Diet induced obesity and type 2 diabetes drives exacerbated sex-associated disease profiles in K18-hACE2-mice challenged with SARS-CoV-2

  1. Katherine S. Lee
  2. Brynnan P. Russ
  3. Ting Y. Wong
  4. Alexander M. Horspool
  5. Michael T. Winters
  6. Mariette Barbier
  7. Justin R. Bevere
  8. Ivan Martinez
  9. F. Heath Damron
  10. Holly A. Cyphert

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Abstract

SARS-CoV-2 infection results in wide-ranging disease manifestation from asymptomatic to potentially lethal. Infection poses an increased threat of severity to at-risk populations including those with hypertension, diabetes, and obesity. Type 2 Diabetes (T2DM), is characterized, in part, by insulin insensitivity and impaired glucose regulation. T2DM patients have increased disease severity and poorer outcomes with COVID-19. We utilized the diet-induced obesity (DIO) model of Type 2 Diabetes in SARS-CoV-2-susceptible K18-hACE2 transgenic mice to better understand the obesity co-morbidity. Female DIO, but not male DIO mice challenged with SARS-CoV-2 were observed to have shortened time to morbidity compared to normal diet mice. Increase in susceptibility to SARS-CoV2 in female DIO was associated with increased total viral RNA burden compared to male mice. RNAseq analysis was performed on the lungs of non-challenged, challenged, females, males, of either normal diet or DIO cohorts to determine the disease specific transcriptional profiles. DIO female mice had more total activated genes than normal diet mice after challenge; however, male mice experienced a decrease. GO term analysis revealed the DIO condition increased interferon response signatures and interferon gamma production following challenge. Male challenged mice had robust expression of antibody-related genes suggesting antibody producing cell localization in the lung. DIO reduced antibody gene expression in challenged males. Collectively this study establishes a preclinical T2DM/obesity co-morbidity model of COVID-19 in mice where we observed sex and diet specific responses that begin to explain the effects of obesity and diabetes on COVID-19 disease.

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    SciScore for 10.1101/2022.04.26.489580: (What is this?)

    Please note, not all rigor criteria are appropriate for all manuscripts.

    Table 1: Rigor

    EthicsIACUC: Animal, Ethics, Biosafety statement: All research performed was approved by West Virginia University IACUC protocol number 2004034204.
    Field Sample Permit: All SARS-CoV-2 viral propagation or challenge studies were conducted in the West Virginia University Biosafety Laboratory Level 3 facility under the IBC protocol number 20-04-01.
    Sex as a biological variableHigh fat diet induced obesity and Type 2 Diabetes model and intraperitoneal glucose tolerance test (IPGTT): Diet induction of obesity was achieved through feeding a high fat diet (Bio-serv Mouse diet high fat 60% kCAL from fat #S3282) for 8 weeks to cohorts of 6-week-old female and male K18-hACE2-mice.
    Randomizationnot detected.
    Blindingnot detected.
    Power Analysisnot detected.
    Cell Line Authenticationnot detected.

    Table 2: Resources

    Experimental Models: Cell Lines
    SentencesResources
    Viral doses were prepared from the first passage collections from infected Vero E6 cells.
    Vero E6
    suggested: RRID:CVCL_XD71)
    Experimental Models: Organisms/Strains
    SentencesResources
    K18-hACE2-mice (B6.Cg-Tg(K18-ACE2)2Prlmn/J; JAX strain number #034860).
    B6.Cg-Tg(K18-ACE2)2Prlmn/J; JAX
    suggested: RRID:IMSR_JAX:034860)
    Concurrently, control age-matched K18-hACE2-mice remained on a standard chow diet.
    K18-hACE2-mice
    suggested: None
    K18-hACE2 mice were challenged with virus by intranasal administration of 25μL dose per nare (50μL total).
    K18-hACE2
    suggested: RRID:IMSR_GPT:T037657)
    Software and Algorithms
    SentencesResources
    Ingenuity Pathway Analysis: RNAseq fold change gene expression data was submitted to Ingenuity Pathway analysis using a cut off of P= 0.05.
    Ingenuity Pathway analysis
    suggested: (Ingenuity Pathway Analysis, RRID:SCR_008653)
    Statistical analyses: Tests to determine statistical significance were performed using GraphPad Prism version 9.
    GraphPad Prism
    suggested: (GraphPad Prism, RRID:SCR_002798)

    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: We detected the following sentences addressing limitations in the study:
    Our study does have some caveats that warrant discussion. In our experiment, we only evaluated one SARS-CoV-2 challenge strain, Alpha, and there have now been three VOC strain surges (Beta, Delta, Omicron) since Alpha was dominantly circulating. In additional studies since then, we have observed enhanced airway inflammation due to challenge with the Delta variant (68). We anticipate that different strains would result in variable host responses to what was identified using Alpha; however, additional studies will need to be performed. Another caveat is that only one challenge dose was evaluated (1,000 PFU). If lower or higher challenge doses were to be studied, we would expect to have either shorter or longer time to morbidity during which host response profiles may further develop or remain hidden due to the disease timeline. Finally, our study focused on defining transcriptomic responses to characterize the altered host responses to SARS-CoV-2 challenge. We did not analyze specific cell populations through cell isolation and flow cytometry, nor did we evaluate potential mechanisms responsible for this co-morbidity. SARS-CoV-2 infection in humans is generally heterogenous in symptomology, however, increased susceptibility to severe infection requiring hospitalization are common across patients with T2DM metabolic disease and increased adiposity (obesity) (89). The role of elevated glucose and fat accumulation downstream of metabolic dysfunction has been shown in other setting...

    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.

    Results from scite Reference Check: We found no unreliable references.

    About SciScore

    SciScore is an automated tool that is designed to assist expert reviewers by finding and presenting formulaic information scattered throughout a paper in a standard, easy to digest format. SciScore checks for the presence and correctness of RRIDs (research resource identifiers), and for rigor criteria such as sex and investigator blinding. For details on the theoretical underpinning of rigor criteria and the tools shown here, including references cited, please follow this link.

  2. Version published to 10.1101/2022.04.26.489580v1 on bioRxiv