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Physiol Genomics. 2016 Jul 1;48(7):502-12. doi: 10.1152/physiolgenomics.00031.2016. Epub 2016 May 13.

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

Author information

1
Epithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland; Vitreous State Laboratory, The Catholic University of America, Washington, District of Columbia; and.
2
Epithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland;
3
Epithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland; Systems Biology Center, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand.
4
Vitreous State Laboratory, The Catholic University of America, Washington, District of Columbia; and.
5
Epithelial Systems Biology Laboratory, Systems Biology Center, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland; knepperm@nhlbi.nih.gov.

Abstract

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.

KEYWORDS:

E3 ubiquitin ligases; aquaporin-2; collecting duct; kidney

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