Ketogenesis restrains aging-induced exacerbation of COVID in a mouse model
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Abstract
Increasing age is the strongest predictor of risk of COVID-19 severity. Unregulated cytokine storm together with impaired immunometabolic response leads to highest mortality in elderly infected with SARS-CoV-2. To investigate how aging compromises defense against COVID-19, we developed a model of natural murine beta coronavirus (mCoV) infection with mouse hepatitis virus strain MHV-A59 (mCoV-A59) that recapitulated majority of clinical hallmarks of COVID-19. Aged mCoV-A59-infected mice have increased mortality and higher systemic inflammation in the heart, adipose tissue and hypothalamus, including neutrophilia and loss of γδ T cells in lungs. Ketogenic diet increases beta-hydroxybutyrate, expands tissue protective γδ T cells, deactivates the inflammasome and decreases pathogenic monocytes in lungs of infected aged mice. These data underscore the value of mCoV-A59 model to test mechanism and establishes harnessing of the ketogenic immunometabolic checkpoint as a potential treatment against COVID-19 in the elderly.
Highlights
Natural MHV-A59 mouse coronavirus infection mimics COVID-19 in elderly.
Aged infected mice have systemic inflammation and inflammasome activation
Murine beta coronavirus (mCoV) infection results in loss of pulmonary γδ T cells.
Ketones protect aged mice from infection by reducing inflammation.
eTOC Blurb
Elderly have the greatest risk of death from COVID-19. Here, Ryu et al report an aging mouse model of coronavirus infection that recapitulates clinical hallmarks of COVID-19 seen in elderly. The increased severity of infection in aged animals involved increased inflammasome activation and loss of γδ T cells that was corrected by ketogenic diet.
Article activity feed
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SciScore for 10.1101/2020.09.11.294363: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank …
SciScore for 10.1101/2020.09.11.294363: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
NIH rigor criteria are not applicable to paper type.Table 2: Resources
No key resources detected.
Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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