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Abstract: PO1399

Bayesian Identification of Transcription Factors That Regulate Aqp2 Transcription

Session Information

Category: Fluid, Electrolyte, and Acid-Base Disorders

  • 901 Fluid, Electrolyte, and Acid-Base Disorders: Basic

Authors

  • Kikuchi, Hiroaki, National Institutes of Health, Bethesda, Maryland, United States
  • Jung, Hyun Jun, Johns Hopkins University, Baltimore, Maryland, United States
  • Park, Euijung, National Institutes of Health, Bethesda, Maryland, United States
  • Chen, Lihe, National Institutes of Health, Bethesda, Maryland, United States
  • Raghuram, Viswanathan, National Institutes of Health, Bethesda, Maryland, United States
  • Knepper, Mark A., National Institutes of Health, Bethesda, Maryland, United States
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.

Methods

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 select the TFs most strongly expressed in mpkCCD cells. Then, 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.

Results

The analysis showed that the TFs most likely involved in regulation of Aqp2 gene expression are associated with six enhancer regions in the CTCF loop surrounding the Aqp2 gene. Of the six enhancers, of particular interest is a 517 bp region identified 5.0 kb upstream from the Aqp2 transcription start site (TSS) that is predicted to bind Tcf7l2 (Wnt signaling), Tead2 (Hippo signaling) and Gli3 (Hedgehog signaling). Also within this enhancer region are high probability binding sites for TFs previously identified to regulate Aqp2 gene transcription, viz. Nfat5/NfatC3, Nfkb1/Rela, and Grhl2. Another enhancer is 5.8 kb downstream from the Aqp2 TSS and contains binding sites for three TFs already implicated in Aqp2 transcriptional regulation, namely Cebpb, AP-1 (Jun/Fosl2) and Elf1/Elf3, as well as sites for several TFs that are so far unstudied with respect to Aqp2 regulation.

Conclusion

The Bayesian analysis has defined the enhancer regions within the CTCF loop surrounding the Aqp2 gene and identified the TFs most likely to bind to these regions, providing a roadmap for future studies to understand regulation of Aqp2 gene expression.

Funding

  • Other NIH Support