Associations between sexual habits, menstrual hygiene practices, demographics and the vaginal microbiome as revealed by Bayesian network analysis

<div><p>The vaginal microbiome plays an influential role in several disease states in reproductive age women, including bacterial vaginosis (BV). While demographic characteristics are associated with differences in vaginal microbiome community structure, little is known about the influence of sexual and hygiene habits. Furthermore, associations between the vaginal microbiome and risk symptoms of bacterial vaginosis have not been fully elucidated. Using Bayesian network (BN) analysis of 16S rRNA gene sequence results, demographic and extensive questionnaire data, we describe both novel and previously documented associations between habits of women and their vaginal microbiome. The BN analysis approach shows promise in uncovering complex associations between disparate data types. Our findings based on this approach support published associations between specific microbiome members (e.g., <i>Eggerthella</i>, <i>Gardnerella</i>, <i>Dialister</i>, <i>Sneathia</i> and <i>Ruminococcaceae</i>), the Nugent score (a BV diagnostic) and vaginal pH (a risk symptom of BV). Additionally, we found that several microbiome members were directly connected to other risk symptoms of BV (such as vaginal discharge, odor, itch, irritation, and yeast infection) including <i>L</i>. <i>jensenii</i>, <i>Corynebacteria</i>, and <i>Proteobacteria</i>. No direct connections were found between the Nugent Score and risk symptoms of BV other than pH, indicating that the Nugent Score may not be the most useful criteria for assessment of clinical BV. We also found that demographics (i.e., age, ethnicity, previous pregnancy) were associated with the presence/absence of specific vaginal microbes. The resulting BN revealed several as-yet undocumented associations between birth control usage, menstrual hygiene practices and specific microbiome members. Many of these complex relationships were not identified using common analytical methods, i.e., ordination and PERMANOVA. While these associations require confirmatory follow-up study, our findings strongly suggest that future studies of the vaginal microbiome and vaginal pathologies should include detailed surveys of participants’ sanitary, sexual and birth control habits, as these can act as confounders in the relationship between the microbiome and disease. Although the BN approach is powerful in revealing complex associations within multidimensional datasets, the need in some cases to discretize the data for use in BN analysis can result in loss of information. Future research is required to alleviate such limitations in constructing BN networks. Large sample sizes are also required in order to allow for the incorporation of a large number of variables (nodes) into the BN, particularly when studying associations between metadata and the microbiome. We believe that this approach is of great value, complementing other methods, to further our understanding of complex associations characteristic of microbiome research.</p></div

Nested PCR Biases in Interpreting Microbial Community Structure in 16S rRNA Gene Sequence Datasets

<div><p>Background</p><p>Sequencing of the PCR-amplified 16S rRNA gene has become a common approach to microbial community investigations in the fields of human health and environmental sciences. This approach, however, is difficult when the amount of DNA is too low to be amplified by standard PCR. Nested PCR can be employed as it can amplify samples with DNA concentration several-fold lower than standard PCR. However, potential biases with nested PCRs that could affect measurement of community structure have received little attention.</p><p>Results</p><p>In this study, we used 17 DNAs extracted from vaginal swabs and 12 DNAs extracted from stool samples to study the influence of nested PCR amplification of the 16S rRNA gene on the estimation of microbial community structure using Illumina MiSeq sequencing. Nested and standard PCR methods were compared on alpha- and beta-diversity metrics and relative abundances of bacterial genera. The effects of number of cycles in the first round of PCR (10 vs. 20) and microbial diversity (relatively low in vagina vs. high in stool) were also investigated. Vaginal swab samples showed no significant difference in alpha diversity or community structure between nested PCR and standard PCR (one round of 40 cycles). Stool samples showed significant differences in alpha diversity (except Shannon’s index) and relative abundance of 13 genera between nested PCR with 20 cycles in the first round and standard PCR (P<0.01), but not between nested PCR with 10 cycles in the first round and standard PCR. Operational taxonomic units (OTUs) that had low relative abundance (sum of relative abundance <0.167) accounted for most of the distortion (>27% of total OTUs in stool).</p><p>Conclusions</p><p>Nested PCR introduced bias in estimated diversity and community structure. The bias was more significant for communities with relatively higher diversity and when more cycles were applied in the first round of PCR. We conclude that nested PCR could be used when standard PCR does not work. However, rare taxa detected by nested PCR should be validated by other technologies.</p></div

Evaluation of Methods for the Extraction and Purification of DNA from the Human Microbiome

<div><h3>Background</h3><p>DNA extraction is an essential step in all cultivation-independent approaches to characterize microbial diversity, including that associated with the human body. A fundamental challenge in using these approaches has been to isolate DNA that is representative of the microbial community sampled.</p> <h3>Methodology/Principal Findings</h3><p>In this study, we statistically evaluated six commonly used DNA extraction procedures using eleven human-associated bacterial species and a mock community that contained equal numbers of those eleven species. These methods were compared on the basis of DNA yield, DNA shearing, reproducibility, and most importantly representation of microbial diversity. The analysis of 16S rRNA gene sequences from a mock community showed that the observed species abundances were significantly different from the expected species abundances for all six DNA extraction methods used.</p> <h3>Conclusions/Significance</h3><p>Protocols that included bead beating and/or mutanolysin produced significantly better bacterial community structure representation than methods without both of them. The reproducibility of all six methods was similar, and results from different experimenters and different times were in good agreement. Based on the evaluations done it appears that DNA extraction procedures for bacterial community analysis of human associated samples should include bead beating and/or mutanolysin to effectively lyse cells.</p> </div

Histogram of node degree for the final BN.

<p>The majority of nodes had fewer than 8 connections (i.e., degrees), while a few were more highly connected.</p

Comparison of DNA yields of type strains obtained using six DNA extraction methods.

a<p>DNA concentrations are means calculated using data from eight replicates.</p>b<p>Means with the same letter are not significantly different.</p

16S rRNA gene copy numbers, expected and observed proportions of 16S rRNA gene sequence reads for each type strain.

a<p>If more than one possible copy numbers are available for one species in <i>rrn</i> database (<a href="http://ribosome.mmg.msu.edu/rrndb/index.php" target="_blank">http://ribosome.mmg.msu.edu/rrndb/index.php</a>), the larger one was chosen.</p>b<p>Average proportions and standard deviations are calculated based on eight replicates.</p

Features of the six DNA extraction methods used.

a<p>Cell lysis method: B, bead beating; E1, lysozyme; E2, mutanolysin; E3, lysostaphin; C, chemical.</p>b<p>Phenol-chloroform purification and isopropanol precipitation.</p

Network depicting only microbiome nodes, colored by modularity-based community type, as determined using a modularity optimizing algorithm [44].

<p>Node size is proportional to betweenness centrality, and edge thickness is proportional to bootstrap support. Node label abbreviations can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0191625#pone.0191625.s005" target="_blank">S4 File</a>.</p

Network depicting modularity-based communities (node colors) as determined by a modularity optimization algorithm [44].

<p>Size of node is proportional to node’s betweenness centrality. Node label abbreviations can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0191625#pone.0191625.s005" target="_blank">S4 File</a>.</p

Network depicting nodes related to demographics (orange) and microbial taxa (green), as well as all edges with at least 30% bootstrap support.

<p>Node size is proportional to node degree (i.e., number of incoming and outgoing edges). Arrow thickness is proportional to bootstrap support. Red arrows bridge demographic and microbiome nodes, while green and orange arrows connect nodes of the same type (i.e., microbiome-to-microbiome or demographic-to-demographic). Node label abbreviations can be found in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0191625#pone.0191625.s005" target="_blank">S4 File</a>.</p