Systematic discovery and functional dissection of enhancers needed for cancer cell fitness and proliferation

Chen, Poshen B.; Fiaux, Patrick C.; Zhang, Kai; Li, Bin; Kubo, Naoki; Jiang, Shan; Hu, Rong; Rooholfada, Emma; Wu, Sihan; Wang, Mengchi; Wang, Wei; McVicker, Graham; Mischel, Paul S.; Ren, Bing

doi:10.1016/j.celrep.2022.111630

Cited by 19 publications

(12 citation statements)

References 105 publications

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“…To compare MChIP-C data with a similar restriction-endonuclease based C-method, we reanalyzed publicly available H3K4me3 PLAC-seq experiments (Chen et al, 2022). Raw sequencing reads were downloaded from the 4D Nucleome Data portal (https://data.4dnucleome.org/experiment-set-replicates/4DNESWX1J3QU/#raw-files) and processed similarly to MChIP-C reads.…”

Section: Methodsmentioning

confidence: 99%

“…HiChIP and PLAC-seq are directly comparable to MChIP-C, as both HiChIP and PLAC-seq combine Hi-C with chromatin immunoprecipitation. Specifically, we compared our data to those of Chen et al (Chen et al, 2022), who used PLAC-seq with anti-H3K4me3 antibodies in K562 cells. We first visually compared the PLAC-seq and MChIP-C proximity ligation profiles of a number of genes with well-described regulatory landscape in K562 cells: MYC, GATA1 and HBG2.…”

Section: Mchip-c Provides a Sensitive Genome-wide View Of Promoter-ce...mentioning

confidence: 99%

“…S5a,b; Datasets S08 and S09). We used PLAC-seq interactions reported reviously by Chen et al (Chen et al, 2022), and for Hi-C and Micro-C we used the entire genome-wide datasets to identify interactions using Mustache (Roayaei Ardakany et al, 2020). With respect to recall/sensitivity, the recall of MChIP-C (61.5%, as mentioned above) was superior to those of PLAC-seq (15.0%), Micro-C (19.7%), and Hi-C (2.3%).…”

Section: Mchip-c Data Are Largely Consistent With the Looping Model O...mentioning

confidence: 99%

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A genome-wide nucleosome-resolution map of promoter-centered interactions in human cells corroborates the enhancer-promoter looping model

Golov

Гаврилов

Kaplan

et al. 2023

Preprint

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The enhancer-promoter looping model, in which enhancers activate their target genes via physical contact, has long dominated the field of gene regulation. However, the ubiquity of this model has been questioned due to evidence of alternative mechanisms and the lack of its systematic validation, primarily owing to the absence of suitable experimental techniques. In this study, we present a new MNase-based proximity ligation method called MChIP-C, allowing for the measurement of protein-mediated chromatin interactions at single-nucleosome resolution on a genome-wide scale. By applying MChIP-C to study H3K4me3 promoter-centered interactions in K562 cells, we found that it had greatly improved resolution and sensitivity compared to restriction endonuclease-based C-methods. This allowed us to identify EP300 histone acetyltransferase and the SWI/SNF remodeling complex as potential candidates for establishing and/or maintaining enhancer-promoter interactions. Finally, leveraging data from published CRISPRi screens, we found that most functionally-verified enhancers do physically interact with their cognate promoters, supporting the enhancer-promoter looping model.

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Section: Methodsmentioning

confidence: 99%

Section: Mchip-c Provides a Sensitive Genome-wide View Of Promoter-ce...mentioning

confidence: 99%

Section: Mchip-c Data Are Largely Consistent With the Looping Model O...mentioning

confidence: 99%

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A genome-wide nucleosome-resolution map of promoter-centered interactions in human cells corroborates the enhancer-promoter looping model

Golov

Гаврилов

Kaplan

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…For the CRISPRi screens, we employed the dual-gRNA CRISPRi approach as we previously demonstrated more effective dCas9-KRAB-mediated epigenetic silencing using dual-gRNAs targeting a cCRE than using a single gRNA 23 . For each cCRE we designed 5 pairs of gRNAs with an average genomic distance of ~300 bp between each pair of gRNAs.…”

Section: Genome-scale Crispri Screens For Ipsc Proliferation and Neur...mentioning

confidence: 99%

Functional characterization of gene regulatory elements and neuropsychiatric disease-associated risk loci in iPSCs and iPSC-derived neurons

Yang,

Jones,

Chen

et al. 2023

Preprint

Self Cite

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Genome-wide association studies (GWAS) have identified thousands of non-coding variants that contribute to psychiatric disease risks, likely by perturbingcis-regulatory elements (CREs). However, our ability to interpret and explore their mechanisms of action is hampered by a lack of annotation of functional CREs (fCREs) in neural cell types. Here, through genome-scale CRISPR screens of 22,000 candidate CREs (cCREs) in human induced pluripotent stem cells (iPSCs) undergoing differentiation to excitatory neurons, we identify 2,847 and 5,540 fCREs essential for iPSC fitness and neuronal differentiation, respectively. These fCREs display dynamic epigenomic features and exhibit increased numbers and genomic spans of chromatin interactions following terminal neuronal differentiation. Furthermore, fCREs essential for neuronal differentiation show significantly greater enrichment of genetic heritability for neurodevelopmental diseases including schizophrenia (SCZ), attention deficit hyperactivity disorder (ADHD), and autism spectrum disorders (ASD) than cCREs. Using high-throughput prime editing screens we experimentally confirm 45 SCZ risk variants that act by affecting the function of fCREs. The extensive and in-depth functional annotation of cCREs in neuronal types therefore provides a crucial resource for interpreting non-coding risk variants of neuropsychiatric disorders.

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“…By treating the DNN as a surrogate for the experimental assay, CREME can be queried with new sequences and provide in silico "measurements", albeit through the lens of the DNN. Inspired by wetlab experiments, such as CRISPRi 23,24,73 , that perturb genomic loci to uncover how CREs 13/23…”

mentioning

confidence: 99%

InterpretingCis-Regulatory Interactions from Large-Scale Deep Neural Networks for Genomics

Toneyan¹,

Koo²

2023

Preprint

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The rise of large-scale, sequence-based deep neural networks (DNNs) for predicting gene expression has introduced challenges in their evaluation and interpretation. Current evaluations align DNN predictions with experimental perturbation assays, offering a limited perspective of the DNN's capabilities within the studied loci. Moreover, existing model explainability tools mainly focus on motif analysis, which becomes complex to interpret for longer sequences. Here we introduce CREME, an in silico perturbation toolkit that interrogates large-scale DNNs to uncover rules of gene regulation that it has learned. Using CREME, we investigate Enformer, a prominent DNN in gene expression prediction, revealing cis-regulatory elements (CREs) that directly enhance or silence target genes. We explore the relationship between CRE distance from transcription start sites and gene expression, as well as the intricate complexity of higher-order CRE interactions. This work advances the ability to translate the powerful predictions of large-scale DNNs to study open questions in gene regulation.

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Systematic discovery and functional dissection of enhancers needed for cancer cell fitness and proliferation

Cited by 19 publications

References 105 publications

A genome-wide nucleosome-resolution map of promoter-centered interactions in human cells corroborates the enhancer-promoter looping model

A genome-wide nucleosome-resolution map of promoter-centered interactions in human cells corroborates the enhancer-promoter looping model

Functional characterization of gene regulatory elements and neuropsychiatric disease-associated risk loci in iPSCs and iPSC-derived neurons

InterpretingCis-Regulatory Interactions from Large-Scale Deep Neural Networks for Genomics

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