The majority of genetic variants associated with common human diseases map to enhancers, non-coding elements that shape cell-type-specific transcriptional programs and responses to extracellular cues1,2,3. Systematic mapping of functional enhancers and their biological contexts is required to understand the mechanisms by which variation in non-coding genetic sequences contributes to disease. Functional enhancers can be mapped by genomic sequence disruption4,5,6, but this approach is limited to the subset of enhancers that are necessary in the particular cellular context being studied. We hypothesized that recruitment of a strong transcriptional activator to an enhancer would be sufficient to drive target gene expression, even if that enhancer was not currently active in the assayed cells. Here we describe a discovery platform that can identify stimulus-responsive enhancers for a target gene independent of stimulus exposure. We used tiled CRISPR activation (CRISPRa)7 to synthetically recruit a transcriptional activator to sites across large genomic regions (more than 100 kilobases) surrounding two key autoimmunity risk loci, CD69 and IL2RA. We identified several CRISPRa-responsive elements with chromatin features of stimulus-responsive enhancers, including an IL2RA enhancer that harbours an autoimmunity risk variant. Using engineered mouse models, we found that sequence perturbation of the disease-associated Il2ra enhancer did not entirely block Il2ra expression, but rather delayed the timing of gene activation in response to specific extracellular signals. Enhancer deletion skewed polarization of naive T cells towards a pro-inflammatory T helper (TH17) cell state and away from a regulatory T cell state. This integrated approach identifies functional enhancers and reveals how non-coding variation associated with human immune dysfunction alters context-specific gene programs.
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We thank members of Marson and Corn laboratories, as well as A. Abbas, S. Qi, L. Gilbert, J. Hiatt, M. Lee, V. Nguyen, J. Weissman, J. Roose, M. Gavin and W. Leonard for suggestions and technical assistance. This research was supported by NIH grants DP3DK111914-01 (A.M., M.S.A., C.Y.), R01HG0081410-01 (A.M, W.G.), R01HL109102 (K.M.A., A.M.), P50-HG007735 (H.Y.C., W.J.G.), Scleroderma Research Foundation (H.Y.C.), the UCSF Sandler Fellowship (A.M.), a gift from Jake Aronov (A.M.), a National Multiple Sclerosis Society grant (A.M.; CA 1074-A-21), and the Marcus Program in Precision Medicine Innovation (A.M.). A.M. holds a Career Award for Medical Scientists from the Burroughs Wellcome Fund and is a Chan Zuckerberg Biohub Investigator. J.E.C. is supported by the Li Ka Shing Foundation. B.G.G. is supported by the IGI-AstraZeneca Postdoctoral Fellowship. K.M.A. is a Leukemia & Lymphoma Society Scholar. J.D.G. is a National Science Foundation Predoctoral Fellow. K.S. is supported by a DFG Postdoctoral Fellowship. We thank Jackson Laboratories for generating the SNP and EDEL mice and Agilent for generating oligo pools for cloning of the CRISPRa gRNA library. We thank UC Berkeley High Throughput Screening Facility and Flow Cytometry Facility. This work used the Vincent J. Coates Genomics Sequencing Laboratory at UC Berkeley, supported by NIH S10 Instrumentation Grants S10RR029668 and S10RR027303. We also relied on the Flow Cytometry Core at UCSF, supported by the Diabetes Research Center grant NIH P30 DK063720.
Extended data figures
Differentially expressed genes from CRISPRa of IL2RA TSS, CaRE3 and CaRE4. This table lists the genes called as differentially expressed, along with q-value and log2 fold-change.
Full RNA sequencing data from CRISPRa of IL2RA TSS, CaRE3 and CaRE4. This table contains the q-value and log2 fold-change for all genes analyzed in the RNA-Seq experiment.
Analysis matrix for CRISPRa RNA sequencing in Extended Data Figure 3. This table contains the analysis matrix used by sleuth to determine differential gene expression.
About this article
Nature Reviews Immunology (2018)