Nutrigenomics: The path towards nutritional science 2.0

1. ICN Cascade lecture Oct-7-2009 Nutrigenomics: The path towards nutritional science 2.0 Michael Müller Netherlands Nutrigenomics Consortium, TIFN & Nutrition, Metabolism and Genomics Group Division of Human Nutrition, Wageningen University

2. Our scientific challenge: What's healthy?

3. What we eat in one week

4. Our “paleolithic” genes + modern diets Paleolithic era Modern Times 1.200.000 Generations 2-3 Generations between feast en famine in energy abundance % Energy % Energy 100 100 Grain Low-fat meat Milk/-products Chicken Isolated Carbohydrates Eggs Isolated Fat/Oil Fish Alcohol 50 50 Meat Fruit Chicken Vegetables (carrots) Fish Nuts Honey Fruit Vegetables 0 0 Beans Less ligands for sensing TFs/NRs

5. Nutrigenomics allows the quantification of the nutritional genotype-phenotype plasticity Phenotype Metabolome Lifestyle Proteome Nutrition Environment Transcriptome Epigenome Genotype

6. Nutrigenomics: Genome wide transcriptome analysis • Complementary to proteomics and metabolomics • Identification of target 1 3 6 33 3057 9.99 0 genes of nutrients and sensing transcription factors • Identification of biomarkers for organ vitality (adaptive response capacity) SL_W1H_L SL_W2_HL SL_W3_HL SL_W4_HL SL_10_HL SL_16_HL

7. Organ and systemic responses to dietary lipids Lipids FFA Remnant VLDL LPL Chylomicrons

8. Nutrigenomics – molecular nutrition and genomics 5 -10,000 (?) metabolites 90,000 (?) proteins 100,000 (?) transcripts 20,210 genes Müller & Kersten Nature Reviews Genetics 2003

9. The mouse: A toolbox with 20,210 genes

10. How nutrients regulate our genes Improved organ capacity by PUFAs BMC Genomics 2007 8:267 J Biol Chem. 2008 ;283(33):22620-7 J Clin Invest. 2004 ;114(1):94-103 Arterioscler Thromb Vasc Biol. 2007;27(11):2420-7 Arterioscler Thromb Vasc Biol. 2009 Apr 2. J Biol Chem. 2006 ;281(2):934-44 Am J Clin Nutr. 2007 ;86(5):1515-23 Circulation 2009 in press Endocrinology. 2006 ;147:1508-16 PLOS ONE 2008;3(2):e1681. Plos One 2009 Aug Physiol Genomics. 2007 ;30(2):192-204 BMC Genomics 2008, 9:231 Hepatology 2009 in press Endocrinology. 2007 ;148(6):2753-63 BMC Genomics. 2008 ;9(1):262. Am J Clin Nutr. 2009 Aug; 90:415-24 Am J Clin Nutr. 2009 in press

11. PPAR controls lipid metabolism & is the hepatic sensor for dietary fatty acids in mice & men Rakhshandehroo M, Hooiveld G, Müller M, Kersten S (2009) Comparative Analysis of Gene Regulation by the Transcription Factor PPAR between Mouse and Human. PLoS ONE 4(8): e6796

12. Organ-specific gene expression signatures of the early phase (metabolic stress) & the late phase of metabolic syndrome 1 5 20 14464 1 3 6 33 3057 1 3 10 50 2636 1 12 87 6375 9.99 0 8.69 0 15.2 0 8.28 0 SL_W1H_L SL_W2_HL SL_W3_HL SL_W4_HL SL_10_HL SL_16_HL SL_W1H_L SL_W2_H_ SL_W3_H_ SL_W4HH_ SL_10_H_ SL_16_H_ SL_W1H_L SL_W2_H_ SL_W3_H_ SL_W4_H_ SL_10_H_ SL_16_H_ 1 2 3 4 10 16 1 2 3 4 10 16 1 2 3 4 10 16 1 2 3 4 10 16 Weeks Weeks Weeks Weeks Intestine Liver Muscle WAT Healthy Unhealthy Healthy Unhealthy Healthy Unhealthy Healthy Unhealthy

13. Conclusions • Whole genome transcriptome analysis is a powerful tool – For high through-put screening of target genes of nutrients and sensing transcription factors. – For genome wide searches for biomarkers of homeostasis and resilience capacity.

14. Nutrigenomics: From Mice to Humans Use of transcriptomics for the identification of biomarkers for organ function / vitality

15. Human Nutrigenomics: What is possible now ? • Muscle biopts • Adipose tissue biopts • Intestinal biopts • White blood cells

16. You are what you (not) eat 24h 48h PATHWAYS up down up down carnitine O-palmitoyltransferase activity O-palmitoyltransferase activity palmitoyltransferase activity 3-oxoacyl-[acyl-carrier protein] reductase activity Fatty acid metabolism fatty-acid synthase activity carnitine O-acyltransferase activity fatty acid beta-oxidation fatty acid oxidation acetyl-CoA C-acyltransferase activity lipoate biosynthesis Pyruvate metabolism pyruvate transporter activity heterogeneous nuclear ribonucleoprotein complex regulation of viral genome replication RNA processing RNA/DNA metabolism B-cell differentiation negative regulation of transcription small ribosomal subunit cytoplasmic microtubule glyoxylate metabolism heme biosynthesis Other histone-lysine N-methyltransferase activity procollagen N-endopeptidase activity alpha-ketoglutarate dehydrogenase complex TCA cycle TCA cycle enzyme complex (sensu Eukarya) up_24h down_24h neg up_48h down_48h neg Bouwens et al. Am J Clin Nutr. 2007

17. We are different! Robust and individual gene responses Nutrigenomics challenge test Bouwens et al. BMC Genomics 2008

18. Human nutrigenomics study 1 “Old” & “new” biomarkers

19. Changes in lipid composition due to PUFA intake Low = 0.4 g EPA+DHA/d; high = 1.8 g EPA+DHA/d

20. Fish-oil supplementation induces anti-inflammatory gene expression profiles in human blood mononuclear cells Less inflammation & decreased pro-arteriosclerosis markers = Anti-immuno-senescence Bouwens et al. Am J Clin Nutr. 2009

21. Concentration-dependent changes in gene expression with EPA/DHA intervention Bouwens et al. Am J Clin Nutr. 2009

22. Conclusions • Transcriptomics analysis of PBMC gene expression profiles allows sensitive monitoring of subtle (but likely important) changes in resilience (metabolic flexibility) capacity as important markers of human health.

23. Human nutrigenomics study 2: Dietary fat and inflammation in adipose tissue Change in ? diet composition de Luca, C and Olefsky JM, Nature Medicine 12, 41 - 42 (2006) Van Dijk et al. AJCN 2009

24. Design of the SFA vs MUFA-rich intervention study T=0 wks T=2 wks T=10 wks Run-in SFA-rich diet (n=10) SFA-rich diet (n=20) MUFA-rich diet (n=10) Baseline After intervention - Clamp - Clamp - Adipose tissue biopsy - Adipose tissue biopsy - Blood sampling - Blood sampling Van Dijk et al. AJCN 2009

25. „Obese-linked‟ pro-inflammatory gene expression profile by SFAs SFA diet MUFA diet • The SFA-rich diet: • Induces a pro- inflammatory obese-linked gene expression profile • Decreases expression and plasma level of the anti- inflammatory cytokine adiponectin • “Personal Transcriptomes” Van Dijk et al. AJCN 2009

26. Human intervention study 3: Caloric restriction in patients with metabolic syndrome • Do metabolic syndrome patients differentially respond to a challenge of CR compared to healthy controls? • Does a challenge of CR increase differences in gene expression profiles?

27. The answer: Yes “unhealthy” “healthy” Afman et al. 2009 unpublished

28. Human nutrigenomics study 4 Scientific basis for health effects of probiotics found with Nutrigenomics

29. Applications of transcriptomics in nutrigenomics • Functional genomics approach: genome-wide analysis of gene expression to answer questions that are unattainable using conventional tools. • Transcriptomics is a complementary nutrigenomics technology that uniquely allows the assessment of genome-wide effects of nutritional components and food bioactives on organ vitality and resilience capacity. • It nicely allows to discriminate between effects of nutritional interventions and their impacts on metabolic health & stress.

30. DIETome database for evidence-based nutrition Evidence-based Nutrition Genes regulated by fatty acids Genes regulated by high fat Genes also regulated by inflammation DIET Query Genome Epigenome Transcriptome “DIETome” Proteome Metabolome database Nutrigenomics Query Potential Biomarkers Organ-specific secreted proteins

31. Netherlands Nutrigenomics Centre Transgenic WWW PBMC models Data DB EU & NL EU & NL Grants Grants NGI-GCs NGI-GCs Databases Mouse Molecular Human Life Clinic Nutrition Intervention Lines on Studies Studies cohort ageing Technology Integration Deliverables Deliverables Deliverables Public (health) Nutritional Science 2.0 Industry

32. Nutrigenomics: The path towards Nutritional Science 2.0 Comprehensive Research & integrated Bio-banking questions nutrigenomics applications MADMAX DB DIETome DB PBMCs DB NutBiobank DB System biology Microbiome DB Nutrigenomics Challenge tests analyses HealthyPheno DB DB mining Modeling DIETage DB GenoPheno DB Secretome DB Improved Controlled High impact study design nutritional publications intervention Phenotyping Industrial Imaging (MRI) Challenge tests relevance Metabolic Ward and applications

33. Nutrigenomics enables us  To understand how nutrition precisely works (evidence-based nutrition);  To quantify the nutritional needs for optimized fitness at different life stages (“personalized” nutrition);  To improve early diagnostics of nutrition related disorders (“challenge tests”);  To support the development of “smart foods” for modern mankind (healthy and tasty, sustainable, affordable)  To enable the transition of nutritional science to nutritional science 2.0.

34. Sander Kersten Linda Sanderson Natasha Georgiadi Mark Bouwens Lydia Afman Guido Hooiveld Meike Bunger Philip de Groot Mark Boekschoten Lisette de Groot Marianne Geleijnse Caroline Duval Nicole de Wit Edith Feskens Christian Trautwein Folkert Kuipers Ben van Ommen + many more More info: michael.muller@wur.nl