4.10. Protein Identification, Relative Quantification, Bioinformatic Functional Profiling and Interaction Analysis

Proteins were identified by searching the mass spectrometry data files against the Homo sapiens proteome database (www.uniprot.org/proteomes/UP000005640, downloaded, 2 August 2021; 78120 entries) using MaxQuant v. 1.6.4.0 with the label free quantification (LFQ) and intensity-based absolute quantification options selected [45,46,47]. Default settings were used with search parameters set to include the following modifications: carbamidomethyl-Cys (fixed); Met oxidation; protein N-terminal acetylation (variable); maximum of two missed tryptic cleavages. Peptide-spectrum matches and protein identifications were filtered using a target-decoy approach at a false discovery rate (FDR) of 1%. Statistical analyses were performed using LFQAnalyst (bioinformatics.erc.monash.edu/apps/LFQ-Analyst/, accessed 18 October 2021), where the LFQ intensity values were used for protein quantification [48]. Missing values were replaced by values drawn from a normal distribution of 1.8 standard deviations and a width of 0.3 for each sample (Perseus-type). Protein-wise linear models combined with empirical Bayesian statistics were used for differential expression analysis using the Bioconductor package limma, whereby the adjusted P-value cut-off was set at 0.05 and log2 fold change cut-off was set at 1. The Benjamini–Hochberg method of FDR correction was applied. The g: Profiler tool was used for functional enrichment analysis g:Profiler (version e104_eg51_p15_3922dba) with the Benjamini-Hochberg FDR method applying a significance threshold of 0.05 [44]. The Min and Max size settings of the functional category were set to 3 and 500, respectively, with No electronic gene ontology (GO) annotations selected. GO terms for Molecular Function, Biological Process, Cellular Compartment, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome were assigned. The species was set to Homo sapiens.