The Dynamics of the Microbiome: The Human Vagina

Jacques Ravel Institute for Genome Sciences Dept. of Microbiology and Immunology University of Maryland School of Medicine

Human Microbiome Science: Vision for the Future - July 24, 2013

Definitions Dynamics

The dynamics of microbial communities can be defined as temporal and spatial changes in community structure or function Definitions Stability/Instability

• In 1997 Grimm and Wissel catalogued 167 definitions of community stability in the field of ecology

resistance (or constancy) – staying essentially unchanged over time resilience – returning to the reference state after a disturbance persistence – persistence of an ecological state through time.

• Stability quantifies the extend to which a community “stays the same” over a long period of time (depending on the time scale) or in the face of some disturbances.

V. Grimm and C. Wissel. Babel, or the ecological stability discussions: an inventory and analysis of terminology and a guide for avoiding confusion. Oecologia, 109:323–334, 1997.

Definitions Measures used to study stability/instability

• Culture/physical measurement (i.e., pH), microscopy

• Microbiota compositional change (16S rRNA gene survey) - Relative abundance

• Microbiota compositional change (species specific qPCR - pan-bacterial qPCR)

• Estimates of diversity (evenness and richness, relative entropy)

• Metagenomic characterization (microbiome: genes/genomes content, SNP)

• Functional analysis of metagenomic data (metabolic potential)

• Metatranscriptomics/metaproteomics (function of a community)

• Metabolomics (functional output of a community)

Communities can appear stable given one metrics and unstable given another one.

McNulty, N. P., Yatsunenko, T., Hsiao, A., Faith, J. J., Muegge, B. D., Goodman, A. L., et al. (2011). The impact of a consortium of fermented milk strains on the gut microbiome of gnotobiotic mice and monozygotic twins. Science translational medicine, 3(106), 106ra106. Definitions Disturbance/Resilience

• A disturbance may be defined as an event that disrupts community structure and/or simultaneously changes resources, substrate availability, or the physical environment.

• A disturbance can also be defined as a killing, displacement, or damage to one or more populations in a community that directly or indirectly creates opportunities for new individuals to become established.

• Resilience is the amount of disturbance that an ecosystem can withstand without changing its self-organizing processes.

• Communities with fundamental differences in species composition and structure will differ in resilience.

Putman, R.J. (1994). Community Ecology (Chapman and Hall, New York). Ma, Z. S., Geng, J. W., Abdo, Z., & Forney, L. J. (2012). A Bird’s Eye View of Microbial Community Dynamics. Microbial Ecology Theory: Current Perspectives. Norwich, UK: Horizon Scientific Press, 57–70.

Ecosystem disturbances/resilience

Disturbed Increased Risk to Disease? Ecosystem Disease itself? Cause/effects?

Disturbances to the microbiome are often imposed by human actions: - hygiene - diet Intensity - behaviors - antibiotics

But also include: - natural states (menstruation) - hormonal variations - immunological changes Frequency or Duration

What are the drivers of change and resilience in a microbial community? Issues with current study

• While key studies, most looked at either low number of time points (short sampling period, long period between samples) or very few subjects (sampled frequently) - Difficulties in generalizing the findings/conclusions • Feasibility and cost have limited these studies

• Principles of community dynamics are certainly body site specific and not applicable across the spectrum of community types on the human body

• Compositional analysis often done at low taxonomic resolution (Phylum, Class, Order)

The dynamics of the vaginal microbiota in reproductive age women The Vaginal Community Space

CST V (L. jensenii) • Cross sectional study of 410 asymptomatic healthy women

• Five community state types (CST) that differ in their microbial CST IV composition and abundance CST II (L. gasseri) • Community state type IV lacks significant number of Lactobacillus - higher diversity - (IV-A and IV-B)

CST I (L. crispatus) CST III (L. iners) A plot of principle component analysis (PCA) shows distribution of community states in 3-D space. Graphics generated with inVUE (inVUE.sourceforge.net)

Longitudinal studies of the vaginal microbiota Study design

160 women cohort - prospective longitudinal study

• Self-collect vaginal smears, pH and swabs daily for 10 weeks • Three swabs a day: Amies, RNAlater and dry • Daily diary and weekly clinic visits • Clinical assessment at enrollment, week 5 and 10 • Determine bacterial community composition by 454 pyrosequencing of barcoded V1-V3 16S rRNA gene of 50 women Longitudinal profiles - stability/instability

100 100 L crispatus L iners e (%) e (%) 80 L iners 80 L otu4 Corynebacterium L gasseri 60 60 L vaginalis L crispatus e abundan c e abundan c ti v ti v 40 L crispatus 1 40 el a L jensenii el a Peptoniphilus Streptococcus Anaerococcus ype r 20 ype r 20 L gasseri ylo t ylo t h h L jensenii P P 0 0

10 10 7 Nugent Score 7 Nugent Score 3 3 0 0 7 7 6 pH 6 pH 5 5 4 4

0 1 2345 6789 10 0 1 2345 6789 10 weeks weeks weeks

100 100 Atopobium vaginae Gardnerella vaginalis Gardnerella vaginalis

BVAB2 e (%) Others 80 80 e (%) BVAB3 Bifidobacteriaceae Megasphaera sp type 1 Atopobium vaginae Others BVAB2 60 60 Mobiluncus Lactobacillus iners candidate division TM7 e abundan c e abundan c

ti v Aerococcus christensenii

ti v Parvimonas micra Megasphaera sp type 1 Peptoniphilus el a 40 el a 40 Lactobacillus crispatus Bifidobacteriaceae ype r Bifidobacterium bifidum

ype r Anaerococcus tetradius Lactobacillus jensenii ylo t

Prevotella genogroup 1 h 20 ylo t

20 P

h P Finegoldia magna Anaerococcus 0

0 10 10 7 7 Nugent Score Nugent Score 3 3 0 0 7 7 6 pH 6 pH 5 5 4

012 345 6789 10 012 345 6789 10 weeks weeks

100 100 Gardnerella vaginalis Gardnerella vaginalis Others Lactobacillus iners Megasphaera sp type 2 Lactobacillus crispatus Atopobium vaginae Others 80 Lactobacillus iners 80 Atopobium vaginae e (%) e (%) Prevotella genogroup 1 Bifidobacteriaceae BVAB2 Anaerococcus Megasphaera sp type 2 60 BVAB1 60 Veillonella montpellierensis Peptoniphilus BVAB2 e abundan c Peptoniphilus e abundan c BVAB3 ti v Finegoldia magna

ti v Parvimonas micra

Peptostreptococcus el a 40 BVAB3

el a 40 Streptococcus Prevotella Aerococcus christensenii Clostridiales ype r

ype r Finegoldia magna Prevotella genogroup 2

Peptoniphilus lacrimalis ylo t 20 Anaerococcus tetradius h ylo t 20 Lactobacillus gasseri Aerococcus christensenii P h

P Megasphaera sp type 1 Bacteria Lactobacillus jensenii Streptococcus anginosus 0 Bacillales 0 Bacillales candidate division TM7 Streptococcus salivarius Streptococcus anginosus BVAB1 faecalis 0 1 2345 6789 10 Streptococcus agalactiae 0 1 2345 6789 10 Prevotella bivia weeks Mobiluncus curtisii weeks class 10 10 7 Nugent Score 7 Nugent Score 3 3 0 0 7 7 6 pH 6 pH 5 5 4 4

Drivers of instability

Modeling the dependence of the log of Jensen-Shannon divergence rate of change (estimate of stability) on the menstrual time (normalized time) - Bayesian Markov Chain Monte Carlo methods using linear mixed effect models.

Estrogen 200200 ) ) t t JS JS D D ( ( log log

100100

Concentration of Progesterone [4x] and Estradiol [pg/ml] Progesterone Concentration of Progesterone [4x] and Estradiol [pg/ml] 0 7 1414 2121 2828 Menstrual cycle (days)

The rate of community change is affected by time in the menstrual cycle (natural change) and sexual activities to some extend

Gajer et al. The temporal dynamics of the vaginal microbiota. Science Translational Medicine. 2012. 4(132): 132ra52. Vaginal community dynamics

pH, Nugent score, microbial community state

0-3 Nugent Score CST V CST V 4.0-4.5 pH 4-6 (L. jensenii) (L. jensenii) 4.6-5.0 7-10 5.1-5.5 >5.5

CST IV CST II CST IV CST II (L. gasseri) (L. gasseri)

CST I CST I (L. crispatus) CST III (L. crispatus) CST III (L. iners) (L. iners)

In these healthy, asymptomatic women, CST IV is associated with higher Nugent scores and higher pH

Ravel et al. The vaginal microbiome of reproductive age women. PNAS. 2011. 108 Suppl 1, 4680–4687. CST IV: an normal state that carry risks?

• At any given time, >25% of women are in a non-Lactobacillus dominated state.

• This state is associated with higher Nugent scores and higher pH

• High Nugent score is strongly associated with increased risk of sexually transmitted infection acquisition and transmission, including HIV, as well as preterm birth

• These women are asymptomatic and apparently healthy, but potentially at increased risk of STI or other adverse outcomes

Wiesenfeld HC, et al. : Bacterial vaginosis is a strong predictor of Neisseria gonorrhoeae and Chlamydia trachomatis infection. Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 2003, 36(5):663-668. Taha TE, et al. : Bacterial vaginosis and disturbances of vaginal flora: association with increased acquisition of HIV. AIDS (London, England) 1998, 12(13):1699-1706. Cohen, C. R. et al. (2012). Bacterial Vaginosis Associated with Increased Risk of Female-to-Male HIV-1 Transmission: A Prospective Cohort Analysis among African Couples. (H. Watts, Ed.)PLoS Medicine, 9(6), e1001251.

CST IV: an healthy state that carry risks?

There are windows of higher risk that open and close on a temporal scale Vaginal community dynamics Windows of higher risk that open and close on a temporal scale

Community stability/dynamics (frequency and duration of CST IV) might better represent risks to disease

Low resilience = increased risk

Vaginal community dynamics Windows of higher risk that open and close on a temporal scale

Still, we do not understand the underlying causes for stability/ dynamics (frequency and duration of CST IV)

Understand the molecular basis of the association between stability and susceptibility Stability and the community genome

Is there a correlation between the gene content of some of the species and community stability?

Use metagenomics analysis of microbial communities Establish community composition and assemble the genomes of community members Perform comparative genome analysis with known genomes

Metagenomics analysis: L. iners genomics

Lactobacillus_iners Gardnerella_vaginalis Lactobacillus_jensenii Lactobacillus_crispatus Atopobium_vaginae Bifidobacterium_unclassified Prevotella_bivia Prevotella_timonensis Lactobacillus_gasseri Streptococcus_agalactiae Prevotella_buccalis Prevotella_amnii Peptoniphilus_unclassified Streptococcus_mitis Anaerococcus_tetradius Lactobacillus_brevis Peptoniphilus_harei Bacteroides_unclassified Candidatus_Zinderia_unclassified Dialister_microaerophilus Finegoldia_magna Corynebacterium_amycolatum Anaerococcus_unclassified Mobiluncus_mulieris Peptostreptococcus_anaerobius Candidatus_Carsonella_ruddii Leuconostoc_unclassified Lactobacillus_vaginalis Ureaplasma_parvum Mycoplasma_hominis CHARM150_V CHARM198_V 445_020507 CHARM110_V CHARM144_V CHARM180_V CHARM170_V CHARM154_V CHARM170_V 414_050806 CHARM066_V CHARM066_V HMP40_W6D CHARM154_V HMP40_W5D HMP40_W3D HMP40_W1D HMP40_W3D CHARM068_V HMP40_W4D CHARM148_V EM10W6D 7 VM077 HMP10_W3D HMP10_W4D VM009 VM177 CHARM140_V CHARM148_V CHARM140_V EMO9W9D3 EM12W4D 3 CHARM014_V VM255 401_030106 AY 401_022506 CHARM150_V AC 05W3D 7 2 1 6 2 7 2 7 7 2 2 1 2 2 2 1 1 2 1 1 1 2 2 1 1 1 2 L. iners and community stability

100

74.1

100 64.8

100 100 100 83.5 100 56.6 100 88.5 82 100 82.8 85

60.7 100 87.1 65.6 89.1 52.7 100 100 66.4 94.8 100

100 100

100

100 100

Drivers community stability

• Certain kinds of L. iners genomes appear associated with community resilience to enter CST IV

• Does the “fragility” of a single keystone species drive the community into dysbiotic states that carry risk to disease?

• We hypothesize that vaginal microbiome stability/dynamics (frequency and duration of CST IV) might better represent estimates of risks to infection and that stability/instability can be driven by the lack of resilience of certain keystone Lactobacillus sp. Gaps and Challenges

• The issue of cause or effects - Need for prospective longitudinal studies

• Better understand the drivers of dynamics/stability or instability in health (risk to disease) over a women’s lifespan

• Understand what a shifting microbiome means functionally (role of microbiota and host).

• Need to evaluate the dynamics using different measures (metabolomics, metatranscriptomics [host and microbes] and immunological)

• Expand the studies to dynamics of phages, viruses and fungi

• Develop predictive models of stability/instability/susceptibility that accounts for health practices and behaviors. • Translate this information in better diagnostics, prognostics, and treatments - moving toward personalized medicine

Acknowledgments

Pawel Gajer Bing Ma Ligia Peralta Reshma Gorle Rebecca Brotman Doug Fadrosh Larry Forney Mishka Terplan Stacey McCulle Sara Koenig Zaid Abdo Esther Colinetti Guoyun Bai Joyce Sakamoto Maria Schneider Li Fu Melissa Nandy Xia Zhou Bishoy Michael Hongqiu Yang Jane Schwebke Xue Zhong Charles Rivers Ursel Shütte Anup Mahurkar Owen White Molly Flynn Matt Settles Roxana Hickey

Patrik Bavoil National Institutes of Health NIAID U01 - AI070921- R03-AI061131 NIAID UH2 - AI083264 - U19 AI084044