bioRxivGenomicsPreprint
Synonymous codon usage is biased for and against m⁶A RRACH motifs in mammals
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Abstract
RNA methylation at N6-adenosine (m6A) predominantly occurs within RRACH motifs, yet the forces shaping these motifs in coding regions remain unclear. Here we show that the synonymous codon combinations able to form or disrupt RRACH sites are used non-randomly across mammals. Using 13,491 protein-coding genes from 261 species, we identified genes significantly enriched or depleted in RRACH motifs, a pattern consistent with gene-specific selection for or against m6A potential. Genes enriched in RRACH sites were linked to ubiquitin-like conjugation and cell cycle regulation, whereas transmembrane and HOX genes were RRACH-poor, likely reflecting sequence incompatibility with CpG dinucleotides. Cross-species comparison with Caenorhabditis elegans, which lacks mRNA m6A methylation, revealed reciprocal RRACH frequencies, as expected if these motifs are under selection in m6A-competent genomes but evolve without this constraint otherwise. At the codon level, specific amino acid pairs, particularly threonine-ending dyads, were biased toward RRACH-forming codons while others were depleted, indicating that synonymous codon choice is skewed for and against motif formation. RRACH motifs were also non-randomly distributed along coding sequences, depleted near start codons and enriched toward the 3' end, consistent with known m6A profiles. Finally, analysis of cancer mutations revealed tissue-specific gain and loss of RRACH sites, reflecting context-dependent remodeling of methylation potential. Together, these results show that synonymous codon usage is systematically biased for and against m6A RRACH motifs, pointing to an evolutionary coupling between the genetic code and the epitranscriptomic landscape.
- organism: human
- organism: mouse
- organism: rat
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: database / resource
- approach: statistical model
- approach: benchmark study
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