Long noncoding RNA expression profile and association with SLEDAI score in monocyte-derived dendritic cells from patients with systematic lupus erythematosus

Yilun Wang
Shuang Chen
†
Sunyi Chen
Juan Du
Jinran Lin
Haihong Qin
Jie Wang
Jun Liang
Jinhua Xu
Long noncoding RNA expression profile and association with SLEDAI score in monocyte-derived dendritic cells from patients with systematic lupus erythematosus
10.1186/s13075-018-1640-xR E S E A R C H A R T I C L E Open Access
Background: Monocyte-derived dendritic cells (moDCs) play important roles in the pathogenesis of systemic lupus erythematosus (SLE). Aberrant expression of long noncoding RNAs (lncRNAs) could affect the function of moDCs. The aim of this study was to explore the lncRNA expression profile in moDCs of SLE patients to provide new insights into SLE.Methods: LncRNA and mRNA microarrays were performed to identify differentially expressed lncRNAs and mRNAs in moDCs of SLE patients compared with normal controls. Bioinformatics analysis was also performed. Quantitative polymerase chain reaction (qPCR) was used to validate the results, and correlation analysis was used to analyze the relationship between these aberrantly expressed lncRNAs and SLE disease activity index (SLEDAI) scores.Results: According to the gene expression profiles, 163 lncRNAs were differentially expressed between SLE and normal controls, including 118 that were upregulated and 45 that were downregulated. A total of 137 mRNAs were differentially expressed in moDCs of patients with SLE, including 83 that were upregulated and 54 that were downregulated. Furthermore, qPCR data showed that lncRNA ENST00000604411.1 (18.23-fold, P < 0.001) and ENST00000501122.2 (1.96-fold, P < 0.001) were upregulated and the other two lncRNAs, lnc-HSFY2-3:3 (0.42-fold, P < 0.001) and lnc-SERPINB9-1:2 (0.50-fold, P = 0.040), were downregulated in moDCs of SLE patients. The expression levels of ENST00000604411.1 (r = 0.593, P = 0.020) and ENST00000501122.2 (r = 0.539, P = 0.038) were positively correlated with the SLEDAI score, respectively.Conclusions:The results indicate that the abnormal expression of lncRNAs in moDCs may be involved in the pathological processes of SLE. The expression level of ENST00000604411.1 and ENST00000501122.2 may have potential value for the assessment of disease activity in SLE.

Background

Systematic lupus erythematosus (SLE) is an autoimmune disease that may damage multiple organs by autoantibodies and immune complexes. The precise etiology of SLE is still unclear and it might involve the regulation of genes, environments, and immune imbalance. Experimental evidence suggests that the pathogenesis of SLE is related to the failure of T-and B-cell suppression mediated by defects in cell signaling, immune tolerance, and apoptotic mechanisms promoting autoimmunity [1].

Although T and B cells have been widely studied in SLE, the upper stream cells which could present autoantigens to them have only been emphasized more recently. Autoantigens are released mainly from secondary necrotic cells because of a defective clearance of apoptotic cells in patients with SLE [2]. Dendritic cells (DCs) are the most efficient antigen-presenting cells (APCs) in the human body. Recent research has associated lupus development with changes in the DC compartment, including altered DC subset frequency, localization, phenotype, and functional defects [3]. The dysfunction of DCs is related to the overreaction of T cells and B cells in SLE patients [4], including presenting autoantigens to autoreactive T cells, overproduction of proinflammatory cytokines and chemokines, suppression of Tregs, and promoting B cells to secret autoantibodies [5][6][7][8], which results in the loss of self-tolerance and production of autoantibodies.

Recent advances suggest that long noncoding RNAs (lncRNAs) regulate gene expression at the pretranscriptional, transcriptional, and post-transcriptional levels [9]. LncRNAs mediate their molecular functions through a multitude of mechanisms [10]. An lncRNA could interact with proteins in the cytoplasm as a guide, scaffold, or decoy molecule [10]. LncRNAs could also promote or repress the translation of mRNAs in the cytoplasm. For example, the antisense lncRNA BACE1-AS rapidly and reversibly upregulates BACE1 levels in response to a variety of stresses. Since BACE1 and BACE1-AS form an RNA duplex, the duplex may act to alter the secondary or tertiary structure of BACE1 and thereby increase its stability [11]. In the nucleus, lncRNAs can act in cis to control local allele-specific functions or in trans at one or more genomic loci to regulate gene expression.

LncRNAs are important in regulating the differentiation and function of DCs. Lnc-DC, which is exclusively expressed in human conventional DCs, can affect cellular differentiation (monocytes into dendritic cells) and reduce the capacity of DCs to stimulate T-cell activation by activating the transcription factor STAT3 [12]. The expression of the lncRNA HOTAIRM1 was downregulated when monocytes differentiated into DCs, and silencing of HOTAIRM1 caused changes in the expression of several monocyte differentiation markers such as CD14 and B7H2 [13]. As a result, lncRNAs are able to cause clinical diseases involving the dysfunction of DCs. However, it is largely unknown whether lncRNAs participate in the pathogenesis of SLE by regulating the function of DCs; this is therefore the focus of this study.

Methods

Subjects

Fifteen female SLE patients were recruited from the inpatient service in Huashan Hospital, Fudan University. The diagnostic criteria were in accordance with the 1997 American College Rheumatology revised criteria for the classification of SLE. Relevant clinical and laboratory information regarding the patients is shown in Table 1. Fifteen age-matched female healthy controls were also recruited. The study was approved by the Independent Ethics Committee of Huashan Hospital and written informed consent was obtained from all subjects. All the experiments were carried out in accordance with relevant guidelines and regulations of Huashan Hospital.

Cell culture

In-vitro differentiation of human monocytes into DCs was performed as described previously [8]. Briefly, peripheral blood mononuclear cells (PBMCs) were isolated and CD14 + monocytes were sorted by positive selection using magnetic beads (Miltenyi Biotec, Germany). The cells were then cultured for 5-7 days in RPMI 1640 supplemented with 1000 U/ml granulocyte/macrophage colony-stimulating factor (GM-CSF) and 1000 U/ml interleukin (IL)-4 (PeproTech, Rocky Hill). For monocyte-derived dendritic cell (moDC) maturation, 1 μg/ml lipopolysaccharide (LPS; Escherichia coli type 055:B6; Sigma) was added to the medium at day 6. The following antigens were used to identify the phenotype of moDCs: CD11c, HLA-DR, CD40, CD86, CD83, and

LncRNA and mRNA microarray

LncRNA expression profiling was determined by Human 4*180 K lncRNA arrays manufactured by Agilent Technologies (Santa Clara, CA), including 63,431 lncRNA probes and 39,887 mRNA probes. Each transcript was represented using 1-5 probes to improve statistical confidence. The lncRNA and mRNA expression data have been deposited into the Gene Expression Omnibus (GEO) under accession number GSE89240.

Gene function analysis

The Database for Annotation, Visualization and Integrated Discovery (DAVID; https://david.ncifcrf.gov) was utilized to identify the molecular function represented in the gene profile. Furthermore, the Kyoto Encyclopedia of Genes and Genomes (KEGG) database (https:// www.genome.jp/kegg/) was also used to analyze the potential functions of these target genes in the pathways. The cut-off criterion for false discovery rate (FDR) was set at less than 0.05.

Coexpression network construction

The lncRNA-mRNA coexpression network was constructed based on the correlation between the differentially expressed lncRNAs and mRNAs using Cytoscape. Pearson correlation analysis was used to evaluate the significance of the correlation between the expression levels between each pair of genes. Pearson correlation coefficients were selected for inclusion in the network when they were above 0.95.

Cis and trans target gene analysis of lncRNAs

The gene location for different lncRNAs on the chromosome was determined. Subsequently, the common lncRNA coexpression genes were intersected to identify the genes 10 kbp upstream or downstream of the lncRNAs as potential 'cis' genes. For the trans analyses, we predicted the trans-associated genes of the differentially expressed lncRNAs with RNAplex v0.2. The RNAplex parameters were set as e ≤ −30 in the current study to identify the trans-associated genes, and genes that were found to be located on the same chromosome as the lncRNA were excluded.

Quantitative real-time polymerase chain reaction (qRT-PCR)

Total RNA was reverse transcribed using a PrimeScript RT reagent Kit with gDNA Eraser (Perfect Real Time; TaKaRa, Dalian, China) in accordance with the manufacturer's instructions. The expressions of selected lncRNAs were analyzed using qRT-PCR with an ABI Power SYBR Green PCR Master Mix (ABI, USA). Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) mRNA was used as an internal control. The primers are listed in Table 2.

For quantitative results, expression of each lncRNA was represented as a fold change using the 2 -ΔΔCt method and then statistically analyzed.

Statistics

Continuous variables are expressed as means (SD) and categorical variables as frequencies (%). The Student t test or one-way analysis of variance was used to compare continuous variables. All P values were estimated in a two-tailed fashion. Differences were considered to be statistically significant at P < 0.05. Data were analyzed using SPSS 13.0 (SPSS Inc., IL, USA). The relationships between the expression levels of lncRNAs and systemic lupus erythematosus disease activity index (SLEDAI) were analyzed by Pearson's correlation coefficient.

Results

Differential expression profile of lncRNAs in moDCs of SLE

Dendritic cells were differentiated from CD14 + monocytes. MoDCs were identified using flow cytometry. The phenotype of moDCs displayed high expression of HLA-DR, CD83, CD11c, CD86, and CD40, and low expression of CD14 ( Fig. 1). To profile differentially expressed lncRNAs in moDCs of patients with SLE, we performed a genome-wide analysis of lncRNA expression in moDCs between SLE patients and normal controls. Volcano plot analysis showed that 163 (0.22%) of the lncRNAs were significantly differentially expressed in moDCs of patients with SLE ( Fig. 2a), including 118 upregulated and 45 downregulated lncRNAs (fold change ≥ 2.0, P < 0.05) (Fig. 2b). We categorized these differentially expressed lncRNAs into six classes: intergenic, intronic sense, intronic antisense, bidirectional, exonic sense, and exonic antisense. We  Table S1).

Differential expression profile of mRNAs in moDCs of SLE

A total of 137 (0.34%) mRNAs were differentially expressed in moDCs of patients with SLE compared with normal controls (fold change ≥ 2.0, P < 0.05) (Fig. 2c). Among them, 83 mRNAs were upregulated and 54 mRNAs were downregulated (Fig. 2d, Additional file 2: Table S2). Gene ontology (GO) analysis was performed to classify these differentially expressed mRNAs into three domains: biological process, molecular function, and cellular component. In the biological process domain, the GO terms for the differentially expressed mRNAs included cell migration, cell-cell adhesion, and T-cell costimulation, etc. (Fig. 3a). In the cellular component domain, the top five GO terms were extracellular space, protein complex, transcription factor complex, endoplasmic reticulum lumen, and extracellular matrix (Fig. 3b). In the molecular function domain, the GO terms contained signal transducer activity, chemokine activity, and ubiquitin binding (Fig. 3c). KEGG pathway analysis was also conducted to identify the key signaling pathways and the relationships among the differentially expressed mRNAs. We identified 19 signaling pathways that were enriched in moDCs of SLE. The top five pathways were PI3K-Akt signaling pathway, cytokine-cytokine receptor interaction, protein digestion and absorption, extracellular matrix-receptor interaction, and hematopoietic cell lineage (Fig. 3d).

Construction of the coexpression network with differentially expressed lncRNAs and mRNAs

Next, we constructed a coexpression network of these coding-noncoding genes that included the differentially expressed lncRNAs and mRNAs. Our data showed that the coexpression network was composed of 127 network nodes and 316 connections between 62 lncRNAs and 65 mRNAs. This coexpression network indicated that one lncRNA could target, at most, 16 coding genes and that one coding gene could correlate with at most 12 lncRNAs (Additional file 3: Figure S1). NR_024243 and lnc-BSPH1-2:1 were the most connected lncRNAs. NMT1, NPM3, and UPB1 were the most connected mRNAs. In a and c, each point represents a different transcript. Red points represent genes that were significantly upregulated and green points represent genes that were significantly downregulated. In b and d, relatively high expression is indicated by red shading and relatively low expression is indicated by green shading. N1-5 represents normal controls and S1-5 represents the patients with SLE

Analysis of lncRNA-target gene regulatory network

We identified the chromosomal coexpression genes 10 kbp upstream and downstream of the differentially expressed lncRNAs to determine potential lncRNA "cis" genes. The results of the "cis" analyses are shown in Additional file 4 (Table S3). Then we predicted the trans-associated genes of the differentially expressed lncRNAs with RNAplex v0.2, and the results are shown in Additional file 5 (Table S4). Matured moDCs could secrete an abundant source of both inflammatory and lymphoid chemokines, sustaining interaction of naive and activated T cells with antigen-presenting matured moDCs. We then constructed lncRNA-target genes of cytokines and chemokines using Cytoscape software (Additional file 6: Figure S2). Our data show that the lncRNA-target gene network was composed of 83 network nodes and 318 connections between 55 lncRNAs and 28 mRNAs. Within this coexpression network, CXCL10 was the most chemokine connected by lncRNAs and genes and IL-6 was the most cytokine connected by lncRNAs and genes. Lnc-BSPH1-1:1, NR_024214, and ENST00000501122.2 were the most connected lncRNAs.

Confirmation of differentially expressed lncRNAs by qRT-PCR

To confirm the reliability of the microarray data and considering the fold changes and the potential target genes of lncRNAs, we enlarged the sample sizes and selected five differentially expressed lncRNAs (three upregulated and two downregulated) and measured their expression level using qRT-PCR among 15 patients and 15 healthy controls including those 5 patients and 5 normal controls in the microarray. Consistent with the microarray data, the expression level of ENST00000604411.1 and ENST00000501122.2 were upregulated, and the expression level of lnc-HSFY2-3:3 and lnc-SERPINB9-1:2 were downregulated. However, the expression level of ENST00000568394.1 showed the same changed pattern as seen in the microarray analysis with no statistical significance (Fig. 4).

Correlation between the aberrantly expressed lncRNAs and SLEDAI score of patients with SLE

We further analyzed the correlation between the expression levels of the lncRNAs ENST00000604411.1, ENST00000501122.2, lnc-HSFY2-3:3, lnc-SERPINB9-1:2 and SLEDAI score. The expression levels of ENST00000604411.1 (r = 0.593, P = 0.020) and ENST00000501122.2 (r = 0.539, P = 0.038) were positively correlated with the SLEDAI score. However, neither lnc-HSFY2-3:3 nor lnc-SERPINB9-1:2 was significantly correlated with SLEDAI score (Fig. 5).

Discussion

Long noncoding RNAs, more than 200 nucleotides in length, are nonprotein-coding transcripts with a lack of an open reading frame. They can regulate gene expression at the level of chromatin remodeling, gene transcription, protein transport, and trafficking [14]. There is increasing interest in the potential involvement of lncRNAs in a number of complex human diseases, including autoimmune diseases, neurological disorders, coronary artery disease, and various cancers [11,14,15]. Genetic evidence suggests that lncRNA GAS5, a prime candidate for the chromosome 1q25 SLE locus, is related to susceptibility for SLE [16]. GAS5 also has been linked with an increased susceptibility to SLE in mouse models, presumably as a result of its effect on the immunosuppressant role of glucocorticoids [17]. The increased lncRNA NEAT1 expression in monocytes is related to the elevated production of a number of cytokines and chemokines in SLE patients [18]. LncRNA MALAT-1 expression was abnormally increased in monocytes of SLE patients, and silencing MALAT-1 significantly reduced the expression of IL-21 in primary monocytes of SLE patients by regulating SIRT1 signaling [19]. This all suggests that lncRNAs could contribute to the pathogenesis of SLE. Furthermore, lncRNAs could also serve as potential biomarkers in SLE. For instance, Linc0949 is decreased in PBMCs of patients with SLE. It can significantly increase following effective treatment for lupus, suggesting its potential as a biomarker for diagnosis, disease activity, and therapeutic response in SLE [20]. GAS5, linc0597, and lnc-DC in plasma may also specifically identify patients with SLE [21].

Published studies involving aberrantly expressed lncRNAs in SLE patients have mainly focused on PBMCs [20,22], T cells [23], monocytes [18], and plasma [21]. However, there are no current studies regarding lncRNAs of DCs in SLE patients. Since lncRNAs appear to be expressed in a much more cell type-specific manner than transcription factors and other protein-coding genes, the aim of our study was to explore aberrant lncRNA expression in moDCs of SLE patients to provide new insight into the pathogenesis of SLE.

We analyzed five moDC samples from individual SLE patients and five moDC samples from normal controls using lncRNA and mRNA microarrays. Based on the microarray data, we found 163 lncRNAs and 137 mRNAs that were differentially expressed. GO and KEGG pathway analyses showed that the differentially expressed mRNAs on moDCs mainly related to T-cell costimulation, chemokine activity, and cytokine-cytokine receptor interaction that are clearly associated with SLE pathogenesis.

We used qRT-PCR to validate the lncRNA microarray results in 15 patients and 15 normal controls, including those in the microarrays. Based on the qRT-PCR results, ENST00000604411.1, ENST00000501122.2, lnc-HSFY2-3:3, and lnc-SERPINB9-1:2 were differentially expressed, which was in agreement with the microarray results. The expression level of ENST00000568394.1 showed the same change pattern as shown in the microarray analysis with no statistical significance, which is likely due to the fact that the expanded test sample size for the qRT-PCR might have excluded some of the false positive results obtained in the microarray or due to technical limitations, such as cross-hybridization, signal saturation, and limited dynamic range in the microarray.

ENST00000604411.1, known as TSIX or LINC00013, expresses a noncoding antisense transcript across the 3′ end of the XIST locus. TSIX was overexpressed in systemic sclerosis (SSc) dermal fibroblasts both in vivo and in vitro, and is higher in SSc sera. TSIX is a new regulator of collagen expression which stabilizes the collagen mRNA [24]. It also protects the active-X from ectopic silencing once X-inactivation has commenced [25]. There is an increased incidence and prevalence of systemic lupus erythematosus in females, which might involve X chromosome inactivation [26]. In our study, we found ENST00000604411.1 was increased in moDCs of SLE patients and the expression level of ENST00000604411.1 was positively correlated with the SLEDAI score. Therefore, the upregulated ENST00000604411.1 might facilitate X chromosome inactivation through protecting the active-X from ectopic silencing and take part in the pathogenesis of SLE; however, further studies need to be performed to know exactly what role TSIX plays in the processes of the disease.

ENST00000501122.2 is a 22.74-kb intergenic lncRNA transcript, known as NEAT1. This lncRNA is retained in the nucleus where it forms the core structural component of the paraspeckle suborganelles. Zhang et al. [27] found NEAT1 expression was abnormally increased in SLE patients and predominantly expressed in human monocytes. There was also a positive correlation between NEAT1 and clinical disease activity in SLE patients. Furthermore, silencing NEAT1 significantly reduced the expression of a group of chemokines and cytokines, including IL-6, CXCL10, etc., which were induced by LPS continuously and in late stages [27]. NEAT1 is also critical for the expression of IL-8 [28]. In our study, we found NEAT1 expression was increased in moDCs from SLE patients. Therefore, NEAT1 was upregulated in both moDCs and their parent monocytes in SLE. We have also previously found IL-6 expression was increased in moDCs of SLE patients [8]. Future studies should focus on whether the increased NEAT1 expression in moDCs impact the cells to produce increased cytokines and chemokines in SLE patients.

Widespread change in lncRNAs might regulate the gene expression and production of inflammatory mediators in moDCs. Lnc-DC knockdown impacted the antigen uptake function of moDCs, impaired allogenic CD4 + T-cell proliferation, and reduced the strength of cytokine release [12]. A previous study demonstrated that lincRNA-Cox2, a critical inflammation mediator, was induced in bone marrow-derived DCs after stimulation with LPS [29]. A further study revealed that lincRNA-Cox2 mediated both the activation and repression of distinct classes of immune genes, including Irf7, CCL5, and IL-6, etc. [30]. Moreover, lncRNAs, such as THRIL, PACER, and lincRNA-EPS, can also regulate the inducible expression of cytokines following immune activation [31]. In the current study, we found that the predicted target genes of differentially expressed lncRNAs in moDCs included cytokines and chemokines, especially IL-6, CXCL10, IL-10, CXCL2, etc. We also observed that the differentially expressed mRNAs in moDCs of SLE patients were enriched in the process of cell migration and chemokine activity and in the pathways of cytokine-cytokine receptor interaction. This is similar to our previous observation that moDCs in SLE manifested proinflammatory functions such as producing elevated levels of IL-6, CCL2, and CCL5, with the attraction of more CD4 + T cells compared with moDCs of healthy controls [8]. In addition, the majority of patients with SLE display an increased expression of type I interferon (IFN)-regulated genes, also known as an IFN signature [32]. We found that some target genes of the differentially expressed lncRNAs in moDCs are connected to the type I IFN system, such as IRF5 and TREX1. IRF5, the target gene of lncRNA NR_034053.2, is associated with increased serum IFN activity in SLE patients [33]. The target gene of lncRNA n339353 is TREX1, and loss-of-function mutations in this leads to accumulation of intracellular DNA that triggers type I IFN production [34]. The expression level of both lncRNA NR_034053.2 and lncRNA n339353 is elevated in moDCs of SLE patients, which might suggest a regulatory role of moDCs in producing type I IFN in SLE. Since cytokine production is one of the major functions of DCs with great biological importance, more studies are needed to focus on the crosstalk between cytokines and lncRNAs in DCs; the functions of these differentially expressed lncRNAs require further study.

The limitation of our study is that we have not examined lncRNA profiles in circulating myeloid DCs (mDC) and plasmacytoid DCs (pDC) in SLE patients, which is more meaningful in the clinic. However, since the population of mDC and pDC is very small in the peripheral blood, it is difficult to obtain enough cells for study. We therefore utilized the well-accepted model of human DC differentiation from peripheral blood monocytes under inflammatory conditions [12], and we have successfully identified those cells as DCs according to their morphology, phenotype, and function. The potential disadvantage of using moDCs is that it is an artificial system and could be affected by external environments. However, moDCs in our study were differentiated under the same conditions. As a result, the discrepancy between the SLE group and the healthy control group might be due to the intrinsic factors. Another major limitation is that all SLE patients and healthy controls in our study are Chinese females, and so there is gender bias in our findings. Our results may not reflect to male patients and patients of other ethnic backgrounds. Since DCs are the master regulators for initiation, amplification, and perpetuation of SLE [35], targeting DCs may be of benefit for treatment in the future.

Conclusions

Our study provides comprehensive lncRNA and mRNA profiles for moDCs in SLE patients. The differential expression of ENST00000604411.1, ENST00000501122.2, lnc-HSFY2-3:3, and lnc-SERPINB9-1:2 may participate in the pathogenesis of SLE. The expression level of ENST00000604411.1 and ENST00000501122.2 was positively correlated with SLEDAI score and may have disease activity evaluation value for SLE.

Additional files

Additional file 1: Table S1. The specific information about the differentially expressed lncRNAs. (XLSX 76 kb) Additional file 2: Table S2. The specific information about the differentially expressed mRNAs. (XLSX 83 kb) Additional file 3: Figure S1. Coexpression network with differentially expressed lncRNAs and mRNAs. Red triangles represent upregulated lncRNA, green triangles represent downregulated lncRNA, circles with a yellow border represent upregulated mRNA, and circles with a blue border represent downregulated mRNA. Arrows represent positive correlation and terminated lines represent negative correlation. (PDF 33 kb) Additional file 4: Table S3. The predicted cis-associated genes of the differentially expressed lncRNAs. (XLSX 2714 kb) Additional file 5: Table S4. The predicted trans-associated genes of the differentially expressed lncRNAs. (XLSX 7838 kb) Additional file 6: Figure S2. LncRNAs and targeted genes regulating cytokine and chemokine networks. The node color differs from dark green to dark red according to the connection numbers from small to large. Squares represent target genes. Circles represent lncRNAs. Arrows represent positive correlation and terminated lines represent negative correlation. (PDF 32 kb)

Fig. 1
1moDCs were identified using flow cytometry. The phenotype of the cells displayed high expression of a HLA-DR, b CD83, c CD11c, d CD86, and e CD40, and f low expression of CD14

Fig. 2
2Volcano plots and hierarchical clusters of differentially expressed lncRNAs and mRNAs. a LncRNA volcano plots of SLE patients versus normal controls. b A total of 163 lncRNAs were differentially expressed in moDCs of patients with SLE (n = 5) compared with normal controls (n = 5), including 118 upregulated and 45 downregulated lncRNA (fold change (FC) ≥ 2.0, P < 0.05). c mRNA volcano plots of SLE patients versus normal controls. d A total of 137 mRNAs were differentially expressed in moDCs of patients with SLE (n = 5) compared with normal controls (n = 5), including 83 upregulated and 54 downregulated mRNAs (fold change ≥ 2.0, P < 0.05).

Fig. 3 Fig. 5
35Gene ontology (GO) and pathway enrichment analysis for differentially regulated mRNAs. a GO analysis of mRNAs according to biological process. b GO analysis of mRNAs according to cellular component. c GO analysis of mRNAs according to molecular function. d Pathway analysis of differentially expressed mRNAs in moDCs of SLE patients. ECM extracellular matrixFig. 4 Validation of selected lncRNAs by qRT-PCR. The levels of ENST00000604411.1 (*P < 0.001), ENST00000501122.2 (*P < 0.001), ENST00000568394.1 (P = 0.151), lnc-HSFY2-3:3 (*P < 0.001), and lnc-SERPINB9-1:2 (*P = 0.040) were determined in moDCs of 15 normal controls (NC) and 15 patients with systemic lupus erythematosus (SLE) (a-e). Data are shown as mean ± SD. f The change patterns between microarray analysis and quantitative polymerase chain reaction (qPCR) Correlation between lncRNAs and systemic lupus erythematosus disease activity index (SLEDAI). a, b ENST00000604411.1 and ENST00000501122.2 expression was positively correlated with SLEDAI score. c No significantly correlation was observed between lnc-HSFY2-3:3 and SLEDAI score. d No significantly correlation was observed between lnc-SERPINB9-1:2 and SLEDAI score

Abbreviations APC: Antigen-presenting cell; DC: Dendritic cell; FDR: False discovery rate; GAPDH: Glyceraldehyde 3-phosphate dehydrogenase; GO: Gene ontology; IFN: Interferon; IL: Interleukin; KEGG: Kyoto Encyclopedia of Genes and Genomes; lncRNA: Long noncoding RNA; LPS: Lipopolysaccharide; moDC: Monocyte-derived dendritic cell; PBMC: Peripheral blood mononuclear cell; qRT-PCR: Quantitative real-time polymerase chain reaction; SLE: Systemic lupus erythematosus; SLEDAI: Systemic lupus erythematosus disease activity index; SSc: Systemic sclerosis Funding This research was supported by the National Natural Science Foundation of China (grant no. 81703122, 81773324) and the Natural Science Foundation of Shanghai (16ZR1404900).

Table 1
1Clinical and laboratory characteristics of the patients with SLE in the studyCharacteristic
SLE (n = 15)

Sex, male/female (n)
0/15 (15)

Age (years), median (range)
33 (17-57)

Duration (months), median (range)
3 (1-60)

SLEDAI score, median (range)
15 (10-30)

ANA > 1:320, yes/no (n)
15/0 (15)

Anti-dsDNA (IU/ml), median (range)
391.9 (25.9-800)

Hypocomplementemia, yes/no (n)
12/3 (15)

Organ involvement, yes/no (n)
6/9 (15)

Steroids, yes/no (n)
9/6 (15)

Immunosuppressive drugs, yes/no (n)
15/0 (15)

ANA antinuclear antibody, SLE systemic lupus erythematosus, SLEDAI systemic
lupus erythematosus disease activity index

Table 2 The
2sequences of quantitative polymerase chain
reaction primers

Primer name
Sequence (5' to 3')

gapdh(Hs) forward
TGACTTCAACAGCGACACCCA

gapdh(Hs) reverse
CACCCTGTTGCTGTAGCCAAA

ENST00000604411.1 forward
AGCCCCACTTCACATTAGACC

ENST00000604411.1 reverse
TGATGTTGCAGTCCTGTGAGG

ENST00000501122.2 forward
TTCTGCTTTCTGCCCATGTA

ENST00000501122.2 reverse
GTGGCAGTGACAACCTCTCA

ENST00000568394.1 forward
TCTCTCCCCAGTGACAGTACA

ENST00000568394.1 reverse
GCGATCTGTAGTCCAGGTGT

lnc-HSFY2-3:3 forward
TGATTGGAAGATGGAAGTGGA

lnc-HSFY2-3:3 reverse
GCTCCACCTGAAACTCATTTG

lnc-SERPINB9-1:2 forward
GGGGAAAATAAAAGGGATGGT

lnc-SERPINB9-1:2 reverse
TCTCTCTTAGGGAAGGGCATT

Availability of data and materialsThe microarray data have been deposited in Gene Expression Omnibus (GEO) under accession number GSE89240.Authors' contributions YW cultured the cells, performed the microarray, and drafted the manuscript. ShC and SuC participated in the design of the study and contributed to the analyses of the results. JD participated in the qPCR experiments. JL and HQ participated in study design and target gene prediction. JW contributed to data interpretation and manuscript revision. JL and JX conceived the study, participated in its design and coordination, and revised the manuscript. All authors read and approved the final version to be published.Ethics approval and consent to participateThe study was approved by the Independent Ethics Committee of Huashan Hospital and written informed consent was obtained from all subjects.Consent for publicationWritten informed consents were obtained from the patients for publication of their individual details and accompanying images in this manuscript. The consent form is held by the authors and is available for review by the Editor-in-Chief.Competing interestsThe authors declare that they have no competing interests.
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Raising concerns about the Sepsis-3 definitions

Massimo Sartelli
Yoram Kluger
Luca Ansaloni
Timothy C Hardcastle
Jordi Rello
Richard R Watkins
Matteo Bassetti
Eleni Giamarellou
Federico Coccolini
Fikri M Abu-Zidan
Abdulrashid K Adesunkanmi
Goran Augustin
Gian L Baiocchi
Miklosh Bala
Oussema Baraket
Marcelo A Beltran
Asri Che Jusoh
Zaza Demetrashvili
Belinda De Simone
Hamilton P De Souza
Yunfeng Cui
R Justin Davies
Sameer Dhingra
Jose J Diaz
Salomone Di Saverio
Agron Dogjani
Mutasim M Elmangory
Mushira A Enani
Paula Ferrada
Gustavo P Fraga
Sabrina Frattima
Wagih Ghnnam
Carlos A Gomes
Souha S Kanj
Aleksandar Karamarkovic
Jakub Kenig
Faryal Khamis
Vladimir Khokha
Kaoru Koike
Kenneth Y Y Kok
Arda Isik
Francesco M Labricciosa
Rifat Latifi
Jae G Lee
Andrey Litvin
Gustavo M Machain
Ramiro Manzano-Nunez
Piotr Major
Sanjay Marwah
Michael Mcfarlane
Ziad A Memish
Cristian Mesina
Ernest E Moore
Frederick A Moore
Noel Naidoo
Ionut Negoi
Richard Ofori-Asenso
Iyiade Olaoye
Carlos A Ordoñez
Mouaqit Ouadii
Ciro Paolillo
Edoardo Picetti
Tadeja Pintar
Alfredo Ponce-De-Leon
Guntars Pupelis
Tarcisio Reis
Boris Sakakushev
Hossein Samadi Kafil
Norio Sato
Jay N Shah
Boonying Siribumrungwong
Cristian Tranà
Jan Ulrych
Kuo-Ching Yuan
Fausto Catena
Raising concerns about the Sepsis-3 definitions
10.1186/s13017-018-0165-6R E V I E WSepsisSeptic shockOrgan dysfunctionInfections Background
The Global Alliance for Infections in Surgery appreciates the great effort of the task force who derived and validated the Sepsis-3 definitions and considers the new definitions an important step forward in the evolution of our understanding of sepsis. Nevertheless, more than a year after their publication, we have a few concerns regarding the use of the Sepsis-3 definitions.

Background

The definition of sepsis has shifted over time.

Systemic inflammatory response syndrome (SIRS), sepsis, severe sepsis, and septic shock were initially defined in 1991 by a consensus panel convened by the American College of Chest Physicians (ACCP) and the Society of Critical Care Medicine (SCCM) [1]. The conference defined sepsis as a systemic inflammatory response syndrome (SIRS) due to infection. "Severe" sepsis was defined as sepsis associated with organ dysfunction, hypoperfusion, or hypotension, and "septic shock" was defined as sepsis with arterial hypotension despite adequate fluid resuscitation (Appendix 1).

The definitions were revisited in 2001 during the International Sepsis Definitions Conference, which included members from the ACCP, the SCCM, the American Thoracic Society (ATS), the European Society of Intensive Care Medicine (ESICM), and the Surgical Infection Society (SIS). [2] The consensus retained the definitions of sepsis as SIRS due to infection (presumed or confirmed) and severe sepsis as sepsis associated with acute organ dysfunction. However, the new criteria defining SIRS were greatly expanded from the 1991 original, and organ dysfunction variables indicative of severe sepsis were also defined. This new set of sepsis criteria also changed the diagnostic requirement from "more than 1" of the original 4 to "some" of the expanded list (Appendix 2).

In February 2016, the Journal of the American Medical Association (JAMA) published a proposal for new definitions and criteria for sepsis, called Sepsis-3 [3], updating previous sepsis definitions [1,2]. The new definitions were prepared by a task force appointed by the European Society of Intensive Care Medicine (ESICM) and the Society of Critical Care Medicine (SCCM) (Appendix 3). They were aimed at providing a standardized classification to facilitate clinical care, future research, and reporting.

In 2017, the Global Alliance for Infections in Surgery instituted an interdisciplinary task force of 76 experts from 50 different countries with different backgrounds to assess the clinical value of the Sepsis-3 definitions.

The Global Alliance for Infections in Surgery appreciates the great effort of the task force who derived and validated the Sepsis-3 definitions and considers the new definitions an important step forward in the evolution of our understanding of sepsis particularly in regard to what distinguishes sepsis from uncomplicated infection. Nevertheless, more than a year after their publication, we have a few concerns regarding the use of the Sepsis-3 definitions [3]. We hope our comments can encourage further discussion and debate on how to further optimize the definitions of the sepsis continuum.

Sepsis-3 definitions

The process for revising the Sepsis-3 definitions of sepsis and septic shock was a 2-year-long process that involved several components [3]. Critical efforts in this process included a discussion of the concept of sepsis, identification of criteria alerting clinicians for the patient's risk to develop sepsis, and the development of the criteria to identify septic shock.

The clinical criteria for sepsis were formulated by using a data-driven approach. Electronic health record data of 1.3 million encounters at 12 community and academic hospitals within the University of Pittsburgh Medical Center health system in southwestern Pennsylvania were studied, and among them, there were 148,907 patients with suspected infection. The power of the Sequential [sepsis-related] Organ Failure Assessment (SOFA) was equivalent to Logistic Organ Dysfunction Score (LODS) and higher than SIRS in predicting hospital mortality in the intensive care unit (ICU). The choice of the SOFA to measure organ dysfunction was due to its greater simplicity. The task force introduced the rapid bedside quick SOFA (qSOFA) tool for determined patients outside the ICU who likely develop sepsis from the retrospectively derived databases. Confirmatory analyses were performed by using data sets from the USA and Germany.

The clinical criteria for septic shock were formulated by using multiple methods: (a) a systematic literature review and meta-analysis of the criteria used in observational studies reporting sepsis epidemiology, (b) a Delphi process among the 19 members of the task force to achieve consensus on the new definitions, and (c) cohort studies to test variables identified by the Delphi process using the Surviving Sepsis Campaign registry, along with two other data sets from the USA.

Yet when they were published, these definitions, however, had not been prospectively validated in a generalizable population. Moreover, the patient data were all almost exclusively from adults in high-income countries and primarily contained information from patients in the USA and Germany, so the utility of these definitions in other geographic regions and particularly in settings with less resources is unknown.

In recent years, the SIRS criteria were criticized for being too non-specific. The current use of two or more SIRS criteria to identify sepsis was unanimously considered by the task force to be unhelpful.

However, the task force stated that, although SIRS was not helpful in identifying patients with organ dysfunction, non-specific SIRS criteria still might have utility in identifying patients having infection.

Sepsis is now defined as life-threatening organ dysfunction caused by a dysregulated host response to infection. The aim of this definition is to emphasize the primacy of the non-homeostatic host response to infection, the potential lethality, and the need for urgent recognition.

Organ dysfunction can be represented by an increase in the Sequential [sepsis-related] Organ Failure Assessment (SOFA) score of 2 points or more (Table 1) which is associated with an in-hospital mortality greater than 10%.

Septic shock is defined as a subset of sepsis and should be clinically identified by a vasopressor requirement to maintain a mean arterial pressure of 65 mmHg or greater and serum lactate level greater than 2 mmol/l (> 18 mg/ dl) in the absence of hypovolemia. The term "severe sepsis" is now superfluous and has been removed from the current definitions.

The Sepsis-3 definitions suggest that patients with at least two of these three clinical variables may be prone for the poor outcome typical of sepsis: (1) low blood pressure (SBP ≤ 100 mmHg), (2) high respiratory rate (≥ 22 breaths per min), or (3) altered mentation (Glasgow coma scale < 15) (quick SOFA). It is supposed to be useful in out-of-hospital, emergency surgery (ED), or general hospital ward settings, and patients with positive qSOFA should be used to further investigate for organ dysfuntion. The qSOFA score should not be regarded as a diagnostic criterion for defining sepsis. Rather, it should be regarded as a warning for patients with suspected infection who are likely to have poor outcomes.

Raising concerns about the Sepsis-3 definitions

Sepsis is a multifaceted host response to an infecting pathogen that may be significantly amplified by endogenous factors. If left untreated, it may lead to the functional impairment of one or more vital organs or systems [4].There are many well-known risk factors for the infections that most commonly precipitate organ dysfunction, including acquired immunodeficiency syndrome, chronic obstructive pulmonary disease, many cancers, the use of immunosuppressive agents, and advanced age [5]. Although big steps forward have been made, the pathophysiological mechanisms for organ dysfunction are not entirely known, but it has become apparent that infection triggers a much more complex, variable, and prolonged host response, involving early activation of both pro-and anti-inflammatory responses.

Sepsis has variable clinical presentations depending on the initial site of infection, the causative organism, the pattern of acute organ dysfunction, and the underlying health status of the patient [5].

There is general consensus that early recognition and timely treatment largely determine outcome of sepsis.

Since the first classification in 1991 [1], the definition of sepsis, severe sepsis, and septic shock, though imprecise, have provided the clinicians a useful framework for clinical management, stressing the need for early recognition [6], and when these criteria are followed by the application of the Surviving Sepsis Campaign recommendations, they have an impressive history of success in reducing the mortality of sepsis in several areas of the world [7].

Several studies demonstrated that sepsis-related mortality reduced steadily over the years. A meta-analysis reported a reduction of sepsis 28-day mortality rates from 46.9% during the period 1991-1995 to 29% during 2006-2009 [8].

In the USA, mortality due to severe sepsis decreased by 51% from 1988 to 2012 [9]. In Australia and New Zealand, an overall decrease of 16.7% in hospital sepsis mortality was reported between 2000 and 2012 (from 35 to 18.4%) [10]. However, high mortality rates are still reported in low-and middle-income countries [11].

Despite decades of sepsis research, no specific therapies for sepsis have emerged. Without specific therapies, management is based on control of the infection and organ support.

Early antibiotics, source control, and fluid resuscitation support of vital organ function are the cornerstones for the treatment of patients with sepsis [12].

Timing and adequacy of source control are the most important issues in the management of patients with complicated intra-abdominal infections (cIAIs) because inadequate and late operation may have a negative effect on outcome. Source control was considered an essential element in the management of patients with complicated intra-abdominal infections (cIAIs) and should be considered and performed early after the diagnosis is established in most if not all patients [13]. Sotto et al. in 2002 found in a retrospective study that time between diagnosis and operation was associated with mortality [14]. In this study, the period between diagnosis and surgery was predictive of death within 30 days after diagnosis of peritonitis, emphasizing the importance of prompt surgical treatment. In the CIAOW (complicated intra-abdominal infections worldwide) observational study including 1898 consecutive patients older than 18 years undergoing surgery or interventional drainage to address IAI, a delayed initial intervention was found to be an independent variable predictive of mortality. In this study, the overall mortality rate was 10.5% (199/ 1898) [15]. To enable early interventions being effective, the diagnosis must be made as early as possible and treatment must be started early. The ability to identify septic patients who are at high risk for subsequent organ dysfunction and mortality, starting from pre-hospital care and ED, is crucial since timely recognition and appropriate, effective treatment substantially improves survival. This highlights the need for all healthcare workers to be vigilant about sepsis, so that the diagnosis can be made as early as possible [16].

One consequence of the new definitions is elimination of the concept of sepsis without organ dysfunction. Although the task force considered that the new definitions may better reflect the current understanding of sepsis pathophysiology, the literal interpretation of "sepsis" as a problem only when life-threatening organ dysfunction appears may be of limited utility in identifying patients who may benefit from early intervention.

The Sepsis-3 definitions requiring the presence of organ dysfunction to define sepsis may hinder the awareness of the importance of early recognition and treatment of infections before organ dysfunction appears, de-emphasizing intervention at earlier stages when it is most treatable.

Ideally, patients at risk for sepsis should be identified before organ dysfunction is established. Therefore, it may be questionable if it is helpful to have a definition that recognizes a patient once organ dysfunction has occurred and the patient already needs intensive care.

An observational study conducted at 132 medical institutions worldwide over a 4-month study period enrolled 4533 patients to validate a predictive score for patients with intra-abdominal infections [17]. Data from the WSES cIAIs Score Study (WISS) showed that mortality was significantly affected by the previous sepsis definition. Mortality with no sepsis was 1.2%, with sepsis only 4.4%, with severe sepsis 27.8%, and with septic shock was 67.8%. Severity of illness and the inherent mortality risk escalated from no sepsis, through sepsis, severe sepsis, and septic shock.

The previous stratification of the severity and consequent mortality due to infection that progressed from no sepsis to sepsis (infection meeting the criteria for systemic inflammatory response syndrome or SIRS), to severe sepsis (sepsis with organ failure, arterial hypertension, and/or hypoperfusion), to septic shock (arterial hypotension refractory to adequate volume resuscitation) is now reduced to simple infection, sepsis (infection and organ dysfunction), and septic shock (arterial hypotension defined as the use of vasopressors and hyperlactatemia). The previous concept of severe sepsis corresponds now to the definition of sepsis in the Sepsis-3 criteria, although this correlation is not absolute because sepsis, according to the new criteria, can include very different conditions, such as organ failure without hypotension nor hyperlactatemia [7].

The Sepsis-3 definitions exclude patients with isolated hypotension from the definition of sepsis because they would have a SOFA score of 1. Moreover, lactate is not part of the SOFA score, even though it is well documented to be a sensitive marker of severity of illness in patients with infection.

The Sepsis-3 definitions recommend using an increase in the SOFA score of 2 or more points to represent organ dysfunction. The SOFA score is intended to be used in the ICU and, to a lesser extent, the ED. Outside the ICU, SOFA was found only as good as the previous SIRS criteria (AUROC = 0.79 vs. AUROC = 0.76). Moreover, it is a valuable predictor of unfavorable outcome. The SOFA score was proposed in 1996 by the Working Group on Sepsis-Related Problems of the European Society of Intensive Care Medicine [18] to objectively describe the degree of organ dysfunction over time and to evaluate morbidity in patients in the ICU with sepsis. It was demonstrated to be a good indicator of prognosis in critically ill patients during the first few days of ICU admission [19]. The use of the SOFA score in research is commonly performed and constitutes a routine component of data collection for clinical trials in ICUs. However, the SOFA score is not universally accessible, especially for PaO 2 , which would require an arterial blood gas measurement.

Recognizing these practical limitations, the task force described a simplified method termed the "quick SOFA" (qSOFA) score to facilitate easier identification of patients potentially at risk of dying outside of critical care settings [20]. This instrument, which had not been validated in clinical practice at the time of Sepsis-3 publication, comprises three clinical parameters that are easy to assess outside the ICU.

The prognostic accuracy for in-hospital mortality of qSOFA is an area of great debate. A recent multicenter prospective cohort study involving 879 patients with suspected infection treated in the ED reported that qSOFA may be better than previous criteria at predicting inhospital mortality among patients with suspected infection [21]. Nevertheless, we calculated the positive predictive value (PPV) of qSOFA for in-hospital death in this study to be only 0.24. This indicates that only one-quarter of the patients who had a qSOFA equal or more than 2 died in the hospital.

A recent large retrospective cohort analysis of 184,875 patients in 182 Australian and New Zealand intensive care units (ICUs) found that SOFA score had superiority in prediction of in-hospital mortality and that SIRS criteria had a greater prognostic accuracy for in-hospital mortality than qSOFA score [22].

In an observational cohort study performed at one ED at an urban university teaching hospital in Norway [23], qSOFA failed to identify two thirds of the patients admitted to an ED with severe sepsis. Further, qSOFA failed to be a risk stratification tool as the sensitivity to predict 7-day and 30-day mortality was low. The sensitivity was poorer than the other warning scores already in use at the study site and the SIRS criteria.

In a study of 886 patients, Tusgul et al. showed that the qSOFA score, SIRS criteria, and sepsis definition have low identification sensitivity in selecting septic patients in the pre-hospital setting or upon arrival in the ED [24].

An important limitation of the new definitions is the poor sensitivity of the qSOFA scoring system. This leads to a high number of false negatives and, subsequently, to a delayed diagnosis in many patients, which likely excludes its use as a screening tool for early sepsis, the stage in which treatment is most effective.

In a recent analysis of three prospectively collected, observational cohorts of 7754 infected emergency department patients aged 18 years or older, Henning et al. [25] demonstrated that the mortality rate for patients with a qSOFA score greater than or equal to 2 was 14.2%, with a sensitivity of 52% and specificity of 86% to predict mortality. In comparison, the original SIRS-based Sepsis-2 definition had a mortality rate of 6.8%, a sensitivity of 83%, and a specificity of 50%. Both the Sepsis-2 and Sepsis-3 definitions stratified patients at risk for mortality, with differing performances. In terms of mortality prediction, the new definitions had improved specificity but had very low sensitivity.

Williams et al. prospectively studied 8871 consecutive patients who were admitted from the ED with presumed infection and compared the diagnostic accuracy of SIRS with qSOFA and Sepsis-2 with Sepsis-3 definitions for organ dysfunction [26]. SIRS was associated with increased risk of organ dysfunction and mortality in patients without organ dysfunction. SIRS and qSOFA showed similar discrimination for organ dysfunction. qSOFA was specific but poorly sensitive for organ dysfunction. Mortality for patients with organ dysfunction was similar for Sepsis-2 and Sepsis-3, although 29% of patients with Sepsis-3 organ dysfunction did not meet Sepsis-2 criteria. Increasing numbers of Sepsis-2 organ system dysfunctions were associated with greater mortality.

In another study in which 3346 patients with infection outside the ICU and 1058 patients with infection in the ICU were analyzed, qSOFA provided inadequate sensitivity for early risk assessment [27].

Peake et al. performed a post hoc analysis of 1591 adult patients presenting to the ED with early septic shock [28]. At baseline, 1139 patients had a qSOFA score of ≥ 2. In contrast, 1347 participants met the Sepsis-3 criteria for sepsis. Of these, 1010 participants had a qSOFA score of ≥ 2 and met the Sepsis-3 criteria for sepsis. A quarter of participants who met the new sepsis definitions did not fulfill the qSOFA screening criteria, potentially limiting its utility as a screening tool for sepsis in the ED.

Sepsis requires urgent recognition because delayed treatment increases mortality. To optimize the timing of therapy, a screening test should be as sensitive as possible. Thus, it is preferable to have a more sensitive test with lower false negative results in order to not miss cases of serious sepsis.

Although some patients with ongoing sepsis may not have elevated lactate levels at presentation or during their clinical course [29], lactate measurement is advised as an important component of the initial evaluation of patients with sepsis. Elevated lactate levels (even if > 4 mmol/l) are no longer part of organ dysfunction criteria to define organ failure.

In the new definition of septic shock, hyperlactatemia is a required component for septic shock, differently from Sepsis-1 and Sepsis-2 definitions in which just the presence of refractory hypotension to fluid loading was considered shock. Therefore, when lactate measurements are not available, the diagnosis of septic shock can be more challenging and patients with potential shock will be considered as having only sepsis.

Discussion

Sepsis was previously defined as a systemic inflammatory response syndrome (SIRS) in a patient with an infection but now reflects a significantly greater degree of illness, characterized by organ dysfunction, de-emphasizing intervention at earlier stages when it is most treatable.

Definition of organ failure based on SOFA ≥ 2 may be accurate and safe in the ICU, although reliance on the SOFA score can make it more challenging to diagnose a patient with sepsis outside the ICU environment.

The SOFA score is not globally accessible and not well known by emergency or ward healthcare professionals, and its applicability is complex outside the ICU, as it might demand the calculation of SOFA for subsequent days to verify if the patients fulfill the strict criteria and require laboratory tests [30].

The qSOFA is a tool for risk stratification, and it seems necessary to look for options to improve its low sensitivity. Until then, SIRS will still be helpful as a screening tool in ED to identify patients with infections that will most likely benefit from earlier and more aggressive interventions.

Clinicians should keep in mind the difference between a screening tool and a risk-stratification tool. A screening tool aims to identify patients with a particular disease from a larger pool of patients. Once these patients are identified, a risk-stratification tool can be applied to determine their likelihood of meeting a particular outcome.

We probably still need a good screening tool to identify patients at risk of developing organ dysfunction. However, this is not addressed in Sepsis-3.

The Sepsis-3 authors concluded that "These updated definitions and clinical criteria should clarify long-used descriptors and facilitate earlier recognition and more timely management of patients with sepsis or at risk of developing it." However, it seems to us that Sepsis3 definitions have the opposite outcome.

Sepsis 3 definitions exclude from the concept of sepsis those patients who are in transition from infection to sepsis and who have a SOFA score of 2 or more points.

Probably, a pre-sepsis scoring concept is missing [31] and should be mandatory.

Fever, tachypnea, tachycardia, and increased WBC count are consistent features of critical illnesses, including those induced by infection. Although the SIRS criteria have been criticized for their lack of specificity, since 1991, they have gained widespread acceptance among clinicians all over the world and are still used worldwide to recognize early sepsis in clinical practice.

Instead of replacing the SIRS score with the new qSOFA score to identify early patients with sepsis, why not use both of them together, taking advantage of the sensitivity of SIRS and the specificity of qSOFA?

The new definitions are based on a retrospective evaluation of large hospital databases from two countries (the USA and Germany).The majority of sources of infection were hospital patients in referral centers with respiratory and postoperative infections. The target reader is an intensive care unit (ICU) physician. Although these definitions are of help for research purposes, they may not be representative of the wider clinical community [32].

Major international differences exist in the prevalence of infections, types of infecting microorganisms, and mortality rates. EPIC II demonstrated significant differences in Eastern Europe as compared to Western Europe, in Australasia as compared to Asia, and in Latin America as compared to North America [33].

Early recognition of sepsis is a general principle of sepsis management and is very important in low-and middle-income countries where the priorities for improving the quality of care for critically ill patients are different. Documenting the burden of critical illness in low-resource settings is challenging [29]. In these settings, a triage system that quickly recognizes critically ill patients and transfers them immediately to an acute care unit is a vital component of the emergency services. The most important challenges in the management of sepsis in these areas are triage and pre-hospital diagnosis. It should be done by very sensitive and non-invasive methods outside the hospital setting.

As a consequence, any process of improving quality of sepsis care globally should focus on simple diagnostic criteria based on physical examination findings that can recognize patients needing critical care. In these settings, a feasible, low-cost method of rapidly identifying patients requiring critical care is crucial. Early warning system scores utilize physiological, easy-tomeasure parameters, assessing physiological parameters such as systolic blood pressure, pulse rate, respiratory rate, temperature, oxygen saturations, and level of consciousness [34]. They are simple, non-invasive, and easy-torepeat measurement bedside tools. Large multicenter trials will be needed to explore if these findings can be shared all over the world.

Conclusions

The pathogenesis of sepsis involves a complex interaction between the host immune system and the infecting microorganisms. Sepsis describes a broad-based syndrome covering many infectious agents, affecting various sites in patients of differing age, gender, and comorbidity. Its clinical manifestations are highly variable and may lead to severe organ dysfunction and death. Despite remarkable advances in the management of patients with sepsis, its recognition and timely, appropriate treatment remain of utmost importance.

The Sepsis-3 definitions underline the concept of a dysregulated immune response resulting in potentially modifiable life-threatening organ dysfunction. However, they may fail in identifying patients with serious infections before organ dysfunction ensues.

Downplaying infections that do not meet the current Sepsis-3 criteria may hinder their identification, resulting in an unnecessary increase in both morbidity and mortality due to their inexorable progression in the following hours.

Clear definitions for sepsis and septic shock should guide clinicians both to support early recognition of at-risk patients and to facilitate an understanding of the global epidemiology of sepsis.

Sepsis is a burden for global health. Its global nature calls for a global response, both in the geographic sense and across the whole range of sectors involved.

In this paper, we have raised our concerns regarding the Sepsis-3 definitions. We believe that in order for sepsis definitions to be universally accepted, they should facilitate clinical care on a global scale.

On Friday, May 26th, 2017, the World Health Assembly and the World Health Organization made sepsis a global health priority, by adopting a resolution to improve, prevent, diagnose, and manage sepsis [35].

The Global Alliance for Infections in Surgery suggests that future revisions have a more global perspective and include a wider range of representatives and expertise. We also hope our comments can serve as a basis for future discussions on how to further improve the definitions of the sepsis continuum.

Appendix 1

Sepsis-1 definitions

SIRS (systemic inflammatory response syndrome)

Presence of more than 1 of 4 findings:

Sepsis SIRS in the presence of a confirmed or suspected infection.

Severe sepsis Sepsis associated with organ dysfunction, hypoperfusion, or hypotension, Septic shock Sepsis with arterial hypotension despite adequate fluid resuscitation.

Appendix 2

Sepsis-2 definitions

Sepsis

Infection documented or suspected and some of the following parameters:

General parameters Fever (core temperature > 38.3°C) Hypothermia (core temperature < 36°C) Heart rate 90 bpm or > 2 SD above the normal value for age Tachypnea: > 30 bpm Altered mental status, significant edema or positive fluid balance (> 20 ml/kg over 24 h) Hyperglycemia (plasma glucose > 110 mg/dl or 7.7 mM/l) in the absence of diabetes

Inflammatory parameters

Leukocytosis (white blood cell count > 12,000/μl) Leukopenia (white blood cell count < 4000/μl) Normal white blood cell count > 10% immature forms Plasma C reactive protein > 2 SD above the normal value Plasma procalcitonin > 2 SD above the normal value

Hemodynamic parameters

Or a systolic blood pressure decrease > 40 mmHg in adults or < 2 SD below normal for age Mixed venous oxygen saturation > 70% Cardiac index > 3.5 l min − 1 m − 2 Organ dysfunction parameters Arterial hypoxemia (PaO 2 /FIO 2 < 300) Acute oliguria (urine output > 0.5 ml/kg − 1 h − 1 or 45 mM/l for at least 2 h) Creatinine increase ≥ 0.5 mg/dl Coagulation abnormalities (international normalized ratio > 1.5 or activated partial thromboplastin time > 60 s) Ileus (absent bowel sounds) Thrombocytopenia (platelet count 4 mg/dl or 70 mmol/l)

Tissue perfusion parameters

Sepsis-3 definitions

Sepsis

Life-threatening organ dysfunction due to a dysregulated host response to infection.

Sepsis clinical criteria: organ dysfunction is defined as an increase of 2 points or more in the Sequential Organ Failure Assessment (SOFA) score.

Patients with suspected infection who are likely to have a prolonged ICU stay or to die in the hospital can be promptly identified at the bedside with qSOFA. Two or more of:

Hypotension: SBP less than or equal to 100 mmHg Altered mental status (any GCS less than 15) Tachypnoea: RR greater than or equal to 22

Septic shock Subset of sepsis in which underlying circulatory and cellular/metabolic abnormalities are profound enough to substantially increase mortality.

Septic shock clinical criteria: Sepsis and (despite adequate volume resuscitation) both of: Persistent hypotension requiring vasopressors to maintain MAP greater than or equal to 65 mmHg, and lactate greater than or equal to 2 mmol/l.

Abbreviations ED: Emergency department; IAI: Intra-abdominal infection; ICU: Intensive care unit; SIRS: Systemic inflammatory response syndrome; SOFA: Sequential [sepsis-related] Organ Failure Assessment

Table 1
1SOFA scorePaO 2 /FiO 2 (mmHg)
SOFA
score

< 400
1

< 300
2

< 200 and mechanically ventilated
3

< 100 and mechanically ventilated
4

Glasgow coma scale

13-14
1

10-12
2

6-9
3

< 6
4

Mean arterial pressure OR administration of vasopressors
required

SOFA
score

MAP < 70 mm/Hg
1

dop ≤ 5 or dob (any dose)
2

dop > 5 OR epi ≤ 0.1 OR nor ≤ 0.1
3

dop > 15 OR epi > 0.1 OR nor > 0.1
4

Bilirubin (mg/dl) [μmol/l]

1.2-1.9 [> 20-32]
1

2.0-5.9 [33-101]
2

6.0-11.9 [102-204]
3

> 12.0 [> 204]
4

Platelets ×10 3 /μl

< 150
1

< 100
2

< 50
3

< 20
4

Creatinine (mg/dl) [μmol/l] (or urine output)

1.2-1.9 [110-170]
1

2.0-3.4 [171-299]
2

3.5-4.9 [300-440] (or < 500 ml/d)
3

> 5.0 [> 440] (or < 200 ml/d)
AcknowledgementsNot applicable.FundingNone.Availability of data and materialsThe authors are responsible for the data described in the manuscript and assure full availability of the study material upon request to the corresponding author.Authors' contributions MS wrote the first draft of the manuscript. All the authors reviewed the manuscript and approved the final draft.Ethics approval and consent to participate Not applicable.Consent for publicationNot applicable.Competing interestsThe authors declare that they have no competing interests.• We accept pre-submission inquiries • Our selector tool helps you to find the most relevant journal Submit your next manuscript to BioMed Central and we will help you at every step:
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