human crispr knockout pooled library brunello doench Search Results


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Addgene inc human brunello crispr knockout pooled library
a Schematic illustrating difference between classic synthetic lethality and our common genetic architecture . Synthetic lethality consists of many individual Y i functions. These functions are cell-type-specific models with single features. Our proposed common genetic architecture is hypothesized to connect these “private” functions with shared CERES features. A common genetic architecture has many redundant edges, and more interconnected nodes. More nodes suggest that more cell-type-specific phenotypes are predictable, and more edges suggest redundancy. b A network built from the aggregation of all multivariate models. Genes are represented as nodes and feature-target gene relations as edges. Colors represent distinct subnetwork communities that were identified by the Louvain method. c Network communities with (right) and without (left) nodes/edges involving functional <t>CRISPR</t> features for a single Louvain community, and a comparison with our hypothesis from ( a ). Edges are colored based upon the data source; and nodes are colored based on the model score (of top ten feature model) of the corresponding gene as target. d To quantitate the visual similarity between our hypothesis in ( a ) and the data in ( c ) across all Louvain communities, we examined the differences in the clustering coefficient, the average number of neighbors, and the network heterogeneity. e gprofiler2 plots examine the enrichment of functional categories. f Residual plot identifies GO terms that are more (residuals of −log10 P values >10) or less (residuals of -log10 P values < −10) enriched in predictor genes than in target genes. Dots represent shared GO terms among the 100 most significant terms in target and predictor gprofiler2 analysis result. The p- values from gprofiler2 for ( e ) and ( f ) are based on hypergeometric tests with multiple testing corrections using the g:SCS method. Source data are provided as a Source Data file.
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<t>CRISPR</t> knockout screen identifies common host factors required for rVSV-CCHFV pseudovirus infection. ( A ). Bubble plot of genes significantly enriched in a genome-wide CRISPR knockout screen in wild-type A549 (A549-WT) cells challenged with rVSV-CCHFV pseudovirus. The virus-resistant A549-WT cells were collected for analysis, and genes were ranked according to the MAGeCK score. ( B ) KEGG (Kyoto Encyclopedia of Genes and Genomes) and Go (Gene Ontology) analysis of top 100 enriched genes. ( C and D ) Flow cytometry ( C ) and fluorescence imaging ( D ) analysis of A549-WT and A549-BAT (B3GAT3, AXL, and TIM-1 triple-knockout cells) infected with rVSV-CCHFV (MOI 3). The percentage of GFP-positive cells was analyzed at indicated time points using flow cytometer, and images were taken using fluorescence microscope at 24 h post-infection (hpi). Scale bar, 400 µm. Two-way ANOVA with Sidak’s multiple-comparison test. **** P < 0.0001.
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Broad Institute Inc genome- wide knockout sgrna libraries (human brunello crispr ko pooled libraries)
<t>CRISPR</t> knockout screen identifies common host factors required for rVSV-CCHFV pseudovirus infection. ( A ). Bubble plot of genes significantly enriched in a genome-wide CRISPR knockout screen in wild-type A549 (A549-WT) cells challenged with rVSV-CCHFV pseudovirus. The virus-resistant A549-WT cells were collected for analysis, and genes were ranked according to the MAGeCK score. ( B ) KEGG (Kyoto Encyclopedia of Genes and Genomes) and Go (Gene Ontology) analysis of top 100 enriched genes. ( C and D ) Flow cytometry ( C ) and fluorescence imaging ( D ) analysis of A549-WT and A549-BAT (B3GAT3, AXL, and TIM-1 triple-knockout cells) infected with rVSV-CCHFV (MOI 3). The percentage of GFP-positive cells was analyzed at indicated time points using flow cytometer, and images were taken using fluorescence microscope at 24 h post-infection (hpi). Scale bar, 400 µm. Two-way ANOVA with Sidak’s multiple-comparison test. **** P < 0.0001.
Genome Wide Knockout Sgrna Libraries (Human Brunello Crispr Ko Pooled Libraries), supplied by Broad Institute Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Addgene inc human kinome crispr pooled library
<t>CRISPR</t> knockout screen identifies common host factors required for rVSV-CCHFV pseudovirus infection. ( A ). Bubble plot of genes significantly enriched in a genome-wide CRISPR knockout screen in wild-type A549 (A549-WT) cells challenged with rVSV-CCHFV pseudovirus. The virus-resistant A549-WT cells were collected for analysis, and genes were ranked according to the MAGeCK score. ( B ) KEGG (Kyoto Encyclopedia of Genes and Genomes) and Go (Gene Ontology) analysis of top 100 enriched genes. ( C and D ) Flow cytometry ( C ) and fluorescence imaging ( D ) analysis of A549-WT and A549-BAT (B3GAT3, AXL, and TIM-1 triple-knockout cells) infected with rVSV-CCHFV (MOI 3). The percentage of GFP-positive cells was analyzed at indicated time points using flow cytometer, and images were taken using fluorescence microscope at 24 h post-infection (hpi). Scale bar, 400 µm. Two-way ANOVA with Sidak’s multiple-comparison test. **** P < 0.0001.
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Addgene inc human crispr brunello genomewide knockout library
FIGURE 1. <t>CRISPR</t> activation screen identifies novel regulators of PD-L1 expression. (A) Schematic setup of the screen. MelJuSo melanoma cells stably expressing MS2-p65-HSF1 were transduced with a pooled gRNA library containing dCAS9 and sorted by FACS for cells displaying high levels of PD-L1. (B) Genes for which at least two different gRNAs were significantly enriched (greater than fourfold) in the sorted population versus control population in both replicate sorts. Plotted are p val- ues based on RSA analysis. (C) MelJuSo MPH cells stably expressing the SAM vector with or without the indicated activation gRNAs were analyzed for cell surface expression of PD-L1 and MHC class I (HLA-ABC). Data represent three independent experiments (1SD), and statistical significance was determined by paired Student t test (*p < 0.05, **p < 0.01). (D) MelJuSo cells stably expressing FLAG (EV), GATA2-FLAG, or FLAG-VGLL3 were analyzed for cell surface expression of PD-L1 using flow cytometry. (E) MelJuSo cells as in D were either stimulated or not with IFN-g for 48 h, and cell surface expression of PD-L1 and PD-L2 was mea- sured using flow cytometry. (F) MelJuSo cells as in D were either stimulated or not with IFN-g for 24 h, and expression of the indicated proteins was determined by Western blot analysis. (G) MelJuSo cells as in D were treated with IFN-g for 24 h when indicated, and mRNA levels of the indicated genes were analyzed using quanti- tative real-time PCR and normalized to GAPDH. All data represent three independent experiments (1SD), and statistical significance was determined by ANOVA using Dunnett’s multiple comparison test (*p < 0.05, **p < 0.01).
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FIGURE 1. <t>CRISPR</t> activation screen identifies novel regulators of PD-L1 expression. (A) Schematic setup of the screen. MelJuSo melanoma cells stably expressing MS2-p65-HSF1 were transduced with a pooled gRNA library containing dCAS9 and sorted by FACS for cells displaying high levels of PD-L1. (B) Genes for which at least two different gRNAs were significantly enriched (greater than fourfold) in the sorted population versus control population in both replicate sorts. Plotted are p val- ues based on RSA analysis. (C) MelJuSo MPH cells stably expressing the SAM vector with or without the indicated activation gRNAs were analyzed for cell surface expression of PD-L1 and MHC class I (HLA-ABC). Data represent three independent experiments (1SD), and statistical significance was determined by paired Student t test (*p < 0.05, **p < 0.01). (D) MelJuSo cells stably expressing FLAG (EV), GATA2-FLAG, or FLAG-VGLL3 were analyzed for cell surface expression of PD-L1 using flow cytometry. (E) MelJuSo cells as in D were either stimulated or not with IFN-g for 48 h, and cell surface expression of PD-L1 and PD-L2 was mea- sured using flow cytometry. (F) MelJuSo cells as in D were either stimulated or not with IFN-g for 24 h, and expression of the indicated proteins was determined by Western blot analysis. (G) MelJuSo cells as in D were treated with IFN-g for 24 h when indicated, and mRNA levels of the indicated genes were analyzed using quanti- tative real-time PCR and normalized to GAPDH. All data represent three independent experiments (1SD), and statistical significance was determined by ANOVA using Dunnett’s multiple comparison test (*p < 0.05, **p < 0.01).
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Addgene inc paper n a recombinant dna human crispr knockout pooled library brunello
Figure 1. Genome-wide <t>CRISPR-Cas9</t> screen identifies host-encoded regulators of SARS-CoV-2 frameshifting (A) Schematic of the SARS-CoV-2 genome. Dotted box indicates close up of region shown in (B) harboring the coronavirus frameshifting element (FSE). (B) Secondary structure of the SARS-CoV-2 FSE containing the slippery sequence and three-stemmed pseudoknot. Based on structural data from Bhatt et al.13
Paper N A Recombinant Dna Human Crispr Knockout Pooled Library Brunello, supplied by Addgene inc, used in various techniques. Bioz Stars score: 98/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Figure 1. Genome-wide <t>CRISPR-Cas9</t> screen identifies host-encoded regulators of SARS-CoV-2 frameshifting (A) Schematic of the SARS-CoV-2 genome. Dotted box indicates close up of region shown in (B) harboring the coronavirus frameshifting element (FSE). (B) Secondary structure of the SARS-CoV-2 FSE containing the slippery sequence and three-stemmed pseudoknot. Based on structural data from Bhatt et al.13
Human Crispr Knockout Pooled Library Brunello Doench, supplied by Addgene inc, used in various techniques. Bioz Stars score: 96/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Figure 1. Genome-wide <t>CRISPR-Cas9</t> screen identifies host-encoded regulators of SARS-CoV-2 frameshifting (A) Schematic of the SARS-CoV-2 genome. Dotted box indicates close up of region shown in (B) harboring the coronavirus frameshifting element (FSE). (B) Secondary structure of the SARS-CoV-2 FSE containing the slippery sequence and three-stemmed pseudoknot. Based on structural data from Bhatt et al.13
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Figure 1. Genome-wide <t>CRISPR-Cas9</t> screen identifies host-encoded regulators of SARS-CoV-2 frameshifting (A) Schematic of the SARS-CoV-2 genome. Dotted box indicates close up of region shown in (B) harboring the coronavirus frameshifting element (FSE). (B) Secondary structure of the SARS-CoV-2 FSE containing the slippery sequence and three-stemmed pseudoknot. Based on structural data from Bhatt et al.13
Human Crispr Knockout Pooled Library, supplied by Addgene inc, used in various techniques. Bioz Stars score: 93/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Figure 1. Genome-wide <t>CRISPR-Cas9</t> screen identifies host-encoded regulators of SARS-CoV-2 frameshifting (A) Schematic of the SARS-CoV-2 genome. Dotted box indicates close up of region shown in (B) harboring the coronavirus frameshifting element (FSE). (B) Secondary structure of the SARS-CoV-2 FSE containing the slippery sequence and three-stemmed pseudoknot. Based on structural data from Bhatt et al.13
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Image Search Results


a Schematic illustrating difference between classic synthetic lethality and our common genetic architecture . Synthetic lethality consists of many individual Y i functions. These functions are cell-type-specific models with single features. Our proposed common genetic architecture is hypothesized to connect these “private” functions with shared CERES features. A common genetic architecture has many redundant edges, and more interconnected nodes. More nodes suggest that more cell-type-specific phenotypes are predictable, and more edges suggest redundancy. b A network built from the aggregation of all multivariate models. Genes are represented as nodes and feature-target gene relations as edges. Colors represent distinct subnetwork communities that were identified by the Louvain method. c Network communities with (right) and without (left) nodes/edges involving functional CRISPR features for a single Louvain community, and a comparison with our hypothesis from ( a ). Edges are colored based upon the data source; and nodes are colored based on the model score (of top ten feature model) of the corresponding gene as target. d To quantitate the visual similarity between our hypothesis in ( a ) and the data in ( c ) across all Louvain communities, we examined the differences in the clustering coefficient, the average number of neighbors, and the network heterogeneity. e gprofiler2 plots examine the enrichment of functional categories. f Residual plot identifies GO terms that are more (residuals of −log10 P values >10) or less (residuals of -log10 P values < −10) enriched in predictor genes than in target genes. Dots represent shared GO terms among the 100 most significant terms in target and predictor gprofiler2 analysis result. The p- values from gprofiler2 for ( e ) and ( f ) are based on hypergeometric tests with multiple testing corrections using the g:SCS method. Source data are provided as a Source Data file.

Journal: Nature Communications

Article Title: A pan-CRISPR analysis of mammalian cell specificity identifies ultra-compact sgRNA subsets for genome-scale experiments

doi: 10.1038/s41467-022-28045-w

Figure Lengend Snippet: a Schematic illustrating difference between classic synthetic lethality and our common genetic architecture . Synthetic lethality consists of many individual Y i functions. These functions are cell-type-specific models with single features. Our proposed common genetic architecture is hypothesized to connect these “private” functions with shared CERES features. A common genetic architecture has many redundant edges, and more interconnected nodes. More nodes suggest that more cell-type-specific phenotypes are predictable, and more edges suggest redundancy. b A network built from the aggregation of all multivariate models. Genes are represented as nodes and feature-target gene relations as edges. Colors represent distinct subnetwork communities that were identified by the Louvain method. c Network communities with (right) and without (left) nodes/edges involving functional CRISPR features for a single Louvain community, and a comparison with our hypothesis from ( a ). Edges are colored based upon the data source; and nodes are colored based on the model score (of top ten feature model) of the corresponding gene as target. d To quantitate the visual similarity between our hypothesis in ( a ) and the data in ( c ) across all Louvain communities, we examined the differences in the clustering coefficient, the average number of neighbors, and the network heterogeneity. e gprofiler2 plots examine the enrichment of functional categories. f Residual plot identifies GO terms that are more (residuals of −log10 P values >10) or less (residuals of -log10 P values < −10) enriched in predictor genes than in target genes. Dots represent shared GO terms among the 100 most significant terms in target and predictor gprofiler2 analysis result. The p- values from gprofiler2 for ( e ) and ( f ) are based on hypergeometric tests with multiple testing corrections using the g:SCS method. Source data are provided as a Source Data file.

Article Snippet: Human Brunello CRISPR knockout pooled library was a gift from David Root and John Doench (Addgene #73178).

Techniques: Functional Assay, CRISPR, Comparison

a Two separate pooled screens were performed in a cell line (PC9) that was not included in model training and validation. Experiment 1 was the full Brunello library. A 21-day dropout experiment was performed in PC9 cells. Measurements on 18,114 genes were direct and form the gold standard. The L200 can be computationally extracted from the full screen and compared to these gold-standard measurements. A new L200 standalone library of 800 guides targeting 200 genes was cloned. This library can be used to perform a small-scale lossy compression experiment. The data can then be compared to the gold standard. b Correlations of inferred vs measured CERES scores for both screens in ( a ) and a comparison of the predictions between the standalone sets and the computationally extracted L200 set in the Brunello library. c A Venn diagram describes the overlap in “Hits” in the 500 most differentially required genes for growth in PC9 cells. Both of the lossy compression screens from ( a ) and the gold-standard (measured) data are compared. Source data are provided as a Source Data file.

Journal: Nature Communications

Article Title: A pan-CRISPR analysis of mammalian cell specificity identifies ultra-compact sgRNA subsets for genome-scale experiments

doi: 10.1038/s41467-022-28045-w

Figure Lengend Snippet: a Two separate pooled screens were performed in a cell line (PC9) that was not included in model training and validation. Experiment 1 was the full Brunello library. A 21-day dropout experiment was performed in PC9 cells. Measurements on 18,114 genes were direct and form the gold standard. The L200 can be computationally extracted from the full screen and compared to these gold-standard measurements. A new L200 standalone library of 800 guides targeting 200 genes was cloned. This library can be used to perform a small-scale lossy compression experiment. The data can then be compared to the gold standard. b Correlations of inferred vs measured CERES scores for both screens in ( a ) and a comparison of the predictions between the standalone sets and the computationally extracted L200 set in the Brunello library. c A Venn diagram describes the overlap in “Hits” in the 500 most differentially required genes for growth in PC9 cells. Both of the lossy compression screens from ( a ) and the gold-standard (measured) data are compared. Source data are provided as a Source Data file.

Article Snippet: Human Brunello CRISPR knockout pooled library was a gift from David Root and John Doench (Addgene #73178).

Techniques: Biomarker Discovery, Clone Assay, Comparison

CRISPR knockout screen identifies common host factors required for rVSV-CCHFV pseudovirus infection. ( A ). Bubble plot of genes significantly enriched in a genome-wide CRISPR knockout screen in wild-type A549 (A549-WT) cells challenged with rVSV-CCHFV pseudovirus. The virus-resistant A549-WT cells were collected for analysis, and genes were ranked according to the MAGeCK score. ( B ) KEGG (Kyoto Encyclopedia of Genes and Genomes) and Go (Gene Ontology) analysis of top 100 enriched genes. ( C and D ) Flow cytometry ( C ) and fluorescence imaging ( D ) analysis of A549-WT and A549-BAT (B3GAT3, AXL, and TIM-1 triple-knockout cells) infected with rVSV-CCHFV (MOI 3). The percentage of GFP-positive cells was analyzed at indicated time points using flow cytometer, and images were taken using fluorescence microscope at 24 h post-infection (hpi). Scale bar, 400 µm. Two-way ANOVA with Sidak’s multiple-comparison test. **** P < 0.0001.

Journal: mBio

Article Title: Soluble MFGE8 mediates cell entry of Crimean-Congo hemorrhagic fever virus

doi: 10.1128/mbio.01617-25

Figure Lengend Snippet: CRISPR knockout screen identifies common host factors required for rVSV-CCHFV pseudovirus infection. ( A ). Bubble plot of genes significantly enriched in a genome-wide CRISPR knockout screen in wild-type A549 (A549-WT) cells challenged with rVSV-CCHFV pseudovirus. The virus-resistant A549-WT cells were collected for analysis, and genes were ranked according to the MAGeCK score. ( B ) KEGG (Kyoto Encyclopedia of Genes and Genomes) and Go (Gene Ontology) analysis of top 100 enriched genes. ( C and D ) Flow cytometry ( C ) and fluorescence imaging ( D ) analysis of A549-WT and A549-BAT (B3GAT3, AXL, and TIM-1 triple-knockout cells) infected with rVSV-CCHFV (MOI 3). The percentage of GFP-positive cells was analyzed at indicated time points using flow cytometer, and images were taken using fluorescence microscope at 24 h post-infection (hpi). Scale bar, 400 µm. Two-way ANOVA with Sidak’s multiple-comparison test. **** P < 0.0001.

Article Snippet: The human Brunello CRISPR knockout pooled library targeting 19,114 genes (Addgene #73178) or Calabrese activation pooled library targeting 18,885 genes (Addgene #92379) was a gift from David Root and John Doench ( ) and packaged in 293 FT cells after co-transfection with psPAX2 (Addgene #12260) and pMD2.G (Addgene #12259) using FugeneHD (Promega).

Techniques: CRISPR, Knock-Out, Infection, Genome Wide, Virus, Flow Cytometry, Fluorescence, Imaging, Triple Knockout, Microscopy, Comparison

CRISPR activation screen identifies MFGE8 as a proviral host factor for rVSV-CCHFV infection. ( A ) Identification of genes from CRISPR screen in A549-BAT cells. Cells transduced with the CRISPR activation library were infected with rVSV-CCHFV for 24 h. GFP-positive cells were sorted for sgRNA abundance analysis and ranked based on the MAGeCK score and P value. ( B and C ) Validation of MFGE8 gene. Gene expression was activated using two or representative sgRNAs in A549-BAT cells, followed by infection with rVSV-CCHFV (MOI 3, 18 h) ( B ) and rVSV (MOI 0.01, 15 h) ( C ). The percentage of GFP-positive cells were analyzed by flow cytometry. ( D ) Representative fluorescence images of A549-BAT cell infected with respective virus from ( B ) and ( C ) were taken before harvesting the cells. Scale bar, 400 µm. ( E ) Overexpression of MFGE8 enhances rVSV-CCHFV infection in A549-BAT cells. ( F ) Growth kinetics of rVSV-CCHFV in vector control and MFGE8-overexpressing cells. Cells were infected with rVSV-CCHFV at an MOI of 0.3, and viral titers in the supernatants at indicated time points were determined by plaque-forming assay. ( G–I ) Overexpression of MFGE8 enhances rVSV-CCHFV infection in A549-WT ( G ), Hela ( H ), and SW-13 ( I ) cells. The percentage of GFP-positive cells were analyzed by flow cytometry at 16 hpi. (J) Knockout of MFGE8 decreases the rVSV-CCHFV infection. A549-WT cells edited with two different nontargeting control or MFGE8 -specific sgRNAs were infected with rVSV-CCHFV, followed by flow cytometry analysis of GFP-positive cells at 16 hpi. Two-way ANOVA with Sidak’s multiple-comparison test. ns, not significant; *** P < 0.001; **** P < 0.0001.

Journal: mBio

Article Title: Soluble MFGE8 mediates cell entry of Crimean-Congo hemorrhagic fever virus

doi: 10.1128/mbio.01617-25

Figure Lengend Snippet: CRISPR activation screen identifies MFGE8 as a proviral host factor for rVSV-CCHFV infection. ( A ) Identification of genes from CRISPR screen in A549-BAT cells. Cells transduced with the CRISPR activation library were infected with rVSV-CCHFV for 24 h. GFP-positive cells were sorted for sgRNA abundance analysis and ranked based on the MAGeCK score and P value. ( B and C ) Validation of MFGE8 gene. Gene expression was activated using two or representative sgRNAs in A549-BAT cells, followed by infection with rVSV-CCHFV (MOI 3, 18 h) ( B ) and rVSV (MOI 0.01, 15 h) ( C ). The percentage of GFP-positive cells were analyzed by flow cytometry. ( D ) Representative fluorescence images of A549-BAT cell infected with respective virus from ( B ) and ( C ) were taken before harvesting the cells. Scale bar, 400 µm. ( E ) Overexpression of MFGE8 enhances rVSV-CCHFV infection in A549-BAT cells. ( F ) Growth kinetics of rVSV-CCHFV in vector control and MFGE8-overexpressing cells. Cells were infected with rVSV-CCHFV at an MOI of 0.3, and viral titers in the supernatants at indicated time points were determined by plaque-forming assay. ( G–I ) Overexpression of MFGE8 enhances rVSV-CCHFV infection in A549-WT ( G ), Hela ( H ), and SW-13 ( I ) cells. The percentage of GFP-positive cells were analyzed by flow cytometry at 16 hpi. (J) Knockout of MFGE8 decreases the rVSV-CCHFV infection. A549-WT cells edited with two different nontargeting control or MFGE8 -specific sgRNAs were infected with rVSV-CCHFV, followed by flow cytometry analysis of GFP-positive cells at 16 hpi. Two-way ANOVA with Sidak’s multiple-comparison test. ns, not significant; *** P < 0.001; **** P < 0.0001.

Article Snippet: The human Brunello CRISPR knockout pooled library targeting 19,114 genes (Addgene #73178) or Calabrese activation pooled library targeting 18,885 genes (Addgene #92379) was a gift from David Root and John Doench ( ) and packaged in 293 FT cells after co-transfection with psPAX2 (Addgene #12260) and pMD2.G (Addgene #12259) using FugeneHD (Promega).

Techniques: CRISPR, Activation Assay, Infection, Transduction, Biomarker Discovery, Gene Expression, Flow Cytometry, Fluorescence, Virus, Over Expression, Plasmid Preparation, Control, Knock-Out, Comparison

FIGURE 1. CRISPR activation screen identifies novel regulators of PD-L1 expression. (A) Schematic setup of the screen. MelJuSo melanoma cells stably expressing MS2-p65-HSF1 were transduced with a pooled gRNA library containing dCAS9 and sorted by FACS for cells displaying high levels of PD-L1. (B) Genes for which at least two different gRNAs were significantly enriched (greater than fourfold) in the sorted population versus control population in both replicate sorts. Plotted are p val- ues based on RSA analysis. (C) MelJuSo MPH cells stably expressing the SAM vector with or without the indicated activation gRNAs were analyzed for cell surface expression of PD-L1 and MHC class I (HLA-ABC). Data represent three independent experiments (1SD), and statistical significance was determined by paired Student t test (*p < 0.05, **p < 0.01). (D) MelJuSo cells stably expressing FLAG (EV), GATA2-FLAG, or FLAG-VGLL3 were analyzed for cell surface expression of PD-L1 using flow cytometry. (E) MelJuSo cells as in D were either stimulated or not with IFN-g for 48 h, and cell surface expression of PD-L1 and PD-L2 was mea- sured using flow cytometry. (F) MelJuSo cells as in D were either stimulated or not with IFN-g for 24 h, and expression of the indicated proteins was determined by Western blot analysis. (G) MelJuSo cells as in D were treated with IFN-g for 24 h when indicated, and mRNA levels of the indicated genes were analyzed using quanti- tative real-time PCR and normalized to GAPDH. All data represent three independent experiments (1SD), and statistical significance was determined by ANOVA using Dunnett’s multiple comparison test (*p < 0.05, **p < 0.01).

Journal: Journal of immunology (Baltimore, Md. : 1950)

Article Title: CRISPR Activation Screening Identifies VGLL3-TEAD1-RUNX1/3 as a Transcriptional Complex for PD-L1 Expression.

doi: 10.4049/jimmunol.2100917

Figure Lengend Snippet: FIGURE 1. CRISPR activation screen identifies novel regulators of PD-L1 expression. (A) Schematic setup of the screen. MelJuSo melanoma cells stably expressing MS2-p65-HSF1 were transduced with a pooled gRNA library containing dCAS9 and sorted by FACS for cells displaying high levels of PD-L1. (B) Genes for which at least two different gRNAs were significantly enriched (greater than fourfold) in the sorted population versus control population in both replicate sorts. Plotted are p val- ues based on RSA analysis. (C) MelJuSo MPH cells stably expressing the SAM vector with or without the indicated activation gRNAs were analyzed for cell surface expression of PD-L1 and MHC class I (HLA-ABC). Data represent three independent experiments (1SD), and statistical significance was determined by paired Student t test (*p < 0.05, **p < 0.01). (D) MelJuSo cells stably expressing FLAG (EV), GATA2-FLAG, or FLAG-VGLL3 were analyzed for cell surface expression of PD-L1 using flow cytometry. (E) MelJuSo cells as in D were either stimulated or not with IFN-g for 48 h, and cell surface expression of PD-L1 and PD-L2 was mea- sured using flow cytometry. (F) MelJuSo cells as in D were either stimulated or not with IFN-g for 24 h, and expression of the indicated proteins was determined by Western blot analysis. (G) MelJuSo cells as in D were treated with IFN-g for 24 h when indicated, and mRNA levels of the indicated genes were analyzed using quanti- tative real-time PCR and normalized to GAPDH. All data represent three independent experiments (1SD), and statistical significance was determined by ANOVA using Dunnett’s multiple comparison test (*p < 0.05, **p < 0.01).

Article Snippet: For knockout screening, we used the human CRISPR Brunello genomewide knockout library, a gift from David Root and John Doench (Addgene, 73178).

Techniques: CRISPR, Activation Assay, Expressing, Stable Transfection, Transduction, Control, Plasmid Preparation, Flow Cytometry, Western Blot, Real-time Polymerase Chain Reaction, Comparison

FIGURE 4. VGLL3 cooperates with TEAD1 to drive PD-L1 expression. (A) Schematic setup of the screen. MelJuSo cells stably expressing FLAG- VGLL3 were transduced with the Brunello CRISPR knockout library and sorted by FACS twice for cells displaying low PD-L1 surface levels. (B) Results of the RSA analysis of the inserts from the biological duplicates, with three candidates indicated with gray dots. (C) Western blot validation of the knockout effi- ciency of the pooled MelJuSo VGLL3 knockout cells transduced with the indicated gRNAs. (D) MelJuSo FLAG-VGLL3 or FLAG-expressing cells were transduced with the indicated gRNAs, and pooled knockout lines were analyzed for surface PD-L1 expression using flow cytometry. (E) Left: Myc or Myc- TEAD1 were isolated from HEK293T cells using Myc-TRAP beads, and associated FLAG-VGLL3 or FLAG-VGLL3(vhfaaa) was detected by Western blot analysis. Right: MelJuSo cells transduced with the indicated expression constructs were analyzed for expression of PD-L1 using flow cytometry. (F) MelJuSo cells stably expressing FLAG or FLAG-VGLL3 were transfected with the indicated siRNAs and 3 d later were analyzed for PD-L1 expression using flow cytometry. (G) As in F, but 3 d after siRNA transfection. mRNA was isolated, and the expression of PD-L1 transcript was analyzed by qRT-PCR and normal- ized to GAPDH mRNA. All data represent three independent experiments (1SD); statistical significance was determined by ANOVA using Dunnett’s multi- ple comparison test (*p < 0.05, **p < 0.01).

Journal: Journal of immunology (Baltimore, Md. : 1950)

Article Title: CRISPR Activation Screening Identifies VGLL3-TEAD1-RUNX1/3 as a Transcriptional Complex for PD-L1 Expression.

doi: 10.4049/jimmunol.2100917

Figure Lengend Snippet: FIGURE 4. VGLL3 cooperates with TEAD1 to drive PD-L1 expression. (A) Schematic setup of the screen. MelJuSo cells stably expressing FLAG- VGLL3 were transduced with the Brunello CRISPR knockout library and sorted by FACS twice for cells displaying low PD-L1 surface levels. (B) Results of the RSA analysis of the inserts from the biological duplicates, with three candidates indicated with gray dots. (C) Western blot validation of the knockout effi- ciency of the pooled MelJuSo VGLL3 knockout cells transduced with the indicated gRNAs. (D) MelJuSo FLAG-VGLL3 or FLAG-expressing cells were transduced with the indicated gRNAs, and pooled knockout lines were analyzed for surface PD-L1 expression using flow cytometry. (E) Left: Myc or Myc- TEAD1 were isolated from HEK293T cells using Myc-TRAP beads, and associated FLAG-VGLL3 or FLAG-VGLL3(vhfaaa) was detected by Western blot analysis. Right: MelJuSo cells transduced with the indicated expression constructs were analyzed for expression of PD-L1 using flow cytometry. (F) MelJuSo cells stably expressing FLAG or FLAG-VGLL3 were transfected with the indicated siRNAs and 3 d later were analyzed for PD-L1 expression using flow cytometry. (G) As in F, but 3 d after siRNA transfection. mRNA was isolated, and the expression of PD-L1 transcript was analyzed by qRT-PCR and normal- ized to GAPDH mRNA. All data represent three independent experiments (1SD); statistical significance was determined by ANOVA using Dunnett’s multi- ple comparison test (*p < 0.05, **p < 0.01).

Article Snippet: For knockout screening, we used the human CRISPR Brunello genomewide knockout library, a gift from David Root and John Doench (Addgene, 73178).

Techniques: Expressing, Stable Transfection, Transduction, CRISPR, Knock-Out, Western Blot, Biomarker Discovery, Flow Cytometry, Isolation, Construct, Transfection, Quantitative RT-PCR, Comparison

Figure 1. Genome-wide CRISPR-Cas9 screen identifies host-encoded regulators of SARS-CoV-2 frameshifting (A) Schematic of the SARS-CoV-2 genome. Dotted box indicates close up of region shown in (B) harboring the coronavirus frameshifting element (FSE). (B) Secondary structure of the SARS-CoV-2 FSE containing the slippery sequence and three-stemmed pseudoknot. Based on structural data from Bhatt et al.13

Journal: Cell reports

Article Title: CRISPR screening reveals a dependency on ribosome recycling for efficient SARS-CoV-2 programmed ribosomal frameshifting and viral replication.

doi: 10.1016/j.celrep.2023.112076

Figure Lengend Snippet: Figure 1. Genome-wide CRISPR-Cas9 screen identifies host-encoded regulators of SARS-CoV-2 frameshifting (A) Schematic of the SARS-CoV-2 genome. Dotted box indicates close up of region shown in (B) harboring the coronavirus frameshifting element (FSE). (B) Secondary structure of the SARS-CoV-2 FSE containing the slippery sequence and three-stemmed pseudoknot. Based on structural data from Bhatt et al.13

Article Snippet: REAGENT or RESOURCE SOURCE IDENTIFIER Deposited data CRISPR screening data This paper GEO: GSE206101 Experimental models: Cell lines HCT116 ATCC CCL-247; RRID: CVCL_0291 HEK293T ATCC CRL-3216; RRID: CVCL_0063 VeroE6 ATCC CRL-1586; RRID: CVCL_0574 HCT116-SARS-CoV-2-PRF-1 reporter cell line 1 This paper N/A HCT116-SARS-CoV-2-PRF-1 reporter cell line 2 This paper N/A HCT116-SARS-CoV-2-PRF-0 reporter cell line 1 This paper N/A HCT116-SARS-CoV-2-PRF-0 reporter cell line 2 This paper N/A HCT116-ACE2-Blast cell line 1 This paper N/A HCT116-ACE2-Blast cell line 2 This paper N/A Oligonucleotides Sequences of oligonucleotides used in this study are provided in Table S2 This paper N/A Recombinant DNA Human CRISPR Knockout Pooled Library (Brunello) Addgene (David Root, John Doench) Cat# 73179; RRID: Addgene_73179 lentiCas9-Blast Addgene (Feng Zhang) Cat# 52962; RRID: Addgene_52962 lentiCRISPR v2 Addgene (Feng Zhang) Cat# 52961; RRID: Addgene_52961 pMD2.G Addgene (Didier Trono) Cat# 12259; RRID: Addgene_12259 psPAX2 Addgene (Didier Trono) Cat# 12260; RRID: Addgene_12260 pSCRBBL-ACE2-Blasticidin John Schoggins lab N/A lenti-mCh-HIV-PRF-1-P2A-eGFP This paper N/A lenti-mCh-HIV-PRF-0-P2A-eGFP This paper N/A lenti-mCh-SARS-CoV2-PRF-1-P2A-eGFP This paper N/A lenti-mCh-SARS-CoV2-PRF-0-P2A-eGFP This paper N/A lenti-nLuc-HKU1-PRF-1-ffLuc This paper N/A lenti-nLuc-HKU1-PRF-0-ffLuc This paper N/A lenti-nLuc-OC43-PRF-1-ffLuc This paper N/A lenti-nLuc-OC43-PRF-0-ffLuc This paper N/A lenti-nLuc-SARS-CoV1-PRF-1-ffLuc This paper N/A lenti-nLuc-SARS-CoV1-PRF-0-ffLuc This paper N/A lenti-nLuc-SARS-CoV2-PRF-1-ffLuc This paper N/A lenti-nLuc-SARS-CoV2-PRF-0-ffLuc This paper N/A lenti-nLuc-SARS-CoV2-UUA-PRF-1-ffLuc This paper N/A lenti-nLuc-SARS-CoV2-UUA-PRF-0-ffLuc This paper N/A lentiCRISPR-v2-sgRNA-hsa-ABCE1-1 This paper N/A lentiCRISPR-v2-sgRNA-hsa-ABCE1-2 This paper N/A lentiCRISPR-v2-sgRNA-hsa-DENR-1 This paper N/A lentiCRISPR-v2-sgRNA-hsa-DENR-2 This paper N/A lentiCRISPR-v2-gRNA-hsa-DOHH This paper N/A lentiCRISPR-v2-gRNA-hsa-DPH1 This paper N/A lentiCRISPR-v2-gRNA-hsa-DPH3 This paper N/A lentiCRISPR-v2-gRNA-hsa-EIF2D This paper N/A lentiCRISPR-v2-gRNA-hsa-EIF5A Manjunath et al.53 N/A lentiCRISPR-v2-gRNA-hsa-POLR3K This paper N/A lentiCRISPR-v2-sgRNA-hsa-ORAOV1 This paper N/A lentiCRISPR-v2-sgRNA-hsa-YAE1D1 This paper N/A (Continued on next page) Cell Reports 42, 112076, February 28, 2023 15

Techniques: Genome Wide, CRISPR, Sequencing

Figure 6. Loss of ribosome recycling factors inhibits SARS-CoV-2 replication and reduces ribosomal frameshifting during infection (A) Experimental workflow for testing the effect of ribosome recycling on SARS-CoV-2 replication. (1) Lentiviral expression of ACE2 in HCT116 cells. (2) CRISPR- Cas9-mediated knockout of ABCE1 or DENR. (3) Infection with SARS-CoV-2. (4) Sample collection 7 h post-infection and qRT-PCR analysis of nucleocapsid (N) expression. (B) Immunoblotting of ABCE1 and DENR in HCT116-ACE2 CRISPR knockout pools. (C and D) qRT-PCR measurement of nucleocapsid mRNA expression 7 h after SARS-CoV-2 infection in cells transduced with non-target control sgRNA (sgNeg) or sgRNAs targeting ABCE1 (C) or DENR (D). Two distinct sgRNAs were used per gene in two independent ACE2-expressing HCT116 cell lines (ACE2-1 and ACE2-2). Nucleocapsid expression was normalized to host GAPDH expression. (E) Schematic of SARS-CoV-2 ORF1a and ORF1b non-structural proteins (NSPs). Antibody symbols indicate upstream (NSP1) and downstream (NSP16) NSPs that were detected by immunoblotting to assess relative frameshifting rate. (F) Representative western blot for NSP1, NSP16, and nucleocapsid from uninfected cells, infected control cells (sgNeg) and infected ABCE1 knockout pools generated with two independent sgRNAs (sgABCE1-1 and sgABCE1-2). (G) Quantification of NSP1, NSP16, and nucleocapsid protein levels, normalized to host GAPDH expression, from three independent experiments. Data are represented as the mean ± SD with individual replicates plotted. The p values for qRT-PCR experiments were calculated by two-way ANOVA with Dunnett’s multiple comparisons test. The p values for the immunoblotting results were calculated by two-way ANOVA with Tukey’s multiple comparisons test; **p % 0.01, ***p % 0.001; n = 3 biological replicates for all experiments.

Journal: Cell reports

Article Title: CRISPR screening reveals a dependency on ribosome recycling for efficient SARS-CoV-2 programmed ribosomal frameshifting and viral replication.

doi: 10.1016/j.celrep.2023.112076

Figure Lengend Snippet: Figure 6. Loss of ribosome recycling factors inhibits SARS-CoV-2 replication and reduces ribosomal frameshifting during infection (A) Experimental workflow for testing the effect of ribosome recycling on SARS-CoV-2 replication. (1) Lentiviral expression of ACE2 in HCT116 cells. (2) CRISPR- Cas9-mediated knockout of ABCE1 or DENR. (3) Infection with SARS-CoV-2. (4) Sample collection 7 h post-infection and qRT-PCR analysis of nucleocapsid (N) expression. (B) Immunoblotting of ABCE1 and DENR in HCT116-ACE2 CRISPR knockout pools. (C and D) qRT-PCR measurement of nucleocapsid mRNA expression 7 h after SARS-CoV-2 infection in cells transduced with non-target control sgRNA (sgNeg) or sgRNAs targeting ABCE1 (C) or DENR (D). Two distinct sgRNAs were used per gene in two independent ACE2-expressing HCT116 cell lines (ACE2-1 and ACE2-2). Nucleocapsid expression was normalized to host GAPDH expression. (E) Schematic of SARS-CoV-2 ORF1a and ORF1b non-structural proteins (NSPs). Antibody symbols indicate upstream (NSP1) and downstream (NSP16) NSPs that were detected by immunoblotting to assess relative frameshifting rate. (F) Representative western blot for NSP1, NSP16, and nucleocapsid from uninfected cells, infected control cells (sgNeg) and infected ABCE1 knockout pools generated with two independent sgRNAs (sgABCE1-1 and sgABCE1-2). (G) Quantification of NSP1, NSP16, and nucleocapsid protein levels, normalized to host GAPDH expression, from three independent experiments. Data are represented as the mean ± SD with individual replicates plotted. The p values for qRT-PCR experiments were calculated by two-way ANOVA with Dunnett’s multiple comparisons test. The p values for the immunoblotting results were calculated by two-way ANOVA with Tukey’s multiple comparisons test; **p % 0.01, ***p % 0.001; n = 3 biological replicates for all experiments.

Article Snippet: REAGENT or RESOURCE SOURCE IDENTIFIER Deposited data CRISPR screening data This paper GEO: GSE206101 Experimental models: Cell lines HCT116 ATCC CCL-247; RRID: CVCL_0291 HEK293T ATCC CRL-3216; RRID: CVCL_0063 VeroE6 ATCC CRL-1586; RRID: CVCL_0574 HCT116-SARS-CoV-2-PRF-1 reporter cell line 1 This paper N/A HCT116-SARS-CoV-2-PRF-1 reporter cell line 2 This paper N/A HCT116-SARS-CoV-2-PRF-0 reporter cell line 1 This paper N/A HCT116-SARS-CoV-2-PRF-0 reporter cell line 2 This paper N/A HCT116-ACE2-Blast cell line 1 This paper N/A HCT116-ACE2-Blast cell line 2 This paper N/A Oligonucleotides Sequences of oligonucleotides used in this study are provided in Table S2 This paper N/A Recombinant DNA Human CRISPR Knockout Pooled Library (Brunello) Addgene (David Root, John Doench) Cat# 73179; RRID: Addgene_73179 lentiCas9-Blast Addgene (Feng Zhang) Cat# 52962; RRID: Addgene_52962 lentiCRISPR v2 Addgene (Feng Zhang) Cat# 52961; RRID: Addgene_52961 pMD2.G Addgene (Didier Trono) Cat# 12259; RRID: Addgene_12259 psPAX2 Addgene (Didier Trono) Cat# 12260; RRID: Addgene_12260 pSCRBBL-ACE2-Blasticidin John Schoggins lab N/A lenti-mCh-HIV-PRF-1-P2A-eGFP This paper N/A lenti-mCh-HIV-PRF-0-P2A-eGFP This paper N/A lenti-mCh-SARS-CoV2-PRF-1-P2A-eGFP This paper N/A lenti-mCh-SARS-CoV2-PRF-0-P2A-eGFP This paper N/A lenti-nLuc-HKU1-PRF-1-ffLuc This paper N/A lenti-nLuc-HKU1-PRF-0-ffLuc This paper N/A lenti-nLuc-OC43-PRF-1-ffLuc This paper N/A lenti-nLuc-OC43-PRF-0-ffLuc This paper N/A lenti-nLuc-SARS-CoV1-PRF-1-ffLuc This paper N/A lenti-nLuc-SARS-CoV1-PRF-0-ffLuc This paper N/A lenti-nLuc-SARS-CoV2-PRF-1-ffLuc This paper N/A lenti-nLuc-SARS-CoV2-PRF-0-ffLuc This paper N/A lenti-nLuc-SARS-CoV2-UUA-PRF-1-ffLuc This paper N/A lenti-nLuc-SARS-CoV2-UUA-PRF-0-ffLuc This paper N/A lentiCRISPR-v2-sgRNA-hsa-ABCE1-1 This paper N/A lentiCRISPR-v2-sgRNA-hsa-ABCE1-2 This paper N/A lentiCRISPR-v2-sgRNA-hsa-DENR-1 This paper N/A lentiCRISPR-v2-sgRNA-hsa-DENR-2 This paper N/A lentiCRISPR-v2-gRNA-hsa-DOHH This paper N/A lentiCRISPR-v2-gRNA-hsa-DPH1 This paper N/A lentiCRISPR-v2-gRNA-hsa-DPH3 This paper N/A lentiCRISPR-v2-gRNA-hsa-EIF2D This paper N/A lentiCRISPR-v2-gRNA-hsa-EIF5A Manjunath et al.53 N/A lentiCRISPR-v2-gRNA-hsa-POLR3K This paper N/A lentiCRISPR-v2-sgRNA-hsa-ORAOV1 This paper N/A lentiCRISPR-v2-sgRNA-hsa-YAE1D1 This paper N/A (Continued on next page) Cell Reports 42, 112076, February 28, 2023 15

Techniques: Infection, Expressing, CRISPR, Knock-Out, Quantitative RT-PCR, Western Blot, Transduction, Control, Generated