Skip to main content
SpringerLink
Log in

Metabolic adaptations to HFHS overfeeding: how whole body and tissues postprandial metabolic flexibility adapt in Yucatan mini-pigs

  • Original Contribution
  • Published:
European Journal of Nutrition Aims and scope Submit manuscript

Abstract

Purpose

In the present study, we aimed to metabolically characterize the postprandial adaptations of the major tissues involved in energy, lipids and amino acids metabolisms in mini-pigs.

Method

Mini-pigs were fed on high-fat–high-sucrose (HFHS) diet for 2 months and several tissues explored for metabolic analyses. Further, the urine metabolome was followed over the time to picture the metabolic adaptations occurring at the whole body level following overfeeding.

Results

After 2 months of HFHS consumption, mini-pigs displayed an obese phenotype characterized by high circulating insulin, triglycerides and cholesterol levels. At the tissue level, a general (muscle, adipose tissue, intestine) reduction in the capacity to phosphorylate glucose was observed. This was also supported by the enhanced hepatic gluconeogenesis potential, despite the concomitant normoglycaemia, suggesting that the high circulating insulin levels would be enough to maintain glucose homoeostasis. The HFHS feeding also resulted in a reduced capacity of two other pathways: the de novo lipogenesis, and the branched-chain amino acids transamination. Finally, the follow-up of the urine metabolome over the time allowed determining breaking points in the metabolic trajectory of the animals.

Conclusions

Several features confirmed the pertinence of the animal model, including increased body weight, adiposity and porcine obesity index. At the metabolic level, we observed a perturbed glucose and amino acid metabolism, known to be related to the onset of the obesity. The urine metabolome analyses revealed several metabolic pathways potentially involved in the obesity onset, including TCA (citrate, pantothenic acid), amino acids catabolism (cysteine, threonine, leucine).

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Adochio RL, Leitner JW, Gray K, Draznin B, Cornier MA (2009) Early responses of insulin signaling to high-carbohydrate and high-fat overfeeding. Nutr Metab 6:37

    Article  Google Scholar 

  2. Basu A, Basu R, Shah P, Vella A, Johnson CM, Nair KS, Jensen MD, Schwenk WF, Rizza RA (2000) Effects of type 2 diabetes on the ability of insulin and glucose to regulate splanchnic and muscle glucose metabolism: evidence for a defect in hepatic glucokinase activity. Diabetes 49:272–283

    Article  CAS  Google Scholar 

  3. Hoy AJ, Brandon AE, Turner N, Watt MJ, Bruce CR, Cooney GJ, Kraegen EW (2009) Lipid and insulin infusion-induced skeletal muscle insulin resistance is likely due to metabolic feedback and not changes in IRS-1, Akt, or AS160 phosphorylation. Am J Physiol Endocrinol Metab 297:E67–E75

    Article  CAS  Google Scholar 

  4. Kraegen EW, Clark PW, Jenkins AB, Daley EA, Chisholm DJ, Storlien LH (1991) Development of muscle insulin resistance after liver insulin resistance in high-fat-fed rats. Diabetes 40:1397–1403

    Article  CAS  Google Scholar 

  5. Le Roy T, Llopis M, Lepage P, Bruneau A, Rabot S, Bevilacqua C, Martin P, Philippe C, Walker F, Bado A, Perlemuter G, Cassard-Doulcier AM, Gerard P (2013) Intestinal microbiota determines development of non-alcoholic fatty liver disease in mice. Gut 62:1787–1794

    Article  Google Scholar 

  6. Machado MV, Ferreira DM, Castro RE, Silvestre AR, Evangelista T, Coutinho J, Carepa F, Costa A, Rodrigues CM, Cortez-Pinto H (2012) Liver and muscle in morbid obesity: the interplay of fatty liver and insulin resistance. PLoS One 7:e31738

    Article  CAS  Google Scholar 

  7. Turner N, Kowalski GM, Leslie SJ, Risis S, Yang C, Lee-Young RS, Babb JR, Meikle PJ, Lancaster GI, Henstridge DC, White PJ, Kraegen EW, Marette A, Cooney GJ, Febbraio MA, Bruce CR (2013) Distinct patterns of tissue-specific lipid accumulation during the induction of insulin resistance in mice by high-fat feeding. Diabetologia 56:1638–1648

    Article  CAS  Google Scholar 

  8. Kleemann R, van Erk M, Verschuren L, van den Hoek AM, Koek M, Wielinga PY, Jie A, Pellis L, Bobeldijk-Pastorova I, Kelder T, Toet K, Wopereis S, Cnubben N, Evelo C, van Ommen B, Kooistra T (2010) Time-resolved and tissue-specific systems analysis of the pathogenesis of insulin resistance. PLoS One 5:e8817

    Article  Google Scholar 

  9. Turner N, Cooney GJ, Kraegen EW, Bruce CR (2014) Fatty acid metabolism, energy expenditure and insulin resistance in muscle. J Endocrinol 220:T61–T79

    Article  CAS  Google Scholar 

  10. Schmitz-Peiffer C (2000) Signalling aspects of insulin resistance in skeletal muscle: mechanisms induced by lipid oversupply. Cell Signal 12:583–594

    Article  CAS  Google Scholar 

  11. Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, Lewis GD, Fox CS, Jacques PF, Fernandez C, O’Donnell CJ, Carr SA, Mootha VK, Florez JC, Souza A, Melander O, Clish CB, Gerszten RE (2011) Metabolite profiles and the risk of developing diabetes. Nat Med 17:448–453

    Article  Google Scholar 

  12. Xu F, Tavintharan S, Sum CF, Woon K, Lim SC, Ong CN (2013) Metabolic signature shift in type 2 diabetes mellitus revealed by mass spectrometry-based metabolomics. J Clin Endocrinol Metab 98:E1060–E1065

    Article  CAS  Google Scholar 

  13. Polakof S, Rémond D, Rambeau M, Pujos-Guillot E, Sébédio J-L, Dardevet D, Comte B, Savary-Auzeloux I (2014) Postprandial metabolic events in mini-pigs: new insights from a combined approach using plasma metabolomics, tissue gene expression, and enzyme activity. Metabolomics: 1–16

  14. Wishart DS (2007) Current progress in computational metabolomics. Brief Bioinform 8(5):279–293

    Article  CAS  Google Scholar 

  15. Zhu Y, Feng Y, Shen L, Xu D, Wang B, Ruan K, Cong W (2013) Effect of metformin on the urinary metabolites of diet-induced-obese mice studied by ultra performance liquid chromatography coupled to time-of-flight mass spectrometry (UPLC-TOF/MS). J Chromatogr B 925:110–116

    Article  CAS  Google Scholar 

  16. Pedersen R, Ingerslev HC, Sturek M, Alloosh M, Cirera S, Christoffersen BO, Moesgaard SG, Larsen N, Boye M (2013) Characterisation of gut microbiota in Ossabaw and Gottingen minipigs as models of obesity and metabolic syndrome. PLoS One 8:e56612

    Article  CAS  Google Scholar 

  17. Guillerm-Regost C, Louveau I, Sebert SP, Damon M, Champ MM, Gondret F (2006) Cellular and biochemical features of skeletal muscle in obese Yucatan minipigs. Obesity (Silver Spring) 14:1700–1707

    Article  CAS  Google Scholar 

  18. Nielsen KL, Hartvigsen ML, Hedemann MS, Laerke HN, Hermansen K, Bach Knudsen KE (2014) Similar metabolic responses in pigs and humans to breads with different contents and compositions of dietary fibers: a metabolomics study. Am J Clin Nutr 99:941–949

    Article  CAS  Google Scholar 

  19. Litten-Brown JC, Corson AM, Clarke L (2010) Porcine models for the metabolic syndrome, digestive and bone disorders: a general overview. Animal 4:899–920

    Article  CAS  Google Scholar 

  20. Christoffersen B, Ribel U, Raun K, Golozoubova V, Pacini G (2009) Evaluation of different methods for assessment of insulin sensitivity in Göttingen minipigs: introduction of a new, simpler method. Am J Physiol Regul Integr Comp Physiol 297:R1195–R1201

    Article  CAS  Google Scholar 

  21. Spurlock ME, Gabler NK (2008) The development of porcine models of obesity and the metabolic syndrome. J Nutr 138:397–402

    Article  CAS  Google Scholar 

  22. Keppler D, Decker K, Bergmeyer HU (1974) Glycogen determination with amyloglucosidase. Methods of enzymatic analysis. Academic Press, New York, pp 1127–1131

    Google Scholar 

  23. Pereira H, Martin J-F, Joly C, Sébédio J-L, Pujos-Guillot E (2010) Development and validation of a UPLC/MS method for a nutritional metabolomic study of human plasma. Metabolomics 6:207–218

    Article  CAS  Google Scholar 

  24. Benton HP, Wong DM, Trauger SA, Siuzdak G (2008) XCMS2: processing tandem mass spectrometry data for metabolite identification and structural characterization. Anal Chem 80:6382–6389

    Article  CAS  Google Scholar 

  25. Smith CA, Want EJ, O’Maille G, Abagyan R, Siuzdak G (2006) XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78:779–787

    Article  CAS  Google Scholar 

  26. Sumner LW, Amberg A, Barrett D, Beale MH, Beger R, Daykin CA, Fan TW, Fiehn O, Goodacre R, Griffin JL, Hankemeier T, Hardy N, Harnly J, Higashi R, Kopka J, Lane AN, Lindon JC, Marriott P, Nicholls AW, Reily MD, Thaden JJ, Viant MR (2007) Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3:211–221

    Article  CAS  Google Scholar 

  27. Furet JP, Firmesse O, Gourmelon M, Bridonneau C, Tap J, Mondot S, Dore J, Corthier G (2009) Comparative assessment of human and farm animal faecal microbiota using real-time quantitative PCR. FEMS Microbiol Ecol 68:351–362

    Article  CAS  Google Scholar 

  28. Guo X, Xia X, Tang R, Zhou J, Zhao H, Wang K (2008) Development of a real-time PCR method for Firmicutes and Bacteroidetes in faeces and its application to quantify intestinal population of obese and lean pigs. Lett Appl Microbiol 47:367–373

    Article  CAS  Google Scholar 

  29. Matsuki T, Watanabe K, Fujimoto J, Takada T, Tanaka R (2004) Use of 16S rRNA gene-targeted group-specific primers for real-time PCR analysis of predominant bacteria in human feces. Appl Environ Microbiol 70:7220–7228

    Article  CAS  Google Scholar 

  30. Malinen E, Rinttila T, Kajander K, Matto J, Kassinen A, Krogius L, Saarela M, Korpela R, Palva A (2005) Analysis of the fecal microbiota of irritable bowel syndrome patients and healthy controls with real-time PCR. Am J Gastroenterol 100:373–382

    Article  CAS  Google Scholar 

  31. Cloetens L, Broekaert WF, Delaedt Y, Ollevier F, Courtin CM, Delcour JA, Rutgeerts P, Verbeke K (2010) Tolerance of arabinoxylan-oligosaccharides and their prebiotic activity in healthy subjects: a randomised, placebo-controlled cross-over study. Br J Nutr 103:703–713

    Article  CAS  Google Scholar 

  32. Tana C, Umesaki Y, Imaoka A, Handa T, Kanazawa M, Fukudo S (2010) Altered profiles of intestinal microbiota and organic acids may be the origin of symptoms in irritable bowel syndrome. Neurogastroenterol Motil 22(512–519):e114–e515

    Google Scholar 

  33. Ohene-Adjei S, Teather RM, Ivan M, Forster RJ (2007) Postinoculation protozoan establishment and association patterns of methanogenic archaea in the ovine rumen. Appl Environ Microbiol 73:4609–4618

    Article  CAS  Google Scholar 

  34. Ramirez-Farias C, Slezak K, Fuller Z, Duncan A, Holtrop G, Louis P (2009) Effect of inulin on the human gut microbiota: stimulation of Bifidobacterium adolescentis and Faecalibacterium prausnitzii. Br J Nutr 101:541–550

    Article  CAS  Google Scholar 

  35. Collado MC, Derrien M, Isolauri E, de Vos WM, Salminen S (2007) Intestinal integrity and Akkermansia muciniphila, a mucin-degrading member of the intestinal microbiota present in infants, adults, and the elderly. Appl Environ Microbiol 73:7767–7770

    Article  CAS  Google Scholar 

  36. Caraux G, Pinloche S (2005) PermutMatrix: a graphical environment to arrange gene expression profiles in optimal linear order. Bioinformatics 21:1280–1281

    Article  CAS  Google Scholar 

  37. Sebert SP, Lecannu G, Kozlowski F, Siliart B, Bard JM, Krempf M, Champ MM (2005) Childhood obesity and insulin resistance in a Yucatan mini-piglet model: putative roles of IGF-1 and muscle PPARs in adipose tissue activity and development. Int J Obes (Lond) 29:324–333

    Article  CAS  Google Scholar 

  38. Backhed F, Ding H, Wang T, Hooper LV, Koh GY, Nagy A, Semenkovich CF, Gordon JI (2004) The gut microbiota as an environmental factor that regulates fat storage. Proc Natl Acad Sci USA 101:15718–15723

    Article  Google Scholar 

  39. Xi S, Yin W, Wang Z, Kusunoki M, Lian X, Koike T, Fan J, Zhang Q (2004) A minipig model of high-fat/high-sucrose diet-induced diabetes and atherosclerosis. Int J Exp Pathol 85:223–231

    Article  Google Scholar 

  40. Dyson MC, Alloosh M, Vuchetich JP, Mokelke EA, Sturek M (2006) Components of metabolic syndrome and coronary artery disease in female Ossabaw swine fed excess atherogenic diet. Comp Med 56:35–45

    CAS  Google Scholar 

  41. Kulkarni RN, Bruning JC, Winnay JN, Postic C, Magnuson MA, Kahn CR (1999) Tissue-specific knockout of the insulin receptor in pancreatic beta cells creates an insulin secretory defect similar to that in type 2 diabetes. Cell 96:329–339

    Article  CAS  Google Scholar 

  42. Croset M, Rajas F, Zitoun C, Hurot JM, Montano S, Mithieux G (2001) Rat small intestine is an insulin-sensitive gluconeogenic organ. Diabetes 50:740–746

    Article  CAS  Google Scholar 

  43. Li Y, Xu S, Mihaylova MM, Zheng B, Hou X, Jiang B, Park O, Luo Z, Lefai E, Shyy JY, Gao B, Wierzbicki M, Verbeuren TJ, Shaw RJ, Cohen RA, Zang M (2011) AMPK phosphorylates and inhibits SREBP activity to attenuate hepatic steatosis and atherosclerosis in diet-induced insulin-resistant mice. Cell Metab 13:376–388

    Article  CAS  Google Scholar 

  44. Stanhope KL (2012) Role of fructose-containing sugars in the epidemics of obesity and metabolic syndrome. Annu Rev Med 63:329–343

    Article  CAS  Google Scholar 

  45. Hellerstein MK (1999) De novo lipogenesis in humans: metabolic and regulatory aspects. Eur J Clin Nutr 53(Suppl 1):S53–S65

    Article  Google Scholar 

  46. Mithieux G (2009) A novel function of intestinal gluconeogenesis: central signaling in glucose and energy homeostasis. Nutrition 25:881–884

    Article  CAS  Google Scholar 

  47. Sejersen H, Sørensen MT, Larsen T, Bendixen E, Ingvartsen KL (2013) Liver protein expression in young pigs in response to a high-fat diet and diet restriction. J Anim Sci 91:147–158

    Article  CAS  Google Scholar 

  48. Iozzo P, Bucci M, Roivainen A, Någren K, Järvisalo MJ, Kiss J, Guiducci L, Fielding B, Naum AG, Borra R, Virtanen K, Savunen T, Salvadori PA, Ferrannini E, Knuuti J, Nuutila P (2010) Fatty acid metabolism in the liver, measured by positron emission tomography is increased in obese individuals. Gastroenterology 139(846–856):e846

    Article  Google Scholar 

  49. Newgard CB (2012) Interplay between lipids and branched-chain amino acids in development of insulin resistance. Cell Metab 15:606–614

    Article  CAS  Google Scholar 

  50. Lynch CJ, Adams SH (2014) Branched-chain amino acids in metabolic signalling and insulin resistance. Nat Rev.Endocrinol 10(12):723–736

    Article  CAS  Google Scholar 

  51. Shin AC, Fasshauer M, Filatova N, Grundell LA, Zielinski E, Zhou JY, Scherer T, Lindtner C, White PJ, Lapworth AL, Ilkayeva O, Knippschild U, Wolf AM, Scheja L, Grove KL, Smith RD, Qian WJ, Lynch CJ, Newgard CB, Buettner C (2014) Brain insulin lowers circulating BCAA levels by inducing hepatic BCAA catabolism. Cell Metab 20:898–909

    Article  CAS  Google Scholar 

  52. She P, Van Horn C, Reid T, Hutson SM, Cooney RN, Lynch CJ (2007) Obesity-related elevations in plasma leucine are associated with alterations in enzymes involved in branched-chain amino acid metabolism. Am J Physiol Endocrinol Metab 293:E1552–E1563

    Article  CAS  Google Scholar 

  53. Morio B, Comte B, Martin J-F, Chanseaume E, Alligier M, Junot C, Lyan B, Boirie Y, Vidal H, Laville M, Pujos-Guillot E, Sébédio J-L (2014) Metabolomics reveals differential metabolic adjustments of normal and overweight subjects during overfeeding. Metabolomics 11(4):920–938

    Article  Google Scholar 

  54. Lillefosse HH, Clausen MR, Yde CC, Ditlev DB, Zhang X, Du Z-Y, Bertram HC, Madsen L, Kristiansen K, Liaset B (2014) Urinary loss of tricarboxylic acid cycle intermediates as revealed by metabolomics studies: an underlying mechanism to reduce lipid Accretion by whey protein ingestion? J Proteome Res 13:2560–2570

    Article  CAS  Google Scholar 

  55. Adams SH (2011) Emerging perspectives on essential amino acid metabolism in obesity and the insulin-resistant state. Adv Nutr Int Rev J 2:445–456

    Article  CAS  Google Scholar 

  56. Legro RS, Finegood D, Dunaif A (1998) A fasting glucose to insulin ratio is a useful measure of insulin sensitivity in women with polycystic ovary syndrome. J Clin Endocrinol Metab 83:2694–2698

    CAS  Google Scholar 

  57. Sebert SP, Lecannu G, Kozlowski F, Siliart B, Bard JM, Krempf M, Champ MMJ (2005) Childhood obesity and insulin resistance in a Yucatan mini-piglet model: putative roles of IGF-1 and muscle PPARs in adipose tissue activity and development. Int J Obes Relat Metab Disord 29:324–333

    Article  CAS  Google Scholar 

  58. Witczak CA, Mokelke EA, Boullion R, Wenzel J, Keisler DH, Sturek M (2005) Noninvasive measures of body fat percentage in male Yucatan swine. Comp Med 55:445–451

    CAS  Google Scholar 

Download references

Acknowledgments

The authors acknowledge J. David, C. Prolhac, D. Durand and the personnel of the Animal Facility (C. de L’Homme, B. Cohade) for technical assistance

Author information

Authors and Affiliations

  1. Unité de Nutrition Humaine, Clermont Université, Université d’Auvergne, BP 10448, 63000, Clermont-Ferrand, France

    Sergio Polakof, Didier Rémond, Mathieu Rambeau, Estelle Pujos-Guillot, Blandine Comte, Dominique Dardevet & Isabelle Savary-Auzeloux

  2. UMR 1019, UNH, CRNH Auvergne, INRA, 63000, Clermont-Ferrand, France

    Sergio Polakof, Didier Rémond, Mathieu Rambeau, Estelle Pujos-Guillot, Blandine Comte, Dominique Dardevet & Isabelle Savary-Auzeloux

  3. UMR 1019, Plateforme d’Exploration du Métabolisme, UNH, INRA, 63000, Clermont-Ferrand, France

    Estelle Pujos-Guillot

  4. Unité de Microbiologie, UR454, INRA, 63122, Saint Genès Champanelle, France

    Annick Bernalier-Donadille

Authors
  1. Sergio Polakof
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Didier Rémond
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Annick Bernalier-Donadille
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mathieu Rambeau
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Estelle Pujos-Guillot
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Blandine Comte
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Dominique Dardevet
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Isabelle Savary-Auzeloux
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sergio Polakof.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Polakof, S., Rémond, D., Bernalier-Donadille, A. et al. Metabolic adaptations to HFHS overfeeding: how whole body and tissues postprandial metabolic flexibility adapt in Yucatan mini-pigs. Eur J Nutr 57, 119–135 (2018). https://doi.org/10.1007/s00394-016-1302-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00394-016-1302-1

Keywords

Search

Navigation