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      CellPalmSeq: A curated RNAseq database of palmitoylating and de-palmitoylating enzyme expression in human cell types and laboratory cell lines

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          Abstract

          The reversible lipid modification protein S-palmitoylation can dynamically modify the localization, diffusion, function, conformation and physical interactions of substrate proteins. Dysregulated S-palmitoylation is associated with a multitude of human diseases including brain and metabolic disorders, viral infection and cancer. However, the diverse expression patterns of the genes that regulate palmitoylation in the broad range of human cell types are currently unexplored, and their expression in commonly used cell lines that are the workhorse of basic and preclinical research are often overlooked when studying palmitoylation dependent processes. We therefore created CellPalmSeq ( https://cellpalmseq.med.ubc.ca), a curated RNAseq database and interactive webtool for visualization of the expression patterns of the genes that regulate palmitoylation across human single cell types, bulk tissue, cancer cell lines and commonly used laboratory non-human cell lines. This resource will allow exploration of these expression patterns, revealing important insights into cellular physiology and disease, and will aid with cell line selection and the interpretation of results when studying important cellular processes that depend on protein S-palmitoylation.

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          Most cited references54

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          Near-optimal probabilistic RNA-seq quantification.

          We present kallisto, an RNA-seq quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy. Kallisto pseudoaligns reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of individual bases. We use kallisto to analyze 30 million unaligned paired-end RNA-seq reads in <10 min on a standard laptop computer. This removes a major computational bottleneck in RNA-seq analysis.
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            The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity

            The systematic translation of cancer genomic data into knowledge of tumor biology and therapeutic avenues remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacologic annotation is available 1 . Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number, and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacologic profiles for 24 anticancer drugs across 479 of the lines, this collection allowed identification of genetic, lineage, and gene expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Altogether, our results suggest that large, annotated cell line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of “personalized” therapeutic regimens 2 .
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              Next-generation characterization of the Cancer Cell Line Encyclopedia

              Large panels of comprehensively characterized human cancer models, including the Cancer Cell Line Encyclopedia (CCLE), have provided a rigorous backbone upon which to study genetic variants, candidate targets, small molecule and biological therapeutics and to identify new marker-driven cancer dependencies. To improve our understanding of the molecular features that contribute to cancer phenotypes including drug responses, here we have expanded the characterizations of cancer cell lines to include genetic, RNA splicing, DNA methylation, histone H3 modification, microRNA expression and reverse-phase protein array data for 1,072 cell lines from various lineages and ethnicities. Integrating these data with functional characterizations such as drug-sensitivity data, short hairpin RNA knockdown and CRISPR–Cas9 knockout data reveals potential targets for cancer drugs and associated biomarkers. Together, this dataset and an accompanying public data portal provide a resource to accelerate cancer research using model cancer cell lines.
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                Author and article information

                Contributors
                Journal
                Front Physiol
                Front Physiol
                Front. Physiol.
                Frontiers in Physiology
                Frontiers Media S.A.
                1664-042X
                24 January 2023
                2023
                : 14
                : 1110550
                Affiliations
                [1] 1 Bamji Lab , Department of Cellular and Physiological Sciences , Life Sciences Institute and Djavad Mowafaghian Centre for Brain Health , Vancouver, BC, Canada
                [2] 2 Life Sciences Institute Bioinformatics Facility , University of British Columbia , Vancouver, BC, Canada
                Author notes

                Edited by: Rebeca M. Mejias Estevez, Sevilla University, Spain

                Reviewed by: William Fuller, University of Glasgow, United Kingdom

                Shaun S. Sanders, University of Guelph, Canada

                *Correspondence: Shernaz X. Bamji, shernaz.bamji@ 123456ubc.ca

                This article was submitted to Lipid and Fatty Acid Research, a section of the journal Frontiers in Physiology

                Article
                1110550
                10.3389/fphys.2023.1110550
                9904442
                36760531
                1b2e3e89-a674-4a32-bf76-0356005eee24
                Copyright © 2023 Wild, Hogg, Flibotte, Kochhar, Hollman, Haas and Bamji.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 28 November 2022
                : 09 January 2023
                Funding
                Funded by: Canadian Institutes of Health Research , doi 10.13039/501100000024;
                AW, RH, SK, and SB were funded by Canadian Health Services Research Foundation (F18-00650 CIHR Foundation Grant). PH, KH were funded by Canadian Institutes of Health Research (FDN-148468 Foundation Grant). This work was supported by resources made available through the Dynamic Brain Circuits cluster and the NeuroImaging and NeuroComputation Centre at the UBC Djavad Mowafaghian Centre for Brain Health (RRID SCR_019086).
                Categories
                Physiology
                Original Research

                Anatomy & Physiology
                palmitoylation,zdhhc,depalmitoylating enzyme,cancer,cell line,human,rnaseq,expression
                Anatomy & Physiology
                palmitoylation, zdhhc, depalmitoylating enzyme, cancer, cell line, human, rnaseq, expression

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