1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Identification of Autophagy-Related Genes as Targets for Senescence Induction Using a Customizable CRISPR-Based Suicide Switch Screen

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Pro-senescence therapies are increasingly being considered for the treatment of cancer. Identifying additional targets to induce senescence in cancer cells could further enable such therapies. However, screening for targets whose suppression induces senescence on a genome-wide scale is challenging, as senescent cells become growth arrested, and senescence-associated features can take 1 to 2 weeks to develop. For a screen with a whole-genome CRISPR library, this would result in billions of undesirable proliferating cells by the time the senescent features emerge in the growth arrested cells. Here, we present a suicide switch system that allows genome-wide CRISPR screening in growth-arrested subpopulations by eliminating the proliferating cells during the screen through activation of a suicide switch in proliferating cells. Using this system, we identify in a genome-scale CRISPR screen several autophagy-related proteins as targets for senescence induction. We show that inhibiting macroautophagy with a small molecule ULK1 inhibitor can induce senescence in cancer cell lines of different origin. Finally, we show that combining ULK1 inhibition with the senolytic drug ABT-263 leads to apoptosis in a panel of cancer cell lines.

          Implications:

          Our suicide switch approach allows for genome-scale identification of pro-senescence targets, and can be adapted to simplify other screens depending on the nature of the promoter used to drive the switch.

          Related collections

          Most cited references33

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          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.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            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.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Differential expression analysis for sequence count data

              High-throughput sequencing assays such as RNA-Seq, ChIP-Seq or barcode counting provide quantitative readouts in the form of count data. To infer differential signal in such data correctly and with good statistical power, estimation of data variability throughout the dynamic range and a suitable error model are required. We propose a method based on the negative binomial distribution, with variance and mean linked by local regression and present an implementation, DESeq, as an R/Bioconductor package.
                Bookmark

                Author and article information

                Journal
                Mol Cancer Res
                Mol Cancer Res
                Molecular Cancer Research
                American Association for Cancer Research
                1541-7786
                1557-3125
                01 October 2021
                22 June 2021
                : 19
                : 10
                : 1613-1621
                Affiliations
                Division of Molecular Carcinogenesis, Oncode Institute, Netherlands Cancer Institute, Amsterdam, the Netherlands.
                Author notes
                [* ] Corresponding Author: Rene Bernards, Division of Molecular Carcinogenesis, The Netherlands Cancer Institute, Plesmanlaan 121, Amsterdam, 1066 CX, the Netherlands. E-mail: r.bernards@ 123456nki.nl
                Author information
                https://orcid.org/0000-0003-3240-9233
                https://orcid.org/0000-0002-9696-2014
                https://orcid.org/0000-0002-2647-228X
                Article
                MCR-21-0146
                10.1158/1541-7786.MCR-21-0146
                7611779
                34158393
                5d8a15d6-a8d8-4d29-89fd-10af5162e2ea
                ©2021 The Authors; Published by the American Association for Cancer Research

                This open access article is distributed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license.

                History
                : 24 February 2021
                : 07 May 2021
                : 11 June 2021
                Page count
                Pages: 9
                Funding
                Funded by: European Research Council, DOI https://doi.org/10.13039/501100000781;
                Award ID: ERC 787925
                Funded by: Center for Cancer Genomics, DOI ;
                Categories
                Cancer Genes and Networks

                Comments

                Comment on this article