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      ACSL4-mediated lipid rafts prevent membrane rupture and inhibit immunogenic cell death in melanoma

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          Abstract

          Chemotherapy including platinum-based drugs are a possible strategy to enhance the immune response in advanced melanoma patients who are resistant to immune checkpoint blockade (ICB) therapy. However, the immune-boosting effects of these drugs are a subject of controversy, and their impact on the tumor microenvironment are poorly understood. In this study, we discovered that lipid peroxidation (LPO) promotes the formation of lipid rafts in the membrane, which mediated by Acyl-CoA Synthetase Long Chain Family Member 4 (ACSL4) impairs the sensitivity of melanoma cells to platinum-based drugs. This reduction primarily occurs through the inhibition of immunogenic ferroptosis and pyroptosis by reducing cell membrane pore formation. By disrupting ACSL4-mediaged lipid rafts via the removal of membrane cholesterol, we promoted immunogenic cell death, transformed the immunosuppressive environment, and improved the antitumor effectiveness of platinum-based drugs and immune response. This disruption also helped reverse the decrease in CD8 + T cells while maintaining their ability to secrete cytokines. Our results reveal that ACSL4-dependent LPO is a key regulator of lipid rafts formation and antitumor immunity, and that disrupting lipid rafts has the potential to enhance platinum-based drug-induced immunogenic ferroptosis and pyroptosis in melanoma. This novel strategy may augment the antitumor immunity of platinum-based therapy and further complement ICB therapy.

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          Most cited references65

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          HISAT: a fast spliced aligner with low memory requirements.

          HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.
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            StringTie enables improved reconstruction of a transcriptome from RNA-seq reads.

            Methods used to sequence the transcriptome often produce more than 200 million short sequences. We introduce StringTie, a computational method that applies a network flow algorithm originally developed in optimization theory, together with optional de novo assembly, to assemble these complex data sets into transcripts. When used to analyze both simulated and real data sets, StringTie produces more complete and accurate reconstructions of genes and better estimates of expression levels, compared with other leading transcript assembly programs including Cufflinks, IsoLasso, Scripture and Traph. For example, on 90 million reads from human blood, StringTie correctly assembled 10,990 transcripts, whereas the next best assembly was of 7,187 transcripts by Cufflinks, which is a 53% increase in transcripts assembled. On a simulated data set, StringTie correctly assembled 7,559 transcripts, which is 20% more than the 6,310 assembled by Cufflinks. As well as producing a more complete transcriptome assembly, StringTie runs faster on all data sets tested to date compared with other assembly software, including Cufflinks.
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              Is Open Access

              TIMER2.0 for analysis of tumor-infiltrating immune cells

              Abstract Tumor progression and the efficacy of immunotherapy are strongly influenced by the composition and abundance of immune cells in the tumor microenvironment. Due to the limitations of direct measurement methods, computational algorithms are often used to infer immune cell composition from bulk tumor transcriptome profiles. These estimated tumor immune infiltrate populations have been associated with genomic and transcriptomic changes in the tumors, providing insight into tumor–immune interactions. However, such investigations on large-scale public data remain challenging. To lower the barriers for the analysis of complex tumor–immune interactions, we significantly improved our previous web platform TIMER. Instead of just using one algorithm, TIMER2.0 (http://timer.cistrome.org/) provides more robust estimation of immune infiltration levels for The Cancer Genome Atlas (TCGA) or user-provided tumor profiles using six state-of-the-art algorithms. TIMER2.0 provides four modules for investigating the associations between immune infiltrates and genetic or clinical features, and four modules for exploring cancer-related associations in the TCGA cohorts. Each module can generate a functional heatmap table, enabling the user to easily identify significant associations in multiple cancer types simultaneously. Overall, the TIMER2.0 web server provides comprehensive analysis and visualization functions of tumor infiltrating immune cells.

                Author and article information

                Contributors
                guoquanliu@bjmu.edu.cn
                Journal
                Cell Death Dis
                Cell Death Dis
                Cell Death & Disease
                Nature Publishing Group UK (London )
                2041-4889
                29 September 2024
                29 September 2024
                September 2024
                : 15
                : 9
                : 695
                Affiliations
                [1 ]GRID grid.11135.37, ISNI 0000 0001 2256 9319, State Key Laboratory of Natural and Biomimetic Drugs, School of Pharmaceutical Sciences, , Peking University, ; Beijing, China
                [2 ]Department of Pharmaceutical Analysis, School of Pharmaceutical Sciences, Peking University, ( https://ror.org/02v51f717) Beijing, China
                [3 ]GRID grid.24696.3f, ISNI 0000 0004 0369 153X, Department of Pathology, Beijing Friendship Hospital, , Capital Medical University, ; Beijing, China
                [4 ]Department of Biomedical Engineering, Institute of Advanced Clinical Medicine, Peking University, ( https://ror.org/02v51f717) Beijing, 100191 China
                Author information
                http://orcid.org/0000-0003-0680-4811
                Article
                7098
                10.1038/s41419-024-07098-3
                11439949
                39343834
                bb83a1fb-667e-41fa-8332-3b504228bec2
                © The Author(s) 2024

                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/.

                History
                : 26 January 2024
                : 17 September 2024
                : 20 September 2024
                Funding
                Funded by: FundRef https://doi.org/10.13039/501100001809, National Natural Science Foundation of China (National Science Foundation of China);
                Award ID: 22177008
                Award ID: 31971173
                Award Recipient :
                Funded by: the Clinical Medicine Plus X - Young Scholars Project, Peking University, the Fundamental Research Funds for the Central Universities
                Categories
                Article
                Custom metadata
                © Associazione Differenziamento e Morte Cellulare ADMC 2024

                Cell biology
                cancer microenvironment,cancer metabolism,chemotherapy
                Cell biology
                cancer microenvironment, cancer metabolism, chemotherapy

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