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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.

SUBMITTER: Bauer DC

PROVIDER: S-EPMC7264654 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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

Bauer Denis C DC Bauer Denis C DC Tay Aidan P AP Wilson Laurence O W LOW Reti Daniel D Hosking Cameron C McAuley Alexander J AJ Pharo Elizabeth E Todd Shawn S Stevens Vicky V Neave Matthew J MJ Tachedjian Mary M Drew Trevor W TW Vasan Seshadri S SS

Transboundary and emerging diseases 20200525 4

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 epidemi ...[more]

PMID: 32306500

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Project description:BackgroundNosocomial outbreaks of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) are frequent despite implementation of conventional infection control measures. An outbreak investigation was undertaken using advanced genomic and statistical techniques to reconstruct likely transmission chains and assess the role of healthcare workers (HCWs) in SARS-CoV-2 transmission.MethodsA nosocomial SARS-CoV-2 outbreak in a university-affiliated rehabilitation clinic was investigated, involving patients and HCWs, with high coverage of pathogen whole-genome sequences (WGS). The time-varying reproduction number from epidemiological data (Rt) was estimated, and maximum likelihood phylogeny was used to assess genetic diversity of the pathogen. Genomic and epidemiological data were combined into a Bayesian framework to model the directionality of transmission, and a case-control study was performed to investigate risk factors for nosocomial SARS-CoV-2 acquisition in patients.FindingsThe outbreak lasted from 14th March to 12th April 2020, and involved 37 patients (31 with WGS) and 39 employees (31 with WGS), 37 of whom were HCWs. Peak Rt was estimated to be between 2.2 and 3.6. The phylogenetic tree showed very limited genetic diversity, with 60 of 62 (96.7%) isolates forming one large cluster of identical genomes. Despite the resulting uncertainty in reconstructed transmission events, the analyses suggest that HCWs (one of whom was the index case) played an essential role in cross-transmission, with a significantly greater fraction of infections (P<2.2e-16) attributable to HCWs (70.7%) than expected given the number of HCW cases (46.7%). The excess of transmission from HCWs was higher when considering infection of patients [79.0%; 95% confidence interval (CI) 78.5-79.5%] and frail patients (Clinical Frailty Scale score >5; 82.3%; 95% CI 81.8-83.4%). Furthermore, frail patients were found to be at greater risk for nosocomial COVID-19 than other patients (adjusted odds ratio 6.94, 95% CI 2.13-22.57).InterpretationThis outbreak report highlights the essential role of HCWs in SARS-CoV-2 transmission dynamics in healthcare settings. Limited genetic diversity in pathogen genomes hampered the reconstruction of individual transmission events, resulting in substantial uncertainty in who infected whom. However, this study shows that despite such uncertainty, significant transmission patterns can be observed.

Dataset Information

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

Publications

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

Similar Datasets

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