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The organization of the mammalian genome into gene subsets corresponding to specific functional classes has provided key tools for systems biology research. Here, we have created a web-accessible resource called the Mammalian Metabolic Enzyme Database (https://hpcwebapps.cit.nih.gov/ESBL/Database/MetabolicEnzymes/MetabolicEnzymeDatabase.html) keyed to the biochemical reactions represented on iconic metabolic pathway wall charts created in the previous century. Overall, we have mapped 1,647 genes to these pathways, representing ~7 percent of the protein-coding genome. To illustrate the use of the database, we apply it to the area of kidney physiology. In so doing, we have created an additional database (Database of Metabolic Enzymes in Kidney Tubule Segments:https://hpcwebapps.cit.nih.gov/ESBL/Database/MetabolicEnzymes/), mapping mRNA abundance measurements (mined from RNA-Seq studies) for all metabolic enzymes to each of 14 renal tubule segments. We carry out bioinformatics analysis of the enzyme expression pattern among renal tubule segments and mine various data sources to identify vasopressin-regulated metabolic enzymes in the renal collecting duct.

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.