Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex

Abstract

Systematic interrogation of gene function requires the ability to perturb gene expression in a robust and generalizable manner. Here we describe structure-guided engineering of a CRISPR-Cas9 complex to mediate efficient transcriptional activation at endogenous genomic loci. We used these engineered Cas9 activation complexes to investigate single-guide RNA (sgRNA) targeting rules for effective transcriptional activation, to demonstrate multiplexed activation of ten genes simultaneously, and to upregulate long intergenic non-coding RNA (lincRNA) transcripts. We also synthesized a library consisting of 70,290 guides targeting all human RefSeq coding isoforms to screen for genes that, upon activation, confer resistance to a BRAF inhibitor. The top hits included genes previously shown to be able to confer resistance, and novel candidates were validated using individual sgRNA and complementary DNA overexpression. A gene expression signature based on the top screening hits correlated with markers of BRAF inhibitor resistance in cell lines and patient-derived samples. These results collectively demonstrate the potential of Cas9-based activators as a powerful genetic perturbation technology.

Extended Data Figure 1. Structure-guided engineering of Cas9 sgRNA

a, Schematic of the sgRNA stem-loops showing contacts between each stem-loop and Cas9. Contacting amino acid residues are highlighted in yellow. Tetraloop and stem-loop 2 do not make any contacts with Cas9 whereas stem-loops 1 and 3 share extensive contacts with Cas9. b, sgRNA 2.0 with MS2 stem-loops inserted into the tetraloop and stem-loop 2. c, Addition of a second NLS or an alternative HNH domain inactivating point mutation in Cas9 improve efficiency of transcription activation for MYOD1 moderately. d, dCas9-VP64 activators exhibit improved performance by recruitment of MS2-p65 to the tetraloop and stem-loop 2. Addition of an AU flip or extension in the tetraloop does not increase the effectiveness of dCas9-mediated transcription activation. e, Tetraloop and stem-loop 2 are amenable to replacement with MS2 stem-loops. Base changes from the sgRNA 2.0 scaffold are shown at the respective positions, with dashes indicating unaltered bases and bases below dashes indicating insertions. Deletions are indicated by absence of dashes at respective positions. All figures are n = 3 and mean ± SEM.

Extended Data Figure 2. SAM mediates efficient activation of a panel of 12 coding genes and 6 lincRNAs

a, Comparison of the activation levels of 12 genes with dCas9-VP64 in combination with MS2-p65, MS2-p65-HSF1, or MS2-p65-MyoD1. MS2-p65-HSF1 mediated significantly higher levels of activation than MS2-p65 alone for 9 out of 12 genes. The best guide out of 8 tested for each gene (Fig. 2a) was used in this experiment. Activation levels for each type of MS2-fusion is presented as a percentage relative to the activation achieved using MS2-p65. b, Investigation of transcriptional changes in the closest coding transcripts for SAM-mediated activation of 6 lincRNAs. Direction of the coding transcript relative to the lincRNA and distance between transcription start sites are shown. Only targeting of HOTTIP resulted in a significant change in the levels of the closest coding transcript (HOXA13). The best guide out of 8 tested for each gene (Fig. 2e) in combination with dCas9-VP64 and MS2-p65-HSF1 was used in this experiment. All figures are n = 3 and mean ± SEM.

Extended Data Figure 3. Activation of lincRNAs by SAM

Six lincRNAs, three characterized and three uncharacterized, were targeted using SAM. For each lincRNA, 8 sgRNAs were designed to target the proximal promoter region (+1 to −800bp from the TSS) with 4 different MS2 activators (MS2-p65-HSF1, MS2-p65-MyoD1, MS2-p65, and MS2-VP64) in combination with dCas9-VP64. MS2 activators with a combination of 2 different domains (MS2-p65-HSF1 or MS2-p65-MyoD1) consistently provided the highest activation for each lincRNA, * denotes p < 0.01 for MS2-p65-HSF1 or MS2-p65-MyoD1 vs. MS2-p65. N = 3 and mean ± SEM is shown.

Extended Data Figure 4. Multiplexed activation using SAM and activation of a panel of 10 genes as a function of SAM component dosage

a, Activation of a panel of 10 genes by combinations of 2, 4, 6, or 8 sgRNAs simultaneously. The mean fold up-regulation is shown on a log₁₀ scale. MS2-p65-HSF1 and dCas9-VP64 were used in this experiment. b, The relative activation efficiency of individual sgRNAs varies depending on the target gene and the degree of multiplexing. N = 3 and mean ± SEM is shown.

Extended Data Figure 5. The effect of guide and SAM-component dilution on target activation

a, The results for dilution of sgRNA 2.0 on target activation. b, The result for dilution of sgRNA 1.0 on target activation. # denotes an activation of < 2-fold at 1× guide dilution. c, Effect of MS2-p65-HSF1 and dCas9-VP64 dilution, at 1:1, 1:4, 1:10, and 1:50 of the original dosage for each component, on the effectiveness of transcription up-regulation. The amount of sgRNA expression plasmid was kept constant. d, Effect of diluting all three SAM components (dCas9-VP64, MS2-p65-HSF1, and sgRNA) at 1:4, 1:10, and 1:50 of the original dosage for each component. Fold up-regulation is calculated using GFP-transfected cells as the baseline. Error bars indicate S.E.M. and N = 3 for all figures.

Extended Data Figure 6. RNA-seq analysis of transcriptome changes mediated by SAM

a, A heat map of log(TPM) expression values of all statistically significant differentially expressed genes (T-test q-value < 0.05 adjusted with FDR multiple hypothesis correction) found in any of the six experimental conditions compared to the GFP-transfected control. b, Expression levels in log(TPM) values of all detected genes in RNA-seq libraries of GFP-transfected controls (x-axis of all graphs) compared to (from left to right): non-targeting control sgRNA #2 in 1× dilution and 50× dilution (y-axis). Marked are HBG1 (red) and HGB2 (blue).

Extended Data Figure 7. Genome-scale lentiviral screen using Puromycin-resistant SAM sgRNA library

a, Design of three lentiviral vectors for expressing sgRNA, dCas9-VP64, and MS2-p65-HSF1. Each vector contains a distinct selection marker to enable co-selection of cells expressing all three vectors. b, Lentiviral delivery of SAM components was tested by first generating 293FT cell lines stably integrated with dCas9-VP64 and MS2-p65-HSF1, and subsequently transducing these cells with single-gene targeting lentiviral sgRNAs at MOI <0.2. Transcription activation efficiency is measured 4 days post sgRNA lentivirus transduction and selection with Zeocin or Puromycin. Activation is at least as effective as previously observed with transient transfection in all three cases. c, Box plot showing the distribution of sgRNA frequencies at different time points post lentiviral transduction with the Puromycin library, after treatment with DMSO vehicle or PLX-4720. Two infection replicates are shown. d, Identification of top candidate genes using the RIGER P value analysis (KS method) based on the average of both infection replicates. Genes are organized by positions within chromosomes. e, Overlap between the top 20 hits from the Zeo and Puro screens. Genes belonging to the same family are indicated by the same color. There is a 50% overlap between the top hits of each screen as shown in the intersection of the Venn diagram. f, Relevant signaling pathways in BRAF inhibitor resistance. Reactivation of the Ras-ERK pathway as well as the parallel PI3K-Akt pathway have previously been implicated as two alternative resistance mechanisms to BRAF inhibitors^,,–. Both pathways have been described as stimulating proliferation and survival. BAD, FOXO and p27 are common inhibited downstream targets. Recently, stimulation of the cAMP - CREB pathway by GPCRs has been described as a potential additional resistance mechanism. Top candidates from our screen are indicated in blue and putative connections to all three pathways are shown^,,. Candidates previously validated to mediate PLX-4720 resistance are underlined in green^,. COT and CREB are independently validated mediators of resistance^,.

Extended Data Figure 8. Individual validation of PLX-4720 resistance mediation by top screen hits

a, Validation of the top 10 Zeo screen hits and the top 10 shared hits (13 genes total). Every gene was independently activated by all three guides from the screen and tested for the ability to increase survival of A375 cells treated with three different concentrations of PLX-4720 (2µM, 0.5µM and 0.15µM). The z-score based on the % increase in survival relative to control (A375 cells transduced with dCas9-VP64 and MS2-p65-HSF1 alone) is shown for each guide and PLX-4720 concentration. Five cDNAs available from a previous large-scale gain-of-function PLX-4720 resistance screen were also included. Every guide for each top hit mediates significant PLX-4720 resistance. b, The same panel of top hits exhibits a large range of basal expression levels and is effectively activated by all guides. The expression level relative to the housekeeping gene GAPDH is shown both at baseline as well as after activation by each individual guide. c, Ranks of the validated set of genes in the previous ORF screen. Six genes were not part of the cDNA library, five hits are shared (present in the top 3%) and only LPAR5 and ARHGEF1 were present but not highly ranked. Both of these genes had highly ranked members of the same family. d, Levels of overexpression from the five tested cDNA constructs. Transcript levels were higher for these five cDNAs than those mediated by SAM for the same genes. e, Correlation of survival at 2µM PLX-4720 treatment and transcript upregulation achieved by individual guides. For most genes (9 out of 12 shown), the percent survival is very similar across transcript levels achieved by all three guides. Dotted lines indicate control survival.

Extended Data Figure 9. Expression of top hits and screen signatures are elevated in PLX-4720 resistant melanoma cell lines and patient samples

a, Heat map showing sensitivity to different drugs (top), expression of SAM top screen hits (middle), and SAM screen signature scores (bottom; see Online Methods for signature generation) in Cancer Cell Line Encyclopedia cell lines. Drug sensitivities are measured as Activity Areas (AA). The melanoma cell lines are sorted by PLX-4720 drug sensitivity. RAF inhibitors: PLX-4720 and RAF265; MEK inhibitors: AZD6244 and PD-0325901. b, Heat map showing expression of gene/signature markers for BRAF-inhibitor sensitivity (top), expression of SAM top screen hits (middle) and screen signature scores (bottom) in different BRAF^V600 patient melanoma samples (primary or metastatic) from The Cancer Genome Atlas. c, Heat map showing MITF expression (top), screen signature scores (middle), and expression of SAM top screen hits (bottom) in different BRAF^V600E patient melanoma biopsies post-treatment with BRAF inhibitors. d, Bar chart showing the number of patients from (c) with at least a two-fold change (post/pre treatment) in gene expression of the top PLX-4720 screen hits in the post-treatment samples. All associations are measured using the information coefficient (IC) between the index and each of the features and P values are determined using a permutation test. All heat maps show z-scores.

Extended Data Figure 10. Guide depletion analysis to identify gene set enrichment and guide efficiency parameters

a, Heat maps of sgRNA nucleotide content versus depletion after 21 days. sgRNA targeting significantly depleted genes (from RIGER analysis) in sgRNA-zeo (a) or sgRNA-puro (b) screens were analyzed for trends based on G or T content in the sgRNA sequence. sgRNA depletion is positively correlated with G content and negatively correlated with T content. Other bases analyzed (A and C) had significant (p < 0.0007) but weak (r < 0.2) negative correlation. c, 90% of guides analyzed fall within a 100bp window <200bp from the TSS. Boxplots of distance from 5’ end of the guide to the TSS for sgRNA-zeo and sgRNA-puro in same and reverse direction (relative to target transcription). Whiskers span 5^th to 95^th quartile. d, Coefficients and P values for ordinary least squares predicting sgRNA depletion of significantly depleted genes from G content, T content, distance from 5’ end of the guide to the TSS and direction of guide. Only nucleotide content has a significant effect on depletion in this model, consistent with a high efficiency of guides within 200bp of the TSS regardless of strand orientation (Fig. 2d). e, The cumulative frequency of sgRNAs 3 and 21 days after transduction in A375 cells is shown. Shift in the 21-day curve represents the depletion in a subset of sgRNAs. Less than 0.1% of all guides are not detected at day 3 (detected by less than 10 reads). f, Depleted guides (Supplementary Table 3) can be analyzed for significant clustering of gene categories. Gene categories exhibiting significant depletion based on Ingenuity Pathway Analysis (p<0.01 after B-H FDR correction) are shown. Categories based on the 1000 most depleted guides individually (left) and the average of all 3 guides/gene (right). These categories include either positive or negative regulators of each pathway that reduce proliferation and survival.

Figure 1. Structure-guided design and optimization of an RNA-guided transcription activation complex

a, A crystal structure of the Cas9-sgRNA-target DNA tertiary complex (PDB ID: 4OO8) reveals that the sgRNA tetraloop and stem loop 2 are exposed. b, Schematic of the three-component SAM system. c, Design and optimization of sgRNA scaffolds for optimal recruitment of MS2-VP64 transactivators in Neuro-2a cells. d, MS2 stem-loop placement within the sgRNA significantly affects transcription activation efficiency. e, Combinations of different activation domains act in synergy to enhance the level of transcription activation. f, Addition of the HSF1 transactivation domain to MS2-p65 further increases the efficiency of transcription activation. Experiments for d-f were performed in 293FT cells. All values are mean ± SEM with n = 3. * indicates p <0.05 based on Student’s t-test.

Figure 2. Characterization of SAM-mediated gene and lincRNA activation and derivation of selection rules for efficient sgRNAs

a, Fold activation of 12 different genes plotted against the sgRNA location. sgRNA 1.0 with dCas9-VP64 (grey), sgRNA 2.0 with dCas9-VP64 and MS2-p65-HSF1 (blue). b, Comparison of activation efficiency of 12 target genes: dCas9-VP64 and a single sgRNA 1.0; dCas9-VP64 with a single sgRNA 2.0 and MS2-p65-HSF1, and dCas9-VP64 with a mixture of 8 sgRNA 1.0s. c, Efficiency of target gene activation as a function of baseline expression levels. d, Correlation of gene activation efficiency with sgRNA targeting position. Activation efficiency of each sgRNA for the same target gene is normalized against the highest-activating sgRNA. e, Fold activation of six lincRNA transcripts by SAM (best sgRNA out of 8 tested). All experiments were performed in 293FT cells. All values are mean ± SEM with n = 3.

Figure 3. Simultaneous activation of endogenous genes using multiplexed sgRNA expression

a, Activation of individual genes by single sgRNAs with dCas9-VP64 and MS2-p65-HSF1. b, Simultaneous activation of the same ten genes using a mixture of ten sgRNAs each targeting a different gene. c, Effect of sgRNA dilution on gene activation efficiency. d, Correlation between the activation efficiency of a single 10-fold diluted sgRNA and that of the same sgRNA delivered within a mixture of ten different-gene targeting sgRNAs. All values are mean ± SEM with n = 3.

Figure 4. Evaluation of SAM specificity

Expression levels in log(TPM) values of all detected genes in RNA-seq libraries of GFP-transfected controls (x-axis of all graphs) compared to (from left to right): SAM targeting HBG1/2 genes in 1× dilution and 50× dilution, non-targeting control sgRNAs in 1× dilution and 50× dilution (y-axis). Marked are the two statistically significant differentially expressed genes (T-test q-value < 0.05 with FDR correction): HBG1 (red) and HGB2 (blue). The average from n = 3 is shown.

Figure 5. Genome-scale gene activation screening identifies mediators of BRAF inhibitor resistance

a, Flow chart of transcription activation screening using SAM. b, Box plot showing the distribution of sgRNA frequencies post lentiviral transduction for baseline (day 3), vehicle (day 21), and PLX-4720 (day 21) conditions. c, Scatterplot showing enrichment of specific sgRNAs after PLX-4720 treatment. d, Identification of top candidate genes using the RIGER P value analysis based on the average of both infection replicates. e, Comparison of RIGER P values for the top 100 hits from SAM and GeCKO PLX-4720 resistance screens. f, Consistency of sgRNAs for top screening hits. Fraction of unique sgRNAs targeting each gene that are in the top 5% of all sgRNAs is plotted.

Figure 6. Validation of top hits from genome-scale gene activation screen for PLX-4720 resistance mediators

a, Comparison of PLX-4720 resistance, transcription activation and protein upregulation in A375 cells for top screening hits. b, Expression levels of top hits and screen signatures are elevated in the resistant state of short-term BRAF^V600 melanoma cultures (see Methods for signature generation). The subset of samples which were previously tested for PLX-4720 sensitivity and resistance are indicated by blue and red arrows respectively. IC: Information Coefficient. All values are mean ± SEM with n = 3.