Metabolomics by numbers:

lessons from large-scale phenotyping

@RoyGoodacre

@LivUniCMR

@Metabolomics www.biospec.net

Roy Goodacre and friends

Human Metabolism

Metabolite intermediate

of metabolism

“Traditional” linear view

of 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

S

metabolites

metabolomics

proteins/mRNA

Integrate: SNP / genotype

-----> system understanding

Biofluids (exo-metabolome)

Endo-metabolome

Biopsy

Sputum

Volatilome

1y cell culture

cells footprint

www.husermet.org

Funded by and in collaboration with:

Department of Trade

and Industry

GC-MS + RPLC-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

LESSON I Mass Spectrometers & Chromatography DRIFT

QCs allow signal

assessment

QC 1

QC 2

QC 3

QC 4

QC 5

QC 6

QC 7

QC 8

QC 9

QC 10

blank

column test solution

sample 1

sample 2

sample 3

sample 4

sample 5

QC 11

sample 6

sample 7

sample 8

sample 9

sample 10

QC 12

sample 11

Begley P. et al. (2009) Analytical Chemistry 71, 7038-7046

Real:

60 serum (healthy).

QA:

sigma serum.

Spike1:

glutaric acid, citric

acid, alanine, glycine,

leucine,

phenylalanine, and

tryptophan. [each at 0.16 mg mL-1]

Spike 2:

caffeine & nicotine. [each at 0.16 mg mL-1]

LOESS: low-order nonlinear locally estimated smoothing function

QCs allow signal correction

Sample

QC

Instrument annual

maintenance

Before:

After:

Dunn W. et al. (2011) Nature Protocols 6, 1060-1083

QC 1

QC 2

QC 3

QC 4

QC 5

QC 6

QC 7

QC 8

QC 9

QC 10

blank

column test solution

sample 1

sample 2

sample 3

sample 4

sample 5

QC 11

sample 6

sample 7

sample 8

sample 9

sample 10

QC 12

sample 11

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?

Discriminant

analysis:

Attempting to

separate the

10 batches

A: Yes!

Admin

+User

+Experiment

Growth

+Treatment

+Environment

SampleHandling

+Explant

+Sample

Collection

+Collection

+Event

SamplePreparation

+Aliquot

+AnalysisMaterial

AnalysisSpecificSamplePreparation

+PreparationMethod

+Procedure

InstrumentalAnalysis

+Machine

+Run

MetabolomeEstimate

+Output

+DataPoint

BiologicalSource

+Genotype

+Source

HUSERMET pipeline

metadata capture

metabolomics curation

reference map of normal

biomarker

discovery

SOPs

QCs

D

SOP

SOP

N=1000s

Multiple samples -

Biobank for future

studies

Admin

+User

+Experiment

Growth

+Treatment

+Environment

SampleHandling

+Explant

+Sample

Collection

+Collection

+Event

SamplePreparation

+Aliquot

+AnalysisMaterial

AnalysisSpecificSamplePreparation

+PreparationMethod

+Procedure

InstrumentalAnalysis

+Machine

+Run

MetabolomeEstimate

+Output

+DataPoint

BiologicalSource

+Genotype

+Source

reference map of normal

biomarker

discovery

HUSERMET pipeline

and data analysis

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

• Normal populations: • Stockport PCT

• GSK, EMAS

• 1200 subjects: • Took 18 months

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

Dunn, W.B. et al. (2015) Metabolomics 11, 9-26.

med

ian

(IQR

)

n

Data Analysis

RFsorSVM

PLS-DA

MetabolomicsChemometrics

Modeler

Feature

discovery

Combination of:

Multivariate

PLS, RFs, SVMs committee voting

Bootstrapping (n=1000) + permutation testing

Univariate

ANOVA + Q-Q plots (normality)

FDR: Benjamini–Hochberg procedure

Common

Features

Ageing: glycolysis and TCA

Figure: Levine A.J. & Puzio-Kuter, A.M. (2010)

Science 330, 1340-1344

2-way ANOVA; age & gender:

F(1,779)=79.8, p=3.1x10-18

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

body composition

tissue turnover

metabolic rate (at rest)

age

human genotype

health status

reproductive status

diurnal cycle

nutrients

non-nutrients

drugs

physical activity

microbiome

mental status

Intrinsic factors

Extrinsic factors

Metabolic status

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

Dunn, W.B. et al. (2015) Metabolomics 11, 9-26.

med

ian

(IQR

)

n

Q: Is size important?

We measured

1200 subjects

100 bootstraps for

each sample size

selection

Recommend:

at least 300

subjects per class

A: You Bet!

Dunn, W.B. et al. (2015) Metabolomics 11, 9-26.

Trivedi, Hollywood, Goodacre. New Horizons in Translational Medicine 2017: 3, 294-305

Metabolomics and study size

Underpowered many < 50 people in total

Poor Balance

Only 3 studies > 300 Case vs. 300 Control

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

• Longitudinal data from >50 year olds.

• Objective and subjective data relating to

health and disability, biological markers of

disease, economic circumstance, social

participation, networks and well-being.

• Annual 2 h questionnaire (100 Qs) over the

past 15 y following 11,400 people at Wave 1.

Cumulative Frailty Score

No.

of

Subje

cts

0 – 0.1 = 320

0.1 – 0.2 = 571

0.2 – 0.3 = 206

0.3 – 0.4 = 59

Over 0.4 = 36

2006 Rockwood papers in CMAJ and The Gerontologist

Currently there is no single generally accepted clinical definition of frailty.

Metabolomics – wave 4 (n = 1192)

Frail

Non-Frail

Pre-Frail

+

= 52-54%

= 22-28%

= 16-26%

Group Variance

Frailty Index 0 1

Fre

qu

en

cy

Bin Sample Size

0.0 – 0.1 = 320

0.1 – 0.2 = 571

0.2 – 0.3 = 206

0.3 – 0.4 = 95

Discriminant Function 1

Dis

cri

min

an

t F

un

cti

on

2

Mummichog within XCMS

mummichog:

accurate mass of significant metabolites pathway analysis

Huan, T. et al. (2017) Nature Methods 14, 461-426

Mummichog network enrichment

Findings

Several pathways

highlighted

MSI Level 1

LC-MS-MS on Orbitrap

+ Standards

Validation

Targeted analysis in

Wave 6:

50 Frail vs. 50 Resilient

Further 600 individuals

thanks to Reviewer…

Carnitines

Tocotrienols

Tryptophan

degradation

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

Emerging diverse field

Great excitement in the area

Integration with other ’omics

Weaknesses

Lack of Metabolite Id.

Semi-quantitative at best

Poor statistics/validation

Opportunities

Improving Met Id.

Dynamic measurements

Spatial metabolomics

Improved patient care

Biological understanding

Threats

Over cooking results

Working in isolation

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

Aaron Levenstein

Poste, G. (2011) Bring on the biomarkers. Nature 469, 156-157.

Winder, C.L. (2011) Trends Microbiol. 19, 315-322. http://metaspace2020.eu.

www.biospec.net #ScienceIsGlobal