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Regulation of mRNA Level by Synonymous Codon Usage
Although synonymous codons encode the same amino acid, they are used with different frequencies in the genome, a phenomenon that is known as codon usage bias. The observation that codon usage bias is highly correlated with mRNA level has been a hot topic in the field of molecular evolution over the past decades. It is widely accepted that the correlation results from the stronger natural selection on synonymous codon usage in more highly expressed genes to improve their translational efficiency and accuracy. In other words, mRNA level is widely believed to be the cause of codon usage bias. However, the possibility that synonymous codon usage regulates mRNA level has been tested until recently.
Recently, researchers from QIAN Wenfeng’s Lab at the Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, measured the mRNA levels for thousands of synonymous variants of two genes, and surprisingly, found that codon usage bias and mRNA level are highly correlated (in the absence of natural selection). They observed that a single synonymous mutation was sufficient to affect mRNA level, and more importantly, this effect was highly dependent on the sequence context of the mutation.
They provided unambiguous evidence supporting the converse mechanism, that is, codon usage can directly regulate mRNA level. They also found that codon usage bias played the role in regulating transcript concentration likely via regulating mRNA degradation rate. This finding reveals the pleiotropic effects of synonymous codon usage, and calls for a re-evaluation of the theories on the evolution of codon usage bias.
This work entitled “Codon-resolution analysis reveals a direct and context-dependent impact of individual synonymous mutations on mRNA level”, has been published on Molecular Biology and Evolution (DOI:10.1093/molbev/msx229). This work was supported by the National Natural Science Foundation of China.
Figure. The impact of individual synonymous mutation on mRNA level. (Image by IGDB)
Dr. QIAN Wenfeng