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

Area of Science:

• Virology
• Bioinformatics
• Epidemiology

Background:

• Effective pre-clinical research for infectious diseases relies on representative animal models.
• Current methods for selecting virus strains for animal models often lack comprehensive analysis, potentially leading to non-representative models.
• The selection of virus strains for animal models is critical for developing accurate diagnostics, vaccines, and treatments.

Purpose of the Study:

• To propose an integrated approach for selecting virus strains for animal models.
• To evaluate current SARS-CoV-2 strains used in animal models using traditional phylogenetics and a novel alignment-free bioinformatic method.
• To identify potential gaps in current animal models concerning viral evolution and suggest improved strain selection for future models.

Main Methods:

• Utilized epidemiological, experimental, and bioinformatic analyses for virus strain selection.
• Applied phylogenetic analysis to assess SARS-CoV-2 strains.
• Developed and employed a novel alignment-free bioinformatic approach to analyze genome-wide viral functionalities and 'cloud of variances'.

Main Results:

• The study assessed current SARS-CoV-2 strains in international models.
• Phylogenetic analysis and the novel alignment-free approach provided complementary insights into viral evolution.
• While current models adequately cover existing strains, significant ongoing viral evolution may not be represented.

Conclusions:

• A combined epidemiological, experimental, and bioinformatic approach enhances virus strain selection for animal models.
• The alignment-free method offers a dynamic view of RNA virus evolution, complementing traditional phylogenetics.
• Specific SARS-CoV-2 isolates are recommended for future animal models to better reflect viral evolution and improve research outcomes.