Supporting pandemic response using genomics and bioinformatics: A case study on the emergent SARS-CoV-2 outbreak.

Abstract:

:Pre-clinical responses to fast-moving infectious disease outbreaks heavily depend on choosing the best isolates for animal models that inform diagnostics, vaccines and treatments. Current approaches are driven by practical considerations (e.g. first available virus isolate) rather than a detailed analysis of the characteristics of the virus strain chosen, which can lead to animal models that are not representative of the circulating or emerging clusters. Here, we suggest a combination of epidemiological, experimental and bioinformatic considerations when choosing virus strains for animal model generation. We discuss the currently chosen SARS-CoV-2 strains for international coronavirus disease (COVID-19) models in the context of their phylogeny as well as in a novel alignment-free bioinformatic approach. Unlike phylogenetic trees, which focus on individual shared mutations, this new approach assesses genome-wide co-developing functionalities and hence offers a more fluid view of the 'cloud of variances' that RNA viruses are prone to accumulate. This joint approach concludes that while the current animal models cover the existing viral strains adequately, there is substantial evolutionary activity that is likely not considered by the current models. Based on insights from the non-discrete alignment-free approach and experimental observations, we suggest isolates for future animal models.

journal_name

Transbound Emerg Dis

authors

Bauer DC,Tay AP,Wilson LOW,Reti D,Hosking C,McAuley AJ,Pharo E,Todd S,Stevens V,Neave MJ,Tachedjian M,Drew TW,Vasan SS

doi

10.1111/tbed.13588

subject

Has Abstract

pub_date

2020-07-01 00:00:00

pages

1453-1462

issue

4

eissn

1865-1674

issn

1865-1682

journal_volume

67

pub_type

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