Computational protocol: Progressive muscle proteome changes in a clinically relevant pig model of Duchenne muscular dystrophy

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Protocol publication

[…] MS data were processed, using MaxQuant V1.5.1 and the Sus scrofa subset of the UniProt database. For identification, the following parameters were used: i) Enzyme: Trypsin; ii) Mass tolerance precursor: 10 ppm; iii) Mass tolerance MS/MS: 0.8 Da; iv) Fixed modification: Carbamidomethylation of cysteine; v) Variable modifications: acetylation of protein N-terminus and oxidized methionine. FDRs at the peptide and protein level were set to 1%. For label-free quantification (LFQ) the match between runs option was enabled. Hierarchical clustering was performed with the Perseus module of MaxQuant. In case proteins were detected in all replicates of one group, but in no replicate of the other group, the MaxQuant imputation feature was used to allow a statistical evaluation. Further statistics was done with R. A 2-way ANOVA with age and genotype as fixed effects was performed, followed by Tukey Honest Significant Differences post-hoc test to verify differences between the groups. To correct the results of the 2-way ANOVA for multiple testing, we chose a FDR-based approach and calculated the q-value using the R package “qvalue”. This approach avoids false positive results while offering a more liberal correction criterion compared to other methods. For the DAVID analysis as well as for the discussion section, we exclusively used proteins showing an abundance alteration of a log2fold >│0.6│ and a significance level of <0.05 after correction for multiple testing. To facilitate a meta-analysis addressing also less prominent abundance alterations, the quantitative values for all identified proteins are listed in . For functional annotation clustering, the DAVID online platform was used. […]

Pipeline specifications

Software tools MaxQuant, Perseus, DAVID
Application MS-based untargeted proteomics
Organisms Sus scrofa, Homo sapiens, Mus musculus
Diseases Muscular Diseases, Muscular Dystrophies, Muscular Dystrophy, Duchenne, Genetic Diseases, Inborn