Severe coronavirus disease 2019 (COVID-19) infection may lead to lung injury, multi-organ failure, and eventually death. Cytokine storm due to excess cytokine production has been associated with fatality in severe infections. However, the specific molecular signatures associated with the elevated immune response are yet to be elucidated. We performed a mass-spectrometry-based proteomic and metabolomic analysis of COVID-19 plasma samples collected at two time points. Using Orbitrap Fusion LC–MS/MS-based label-free proteomic analysis, we identified around 10 significant proteins, 32 significant peptides, and 5 metabolites that were dysregulated at the severe time points. Few of these proteins identified by quantitative proteomics were validated using the multiple reaction monitoring (MRM) assay. Integrated pathway analysis using distinct proteomic and metabolomic signatures revealed alterations in complement and coagulation cascade, platelet aggregation, myeloid leukocyte activation pathway, and arginine metabolism. Further, we highlight the role of leukocyte activation and arginine metabolism in COVID-19 pathogenesis and targeting these pathways for COVID-19 therapeutics.

An Internet computing server has been developed to identify all the occurrences of the internal sequence repeats in a protein and DNA sequences. Further, an option is provided for the users to check the occurrence(s) of the resultant sequence repeats in the other sequence and structure (Protein Data Bank) databases. The databases deployed in the proposed computing engine are up-to-date and thus the users will get the latest information available in the respective databases. The server is freely accessible over the World Wide Web (WWW).Availabilityhttp://bioserver1.physics.iisc.ernet.in/fair/

Abstract:Distant repeats in protein sequence play an important role in various aspects of protein analysis. A keen analysis of the distant repeats would enable to establish a firm relation of the repeats with respect to their function and three-dimensional structure during the evolutionary process. Further, it enlightens the diversity of duplication during the evolution. To this end, an algorithm has been developed to find all distant repeats in a protein sequence. The scores from Point Accepted Mutation (PAM) matrix has been deployed for the identification of amino acid substitutions while detecting the distant repeats. Due to the biological importance of distant repeats, the proposed algorithm will be of importance to structural biologists, molecular biologists, biochemists and researchers involved in phylogenetic and evolutionary studies.

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Introduction. COVID-19 has become a global impediment by bringing everything to a halt starting from January 2020. India underwent the lockdown starting from 22nd March 2020 with the sudden spike in the number of COVID-19 patients in major cities and states. This study focused on how metabolites play a crucial role in SARSCoV-2 prognosis.Materials and methods. Metabolome profiling of 106 plasma samples and 24 swab samples from symptomatic patients in the Indian population of the Mumbai region was done. COVID-19 positive samples were further segregated under the non-severe COVID-19 and severe COVID-19 patient cohort for both plasma and swab.Results. After analyzing the raw files, total 7,949 and 12,871 metabolites in plasma and swab were found. 11 and 35 significantly altered metabolites were found in COVID-19 positive compared to COVID-19 negative plasma and swab samples, respectively. Also, 9 and 23 significantly altered metabolites were found in severe COVID-19 positive to non-severe COVID-19 positive plasma and swab samples, respectively. The majorly affected pathways in COVID-19 patients were found to be the amino acid metabolism pathway, sphingosine metabolism pathway, and bile salt metabolism pathway.Conclusion. This study facilitates identification of potential metabolite-based biomarker candidates for rapid diagnosis and prognosis for clinical applications.