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Metabolic Syndrome: From the Genetics to the Pathophysiology

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Abstract

The metabolic syndrome (MS) constitutes a combination of underlying risk factors for an adverse outcome, cardiovascular disease. Thus, the clinical behavior of the MS can be regarded as a whole. Nevertheless, from a pathogenic point of view, understanding of the underlying mechanisms of each MS intermediate phenotype is far beyond their understanding as an integrative process. Systems biology introduces a new concept for revealing the pathogenesis of human disorders and suggests the presence of common physiologic processes and molecular networks influencing the risk of a disease. This paper shows a model of this concept to explain the genetic determinants of MS-associated phenotypes. Based on the hypothesis that common physiologic processes and molecular networks may influence the risk of MS disease components, we propose two systems-biology approaches: a gene enrichment analysis and the use of a protein-protein interaction network. Our results show that a network driven by many members of the nuclear receptor superfamily of proteins, including retinoid X receptor and farnesoid X receptor (FXR), may be implicated in the pathogenesis of the MS by its interactions at multiple levels of complexity with genes associated with metabolism, cell differentiation, and oxidative stress. In addition, we review two alternative genetic mechanisms that are gaining acceptance in the physiopathology of the MS: the regulation of transcriptional and post-transcriptional gene expression by microRNAs and epigenetic modifications such as DNA methylation.

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References

Papers of particular interest, published recently, have been highlighted as: • Of importance

  1. Despres JP, Lemieux I: Abdominal obesity and metabolic syndrome. Nature 2006, 444:881–887.

    Article  PubMed  CAS  Google Scholar 

  2. Kahn R: Metabolic syndrome: is it a syndrome? Does it matter? Circulation 2007, 115:1806–1810.

    Article  PubMed  Google Scholar 

  3. Alberti KG, Zimmet PZ: Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus provisional report of a WHO consultation. Diabet Med 1998, 15:539–553.

    Article  PubMed  CAS  Google Scholar 

  4. Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA 2001, 285:2486–2497.

    Google Scholar 

  5. Alberti KG, Zimmet P, Shaw J: The metabolic syndrome—a new worldwide definition. Lancet 2005, 366:1059–1062.

    Article  PubMed  Google Scholar 

  6. Sookoian S, Pirola CJ: Genetics of the cardiometabolic syndrome: new insights and therapeutic implications. Ther Adv Cardiovasc Dis 2007, 1:37–47.

    Article  PubMed  Google Scholar 

  7. Zhang C: Novel functions for small RNA molecules. Curr Opin Mol Ther 2009, 11:641–651.

    PubMed  CAS  Google Scholar 

  8. Weaver IC: Epigenetic programming by maternal behavior and pharmacological intervention. Nature versus nurture: let’s call the whole thing off. Epigenetics 2007, 2:22–28.

    Article  PubMed  Google Scholar 

  9. • Chen J, Bardes EE, Aronow BJ, et al.: ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 2009, 37:W305–W311. This article demonstrates the bioinformatic tool for gene list functional enrichment, candidate gene prioritization, and identification and prioritization of novel disease candidate genes in the interactome.

    Article  PubMed  CAS  Google Scholar 

  10. Sookoian S, Gianotti TF, Schuman M, et al.: Gene prioritization based on biological plausibility over genome wide association studies renders new loci associated with type 2 diabetes. Genet Med 2009, 11:338–343.

    Article  PubMed  CAS  Google Scholar 

  11. Sookoian S, Gemma C, Pirola CJ: Influence of hepatocyte nuclear factor 4α (HNF4α) gene variants on the risk of type 2 diabetes: a meta-analysis in 49,577 individuals. Mol Genet Metab 2010, 99:80–89.

    Article  PubMed  CAS  Google Scholar 

  12. Sookoian S, Gianotti TF, Gemma C, et al.: Role of genetic variation in insulin-like growth factor 1 receptor on insulin resistance and arterial hypertension. J Hypertens 2010, 28:1194–1202.

    PubMed  CAS  Google Scholar 

  13. Hindorff LA, Sethupathy P, Junkins HA, et al.: Potential etiologic and functional implications of genome-wide association loci for human diseases and traits. Proc Natl Acad Sci U S A 2009, 106:9362–9367.

    Article  PubMed  CAS  Google Scholar 

  14. Sookoian S, Gemma C, Garcia SI, et al.: Short allele of serotonin transporter gene promoter is a risk factor for obesity in adolescents. Obesity (Silver Spring) 2007, 15:271–276.

    Article  CAS  Google Scholar 

  15. Sookoian S, Gianotti TF, Gemma C, et al.: Contribution of the functional 5-HTTLPR variant of the SLC6A4 gene to obesity risk in male adults. Obesity (Silver Spring) 2008, 16:488–491.

    Article  CAS  Google Scholar 

  16. Duez H, Staels B: Nuclear receptors linking circadian rhythms and cardiometabolic control. Arterioscler Thromb Vasc Biol 2010, 30:1529–1534.

    Article  PubMed  CAS  Google Scholar 

  17. Sookoian S, Gemma C, Gianotti TF, et al.: Genetic variants of Clock transcription factor are associated with individual susceptibility to obesity. Am J Clin Nutr 2008, 87:1606–1615.

    PubMed  CAS  Google Scholar 

  18. Sookoian S, Castano G, Gemma C, et al.: Common genetic variations in CLOCK transcription factor are associated with nonalcoholic fatty liver disease. World J Gastroenterol 2007, 13:4242–4248.

    PubMed  CAS  Google Scholar 

  19. Li S, Zhao JH, Luan J, et al.: Cumulative effects and predictive value of common obesity-susceptibility variants identified by genome-wide association studies. Am J Clin Nutr 2010, 91:184–190.

    Article  PubMed  CAS  Google Scholar 

  20. Tomaszewski M, Charchar FJ, Lacka B, et al.: Epistatic interaction between β2-adrenergic receptor and neuropeptide Y genes influences LDL-cholesterol in hypertension. Hypertension 2004, 44:689–694.

    Article  PubMed  CAS  Google Scholar 

  21. Baessler A, Fischer M, Mayer B, et al.: Epistatic interaction between haplotypes of the ghrelin ligand and receptor genes influence susceptibility to myocardial infarction and coronary artery disease. Hum Mol Genet 2007, 16:887–899.

    Article  PubMed  CAS  Google Scholar 

  22. Isaacs A, Aulchenko YS, Hofman A, et al.: Epistatic effect of cholesteryl ester transfer protein and hepatic lipase on serum high-density lipoprotein cholesterol levels. J Clin Endocrinol Metab 2007, 92:2680–2687.

    Article  PubMed  CAS  Google Scholar 

  23. Brisson D, St-Pierre J, Santure M, et al.: Genetic epistasis in the VLDL catabolic pathway is associated with deleterious variations on triglyceridemia in obese subjects. Int J Obes (Lond) 2007, 31:1325–1333.

    Article  CAS  Google Scholar 

  24. Sookoian S, Gianotti TF, Burgueño A, Pirola CJ: Gene-gene interaction between serotonin transporter (SLC6A4) and CLOCK modulates the risk of metabolic syndrome in rotating shiftworkers. Chronobiol Int 2010, 27:1202–1218.

    Article  PubMed  CAS  Google Scholar 

  25. Ling C, Del Guerra S, Lupi R, et al.: Epigenetic regulation of PPARGC1A in human type 2 diabetic islets and effect on insulin secretion. Diabetologia 2008, 51:615–622.

    Article  PubMed  CAS  Google Scholar 

  26. Gemma C, Sookoian S, Alvarinas J, et al.: Maternal pregestational BMI is associated with methylation of the PPARGC1A promoter in newborns. Obesity (Silver Spring) 2009, 17:1032–1039.

    Article  CAS  Google Scholar 

  27. Gemma C, Sookoian S, Dieuzeide G, et al.: Methylation of TFAM gene promoter in peripheral white blood cells is associated with insulin resistance in adolescents. Mol Genet Metab 2010, 100:83–87.

    Article  PubMed  CAS  Google Scholar 

  28. Gemma C, Sookoian S, Alvarinas J, et al.: Mitochondrial DNA depletion in small- and large-for-gestational-age newborns. Obesity (Silver Spring) 2006, 14:2193–2199.

    Article  CAS  Google Scholar 

  29. Gianotti TF, Sookoian S, Dieuzeide G, et al.: A decreased mitochondrial DNA content is related to insulin resistance in adolescents. Obesity 2008, 16:1591–1595.

    Article  PubMed  CAS  Google Scholar 

  30. Ling C, Groop L: Epigenetics: a molecular link between environmental factors and type 2 diabetes. Diabetes 2009, 58:2718–2725.

    Article  PubMed  CAS  Google Scholar 

  31. Sethupathy P, Borel C, Gagnebin M, et al.: Human microRNA-155 on chromosome 21 differentially interacts with its polymorphic target in the AGTR1 3′ untranslated region: a mechanism for functional single-nucleotide polymorphisms related to phenotypes. Am J Hum Genet 2007, 81:405–413.

    Article  PubMed  CAS  Google Scholar 

  32. Sookoian S, Castano G, Garcia SI, et al.: A1166C angiotensin II type 1 receptor gene polymorphism may predict hemodynamic response to losartan in patients with cirrhosis and portal hypertension. Am J Gastroenterol 2005, 100:636–642.

    Article  PubMed  CAS  Google Scholar 

  33. Yang Z, Kaye DM: Mechanistic insights into the link between a polymorphism of the 3′UTR of the SLC7A1 gene and hypertension. Hum Mutat 2009, 30:328–333.

    Article  PubMed  Google Scholar 

  34. Yang Z, Venardos K, Jones E, et al.: Identification of a novel polymorphism in the 3′UTR of the L-arginine transporter gene SLC7A1: contribution to hypertension and endothelial dysfunction. Circulation 2007, 115:1269–1274.

    PubMed  CAS  Google Scholar 

  35. • Rayner KJ, Suarez Y, Davalos A, et al.: MiR-33 contributes to the regulation of cholesterol homeostasis. Science 2010, 328:1570–1573. The authors demonstrated that miR-33 appears to regulate both HDL biogenesis in the liver and cellular cholesterol efflux.

    Article  PubMed  CAS  Google Scholar 

  36. Peter ME: Targeting of mRNAs by multiple miRNAs: the next step. Oncogene 2010, 29:2161–2164.

    Article  PubMed  CAS  Google Scholar 

  37. Warde-Farley D, Donaldson SL, Comes O, et al: The GeneMANIA prediction server: biological network integration for gene prioritization and predicting gene function. Nucleic Acids Res. 2010:W214-220.

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Acknowledgments

Supported in part by Grants UBACYT M055 (Universidad de Buenos Aires) and PICT 2006-124 (Agencia Nacional de Promoción Científica y Tecnológica). SS and CJP are members of Consejo Nacional de Investigaciones Científicas (CONICET).

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Correspondence to Carlos J. Pirola.

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Sookoian, S., Pirola, C.J. Metabolic Syndrome: From the Genetics to the Pathophysiology. Curr Hypertens Rep 13, 149–157 (2011). https://doi.org/10.1007/s11906-010-0164-9

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