MERS-CoV and H5N1 influenza virus antagonize antigen presentation by altering the epigenetic landscape

Menachery, Vineet D.; Schäfer, Alexandra; Burnum-Johnson, Kristin E.; Mitchell, Hugh D.; Eisfeld, Amie J.; Walters, Kevin B.; Nicora, Carrie D.; Purvine, Samuel O.; Casey, Cameron P.; Monroe, Matthew E.; Weitz, Karl K.; Stratton, Kelly G.; Webb-Robertson, Bobbie-Jo M.; Gralinski, Lisa E.; Metz, Thomas O.; Smith, Richard D.; Waters, Katrina M.; Sims, Amy C.; Kawaoka, Yoshihiro; Baric, Ralph S.

Processing. Proteomics preparation was carried out as described (37) . Detailed experimental approaches have been included in Supporting Information. Quality control processing was performed to identify and remove contaminant proteins, redundant peptides, peptides with an insufficient amount of data across the set of samples (43) , and LC-MS runs that showed significant deviation from the standard behavior of all LC-MS analyses (44) . Peptides were normalized by using a statistical procedure for the analysis of proteomic normalization strategies that identified the peptide-selection method and data-scaling factor which introduced the least amount of bias into the dataset (45) . The peptide abundance values were normalized across the technical replicates by using a global median centering of the data. Normalized log10 abundance values were averaged across the technical replicates within each biological sample. Peptides were evaluated with a Dunnett adjusted t test and a G test to identify quantitative and qualitative significance patterns, respectively, in the peptide data. Peptide level significance patterns were used for protein roll-up to select appropriate peptides for protein quantification. Proteins were quantified by using a standard R-Rollup method using the most abundant reference peptide (46) after filtering the peptides that were redundant, had low data content, or were outside the dominant significance pattern.