Back

Premature termination codons signal targeted gene repair by nonsense-mediated gene editing in E. coli

Wang, X.; Wang, X.; Peng, H.; Li, C.; Wang, Y.; Chen, G.; Zhang, J.

2019-12-24 genetics
10.1101/069971 bioRxiv
Show abstract

The molecular mechanisms for repairing DNA damages and point mutations have been well understood but it remains unclear how a frameshift mutation is repaired. Here we report that frameshift reversion occurs in E. coli more frequently than expected and appears to be a targeted gene repair signaled by premature termination codons (PTCs), producing high-level variations in the repaired genes. Genome resequencing shows that the revertant genome is highly stable, and the single-molecule variations in the repaired genes are derived from RNA editing. A multi-omics analysis shows that the expression levels change greatly in most the DNA and RNA manipulating genes. DNA replication, transcription, RNA editing, RNA degradation, nucleotide excision repair, mismatch repair, and homologous recombination were upregulated in the frameshift or revertant, but the base excision repair was not. Moreover, genes and transposons in a duplicate region silenced in wild type E. coli were activated in the frameshift. Finally, we propose a nonsense-mediated gene revising (NMGR) model for frame repair, which also acts as a driving force for molecular evolution. In essence, nonsense mRNAs are recognized, edited, and transported to template the repair of the coding gene by RNA-directed DNA repair, nucleotide excision, mismatch repair, and homologous recombination. Thanks to NMGR, the mutation rate temporarily rises in a frameshift gene, bringing genetic diversity while repairing the frameshift mutation and accelerating the evolution process without a high mutation rate in the genome.

Matching journals

The top 8 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.