78
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      Temporal Dynamics of the Human Vaginal Microbiota

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The vaginal microbiome is dynamic, varying over time in composition and function with implications for women’s health.

          What’s Up with Vaginal Microbes?

          The ability to properly identify women at risk of acquiring sexually transmitted infectious diseases or who might suffer from adverse obstetric sequelae is a critical first step in reducing their incidence and the unnecessary use of antibiotics. Currently, patients undergo a clinical examination of the vagina that includes measuring the pH and evaluating the amount and type of discharge and the presence of odor. These criteria are thought to be surrogates for the presence of an “abnormal” vaginal microbiota. Although these kinds of tests, done only once, could be used to diagnose conditions such as bacterial vaginosis, it is debatable whether they are accurate predictors of risk because little is known about how the composition and function of the vaginal microbiome changes over time. Previous studies have established that in healthy asymptomatic women, five types of vaginal microbiota exist that differ in the kinds of microbes they contain. It was thought that each type carries its own risks and particular response to environmental disturbances, such as sexual activity or hygiene practices. In an exciting new study, Gajer and colleagues now describe changes in the identity and abundance of bacteria in the vaginal communities of 32 women by analyzing vaginal samples obtained twice weekly over a 16-week period. The kinds of bacteria present in the samples were identified by classifying thousands of 16 S rRNA gene sequences in each sample using high-throughput next-generation sequencing. The authors further characterized vaginal community function by determining the metabolites produced throughout the 16-week period.

          Gajer and colleagues found that there were five longitudinal patterns of change in vaginal microbial community composition. Moreover, in some women, the vaginal microbial community composition changed markedly and rapidly over time, whereas in others it was relatively stable. Using statistical modeling, the authors showed that the menstrual cycle influenced the stability of the vaginal communities. In many cases, the metabolite profiles indicated that vaginal community function was maintained despite changes in bacterial composition.

          Intervals of increased susceptibility to disease may occur because the vaginal microbiota varies over time. The authors envision that better knowledge of the causes and consequences of these changes to the host will lead to the development of new strategies to manage vaginal microbiomes in ways that promote health and minimize the use of antibiotics.

          Abstract

          Elucidating the factors that impinge on the stability of bacterial communities in the vagina may help in predicting the risk of diseases that affect women’s health. Here, we describe the temporal dynamics of the composition of vaginal bacterial communities in 32 reproductive-age women over a 16-week period. The analysis revealed the dynamics of five major classes of bacterial communities and showed that some communities change markedly over short time periods, whereas others are relatively stable. Modeling community stability using new quantitative measures indicates that deviation from stability correlates with time in the menstrual cycle, bacterial community composition, and sexual activity. The women studied are healthy; thus, it appears that neither variation in community composition per se nor higher levels of observed diversity (co-dominance) are necessarily indicative of dysbiosis.

          Related collections

          Most cited references30

          • Record: found
          • Abstract: not found
          • Article: not found

          QIIME allows analysis of high-throughput community sequencing data.

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Search and clustering orders of magnitude faster than BLAST.

            Biological sequence data is accumulating rapidly, motivating the development of improved high-throughput methods for sequence classification. UBLAST and USEARCH are new algorithms enabling sensitive local and global search of large sequence databases at exceptionally high speeds. They are often orders of magnitude faster than BLAST in practical applications, though sensitivity to distant protein relationships is lower. UCLUST is a new clustering method that exploits USEARCH to assign sequences to clusters. UCLUST offers several advantages over the widely used program CD-HIT, including higher speed, lower memory use, improved sensitivity, clustering at lower identities and classification of much larger datasets. Binaries are available at no charge for non-commercial use at http://www.drive5.com/usearch.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy.

              The Ribosomal Database Project (RDP) Classifier, a naïve Bayesian classifier, can rapidly and accurately classify bacterial 16S rRNA sequences into the new higher-order taxonomy proposed in Bergey's Taxonomic Outline of the Prokaryotes (2nd ed., release 5.0, Springer-Verlag, New York, NY, 2004). It provides taxonomic assignments from domain to genus, with confidence estimates for each assignment. The majority of classifications (98%) were of high estimated confidence (> or = 95%) and high accuracy (98%). In addition to being tested with the corpus of 5,014 type strain sequences from Bergey's outline, the RDP Classifier was tested with a corpus of 23,095 rRNA sequences as assigned by the NCBI into their alternative higher-order taxonomy. The results from leave-one-out testing on both corpora show that the overall accuracies at all levels of confidence for near-full-length and 400-base segments were 89% or above down to the genus level, and the majority of the classification errors appear to be due to anomalies in the current taxonomies. For shorter rRNA segments, such as those that might be generated by pyrosequencing, the error rate varied greatly over the length of the 16S rRNA gene, with segments around the V2 and V4 variable regions giving the lowest error rates. The RDP Classifier is suitable both for the analysis of single rRNA sequences and for the analysis of libraries of thousands of sequences. Another related tool, RDP Library Compare, was developed to facilitate microbial-community comparison based on 16S rRNA gene sequence libraries. It combines the RDP Classifier with a statistical test to flag taxa differentially represented between samples. The RDP Classifier and RDP Library Compare are available online at http://rdp.cme.msu.edu/.
                Bookmark

                Author and article information

                Journal
                Science Translational Medicine
                Sci. Transl. Med.
                American Association for the Advancement of Science (AAAS)
                1946-6234
                1946-6242
                May 02 2012
                May 02 2012
                : 4
                : 132
                Affiliations
                [1 ]Institute for Genome Sciences, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
                [2 ]Department of Microbiology and Immunology, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
                [3 ]Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, MD 21201, USA.
                [4 ]Department of Biological Sciences, University of Idaho, Moscow, ID 83844, USA.
                [5 ]Institute for Bioinformatics and Evolutionary Studies, University of Idaho, Moscow, ID 83844, USA.
                [6 ]Department of Mathematics, University of Idaho, Moscow, ID 83844, USA.
                [7 ]Department of Statistics, University of Idaho, Moscow, ID 83844, USA.
                Article
                10.1126/scitranslmed.3003605
                3722878
                22553250
                a1e244fd-066a-4f84-b2ea-867283865693
                © 2012
                History

                Comments

                Comment on this article