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      The reactome pathway knowledgebase 2022

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          Abstract

          The Reactome Knowledgebase ( https://reactome.org), an Elixir core resource, provides manually curated molecular details across a broad range of physiological and pathological biological processes in humans, including both hereditary and acquired disease processes. The processes are annotated as an ordered network of molecular transformations in a single consistent data model. Reactome thus functions both as a digital archive of manually curated human biological processes and as a tool for discovering functional relationships in data such as gene expression profiles or somatic mutation catalogs from tumor cells. Recent curation work has expanded our annotations of normal and disease-associated signaling processes and of the drugs that target them, in particular infections caused by the SARS-CoV-1 and SARS-CoV-2 coronaviruses and the host response to infection. New tools support better simultaneous analysis of high-throughput data from multiple sources and the placement of understudied (‘dark’) proteins from analyzed datasets in the context of Reactome’s manually curated pathways.

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          UniProt: the universal protein knowledgebase in 2021

          (2020)
          Abstract The aim of the UniProt Knowledgebase is to provide users with a comprehensive, high-quality and freely accessible set of protein sequences annotated with functional information. In this article, we describe significant updates that we have made over the last two years to the resource. The number of sequences in UniProtKB has risen to approximately 190 million, despite continued work to reduce sequence redundancy at the proteome level. We have adopted new methods of assessing proteome completeness and quality. We continue to extract detailed annotations from the literature to add to reviewed entries and supplement these in unreviewed entries with annotations provided by automated systems such as the newly implemented Association-Rule-Based Annotator (ARBA). We have developed a credit-based publication submission interface to allow the community to contribute publications and annotations to UniProt entries. We describe how UniProtKB responded to the COVID-19 pandemic through expert curation of relevant entries that were rapidly made available to the research community through a dedicated portal. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.
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            A SARS-CoV-2 Protein Interaction Map Reveals Targets for Drug-Repurposing

            SUMMARY The novel coronavirus SARS-CoV-2, the causative agent of COVID-19 respiratory disease, has infected over 2.3 million people, killed over 160,000, and caused worldwide social and economic disruption 1,2 . There are currently no antiviral drugs with proven clinical efficacy, nor are there vaccines for its prevention, and these efforts are hampered by limited knowledge of the molecular details of SARS-CoV-2 infection. To address this, we cloned, tagged and expressed 26 of the 29 SARS-CoV-2 proteins in human cells and identified the human proteins physically associated with each using affinity-purification mass spectrometry (AP-MS), identifying 332 high-confidence SARS-CoV-2-human protein-protein interactions (PPIs). Among these, we identify 66 druggable human proteins or host factors targeted by 69 compounds (29 FDA-approved drugs, 12 drugs in clinical trials, and 28 preclinical compounds). Screening a subset of these in multiple viral assays identified two sets of pharmacological agents that displayed antiviral activity: inhibitors of mRNA translation and predicted regulators of the Sigma1 and Sigma2 receptors. Further studies of these host factor targeting agents, including their combination with drugs that directly target viral enzymes, could lead to a therapeutic regimen to treat COVID-19.
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              KEGG: integrating viruses and cellular organisms

              Abstract KEGG (https://www.kegg.jp/) is a manually curated resource integrating eighteen databases categorized into systems, genomic, chemical and health information. It also provides KEGG mapping tools, which enable understanding of cellular and organism-level functions from genome sequences and other molecular datasets. KEGG mapping is a predictive method of reconstructing molecular network systems from molecular building blocks based on the concept of functional orthologs. Since the introduction of the KEGG NETWORK database, various diseases have been associated with network variants, which are perturbed molecular networks caused by human gene variants, viruses, other pathogens and environmental factors. The network variation maps are created as aligned sets of related networks showing, for example, how different viruses inhibit or activate specific cellular signaling pathways. The KEGG pathway maps are now integrated with network variation maps in the NETWORK database, as well as with conserved functional units of KEGG modules and reaction modules in the MODULE database. The KO database for functional orthologs continues to be improved and virus KOs are being expanded for better understanding of virus-cell interactions and for enabling prediction of viral perturbations.
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                Author and article information

                Contributors
                Journal
                Nucleic Acids Res
                Nucleic Acids Res
                nar
                Nucleic Acids Research
                Oxford University Press
                0305-1048
                1362-4962
                07 January 2022
                12 November 2021
                12 November 2021
                : 50
                : D1
                : D687-D692
                Affiliations
                Ontario Institute for Cancer Research , Toronto, ON M5G0A3, Canada
                College of Pharmacy and Health Sciences, St. John’s University , Queens, NY11439, USA
                Ontario Institute for Cancer Research , Toronto, ON M5G0A3, Canada
                Ontario Institute for Cancer Research , Toronto, ON M5G0A3, Canada
                Ontario Institute for Cancer Research , Toronto, ON M5G0A3, Canada
                Ontario Institute for Cancer Research , Toronto, ON M5G0A3, Canada
                Ontario Institute for Cancer Research , Toronto, ON M5G0A3, Canada
                Universidade Federal do Paraná , Curitiba, 80060-000, Brazil
                European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
                Department of Dermatology, Medical University of Vienna , 1090 Vienna, Austria
                European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
                NYU Grossman School of Medicine , New York, NY10016, USA
                European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
                National Center for Protein Sciences Beijing, Beijing Institute of Life Omics , Beijing102206, China
                Chongqing Key Laboratory on Big Data for Bio Intelligence, Chongqing University of Posts and Telecommunications , Chongqing 400065, China
                European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
                Open Targets, Wellcome Genome Campus , Hinxton, Cambridgeshire, CB10 1SD, UK
                European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
                European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
                Ontario Institute for Cancer Research , Toronto, ON M5G0A3, Canada
                NYU Grossman School of Medicine , New York, NY10016, USA
                Ontario Institute for Cancer Research , Toronto, ON M5G0A3, Canada
                Oregon Health and Science University , Portland, OR 97239, USA
                Oregon Health and Science University , Portland, OR 97239, USA
                Oregon Health and Science University , Portland, OR 97239, USA
                Oregon Health and Science University , Portland, OR 97239, USA
                European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
                European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
                Centro Internacional de Entrenamiento e Investigaciones Médicas , Cali 18 # 122-135, Colombia
                European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
                Ontario Institute for Cancer Research , Toronto, ON M5G0A3, Canada
                Ontario Institute for Cancer Research , Toronto, ON M5G0A3, Canada
                The Donnelly Centre, University of Toronto , Toronto, ON M5S 3E1, Canada
                Oregon Health and Science University , Portland, OR 97239, USA
                cBio Center at Dana-Farber Cancer Institute , Boston, MA02115, USA
                Ontario Institute for Cancer Research , Toronto, ON M5G0A3, Canada
                Oregon Health and Science University , Portland, OR 97239, USA
                Ontario Institute for Cancer Research , Toronto, ON M5G0A3, Canada
                Department of Molecular Genetics, University of Toronto , Toronto, ON M5S 1A1, Canada
                European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI) , Wellcome Genome Campus, Hinxton, Cambridgeshire, CB10 1SD, UK
                National Center for Protein Sciences Beijing, Beijing Institute of Life Omics , Beijing102206, China
                NYU Grossman School of Medicine , New York, NY10016, USA
                Author notes
                To whom correspondence should be addressed. Tel: +1 212 263 5779; Email: deustp01@ 123456med.nyu.edu
                Correspondence may also be addressed to Henning Hermjakob. Tel: +44 1223 494 671; Email: hhe@ 123456ebi.ac.uk
                Correspondence may also be addressed to Lincoln Stein. Tel: +1 416 673 8514; Email: Lincoln.Stein@ 123456oicr.on.ca
                Correspondence may also be addressed to Guanming Wu. Tel: +1 503 494 4502; Fax: +1 503 346 6815; Email: wug@ 123456ohsu.edu
                Author information
                https://orcid.org/0000-0002-7876-6503
                https://orcid.org/0000-0002-3288-8599
                https://orcid.org/0000-0003-0185-8861
                https://orcid.org/0000-0001-8479-0262
                https://orcid.org/0000-0002-5494-626X
                Article
                gkab1028
                10.1093/nar/gkab1028
                8689983
                34788843
                f0fb8f19-e8e4-48a9-a86d-e1ac81d942ca
                © The Author(s) 2021. Published by Oxford University Press on behalf of Nucleic Acids Research.

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

                History
                : 13 October 2021
                : 11 October 2021
                : 30 September 2021
                Page count
                Pages: 6
                Funding
                Funded by: National Institutes of Health, DOI 10.13039/100000002;
                Award ID: U41HG003751
                Award ID: U54GM114833
                Award ID: U01CA239069
                Funded by: European Bioinformatics Institute, DOI 10.13039/100012116;
                Funded by: Open Targets;
                Funded by: University of Toronto, DOI 10.13039/501100003579;
                Categories
                AcademicSubjects/SCI00010
                Database Issue

                Genetics
                Genetics

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