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      GOT1 inhibition promotes pancreatic cancer cell death by ferroptosis

      research-article
      1 , 2 , 3 , 1 , 4 , 1 , 3 , 3 , 1 , 1 , 1 , 5 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 6 , 7 , 7 , 8 , 1 , 1 , 9 , 10 , 9 , 10 , 9 , 10 , 11 , 1 , 12 , 13 , 14 , 7 , 15 , 16 , 1 , 7 , 1 , 5 , 7 , 9 , 10 , 1 , 5 , 7 ,
      Nature Communications
      Nature Publishing Group UK
      Cancer metabolism, Pancreatic cancer

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          Abstract

          Cancer metabolism is rewired to support cell survival in response to intrinsic and environmental stressors. Identification of strategies to target these adaptions is an area of active research. We previously described a cytosolic aspartate aminotransaminase (GOT1)-driven pathway in pancreatic cancer used to maintain redox balance. Here, we sought to identify metabolic dependencies following GOT1 inhibition to exploit this feature of pancreatic cancer and to provide additional insight into regulation of redox metabolism. Using pharmacological methods, we identify cysteine, glutathione, and lipid antioxidant function as metabolic vulnerabilities following GOT1 withdrawal. We demonstrate that targeting any of these pathways triggers ferroptosis, an oxidative, iron-dependent form of cell death, in GOT1 knockdown cells. Mechanistically, we reveal that GOT1 inhibition represses mitochondrial metabolism and promotes a catabolic state. Consequently, we find that this enhances labile iron availability through autophagy, which potentiates the activity of ferroptotic stimuli. Overall, our study identifies a biochemical connection between GOT1, iron regulation, and ferroptosis.

          Abstract

          The aspartate aminotransaminase GOT1 is important for maintaining redox balance. Here, the authors show that inhibition of GOT1 in pancreatic cancer cells leads to cell death via ferroptosis.

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          Most cited references57

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            Trimmomatic: a flexible trimmer for Illumina sequence data

            Motivation: Although many next-generation sequencing (NGS) read preprocessing tools already existed, we could not find any tool or combination of tools that met our requirements in terms of flexibility, correct handling of paired-end data and high performance. We have developed Trimmomatic as a more flexible and efficient preprocessing tool, which could correctly handle paired-end data. Results: The value of NGS read preprocessing is demonstrated for both reference-based and reference-free tasks. Trimmomatic is shown to produce output that is at least competitive with, and in many cases superior to, that produced by other tools, in all scenarios tested. Availability and implementation: Trimmomatic is licensed under GPL V3. It is cross-platform (Java 1.5+ required) and available at http://www.usadellab.org/cms/index.php?page=trimmomatic Contact: usadel@bio1.rwth-aachen.de Supplementary information: Supplementary data are available at Bioinformatics online.
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              Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

              Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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                Author and article information

                Contributors
                clyssiot@med.umich.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                11 August 2021
                11 August 2021
                2021
                : 12
                : 4860
                Affiliations
                [1 ]GRID grid.214458.e, ISNI 0000000086837370, Department of Molecular & Integrative Physiology, , University of Michigan, ; Ann Arbor, MI USA
                [2 ]GRID grid.214458.e, ISNI 0000000086837370, Graduate Program in Chemical Biology, , University of Michigan, ; Ann Arbor, MI USA
                [3 ]GRID grid.214458.e, ISNI 0000000086837370, Graduate Program in Cancer Biology, , University of Michigan, ; Ann Arbor, MI USA
                [4 ]GRID grid.214458.e, ISNI 0000000086837370, Immunology Graduate Program, , University of Michigan, ; Ann Arbor, MI USA
                [5 ]GRID grid.214458.e, ISNI 0000000086837370, Department of Internal Medicine, Division of Gastroenterology and Hepatology, , University of Michigan, ; Ann Arbor, MI USA
                [6 ]GRID grid.214458.e, ISNI 0000000086837370, Department of Biostatistics, School of Public Health, , University of Michigan, ; Ann Arbor, MI USA
                [7 ]GRID grid.214458.e, ISNI 0000000086837370, Rogel Cancer Center, , University of Michigan, ; Ann Arbor, MI USA
                [8 ]GRID grid.267313.2, ISNI 0000 0000 9482 7121, Medical Scientist Training Program, , University of Texas Southwestern Medical Center, ; Dallas, TX USA
                [9 ]GRID grid.239585.0, ISNI 0000 0001 2285 2675, Division of Digestive and Liver Diseases, Department of Medicine, , Columbia University Medical Center, ; New York, NY USA
                [10 ]GRID grid.239585.0, ISNI 0000 0001 2285 2675, Herbert Irving Comprehensive Cancer Center, , Columbia University Medical Center, ; New York, NY USA
                [11 ]GRID grid.239585.0, ISNI 0000 0001 2285 2675, Department of Pathology, , Columbia University Medical Center, ; New York, NY USA
                [12 ]GRID grid.239395.7, ISNI 0000 0000 9011 8547, Division of Signal Transduction and Mass Spectrometry Core, , Beth Israel Deaconess Medical Center, ; Boston, MA USA
                [13 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Medicine, , Harvard Medical School, ; Boston, MA USA
                [14 ]GRID grid.267313.2, ISNI 0000 0000 9482 7121, Children’s Medical Center Research Institute and Department of Pediatrics, , University of Texas Southwestern Medical Center, ; Dallas, TX USA
                [15 ]GRID grid.214458.e, ISNI 0000000086837370, Department of Surgery, , University of Michigan, ; Ann Arbor, MI USA
                [16 ]GRID grid.214458.e, ISNI 0000000086837370, Department of Cell and Developmental Biology, , University of Michigan, ; Ann Arbor, MI USA
                Author information
                http://orcid.org/0000-0001-6224-0550
                http://orcid.org/0000-0003-3201-9534
                http://orcid.org/0000-0002-3376-3114
                http://orcid.org/0000-0001-9786-2245
                http://orcid.org/0000-0002-8556-7481
                http://orcid.org/0000-0003-1641-2045
                http://orcid.org/0000-0003-0135-1032
                http://orcid.org/0000-0003-1776-451X
                http://orcid.org/0000-0001-9361-675X
                http://orcid.org/0000-0002-2487-4816
                http://orcid.org/0000-0001-9309-6141
                Article
                24859
                10.1038/s41467-021-24859-2
                8357841
                34381026
                7e39d023-e753-4d0b-ab3e-64d50a105fc1
                © The Author(s) 2021

                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 license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license 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 license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 April 2021
                : 8 July 2021
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                © The Author(s) 2021

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                cancer metabolism,pancreatic cancer
                Uncategorized
                cancer metabolism, pancreatic cancer

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