Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
Baylor College of Medicine Medical Scientist Training Program, Houston, TX, USA.
Nat Microbiol. 2024 May;9(5):1382-1392. doi: 10.1038/s41564-024-01655-4. Epub 2024 Apr 22.
RNA viruses, like SARS-CoV-2, depend on their RNA-dependent RNA polymerases (RdRp) for replication, which is error prone. Monitoring replication errors is crucial for understanding the virus's evolution. Current methods lack the precision to detect rare de novo RNA mutations, particularly in low-input samples such as those from patients. Here we introduce a targeted accurate RNA consensus sequencing method (tARC-seq) to accurately determine the mutation frequency and types in SARS-CoV-2, both in cell culture and clinical samples. Our findings show an average of 2.68 × 10 de novo errors per cycle with a C > T bias that cannot be solely attributed to APOBEC editing. We identified hotspots and cold spots throughout the genome, correlating with high or low GC content, and pinpointed transcription regulatory sites as regions more susceptible to errors. tARC-seq captured template switching events including insertions, deletions and complex mutations. These insights shed light on the genetic diversity generation and evolutionary dynamics of SARS-CoV-2.
RNA 病毒(如 SARS-CoV-2)依赖其 RNA 依赖性 RNA 聚合酶(RdRp)进行复制,而这种复制过程容易出错。监测复制错误对于理解病毒的进化至关重要。目前的方法缺乏检测稀有从头 RNA 突变的精度,特别是在输入量低的样本(如来自患者的样本)中。在这里,我们引入了一种靶向准确 RNA 共识测序方法(tARC-seq),以准确确定 SARS-CoV-2 在细胞培养和临床样本中的突变频率和类型。我们的研究结果表明,每个循环平均会产生 2.68×10 个新的错误,其中 C>T 偏向不能仅归因于 APOBEC 编辑。我们在整个基因组中发现了热点和冷点,与高或低 GC 含量相关,并确定转录调节位点为更容易出错的区域。tARC-seq 捕获了包括插入、缺失和复杂突变在内的模板转换事件。这些发现揭示了 SARS-CoV-2 的遗传多样性产生和进化动态的一些机制。