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      Genome homeostasis defects drive enlarged cells into senescence

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          Summary

          Cellular senescence refers to an irreversible state of cell-cycle arrest and plays important roles in aging and cancer biology. Because senescence is associated with increased cell size, we used reversible cell-cycle arrests combined with growth rate modulation to study how excessive growth affects proliferation. We find that enlarged cells upregulate p21, which limits cell-cycle progression. Cells that re-enter the cell cycle encounter replication stress that is well tolerated in physiologically sized cells but causes severe DNA damage in enlarged cells, ultimately resulting in mitotic failure and permanent cell-cycle withdrawal. We demonstrate that enlarged cells fail to recruit 53BP1 and other non-homologous end joining (NHEJ) machinery to DNA damage sites and fail to robustly initiate DNA damage-dependent p53 signaling, rendering them highly sensitive to genotoxic stress. We propose that an impaired DNA damage response primes enlarged cells for persistent replication-acquired damage, ultimately leading to cell division failure and permanent cell-cycle exit.

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          Highlights

          • Excess G 1 cell size triggers long-term cell-cycle exit via the p53-p21 pathway

          • 53PB1 and other NHEJ machinery are not recruited to DNA breaks in enlarged cells

          • This correlates with inefficient DNA damage repair and blunted p53 signaling

          • Enlarged cells accumulate replication-acquired damage and undergo mitotic errors

          Abstract

          Manohar et al. studied how increased cell size—a hallmark of senescence—impairs long-term proliferation. Excess cell size activates p53-p21 signaling and impairs 53BP1 recruitment to DNA double-stranded breaks. This defect correlates with an impaired DNA damage response, DNA damage sensitivity, and genomic instability, resulting in permanent cell-cycle exit.

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          Most cited references86

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification.

            Efficient analysis of very large amounts of raw data for peptide identification and protein quantification is a principal challenge in mass spectrometry (MS)-based proteomics. Here we describe MaxQuant, an integrated suite of algorithms specifically developed for high-resolution, quantitative MS data. Using correlation analysis and graph theory, MaxQuant detects peaks, isotope clusters and stable amino acid isotope-labeled (SILAC) peptide pairs as three-dimensional objects in m/z, elution time and signal intensity space. By integrating multiple mass measurements and correcting for linear and nonlinear mass offsets, we achieve mass accuracy in the p.p.b. range, a sixfold increase over standard techniques. We increase the proportion of identified fragmentation spectra to 73% for SILAC peptide pairs via unambiguous assignment of isotope and missed-cleavage state and individual mass precision. MaxQuant automatically quantifies several hundred thousand peptides per SILAC-proteome experiment and allows statistically robust identification and quantification of >4,000 proteins in mammalian cell lysates.
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              ShinyGO: a graphical gene-set enrichment tool for animals and plants

              Gene lists are routinely produced from various omic studies. Enrichment analysis can link these gene lists with underlying molecular pathways and functional categories such as gene ontology (GO) and other databases. To complement existing tools, we developed ShinyGO based on a large annotation database derived from Ensembl and STRING-db for 59 plant, 256 animal, 115 archeal and 1678 bacterial species. ShinyGO’s novel features include graphical visualization of enrichment results and gene characteristics, and application program interface access to KEGG and STRING for the retrieval of pathway diagrams and protein–protein interaction networks. ShinyGO is an intuitive, graphical web application that can help researchers gain actionable insights from gene-sets. http://ge-lab.org/go/. Supplementary data are available at Bioinformatics online.
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                Author and article information

                Contributors
                Journal
                Mol Cell
                Mol Cell
                Molecular Cell
                Cell Press
                1097-2765
                1097-4164
                16 November 2023
                16 November 2023
                : 83
                : 22
                : 4032-4046.e6
                Affiliations
                [1 ]Institute for Biochemistry, Department of Biology, ETH Zürich 8093, Zürich, Zürich, Switzerland
                [2 ]Laboratory for Molecular Cell Biology, University College London, London WC1E 6BT, UK
                [3 ]UCL Cancer Institute, University College London, London WC1E 6BT, UK
                Author notes
                []Corresponding author gabriel.neurohr@ 123456bc.biol.ethz.ch
                [4]

                Lead contact

                Article
                S1097-2765(23)00855-9
                10.1016/j.molcel.2023.10.018
                10659931
                37977116
                e9334682-9538-4442-b862-2a0db9a5c25d
                © 2023 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 12 September 2022
                : 30 June 2023
                : 16 October 2023
                Categories
                Article

                Molecular biology
                dna damage,cell size,cell growth,cell cycle,senescence
                Molecular biology
                dna damage, cell size, cell growth, cell cycle, senescence

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