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      Plasma Proteome Profiling of Coronary Artery Disease Patients: Downregulation of Transthyretin—An Important Event

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          Abstract

          Coronary artery disease (CAD) is a prevalent chronic inflammatory cardiac disorder. An early diagnosis is likely to help in the prevention and proper management of this disease. As the study of proteomics provides the potential markers for detection of a disease, in the present investigation, attempt has been made to identify disease-associated differential proteins involved in CAD pathogenesis. For this study, a total of 200 selected CAD patients were considered, who were recruited for percutaneous coronary intervention (PCI) treatment. The proteomic analysis was performed using two-dimensional gel electrophoresis (2-DE) and MALDI-TOF MS/MS. Samples were also subjected to Western blot analysis, enzyme-linked immunosorbent assay (ELISA), peripheral blood mononuclear cells isolation immunofluorescence (IF) analysis, analytical screening by fluorescence-activated cell sorting (FACS), and in silico analysis. The representative data were shown as mean ± SD of at least three experiments. A total of 19 proteins were identified. Among them, the most abundant five proteins (serotransferrin, talin-1, alpha-2HS glycoprotein, transthyretin (TTR), fibrinogen- α chain) were found to have altered level in CAD. Serotransferrin, talin-1, alpha-2HS glycoprotein, and transthyretin (TTR) were found to have lower level, whereas fibrinogen- α chain was found to have higher level in CAD plasma compared to healthy, confirmed by Western blot analysis. TTR, an important acute phase transport protein, was validated low level in 200 CAD patients who confirmed to undergo PCI treatment. Further, in silico and in vitro studies of TTR indicated a downexpression of CAD in plasma as compared to the plasma of healthy individuals. Lower level of plasma TTR was determined to be an important risk marker in the atherosclerotic-approved CAD patients. We suggest that the TTR lower level predicts disease severity and hence may serve as an important marker tool for CAD screening. However, further large-scale studies are required to determine the clinical significance of TTR.

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          Heart Disease and Stroke Statistics—2017 Update: A Report From the American Heart Association

          Circulation, 135(10)
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            STRING v9.1: protein-protein interaction networks, with increased coverage and integration

            Complete knowledge of all direct and indirect interactions between proteins in a given cell would represent an important milestone towards a comprehensive description of cellular mechanisms and functions. Although this goal is still elusive, considerable progress has been made—particularly for certain model organisms and functional systems. Currently, protein interactions and associations are annotated at various levels of detail in online resources, ranging from raw data repositories to highly formalized pathway databases. For many applications, a global view of all the available interaction data is desirable, including lower-quality data and/or computational predictions. The STRING database (http://string-db.org/) aims to provide such a global perspective for as many organisms as feasible. Known and predicted associations are scored and integrated, resulting in comprehensive protein networks covering >1100 organisms. Here, we describe the update to version 9.1 of STRING, introducing several improvements: (i) we extend the automated mining of scientific texts for interaction information, to now also include full-text articles; (ii) we entirely re-designed the algorithm for transferring interactions from one model organism to the other; and (iii) we provide users with statistical information on any functional enrichment observed in their networks.
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              WoLF PSORT: protein localization predictor

              WoLF PSORT is an extension of the PSORT II program for protein subcellular location prediction. WoLF PSORT converts protein amino acid sequences into numerical localization features; based on sorting signals, amino acid composition and functional motifs such as DNA-binding motifs. After conversion, a simple k-nearest neighbor classifier is used for prediction. Using html, the evidence for each prediction is shown in two ways: (i) a list of proteins of known localization with the most similar localization features to the query, and (ii) tables with detailed information about individual localization features. For convenience, sequence alignments of the query to similar proteins and links to UniProt and Gene Ontology are provided. Taken together, this information allows a user to understand the evidence (or lack thereof) behind the predictions made for particular proteins. WoLF PSORT is available at wolfpsort.org
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                Author and article information

                Contributors
                Journal
                Mediators Inflamm
                Mediators Inflamm
                MI
                Mediators of Inflammation
                Hindawi
                0962-9351
                1466-1861
                2020
                10 November 2020
                : 2020
                : 3429541
                Affiliations
                1Council of Industrial Research (CSIR)-Institute of Genomics and Integrative Biology, Mall Road, Delhi University Campus, 110007, Delhi, India
                2Delhi Institute of Pharmaceutical Sciences and Research (DIPSAR), University of Delhi, Pushpvihar, New Delhi 110017, India
                3All India Institute of Medical Sciences, Ansari Nagar, New Delhi 110029, India
                4School of Life Sciences, Takshashila Campus, Devi Ahilya University, 452017, Indore, India
                Author notes

                Academic Editor: Carla Pagliari

                Author information
                https://orcid.org/0000-0002-2017-1670
                Article
                10.1155/2020/3429541
                7707994
                0b69c238-851e-408b-a8e7-82802601a352
                Copyright © 2020 Monu et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 2 July 2020
                : 24 October 2020
                Funding
                Funded by: Council of Scientific and Industrial Research, India
                Award ID: BSC0122
                Categories
                Research Article

                Immunology
                Immunology

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