Analysis of single-cell CRISPR perturbations indicates that enhancers act multiplicatively and provides limited evidence for epistatic-like interactions

Zhou, Jianxun; Guruvayurappan, Karthik; Chen, Hsiuyi V.; Chen, Aaron R; McVicker, Graham

doi:10.1101/2023.04.26.538501

Cited by 5 publications

(5 citation statements)

References 59 publications

(117 reference statements)

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“…But, given the clustering or adjacency we observed in this list, and the fact that a lot of these genes are known to be regulated by super-enhancers according to dbSuper ( Hnisz et al 2013 ), we think these are indeed functional, but atypical EP pairs that function through a different mechanism that relies on the presence of other enhancers or promoters in the genomic neighborhood. A model where enhancers act multiplicatively to regulate a single gene was recently proposed ( Zhou et al 2023 ), and could potentially explain such pattern where a weak enhancer is functional near a stronger enhancer.…”

Section: Resultsmentioning

confidence: 99%

Supervised learning of enhancer–promoter specificity based on genome-wide perturbation studies highlights areas for improvement in learning

Barth,

Van,

Cardwell

et al. 2024

Bioinformatics

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Motivation Understanding the rules that govern enhancer-driven transcription remains a central unsolved problem in genomics. Now with multiple massively parallel enhancer perturbation assays published, there are enough data that we can utilize to learn to predict enhancer–promoter (EP) relationships in a data-driven manner. Results We applied machine learning to one of the largest enhancer perturbation studies integrated with transcription factor (TF) and histone modification ChIP-seq. The results uncovered a discrepancy in the prediction of genome-wide data compared to data from targeted experiments. Relative strength of contact was important for prediction, confirming the basic principle of EP regulation. Novel features such as the density of the enhancers/promoters in the genomic region was found to be important, highlighting our lack of understanding on how other elements in the region contribute to the regulation. Several TF peaks were identified that improved the prediction by identifying the negatives and reducing False Positives. In summary, integrating genomic assays with enhancer perturbation studies increased the accuracy of the model, and provided novel insights into the understanding of enhancer-driven transcription. Availability and implementation The trained models, data, and the source code are available at http://doi.org/10.5281/zenodo.11290386 and https://github.com/HanLabUNLV/sleps.

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Section: Resultsmentioning

confidence: 99%

Supervised learning of enhancer–promoter specificity based on genome-wide perturbation studies highlights areas for improvement in learning

Barth,

Van,

Cardwell

et al. 2024

Bioinformatics

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“…Our observations regarding super-additive effects of enhancers require particular care in interpretation. Previous studies have investigated how enhancers might combine additively, synergistically, or redundantly by assessing effects at the level of gene expression 39,[53][54][55][56][57] , cellular phenotypes 37 , or organismal phenotypes 58,59 . These studies have offered conflicting views, perhaps in part because these higher-order phenotypes might have varying non-linear relationships with quantitative gene expression.…”

Section: Discussionmentioning

confidence: 99%

An encyclopedia of enhancer-gene regulatory interactions in the human genome

Gschwind,

Mualim,

Karbalayghareh

et al. 2023

Preprint

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Identifying transcriptional enhancers and their target genes is essential for understanding gene regulation and the impact of human genetic variation on disease1–6. Here we create and evaluate a resource of >13 million enhancer-gene regulatory interactions across 352 cell types and tissues, by integrating predictive models, measurements of chromatin state and 3D contacts, and large-scale genetic perturbations generated by the ENCODE Consortium7. We first create a systematic benchmarking pipeline to compare predictive models, assembling a dataset of 10,411 element-gene pairs measured in CRISPR perturbation experiments, >30,000 fine-mapped eQTLs, and 569 fine-mapped GWAS variants linked to a likely causal gene. Using this framework, we develop a new predictive model, ENCODE-rE2G, that achieves state-of-the-art performance across multiple prediction tasks, demonstrating a strategy involving iterative perturbations and supervised machine learning to build increasingly accurate predictive models of enhancer regulation. Using the ENCODE-rE2G model, we build an encyclopedia of enhancer-gene regulatory interactions in the human genome, which reveals global properties of enhancer networks, identifies differences in the functions of genes that have more or less complex regulatory landscapes, and improves analyses to link noncoding variants to target genes and cell types for common, complex diseases. By interpreting the model, we find evidence that, beyond enhancer activity and 3D enhancer-promoter contacts, additional features guide enhancer-promoter communication including promoter class and enhancer-enhancer synergy. Altogether, these genome-wide maps of enhancer-gene regulatory interactions, benchmarking software, predictive models, and insights about enhancer function provide a valuable resource for future studies of gene regulation and human genetics.

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“…In another separate study, GLiMMIRS was developed using negative binomial generalized linear models, accounting for sgRNA efficiency and several other covariates. Reanalysis of Gasperini's dataset [10] using this method, which investigated interactions between 3,808 enhancer pairs, showed that enhancers act in a multiplicative manner; however, the results provided no evidence for strong interactions between pairs of enhancers [53]. Most recently, SCREE has been introduced as a comprehensive workflow that facilitates crisprQTL data analysis.…”

Section: Computational and Statistical Toolkitsmentioning

confidence: 99%

“…As discussed above, despite the high efficiency of CRISPRi for perturbation across a cell population, it is susceptible to incomplete inhibition. The variability in sgRNA targeting efficacy and dCas9-KRAB perturbation should be considered [10], and GLiMMIRS uses a statistical model to account for this covariate [53].…”

Section: Pitfalls and Recommendationsmentioning

confidence: 99%

Technological Convergence: Highlighting the Power of CRISPR Single-Cell Perturbation Toolkit for Functional Interrogation of Enhancers

Ghamsari

Rosenbluh

Menon

et al. 2023

Cancers

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Higher eukaryotic enhancers, as a major class of regulatory elements, play a crucial role in the regulation of gene expression. Over the last decade, the development of sequencing technologies has flooded researchers with transcriptome-phenotype data alongside emerging candidate regulatory elements. Since most methods can only provide hints about enhancer function, there have been attempts to develop experimental and computational approaches that can bridge the gap in the causal relationship between regulatory regions and phenotypes. The coupling of two state-of-the-art technologies, also referred to as crisprQTL, has emerged as a promising high-throughput toolkit for addressing this question. This review provides an overview of the importance of studying enhancers, the core molecular foundation of crisprQTL, and recent studies utilizing crisprQTL to interrogate enhancer-phenotype correlations. Additionally, we discuss computational methods currently employed for crisprQTL data analysis. We conclude by pointing out common challenges, making recommendations, and looking at future prospects, with the aim of providing researchers with an overview of crisprQTL as an important toolkit for studying enhancers.

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Analysis of single-cell CRISPR perturbations indicates that enhancers act multiplicatively and provides limited evidence for epistatic-like interactions

Cited by 5 publications

References 59 publications

Supervised learning of enhancer–promoter specificity based on genome-wide perturbation studies highlights areas for improvement in learning

Supervised learning of enhancer–promoter specificity based on genome-wide perturbation studies highlights areas for improvement in learning

An encyclopedia of enhancer-gene regulatory interactions in the human genome

Technological Convergence: Highlighting the Power of CRISPR Single-Cell Perturbation Toolkit for Functional Interrogation of Enhancers

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