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Review 1: "Mutational signatures in countries affected by SARS-CoV-2: Implications in host-pathogen interactome"

Published onNov 03, 2020
Review 1: "Mutational signatures in countries affected by SARS-CoV-2: Implications in host-pathogen interactome"
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Mutational signatures in countries affected by SARS-CoV-2: Implications in host-pathogen interactome
Description

AbstractWe are in the midst of the third severe coronavirus outbreak caused by SARS-CoV-2 with unprecedented health and socio-economic consequences due to the COVID-19. Globally, the major thrust of scientific efforts has shifted to the design of potent vaccine and anti-viral candidates. Earlier genome analyses have shown global dominance of some mutations purportedly indicative of similar infectivity and transmissibility of SARS-CoV-2 worldwide. Using high-quality large dataset of 25k whole-genome sequences, we show emergence of new cluster of mutations as result of geographic evolution of SARS-CoV-2 in local population (≥10%) of different nations. Using statistical analysis, we observe that these mutations have either significantly co-occurred in globally dominant strains or have shown mutual exclusivity in other cases. These mutations potentially modulate structural stability of proteins, some of which forms part of SARS-CoV-2-human interactome. The high confidence druggable host proteins are also up-regulated during SARS-CoV-2 infection. Mutations occurring in potential hot-spot regions within likely T-cell and B-cell epitopes or in proteins as part of host-viral interactome, could hamper vaccine or drug efficacy in local population. Overall, our study provides comprehensive view of emerging geo-clonal mutations which would aid researchers to understand and develop effective countermeasures in the current crisis.SignificanceOur comparative analysis of globally dominant mutations and region-specific mutations in 25k SARS-CoV-2 genomes elucidates its geo-clonal evolution. We observe locally dominant mutations (co-occurring or mutually exclusive) in nations with contrasting COVID-19 mortalities per million of population) besides globally dominant ones namely, P314L (ORF1b) and D164G (S) type. We also see exclusive dominant mutations such as in Brazil (I33T in ORF6 and I292T in N protein), England (G251V in ORF3a), India (T2016K and L3606F in ORF1a) and in Spain (L84S in ORF8). The emergence of these local mutations in ORFs within SARS-CoV-2 genome could have interventional implications and also points towards their potential in modulating infectivity of SARS-CoV-2 in regional population.

RR:C19 Evidence Scale rating by reviewer:

  • Potentially informative. The main claims made are not strongly justified by the methods and data, but may yield some insight. The results and conclusions of the study may resemble those from the hypothetical ideal study, but there is substantial room for doubt. Decision-makers should consider this evidence only with a thorough understanding of its weaknesses, alongside other evidence and theory. Decision-makers should not consider this actionable, unless the weaknesses are clearly understood and there is other theory and evidence to further support it.

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Review:

Major Revisions

The article " Mutational signatures in countries affected by SARS-CoV-2: Implications in host-pathogen interactome" is clear and certainly offers some interesting findings.

The authors, however, recall the viral "quasispecies" model observed mainly among RNA viruses characterized by a high rate of mutation, of which no mention is made. In the quasispecies model a group of related genomes, derived from replication of a single species, changes over time acquiring mutations to respond to a particular stimulus (e.g. an antiviral drug, selection of environmental factors).

Considering a mutation frequency greater than 10%, I would advise the authors not to talk about mutations but rather to focus on genomic variants with possible effects on viral functionality.

The geographical location of the different variants is certainly interesting but it is not considered the possible correlation for example with the rate of contagiousness or symptomatology.

In addition, prediction analysis on the effect of mutations should be implemented with 3D models in order to verify the spatial localization of these mutations.

Surely regions of mutational hotspots have been identified which should be excluded for the production of possible vaccines as well as extremely conserved and specific regions of SARS-Cov2 have been identified.

Minor Revisions

The title does not correspond to the content of the article.

Possible alterations in metabolic pathways are mentioned but are not sufficiently described and the link with the described variants is unclear.

The authors should probably support the detected data with a summary image.

Mutations also do not identify a signature but only a sequence variation due to the evolutionary pressure. (https://doi.org/10.1002/int.22268).

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