Keywords
COVID-19, SARS-CoV-2, Synonymous mutations, RNA secondary structure
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COVID-19, SARS-CoV-2, Synonymous mutations, RNA secondary structure
The title and abstract of the paper have been modified to improve the clarity and to update the results. The introduction section with more relevant literatures has been expanded. The method section has been updated. The frequency of the top 10 synonymous mutations has been updated in Table 1. Additional paragraph in the discussion section has been added to discuss the impact and the relevance of our study. Some major updates are included for RNA secondary structure prediction and base pair probability estimation analysis. For RNAfold analysis, SHAPE reactivity data obtained from the experimental data is incorporated for prediction analysis. Two additional RNA secondary structures analysis, namely IPknot++, MXfold2 are added. The results of four synonymous mutations, which show changes in all 3 RNA secondary structure prediction analysis and base pair probabilities estimation analysis (MutaRNA) are described in Figure 2-5. The results of RNAfold, IPknot++, MXfold2 and MutaRNA for the top 10 synonymous mutations are summarized in Table 2. The reviewer no. 2 suggested that we should apply a more stringent filter to remove bad sequences before performing multiple sequence alignment. In this revision, we applied the filter and performed the multiple sequence alignment in a single file (underlying data), instead of four.The results of 3 prediction analysis of RNA secondary structure and base pair probabilities estimation analysis for all top 10 synonymous mutations are included in Extended data 1-4. Conclusion section has been updated with the revised results. Some citations have been removed, added or updated accordingly.
See the authors' detailed response to the review by Chandran Nithin
See the authors' detailed response to the review by Leyi Wang
See the authors' detailed response to the review by Takahiko Koyama
In December 2019, coronavirus disease 2019 (COVID-19) cases first emerged from Wuhan, China1. Soon after, rapid spread of COVID-19 has resulted in a serious global outbreak. COVID-19 is an infectious and potentially lethal disease caused by a newly found coronavirus strain, known as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The virus causes clinical manifestation ranging from asymptomatic to severe pneumonia and in the worst scenario, death2. SARS-CoV-2 seems to have a higher transmission rate3 but lower mortality rate2 in comparison to Middle East respiratory coronavirus (MERS-CoV) and severe acute respiratory syndrome coronavirus (SARS-CoV).
SARS-CoV-2 is a single-stranded RNA virus with a genome size of 29,903 bases. In general, RNA viruses have a higher mutation rate than DNA viruses and this allows them to evolve rapidly, escaping the host immune defence response4. A study by Kim et al. (2020) identified a total of 1,352 nonsynonymous and 767 synonymous mutations from 4,254 SARS-CoV-2 genomes5. While in another study done by Khailany et al. (2020), 116 mutations had been identified from 95 SARS-CoV-2 genomes6. Different SARS-CoV-2 variants with multiple synonymous and nonsynonymous mutations have been reported since the beginning of the outbreak7. Some variants are classified as variants of concern (VOIs) since they are associated with the change in viral pathogenicity such as, higher disease severity, higher transmission rate, lower immunity response in the host as a consequence of the mutations8. Numerous studies have been carried out to understand the molecular mechanisms of these nonsynonymous mutations on the functions of different SARS-CoV-2 proteins8. However, there are only a few studies on the synonymous mutations of SARS-CoV-2 genome9,10.
In this study, we focus on synonymous mutations instead of nonsynonymous mutations as researchers often overlook their biological importance. Synonymous mutations are also known as silent mutations because the nucleotide mutations result in a change in the RNA sequence without altering the amino acid sequence11. Synonymous mutations have been suggested to have no functional consequence on the fitness of organisms and their evolution in long term12. However, numerous recent studies had showed that synonymous mutations may affect the folding and stability of RNA structures13. Interestingly a recent large scale study of synonymous mutations in multiple yeast genes has shown that most of synonymous mutations are not neutral, affecting the fitness of the cell14. For RNA viruses, even though synonymous mutations generally do not change their pathogenicity directly, some studies reveal that synonymous mutations may affect the RNA secondary structure of the virus15 and also change the codon usage bias of the genes in the virus16,17.
30,229 SARS-CoV-2 genomic sequences were downloaded from GISAID database (Global Initiative on Sharing All Influenza Data, RRID:SCR_018251)18 ranging from 31 December 2019 to 22 March 2021. SARS-CoV-2 genomic sequences were filtered by setting parameters to keep only sequences with complete genome and high coverage. The sequences were further filtered to remove those sequences with higher than 0.1% “N” unresolved nucleotides and ambiguous letters. A total of 3,584 sequences were removed by applying this filter. The reference sequence of SARS-CoV-2 genome (NC_045512.2)19 was retrieved in fasta format from NCBI database (NCBI, RRID:SCR_006472). It is a Wuhan isolate with a complete genome which comprises of 29,903 bases.
The rapid calculation available in MAFFT online server (MAFFT, version 7.467, RRID:SCR_011811)20 was used to perform multiple sequence alignment (MSA) for 26,645 SARS-CoV-2 genomes. This option supports the alignment of more than 20,000 sequences with approximately 30,000 sites. The alignment length was kept, which means the insertions at the mutated sequences were removed, to keep the alignment length the same as the reference sequence. While other parameters were left as default.
A simple Python script was written to identify the mutations in 26,645 SARS-CoV-2 genomes. To determine whether the identified mutations are synonymous or nonsynonymous, MEGA X software, version 10.2.5 build 10210330 (MEGA Software, RRID:SCR_000667)21 was utilized to perform the translation for inspection purposes. The presence of amino acid changes was identified by referring to the genomic position of the nucleotide mutations. Synonymous mutations with the top 10 highest frequencies were generated.
The RNA secondary structure of wild type and mutant sequences were predicted using RNAfold program, version 2.4.18 (Vienna RNA, RRID:SCR_008550)22 with the incorporation of SHAPE reactivity data obtained from the study done by Manfredonia et al. (2020)23. The RNA secondary structure prediction was performed using a sequence length of 250 nucleotides upstream and downstream of the mutation site. Other than RNAfold, another two programs which are IPknot++ version 2.1.0 (SCR_022557)24, and MXFold2 (SCR_022558)25 were also used to perform the RNA secondary structure prediction of SARS-CoV-2 wild type and mutants.
To predict how the mutations affect RNA local folding, base pair probability was estimated by utilizing MutaRNA, version 1.3.0 (MutaRNA, RRID:SCR_021723)26. MutaRNA is a web-based tool that allows prediction and visualization of the structure changes induced by a single nucleotide polymorphism (SNP) in an RNA sequence. It includes the base pair probabilities within RNA molecule of both wild type and mutant. The parameters used in MutaRNA were set as default except the window size was changed to 501nt.
Relative synonymous codon usage (RSCU) represents the ratio of the observed frequency of codons appearing in a gene to the expected frequency under equal codon usage. RSCU is calculated using the formula:
where Xi implies the number of occurrences of codon i and n stands for the number of synonymous codons encoded for that particular amino acid.
A synonymous mutation is a change in the nucleotide that does not cause any changes in the encoded amino acid. Synonymous mutations were previously considered to be less important, but they are now proven to have some effects on RNA folding, RNA stability, miRNA binding and translational efficiency27. Synonymous mutations may have significant effects on the adaptation, virulence, and evolution of RNA viruses28. Another study done also indicated that synonymous mutations have association with more than 50 human diseases such as hemophilia B, tuberculosis (TB), cystic fibrosis (CF), Alzheimer, schizophrenia, chronic hepatitis C and so on29. All these studies show that increasing importance has been associated with synonymous mutations over these years. Hence, it is necessary for us to study the effects of synonymous mutations of SARS-CoV-2 genome.
A total of 381 mutations were found in SARS-CoV-2 genomes by using python script, in which 150 of them are synonymous mutations. The distribution of synonymous mutations in 11 coding regions is shown in Figure 1. Among these mutations, ORF1a and ORF1b have a higher number of synonymous mutations at 76 and 33, respectively, which might be due to their longer sequence length. Besides that, our findings also show high C to U mutation rate in SARS-CoV-2 genome and this mutational skews are in line with the studies done by Rice et al. (2021) and Simmonds (2020)30,31. These mutational skews are necessary to be considered when deducing the selection acting on synonymous variants in SARS-CoV-2 evolution10. Synonymous mutations are assumed subject to a lower selective pressure than nonsynonymous mutations, presumably the purifying selection force has stronger negative impact on the frequencies of nonsynonymous mutations. Interestingly there may be some selection force on synonymous mutations shown by a few studies, suggesting that these synonymous mutations are not random and neutral, may have some biological impact on viral fitness10,32.
The synonymous mutations in SARS-CoV-2 genomes with the top 10 highest frequency were listed in Table 1. As shown in Table 1, synonymous mutations with the highest frequency identified from SARS-CoV-2 genomes is C3037U mutation located in nsp3 of ORF1a, followed by C313U mutation in nsp1 of ORF1a and C9286U mutation in nsp4 of ORF1a. Mutations with higher frequency are mostly found in ORF1a and ORF1b. It is of great interest to find out the effect of these top 10 synonymous mutations on SARS-CoV-2 genome.
Similar to another companion paper, which focuses on the prediction analysis of nonsynonymous mutations of SARS-CoV-2 proteins33, the same SARS-CoV-2 virus genome data from GISAID database ranging from 1st January 20 to 22 March 21 were used in this study. The data collection time was overlapping with the period when the frequency of alpha variant reached the highest numbers around March-May 2134. There are seven synonymous mutations identified as the defining mutations in the alpha variant, of which all except C241T are also reported in our study. Due to the rapid evolution of SARS-CoV-2 genome, it is beyond the scope of our study to keep track SARS-CoV-2 mutational profile and to predict the consequences of these mutations. More recently, two independent studies published preprint articles reported that alpha or alpha-like SARS-CoV-2 variants are circulating among wild deer population in North America in late 202135,36. Although there is no reported case of deer to human transmission of SARS-CoV-2 virus, we can’t simply rule out this possibility yet. Hence, our findings remain relevant despite of not using the latest genome dataset.
SARS-CoV-2 virus can form highly structured RNA elements, which may affect viral replication, discontinuous transcription and translation37,38. For example, SARS-CoV-2 forms a three-stemmed pseudoknot structure to promote programmed -1 ribosomal frameshifting to increase the synthesis of the proteins required for viral replication37,38. There are numerous high throughput studies on the characterization of RNA secondary structure of SARS-CoV-2 genome23,39–42. In these recent high throughput studies, the RNA secondary structures of SARS-CoV-2 genome were determined experimentally using chemical probing methods, such as SHAPE-MaP23,39 or proximity ligation methods, such as RIC-seq41, COMRADES42. Although these data are very useful to determine the RNA secondary structures of SARS-CoV-2 virus, there is very little study on the effect of the synonymous mutations on RNA secondary structure, which may be beneficial or deleterious to the viral fitness. Therefore, we performed RNA secondary structure prediction and base pair probability estimation analysis of these top 10 highest frequency of synonymous mutations.
To improve the outcome of the study, multiple RNA secondary structure prediction tools, namely RNAfold with SHAPE reactivity data22, IPknot++24 and MXfold225 were applied in our study. In addition, MutaRNA analysis tool was used to estimate the base pair potential of the wild type and mutant sequences. RNAfold with SHAPE reactivity data uses thermodynamic approach to calculate the minimum free energy for the most probable RNA secondary structure by incorporating the nucleotide reactivity data derived from the experiments. If the reactivity value is high, the nucleotide is less likely to be paired, or vice versa. SARS-CoV-2 virus can form pseudoknot structures, which promote ribosomal frameshifting38. However, many RNA secondary structure prediction programmes don’t predict pseudoknot structure since the calculation is computationally demanding. IPknot++ is one of the few programmes, which can predict pseudoknot structure. MXfold2 predicts RNA secondary structure using deep learning method with a large amount of training dataset.
In our study, we don’t expect these 3 prediction tools producing identical or similar RNA secondary structures since they are using different algorithms for the calculation. Furthermore, it has been shown that SARS-CoV-2 adopts different RNA secondary structure conformations to have different interactions with host RNAs42. Hence, we are more interested to find out if a mutation results in a change in RNA secondary structure. The prediction results for all 10 synonymous mutations using these 3 tools and the base pair probability estimation results are summarized in the Table 2 (✓ - changes, × - no change). The results for all 10 synonymous mutations predicted with RNAfold, IPknot++ and MXfold2 are available in Extended data 1, 2 and 3, respectively43. The base pair probabilities for all 10 synonymous mutations are shown as circular plots in Extended data 443. The darker the edge is, the more likely the two connected bases to form base pair. C313U mutant is predicted to show no difference between wild type and mutant while C913U, C3037U, U16176C and C18877U mutants show pronounced changes between wild types and mutants in all 3 prediction tools, suggesting these synonymous mutations may have some biological impact on viral fitness. Having say that, it is also possible that other mutants with only one or two changes predicted by these analyses, may also affect RNA secondary structures, having some impact on viral fitness.
RNAfold (SHAPE) | IPknot++ | MXFold2 | MutaRNA | |
---|---|---|---|---|
C313T | × | × | × | × |
C913T | ✓ | ✓ | ✓ | ✓ |
C3037T | ✓ | ✓ | ✓ | ✓ |
C5986T | ✓ | × | ✓ | ✓ |
C9286T | × | ✓ | × | ✓ |
C14676T | ✓ | ✓ | × | ✓ |
C15279T | × | ✓ | × | × |
T16176C | ✓ | ✓ | ✓ | ✓ |
C18877T | ✓ | ✓ | ✓ | ✓ |
C26735T | ✓ | ✓ | × | ✓ |
C913U mutation is found in the Nsp2 in ORF1a in SARS-CoV-2 genome. As shown in Figure 2, C913U mutation has a pronounced effect on RNA secondary structure predicted by RNAfold. C913U mutation results in the appearance or disappearance of multiple loops, not only at the nearby mutated residue, but also at the sites further apart, suggesting this mutation may affect its long-range RNA interaction. Interestingly, U913 mutation results in the formation of a smaller pseudoknot structure compared to C913 wild type as shown in IPknot++ result. While MXfold2 predicts that U913 mutant forms a shorter stem and a larger hairpin loop compared to C913 wild type. Figure 2 shows that the base pair interactions of wild type RNA are changed substantially by U913 mutation. Previously it has been shown that Nsp2 protein suppresses host immune response by inhibiting the mRNA translation of interferon gene44. Although C913U mutation does not alter the amino acid residue of Nsp2 protein, it may be worthwhile to see if this C913U mutation plays a direct or indirect role in host immune response through Nsp2 protein.
C3037U mutation is found in the Nsp3 in ORF1a. As shown in Figure 3, both IPknot++ and MXfold2 predict that U3037 mutant forms longer stem and smaller internal loop compared to wild type. On the contrary, RNAfold predicts that a small internal loop fuses into a bigger internal loop in U3037 mutant. MutaRNA circular plot shows that there is some minor difference in base pair probabilities between C3037 wild type and U3037 mutant. Nsp3 is a papain-like protease, which hydrolyzes several Nsp proteins, involved in viral replication45. Hence, we should investigate the effect of this mutation on its cleavage activity, probably through the change in transcription or translation level of Nsp3.
U16176C mutation is located in the Nsp12 in ORF1b. As shown in Figure 4, U16176C mutation results in a drastic change in RNA secondary structure predicted using RNAfold. IPknot++ predicts C16176 mutant forms a new pseudoknot structure, which is absent in wild type U16176. On the other hand, MXfold2 predicts C16176 mutant forms a larger multi-branched loop and a shorter stem compared to wild type. Similarly, MutaRNA result shows C16176 mutant affects base pair potential at multiple sites. Nsp12 is one of the subunits of RNA-dependent RNA polymerase (RdRp), which is required for RNA synthesis46. U16176C together with C14676U and C15279U have very similar number of frequencies as shown in Table 1. Interestingly IPknot++ predicted all of them result in changes in pseudoknot structure as shown in Extended data 2. We speculated that these three sSNPs may be functionally related. These mutations are located downstream of the frameshifting element (residues 13405–13488) and this element forms a pseudoknot to promote ribosomal frameshifting during viral replication47. It has been demonstrated that synonymous mutations affect both RNA secondary structure of the ribosomal frameshift signal and frameshifting efficiency in SARS-CoV virus48. Another study had shown that this ribosomal frameshifting structure in SARS-CoV-2 virus involves long-range sequence interaction of 1.5 kb42. It remains to be seen whether the long-range sequence interaction for ribosomal frameshifting can go beyond 1.5kb long. If yes, do these 3 mutations affect the formation of pseudoknot during ribosomal frameshifting?
C18877U mutation is located in Nsp14 in ORF1b. As shown in Figure 5, an additional internal loop is formed in U18877 mutant predicted by RNAfold. IPknot++ predicts U18877 mutant forms extra internal loops and longer hairpin near the mutated residue and it also affects the pseudoknot structure at 2 different sites further from the mutated residue. While MXfold2 predicts U18877 mutant forms one hairpin with multiple loops instead of one hairpin as seen in wild type. The changes at multiple base pairing sites due to the U18877 mutation is also observed in MutaRNA circular plot. Nsp14 is important to maintain high fidelity during viral RNA synthesis49.
Codon usage bias (CUB), which is non-random usage of synonymous codons, is common in all species. It is a phenomenon where some codons are preferred over others for a specific amino acid. SARS-CoV-2 replicates using host cell’s machinery and synthesizes its protein by utilizing host cellular components. Hence, codon usage bias may affect the replication of viruses50.
Relative synonymous codon usage (RSCU) is a widely used statistical approach51 that can be used to measure codon usage bias in coding sequences. The RSCU values of SARS-CoV-2 are shown in Table 3 and the most preferred codons for each amino acid are marked in bold. Stop codons (UAA, UAG, UGA) and codons which code for an amino acid uniquely (AUG, UGG) are excluded from RSCU analysis.
Based on the RSCU values, the synonymous codons can be classified into five groups: i) codons with RSCU value equals to 1.0 are unbiased codons; ii) codons with RSCU value > 1.0 are codons preferred in a genome; iii) codons with RSCU value < 1.0 are codons less preferred in a genome; iv) codons with RSCU value > 1.6 are codons which are over-represented in a genome; v) codons with RSCU value < 1.6 are codons which are under-represented in a genome50. There are 15 preferred codons (RSCU value > 1.0) and 11 over-represented codons (RSCU value > 1.6) in SARS-CoV-2 genome as shown in Table 3. The preferred codons in SARS-CoV-2 genome are GCA (Ala), CGU (Arg), AAU (Asn), GAU (Asp), UGU (Cys), CAA (Gln), GAA (Glu), CAU (His), AUU (Ile), UUG (Leu), AAA (Lys), UUU (Phe), CCA (Pro), AGU (Ser) and UAU (Tyr) while the over-represented codons are GCU (Ala), AGA (Arg), GGU (Gly), CUU (Leu), UUA (Leu), CCU (Pro), UCA (Ser), UCU (Ser), ACA (Thr), ACU (Thr), and GUU (Val). The presence of the preferred and over-presented codons in a genome increases the protein synthesis rate.
Table 4 shows the RSCU analysis of the top 10 synonymous mutations. The codons in bold in the ‘codon change’ column are the codons with higher RSCU value, which means they are more preferred in SARS-CoV-2 genome. Most of the mutations change the codon to a more preferred codon as shown in Table 4. Since it is presumed that preferred codons have a higher translation rate compared to nonpreferred codons52, it is possible that most of the mutations may increase the translation efficiency of SARS-CoV-2, which may affect virus replication, transmission, and evolution.
The effects of SARS-CoV-2 synonymous mutations in various aspects such as RNA secondary structure and codon usage bias were studied, even though they do not cause changes in amino acid residue of the protein. C913U, C3037U, U16176C and C18877U mutants show pronounced changes between wild type and mutant predicted in all 3 RNA secondary structure prediction tools, suggesting these mutations may have some biological impact on viral fitness. In addition, these mutations showed changes in base pair potential estimated by MutaRNA. All mutations except U16176C change the codon to a more preferred codon, which may result in higher translation efficiency. Due to the shortcomings of prediction tools, experimental studies are needed to give a more comprehensive understanding of the biological consequences of synonymous mutations on SARS-CoV-2 virus.
No ethical approval is required for data analysis in this study (EA2702021).
SARS-CoV-2 virus genome sequence data were obtained from the GISAID Database. The multiple alignment data can be assessed through FigShare.
Figshare: MSA (SARS-CoV-2). https://doi.org/10.6084/m9.figshare.20486178.v153
Figshare: RNA secondary structure prediction and base pair probability estimation analysis
https://doi.org/10.6084/m9.figshare.20486166.v443
Extended data 1. The RNA secondary structure of SARS-CoV-2 genome predicted using RNAfold.
Extended data 2. The RNA secondary structure of SARS-CoV-2 genome predicted using IPknot++.
Extended data 3. The RNA secondary structure of SARS-CoV-2 genome predicted using MXfold2.
Extended data 4. The base pair probabilities of SARS-CoV-2 genome estimated using MutaRNA
Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).
The python code for the identification of SARS-CoV-2 genome mutations can be assessed through GitHub.
CHN contributes to the concept, design, supervision of the project. WXB and SBZ contribute to the design, methodology, and data collection. WXB contributed to the analysis, and interpretation of data. All authors were involved in drafting and revising the manuscript and approved the final version.
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Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
Partly
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Computational Biology, Structural Genomics, RNA biology, Virology
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: RNA structure prediction; Computational Structural Biology; RNA-protein complexes.
Is the work clearly and accurately presented and does it cite the current literature?
No
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Partly
If applicable, is the statistical analysis and its interpretation appropriate?
Partly
Are all the source data underlying the results available to ensure full reproducibility?
Partly
Are the conclusions drawn adequately supported by the results?
No
References
1. Lan T, Allan M, Malsick L, Woo J, et al.: Secondary structural ensembles of the SARS-CoV-2 RNA genome in infected cells. Nature Communications. 2022; 13 (1). Publisher Full TextCompeting Interests: No competing interests were disclosed.
Reviewer Expertise: RNA structure prediction; Computational Structural Biology; RNA-protein complexes.
Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
Yes
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Clinical Virology Diagnosis
Is the work clearly and accurately presented and does it cite the current literature?
Partly
Is the study design appropriate and is the work technically sound?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
If applicable, is the statistical analysis and its interpretation appropriate?
Yes
Are all the source data underlying the results available to ensure full reproducibility?
Yes
Are the conclusions drawn adequately supported by the results?
Partly
Competing Interests: No competing interests were disclosed.
Reviewer Expertise: Genome Analysis, SARS-CoV-2, Cancer, Immunology, Stem cell
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