Systematic mapping of functional enhancer–promoter connections with CRISPR interference
Open accessAbstract
CRISPR screens illuminate enhancer function: The noncoding regions around a gene that control the transcription of the protein-coding region are difficult to identify. Leveraging a CRISPR interference system (CRISPRi), Fulco et al. identified enhancer-promoter connections to map specific noncoding regions affecting gene regulation for the GATA1 and MYC loci (see the Perspective by Einstein and Yeo). Going forward, such CRISPRi-mapping can be used to evaluate promoter-enhancer screens functionally in an unbiased way. Science , this issue p. 769 ; see also p. 705
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Enhancers E1-E4 control expression of the PVT1 long noncoding RNA.
“Finally, we explored the potential utility of CRISPRoff for silencing promoter-distal elements by targeting enhancers that control the expression of the PVT1 long noncoding RNA (Cho et al., 2018; Fulco et al., 2016).”
From Genome-wide programmable transcriptional memory by CRISPR-based epigenome editing (2021) · cites this paper as Fulco et al., 2016
The passage describing the seven elements (section labeled Abstract; page unavailable) says e1–e7 are enhancers that control MYC expression, not PVT1 expression. It identifies e3 and e4 as alternative transcription start sites for PVT1, but says they likely affect cellular proliferation through direct regulation of MYC. (LLM, conf=0.88)
Checked by a reasoning model — may be wrong.
Experimental — accuracy not yet measuredHistone modification and cofactor binding are enriched but not completely predictive of enhancer activity.
“Although enriched, histone modification and cofactor binding is not completely predictive of enhancer activity54,58,72,77,105.”
From Towards a comprehensive catalogue of validated and target-linked human enhancers (2020) · cites this paper as [105]
The supplied Abstract passages report that H3K27ac signal distinguished most functional MYC enhancers but did not rank their effects correctly, supporting the point that a histone modification is informative but not fully predictive. They also report transcription-factor binding at the seven enhancers, but do not establish that cofactor binding is enriched or test its predictive value. (LLM, conf=0.83)
Experimental — accuracy not yet measuredCRISPRoff can perturb enhancers.
“Our initial experiments demonstrate CRISPRoff can perturb enhancers, opening the potential to target genome elements that control tissue-specific gene expression (Fulco et al., 2016; Tarjan et al., 2019).”
From Genome-wide programmable transcriptional memory by CRISPR-based epigenome editing (2021) · cites this paper as Fulco et al., 2016
The paper reports that targeting enhancers with CRISPR interference (CRISPRi) reduced GATA1 expression, supporting the general point that enhancers can be perturbed. It does not report experiments with CRISPRoff, so it does not establish that CRISPRoff specifically can perturb enhancers (results described under the Abstract section; page not supplied). (LLM, conf=0.88)
Experimental — accuracy not yet measuredCRISPR-based approaches have confirmed that promoters often receive signals from multiple functional enhancers.
“CRISPR-based approaches have confirmed that promoters often receive signals from multiple functional enhancers [479, 480].”
From Transcriptional enhancers and their communication with gene promoters (2021) · cites this paper as [479]
The paper’s concluding summary (page not specified in our copy) says CRISPRi revealed coordinated activity of multiple enhancers controlling a single gene at the MYC and GATA1 loci. That supports the existence of such connections, but the study of these loci does not establish that promoters often receive signals from multiple functional enhancers generally. (LLM, conf=0.85)
Experimental — accuracy not yet measuredWhen comparing Borzoi, Enformer and a distance-to-TSS baseline on classifying measured positive from negative enhancer-gene interactions, Borzoi has superior AUPRC and AUROC at all distances.
“When comparing Borzoi, Enformer and a distance-to-TSS baseline on their ability to classify measured positive from negative enhancer–gene interactions in data from previous works60–65, we find that Borzoi has superior average precision (AUPRC) and area under the receiver operating characteristic curve (AUROC) at all distances (Fig. 4c and Extended Data Fig. 5a).”
From Predicting RNA-seq coverage from DNA sequence as a unifying model of gene regulation (2025) · cites this paper as [61]
CRISPRi has been used in multiple single-gene screens of regulatory elements.
“CRISPRi has subsequently been used in multiple single-gene screens of regulatory elements105,208,209.”
From Towards a comprehensive catalogue of validated and target-linked human enhancers (2020) · cites this paper as [105]
To date, all pooled CRISPR-based enhancer screens for in-genome perturbation use Cas9-induced perturbations, including active Cas9 for sequence disruption or nuclease dead-Cas9 tethered to an epigenetic repressor or activator domain.
“To date, all such screens use Cas9-induced perturbations, including active Cas9 for sequence disruption195, or nuclease dead-Cas9 (dCas9) tethered to an epigenetic repressor105 or activator domain92.”
From Towards a comprehensive catalogue of validated and target-linked human enhancers (2020) · cites this paper as [105]
TAP-seq is useful for functional genomics applications where phenotypes appear as expression changes in small gene sets.
“TAP-seq is applicable to a broad range of functional genomics applications, including studies of gene regulatory networks7,19,31, signaling pathways6,32, and combinatorial regulatory logic7,32,33, where phenotypes of interest are represented by expression changes in small sets of genes.”
From Targeted Perturb-seq enables genome-scale genetic screens in single cells (2020) · cites this paper as [19]
A known enhancer 3.6 kb upstream of GATA1 was re-identified.
“Additionally, we re-identified a known enhancer 3.6 kb upstream of GATA1 (Fulco et al., 2016).”
From A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens (2019) · cites this paper as Fulco et al., 2016
Nine recently published CRISPR screens of noncoding sequences cumulatively studied regulatory effects on a total of 17 genes.
“In contrast, nine recently published CRISPR screens of noncoding sequences cumulatively studied regulatory effects on a total of 17 genes (Canver et al., 2015; Diao et al., 2016, 2017; Fulco et al., 2016; Gasperini et al., 2017; Klann et al., 2017; Korkmaz et al., 2016; Rajagopal et al., 2016; Sanjana et al., 2016).”
From A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens (2019) · cites this paper as Fulco et al., 2016
Prior cell-based CRISPR screens for regulatory sequences focus on regulation of a single gene per experiment, typically requiring a gene-specific assay.
“First, they focus on the regulation of a single gene per experiment, typically entailing the development of a gene-specific assay.”
From A Genome-wide Framework for Mapping Gene Regulation via Cellular Genetic Screens (2019) · cites this paper as Fulco et al., 2016
Enhancer proximity to the transcriptional start site scales directly with expression level.
“it has been posited that the proximity of the enhancer to the transcriptional start site (TSS) of a gene scales directly to the level of expression20.”
From Viral manipulation of functionally distinct interneurons in mice, non-human primates and humans (2020) · cites this paper as [20]
Scalable approaches using genome editing tools, such as CRISPR/Cas9, enable cis-regulatory elements to be dissected in cell lines and even in primary cell contexts.
“Scalable approaches using genome editing tools, such as CRISPR/Cas9, enable cis-regulatory elements to be dissected in cell lines and even in primary cell contexts (Fulco et al., 2016; Simeonov et al., 2017).”
From Unraveling Hematopoiesis through the Lens of Genomics (2020) · cites this paper as Fulco et al., 2016
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- Authors
- Fulco, C. P., Munschauer, M., Anyoha, R., Munson, G., Grossman, S. R., Perez, E. M., Kane, M., Cleary, B., Lander, E. S., & Engreitz, J. M.
- Affiliations
- Broad Institute (US)
- Corresponding author
- Lander, Eric S.
- Engreitz, Jesse M.
- ORCID
- Venue
- Science
- Published
- 2016-11-11
- Volume
- 354
- Issue
- 6313
- Pages
- 769-773
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC5438575
- OpenAlex
- W2524507363
- Cited by
- 643
- References
- 86
- Citations by year
- 2026: 50 · 2025: 65 · 2024: 57 · 2023: 68 · 2022: 77 · 2021: 72 · 2020: 72 · 2019: 66 · 2018: 62 · 2017: 49 · 2016: 3
- FWCI
- 31.19
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Broad Institute
- National Defense Science and Engineering Graduate Fellowship
- Fannie and John Hertz Foundation
- Deutsche Forschungsgemeinschaft Research Fellowship
- National Institute of General Medical Sciences (T32GM007753)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Chromatin Dynamics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › CRISPR and Genetic Engineering
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Chromatin Remodeling and Cancer
- Keywords
- distal enhancers, CRISPR interference, non-coding regulatory elements, c-Myc, GATA-1, chromatin state, chromosome conformation, transcriptional regulatory networks, cell proliferation, target gene identification
- MeSH terms
- Chromosome Mapping, Disease, Enhancer Elements, Genetic, Gene Expression Regulation, Humans, Promoter Regions, Genetic, Proto-Oncogene Proteins c-myc, K562 Cells, Cell Proliferation, GATA1 Transcription Factor, High-Throughput Nucleotide Sequencing, Real-Time Polymerase Chain Reaction, Clustered Regularly Interspaced Short Palindromic Repeats, CRISPR-Cas Systems
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