Supporting pandemic response using genomics and bioinformatics: a case study on the emergent SARS-CoV-2 outbreak.
Identifieur interne : 000195 ( 2020/Analysis ); précédent : 000194; suivant : 000196Supporting pandemic response using genomics and bioinformatics: a case study on the emergent SARS-CoV-2 outbreak.
Auteurs : Denis C. Bauer [Australie] ; Aidan P. Tay [Australie] ; Laurence O W. Wilson [Australie] ; Daniel Reti [Australie] ; Cameron Hosking [Australie] ; Alexander J. Mcauley [Australie] ; Elizabeth Pharo [Australie] ; Shawn Todd [Australie] ; Vicky Stevens [Australie] ; Matthew J. Neave [Australie] ; Mary Tachedjian [Australie] ; Trevor W. Drew [Australie] ; S S Vasan [Australie]Source :
- Transboundary and emerging diseases [ 1865-1682 ] ; 2020.
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 bioinformatics 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 bioinformatics 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.
DOI: 10.1111/tbed.13588
PubMed: 32306500
Affiliations:
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Mcauley</name><affiliation wicri:level="1"><nlm:affiliation>Commonwealth Scientific and Industrial Research Organisation, Health and Biosecurity, Geelong, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Commonwealth Scientific and Industrial Research Organisation, Health and Biosecurity, Geelong</wicri:regionArea><wicri:noRegion>Geelong</wicri:noRegion></affiliation></author><author><name sortKey="Pharo, Elizabeth" sort="Pharo, Elizabeth" uniqKey="Pharo E" first="Elizabeth" last="Pharo">Elizabeth Pharo</name><affiliation wicri:level="1"><nlm:affiliation>Commonwealth Scientific and Industrial Research Organisation, Health and Biosecurity, Geelong, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Commonwealth Scientific and Industrial Research Organisation, Health and Biosecurity, Geelong</wicri:regionArea><wicri:noRegion>Geelong</wicri:noRegion></affiliation></author><author><name sortKey="Todd, Shawn" sort="Todd, Shawn" uniqKey="Todd S" first="Shawn" last="Todd">Shawn Todd</name><affiliation wicri:level="1"><nlm:affiliation>Commonwealth Scientific and Industrial Research Organisation, Health and Biosecurity, Geelong, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Commonwealth Scientific and Industrial Research Organisation, Health and Biosecurity, Geelong</wicri:regionArea><wicri:noRegion>Geelong</wicri:noRegion></affiliation></author><author><name sortKey="Stevens, Vicky" sort="Stevens, Vicky" uniqKey="Stevens V" first="Vicky" last="Stevens">Vicky Stevens</name><affiliation wicri:level="1"><nlm:affiliation>Commonwealth Scientific and Industrial Research Organisation, Australian Animal Health Laboratory, Geelong, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Commonwealth Scientific and Industrial Research Organisation, Australian Animal Health Laboratory, Geelong</wicri:regionArea><wicri:noRegion>Geelong</wicri:noRegion></affiliation></author><author><name sortKey="Neave, Matthew J" sort="Neave, Matthew J" uniqKey="Neave M" first="Matthew J" last="Neave">Matthew J. Neave</name><affiliation wicri:level="1"><nlm:affiliation>Commonwealth Scientific and Industrial Research Organisation, Australian Animal Health Laboratory, Geelong, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Commonwealth Scientific and Industrial Research Organisation, Australian Animal Health Laboratory, Geelong</wicri:regionArea><wicri:noRegion>Geelong</wicri:noRegion></affiliation></author><author><name sortKey="Tachedjian, Mary" sort="Tachedjian, Mary" uniqKey="Tachedjian M" first="Mary" last="Tachedjian">Mary Tachedjian</name><affiliation wicri:level="1"><nlm:affiliation>Commonwealth Scientific and Industrial Research Organisation, Health and Biosecurity, Geelong, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Commonwealth Scientific and Industrial Research Organisation, Health and Biosecurity, Geelong</wicri:regionArea><wicri:noRegion>Geelong</wicri:noRegion></affiliation></author><author><name sortKey="Drew, Trevor W" sort="Drew, Trevor W" uniqKey="Drew T" first="Trevor W" last="Drew">Trevor W. Drew</name><affiliation wicri:level="1"><nlm:affiliation>Commonwealth Scientific and Industrial Research Organisation, Australian Animal Health Laboratory, Geelong, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Commonwealth Scientific and Industrial Research Organisation, Australian Animal Health Laboratory, Geelong</wicri:regionArea><wicri:noRegion>Geelong</wicri:noRegion></affiliation></author><author><name sortKey="Vasan, S S" sort="Vasan, S S" uniqKey="Vasan S" first="S S" last="Vasan">S S Vasan</name><affiliation wicri:level="1"><nlm:affiliation>Commonwealth Scientific and Industrial Research Organisation, Health and Biosecurity, Geelong, Australia.</nlm:affiliation><country xml:lang="fr">Australie</country><wicri:regionArea>Commonwealth Scientific and Industrial Research Organisation, Health and Biosecurity, Geelong</wicri:regionArea><wicri:noRegion>Geelong</wicri:noRegion></affiliation></author></analytic><series><title level="j">Transboundary and emerging diseases</title><idno type="eISSN">1865-1682</idno><imprint><date when="2020" type="published">2020</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">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 bioinformatics 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 bioinformatics 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.</div></front></TEI><affiliations><list><country><li>Australie</li></country></list><tree><country name="Australie"><noRegion><name sortKey="Bauer, Denis C" sort="Bauer, Denis C" uniqKey="Bauer D" first="Denis C" last="Bauer">Denis C. Bauer</name></noRegion><name sortKey="Drew, Trevor W" sort="Drew, Trevor W" uniqKey="Drew T" first="Trevor W" last="Drew">Trevor W. Drew</name><name sortKey="Hosking, Cameron" sort="Hosking, Cameron" uniqKey="Hosking C" first="Cameron" last="Hosking">Cameron Hosking</name><name sortKey="Mcauley, Alexander J" sort="Mcauley, Alexander J" uniqKey="Mcauley A" first="Alexander J" last="Mcauley">Alexander J. Mcauley</name><name sortKey="Neave, Matthew J" sort="Neave, Matthew J" uniqKey="Neave M" first="Matthew J" last="Neave">Matthew J. Neave</name><name sortKey="Pharo, Elizabeth" sort="Pharo, Elizabeth" uniqKey="Pharo E" first="Elizabeth" last="Pharo">Elizabeth Pharo</name><name sortKey="Reti, Daniel" sort="Reti, Daniel" uniqKey="Reti D" first="Daniel" last="Reti">Daniel Reti</name><name sortKey="Stevens, Vicky" sort="Stevens, Vicky" uniqKey="Stevens V" first="Vicky" last="Stevens">Vicky Stevens</name><name sortKey="Tachedjian, Mary" sort="Tachedjian, Mary" uniqKey="Tachedjian M" first="Mary" last="Tachedjian">Mary Tachedjian</name><name sortKey="Tay, Aidan P" sort="Tay, Aidan P" uniqKey="Tay A" first="Aidan P" last="Tay">Aidan P. Tay</name><name sortKey="Todd, Shawn" sort="Todd, Shawn" uniqKey="Todd S" first="Shawn" last="Todd">Shawn Todd</name><name sortKey="Vasan, S S" sort="Vasan, S S" uniqKey="Vasan S" first="S S" last="Vasan">S S Vasan</name><name sortKey="Wilson, Laurence O W" sort="Wilson, Laurence O W" uniqKey="Wilson L" first="Laurence O W" last="Wilson">Laurence O W. Wilson</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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