Approaches to understanding susceptibility to nephropathy: From genetics to genomics
2002; Elsevier BV; Volume: 61; Issue: 1 Linguagem: Inglês
10.1046/j.1523-1755.2002.0610s1061.x
ISSN1523-1755
AutoresSudha K. Iyengar, Jeffrey R. Schelling, John R. Sedor,
Tópico(s)Renin-Angiotensin System Studies
ResumoApproaches to understanding susceptibility to nephropathy: From genetics to genomics. The incidence of end-stage renal disease (ESRD) is increasing worldwide despite efforts to slow the progression of chronic renal failure (CRF) by controlling blood pressure and hyperglycemia. Two available therapies for ESRD, dialysis and transplantation, are expensive and are at best palliative. Recently, data from several laboratories have demonstrated that ESRD is under substantial genetic control, and efforts to identify these genetic determinants are underway. Identifying genes for ESRD pathogenesis has several goals. First, understanding the genetic basis of ESRD offers a means to clarify the mechanisms that result in kidney pathobiology. Second, better and new treatments for prevention of progression of CRF to ESRD may be developed. Third, individuals at risk could be identified early in their course and targeted for intensive therapy. Finally, the products of genes causing disease become target molecules for gene therapy. In this article, we discuss data from our laboratories, which employ two different molecular genetic strategies for identifying ESRD pathogenesis genes. In contrast to traditional experimental design, both approaches are hypothesis generating, identifying candidate molecules for further study, rather than hypothesis driven and may provide novel insights into mechanisms of renal disease progression. Approaches to understanding susceptibility to nephropathy: From genetics to genomics. The incidence of end-stage renal disease (ESRD) is increasing worldwide despite efforts to slow the progression of chronic renal failure (CRF) by controlling blood pressure and hyperglycemia. Two available therapies for ESRD, dialysis and transplantation, are expensive and are at best palliative. Recently, data from several laboratories have demonstrated that ESRD is under substantial genetic control, and efforts to identify these genetic determinants are underway. Identifying genes for ESRD pathogenesis has several goals. First, understanding the genetic basis of ESRD offers a means to clarify the mechanisms that result in kidney pathobiology. Second, better and new treatments for prevention of progression of CRF to ESRD may be developed. Third, individuals at risk could be identified early in their course and targeted for intensive therapy. Finally, the products of genes causing disease become target molecules for gene therapy. In this article, we discuss data from our laboratories, which employ two different molecular genetic strategies for identifying ESRD pathogenesis genes. In contrast to traditional experimental design, both approaches are hypothesis generating, identifying candidate molecules for further study, rather than hypothesis driven and may provide novel insights into mechanisms of renal disease progression. End-stage renal disease (ESRD) is a serious health problem in the United States and worldwide, with a 7% annual increase in incidence over the last decade. The major emphasis in kidney disease research has been in determining hemodynamic alterations caused by reduction in nephron number or mechanisms that result in fibrosis, which subsequently leads to initiation and progression of chronic renal failure (CRF). However, molecular mechanisms of CRF remain incompletely understood. Family- and population-based epidemiology, as well as the cloning of several genes responsible for familial aggregation of renal disease, suggests that renal disease initiation and progression is genetically determined. Identifying genetic determinants is easier for single gene disorders that show Mendelian modes of inheritance, such as polycystic kidney disease, because of the availability of families with multiple affected members. In contrast, the complexity in the etiology of CRF and the variation in rate and number of individuals who progress to ESRD suggest that it is unlikely that the phenotype for most etiologies of CRF will be attributed to a single gene. Because high-throughput technologies for genetic mapping are now accessible to many researchers, recruitment of sib pairs or families with nephropathy has been undertaken with the goal of identifying genes for the more common forms of CRF initiation or progression by candidate gene and/or whole genome analyses. As an alternative strategy to family collection for identifying nephropathy susceptibility genes, gene expression analyses that use novel molecular techniques, such as expression microarrays or serial analysis of gene expression (SAGE), have been proposed. These assays simultaneously determine the expression patterns of multiple genes in a specific tissue or cell type. The ultimate goal of either approach is to identify molecules that will aid in early prognosis or that can identify novel targets for ESRD therapy. Strategies for collection, phenotyping, and analysis of families, in which nephropathy is clustered, have been published [1.Covic A.M. Iyengar S.K. Olson J.M. et al.A family-based strategy to identify genes for diabetic nephropathy.Am J Kidney Dis. 2001; 37: 638-647Abstract Full Text Full Text PDF PubMed Scopus (13) Google Scholar, 2.Covic A.M. Schelling J.R. Constantiner M. et al.Serum C-peptide concentrations poorly phenotype type 2 diabetic end-stage renal disease patients.Kidney Int. 2000; 58: 1742-1750Abstract Full Text Full Text PDF PubMed Scopus (38) Google Scholar]. Briefly, ESRD patients were screened by questionnaire and medical record review (N = 2900). To quantify the strength of familial clustering, we have determined two measures, sibling recurrence risk (SRR) and the SSR ratio (λs), in our population of African American (AA) and Caucasian (CA) cases using interview-based family history data. SRR was computed by dividing the total number of affected sibs by the total number of sibs. λs was determined by dividing the SRR by the point prevalence of ESRD from the USRDS data. These variables were computed in a subset (N = 1148) of the ESRD patients completing a screening questionnaire (available at http://kidney.metrohealth.org) [3.Iyengar S.K. Jedrey C.M. Olson J.M. et al.Familial risk of end-stage renal disease is higher in Caucasians than African Americans: Evidence from a bi-racial population.J Am Soc Nephrol. 2000; 11 (abstract): 63AGoogle Scholar]. Chi-square tests were used to determine significant ethnic differences between gender, age at first dialysis, and diagnosis distribution in the entire population. To identify genes that regulate renal disease progression, SAGE kidney mRNA profiles were contrasted from sclerosis-prone ROP-Os/+ and sclerosis-resistant C57BL/6-Os/+ (C57-Os/+) mice (abstract; El-Meanawy et al, J Am Soc Nephrol 11:617A, 2000) [4.Striker L.J. Nephron reduction in man: Lessons from the Os mouse.Nephrol Dial Transplant. 1998; 13: 543-545Crossref PubMed Scopus (4) Google Scholar, 5.Zheng F. Striker G.E. Esposito C. et al.Strain differences rather than hyperglycemia determine the severity of glomerulosclerosis in mice.Kidney Int. 1998; 54: 1999-2007Abstract Full Text Full Text PDF PubMed Scopus (78) Google Scholar]. SAGE permits comparative, quantitative analysis of gene-specific, 9 to 13 bp sequence tag libraries, and methods have been published [6.El-Meanawy M.A. Schelling J.R. Pozuelo F. et al.Use of serial analysis of gene expression to generate kidney expression libraries.Am J Physiol. 2000; 279: F383-F392PubMed Google Scholar, 7.Velculescu V.E. Zhang L. Vogelstein B. Kinzler K.W. Serial analysis of gene expression.Science. 1995; 270: 484-487Crossref PubMed Scopus (3474) Google Scholar]. P values were determined for the difference in tag counts between the two libraries using the winflat program [8.Audic S. Claverie J.M. The significance of digital gene expression profiles.Genome Res. 1997; 7: 986-995Crossref PubMed Scopus (2264) Google Scholar], which assumes a Poisson distribution for the number of observed tags. Patients from 13 dialysis units in the Greater Cleveland Metropolitan area (Network 9) were approached for the study (N = 1148). Informed consent was obtained from 952 index cases with ESRD. The remaining patients either declined or were unable to participate because of reduced mental capacity or inability to communicate. We restricted our analyses to CA (N = 300) and AA (N = 621), the predominant ethnic populations in our sample Figure 1. A comparison of Health Care Financing Corporation (HCFA) 2728 diagnostic categories showed significant differences between CA and AA (χ2 = 78.097, P < 0.0001). Hypertensive nephrosclerosis was more prevalent among the AA index cases, but in this sample, type 2 diabetes as a cause of ESRD was equally frequent in the two ethnic groups (Fig. 1). Family history of ESRD in first-, second-, or third-degree relatives was reported by 33.9% (N = 312) of ESRD index cases. Of these, 91% (24.9% of the total) reported that ESRD affected at least one first-degree relative. Family members of AA index cases showed a greater risk of developing ESRD, with 39% of AA compared with 29% of CA reporting a family history of ESRD in first, second-, or third-degree relatives (Fig. 2). To quantify the strength of familial clustering, we have determined SRR and λs in our population of AA and CA cases using interview-based family history data. We observed that SRR was high in both AAs and CA (0.062 ± 0.011 vs. 0.056 ± 0.011) but in a range expected for a complex disease. In contrast, the λs for ESRD was extremely high in AA (λs = 18.6) but was even higher in CA (λs = 58.6) [3.Iyengar S.K. Jedrey C.M. Olson J.M. et al.Familial risk of end-stage renal disease is higher in Caucasians than African Americans: Evidence from a bi-racial population.J Am Soc Nephrol. 2000; 11 (abstract): 63AGoogle Scholar]. The increased prevalence of ESRD in AA suggests that ESRD susceptibility alleles have obtained a high frequency in the AA population. Alternatively, the relatively greater λs in AA may be due to more nongenetic causes of ESRD occurring in AA compared with CA.Figure 2Data from the ESRD genetics project demonstrating that aggregation of family history among AAs is greater than among CAs. We observed that independent of race females demonstrated greater familial clustering.View Large Image Figure ViewerDownload Hi-res image Download (PPT) The ROP-Os/+ mouse demonstrates progressive renal failure from FSGS that cosegregates with oligosyndactaly [9.Zalups R.K. The Os/+ mouse: A genetic animal model of reduced renal mass.Am J Physiol. 1993; 264: F53-F60PubMed Google Scholar]. The Os mutation, originally generated by random radiation mutagenesis, has been mapped to mouse chromosome 8 [10.Becker-Follmann J. Gaa A. Bausch E. et al.High-resolution mapping of a linkage group on mouse chromosome 8 conserved on human chromosome 16Q.Mamm Genome. 1997; 8: 172-177Crossref PubMed Scopus (11) Google Scholar], but the specific gene has not been isolated. Development of glomerulosclerosis is dependent on the genetic background of the animal [11.Lenz O. Zheng F. Vilar J. et al.The inheritance of glomerulosclerosis in mice is controlled by multiple quantitative trait loci.Nephrol Dial Transplant. 1998; 13: 3074-3078Crossref PubMed Scopus (27) Google Scholar]. C57BL6 mice are resistant to development of glomerulosclerosis despite carrying the Os mutation, whereas ROP mice carrying the same mutation develop glomerulosclerosis and die of renal failure. To identify important CRF-related genes, we have used SAGE, which is an empiric approach that makes no a priori assumptions about disease pathogenesis, rather than testing the roles of a finite number of predetermined molecular pathways that may have a limited application to renal failure. Renal function and histology were assessed in ROP-Os/+ and C57-Os/+ mice from 6 to 16 weeks of age to establish the earliest time point of renal disease onset and maximize isolation of pathogenesis genes. Glomerular sclerosis scores were determined by computer-aided quantitative morphometry, and urine albumin/creatinine demonstrated that nephropathy began as early as six weeks in ROP-Os/+ mice but was not manifest in C57-Os/+ mice (data not shown). Thus far, 17,042 and 21,419 tags have been identified in six-week ROP-Os/+ and C57-Os/+ kidney expression libraries, respectively, and compared as described in the Methods section. Only 25 tags are expressed at significantly different levels between libraries (Fig. 3); 12 are overexpressed and 13 are underexpressed in the ROP-Os/+ library. For example, enhanced expression (2- to 3-fold) of glutathione peroxidase in ROP-Os/+ mouse kidney suggests a compensatory response to increased oxidant stress, which may be an early mechanism of sclerosis pathogenesis in this model. These data suggest that comprehensive characterization and comparison of gene expression patterns in normal and diseased kidneys will provide novel targets for therapeutic intervention by identifying candidate pathways, which regulate nephropathy pathogenesis. Although mechanisms of renal disease progression have been clarified, ESRD pathogenesis remains incompletely understood. Interindividual variability in the renal responses to stochastic events, for example, high blood pressure or hyperglycemia, has suggested that genes may regulate mechanisms of CRF. We have applied two types of molecular genetic strategies to test this hypothesis. First, we designed a study that will use genetic or positional cloning approaches to identify disease susceptibility genes. Positional cloning methods correlate disease or trait status in families or in cases and controls with anonymous genetic markers without assuming any knowledge about the function or location of the putative susceptibility gene. Second, we used novel gene expression technology (SAGE) to conduct analyses of gene function on a global scale (genomics). Simultaneous examination of gene expression profiles for a large number of genes from diseased and normal kidneys should yield differentially expressed genes that play a role in the disease process. Using a genetic approach, we demonstrate a substantial genetic risk for nephropathy in first-degree relatives of both CA and AA ESRD probands. Using a genomic approach, we have identified candidate nephropathy susceptibility genes in an animal model of focal segmental glomerulosclerosis (FSGS). There is substantial evidence that ESRD aggregates in families [12.Freedman B.I. Bowden D.W. Rich S.S. Appel R.G. Genetic initiation of hypertensive and diabetic nephropathy.Am J Hypertens. 1998; 11: 251-257Crossref PubMed Scopus (25) Google Scholar, 13.Schelling J.R. Zarif L. Sehgal A. et al.Genetic susceptibility to end-stage renal disease.Curr Opin Nephrol Hypertens. 1999; 8: 465-472Crossref PubMed Scopus (42) Google Scholar, 14.Krolewski A.S. Fogarty D.G. Warram J.H. Hypertension and nephropathy in diabetes mellitus: What is inherited and what is acquired?.Diabetes Res Clin Pract. 1998; 39: S1-S14Abstract Full Text Full Text PDF PubMed Scopus (25) Google Scholar]. The increased aggregation of ESRD in families cannot be completely accounted for by greater prevalence of co-morbid phenotypes, such as diabetes mellitus, hypertension, hypertension severity, inadequate antihypertensive therapy, or socioeconomic status [15.Byrne C. Nedelman J. Luke R.G. Race, socioeconomic status, and the development of end-stage renal disease.Am J Kidney Dis. 1994; 23: 16-22Abstract Full Text PDF PubMed Scopus (100) Google Scholar, 16.Freedman B.I. Iskandar S.S. Appel R.G. The link between hypertension and nephrosclerosis.Am J Kidney Dis. 1995; 25: 207-221Abstract Full Text PDF PubMed Scopus (180) Google Scholar, 17.Kasinath B.S. Mujais S.K. Spargo B.H. Katz A.I. Nondiabetic renal disease in patients with diabetes mellitus.Am J Med. 1983; 75: 613-617Abstract Full Text PDF PubMed Scopus (85) Google Scholar, 18.Lei H.H. Perneger T.V. Klag M.J. et al.Familial aggregation of renal disease in a population-based case-control study.J Am Soc Nephrol. 1998; 9: 1270-1276PubMed Google Scholar, 19.Qualheim R.E. Rostand S.G. Kirk K.A. et al.Changing patterns of end-stage renal disease due to hypertension.Am J Kidney Dis. 1991; 18: 336-343Abstract Full Text PDF PubMed Scopus (45) Google Scholar]. We conducted a cross-sectional study of familial aggregation of ESRD in a dialysis population from Cleveland, Ohio. Using HCFA 2728 diagnosis, we observed that hypertensive nephrosclerosis was more frequent in AA, whereas type 2 diabetes was equally frequent in both ethnic groups (Fig. 1). Relative to males, we observed that both AA and CA females report a 1.5-fold increased risk of reporting an additional family member with ESRD. Surprisingly, when the risk to siblings of AA and CA index cases was quantified, λs was high in AA but even higher in CA. Although data from our and other laboratories indicate that nephropathy demonstrates strong familial aggregation, segregation and co-mingling analyses are the only established methods for determining whether a phenotype (clinical measure or trait) fits a particular genetic model. Two recent segregation analyses have suggested a major locus controls albumin excretion rate [20.Fogarty D.G. Hanna L.S. Wantman M. et al.Segregation analysis of urinary albumin excretion in families with type 2 diabetes.Diabetes. 2000; 49: 1057-1063Crossref PubMed Scopus (58) Google Scholar, 21.Imperatore G. Knowler W.C. Pettitt D.J. et al.Segregation analysis of diabetic nephropathy in Pima Indians.Diabetes. 2000; 49: 1049-1056Crossref PubMed Scopus (75) Google Scholar]. In one study, proteinuria was analyzed as a continuous variable, with the conclusion that proteinuria was influenced by multiple genes with variable effects [20.Fogarty D.G. Hanna L.S. Wantman M. et al.Segregation analysis of urinary albumin excretion in families with type 2 diabetes.Diabetes. 2000; 49: 1057-1063Crossref PubMed Scopus (58) Google Scholar]. The report by Imperatore et al in diabetic Pima families considered proteinuria as a discrete variable and determined that it was influenced by a major gene effect [21.Imperatore G. Knowler W.C. Pettitt D.J. et al.Segregation analysis of diabetic nephropathy in Pima Indians.Diabetes. 2000; 49: 1049-1056Crossref PubMed Scopus (75) Google Scholar]. Two studies of type 2 diabetic nephropathy partitioned the genetic and environmental influences in albumin excretion rate and estimated heritability (h2), a measure of genetic predisposition [22.Fogarty D.G. Rich S.S. Hanna L. et al.Urinary albumin excretion in families with type 2 diabetes is heritable and genetically correlated to blood pressure.Kidney Int. 2000; 57: 250-257Abstract Full Text Full Text PDF PubMed Scopus (93) Google Scholar, 23.Forsblom C.M. Kanninen T. Lehtovirta M. et al.Heritability of albumin excretion rate in families of patients with type II diabetes.Diabetologia. 1999; 42: 1359-1366Crossref PubMed Scopus (69) Google Scholar]. Both studies estimated the heritability for urinary albumin excretion to be approximately 30%. The estimated heritability for urine albumin excretion was statistically significant, even after adjusting for potential confounding covariables, such as age, gender, body weight, diabetes duration, and environment, suggesting a major genetic effect for proteinuria [22.Fogarty D.G. Rich S.S. Hanna L. et al.Urinary albumin excretion in families with type 2 diabetes is heritable and genetically correlated to blood pressure.Kidney Int. 2000; 57: 250-257Abstract Full Text Full Text PDF PubMed Scopus (93) Google Scholar]. Finally, a biopsy study of resemblance of type 1 diabetic sibs demonstrated a high degree of correlation between severity and patterns of glomerular lesions despite lack of concurrence in the course of glycemia between sibs [24.Fioretto P. Steffes M.W. Barbosa J. et al.Is diabetic nephropathy inherited? Studies of glomerular structure in type 1 diabetic sibling pairs.Diabetes. 1999; 48: 865-869Crossref PubMed Scopus (64) Google Scholar]. Thus, there is significant evidence from a number of studies that ESRD pathogenesis is partly regulated by genes. Mutations in specific genes have been identified in families with Mendelian inheritance patterns of renal disease [25.Patrakka J. Kestila M. Wartiovaara J. et al.Congenital nephrotic syndrome (NPHS1): Features resulting from different mutations in Finnish patients.Kidney Int. 2000; 58: 972-980https://doi.org/10.1046/j.1523-1755.2000.00254.xAbstract Full Text Full Text PDF PubMed Scopus (204) Google Scholar, 26.Lenkkeri U. Mannikko M. McCready P. et al.Structure of the gene for congenital nephrotic syndrome of the Finnish type (NPHS1) and characterization of mutations.Am J Hum Genet. 1999; 64: 51-61Abstract Full Text Full Text PDF PubMed Scopus (299) Google Scholar, 27.Kestila M. Lenkkeri U. Mannikko M. et al.Positionally cloned gene for a novel glomerular protein—nephrin—is mutated in congenital nephrotic syndrome.Mol Cell. 1998; 1: 575-582Abstract Full Text Full Text PDF PubMed Scopus (1480) Google Scholar, 28.Boute N. Gribouval O. Roselli S. et al.NPHS2, encoding the glomerular protein podocin, is mutated in autosomal recessive steroid-resistant nephrotic syndrome.Nat Genet. 2000; 24: 349-354Crossref PubMed Scopus (1134) Google Scholar, 29.Tsukaguchi H. Yager H. Dawborn J. et al.A locus for adolescent and adult onset familial focal segmental glomerulosclerosis on chromosome 1q25-31.J Am Soc Nephrol. 2000; 11: 1674-1680PubMed Google Scholar, 30.Vats A. Nayak A. Ellis D. et al.Familial nephrotic syndrome: Clinical spectrum and linkage to chromosome 19q13.Kidney Int. 2000; 57: 875-881Abstract Full Text Full Text PDF PubMed Scopus (31) Google Scholar, 31.Winn M.P. Conlon P.J. Lynn K.L. et al.Linkage of a gene causing familial focal segmental glomerulosclerosis to chromosome 11 and further evidence of genetic heterogeneity.Genomics. 1999; 58: 113-120Crossref PubMed Scopus (111) Google Scholar, 32.Kaplan J.M. Kim S.H. North K.N. et al.Mutations in ACTN4, encoding alpha-actinin-4, cause familial focal segmental glomerulosclerosis.Nat Genet. 2000; 24: 251-256Crossref PubMed Scopus (989) Google Scholar]. In comparison, genes for other main causes of ESRD, such as diabetic nephropathy and hypertensive nephrosclerosis, have yet to be characterized. The primary disadvantage in the case of diabetic nephropathy has been the lack of availability of large families and complex (nonmendelian) modes of inheritance. Thus, most researchers have resorted to collecting affected or discordant sib pairs to map genes for this disease. A collection of sib pairs for mapping complex diseases has been fostered by development of analytic methods that do not require assumptions about specific genetic models (model-free linkage analysis) [reviewed in [33.Elston R.C. The genetic dissection of multifactorial traits.Clin Exp Allergy. 1995; 25: 103-106Crossref PubMed Google Scholar, 34.Lander E.S. Schork N.J. Genetic dissection of complex traits.Science. 1994; 265: 2037-2048Crossref PubMed Scopus (2678) Google Scholar]. Using the affected sib pair method, a genome-wide scan in Pima with diabetic nephropathy yielded three potential loci (two on chromosome 7, one on chromosome 20) [35.Imperatore G. Hanson R.L. Pettitt D.J. et al.Sib-pair linkage analysis for susceptibility genes for microvascular complications among Pima Indians with type 2 diabetes: Pima Diabetes Genes Group.Diabetes. 1998; 47: 821-830Crossref PubMed Scopus (293) Google Scholar]. Similarly, using type 1 diabetic sib pairs discordant for nephropathy, Moczulski et al identified a 20 cM region on chromosome 3q near the type I angiotensin II receptor as a major locus for nephropathy susceptibility [36.Moczulski D.K. Rogus J.J. Antonellis A. et al.Major susceptibility locus for nephropathy in type 1 diabetes on chromosome 3q: Results of novel discordant sib-pair analysis.Diabetes. 1998; 47: 1164-1169Crossref PubMed Scopus (157) Google Scholar], but the specific gene(s) within this locus that regulates nephropathy has not been identified. Our group has focused on mapping genes for the diabetic nephropathy phenotype [1.Covic A.M. Iyengar S.K. Olson J.M. et al.A family-based strategy to identify genes for diabetic nephropathy.Am J Kidney Dis. 2001; 37: 638-647Abstract Full Text Full Text PDF PubMed Scopus (13) Google Scholar, 2.Covic A.M. Schelling J.R. Constantiner M. et al.Serum C-peptide concentrations poorly phenotype type 2 diabetic end-stage renal disease patients.Kidney Int. 2000; 58: 1742-1750Abstract Full Text Full Text PDF PubMed Scopus (38) Google Scholar, 3.Iyengar S.K. Jedrey C.M. Olson J.M. et al.Familial risk of end-stage renal disease is higher in Caucasians than African Americans: Evidence from a bi-racial population.J Am Soc Nephrol. 2000; 11 (abstract): 63AGoogle Scholar] and is collecting CA and AA sib pairs with and without type 2 diabetic nephropathy. Additionally, we are members of the Family Investigation of Diabetes and Nephropathy consortium, which is a joint mapping effort with the National Institute of Digestive, Diabetes and Kidney Diseases and seven other member institutions to map genes for diabetic nephropathy (http://darwin.cwru.edu/research/find/). Traditional genetic analyses of humans and model organisms attempt to identify loci that cosegregate with a specific disease phenotype [36.Moczulski D.K. Rogus J.J. Antonellis A. et al.Major susceptibility locus for nephropathy in type 1 diabetes on chromosome 3q: Results of novel discordant sib-pair analysis.Diabetes. 1998; 47: 1164-1169Crossref PubMed Scopus (157) Google Scholar]. However these techniques, although valuable, focus on a limited number of genes at a time and do not examine mitochondrial genomes. Recently, alternative methods have been developed to determine the mechanism by which genes act in concert. These techniques include differential display, subtraction hybridization, gene expression analysis using microarrays, and SAGE [reviewed in [37.Maser R.L. Calvet J.P. Analysis of differential gene expression in the kidney by differential cDNA screening, subtractive cloning, and mRNA differential display.Semin Nephrol. 1995; 15: 29-42PubMed Google Scholar, 38.Carulli J.P. Artinger M. Swain P.M. et al.High throughput analysis of differential gene expression.J Cell Biochem Suppl. 1998; 30–31: 286-296Crossref PubMed Google Scholar, 39.Hsiao L.L. Stears R.L. Hong R.L. Gullans S.R. Prospective use of DNA microarrays for evaluating renal function and disease.Curr Opin Nephrol Hypertens. 2000; 9: 253-258Crossref PubMed Scopus (27) Google Scholar]. Although these techniques represent technological advances, with the exception of SAGE, all suffer from lack of quantification. Differential display and subtraction hybridization are well-established techniques, whereas expression microarrays and SAGE are recent discoveries. The advantage of the latter two techniques is that they are high-throughput methods. Each of these techniques permits comparison of expression profiles between multiple samples. Differential display, subtractive hybridization, and microarrays provide some form of comparative ratio between two profiles, whereas SAGE provides absolute numbers (counts) of cDNA molecules. Even in diseases that are caused by single gene defects, expression patterns of multiple genes are often altered [40.Dietz H.C. Pyeritz R.E. Mutations in the human gene for fibrillin-1 (FBN1) in the Marfan syndrome and related disorders.Hum Mol Genet. 1995; 4: 1799-1809Crossref PubMed Scopus (413) Google Scholar, 41.Fisher G.J. Datta S.C. Talwar H.S. et al.Molecular basis of sun-induced premature skin ageing and retinoid antagonism.Nature. 1996; 379: 335-339Crossref PubMed Scopus (1120) Google Scholar, 42.Soukas A. Cohen P. Socci N.D. Friedman J.M. Leptin-specific patterns of gene expression in white adipose tissue.Genes Dev. 2000; 14: 963-980PubMed Google Scholar, 43.Srivastava M. Eidelman O. Pollard H.B. Pharmacogenomics of the cystic fibrosis transmembrane conductance regulator (CFTR) and the cystic fibrosis drug CPX using genome microarray analysis.Mol Med. 1999; 5: 753-767PubMed Google Scholar, 44.Sudarsanam P. Iyer V.R. Brown P.O. Winston F. Whole-genome expression analysis of snf/swi mutants of Saccharomyces cerevisiae.Proc Natl Acad Sci USA. 2000; 97: 3364-3369Crossref PubMed Scopus (331) Google Scholar]. Therefore, a cohesive gene expression approach may be especially valuable in identifying novel pathways that generate disease in complex disorders. Table 1 summarizes published kidney gene expression profiles (Table 1). Because expression profiles of some genes are transient, hypotheses generated by these methods require further validation with additional samples and other methods.Table 1A synopsis of renal gene expression analyses collated from the literatureMethodExperimentOrganismReferenceSubtractive hybridizationNephrogenesisMouse48.Abidari J.M. Gonzales E.T. Inoue K. et al.Identification of novel genes expressed during metanephric induction through single-cell library screening.Kidney Int. 2000; 57: 2221-2228Abstract Full Text Full Text PDF PubMed Scopus (5) Google ScholarSubtractive hybridizationAndrogen regulationMouse49.Melia M.J. Bofill N. Hubank M. Meseguer A. Identification of androgen-regulated genes in mouse kidney by representational difference analysis and random arbitrarily primed polymerase chain reaction.Endocrinology. 1998; 139: 688-695Crossref PubMed Scopus (41) Google Scholar, 50.Cornwall G.A. Orgebin-Crist M.C. Hann S.R. Differential expression of the mouse mitochondrial genes and the mitochondrial RNA-processing endoribonuclease RNA by androgens.Mol Endocrinol. 1992; 6: 1032-1042PubMed Google ScholarSubtractive hybridizationRenal cell carcinomaHuman51.Pitzer C. Stassar M. Zoller M. Identification of renal-cell-carcinoma-related cDNA clones by suppression subtractive hybridization.J Cancer Res Clin Oncol. 1999; 125: 487-492Crossref PubMed Scopus (19) Google ScholarSubtractive hybridizationDiabetic nephropathyHuman52.Murphy M. Godson C. Cannon S. et al.Suppression subtractive hybridization identifies high glucose levels as a stimulus for expression of connective tissue growth factor and other genes in human mesangial cells.J Biol Chem. 1999; 274: 5830-5834Crossref PubMed Scopus (351) Google Scholar, 53.Peraldi M.N. Berrou J. Hagege J. et al.Subtractive hybridization cloning: an efficient technique to detect overexpressed mRNAs in diabetic nephropathy.Kidney Int. 1998; 53: 926-931Abstract Full Text Full Text PDF PubMed Scopus (1) Google ScholarSubtractive hybridizationWilms' tumorHuman54.Austruy E. Cohen-Salmon M. Antignac C. et al.Isolation of kidney complementary DNAs down-expressed in Wilms' tumor by a subtractive hybridization approach.Cancer Res. 1993; 53: 2888-2894PubMed Google ScholarDifferential displayNephrolithiasisMouse55.Wang L. Raikwar N. Deng L. et al.Altered gene expression in kidneys of mice with 2,8-dihydroxyadenine nephrolithiasis.Kidney Int. 2000; 58: 528-536Abstract Full Text Full Text PDF PubMed Scopus (26) Google ScholarDifferential displayNephrogenesisRat explants, mouse56.Plisov S.Y. Ivanov S.V. Yoshino K. et al.Mesenchymal-epithelial transition in the developing metanephric kidney: Gene expression study by differential display.Genesis. 2000; 27: 22-31Crossref PubMed Scopus (36) Google Scholar, 57.Leimeister C. Bach A. Woolf A.S. Gessler M. Screen for genes regulated during early kidney morphogenesis.Dev Genet. 1999; 24: 273-283Crossref PubMed Scopus (19) Google Scholar, 58.Rosenblum N.D. Yager T.D. Changing patterns of gene expression in developing mouse kidney, as probed by differential mRNA display combined with cDNA library screening.Kidney Int. 1997; 51: 920-925Abstract Full Text PDF PubMed Scopus (8) Google ScholarDifferential displayAgingMouse59.Iida R. Yasuda T. Aoyama M. et al.Age-related changes of gene expression in mouse kidney: Fluorescence differential display: PCR analyses.Mech Ageing Dev. 2000; 113: 135-144Crossref PubMed Scopus (22) Google ScholarDifferential displayNephrotic syndromeHuman60.Holthofer H. Kretzler M. Haltia A. et al.Altered gene expression and functions of mitochondria in human nephrotic syndrome.FASEB J. 1999; 13: 523-532PubMed Google Scholar, 61.Haltia A. Solin M. Luimula P. et al.mRNA differential display analysis of nephrotic kidney glomeruli.Exp Nephrol. 1999; 7: 52-58Crossref PubMed Scopus (11) Google ScholarDifferential displayDiabetic nephropathyCultured human cells62.Holmes D.I. Abdel W.N. Mason R.M. Identification of glucose-regulated genes in human mesangial cells by mRNA differential display.Biochem Biophys Res Commun. 1997; 238: 179-184Crossref PubMed Scopus (34) Google ScholarDifferential displayDiabetic nephropathyRat63.Page R. Morris C. Williams J. et al.Isolation of diabetes-associated kidney genes using differential display.Biochem Biophys Res Commun. 1997; 232: 49-53Crossref PubMed Scopus (31) Google ScholarSAGENormal kidney and nephron tubule segmentsMouse46.Virlon B. Cheval L. Buhler J.M. et al.Serial microanalysis of renal transcriptomes.Proc Natl Acad Sci USA. 1999; 96: 15286-15291Crossref PubMed Scopus (151) Google ScholarSAGENormal kidneyMouse6.El-Meanawy M.A. Schelling J.R. Pozuelo F. et al.Use of serial analysis of gene expression to generate kidney expression libraries.Am J Physiol. 2000; 279: F383-F392PubMed Google ScholarMicroarrayRenal cell carcinomaHuman64.Moch H. Schraml P. Bubendorf L. et al.Identification of prognostic parameters for renal cell carcinoma by cDNA arrays and cell chips.Verh Dtsch Ges Pathol. 1999; 83: 225-232PubMed Google Scholar, 65.Moch H. Schraml P. Bubendorf L. et al.High-throughput tissue microarray analysis to evaluate genes uncovered by cDNA microarray screening in renal cell carcinoma.Am J Pathol. 1999; 154: 981-986Abstract Full Text Full Text PDF PubMed Scopus (366) Google ScholarMicroarrayNephrogenesisCultured cells47.Pavlova A. Stuart R.O. Pohl M. Nigam S.K. Evolution of gene expression patterns in a model of branching morphogenesis.Am J Physiol. 1999; 277: F650-F663PubMed Google Scholar, 66.Qiao J. Sakurai H. Nigam S.K. Branching morphogenesis independent of mesenchymal-epithelial contact in the developing kidney.Proc Natl Acad Sci USA. 1999; 96: 7330-7335Crossref PubMed Scopus (153) Google ScholarMicroarrayRenal cortexHuman67.Yano N. Endoh M. Fadden K. et al.Comprehensive gene expression profile of the adult human renal cortex: analysis by cDNA array hybridization.Kidney Int. 2000; 57: 1452-1459Abstract Full Text Full Text PDF PubMed Scopus (25) Google ScholarMicroarrayRenal inner medullaMouse68.Takenaka M. Imai E. Nagasawa Y. et al.Gene expression profiles of the collecting duct in the mouse renal inner medulla.Kidney Int. 2000; 57: 19-24Abstract Full Text Full Text PDF PubMed Scopus (22) Google Scholar Open table in a new tab We are contrasting kidney gene expression profiles generated by SAGE from sclerosis-prone (ROP Os/+) and sclerosis-resistant (C57BL6 Os/+) animals to identify genes that regulate renal disease progression [45.Zhang L. Zhou W. Velculescu V.E. et al.Gene expression profiles in normal and cancer cells.Science. 1997; 276: 1268-1272Crossref PubMed Scopus (1187) Google Scholar]. We previously constructed a SAGE library from the normal ROP-+/+ mouse kidney [6.El-Meanawy M.A. Schelling J.R. Pozuelo F. et al.Use of serial analysis of gene expression to generate kidney expression libraries.Am J Physiol. 2000; 279: F383-F392PubMed Google Scholar]. Analyses of 3868 tags from this library yielded 1453 unique kidney transcripts. Forty-two percent of these transcripts matched mRNA sequence entries with known function, 35% of the transcripts corresponded to expressed sequence tag entries or cloned genes, whose function has not been established, and 23% represented unidentified genes. The profile obtained from our kidney library was very similar to that obtained by Virlon et al, who microdissected normal mouse kidney and used a miniaturized version of SAGE, which required less input RNA [46.Virlon B. Cheval L. Buhler J.M. et al.Serial microanalysis of renal transcriptomes.Proc Natl Acad Sci USA. 1999; 96: 15286-15291Crossref PubMed Scopus (151) Google Scholar]. A number of other laboratories have published expression analyses of normal kidney, nephron segments, and both in vitro and animal models of nephropathy (Table 1). Currently, bioinformatics, analytical approaches for data mining, are a major limitation in maximizing the knowledge to be gained from expression libraries. Data analysis of expression profiles ultimately will depend on integrating kidney mRNA libraries with external information resources and will require software development, such as the VectorArray application used to analyze the gene expression profiles during branching morphogenesis [47.Pavlova A. Stuart R.O. Pohl M. Nigam S.K. Evolution of gene expression patterns in a model of branching morphogenesis.Am J Physiol. 1999; 277: F650-F663PubMed Google Scholar]. Necessary bioinformatics tools include links between kidney genes identified in an expression profile and Genbank, links to user-friendly biological pathway databases, and access to databases that can identify functionally important nucleotide (for example, regulatory elements) or protein (for example, kinase domain) motifs. In the future, we anticipate that results from genetic and genomic technologies will be used in tandem to comprehend the pathobiology of complex diseases such as nephropathy. For example, once a candidate gene is identified through genetic analyses, the interaction of this gene with others that causes disease can be clarified using expression profiling. In conclusion, both techniques have weaknesses that can be overcome by pairing the technologies in future research. This work was supported by grants from the National Institutes of Health (DK54644, DK54178, DK38558, DK51472, DK02281, and DK57329), the Northeastern Ohio chapter of the American Heart Association, Central Ohio Diabetes Association, Leonard Rosenberg Foundation, Juvenile Diabetes Foundation, and Baxter Extramural Grant Program. Portions were presented in abstract form at the Annual Meeting of the American Society of Nephrology and the American Society of Human Genetics. Dr. Schelling is an Established Investigator of the American Heart Association.
Referência(s)