Data integration in physiology using Bayes' rule and minimum Bayes' factors: deubiquitylating enzymes in the renal collecting duct.

A major challenge in physiology is to exploit the many large-scale data sets available from "-omic" studies to seek answers to key physiological questions. In previous studies, Bayes' theorem has been used for this purpose. This approach requires a means to map continuously distributed experimental data to probabilities (likelihood values) to derive posterior probabilities from the combination of prior probabilities and new data. Here, we introduce the use of minimum Bayes' factors for this purpose and illustrate the approach by addressing a physiological question, "Which deubiquitylating enzymes (DUBs) encoded by mammalian genomes are most likely to regulate plasma membrane transport processes in renal cortical collecting duct principal cells?" To do this, we have created a comprehensive online database of 110 DUBs present in the mammalian genome (https://hpcwebapps.cit.nih.gov/ESBL/Database/DUBs/). We used Bayes' theorem to integrate available information from large-scale data sets derived from proteomic and transcriptomic studies of renal collecting duct cells to rank the 110 known DUBs with regard to likelihood of interacting with and regulating transport processes. The top-ranked DUBs were OTUB1, USP14, PSMD7, PSMD14, USP7, USP9X, OTUD4, USP10, and UCHL5. Among these USP7, USP9X, OTUD4, and USP10 are known to be involved in endosomal trafficking and have potential roles in endosomal recycling of plasma membrane proteins in the mammalian cortical collecting duct.

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

The gene encoding the aquaporin-2 water channel is regulated transcriptionally in response to vasopressin. In the renal collecting duct, vasopressin stimulates the nuclear translocation and phosphorylation (at Ser552) of ß-catenin, a multifunctional protein that acts as a transcriptional coregulator in the nucleus. The purpose of this study was to identify ß-catenin-interacting proteins that might be involved in transcriptional regulation in rat inner medullary collecting duct (IMCD) cells, using experimental and computational approaches. We used a standard chromatin immunoprecipitation procedure coupled to mass spectrometry (ChIP-MS) in a nuclear fraction isolated from rat IMCD suspensions. Over four biological replicates, we reproducibly identified 43 ß-catenin-binding proteins, including several known ß-catenin-binding partners as well as novel interacting proteins. Multiple proteins involved in transcriptional regulation were identified (Taf1, Jup, Tdrd3, Cdh1, Cenpj, and several histones). Many of the identified ß-catenin-binding partners were found in prior studies to translocate to the nucleus in response to vasopressin. There was only one DNA-binding transcription factor (TF), specifically Taf1, part of the RNA-polymerase II preinitiation complex. To identify sequence-specific TFs that might interact with ß-catenin, Bayes' theorem was used to integrate data from several information sources. The analysis identified several TFs with potential binding sites in the Aqp2 gene promoter that could interact with ß-catenin in the regulation of Aqp2 gene transcription, specifically Jun, Junb, Jund, Atf1, Atf2, Mef2d, Usf1, Max, Pou2f1, and Rxra. The findings provide information necessary for modeling the transcriptional response to vasopressin.

Comprehensive database of human E3 ubiquitin ligases: application to aquaporin-2 regulation.

Aquaporin-2 (AQP2) is regulated in part via vasopressin-mediated changes in protein half-life that are in turn dependent on AQP2 ubiquitination. Here we addressed the question, "What E3 ubiquitin ligase is most likely to be responsible for AQP2 ubiquitination?" using large-scale data integration based on Bayes' rule. The first step was to bioinformatically identify all E3 ligase genes coded by the human genome. The 377 E3 ubiquitin ligases identified in the human genome, consisting predominant of HECT, RING, and U-box proteins, have been used to create a publically accessible and downloadable online database (https://hpcwebapps.cit.nih.gov/ESBL/Database/E3-ligases/). We also curated a second database of E3 ligase accessory proteins that included BTB domain proteins, cullins, SOCS-box proteins, and F-box proteins. Using Bayes' theorem to integrate information from multiple large-scale proteomic and transcriptomic datasets, we ranked these 377 E3 ligases with respect to their probability of interaction with AQP2. Application of Bayes' rule identified the E3 ligases most likely to interact with AQP2 as (in order of probability): NEDD4 and NEDD4L (tied for first), AMFR, STUB1, ITCH, ZFPL1. Significantly, the two E3 ligases tied for top rank have also been studied extensively in the reductionist literature as regulatory proteins in renal tubule epithelia. The concordance of conclusions from reductionist and systems-level data provides strong motivation for further studies of the roles of NEDD4 and NEDD4L in the regulation of AQP2 protein turnover.

Proteomic profiling of nuclear fractions from native renal inner medullary collecting duct cells.

The control of renal water excretion occurs in part by regulation of transcription in response to vasopressin in cells of the collecting duct. A systems biology-based approach to understanding transcriptional control in renal collecting duct cells depends on knowledge of what transcription factors and other regulatory proteins are present in the cells' nuclei. The goal of this article is to report comprehensive proteomic profiling of cellular fractions enriched in nuclear proteins from native inner medullary collecting duct (IMCD) cells of the rat. Multidimensional separation procedures and state-of-the art protein mass spectrometry produced 18 GB of spectral data that allowed the high-stringency identification of 5,048 proteins in nuclear pellet (NP) and nuclear extract (NE) fractions of biochemically isolated rat IMCD cells (URL: https://helixweb.nih.gov/ESBL/Database/IMCD_Nucleus/). The analysis identified 369 transcription factor proteins out of the 1,371 transcription factors coded by the rat genome. The analysis added 1,511 proteins to the recognized proteome of rat IMCD cells, now amounting to 8,290 unique proteins. Analysis of samples treated with the vasopressin analog dDAVP (1 nM for 30 min) or its vehicle revealed 99 proteins in the NP fraction and 88 proteins in the NE fraction with significant changes in spectral counts (Fisher exact test, P < 0.005). Among those altered by vasopressin were seven distinct histone proteins, all of which showed decreased abundance in the NP fraction, consistent with a possible effect of vasopressin to induce chromatin remodeling. The results provide a data resource for future studies of vasopressin-mediated transcriptional regulation in the renal collecting duct.

Sequence-based searching of custom proteome and transcriptome databases.

A long-term goal in renal physiology is to understand the mechanisms involved in collecting duct function and regulation at a cellular and molecular level. The first step in modeling of these mechanisms, which can provide a guide to experimentation, is the generation of a list of model components. We have curated a list of proteins expressed in the rat renal inner medullary collecting duct (IMCD) from proteomic data from 18 different publications. The database has been posted as a public resource at https://hpcwebapps.cit.nih.gov/ESBL/Database/IMCD_Proteome_Database/. It includes 8956 different proteins. To search the IMCD Proteomic Database efficiently, we have created a Java-based program called curated database Basic Local Alignment Search Tool (cdbBLAST), which uses the NCBI BLAST kernel to search for specific amino acid sequences corresponding to proteins in the database. cdbBLAST reports information on the matched protein and identifies proteins in the database that have similar sequences. We have also adapted cdbBLAST to interrogate our previously published IMCD Transcriptome Database. We have made the cdbBLAST program available for use either as a web application or a downloadable .jar file at https://hpcwebapps.cit.nih.gov/ESBL/Database/cdbBLAST/. Database searching based on protein sequence removes ambiguities arising from the standard search method based on official gene symbols and allows the user efficient identification of related proteins that may fulfill the same functional roles.