JACKS: joint analysis of CRISPR/Cas9 knockout screens.
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ABSTRACT:
SUBMITTER: Allen F
PROVIDER: S-EPMC6396427 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:We propose the Model-based Analysis of Genome-wide CRISPR/Cas9 Knockout (MAGeCK) method for prioritizing single-guide RNAs, genes and pathways in genome-scale CRISPR/Cas9 knockout screens. MAGeCK demonstrates better performance compared with existing methods, identifies both positively and negatively selected genes simultaneously, and reports robust results across different experimental conditions. Using public datasets, MAGeCK identified novel essential genes and pathways, including EGFR in vemurafenib-treated A375 cells harboring a BRAF mutation. MAGeCK also detected cell type-specific essential genes, including BCR and ABL1, in KBM7 cells bearing a BCR-ABL fusion, and IGF1R in HL-60 cells, which depends on the insulin signaling pathway for proliferation.
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Project description:MotivationGenome-wide clustered, regularly interspaced, short palindromic repeat (CRISPR)-Cas9 screen has been widely used to interrogate gene functions. However, the rules to design better libraries beg further refinement.ResultsWe found single guide RNA (sgRNA) outliers are characterized by higher G-nucleotide counts, especially in regions distal from the PAM motif and are associated with stronger off-target activities. Furthermore, using non-targeting sgRNAs as negative controls lead to strong bias, which can be mitigated by using sgRNAs targeting multiple 'safe harbor' regions. Custom-designed screens confirmed our findings and further revealed that 19 nt sgRNAs consistently gave the best signal-to-noise ratio. Collectively, our analysis motivated the design of a new genome-wide CRISPR/Cas9 screen library and uncovered some intriguing properties of the CRISPR-Cas9 system.Availability and implementationThe MAGeCK workflow is available open source at https://bitbucket.org/liulab/mageck_nest under the MIT license.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:High-throughput CRISPR-Cas9 knockout screens using a tiling-sgRNA design permit in situ evaluation of protein domain function. Here, to facilitate de novo identification of essential protein domains from such screens, we propose ProTiler, a computational method for the robust mapping of CRISPR knockout hyper-sensitive (CKHS) regions, which refer to the protein regions associated with a strong sgRNA dropout effect in the screens. Applied to a published CRISPR tiling screen dataset, ProTiler identifies 175 CKHS regions in 83 proteins. Of these CKHS regions, more than 80% overlap with annotated Pfam domains, including all of the 15 known drug targets in the dataset. ProTiler also reveals unannotated essential domains, including the N-terminus of the SWI/SNF subunit SMARCB1, which is validated experimentally. Surprisingly, the CKHS regions are negatively correlated with phosphorylation and acetylation sites, suggesting that protein domains and post-translational modification sites have distinct sensitivities to CRISPR-Cas9 mediated amino acids loss.
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Project description:Targeting the MAPK signaling pathway has transformed the treatment of metastatic melanoma. CRISPR-Cas9 genetic screens provide a genome-wide approach to uncover novel genetic dependencies that might serve as therapeutic targets. Here, we analyzed recently reported CRISPR-Cas9 screens comparing data from 28 melanoma cell lines and 313 cell lines of other tumor types in order to identify fitness genes related to melanoma. We found an average of 1,494 fitness genes in each melanoma cell line. We identified 33 genes, inactivation of which specifically reduced the fitness of melanoma. This set of tumor type-specific genes includes established melanoma fitness genes as well as many genes that have not previously been associated with melanoma growth. Several genes encode proteins that can be targeted using available inhibitors. We verified that genetic inactivation of DUSP4 and PPP2R2A reduces the proliferation of melanoma cells. DUSP4 encodes an inhibitor of ERK, suggesting that further activation of MAPK signaling activity through its loss is selectively deleterious to melanoma cells. Collectively, these data present a resource of genetic dependencies in melanoma that may be explored as potential therapeutic targets.
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Project description:H1-HeLa cells were stably transduced with lentiCas9-Blast (Addgene, Plasmid #52962) and subsequently selected using blasticidin to generate constitutively expressing Cas9 H1-HeLa cells. A single Cas9-expressing H1-HeLa clone was then transduced with lentivirus without a selection marker to stably express CDHR3 C529Y (H1-HeLa+CDHR3). A single CDHR3-expressing H1-HeLa clone was then chosen based on RT-qPCR of CHDR3 expression and RV-C15 RNA levels for mutagenesis. 300 million of the H1-HeLa cells constitutively expressing CDHR3 and Cas9 were transduced with the lentiGuide-Puro from the GeCKO v2 library at a MOI of 0.3. Cells were selected using puromycin and heterogeneous H1-HeLa knockout cell populations were subsequently pooled together. The CRISPR genetic screens were started 10 days post transduction. >1000-fold coverage of mutagenized cells (libraries A and B) was infected with either RV-C15 (MOI=1 PFU/cell) or EV-D68 Missouri (MOI=1 PFU/cell). RV-C15 infection was repeated for an additional round at 6 days post-infection. As soon as appearance of visibly viable colonies was observed, populations of virus-resistant cells were pooled and harvested. Uninfected starting populations of mutagenized cells were used as the unselected reference. Total genomic DNA from both virus-resistant and uninfected cells was respectively extracted using QIAamp DNA Mini Kit (Qiagen). The inserted guide RNA sequences were retrieved from the genomic DNA by PCR amplification. The PCR products were then purified and subjected to NextSeq platform (Illumina) next-generation sequencing.
2019-07-10 | E-MTAB-8125 | biostudies-arrayexpress
Project description:CRISPR-derived RNA guided endonucleases (RGENs) have been widely used for both gene knockout and knock-in at the level of single or multiple genes. RGENs are now available for forward genetic screens at genome scale, but single guide RNA (sgRNA) selection at this scale is difficult.We develop an online tool, Cas-Database, a genome-wide gRNA library design tool for Cas9 nucleases from Streptococcus pyogenes (SpCas9). With an easy-to-use web interface, Cas-Database allows users to select optimal target sequences simply by changing the filtering conditions. Furthermore, it provides a powerful way to select multiple optimal target sequences from thousands of genes at once for the creation of a genome-wide library. Cas-Database also provides a web application programming interface (web API) for advanced bioinformatics users.Free access at http://www.rgenome.net/cas-database/sangsubae@hanyang.ac.kr or jskim01@snu.ac.krSupplementary data are available at Bioinformatics online.
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Project description:Combinatorial genetic screening using CRISPR-Cas9 is a useful approach to uncover redundant genes and to explore complex gene networks. However, current methods suffer from interference between the single-guide RNAs (sgRNAs) and from limited gene targeting activity. To increase the efficiency of combinatorial screening, we employ orthogonal Cas9 enzymes from Staphylococcus aureus and Streptococcus pyogenes. We used machine learning to establish S. aureus Cas9 sgRNA design rules and paired S. aureus Cas9 with S. pyogenes Cas9 to achieve dual targeting in a high fraction of cells. We also developed a lentiviral vector and cloning strategy to generate high-complexity pooled dual-knockout libraries to identify synthetic lethal and buffering gene pairs across multiple cell types, including MAPK pathway genes and apoptotic genes. Our orthologous approach also enabled a screen combining gene knockouts with transcriptional activation, which revealed genetic interactions with TP53. The "Big Papi" (paired aureus and pyogenes for interactions) approach described here will be widely applicable for the study of combinatorial phenotypes.
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Project description:Large-scale genetic screens using CRISPR/Cas9 technology have emerged as a major tool for functional genomics. With its increased popularity, experimental biologists frequently acquire large sequencing datasets for which they often do not have an easy analysis option. While a few bioinformatic tools have been developed for this purpose, their utility is still hindered either due to limited functionality or the requirement of bioinformatic expertise. To make sequencing data analysis of CRISPR/Cas9 screens more accessible to a wide range of scientists, we developed a Platform-independent Analysis of Pooled Screens using Python (PinAPL-Py), which is operated as an intuitive web-service. PinAPL-Py implements state-of-the-art tools and statistical models, assembled in a comprehensive workflow covering sequence quality control, automated sgRNA sequence extraction, alignment, sgRNA enrichment/depletion analysis and gene ranking. The workflow is set up to use a variety of popular sgRNA libraries as well as custom libraries that can be easily uploaded. Various analysis options are offered, suitable to analyze a large variety of CRISPR/Cas9 screening experiments. Analysis output includes ranked lists of sgRNAs and genes, and publication-ready plots. PinAPL-Py helps to advance genome-wide screening efforts by combining comprehensive functionality with user-friendly implementation. PinAPL-Py is freely accessible at http://pinapl-py.ucsd.edu with instructions and test datasets.
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Project description:BACKGROUND:Pooled library CRISPR/Cas9 knockout screening across hundreds of cell lines has identified genes whose disruption leads to fitness defects, a critical step in identifying candidate cancer targets. However, the number of essential genes detected from these monogenic knockout screens is low compared to the number of constitutively expressed genes in a cell. RESULTS:Through a systematic analysis of screen data in cancer cell lines generated by the Cancer Dependency Map, we observe that half of all constitutively expressed genes are never detected in any CRISPR screen and that these never-essentials are highly enriched for paralogs. We investigated functional buffering among approximately 400 candidate paralog pairs using CRISPR/enCas12a dual-gene knockout screening in three cell lines. We observe 24 synthetic lethal paralog pairs that have escaped detection by monogenic knockout screens at stringent thresholds. Nineteen of 24 (79%) synthetic lethal interactions are present in at least two out of three cell lines and 14 of 24 (58%) are present in all three cell lines tested, including alternate subunits of stable protein complexes as well as functionally redundant enzymes. CONCLUSIONS:Together, these observations strongly suggest that functionally redundant paralogs represent a targetable set of genetic dependencies that are systematically under-represented among cell-essential genes in monogenic CRISPR-based loss of function screens.
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Project description:Several groups have used genome-wide libraries of lentiviruses encoding small guide RNAs (sgRNAs) for genetic screens. In most cases, sgRNA expression cassettes are integrated into cells by using lentiviruses, and target genes are statistically estimated by the readout of sgRNA sequences after targeted sequencing. We present a new virus-free method for human gene knockout screens using a genome-wide library of CRISPR/Cas9 sgRNAs based on plasmids and target gene identification via whole-genome sequencing (WGS) confirmation of authentic mutations rather than statistical estimation through targeted amplicon sequencing. We used 30,840 pairs of individually synthesized oligonucleotides to construct the genome-scale sgRNA library, collectively targeting 10,280 human genes (i.e. three sgRNAs per gene). These plasmid libraries were co-transfected with a Cas9-expression plasmid into human cells, which were then treated with cytotoxic drugs or viruses. Only cells lacking key factors essential for cytotoxic drug metabolism or viral infection were able to survive. Genomic DNA isolated from cells that survived these challenges was subjected to WGS to directly identify CRISPR/Cas9-mediated causal mutations essential for cell survival. With this approach, we were able to identify known and novel genes essential for viral infection in human cells. We propose that genome-wide sgRNA screens based on plasmids coupled with WGS are powerful tools for forward genetics studies and drug target discovery.
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