Synonymous Mutations that Increase the tRNA Adaptation Index (tAI) are Generally Suppressed in Human Oncogenes

Background: Different synonymous codons are decoded at distinct rates during translation elongation due to the difference in tRNA availability, term tRNA adaptation index (tAI). Codons with higher tAI values are translated faster. Synonymous mutations do not change the amino acid (AA) but do sometimes change the tAI. That means synonymous mutations are able to alter the translation efficiency of host genes. In the cancer field, synonymous mutations are often automatically ignored. However, the increased translation of oncogenes or the decreased translation of tumor suppressor genes (TSG) might also lead to oncogenesis even when the protein sequences are unchanged. These changes in translation level could be induced by synonymous mutations. Results and Discussion: We downloaded the single nucleotide polymorphisms (SNPs) in human populations. The ancestral state is parsed according to genomic alignments between human, macaque and mouse. We found that in the normal human populations, derived synonymous mutations that increase tAI are strongly suppressed in oncogenes but slightly favored in TSG. In oncogenes, mutation sites with higher conservation levels are more likely to decrease the tAI. Conclusion: Our results indicate that the synonymous mutations in the human genome are not strictly neutral. The potentially increased translation of oncogenes and the decreased translation of TSG caused by synonymous mutations are suppressed in normal human populations. This is an indirect evidence that the synonymous-induced translational changes might be related to oncogenesis and should not be ignored in the cancer studies. Citation: Chu D, Wei L (2019) Synonymous Mutations that Increase the tRNA Adaptation Index (tAI) are Generally Suppressed in Human Oncogenes. J Cancer Sci Ther 11: 208-212. J Cancer Sci Ther, an open access journal ISSN: 1948-5956 Volume 11(6) 208-212 (2019) 209 314 tumor suppressor genes (TSG) (Additional file 1: Table S1). The reference genome sequences of human (H. sapiens, version hg19) and mouse (M. musculus, version mm10) were downloaded from UCSC Genome Browser (genome.ucsc.edu), and the reference genome of rhesus macaque (M mulatta, Ensembl version v89) was downloaded from Ensembl Genome Browser (www.ensembl.org). Annotation of human SNPs We annotated the SNP sites using the hg19 human genome downloaded from UCSC Genome Browser (genome.ucsc.edu). If a SNP hits multiple isoforms of the same gene, the transcript with the longest CDS (canonical transcript) was retained. The canonical transcript of each gene was defined by the software SnpEff (version 4.2) [9]. If a SNP does not hit any genes, it is annotated as intergenic. All the information of a given SNP in CDS including the position on CDS, the amino acid before and after mutation, were inferred from the output file of SnpEff. The mutations related to splicing effects (indicated by the software) were discarded because we should separate the selection on synonymous mutations from the constrain on splicing changes. Conservation analysis Conservation level of genomic positions is measured by phyloP score (file hg19.100way.phyloP100way.bw, downloaded from UCSC Genome Browser, genome.ucsc.edu). Briefly, sites with higher conservation level have higher phyloP scores. The orthologous sites (genomic coordinates) between human and mouse or between human and rhesus macaque were transferred with liftOver [8] based on the pairwise genomic alignments. The lift Over chain files were downloaded from UCSC Genome Browser website(http://hgdownload.soe.ucsc. edu/goldenPath/hg19/liftOver/). Bedtools (version 2.25) [10] was used to extract sequences of a give region according to the reference genome. Calculation of gene expression level in human HeLa cells We searched for the public database and chose an NGS dataset conducted in human HeLa cells (GES63591) [11]. The mRNA-Seq library (si-control) was used to define the gene expression level. We aligned the mRNA-Seq NGS reads to the hg19 reference genome using STAR (version 2.7) [12]. The uniquely mapped reads were kept for downstream analysis. The read counts of each gene were calculated by htseq-count (version 0.5.4) [13]. In this gene expression calculation, the canonical transcript of each gene was chosen, and all the reads overlapped with exon regions were counted. Highly expressed genes (7712 genes in total) are defined as gene with reads count > 200. Among the 7712 highly expressed genes in HeLa cells, 104 are oncogenes and 157 are TSG, the remaining 7451 genes are termed “other genes”. We focused on highly expressed genes because they are better annotated (since poorly annotated genes are not possible to have many mapped NGS reads). History and definition of tAI To quantitatively measure the extent of codon bias, the codon adaptation index (CAI) was first invented [14]. CAI considers the codon frequencies appearing in the highly expressed genes. The factors that are ignored by CAI are 1) the tRNA pool that could actually translate each codon and 2) the wobble interactions between codon and anticodon. These factors could indeed affect the decoding efficiency. For example, the adenosines located at the first anticodon positions (A34NN) are deaminated into inosines (I34NN), which could form wobble (and also weaker) interactions with C, U or A [15]. To fill the gap left by CAI, tRNA adaptation index (tAI) was then created [7]. tAI could either describe a codon or a gene. The codon level tAI was name wi in the original literature [7]. tAI of a codon is calculated by the following steps, 1) seek for the corresponding tRNA copy numbers of a codon; 2) weight each tRNA copy number if wobble base pairing appears; 3) sum up the weighted tRNA copy numbers; 4) normalize by the maximum value within the same amino acid (within synonymous codons). The weight of each type of wobble interaction is decided by the selection constraint (sij) [7]. Distinct sij values suitable for different evolutionary clades were also defined [16]. tAI of each codon is generally in proportion to the tRNA that can translate this codon. This could be an estimate of translation efficiency at codon level. The tAI of each gene is the geometric mean of the tAI values of each codon. Similarly, tAI of a gene could be an estimate of gene level translation efficiency. Statistical analysis and code availability All statistical analyses (correlation tests, Fisher’s exact tests) and the graphic work were conducted in R environment (http://www.Rproject.org/). The codes used in this study are available under request.