Changes in vaginal community state types reflect major shifts in the microbiome

ABSTRACT Background: Recent studies of various human microbiome habitats have revealed thousands of bacterial species and the existence of large variation in communities of microorganisms in the same habitats across individual human subjects. Previous efforts to summarize this diversity, notably in the human gut and vagina, have categorized microbiome profiles by clustering them into community state types (CSTs). The functional relevance of specific CSTs has not been established. Objective: We investigate whether CSTs can be used to assess dynamics in the microbiome. Design: We conduct a re-analysis of five sequencing-based microbiome surveys derived from vaginal samples with repeated measures. Results: We observe that detection of a CST transition is largely insensitive to choices in methods for normalization or clustering. We find that healthy subjects persist in a CST for two to three weeks or more on average, while those with evidence of dysbiosis tend to change more often. Changes in CST can be gradual or occur over less than one day. Upcoming CST changes and switches to high-risk CSTs can be predicted with high accuracy in certain scenarios. Finally, we observe that presence of Gardnerella vaginalis is a strong predictor of an upcoming CST change. Conclusion: Overall, our results show that the CST concept is useful for studying microbiome dynamics.

[1]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[2]  S. G. Chadwick,et al.  Development and Validation of a Highly Accurate Quantitative Real-Time PCR Assay for Diagnosis of Bacterial Vaginosis , 2016, Journal of Clinical Microbiology.

[3]  Jennifer M. Fettweis,et al.  Differences in vaginal microbiome in African American women versus women of European ancestry. , 2014, Microbiology.

[4]  Jennifer M. Fettweis,et al.  The truth about metagenomics: quantifying and counteracting bias in 16S rRNA studies , 2015, BMC Microbiology.

[5]  Hadley Wickham,et al.  Reshaping Data with the reshape Package , 2007 .

[6]  K. Holmes,et al.  Nonspecific vaginitis: Diagnostic criteria and microbial and epidemiologic associations , 1983 .

[7]  J. Castro,et al.  BV and non-BV associated Gardnerella vaginalis establish similar synergistic interactions with other BV-associated microorganisms in dual-species biofilms. , 2015, Anaerobe.

[8]  Susan Holmes,et al.  phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data , 2013, PloS one.

[9]  Yi‐Hui Zhou,et al.  A Two-Stage Hidden Markov Model Design for Biomarker Detection, with Application to Microbiome Research , 2018, Statistics in biosciences.

[10]  Davis J. McCarthy,et al.  Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation , 2012, Nucleic acids research.

[11]  F. Bushman,et al.  Linking Long-Term Dietary Patterns with Gut Microbial Enterotypes , 2011, Science.

[12]  Jacques Ravel,et al.  Daily temporal dynamics of vaginal microbiota before, during and after episodes of bacterial vaginosis , 2013, Microbiome.

[13]  N. Angelopoulos,et al.  The vaginal microbiome during pregnancy and the postpartum period in a European population , 2015, Scientific Reports.

[14]  Daniel B. DiGiulio,et al.  Microbial Prevalence, Diversity and Abundance in Amniotic Fluid During Preterm Labor: A Molecular and Culture-Based Investigation , 2008, PloS one.

[15]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[16]  Christine L. Sun,et al.  Temporal and spatial variation of the human microbiota during pregnancy , 2015, Proceedings of the National Academy of Sciences.

[17]  J. A. Lambert,et al.  Longitudinal Analysis of Vaginal Microbiome Dynamics in Women with Recurrent Bacterial Vaginosis: Recognition of the Conversion Process , 2013, PloS one.

[18]  Daphne Koller,et al.  Continuous Time Bayesian Networks , 2012, UAI.

[19]  Mardge H. Cohen,et al.  Utility of Amsel Criteria, Nugent Score, and Quantitative PCR for Gardnerella vaginalis, Mycoplasma hominis, and Lactobacillus spp. for Diagnosis of Bacterial Vaginosis in Human Immunodeficiency Virus-Infected Women , 2005, Journal of Clinical Microbiology.

[20]  D. Scharfstein,et al.  The effect of vaginal douching cessation on bacterial vaginosis: a pilot study. , 2008, American journal of obstetrics and gynecology.

[21]  Hadley Wickham,et al.  ggplot2 - Elegant Graphics for Data Analysis (2nd Edition) , 2017 .

[22]  Daniela M. Witten,et al.  Classification and clustering of sequencing data using a poisson model , 2011, 1202.6201.

[23]  S. Garland,et al.  High recurrence rates of bacterial vaginosis over the course of 12 months after oral metronidazole therapy and factors associated with recurrence. , 2006, The Journal of infectious diseases.

[24]  Yi-Hui Zhou,et al.  Hypothesis testing at the extremes: fast and robust association for high-throughput data. , 2014, Biostatistics.

[25]  Raphael Gottardo,et al.  Orchestrating high-throughput genomic analysis with Bioconductor , 2015, Nature Methods.

[26]  J. Marrazzo,et al.  Temporal Variability of Human Vaginal Bacteria and Relationship with Bacterial Vaginosis , 2010, PloS one.

[27]  Elad Eban,et al.  Structured Proportional Jump Processes , 2014, UAI.

[28]  Andy Liaw,et al.  Classification and Regression by randomForest , 2007 .

[29]  D.,et al.  Regression Models and Life-Tables , 2022 .

[30]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[31]  Duy Tin Truong,et al.  MetaPhlAn2 for enhanced metagenomic taxonomic profiling , 2015, Nature Methods.

[32]  Christopher E. McKinlay,et al.  Rethinking "enterotypes". , 2014, Cell host & microbe.

[33]  J. Sobel,et al.  Identification of intrinsically metronidazole-resistant clades of Gardnerella vaginalis. , 2016, Diagnostic microbiology and infectious disease.

[34]  J. Marrazzo,et al.  Molecular identification of bacteria associated with bacterial vaginosis. , 2005, The New England journal of medicine.

[35]  P. Gajer,et al.  Vaginal microbiome of reproductive-age women , 2010, Proceedings of the National Academy of Sciences.

[36]  Georg K Gerber,et al.  The dynamic microbiome , 2014, FEBS letters.

[37]  J. H. Ward Hierarchical Grouping to Optimize an Objective Function , 1963 .

[38]  M A Krohn,et al.  Reliability of diagnosing bacterial vaginosis is improved by a standardized method of gram stain interpretation , 1991, Journal of clinical microbiology.

[39]  C. Huttenhower,et al.  Metagenomic biomarker discovery and explanation , 2011, Genome Biology.

[40]  Jennifer M. Fettweis,et al.  Genomic sequence analysis and characterization of Sneathia amnii sp. nov , 2012, BMC Genomics.

[41]  Gregor Reid,et al.  Characterization of the vaginal microbiota of healthy Canadian women through the menstrual cycle , 2014, Microbiome.

[42]  Jennifer M. Fettweis,et al.  The Vaginal Microbiome: Disease, Genetics and the Environment , 2010, Nature Precedings.

[43]  Christopher H. Jackson,et al.  Multi-State Models for Panel Data: The msm Package for R , 2011 .

[44]  P. Bork,et al.  Enterotypes of the human gut microbiome , 2011, Nature.

[45]  Jean M. Macklaim,et al.  Comparative meta-RNA-seq of the vaginal microbiota and differential expression by Lactobacillus iners in health and dysbiosis , 2013, Microbiome.

[46]  Katherine H. Huang,et al.  Structure, Function and Diversity of the Healthy Human Microbiome , 2012, Nature.

[47]  Wenguang Sun,et al.  Large‐scale multiple testing under dependence , 2009 .

[48]  W. Huber,et al.  Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , 2014, Genome Biology.

[49]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[50]  Jennifer M. Fettweis,et al.  The changing landscape of the vaginal microbiome. , 2014, Clinics in laboratory medicine.

[51]  H. L. Gardner,et al.  Haemophilus vaginalis vaginitis: a newly defined specific infection previously classified non-specific vaginitis. , 1955, American journal of obstetrics and gynecology.

[52]  Xavier Robin,et al.  pROC: an open-source package for R and S+ to analyze and compare ROC curves , 2011, BMC Bioinformatics.

[53]  Zaid Abdo,et al.  Temporal Dynamics of the Human Vaginal Microbiota , 2012, Science Translational Medicine.