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      Lysosomal lipid peroxidation regulates tumor immunity

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          Abstract

          Lysosomal inhibition elicited by palmitoyl-protein thioesterase 1 (PPT1) inhibitors such as DC661 can produce cell death, but the mechanism for this is not completely understood. Programmed cell death pathways (autophagy, apoptosis, necroptosis, ferroptosis, and pyroptosis) were not required to achieve the cytotoxic effect of DC661. Inhibition of cathepsins, or iron or calcium chelation, did not rescue DC661-induced cytotoxicity. PPT1 inhibition induced lysosomal lipid peroxidation (LLP), which led to lysosomal membrane permeabilization and cell death that could be reversed by the antioxidant N-acetylcysteine (NAC) but not by other lipid peroxidation antioxidants. The lysosomal cysteine transporter MFSD12 was required for intralysosomal transport of NAC and rescue of LLP. PPT1 inhibition produced cell-intrinsic immunogenicity with surface expression of calreticulin that could only be reversed with NAC. DC661-treated cells primed naive T cells and enhanced T cell–mediated toxicity. Mice vaccinated with DC661-treated cells engendered adaptive immunity and tumor rejection in “immune hot” tumors but not in “immune cold” tumors. These findings demonstrate that LLP drives lysosomal cell death, a unique immunogenic form of cell death, pointing the way to rational combinations of immunotherapy and lysosomal inhibition that can be tested in clinical trials.

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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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            Genome engineering using the CRISPR-Cas9 system.

            Targeted nucleases are powerful tools for mediating genome alteration with high precision. The RNA-guided Cas9 nuclease from the microbial clustered regularly interspaced short palindromic repeats (CRISPR) adaptive immune system can be used to facilitate efficient genome engineering in eukaryotic cells by simply specifying a 20-nt targeting sequence within its guide RNA. Here we describe a set of tools for Cas9-mediated genome editing via nonhomologous end joining (NHEJ) or homology-directed repair (HDR) in mammalian cells, as well as generation of modified cell lines for downstream functional studies. To minimize off-target cleavage, we further describe a double-nicking strategy using the Cas9 nickase mutant with paired guide RNAs. This protocol provides experimentally derived guidelines for the selection of target sites, evaluation of cleavage efficiency and analysis of off-target activity. Beginning with target design, gene modifications can be achieved within as little as 1-2 weeks, and modified clonal cell lines can be derived within 2-3 weeks.
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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 Clin Invest
                J Clin Invest
                J Clin Invest
                The Journal of Clinical Investigation
                American Society for Clinical Investigation
                0021-9738
                1558-8238
                17 April 2023
                17 April 2023
                17 April 2023
                : 133
                : 8
                : e164596
                Affiliations
                [1 ]Abramson Cancer Center and Department of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
                [2 ]The Wistar Institute, Philadelphia, Pennsylvania, USA.
                [3 ]Department of Cancer Biology, Sidney Kimmel Cancer Center, Thomas Jefferson University, Philadelphia, Pennsylvania, USA.
                [4 ]Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
                Author notes
                Address correspondence to: Ravi Kumar Amaravadi, University of Pennsylvania, 852 BRB 2/3, 421 Curie Blvd., Philadelphia, Pennsylvania 19104, USA. Phone: 215.796.5159; Email: Ravi.Amaravadi@ 123456pennmedicine.upenn.edu . Or to: David W. Speicher, The Wistar Institute, 3601 Spruce Street, Philadelphia, Pennsylvania 19104, USA. Phone: 215.898.3972; Email: speicher@ 123456wistar.org .

                Authorship note: MB, JJL, and AMV contributed equally to this work.

                Author information
                http://orcid.org/0000-0002-4358-6393
                http://orcid.org/0000-0003-0067-0365
                http://orcid.org/0000-0002-3036-6893
                http://orcid.org/0000-0001-7605-9592
                http://orcid.org/0000-0002-6952-5601
                http://orcid.org/0000-0002-9235-6236
                http://orcid.org/0000-0002-0953-6819
                http://orcid.org/0000-0002-2734-3244
                http://orcid.org/0000-0002-8309-0624
                http://orcid.org/0000-0001-8264-5491
                http://orcid.org/0000-0001-9964-580X
                Article
                164596
                10.1172/JCI164596
                10104903
                36795483
                693772f9-18ae-488f-ba1b-746feb01161e
                © 2023 Bhardwaj et al.

                This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 19 August 2022
                : 14 February 2023
                Funding
                Funded by: HHS/NIH/NCI
                Award ID: P30 CA016520-45
                Award ID: PO1 CA114046
                Funded by: HHS/NIH/NCI
                Award ID: RO1 CA266404
                Award ID: P50 CA174523
                Funded by: HHS/NIH/NCI
                Award ID: U5CA4224070,PO1CA114046,P50CA261608,RO1CA238237
                Funded by: Cancer Support Grants
                Award ID: CA010815
                Funded by: NIH
                Award ID: S10 OD023586
                Funded by: HHS/NIH/NCI
                Award ID: R01CA256945,P01 CA114046
                Funded by: NIH
                Award ID: P30DK050306
                Grants to Ravi Amaravadi
                Grants to Ravi Amaravadi and David Speicher.
                The Wistar Proteomics and Metabolomics Core Facility
                Grants to Andrew E. Aplin
                Grant to The Center for Molecular Studies in Digestive and Liver Diseases, Host-Microbial Analytic and Repository Core (H-MARC), University of Pennsylvania.
                Categories
                Research Article

                oncology,cancer,cellular immune response,lysosomes
                oncology, cancer, cellular immune response, lysosomes

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