{"doi":"10.1101/2020.09.11.294363","title":"Ketogenesis restrains aging-induced exacerbation of COVID in a mouse model","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.","journal":"bioRxiv (Cold Spring Harbor Laboratory)","year":2020,"id":120040,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":13,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9574,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":280215,"name":"Irina Shchukina","orcid":null,"position":1,"is_corresponding":false},{"id":556361,"name":"Yun‐Hee Youm","orcid":"0000-0002-8098-8527","position":2,"is_corresponding":false},{"id":235358,"name":"Hua Qing","orcid":"0000-0002-2693-0682","position":3,"is_corresponding":false},{"id":556362,"name":"Brandon K. Hilliard","orcid":"0000-0001-5030-4828","position":4,"is_corresponding":false},{"id":556872,"name":"Tamara Dlugos","orcid":null,"position":5,"is_corresponding":false},{"id":245124,"name":"Xinbo Zhang","orcid":"0000-0002-5806-159X","position":6,"is_corresponding":false},{"id":107010,"name":"Yuki Yasumoto","orcid":"0000-0001-9882-4478","position":7,"is_corresponding":false},{"id":374037,"name":"Carmen J. Booth","orcid":"0000-0002-2153-738X","position":8,"is_corresponding":false},{"id":245129,"name":"Carlos Fernández‐Hernando","orcid":"0000-0002-3950-1924","position":9,"is_corresponding":false},{"id":556363,"name":"Yajaira Suárez","orcid":"0000-0003-4549-2953","position":10,"is_corresponding":false},{"id":75650,"name":"Kamal M. Khanna","orcid":"0000-0002-9328-3817","position":11,"is_corresponding":false},{"id":107023,"name":"Tamás L. Horváth","orcid":"0000-0002-7522-4602","position":12,"is_corresponding":false},{"id":556364,"name":"Marcelo O. Dietrich","orcid":"0000-0001-9781-2221","position":13,"is_corresponding":false},{"id":52958,"name":"Maxim N. Artyomov","orcid":"0000-0002-1133-4212","position":14,"is_corresponding":false},{"id":66189,"name":"Andrew Wang","orcid":"0000-0002-6951-8081","position":15,"is_corresponding":false},{"id":269782,"name":"Vishwa Deep Dixit","orcid":"0000-0002-5341-6494","position":16,"is_corresponding":false},{"id":556360,"name":"Seungjin Ryu","orcid":"0000-0001-6353-8789","position":0,"is_corresponding":true}],"reference_count":69,"raw_metadata":null,"created_at":"2026-07-18T23:14:13.002105Z","pmid":"33236006","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}