Use of LC-MS/MS and Bayes' theorem to identify protein kinases that phosphorylate aquaporin-2 at Ser256

使用 LC-MS/MS 和贝叶斯定理鉴定在 Ser256 位点磷酸化水通道蛋白 2 的蛋白激酶

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作者:Davis Bradford, Viswanathan Raghuram, Justin L L Wilson, Chung-Lin Chou, Jason D Hoffert, Mark A Knepper, Trairak Pisitkun

Abstract

In the renal collecting duct, binding of AVP to the V2 receptor triggers signaling changes that regulate osmotic water transport. Short-term regulation of water transport is dependent on vasopressin-induced phosphorylation of aquaporin-2 (AQP2) at Ser256. The protein kinase that phosphorylates this site is not known. We use Bayes' theorem to rank all 521 rat protein kinases with regard to the likelihood of a role in Ser256 phosphorylation on the basis of prior data and new experimental data. First, prior probabilities were estimated from previous transcriptomic and proteomic profiling data, kinase substrate specificity data, and evidence for kinase regulation by vasopressin. This ranking was updated using new experimental data describing the effects of several small-molecule kinase inhibitors with known inhibitory spectra (H-89, KN-62, KN-93, and GSK-650394) on AQP2 phosphorylation at Ser256 in inner medullary collecting duct suspensions. The top-ranked kinase was Ca2+/calmodulin-dependent protein kinase II (CAMK2), followed by protein kinase A (PKA) and protein kinase B (AKT). Liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based in vitro phosphorylation studies compared the ability of three highly ranked kinases to phosphorylate AQP2 and other inner medullary collecting duct proteins, PKA, CAMK2, and serum/glucocorticoid-regulated kinase (SGK). All three proved capable of phosphorylating AQP2 at Ser256, although CAMK2 and PKA were more potent than SGK. The in vitro phosphorylation experiments also identified candidate protein kinases for several additional phosphoproteins with likely roles in collecting duct regulation, including Nedd4-2, Map4k4, and 3-phosphoinositide-dependent protein kinase 1. We conclude that Bayes' theorem is an effective means of integrating data from multiple data sets in physiology.

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