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Nutrigenomics
Prof:Rui Alves
[email protected]
973702406
Dept Ciencies Mediques Basiques,
1st Floor, Room 1.08
Website of the Course:http://web.udl.es/usuaris/pg193845/Courses/Bioinformatics_2007/
Course: http://10.100.14.36/Student_Server/
What is Nutrigenomics?

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Nutrigenomics is the science that examines the
response of individuals to food compounds using
post-genomic and related technologies.
The long-term aim of nutrigenomics is to
understand how the whole body responds to real
foods using an integrated approach.
Studies using this approach can examine people
(i.e. populations, sub-populations - based on
genes or disease - and individuals), food, lifestage and life-style without preconceived ideas.
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Problem 1: Nutrition – tasty + complex
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Genes – Lifestyle – Calories
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The same genes – The changed
diet
Paleolithic era
Modern Times
1.200.000 Generations between
feast en famine
% Energy
100
50
0
2-3 Generations in energy abundance
% Energy
Low-fat meat
Chicken
Eggs
Fish
Fruit
Vegetables (carrots)
Nuts
Honey
100
50
0
Grain
Milk/-products
Isolated Carbohydrates
Isolated Fat/Oil
Alcohol
Meat
Chicken
Fish
Fruit
Vegetables
Beans
Molecular nutrition
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Problem 2:
Our “gene passports” and nutrition
Optimal Nutrition
Individual genotype
Functional phenotype
AA
AB
BB
Lifestyle
Improvement
of Health
Maintenance
“Eat right for your genotype??”
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Personalized diets?
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Nutrigenomics
Target Genes
Mechanisms
Pathways
Foods
Nutrition
Molecular Nutrition
& Genomics
Signatures
Profiles
Biomarkers
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
Large research consortia
Big money
Small research groups
Small budgets
Complexity
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Nutrients acts as dietary
signals
Nutritional factors
Transcription factors
Gene transcription
Energy
homeostasis
Cell
proliferation
Nutrient
absorption
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“Molecular Nutrition & Genomics”
The strategy of Nutrigenomics
50000 (?)
metabolites
80-100000
proteins
100000
transcripts
20-25000
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Transcription-factor pathways
mediating nutrient-gene interaction
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A key instrument in Nutrigenomics
research:
The GeneChip® System
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Functions of PPARs
PPARa
-Nutrient metabolism
(lipid, glucose, AAs)
PPARg
- Lipid and glucose
metabolism
PPARb
- Lipid metabolism
- Proliferation
- Cell cycle control
- Keratinocyte
differentiation
- Inflammation
- Inflammation
- Inflammation
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PPARs are ligand activated transcription
factors
fatty acids
Function
9 cis retinoic acid
Protein
synthesis
PPAR
Gene
Response element
AGGTCAaAGGTCA
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+
DNA transcription
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Why are PUFAs healthy?
PPAR
-
+
SREBP1
SP1/NF-Y
PPRE
Fatty acid oxidation
genes
b-Oxidation
Lipogenic genes
FA synthesis
Triglyceride synthesis
VLDL-TG
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Pharmacological
activation
WY14643
PPARa+/+
PPARa-/-
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Physiological
activation
Fasting
PPARa+/+
PPARa-/-
Nutritional
activation
High fat diet
PPARa+/+
PPARa-/-
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Pharmacological
activation
Physiological
activation
WY14643
Nutritional
activation
Fasting
 PPARa-/-
High fat diet
 PPARa-/-
 PPARa-/4  PPARa+/+
4  PPARa+/+
3
3
3
2
2
2
1
1
1
0
0
0
4
 PPARa+/+
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Kersten et al.
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Role of PPARa in the hepatic
response to fasting
FFA
Elucidation by employing:
1) k.o.-mice
2) specific ligands
3) transcriptome analysis
4) In vitro studies (Promoter
studies, ChIP, etc)
Liver
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CMLS, Cell. Mol. Life Sci. 61 (2004) 393–416
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Metabolic Syndrome and Diabetes
Genes
Muscle insulin
resistence
Obesity
Increased
lipolysis in
visceral fat
Ageing
Increased
fatty acids levels
hyperinsulemia
b Cell
compensation
Increased
glucose output
Decreased
glucose
tolerance
b Cell
decompensation
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Increased
gluconeogenesis
in liver
Decreased
insulin
secretion
Diabetes
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Gene regulation by fatty acids
WAT
Fatty acid oxidation
Fatty acid hydroxylation
Hydrolysis of Acyl-CoA
Fatty acid transport
Hepatobiliary lipid transport
Ppara
PC
FFA
TG
+
+ Fxr/Lxr
+
+
ABCG5/G8
Mdr2
-Acute phase
response
Gluconeogenesis
Portal blood
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Hepatocyte
Bile
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What happens during fasting?
glucose
Blood
DHAP
FFA
Glycerol
TG
G3P
FFA
WAT
Liver
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Mouse liver gene expression
signatures during fasting
Metabolic reprogramming during fasting
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2351.5
168.5
3248.1
143.4
D1
D2
D3
D1
3.3
2.3
2.2
1.6
2.2
1.7
2
335.2
3615.5
4171.4
783.6
177.9
4116.4
925
D1
D2
D2
D4
D4
D5
D5
1.8
2.2
2
395.9 D1
2848.7 D5
1149.7 D2
4.4
2.9
1.7
2.3
3.3
8.7
1.7
4.5
1.8
2.2
2.5
2.6
456.7
913.2
1678.7
142
106.6
4283.8
787.4
3997.4
1587.7
3607.4
1842.4
4177.9
cluster
4.3
3.8
3.1
2.7
3.3
2.3
2.9
2.3
2
2.6
3.8
73
261.3
134.8
531.7
110.3
217.8
100.2
U1
U1
U1
U1
U2
U5
U3
5.9
3.4
2.3
3.2
3.2
4.4
2.4
300.7
1993.4
462.8
166.2
34.4
504.1
486.5
U4
U1
U2
U4
U3
U1
U3
transcription factors
3
10.4
8.5
4.5
1.8
3.5
X61800 C/EBPd
X62600 C/EBPb
AA106163 CAR
U09416 FXR
U09419 LXRb
X57638 PPAR a
M34476 RARg
receptors and binding proteins
X70533 corticosteroid binding globulin
M33324 high molecular weight growth hormone receptor
AA038239 plasma retinol binding protein RBP
X14961 heart fatty acid binding protein H-FABP
Avg
Diff
SREBP-1
SREBP-1
SREBP-1
retinoid O receptor RORgamma
retinoid O receptor RORalpha1
hepatic nuclear factor HNF3alpha
FoldChange
cluster
D1
D1
D1
D1
D2
D2
transcription factors
AA061461
AA068578
AA067092
U39071
Y08640
U44752
up
Acc.
No.
Avg
Diff
104.3
580.3
172.4
267.3
266.6
72
FoldChange
Acc.
No.
down
receptors and binding proteins
X81579
L05439
L38613
X57796
U40189
J03398
M65034
insulin-like growth factor binding protein 1
insulin-like growth factor binding protein 2
glucagon receptor
tumor necrosis factor receptor 55 kD
pancreatic polypeptide/neuropeptide Y receptor
Abcb4 (Mdr2)
intestinal fatty acid binding protein I-FABP
amino acid metabolism
Z14986
M17030
X51942
J02623
U38940
U24493
X16314
adenosylmethionine decarboxylase
*ornithine transcarbamylase
phenylalanine hydroxylase
aspartate aminotransferase
asparagine synthetase
tryptophan 2,3-dioxygenase
glutamine synthetase
Metabolic reprogramming
during fasting
nucleotide metabolism
X75129
xanthine dehydrogenase
M27695.0 urate oxidase
X56548
purine nucleoside phosphorylase
other enzymes
other enzymes
W54790
W91222
X01756
U39200
W41963
M27796
X51971
AA106634
U00445
U27014
M63245
M74570
ATP synthase A chain
cytochrome c oxidase subunit VIIa
cytochrome c
epidermal 12(S)-lipoxygenase
acetyl-CoA synthetase
carbonic anhydrase III
carbonic anhydrase V
cis-retinol/3-alpha-hydroxysterol short chain dehydr.
glucose-6-phosphatase
sorbitol dehydrogenase
amino levulinate synthase (ALAS-H)
aldehyde dehydrogenase II
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D4
D5
D5
D2
D2
D3
D1
D5
D4
D2
D4
D4
X80899
U14390
Z37107
U33557
D49744
U12922
J03733
D16333
J02652
SIG81 (cytochrome c oxidase VIIa homologue)
aldehyde dehydrogenase (Ahd3)
epoxide hydrolase
folylpolyglutamate synthetase
farnesyltransferase alpha
CD1 geranylgeranyl transferase beta subunit
ornithine decarboxylase
coproporphyrinogen oxidase
malate NADP oxidoreductase
2
3.6
1.8
2.1
1.9
2.1
1.6
2.5
1.7
762.5
660.9
3012.6
648.8
475.8
260.1
257.8
216.9
249
U2
U3
U3
U5
U3
U3
U3
U3
U3
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How to crack the code?
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Rosetta Resolver 5/Base 2
Bioconductor et al. (WWW)
Spotfire
MS Excel
Pathway assist
GeneGo
Ingenuity
Thinking!!
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The common diseases are complex:
Factors influencing the development of metabolic
syndrome
Obesity
Hypertension
Diabetes 1 2
3
Inflammation
Hyperlipidemia
MSX
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DISEASE STATE (arbitrary units)
Prevention versus Therapy – Nutrition
versus Pharma
120
Complex Disease
100
80
Different targets
60
40
20
0
Homeostasis
Health
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TIME (months/years)
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Interplay between diet, organs and
metabolic stress
Adipose
tissue
Absorbed
nutrients
Diet
Digestion
and
absorption
Unabsorbed
nutrients
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Muscle
Lipids
Homeostasis
by liver
Systemic effects:
• Glucose intolerance
• Insulin resistance
• Lipid disorders
EnteroHepatic
Cycle
 Gut
contents
Signals gut mucosa:
• satiety hormones
• cytokines
•  barrier
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Signatures of health & stress -The “two hits”:
Metabolic and pro-inflammatory stress
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Nutritional Systems Biology
Gene
Sample Types:
protein index
metabolite index
Protein
gen
ei
nd
ex
• 10 ApoE3 mice
• 10 wildtype mice
• liver tissue
• plasma
• urine
Biostatistics
Biostatistics
Bioinfomatics
Bioinfomatics
Metabolite
9
8
7
6
5
4
3
2
1
0 ppm
Targets
Targets
and
and
Biomarkers
Biomarkers
Figure 1. A typical Systems Biology strategy for study of atherosclerosis [1] using
a transgenic ApoE3 Leiden mouse model.
Onset of
disease
Predisposition
Genotype
Surrogate
Biomarkers
Late biomarkers
of disease
Early biomarkers
of disease
Diagnostic
markers
Prognostic
markers
Changes in pathway dynamics
to maintain homeostasis
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Gene regulation
by nutrients
Gene expression
Prevention of
Signatures
Metabolic Syndrome
Dietary
Programming
Nutrient-related cellular sensing + Metabolic stress
Nutrients
Signaling
Target
genes
of nutrients
Transporters
Transcription
factors
Lipids
Fatty acids
Sugars
Calcium
Enterocytes
Hepatocytes
Adipocytes
Lymphocytes
Signaling
Cells
Functions
Proteins
Genes
Cells
Metabolic
Implications
Metabolites
Proteins
Posttranslational
Regulation
Genes
Organs
Proteins
Animal
Healthy Food
Mouse
Models
Intestine
Liver, Muscle
Blood
Adipose tissue
Functions
Humans
Organs
Animal
Intervention
Studies
Humans
Diet-related organ sensing, Sensitivity genes + Molecular Phenotype
Molecular Biology
Tools
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Transcriptome
Proteome
Metabolomics
Systems Biology
Early Molecular
Biomarkers
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Linking to other EU programs
NuGO
DIOGENES
obesity
(EU, 12M€)
Proliferation
Differentiation
Apoptosis
Absorption
Host-microbe
interaction
Carotenoids
Metabolic stress
Gut Health
Metabolic health
Life stage nutrition
Risk Benefit analysis
Adipocyte
fat oxidation
Periconceptual
nutrition
Inflammation
Muscle insulin
resistance
Systems biology
Nutrigenetics
Genetic epidemiology
Toxicogenomics
EARNEST
early life nutrition
(EU, 14M€)
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Lipid metabolism
Early biomarkers
Nuclear
transcription
factors
LIPGEN
Lipids & genes
(EU, 14M€)
Diabetes II
Innovative Cluster Nutrigenomics
Chronic metabolic stress
(Dutch, 21M€)
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Two Strategies
(1) The traditional hypothesis-driven approach: specific genes and
proteins, the expression of which is influenced by nutrients, are
identified using genomics tools — such as transcriptomics, proteomics
and metabolomics — which subsequently allows the regulatory
pathways through which diet influences homeostasis to be identified .
Transgenic mouse models and cellular models are essential tools .
provide us with detailed molecular data on the interaction
between nutrition and the genome .
(2) The SYSTEMS BIOLOGY approach: gene, protein and metabolite
signatures that are associated with specific nutrients, or nutritional
regimes, are catalogued, and might provide ‘early warning’molecular
biomarkers for nutrient-induced changes to homeostasis.
Be more important for human nutrition, given the difficulty of
collecting tissue samples from ‘healthy’ individuals.
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Use model organisms in nutrition
research
Caenorhaboditis
elegans
(completed genome
segence)
Zebrafish
(Danio rerino)
Mouse
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Role of nutrients in Alzhelmer and
Parkinson diseases.
Role of nutrients in development and
organ functions.
Role of nutrition in development and
organ functions.
35
Use model organisms in nutrition
research
Knockout mice
is useful ！
HNF, hepatocyte nuclear factor; LXR, liver X receptor; MTF1, metal-responsive
transcription factor; PPAR,peroxisome proliferator-activated receptor; TGF,
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Nature reviews/genetics (2003) , 4:315-322
transforming growth factor.
Nutrigenomics and nutritional systems
biology apply the same set of technologies
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Nutrition (2004) , 20: 4-8
37
The ‘smart’ combination of molecular nutrition
and nutrigenomics.
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Nature reviews/genetics (2003) , 4:315-322
Strategies we need in gene-nutrient
interactions
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Integration of enabling technologies in
nutrigenomics
Microarray & SAGE
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Aging-related changes in gene
expression in gastrocnemius muscle
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Science (1999) 285:1390-139341
Caloric restriction–induced alterations in
gene expression
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Science (1999) 285:1390-1393
42
Conclusion of gene expression profile of
aging and its retardation by caloric restriction
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Science (1999) 285:1390-1393
Conclusion and future perspective
(1) Nutrigenomics researchers must know the challenge of
understanding polygenic diet related diseases.
(2) Short-term goals:
1. to identify the dietary signals.
2. to elucidate the dietary sensor mechanisms.
3. to characterize the target genes of these sensors.
4. to understand the interaction between these signalling
pathways and pro-inflammatory signalling to search for
sensitizing genotypes.
5. to find ‘signatures’ (gene/protein expression and metabolite
profiles).
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(3) Long-term goals:
Nutrigenomics is to help to understand how we can use
nutrition to prevent many of the same diseases for which
pharmacogenomics is attempting to identify cures.
SNP database will be effect on disease risk.
Future
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personalized diets
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To Do
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Find examples in the literature of nutrigenomic
studies.
Review their finding
Prepare a presentation about it.
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