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      CRISPR activation screen identifies TGFβ-associated PEG10 as a crucial tumor suppressor in Ewing sarcoma

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          Abstract

          As the second most common pediatric bone and soft tissue tumor, Ewing sarcoma (ES) is an aggressive disease with a pathognomonic chromosomal translocation t(11;22) resulting in expression of EWS-FLI1, an “undruggable” fusion protein acting as transcriptional modulator. EWS-FLI1 rewires the protein expression in cancer cells by activating and repressing a multitude of genes. The role and contribution of most repressed genes remains unknown to date. To address this, we established a CRISPR activation system in clonal SKNMC cell lines and interrogated a custom focused library covering 871 genes repressed by EWS-FLI1. Among the hits several members of the TGFβ pathway were identified, where PEG10 emerged as prime candidate due to its strong antiproliferative effect. Mechanistic investigations revealed that PEG10 overexpression caused cellular dropout via induction of cell death. Furthermore, non-canonical TGFβ pathways such as RAF/MEK/ERK, MKK/JNK, MKK/P38, known to lead to apoptosis or autophagy, were highly activated upon PEG10 overexpression. Our study sheds new light onto the contribution of TGFβ signalling pathway repression to ES tumorigenesis and suggest that its re-activation might constitute a novel therapeutic strategy.

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          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
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            Metascape provides a biologist-oriented resource for the analysis of systems-level datasets

            A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists. In terms of design features, Metascape combines functional enrichment, interactome analysis, gene annotation, and membership search to leverage over 40 independent knowledgebases within one integrated portal. Additionally, it facilitates comparative analyses of datasets across multiple independent and orthogonal experiments. Metascape provides a significantly simplified user experience through a one-click Express Analysis interface to generate interpretable outputs. Taken together, Metascape is an effective and efficient tool for experimental biologists to comprehensively analyze and interpret OMICs-based studies in the big data era.
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              Optimized sgRNA design to maximize activity and minimize off-target effects of CRISPR-Cas9

              CRISPR-Cas9-based genetic screens are a powerful new tool in biology. By simply altering the sequence of the single-guide RNA (sgRNA), Cas9 can be reprogrammed to target different sites in the genome with relative ease, but the on-target activity and off-target effects of individual sgRNAs can vary widely. Here, we use recently-devised sgRNA design rules to create human and mouse genome-wide libraries, perform positive and negative selection screens and observe that the use of these rules produced improved results. Additionally, we profile the off-target activity of thousands of sgRNAs and develop a metric to predict off-target sites. We incorporate these findings from large-scale, empirical data to improve our computational design rules and create optimized sgRNA libraries that maximize on-target activity and minimize off-target effects to enable more effective and efficient genetic screens and genome engineering.
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                Author and article information

                Contributors
                Beat.Schaefer@kispi.uzh.ch
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                23 June 2022
                23 June 2022
                2022
                : 12
                : 10671
                Affiliations
                [1 ]GRID grid.412341.1, ISNI 0000 0001 0726 4330, Department of Oncology and Children’s Research Center, , University Children’s Hospital, ; Steinwiesstrasse 32, 8032 Zurich, Switzerland
                [2 ]GRID grid.7400.3, ISNI 0000 0004 1937 0650, Experimental Infectious Diseases and Cancer Research, Children’s Research Center, University Children’s Hospital of Zurich, , University of Zurich, ; Zurich, Switzerland
                Article
                12659
                10.1038/s41598-022-12659-7
                9225990
                35739280
                7a2cfdf0-8ea1-4781-b526-d74b138d74f0
                © The Author(s) 2022

                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
                : 18 January 2022
                : 19 April 2022
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100006447, Universität Zürich;
                Award ID: FK20-039
                Award Recipient :
                Funded by: FundRef http://dx.doi.org/10.13039/501100001711, Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung;
                Award ID: 31003A_170026
                Categories
                Article
                Custom metadata
                © The Author(s) 2022

                Uncategorized
                cancer,oncogenes,paediatric cancer,sarcoma
                Uncategorized
                cancer, oncogenes, paediatric cancer, sarcoma

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