Environmental impacts on the internal ecosystem: Antibiotics and the

human gut microbiota

Les Dethlefsen Relman Lab

dethlefs@stanford.edu

Superfund Research Program Annual Meeting Lexington, Kentucky

October 24-25, 2011

Start at the beginning...

Complex histories of colonization and succession...

Figure 6. Temporal Patterns in Average Pairwise Similarity of Infant Stool Microbiota Profiles (A) Similarity between infants over time. For each time point for which at least six babies were profiled, we calculated the mean pairwise Pearson correlation between the level 4 taxonomic profiles of all babies at that time point. The mean pairwise Pearson correlation between these profiles in 18 adult participants in this study (nine males and nine females) is also shown (open circle indicated by the arrow). (B) Progression towards adult-like flora over time. For each time point for which at least four babies were profiled, we calculated the mean Pearson correlation between the level 4 taxonomic profiles of all babies at that

Figure 7. Temporal Profiles of the Most Abundant Level 3 Taxonomic Groups time point and a ‘‘generic adult’’ profile. The generic adult profile is the Level 3 taxonomic groups were selected for display if their mean (normalized) relative abundance across all baby samples was greater than 1%. The x- centroid of 18 (nine male and nine female) adults (parents in this study). axis indicates days since birth and is shown on a log scale, and the y-axis shows estimated (normalized) relative abundance. For some babies, no values doi:10.1371/journal.pbio.0050177.g006 are plotted for the first few days because the total amount of bacteria in the stool samples collected on those days was insufficient for microarray-based analysis. doi:10.1371/journal.pbio.0050177.g007

Palmer et al., PLoS Biology, 2007

...lead to stable, strain-diverse, individualized communities...

y-is ree of his lo-n­ear i-o-are 1. ct or ce

Eckburg et al., Science, 2005 Dethlefsen & Relman, PNAS, 2010

...which are ecologically diverse and interdependent.

FIG. 2. Nutritional interactions between polysaccharide degrading microorganisms in the rumen. This figure has been modified from Flint (1997), with permission.

Flint et al., Adv. Appl. Microbiol., 2004

But the real beginning was somewhat earlier...

National Geographic

a Bacterial phylum b COG categories100

Rel

ativ

e ab

unda

nce

(%) 80

60

40

20

0

F1T1

LeF1

T2Le

F1M

Ov

F2T1

LeF2

T2Le

F2M

Ob

F3T1

LeF3

T2Le

F3M

Ov

F4T1

Ob

F4T2

Ob

F4M

Ob

F5T1

Ob

F5T2

Ob

F5M

Ov

F6T1

Ob

F6T2

Ob

F6M

Ob

Firmicutes Bacteroidetes Actinobacteria [Q] [H] [G] [R] [W] [M] [Y] [L] [J] Proteobacteria Other [P] [F] [C] [O] [Z] [T] [D] [K]

[I] [E] [S] [U] [N] [V] [B] [A]

F1T1

LeF1

T2Le

F1M

Ov

F2T1

LeF2

T2Le

F2M

Ob

F3T1

LeF3

T2Le

F3M

Ov

F4T1

Ob

F4T2

Ob

F4M

Ob

F5T1

Ob

F5T2

Ob

F5M

Ov

F6T1

Ob

F6T2

Ob

F6M

Ob

Figure 3 | Comparison of taxonomic and functional variations in the human gut microbiome. a, Relative abundance of major phyla across 18 faecal microbiomes from monozygotic twins and their mothers, based on BLASTX comparisons of microbiomes and the National Center for Biotechnology Information non-redundant database. b, Relative abundance of categories of genes across each sampled gut microbiome (letters correspond to categories in the COG database).

a

PC1

PC2

DA.AD.1

DA.AD.4

ES.AD.1

ES.AD.2

ES.AD.3

FR.AD.3

FR.AD.6

IT.AD.4

JP.AD.1

JP.AD.4

JP.AD.6

JP.AD.7

JP.AD.8

JP.AD.9

AM.F10.T2

DA.AD.2 DA.AD.3

ES.AD.4

FR.AD.1

FR.AD.2

FR.AD.4

FR.AD.5

FR.AD.7

FR.AD.8

IT.AD.2

IT.AD.3

IT.AD.5

IT.AD.6

JP.AD.2

JP.AD.3 JP.AD.5

IT.AD.1AM.F10.T1

RoseburiaRoseburia

BacteroidesBacteroides

PrevotellaPrevotellaAkkermansiaAkkermansia

RuminococcusRuminococcus

AlistipesAlistipes

Roseburia

Bacteroides

Prevotella Akkermansia

Ruminococcus

Alistipes

Obese

IBD

Phylogenetic diversity but overall functional similarity...

Arumugam et al., Nature, 2011 Turnbaugh et al., Nature, 2008

...but diet appears to drive some community differences.

De Filippo et al., PNAS, 2010 Wu et al., Science, 2011

Actually the real beginning was even earlier...

Actually the real beginning was even earlier...

Actually the real beginning was even earlier...

Terrestrial Vertebrates

Gut Microbiota

Human-microbe mutualism

Humans get

• Resources

• Pathogen resistance

• Developmental signals

• Immune regulation

• Metabolic regulation

• Cometabolism

Microbes get

• Resources

• Habitat

• Dispersal

Immune regulation

Hill et al., Mucosal Immunol., 2010

Metabolic regulation

Björkholm et al., PLoS One, 2009

Cometabolism

Martin et al., J. Proteome Res., 2008

Rhinos and oxpeckers, a no-cost mutualism...

Some human-microbe mutualisms are costly...

Anti-pathogen bacteroicin activity

Figure 4 Model of how Bacteroides thetaiotaomicron induces production of Fucα1,2Galβ-ng glycans in the distal intestinal epithelium. L-fucose induces the bacterial fucose

that encodes FucR plus the enzymes involvedycansin fucose metabolism (note n operonHost fucosylation of glse permease is constitutively expressed and is not a member of the operon). In this ucR also functions as a corepressor at another locus, control of signal production

rptive enterocytes. These glycans are postulated to function as a nutrient etaiotaomicron.

...driven by a shared evolutionary fate.

Hooper et al., Ann. Rev. Nutrition 2002; Lux et al., J. Bact., 2007

Gut microbes share our fate because...

• Transmission to next generation easy unused resources open niches repeated opportunities

• Transmission within generations hard niches filled retention of existing community newcomers at numerical disadvantage

--

---

...which permits community-level selection on human + microbita.

Costly mutualism...

• Exploited by cheaters

- loss of mutualism gives higher microbial fitness within a host

• Supported by community selection on fitter hosts with mutualists

- acts across host generations

...equilibrium between cheater and mutualist strains.

Context of perturbation...

• Potential loss of mutualist functions

- specific pathogen interference

- immune & hormonal regulation

- some cometabolic interactions

• vertical, horizontal transmission

- weaker community selection

Antibiotics

• -

•--

•--

235 million doses annually in U.S.

perhaps half unnecessary

Acute adverse effects

antibiotic associated diarrhea

pseudomembranous colitis

Chronic adverse effects

allergies, atopic disease

??

Study design

Amplify V1-V3 region 454 Titanium 16S rDNA Pyrosequencing

3 subjects

Study design

Amplify V1-V3 region 454 Titanium 16S rDNA Pyrosequencing

Randomly sheared 454 Titanium genomic DNA Pyrosequencing

2 subjects

16S rDNA

Diversity over tim e

PNAS, 2010Dethlefsen & Relman,

16S rDNA

Heat map o f taxon

abundance

Dethlefsen & Relman, PNAS, 2010

16S rDNA Heat map of taxon abundance D E F

Clo

s. X

IVa

Clo

s. IV

Blautia

MarvinbrantiaMoreyella

Lachnospira Coprococcus II

Roseburia

Faecalibacterium Subdoligranulum Ruminococcus I Ruminococcus II

Oscillobacter Oscillospira

Coprococcus I

Dethlefsen & Relman, PNAS, 2010

16S rDNA dbRDA of Bray-Curtis distances

Dethlefsen & Relman, PNAS, 2010

16S rDNA dbRDA of Bray-Curtis distances

Dethlefsen & Relman, PNAS, 2010

16S rDNA Bray-Curtis distance

relative to last day pre-C p

Sampling day

16S rDNA Bray-Curtis distance

relative to last day pre-Cp

Sampling day

Metagenomic

Species-level taxonomic assignments pre-Cp

PCO1 - 39.0%

PCO

2 -

22.6

%

D D-Cp E E-Cp

Metagenomic Species-level taxonomic assignments

1st Cp

PCO1 - 39.0%

PCO

2 -

22.6

%

D D-Cp E E-Cp

Metagenomic

Species-level taxonomic assignments Interim

PCO1 - 39.0%

PCO

2 -

22.6

%

D D-Cp E E-Cp

Metagenomic Species-level taxonomic assignments

2nd Cp

PCO1 - 39.0%

PCO

2 -

22.6

%

D D-Cp E E-Cp

Metagenomic

Species-level taxonomic assignments Post

PCO1 - 39.0%

PCO

2 -

22.6

%

D D-Cp E E-Cp

Species level taxonomy - Pre 0.25

0.2

0.15

0.1

0.05 ~ 0 co N 0 N

I

N 0

-0.05 u

•1 1,2

• D

• D-Cp

• E

• E-Cp a..

-0.1

-0.15

• • -0.2 1 2

-0.25

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3

PC01- 39.0%

0.25

0.2

0.15

0.1

~ 0.05 0 co ~ ......

N 0 0 u a..

-0.05

-0.1

-0.15

-0.2 -0.2 -0.15

Functional gene categories - Pre

• 2

• • 1

1

• 2

-0.1 -0.05 0 0.05 0.1

PC01 -15.6%

0.15

• D • D-Cp

• E • E-Cp

Metagenomic

Species level taxonomy - 1st Cp 0.25

0.2

0.15 •• 0.1 3

3 • 0.05 4 •

::::!:! 0 <O N 0 N

4 ., • O • 0-Cp

I

N • E 0

-0.05 () • E-Cp a..

-0.1

-0.15 • • -0.2

-0.25

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3

PC01 - 39.0%

0.25

0.2

0.15

0.1

::::!:! 0.05 0 <O v T""

N 0 0 () a..

-0.05

-0.1

-0.15

-0.2

-0.2

Functional gene categories - 1st Cp

• 3 • 3 •

• • • • 4 4

•

-0.15 -0.1 -0.05 0 0.05 0.1

PC01 -15.6%

0.15

• O • 0-Cp • E • E-Cp

Metagenomic

Species level taxonomy- Interim 0.25

0.2

0.15 •• 0.1 • • 0.05

'#. co N 0 N

I

N

~ - 7 •1 8

6

• D

• D-Cp

• E 0

-0.05 () • E-Cp a..

-0.1 5 •

-0.15

• • 6 -0.2 7 •

8 • • -0.25

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3

PC01-39.0%

0.25

0.2

0.15

0.1

~ 0.05 0

co ~ ..... N 0 0 () a..

-0.05

-0.1

-0.15

-0.2

-0.2

Functional gene categories - Interim

• • . s • • 7

• 1.• 6

• • • 5

• • • 7 8 5 •

• 6

-0.15 -0.1 -0.05 0 0.05 0.1

PC01 - 15.6%

0.15

• D • D-Cp

• E • E-Cp

Metagenomic

Metagenomic

Text

Metagenomic

Species level taxonomy - Post 0.25

0.2

• 0.15 • •• •

0.1 • • • 0.05 ::::!:? 0 co N 0 N

I

12 11 •.·J· • D

• D-Cp

N 0

-0.05 u a..

-0.1 11 oi12

• E

• E-Cp

• -0.15

• • -0.2 •

• • -0.25

-0.4 -0.3 -0.2 -0.1 0 0.1 0.2 0.3

PC01- 39.0%

::::!:? 0

co ~ .....

I

N 0 u a..

0.25

0.2 • 0.15

0.1

0.05

0

-0.05

-0.1

-0.15

-0.2

-0.2

Functional gene categories - Post

• • • • • 12 • 11 11 • •

•1~ 11 L•

• • • • • • •

•

-0.15 -0.1 -0.05 0 0.05 0.1

PC01 -15.6%

0.15

• D • D-Cp

• E • E-Cp

D

E

-100 0 100 200 300

Distance relative to last day pre-Cp for 16S rDNA

Distances relative to last day pre-Cp for phylogenetic and functional

classification of metagenomic reads

Sampling day

A B Untreated Treated

Incr

easi

ng m

ass

valu

es

>1000

100 - 1000

50 - 100

25 - 50

10 - 25

5 - 10

2 - 5

Fold change

Induced by treatment

Repressed by treatment

0.78 3.19

3.96

8.17

23.6

29.9

30.4

0.38 1.37

2.62

7.33

16.7

26.1

45.5

A B C D A B C D

Antibiotics affect the metabolome

Antunes et al., Antimicrob.Agents Chemo., 2011

Conclusions •

•--

•--

--

•

Gut microbiota fluctuates around stable long term averages in both composition and function

Cp is dramatic perturbation to community composition

responds and rebounds quickly, perhaps not completely

idiosyncratic

Cp is less dramatic perturbation to community function

unclear whether any persistent effects

idiosyncratic

Perturbation lacks any immediate symptoms (for these subjects)

nature of restoring force on community isn’t clear

probably not based on selection for mutualist functions

THANKS!

••

•

•

•

-

-

Doris Duke Foundation

Tanya Yatsunyenko & Jeff Gordon (Wash. U.)

Metagenomic sequencing

Mani Arumugam & Peer Bork (EMBL)

SmashCommunity bioinformatics

Liz Costello, Eoghan Harrington & David Relman (Stanford)

NIEHS Superfund Research Program

Abundant functional categories of genes affected by ciprofloxacin

Subject D COG 60 - Isoleucyl-tRNA synthetase

COG 513 - Superfamily II DNA and RNA helicases COG 768 - Cell division protein FtsI/penicillin-binding protein 2 COG 3292 - Predicted periplasmic ligand-binding sensor domain

COG 3507 - Beta-xylosidase COG 4646 - DNA methylase

Subject E COG 1506 - Dipeptidyl aminopeptidases/acylaminoacyl-peptidases

COG 4799 - Acetyl-CoA carboxylase, carboxyltransferase component NOG 44491 – Oxidoreductase

Operational Taxonomic Unit:

• OTUs by sequence similarity potentially problematic w/pyrosequencing errors

• Clustered Silva database 97% similarity

• Reference database = ‘seeds’ for pyro reads

• Reads clustering w/same reference seq at 95% similarity = refOTU

• Reads not clustering were discarded

• 3 Subjects

• 52-56 samples/subject

• 1-2 DNA extractions/sample

• 1-3 PCR amplifications/extraction

• 4 pyrosequencing runs

• Quality filtering & trimming

• 1.76 million reads analyzed

• 2,582 refOTUs

Dethlefsen & Relman, PNAS, 2010

Metagenomic reads classified according to phylogenetic or functional categories Jensen-Shannon distance to last pre-Cp sample

Jens

en-S

hann

on d

ista

nce

Phylogenetic Functional

Sampling day

Migrations recorded by Helicobacter pylori...

Fig. 3. Putative modern and ancient migrations of H. pylori. (A) Average proportion of ancestral nucleotides by source. Numbers correspond to the codes in Table 1 and colors are as in Fig. 1C. (B) Interpretation. Arrows indicate specific migrations of humans and H. pylori populations. BP, years before present.

Falush et al., Science, 2003