Nanjing1 2013 Lecture "Nutrigenomics part 1"


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Nanjing1 2013 Lecture "Nutrigenomics part 1"

  1. 1. Lecture 1NutrigenomicsWhat is Nutrigenomics & molecular nutrition research?From challenges to solutionsMichael MüllerNutrition, Metabolism and Genomics Group, Division of Human Nutrition, Wageningen University
  2. 2. Nutrition, Metabolism & Genomics groupScientific objectives• To unravel the molecular details of how nutrition influencesmetabolic health of individuals, basically to answer thequestion of what is behind “You are what you eat and haveeaten”.• We are aiming to characterize the quantitative role ofnutrition (and lifestyle factors as exercise) on thetranslation of an individual genotype into a healthyphenotype.• Essential for the success of this approach is thecombination of functional genomics research usingtransgenic animals and translational human nutrigenomicsstudies where mechanistic concepts derived from modelstudies are validated in controlled human interventionstudies.
  3. 3. NMG research linesMetabolic HealthMolecular nutrition of fatty acid sensingHuman Metabolic PlasticityIntestine as GatekeeperRole of Epigenetics in AgeingNNC infrastructure (databases, OMICs)
  4. 4. Nutrigenomics: Research with impact
  5. 5. Challenge 1: Successful ageingStay healthy as long as possible100 %timeHealth/“Qualityoflife”
  6. 6. Challenge 2: Whats healthy?
  7. 7. Challenge 3:We have a tsunami of health problems
  8. 8. 100500% EnergyLow-fat meatChickenEggsFishFruitVegetables (carrots)NutsHoney100500% EnergyFruitVegetablesBeansMeatChickenFishGrainMilk/-productsIsolated CarbohydratesIsolated Fat/OilAlcohol1.200.000 Generationsbetween feast en faminePaleolithic era2-3 Generationsin energy abundanceModern TimesChallenge 4:Our “paleolithic” genes + modern diets
  9. 9. You are what you eat
  10. 10. How many human genes do we have?Not so many but….
  11. 11. Genotype• A genotype is an individuals collection ofgenes. The term also can refer to the twoalleles inherited for a particular gene.• The genotype is expressed when theinformation encoded in the genes DNA isused to make protein and RNA molecules.• The expression of the genotypecontributes to the individuals observabletraits, called the phenotype.
  12. 12. Our genes• 23 chromosomes• ≈ 20,000+ genes
  13. 13. Genes and Nutrition => PhenotypeIts not that easy
  14. 14. Classification of hereditary diseases
  15. 15. Genetic “mistakes” makes us “vulnerable”
  16. 16. Phenotype• A phenotype is an individuals observabletrait, such as height, eye color, and bloodtype.• The genetic contribution to the phenotype iscalled the genotype.• Some traits are largely determined by thegenotype, while other traits are largelydetermined by environmental factors(including nutrition). => Nutritional Phenotype
  17. 17. Phenotype plasticityPhenotypic plasticity is the ability of an organism tochange its phenotype in response to changes in theenvironment (e.g. nutrition or exercise).CYP4A1002468101214WT KO WT KO WT KO WT KO WT KO WT KO WT KO WT KO WT KOctrl WY feno C10:0TG C18:1TG C18:2TG C18:3TG C20:5TG C22:6TGFCvsWTctrl
  18. 18. Genome plasticity
  19. 19. Sequencingtechnologiesand their usesTogether, these methods can be usedfor integrated personal omics profilingto map all regulatory and functionalelements in an individual. Using thisbasal profile, dynamics of the variouscomponents can be studied in thecontext of disease, infection, treatmentoptions, and so on. Such studies willbe the cornerstone of personalized andpredictive medicine
  20. 20. Timely relatively modest interventions in earlylife can have a large effect on disease risk later
  21. 21. You are what you eat and have eaten:Received, Recorded, Remembered & Revealed
  22. 22. NutrigenomicsQuantification of the nutritional genotype-phenotypePhenotypeMetabolomeProteomeTranscriptomeEpigenomeGenotypeLifestyleNutritionMicrobiotaEnvironment
  23. 23. Genomics/Transcriptomics Proteomics MetabolomicsBioinformaticsBioinformaticsGC-MSGenotyping (polymorphisms)Foods (functionality)Physiology (phenotyping)Species (genotyping, traits)ResistanceFoods (GMO)Adaptation (stress response)GMO (allergens)GenotypingFoods (traits)Foods (starter cultures)Plant foods (contaminants)Hygiene (contaminants)Nutrition (GI flora)Microorganisms Plants Animals HumansFood and NutritionPhysiology (phenotyping)BiomarkersHuman nutrition and the new technologies
  24. 24. Why Nutrigenomics• To understand nutrition &metabolic health/plasticity• To comprehensivelyphenotype• To validate FFQ• To enable strategies tooptimize personal health• To provide scientificevidence for healthclaims of “functional”foods Mechanisms Biomarkers Nutritional Science 2.0 Personal Nutrition Health claim support
  25. 25. Nutrigenomics: Two strategiesTarget GenesMechanismsPathwaysSignaturesProfilesBiomarkersMolecular Nutrition& GenomicsNutritionalSystems 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 proteomeand metabolomeComplexity++++ +L. Afman & M. Müller J Am Diet Assoc. 2006;106:569-576.
  26. 26. What is the background? What the problem?WHYHealth claims(EFSA)Insufficient evidenceto support claimse.g. microbiotaCollaboration withFood industriesPPPNutritional interventionOften not effective:Hard to demonstrateeffects of “healthy”foodsBiomarkers notSensitive enoughFFQ not hard evidence“fuzzy” phenotypeComplex genotypesPersonalizedNutritionSaturated fat = badUnsaturated fat = goodTrue? Why?Mechanisms?All sat. fats? ω3/6Important toDifferentiate! ForConsumer/ Industry &ScienceObesity:Role of “too much”calories“Modern” foods too„Tasty‟ & not satietyinducingComprehensiveUnderstanding of nutrientsensing & satiety
  27. 27. What is the specific aim?AIMHealth effect of foodsFunctional foodsNew smart foodsfor specificpopulationsPrevention ofdiseasesEarly diagnosisEarly biomarkersImproved & moreeffective prevention ofDiet-related diseasesMechanismRole of nuclearreceptorsEvidence-basedNutritionComprehensiveunderstanding ofnutrition =>NutritionalSystems BiologyDisease relatedLink Nutrition &Obesity, Cancer,Diabetes, CVDUnderstanding of earlypathology of diseasesIdentification of targetsImproved intervention
  28. 28. Which materials and methods?MaterialsMethodsHealth effect of foodsFunctional foodsNew Cell based assaysMicroarray analysisK.O. miceHuman StudiesMouse/HumanIn vivo/ vitroPrevention ofdiseasesEarly diagnosisEarly biomarkersMouse studiesHuman StudiesBlood, Urine, tissueOMICs analysisMouse / HumanIn vivoControlledinterventionsMechanismRole of nuclearreceptorsEvidence-basedNutritionFunctional genomicsK.O. miceMicroarray analysis of organsMetabolomics, SystemsBiologyMouse (models)In vivo/ vitroDisease relatedLink Nutrition &Obesity, Cancer,Diabetes, CVDDisease modelsComprehensivephenotypingTime seriesMouse (models) orHuman (control/case)Well phenotyped
  29. 29. What are the specific deliverables?DeliverablesHealth effect of foodsFunctional foodsNew bioassays to testFood functionality (HCS)New in vivo modelsClaim supportMechanisticbasis of foodfunctionalityPrevention ofdiseasesEarly diagnosisEarly biomarkersDatabase withOMICS basedwell annotated data setsRelated to Organ healthversus Systemic healthNutritionalPhenotype DB forsmart query &biomarker discoveryMechanismRole of nuclearreceptorsEvidence-basedNutritionOrgan-specificDatabases(transcriptome,secretome, etc…)Systems Biology modelsPrediction of metabolicconsequences ofnutrients/ bioactivesDisease relatedLink Nutrition &Obesity, Cancer,Diabetes, CVDElucidation of pathwaysInvolved in earlyPathology (liver, intestine,WAT)New anti-inflammatory targetse.g. preventivedietary modulationOf interaction of organcells with macrophages
  30. 30. Your are what you eatHealthy 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
  31. 31. VLDL LPLRemnantFFAFatsChylomicronsWe are what we eat
  32. 32. Transcription-factor pathwaysmediating nutrient-gene interaction, RXRsTLR4
  33. 33. EndocrinereceptorsSteroid hormonesHigh affinityEndocrinereceptorsLipidsLow affinityOrphans?Chawla, Science 2001Nuclear hormone receptors
  34. 34. Regulation of Cholesterol andLipid Handling in Metabolic OrganSystems by Nuclear ReceptorsIntestineLXRDecreased cholesterol absorptionFXRIncreased bile salt recirculationPPARImproved lipid handling
  35. 35. Nutrigenomics: “Molecular Nutrition & Genomics”Essential role of nutrient sensing transcription factors
  36. 36. Understanding NutritionHow nutrients regulate our genes: via sensing molecular switchesChangedorganmetaboliccapacityJ Clin Invest. 2004;114:94-103J Biol Chem. 2006;28:934-44Endocrinology. 2006;147:1508-16Physiol Genomics. 2007;30:192-204Endocrinology. 2007;148:2753-63BMC Genomics 2007; 8:267Arterioscler Thromb Vasc Biol. 2007;27:2420-7Am J Clin Nutr. 2007;86(5):1515-23PLOS ONE 2008;3(2):e1681BMC Genomics 2008; 9:231BMC Genomics 2008; 9:262J Biol Chem. 2008;283:22620-7Arterioscler Thromb Vasc Biol. 2009;29:969-74.Plos One 2009;4(8):e6796Hepatology 2010;51:511-522Am J Clin Nutr. 2009; 90:415-24Am J Clin Nutr. 2009;90:1656-64Mol Cell Biology 2009;29:6257-67Am J Clin Nutr. 2010;91:208-17BMC Genomics 2009Physiol. Genomics 2009Circulation 2010Diabetes 2010Cell Metabolism 2010Physiol Genomics. 2011;43(23):1307-18.PLoS One. 2011;6(4):e19145.Nature 2011 May 22PLoS One. 2012;7(12):e49868.PLoS One. 2012;7(11):e51066.PLoS One. 2012;7(10):e47303.BMC Med Genomics. 2012 Aug 28PLoS One. 2012;7(8):e43260.J Hepatol. 2012 Dec;57(6):1370-3.Am J Physiol Gastrointest Liver Physiol. 2012Physiol Genomics. 2012 Mar 19;44(6):352-61.Am J Physiol Endocrinol Metab. 2012Prog 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
  37. 37. PPARs are ligand activated transcription factorsPPAR9 cis retinoic acidfatty acidsDNA transcriptionAGGTCAaAGGTCA+GeneResponse elementProteinsynthesisFunctionPPARRXR
  38. 38. Structures of PPAR ligands
  39. 39. Three sources of fatty acids in liver& the role of fatty acid sensors PPAR and PPARPPAR
  40. 40. Murine PPARSI Liver02.0 1054.0 1056.0 1058.0 105Human PPARSI Liver025%50%75%100%Molecules/µgRNAExpression(%) Expression levels of human and murine PPAR0 25 50 75 100placentatracheathymusbladderprostatetestescervixthyroidadiposelungesophaguscolonspleenovarybrainskeletal musclekidneyliverheartsmall intestine%%%%Human PPARBunger et al.Physiol. Genomics 2007
  41. 41. Is PPAR the hepatic fatty acid sensor?YES!Sanderson et al PLoS One. 2008 Feb 27;3(2):e1681
  42. 42. Difference between Food & PharmaMultiple targets versus specificityA CPPARPPARPPARBFatty acidDrugsFatty acidsReceptorMultiple targetsC6C5C4C3C2C1Physiol Genomics. 2011 Sep 27
  43. 43. BloodtriglyceridesNutrigenomics & molecular nutrition allowsus to define the mechanistic framework
  44. 44. Response to the intestine to differentdoses of dietary fatDe Wit PLOS one 2011
  45. 45. Study to show metabolic plasticity of the gutDose-dependent effects of dietary fat on development of obesity inrelation to intestinal differential gene expression in C57BL/6J miceDe Wit PLOS one 2011
  46. 46. Robust & concentration dependent effects in small intestineDifferentially regulated intestinal genes by high fat dietC1 C2 C3 C4 C5 C6 C7 C8 C9 C10De Wit PLOS one 2011
  47. 47. Cellular localization and specific lipid metabolism-relatedfunction of fat-dose dependently regulated genesDe Wit PLOS one 2011
  48. 48. Conclusion: Do not overload the gut45% FAT10% FATC1 C2 C3 C4 C5 C6 C7 C8 C9 C1040 cm4 cmchronically
  49. 49. Nutrigenomics: From Mice to HumansUse of transcriptomics for the identificationof biomarkers for organ function / vitality
  50. 50. Human Nutrigenomics:What is possible now ?• Muscle biopts• Adipose tissue biopts• Intestinal biopts• White blood cells
  51. 51. Human nutrigenomics study 1“Old” & “new” biomarkers
  52. 52. Changes in lipid composition due to PUFA intakeLow = 0.4 g EPA+DHA/d; high = 1.6 g EPA+DHA/d
  53. 53. Fish-oil supplementation induces anti-inflammatory geneexpression profiles in human blood mononuclear cellsLess inflammation & decreasedpro-arteriosclerosis markers= Anti-immuno-senescenceBouwens et al. Am J Clin Nutr. 2009
  54. 54. Human nutrigenomics study 2:Dietary fat and inflammation in adipose tissueChange indietcomposition?de Luca, C and Olefsky JM, Nature Medicine 12, 41 - 42 (2006)Van Dijk et al. AJCN 2009
  55. 55. Design of the SFA vs MUFA-richintervention studyRun-inSFA-rich diet(n=20)SFA-rich diet (n=10)MUFA-rich diet (n=10)Baseline- Clamp- Adipose tissue biopsy- Blood samplingAfter intervention- Clamp- Adipose tissue biopsy- Blood samplingT=0 wks T=2 wks T=10 wksVan Dijk et al. AJCN 2009
  56. 56. „Obese-linked‟ pro-inflammatorygene expression profile by SFAs• The SFA-rich diet:• Induces a pro-inflammatory obese-linkedgene expression profile• Decreases expression andplasma level of the anti-inflammatory cytokineadiponectin• “Personal Transcriptomes”SFA diet MUFA dietVan Dijk et al. AJCN 2009
  57. 57. Human study 3:Plasma Protein Profiling RevealsProtein Clusters Related to BMI and InsulinLevels in Middle-Aged Overweight SubjectsAIM• Associate plasma protein profiles with BMI• Identify potential marker profile of earlydisease state. PLoS One. 2010 Dec 23;5(12):e14422
  58. 58. 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
  59. 59. We are different: improved phenotypingnecessary to reveal phenotype clusters. PLoS One. 2010 Dec 23;5(12):e14422
  60. 60. Conclusion• We identified clusters of plasma proteins associated withBMI and insulin in a healthy population.• These clusters included earlier identified biomarkers forobesity-related disease as well as potential newbiomarkers.• These plasma protein clusters could have potentialapplications for improved phenotypic characterization ofvolunteers in nutritional intervention studies or asbiomarkers in the early detection in obesity-linkeddisease development and progression.van Dijk SJ, Feskens EJM, Heidema AG, Bos MB, van de Rest O, Geleijnse JM, deGroot CPGM, Müller M, Afman LA. Plasma Protein Profiling Reveals Protein ClustersRelated to BMI and Insulin Levels in Middle-Aged Overweight Subjects. PLoS One. 2010Dec 23;5(12):e14422
  61. 61. Some future perspectives
  62. 62. DIETGenomeEpigenomeTranscriptomeProteomeMetabolome“DIETome”databaseNutrigenomicsEvidence-basedNutritionGenes regulated by fatty acidsGenes regulated by high fatGenes also regulated by inflammationPotential BiomarkersOrgan-specific secreted proteinsQueryQueryDIETome database for evidence-based nutrition
  63. 63. Nutrigenomics& Systems Biology
  64. 64. NutrigenomicsPlatformTIFN-nextNWO-TopDiabetesfoundationNUGONutriTechIPOPGut fermentation+ SatietyIPOP SysBiolEU IdealAgeingEpigenomicsTIFNA-1001A-1004TIFN-nextprojects-Cardiovascular-Gastrointestinal health-Weight managementNCSB projectSCFA metabolism in the GutBelly FatCohortWageningenNGC projectsTIFN-projectsSysBiol Project GatekeeperLifeLinesGroningen
  65. 65. ResearchquestionsNutrigenomicsDB miningImprovedstudy designNutrigenomicsPhenotypingChallengetestsControllednutritionalinterventionChallengetestsNutrigenomicsPhenotypingQuantitativeModelingSystems BiologyDatabasingNutrigenomics PlatformNNCHigh StandardizationComprehensive PhenotypingData capturing, basing, miningMADMAX DB / DIETome DBPBMCs DB / NutriPheno DBMicrobiome DBSecretome DBNutritional Science 2.0
  66. 66. Cohort: 500 men (45/50 y with/withoutabdominal overweight (BMI 28-30 kg/m2)MeasurementsMRIHyperinsulinemic euglycemic clampFunctional vascular measurementsPlasma glucose & lipid- & cytokine profilesPBMCs, WAT & muscle bioptsPlasma, Urine, FecesFFQMetabolite profiling in plasma & urineMicrobiotaMRI neuro-imaging
  67. 67. I have a dream“I track, therefore I am”
  68. 68. Key questions for nutrigenomics1. What is your scientific problem? Why do you neednutrigenomics?2. What are the best suitable genomics tools for yournutrition research and how to apply them?3. What is the role of nutrition in the genotype-phenotyperelationship?4. What is healthy and how to measure and quantify thehealth status?5. What are feasible (human) applications?6. What is the impact of nutrigenomics for the food (&pharma) industry?
  69. 69. General conclusions• Nutrigenomics is the combination of molecular nutritionand multi-Omics applications.• There is not one gen-Omics tool that can “do everything”.• In mice it is possible to perform most comprehensivenutritional systems biology studies to elucidate the impactof nutritional strategies on metabolic plasticity & organhealth.• The challenge remains to get useful human data for theindividual characterization of organ function (metabolichealth) versus systemic health.
  70. 70. Its easy (if your genes are ok)2 Meals a day, work as long as possible & embracechallengesWalter Breuning (1896 - 2011)