Mass spectrometry (MS)-based quantitative proteomics experiments frequently generate data with missing values, which may profoundly affect downstream analyses. A wide variety of imputation methods have been established to deal with the missing-value issue. To date, however, there is a scarcity of efficient, systematic, and easy-to-handle tools that are tailored for proteomics community. Herein, we developed a user-friendly and powerful stand-alone software, NAguideR, to enable implementation and evaluation of different missing value methods offered by 23 widely used missing-value imputation algorithms. NAguideR further evaluates data imputation results through classic computational criteria and, unprecedentedly, proteomic empirical criteria, such as quantitative consistency between different charge-states of the same peptide, different peptides belonging to the same proteins, and individual proteins participating protein complexes and functional interactions. We applied NAguideR into three label-free proteomic datasets featuring peptide-level, protein-level, and phosphoproteomic variables respectively, all generated by data independent acquisition mass spectrometry (DIA-MS) with substantial biological replicates. The results indicate that NAguideR is able to discriminate the optimal imputation methods that are facilitating DIA-MS experiments over those sub-optimal and low-performance algorithms. NAguideR further provides downloadable tables and figures supporting flexible data analysis and interpretation. NAguideR is freely available at http://www.omicsolution.org/wukong/NAguideR/ and the source code: https://github.com/wangshisheng/NAguideR/.

With an exponential growth in applications identifying protein post‐translational modifications via mass spectrometry, discovery and presentation of motifs surrounding those modification sites have become increasingly desirable. Despite a few tools being designed, there is still a scarcity of effective and polyfunctional software for such analysis and illustrations. In this study, a versatile and user‐friendly web tool is developed, motifeR, for extracting and visualizing statistically significant motifs from large datasets. Particularly, several functions are also integrated for processing multi‐modification sites enrichment. Public datasets are applied to test their usability, indicating that some concurrent modification sites may form motifs and that peptides with low location probability may be not identified randomly and can be included to support motif discovery. In addition, for human phosphoproteomics datasets, the characterization of differential kinase signaling networks can be estimated and modeled by combining kinase‐substrate relations based on the NetworKIN database as an optional feature for users. The motifeR toolkit can be conveniently operated by any scientific community or individuals, even those without any bioinformatics background and is freely available at https://www.omicsolution.org/wukong/motifeR.

Blood–brain barrier (BBB) breakdown and inflammation occurring at the BBB have a key, mainly a deleterious role in the pathophysiology of ischemic stroke. Neddylation is a ubiquitylation-like pathway that is critical in various cellular functions by conjugating neuronal precursor cell-expressed developmentally down-regulated protein 8 (NEDD8) to target proteins. However, the roles of neddylation pathway in ischemic stroke remain elusive. Here, we report that NEDD8 conjugation increased during acute phase after ischemic stroke and was present in intravascular and intraparenchymal neutrophils. Inhibition of neddylation by MLN4924, also known as pevonedistat, inactivated cullin-RING E3 ligase (CRL), and reduced brain infarction and improved functional outcomes. MLN4924 treatment induced the accumulation of the CRL substrate neurofibromatosis 1 (NF1). By using virus-mediated NF1 silencing, we show that NF1 knockdown abolished MLN4924-dependent inhibition of neutrophil trafficking. These effects were mediated through activation of endothelial P-selectin and intercellular adhesion molecule-1 (ICAM-1), and blocking antibodies against P-selectin or anti–ICAM-1 antibodies reversed NF1 silencing-induced increase in neutrophil infiltration in MLN4924-treated mice. Furthermore, we found that NF1 silencing blocked MLN4924-afforded BBB protection and neuroprotection through activation of protein kinase C δ (PKCδ), myristoylated alanine-rich C-kinase substrate (MARCKS), and myosin light chain (MLC) in cerebral microvessels after ischemic stroke, and treatment of mice with the PKCδ inhibitor rottlerin reduced this increased BBB permeability. Our study demonstrated that increased neddylation promoted neutrophil trafficking and thus exacerbated injury of the BBB and stroke outcomes. We suggest that the neddylation inhibition may be beneficial in ischemic stroke.

Because of inevitable and complicated signal variations in LC-MS-based nontargeted metabolomics, normalization of metabolites data is a highly recommended procedure to assist in improving accuracies in metabolic profiling and discovery of potential biomarkers. Despite various normalization methods having been developed and applied for processing these data sets, it is still difficult to assess their performance. Moreover, such methods are elusive and difficult to choose for users, especially those without bioinformatics training. In this study, we present a powerful and user-friendly web platform, named MetaboGroup S, for comparison and evaluation of seven popular normalization methods and provide an optimal one automatically for end users based on the group entropies of every sample data point. For examination and application of this tool, we analyzed a complex clinical human data set from maintenance hemodialysis patients with erythrin resistance. Metabolite peaks (11 027) were extracted from the experimental data and then imported into this platform; the entire analysis process was completed sequentially within 5 min. To further test the performance and universality of MetaboGroup S, we analyzed two more published data sets including a nuclear magnetic resonance (NMR) data set on this platform. The results indicated that the method with a lower intragroup entropy and higher intergroup entropy would be preferable. In addition, MetaboGroup S can be quite conveniently operated by users and does not require any profound computational expertise or background for scientists in many fields. MetaboGroup S is freely available at https://omicstools.shinyapps.io/MetaboGroupSapp/ .

Evolutionary profiling has been largely limited to the nucleotide level. Using consistent proteomic methods, we quantified proteomic and phosphoproteomic layers in fibroblasts from 11 common mammalian species, with transcriptomes as reference. Covariation analysis indicates that transcript and protein expression levels and variabilities across mammals remarkably follow functional role, with extracellular matrix–associated expression being the most variable, demonstrating strong transcriptome-proteome coevolution. The biological variability of gene expression is universal at both interindividual and interspecies scales but to a different extent. RNA metabolic processes particularly show higher interspecies versus interindividual variation. Our results further indicate that while the ubiquitin-proteasome system is strongly conserved in mammals, lysosome-mediated protein degradation exhibits remarkable variation between mammalian lineages. In addition, the phosphosite profiles reveal a phosphorylation coevolution network independent of protein abundance.