bioRxivGenomicsPreprint
The genetic architecture of human programmed stop codon readthrough
Abstract
Programmed translational readthrough produces C-terminally extended protein isoforms via decoding of stop codons by near-cognate tRNAs. Human genes experimentally validated as readthrough targets share a CUAG motif downstream of a UGA stop codon. However, the full sequence determinants of readthrough efficiency, how they combine, and how generalisable they are across genes remain largely unexplored. Here we use deep mutational scanning to quantify [~]1,400 sequence variants for each of the three examples of human readthrough in the genes AQP4, MAPK10 and OPRK1. In addition to the core CUAG motif, mutations that modulate readthrough elements extend up to +27 nucleotides downstream of the stop codon and across six codons (18 nucleotides) upstream. For the downstream sequence, an additive model with a sigmoidal global epistasis function captures most of the within-gene readthrough variance for double mutants (R{superscript 2}=0.84-0.96), with additional contributions from a small number of strong pairwise interactions. Mutational effects nonetheless generalise poorly between genes: only the immediate -3 to +4 nucleotide window shows consistent behaviour, while mutations in more distal positions have context-dependent effects. Combinatorial assembly of sequence blocks from different genes into chimeras reveals strong interactions (epistasis) between sequences upstream and downstream of the stop codon. This study provides comprehensive quantitative maps of the sequence determinants of human programmed readthrough and suggests that three examples of programmed readthrough are located on distinct local fitness peaks, each defined by different upstream and downstream architectures built around a shared CUAG core motif.
- organism: human
- assay: DNA methylation
- research focus: gene regulatory network inference
- research focus: differential expression
- research focus: variant effect prediction
- research focus: benchmarking
- approach: deep learning
- approach: statistical model
- Citations
- 0
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