Synonymous mutations that regulate translation speed might play a non-negligible role in liver cancer development

The human and macaque reference genomes were downloaded from Ensembl website (http://​ensemblgenomes.​org/​). The sequencing data (RNA-seq and ribosome profiling) of normal/tumor tissues from ten patients were downloaded from NCBI via accession number GSE112705 [42]. According to the original literature, the ten patients suffered from hepatocellular carcinoma (HCC). The liver tumor tissues and their adjacent normal liver tissues were collected.

The lists of oncogenes and tumor suppressor genes (TSG) were downloaded from the cancer gene consortium website (CGC, https://​cancer.​sanger.​ac.​uk/​census/​). Totally 240 oncogenes and 242 TSG were obtained. This final gene list did not include the ambiguously annotated genes, for example, some genes were annotated as both oncogene and TSG. These genes were not included in our list.

The SNP data of the 1000 genome project were downloaded from official website (ftp://​ftp.​1000genomes.​ebi.​ac.​uk/​). The genome version is “phase2 reference assembly sequence hs37d5”.

We mapped the sequencing reads to the human reference genome by using STAR [43] with default parameters (version 2.7.3a). The gene expression profile was directly downloaded from the original article [42]. Mutations were called by Samtools [44] with parameter -Q 30 -q 20 (version 1.10). The parameters require the base quality to be higher than 30 and mapping quality higher than 20. Higher base quality represents lower sequencing error probability. Q > 30 means error probability <1e-3. Mapping quality increases with the reliability of the sequence alignment. Mapping quality q > 20 almost ensures a read to be uniquely mapped to the reference genome.

As we have defined in the main text, the direction of mutations should be “from ancestral allele to derived allele”. To obtain the tumor-specific SNP, we required the mutations to have both the ancestral and derived alleles in tumor RNAs. Meanwhile, this position in normal sample should be covered by at least five reads (of ancestral allele) to prove that this site did not have mutation in normal tissues. This pipeline was first applied to the data of each of the ten patients to get the SNP profiles of ten individuals. Next, these ten SNP sets were merged to get a union of SNP sites, which is approximately 400,000 unique sites. Functional annotation was done by software SnpEff [38].

For the so-called tumor-specific mutations in this study, there are two layers. In the section that displayed the mutation landscape in RNA-seq, we only required (1) sequencing coverage ≥5 in normal tissues and no mutations were detected; (2) coverage ≥5 in tumor samples and mutations were detected. In the section that compared translational speed of different alleles (which is sensitive to low sequencing coverage), we further required the mutation sites to have (1) RNA-seq coverage ≥20 in normal tissues and no mutation was detected; (2) RPF (A-site) coverage ≥20 in normal tissues and no mutation was detected in A-site tri-nucleotide; (3) RNA-seq coverage ≥20 in tumors and both two alleles were detected with allele count ≥3; (4) RPF (A-site) coverage ≥20 in tumors and both two alleles were detected in A-site tri-nucleotide with allele count ≥3; (5) absent in 1000-genomes. We have emphasized that we do not intend to obtain an accurate list of tumor-specific mutations. Instead, we just want to identify the bona fide ones to perform translational analysis and prove the notion that synonymous mutations could affect translational speed.

With similar idea and methodology to previous studies [45, 46], we used the A-site tri-nucleotide to calculate the ribosome density and translation elongation speed of each codon. We obtained the RPF alignment file and ran with software plastid [47]. The outcome of this pipeline would tell us the P-site offset values for each length of the RPF reads. By trimming the offset from the 5′ end, one could determine the P-site tri-nucleotide in each RPF read. The A-site tri-nucleotide is just located downstream the P-site in each RPF read. For the ribosome density used to calculate translational speed and the mutations detected in RPF reads, only the A-site tri-nucleotide was used. Therefore, the RPF density on each codon was also termed A-site density in this article.

The TE of genes was calculated as RPKM_RPF/RPKM_mRNA. RPKM stood for “reads per kilobase per million mapped reads”. The raw reads count of each gene was accomplished by software package htseq [48].

For each mutation site in human, the orthologous nucleotides in macaque were transferred by liftOver chain downloaded from Ensembl. The liftOver tool allows the transfer of genomic coordinates of different species. Command “liftOver human_sites human_to_macaque_chain macaque_sites”. When the orthologous location in the macaque genome is known, then we could extract the corresponding nucleotide using Bedtools [49]. Command “bedtools getfasta -fi macaque.fa -bed macaque_sites -fo macaque_nucleotide”. The dN (nonsynonymous substitution rate) was calculated by software yn00 [50]. When running yn00, first we need to have the orthologous gene table between human and macaque. This could be obtained by transferring the genomic coordinates of the two species and retrieve the gene ID with the help of the genome annotation file (gft format).

The graphic works and statistics were realized in the R environment (version 3.5.2). KS tests were realized with command “ks.test()”. Correlation tests were performed with command “cor.test()”.

By mapping the reads to the human reference genome and performing variant calling, we defined the mutations appearing in cancer tissues but absent in normal tissues (Fig. 1a, b). We stress that this study does not aim to retrieve the accurate full list of specific mutations in tumors. We only aim to find solid evidence that the synonymous mutations indeed affect the translational speed. To accomplish this, we wish to find the “high-confidence sites” where no derived mutations are observed in normal tissues while the tumor samples are heterozygous (Fig. 1b). Sites with low sequencing coverage are not informative to determine whether they are mutated or not, so we will perform stricter filtering criteria in the following sections when using ribosome profiling data, where the 1000-genomes SNPs are also excluded (see Methods). Here, we first show the landscape of mutations in RNA-seq.

The nucleotides in human reference genome might not reflect the ancestral state, and therefore we only keep the derived mutations by looking at the orthologous site in macaque (Macaca mulatta) (Fig. 1a). This step is necessary since the direction of mutations should not be based on the reference genome and could only be inferred from the sequence of outgroup species. In the comparison of normal and tumor tissues, we require the reference genome and normal tissue to be ancestral nucleotide and tumor tissue to have both the ancestral and derived nucleotides (Fig. 1b). The mutations in tumors with extreme high level of 100% were unreliable. Moreover, a SNP might be missed in normal tissues due to insufficient sequencing coverage. Therefore, when searching for these tumor tissue-specific mutations, we require them to have at least 5 RNA-seq reads covered in normal tissues (see Methods for details). Again, we re-emphasize that the so-called tumor-specific mutations might be overestimated due to insufficient sequencing coverage in normal tissues. However, we do not aim to acquire the accurate list of tumor-specific mutations. Instead, these mutations just serve as candidate sites for our downstream analysis on ribosome profiling data. In the following sections (where stricter cutoffs are used and the 1000-genomes SNPs are excluded. See Methods), we will observe that those synonymous sites are un-mutated in our normal tissues (coverage ≥20) but mutated in tumor samples with high confidence (coverage ≥20 & alternative allele ≥3), and that they indeed affect the translational speed by comparing different alleles. This is our ultimate purpose.

Here, under coverage ≥5 in RNA-seq of normal tissues and mutations detected in tumor sample, 9567 ~ 66,173 SNPs were found in the ten patients, with individual LC034 having the fewest SNPs and individual LC502 having the most SNPs. The union of these SNPs is 399,727 unique sites. Among these 399,727 unique SNPs in patients, 146,595 (43.2%) were missense, 61,259 (18.1%) were synonymous, ~ 20% were located in UTRs (untranslated regions), and ~ 20% were located in intron or other noncoding regions (Fig. 1c). The SNP profile show that transitions take place much more frequently than transversions (Fig. 1d), which confirms with our commonsense. These results represent the basic landscape of mutations detected in RNA-seq. The next step is to identify the signal of translation of these mutations, where stricter criteria and higher cutoffs will be used.

The ribosome protected fragment, termed RPF, is typically around 30 nt. The position being translated is the P-site and A-site (Fig. 2a). P-site is more relevant to the amino acid property and A-site usually reflects the translational speed of the codon. The distance from the 5′ end of RPF read to the 5′ of P-site is termed P-site offset. Multiple tools [47, 51] are able to find out the P-site offset in the ribosome profiling data with good phase.

We looked at the phase of the ribosome profiling data in hand. For different RPF length, most of the reads have 5′ end mapped to the first codon position (Fig. 2b), suggesting the success of nucleotide digestion in the ribosome profiling protocol. Next, we employed software plastid [47] to determine the P-site offset RPF reads with different length (Fig. 2c, d). By trimming the offset from the 5′ end, we determine the P-site tri-nucleotide in each RPF read. The A-site tri-nucleotide is just located downstream the P-site in each RPF read (see Methods for details). In the following sections, only the A-site tri-nucleotide was used to calculate the ribosome density (translational speed) and the mutations detected in RPF reads.

For all the mutations we selected, the nucleotides in normal tissues are the ancestral alleles and the nucleotides in tumors have both ancestral and derived alleles (Fig. 3). For each of these alleles, it has RNA coverage and RPF (A-site) coverage, and the RPF (A-site) density could be calculated to represent translation elongation velocity (Fig. 3). Higher A-site density represents slower local translation velocity. In normal tissues, the A-site densities of the ancestral alleles are calculated, and in tumors, both the ancestral and derived alleles have their own RPF densities (Fig. 3). We calculated the A-site densities of the alleles for all SNP sites. Next, we retrieved the synonymous sites and check whether the synonymous mutations increase or decrease the codon optimality. Synonymous mutations from A/T to C/G increase codon optimality and those from C/G to A/T decrease codon optimality (Fig. 3). Our aim is to examine if the alteration in codon optimality could impact the A-site density, that is, the local translation elongation speed. As shown in Fig. 3, the comparison of A-site densities between normal and tumor samples might be questionable as we did not perform “normalization by library size”. However, the comparison of A-site densities between the two alleles in tumor sample is completely unbiased and valid because they are produced from the same sequencing library. Moreover, heterozygous SNPs within the same sample are exposed to the same trans environment, which provides the perfect condition to study the effects of cis elements like sequence features. Nevertheless, although the cross-sample comparison is affected by library size and other variables, we intend to add it as supporting evidence.

In the previous section that displayed the mutation landscape in RNA-seq, we only required sequencing coverage ≥5 and mutation detected in tumor samples. In the following comparison of translational speed, which would be sensitive to low sequencing coverage, we further required the mutation sites to have (1) RNA-seq coverage ≥20 in normal tissues and no mutation was detected; (2) A-site coverage ≥20 in normal tissues and no mutation was detected in A-site tri-nucleotide; (3) RNA-seq coverage ≥20 in tumors and both two alleles were detected with allele count ≥3; (4) A-site coverage ≥20 in tumors and both two alleles were detected in A-site tri-nucleotide with allele count ≥3; (5) absent in 1000-genome. We believe these criteria would ensure the high-confidence mutation sites we want. Now that we have defined the synonymous mutations in RNA-seq and RPF data, the next step would be the comparison of translational speed on the different alleles of the mutation sites.

We plotted the A-site density on each allele in normal and tumor samples. We first looked at the optimal synonymous mutations from A/T to G/C. Apparently, the A-site densities on ancestral alleles in normal and tumor samples are comparably high, while the A-site density on derived allele in tumors is lower (Fig. 4a). Since lower A-site density denotes faster translation elongation speeds, this result demonstrates that the optimal synonymous mutations really change the local translation rate. One may argue that the comparison between two samples did not consider the library size. Therefore, we display the A-site density of two alleles within the tumor samples (Fig. 4b). In the figure, each dot represents one optimal synonymous mutation site, and we could clearly see that the A-site density on ancestral allele (A/T) is significantly higher than that on derived allele (C/G). This is direct observation of the different translational speed on two alleles with different codon optimality (within the same library).

Similarly, we then looked at the non-optimal synonymous mutations from C/G to A/T. The A-site densities on ancestral alleles in normal and tumor samples are low, while the A-site density on derived allele in tumors is high (Fig. 4c). Again, to cancel the potential bias caused by library size, we compared the A-site densities on ancestral and derived alleles in tumors. The translation on ancestral alleles (C/G) is significantly faster than that on derived alleles (A/T) (Fig. 4d). This consolidates the notion that synonymous mutations in tumors altered the codon optimality and local translation elongation speed.

Note that the pre-filters on sequencing coverage (≥ 20) ensured the high confidence of these mutation sites where the normal tissues do not have mutations and the tumor samples have both alleles sequenced with adequate coverage (≥ 3). These criteria should make our results and conclusions more reliable.

By merely observing the differential translation speed on two alleles in tumors does not prove the causal relationship between synonymous mutations and oncogenesis. However, the direct evidence could only be obtained from molecular and cellular experiments or animal models. Given the transcriptome and translatome data in hand, what we could do is to examine the enrichment of these synonymous mutations in different groups of genes. We denote the optimal (A/T to C/G) and non-optimal (C/G to A/T) synonymous mutations as the speed-controlling synonymous mutations (as proved by the results above).

The synonymous mutations used in this section was those with RNA-seq coverage ≥5. Although in the translational speed comparison we used a more stringent filter to obtain speed-affecting mutations with higher confidence, we believe that many of the discarded sites in the filtering steps would also affect translational speed and they are removed solely due to the technical issue (insufficient sequencing coverage). Therefore, in this section that compares the synonymous SNP density in different genes, to increase statistic power, we used the larger set of mutation sites with RNA-seq coverage ≥5. The results of high-confidence mutation sites will be displayed in the next section.

We defined SNP density as the number of SNPs per Kb of CDS. Here, SNP means the tumor-specific SNPs as defined previously (and throughout this study). Meanwhile, we retrieved the lists of oncogenes and tumor suppressor genes (TSG) from cancer gene consortium (CGC, https://​cancer.​sanger.​ac.​uk/​census/​). Totally 240 oncogenes and 242 TSG were obtained. We found that the density of optimal synonymous mutations is significantly higher in oncogenes than in TSG (Fig. 5a, pooled samples are shown), and the density of non-optimal synonymous mutations is significantly higher in TSG than in oncogenes (Fig. 5b). Apart from oncogenes and TSG, the remaining genes also have these synonymous mutations. However, we did not see differential SNP densities (optimal versus non-optimal) on these genes (Fig. 5c).

These results make sense and seem to connect synonymous mutations to liver cancer development. In tumor samples, optimal synonymous mutations increase translational speed and are enriched in oncogenes, and non-optimal synonymous mutations decrease translational speed and are enriched in TSG, while other genes do not exhibit different densities of these optimal or non-optimal synonymous mutations.

To visualize our results and provide more specific examples, we listed the genes with the highest synonymous SNP density (Fig. 5d). Oncogenes with the highest density of optimal synonymous mutations and TSG with the highest density of non-optimal synonymous mutations were listed. Among the top genes, the oncogenes CALR (Calreticulin), HNRNPA2B1 (Heterogeneous Nuclear Ribonucleoprotein A2/B1), and TSG including B2M (Beta-2-Microglobulin), BTG1 (B-Cell Translocation Gene 1) may all play crucial roles in maintaining or disrupting the equilibrium status in normal cells. For example, mutations in gene CALR were reported to cause breast and colorectal cancer [52], ovarian carcinoma [53], and prostate cancer [54], and the mutations reported were missense mutations. Now that we found many optimal synonymous mutations in liver cancer tissues, this might broaden the knowledge about this gene and serve as guidance in diagnosis in the future.

To show the potential effect of the synonymous mutations on the global translation efficiency (TE) of genes, we first compared the TE in tumor samples and normal samples. For oncogenes, the TE was significantly higher in tumor samples than normal samples (Fig. 6a). For TSG, the TE was significantly higher in normal samples than tumor samples (Fig. 6b). Since we already observed the enrichment of “speed-controlling” synonymous mutations in these gene sets, it was reasonable to attribute the TE foldchange to the effect of synonymous mutations. We performed multiple regression analysis “Y ~ X1 + X2 + ... + Xn”, where Y was the TE foldchange between tumor and normal samples, and X1 to Xn were the variables. We aimed to see which variable had the strongest effect on TE foldchange. We found that the number of optimal synonymous mutations positively contributed to TE foldchange and that the number of non-optimal synonymous mutations negatively contributed to TE foldchange (Fig. 6c). For other variables like gene length, conservation level (dN), expression, and GC content played minor roles in determining the tumor versus normal translational changes. This observation strongly indicated that synonymous mutations did have impact on the local and global translational changes between tumor and normal samples.

As stated, the so-called tumor-specific mutations have two layers in this article. The results of the mutations with loose criteria (coverage ≥5) were shown above. Here, we try to show the robustness of the patterns by using the high-confidence mutations with much stricter cutoffs (coverage ≥20, alternative allele ≥3, and also absent in the 1000-genomes SNPs. See Methods for details).

We validated the following patterns. The density of optimal synonymous mutations is significantly higher in oncogenes than in TSG (Fig. 7a), and the density of non-optimal synonymous mutations is significantly higher in TSG than in oncogenes (Fig. 7b). No significant difference of SNP densities (optimal versus non-optimal) was observed on the remaining genes (Fig. 7c). The optimal synonymous mutations contribute positively to the gene TE while the non-optimal synonymous mutations negatively affect the gene TE (Fig. 7D). All these patterns suggest that our observation of synonymous mutations and codon optimality affecting the translation elongation speed is robust.