Conserved in VivoPhosphorylation of Calnexin at Casein Kinase II Sites as Well as a Protein Kinase C/Proline-directed Kinase Site

Calnexin is a lectin-like chaperone of the endoplasmic reticulum (ER) that couples temporally and spatially N-linked oligosaccharide modifications with the productive folding of newly synthesized glycoproteins. Calnexin was originally identified as a major type I integral membrane protein substrate of kinase(s) associated with the ER. Casein kinase II (CK2) was subsequently identified as an ER-associated kinase responsible for the in vitro phosphorylation of calnexin in microsomes (Ou, W-J., Thomas, D. Y., Bell, A. W., and Bergeron, J. J. M. (1992) J. Biol. Chem. 267, 23789-23796). We now report on the in vivo sites of calnexin phosphorylation. After 32PO4 labeling of HepG2 and Madin-Darby canine kidney cells, immunoprecipitated calnexin was phosphorylated exclusively on serine residues. Using nonradiolabeled cells, we subjected calnexin immunoprecipitates to in gel tryptic digestion followed by nanoelectrospray mass spectrometry employing selective scans specific for detection of phosphorylated fragments. Mass analyses identified three phosphorylated sites in calnexin from either HepG2 or Madin-Darby canine kidney cells. The three sites were localized to the more carboxyl-terminal half of the cytosolic domain: S534DAE (CK2 motif), S544QEE (CK2 motif), and S563PR. We conclude that CK2 is a kinase that phosphorylates calnexin in vivo as well as in microsomes in vitro. Another yet to be identified kinase (protein kinase C and/or proline-directed kinase) is directed toward the most COOH-terminal serine residue. Elucidation of the signaling cascade responsible for calnexin phosphorylation at these sites in vivo may define a novel regulatory function for calnexin in cargo folding and transport to the ER exit sites

Different methods for handling incomplete longitudinal binary outcome due to missing at random dropout

© 2014 Elsevier B.V. This paper compares the performance of weighted generalized estimating equations (WGEEs), multiple imputation based on generalized estimating equations (MI-GEEs) and generalized linear mixed models (GLMMs) for analyzing incomplete longitudinal binary data when the underlying study is subject to dropout. The paper aims to explore the performance of the above methods in terms of handling dropouts that are missing at random (MAR). The methods are compared on simulated data. The longitudinal binary data are generated from a logistic regression model, under different sample sizes. The incomplete data are created for three different dropout rates. The methods are evaluated in terms of bias, precision and mean square error in case where data are subject to MAR dropout. In conclusion, across the simulations performed, the MI-GEE method performed better in both small and large sample sizes. Evidently, this should not be seen as formal and definitive proof, but adds to the body of knowledge about the methods' relative performance. In addition, the methods are compared using data from a randomized clinical trial.publisher: Elsevier articletitle: Different methods for handling incomplete longitudinal binary outcome due to missing at random dropout journaltitle: Statistical Methodology articlelink: http://dx.doi.org/10.1016/j.stamet.2014.10.002 content_type: article copyright: Copyright © 2014 Elsevier B.V. All rights reserved.status: publishe