1. Immunology and Inflammation
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Ketogenic diet restrains aging-induced exacerbation of coronavirus infection in mice

  1. Seungjin Ryu
  2. Irina Shchukina
  3. Yun-Hee Youm
  4. Hua Qing
  5. Brandon Hilliard
  6. Tamara Dlugos
  7. Xinbo Zhang
  8. Yuki Yasumoto
  9. Carmen J Booth
  10. Carlos Fernández-Hernando
  11. Yajaira Suárez
  12. Kamal Khanna
  13. Tamas L Horvath
  14. Marcelo O Dietrich
  15. Maxim Artyomov
  16. Andrew Wang  Is a corresponding author
  17. Vishwa Deep Dixit  Is a corresponding author
  1. Department of Comparative Medicine, Yale School of Medicine, United States
  2. Department of Immunobiology, Yale School of Medicine, United States
  3. Department of Pathology and Immunology, Washington University School of Medicine, United States
  4. Department of Internal Medicine, Yale School of Medicine, United States
  5. Program in Integrative Cell Signaling and Neurobiology of Metabolism, Yale School of Medicine, United States
  6. Department of Microbiology, New York University Langone Health, United States
  7. Yale Center for Research on Aging, United States
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Cite this article as: eLife 2021;10:e66522 doi: 10.7554/eLife.66522

Abstract

Increasing age is the strongest predictor of risk of COVID-19 severity and mortality. Immunometabolic switch from glycolysis to ketolysis protects against inflammatory damage and influenza infection in adults. To investigate how age compromises defense against coronavirus infection, and whether a pro-longevity ketogenic diet (KD) impacts immune surveillance, we developed an aging model of natural murine beta coronavirus (mCoV) infection with mouse hepatitis virus strain-A59 (MHV-A59). When inoculated intranasally, mCoV is pneumotropic and recapitulates several clinical hallmarks of COVID-19 infection. 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. Activation of ketogenesis in aged mice expands tissue protective γδ T cells, deactivates the NLRP3 inflammasome, and decreases pathogenic monocytes in lungs of infected aged mice. These data establish harnessing of the ketogenic immunometabolic checkpoint as a potential treatment against coronavirus infection in the aged.

Introduction

Aging-driven reduced resilience to infections is dependent in part on the restricted T cell repertoire diversity together with impaired T and B cell activation as well as inflammasome-driven low-grade systemic inflammation that compromises innate immune function (Akbar and Gilroy, 2020; Camell et al., 2017; Youm et al., 2013). Consequently, 80% of deaths due to COVID-19 in USA are in adults >65 years old (https://www.cdc.gov/), and aging is the strongest factor to increase infection fatality (Pastor-Barriuso et al., 2020; Perez-Saez et al., 2021; Ward et al., 2020). Lack of an aging animal model that mimics SARS-CoV-2 immunopathology has been a major limitation in the effort to determine the mechanism of disease and to develop effective therapeutics for the elderly. Inability of mouse ACE2 to bind SARS-CoV-2 is a significant hurdle in understanding the basic mechanism of COVID-19. Accordingly, several approaches have been employed to develop models including introduction of human-ACE2 in mice and transient induction of hACE2 through adenoviral-associated vectors. These models have begun to yield important information on the mechanism of disease development. For example, epithelial cell-specific induction of hACE2 (K18-hACE2) as a model of SARS-CoV-2 infection demonstrated that post-intranasal inoculation, animals develop lung inflammation and pneumonia driven by infiltration of monocytes, neutrophils, and T cells (Winkler et al., 2020). Also, initial studies that employ lung-ciliated epithelial cell-specific HFH4/FOXJ1 promoter-driven hACE2 transgenic mice show that SARS-CoV-2 infection induces weight loss, lung inflammation, and approximately 50% mortality rate, suggesting the usefulness of this model to understand the mechanism of immune dysregulation (Jiang et al., 2020). However, significant hurdles remain to understand the mechanism and test therapeutic interventions that are relevant to disease severity in elderly, as complicated breeding and specific mutations need to be introduced in hACE2 transgenic strains in addition to the time required to age these models. The mouse model of SARS-CoV-2 based on adeno-associated virus (AAV)–mediated expression of hACE2 may allow circumvention of the above constrains. The delivery of hACE2 into the respiratory tract of C57BL/6 mice with AAV9 causes a productive infection as revealed by >200-fold increase in SARS-CoV-2 RNA and shows similar interferon gene expression signatures as COVID-19 patients (Israelow et al., 2020). However, in young wild-type mice, this model induces mild acute respiratory distress syndrome (ARDS) and does not cause neutrophilia, weight loss, or lethality (Israelow et al., 2020). Other studies using replication-deficient adenovirus-mediated transduction of hACE in mice and infection with SARS-CoV-2 produced 20% weight loss including lung inflammation (Hassan et al., 2020; Sun et al., 2020). Furthermore, genetic remodeling of the SARS-CoV-2 spike receptor binding domain that allow interaction with mACE demonstrated peribronchiolar lymphocytic inflammatory infiltrates and epithelial damage but no weight loss in infected mice (Dinnon et al., 2020). Moreover, middle-aged female mice that are 1 year old, (analogous to approximately 43 year old human), display greater lung pathology and loss of function post-infection with 10% weight loss followed by spontaneous recovery 7 days post-infection (Dinnon et al., 2020).

The mouse hepatitis virus (MHV) and SARS-CoV-2 are both ARDS-related beta coronaviruses with a high degree of homology (Gorbalenya et al., 2020). Importantly, mCoV-A59 utilizes the entry receptor CEACAM1, which is expressed not only on respiratory epithelium, but also on enterocytes, endothelial cells, and neurons, much like ACE2 (Godfraind et al., 1995), thus allowing the study of wide-ranging systemic impacts of infection. The MHV infection is known to cause hepatitis and encephalomyelitis (Lavi et al., 1984). Importantly, however, the intranasal infection with mCoV-A59 is pneumotropic and causes ARDS in C57BL/6J animals, while all other MHV strains require the A/J or type-I interferon-deficient background, for the development of severe disease (De Albuquerque et al., 2006; Khanolkar et al., 2009; Yang et al., 2014) limiting their use.

Aging-induced chronic inflammation in the absence of overt infections is predominantly driven by the NLRP3 inflammasome (Bauernfeind et al., 2016; Camell et al., 2017; Youm et al., 2013), a myeloid cell-expressed multiprotein complex that senses pathogen-associated molecular patterns (PAMPs) and danger-associated molecular patterns (DAMPs) to cause the processing and secretion of IL-1β and IL-18. There is increasing evidence that SARS-CoV-2 infection activates the NLRP3 inflammasome with increased levels of IL-18 and lactate dehydrogenase (LDH) levels due to inflammasome-mediated pyroptotic cell death (Lucas et al., 2020; Zhou et al., 2020). It is now known that increased glycolysis, which activates inflammasome, is associated with worsened COVID-19 outcome (Codo et al., 2020). This raises the question whether the substrate switch from glycolysis-to-ketogenesis can be employed to stave off COVID-19 in high-risk elderly population. Here, we establish that intranasal infection with mCoV-A59 recapitulates clinical features of COVID-19 seen in elderly and demonstrate that ketone metabolites protect against disease through inhibition of NLRP3 inflammasome and expansion of protective γδ T cells in lungs.

Results

mCoV-A59 infection in aged mice mimics COVID-19 severity

To determine the underlying deficits in immune and inflammatory response in aging, we investigated the impact of mCoV-A59 intranasal inoculation on adult (2–6 months) and old male mice (20–24 months) (Figure 1A). The LD-0 infectious dose of mCoV-A59 in adult (PFU 7e3) caused 100% lethality in aged mice (Figure 1B). Aged mice displayed greater weight loss (Figure 1C), hypoxemia (Figure 1D), and anorexia (Figure 1E), without a significant difference in viral load in lungs (Figure 1F) after infection (PFU 7e2) when compared to adults.

Figure 1 with 1 supplement see all
Aging exacerbates A59 (mCoV) infection.

(A) Schematic of A59 (mCoV) infection experiment with young (2–6 months) and old mice (20–24 months). (B) Probability of survival of young (n = 6) and old (n = 6) infected mice. Survival of mice was examined after infection with high dose of virus (PFU 7e3) up to 14 days. (C–E) Young (n = 8) and old mice (n = 8) were infected with low dose of virus (PFU 7e2), and weight change (%) (C), O2 saturation (D), and daily food intake (E) were recorded. (F) Plaque assay of lung from uninfected, infected young and old mice. (G–L) Flow cytometry analysis of CD4 T cell (G), CD4/CD8 T cell ratio (H), γδ T cell (I), neutrophil (J), Ly6Chi cell (K), and CD64+ MerTK+ cell (L) on day 8 post (PFU 7e2) infection. Error bars represent the mean ± S.E.M. Two-tailed unpaired t-tests were performed for statistical analysis. *p<0.05; **p<0.01; ***p<0.001; ****p<0.0001.

Immune profiling of the lung revealed that aged mice had significant reductions both in the percentage of CD4 cells and in the CD4:CD8 ratio in the lung compared to adult controls at steady state and after infection (Figure 1G,H, Figure 1—figure supplement 1A). Interestingly, while ϒδ T cell numbers in the lung were significantly increased at steady state in aged mice compared to adult mice, both the proportion and number of ϒδ T cells in the lung were dramatically reduced only in aged mice after infection compared to adult mice (Figure 1I, Figure 1—figure supplement 1A). In the spleen, ϒδ T cells were found to be reduced at steady state in aged mice (Figure 1—figure supplement 1B).

Analyses of the myeloid compartment revealed that aged mice had significantly increased neutrophils in the lung post-infection with a trend toward increase in neutrophils at steady state (Figure 1J). We did not detect age-associated changes in eosinophils in lungs (Figure 1—figure supplement 1C). Aging increased Ly6Chi monocytes, which rose further post-infection (Figure 1K, Figure 1—figure supplement 1D). Moreover, the frequency and number of CD64+MerTK+ cells were highest in infected aged mice (Figure 1L, Figure 1—figure supplement 1D), while no significant differences were observed in the total population of alveolar or interstitial macrophages (Figure 1—figure supplement 1E,F) in lungs. These data suggest that post-sub-lethal infection, when young animals recover, aging is associated with delayed resolution of infection with prolonged infiltration of inflammatory myeloid cells and a dramatic reduction in γδ T cells in the lung.

Transmission electron microscopy confirmed the dissemination of the viral particles in pneumocytes in lungs (Figure 2A). The pneumotropism of mCoV-A59 was also validated by demonstration of viable mCoV-A59 in lungs using plaque assays, and aged mice did not show significant differences in viral load in lungs (Figure 1F). Interestingly, following mCoV-A59 inoculation, pathology analyses by hematoxylin and eosin (H and E) and MSB staining, in both 6 month and 20–24 month old mice, revealed perivascular inflammation (arrows, arrowhead) as well as perivascular edema (*) and increased perivascular collagen/fibrosis (MSB, blue) that is more severe in the 20–24 month mCoV-A59 infected mice (Figure 2B). Furthermore, 20–24 month old mice intranasally inoculated with mCoV-A59 have dense foci visible at low power (box) and amphophilic material (fibrosis) with few scattered brightly eosinophilic erythrocytes (gray arrowhead) admixed with lymphocytes and plasma cells. By MSB stain, at higher power, this same focus (***) in the 20–24 month infected mice revealed that the end of a small blood vessel (BV) terminates into a mass of collapsed alveoli, without obvious septa admixed with inflammatory cells, disorganized fibrin/collagen fibers (blue) suggestive of ante mortem pulmonary thrombosis in contrast with post-mortem blood clots where erythrocytes are yellow (** MSB, yellow) (Figure 2B). Taken together, consistent with ARDS, the lungs of aged mice infected with mCoV-A59 had increased foci of inflammation, immune cell infiltration, perivascular edema, hyaline membrane formation and type II pneumocyte hyperplasia, organizing pneumonia, interstitial pneumonitis, and occasional hemorrhage and microthrombi, affecting approximately 75% of the lungs (Figure 2B).

A59 (mCoV) infection significantly affects lung phenotype in old mice.

(A) Transmission electron microscopic images of A59 (mCoV) particles in pneumocytes. Left panels show pneumocyte of an infected young (left) and old (right) mice. Bar scale represents 500 nm. Right panels show zoomed-in images of boxed areas of left panels. Pneumocyte with budding viral particles was indicated by red arrowheads. Bar scale represents 100 nm. Apoptotic pneumocyte of an infected old mouse showed the shrunken and degraded nucleus (white arrow) and chromatin condensation (white asterisk). (B) Representative photomicrographs of hematoxylin and eosin (H and E)-stained (left) and Martius scarlet blue trichrome (MSB)-stained (right) sections of lung from young and old mice 8 days post infection with A59 (mCoV), along with lung from uninfected young and old mice. There are foci of inflammation (arrow), perivascular edema (*), and perivascular lymphocytes and plasma cells (arrow head). Boxed areas in low power images were used for high power imaging. BV indicates small blood vessel.

Aging enhances systemic inflammatory response in mCoV-A59 infected mice

We next investigated whether mCoV-A59 infection in aged mice mimics the hyperinflammatory systemic response seen in elderly patients infected with COVID-19. Compared to young animals, old mice infected with equivalent doses of mCoV-A59 displayed significant increase in circulating IL-1β, TNFα, IL-6, and MCP-1 (Figure 3A–C, Figure 3—figure supplement 1A–D), without affecting MIP-1β (Figure 3—figure supplement 1E). Similar to COVID-19, infection with mCoV-A59 caused increased cardiac inflammation in old mice as evaluated by greater number of infiltrating CD68+ myeloid cells (Figure 3D, Figure 3—figure supplement 1F).

Figure 3 with 1 supplement see all
Aging induces systemic hyperinflammatory response in A59 (mCoV) infection.

(A–C) Serum levels of inflammatory cytokines IL-1β (A), TNFα (B), and IL-6 (C) of young (6 months old) and old (24 months old) infected mice on day 8 post (PFU 7e2) infection. (D) Representative immunofluorescence analysis of CD68 expression, phalloidin, and DAPI in hearts isolated from young and old mice. CD68+ cells/heart area analysis was shown in right panel. (E) Quantification of MHV-A59 (mCoV) in visceral adipose tissue (VAT) of young and old infected mice by quantitative PCR (qPCR) of a gene expressing A59 M protein. (F–H) qPCR analysis of Il1b (F), Tnf (G), and Il6 (H) in VAT of young and old infected mice. (I) Western blot analysis of caspase-1 inflammasome activation in VAT of uninfected young and old mice. (J) Immunoblot analysis of caspase-1 cleavage showing higher inflammasome activation in VAT in aged mice post-infection. (K) Quantification of gene expression of Casp1 in VAT by qPCR. (L) Quantification of MHV-A59 (mCoV) in hypothalamus of young and old infected mice by qPCR. (M–P) Gene expression analysis of Tnf (M), Casp1 (N), Il1b (O), and Il6 (P) in hypothalamus of young and old mice 8 days post-infection. Error bars represent the mean ± S.E.M. Two-tailed unpaired t-tests were performed for statistical analysis. *p<0.05; **p<0.01.

Given that increased visceral adiposity is a risk factor for COVID-19 severity and expression of ACE2 is upregulated in adipocytes of obese and diabetic patients infected with SARS-CoV-2 (Kruglikov and Scherer, 2020), we next studied whether mCoV-A59 infection affects adipose tissue. Given the prevalence of obesity is 10% among younger adults aged 20–39, 45% among adults aged 40–59 years, and 43% among older adults aged 60 and over (Hales et al., 2020), we investigated adipose tissue inflammation as a potential mechanism that contributes to infection severity in the aged. Interestingly, consistent with the prior findings that adipose tissue can harbor several viruses (Damouche et al., 2015), the mCoV-A59 RNA was detectable in visceral adipose tissue (VAT) (Figure 3E, Figure 3—figure supplement 1G). Despite similar viral loads, VAT of aged infected mice had significantly higher mRNA levels of the pro-inflammatory cytokines IL-1β, TNFα, and IL-6 (Figure 3F–H, Figure 3—figure supplement 1H–J). Moreover, consistent with prior work, aging is associated with increases in inflammasome activation (Figure 3I). Infection with mCoV-A59 further enhanced caspase-1 cleavage (p20 active heterodimer) as well as expression of inflammasome components, Casp1 and Nlrp3, in old mice (Figure 3I–K, Figure 3—figure supplement 1K). In addition, similar to SARS-CoV-2 invasiveness in central nervous system (CNS), the mCoV-A59 was detectable in the hypothalamus (Figure 3L, Figure 3—figure supplement 1L). Compared to adults, the hypothalamus of aged infected mice showed increased mRNA expression of TNFα and caspase-1 (Figure 3M,N, Figure 3—figure supplement 1M,N) with no significant differences in IL-1β, IL-6 (Figure 3O,P, Figure 3—figure supplement 1O,P), and NLRP3 (Figure 3—figure supplement 1Q). Infection in both young and aged mice caused significant increases in markers of astrogliosis and microglia activation (Figure 3—figure supplement 1R,S). Interestingly, mCoV-A59 reduced the mRNA expression of orexigenic neuropeptide-Y (Figure 3—figure supplement 1R,S), consistent with the fact that infected mice display anorexia (Figure 1C,E). However, mCoV-A59 infection completely abolished the expression of pre-opiomelanocortin (POMC) in the hypothalamus, a transcript expressed by POMC neurons, which is involved in the control of the autonomic nervous system and integrative physiology. Further investigation will be necessary to test the involvement of the hypothalamus in the pathogenesis of COVID-19 and organ failure due to alterations in the autonomic nervous system.

Ketogenic diet-mediated protection against mCoV-A59 infection in aging is coupled with inflammasome deactivation

Given the switch from glycolysis to fatty acid oxidation reprograms the myeloid cell from pro-inflammatory to tissue reparative phenotype during infections (Ayres, 2020; Buck et al., 2017; Galván-Peña and O'Neill, 2014), we next investigated whether mCoV-A59-driven hyperinflammatory response in aging can be targeted through immunometabolic approaches. Hepatic ketogenesis, a process downstream of lipolysis that converts long-chain fatty acids into short chain β-hydroxybutyrate (BHB) as a preferential fatty acid fuel during starvation or glucoprivic states, inhibits the NLRP3 inflammasome activation (Youm et al., 2015) and protects against influenza infection-induced mortality in mice (Goldberg et al., 2019). We infected bone marrow-derived macrophages (BMDMs) with mCoV-A59 in vitro in TLR4 (Figure 4A, Figure 4—figure supplement 1A) and TLR1/2 primed cells (Figure 4B, Figure 4—figure supplement 1B). Infection with mCoV-A59 caused robust activation of inflammasome as measured by cleavage of active IL-1β (p17) in BMDM supernatants (Figure 4A,B) as well as in cell lysates (Figure 4—figure supplement 1A,B). Given our prior findings that ketone metabolites specifically inhibit the NLRP3 inflammasome in response to sterile DAMPs such as ATP, ceramides, silica, and urate crystals (Youm et al., 2015), we next tested whether BHB impacts inflammasome activation caused by mCoV-A59.

Figure 4 with 1 supplement see all
Ketogenic diet reduces the severity of A59 (mCoV) infection in old mice.

(A, B) Western blot analysis about pro and active cleaved p17 form of IL-1β from supernatant of A59 (mCoV)-infected BMDMs co-treated with priming reagents such as LPS (A) and Pam3CSK4 (B), and BHB with indicated concentration. (C, D) Western blot analysis of ASC monomer, dimer, and oligomers from insoluble pellet of A59 (mCoV) infected BMDMs co-treated with priming reagents such as LPS (C) and Pam3CSK4 (D), and BHB with indicated concentration. (E) Schematic of non-lethal dose (PFU 7e2) of A59 (mCoV) infection experiment with old mice (20–21 month) fed chow (Old-Chow, n = 6) or ketogenic diet (Old-KD, n = 5). The mice were provided with diet from 5 days before infection. (F, G) After infection, the phenotype was evaluated until 7 days post-infection. Weight change (%) (F), and % O2 saturation (G) in old mice fed chow or KD. (H) Schematic of lethal dose (PFU 1e6) of A59 (mCov) infection experiment with young (4 months) and old (24 months) mice fed chow (Young-Chow, n = 11; Old-Chow, n = 7) or KD (Young-KD, n = 11; Old-KD, n = 6). The mice were provided with diet from 5 days before infection. After infection, the survival was evaluated until 7 days post-infection. (I) Probability of survival of lethal dose A59 (mCoV)-infected young mice fed chow or KD. (J) Probability of survival of lethal dose A59 (mCoV)-infected old mice fed chow or KD. Error bars represent the mean ± S.E.M. Two-tailed unpaired t-tests were performed for statistical analysis. Gehan–Breslow–Wilcoxon tests were performed for survival analysis. *p<0.05.

Interestingly, BHB treatment reduced pro and active cleaved IL-1β (p17) in both conditions when protein level was measured in the supernatant (Figure 4A,B) and cell lysate (Figure 4—figure supplement 1A,B). Mechanistically, post-mCoV-A59 infection, the BHB reduced the oligomerization of ASC, which is an adaptor protein required for the assembly of the inflammasome complex (Figure 4C,D). This data provides evidence that the ketone metabolite BHB can lower inflammation in response to coronavirus infection and deactivate the inflammasome. However, inflammasome activation is also required for mounting adequate immune response against pathogens including certain viruses. Therefore, we next investigated if induction of ketogenesis and ketolysis in vivo by feeding a diet rich in fat and low in carbohydrates that elevates BHB level impacts inflammasome and host defense against mCoV-A59 infection in aged mice.

We next tested whether ketogenic diet that increases BHB via mitochondrial metabolism affects the outcome of coronavirus infection in mice. Ketogenesis is dependent on hydrolysis of triglycerides and conversion of long-chain fatty acids in liver into short chain fatty acid BHB that serves as primary source of ATP for heart and brain when glucose is limiting. Aging is associated with impaired lipid metabolism which includes reduced lipolysis that generates free fatty acids that are essential substrates for BHB production. Thus, it is unclear whether in context of severe infection and aging, if sufficient ketogenesis can be induced. To test this, the aged male mice (20–21 months old) were fed a ketogenic diet (KD) or control diet for 5 days and then intranasally infected with mCoV-A59 (Figure 4E). Despite mCoV-A59’s known effects in causing hepatic inflammation (Navas et al., 2001), we observed that compared to chow-fed animals, old KD-fed mice achieved mild physiological ketosis between 0.6 and 1 mM over the course of infection for 1 week (Figure 4—figure supplement 1C).

Compared to chow-fed animals, the mCoV-A59-infected mice fed KD displayed similar levels of food intake, blood glucose, core body temperature, heart rate, and respiration (Figure 4—figure supplement 1D–I). Interestingly, mCoV-A59-infected KD-fed mice were protected from infection-induced weight loss and hypoxemia (Figure 4F,G). Furthermore, we infected young and old mice fed either chow or KD with a lethal dose of mCoV-A59 (PFU 1e6) to investigate whether KD affects mortality caused by mCoV-A59 infection (Figure 4H). Interestingly, compared to chow-fed controls, young mice fed KD prior to infection showed improved survival (Figure 4I). However, old mice fed KD were not protected from death caused by high-dose mCoV-A59-induced infection (Figure 4J). Together, these data show that KD is protective in aged mice in sub-lethal infections and partially protects adult, but not aged animals, from lethal coronavirus infections.

Induction of ketogenic substrate switch inhibits systemic inflammation in aged mCoV-A59 infection

Elderly COVID-19 patients exhibit multi-organ failure with systemic viremia and inflammation. Therefore, we next investigated the impact of KD on the inflammatory response in lungs, adipose tissue, and hypothalamus in old mice post-sub-lethal mCoV-A59 infection. Consistent with the improved clinical outcome and protection afforded by ketone bodies in infected mice, we found that KD-fed mice inoculated with mCoV-A59 had significantly reduced mRNA expression of pro-inflammatory cytokines IL-1β, TNFα, and IL-6 in lung, VAT, and hypothalamus (Figure 5A–C). Aging and mCoV-A59 increases inflammasome activation, which is increasingly implicated in the pathogenesis of COVID-19 (Vijay et al., 2017; Youm et al., 2013). The SARS-CoV open-reading frame 3a (ORF3a) and ORF8b activates the NLRP3 inflammasome (Siu et al., 2019) by inducing ER stress and lysosomal damage (Shi et al., 2019). Moreover, ability of bats to harbor multiple viruses including coronaviruses is due to splice variants in the LRR domain of NLRP3, which prevents inflammasome-mediated inflammatory damage (Ahn et al., 2019). Interestingly, in the aging mouse model of mCoV-A59 infection, KD significantly lowered NLRP3 and caspase-1 mRNA in lung, VAT, and hypothalamus (Figure 5D,E), inflammasome activation in VAT (Figure 5F,G), and decreased myeloid cell infiltration in heart (Figure 5H). The ketogenesis in infected old mice did not affect the frequency of CD4, CD8 effector memory or macrophage subsets in lungs, suggesting that reduction in pro-inflammatory cytokines was not a reflection of reduced infiltration of these cell types (Figure 5—figure supplements 1A–L and 2A–H). Interestingly, we found that KD feeding rescued mCoV-A59-induced depletion of ϒδ T cell in lungs of aged mice (Figure 5I, Figure 5—figure supplement 1A).

Figure 5 with 2 supplements see all
Ketogenic diet protects old mice from A59 (mCoV) infection by alleviation of inflammation.

(A–E) Gene expression analysis of inflammatory cytokines (A–C) and components of inflammasome (D, E) in lung, VAT, and hypothalamus of A59 (mCoV)-infected old mice fed chow (Old-Chow, 20–21 months, n = 6) or KD (Old-KD, 20–21 months, n = 5). (F, G) Western blot analysis of caspase-1 inflammasome activation (F) in VAT of infected Old-Chow and Old-KD mice with quantification (G). (H) Representative immunofluorescence images of CD68 expression, phalloidin, and DAPI in hearts isolated from Old-Chow and Old-KD mice (left) and CD68+ cells/heart area analysis in heart of infected Old-Chow and Old-KD mice (right). (I) Flow cytometry analysis of γδ T cell in lung of infected Old-Chow and Old-KD mice. Error bars represent the mean ± S.E.M. Two-tailed unpaired t-tests were performed for statistical analysis. *p<0.05; **p<0.01.

Ketogenesis induces protective γδ T cells and decreases myeloid cell subset in mCoV-A59-infected old mice

To determine the mechanism of ketogenesis-induced protection from mCoV-A59-driven inflammatory damage in aging, we next investigated the transcriptional changes in lung at the single-cell level. The scRNA-sequencing of whole-lung tissues (Figure 6A, Figure 6—figure supplement 2A) found that KD feeding in old infected mice caused significant increase in goblet cells (Figure 6B), expansion of ϒδ T cells (Figure 6B) and significant decrease in proliferative cell subsets and monocyte populations (Figure 6B). When differential gene expression analyses were performed with the clusters, we observed only very modest changes within few clusters and no shared signature (Figure 6—figure supplement 2B). Comparison with scRNA-seq of the lungs from young and old infected animals highlighted the largest increase in B cells and club cells and reduction of proliferating myeloid cells, including Trem2+ macrophages and NK cells (Figure 6—figure supplement 1A–D). The fraction of Foxp3-positive Treg cells were not different between young infected and old infected mice (Figure 6—figure supplement 1E–G). Notably, when differential gene expression and pathway analyses were performed (Figure 6—figure supplement 1H–K), the old infected mice showed a reduced interferon signature, suggesting increased vulnerability to the viral infection (Figure 6—figure supplement 1I,J). Interestingly, some of the most striking changes occurred in T cells, where ketogenesis led to a substantial increase in ϒδ but not αβ T cells (Figure 6C, Figure 6—figure supplement 2C). To understand whether expansion of ϒδ T cells was also accompanied by the changes in their regulatory programs, we sorted the lung ϒδ T cells from aged mice fed chow diet and KD and conducted bulk RNA-sequencing to determine the mechanism of potential tissue protective effects of these cells in mCoV-A59 infection. We found that KD in aging significantly increased the genes associated with reduced inflammation (Figure 6D), increased lipoprotein remodeling and downregulation of TLR signaling, Plk1 and aurora B signaling pathways in ϒδ T cells (Figure 6E). However, there was no difference in mRNA expression levels of Vγ chain subtypes, IL-17, or IFN-γ (Figure 6—figure supplement 2D–F). Furthermore, RNA-sequencing revealed that lung ϒδ T cells from ketogenic mCoV-A59-infected old mice displayed elevated respiratory electron transport and complex I biogenesis (Figure 6E). In addition, Golgi to ER retrograde transport and cell cycle are downregulated, suggesting the reduced activation status of ϒδ T cell (Figure 6E). These data suggest that ϒδ T cells expanded with KD are functionally more homeostatic and immune protective against mCoV-A59 infection.

Figure 6 with 2 supplements see all
Ketogenesis induces protective γδ T cell expansion and inhibits myeloid cell activation in aged mice with mCoV infection.

(A) Uniform Manifold Approximation and Projection (UMAP) plot of lung cells from Old-KD and Old-Chow samples as described in Figure 4E. (B) Bar chart shows population fold-changes in relative abundance of each cluster. (C) Zoom into UMAP plot from A showing T cells (cluster 1) split by sample. Color represents expression of Trdc. (D) Volcano plot identifying significantly regulated genes (5% FDR) within sorted γσ T cells from lungs of Old-KD and Old-Chow mice. Increase in expression corresponds to ketogenic diet-induced genes. (E) GSEA enrichment curve based on differential expression analysis results shown in (D). (F) Monocyte cluster 0 and 1 from (A) were subset and analyzed separately. UMAP plot of monocytes split by samples. (G) Heatmap of normalized within row gene expression values of selected markers of three monocyte subsets. (H) UMAP as in (F). Color represents expression of selected genes. For (A–C, F–H), expression values were obtained by pooling data from Old-Chow and Old-KD samples (each containing n = 6 chow and n = 5 KD pooled biological samples into one technical sample for each diet).

Zooming in into monocyte sub-population, we observed three distinct monocyte clusters (Figure 6F), characterized by Ifi44, Lmna, and Cd300e, respectively (Figure 6G). Strikingly, ketogenesis-induced change in the monocyte compartment was driven by a loss of cluster 1 (characterized by high levels of Chil3, Lmna, Il1r2, Lcn2, Cd33, Cd24a, Figure 6H, Figure 6—figure supplement 2G,H). The same clusters were identified in scRNA-sequencing with young and old infected mice and aging was only associated with reduction of the cluster 0 (Figure 6—figure supplement 2I–K). In addition, the loss of monocyte sub-population was observed in cells with low interferon response, further suggesting the immune protective response induction post-ketogenesis in infected mice. This is an intriguing finding that is consistent with recent observations that dietary interventions can impact plasticity of the monocyte pool in both mouse and human (Collins et al., 2019; Jordan et al., 2019).

Discussion

Immune-senescence exemplified by inflammasome-mediated basal activation of myeloid cells, expansion of pro-inflammatory aged B cells, impaired germinal center, and antibody responses together with thymic demise and restriction of T cell repertoire diversity all contribute to increased risk of infections and vaccination failures in elderly (Akbar and Gilroy, 2020; Frasca et al., 2020; Goldberg and Dixit, 2015; Goronzy and Weyand, 2019). It is likely that multiple mechanisms partake in aging-induced mortality and morbidity to SARS-CoV-2. However, study of immunometabolic mechanisms that control aberrant inflammatory response in elderly COVID-19 patients is incompletely understood. Epidemiological data strongly support that elderly and aged individuals with late-onset chronic diseases – including diabetes, obesity, heart conditions, pulmonary dysfunctions, and cancer – present a much higher disease severity compared to young healthy adults (Cai et al., 2020; Chen et al., 2020). These observations suggest that it is the vulnerability of the various tissues that occur in these chronic conditions that predispose elderly to develop severe forms of COVID-19.

Rodent CoVs are natural, highly contagious pathogens of mice and rats (Compton et al., 2003; Compton et al., 1993). These viruses also offer the possibility of efficient and safe platforms for modeling COVID-19 and examining immunometabolic mechanisms and interventions that impact disease. Of particular interest for COVID-19 are the strains of MHV that are respiratory tropic (Yang et al., 2014). Given, these advantages, MHV mCoV-A59 infection in C57BL/6 mice can be a powerful tool to rapidly study the disease as well as test therapeutic interventions. We demonstrate that MHV mCoV-A59 infection in aged mice recapitulates severe features of COVID-19 that includes up to 30% weight loss, sickness behavior exemplified by anorexia, loss of oxygen saturation, lung pathology including neutrophilia, monocytosis, loss of γδ T cells, lymphopenia, increase in circulating pro-inflammatory cytokines, hypothalamic, adipose and cardiac inflammation, and inflammasome activation. Importantly, LD-0 dose of MHV mCoV-A59 induces 100% lethality in 2 year old male mice, suggesting that this model allows investigation of COVID-19-relevant immunometabolic mechanisms that control disease development and severity with aging.

Mechanistically, NLRP3 inflammasome has been demonstrated to be an important driver of aging-induced chronic inflammation and organ damage (Bauernfeind et al., 2016; Camell et al., 2017; Youm et al., 2013). COVID-19 patients have inflammasome-dependent pyroptosis and increase in IL-18 (Lucas et al., 2020; Zhou et al., 2020). Consistent with the hypothesis that aging may exacerbate inflammasome activation in SARS-CoV-2 infection, our data demonstrates that in vivo, mCoV infection increases NLRP3 inflammasome-mediated inflammation. Recent study shows that MHV-A59 also activates the NLRP3 inflammasome in vitro BMDM (Zheng et al., 2020). In addition, severe cases of COVID-19 are accompanied with dysregulation of monocyte populations with increased level of S100A8/A9 or calprotectin (Schulte-Schrepping et al., 2020; Silvin et al., 2020), which can prime and induce the inflammasome activation (Goldberg et al., 2017). Interestingly, the KD feeding blocked infiltration of pathogenic monocyte subset in lungs that has high S100A8/9 and low interferon expression. These data underscore that enhanced innate immune tolerance mediated by inflammasome deactivation maybe an important strategy against COVID-19.

The integrated immunometabolic response (IIMR) is critical in regulating the setpoint of protective versus pathogenic inflammatory response (Lee and Dixit, 2020). The IIMR involves sensing of nutrient balance by neuronal (sympathetic and sensory innervation) and humoral signals (e.g. hormones and cytokines) between the CNS and peripheral tissues that allow the host to prioritize storage and/or utilize substrates for tissue growth, maintenance, and protective inflammatory responses. Peripheral immune cells, both in circulation and those residing within tissues, are subject to regulation by the metabolic status of the host. Ketone bodies, BHB, and acetoacetate are produced during starvation to support the survival of host by serving as an alternative energy substrate when glycogen reserves are depleted (Newman and Verdin, 2017). Classically, ketone bodies are considered essential metabolic fuels for key tissues such as the brain and heart (Puchalska and Crawford, 2017; Veech et al., 2017). However, there is increasing evidence that immune cells can also be profoundly regulated by ketone bodies (Goldberg et al., 2020; Youm et al., 2015). For example, stable isotope tracing revealed that macrophage oxidation of liver-derived AcAc was essential for protection against liver fibrosis (Puchalska et al., 2019). Given our past findings that ketone bodies inhibit NLRP3 inflammasome activation induced by sterile DAMPs, we next hypothesized that coronavirus mediated inflammasome activation and disease severity in aging could be improved by BHB driven improved metabolic efficiency and NLRP3 deactivation. In support of this hypothesis, we found that BHB inhibits the mCoV-A59-induced NLRP3 inflammasome assembly and KD reduces caspase-1 cleavage as well as decreases gene expression of inflammasome components. We next investigated the mechanism of protection elicited by KD that is relevant to aging. Interestingly, scRNA-sequencing analyses of lung homogenates of old mice fed KD revealed robust expansion of immunoprotective γδ T cells, which are reported to decline in COVID-19 patients (Lei et al., 2020; Rijkers et al., 2020). The KD activated the mitochondrial function as evidenced by enhanced complex-1 biogenesis and upregulation of ETC in immunoprotective γδ T cells. These results are consistent with a previous study reporting that KD-mediated enhancement of mitochondrial fatty acid oxidation is required for protection against flu infection. In the study, increasing BHB by feeding ketone esters could not mimic the effect of KD (Goldberg et al., 2019). This suggests an important role for mitochondrial fatty acid oxidation and hepatic ketogenesis in protection against viral infections.

Our findings assume strong clinical significance as recent studies demonstrate that γδ T cells were severely depleted in COVID-19 patients in two highly variable cohorts, and disease progression was correlated with near ablation of Vγ9Vδ2 cells that are dominant subtype of circulating γδ T cells (Laing et al., 2020). Taken together these data demonstrate that a ketogenic immunometabolic switch protects against mCoV-A59-driven infection in mice and this involves anti-inflammatory response in lung which is coupled with reduction of NLRP3 inflammasome, restoration of protective ϒδ T cells, and remodeling of the pool of the inflammatory monocytes. Finally, our results suggest that acutely switching infected or at-risk elderly patients to a KD may ameliorate COVID-19 and, therefore, is a relatively accessible and affordable intervention that can be promptly applied in most clinical settings.

Materials and methods

Key resources table
Reagent type
(species) or
resource
DesignationSource or
reference
IdentifiersAdditional
information
Strain, strain background (mouse, male)C57BL/6NIA
Strain, strain background (Murine Coronavirus)MHV-A59Bei resourcesNR-43000
Cell line (Rattus norvegicus)L2ATCCCCL-149
Cell line (Mus musculus)L929ATCCCCL1
Biological sample (Mus musculus)Primary BMDMThis paperFreshly isolated from mouse
AntibodyRat anti-Caspase-1GenentechN/AWB (1:250)
AntibodyRabbit polyclonal anti-β actinCell Signaling TechnologyCat#4967L; RRID:AB_330288WB (1:1000)
AntibodyRabbit polyclonal anti-IL-1βGeneTexCat#GTX74034; RRID:AB_378141WB (1:1000)
AntibodyRabbit polyclonal anti-ASCAdipoGenCat#AG-25B-0006; RRID:AB_2490440WB (1:1000)
AntibodyBV711 Rat monoclonal anti-CD45BioLegendCat#103147; RRID:AB_2564383FACS (1:100)
AntibodyFITC Rat monoclonal anti-MERTKBioLegendCat#151504; RRID:AB_2617035FACS (1:100)
AntibodyBV605 Mouse monoclonal anti-CD64BioLegendCat#139323; RRID:AB_2629778FACS (1:100)
AntibodyeFluor 450 Rat monoclonal anti-F4/80Thermo Fisher ScientificCat#48-4801-82; RRID:AB_1548747FACS (1:100)
AntibodyPerCP-Cy5.5 Rat monoclonal anti-Ly-6CThermo Fisher ScientificCat#45-5932-82; RRID:AB_2723343FACS (1:100)
AntibodyAPC Hamster monoclonal anti-CD11cBioLegendCat#117310; RRID:AB_313779FACS (1:100)
AntibodyPE Rat monoclonal anti-CD169Thermo Fisher ScientificCat#12-5755-82; RRID:AB_2572625FACS (1:100)
AntibodyPE/Cy7 Rat monoclonal anti-CD86BioLegendCat#105014; RRID:AB_439783FACS (1:100)
AntibodyBV605 Rat monoclonal anti-CD3BioLegendCat#100237; RRID:AB_2562039FACS (1:100)
AntibodyPE-Cy7 Rat monoclonal anti-CD4Thermo Fisher ScientificCat#25-0042-82; RRID:AB_469578FACS (1:100)
AntibodyeFluor 450 Rat monoclonal anti-CD8aThermo Fisher ScientificCat#48-0081-82; RRID:AB_1272198FACS (1:100)
AntibodyPE Hamster monoclonal anti-TCR γ/δBioLegendCat#118108; RRID:AB_313832FACS (1:100)
AntibodyAPC Rat monoclonal anti-Ly-6GThermo Fisher ScientificCat#17-9668-82; RRID:AB_2573307FACS (1:100)
AntibodyPerCP-Cy5.5 Rat monoclonal anti-Siglec-FBD BiosciencesCat#565526; RRID:AB_2739281FACS (1:100)
AntibodyPerCP-Cy5.5 Rat monoclonal anti-CD62LBioLegendCat#104432; RRID:AB_2285839FACS (1:100)
AntibodyAPC/Cy7 Rat monoclonal anti-CD44BioLegendCat#103028; RRID:AB_830785FACS (1:100)
AntibodyRat monoclonal anti-CD68Bio-RadCat#MCA1957; RRID:AB_322219IF (1:100)
Peptide, recombinant proteinRecombinant Mouse M-CSF ProteinR and D SystemsCat#416 ML-050
Commercial assay or kitRNeasy Plus micro kitQiagenCat#74034
Commercial assay or kitDirect-zol RNA Miniprep Plus kitZymo ResearchCat#R2072
Commercial assay or kitiScript cDNA synthesis kitBio-RadCat#1708891
Commercial assay or kitPower SYBR Green PCR Master MixThermo Fisher ScientificCat#4367659
Commercial assay or kitProcartaPlex multiplex assayThermo Fisher ScientificCat#PPX
Chemical compound, drugAlexa Fluor 594 PhalloidinThermo Fisher ScientificCat#A12381, RRID:AB_2315633
Chemical compound, drugLPSSigmaCat#L3024
Chemical compound, drugPam3CSK4InvivogenCat#tlrl-pms
Chemical compound, drug(R)-Hydroxybutyric Acid (BHB)SigmaCat#54920–1 G-F
Chemical compound, drugDisuccinimidyl suberate (DSS)Thermo Fisher ScientificCat#21655
Software, algorithmImage JNIHhttps://imagej.nih.gov/ij/
Software, algorithmPrism 7Graphpadhttps://www.graphpad.com/
Software, algorithmFlowJoTreestarhttps://www.flowjo.com/
Software, algorithmCell Ranger Single-Cell Software
Suite (v3.0.2)
10x genomicshttps://support.10xgenomics.com
Software, algorithmR (v3.5.0) package Seurat (v3.1.1)Butler et al., 2018https://cran.r-project.org/web/packages/Seurat/index.html
Software, algorithmMASTFinak et al., 2015https://github.com/RGLab/MAST
Software, algorithmclusterProfiler package (v3.12.0)Yu et al., 2012http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html
Software, algorithmSTAR (v2.7.3a)Dobin et al., 2013http://code.google.com/p/rna-star/
Software, algorithmMultiQC (v1.9)Ewels et al., 2016http://multiqc.info
Software, algorithmHTSeq framework (v0.11.2)Anders et al., 2015http://www-huber.embl.de/HTSeq
Software, algorithmDeSeq2 package (v1.24.0)Love et al., 2014http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html
Software, algorithmfgsea R package (v1.10.0)Sergushichev, 2016https://github.com/ctlab/fgsea

Study design

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The object of this study was to investigate whether KD affects defense response of mice against MHV-A59 along with identification of underlying mechanism. Mice were randomized for all experiments groups. For experiments with mice provided with different diets, mice were fed with chow diet or KD for 5 days before infection following phenotype observation. It was not possible that investigators were blinded for KD experiments because appearance of KD was different from chow diet, while investigators were blinded for experiments and data analysis. Number of samples are indicated in each figure legend. Sample size for experiments was decided based on power calculation from previous experiments and experiences. No data points were excluded in this study. All experiments were repeated independently at least two times. Bulk RNA-sequencing experiment was performed once with biologically independent samples. Single-cell RNA-sequencing experiment was done once with pooling biologically independent samples per group.

Animal models

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All mice used in this study were C57BL/6 mice. Old mice (20–24 month old) were received from NIA, maintained in our laboratory. Young mice (2–6 months old) were from NIA or purchased from Jackson Laboratories or bred in our laboratory. The mice were housed in specific pathogen-free facilities with free access to sterile water through Yale Animal Resources Center. Mice were fed a standard vivarium chow (Harlan 2018s) or a ketogenic diet (Envigo, TD.190049) for indicated time. The mice were housed under 12 hr light/dark cycles. All experiments and animal use were conducted in compliance with the National Institute of Health Guide for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee (IACUC) at Yale University.

Viral infection

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MHV-A59 was purchased from Bei resources (NR-43000) and grown in BV2 cells. Mice were anesthetized by intraperitoneal injection of ketamine/xylazine. 700, 7000, or 106 PFU of MHV-A59 was delivered in 40 µl PBS via intranasal inoculation. Vital signs were measured before and after infection. Arterial oxygen saturation, breath rate, heart rate, and pulse distention were measured in conscious, unrestrained mice via pulse oximetry using the MouseOx Plus (Starr Life Sciences Corp.).

Electron microscopy

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Lungs were fixed in 10% formaldehyde, osmicated in 1% osmium tetroxide, and dehydrated in ethanol. During dehydration, 1% uranyl acetate was added to the 70% ethanol to enhance ultrastructural membrane contrast. After dehydration, the lungs were embedded in Durcupan, and ultrathin sections were cut on a Leica Ultra-Microtome, collected on Formvar-coated single-slot grids, and analyzed with a Tecnai 12 Biotwin electron microscope (FEI).

Histology and immunohistochemistry

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H and E and MSB staining of lung tissues were performed on sections of formalin-fixed paraffin-embedded at the Comparative Pathology Research core at Yale School of Medicine. For immunohistochemistry, the hearts were harvested from MHV-A59-infected mice, fixed in 4% PFA overnight, and embedded in OCT after dehydration with 30% sucrose, and serial sections of aortic root were cut at 6 μm thickness using a cryostat. Sections were incubated at 4°C overnight with CD68 (Serotec; #MCA1957) and Alexa Fluor 594 Phalloidin (ThermoFisher, A12381) after blocking with blocker buffer (5% Donkey Serum, 0.5% BSA, 0.3% Triton X-100 in PBS) for 1 hr at RT, followed by incubation with Alexa Fluor secondary antibody (Invitrogen, Carlsbad, CA) for 1 hr at RT. The stained sections were captured using a Carl Zeiss scanning microscope Axiovert 200M imaging system, and images were digitized under constant exposure time, gain, and offset. Results are expressed as the percent of the total plaque area stained measured with the Image J software (ImageJ version 1.51).

Plaque assay

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L2 cell (1.5 ml of 6 × 105 cells/ml) were seeded on six-well plates (Corning, 3516) in supplemented DMEM and allowed to adhere overnight. Tissue samples were homogenized in unsupplemented DMEM and spun down at 2000 rpm for 5 min. Supernatant was serially diluted and 200 μL of each sample was added to aspirated L2 cells in six-well plates. Plates were agitated regularly for 1 hr before adding overlay media consisting of 1 part 1.2% Avicel and 1 part 2× DMEM (Thermo Fisher, 12800) supplemented with 4% FBS (Thermo Fisher, A3840301), penicillin-streptomycin (Gibco, 15140122), MEM Non-Essential Amino Acids Solution (Gibco, 11140050), and HEPES (Gibco, 15630080). After a 4-day incubation, cells were fixed in 10% formaldehyde (Sigma-Aldrich, 8187081000) diluted with PBS for 1 hr. Cells were then stained in 1% (w/v) crystal violet (Sigma Aldrich, C0775) for 1 hr, washed once in distilled water, and then quantified for plaque formations.

Multiplex cytokine analyses

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Serum cytokine and chemokine level was measured by ProcartaPlex multiplex assay (Thermo Fisher Scientific). Assay was prepared following manufacture’s instruction. Twenty-five microliters of collected serum from each mice in this study was used. Customized assay including IL-1β, TNFα, IL-6, MCP-1, and MIP-1β was used. Luminex xPONENT system was used to perform the assay.

Quantitative PCR

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To extract and purify RNA from tissues, RNeasy Plus micro kit (Qiagen) and Direct-zol RNA Miniprep Plus kit (Zymo Research) were used according to manufacturer’s instructions. cDNA was synthesized with isolated RNA using iScript cDNA synthesis kit (Bio-Rad). To quantify amount of mRNA, real-time quantitative PCR was done with the synthesized cDNA, gene-specific primers, and Power SYBR Green detection reagent (Thermo Fischer Scientific) using the LightCycler 480 II (Roche). Analysis was done by ΔΔCt method with measured values from specific genes, the values were normalized with Gaphd gene as an endogenous control. Primer information is described in Supplementary file 1.

BMDM culture and in vitro viral infection

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BMDM was cultured by collecting mouse femurs and tibias in complete collecting media containing RPMI (Thermo Fischer Scientific), 10% FBS (Omega Scientific), and 1% antibiotics/antimycotic (Thermo Fischer Scientific). Using needle and syringe, bone marrow was flushed into new complete media, followed by red blood cells lysis by ACK lyses buffer (Quality Biological). In six-well plate, the collected cells were seeded to be differentiated into macrophages incubated with 10 ng/ml M-CSF (R and D) and L929 (ATCC) conditioned media. Cells were harvested on day 7 and seeded as 1 × 106 cell/well in 24-well plate for experiments. To infect BMDM, MHV-A59 was incubated with BMDM as a MOI 1 (1:1) for 24 hr. For inflammasome activation, LPS (1 µg/ml) or Pam3CSK4 (1 µg/ml) was pre-treated with or without BHB (1, 5, and 10 mM) for 4 hr before MHV-A59 infection for 24 hr or ATP (5 mM) treatment for 1 hr.

Western blotting

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To prepare samples for western blotting, tissues were snap frozen in liquid nitrogen. RIPA buffer with protease inhibitors was used to homogenize the tissues. After cell supernatant was collected, cells were harvested by directly adding RIPA buffer on cell culture plate. After quantification of protein amount by the DC protein assay (Bio-Rad), the same amount of protein was run on SDS–PAGE gel followed by transferring to nitrocellulose membrane. Specific primary antibodies and appropriate secondary antibodies (Thermo Fisher Scientific) were used to probe blots, and bands were detected by ECL Western Blotting Substrate (Pierce). The following primary antibodies were used for experiments: Antibodies to caspase-1 (1:250, Genentech), β-actin (1:1,000, 4967L; Cell Signaling), IL-1β (1:1000, GTX74034, GeneTex), and ASC (1:1000, AG-25B-0006, AdipoGen).

ASC oligomerization assay

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To detect ASC oligomers, cells were harvested in NP-40 lysis buffer that contains 20 mM HEPES–KOH (pH 7.5), 150 mM KCl, 1% NP-40, 0.1 mM PMSF, and protease inhibitors. The cells in lysis buffer were incubated on ice for 15 min and centrifuged at 6000 rpm at 4°C for 10 min. Supernatant was collected and kept for cell lysate western blotting. The pellet was vortexed with 1 ml of NP-40 lysis buffer and centrifuged at 6000 rpm at 4°C for 10 min. The pellet was incubated with 50 µl of NP-40 lysis buffer and 1 µl of 200 mM disuccinimidyl suberate for 30 min at room temperature and then centrifuged at 6000 rpm at 4°C for 10 min. The pellet with SDS sample buffer and reducing reagent was loaded for western blotting.

Flow cytometry

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Lung was digested in RPMI (Thermo Fisher) with 0.5 mg/ml Collagenase I (Worthington) and 0.2 mg/ml DNase I (Roche) for 1 hr. Digested lung tissues were minced through 100 μm strainer. Spleen was directly minced through 100 μm strainer. Minced tissues were additionally filtered with 40 μm strainer after red blood cell lysis by ACK lysing buffer (Quality Biological). After incubation with Fc Block CD16/32 antibodies (Thermo Fisher Scientific), the cells from lung and spleen were further incubated with surface antibodies for 30 min on ice in the dark. Washed cells were stained with LIVE/DEAD Fixable Aqua Dead Cell Stain Kit (Thermo Fisher Scientific). BD LSRII was used for flow cytometry, and results were analyzed by FlowJo software. The following antibodies were used for flow cytometry analysis to detect CD4 T cell, CD8 T cell, γδ T cell, neutrophil, eosinophil, and macrophage: CD45-BV711, MerTK-FITC, CD64-BV605, F4/80-eFluor450, Ly6C-PerCP-Cy5.5, CD11c-APC, CD169-PE, CD86-PE-Cy7, CD3-BV605, CD4-PE-Cy7, CD8-eFluor450, TCR γ/δ-PE, Ly6G-APC, SiglecF-BV605, CD62L-PerCP-Cy5.5, CD44-APC-Cy7.

Single-cell RNA-sequencing

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Lung cells were prepared as mentioned above for flow cytometry and equal amount of cells were pooled as indicted in the experiments. Single-cell RNA-sequencing libraries were prepared at Yale Center for Genome Analysis following manufacturer’s instruction (10× Genomics). NovaSeq6000 was used for sequencing library read.

Alignment, barcode assignment, and unique molecular identifier counting

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The Cell Ranger Single-Cell Software Suite (v3.0.2) (available at https://support.10xgenomics.com/single-cell-gene-expression/software/pipelines/latest/what-is-cell-ranger) was used to perform sample demultiplexing, barcode processing, and single-cell 3’ counting. Cellranger mkfastq was used to demultiplex raw base call files from the NovaSeq6000 sequencer into sample-specific fastq files. Subsequently, fastq files for each sample were processed with cellranger counts to align reads to the mouse reference (version mm10-3.0.0) with default parameters.

Preprocessing analysis with Seurat package

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For the analysis, the R (v3.5.0) package Seurat (v3.1.1) (Butler et al., 2018) was used. Cell Ranger filtered genes by barcode expression matrices were used as analysis inputs. Samples were pooled together using the merge function. The fraction of mitochondrial genes was calculated for every cell, and cells with high (>5%) mitochondrial fraction were filtered out. Expression measurements for each cell were normalized by total expression and then scaled to 10,000, after that log normalization was performed (NormalizeData function). Two sources of unwanted variation – unique molecular identifier (UMI) counts and fraction of mitochondrial reads – were removed with ScaleData function. For both datasets, platelet clusters as well as a cluster of degraded cells (no specific signature and low UMI count) were removed and data was re-normalized without them. In case of Old-Keto and Old-Chow dataset, we additionally removed neutrophils, doublets, and red blood cells. Doublets were manually defined as cells that were positive for two or more marker sets.

Dimensionality reduction and clustering

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The most variable genes were detected using the FindVariableGenes function. Principal components analysis (PCA) was run only using these genes. Cells are represented with >Uniform Manifold Approximation and Projection (UMAP) plots. We applied RunUMAP function to normalized data, using first 15 PCA components. For clustering, we used functions FindNeighbors and FindClusters that implement shared nearest neighbor modularity optimization-based clustering algorithm on top 20 PCA components using resolution of 0.3 for both datasets.

Identification of cluster-specific genes and marker-based classification

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To identify marker genes, FindAllMarkers function was used with likelihood-ratio test for single-cell gene expression. For each cluster, only genes that were expressed in more than 10% of cells with at least 0.1-fold difference (log-scale) were considered. For heatmap representation, mean expression of markers inside each cluster calculated by AverageExpression function was used.

Single-cell RNA-seq differential expression

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To obtain differential expression between clusters, MAST test was performed via FindMarkers function on genes expressed in at least 1% of cells in both sets of cells, and p-value adjustment was done using a Bonferroni correction (Finak et al., 2015). Pathway analysis was performed using clusterProfiler package (v3.12.0) (Yu et al., 2012), with Hallmark gene sets from MSigDB. Significantly different genes were used (padj<0.05) if percent difference between conditions (|pct.1 – pct.2|) was over 1%. To visualize pathway expression for each cell z-scores of all pathways, genes were averaged.

Cell subsets analysis

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To separate αβ T cells and γσ T cells, we subset raw values of T cell (cluster 2), normalized and clustered corresponding data separately as described above with clustering resolution 0.2. Obtained subclusters were projected on the original UMAP, splitting cluster 2 in 2_1 (αβ T cells) and 2_2 (γσ T cells). Monocyte clusters 0 and 1 were subset and re-analyzed in the same manner with clustering resolution 0.3. UMAP was recalculated for monocytes only as shown in Figure 6F.

Bulk RNA-sequencing of sorted γδ T cells

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Cells from lung were prepared as mentioned above for flow cytometry, and γδ T cell was sorted by flow cytometry (live CD45+CD3+CD4 CD8− TCR γ/δ+). RNA was isolated from sorted cells using RNeasy Plus micro kit (Qiagen). Quality checked RNA was used for RNA-sequencing library preparation at Yale Center for Genome Analysis following manufacturer’s instruction (Illumina). NovaSeq6000 was used for sequencing library read.

Fastq files for each sample were aligned to the mm10 genome (Gencode, release M25) using STAR (v2.7.3a) with the following parameters: STAR --genomeDir $GENOME_DIR --readFilesIn $WORK_DIR/$FILE_1 $WORK_DIR/$FILE_2 --runThreadN 12 --readFilesCommand zcat --outFilterMultimapNmax 15 --outFilterMismatchNmax 6 --outReadsUnmapped Fastx --outSAMstrandField intronMotif --outSAMtype BAM SortedByCoordinate --outFileNamePrefix. /$ (Dobin et al., 2013). Quality control was performed by FastQC (v0.11.8), MultiQC (v1.9) (Ewels et al., 2016), and Picard tools (v2.21.6). Quantification was done using htseq-count function from HTSeq framework (v0.11.2): htseq-count -f bam -r pos -s no -t exon $BAM $ANNOTATION > $OUTPUT (Anders et al., 2015). Differential expression analysis was done using DESeq function from DeSeq2 package (Love et al., 2014) (v1.24.0) with default settings. Significance threshold was set to adjusted p-value<0.05. Gene set enrichment analysis via fgsea R package (Sergushichev, 2016) (v1.10.0) was used to identify enriched pathways and plot enrichment curves.

Statistical analysis

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To calculate statistical significance, two-tailed Student’s t-test was used. Level of significance was indicated as follows: *p<0.05; **p<0.005; ***p<0.001; ****p<0.0001. All statistical tests used 95% confidence interval, and normal distribution of data was assumed. Biological replication numbers for each experiment were indicated in each figure and figure legend. Data were shown as mean ± S.E.M. GraphPad Prism software was used for all statistical tests to analyze experimental results.

Data availability

The single cell RNA-sequencing and bulk RNA-sequencing data has been uploaded to Gene Expression Omnibus, GSE155346 and GSE155347, respectively.

The following data sets were generated

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Decision letter

  1. Chyung-Ru Wang
    Reviewing Editor; Northwestern University, United States
  2. Tadatsugu Taniguchi
    Senior Editor; Institute of Industrial Science, The University of Tokyo, Japan
  3. Berislav Bosnjak
    Reviewer

In the interests of transparency, eLife publishes the most substantive revision requests and the accompanying author responses.

Acceptance summary:

This study used an aging model of natural murine beta coronavirus infection to demonstrate a protective effect of ketogenic diet on viral infection through expanding protective γδ T cells and decreasing myeloid cell-mediated inflammatory responses. As this model recapitulates several hallmarks of human COVID-19, the findings of this study support the use of ketogenic diet as a potential treatment against SARS-CoV-2 infection for older adults who are more vulnerable to this disease.

Decision letter after peer review:

Thank you for submitting your article "Ketogenic diet restrains aging-induced exacerbation of coronavirus infection in mice" for consideration by eLife. Your article has been reviewed by 3 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Tadatsugu Taniguchi as the Senior Editor. The following individual involved in review of your submission has agreed to reveal their identity: Berislav Bosnjak (Reviewer #2).

The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.

Essential revisions:

1. The flow cytometry data presented in figure 1 is better understood with the entirety of the data. For example, CD4 T cells in uninfected and infected old mice are at similar levels, however as presented the data in Figure 1 only show infected young v infected old with a significant reduction. Include the Figure 1 supplementary data B-D in Figure 1 and include these complexities in the Results section. Viral load should also be included in the main figure.

2. The authors should include the absolute cell numbers (Figure 1F-J) and indicate whether these data were from lung or spleen. In addition, please comment on whether Treg were examined in the CD4 T cell population.

3. Similar comment for Figure 3 as old mice have elevated IL1-β already, so the difference is not necessarily due to infection, but old age. Include supplementary data in Figure 3 instead of the partial data shown and explain in Results section.

4. The authors should provide data on whether the ketogenic diet saved the mice from mortality due to infection (Figure 4).

5. The authors should comment on whether the reductions in inflammatory mediators identified in Figure 5 were equivalent to those observed in young mice.

6. The authors should comment on which Vγ chains were more prevalent in the infected aged vs young populations, as different γδ T cell populations play differing roles in the lung (Figure 1G, 5G). In addition, please comment on any cytokine genes were noted in γδT cell RNA-seq data (Figure 6D)

7. The gating strategy for lung leukocyte populations depicted in Figure 1—figure supplement is not complete and unclear. It seems that cells positive for live/dead staining were included in the analysis (as depicted in Figure 1—figure supplement E and H). Moreover, the authors do not show gating for all populations included in the study (e.g. eosinophils or neutrophils). The same applies to the data shown in Figure 5—figure supplement. Please re-adjust the gating strategy, assuring that all dead cells are excluded from the analysis. Further, it is crucial that the gating strategy is clearly depicted for all investigated subsets.

8. Figure 5 and Figure 1—figure supplement: as for figure 1, please express the data as cell numbers rather than as cell percentages.

9. it is not clear whether the authors compare aged to adult mice without or with infection. Please re-phrasing the paragraph (Line 106-113) and clearly stating differences (i) induced by aging and (ii) by an infection in each age group.

10. Please comment on whether the effect observed with a ketogenic diet could be mimicked by BHB alone.

11. Regarding single-cell RNA sequencing analysis: (i) are there any differences in monocytes between adult and aged mice without infection? (ii) what are the differences between uninfected and infected mice of the same age group? (iii) what are the differences in gene expression per cell type when aged mice fed with normal or KD are compared? In other words, are there any common changes induced by KD feeding that are common to all cell types? (iv) please specify the number of cells per sample that were analyzed and the sequencing depth. Why were neutrophils excluded from the "Old-Keto and Old-Chow dataset"? How were the doublets detected? (v) Why was a % of mitochondrial genes set at 5%, as it is possible that highly activated cells have higher expression of mitochondrial genes? Does the KD have an effect on mitochondrial gene expression?

12. Did the authors also analyze with single cell-RNA sequencing of infected young mice? Do those mice also miss cluster 1 monocyte population (referring to Figure 6F)? This would imply the relative importance of those cells in the immunopathology of the lungs in aged mice. Further, would it be possible to characterize the monocytes from cluster 1 with extracellular markers within the flow cytometry data?

https://doi.org/10.7554/eLife.66522.sa1

Author response

Essential revisions:

1. The flow cytometry data presented in figure 1 is better understood with the entirety of the data. For example, CD4 T cells in uninfected and infected old mice are at similar levels, however as presented the data in Figure 1 only show infected young v infected old with a significant reduction. Include the Figure 1 supplementary data B-D in Figure 1 and include these complexities in the Results section. Viral load should also be included in the main figure.

We sincerely thank the Reviewer for the suggestion. In the revised Figure 1, we included the data in Figure 1—figure supplement 1B-D to show all data to include uninfected and infected young and old mice (now Figure 1G-L). The viral load data from Figure 1—figure supplement 1A was also included to improve clarity (now Figure 1F). We also included the description of the updated figures in Results section of manuscript (Line 106-124).

2. The authors should include the absolute cell numbers (Figure 1F-J) and indicate whether these data were from lung or spleen. In addition, please comment on whether Treg were examined in the CD4 T cell population.

We thank the Reviewer for the suggestion. We additionally included the flow cytometry data indicated as absolute cell numbers for Figure 1F-J (now Figure 1G-L) and clearly indicated that the cells were derived from lungs in figure 1 and in the legend. We included the interpretation of the data in Results section of manuscript (Line 106-124).

We also thank the Reviewer for the suggestion about clarifying whether CD4 T cell population analyses included analyses of Tregs. Tregs are known to increase with aging in spleen and adipose tissue (Bapat et al., 2015, PMID: 26580014). As suggested by the Reviewers, the Treg population could be identified by additional scRNA-seq analysis with young and old infected mice. These analyses revealed that Treg cells in lung between young and old infected mice were not significantly different and comparisons of fraction of Foxp3 positive T cell population are included now in Figure 6—figure supplement 1E-G. The manuscript is revised to include these data in Results section (Line 272-273).

3. Similar comment for Figure 3 as old mice have elevated IL1-β already, so the difference is not necessarily due to infection, but old age. Include supplementary data in Figure 3 instead of the partial data shown and explain in Results section.

We thank the Reviewer for the suggestion. Indeed, we and others have shown that Nlrp3 inflammasome-dependent IL-1β increases with aging. As per Reviewer’s suggestion, we have included the Western blot results (Figure 3I). We agree that basal inflammasome activation increases in aging and show that it is further exacerbated post infection; higher transcriptional levels of Casp1 and Nlrp3 in VAT from old infected mice compared to old uninfected mice support the results (Figure 3K and Figure 3—figure supplement 1K). We have revised the manuscript by including these new details in Results section (Line 165-168).

4. The authors should provide data on whether the ketogenic diet saved the mice from mortality due to infection (Figure 4).

As suggested by the Reviewer, we performed additional experiments to examine the impact of ketogenesis on coronavirus infection-induced mortality in both young and old mice upon infection with lethal doses of MHV (PFU 1e6). These results show that ketogenic diet-fed young mice are partially protected from death post infection; however, KD is unable to rescue old mice from high dose MHV-A59 viral infection-induced death. We have included these data in Figure 4 (H-J). These data are an important addition to the manuscript that shows that the role of ketogenesis in protecting old mice against disease severity and recovery but not in mortality caused by lethal infection. We included the description of new data in Results section (Line 228-235).

5. The authors should comment on whether the reductions in inflammatory mediators identified in Figure 5 were equivalent to those observed in young mice.

Our work primarily focused on aging. We have included new data that KD-fed young mice are protected from infection-induced lethality while KD-fed old mice are not (Figure 4I, J). Also, we have included data on the impact of MHV-A59 on inflammatory gene expression in young and old mice in visceral adipose tissue (VAT) and hypothalamus (Figure 3K and Figure 3—figure supplement 1H-Q). These results show that infection significantly increases Casp1, Tnf, Nlrp3 and Il6 expression in VAT and Casp1 and Tnf in hypothalamus.

6. The authors should comment on which Vγ chains were more prevalent in the infected aged vs young populations, as different γδ T cell populations play differing roles in the lung (Figure 1G, 5G). In addition, please comment on any cytokine genes were noted in γδT cell RNA-seq data (Figure 6D)

As suggested by the Reviewer, we re-analyzed the subtypes of γ chain and cytokines represented in the RNA-sequencing analysis of γδT cell in aged mice on chow and ketogenic diet. Compared to chow, there is no significant difference in representation of Vγ chains in the γδT cells expanded by ketogenic data. We have included these data in Figure 6—figure supplement 2D, E. and it was described in Results section (Line 284-286).

Also, Ifng and Il17a expression was examined by RNA-sequencing of γδT cells in aged mice on chow and ketogenic diet. These data also show that ketogenic diet does not significantly change the transcription of cytokine genes. These data are now included in Figure 6—figure supplement 2F and it was described in Results section (Line 284-286).

7. The gating strategy for lung leukocyte populations depicted in Figure 1—figure supplement is not complete and unclear. It seems that cells positive for live/dead staining were included in the analysis (as depicted in Figure 1—figure supplement E and H). Moreover, the authors do not show gating for all populations included in the study (e.g. eosinophils or neutrophils). The same applies to the data shown in Figure 5—figure supplement. Please re-adjust the gating strategy, assuring that all dead cells are excluded from the analysis. Further, it is crucial that the gating strategy is clearly depicted for all investigated subsets.

We appreciate the Reviewer’s remarks and have accordingly re-analyzed and corrected/clarified the gating and, in particular, the live/dead gating plots. We have accordingly revised the manuscript and revised the gating strategy figures in Figure 1—figure supplement 1 and Figure 5—figure supplement 1, 2. In addition, gating plots for eosinophils and neutrophils were also included in the gating schematic in Figure 1—figure supplement 1 and Figure 5—figure supplement 1 for clarity.

8. Figure 5 and Figure 1—figure supplement: as for figure 1, please express the data as cell numbers rather than as cell percentages.

As suggested by the Reviewer, he have revised the manuscript and included the absolute cell numbers in Figure 5I, Figure 5—figure supplement 1, 2, and Figure 1—figure supplement 1.

9. It is not clear whether the authors compare aged to adult mice without or with infection. Please re-phrasing the paragraph (Line 106-113) and clearly stating differences (i) induced by aging and (ii) by an infection in each age group.

We thank the Reviewer for the suggestion. Together with figure changes made in response to #1, we have edited the paragraph to clearly indicate that the old infected mice were compared to young infected as well as old uninfected mice. We included the description of the updated figures in Results section of manuscript (Line 106-124).

10. Please comment on whether the effect observed with a ketogenic diet could be mimicked by BHB alone.

In a previous study in our lab (Goldberg et al., 2019, PMID: 31732517), we showed that increasing BHB by feeding ketone esters—bypassing hepatic ketogenesis and mitochondrial fatty acid oxidation—is not sufficient to confer protection against influenza infection. We have included this important point in the Discussion section (Line 371-375).

11. Regarding single-cell RNA sequencing analysis: (i) are there any differences in monocytes between adult and aged mice without infection? (ii) what are the differences between uninfected and infected mice of the same age group? (iii) what are the differences in gene expression per cell type when aged mice fed with normal or KD are compared? In other words, are there any common changes induced by KD feeding that are common to all cell types? (iv) please specify the number of cells per sample that were analyzed and the sequencing depth. Why were neutrophils excluded from the "Old-Keto and Old-Chow dataset"? How were the doublets detected? (v) Why was a % of mitochondrial genes set at 5%, as it is possible that highly activated cells have higher expression of mitochondrial genes? Does the KD have an effect on mitochondrial gene expression?

(i) Even though we did not perform scRNA-seq with uninfected mice, based on Reviewer’s suggestions, we have re-analyzed the scRNA-seq data in young and old infected mice, focusing on responses to infection associated with aging. We identified three monocyte populations in young and old chow-fed infected mice that match populations described in Figures 6F-H. Aging was mostly associated with a decrease in the Ifi44-expressing monocyte subpopulation (cluster 0), while cluster 1, affected the most by ketogenic diet, was not subject to age-associated changes. These data are included in the Figure 6—figure supplement 2I-K. We also described the data in Results section (Line 296-298).

(ii) We didn’t perform scRNA-seq with uninfected young and old mice in this study. We have performed these analyses in a separate study and posted the data “Dysregulation of adipose ILC2 underlies thermogenic failure in aging | bioRxiv” Indeed, as predicted by the Reviewer, there are dramatic changes in tissue-resident immune system in aging. We believe these analyses will detract from the main point of the paper that ketogenic diet in aging protects against coronavirus infection.

(iii) Results of per cell type differential expression analysis between old infected chow- and KD-fed mice are summarized in Figure 6—figure supplement 2 panel B. While population structure was substantially affected by ketogenic diet (Figure 6B), we observed only very modest changes within few clusters. No shared signature was found. We included the description in Results section of manuscript (Line 267-269).

(iv) Numbers of cells used in the analysis are specified for each sample in Figure 6A and Figure 6—figure supplement 1 panel A. Quality control parameters, including sequencing depth, are summarized in Author response image 1.

Author response image 1

Doublets were manually defined as cells that were positive for two or more marker sets (e.g. highly expressing both Cd3e (T cell) and Lyz2 (myeloid)). Corresponding clarification has been added to the methods section (Line 569-570). Neutrophils have extremely low RNA content, and generation of representative single-cell data for this cell type, as well as for other granulocytes, remains challenging (https://kb.10xgenomics.com/hc/en-us/articles/360004024032-Can-I-process-neutrophils-or-other-granulocytes-using-10x-Single-Cell-applications- ). Accordingly, neutrophils and red blood cells demonstrated very low gene counts in our data. Additionally, we previously observed that estimation of neutrophil abundances based on single-cell RNA-seq data cannot always be reproduced using extracellular markers (Gubin et al., 2018, PMID: 30445041).(v) Fraction of reads mapping to mitochondrial genes (mtDNA) is a widely used quality control metric to filter out dead cells. In a cell with disrupted membranes, cytosolic RNA will leak out while mitochondria will stay within the cell. Therefore, the fraction of mitochondrial genes will be increased in dying cells (Osorio et al., 2020, PMID: 32840568). 5% is a standard threshold accepted by the field, as per 10x technical note (https://assets.ctfassets.net/an68im79xiti/4tVumiyINGgAeoCg8SiWGG/1cf0888200d668142612c8d3f3679cf4/CG000130_10x_Technical_Note_DeadCell_Removal_RevA.pdf ) samples with ~80% viable cells have fraction of mitochondrial UMI around 3%. Per cluster differential expression analysis comparing old chow- and KD-fed mice identified only three mitochondrial genes that change with ketogenic feeding in at least one cell type. No coordinated increase in mitochondrial genes or pathways was observed. We have included these details in Author response image 2.

Author response image 2

12. Did the authors also analyze with single cell-RNA sequencing of infected young mice? Do those mice also miss cluster 1 monocyte population (referring to Figure 6F)? This would imply the relative importance of those cells in the immunopathology of the lungs in aged mice. Further, would it be possible to characterize the monocytes from cluster 1 with extracellular markers within the flow cytometry data?

As discussed above (11i), based on Reviewer’s suggestions, we have re-analyzed the scRNA-seq data. We identified three monocyte populations in young and old chow-fed infected mice that match populations described in Figures 6F-H. Aging was mostly associated with a decrease in Ifi44-expressing monocyte subpopulation (cluster 0), while cluster 1, affected the most by ketogenic diet, was not subject to age-associated changes. These data are included in the Figure 6—figure supplement 2I-K. We also described the data in Results section (Line 296-298).

https://doi.org/10.7554/eLife.66522.sa2

Article and author information

Author details

  1. Seungjin Ryu

    1. Department of Comparative Medicine, Yale School of Medicine, New Haven, United States
    2. Department of Immunobiology, Yale School of Medicine, New Haven, United States
    Contribution
    Formal analysis, Investigation, Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-6353-8789
  2. Irina Shchukina

    Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, United States
    Contribution
    Data curation, Formal analysis, Writing - review and editing
    Competing interests
    No competing interests declared
  3. Yun-Hee Youm

    1. Department of Comparative Medicine, Yale School of Medicine, New Haven, United States
    2. Department of Immunobiology, Yale School of Medicine, New Haven, United States
    Contribution
    Investigation, Performed experiments regarding mouse tissue processing and qPCR
    Competing interests
    No competing interests declared
  4. Hua Qing

    Department of Internal Medicine, Yale School of Medicine, New Haven, United States
    Contribution
    Investigation, Writing - review and editing, Performed infections and phenotyping of mice
    Competing interests
    No competing interests declared
  5. Brandon Hilliard

    Department of Internal Medicine, Yale School of Medicine, New Haven, United States
    Contribution
    Investigation, Performed plaque assay
    Competing interests
    No competing interests declared
  6. Tamara Dlugos

    1. Department of Comparative Medicine, Yale School of Medicine, New Haven, United States
    2. Department of Immunobiology, Yale School of Medicine, New Haven, United States
    Contribution
    Investigation, Performed experiments regarding mouse tissue processing
    Competing interests
    No competing interests declared
  7. Xinbo Zhang

    Department of Comparative Medicine, Yale School of Medicine, New Haven, United States
    Contribution
    Investigation, Performed experiments about impact of infection on heart
    Competing interests
    No competing interests declared
  8. Yuki Yasumoto

    Department of Comparative Medicine, Yale School of Medicine, New Haven, United States
    Contribution
    Investigation, Executed electron microscopy
    Competing interests
    No competing interests declared
  9. Carmen J Booth

    Department of Comparative Medicine, Yale School of Medicine, New Haven, United States
    Contribution
    Investigation, Performed the histological analyses
    Competing interests
    No competing interests declared
  10. Carlos Fernández-Hernando

    1. Department of Comparative Medicine, Yale School of Medicine, New Haven, United States
    2. Program in Integrative Cell Signaling and Neurobiology of Metabolism, Yale School of Medicine, New Haven, United States
    Contribution
    Resources, Supervision, Writing - review and editing
    Competing interests
    No competing interests declared
  11. Yajaira Suárez

    1. Department of Comparative Medicine, Yale School of Medicine, New Haven, United States
    2. Program in Integrative Cell Signaling and Neurobiology of Metabolism, Yale School of Medicine, New Haven, United States
    Contribution
    Resources, Supervision, Writing - review and editing
    Competing interests
    No competing interests declared
  12. Kamal Khanna

    Department of Microbiology, New York University Langone Health, New York, United States
    Contribution
    Conceptualization, Writing - review and editing, Designed lung immune cell analyses experiments
    Competing interests
    No competing interests declared
  13. Tamas L Horvath

    1. Department of Comparative Medicine, Yale School of Medicine, New Haven, United States
    2. Program in Integrative Cell Signaling and Neurobiology of Metabolism, Yale School of Medicine, New Haven, United States
    3. Yale Center for Research on Aging, New Haven, United States
    Contribution
    Resources, Supervision, Writing - review and editing, Designed electron microscopy
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-7522-4602
  14. Marcelo O Dietrich

    1. Department of Comparative Medicine, Yale School of Medicine, New Haven, United States
    2. Program in Integrative Cell Signaling and Neurobiology of Metabolism, Yale School of Medicine, New Haven, United States
    Contribution
    Resources, Supervision, Writing - review and editing, Designed experiments and analyzed hypothalamic inflammation
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-9781-2221
  15. Maxim Artyomov

    Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, United States
    Contribution
    Resources, Formal analysis, Supervision, Writing - review and editing
    Competing interests
    No competing interests declared
  16. Andrew Wang

    1. Department of Immunobiology, Yale School of Medicine, New Haven, United States
    2. Department of Internal Medicine, Yale School of Medicine, New Haven, United States
    Contribution
    Conceptualization, Resources, Supervision, Writing - review and editing
    For correspondence
    andrew.wang@yale.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-6951-8081
  17. Vishwa Deep Dixit

    1. Department of Comparative Medicine, Yale School of Medicine, New Haven, United States
    2. Department of Immunobiology, Yale School of Medicine, New Haven, United States
    3. Program in Integrative Cell Signaling and Neurobiology of Metabolism, Yale School of Medicine, New Haven, United States
    4. Yale Center for Research on Aging, New Haven, United States
    Contribution
    Conceptualization, Supervision, Funding acquisition, Writing - original draft, Writing - review and editing
    For correspondence
    vishwa.dixit@yale.edu
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-5341-6494

Funding

National Institute on Aging (P01AG051459)

  • Vishwa Deep Dixit

National Institute of Arthritis and Musculoskeletal and Skin Diseases (AR070811)

  • Vishwa Deep Dixit

American Federation for Aging Research (Glenn Foundation for Medical Research Postdoctoral Fellowships in Aging Research)

  • Seungjin Ryu

National Institute of Allergy and Infectious Diseases (1K08AI128745)

  • Andrew Wang

G. Harold and Leila Y. Mathers Charitable Foundation (MF200400809)

  • Andrew Wang

Ludwig Family Foundation

  • Andrew Wang

Knights of Columbus

  • Andrew Wang

The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.

Acknowledgements

SR is supported by American Federation of Aging Research (AFAR) postdoctoral fellowship award. The Dixit lab is supported in part by NIH grants P01AG051459, AR070811 and Cure Alzheimer’s Fund. The Wang lab is supported in part by NIH grant 1K08AI128745 and by gifts from the Knights of Columbus, G Harold and Leila Y Mathers Charitable Foundation, and the Ludwig Family Foundation. We also thank the Yale Center on Genomic Analysis (YCGA) for RNA-seq studies and Genentech Inc for providing the anti-caspase-1 antibody.

Ethics

Animal experimentation: All experiments and animal use were conducted in compliance with the National Institute of Health Guide for the Care and Use of Laboratory Animals and were approved by the Institutional Animal Care and Use Committee (IACUC) protocol (#2019-11572 and 2020-20149) of Yale University.

Senior Editor

  1. Tadatsugu Taniguchi, Institute of Industrial Science, The University of Tokyo, Japan

Reviewing Editor

  1. Chyung-Ru Wang, Northwestern University, United States

Reviewer

  1. Berislav Bosnjak

Publication history

  1. Received: January 13, 2021
  2. Accepted: June 15, 2021
  3. Accepted Manuscript published: June 21, 2021 (version 1)
  4. Version of Record published: June 30, 2021 (version 2)

Copyright

© 2021, Ryu et al.

This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.

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