Environmental Metabolomics in Humans Overcoming the Barrier Imposed by Variable Diet

1. Ohio Valley Society of Toxicology Webcast Environmental Metabolomics in Humans 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 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 EMORY SCHOOL OF MEDICINE

2. Metabolomics Discipline/Methods to understand the dynamics of small molecules in living systems Environmental Metabolomics 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

3. 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

4. Approach complements other information-rich methods DNA RNA Reproduce Proteins Extract energy Maintain physical and chemical organization Maintain delineation from environment Slide 4

5. Metabolomics is focused on the chemical homeostasis and dynamics DNA RNA Reproduce Proteins Extract energy Maintain physical and chemical organization Maintain delineation from environment Slide 5

6. Metabolomic principles 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

7. 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

8. Redox couples Multiple Proteins with -SH Multiple altered functions Oxidative Stress Conjugated aldehydes 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 Metabolic response patterns provide a means to identify conditions of risk Slide 8

9. Environmental Metabolomics: Approach • Define scope of needs • Investigation and discovery of mechanism • Diagnosis of toxicologic outcome • 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

10. 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

11. 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

12. Dietary contributions to the human metabolome Macronutrient energy Sources Essential micronutrients Non-essential, beneficial dietary components Metabolically neutral dietary components Dietary toxins and toxicants Genome Transcriptome Proteome Metabolome Biologic Function/Health Slide 12

13. 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 39:1-40 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

14. An important feature of 1H-NMR spectrum of human plasma is that it provides a simple means to measure macronutrients Lipid DSS Slide 14

15. 1H-NMR-based Metabolomics • 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

16. 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

17. Spectral analysis 1. 600 MHz Varian INOVA 600 with water presaturation at 25º. 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

18. 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

19. 3000 2900 2800 2700 2600 Total metabolites 2500 2400 2300 2200 2100 2000 8:30 9:30 0:30 1:30 2:30 3:30 4:30 5:30 6:30 7:30 8:30* 10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30 18:30 19:30 20:30 21:30 22:30 23:30 Time of day Total plasma NMR signal varies 30% over time of day (mean of 8 individuals over 24 h) 3000 Morning Morning Afternoon / Evening Afternoon / Evening Night Night Morning Morning Snack Snack (21:30) (21:30) 2900 2800 2700 2600 Total metabolites 2500 Lunch Lunch 2400 Dinner Dinner (13:30) (13:30) (17:30) (17:30) 2300 2200 Breakfast Breakfast 2100 (9:30) (9:30) 2000 8:30 9:30 0:30 1:30 2:30 3:30 4:30 5:30 6:30 7:30 8:30* 10:30 11:30 12:30 13:30 14:30 15:30 16:30 17:30 18:30 19:30 20:30 21:30 22:30 23:30 Time of day Conclusion: Normalization of total signal introduces error in individual metabolites and is therefore inappropriate Slide 19

20. Prediction / Classification A range of statistical techniques are available to reduce complexity of data, recognize patterns and develop predictive models Unsupervised Unsupervised Hierarchical Clustering Visualization Learning Learning 1H NMR Spectra Variable Supervised Supervised Validation Validation Validation Validation Selection Learning Learning Environmental metabolomics needs a working partnership between data collection and data analysis teams Slide 20

21. 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

22. Principal Component Analysis (PCA): Proportion of variability of spectra in diurnal variation study explained by first 10 Principal Components 0.8 0.6 Proportion 0.4 0.2 0 1 2 3 4 5 6 7 8 9 10 PCs Slide 22

23. PCA shows that metabolic profiles separate into 3 classes according to time of day Afternoon/Evening Morning Night Slide 23

24. Same classification is obtained with first 2 Principal Components Slide 24

25. 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 14:30 15:30 18:30 16:30 19:30 8:30* 17:30 22:30 -6 20:30 2:30 13:30 00:30 3:30 PC2 7:30 1:30 12:30 -8 23:30 21:30 11:30 -10 10:30 5:30 4:30 9:30 -12 8:30 6:30 Slide 25

26. For diurnal variation study, the same classification is obtained with k-Means clustering (partitioning method, 3 clusters) as with PCA Slide 26

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

28. 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

29. 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

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

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

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

33. 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

34. 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

35. Total ion chromatogram for 8-min chromatographic separation of 10 μl of human plasma Thermo FT/MS detection of ions with m/z between 100 and 1000 Slide 35

36. Summation of m/z spectra collected at 1/s over 5.4 min span indicated by red Slide 36

37. 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

38. S-cmGSH x50 x10 x10 Slide 38

39. Accuracy of measured m/z is sufficient to correctly identify elemental composition, thereby providing virtual certainty of correct identification for many metabolites Slide 39

40. Re-analysis of plasma chromatogram with 10 ppm windows show detection of only S-cmGSH, which co-eluted with authentic standard Slide 40

41. 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 m/z = 366 CM-GSH m/z = 613 GSSG m/z = 427 CySSG m/z = 180 CM-Cys m/z = 241 CySS 2 4 6 Min 0 Slide 41

42. 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

43. Goals for Environmental Metabolomics 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

44. Environmental Metabolomics in Humans 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