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‘From Molecular to Systems Nutrition. Lessons from mouse to man’ NUGO Dublin 2 June 2015

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A lecture about how to apply genomics tools to molecular nutrition studies.....

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‘From Molecular to Systems Nutrition. Lessons from mouse to man’ NUGO Dublin 2 June 2015

  1. 1. ‘From Molecular to Systems Nutrition Lessons from mouse to man’ Michael Müller Professor of Nutrigenomics & Systems Nutrition Norwich Medical School @nutrigenomics
  2. 2. You are what you eat, have eaten, host & how you lived 2 Meals/day, work as long as possible & embrace challenges Walter Breuning (1896 – 2011, aged 114 years, 205 days) (But Breuning was also a lifelong cigar smoker, but quit in 1999 when he was 103 because it became too expensive)
  3. 3. 100 50 0 % Energy Low-fat meat Chicken Eggs Fish Fruits Vegetables (carrots) Nuts Honey 100 50 0 % Energy Fruits Vegetables Beans Meat Chicken Fish Grain Milk/-products Isolated Carbs Isolated Fat/Oil Alcohol 1.200.000 Generations between feast en famine Paleolithic era 3-4 Generations in energy abundance Modern Times Our “paleolithic” genes + modern diets Real Foods with ‘challenges’ “Safe, processed” foods = Less challenges
  4. 4. My talk • Challenges & sensing mechanisms • Biological Systems Multi-omics - challenges • From molecular nutrition to the mode of action • Complex diseases: role of the inter-organ crosstalk • The role of organ capacity • From the liver and adipose tissue back to the gut • The gut as a gatekeeper • Opportunities for customized / precision nutrition
  5. 5. No pain, no gain The molecular basis of adaptation to challenges
  6. 6. “We are what we eat, have eaten and what we host” 4Rs: Received, Recorded, Remembered & Revealed Mathers JC (2008) Proc.Nutr.Soc.67,9390-394
  7. 7. Biological Systems Multi-omics to elucidate the Role of Nutrition in the Genotype-Phenotype Relationship Nature Reviews Genetics | AOP, published online 13 January 2015 Phenome • Metabolic Syndrome CVD NAFLD • Inflammatory Diseases • Cancer
  8. 8. But
  9. 9. Genomics Data Functional Knowledge Base Analytical Tools (Galaxy, R, Bioconductor, Subio, Ingenuity, Genomatix) Single Cell Data Epigenetic regulation Text Mining Interaction Networks KEGG/GO pathways Transcriptomics Metabolomics “Multi”Omics Epigenomics (incl. miRNA) Microbiome Genome analysis Transcription factor analysis New functional knowledge New physiological understanding New testable hypothesis ‘Virtual gut model’ Statistical Analysis Machine learning Visualization Gut- Microbe- Dietome Integration and Mining of Big Data from Omics Applications
  10. 10. Expression heatmapping of PPAR target genes & inflammatory genes in human PBMCs after SFA/MUFA consumption Esser et al Mol Food Nutr. Res 2015 In a cross-over study, 17 lean and 15 obese men (50-70y) received two 95g fat shakes, high in SFAs or MUFAs. PBMC gene-expression profiles were assessed fasted and 4h postprandially. Comparisons were made between groups and shakes.
  11. 11. Expression heatmap of significantly changed genes in obese relative to lean subjects after the MUFA challenge Esser et al Mol Food Nutr. Res 2015
  12. 12. What do we know about the mechanisms? Healthy food (pattern)s have a large impact on our gene expression & phenotype • (Micro & Macro) Nutrients – High in Mono & (N-3) polyunsaturated fatty acids – Sufficient high-quality protein (optimal macro-nutrient ratio) – Vitamins (e.g. vitamin A & D) , minerals (e.g. Zn) • Microbiota (from foods) – Vegetarians / omnivores / carnivores => different microbiota – “Raw” or fermented food (e.g. diary, cheese) consumption => food- borne microbiota – Dietary diversity => microbiota diversity & genetic richness • Plant food components – Fibers or secondary plant metabolites (e.g. resveratrol, glucosinolates) e.g. bitter => “healthy stressors” & impact on microbiota • Less foods/calories & diet-related stress (caloric restriction) – “Chromatin exercise” & other epigenetic mechanisms – “Cell exercise” (e.g. via autophagy)
  13. 13. Ronald M. Evans , David J. Mangelsdorf Nuclear Receptors, RXR, and the Big Bang Cell, Volume 157, Issue 1, 2014, 255 - 266 Metabolic homeostasis is regulated by nutrient sensors
  14. 14. Zooming in – zooming out Expression atlas http://www.ebi.ac.uk/gxa/home
  15. 15. Understanding Nutrition: Identifying the mechanisms involved in the regulation of chromatin activity and gene transcription Impact on metabolic capacity & health of organs & the epigenetic memory Transgenic mice (e.g. NRF2, SIRT1 HIF1, AHR, PPARs etc)
  16. 16. Nuclear receptors are linking cellular biology to systems biology PPAR Liver Heart Intestine 0 25 50 75 100 placenta trachea thymus bladder prostate testes cervix thyroid adipose lung esophagus colon spleen ovary brain skeletal muscle kidney liver heart small intestine %%%% Apolipoproteins & related TAG (re)-synthesis Pnliprp2 ,Pnliprp1, Mgll, Lipe,Lipa,Pla2g6, Pnpla2,Pnpla8, Daglb, Ces1,Ces3 Chylomicron assembly & secretion Ketone body synthesis Intestinal lipases & phospholipases Fat/Cd36,Slc27a2, Slc27a4,Acsl1,Acsl3, Acsl5,Fabp2,Fabp1, Scarb2,Scarb1 Gpat1, Mogat2, Dgat1,Dgat2, Gpat3,Agpat3 Mttp, Stx5a,Vti1a, Bet1,Sar1a Apob,Apoa1 ,Apoa2,Angptl4, Apoa4,Apoc2,Vldlr,Apoc3, Apoe,Apol3,Apool Acaa2,Acad10,Acad8,Acad9, Acadl,Acadm,Acads,Acadsb, Acadvl,Acot10,Acot2,Acot9, Aldh9a1,Cpt1a,Cpt2,Crat,Dci, Decr1,Hadha,Hadhb,Hibch, Slc22a5,Slc25a20,Aldh3a2, Cyp4a10,Abcd3,Acaa1a, Acaa1b,Acot3,Acot4,Acot5, Acot8,Acox1,Acox2,Crot, Decr2,Ech1,Ehhadh, Hsd17b4,Peci,Pecr, Ppara Acat1, Hmgcl,Hmgcs2 Mitochondrial, microsomal, peroxisomal fatty acid oxidation Fatty acid transport & binding P P A R α PPARa
  17. 17. Context-dependent gene regulation by the nutrient-sensing transcription factor PPARa WY Fibrate “Drug” DHA PUFA “Food” Liver Intestine
  18. 18. Non-communicable diseases are complex “Too much non-resolving metabolic & pro-inflammatory stress” Non-communicable diseases are caused by chronic organ overload & dysregulation
  19. 19. LOCAL effects/inflammation) (first!) Systemic effects/inflammation (second) Br J Nutr. 2013 Jan;109 Suppl 1:S1-34.
  20. 20. de Wit NJ, Afman LA, Mensink M, Müller M Phenotyping the effect of diet on non-alcoholic fatty liver disease J Hepatol 2012 . A systems nutrition view of health & disease
  21. 21. Interaction between WAT and liver tissue essential for NASH/NAFLD in C57Bl/6 mice • stratification on body weight • liver• plasma collection multiple protein assays RNA extraction: Affx microarrays tissue collectionrun-in diet 20 weeks diet intervention frozen sections: histological feat. • ep. white adipose tissue 10% low fat diet (palm oil) 10 LFD 10 HFD 45% high fat diet (palm oil) 20 LFD RNA extraction: real-time PCR paraffin sections: histological feat. lipid content quality control & data analysis pipeline Mouse genome 430 2.0 0 2 4 8 12 16 20 weeks-3
  22. 22. High fat diet-induced obesity 0 5 10 15 20 25 0 2 4 8 12 16 20 weeks under diet intervention BWgain(g) * * * ** * * * * LFL LFH HFL HFH ** * *** Liver TG content 0 40 80 120 160 200 mgTG/gliver ALTactivity(UI) ALT plasma activity RatioLW/BW(%) Hepatomegaly ** 0 2 4 6 8 10 *** 0 20 40 60 80 100 * * LFL LFH HFL HFH
  23. 23. A subpopulation of mice fed HFD develops NASH
  24. 24. Immunohistochemical staining confirms enhanced liver inflammation and early fibrosis in HFH mice Macrophage CD68 Collagen Stellate cell GFAP HFL HFH
  25. 25. Upregulation of inflammatory and fibrotic gene expression in HFH responder mice
  26. 26. Adipose dysfunction in HFH mice
  27. 27. Change in adipose gene expression indicate adipose tissue dysfunction
  28. 28. Conclusions • The data support the existence of a tight relationship between adipose tissue dysfunction (because of chronic organ overload & unbalanced expansion) and NASH pathogenesis. • It also demonstrates the time-dependent progression of local (organ) towards systemic inflammation. Duval et al. Diabetes 2010
  29. 29. Mice are not Humans? No, they are Models for Systems Responses to Food Patterns Natural Variation in Gene-by-Diet Interactions
  30. 30. A weekly alternating diet between caloric restriction and medium-fat protects the liver from fatty liver development in middle-aged C57BL/6J mice • Can a novel dietary intervention consisting of an every- other-week calorie-restricted diet prevent nonalcoholic fatty liver disease (NAFLD) development induced by a medium-fat (MF) diet? • 9-week-old male C57BL/6J mice received either – a control (C), 30E% calorie restricted (CR), MF (25E% fat), an intermittent (INT) diet, a diet alternating weekly between 40E% CR and an ad libitum MF diet until the age of 12 months. • The metabolic, morphological, and molecular features of NAFLD were examined. Rusli F, Boekschoten MV, Zubia AA, Lute C, Müller M, Steegenga WT.. Mol Nutr Food Res. 2014 Dec 15
  31. 31. A B C *** *** *** ED F *** *** * *** * * r = 0.83 P-value < 0.0001 ** *** Body w eight (g) Liverweight(g) 20 30 40 50 0.5 1.0 1.5 2.0 2.5 C CR MF ID r = 0.82 P-value < 0.0001 *** *** *** *** Age (w eeks) Bodyweight(g) 10 20 30 40 50 10 20 30 40 50 C CR MF INT WATweight(g) C CR MF INT 0.0 0.5 1.0 1.5 2.0 RelativeWATweight(%) C CR MF INT 0 1 2 3 4 5 Liverweight(g) C CR MF INT 0.0 0.5 1.0 1.5 2.0 2.5 Relativeliverweight(%) C CR MF INT 0 1 2 3 4 5 6 WAT w eight (g) Liverweight(g) 0.0 0.5 1.0 1.5 2.0 0.5 1.0 1.5 2.0 2.5 C CR MF INT kcal/day/mouse D ay 1 D ay 2 D ay 3 D ay 4D ay 5-7 0 2 4 6 8 10 C CR MF INT-CR w eek INT-MF w eek kcal/week/mouse C CR MF INT 0 20 40 60 80 100 120 Carbohydrate Protein Fat Time (min) Bloodglucoselevel(mmol/L) 0 50 100 150 5 10 15 20 25 30 C CR MF INT Plasmainsulinlevel(ng/ml) C CR MF INT 0 1 2 3 Beneficial effects of an INT diet regimen on body, WAT, and liver weight, food intake, and glucose tolerance Rusli F, Boekschoten MV, Zubia AA, Lute C, Müller M, Steegenga WT.. Mol Nutr Food Res. 2014
  32. 32. F C CR MF INT LowBWHighBWHighBW E C CR MF INT LowBW NAFLD development in C- and MF-fed mice, but not in mice exposed to the CR and INT diet Rusli F, Boekschoten MV, Zubia AA, Lute C, Müller M, Steegenga WT.. Mol Nutr Food Res. 2014 Dec 15
  33. 33. Conclusions • Our study reveals that the INT diet maintains metabolic health and reverses the adverse effects of the MF diet, thus effectively prevents the development of NAFLD in 12-month-old male C57BL/6J mice. Age (weeks) Survival(%) 52 65 78 91 104 50 75 100 C CR MF INT * * ** Rusli F, Boekschoten MV, Zubia AA, Lute C, Müller M, Steegenga WT.. Mol Nutr Food Res. 2014 Dec 15
  34. 34. From local problems to systemic diseases – the contribution of the gut
  35. 35. Robust & concentration dependent effects in small intestine Differentially regulated intestinal genes by high fat diet C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 De Wit et al Plos ONE 2011
  36. 36. Chronic overload of organs => NCDs • Saturated fat (but not or to a less extent unsaturated fat) stimulates obesity and the development of fatty liver disease and affects gut microbiota composition & diversity by an enhanced overflow of dietary fat to the distal intestine. • Unsaturated fats are more effectively taken up by the small intestine, likely by more efficiently activating nutrient sensing systems (PPARs) and thereby contributing to the prevention of organ overload & the development of early pathology (e.g. NASH). Food Colon
  37. 37. Gene expression intensity Differential gene expression 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Triglyceride/FFA metabolism Transport 12491_at Cd36 26458_at Slc27a2 26569_at Slc27a4 14080_at Fabp1 14079_at Fabp2 Oxidation . omega 13117_at Cyp4a10 11522_at Adh1 26876_at Adh4 11668_at Aldh1a1 beta 11363_at Acadl 11370_at Acadvl 14081_at Acsl1 12894_at Cpt1a 12896_at Cpt2 51798_at Ech1 97212_at Hadha 57279_at Slc25a20 TG/Chylomicron synthesis 110446_at Acat1 238055_at Apob 11813_at Apoc2 13350_at Dgat1 67800_at Dgat2 17777_at Mttp FA synthesis 104112_at Acly 14104_at Fasn 20249_at Scd1 20250_at Scd2 Transcription regulation 19013_at Ppara 19015_at Ppard 19016_at Pparg 19017_at Ppargc1a Cholesterol/oxysterol metabolism Transport 11303_at Abca1 27409_at Abcg5 67470_at Abcg8 237636_at Npc1l1 Cholesterol synthesis 74754_at Dhcr24 15357_at Hmgcr Transcription regulation 22260_at Nr1h2/Lxrb 22259_at Nr1h3/Lxra 20787_at Srebf1 20788_at Srebf2 Bile acid metabolism Transport 16204_at Fabp6 106407_at Osta 330962_at Ostb 20494_at Slc10a2 Transcription regulation 23957_at Nr0b2/Shp 20186_at Nr1h4/Fxr Chow LF HF 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 Triglyceride/FFA metabolism Transport 12491_at Cd36 26458_at Slc27a2 26569_at Slc27a4 14080_at Fabp1 14079_at Fabp2 Oxidation omega 13117_at Cyp4a10 11522_at Adh1 26876_at Adh4 11668_at Aldh1a1 beta 11363_at Acadl 11370_at Acadvl 14081_at Acsl1 12894_at Cpt1a 12896_at Cpt2 51798_at Ech1 97212_at Hadha 57279_at Slc25a20 TG/Chylomicron synthesis 110446_at Acat1 238055_at Apob 11813_at Apoc2 13350_at Dgat1 67800_at Dgat2 17777_at Mttp FA synthesis 104112_at Acly 14104_at Fasn 20249_at Scd1 20250_at Scd2 Transcription regulation 19013_at Ppara 19015_at Ppard 19016_at Pparg 19017_at Ppargc1a Cholesterol/oxysterol metabolism Transport 11303_at Abca1 27409_at Abcg5 67470_at Abcg8 237636_at Npc1l1 Cholesterol synthesis 74754_at Dhcr24 15357_at Hmgcr Transcription regulation 22260_at Nr1h2/Lxrb 22259_at Nr1h3/Lxra 20787_at Srebf1 20788_at Srebf2 Bile acid metabolism Transport 16204_at Fabp6 106407_at Osta 330962_at Ostb 20494_at Slc10a2 Transcription regulation 23957_at Nr0b2/Shp 20186_at Nr1h4/Fxr HF-Chow HF-LF Chow-LF Dietary impact of on intestinal gene expression involved in lipid metabolism
  38. 38. 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 1 2 3 4 5 6 7 8 9 10 AHR activation 11622_at Ahr Detoxification 13076_at Cyp1a1 14858_at Gsta2 14859_at Gsta3 14862_at Gstm1 18104_at Nqo1 Inflammation (ILCs and IELs) 19885_at Rorc (ILC) 12501_at Cd3e (IEL) 12502_at Cd3g (IEL) 12525_at Cd8a (IEL) 20302_at Ccl3 (IEL) 20304_at Ccl5 (IEL) 432729_at Tcrg-C (IEL) 17067_at Ly6c1 (IEL, type a) 16636_at Klra5 (IEL, type b) 12504_at Cd4 (T helper) 12475_at Cd14 (Monocytes) 12478_at Cd19 (B cells) HF-Chow HF-LF Chow-LF Role of 3 diets on gut phenotypes Dietary impact on the activation of the AhR essential for the gut immune system 3 Diets = 3 functional states of the gut
  39. 39. Role of dietary fibres on gut function SCFA INULIN, FOS, GuarGum, NAXUS (Arabinoxylan), Resistant Starch, Ctrl (Starch) microbiota 10 days Lange K, Hugenholtz F, Jonathan MC, Schols HA, Kleerebezem M, Smidt H, Müller M, Hooiveld GJ. Mol Nutr Food Res. 2015 Apr 25.
  40. 40. Integration of epithelial cell gene expression with luminal microbiota composition Bacterial groups within Clostridium cluster XIVa positively correlated with genes involved in energy metabolism (1) Lange K, Hugenholtz F, Jonathan MC, Schols HA, Kleerebezem M, Smidt H, Müller M, Hooiveld GJ. Mol Nutr Food Res. 2015 Apr 25.
  41. 41. PPARg targetsUpstream regulator Role of Pparg in fibre-dependent gene regulation 1 Activation score per dietary fiber RS FOS AX IN GG PPARG 2.83 2.01 4.23 3.07 HNF4A 2.58 3.50 TP53 2.36 2.82 ATF4 2.61 2.43 PPARGC1A 2.39 2.08 XBP1 2.93 NR5A2 2.61 SREBF1 2.58 FOXC2 2.43 SREBF2 2.22 PTTG1 2.21 NR1I2 2.09 CEBPB 2.02 KDM5B 2.00 NCOA2 2.00 TP63 -2.15 STAT5B -2.16 MBD2 -2.23 STAT5A -2.36 MYC -2.63 Lange K, Hugenholtz F, Jonathan MC, Schols HA, Kleerebezem M, Smidt H, Müller M, Hooiveld GJ. Mol Nutr Food Res. 2015 Apr 25.
  42. 42. Role of dietary fibres in the colon • Differential regulation of genes involved in metabolic, energy-generating and oxidative processes & those involved in adhesion dynamics and signalling by dietary fibres. • Strongly linked to Clostridium cluster XIVa bacteria (butyrate producers) & likely governed by the transcription factor PPARg (MCB 2013; & recent data with organoids from gut-specific Pparg-k.o.. mice). • Because of different fermentation behaviour fibres will have a diverse location-specific impact on the microbiome and the host immune-metabolic responses. • Not ‘one fibre fits all’: Diverse food patterns are recommended to keep our guts ‘flexible and healthy’! Lange K, Hugenholtz F, Jonathan MC, Schols HA, Kleerebezem M, Smidt H, Müller M, Hooiveld GJ. Mol Nutr Food Res. 2015 Apr 25.
  43. 43. We are what we fed them…? How strong is the science yet? ‘our gastrointestinal tract is not only the body's most under-appreciated organ, but "the brain's most important adviser”’.
  44. 44. Back to Humans: Identification and Personalized Treatments of Patients at Risk for Developing NCD (e.g.T2D) Based on the Microbiota Microbial Modulation of Insulin Sensitivity Khan, Muhammad Tanweer et al. Cell Metabolism , Volume 20 , Issue 5 , 753 - 760
  45. 45. The Norwich Centre for Food and Health (2017)
  46. 46. Plant and Crop Science for Health (JIC/IFR/UEA/TGAC) Human Nutrition With Controlled Interventions to Evidence for Prevention of NCDs (UEA/IFR/NNUH) Molecular Nutrition From Association to Causality (UEA/IFR/ JIC/TGAC) Liver-Gut Crosstalk in Health & Disease (IFR/UEA/NNUH) Gut-Food-Microbe Interactions in Health & Disease (IFR/UEA/JIC/TGAC)Food-borne Pathogens Food Safety (IFR/UEA) Gut Mucosal Immunity & Inflammatory Diseases (IFR/UEA/NNUH) Impact of Gut & Liver for Systemic Diseases (UEA/NNUH/IFR) Nutrition & Organ Memory Stem cells From Health to Disease (UEA/IFR/NNUH) Big Data Network analysis & Systems integration (TGAG/UEA/IFR) Food and Immuno-Metabolic Health Alliance Opportunities for an Integrated Approach
  47. 47. Take home messages • A systems nutrition approach (not correlation science) is necessary to understand causal relationships between our food(s) patterns & organ and systemic health => next-generation nutritional sciences. – Challenges & sensing mechanisms – From molecular nutrition to mode of action – Complex diseases: The role of interorgan crosstalk & of organ capacity – Opportunities for customized / precision nutrition – Biological Systems Multi-omics to elucidate the Role of Nutrition in the Genotype-Phenotype Relationship A healthy gut is an essential gatekeeper for a human health => we need a comprehensive understanding of the host-microbe-food interaction for more healthy modern (reformulated) foods and improved (customized) therapies. "Eat food, not too much, mostly plants”

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