Systematic mapping of functional enhancer-promoter connections with CRISPR interference

Abstract

Gene expression in mammals is regulated by noncoding elements that can affect physiology and disease, yet the functions and target genes of most noncoding elements remain unknown. We present a high-throughput approach that uses clustered regularly interspaced short palindromic repeats (CRISPR) interference (CRISPRi) to discover regulatory elements and identify their target genes. We assess >1 megabase of sequence in the vicinity of two essential transcription factors, MYC and GATA1, and identify nine distal enhancers that control gene expression and cellular proliferation. Quantitative features of chromatin state and chromosome conformation distinguish the seven enhancers that regulate MYC from other elements that do not, suggesting a strategy for predicting enhancer-promoter connectivity. This CRISPRi-based approach can be applied to dissect transcriptional networks and interpret the contributions of noncoding genetic variation to human disease.

Fig. 1. Systematic mapping of noncoding elements that regulate GATA1

(A) CRISPRi method for identifying gene regulatory elements. Cells expressing KRAB-dCas9 from a dox-inducible promoter are infected with a pool of single guide RNAs (sgRNAs) targeting every possible site across a region of interest. In a proliferation-based screen, cells expressing sgRNAs that target essential regulatory elements will be depleted in the final population. (B) CRISPRi screen results in the GATA1 locus. A high CRISPRi score indicates strong depletion over the course of the screen. Red boxes: Windows showing significant depletion compared to negative control sgRNAs (13). DNase I hypersensitivity, H3K27ac ChIP-Seq, and histone modification annotations (ChromHMM) in K562 cells are from ENCODE (4). (C) Close-up of e-GATA1 and e-HDAC6. sgRNA track shows CRISPRi scores for each individual sgRNA in the region. White bar in GATA1 ChIP-seq track represents the GATA1 motif. (D) qPCR for GATA1 and HDAC6 mRNA in cells expressing individual sgRNAs. KRAB-dCas9 expression was activated for 24 hours before measurement. Gray bars: different sgRNAs for each target. Ctrl: negative control sgRNAs without a genomic target. Error bars: 95% confidence intervals (CI) for the mean of 3 biological replicates (13). *: p < 0.05 in T-test versus Ctrl.

Fig. 2. Identification and prediction of elements that regulate MYC

(A) CRISPRi screening identifies 7 distal enhancers (e1–e7) that activate MYC and two repressive elements (r1, r2) that may act to repress MYC. NS1: an element that does not score in the screen. (B) 18-kb windows around each of the 7 distal enhancers. Y-axis scales are equivalent between panels. (C) qPCR for MYC mRNA in cells expressing individual sgRNAs 24 hours after KRAB-dCas9 activation. Gray bars: 2 different sgRNAs per target, or 5 for non-targeting controls (Ctrl). Error bars: 95% CI for the mean of 12 biological replicates (13). *: p < 0.05 in T-test versus negative controls. (D) Correlation between MYC expression and relative cell viability for e1–e7, MYC TSS, NS1, and Ctrl sgRNAs (13). Pearson’s R = 0.92 includes e1–e7 sgRNAs only; with the others, R = 0.95. (E) Predicted impact of DHS elements on MYC expression (a function of quantitative DHS, H3K27ac, and Hi-C signal) versus their experimentally derived CRISPRi scores (13).

Fig. 3. A heuristic model predicts disease-associated MYC enhancers across cell types

(A) H3K27ac occupancy around MYC varies among 8 cell types and primary tissues. Black arrows: elements highlighted in panels below. (B) Locations of 4 enhancers previously shown to regulate MYC expression in other cell types and their predicted impact in a corresponding cell type. Points show predicted impact of 2-kb windows tiled in 100-bp increments across the MYC locus (13). T-ALL: T-cell acute lymphoblastic leukemia. AML: Acute myeloid leukemia. For each cell type, predicted impact is calculated based on available data (13). (C) Haplotype blocks of SNPs linked to human diseases and phenotypes (R² > 0.8 with index SNP in genome-wide association study). (D) SNPs associated with bladder cancer and Hodgkin’s lymphoma overlap regulatory elements predicted by our metric to regulate MYC in a corresponding cell type or tissue. A SNP associated with height overlaps a conserved element that is active only in chondrocytes. Karpas422: diffuse large B cell lymphoma cell line.