Using nutrigenomics to study ranges and plasticity in homeostasis
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"Using nutrigenomics to study ranges and plasticity in homeostasis" Lecture on 6 Oct. 2011 at ILSI conference in Prague "Health benefits of foods"
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Using nutrigenomics to study ranges and plasticity in homeostasis
- 1. Using nutrigenomics to study ranges and plasticity in homeostasis http://twitter.com/nutrigenomics Michael MüllerNetherlands Nutrigenomics Centre & Nutrition, Metabolism and Genomics GroupDivision of Human Nutrition, Wageningen University
- 2. Our scientific challenge: What's healthy?
- 3. Metabobolic homeostasis & syndrome
- 4. Metabolic health = plasticity / flexibility The personal genome is the starting point & we can get comprehensive information about it but we should not underestimate the challenges of“bioinformatics & databasing” Health is dynamic: The property to adapt to metabolic perturbations / challenges Feeding / fasting => autophagy => cellular homeostasis & “exercise” Caloric restriction => chromatin “exercise” Food bioactives that modulate transcription (e.g. via nuclear receptors) or chromatin activity (nutriepigenome) => cell & organ “exercise” 4
- 5. Phenotype plasticity Phenotypic plasticity is the ability of an organism to change its phenotype in response to changes in the environment => nutrition, lifestyle
- 6. Duality of biological information:Epigenetic & Genetic
- 7. Modern Nutritional Science with Nutrigenomics Quantification of the nutritional phenotype Lifestyle NutritionFoods Microbiota Environment
- 8. Timelyrelatively modest interventions in early life can have a large effect on disease risk later
- 9. Nutrigenomics – molecular nutrition and genomics 5 -10,000 (?)metabolites 90,000 (?)proteins 100,000 (?) transcripts 20,210 genes Müller & Kersten Nature Reviews Genetics 2003
- 10. Understanding NutritionHow nutrients regulate our genes: via sensing molecular switches Changed organ metabolic capacity Am J ClinNutr. 2009; 90:415-24Am J ClinNutr. 2009;90:1656-64Mol CellBiology2009;29:6257-67 Am J ClinNutr. 2010;91:208-17BMC Genomics2009 Physiol. Genomics2009Circulation 2010Diabetes 2010 Cell Metabolism 2010Nature 2011 Am J Clin Nutr. 2007;86(5):1515-23 PLOS ONE 2008;3(2):e1681 BMC 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;51:511-522 J Clin Invest. 2004;114:94-103 J Biol Chem. 2006;28:934-44 Endocrinology. 2006;147:1508-16 Physiol Genomics. 2007;30:192-204Endocrinology. 2007;148:2753-63 BMC Genomics 2007; 8:267 Arterioscler Thromb Vasc Biol. 2007;27:2420-7
- 11. Dose-dependent effects of dietary fat on development of obesity in relation to intestinal differential gene expression in C57BL/6J mice PLOS one 2011
- 12. Robust & concentration dependent effects in small intestineDifferentially regulated intestinal genes by high fat diet C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 PLOS one 2011
- 13. Cellular localization and specific lipid metabolism-related function of fat-dose dependently regulated genes PLOS one 2011
- 14. Intestinal capacity for lipid absorption 40 cm 4 cm C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 Microbiota 10% FAT 45% FAT
- 16. Dangerous interaction - The two hitsStress from Metabolism & Inflammation
- 18. Different stages in NAFLD progression:
- 19. Molecular events involved in NASH pathogenesis:
- 20. Role of PPARa (Endocrinology 2008 & Hepatology 2010)
- 21. Role Kupffer cells (Hepatology 2010)
- 22. Role of macrophages in lipid metabolism (JBC 2008; Cell Metabolism 2010)hepatic steatosis steatohepatitis (NASH) & fibrosiscirrhosis
- 25. High fat diet-induced obesity 0 2 4 8 12 16 20 HFL LFL HFH LFH 25 20 * * 15 ** BW gain (g) * 10 * * * * 5 0 weeks under diet intervention Liver TG content Hepatomegaly ALT plasma activity 200 10 100 *** *** ** 160 8 80 ** 120 6 60 * Ratio LW/BW (%) mg TG/g liver ALT activity (UI) 80 4 40 * * 40 2 20 0 0 0 LFL LFH HFL HFH
- 26. The HFH mice develops NASH
- 27. Immunohistochemicalstaining confirms enhanced liver inflammation and early fibrosis in HFH mice Macrophage CD68 Collagen Stellate cell GFAP
- 28. Upregulation of inflammatory and fibrotic gene expression in HFH responder mice
- 29. Adipose dysfunction in HFH mice
- 30. Change in adipose gene expression indicate adipose tissue dysfunction
- 31. Plasma proteins as early predictive biomarker for NASH in C57Bl/6 mice
- 32. Plasma proteins as early predictive biomarker for NASH in C57Bl/6 mice Multivariate analysis of association of protein plasma concentrations with final liver triglyceride content
- 33. Conclusions Our data support the existence of a tight relationship between adipose tissue dysfunction and NASH pathogenesis. It points to several novel potential predictive biomarkers for NASH. Duval C, Thissen U, Keshtkar S, Accart B, Stienstra R, Boekschoten MV, Roskams T, Kersten S, Müller M. Adipose tissue dysfunction signals progression of hepatic steatosis towards nonalcoholic steatohepatitis in C57BL/6 mice. Diabetes. 2010;59:3181-91.
- 34. Plasma Protein Profiling Reveals Protein Clusters Related to BMI and Insulin Levels in Middle-Aged Overweight Subjects AIM Associate plasma protein profiles with BMI Identifypotential marker profile of earlydisease state . PLoS One. 2010 Dec 23;5(12):e14422
- 35. Measurements RulesBasedMedicine (Austin, USA) Multiplex immunoassay In total 124 proteinsmeasured Involved in diseases, inflammation, endothelialfunction and metabolism . PLoS One. 2010 Dec 23;5(12):e14422
- 36. We are different: improved phenotyping necessary to reveal phenotype clusters . PLoS One. 2010 Dec 23;5(12):e14422
- 37. 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 ClinNutr. 2009
- 38. Very personal conclusionsHow to keep our metabolic plasticity/health Identify chronic (non-resolving) stress using systems “perturbation” tests & deep genomics-based phenotyping Solve it! Less Inflammation Less Metabolic Stress (sat. fat, lipogenic foods) More Exercise (muscle & other organs) with a “challenging” lifestyle & food pattern Eat less from time to time 32
- 39. 2 Meals a day, work as long as possible & embrace challenge Walter Breuning (1896 - 2011)
- 40. Sander KerstenLinda SandersonNatasha Georgiadi Mark BouwensLydia Afman Guido Hooiveld Meike Bunger Philip de Groot Mark Boekschoten Nicole de Wit Mohammad Ohid Ullah Christian Trautwein Folkert Kuipers Ben van Ommen Hannelore Daniel Bart Staels Edith Feskens …..
Notes de l'éditeur
- Inflammation has been associated with many disease phenotypes including steatohepatitis or diabetes. This relationship is in particular when inflammation is chronic or non-resolving. There is an interaction between metabolism and inflammation with positive or negative consequences with respect to organ and systemic health.In my talk I will briefly discuss two unpublished studies, one investigating the important interaction of WAT and liver in particular under conditions of diet-induced obesity. Organ-specific macrophages in WAT and liver play an crucial role in progressing organ-specific inflammatory phenotypes. In the second study we found very interesting interaction between dietary fat and macrophages in mesenteric lymph nodes that are exposed postprandially to very high concentrations of chylomicrons. We used a k.o. mouse for ANGPTL4 and could show that chronic consumption of saturated fat can be deadly.
- Haematoxylin and eosin staining (D) and oil red O staining (E) of representative liver sections of the 4 subgroups
- (Immuno)histochemical staining confirms enhanced inflammation and early fibrosis in HFH miceImmunohistochemical staining of macrophage activation in representative liver section of HFL and HFH mice using antibody against the specific macrophagemarker Cd68Collagen staining using fast green FCF/sirius red F3B. Staining of stellate cell activation using antibody against GFAP.
- - Number of genes up- or down-regulated in the various subgroups in comparison to the LFL mice, as determined by Affymetrix GeneChip analysis. Genes with a p-value below 0.05 were considered significantly regulated. - Heat map showing changes in expression of selected genes involved in lipid metabolism, inflammation and fibrosis in liver. Changes in gene expression of selected genes as determined by real-time quantitative PCR. Mean expression in LFL mice was set at 100%. Error bars reflect standard deviation. Bars with different letters are statistically different (P<0.05 according to Student’s t-test). Number of mice per group: n=4 (LFL, HFL, HFH), n=6 (LFH).
- Haematoxylin and eosin staining of representative adipose tissue sections. Immunohistochemical staining of macrophages using antibody against Cd68. Collagen staining using fast green FCF/sirius red F3B.
- Adipose tissue mRNA expression of a selected group of genes was determined by quantitative real-time PCR after 21 weeks of dietary intervention. Mean expression in LFL mice was set at 100%. Error bars reflect standard deviation. * = significantly different from HFL mice according to Student’s t-test (P<0.05). Number of mice per group: n=4 (LFL, HFL, HFH), n=6 (LFH).
- . A) Plasma concentration of haptoglobin, TIMP-1, IL-1β, leptin and insulin were determined by multiplex assay at specific time points during the 21 weeks of dietary intervention after a 6h fast. White squares: LFL, Light grey squares: LFH, dark grey squares: HFL, black squares: HFH. Error bars reflect standard deviation. * = significantly different from HFL mice according to Student’s t-test (P<0.05). Number of mice per group: n=4 (LFL, HFL, HFH), n=6 (LFH).
- Graphs illustrating the result of multivariate analysis showing the association of protein plasma concentrations at various time points with final liver triglyceride content. Significant proteins display an inverse RSD value higher than 2 (bold line indicates the inverse RSD threshold value of 2).RSD = Relative standard deviation.