Abstract
Over the past 15 years, sequencing of the human genome and The Cancer Genome Atlas (TCGA) project have led to comprehensive lists of single-nucleotide polymorphisms (SNPs) and gene mutations across a large number of human samples. However, our ability to predict the functional impact of SNPs and mutations on gene expression is still in its infancy. Here, we provide key examples to help understand how mutations present in genes can affect translational output.
Highlights
LEADER SEQUENCE VARIATION AND START SITE SELECTIONThe mechanism by which 43S PICs locate an initiation codon has consequences on how single-nucleotide polymorphisms (SNPs) that generate new, or remove existing, start codons affect translation initiation
Reviewed by: Jun Yasuda, Miyagi Cancer Center, Japan Eric Londin, Thomas Jefferson University, United States Tommy Alain, University of Ottawa, Canada
There has been a bloom in genome-wide association studies (GWAS) where the prevalence of specific single-nucleotide polymorphisms (SNPs) is linked to phenotypes or disease (Srinivasan et al, 2016)
Summary
The mechanism by which 43S PICs locate an initiation codon has consequences on how SNPs that generate new, or remove existing, start codons affect translation initiation. Mutations arising within the coding region of uORFs have the potential to exert two types of effects on translation – by affecting the nature of an encoded regulatory peptide and by altering elongation rates If they perturb the function of a regulatory peptide involved in dictating ribosome re-initiation rates, they can affect the output from the major ORF. The authors tested the consequences of this SNP in a luciferase-based transfection assay and found that the T allele caused a 2.5- to 2.9-fold increase in luciferase expression without altering mRNA levels One interpretation of these results is that the uORF-encoded peptide plays an inhibitory role in translation and the G to A change impairs activity of this small polypeptide. Whether SNPs affect splicing or transcription, can only be assessed through sequence characterization of mRNA 5 leader regions, an analysis that is all too frequently omitted
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