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      Valosin containing protein (VCP): initiator, modifier, and potential drug target for neurodegenerative diseases

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          Abstract

          The AAA + ATPase valosin containing protein (VCP) is essential for cell and organ homeostasis, especially in cells of the nervous system. As part of a large network, VCP collaborates with many cofactors to ensure proteostasis under normal, stress, and disease conditions. A large number of mutations have revealed the importance of VCP for human health. In particular, VCP facilitates the dismantling of protein aggregates and the removal of dysfunctional organelles. These are critical events to prevent malfunction of the brain and other parts of the nervous system. In line with this idea, VCP mutants are linked to the onset and progression of neurodegeneration and other diseases. The intricate molecular mechanisms that connect VCP mutations to distinct brain pathologies continue to be uncovered. Emerging evidence supports the model that VCP controls cellular functions on multiple levels and in a cell type specific fashion. Accordingly, VCP mutants derail cellular homeostasis through several mechanisms that can instigate disease. Our review focuses on the association between VCP malfunction and neurodegeneration. We discuss the latest insights in the field, emphasize open questions, and speculate on the potential of VCP as a drug target for some of the most devastating forms of neurodegeneration.

          Supplementary Information

          The online version contains supplementary material available at 10.1186/s13024-023-00639-y.

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          Most cited references380

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          The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

          Abstract Cellular life depends on a complex web of functional associations between biomolecules. Among these associations, protein–protein interactions are particularly important due to their versatility, specificity and adaptability. The STRING database aims to integrate all known and predicted associations between proteins, including both physical interactions as well as functional associations. To achieve this, STRING collects and scores evidence from a number of sources: (i) automated text mining of the scientific literature, (ii) databases of interaction experiments and annotated complexes/pathways, (iii) computational interaction predictions from co-expression and from conserved genomic context and (iv) systematic transfers of interaction evidence from one organism to another. STRING aims for wide coverage; the upcoming version 11.5 of the resource will contain more than 14 000 organisms. In this update paper, we describe changes to the text-mining system, a new scoring-mode for physical interactions, as well as extensive user interface features for customizing, extending and sharing protein networks. In addition, we describe how to query STRING with genome-wide, experimental data, including the automated detection of enriched functionalities and potential biases in the user's query data. The STRING resource is available online, at https://string-db.org/.
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            The GeneCards Suite: From Gene Data Mining to Disease Genome Sequence Analyses.

            GeneCards, the human gene compendium, enables researchers to effectively navigate and inter-relate the wide universe of human genes, diseases, variants, proteins, cells, and biological pathways. Our recently launched Version 4 has a revamped infrastructure facilitating faster data updates, better-targeted data queries, and friendlier user experience. It also provides a stronger foundation for the GeneCards suite of companion databases and analysis tools. Improved data unification includes gene-disease links via MalaCards and merged biological pathways via PathCards, as well as drug information and proteome expression. VarElect, another suite member, is a phenotype prioritizer for next-generation sequencing, leveraging the GeneCards and MalaCards knowledgebase. It automatically infers direct and indirect scored associations between hundreds or even thousands of variant-containing genes and disease phenotype terms. VarElect's capabilities, either independently or within TGex, our comprehensive variant analysis pipeline, help prepare for the challenge of clinical projects that involve thousands of exome/genome NGS analyses. © 2016 by John Wiley & Sons, Inc.
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              Molecular chaperones in protein folding and proteostasis.

              Most proteins must fold into defined three-dimensional structures to gain functional activity. But in the cellular environment, newly synthesized proteins are at great risk of aberrant folding and aggregation, potentially forming toxic species. To avoid these dangers, cells invest in a complex network of molecular chaperones, which use ingenious mechanisms to prevent aggregation and promote efficient folding. Because protein molecules are highly dynamic, constant chaperone surveillance is required to ensure protein homeostasis (proteostasis). Recent advances suggest that an age-related decline in proteostasis capacity allows the manifestation of various protein-aggregation diseases, including Alzheimer's disease and Parkinson's disease. Interventions in these and numerous other pathological states may spring from a detailed understanding of the pathways underlying proteome maintenance.
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                Author and article information

                Contributors
                siwei.chu@mail.mcgill.ca
                xinyi.xie@mail.mcgill.ca
                carlapayan5@gmail.com
                ursula.stochaj@mcgill.ca
                Journal
                Mol Neurodegener
                Mol Neurodegener
                Molecular Neurodegeneration
                BioMed Central (London )
                1750-1326
                7 August 2023
                7 August 2023
                2023
                : 18
                : 52
                Affiliations
                [1 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, Department of Physiology, , McGill University, ; Montreal, HG3 1Y6 Canada
                [2 ]GRID grid.14709.3b, ISNI 0000 0004 1936 8649, Quantitative Life Sciences Program, , McGill University, ; Montreal, Canada
                Author information
                http://orcid.org/0000-0003-4040-8695
                Article
                639
                10.1186/s13024-023-00639-y
                10405438
                37545006
                a484b985-a955-4997-9de3-a734f5a69f01
                © Editorial Group and BioMed Central Ltd., part of Springer Nature 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 20 February 2023
                : 27 June 2023
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000038, Natural Sciences and Engineering Research Council of Canada;
                Funded by: FundRef http://dx.doi.org/10.13039/501100003150, Fonds Québécois de la Recherche sur la Nature et les Technologies;
                Funded by: FundRef http://dx.doi.org/10.13039/501100004489, Mitacs;
                Categories
                Review
                Custom metadata
                © Editorial Group and BioMed Central Ltd., part of Springer Nature 2023

                Neurosciences
                proteostasis,neurodegeneration,chaperone networks,stress granules,inclusion bodies,valosin containing protein

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