Environmental Metabolomics in Humans Overcoming the Barrier Imposed by Variable Diet

Dean P. Jones, Ph.D., DirectorClinical Biomarkers Laboratory

Emory University, Atlanta

EMORYSCHOOL OF

MEDICINE

Ohio Valley Society of Toxicology Webcast

Research funding provided by the National Institute for Environmental Health Sciences, National Center for Research Resources, National Institute for Diabetes, Digestive and Kidney Diseases, Georgia Research Alliance and Emory University

Collaborators: Youngja Park, PhD; Thomas Ziegler, MD; Seoung Kim, PhD; Bing Wang, PhD; Roberto Blanco, MD, Nana Gletsu, PhD, and Shaoxiong Wu, PhD, in conjunction with the Emory GCRC and Emory NMR Center

MetabolomicsDiscipline/Methods to understand the dynamics of

small molecules in living systems

Environmental MetabolomicsDiscipline/Methods to understand environmental, especially toxicologic, influences on the dynamics

of small molecules in living systems

Note that this approach expands the concept of toxicokinetics from a toxicant and its direct metabolites to

include ALL small molecules perturbed by the toxicant

Slide 2

Metabolomics can support the NIH Roadmap concept for biological data of the future

Non-destructive, minimally invasive

Quantitative

Multidimensional and spatially resolved

High temporal resolution

High-density data, information rich

Common standards

Cumulative (Publicly accessible)

Slide 3

Approach complements other information-rich methods

DNA

RNA

Proteins

Extract energyMaintain physical and chemical organizationMaintain delineation from environment

Reproduce

Slide 4

Metabolomics is focused on the chemical homeostasis and dynamics

DNA

RNA

Proteins

Extract energyMaintain physical and

chemical organizationMaintain delineation from environment

Reproduce

Slide 5

Metabolomic principles

Each catalyzed chemical reaction is determined by one or more proteins and relevant regulation, which can be linked to products of specific genes

The chemical requirements, chemical use and chemical products of a living organism can be defined

Therefore, with appropriate methods, comprehensive static descriptions of the metabolome of an organism can be defined, and a systems biological description of the dynamics of the metabolome can be developed

Slide 6

Progress in mapping the entire metabolome of microorganisms: Genome defined, complete series of mutants available. With defined growth media,

possible to link metabolic changes to specific genetic change

Capillary electrophoresisMass spectrometrySoga et al, 2002

>1500 metabolites detectedLimits:-Dynamic range-Multiple separation and ionization methods needed-Quantification is relatively poor

Slide 7

Redox Metabolomics to study oxidative stress

Most toxicants have multiple metabolic effects

Multiple factors affect toxicity of toxicants

Metabolomics provides a very general approach for discovery of sensitive metabolic pathways

OxidativeStress

MultipleProteins with -SHConjugated

aldehydes

Redoxcouples Multiple

alteredfunctions

Metabolic response patterns provide a means to identify conditions of risk

Slide 8

Environmental Metabolomics: Approach

A. Define scope of needs• Investigation and discovery of mechanism• Diagnosis of toxicologic outcome

B. Biologic system for study• Cell models• Animal models• Human subjects or populations

C. Profiling tools (many available)• 1H-NMR• Mass spectrometry

D. Informatic tools Slide 9

Application of environmental metabolomics to human research

Human urine largely reflects waste products of diet24-h urine collections are not convenient

Human plasma contains broad spectrum of normal metabolites that are maintained by homeostatic mechanisms

Metabolic profiles in blood could provide a sensitive way to detect toxicologic perturbations

Slide 10

Major complication for metabolomics is variability of diet

1. Food is consumed intermittently

2. Quantity of food consumed is variable

3. Composition of diet is variable

4. Individual food items vary in chemical composition

Slide 11

Dietary contributions to the human metabolome

Genome

Proteome

Metabolome

Macronutrient energySources

Essential micronutrients

Non-essential, beneficial dietary components

Metabolically neutral dietary components

Dietary toxins and toxicants

Biologic Function/Health

Transcriptome

Slide 12

1H-NMR spectroscopy of biologic fluids provides useful approach for metabolic profiling

Methods pioneered by Nicholson, Lindon, Holmes and colleagues

Many references for methods: J.K. Nicholson et al (1995) 750 MHz NMR spectroscopy of human

blood plasma. Anal. Chem 67: 793-811.J.C. Lindon et al (2001) Pattern recognition methods and

applications….Progr NMR Spectroscopy 39:1-40D. Robertson et al (2002) Metabonomic technology as a tool for rapid

throughput in vivo toxicity screening. In Comprehensive Toxicology; Cell and molecular toxicology, pp 583-610

Used for broad range of studies in laboratory animals; numerous studies of human urine, plasma, saliva, amnionic fluid, tissue extracts

We focused on 2 aspects, minimum processing and maximum throughput—consequently the resolution in our spectra is not as good as is possible with other processing and analysis approaches

Slide 13

An important feature of 1H-NMR spectrum of human plasma is that it provides a simple means to measure macronutrients

Slide 14

citrate

lipid

albumin lysyl

choline

EDTA

Ca-EDTA

3.4 3.3 3.2 3.1 3.0 2.9 2.8 2.7 2.6 PPM

glucose

6 4 2 0 PPM

citrate

lipid

albumin lysyl

choline

EDTA

Ca-EDTA

3.4 3.3 3.2 3.1 3.0 2.9 2.8 2.7 2.6 PPM

glucose

citrate

lipid

albumin lysyl

choline

EDTA

Ca-EDTA

3.4 3.3 3.2 3.1 3.0 2.9 2.8 2.7 2.6 PPM

glucose

6 4 2 0 PPM6 4 2 0 PPM

Lipid

DSS

1H-NMR-based Metabolomics

1. Reproducible spectral method could be ideal for cumulative human metabolomic reference library

a. define common variables, time of day, fasting, aging, obesity, disease

b. perform series of studies with chemically defined, semisynthetic diets to determine effects of nutritional deficiency and excess

c. use this library to assess metabolic effects of real foods, drugs etc.

2. Use this library for development of predictive algorithms to assess environmental exposures, nutritional deficiencies and excesses, etc

Slide 15

Purpose: to determine extent of diurnal variation in 1H-NMR spectra of plasma in healthy adults in a controlled environment fed standardized diet at timed intervals

Design: 8 healthy, non smoking individuals (4 males, 4 females; 4 subjects each 18-39 y and 60-85 y)

Emory GCRC study; following informed consent had complete medical history and physical exam

Admitted for 24-h period with hourly blood draws;Standardized, nutritionally balanced meals to provide energy requirement based upon Harris Benedict equation and protein at 0.8 g/kg per day

Meals given at 9:30 (30%), lunch at 13:30 (30%), dinner at 17:30 (30%) and evening snack at 21:30 (10%)

Slide 16

Spectral analysis

1. 600 MHz Varian INOVA 600 with water presaturation at 25º. Data simplified to 10,000 data points per spectrum

3. Polynomial regression for baseline correction

2. Frequency referenced to internal standard DSS

4. Beam search algorithm used for spectral alignment

Slide 17

Data for analysis: 200 spectra representing 25 time points from each of 8 subjects

Nested analysis of variance showed that

21% of variation was associated with subjects

79% of variation was associated with time of day

Conclusion: Sampling time is critical to interpret environmental perturbations on metabolome

Slide 18

Total plasma NMR signal varies 30% over time of day (mean of 8 individuals over 24 h)

Conclusion: Normalization of total signal introduces error in individual metabolites and is therefore inappropriate

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Time of day

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Breakfast(9:30)

Lunch(13:30)

Dinner (17:30)

Snack (21:30)

Morning Afternoon / Evening Night Morning

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Time of day

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tal

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Breakfast(9:30)

Lunch(13:30)

Dinner (17:30)

Snack (21:30)

Morning Afternoon / Evening Night Morning

Slide 19

A range of statistical techniques are available to reduce complexity of data, recognize patterns

and develop predictive models

Visualization

ValidationValidationSupervised Learning

Unsupervised Learning

SelectionValidation

1H NMR Spectra

HierarchicalClustering

Variable Prediction / Classification

ValidationSupervised Learning

Unsupervised Learning

Environmental metabolomics needs a working partnership between data collection and data

analysis teams Slide 20

Factor analysis is one of the most widely used (and misused) multivariate statistical methods

Used to explore data, test hypotheses and to reduce complexity of data

With Principal Component Analysis (PCA), most of the variation in a series of complex spectra can be described by a few Principal Components

Slide 21

Principal Component Analysis (PCA): Proportion of variability of spectra in diurnal variation study explained by first 10 Principal Components

1 2 3 4 5 6 7 8 9 100

0.2

0.4

0.6

0.8

PCs

Pro

port

ion

Slide 22

5560

6570

7580

85-15

-10

-5

0

2

4

6

8

10

PC2

00:30 23:30

5:30

2:30

4:30

1:30

14:30

3:30

16:30 17:30

15:30

6:30

18:30

13:30

20:30

19:30

22:30

PC1

21:30

8:30*

11:30

8:30

12:30

10:30

7:30 9:30

PC

3

PCA shows that metabolic profiles separate into 3 classes according to time of day

Morning

Night

Afternoon/Evening

Slide 23

Same classification is obtained with first 2 Principal Components

55 60 65 70 75 80 85-14

-12

-10

-8

-6

-4

-2

0

PC1

PC

2

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8:30*

morning (7:30-12:30)

afternoon/evening(13:30-22:30)

night(23:30-6:30)

Slide 24

Clustering methods

Hierarchical methods provide index of similarity: Multiple ways that two curves can have a correlation of 1.0

Partitioning methods assume that unique groups exist

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-10

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Slide 25

For diurnal variation study, the same classification is obtained with k-Means clustering (partitioning method, 3 clusters) as with PCA

Index Time Classthree-means (predicted)

Index Time Classthree-means

(predicted)

1 08:30 M M 14 21:30 A/E M

2 09:30 M M 15 22:30 A/E A/E

3 10:30 M M 16 23:30 N N

4 11:30 M M 17 00:30 N N

5 12:30 M M 18 1:30 N N

6 13:30 A/E A/E 19 2:30 N N

7 14:30 A/E A/E 20 3:30 N N

8 15:30 A/E A/E 21 4:30 N N

9 16:30 A/E A/E 22 5:30 N N

10 17:30 A/E A/E 23 6:30 N N

11 18:30 A/E A/E 24 7:30 M M

12 19:30 A/E A/E 25 8:30* M M

1320:30 A/E A/E

26

Slide 26

VLDL1

LDL1

VLDL2LDL2Lactate

LipidNAC1NAC2

LipidLipid

-glucose

M – AM – N

A – N

M – AM – N

M – AA – N

M – AM – N

M – A

Lipid

A – N

VLDL1

LDL1

VLDL2LDL2Lactate

LipidNAC1NAC2

LipidLipid

-glucose

M – AM – N

A – N

M – AM – N

M – AA – N

M – AM – N

M – A

Lipid

A – N

False Discovery Rates provides approach to identify metabolites that contribute to time-of-day classifications

Slide 27

Conclusions: Diurnal variation of 1H-NMR spectra of human plasma

Spectra should be normalized relative to an added standard rather than according to total signal

Diurnal variations within an individual are greater than spectral differences between individuals—time of day is critical for comparative studies

Blood lipids represent major diurnal change

1H-NMR spectra of plasma may be suitable to characterize environmental effects on macronutrient metabolism, especially effects on lipid metabolism

Slide 28

Xenobiotic-Nutrient Interactions

Many toxicants and drugs are metabolized through pathways that utilize cysteine, eg. GSH conjugation

Many biologic functions are dependent upon thiol/disulfide redox state, which depends upon cysteine

Thus, one may anticipate that xenobiotic exposure may interact with cysteine in effects on metabolic patterns

To test this concept, we have initiated studies of short-term cysteine insufficiency and acetaminophen effects on metabolism

Slide 29

Currently only have data for first part:Sulfur amino acid deficiency protocol

Semisynthetic, chemically defined diet given at specific times under controlled conditions in the Emory GCRC with 2-d equilibration

Eliminates variables of free-living diet

The approach allows controlled addition of specific chemicals or combinations, with the same individual as control, thereby allowing detection of effects of a specific agent on metabolism

Day 1 2 3 4 5 6 7 8 9 10

Time 89

101112

24

89

101112

24

89

101112

24

89

101112

24

8 8 8 8 8 8

SAA-free diet SAA-containing diet

Samplingtimes

Slide 30

PCA separates plasma 1H-NMR spectra following sulfur amino acid deficiency and excess: 8 am

3

1

2 Day9Day2

Day10

Day3

Day4

Day5

Day6Day7

Day8Day1

SAA deficient

SAA excess

Slide 31

Spectra for sulfur amino acid deficiency and excess are classified according to time of day

2

1

3 D830D930

E1030

D1030D1130

E1230

D1230

E1430

D1430

E1630 E830

E930

E1130

D1630

Day 10117 mg/kg

Day 5SAA-Free

Slide 32

Conclusions: 1H-NMR spectra of human plasma following SAA deficiency

Metabolic changes linked to SAA intake are detected by NMR spectroscopy even when taurine (major detected SAA metabolite) is excluded from spectrum

PCA of fasting morning samples shows less discrimination than responses after a meal

False discovery rates shows that blood lipids represent major metabolic effects of SAA intake

1H-NMR spectra of plasma following response to challenge may be more powerful than fasting morning samples to detect metabolic effects of xenobiotics

Slide 33

LC-Fourier-transform mass spectrometry for high-throughput environmental metabolomics

NMR spectroscopy has limited sensitivity to measure metabolites in biologic fluids

Mass spectrometry-based methods are more sensitive but limited by need for separation of metabolites prior to analysis

FT/MS and Orbitrap (Thermo) have higher mass resolution and better mass accuracy, thus decreasing separation requirements for many metabolites

We have begun to develop techniques for metabolic profiling based principally upon the high mass accuracy of FT/MS

Slide 34

RT: 0.00 - 7.99

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3.984.973.675.053.56 4.75 5.313.172.64

2.32 5.960.36 6.56 7.34

NL:3.76E6

TIC F: MS 12150501

Total ion chromatogram for 8-min chromatographic separationof 10 μl of human plasma

Thermo FT/MS detection of ions with m/z between 100 and 1000

Slide 35

12150501 #42-495 RT: 0.67-6.38 AV: 454 NL: 1.84E4T: FTMS + p ESI Full ms [ 100.00-1000.00]

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208.0393

758.5687

296.9707180.0444 780.5505

804.5507

634.8761828.5504

702.8631

430.9140 556.8866

362.9265

848.5384

906.8258

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3.984.973.675.053.56 4.75 5.313.172.64

2.32 5.960.36 6.56 7.34

NL:3.76E6

TIC F: MS 12150501

Summation of m/z spectra collected at 1/s over 5.4 min span indicated

by red

Slide 36

Expansion of spectrum (next figure) shows resolving power of instrumentation

With 10 ppm resolution, many ions in human plasma can be identified because the spectrum of chemicals normally found in blood is limited

For S-carboxymethylGSH, only 2 other ions are detected within a 10 ppm window; both are minor, and both are separated from S-cmGSH if the 5.4 min spectrum is integrated over 30 s intervals.

Slide 37

12150501 #42-495 RT: 0.67-6.38 AV: 454 NL: 1.84E4T: FTMS + p ESI Full ms [ 100.00-1000.00]

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Rel

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bund

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208.0393

758.5687

296.9707180.0444 780.5505

804.5507

634.8761828.5504

702.8631

430.9140 556.8866

362.9265

848.5384

906.8258

12150501 #42-495 RT: 0.67-6.38 AV: 454 NL: 3.45E3T: FTMS + p ESI Full ms [ 100.00-1000.00]

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m/z

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391.9191356.9097323.9316

376.0205320.9372

304.8960 340.9969 354.9613 382.9559318.9058

329.9486 366.0965

12150501 #42-495 RT: 0.67-6.38 AV: 454 NL: 3.45E3T: FTMS + p ESI Full ms [ 100.00-1000.00]

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m/z

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362.9265

363.8145361.8170 366.0965

365.8131360.2207 364.7799 366.7780 367.8091 368.5531362.0696

363.0994

12150501 #42-495 RT: 0.67-6.38 AV: 454 NL: 3.25E2T: FTMS + p ESI Full ms [ 100.00-1000.00]

366.00 366.05 366.10 366.15 366.20

m/z

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366.0965

366.1058

366.0875

366.0617 366.1509 366.1776

S-cmGSH

x10

x10

x50

Slide 38

12150501 #42-495 RT: 0.67-6.38 AV: 454 NL: 3.25E2T: FTMS + p ESI Full ms [ 100.00-1000.00]

366.00 366.05 366.10 366.15 366.20

m/z

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366.0965

366.1058

366.0875

366.0617 366.1509 366.1776

Accuracy of measured m/z is sufficient to correctly identify elemental composition, thereby providing virtual certainty of correct identification for many metabolites

Slide 39

RT: 0.00 - 7.99

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5.96

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NL:8.06E4

m/z= 366.09290-366.10020 F: MS 12150501

Re-analysis of plasma chromatogram with 10 ppm windows show detection of only S-cmGSH,

which co-eluted with authentic standard

Slide 40

Approach can be expanded to measure multiple metabolites by LC-FT/MS based upon mass accuracy (10 ppm) with minimal LC resolving

power in 8 min chromatographyRT: 0.00 - 7.96

0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5

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m/z= 366.09500-366.10000 MS 1201058c

NL:1.49E4

m/z= 613.15750-613.16050 MS 1201058c

NL:9.32E4

m/z= 427.09400-427.09600 MS 1201058c

NL:7.14E3

m/z= 180.03200-180.03300 MS 1201058c

NL:4.10E3

m/z= 241.03100-241.03300 MS 1201058c

0 2 4 6 Min

CM-GSH

GSSG

CySSG

CM-Cys

CySS

m/z = 366

m/z = 613

m/z = 427

m/z = 180

m/z = 241

Slide 41

Conclusions: LC-FT/MS for high-throughput environmental metabolomics

Analysis at 10 ppm with a short (<10 min) separation by LC provides sufficient mass resolution and accuracy for profiling hundreds of metabolites in human plasma

In principle, analysis of such information-rich MS spectra by advanced statistical methods provides a means to identify previously unknown effects of environmental exposures

Introduction of such information-rich MS spectra into cumulative libraries would allow future in silico studies of specific metabolites from data collected for other purposes.

Slide 42

Goals for Environmental Metabolomics

1. Identify metabolic patterns or change in pattern in response to environmental challenge

3. Predict toxicity or increased disease risk from metabolic patterns or change in pattern in response to environmental/occupational/drug exposures

2. Develop sensitive methods to detect drug-drug, drug-environment and diet-environment interactions

Distinguish these patterns from variations due to genetics, disease, infection, age, diet and behavioral factors

4. Use metabolic profiles to guide therapeutic interventions to compensate for early life exposures

Develop methods to identify early life exposures that result in metabolic perturbations leading to chronic toxicity

Slide 43

Environmental Metabolomics in Humans

To overcome the barrier imposed by variable diet, chemically defined, semisynthetic diets should be used

Does not address problem of variable enteric bacteria

Studies are needed to address equilibration time and frequency of eating for use of semisynthetic diets

Cumulative human metabolomic data libraries are essential to address the complexity of environmental effects on human health

Standardized data acquisition procedures are needed for creation of cumulative human metabolomic data libraries

Slide 44