Baldur: Bayesian Hierarchical Modeling for Label-Free Proteomics with Gamma Regressing Mean-Variance Trends.

Label-free proteomics is a fast-growing methodology to infer abundances in mass spectrometry proteomics. Extensive research has focused on spectral quantification and peptide identification. However, research toward modeling and understanding quantitative proteomics data is scarce. Here we propose a Bayesian hierarchical decision model (Baldur) to test for differences in means between conditions for proteins, peptides, and post-translational modifications. We developed a Bayesian regression model to characterize local mean-variance trends in data, to estimate measurement uncertainty and hyperparameters for the decision model. A key contribution is the development of a new gamma regression model that describes the mean-variance dependency as a mixture of a common and a latent trend-allowing for localized trend estimates. We then evaluate the performance of Baldur, limma-trend, and t test on six benchmark datasets: five total proteomics and one post-translational modification dataset. We find that Baldur drastically improves the decision in noisier post-translational modification data over limma-trend and t test. In addition, we see significant improvements using Baldur over the other methods in the total proteomics datasets. Finally, we analyzed Baldur's performance when increasing the number of replicates and found that the method always increases precision with sample size, while showing robust control of the false positive rate. We conclude that our model vastly improves over popular data analysis methods (limma-trend and t test) in several spike-in datasets by achieving a high true positive detection rate, while greatly reducing the false-positive rate.

American Medical Society for Sports Medicine Position Statement on the Care of the Athlete and Athletic Person With Diabetes.

ABSTRACT: The American Medical Society for Sports Medicine (AMSSM) developed this position statement to assist physicians and other health professionals in managing athletes and active people with diabetes. The AMSSM selected the author panel through an application process to identify members with clinical and academic expertise in the care of active patients with diabetes. This article reviews the current knowledge and gaps regarding the benefits and risks of various types of exercise and management issues for athletes and physically active people with diabetes, including nutrition and rehabilitation issues. Resistance exercises seem to be beneficial for patients with type 1 diabetes, and the new medications for patients with type 2 diabetes generally do not need adjustment with exercise. In preparing this statement, the authors conducted an evidence review and received open comment from the AMSSM Board of Directors before finalizing the recommendations.

Proteome-Wide Analysis of Cysteine Reactivity during Effector-Triggered Immunity.

A surge in the accumulation of oxidants generates shifts in the cellular redox potential during early stages of plant infection with pathogens and activation of effector-triggered immunity (ETI). The redoxome, defined as the proteome-wide oxidative modifications of proteins caused by oxidants, has a well-known impact on stress responses in metazoans. However, the identity of proteins and the residues sensitive to oxidation during the plant immune response remain largely unknown. Previous studies of the thimet oligopeptidases TOP1 and TOP2 placed them in the salicylic acid dependent branch of ETI, with a current model wherein TOPs sustain interconnected organellar and cytosolic pathways that modulate the oxidative burst and development of cell death. Herein, we characterized the ETI redoxomes in Arabidopsis (Arabidopsis thaliana) wild-type Col-0 and top1top2 mutant plants using a differential alkylation-based enrichment technique coupled with label-free mass spectrometry-based quantification. We identified cysteines sensitive to oxidation in a wide range of protein families at multiple time points after pathogen infection. Differences were detected between Col-0 and top1top2 redoxomes regarding the identity and number of oxidized cysteines, and the amplitude of time-dependent fluctuations in protein oxidation. Our results support a determining role for TOPs in maintaining the proper level and dynamics of proteome oxidation during ETI. This study significantly expands the repertoire of oxidation-sensitive plant proteins and can guide future mechanistic studies.

Evaluation of linear models and missing value imputation for the analysis of peptide-centric proteomics.

BACKGROUND: Several 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. RESULTS: Our 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. CONCLUSION: Identifying 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.