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Metabolomics @RoyGoodacre @LivUniCMR www.biospec.net @Metabolomics Metabolomics by numbers: lessons from large-scale phenotyping Roy Goodacre and friends 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 of a metabolic pathway A “scale-free” metabolic network Metabolomics & biological systems Volatilome metabolites Endo-metabolome Biopsy metabolomics Sputum 1y cell culture S cells footprint Biofluids (exo-metabolome) pathways networks proteins/mRNA Integrate: SNP / genotype -----> system understanding www.husermet.org Funded by and in collaboration with: Department of Trade and Industry GC-MS + RPLC-MS METABOLIC METABOLIC PATHWAYS PATHWAYS Glycolysis, TCA cycle Lipid and fatty acid Pentose Phosphate metabolism Amino acid metabolism Secondary metabolite synthesis Gluconeogenesis Metabolism of co- Urea cycle factors and vitamins Inositol metabolism Metabolism of Xenobiotics Carbohydrate metabolism PROVIDES GOOD METABOLITE COVERAGE IN COMPLEMENTARY PATHWAYS LESSON I Mass Spectrometers & Chromatography DRIFT QCs allow signal assessment QC 1 QC 2 Real: QC 3 QC 4 60 serum (healthy). QC 5 QC 6 QA: QC 7 sigma serum. QC 8 QC 9 Spike1: QC 10 blank glutaric acid, citric column test solution acid, alanine, glycine, sample 1 sample 2 leucine, sample 3 phenylalanine, and sample 4 sample 5 tryptophan. QC 11 -1 [each at 0.16 mg mL ] sample 6 sample 7 Spike 2: sample 8 caffeine & nicotine. sample 9 sample 10 [each at 0.16 mg mL-1] QC 12 sample 11 Begley P. et al. (2009) Analytical Chemistry 71, 7038-7046 QCs allow signal correction Instrument annual maintenance Sample QC QC 1 Before: QC 2 QC 3 QC 4 QC 5 QC 6 QC 7 QC 8 QC 9 QC 10 blank After: column test solution sample 1 sample 2 sample 3 sample 4 sample 5 QC 11 sample 6 sample 7 sample 8 sample 9 LOESS: low-order nonlinear locally estimated smoothing function sample 10 QC 12 sample 11 Dunn W. et al. (2011) Nature Protocols 6, 1060-1083 QC guidelines Broadhurst, D. et al. (2018) Guidelines and considerations for the use of system suitability and quality control samples in mass spectrometry assays applied in untargeted clinical metabolomic studies. Metabolomics 14: 72 Q: Did the QC correction work? A: Yes! Discriminant analysis: Attempting to separate the 10 batches HUSERMET pipeline reference map of normal Admin BiologicalSource Growth +User +Genotype +Treatment +Experiment +Source +Environment SampleHandling Collection Multiple samples - biomarker +Explant +Collection +Sample +Event Biobank for future discovery SamplePreparation AnalysisSpecificSamplePreparation studies +Aliquot +PreparationMethod +AnalysisMaterial +Procedure InstrumentalAnalysis +Machine +Run metadata capture MetabolomeEstimate +Output +DataPoint SOP metabolomics curation N=1000s SOP D SOPs QCs HUSERMET pipeline reference map of normal Admin BiologicalSource Growth +User +Genotype +Treatment +Experiment +Source +Environment and data analysis SampleHandling Collection biomarker +Explant +Collection +Sample +Event discovery SamplePreparation AnalysisSpecificSamplePreparation +Aliquot +PreparationMethod +AnalysisMaterial +Procedure InstrumentalAnalysis +Machine +Run MetabolomeEstimate +Output +DataPoint • Normal populations: • Stockport PCT • GSK, EMAS • 1200 subjects: • Took 18 months Brown, M. et al. (2005) Metabolomics 1, 39-51 Mamas, M. et al. (2011) Arch. Toxicology 85, 5-17 Goodacre, R. et al. (2007) Metabolomics 3, 231-241 Clinical Chemistry versus LC-MS Correlation analysis based on 1,200 individuals Clinical Chemistry versus GC-MS microbial drugs food Many molecular phenotypes Clinical characteristics of the Husermet cohort n median median (IQR) Dunn, W.B. et al. (2015) Metabolomics 11, 9-26. Data Analysis Metabolomics Chemometrics Modeler PLS-DA RFs or SVM Feature discovery Combination of: Multivariate PLS, RFs, SVMs committee voting Bootstrapping (n=1000) + permutation testing Common Univariate Features ANOVA + Q-Q plots (normality) FDR: Benjamini–Hochberg procedure Ageing: glycolysis and TCA 2-way ANOVA; age & gender: F(1,779)=79.8, p=3.1x10-18 Figure: Levine A.J. & Puzio-Kuter, A.M. (2010) Science 330, 1340-1344 Gender effects Seen previously 4-hydroxyphenyllactic acid, creatinine, citrate, urate, glycerol, hexadecenoic acid Higher in Females Caffeine: food consumption 2-aminomalonic acid: associated with atherosclerotic plaques glycerol, + glyceric acid, glycerol-3P: glycerolipid and glycerophospholipid synthesis Marker of oxidative stress; oxidation product of methionine 2-way ANOVA on gender: F(1,901)=20.3, p=7.7x10-6 Gender and age effects 2-way ANOVA Age (<50 vs. >64 y): F(1,788)=39.1 F(1,778)=11.7 p=6.8x10-10 p=0.0007 Gender F(1,788)=55.4 p=2.6x10-13 Q: How fast did we get to this point? A: A lot slower than expected!!! http://searchengineland.com/figz/wp-content/seloads/2014/08/speed-slow-snails-ss-1920-800x450.jpg LESSON II Need to control experimental design Factors affecting the human metabolome Intrinsic factors Extrinsic factors body composition tissue turnover nutrients metabolic rate (at rest) non-nutrients age drugs human genotype physical activity health status microbiome reproductive status mental status Metabolic status diurnal cycle Goodacre, R. (2007) J. Nutrition 137, 259S-266S. Metabolomics of a superorganism Complex! Its metabolites include: Human derived metabolites Microbial derived one Nutritional metabolites Xenometabolites To do it properly… Need to control diet Sampling time (diurnal rhythm) Need to have matched controls Many molecular phenotypes Clinical characteristics of the Husermet cohort n median median (IQR) Dunn, W.B. et al. (2015) Metabolomics 11, 9-26. Q: Is size important? We measured 1200 subjects 100 bootstraps for each sample size selection A: You Bet! Recommend: at least 300 subjects per class Dunn, W.B. et al. (2015) Metabolomics 11, 9-26. Metabolomics and study size Underpowered many < 50 people in total Poor Balance Only 3 studies > 300 Case vs. 300 Control Trivedi, Hollywood, Goodacre. New Horizons in Translational Medicine 2017: 3, 294-305 LESSON III Biological inference is possible fRaill: Frailty, Resilence And Inequality in Later Life Nicholas J W Rattray, Drupad K Trivedi, Yun Xu, Tarani Chandola, Robert J A H Eendebak, Caroline H Johnson, Alan D Marshall, Kris Mekli, Zahra Rattray, Gindo Tampubolon, Bram Vanhoutte, Iain R White, Frederick C W Wu, Neil Pendleton, James Nazroo & Royston Goodacre Unpublished data ELSA, Fraility and Rockwood Index 0 – 0.1 = 320 0.1 – 0.2 = 571 0.2 – 0.3 = 206 • Longitudinal data from >50 year olds. 0.3 – 0.4 = 59 • Objective and subjective data relating to Over 0.4 = 36 health and disability, biological markers of disease, economic circumstance, social No. of Subjects participation, networks and well-being. Cumulative Frailty Score • Annual 2 h questionnaire (100 Qs) over the past 15 y following 11,400 people at Wave 1. Currently there is no single generally accepted clinical definition of frailty. 2006 Rockwood papers in CMAJ and The Gerontologist Metabolomics – wave 4 (n = 1192) Bin Sample Size Pre-Frail 0.0 – 0.1 = 320 0.1 – 0.2 = 571 0.2 – 0.3 = 206 0.3 – 0.4 = 95 Frequency Frail Frailty Index 0 1 Non-Frail Group Variance Discriminant Function 2 Function Discriminant = 52-54% = 22-28% + = 16-26% Discriminant Function 1 Mummichog within XCMS mummichog: Huan, T. et al. (2017) Nature Methods 14, 461-426 accurate mass of significant metabolites pathway analysis Mummichog network enrichment Findings Several pathways Tryptophan highlighted degradation MSI Level 1 LC-MS-MS on Orbitrap + Standards Validation Tocotrienols Targeted analysis in Wave 6: Carnitines 50 Frail vs. 50 Resilient Further 600 individuals thanks to Reviewer… Links to ageing Antioxidant properties of Vitamin E analogues Protect fatty acid groups from lipid peroxidation. With more free fatty acids Carnitine shuttle generates more acetyl-Co, vital for electron transport chain. Within the frail-metabotype: Lower abundance of Vitamin E analogues and carnitines Indicates a down-regulation of this system and hence a lower energy output. Mebo: SWOT analysis Strengths Weaknesses Emerging diverse field Lack of Metabolite Id. Great excitement in the area Semi-quantitative at best Integration with other ’omics Poor statistics/validation Opportunities Threats Improving Met Id. Over cooking results Dynamic measurements Working in isolation “Statistics are like bikinis. Spatial metabolomics Technology expensive What they reveal is suggestive, ImprovedPoste , patientG. (2011) Brin gcare on the bioma rke butrs. Natu rewhat 469, 156-1 57they. conceal is vital” Biological understanding Winder, C.L. (2011) http://metaspace2020.eu. AaronTrends Microbiol Levenstein. 19 , 315-322. www.biospec.net #ScienceIsGlobal .
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