한빛사논문
- 조회738
Jayoung Ryu 1,2,3, Sam Barkal 4, Tian Yu 4, Martin Jankowiak 3, Yunzhuo Zhou 5,6, Matthew Francoeur 4, Quang Vinh Phan 4, Zhijian Li 1,3, Manuel Tognon 1,3,7, Lara Brown 4, Michael I. Love 8, Vineel Bhat 4, Guillaume Lettre 9,10, David B. Ascher 5,6, Christopher A. Cassa 4,*, Richard I. Sherwood 4,* & Luca Pinello 1,3,11,*
1Molecular Pathology Unit, Krantz Family Center for Cancer Research, Massachusetts General Hospital, Boston, MA, USA.
2Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA.
3Gene Regulation Observatory, The Broad Institute of Harvard and MIT, Cambridge, MA, USA.
4Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA.
5School of Chemistry and Molecular Biosciences, University of Queensland, Brisbane, Queensland, Australia.
6Computational Biology and Clinical Informatics, Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
7Computer Science Department, University of Verona, Verona, Italy.
8Department of Genetics, Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
9Montreal Heart Institute, Montréal, Quebec, Canada.
10Faculté de Médecine, Université de Montréal, Montréal, Quebec, Canada.
11Department of Pathology, Harvard Medical School, Boston, MA, USA.
*Corresponding authors: correspondence to Christopher A. Cassa, Richard I. Sherwood or Luca Pinello
Abstract
CRISPR base editing screens enable analysis of disease-associated variants at scale; however, variable efficiency and precision confounds the assessment of variant-induced phenotypes. Here, we provide an integrated experimental and computational pipeline that improves estimation of variant effects in base editing screens. We use a reporter construct to measure guide RNA (gRNA) editing outcomes alongside their phenotypic consequences and introduce base editor screen analysis with activity normalization (BEAN), a Bayesian network that uses per-guide editing outcomes provided by the reporter and target site chromatin accessibility to estimate variant impacts. BEAN outperforms existing tools in variant effect quantification. We use BEAN to pinpoint common regulatory variants that alter low-density lipoprotein (LDL) uptake, implicating previously unreported genes. Additionally, through saturation base editing of LDLR, we accurately quantify missense variant pathogenicity that is consistent with measurements in UK Biobank patients and identify underlying structural mechanisms. This work provides a widely applicable approach to improve the power of base editing screens for disease-associated variant characterization.
논문정보
- Research article
- 2024년 04월 (BRIC 등록일 2024-04-30)
- 국외 연구진
- 의약학 > 유전 및 유전체의학
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