Ohio Valley Society of Toxicology Webcast
Overcoming the Barrier Imposed by Variable Diet
Dean P. Jones, Ph.D., Director
Clinical Biomarkers Laboratory
Emory University, Atlanta
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
EMORY
SCHOOL OF
MEDICINE
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

Discipline/Methods to understand the dynamics of small molecules in living systems
Discipline/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

Slide 3

DNA
Reproduce
RNA
Proteins
Extract energy
Maintain physical and chemical organization
Maintain delineation from environment
Slide 4

DNA
Reproduce
RNA
Proteins
Extract energy
Maintain physical and
Maintain delineation from environment
Slide 5

The chemical requirements, chemical use and chemical products of a living organism can be defined
Each catalyzed chemical reaction is determined by one or more proteins and relevant regulation, which can be linked to products of specific genes
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 electrophoresis
Mass spectrometry
Soga et al, 2002
>1500 metabolites detected
Limits:
-Dynamic range
-Multiple separation and ionization methods needed
-Quantification is relatively poor
Slide 7

Oxidative
Stress
Most toxicants have multiple metabolic effects
Multiple factors affect toxicity of toxicants
Redox couples
Conjugated aldehydes
Multiple
Proteins with -SH
Multiple altered functions
Metabolomics provides a very general approach for discovery of sensitive metabolic pathways
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)
• 1 H-NMR
• Mass spectrometry
D. Informatic tools
Slide 9

Application of environmental metabolomics to human research
Human urine largely reflects waste products of diet
24-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

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

Macronutrient energy
Sources
Essential micronutrients
Non-essential, beneficial dietary components
Metabolically neutral dietary components
Dietary toxins and toxicants
Slide 12

1 H-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 39:1-40
D. 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 1 H-NMR spectrum of human plasma is that it provides a simple means to measure macronutrients
6 4
3.4
3.3
3.2
3.1
3.0
2.9
2.8
2.7
2.6
Lipid
2
DSS
0 PPM
Slide 14

1
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 1 H-
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

1. 600 MHz Varian INOVA 600 with water presaturation at 25 º. Data simplified to 10,000 data points per spectrum
2. Frequency referenced to internal standard
DSS
3. Polynomial regression for baseline correction
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
Slide 19

A range of statistical techniques are available to reduce complexity of data, recognize patterns and develop predictive models
Visualization
Hierarchical
Clustering
1 H NMR
Spectra
Variable
Selection
Prediction /
Classification
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
0.8
0.6
0.4
0.2
0
1 2 3 4 5 6 7 8 9 10
PCs
Slide 22

PCA shows that metabolic profiles separate into
3 classes according to time of day
14:30
Afternoon/Evening
19:30 18:30
10:30
Morning
10
9:30
7:30
8:30
8:30*
8
21:30
15:30
20:30
22:30
13:30
16:30
17:30
2:30
23:30
00:30
3:30
1:30
5:30
6:30
6
Night
4:30
4
-5
PC2
0
-10 2
55
60
65
PC1
70
75
80
85
-15
Slide 23

0
14:30
-2 morning (7:30-12:30) af ternoon/evening(13:30-22:30) night(23:30-6:30)
-4
19:30
15:30
18:30
16:30
8:30*
-6
22:30
20:30
13:30
17:30
2:30
3:30
1:30
00:30
-8
7:30
12:30
11:30
21:30
23:30
-10
10:30
4:30
5:30
-12
9:30
8:30
6:30
-14
55 60 65 70
PC1
75 80 85
Slide 24

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
14:30
-6
-8
-10
8:30*
7:30
12:30
11:30
21:30
10:30
19:30
15:30
18:30
16:30
22:30
20:30
13:30
17:30
3:30
1:30
2:30
-12
9:30
8:30
6:30
00:30
23:30
4:30
5:30
Slide 25

For diurnal variation study, the same classification is obtained with k-Means clustering (partitioning method, 3 clusters) as with PCA
Index
5
6
7
3
4
1
2
8
9
10
11
12
13
Time
08:30
09:30
10:30
11:30
12:30
13:30
14:30
15:30
16:30
17:30
18:30
19:30
20:30
Class
M
M
M
M
M
A/E
A/E
A/E
A/E
A/E
A/E
A/E
A/E three-means
(predicted)
M
M
M
M
M
A/E
A/E
A/E
A/E
A/E
A/E
A/E
A/E
Index
14
15
16
17
18
19
20
21
22
23
24
25
26
Time
21:30
22:30
4:30
5:30
6:30
7:30
8:30*
23:30
00:30
1:30
2:30
3:30
Class
A/E
A/E
N
N
N
N
N
N
M
N
N
M three-means
(predicted)
M
A/E
N
N
N
N
N
N
M
N
N
M
Slide 26

False Discovery Rates provides approach to identify metabolites that contribute to time-of-day classifications
Slide 27

1
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
1 H-NMR spectra of plasma may be suitable to characterize environmental effects on macronutrient metabolism, especially effects on lipid metabolism
Slide 28

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
Sampling times
SAA-free diet SAA-containing diet
Day 1 2 3 4 5 6 7 8 9 10
Time 8
9
10
11
12
2
4
8 8 8 8 8
9
10
9
10
11
12
2
4
11
12
2
4
8
9
10
11
12
2
4
Slide 30

PCA separates plasma 1 H-NMR spectra following sulfur amino acid deficiency and excess: 8 am
3
Day3
Day6
2
Day7
Day1
Day8
Day9
Day4
Day2
Day10
SAA excess
1
Day5
SAA deficient
Slide 31

Spectra for sulfur amino acid deficiency and excess are classified according to time of day
Day 10
117 mg/kg
E930
2
E1630
E830
3
E1030
E1130
E1230
E1430
D930
D830
D1630
D1130
D1030
D1230
D1430
Day 5
SAA-Free
1
Slide 32

1
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
1 H-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

Total ion chromatogram for 8-min chromatographic separation of 10 μl of human plasma
RT: 0.00 - 7.99
100
0.88
0.85
90
80
70
60
50
40
30
20
10
0
0
0.36
1
1.29
4
Time (min)
4.82
3.84
3.91
3.98
2.32
2.64
3.17
3.67
3.56
4.75
4.97
5.05
5.31
5.96
6.56
2 3 5 6 7
7.34
NL:
3.76E6
TIC F: MS
12150501
Thermo FT/MS detection of ions with m/z between 100 and 1000
Slide 35

RT: 0.00 - 7.99
100
0.88
0.85
90
80
12150501 # 42-495 RT: 0.67-6.38
AV: 454 NL: 1.84E4
T: FTMS + p ESI Full ms [ 100.00-1000.00]
60
208.0393
70
100
50
40
90
30
80
20
70
180.0444
296.9707
10
60
0
0
0.36
1
1.29
4.82
3.84
3.91
2.32
2.64
758.5687
3.67
3.56
3.17
3.98
780.5505
4.75
4.97
5.05
5.31
5.96
6.56
2 3 5 6
50
4
Time (min)
804.5507
7
7.34
40
30
362.9265
430.9140
556.8866
702.8631
634.8761
828.5504
848.5384
906.8258
20
10
0
100 200 300 400 500 m/z
600 700 800 900 1000
NL:
3.76E6
TIC F: MS
12150501
Slide 36

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: 3.45E3
T: FTMS + p ESI Full ms [ 100.00-1000.00]
362.9265
100
90
80
70
60
50
40
30
12150501 # 42-495 RT: 0.67-6.38
AV: 454 NL: 3.45E3
T: FTMS + p ESI Full ms [ 100.00-1000.00]
100
90
80
363.0994
12150501 # 42-495 RT: 0.67-6.38
AV: 454 NL: 3.25E2
T: FTMS + p ESI Full ms [ 100.00-1000.00]
366.0965
100
90
80
70
60
50
362.9265
366.0875
40
30
20
10
0
366.00
366.0617
366.05
366.1058
366.10
m/z
S-cmGSH
366.1509
366.15
366.1776
366.20
x50
70
20
10
60
361.8170
363.8145
12150501
T:
# 42-495
0
360.2207
AV:
362.0696
1.84E4
364.7799
100
361
40
362 363 364
323.9316
365.8131
365 m/z
366.0965
366
366.7780 367.8091 368.5531
367
356.9097
368 369
90 30
758.5687
376.0205
320.9372
80
70
180.0444
20
304.8960
296.9707
10
318.9058
329.9486
340.9969 354.9613
780.5505
366.0965
382.9559
x10
391.9191
x10
60
50
0
300 310 320 330 340 350 m/z
370 380 390
40
30
20
362.9265
430.9140
702.8631
556.8866
634.8761
828.5504
848.5384
906.8258
10
0
100 200 300 400 500 m/z
600 700 800 900 1000 Slide 38

Accuracy of measured m/z is sufficient to correctly identify elemental composition, thereby providing virtual certainty of correct identification for many metabolites
12150501 # 42-495 RT: 0.67-6.38
AV: 454 NL: 3.25E2
T: FTMS + p ESI Full ms [ 100.00-1000.00]
366.0965
100
90
80
70
60
50
40
30
20
10
0
366.00
366.0617
366.05
366.0875
366.10
m/z
366.1058
366.1509
366.15
366.1776
366.20
Slide 39

Re-analysis of plasma chromatogram with 10 ppm windows show detection of only S-cmGSH, which co-eluted with authentic standard
RT: 0.00 - 7.99
100
90
80
30
20
10
0
0
70
60
50
40
1 2 3 4
Time (min)
5
5.96
6
6.03
7
NL:
8.06E4
m/z=
366.09290-
366.10020
F: MS
12150501
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 chromatography
20
0
100
80
60
40
80
60
40
20
0
100
80
60
40
20
0
100
80
60
40
20
0
0.0
0
RT: 0.00 - 7.96
100
80
60
40
20
0
100 m/z = 366
0.5
m/z = 613 m/z = 427 m/z = 180 m/z = 241
1.0
1.5
2.0
2
2.5
3.0
3.5
4.0
4.31
4.70
4.64
4.53
4.5
4.94
5.0
5.5
CM-GSH
GSSG
CySSG
CM-Cys
6.0
6
CySS
6.5
7.0
7.5
Min
NL:
3.52E4
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
Slide 41

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

1. Identify metabolic patterns or change in pattern in response to environmental challenge
Distinguish these patterns from variations due to genetics, disease, infection, age, diet and behavioral factors
2. Develop sensitive methods to detect drug-drug, drug-environment and diet-environment interactions
3. Predict toxicity or increased disease risk from metabolic patterns or change in pattern in response to environmental/occupational/drug exposures
Develop methods to identify early life exposures that result in metabolic perturbations leading to chronic toxicity
4. Use metabolic profiles to guide therapeutic interventions to compensate for early life exposures
Slide 43

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
To overcome the barrier imposed by variable diet, chemically defined, semisynthetic diets should be used
Studies are needed to address equilibration time and frequency of eating for use of semisynthetic diets
Does not address problem of variable enteric bacteria
Slide 44