From Nutrigenomics to nutritional systems biology of fatty acid sensing
Nutrigenomics of the two hits: non-resolving metabolic and pro-inflammatory stress
1. Nutrigenomics of the two hits: non-resolving
metabolic and pro-inflammatory stress
Michael Müller
Netherlands Nutrigenomics Centre
& Nutrition, Metabolism and Genomics Group
Division of Human Nutrition, Wageningen University
@nutrigenomics
3. Content
The two hits:
Non-resolving metabolic & pro-inflammatory stress
The liver & the two hits
Obesity & NASH - Interaction of the white adipose
tissue & the liver
Too much saturated fat & macrophages
Human Nutrigenomics examples
Conclusions
4. A consideration of biomarkers to be used for evaluation of inflammation in human
nutritional studies ILSI working group BJN 2012 submitted
8. More steatosis in mouse livers from
PPARa -/- mice on a high fat diet
Stienstra R, Mandard S, Patsouris D, Maass C, Kersten S, Müller M
PPAR{alpha} protects against obesity-induced hepatic inflammation
Endocrinology. 2007 Jun;148(6):2753-63
21. 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
22. Conclusions
• The data support the existence of a
tight relationship between adipose
tissue dysfunction and NASH
pathogenesis.
Duval et al. Diabetes 2010
23. How inflammation is initiated and
developed in obesity
Annu Rev Nutr. 2012 Mar 9.
34. Conclusion
• A high saturated fat diet causes massive inflammation in
Angptl4-/- mice originating in mesenteric lymph nodes.
• In the absence of this protective mechanism, feeding a
diet rich in saturated fat rapidly leads to enhanced lipid
uptake into MLN-resident macrophages, triggering foam
cell formation and a massive inflammatory response.
Lichtenstein et al. Cell Metab. 2010
35. Nutritional regulation of immune capacity
Veldhoen M, Brucklacher-Waldert V.
Dietary influences on intestinal immunity.
Nat Rev Immunol. 2012; 12 (10):696-708
36. Human study:
Plasma Protein Profiling Reveals
Protein Clusters Related to BMI and Insulin
Levels in Middle-Aged Overweight Subjects
AIM
• Associate plasma protein profiles with BMI
• Identify potential marker profile of early
disease state
. PLoS One. 2010 Dec 23;5(12):e14422
39. Human nutrigenomics study
Dietary fat and inflammation in adipose tissue
?
Change in
diet
composition
Van Dijk et al. AJCN 2009
de Luca, C and Olefsky JM, Nature Medicine 12, 41 - 42 (2006)
40. Design of the SFA vs MUFA-rich
intervention study
T=0 wks T=2 wks T=10 wks
Run-in SFA-rich diet (n=10)
SFA-rich diet
(n=20)
MUFA-rich diet (n=10)
Baseline After intervention
- Clamp - Clamp
- Adipose tissue biopsy - Adipose tissue biopsy
- Blood sampling - Blood sampling
Van Dijk et al. AJCN 2009
41. “Obese-linked” pro-inflammatory
gene expression profile by saturated fat
SFA diet MUFA diet
• The SFA-rich diet:
• Induces a pro-
inflammatory obese-linked
gene expression profile
• Decreases expression and
plasma level of the anti-
inflammatory cytokine
adiponectin
• “Personal Transcriptomes”
Van Dijk et al. AJCN 2009
42. 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 Clin Nutr. 2009
43. General conclusions
• (Over)nutrition and inflammation are intimately linked =>
non-resolving metabolic and pro-inflammatory stress (the
two hits).
• It will be essential to get a better understanding of the very
early events that lead to non-resolving organ inflammation
and the precise role of nutrition (causal or preventive) in
this pathophysiological development.
• We need biomarkers specifically for organ function (“2 hit
state”) to be able to specifically target and modulate.
• The challenge will be the translation of the findings from
mice studies to the human situation (“individual” health).
44. Sander Kersten
Rinke Stienstra
Lydia Afman
Guido Hooiveld
Mark Boekschoten
Laetitia Lichtenstein
Caroline Duval
& NMG group
Christian Trautwein
Folkert Kuipers
Ben van Ommen
& many more
Notes de l'éditeur
A subpopulation of mice fed HFD develops NASH. 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.