The MSstats R-Bioconductor family of packages is widely used for statistical analyses of quantitative bottom-up mass spectrometry-based proteomic experiments to detect differentially abundant proteins. It is applicable to a variety of experimental designs and data acquisition strategies and is compatible with many data processing tools used to identify and quantify spectral features. In the face of ever-increasing complexities of experiments and data processing strategies, the core package of the family, with the same name MSstats, has undergone a series of substantial updates. Its new version MSstats v4.0 improves the usability, versatility, and accuracy of statistical methodology, and the usage of computational resources. New converters integrate the output of upstream processing tools directly with MSstats, requiring less manual work by the user. The package's statistical models have been updated to a more robust workflow. Finally, MSstats' code has been substantially refactored to improve memory use and computation speed. Here we detail these updates, highlighting methodological differences between the new and old versions. An empirical comparison of MSstats v4.0 to its previous implementations, as well as to the packages MSqRob and DEqMS, on controlled mixtures and biological experiments demonstrated a stronger performance and better usability of MSstats v4.0 as compared to existing methods.

Liquid chromatography coupled with bottom-up mass spectrometry (LC-MS/MS)–based proteomics is increasingly used to detect changes in posttranslational modifications (PTMs) in samples from different conditions. Analysis of data from such experiments faces numerous statistical challenges. These include the low abundance of modified proteoforms, the small number of observed peptides that span modification sites, and confounding between changes in the abundance of PTM and the overall changes in the protein abundance. Therefore, statistical approaches for detecting differential PTM abundance must integrate all the available information pertaining to a PTM site and consider all the relevant sources of confounding and variation. In this manuscript, we propose such a statistical framework, which is versatile, accurate, and leads to reproducible results. The framework requires an experimental design, which quantifies, for each sample, both peptides with PTMs and peptides from the same proteins with no modification sites. The proposed framework supports both label-free and tandem mass tag-based LC-MS/MS acquisitions. The statistical methodology separately summarizes the abundances of peptides with and without the modification sites, by fitting separate linear mixed effects models appropriate for the experimental design. Next, model-based inferences regarding the PTM and the protein-level abundances are combined to account for the confounding between these two sources. Evaluations on computer simulations, a spike-in experiment with known ground truth, and three biological experiments with different organisms, modification types, and data acquisition types demonstrate the improved fold change estimation and detection of differential PTM abundance, as compared to currently used approaches. The proposed framework is implemented in the free and open-source R/Bioconductor package MSstatsPTM.

Defective autophagy is strongly associated with chronic inflammation. Loss-of-function of the core autophagy gene Atg16l1 increases risk for Crohn’s disease in part by enhancing innate immunity through myeloid cells such as macrophages. However, autophagy is also recognized as a mechanism for clearance of certain intracellular pathogens. These divergent observations prompted a re-evaluation of ATG16L1 in innate antimicrobial immunity. In this study, we found that loss of Atg16l1 in myeloid cells enhanced the killing of virulent Shigella flexneri (S.flexneri), a clinically relevant enteric bacterium that resides within the cytosol by escaping from membrane-bound compartments. Quantitative multiplexed proteomics of murine bone marrow-derived macrophages revealed that ATG16L1 deficiency significantly upregulated proteins involved in the glutathione-mediated antioxidant response to compensate for elevated oxidative stress, which simultaneously promoted S.flexneri killing. Consistent with this, myeloid-specific deletion of Atg16l1 in mice accelerated bacterial clearance in vitro and in vivo. Pharmacological induction of oxidative stress through suppression of cysteine import enhanced microbial clearance by macrophages. Conversely, antioxidant treatment of macrophages permitted S.flexneri proliferation. These findings demonstrate that control of oxidative stress by ATG16L1 and autophagy regulates antimicrobial immunity against intracellular pathogens.