On June 24, 2025, the research team led by Prof. Wensheng Wei at Peking University and Changping Laboratory published a research paper titled “Prime editor-based high-throughput screening reveals functional synonymous mutations in human cells” in Nature Biotechnology. The study systematically reveals that although synonymous mutations diverge from nonsynonymous mutations in fitness effects yet exhibit similar phenotypic distributions relative to negative controls, a small subset of them demonstrated measurable effects. This work constructed an innovative experimental framework for precise mutation research, providing a novel tool for human genetics research.

　　

 

　　Synonymous mutation refers to a change in the DNA sequence that does not alter the amino acid sequence of the protein, and thus is typically regarded as neutral in traditional genetic theory¹. However, several studies in viruses and prokaryotes suggested that synonymous mutations could affect the fitness of these organisms^2-5. A recent study in Saccharomyces cerevisiae reported that synonymous and nonsynonymous mutations can similarly disrupt cell fitness, claiming that synonymous mutations may have non-neutral phenotypes⁶, though this finding remains controversial⁷. Yet, it remains unclear whether these findings in noneukaryotic organisms and yeast are applicable to mammals, especially humans.

　　

　　To address this issue, Wei’s laboratory employed the PEmax system to construct a library containing 297,900 epegRNAs targeting 3,644 human protein-coding genes. Using this library, this study performed the most systematic high-throughput screening of synonymous mutations to date in the human colon cancer cell line HCT116. The screen identified 409 synonymous mutations that significantly impacted cell proliferation—representing 0.43% of all tested synonymous mutations—compared to 1,505 functional nonsynonymous mutations (3.83%). Overall, synonymous mutations displayed weaker and more neutral fitness effects than missense, nonsense, or frameshift mutations (Figure 1), a finding that contrasts sharply with yeast-based observations.

　　

　　

　　Figure 1. High-throughput screening unveils functional synonymous mutations in the human genome.

 

　　Although the majority of synonymous mutations exhibit no significant functional effects, the study identified a small fraction with deleterious effects. To explore their potential mechanisms, this study developed a machine learning model named DS Finder (Figure 2). This model revealed that functional synonymous mutations tend to disrupt splicing sites, alter mRNA secondary structures, or interfere with translation initiation. For example, the mutation PLK1_S2 (AGT>AGC) enhances local mRNA stability while repressing translation initiation, whereas BUB1B_R322 (AGG>AGA) causes aberrant splicing that leads to mRNA degradation.

　　

　　

　　Figure 2. Investigating mechanisms of functional synonymous mutations using machine learning and single-cell sequencing

 

　　By integrating the screening results with DS Finder, the researchers conducted a systematic analysis of clinical databases and identified several potentially pathogenic synonymous variants. One notable example is G6PC3 c.G399A, which is associated with autosomal recessive severe congenital neutropenia. Although annotated as “likely benign” in ClinVar, this mutation received the highest DS Finder score in our analysis and was further supported by SilVA and CADD predictions, suggesting potential disease relevance under specific conditions (Figure 3).

 

　　

　　Figure 3. DS Finder identifies potentially functional synonymous mutations in clinical datasets

 

　　This research deepens our understanding of synonymous mutations, providing insights for clinical disease studies. The mutation screening platform and prediction model have been made publicly accessible. For more information and tools, please visit the project website “Hearing Silence” at: https://search-synonymous-mutations.streamlit.app.

 

　　Xuran Niu (Ph.D. student at the School of Life Sciences, Peking University), Wei Tang (Ph.D. student at the Academy for Advanced Interdisciplinary Studies, Peking University) and Dr. Yongshuo Liu (former postdoctoral fellow at Peking University, now Associate Researcher in the Department of Laboratory Medicine at Shandong Cancer Hospital) are co-first authors of the study. Dr. Ying Liu (Associate Researcher at Changping Laboratory) is the co-corresponding author. Binrui Mo (undergraduate student at the School of Life Sciences, Peking University) and Dr. Ying Yu (Research Assistant at Peking University) also made important contributions to this work. The project was supported by the National Natural Science Foundation of China, the Peking-Tsinghua Center for Life Sciences, Changping Laboratory, and the Taishan Scholars Program.

　　

      Paper link: https://www.nature.com/articles/s41587-025-02710-z

 

　　References：

　　1. Kimura, M. Preponderance of synonymous changes as evidence for the neutral theory of molecular evolution. Nature 267, 275–276 (1977).

　　2. Cuevas, J. M., Domingo-Calap, P. & Sanjuan, R. The fitness effects of synonymous mutations in DNA and RNA viruses. Mol. Biol. Evol. 29, 17–20 (2012).

　　3. Kristofich, J. et al. Synonymous mutations make dramatic contributions to fitness when growth is limited by a weak-link enzyme. PLoS Genet. 14, e1007615 (2018).

　　4. Walsh, I. M., Bowman, M. A., Soto Santarriaga, I. F., Rodriguez, A. & Clark, P. L. Synonymous codon substitutions perturb cotranslational protein folding in vivo and impair cell fitness. Proc. Natl. Acad. Sci. U S A 117, 3528–3534 (2020).

　　5. Lebeuf-Taylor, E., McCloskey, N., Bailey, S. F., Hinz, A. & Kassen, R. The distribution of fitness effects among synonymous mutations in a gene under directional selection. eLife 8, e45952 (2019).

　　6. Shen, X., Song, S., Li, C. & Zhang, J. Synonymous mutations in representative yeast genes are mostly strongly non-neutral. Nature 606, 725–731 (2022).

　　7. Kruglyak, L. et al. Insufficient evidence for non-neutrality of synonymous mutations. Nature 616, E8–E9 (2023).