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Use of Rat Genomics for Investigating the Metabolic Syndrome

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Rat Genomics

Part of the book series: Methods in Molecular Biology ((MIMB,volume 597))

Abstract

The spontaneously hypertensive rat (SHR) is the most widely used animal model of essential hypertension and accompanying metabolic disturbances. In this model, the use of whole genome sequencing and gene expression profiling techniques, linkage and correlation analyses in recombinant inbred strains, and in vitro and in vivo functional studies in congenic and transgenic lines has recently enabled molecular identification of quantitative trait loci (QTLs) relevant to the metabolic syndrome: (1) a deletion variant in Cd36 (fatty acid translocase) responsible for QTLs on chromosome 4 associated with dyslipidemia, insulin resistance and hypertension, (2) mutated Srebf1 (sterol regulatory element binding factor 1) as a QTL on chromosome 10 influencing dietary-induced changes in hepatic cholesterol levels, and (3) Ogn (osteoglycin) as a QTL on chromosome 17 associated with left ventricular hypertrophy. In addition, selective replacement of the mitochondrial genome of the SHR with the mitochondrial genome of the Brown Norway rat influenced several major metabolic risk factors for type 2 diabetes and provided evidence that spontaneous variation in the mitochondrial genome per se can promote systemic metabolic disturbances relevant to the pathogenesis of metabolic syndrome. Owing to recent progress in the development of rat genomic resources, the pace of QTL identification and discovery of new disease mechanisms can be expected to accelerate in the near future.

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References

  1. Lakka HM, Laaksonen DE, Lakka TA, Niskanen LK, Kumpusalo E, Tuomilehto J et al (2002) The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA 288:2709–2716

    Article  PubMed  Google Scholar 

  2. Park YW, Zhu S, Palaniappan L, Heshka S, Carnethon MR, Heymsfield SB (2003) The metabolic syndrome: prevalence and associated risk factor findings in the US population from the Third National Health and Nutrition Examination Survey, 1988–1994. Arch Intern Med 163:427–436

    Article  PubMed  Google Scholar 

  3. Laaksonen DE, Lakka HM, Niskanen LK, Kaplan GA, Salonen JT, Lakka TA (2002) Metabolic syndrome and development of diabetes mellitus: application and validation of recently suggested definitions of the metabolic syndrome in a prospective cohort study. Am J Epidemiol 156:1070–1077

    Article  PubMed  Google Scholar 

  4. Groop L, Lyssenko V (2008) Genes and type 2 diabetes mellitus. Curr Diab Rep 8:192–197

    Article  CAS  PubMed  Google Scholar 

  5. Zeggini E, Scott LJ, Saxena R, Voight BF, Marchini JL, Hu T et al (2008) Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 40:638–645

    Article  CAS  PubMed  Google Scholar 

  6. Bodmer W, Bonilla C (2008) Common and rare variants in multifactorial susceptibility to common diseases. Nat Genet 40:695–701

    Article  CAS  PubMed  Google Scholar 

  7. Glazier AM, Nadeau JH, Aitman TJ (2002) Finding genes that underlie complex traits. Science 298:2345–2349

    Article  CAS  PubMed  Google Scholar 

  8. Jacob HJ (1999) Functional genomics and rat models. Genome Res 9:1013–1016

    Article  CAS  PubMed  Google Scholar 

  9. Aitman TJ, Critser JK, Cuppen E, Dominiczak A, Fernandez-Suarez XM, Flint J et al (2008) Progress and prospects in rat genetics: a community view. Nat Genet 40:516–522

    Article  CAS  PubMed  Google Scholar 

  10. Consortium STAR, Saar K, Beck A, Bihoreau MT, Birney E, Brocklebank D, Chen Y et al (2008) SNP and haplotype mapping for genetic analysis in the rat. Nat Genet 40:560–566

    Article  Google Scholar 

  11. Twigger SN, Pruitt KD, Fernández-Suárez XM, Karolchik D, Worley KC, Maglott DR et al (2008) What everybody should know about the rat genome and its online resources. Nat Genet 40:523–527

    Article  CAS  PubMed  Google Scholar 

  12. Butcher LM, Beck S (2008) Future impact of integrated high-throughput methylome analyses on human health and disease. J Genet Genomics 35:391–401

    Article  CAS  PubMed  Google Scholar 

  13. Saad Y, Garrett MR, Manickavasagam E, Yerga-Woolwine S, Farms P, Radecki T et al (2007) Fine-mapping and comprehensive transcript analysis reveals nonsynonymous variants within a novel 1.17 Mb blood pressure QTL region on rat chromosome 10. Genomics 89:343–353

    Article  CAS  PubMed  Google Scholar 

  14. Lee SJ, Liu J, Westcott AM, Vieth JA, DeRaedt SJ, Yang S et al (2006) Substitution mapping in dahl rats identifies two distinct blood pressure quantitative trait loci within 1.12- and 1.25-mb intervals on chromosome 3. Genetics 174:2203–2213

    Article  CAS  PubMed  Google Scholar 

  15. Seda O, Liska F, Sedová L, Kazdová L, Krenová D, Kren V (2005) A 14-gene region of rat chromosome 8 in SHR-derived polydactylous congenic substrain affects muscle-specific insulin resistance, dyslipidaemia and visceral adiposity. Folia Biol (Praha) 51:53–61

    CAS  Google Scholar 

  16. Fournie GJ (2009) Generation of consomic and congenic rat strains (including speed congenics). Method Mol Biol current issue

    Google Scholar 

  17. Landa V, Zidek V, Pravenec M (2009) Generation of rat conplastic strains using superovulation. Method Mol Biol current issue

    Google Scholar 

  18. Jansen RC, Nap JP (2001) Genetical genomics: the added value from segregation. Trends Genet 17:388–391

    Article  CAS  PubMed  Google Scholar 

  19. Passador-Gurgel G, Hsieh WP, Hunt P, Deighton N, Gibson G (2007) Quantitative trait transcripts for nicotine resistance in Drosophila melanogaster. Nat Genet 39:264–268

    Article  CAS  PubMed  Google Scholar 

  20. Hübner N, Wallace CA, Zimdahl H, Petretto E, Schulz H, Maciver F et al (2005) Integrated transcriptional profiling and linkage analysis for identification of genes underlying disease. Nat Genet 37:243–253

    Article  PubMed  Google Scholar 

  21. Pravenec M, Zídek V, Landa V, Simáková M, Mlejnek P, Kazdová L et al (2004) Genetic analysis of “metabolic syndrome” in the spontaneously hypertensive rat. Physiol Res 53(Suppl 1):S15–S22

    CAS  PubMed  Google Scholar 

  22. Srinivasan K, Ramarao P (2007) Animal models in type 2 diabetes research: an overview. Indian J Med Res 125:451–472

    CAS  PubMed  Google Scholar 

  23. Chen D, Wang MW (2005) Development and application of rodent models for type 2 diabetes. Diabetes Obes Metab 7:307–317

    Article  PubMed  Google Scholar 

  24. Weksler-Zangen S, Yagil C, Zangen DH, Ornoy A, Jacob HJ, Yagil Y (2001) The newly inbred cohen diabetic rat: a nonobese normolipidemic genetic model of diet-induced type 2 diabetes expressing sex differences. Diabetes 50:2521–2529

    Article  CAS  PubMed  Google Scholar 

  25. Sedová L, Kazdová L, Seda O, Krenová D, Kren V (2000) Rat inbred PD/cub strain as a model of dyslipidemia and insulin resistance. Folia Biol (Praha) 46:99–106

    Google Scholar 

  26. Vrána A, Kazdová L (1990) The hereditary hypertriglyceridemic nonobese rat: an experimental model of human hypertriglyceridemia. Transplant Proc 22:2579

    PubMed  Google Scholar 

  27. Williams RW, Gu J, Qi S, Lu L (2001) The genetic structure of recombinant inbred mice: high-resolution consensus maps for complex trait analysis. Genome Biol 2:RESEARCH004

    Google Scholar 

  28. Aitman TJ, Glazier AM, Wallace CA, Cooper LD, Norsworthy PJ, Wahid FN et al (1999) Identification of Cd36 (Fat) as an insulin-resistance gene causing defective fatty acid and glucose metabolism in hypertensive rats. Nat Genet 21:76–83

    Article  CAS  PubMed  Google Scholar 

  29. Pravenec M, Zidek V, Simakova M, Kren V, Krenova D, Horky K et al (1999) Genetics of Cd36 and the clustering of multiple cardiovascular risk factors in spontaneous hypertension. J Clin Invest 103:1651–1657

    Article  CAS  PubMed  Google Scholar 

  30. Pravenec M, Landa V, Zidek V, Musilova A, Kren V, Kazdova L et al (2001) Transgenic rescue of defective Cd36 ameliorates insulin resistance in spontaneously hypertensive rats. Nat Genet 27:156–158

    Article  CAS  PubMed  Google Scholar 

  31. Pravenec M, Churchill PC, Churchill MC, Viklicky O, Kazdova L, Aitman TJ et al (2008) Identification of renal Cd36 as a determinant of blood pressure and risk for hypertension. Nat Genet 40:952–954

    Article  CAS  PubMed  Google Scholar 

  32. Pravenec M, Kazdova L, Landa V, Zidek V, Mlejnek P, Simakova M et al (2008) Identification of mutated Srebf1 as a QTL influencing risk for hepatic steatosis in the spontaneously hypertensive rat. Hypertension 51:148–153

    Article  CAS  PubMed  Google Scholar 

  33. Petretto E, Sarwar R, Grieve I, Lu H, Kumaran MK, Muckett PJ et al (2008) Integrated genomic approaches implicate osteoglycin (Ogn) in the regulation of left ventricular mass. Nat Genet 40:546–552

    Article  CAS  PubMed  Google Scholar 

  34. Pravenec M, Klír P, Kren V, Zicha J, Kunes J (1989) An analysis of spontaneous hypertension in spontaneously hypertensive rats by means of new recombinant inbred strains. J Hypertens 7:217–221

    Article  CAS  PubMed  Google Scholar 

  35. Wang J, Williams RW, Manly KF (2003) WebQTL: web-based complex trait analysis. Neuroinformatics 1:299–308

    Article  PubMed  Google Scholar 

  36. Darvasi A (1998) Experimental strategies for the genetic dissection of complex traits in animal models. Nat Genet 18:19–24

    Article  CAS  PubMed  Google Scholar 

  37. Chesler EJ, Lu L, Shou S, Qu Y, Gu J, Wang J et al (2005) Complex trait analysis of gene expression uncovers polygenic and pleiotropic networks that modulate nervous system function. Nat Genet 37:233–242

    Article  CAS  PubMed  Google Scholar 

  38. Nadeau JH, Burrage LC, Restivo J, Pao YH, Churchill G, Hoit BD (2003) Pleiotropy, homeostasis, and functional networks based on assays of cardiovascular traits in genetically randomized populations. Genome Res 13:2082–2091

    Article  CAS  PubMed  Google Scholar 

  39. Aitman TJ, Gotoda T, Evans AL, Imrie H, Heath KE, Trembling PM et al (1997) Quantitative trait loci for cellular defects in glucose and fatty acid metabolism in hypertensive rats. Nat Genet 16:197–201

    Article  CAS  PubMed  Google Scholar 

  40. Bottger A, van Lith HA, Kren V, Krenová D, Bílá V, Vorlícek J et al (1996) Quantitative trait loci influencing cholesterol and phospholipid phenotypes map to chromosomes that contain genes regulating blood pressure in the spontaneously hypertensive rat. J Clin Invest 98:856–862

    Article  CAS  PubMed  Google Scholar 

  41. Pravenec M, Gauguier D, Schott JJ, Buard J, Kren V, Bila V et al (1995) Mapping of quantitative trait loci for blood pressure and cardiac mass in the rat by genome scanning of recombinant inbred strains. J Clin Invest 96:1973–1978

    Article  CAS  PubMed  Google Scholar 

  42. Glazier AM, Scott J, Aitman TJ (2002) Molecular basis of the Cd36 chromosomal deletion underlying SHR defects in insulin action and fatty acid metabolism. Mamm Genome 13:108–113

    Article  CAS  PubMed  Google Scholar 

  43. Behringer R (1998) Supersonic congenics? Nat Genet 18:108

    Article  CAS  PubMed  Google Scholar 

  44. Pravenec M, Hyakukoku M, Houstek J, Zidek V, Landa V, Mlejnek P et al (2007) Direct linkage of mitochondrial genome variation to risk factors for type 2 diabetes in conplastic strains. Genome Res 17:1319–1326

    Article  CAS  PubMed  Google Scholar 

  45. Manfredi G, Fu J, Ojaimi J, Sadlock JE, Kwong JQ, Guy J et al (2002) Rescue of a deficiency in ATP synthesis by transfer of MTATP6, a mitochondrial DNA-encoded gene, to the nucleus. Nat Genet 30:394–399

    Article  CAS  PubMed  Google Scholar 

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Acknowledgments

This work was supported by grants 1P05ME791 and 1M6837805002 from the Ministry of Education of the Czech Republic, grants 301/06/0028 and 301/08/0166 from the Grant Agency of the Czech Republic, grant IAA500110604 from the Grant Agency of the Academy of Sciences of the Czech Republic, and by the European Commission within the Sixth Framework Programme through the Integrated Project EURATools (contract no. LSHG-CT-2005-019015). M.P. is an international research scholar of the Howard Hughes Medical Institute.

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Correspondence to Michal Pravenec .

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Pravenec, M. (2010). Use of Rat Genomics for Investigating the Metabolic Syndrome. In: Anegon, I. (eds) Rat Genomics. Methods in Molecular Biology, vol 597. Humana Press. https://doi.org/10.1007/978-1-60327-389-3_28

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  • DOI: https://doi.org/10.1007/978-1-60327-389-3_28

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  • Print ISBN: 978-1-60327-388-6

  • Online ISBN: 978-1-60327-389-3

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