Use of serial analysis of gene expression to generate kidney expression libraries

Abstract

Chronic renal disease initiation and progression remain incompletely understood. Genome-wide expression monitoring should clarify mechanisms that cause progressive renal disease by determining how clusters of genes coordinately change their activity. Serial analysis of gene expression (SAGE) is a technique of expression profiling, which permits simultaneous, comparative, and quantitative analysis of gene-specific, 9- to 13-bp sequence tags. Using SAGE, we have constructed a tag expression library from ROP-+/+ mouse kidney. Tag sequences were sorted by abundance, and identity was determined by sequence homology searching. Analyses of 3,868 tags yielded 1,453 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 (EST) entries or cloned genes, whose function has not been established, and 23% represented unidentified genes. Previously characterized transcripts were clustered into functional groups, and those encoding metabolic enzymes, plasma membrane proteins (transporters/receptors), and ribosomal proteins were most abundant (39, 14, and 12% of known transcripts, respectively). The most common, kidney-specific transcripts were kidney androgen-regulated protein (4% of all transcripts), sodium-phosphate cotransporter (0.3%), renal cytochrome P-450 (0.3%), parathyroid hormone receptor (0.1%), and kidney-specific cadherin (0.1%). Comprehensively characterizing and contrasting gene expression patterns in normal and diseased kidneys will provide an alternative strategy to identify candidate pathways, which regulate nephropathy susceptibility and progression, and novel targets for therapeutic intervention.