The detected clustering suggests a selection advantage of the mutated CD8+ clones and calls for further research on possible phenotypic effects.ĭisease-associated variants (DAVs) are commonly considered either through a genomic lens that describes variant function at the DNA level, or at the protein function level if the variant is translated. Known activating STAT3 mutations were found both in MS patients and controls and overall 1/5 of the mutations were previously described cancer mutations. The mutations showed statistically significant clustering especially to the STAT3 gene, and also enrichment to the SMARCA2, DNMT3A, SOCS1 and PPP3CA genes. We discovered nonsynonymous somatic mutations in all MS patients’ and controls’ CD8+ cell DNA samples, with no significant difference in number between the groups (p = 0.60), at a median allelic fraction of 0.5% (range 0.2–8.6%). Here we concentrated on CD8+ cells in more detail and tested (i) how commonly somatic mutations are detectable, (ii) does the overall mutation load differ between MS patients and controls, and (iii) do the somatic mutations accumulate non-randomly in certain genes? We separated peripheral blood CD8+ cells from newly diagnosed relapsing MS patients (n = 21) as well as matched controls (n = 21) and performed next-generation sequencing of the CD8+ cells’ DNA, limiting our search to a custom panel of 2524 immunity and cancer related genes, which enabled us to obtain a median sequencing depth of over 2000x. Previously we and others have demonstrated that especially CD8+ T cells in blood can harbor persistent somatic mutations in some patients with multiple sclerosis (MS) and rheumatoid arthritis. Somatic mutations have a central role in cancer but their role in other diseases such as common autoimmune disorders is not clear. This model may explain the documented arginine substitution bias in cancers. We propose that base pair substitution bias and amino acid physiology both play a role in purifying selection.
SUMMARIZE ANTONYM DRIVER
These include known factors such as IDH1, as well as previously unreported genes, including four cancer driver genes (FGFR3, PPP6C, MAX, GNAQ). Our analysis identifies several genes with an arginine substitution bias. Here, we provide a review of the available literature and reanalyze publicly available data from the Catalogue of Somatic Mutations in Cancer (COSMIC).
This bias appears to be driven by C > T and G > A transitions in four of the six arginine codons, a signature that is universal and independent of cancer tissue of origin or histology. This suggests that a mutational bias, or “purifying selection”, mechanism is at work. Four amino acids (histidine, cysteine, glutamine, and tryptophan) account for over 75% of amino acid substitutions of arginine. Base pair changes in any of these codons can have a broad spectrum of effects including substitutions to twelve different amino acids, eighteen synonymous changes, and two stop codons. Across all 4 summarized metrics, a clear correlation between global constraint and increasing score can be observed.Īrginine is encoded by six different codons. The dashed arrow indicates our suggested minimum cut-off of 5 for any given metric.
Dashed red line indicates the average P(MAF > 0) value of 13.8% seen in sSNVs globally. Shaded area represents 90th percentile confidence intervals for the given summary metric.
The corresponding value of P(MAF > 0) was plotted against the SURF metric for each bin (red circles) and fitted with a smoothed loess curve (red line). For each plot, variants are grouped by integer values into 36 bins (ranging from 0 to 40, i.e., the 99.99th percentile). For each SNV in our dataset, the maximum Phred score was determined across (A) all 11 metrics-SURF, (B) the 4 stability metrics (MFE, CFE, MEAFE, and EFE)-SURF Stability, (C) the 4 edge distance metrics (MFEED, CED, MEAED, and EED)-SURF Edit Distance, or (D)-the 2 diversity metrics (CD and END)-SURF Diversity. SPI and each of the 10 RNA-folding metrics were percentile ranked and Phred-scaled, such that the larger the Phred-scaled value the greater the predicted change in RNA structure. SUmmarized RNA Folding (SURF) metrics correlate with constraint in synonymous variants.
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