What do gut bugs and

metabonomics tell us about

disease? Jonathan Swann

BAPEN 2010: From Cutting Edge Research to Clinical Practice

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

Nature March 4th 2010

3.3 Million Genes in the Symbiotic Microbiome = We are all < 0.7% Human!

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

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

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.

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

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

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

‘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

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

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

13

- 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

ANTIBIOTIC MODEL: - MICE TREATED WITH VANCOMYCIN

treated

control

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

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

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

Statistical linkage of metagenomics with NMR-generated metabolic data

THE PARTIAL (CHINESE) HUMAN MICROBIOME-METABOLIC AXIS

(+/-) (library match %)

CLASSIFICATION

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

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

Suggested that Obese Individuals may have a lower Bacteroidetes: Firmicutes ratio than Lean Individuals – and this can be modulated by diet.

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/-)

Bifidobacteria

hippurate

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

MICROBIAL METABOLISM IS

TOXICOLOGICALLY SIGNIFICANT!

LOW ABUNDANCE GUT MICROFLORAL METABOLITE

RESPONSIBLE FOR CARCINOGENICITY?!-

No difference in hydrazine metabolism

between germ-free and conventional rats

Acetyl-hydrazine

Diacetyl-hydrazine

THOPC

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

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.

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

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.

Questions?

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

Diagnostic Power and Biomarker Recovery

GC-MS of Derivatized Colonic Biopsy Extracts

(minimum time from sample collection to analysis = 4 hours)

MAS-NMR of Intact Colonic Biopsies

(minimum time from sample collection to analytical result= 5 minutes)

Triglycerides

Triglycerides

HR-MAS-NMR Detected Biomarkers of Colonic Cancer in intact biopsies

*

*

Triglycerides

* = also detected by GC-MS