Este artigo é um Preprint
Preprints são relatos preliminares de pesquisa que não foram certificados pela revisão por pares. Eles não devem ser considerados para orientar a prática clínica ou comportamentos relacionados à saúde e não devem ser publicados na mídia como informação estabelecida.
Preprints publicados online permitem que os autores recebam feedback rápido, e toda a comunidade científica pode avaliar o trabalho independentemente e responder adequadamente. Estes comentários são publicados juntamente com os preprints para qualquer pessoa ler e servir como uma avaliação pós-publicação.
Genetically diverse mouse models of SARS-CoV-2 infection model clinical variation and cytokine responses in COVID-19
Preprint
em En
| PREPRINT-BIORXIV
| ID: ppbiorxiv-460664
ABSTRACT
Inflammation in response to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection drives severity of coronavirus disease 2019 (COVID-19), with effective versus dysregulated responses influenced by host genetics. To understand mechanisms of inflammation, animal models that reflect genetic diversity and clinical outcomes observed in humans are needed. We report a mouse panel comprising the diverse genetic backgrounds of the Collaborative Cross founder strains crossed to K18-hACE2 transgenic mice that confers high susceptibility to SARS-CoV-2. Infection of CC x K18-hACE2 F1 progeny resulted in a spectrum of weight loss, survival, viral replication kinetics, histopathology, and cytokine profiles, some of which were sex-specific. Importantly, survival was associated with early type I interferon (IFN) expression and a phased proinflammatory response distinct from mice with severe disease. Thus, dynamics of inflammatory responses observed in COVID-19 can be modeled in diverse mouse strains that provide a genetically tractable platform for understanding antiviral immunity and evaluating countermeasures. One Sentence SummaryGenetically diverse mice display a broad spectrum of clinically relevant responses to SARS-CoV-2 infection, reflecting variability in COVID-19 disease.
cc_no
Texto completo:
1
Coleções:
09-preprints
Base de dados:
PREPRINT-BIORXIV
Tipo de estudo:
Experimental_studies
/
Prognostic_studies
/
Rct
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Preprint