Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome

Wan Xin Boon; Boon Zhan Sia; Chong Han Ng

doi:10.12688/f1000research.72896.1

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Research Article

Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome

[version 1; peer review: 1 approved with reservations, 2 not approved]

Wan Xin Boon¹, Boon Zhan Sia¹, Chong Han Ng ¹

PUBLISHED 18 Oct 2021

Author details Author details

¹ Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, 75450, Malaysia

Wan Xin Boon
Roles: Formal Analysis, Investigation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Boon Zhan Sia
Roles: Investigation, Methodology

Chong Han Ng
Roles: Conceptualization, Funding Acquisition, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

OPEN PEER REVIEW

REVIEWER STATUS

This article is included in the Emerging Diseases and Outbreaks gateway.

This article is included in the Research Synergy Foundation gateway.

This article is included in the Coronavirus (COVID-19) collection.

Abstract

Background: The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) had led to a global pandemic since December 2019. SARS-CoV-2 is a single-stranded RNA virus, which mutates at a higher rate. Multiple studies had been done to identify and study nonsynonymous mutations, which change amino acid residues of SARS-CoV-2 proteins. On the other hand, there is little study on the effects of SARS-CoV-2 synonymous mutations. Although these mutations do not alter amino acids, some studies suggest that they may affect viral fitness. This study aims to predict the effect of synonymous mutations on the SARS-CoV-2 genome.
Methods: A total of 30,229 SARS-CoV-2 genomic sequences were retrieved from Global Initiative on Sharing all Influenza Data (GISAID) database and aligned using MAFFT. Then, the mutations and their respective frequency were identified. A prediction of RNA secondary structures and their base pair probabilities was performed to study the effect of synonymous mutations on RNA structure and stability. Relative synonymous codon usage (RSCU) analysis was also performed to measure the codon usage bias (CUB) of SARS-CoV-2.
Results: A total of 150 synonymous mutations were identified. The synonymous mutation identified with the highest frequency is C3037U mutation in the nsp3 of ORF1a, followed by C313U and C9286U mutation in nsp1 and nsp4 of ORF1a, respectively.
Conclusion: Among the synonymous mutations identified, C913U mutation in ORF1a and C26735U in membrane (M) protein may affect RNA secondary structure, reducing the stability of RNA folding and possibly resulting in a higher translation rate. However, lab experiments are required to validate the results obtained from prediction analysis.

Keywords

COVID-19, SARS-CoV-2, Synonymous mutations, RNA secondary structure

Corresponding author: Chong Han Ng

Competing interests: No competing interests were disclosed.

Grant information: This research is supported by Multimedia University, Malaysia, IRFund 2.0 (grant number MMUI/210119 awarded to Chong Han, Ng). The funder has no role in study design, data analysis, decision to publish or manuscript preparation.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2021 Boon WX et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

How to cite: Boon WX, Sia BZ and Ng CH. Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome [version 1; peer review: 1 approved with reservations, 2 not approved]. F1000Research 2021, 10:1053 (https://doi.org/10.12688/f1000research.72896.1) First published: 18 Oct 2021, 10:1053 (https://doi.org/10.12688/f1000research.72896.1) Latest published: 18 Sep 2024, 10:1053 (https://doi.org/10.12688/f1000research.72896.4)

Introduction

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 eventually 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.

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 sequence⁷. Synonymous mutations have been suggested to have no functional consequence on the fitness of organisms and their evolution in long term⁸. However, numerous recent studies had showed that synonymous mutations may affect the folding and stability of RNA structures⁹. For RNA viruses, even though synonymous mutations generally do not change their pathogenicity, some studies reveal that synonymous mutations may affect the RNA secondary structure of the virus¹⁰ and also change the codon usage bias of the genes in the virus^11,12.

Methods

Sequence retrieval

30,229 SARS-CoV-2 genomic sequences were retrieved from GISAID database (Global Initiative on Sharing All Influenza Data, RRID:SCR_018251)¹³ 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 reference sequence of SARS-CoV-2 genome (NC_045512.2)¹⁴ 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.

Multiple sequence alignment

The rapid calculation available in MAFFT online server (MAFFT, version 7.467, RRID:SCR_011811)¹⁵ was used to perform multiple sequence alignment (MSA) for 30,229 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.

Identification of mutations and their frequency in SARS-CoV-2 genomes

A simple Python script was written to identify the mutations in 30,229 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)¹⁶ 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.

SARS-CoV-2 RNA secondary structure prediction

The RNA secondary structure of wild type and mutant sequences were predicted using RNAfold program, version 2.4.18 (Vienna RNA, RRID:SCR_008550) to show how mutations affect RNA secondary structure. The RNA secondary structure prediction was performed using a sequence length of 250 nucleotides upstream and downstream of the mutation site. The minimum free energy (MFE) was also calculated in the RNAfold program for both wild type and mutant sequences to show comparison of the RNA folding stability between them. The comparison on the value of minimum free energy (MFE) is important to indicate whether the mutations affect the folding stability of the respective RNA structure.

Base pair probability estimation and analysis

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)¹⁷. 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, in which the window size is 200nt and the maximal base pair span is 150nt.

Relative synonymous codon usage (RSCU)

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:

R S C U_{i} = \frac{X_{i}}{\frac{1}{n} \sum_{i = 1}^{n} X_{i}},

where X_i implies the number of occurrences of codon i and n stands for the number of synonymous codons encoded for that particular amino acid.

Results and discussion

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 efficiency¹⁸. Synonymous mutations may have significant effects on the adaptation, virulence, and evolution of RNA viruses¹⁹. 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 on²⁰. 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.

Identification of SARS-CoV-2 synonymous mutations

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 genetic variation and this mutational skews are in line with the studies done by Rice et al. (2021) and Simmonds (2020)^21,22. These mutational skews are necessary to be considered when deducing the selection acting on synonymous variants in SARS-CoV-2 evolution²³.

Figure 1. Distribution of SARS-CoV-2 synonymous mutations in 11 coding regions.

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.

Table 1. SARS-CoV-2 synonymous mutations with the top 10 highest frequency.

ORF		Position	Nucleotide Variation	Frequency
1a	nsp1	313	C > U	8569
	nsp2	913	C > U	3213
	nsp3	3037	C > U	28054
	nsp3	5986	C > U	3261
	nsp4	9286	C > U	4030
1b	nsp12	14676	C > U	3262
		15279	C > U	3244
		16176	U > C	3236
	nsp14	18877	C > U	2944
M		26735	C > U	2701

RNA secondary structure and base pair probability

SARS-CoV-2 virus can form highly structured RNA elements, which may affect viral replication, discontinuous transcription and translation^24–28

The RNA secondary structures of wild type and mutant sequences were predicted using RNAfold program. Minimum free energy (MFE) of each structure was also calculated to show the folding stability of the respective RNA structure. Among all, C913U mutation and C26735U mutation were found to have a more obvious effect on the predicted RNA secondary structure compared to the wild type.

As shown in Figure 2(A), a single hairpin is formed around the mutant with C913U mutation instead of a multiloop in the wild type. The minimum free energy value of the mutant (- 146.90 kcal/mol) is slightly less negative than that of the wild type (- 147.40 kcal/mol), which makes it a less thermodynamically stable structure compared to the wild type.

Figure 2. The effect of C913U mutation on RNA secondary structure of nsp2 in ORF1a.

(A) RNA secondary structure of wild type C913 and mutant U913. (B) Circular plots showing the base pairing probabilities with darker shades indicating higher base pairing probabilities and a red arrow pointing to the mutation site.

To visualize the differences in the base pairing potential induced by the mutation, base pairing probability was estimated using MutaRNA. The circular plots in Figure 2(B) show the base pairing probabilities of both wild type and mutant. As shown in the circular plots, C913U mutation decreased the Watson–Crick base pairing probability near the mutation site, which led to a less stable predicted RNA secondary structure. C913U mutation is found in the nsp2 of ORF1a in SARS-CoV-2 genome. Nsp2 in SARS-CoV interacts with two human host proteins specifically, which are prohibitin 1 (PHB1) and prohibitin 2 (PHB2)²⁹. This interaction may disrupt the intracellular host signalling during the viral infections, rather than taking part in the viral replication²⁹. It is yet to see if nsp2 of SARS-CoV-2 shares same or similar function as that of SARS-CoV.

C26735U mutation is located at the membrane (M) protein. M protein may suppress host immune responses via the interference of Type I interferon production³⁰. C26735U mutation induces changes in the predicted RNA secondary structure by forming an extra multibranch loop at the mutation site as shown in Figure 3(A). The RNA secondary structure formed by the mutant (-134.50 kcal/mol) has a less negative minimum free energy value compared to the wild type (-136.30 kcal/mol), which makes it a less stable structure. While for the circular plots in Figure 3(B), C26735U mutation decreases the base pairing probabilities in flanking regions, which again predicted that the mutation decreases the folding stability of the RNA secondary structure.

Figure 3. The effect of C26735U mutation on RNA secondary structure of M protein.

(A) RNA secondary structure of wild type C26735 and mutant U26735. (B) Circular plots showing the base pairing probabilities with darker shades indicating higher base pairing probabilities and a red arrow pointing to the mutation site.

In short, both C913U and C26735U mutations cause a more drastic change in RNA secondary structure. Given the shortcomings of prediction tools, it is necessary to check if the changes in RNA secondary structure affect the pathogenicity of SARS-CoV-2 using experimental approaches. Besides that, these two mutations also reduce the folding stability of the RNA secondary structure, which then affects the polypeptide translation and folding. There is evidence suggesting that stable RNA structures play a key role in reducing the translation speed to prevent “ribosomal traffic jams” so that the newly translated polypeptides can fold properly³¹. Hence, both C913U and C26735U mutations increase the translation speed of SARS-CoV-2 RNA but they might cause the nascent polypeptide folding more prone to error during translation.

RSCU analysis of SARS-CoV-2

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 viruses³².

Relative synonymous codon usage (RSCU) is a widely used statistical approach³³ that can be used to measure codon usage bias in coding sequences. The RSCU values of SARS-CoV-2 are shown in Table 2 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.

Table 2. RSCU values of SARS-CoV-2 genome.

Amino Acid	Synonymous Codons	RSCU
Ala	GCA	1.09
	GCC	0.58
	GCG	0.16
	GCU	2.17
Arg	AGA	2.67
	AGG	0.81
	CGA	0.29
	CGC	0.58
	CGG	0.19
	CGU	1.46
Asn	AAC	0.65
Asn	AAU	1.35
Asp	GAC	0.72
Asp	GAU	1.28
Cys	UGC	0.45
Cys	UGU	1.55
Gln	CAA	1.39
Gln	CAG	0.61
Glu	GAA	1.44
Glu	GAG	0.56
Gly	GGA	0.82
	GGC	0.71
	GGG	0.12
	GGU	2.34
His	CAC	0.61
His	CAU	1.39
Ile	AUA	0.92
	AUC	0.56
	AUU	1.53
Leu	CUA	0.66
	CUC	0.59
	CUG	0.30
	CUU	1.75
	UUA	1.63
	UUG	1.06
Lys	AAA	1.31
Lys	AAG	0.69
Phe	UUC	0.59
Phe	UUU	1.41
Pro	CCA	1.59
	CCC	0.29
	CCG	0.17
	CCU	1.94
Ser	AGC	0.36
	AGU	1.43
	UCA	1.67
	UCC	0.46
	UCG	0.11
	UCU	1.96
Thr	ACA	1.64
	ACC	0.38
	ACG	0.20
	ACU	1.78
Tyr	UAC	0.78
Tyr	UAU	1.22
Val	GUA	0.91
	GUC	0.56
	GUG	0.58
	GUU	1.95

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 genome³². 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 2. 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 3 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 3. Since it is presumed that preferred codons have a higher translation rate compared to nonpreferred codons³⁴, 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.

Table 3. RSCU analysis of the top 10 synonymous mutations of SARS-CoV-2 genome.

ORF		Mutation	Codon Change
1a	nsp1	C313U	CUC -> CUU
	nsp2	C913U	UCC -> UCU
	nsp3	C3037U	UUC -> UUU
	nsp3	C5986U	UUC -> UUU
	nsp4	C9286U	AAC -> AAU
1b	nsp12	C14676U	CCC -> CCU
		C15279U	CAC -> CAU
		U16176C	ACU -> ACC
	nsp14	C18877U	CUA -> UUA
M		C26735U	UAC -> UAU

Conclusions

The effects of SARS-CoV-2 synonymous mutations in various aspects such as RNA folding and RNA stability of the virus were studied, even though they do not cause changes in amino acid residue of the protein. Most of the synonymous mutations identified in the SARS-CoV-2 genome are found to have a minor effect on RNA folding and RNA stability of the virus except for C913U and C26735U mutations. 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.

Ethics and dissemination

No ethical approval is required for data analysis in this study (EA2702021).

Data and software availability

Underlying data

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.16681900.v1³⁵

This project contains the following underlying data.

• MSA_0.fasta (multiple sequence alignment of SARS-CoV-2 sequences obtained between 31-12-2019 and 31-05-2020.)

• MSA_1.fasta (multiple sequence alignment of SARS-CoV-2 sequences obtained between 01-06-2020 and 15-10-2020.)

• MSA_2.fasta (multiple sequence alignment of SARS-CoV-2 sequences obtained between 16-10-2020 and 31-01-2021.)

• MSA_3.fasta (multiple sequence alignment of SARS-CoV-2 sequences obtained between 01-02-2021 to 22-03-2021.)

Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0).

Software

The python code for the identification of SARS-CoV-2 genome mutations can be assessed through GitHub.

Author contributions

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.

Faculty Opinions recommended

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VERSION 4 PUBLISHED 18 Oct 2021

Author details Author details

¹ Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, Melaka, 75450, Malaysia

Wan Xin Boon
Roles: Formal Analysis, Investigation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Boon Zhan Sia
Roles: Investigation, Methodology

Chong Han Ng
Roles: Conceptualization, Funding Acquisition, Project Administration, Supervision, Writing – Original Draft Preparation, Writing – Review & Editing

Competing interests

No competing interests were disclosed.

Grant information

This research is supported by Multimedia University, Malaysia, IRFund 2.0 (grant number MMUI/210119 awarded to Chong Han, Ng). The funder has no role in study design, data analysis, decision to publish or manuscript preparation.
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

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version 4

Revised

Published: 18 Sep 2024, 10:1053

https://doi.org/10.12688/f1000research.72896.4

version 3

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Published: 29 Feb 2024, 10:1053

https://doi.org/10.12688/f1000research.72896.3

version 2

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Published: 05 Sep 2022, 10:1053

https://doi.org/10.12688/f1000research.72896.2

version 1

Published: 18 Oct 2021, 10:1053

https://doi.org/10.12688/f1000research.72896.1

© 2021 Boon WX et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Boon WX, Sia BZ and Ng CH. Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome [version 1; peer review: 1 approved with reservations, 2 not approved]. F1000Research 2021, 10:1053 (https://doi.org/10.12688/f1000research.72896.1)

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ApprovedThe paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approvedFundamental flaws in the paper seriously undermine the findings and conclusions

Version 1

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PUBLISHED 18 Oct 2021

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Reviewer Report 28 Apr 2022

Chandran Nithin, Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland

Not Approved

https://doi.org/10.5256/f1000research.76505.r135257

The manuscript titled "Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome" analyzes 30,299 SARS-CoV-2 genomes retrieved from GISAID for the period between 31st December 2019 and 22nd March 2021. The study identifies 381 mutations in the genome, of ... Continue reading

The dataset used in this study is outdated and represents only a short period between 31st December 2019 and 22nd March 2021. It will be necessary to update the data to include more sequences from a wider time frame.

The authors had indicated (through the editorial manager) that they are unwilling to update the dataset; the authors need to indicate the same in the title and detail the limitations of the dataset in the introduction section.
The authors have used the RNAfold program to predict the secondary structure of wild-type and mutant sequences. The authors need to use additional prediction tools and check for consensus between the predicted structure. Moreover, authors need to employ prediction tools to predict pseudoknots in the RNA secondary structure.
The secondary structure of SARS-CoV2 determined with the help of data from SHAPE-MaP, icSHAPE, and DMS-MaPseq experiments is available in the literature.¹,²,³ The authors should utilize this data to augment this study.
The study requires the inclusion of appropriate controls which quantify the effect of mutations on the stability of the RNA structure. The authors state that "The minimum free energy value of the mutant (- 146.90 kcal/mol) is slightly less negative than that of the wild type (- 147.40 kcal/mol), which makes it a less thermodynamically stable structure compared to the wild type." This claim needs to be explored in detail and supported with evidence from experimental data and literature.
The authors state that C26735U mutation induces changes in the predicted RNA secondary structure by forming an extra multibranch loop at the mutation site. The authors should compare the same with secondary systems mentioned in point 3 (See Ref 1, 2, and 3). The authors need to perform a similar analysis for C913U.
The study identifies 381 mutations in the genome, of which 150 are synonymous; however, it discusses only two. The authors need to provide the data on the remaining 148 synonymous mutations and briefly discuss some of them in the manuscript.
The authors need to expand and include relevant literature in the introduction section.
The manuscript needs to be edited for language errors.

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 Text
2. Huston N, Wan H, Strine M, de Cesaris Araujo Tavares R, et al.: Comprehensive in vivo secondary structure of the SARS-CoV-2 genome reveals novel regulatory motifs and mechanisms. Molecular Cell. 2021; 81 (3): 584-598.e5 Publisher Full Text
3. Manfredonia I, Nithin C, Ponce-Salvatierra A, Ghosh P, et al.: Genome-wide mapping of SARS-CoV-2 RNA structures identifies therapeutically-relevant elements. Nucleic Acids Research. 2020; 48 (22): 12436-12452 Publisher Full Text

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: RNA structure prediction; Computational Structural Biology; RNA-protein complexes.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

CITE

Report a concern

Author Response 05 Sep 2022

Chong Han Ng, Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, 75450, Malaysia

05 Sep 2022

Author Response
The manuscript titled "Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome" analyzes 30,299 SARS-CoV-2 genomes retrieved from GISAID for the period between 31st December 2019 and 22nd March ... Continue reading
The manuscript titled "Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome" analyzes 30,299 SARS-CoV-2 genomes retrieved from GISAID for the period between 31st December 2019 and 22nd March 2021. The study identifies 381 mutations in the genome, of which 150 are synonymous. The study discusses two mutations, C913U and C26735U.

The dataset used in this study is outdated and represents only a short period between 31st December 2019 and 22nd March 2021. It will be necessary to update the data to include more sequences from a wider time frame.

The authors had indicated (through the editorial manager) that they are unwilling to update the dataset; the authors need to indicate the same in the title and detail the limitations of the dataset in the introduction section.

Response: The landscape of mutational profile of SARS-CoV-2 genome is very dynamic, changing rapidly. As of 14 August 2022, there are more than 12 million SARS-CoV-2 virus genomes sequences deposited in GISAID database. Although it is useful to get more recent dataset, it is out of the scope of our study to update the data. Both identification of the SARS-CoV-2 mutations and the prediction analysis of the biological consequences of these mutations are very time-consuming processes. Based on the timelines of emergence of different variants, the cut-off date of our data collection overlaps with the period when the frequency of the alpha variant peaked. All the synonymous mutations except C241T reported in the alpha variant have been identified in our study as well. Although the alpha variant has been replaced by other variants in human population, it is detected from wildlife animals based on a few recent genomic surveillance analysis. It is possible that these alpha variant from wildlife animal may jump back to human, hence it remains relevant to predict the biological consequences of these synonymous mutations. The title of the paper has been revised accordingly. The limitation of the dataset was included in the discussion.

The authors have used the RNAfold program to predict the secondary structure of wild-type and mutant sequences. The authors need to use additional prediction tools and check for consensus between the predicted structure. Moreover, authors need to employ prediction tools to predict pseudoknots in the RNA secondary structure.

Response: To improve the outcome of the RNA secondary structure analysis, 3 prediction tools, namely RNAfold, IPknot++ and MXfold2 are used in our study. Of these, IPknot++ predicted some synonymous mutations may affect the pseudoknot formation. The prediction results for all top 10 synonymous mutations using these 3 tools are summarized in the Table 2

The secondary structure of SARS-CoV2 determined with the help of data from SHAPE-MaP, icSHAPE, and DMS-MaPseq experiments is available in the literature.1,2,3 The authors should utilize this data to augment this study

Response: To improve the outcome of the prediction analysis, the RNA secondary structure of wild type and mutant sequences were predicted using RNAfold program with the incorporation of SHAPE reactivity data obtained from the study done by Manfredonia et al. (2020).

The study requires the inclusion of appropriate controls which quantify the effect of mutations on the stability of the RNA structure. The authors state that "The minimum free energy value of the mutant (- 146.90 kcal/mol) is slightly less negative than that of the wild type (- 147.40 kcal/mol), which makes it a less thermodynamically stable structure compared to the wild type." This claim needs to be explored in detail and supported with evidence from experimental data and literature.

Response: Instead of comparing minimum free energy between the wild type and mutant, we applied three RNA secondary structure prediction tools to predict the effect of the mutation on RNA secondary structure.

The authors state that C26735U mutation induces changes in the predicted RNA secondary structure by forming an extra multibranch loop at the mutation site. The authors should compare the same with secondary systems mentioned in point 3 (See Ref 1, 2, and 3). The authors need to perform a similar analysis for C913U.

Response: The results of the 4 mutations showing changes in all 3 prediction tools are included in Figure 2-5.

The study identifies 381 mutations in the genome, of which 150 are synonymous; however, it discusses only two. The authors need to provide the data on the remaining 148 synonymous mutations and briefly discuss some of them in the manuscript.

Response: The RNA secondary structures prediction results using 3 prediction tools for the top 10 synonymous mutations have been included in Extended data 1-3. There were 4 mutations showing changes in all 3 prediction tools and the results are discussed in the manuscript.

The authors need to expand and include relevant literature in the introduction section.

Response: The relevant literature in the introduction section has been updated.

The manuscript needs to be edited for language errors.

Response: The language errors have been edited accordingly.
The manuscript titled "Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome" analyzes 30,299 SARS-CoV-2 genomes retrieved from GISAID for the period between 31st December 2019 and 22nd March 2021. The study identifies 381 mutations in the genome, of which 150 are synonymous. The study discusses two mutations, C913U and C26735U.

The dataset used in this study is outdated and represents only a short period between 31st December 2019 and 22nd March 2021. It will be necessary to update the data to include more sequences from a wider time frame.

The authors had indicated (through the editorial manager) that they are unwilling to update the dataset; the authors need to indicate the same in the title and detail the limitations of the dataset in the introduction section.

Response: The landscape of mutational profile of SARS-CoV-2 genome is very dynamic, changing rapidly. As of 14 August 2022, there are more than 12 million SARS-CoV-2 virus genomes sequences deposited in GISAID database. Although it is useful to get more recent dataset, it is out of the scope of our study to update the data. Both identification of the SARS-CoV-2 mutations and the prediction analysis of the biological consequences of these mutations are very time-consuming processes. Based on the timelines of emergence of different variants, the cut-off date of our data collection overlaps with the period when the frequency of the alpha variant peaked. All the synonymous mutations except C241T reported in the alpha variant have been identified in our study as well. Although the alpha variant has been replaced by other variants in human population, it is detected from wildlife animals based on a few recent genomic surveillance analysis. It is possible that these alpha variant from wildlife animal may jump back to human, hence it remains relevant to predict the biological consequences of these synonymous mutations. The title of the paper has been revised accordingly. The limitation of the dataset was included in the discussion.

The authors have used the RNAfold program to predict the secondary structure of wild-type and mutant sequences. The authors need to use additional prediction tools and check for consensus between the predicted structure. Moreover, authors need to employ prediction tools to predict pseudoknots in the RNA secondary structure.

Response: To improve the outcome of the RNA secondary structure analysis, 3 prediction tools, namely RNAfold, IPknot++ and MXfold2 are used in our study. Of these, IPknot++ predicted some synonymous mutations may affect the pseudoknot formation. The prediction results for all top 10 synonymous mutations using these 3 tools are summarized in the Table 2

The secondary structure of SARS-CoV2 determined with the help of data from SHAPE-MaP, icSHAPE, and DMS-MaPseq experiments is available in the literature.1,2,3 The authors should utilize this data to augment this study

Response: To improve the outcome of the prediction analysis, the RNA secondary structure of wild type and mutant sequences were predicted using RNAfold program with the incorporation of SHAPE reactivity data obtained from the study done by Manfredonia et al. (2020).

The study requires the inclusion of appropriate controls which quantify the effect of mutations on the stability of the RNA structure. The authors state that "The minimum free energy value of the mutant (- 146.90 kcal/mol) is slightly less negative than that of the wild type (- 147.40 kcal/mol), which makes it a less thermodynamically stable structure compared to the wild type." This claim needs to be explored in detail and supported with evidence from experimental data and literature.

Response: Instead of comparing minimum free energy between the wild type and mutant, we applied three RNA secondary structure prediction tools to predict the effect of the mutation on RNA secondary structure.

The authors state that C26735U mutation induces changes in the predicted RNA secondary structure by forming an extra multibranch loop at the mutation site. The authors should compare the same with secondary systems mentioned in point 3 (See Ref 1, 2, and 3). The authors need to perform a similar analysis for C913U.

Response: The results of the 4 mutations showing changes in all 3 prediction tools are included in Figure 2-5.

The study identifies 381 mutations in the genome, of which 150 are synonymous; however, it discusses only two. The authors need to provide the data on the remaining 148 synonymous mutations and briefly discuss some of them in the manuscript.

Response: The RNA secondary structures prediction results using 3 prediction tools for the top 10 synonymous mutations have been included in Extended data 1-3. There were 4 mutations showing changes in all 3 prediction tools and the results are discussed in the manuscript.

The authors need to expand and include relevant literature in the introduction section.

Response: The relevant literature in the introduction section has been updated.

The manuscript needs to be edited for language errors.

Response: The language errors have been edited accordingly.
Competing Interests: No competing interests were disclosed. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 05 Sep 2022

Chong Han Ng, Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, 75450, Malaysia

05 Sep 2022

Author Response
The manuscript titled "Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome" analyzes 30,299 SARS-CoV-2 genomes retrieved from GISAID for the period between 31st December 2019 and 22nd March ... Continue reading
The manuscript titled "Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome" analyzes 30,299 SARS-CoV-2 genomes retrieved from GISAID for the period between 31st December 2019 and 22nd March 2021. The study identifies 381 mutations in the genome, of which 150 are synonymous. The study discusses two mutations, C913U and C26735U.

The dataset used in this study is outdated and represents only a short period between 31st December 2019 and 22nd March 2021. It will be necessary to update the data to include more sequences from a wider time frame.

The authors had indicated (through the editorial manager) that they are unwilling to update the dataset; the authors need to indicate the same in the title and detail the limitations of the dataset in the introduction section.

Response: The landscape of mutational profile of SARS-CoV-2 genome is very dynamic, changing rapidly. As of 14 August 2022, there are more than 12 million SARS-CoV-2 virus genomes sequences deposited in GISAID database. Although it is useful to get more recent dataset, it is out of the scope of our study to update the data. Both identification of the SARS-CoV-2 mutations and the prediction analysis of the biological consequences of these mutations are very time-consuming processes. Based on the timelines of emergence of different variants, the cut-off date of our data collection overlaps with the period when the frequency of the alpha variant peaked. All the synonymous mutations except C241T reported in the alpha variant have been identified in our study as well. Although the alpha variant has been replaced by other variants in human population, it is detected from wildlife animals based on a few recent genomic surveillance analysis. It is possible that these alpha variant from wildlife animal may jump back to human, hence it remains relevant to predict the biological consequences of these synonymous mutations. The title of the paper has been revised accordingly. The limitation of the dataset was included in the discussion.

The authors have used the RNAfold program to predict the secondary structure of wild-type and mutant sequences. The authors need to use additional prediction tools and check for consensus between the predicted structure. Moreover, authors need to employ prediction tools to predict pseudoknots in the RNA secondary structure.

Response: To improve the outcome of the RNA secondary structure analysis, 3 prediction tools, namely RNAfold, IPknot++ and MXfold2 are used in our study. Of these, IPknot++ predicted some synonymous mutations may affect the pseudoknot formation. The prediction results for all top 10 synonymous mutations using these 3 tools are summarized in the Table 2

The secondary structure of SARS-CoV2 determined with the help of data from SHAPE-MaP, icSHAPE, and DMS-MaPseq experiments is available in the literature.1,2,3 The authors should utilize this data to augment this study

Response: To improve the outcome of the prediction analysis, the RNA secondary structure of wild type and mutant sequences were predicted using RNAfold program with the incorporation of SHAPE reactivity data obtained from the study done by Manfredonia et al. (2020).

The study requires the inclusion of appropriate controls which quantify the effect of mutations on the stability of the RNA structure. The authors state that "The minimum free energy value of the mutant (- 146.90 kcal/mol) is slightly less negative than that of the wild type (- 147.40 kcal/mol), which makes it a less thermodynamically stable structure compared to the wild type." This claim needs to be explored in detail and supported with evidence from experimental data and literature.

Response: Instead of comparing minimum free energy between the wild type and mutant, we applied three RNA secondary structure prediction tools to predict the effect of the mutation on RNA secondary structure.

The authors state that C26735U mutation induces changes in the predicted RNA secondary structure by forming an extra multibranch loop at the mutation site. The authors should compare the same with secondary systems mentioned in point 3 (See Ref 1, 2, and 3). The authors need to perform a similar analysis for C913U.

Response: The results of the 4 mutations showing changes in all 3 prediction tools are included in Figure 2-5.

The study identifies 381 mutations in the genome, of which 150 are synonymous; however, it discusses only two. The authors need to provide the data on the remaining 148 synonymous mutations and briefly discuss some of them in the manuscript.

Response: The RNA secondary structures prediction results using 3 prediction tools for the top 10 synonymous mutations have been included in Extended data 1-3. There were 4 mutations showing changes in all 3 prediction tools and the results are discussed in the manuscript.

The authors need to expand and include relevant literature in the introduction section.

Response: The relevant literature in the introduction section has been updated.

The manuscript needs to be edited for language errors.

Response: The language errors have been edited accordingly.
The manuscript titled "Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome" analyzes 30,299 SARS-CoV-2 genomes retrieved from GISAID for the period between 31st December 2019 and 22nd March 2021. The study identifies 381 mutations in the genome, of which 150 are synonymous. The study discusses two mutations, C913U and C26735U.

The dataset used in this study is outdated and represents only a short period between 31st December 2019 and 22nd March 2021. It will be necessary to update the data to include more sequences from a wider time frame.

The authors had indicated (through the editorial manager) that they are unwilling to update the dataset; the authors need to indicate the same in the title and detail the limitations of the dataset in the introduction section.

Response: The landscape of mutational profile of SARS-CoV-2 genome is very dynamic, changing rapidly. As of 14 August 2022, there are more than 12 million SARS-CoV-2 virus genomes sequences deposited in GISAID database. Although it is useful to get more recent dataset, it is out of the scope of our study to update the data. Both identification of the SARS-CoV-2 mutations and the prediction analysis of the biological consequences of these mutations are very time-consuming processes. Based on the timelines of emergence of different variants, the cut-off date of our data collection overlaps with the period when the frequency of the alpha variant peaked. All the synonymous mutations except C241T reported in the alpha variant have been identified in our study as well. Although the alpha variant has been replaced by other variants in human population, it is detected from wildlife animals based on a few recent genomic surveillance analysis. It is possible that these alpha variant from wildlife animal may jump back to human, hence it remains relevant to predict the biological consequences of these synonymous mutations. The title of the paper has been revised accordingly. The limitation of the dataset was included in the discussion.

The authors have used the RNAfold program to predict the secondary structure of wild-type and mutant sequences. The authors need to use additional prediction tools and check for consensus between the predicted structure. Moreover, authors need to employ prediction tools to predict pseudoknots in the RNA secondary structure.

Response: To improve the outcome of the RNA secondary structure analysis, 3 prediction tools, namely RNAfold, IPknot++ and MXfold2 are used in our study. Of these, IPknot++ predicted some synonymous mutations may affect the pseudoknot formation. The prediction results for all top 10 synonymous mutations using these 3 tools are summarized in the Table 2

The secondary structure of SARS-CoV2 determined with the help of data from SHAPE-MaP, icSHAPE, and DMS-MaPseq experiments is available in the literature.1,2,3 The authors should utilize this data to augment this study

Response: To improve the outcome of the prediction analysis, the RNA secondary structure of wild type and mutant sequences were predicted using RNAfold program with the incorporation of SHAPE reactivity data obtained from the study done by Manfredonia et al. (2020).

The study requires the inclusion of appropriate controls which quantify the effect of mutations on the stability of the RNA structure. The authors state that "The minimum free energy value of the mutant (- 146.90 kcal/mol) is slightly less negative than that of the wild type (- 147.40 kcal/mol), which makes it a less thermodynamically stable structure compared to the wild type." This claim needs to be explored in detail and supported with evidence from experimental data and literature.

Response: Instead of comparing minimum free energy between the wild type and mutant, we applied three RNA secondary structure prediction tools to predict the effect of the mutation on RNA secondary structure.

The authors state that C26735U mutation induces changes in the predicted RNA secondary structure by forming an extra multibranch loop at the mutation site. The authors should compare the same with secondary systems mentioned in point 3 (See Ref 1, 2, and 3). The authors need to perform a similar analysis for C913U.

Response: The results of the 4 mutations showing changes in all 3 prediction tools are included in Figure 2-5.

The study identifies 381 mutations in the genome, of which 150 are synonymous; however, it discusses only two. The authors need to provide the data on the remaining 148 synonymous mutations and briefly discuss some of them in the manuscript.

Response: The RNA secondary structures prediction results using 3 prediction tools for the top 10 synonymous mutations have been included in Extended data 1-3. There were 4 mutations showing changes in all 3 prediction tools and the results are discussed in the manuscript.

The authors need to expand and include relevant literature in the introduction section.

Response: The relevant literature in the introduction section has been updated.

The manuscript needs to be edited for language errors.

Response: The language errors have been edited accordingly.
Competing Interests: No competing interests were disclosed. Close
Report a concern

Views

Reviewer Report 20 Dec 2021

Leyi Wang, Veterinary Diagnostic Laboratory and Department of Veterinary Clinical Medicine, College of Veterinary Medicine, University of Illinois, Urbana, IL, USA

Approved with Reservations

https://doi.org/10.5256/f1000research.76505.r101828

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

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above.

CITE

Report a concern

Author Response 05 Sep 2022

Chong Han Ng, Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, 75450, Malaysia

05 Sep 2022

Author Response

The manuscript entitled “Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome” reports an analysis of the SARS-CoV-2 genome obtained in the GISAID database. The authors analyzed synonymous changes ... Continue reading The manuscript entitled “Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome” reports an analysis of the SARS-CoV-2 genome obtained in the GISAID database. The authors analyzed synonymous changes of the 30,229 SARS-CoV-2 sequences available online, and the effects of these changes on RNA secondary structures and base-pair probability. They identified 150 synonymous mutations in 11 coding regions. Mutations with higher frequency are mostly found in ORF1a and ORF1b and two mutations (C913U and C26735U) found with effect on the predicted RNA secondary structure are not those with higher frequency.

This study collected 30,229 sequences in the database in the period of 31 Dec 2019 to 22 March 2021. As of 17 Dec 2021, there are over 4.4 million complete genomes with high coverage. Will these sequences used in the study include all variants reported or what sequences of variants are included? A table to summarize all variants with these mutations or not is needed.
Response: We downloaded the raw data on 23^rd March 2021 and completed the data analysis in late June 2021. We submitted the manuscript in late August 2021. The landscape of mutational profile of SARS-CoV-2 genome is very dynamic, changing rapidly. It is far beyond the scope of this study to include all variants reported. Based on the timelines of emergence of different variants, the cut-off date of our data collection overlaps with the period when the frequency of the alpha variant peaked. All the synonymous mutations except C241T reported in the alpha strain have been identified in our study as well. We added new paragraph in the discussion section to discuss the impact of our findings and to explain why the study of alpha variant remains relevant now. In addition, we used 3 RNA secondary prediction analysis tool, instead of one, to improve the outcome of the analysis. The prediction results for all top 10 synonymous mutations using these 3 tools and the base pair probability estimation results are summarized in the Table 2.

A major concern is that the sequences even filtered by setting parameters to keep only sequencing with complete genome and high coverage still contain bad sequences, how did the authors exclude those sequences?

Response: In our original method, we used the high coverage filter when downloading from GISAID database, in which only entries with less 1% N and 0.05% unique amino acids mutations are included. To further reduce bad sequences, we filtered to remove those sequences with higher than 0.1% N unresolved nucleotides and ambiguous letters. A total of 3584 sequences were removed by applying this filter. The list of the synonymous mutations remains unchanged despite of the revision of the mutation frequency.

In the abstract section, the results part only included those mutations with high frequency but did not contain results of other analyses. Instead, they included the results of RNA secondary structure analysis in the conclusion.

Response: The abstract has been revised to update the results and conclusion.
The manuscript entitled “Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome” reports an analysis of the SARS-CoV-2 genome obtained in the GISAID database. The authors analyzed synonymous changes of the 30,229 SARS-CoV-2 sequences available online, and the effects of these changes on RNA secondary structures and base-pair probability. They identified 150 synonymous mutations in 11 coding regions. Mutations with higher frequency are mostly found in ORF1a and ORF1b and two mutations (C913U and C26735U) found with effect on the predicted RNA secondary structure are not those with higher frequency.

This study collected 30,229 sequences in the database in the period of 31 Dec 2019 to 22 March 2021. As of 17 Dec 2021, there are over 4.4 million complete genomes with high coverage. Will these sequences used in the study include all variants reported or what sequences of variants are included? A table to summarize all variants with these mutations or not is needed.
Response: We downloaded the raw data on 23^rd March 2021 and completed the data analysis in late June 2021. We submitted the manuscript in late August 2021. The landscape of mutational profile of SARS-CoV-2 genome is very dynamic, changing rapidly. It is far beyond the scope of this study to include all variants reported. Based on the timelines of emergence of different variants, the cut-off date of our data collection overlaps with the period when the frequency of the alpha variant peaked. All the synonymous mutations except C241T reported in the alpha strain have been identified in our study as well. We added new paragraph in the discussion section to discuss the impact of our findings and to explain why the study of alpha variant remains relevant now. In addition, we used 3 RNA secondary prediction analysis tool, instead of one, to improve the outcome of the analysis. The prediction results for all top 10 synonymous mutations using these 3 tools and the base pair probability estimation results are summarized in the Table 2.

A major concern is that the sequences even filtered by setting parameters to keep only sequencing with complete genome and high coverage still contain bad sequences, how did the authors exclude those sequences?

Response: In our original method, we used the high coverage filter when downloading from GISAID database, in which only entries with less 1% N and 0.05% unique amino acids mutations are included. To further reduce bad sequences, we filtered to remove those sequences with higher than 0.1% N unresolved nucleotides and ambiguous letters. A total of 3584 sequences were removed by applying this filter. The list of the synonymous mutations remains unchanged despite of the revision of the mutation frequency.

In the abstract section, the results part only included those mutations with high frequency but did not contain results of other analyses. Instead, they included the results of RNA secondary structure analysis in the conclusion.

Response: The abstract has been revised to update the results and conclusion.
Competing Interests: No competing interests were disclosed. Close
Report a concern
Respond or Comment

COMMENTS ON THIS REPORT

Author Response 05 Sep 2022

Chong Han Ng, Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, 75450, Malaysia

05 Sep 2022

Author Response

The manuscript entitled “Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome” reports an analysis of the SARS-CoV-2 genome obtained in the GISAID database. The authors analyzed synonymous changes ... Continue reading The manuscript entitled “Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome” reports an analysis of the SARS-CoV-2 genome obtained in the GISAID database. The authors analyzed synonymous changes of the 30,229 SARS-CoV-2 sequences available online, and the effects of these changes on RNA secondary structures and base-pair probability. They identified 150 synonymous mutations in 11 coding regions. Mutations with higher frequency are mostly found in ORF1a and ORF1b and two mutations (C913U and C26735U) found with effect on the predicted RNA secondary structure are not those with higher frequency.

This study collected 30,229 sequences in the database in the period of 31 Dec 2019 to 22 March 2021. As of 17 Dec 2021, there are over 4.4 million complete genomes with high coverage. Will these sequences used in the study include all variants reported or what sequences of variants are included? A table to summarize all variants with these mutations or not is needed.
Response: We downloaded the raw data on 23^rd March 2021 and completed the data analysis in late June 2021. We submitted the manuscript in late August 2021. The landscape of mutational profile of SARS-CoV-2 genome is very dynamic, changing rapidly. It is far beyond the scope of this study to include all variants reported. Based on the timelines of emergence of different variants, the cut-off date of our data collection overlaps with the period when the frequency of the alpha variant peaked. All the synonymous mutations except C241T reported in the alpha strain have been identified in our study as well. We added new paragraph in the discussion section to discuss the impact of our findings and to explain why the study of alpha variant remains relevant now. In addition, we used 3 RNA secondary prediction analysis tool, instead of one, to improve the outcome of the analysis. The prediction results for all top 10 synonymous mutations using these 3 tools and the base pair probability estimation results are summarized in the Table 2.

A major concern is that the sequences even filtered by setting parameters to keep only sequencing with complete genome and high coverage still contain bad sequences, how did the authors exclude those sequences?

Response: In our original method, we used the high coverage filter when downloading from GISAID database, in which only entries with less 1% N and 0.05% unique amino acids mutations are included. To further reduce bad sequences, we filtered to remove those sequences with higher than 0.1% N unresolved nucleotides and ambiguous letters. A total of 3584 sequences were removed by applying this filter. The list of the synonymous mutations remains unchanged despite of the revision of the mutation frequency.

In the abstract section, the results part only included those mutations with high frequency but did not contain results of other analyses. Instead, they included the results of RNA secondary structure analysis in the conclusion.

Response: The abstract has been revised to update the results and conclusion.
The manuscript entitled “Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome” reports an analysis of the SARS-CoV-2 genome obtained in the GISAID database. The authors analyzed synonymous changes of the 30,229 SARS-CoV-2 sequences available online, and the effects of these changes on RNA secondary structures and base-pair probability. They identified 150 synonymous mutations in 11 coding regions. Mutations with higher frequency are mostly found in ORF1a and ORF1b and two mutations (C913U and C26735U) found with effect on the predicted RNA secondary structure are not those with higher frequency.

This study collected 30,229 sequences in the database in the period of 31 Dec 2019 to 22 March 2021. As of 17 Dec 2021, there are over 4.4 million complete genomes with high coverage. Will these sequences used in the study include all variants reported or what sequences of variants are included? A table to summarize all variants with these mutations or not is needed.
Response: We downloaded the raw data on 23^rd March 2021 and completed the data analysis in late June 2021. We submitted the manuscript in late August 2021. The landscape of mutational profile of SARS-CoV-2 genome is very dynamic, changing rapidly. It is far beyond the scope of this study to include all variants reported. Based on the timelines of emergence of different variants, the cut-off date of our data collection overlaps with the period when the frequency of the alpha variant peaked. All the synonymous mutations except C241T reported in the alpha strain have been identified in our study as well. We added new paragraph in the discussion section to discuss the impact of our findings and to explain why the study of alpha variant remains relevant now. In addition, we used 3 RNA secondary prediction analysis tool, instead of one, to improve the outcome of the analysis. The prediction results for all top 10 synonymous mutations using these 3 tools and the base pair probability estimation results are summarized in the Table 2.

A major concern is that the sequences even filtered by setting parameters to keep only sequencing with complete genome and high coverage still contain bad sequences, how did the authors exclude those sequences?

Response: In our original method, we used the high coverage filter when downloading from GISAID database, in which only entries with less 1% N and 0.05% unique amino acids mutations are included. To further reduce bad sequences, we filtered to remove those sequences with higher than 0.1% N unresolved nucleotides and ambiguous letters. A total of 3584 sequences were removed by applying this filter. The list of the synonymous mutations remains unchanged despite of the revision of the mutation frequency.

In the abstract section, the results part only included those mutations with high frequency but did not contain results of other analyses. Instead, they included the results of RNA secondary structure analysis in the conclusion.

Response: The abstract has been revised to update the results and conclusion.
Competing Interests: No competing interests were disclosed. Close
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Reviewer Report 02 Nov 2021

Takahiko Koyama, IBM TJ Watson Research Center, Yorktown Heights, NY, USA

Not Approved

https://doi.org/10.5256/f1000research.76505.r97299

Boon et al. made an investigation of synonymous mutations of SARS-CoV-2 genomes. Authors first identified common synonymous mutations such as 3037C>T followed by secondary structure predictions using RNAfold.

First, the number of genomes authors used is too small and too old. Currently, nearly 5 million genomes are uploaded at GISAID; however, authors used only 30,229. The set does not represent the current mutational landscape of SARS-CoV-2. Authors need to update the data. I have 3 million genomes analyzed and 18108 distinct synonymous mutations identified. The list of highest frequency in Table 1 looks completely different with the latest dataset.

Secondly, authors employed RNAfold for RNA secondary structure prediction. Although RNAfold is a standard software for the task, the accuracy of the prediction is not impressive. Authors need to explore various other tools for the secondary structure or justify the usage of RNAfold.

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

CITE

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Author Response 08 Nov 2021

Chong Han Ng, Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, 75450, Malaysia

08 Nov 2021

Author Response

Boon et al. made an investigation of synonymous mutations of SARS-CoV-2 genomes. Authors first identified common synonymous mutations such as 3037C>T followed by secondary structure predictions using RNAfold.

First, ... Continue reading Boon et al. made an investigation of synonymous mutations of SARS-CoV-2 genomes. Authors first identified common synonymous mutations such as 3037C>T followed by secondary structure predictions using RNAfold.

First, the number of genomes authors used is too small and too old. Currently, nearly 5 million genomes are uploaded at GISAID; however, authors used only 30,229. The set does not represent the current mutational landscape of SARS-CoV-2. Authors need to update the data. I have 3 million genomes analyzed and 18108 distinct synonymous mutations identified. The list of highest frequency in Table 1 looks completely different with the latest dataset.

RE: Thank you for the comments and feedbacks. We downloaded SARS-CoV-2 virus genome data submitted from 31 December 19 to 22 March 21 on 23 March 21. There were about a total of 842,603 SARS-CoV-2 virus genomes sequences before applying filters. SARS-CoV-2 genomic sequences were filtered by setting parameters to keep only sequences with complete genome, high coverage, low coverage excluded and patient status options. The use of the low coverage excluded and patient status filters were unintentionally left out in the manuscript and they will be added later. In our study, the use of these filters allows us to analyze high quality data to identify SARS-CoV-2 mutations without the requirement of high computing power. Our data is representative of the mutational landscape of SARS-CoV-2 for this specified period. Same synonymous mutations, including C913T, C3037T, C5986T, C14676T, C15279T and T16176C were identified in alpha strain by other research groups (https://covariants.org/variants/20I.Alpha.V1) and (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/959438/Technical_Briefing_VOC_SH_NJL2_SH2.pdf). The frequency of alpha strain peaked in April-May 21 and subsequently it has been quickly replaced by delta strain. Interestingly the defining synonymous mutations of delta strain (https://covariants.org/variants/21A.Delta) are very different from those of alpha strain. The mutational landscape of SARS-CoV-2 genome is very dynamic as shown by other research groups. Bear in mind that the main objective of our paper is not to monitor the mutational changes of SARS-CoV-2 genome. Although it is useful to get more recent dataset, it is out of the scope of our study to update the data. Both identification of the SARS-CoV-2 mutations and the prediction analysis of the biological consequences of these mutations are very time-consuming processes. The first draft of the manuscript was prepared in late June 21 and submitted in late August 21. Since different SARS-CoV-2 variants may have different sets of mutations, we will gain more insight to study the effects of different mutations in different SARS-CoV-2 variants. In our follow-up study, we plan to predict and compare the effect of the mutations of different variants. These analyses may provide some plausible explanation why some variants are replaced by other variants eventually.

Secondly, authors employed RNAfold for RNA secondary structure prediction. Although RNAfold is a standard software for the task, the accuracy of the prediction is not impressive. Authors need to explore various other tools for the secondary structure or justify the usage of RNAfold.

RE: We chose RNAfold because it is the most commonly used tools to predict the RNA secondary structure. Other than RNAfold, we also considered using Mfold to perform the RNA secondary structure prediction as well. However, the Mfold server was down during the time of the study. RNAfold computes an optimal structure over the whole sequence length, which means its performance and accuracy can be affected as the sequences used get longer. However, in our case, the accuracy of the prediction is still acceptable since the sequence we used to do the structure prediction is relatively short. Besides, the prediction results we obtained from RNAfold is further supported with the prediction results obtained from MutaRNA, which predicts the structural changes induced by the mutation by estimating the base pairing probabilities. In our results, the circular plot from MutaRNA shows the changes in the base pairing probabilities near the mutation site correlates well with the RNA secondary structure predicted by RNAfold.
Boon et al. made an investigation of synonymous mutations of SARS-CoV-2 genomes. Authors first identified common synonymous mutations such as 3037C>T followed by secondary structure predictions using RNAfold.

First, the number of genomes authors used is too small and too old. Currently, nearly 5 million genomes are uploaded at GISAID; however, authors used only 30,229. The set does not represent the current mutational landscape of SARS-CoV-2. Authors need to update the data. I have 3 million genomes analyzed and 18108 distinct synonymous mutations identified. The list of highest frequency in Table 1 looks completely different with the latest dataset.

RE: Thank you for the comments and feedbacks. We downloaded SARS-CoV-2 virus genome data submitted from 31 December 19 to 22 March 21 on 23 March 21. There were about a total of 842,603 SARS-CoV-2 virus genomes sequences before applying filters. SARS-CoV-2 genomic sequences were filtered by setting parameters to keep only sequences with complete genome, high coverage, low coverage excluded and patient status options. The use of the low coverage excluded and patient status filters were unintentionally left out in the manuscript and they will be added later. In our study, the use of these filters allows us to analyze high quality data to identify SARS-CoV-2 mutations without the requirement of high computing power. Our data is representative of the mutational landscape of SARS-CoV-2 for this specified period. Same synonymous mutations, including C913T, C3037T, C5986T, C14676T, C15279T and T16176C were identified in alpha strain by other research groups (https://covariants.org/variants/20I.Alpha.V1) and (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/959438/Technical_Briefing_VOC_SH_NJL2_SH2.pdf). The frequency of alpha strain peaked in April-May 21 and subsequently it has been quickly replaced by delta strain. Interestingly the defining synonymous mutations of delta strain (https://covariants.org/variants/21A.Delta) are very different from those of alpha strain. The mutational landscape of SARS-CoV-2 genome is very dynamic as shown by other research groups. Bear in mind that the main objective of our paper is not to monitor the mutational changes of SARS-CoV-2 genome. Although it is useful to get more recent dataset, it is out of the scope of our study to update the data. Both identification of the SARS-CoV-2 mutations and the prediction analysis of the biological consequences of these mutations are very time-consuming processes. The first draft of the manuscript was prepared in late June 21 and submitted in late August 21. Since different SARS-CoV-2 variants may have different sets of mutations, we will gain more insight to study the effects of different mutations in different SARS-CoV-2 variants. In our follow-up study, we plan to predict and compare the effect of the mutations of different variants. These analyses may provide some plausible explanation why some variants are replaced by other variants eventually.

Secondly, authors employed RNAfold for RNA secondary structure prediction. Although RNAfold is a standard software for the task, the accuracy of the prediction is not impressive. Authors need to explore various other tools for the secondary structure or justify the usage of RNAfold.

RE: We chose RNAfold because it is the most commonly used tools to predict the RNA secondary structure. Other than RNAfold, we also considered using Mfold to perform the RNA secondary structure prediction as well. However, the Mfold server was down during the time of the study. RNAfold computes an optimal structure over the whole sequence length, which means its performance and accuracy can be affected as the sequences used get longer. However, in our case, the accuracy of the prediction is still acceptable since the sequence we used to do the structure prediction is relatively short. Besides, the prediction results we obtained from RNAfold is further supported with the prediction results obtained from MutaRNA, which predicts the structural changes induced by the mutation by estimating the base pairing probabilities. In our results, the circular plot from MutaRNA shows the changes in the base pairing probabilities near the mutation site correlates well with the RNA secondary structure predicted by RNAfold.
Competing Interests: No competing interests were disclosed. Close
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Author Response 30 Nov 2022

Chong Han Ng, Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, 75450, Malaysia

30 Nov 2022

Author Response

First, the number of genomes authors used is too small and too old. Currently, nearly 5 million genomes are uploaded at GISAID; however, authors used only 30,229. The set does ... Continue reading First, the number of genomes authors used is too small and too old. Currently, nearly 5 million genomes are uploaded at GISAID; however, authors used only 30,229. The set does not represent the current mutational landscape of SARS-CoV-2. Authors need to update the data. I have 3 million genomes analyzed and 18108 distinct synonymous mutations identified. The list of highest frequency in Table 1 looks completely different with the latest dataset.

RE: We submitted a revised version in Sep 2022. Although we didn’t use the latest dataset, we provide some justification in the discussion to explain why our data may remain relevant. We hope you will consider to review our manuscript again. The landscape of mutational profile of SARS-CoV-2 genome is very dynamic, changing rapidly. It is far beyond the scope of this study to include all variants reported. Based on the timelines of emergence of different variants, the cut-off date of our data collection overlaps with the period when the frequency of the alpha variant peaked. All the synonymous mutations except C241T reported in the alpha strain have been identified in our study as well. Although the alpha variant has been replaced by other variants in human population, it is detected from wildlife animals based on a few recent genomic surveillance analysis. It is possible that these alpha variant from wildlife animal may jump back to human, hence it remains relevant to predict the biological consequences of these synonymous mutations. The title of the paper has been revised accordingly. The limitation of the dataset was included in the discussion.

Secondly, authors employed RNAfold for RNA secondary structure prediction. Although RNAfold is a standard software for the task, the accuracy of the prediction is not impressive. Authors need to explore various other tools for the secondary structure or justify the usage of RNAfold.

RE: In the revised version, we used 3 RNA secondary prediction analysis tool, namely RNAfold, IPknot++, to improve the outcome of the analysis. The prediction results for all top 10 synonymous mutations using these 3 tools and the base pair probability estimation results are summarized in the Table 2.
First, the number of genomes authors used is too small and too old. Currently, nearly 5 million genomes are uploaded at GISAID; however, authors used only 30,229. The set does not represent the current mutational landscape of SARS-CoV-2. Authors need to update the data. I have 3 million genomes analyzed and 18108 distinct synonymous mutations identified. The list of highest frequency in Table 1 looks completely different with the latest dataset.

RE: We submitted a revised version in Sep 2022. Although we didn’t use the latest dataset, we provide some justification in the discussion to explain why our data may remain relevant. We hope you will consider to review our manuscript again. The landscape of mutational profile of SARS-CoV-2 genome is very dynamic, changing rapidly. It is far beyond the scope of this study to include all variants reported. Based on the timelines of emergence of different variants, the cut-off date of our data collection overlaps with the period when the frequency of the alpha variant peaked. All the synonymous mutations except C241T reported in the alpha strain have been identified in our study as well. Although the alpha variant has been replaced by other variants in human population, it is detected from wildlife animals based on a few recent genomic surveillance analysis. It is possible that these alpha variant from wildlife animal may jump back to human, hence it remains relevant to predict the biological consequences of these synonymous mutations. The title of the paper has been revised accordingly. The limitation of the dataset was included in the discussion.

Secondly, authors employed RNAfold for RNA secondary structure prediction. Although RNAfold is a standard software for the task, the accuracy of the prediction is not impressive. Authors need to explore various other tools for the secondary structure or justify the usage of RNAfold.

RE: In the revised version, we used 3 RNA secondary prediction analysis tool, namely RNAfold, IPknot++, to improve the outcome of the analysis. The prediction results for all top 10 synonymous mutations using these 3 tools and the base pair probability estimation results are summarized in the Table 2.
Competing Interests: No competing interests were disclosed. Close
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COMMENTS ON THIS REPORT

Author Response 08 Nov 2021

Chong Han Ng, Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, 75450, Malaysia

08 Nov 2021

Author Response

Boon et al. made an investigation of synonymous mutations of SARS-CoV-2 genomes. Authors first identified common synonymous mutations such as 3037C>T followed by secondary structure predictions using RNAfold.

First, ... Continue reading Boon et al. made an investigation of synonymous mutations of SARS-CoV-2 genomes. Authors first identified common synonymous mutations such as 3037C>T followed by secondary structure predictions using RNAfold.

First, the number of genomes authors used is too small and too old. Currently, nearly 5 million genomes are uploaded at GISAID; however, authors used only 30,229. The set does not represent the current mutational landscape of SARS-CoV-2. Authors need to update the data. I have 3 million genomes analyzed and 18108 distinct synonymous mutations identified. The list of highest frequency in Table 1 looks completely different with the latest dataset.

RE: Thank you for the comments and feedbacks. We downloaded SARS-CoV-2 virus genome data submitted from 31 December 19 to 22 March 21 on 23 March 21. There were about a total of 842,603 SARS-CoV-2 virus genomes sequences before applying filters. SARS-CoV-2 genomic sequences were filtered by setting parameters to keep only sequences with complete genome, high coverage, low coverage excluded and patient status options. The use of the low coverage excluded and patient status filters were unintentionally left out in the manuscript and they will be added later. In our study, the use of these filters allows us to analyze high quality data to identify SARS-CoV-2 mutations without the requirement of high computing power. Our data is representative of the mutational landscape of SARS-CoV-2 for this specified period. Same synonymous mutations, including C913T, C3037T, C5986T, C14676T, C15279T and T16176C were identified in alpha strain by other research groups (https://covariants.org/variants/20I.Alpha.V1) and (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/959438/Technical_Briefing_VOC_SH_NJL2_SH2.pdf). The frequency of alpha strain peaked in April-May 21 and subsequently it has been quickly replaced by delta strain. Interestingly the defining synonymous mutations of delta strain (https://covariants.org/variants/21A.Delta) are very different from those of alpha strain. The mutational landscape of SARS-CoV-2 genome is very dynamic as shown by other research groups. Bear in mind that the main objective of our paper is not to monitor the mutational changes of SARS-CoV-2 genome. Although it is useful to get more recent dataset, it is out of the scope of our study to update the data. Both identification of the SARS-CoV-2 mutations and the prediction analysis of the biological consequences of these mutations are very time-consuming processes. The first draft of the manuscript was prepared in late June 21 and submitted in late August 21. Since different SARS-CoV-2 variants may have different sets of mutations, we will gain more insight to study the effects of different mutations in different SARS-CoV-2 variants. In our follow-up study, we plan to predict and compare the effect of the mutations of different variants. These analyses may provide some plausible explanation why some variants are replaced by other variants eventually.

Secondly, authors employed RNAfold for RNA secondary structure prediction. Although RNAfold is a standard software for the task, the accuracy of the prediction is not impressive. Authors need to explore various other tools for the secondary structure or justify the usage of RNAfold.

RE: We chose RNAfold because it is the most commonly used tools to predict the RNA secondary structure. Other than RNAfold, we also considered using Mfold to perform the RNA secondary structure prediction as well. However, the Mfold server was down during the time of the study. RNAfold computes an optimal structure over the whole sequence length, which means its performance and accuracy can be affected as the sequences used get longer. However, in our case, the accuracy of the prediction is still acceptable since the sequence we used to do the structure prediction is relatively short. Besides, the prediction results we obtained from RNAfold is further supported with the prediction results obtained from MutaRNA, which predicts the structural changes induced by the mutation by estimating the base pairing probabilities. In our results, the circular plot from MutaRNA shows the changes in the base pairing probabilities near the mutation site correlates well with the RNA secondary structure predicted by RNAfold.
Boon et al. made an investigation of synonymous mutations of SARS-CoV-2 genomes. Authors first identified common synonymous mutations such as 3037C>T followed by secondary structure predictions using RNAfold.

First, the number of genomes authors used is too small and too old. Currently, nearly 5 million genomes are uploaded at GISAID; however, authors used only 30,229. The set does not represent the current mutational landscape of SARS-CoV-2. Authors need to update the data. I have 3 million genomes analyzed and 18108 distinct synonymous mutations identified. The list of highest frequency in Table 1 looks completely different with the latest dataset.

RE: Thank you for the comments and feedbacks. We downloaded SARS-CoV-2 virus genome data submitted from 31 December 19 to 22 March 21 on 23 March 21. There were about a total of 842,603 SARS-CoV-2 virus genomes sequences before applying filters. SARS-CoV-2 genomic sequences were filtered by setting parameters to keep only sequences with complete genome, high coverage, low coverage excluded and patient status options. The use of the low coverage excluded and patient status filters were unintentionally left out in the manuscript and they will be added later. In our study, the use of these filters allows us to analyze high quality data to identify SARS-CoV-2 mutations without the requirement of high computing power. Our data is representative of the mutational landscape of SARS-CoV-2 for this specified period. Same synonymous mutations, including C913T, C3037T, C5986T, C14676T, C15279T and T16176C were identified in alpha strain by other research groups (https://covariants.org/variants/20I.Alpha.V1) and (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/959438/Technical_Briefing_VOC_SH_NJL2_SH2.pdf). The frequency of alpha strain peaked in April-May 21 and subsequently it has been quickly replaced by delta strain. Interestingly the defining synonymous mutations of delta strain (https://covariants.org/variants/21A.Delta) are very different from those of alpha strain. The mutational landscape of SARS-CoV-2 genome is very dynamic as shown by other research groups. Bear in mind that the main objective of our paper is not to monitor the mutational changes of SARS-CoV-2 genome. Although it is useful to get more recent dataset, it is out of the scope of our study to update the data. Both identification of the SARS-CoV-2 mutations and the prediction analysis of the biological consequences of these mutations are very time-consuming processes. The first draft of the manuscript was prepared in late June 21 and submitted in late August 21. Since different SARS-CoV-2 variants may have different sets of mutations, we will gain more insight to study the effects of different mutations in different SARS-CoV-2 variants. In our follow-up study, we plan to predict and compare the effect of the mutations of different variants. These analyses may provide some plausible explanation why some variants are replaced by other variants eventually.

Secondly, authors employed RNAfold for RNA secondary structure prediction. Although RNAfold is a standard software for the task, the accuracy of the prediction is not impressive. Authors need to explore various other tools for the secondary structure or justify the usage of RNAfold.

RE: We chose RNAfold because it is the most commonly used tools to predict the RNA secondary structure. Other than RNAfold, we also considered using Mfold to perform the RNA secondary structure prediction as well. However, the Mfold server was down during the time of the study. RNAfold computes an optimal structure over the whole sequence length, which means its performance and accuracy can be affected as the sequences used get longer. However, in our case, the accuracy of the prediction is still acceptable since the sequence we used to do the structure prediction is relatively short. Besides, the prediction results we obtained from RNAfold is further supported with the prediction results obtained from MutaRNA, which predicts the structural changes induced by the mutation by estimating the base pairing probabilities. In our results, the circular plot from MutaRNA shows the changes in the base pairing probabilities near the mutation site correlates well with the RNA secondary structure predicted by RNAfold.
Competing Interests: No competing interests were disclosed. Close
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Author Response 30 Nov 2022

Chong Han Ng, Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, 75450, Malaysia

30 Nov 2022

Author Response

First, the number of genomes authors used is too small and too old. Currently, nearly 5 million genomes are uploaded at GISAID; however, authors used only 30,229. The set does ... Continue reading First, the number of genomes authors used is too small and too old. Currently, nearly 5 million genomes are uploaded at GISAID; however, authors used only 30,229. The set does not represent the current mutational landscape of SARS-CoV-2. Authors need to update the data. I have 3 million genomes analyzed and 18108 distinct synonymous mutations identified. The list of highest frequency in Table 1 looks completely different with the latest dataset.

RE: We submitted a revised version in Sep 2022. Although we didn’t use the latest dataset, we provide some justification in the discussion to explain why our data may remain relevant. We hope you will consider to review our manuscript again. The landscape of mutational profile of SARS-CoV-2 genome is very dynamic, changing rapidly. It is far beyond the scope of this study to include all variants reported. Based on the timelines of emergence of different variants, the cut-off date of our data collection overlaps with the period when the frequency of the alpha variant peaked. All the synonymous mutations except C241T reported in the alpha strain have been identified in our study as well. Although the alpha variant has been replaced by other variants in human population, it is detected from wildlife animals based on a few recent genomic surveillance analysis. It is possible that these alpha variant from wildlife animal may jump back to human, hence it remains relevant to predict the biological consequences of these synonymous mutations. The title of the paper has been revised accordingly. The limitation of the dataset was included in the discussion.

Secondly, authors employed RNAfold for RNA secondary structure prediction. Although RNAfold is a standard software for the task, the accuracy of the prediction is not impressive. Authors need to explore various other tools for the secondary structure or justify the usage of RNAfold.

RE: In the revised version, we used 3 RNA secondary prediction analysis tool, namely RNAfold, IPknot++, to improve the outcome of the analysis. The prediction results for all top 10 synonymous mutations using these 3 tools and the base pair probability estimation results are summarized in the Table 2.
First, the number of genomes authors used is too small and too old. Currently, nearly 5 million genomes are uploaded at GISAID; however, authors used only 30,229. The set does not represent the current mutational landscape of SARS-CoV-2. Authors need to update the data. I have 3 million genomes analyzed and 18108 distinct synonymous mutations identified. The list of highest frequency in Table 1 looks completely different with the latest dataset.

RE: We submitted a revised version in Sep 2022. Although we didn’t use the latest dataset, we provide some justification in the discussion to explain why our data may remain relevant. We hope you will consider to review our manuscript again. The landscape of mutational profile of SARS-CoV-2 genome is very dynamic, changing rapidly. It is far beyond the scope of this study to include all variants reported. Based on the timelines of emergence of different variants, the cut-off date of our data collection overlaps with the period when the frequency of the alpha variant peaked. All the synonymous mutations except C241T reported in the alpha strain have been identified in our study as well. Although the alpha variant has been replaced by other variants in human population, it is detected from wildlife animals based on a few recent genomic surveillance analysis. It is possible that these alpha variant from wildlife animal may jump back to human, hence it remains relevant to predict the biological consequences of these synonymous mutations. The title of the paper has been revised accordingly. The limitation of the dataset was included in the discussion.

Secondly, authors employed RNAfold for RNA secondary structure prediction. Although RNAfold is a standard software for the task, the accuracy of the prediction is not impressive. Authors need to explore various other tools for the secondary structure or justify the usage of RNAfold.

RE: In the revised version, we used 3 RNA secondary prediction analysis tool, namely RNAfold, IPknot++, to improve the outcome of the analysis. The prediction results for all top 10 synonymous mutations using these 3 tools and the base pair probability estimation results are summarized in the Table 2.
Competing Interests: No competing interests were disclosed. Close
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Version 4

VERSION 4 PUBLISHED 18 Oct 2021

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Takahiko Koyama, IBM TJ Watson Research Center, Yorktown Heights, USA
Leyi Wang, University of Illinois, Urbana, USA
Chandran Nithin, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland
Roland Huber, Bioinformatics Institute, A*STAR, Singapore
Diego Forni, Scientific Institute IRCCS E Medea, Bosisio Parini, Italy
Tamar Schlick, New York University, New York, USA

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27 Sep 2024 | for Version 4

Diego Forni, Scientific Institute IRCCS E Medea, Bosisio Parini, Italy

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Approved With Reservations

I would like to thank the authors for answering my questions.
I still believe that it is not optimal to report counts without taking into account ORD length in figure 1.
I still also believe that differences among RNA secondary structures are relevant and I am not really sure that "C913U, C3037U, U16176C and C18877U mutants show pronounced changes between wild type and mutant" as stated in the conclusion.

Finally the two sentences starting with "Synonymous mutations are assumed subject to a lower selective pressure than nonsynonymous mutations, presumably.." in the introduction are not clear and need to be better explained.

Competing Interests

No competing interests were disclosed.

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viral genomics, viral evolution

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11 Sep 2024 | for Version 3

Tamar Schlick, Department of Chemistry and Courant Institute of Mathematical Sciences, New York University, New York, New York, USA

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Approved With Reservations

In this work, synonymous mutations of the SARS-CoV-2 genome are explored, with the rationale that these mutations impact function through altered RNA folding, despite unaltered protein products. Specifically, the researchers find 150 synonymous mutations after performing multiple sequence alignment and examining the protein products, and mapped the distributions in the coding regions. RNA secondary structure predictions and base pair probability calculations are then presented for 4 mutations C913U, C3037U, U16176C and C18877U that show pronounced changes between wildtype and mutant structures. Different prediction tools were used to confirm the structure predictions. The value of the study, while interesting, is limited because the long RNA lengths used make the disparate predicted structures unreliable. If the Information Classification: General authors examine some mutant systems experimentally with chemical reactivity like SHAPE or DMS, the results may be more meaningful.

Is the work clearly and accurately presented and does it cite the current literature?

Yes
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.

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27 Jul 2024 | for Version 3

Diego Forni, Scientific Institute IRCCS E Medea, Bosisio Parini, Italy

13 Views Cite this report Responses(1)

Not Approved

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?

Partly
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

viral genomics, viral evolution

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Author Response

18 Sep 2024

Chong Han Ng, Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, 75450, Malaysia

In this manuscript, Dr Boon et al. analyzed the most common synonymous mutations of early pandemic SARS-CoV-2 genomes. They identified substitutions that influence the RNA secondary structure surrounding these positions. The authors have already modified and updated their manuscript based on previous comments, still there are few open questions in my opinion.
First of all, it is clear that the dataset is not up to date and it will not be changed, still this should be clearly stated, starting from the title, reporting or the time period or something like "at the beginning of the pandemic''.
Author response: The title has been revised to “Prediction of the effects of the top 10 synonymous mutations from 26645 SARS-CoV-2 genomes of early pandemic phase”.

Reviewer comment: Following this, also the introduction section should be focused on what we know about genomic variability and viral evolution in the time period being analyzed herein. If I understood well, most of these sequences belong to the alpha variant.
Author response: We added a few lines about Alpha variant in introduction, and result and discussion section (Identification of SARS-CoV-2 synonymous mutations).

Reviewer comment: Moreover, " variant of concern" (abbreviated VOC not VOI as stated in the text) refers to a specific lineage that carries several and distinct mutations. As it is stated in the text now it seems that a single mutation is a VOC. Please check it.
Author response: The abbreviation has been corrected from VOI to VOC. In the text, we mentioned that different SARS-CoV-2 variants with multiple synonymous and nonsynonymous mutations have been reported since the beginning of the outbreak. Therefore, it is clear that VOCs contain multiple mutations.

Reviewer comment: It is expected that most mutations are found in ORF1ab, as this covers most of the genome, so counts should be normalized by ORF length (e.g. fig.1).
Author response: Figure 1 show the distribution of 150 different types of synonymous mutations identified from 26645 SARS-CoV-2 genomes. It is not about the count or the average of the mutations in each ORFs, thus it should not be normalized.

Reviewer comment: Table 1 should report mutation frequency not count.
Author response: Table 1 shows the mutation frequency as reported in this paper.

Reviewer comment: Authors should discuss more why different predictors give very different results in terms of rna secondary structure, and how this can affect their analysis.
Author response: In the paper, there is about two paragraphs on comparison of these RNA secondary structure tools and we highlighted their similarities and differences of these tools. As mentioned in the discussion, we expected some differences in the outcomes obtained from different RNA secondary structure prediction tools due to the differences in algorithms, thermodynamic parameter settings, inclusion of pseudoknot calculation, incorporation of experimental data and the assumptions of each tool. Furthermore, SARS-CoV-2 may adopt different RNA secondary structure conformations. However, our major focus is to predict if the sSNP may affect/change RNA secondary structure, rather than the predicted structures per se. Importantly the outcomes allow us to prioritize variants for the experiment functional studies in the future. In addition, we demonstrated the usability of prediction tools by using 5’ UTR (1-480 nt) of SARS-CoV-2, which is well-characterized in other studies in the Extended data 1. For the four sSNPs which showed pronounced changes, we also compared the differences of these structures predicted by different tools.

Reviewer comment: Why did the author select 250 bp flanking positions for their prediction? Which is the rationale? How does this influence the results?
Author response: Some sSNPs affect only RNA secondary structures of neighboring nucleotides while some sSNPs may have long-ranging effects, in addition to the changes in the neighboring nucleotides. The length of RNA sequences used should not be too short since we may miss out the long-ranging effects. On the other hand, we need to bear in mind that most of RNA secondary structure prediction tools are not suitable for long sequences analysis. We tried out different lengths of flanking sequences, including 100b, 250b, 500b and 1000b, and we found out that 250b flanking sequences produce the optimal results.

Reviewer comment: Are there any other mutations in the 501 nucleotide regions? Could mutations have an impact on predictions?
Author response: Yes, there may be other synonymous mutations within the 501 nucleotide regions, but they are not from the top 10 synonymous mutations. We are not doing epitasis analysis of different mutations since it is out of the scope of this study.

Reviewer comment: How did the authors handle overlapping/internal ORFs? In these situations a syn mutations could also be non-synonymous for the other ORF. I do not think this is the case for the top 10 mutations, but still this should be explained.
Author response: Yes, we don’t find any synonymous mutation of one ORF, which is also a synonymous mutation for another ORF. We included one sentence about the overlapping ORFs in the result and discussion section in Identification of SARS-CoV-2 synonymous mutation. Since some mutations, including C913U, U16176C are located near the boundaries of different ORFs, we postulated their potential long ranging effects at the neighboring ORFs in the discussion section.

Reviewer comment: The CUB section seems to me not really linked to the rest of the analyses and could be expanded more.
Author response: We expanded CUB section, including additional relevant references, elaborating the results further to make it clearer to the reader.

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Competing Interests

No competing interests were disclosed.

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Reviewer Report

12 Views

18 Oct 2023 | for Version 2

Roland Huber, Bioinformatics Institute, A*STAR, Singapore

12 Views Cite this report Responses(0)

Not Approved

I agree with previous reviewers that the analyzed sequences represent only a limited sample of variation in SARS-CoV-2, specifically from early in the pandemic. This might introduce unexpected biases in the analysis. E.g. it would be more likely to observe host adaption early on which would be consistent with more favourable codon usage.

With regard to the data that was analysed, the structure models obtained show limited consistency. The authors state that they do not expect the used tools to concur on the structures since they use different algorithms. This is concerning, as one would expect unambiguous structures to be consistent, even using different methodologies. Other tools, e.g. RNAstructure, also allow the inclusion of shape data and the prediction of pseudoknots. We are thus left with a series of diverging structure predictions and unsure what, if any, effect these specific mutations have. This is not helped by inconsistent presentation of the results. Figures 2-5 use different visualisations for the results of the 4 tools employed, which makes comparisons of the structures difficult for the reader.

The study unfortunately does nothing to associate the regions or structural elements with any type of functional or biological information. We are thus left with a study that analyses a limited set of synonymous mutations using inconsistent structure predictions and offers no additional biological insight.

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

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Reviewer Report

22 Views

06 Sep 2022 | for Version 2

Chandran Nithin, Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland

22 Views Cite this report Responses(0)

Approved

Authors have addressed the comments. However, the data set used in the study is outdated.

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

RNA structure prediction; Computational Structural Biology; RNA-protein complexes.

I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.

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Reviewer Report

38 Views

28 Apr 2022 | for Version 1

Chandran Nithin, Laboratory of Bioinformatics and Protein Engineering, International Institute of Molecular and Cell Biology in Warsaw, Warsaw, Poland

38 Views Cite this report Responses(1)

Not Approved

The dataset used in this study is outdated and represents only a short period between 31st December 2019 and 22nd March 2021. It will be necessary to update the data to include more sequences from a wider time frame.

The authors had indicated (through the editorial manager) that they are unwilling to update the dataset; the authors need to indicate the same in the title and detail the limitations of the dataset in the introduction section.
The authors have used the RNAfold program to predict the secondary structure of wild-type and mutant sequences. The authors need to use additional prediction tools and check for consensus between the predicted structure. Moreover, authors need to employ prediction tools to predict pseudoknots in the RNA secondary structure.
The secondary structure of SARS-CoV2 determined with the help of data from SHAPE-MaP, icSHAPE, and DMS-MaPseq experiments is available in the literature.¹,²,³ The authors should utilize this data to augment this study.
The study requires the inclusion of appropriate controls which quantify the effect of mutations on the stability of the RNA structure. The authors state that "The minimum free energy value of the mutant (- 146.90 kcal/mol) is slightly less negative than that of the wild type (- 147.40 kcal/mol), which makes it a less thermodynamically stable structure compared to the wild type." This claim needs to be explored in detail and supported with evidence from experimental data and literature.
The authors state that C26735U mutation induces changes in the predicted RNA secondary structure by forming an extra multibranch loop at the mutation site. The authors should compare the same with secondary systems mentioned in point 3 (See Ref 1, 2, and 3). The authors need to perform a similar analysis for C913U.
The study identifies 381 mutations in the genome, of which 150 are synonymous; however, it discusses only two. The authors need to provide the data on the remaining 148 synonymous mutations and briefly discuss some of them in the manuscript.
The authors need to expand and include relevant literature in the introduction section.
The manuscript needs to be edited for language errors.

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

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

RNA structure prediction; Computational Structural Biology; RNA-protein complexes.

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Author Response

05 Sep 2022

Chong Han Ng, Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, 75450, Malaysia

The dataset used in this study is outdated and represents only a short period between 31st December 2019 and 22nd March 2021. It will be necessary to update the data to include more sequences from a wider time frame.

The authors had indicated (through the editorial manager) that they are unwilling to update the dataset; the authors need to indicate the same in the title and detail the limitations of the dataset in the introduction section.

Response: The landscape of mutational profile of SARS-CoV-2 genome is very dynamic, changing rapidly. As of 14 August 2022, there are more than 12 million SARS-CoV-2 virus genomes sequences deposited in GISAID database. Although it is useful to get more recent dataset, it is out of the scope of our study to update the data. Both identification of the SARS-CoV-2 mutations and the prediction analysis of the biological consequences of these mutations are very time-consuming processes. Based on the timelines of emergence of different variants, the cut-off date of our data collection overlaps with the period when the frequency of the alpha variant peaked. All the synonymous mutations except C241T reported in the alpha variant have been identified in our study as well. Although the alpha variant has been replaced by other variants in human population, it is detected from wildlife animals based on a few recent genomic surveillance analysis. It is possible that these alpha variant from wildlife animal may jump back to human, hence it remains relevant to predict the biological consequences of these synonymous mutations. The title of the paper has been revised accordingly. The limitation of the dataset was included in the discussion.
The authors have used the RNAfold program to predict the secondary structure of wild-type and mutant sequences. The authors need to use additional prediction tools and check for consensus between the predicted structure. Moreover, authors need to employ prediction tools to predict pseudoknots in the RNA secondary structure.

Response: To improve the outcome of the RNA secondary structure analysis, 3 prediction tools, namely RNAfold, IPknot++ and MXfold2 are used in our study. Of these, IPknot++ predicted some synonymous mutations may affect the pseudoknot formation. The prediction results for all top 10 synonymous mutations using these 3 tools are summarized in the Table 2
The secondary structure of SARS-CoV2 determined with the help of data from SHAPE-MaP, icSHAPE, and DMS-MaPseq experiments is available in the literature.1,2,3 The authors should utilize this data to augment this study

Response: To improve the outcome of the prediction analysis, the RNA secondary structure of wild type and mutant sequences were predicted using RNAfold program with the incorporation of SHAPE reactivity data obtained from the study done by Manfredonia et al. (2020).
The study requires the inclusion of appropriate controls which quantify the effect of mutations on the stability of the RNA structure. The authors state that "The minimum free energy value of the mutant (- 146.90 kcal/mol) is slightly less negative than that of the wild type (- 147.40 kcal/mol), which makes it a less thermodynamically stable structure compared to the wild type." This claim needs to be explored in detail and supported with evidence from experimental data and literature.

Response: Instead of comparing minimum free energy between the wild type and mutant, we applied three RNA secondary structure prediction tools to predict the effect of the mutation on RNA secondary structure.
The authors state that C26735U mutation induces changes in the predicted RNA secondary structure by forming an extra multibranch loop at the mutation site. The authors should compare the same with secondary systems mentioned in point 3 (See Ref 1, 2, and 3). The authors need to perform a similar analysis for C913U.

Response: The results of the 4 mutations showing changes in all 3 prediction tools are included in Figure 2-5.
The study identifies 381 mutations in the genome, of which 150 are synonymous; however, it discusses only two. The authors need to provide the data on the remaining 148 synonymous mutations and briefly discuss some of them in the manuscript.

Response: The RNA secondary structures prediction results using 3 prediction tools for the top 10 synonymous mutations have been included in Extended data 1-3. There were 4 mutations showing changes in all 3 prediction tools and the results are discussed in the manuscript.
The authors need to expand and include relevant literature in the introduction section.

Response: The relevant literature in the introduction section has been updated.
The manuscript needs to be edited for language errors.

Response: The language errors have been edited accordingly.

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Competing Interests

No competing interests were disclosed.

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Reviewer Report

54 Views

20 Dec 2021 | for Version 1

Leyi Wang, Veterinary Diagnostic Laboratory and Department of Veterinary Clinical Medicine, College of Veterinary Medicine, University of Illinois, Urbana, IL, USA

54 Views Cite this report Responses(1)

Approved With Reservations

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

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Author Response

05 Sep 2022

Chong Han Ng, Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, 75450, Malaysia

The manuscript entitled “Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome” reports an analysis of the SARS-CoV-2 genome obtained in the GISAID database. The authors analyzed synonymous changes of the 30,229 SARS-CoV-2 sequences available online, and the effects of these changes on RNA secondary structures and base-pair probability. They identified 150 synonymous mutations in 11 coding regions. Mutations with higher frequency are mostly found in ORF1a and ORF1b and two mutations (C913U and C26735U) found with effect on the predicted RNA secondary structure are not those with higher frequency.

This study collected 30,229 sequences in the database in the period of 31 Dec 2019 to 22 March 2021. As of 17 Dec 2021, there are over 4.4 million complete genomes with high coverage. Will these sequences used in the study include all variants reported or what sequences of variants are included? A table to summarize all variants with these mutations or not is needed.
Response: We downloaded the raw data on 23^rd March 2021 and completed the data analysis in late June 2021. We submitted the manuscript in late August 2021. The landscape of mutational profile of SARS-CoV-2 genome is very dynamic, changing rapidly. It is far beyond the scope of this study to include all variants reported. Based on the timelines of emergence of different variants, the cut-off date of our data collection overlaps with the period when the frequency of the alpha variant peaked. All the synonymous mutations except C241T reported in the alpha strain have been identified in our study as well. We added new paragraph in the discussion section to discuss the impact of our findings and to explain why the study of alpha variant remains relevant now. In addition, we used 3 RNA secondary prediction analysis tool, instead of one, to improve the outcome of the analysis. The prediction results for all top 10 synonymous mutations using these 3 tools and the base pair probability estimation results are summarized in the Table 2.

A major concern is that the sequences even filtered by setting parameters to keep only sequencing with complete genome and high coverage still contain bad sequences, how did the authors exclude those sequences?

Response: In our original method, we used the high coverage filter when downloading from GISAID database, in which only entries with less 1% N and 0.05% unique amino acids mutations are included. To further reduce bad sequences, we filtered to remove those sequences with higher than 0.1% N unresolved nucleotides and ambiguous letters. A total of 3584 sequences were removed by applying this filter. The list of the synonymous mutations remains unchanged despite of the revision of the mutation frequency.

In the abstract section, the results part only included those mutations with high frequency but did not contain results of other analyses. Instead, they included the results of RNA secondary structure analysis in the conclusion.

Response: The abstract has been revised to update the results and conclusion.

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No competing interests were disclosed.

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Reviewer Report

71 Views

02 Nov 2021 | for Version 1

Takahiko Koyama, IBM TJ Watson Research Center, Yorktown Heights, NY, USA

71 Views Cite this report Responses(2)

Not Approved

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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Author Response

08 Nov 2021

Chong Han Ng, Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, 75450, Malaysia

Boon et al. made an investigation of synonymous mutations of SARS-CoV-2 genomes. Authors first identified common synonymous mutations such as 3037C>T followed by secondary structure predictions using RNAfold.

First, the number of genomes authors used is too small and too old. Currently, nearly 5 million genomes are uploaded at GISAID; however, authors used only 30,229. The set does not represent the current mutational landscape of SARS-CoV-2. Authors need to update the data. I have 3 million genomes analyzed and 18108 distinct synonymous mutations identified. The list of highest frequency in Table 1 looks completely different with the latest dataset.

RE: Thank you for the comments and feedbacks. We downloaded SARS-CoV-2 virus genome data submitted from 31 December 19 to 22 March 21 on 23 March 21. There were about a total of 842,603 SARS-CoV-2 virus genomes sequences before applying filters. SARS-CoV-2 genomic sequences were filtered by setting parameters to keep only sequences with complete genome, high coverage, low coverage excluded and patient status options. The use of the low coverage excluded and patient status filters were unintentionally left out in the manuscript and they will be added later. In our study, the use of these filters allows us to analyze high quality data to identify SARS-CoV-2 mutations without the requirement of high computing power. Our data is representative of the mutational landscape of SARS-CoV-2 for this specified period. Same synonymous mutations, including C913T, C3037T, C5986T, C14676T, C15279T and T16176C were identified in alpha strain by other research groups (https://covariants.org/variants/20I.Alpha.V1) and (https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/959438/Technical_Briefing_VOC_SH_NJL2_SH2.pdf). The frequency of alpha strain peaked in April-May 21 and subsequently it has been quickly replaced by delta strain. Interestingly the defining synonymous mutations of delta strain (https://covariants.org/variants/21A.Delta) are very different from those of alpha strain. The mutational landscape of SARS-CoV-2 genome is very dynamic as shown by other research groups. Bear in mind that the main objective of our paper is not to monitor the mutational changes of SARS-CoV-2 genome. Although it is useful to get more recent dataset, it is out of the scope of our study to update the data. Both identification of the SARS-CoV-2 mutations and the prediction analysis of the biological consequences of these mutations are very time-consuming processes. The first draft of the manuscript was prepared in late June 21 and submitted in late August 21. Since different SARS-CoV-2 variants may have different sets of mutations, we will gain more insight to study the effects of different mutations in different SARS-CoV-2 variants. In our follow-up study, we plan to predict and compare the effect of the mutations of different variants. These analyses may provide some plausible explanation why some variants are replaced by other variants eventually.

Secondly, authors employed RNAfold for RNA secondary structure prediction. Although RNAfold is a standard software for the task, the accuracy of the prediction is not impressive. Authors need to explore various other tools for the secondary structure or justify the usage of RNAfold.

RE: We chose RNAfold because it is the most commonly used tools to predict the RNA secondary structure. Other than RNAfold, we also considered using Mfold to perform the RNA secondary structure prediction as well. However, the Mfold server was down during the time of the study. RNAfold computes an optimal structure over the whole sequence length, which means its performance and accuracy can be affected as the sequences used get longer. However, in our case, the accuracy of the prediction is still acceptable since the sequence we used to do the structure prediction is relatively short. Besides, the prediction results we obtained from RNAfold is further supported with the prediction results obtained from MutaRNA, which predicts the structural changes induced by the mutation by estimating the base pairing probabilities. In our results, the circular plot from MutaRNA shows the changes in the base pairing probabilities near the mutation site correlates well with the RNA secondary structure predicted by RNAfold.

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Competing Interests

No competing interests were disclosed.

Author Response

30 Nov 2022

Chong Han Ng, Faculty of Information Science and Technology, Multimedia University, Bukit Beruang, 75450, Malaysia

First, the number of genomes authors used is too small and too old. Currently, nearly 5 million genomes are uploaded at GISAID; however, authors used only 30,229. The set does not represent the current mutational landscape of SARS-CoV-2. Authors need to update the data. I have 3 million genomes analyzed and 18108 distinct synonymous mutations identified. The list of highest frequency in Table 1 looks completely different with the latest dataset.

RE: We submitted a revised version in Sep 2022. Although we didn’t use the latest dataset, we provide some justification in the discussion to explain why our data may remain relevant. We hope you will consider to review our manuscript again. The landscape of mutational profile of SARS-CoV-2 genome is very dynamic, changing rapidly. It is far beyond the scope of this study to include all variants reported. Based on the timelines of emergence of different variants, the cut-off date of our data collection overlaps with the period when the frequency of the alpha variant peaked. All the synonymous mutations except C241T reported in the alpha strain have been identified in our study as well. Although the alpha variant has been replaced by other variants in human population, it is detected from wildlife animals based on a few recent genomic surveillance analysis. It is possible that these alpha variant from wildlife animal may jump back to human, hence it remains relevant to predict the biological consequences of these synonymous mutations. The title of the paper has been revised accordingly. The limitation of the dataset was included in the discussion.

Secondly, authors employed RNAfold for RNA secondary structure prediction. Although RNAfold is a standard software for the task, the accuracy of the prediction is not impressive. Authors need to explore various other tools for the secondary structure or justify the usage of RNAfold.

RE: In the revised version, we used 3 RNA secondary prediction analysis tool, namely RNAfold, IPknot++, to improve the outcome of the analysis. The prediction results for all top 10 synonymous mutations using these 3 tools and the base pair probability estimation results are summarized in the Table 2.

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Competing Interests

No competing interests were disclosed.

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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Prediction of the Effects of Synonymous Variants on SARS-CoV-2 Genome

Abstract

Keywords

Introduction

Methods

Sequence retrieval

Multiple sequence alignment

Identification of mutations and their frequency in SARS-CoV-2 genomes

SARS-CoV-2 RNA secondary structure prediction

Base pair probability estimation and analysis

Relative synonymous codon usage (RSCU)

Results and discussion

Identification of SARS-CoV-2 synonymous mutations

Figure 1. Distribution of SARS-CoV-2 synonymous mutations in 11 coding regions.

Table 1. SARS-CoV-2 synonymous mutations with the top 10 highest frequency.

RNA secondary structure and base pair probability

Figure 2. The effect of C913U mutation on RNA secondary structure of nsp2 in ORF1a.

Figure 3. The effect of C26735U mutation on RNA secondary structure of M protein.

RSCU analysis of SARS-CoV-2

Table 2. RSCU values of SARS-CoV-2 genome.

Table 3. RSCU analysis of the top 10 synonymous mutations of SARS-CoV-2 genome.

Conclusions

Ethics and dissemination

Data and software availability

Underlying data

Software

Author contributions

References

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Open Peer Review

Reviewer Status

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