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Jacques RavelInstitute for Genome Sciences
Dept. of Microbiology and ImmunologyUniversity of Maryland School of Medicine
Human Microbiome Science: Vision for the Future - July 24, 2013
The Dynamics of the Microbiome:
The Human Vagina
The dynamics of microbial communities can
be defined as temporal and spatial changes in
community structure or function
DefinitionsDynamics
• In 1997 Grimm and Wissel catalogued 167 definitions of community
stability in the field of ecology
DefinitionsStability/Instability
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.
• 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.
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.
• 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)
• Metabolomics (functional output of a community)
• Metatranscriptomics/metaproteomics (function of a community)
• Culture/physical measurement (i.e., pH), microscopy
DefinitionsMeasures used to study stability/instability
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.
• A disturbance may be defined as an event that disrupts community structure and/or
simultaneously changes resources, substrate availability, or the physical environment.
DefinitionsDisturbance/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.
• 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.
Inte
nsity
Frequency or Duration
DisturbedEcosystem
Ecosystem disturbances/resilience
Increased Risk to Disease?Disease itself? Cause/effects?
Disturbances to the microbiome are often imposed by human actions:- hygiene- diet- behaviors- antibiotics
But also include: - natural states (menstruation)- hormonal variations - immunological changes
What are the drivers of change and resilience in a microbial community?
•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
Issues with current study
•Compositional analysis often done at low taxonomic resolution (Phylum,
Class, Order)
• Principles of community dynamics are certainly body site specific and not
applicable across the spectrum of community types on the human body
The dynamics of the vaginal microbiota in reproductive age women
The Vaginal Community Space
A plot of principle component analysis (PCA) shows distribution of community states in 3-D space.
Graphics generated with inVUE (inVUE.sourceforge.net)
CST III(L. iners)
CST I(L. crispatus)
CST V(L. jensenii)
CST II(L. gasseri)
CST IV
• Five community state types (CST) that differ in their microbial composition and abundance
• Community state type IV lacks significant number of Lactobacillus - higher diversity - (IV-A and IV-B)
• Cross sectional study of 410 asymptomatic healthy women
Longitudinal studies of the vaginal microbiotaStudy 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
0
20
40
60
80
100 Gardnerella vaginalisLactobacillus inersLactobacillus crispatusOthersAtopobium vaginaeBifidobacteriaceaeBVAB2Megasphaera sp type 2Veillonella montpellierensisPeptoniphilusBVAB3Parvimonas micraPeptostreptococcusStreptococcusAerococcus christenseniiFinegoldia magnaPeptoniphilus lacrimalisLactobacillus gasseriMegasphaera sp type 1Lactobacillus jenseniiStreptococcus anginosusBacillalesStreptococcus salivariusBVAB1Streptococcus agalactiaeMobiluncus curtisii
037
10Nugent Score
4567
pH
weeks0 1 2 3 4 5 6 7 8 9 10
Phyl
otyp
e re
lativ
e ab
unda
nce
(%)
0
20
40
60
80
100 Gardnerella vaginalisOthersMegasphaera sp type 2Atopobium vaginaeLactobacillus inersPrevotella genogroup 1Streptococcus agalactiaeAnaerococcusBVAB1BVAB2PeptoniphilusFinegoldia magnaBVAB3PrevotellaClostridialesPrevotella genogroup 2Anaerococcus tetradiusAerococcus christenseniiBacteriaPeptostreptococcusBacillalescandidate division TM7Streptococcus anginosusEnterococcus faecalisPrevotella biviaActinobacteria class
037
10Nugent Score
4567
pH
weeks0 1 2 3 4 5 6 7 8 9 10
Phyl
otyp
e re
lativ
e ab
unda
nce
(%)
0
20
40
60
80
100
L crispatusL inersL otu4L gasseriL vaginalisClostridiumL crispatus 1L jenseniiStreptococcusGemella
Nugent Score
pH
Phyl
otyp
e re
lativ
e ab
unda
nce
(%)
037
10
4567
weeks0 1 2 3 4 5 6 7 8 9 10
Nugent Score
pH
0
20
40
60
80
100
L inersStreptococcusCorynebacteriumFinegoldiaL crispatusStaphylococcusPeptoniphilusAnaerococcusL gasseriL jensenii
Phyl
otyp
e re
lativ
e ab
unda
nce
(%)
037
10
4567
weeks0 1 2 3 4 5 6 7 8 9 10
0
20
40
60
80
100
Gardnerella vaginalisOthersBifidobacteriaceaeAtopobium vaginaeBVAB2Lactobacillus inersAerococcus christenseniiMegasphaera sp type 1Lactobacillus crispatusBifidobacterium bifidumLactobacillus jensenii
037
10Nugent Score
4567
pH
weeks0 1 2 3 4 5 6 7 8 9 10
Phyl
otyp
e re
lativ
e ab
unda
nce
(%)
0
20
40
60
80
100Atopobium vaginaeGardnerella vaginalisBVAB2BVAB3Megasphaera sp type 1OthersMobiluncuscandidate division TM7Parvimonas micraPeptoniphilusBifidobacteriaceaeAnaerococcus tetradiusPrevotella genogroup 1BacteriaFinegoldia magnaAnaerococcus
037
10Nugent Score
567
pH
weeks0 1 2 3 4 5 6 7 8 9 10
Phyl
otyp
e re
lativ
e ab
unda
nce
(%)
weeks
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.
Drivers of instability
Gajer et al. The temporal dynamics of the vaginal microbiota. Science Translational Medicine. 2012. 4(132): 132ra52.
Menstrual cycle (days)
log(6
DJS
6t)
0 7 14 21 28
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Con
cent
ratio
n of
Pro
gest
eron
e [4
x] a
nd E
stra
diol
[pg/
ml]
Menstrual cycle (days)
log(6
DJS
6t)
0 7 14 21 28
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Con
cent
ratio
n of
Pro
gest
eron
e [4
x] a
nd E
stra
diol
[pg/
ml]
Estrogen
Progesterone
The rate of community change is affected by time in the menstrual cycle (natural change) and sexual activities to some extend
Vaginal community dynamics
pH, Nugent score, microbial community state
0-3
4-6
7-10
Nugent Score
CST III(L. iners)
CST I(L. crispatus)
CST V(L. jensenii)
CST II(L. gasseri)
CST IV
4.0-4.5
4.6-5.0
5.1-5.5
>5.5
pH
CST III(L. iners)
CST I(L. crispatus)
CST V(L. jensenii)
CST II(L. gasseri)
CST IV
Ravel et al. The vaginal microbiome of reproductive age women. PNAS. 2011. 108 Suppl 1, 4680–4687.
In these healthy, asymptomatic women, CST IV is associated with higher Nugent scores and higher pH
•At any given time, >25% of women are in a non-Lactobacillus dominated state.
CST IV: an normal state that carry risks?
• 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.
There are windows of higher risk that open and close on a temporal scale
CST IV: an healthy state that carry risks?
Vaginal community dynamics
Community stability/dynamics (frequency and duration of CST IV) might better
represent risks to disease
Low resilience = increased risk
Windows of higher risk that open and close on a temporal scale
Understand the molecular basis of the association between stability and susceptibility
Still, we do not understand the underlying causes for stability/dynamics (frequency and duration of CST IV)
Vaginal community dynamicsWindows of higher risk that open and close on a temporal scale
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 genomicsLactobacillus_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
CH
ARM
198_V2445_020507C
HAR
M110_V 2
CH
ARM
144_V1C
HAR
M180_V2
CH
ARM
170_V2C
HAR
M154_V2
CH
ARM
170_V1414_050806C
HAR
M066_V 1
CH
ARM
066_V2H
MP40_W
6D2
CH
ARM
154_V 1H
MP40_W
5D1
HM
P40_W3D
6H
MP40_W
1D2
HM
P40_W3D
7C
HAR
M068_V 1
HM
P40_W4D
2C
HAR
M148_V 1
EM10W
6D7
VM077
HM
P10_W3D
7H
MP10_W
4D7
VM009
VM177
CH
ARM
140_V 2C
HAR
M148_V2
CH
ARM
140_V1EM
O9W
9D3
EM12W
4D3
CH
ARM
014_V 1VM
255401_030106AYAC
05W3D
7401_022506C
HAR
M150_V 1
CH
ARM
150_V2
L. iners and community stability
60.7
65.6
52.7
94.8
100
100
100 100
66.4
100
100100
100
100
82
100
100
83.556.6
64.8
74.1
82.8
88.5
85
100
100 87.1
89.1
100100
100
100
• Certain kinds of L. iners genomes appear associated with community resilience
to enter CST IV
Drivers community stability
• 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
• Develop predictive models of stability/instability/susceptibility that accounts for health practices
and behaviors.
• 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
• Understand what a shifting microbiome means functionally (role of microbiota and host).
• Translate this information in better diagnostics, prognostics, and treatments - moving toward
personalized medicine
Acknowledgments
Larry Forney
National Institutes of Health NIAID U01 - AI070921- R03-AI061131NIAID UH2 - AI083264 - U19 AI084044
Pawel Gajer
Sara KoenigStacey McCulle
Guoyun Bai
Li Fu
Rebecca Brotman
Zaid Abdo
Maria Schneider
Xia Zhou
Joyce Sakamoto
Melissa Nandy
Hongqiu YangXue Zhong
Bishoy Michael
Bing Ma
Doug Fadrosh
Anup MahurkarUrsel Shütte
Ligia Peralta Reshma Gorle
Matt Settles
Roxana Hickey
Patrik Bavoil
Mishka TerplanEsther Colinetti
Owen White
Jane Schwebke
Molly Flynn
Charles Rivers
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