This document discusses integrating "omic" approaches to investigate the gut microbiota and its relationship to human health and disease. It summarizes various studies that have used metagenomic, metabonomic, and metataxonomic approaches to characterize the gut microbiome and define its core functions. By correlating microbial taxa with metabolic profiles, these integrated studies aim to understand how specific microbes and microbial functions influence health outcomes. The document also discusses using such approaches to explore how bariatric surgery impacts the host through changes in the gut microbiome and metabolome.

1. Integrating “omic”
approaches to investigate
the gut microbiota
Julian R. Marchesi
School of Biosciences

2. IBD
Many claims
have been Fibromyalgia Colon Cancer
made for gut
microbiota and
diseases
Diabetes
T I & T II
Driving force behind integrating the gut
CVD
microbiota into host biology is to
understand how it maintains health and
Obesity
initiates or supports disease
Depression
Normal Fatty
liver liver
Non-alcoholic
fatty liver disease
Atopic disease Kidney stones

3. It is also politically important to integrate the microbiota
into the host biology.
The HGMP and associated claims – the search for SNPs with
links to diseases - GWAS.
NY Time 12 June 2010

4. “Omic” approaches available to investigate the gut
Inventories of
16S rRNA genes

7. Using omic approaches was defended on the premise we
can’t grow 20-30% of the bacteria.
DIET
PCoA of fecal samples from gnotobiotic
mice, colonized with complete or
cultured human fecal communities from
two unrelated donors and sampled over
time
Cultured bacteria
recapitulate total
community functions

8. METAHIT consortium
Abstract
Here we describe the Illumina-based metagenomic sequencing, assembly and characterization of
3.3 million non-redundant microbial genes, derived from 576.7 gigabases of sequence, from
faecal samples of 124 European individuals. The gene set, ~150 times larger than the human
gene complement, contains an overwhelming majority of the prevalent (more frequent)
microbial genes of the cohort and probably includes a large proportion of the prevalent human
intestinal microbial genes. The genes are largely shared among individuals of the cohort. Over
99% of the genes are bacterial, indicating that the entire cohort harbours between 1,000 and
1,150 prevalent bacterial species and each individual at least 160 such species, which are also
largely shared. We define and describe the minimal gut metagenome and the minimal gut
bacterial genome in terms of functions present in all individuals and most bacteria, respectively.

9. One of their aims was
to define the core
microbiome of the gut
In 85 healthy
Europeans (Danish to
We want determine
and Spanish).
• What are the keystone functions?
• Which ones are variable?
A large proportion of
• Which
unknowns and ones are druggable?
fundamental bacterial
functions indentified,
but are these core
functions of gut
microbiota?

10. In this study when the supplementary data is searched
there are no reported hits to genes involved in:-
We need to re-define what can be
• butyrate synthesis
• bile catabolismcore function:
considered a
• glucuronidases
Needs to which are not easily microbial host, but
Or functions
useful to the classified, but maybe
important to the host show an interaction with
at the same time
the eukaryotic host.
• indole-3-propionic acid synthesis - depression
• choline catabolism – cardiovascular disease
• NF-κB modulators – innate immunity

11. How can we start to define the core microbiome/bacteriome?
Change the strategy to a top-down rather than bottom-up
investigation.
Use metabonomics/metaproteomics to determine the core
In the healthy host the dialogue between
metabonome and from this isolate, functions or species which are
responsible for these bioactive molecules.
the microbiome and the karyome is via the
proteome and metabonome.
Studies providing links between bacterial
species and molecules – from correlations
to cause and effect.

12. Metabonomics 21
3
1
22 15 15 10
16 16 10
A
17
1H
7
NMR
2,4,5
8 11 8
B 6
13 12
20 1&3 1
9
19 19
C 14
8.0 7.5 ppm 4.0 3.5 3.0 2.5 2.0 1.5 ppm
Chemical shift (ppm)
UPLC-MS
Sample:
Faecal water
Faecal
extracted Data reduction
•Metabolic profiles Analysis
•Biomarkers
Collaboration with Prof. Elaine
Holmes, Imperial College London

13. Marrying together “omic” datasets
NMR/MS OTUs
observations Metabonomics Metataxonomics
observations
X Y
Pearson’s correlation
OTUs
Correlation
NMR/MS
matrix

14. NMR/ OTU
MS s
X Y
Observatio
Observatio
ns
ns
Pearson’s correlation
OTU
s
NMR/M
Or function
e.g. BSH
S
genes

15. Bacteroides enterotype
Enterotype ≠ metabotype
Are enterotypes real and biologically
significant?
Prevotella enterotype

16. PCA plot of genomes based on their functions (COGs)
30 Alistipes
Atopobium
Bacillus
Bacteroides
Bacteroides Bifidobacterium
20
Blautia
Clostridium
How can Prevotella Collinsella
Desulfovibrio
we move to Enterobacter
10
PC1
here? Enterococcus
Escherichia
Lactobacillus
Archeae
0 Parabacteroides
Porphyromonas
Prevotella
Escherichia/ Propionibacterium
-10 Enterobacter
-20 -15 -10 -5 0 5 10
PC2

17. RAT MODEL
PCA of metabolite profiles PCA of Rat model profiles
bacterial
Metabolic Roux-en-Y Gastric Bypass Surgery (RYGB) and gut microbiota/metabolites
Li et al., (2011) Gut

18. Rat (Li et al. 2011 Gut) Human (Zhang et al. 2009 PNAS)
2-week post 2-week post
Normal Obese RYGB
SHAM RYGB
weight

19. p = 0 .0 1
Aerococcaceae
Alcaligenaceae 0. 86
Alterom onadaceae
Bacillaceae
Bacteroidaceae
Bifidobacteriaceae 0. 69
Carnobacteriaceae
Clostridiaceae
Coriobacteriaceae
Corynebacteriaceae 0. 52
Deferribacteraceae
Desulfov ibrionaceae
Enterobacteriaceae 0. 35
Enterococcaceae
Erysipelotrichaceae
Eubacteriaceae
Incertae Sedis X II 0. 18
Incertae Sedis X III
Lachnospiraceae
Lactobacillaceae
Methylobacteriaceae 0. 01
Methylococcaceae
Microbacteriaceae
Micrococcaceae
-0. 15
Moraxellaceae
Pasteurellaceae
Peptococcaceae
Peptostreptococcaceae -0. 32
Planococcaceae
Porphyrom onadaceae
Prev otellaceae
Pseudom onadaceae -0. 49
Rikenellaceae
Rum inococcaceae
Staphylococcaceae
Streptococcaceae -0. 66
Veillonellaceae
PG PAG creatine PS m ylam e p trescin
eth in u e uracil
Cross correlation plots derived from selected urinary and fecal metabolites and 37
bacterial families at the levels of p= 0.01.
Key: PS, p-cresyl sulfate; PG, p-cresyl glucuronide; PAG, phenylacetylglycine.

21. Exploring the impact of GB on the host using
metabonomic and metataxonomics

22. Thank you for listening
Thanks to colleagues at University College Cork, Cardiff
University, Nijmegen University, Liverpool
University, and especially the metabonomics group at
Imperial College London.
Science Foundation Ireland, Enterprise Ireland, The
Royal Society, ESF and BBSRC studentship for funding my
work