Charting the function of microbesand microbial communities

Curtis Huttenhower

11-17-11Harvard School of Public HealthDepartment of Biostatistics

Valm et al, PNAS 2011

What to do with your metagenome?

3

Diagnostic or prognostic

biomarker for host disease

Public health tool monitoring

population health and interactions

Comprehensive snapshot of

microbial ecology and evolution

Reservoir of gene and protein

functional information

Who’s there?What are they doing?

Who’s there varies: your microbiota is plastic and personalized.

This personalization is true at the level of phyla, genera, species, strains, and

sequence variants.

What they’re doing is adapting totheir environment:

you, your body, and your environment.

The NIH Human Microbiome Project (HMP):A comprehensive microbial survey

• What is a “normal” human microbiome?• 300 healthy human subjects• Multiple body sites

• 15 male, 18 female• Multiple visits• Clinical metadata

www.hmpdacc.org

Slides by Dirk Gevers

A three-tier study design…

16S WGS

ref

…for mining metagenomic data

contigs

pathways

~100M readsper sample

Assembly

Annotation

Map on

~50%

~90M proteins

16S WGS

Filtering/trimming

Chimera removal

>3k readsper sample

BLASTagainst

functionalDBs

Organismal censusat different taxonomic levels

ref

Taxonomicclassification

(RDP)

Clusteringinto OTUs

census...

~36%

~57%

genes

Buccal mucosa Tongue dorsum Supragingival plaque

Stool Posterior fornix Anterior nares Retroauricular crease

0

0.02

0.04

0.06

0.08

0.1

0.12 Gardnerella vaginalisAlistipes putredinisGemella haemolysansActinomyces odontolyticusCapnocytophaga sputigenaCapnocytophaga gingivalisCapnocytophaga ochraceaEikenella corrodensBurkholderiales bacteriumPropionibacterium acnesParvimonas micraPorphyromonas gingivalisProteus mirabilisStreptobacillus moniliformisAtopobium rimaeUreaplasma urealyticumEggerthella lentaProteus penneriArcobacter butzleriSalmonella entericaNocardia farcinicaCryptobacterium curtumAv

erag

e Re

lativ

e Ab

unda

nce

“Pathogen” carriage varies a lot

7

Gardnerella

Alistipes

Capnocytophaga

Actinomyces

Gemella

22 ***uniquely identifiable*** nonzero abundance “pathogens” from NIAID’s list of 135

+Propionibacterium>0.66

00.020.040.060.080.1

0.120.14

Supragingival plaqueCapnocytophaga gingivalisCapnocytophaga sputigenaCapnocytophaga ochracea

124 Samples

Rela

tive

Abun

danc

e

00.050.1

0.150.2

0.250.3

0.350.4

Stool

Alistipes putredinis

146 Samples

Rela

tive

Abun

danc

e

0

0.2

0.4

0.6

0.8

1

Posterior fornix

Gardnerella vaginalis

60 Samples

Rela

tive

Abun

danc

e

8

Phenotypes that explain variation(or not) can be surprising

Nor

mal

ized

rela

tive

abun

danc

e

9

Phenotypes that explain variation(or not) can be surprising

Nor

mal

ized

rela

tive

abun

danc

e

10

Phenotypes that explain variation(or not) can be surprising

Nor

mal

ized

rela

tive

abun

danc

e

GeneexpressionSNPgenotypes

A functional perspective on thehuman microbiome

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Healthy/IBDBMIDiet

Taxon abundancesEnzyme family abundancesPathway abundances

Functional seq.KEGG + MetaCYC

CAZy, TCDB,VFDB, MEROPS…

100 subjects1-3 visits/subject~7 body sites/visit

10-200M reads/sample100bp reads

Metagenomic reads

Enzymes and pathways

?

HUMAnNHMP Unified Metabolic

Analysis Networkhttp://huttenhower.sph.harvard.edu/humann

BLAST

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HUMAnN: Metabolic reconstruction

Pathway coverage Pathway abundance

← Samples →←

Pat

hway

s→

Vaginal Skin NaresGut Oral (SupP)Oral (BM) Oral (TD)

← P

athw

ays→

← Samples →

Vaginal Skin Nares GutOral (SupP) Oral (BM) Oral (TD)

← Subjects →

← P

athw

ay a

bund

ance

→←

Phy

loty

pe a

bund

ance

→

A portrait of the healthy human microbiome:Who’s there vs. what they’re doing

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Vaginal SkinNares Gut Oral (SupP)Oral (BM) Oral (TD)

← P

hylo

type

abu

ndan

ce →

← Subjects →

← P

athw

ay a

bund

ance

→

← P

athw

ay a

bund

ance

→

← ~700 HMP communities→

Niche specialization in human microbiome function

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Metabolic modules in theKEGG functional catalogenriched at one or more

body habitats

• 16 (of 251) modules strongly “core” at 90%+ coverage in 90%+ individuals at 7 body sites

• 24 modules at 33%+ coverage• 71 modules (28%) weakly “core” at 33%+ coverage in 66%+ individuals at 6+ body sites• Contrast zero phylotypes or OTUs meeting this threshold!• Only 24 modules (<10%) differentially covered by body site• Compare with 168 modules (>66%) differentially abundant by body site

Proteoglycan degradationby the gut microbiota

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AA coreGlycosaminoglycans(Polysaccharide chains)

Proteoglycan degradation:From pathways to enzymes

16

10-310-8

Enzyme relative abundance

• Heparan sulfate degradation

missing due to the absence of

heparanase, a eukaryotic enzyme

• Other pathways not bottlenecked

by individual genes

• HUMAnN links microbiome-wide

pathway reconstructions →

site-specific pathways →

individual gene families

Patterns of variation in human microbiome function by niche

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Patterns of variation in human microbiome function by niche

18

• Three main axes of variation

• Eukaryotic exterior• Low-diversity vaginal• Gut metabolism• Oral vs. tooth hard

surface• Only broad patterns:

every human-associated habitat

is functionally distinct!

Normal varies a lot at the genus level (16S)

200 subjects

Bacteroides

AlistipesFaecalibacterium

Parabacteroides

343 genera

Rela

tive

frequ

ency

Relative frequency of genera within Stool

Dirk Gevers

Bacteroides vulgatus

Bacteroides sp.

Bacteroides uniformis

Bacteroides sp.Bacteroides stercorisBacteroides caccae

Relative frequency of Bacteroides species within Stool

123 samples

Rela

tive

frequ

ency

Normal varies a lot at the species level (WGS)

Dirk Gevers

What’s wrong with this picture?

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Lactobacillus crispatus MV-1A-USLactobacillus crispatus JV-V01Lactobacillus crispatus 125-2-CHNLactobacillus crispatus 214-1Lactobacillus crispatus MV-3A-USLactobacillus crispatus ST1Lactobacillus gasseri JV-V03Lactobacillus gasseri 202-4Lactobacillus gasseri 224-1Lactobacillus gasseri MV-22Bifidobacterium breve DSM 20213Bifidobacterium dentium ATCC 27679Mycoplasma hominisClostridiales genomosp BVAB3 str UPII9-5Clostridiales genomosp BVAB3 UPII9-5Gardnerella vaginalis AMDPrevotella timonensis CRIS 5C-B1Megasphaera genomosp type 1 str 28LPorphyromonas uenonis 60-3Gardnerella vaginalis 409-05Gardnerella vaginalis 5-1Atopobium vaginae DSM 15829Gardnerella vaginalis ATCC 14019Lactobacillus jensenii 1153Lactobacillus jensenii 269-3Lactobacillus jensenii SJ-7A-USLactobacillus jensenii 208-1Lactobacillus jensenii JV-V16Lactobacillus jensenii 27-2-CHNLactobacillus jensenii 115-3-CHNLactobacillus iners AB-1Lactobacillus iners DSM 13335

52 posterior fornix microbiomes →

Species and strains matter – but so does your method for

identifying them in a community!

Core gene families

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Gene X is a core gene for Clade Y

All subclades of Clade Y must have Gene X as core gene (strict definition)

Gene X may be a core gene of several (unrelated) clades

We have to relax the definition for taking into account:• Low-level gene losses• Sequencing errors• Gene calls errors

Gene XA core gene is a gene strongly conserved within a clade

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Examples of core genes

Clade-specific marker genes

24

Gene XGene X is a marker gene (for Clade Y) if X is a core gene for Y and X never appears outside Clade Y

Examples of marker genes

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The BactoChip: high-throughput microbial species identification

With Olivier Jousson, Annalisa Ballarini

27

BactoChip: detecting single speciesWith Olivier Jousson, Annalisa Ballarini

MetaPhlAn: inferring microbial abundancesfrom metagenomic data using marker genes

28

• Map metagenomic reads to marker genes to infer microbial abundances– Normalizing for copy number, gene length, etc.

Much faster than existing approaches as the marker gene database is ~50 times smaller than the whole microbial sequence DB

Few hours instead of weeks for Illumina samples with 100Gb of sequence data

MetaPhlAn: Metagenomic Phylogenetic Analysishttp://huttenhower.sph.harvard.edu/metaphlan

MetaPhlAn: synthetic validation on log-normal abundances

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Summary of 8 synthetic communities composed by 2M reads coming from 200 organisms with log-normal distributed abundances concentrations

Species-level Class-levelSpecies level Class level

Matching 16S and more

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The human microbiome atspecies-level resolution

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Whence enterotypes?

32

Gen

era

Spec

ies

Microbial community function and structure in the human microbiome: the story so far?

• Who’s there varies even in health– What they’re doing doesn’t (as much)– Both correlate with niche– By the way: both change during disease and treatment

• There are patterns in this variation– Function correlates with membership and phenotype– “Pathogenicity” correlates with lower prevalence– Membership means species, strains, or variants– Patterns aren’t always as simple as enterotypes

• ~1/3 to 2/3 of human metagenome characterized– Job security!

33

Ask both what you can do for your microbiomeand what your microbiome can do for you

Wendy GarrettMichelle Rooks

Ramnik XavierHarry Sokol

Thanks!

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Nicola Segata Levi Waldron

Fah Sathira

Human Microbiome Project

HMP Metabolic Reconstruction

Owen WhiteGeorge WeinstockKaren NelsonJoe PetrosinoMihai PopPat SchlossMakedonka MitrevaErica SodergrenVivien Bonazzi Jane PetersonLita Proctor

Sahar AbubuckerYuzhen Ye

Beltran Rodriguez-MuellerJeremy ZuckerQiandong Zeng

Mathangi ThiagarajanBrandi Cantarel

Maria RiveraBarbara Methe

Bill KlimkeDaniel Haft

Dirk Gevers

Bruce Birren Mark DalyDoyle Ward Eric AlmAshlee Earl Lisa Cosimi

http://huttenhower.sph.harvard.edu

Joseph Moon

VagheeshNarasimhan

Tim Tickle

Xochi Morgan

Josh Reyes

Jeroen RaesKaroline Faust

Jacques Izard

Olivier JoussonAnnalisa Ballarini

Linking function to community composition

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← T

axa

and

corr

elat

ed m

etab

olic

pat

hway

s →

← 52 posterior fornix microbiomes →

F-type ATPase, THF

Sugar transport

Phosphate and peptide transport

AA and small molecule biosynthesis

Embden-Meyerhof glycolysis, phosphotransferases

Eukaryotic pathways

Plus ubiquitous pathways: transcription, translation, cell wall, portions of central carbon metabolism…

Lactobacillus crispatus

Lactobacillus jensenii

Lactobacillus gasseri

Lactobacillus iners

Gardnerella/Atopobium

Candida/Bifidobacterium

Linking communities to host phenotype

38

Nor

mal

ized

rela

tive

abun

danc

e

Vaginal pH (posterior fornix)

Body Mass Index

Top correlates with BMI in stool

Vaginal pH, community metabolism, and community composition represent a strong, direct link between

phenotype and function in these data.Vaginal pH (posterior fornix)