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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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  • 13 June 2018

    In this Letter, the data used to generate Extended Data Fig. 9d contained errors and were not correctly analysed. The corrected and reanalysed Extended Data Fig. 9d is shown in the Supplementary Information to the accompanying Amendment. The original Letter has not been corrected.


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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.

Author information

Author notes

    • Dimitre R. Simeonov
    •  & Benjamin G. Gowen

    These authors contributed equally to this work.

    • Jacob E. Corn
    •  & Alexander Marson

    These authors jointly supervised this work.


  1. Biomedical Sciences Graduate Program, University of California, San Francisco, California 94143, USA

    • Dimitre R. Simeonov
    • , Theodore L. Roth
    •  & John D. Gagnon
  2. Department of Microbiology and Immunology, University of California, San Francisco, California 94143, USA

    • Dimitre R. Simeonov
    • , Theodore L. Roth
    • , John D. Gagnon
    • , Youjin Lee
    • , Michelle L. Nguyen
    • , Zhongmei Li
    • , Jonathan M. Woo
    • , Victoria R. Tobin
    • , Kathrin Schumann
    • , K. Mark Ansel
    •  & Alexander Marson
  3. Diabetes Center, University of California, San Francisco, California 94143, USA

    • Dimitre R. Simeonov
    • , Theodore L. Roth
    • , Youjin Lee
    • , Alice Y. Chan
    • , Michelle L. Nguyen
    • , Zhongmei Li
    • , Jonathan M. Woo
    • , Eric Boyer
    • , Frederic Van Gool
    • , Victoria R. Tobin
    • , Kathrin Schumann
    • , Mark S. Anderson
    • , Jeffrey A. Bluestone
    •  & Alexander Marson
  4. Innovative Genomics Institute, University of California, Berkeley, California 94720, USA

    • Dimitre R. Simeonov
    • , Benjamin G. Gowen
    • , Mandy Boontanrart
    • , Theodore L. Roth
    • , Youjin Lee
    • , Nicolas L. Bray
    • , Michelle L. Nguyen
    • , Zhongmei Li
    • , Jonathan M. Woo
    • , Therese Mitros
    • , Graham J. Ray
    • , Gemma L. Curie
    • , Nicki Naddaf
    • , Julia S. Chu
    • , Hong Ma
    • , Eric Boyer
    • , Victoria R. Tobin
    • , Kathrin Schumann
    • , Jacob E. Corn
    •  & Alexander Marson
  5. Department of Molecular and Cell Biology, University of California, Berkeley, Berkeley, California 94720, USA

    • Benjamin G. Gowen
    • , Mandy Boontanrart
    • , Nicolas L. Bray
    • , Therese Mitros
    • , Graham J. Ray
    • , Gemma L. Curie
    • , Nicki Naddaf
    • , Julia S. Chu
    • , Hong Ma
    •  & Jacob E. Corn
  6. Sandler Asthma Basic Research Center, University of California, San Francisco, California 94143, USA

    • John D. Gagnon
    •  & K. Mark Ansel
  7. Center for Personal Dynamic Regulomes, Stanford University School of Medicine, Stanford, California 94305, USA

    • Maxwell R. Mumbach
    • , Ansuman T. Satpathy
    • , William J. Greenleaf
    •  & Howard Y. Chang
  8. Program in Epithelial Biology, Stanford University School of Medicine, Stanford, California 94305, USA

    • Maxwell R. Mumbach
    •  & Howard Y. Chang
  9. Department of Genetics, Stanford University School of Medicine, Stanford, California 94305, USA

    • Maxwell R. Mumbach
    • , Ansuman T. Satpathy
    •  & William J. Greenleaf
  10. Department of Pediatrics, University of California, San Francisco, California 94143, USA

    • Alice Y. Chan
  11. Department of Epidemiology and Biostatistics, Department of Bioengineering and Therapeutic Sciences, Institute for Human Genetics (IHG), University of California, San Francisco, California 94143, USA

    • Dmytro S. Lituiev
    • , Rachel E. Gate
    • , Meena Subramaniam
    •  & Chun J. Ye
  12. Biological and Medical Informatics Graduate Program, University of California, San Francisco, California 94158, USA

    • Rachel E. Gate
    •  & Meena Subramaniam
  13. Broad Institute of MIT and Harvard, Cambridge, Massachusetts 02142, USA

    • Hailiang Huang
    • , Ruize Liu
    •  & Mark J. Daly
  14. Analytic and Translational Genetics Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts 02114, USA

    • Hailiang Huang
    • , Ruize Liu
    •  & Mark J. Daly
  15. Illumina Inc., 5200 Illumina Way, San Diego, California 92122, USA

    • Kyle K. Farh
  16. Department of Applied Physics, Stanford University, Stanford, California 94025, USA

    • William J. Greenleaf
  17. Chan Zuckerberg Biohub, San Francisco, California 94158, USA

    • William J. Greenleaf
    •  & Alexander Marson
  18. Department of Medicine, University of California, San Francisco, California 94143, USA

    • Mark S. Anderson
    •  & Alexander Marson
  19. UCSF Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, California 94158, USA

    • Alexander Marson


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D.R.S., B.G.G, J.E.C. and A.M. designed the study and wrote the manuscript. B.G.G., M.B., N.L.B., T.M., G.J.R. and G.L.C. performed and analysed CRISPRa screens. B.G.G., D.R.S., N.N., J.S.C. and H.M. performed luciferase reporter cloning and experiments. D.R.S., T.L.R., J.D.G., Y.L., A.C., M.L.N., Z.L., J.M.W, E.B., F.V.G, V.R.T, R.E.G., M.S. and K.S. contributed to functional experiments on CaRE4 and rs61839660. M.R.M., A.T.S., W.J.G. and H.Y.C. generated and analysed HiChIP data. D.S.L. and C.Y. performed ImmVar QTL analysis. H.H., R.L., K.K.F. and M.J.D. contributed to fine-mapping analysis of rs61839660 disease association. D.R.S, J.D.G. and K.M.A. contributed to T cell differentiation. M.S. and J.A.B. advised on functional studies in murine models. D.R.S. and B.G.G. are joint first authors. J.E.C. and A.M. are co-corresponding and co-senior authors.

Competing interests

H.Y.C. and W.J.G. are co-founders of Epinomics. A.M. and J.E.C. are co-founders of Spotlight Therapeutics. J.E.C. serves as an advisor to Mission Therapeutics and the Corn laboratory has received sponsored research support from AstraZeneca and Pfizer. A.M. serves as an advisor to Juno Therapeutics and PACT Therapeutics and the Marson laboratory has received sponsored research support from Juno Therapeutics and Epinomics.

Corresponding authors

Correspondence to Jacob E. Corn or Alexander Marson.

Reviewer Information Nature thanks F. Urnov and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Supplementary information

PDF files

  1. 1.

    Supplementary Figure

    Flow cytometry gating strategies. (a) Gating strategy for mouse immunophenotyping of spleen. Data for peripheral and mesenteric lymph nodes, as well as large intestine were acquired and analyzed in a similar manner. (b) Thymus flow cytometry data. (c) Sort sort strategy for naive CD4+ T cells from spleen and peripheral lymph node negatively enriched for CD4+ T cells. (d) Example of naive CD4+ T cell activation staining. (e) FACS from anti-CD3 treated mice. (f) NOD EDEL naive CD4+ T cell differentiation flow cytometry gating.

  2. 2.

    Reporting Summary

Excel files

  1. 1.

    Supplementary Table 1

    CRISPRa screen raw data and analysis. This table contains the following information for each sgRNA in the CD69 and IL2RA libraries: raw read counts for each replicate, normalized read counts for each replicate, log2 fold-change over background for each replicate, and the mean log2 fold-change across screen replicates.

  2. 2.

    Supplementary Table 4

    A list of antibodies used in the study.

  3. 3.

    Supplementary Table 5

    A list of primer sequences used in the study.

  4. 4.

    Supplementary Table 6

    A guide to RNA sequences used in the study.

  5. 5.

    Supplementary Table 7

    HiChIP interaction matrices for IL2RA TSS and CaREs.

  6. 6.

    Supplementary Table 8

    Constructs for luciferase assays. This table contains details about each luciferase reporter construct used.

  7. 7.

    Supplementary Table 10

    Epigenetic track documentation. This table contains information on each track of epigenetic data shown in Extended Data Figures 4 and 5.

Text files

  1. 1.

    Supplementary Table 2

    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.

  2. 2.

    Supplementary Table 3

    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.

  3. 3.

    Supplementary Table 9

    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.

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