Manchester Institute of Biotechnology Discovery through innovation - - PowerPoint PPT Presentation

Manchester Institute of Biotechnology

Discovery through innovation

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.

@RoyGoodacre @Metabolomics

Metabolomics the way forward Roy Goodacre and friends

www.biospec.net

‘Menu’

u Introduction to metabolomics u 6 papers that ‘rocked’ the world of metabolomics u Conclusions and outlook

Disease development & progression

Healthy Disease progression Mortality

{

Therapeutic intervention }

Predisposition markers Prognostic markers Diagnostic markers Onset of disease/disorder Early biomarkers of disease/disorder Late biomarkers of disease/disorder Perturbation in pathway dynamics Nutrition Nutrition Pharma Pharma Surgery Surgery

Ellis, D.I. et al. (2007) Pharmacogenomics 8, 1243-1266.

Genome Transcriptome Proteome Metabolome Function (phenotype) Feedback

Alternative splicing Post translational modifications

Gene mRNA Enzyme Central dogma of molecular biology Substrate Product Metabolism

Central dogma of molecular biology and ’omic organisation

Different levels of functional analysis ~28,000 ~106 (~3,500) ~10,000*

*human derived

dynamic static

http://www.thefreedictionary.com/metamorphosis

Analytical technologies

‘‘Progress in science depends on new techniques, new discoveries and new ideas, probably in that order’’

Sydney Brenner, Nature, 5 June 1980

Human Metabolism

Metabolite

intermediate

• f metabolism

“Traditional” linear view

• f a metabolic pathway

From metabolites to metabolomics

Metabolomics

defined as the metabolic complement (metabolite pool) of a cell or tissue type under a given set of conditions A “scale-free” metabolic network

Metabolomics & biological systems

pathways networks Σ metabolites metabolomics Biofluids (exo-metabolome) Endo-metabolome Biopsy Sputum Volatilome proteins/mRNA Integrate: SNP / genotype system understanding 1y cell culture cells footprint

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 1H, 13C, 15N, 19F and 31P

u Highly quantitative

Ä Area ∝ #nuclei absorbing

• r 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.

Higher affinity to stationary phase Lower affinity to stationary phase DETECTOR

lactate monosaccharides disaccharides Amino and organic acids

GC-MS Yeast footprint Ø 70-140 metabolite peaks

Serum: GC-MS or LC-MS Urine: GC-MS or LC-MS

GC-MS (electron impact) Much greater degree of fragmentation as higher energy ionisation process Hippuric acid UHPLC-MS (electrospray (ESI)) Soft ionisation technique, intact parent mass ion detected, but many adducts can be produced 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

Higher affinity to stationary phase Lower affinity to stationary phase DETECTOR Sumner L.W. et al. (2007) Metabolomics 3, 211-221

Chromatography linked to Mass Spec.

GC-MS vs. LC-MS

METABOLIC PATHWAYS Glycolysis TCA cycle Pentose Phosphate Amino acid metabolism Gluconeogenesis Urea cycle Inositol metabolism Carbohydrate metabolism

PROVIDES GOOD METABOLITE COVERAGE IN COMPLEMENTARY PATHWAYS

METABOLIC PATHWAYS Lipid and fatty acid metabolism Secondary metabolite synthesis Metabolism of co-factors and vitamins Metabolism of Xenobiotics

Mega- & Multi- variate data

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

500 1000 1500 2000 2500 3000 3500 4000 4500 200 400 600 800 1000 1200 1400

n N gb GB l L k K N N GB n N gb GB l L k K N N GB n N gb GB l L k K N N GB f f f F F F t t t T T T s s s S S S

Caffiene Chlorogenic acid

2000 4000 6000 500 1000 1500 2000 4000 6000 8000 Caffiene

T T T S S S t t t N N GB N GB N GB L N GB GB N L GB N K K N s N L s s K k F F F l n gb k k gb n l l

Chlorogenic acid

n gb f f f

sugar 2000 4000 6000 500 1000 1500 2000 4000 6000 8000 Caffiene

T T T S S S t t t N N GB N GB N GB L N GB GB N L GB N K K N s N L s s K k F F F l n gb k k gb n l l

Chlorogenic acid

n gb f f f

sugar

Data floods Data outputs

Pairs: Identifier: transcript / protein / metabolite Quant Info: concentration or ratio

Defence against data floods

A metabolomics pipeline and data analysis

Brown, M. et al. (2005) Metabolomics 1, 39-51 Mamas, M. et al. (2011) Archive in Toxicology 85, 5-17

BIOLOGICAL EXPERIMENT ANALYTICAL EXPERIMENT ANALYTICAL EXPERIMENT DATA INTEGRATION, ANALYSIS AND METABOLITE IDENTIFICATION BIOLOGICAL INTERPRETATION

Metabolomics is…

J

Analytical Chemistry Informatics 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 samples

Ä % s.d. range 2 to 12

u Biological variation for 18 samples

Ä % s.d. range 17 to 56

Plant-to-plant variation high. ∴ do lot of reps Instrument reproducibility is good

Plants studied

u Parental ecotypes

Ä Col-2 and C24 [700 allelic differences]

u Mutants

Ä dgd1 mutant in Col-2 [severe phenotype]

ð 90% reduction in galactolipid digalactosyldiacylglycerol ð Impaired in photosynthesis, hypersensitive to light

Ä sdd1-1 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 C24 WT dgd1 sdd1-1 PC1 = allelic differences Severe phenotype Mild phenotype

Plant metabolomics

ADVANTAGES u Robust u Relatively inexpensive u Highly reproducible u Standardized spectral libraries DISADVANTAGES u Limited chemistry u Derivatisation u Deconvolution issues u Not all metabolites in library u GC-ToF-MS is currently dominant Now routinely applied in all areas of metabolomics

Phenotypes of silent mutations

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 Mixtures of continuous cultures

a FANCY approach

u Concentrations of intracellular metabolites have altered so as to compensate for the effect

• f the mutation

Ä ∴ use metabolomics → 1H-NMR spectroscopy

u Functional analysis

Ä Using comparative metabolomics Ä Functional ANalysis by Co-responses in Yeast.

Co-response analysis of metabolites relative to G6P

[ plots: arc cos of ratio of ln of changes]

• 250
• 200
• 150
• 100
• 50

50 100 150

• 150
• 100
• 50

50 100 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6

• Fig. 3

DF1 DF2

PFK KOs PFK26 and PFK27 encode the same enzyme, 6-phosphofructo-2-kinase, catalyzes the conversion of fructose-6-phosphate into fructose-2,6-bisphosphate

Clustering of full NMR spectra

Partially resp. def 100%

• resp. def

Coronary heart disease

u Two patient groups

Ä Severe atherosclerosis including 3VD (triple vessel disease) Ä Normal coronary arteries (NCA)

u Serum → 1H NMR + pattern recognition

PLS-DA results on CHD

u Also looked at different severity of coronary atherosclerosis

Ä Mild Ä Moderate Ä Severe

u PLS-DA worked on pairwise comparison

NCA TVD

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.

Progressiveness of prostate cancer

u PSA (prostate specific antigen) is a weak indicator of this, and biopsies invasive, so alternative methods needed. u Large study. u Used GC-MS and LC-MS to measure >1000 metabolites related to prostate cancer in tissues, urine and plasma.

u Sarcosine

Ä an N-methyl derivative of the amino acid glycine Ä identified in urine and raised in metastasis. Ä could not be measured in plasma.

u Biochemistry

Ä Investigated role of sarcosine further

ð Culture based studies: KOs and sarcosine spikes

Ä Overflow of amino acid metabolism in invasive cancer has been reported previously

‘Menu’

u Introduction to metabolomics u 6 papers that ‘rocked’ the world of metabolomics u Conclusions and outlook

SWOT analysis

Strengths u Emerging diverse field u Great excitement in the area u Integration with other ’omics Weaknesses u Lack of Metabolite Id. u Semi-quantitative at best u Poor statistics/validation Opportunities u Spatial metabolomics u Dynamic measurements u Improved patient care u Biological understanding Threats u Over cooking results u Working in isolation u Technology expensive

“Statistics are like bikinis. What they reveal is suggestive, but what they conceal is vital” Aaron Levenstein

www.hmdb.ca

www.hmdb.ca

Data Standards: MetabolomeXchange

Experimental, Design, Biological, System, Chemical, Analysis, Data, Processing, Mammalian,/,in#vitro# Microbial,/,in#vitro# Plant,systems, Environmental, Griffin,et#al.,(2007), van,der,Werf,et#al.,(2007),, Fiehn,et#al.,(2007), Morrison,et#al.,(2007),, Hardy,&, Taylor,(2007),, Goodacre,et#al.,(2007),, Rubtsov#et#al.,(2007),, NMR,spectroscopy, General,overview, Sumner,et#al.,(2007),, MSI,overview, Fiehn,et#al.,(2007),, Sansone,et# al.,(2007),, Data, Exchange, Ontology,

MSI

“The aim is not to suggest anyone how to conduct experiments but to provide a common language and platform for describing and sharing experimental data” Why should you deposit data?

• i. Free access to data
• ii. Transparency of studies
• iii. Depositors: boost citations
• iv. Publishers: increase impact
• v. Automated data consistency

checks and metadata capture and transmission

• vi. Public-funded work

Raw$data$ Pre*processing$ Pre*treatment$ Processing$ Post*processing$ Valida5on$ Interpreta5on$ Chemical$analysis$ Metadata$

Metabolomics in space

Dill, A.J. et al. (2011) Chem. Commun. 47, 2741–46 Chen, Y. et al. (2008) Anal. Chem. 80, 2780-2788 Nick Lockyer: www.sarc.manchester.ac.uk

Tay-Sachs disease

Lipid storage disorder in brain ↑ Ganglioside GM2

Metabolomics in time

Winder, C.L. et al. (2011) Trends Microbiol. 19, 315-322 Glc-­‑6-­‑P ¡ Fru-­‑6-­‑P ¡ Fru-­‑1,6-­‑P ¡ DHAP ¡ Gly-­‑3-­‑P ¡ PEP ¡ Pyr ¡ Glc ¡

1 ¡ 2 ¡ 3 ¡ 4 ¡ 5 ¡ 6 ¡

12C ¡ 13C ¡ 12C ¡or ¡13C, ¡needs ¡mass ¡

¡ ¡ ¡ ¡isotopomer ¡analysis ¡

V

peristaltic ¡pump

f

100 200 300 400 500 600 1560 2 4 6 8 10 12 time (min)

12C/13C ratio

U13C glucose U12C glucose Oroticacid Citric acid Putrescine

Increasing sample numbers and throughput

Biomarker Discover: From lab to bedside

Conception (objectives, collaborations, design of experiment)

Set of candidate metabolites Representative Cohort study (n=1000s) with analysis of candidates (LC-MS and/or assay) To the clinic Hypothesis generation study 1 (independent sample set 1) Hypothesis validation (independent sample set 2)

Metabolomics IS the way forward

u Builds on traditional clinical chemistry u Metabolites are highly dynamic u Many diseases have underlying metabolic effect u Allows metabolism to be probed: expected v. off-target u Growth in field is exponential

Ä Approaching 10,000 articles per year

Biological(ques-on(

GC1MS( LC1MS( FT1IR( NMR(

101101110100011010(

DATA( Discriminant(analysis( Classifica-on(trees( Machine(learning(

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Results(

Experimental( design(

Many thanks to the Team