@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 [each at 0.16 mg mL-1] sample 10 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 and data analysis +Experiment +Source +Environment
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 (IQR) median
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 (IQR) median
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“Statistics are Workinglike bikinis. in isolation Spatial metabolomics What they reveal isTechnology suggestive, expensive 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