Identification of PKA-dependent signaling network using CRISPR-Cas9 coupled with quantitative transcriptomics, proteomics and phosphoproteomics

Purpose: PKA plays a crucial role in vasopressin signaling of renal collecting duct cells. To understand regulation of mRNA expression mediated by vasopressin/PKA signaling, mRNA expression was profiled by RNA-Seq in double knockout cells (both PKA catalytic genes) generated from mouse cortical collecting duct mpkCCD cell line versus control lines with intact PKA expression. Methods: PKA double knockout (dKO) cell lines were generated from mouse cortical collecting duct mpkCCDc11 cells by CRISPR/Cas-9 genome editing method. For mRNA profiling using RNA-Seq analysis, three biological replicates of control (not mutated in PKA two catalytic subunits) cell lines and PKA double knockout cell lines were used. The reads uniquely mapped on GENCODE mouse gene set were analyzed with HOMER (v4.8) and edgeR (v3.10.5). Results and conclusion: About 40-50 million sequence reads per sample were sucessfully mapped in the mouse genome (GENCODE, GPCm38.p5). Among total transcripts of the mouse genome, 10,190 transcripts (cutoff: Counts Per Million > 4 by edgeR) were considered as genes expressed in the cell lines. In differential expression analysis by standard edgeR analysis, 354 transcripts were differentially expressed between control cell lines and PKA dKO cell lines (FDR < 0.05). We also identified nine genes that were markedly decreased in PKA dKO cell lines (log2 PKA dKO/Control < -2, FDR < 0.05) including aquaporin-2 (Aqp2) and two genes that were markedly increased in PKA dKO cell lines (log2 PKA dKO/Control > 2, FDR < 0.05). These results suggest PKA signaling is important for regulation of expression of a very limited number of genes in vasopressin-responsive renal collecting duct cells. Overall design: Total mRNA profiling of three control cell lines and three PKA double knockout cell lines generated from mpkCCDc11 cell line were carried out by standard RNA-Seq protocols with deep sequencing on an Illumina HiSeq 3000.

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Isobe K, Jung HJ, Yang CR, Claxton J, Sandoval P, Burg MB, Raghuram V, Knepper MA