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http://creativecommons.org/licens es/by-sa/2.0/ 7/17/2015 1 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? 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. 7/17/2015 3 Problem 1: Nutrition – tasty + complex 7/17/2015 4 Genes – Lifestyle – Calories 7/17/2015 5 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 7/17/2015 7 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??” 7/17/2015 8 Personalized diets? 7/17/2015 9 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 7/17/2015 10 Nutrients acts as dietary signals Nutritional factors Transcription factors Gene transcription Energy homeostasis Cell proliferation Nutrient absorption 7/17/2015 11 “Molecular Nutrition & Genomics” The strategy of Nutrigenomics 50000 (?) metabolites 80-100000 proteins 100000 transcripts 20-25000 7/17/2015 genes 12 Transcription-factor pathways mediating nutrient-gene interaction 7/17/2015 13 A key instrument in Nutrigenomics research: The GeneChip® System 7/17/2015 14 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 7/17/2015 15 PPARs are ligand activated transcription factors fatty acids Function 9 cis retinoic acid Protein synthesis PPAR Gene Response element AGGTCAaAGGTCA 7/17/2015 + DNA transcription 16 Why are PUFAs healthy? PPAR - + SREBP1 SP1/NF-Y PPRE Fatty acid oxidation genes b-Oxidation Lipogenic genes FA synthesis Triglyceride synthesis VLDL-TG 7/17/2015 17 Pharmacological activation WY14643 PPARa+/+ PPARa-/- 7/17/2015 Physiological activation Fasting PPARa+/+ PPARa-/- Nutritional activation High fat diet PPARa+/+ PPARa-/- 18 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+/+ 7/17/2015 Kersten et al. 19 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 7/17/2015 CMLS, Cell. Mol. Life Sci. 61 (2004) 393–416 20 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 7/17/2015 Increased gluconeogenesis in liver Decreased insulin secretion Diabetes 21 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 7/17/2015 Hepatocyte Bile 22 What happens during fasting? glucose Blood DHAP FFA Glycerol TG G3P FFA WAT Liver 7/17/2015 23 Mouse liver gene expression signatures during fasting Metabolic reprogramming during fasting 7/17/2015 24 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 7/17/2015 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 25 How to crack the code? 7/17/2015 Rosetta Resolver 5/Base 2 Bioconductor et al. (WWW) Spotfire MS Excel Pathway assist GeneGo Ingenuity Thinking!! 26 The common diseases are complex: Factors influencing the development of metabolic syndrome Obesity Hypertension Diabetes 1 2 3 Inflammation Hyperlipidemia MSX 7/17/2015 27 DISEASE STATE (arbitrary units) Prevention versus Therapy – Nutrition versus Pharma 120 Complex Disease 100 80 Different targets 60 40 20 0 Homeostasis Health 7/17/2015 TIME (months/years) 28 Interplay between diet, organs and metabolic stress Adipose tissue Absorbed nutrients Diet Digestion and absorption Unabsorbed nutrients 7/17/2015 Muscle Lipids Homeostasis by liver Systemic effects: • Glucose intolerance • Insulin resistance • Lipid disorders EnteroHepatic Cycle Gut contents Signals gut mucosa: • satiety hormones • cytokines • barrier 29 Signatures of health & stress -The “two hits”: Metabolic and pro-inflammatory stress 7/17/2015 30 Nutritional Systems Biology Gene Sample Types: protein index metabolite index Protein ge ne ind 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 7/17/2015 31 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 7/17/2015 Transcriptome Proteome Metabolomics Systems Biology Early Molecular Biomarkers 32 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€) 7/17/2015 Lipid metabolism Early biomarkers Nuclear transcription factors LIPGEN Lipids & genes (EU, 14M€) Diabetes II Innovative Cluster Nutrigenomics Chronic metabolic stress (Dutch, 21M€) 33 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. 7/17/2015 34 Use model organisms in nutrition research Caenorhaboditis elegans (completed genome segence) Zebrafish (Danio rerino) Mouse 7/17/2015 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, 7/17/2015 36 Nature reviews/genetics (2003) , 4:315-322 transforming growth factor. Nutrigenomics and nutritional systems biology apply the same set of technologies 7/17/2015 Nutrition (2004) , 20: 4-8 37 The ‘smart’ combination of molecular nutrition and nutrigenomics. 7/17/2015 38 Nature reviews/genetics (2003) , 4:315-322 Strategies we need in gene-nutrient interactions 7/17/2015 39 Integration of enabling technologies in nutrigenomics Microarray & SAGE 7/17/2015 40 Aging-related changes in gene expression in gastrocnemius muscle 7/17/2015 Science (1999) 285:1390-139341 Caloric restriction–induced alterations in gene expression 7/17/2015 Science (1999) 285:1390-1393 42 Conclusion of gene expression profile of aging and its retardation by caloric restriction 7/17/2015 43 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). 7/17/2015 44 (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 7/17/2015 personalized diets 45 To Do Find examples in the literature of nutrigenomic studies. Review their finding Prepare a presentation about it. 7/17/2015 46