BACKGROUND: Profiling of gene expression in healthy and diseased renal tissue is important for elucidating the pathogenesis of renal diseases. Comprehensive information about the genes expressed in renal tissue is unavailable. The recently developed cDNA array hybridization methodology allows simultaneous monitoring of thousands of genes expressed renal tissue. METHODS: Complex [alpha-33P]-labeled cDNA probes were prepared from histopathologically uninvolved remnants of nine renal tissues obtained by nephrectomy. Each probe was hybridized to a high-density array of 18,326 paired target genes. The radioactive hybridization signals by phosphorimager screens were quantitated by special software. Bioinformatics from public genomic databases were used to assign a chromosomal location of each expressed transcript and gene function. Cluster analysis was used to arrange genes according to the similarity in pattern of gene expression. RESULTS: A total of 7563 different gene transcripts was detected in the nine tissue samples. Approximately 870 of these genes were full-length mRNA human transcripts (HT), and the remaining 6693 were expressed sequence tags (ESTs). The full-length transcripts were classified by function of the gene product and were listed with information of their chromosomal positions. To allow a comparison between gene expression in clinical and experimental studies, the mouse genes with known similar function to the human counterpart were included in the bioinformatics analysis. Cluster analysis of 502 full-length genes that are expressed in four or more renal tissues revealed more than 110 genes that are highly expressed in all the renal specimens. CONCLUSIONS: The presented data constitute a comprehensive preliminary transcriptional map of the adult human renal cortex. The information may serve as a resource for speeding up the discovery of genes underlying human renal disease. The integrated listing of the full-length expressed human and mouse genes is available through e-mail (Abdalla_Rifai@Brown.edu).
BACKGROUND: Profiling of gene expression in healthy and diseased renal tissue is important for elucidating the pathogenesis of renal diseases. Comprehensive information about the genes expressed in renal tissue is unavailable. The recently developed cDNA array hybridization methodology allows simultaneous monitoring of thousands of genes expressed renal tissue. METHODS: Complex [alpha-33P]-labeled cDNA probes were prepared from histopathologically uninvolved remnants of nine renal tissues obtained by nephrectomy. Each probe was hybridized to a high-density array of 18,326 paired target genes. The radioactive hybridization signals by phosphorimager screens were quantitated by special software. Bioinformatics from public genomic databases were used to assign a chromosomal location of each expressed transcript and gene function. Cluster analysis was used to arrange genes according to the similarity in pattern of gene expression. RESULTS: A total of 7563 different gene transcripts was detected in the nine tissue samples. Approximately 870 of these genes were full-length mRNA human transcripts (HT), and the remaining 6693 were expressed sequence tags (ESTs). The full-length transcripts were classified by function of the gene product and were listed with information of their chromosomal positions. To allow a comparison between gene expression in clinical and experimental studies, the mouse genes with known similar function to the human counterpart were included in the bioinformatics analysis. Cluster analysis of 502 full-length genes that are expressed in four or more renal tissues revealed more than 110 genes that are highly expressed in all the renal specimens. CONCLUSIONS: The presented data constitute a comprehensive preliminary transcriptional map of the adult human renal cortex. The information may serve as a resource for speeding up the discovery of genes underlying humanrenal disease. The integrated listing of the full-length expressed human and mouse genes is available through e-mail (Abdalla_Rifai@Brown.edu).
Authors: John P T Higgins; Lingli Wang; Neeraja Kambham; Kelli Montgomery; Veronica Mason; Stefanie U Vogelmann; Kevin V Lemley; Patrick O Brown; James D Brooks; Matt van de Rijn Journal: Mol Biol Cell Date: 2003-12-02 Impact factor: 4.138
Authors: Facundo M Giorello; Matias Feijoo; Guillermo D'Elía; Lourdes Valdez; Juan C Opazo; Valeria Varas; Daniel E Naya; Enrique P Lessa Journal: BMC Genomics Date: 2014-06-08 Impact factor: 3.969