The host response to SARS-CoV-2 infection provide insights into both viral pathogenesis and patient management. The host-encoded microRNA (miRNA) response to SARS-CoV-2 infection, however, remains poorly defined. Here we profiled circulating miRNAs from ten COVID-19 patients sampled longitudinally and ten age and gender matched healthy donors. We observed 55 miRNAs that were altered in COVID-19 patients during early-stage disease, with the inflammatory miR-31-5p the most strongly upregulated. Supervised machine learning analysis revealed that a three-miRNA signature (miR-423-5p, miR-23a-3p and miR-195-5p) independently classified COVID-19 cases with an accuracy of 99.9%. In a ferret COVID-19 model, the three-miRNA signature again detected SARS-CoV-2 infection with 99.7% accuracy, and distinguished SARS-CoV-2 infection from influenza A (H1N1) infection and healthy controls with 95% accuracy. Distinct miRNA profiles were also observed in COVID-19 patients requiring oxygenation. This study demonstrates that SARS-CoV-2 infection induces a robust host miRNA response that could improve COVID-19 detection and patient management.

The global response to Coronavirus Disease 2019 (COVID-19) is now facing new challenges such as vaccine inequity and the emergence of SARS-CoV-2 variants of concern (VOCs). Preclinical models of disease, in particular animal models, are essential to investigate VOC pathogenesis, vaccine correlates of protection and postexposure therapies. Here, we provide an update from the World Health Organization (WHO) COVID-19 modeling expert group (WHO-COM) assembled by WHO, regarding advances in preclinical models. In particular, we discuss how animal model research is playing a key role to evaluate VOC virulence, transmission and immune escape, and how animal models are being refined to recapitulate COVID-19 demographic variables such as comorbidities and age.

The ‘D614G’ mutation (Aspartate-to-Glycine change at position 614) of the SARS-CoV-2 spike protein has been speculated to adversely affect the efficacy of most vaccines and countermeasures that target this glycoprotein, necessitating frequent vaccine matching. Virus neutralisation assays were performed using sera from ferrets which received two doses of the INO-4800 COVID-19 vaccine, and Australian virus isolates (VIC01, SA01 and VIC31) which either possess or lack this mutation but are otherwise comparable. Through this approach, supported by biomolecular modelling of this mutation and the commonly-associated P314L mutation in the RNA-dependent RNA polymerase, we have shown that there is no experimental evidence to support this speculation. We additionally demonstrate that the putative elastase cleavage site introduced by the D614G mutation is unlikely to be accessible to proteases.

Vaccines against SARS-CoV-2 are likely to be critical in the management of the ongoing pandemic. A number of candidates are in Phase III human clinical trials, including ChAdOx1 nCoV-19 (AZD1222), a replication-deficient chimpanzee adenovirus-vectored vaccine candidate. In preclinical trials, the efficacy of ChAdOx1 nCoV-19 against SARS-CoV-2 challenge was evaluated in a ferret model of infection. Groups of ferrets received either prime-only or prime-boost administration of ChAdOx1 nCoV-19 via the intramuscular or intranasal route. All ChAdOx1 nCoV-19 administration combinations resulted in significant reductions in viral loads in nasal-wash and oral swab samples. No vaccine-associated adverse events were observed associated with the ChAdOx1 nCoV-19 candidate, with the data from this study suggesting it could be an effective and safe vaccine against COVID-19. Our study also indicates the potential for intranasal administration as a way to further improve the efficacy of this leading vaccine candidate.

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.