| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
|
|
|
|||||||
|
|||||||||
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Themed Issue papers |
en121 Institute of Physiology, Czech Academy of Sciences and Center for Applied Genomics, Prague, Czech Republic2 Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, Prague, Czech Republic
 |
Abstract |
|---|
 Top Abstract IntroductionReferences |
|---|
(Received 12 January 2005;
accepted after revision 17 February 2005; first published online 22 February 2005)
Corresponding author M. Pravenec: Institute of Physiology, Czech Academy of Sciences, Videnska 1083, 14220 Prague 4, Czech Republic. Email: pravenec{at}biomed.cas.cz
|
Introduction |
|---|
|
TopAbstractIntroductionReferences |
|---|
Animal models of multifactorially determined cardiovascular diseases
Comparative genomics is an essential tool for genetic dissection of multifactorial diseases and much of our present knowledge concerning the genes that regulate clinically important pathophysiological traits has been derived from rodents (Glazier et al. 2002a). For instance, the spontaneously hypertensive rat (SHR) develops spontaneous hypertension and, under the appropriate environmental conditions, also insulin resistance and dyslipidaemia and could therefore be considered a model of human metabolic syndrome (Okamoto & Aoki, 1963; Reaven, 1988; Aitman et al. 1997; Pravenec et al. 2004). The SHR has been studied for over 30 years and is a very useful animal model for the testing of antihypertensive drugs. At present, almost 200 quantitative trait loci (QTLs) associated with blood pressure have been reported in the SHR and other rat models (Rat Genome Database at http://www.rgd.mcw.edu/). However, identification of these putative QTLs at the molecular level is a difficult task. The problem lies in the complexity of phenotypes with which ‘candidate’ genes are associated. It is therefore hoped that complex phenotypes might be subdivided into intermediate phenotypes that are less complex, with oligogenic determination and therefore exhibiting higher heritability. The intermediate phenotypes with a simple genetic determination are gene expression profiles that are regulated in cis by the genes themselves (Brem et al. 2002; Cheung et al. 2003; Schadt et al. 2003; Yvert et al. 2003; Morley et al. 2004). Additional levels of complexity represent physiological phenotypes at the cellular, tissue and organ levels. Systemic phenotypes such as blood pressure or plasma lipids then exhibit the highest level of complexity. Linkage analyses can be performed at each level of complexity and cumulative data from these linkages may provide sufficient information about the specific roles of given gene variants in determination of complex traits. This approach requires a model system that allows cumulativeness of linkage data obtained at several levels of complexity. Recombinant inbred (RI) strains represent such a system for genetic analysis of complex traits (Williams et al. 2001).
Recombinant inbred (RI) strains for the analysis of genes and functional networks predisposing to cardiovascular disease
Recombinant inbred strains combine the advantages of inbreeding and gene segregation. An important feature of RI strains is that, because they are inbred and genetically defined populations, repeated assays can be made so that the phenotypes of each strain can be systematically characterized with a rigor that is often not feasible in conventional segregating populations. Moreover, the acquired data are cumulative across assays, studies and research groups. So, large datasets can be analysed and trait relationships discovered that might not have been suspected otherwise. This cumulativeness is an enormous advantage and an essential feature for the analysis of complex pathophysiological traits. For genetic and correlation analyses of spontaneous hypertension and metabolic defects in the SHR, the BXH/HXB sets of RI strains were developed by reciprocal crossings of the SHR and the Brown Norway (BN-Lx) strains (Pravenec et al. 1989). At present, 21 HXB and 10 BXH RI strains are available. The present map of RI strains contains more than 1000 gene markers (Pravenec et al. 1996; Jirout et al. 2003; Hübner et al. 2005) and over 80 phenotypes were determined in the RI strains (WebQTL at http://www.genenetwork.org/). Genetic analysis of the RI strains revealed QTLs for blood pressure regulation on chromosomes 1, 2, 4, 19 and 20, QTLs for heart weight on chromosomes 12, 17 and 20, QTLs for renal weight on chromosomes 1, 3 and 17, a QTL for compensatory renal growth on chromosome 4, QTLs for dyslipidaemia on chromosomes 4, 7, 10 and 19, QTLs for insulin resistance on chromosomes 3, 4, 7 and 19, a QTL for haematocrit on chromosome 4, and QTLs for response to stress on chromosomes 10 and 12 (reviewed by Pravenec et al. 2004).
Recent advances in simultaneous analysis of the expression of thousands of genes using the Affymetrix microarrays platform might aid the use of expression phenotypes as useful intermediates between genes and complex physiological phenotypes. Recently, the BXH/HXB panel of RI strains was used to map the major genetic determinants of gene expression in SHR for 15 923 genes in two of the key tissues in the pathophysiology of the metabolic syndrome. After assessment of genome-wide significance and accounting for multiple testing using false discovery rate (FDR), well in excess of 1000 expression QTLs (eQTLs) were found in kidney and fat, of which several hundred were common to both tissues. These eQTLs represent a large source of attractive candidate genes for the scores of physiological QTLs (pQTLs) that have been mapped in SHR (Hübner et al. 2005). A similar ‘genetical genomics’ approach (Jansen & Nap, 2001) was used by Chesler et al. (2005) to uncover genes that modulate nervous system function and by Bystrykh et al. (2005) to analyse regulatory pathways that affect haematopoietic stem cell function using mouse BXD RI strains.
On the one hand, the limitations of RI strains for the mapping of QTLs associated with complex traits are widely recognized, especially in regard to progenitor strains and to small numbers of RI strains in individual sets (Darvasi, 1998). On the other hand, RI strains can be used to map QTLs with relatively large effects and for the mapping of less complex, essentially monogenic cis-acting eQTLs (Chesler et al. 2005; Hübner et al. 2005). In addition, the unique genetic constitution of RI strains can be used in a powerful way for network (correlation) analyses, because the primary goal in such studies is to measure the tendency of different traits to co-segregate rather than to map genes. Thus, because of their unique patterns of genetic randomization, RI strains provide remarkable statistical power for the measurement of the tendencies of traits to co-segregate (Nadeau et al. 2003). Because of these advantages of RI strains the Complex Trait Consortium recently proposed to derive a ‘collaborative cross’, a large set of mouse RI strains as a community resource for the genetic analysis of complex traits (Churchill et al. 2004).
Proof of the principle: identification of Cd36 as an insulin-resistance gene causing defective fatty acid and glucose metabolism in hypertensive rats
Linkage analysis of the BXH/HXB RI strains revealed significant QTLs near the centromeric end of rat chromosome 4 that were associated with in vitro phenotypes of insulin resistance and dyslipidaemia, specifically with insulin-stimulated glucose uptake and isoproterenol-induced lipolysis in isolated adipocytes (Aitman et al. 1997). Additional suggestive QTLs associated with serum HDL2 (high density lipoproteins) phospholipids (Bottger et al. 1996) and blood pressure (Pravenec et al. 1995) were mapped to the same chromosome region. In the follow-up studies, a combination of cDNA microarrays and congenic mapping was used to identify a defective SHR gene, Cd36 (also known as Fat because it encodes fatty acid translocase), at the peak of these QTL linkages (Aitman et al. 1999; Pravenec et al. 1999). Sequence analysis revealed that the SHR Cd36 cDNA contains multiple variants, caused by unequal genomic recombination of a duplicated ancestral gene (Glazier et al. 2002b). The SHR defect in Cd36 has been conclusively shown to result in pathophysiological phenotypes in complementation studies (Pravenec et al. 2001). Additional linkage studies, using integrated transcriptional profiling with Affymetrix microarrays and linkage analysis in RI strains, revealed a strong cis-acting ‘Cd36 eQTL’ (where eQTL is expression quantitative trait locus) (P < 10–6) in both fat and kidney (Hübner et al. 2005; Fig. 1).
|
|
In conclusion, a combination of linkage analyses of essentially monogenic cis-acting expression phenotypes and oligogenic intermediate physiological phenotypes represents a promising approach to identify QTLs underlying complex physiological traits at the molecular level.
|
References |
|---|
|
TopAbstractIntroductionReferences |
|---|
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. 10.1038/ng0697-197
Bottger A, van Lith HA, Kren V, Krenova D, Bila V, Vorlicek 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.[Medline]
Brem
RB, Yvert
G, Clinton
R
&
Kruglyak
L (2002). Genetic dissection of transcriptional regulation in budding yeast. Science
296, 752–755.
Bystrykh L, Weersing E, Dontje B, Sutton S, Pletcher MT, Wiltshire T et al. (2005). Uncovering regulatory pathways that affect hematopoietic stem cell function using ‘genetical genomics’. Nat Genet; DOI: 10.1038/ng1497.[CrossRef][Medline]
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; DOI: 10.1038/ng1518.[CrossRef][Medline]
Cheung VG, Conlin LK, Weber TM, Arcaro M, Jen KY et al. (2003). Natural variation in human gene expression assessed in lymphoblastoid cells. Nat Genet 33, 422–425.[CrossRef][Medline]
Churchill GA et al. (2004). The Collaborative Cross, a community resource for the genetic analysis of complex traits. Nat Genet 36, 1133–1137.[CrossRef][Medline]
Darvasi A (1998). Experimental strategies for the genetic dissection of complex traits in animal models. Nat Genet 18, 19–24. 10.1038/ng0198-19[CrossRef][Medline]
Glazier
AM, Nadeau
JH
&
Aitman
TJ (2002a). Finding genes that underlie complex traits. Science
298, 2345–2349. 10.1126/science.1076641
Glazier AM, Scott J & Aitman TJ (2002b). Molecular basis of the Cd36 chromosomal deletion underlying SHR defects in insulin action and fatty acid metabolism. Mamm Genome 13, 108–113. 10.1007/s00335-001-2132-9[CrossRef][Medline]
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. 10.1038/ng1522[CrossRef][Medline]
Jansen RC & Nap JP (2001). Genetical genomics: the added value from segregation. Trends Genet 17, 388–391. 10.1016/S0168-9525(01)02310-1[CrossRef][Medline]
Jirout M, Krenova D, Kren V, Breen L, Pravenec M et al. (2003). A new framework marker-based linkage map and SDPs for the rat HXB/BXH strain set. Mamm Genome 14, 537–546. 10.1007/s00335-003-2266-z[CrossRef][Medline]
Mlejnek P, Kren V, Liska F, Zidek V, Landa V et al. (2003). The CD36 protein functions as an immunogenic domain of the RT8 alloantigen. Eur J Immunogenet 30, 325–327. 10.1046/j.1365-2370.2003.00410.x[Medline]
Morley M, Molony CM, Weber TM, Devlin JL, Ewens KG et al. (2004). Genetic analysis of genome-wide variation in human gene expression. Nature 430, 743–747. 10.1038/nature02797[CrossRef][Medline]
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. 10.1101/gr.1186603
Okamoto K & Aoki K (1963). Development of a strain of spontaneously hypertensive rats. Jpn Circ J 27, 282–293.[Medline]
Pravenec M, Gauguier D, Schott J-J, 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.
Pravenec M, Gauguier D, Schott JJ, Buard J, Kren V, Bila V et al. (1996). A genetic linkage map of the rat derived from recombinant inbred strains. Mamm Genome 7, 117–127. 10.1007/s003359900031[CrossRef][Medline]
Pravenec M, Klir 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.[Medline]
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. 10.1038/84777
Pravenec M, Zidek V, Landa V, Simakova M, Mlejnek P, Kazdova L et al. (2004). Genetic analysis of ‘metabolic syndrome’ in the spontaneously hypertensive rat. Physiol Res 53 (Suppl. 1), S15–S22.
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.[Medline]
Reaven GM (1988). Banting Lecture 1988. Role of insulin resistance in human disease. Diabetes 37, 1595–1607.[Abstract]
Schadt EE, Monks SA, Drake TA, Lusis AJ, Che N, Colinayo V et al. (2003). Genetics of gene expression surveyed in maize, mouse and man. Nature 422, 297–302. 10.1038/nature01434[CrossRef][Medline]
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, RESEARCH0046.[Medline]
Yvert G, Brem RB, Whittle J, Akey JM, Foss E, Smith EN et al. (2003). Trans-acting regulatory variation in Saccharomyces cerevisiae and the role of transcription factors. Nat Genet 35, 57–64. 10.1038/ng1222[Medline]
|
Acknowledgements |
|---|
This article has been cited by other articles:
|
|
 |
|
  L. Sedova, O. Seda, L. Kazdova, B. Chylikova, P. Hamet, J. Tremblay, V. Kren, and D. Krenova Sucrose feeding during pregnancy and lactation elicits distinct metabolic response in offspring of an inbred genetic model of metabolic syndrome Am J Physiol Endocrinol Metab, May 1, 2007; 292(5): E1318 - E1324. [Abstract] [Full Text] [PDF]
|
|
|
|
 |
|
 M. Buresova, V. Zidek, A. Musilova, M. Simakova, A. Fucikova, V. Bila, V. Kren, L. Kazdova, R. Di Nicolantonio, and M. Pravenec Genetic relationship between placental and fetal weights and markers of the metabolic syndrome in rat recombinant inbred strains Physiol Genomics, September 14, 2006; 26(3): 226 - 231. [Abstract] [Full Text] [PDF]
|
|
| ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| HOME | HELP | FEEDBACK | SUBSCRIPTIONS | ARCHIVE | SEARCH | TABLE OF CONTENTS |
