Data visualization plays critical roles in proteomics studies, ranging from quality control of MS/MS data to validation of peptide identification results. Herein, we present PDV, an integrative proteomics data viewer that can be used to visualize a wide range of proteomics data, including database search results, de novo sequencing results, proteogenomics files, MS/MS data in mzML/mzXML format and data from public proteomics repositories. PDV is a lightweight visualization tool that enables intuitive and fast exploration of diverse, large-scale proteomics datasets on standard desktop computers in both graphical user interface and command line modes. PDV software and the user manual are freely available at http://pdv.zhang-lab.org. The source code is available at https://github.com/wenbostar/PDV and is released under the GPL-3 license. Supplementary data are available at Bioinformatics online.

The outbreak of coronavirus disease 2019 (COVID-19) is a global health emergency. Various omics results have been reported for COVID-19, but the molecular hallmarks of COVID-19, especially in those patients without comorbidities, have not been fully investigated. Here we collect blood samples from 231 COVID-19 patients, prefiltered to exclude those with selected comorbidities, yet with symptoms ranging from asymptomatic to critically ill. Using integrative analysis of genomic, transcriptomic, proteomic, metabolomic and lipidomic profiles, we report a trans-omics landscape for COVID-19. Our analyses find neutrophils heterogeneity between asymptomatic and critically ill patients. Meanwhile, neutrophils over-activation, arginine depletion and tryptophan metabolites accumulation correlate with T cell dysfunction in critical patients. Our multi-omics data and characterization of peripheral blood from COVID-19 patients may thus help provide clues regarding pathophysiology of and potential therapeutic strategies for COVID-19.

High-throughput metabolite profiling provides the opportunity to reveal metabolic mechanisms and identify biomarkers. Psoriasis is an immune-mediated chronic inflammatory disease. However, the role of metabolism in psoriasis pathogenesis remains unclear.

Disease progression prediction and therapeutic drug target discovery for Coronavirus disease 2019 (COVID-19) are particularly important, as there is still no effective strategy for severe COVID-19 patient treatment. Herein, we performed multi-platform omics analysis of serial plasma and urine samples collected from patients during the course of COVID-19. Integrative analyses of these omics data revealed several potential therapeutic targets, such as ANXA1 and CLEC3B. Molecular changes in plasma indicated dysregulation of macrophage and suppression of T cell functions in severe patients compared to those in non-severe patients. Further, we chose 25 important molecular signatures as potential biomarkers for the prediction of disease severity. The prediction power was validated using corresponding urine samples and plasma samples from new COVID-19 patient cohort, with AUC reached to 0.904 and 0.988, respectively. In conclusion, our omics data proposed not only potential therapeutic targets, but also biomarkers for understanding the pathogenesis of severe COVID-19.

The N-glycans of mammalian glycoproteins vary greatly in structure, and the biological importance of these variations is mostly unknown. It is widely acknowledged that the bisecting N-acetylglucosamine (GlcNAc) structure, a β1,4-linked GlcNAc attached to the core β-mannose residue, represents a special type of N-glycosylated modification, and it has been reported to be involved in various biological processes, such as cell adhesion, fertilization and fetal development, neuritogenesis, and tumor development. In particular, the occurrence of N-glycans with a bisecting GlcNAc modification on proteins has been proven, with many implications for immune biology. Due to the essential functions of bisecting GlcNAc structures, analytical approaches to this modification are highly required. The traditional approach that has been used for bisecting GlcNAc determinations is based on the lectin recognition of Phaseolus vulgaris erythroagglutinin (PHA-E); however, poor binding specificity hinders the application of this method. With the development of mass spectrometry (MS) with high resolution and improved sensitivity and accuracy, MS-based glycomic analysis has provided precise characterization and quantification for glycosylation modification. In this review, we first provide an overview of the bisecting GlcNAc structure and its biological importance in neurological systems, immune tolerance, immunoglobulin G (IgG), and tumor metastasis and development and then summarize approaches to its determination by MS for performing precise functional studies. This review is valuable for those readers who are interested in the importance of bisecting GlcNAc in cell biology.