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      Mitochondrial metabolism promotes adaptation to proteotoxic stress

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          Abstract

          The mechanisms by which cells adapt to proteotoxic stress are largely unknown, but key to understanding how tumor cells, particularly in vivo, are largely resistant to proteasome inhibitors. Analysis of cancer cell lines, mouse xenografts and patient-derived tumor samples all showed an association between mitochondrial metabolism and proteasome inhibitor sensitivity. When cells were forced to use oxidative phosphorylation rather than glycolysis, they became proteasome inhibitor-resistant. This mitochondrial state, however, creates a unique vulnerability: sensitivity to the small-molecule compound elesclomol. Genome-wide CRISPR/Cas9 screening showed that a single gene, encoding the mitochondrial reductase FDX1, could rescue elesclomol-induced cell death. Enzymatic function and NMR-based analyses further showed that FDX1 is the direct target of elesclomol, which promotes a unique form of copper-dependent cell death. These studies elucidate a fundamental mechanism by which cells adapt to proteotoxic stress and suggests strategies to mitigate proteasome inhibitor-resistance.

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          Most cited references61

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          STAR: ultrafast universal RNA-seq aligner.

          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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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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              Cytoscape: a software environment for integrated models of biomolecular interaction networks.

              Cytoscape is an open source software project for integrating biomolecular interaction networks with high-throughput expression data and other molecular states into a unified conceptual framework. Although applicable to any system of molecular components and interactions, Cytoscape is most powerful when used in conjunction with large databases of protein-protein, protein-DNA, and genetic interactions that are increasingly available for humans and model organisms. Cytoscape's software Core provides basic functionality to layout and query the network; to visually integrate the network with expression profiles, phenotypes, and other molecular states; and to link the network to databases of functional annotations. The Core is extensible through a straightforward plug-in architecture, allowing rapid development of additional computational analyses and features. Several case studies of Cytoscape plug-ins are surveyed, including a search for interaction pathways correlating with changes in gene expression, a study of protein complexes involved in cellular recovery to DNA damage, inference of a combined physical/functional interaction network for Halobacterium, and an interface to detailed stochastic/kinetic gene regulatory models.
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                Author and article information

                Journal
                101231976
                32624
                Nat Chem Biol
                Nat Chem Biol
                Nature chemical biology
                1552-4450
                1552-4469
                29 May 2019
                27 May 2019
                July 2019
                07 June 2021
                : 15
                : 7
                : 681-689
                Affiliations
                [1 ]Broad Institute of Harvard and MIT, Cambridge, MA 02142
                [2 ]Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA 02215
                [3 ]Biochemistry Department, University of Wisconsin-Madison, Madison, WI 53706
                [4 ]Whitehead Institute for Biomedical Research, Cambridge, MA 02142
                [5 ]OnTarget Pharmaceutical Consulting LLC, Lexington, MA 02421
                [6 ]Department of Pathology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115
                [7 ]Ludwig Center at Harvard, Harvard Medical School, Boston, MA 02132
                [8 ]Deceased
                [9 ]Howard Hughes Medical Institute, Chevy Chase, MD 20815
                [10 ]Harvard Medical School, Boston, MA 02115
                [11 ]Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
                Author notes

                Author Contributions

                Conceptualization, P.T.; Investigation P.T., A.D., K.C., H.R.K, M.R; Formal analysis, P.T., Pr.T., G.K., J.R., M.K., H.R.K.; Resources, Z.B. W.Y., A.T. N.K. ; Writing original-draft P.T. Writing review & editing, P.T., H.R.K, S.S., L.W., K.C., J.L.M., T.R.G; Funding acquisition, S.L., J.L.M, I.M.G., T.R.G. ; Supervision, S.L, L.W., J.L.M, I.M.G, T.R.G ;

                Article
                NIHMS1527246
                10.1038/s41589-019-0291-9
                8183600
                31133756
                d3a5781e-7c05-477c-8f47-25761b50174a

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                Biochemistry
                Biochemistry

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