what do gut bugs and metabonomics tell us about disease? · 2010-11-12 · what do gut bugs and...
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What do gut bugs and
metabonomics tell us about
disease? Jonathan Swann
BAPEN 2010: From Cutting Edge Research to Clinical Practice
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Microbiota impacts on human physiology
Impacts on: • Immune system
• Digestive function
• Metabolism
Associated with disorders, such as:
• Gastroenteritis
• Bone health
• IBD (UC, CD)
• AAD
• IBS
• Colorectal cancer
• PCI
• Obesity
• ASD‟s
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Nature March 4th 2010
3.3 Million Genes in the Symbiotic Microbiome = We are all < 0.7% Human!
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Bacteroidetes
Firmicutes
Actinobacter
Proteobacteria
Eckburg, et al. SCIENCE VOL 308 10 JUNE 2005
395 unique bacterial phylotypes 3 healthy individuals 6 sampling sites: Faeces + mucosa
Microbial colonization: Individuals differ Faeces differs from mucosal samples
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A B C D E F
Secretory Metabolomes
HOST GENOME
Cellular transcriptomes
Cellular proteomes
1o
Intracellular metabolomes
Extracellular metabolite pool
The Primary Metabolome and Co-metabolome interactions
(Nicholson, J. et al Nature, Rev. Microbiology, 2005, 3, 2-8)
Humans: > 500 functionally distinct NORMAL cell types/ca.10 trillion
parenchymal cells
GUT MICROBIOME
Species transcriptomes
Species proteomes
Species metabolomes
Enteron
Co-metabolome enters via hepatic portal
+ mesenteric veins
1 2 3 4 5 6
microbial and dietary 2
o metabolites
Biliary secretions enter duodenum from
common bile duct
ENTEROHEPATIC CIRCULATION
Humans: > 1000 Species. > 100 trillion cells
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Conditional metabolic phenotype of the host
Taken from Nicholson, J.K., Holmes, E., Lindon, J.C., & Wilson, I.D (2004). „The challenges of modeling mammalian biocomplexity‟. Nat.
Biotechnol. 22, 1268-1274.
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Metabonomics
“Quantitative measurement of time-related multiparametric
metabolic responses of multicellular systems to pathophysiological
stimuli or genetic modification”.
(SYSTEMS APPROACH) INCLUDES BIOFLUIDS AND TISSUES
© Imperial College, 2002
Move away from HYPOTHESIS-LED towards HYPOTHESIS-GENERATION
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NMR Strategy 1H,
13C,
31P
Intact Tissues
(MAS)
Biofluids
2 D methods
Diffusion
analysis
LC-NMR
etc.
MS Strategy
Biofluids and Extracts
LC-MSn
Other stuff
GC-MS
LC-MSn-NMR
Metabolite
Identification/
Quantitation
and
Interactions
Metabonomic Analysis Strategies
Chemometrics and Disease Modelling
UPLC-MS NMR
CE
GC-MS
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900 MHz spectrum of human urine
Ar-OH
C-OH
C-SH
C-NH2
Pyr-a-H
Pyr-b,g-H
Ph-H
HC=C
H2C=C
OCH
OCH2
OCH3
NCH3
SCH3
PhCH3
COCH3
COCH2
=C.CH3
-C.CH3
NMR spectroscopy is a powerful structure elucidation tool- we use it
as our primary metabonomic “pathfinder” technology
Contains latent
Biomarker information on:
Genotype
Physiological state
Nutritional state
Gut microflora
„Biological‟ Age
Presence of Disease
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‘Omics’ data are variable heavy and therefore require multivariate
analysis Example of PCA Analysis
- 2
- 1
0
1
2
- 3 - 2 - 1 0 1 2 3
t[2
]
t[1]
1
2 3
4 5
6
- 0 . 4 0
- 0 . 3 0
- 0 . 2 0
- 0 . 1 0
0 . 0 0
0 . 1 0
0 . 2 0
0 . 3 0
- 0 . 2 0 - 0 . 1 0 0 . 0 0 0 . 1 0 0 . 2 0 0 . 3 0 0 . 4 0 0 . 5 0 0 . 6 0 0 . 7 0
p[2
]
p[1]
9.98 9.94 9.9 9.86 9.82 9.78 9.74 9.7 9.66 9.62 9.58
9.54 9.5 9.46 9.42 9.38 9.34 9.3 9.26 9.22 9.18 9.14 9.1 9.06 9.02 8.98 8.94 8.9 8.86 8.82 8.78 8.74 8.7 8.66 8.62 8.58 8.54 8.5 8.46 8.42 8.38
8.34 8.3 8.26
8.22 8.18 8.14 8.1 8.06
8.02
7.98 7.94 7.9 7.86 7.82 7.78 7.74 7.7 7.66 7.62
7.58 7.54 7.5 7.46 7.42 7.38 7.34 7.3 7.26 7.22 7.18 7.14 7.1 7.06 7.02 6.98
6.94
6.9 6.86 6.82 6.78 6.74 6.7 6.66 6.62 6.58 6.54 6.5 6.46 6.42 6.38 6.34 6.3 6.26 6.22 6.18 6.14 6.1 6.06 6.02 4.5 4.46
4.42 4.38
4.34 4.3 4.26
4.22
4.18
4.14
4.1
4.06 4.02
3.98
3.94 3.9 3.86 3.82
3.78
3.74
3.7 3.66
3.62
3.58 3.54 3.5
3.46
3.42
3.38
3.34 3.3
3.26
3.22
3.18
3.14 3.1 3.06
3.02 2.98
2.94 2.9 2.86 2.82
2.78 2.74 2.7 2.66 2.62 2.58
2.54
2.5
2.46
2.42
2.38
2.34
2.3 2.26
2.22 2.18
2.14
2.1 2.06
2.02
1.98 1.94
1.9
1.86 1.82 1.78 1.74
1.7
1.66
1.62
1.58
1.54 1.5 1.46
1.42
1.38
1.34
1.3
1.26
1.22
1.18
1.14 1.1
1.06
1.02 0.98
0.94
1.78
0.86
0.82
0.78 0.74 0.7 0.66 0.62 0.58 0.54 0.5 0.46 0.42
Spectrum 2
Spectrum 1
Spectrum 3
Spectrum 4
Spectrum 5
Spectrum 6
1.78
( a)
( b)
Spectra scores loadings spectra
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Metabolic Hyperspace Modelling:
PCA and The “Influence Vector Concept”
Age
vector Sexual
Dimorphism
vector
Other Physiological or
Disease Vectors
Microfloral composition
Nutrition vector
Each dot is an individual
(n = ca. 5,000 Biofluid NMR spectra) © Imperial College, 2003
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Influence of the microbiota on
the host metabolic system
Impact on host endogenous metabolism
Control of phase II bile acid conjugations and effects on gene expression
Participation in host pathologies
Influence host xenobiotic metabolism
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13
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- 1 0
0
1 0
- 2 0 - 1 0 0 1 0 2 0
PC
2
PC1
Day 0
(T1)
Days 1-2
(T2)
Days 4-9
(T3)
Days 12-17
(T4)
Day 21
(T5)
*
Imidazole metabolite
x2
T1
phenylacetylglycine
x1.5
T2
4-hydroxy-phenylpropionate
x1.5
T3 3-hydroxy-phenylpropionate
x1.5
T4 hippurate
x1
T5
Gnotobiotic rats on exposure to the environment
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ANTIBIOTIC MODEL: - MICE TREATED WITH VANCOMYCIN
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treated
control
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Influence of the microbiota on
the host metabolic system
Impact on host endogenous metabolism
Control of phase II bile acid conjugations and effects on gene expression
Participation in host pathologies
Influence host xenobiotic metabolism
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Normal Rats Germ Free Rats
UPLC-QToF-MS Quantified Bile Acids
(Taurine Conjugation Dominance in Germ Free)
Swann et al. (2010) „Systemic gut microbial modulation of bile
acid metabolism in host tissue compartments. Proc Natl Acad Sci.
Gut Microbial Effects on Bile Acid Conjugate Patterns in Multiple Compartments
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Hepatic Metabolic Pathway Transcriptomic Patterns Show Major Differential Metabolic Regulating Effects of Microbiome (Especially for Lipids)
Higher in Germ Free Rats
Lower in Germ Free Rats
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Statistical linkage of metagenomics with NMR-generated metabolic data
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THE PARTIAL (CHINESE) HUMAN MICROBIOME-METABOLIC AXIS
(+/-) (library match %)
CLASSIFICATION
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Influence of the microbiota on
the host metabolic system
Impact on host endogenous metabolism
Control of phase II bile acid conjugations and effects on gene expression
Participation in host pathologies
Influence host xenobiotic metabolism
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Diabetic outliers (mainly obese)
Metabolic signatures of human obesity
US population (n = ca. 800 lean and 1200 obese)
obese
lean P < 10-9
P < 10-9
P < 10-5
P < 10-4
Phenylacetylglutamine
Hippurate
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Suggested that Obese Individuals may have a lower Bacteroidetes: Firmicutes ratio than Lean Individuals – and this can be modulated by diet.
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Zucker animal model of obesity and
insulin resistance
Mutation of the leptin receptor ‘fa’, resulting in diminished sensitivity to leptin
Leptin – acts as an appetite suppressant
Three strains: (fa/fa), (-/-) and (fa/-)
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Bifidobacteria
hippurate
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Influence of the microbiota on
the host metabolic system
Impact on host endogenous metabolism
Control of phase II bile acid conjugations and effects on gene expression
Participation in host pathologies
Influence host xenobiotic metabolism
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MICROBIAL METABOLISM IS
TOXICOLOGICALLY SIGNIFICANT!
LOW ABUNDANCE GUT MICROFLORAL METABOLITE
RESPONSIBLE FOR CARCINOGENICITY?!-
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No difference in hydrazine metabolism
between germ-free and conventional rats
Acetyl-hydrazine
Diacetyl-hydrazine
THOPC
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Gut microbial variation may modulate host susceptibility
to xenobiotic toxicity
Taken from Roth, R.A., Luyendyk, J.P., Maddox, J.F., & Ganey, P.E. (2003). „Inflammation and drug idiosyncrasy – is there a connection?‟. J.
Pharmacol. Exp. Ther. 307, 1-8.
Lipopolysaccharide (LPS) is a cell wall
component of Gram negative bacteria
cytoplasmic
membrane
phospholipid
O polysaccharide
Lipid A
outer
membrane
peptidoglycan
Lipopolysaccharide (LPS) is a potent
inflammagen and modest inflammation from LPS
exposure can lower the threshold for drug toxicity
LPS
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Microbiota influence the conditional metabolic
phenotype of the host
Gut microbiome
Direct interaction
Modulation of host xenobiotic metabolism
Influence of the overall metabolic state of the host
Host susceptibility
Taken from Nicholson, J.K., Holmes, E., Lindon, J.C., & Wilson, I.D (2004). „The challenges of modeling mammalian biocomplexity‟. Nat.
Biotechnol. 22, 1268-1274.
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Integrated Surgical Metabonomics
Solid State NMR for Real Time Surgical Diagnostics
Peri-operative predictive profiling
to model surgical outcomes
Critical Care Phenotyping
“The Intelligent Knife”
MALDI-TOF-MS Tissue Imaging
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ACKNOWLEDGEMENTS Academic: Prof. Jeremy Nicholson, Prof. Elaine Holmes, Prof John C. Lindon,
Dr H. Keun, Dr T.Ebbels, Dr J. Bundy, Prof James Scott, Prof Tim Aitman, Prof Paul Elliot, Dr H. Tang, Dr J. Saric, Dr S. Mitchell. Thanos Athanasiou, Ara Darzi(Imperial)
Post Doctoral Group: Drs, M. Bollard, O. Cloarec, M. Dumas, A. Craig, Severine, Zirah, A. Maher, B. Beckwith-Hall, E.Stanley, A. Clayton, R. Barton, J., Y. Wang, M.Mehta, E. Meibaum, I. Douarte, S. Bruce. T. Tsang, C.Stella, M. Coen. J. Sidhu, E.Skiordi. S. Claus, J.Li, J. Kinross, H. Ashrafian
Graduate Students: T. Athersuch, I. Yap, R. Stolanova. P. Bond, C. Bailey, C. Teague, D. Parker, A. Tregay. J. Pearce, J. Bowen, S. Lowdell, L.Smith, A. Cooray, N.Jones, G. McLaughlin, D. O‟Connor, R.Liu, M.Ratalainen, K. Veselkov, F.P. Martin.
Collaborators: Prof G. Gibson (University of Reading), Prof I.D. Wilson, Drs J. Sidaway and T.Orton (AZ ), Prof J. Everett, Drs M. Reily and D. Robertson, (Pfizer), Prof Jose Ordovas (Tufts University), Prof Burt Singer (Princeton University), Drs M. Spraul (Bruker), Dr Rob Plum, John Shockcor (Waters), Dr Sunil Kochhar (Nestle), Frans van D‟Ouderra,J. Powell, M. Faughan et al (Unilever). Dr D. Gaugier (Oxford University), Prof D.Withers (UCL).
FUNDING: NIH, The Wellcome Trust, BBSRC, MRC, EPSRC, NERC, The Royal Society, Roche Foundation, Servier, Lilly, P&G, Pfizer, AstraZeneca, Nestle, Unilever, Novo Nordisk, Roche Foundation, BMS, Hi-Q, Metabometrix, METAGRAD, WATERS CORPORATION.
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Questions?
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Nutrition Caloric Restriction
Saturated fat
Total diet
Salt and Fibre
Vitamin supplements
Flavenoids
GUT MICROBIOTA Axenia/Gut development
IMMUNE STATUS
Gastric ulcers/Colon cancer
IBD/Crohn‟s disease
Hepatitis
Neuropsychiatric
Disease Schizophrenia
Huntington‟s
Parkinson‟s
Neurotoxicity
Metabolic Syndrome Type 2 Diabetes
Hypertension
Cardiovascular disease
Insulin Resistance
Obesity
Parasitology & Infection Geohelminths
Malaria
Trypanosomes
TB
Toxicology & Drug Metabolism Idiosyncratic toxicity
Interspecies differences
Drug Efficacy
pharmacometabonomics
Renal & Hepatic Disease Organ transplantation
Haemodialysis
Fanconi syndrome
Alcohol metabolism/use
Osteopathies Osteoporosis
Osteoarthritis
Herbal Medicines Toxicity
Quality control TCMs
Metabolic effects of TCMs
Fertility/gender specific disease Dysmenorrhea
Fertility
Spinal injury
Ageing
Personalized
Medicine
© Imperial College, 2007
SUPERORGANISM MEDICINE CONNECTS VIA THE EXTENDED GENOME
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Diagnostic Power and Biomarker Recovery
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GC-MS of Derivatized Colonic Biopsy Extracts
(minimum time from sample collection to analysis = 4 hours)
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MAS-NMR of Intact Colonic Biopsies
(minimum time from sample collection to analytical result= 5 minutes)
Triglycerides
Triglycerides
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HR-MAS-NMR Detected Biomarkers of Colonic Cancer in intact biopsies
*
*
Triglycerides
* = also detected by GC-MS