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      Endolysosomal TRPML1 channel regulates cancer cell migration by altering intracellular trafficking of E-cadherin and β 1-integrin

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          Abstract

          Metastasis still accounts for 90% of all cancer-related death cases. An increase of cellular mobility and invasive traits of cancer cells mark two crucial prerequisites of metastasis. Recent studies highlight the involvement of the endolysosomal cation channel TRPML1 in cell migration. Our results identified a widely antimigratory effect upon loss of TRPML1 function in a panel of cell lines in vitro and reduced dissemination in vivo. As mode-of-action, we established TRPML1 as a crucial regulator of cytosolic calcium levels, actin polymerization, and intracellular trafficking of two promigratory proteins: E-cadherin and β 1-integrin. Interestingly, KO of TRPML1 differentially interferes with the recycling process of E-cadherin and β 1-integrin in a cell line–dependant manner, while resulting in the same phenotype of decreased migratory and adhesive capacities in vitro. Additionally, we observed a coherence between reduction of E-cadherin levels at membrane site and phosphorylation of NF-κB in a β-catenin/p38-mediated manner. As a result, an E-cadherin/NF-κB feedback loop is generated, regulating E-cadherin expression on a transcriptional level. Consequently, our findings highlight the role of TRPML1 as a regulator in migratory processes and suggest the ion channel as a suitable target for the inhibition of migration and invasion.

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          Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries

          This article provides an update on the global cancer burden using the GLOBOCAN 2020 estimates of cancer incidence and mortality produced by the International Agency for Research on Cancer. Worldwide, an estimated 19.3 million new cancer cases (18.1 million excluding nonmelanoma skin cancer) and almost 10.0 million cancer deaths (9.9 million excluding nonmelanoma skin cancer) occurred in 2020. Female breast cancer has surpassed lung cancer as the most commonly diagnosed cancer, with an estimated 2.3 million new cases (11.7%), followed by lung (11.4%), colorectal (10.0 %), prostate (7.3%), and stomach (5.6%) cancers. Lung cancer remained the leading cause of cancer death, with an estimated 1.8 million deaths (18%), followed by colorectal (9.4%), liver (8.3%), stomach (7.7%), and female breast (6.9%) cancers. Overall incidence was from 2-fold to 3-fold higher in transitioned versus transitioning countries for both sexes, whereas mortality varied <2-fold for men and little for women. Death rates for female breast and cervical cancers, however, were considerably higher in transitioning versus transitioned countries (15.0 vs 12.8 per 100,000 and 12.4 vs 5.2 per 100,000, respectively). The global cancer burden is expected to be 28.4 million cases in 2040, a 47% rise from 2020, with a larger increase in transitioning (64% to 95%) versus transitioned (32% to 56%) countries due to demographic changes, although this may be further exacerbated by increasing risk factors associated with globalization and a growing economy. Efforts to build a sustainable infrastructure for the dissemination of cancer prevention measures and provision of cancer care in transitioning countries is critical for global cancer control.
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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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              The Perseus computational platform for comprehensive analysis of (prote)omics data.

              A main bottleneck in proteomics is the downstream biological analysis of highly multivariate quantitative protein abundance data generated using mass-spectrometry-based analysis. We developed the Perseus software platform (http://www.perseus-framework.org) to support biological and biomedical researchers in interpreting protein quantification, interaction and post-translational modification data. Perseus contains a comprehensive portfolio of statistical tools for high-dimensional omics data analysis covering normalization, pattern recognition, time-series analysis, cross-omics comparisons and multiple-hypothesis testing. A machine learning module supports the classification and validation of patient groups for diagnosis and prognosis, and it also detects predictive protein signatures. Central to Perseus is a user-friendly, interactive workflow environment that provides complete documentation of computational methods used in a publication. All activities in Perseus are realized as plugins, and users can extend the software by programming their own, which can be shared through a plugin store. We anticipate that Perseus's arsenal of algorithms and its intuitive usability will empower interdisciplinary analysis of complex large data sets.
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                Author and article information

                Contributors
                Journal
                J Biol Chem
                J Biol Chem
                The Journal of Biological Chemistry
                American Society for Biochemistry and Molecular Biology
                0021-9258
                1083-351X
                21 December 2023
                January 2024
                21 December 2023
                : 300
                : 1
                : 105581
                Affiliations
                [1 ]Department of Pharmacy, Pharmaceutical Biology, Ludwig-Maximilians-University Munich, Munich, Germany
                [2 ]Department of Pharmacy, Ludwig-Maximilians-University Munich, Munich, Germany
                [3 ]Gene Center, Laboratory for Functional Genome Analysis, Ludwig Maximilians-University Munich, Munich, Germany
                [4 ]Walther-Straub-Institute of Pharmacology and Toxicology, Ludwig-Maximilians-University Munich, Munich, Germany
                Author notes
                []For correspondence: Karin Bartel karin.bartel@ 123456cup.uni-muenchen.de
                [‡]

                These authors contributed equally to this work.

                Article
                S0021-9258(23)02609-1 105581
                10.1016/j.jbc.2023.105581
                10825694
                38141765
                ffcbe285-dbf7-4bc5-989c-f8170786e0ed
                © 2023 The Authors

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

                History
                : 26 May 2023
                : 23 November 2023
                Categories
                Research Article

                Biochemistry
                cancer biology,migration,adhesion,lysosome,ion channel
                Biochemistry
                cancer biology, migration, adhesion, lysosome, ion channel

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