Effects of mirror-image nucleosides on DNA replication and transcription in human cells

Mirror-image nucleosides, as potential antiviral drugs, can inhibit virus DNA polymerase to prevent virus replication. Conversely, they may be inserted into the DNA strands during DNA replication or transcription processes, leading to mutations that affect genome stability. Accumulation of significant mutation damage in cells may result in cell aging, apoptosis, and even uncontrolled cell division. We have previously explored replicative repair of mirror-image nucleosides within Escherichia coli, and this study focuses on human cells. We constructed several plasmid substrates, each carrying a specific mirror-image nucleoside, to investigate their impact on intracellular DNA replication and transcription processes. The results showed that in HepG2 cells, L-adenosine (L-dA) was the most potent substrate in inhibiting cell replication and transcription. L-cytidine (L-dC) exhibited the highest bypass efficiency in both template and non-template strands and had the most diverse mutation types. We also observed that L-dC induced immunoregulation of the JAK-STAT signaling pathway. Therefore, our results provide a theoretical basis for the disruptions caused by mirror-image nucleosides in replication and transcription and give us some understanding that mirror-image nucleoside drugs can cause cytotoxicity. Overall design: Cells were transfected with recombinant plasmids containing L-dC (A207P, S206A), with cells transfected with normal plasmids serving as the control group. Total cellular RNA was isolated using TRIZOL reagent (Invitrogen, Karlsruhe, Germany) following the manufacturer's instructions. The isolated RNA was then shipped to BGI for transcriptome sequencing and basic data analysis, preserved on dry ice. After quality control of the samples, the clean data were aligned with the reference genome, generating 1.15-1.19Gb of data per sample. Gene expression quantification was conducted based on the alignment results. Differential analysis of cell gene expression was performed using DESeq to identify differentially expressed genes. Functional annotation databases such as GO and KEGG were utilized to obtain significant enrichment of functional information and related pathway information based on the genes differentially expressed between samples or groups.