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Synthego Inc arrayed sgrna library

(A) Rolling circle assay (RCA) analysis of genomic DNA isolated from U2OS cells transfected with the <t>sgRNA</t> and relative quantification. (B) Example of TAILS data and quantification; U2OS cells transfected with indicated sgRNA were processed as described in the TAILS pipeline. A minimum of 2,200 cells were analyzed per condition. The scale bar represents 10 μm. (C) TAILS analysis in ALT-positive U2OS cells. The ssTelo intensity derived from two biological replicates of the arrayed <t>sgRNA</t> <t>library.</t> Linear regression analysis by Pearson correlation coefficient (R) was calculated, and it is 0.701. (D) Scatterplot displaying mean Z score for each gene assayed (y axis) and the relative ranking based on descending Z score (x axis). Dotted lines indicate the cutoff value (±2.4) chosen to identify putative hits. Genes that have previously been reported to affect ALT activity are indicated. (E) Images obtained by TAILS of hits selected known modulators of ALT. The scale bar represents 10 μm. (F) Gene function classification of all the genes contained in sgRNA library (left diagram) and the hits identified by TAILS (right diagrams). For more information, see .

Arrayed Sgrna Library, supplied by Synthego 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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1) Product Images from "Identification of modulators of the ALT pathway through a native FISH-based optical screen"

Article Title: Identification of modulators of the ALT pathway through a native FISH-based optical screen

Journal: Cell reports

doi: 10.1016/j.celrep.2024.115114

(A) Rolling circle assay (RCA) analysis of genomic DNA isolated from U2OS cells transfected with the sgRNA and relative quantification. (B) Example of TAILS data and quantification; U2OS cells transfected with indicated sgRNA were processed as described in the TAILS pipeline. A minimum of 2,200 cells were analyzed per condition. The scale bar represents 10 μm. (C) TAILS analysis in ALT-positive U2OS cells. The ssTelo intensity derived from two biological replicates of the arrayed sgRNA library. Linear regression analysis by Pearson correlation coefficient (R) was calculated, and it is 0.701. (D) Scatterplot displaying mean Z score for each gene assayed (y axis) and the relative ranking based on descending Z score (x axis). Dotted lines indicate the cutoff value (±2.4) chosen to identify putative hits. Genes that have previously been reported to affect ALT activity are indicated. (E) Images obtained by TAILS of hits selected known modulators of ALT. The scale bar represents 10 μm. (F) Gene function classification of all the genes contained in sgRNA library (left diagram) and the hits identified by TAILS (right diagrams). For more information, see .

Figure Legend Snippet: (A) Rolling circle assay (RCA) analysis of genomic DNA isolated from U2OS cells transfected with the sgRNA and relative quantification. (B) Example of TAILS data and quantification; U2OS cells transfected with indicated sgRNA were processed as described in the TAILS pipeline. A minimum of 2,200 cells were analyzed per condition. The scale bar represents 10 μm. (C) TAILS analysis in ALT-positive U2OS cells. The ssTelo intensity derived from two biological replicates of the arrayed sgRNA library. Linear regression analysis by Pearson correlation coefficient (R) was calculated, and it is 0.701. (D) Scatterplot displaying mean Z score for each gene assayed (y axis) and the relative ranking based on descending Z score (x axis). Dotted lines indicate the cutoff value (±2.4) chosen to identify putative hits. Genes that have previously been reported to affect ALT activity are indicated. (E) Images obtained by TAILS of hits selected known modulators of ALT. The scale bar represents 10 μm. (F) Gene function classification of all the genes contained in sgRNA library (left diagram) and the hits identified by TAILS (right diagrams). For more information, see .

Techniques Used: Isolation, Transfection, Quantitative Proteomics, Derivative Assay, Activity Assay

(A) Gene function composition of the arrayed sgRNA library (“Library”) and the number genes that were identified as putative ALT suppressors by TAILS (“Enriched”). (B) List of ALT-suppressor genes that were further characterized in this study. The table reports the average Z score (“Score”) and gene function category (“Function”). (C and D) Rolling circle assay (RCA) analysis of genomic DNA isolated from 3 independent DDX39A −/− clones (C1, C2, and C3) and the parental U2OS cells (WT). (E and F) Representative images and quantification of DDX39A-deficient and -proficient cells in ALT-positive U2OS and ALT-negative HeLa backgrounds. Each dot represents one cell, with a minimum of 200 cells per condition analyzed across 2 independent experiments. The scale bar represents 10 μm. (G) ssTelo staining of U2OS cells transfected with sgRNAs against FANCM, DDX39A, DDX39B, and a non-targeting control (sgCtrl) in the presence of absence of sgRNA against BLM. A minimum of 1,800 cells were analyzed per condition. The scale bar represents 10 μm. (H) Quantification of the data shown in (G). (I and J) Representative images and quantification of ssTelo analysis ALT-positive U2OS, G292, and SAOS-2 cells treated with transcription inhibitor DRB. Each dot represents one cell, with a minimum of 250 cells per condition analyzed across 2 independent experiments. The scale bar represents 10 μm. (K) Quantification of ssTelo analysis U2OS cells treated with DRB in the presence of absence of sgRNA against BLM. Each dot represents one cell, with a minimum of 300 cells per condition analyzed across 2 independent experiments. For representative images, see . Data are represented as mean ± SEM. An unpaired t test was used for statistical analysis; ** p ≤ 0.01 and **** p ≤ 0.0001 on the graphs.

Figure Legend Snippet: (A) Gene function composition of the arrayed sgRNA library (“Library”) and the number genes that were identified as putative ALT suppressors by TAILS (“Enriched”). (B) List of ALT-suppressor genes that were further characterized in this study. The table reports the average Z score (“Score”) and gene function category (“Function”). (C and D) Rolling circle assay (RCA) analysis of genomic DNA isolated from 3 independent DDX39A −/− clones (C1, C2, and C3) and the parental U2OS cells (WT). (E and F) Representative images and quantification of DDX39A-deficient and -proficient cells in ALT-positive U2OS and ALT-negative HeLa backgrounds. Each dot represents one cell, with a minimum of 200 cells per condition analyzed across 2 independent experiments. The scale bar represents 10 μm. (G) ssTelo staining of U2OS cells transfected with sgRNAs against FANCM, DDX39A, DDX39B, and a non-targeting control (sgCtrl) in the presence of absence of sgRNA against BLM. A minimum of 1,800 cells were analyzed per condition. The scale bar represents 10 μm. (H) Quantification of the data shown in (G). (I and J) Representative images and quantification of ssTelo analysis ALT-positive U2OS, G292, and SAOS-2 cells treated with transcription inhibitor DRB. Each dot represents one cell, with a minimum of 250 cells per condition analyzed across 2 independent experiments. The scale bar represents 10 μm. (K) Quantification of ssTelo analysis U2OS cells treated with DRB in the presence of absence of sgRNA against BLM. Each dot represents one cell, with a minimum of 300 cells per condition analyzed across 2 independent experiments. For representative images, see . Data are represented as mean ± SEM. An unpaired t test was used for statistical analysis; ** p ≤ 0.01 and **** p ≤ 0.0001 on the graphs.

Techniques Used: Isolation, Clone Assay, Staining, Transfection, Control

Figure Legend Snippet:

Techniques Used: Recombinant, Extraction, SYBR Green Assay, Control, Blocking Assay, Reverse Transcription, shRNA, Plasmid Preparation, Software

Image Search Results

A Schematic of the experimental strategy for performing in vivo genome-wide sgRNA screens to identify candidate tumor suppressors. Fetal liver cells (FLCs), a rich source of hematopoietic stem/progenitor cells (HSPCs), from E13.5 Eµ-Myc ; Cas9 embryos (C57BL/6-Ly5.2 background) were transduced with lentiviruses containing sgRNAs targeting p53 ( sgp53; positive control), a negative control sgRNA targeting human BIM ( sgControl) or a whole-genome sgRNA library 28 . The transduced FLCs were injected intravenously (i.v.) into lethally irradiated (2 × 5.5 Gy, 3 h apart) congenic recipient C57BL/6-Ly5.1 mice. Lymphoma bearing mice displayed enlarged spleen, lymph nodes and/or thymus. Genomic DNA was isolated from the spleen, comprising mostly of lymphoma cells but also containing non-transformed hematopoietic cells. The enriched sgRNAs were identified by NGS. Schematic created in BioRender. Potts, M. ( https://BioRender.com/smdpt7f ). B Tumor-free survival of mice transplanted with Eµ-Myc;Cas9 FLCs that had been lentivirally transduced with the positive control sgRNA ( sgp53) , the negative control sgRNA ( sgControl) or a whole-genome sgRNA library. The dotted line represents cut-off for lymphomas from the whole genome sgRNA library cohort that were arbitrarily deemed to be accelerated and therefore selected for further analysis. n represents total number of transplanted mice per sgRNA from 6 reconstitution cohorts. Median survival is indicated in brackets. Log-rank (Mantel-Cox) statistical test for survival curve comparison to sgControl . C Top 10 tumor suppressor genes identified as hits, determined by frequency of their sgRNAs detected in independent lymphomas from mice from the whole-genome sgRNA library cohort that showed accelerated lymphoma, with the corresponding sgRNAs found to be highly enriched ( > 50% of reads within a given lymphoma) by sequencing. Genes emboldened (five of the top 10) represent those encoding proteins with functions in the mTORC1 inhibitory pathway. Source data provided as Supplementary Data and as Source Data File.

Journal: Nature Communications

Article Title: Genome-wide in vivo CRISPR screens identify GATOR1 complex as a tumor suppressor in Myc-driven lymphoma

doi: 10.1038/s41467-025-62615-y

Figure Lengend Snippet: A Schematic of the experimental strategy for performing in vivo genome-wide sgRNA screens to identify candidate tumor suppressors. Fetal liver cells (FLCs), a rich source of hematopoietic stem/progenitor cells (HSPCs), from E13.5 Eµ-Myc ; Cas9 embryos (C57BL/6-Ly5.2 background) were transduced with lentiviruses containing sgRNAs targeting p53 ( sgp53; positive control), a negative control sgRNA targeting human BIM ( sgControl) or a whole-genome sgRNA library 28 . The transduced FLCs were injected intravenously (i.v.) into lethally irradiated (2 × 5.5 Gy, 3 h apart) congenic recipient C57BL/6-Ly5.1 mice. Lymphoma bearing mice displayed enlarged spleen, lymph nodes and/or thymus. Genomic DNA was isolated from the spleen, comprising mostly of lymphoma cells but also containing non-transformed hematopoietic cells. The enriched sgRNAs were identified by NGS. Schematic created in BioRender. Potts, M. ( https://BioRender.com/smdpt7f ). B Tumor-free survival of mice transplanted with Eµ-Myc;Cas9 FLCs that had been lentivirally transduced with the positive control sgRNA ( sgp53) , the negative control sgRNA ( sgControl) or a whole-genome sgRNA library. The dotted line represents cut-off for lymphomas from the whole genome sgRNA library cohort that were arbitrarily deemed to be accelerated and therefore selected for further analysis. n represents total number of transplanted mice per sgRNA from 6 reconstitution cohorts. Median survival is indicated in brackets. Log-rank (Mantel-Cox) statistical test for survival curve comparison to sgControl . C Top 10 tumor suppressor genes identified as hits, determined by frequency of their sgRNAs detected in independent lymphomas from mice from the whole-genome sgRNA library cohort that showed accelerated lymphoma, with the corresponding sgRNAs found to be highly enriched ( > 50% of reads within a given lymphoma) by sequencing. Genes emboldened (five of the top 10) represent those encoding proteins with functions in the mTORC1 inhibitory pathway. Source data provided as Supplementary Data and as Source Data File.

Article Snippet: Two independent sgRNAs for in vivo validation experiments of hits and a negative control sgRNA targeting human NLRC5 , (Supplementary Table ) were obtained from the Merck CRISPR glycerol stock arrayed mouse sgRNA library available at WEHI (Sigma Aldrich # MSANGERG ).

Techniques: In Vivo, Genome Wide, Transduction, Positive Control, Negative Control, Injection, Irradiation, Isolation, Transformation Assay, Comparison, Sequencing

A Schematic of the GATOR1 complex, consisting of three proteins, NPRL3, DEPDC5 (sgRNAs targeting their genes identified as hits in our genome-wide CRISPR screen), and NPRL2. The GATOR1 complex negatively regulates mTORC1 signaling in response to the availability of the amino acids leucine (Leu), methionine (Met) and arginine (Arg). B – D Tumor-free survival of mice transplanted with FLCs from Eµ-Myc;Cas9 E13.5 embryos that had been transduced with either the sg p53 (positive control), the negative control sgRNA ( sgControl ) or two independent sgRNAs each for targeting either Nprl3 ( B ) Depdc5 ( C ) or Nprl2 ( D ). n represents the number of transplanted mice per sgRNA across two transplanted mouse cohorts. Median survival is indicated in brackets. Two-sided log-rank (Mantel-Cox) test was used for comparison of mouse survival curves to sgControl . E , Proportions of frameshift, in frame InDels or wildtype (wt) sequence reads for the target gene of each sgRNA, analyzed by NGS. Each bar represents one lymphoma cell line derived from lymphomas of recipient mice that had been transplanted with sgNrpl3, sgDepdc5 or sgNprl2 Eµ-Myc;Cas9 FLCs ( n = 3 cell lines per genotype). Source data provided as Supplementary Data . F Survival of human patients with diffuse large B cell lymphoma (DLBCL) , a cancer driven by abnormally high c-MYC expression, stratified by GATOR1 mRNA expression, where the GATOR1 -low ( n = 103) strata is defined as expression of either the NPRL3 , DEPDC5 or NPRL2 mRNA in the lowest quartile. The others were grouped into the GATOR1 -high strata ( n = 104). Two-sided log-rank Kaplan-Meier statistical test, P = 0.0021. Source data are provided as a Source Data file.

Journal: Nature Communications

Article Title: Genome-wide in vivo CRISPR screens identify GATOR1 complex as a tumor suppressor in Myc-driven lymphoma

doi: 10.1038/s41467-025-62615-y

Figure Lengend Snippet: A Schematic of the GATOR1 complex, consisting of three proteins, NPRL3, DEPDC5 (sgRNAs targeting their genes identified as hits in our genome-wide CRISPR screen), and NPRL2. The GATOR1 complex negatively regulates mTORC1 signaling in response to the availability of the amino acids leucine (Leu), methionine (Met) and arginine (Arg). B – D Tumor-free survival of mice transplanted with FLCs from Eµ-Myc;Cas9 E13.5 embryos that had been transduced with either the sg p53 (positive control), the negative control sgRNA ( sgControl ) or two independent sgRNAs each for targeting either Nprl3 ( B ) Depdc5 ( C ) or Nprl2 ( D ). n represents the number of transplanted mice per sgRNA across two transplanted mouse cohorts. Median survival is indicated in brackets. Two-sided log-rank (Mantel-Cox) test was used for comparison of mouse survival curves to sgControl . E , Proportions of frameshift, in frame InDels or wildtype (wt) sequence reads for the target gene of each sgRNA, analyzed by NGS. Each bar represents one lymphoma cell line derived from lymphomas of recipient mice that had been transplanted with sgNrpl3, sgDepdc5 or sgNprl2 Eµ-Myc;Cas9 FLCs ( n = 3 cell lines per genotype). Source data provided as Supplementary Data . F Survival of human patients with diffuse large B cell lymphoma (DLBCL) , a cancer driven by abnormally high c-MYC expression, stratified by GATOR1 mRNA expression, where the GATOR1 -low ( n = 103) strata is defined as expression of either the NPRL3 , DEPDC5 or NPRL2 mRNA in the lowest quartile. The others were grouped into the GATOR1 -high strata ( n = 104). Two-sided log-rank Kaplan-Meier statistical test, P = 0.0021. Source data are provided as a Source Data file.

Techniques: Genome Wide, CRISPR, Transduction, Positive Control, Negative Control, Comparison, Sequencing, Derivative Assay, Expressing

Journal: Cell reports

Article Title: Identification of modulators of the ALT pathway through a native FISH-based optical screen

doi: 10.1016/j.celrep.2024.115114

Figure Lengend Snippet: (A) Rolling circle assay (RCA) analysis of genomic DNA isolated from U2OS cells transfected with the sgRNA and relative quantification. (B) Example of TAILS data and quantification; U2OS cells transfected with indicated sgRNA were processed as described in the TAILS pipeline. A minimum of 2,200 cells were analyzed per condition. The scale bar represents 10 μm. (C) TAILS analysis in ALT-positive U2OS cells. The ssTelo intensity derived from two biological replicates of the arrayed sgRNA library. Linear regression analysis by Pearson correlation coefficient (R) was calculated, and it is 0.701. (D) Scatterplot displaying mean Z score for each gene assayed (y axis) and the relative ranking based on descending Z score (x axis). Dotted lines indicate the cutoff value (±2.4) chosen to identify putative hits. Genes that have previously been reported to affect ALT activity are indicated. (E) Images obtained by TAILS of hits selected known modulators of ALT. The scale bar represents 10 μm. (F) Gene function classification of all the genes contained in sgRNA library (left diagram) and the hits identified by TAILS (right diagrams). For more information, see .

Article Snippet: The arrayed sgRNA library (Synthego) used in this study targets 1064 human genes involved in DNA transactions (see for details).

Techniques: Isolation, Transfection, Quantitative Proteomics, Derivative Assay, Activity Assay

Journal: Cell reports

Article Title: Identification of modulators of the ALT pathway through a native FISH-based optical screen

doi: 10.1016/j.celrep.2024.115114

Figure Lengend Snippet: (A) Gene function composition of the arrayed sgRNA library (“Library”) and the number genes that were identified as putative ALT suppressors by TAILS (“Enriched”). (B) List of ALT-suppressor genes that were further characterized in this study. The table reports the average Z score (“Score”) and gene function category (“Function”). (C and D) Rolling circle assay (RCA) analysis of genomic DNA isolated from 3 independent DDX39A −/− clones (C1, C2, and C3) and the parental U2OS cells (WT). (E and F) Representative images and quantification of DDX39A-deficient and -proficient cells in ALT-positive U2OS and ALT-negative HeLa backgrounds. Each dot represents one cell, with a minimum of 200 cells per condition analyzed across 2 independent experiments. The scale bar represents 10 μm. (G) ssTelo staining of U2OS cells transfected with sgRNAs against FANCM, DDX39A, DDX39B, and a non-targeting control (sgCtrl) in the presence of absence of sgRNA against BLM. A minimum of 1,800 cells were analyzed per condition. The scale bar represents 10 μm. (H) Quantification of the data shown in (G). (I and J) Representative images and quantification of ssTelo analysis ALT-positive U2OS, G292, and SAOS-2 cells treated with transcription inhibitor DRB. Each dot represents one cell, with a minimum of 250 cells per condition analyzed across 2 independent experiments. The scale bar represents 10 μm. (K) Quantification of ssTelo analysis U2OS cells treated with DRB in the presence of absence of sgRNA against BLM. Each dot represents one cell, with a minimum of 300 cells per condition analyzed across 2 independent experiments. For representative images, see . Data are represented as mean ± SEM. An unpaired t test was used for statistical analysis; ** p ≤ 0.01 and **** p ≤ 0.0001 on the graphs.

Article Snippet: The arrayed sgRNA library (Synthego) used in this study targets 1064 human genes involved in DNA transactions (see for details).

Techniques: Isolation, Clone Assay, Staining, Transfection, Control

Journal: Cell reports

Article Title: Identification of modulators of the ALT pathway through a native FISH-based optical screen

doi: 10.1016/j.celrep.2024.115114

Figure Lengend Snippet:

Article Snippet: The arrayed sgRNA library (Synthego) used in this study targets 1064 human genes involved in DNA transactions (see for details).

Techniques: Recombinant, Extraction, SYBR Green Assay, Control, Blocking Assay, Reverse Transcription, shRNA, Plasmid Preparation, Software

Genome-scale CRISPRi fitness profiling in Mtb (A) Experimental design to quantify Mtb gene vulnerability. (i) The Mtb CRISPRi library was built by cloning an sgRNA oligo array into an anhydrotetracycline (ATc)-inducible Sth1dCas9 vector. The library was designed to target all possible Mtb genes with sgRNAs of varying predicted knockdown efficiencies. (ii) Cultures were passaged for approximately 30 generations in the presence (CRISPRi on) or absence of ATc. At the indicated time points, genomic DNA was harvested and sgRNA targeting sequences amplified for next-generation sequencing. (iii) The relative fitness of individual strains was quantified by the sgRNA log2 fold change (L2FC) over time (+ATc/–ATc). Relative fitness values were then used to quantify three parameters that define target vulnerability: (1) maximum fitness cost, (2) sensitivity to partial knockdown, and (3) the phenotypic lag between the timing of CRISPRi induction and onset of a fitness defect. (B) Boxen plots (mean and quantiles) comparing time-dependent changes in L2FC values of sgRNAs targeting genes defined as Essential (n = 63,867) or Non-Essential (n = 29,609) by TnSeq and of control Non-Targeting sgRNAs (n = 1,658). sgRNAs targeting TnSeq Uncertain genes (n = 563) are not shown. (C) Hierarchical clustering of gene level depletion from the experiment described in (A). Each row represents a single targeted Mtb gene. (D) Bar chart showing the overlap between gene calls by TnSeq and CRISPRi. 42 genes in the Mtb genome cannot be called by either method. See also <xref ref-type=

Figure S1 and . " width="100%" height="100%">

Journal: Cell

Article Title: Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis

doi: 10.1016/j.cell.2021.06.033

Figure Lengend Snippet: Genome-scale CRISPRi fitness profiling in Mtb (A) Experimental design to quantify Mtb gene vulnerability. (i) The Mtb CRISPRi library was built by cloning an sgRNA oligo array into an anhydrotetracycline (ATc)-inducible Sth1dCas9 vector. The library was designed to target all possible Mtb genes with sgRNAs of varying predicted knockdown efficiencies. (ii) Cultures were passaged for approximately 30 generations in the presence (CRISPRi on) or absence of ATc. At the indicated time points, genomic DNA was harvested and sgRNA targeting sequences amplified for next-generation sequencing. (iii) The relative fitness of individual strains was quantified by the sgRNA log2 fold change (L2FC) over time (+ATc/–ATc). Relative fitness values were then used to quantify three parameters that define target vulnerability: (1) maximum fitness cost, (2) sensitivity to partial knockdown, and (3) the phenotypic lag between the timing of CRISPRi induction and onset of a fitness defect. (B) Boxen plots (mean and quantiles) comparing time-dependent changes in L2FC values of sgRNAs targeting genes defined as Essential (n = 63,867) or Non-Essential (n = 29,609) by TnSeq and of control Non-Targeting sgRNAs (n = 1,658). sgRNAs targeting TnSeq Uncertain genes (n = 563) are not shown. (C) Hierarchical clustering of gene level depletion from the experiment described in (A). Each row represents a single targeted Mtb gene. (D) Bar chart showing the overlap between gene calls by TnSeq and CRISPRi. 42 genes in the Mtb genome cannot be called by either method. See also Figure S1 and .

Article Snippet: sgRNA oligo arrays to clone CRISPRi libraries RLC11 and RLC12 , This paper , Addgene #163955 and #163954; Github: https://github.com/rock-lab/vulnerability_2021.

Techniques: Cloning, Plasmid Preparation, Knockdown, Amplification, Next-Generation Sequencing, Control

Genome-scale CRISPRi fitness profiling in Mtb H37Rv, related to and , and (A) Histogram depicting the number of sgRNAs per gene in the Mtb CRISPRi library (RLC12; Addgene #163954). (B) Next generation sequencing quality-control metrics for the Mtb CRISPRi library. The “Plasmid” column depicts metrics for the RLC12 plasmid library following cloning and isolation from E. coli . The “H37Rv Mtb” column depicts library metrics following transformation and expansion in Mtb H37Rv. Skew ratio represents the ratio between top and bottom 10% of sgRNA counts. (C-F) Correlation heatmap of the triplicate screens depicted in <xref ref-type=

Figure 1 A. Panel (C) depicts the correlation between non-targeting sgRNAs in the –ATc cultures; panel (D) depicts the correlation between non-targeting sgRNAs in the +ATc cultures; panel (E) depicts the correlation between TnSeq essential gene targeting sgRNAs in the –ATc cultures; panel (F) depicts the correlation between TnSeq essential gene targeting sgRNAs in the +ATc cultures. G, generation. (G) Boxen plots comparing time-dependent changes in sgRNA L2FC values (mean ± quantiles) comparing –ATc to Input (i.e., generation 0). sgRNAs are grouped according to whether they target genes defined as Essential by TnSeq (n = 63,867 sgRNAs) or Non-Targeting sgRNAs (n = 1,658). ns, not significant. (H) Density plot to detect potential new “bad-seed” sequences. The plot shows the L2FC (+ATc/–ATc at generation 24.3) of all sgRNAs targeting non-essential genes (dashed line), and sgRNAs targeting non-essential genes that contain the indicated sgRNA seed sequences (defined as the five PAM-proximal nucleotides of the sgRNA targeting sequence) displaying the strongest depletion from the library. See “ ” for more detail. (I) Violin plot showing the behavior of sgRNAs containing the strongest “bad-seed” sequences identified for SpydCas9 ( Cui et al., 2018 ). Only sgRNAs targeting a CRISPRi non-essential gene were analyzed. sgRNAs with a PAM-proximal ‘ACCCA’ sequence (n = 24) show some evidence for target-independent depletion (i.e., "bad-seed" behavior). Dot and error bars represent mean and SD. ∗ p = 0.021; ns, not significant. (J) Heatmap showing the behavior of mismatched sgRNAs in the competitive fitness experiment depicted in Figure 1 A. ΔL2FC represents the difference in depletion between essential gene-targeting sgRNAs with perfectly matching targeting sequences and the corresponding mismatched sgRNAs. Mismatched sgRNAs contain mismatches between the sgRNA targeting sequence and the gene target at the indicated position (x axis; 22 is the sgRNA nucleotide furthest from the PAM). Mismatched sgRNAs were not designed but were the result of errors during library synthesis or cloning. (K) Frequency of ATc-resistant colonies that occur after transformation of four unique sgRNAs targeting the essential genes gyrB ( ms0005) , dnaE1 ( ms3178) , mmpL3 ( ms0250), and pptT ( ms2648) in Msmeg. Dots represent transformations performed in biological duplicate; error bars indicate median ± 95% CI. CFU, colony forming unit; NT, non-targeting. (L) Table summarizing the mutations observed in the CRISPRi plasmid in independent ATc-resistant colonies. All but two isolates show unique deletions, duplications, or an inversion (all generically marked as Δ to indicate lack of CRISPRi functionality) within the sgRNA, Cas9, or both. WT, wild-type; TetR, Tet repressor protein; oriE, E. coli origin of replication. (M) Line plot showing all sgRNAs targeting dnaA ( rv0001 ) in the Mtb H37Rv CRISPRi fitness experiment. “Flatliner” sgRNAs of presumed CRISPRi-resistant subpopulations are indicated in green. See for details. (N and O) Distribution of sgRNA depletion slopes (β e ) for sgRNAs targeting essential genes (n = 63,867 sgRNAs) stratified by targeted PAM sequence (N) or sgRNA targeting sequence length (O). Black dots and lines show the median and 25%–75% percentiles. Dot and error bars represent mean and SD. NT, non-targeting. " width="100%" height="100%">

Journal: Cell

Article Title: Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis

doi: 10.1016/j.cell.2021.06.033

Figure Lengend Snippet: Genome-scale CRISPRi fitness profiling in Mtb H37Rv, related to and , and (A) Histogram depicting the number of sgRNAs per gene in the Mtb CRISPRi library (RLC12; Addgene #163954). (B) Next generation sequencing quality-control metrics for the Mtb CRISPRi library. The “Plasmid” column depicts metrics for the RLC12 plasmid library following cloning and isolation from E. coli . The “H37Rv Mtb” column depicts library metrics following transformation and expansion in Mtb H37Rv. Skew ratio represents the ratio between top and bottom 10% of sgRNA counts. (C-F) Correlation heatmap of the triplicate screens depicted in Figure 1 A. Panel (C) depicts the correlation between non-targeting sgRNAs in the –ATc cultures; panel (D) depicts the correlation between non-targeting sgRNAs in the +ATc cultures; panel (E) depicts the correlation between TnSeq essential gene targeting sgRNAs in the –ATc cultures; panel (F) depicts the correlation between TnSeq essential gene targeting sgRNAs in the +ATc cultures. G, generation. (G) Boxen plots comparing time-dependent changes in sgRNA L2FC values (mean ± quantiles) comparing –ATc to Input (i.e., generation 0). sgRNAs are grouped according to whether they target genes defined as Essential by TnSeq (n = 63,867 sgRNAs) or Non-Targeting sgRNAs (n = 1,658). ns, not significant. (H) Density plot to detect potential new “bad-seed” sequences. The plot shows the L2FC (+ATc/–ATc at generation 24.3) of all sgRNAs targeting non-essential genes (dashed line), and sgRNAs targeting non-essential genes that contain the indicated sgRNA seed sequences (defined as the five PAM-proximal nucleotides of the sgRNA targeting sequence) displaying the strongest depletion from the library. See “ ” for more detail. (I) Violin plot showing the behavior of sgRNAs containing the strongest “bad-seed” sequences identified for SpydCas9 ( Cui et al., 2018 ). Only sgRNAs targeting a CRISPRi non-essential gene were analyzed. sgRNAs with a PAM-proximal ‘ACCCA’ sequence (n = 24) show some evidence for target-independent depletion (i.e., "bad-seed" behavior). Dot and error bars represent mean and SD. ∗ p = 0.021; ns, not significant. (J) Heatmap showing the behavior of mismatched sgRNAs in the competitive fitness experiment depicted in Figure 1 A. ΔL2FC represents the difference in depletion between essential gene-targeting sgRNAs with perfectly matching targeting sequences and the corresponding mismatched sgRNAs. Mismatched sgRNAs contain mismatches between the sgRNA targeting sequence and the gene target at the indicated position (x axis; 22 is the sgRNA nucleotide furthest from the PAM). Mismatched sgRNAs were not designed but were the result of errors during library synthesis or cloning. (K) Frequency of ATc-resistant colonies that occur after transformation of four unique sgRNAs targeting the essential genes gyrB ( ms0005) , dnaE1 ( ms3178) , mmpL3 ( ms0250), and pptT ( ms2648) in Msmeg. Dots represent transformations performed in biological duplicate; error bars indicate median ± 95% CI. CFU, colony forming unit; NT, non-targeting. (L) Table summarizing the mutations observed in the CRISPRi plasmid in independent ATc-resistant colonies. All but two isolates show unique deletions, duplications, or an inversion (all generically marked as Δ to indicate lack of CRISPRi functionality) within the sgRNA, Cas9, or both. WT, wild-type; TetR, Tet repressor protein; oriE, E. coli origin of replication. (M) Line plot showing all sgRNAs targeting dnaA ( rv0001 ) in the Mtb H37Rv CRISPRi fitness experiment. “Flatliner” sgRNAs of presumed CRISPRi-resistant subpopulations are indicated in green. See for details. (N and O) Distribution of sgRNA depletion slopes (β e ) for sgRNAs targeting essential genes (n = 63,867 sgRNAs) stratified by targeted PAM sequence (N) or sgRNA targeting sequence length (O). Black dots and lines show the median and 25%–75% percentiles. Dot and error bars represent mean and SD. NT, non-targeting.

Article Snippet: sgRNA oligo arrays to clone CRISPRi libraries RLC11 and RLC12 , This paper , Addgene #163955 and #163954; Github: https://github.com/rock-lab/vulnerability_2021.

Techniques: Next-Generation Sequencing, Control, Plasmid Preparation, Cloning, Isolation, Transformation Assay, Sequencing

Identification of features that dictate sgRNA strength (A) Two-line model fits for three different sgRNAs targeting mmpL3 . (B) Bar plot showing the regression coefficients (mean ± SEM) for each sgRNA feature identified by the linear model, colored by feature type. All features were represented by more than 500 sgRNAs except for the 20%–30% GC (n = 18) and 90%–100% GC (n = 458) bins. (C) Comparison of measured versus linear model predicted CRISPRi activity (mean ± SEM) of 29 sgRNAs against a Renilla luciferase target in Msmeg; sgRNAs are color coded from blue (strength = 0) to red (strength = 1). The green dot indicates a control non-targeting sgRNA. RLU, relative light unit. (D) Line plot showing the behavior of sgRNAs targeting the essential gene mmpL3 and non-essential gene clgR . sgRNAs are color coded by predicted strengths as in (C). Circles represent our sequencing limit of detection. Triangles represent the point of observation of rare CRISPRi-resistant subpopulations, beyond which sgRNA L2FC values are not plotted (see for details). See also <xref ref-type=

Figure S1 and . " width="100%" height="100%">

Journal: Cell

Article Title: Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis

doi: 10.1016/j.cell.2021.06.033

Figure Lengend Snippet: Identification of features that dictate sgRNA strength (A) Two-line model fits for three different sgRNAs targeting mmpL3 . (B) Bar plot showing the regression coefficients (mean ± SEM) for each sgRNA feature identified by the linear model, colored by feature type. All features were represented by more than 500 sgRNAs except for the 20%–30% GC (n = 18) and 90%–100% GC (n = 458) bins. (C) Comparison of measured versus linear model predicted CRISPRi activity (mean ± SEM) of 29 sgRNAs against a Renilla luciferase target in Msmeg; sgRNAs are color coded from blue (strength = 0) to red (strength = 1). The green dot indicates a control non-targeting sgRNA. RLU, relative light unit. (D) Line plot showing the behavior of sgRNAs targeting the essential gene mmpL3 and non-essential gene clgR . sgRNAs are color coded by predicted strengths as in (C). Circles represent our sequencing limit of detection. Triangles represent the point of observation of rare CRISPRi-resistant subpopulations, beyond which sgRNA L2FC values are not plotted (see for details). See also Figure S1 and .

Article Snippet: sgRNA oligo arrays to clone CRISPRi libraries RLC11 and RLC12 , This paper , Addgene #163955 and #163954; Github: https://github.com/rock-lab/vulnerability_2021.

Techniques: Comparison, Activity Assay, Luciferase, Control, Sequencing

Individual vulnerability model parameters are gene specific, and vulnerability is not correlated with gene expression levels, related to <xref ref-type=

Figure 3 and (A) Histogram showing the per-gene Spearman correlation between the rate of depletion ( β e ) estimated from the Bayesian vulnerability model and the predicted strength for targeting sgRNAs. All CRISPRi essential genes with confident vulnerability calls in H37Rv (n = 552) are included in this analysis. (B) Histogram of the vulnerability indices estimated from 5,000 parameter samples for mmpL3 ( rv0206c ), embB ( rv3795 ), rv2477c , and moeB1 ( rv3206c ). The vulnerability index 95% credible regions are depicted by dashed lines. (C) Histogram showing the potential influence of the CRISPRi polar effect on vulnerability. The difference in vulnerability index between any downstream gene and its respective upstream gene in the operon is depicted (VI downstream gene – VI upstream gene; n = 657 comparisons). Dashed line depicts the mean difference in VI (mean, 1.658). (D) Histogram of vulnerability indices for genes predicted to be essential by CRISPRi and with confident vulnerability calls, highlighting genes predicted to have an essential domain according to TnSeq ( DeJesus et al., 2017 ). (E) Violin plot depicting the vulnerability index for different groups of genes: all CRISPRi essential genes with confident vulnerability calls (All Ess; n = 552), genes predicted to have an essential domain (Domain Ess; n = 26), genes without an essential domain (Not Domain Ess; n = 526), and genes in the top (n = 138) and bottom (n = 138) quartiles of vulnerability index. Dot and error bars represent mean and SD. Significance (p-value) is calculated using a two-sided t-test. (F-H) Scatterplot of gene vulnerability ratios and/or individual gene parameter estimates. Only confident vulnerability index estimates are shown (see main text for details). (F) depicts the relationship between γ and M ; (G) depicts the relationship between β m a x and M ; (H) depicts the relationship between β m a x and γ . (I and J) Scatterplot showing the relationship between gene mRNA levels as quantified by RNaseq (I) or protein levels as quantified by mass spectrometry (J) ( Schubert et al., 2015 ) and gene vulnerability. Only confident vulnerability index estimates are shown (see for details). " width="100%" height="100%">

Journal: Cell

Article Title: Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis

doi: 10.1016/j.cell.2021.06.033

Figure Lengend Snippet: Individual vulnerability model parameters are gene specific, and vulnerability is not correlated with gene expression levels, related to Figure 3 and (A) Histogram showing the per-gene Spearman correlation between the rate of depletion ( β e ) estimated from the Bayesian vulnerability model and the predicted strength for targeting sgRNAs. All CRISPRi essential genes with confident vulnerability calls in H37Rv (n = 552) are included in this analysis. (B) Histogram of the vulnerability indices estimated from 5,000 parameter samples for mmpL3 ( rv0206c ), embB ( rv3795 ), rv2477c , and moeB1 ( rv3206c ). The vulnerability index 95% credible regions are depicted by dashed lines. (C) Histogram showing the potential influence of the CRISPRi polar effect on vulnerability. The difference in vulnerability index between any downstream gene and its respective upstream gene in the operon is depicted (VI downstream gene – VI upstream gene; n = 657 comparisons). Dashed line depicts the mean difference in VI (mean, 1.658). (D) Histogram of vulnerability indices for genes predicted to be essential by CRISPRi and with confident vulnerability calls, highlighting genes predicted to have an essential domain according to TnSeq ( DeJesus et al., 2017 ). (E) Violin plot depicting the vulnerability index for different groups of genes: all CRISPRi essential genes with confident vulnerability calls (All Ess; n = 552), genes predicted to have an essential domain (Domain Ess; n = 26), genes without an essential domain (Not Domain Ess; n = 526), and genes in the top (n = 138) and bottom (n = 138) quartiles of vulnerability index. Dot and error bars represent mean and SD. Significance (p-value) is calculated using a two-sided t-test. (F-H) Scatterplot of gene vulnerability ratios and/or individual gene parameter estimates. Only confident vulnerability index estimates are shown (see main text for details). (F) depicts the relationship between γ and M ; (G) depicts the relationship between β m a x and M ; (H) depicts the relationship between β m a x and γ . (I and J) Scatterplot showing the relationship between gene mRNA levels as quantified by RNaseq (I) or protein levels as quantified by mass spectrometry (J) ( Schubert et al., 2015 ) and gene vulnerability. Only confident vulnerability index estimates are shown (see for details).

Article Snippet: sgRNA oligo arrays to clone CRISPRi libraries RLC11 and RLC12 , This paper , Addgene #163955 and #163954; Github: https://github.com/rock-lab/vulnerability_2021.

Techniques: Gene Expression, Mass Spectrometry

Genome-scale CRISPRi in Msmeg, related to <xref ref-type=

Figure 4 , , and (A) Histogram depicting the number of sgRNAs per gene in the Msmeg CRISPRi library (RLC11; Addgene #163955). The library targets 6,642 of the 6,679 annotated Msmeg genes. (B) Next generation sequencing quality-control metrics for the Msmeg CRISPRi library. The “Plasmid” column depicts metrics for the RLC11 plasmid library following cloning and isolation from E. coli . The “Msmeg” column depicts library metrics following transformation and expansion in Msmeg. Skew ratio represents the ratio between top and bottom 10% of sgRNA counts. (C and D) Correlation heatmap of the triplicate screens performed in Msmeg depicting TnSeq essential gene ( Dragset et al., 2019 ) targeting sgRNAs in the –ATc (C) and +ATc (D) cultures. (E) Boxen plots comparing time-dependent changes in sgRNA L2FC values targeting genes defined as Essential (n = 27,702 sgRNAs) and Non-Essential (n = 120,429) by TnSeq ( Dragset et al., 2019 ). Mean L2FC (solid line) and quantiles beyond the 25 th and 75 th percentiles are shown (boxes). Also depicted are control Non-Targeting sgRNAs (n = 7,421). (F) Hierarchical clustering of gene level depletion from the Msmeg CRISPRi fitness screen. Each row represents a single targeted Msmeg gene. (G) Bar chart showing the overlap between gene calls by TnSeq ( Dragset et al., 2019 ) and CRISPRi. 73% of TnSeq essential calls (291 of 401) are shared with CRISPRi. (H) Histogram showing the per-gene Spearman correlation between the rate of depletion ( β e ) estimated from the Bayesian vulnerability model and the predicted strength for targeting sgRNAs. All CRISPRi essential genes with confident vulnerability calls in Msmeg are included in this analysis. (I) Growth kinetics of the hypomorphic sgRNAs (mean ± SD) shown in Figure 4 C. The linear model predicted sgRNA strengths are listed in parentheses next to each gene name. All strains were grown for 15 generations in the presence or absence of ATc and then used to seed cultures for the time-course experiment shown here. Growth for 15 generations ± ATc ensures all strains have reached steady-state growth in response to CRISPRi target gene knockdown. NT, non-targeting. (J) Quantification of target gene mRNA levels by qRT-PCR (biological triplicates; mean ± SEM) of the hypomorphic strains depicted in Figure 4 C. (K) Effect of titrating the ATc concentration (range 0-500 ng/mL) on growth (mean ± SD) of the indicated strains from Figure 4 C. These strains encode either a non-targeting (NT) sgRNA or a strong sgRNA (predicted strength range, 0.94 – 1.00) against the indicated target. Strains are color coded by vulnerability as in Figure 4 D. " width="100%" height="100%">

Journal: Cell

Article Title: Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis

doi: 10.1016/j.cell.2021.06.033

Figure Lengend Snippet: Genome-scale CRISPRi in Msmeg, related to Figure 4 , , and (A) Histogram depicting the number of sgRNAs per gene in the Msmeg CRISPRi library (RLC11; Addgene #163955). The library targets 6,642 of the 6,679 annotated Msmeg genes. (B) Next generation sequencing quality-control metrics for the Msmeg CRISPRi library. The “Plasmid” column depicts metrics for the RLC11 plasmid library following cloning and isolation from E. coli . The “Msmeg” column depicts library metrics following transformation and expansion in Msmeg. Skew ratio represents the ratio between top and bottom 10% of sgRNA counts. (C and D) Correlation heatmap of the triplicate screens performed in Msmeg depicting TnSeq essential gene ( Dragset et al., 2019 ) targeting sgRNAs in the –ATc (C) and +ATc (D) cultures. (E) Boxen plots comparing time-dependent changes in sgRNA L2FC values targeting genes defined as Essential (n = 27,702 sgRNAs) and Non-Essential (n = 120,429) by TnSeq ( Dragset et al., 2019 ). Mean L2FC (solid line) and quantiles beyond the 25 th and 75 th percentiles are shown (boxes). Also depicted are control Non-Targeting sgRNAs (n = 7,421). (F) Hierarchical clustering of gene level depletion from the Msmeg CRISPRi fitness screen. Each row represents a single targeted Msmeg gene. (G) Bar chart showing the overlap between gene calls by TnSeq ( Dragset et al., 2019 ) and CRISPRi. 73% of TnSeq essential calls (291 of 401) are shared with CRISPRi. (H) Histogram showing the per-gene Spearman correlation between the rate of depletion ( β e ) estimated from the Bayesian vulnerability model and the predicted strength for targeting sgRNAs. All CRISPRi essential genes with confident vulnerability calls in Msmeg are included in this analysis. (I) Growth kinetics of the hypomorphic sgRNAs (mean ± SD) shown in Figure 4 C. The linear model predicted sgRNA strengths are listed in parentheses next to each gene name. All strains were grown for 15 generations in the presence or absence of ATc and then used to seed cultures for the time-course experiment shown here. Growth for 15 generations ± ATc ensures all strains have reached steady-state growth in response to CRISPRi target gene knockdown. NT, non-targeting. (J) Quantification of target gene mRNA levels by qRT-PCR (biological triplicates; mean ± SEM) of the hypomorphic strains depicted in Figure 4 C. (K) Effect of titrating the ATc concentration (range 0-500 ng/mL) on growth (mean ± SD) of the indicated strains from Figure 4 C. These strains encode either a non-targeting (NT) sgRNA or a strong sgRNA (predicted strength range, 0.94 – 1.00) against the indicated target. Strains are color coded by vulnerability as in Figure 4 D.

Article Snippet: sgRNA oligo arrays to clone CRISPRi libraries RLC11 and RLC12 , This paper , Addgene #163955 and #163954; Github: https://github.com/rock-lab/vulnerability_2021.

Techniques: Next-Generation Sequencing, Control, Plasmid Preparation, Cloning, Isolation, Transformation Assay, Knockdown, Quantitative RT-PCR, Concentration Assay

Pathway analysis identifies differentially vulnerable processes in mycobacteria (A) Heatmap of fitness cost (scaled β e ) as a function of increasing sgRNA strength. Each row represents a single Mtb gene for which a high-confidence VI is available. (B) Table depicting evolutionary conservation between Mtb and eight other bacterial species. For the most vulnerable (VUL; n = 138) and invulnerable (INV; n = 138) H37Rv Mtb genes, the frequency with which a homolog was identified (“genes with homolog”) and the average amino acid similarity (“average similarity of homologs”; % ± SEM) are reported. For the four bacterial species for which genome-wide essentiality calls are available, conservation of essentiality (%) is also listed. M. smeg , M. smegmatis ; M. abs , M. abscessus ; C. glut , C. glutamicum ; B. sub , B. subtilis . ∗∗∗∗ p < 0.0001. ns, not significant. (C) Bubble plot of the enriched (p < 0.05) PATRIC subclasses for the top quartile VUL and bottom quartile INV Mtb and Msmeg ( Msm ) genes. Conserved subclass enrichment is depicted in bold type. The star represents subclasses where some or all of the corresponding Msmeg homologs are non-essential ( <xref ref-type=

Figure S4 C), which, for the purposes of this analysis, were considered INV. (D) Logistic regression curves of the indicated Mtb gene groups. Each colored line represents a single gene. The solid black line represents the locally estimated scatterplot smoothing (LOESS) fit of the individual mean logistic regressions. (E) Detailed view of the different vulnerabilities of Mtb genes involved in DNA replication. Genes are color coded by their VI. Darker shades of purple indicate higher vulnerability. The density scale represents the fraction of CRISPRi essential genes with confident VI calls. Figure adapted from . ∗ , low-confidence call. See also and and . " width="100%" height="100%">

Journal: Cell

Article Title: Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis

doi: 10.1016/j.cell.2021.06.033

Figure Lengend Snippet: Pathway analysis identifies differentially vulnerable processes in mycobacteria (A) Heatmap of fitness cost (scaled β e ) as a function of increasing sgRNA strength. Each row represents a single Mtb gene for which a high-confidence VI is available. (B) Table depicting evolutionary conservation between Mtb and eight other bacterial species. For the most vulnerable (VUL; n = 138) and invulnerable (INV; n = 138) H37Rv Mtb genes, the frequency with which a homolog was identified (“genes with homolog”) and the average amino acid similarity (“average similarity of homologs”; % ± SEM) are reported. For the four bacterial species for which genome-wide essentiality calls are available, conservation of essentiality (%) is also listed. M. smeg , M. smegmatis ; M. abs , M. abscessus ; C. glut , C. glutamicum ; B. sub , B. subtilis . ∗∗∗∗ p < 0.0001. ns, not significant. (C) Bubble plot of the enriched (p < 0.05) PATRIC subclasses for the top quartile VUL and bottom quartile INV Mtb and Msmeg ( Msm ) genes. Conserved subclass enrichment is depicted in bold type. The star represents subclasses where some or all of the corresponding Msmeg homologs are non-essential ( Figure S4 C), which, for the purposes of this analysis, were considered INV. (D) Logistic regression curves of the indicated Mtb gene groups. Each colored line represents a single gene. The solid black line represents the locally estimated scatterplot smoothing (LOESS) fit of the individual mean logistic regressions. (E) Detailed view of the different vulnerabilities of Mtb genes involved in DNA replication. Genes are color coded by their VI. Darker shades of purple indicate higher vulnerability. The density scale represents the fraction of CRISPRi essential genes with confident VI calls. Figure adapted from . ∗ , low-confidence call. See also and and .

Article Snippet: sgRNA oligo arrays to clone CRISPRi libraries RLC11 and RLC12 , This paper , Addgene #163955 and #163954; Github: https://github.com/rock-lab/vulnerability_2021.

Techniques: Genome Wide

tRNA synthetases are choke points in Mtb translation, related to <xref ref-type=

Figure 5 and Vulnerability estimates for Mtb H37Rv amino acid metabolic genes and tRNA synthetases. Only genes that are CRISPRi essential and have a vulnerability call with high confidence are shown. Genes are color coded as in Figure 5 E. The density scale in the figure legend represents the fraction of CRISPRi essential genes with certain vulnerability calls. " width="100%" height="100%">

Journal: Cell

Article Title: Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis

doi: 10.1016/j.cell.2021.06.033

Figure Lengend Snippet: tRNA synthetases are choke points in Mtb translation, related to Figure 5 and Vulnerability estimates for Mtb H37Rv amino acid metabolic genes and tRNA synthetases. Only genes that are CRISPRi essential and have a vulnerability call with high confidence are shown. Genes are color coded as in Figure 5 E. The density scale in the figure legend represents the fraction of CRISPRi essential genes with certain vulnerability calls.

Article Snippet: sgRNA oligo arrays to clone CRISPRi libraries RLC11 and RLC12 , This paper , Addgene #163955 and #163954; Github: https://github.com/rock-lab/vulnerability_2021.

Techniques:

Genome-scale CRISPRi in Mtb HN878, related to <xref ref-type=

Figure 6 and (A) Read depth plot of Mtb HN878 whole genome sequencing mapped to the H37Rv genome (GenBank: NC_018143). The 248 SNPs affecting 664 sgRNAs of our CRISPRi library are indicated in red. Significant decreases and increases in read depth mark a genomic deletion and duplication, respectively, in our HN878 clone and are highlighted in gray. (B) Next generation sequencing quality-control metrics for the Mtb HN878 CRISPRi library. The “Plasmid” column depicts metrics for the RLC12 plasmid library following cloning and isolation from E. coli . The “Mtb HN878” column depicts library metrics following transformation and expansion in Mtb HN878. Skew ratio represents the ratio between top and bottom 10% of sgRNA counts. (C) Correlation heatmap of the triplicate screens performed in Mtb HN878 depicting TnSeq essential gene ( DeJesus et al., 2017 ) targeting sgRNAs in the –ATc cultures. (D) Correlation heatmap of the triplicate screens performed in Mtb HN878 depicting TnSeq essential gene ( DeJesus et al., 2017 ) targeting sgRNAs in the +ATc cultures. (E) Scatterplot of the linear model coefficients (as in Figure 2B) for Mtb H37Rv (x axis) and Mtb HN878 (y axis). (F) Histogram showing the per-gene Spearman correlation between the rate of depletion ( β e ) estimated from the Bayesian vulnerability model and the predicted strength for targeting sgRNAs. All CRISPRi essential genes with confident vulnerability calls in HN878 are included in this analysis. (G) Liquid growth assay (mean ± SD) using the sgRNAs targeting two differentially essential genes used in Figure 6 B. NT, non-targeting. (H) Quantification of target gene mRNA levels by qRT-PCR (n = 6 technical replicates; mean ± SEM) following CRISPRi silencing of rv2017 and rv2228c in H37Rv and HN878. Gene expression levels were normalized to the non-targeting control for each strain. (I) Quantification of target gene mRNA levels by qRT-PCR (technical triplicates of biological duplicates) of cydABCD and qcrCAB in H37Rv and HN878. For each gene, HN878 expression levels were compared to H37Rv (control). " width="100%" height="100%">

Journal: Cell

Article Title: Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis

doi: 10.1016/j.cell.2021.06.033

Figure Lengend Snippet: Genome-scale CRISPRi in Mtb HN878, related to Figure 6 and (A) Read depth plot of Mtb HN878 whole genome sequencing mapped to the H37Rv genome (GenBank: NC_018143). The 248 SNPs affecting 664 sgRNAs of our CRISPRi library are indicated in red. Significant decreases and increases in read depth mark a genomic deletion and duplication, respectively, in our HN878 clone and are highlighted in gray. (B) Next generation sequencing quality-control metrics for the Mtb HN878 CRISPRi library. The “Plasmid” column depicts metrics for the RLC12 plasmid library following cloning and isolation from E. coli . The “Mtb HN878” column depicts library metrics following transformation and expansion in Mtb HN878. Skew ratio represents the ratio between top and bottom 10% of sgRNA counts. (C) Correlation heatmap of the triplicate screens performed in Mtb HN878 depicting TnSeq essential gene ( DeJesus et al., 2017 ) targeting sgRNAs in the –ATc cultures. (D) Correlation heatmap of the triplicate screens performed in Mtb HN878 depicting TnSeq essential gene ( DeJesus et al., 2017 ) targeting sgRNAs in the +ATc cultures. (E) Scatterplot of the linear model coefficients (as in Figure 2B) for Mtb H37Rv (x axis) and Mtb HN878 (y axis). (F) Histogram showing the per-gene Spearman correlation between the rate of depletion ( β e ) estimated from the Bayesian vulnerability model and the predicted strength for targeting sgRNAs. All CRISPRi essential genes with confident vulnerability calls in HN878 are included in this analysis. (G) Liquid growth assay (mean ± SD) using the sgRNAs targeting two differentially essential genes used in Figure 6 B. NT, non-targeting. (H) Quantification of target gene mRNA levels by qRT-PCR (n = 6 technical replicates; mean ± SEM) following CRISPRi silencing of rv2017 and rv2228c in H37Rv and HN878. Gene expression levels were normalized to the non-targeting control for each strain. (I) Quantification of target gene mRNA levels by qRT-PCR (technical triplicates of biological duplicates) of cydABCD and qcrCAB in H37Rv and HN878. For each gene, HN878 expression levels were compared to H37Rv (control).

Article Snippet: sgRNA oligo arrays to clone CRISPRi libraries RLC11 and RLC12 , This paper , Addgene #163955 and #163954; Github: https://github.com/rock-lab/vulnerability_2021.

Techniques: Sequencing, Next-Generation Sequencing, Control, Plasmid Preparation, Cloning, Isolation, Transformation Assay, Growth Assay, Quantitative RT-PCR, Gene Expression, Expressing

Differential VI predicts strain-specific susceptibility to antibacterial agents (A) Bar chart showing the overlap between CRISPRi gene essentiality calls in H37Rv and HN878. (B) CRISPRi knockdown of two genes predicted to be essential in H37Rv and non-essential in HN878. NT, non-targeting. (C) Correlation between VI in H37Rv and HN878 for all genes (black) and CRISPRi essential genes for which high-confidence VI calls are available (blue). (D) Histogram showing the normalized differential VI between HN878 and H37Rv for genes with a high-confidence call in both strains. Quartiles are delineated by a dotted line. (E) Logistic regression fits for accD6 in H37Rv (black) and HN878 (turquoise). Lines represent fits generated by the sampling procedure with the dark line representing the mean fit. (F) Phenotypic consequences of accD6 knockdown. The optical density 600 (OD 600 ) L2FC (+ATc/–ATc; mean ± SD) was calculated for three accD6 sgRNAs (1–3) and a non-targeting control sgRNA in H37Rv and HN878. Strains were pre-treated with ATc for 3 days prior to starting the depicted time course. (G) Bubble plot of the enriched (p < 0.05) PATRIC subclasses for genes more VUL in HN878 versus H37Rv. The star represents a subclass where some or all of the corresponding H37Rv homologs are non-essential, which, for the purposes of this analysis, were considered INV. (H–L) Effect of rifampicin (H), ethambutol (I), isoniazid (J), Q203 (K), and ND-10885 (L) on growth (mean ± SD) of H37Rv and HN878. (M) Gene-level L2FC measurements for cydABCD and inhA from the H37Rv and HN878 CRISPRi screens at ~29 generations. (N and O) Effect of novobiocin (N) and SPR719 (O) on growth (mean ± SD) of H37Rv and HN878. See also <xref ref-type=

Figure S6 and . " width="100%" height="100%">

Journal: Cell

Article Title: Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis

doi: 10.1016/j.cell.2021.06.033

Figure Lengend Snippet: Differential VI predicts strain-specific susceptibility to antibacterial agents (A) Bar chart showing the overlap between CRISPRi gene essentiality calls in H37Rv and HN878. (B) CRISPRi knockdown of two genes predicted to be essential in H37Rv and non-essential in HN878. NT, non-targeting. (C) Correlation between VI in H37Rv and HN878 for all genes (black) and CRISPRi essential genes for which high-confidence VI calls are available (blue). (D) Histogram showing the normalized differential VI between HN878 and H37Rv for genes with a high-confidence call in both strains. Quartiles are delineated by a dotted line. (E) Logistic regression fits for accD6 in H37Rv (black) and HN878 (turquoise). Lines represent fits generated by the sampling procedure with the dark line representing the mean fit. (F) Phenotypic consequences of accD6 knockdown. The optical density 600 (OD 600 ) L2FC (+ATc/–ATc; mean ± SD) was calculated for three accD6 sgRNAs (1–3) and a non-targeting control sgRNA in H37Rv and HN878. Strains were pre-treated with ATc for 3 days prior to starting the depicted time course. (G) Bubble plot of the enriched (p < 0.05) PATRIC subclasses for genes more VUL in HN878 versus H37Rv. The star represents a subclass where some or all of the corresponding H37Rv homologs are non-essential, which, for the purposes of this analysis, were considered INV. (H–L) Effect of rifampicin (H), ethambutol (I), isoniazid (J), Q203 (K), and ND-10885 (L) on growth (mean ± SD) of H37Rv and HN878. (M) Gene-level L2FC measurements for cydABCD and inhA from the H37Rv and HN878 CRISPRi screens at ~29 generations. (N and O) Effect of novobiocin (N) and SPR719 (O) on growth (mean ± SD) of H37Rv and HN878. See also Figure S6 and .

Article Snippet: sgRNA oligo arrays to clone CRISPRi libraries RLC11 and RLC12 , This paper , Addgene #163955 and #163954; Github: https://github.com/rock-lab/vulnerability_2021.

Techniques: Knockdown, Generated, Sampling, Control

Journal: Cell

Article Title: Genome-wide gene expression tuning reveals diverse vulnerabilities of M. tuberculosis

doi: 10.1016/j.cell.2021.06.033

Figure Lengend Snippet:

Article Snippet: sgRNA oligo arrays to clone CRISPRi libraries RLC11 and RLC12 , This paper , Addgene #163955 and #163954; Github: https://github.com/rock-lab/vulnerability_2021.

Techniques: Virus, Recombinant, Luciferase, Sequencing, Mass Spectrometry, Library Amplification, Plasmid Preparation, Software

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