Deep Mutational Scanning in Disease-related Genes with Saturation Mutagenesis-Reinforced Functional Assays (SMuRF)

Kaiyue Ma; Kenneth K Ng; Shushu Huang; Nicole J Lake; Jenny Xu; Angela Lek; Lin Ge; Keryn G Woodman; Katherine E Koczwara; Vincent Ho; Christine L O'Connor; Soumya Joseph; Melinda A Brindley; Kevin P Campbell; Monkol Lek

doi:10.1101/2023.07.12.548370

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Deep Mutational Scanning in Disease-related Genes with Saturation Mutagenesis-Reinforced Functional Assays (SMuRF)

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Deep Mutational Scanning in Disease-related Genes with Saturation Mutagenesis-Reinforced Functional Assays (SMuRF)

Kaiyue Ma, Kenneth K Ng, Shushu Huang, Nicole J Lake, Jenny Xu, Angela Lek, Lin Ge, Keryn G Woodman, Katherine E Koczwara, Vincent Ho, …

bioRxiv : the preprint server for biology

Cold Spring Harbor Laboratory

11/27/2023

DOI: 10.1101/2023.07.12.548370

PMCID: PMC10592615

PMID: 37873263

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https://doi.org/10.1101/2023.07.12.548370View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

Abstract

Interpretation of disease-causing genetic variants remains a challenge in human genetics. Current costs and complexity of deep mutational scanning methods hamper crowd-sourcing approaches toward genome-wide resolution of variants in disease-related genes. Our framework, Saturation Mutagenesis-Reinforced Functional assays (SMuRF), addresses these issues by offering simple and cost-effective saturation mutagenesis, as well as streamlining functional assays to enhance the interpretation of unresolved variants. Applying SMuRF to neuromuscular disease genes and , we generated functional scores for over 99.8% of all possible coding single nucleotide variants and resolved 310 clinically reported variants of uncertain significance with high confidence, enhancing clinical variant interpretation in dystroglycanopathies. SMuRF also demonstrates utility in predicting disease severity, resolving critical structural regions, and providing training datasets for the development of computational predictors. Our approach opens new directions for enabling variant-to-function insights for disease genes in a manner that is broadly useful for crowd-sourcing implementation across standard research laboratories.

Details

Title: Subtitle: Deep Mutational Scanning in Disease-related Genes with Saturation Mutagenesis-Reinforced Functional Assays (SMuRF)
Creators: Kaiyue Ma - Yale School of Medicine
Kenneth K Ng - Yale School of Medicine
Shushu Huang - Yale School of Medicine
Nicole J Lake - Yale School of Medicine
Jenny Xu - University of New Haven
Angela Lek - Muscular Dystrophy Association
Lin Ge - Capital Medical University
Keryn G Woodman - Yale School of Medicine
Katherine E Koczwara - Yale School of Medicine
Vincent Ho - Yale School of Medicine
Christine L O'Connor - Yale School of Medicine
Soumya Joseph - Howard Hughes Medical Institute, Senator Paul D. Wellstone Muscular Dystrophy Specialized Research Center, Department of Molecular Physiology and Biophysics and Department of Neurology, Roy J. and Lucille A. Carver College of Medicine, The University of Iowa, Iowa City, IA, USA
Melinda A Brindley - Senior Authors
Kevin P Campbell - Senior Authors
Monkol Lek - Lead Contact
Resource Type: Preprint
Publication Details: bioRxiv : the preprint server for biology
DOI: 10.1101/2023.07.12.548370
PMID: 37873263
PMCID: PMC10592615
Publisher: Cold Spring Harbor Laboratory; United States
Grant note: U54 NS053672 / NINDS NIH HHS R01 AI139238 / NIAID NIH HHS P30 CA016359 / NCI NIH HHS
Language: English
Date posted: 11/27/2023
Academic Unit: Neurology; Molecular Physiology and Biophysics; Iowa Neuroscience Institute
Record Identifier: 9984532183402771

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