Prime editor-based high-throughput screening reveals functional synonymous mutations in human cells.

Area of Science:

• Genomics
• Molecular Biology
• Bioinformatics

Background:

• Synonymous mutations are typically regarded as neutral, with their functional impact in the human genome under-investigated.
• Previous research has not fully explored the phenotypic consequences of synonymous genetic variations.

Purpose of the Study:

• To identify synonymous mutations that influence cell fitness using a large-scale screening approach.
• To investigate the molecular mechanisms underlying the functional effects of synonymous mutations.
• To develop predictive models for clinically significant synonymous mutations.

Main Methods:

• Engineered a library of 297,900 prime-editing guide RNAs (PEmax system) for extensive screening.
• Performed group-level analyses comparing fitness effects of synonymous and nonsynonymous mutations.
• Developed a machine learning tool integrating screening data to analyze mutation impact.
• Utilized multifaceted experimental evidence, including RNA folding and translation assays (e.g., PLK1_S2).

Main Results:

• Identified a subset of synonymous mutations with measurable effects on cell fitness.
• Synonymous mutations showed distinct fitness effects compared to nonsynonymous mutations but similar phenotypic distributions to controls.
• Functional mutations were found to impact messenger RNA splicing and transcription.
• Demonstrated that synonymous mutations can alter RNA folding and affect translation.

Conclusions:

• Synonymous mutations are not universally neutral and can have significant functional consequences.
• A machine learning approach can predict clinically deleterious synonymous mutations.
• This research provides novel insights into the roles of synonymous mutations in biological processes and disease.