Evaluating dosage compensation as a cause of duplicate gene retention in Paramecium tetraurelia
2007

Timothy Hughes
Diana Ekman
Department of Biochemistry and Biophysics
Stockholm University
10691StockholmSweden

Himanshu Ardawatia
Department of Molecular Biology
University of Wyoming
82071LaramieWYUSA

Arne Elofsson
Department of Biochemistry and Biophysics
Stockholm University
10691StockholmSweden

David A Liberles email@example.com
Department of Molecular Biology
University of Wyoming
82071LaramieWYUSA

Center for Computational Science
Addresses: *Computational Biology Unit
University of Bergen
5020Bergen, BergenNorway

Evaluating dosage compensation as a cause of duplicate gene retention in Paramecium tetraurelia

Genome Biology
8213200710.1186/gb-2007-8-5-213)Minireview Correspondence: David A Liberles. The electronic version of this article is the complete one and can be found online at
The high retention of duplicate genes in the genome of Paramecium tetraurelia has led to the hypothesis that most of the retained genes have persisted because of constraints due to gene dosage. This and other possible mechanisms are discussed in the light of expectations from population genetics and systems biology.

Many genomes display extensive gene duplication, which may result from either small-scale duplications or from duplication of the whole genome. What determines whether both copies of a duplicate gene are retained in the genome, and their subsequent evolutionary fate, is still a matter of debate. Aury et al. [1] have recently characterized gene duplication in the ciliate Paramecium tetraurelia, a unicellular eukaryote, which appears to have undergone multiple rounds of whole-genome duplication with a high level of retention of the duplicate copies. They suggest that this high level of retention is due to constraints arising from gene dosage, rather than other proposed mechanisms. Here we discuss these results in relation to the various models proposed for gene duplication and retention.

When duplication of a gene, or genome, occurs in an individual organism, it will only become part of the species genome if it becomes 'fixed' in the population (that is, becomes part of the genome of all members of the population). If the initial duplication event is evolutionarily neutral, the duplicated genes will become fixed in the population with a probability dependent on the inverse of the effective population size. It has been suggested, however, that the initial duplication event is likely to be deleterious for gene duplicates with functional regulatory regions, because of the metabolic cost of producing extra protein [2]. This would reduce the probability of fixation.

Given that fixation probably occurs much more quickly than the resolution of the fates of the duplicate copies, most work has considered fate determination as an independent step that occurs after the random process of fixation. Once fixation occurs, if there is purely neutral evolution at the protein level, one copy of a duplicated gene will quickly become a pseudogene, leaving a single ancestral copy with an ancestral function. While relaxation of selective constraint is generally thought to occur after gene duplication, negative selection, which discards changes, apparently returns quickly. Negative selection on parts of the gene may also be coupled to positive selection for the evolution of new functions or levels of expression. Relaxation of selective constraint (or a combination of negative and positive selection) that quickly gives way to stronger negative selection has been observed both in Paramecium [1] and in computer simulations of the evolution of gene duplicates [3].

Models that aim to explain the retention of duplicated genes include the subdivision of expression profiles or functions of the ancestral gene between the duplicates (subfunctionalization) [4]; the acquisition of new functions by one or both duplicated copies (neofunctionalization) [5]; selection to increase robustness by maintaining a highly conserved backup copy [6]; and selection for increased gene dosage or for dosage-compensation effects, as suggested for Paramecium (see also [7]).

Selection that depends on gene dosage can involve two different mechanisms. Selection for increased gene dosage involves a positive selection pressure to increase expression from a locus that is already highly expressed and has little mutational capacity to increase its expression or concentration-dependent activity. The dosage-compensation model, on the other hand, invokes a negative selection pressure to retain the function and expression levels of both copies in order to preserve the correct stoichiometry -the appropriate amounts or activity of the proteins in relation to each other or other proteins. Subfunctionalization is a nearly neutral model, with neither positive nor negative selection on gene function during the initial period of preservation, whereas neofunctionalization involves positive selection for the generation of new functions in the retained genes. Selection for redundancy, like that for dosage compensation, is characterized by negative selection. Several of these processes can act at different levels of biological regulation: for example, neofunctionalization and subfunctionalization can occur through changes in protein expression, changes in protein function, or changes in alternative or constitutive splicing. Dosage compensation, on the other hand, is a model in which conservation acts simultaneously on all of these processes.

Genome duplication favors the retention of duplicate genes

From examination of a variety of genomes, tandem and segmental gene duplications are known to occur at very high rates (on average 0.01 per gene per million years), similar in magnitude to the rate of mutation per nucleotide site [8,9]. Following such duplications, the average half-life of a gene copy is of the order of a few million years, with only a small fraction of duplicates surviving beyond a few tens of millions of years (TH and DAL, unpublished observations). Following whole-genome duplication, on the other hand, a large proportion of duplicate genes is retained after tens of millions of years (as in Xenopus laevis [10]) or even hundreds of millions of years (in teleost fish [11]). For teleost fish, the rate of retention has been reported to be much higher for the products of whole-genome duplication than for those of small-scale duplication [11].

One possible explanation for these differences is that gene fate is shaped by different evolutionary forces, depending on whether a gene is duplicated in a whole-genome event or not. In a whole-genome duplication, unlike a smaller-scale duplication, the entire network of interacting partners is duplicated together (Figure 1). It is unclear to what degree this build-up of pleiotropic constraints is a limitation as duplicates diverge, and this question needs to be addressed, potentially using protein structural models. The dosagecompensation model would predict that the build-up of pleiotropic constraint is difficult to resolve without deleterious effects, thus introducing a strong negative selection initially against the loss of genes or interactions. This would lead to gene retention and initial conservation of sequence and expression after whole-genome duplication.

Gene duplication in the Paramecium genome

With the sequencing of the genome of P. tetraurelia by Aury et al. [1], it was found to contain 39,642 genes, more genes than many other completely sequenced genomes. Furthermore, these genes can be grouped into families whose members are very closely related in sequence. Phylogenetic analysis of these gene families points to a recent whole-genome duplication in P. tetraurelia, in addition to several older genome duplications. The most recent duplication occurred long enough ago for negative selection to have set in, however.

Aury et al. [1] find that duplicate genes for signaling proteins and transcription factors are preferentially retained in the genome, as are duplicated genes for proteins known to form multicomponent complexes, with a positive correlation between retention and the number of components in the complex. A similar correlation between retention and complexity was observed for genes involved in metabolic pathways. More highly expressed genes were also more likely to have been retained.

Interestingly, the co-retained duplicates did not always originate from the same whole-genome duplication. In regard to complex-forming proteins, genes that were coretained after the most recent whole-genome duplication were not found to be those preferentially retained in the older duplications. In all, Aury et al. [1] found that patterns of retention across whole-genome duplications were affected by gene function, and showed a preference for retention of duplicated genes that had not retained a duplicate in an older whole-genome duplication.

The authors conclude that dosage compensation to maintain the stoichiometry of protein complexes and metabolic pathways and keep them functioning correctly plays an important part in the retention of duplicate genes after a whole-genome duplication. From consideration of the traces of the preceding whole-genome duplications they also propose that over time there is a slow progressive loss of duplicates, as gene-expression levels become adapted for stoichiometric reasons, for example.

The dosage-compensation model predicts that duplicates of genes for proteins that do not form complexes or do not have concentration-dependent roles in metabolism will be rapidly lost. In the case of duplicated genes encoding interacting proteins, it predicts strong selection for retention, but if one of the interacting duplicates is lost from the genome, the model predicts that the loss of the remaining duplicate will now be positively selected for. The first part of this prediction is qualitatively satisfied by the observations from the P. tetraurelia genome of the retention of genes for complexforming proteins. On the other hand, the retention patterns and differing profiles of nonsynonymous (K a ) and synonymous (K s ) substitutions (K a /K s profiles) for duplicates of different ages do not seem to support dosage compensation as the driving force for keeping them in the genome. Selection as a result of dosage compensation thus appears to be complex and may have a role in modulating other evolutionary mechanisms. The apparent burst of either positive selection or relaxation of selective constraint in the period shortly after genome duplication implies that selective mechanisms other than dosage compensation are also acting.

Following the most recent whole-genome duplication in P. tetraurelia, species radiation occurred, resulting in the P. tetraurelia complex of 15 sibling species. Aury et al. [1] propose that this burst of speciation is a side-effect of the whole-genome duplication, occurring as a result of differential gene loss in different populations, leading to inviable hybrids and reproductive isolation by Dobzhansky-Muller incompatibility [12]. Such a proposition is consistent with the loss of proteins not under dosage-balance constraint under the dosage-compensation model and in our opinion is most consistent with speciation accompanied by neofunctionalization or subfunctionalization.

In evaluating alternative explanations of the retention profiles for duplicates in the paramecium genome, effective population size may be an important consideration. Effective population size (together with mutation rate) as a modulator of the strength of selection has been implicated as an important switch between subfunctionalization as a purely neutral process and neofunctionalization or, potentially, dosage compensation as mechanisms involving selection [4,8,9]. Paramecium has been shown to have a relatively large effective population size, making mechanisms that involve selection possible [13]. However, it has been shown that binding interactions as well as regulatory modules can subfunctionalize in the preservation of duplicate genes [3,14], and so the subfunctionalization model for gene duplicate retention may also be consistent with a dependence on the number of interacting protein partners, where the probability of subfunctionalization might be expected to be proportional to the number of ways of subfunctionalizing the interactions with partners. This is a different mechanism of gene retention from dosage compensation, but this characteristic of subfunctionalization has not been evaluated to show that it has the same potential to retain duplicate genes in such high numbers as dosage compensation appears to be able to do. Eventually, quantitative models characterizing these various processes can be tested against the data to extend our understanding of the process of gene retention.

Where does dosage compensation fit in?

Dosage compensation may indeed affect the short-term retention rate of duplicate genes after whole-genome duplication. Over longer time frames, however, proteins involved in complexes and pathways are not preferentially retained in the duplicate pairs originating from wholegenome duplications, neither in P. tetraurelia, as indicated by Aury et al. [1], nor in yeast [15] (except for ribosomal proteins [16]). In fact, whereas 17% of highly connected proteins (hubs) in the yeast protein-protein interaction network belong to a pair originating from the relatively ancient whole-genome duplication that has occurred in Saccharomyces cerevisiae, only 5% of the party hubs, which are coexpressed with their interaction partners, are part of such a pair [15]. Homologous complexes in yeast appear to have been created through stepwise partial duplications and not through whole-genome duplication [17].

The results of Aury et al. [1] do suggest that after more recent whole-genome duplication events, the duplicate proteins belonging to complexes and pathways are initially retained to a greater extent than other proteins. According to this view, although dosage sensitivity is not sufficient for the long-term fixation of duplicates in the genome, it may be important in the first phase following the whole-genome duplication. One might postulate dosage compensation as a mechanism for holding duplicated genes in the genome for some time, to give an opportunity for eventual neofunctionalization (as has been suggested for subfunctionalization [3]). However, even in the period immediately following duplication, stoichiometric issues will be dependent on the interplay between expression and sequence as well as selective pressures for concentration dictated by metabolism and systems-level constraints. Further modeling work is needed to understand the mechanism, as the suggestions by Aury et al. [1] and alternative suggestions (such as subfunctionalization of binding interactions) are part of an ongoing synthesis to understand the process of gene duplication and its relationship to the evolution of gene function.

Considering the case of metabolic networks, the patterns of retention or modification have been observed to be influenced by network structure, topology and function, and the positioning of duplicate genes at key points in the network. Genes coding for enzymes involved in directing higher metabolic fluxes are subject to greater evolutionary constraints as a gene duplication event would increase the flux through an enzyme-catalyzed reaction. It has been observed in S. cerevisiae that genes encoding highly connected enzymes in metabolic pathways have a higher likelihood of maintaining duplicates [18]. Thus, duplication of genes encoding enzymes carrying high metabolic fluxes are more likely to be retained compared to genes encoding enzymes carrying lower metabolic fluxes.

Enzymes in a pathway can evolve with different functional requirements, which can lead to mismatches in the enzyme activities upon duplication [19]. This means that upregulation of individual enzymes can increase or decrease the flux capacity of the pathway and by different amounts. Hence, if only certain proteins increase the performance of the pathway, the duplicates of the other proteins in the pathway will not provide extra fitness to the organism. This also has implications for the retention of duplicate copies based upon an entire pathway being duplicated, indicating that the negative selective pressure for retention of each duplicate in a pathway would not be equally strong. Interestingly, it has been argued that the neutral expectation for biological networks involves a more complex network than that minimally required for function, without necessarily invoking robustness as a driving force for this non-minimal network [20].

The findings by Aury et al. [1] lend further support to the idea that dosage compensation can play a role in the retention of duplicated genes in a genome. Whole-genome duplication events in additional lineages representing different time points will enable a fuller testing of this and other hypotheses, as well as their functional implications for systems biology.

Figure 1
1Possible outcomes for gene retention after whole-genome duplication. An ancestral network of interacting proteins is shown. Following a wholegenome duplication event, all of the proteins together with their interactions are duplicated. Over time, depending upon the evolutionary forces that are operating on the genome, different interactions are retained, gained or lost. Under the dosage-compensation model (bottom left), all interactions are retained. Under the subfunctionalization model (bottom center), redundant interactions become nonredundant (blue).When this is combined with the neofunctionalization model (bottom right), new interactions are also gained (red). In this figure, all of the duplicated copies have been retained as functional genes, but that is not the most likely outcome with increasing evolutionary time.

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Tutorial Voices in Evidence-Based Newborn Care: A How-to-Guide on Developing a Parent-Facing Podcast

Joanna Parga-Belinkie
Children's Hospital of Philadelphia
PhiladelphiaPAUnited States

MD;Raina M Merchant
Children's Hospital of Philadelphia 3401 Civic Center Blvd Division of Neonatology
University of Pennsylvania
2NW35, 19104Philadelphia, PhiladelphiaPAUnited States, United States

MDJoanna Parga-Belinkie
Tutorial Voices in Evidence-Based Newborn Care: A How-to-Guide on Developing a Parent-Facing Podcast
10.2196/16335Corresponding Author: (JMIR Pediatr Parent 2019;2(2):e16335)neonatologysocial mediamedical educationpatient education
Podcasting is becoming a more popular form of media. Its use in medical education is being researched-but what about its use in public education? In this study, the authors offer a how-to-guide on starting a public or patient-facing podcast. The authors hope to inspire more physicians to utilize this type of media to share evidence-based information. More research is needed looking into how podcasting can be used to help with patient education.

Introduction

As a neonatologist and new parent, the birth of my daughter brought with it a search for the right way to raise my healthy newborn. Although well versed in infant pathology, I did not know the differences in pacifier brands, how to work a breast pump, or if there was an ergonomically superior baby carrier. Parenting took my pediatric training, mixed it with a chronic as opposed to episodic sleep deprivation, upset my understanding of practical knowledge in my field, and generally upended me. I know I am not alone in feeling this way. Being disoriented and distressed is common after delivery. Baby blues strike 80% of new parents [1]. If symptoms of sadness persist for 2 weeks, concerns for postpartum depression arise [2]. Postpartum depression affects an estimated 15% of new parents [2]. To ease my unrest, I searched for solid and easily available evidence-based parenting resources. Despite being educated about scientific studies and research, I found myself confused by infant care claims on the internet and social media. I started to wonder how parents without a medical background were navigating the highly commercialized and often unevidenced world of modern parenting. After researching different forms of media, it became evident that podcasting was gaining a growing listenership [3]. For the first time in history, more than half of the Americans (51%) have reported listening to podcasts, that is, 144 million people up from approximately 1.4 million a decade ago in 2009 [3]. However, within the podcasting landscape there were no prominent female physician voices leading medical education for pregnancy. I saw this as an opportunity to create something in this space. I looked to starting a podcast as a way to humanize my experiences in parenting and provide factual information and commentary on infant development, practices, and products when applicable. To accomplish this, I enlisted the help of another neonatologist to cohost the episodes, and began utilizing resources within my academic institution to help with podcast growth. This study is about how to start a medical podcast, and it outlines the successes and pitfalls my team has experienced as physician podcasters.

Reviewing the Parenting Podcast Landscape

Before starting a podcast, we looked at what was already available for parents regarding pregnancy and the first 3 to 4 months of an infant's life [4,5]. Although podcasts existed on parenting experiences and breastfeeding, there were none we could find by physicians dedicated to the postpartum period. For a lot of the podcasts, the hosts were not experts in infant care and relied on recounting their personal experiences or bringing on experts for topic interviews. Physicians did host podcasts, but the majority were aimed at educating other clinicians as opposed to the public [6]. There was room for a podcast that allowed for medical providers to recount their clinical experiences to those in nonmedical professions. This would allow for the creation of a trusted voice for medical information that could be listened to outside of the doctor's office.

Preparing to Launch and Financial Considerations

Purveying medical information can be dry-a hosting format between 2 women who are both doctors and mothers was devised to keep topics conversational, avoid monotony, and give different perspectives from providers within the field. Use of a pseudonym, in this case Baby Doctor Mamas, was also chosen. This would allow for various neonatologists, obstetricians, or gynecologists to act as hosts on the podcast. The hope was that this would ensure longevity and marketability of the podcast.

Launching the podcast required some startup capital that we paid out of pocket (Table 1). Startup equipment included a Tascam recorder and Rode microphones. The recording space was a spare guest bedroom in one of the host's house, with pillow padding around the microphones to buffer sounds. For help with logo creation and website development [7], another neonatologist, Dr Juanita Lewis, volunteered her services. Website development required purchase of a domain name and server space. A platform for hosting the podcast was also required, and Blubrry [8] was chosen for cost and statistical capabilities.

We wanted the podcast to sound professional as audio quality is an important metric for listener engagement. Instead of purchasing equipment for audio editing, we hired an audio editor who charged an hourly fee for edits. When launching, we decided on a regular schedule of once weekly release of the podcast to allow for predictability and growth-that way our listeners knew when to expect episode releases to follow them. We also had 4 episodes prerecorded before our launch, releasing 2 at once and allowing the other 2 to be released in the coming weeks. This allowed for varied content at the outset of the podcast, for effective time management, and for us to be prepared for future episodes.

Use of Multiple Podcast Platforms and Social Media Engines for Promotion

To enhance use, we utilized several platforms and launched simultaneously on all of them to allow for listeners to find the episodes easily. Baby Doctor Mama podcasts are found on Apple iTunes, Spotify, Stitcher, iheartradio, and Soundcloud. Promotion of the podcast was done almost exclusively on Web-based social media platforms such as Instagram, Twitter, and Facebook, all under the name Baby Doctor Mamas. Posting on these platforms occurred before launch, and the accounts were all public facing to allow for feedback and discovery. The only other promotional move made was to launch the podcast during a national conference for pediatrics. Flyers were printed and distributed at this conference to gain listenership.

Thoughts on Expansion and Audience Reach

Initial episodes of the podcast engaged 200 to 400 listeners, and this has steadily grown to anywhere from 450 to more than 800 listeners per episode, with a maximum of 958 listeners on a sleep training episode (see Figure 1). In less than 1 year, we have 719 followers on Instagram, 494 on Facebook, and 97 on Twitter. Tactics that have worked to increase social media following and downloads of the podcast/listens per episode include the following: having an expert or celebrity on infant topics (ie, Dr Harvey Karp or Dr Jennifer Arnold), interviewing an Instagram influencer with tens of thousands of followers and having them post on social media regarding the episode (ie, Dr Danielle Jones aka Mama Doctor Jones @mamadoctorjones with 55.5K followers on Instagram), and linking in an expert with a high twitter following and ability to promote through a department within the hospital (ie, Dr Ariel Williamson on sleep training who posted about the episode through the Health Policy Lab at the Children's Hospital of Philadelphia).

Conclusions

As medical podcasters and mothers, our goals are to disseminate evidence-based medicine and provide entertaining and accessible content to listeners. Although there has been steady growth of the Baby Doctor Mama podcast through social media promotion, there is still room to grow. Eventually, working in conjunction with an academic university for podcast promotion and listenership may be a key strategy for the growth and expansion of listenership to more patients/parents. Marketing the podcast differently with a focus on creating a media packet is also an option. More research is needed into how patients/parents are utilizing podcasts for medical information.

Conflicts of Interest

Figure 1 .
1The number of listeners per episode of the Baby Doctor Mamas podcast from October 29, 2018, to September 1, 2019. The graph shows an increase in listeners over time and reviews the topics discussed in each episode. The blue solid line represents the number of listeners for each topic over time. The purple dotted line represents the mean number of listeners over time for all episodes. AAP: American Academy of Pediatrics; NICU: Neonatal Intensive Care Unit.

Table 1 .
1Startup costs for launch of the podcast Baby Doctor Mamas. Use of wordpress.com but ultimately used wordpress.org given better user rights US $132.00; Wordpress website with Bluehost server space Website hosting Use of Libsyn or soundcloud services US $89.00; Blubrry hosting service Podcast hosting Self-editing with Audacity, freelance sound designer US $20-30 per hour Audio editing Not applicable US $198.72+ ongoing editing costs Total startup costsSupplies/other services considered
Annual costs
Item

Basic supplies: recorder, 2 professional microphones
Approximately US $2000-US $3000; one-time
fee

Equipment

Use of 99designs company
Free, services offered by another neonatologist
Website design and logo cre-
ation

Use of freelance composers
US $46.55; Jamendo website, full rights to content
Jingle for Introduction

None
US $12.17; GoDaddy website
Domain name

JMIR Pediatr Parent 2019 | vol. 2 | iss. 2 | e16335 | p. 1 http://pediatrics.jmir.org/2019/2/e16335/ (page number not for citation purposes)
JMIR Pediatr Parent 2019 | vol. 2 | iss. 2 | e16335 | p. 2 http://pediatrics.jmir.org/2019/2/e16335/ (page number not for citation purposes)

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Please cite as: Parga-Belinkie J, Merchant RM Voices in Evidence-Based Newborn Care: A How-to-Guide on Developing a Parent-Facing Podcast JMIR Pediatr Parent. 10.2196/16335216335Please cite as: Parga-Belinkie J, Merchant RM Voices in Evidence-Based Newborn Care: A How-to-Guide on Developing a Parent-Facing Podcast JMIR Pediatr Parent 2019;2(2):e16335 URL: http://pediatrics.jmir.org/2019/2/e16335/ doi: 10.2196/16335 PMID:

), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Pediatrics and Parenting, is properly cited. The complete bibliographic information. Raina M ©joanna Parga-Belinkie, Merchant, 14.12JMIR Pediatrics and Parenting. This is an open-access article distributed under the terms of the Creative Commons Attribution License. as well as this copyright and license information must be included©Joanna Parga-Belinkie, Raina M Merchant. Originally published in JMIR Pediatrics and Parenting (http://pediatrics.jmir.org), 14.12.2019. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Pediatrics and Parenting, is properly cited. The complete bibliographic information, a link to the original publication on http://pediatrics.jmir.org, as well as this copyright and license information must be included.

Jmir Pediatr, Parent, | iss. 2 | e16335 | p. 24JMIR Pediatr Parent 2019 | vol. 2 | iss. 2 | e16335 | p. 4


Comparison of microbial diversity determined with the same variable tag sequence extracted from two different PCR amplicons

Yan He
†
Ben-Jie Zhou
†
Guan-Hua Deng
Xiao-Tao Jiang
Hai Zhang
Hong-Wei Zhou
Comparison of microbial diversity determined with the same variable tag sequence extracted from two different PCR amplicons
R E S E A R C H A R T I C L E Open Access
Background: Deep sequencing of the variable region of 16S rRNA genes has become the predominant tool for studying microbial ecology. As sequencing datasets have accumulated, meta-analysis of sequences obtained with different variable 16S rRNA gene targets and by different sequencing methods has become an intriguing prospect that remains to be evaluated experimentally. Results: We amplified a group of fecal samples using both V4F-V6R and V6F-V6R primer sets, excised the same V6 fragment from the two sets of Illumina sequencing data, and compared the resulting data in terms of the α-diversity, β-diversity, and community structure. Principal component analysis (PCA) comparing the microbial community structures of different datasets, including those with simulated sequencing errors, was very reliable. Procrustes analysis showed a high degree of concordance between the different datasets for both abundance-weighted and binary Jaccard distances (P < 0.05), and a meta-analysis of individual datasets resulted in similar conclusions. The Shannon's diversity index was consistent as well, with comparable values obtained for the different datasets and for the meta-analysis of different datasets. In contrast, richness estimators (OTU and Chao) varied significantly, and the meta-analysis of richness estimators was also biased. The community structures of the two datasets were obviously different and led to significant changes in the biomarkers identified by the LEfSe statistical tool. Conclusions: Our results suggest that beta-diversity analysis and Shannon's diversity are relatively reliable for meta-analysis, while community structures and biomarkers are less consistent. These results should be useful for future meta-analyses of microbiomes from different data sources.

Background

Determining 16S rRNA gene tag sequences using next generation sequencing (NGS) techniques, mainly the 454 and Illumina system platforms, has become a revolutionary tool in the field of microbiome research [1][2][3][4]. The major advantages of NGS methods are highthroughput capabilities and cost-effectiveness. Thousands of sequences per microbiome sample can be obtained easily, and hundreds to thousands of samples can be sequenced simultaneously [5]. However, the sequencing lengths obtained by NGS are shorter than those obtained by the Sanger sequencing method, and only part of the 16S rRNA gene spanning one or more of the nine hypervariable regions can be determined [4]. The first published study using NGS to study microbiomes determined the V6 tag of the 16S rRNA gene, and this region was short enough to be analyzed by the 454 Genome Sequencer 20 system at that time [6]. With the improvement of NGS techniques, sequencing lengths have grown to hundreds of bases per read, with even longer tags expected in the near future [5]. Although the short tag has proven useful for taxonomy assignment [7], longer tags may provide higher resolution for differentiating microbes and better taxonomy results. Until now, there has been no standard or best choice for choosing the variable regions, and researchers have historically determined diverse variable regions with varied sequencing lengths both within and across labs [3,4,[8][9][10].

One of the major advantages of using DNA sequences to analyze microbiome diversity is that sequencing data obtained from different studies can be analyzed together, constituting a more cumulative approach than comparing DNA fingerprinting results [5]. However, if and how datasets from different sequencing projects can be combined for meta-analysis has not been evaluated because few studies have sequenced and compared actual microbiome samples processed by different experimental methods. One of the most straightforward ideas is to use the same variable region for different PCR amplicons and extract sequences of that specific region from different studies for direct comparison. Theoretically, the same tag region allows for a consistent clustering of operational taxonomic units (OTUs) and taxonomy assignment; therefore, subsequent parameters, including αand β-diversities and community structures, can be analyzed. However, experimental conditions such as primer bias and sequencing quality might affect these analyses [11]. Until now, there have been no reports addressing this approach by amplifying real samples with different primers and extracting the same variable tag for direct comparison. In this study, we determined a total of 28 fecal microbiome samples from four individuals and amplified each sample independently with two primer sets (V4F-V6R and V6F-V6R). We analyzed the α-diversity, β-diversity, microbial community structure, and biomarkers and focused on the following two questions: First, do the results from the two datasets agree with one another? Second, can the two datasets be combined to produce reliable results? The present study provides useful information for evaluating the feasibility of meta-analysis for the study of microbiomes.

Methods

Ethical statement

This study was approved by the Ethical Committee of Southern Medical University, and all participants provided written informed consent.

Sample processing and sequencing

Fecal samples were obtained from four individuals. For each individual, one sample was collected every two days for a period of two weeks. All of the samples were stored at -80°C until DNA extraction, and 200 mg of each sample was used for DNA extraction. DNA was extracted using the PowerSoil DNA kit (MoBio, USA) according to the manufacturer's instructions. The high fidelity ExTaq cocktail (Takara, China) was used to amplify the 16S rRNA gene tags. Each DNA sample was amplified by 2 barcoded primer sets, one of which included the primers V4F 5′ GTGCCAGCMGCCGCGGTAA 3′ and V6R 5′ ACAGCCATGCANCACCT 3′, while the other included the primers V6F 5′ CNACGCGAAGAACC TTANC 3′ and V6R 5′ ACAGCCATGCANCACCT 3′. The PCR conditions included an initial denaturation step at 94°C for 2 min, 25 cycles of 94°C for 30 s, 52~59°C for 30 s and 72°C for 30 s, and a final extension at 72°C for 5 min. Each 25-μl reaction consisted of 2.5 μl of Takara 10× Ex Taq Buffer (Mg 2+ free), 2 μl of dNTP Mix (2.5 mM), 1.5 μl of Mg 2+ (25 mM), 0.25 μl of Takara Ex Taq DNA polymerase (2.5 units), 1 μl of template DNA, 0.5 μl of 10 μM barcode primer 967 F, 0.5 μl of 10 μM primer 1406R, and 16.75 μl of ddH 2 O. The two PCR products were sequenced independently in two sequencing batches at the Beijing Genomic Institute using paired-end sequencing with an Illumina HiSeq 2000 platform, and 101 bp were sequenced from each end. The sequences have been deposited in the sequence read archive (SRA) with accession number from ERS346316 to ERS346371.

Sequence processing and analysis

We wrote a Perl script to separate tags according to their barcodes with the following steps: the primer region of each tag was first identified with no mismatches allowed; tags which failed to match primers were replaced by their reverse complements, and the primer region was identified again; the barcodes (region before the primer) and target V6 region (region after the primer) were stored for each tag; tags were separated according to their barcodes, and tags without any matching samples were discarded. For quality control purposes, no mismatches were allowed in the primer or barcode regions (see above). Furthermore, we removed tags with ambiguous bases (N) and screened potential chimeras with UCHIME (de novo mode, parameters set as follows: --minchunk 20 --xn 7 -noskipgaps 2 [12]. To unify the target region of the tags from the two primer sets, we extracted the V6 region of each tag by cutting 60 bp from the right end of the sequences from V6R primers (960 bp to 1,028 bp in E. coli). To avoid the effects of different sequencing depths, all samples were normalized to 5,000 sequences for subsequent analyses. We calculated the Good's coverage of each sample at this depth. The formula used was C ¼ 1− n N , where C is the Good's coverage, n is the number of OTUs with only one tag per sample, and N is the number of all tags in that sample. TSC was used to cluster the tags into OTUs, with the similarity threshold set to 0.97 [13]. GAST was used to assign these sequences into taxa with the V6 database [7]. The α-diversity indices, including Chao, Ace, Shannon and observed OTUs, were calculated using the MOTHUR [14]. PCA was implemented using QIIME based on the Jaccard distance [15]. LEfSe was used to determine the biomarkers with LDA = 3 [16]. Statistical analysis was performed using SigmaPlot 12.0.

Results and discussion

Illumina paired-end sequencing results

In total, we determined 417,821 tags with the V4F-V6R primer set (an average of 14,992 tags per sample) and 756,514 tags with the V6F-V6R primer set (an average of 27,018 tags per sample). We discarded tags with mismatches in the primer regions, which reduced the total number of tags to 306,328 for the V4F-V6R dataset (an average of 10,940 tags per sample) and to 715,634 for the V6F-V6R dataset (an average of 25,558 tags per sample). More sequences were discarded from the V4F-V6R than the V6F-V6R dataset, indicating that the sequencing quality of the V4F-V6R dataset was inferior to that of the V6F-V6R. This difference in sequencing quality affected the α-diversity estimations, which will be discussed below. Secondly, we screened the chimeras with UCHIME. Because the sequencing of 101 bp from both ends could not sequence through the whole V4 to V6 region of the 16S rRNA, we linked each pair of tags with 30 Ns to allow screening of the chimeras. After this step, we acquired 263,127 tags from the V4F-V6R primer set (an average of 9,398 tags per sample) and 714,938 tags from the V6F-V6R primer set (an average of 25,533 tags per sample). Once again, many more chimeras were found with the V4F-V6R than the V6F-V6R dataset. This result is reasonable, as the V4 to V6 region (approximately 550 bp) is much longer than the V6 region (approximately 65 bp) and spans conservative sequences in the 16S rRNA, thus being more likely to form chimeras during the process of PCR amplification [17]. Finally, to unify the region and length of the tag, the same 60 bp sequence next to the V6R primer was extracted from both primer sets. To avoid the influence of different sequencing depths, we rarefied all samples to 5,000 tags for a consistent sequencing depth. The Good's coverage of all samples with 5,000 tags was higher than 0.95 with 0.96 ± 0.005 (mean ± SEM) for samples from the V4F-V6R datasets and 0.98 ± 0.004 for the V6F-V6R datasets, indicating that the sequencing depth was sufficient for reliable analysis of these fecal microbial community samples. Based on these data, analyses including α-diversity (within-community diversity), β-diversity (between-communities diversity), microbial structure and biomarker determination were evaluated, as they are fundamental for microbiome research.

In addition to the quality filtering results, four external standards were sequenced simultaneously with each of the two libraries for a direct comparison of the sequencing quality. The external standards were samples with only one known cloned sequence as the PCR template, and the accuracy was checked at each base position. By comparing the sequencing results of the external standards with the known sequence, we could, to some extent, evaluate the sequencing quality of the library. All external standards were also filtered to remove ambiguous bases (N) and chimeras as above. As shown in Additional file 1: Figure S1, the proportion of sequences which have 100% identity with the external standard in the V6F-V6R library was higher than that of the V4F-V6R library (0.939 vs. 0.879, t-test, P < 0.001), while the proportion of error sequences was significantly lower in the V6F-V6R than the V4F-V6R library, indicating that the sequencing quality of the former was superior to that of the latter.

α-diversity indices

We evaluated four α-diversity indices, including the ACE, Chao, number of observed OTUs and Shannon index. The three species richness estimates (ACE, Chao, and observed OTUs) calculated using the V6 tag extracted from the V4F-V6R dataset were significantly higher than those calculated from the V6F-V6R dataset (P < 0.001) (Figure 1). It is reasonable to expect that all errors including PCR biases, PCR errors (mutations and chimeras), and sequencing errors could contribute to differences in the richness estimates. According to our quality control analysis, the sequencing quality of the V4F-V6R dataset was significantly inferior to that of the V6F-V6R dataset, and chimeras were also more prevalent in the former. These error sequences tend to be rare, as the same error is unlikely to occur multiple times [18,19]. Because species richness estimators such as ACE and Chao mainly depend on the number of rare OTUs (for example, the Chao is calculated only with the number of singletons and doubletons), the V6 tag from the V4F-V6R dataset, which contained more errors, obtained significantly higher richness estimates. The fact that each library was only sequenced once reduced the statistical power for evaluating the adverse effects of sequencing errors.

Not surprisingly, the meta-analysis of species richness was significantly biased by the data source. For example, if we chose sequences from the V4F-V6R dataset for individuals A and B and sequences from the V6F-V6R dataset for individuals C and D (simulating a situation where sequences are obtained by various methods from individuals A and B in one experiment and from individuals C and D in another experiment prior to combination of the data), then A and B had much higher species richness estimates than C and D, a result which actually reflects differences in the generation of the two datasets (sequencing and PCR errors) rather than the diversity of the samples. Although we used the same HiSeq 2000 instrument for both of the datasets, the sequencing quality of the two sequencing batches was obviously different. For those datasets preserved in databases, individuals using various 454 and Illumina instruments obtained different sequencing qualities, a factor which is problematic for meta-analysis of richness estimates.

In contrast, Shannon's diversity index showed no significant difference between the two datasets (3.77 ± 0.10 for V4F-V6R versus 4.06 ± 0.06 for V6F-V6R, P = 0.056), indicating that this index was more stable than the richness estimators and more reliable for comparison across various studies. In addition, we randomly changed the bases of these sequences to simulate sequencing errors rates of 0.1% and 1% to evaluate the effect of sequencing errors on the estimation of alpha indices. The results demonstrated that at an error rate of 0.1%, all indices including the richness estimates and Shannon index were hardly influenced (One Way ANOVA on ranks, P < 0.05, Dunn's test for pair-wise comparisons between 0% error rate and 0.1% error rate, P > 0.05), but raising the error rate to 1% inflated the species richness estimates significantly (One Way ANOVA on ranks, P < 0.05, Dunn's test for pair-wise comparisons between 0% error rate and 1% error rate: ACE, 546 vs. 2435, P < 0.05; Chao1, 886 vs. 3680, P < 0.05; observed species, 285 vs. 577, P < 0.05). By comparison, although the Shannon index was also inflated (5.37 vs. 5.90, P < 0.05), the extent of inflation was much smaller than that of the species richness estimators, and no significant differences were observed between the two datasets (Additional file 1: Figure S3). The explanation for this result is that Shannon diversity index depends more on highly abundant OTUs compared to species richness estimates [20], is consequently less sensitive to sequencing errors and was therefore able to produce similar values for both of the datasets in the present study. In support of this theory, we found in a recent study [20] that Shannon diversity index of freshwater and marine sediments were comparable across multiple studies.

PCA using the Jaccard distances

We next compared the two datasets in terms of β-diversity obtained using Principal Component Analysis (PCA) with Jaccard distances (Figure 2a, b). The rationale for using the Jaccard, rather than the phylogenetic-based UniFrac, distances is that the V6 tag is very short with high variability, leading to a relatively lower resolution of the UniFrac distance after alignment and filtering of unmatched sequences. Procrustes analysis illustrates two PCA analyses in one plot, transforming one of the coordinate sets by rotating, scaling, and translating it to minimize the distances between two corresponding points of the same sample. The results of the two datasets (the V6 fragment extracted from two different PCR and sequencing runs) were in accordance with each other based on the abundance-weighted and binary Jaccard distances (p = 0.000), with obvious clustering of samples from each individual.

Subsequently, we combined all sequences from these two datasets to simulate a meta-analysis (Figure 2c, d). PCA of both binary and abundance-weighted Jaccard distances demonstrated that the pooled analysis resulted in almost the same clustering as the single dataset, and the points from the two data sources could hardly be differentiated, suggesting that meta-analysis using the Jaccard distance is relatively stable across different PCR and sequencing runs. To further understand the effect of sequencing errors on PCA, we performed procrustes analysis with the original datasets vs. datasets with simulated base error rates of 1% (Additional file 1: Figure S4). All pair-wise comparisons show that sequencing errors did not greatly affect the PCA based on the Jaccard distance, in support of our conclusions detailed above.

Microbial composition and biomarker determination

The two datasets showed significantly different community structures (Figure 3a). Although the gut flora of all subjects consisted primarily of Firmicutes, Bacteroidetes and Proteobacteria, the relative abundance of these microbes varied significantly. Compared to the V6F-V6R dataset, the V4F-V6R dataset identified higher levels of Bacteroidetes and lower levels of Firmicutes (Figure 3c). Interestingly, the categories of genera identified by the two primer sets were similar to each other, while the relative abundance of the genera differed (Figure 3b). We suggest that both the primer bias and sequencing errors contributed to these differences, but the former may have contributed more because sequencing errors usually occur at a very low frequency and do little to change the overall relative abundance. Several studies have compared microbial community structures using different primer sets [11,21]. These studies usually found significant primer biases in the evaluation of microbial ecology. However, here we demonstrated for the first time that PCA using the Jaccard distance was minimally affected by primer bias and differences in sequencing quality, suggesting the feasibility of performing metaanalysis for sequences obtained from different sources.

We used LEfSe for the quantitative analysis of biomarkers within different groups (Figure 4 and Additional file 1: Figure S2). This method was designed to analyze data in which the number of species is much higher than the number of samples and to provide biological class explanations to establish statistical significance, biological consistency, and effect-size estimation of predicted biomarkers [16]. To simulate a simple meta-analysis, we compared the microbiomes of four individuals two at a time (e.g., A vs. C and B vs. D). The results demonstrated that when the data from the two individuals came from the same dataset, their biomarkers were generally similar. For example, the biomarkers identified using the V4F-V6R dataset for individuals A and C were similar to those obtained using the V6F-V6R dataset (Proteobacteria, Bacteroides, Lachnospiraceae, and Enterobacteriaceae for individual A and Prevotella, Faecalibacterium, and Erysipelotrichaceae for individual C) (Figure 4a, b). However, when we analyzed the microbiome data of individual A from the V4F-V6R dataset and the data of individual C from the V6F-V6R dataset, the Firmicutes phylum was identified for individual C, and Proteobacteria was no longer identified as a biomarker for individual A (Figure 4c). Surprisingly, when we analyzed the microbiome data for individual A from the V6F-V6R dataset and the data for individual C from the V4F-V6R dataset, no biomarkers were identified for the two groups (not shown in Figure 4, as no biomarkers were identified). A similar situation occurred when analyzing the data from individuals B and D, as there were no biomarkers identified when the V6F-V6R dataset was used for individual B and the V4F-V6R dataset was used for individual D (Additional file 1: Figure S2). Taken together, these results suggest that while similar biomarkers can be obtained even when different primer sets and sequencing batches are used, meta-analysis  should be performed cautiously when using data obtained from different sources.

Conclusions

For the purposes of meta-analysis, PCA using both the binary and abundance-weighted Jaccard distance is reliable, and Shannon diversity index is also relatively stable across different studies. However, the richness estimators, especially those depending primarily on rare tags (e.g., Chao and ACE) are significantly affected by the experimental procedures unique to individual studies. The community structure, especially the relative abundance, also varies significantly between different datasets. Biomarkers between different groups are comparable between multiple experiments if the input data for the LEfSe analysis is obtained from a single experiment, but meta-analyses using combined datasets should be performed cautiously. In the present study, we only take into account primer bias and sequencing quality, and their effect on microbiota analyses from combined studies, variations in the experimental procedures of different laboratories could also affect the meta-analyses. Additional studies verifying the PCR conditions, particularly the enzyme system, DNA extraction, DNA storage effect, etc., are needed in future.

Additional file

Additional file 1: Figure S1. Using external standards to compare the sequencing qualities between the two libraries. The identity with external standard sequence is split into four groups and we calculated the proportion of sequences in each sequencing batch fall into each group. Figure S2. LEfSe comparison of microbial communities between individuals B and D with different data sources. Figure S3. Alpha diversity index calculated from the V6F-V6R and V4F-V6R datasets at error rates of 0%, 0.1% and 1%. Figure S4. Procrustes analysis of datasets from the two libraries and error rates.

Figure 1 α
1-diversity comparisons between the two datasets. Mean values and 95% SEM are shown for each individual. Statistical analysis was performed using Mann-Whitney rank sum tests. Three species richness estimators, including (a) ACE (b) Chao and (c) number of OTUs, and one species evenness estimator, (d) Shannon's diversity index, were included.

Figure 2
2Principal component analysis of binary and abundance-weighted Jaccard distances between samples. (a and b) Procrustes analysis of PCA results based on binary (a) or abundance-weighted (b) Jaccard distances of the two datasets. Points linked with bars were obtained from the same individual but from two different datasets. (c) and (d) Two datasets were combined for meta-analysis based on binary (c) or abundance-weighted (d) Jaccard distance.

Figure 3
3Microbial structure at phylum and genus level. (a) Microbial structures of each individual determined at the phylum level by the two primer sets. (b) Microbial structures of each individual determined at the genus level by the two primer sets. (c) Relative abundance of Firmicutes and Bacteroidetes determined by the two primer sets.

Figure 4
4LEfSe comparison of microbial communities between individuals A and C with different data sources. (a) Individual A and C are both from V46 library. (b) Individual A and C are both from V6 library. (c) Individual A is from V46 library and Individual C is from V6 library.
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Competing interestThe authors declare no competing financial interests.Authors' contributions YH, XTJ and HWZ conceived of the study. BJZ and GHD performed the experiments. YH, XTJ and HZ analyzed the data. YH and HWZ wrote the manuscript. All authors read and approved the final manuscript.
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Comparison of microbial diversity determined with the same variable tag sequence extracted from two different PCR amplicons. He, BMC Microbiology. 13208Cite this article asCite this article as: He et al.: Comparison of microbial diversity determined with the same variable tag sequence extracted from two different PCR amplicons. BMC Microbiology 2013 13:208.