VHIR Seminar led by Joel Doré. Research Director. Institut National de la Recherche Agronomique (INRA). Jouy-en-Josas, France.
Abstract: The human intestinal tract harbours a complex microbial ecosystem which plays a key role in nutrition and health. Interactions between food constituents, microbes and the host organism derive from a long co-evolution that resulted in a mutualistic association.
Current investigations into the human faecal metagenome are delivering an extensive gene repertoire representative of functional potentials of the human intestinal microbiota. The most redundant genomic traits of the human intestinal microbiota are identified and thereby its functional balance. These observation point towards the existence of enterotypes, i.e. microbiota sharing specific traits but yet independent of geographic origin, age, sex etc.. It also shows a unique segregation of the human population into individuals with low versus high gene-counts. In the end, it not only gives an unprecedented view of the intestinal microbiota, but it also significantly expands our ability to look for specificities of the microbiota associated with human diseases and to ultimately validate microbial signatures of prognostic and diagnostic value in immune mediated diseases.
Metagenomics of the human intestinal tract was applied to specifically compare obese versus lean individuals as well as to explore the dynamic changes associated with a severe calory-restricted diet. Microbiota structure differs with body-mass index and a limited set of marker species may be used as diagnostic model with a >85% predictive value. Among obese subjects; the overall phenotypic characteristics are worse in individuals with low gene counts microbiota, including a worse evolution of morphometric parameters over a period of 10 years, a low grade inflammatory context also associated with insulin-resistance, and the worst response to dietary constraints in terms of weight loss or improvement of biological and inflammatory characteristics. Low gene count microbiota is also associated with less favourable conditions in inflammatory bowel disease, such as higher relapse rate in ulcerative colitis patients.
Finally, microbiota transplantation has seen a regain of interest with applications expanding from Clostridium difficile infections to immune mediated and metabolic diseases.
The human intestinal microbiota should hence be regarded as a true organ, amenable to rationally designed modulation for human health.
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Gut microbiota for health: lessons of a metagenomic scan (by Joel Doré)
1. Gut microbiota for health
- lessons of a metagenomic scan Joël Doré
Deputy head UMR 1319 Micalis Institute &
Scientific Director US 1367 MetaGenoPolis
INRA, Jouy en Josas, France
2. Gut microbiota for health: lessons of a metagenomic scan
Joël Doré, INRA.fr
Photos :
INRA UEPSD
From the
romantic
« flora »
Faecalibacterium prausnitzii
Ruminococcus spp
Clostridium difficile in a mouse caecum
to the
pragmatic
‘microbiota’
Bacteroides dorei
Escherichia coli
Segmented filamentous bacteria
anchored in a Peyer’s Patch of mouse
intestin
Microscopic counts >> culture counts : great plate count anomaly
3. The human intestinal microbiota
100 trillion microorganisms ; 10 times the number of
human cells in our body (Savage 1977) ; >150 fold more
genes than in the human genome
Predominantly not yet cultured to date (~70% of
dominant species)
Central to Food-Microbiota-Host interactions
(crosstalk between microbiome and human
genome impact immune, neural and endocrine functions)
Mutualistic association & true organ, « protecting our
health and well-being »« throughout all stages of our
life » ; and amenable to modulations
4. Phylogenetic view : gut bacteria in the ‘family’ tree of life
Bacteroidetes
Classification memo:
Domain
Phylum
Class
Order
Family
Genus species (strain)
Actinobacteria
Single gene 16S rDNA sequence :
3 major phyla among
the >50 within currently
known bacterial diversity
Eckburg et al. 2005
(11831 séquences ; 391 espèces)
Firmicutes
5. Phylogenetics of the human intestinal tract
Sequence-based phylogenetics of the dominant human
intestinal microbiota was initiated in the mid 1990’s
DNA
amplification
&
extraction
of SSUrDNA
Sequencing
&
phylogenetic
profiling
Single gene - 16S rDNA sequence based approaches :
• A few dominant phyla
• High species diversity
• Resistance and resilience = homeostasis
• A few prevalent & dominant species = Core microbiota
6. ‘sterile’ in utero,
the intestine is colonized at birth
• colonization is affected by:
–
–
–
–
–
Gestational term
Mode of delivery (vaginal delivery or caesarean section)
Maternal nutrition and maternal microbiome
Hygiene of neonatal environment and antibiotic administration
Mode of feeding (breast milk versus bottled milk) and weaning diet
• early colonisation & hygiene hypothesis:
exposure to low bacterial diversity in the neonatal period would prevent
or delay maturation of the mucosal immune system and favor aberrant
responses to allergens or auto-antigens and onset of associated
pathologies
Bach JF. N Engl J Med. 2002;347:911-920
Okada et al Clin Exp Immunol 2010 ; 160:1-9
7. microbiome diversity is low in north-Americans
compared to Amerindians and Malawians
after the age of 3
Yatsunenko et al. Nature 2012
8. Metagenomics of the human intestinal tract
The metagenome is made of the combined genomes
of all dominant microbes within a given ecosystem
DNA
extraction
Whole
Genome
Shotgun
sequencing
Assembly and annotation
Initial pilot studies in sequence based metagenomics:
Manichanh Gut 2006 => Healthy versus Crohn
Gill Science 2006
Kurokawa DNA Res 2008
Manichanh Nucl Acids Res 2008
Reference gene catalog
and gene counts
Qin, Raes et al, Nature 2010
9. S.D. Ehrlich
J. Doré
L. Zhao
Ashler Mullard. The inside story, Nature 453. May 2008. …
International Human Microbiome Consortium - IHMC :
INRA-Paris oct 2005 ; EMBL-Heidelberg nov 2008
10. Humans share a core microbiome, and yet differ at
the level of metagenomic species
On average, each individual carries ~540 000 genes
of the initial 3.3 million genes catalog (Qin, Raes et al, Nature 2010)
Similarity:
Yet, individuality:
Core metagenome genes :
~50 % of an individual’s genes
are shared by at least 50 % of
individuals of the cohort
Rare genes :
genes shared by less than
20 % of individuals
= 2.4 million genes
We are all rather similar!,
but not identical!!
11. Humans differ at the level of ecological make-up of
their intestinal microbiota
In an attempt to characterize the ‘average’
human intestinal microbiota, we observed
…
An organisation of intestinal microbiomes
into three assemblages of genes and
microbial taxa that were named enterotypes:
Arumugam, Raes et al, Nature 2011
… order in chaos !?!
…
les ‘entérotypes’
12. Arumugam,
Raes et al,
Nature 2011
Humans studied so far belong to
one of three enterotypes
Danes
n=85;
Illumina
Europeans,
Americans,
Asians.
n=33
Sanger
US
n=154;
454
Bacteroides
Prevotella
Ruminococcus
3 enterotypes ; 3 microbial drivers ; 3 ecological settings
13. Enterotypes may be regarded as preferred patterns in the
ecological landscape of human intestinal microbiome
Scheffer, Nature 2001
Data density (Fraction of data close to a central point )
‘Density plots’ pour ~400
échantillons
Enterotypes appear as densely
populated zones within the
ecological landscape of all
possible compositions.
They strongly suggest
ecological driving forces.
Arumugam et al. Nature 2012
Each metagenome appears
quite stable, even at the finest
level of nucleotide sequence at
which variants (SNPs) remain
over time within a person’s
microbiome.
Schloissnig et al. Nature 2012
14. Sambo-MetaQuant
- Quantitative Metagenomics SAMBO samples
processing
MetaQuant
NGS
Identification
quantification
50+ million
tags/sample
Stool
sample
Gene
counts
profiles
Total
DNA
www.microbiome-standards.org/
Cardona et al BMC Microbiol. 2012
MetaHIT & iMOMi Databases
• 4 - 8 million genes
• 6000+ genomes
Gene Catalog
• Sub-populations
• Client Specific
• Environment
Specific
15. Humans differ by species, by enterotypes
and also by gut bacterial gene counts
n=277
Marker species for low/high
gene-count microbiota
Known
species
n=10
Low
Gene count
High
Humans intestinal microbiota share large
similarities but also differences that permit
stratification, with potential applications in
personalized / digitized medicine and nutrition
Unknown
species
n=58
Each column is an individual
Each row is a gene, 50 are displayed
Colors reflect gene abundance
low
high
16. Gut microbiota is an organ of the host !
Although it has a genome of its own, the microbiota
• exerts unique functionalities, essentially protective, many of which are
conserved in humans & complementary to human-gene encoded functions
• intimately interacts with food and with human cells, with the immune &
neural systems, and organs far beyond the gut (liver, adipose tissue, brain)
• is markedly distorted in many immune mediated diseases = dysbiosis
• is a great source of biomarkers with use in stratification of disease/risk
Because it has a genome of its own, it may be modulated,
with perspective to maintain or restore normobiosis/homeostasis
in disease or risk
• in structure and probably even more so in functions
• by diet, by functional foods
• by full fecal microbiota transplantation (FMT), currently tested in immune
disorders (A. Vrieze et al Gastroenterology 2012)
17. 0.1
Metagenomic signatures of dysbiosis
in immune mediated diseases
inflammatory bowel diseases
Guarner (HUVH, Barcelona)
Wang Jun (BGI, Shenzen)
Ehrlich, Lepage, Tap (INRA)
842
1
5586
aeruginosa.LESB58
ulum.v ariabile
us.bromii.L2.63
ubacterium.rectale.M104.1
H1
P76
ndidatus.Sulcia.muelleri.GWSS
olescentis
ctus
anisolv ens.XB1A ens.16.4
.subsp..multocida.str..Pm70
nalis.M50.1
y riv
cola ibrio.f ibrisolv
L2.50
pillosus
ccus.obeum.A2.162
occus.comes.SL7.1
es.merdae
oncisus.13826
ardii.ATCC.8290
cae
gum.subsp..inf antis.CCUG.52486
uinis.SK36
f ormis
p..D1
ves.odontoly ticus
ibacter.smithii.DSM2375
mentum.IFO.3956
rev e
nensis.DSM.16047 .sp.Nov
eticus.DPC.4571
.pamelaeae.gen.nov
prophilus
eri.SD2112
senteroides.subsp..mesenteroides.ATCC.8293
a.stadtmanae.DSM.3091
generans
mutans.UA159
eroides.distasonis.ATCC.8503
ccus.lactaris
.thermophilus.LMD.9
coccus.torques.L2.14
antella.f
rof orme ormatexigens
TCC.25302
us.ATCC.15305
ium.v entriosum
us.colihominis
is.stercorihominis
idium.phy tof ermentans.ISDg
AA.381
dium.sp.SS2.1
C.BAA.835
5
d=5
BMI
UC Patients
Y :UC
Crohn Patients
N:N
Y :CD
842
.siraeum.70.3
1
5586
aeruginosa.LESB58
ulum.v ariabile
us.bromii.L2.63
H1
ubacterium.rectale.M104.1
P76
ndidatus.Sulcia.muelleri.GWSS
is.SH0165
olescentis
ctus
anisolv ens.XB1A ens.16.4
.subsp..multocida.str..Pm70
nalis.M50.1
cola ibrio.f ibrisolv
y riv
pillosus
L2.50
ccus.obeum.A2.162
occus.comes.SL7.1
oncisus.13826 antis.CCUG.52486
es.merdae
ardii.ATCC.8290
cae
gum.subsp..inf
uinis.SK36
f ibacter.smithii.DSM2375
p..D1
ormis
veticus.DPC.4571
mentum.IFO.3956
rev e
es.odontoly
nensis.DSM.16047 .sp.Nov
.pamelaeae.gen.nov
prophilus ormatexigens
senteroides.subsp..mesenteroides.ATCC.8293
eri.SD2112 ticus
generans
us
a.stadtmanae.DSM.3091
mutans.UA159
eroides.distasonis.ATCC.8503
.thermophilus.LMD.9
ccus.lactaris
coccus.torques.L2.14
antella.f
aracasei.subsp..paracasei.ATCC.25302
ssotus
rof orme ticus.subsp..saprophy ticus.ATCC.15305
plei.str..Twist
nsonii.NCC.533
dens
saprophy
ium.v
tena entriosum
us.colihominis
is.stercorihominis
cter.hominis.ATCC.BAA.381
m.hallii
idium.phy tof ermentans.ISDg
ansia.muciniphila.ATCC.BAA.835
dium.sp.SS2.1
5
s
and obesity
Scores and classes
Healthy
Controls
Pedersen (SDC, Copenhagen)
Wang Jun (BGI, Shenzen)
Ehrlich (INRA, Paris)
p-value: 0.031
d=2
We identify bacterial genes & genomes specific of the microbiome of patients
18. Mucosal Dysbiosis in Crohn’s Disease
20 patients with active CD,
requiring ileo-caecal resection :
Harry Sokol,
Philippe Langella et al.
PNAS 2008
M0
surgical resection
FISH analysis of biopsies
•Eub338 (Eubactia)
• Bac303 (Bacteroides-Prevotella)
• Ent1458 (Enterobacteria)
• Erec482 (Clostridium coccoides)
• Lab158 (Lactobacillus-Enterococcus)
• Bif164 (Bifidobacterium)
• Fprau645 (Faecalibacterium prausnitzii)
M6
colonoscopy
Still in remission
or
Endoscopic relapse
F. prausnitzii at M0 (p=0.027)
3.3%
0.3%
Remission at M6
Relapse at M6
Faecalibacterium prausnitzii is associated with protection
from endoscopic inflammation relapse 6 months after surgery.
… It’s a bacterial signature of high gene count microbiota …
19. Gut microbial dysbiosis in Crohn’s Disease
beyond Faecalibacterium prausnitzii
Reference
F. prausnitzii
under-represented
Other species under-represented
Sokol et al, PNAS 2008
yes
Not explored
Willing et al. 2009
yes
Subdoligranulum sp, Roseburia sp.
Qin et al, Nature 2010
yes
yes
Kang et al, IBD 2010
yes
Ruminococcus sp, Bacteroides group.
Mondot et al, IBD 2010
yes
Subdoligranulum sp, Ruminococcus sp.
Oscillibacter sp, Bifidobacterium sp,..
Joossens et al. 2011
yes
Ruminococcus sp, Bifidobacterium sp,..
For review: Legage et al Gut 2012
Many are bacterial signatures of high gene count microbiota …
20. Faecalibacterium prausnitzii in UC:
associated with Relapse Rate
10
10
*
9
8
10 7
10 6
10 5
High
RELATIVE ABUNDANCE
Copies Fp / 1000 Bacteria
Copies Fp / g stool
CONCENTRATION IN FECAL SAMPLES
*
6
4
2
0
Low
relapse rate
Below 108 copies per g:
OR 2.29 (1.07-4.90) for
relapsing condition (p<0.05)
*
p<0.05 vs. High
High relapse rate: > 1 per year
Low relapse rate: < 1 per year
High
Low
relapse rate
Below 3 copies per 1000:
OR 3.13 (1.40-6.96) for frequent
relapsing condition (p<0.01)
Recovery of the F. prausnitzii population after relapse was associated with
maintenance of clinical remission
Varela, Manichanh et al, APT 2013
… It’s a bacterial signature of high gene count microbiota …
21. associated to time since last relapse:
associated to relapse frequency:
most frequent relapses in low gene counts
Nulber of genes per dominant metagenome
Low Gene-counts in UC:
associated with higher Relapse Rate
22. Low Gene-counts in UC: predictive of non-response
to microbiota stabilization by a probiotic
2 weeks run-in
Probiotic versus placebo consumption
T0 (baseline)
T2 (12 sem.)
T1 (6 sem.)
Randomized Double-Blind Placebo-Controlled Trial
Microbiome stability computed based on quantitative metagenomic profiling
p=0.01
p=0.06
p=0.01
Microbiota stability
Microbiota stability
p=0.06
controls
Placebo
Probiotic
All patients
Placebo
Probiotic
High gene-count microbiome patients
Microbiome diversity permits stratification in responders/non-responders.
HUVH, Barcelona, Guarner et al.; Danone, Derrien et al.
23. Intestinal Microbiota and Obesity in human
Metagenomic species show a good discrimination power between obese
and lean, in contrast to human genome
12 MGS
AUC = 0.84
n= 154 Danes
True positive
Linear additive model
ROCs for
individual MGS
18 obesity risk loci
AUC = 0.58
n = 6,510 middleaged Danes
False positive rate
Andreasen et al. Diabetes 2010
Le chatelier et al, Nature, 2013
False positive
24. Low gene count
n=31
High gene count
n=68
∆ 240 K genes, 40%
Low gene count
n=13
High gene count
n=23
∆ 230 K genes, 35%
Micro
Obes
The low gene count individuals display increased adiposity, insulin resistance,
dyslipidaemia, and inflammation
Le chatelier et al, Nature, 2013;
Cottillard et al, Nature, 2013
25. Obese people differ by gut bacterial gene counts,
and species
Each column is an individual
Each row is a set of 50 gene per species
Colors reflect gene abundance
low
high
6 weeks hyper low caloric diet
Le chatelier et al, Nature, 2013;
Cottillard et al, Nature, 2013
26. Low Gene-counts in obesity: predictive of a poor
response to nutritional intervention
intervention
stabilization
Low
High
High gene count patients
Low gene count patients
Time (weeks)
Although partly corrected by calory-restriction, a low gene count of
the microbiota predicts a lesser response in terms of weight loss,
insulin resistance and correction of inflammatory tone
intervention
1200-1500 Kcal
: low fat, high protein and low glycemic index carbohydrates
with a large variety of fibers from fruits and vegetables.
Cotillard et al,
Nature 2013
28. Microbiome diversity is a key stratifier :
A low gene count (low species richness) microbiome
may predict less healthy outcome
in Spanish UC patients (F Guarner, Barcelona):
• diversity is consistently lower in patients microbiota
• Lower gene count predicts higher relapse rate of chronic acute phases
in Danish obese patients (O Pederson, Copenhagen):
• indicates higher weight gain over time
• higher inflammatory context and biomarkers of risk of comorbidities
in French obese patients (K Clément, Paris):
• higher inflammatory context and biomarkers of risk of comorbidities
• Low gene count predicts worst response to calory-restricted diet in
terms of weight loss, improvement of inflammatory tone, Micro
Obes
biology and adiposity.
29. Dysbiosis/loss of diversity (richness) =>
also loss of host-microbiome crosstalk?
Microbiome-transcriptome correlation analysis in genetically identical humans:
healthy
individuals
UC-affected
discordant twins
host transcripts
quantitative data
spearman rank correlation
-0.5<r<0.5
false discovery rate ≤ 5%
bacterial genera
no correlation
positive correlation
negative correlation
Lepage & Häsler et al., Gastroenterol 2011
Stefan Schreiber Lab
30. Why bother ? Dysbiosis/loss of diversity =>
loss of host-microbiome crosstalk?
Microbiome-transcriptome correlation analysis in genetically identical humans:
healthy
individuals
UC-unaffected
UC-affected
discordant twins discordant twins
host transcripts
Loss of correlation
Genetic effect
Non-genetic effect
bacterial genera
no correlation
positive correlation
negative correlation
Lepage & Häsler et al., Gastroenterol 2011
Stefan Schreiber Lab
31. Dysbiosis in chronic immune diseases:
the vicious circle should be tackled and broken
chicken or egg ; does it matter?
…
by a combined modulation of:
Environment,
Environment,
microbiota Genetic Predisposition inflammation !
and
diet, life-style
diet, life-style
Stressors
Dysbiosis of the Gut
Microbiota
& crosstalk
Low grade inflammation
Stressors
Altered intestinal ecology
vicious
circle
… with fascinating questions
altered host physiology
of intestinal ecology
32. Microbiota remodeling may be associated with
a resolution of insulin resistance – 2 examples
Fecal Microbiota Transplantation
in T2D patients
Bariatric Surgery in morbid obesity :
gut microbiota & crosstalk modulation
Increased diversity
& restored crosstalk
Vrieze et al, Gy, 2012
Kong et al, Am J Clin Nutr, 2013
33. Causal agents, contributors, consequence ?
Play a role in chronicity ?
Mechanisms ?
As opposed to pathogens - host interactions,
the cross-talk mechanisms with the commensal microbiota
are poorly understood
Which are the genes (and from which bacterial species) that are
responsible for interactions with the host
and what are their role ?
(Inflammatory or anti-inflammatory effects ? …)
How to study these interactions when 70 to 80 % of the
commensals are not yet cultured ?
Functional metagenomics
34. Functional Metagenomic - 1
Selection
Bacterial
Fraction
Metagenomic
DNA
Picking
Epi
FOS-5
Cloning in
E. coli
Gloux et al., AEM, 2007
Lakhdari et al., PLoS one, 2010
Metagenomic
Library
36. Commensal bacteria develop functional crosstalk
with human cells (epithelial, immune cells, beyond..)
Modulation of immune functions
- NF-kB & AP1 pathways, TSLP, …
Transcription factors
Genes of interest
Modulation of epithelial cell turnover
- AP1, …
Modulation of cellular metabolism
- PPAR gamma, Fiaf, …
20 screens developed ; >50,000 clones or strains screened ;
~ 30 bioactive clones and strains identified
Lakhdari 2010, 2011, Madi 2010, Gloux 2011, Kaci 2011, Nepelska 2012, Santos Rocha
2012, Cultrone 2013
37. Immunomodulatory metagenomic clones on NF-kB
Activators of immune defenses
NF-kB activity
stimulators
Metagenomic
Clones
Control E. coli
inhibitors
Inhibitors, anti-inflammatory
Growth of metagenomic clones
Growth of metagenomic clones (DO 600nm)
Lakhdari et al, PLoS One 2010
38. Clone 5A LAB F4 (from Healthy library)
M.Nepelska
Stimulates NF-kB, AP1 pathways & TSLP in HT-29,
and PIgR & TSLP in Caco-2
Sequence related to Firmicutes (C. Leptum, F. prausnitzii)
Secreted factor: trypsin sensitive, heat resistant, 2-3 kDa
Key genes for bioactivity encode ABC transporters
Bioactivity is MyD88 independent hence TLR independent
2500
***
*
Il-8 (pg/ml)
control
PMA
LB
Epi
F4
6A5
D5F4t
*
2000
1500
1000
500
THP-1
MyD88-/-
t
D5
F4
6A
5
F4
Ep
i
LB
co
nt
ro
l
PM
A
0
1.0
0.5
2
3
4
LP
S
Transposon insertions are in ABC transporters
(18 KO/200 mutants)
EZ-TN5
1
Il1
TN
F
A
ep
i
F4
5
0.0
co
nt
ro
l
OD(600)
THP-1
***
1.5
Also stimulates IL-8 secretion
5
6
7 8 9 10 11 12 13 14 15 16
17 181920 21 23 24
22
25
26
27
28 29 30 31 32
33
34
35 36 38
37
39
EZ-TN5
40
41
42 43 44 45
46
47
39. New experimental approach to study the properties
of probiotics and bioactive metagenomic clones
Tsilingiri et al Gut 2012
luminal
compartment
Sealed cylinder
Glue
Tissue
specimen
Organ culture
inset
Collab. with Maria Rescigno et al, IEO - Milan
40. Bioactive metagenomic clone F4 protects against
Salmonella (FB62)-induced tissue destruction
Tissue with
without
Salmonella
control medium
control E.coli
CTRL
EPI300
clone F4
F4
luminal
compartment
Sealed cylinder
Glue
Tissue
specimen
with
Salmonella
FB62
Organ culture
inset
Tsilingiri 2012
FB62
EPI300 + FB62
Collab. with Maria Rescigno et al, IEO - Milan
F4 + FB62
human tissues
41. Key messages :
1) Reduced microbial diversity (species richness)
is a robust indicator of altered intestinal ecology and physiology
2) Altered intestinal ecology associated with immune-mediated disease
conditions may correspond to alternative stable states
3) Whether cause or consequence, altered intestinal ecology may
contribute to the maintenance of chronic conditions
with altered crosstalk between the gut and the microbiota
4) A dietary intake of diverse plant fibers may promote microbiota
diversification
5) Non-empirical interventions to restore normobiosis and healthy
crosstalk will require a thorough understanding of gut ecology…
6) Functional metagenomics, a new window into microbe-cell crosstalk
7) Microbiome-based stratification appears promissing --/--
42. Stratification based on microbiome - future perspectives
Relevant to the push for personalized and digital medicine
Relevant for health, preventive nutrition and medical
applications
Prediction of responders / non-responders
Prediction of relative risk of disease onset in healthy subjects
Prediction of risk of aggravation and co-morbidities in patients
Useful to assist in diagnosis/prognosis, in prescription and
clinical management of patients
Useful to provide rationale targets and strategies for
microbiota modulation
43. Full and Complete understanding of
Human Physiology
Blottière, De Vos, Ehrlich and Doré, COMICR, 2013
44. Merci
de votre attention
INRA Jouy-en-Josas
Christel Béra-Maillet
Hervé Blottière
Marion Leclerc
Patricia Lepage
Catherine Juste
Nicolas Lapaque
Tomas de Wouters
Antonella Cultrone
Malgorsata Nepelska
Elsa Jacouton
ChenHong Zhang
Julien Tap
Stanislas Mondot
Omar Lakhdari
European Community
& ANR-France
S Dusko Ehrlich, Jean Weissenbach (Genoscope, Evry), Wang Jun (BGI,
Shenzhen), Peer Borck (EMBL Heidelberg), Francisco Guarner (Val d’Hebron
Hospital Barcelona), Oluf Pedersen (SDC Copenhagen), Maria Rescigno (IEO
Milan), Liping Zhao (Shanghai JiaoTong University), Jim Versalovic (Baylor
College of Medicine, Houston), Baghi Singh (Western Ontario, London) and EUMetaHIT and IHMS Consortia
Karine Clément (INSERM U972, CR des Cordeliers), Denis Le Paslier & Eric
Pelletier, (CEA-Genoscope), Liping Zhao (Shanghai JiaoTong University) and
ANR MicroObese consortium
A PLATFORM OF EXCELLENCE DEDICATED TO
QUANTITATIVE AND FUNCTIONAL METAGENOMICS,
FUNDED BY FRENCH GOVERNMENT’S FUTURES INVESTMENTS
Philippe Langella
and col.
Bruno Pot
Corinne grangette
and col.
Micro
Obes
Philippe Seksik
Harry Sokol
Philippe Marteau
http:// www.gutmicrobiotaforhealth.com/