Bayesian Molecular Dating Analyses Combined with Mutational Profiling Suggest an Independent Origin and Evolution of SARS-CoV-2 Omicron BA.1 and BA.2 Sub-Lineages
, Rahul Kaushik 2,3,†, Ashutosh Singh 1, Vladimir N. Uversky 4,5, Kam Y. J. Zhang 3, Upasana Sahu 1, Sandeep Bhatia 1 and Aniket Sanyal 1
Round 1
Reviewer 1 Report
Kumar et al. has discussed in their manuscript about the origin and evolution of SARS-CoV-2 Omicron BA.1 and BA.2 sub-lineages using bioinformatics tools. The manuscript is well written and has been pleasant to read. However, I have some comments listed below:
Please follow the classification of SARS-CoV-2 variants based on either WHO or CDC. For example, some of the variants are not VOC and have been referred to as VBM or something else. Please see the following https://www.cdc.gov/coronavirus/2019-ncov/variants/variant-classifications.html
Author Response
Comment 1: Please follow the classification of SARS-CoV-2 variants based on either WHO or CDC. For example, some of the variants are not VOC and have been referred to as VBM or something else. Please see the following https://www.cdc.gov/coronavirus/2019-ncov/variants/variant-classifications.html
Reply: We sincerely thank the reviewer for this clarity in SARS-CoV-2 variant’s classification. Though CDC in its recent SARS-CoV-2 classification has put Alpha, Beta, Gamma and Delta VOC in the VBM category, however, WHO has retained its classification and therefore, Alpha, Beta, Gamma and Delta are classified as previous VOCs. To avoid this discrepancy and for the sake of clarity for the readers, we have updated this information in the introduction section of the manuscript (please see lines 66-68).
Reviewer 2 Report
Naveen Kumar and coworkers have reported the Bayesian molecular dating analyses combined with mutational profiling to understand the origin and evolution of 3 SARS-CoV-2 Omicron BA.1 and BA.2 sub-lineages. The study explores the origins of Omicron BA.1 and BA.2 sublineages. The authors applied various molecular analyses to find out the evolutionary mechanisms of Omicron BA.1 and BA.2. This study found recombinant signals in BA.2. This study concentrated on these two variants which have been dominant during late 2021 and early 2022. However, it must be noted that these variants are now replaced by BA.4, BA.5, and other recombinants like XBB. Why authors have not included them?
In introduction, The WHO named this variant as the Omicron variant—the fifth VOC 69 of SARS-CoV-2.---Add reference.
The introduction needs to be updated with the suggested articles---
- DOI:
- 10.1111/CBDD.14035;
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In 2.1, genome sequences of SARS-CoV-2 Omicron variant deposited between November 2021 102 and January 2022. Why?
Why in Fig. 2, authors have not compared BA.1 and BA.2 with original Omicron.B.1.1.529. - The recombination signals for BA.1 sub-lineage dataset is not properly discussed with evidence. Please improve this part. It is very essential.
- In Fig. 3 & 5, letter size is very small. not readable.
- The conclusion should be improved as per the findings.
- As per ref. 1, (GISIAD data accessed on 21/10/2022), then why only BA.1 & BA.2?
- In conclusion, improve it---Omicron may have been influenced by the long-term persistence of infections in human 430 and/or animal hosts (mainly as an intermediate host for reverse-zoonosis).
- Check the references as per journal format.
- In references, (accessed on 21/10/2022) and (accessed on 10/21/2022).---make it one format.
Author Response
Please find attached response to reviewer's suggestions.
Author Response File: 
Author Response.pdf
Reviewer 3 Report
Kumar et al. performed Bayesian molecular dating analyses and mutational profiling of SARS-CoV-2 Omicron BA.1 and BA.2 sublineages. The authors used 32,170 BA.1 and BA.2 whole genome sequences obtained from the GISAID database and sampled globally between November 2021 and January 2022. They performed a mutational scanning of SARS-CoV-2 Omicron BA.1 and BA.2 lineages and estimated time to the most recent common ancestor and substitution rates for each lineage. Finally, they identified specific sites undergoing positive and negative selection. This is a generally well-written paper presented in a well-structured manner. The cited references are recent and relevant, allowing an interesting in-depth discussion.
The analyses performed lead to descriptive results with no answer to specific hypotheses or key conclusion. The observations made (such as mutational profiles and selection results) are commented based on functional results obtained in other studies. The main conclusion suggests an independent origin and evolution of SARS-CoV-2 Omicron BA.1 and BA.2 sub-lineages as stated in the title and several sections of the article. However, the evidence supporting this hypothesis and the scientific significance are unclear.
I have the following specific comments:
This study is entirely based on SARS-CoV-2 full-length genomic sequences obtained from GISAID. As stated by GISAID, authors should properly acknowledge data contributors by adding the required information in the Acknowledgement and Materials & Methods sections (text, EPI_SET identifier, supplemental table). More details concerning acknowledgement of data contributors can be found in the GISAID citation and acknowledgement guide.
Lines 206-207: S371L is specific to BA.1 while S371F is found in BA.2. G446S and G496S are not present in BA.2 and may not be listed as Omicron defining changes.
Figure 2: I found it difficult to read the text of the figure, in particular aa substitutions. Would it be possible to increase font size and resolution?
Lines 300-301: The authors identified and removed outliers. What method did they use to select outliers? How many outlier sequences did they remove? I would suggest that the authors add this information in the Materials & Methods section.
Lines 304-305: How many sequences were left for the analyses with one, two (75 for BA.1 and 86 for BA.2?) or three sequences from each sampling date? Adding this information could give more insight into the methodology used.
Lines 302-303: The removal of outliers and temporal selection led to a drastic selection from 1770 to 75 sequences for BA.1 and from 1037 to 86 sequences for BA.2. The use of only one randomly selected dataset may introduce sampling bias. Did the authors perform parallel runs with 5 or more random subsets in order to assess the variation across replicates? Adding the results of replicate analyses may be useful to confirm the results.
Lines 375-386: The authors comment on the higher per-day exponential growth rate for BA.2 compared to BA.1. It would be useful to add information concerning the method applied to estimate the growth rate in the corresponding Materials & Methods section. Is the difference between the estimated growth rates for BA.1 (0.151, 95% HPD 0.047-0.243) and BA.2 (0.155, 95% HPD 0.007-0.392) statistically significant? Why are the 95% HPD intervals so important? If the difference is not significant, it would be appropriate to remove the conclusion “that BA.2 has a slight growth advantage over BA.1”. and to modify the discussion of this finding accordingly (lines 378-383).
Lines 476-478: This medRxiv reference has been published in Science and can be updated.
The authors write that their results “suggest that the Omicron BA.1 and BA.2 sublineages originated independently and evolved over time” (lines 3-4, 35-36, 236-237, 356-358 and 423-424). This conclusion is based on the different tMRCA and mutational profiles observed for BA.1 and BA.2. However, sublineages are generally not expected to share the same tMRCA and the later tMRCA observed for BA.2 is in line with its more recent emergence compared to BA.1, as stated in the introduction (lines 84-85). By definition, BA.1 and BA.2 are also expected to bear sublineage-specific mutations and to have evolved “independently”. Based on these observations, the interpretation of the results as well as the meaning and significance of an “independent origin and evolution of BA.1 and BA.2 sub-lineages” are unclear. I would suggest that the authors rephrase and clarify this conclusion throughout the manuscript.
Author Response
Please find attached response to reviewer's suggestions.
Author Response File: Author Response.pdf
Reviewer 4 Report
The article is well written, easy to follow and interesting work related to molecular dating analyses for origin and evolution of Omicron sub-lineages. I enjoyed reading it. The work uses a comprehensive dataset to support its conclusions, and is presented in an appropriate manner. I recommend it for publication. There are a couple of minor suggestions as follows.
1. Line 53, “the former carries genetic changes” should be changed to “the former carries some peculiar genetic changes”
2. Line 385 to 389, the sentence “This study also advocates…” is very long. I suggest breaking that sentence into two.
3. Line 83-84: Continued evolution of SARS-CoV-2 Omicron VOC has already generated at least five sub-lineages, however, it is not clear to me why the authors prefered only the two sub-lineages of Omicron VOC for deciphering its possible origin and evolution.
4. Line 163-164: Performing Bayesian analyses on a large dataset is computing-intensive. I would suggest that the authors should mention here the computing facility employed for Bayesian analyses. This would make it easier for readers to follow and understand.
5. Line 394-395: What can a reader deduce from 'Omicron signature mutational profiles'? Is it a spike protein signature mutational profile?
Author Response
Please find attached response to reviewer's suggestions.
Author Response File: Author Response.pdf
Round 2
Reviewer 2 Report
It may be accepted for publication.
Author Response
We really appreciate the reviewer's insightful comments, which helped to make the manuscript better.
Reviewer 3 Report
I thank the authors for their answers and modifications to the manuscript. I would recommend the following minor revisions related to comments 1 and 7.
Comment 1: This study is entirely based on SARS-CoV-2 full-length genomic sequences obtained from GISAID. As stated by GISAID, authors should properly acknowledge data contributors by adding the required information in the Acknowledgement and Materials & Methods sections (text, EPI_SET identifier, supplemental table). More details concerning acknowledgement of data contributors can be found in the GISAID citation and acknowledgement guide.
Reply: We are grateful to the reviewer for highlighting the acknowledgement of data contributors. In the revised manuscript, we have added GISAID acknowledgement statement in recognition of data contributors in the acknowledgement section of the manuscript. In addition, we have provided a separate supplementary table (Table S1) providing details of the genome sequences with their EPI_SET identifier used in this study.
I would recommend that the authors create and include an EPI_SET Identifier (as indicated in the GISAID citation and acknowledgement guide). The EPI_SET ID corresponds to the aggregation of GISAID accession numbers into a single identifier that can be used for both the acknowledgement of data contributors and to facilitate reproducibility analysis.
Comment 7: Lines 375-386: The authors comment on the higher per-day exponential growth rate for BA.2 compared to BA.1. It would be useful to add information concerning the method applied to estimate the growth rate in the corresponding Materials & Methods section. Is the difference between the estimated growth rates for BA.1 (0.151, 95% HPD 0.047-0.243) and BA.2 (0.155, 95% HPD 0.007-0.392) statistically significant? Why are the 95% HPD intervals so important? If the difference is not significant, it would be appropriate to remove the conclusion “that BA.2 has a slight growth advantage over BA.1”. and to modify the discussion of this finding accordingly (lines 378-383).
Reply: We estimated the per-day exponential growth using exponential growth tree prior with strict clock model, which we have updated in the Materials & Methods section. The 95% HPD is important because it shows the credible interval in which growth rates estimates can vary and this credible interval falls in posterior probability distribution. In our opinion, these are estimates that may likely influenced by several factors as discussed in the manuscript. Since our results are in line with an in vitro study that found BA.2 to form the larger syncytia and be more contagious in human nasal epithelial cells than BA.1 (Yamasoba et al., 2022, doi:10.1016/j.cell.2022.04.035.), we believe that BA.2 may have growth advantage as compared to BA.1.
As mentioned by the authors, other studies have shown a growth advantage for BA.2 over BA.1. Importantly, the confidence intervals obtained in these studies support these conclusions (Tegally et al.). In contrast, the results presented in this work show a differential per-day exponential growth rate of 0.004 with 95% HPD intervals of 0.2 and 0.38, precluding any interpretation and conclusion about a growth advantage of BA.2 over BA.1. These results do not indicate that there is no growth advantage but rather that growth rate comparisons cannot be performed with the dataset used in this study, probably due to its small sample size. The statistical significance and interpretation of the data obtained in this study should be independent of the results obtained by others, including the in vitro study mentioned by the authors (Yamasoba et al.). It would therefore be appropriate to remove the conclusions of a “slight growth advantage of BA.2 over BA.1”. I would recommend to exclude the growth rate estimates from the manuscript since the dataset used is not appropriate for accurate calculations.
Author Response
Comment 1: I would recommend that the authors create and include an EPI_SET Identifier (as indicated in the GISAID citation and acknowledgement guide). The EPI_SET ID corresponds to the aggregation of GISAID accession numbers into a single identifier that can be used for both the acknowledgement of data contributors and to facilitate reproducibility analysis.
Reply: We have generated an EPI_SET Identifier for the metadata of high-quality SARS-CoV-2 genomic sequences used in this study and incorporated it in the revised manuscript (Materials & Methods Section). It can be read as “The findings of this study are based on metadata associated with 1769 high – quality SARS-CoV-2 Omicron sequences available on GISAID from November 2021 and January 2022, via EPI_SET_221127gp (accessible at doi.org/10.55876/gis8.221127gp)”.
Comment 2: As mentioned by the authors, other studies have shown a growth advantage for BA.2 over BA.1. Importantly, the confidence intervals obtained in these studies support these conclusions (Tegally et al.). In contrast, the results presented in this work show a differential per-day exponential growth rate of 0.004 with 95% HPD intervals of 0.2 and 0.38, precluding any interpretation and conclusion about a growth advantage of BA.2 over BA.1. These results do not indicate that there is no growth advantage but rather that growth rate comparisons cannot be performed with the dataset used in this study, probably due to its small sample size. The statistical significance and interpretation of the data obtained in this study should be independent of the results obtained by others, including the in vitro study mentioned by the authors (Yamasoba et al.). It would therefore be appropriate to remove the conclusions of a “slight growth advantage of BA.2 over BA.1”. I would recommend to exclude the growth rate estimates from the manuscript since the dataset used is not appropriate for accurate calculations.
Reply: We really appreciate the reviewer's insightful comment. We have removed the growth rate estimates from the manuscript.
