# Presentation: 723273

## What is metabolomics/metabonomics?

- Jules Griffin
- Department of Biochemistry,
- University of Cambridge

## An overview

- Some definitions
- A brief overview of key literature
- Given the subsequent talks this will focus on non-cancer related applications
- Phenotyping yeast by NMR
- GC-MS and plant phenotypes
- CAD and screening patients using blood plasma
- Future directions

## What’s in a name?

- Metabo_l_omics *“...the complete set of metabolites/low-molecular-weight intermediates, which are context dependent, varying according to the physiology, developmental or pathological state of the cell, tissue, organ or organism...” *Oliver* *2002

**What’s in a name?**

## Systems Biology and the rise of the “-omes”

- Genomics
- Study of genes – the only -ome which is not context dependent
- Transcriptomics
- All the mRNA in a cell/tissue/organism
- Proteomics
- All the proteins in a cell/tissue/organism
- Metabonomics/Metabolomics
- All the metabolites in a cell/tissue/organism

## The challenges of metabolomics

- How many metabolites?
- Just considering one class there are a huge number of permutations
- 40 common fatty acids
- 40 FA acyl CoA
- 64000 TAGs
- 120 1-, 2-, 3- MAG
- 4800 1,2-, 1,3-, 2,3- DAGs
- Total = 69000

- Polarity
- Log -6 to 14

- Mass < 1500 amu

- Conc. Range 109

- NMR
- GC-MS
- LC-MS
- Custom assays

- Global
- profiles

- From a talk by J van der Greef

## Open or Closed?

- Open analysis
- An analysis of the total detectable content of the sample (e.g. an NMR spectrum of urine)
- Primarily used for the detection of novel entities
- Closed analysis
- An analysis focused onto a specific molecule or molecules (e.g. measurement of a specific *m/z*)
- Used for the measurement of known variables for a model

## A procedure for Metabonomics

- Measure small molecule concentrations through a global approach
- Use pattern recognition to define metabolism in a multidimensional space
- Define a metabolic phenotype (metabotype)
- Use this information to determine an end point (e.g. drug toxicity, disease state) or use to data mine another –omic technology.

## Global Profiling Tools

- NMR spectroscopy
- Solution state, solid state, *in vivo*
- High throughput
- Relatively robust
- GC- and LC-Mass spectroscopy
- More analytically sensitive
- Potentially truly global
- Problems with ionisation though?

- Coulombic arrays
- FT-IR spectroscopy
- TLC
- Metabolite arrays
- Used to monitor E.Coli strains
- Use biochemical assays

## Key Publications

- Examples Showing The Potential Of Metabolomics

## Genomics v. Metabonomics Listening to Silent Phenotypes

- Yeast was the first eukaryote to be sequenced
- Mutants for the 6000 genes in yeast can now be taken from banks such as EUROFAN
- Suggests we can completely phenotype all the genes in yeast
- Have a significant impact on human disease through comparison of gene sequence/similarities

*Raamsdonk et al. (2001) Nat. Biotechnol. 19, 45-50.*

- Standard way to phenotype yeast strains is to see how rapidly a strain grows on a given substrate mixture
- If the mutation does not alter the rate of growth it is said to be a silent mutation
- Can we use metabolomics to distinguish these silent phenotypes?
- Can we cluster similar genes together?

## FANCY - Functional ANalysis by Co-responses in Yeast 

- 1H NMR spectroscopy to study the metabolic changes induced in the different yeast strains
- Metabolic perturbations can be used to classify strains
- clusters mutants from similar deletions together.
- Two mutants involving 6-phosphofructo-2-kinase, and oxidative phosphorylation made up two clusters

- PLS Component 2

- PLS Component 3

*Raamsdonk et al. (2001) Nat. Biotechnol. 19, 45-50.*

## GC/MS and plant metabolomics

- Huge challenge
- plant genomes contain 20,000-50,000 genes,
- currently 50,000 metabolites identified
- number set to rise ~200,000
- Current plant metabolomics uses metabolic profiling through GC-MS of plant extracts.

*Fiehn O et al. 2000 Nature Biotechnology, 18, 1157-1161.*

## GC/MS and plant metabolomics

- Fiehn et al. - GC-MS quantifies 326 distinct compounds in *Arabidopsis thaliana* leaf extracts
- chemical structure to half of these.
- PCA separates 4 genotypes
- GC-TOF-MS now detected & characterised ~1000 metabolites.
- Since used these data bases to identify metabolic cliques

*Fiehn O et al. 2000 Nature Biotechnology, 18, 1157-1161.*

## Predicting Coronary Artery Disease In Humans

- Predict the occurrence and severity of coronary artery disease using blood plasma.
- Models could be built that distinguish the different disease groups
- e.g. NCA vs. single vessel,  single vessel vs. double vessel
- Disease presence and severity can be predicted
- Such systems may produce significant financial savings
- angiography, currently the gold standard for diagnosis.

*Brindle JT et al., 2002. Nat Med. ****8(12)****, 1439-45.*

## Future directions

- A wish list from current research....

## Future directions I: Rapid Phenotyping 

- As part of a large scale mutagenesis program at the MRC Mammalian Genetics unit, Harwell
- Harwell Mutagenesis program
- Use N-ethyl n-nirosourea to induce mutations
- Aim to generate 100 F1 progeny of mutagenised animals per week!
- Mice with interesting phenotypes will then be posted for the wider research community
- Need a high through put phenotyping tool to correlate with the genotype information

- PCA of 160 urine samples from a diabetic mouse model (dbdb mouse maintained at MRC Harwell). Class 1 – Male Wild Type/Heterozygous; Class 2 - Male Homozygous; Class 3 - Female WT/Heterozygous; Class 4 - Female Homozygous.

## Future directions II

- Improvements in metabolomic technology
- Cryoprobes
- LC-NMR
- LC-MS, GC-MS
- HRMAS
**Integration of these approaches**

## Rat Urine baseline study: A Combined NMR and LC-MS study

**Rat Urine baseline study: A Combined NMR and LC-MS study**

## LC/MS

**LC/MS**

## LC/MS

**LC/MS**

- Male

- Male

- Female

- Female

## Results: LC/MS

**Results: LC/MS**

## Increase in PUFAs during PCD

- Used a combined MRI, MRS, HRMAS and high res NMR approach to following PCD in tumours (see Prof Kauppinen’s talk)
- During PCD lipids increase in intensity for both saturated and unsaturated resonances
-  5.3, 2.8  3-fold
-   1.3  2-fold
- This increase in PUFAs also detected in T2 hyperintensive core of tumours
- Lipid changes associated both with PCD and cell debris region

- STEAM

- Entire region

- Hyper-intensive region

## Metabolomics and transcriptomics – Fatty liver disease

- Non-alcoholic steatohepatisis is a common feature of the Metabolic Syndrome & toxic reactions to pharmacological drugs.
- Orotic acid supplementation induces fatty liver
- disruption of Apo proteins production?
- Applied a genomic, proteomic and metabolomics approach to the problem

*Griffin et al., Physiol Genomics 2004*

## A simple system, but polygenic challenge

- Two rat strains used
- Wistar - classically used - Out bred strain
- Kyoto - prone to fatty liver accumulation -In bred
- Comparable to pharmacogenomics

- Wistar

- Kyoto

**Kyoto**

**Wistar**

*Griffin et al., Physiol Genomics 2004 *

## Griffin et al., Physiol Genomics 2004

- Glycolysis

- Sn-Glycerol 3-phosphate

- Glycerol

- ATP

- ADP

- NAD

- NADH

*Glycerol 3 phosphate acyltransferase*

**Deposition of hepatic lipid triglyceride Fatty acid synthesis:**
*ATP citrate lyase*
*Acyl carrier protein domain of fatty acid synthetase*
*Stearyl-CoA Desaturase*
******-Oxidation*** *
- -hydroxy-butyrate
*Fatty acid Coenzyme A ligase*
**Transport**
*ApoB  *
*Apo C III*

- 1-Acylglycerol 3-phosphate

- 1,2-diacylglycerol phosphate

- 1,2 Diacylglycerol

- Triacylglycerol

- Phosphatidylcholine

- Phosphatidyserine & Phosphatidylethanolamine

- CMP

- CDP-choline

- Phosphocholine

- CTP

- PP

- Choline

- 1,2 Diacylglycerol

- TMA

- TMAO

- Betaine
- aldehyde

- Betaine

- Dimethylglycine

- Sarcosine

- Glycine

- Serine

- Phosphoserine

- 3P-Hydroxypyruvate

- 3P-D glycerate

- Glyoxylate

- Adenosine

*Uricase*

- Glycolate

- Glucose

- Glycogen

*Glycogen phosphorylase*

*D-3-phosphoglycerate dehydrogenase*

*Phosphoserine aminotransferase*

**OROTIC ACID**

- UTP

- Creatine

*Griffin et al., Physiol Genomics 2004*

## Future Directions III

- NMR derived metabolic profiles ideal for phenotyping animals
- FANCY in yeast
- Can we mine information from a number of mouse models simultaneously (PIMP)?
- Development of metabolomic databases
- MIAME for metabolomics?

## Acknowledgements

- JLG Group
- Helen Atherton
- Melanie Gulston
- Mark Hodson
- Oliver Jones
- Mahon Maguire
- Michael Pears
- Denis Rubtsov
- Reza Salek
- Jeff Troke
- MRC Harwell
- Steve Brown
- Michael Cheeseman
- Tertius Hough

- GlaxoSmithKline
- Brian Sweatman
- John Haselden
- Andy Nicholls
- Sue Connor
- Imperial College
- James Scott
- Jeremy Nicholson
- University of Birmingham
- Risto Kauppinen
- Waters
- John Shockcor