Synonymous variants that disrupt messenger RNA structure are significantly constrained in the human population

Abstract

Background The role of synonymous single-nucleotide variants in human health and disease is poorly understood, yet evidence suggests that this class of “silent” genetic variation plays multiple regulatory roles in both transcription and translation. One mechanism by which synonymous codons direct and modulate the translational process is through alteration of the elaborate structure formed by single-stranded mRNA molecules. While tools to computationally predict the effect of non-synonymous variants on protein structure are plentiful, analogous tools to systematically assess how synonymous variants might disrupt mRNA structure are lacking. Results We developed novel software using a parallel processing framework for large-scale generation of secondary RNA structures and folding statistics for the transcriptome of any species. Focusing our analysis on the human transcriptome, we calculated 5 billion RNA-folding statistics for 469 million single-nucleotide variants in 45,800 transcripts. By considering the impact of all possible synonymous variants globally, we discover that synonymous variants predicted to disrupt mRNA structure have significantly lower rates of incidence in the human population. Conclusions These findings support the hypothesis that synonymous variants may play a role in genetic disorders due to their effects on mRNA structure. To evaluate the potential pathogenic impact of synonymous variants, we provide RNA stability, edge distance, and diversity metrics for every nucleotide in the human transcriptome and introduce a “Structural Predictivity Index” (SPI) to quantify structural constraint operating on any synonymous variant. Because no single RNA-folding metric can capture the diversity of mechanisms by which a variant could alter secondary mRNA structure, we generated a SUmmarized RNA Folding (SURF) metric to provide a single measurement to predict the impact of secondary structure altering variants in human genetic studies.

CD8+ cell somatic mutations in multiple sclerosis patients and controls—Enrichment of mutations in STAT3 and other genes implicated in hematological malignancies

“…However, we also utilized the SURF RNA structural predictivity index that combines different secondary structure effects into one summary metric, and this algorithm did predict the mutation to be deleterious. The somatic MAP3K12 synonymous mutation had a SURF PHRED score of 21.46, which is inside the 99 th percentile and is higher than the scores obtained by the SURF authors for nine known pathogenic mutations affecting RNA structure [ 36 ]. This suggests that the synonymous mutation may have a deleterious effect on the stability of MAP3K12 RNA, which is a gene associated with JNK signaling [ 37 ].…”

“…Enrichment of somatic mutations on certain genes was assessed using a Poisson test, where the per-gene expected mutation rate parameter was obtained from the size of each gene, the size of the full gene panel target area, and the total number of mutations found in all participants. Synonymous somatic variants were evaluated using the TraP [ 35 ] and SURF [ 36 ] algorithms.…”

Arginine Depletion in Human Cancers

“…Organisms commonly display preferences for certain synonymous codons over others [ 25 , 26 , 27 ] ( Figure 1 B). This codon usage bias has a major role in gene expression, regulating translation speed and protein folding [ 28 , 29 , 30 ], as well as mRNA structure, processing, and stability [ 31 , 32 , 33 , 34 ]. Additionally, in cancer cells, codon usage is optimized to accommodate high translation of cell cycle regulatory genes [ 35 ].…”

IndelsRNAmute: predicting deleterious multiple point substitutions and indels mutations

“…More recently, RNAsnp was developed [ 17 , 18 ], with applications such as in studying gene variants [ 19 ] by utilizing dot plot representations that can be analyzed in a variety of ways (e.g., [ 20 ]). RNAsnp offers an efficient method to predict the effect of SNPs on local RNA secondary structure [ 21 , 22 ] whereas the approaches reviewed in [ 16 ] are for global RNA secondary structure rearrangements.…”