1. Lecture 1
Nutrigenomics
What is Nutrigenomics & molecular nutrition research?
From challenges to solutions
Michael Müller
Nutrition, Metabolism and Genomics Group, Division of Human Nutrition, Wageningen University

2. Nutrition, Metabolism & Genomics group
Scientific objectives
• To unravel the molecular details of how nutrition influences
metabolic health of individuals, basically to answer the
question of what is behind “You are what you eat and have
eaten”.
• We are aiming to characterize the quantitative role of
nutrition (and lifestyle factors as exercise) on the
translation of an individual genotype into a healthy
phenotype.
• Essential for the success of this approach is the
combination of functional genomics research using
transgenic animals and translational human nutrigenomics
studies where mechanistic concepts derived from model
studies are validated in controlled human intervention
studies.

3. NMG research lines
Metabolic Health
Molecular nutrition of fatty acid sensing
Human Metabolic Plasticity
Intestine as Gatekeeper
Role of Epigenetics in Ageing
NNC infrastructure (databases, OMICs)

4. Nutrigenomics: Research with impact

5. Challenge 1: Successful ageing
Stay healthy as long as possible
100 %
time
Health/“Qualityoflife”

6. Challenge 2: What's healthy?

7. Challenge 3:
We have a tsunami of health problems

8. 100
50
0
% Energy
Low-fat meat
Chicken
Eggs
Fish
Fruit
Vegetables (carrots)
Nuts
Honey
100
50
0
% Energy
Fruit
Vegetables
Beans
Meat
Chicken
Fish
Grain
Milk/-products
Isolated Carbohydrates
Isolated Fat/Oil
Alcohol
1.200.000 Generations
between feast en famine
Paleolithic era
2-3 Generations
in energy abundance
Modern Times
Challenge 4:
Our “paleolithic” genes + modern diets

9. You are what you eat

10. How many human genes do we have?
Not so many but….

11. Genotype
• A genotype is an individual's collection of
genes. The term also can refer to the two
alleles inherited for a particular gene.
• The genotype is expressed when the
information encoded in the genes DNA is
used to make protein and RNA molecules.
• The expression of the genotype
contributes to the individual's observable
traits, called the phenotype.

12. Our genes
• 23 chromosomes
• ≈ 20,000+ genes

13. Genes and Nutrition => Phenotype
Its not that easy

14. Classification of hereditary diseases

15. Genetic “mistakes” makes us “vulnerable”

16. Phenotype
• A phenotype is an individual's observable
trait, such as height, eye color, and blood
type.
• The genetic contribution to the phenotype is
called the genotype.
• Some traits are largely determined by the
genotype, while other traits are largely
determined by environmental factors
(including nutrition). => Nutritional Phenotype

17. Phenotype plasticity
Phenotypic plasticity is the ability of an organism to
change its phenotype in response to changes in the
environment (e.g. nutrition or exercise).
CYP4A10
0
2
4
6
8
10
12
14
WT KO WT KO WT KO WT KO WT KO WT KO WT KO WT KO WT KO
ctrl WY feno C10:0TG C18:1TG C18:2TG C18:3TG C20:5TG C22:6TG
FCvsWTctrl

18. Genome plasticity

20. Sequencing
technologies
and their uses
Together, these methods can be used
for integrated personal omics profiling
to map all regulatory and functional
elements in an individual. Using this
basal profile, dynamics of the various
components can be studied in the
context of disease, infection, treatment
options, and so on. Such studies will
be the cornerstone of personalized and
predictive medicine

21. Timely relatively modest interventions in early
life can have a large effect on disease risk later

22. You are what you eat and have eaten:
Received, Recorded, Remembered & Revealed

23. Nutrigenomics
Quantification of the nutritional genotype-phenotype
Phenotype
Metabolome
Proteome
Transcriptome
Epigenome
Genotype
Lifestyle
Nutrition
Microbiota
Environment

24. Genomics/Transcriptomics Proteomics MetabolomicsBioinformaticsBioinformatics
GC-MS
Genotyping (polymorphisms)
Foods (functionality)
Physiology (phenotyping)
Species (genotyping, traits)
Resistance
Foods (GMO)
Adaptation (stress response)
GMO (allergens)
Genotyping
Foods (traits)
Foods (starter cultures)
Plant foods (contaminants)
Hygiene (contaminants)
Nutrition (GI flora)
Microorganisms Plants Animals Humans
Food and Nutrition
Physiology (phenotyping)
Biomarkers
Human nutrition and the new technologies

25. Why Nutrigenomics
• To understand nutrition &
metabolic health/plasticity
• To comprehensively
phenotype
• To validate FFQ
• To enable strategies to
optimize personal health
• To provide scientific
evidence for health
claims of “functional”
foods
 Mechanisms
 Biomarkers
 Nutritional Science 2.0
 Personal Nutrition
 Health claim support

26. Nutrigenomics: Two strategies
Target Genes
Mechanisms
Pathways
Signatures
Profiles
Biomarkers
Molecular Nutrition
& Genomics
Nutritional
Systems Biology
•Identification of dietary signals
•Identification of dietary sensors
•Identification of target genes
•Reconstruction of signaling pathways
•Measurement of stress signatures
•Identification of early biomarkers
•Nutritional plasma proteome
and metabolome
Complexity
++++ +
L. Afman & M. Müller J Am Diet Assoc. 2006;106:569-576.

27. What is the background? What the problem?
WHY
Health claims
(EFSA)
Insufficient evidence
to support claims
e.g. microbiota
Collaboration with
Food industries
PPP
Nutritional intervention
Often not effective:
Hard to demonstrate
effects of “healthy”foods
Biomarkers not
Sensitive enough
FFQ not hard evidence
“fuzzy” phenotype
Complex genotypes
Personalized
Nutrition
Saturated fat = bad
Unsaturated fat = good
True? Why?
Mechanisms?
All sat. fats? ω3/6
Important to
Differentiate! For
Consumer/ Industry &
Science
Obesity:
Role of “too much”
calories
“Modern” foods too
„Tasty‟ & not satiety
inducing
Comprehensive
Understanding of nutrient
sensing & satiety

28. What is the specific aim?
AIM
Health effect of foods
Functional foods
New smart foods
for specific
populations
Prevention of
diseases
Early diagnosis
Early biomarkers
Improved & more
effective prevention of
Diet-related diseases
Mechanism
Role of nuclear
receptors
Evidence-based
Nutrition
Comprehensive
understanding of
nutrition =>
Nutritional
Systems Biology
Disease related
Link Nutrition &
Obesity, Cancer,
Diabetes, CVD
Understanding of early
pathology of diseases
Identification of targets
Improved intervention

29. Which materials and methods?
Materials
Methods
Health effect of foods
Functional foods
New Cell based assays
Microarray analysis
K.O. mice
Human Studies
Mouse/Human
In vivo/ vitro
Prevention of
diseases
Early diagnosis
Early biomarkers
Mouse studies
Human Studies
Blood, Urine, tissue
OMICs analysis
Mouse / Human
In vivo
Controlled
interventions
Mechanism
Role of nuclear
receptors
Evidence-based
Nutrition
Functional genomics
K.O. mice
Microarray analysis of organs
Metabolomics, Systems
Biology
Mouse (models)
In vivo/ vitro
Disease related
Link Nutrition &
Obesity, Cancer,
Diabetes, CVD
Disease models
Comprehensive
phenotyping
Time series
Mouse (models) or
Human (control/case)
Well phenotyped

30. What are the specific deliverables?
Deliverables
Health effect of foods
Functional foods
New bioassays to test
Food functionality (HCS)
New in vivo models
Claim support
Mechanistic
basis of food
functionality
Prevention of
diseases
Early diagnosis
Early biomarkers
Database with
OMICS based
well annotated data sets
Related to Organ health
versus Systemic health
Nutritional
Phenotype DB for
smart query &
biomarker discovery
Mechanism
Role of nuclear
receptors
Evidence-based
Nutrition
Organ-specific
Databases
(transcriptome,
secretome, etc…)
Systems Biology models
Prediction of metabolic
consequences of
nutrients/ bioactives
Disease related
Link Nutrition &
Obesity, Cancer,
Diabetes, CVD
Elucidation of pathways
Involved in early
Pathology (liver, intestine,
WAT)
New anti-inflammatory targets
e.g. preventive
dietary modulation
Of interaction of organ
cells with macrophages

31. Your are what you eat
Healthy food (pattern)s have large impact on our gene expression & phenotype
• (Micro & Macro) Nutrients
– Mono & polyunsaturated fatty acids
– Vitamines (e.g. vitamine A & D) , minerals (e.g. Zn)
• Microbiota (from foods)
– Vegetarians / omni- /carnivores => different microbiota
– “Raw” (e.g. “Sushi”) or fermented food consumption => food-
specific microbiota
• Food components (bitter, toxic, “healthy”)
– Secondary plant metabolites (e.g. resveratrol, glucosinolates,
cafestol....)
– MicroRNA (e.g. rice) => “nutrient”?
• Less foods/calories (caloric restriction) => “chromatin”
exercise

32. VLDL LPL
Remnant
FFA
Fats
Chylomicrons
We are what we eat

33. Transcription-factor pathways
mediating nutrient-gene interaction
, RXRs
TLR4

34. Endocrine
receptors
Steroid hormones
High affinity
Endocrine
receptors
Lipids
Low affinity
Orphans?
Chawla, Science 2001
Nuclear hormone receptors

35. Regulation of Cholesterol and
Lipid Handling in Metabolic Organ
Systems by Nuclear Receptors
Intestine
LXR
Decreased cholesterol absorption
FXR
Increased bile salt recirculation
PPAR
Improved lipid handling

36. Nutrigenomics: “Molecular Nutrition & Genomics”
Essential role of nutrient sensing transcription factors

37. Understanding Nutrition
How nutrients regulate our genes: via sensing molecular switches
Changed
organ
metabolic
capacity
J Clin Invest. 2004;114:94-103
J Biol Chem. 2006;28:934-44
Endocrinology. 2006;147:1508-16
Physiol Genomics. 2007;30:192-204
Endocrinology. 2007;148:2753-63
BMC Genomics 2007; 8:267
Arterioscler Thromb Vasc Biol. 2007;27:2420-7
Am J Clin Nutr. 2007;86(5):1515-23
PLOS ONE 2008;3(2):e1681
BMC Genomics 2008; 9:231
BMC Genomics 2008; 9:262
J Biol Chem. 2008;283:22620-7
Arterioscler Thromb Vasc Biol. 2009;29:969-74.
Plos One 2009;4(8):e6796
Hepatology 2010;51:511-522
Am J Clin Nutr. 2009; 90:415-24
Am J Clin Nutr. 2009;90:1656-64
Mol Cell Biology 2009;29:6257-67
Am J Clin Nutr. 2010;91:208-17
BMC Genomics 2009
Physiol. Genomics 2009
Circulation 2010
Diabetes 2010
Cell Metabolism 2010
Physiol Genomics. 2011;43(23):1307-18.
PLoS One. 2011;6(4):e19145.
Nature 2011 May 22
PLoS One. 2012;7(12):e49868.
PLoS One. 2012;7(11):e51066.
PLoS One. 2012;7(10):e47303.
BMC Med Genomics. 2012 Aug 28
PLoS One. 2012;7(8):e43260.
J Hepatol. 2012 Dec;57(6):1370-3.
Am J Physiol Gastrointest Liver Physiol. 2012
Physiol Genomics. 2012 Mar 19;44(6):352-61.
Am J Physiol Endocrinol Metab. 2012
Prog Lipid Res. 2012 Jan;51(1):63-70.
Mol Cell Biol. 2013 Jan 22.
Hepatology. 2013 Jan 21.
J Nutr. 2013 Jan 16.
Carcinogenesis. 2013 March

38. PPARs are ligand activated transcription factors
PPAR
9 cis retinoic acidfatty acids
DNA transcription
AGGTCAaAGGTCA
+
Gene
Response element
Protein
synthesis
Function
PPAR
RXR

39. Structures of PPAR ligands

40. Three sources of fatty acids in liver
& the role of fatty acid sensors PPAR and PPAR
PPAR

41. Murine PPAR
SI Liver
0
2.0 10
5
4.0 10
5
6.0 10
5
8.0 10
5
Human PPAR
SI Liver
0
25%
50%
75%
100%
Molecules/µgRNA
Expression(%) Expression levels of human and murine PPAR
0 25 50 75 100
placenta
trachea
thymus
bladder
prostate
testes
cervix
thyroid
adipose
lung
esophagus
colon
spleen
ovary
brain
skeletal muscle
kidney
liver
heart
small intestine
%%%%
Human PPAR
Bunger et al.
Physiol. Genomics 2007

42. Is PPAR the hepatic fatty acid sensor?
YES!
Sanderson et al PLoS One. 2008 Feb 27;3(2):e1681

43. Difference between Food & Pharma
Multiple targets versus specificity
A C
PPARPPARPPAR
B
Fatty acid
Drugs
Fatty acids
Receptor
Multiple targets
C6C5C4
C3C2C1
Physiol Genomics. 2011 Sep 27

44. Blood
triglycerides
Nutrigenomics & molecular nutrition allows
us to define the mechanistic framework

45. Response to the intestine to different
doses of dietary fat
De Wit PLOS one 2011

46. Study to show metabolic plasticity of the gut
Dose-dependent effects of dietary fat on development of obesity in
relation to intestinal differential gene expression in C57BL/6J mice
De Wit PLOS one 2011

47. Robust & concentration dependent effects in small intestine
Differentially regulated intestinal genes by high fat diet
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10
De Wit PLOS one 2011

48. Cellular localization and specific lipid metabolism-related
function of fat-dose dependently regulated genes
De Wit PLOS one 2011

49. Conclusion: Do not overload the gut
45% FAT
10% FAT
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10
40 cm
4 cm
chronically

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

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

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

53. Changes in lipid composition due to PUFA intake
Low = 0.4 g EPA+DHA/d; high = 1.6 g EPA+DHA/d

54. 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

55. 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

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

57. „Obese-linked‟ pro-inflammatory
gene expression profile by SFAs
• 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”
SFA diet MUFA diet
Van Dijk et al. AJCN 2009

58. Human study 3:
Plasma Protein Profiling Reveals
Protein Clusters Related to BMI and Insulin
Levels in Middle-Aged Overweight Subjects
AIM
• Associate plasma protein profiles with BMI
• Identify potential marker profile of early
disease state
. PLoS One. 2010 Dec 23;5(12):e14422

59. Measurements
• Rules Based Medicine (Austin, USA)
• Multiplex immunoassay
• In total 124 proteins measured
– Involved in diseases, inflammation,
endothelial function and metabolism
. PLoS One. 2010 Dec 23;5(12):e14422

60. We are different: improved phenotyping
necessary to reveal phenotype clusters
. PLoS One. 2010 Dec 23;5(12):e14422

61. Conclusion
• We identified clusters of plasma proteins associated with
BMI and insulin in a healthy population.
• These clusters included earlier identified biomarkers for
obesity-related disease as well as potential new
biomarkers.
• These plasma protein clusters could have potential
applications for improved phenotypic characterization of
volunteers in nutritional intervention studies or as
biomarkers in the early detection in obesity-linked
disease development and progression.
van Dijk SJ, Feskens EJM, Heidema AG, Bos MB, van de Rest O, Geleijnse JM, de
Groot CPGM, Müller M, Afman LA. Plasma Protein Profiling Reveals Protein Clusters
Related to BMI and Insulin Levels in Middle-Aged Overweight Subjects. PLoS One. 2010
Dec 23;5(12):e14422

62. Some future perspectives

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

64. Nutrigenomics
& Systems Biology

65. Nutrigenomics
Platform
TIFN-next
NWO-Top
Diabetes
foundation
NUGO
NutriTech
IPOP
Gut fermentation
+ Satiety
IPOP SysBiol
EU Ideal
Ageing
Epigenomics
TIFN
A-1001
A-1004
TIFN-next
projects
-Cardiovascular
-Gastrointestinal health
-Weight management
NCSB project
SCFA metabolism in the Gut
Belly Fat
Cohort
Wageningen
NGC projects
TIFN-projects
SysBiol Project Gatekeeper
LifeLines
Groningen

66. Research
questions
Nutrigenomics
DB mining
Improved
study design
Nutrigenomics
Phenotyping
Challenge
tests
Controlled
nutritional
intervention
Challenge
tests
Nutrigenomics
Phenotyping
Quantitative
Modeling
Systems Biology
Databasing
Nutrigenomics Platform
NNC
High Standardization
Comprehensive Phenotyping
Data capturing, basing, mining
MADMAX DB / DIETome DB
PBMCs DB / NutriPheno DB
Microbiome DB
Secretome DB
Nutritional Science 2.0

67. Cohort: 500 men (45/50 y with/without
abdominal overweight (BMI 28-30 kg/m2)
Measurements
MRI
Hyperinsulinemic euglycemic clamp
Functional vascular measurements
Plasma glucose & lipid- & cytokine profiles
PBMCs, WAT & muscle biopts
Plasma, Urine, Feces
FFQ
Metabolite profiling in plasma & urine
Microbiota
MRI neuro-imaging

68. I have a dream
“I track, therefore I am”

69. Key questions for nutrigenomics
1. What is your scientific problem? Why do you need
nutrigenomics?
2. What are the best suitable genomics tools for your
nutrition research and how to apply them?
3. What is the role of nutrition in the genotype-phenotype
relationship?
4. What is healthy and how to measure and quantify the
health status?
5. What are feasible (human) applications?
6. What is the impact of nutrigenomics for the food (&
pharma) industry?

70. General conclusions
• Nutrigenomics is the combination of molecular nutrition
and multi-Omics applications.
• There is not one gen-Omics tool that can “do everything”.
• In mice it is possible to perform most comprehensive
nutritional systems biology studies to elucidate the impact
of nutritional strategies on metabolic plasticity & organ
health.
• The challenge remains to get useful human data for the
individual characterization of organ function (metabolic
health) versus systemic health.

71. Its easy (if your genes are ok)
2 Meals a day, work as long as possible & embrace
challenges
Walter Breuning (1896 - 2011)