Back

ARDS and Cytokine Storm in SARS-CoV-2 Infected Caribbean Vervets

Blair, R. V.; Vaccari, M.; Doyle-Meyers, L. A.; Roy, C. J.; Russell-Lodrigue, K.; Fahlberg, M.; Monjure, C. J.; Beddingfield, B.; Plante, K. S.; Plante, J. A.; Weaver, S. C.; Qin, X.; Midkiff, C. C.; Lehmicke, G.; Golden, N.; Threeton, B.; Penney, T.; Allers, C.; Barnes, M. B.; Pattison, M.; Datta, P. K.; Maness, N. J.; Birnbaum, A.; Bohm, R. P.; Rappaport, J.

2020-06-19 pathology
10.1101/2020.06.18.157933 bioRxiv
Show abstract

SARS-CoV-2 induces a wide range of disease severity ranging from asymptomatic infection, to a life-threating illness, particularly in the elderly and persons with comorbid conditions. Among those persons with serious COVID-19 disease, acute respiratory distress syndrome (ARDS) is a common and often fatal presentation. Animal models of SARS-CoV-2 infection that manifest severe disease are needed to investigate the pathogenesis of COVID-19 induced ARDS and evaluate therapeutic strategies. Here we report ARDS in two aged African green monkeys (AGMs) infected with SARS-CoV-2 that demonstrated pathological lesions and disease similar to severe COVID-19 in humans. We also report a comparatively mild COVID-19 phenotype characterized by minor clinical, radiographic and histopathologic changes in the two surviving, aged AGMs and four rhesus macaques (RMs) infected with SARS-CoV-2. We found dramatic increases in circulating cytokines in three of four infected, aged AGMs but not in infected RMs. All of the AGMs showed increased levels of plasma IL-6 compared to baseline, a predictive marker and presumptive therapeutic target in humans infected with SARS-CoV-2 infection. Together, our results show that both RM and AGM are capable of modeling SARS-CoV-2 infection and suggest that aged AGMs may be useful for modeling severe disease manifestations including ARDS.

Matching journals

The top 7 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.