Why are synonymous mutations more frequent

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Front Physiol. Cancer Res. Download references. You can also search for this author in PubMed Google Scholar. YB performed the analysis and drafted the manuscript. JX designed the study, performed the analysis, and drafted the manuscript.

All authors read and approved the final manuscript. Correspondence to Junfeng Xia. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Illustration of analysis procedure of cancer associated synonymous mutations.

Figure S2. Correlation between percentages of synonymous mutations and codon numbers of amino acids in TCGA and G. Figure S3. Distribution of synonymous hotspot mutations across cancer types and genes. Table S1. Synonymous codons of amino acids with optimal and non-optimal codons for human genome.

Table S2. Hotspot synonymous mutations across different cancer types in TCGA dataset. Reprints and Permissions. Bin, Y. An analysis of mutational signatures of synonymous mutations across 15 cancer types. BMC Med Genet 20, Download citation. Published : 09 December Anyone you share the following link with will be able to read this content:. Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all BMC articles Search. Download PDF. Volume 20 Supplement 2. Abstract Background Synonymous mutations have been identified to play important roles in cancer development, although they do not modify the protein sequences. Results We investigated the nucleotide-based and amino acid-based features of synonymous mutations across 15 cancer types from The Cancer Genome Atlas TCGA , and revealed novel driver candidates by identifying hotspot mutations.

Conclusions We illustrated the preferences of cancer associated synonymous mutations, especially hotspots, and laid the groundwork for understanding the synonymous mutations act as drivers in cancer. Background Synonymous mutations, which occur in the gene-coding regions without changing the encoded amino acids, have long been supposed to be silent for the fitness of organisms and neutral during evolution [ 1 ].

Hotspot mutations identification Here we used the Hot-Driver package [ 26 ] to identify the hotspot mutations that are positively correlated with the number of mutations across all cancer samples for all 15 cancer types. Protein domain annotation We mapped the hotspot mutations to conserved protein domains obtained from Pfam-A version Results and discussion Synonymous mutation distribution across cancer types From TCGA, we obtained , synonymous mutations of tumor samples from 15 types of cancer.

Full size image. Conclusions In this study, we not only investigated the distribution and mutational nucleotide changes of synonymous mutations across 15 cancer types, but also made the comparison of synonymous mutational signatures between TCGA and G at nucleotide and amino acid levels. Availability of data and materials The datasets supporting the conclusions of this article are included within the article and its additional files. References 1. Article Google Scholar Acknowledgements The authors thank all members of our laboratory for their valuable discussions.

View author publications. Ethics declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Supplementary information. Additional file 1: Figure S1. Additional file 2: Table S3. Additional file 3: Table S4. About this article. Codons 1 and 5 specify Met, and there is only one codon choice for this amino acid. The whole genome of each constructed strain was sequenced to identify any adventitious mutations introduced during genome editing Table 2.

In contrast, there is a surprising anti-correlation between growth rate and the level of ProB. Black, no other mutations in head region; blue, intergenic mutation M2; red, synonymous mutations in codon 2; white, synonymous mutation in codon 3; magenta, synonymous mutation in codon 4; cyan, synonymous mutations in codon 6; yellow, non-synonymous mutation M5.

Data points representing strains with M1 and two additional mutations are shown with colored stripes corresponding to the colors used for individual mutations.

We utilized the RNAstructure package version 5. The choice of the fragment length for the modeling was considered carefully. Because the ribosome occludes about 11 codons of mRNA [ 21 ], a new initiation cycle cannot be started until the preceding ribosome moves at least 11 codons away from the start codon. Thus, we chose to model the structures of nucleotide mRNA fragments beginning 4 nucleotides upstream of the Shine-Dalgarno sequence and continuing 34 nucleotides past the AUG start codon.

The color used for each nucleotide conveys the probability that the nucleotide is found in the depicted state. The Shine-Dalgarno sequence [ 22 ] is predicted to be single-stranded and therefore accessible for binding to the 30S subunit of the ribosome in this structure, as well as all of the mutant structures to be discussed below.

Notably, the AUG start codon is sequestered in a 5-bp stem that includes parts of codons 2 and 7 and all of codon 6. The mutations discussed below do not alter the structure of the second stem-loop at the lower right of Fig 8A , so this part of the structure will not be shown in the following figures. Minimal free energies for each calculated structure are shown in Table 3. The Shine-Dalgarno sequence is indicated by the red box and the start codon by the blue box.

The colors of the bases indicate the probability that each base is found in the depicted state. Fig 9 shows the effect of mutations M3, M4 and two of the synonymous mutations in codons 2 and 6 that increase growth rate on the lowest energy structure predicted by the Fold algorithm. M3 and M4 destabilize the structure by 0. The effect of combining M3 and M4 is additive, suggesting that the effects of the two mutations are independent.

The most dramatic effects were seen for mutations in codon 6 that disrupt the middle base pair in the 5-base-pair stem. Equally dramatic effects are caused by synonymous mutations that increase the stability of the stem structure Fig A synonymous mutation that adds a GU base pair increases stability by 0.

Fig 11 shows that growth rate is linearly related to the stability of the minimal free energy structure for this set of synonymous mutations in codons 2 and 6.

In contrast to synonymous mutations in codons 2 and 6, the intergenic mutation M2 and synonymous mutations in codons 3 and 4 do not change the minimal folding energy Fig However, they have a striking effect on the probabilities that nucleotides preceding the start codon are single-stranded.

The minimal folding energy of all four structures is RGR, relative growth rate. Deletion of argC requires S. This system provides a good model for many situations in which a new enzyme is needed, such as the presence of a toxin or the availability of a new source of carbon, nitrogen or phosphorus. Recruitment of a mutant version of ProA solves the immediate problem of providing an enzyme that is newly required for growth.

Wild-type ProA cannot substitute for the missing ArgC. This situation is particularly interesting because both the new and the original functions of the enzyme are required to support growth.

When growth of microbes is limited by the inefficiency of a weak-link bifunctional enzyme, selection favors emergence of mutants that have managed to increase the level of one or both growth-restricting activities.

This is often accomplished, at least initially, by promoter mutations or gene amplification. The lack of amplification is surprising, as previous studies have shown that duplications the precursors of amplifications occur readily in many regions of the S.

These results suggest that the abilities of even relatively closely related microbes to evolve a new enzyme in the face of an environmental challenge may be dramatically different. This mutation changes Glu34 to Gly. Efforts to determine the effect of this mutation are in progress. These mutations had surprisingly large effects on fitness, ranging from a doubling of growth rate to complete inhibition of growth Fig 7 and Table 3. Although six mutations in this region increased growth rate, we found only two in the adapted lineages.

This discrepancy is likely due to the limited number of clones seven derived from only four evolved lineages whose genomes were sequenced. Further, three of the four mutations that were not found are transversions, which are fold less common than transitions such as M3 and M4 [ 25 ], and the other is unlikely to occur over a short period of selection because it requires two point mutations.

We can also dismiss effects on protein folding. The use of rare codons at specific points in a mRNA has been suggested to allow folding of translated polypeptide sequences in the absence of downstream sequences that might interfere with proper folding. We identified high-impact synonymous mutations within the first six codons.

Any effects of translation rate on protein folding would be irrelevant in the initial six amino acids. These effects might be due to alterations in binding to a small regulatory RNA. However, of the recognized small regulatory RNAs in S. A final possibility is that differences in degradation are due to the well-established link between mRNA degradation and translation efficiency.

Ribosome binding also prevents premature transcription termination, which occurs when translation is not initiated promptly after the initial part of the transcript is produced by RNA polymerase [ 29 ]. Translation efficiency is determined by the efficiency of initiation [ 30 ], which depends on a host of factors, including the strength of the Shine-Dalgarno sequence, the strength of the start codon, the spacing between these elements, the nature of the codons in the head of the mRNA, and the accessibility of the region between the Shine-Dalgarno sequence and start codon.

Biases in codon usage in the heads of mRNAs have been recognized since the s [ 31 , 32 ] and have been ascribed to either the importance of minimizing secondary structure in the region that must bind to the 30S ribosomal subunit to initiate ribosome assembly [ 31 — 33 ], or to the benefits of a slow ramp in translation speed due to rare codons at the beginning of transcripts 30—50 codons that prevents ribosome traffic jams [ 34 ]. Both factors may be important, and cannot always be deconvoluted.

In our case, every synonymous mutation except one—both beneficial and detrimental—changed a common codon into a rare codon S4 Table , suggesting that the beneficial effects cannot be attributed to the latter mechanism. Our modeling results suggest that the intergenic mutation at -3 and synonymous mutations in the first six codons alter the propensity for secondary structure around the Shine-Dalgarno sequence and start codon.

This region must be single-stranded to bind to the 30S subunit of the ribosome prior to assembly of the full ribosome [ 35 ], as the 30S initiation complex does not contain a competent GTPase that can use energy to unwind secondary structures. IF2, which is present in the initiation complex, is not activated to hydrolyze GTP until after the 50S subunit binds [ 36 , 37 ].

Other beneficial mutations do not affect the stability of the stem-loop structure, but increase the probability that the 10 nucleotides preceding the start codon will be single-stranded. In contrast, detrimental mutations either decrease the probability that the 10 nucleotides preceding the start codon will be single-stranded or increase the stability of the stem-loop structure that sequesters the start codon.

Notably, a mutation that adds an extra GC base pair to the stem-loop structure is sufficient to prevent growth entirely. Our results are consistent with recent computational studies showing that folding energies in nucleotide windows in every mRNA encoded by bacterial genomes are less negative around the start codon than in the rest of the transcripts; this decrease in stability is associated with the use of rare codons that tend to be AU-rich [ 38 ].

The importance of low secondary structure around the ribosome binding site was reinforced by a study of a library of recoded GFP variants in which synonymous mutations were introduced randomly throughout the gene with an average of differences between pairs of sequences.

Expression was highest for sequences for which secondary structure was minimal around the ribosome binding site 30 nucleotides centered around the start codon [ 39 ]. Substantial increases in growth rate due to synonymous mutations in a weak-link enzyme have been reported in two other cases.

Agashe et al. Every re-coded version caused a significant decrease in growth rate on methanol and in the level of FAE [ 41 ]. In a subsequent adaptive evolution experiment, growth of three strains carrying recoded versions of fae was substantially improved by synonymous mutations in codons 4, 9 and 13 [ 42 ]. Further analysis of a set of 37 mutants showed no correlation between growth rate and either the computed folding energies of a nucleotide fragment surrounding the start codon or the affinity between the Shine-Dalgarno sequence and the anti-Shine-Dalgarno sequence on the 30S ribosomal subunit, suggesting that epistatic interactions within the widely different fae alleles influenced the effects of single synonymous mutations on fitness.

Measure content performance. Develop and improve products. List of Partners vendors. Share Flipboard Email. Heather Scoville. Science Expert. Heather Scoville is a former medical researcher and current high school science teacher who writes science curriculum for online science courses. Featured Video. Cite this Article Format. Scoville, Heather. Nonsynonymous Mutations.

Synonymous vs. What Is a Peptide? Definition and Examples. Ribosomes - The Protein Builders of a Cell.