Serine/threonine phosphatases and aquaporin-2 regulation in renal collecting duct

Abstract

Phosphorylation of the aquaporin-2 (AQP2) water channel at four COOH-terminal serines plays a central role in the regulation of water permeability of the renal collecting duct. The level of phosphorylation at these sites is determined by a balance between phosphorylation by protein kinases and dephosphorylation by phosphatases. The phosphatases that dephosphorylate AQP2 have not been identified. Here, we use large-scale data integration techniques to identify serine-threonine phosphatases likely to interact with AQP2 in renal collecting duct principal cells. As a first step, we have created a comprehensive list of 38 S/T phosphatase catalytic subunits present in the mammalian genome. Then we used Bayes' theorem to integrate available information from large-scale data sets from proteomic and transcriptomic studies to rank the known S/T phosphatases with regard to the likelihood that they interact with AQP2 in renal collecting duct cells. To broaden the analysis, we have generated new proteomic data (LC-MS/MS) identifying 4538 distinct proteins including 22 S/T phosphatases in cytoplasmic fractions from native inner medullary collecting duct cells from rats. The official gene symbols corresponding to the top-ranked phosphatases (common names in parentheses) were: Ppp1cb (PP1-β), Ppm1g (PP2C), Ppp1ca (PP1-α), Ppp3ca (PP2-B or calcineurin), Ppp2ca (PP2A-α), Ppp1cc (PP1-γ), Ppp2cb (PP2A-β), Ppp6c (PP6C), and Ppp5c (PP5). This ranking correlates well with results of prior reductionist studies of ion and water channels in renal collecting duct cells.

Sequence-based searching of custom proteome and transcriptome databases

“…; LeMaire et al. ) done within our laboratory. Some of these proteomic studies include multiple experimental methods, which results in 20 “sources” describing which experimental method identified a specific protein.…”

“…We curated a comprehensive list of proteins found in the rat IMCD cells from 18 studies Hoffert et al 2004Hoffert et al , 2006Hoffert et al , 2012Hoffert et al , 2014Barile et al 2005;Hoorn et al 2005;Pisitkun et al 2006;Simons et al 2006;Yu et al 2006;Sachs et al 2008;Bansal et al 2010;Tchapyjnikov et al 2010;Zhao et al 2012;Bradford et al 2014;Trepiccione et al 2014;Pickering et al 2016;LeMaire et al 2017) done within our laboratory. Some of these proteomic studies include multiple experimental methods, which results in 20 "sources" describing which experimental method identified a specific protein.…”

Identification of β-catenin-interacting proteins in nuclear fractions of native rat collecting duct cells

“…We carried out a large-scale data integration analysis using Bayes' Theorem to answer the question, "Which among the 1,375 DNAbinding transcription factors annotated in the rat genome are most likely to regulate Aqp2 gene expression in a ␤-catenin-dependent manner?" The method, as applied to physiological questions, was described previously in detail (40,79). Briefly, to address the question, the method begins with a set of unbiased prior probabilities for each transcription factor (each 1/1,375).…”

“…To identify sequence-specific transcription factors that might interact with ␤-catenin in the promoter region of the Aqp2 gene, we followed two steps. 1) We utilized large-scale data integration techniques using Bayes' theorem (40,79) to identify which of the 1,375 transcription factors annotated in the rat genome were most likely to bind to the Aqp2 promoter; and 2) we looked for prior evidence of interactions between these transcription factors and ␤-catenin. The algorithm for the calculation is summarized in Table 7, and the 23 top-ranked transcription factors are listed in order in Table 8.…”

From 20th century metabolic wall charts to 21st century systems biology: database of mammalian metabolic enzymes

“…This list was an essential component of Bayesian analysis of cell signaling in the renal collecting duct (3,27). Similar lists of transcription factors (http://www.bioguo.org/AnimalTFDB/), proteases (http://merops.sanger.ac.uk/), protein phosphatases (https://hpcwebapps.cit.nih.gov/ESBL/Database/Phosphatases/) (12), and ubiquitin E3 ligases (https://hpcwebapps.cit.nih. gov/ESBL/Database/E3-ligases/) (14) have been crucial to other systems-level studies.…”