Bayesian Identification of Transcription Factors that Regulate Aqp2 Transcription

Background Renal collecting duct and connecting tubule cells selectively express the water channel aquaporin-2 (AQP2) and Aqp2 gene transcription is strongly regulated by vasopressin. However, the transcription factors (TFs) responsible for regulation of expression of AQP2 remain largely unknown. Here, we used Bayesian data integration techniques to identify these TFs. Method The general strategy is to use Bayes’ Rule to integrate several -omic datasets to stratify a curated list of 1344 TFs present in the mouse genome with regard to probability of regulating Aqp2 gene transcription. First, existing proteomic and transcriptomic data were used to prioritize TFs according to expression level in mpkCCD and native IMCD cells. Second, we used our existing ATAC-Seq, histone H3K27-acetylation ChIP-Seq, and RNA-polymerase II ChIP-Seq data to identify enhancer regions in the CTCF loop surrounding the Aqp2 gene. The sequences within these enhancers were analyzed to identify recognized TF binding motifs within them; and these motifs were matched to TFs on the Bayesian list to identify the TFs most likely to bind Aqp2 regulatory regions. Beyond this, we use additional -omic datasets to prioritize TFs that are regulated by vasopressin. Finally, we carried out new RNA-Seq experiments mapping the time course of vasopressin induced changes in the transcriptome of mouse mpkCCD cells to further prioritize TFs that change in tandem with AQP2. Results The analysis identified 21 TFs out of 1344 in the mouse genome that are most likely to be involved in regulation of Aqp2 gene transcription. These TFs included nine that have been proposed in prior studies to play a role in Aqp2 regulation, viz. Cebpb, Elf1, Elf3, Ets1, Hes1, Jun, Junb, Nfkb1, and Sp1. The remaining 12 represent new candidates for future studies (Atf1, Cebpg, Fosl2, Irf3, Klf5, Klf6, Mef2d, Nfib, Nfyb, Nfyc, Nr2f6, Stat3). Conspicuously absent is CREB, i.e. Creb1, which has been widely proposed to mediate vasopressin-induced regulation of Aqp2 gene transcription. Instead, another CREB-like TF, Atf1, with a pKID PKA-phosphorylation domain, ranked second among all transcription factors, representing an apt target for future studies. The RNA-Seq time course experiments in mpkCCD cells showed a rapid increase in Aqp2 mRNA, within 3 hour of vasopressin exposure. This response was matched by an equally rapid increase in the abundance of the mRNA coding for Cebpb, a so-called pioneer TF, which we have shown by ChIP-seq studies to bind to an enhancer 938 bp downstream from the Aqp2 gene. Conclusions The Bayesian analysis has identified the TFs most likely to bind to these regions and likely to be regulated by vasopressin stimulation, providing a roadmap for future studies to understand regulation of Aqp2 gene expression.