Developing strong animal models is essential for furthering our understanding of how the immune system functions in response to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. The alarming speed at which SARS-CoV-2 has spread, and the high mortality rate of severe Coronavirus Disease 2019 (COVID-19), has required both basic science and clinical research to move at an unprecedented pace. Models previously developed to study the immune response against SARS-CoV have been rapidly deployed to now study SARS-CoV-2. To date, both small and large animal models are remarkably consistent when infected with SARS-CoV-2; however, certain models have proven more useful when answering specific immunological questions than others. Small animal models, such as Syrian hamsters, ferrets, and mice carrying the hACE2 transgene, appear to reliably recapitulate the initial cytokine surge seen in COVID-19 as well as show significant innate and adaptive cell infiltration in to the lung early in infection. Additionally, these models develop strong antibody responses to the virus, are protected from reinfection, and genetically modified versions exist that can be used to ask specific immunological questions. Large animal models such as rhesus and cynomologus macaques and African green monkeys are critical to understanding how the immune system responds to SARS-CoV-2 infection because they are considered to be the most similar to humans. These models are considered the gold standard for assessing vaccine efficacy and protection, and recapitulate the initial cytokine surge, immune cell infiltration into the lung, certain aspects of thrombosis, and the antibody and T-cell response to the virus. In this review, we discuss both small and large animal model studies previously used in SARS-CoV-2 research that may be useful in elucidating the immunological contributions to hallmark syndromes observed with COVID-19.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8135340 | PMC |
http://dx.doi.org/10.1093/ilar/ilab010 | DOI Listing |
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