BackgroundSeveral methods to handle data generated from bottom-up proteomics via liquid chromatography-mass spectrometry, particularly for peptide-centric quantification dealing with post-translational modification (PTM) analysis like reversible cysteine oxidation are evaluated. The paper proposes a pipeline based on the R programming language to analyze PTMs from peptide-centric label-free quantitative proteomics data.ResultsOur methodology includes variance stabilization, normalization, and missing data imputation to account for the large dynamic range of PTM measurements. It also corrects biases from an enrichment protocol and reduces the random and systematic errors associated with label-free quantification. The performance of the methodology is tested by performing proteome-wide differential PTM quantitation using linear models analysis (limma). We objectively compare two imputation methods along with significance testing when using multiple-imputation for missing data.ConclusionIdentifying PTMs in large-scale datasets is a problem with distinct characteristics that require new methods for handling missing data imputation and differential proteome analysis. Linear models in combination with multiple-imputation could significantly outperform a t-test-based decision method.Electronic supplementary materialThe online version of this article (10.1186/s12859-019-2619-6) contains supplementary material, which is available to authorized users.

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Targeted proteolysis activities activated during the plant immune response catalyze the synthesis of stable endogenous peptides. Little is known about their biogenesis and biological roles. Herein, we characterize an Arabidopsis thaliana mutant top1top2 in which targeted proteolysis of immune-active peptides is drastically impaired during effector-triggered immunity (ETI). For effective ETI, the redox-sensitive thimet oligopeptidases TOP1 and TOP2 are required. Quantitative mass spectrometry-based peptidomics allowed differential peptidome profiling of wild type (WT) and top1top2 mutant at the early ETI stages. Biological processes of energy-producing and redox homeostasis were enriched, and TOPs were necessary to maintain the dynamics of ATP and NADP(H) accumulation in the plant during ETI. Subsequently, a set of novel TOPs substrates validated in vitro enabled the definition of the TOP-specific cleavage motif and informed an in-silico model of TOP proteolysis to generate bioactive peptide candidates. Several candidates, including those derived from proteins associated with redox metabolism, were confirmed in planta. The top1top2 background rescued WT’s ETI deficiency caused by treatment with peptides derived from targeted proteolysis of the negative immune regulator FBR12, the reductive enzyme APX1, the isoprenoid pathway enzyme DXR, and ATP-subunit β. These results demonstrate TOPs role in orchestrating the production and degradation of phytocytokines.

Due to its simplicity in sample preparation, label-free quantification has become de facto in proteomics research at the expense of precision. We propose a Bayesian hierarchical decision model to test for differences in means between conditions for proteins, peptides, and post-translation modifications. We introduce a novel Bayesian regression model to characterize local mean-variance trends in the data to describe measurement uncertainty and to estimate the decision model hyperparameters. Our model vastly improves over state-of-the-art methods (Limma-Trend and t-test) in several spike-in datasets by having competitive performance in detecting true positives while showing superiority by greatly reducing false positives.