downregulation of mesenchymal markers and upregulation of epithelial markers were detected in the ESCCs. Our results demonstrate that EZH2 regulates the expression of miR-200c and critical EMT genes, implying that overexpression of Zeb2, Fibronectin, N-cadherin, and Vimentin lead to a mesenchymal phenotype and morphology while underexpression of epithelial genes, enhance cell migration after enforced expression of EZH2 in ESCCs. EZH2 gene can be a beneficial treatment marker for patients with esophageal cancer through decrease invasiveness of the disease and efficient response to neoadjuvant therapy.
Esophageal cancer (EC) is the seventh most common cancer and the sixth leading cause of cancer-related mortality worldwide with five-year survival rates of less than 20%. EC presents two major histological types including esophageal adenocarcinoma and esophageal squamous cell carcinoma (ESCC). ESCC is considered as the main histological type (90% of patients) with poor prognosis in the East and the Middle East of Asia, Africa, South America, and Southern Europe. Although extensive studies have been performed on the ESCC tumorigenesis, the cellular and molecular mechanisms of ESCC progression and development are inadequately identified. Metastasis, tumor relapses and drug resistance are three major difficulties in the treatment of the ESCC malignancy which are associated with high morbidity and mortality of the disease. Presented treatments for ESCC are including esophagectomy, local mucosal resection or ablation therapies, chemotherapy, and radiation therapy which are approved by the National Comprehensive Cancer Network (NCCN). For many clinical consequences, there are no sufficient conclusive results to assist EC treatment. Esophagectomy, as one of the most complicated cancer surgeries, has a 5% in-hospital mortality rate and a recovery period of nearly a year. Due to the poor prognoses, esophageal cancer almost found at an advanced stage when traditional treatment modalities cannot accomplish the therapy. Furthermore, a large proportion of treated patients (60–70%) do not respond well to neoadjuant therapies and experience serious side effects (vomiting, fluid and electrolyte imbalance, stomatitis/mucositis, renal, hearing, and peripheral neuropathy. As a consequence of ESCC micro-metastasis at the time of clinical examination and diagnosis, most patients with resectable esophageal cancer have a minimum prospect of cure, And ESCC tumor cells had already migrated to distant organs or tissues after surgery. Determining the most effective approach for ESCC patients continues to be extensively investigated as prognoses are frequently rather dismal with many different treatment strategies. There are different protein-coding genes and key regulators of cancer gene networks which may be involved in ESCC carcinogenesis. Though numerous oncoproteins have been involved in development of ESCC, an increasing extent of research indicates that other types of biological molecules, specially non-coding RNA (ncRNA), are correspondingly essential. Hence, it is critical to identify novel non-invasive biomarkers that can improve diagnosis, prognosis, diagnosis, and treatment of ESCC. microRNAs (miRNAs) are small (20–24 nt) non-coding RNAs that are contributed in post-transcriptional regulation of gene expression in multicellular organisms by affecting both the permanency and translation of mRNAs. According to their target genes and the tissues in which they are expressed, miRNAs are classified into different groups such as oncomiRs and tumor suppressors. Specific miRNAs have been found in various cancers having a strong impact on the development of human tumors and the prognosis of patients and abnormal miRNA expression has been identified in a variety of cancers, implying miRNAs as a possible therapeutic target for cancer care. miRNA expression monitoring is important as either a valuable diagnostic or prognostic tool. Moreover, it has been demonstrated that miRNAs are present in human plasma in an extraordinarily stable shape that is protected from endogenous RNase activity. MicroRNA (miR)-200 family members are consisting of miR-200a, miR-200b, miR200c, miR-141, and miR-429, which controlled invasion and metastasis in different advanced tumors. miR-200c has key functions in cancer proliferation, metastasis, cell cycle regulation, invasion, and apoptosis in many cancer cells. In addition, miR-200c is a well-known prognostic and diagnostic marker in a variety of cancer types. The oncogenic microRNA, miR-200c has been discovered recently in ESCC. Since ESCC is known as an aggressive disease, not only most patients are diagnosed at late stages of carcinogenesis, but also tumor migration to distant organs and tissues is detected after surgery. Therefore, investigation of probable components involved in this process may help to a better inhibition of ESCC invasiveness and aggressiveness. EMT is considered as one of the main promoters of invasion and metastasis in cancer. Through EMT, epithelial cancer cells acquire migration and invasive mesenchymal cell-like features, allowing them to emigrate from the initial tumor mass and spread to distant locations. The Polycomb proteins (PcG) are important components of histone modifier. They are known as transcriptional suppressors primarily by causing chromatin remodeling and controlling the expression of a variety of developmentally regulated genes. The PcG forms at least two main repressive complexes: Polycomb repressive complex 1 (PRC1) and Polycomb repressive complex 2 (PRC2). Enhancer of zeste homolog 2 (EZH2) is a component of the polycomb repressive complex 2 (PRC2), which catalyzes the trimethylation of lysine 27 in histone 3 (H3K27me3) it mediates chromatin compaction that induces transcriptional repression of target genes. EZH2 controls genes involved in EMT and invasion targeting PRC2 potentially affects tumor metastasis and angiogenesis. EZH2, can regulate key genes involved in the recurrence and progression of cancer. EZH2 promotes and regulates EMT, as well as migration and invasion in several cancers. In a variety of malignant tumor models, EZH2 mediates H3K27me3 and plays a key role in driving tumor growth and metastasis. Furthermore, gain or loss of function mutations in EZH2 have been found in different cancers. Since epigenetics regulatory pathways and polycomb proteins such as EZH2 are associated with EMT and tumorigenesis of different malignancies, we aimed in this study to explore the functional correlation between EZH2 and Mir200c in ESCCs as well as evaluate the impact of EZH2 on EMT genes (E-cadherin, Occludin, Vimentin, Fibronectin, N-cadherin, Zeb2) main components to reveal a mechanistic route for EZH2 in this process.
ESCC lines YM-1 and KYSE30 were cultured in RPMI-1640 and Dulbecco’s modified Eagle’s medium/-F12 media medium (PAA, Pasching, Austria, and Gibco, Grand Island, NY), respectively, supplemented with 100 U/ml streptomycin/penicillin (Gibco) and 10% heat-inactivated fetal bovine serum (FBS, Invitrogen, Carlsbad, CA) at 37 °C in a humidified atmosphere containing 5% CO2. KYSE-30 cell line was purchased from the Pasteur Institute Cell Bank of Iran (http://ncbi.Pasteur.ac.ir/) and YM1 cells were purchased from Department of Molecular Medicine, Faculty of Advanced Medical Technologies, Golestan University of Medical Sciences, Gorgan, Iran.
EZH2-specific short hairpin RNA (shRNA) (RNAi-Ready pSIREN-RetroQ Vector, kindly provided by Dr. Moein Farshchian, (Molecular Medicine Research Department, ACECR-Khorasan Razavi Branch, Mashhad, Iran) was used to downregulate EZH2 in YM-1 and KYSE-30 cells. In addition, the Human EZH2 ORF mammalian expression plasmid, C-HA tag (Sino Biological Inc. Catalog Number: HG11337-CY) was used to upregulate EZH2 in the cells. Green fluorescent DNA plasmid (GFP) expression vector pCDH-513b (System Bioscience, Palo Alto, CA) was also used as a control plasmid for transfection efficiency. Cells were transfected using X-treme Gene HP DNA transfection reagent (Roche, Basel, Switzerland). Briefly, approximately 600,000 cells were seeded per six-well plate 18 h before transfection. The growth medium was exchanged with serum/antibiotic-free RPMI-1640 and DMEM medium 3 h before transfection. The cells were transfected with a total of 1 μg DNA per well. 6 h post-transfection the cells were supplemented with FBS, and incubated for 48 h. After separately transfection of EZH2 silencing and inducing vectors, transfection efficiency was checked in YM-1 and KYSE-30 GFP‐control cells (Fig. 1). Consequently; cells were treated with trypsin and EDTA (brand trypsin and EDTA CegrogenN0100-751) 48 h after transfection and subjected to RNA extraction.
The total RNA, including mRNA and miRNA, was isolated from 1 × 106 of both EZH2-silenced and EZH2-overexpressed KYSE-30 and YM1 as well as the control cells using the total RNA extraction kit (Pars Tous, Iran), according to the manufacturer’s instruction. Purity and amount of total RNA were measured on a Nano Drop spectrophotometer (WPA, Biowave II, and Germany). In addition, RNA integrity was assessed by 1% agarose gel electrophoresis, and 28S and 18S ribosomal RNA bands were observed accordingly. Afterward, the DNA decontamination steps were performed using Thermo Fisher Scientific kit (USA) according to the manufacturer’s protocol. Thermo fisher kit (Catalog number: K1621) and micro Script microRNA cDNA synthesis kit (Norgen Cat. 54410) were used for cDNA synthesis based on the manufacturer’s instruction. The expression of EZH2, mir200-c, E-cadherin, Occludin, Vimentin, Fibronectin, N-cadherin and Zeb2, was evaluated in EZH2-silenced and EZH2-overexpressed cells compared to control using relative comparative real-time PCR analysis. Comparative real-time PCR (SYBR® Premix Ex Taq II Kit, TaKaRa) was performed in duplicate reactions (Light Cycler, Roche, Germany) with specific primers (Table 1) to determine EZH2, miR-200c, E-cadherin and Zeb2 mRNA expression levels. Data were normalized by glyceraldehyde-3-phosphate dehydrogenase (GAPDH) for EZH2, EMT genes and Zeb2, while U6snRNA was used as normalizer for miR-200c. The thermal profile for EZH2 and EMT genes was consisted of an initial denaturation step at 95 °C for 2 min, 45 cycles (95 °C for 30 s and 60 °C for 30 s) for EZH2 and (95 °C for 30 s and 61 °C for 30 s) for E-cadherin, Occludin, Vimentin, Fibronectin, N-cadherin and Zeb2, a final extension step of 72 °C for 30 s. The used thermal profile for miR-200c was a first denaturation step of 95 °C for 3 min, after that 40 cycles of (94 °C for 30 s and 60–63 °C for 30 s) and an ending extension step of 72 °C for 45 s. The 2 − ΔΔCT method was used to analyze the expression level of the genes. (Table.1).Tumors with more and less than 2 and − 2 log2 fold change was considered as overexpressed and under expressed, respectively. The log2 fold change between 2 and − 2 was defined as normal or unchanged expression.
Cell migration was examined using wound healing assay. For this reason, after transfection, cells were plated in 6-well and allowed to forming a monolayer, and then the cell surface was, wounded by a p-200 pipette tip. The wound closure was monitored at baseline, 12 and 24 h later, using an inverted microscope (Nikon, Tokyo, Japan). Image J 7.0 was used to analyze the minimum distance of wound borders, and all tests were executed in triplicate.
YM-1 and KYSE-30 cell lines were transfected as described above and collected in Immunocytochemistry fixing solution (FS) (4% paraformaldehyde and 0.05% v/v F68). Cells smear was prepared by celltrazone huro (path filter-Korea). Concisely, following peroxidase blocking specific monoclonal antibody incubation was started for 30 min at 25 centigrade degree. The used antibodies include Cadherin-E master diagnostics (Mouse anti-human Cadherin E Monoclonal Antibody/Clone HECD-1) and. Anti-beta Actin (antibody (ab8227) Abcam). After rinsing for removing excess antibody, secondary antibodies (Sigma, RE7111, US) was applied. Immunocytochemical identification was performed by the Novolink™ DAB (Polymer) (Product No: RE7230-K, RE7270-K) chromogen according to the manufactures’ protocol. Lastly, hematoxylin was applied as a counterstained. All photos were captured via a digital camera (Olympus image analysis system, Japan). Two expert pathologists who were blinded to the primary research, independently scored the immunocytochemical staining of, E-cadherin, according to the semi-quantitative immunoreactivity score. The intensity of immunostaining was scored as 0–3 (0, negative; 1, weak; 2, moderate; and 3, strong), and the percentage of immunoreactive cells was analyzed utilizing Image J software.
Thermo Fisher’s Pierce RIPA Buffer was used to lyse the cells, along with a phosphatase and protease inhibitors cocktail (Roche-04906845001, Sigma-Aldrich). The cell lysates were firstly separated on SDS-PAGE gels, transferred to polyvinylidene fluoride (PVDF) membranes from Millipore, and then blocked with 5% milk for 1 h at room temperature. After washing, PVDF membranes were treated with the primary antibody (Cadherin E master diagnostics (Mouse anti-human Cadherin E Monoclonal Antibody/Clone HECD-1) and Anti-beta Actin antibody (ab8227) Abcam) for one hour at room temperature and then incubated with the secondary antibody overnight at 4 °C. Luminescence substrate was used for visualizing band and G-Box gel documentation system (Syngene, Cambridge, UK) detected and captured images. Densitometry quantification was carried out via ImageJ software.
GeneMANIA web base database was used to predict potential targets of EZH2. For the has-miR-200c board, MiRWalk3.0 (http://mirwalk.umm.uni-heidelberg.de/) was used to predict has-miR-200c target genes. The miRWalk platform is based on projected mRNA targets and combines predictions from several prediction tools, including miRDB, TransmiR, target scan Human, and miRTarbase. Also, RPISeq website was used to predict interaction between miR-200c and EZH2.
The statistical analyses were performed using the Graf Pad PRISM 8 statistics software (Graph Pad Software, San Diego, CA, US). P value < 0.05 was regarded as statistically significant. Sample t-test and Pearson association tests were applied to determine the correlation between the genes.
The study was approved by the Damghan branch, Islamic Azad University ethical guidelines.
In order to determine EZH2 interaction with miR-200c and EMT genes we recruited bioinformatics tools. Based on GeneMANIA web-based bioinformatics resource prediction, EMT genes interact with EZH2. GeneMANIA predicted a direct association between EZH2 and E-cadherin, and Zeb2 (Fig. 2a). For investigating EZH2 and has-miR-200c correlation TransmiR was utilized. The results predicted that has-miR-200c interacts with 136 transcription regulators, including EZH2 and Zeb2. According to TransmiR has-mir-200c interacts with EZH2 with 3.3835 Fold, 0.2959 P-value, and 0.4968 FDR (Fig. 2b). Web-based bioinformatics target scan Human showed that the E-cadherin, Fibronectin, and Zeb2 genes may be targets for has-miR-200c 3p and 5p. Furthermore, interaction between EZH2 and hsa-miR-200c was predicted by using miRWALK and RPISeq websites. We observed a significant score prediction in miRWALK (0.84) that demonstrated has-miR-200c3P may bind to the coding sequence (CDS) of EZH2 mRNA, consists of the score 0.92 binding sites 18,691,886 nt positions in the CDS of EZH2. In 2D form with Energy − 20.9 G and Bind Length of 17 nucleotide to sha-mir-200c. Having used PDB and Frona (http://rna.tbi.univie.ac.at/forna) tools we found that EZH2 has an affinity to bind TAATA 5ʹ end of has-miR-200c in A form. (Fig. 2c,d) Another 2D form revealed that mir-200c3P may bind to the CDS site of the EZH2 gene with Energy − 18.9 G and Binding Length of 28 nucleotides including 9 to 15 nucleotide B form of has-miR-200c CCGGGTA (Fig. 2e,f). We also found has-miR-200c can bind to EZH2 gene according to RF classifier with 0.6 scores and SVM classifier with 0.91 score in RPISeq website. (http://pridb.gdcb.iastate.edu/RPISeq/results.php), it predicts interaction probabilities ranging from 0 to 1. Predictions with probability > 0.5 were considered “positive” in performance evaluation experiments, indicating that the related has-miR-200c and EZH2 are likely to interact. In cross-validation evaluation studies on data sets, classifier accuracies range from 87 to 90%.
Following silencing of EZH2 mRNA in YM-1 and KYSE-30 cells, quantitative real-time PCR (qRT-PCR) was used and we found that miR-200C level was repressed in both EZH2 silenced ESCC lines (Fig. 3). The results showed that has miR-200c may be regulated by EZH2 in the cells. Real-time PCR verification was performed to verify the miR-200C level after EZH2 enforced expression in the ESCC cell lines. We found that in EZH2 induced cells, has-miR-200c was also overexpressed compared to controls cells (Fig. 4). These findings clearly revealed that EZH2 has a significant impact on miR-200c expression in ESCCs.
Tumor metastasis and cell migration modulated by the epithelial mesenchymal transition (EMT) pathway are important in the development of various cancers. Since many aggressive tumors were reported to overexpress EZH2, it was suggested that this protein act as an important regulator of the development of EMT. Consistent with the previous prediction with web-based bioinformatics, we examined the mRNA level of the EMT genes in EZH2-treated YM-1 and KYSE-30 cells. While EZH2 silencing caused a significant decreased expression of Vimentin, Fibronectin, N-cadherin, and Zeb2, it caused a meaningful increase in expression of E-cadherin and Occludin (Fig. 3, Table 2). In EZH2-overexpressed cells we identified downregulation of E-cadherin and Occludin expression and upregulation of Vimentin, Fibronectin, N-cadherin and Zeb2 in YM-1 and KYSE-30 cells (Fig. 4, Table 2).
To substantiate the results of q-RT-PCR and protein expression assays, in vitro migration was assessed through wound healing assay in esophageal cancer cells. After scratching, the area of gap was measured through 24–72 h. The results revealed that EZH2-overexpressed YM-1 cells close the scratched gap approximately three times faster than control cells. Besides, wound healing assay experiments demonstrated a significant decrease in migration of both EZH2-silenced YM-1 and KYSE-30 cells in comparison with the controls, which demonstrated the impact of EZH2 on migration process in ESCCs (Fig. 5). We alternatively examined E-cadherin and Zeb2 in protein level confirming the q-RT-PCR results. As expected, immunocytochemistry staining and western blotting outcome indicated E-cadherin repression in EZH2-overexpressed cell lines while its enhanced expression in EZH2-silenced cells (Fig. 6). The epithelial-to-mesenchymal transition is regarded as one of the crucial constituents of metastatic progression in vitro. The epithelial phenotype is distinguished by a rounded morphology and a spindle-shaped morphology modified to facilitate invasion and migration. EZH2 can control the plasticity of the transition between epithelial and mesenchymal escapes, which is increasingly recognized as a dynamic process in the development of cancer. EMT induction through EZH2 overexpression in ESCC was demonstrated by changes in cellular morphology from epithelial to mesenchymal which led to metastasis and invasion in EC (Fig. 7), a reduction in E-cadherin protein levels likewise Occludin at mRNA level, and an increase in Vimentin, Fibronectin, N-cadherin and Zeb2 expression levels (Fig. 4). The morphological modifications started to be noticeable between 12 and 24 h after EZH2 induction and progressed during EZH2 transfection up to 48 h (Fig. 7).
E-cadherin is a key homophilic cell–cell adhesion protein that prevents the migration of individual cells on a matrix. In metastatic cells E-cadherin expression is down-regulated. To further demonstrate that EZH2 mediates migration of ESCC, we performed western blot and immunocytochemistry to assess protein level of E-cadherin and Zeb-2. According to western blotting, E-cadherin was notably upregulated in the EZH2-silenced cells, while it was downregulated in EZH2-overexpressed cells (Fig. 4E). Furthermore, immunocytochemistry staining revealed E-cadherin overexpression and Zeb2 underexpression in EZH2-silenced cells. These findings suggest that silencing EZH2 suppressed EMT and metastatic progression in ESCC by regulating E-cadherin and other EMT key genes.
Esophageal cancer is one of the most prevalent malignancis in both gender men and women. The most prevalent histological type is ESCC, identified as tumor present with extensive metastases. The tumor initiation, development, metastasis, and tumor recurrence in esophageal cancer, significantly influenced through EMT pathway. In addition, MiRNAs have been considered to regulate cell proliferation, apoptosis, epithelial-mesenchymal transition (EMT), metastasis, chemotherapy and radiotherapy through playing an oncogenic role. Considering metastasis as the main cause of more than 90% of cancer-related mortalities, elucidating the molecular processes by which microRNAs promote metastasis and identifying new therapeutic targets is critical miR-200c plays the role of an oncomir in ESCC, and many malignant cancers (thyroid carcinoma, testicular germ cell tumors, rectum adenocarcinoma, pancreatic adenocarcinoma, kidney renal papillary cell carcinoma, and cervical squamous cell carcinoma). Moreover, miR-200c regulated EZH2 through BMI-1 and E2F in breast, lung, prostate, bladder cancers and hepatocarcinoma. MiR-200c was regulated by EZH2 through epigenetic repression in prostate cancer, and miR-200c directly controlled the expression of E2F3 by targeting 3′UTR. E2F3 positively conducted the transcriptional level of EZH2 (Fig. 8). In gastric cancer SNHG22 LncRNA recruits EZH2 and sponge mir-200c-3p to suppress tumor suppressor genes and promote the cancer progression. In lung cancer cells, miR-200c overexpression significantly prevented cell migration and invasion by increasing the level of E-cadherin and decreasing the expression of EZH2. MiR-200c is overexpressed in esophageal cancer and promotes chemotherapy and radiotherapy resistance in tumor cells. ESCC patients with high-serum level miR-200c may have a higher risk of death than someone with low expression. Hence, serum miR-200c levels can be used to predict chemotherapy responses and prognosis in patients with esophageal cancer. Previous studies reported the correlation between EZH2 and miR-200 family in a few cancer types including hepatocellular, prostate, and OSCC malignancies (Fig. 8). Although, the effect of the miR-200 expression on EZH2 has been examined, no study investigated inversely describing that how EZH2 can regulate miR-200 family. In this study, we have evaluated the effect of the EZH2 gene on miR-200c and EMT genes in esophageal squamous cell carcinoma. EZH2 can directly regulate EMT by inhibiting E-cadherin and Occludin expression. EZH2 is one of the most important members of polycomb family which is upregulated in ESCC and promotes tumor development. EZH2 is found in a variety of cancers, including breast, prostate, bladder, colon, lung, and pancreatic cancers, as well as anaplastic thyroid, nasopharyngeal, endometrial carcinomas, and lymphomas. Overexpression of EZH2 is strongly associated with advanced stages of cancer progression, and tumor metastasis by regulating EMT pathway in a variety of cancers. For instance, EZH2 knockdown upregulated the expression of E-cadherin in ovarian cancer cells. Likewise, in lung cancer cells with enhancing EZH2 protein expression, EMT was regulated through upregulation of Vimentin and reducing of E-cadherin. EZH2 can directly associate with EMT in downregulating E-cadherin expression by histone H3K27 trimethylation and indirectly through miR-361 suppressing endometriosis. Moreover, EZH2 enhances expression of mesenchymal markers (Vimentin, N-cadherin, and Fibronectin) leading the promotion of EMT pathway in endometriosis. In gastric cancer cells EZH2 promotes EMT, upregulates Vimentin and downregulates E-cadherin. By direct binding to the PTEN locus, EZH2 reduces PTEN expression subsequently triggers the Akt pathway, and ultimately leads to the acquisition of metastasis and pluripotent phenotype. Different EZH2-mediated gene promoter methylation have been demonstrated to increase the malignant phenotypes of cancer cells, which is related to the loss of tumor suppressor gene activities and results in stem cells or progenitor cells developing to cancer cells. Our results demonstrated a close correlation between EZH2 expression and epithelial-mesenchymal markers expression, supporting a critical aspect of EZH2 in cellular EMT process. Following EZH2 downregulation in ESCCs, the level of miR-200c and mesenchymal genes dropped significantly and epithelial markers (E-cadherin, and Occludin) expression was promoted, while after ectopic expression of EZH2 in esophageal squamous carcinoma cell lines, expression of miR-200c and mesenchymal genes (Vimentin, Fibronectin, N-cadherin and Zeb2) were increased and epithelial markers expression were suppressed compared to control cells. EZH2 is recognized to play an important role in these two crucial aspects of cancer biology. Downregulating EZH2 may prevent the progression of ESCC. To the best of our knowledge, the current study was the first to evaluate the effect of EZH2 gene expression on miR-200 in ESCC and show a connection between EZH2/miR-200c and prognosis in ESCC. The molecular mechanism between miR-200c and EZH2 is complicated and further research is necessary to clarify the role of the EZH2/miR-200c and probable involved proteins in ESCC carcinogenesis. The findings in this study may help therapeutic approaches in two ways. First, reducing the expression of the EZH2 gene, which reduces cancer cell progression, invasion and metastasis trough EMT pathway, and second, by reducing miR-200c expression, which increases patients’ survival and response to chemo and radiotherapy. In conclusion, EZH2 can be an effective therapeutic marker for esophageal cancer patients. Our results demonstrate that by repressing the expression of EZH2 in esophageal cancer cells, the EMT pathway and as a result, cell migration and metastasis are reduced, which can increase the hopefulness of esophageal cancer patients for treatment without recurrence and increase the survival rate in patients. This study provided an understanding of the critical activities of the EZH2, which may be affected by miR-200, in ESCCs, although its regulating cellular mechanisms are still ambiguous and required further investigation.
Supplementary Information. | true | true | true |
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PMC9622883 | Shantanu Gupta,Pritam Kumar Panda,Wei Luo,Ronaldo F. Hashimoto,Rajeev Ahuja | Network analysis reveals that the tumor suppressor lncRNA GAS5 acts as a double-edged sword in response to DNA damage in gastric cancer | 31-10-2022 | Bioinformatics,Biological models,Computer modelling,Dynamic networks,Regulatory networks,Computational biology and bioinformatics,Systems biology | The lncRNA GAS5 acts as a tumor suppressor and is downregulated in gastric cancer (GC). In contrast, E2F1, an important transcription factor and tumor promoter, directly inhibits miR-34c expression in GC cell lines. Furthermore, in the corresponding GC cell lines, lncRNA GAS5 directly targets E2F1. However, lncRNA GAS5 and miR-34c remain to be studied in conjunction with GC. Here, we present a dynamic Boolean network to classify gene regulation between these two non-coding RNAs (ncRNAs) in GC. This is the first study to show that lncRNA GAS5 can positively regulate miR-34c in GC through a previously unknown molecular pathway coupling lncRNA/miRNA. We compared our network to several in-vivo/in-vitro experiments and obtained an excellent agreement. We revealed that lncRNA GAS5 regulates miR-34c by targeting E2F1. Additionally, we found that lncRNA GAS5, independently of p53, inhibits GC proliferation through the ATM/p38 MAPK signaling pathway. Accordingly, our results support that E2F1 is an engaging target of drug development in tumor growth and aggressive proliferation of GC, and favorable results can be achieved through tumor suppressor lncRNA GAS5/miR-34c axis in GC. Thus, our findings unlock a new avenue for GC treatment in response to DNA damage by these ncRNAs. | Network analysis reveals that the tumor suppressor lncRNA GAS5 acts as a double-edged sword in response to DNA damage in gastric cancer
The lncRNA GAS5 acts as a tumor suppressor and is downregulated in gastric cancer (GC). In contrast, E2F1, an important transcription factor and tumor promoter, directly inhibits miR-34c expression in GC cell lines. Furthermore, in the corresponding GC cell lines, lncRNA GAS5 directly targets E2F1. However, lncRNA GAS5 and miR-34c remain to be studied in conjunction with GC. Here, we present a dynamic Boolean network to classify gene regulation between these two non-coding RNAs (ncRNAs) in GC. This is the first study to show that lncRNA GAS5 can positively regulate miR-34c in GC through a previously unknown molecular pathway coupling lncRNA/miRNA. We compared our network to several in-vivo/in-vitro experiments and obtained an excellent agreement. We revealed that lncRNA GAS5 regulates miR-34c by targeting E2F1. Additionally, we found that lncRNA GAS5, independently of p53, inhibits GC proliferation through the ATM/p38 MAPK signaling pathway. Accordingly, our results support that E2F1 is an engaging target of drug development in tumor growth and aggressive proliferation of GC, and favorable results can be achieved through tumor suppressor lncRNA GAS5/miR-34c axis in GC. Thus, our findings unlock a new avenue for GC treatment in response to DNA damage by these ncRNAs.
Gastric cancer (GC) is responsible for the second-highest number of cancer-related deaths worldwide, making it a major medical issue and an attractive target for drug development. Dysregulation of dominant signaling pathways through alterations in key oncogenes and tumor suppressors is one of the hallmarks of GC, which initiates aberrant cell proliferation and survival. In this context, Zheng et al. provide evidence that E2F transcription factor 1 (E2F1) remains highly expressed in GC and is accountable for GC tumorigenesis in BGC-823, MGC-803, and SGC-7901 cell lines. Furthermore, Zheng et al. found that microRNA-34c-5p (hereinafter referred to as miR-34c) is downregulated in the same GC cell lines. Interestingly, Zheng et al. determined that E2F1 directly inhibits miR-34c expression at the transcriptional level. Furthermore, Zheng et al. confirmed that knockdown (KO) of E2F1 triggers miR-34c expression, whereas overexpression (E1) of E2F1 shows the opposite result. In conclusion, Zheng et al. showed that upregulated miR-34c could suppress GC proliferation through induction of cell cycle arrest and apoptosis at the G1/S checkpoint following E2F1 knockdown. The miR-34 family (miR-34a, miR-34b, and miR-34c) is a master regulator of tumor suppression and plays an essential role in the DNA damage response. MiR-34c is downregulated in several types of cancer, including GC. The transactivation of miR-34c by p53 is well known, although new evidence has shown that miR-34c activation occurs through the ATM or ATM/p38 MAPK pathway, which is independent of de-novo p53-mediated transcription. Furthermore, the study of Suzuki et al. provides evidence that miR-34b and miR-34c are found to be epigenetically silenced in GC cell lines. Moreover, Suzuki et al. further reported that there is no correlation found between miR-34b/c and p53 functionality in GC. Interestingly, it is well-recognized that cyclin-dependent kinase inhibitor 1 (p21) is transactivated through p53. Nevertheless, in GC cell lines, p21 was found to be governed by ATM/p38 pathway. In more detail, Liu et al. found that activation of p21 was strongly detected in GC cell lines with Wildtype (WT) p53 (MKN-45) and cells lacking WT p53 (MKN-28). Moreover, Liu et al. further explained that p21 was found to act as a switch between cell cycle arrest and apoptosis due to its ability to inhibit caspase 3 activity in GC cell lines. Furthermore, upregulation of p21 was determined through the p38 MAPK signaling pathway rather than p53. On the other hand, Subhash et al. provide evidence that p53 is not essential for cell cycle arrest and apoptosis in the G1/S checkpoint in GC. In more detail, Subhash et al. observed that ATM is required to induce cell fate decisions such as cell cycle arrest and apoptosis in GC cell lines (IM95, IST1, NUGC4, TMK1, YCC3, MKN45, and MKN1) at the G1/S checkpoint. Moreover, Subhash et al. found that overexpression of ATM in response to DNA damage induces robust induction of p21 and PUMA in CG cell lines, independently of p53. Interestingly, miR-34c regulates p21 expression independent of p53 in various cancer cell lines. It is well known that protein-coding genes account for above than 1% of the total genome, whereas large portions of the human genome can be transcribed into non-coding RNAs (ncRNAs). Indeed, miRNAs may be sufficient to inhibit proliferation in GC cells in response to DNA damage. miRNAs are a class of small ncRNAs often associated with several biological processes such as cancer progression. Recent studies identified a novel class of small ncRNAs, i.e., long noncoding RNAs (lncRNAs), which may regulate cell proliferation in GC through downstream DNA damage mechanisms. lncRNAs have recently emerged as important players in cancer biology and can be used for cancer diagnosis, prognosis, and potential therapeutic targets. In this context, a tumor suppressor lncRNA-GAS5 (growth arrest-specific transcript) was found to be dysregulated in various cancers, including GC. Furthermore, in response to DNA damage, overexpression of the lncRNA-GAS5 can suppress cancer progression by induction of cell-cycle arrest or cellular apoptosis in many types of cancer, including GC. Interestingly, recently, Sun et al. exposed the role of lncRNA-GAS5 in (SGC7901, BGC823, MGC803, MKN45, MKN28) GC cell lines. In detail, Sun et al. found that lncRNA-GAS5 expression was markedly downregulated in GC tissues and correlated with larger tumor size. Moreover, these authors further demonstrated through the Vivo/Vitro experiments that gain-of-function (GoF) of lncRNA-GAS5 inhibits gastric cancer cell proliferation by inducing cell fates at the G1/S checkpoint, while downregulation of lncRNA-GAS5 could promote cell proliferation. Furthermore, Sun et al. confirmed that E2F1 and G1/S-specific cyclin-D1 (Cyclin D1) were functional targets of lncRNA-GAS5 in GC cells. However, the advanced aspect of the lncRNA-GAS5 in the coordination of miR-34c expression in response to DNA damage at the G1/S checkpoint remains unexplained in GC cells. Boolean models of regulatory networks were meant to simplify the dynamics of complex biological systems. This method provides a qualitative description that captures advanced features of network dynamics. For example, enclosed pathways connecting two or more nodes in a network (similar to feedback loops in the continuous model) may serve as regulatory circuits controlling the dynamics of the network. Classification of molecular regulatory networks in a rational framework by a computational-experimental combinatorial process is recognized as a valuable approach for cell fate choice and the study of various biological processes. For more details about Boolean modeling, see the Methods section. Inspired by the facts mentioned above, in this work, we proposed a mathematical model to uncover the unique function of the lncRNA-GAS5 in the gene regulation of GC at the G1/S checkpoint (see Fig. 1). Indeed, to our knowledge, this is the first fundamental study that uncovers a positive concrete nexus between lncRNA versus miRNAs, i.e., lncRNA-GAS5 controls miR-34c expression in GC.
There are 26 nodes in our Boolean network. The blue rectangular node represents lncRNA-GAS5, the yellow node signifies miR-34c, and the red one defines DNA damage which is the input of the Boolean network. Dashed hammer-head arches are highlighted by orange-colored oval nodes delineating the targets of miR-34c, while other proteins are in grey-colored nodes. The three outputs (in white nodes) of the network are proliferation, cell cycle arrest, and apoptosis, respectively. There are a total of 73 direct interactions within these nodes. See Fig. 1. Our network simulations show 3 fixed points for the wild-type case dynamics (WT) that are associated with multiple phenotypes, Fig. 2. The first fixed point indicates a proliferative state (corresponding to input: DNA damage = OFF), i.e., no cycle arrest is identified, only cell cycle enhancers are active: cdk4/6-CycD, cdk2/CycE, Cdc25A, HDAC1, Myc and E2F1. Additional two fixed points (produced by the same input: DNA damage = ON), i.e., cell cycle arrest and apoptosis, respectively. Interestingly, all these three fixed points are consistent with in-vivo/in-vitro experiments in GC.
To uncover the advanced aspect of lncRNA-GAS5 in the regulation of miR-34c expression through the knockdown of E2F1, which is the main focus here. We interrogated lncRNA-GAS5/E2F1 and miR-34c, i.e., we performed GoF/LoF perturbation. The results are presented in Fig. 2. We found that overexpression (E1) of lncRNA GAS5 diminished E2F1 expression and increased the expressions of miR-34c, RB1, and p21. In comparison, knockdown (KO) of lncRNA GAS5 gives the opposite effect. On the other hand, overexpression of E2F1 declined miR-34c, RB1, and p21 expressions. At the same time, the knockdown of E2F1 presents opposite outcomes. In the last, overexpression (E1) of miR-34c inhibits proliferation through the induction of cell cycle arrest and apoptosis. Whereas knockdown (KO) of miR-34c enhanced apoptosis. As can be seen in Fig. 2, overexpression (E1) or knockdown (KO) of miR-34c did not affect the lncRNA-GAS5 expression. Interestingly, all these results are in excellent agreement with in-vivo/in-vitro experiments in GC. Our results suggest that lncRNA-GAS5 is an important regulator of miR-34c activity in GC. Additionally, these results further pinpointed that lncRNA-GAS5 can also regulate RB1 and p21 expressions in GC.
HDAC1 is a well-known tumor promoter in GC cells. Growing evidence suggests that targeting HDAC1 can upregulate p21 by miR-34c. Therefore, we conducted the node perturbations to examine the similarity between phenotypes (model attractors) and experimental observations. Results are presented in Fig. 2. We found that the direct suppression of HDAC1 due to overexpression of miR-34c induces p21 expression, while the gain-of-function (GoF) of HDAC1 downregulates p21 (see Fig. 2). Additionally, Liu et al. provide evidence that p21 acts as a switch between cell cycle arrest and apoptosis, and the functionality of this switch does not depend on p53 status, implying that p21 can regulate cell cycle arrest and apoptosis in p53-deficient GC cell lines. To test it, we carried out another perturbation of loss-of-function (LoF) of p53. Results are presented in Fig. 2. These results show that miR-34c indirectly regulates p21 in GC. Thus, miR-34c functions to regulate cycle arrest and apoptosis through the HDAC1/p21 pathway that does not depend on the p53 functionality. Our results are in the finest agreement with these in-vivo/in-vitro experiments.
Indeed, Liu et al. confirmed that G1/S arrest and apoptosis are independent of p53 but dependent on the p38 MAPK pathway in GC cell lines. Furthermore, Subhash et al. provide further evidence that ATM mediates p53-independent regulation of apoptosis and cell-cycle arrest at the G1/S checkpoint in GC cells. Interestingly, various studies have recently shown that lncRNA-GAS5 is associated with the ATM/p38 MAPK pathway. To address whether this ATM/p38 MAPK signaling pathway, independent of p53 and can regulate cell cycle arrest and apoptosis at the G1/S checkpoint. We performed node perturbation to examine the agreement between the phenotypes (fixed points of the model) and in-vivo/in-vitro experiments. The results are presented in Fig. 3. As expected, we found that loss-of-function (LoF) of p38 MAPK or ATM, i.e., knockdown of p38 MAPK or ATM, induces GC proliferation. Whereas gain-of-function (GoF) of p38 MAPK and ATM triggers cell cycle arrest and apoptosis in GC cell lines. We conducted two separate perturbations of each node. In more detail, first, we performed the single node perturbation, i.e., we overexpressed (E1) of p38 MAPK, and second, double node perturbation, which means we performed loss-of-function (LoF) of p53 together with gain-of-function (GoF) of p38 MAPK. We repeated the same technique with ATMs. First, we overexpressed (E1) ATM alone, and next, we overexpressed (E1) ATM along with knockdown (KO) of p53. In this way, we can determine that ATM/p38 MAPK may regulate cell fate decisions such as cell cycle arrest and apoptotic cell death beyond the functionality of p53 in GC cell lines. Interestingly (see Fig. 3). We observed that p38 MAPK and ATM knockdown (KO) cells, lncRNA-GAS5, were stimulated in the presence of DNA damage; however, lncRNA-GAS5 failed to regulate cycle arrest and/or apoptosis. These results provide two important conclusions. First, the ATM/p38 signaling pathway, rather than p53, is required for modulating tumor growth and proliferation in GCs. Second, the lncRNA-GAS5 regulates cell fates in an ATM/p38 signaling pathway-dependent manner. Our results are in excellent agreement with the in-vivo/in-vitro experiments.
The concept of experimental design is driven by Guo et al.. Who found that lncRNA-GAS5 blocks the G1/S checkpoint by modulating CDK6. Indeed, forced expression of lncRNA-GAS5 enhanced p21 activation, which directly inhibits CDK4/6-CyclinD and CDK2-CyclinE at the G1/S checkpoint. However, so far, there is no study/evidence yet that can uncover the functional concrete relationship between lncRNA-GAS5 and miR-34c. Therefore, we propose a new clinical framework defined by the network that a lncRNA-GAS5 functioning as an inhibitor of E2F1 may inhibit cancer progression through the establishment of miR-34c and can induce cell cycle arrest and apoptosis at the G1/S checkpoint. A conceivable outcome of this experiment would be the suppression of tumor growth and proliferation in GC. To test this development, we insist on these potential perturbations scenarios predicted by the network. lncRNA-GAS5 (E1) → E2F1 (KO) → miR-34c ↑ lncRNA-GAS5 (E1) → E2F1 (KO) → RB1 ↑ lncRNA-GAS5 (E1) → E2F1 (KO) → p21 ↑
It is widely known that E2F1 is involved in both cancer cell growth and apoptosis. Petrocca et al. went into further depth, showing that transfection of E2F1 overexpression and suppressing expression in GC cells might promote cell proliferation and prevent apoptosis. Similar to this, Guo et al. recently discovered that miR-537/E2F1 has a positive (double negative) feedback loop which inhibits GC development and proliferation by activating cell cycle arrest and apoptosis. Furthermore, Zhang and colleagues discovered that overexpression of E2F1 and p53 cause cancer cells to undergo apoptotic cell death in response to DNA damage. We have also revealed the dual functionality of E2F1 in both proliferation and apoptosis in glioblastoma multiform (GBM). High expression of E2F1 has been linked to poor outcomes in GC. As no genetic aberrations have been found so far that could describe the upregulation of E2F1 in GC. The lncRNA-GAS5 is a key regulator of E2F1 expression and is downregulated in GC. Similarly, miR-34c is downregulated in GC. Interestingly, miR-34c was inhibited by E2F1 in GC. It was unknown whether the lncRNA-GAS5/E2F1 axis plays an important role in the regulation of miR-34c in GC. Here, we constructed a Boolean network to identify lncRNA-GAS5 interactions in GC (see Fig. 1). The model suggested the lncRNA-GAS5 as a key regulator of miR-34c through its ability to repress E2F1 expression. Consistent with this concept, high expression of the lncRNA-GAS5/miR-34c in GC was correlated with suppression of GC progression and proliferation through induction of cell cycle arrest and/or apoptotic in GC. Our results suggest the mechanism by which increased expression of lncRNA-GAS5 may enhance miR-34c expression in GC. lncRNA-GAS5 targets E2F1 expression, which is the main inhibitor of miR-34c activity in GC. Knockdown of E2F1 was followed by increased expression of the tumor suppressor miR-34c, i.e., inhibition of tumor growth and proliferation at the G1/S checkpoint. The following assumption is confirmed by the observation of lncRNA-GAS5 expression at three fixed points (attractors). The results are shown in Fig. 2. In the first fixed point, lncRNA-GAS5/miR-34c was downregulated, whereas E2F1, CDK4/6-CyclinD, CDK2-CyclinE, Cdc25A were highly expressed. In response to DNA damage, two fixed points were found to be associated with lncRNA-GAS5, conferring constitutive activation of miR-34c in both fixed points. On the other hand, E2F1 was downregulated in both fixed points, which was expected. Indeed, our Boolean network captured a specific molecular mechanism related to the lncRNA-GAS5 that had never been reported before. Nevertheless, all these marked results are established on the discrete core of the model elements. Therefore, the prediction of time-dependent features as the evolution of precise expression levels over time is one of the limitations of our model. Plenty of studies are available that highlight the essential role of lncRNAs sponging miRNAs in controlling mRNAs. For example, numerous studies found that the lncRNA-GAS5 sponged miR-21, miR-222, and miR-106a-5p in several cancers, including GC. On the other hand, the lncRNA-GAS5 is also known to directly (positively or negatively) regulate mRNAs expressions such as E2F1, p53, and mTOR. However, there is no evidence yet that lncRNAs can positively regulate miRNAs expression by sponging/decoys of other molecules (e.g., other lncRNAs, miRNAs, mRNA). Our results highlighted a very convincing relationship between the lncRNA-GAS5 and miR-34c via E2F1. Unraveling this novel molecular mechanism associating lncRNA-GAS5/miR-34c for the first time confirms that lncRNAs can positively modify miRNAs and that collectively they can effectively inhibit the tumor growth and proliferation in GC. Additionally, our results revealed that the ATM/p38 MAPK pathways are essential for regulating cell cycle arrest and apoptosis at the G1/S checkpoint in GC cell lines. It is well known that p53 is the main regulator of cell fate in response to DNA damage and plays an important role in controlling tumor growth. However, in GC cell lines, p53 mutations have been found in up to 70%, and there is much evidence confirming that the ATM/p38 MAPK pathway compensates for p53 deficiency in GC cells. So, we tested it, and the results are shown in Fig. 3. We found that cell fate decisions such as cell cycle arrest and/or apoptosis are independent of p53 status in GC cells as suggested by these in-vivo/in-vitro studies. Although WT p53 is defined in some GC cell lines, it will induce cell cycle arrest and/or apoptosis, as both ATM/p38 MAPK are upstream regulators of p53. Whereas p53-deficient GC cell lines, ATM/ p38MAPK pathway eliminates the tumor growth and proliferation by the activation of cell cycle arrest and apoptosis in GC cell lines. Thus, lncRNA-GAS5 acts as a double-edged sword in GC progression through the activation of miR-34c expression as well as p21 and RB1. For more detail, see Fig. 4. Consequently, Our results provide a broad and relevant background in this new direction. miRNAs are already well-known players in cancer research. The molecular mechanisms associated with lncRNAs are not yet fully understood, and there is still much to be revealed. The results presented in this work will provide a new direction to many researchers with diverse expertise in fighting cancer (development of new technologies and new experiments) based on accumulated knowledge. In summary, we developed a Boolean model of lncRNA-GAS5 at the G1/S checkpoint based on the literature and public databases in GC. Our Boolean model simulations uncover very complex scenarios between lncRNA-mRNA-miRNA, i.e., lncRNA-GAS5, E2F1, and miR-34c in response to DNA damage. Our results suggested that lncRNA-GAS5 is an important regulator of miR-34c activity in GC. Thus, our approach may contribute to proposing alternative therapeutic strategies for cancer through drugs targeting this lncRNA-mRNA-miRNA axis that might significantly reduce the tumor growth and proliferation in GC cancer cells.
Building a gene regulatory network of lncRNA-GAS5 activity GC cells. We have only used PubMed studies and databases such as BioGrid. The focus was to identify genes or proteins that were targeted by the lncRNA-GAS5 (Fig. 1), such as E2F1 and cyclin D1. Furthermore, we identify genes or proteins that are directly targeted through the miR-34c. For that, we have used public databases like; miRTargetLink and TargetScan, respectively. GINsim 3.0.0b was used for the construction and simulation of the Boolean network and visualization of the results, which is a Java-based software and is freely available to researchers (http://www.ginsim.org/downloads). GINsim algorithms recognize all the attractors for the wild-type case as well as for various mutant situations. The model file is available in the “Code availability” section.
The Boolean method is based on the characterization of a regulatory graph, where an individual node defines a molecule, and each directed edge (or arc) signifies an activation or inhibition among two nodes. Nodes are Boolean variables that only consider 0/OFF and 1/ON values. Based on the description of the biochemical information, each node in the network is assigned a logical rule, which determines its activation level concerning the position of its regulators. Our Boolean network model of lncRNA-GAS5 was produced by translating the biological interactions of the DNA damage-induced G1/S checkpoint, which is an ATM/p53-dependent, described in the gene regulatory network (Fig. 1) into Boolean rules. For more details, see Supplementary Table S1 (PubMed links included). Classical Boolean operators were used to write these rules “AND,” “OR,” and “NOT”. Attractors are the main outcome of simulations using a Boolean network. The dynamical performance of a Boolean model can be interpreted by a state transition graph (STG). In this graph, each node describes the state of the network variables, and the arcs signify transitions between these states. The STG serves all possible trajectories that one initial state can drive to a final state. Terminal nodes that have no outgoing edges are called stable states (or fixed points), while a set of transitions trapped among a fixed group of states in the STG defines a cyclic state. For the updates of states, synchronous updates were considered, which has the potential to describe deterministic behavior observed in molecular networks. In addition, in silico Gain-of-Function (GoF) or Loss-of-function (LoF) perturbations, we force node values to be ON or OFF, respectively, to examine the effect of particular nodes on network dynamics and the resulting phenotype.
Simulations using the lncRNA-GAS5 Boolean network should describe the biology of a cancer cell (with or without DNA damage in GC). Indeed, cancer cells can be simplified as either remains a proliferative state due to downregulation of lncRNA-GAS5/miR-34c i.e., upregulation of E2F1 (in the absence of DNA damage). Whereas, in the case when DNA damage is present in the cell, upregulation of lncRNA-GAS5/miR-34c is required that can repress the proliferation by the induction of cell fate decisions such as cell cycle arrest and/or apoptosis at the G1/S checkpoint. Consequently, at least three fixed points/stable states (attractors) are expected.
It is well-known that cell fate decisions such as cell cycle arrest or apoptosis are induced by DNA damage response at both G1/S and G2/M checkpoints. Interestingly, lncRNA-GAS5 can be induced by DNA damage as suggested by Liu et al. and Zhou et al.. In addition, in the event of the G1/S checkpoint, about 20 molecules are involved in its main regulatory network. DNA double-strand breaks (DSBs) can be affected by the radiomimetic chemicals or reactive oxygen species (ROS) or radiation, which triggers autophosphorylation of ATM at serine 1981 by initiating its kinase activity. Downstream phosphorylation at the ATM pathway leads to the activation of p53 in response to DNA damage. In GC cells, lncRNA-GAS5 is required for the induction of the G1/S checkpoint, which is our focus here; in fact, GC cells knocked down for lncRNA-GAS5 cannot arrest at the G1/S phase. In DNA damage response, In this model, based on its different phosphorylation events, p53 is denoted by different nodes. The p53 node is linked with its interaction with Mdm2, which is required to initiate p53-A and p53-K. p53-A represents p53 phosphorylated at Ser-15 and Ser-20 that activated p21, while p53-K represents p53 further phosphorylation at Ser-46, which leads to the activation of the apoptotic pathway via BAX. P53-A and p53-K are attached by a positive circuit, and the conversion between p53-A and p53-K is ruled by the protein phosphatase 1D (Wip1) and the tumor protein p53 inducible nuclear protein 1 (p53-INP1). In the network, apoptosis is triggered by caspase, which is regulated by Bcl2, Bax, and p21. In more detail, activation of Bax and inactivation of Bcl2 and p21 are associated with caspase activation, which causes apoptotic cell death. In addition, p53 is a well-known activator of the miR-34 family (miR-34a, miR-34b, and miR-34c) in the DNA damage response. However, the expression of miR-34c modulates by ATM and p38 MAPK pathway, which is independent of de-novo p53. Therefore, in our network, miR-34c is directly activated by the ATM/p38 MAPK pathway or indirectly by the lncRNA GAS5/E2F1 pathway. miR-34c directly targets E3 ubiquitin-protein ligase Mdm2 (Mdm2), Myc proto-oncogene protein (Myc), Cyclin-dependent kinase 4/6 (cdk4/6-Cyclin-D), and the Cyclin-dependent kinase 2-G1/S-specific cyclin-E1 (cdk2/Cyclin-E) complex, Cell division cycle 25A (Cdc25A), NAD-dependent protein deacetylase sirtuin-1 (Sirt1), Histone deacetylase 1 (HDAC1), and Apoptosis regulator Bcl-2 (Bcl2), for more details about miR-34c targets see review by Xiong et al.. Worth noting is that in the lncRNA-GAS5 Boolean network, we selected only experimentally verified targets of miR-34c. Forced expression of miR-34c induces cycle arrest by targeting cdk4/6-Cyclin-D, cdk2/Cyclin-E complex, HDAC1, Myc, and Cdc25A, whereas it triggers apoptotic phenotype by knockdown Bcl2.
Supplementary Table S1. | true | true | true |
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PMC9622897 | Wei Huang,Yu-Meng Sun,Qi Pan,Ke Fang,Xiao-Tong Chen,Zhan-Cheng Zeng,Tian-Qi Chen,Shun-Xin Zhu,Li-Bin Huang,Xue-Qun Luo,Wen-Tao Wang,Yue-Qin Chen | The snoRNA-like lncRNA LNC-SNO49AB drives leukemia by activating the RNA-editing enzyme ADAR1 | 01-11-2022 | Non-coding RNAs,Leukaemia | Long noncoding RNAs (lncRNAs) are usually 5′ capped and 3′ polyadenylated, similar to most typical mRNAs. However, recent studies revealed a type of snoRNA-related lncRNA with unique structures, leading to questions on how they are processed and how they work. Here, we identify a novel snoRNA-related lncRNA named LNC-SNO49AB containing two C/D box snoRNA sequences, SNORD49A and SNORD49B; and show that LNC-SNO49AB represents an unreported type of lncRNA with a 5′-end m7G and a 3′-end snoRNA structure. LNC-SNO49AB was found highly expressed in leukemia patient samples, and silencing LNC-SNO49AB dramatically suppressed leukemia progression in vitro and in vivo. Subcellular location indicated that the LNC-SNO49AB is mainly located in nucleolus and interacted with the nucleolar protein fibrillarin. However, we found that LNC-SNO49AB does not play a role in 2′-O-methylation regulation, a classical function of snoRNA; instead, its snoRNA structure affected the lncRNA stability. We further demonstrated that LNC-SNO49AB could directly bind to the adenosine deaminase acting on RNA 1(ADAR1) and promoted its homodimerization followed by a high RNA A-to-I editing activity. Transcriptome profiling shows that LNC-SNO49AB and ADAR1 knockdown respectively share very similar patterns of RNA modification change in downstream signaling pathways, especially in cell cycle pathways. These findings suggest a previously unknown class of snoRNA-related lncRNAs, which function via a manner in nucleolus independently on snoRNA-guide rRNA modification. This is the first report that a lncRNA regulates genome-wide RNA A-to-I editing by enhancing ADAR1 dimerization to facilitate hematopoietic malignancy, suggesting that LNC-SNO49AB may be a novel target in therapy directed to leukemia. | The snoRNA-like lncRNA LNC-SNO49AB drives leukemia by activating the RNA-editing enzyme ADAR1
Long noncoding RNAs (lncRNAs) are usually 5′ capped and 3′ polyadenylated, similar to most typical mRNAs. However, recent studies revealed a type of snoRNA-related lncRNA with unique structures, leading to questions on how they are processed and how they work. Here, we identify a novel snoRNA-related lncRNA named LNC-SNO49AB containing two C/D box snoRNA sequences, SNORD49A and SNORD49B; and show that LNC-SNO49AB represents an unreported type of lncRNA with a 5′-end m7G and a 3′-end snoRNA structure. LNC-SNO49AB was found highly expressed in leukemia patient samples, and silencing LNC-SNO49AB dramatically suppressed leukemia progression in vitro and in vivo. Subcellular location indicated that the LNC-SNO49AB is mainly located in nucleolus and interacted with the nucleolar protein fibrillarin. However, we found that LNC-SNO49AB does not play a role in 2′-O-methylation regulation, a classical function of snoRNA; instead, its snoRNA structure affected the lncRNA stability. We further demonstrated that LNC-SNO49AB could directly bind to the adenosine deaminase acting on RNA 1(ADAR1) and promoted its homodimerization followed by a high RNA A-to-I editing activity. Transcriptome profiling shows that LNC-SNO49AB and ADAR1 knockdown respectively share very similar patterns of RNA modification change in downstream signaling pathways, especially in cell cycle pathways. These findings suggest a previously unknown class of snoRNA-related lncRNAs, which function via a manner in nucleolus independently on snoRNA-guide rRNA modification. This is the first report that a lncRNA regulates genome-wide RNA A-to-I editing by enhancing ADAR1 dimerization to facilitate hematopoietic malignancy, suggesting that LNC-SNO49AB may be a novel target in therapy directed to leukemia.
Long noncoding RNAs (lncRNAs) constitute a class of noncoding transcripts longer than 200 nucleotides with no apparent protein-coding potential. They are emerging as potential key regulators in numerous biological processes across every branch of life, exhibiting a surprising range of shapes and sizes. Remarkably, recent studies have reported that the unusual biogenic processes of small nucleolar RNAs (snoRNAs) generate a group of lncRNAs, named snoRNA-related lncRNAs, adding a novel layer to lncRNA biogenesis. SnoRNAs are evolutionarily conserved ncRNAs which are 60–300 nt in length and function mainly as guide RNAs during site-specific rRNA modification. They are mostly processed from introns of protein-coding or noncoding genes called “host genes”. Two types of snoRNA-related lncRNAs, sno-lncRNAs with a snoRNA at both ends and SPAs (5′ snoRNA capped and 3′ polyadenylated lncRNAs), have been identified. The high abundance of these lncRNAs indicates that they are unlikely to be precursors of snoRNAs, suggesting previously unrecognized regulatory potential of snoRNA sequences. To date, a set of putative snoRNA-related lncRNAs have been identified in human cells, with only a small fraction characterized. For example, sno-lncRNA1-5 and SPAs, encoded in the deletion chromosome region of Prader-Willi syndrome (PWS), a neurodevelopmental genetic disorder, sequester multiple RNA-binding proteins away from their normal functional sites to affect mRNA metabolism and contribute to PWS pathogenesis. A sno-lncRNA named SLERT was reported to control rRNA synthesis by directly binding to the DEAD-box RNA helicase DDX21 via a 143-nt non-snoRNA sequence to alter the conformation of DDX21 in cancer. SnoRD86-cSPA is speculated to operate as a decoy for snoRNP core proteins, sequestering them away from the nuclear. These studies suggested the potential functional diversity of these ncRNAs. Given their important roles in cellular processes, additional snoRNA-related lncRNAs will be of great interest to be characterized and investigated for their roles in the disease context. Leukemia is the most prevalent and aggressive blood cancer and can be subdivided into different subtypes according to cell maturity (acute or chronic) and cell type (lymphocytic or myeloid). With standard chemotherapies, the event-free survival of acute lymphocytic leukemia (ALL) is ~60%–80%, and only 35%–40% of younger (aged < 60 years) and 5%–15% of older (aged > 60 years) patients with acute myeloid leukemia (AML) survive more than 5 years. Thus, there is an ever-present need to better understand the genetic and molecular mechanisms of leukemia biogenesis and progression. Recent advances in understanding the noncoding RNA (ncRNA) transcriptome have highlighted the importance of various ncRNA species in leukemia. Interestingly, functional validation studies have indicated that snoRNAs are required for leukemogenesis, which has challenged the view that snoRNAs merely function as housekeeping genes for the posttranscriptional modification of rRNAs. Among these snoRNAs, two snoRNAs, SNORD49A and SNORD49B, attracted our attention. Although they are classical C/D box snoRNAs, both function independent of rRNA 2′-O-methylation, suggesting alternative mechanisms by which they play oncogenic roles in leukemia. Interestingly, they were predicted to generate a snoRNA-related lncRNA. Thus, we questioned whether SNORD49A/B can also function as snoRNA-related lncRNAs and contribute to hematopoietic malignancy. In this study, we identified a novel and highly expressed snoRNA-related lncRNA, named as LNC-SNO49AB, in leukemia patient samples. Sequence and structure analyses showed that LNC-SNO49AB contains two C/D box snoRNA sequences, SNORD49A and SNORD49B, and a 5′-end m7G and a 3′-end SNORD49A, which is a new type of snoRNA-related lncRNA. We further showed that LNC-SNO49AB directly binds to adenosine deaminase acting on RNA 1 (ADAR1) and promotes its dimerization and function to elevate the global rate of RNA A-to-I editing, thereby regulating hematopoietic malignancy. Our study reveals a pivotal role for a snoRNA-related lncRNA with a previously unknown structure, LNC-SNO49AB, in ADAR-mediated RNA editing and leukemia progression.
To determine whether SNORD49A/B can function as a snoRNA-related lncRNA, we first validated the existence of these lncRNAs by northern blotting and rapid amplification of cDNA ends (RACE). As shown in Fig. 1a, SNORD49A and SNORD49B are both located in the second intron of the noncoding gene SNHG29 on p11.2 of chromosome 17. Northern blot analysis with a probe for detecting the sequence between SNORD49A and SNORD49B confirmed the existence of only one mature transcript in four leukemic cell lines (Fig. 1b). We next performed 5′ and 3′ RACE and then sequenced to conform the length of the transcript. Sequence analysis revealed that the transcript contains the first exon of SNHG29 and the 5′ end of second exon to the 3′ end of SNORD49A of SNHG29 and is 804 nt (Fig. 1c and Supplementary Fig. S1a, b). We also overexpressed LNC-SNO49AB into 293T cells to further support the length of mature LNC-SNO49AB (Supplementary Fig. S1c, d). These results suggest that this gene locus forms a snoRNA-related lncRNA that has SNORD49A/B sequences, thus, we named this lncRNA LNC-SNO49AB (Fig. 1a). We then characterized the structure of LNC-SNO49AB. LNC-SNO49AB lacks the poly(A) tail (Fig. 1d) and is terminated by SNORD49A at the 3′-end (Fig. 1a). Unexpectedly, we found that SNORD49B is in the middle of LNC-SNO49AB (Fig. 1a), not at the 5′-end as those previous reports, showing that LNC-SNO49AB has a unique structure. We further showed that LNC-SNO49AB can be precipitated by anti-m7G antibody (Fig. 1e), indicating that it is capped by m7G, similar to most mRNAs and lncRNAs, not m3G or any other modification of classical eukaryotic snoRNA. We name the lncRNAs “lnc-snoRNAs”, with a 5′-end m7G and a 3′-end snoRNA structure (Fig. 1f). Next, we characterized the features of LNC-SNO49AB. The relative expression level of LNC-SNO49AB is lower than SNHG29 but higher than the precursor pre-SNHG29 (Supplementary Fig. S1e). The addition of siRNAs that targeted the non-snoRNA regions of LNC-SNO49AB did not alter the expression of SNORD49A or SNORD49B (Fig. 1g), suggesting that LNC-SNO49AB is a stable lncRNA, not a snoRNA precursor. As LNC-SNO49AB was terminated by a snoRNA at the 3′ end, we sought to determine whether the 3′-end snoRNA can provide lncRNAs with stability comparable to that of an RNA with a poly(A) tail. We examined the RNA stability with Actinomycin D treatment and results showed that the half-life of LNC-SNO49AB is similar to that of GAPDH mRNA and lncRNAs, which have poly(A) tails (Supplementary Fig. S1f). In addition, the DNA sequence of LNC-SNO49AB is relatively conserved among primates but not in other mammals; SNORD49A and SNORD49B are also conserved in mice (Supplementary Fig. S1g). Finally, bioinformatics and polysome profiling analysis suggested that LNC-SNO49AB has no coding potential (Fig. 1h, i and Supplementary Fig. S1h). In brief, LNC-SNO49AB represents an unreported type of lncRNA with a 5′-end m7G and a 3′-end snoRNA structure.
The unusual structure of LNC-SNO49AB leads to the question whether snoRNA is involved in the processing of LNC-SNO49AB. SNORD49A and SNORD49B belong to C/D box snoRNAs, which contain two boxes near their termini (box C and box D) and two boxes away from their termini (boxes D’ and C’) (Fig. 2a). Typically, the conserved motifs of C/D box snoRNAs directly bind core proteins (snoRNPs), including fibrillarin (FBL), NOP58, NOP56, and 15.5 K (Fig. 2a), which are essential for snoRNA processing and function. Therefore, we asked if these core proteins interact with LNC-SNO49AB. RNA immunoprecipitation (RIP) assays showed that LNC-SNO49AB could be enriched by FBL, which is similar to SNORD49A and SNORD49B, while lncRNA SNHG29 transcribed by its host gene showed a lack of enrichment of FBL binding (Fig. 2b). Consistently, a tRSA-based RNA pull-down confirmed that LNC-SNO49AB could bind to the four core proteins that bind C/D box snoRNAs (Fig. 2c–e). In addition, when deleting both SNORD49A and SNORD49B domains of LNC-SNO49AB, the lnc-snoRNA could no longer interact with FBL (Fig. 2f), suggesting that these two snoRNA sequences determined the interaction between LNC-SNO49AB and the core C/D box snoRNA proteins. Importantly, suppression of FBL expression resulted in the dramatic downregulation of both snoRNAs and the lnc-snoRNA (Fig. 2g), indicating that the SNORD49A/B sequences could mediate snoRNP complex formation and is critical for the stability of LNC-SNO49AB. It was previously reported that snoRNAs at the ends of lncRNAs protect the intronic sequences from exonuclease trimming after splicing, leading to the formation of snoRNA-related lncRNAs. However, different from the previous findings, SNORD49A and B are at the 3′-end and in the middle of LNC-SO49AB respectively, raising the question of how these two snoRNAs participate in LNC-SNO49AB formation. We sequentially deleted the C, D, C’ and D’ boxes of SNORD49B and SNORD49A to see which one is important for the lnc-snoRNA. We found that LNC-SNO49AB formation was completely eliminated only when deleting the D box of SNORD49A, the rest boxes did not show significant effects (Fig. 2h). In particular, deletion of the conserved motifs of SNORD49B did not impair the generation of LNC-SNO49AB. These results indicate that only snoRNAs at the ends are crucial for the lnc-snoRNA formation. Therefore, we proposed a model in which LNC-SNO49AB biogenesis is associated with intronic cleavage, a splicing-independent mechanism. During transcription initiation, m7G is installed at the 5′ cap of LNC-SNO49AB cotranscriptionally. The sequence of unspliced intron 2 is retained in the pre-LNC-SNO49AB and is trimmed to the point where the exonuclease reaches the SNORD49A snoRNP, which prevents further degradation, thereby generating a snoRNA at the 3′-end (Fig. 2i). At the current stage of study, we do not know why lnc-snoRNA needs a snoRNA positioned in the middle.
We next investigated the expression pattern and function of LNC-SNO49AB. Both SNORD49A and SNORD49B have been shown abnormally expressed in leukemia, and thus we mainly investigated the potential role of LNC-SNO49AB in leukemia. In analysis of our in-house clinical samples with 15 normal control and 94 leukemia samples with 84 AML and 10 ALL (the detailed clinical parameters are presented in Supplementary Table S1), we found that LNC-SNO49AB displayed a significantly higher expression level both in the ALL and AML patient groups than that in the normal group (Fig. 3a and Supplementary Fig. S2a). A high copy number of LNC-SNO49AB was also detected in a panel of leukemia cell lines (Supplementary Fig. S2b, c). When classifying patient samples into different subtypes according to mutational background with different types of chromosomal rearrangements, we found that LNC-SNO49AB expression did not correlate with a specific mutational background, instead, it shows a widely high expression level in leukemia (Supplementary Fig. S2d). In a 19 paired preliminary diagnosis-complete remission (CR) samples, we showed that CR samples showed a lower expression level of LNC-SNO49AB (Fig. 3b). ROC analysis indicated that LNC-SNO49AB could efficiently predict CR (AUC = 0.8366, 95% CI: 0.7035–0.9696, P < 0.001), with 78.95% sensitivity and 84.21% specificity at the optimal likelihood ratio (Supplementary Fig. S2e). Moreover, high level of LNC-SNO49AB was correlated with high-risk, poor-prednisone response and unfavorable recurrence-free survival (RES) (Supplementary Fig. S2f–h). Together, these results indicate that LNC-SNO49AB was generally highly expressed in various types of leukemia and associated with leukemia progression and therapeutic outcomes, highlighting its potential oncogenic role. We next investigated the function of LNC-SNO49AB in leukemia. Introduction of siRNAs targeting LNC-SNO49AB in multiple leukemia cells downregulated LNC-SNO49AB expression (Supplementary Fig. S3a) and significantly reduced cell proliferation in the cell counting test (Fig. 3c and Supplementary Fig. S3b) and EdU incorporation assay (Supplementary Fig. S3c). We also found that silencing LNC-SNO49AB caused cell cycle arrest (Fig. 3d and Supplementary Fig. S3d) and induced the apoptosis of various leukemia cells (Supplementary Fig. S3e). Similar phenomena were observed when LNC-SNO49AB was knocked down with a mix of additional siRNAs and antisense oligonucleotides (smart silencer) targeting sequences different from that of by si-LNC-SNO49AB that we previously used (Fig. 3e, f and Supplementary Fig. S3f–h). In addition, clonogenicity was significantly impaired after LNC-SNO49AB expression was reduced (Fig. 3g and Supplementary Fig. S3i). Moreover, overexpressing LNC-SNO49AB in leukemia cell lines (Supplementary Fig. S4a showed the overexpression efficiency), including NB4, MV4-11 and RS4;11 cells, promoted cell proliferation (Fig. 3h and Supplementary Fig. S4b), released cell cycle arrest (Fig. 3i and Supplementary Fig. S4c), and inhibited apoptosis (Supplementary Fig. S4d). We further investigated the ability of LNC-SNO49AB to regulate leukemia progression in patient-derived leukemia samples and a mouse model. Primary leukemia cells were first isolated from the bone marrow of four patients with newly diagnosed acute leukemia (numbers H485, H521, H527 and H686) (Supplementary Fig. S4e). Silencing LNC-SNO49AB expression by siRNAs significantly impaired the DNA synthesis rate and increased the cell apoptosis rate of all the primary leukemia cells (Supplementary Fig. S4f–h), which is concordant with the phenotypes observed in cell lines. We also examined the effect of LNC-SNO49AB depletion on leukemic progression in vivo by injecting GFP+ RS4;11 cells transduced with sh-NC or sh-LNC-SNO49AB in the tail vein of mice (Supplementary Fig. S5a). Supplementary Fig. S5b, c showed knocked down efficiencies of LNC-SNO49AB. The success of mouse model establishment was confirmed by the appearance of RS4;11 cells in blood and bone marrow (BM) (Fig. 3j). Reducing LNC-SNO49AB caused a decrease in leukemia engraftment in the spleen, BM, and liver, as determined by haematoxylin & eosin (H&E) staining (Supplementary Fig. S5d) and the reduced spleen size and weight of xenograft tumors (Fig. 3k). Similarly, flow cytometry analysis revealed a sharp decrease in the infiltration of RS4;11 cells in blood, BM, spleen, and liver (Fig. 3l and Supplementary Fig. S5e). Furthermore, mice injected with sh-LNC-SNO49AB cells displayed better survival outcomes (Fig. 3m). In contrast, overexpression of LNC-SNO49AB by PCDH-LNC-SNO49AB significantly promoted hematopoietic malignancy in the recipient mice (Supplementary Fig. S5f–h). Taken together, both in vitro and in vivo experimental results have demonstrated that LNC-SNO49AB plays an important oncogenic role in leukemia progression.
Next, we explored the molecular mechanism underlying the oncogenic activity of LNC-SNO49AB in the disease. We first examined the subcellular localization of LNC-SNO49AB. RNA imaging by CRISPR-Cas13 system (Fig. 4a) and subcellular fraction analysis (Fig. 4b and Supplementary Fig. S6a) showed that LNC-SNO49AB was mainly located in the nucleus, especially in the nucleolus. NEAT1, enriched in paraspeckle, was used as a negative control for nucleolus-located lncRNAs (Supplementary Fig. S6b). Although the canonical role of C/D box snoRNA is mediating the 2′-O-methylation of rRNA in the nucleolus, we found no alteration of the methylation abundance at 28 S rRNA cytidine4426 (predicted to be guided by SNORD49A and SNORD49B) or global rRNA when silencing LNC-SNO49AB (Supplementary Fig. S6c–e). These results suggest that LNC-SNO49AB might function through a novel molecular mechanism in the nucleolus, independent of snoRNA-guide rRNA modification. We then performed tRSA RNA pull-down assays to identify the potential proteins associated with LNC-SNO49AB in leukemia. The tRSA-LNC-SNO49AB-specific bands at 100~130 kDa were repeatedly observed in two independent RNA pull-down experiments using leukemia cell lines (Fig. 4c and Supplementary Fig. S6f), then they were subjected to mass spectrometry (MS) (Supplementary Table S2). Among the proteins identified by MS, one specific protein, ADAR1, a known RNA-editing enzyme located in the nucleolus, attracted our attention (Supplementary Fig. S6g). The interaction between ADAR1 and LNC-SNO49AB was further validated by RNA pull-down followed by western blotting. As shown in Fig. 4d, endogenous ADAR1 interacted with tRSA-LNC-SNO49AB but not tRSA. A similar interaction was readily detected by anti-Flag antibodies in RIP assays of 293T cells transiently expressing the Flag-ADAR1 protein (Supplementary Fig. S6h). Moreover, in vitro transcribed LNC-SNO49AB could bind to purified Flag-ADAR1 protein in a cell-free system (Fig. 4e). Immunofluorescence assays also showed that ADAR1 locates in the nucleolus, and LNC-SNO49AB and ADAR1 predominantly colocalized in the nucleolus (Fig. 4f and Supplementary Fig. S6i). Together, these data indicate that LNC-SNO49AB binds directly to ADAR1. ADAR1, belonging to the ADAR family, is critical for the majority of A-to-I editing activity in mammals. ADAR1 contains three main domains, including a Z-DNA-binding domain in the N-terminus; three dsRNA-binding domains (dsRBDs), namely, RI, RII and RIII, in the middle; and a catalytic deaminase domain in the C-terminus. We next conducted an RNA pull-down assay using a series of Flag-tagged ADAR1 with deletions based on structural features to map its functional motif, which was associated with LNC-SNO49AB (Fig. 4g). The results showed that deletion of dsRBDs of ADAR1 strongly impaired the ADAR1 interaction with LNC-SNO49AB (Fig. 4h), while the remaining two domains exerted negligible effects. Accordingly, a RIP analysis revealed the high affinity of dsRBDs for LNC-SNO49AB (Fig. 4i), and a truncated ADAR1 fragment with three dsRBDs bound to LNC-SNO49AB efficiently (Fig. 4j, k), suggesting that dsRBDs are necessary and sufficient to mediate the interaction of ADAR1 and LNC-SNO49AB. Within the dsRBDs, deletion of RI significantly decreased the interaction between ADAR1 and LNC-SNO49AB, while RII and RIII showed less efficiency in binding LNC-SNO49AB (Fig. 4l, m), indicating that RI of the dsRBDs in ADAR1 is critical for LNC-SNO49AB binding. It is known that the dsRBDs of ADAR1 can make direct contact with dsRNAs to edit these dsRNAs. However, no A-to-I editing sites of LNC-SNO49AB were found in three major RNA-editing databases RADAR, http://rnaedit.com/; REDIportal, http://srv00.recas.ba.infn.it/atlas/; and DARNED, http://darned.ucc.ie/about/, indicating that LNC-SNO49AB was not the gene targeted for editing by ADAR1. In agreement with this result, deletion of RIII diminished ADAR1 binding of its known RNA-editing substrate but had less effect on its binding to LNC-SNO49AB, as shown in the RIP assay (Fig. 4l). In addition, LNC-SNO49AB did not regulate the protein levels of ADAR1 (Supplementary Fig. S6j), and silencing ADAR1 did not affect the LNC-SNO49AB expression (Supplementary Fig. S6k). Together, these results raise a question, what is the biological role of specific LNC-SNO49AB binding to dsRBDs in ADAR1?
The RNA-editing activity of ADAR1 depends on homodimerization (Fig. 5a). Indeed, we found that ectopically expressed Flag-ADAR1 coprecipitated with HA-tagged ADAR1 in 293T cells (Fig. 5b). Importantly, deleting the dsRBDs of Flag-ADAR1 severely impaired of the Flag-ADAR1 interaction with full-length HA-ADAR1 (Fig. 5c), suggesting a pivotal role of dsRBDs in dimerization. Thus, we hypothesized that LNC-SNO49AB may affect the homodimerization of ADAR1 by binding the dsRBDs. The premise of this hypothesis is that LNC-SNO49AB can form a complex with the ADAR1 homodimer. We first overexpressed FLAG-ADAR1, HA-ADAR1 and LNC-SNO49AB simultaneously in 293T cells, and then an HA-ADAR1 coimmunoprecipitation assay showed that both FLAG-ADAR1 and LNC-SNO49AB were significantly enriched (Fig. 5d). Moreover, when performing a two-step RIP assay, we found that the first-phase of the RIP using anti-FLAG antibodies captured high levels of HA-ADAR1 and LNC-SNO49AB in addition to FLAG-ADAR1, whereas antibodies against the HA epitope tag precipitated HA-ADAR1 together with FLAG-ADAR1 and LNC-SNO49AB (Fig. 5e), indicating that LNC-SNO49AB and dimerized ADAR1 formed a functional structure. To further explore the potential motifs of LNC-SNO49AB that function in ADAR1 homodimerization, we dissected the LNC-SNO49AB sequence (Fig. 5f). Strikingly, an RNA pull-down assay showed that every truncated LNC-SNO49AB fragment interacted with ADAR1 in MOLM13 leukemia cells (Fig. 5g), implying that LNC-SNO49AB contains several ADAR1 binding sites over its entire sequence. Next, we explored whether LNC-SNO49AB affected the dimerization of ADAR1. As a result, LNC-SNO49AB knockdown decreased the relative amount of HA-ADAR1 associated with FLAG-ADAR1 in 293T cells, whereas LNC-SNO49AB overexpression increased the interaction between Flag-ADAR1 and HA-ADAR1 in a cell-free system (Fig. 5h, i), indicating that LNC-SNO49AB stabilized ADAR1 dimerization. Since the RNA-editing activity of ADAR1 requires dimerization, we designed an editing reporter vector based on a dual luciferase system to investigate whether LNC-SNO49AB affects the activity of ADAR1 (Fig. 5j). A stop codon was embedded near the start codon of Renilla luciferase (hRluc) in a short stretch of dsRNA, resulting in a premature stop to translation. Importantly, this dsRNA structure was recognized as an editing substrate by ADAR1, and RNA A-to-I editing converted the stop codon (UAG) into UIG, allowing the translation of mature hRluc. Thus, the expression of hRluc was editing dependent. The reporter vector was transfected with either pcDNA3.1 or pcDNA3.1-LNC-SNO49AB into 293T cells, and the frequency of A-to-I RNA editing was measured by luciferase activity. We found that LNC-SNO49AB overexpression produced more hRluc than the control (Fig. 5k), suggesting a role of LNC-SNO49AB in regulating ADAR1 activity. Together, these data demonstrate that LNC-SNO49AB elevates ADAR1 activity by promoting its homodimerization.
Elevated activity of RNA A-to-I editing is observed in various cancer types and is associated with poor prognosis, and systematic exploration of cancer vulnerabilities informs the dependency of ADAR1 in a subset of cancer cells. Thus, we hypothesized that LNC-SNO49AB might elevate the RNA A-to-I editing rate by promoting ADAR1 homodimerization to exert its oncogenic effects. Unbiased transcriptome profiling was performed in RS4;11 cells transfected with si-NC or si-LNC-SNO49AB to investigate the change in RNA A-to-I editing rates (Fig. 6a). We observed a global decrease (P = 5.5e−13) in the RNA A-to-I editing rate when LNC-SNO49AB was silenced (Fig. 6b), confirming that LNC-SNO49AB regulates RNA-editing activity in leukemia. Candidates showing major variation in editing were experimentally verified (Supplementary Fig. S7a). We then analysed the potential signaling pathway of 499 genes with significant variation in RNA-editing rate and found that most of the genes were significantly enriched in the cell cycle and DNA repair process (Supplementary Fig. S7b, Table S3). Previous studies reported that RNA A-to-I editing has a profound impact on RNA translation, splicing and stability. It has also been shown that the A-to-I sites are largely distributed in the 3′-UTR of mature mRNA. Remarkably, editing the 3′-UTR can create or destroy microRNA recognition, which may be involved in either translation repression or mRNA degradation. Combined with the RNA expression level identified in the RNA-seq data, we found that oncogenes, BRI3BP, involved in apoptosis, was hypoedited in the 3′-UTR in the si-LNC-SNO49AB group and were significantly downregulated in both si-LNC-SNO49AB and si-ADAR1 groups (Supplementary Fig. S7c). To further investigate the potential downstream genes of LNC-SNO49AB and ADAR1 in leukemia, we compared the RNA-seq data between si-ADAR1 group and si-LNC-SNO49AB group. 162 of 384 dysregulated genes in the si-ADAR1 group were also found to be dysregulated in the si-LNC-SNO49AB group (|fold change (FC)| > 2, FDR < 0.05) (Fig. 6c and Supplementary Tables S4, S5), further confirming that LNC-SNO49AB can promote leukemic progression by regulating ADAR1 activity. Interestingly, many common downstream genes of LNC-SNO49AB and ADAR1 were significantly enriched in gene ontology (GO) terms, including the cell cycle, Hippo signaling, and lipoprotein metabolic processes (Fig. 6d). We also found that knocking down LNC-SNO49AB or ADAR1 significantly regulated the expression of these potential target genes, further suggesting the common roles of SNO49AB and ADAR1 in cell viability (Fig. 6e). To further investigate whether the function of LNC-SNO49AB in the disease is ADAR1 dependent, we next knocked down ADAR1 in leukemia cells. Specific siRNAs targeting ADAR1 reduced the levels of ADAR1 protein (Supplementary Fig. S7d) and resulted in cell cycle arrest (Fig. 6f) and impaired clonogenic growth (Fig. 6g), confirming that ADAR1 is a tumor-promoting gene in leukemia. Similarly, forced expression of ADAR1 recapitulated the phenotypes acquired through LNC-SNO49AB overexpression (Supplementary Fig. S7e, f). Furthermore, the effects of LNC-SNO49AB knockdown were largely rescued by forced expression of ADAR1 (Fig. 6h) and ADAR1 ablation partially counteracted the effect of LNC-SNO49AB overexpression on cell cycle promotion (Supplementary Fig. S7g). Collectively, our data demonstrate that LNC-SNO49AB promotes hematopoietic malignancy by enhancing the activation of ADAR1-mediated RNA A-to-I editing. The proposed working model is summarized in Fig. 7.
LncRNAs, potential key elements in gene expression regulation networks, are reshaping modern oncology. Due to the association between lncRNA signatures and pathological processes and the ability to profoundly influence carcinogenesis, lncRNAs can be utilized as potential cancer biomarkers and therapeutic targets in the clinic. In this study, we identified an abundant lncRNA, LNC-SNO49AB, containing the sequences of SNORD49A and SNORD49B in leukemia. The expression of LNC-SNO49AB was correlated with therapeutic outcomes, and silencing LNC-SNO49AB impaired leukemia progression in vitro and in vivo. We further determined that LNC-SNO49AB is a stable lncRNA and interfering the lncRNA did not impair the expression level of its host gene and mature snoRNAs in the same gene locus, indicating that LNC-SNO49AB may represent a potential prognostic biomarker and has potential application values of targeted therapy in leukemia. As a class of newly identified stable lncRNAs with unique structures, snoRNA-related lncRNAs have attracted much attention. While most lncRNAs are processed to be 5′ m7G capped and 3′ polyadenylated, the ends of snoRNA-related lncRNAs contain snoRNA sequences. The majority of snoRNAs are encoded within the introns of their host genes, which are trimmed. When two snoRNAs are embedded within a single intron, trimming can result in a sno-lncRNA, which consists of an intronic lncRNA flanked by two snoRNAs. Notably, only a small fraction of known snoRNAs appear as a pair in the same intron, whereas the majority, ~85%, of snoRNAs are embedded in introns individually. However, whether the snoRNAs located in introns individually are capable of generating snoRNA-related lncRNAs is largely unknown. Here, we identified a 5′-end m7G and 3′-end snoRNA lncRNA, LNC-SNO49AB, with SNORD49B in the middle and SNORD49A at the 3′ end. In particular, only one snoRNA (SNORD49A) is required for LNC-SNO49AB biogenesis, raising the possibility that snoRNAs embedded in introns individually might mediate snoRNA-related lncRNA generation. Interestingly, in addition to snoRNAs, microRNAs, other types of small RNAs located within introns, were reported to form lncRNAs called lnc-pri-miRNAs. Lnc-pri-miRNAs are flanked by miRNAs at the 3′ end and thus lack poly(A)-tails. We believe that the total number and diversity of lncRNAs with small RNA-ends will continue to climb, because of more-sensitive RNA sequencing and new computational pipelines, especially the improvement in non-polyadenylated RNA-seq. Assembly into macromolecular complexes is crucial for most proteins to become functional, which requires that subunits find each other precisely and efficiently in the crowded cellular environment. Recent studies indicated that oligomerization is facilitated by timely coupling translation and subunit interaction (cotranslational assembly). Here, our results demonstrated that LNC-SNO49AB promotes ADAR1 dimerization at the posttranslational level, as shown by (1) LNC-SNO49AB interacting with ADAR1 in nucleoli; (2) LNC-SNO49AB forming a functional structure with an ADAR1 dimer; and (3) silencing of LNC-SNO49AB diminishing the interaction between the ADAR1s, inhibiting dimerization. Thus, LNC-SNO49AB acts as an RNA scaffold to facilitate the association between ADAR1s forming dimers posttranslationally. Similar to LNC-SNO49AB, a few lncRNAs have also been reported to be involved in protein complex formation. Although co-translational assembly is an efficient method, lncRNA-mediated homodimerization or multimerization may be an important mechanism for protein activity control at the posttranslational level in response to cellular stresses and cancer progression since lncRNA expression levels vary. This work will inspire future investigations to dissect the effects of lncRNAs on protein assembly at different developmental stages and in various diseases. Cancer is driven by alterations in genomic information. The recently coined term ‘epitranscriptome’ describes RNA modifications, including RNA editing and methylation, that induce relevant changes to the transcriptome. Despite increasing evidence showing that the epitranscriptome contributes to oncogenesis through the determination of RNA function and gene expression diversity, our understanding of how it can be dynamically controlled is limited. Two antisense lncRNAs, FOXM1-AS and PCA3, have been reported to specifically regulate the m6A and A-to-I editing of their sense protein-coding genes by recruiting the m6A demethylases ALKBH5 and ADAR1, respectively, showing that lncRNAs can give rise to the specificity of RNA modification. However, the epitranscriptome (i.e., m6A, A-to-I editing, pseudouridine, etc.) has often been shown to be globally dysregulated in cancer. Therefore, it is important to determine the global effects of the epitranscriptome and its associated machinery. In this study, we identified that targeting LNC-SNO49AB decreases the global A-to-I RNA-editing rate by impairing ADAR1 dimerization, revealing a previously unrecognized strategy by which lncRNAs regulate the global epitranscriptome in cancer. Notably, very recent study demonstrated that endogenous ADAR-mediated RNA editing can persistently edit the target RNAs with oligonucleotides AIMers in non-human primate liver and has enormous potentials for specific cancer therapies in the nucleic acid-editing level. In this study, we revealed that LNC-SNO49AB can specifically regulate the activity of ADAR1, which may provide a novel insight into the precision regulation of ADAR1-based RNA editing therapeutic strategy. It may be promising method of combining AIMers and LNC-SNO49AB overexpression to improve the efficiency in this ADAR1-based approach. In summary, we identified a novel lncRNA, LNC-SNO49AB, with a 5′ m7G and 3′ snoRNA structure and revealed the mechanism of LNC-SNO49AB dependence on its ADAR1 interaction that promotes ADAR1 dimerization and elevates RNA A-to-I editing rates to enhance leukaemogenesis. These findings broaden our understanding of lncRNA diversity and provide a potential therapeutic target for leukemia treatment.
Human leukemia cell lines RS4;11, SUPB15, CCRF-CEM, MOLM13, THP1, MV4-11, NB4, HL60 and human embryonic kidney cell line HEK293T were obtained from American Type Culture Collection (ATCC) and grown according to standard protocols. The primary cells were culture in IMDM (HyClone) supplemented with 10% FBS. All cells were culture at 37 °C in a 5% CO2 atmosphere. Transcription was blocked by adding 2 μg/mL actinomycin D or dimethylsulphoxide (Sigma) as a control to the cell culture medium.
The clinical leukemia samples were obtained at the time of diagnosis and with informed consent from the First Affiliated Hospital of Sun Yat-sen University. Sample collection was approved by the Ethics Committee of the First Affiliated Hospital of Sun Yat-sen University. The study was conducted in accordance with the Declaration of Helsinki. The detail clinicopathological characteristics of the patients were summarized in Supplementary Table S1. The leukemia samples were stored in liquid nitrogen until used.
Total RNA was extracted from bone marrow and cell samples using an Invitrogen™ TRIZOL according to the manufacturer’s instructions. All RNA samples were stored at –80 °C before reverse transcription and quantitative RT-PCR. RNA was reverse transcribed into cDNA with the PrimeScript® RT reagent Kit with Gdna Eraser (Takara, Japan). Quantitative RT-PCR was performed using the SYBR Premix ExTaq real-time PCR Kit (Takara, Japan) according to the manufacturer’s instructions. Data were normalized to GAPDH expression as a control. The relative expression level for LNC-SNO49AB and other lncRNA or mRNA was determined using the 2−ΔΔCt method. The primers are listed in Supplementary Table S6.
Total RNA from RS4;11 cells was extracted using TRIzol according to the manufacturer’s guidelines. The 5′ and 3′ ends of Cdna were acquired using a FirstChoice® RLM-RACE Kit (Invitrogen™) according to the manufacturer’s instructions. Notably, in 3′ RACE assay, RNA was reverse transcribed by TransScript® miRNA First-Strand cDNA Synthesis SuperMix (TransGen Biotech) to add polyA tails. PCR products were obtained and then cloned into Peasy-T3 (TransGen Biotech, China) for further sequencing.
Cell proliferation was measured using Cell Counting Kit-8 (Dojindo Molecular Technologies, China). Cells were seeded at a density of 20,000 cells per well in 100 μL of complete medium in 96-well plates. Absorbance was measured by a VICTOR™ X5 Multilabel Plate Reader (PerkinElmer, USA) at wavelengths of 480 and 630 nm at 0, 24, 48, 72, and 96 h. For the cell cycle and apoptosis assay, cells were collected and washed once by PBS. Cell pellets were resuspended in 0.5 μL of PI/Rnase staining buffer or Annexin V/PI staining buffer (Dojindo Molecular Technologies, China), respectively, and incubated for 30 min at room temperature. Cells were immediately measured and analyzed using flow cytometer (BD Biosciences, USA). For CFU assay, leukemia cells were plated into methylcellulose at a density of 500–1000 cells per 1.5 μL methylcellulose per 35 mm dish. Colonies (> 50 cells) were scanned after 10 days in culture.
The nuclear and cytoplasmic fractions were extracted using NE-PER Nuclear and Cytoplasmic Extraction Reagents (Thermo Scientific) according to the manufacturer’s instructions. Total RNA from whole-cell lysates or the nuclear and cytoplasmic fractions were isolated using TRIzol (Life Technologies, Carlsbad, CA). Nucleoli isolation was performed as described with modification. Briefly, 2 × 107 NB4 cells were collected and suspended in 200 μL lysis buffer (10 mM Tris, pH 8.0, 140 mM NaCl, 1.5 mM, MgCl2, 0.5% Igepal, add RNAse inhibitor before use) and incubated on ice for 10 min. The lysate was centrifuged at 1000× g for 5 min. The supernatant (cytoplasmic extract) was transfered to a clean tube immediately. The insoluble pellet (nuclear extract) was suspended in 200 μL 340 mM sucrose solution containing 5 mM MgCl2. To obtain nucleoplasmic and nucleolar fraction, nuclei were broken by sonication until intact nuclei cannot be detected by microscope. 200 μL 880 mM sucrose solution containing 5 mM MgCl2 was added gently to the sonicated nuclei and then centrifuged 20 min at 2000× g. The supernatant was the nucleoplasmic fraction and the pellet was nucleolar fraction. All centrifugation steps were performed at 4 °C and cell samples and extracts were always kept on ice during experiment. Fractioned RNAs from the same number of cells were used for reverse transcription and qRT-PCR.
NOD-SCID mice were maintained under specific pathogen-free conditions in the Laboratory Animal Center of Sun Yat-sen University. All experiments on animals were performed according to the institutional ethical guidelines for animal experiments. RS4;11 cells stably expressing sh-NC, sh-LNC-SNO49AB, PCDH or PCDH-LNC-SNO49AB (GFP+ cell populations) were tail vein injected into the mice (5 × 106 cells for LNC-SNO49AB knockdown groups and 3 × 106 cells for overexpressed ones in 150 Μl PBS per mice). Here, PCDH refers to the overexpression vector, PCDH-MSCV-MCS-EF1-Puro-copGFP. For the control, 150 μL of PBS without cells was injected. Thirty days after inoculation, xenografted mice were sacrificed for analysis. Human cell engraftment (GFP+ cell populations) in bone marrow was evaluated by flow cytometry. The remaining mice were used to perform the survival assay.
Biotin-labeled RNA probes were prepared by adding biotin-labeled UTP (Roche) in in vitro transcription using TranscriptAid T7 High Yield Transcription Kit (Thermo Scientific). The PCR primers for RNA probe are listed in Supplementary Table S6. A total of 20 μg RNA run on formaldehyde denaturing agarose gel and transferred to a Hybond-N+ membrane (GE Healthcare) capillary transfer. Membranes were dried and ultraviolet-crosslinked. Pre-hybridization was done at 42 °C for 1 h and hybridization was performed at 42 °C overnight.
A serial dilution of the plasmin Blunt-LNC-SNO49AB was used in qPCR to generate a standard curve. To measure the copy number of LNC-SNO49AB in leukemia cells, total RNA extracted from 1 × 106 cells of each line was reverse transcribed into cDNAs for qPCR analysis and the copy number was quantitated from the standard curve.
Protein recombination and purification were performed as described previously. Briefly, recombinant proteins were expressed in E. coli strain BL21 [Transetta (DE3) chemically competent cell (Transgen biotech, CD801)]. In brief, 5 μL Luria-Bertani (LB) culture supplemented with 100 μg/μL ampicillin was inoculated with a single colony at 37 °C. After overnight growth, the culture was diluted 100-fold into 300 mL LB supplemented with 100 μg/mL ampicillin. Protein expression was induced in the presence of 0.4 mM IPTG at 16 °C overnight. Then the cell pellets were collected by centrifugation at 5000 rpm, 4 °C for 10 min and purified recombinant proteins using a His or Flag tag Protein Purification Kit (BeaverBeads™) according to the manufacturer’s instruction.
In the RIP experiment, anti-FLAG, anti-HA, or anti-IgG antibodies were used along with an EZ-Magna RIP™ RNA-Binding Protein Immunoprecipitation Kit (17–701) (Merck Millipore, Germany) according to the manufacturer’s instructions. In HA-tagged Flag-ADAR1 fragments RIP assays, 1 × 106 293T cells were transfected into 5 μg indicated plasmid and collected after 48 h. All proteins for RIP were lysed with cell lysis buffer supplemented with Thermo Scientific™ Halt™ Protease Inhibitor Cocktail (Thermo Fisher, USA). To prepare antibody-coated beads, 50 μL Protein A/G magnetic beads were incubated with 3 μg antibody or control IgG in 500 μL wash buffer at 4 °C for 1 h. Then the beads were washed three times and mixed with the cell lysates in new tubes. The tubes were rotated at 4 °C overnight. Finally, RNA extraction from the beads was further collected by using Trizol according to the manufacturer’s instructions. Reverse transcription and qPCR were performed as previously described.
The LNC-SNO49AB sequence was cloned into the BLUNT plasmid with the tRSA tag at its 5′ end. RNA products were transcribed in vitro using the TranscriptAid T7 High Yield Transcription Kit (Thermo, USA) and were then purified using the GeneJET RNA Purification Kit (Thermo, USA). The RNA pull-down assay was performed using 50 pmol RNA for each sample with the manufacturer’s instructions of Pierce Magnetic RNA-Protein Pull-down Kit (Thermo, USA). Briefly, folded tRSA and tRSA-labeled LNC-SNO49AB were mixed with RS4;11 cell extract (containing 2 mg total protein) in 400 µL RIP buffer and incubated at RT (room temperature) for one hour. Next, 50 µL of washed streptavidin magnetic beads was added to each reaction and further incubated at RT for another hour. Beads were washed briefly with wash buffer six times and then boiled in SDS loading buffer. Finally, the enriched proteins were resolved via SDS-PAGE and silver stained followed by mass spectrometry (MS) identification (FitGene Biotechnology, China) and western blot.
The 50 pmol tRSA-RNA products were first mixed with 1–3 μg recombinant proteins, and then the mixture was rotated at 4 °C for 1 h in RNA binding buffer according to Pierce Magnetic RNA-Protein Pull-down Kit. Beads were washed three times with RNA wash buffer and then boiled in SDS loading buffer. Finally, the enriched proteins were resolved via SDS-PAGE and analyzed western blotting.
Total protein was extracted from cells using RIPA lysis buffer (Beyotime, China) with 1× complete ULTRA (Roche, USA). Proteins were resolved by 10% or 12% BisTrispolyacrylamide gels and then transferred to polyvinylidene fluoride membranes. Membranes were blocked in 5% BSA for 1 h, and followed by the appropriate antibody overnight at 4 °C and then incubated with horseradish peroxidase-conjugated secondary antibodies at room temperature for 1 h. Membranes were visualized with an enhanced chemoluminescence detection system.
The si-NC, si-LNC-SNO49AN, and si-ADAR1 samples (5 × 106 cells/sample) were obtained from RS4;11 cells. The next-generation sequencing in this study was performed by poly-A RNA-seq on an illumina noveSeq instrument. The sequencing depth of the RNA-Seq is up to 6 G in 150-base paired-end mode. For the data analysis, pair-end reads were adapter and quality trimmed using Trim Galore (v0.6.4), and high-quality reads were aligned to reference transcriptome (Gencode v34) and quantified using Salmon (v1.3.0) with fragment GC bias and positional bias corrections. DESeq2 was used for normalizing gene expression level and identifying differentially expressed genes. Fragments Per Kilobase of exon model per Million mapped fragments (FPKM) were used to quantify the gene expression. False discovery rate (FDR) was used to compare the si-LNC-SNO49AB, si-ADAR1 and si-NC. A-to-I editing analysis was performed as described previously. Briefly, we collected all mismatches between the aligned reads and the reference genome, discarding mismatches in read positions with quality Phred score < 30 and those located at sites reported as genomic SNPs in dbSNP (SNP build 135).
2-OMe-seq was carried out as previously described with some modifications. Briefly, for each experiment, 1 μg of total RNA were reverse transcribed using either 1 mM final dNTP (high dNTP sample), or 0.004 mM final dNTP (low dNTP sample), 1 U/μL AMV Reverse Transcriptase (NEB) and random primers. Template RNA was degraded by the addition of 1 μL RNase H (BioLegend) and 1 μL of RNase A (NEB). Following purification using VAHTSTM DNA Beads (Vazyme), cDNAs were converted into a dedicated library by VAHTS ssDNA library prep kit for Illumina (Vazyme). This approach allowed us to keep the strand-specificity of the library, so that each read started 1 nt downstream of the RT stopping point. Libraries were subjected to sequencing on the Illumina™ NextSeq 500 Sequencer. Then, we aligned the sequence data to GRCh38.p12 genome by bowtie2 v2.4.5 (default parameter), and normalized each same treatment sample by mapped reads count. Then, we made 5 bp windows by bedtools v2.30.0 and summed the total counts in the window. Finally, we calculate 2-OMe ratio following the previous method.
To validate whether the identified editing sites are bona fide we performed regular PCR to amplify a selection of sites. We used SYBR Premix ExTaq real-time PCR Kit (Takara, Japan) for the PCR reactions. All Sanger sequencing was carried out by RuiBiotech (Beijing, China).
Mann-Whitney test was used to analyze the LNC-SNO49AB level between patients with or without leukemia. Kruskal-Wallis test was performed to compare multiple patient groups, and the Dunn’s multiple comparisons test was used to analyze multiple comparisons. Fisher’s exact test was used to determine the significance of differentially expressed lncRNA and mRNA levels between two groups. Data are expressed as the means ± SEM or ±SD of three independent experiments. Kaplan–Meier method with a log-rank test was used to analyze the mice survival. Two-tailed tests were used for univariate comparisons. Throughout the text: ns, not significant; ∗P < 0.05; ∗∗P < 0.01; ∗∗∗P < 0.001.
Supplemental Fig S1 Supplemental Fig S2 Supplemental Fig S3 Supplemental Fig S4 Supplemental Fig S5 Supplemental Fig S6 Supplemental Fig S7 Supplemental Tab S1 Supplemental Tab S2 Supplemental Tab S3 Supplemental Tab S4 Supplemental Tab S5 Supplemental Tab S6 | true | true | true |
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PMC9622915 | Junfang Zhang,Xinli Guo,Zhangyin Cai,Yan Pan,Hao Yang,Yali Fu,Zixuan Cao,Yaxian Wen,Chao Lei,Chenshan Chu,Yu Yuan,Dongyang Cui,Pengyu Gao,Bin Lai,Ping Zheng | Two kinds of transcription factors mediate chronic morphine-induced decrease in miR-105 in medial prefrontal cortex of rats | 31-10-2022 | Molecular neuroscience,Epigenetics in the nervous system | Chronic morphine administration alters gene expression in different brain regions, an effect which may contribute to plastic changes associated with addictive behavior. This change in gene expression is most possibly mediated by addictive drug-induced epigenetic remodeling of gene expression programs. Our previous studies showed that chronic morphine-induced decrease of miR-105 in the medial prefrontal cortex (mPFC) contributed to context-induced retrieval of morphine withdrawal memory. However, how chronic morphine treatment decreases miR-105 in the mPFC still remains unknown. The present study shows that chronic morphine induces addiction-related change in miR-105 in the mPFC via two kinds of transcription factors: the first transcription factor is CREB activated by mu receptors-ERK-p90RSK signaling pathway and the second transcription factor is glucocorticoid receptor (GR), which as a negative transcription factor, mediates chronic morphine-induced decrease in miR-105 in the mPFC of rats. | Two kinds of transcription factors mediate chronic morphine-induced decrease in miR-105 in medial prefrontal cortex of rats
Chronic morphine administration alters gene expression in different brain regions, an effect which may contribute to plastic changes associated with addictive behavior. This change in gene expression is most possibly mediated by addictive drug-induced epigenetic remodeling of gene expression programs. Our previous studies showed that chronic morphine-induced decrease of miR-105 in the medial prefrontal cortex (mPFC) contributed to context-induced retrieval of morphine withdrawal memory. However, how chronic morphine treatment decreases miR-105 in the mPFC still remains unknown. The present study shows that chronic morphine induces addiction-related change in miR-105 in the mPFC via two kinds of transcription factors: the first transcription factor is CREB activated by mu receptors-ERK-p90RSK signaling pathway and the second transcription factor is glucocorticoid receptor (GR), which as a negative transcription factor, mediates chronic morphine-induced decrease in miR-105 in the mPFC of rats.
Drug addiction is a chronic brain disorder characterized by compulsive repeated use of drugs [1]. Chronic exposure to addictive drugs, such as morphine, causes long-term changes in different brain regions called “addictive brain”, leading vulnerable individuals to engage in pathological drug seeking and drug taking that can remain a lifelong struggle [2]. Therefore, the study of the mechanism underlying addictive drug-induced long-term changes in the brain is of importance for developing new therapeutic approaches to prevent drug addiction. Previous studies have found that chronic morphine administration alters gene expression in different brain regions, which may contribute to plastic changes associated with addictive behavior [3–6]. This change in gene expression is most possibly mediated by addictive drug-induced epigenetic remodeling of gene expression programs, which alters gene expression without a change in nucleotide sequence of DNA [7], in discrete brain regions. Moreover, the kinds of involved genes and the brain regions where the genes lie are closely related to addiction. For example, Ferguson et al. found that chronic morphine treatment selectively upregulated SIRT1, a kind of deacetylases that targeted histone in the nucleus accumbens (NAc) without altering other sirtuins. Overexpression of SIRT1 within the NAc potentiated morphine-induced retrieval of reward memory, whereas the knockdown of SIRT1 had an opposite effect [8]. The studies from our lab showed that (1) chronic morphine-induced increases in the expression of D1 receptors at presynaptic terminals coming from other structures to the basolateral amygdala (BLA) played an important role in conditioned context-induced retrieval of morphine withdrawal memory [9]; (2) the downregulation of miR-105 in neurons projecting from the mPFC to the BLA was the reason for chronic morphine-induced increases in the expression of D1 receptors at presynaptic terminals coming from mPFC to BLA and the overexpression of miR-105 in the mPFC inhibited conditioned context-induced retrieval of morphine withdrawal memory [10], suggesting that chronic morphine-induced decrease of miR-105 in the mPFC contributed to context-induced retrieval of morphine withdrawal memory. However, how chronic morphine treatment decreases miR-105 in the mPFC remains still unknown. In this paper, using western blotting, real-time PCR, RNAi technology, and behavioral assay method, we studied intracellular molecular mechanism underlying chronic morphine-induced decrease in the expression of miR-105 in the mPFC and its functional significance in conditioned context-induced retrieval of morphine withdrawal memory.
Male Sprague-Dawley (SD) rats (220–250 g) were housed under a 12:12 h light/dark cycle in a temperature- and humidity-controlled environment with free access to food and water. All experimental procedures conformed to the guidelines of the Institutional Animal Care and Use Committee of Fudan University, Shanghai Medical College. The morphine dependence of rat was induced according to previously described procedures [10, 11]. Briefly, rats were intraperitoneal injected by an increment dose of morphine twice daily at 08.00 h and 19.00 h, and morphine doses were progressively increased from 10 mg/kg to 40 mg/kg (2× 10 mg/kg on day 1; 2× 20 mg/kg on day 2; 2× 30 mg/kg on day 3; and 2× 40 mg/kg on days 4 and 5). Control rats were treated with saline following the same procedure. The rats were sacrificed 2 h after the last time of morphine administration. At this time, morphine should be on board because there were reports that there was no significant decline of morphine concentration in plasma at 2 h after s.c. injection of morphine [12].
The mPFC neurons were isolated and cultured as described previously [9, 13]. Briefly, newborn (0–1 d) SD rats were euthanized by decapitation. The mPFC was dissected and dissociated with 0.125% trypsin at 37 °C for 15 min; digestion was terminated by adding DMEM containing 20% fetal bovine serum (FBS). Tissue was puffed, filtered, centrifuged, and the cell pellets collected. The cells were then resuspended in complete culture medium and plated in 6-well culture plates coated with poly-D-lysine (100 µg/mL; Sigma) at a density of 2 × 106 cells/well. The cells were incubated in DMEM supplemented with penicillin (100 U/mL) and streptomycin (100 U/mL). After 4 h, the medium was replaced with a neurobasal medium (Gibco, Auckland, New Zealand) supplemented with 2% B27 (Gibco) and 0.5 mM glutamine (Gibco). A cytosine arabinoside solution (0.5 μM) was added to the culture 24 h after plating to minimize glial cell proliferation. During the maintenance phase, half the medium in each well was replaced with fresh medium every 2 days. Five-day-old mPFC neurons were subjected to morphine treatment at the indicated times. Primary cultured mPFC neurons were pretreated with naloxone (10 μM), U0126 (10 μM), and LJI308 (1 μM) for 3 h followed by 72 h morphine (10 μM) treatment. The primary cultured mPFC neurons were collected 2 h after the last morphine administration. The nonselective opioid receptor antagonist naloxone was purchased from Sigma. The ERK1/2 inhibitor U0126 and the p90RSK inhibitor LJI308 were obtained from Selleck.
GR knockdown was achieved using lentiviral RNAi technology (GeneChem, Shanghai, China). For GR knockdown, a sequence targeting GR mRNA was inserted into the GV248 vector (LV-GR-RNAi). The GR mRNA target sequence was 5′-GGTCTGAAGAGCCAAGAGTTA-3′. To generate the negative control lentivirus (LV-NC-RNAi), the sequence 5′-TTCTCCGAACGTGTCACGT-3′ was inserted into the GV248 vector. LV-GR-RNAi and LV-NC-RNAi were transfected into primary cultured mPFC neurons for 48 h before morphine treatment.
Male SD rats (220–250 g) were anesthetized with ketamine and xylazine (160 mg/kg and 2 mg/kg body weight, respectively) and secured in a stereotaxic instrument (Stoelting). Microinjections were performed using needles connected to a 1-μl microsyringe (Hamilton) by polyethylene tubing and controlled by a syringe pump (Harvard Apparatus). The intended stereotaxic coordinates for the mPFC were as follows: AP, +3.2 mm; ML, ±0.8 mm; DV, −3.8 mm. Rats were injected with LV-GR-RNAi and LV-NC-RNAi bilaterally into the mPFC in a 1 μl volume within 10 min. After the injection, the needles were retained in place for an additional 10 min to allow the diffusion of the virus. The rats were allowed at least 7 days to recover before the behavioral experiment and the efficiency of virus was verified in mPFC slices by analyzing the expression of EGFP.
Total RNA, including mRNA and microRNA (miRNA), was extracted from cells and tissue using the miRcute miRNA Isolation Kit (Tiangen, Shanghai, China) according to the manufacturer’s manual. Reverse-transcription was performed with FastKing gDNA Dispelling RT SuperMix (Tiangen). Quantitative real-time PCR (qPCR) analysis of pri-miR-105, and GR levels were performed with a SuperReal PreMix Plus (SYBR Green) using 40 cycles of amplification (95 °C for 10 s, 60 °C for 25 s, and 72 °C for 20 s). MiR-105 was reverse-transcribed using a microRNA first-strand cDNA synthesis kit from Tiangen. qPCR was performed on a Mastercycler ep realplex Real-time PCR System of eppendorf (40 cycles of amplification: 94 °C for 2 min, 94 °C for 20 s, and 60 °C for 34 s) using a miRcute microRNA qPCR detection kit (Tiangen). The primers for miR-105, pri-miR-105, and U6 were synthesized by Tiangen. Those for GR (RQP048912), and Gapdh (RQP049537) were purchased from GeneCopoeia (Guangzhou, China). To obtain the fold-change in mRNA and miRNA levels, the data were analyzed using the 2–ΔΔCT method. The final gene expression levels were normalized to that of Gapdh for mRNA and U6 for miRNA. Triplicate reactions were carried out in three separate experiments.
Total protein was extracted from cells and brain tissue using cold RIPA lysis buffer (50 mM Tris-HCl, pH 7.4, 150 mM NaCl, 1% NP-40, 0.5% sodium deoxycholate, 0.1% SDS, and 1% protease inhibitor), kept on ice for 30 min, and then centrifuged at 12,000 rpm for 10 min at 4 °C. The concentration of the protein extraction was quantified using a BCA kit (Pierce Chemicals). Equal amounts of protein were loaded and separated by SDS–PAGE and transferred to nitrocellulose membranes. After blocking in nonfat milk, the membranes were incubated with the following primary antibodies: rabbit polyclonal anti-GR (1:800, Abcam, ab3579) and mouse monoclonal anti-GAPDH (1:3000, Abclonal, AC002); and rabbit polyclonal anti-ERK (1:1000, Cell Signaling Technology, 4695), rabbit polyclonal anti-p-ERK (1:1000, Cell Signaling Technology, 4370), rabbit polyclonal anti-p90RSK (1:500, Cell Signaling Technology, 9355), rabbit polyclonal anti-pp90RSK (1:500, Cell Signaling Technology, 9341), rabbit polyclonal anti-CREB (1:1000, Cell Signaling Technology, 9197), and rabbit polyclonal anti-p-CREB (1:1000, Cell Signaling Technology, 9198). HRP-conjugated goat anti-mouse/rabbit IgG (1:10,000, LI-COR Bioscience, 925-32210 and 926-68071) were used as the secondary antibodies. The signals were detected using an Odyssey infrared imaging system (LI-COR Bioscience). Densities were quantified using Image J and normalized to that of GAPDH, which served as loading control. Each experiment was repeated at least three times.
Conditioned place aversion (CPA) was assessed using a three-chamber (15 × 15 × 20 cm) apparatus (Med Associates, USA) with distinct visual and tactile environments to maximize contextual differences. The procedure for CPA was similar to that previously described [14]. First, rats were given a pre-test and allowed to freely explore the entire apparatus for 15 min. Rats with a strong unconditioned preference (>75% of the session time) for any compartment were excluded from the study. Microinjections of LV-GR-RNAi or LV-NC-RNAi into the mPFC were performed after the pre-test. After injection, the rats were allowed to recover for at least 7 days before the administration of chronic morphine treatment. Morphine dependence was induced in the animals by repeated intraperitoneal injections of morphine twice daily at 08.00 h and 19.00 h for 5 days, as described above. On days 6 and 8, 2 h after the administration of 40 mg/kg morphine, each rat was confined in its morphine withdrawal-paired compartment for 20 min immediately after an intraperitoneal injection of naloxone (0.1 mg/kg). On alternating days 7 and 9, two hours after the administration of 40 mg/kg morphine, the rats were confined in the opposite compartment (saline-paired compartment) for 20 min immediately after an intraperitoneal injection of saline. The post-test was conducted 24 h after conditioning on day 9; the rats were allowed to freely explore the three compartments for 15 min and the CPA score was calculated as the time in the morphine withdrawal-paired compartment minus the time in the saline-paired compartment.
All data were analyzed using GraphPad Prism 8. Numerical data were expressed as means ± SEM. For two-group comparisons, two-tailed unpaired Student’s t-tests were used. For comparisons among multiple groups, analyses of variance (ANOVAs) were used. One-way ANOVA and two-way ANOVA were followed by Bonferroni’s post hoc test. P-value <0.05 was considered statistically significant.
Our previous studies showed that chronic morphine downregulated miR-105 expression in the mPFC of rats [10]. To confirm this statement, here we repeated this experiment. The rats were randomly divided into two groups: saline group and morphine group. The level of miR-105 was detected by qPCR in the mPFC of rats treated with saline or morphine for 5 days. A significant decrease in miR-105 level was observed in morphine group (0.639 ± 0.050, n = 8, unpaired Student’s t-tests, t = 3.804, P = 0.0019), compared with saline group (1.00 ± 0.081, n = 8) (Fig. 1a). The miRNAs were transcribed as part of primary miRNAs (pri-miRNAs). To study whether chronic morphine-induced decrease in miR-105 was due to a decrease of primary miR-105 (pri-miR-105), we examined the influence of chronic morphine on pri-miR-105 expression by qPCR. Result showed that chronic morphine induced a significant decrease of pri-miR-105 level in the mPFC of rats (0.736 ± 0.080, n = 6, unpaired Student’s t-tests, t = 2.593, P = 0.0268), compared with saline group (1.015 ± 0.072, n = 6) (Fig. 1b). We also tested the effect of chronic morphine on the expression of miR-105 in primary cultured mPFC neurons of rats. Neurons were divided into two groups, saline group treated with saline for 3 days and morphine group treated with morphine (10 μM) for 3 days. The result showed that the miR-105 level significantly decreased after chronic morphine treatment (0.484 ± 0.038, n = 3, unpaired Student’s t-tests, t = 13.64, P < 0.0001), compared with saline group (1.010 ± 0.009, n = 3) (Fig. 1c). These results suggest that chronic morphine indeed downregulates the expression of miR-105 in the mPFC of rats. This statement is consistent with that of our previous report [10]. We also examined the effect of acute morphine on the expression of miR-105 in the mPFC of rats and in primary cultured mPFC neurons of rats. The result showed no significant change in the expression of miR-105 in the mPFC of rats at 2 h after one dose morphine (10 mg/kg) injection (1.066 ± 0.078, n = 4, unpaired Student’s t-tests, t = 0.1165, P > 0.05), compared with saline group (1.044 ± 0.173, n = 4) (Supplementary data Fig. 1a). Moreover, 2 h morphine treatment (10 μM) also had no significant effect on the expression of miR-105 in primary cultured mPFC neurons of rats (0.749 ± 0.183, n = 3, unpaired Student’s t-tests, t = 1.089, P > 0.05), compared with saline group (1.029 ± 0.181, n = 3) (Supplementary data Fig. 1b). These results suggest that acute morphine has no significant influence on the expression of miR-105 in the mPFC of rats.
It has been known that morphine is an agonist of opiate receptors. As the original site acted by morphine, opiate receptors should play a key role in chronic morphine-induced change in the expression of miR-105 in the mPFC of rats. To confirm this statement, we examined the influence of opiate receptor antagonist naloxone on chronic morphine-induced change in miR-105 in primary cultured mPFC neurons. Neurons were divided into two groups: morphine group treated with saline and morphine (10 μM); morphine + naloxone group treated with naloxone (10 μM) and morphine (10 μM). The result showed that after naloxone treatment, the change in miR-105 induced by chronic morphine disappeared (2.599 ± 0.038, n = 3, unpaired Student’s t-tests, t = 18.26, P < 0.0001), compared with morphine group (1.000 ± 0.079, n = 3) (Fig. 2a). We also examined whether naloxone alone had an influence on chronic morphine-induced change in the expression of miR-105. The result showed that naloxone alone had no a significant effect on the expression of miR-105 in primary cultured mPFC neurons (1.589 ± 0.161, n = 3, unpaired Student’s t-tests, t = 2.315, P = 0.082), compared with control group (1.029 ± 0.181, n = 3) (Supplementary data Fig. 2). This result suggests that opiate receptors may mediate chronic morphine-induced decrease in the expression of miR-105 in the mPFC in rats. Opiate receptors mainly include three classes: the delta, kappa, and mu receptors [15]. Among them, morphine has a high selectivity for mu receptor [15]. Upon the activation by morphine, mu receptors are known to experience conformational changes, subsequently leading to different corresponding signaling pathways. Hui Zheng et al. proposed that the continued activation of Gi by mu receptor led to ERK phosphorylation, which then induced the subsequent activation of p90RSK and CREB [16]. To study the role of CREB in the change of miR-105 expression after chronic morphine treatment, the phosphorylation of CREB (p-CREB) was detected by western blotting. Rats and primary cultured mPFC neurons were divided into two groups: saline group and morphine group. The results showed that chronic morphine treatment could activate CREB in both the mPFC of rats (1.302 ± 0.0912, n = 3, unpaired Student’s t-tests, t = 2.869, P = 0.0455) and primary cultured mPFC neurons (1.453 ± 0.1164, n = 3, unpaired Student’s t-tests, t = 2.792, P = 0.0492), compared with the control group (Fig. 2b). We also examined the influence of chronic morphine on the phosphorylation of ERK1/2 and p90RSK. The result showed that chronic morphine increased the expression of p-ERK and pp90RSK in both the mPFC of rats (1.301 ± 0.0166, n = 3, P = 0.0009 and 1.794 ± 0.1008, n = 3, P = 0.0027) and primary cultured mPFC neurons (1.21 ± 0.0326, n = 3, P = 0.0136 and 1.40 ± 0.1021, n = 3. P = 0.0365) (Fig. 2c). Moreover, the pretreatment with U0126 (ERK1/2 inhibitor, 10 μM) or LJI308 (p90RSK inhibitor, 1 μM) blocked chronic morphine-induced decrease in the expression of miR-105 in primary cultured mPFC neurons (U0126: 0.8873 ± 0.0319, n = 3, P < 0.001; LJI308: 1.143 ± 0.0466, n = 3, P = 0.006, compared with morphine group (0.6436 ± 0.0396, n = 3) (Fig. 2d). These results suggest that CREB activated by mu receptors-ERK-p90RSK signaling pathway may be the first transcription factor that mediates chronic morphine induced-decrease in the expression of miR-105 in the mPFC.
GR is another important transcription factor [17]. In addition to the activation of the transcription of genes, GR is also able to repress the transcription of genes [18]. The binding of GR to GRE, a specific DNA motif, can suppress the expression of genes. Surjit et al. characterized the direct binding and repression of GR to widespread gene loci and reported that negative GREs were present in over 1000 mouse and human gene loci [19]. Therefore, it is possible that GR, as a negative transcription factor, mediates chronic morphine-induced decrease in miR-105 in the mPFC of rats. To test this hypothesis, first, we examined the influence of chronic morphine on the expression of GR mRNA and GR protein. The rats were randomly divided into two groups: saline group and morphine group. The level of GR mRNA and protein were detected by qRT-PCR and western blotting. The results showed that GR mRNA and protein level were significantly upregulated to 1.653 ± 0.2382 (n = 6, unpaired two-tailed Student’s t-test, t = 2.3, P = 0.0442) and 1.639 ± 0.2211 (n = 3, unpaired two-tailed Student’s t-test, t = 2.953, P = 0.0419) in morphine group, compared with that in saline group (GR mRNA, n = 6, 1.034 ± 0.1245 and GR protein, n = 3, 1.00 ± 0.1747) (Fig. 3a, b). Then, we examined the influence of the inhibition of GR using RNAi technology on chronic morphine-induced decrease in the expression of miR-105 in the mPFC of rats and in primary cultured mPFC neurons of rats. The neurons were divided into four groups: saline + LV-NC-RNAi group, saline + LV-GR-RNAi group, morphine + LV-NC-RNAi group, and morphine + LV-GR-RNAi group. Two-way ANOVA showed a statistically significant interaction between the effects of morphine and RNAi treatment on miR-105 expression (morphine × RNAi: F(1,20) = 11.43, P = 0.003; morphine factor: F(1,20) = 52.75, P < 0.0001; RNAi factor: F(1,20) = 227.3, P < 0.0001. Bonferroni’s post-tests: saline + LV-NC-RNAi group vs. saline + LV-GR-RNAi group, P < 0.0001, t = 8.27; morphine + LV-NC-RNAi group vs. morphine + LV-GR-RNAi group, P < 0.0001, t = 13.050; saline + LV-GR-RNAi group vs morphine + LV-GR-RNAi group, P = 0.0749, t = 2.745, n = 6 in each group). This result indicated that the inhibition of GR expression increased miR-105 expression both in saline + LV-GR-RNAi and morphine + LV-GR-RNAi group, but the increase in miR-105 expression after the inhibition of GR from morphine + LV-NC-RNAi group to morphine + LV-GR-RNAi group was much higher than that from saline + LV-NC-RNAi group to saline + LV-GR-RNAi group (Fig. 3c). Moreover, there was a statistically significant interaction between the effects of morphine and RNAi treatment on miR-105 expression. Therefore, although under basal conditions, GR had some extent of inhibitory control on miR-105 expression, morphine-induced GR increase might have a much stronger inhibitory effect on miR-105 expression under chronic morphine treatment. We also studied the effect of GR inhibition by RNAi on miR-105 expression in the rats treated by chronic morphine. The rats were divided into two groups: morphine + LV-NC-RNAi group and morphine + LV-GR-RNAi group. The rats were injected with LV-GR-RNAi or LV-NC-RNAi as control into the mPFC of rats 7 days before chronic morphine treatment. The results showed that GR inhibition led to a significant increase in the level of miR-105 (1.695 ± 0.080, n = 3, unpaired two-tailed Student’s t-test, t = 6.185, P = 0.0035) in morphine + LV-GR-RNAi group, compared with that in morphine + LV-NC-RNAi group (1.000 ± 0.079, n = 3) (Fig. 3d). Next, we explored the possible upstream mechanism underlying chronic morphine-induced increase in the expression of GR in the mPFC of rats. Based on the above results, CREB activated by mu receptors-ERK-p90RSK signaling pathway is the first transcription factor that mediates chronic morphine induced-decrease in the expression of miR-105 in the mPFC of rats. Therefore, we propose a hypothesis that this signaling pathway may be an upstream mechanism underlying chronic morphine-induced increase in GR expression in the mPFC of rats. To test this hypothesis, we examined the influence of the inhibition of ERK or p90RSK using the ERK inhibitor U0126 [20] or the p90RSK inhibitor LJI308 [21] on chronic morphine-induced increase of GR in primary cultured mPFC neurons. Neurons were divided into four groups: saline + DMSO group, morphine + DMSO group, U0126 + morphine group, and LJI308 + morphine group. GR protein levels in primary cultured mPFC neurons were subsequently detected by Western blotting. The result showed that morphine treatment increased GR protein level from 1.000 ± 0.05476 in saline + DMSO group to 2.048 ± 0.0746 in morphine + DMSO group (n = 3, P = 0.031, saline + DMSO group vs morphine + DMSO group, one-way ANOVA), but in the presence of U0126 or LJ1308, morphine-induced increase in GR protein level was decreased to 0.7853 ± 0.050 in U0126 + morphine group (n = 3, P < 0.0001, compared with morphine group, one-way ANOVA) or to 1.383 ± 0.0184 in LJI308 + morphine group (n = 3, P = 0.001, compared with morphine + DMSO group, one-way ANOVA), respectively (Fig. 4a). We also examined whether U0126 or LJI 308 alone had an influence on the expression of GR in primary cultured mPFC neurons. The results showed that U0126 or LJI 308 alone had no effect on GR expression in primary cultured mPFC neurons (U0126: 1.246 ± 0.132 and LJI 308: 0.874 ± 0.153, n = 3 in each group), compared with the control group (1.000 ± 0.046, n = 3, P = 0.1611, one-way ANOVA) (Supplementary data Fig. 3). We further investigated the effect of GR deletion on the phosphorylation of CREB induced by morphine. Primary cultured neurons were transfected with LV-GR-RNAi or LV-NC-RNAi and then treated with morphine for 72 h. Neurons were divided into four groups: LV-NC-RNAi group, LV-GR-RNAi group, morphine + LV-NC-RNAi group, and morphine + LV-GR-RNAi group. The results showed that GR deletion did not alter the upregulation of p-CREB induced by morphine (1.237 ± 0.073 vs 1.174 ± 0.058, n = 3, P = 0.097, one-way ANOVA) (Fig. 4b). These results suggest that mu receptor-ERK-p90RSK-CREB signaling pathway may mediate chronic morphine-induced increase in the expression of GR in the mPFC of rats.
In our previous study, we demonstrated that (1) chronic morphine-induced increases in the expression of D1 receptors at presynaptic terminals coming from other structures to the BLA played an important role in conditioned context-induced retrieval of morphine withdrawal memory;[9] (2) the downregulation of miR-105 in neurons projecting from the mPFC to the BLA was the reason for chronic morphine-induced increases in the expression of D1 receptors at presynaptic terminals coming from mPFC to the BLA and the overexpression of miR-105 in the mPFC inhibited context-induced retrieval of morphine withdrawal memory [10]. In this study, we characterized intracellular mechanisms underlying chronic morphine-induced decrease in the expression of miR-105 after chronic morphine and found that GR in the mPFC played an important role in chronic morphine-induced decrease in the expression of miR-105 in the mPFC. Therefore, it was most likely that chronic morphine-induced increase in the expression of GR in the mPFC also played an important role in conditioned context-induced retrieval of morphine withdrawal memory. To test this hypothesis, we examined the influence of the inhibition of GR using RNAi method on CPA, which was a typical animal model of conditioned context-induced retrieval of morphine withdrawal memory [22]. The rats were divided into two groups: Morphine + LV-GR-RNAi group and Morphine + LV-NC-RNAi group. The CPA training process (Fig. 5a) was executed 7 days after the rats were injected with LV-GR-RNAi or LV-NC-RNAi into the mPFC (Fig. 5b) before chronic morphine treatment. The results showed the rats in the morphine + LV-NC-RNAi group exhibited a strong aversion to withdrawal-paired compartment, while the rats in the morphine + LV-GR-RNAi group had no significant aversive responses for the two compartments (two-way ANOVA, virus treatment factor, F(1,38) = 9.996, P = 0.0031; test condition factor, F(1,38) = 35.56, P < 0.0001; virus treatment x condition, F(1,38) = 7.594, P = 0.0089; Bonferroni post hoc analysis). Average post-test CPA score of the morphine + LV-GR-RNAi group (18.88 ± 28.19 s, n = 11) was significantly lower than that of the morphine + LV-NC-RNAi group (−163.7 ± 44.49 s, n = 10; two-way ANOVA, Bonferroni post hoc analysis, t = 4.184, P = 0.0003, compared to the post-test CPA score in morphine + LV-GR-RNAi group) (Fig. 5c). This result suggests that chronic morphine-induced increase of GR in the mPFC plays an important role in conditioned context-induced retrieval of morphine withdrawal memory in rats.
Our previous study showed that chronic morphine significantly decreased miR-105 in the mPFC, which was closely related to conditioned context-induced retrieval of morphine withdrawal memory [10]. In this paper, we repeated this experiment and confirmed that chronic morphine indeed could decrease miR-105 in the mPFC. Moreover, here we examined the influence of chronic morphine on the expression of the precursor of miR-105 (pri-miR-105) in the mPFC and the result showed that chronic morphine could decrease the expression of pri-miR-105, suggesting that the decrease of the precursor of miR-105 might be the main reason of chronic morphine-induced decrease of miR-105 in the mPFC. We further studied signaling pathways underlying chronic morphine-induced decrease of miR-105 in the mPFC. The classical signaling pathways underlying the analgesic effect of morphine is that the activation of opioid receptors leads to an inhibition of adenylate cyclase activity and a reduction of cAMP levels as well as a suppression of the activity of protein kinase A [23]. However, based on our present results, it appears that the signaling pathways after the activation of opioid receptors by morphine to induce addiction-related change in miR-105 are different to that of morphine-induced analgesia, that is, morphine decreases the expression of miR-105 in the mPFC via mu receptors-ERK-p90RSK-CREB signaling pathway. It has been known that CREB is a transcription factor that binds to cAMP response element (CRE) as a dimer to activate transcription [3]. Therefore, the direct effect of CREB should not be able to inhibit the expression of miR-105. GR is a transcription factor that can bind to promoter regions to induce transrepression [19]. So, we propose a hypothesis that chronic morphine may decrease the expression of miR-105 in the mPFC by two kinds of transcription factors: CREB as the first transcription factor and GR as the second transcription factor. This hypothesis is confirmed by our present results. To our knowledge, this is the first report showing that morphine first activates one kind of transcription by CREB and then activates a negative transcription factor GR that inhibits the transcription of targeted gene to modulate the expression of genes. In addition to be a transcription factor, GR is also the receptor of glucocorticoids (GCs). Previous studies showed that acutely administered morphine significantly increased the plasma levels of GCs via the action on the hypothalamo-pituitary-adrenocortical system [24, 25]. This increase of GCs plays a key role in mediating the biological effects of morphine, such as the development of behavioral sensitization [26], physical dependence [5], and reward learning [27–29]. However, Milanés et al. reported that in chronically morphine-treated rats, there was no significant change in plasma GCs levels [24]. This result combined with our present finding suggests that chronic morphine do not induce biological effects through increasing local GCs concentration, but may do it by enhancing the effect of GR as a negative transcription factor and this effect of GR may play an important role in the retrieval of drug withdrawal memory. In conclusion, the present results suggest that two kinds of transcription factors, CREB as the first transcription factor and GR as the second transcription factor, mediate chronic morphine-induced decrease in miR-105 in the mPFC of rats and this pathway mediates conditioned context-induced retrieval of morphine withdrawal memory in rats.
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PMC9623242 | Woo Hyun Shin,Kwang Chul Chung | Tollip negatively regulates mitophagy by promoting the mitochondrial processing and cytoplasmic release of PINK1 | 31-10-2022 | Mitochondria,Mitophagy,Parkinson’s disease,PINK1,Tollip | PTEN-induced putative kinase 1 (PINK1) is a serine/threonine kinase that phosphorylates several substrates and exerts neuroprotective effects against stress-induced apoptotic cell death. Mutations in PINK1 have been linked to autosomal recessive forms of Parkinson’s disease (PD). Mitophagy is a type of autophagy that selectively promotes mitochondrial turnover and prevents the accumulation of dysfunctional mitochondria to maintain cellular homeostasis. Toll-interacting protein (Tollip) was initially identified as a negative regulator of IL-1β receptor signaling, suppressing inflammatory TLR signaling cascades. Recently, Tollip has been reported to play a role in autophagy and is implicated in neurodegeneration. In this study, we determined whether Tollip was functionally linked to PINK1-mediated mitophagy. Our results demonstrated that Tollip promoted the mitochondrial processing of PINK1 and altered the localization of PINK1, predominantly to the cytosol. This action was attributed to increased binding of PINK1 to mitochondrial processing peptidase β (MPPβ) and the subsequent increase in MPPβ-mediated mitochondrial PINK1 cleavage. Furthermore, Tollip suppressed mitophagy following carbonyl cyanide m-chlorophenylhydrazone-induced mitochondrial dysfunction. These findings suggest that Tollip inhibits mitophagy via the PINK1/parkin pathway upon mitochondrial damage, leading to the blockade of PINK1-mediated neuroprotection. | Tollip negatively regulates mitophagy by promoting the mitochondrial processing and cytoplasmic release of PINK1
PTEN-induced putative kinase 1 (PINK1) is a serine/threonine kinase that phosphorylates several substrates and exerts neuroprotective effects against stress-induced apoptotic cell death. Mutations in PINK1 have been linked to autosomal recessive forms of Parkinson’s disease (PD). Mitophagy is a type of autophagy that selectively promotes mitochondrial turnover and prevents the accumulation of dysfunctional mitochondria to maintain cellular homeostasis. Toll-interacting protein (Tollip) was initially identified as a negative regulator of IL-1β receptor signaling, suppressing inflammatory TLR signaling cascades. Recently, Tollip has been reported to play a role in autophagy and is implicated in neurodegeneration. In this study, we determined whether Tollip was functionally linked to PINK1-mediated mitophagy. Our results demonstrated that Tollip promoted the mitochondrial processing of PINK1 and altered the localization of PINK1, predominantly to the cytosol. This action was attributed to increased binding of PINK1 to mitochondrial processing peptidase β (MPPβ) and the subsequent increase in MPPβ-mediated mitochondrial PINK1 cleavage. Furthermore, Tollip suppressed mitophagy following carbonyl cyanide m-chlorophenylhydrazone-induced mitochondrial dysfunction. These findings suggest that Tollip inhibits mitophagy via the PINK1/parkin pathway upon mitochondrial damage, leading to the blockade of PINK1-mediated neuroprotection.
A hallmark of Parkinson’s disease (PD) is the slow and gradual degeneration of dopaminergic neurons in the substantia nigra (1). Although the cause of neurological loss in PD was not well known, several genetic mutations related to familial PD have been discovered, including in SNCA, PINK1, PARK2, LRRK2, PARK7, and FBXO7 (2). Recent findings suggest that inadequate mitochondrial quality control is implicated in the pathogenesis of PD and that PINK1 and E3 ligase parkin play an important role in mitophagy, which is a selective autophagy of mitochondria that eliminates damaged mitochondria (3). Since PINK1 has a mitochondrial targeting sequence, it migrates into the mitochondria through the TOM and TIM complex immediately after being synthesized in the cytosol (4). The translocated PINK1 is then consecutively processed by mitochondrial proteases like MPP and PARL (5, 6). The processed cytosolic PINK1 is then targeted to the N-end rule pathway (7) and ultimately degraded by the proteasome (8, 9). Mitophagy is a complexly controlled process in which cells break down defective mitochondria to maintain a mitochondria population. When the mitochondrial membrane potential is depleted, PINK1 accumulates on the outer membrane (OMM) and forms a large complex with parkin on the OMM surface (10). Parkin phosphorylated by PINK1 (11, 12) links ubiquitin chains to various substrates on the mitochondria. These ubiquitinated proteins can act as adaptors to sequestosome-1 (SQSTM1 or p62) and facilitate the removal of defective mitochondria by autophagosome (13). The ubiquitination of substrates is delicately controlled by the ubiquitin proteasome system (UPS) and deubiquitin proteases (DUBs), and dysfuction of UPS and DUBs is directly related to PD (14). Toll-like receptors (TLRs) are evolutionarily conserved receptor groups that induce interleukins and other inflammatory proteins to cause inflammatory reactions (15). In TLR signaling cascades, adaptor protein Tollip acts as an inhibitory factor (16-18). When stimulated by IL-1 or LPS, Tollip forms a complex with IL-1 receptor (IL-1R) and IRAK1 and suppresses the kinase activity of IRAK1 (19). We previously found a new mechanism for PINK1-mediated regulation of TAK1 and TRAF6 activation during sequential inflammatory signal cascades (20). In addition, we further clarified that PINK1 binds directly to Tollip and IRAK1 under IL-1β stimulation and accelerates the separation of Tollip from IRAK1, ultimately promoting IL-1β-mediated inflammatory signals (21). Interestingly, Tollip has recently been reported to play a role in autophagy and its alteration is implicated in neurodegeneration, such as in Alzheimer’s disease (22, 23) and Huntington’s disease (24). Tollip plays an important role in the autophagic clearance of cytotoxic protein aggregates by linking ubiquitin-modified protein aggregates to autophagosome (24). Based on these findings, it is probable that Tollip somehow affects PINK1 function within the mitochondria, possibly affecting PINK1-mediated mitophagy. In the present study, we investigated the effect of Tollip on PINK1-mediated mitophagy following mitochondrial depolarization. We found that Tollip suppressed mitophagy by increasing mitochondrial processing of PINK1 and the release of cleaved PINK1 into the cytosol. These results suggest that Tollip negatively regulates mitophagy by affecting the PINK1 processing.
To investigate the regulatory role of Tollip in mitophagy, we examined whether PINK1 binds to Tollip in mammalian cells. Ectopically overexpressed PINK1 was bound to Tollip in HEK293 cells (Supplementary Fig. 1A), and the binding between endogenous PINK1 and Tollip in human neuroblastoma SH-SY5Y cells was also confirmed (Supplementary Fig. 1B). In addition, endogenous Tollip and PINK1 was colocalized primarily outside the nuclei of SH-SY5Y cells (Supplementary Fig. 1C). These data suggest that PINK1 binds to Tollip in a specific way, and this binding mainly occurs in the cytosolic area.
As shown in Supplementary Fig. 1A, co-transfection of Tollip and PINK1 resulted in a noticeable increase in the level of cleaved PINK1 accompanied by a decrease of the larger precursor PINK1 form compared with that of cells transfected with PINK1 alone. Based on this finding, we hypothesized that Tollip may affect PINK1 processing, thereby enhancing the mitochondrial processing of PINK1 and consequently leading to the accumulation of cleaved PINK1 in the cytosol. The cleavage of exogenous PINK1 was significantly increased in a dose-dependent manner by exogenous Tollip (Supplementary Fig. 2A). Also, the increase in the extent of PINK1 cleavage induced by Tollip was not recovered by MG132 treatment (Supplementary Fig. 2B, C), indicating that Tollip affected the cleavage of PINK1, but not its degradation through the proteasome machinery. This finding was further confirmed by comparing the relative levels of PINK1 in Tollip-null and control MEFs (Supplementary Fig. 2D). When treated with CCCP, full-length PINK1 level was increased in both Tollip+/+ MEF and Tollip−/− MEF cells (Supplementary Fig. 2D, E). However, the amount of full-length PINK1 in Tollip+/+ MEFs was markedly reduced compared with that in Tollip−/− MEFs. These results indicate that Tollip increases the mitochondrial processing of PINK1 in mammalian cells.
The cleavage of PINK1 is closely related to its subcellular localization (8). Co-expression of PINK1 and Tollip increased cleaved PINK1 levels in the cytosol fraction compared to PINK1 alone (Fig. 1A). In addition, Tollip overexpression in cells treated with MG132 also caused increased levels of cleaved endogenous PINK1 within the cytosol fraction (Fig. 1B). Moreover, the amount of full-length PINK1 was markedly increased in the cell fraction containing membrane organelles, whereas cleaved PINK1 level was reduced in the cytosol of cells transfected with Tollip-specific siRNA (Fig. 1C). We also compared the intracellular localization of PINK1 in Tollip−/− and control Tollip+/+ MEFs. Tollip induced a marked increase in cytosolic PINK1 levels, which had an equivalent loss in mitochondrial PINK1 levels (Fig. 1D). Overall, these data indicate that Tollip played an important role in the modulation of PINK1 cleavage and localization.
It is widely known that MPP and PARL are involved in the mitochondrial processing of PINK1 through sequential proteolytic cleavage (5, 6). We previously reported that hTERT inhibits the processing of mitochondrial PINK1 and its cytoplasmic release, positively affecting mitophagy (25), which is in contrast to the role of Tollip. Based on these previous findings, we aimed to determine whether a similar mechanism may apply to the Tollip-mediated increase in PINK1 processing. Ectopically expressed MPPβ physically bound to Tollip (Supplementary Fig. 3A). In addition, Tollip overexpression markedly increased the binding affinity between PINK1 and MPPβ (Supplementary Fig. 3B). Conversely, the binding of PINK1 to MPPβ decreased in Tollip siRNA-transfected cells (Supplementary Fig. 3C). These data indicate that Tollip increases the processing of mitochondrial PINK1 by enhancing the interaction between PINK1 and MPPβ, which may consequently promote the MPP-mediated cleavage of PINK1.
We investigated whether Tollip-mediated increases in mitochondrial PINK1 cleavage and its cytoplasmic release led to reduced mitophagy rates. CCCP treatment increased levels of LC3-II, the secondary form of autophagy marker LC3, while the accumulation of LC3-II was significantly decreased with Tollip overexpression (Fig. 2A). In addition, Tollip had a negative effect on the formation of endogenous LC3-II, similar to that observed when LC3 was added exogenously (Fig. 2B). CCCP treatment increased LC3-II formation in both Tollip−/− and Tollip+/+ MEFs, but the band intensity of LC3-II in Tollip+/+ MEFs was significantly lower than that in Tollip−/− MEFs (Fig. 2C). Furthermore, although the formation of LC3-II was considerably decreased by Tollip in PINK1+/+ MEFs, there was no comparable change in PINK1−/− MEFs (Fig. 2D), suggesting an essential role in PINK1 triggering autophagy. The protein BNIP3L is also commonly used as a marker of mitophagy (26). Tollip overexpression decreased endogenous BNIP3L levels, comparable to the outcome observed in LC3-II (Fig. 2E). Collectively, these data indicate that Tollip promotes PINK1 cleavage, thereby negatively modulating the levels of autophagy and mitophagy markers.
We investigated whether the PINK1-binding to both TOM20 and parkin could be affected by Tollip. As shown in Fig. 3A and B, the interaction between PINK1 and TOM20/parkin were all increased under CCCP treatment. The binding affinity of PINK1 to these two proteins was significantly decreased by the co-expression of Tollip. Under CCCP treatment the binding of PINK1 to TOM20 was also significantly increased by knockdown of endogenous Tollip (Fig. 3C). Moreover, PINK1 binding to TOM20 was stronger in Tollip−/− MEFs compared with that in Tollip+/+ MEFs (Fig. 3D). These data indicate that Tollip decreases the tight binding of PINK1 to parkin, as well as to TOM20, following CCCP treatment.
Finally, we investigated whether Tollip would inhibit mitophagy and eliminate damaged mitochondria. As shown in Fig. 4A, Tollip overexpression resulted in reduced rates of mitophagy, but it was not shown in PINK1-knockdown cells. CCCP treatment triggered mitochondrial depolarization, while Tollip overexpression exacerbated the CCCP-induced loss of ΔΨm in PINK1+/+ MEFs (Fig. 4B). Further, reduction of intracellular ATP induced by CCCP was exacerbated by Tollip 72 h after CCCP treatment (Fig. 4C). Because mitochondrial biogenesis is closely associated with the process of proper mitophagy and the removal of damaged mitochondria, it was expected that Tollip could also suppress mitophagy, resulting in a decrease in mitochondrial biogenesis and ATP production. Finally, we investigated whether Tollip affected the levels of typical mitochondrial proteins under CCCP treatment and its potential mechanism. Overexpression of Tollip followed by CCCP treatment rescued the decreased mitochondrial protein levels, restoring them to that of cells undergoing CCCP treatment alone. (Fig. 4D). In conclusion, the present study proposes that Tollip acts as a novel regulator of PINK1 processing and decreases mitophagy in response to mitochondrial damage caused by CCCP.
Mitophagy plays an important role in the quality control of mitochondria and serves as the major process for maintaining mitochondrial network homeostasis (3). Mitochondrial dysfunction can induce autophagy-dependent cell death (27), and mitophagy protects cells from mitotoxicity by removing damaged mitochondria. Various mutations in PINK1 gene are associated with autosomal recessive early onset PD, and mitochondrial dysfunction has been observed in PINK1-null animal model (28). PINK1, along with parkin, plays an important role in the execution and regulation of mitophagy (29). Although the sequential events of PINK1 activation and its regulatory function in mitophagy are well known, the regulators controlling the binding between PINK1 and these targets are unclear. PINK1 is also involved in the inflammatory signaling pathway of IL-1β by upregulating the components of TRAF6 and TAK1 (20). Furthermore, PINK1 positively modulates the interaction between Tollip and IRAK1, promoting IL-1β-mediated signaling (21). The modulatory function of PINK1 in the mitochondria is also associated with the components of the neuroinflammatory signaling cascade, such as TRAF6. PINK1 stabilization on damaged mitochondria requires TRAF6-mediated and Lys63-linked ubiquitination of PINK1 and the complex formation with SARM1 and TRAF6 (30). Many recent studies have demonstrated a crucial role of Tollip in the progression and control of autophagy. Tollip also acts as a core regulator of endosomal compartment and regulates cargo trafficking by interacting with the TOM1 (31). Closely associated with these functions, Tollip has been implicated in many neurodegenerative diseases. For example, Tollip is associated with autophagic dysfunction in Alzheimer’s disease (22, 23). Moreover, autophagic clearance of Huntington’s disease-related polyQ protein is regulated by Tollip (24) and Tollip mediates parkin-dependent trafficking of mitochondrial-derived vesicles (MDV) (32). Based on these findings, we explored the possible effects of Tollip on mitophagy. Our findings displayed that Tollip increases the cleavage of mitochondrial PINK1 and its cytosolic release, resulting in the negative regulation of mitophagy. MPP and PARL are two mitochondrial proteases involved in sequential processing of PINK1 (5). Mutations in MPP and PARL result in dysfunction of proper processing of many mitochondrial proteins, consequently resulting in various mitochondria-related diseases (33). Here, we demonstrated that Tollip directly binds to MPPβ and promotes the interaction of PINK1 and MPPβ, facilitating the cytosolic release of cleaved PINK1. As described previously, the cleavage status of PINK1 is important for activation of the PINK1/parkin pathway. Since only full-length PINK1 can induce parkin recruitment and subsequent recruitment of autophagy adapters and LC3-II, we hypothesized that enhanced cleavage of PINK1 by Tollip may be linked with a decrease in mitophagy levels. Our hypothesis was supported by the finding that Tollip markedly reduces the formation of LC3-II and the amount of BNIP3L. Considering the role of PINK1 in mitophagy, elucidation of the underlying mechanisms of PINK1 cleavage and stability are very important. Although many studies have investigated the subcellular localization of PINK1 (34-36), little is known regarding the mechanisms regulating these processes and the specific factors involved. One of our findings is that CHIP, is an E3 ligase of PINK1, which promotes PINK1 ubiquitination and degradation (37). Moreover, we previously demonstrated that hTERT decreases the processing of PINK1 and regulates mitophagy (25). In the present study, we proposed an additional regulator for the PINK1 processing and localization. We also demonstrated that Tollip-mediated processing and cytosolic release of PINK1 reduced mitophagy, suggesting that Tollip may act as a novel factor involved in PD progression. In conclusion, based on the results from the present study, we propose a novel regulatory pathway for PINK1 processing and localization. Furthermore, our findings demonstrated that Tollip-mediated increase of mitochondrial PINK1 cleavage and its cytoplasmic release could reduce mitophagy, and defect in those modulation of PINK1 activity might contribute to the progression of PD.
The mammalian construct encoding Myc-tagged human wild-type PINK1 (pBOS-3X-Myc-hPINK1-WT) was kindly provided by J. Chung (Seoul National University, Seoul, Korea). The plasmid encoding Xpress-tagged Tollip and FLAG-tagged MPPβ were generated by PCR amplification using PrimeSTAR HS DNA polymerase (TAKARA, Shiga, Japan) and subcloned into a pcDNA3 or pRK5 vector. Small interfering RNAs (siRNAs) targeting human Tollip and control scrambled siRNAs were designed and synthesized by Thermo Fisher Scientific (Waltham, Massachusetts, USA).
Mouse embryonic fibroblasts (MEFs) derived from PINK1-null (PINK1−/−) and control (PINK1+/+) mice were provided by J. Shen (Harvard Medical School, Boston, MA, USA). Human embryonic kidney 293 (HEK293) cells and human neuroblastoma SH-SY5Y cells were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). Cells were maintained in Dulbecco’s Modified Eagle Medium (DMEM) containing 10% fetal bovine serum and 100 U/ml penicillin-streptomycin (Invitrogen, Carlsbad, CA, USA) and grown at 37°C in 5% CO2. All DNA transfections were performed using Lipofectamine and PLUSTM Reagent (Invitrogen), according to the manufacturer’s protocol. The cells were rinsed twice with ice-cold PBS and lysed in lysis buffer containing 50 mM Tris (pH 7.4), 1.0% Nonidet P-40, 150 mM NaCl, 10% glycerol, and a protease inhibitor cocktail.
Cell lysates containing 1 mg protein were incubated with 0.5 μg of appropriate antibody overnight at 4°C, and then with an equal volume of Protein A-Sepharose beads for 2 h at 4°C with gentle rotation. The beads were pelleted by centrifugation and washed five times with lysis buffer. The immunocomplexes were dissociated by boiling in sample buffer, resolved by SDS-PAGE, and transferred to nitrocellulose membranes. The membranes were blocked in Tris-buffered saline with Tween 20 (TBST) buffer containing 5% nonfat dry milk, and then incubated with the primary antibodies overnight at 4°C in 3% nonfat dry milk. The membranes were then washed with TBST, incubated for 1 h with HRP-conjugated secondary IgG, washed again with TBST, and visualized using ECL reagent (Abclon, Seoul, Korea).
All statistical analyses were performed using an unpaired Student’s t-test and IBM SPSS statistical analysis software (version 23.0). All values are expressed as the mean ± standard error of the mean (SEM).
Immunocytochemistry analysis, analysis of mitochondrial membrane potential, and determination of intracellular ATP level are described in the supplementary information. | true | true | true |
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PMC9623364 | 36305015 | Andrea Brancale,Kara Carter,Leen Delang,Jerome Deval,David Durantel,Brian G Gentry,Robert Jordan,Justin G. Julander,Michael K. Lo,Maria-Jesús Pérez-Pérez,Luis M. Schang,Katherine L. Seley-Radtke,Pei-Yong Shi,Subhash G. Vasudevan,Richard J. Whitley,Jessica R. Spengler | Meeting report: 34th international conference on antiviral research | 27-10-2022 | Virus,antiviral,therapeutics,vaccines,ICAR,ISAR meeting | As a result of the multiple gathering and travels restrictions during the SARS-CoV-2 pandemic, the annual meeting of the International Society for Antiviral Research (ISAR), the International Conference on Antiviral Research (ICAR), could not be held in person in 2021. Nonetheless, ISAR successfully organized a remote conference, retaining the most critical aspects of all ICARs, a collegiate gathering of researchers in academia, industry, government and non-governmental institutions working to develop, identify, and evaluate effective antiviral therapy for the benefit of all human beings. This article highlights the 2021 remote meeting, which presented the advances and objectives of antiviral and vaccine discovery, research, and development. The meeting resulted in a dynamic and effective exchange of ideas and information, positively impacting the prompt progress towards new and effective prophylaxis and therapeutics. | Meeting report: 34th international conference on antiviral research
As a result of the multiple gathering and travels restrictions during the SARS-CoV-2 pandemic, the annual meeting of the International Society for Antiviral Research (ISAR), the International Conference on Antiviral Research (ICAR), could not be held in person in 2021. Nonetheless, ISAR successfully organized a remote conference, retaining the most critical aspects of all ICARs, a collegiate gathering of researchers in academia, industry, government and non-governmental institutions working to develop, identify, and evaluate effective antiviral therapy for the benefit of all human beings. This article highlights the 2021 remote meeting, which presented the advances and objectives of antiviral and vaccine discovery, research, and development. The meeting resulted in a dynamic and effective exchange of ideas and information, positively impacting the prompt progress towards new and effective prophylaxis and therapeutics.
As a result of the SARS-CoV-2/COVID-19 pandemic, ICAR 2021 was held virtually from Monday, March 22, through Friday, March 26, 2021, using the OnAIR virtual event platform. ISAR was fully committed to make this virtual meeting an interactive experience by preserving as many of the components of past in-person ICAR meetings as possible. Of course, the attendees could not enjoy casual (and not so casual) conversations over coffee breaks, cheese and wine poster sessions, lunches, dinners, or banquets. Nonetheless, they were given ample access to all speakers and to each other, and the sessions were marked by lively interactions between attendees, presenters, and panel members. The virtual platform used, and the decision of having the sessions pre-recorded and available for pre- (and post-) viewing, were conducive to many productive interactions. The interactivity was particularly highlighted during the speed dating, women in science, poster awards, and PechaKucha competition activities. This last activity was as lively and enjoyable as usual, although some presenters came to realize the challenges of having complex videos in their slides during remote presentations. As ISAR is an international society, the interactive live sessions were organized around two prime-time periods to accommodate speakers and attendees from around the world, held 11:00 am through 1:00 pm and 8:00 pm through 10:00pm EST In addition, attendees were able to listen to the talks, which were all recorded, at their leisure. Similar to in-person meetings of previous years, the virtual ICAR2021 program was outstanding, including topical keynote lectures on SARS-CoV-2 vaccines, antivirals, and a variety of models, as well as on pandemic preparedness. As other viruses are not going away, the problems caused by them still demand new antivirals. The sessions on influenza, respiratory syncytial virus (RSV), herpes, hepatitis, retroviruses, and arboviruses were complemented with sessions on therapeutics and innovations. Altogether, the program provided an excellent update on the many advances in the field, from understanding the pathogenesis of viral diseases to optimizing new chemical entities targeting them. Below are summaries of the presentations prepared by the session Chairs and other contributors to provide an overview of the excellence of ICAR2021.
Louis Bont discussed the incidence and impact of RSV in the Netherlands and globally, noting severe disease in both pediatric and elderly patients. While currently no treatment is approved for RSV bronchiolitis, Louis presented several considerations that may improve intervention options: timing of treatment, simultaneous use of immunomodulators, and the promise of new antibodies that are both efficacious and stable over extended time periods. The F protein of RSV, a key target for antivirals, has two conformational variants: a functional pre-fusion confirmation and an inert post-fusion confirmation. Epitopes differ between conformations; antibodies directed at the pre-fusion epitopes are highly neutralizing, while those directed against post-fusion epitopes are not. Numerous RSV antivirals are under development, including antibodies (mostly against F), fusion inhibitors (also targeting F), and nucleoside analogues targeting intracellular RSV. A healthy human challenge model for RSV is used to evaluate antiviral-mediated replication inhibition in low-risk populations. However, high efficacy in vitro often does not translate to improved clinical outcomes. For example, presatovir, a fusion inhibitor, does not decrease viral loads or improve the clinical response in patients treated 4–5 days after symptom onset. Similarly, while inhaled ALX-0171, a formulation of antibodies against RSV F, dose-dependently decrease RSV replication in airways of infected infants treated 3 days after onset of symptoms, it did not lead to a positive clinical response. These studies and data from other respiratory virus antivirals suggest that clinical development of RSV treatments should focus on initiating treatment earlier, within 24 h of symptom onset. Is stopping viral replication the way forward, or should other contributors to viral pathogenesis also be modulated? Louis discussed how immunopathogenesis may explain challenges in developing antivirals, and suggested immune system suppression in conjunction with antiviral treatment. In particular, high levels of neutrophils are seen in the airways of RSV-infected children; these are phagocytosing cells that degranulate and form neutrophil extracellular traps (NETs). Checkpoint treatment can decrease formation of NETosis and may work in conjunction with antivirals to improve outcome. Finally, Louis discussed vaccines and immunoprophylaxis. He highlighted next-generation monoclonal antibodies (mAb) with demonstrated potency and notably long half-lives (MEDI8897), which allow a single prophylactic injection to effectively prevent severe RSV in the first year of life. However, he cautioned that mAb-resistant RSV mutants have been selected in vitro to these newly highly promising treatments, suggesting that though they are not yet seen in nature, they may occur, as has happened in clinical trials with other candidate mAb.
Rachel Fearns centered her lecture in the context of the work of her lab, which studies RNA-dependent RNA polymerases (RdRP) of non-segmented, negative-strand RNA viruses (nsNSVs), particularly that of RSV. In RSV, as in other nsNSVs, the genome is used both for transcription, providing capped and polyadenylated mRNAs, and for replication, which produces the encapsidated antigenome RNA. Her group has found that RSV polymerase initiates RNA synthesis from two different sites: position 1U in the promoter region begins RNA replication, while position 3C begins transcription, the latter being dominant. Rachel's lab is studying in detail how start site usage is controlled using cell-based minigenome and cell-free biochemical assays. The evidence to date points to relative concentrations of NTP, particularly ATP and GTP, and promoter sequences as the main determining factors for start site usage. The group has also addressed whether similar control strategies are used by other nsNSVs. So far, Rachel's group has found that human metapneumovirus (HMPV), which, like RSV, belongs to the family Pneumoviridae, has a similar promoter sequence and probably uses a similar mechanism as RSV. However, viruses in the families Paramyxoviridae or Rhabdoviridae (e.g., vesicular stomatitis virus [VSV]) appear to have a single initiation site at 1U. These data from the biochemical assays examining RSV initiation mechanisms were further analysed at the structural level. During the formation of the initiation complex, the priming loop contains an aromatic or proline residue that interacts through stacking interactions with the incoming NTP. The VSV priming loop extends into the active site, whereas the putative RSV priming loop does not. Thus, Rachel's group performed mutational analysis of the aromatic and proline residues in this presumed RSV priming loop. The data indicated that Pro1261 indeed stabilizes the initiation complex and may also function as a priming residue. When mutated to Ala, the LP1261A defect was overcome with high concentrations of NTPs or by changing the divalent cation from Mg2+ to Mn2+. The more open structure of RSV, in which the priming loop does not project into the active site, might be required for the initiation complex to be able to start from two different positions (1U or 3C). Dissecting the similarities and differences among the polymerases of nsNSVs will be very helpful for the development of broad antiviral strategies.
In his talk, Ron Fouchier emphasized the zoonotic nature of many respiratory viruses and the importance of animal hosts in pandemics, and discussed vaccination of animals and limiting exposure to potentially infectious hosts as approaches to mitigate human disease. He spoke about a generalized lack of global preparedness for pandemics and a need for pro-active programs, preferably intervening when changes occur in animal populations, or at least shortly after disease spreads to humans. Ideally, spillover into domestic animals and humans could be prevented, but if it did occur, broadly acting antivirals would be available. Influenza is an important example of a zoonotic agent with pandemic potential. Influenza pandemics are frequent and range in severity, and mortality is high in interpandemic years. A single zoonotic event becomes a threat when it acquires the ability for human-to-human transmission. Ron presented ongoing research on influenza to identify high-risk viruses among a diverse virus family and to design broadly active vaccine candidates against these viruses. When investigating whether bird influenza virus could acquire the ability to transmit between mammalian models through droplets and aerosols, Ron's group identified three traits required to confer this phenotype: (1) a switch in receptor specificity; (2) pH stabilization of the HA protein; and (3) polymerase adaptation that allows replication at lower temperatures in mammalian upper airways. This knowledge can now be applied in “smart surveillance” to permit early intervention, stopping outbreaks in domestic species and preventing putative pandemics when changes detected in surveyed virus samples indicate increased pandemic potential. Ron finished by discussing pre-pandemic vaccine design and development of a universal H5 vaccine candidate. He noted potential added value in using biological response modifiers in treatment regimens and a desire to develop improved options for use in pandemic scenarios. In summary, Ron expressed the need for better preparedness for future pandemics via the development and use of diagnostics and surveillance networks, basic research of infectious disease pandemic threats, preventive actions (e.g., at the infected host-human interface), novel vaccine and antiviral development, and availability of generic drugs that may prevent co-morbidities and improve speed of recovery.
Carlos Bueno presented the fourth talk of the session. He started by highlighting that RSV is able to interfere with the immune system and the host response. Interfering with the immune response could thus be exploited for therapeutic strategies against RSV. Carlos’ presentation centered on cyanidin, a flavonoid present in red berries and other fruits. The anti-inflammatory effects of this natural product have been described, and in his presentation, Carlos analyzed its antiviral activity and immunomodulatory properties against RSV in vitro and in vivo. In particular, he showed that cyanidin modulated cytokine production in RSV-infected epithelial cells and TLR-stimulated epithelial cells. Treatment with cyanidin in a murine model of RSV improved the course of the acute disease, particularly evidenced by reduced RSV titers in the lungs and attenuated airway inflammation. The presented data support considering cyanidin as a potential therapeutic alternative against RSV infection.
The presentation by Scott Gibson illustrated different outcomes of drug combinations in treating influenza depending on the pre-existence of drug-resistant mutations. Scott used two drugs with different mechanisms of action: baloxavir marboxil, an inhibitor of influenza cap-dependent endonuclease, and oseltamivir, a well-established neuraminidase inhibitor. A baloxavir marboxil-resistant influenza A/California/04/2009 (H1N1pdm) virus, selected by passaging the virus in increasing concentrations of baloxavir marboxil, and an oseltamivir-resistant clinical isolate influenza, A/Hong Kong/2369/2009 (H1N1pdm), were used to challenge BALB/c mice. Combination therapy was less protective than oseltamivir monotherapy against the baloxavir-resistant virus, suggesting strong antagonism of the oseltamivir-baloxavir combination against a baloxavir-resistant virus. Conversely, combination therapy provided higher protection against oseltamivir-resistant influenza virus compared to oseltamivir monotherapy. Baloxavir marboxil alone or in combination with oseltamivir showed a potent antiviral effect against an oseltamivir-resistant, highly pathogenic avian influenza virus, A/Taiwan/1/2017 H7N9.
Lorena Sanchez-Felipe presented the results of a large collaboration between multiple institutions testing the efficacy of a SARS-CoV-2 vaccine candidate based on the live-attenuated YF17D vaccine. Three constructs containing the wild-type protein expressing S1 and S2 and a cleavage mutant version that only expresses S9 or one that expresses only S1, all inserted between E and NS1-5, were produced. All constructs expressed the recombinant genes and all produced small plaque phenotype, demonstrating attenuation. The vaccines were tested in a hamster challenge model; the S1-expressing construct was not highly immunogenic or protective, but the other two were, stimulating strong antibody responses and protecting against a high dose challenge (2 × 105 PFU/animal). Large reductions in viral infectivity and RNA levels in the lungs and reduced pathology were noted at necropsy. Cell responses were evaluated in a mouse model and, interestingly, the S1-expressing construct, which was not efficacious in hamsters, induced the highest levels of CD4 and CD8 cells expressing interferon (IFN)-γ and tumor necrosis factor (TNF)-α. As expected, the vaccine also protects mice against lethal YFV challenge. In non-human primates (NHP), the S0 vaccine also induced high levels of antibodies, particularly neutralizing antibodies, and decreased viral replication. In a second hamster experiment, a high single dose of vaccine (104 PFU) produced strong antibody responses and protection in as little as 10 days. These results were published in Nature.
William Lee reflected on thirty years of antiviral discovery and development at Gilead Sciences. As Head of Research for over twenty years, he defined innovation as the fusion of ideas, invention, and execution that comes together to create a product that improves the human experience. Innovation has many origins and takes focus, time, resources, and passion. Over the past three decades, such innovations have led to treatments for influenza, hepatitis B virus (HBV), HIV, and SARS-CoV-2 infections, as well as to the cure for hepatitis C virus (HCV). To illustrate his personal experience in antiviral innovation, he reviewed the evolution of single-tablet HIV drug discovery and treatment, including Atripla in 2006, Stribild in combination in 2012, Genvoya in 2015, and Biktarvy in 2018. William emphasized that besides developing new medicine, innovation should also include access to medicine in low- and middle-income populations. Finally, he presented some exciting results on using a broad neutralizing antibody (PGT121) and TLR7 agonist to induce a simian-human immunodeficiency virus (SHIV) cure in NHPs.
Tomas Cihlar discussed how countermeasures for pandemic preparedness should include surveillance, diagnostics, prevention, and treatment. Besides influenza virus, the WHO has prioritized other viral pathogens with pandemic potential, including filoviruses (Ebola and Marburg viruses), coronaviruses (MERS-CoV, SARS-CoV-1, and SARS-CoV-2), paramyxoviruses (Nipah virus and henipaviruses), arenaviruses (Lassa virus); flaviviruses (Zika, dengue, West Nile, and yellow fever viruses), alphaviruses (chikungunya virus), poxviruses (smallpox virus), and “disease X,” the next emerging pathogen. He reviewed (i) the available treatments for WHO priority pathogens; (ii) two regulatory approval paths for medicine (regular approval based on safety and efficacy from clinical trials and “Animal Rule”); (iii) then-current COVID-19 therapy (antibody cocktails and small molecule drugs such as remdesivir); (iv) two complementary approaches for developing antivirals (antibody and small molecule inhibitors); (v) antiviral approaches targeting viral proteins and host proteins/pathways; (vi) options for developing virus-specific and broad-spectrum antivirals; and (vii) pros and cons of repurposing clinical drugs for new indications. To achieve end-to-end drug discovery, consortia, coalitions, and partnerships are needed to coordinate funding and execution between government, industry, academia, and non-profit organizations.
Philip Dormitzer reviewed the lightspeed journey of Pfizer/BioNTech COVID-19 vaccine (BTN162b2) development. Five innovations have contributed to the rapid development of the COVID-19 mRNA vaccine: synthetic biology, RNA platform, lipid nanoparticles for RNA delivery, spike antigen stabilized in the prefusion conformation, and innovative clinical trials in both USA and Germany using different age groups (18–55 and 65–85 years of age) in parallel. The speed of vaccine development is essential for rapid control of epidemics and pandemics. For example, vaccine development was too slow during the 2009 H1N1 pandemic. Philip showcased Novartis's pre-developed self-amplifying RNA platform, which allows quick insertion of a viral antigen, that was deployed when responding to the H7N9 flu outbreak in 2013, just eight days after the viral sequence became available. Finally, he discussed the approaches to tackle then newly emerged SARS-CoV-2 variants to safeguard the efficacy of COVID-19 vaccine. The neutralizing antibody results, together with real-world effectiveness, suggest that BTN162b2 vaccine remained efficacious against those variants. If needed, boosting with the same BTN162b2 vaccine could enhance immune protection against new variants.
Pei-Yong Shi presented two reverse-genetics systems of SARS-CoV-2, an infectious cDNA clone and a trans-complementation system for single-round SARS-CoV-2 infection. The single-round SARS-CoV-2 infection system has the potential to be used at biosafety level 2 (BSL2), which would accelerate research on SARS-CoV-2 antivirals, vaccines, and virology by opening the field to researchers without access to BSL3 containment. Using these systems, Pei-Yong's team has developed reporter SARS-CoV-2 constructs expressing luciferase and other reporter genes, enabling high-throughput antiviral screening and testing neutralizing antibodies. These assays enabled the rapid development of Pfizer/BioNTech's vaccine. These genetic systems have also allowed studies of the biology of SARS-CoV-2 variants and efficacy of vaccine-elicited antibody neutralization against these variants. For example, the D614G substitution in the spike protein was the first prevalent variant discovered in SARS-CoV-2. Using hamster and human primary airway cultures, Pei-Yong and his collaborators showed that this substitution increases viral replication in the upper respiratory tract in infected hamsters, promoting viral transmission. Their results also showed that the spike N501Y substitution of variant B.1.1.7 (alpha), the prevalent variant at the time, enhanced viral spike/hACE2 receptor affinity, leading to increased viral transmission.
Lassa fever and other arenaviral haemorrhagic fevers are listed as priority viral diseases by the WHO. Brian Gowen showed that EIDD-2749, a uridine ribonucleoside analog, is a broad-spectrum antiviral with good oral pharmacokinetics and once daily dosage. EIDD-2749 has EC90 of 3.4 nM and 6.2 nM against Tacaribe virus and Junin virus in Vero cell culture. Doses of 10 mg/kg EIDD-2749 fully protected Tacaribe virus-infected AG129 mice from death even when treatment was initiated 7 days post exposure. Infected animals treated with the compound had undetectable viral loads in organs and sera. Moreover, even treatment with as low as 0.5 mg/kg EIDD-2749 fully protected AG129 mice infected with Tacaribe virus. Additionally, treatment with 10 mg/kg EIDD-2749 starting 7 days post exposure fully protected AG129 mice infected with Junin virus from death, viremia, disease, and weight loss. Overall, the study shows that EIDD-2749 is a promising potential therapeutic for treating disease caused by arenaviruses.
To review alphavirus replication, Dahai Luo first presented the structures and functions of viral nsP1 (MTase/GTPase), nsP2 (helicase/protease), nsP3 (with yet undefined roles in replication and interactions with host proteins), and nsP4 (RdRP). Next, he presented the crystal structure of Ross River virus snP4 polymerase domain at a 2.6 Å resolution. The structure showed considerable flexibility, and even conserved domains were disordered. Although the polymerase motifs were not tightly folded, the protein retained robust RNA polymerization activity, albeit weaker than that of dengue NS5. Finally, Dahai presented the cryogenic electron microscopy structure of chikungunya virus nsP1 protein. The structure showed twelve NSP1 molecules forming a crown-like ring with a central channel ∼7 nM in diameter that allows the transportation of viral RNA and proteins. The top ring contains the catalytic domains (MTase/GTPase), and the bottom ring contains the membrane-associated domain serving as the neck for assembly of the viral replication complex. These studies are breakthroughs towards obtaining high-resolution structures of the nsP1-4 replication complex, which could provide new approaches for antiviral discovery and rational drug design.
No antiviral or vaccine is clinically approved for norovirus treatment and prevention. The goal of the study presented by Jana Van Dycke was to identify inhibitors of norovirus. Toward this goal, they found that compound DC40_2267 acts as a viral protease inhibitor. It inhibited the replication of mouse norovirus and human norovirus replicon with EC50 of 40 nM and 10 nM, respectively. The compound also showed antiviral activity in a cell-based protease assay. Resistance selection mapped escape mutations to the viral protease. Micro-injection of human norovirus into the yolk of zebrafish larvae showed viral replication, allowing in vivo testing of antivirals. Treating the infected zebrafish larvae with compound DC40_2267 in water led to a reduction of viral RNA. The compound also showed antiviral activity in a mouse model, suggesting that DC40_2267 warrants further development for treating norovirus.
The coronavirus 2019 (COVID-19) vaccines session was an exciting discussion of both considerations and evaluation of antibody responses to SARS-CoV-2 and vaccines, as well as presentation of the recent data on vaccines developed by Moderna and Janssen.
Ann Arvin provided a timely presentation on vaccine enhanced disease (VAED) in light of considerations for COVID-19 vaccine development and use. Broadly, she discussed potential mechanisms of immune enhancement, clinical experience with immune enhancement, and implications for COVID-19 vaccine development. Mechanisms of humoral and cellular immunity that protect against viruses may also theoretically enhance illness when an individual with pre-existing immunity encounters the pathogen; this can occur via antibody-dependent enhancement (Fab-dependent or Fc-mediated) and/or cellular immunopathology (T cell responses that trigger exaggerated inflammatory cytokines or skewed T cell responses that interfere with effective immune control). However, clinical evidence for VAED is very rare. Noted exceptions include antibody-dependent enhancement in rare cases of secondary dengue infection, and VAED in children given formalin-inactivated RSV and measles vaccines in the 1960s. Ann also spoke about influenza, a virus against which pre-existing immunity is only partially protective and natural immunity generally does not result in disease enhancement. She cited the exception of the 2009 H1N1 pandemic, during which cases of potential immune enhancement were reported. Ann finished by focusing on the implications for COVID-19 vaccine development. She provided evidence that cross-reactivity between coronaviruses confers a protective effect, and as additional support for the benefits of pre-existing immunity, she noted that more rapid adaptive immune responses correlate with improved outcomes. She discussed multi-inflammatory syndrome in children, which was poorly understood early during the early months of the COVID-19 pandemic; however, the data do not suggest that aberrant adaptive immune responses induce this disorder. Ann emphasized that preventing SARS-CoV2 infection remains the best approach. Antibody- or T cell-dependent responses have not been associated with any negative findings in COVID-19 vaccine studies up to this point, and no immune pathology was associated with vaccine candidates under investigation. Importantly, with the above examples, Ann also discussed the limitations of in vitro and animal model systems in investigating VAED potential. She emphasized that VAED due to pre-existing immunity cannot be differentiated by clinical signs or biomarkers. Therefore, evaluating the incidence and respective risk of these events requires rigorous, large-scale clinical trials to assess vaccine efficacy and adverse events, and these evaluations must continue in post-licensing surveillance efforts.
Florian Krammer lectured about antibody responses to SARS-CoV-2 spike protein after infection and vaccination. He started with a review of the structure of the virus, focusing on the spike protein, and described how his group had started optimizing antibody tests very early. By February 2020 an ELISA test was ready for clinical use. The test proved highly specific and sensitive, detecting 99.5% of the PCR + donors and about 40% of the suspected positives. More than 100,000 donors were screened by October, with some 30,000 scoring as positive, of whom more than 90% had robust anti-spike antibody responses. The ELISA titres correlated well with neutralizing antibody responses and showed that anti-spike antibodies are stable for ∼3 months, declining somewhat by 5 months to stable levels until stabilizing at month 7. Not surprisingly, the levels of antibody responses were highly heterogenous among individuals. Florian discussed protection against reinfection in NHPs, with little detectable SARS-CoV-2 RNA in the upper respiratory tract and none in the lungs. However, no antibody titer correlates of protection are yet known for SARS-CoV-2 as there are for other viral infections or toxoids. Florian then described the longitudinal PARIS cohort that was originally designed to follow the long-term immunology of infected patients; when the SARS-CoV-2 vaccine became available, this cohort became well suited for evaluating the effects of vaccines in recovered individuals. In brief, one dose of mRNA vaccine increased and homogenized the antibody titres in all recovered patients, reaching high titres, while the second dose did not provide obvious additional benefit. The ratio of neutralizing to total antibodies was lower in vaccinated persons; however, this resulted from increased levels of both neutralizing antibodies and total antibodies, with the later increasing more, and thus poses no obvious concern. Florian completed his talk by discussing variants of concern and their effects on neutralizing antibodies. No simple relationship is evident between variants and neutralization, with some variants being more or less resistant to neutralization by some antibodies. Interestingly, the N-terminal domain (NTD), not the receptor binding domain (RBD), was identified as the most important for the ability of antibodies to neutralize the virus. Florian's talk presented a tour-de-force effort to develop the tools for evaluating serological responses and protection against infection and to apply these tools to a rapidly progressing pandemic, garnering important and useful data used for decision making in real time.
Hanneke Schuitemaker presented the development of Janssen's SARS-CoV-2 vaccine, Ad26.COV2.S. This vaccine is based on the adenovirus Ad26 platform, which uses a non-replicating vector to shuttle genes of interest into the recipient. Various sequence modifications of the SARS-CoV-2 spike protein were evaluated, including those optimizing expression, immunogenicity, and manufacturability. Variations in signal peptides and prolines and the use of membrane-associated or soluble protein were investigated. The final construct was based on the pre-fusion structure of the spike protein and included a mutation of the furin cleavage site, a stabilizing double proline substitution in the hinge region, and a wild-type signal peptide; the final construct is a membrane-associated protein. Extensive testing in preclinical models, including NHP and Syrian hamsters, demonstrated safety and efficacy as well as assisted in dose determination. In the NHP model, infectious virus was found in lungs of all infected, unvaccinated animals, but in none of the 6 vaccinated animals. In hamsters, significant weight loss (20% or more) was seen in all unvaccinated animals after viral challenge, but no weight loss was observed in animals vaccinated with either dose evaluated. Lung tissue from unvaccinated infected hamsters showed virus and infiltrating immune cells, while neither was seen in vaccinated hamsters. Importantly, no vaccine-associated respiratory disease was observed. The design of the phase 1/2a study was complex and meant to provide sufficient data at the interim analysis to support the start of a phase 3 study. There were 3 cohorts; cohort 1 included 400 subjects aged 18–55 to demonstrate safety and immunogenicity, cohort 2 included 270 subjects aged 18–55 to study duration of response and boosting, and cohort 3 included 375 subjects 65 and older to demonstrate safety and immunogenicity in this age group. Overall, the profile at interim analysis showed acceptable safety and immunogenicity in all age groups, with lower reactivity in cohort 3. Immunogenicity was demonstrated by ELISA, neutralizing antibody assessment, antibody-dependent cellular phagocytosis, and CD4+ and CD8+ response assessment. The phase 3 ENSEMBLE study started on September 21, 2020, with administering a single dose of 5 × 1010 viral particles. The study was conducted on three continents and eight countries, including Brazil and South Africa, where emerging variants of concern beta and gamma were present at the time. The key findings included 66% vaccine efficacy in protecting against moderate to severe disease in all countries starting 2 weeks post vaccination. In the US, vaccine efficacy was 72% against moderate to severe disease, and globally, the study found 85% protection against severe disease. Protection levels were similar in South Africa, which at the time of the trial had high circulating levels of B.1.351 (beta) variant. Vaccine efficacy was similar across ages, comorbidity status, gender, race, and ethnicity. Acceptable vaccine safety was observed, with most adverse events being mild to moderate and resolving in 1–2 days. Hanneke noted that Janssen has a significant focus on ensuring global access to this vaccine. She noted that it takes many partners to make such a rapid and robust development program successful, citing many of the same collaborators mentioned by Tal Zaks.
Tal Zaks gave an update on the preclinical and clinical development of Moderna's mRNA-based COVID-19 vaccine. Prior to the pandemic, Moderna had developed multiple viral vaccine programs, including those against influenza virus, RSV, Zika virus, chikungunya virus, human metapneumovirus/human parainfluenza virus 3, and cytomegalovirus, which provided the company with tremendous experience and allowed them to rapidly apply their platform to developing a COVID-19 vaccine. Tal asserted that this was essentially a “digital medicine” approach, as time from sequencing the virus to initiating vaccine development was 48 h. Working closely with NIH and the FDA in their phase I studies, Moderna scientists were able to rapidly acquire the data needed to support phase 2 studies. Initial safety data from phase 1 informed phase 2 study design, and dose determination from phase 1 triggered implementation of the phase 2 studies. Phase 1 studies demonstrated a tolerable safety profile of the vaccine and showed consistent development of neutralizing antibody titers after a single dose, with titers increasing even further after the second dose. The response was independent of age and, on average, post-vaccination antibody titers were higher than antibody levels in convalescent serum. Phase 3 studies had a simple design and were executed with strong collaboration with NIH and FDA. Entry criteria included individuals at high risk of infection, encompassing a significant proportion of patients over 65 and those with comorbid conditions, and who were representative of US demographics. Tal noted the difficulty of balancing the desire for a rapid trial with including individuals that fully reflect the diversity of the US population. Analysis of the study data revealed an overall 94.1% efficacy rate, with data at the time of analysis demonstrating 100% protection against severe disease. Of note, data from Pfizer's independently developed mRNA-based vaccine phase 3 trial, released a week later, demonstrated efficacy within 1% of the Moderna results. Local adverse events were mostly associated with pain at the injection site. Systemic adverse events included fatigue, headache, myalgia, and arthralgia; these effects increased with the second dose, but were transient, expected, and correlated with the immune response. Thus, the safety profile was determined to be tolerable enough to warrant use for clinical benefit. A significant attribute of the Moderna mRNA vaccine is its relative stability. The vaccine is stable for 6 months at −20°C, for 30 days at 4°C, and for 12 h at room temperature. Additionally, a multi-dose vial is stable for 6 h after the first puncture. At the time of the talk, millions of individuals had been dosed with real-world data confirming the tolerability and efficacy seen in the phase 3 study. Evaluation of efficacy against variants of concern B.1.1.7 (alpha), B.1.351 (beta), and P.1 (gamma) also had shown protection, with neutralizing titers against B.1.1.7 equivalent to titers against the targeted original Wuhan variant. Neutralizing titers against beta and gamma were 6-fold lower than against the Wuhan variant, but were still higher than those in convalescent sera. Moderna was preparing a new vaccine based on the beta variant as a booster for those previously vaccinated, as well as a primary vaccine. Tal noted that this rapid success in vaccine development could only be achieved with significant collaboration amongst many individuals and organizations, including numerous ISAR members and other investigators and organizations, such as BARDA, OWS, a diversity and inclusion panel, principal investigators, study sites, and study participants. Overall, like for SARS-CoV-2 antivirals, the rapid, coordinated, and robust efforts to understand the immune response and bring forward safe and effective vaccines has been unparalleled.
The annual 2021 ICAR career event was an interactive workshop given by Jen Heemstra, a full professor of chemistry with a very popular Twitter account (@jenheemstra), on which she regularly shares advice on mentoring and science careers. The topic of the workshop was impostor syndrome, the feeling of doubting one's abilities and accomplishments and feeling like a fraud. Nearly everyone struggles with self-doubt or impostor syndrome in some form at various points in life, but the changes and struggles brought on by the COVID-19 pandemic have exacerbated them. Although eliminating these thoughts entirely may just not be possible, handles and tools to recognize and manage them exist Jen first reflected on her career path, confessing her own feelings of self-doubt. At times she felt like she was moving backwards, further away from her career goals. However, those times were not wasted: they gave her skills and perspectives that made her even more successful. Her main point was to emphasize that while the career path of others may look like a smooth, straight line, it really is not. She also underscored the importance of having great mentors. The interactive part of the workshop started after this interesting reflection on Jen's career path. First, participants were asked to think about times when they felt confident in their ability to do something well, how they felt during the activity or event, and whether this confidence and attitude impacted the outcome. Next, participants reflected on times when they doubted their ability do something well, and why they felt that way. Jen pointed out that two things can undermine confidence: what other people tell us and what we tell ourselves. We only have full control over the latter category. As a tool to refute such thoughts, Jen proposed a framework of facts and stories. Based on facts, multiple stories can be crafted. Jen gave the example of starting a postdoc in a new lab, needing to learn new techniques and not understanding much of what is presented at group meetings. One story that can be made of this situation is, “I am not good enough at research to belong here.” However, other facts could tell a different story: “Other group members also had to learn these techniques when they started; I have worked hard before to learn, and I can do it again.” With these different facts, one can build a story of belonging and confidence of eventually rising to the level of the other group members. Jen provided some common stories people tend to tell themselves and solutions for fighting them. For example, we tend to attribute positives to luck and negatives to objective measures. In addition, we tend to overemphasize the importance of the areas in which we are weak while underemphasizing the importance of our strong areas. Jen concluded that our confidence level can impact our enjoyment of activities and, to some extent, their outcome. Importantly, she emphasized that individuals can change their stories by taking a broader look at the facts. Finally, she advised surrounding ourselves with people who help us to tell positive stories, and to reciprocate such stories for them.
Craig Cameron was the recipient of the first ISAR's Diversity Speaker Award, an award developed last year and initially funded by Dr Ann Kwong and Dr Kathie Seley-Radtke. The purpose of the award is to recognize outstanding scientists who have overcome adversity in their careers, including belonging to any demographic underrepresented in science. Craig gave an excellent overview of his career path and his current research focus. He began his career in 1997 at Penn State University and remained there until a few years ago, when he relocated to the University of North Carolina School of Medicine. Throughout his career, he has had two major goals: developing antiviral strategies with broad-spectrum potential and developing strategies based on viral enzyme mechanisms. At the beginning of his career, Craig noted, information about RdRPs, particularly poliovirus RdRP, was limited. To address that gap, he developed a model system that remains an important tool for studying many RNA viruses to this day. In the 2000s, Craig's attention turned to ribavirin (RBV), an apparently typical nucleoside chain terminator that seemed to have unique properties. His group discovered a new mechanism of action for RBV, termed lethal mutagenesis, adding a second mechanism of action for nucleosidic antivirals. As an extension of those studies, Craig's group also developed a number of new assays to study mitochondrial polymerases, which are typically off targets for nucleoside analogues. As a result, the group was able to pursue new nucleoside analogues that targeted these polymerases directly. Craig next focused on developing magnetic tweezers, which allowed his group to study nucleotide incorporation by RdRP into extending RNA and its inhibition. Using this tool, he and others were able to obtain incorporation rates over thousands of cycles of addition at single-nucleotide resolution. Using these magnetic tweezers, his group studied nucleosides like favipiravir and remdesivir, observing a phenomenon they termed pausing. Pausing subsequently leads to backtracking from double-stranded RNA to single-stranded RNA, and back to double-stranded, without dissociation. Thus, the group identified a third type of mechanism if RdRP inhibition for nucleoside derivatives, pausing, in addition to chain termination and lethal mutagenesis. Finally, Craig discussed his current focus: developing tools for single cell analysis. This approach is not only innovative, but needed due to the genetic heterogenicity of virus and cell populations, variability of drug uptake, metabolism, and other factors. Such tools would allow new insights into infection, replication, antiviral activity, and metabolism of different viruses, as well as infection outcomes for different populations. In short, Craig is an impressive scientist who has long served as a role model for many!
Bruno Canard provided an excellent background on viral polymerases and how they have served as one of the best targets for nucleoside antiviral drug design for decades. Unsurprisingly, the RdRP of coronaviruses, including SARS-CoV-2, are currently being studied as antiviral targets. Several nucleosides are being pursued as potential therapeutics against COVID-19. Uniquely among human pathogenic viruses, the coronaviruses a encode proof-reading exonuclease ExoN, nsp14 in SARS-CoV-2, which has rendered many nucleoside analogues ineffective. Despite this challenge, the SARS-CoV RdRP is endowed with several properties that still make it an attractive target. It is at least 10-fold more active than any other known viral RdRP and has unusually high nucleotide incorporation rate. Consequently, it is highly error prone, and some nucleoside analogues, particularly 2ʹ-modified ones, are incorporated efficiently. The RdRP and exonuclease activities lead to a trade-off between insertion ability and the excision rate of the ExoN. Bruno's studies have shown that the active site of SARS-CoV-2 polymerase has some peculiar features that can be exploited in developing antiviral therapeutics. Among the nucleosides tested against SARS-CoV-2 reviewed by Bruno is favipiravir, a nucleobase that is transformed in vivo to a nucleoside triphosphate. Favipiravir is classified as a lethal mutagenic nucleoside analogue. Another one is remdesivir, which appears to work by several mechanisms including the typical delayed chain termination of the RdRP and by inhibiting second strand synthesis. Interestingly, though remdesivir is an ATP analogue, it is significantly incorporated as a GTP analogue, and is indeed excised by nsp14 ExoN. In addition, some reports also found mutagenic mechanisms for remdesivir. Sofosbuvir was also discussed; however, Bruno mentioned that it is readily discriminated against by the SARS-CoV-2 polymerase and is also excised by nsp14. Bruno discussed the new ExoN assay developed by his group and its use to search for ExoN inhibitors. The last compound discussed was AT-527 from Atea, which was the focus of J.P. Sommadossi's talk (Session 4). Bruno showed the results of the structural studies currently conducted by his lab, which revealed that two molecules of the AT-527 triphosphate interact with the SARS-CoV-2 polymerase, one at the NIRAN site and the other at the RdRP active site, highlighting how unique this compound is in the fight against SARS-CoV-2.
Kyeong-Ok Chang presented a collaborative work between his group and those of Dr. Scott Lovell (Kansas State University), Dr. Groutas (Wichita State University), and Dr. Stanley Perlman (University of Iowa) developing small molecule protease inhibitors against SARS-CoV-2 and other coronaviruses. He started by highlighting the significance of coronaviruses in human health, describing the 3C-Like protease (3CLpro) as an antiviral target, and providing its structural analysis. He highlighted that 3CLpro is a dimer and reviewed its substrate specificity. In over 10 years of previous work with di- and tri-peptidyl compounds, this collaboration had developed the dipeptidyl GC376, which is active against many human and animal coronaviruses and has shown activity in clinical trials against feline infectious peritonitis, a fatal disease produced by a feline coronavirus. An FDA Investigational New Animal Drug had already been filed for using GC376 to treat feline infectious peritonitis at the time of the meeting. Further optimization has been pursued through target, cell-based, and structural assays with a strong focus on the cap modifications. Overall, MERS-CoV and SARS-CoV-2 were more potently inhibited than SARS-CoV by most compounds. Two compounds were tested in infected primary human airway endothelial cells. Several compounds were co-crystalized with MERS-CoV, SARS-CoV, and SARS-CoV-2 3CLpro. The structures show that the cap of one compound interacted differently with the protease of MERS-CoV, which was correlated to higher potency of the compound. One compound was shown to be active in mice infected with a mouse-adapted strain of MERS-CoV. Treatment was effective, though less potent, if started as late as 48 h after infection, but starting later was ineffective. Other compounds were optimized for SARS-CoV-2 and tested in K18 hACE2 transgenic mice. In a model that induces about 50% mortality, treatment resulted in survival of all animals, whereas in a model that induces 100% lethality, 90% of treated mice survived. These studies have shown that dipeptidyl compounds provide a solid foundation for the development of antivirals against coronaviruses, including SARS-CoV-2.
Christoph Nitsche discussed targeting the proteases of several RNA viruses, particularly flaviviruses, alphaviruses, and now coronaviruses. He began by describing Zika virus NS2B-NS3 protease in detail, then discussing boronic acid peptide derivatives that potently inhibit NS2B-NS3 of Zika, dengue, and West Nile viruses. However, NS2B-NS3 cleaves a dibasic peptide, which poses a challenge to developing a peptide-based inhibitor with good activity in cells. Using novel Click chemistry, Christoph's group developed a platform to generate and screen macrocyclic peptides as an alternative class of high affinity NS2B-NS3 inhibitors. The best compound had a Ki of 0.14 µM and a half-life of about 20 h in the presence of the protease. Nonetheless, the protease activity has to date precluded co-crystalizing the protease with the macrocyclic uncleaved inhibitor. Christoph then discussed de novo selection of NS2B-NS3 inhibitors using mRNA display, in which an RNA library is translated while the peptide remains attached to the RNA encoding it via puromycin ligation. The peptides include unnatural amino acids and cyclize spontaneously. Interestingly, none of the enriched peptide sequences that were selected to be synthesized in larger scale and tested bound to the active site. Two types of molecules were identified. Some were potent inhibitors acting most likely via an allosteric inhibition mechanism. Other compounds bound to the active site with high affinity but did not inhibit. Christoph then moved on to discuss the structural similarities between 3CLpro of SARS-CoV, MERS-CoV, and SARS-CoV-2, as well as the commonalities of their cleavage sites, which are mostly uncharged. He also discussed the dimerization interface of 3CLpro as a potential drug target. Next, Christoph reviewed several published SARS-CoV-2 3CLpro inhibitors and their properties, highlighting that all of them attach covalently. Rational design of macrocyclic peptide inhibitors that cannot covalently attach themselves to the protease proved challenging. The group also attempted the above de novo selection approach, which had successfully identified inhibitors of Zika virus protease, and found some inhibitors made of canonical amino acids that need to be further characterized and improved.
Jean-Pierre Sommadossi presented new biological data for AT-527, a new nucleoside/tide therapeutic entering Phase 3 clinical trials for COVID-19. As his group's studies have shown, AT-527 offers several advantages over remdesivir, another nucleoside analogue in the same mechanistic class which has received emergency authorization for treating COVID-19. Both drugs disrupt viral replication by inhibiting the RdRP. Remdesivir must be administered intravenously, while AT-527 is orally bioavailable. Moreover, levels of the triphosphate of AT-527 (AT-9010) are 7-fold higher in human epithelial cells than those of triphosphate remdesivir. In addition, AT-527 has a favorable safety profile, enhanced potency, and better selectivity. Also of importance, the synthetic route to produce AT-527 is quite facile and highly amenable to scale-up, whereas remdesivir is produced via a tedious synthetic route and scaling up its production has been challenging due to the hazardous steps needed to add the 1ʹ-cyano group. AT-527 is similar to sofosbuvir, as both compounds feature a fluorine and a methyl group at the 2ʹ-position of the nucleoside scaffold, and both are McGuigan ProTide prodrugs. However, the nucleobase of AT-527 possesses a diamino functionality, with the N6-NH2 group masked by a methyl group. This group essentially serves as a second prodrug moiety, as it is removed in vivo to yield the guanosine analogue. This is a highly advantageous feature of this nucleoside, as other O-alkylated nucleoside analogues heretofore have exhibited significant mutagenic toxicity, while the AT-527 N-alkyl diamino base has not. Finally, in addition to being active against COVID-19, AT-527 has also shown pan-genotypic activity against HCV and, in combination with daclatasvir, is more effective and can potentially be given for less time than sofosbuvir. The first HCV patient enrolled in the phase 3 clinical trial started treatment with AT-527 on April 30th, 2021, just before this virtual ICAR meeting. Jean-Pierre hoped to report the results of the ongoing clinical trials for AT-527 soon.
Benjamin Bailly described a drug repurposing screen that used molecular docking to identify compounds that can inhibit the binding of SARS-CoV-2 S to ACE2, followed by biophysical surface plasmon resonance (SPR) testing of selected hits and testing antiviral activity in cell culture. More than 50,000 compounds were screened in silico, identifying several potential binders to ACE2 or to the RBD of the SARS-CoV-2 S protein. Of these hits, Evans, Blue, EGCG, Levodopa, velapatasir and albrutinib scored as ACE2 binders in SPR, and several of them inhibited interactions between S RBD and ACE2. Only a handful of compounds bound to S on virus-like particles as evaluated by SPR, but all of them inhibited the interaction between SARS-CoV-2 S RBD and ACE2. The group then further evaluated the efficacy of selected compounds to inhibit infection of Vero-E6 cells by an early Australian SARS-CoV-2 isolate closely related to the original Wuhan isolate. Some compounds, including Evans, Blue, and suramin (which had been previously identified as active against SARS-CoV-2) inhibited SARS-CoV-2 infection with EC50 in the tens of micrograms, and two others, lifitegrast and lumacaftor, also inhibited infection but only at higher concentrations. Other compounds that inhibit S binding to ACE2 did not inhibit infection. Benjamin finished the talk by discussing current and future work continuing this project.
This session consisted of six outstanding talks that focused, as the name of the session implies, on diseases produced by retroviruses and herpesviruses, and on treatments of such diseases. The talks can be divided into two categories based on the virus discussed. We will first summarize talks covering herpesviruses, followed by the lectures discussing retroviruses.
Christine Johnston who provided sound evidence for the role that herpes simplex virus 1 (HSV-1) may play in the development of Alzheimer's disease (AD). The hypothesis that HSV-1 may contribute to or even cause AD has been proposed for decades, but actual causality has yet to be established. While Christine's talk did not discuss evidence that establishes actual causality, she did provide evidence from multiple disciplines (epidemiology, in vitro studies, and animal models, to name the most important ones) that strengthen the case for a causal link between HSV-1 and AD. One of the more prominent examples demonstrates that HSV-1 infection is associated with amyloid beta production and induces abnormal phosphorylation of tau protein, both of which are associated with the development of AD.
Gerald Kleymann provided information about IM-250, a helicase-primase drug for the treatment of HSV infections. IM-250 is ∼100-fold more potent in vitro against wild-type HSV than the current standard of treatment (acyclovir). In addition, this drug maintains a high level of activity against HSV-1 isolates in a lethal challenge mouse model and against HSV-2 isolates in a non-lethal guinea pig model. IM-250 has a long in vivo half-life, as well as good bioavailability, excellent target tissue exposure, and a broad volume of distribution—all excellent qualities in a drug for clinical use.
Megan Lloyd discussed the potential for H84T BanLec, a molecularly engineered lectin, as treatment for diseases caused by the herpes family of viruses. H84T BanLec binds to N-linked glycans on the viral envelope, preventing viral entry, uncoating, and spread. Since N-linked glycans are present on the envelope of herpesviruses, this compound was postulated to inhibit these viruses. Indeed, H84T BanLec demonstrated broad-spectrum anti-herpesvirus effects with little to no observed toxicity in vitro. In addition, H84T BanLec was as effective as cidofovir (CDV) against varicella zoster virus (VZV) in vivo but was better tolerated. Further experimentation demonstrated that H84T BanLec prevented herpesvirus spread but not virion production in infected cells.
Jennifer Moffat discussed data demonstrating that oral USC-373, a CDV analog/prodrug, is highly potent against VZV (both wild-type and acyclovir-resistant variants). In addition, USC-373 prevented replication and spread in vivo compared to vehicle controls regardless of route of administration (injection or oral). Importantly, oral administration of the drug had highly significant, long-lasting effects on viral replication even at the lowest doses.
Robert Siliciano discussed the problems associated with curing HIV infections. Current anti-retroviral therapy prevents HIV replication but does not eliminate the latent reservoirs in the long-lived memory CD4 T-cells from which the virus can rebound, resulting in disease progression. Thus, any curative HIV measure must include a means to remove or even eliminate extremely stable latent HIV reservoirs. To achieve this goal, certain fundamental parameters must be evaluated, such as accurately measuring reservoirs with intact proviruses that can cause infection separately from defective proviruses that cannot. In addition, the majority of cells in the reservoir are not generated as a result of new infections, but rather by proliferation of previously uninfected cells. Unfortunately, proliferation of these cells is controlled by normal antigenic responses rather than viral control, meaning that suppressing their proliferation can result in a severely compromised immune system.
Jennifer Zhang discussed the use of lenacapavir as a long-term (low-dosing) agent. After several rounds of chemical modifications to optimize potency and metabolic stability, the result was a compound that affects capsid function at multiple points during the HIV lifecycle and can potentially be used as a long-lasting therapeutic option. The ultimate driver for this project is improving patient compliance, through the requirement of only an injection once every 6 months instead of taking a pill daily without interruptions.
Graciela Andrei provided an overview of herpesviruses, classical anti-herpesvirus agents, and new antivirals with novel targets (helicase-primase inhibitors, terminase inhibitors, and a UL97 protein kinase inhibitor). She discussed that failure of antiviral therapy is largely due to presence of mutations in viral kinases and herpesvirus DNA polymerases, and that most resistant mutations map to conserved regions of these viral genes. She then provided a summary of factors leading to herpesvirus drug resistance, including viral factors, immune suppression, host factors, and drug factors. Graciela suggested viewing antiviral drug resistance as an evolutionary process and emphasized what can be learned from translational drug resistance research. She then presented work with the Research Group for Antiviral Resistance (RegaVir) platform, which provides rapid genotyping and/or phenotyping of clinical isolates to analyse different aspects of drug resistance (e.g., novel mutations, multi-drug resistance, compartmentalization, dynamic evolution, heterogeneity). The aim is to identify viral drug resistance as reason for therapy failure, optimize antiviral therapy, avoid drug toxicity, improve patient care, and reduce cost of antiviral treatment. Graciela used several patient-based examples to demonstrate the utility of the platform in providing insights into herpesvirus diversity and rapid evolution in the immunocompromised host, and into adjusting therapy. She highlighted the advantage of next-generation sequencing (NGS) to detect minor viral populations and emergence of drug resistance. She provided other examples in which RegaVir identified contributors to increased risk for developing (multi)drug resistance in immune-privileged sites, compartmentalization of (multi)drug resistance, and simultaneous and concomitant herpesvirus infections. Graciela completed the talk by discussing her laboratory work investigating a novel mechanism that may contribute to herpesvirus genetic diversity: mutations in DNA polymerase (DNApol) that affect DNA replication fidelity. Her lab identified two novel amino acid changes in the C297W (3ʹ–5ʹ exonuclease domain) and C981Y (thumb domain) of murine gammaherpesvirus 68 (MHV-68) DNApol related to a mutator phenotype; association of C297W with a mutator phenotype was validated by CRISPR/Cas9 genome editing. Finally, studying population evolution by NGS indicated that the competitive fitness of MHV-68 mutator phenotype viruses with and without antivirals was significantly impaired.
Judith Breuer discussed the challenges and approaches to clinical treatment of Othornaviridae infection, including measuring efficacy in vivo and finding the cause of failed treatment in a talk entitled, Antiviral agents for serious RNA virus infections; a personalized medicine approach. Favipiravir is an RdRP-targeting drug with known liver toxicity but broad-spectrum antiviral activity. It acts as a chain terminator when administered at high concentrations, and has a lethal transition mutagenesis effect when administered at low concentrations. Judith's group aimed to evaluate if favipiravir treatment was associated with clinical improvement and if a biomarker could be used to identify any clinical improvement. Treating patients infected with norovirus, influenza B, or RSV indicated that clinical improvement coincided with detection of lethal mutagenesis in these viruses. To better understand how and why mutational frequency was associated with clinical improvement, a mathematical model of the RSV mutational threshold was developed, showing that ∼11 mutations/genome resulted in a 4–8 fold reduction in viral fitness. Finally, in collaboration with Dr Joana Rocha-Pereira's laboratory, a hamster model of SARS-CoV-2 infection was developed to address whether loss of fitness correlated with clinical improvement. The collaborators observed that low-frequency C→T and G→A mutations in the virus increased over time in a dose-dependent manner when the hamsters were treated with favipiravir. No change in viral load was detected, but a dose-dependent loss of viral replication (fitness) and decreased host tissue damage were observed. Overall, Judith concluded that favipiravir has a modest but important effect on clinical status; treatment with favipiravir is associated with increased mutagenesis; mutagenesis is shown in models to reduce viral fitness; and even significant reduction in viral fitness is not associated with large reductions in viral load. Judith continued by discussing combination therapy using an example of influenza B treatment with favipiravir and zanamivir, in which favipiravir was found to work synergistically. Finally, she discussed studies of remdesivir treatment in patients, which did not produce evidence of mutagenesis; remdesivir was found to suppresses viral load in some patients but not in others. She emphasized the benefit of remdesivir but also the need for combination therapy to overcome tissue penetration limitations of remdesivir used alone. Overall, RdRP inhibitors have the potential to act as broad-spectrum antiviral agents against RNA viruses, though their clinical benefit may not be associated with reduced viral loads. Instead, some RdRP inhibitors induce lethal viral mutagenesis; lethal mutagenesis levels of ∼15–20% appear to be associated with clinical improvement and reduction in viral fitness. Finally, combination therapy, including combinations with other RdRP inhibitors, is likely to produce synergistic effects.
Jeroen Kortekaas presented ongoing work on a novel Rift Valley fever virus (RVFV) vaccine platform in a talk entitled, Four-segmented Rift Valley fever virus as a novel live-attenuated vaccine for animal and human use. He first reported on innovations in animal model development, describing a new model system in which RVFV is transmitted from lamb to lamb by laboratory-reared Aedes aegypti mosquitoes. These models can be used to study mosquito-mediated transmission and to evaluate vaccine efficacy. He then introduced the four-segmented RVFV vaccine platform being developed for human (hRVFV-4s) and veterinary (vRVFV-4s) use. The vaccines are generated by splitting the M genome segment into two M-type segments, each encoding one of the two structural glycoproteins, Gn or Gc. For added safety, the NSs gene is either completely deleted (vRVFV-4s) or partially deleted (69%, hRVFV-4s). RVFV-4s replicates efficiently in cell culture but is completely avirulent in mice (intraperitoneal or intranasal inoculation models), pregnant ewes, and lambs (intramuscular and subcutaneous inoculation). Jeroen presented a series of studies demonstrating that vRVFV-4s does not disseminate in vaccinated animals, sheds or spreads to the environment, or reverts to virulence. Single-dose vaccination efficacy was evaluated in lambs, goats, calves, and pregnant ewes; such vaccination induced protection against homologous and heterologous challenge. Based on the success of the veterinary vaccine, the Live-Attenuated Rift Valley Fever Vaccine for Single Shot Application (LARISSA) consortium was formed to evaluate the platform for human use. The human candidate vaccine was shown to be immunogenic and efficacious in both mouse and lamb models. Safety studies in marmosets showed no weight loss in vaccinated animals. However, vaccinated animals developed elevated body temperatures that were dose-dependent and began 24 h earlier than in control animals infected with wild-type RVFV. This observation was ascribed to the robust innate immune responses to vaccination and was supported by concurrent neutrophilia in vaccinated animals. In conclusion, hRVFV-4s was found to be safe in marmosets, was not shed or spread to the environment, and induced a rapid innate immune response and neutralizing antibodies within 7–14 days after single-dose vaccination. Altogether, RVFV-4s-derived vaccines were shown to be safe and efficacious, supporting their continued development for both animal and human use.
Maria Rosenthal summarized current knowledge of the role of bunyavirus L protein, a multi-functional and multi-domain protein that contains the viral RdRP and serves as a good drug target, in the talk entitled, Understanding the multiple functions of the bunyavirus polymerase protein. She provided helpful analyses comparing bunyavirus L protein with the polymerase complexes of other viruses (e.g., influenza virus, order Articulavirales). Maria emphasized that while our knowledge of L protein structure and functions has increased over the years, it remains a challenging target due to its size and flexibility, as well as its diversity among bunyaviruses. Maria described the advances in our understanding of the L protein structure and functions attained by her lab through studies combining biochemistry, structural biology, and virology methods. By solving structures of a C-terminal domain in the L protein using X-ray crystallography, the group identified a cap-binding domain essential for viral transcription. She highlighted that the details of cap binding differ within the order Bunyavirales and compared to other viruses, even those with structurally similar polymerases. Furthermore, her group established expression and purification procedures for full-length L proteins to investigate their structure using single-particle cryo-electron microscopy and characterize their diverse functions in biochemical assays. These results, along with data published by other groups, demonstrate the functional and structural similarities and differences of the L proteins within Bunyavirales and compare them to those of influenza virus polymerase complex. These studies furthermore support targeted drug development strategies against bunyaviruses.
Salvatore Ferla discussed work on the development of novel norovirus antivirals that target the RdRP in a talk titled, Structure and ligand-based virtual screening using different RdRP crystal structures of human and murine norovirus identified two compounds that inhibit RdRP in the micromolar range, though neither showed interesting antiviral activity in cell-based assays due to poor solubility. To improve solubility, rational modifications were introduced in the hydrophobic ring portion of these molecules, including inserting carboxylic substituents in ring 1 and replacing with heteroaromatics in ring 2. These changes improved water solubility without affecting the polymerase-inhibitory activity of the compounds, yet no antiviral activity was observed in cell-based assays. To confer antiviral activity, a scaffold replacement procedure was applied; unfortunately, the new compound only inhibited RdRP by 20% at 100 µM. A computer-aided flexible alignment approach was subsequently used, identifying the TPB compound as overlapping best with the group's original first-hit structure. Twelve new compounds were designed and synthesized based on combining TBP attributes with key structural aspects from the first hit; four displayed antiviral properties. Two molecules were subsequently selected and analysed by a synthetic chemistry approach. Twenty-three additional compounds were synthesized based on this new scaffolding. The new compounds were very promising antivirals but also unstable in aqueous solution. Since these compounds represent one of the few examples of non-nucleoside polymerase inhibitors with significant antiviral activity against human norovirus replication in a cell-based system, the future goal is to increase their stability and solubility in water. Salvatore's group is also planning mutational studies and the use of in silico techniques to design more potent compounds.
Nora Fohrmann presented work on improving the metabolic stability of the lead structure in a novel series of compounds with strong antiviral activity against a panel of RNA viruses, including several bunyaviruses, Lassa virus, and Ebola virus, in a talk entitled, Metabolic stabilization of a novel inhibitor of human dihydroorotate dehydrogenase (hDHODH) with potent broad spectrum antiviral activity. The antiviral activity of these compounds is based on inhibiting hDHODH, a cellular enzyme involved in de novo biosynthesis of the pyrimidine nucleotides crucial for viral RNA replication. The lead structure of these compounds, containing an elongated alkyl chain, was identified using a hit-to-lead approach evaluating the structure/activity relationship. However, a pharmacokinetic investigation of this lead structure showed metabolic stability (S9, rat) of only 22%, which is insufficient for in vivo evaluation. To improve stability, the lead compound main metabolite was identified via liquid chromatography–mass spectrometry and the site of metabolism was stabilized via rational derivatization. Three lead optimization cycles were conducted during which the redesigned structures were synthesized and their metabolic stability and enzymatic inhibition were biologically evaluated. The initial cycle of modifications increased stability, but reduced activity mildly to significantly. Of the modified structures, the urea compound was selected for subsequent optimization cycles. The urea analogue was synthesized in 6 steps with good yield; this approach was used to generate further derivatives for the next two optimization cycles. The structure was modified to stabilize against hydroxylation, increasing stability to 90% but resulting in strong reduction of activity. The final optimization cycle focused on reducing IC50 by modifying the linker fragment. This improved IC50 and only reduced the stability to 70%. Overall, while stability of the lead compound was improved, a decrease in IC50 against certain agents (e.g., Lassa virus) was observed in culture. Future work will continue to optimize stability and potency.
Sebastiaan ter Horst discussed the work on the endonuclease domain of RdRP as an antiviral target for bunyaviruses. Structural studies of the domain support the concept that the chelating agent class of diketo acids could serve as starting point for creating broad-acting antiviral candidates. The group found the endonuclease domain to be well conserved, more so than the polymerase domain, especially within bunyavirus families. This domain is present in and shares sequence similarities with other negative-strand RNA viruses like influenza. Based on this rationale, the group investigated drugs with known activity against influenza as candidates for bunyavirus treatment. Interaction studies were performed using a panel of bunyavirus cap-snatching endonucleases in a thermal shift fluorescence resonance energy transfer- (FRET) based nuclease monitoring assay and GlideSP docking simulations. In addition, in vitro inhibition assays were performed with BUNV-mCherry reporter virus. The first-in-class FDA-approved diketo acid-based influenza drug baloxavir efficiently bound to the endonuclease of La Crosse virus (LACV), though with lower affinity than it bound to influenza virus endonuclease. Furthermore, baloxavir inhibited BUNV-mCherry replication in vitro with an EC50 of 0.7 μM. This inhibition profile was similar to that of RBV, a last-resort option for treating bunyavirus infections, but was at lower compound concentrations. The group also demonstrated a synergistic effect of baloxavir and RBV at certain concentrations. To see if other anti-influenza antivirals may also be suitable for treating bunyavirus infections, the group evaluated L-742,001. Interestingly, a common hydrophobic sub-pocket was identified in binding assays with LACV, Andes virus, and RVFV; L-742,001 and its derivatives demonstrated EC50 values between 5.6 and 6.9 μM in vitro. Overall, this group found that the cap-snatching endonuclease domain is a valid target that can be exploited by chelating the metal ions in the active site of the domain. Future work will focus on rational design of a molecule to better fit the extensive and shallow binding site of bunyaviral endonuclease with the aim of developing efficacious broad-spectrum inhibitors.
While the COVID-19 pandemic continues to impact all our lives, the session on arboviruses at the 34th ICAR covered the field of vector-borne viral infections well. The session had three invited speakers -- Dr Jenny Low (Singapore), Dr Johan Neyts (Belgium), and Dr Mark Heise (USA) -- and three speakers selected from the submitted abstracts: Dr Justin Julander (USA), Dr Gerry Rassias (Greece), and Dr Jinhong Chang (USA).
Mark Heise provided an impressive update on the use of mouse strains developed in the Collaborative Cross to identify factors that contribute to the severity of chikungunya virus infection. Through these efforts, different host pathways were identified that contribute to different disease phenotypes. These results highlight the contributions of the Collaborative Cross project to further our understanding of the pathogenesis of emerging viruses.
Jenny Low shared a perspective of the ongoing arbovirus infections. Singapore experienced its highest ever dengue case numbers in 2020, recording more fatalities than from COVID-19 at that stage. Jenny also discussed the swift bench-to-bedside development of therapeutic antibodies against Zika virus, which was made possible by advances in identifying potent antibodies and development for scale-up of biotherapeutics to be used in human clinical trials together with the adaptive trial designs that allow overlapping studies addressing key clinical development steps. The complexities associated with the roll out of Dengvaxia were touched upon by Drs. Neyts, Low, and Rassias, and the need for effective antiviral small molecules and therapeutic antibodies was discussed.
Johan Neyts presented exciting data on JNJ-A07, a nM to pM range, pan-serotype inhibitor of dengue virus replication developed together with Janssen Pharmaceuticals. This compound is active in vitro in several cell types. Its mechanism of action appears to involve blocking of NS2B-NS3 interactions with NS4B. The compound was also active in AG129 mouse infection models, including during infections under antibody-dependent enhancement conditions. A high barrier to the selection for resistance was demonstrated. Although escape mutants with signature mutations in NS4B eventually appeared, they arose after months of passaging the virus supernatant. The excellent preclinical data, including compelling pharmacokinetics/pharmacodynamics (PK/PD) information, support this compound, a first-in-class directly acting antiviral against the four serotypes of dengue virus, reaching early phase human trials.
Continuing with the development of inhibitors of non-structural proteins, Jinhong Chang's talk focused on mechanistic studies of BDAA, a small molecule inhibitor of NS4B, in yellow fever virus infection. After screening nearly 200 analogs of BDAA, the group discovered a compound with potency in the nanomolar range. Detergent treatment followed by IFA revealed that the mechanism of the compound may involve disruption of the membrane invagination enclosing the viral replication complex to expose the dsRNA, which are then cleaved by cellular RNases. The role in dsRNA protection by NS4B suggests that this protein has a key role in the replication vesicles formed by the replication complex.
Gerry Rassias described the activity of a NS2B-NS3 prodrug, SP-471P, with low micromolar range activity against all four serotypes of dengue virus. SP-471P inhibits viral RNA replication and production of infectious viral particles even when administered 6 h post infection. Mechanistically, SP-471 appears to inhibit both normal intermolecular protease processes and intramolecular cleavage events at the NS2B-NS3 junction, as well as at NS3 internal sites, which are critical for virus replication. The importance of the internal cleavage site and its dominant negative effect on virus RNA replication appears intriguing and is being investigated in more detail. The protease is a challenging target, but the team is focused on exploiting this new finding and the unique prodrug approach to discover more potent inhibitors.
Justin Julander presented work on a potent, broadly active nucleoside analog. Treatment of Eastern equine encephalitis (EEEV) or chikungunya virus infection in mouse models with the ribonucleoside EIDD-2749 resulted in improved outcomes. This compound was also effective when treatment was delayed until 48 h after virus challenge. The potent small molecule inhibitors targeting non-structural proteins of dengue virus and yellow fever virus, a nucleoside analog with activity against EEEV and chikungunya virus, and a monoclonal therapeutic antibody against yellow fever virus described by Dr. Low together demonstrate the efforts of the antiviral research community in responding to visible gaps in the availability or access to countermeasures for various emerging viruses. Even when an effective vaccine is available, the need for such countermeasures is still of high importance, as evidenced during the 2016–17 yellow fever outbreak in Angola and Democratic Republic of Congo.
David Durantel began his award lecture by profusely thanking his mentors, particularly Dr Fabien Zoulim, and the people who had worked with him, especially Dr Julie Lucifora, highlighting how important these interactions and collaborations have been in his career. He then introduced the major problems resulting from HBV and hepatitis delta virus (HDV) infections, including the approximately 1 million deaths per year, mostly due to the resulting hepatocellular carcinoma. Both viruses persist as nuclear episomes, but HDV is a satellite virus that uses HBsAg-coated particles vesicles to spread between cells. Although there are approved antivirals against HBV and many more are being studied, none can completely eliminate the infection and thus none can fully protect against the residual risk. Several innovative approaches are being pursued to decrease the residual risk, from host-targeting agents with combined antiviral and anticancer activities through nucleic acid polymers or siRNA to decrease antigenemia, to restoring T cell functionality with checkpoint inhibitors or therapeutic vaccines, to epigenetic regulation to lose the cccDNA episomes. David also reviewed the variety of models used in the development and evaluation of anti-HBV or D antivirals, from cultures of primary human hepatocytes to progenitor cell lines that differentiate into hepatocytes in culture (HepaRG) to mouse models. Chimeric immunodeficient mice expressing hepatotoxic proteins reconstituted with human primary hepatocytes are most useful in evaluating antivirals but also limited because they cannot be used to study immunological processes. Immunocompetent mice can be transduced with adeno-associated virus (AAV) vectors that efficiently deliver the vectored HBV genomes into the liver, thus allowing the evaluation of the roles of immune responses during HBV therapy. David then discussed the standard antiviral treatments based on the direct acting antivirals (DAA) entecavir, TDF, and TAF. These potent DAA potently reduce viremia while having little effect on HBsAg and cccDNA, although the models suggest they should decrease cccDNA. Considering that HIV/HBV co-infections are common, having nucleosidic prodrugs that only get activated in the liver to treat HBV without risking selection for HIV resistance would be an asset; these nucleosides are a goal of HBV medicinal chemistry. Another goal is the development of combination therapy using nucleosidic and capsid assembly modulators (CAMs) ; there is thus a strong focus on these inhibitors. CAMs can result in the formation of empty or aberrant capsids, and also have secondary mechanisms of action affecting uncoating and cccDNA establishment, for example. At higher concentrations, capsid inhibitors may inhibit HBeAg secretion. The core antigen HBc also has regulatory functions, having a complex interactome. It plays a role in cccDNA and HBV RNA biology through interactions with both molecules and many host factors including RNA binding proteins (RBPs). David then discussed the latest developments and directions in HBV antiviral therapy. Capsid inhibitors resulting in aberrant capsids have been found to decrease RNA biogenesis, leading to decreases in pregenomic (pg) and total RNA accumulation, but not in cccDNA, after 75 days of treatment in culture. Consistently, David advocated for combination treatment with long-term nucleosidic and capsid inhibitors to enable mutual potentiation between these two drugs. Another approach would be to inhibit protein kinases that modulate HBc activity and oligomerization. For example, targeting of PLK1, which is involved in HBc phosphorylation and HBc assembly, with small molecules or siRNA inhibits HBV replication in culture or mouse models, and could therefore synergistically complement nucleosidic and capsid inhibitors. Another protein kinase has been recently identified to phosphorylate the capsid and to be required for HBV replication, although the identity of the kinase could not be disclosed at the time. Yet another approach is to use immunomodulators to activate innate immunity, thus replacing interferon. RIG-I agonists were preferred early on, but the adverse effects that resulted in the halting of the clinical trials of inarigivir have since tempered the enthusiasm for this approach. The focus has thus shifted to the Toll-like receptors, mainly TLR2 and 3 (TLR 7 and 8 are not expressed in the liver). Their agonists have been shown to decrease cccDNA to some extent, but their major effect is on the production and accumulation of HBV RNA. An additional advantage of these agonists is that they are also active against HDV. Although their mode of action is still being evaluated, these agonists have shown good activity in the AAV mouse model as nanoparticles with polylactic acid, which home to the liver where they are maintained for weeks. These formulations have proven active in the AAV mouse model and, in contrast to lamiduvine, they resulted in no rebound. David concluded that combination therapies are needed for HBV and directed our attention to the presentations by John Tavis and Adam Ghering (both Session 8). He indicated the need to target RNA biogenesis, accumulation, and stability, and stated that several drugs achieve these goals, including DAA, immunomodulators, and antimetabolites like the FXR agonist vonafexor, which also inhibit HDV replication (also discussed by Julie Lucifora, see Session 8).
John Tavis discussed HBV therapy. HBV is an enveloped, partially double-stranded DNA virus that replicates in hepatocytes. HBV replicates within the viral capsid by reverse transcription of a pgRNA, which is catalyzed by a polyfunctional polymerase that bears both reverse transcriptase and RNase H activities. HBV chronically infects ∼250 million people worldwide and results in >850,000 deaths per year. Currently approved HBV therapies include PEGylated IFNα (pegIFNα), which is associated with serious side effects. Nucleos(t)ide analogs (NAs) like lamivudine, adefovir, entecavir, telbivudine, and tenofovir strongly suppress viral replication and normalize ALT levels in most patients. However, they do not fully abrogate disease progression and are life-long treatments, with a functional cure rate below 10% and quasi-universal rebound upon treatment arrest More efficacious curative therapies for chronic hepatitis B (CHB) infections will almost certainly require combinations of multiple drugs acting by complementary mechanisms. The biggest obstacle to curing HBV is the elimination of its nuclear episome, cccDNA, which is the template for all replication intermediates of HBV. It persists in liver cells due to long apparent half-life and replenishment by de novo infection and intracellular amplification. Eliminating or permanently silencing cccDNA is key to curing HBV. Obtaining a sterilizing cure involving complete elimination of cccDNA seems unlikely at the moment. Therefore, a functional cure that suppresses both HBV DNA and serum-secreted HBsAg by restoring the anti-HBV immune response and reversing disease progression is a more realistic goal for future therapies. Given the excellent safety profile of already approved nucleoside therapies, future curative drugs should maintain this favorable safety profile, both as single agents and combination therapies. To achieve this goal, a very wide variety of treatment strategies are under development. The major classes of drugs being explored include DAA that interrupt production or intracellular maintenance of HBV. The most advanced agents of this class are entry inhibitors, CAMs, siRNAs, and replication inhibitors (mainly improved nucleosides). Host targeting approaches aim to suppress HBV by interrupting cellular mechanisms and immune enhancement by exploiting the power of the adaptive immune response against HBV. At this point, it is unclear which drug combinations are most promising for achieving a functional cure in the highly diverse hepatitis B patient population. Therefore, combinations studies should not only be rationalized based on preclinical demonstration of synergy, but also be empirically tested in the clinic.
Adam Gehring presented the counterpoint to the virology perspective of the previous lecture in his talk entitled, Towards combination treatments for chronic hepatitis B: an immunologist's point of view. One of the major limitations of all DAA is that they only target steps of the viral replication cycle downstream of cccDNA, including viral RNA biogenesis, protein synthesis, and rcDNA production. Therefore, HBV DNA and HBsAg rebound after treatment termination, posing a major challenge even for new DAA candidates like antisense oligonucleotides (ASO) and siRNAs. A coordinated immune response is required for clearance of HBV infection. However, HBV-specific T and B cell immunity display a profile of profound exhaustion in CHB patients. Immunotherapeutic drugs being developed for CHB target both innate and adaptive immunity. These include therapeutic vaccines, checkpoint inhibitors, and small molecules targeting host pattern recognition receptors. Therapeutic vaccines have the longest history as immunotherapeutic interventions in CHB but have thus far proven ineffective, having no significant impact on HBV replication despite inducing T cell immunity associated with production of anti-HBs antibodies. Newer vaccine candidates currently in early phases of clinical development include DNA vaccines, adenovirus or modified vaccinia vectors, and peptides. These candidates aim at inducing a stronger T cell response with more immunogenic adjuvants. Checkpoint inhibitors are gaining attention, with increasing data on safety profiles from cancer patients, but have only entered small pilot studies. Innate immunomodulators targeting pattern recognition receptors have demonstrated target engagement but only modestly impact viral replication in monotherapy. Selgantolimod (SGLN), a TLR-8 agonist that effectively engages its receptor, induced a strong innate immune response causing a moderate decline in HBsAg levels in a phase-2 clinical study. However, SGLN failed to demonstrate additional reduction of viral DNA over standard-of-care nucleosdic drugs. Similarly, 24 weeks of treatment with TLR-7 agonist GS-9620 induced cytokine production but not DNA decline. Given the limited benefit of immunotherapeutic drugs used as single agents, the expectation is that combination therapy will be required to achieve hepatitis B cure. This combination will likely include DAA in combination with immunomodulatory drugs inducing complementary mechanisms of action to facilitate antiviral immunity in the liver. Perhaps the most promising immune combination so far is therapeutic vaccine administered together with checkpoint inhibitor PD-L1, which results in sustained decrease of HBsAg in the woodchuck model. Combining immune drugs with DAA that reduce HBsAg could be a potential avenue to restoring T cell functionality. In the AAV-HBV mouse model, the combination of TherVacB therapeutic vaccine with anti-HBV siRNA resulted in sustained reduction in HBsAg levels long after cessation of drug treatment. This approach is currently being pursued in the clinic. Finally, nucleic acid polymers acting as secretion inhibitors have clinically demonstrated strong HBsAg reduction when combined with TDF and pegIFN, resulting in a 35% rate of functional cure in all patients treated. Given the currently limited data showing benefit of combining two modalities, three agents may be needed to achieve higher levels of functional cure.
Eike Steinmann's talk focused on hepatitis E virus (HEV). HEV is the causative agent of hepatitis E and the leading cause of acute viral hepatitis, affecting approximately 20 million individuals and resulting in 3.3 million symptomatic infections and 44,000–70,000 deaths per year. HEV is a zoonotic, single-stranded, positive-sense RNA virus of about 7 kb sub-classified into eight genotypes. The main transmission route to humans in Europe and the US is through consuming undercooked pork contaminated with HEV genotype 3. Other sources of infection include consumption of contaminated water, shellfish, crops, and other meat preparations. In Africa and large parts of Asia, the main mode of transmission is drinking water contaminated with genotypes 1 or 2. Although HEV is usually a self-limiting disease, immunocompromised individuals are at risk of developing chronic infection that rapidly progresses to fibrosis, cirrhosis, or even liver failure. Current therapy options to treat hepatitis E are limited to the unspecific antivirals RBV and pegIFN. RBV leads to viral clearance in only 80% of chronically infected patients. However, RBV has not been evaluated in acutely infected patients and is contraindicated in pregnant women, a major high-risk group, emphasizing the urgency of developing new therapy options. The mechanism of action of RBV is complex and involves both direct antiviral effects and immune stimulation. In responders, RBV increases mutations in the viral genome, leading to amino acid changes. Although these mutations are not associated with RBV resistance, some of them increase viral fitness. Strains with increased fitness have been used to improve in vitro cultivation of HEV in hepatoma cells. Both host and viral targets have been evaluated in vitro for future HEV therapies. The RNA polymerase inhibitor sofosbuvir, already approved to treat HCV infections, failed to inhibit HEV in the clinic. Currently, the only drug candidate with demonstrated in vivo anti-HEV effect is the natural product silvestrol, which acts as a host translation inhibitor. Derivatives of silvestrol are currently being evaluated in vitro.
Antoine Alam discussed the continued need for improved therapeutics for CHB. The combination of CD40 agonism and type-I IFN stimulation (IFN-β, specifically) in inhibiting HBV infection was explored both in vitro and in vivo. CD40L boosts the anti-viral effects of IFN-β in HBV-infected primary human hepatocytes, leading to decreased HBeAg and pgRNA. This combination also increased the release of the IFN-responsive protein CXCL10, but not the inflammatory protein IL-8. The combination boosted other interferon stimulated genes, such as CXCL9, CXCL11, and ISG20, a key player in innate antiviral immunity. Furthermore, co-administering CD40L and IFN-β to AAV/HBV-infected mice led to significant and synergistic reduction of all viral parameters, including circulating HBV DNA, HBeAg, and HBsAg, as well as pgRNA and intra-hepatic HBV DNA. Importantly, ex vivo treatment of either human or murine whole blood cells with CD40L and IFN-β did not significantly induce inflammatory markers, such as IL-6 or TNF-α. Together, these results show that the combination of CD40L and IFN-β has potent anti-HBV activity in vitro and in vivo with minimal inflammation. Such a combination may have important therapeutic effect in CHB patients.
Ranjit Chauhan discussed HBV and antiviral targeting of the viral ribonuclease H (RNaseH). Establishment of HBV cccDNA early after HBV infection through recircularization of viral replicative intermediates contributes to HBV persistence. RNaseH is a promising drug target, but how its inhibition impacts cccDNA formation is unknown. Three RNaseH inhibitors from different chemotypes, 1133 (N-hydroxypyridinedione, EC50 = 0.11 µM), 110 (hydroxytropolone EC50 = 0.30 µM), and 1073 (N-hydroxynapthyridinone, EC50 = 1.5 µM), efficacious against intracellular HBV DNA accumulation in inducible replication systems, were tested for effects on HBV product formation in infected HepG2- NTCP cells. Cells were infected for 12 h and compounds were added immediately following infection at concentrations of 0.05 µM, 0.5 µM, and 5 µM. Total intracellular HBV DNA, cccDNA, pgRNA, and total HBV RNA accumulation were evaluated 7 days post infection. The inhibition of total intracellular HBV DNA was dose responsive, with ∼99% inhibition (compared to vehicle control) achieved by all compounds at 0.5 µM. All compounds inhibited cccDNA formation by 75–95% at 0.5 µM. Inhibition of cccDNA was reflected in suppression of pgRNA levels by >90% (by 1133 and 110) and >50% (by 1073). Similar inhibition was detected for all HBV RNAs using primers targeting the HBx region. In conclusion, HBV RNaseH inhibitors can efficiently suppress cccDNA formation in vitro. Viral suppression was more pronounced than predicted by EC50s in stably transfected cells, presumably due to suppression of cccDNA amplification. These data support progression of RNaseH inhibitors as therapeutic candidates for the treatment of CHB.
Julie Lucifora spoke about HDV, a satellite of HBV. Both HBV and HDV use the human sodium taurocholate co-transporting polypeptide (hNTCP), the main transporter of bile acids (BA) in the liver, to enter hepatocytes. Links between BA and HBV infection are not limited to the entry step. Indeed, the farnesoid X receptor alpha (FXR), the nuclear receptor of BA, is a proviral factor for HBV and FXR ligands act as inhibitors of HBV replication. The putative links between BA metabolism, FXR, and HDV replication have not been explored. In HepaRG and primary human hepatocytes co- or super-infected with HDV/HBV, treatment with FXR ligands like GW4064, ECDCA, or tropifexor significantly decreased the levels of total intracellular HDV RNAs by ∼50%. The effect was reversed in FXR loss-of-function experiments, confirming the specificity of action. Immunofluorescent staining and western blot analyses of infected cells showed that FXR ligands also modestly decreased the amount of intracellular delta antigens. The effect on viral progeny was very strong, with >98% loss of infectivity, as assessed by reinfection of Huh7.5-hNTCP cells. FXR ligands potently inhibit HDV replication and propagation in vitro, independently of their effect on HBV. The antiviral effect was far superior to that of IFNα, the current standard of care for chronic HDV patients. Although the precise mechanism of HDV inhibition associated with FXR agonists has yet to be elucidated, FXR appears to represent an attractive target for HDV antiviral therapy.
The last session on Wednesday, March 25th, provided the opportunity to get updated on some new and interesting technologies that are just starting to be applied to antiviral research and will likely have an increasing impact in the near future. The session was chaired by Dr. Jennifer Moffat and Dr. Andrea Brancale, and included three outstanding keynote presentations by Dr. Matthew Disney, Dr. Chris Meier, and Dr. Christiane Wobus. These invited talks were preceded by the Presentation of the Antonín Holý Memorial Award Lecture, delivered by this year's Award recipient Dr. Eddy Arnold. The presentations were all pre-recorded; however, a most interesting and lively live Q&A with all speakers concluded the session.
Eddy Arnold gave a very personal account of his remarkable scientific career. Eddy is well known for his incredible contributions to HIV research, especially in the fields of structural biology and drug design. His talk showed that he was a true pioneer in understanding on mechanism by which non-nucleoside reverse transcriptase inhibitors (NNRTI) bind to the reverse transcriptase of HIV-1. These studies were a challenging endeavor, partly because the enzyme is very flexible and adopts multiple conformations; the NNRTI binding pocket is not even visible in the apo reverse transcriptase (RT) structures. With his skills and knowledge, Eddy contributed significantly to the discovery of two FDA-approved NNRTI drugs (etravirine and rilpivirine) and development of six licensed medicines. In his talk, Eddy presented his scientific achievements as a journey. More importantly, he fondly acknowledged the remarkable people he met along his path. His talk was very warm and personal, loaded with anecdotes that made the story about the discovery and development of NNRTIs compelling and real. Indeed, during the Q&A session, he also answered a question about his interactions with Dr Paul Janssen, and his words were still full of admiration and gratitude, recognizing Dr Janssen as a leader and innovator in the field. A modest, yet outstanding, scientist, Eddy proved to be a very worthy Holý awardee.
Matt Disney began the presentations on new technologies on antiviral research. His talk discussed the development of new molecules and strategies to target viral RNA, revealing the potential of targeting RNA in antiviral drug discovery. This approach could rival or, more likely, complement, the standard antiviral approaches that target viral proteins. Matt described two strategies on which his group is working. One strategy is to understand the three-dimensional structure of viral RNA using available sequences and then using this knowledge to identify compounds that could bind to specific conserved structural motifs. In this context, he discussed his work on identifying new compounds that bind SARS-CoV-2 RNA. The second strategy focusses on recruiting ribonuclease enzymes to facilitate the processing of viral RNAs. This intriguing approach is in some ways similar to PROTAC, which is becoming increasingly popular in drug discovery. During the Q&A session, Matt answered a few questions about selectivity and resistance barrier of RNA binding compounds. He also highlighted that preprint servers are becoming an increasingly important way to share information efficiently and quickly, and emphasized the importance of these servers to his group's work.
Nucleosides are the most prevalent class of antiviral drugs, generally active as triphosphate analogs. However, this highly charged form does not cross biological membranes, and thus cannot be administered as a drug as such. A prodrug approach (the ProTide strategy) was successfully applied to deliver nucleoside monophosphate into cells in recent years, but phosphorylation to achieve the active triphosphate form still requires the participation of cellular enzymes, which discriminate against certain types of modified nucleosides. With the new approach developed by Chris Meier, the active triphosphate form is cleverly masked and thus able to cross cellular membranes, and is then released inside the cell to exert its activity. The new approach, TriPPPro, which is currently state-of-the art, has the potential to open a new era in nucleoside drug discovery. Chris discussed this point during the Q&A, and although it is difficult to predict whether this approach will be clinically successfully in the end, it is already showing the maturity to be developed towards clinical applications in the near future.
Christiane Wobus discussed her work on organoids and 3D cultures in drug discovery. Organoids are becoming an important tool available for drug discovery and development. They are often considered to be more representative of physiological conditions than standard two-dimensional cell culture, and more accurately mimic in vivo results. Although these cultures may appear cumbersome and with limited reproducibility, Christiane discussed how flexible they truly are and showed that they can be generated from patient-derived cells. A very interesting work described by Christiane centers around enteroids. She discussed the ability to create these “mini-guts” with an inner cavity and described how useful they are in studying viruses that infect the GI tract, including SARS-CoV-2 (50% of COVID-19 patients have GI tract symptoms). During the Q&A, Christiane gave her view of the future of more widespread organoid use in antiviral drug discovery. This approach is now also establishing itself as an extremely useful tool in other research fields. Overall, this was a truly exciting and inspirational session, which presented a view of some fascinating new technologies that will most likely become familiar in the near future. The audience was inspired to continuously evaluate these and similar new technologies for their potential applications in antiviral drug discovery and development.
The COVID-19 pandemic has emphasized the ongoing need to study emerging zoonotic pathogens. Hector Aguilar-Carreno and his laboratory have spent over two decades studying the glycoproteins of such pathogens, including Nipah, Hendra, Ebola, and influenza viruses, and, more recently, SARS-CoV-2. Utilizing both classical and molecular virological methods, his group's work exemplifies the potential translation of basic science to the future development of novel vaccines and therapeutics against these viruses. Hector discussed the development and evaluation of a multi-valent VSV vaccine concurrently pseudotyped with the viral entry glycoproteins of Nipah, Hendra, and Ebola viruses and showed that this approach provides complete protection against lethal challenge with each virus in Syrian hamsters. The viability of this platform to address infections with ecological and potentially epidemiological overlap can be applied to generating potential pan-coronavirus vaccines, an approach that Hector's group is currently pursuing. Through the discovery and characterization of a series of membrane-intercalating compounds, Hector's group has developed a compound (XM-01) as an inactivation agent that preserves viral glycoproteins in their native conformations. Mice immunized with XM-01-inactivated influenza showed improved survival and decreased morbidity from viral challenge when compared to mice immunized with influenza inactivated by formalin fixation. Moreover, sera from mice immunized with XM-01-inactivated influenza virus developed comparably more potent anti-HA and anti-NA neutralizing antibodies. Hector's group has also recently established multiple animal models for studying coronaviral infections, including K18 hACE2 mice and Syrian hamsters for SARS-CoV-2 research, and is currently investigating an intranasally administered TMPRSS2 protease inhibitor that significantly protects K18 ACE2 mice from SARS-CoV-2-induced morbidity and mortality.
Emmie de Wit presented work with colleagues evaluating remdesivir in NHP models of Nipah virus, MERS-CoV, and SARS-CoV-2 infection. Remdesivir is an adenosine nucleotide prodrug approved for treatment of COVID-19. The prodrug is metabolized to the active triphosphate form used as a substrate by the viral polymerase. Incorporation of the nucleotide analog into the viral RNA during replication inhibits the viral polymerase, leading to reduced virus replication. Remdesivir has broad-spectrum activity and is active in cell culture against filoviruses, coronaviruses, and paramyxoviruses. African green monkeys (n = 4 per group) were inoculated with 2 × 105 TCID50 of Nipah virus by intranasal and intratracheal routes. Remdesivir (10 mg/kg) or placebo (vehicle) was administered once daily by IV bolus for 12 days starting at 24 h post inoculation. Placebo-treated animals exhibited signs of severe disease and were euthanized by day 8 when they reached signs requiring humane euthanasia. Signs of disease in animals treated with remdesivir were notably less severe compared to placebo treated animals. Viral loads in the upper respiratory tract (nose and throat) were similar in all treatment groups, but no viremia or severe lung disease was observed in animals treated with remdesivir; all remdesivir-treated animals survived to day 92 of the study. A single remdesivir-treated animal had histopathological evidence of meningoencephalitis but no neurological signs were observed. Remdesivir was also evaluated in a rhesus macaque model of MERS-CoV infection. Animals (n = 6 per group) were inoculated with 1 × 106 TCID50 of MERS-CoV-2 by intranasal, intratracheal, or ocular routes. Animals were treated with 5 mg/kg of remdesivir or placebo by IV administration either one day prior to inoculation or 12 h post inoculation, followed by daily treatments on days 1–6. Animals were euthanized on day 6 post inoculation and antiviral efficacy was measured. Remdesivir treatment reduced clinical signs of disease, viral load in the lungs, and lung lesions measured by histopathology compared to placebo. The antiviral effects were more pronounced in animals treated 24 h prior to inoculation than in animals treated 12 h post inoculation. A rhesus macaque model of SARS-CoV-2 infection was developed that resembled mild human disease with measurable pulmonary infiltrates observed on radiographs and by histopathology. Animals inoculated with SARS-CoV-2 by intranasal, intratracheal, oral, or ocular routes exhibited high levels of viral shedding in the nose and throat and intermediate detection of virus in rectal swabs. No viral RNA was detectable in urogenital swabs or in blood and urine. Viral loads peaked 1 day post inoculation and infectious virus was cleared more quickly than viral RNA from the lungs. Animals (n = 6 per group) were treated with placebo or remdesivir starting at 10 mg/kg 12 h post inoculation followed by 6 consecutive daily treatments of 5 mg/kg. Animals were euthanized on day 7 post inoculation. Remdesivir did not impact shedding of virus in the nose or throat or in rectal swabs compared to placebo-treated animals. However, relative to placebo-treated animals, remdesivir treatment reduced clinical disease, viral lung pathology, and viral titers in bronchoalveolar lavages (BAL) 12 h after the initial treatment and in lung tissue on day 7 post inoculation. These data support continued evaluation of remdesivir for treating paramyxovirus and coronavirus infections.
James Gern presented work with colleagues to set the stage for developing vaccines against rhinovirus. Rhinovirus infections are highly associated with wheezing illness in children over 1 year of age and are an important risk factor for developing asthma later in life. Rhinoviruses are also an important trigger exacerbating asthma in adults and children. Three main genotypes of rhinovirus exist, RV-A, RV-B, and RV-C, with approximately 170 associated types that co-circulate in the human population causing a spectrum of respiratory disease from asymptomatic infection or mild upper respiratory disease to pneumonia. RV-A and RV-C are most often associated with clinical disease. The antigenic diversity associated with rhinoviruses is a primary reason rhinovirus infections are maintained in the human population causing seasonal infections year after year. The antigenic diversity of rhinovirus strains presents a challenge for developing effective vaccines to prevent or reduce disease associated with rhinovirus infection. Longitudinal data from the Childhood Origins of Asthma (COAST) birth cohort study were analysed to determine the relationships between age and RV-C infections. Neutralizing antibodies specific for RV-A and RV-C were determined using a novel PCR-based assay. Data were pooled from 14 study cohorts in the United States, Finland, and Australia, and mixed-effects logistic regression was used to identify factors related to the proportion of RV-C versus RV-A detection. RV-A and RV-C infections were common in infancy, whereas RV-C was detected much less often than RV-A in older children. The prevalence of neutralizing antibodies to RV-A or RV-C types was low in children 2 years of age and increased through the teen years. At each age, RV-C seropositivity was 3–5 times more prevalent. The ratio of RV-C to RV-A titers during illnesses was significantly related to age, CDHR3 genotype (putative RV-C receptor and asthma susceptibility allele), and wheezing illnesses. These data suggest the RV-C may be more immunogenic than RV-A causing more disease in early life but less disease as children age due to development of neutralizing antibodies. Identifying rhinovirus types associated with symptomatic disease and recognizing that RV-C neutralizing antibodies are protective reduces the complexity of antigens required for development of a polyvalent vaccine.
Malgorzata (Gosia) Rychlowska described the pathogenicity of Zika virus to neuronal cell progenitors. Zika virus is a mosquito-borne flavivirus that can be both sexually and vertically transmitted in humans, with the latter sometimes resulting in neonatal microcephaly due to Zika virus-induced DNA damage in human neural progenitor cells. Given that rare mutations in the DNA damage repair protein polynucleotide kinase (PNKP) also results in microcephaly, Gosia and colleagues sought to evaluate whether Zika virus infection affected PNKP. A PNKP inhibitor showed dose-dependent inhibition of Zika virus infection. Through fluorescence microscopy, Zika virus infection induced cytoplasmic co-localization of PNKP with Zika virus NS1. Interestingly, a mutant with cytoplasmic PNKP phenotype results in microcephaly. Zika virus induced mitotic abnormalities consistent with the morphological hallmarks of mitotic catastrophe in neural progenitor cells, and a cell cycle inhibitor blocking CDK1 (roscovitine) was shown to inhibit Zika virus replication, likely by inhibiting formation of replication complexes from the endoplasmic reticulum. Since CDK1 colocalized with Zika virus NS1 by fluorescence microscopy, the accumulation of CDK1 and the CDK1 activator Cyclin A was tested and the complexes were immunoprecipitated from infected cells to assess their biochemical activity by kinase assays. The results from this study indicate that Zika virus likely induces CDK1 to form replication complexes while displacing PNKP into the cytoplasm, thereby resulting in mitotic catastrophe.
Xinyu Wang focused on strategies for treating rabies virus. Rabies virus is transmitted to humans through the bite of an infected animal; left untreated, rabies is almost uniformly fatal. Current treatments involve early administration of rabies immunoglobulin and vaccination. While these treatments are effective, they are expensive and have limited availability. Xinyu and colleagues screened a lectin library to identify inhibitors of rabies virus infection in cell culture. Two lectins, UDA and BanLec, were identified as inhibitors of rabies virus replication with EC50 values of 8.2 and 7.2 μg/mL, respectively. Time of addition studies demonstrated that UDA binds to the cell surface to block virus entry. UDA pre-treatment reduced viral yield in cell culture 5-fold. A model using isolated pig muscle explant was developed to measure rabies virus replication. Treating pig muscle tissues with UDA (3 × EC50 value) reduced rabies virus replication over 100-fold relative to vehicle treatment. Replication was quantified using a rabies reporter virus expressing mCherry protein and by immunohistochemical staining for the rabies virus N protein. These data suggest that lectins could be an alternative treatment for rabies virus infection.
While transmission of arboviruses (e.g., Zika, dengue, yellow fever, and chikungunya viruses) through mosquito bites at the skin has been well characterized, little is known about the interactions of arboviruses with skin microbiota present at the site of infection. To investigate this question, Lana Langendries studied the effects of bacterial cell wall components on the infectivity of such arboviruses. Incubating alphaviruses (chikungunya and Semliki Forest viruses) with lipopolysaccharides (LPS) or lipoteichoic acids and peptidoglycans of Gram-positive bacteria significantly reduced the infectivity of these viruses in vitro. This was not due to cell-dependent effects, as pre-incubating cells with LPS prior to infection did not affect viral infectivity. Furthermore, treating cells with Toll-like receptor 4 inhibitor did not ablate the LPS-dependent inhibition of viral infectivity. A virucidal assay involving incubating LPS-treated viruses with or without RNAse showed that LPS incubation rendered alphavirus genomes more susceptible to RNase degradation, suggesting that LPS compromises the structural integrity of the viral envelope. Transmission electron micrographs of Semliki Forest virus incubated with LPS showed progressive changes in virion morphology, which correlated with decreasing viral infectivity, thus further supporting the case for a virucidal effect of bacterial LPS against arboviruses.
ISAR actively addressed the challenges posed by a viral pandemic by supporting antiviral and vaccine discovery and the research and development community through sharing of curated pre-prints and other information early in the pandemic. These and other efforts, including ICAR 2021, supported the lively and timely exchange of information about researchers, developers and other stakeholders involved in curtailing the effects of the pandemic. Although the virtual format of the 2021 annual meeting prevented some of the exchanges that occur at live ICAR meetings, overall, the goals of the annual meeting were achieved and the event was extremely successful at supporting the Society's mission. | true | true | true |
PMC9623429 | Shuang Liu,Xiuyuan Wang,Qianqian Li,Wentao Peng,Zunmian Zhang,Pengfei Chu,Shangjing Guo,Yinglun Fan,Shanhua Lyu | AtGCS promoter-driven clustered regularly interspaced short palindromic repeats/Cas9 highly efficiently generates homozygous/biallelic mutations in the transformed roots by Agrobacterium rhizogenes–mediated transformation | 18-10-2022 | CRISPR/Cas9,agrobacterium rhizogenes–mediated transformation (ARM),genome editing,homozygous/biallelic mutation,hairy root,gamma-glutamylcysteine synthetase gene | Agrobacterium rhizogenes–mediated (ARM) transformation is an efficient and powerful tool to generate transgenic roots to study root-related biology. For loss-of-function studies, transgenic-root-induced indel mutations by CRISPR/Cas9 only with homozygous/biallelic mutagenesis can exhibit mutant phenotype(s) (excluding recessive traits). However, a low frequency of homozygous mutants was produced by a constitutive promoter to drive Cas9 expression. Here, we identified a highly efficient Arabidopsis thaliana gamma- g lutamyl c ysteine s ynthetase promoter, termed AtGCSpro, with strong activity in the region where the root meristem will initiate and in the whole roots in broad eudicots species. AtGCSpro achieved higher homozygous/biallelic mutation efficiency than the most widely used CaMV 35S promoter in driving Cas9 expression in soybean, Lotus japonicus, and tomato roots. Using the pAtGCSpro-Cas9 system, the average homozygous/biallelic mutation frequency is 1.7-fold and 8.3-fold higher than the p2 × 35Spro-Cas9 system for single and two target site(s) in the genome, respectively. Our results demonstrate the advantage of the pAtGCSpro-Cas9 system used in ARM transformation, especially its great potential in diploids with multiple-copy genes targeted mutations and polyploid plants with multiplex genome editing. AtGCSpro is conservatively active in various eudicots species, suggesting that AtGCSpro might be applied in a wide range of dicots species. | AtGCS promoter-driven clustered regularly interspaced short palindromic repeats/Cas9 highly efficiently generates homozygous/biallelic mutations in the transformed roots by Agrobacterium rhizogenes–mediated transformation
Agrobacterium rhizogenes–mediated (ARM) transformation is an efficient and powerful tool to generate transgenic roots to study root-related biology. For loss-of-function studies, transgenic-root-induced indel mutations by CRISPR/Cas9 only with homozygous/biallelic mutagenesis can exhibit mutant phenotype(s) (excluding recessive traits). However, a low frequency of homozygous mutants was produced by a constitutive promoter to drive Cas9 expression. Here, we identified a highly efficient Arabidopsis thaliana gamma- glutamylcysteine synthetase promoter, termed AtGCSpro, with strong activity in the region where the root meristem will initiate and in the whole roots in broad eudicots species. AtGCSpro achieved higher homozygous/biallelic mutation efficiency than the most widely used CaMV 35S promoter in driving Cas9 expression in soybean, Lotus japonicus, and tomato roots. Using the pAtGCSpro-Cas9 system, the average homozygous/biallelic mutation frequency is 1.7-fold and 8.3-fold higher than the p2 × 35Spro-Cas9 system for single and two target site(s) in the genome, respectively. Our results demonstrate the advantage of the pAtGCSpro-Cas9 system used in ARM transformation, especially its great potential in diploids with multiple-copy genes targeted mutations and polyploid plants with multiplex genome editing. AtGCSpro is conservatively active in various eudicots species, suggesting that AtGCSpro might be applied in a wide range of dicots species.
Agrobacterium rhizogenes–mediated (ARM) transformation has revolutionized biological research through its ability to rapidly, simply, and conveniently generate transgenic roots of plant species, including in species recalcitrant to genetic transformation mediated by A. tumefaciens (Fan et al., 2020a; Fan et al., 2020b). Transgenic hairy roots co-transformed with the T-DNA from both the Ri plasmid of A. rhizogenes (carrying root locus [rol] genes, inducing the production of hairy roots) and the binary vector (Chilton et al., 1982; Irigoyen et al., 2020) can be generated. ARM transformation has already been established in a wide variety of plant taxa of more than 100 species and has widely been used for the modification of root traits, either because no protocols for stable A. tumefaciens–mediated transformation to generate transgenic plant (whole plant is genetically modified) or because root-related biological traits were analyzed. The composite plant generated by ARM transformation is composed of transgenic roots and wild shoot, which has already broadly applied for interactions between roots and microbes (e.g., rhizobia, arbuscular mycorrhizal fungi, pathogens, and nematode), signal transduction between root and shoot, and interactions between plant roots and environment (biotic/abiotic stresses). In addition, transgenic hairy roots can be rapidly induced and produced higher biomass by ARM transformation for the fast production of secondary metabolites and phytoremediation (e.g., Plasencia et al., 2016; Fan et al., 2017; Wang et al., 2017; Yang et al., 2017; Jenei et al., 2020; Fan et al., 2020a; Zhang et al., 2021). To knock out gene(s) for loss-of-function studies in roots by ARM transformation, clustered regularly interspaced short palindromic repeats (CRISPR)–associated Cas (CRISPR/Cas) systems provide a convenient and powerful tool. The CRISPR/Cas9 system is the most frequently and widely employed targeted genome editing tool due to its simplicity, high specificity, efficiency, and multiplexing capacity (Hua et al., 2019). The CRISPR/Cas9 system is composed of the single-guide RNA (sgRNA) for target DNA recognition and the Cas9 nuclease for DNA cleavage. Previous studies had shown that the editing efficiency and mutation types (homozygous, heterozygous, or bi-allelic) mediated by the CRISPR/Cas9 system varied considerably in different plant tissues and species when different promoters were used to drive the expression of Cas9 via A. tumefaciens–mediated stable genetic transformation (Wang et al., 2015; Yan et al., 2015; Eid et al., 2016; Gao et al., 2016; Mao et al., 2016; Tsutsui and Higashiyama, 2017; Feng et al., 2018). However, genome editing efficiencies mediated by CRISPR/Cas9 using different promoters to drive the expression of Cas9 by ARM transient transformation have not been evaluated. In most cases, cauliflower mosaic virus (CaMV) 35S is used to drive the expression of Cas9 (Yan et al., 2015; Tang et al., 2016; Ma et al., 2016; Fan et al., 2017; Feng et al., 2018; Zhang et al., 2020) in ARM transformation but with a low genome editing efficiency. In the transformed hairy roots mediated by ARM transformation, except for recessive traits, mutant phenotype(s) can be observed only when all alleles are edited and homozygous/biallelic mutations (H/BM) generated. This is a challenge and bottleneck to achieving multiple targeted loci simultaneous homozygous/biallelic mutagenesis in the transient expression of the CRISPR/Cas9 system in diploid and polyploid plants with multiple gene copies. Large numbers of transformants need to be selected and further identified whether homozygous/biallelic mutagenesis was induced at all the target sites, which is labor intensive, time consuming, tedious, and costly. Here, we describe a highly efficient Arabidopsis thaliana gamma- glutamylcysteine synthetase promoter named AtGCSpro (7-GCS; EC 6.3.2.2; May and Leaver, 1994). pAtGCSpro::GUSPlus transformed whole roots showed a GUS signal and a high level of GUS activity in the initiation region of root meristem undergoing active cell division in broad eudicots diploid soybean (Glycine max), tomato (Solanum lycopersicum), cucumber (Cucumis sativus L.), Lotus japonicus, as well as in polyploid tobacco (Nicotiana tabacum L.), cotton (Gossypium spp), and sweet potato (Ipomoea batatas). Our results indicate the advantage of using AtGCSpro for CRISPR/Cas9 genome editing in inducing H/BM rate applied in ARM transformation of L. japonicus (a model leguminous plant species), soybean, and tomato. This approach has great potential in research addressing multiplex gene copies or gene families with functional redundancy. The conserved and high activity of AtGCSpro in roots covering a wide range of dicots species suggests that AtGCSpro might have great potential to be applied broadly to achieve high H/BM rates at target sites by CRISPR/Cas9 via ARM transformation.
Soybean (Glycine max) Williams 82, L. japonicus (Gifu-129), cucumber (Cucumis sativus L.) “Chinese long” inbred line 9930, tomato (Solanum lycopersicum) local variety Maofeng802, sweet potato (Ipomoea batatas) local variety Jishu25, cotton (Gossypium spp.) local variety Lumianyan28, tobacco (Nicotiana benthamiana), and Arabidopsis thaliana Columbia (Col-0) were used in this study. The plants were grown in a greenhouse under a photoperiod of 16h light (80 µM photons m-2 s-1)/8h dark at 24 ± 2C.
To isolate AtGCSpro, a 2411-bp upstream promoter region of the translation start site of gamma-glutamylcysteine synthetase gene (GenBank accession no. AF068299.1) was amplified by PCR using a GaBa1 primer containing a BamHI site combined with a GaBNR primer containing a BsaI site (produced 5′-CATG sticky end) from A. thaliana Columbia (Col-0) genomic DNA as a template. All primers sequences used in this paper are listed in Supplementary Table S1 . To generate GUSPlus expressing vectors with various AtGCSpro promoter lengths, a recombinant binary vector pRed1305 (Fan et al., 2020b) harboring a GUS gene driven by CaMV35S with an intron from the catalase gene was used as the backbone. The CaMV35S promoter in pRed1305 was replaced by various lengths of AtGCSpro, respectively. Shortened lengths of AtGCSpro were PCR-amplified from the 2411-bp AtGCSpro with a reverse primer (GaBNR) and a forward primer (GaBa2, GaBa3, GaBa4, or GaBa5). The PCR amplification products, including a BamHI restriction site at the 5′ end and a BsaI restriction site at the 3′ end, were digested and directly ligated into pRed1305 previously digested with BamHI and NcoI, thus producing the pRedGa1 (AtGCSpro 2411:: GUSPlus), pRedGa2 (AtGCSpro 1977:: GUSPlus), pRedGa3 (AtGCSpro 1629:: GUSPlus), pRedGa4 (AtGCSpro 1178:: GUSPlus), and pRedGa5 (AtGCSpro 833:: GUSPlus) vectors. Schematic diagrams of the constructs are shown in Figure 1A . All constructs mentioned in the paper were confirmed by Sanger sequencing. The DNA ladder DL2000 in this paper was bought from Sangon Biotech (China, Shanghai). Histological GUS staining was performed as previously described (Fan et al., 2020b). Relative expression levels of GUS were performed by a quantitative Real-time PCR (qRT-PCR) assay according to Lü et al. (2010) with the following minor modifications. The amplification of the soybean GmActin gene was used for normalization, and the primer pairs GmActinF and GmActinR were used according to Fan et al. (2020a). The gene-specific primer pairs GUSPF and GUSPR for GUSPlus gene were used. qRT-PCR experiments were performed with three replicates. In a biological replicate, for each independent transformed event (transformed pRedGa1, pRedGa2, pRedGa3, pRedGa4, or pRedGa5 construct), total 10 independent transgenic positive roots (with ~4 cm root lengths) for each independent construct were individually sampled, ground in liquid nitrogen, and used for total RNA extraction, respectively.
We first generated a series of CRISPR/Cas9-mediated gene knockout vector backbones: pRd35Cas9 (p2×35Spro-Cas9), pRdGa1Cas9 (pAtGCSpro 2411-Cas9), pRdGa4Cas9 (pAtGCSpro 1178-Cas9), pRdGa5Cas9 (pAtGCSpro 833-Cas9), pRdUbiCas9 (pUbiquitinpro -Cas9), pRdYCas9 (pYAOpro -Cas9), and pMd35Cas9 ( Figure 1B ). We first recombined an intermediate vector pRSE401 ( Supplementary Figure S1 ) based on the backbone of pHSE401 (Xing et al., 2014), a DsRed reporter gene driven by CaMV35S promoter that replaced the Hpt II (Hygromycin Phosphotransferase II), which can be easily screened for transgenic positive hairy roots ( Supplementary Figure S2 ). To introduce the DsRed driven by the CaMV35S promoter into pHSE401, the p35S-DsRed-CaMV poly(A)-LB cassettes (primary regions from 8,392 to 11,034 in pCAMBIA1305) were produced from the vector pRed1305 (Fan et al., 2020b) digested by EcoRI and SacII and then inserted into pHSE401. In addition, to shorten the vector sizes, the AtU6-26 promoter (424 bp) was replaced by the AtU3d promoter (121 bp) to drive the gRNA expression (Ma et al., 2015). The complete gRNA expression cassettes of AtU3d-gRNA-Sc-U6-26t were generated by recombinant PCR. To substitute AtU6-26 for AtU3d in pRSE401, the AtU3d promoter (primary regions 144–264 bp in pYLsgRNA-AtU3d) was amplified by PCR with primers Sap401 and Rd4012 using pYLsgRNA-AtU3d plasmid (Ma et al., 2015) as the template. BsaI-gRNA-Sc-U6-26t cassettes (primary regions 1327–2190 bp in pHSE401, Addgene No. 62201) were amplified by PCR with primers Rd4013 and Sap402 using pHSE401 plasmid (Ma et al., 2015) as the template. The two PCR fragments were recombined to generate the sgRNA expression cassettes of AtU3d-BsaI-SpR-BsaI-gRNA-Sc-U6-26t by recombinant PCR using primers Sap401 and Sap402, followed by digestion using SapI, which was cloned into pRSE401 ( Supplementary Figure S1 ) and pPG35Cas9 (Fan et al., 2020c), respectively, previously digested by HindIII followed by added with an “A” at the 3′ end of cohesive ends using KOD DNA polymerase with dATP. Therefore, the recombinant CRISPR-Cas9 vectors pRd35Cas9 and pMd35Cas9 were generated ( Figure 1B ). Based on the backbone of pRd35Cas9, an AtGCSpro 2411 promoter replaced the 2 × 35S and produced the pRdGa1Cas9 ( Figure 1B ). pRdGa1Cas9 generation was as follows. Full-length AtGCSpro 2411 was amplified by PCR with primer GaK1 with a BsaI restriction enzyme digestion site (produce 5′-GTAC sticky end) at the 5′ end and primer GaX2 with an XbaI at the 3′ end and then digested using restriction enzymes for cloning into pRd35Cas9 previously digested by Acc651 and XbaI and, therefore, produced vector pRdGa1Cas9 ( Figure 1B ). The Ubiquitin promoter, YAO promoter, AtGCSpro 1178, and AtGCSpro 833 were amplified by PCR with the primers Ubikp/Ubixp (for Ubiquitinpro ), YAOF18/PYao2 (for YAOpro ), GaBa4/GaX2 (for AtGCSpro 1178), and GaK5/GaX2 (for AtGCSpro 833) using pYLCRISPR/Cas9Pubi-B (Ma et al., 2015), pYGUS1305 (Fan et al., 2020b), AtGCSpro 2411, and AtGCSpro 2411 as the templates, respectively, and digested by KpnI/BsaI, KpnI/BsaI, KpnI/XbaI, and KpnI/XbaI, respectively, for cloning into the KpnI/XbaI restriction sites of the pRd35Cas9, and therefore generated pRdUbiCas9, pRdYCas9, pRdGa4Cas9, and pRdGa5Cas9 ( Figure 1B ).
To construct genome editing vectors to knockout soybean Rj7, pRdGa1Cas9, pRdUbiCas9, pRdYCas9, pRdGa4Cas9, pRdGa5Cas9, and pRd35Cas9 were used as a backbone, respectively. Oligos Ktrj71 and Ktrj72 specifically targeted the soybean Rj7 for construction of p2×35Spro-Cas9-Rj7, pUbiquitinpro -Cas9-Rj7, pYAOpro-Cas9-Rj7, pAtGCSpro 2411-Cas9-Rj7, pAtGCSpro 1178-Cas9-Rj7, and AtGCSpro 833-Cas9-Rj7 vector, respectively. To construct genome editing vectors to knockout L. japonicus LjNLP4, oligos KtLjNL1 and KtLjNL2 were designed and located in the exon of the open reading frame of LjNLP4 (position: 28146509-28146531). The CRISPR/Cas9 vectors pMd35Cas9, pRdGa1Cas9, pRdUbiCas9, pRdYCas9, and pRdGa4Cas9 and pPG35Cas9 ( Figure 1B ) were used and generated the p2×35Spro-Cas9-LjNLP4, pAtGCSpro 2411-Cas9-LjNLP4, pUbiquitinpro -Cas9-LjNLP4, pYAOpro -Cas9-LjNLP4, and pAtGCSpro 1178-Cas9-LjNLP4 vectors, respectively. To construct simultaneously targeting two genome sites, oligos ktGmR11 and ktGmR12 were designed to specifically target soybean GmNNL1 (Zhang et al., 2021) and Rfg1 (Fan et al., 2017) using pRd35Cas9 and pRdGa4Cas9 ( Figure 1B ) as backbones for generating p2×35Spro-Cas9-GmNNL1Rfg1 and pAtGCSpro 1178-Cas9-GmNNL1Rfg1 vectors, respectively. Oligos ktLjSNF and ktLjSNR were designed to specifically target L. japonicus LjNLP4 and LjSYMRK (Wang et al., 2016) using pMd35Cas9 and pRdGa4Cas9 ( Figure 1B ) as backbones for construction of the p2×35Spro-Cas9-LjNLP4LjSYMRK and AtGCSpro 1178-Cas9-LjNLP4LjSYMRK, respectively. Oligos ktSlTRY1 and ktSlTRY2 were designed to specifically target two different sites within the first and second exon of tomato SlTRY (Tominaga-Wada et al., 2013), respectively, using pRd35Cas9 and pRdGa4Cas9 ( Figure 1B ) as backbones for generating p2×35Spro-Cas9-SlTRY and pAtGCSpro 1178-Cas9-SlTRY, respectively. The Optimized CRISPR Plant Design Tool (http://cbi.hzau.edu.cn/cgi-bin/CRISPR) was used to design the oligos for constructing of CRISPR/Cas9 vector(s) (Lei et al., 2014). CRISPR/Cas9-mediated gene mutation vectors were constructed according to the procedure described previously by Xing et al. (2014). Specifically, for cloning a single gRNA into BsaI sites of Cas9 expression vector, oligo primers annealing was carried out; for construction of two-gRNA-expressing vectors for gene targeting, the fragments were amplified via over-lapping PCR with designed primers using the vector pCBC-DT1T2 (Xing et al., 2014) as a template, and then inserted into Cas9 expression vector that was linearized by BsaI through the Golden Gate cloning method.
The constructs were transformed into the A. rhizogenesis strain K599 (for soybean, cucumber, tomato, cotton, sweet potato, and tobacco) and ARqual (for L. japonicus) by electroporation, respectively. Composite soybean, cucumber, tomato, cotton, sweet potato, and tobacco plants were generated by one-step ARM transformation (Fan et al., 2020a; Fan et al., 2020b). Composite L. japonicus was generated according to the protocol (Okamoto et al., 2013). L. japonicus nodulation assay was performed as described by Fan et al. (2020c). L. japonicus composite plants with ~5 cm root lengths were inoculated with Mesorhizobium loti MAFF303099. For the nitrate response assay, 10 mM KNO3 was used and watered the transformed roots.
Previously, we successfully generated purple/red anthocyanin accumulation by overexpression of AtMyb75 in transgenic L. japonicus hairy roots, which can be tracked as a directly visual selection marker of transgenic roots with the naked eyes in the study of rhizobia-legume symbiosis (Fan et al., 2020c). The transgenic positive hairy roots were screened by the purple/red anthocyanin accumulation on roots depending on the expression of AtMyb75 or by visual DsRed fluorescence produced from the expression of DsRed reporter gene due to the different the CRISPR/Cas9 genome editing vector backbones used ( Figure 1B and Supplementary Figure S2 ). To analyze the mutations caused by CRISPR/Cas9, PCR/RE, (restriction enzyme) and Sanger sequencing assays were performed. Genomic DNA was extracted from independent transgenic positive hairy roots (co-transformed primary root) of 5–10 cm in length. The DNA sequences covering the CRISPR target sites of the transformed plants were amplified by PCR using gene-specific primers ( Supplementary Table S1 ). Rj7, GmNNL1, Rfg1, LjNLP4, LjSYMRK, and SlTRY-specific fragments were amplified using pairs of primers Rj71/Rj72 (for Rj7), GmRHin1/GmRHin2 (for GmNNL1), GmRNco1/GmRNc4 (for Rfg1), LjNLP1F/LjNLP1R (for LjNLP4), LjSYF/LjSYR (for LjSYMRK), and SLTRY1/SLTRY2 (for SlTRY) and subsequently subjected to restriction enzyme digestion analyses and sequenced to identify the gene-edited type(s). About 10 clones for each amplicon were individually sequenced to further determine the mutation type. The experiments were replicated for three biological replicates for each transformed construct. The mean values were used for statistical analysis.
To assess the promoter activity of AtGCS at an earlier stage of the initiation of hairy roots and in developing root, a 2411-bp upstream promoter region of AtGCS was cloned and used to drive the expression of GUS (β-Glucuronidase). pAtGCSpro:: GUSPlus was transformed into soybean by ARM hairy roots transformation. A high level of GUS activity is found in the teratoma that is formed, from which hairy roots can emerge, and ubiquitously in the roots ( Figures 2A, B ). In addition to experiments in soybean, we also tested the AtGCSpro activity in broad eudicot species, including diploid species tomato ( Figures 2C, D ), cucumber ( Figures 2E, F ), and L. japonicus ( Figure 2G ), as well as polyploid species cotton ( Figure 2H ), tobacco ( Figures 2I, K ), and sweet potato ( Figures 2J, L ). These results are in agreement with that of AtGCSpro in soybean ( Figures 2A, B ). Whole transformed pAtGCSpro::GUSPlus roots show a strong GUS signal in the initiation emergence regions of hairy roots and the whole roots ( Figures 2C–L ). These results indicate that AtGCSpro activity is broadly conserved in eudicots. To further analyze the promoter activity with different shortened lengths, truncated lengths AtGCSpro with 5′ deletion fragments were produced and used to drive the expression of GUS in soybean hairy roots. Here, we designated the full length 2411-bp sequences as AtGCSpro 2411 and truncated lengths 1977-bp, 1629-bp, 1178-bp, and 833-bp sequences as AtGCSpro 1977, AtGCSpro 1629, AtGCSpro 1178, and AtGCSpro 833, respectively. There were no distinct differences in the GUS signals when comparing the transformed pAtGCSpro 1977::GUSPlus ( Figures 3A, B ), pAtGCSpro 1629::GUSPlus ( Figures 3C, D ), and pAtGCSpro 2411::GUSPlus roots ( Figures 2A, B ; Supplementary Figure S3 ). In contrast, a little bit low GUS activity was observed in the transformed AtGCSpro 1178::GUSPlus roots by histological GUS staining ( Figures 3E, F ). This result was in accordance with the relative expression analysis of GUS by qRT-PCR in transgenic roots, showing a little bit low expression levels of GUS but with no significant difference with those of AtGCSpro 2411::GUSPlus, AtGCSpro 1977::GUSPlus, and AtGCSpro 1629::GUSPlus roots ( Supplementary Figure S3 ). The expression level of GUS was the lowest in the hairy roots transformed with pAtGCSpro 833::GUSPlus ( Figures 3G–I and Supplementary Figure S3 ). No GUS expression is in the root tips, indicating that AtGCSpro 833 has no activity in the root tip tissues ( Figures 3G, I ). In addition, we also analyzed the activity of AtGCSpro 1178 in cucumber and tomato by ARM transformation ( Figures 4A–D ). The AtGCSpro 1178 activity in cucumber ( Figures 4A–C ) and tomato ( Figures 4B, D ) is in agreement with those in soybean ( Figures 3E, F ). The GUS signals are strong and can be detected in the region that will develop into root meristem and in the whole developing roots.
Based on AtGCSpro activity analyses, we reasoned that Cas9 driven by the AtGCSpro could be specifically transcribed in the meristematic region where the root meristem will initiate and in the whole root. To test whether AtGCSpro might improve H/BM frequencies in ARM transformation, we first aimed to knockout a single target site in two leguminous plant species, the important crop soybean and the model plant L. japonicus. The dominant traits are the most prevalent in the genome, such as, in soybean, most of characterized genes are dominant genes (Zhang et al., 2022). Due to only the transgenic roots generated H/BM at the dominant target gene site can result in loss-of-function mutation phenotype(s) in the ARM transient transformation; here, we only analyzed the H/BM-induced frequency. The soybean GmNARK (Rj7) played a crucial role in the autoregulation of nodulation (Searle et al., 2003; Lin et al., 2010) and was selected as a target site. To knock out Rj7, one target site in the first exon of Rj7 containing an EcoRI restriction endonuclease digestion site next to the NGG region ( Figure 5A ) was selected to identify the mutation genotypes. To determine and quantify gene editing efficiency, genomic DNA was extracted from 30 independently transformed hairy roots. PCR amplicon spanning the target site was subjected to digestion by the EcoRI restriction enzyme, and sequenced to verify mutations-type ( Figures 5B–F and Table 1 ). The hairy roots transformed with the p2×35Spro-Cas9-Rj7 vector, 17 lines (#1, #4–7, #9–12, #15–17, #21, #24–26, and #30) among 30 independent transgenic lines are homozygous or biallelic mutations. Compared with the transgenic hairy roots transformed with the p2×35Spro-Cas9-Rj7, in the pAtGCSpro 2411-Cas9-Rj7 roots, 23 lines (#1–2, #4–14, #17–19, #21–22, #24–26, and #29–30) among 30 independent transgenic lines are homozygous or biallelic mutations ( Figure 5D ). The results indicate that the pAtGCSpro 2411-Cas9 system yields 76.7% (23/30) homozygous/biallelic mutants compared with 56.7% (17/30) for the p2×35Spro-Cas9 system ( Figures 5B–E ; Table 1 ). AtGCSpro 2411 exhibits higher efficiency than 2×35Spro in inducing the H/BM in soybean. Various types of insertions or deletions (indels) at the target site are shown. Most of the mutation events were small indels (± 1–8 bp). No large indels (> 50 bp) were observed in 30 randomly selected sequencing mutants. Noticeably, some H/BM mutants with 3n indels ( Figures 5C, E and Supplementary Figure S4 ). To determine the shortest AtGCSpro length used to drive the expression of Cas9 without sacrificing H/BM efficiency, we also generated pAtGCSpro 1178-Cas9 and pAtGCSpro 833-Cas9 systems to knockout Rj7. There was a slightly decreased H/BMs efficiency by pAtGCSpro 1178-Cas9 (21/30; 70%) compared with pAtGCSpro 2411-Cas9 (76.7%) ( Supplementary Figures S5A , Figure 5D , and Table 1 ). However, the pAtGCSpro 833-Cas9 system substantially affects the genome editing efficiency of H/BM at a rate of 50% (10/20) compared with 76.7% (23/30) for the pAtGCSpro 2411-Cas9 system, which is even less than the p2×35Spro-Cas9 system ( Supplementary Figures S5A–C , Figure 5D , and Table 1 ). Therefore, to minimize the construct’s size, for genome editing, we recommend using pAtGCSpro 1178-Cas9 system. In L. japonicus, the NRSYM1/LjNLP4 (Lj5g3v1999250.1) functions as a master regulator for nitrate-dependent symbiotic gene expression and inhibits nodulation when surplus nitrate is in soil. LjNLP4 was selected as the targeted gene because mutations in LjNLP4 result in conveniently observable “nodule” phenotypes, such as defects in high nitrate concentrations. The ljnlp4 mutant can form mature nitrogen-fixing nodules in the presence of a high nitrate concentration (Suzuki et al., 2013; Nishida and Suzaki, 2018; Nishida et al., 2021). One sgRNA was designed to target the LjNLP4 in L. japonicus ( Figure 6A ). To estimate the H/BM rate for ljnlp4, as the criterion of success, we used whether leghemoglobin-rich pink mature nodules formed on transgenic hairy roots in the presence of a high nitrate concentration. Using this classification, 40 transgenic plants were analyzed, and 33 of them (82.5%) were independent transgenic hairy roots lines transformed with the pAtGCSpro 1178-Cas9-LjNLP4 showing mature nodules in the presence of high nitrate concentrations, compared with 32.5% (13/40) for p2×35Spro 1178-Cas9-LjNLP4 ( Figure 6B ). To further accurately determine the gene-editing efficiency, PCR amplicons amplified from each independent transgenic hairy root covering the target site were subjected to digestion by restriction enzyme BamHI and Sanger sequencing. Sixteen independent transgenic roots were tested. The LjNLP4 targeted site was successfully edited using the p2×35Spro-Cas9 and pAtGCSpro 1178-Cas9 systems ( Figures 6C–E ). However, the editing efficiencies are distinct between the p2×35Spro-Cas9-LjNLP4 and pAtGCSpro 1178-Cas9-LjNLP4 systems ( Figures 6C–E and Table 2 ). The H/BM rate for ljnlp4 was 31.3% (5/16) for the p2×35Spro-Cas9 system; much lower than the 81.3% (13/16) achieved using the pAtGCSpro-Cas9 system ( Figures 6B–D and Table 2 ). Based on the results and previous mutation frequency results in soybean, on average, pAtGCSpro 1178-Cas9 system shows a 1.7 times higher H/BM frequency than the p2×35Spro-Cas9 system for a single target site in the genome (proportion of H/BM-induced frequency using pAtGCSpro 1178-Cas9 system compared with that of the p2×35Spro-Cas9 system in soybean and L. japonicus). Besides AtGCSpro, we also evaluated the ubiquitin promoter (Ma et al., 2015) and YAO promoter (a high-efficiency germ cell-specific promoter in Agrobacterium-mediated genetic transformation and with high activity in roots) (Li et al., 2010; Yan et al., 2015; Feng et al., 2018; Fan et al., 2020a; Fan et al., 2020b). The results indicate that AtGCSpro is the most efficient promoter for inducing H/BM, outperforming the ubiquitin, YAO, and CaMV 35S promoters in both transgenic soybean and L. japonicus hairy roots ( Tables S2 and S3 ).
In plants, multiple genomic sites need to be edited simultaneously, resulting in the observable phenotype(s), such as studying multiple functionally related genes or the knockout of functionally redundant genes (Ma et al., 2016). Due to high-efficiency H/BM induction rates for a single target site using the AtGCSpro-Cas9 system relative to other systems, we next assessed the efficiency of pAtGCSpro 1178-Cas9-induced H/BMs when simultaneously targeting two genome sites in soybean, L. japonicus, and tomato. In soybean, which is resistant to nodulation, GmNNL1 (Glyma.02g076900) (Zhang et al., 2021) and Rfg1 gene (Fan et al., 2017) were targeted simultaneously. p2×35Spro-Cas9-GmNNL1Rfg1 and pAtGCSpro 1178-Cas9-GmNNL1Rfg1 achieved H/BM frequencies of 0% (0/30) and 6.7% (2/30) at the GmNNL1 site, 33.3% (10/30) and 83.3% (25/30) at the Rfg1 site, respectively ( Supplementary Figures S6A–H and Table 3 ). The H/BM frequencies for gmnnl1gmnnl1rfg1rfg1 were 0% (0/30) (p2×35Spro-Cas9-GmNNL1Rfg1) and 6.7% (2/30) (pAtGCSpro 1178-Cas9-GmNNL1Rfg1), respectively ( Supplementary Figures S6A–H and Table 3 ). In L. japonicus, two genomic target sites were analyzed, LjNLP4 (Suzuki et al., 2013; Nishida and Suzaki, 2018; Nishida et al., 2021) and LjSYMRK (Wang et al., 2016). Compared with p2×35Spro-Cas9-LjNLP4LjSYMRK, H/BM frequencies of the two LjNLP4 and LjSYMRK target sites were significantly increased from 30.0% (9/30) to 83.3% (25/30) at the LjNLP4 site, from 26.7% (8/30) to 83.3% (25/30) at the LjSYMRK site, when using pAtGCSpro 1178-Cas9-LjNLP4LjSYMRK. The H/BM frequencies for ljnlp4ljnlp4ljsymrkljsymrk were 10.0% (3/30) (p2×35Spro-Cas9-LjNLP4LjSYMRK) and 66.7% (20/30) (pAtGCSpro 1178-Cas9-LjNLP4LjSYMRK), respectively ( Supplementary Figures S7A–G and Table 4 ). In tomato (Solanum lycopersicum), two gRNAs were designed to introduce mutations into the tomato endogenous gene TRYPTICHON (SlTRY, Solyc01g095640.1.1) (Tominaga-Wada et al., 2013). Consistent with these previous observations, using the AtGCSpro 1178 promoter to direct Cas9 expression can lead to a higher H/BM induction rates. At the sgRNA1 target site, p2×35Spro-Cas9-SlTRY and pAtGCSpro 1178-Cas9-SlTRY result in H/BM frequencies of 23.3% (7/30) and 91.3% (21/23), and at the sgRNA2 target site, 13.3% (4/30) and 78.3% (18/23), respectively. The H/BM induction rates for the two simultaneously targeted sites were 6.7% (2/30) (p2×35Spro-Cas9-SlTRY) and 65.2% (15/23) (pAtGCSpro 1178-Cas9-SlTRY), respectively ( Supplementary Figures S8A–D and Table 5 ). Based on the above results, the pAtGCSpro-Cas9 system always substantially enhances the H/BM-induced frequency over the p2×35Spro-Cas9 system in soybean, L. japonicus, and tomato. By using the pAtGCSpro-Cas9 system, we achieved an average H/BM frequency 8.3-fold (proportion of H/BM-induced frequency using pAtGCSpro 1178-Cas9 system compared with that of the p2×35Spro-Cas9 system in soybean, L. japonicus and tomato) higher than the p2×35Spro-Cas9 system for two targeted site(s) in the genome.
In Arabidopsis, AtGCS encodes the first enzyme of glutathione (GSH; 7-glutamylcys teinyl glycine) biosynthesis, γ-glutamylcysteine synthetase (7-GCS; EC 6.3.2.2; May and Leaver, 1994). AtGCSpro is involved in the control of mitosis cell cycle during the G1 to S phase and regulates the initiation and maintenance of cell division in the root apex (Vernoux et al., 2000). However, the promoter activity of AtGCS has not been reported. In this study, our results indicated that AtGCSpro had a high activity in the initiation emergence regions of hairy roots, later, in the root meristem, and in the developing roots. The pAtGCSpro-Cas9 system markedly improves H/BM efficiencies relative to the p2×35Spro-Cas9 system in soybean, L. japonicus, and tomato by ARM transformation. Combined the expression of AtGCSpro in this study with previous studies on the functions of that in specific cell cycle (Cheng et al., 1995; Vernoux et al., 2000), we reasoned that large numbers of Cas9-driven by AtGCSpro expressed in the roots during the G1-to-S phase and loosened chromatin DNA structure and single-strand DNA condition contribute Cas9’s cutting of the DNA strands to generate a DNA-strand break. In particular, single-strand chromatin DNA is subjected to cutting and is introduced to the mutations, which will result in homozygous mutants following cell mitosis cycles. Therefore, it is reasonable that the AtGCSpro-Cas9 system can produce higher H/BM rates than p2×35Spro-Cas9. Previous studies have indicated that using the YAO promoter to drive Cas9 expression in CRISPR/Cas9 constructs leads to high-efficiency genome editing in Arabidopsis by A. tumefaciens–mediated genetic transformation (Yan et al., 2015; Feng et al., 2018). In contrast, in this study, the YAO promoter showed a much lower efficiency than the 2×35S promoter in driving Cas9 expression in soybean and L. japonicus roots mediated by ARM transformation ( Supplementary Tables S2 and S3 ), despite high YAO promoter activity in roots (Fan et al., 2020a; Fan et al., 2020b). This might be because of the target of the T-DNA in ARM transformation but not the germline cells in A. tumefaciens–mediated stable genetic transformation in Arabidopsis. Additionally, AtGCSpro is higher efficient promoter in inducing H/BM, outperforming the constitutive expression promoter ubiquitin in both transgenic soybean and L. japonicus hairy roots ( Supplementary Tables S2 and S3 ). Based on these results, we concluded that Cas9 expression timing and tissue specificity are crucial to the editing efficiency of the CRISPR/Cas9 system in ARM transformation.
Gene duplications are especially prevalent in plants, and the genomes of most extant angiosperm species result from a series of segmental or whole-genome duplication events. At least 70% of all angiosperms underwent at least one episode of polyploidization in their evolutionary history (Leitch and Bennett, 1997; Comai, 2000; Soltis and Soltis, 2000; Wendel, 2000; Qiao et al., 2019). Some species have undergone multiple occurrences of polyploidization in the coding portions of the genome, which are organized hierarchically into families or superfamilies. More than 50% of genes belong to gene family members in eukaryotes (Chervitz et al., 1998; Koonin et al., 1998; Semple and Wolfe, 1999; Thornton and DeSalle, 2000; Blanc and Wolfe, 2004; Xu et al., 2022). Many agriculturally important crops are polyploid plants, such as tetraploid potato (Solanum tuberosum), oilseed rape (Brassica napus), tobacco, cotton (Gossypium spp.), hexaploid bread wheat, sweet potato, and octoploid strawberry (Fragaria × ananassa) (Yang et al., 2017; Abe et al., 2019; Edger et al., 2019; Gao, 2021). To analyze the mutant phenotype(s), these duplicated genes with redundant functions must be simultaneously mutated to generate homozygous/multi-allelic changes for dominant target genes at alleles site. Using the pAtGCSpro-Cas9 system, the average H/BM frequency is 8.3-fold higher than the p2×35Spro-Cas9 system for two simultaneously targeted sites in the genome. Compared with targeting a single genomic site (1.7-fold increased), the efficiency of simultaneous homozygous/biallelic mutagenesis in a single event is significantly increased for targeting two genomic sites using the pAtGCSpro-Cas9 system. Therefore, with the increasing of genomic targeted sites, the H/BM frequency is more significantly increased using the pAtGCSpro-Cas9 system. The pAtGCSpro-Cas9 system provides a powerful tool for analyzing the loss-of-function phenotypes of duplicated genes in the diploid and polyploids plants for multiple genomic targeted editing. In the traditional genetic transformation mediated by Agrobacterium, generating recessive change at multiple target sites is also very important. Although homozygous mutants can also be obtained from heterozygotes mutants at the sgRNA target site by plant self-crossing, a longer experiment was required, and it was laborious to screen and identify the homozygous mutants. Previous research has indicated that regeneration plants using the root or root tip as explants had been reported in some plants, such as L. japonicus (Lombari et al., 2003), tomato (Peres et al., 2001), Chicory (Matvieieva et al., 2011), and Medicago truncatula (Iantcheva et al., 2005). This suggests that the transgenic hairy roots with H/BM at multiple targeted sites could be used as explants to induce the regeneration plants in some plants. Bernard et al. (2019) reported that the edited hairy roots can be used for explants to generate the whole transgenic plant in chicory. The pAtGCSpro-Cas9 system is a greatly convenient for plant genetic engineering breeding involving the simultaneous alteration of multiple homoeologs with H/BM in the transformation of T0 generation. This is a promising technical breakthrough for accelerating plant breeding for simultaneous H/BM at multiple genome target sites to eliminate “deleterious” genes with establishing regeneration plants using the root or root tip as explants in some plant species. The pAtGCSpro-Cas9 system would propel plant breeding and accelerate the generation of homologous mutants with multiplexed genome modifications of homologous genes or gene families in a much shorter time than conventional breeding techniques. Additionally, the genotyping screening of H/BM will greatly reduce the working burden at multiple sites.
In this study, the AtGCSpro-Cas9 system always indicates notably increased homozygous/biallelic targeted mutation efficiency in selected species soybean, L. japonicus, and tomato tested than the p2×35Spro -Cas9 system, although the rates of H/BM-induced are different in different species at different target sites. Furthermore, AtGCSpro indicates a strong activity in broad eudicots species, such as soybean, tomato, cucumber, L. japonicus, tobacco, sweet potato, and cotton. The conserved activity of AtGCSpro in eudicots species suggests that the AtGCSpro-Cas9 system might induce higher H/BM in a wide range of dicots plant species in ARM transformation. In this study, as expected, in the H/BM-induced mutants, we found that some homozygous/biallelic mutants with 3n indels at target site. As protein coding genes are read in units of three (codons), the 3n indels would result in only insert or delete 1 or several amino acids in the corresponding coding protein, and some homozygous/biallelic mutants with 3n indels (such as indels of 3bp, 6bp, 9bp, …) may not be loss-of-function mutants. The generated homozygous/biallelic mutants with non-3n indels at the dominant target gene site are required because they can produce complete loss-of function mutants. Therefore, it is crucial to establish a high-efficiency CRISPR/Cas9 system with higher H/BM mutation efficiency applied for ARM transformation because a certain ratios 3n indels mutants (randomly generated) are produced in the homozygous/biallelic mutants. Besides the AtGCS promoter-driven CRISPR/Cas9, in the future, H/BM efficiency may be improved by optimizing the AtGCSpro-Cas9 system, such as using plant endogenous GCS gene promoter to drive the Cas9 expression, endogenous U6 promoter-driven sgRNA, codon-optimized Cas9, tRNA for multiplexing, a modified sgRNA scaffold, and intronized Cas9 (Li et al., 2014; Dang et al., 2015; Xie et al., 2015; Grützner et al., 2020; Huang et al., 2022). For example, in soybean, the genome editing efficiency was increased by 1.8-fold to 6.3-fold when the GmU6-10 promoter drove the sgRNA expression by replacing the Arabidopsis AtU6-26 gene promoter with CRISPR/Cas9 (Sun et al., 2015). This study shows that the AtGCSpro-Cas9 system is a viable tool for use in inducing H/BMs in a wide scope of plant species in the ARM transformation.
The original contributions presented in the study are included in the article/ Supplementary Material . Further inquiries can be directed to the corresponding authors.
YF and SHL conceived and designed the experiments, and wrote the paper. SL, XW, QL, WP, ZZ, PC, and SG performed the work and analyzed data. All authors contributed to the article and approved the submitted version.
This work was supported by National Natural Science Foundation of China (no. 31271751), and partially funded by Natural Science Foundation of Shandong province (no. ZR2012CL16 and ZR2010CQ025) and Industrial Upgrading Project of Shandong Agricultural Science and Technology Park (2019YQ035).
We thank Prof. Zhe Yan (Northeast Institute of Geography and Agroecology, CAS, China) for kindly providing the L. japonicas seeds and strains Mesorhizobium loti MAFF303099, Prof. Yaoguang Liu and Letian Chen (South China Agricultural University) for pYLsgRNA-AtU3d and pYLCRISPR/Cas9Pubi-B, and Prof. Qijun Chen (China Agricultural University) for pHSE401.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. | true | true | true |
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PMC9623729 | Li-ping Su,Min Ji,Li Liu,Wei Sang,Jing Xue,Bo Wang,Hong-Wei Pu,Wei Zhang | The expression of ASAP3 and NOTCH3 and the clinicopathological characteristics of adult glioma patients | 31-10-2022 | glioma,bioinformatics,clinicopathologic characteristics,ASAP3,NOTCH3 | Abstract ASAP3 is involved in a variety of biological activities, including cancer progression in humans. In adult glioma, we explore the effects of ASAP3 and NOTCH3 and their relationships on prognosis. The Oncomine, TIMER, and Gene Expression Profiling Interactive Analysis databases were used to investigate ASAP3 expression. Immunohistochemistry was used to assess the levels of ASAP3 and NOTCH3 expressions. The effects of ASAP3 and NOTCH3 on prognosis were assessed using survival analysis. The results revealed that the amount of ASAP3 mRNA in gliomas was much higher than in normal tissue (P < 0.01). Glioma patients with high ASAP3 mRNA expression had a worse overall survival and progression-free survival. ASAP3 overexpression is directly associated with the NOTCH signaling system. Immunohistochemistry revealed that ASAP3 and NOTCH3 were overexpressed in glioblastomas (GBMs). ASAP3 expression was associated with age, recurrence, tumor resection, postoperative chemoradiotherapy, World Health Organization (WHO) grade, and Ki-67 expression. ASAP3 expression was related to the isocitrate dehydrogenase-1 mutation in low-grade glioma. Gender, local recurrence, tumor resection, postoperative radio-chemotherapy, WHO grade, recurrence, and ATRX expression were all associated with NOTCH3 expression. ASAP3 was shown to be positively associated with NOTCH3 (r = 0.337, P = 0.000). Therefore, ASAP3 and NOTCH3 as oncogene factors have the potential to be prognostic biomarkers and therapeutic targets in adult glioma. | The expression of ASAP3 and NOTCH3 and the clinicopathological characteristics of adult glioma patients
ASAP3 is involved in a variety of biological activities, including cancer progression in humans. In adult glioma, we explore the effects of ASAP3 and NOTCH3 and their relationships on prognosis. The Oncomine, TIMER, and Gene Expression Profiling Interactive Analysis databases were used to investigate ASAP3 expression. Immunohistochemistry was used to assess the levels of ASAP3 and NOTCH3 expressions. The effects of ASAP3 and NOTCH3 on prognosis were assessed using survival analysis. The results revealed that the amount of ASAP3 mRNA in gliomas was much higher than in normal tissue (P < 0.01). Glioma patients with high ASAP3 mRNA expression had a worse overall survival and progression-free survival. ASAP3 overexpression is directly associated with the NOTCH signaling system. Immunohistochemistry revealed that ASAP3 and NOTCH3 were overexpressed in glioblastomas (GBMs). ASAP3 expression was associated with age, recurrence, tumor resection, postoperative chemoradiotherapy, World Health Organization (WHO) grade, and Ki-67 expression. ASAP3 expression was related to the isocitrate dehydrogenase-1 mutation in low-grade glioma. Gender, local recurrence, tumor resection, postoperative radio-chemotherapy, WHO grade, recurrence, and ATRX expression were all associated with NOTCH3 expression. ASAP3 was shown to be positively associated with NOTCH3 (r = 0.337, P = 0.000). Therefore, ASAP3 and NOTCH3 as oncogene factors have the potential to be prognostic biomarkers and therapeutic targets in adult glioma.
Adult diffuse glioma is the most prevalent malignant brain tumor, accounting for over 80% of all malignant brain and central nervous system (CNS) cancers. Glioma is an aggressive and lethal solid tumor generated by glial cells and among the most prevalent malignant tumors in the brain [1]. According to epidemiological statistics, glioblastomas (GBMs) are malignant tumors, which account for 30% of CNS tumors and 80% of malignant brain tumors in the whole world [2]. Glioma is a refractory solid tumor that is resistant to chemo and radiotherapy. Although combination therapy has improved the prognosis of adult glioma, the prognosis of adult glioma remains dismal, with a median survival of 14–16 months. Following the World Health Organization's (WHO) reclassification of CNS tumors in 2021, a new diagnostic concept was adopted, combining tumor histology and molecular genetics, such as isocitrate dehydrogenase (IDH) mutations and 1P/19Q co-deletion states, TRET, Alpha Thalassemia/Mental Retardation Syndrome X-Linked (ATRX), and morphologic changes, to form a more accurate diagnosis [3]. Patients who were graded as 2–3 low-grade glioma (LGG) survived for more than 10 years before developing highly invasive grade glioma due to aberrant activation of several oncogenes and signaling pathways [4]. With the advancement of genomics and bioinformatics databases, the cancer genome atlas (TCGA), Oncomine, and TIMER data sites have been promoted to deeply understand the genomes of glioma occurrence and progression, and targeted gene therapy is emerging as a promising glioma-accurate drug treatment strategy. However, the intricacy of the signaling pathways involved in gliomagenesis raises confusion regarding the optimal target and limits the utility of targeted treatment in glioma. The processes that cause glioma cell invasion are mostly unknown. Further understanding the mechanism of the malignant invasion and molecular targeted therapy of glioma can lay a theoretical foundation for finding effective biomarkers and potential carcinogenic pathways to fight against this invasive tumor. ASAP3, also known as ACAP4, is a GTPase-activating protein (GAP) for the ADP-ribosylation factor 6 (ARF6) and possesses the BAR, PH, ankyrin repeat, and GAP domains. Okabe et al. initially identified it as a development and differentiation enhancing factor (called DDEFL1) and demonstrated that it promoted the proliferation of hepatocellular carcinoma cells [5]. Given that DDEFL1 and ACAPs family proteins share a similar domain structure organization and substrate, DDEFL1 was renamed ACAP4 and found to be a particular GAP protein for ARF6 and a critical participant in cell migration [6]. ASAP3 expression is minimal or nonexistent in normal epithelia, but it has been reported to be significantly increased in a variety of human carcinomas, including lung carcinomas, colon cancers, and breast cancers, and ASAP3 expression may contribute to a poor clinical outcome in non-small cell lung carcinoma and colon cancer [7,8,9]. These effects may be attributable to the role of ASAP3 in regulating cell migration and, by extension, cancer cell invasion. Epidermal growth factor (EGF) activation causes EGF receptor (EGFR) kinase to phosphorylate ASAP3 at Tyr34, and CCL18 therapy causes ASAP3 Lys311 to be acetylated. These post-translational changes play a vital role in regulating the localization of ASAP3 at focal adhesions during cell migration [10]. However, the molecular processes underlying ASAP3 overexpression and the role of ASAP3 in glioma progression remain largely unknown. This study is intended to evaluate the potential biological function of ASAP3 in the progression of adult glioma. The NOTCH signaling pathway is an evolutionarily conserved pathway that is crucial for both normal embryonic development and malignancy. The pathway is also frequently implicated in neoplasia and promotes neoplastic growth in most situations [11]. Notch3 is a component of the signaling cascade, including NOTCH ligands, NOTCH receptors, and transcription factors. The oncogenic function of NOTCH3 has been documented in esophageal cancer [12], ovarian cancer [13], hepatocellular carcinoma [14], and so on. Furthermore, it has been proven that cross-regulation between EGFR and NOTCH3 has long been detected in genetic studies and that, depending on the cellular context, it can be both cooperative and antagonistic [15]. NOTCH3 promotes glioma cell invasion and proliferation through activation of cell cycle protein D1 (CCND1) and EGFR gene expression. Studies have shown that NOTCH3 gene polymorphisms have the potential to be diagnostic and therapeutic biomarkers for gliomas. Our team has been investigating the glioma molecular mechanism. In our early studies, Ingenuity Pathway Analysis was used to confirm that Notch signaling was activated in GBM with a Z score of 1.342 and P-value of 0.029, and that ASAP3 was activated with a fold change of 1.54 and a P-value of 0.0009. Gene Expression Profiling Interactive Analysis (GEPIA) was used to validate the expression of ASAP3 as well as the correlation of NOTCH signaling pathway proteins in gliomas. Although it has been reported that the expression of ASAP3 is elevated in glioma, its prognostic value and relationship with the expression of the cooperative protein NOTCH3 remain unclear. We intend to investigate the biological function of ASAP3 and its closely related protein NOTCH3, as well as provide new hints for molecularly targeted glioma therapy, by elucidating the expression and clinical significance of ASAP3 in adult glioma.
The Oncomine database (https://www.oncomine.org/resource/login.html), the TIMER database (https://cistrome.shinyapps.io/timer/), and the GEPIA database (http://gepia2.cancer-pku.cn/#analysis) were utilized to evaluate ASAP3 expression between human tumor and paired normal tissue. The data on tumors and normal tissues are analyzed by the GEPIA database, which is a website. This database looks at the data from the TCGA database.
Using the GEPIA database (http://gepia2.cancer-pku.cn/#analysis), the prognostic significance of ASAP3 expression in glioma was investigated. We examined the relationship between ASAP3 expression and overall survival (OS) and disease-free survival (DFS) in glioma using the GEPIA database. In the GEPIA database, the median ASAP3 expression was applied to classify groups as the cutoff value.
The glioma data, mRNA expression profiles, and survival information for 169 glioma patients were downloaded from the TCGA Genomic Data Commons data portal (https://portal.gc.cancer.gov/repository). GSEA is a computer program that determines if a priori-defined collection of genes exhibits statistically significant differential expression between high expression and low expression groups. Generated datasets and phenotype label files were submitted to the GSEA software. The phenotypes were labeled as ASAP3-high and ASAP3-low. For each analysis, 1,000 permutations of the gene set were performed. Generally regarded as enriched were gene sets with a normal P-value <0.05 and a false discovery rate <0.25.
The GEPIA database was used to generate a scatter plot and related genes. The GeneMANIA database was used to analyze protein–protein interactions, genetic pathways, and protein co-expression networks in the ASAP3 gene with the NOTCH signaling pathway.
WebGestalt is a website that analyses gene function enrichment. WebGestalt analyzed the key genes of the NOTCH signaling pathway that interact with ASAP3 to investigate the involved cell components, biological processes, and biological functions.
The Department of Pathology at the First Affiliated Hospital of Xinjiang Medical University constructed tissue microarrays (TMAs), while Shanghai Outdo Biotech Company provided technical support (Shanghai, China). The TMA was built from formalin-fixed paraffin-embedded blocks of 211 adult glioma surgical resections, which were reviewed in each case to confirm the original diagnosis and select the most representative sections using Hematoxylin and Eosin (HE) stained slides. All the surgical resections came from the First Affiliated Hospital of Xinjiang Medical University between July 2010 and November 2020. All glioma biopsies were reviewed by two professional pathologists, and any inconsistent diagnosis was further reviewed by a third professional pathologist in order to reach a final determination. The gliomas of grades 2 and 3 were classified as LGG, whereas the glioblastoma was classified as GBM. Among all the 211 cases of gliomas, 138 were LGG and 73 were GBM. The detection kit was used for immunostaining (ZSGB-BIO, SP-9001, China). Anti-ASAP3 (SC-365840, 1:100, Santa) and anti-NOTCH3 (ab23426, 1:100, Abcam) antibodies were incubated overnight at 4°C on the sections. All slides were dehydrated and counterstained with diaminobenzidine solution (ZSGB-BIO, ZLI-9017, China) for 2 min and hematoxylin (Solarbio, G1140, China). The intensity of the IHC staining was graded as 0 (no), 1 (weak), 2 (medium), and 3 (strong). The staining extent was graded from 0 to 3 based on the percentage of immune-reactive tumor cells (0–10%, 21–75%, and 76–100%). For each example, a score ranging from 0 to 9 was calculated by multiplying the staining extent score by the staining intensity score, resulting in low (0–4) or high (5–9) staining. ASAP3 staining expresses cytoplasm. NOTCH3 staining revealed the cell membrane and nucleus. Ethics approval and consent to participate: This study was approved by the ethics committee of the First Affiliated Hospital of Xinjiang Medical University. A Signed written informed consent was obtained from all participants before the study.
Statistical analysis was performed using SPSS 26.0 Software (SPSS Inc., Chicago, IL, USA) and GraphPad Prism 8.0 Software (GraphPad Software Inc., San Diego, CA, USA). The χ 2 test and Pearson correlation coefficient were used to examine the expression of ASAP3 and NOTCH3 in adult gliomas. The OS and PFS were analyzed by the Kaplan–Meier method and the log-rank method. The cox proportional risk regression model was used for multivariate analysis. Statistical analysis results with P < 0.05 were considered statistically significant, offering credibility for the above data analysis.
We determined the expression difference of ASAP3 between tumor tissues and normal tissues through multiple databases. According to the Oncomine database, ASAP3 expression was higher in cervical cancer, colorectal cancer, gastric cancer, kidney cancer, melanoma, and lymphoma tumors in cancer histology. It is also higher in multicancer with Brain and CNS cancer, kidney, melanoma, and breast cancer in some datasets (Figure 1a). The results of the TIMER database analysis showed that ASAP3 expression was significantly higher in GBM (Glioblastoma multiforme), kidney chromophobe (KICH), and liver hepatocellular carcinoma (LIHC) compared with adjacent normal tissues. However, ASAP3 expression was significantly lower in bladder urothelial carcinoma (BLCA), colon adenocarcinoma (COAD), esophageal carcinoma (ESCA), head and neck cancer (HNSC), kidney renal clear cell carcinoma (KIRC), lung adenocarcinoma (LUAD), Kidney renal papillary cell carcinoma (KIRP), lung squamous cell carcinoma (LUSC), Pheochromocytoma and Paraganglioma (PCPG), rectum adenocarcinoma (READ), stomach adenocarcinoma (STAD), thyroid carcinoma (THCA), and uterine corpus endometrial carcinoma (UCEC) when compared with adjacent normal tissues (Figure 1b). The results of the GEPIA database analysis were used as supplementary results for cancers without paired normal tissues in the TIMER database. Meanwhile, the results showed that the expression of ASAP3 mRNA was also significantly higher in other cancer types: Lymphoid Neoplasm Diffuse Large B-Cell Lymphoma (DLBC), Brain LGG, and Thymoma (THYM) (Figure 1c). These results suggest that the expression levels of ASAP3 are inconsistent in different tumor tissues, which may be related to the different pathogenesis processes of tumors. However, ASAP3 may be involved in the process of glioma regulation.
We used the GEPIA database to analyze the prognostic value of ASAP3 expression in glioma. We analyzed the correlation between ASAP3 expression levels and the OS and PFS of 676 glioma patients. According to the median expression, the 676 glioma patients were split into the ASAP3 high expression group (n = 338) and the ASAP3 low expression group (n = 338). These results of the GEPIA database showed that higher ASAP3 expression was associated with OS and DFS in gliomas (n = 338, OS: Hazard Ratio (HR) = 1.4, P = 0.018; n = 338, DFS: HR = 1.5, P = 0.0023). This was determined by HR, which is a statistical measure of the likelihood of an event occurring. There was a statistical significance to the results (Figure 2a and b).
On the basis of the TCGA-STAD dataset, we used GSEA to identify biological pathways that may be influenced by ASAP3 in the tumors. Using GSEA (h.all.v6.2.symbols.gmt), significant differences in the enrichment of the MSigDB dataset were discovered. The analysis result displayed that the ASAP3 high expression phenotype was associated with the Rig like receptor signaling pathway (Figure 3a), Inositol phosphate metabolism (Figure 3b), NOTCH signaling pathway (Figure 3c), Adherens junction, Alpha linolenic acid metabolism, Phosphatidylinositol signaling system, ABC transporters, Acute myeloid leukemia, Basal cell carcinoma, Pancreatic cancer, etc. The Ribosome (Figure 3d), Oxidative Phosphorylation (Figure 3e), Parkinson disease (Figure 3f), Alzheimer’s disease, Huntington disease, and Proteasome were differentially associated with the ASAP3 low expression phenotype (Table 1).
Using GeneMANIA analysis, we screened a total of 21 genes interacting with ASAP3 (The core enrichment of GSEA was yes in the NOTCH signaling pathway): DTX3L, NUMBL, NOTCH2, ADAM17, PSEN2, CREBBP, DVL1, EP300, CTBP1, LFNG, MAML2, NCSTN, NUMB, NOTCH1, DVL2, NOTCH3, DVL3, DTX1, DTX4, MAML1, and DTX2. We focused on analyzing the interaction relationship between ASAP3 and NOTCH signaling pathway proteins, where the main components are physical interactions, co-expression, shared protein domains, and co-localization. The results showed the correlation scatter plots for ASAP3 and each gene individually in Figure 4. The interaction network showed that these proteins were found to be mainly engaged in biological regulation, metabolic processes, cellular communication, etc. (Figure 5). Genes associated with ASAP3 may be found in many different parts of the cell, such as the nucleus, membrane-enclosed lumens, and membranes. Among the several molecular roles are protein binding, ion binding, and transferase activity (Figure 6).
The clinicopathologic characteristics of 211 patients with adult glioma are shown in supplement Table A1. We mainly analyzed the composition of LGG (65.4%; 138/211) and GBM (34.6%; 73/211) in this cohort. The overall cohort's clinical information included tumor size ≥ 3 cm (82.46%; 174/211), total tumor resection (67.3%; 142/211), and no local recurrence (65.4%; 138/211). There were significant differences in age, tumor location, local recurrence, tumor resection, Ki-67 expression, and p53 expression between LGG and GBM patients.
Immunohistochemistry was used to detect the protein expression of ASAP3, NOTCH3, and their relationship with clinicopathological features in all cohorts in order to study the potential function of ASAP3 and NOTCH3 in the progression of adult glioma (Figure 7). In this cohort, the expression of ASAP3 was found to be high in 123 tumors (58.29%), whereas it was low in 88 tumors (41.71%). The high expression of ASAP3 in GBM (80.82%) suggested that ASAP3 is activated more frequently in the most aggressive and malignant tumors (in Table 2). The results of the statistical analysis showed that ASAP3 expression was related to age, recurrence, tumor resection, postoperative radio-chemotherapy, WHO grade, and Ki-67 expression; however, its expression was not related to gender, ethnicity, tumor size, tumor location, p53 expression, and ATRX expression (Table 3). Meanwhile, the relationship between ASAP3 and 1p19q codeletion in LGG had no statistical significance (P = 0.798). We analyzed the IDH1 mutation status of LGG and found 113 cases of IDH1 mutation (termed IDH1mut) and 25 cases of IDH1 wild type (termed IDH1wt). We detected high ASAP3 expression in 40.71% of IDH1mut (46/113) and 72.00% of IDH1wt (18/25), indicating that ASAP3 was expressed significantly differently between IDH1mut and IDH1wt (P = 0.004). Spearman results revealed the negative relationship between IDH1 mutation and ASAP3 expression (P = 0.004, r = −0.242) (Table 4). According to the results, there may be a correlation between ASAP3 expression and the molecular subtypes of adult glioma. Meanwhile, NOTCH3 was found to be expressed low in 102 tumors (48.14%) and high in 109 tumors (51.66%). High expression of NOTCH3 was predominant in GBM (73.97%) (Table 2). Statistical analysis showed that NOTCH3 expression was associated with gender, local recurrence, tumor resection, postoperative radiotherapy, WHO classification, and ATRX (P < 0.05). But there was no relationship with age, ethnicity, tumor size, tumor location, P53 expression, and Ki-67 expression in glioma (P > 0.05). There was no statistically significant relationship between NOTCH3 expression and 1p19q codeletion in LGG (P = 0.276). In addition, there was no significant difference in the expression of NOTCH3 between IDH1mut and IDH1wt (P = 0.328) (in Table 4). The above results suggested that the expression of NOTCH3 is not closely related to the molecular subtype of glioma. The correlation between the ASAP3 and NOTCH3 protein markers was clarified by Spearman correlation analysis. The ASAP3 expression was found to be associated with the NOTCH3 expression (r = 0.337; P = 0.000) (in Table 5). In adult glioma, the high expression of NOTCH3 is related to the overexpression of ASAP3. Therefore, ASAP3 and NOTCH3 are interconnected with each other and associated with the development of adult glioma.
We constructed univariate and multivariate analyses of OS and PFS in adult glioma patients so that we could evaluate the clinical characteristics of these patients and the prognostic significance of the expression of the ASAP3 and NOTCH3 proteins. Two were lost to follow-up in the 211 cases. The Kaplan–Meier method was used to perform a univariate survival analysis. The following prognostic factors influence the OS in adult glioma (Figure 8a–j): Age, tumor location, local recurrence, tumor resection, WHO grade, Ki-67 expression, ATRX expression, ASAP3 expression, and NOTCH3 expression. Multivariate analysis (Cox’s proportional hazards regression model) showed that the possible independent prognostic factors for OS were as follows: Age, tumor location, local recurrence, tumor resection, WHO grade, Ki-67 expression, and ATRX expression, ASAP3 expression, and NOTCH3 expression (in Table 6). In univariate survival analysis, Age, tumor location, local recurrence, tumor resection, WHO grade, Ki-67 expression, ATRX expression, ASAP3 expression, and NOTCH3 expression were the significant prognostic factors for PFS in adult gliomas (Figure 8k–t). In multivariate analysis, the prognostic factors were the same as those of OS and ASAP3 expression. Also, NOTCH3 expression was independent of prognostic factors for PFS in adult glioma (in Table 6). In conclusion, we analyzed that the independent factors which can predict short OS and PFS in the adult glioma cohort were >50 years old, frontal location, recurrence, tumor total resection, WHO grade IV, Ki-67 expression >5%, IDH1 expression, and ATRX expression. The high expression of ASAP3 and NOTCH3 could predict the short OS in adult glioma. Therefore, ASAP3 and NOTCH3 may be the potential biomarkers of poor prognosis in adult glioma. According to the results of our study, further analysis of ASAP3 and NOTCH3 co-expression in survival analysis in adult gliomas (in Table 7). In the LGG group, the results showed that patients with high ASAP3 and NOTCH3 co-expression had shorter OS and PFS. For the GBM group, the expression levels of ASAP3 and NOTCH3 were not statistically significant with OS and PFS. In conclusion, there was an inverse relationship between the expression of ASAP3, NOTCH3 as well as ASAP3 and NOTCH3 co-expression with poor OS and DFS in the whole cohort. Cox regression analysis showed that ASAP3 and NOTCH3 could be independent prognostic factors for OS in the LGG group (Figure 9).
Glioma in adults remains a disease for which there are no specific therapy targets or prognostic biomarkers. Studying the special biology of glioma through molecular profiling might contribute to new diagnoses that improve therapeutic efficacy. High-throughput sequencing and microarray hybridization technologies have resulted in the development of a variety of gene databases, which have been widely utilized in gene expression quantitative analysis, giving a strong platform for cancer research. The TCGA database was used to derive ASAP3 expression levels in glioma from the TMA data in the library. The function of ASAP3 in glioma has not been deeply comprehended in recent years, despite a number of studies on the abnormal expression of RNA and proteins in malignancies. ASAP3 was initially found as a widely upregulated gene that leads to cell proliferation in hepatocellular carcinoma. Then, it was identified as a member of the ArfGAP family, which may be involved in the functional role of cell migration in the invasion of normal tissues by cancer cells [6,7]. Subsequently, Ha et al. revealed that ASAP3 may regulate the filamentous actin of NIH 3T3 cells and perform essential functions in cell migration [16]. ASAP3 is increased in lung and colorectal cancers with metastasis, which promotes the course of malignant disease and indicates poor patient survival [9]. It is possible that ASAP3 is involved in the regulation of cell migration and, as a result, the invasion of cancer cells. In response to EGF stimulation, CrkII recruited ASAP3 to the plasma membrane, where it cooperated with Grb2 and guanine nucleotide exchange factors to promote the recycling of integrin β1 [17]. In addition to regulating EGF-stimulated membrane and cytoskeleton remodeling and the formation of actin-containing stress fibers, ASAP3 plays an essential role in the process of EGF-elicited cell migration and invasion. ASAP3 is phosphorylated at Tyr34 in the BAR domain by EGFR receptor kinase in response to EGF stimulation, and CCL18 therapy induces acetylation of Lys311 in ASAP3. The location of ASAP3 at the focal adhesion during cell migration is determined by these post-translational modifications [10]. The interaction between ASAP3 and EZRIN was thought to be important for acid secretion in gastric parietal cells because it controls the movement of K-ATPase-containing tubulovesicles to the apical plasma membrane [18]. On the contrary, upregulation of ASAP3 also showed a negative effect on the cell adhesion, spreading, and migration on fibronectin. It has been revealed that ASAP3 was unable to bind to invadopodia in breast cancer cells or podosomes in NIH3T3 mouse fibroblasts. In breast cancer cells transfected with plasmids overexpressing active or inactive ASAP3, cellular vinculin or paxillin in focal adhesion and the distribution of invadopodia were not affected. Ha et al. demonstrated that overexpression of ASAP3 or GAP-inactive mutant ASAP3, or ASAP3 knockdown did not affect vinculin or paxillin distribution, suggesting that ASAP3 does not affect focal adhesions in cancerous tissues [16]. MST4 is activated by histamine stimulation, which enhances MST4-ASAP3 interaction. Furthermore, MST4 causes a conformational change in ASAP3, allowing ASAP3 to associate with PKA-phosphorylated EZRIN at the apical membrane of gastric parietal cells [18]. Previous studies have shown that ASAP3 is associated with glioma biology [19]. However, there is scarcely any research that investigates whether or not there is a correlation between the expression of ASAP3 and the prognosis of glioma patients. Through the bioinformatics database, we found that ASAP3 is closely related to the NOTCH signaling pathway. We analyzed the relationship between the expression of ASAP3 and NOTCH3 and the clinicopathological parameters of 211 adult gliomas using TMA. When we compared the expression of ASAP3 in GBM and LGG, we found that the expression of ASAP3 in GBM was much higher. This result indicated a favorable connection between the ASAP3 marker and the glioma grade. Our study demonstrated that the high expression of ASAP3 in gliomas was closely related to age, high WHO grade, recurrence, resection, postoperative radio-chemotherapy, and Ki-67 expression ≥10%, suggesting that ASAP3 was strongly associated with cell proliferation and migration. The high expression rate of ASAP3 in the IDH1 mutant in LGG was greater than that of the IDH1 wild-type patients. According to the findings of a multivariable study of prognosis, patients whose ASAP3 expression was high had a considerably worse probability of PFS and OS than patients whose ASAP3 expression was low. Multivariate prognostic analysis showed that age, tumor site, WHO grade, recurrence, Ki-67 expression, IDH1 mutant, ATRX, ASAP3, and NOTCH3 expression were shown to be independent predictive determinants of overall survival in gliomas. The high expression of ASAP3 was positively correlated with the Ki-67 expression rate ≥10% and the high WHO grade. Ki-67 is a marker of cell proliferation. IDH1 mutation is associated with a good prognosis and can be used as a predictor of survival. ASAP3 expression was found to be different in the IDH1 mutant and wild-type. ASAP3 expression is negatively correlated with IDH1 mutation. Therefore, we speculated that ASAP3 is related to the proliferation and invasion of adult gliomas, and the effect of ASAP3 expression on the prognosis of gliomas may be realized by promoting the proliferation of glioma cells and thus changing the tissue grading to achieve the malignant characteristics of tumors. So, there was no connection between ASAP3 expression and poor OS and PFS in the GBM cohort. Finally, in multivariate analyses between OS and PFS, we determined that ASAP3 expression was an independent predictive factor for OS. Our findings showed that ASAP3 may play a significant role in the aggressiveness and progression of gliomas. Accordingly, ASAP3 may serve as a prognostic indicator and a potential treatment target in adult glioma. Based on database comparison and literature assessment, the role of the relationship between ASAP3 and NOTCH3 in glioma has attracted the research group's interest among the 21 ASAP3 interacting proteins evaluated in the NOTCH signaling pathway. According to a wide range of studies, NOTCH signaling is involved in regulating biological activity, such as tumor cell adhesion and migration. A wide variety of hematological and solid tumors are associated with the NOTCH signal pathway, which often promotes tumor growth, but it can serve as a tumor suppressor in some cell types [20]. It is still not entirely clear how different NOTCH receptors play a role in the growth of tumors in organisms. The fourmammalian NOTCH paralogs (Notch1–4) are not functionally comparable under all conditions, despite their structural similarities. Studies have shown that binding sites on the promoters of target genes can be linked to different Notch receptors in different ways [20]. The molecular basis for these variances is currently under investigation. Because selectively targeting individual Notch receptors in tumors could reduce treatment side effects, it is important to know which receptor paralogs play key roles in the initial stage and growth of different types of cancer. Increasing evidence suggests that the NOTCH receptor may be responsible for the development of gliomas. However, the majority of research has focused on the importance of how Notch1-mediated signaling pathways contribute to the development, invasion, and recurrence of glioma tumors. NOTCH3 was recently verified as a prognostic factor in the regulation of biological activity, including tumor cell adhesion, migration, invasion, and survival [21]. NOTCH3 belongs to a family of proteins essential for cellular differentiation in a variety of developing tissues. In tumorigenesis, NOTCH3 has been shown to induce T cell leukemia through the activation of NF-kB. NOTCH3 amplified is associated with worse survival compared to tumors with non-amplified locus for gliomas in Chinese patients [22]. Rutten et al. also found that EGFR and NOTCH signaling have vital functions throughout normal development, and they regularly interact in cooperative and antagonistic manners depending on the cellular context [15]. EGFR opposes NOTCH signaling in various developmental stages, and the NOTCH system can also compensate for hypomorphic alleles of EGFR loss of function mutations in Drosophila. Genetic studies demonstrate that EGFR and NOTCH signaling pathways interact complexly. For instance, blocking the epidermal growth factor receptor leads to an increase in the number of lung cancer stem cells that are dependent on NOTCH signaling [23]. EGFR and LIN-12/NOTCH have conflicting impacts on cell fate determination in C. elegans vulva development. The combination of Notch inhibitors with EGFR inhibitors, gefitinib or osimertinib, was found to be effective in EGFR tyrosine kinase inhibitor-resistant lung cancer [24]. NOTCH1 and EGFR have been demonstrated to have antagonistic effects in skin cancer, where suppression of EGFR results in enhanced differentiation of squamous cell carcinoma cells and increased resistance to apoptosis. These results provide additional support for the combined targeting of EGFR and NOTCH3 signaling to inhibit tumor development. Using bioinformatics and functional tests, it was revealed that the NOTCH pathway is significantly associated with increased ASAP3 expression in the TCGA GBM cohort (NES = 1.89; P = 0.002). NOTCH3 is a NOTCH family member that promotes glioma proliferation and invasion [21]. Our studies showed that high NOTCH3 expression in adult gliomas was related to several factors, including gender, recurrence, tumor resection by surgery or resection, postoperative radio-chemotherapy, higher WHO grade, Ki-67 expression ≥10%, and ATRX expression. Patients with high NOTCH3 expression showed substantially shorter PFS and OS than patients with low NOTCH3 expression, indicating that it may emerge as a crucial driver for the malignant development of glioma in a univariate prognostic analysis. Glioma treatment research is now focused on trying to gain a better understanding of the precise processes that are involved in the NOTCH3 pathway. The chromatin remodeler protein ATRX is frequently mutated in H3F3A-mutant pediatric glioblastoma and IDH-mutant grade 2/3 adult glioma [25]. ATRX mutation affects DNA damage repair to render these cells more amenable to therapy, which may contribute to the survival advantage of glioma patients with ATRX mutations. NOTCH3 expression was also detected in the majority of vascular endothelial cells associated with tumors, which may facilitate tumor angiogenesis. ASAP3 may be combined with EGFR to arouse NOTCH3 expression to promote adult glioma proliferation and invasion. We demonstrated for the first time that ASAP3 and NOTCH3 are substantially associated in human glioma samples. The expression of ASAP3 was an independent prognostic factor for the OS of glioma, and the expression of ASAP3 was positively correlated with that of NOTCH3 and Ki-67 expression rate. We speculated that ASAP3, as an upstream factor of the NOTCH3 signaling pathway, may promote glioma proliferation by regulating the expression of NOTCH3, thus affecting the development and prognosis of glioma. Meanwhile, ASAP3 and NOTCH3 co-expression correlated with poorer OS and PFS in glioma patients. These results suggest that ASAP3 may cooperate with NOTCH3 in the malignant progression of glioma. ASAP3 and NOTCH3 co-expression may be a reliable prognostic biomarker. Inhibiting ASAP3 and NOTCH3 co-expression may improve the prognosis of glioma patients. However, due to the single experimental method in this study, the mechanism of ASAP3 in glioma through regulation of the NOTCH3 signaling pathway needs to be further verified at the cellular level and in animal models so as to further confirm the mechanism of ASAP3 in glioma. Future studies on the molecular interaction of ASAP3 and its potential role in the development of glioma will be helpful for a better understanding of the development of this malignant tumor and the clinical tactics of therapy. | true | true | true |
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