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Enhancer network interprets disease risk

Many important genes in human cells have multiple enhancers, noncoding DNA elements that regulate gene expression. It has been a puzzle why many enhancers exist and how they work together over long genomic distances. Combining multiplexed CRISPR interference and machine learning, Lin et al. reveal that multiple enhancers form a nested, multilayer architecture that is important to maintain robust gene expression. Enhancers that are far away (more than 1 million bases) cooperate in three-dimensional space and act as synergistic regulators of gene expression when being perturbed. Their long distance reduces co-mutagenesis and confers a mechanism of robustness. The authors built a model to predict enhancer variants that synergistically control disease-relevant genes, which better links multiple noncoding elements to disease risk prediction beyond genome-wide association studies. —DJ

Abstract

Mammalian genomes have multiple enhancers spanning an ultralong distance (>megabases) to modulate important genes, but it is unclear how these enhancers coordinate to achieve this task. We combine multiplexed CRISPRi screening with machine learning to define quantitative enhancer-enhancer interactions. We find that the ultralong distance enhancer network has a nested multilayer architecture that confers functional robustness of gene expression. Experimental characterization reveals that enhancer epistasis is maintained by three-dimensional chromosomal interactions and BRD4 condensation. Machine learning prediction of synergistic enhancers provides an effective strategy to identify noncoding variant pairs associated with pathogenic genes in diseases beyond genome-wide association studies analysis. Our work unveils nested epistasis enhancer networks, which can better explain enhancer functions within cells and in diseases.

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Supplementary Materials

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References and Notes

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Science
Volume 377 | Issue 6610
2 September 2022

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Received: 7 July 2021
Accepted: 28 July 2022
Published in print: 2 September 2022

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Acknowledgments

The authors thank all members from the Lei Stanley Qi laboratory for useful comments and help on experiments and manuscript preparation, S. Shang and X. Chen for helping with the fluorescence-activated cell sorting, M. Han for helping with the imaging, and J. Magnusson for comments. We thank Y. Ye and L. Han from UTHealth Houston for helping with the LAML linear model. We thank W. Li and J. Zhang from the University of California, Irvine; Y. Ruan and M. Kim from the Jackson laboratory; and F. Ye from Northwestern University for helpful comments. We thank Z. Duren, S. Ma, and S. Wang for helpful discussion. We thank F. Wang and C. Yu for experimental assistance. We acknowledge the data generated by the TCGA Research Network (https://www.cancer.gov/tcga), which allowed us to generate the results of variant interaction. We acknowledge the CD GWAS dataset generated by the Wellcome Trust Case Control Consortium. The ALL Relapse GWAS dataset used for the analyses described in this manuscript were obtained from dbGaP at phs000638.v1.p1. The ALL Relapse GWAS dataset was generated at St. Jude Children’s Research Hospital and by the Children’s Oncology Group, supported by NIH grants CA142665, CA21765, CA158568, CA156449, CA36401, CA98543, CA114766, CA140729, and U01GM92666, Jeffrey Pride Foundation, the National Childhood Cancer Foundation, and by ALSAC.
Funding: X.Z. acknowledges supports by the Stein Fellowship from Stanford University and Institute for Computational and Data Sciences Seed Grant from the Pennsylvania State University. W.H.W. acknowledges support from NIH R01 HG010359. L.S.Q. acknowledges support from the Li Ka Shing Foundation and National Science Foundation. The project is supported by the Li Ka Shing Foundation, the National Cancer Institute of the National Institutes of Health under award no. R01CA266470, and a National Science Foundation CAREER award (L.S.Q., award 2046650). L.S.Q. is a Chan Zuckerberg Biohub investigator.
Author contributions: X.L., Y.L., and L.S.Q. conceived of the concept. Y.L., X.L., S.L., and L.S.Q. planned and designed the experiments. Y.L. and X.L. designed the sgRNA library. Y.L. and L.W. constructed the double sgRNA library. Y.L. performed the CRISPRi screens. D.Z. cloned 192 plasmids in the library and helped with deep sequencing. X.X. cloned 96 plasmids in the library and helped with deep sequencing. X.L. analyzed the CRISPRi screen data and built the SRE model. X.L. applied the model to predict SREs of other genes and designed sgRNAs. Y.L. generated sgRNAs and performed qPCR experiments. S.L. performed Trac-looping, ATAC-seq, and ChIP-seq. X.L. and Y.C. analyzed Trac-looping, ATAC-seq, and ChIP-seq data. Y.L. and Y.Z. performed imaging experiments and 2D image analysis. H.W. performed the 3D image analysis and generated supplementary movies. Y.L. performed the JQ1 experiment. X.Z. mentored X.L. on the SRE variant analysis. A.C. and X.L. developed the enhancer website. X.L., Y.L., and L.S.Q. wrote the manuscript. M.N., H.W., M.L.R., and J.N.N. provided critical comments on the manuscript. L.S.Q. initiated the project. W.H.W., K.Z., and L.S.Q. supervised the project.
Competing interests: L.S.Q. is a founder and scientific advisor of Epicrispr Biotechnologies, and a scientific advisor of Laboratory of Genomics Research. The roles are unrelated to this study.
Data and materials availability: The CRISPRi functional tiling screen, Trac-looping data, ChIP-seq data, and ATAC-seq data have been deposited in the Gene Expression Omnibus under the accession ID GSE160768. The codes for the analysis of CRISPRi screen and the SRE prediction model are publicly accessible at Zenodo (51, 52). The CRISPRi double sgRNA library and key plasmids will be available on Addgene (https://www.addgene.org/Stanley_Qi/).
License information: Copyright © 2022 the authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original US government works. https://www.sciencemag.org/about/science-licenses-journal-article-reuse

Authors

Affiliations

Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Roles: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Resources, Software, Validation, Visualization, Writing - original draft, and Writing - review & editing.
Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Roles: Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Validation, Visualization, Writing - original draft, and Writing - review & editing.
Laboratory of Epigenome Biology, Systems Biology Center, National Heart, Lung and Blood Institute NIH, Bethesda, MD 20892, USA.
Roles: Conceptualization, Data curation, Investigation, Methodology, Resources, Validation, Visualization, and Writing - review & editing.
Department of Statistics, Stanford University, Stanford, CA 94305, USA.
Department of Statistics, The Pennsylvania State University, University Park, PA 16802, USA.
Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, PA 16802, USA.
Roles: Conceptualization, Formal analysis, Methodology, Software, and Writing - review & editing.
Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Roles: Conceptualization, Investigation, Methodology, Validation, and Visualization.
Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Roles: Data curation, Investigation, Resources, Validation, and Writing - review & editing.
Dehua Zhao
Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Role: Investigation.
Xiaoshu Xu
Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Roles: Investigation and Resources.
Augustine Chemparathy
School of Medicine, Stanford University, Stanford, CA 94305, USA.
Roles: Formal analysis and Software.
Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Roles: Formal analysis, Validation, Visualization, and Writing - review & editing.
Laboratory of Epigenome Biology, Systems Biology Center, National Heart, Lung and Blood Institute NIH, Bethesda, MD 20892, USA.
Roles: Formal analysis, Software, and Validation.
Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Roles: Conceptualization, Writing - original draft, and Writing - review & editing.
Jasprina N. Noordermeer
Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Roles: Conceptualization, Methodology, Validation, Writing - original draft, and Writing - review & editing.
Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Roles: Conceptualization, Funding acquisition, Visualization, and Writing - review & editing.
Department of Statistics, Stanford University, Stanford, CA 94305, USA.
Department of Biomedical Data Science, Stanford University, Stanford, CA 94305, USA.
Roles: Resources and Supervision.
Keji Zhao
Laboratory of Epigenome Biology, Systems Biology Center, National Heart, Lung and Blood Institute NIH, Bethesda, MD 20892, USA.
Roles: Conceptualization, Funding acquisition, Project administration, and Supervision.
Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.
Sarafan ChEM-H, Stanford University, Stanford, CA 94305, USA.
Chan Zuckerberg BioHub, San Francisco, CA 94158, USA.
Roles: Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing - original draft, and Writing - review & editing.

Funding Information

Notes

These authors contributed equally to this work.
*
Corresponding author. Email: [email protected]

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