Michael Müller

Nutrigenomics: Providing Early Warning Molecular Biomarkers For

Nutrient-Induced Changes to Homeostasis

Outline

Nutrigenomics: From complex problems to simple questionsFree fatty acids as a “need for glucose” signal“Metabolic stress” and its signaturesHuman studies and the use of PBMCsConclusions

Nutrigenomics

Target GenesMechanisms

Pathways

SignaturesProfiles

Biomarkers

FoodsNutrition

Molecular Nutrition& Genomics

NutritionalSystems 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

Small research groupsSmall budgets

Large research consortiaBig money

Complexity

Nutrigenomics

Complex ProblemsComplex Problems

Simple QuestionsSimple Questions

Transcription-factor pathwaysmediating nutrient-gene interaction

“Molecular Nutrition & Genomics”The strategy of Nutrigenomics

80-100000proteins

20-25000 genes

100000 transcripts

50000 (?)metabolites

Functions of PPARs

“Fat sensors”

PPARα PPARγ PPARβ

-Nutrient metabolism (lipid, glucose, AAs)

- Proliferation

- Inflammation

- Lipid and glucosemetabolism

- Cell cycle control

- Inflammation

- Lipid metabolism

- Keratinocytedifferentiation

- Inflammation

Role of PPARα in the hepatic response to fasting

Liver

FFAFFAElucidation by employing:1) k.o.-mice2) specific ligands3) transcriptome analysis4) In vitro studies (Promoter

studies, ChIP, etc)

Elucidation by employing:1) k.o.-mice2) specific ligands3) transcriptome analysis4) In vitro studies (Promoter

studies, ChIP, etc)

CMLS, Cell. Mol. Life Sci. 61 (2004) 393–416CMLS, Cell. Mol. Life Sci. 61 (2004) 393–416

The role of free fatty acids

.00

.20

.40

.60

.80

0 6 12 18 24

mmol/L

2

4

6

8

10mmol/Lfree fatty acid

blood glucose

“Hunger” signal or“Need for glucose” signal

time (hours)

• During fasting• During long

endurance activity

• In obese people

Factors influencing the development of metabolic syndrome

MSX

13

Diabetes

ObesityHypertension

Inflammation Hyperlipidemia

2

Pharma

Nutrition

0

20

40

60

80

100

120

TIME (months/years)

DIS

EA

SE

STA

TE

HomeostasisHealth

Complex Disease

Different targets

Metabolic stress

Metabolic syn

drome

Signatures of health & stress -The “two hits”: Metabolic and pro-inflammatory stress

Mouse study: 45% High Fat versus 10% Low Fat

05

1015202530354045

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16

Time on diet (weeks)

Wei

ght (

gram

)

C57Bl/6 10% fat

C57Bl/6 45% fat

-5

0

5

10

15

20

25

0 15 30 45 60 90 150

Time (minutes)

Cha

nge

in g

luco

se (m

mol

/L)

10% fat / 9 wk (n=12)45% fat / 9 wk (n=11)10% fat / 15 wk (n=6)45% fat / 15 wk (n=6)

Mean weight per mouseGlucose tolerance test C57Bl/6 mice

IntestineLiverMuscleWAT

+ “omics” analysis

Hierarchical Clustering ‘Liver and Intestine’Liver Intestine

-0.281 1

1 3057 3 13 63

SL_W1H_L SL_W2_HL SL_W3_HL SL_W4_HL SL_10_HL SL_16_HL

-0.255 1

1 2636 3 14 63

SL_W1H_L SL_W2_H_ SL_W3_H_ SL_W4HH_ SL_10_H_ SL_16_H_

Weeks 1 2 3 4 10 16 Weeks 1 2 3 4 10 16

Clustering method: UPGMA (unweighted average)Similarity measure: CorrelationOrdering function: Average value

Pathway analysis supports searches for common regulators

Liver data weeks 1,2,3,4,10 + 16 HF vs LF –0.5<SLR> 0.5

Human Studies “Fasting signatures”

Person 1

Person 2

Person 3

Person 4

0h 24h 48h 0h 24h 48h

DesignDecember February

Blood samples Blood samples

4 pm: meal 5 pm 5 pm 5 pm

Person 1

Person 2

Person 3

Person 44 pm: meal 5 pm 5 pm 5 pm

Get insight in basic gene expression of human PBMCs (inter- and intra-individual variation)Explore the possibilities of using PBMCs as markers for nutritional statusFocus on fatty acid dependent gene expression signatures

Clustering method: UPGMA (unweighted average)Similarity measure: Euclidean distanceOrdering function: Average valueHierarchical Clustering

N3_24N3_0

N11_0N10_0

N11_24

N10_24

N1_0N1_48

N11_48

N10_48

N3_48 0 h 48 h

“Zoom-in”: Common regulators (slr>0.5 or <-0.5)

Conclusions• Nutrigenomics is the application of high-throughput genomics tools

in nutrition research. • Applied wisely, it will promote an increased understanding of how

nutrition influences metabolic pathways and homeostatic control,how this regulation is disturbed in the early phase of a diet-related disease and to what extent individual sensitizing genotypes contribute to such diseases.

• Ultimately, nutrigenomics will allow effective dietary-intervention strategies to recover normal homeostasis and to prevent diet-related diseases.

• However, huge efforts (money + smart ideas) are needed to solve already identified and future problems:– Extract biological relevant data from nutrigenomics datasets (“signal

versus noise”)– Translate animal data to the human situation (in particular for complex

diseases)– Is nutritional “systems biology” the method to identify biomarkers?– How tailored-made can nutrition be in the future?

Collaboration - Linking to EU programs

LIPGENLipids & genes

(EU, 14M€)

DIOGENESobesity

(EU, 12M€)

Innovative Cluster NutrigenomicsChronic metabolic stress

(Dutch, 21M€)

EARNESTearly life nutrition

(EU, 14M€)

Lipid metabolism

Life stage nutritionRisk Benefit analysis

Metabolic health

ProliferationDifferentiation

Apoptosis

Inflammation

Muscle insulin resistance

Nutrigenetics

Early biomarkers

Periconceptualnutrition

Nuclear transcription

factors

Systems biology

Host-microbeinteraction

Gut Health

Absorption

Genetic epidemiology

Toxicogenomics

Metabolic stress

Adipocytefat oxidation

Diabetes II

Carotenoids

Lipid metabolism

Life stage nutritionRisk Benefit analysis

Metabolic health

ProliferationDifferentiation

Apoptosis

Inflammation

Muscle insulin resistance

Nutrigenetics

Early biomarkers

Periconceptualnutrition

Nuclear transcription

factors

Systems biology

Host-microbeinteraction

Gut Health

Absorption

Genetic epidemiology

Toxicogenomics

Metabolic stress

Adipocytefat oxidation

Diabetes II

Carotenoids

NuGO

Thanks to:

Members of the Division of Human Nutrition

Wageningen University

Ben van Ommen

Christian Trautwein

Diabetes-fondsNWO

IOP-Genomics

Members of the IOP Genomics “Gut Health”

Centre for Human NutrigenomicsNutrigenomics Consortium/WCFS

Annibale BiggeriMarta Blangiardo

(NUGO)