Manchester Institute of Biotechnology Discovery through innovation @RoyGoodacre www.biospec.net @Metabolomics Metabolomics the way forward Roy Goodacre and friends The Manchester Institute of Biotechnology is committed to the pursuit of research excellence, education, knowledge transfer and discovery through innovation whereby a coherent and integrated interdisciplinary research community work towards developing new biotechnologies that will find applications in areas such as human health, the energy economy, food security, industrial transformations and the environment.
‘Menu’ u Introduction to metabolomics u 6 papers that ‘ rocked ’ the world of metabolomics u Conclusions and outlook
Disease development & progression Disease progression Mortality Late biomarkers of disease/disorder Early biomarkers of disease/disorder Diagnostic Onset of markers disease/disorder Perturbation in pathway dynamics Healthy Nutrition Nutrition Pharma Pharma Surgery Surgery { Therapeutic intervention } Predisposition Prognostic markers markers Ellis, D.I. et al . (2007) Pharmacogenomics 8 , 1243-1266.
Central dogma of molecular biology and ’omic organisation ~28,000 static Genome Different levels of Gene molecular biology Central dogma of functional analysis Transcriptome mRNA Alternative splicing Feedback dynamic http://www.thefreedictionary.com/metamorphosis ~10 6 (~3,500) Proteome Enzyme Post translational modifications ~10,000* Metabolism Metabolome Substrate Product Function (phenotype) *human derived
Analytical technologies ‘‘Progress in science depends on new techniques, new discoveries and new ideas, probably in that order’’ Sydney Brenner, Nature, 5 June 1980
From metabolites to metabolomics Human Metabolism Metabolite Metabolomics intermediate defined as the metabolic of metabolism complement (metabolite pool) of a cell or tissue type under a given set of conditions “ Traditional ” linear view A “ scale-free ” of a metabolic pathway metabolic network
Metabolomics & biological systems metabolites Volatilome Endo-metabolome metabolomics Biopsy Sputum Σ 1 y cell culture cells footprint Biofluids (exo-metabolome) pathways networks proteins/mRNA Integrate: SNP / genotype system understanding
Targeted Analysis vs . Metabolic Profiling Dunn, W.B. et al . (2011) Chemical Society Reviews 40 , 387-426
NMR spectroscopy u Universal sensing Ä Many atoms can be used ð 1 H, 13 C, 15 N, 19 F and 31 P u Highly quantitative Ä Area ∝ #nuclei absorbing or emitting at that frequency u A little insensitive Ä Hyperpolarised approaches Ellis, D.I. et al . (2012) Chemical Society Reviews 41 , in press
Chromatography linked to Mass Spec. DETECTOR Higher affinity to Lower affinity to stationary phase stationary phase Amino and organic acids GC-MS Yeast footprint Serum: GC-MS or LC-MS Urine: GC-MS or LC-MS monosaccharides Ø 70-140 metabolite peaks lactate disaccharides
Chromatography linked to Mass Spec. DETECTOR Higher affinity to Lower affinity to stationary phase stationary phase Hippuric acid UHPLC-MS (electrospray (ESI)) GC-MS (electron impact) Soft ionisation technique, intact Much greater degree of parent mass ion detected, but many fragmentation as higher adducts can be produced energy ionisation process m / z =179.17 Matching of the chromatographic retention time and fragmentation mass spectra between a sample analyte and a reference standard is required for definitive id. We have ca . 1600 analytes in our GC-MS library Sumner L.W. et al . (2007) Metabolomics 3 , 211-221
GC-MS vs . LC-MS METABOLIC METABOLIC PATHWAYS PATHWAYS Glycolysis Lipid and fatty acid TCA cycle metabolism Pentose Phosphate Secondary metabolite Amino acid metabolism synthesis Gluconeogenesis Metabolism of co-factors and vitamins Urea cycle Metabolism of Inositol metabolism Xenobiotics Carbohydrate metabolism PROVIDES GOOD METABOLITE COVERAGE IN COMPLEMENTARY PATHWAYS
8000 8000 1400 F F F 6000 6000 S S S S Mega- & Multi- s s s s 1200 s s f S S f f N sugar sugar gb l N N N l n K L N L 4000 4000 n N GB GB N K GB K k gb k Chlorogenic acid 1000 GB N variate data 2000 2000 n gb l F F F F F F N N N N N 800 N N N N N N L L N N N N K K L L GB GB GB GB GB GB K K K K GB GB GB T T T T 0 0 L N N f f f f f f l l l l T T gb gb n n n n GB k k k gb gb k k 1500 1500 N N gb gb n n GB GB L L l l GB GB k k S 6000 6000 600 S T Chlorogenic acid Chlorogenic acid T 1000 1000 T t t t t t t S 4000 4000 s s 500 500 s 2000 2000 Caffiene Caffiene 400 0 0 0 0 t t t 200 500 1000 1500 2000 2500 3000 3500 4000 4500 Caffiene A sample resides somewhere in 2D or 3D space But if one collects 100 variables … Need to visualise 100 D space! underlying theme of multivariate analysis (MVA) is thus simplification or dimensionality reduction
Data floods Data outputs Pairs: Identifier: transcript / protein / metabolite Quant Info: concentration or ratio
Defence against data floods
A metabolomics pipeline and data analysis BIOLOGICAL EXPERIMENT ANALYTICAL ANALYTICAL EXPERIMENT EXPERIMENT DATA INTEGRATION, ANALYSIS AND METABOLITE IDENTIFICATION BIOLOGICAL INTERPRETATION Brown, M. et al . (2005) Metabolomics 1 , 39-51 Mamas, M. et al . (2011) Archive in Toxicology 85 , 5-17
Metabolomics is… Analytical Informatics Chemistry J Biology Multidisciplinary science usually conducted by groups of interdisciplinary scientists
‘Menu’ u Introduction to metabolomics u 6 papers that ‘ rocked ’ the world of metabolomics u Conclusions and outlook
Put GC-MS metabolomics on the map u Max Planck Inst Mol Plant Physiol: 1990s u Robust bench-top systems u Characterizing metabolism in plant metabolite engineering projects Ä Separate extracts → polar and non-polar Ä Two-step derivatization procedure adopted
Reproducibility wrt plants u 11 metabolites quantified u Analytical reproducibility for 7 Instrument reproducibility samples is good Ä % s.d. range 2 to 12 u Biological variation for 18 Plant-to-plant samples variation high. ∴ do lot of reps Ä % s.d. range 17 to 56
Plants studied u Parental ecotypes Ä Col-2 and C24 [700 allelic differences] u Mutants Ä dgd 1 mutant in Col-2 [severe phenotype] ð 90% reduction in galactolipid digalactosyldiacylglycerol ð Impaired in photosynthesis, hypersensitive to light Ä sdd 1-1 mutant in C24 [mild phenotype] ð Point mutation in regulatory gene for stomatal development.
PCA on all 326 metabolites 62% of variance Col-2 WT dgd1 PC1 = allelic Severe phenotype differences Mild phenotype C24 WT sdd1-1
Plant metabolomics u GC-ToF-MS is currently dominant ADVANTAGES DISADVANTAGES u Robust u Limited chemistry u Relatively inexpensive u Derivatisation u Highly reproducible u Deconvolution issues u Standardized spectral u Not all metabolites in libraries library Now routinely applied in all areas of metabolomics
Phenotypes of silent mutations Mixtures of continuous cultures u Silent genes Ä No visible phenotype u Usual phenotype for yeast is based on growth rate u But in a mutant (deletant) this is not changed Ä Silent mutation ð scored on the basis of metabolic fluxes
a FANCY approach u Concentrations of intracellular metabolites have altered so as to compensate for the effect of the mutation Ä ∴ use metabolomics → 1 H-NMR spectroscopy u Functional analysis Ä Using comparative metabolomics Ä F unctional AN alysis by C o-responses in Y east.
Co-response analysis of metabolites relative to G6P [ plots: arc cos of ratio of ln of changes]
Clustering of full NMR spectra Fig. 3 100 Partially resp. def 1 1 50 1 2 2 2 2 2 2 2 2 4 2 2 2 2 4 2 2 2 2 2 4 0 3 100% 3 DF2 resp. def 5 6 -50 PFK KOs 5 6 6 -100 5 3 -150 -250 -200 -150 -100 -50 0 50 100 150 DF1 PFK26 and PFK27 encode the same enzyme, 6-phosphofructo-2-kinase, catalyzes the conversion of fructose-6-phosphate into fructose-2,6-bisphosphate
Coronary heart disease u Two patient groups Ä Severe atherosclerosis including 3VD (triple vessel disease) Ä Normal coronary arteries (NCA) u Serum → 1 H NMR + pattern recognition
PLS-DA results on CHD u Also looked at different TVD severity of coronary NCA atherosclerosis Ä Mild Ä Moderate Ä Severe u PLS-DA worked on pairwise comparison u >90% accurate and specificity for patients with disease
Potential confounders u Gender confounders Ä NCAs most were female Ä 3VDs most were male u Drug confounders Ä Most patients in the analyses that compared groups using the severity of CAD (1VD, 2VD or 3VD) were taking cholesterol-lowering statins.
Metabolomics standards initiative u MSI formed in 2005 to unify and to engage with the growing metabolomics community so that experiments can be reproduced by others and are based on solid sample collection, analysis and data processing. u Working group now working on how to perform experimental design better.
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