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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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“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
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“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]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“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]
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“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]
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“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]
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In the citing paper
“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]
In the citing paper
“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]
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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
In the citing paper
“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]
In the citing paper
“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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Histone deacetylases and their inhibitors in cancer, neurological diseases and immune disorders
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Neighborhood regulation by lncRNA promoters, transcription, and splicingPreprint
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Self-citationA putative role for histone deacetylase in the differentiation of human erythroid cells
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Flagged first, then by citations
1 of the 86 references couldn’t be matched to a known paper.
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- 10.1128/MCB.13.2.911 · DOI
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Functional Classification and Experimental Dissection of Long Noncoding RNAs
Kopp, F. B., & Mendell, J. T. (2018) · Cell · doi:10.1016/j.cell.2018.01.011 · Cited by 3,574
The next generation of CRISPR–Cas technologies and applications
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Genome-scale CRISPR-Cas9 Knockout and Transcriptional Activation Screening
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Activity-by-contact model of enhancer–promoter regulation from thousands of CRISPR perturbations
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Self-citationThe chromatin accessibility landscape of primary human cancers
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Metadata
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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