This document summarizes a lecture on nutrigenomics given by Michael Müller. It discusses how nutrigenomics allows quantification of the interplay between genetics, lifestyle, nutrition, and the environment in determining phenotypes. Specifically, it highlights how genome-wide transcriptome analysis can identify target genes of nutrients and biomarkers of organ health and resilience. It also provides examples of human nutrigenomics studies examining how diets high in saturated fat or polyunsaturated fat alter gene expression and inflammation. The summary concludes that nutrigenomics is enabling a transition to nutritional science 2.0 through comprehensive, integrated applications and system biology analyses.

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