Contribution of Lipid Mediators in Divergent Outcomes following Acute Bacterial and Viral Lung Infections in the Obese Host

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Obesity represents a serious and growing global health problem. As of 2015, the mean prevalence of obesity around the world was 19.5% and with some countries reporting >30% obesity among their citizens (1). Obesity is one of the primary contributors to the development of metabolic syndrome, a group of clinical factors that increase the risk of hypertension, diabetes, stroke, dementia, and some types of cancer (1). Moreover, obesity is also considered a significant comorbidity for infectious diseases including influenza, Middle Eastern respiratory virus, SARS-CoV-2, and Streptococcus pneumoniae (2–5). In contrast, there is an interesting paradox in which obesity was correlated with patients having increased risk of developing sepsis but ultimately experiencing better outcomes compared with lean controls (6). Taken together, this suggests that obesity impacts developing immune responses that may lead to either positive or negative outcomes during infection.

The mechanism by which obesity influences immune responses is not completely understood and is often restricted to examination of changes in the cellular and molecular components of the adipose tissue itself. Typically, obesity is considered to promote a hyperinflammatory state. Adipose tissue develops populations of inflammatory macrophages that rapidly and potently respond to microbial stimulus via the production of IL-6 (7–9). Obesity also alters levels of hormones produced primarily by adipocytes that play important roles in both tissue homeostasis and regulation of inflammatory responses. For example, leptin acts to control food intake and energy expenditures but also can promote inflammatory responses (10). In contrast, the hormone adiponectin (which is routinely lower in the obese host) has potent anti-inflammatory activity in addition to being cardioprotective and a negative regulator of glucose metabolism (11).

Despite these clear connections between obesity and unconstrained inflammation there is contrasting evidence of organ-dependent impairment of immune responses in the obese host. It has been shown that pulmonary dendritic cells in obese hosts have dampened responsiveness to inhaled Ags and stimulation with TLR agonists as well as a decreased ability to stimulate T cell responses compared with lean controls (12, 13). It has also been reported that B cells among obese hosts have decreased Ab effector functions (14). Thus, there is evidence for both exacerbated inflammatory responses and dampened function of host cells to infection in obese individuals. Possible contributing features of these dichotomous findings include the tissue that is being studied, for example, adipose tissue versus lung tissue, infecting agent (virus versus bacteria), and/or type of infection (chronic versus acute and localized versus systemic). Understanding shared and unique elements that contribute to disease outcome in obese individuals is required to develop novel effective and targeted therapeutics for this population.

To begin to understand the connection between obesity and disease outcome after pulmonary infection, we compared two pathogens that cause acute disease, SARS-CoV-2 and virulent Francisella tularensis subspecies tularensis strain SchuS4 (SchuS4) in a mouse model of diet-induced obesity (DIO). The strength of comparing infection mediated by these two pathogens lies in both the dynamics of the infection and their primary interaction with the host immune response. Specifically, both infections are rapidly lethal (4–6 d) in mice and during early stages of infection evade triggering innate responses (15, 16). Moreover, both pathogens have been shown to interact and/or trigger changes in host metabolic responses that may be further influenced in the obese host (17, 18). Given the documented influence these microbes have on the host metabolic response, we compared SchuS4 and SARS-CoV-2 to interrogate the role of obesity on pulmonary immune responses to determine how this metabolic syndrome influences the outcome of infection.

In this study, we demonstrate that progression of infection with SchuS4 or SARS-CoV-2 was both impacted by obesity but with opposing outcomes. Consistent with clinical findings, obesity increased morbidity among SARS-CoV-2–infected animals (19, 20). However, DIO mice displayed statistically significant less morbidity following SchuS4 infection as exhibited by both extended mean time to death and increased survival compared with regular weight (RW) controls. Surprisingly, these differences were not attributable to changes in viral or bacterial burdens, significant alterations in immune cell composition of target organs, or exacerbated inflammatory responses in target tissues of DIO mice in either model. Rather, survival was associated with alterations in the responsiveness of immune lipid mediator (LM) pathways. Early increases in cycloxygenase-2 (COX-2)–derived PGs were shown to correlate with resistance to both SARS-CoV-2 and SchuS4 infection in RW or DIO animals, respectively. Inhibition of this early COX-2 response partially reversed resistance to infection in both models. Taken together, these findings provide novel insight into the surprising differential regulation of LMs following bacterial or viral infection and how the temporal responses of these molecules contribute to the response to infection in the obese host.

SchuS4 was provided by Jeannine Peterson (Centers for Disease Control and Prevention, Fort Collins, CO). Bacterial stocks were generated following culture in modified Mueller–Hinton (MMH) broth at 37°C with constant shaking overnight as previously described (15). Cultures were aliquoted into 1-ml samples and frozen at −80°C. For infection, bacteria were thawed and diluted as described below immediately prior to use.

SARS-CoV-2 USA/WA1/2020 was obtained from BEI Resources. Viral stocks were generated as previously described following inoculation of Vero cells (16). Cultures were aliquoted into 250-µl samples and frozen at −80°C. For infection, virus was thawed and diluted as described below immediately prior to use.

Specific-pathogen free 5-wk-old male C57BL/6J and B6.Cg-Tg(k18-hACE2)2PRLmn/J mice were purchased from The Jackson Laboratory. Mice were supplied either normal mouse chow (D12450B) containing 10% kcal from fat or high-fat chow (D12492) containing 60% kcal from fat, both obtained from Research Diets (New Brunswick, NJ). With the exception of the percent of kilocalories from fat, all other components of each diet were exactly the same. Mice were maintained on the indicated diets for 12 wk to achieve obesity (Fig. 1A). Additional male C57BL/6J mice that were maintained on either regular or high-fat chow at The Jackson Laboratory beginning at 5 wk of age were purchased at 14 wk of age. These mice were held for an additional 3 wk at the Rocky Mountain Laboratories on appropriate chow prior to use. Mice were provided food and water ad libitum. Obesity was indicated by a minimum of 30% increased weight over mice fed normal chow and statistically significant increased concentrations of leptin in target tissues (Fig. 1B, 1C). All experiments involving animals were conducted in accordance with a protocol approved by the Rocky Mountain Laboratories Animal Care and Use Committee (ASP#2020-60E). The number of mice used in each experiment is indicated in the corresponding figure legend.

Mice were infected intranasally with either SchuS4 or SARS-CoV-2. Immediately prior to infection, bacteria and virus were rapidly thawed and serially diluted into sterile PBS. Inocula for SchuS4 were confirmed by serial dilution and plating on MMH agar. Mice were anesthetized with 50–150 mg/kg ketamine + 3.5–30 mg/kg xylazine and immediately infected with 25 CFU of SchuS4 or 1000 PFU of SARS-CoV-2 in 25 μl into a single nare using a P200 pipette. Mice receiving PBS served as mock controls for SchuS4-infected animals. Mice that received tissue culture medium diluted with PBS similarly as SARS-CoV-2 inoculum served as mock controls for viral infection.

At the indicated time points mice were humanely euthanized in accordance with the American Veterinary Medical Association guidelines, and lungs, livers, and spleens were aseptically removed and assessed for pathogen burden, changes in cellular populations by flow cytometry, metabolites, and/or histopathological changes. For enumeration of bacterial loads, tissues were homogenized in tissue lysis buffer (150 mM Tris-HCl, 5 mM EDTA, and 10 mM Trizma base [pH 7.2]) containing phosphatase inhibitors I and II and protease inhibitor III (AG Scientific). For enumeration of viral loads, tissues were collected in DMEM (Thermo Fisher Scientific) supplemented with 5% heat-inactivated FBS (Atlas), 0.2 mM l-glutamate, 1 mM sodium pyruvate, 0.1 mM nonessential amino acids, and 1 mM HEPES (cDMEM5; all from Thermo Fisher Scientific).

Tissues were evaluated for bacterial loads as previously described (21). Briefly, tissues were homogenized by grinding tissues through sterile wire mesh using a syringe plunger. A portion of the homogenate was serially diluted in PBS and plated on MMH agar. Plates were incubated for 48 h prior to counting CFU. The remaining homogenate was clarified by centrifugation at 14,000 × g for 20 min and 4°C and then supernatant was stored at −80°C for subsequent analysis.

Viral burdens were determined using a 50% tissue culture infective dose assay as previously described (16). Briefly, homogenates were clarified by centrifugation at 14,000 × g for 20 min at 4°C. Clarified supernatants were stored at −80°C. Vero cells were seeded at 1 × 10⁴ cells per well in flat-bottom 96-well tissue culture plates (Corning) in cDMEM5. Twenty-four hours later medium was removed and homogenates serially diluted in cDMEM5 were added to each well. Plates were incubated for 1 h at 37°C/5% CO₂. Samples were removed and fresh cDMEM5 was added to each well. Plates were incubated at 37°C/5% CO₂ for an additional 72 h. Supernatants were then removed and 100 µl of 4% formalin was added per well. Cells were fixed for 30 min at room temperature. Formalin was removed and replaced with 25 µl of 0.1% crystal violet in methanol. Cells were incubated for 15 min at room temperature. Cells were then washed twice with distilled water and the plates were blotted dry. The 50% tissue culture infective dose was calculated using the Reed–Muench method.

Single-cell suspensions from lungs and adipose were generated as previously described with minor modifications (22, 23). Briefly, 2 g of adipose tissue from uninfected mice or lungs from the indicated animals in 2 ml of PBS were minced and incubated with 700 µg of Liberase TM (thermolysin medium) (Sigma-Aldrich) diluted in PBS. Tissues were incubated for 1 h at 37°C/5% CO₂ with intermittent shaking every 15 min. Tissues were triturated using an 18G needle and 3-ml syringe and centrifuged at 1200 rpm for 5 min at 4°C. Resulting pellets were incubated with ACK (ammonium, chloride, potassium) lysis buffer (Life Technologies) to lyse RBCs. Cells were filtered through 70-µm filters and centrifuged as above. Pellets were resuspended in PBS/2% FBS. Viable cells were enumerated using trypan blue exclusion and counted using a TC20 automated cell counter (Bio-Rad).

Single-cell suspensions generated as described above were phenotyped for specific cellular populations using flow cytometry. To identify live versus dead cells, single-cell suspensions were stained with Zombie NIR (BioLegend) according to the manufacturer’s instructions. Lung cells were then stained with the following Abs: CD11b BUV510, CD11c PerCP-Cy5.5, CD45 BUV395, CD103 BV711, CD115 BV065, CD206 PE-Cy7, CX3CR1 BV785, Ly6C allophycocyanin, Ly6G AF700, F4/80 Pacific Blue, and Siglec-F PE in the presence of 2.4G2 hybridoma supernatant to block nonspecific staining (anti-CD16/CD32; cell line provided by Dr. Jeffrey Frelinger, University of Arizona). Adipose cells were stained with the following Abs: CD11b BV510, CD45 BUV395, Ly6C PE/Dazzle 594, F4/80 AF700, CD64 PerCP-Cy5.5, and CD9 PE-Cy7 in the presence of 2.4G2 hybridoma supernatant. All Abs were purchased from BD Biosciences or BioLegend. Cells were stained for 20 min on ice and then washed twice to remove unbound Ab. Samples were then fixed with 3% paraformaldehyde, washed, and resuspended in PBS/2% FBS prior to analysis. Acquisition of cellular populations was performed on a Symphony flow cytometer (BD Biosciences) and data were analyzed using FlowJo 10 software (BD Biosciences). Representative gating schemes for the lung and adipose tissue are depicted in Supplemental Fig. 1.

Lungs, livers, and spleens were removed and fixed in 10% neutral buffered formalin with two changes of formalin for a minimum of 24 h prior to processing. Tissues were processed with a Sakura Tissue-Tek VIP-5 processor on a 12-h automated schedule using a graded series of ethanol, xylene, and Paraplast X-TRA. Paraffin-embedded tissues were sectioned at 5-µm thickness and dried overnight at 42°C prior to staining. Fixed tissue sections were stained with H&E or, as indicated, for COX-2 using anti–COX-2 Ab (Cell Signaling Technologies; clone D5H5) diluted 1:1000 with an anti-rabbit IgG polymer (ImmPRESS VR from Vector Laboratories). Tissues were processed for immunohistochemistry using a Discovery Ultra automated processor (Ventana Medical Systems) with a ChromoMap diaminobenzidine (DAB) kit (Roche Tissue Diagnostics). Stained slides were examined on an Olympus BX53 light microscope equipped with an Olympus DP74 camera and associated cellSens Dimension 1.4.1 software. Pathological analysis was performed blinded by a board-certified pathologist. Lesions were scored from 0 (no lesions) to 5 (severe).

Cells obtained from single-cell suspensions of adipose tissue were adjusted to 5 × 10⁵ cells/ml in DMEM supplemented with 10% heat-inactivated FBS, 0.2 mM l-glutamine, 1 mM HEPES buffer, and 0.1 mM nonessential amino acids (cDMEM10; all from Thermo Fisher Scientific). Cells were cultured at 1 ml/well in a 24-well tissue culture plate (Corning) overnight at 37°C/5% CO₂. Supernatants were collected and analyzed for IL-6 as described below.

IL-6 from cultured adipose tissue cells was assessed using commercially available ELISA following the manufacturer’s instructions (BD Biosciences). Cytokines in clarified homogenates from SchuS4-infected tissues were quantified utilizing cytometric bead array (BD Biosciences) according to the manufacturer’s instructions. Cytokines in clarified homogenates from SARS-CoV-2–infected samples were quantified using a Meso Scale Discovery multiplex cytokine array following the manufacturer’s instructions (U-PLEX biomarker group 1, 50-plex). Leptin in clarified homogenates was quantitated using a mouse leptin DuoSet ELISA kit following the manufacturer’s instructions (R&D Systems).

For liquid chromatography–mass spectrometry-associated processes, liquid chromatography–mass spectrometry- or HPLC-grade water, methanol, isopropanol, hexanes, methyl formate, chloroform, and acetic acid were purchased through Fisher Scientific (Waltham, MA). All LM standards were purchased from Cayman Chemical (Ann Arbor, MI).

Immune LMs were extracted from an organ section collected directly into 500-µl aliquots of ice-cold methanol containing a heavy isotope–labeled standard mix (1 ng each of d8-5-HETE, d5-RvD2, d5-lipoxin (LX)A₄, d4- leukotriene (LT)B₄, d4-PGE₂). Samples were bead homogenized using a Qiagen TissueLyser II and then centrifuged at >10,000 × g for 10 min at 4°C. The supernatant was collected and carried over to solid-phase extraction. Solid-phase extraction used a C18 column, Sep-Pak 3-ml, 200-mg, C18 cartridges (Waters, Milford, MA). Columns were conditioned with 10 ml of methanol followed by 10 ml of water. Samples were pH adjusted to increase binding by addition of 9 ml of acidified water (pH 3.5 with hydrochloric acid) and then quickly loaded. Each sample was washed with 3 ml of water prior to proceeding to the next sample. Loaded samples were washed with an additional 5 ml of water followed by 5 ml of hexanes. Samples were eluted with 10 ml of methyl formate and dried under nitrogen at 55°C. Each sample was resuspended in 200 µl of 1:1 water/methanol, and 30–40 µl of each sample was injected for analysis.

All analyses used previously established multi-reaction monitoring strategies (24). All chromatography used a Sciex ExionLC AC system, and data were acquired using a Sciex 6500+ QTRAP mass spectrometer.

LM samples were separated on a Kinetex Polar C18 (100 Å, 2.6 µm, 3 × 100 mm) using a binary gradient of A) 0.01% acetic acid in water and B) 0.01% acetic acid in methanol as previously described (24). A 20-min gradient from 40 to 100% B was used to separate species. Samples were detected in a negative multi-reaction monitoring mode with triggered enhanced-product ion scans for post hoc spectral identification. Spectral information was compared with standards and a spectral library for identification. A blank and a standard mix were serially injected every 10 injections. Standard mix consisted of each of the following compounds at 10 ng/ml: RvE1, LXA₄, LXA₅, LXB₄, PGE₂, PGD₂, PGF_2a, PGJ₂, TxB2, PD1, PDX, RvD5, Maresin 1, LTB₄, 5,15-DiHETE, 14-HDHA, 18-HEPE, 13-HODE, 12(13)-EpOME, 9(10)-EpOME, 12,13-DiHOME, 9,10-DiHOME, arachidonic acid (AA), EPA, DPA, DHA.

All data were processed using Sciex OS software 2.0.0. LM datasets were assessed for signal quality visually with a minimum signal-to-noise rubric of 3. Signal stability was assessed by repeat injection of the standard mix. LM data were normalized to internal heavy isotope standards as previously described (24). All multivariate and univariate analyses were performed in MarkerView software 1.3.1 or MetaboAnalyst 5.0.

To inhibit COX-2 in vivo, mice were treated with 100 mg/kg of the COX-2 specific inhibitor ns-398 (Cayman Chemical) via i.p. injection. Mice infected with SchuS4 were treated on day 1 postinfection and mice infected with SARS-CoV-2 were treated on days 1 and 2 postinfection. Mice treated with DMSO diluted similarly to ns-398 served as vehicle-treated controls.

Statistical analysis between group means was determined using an unpaired t test or one-way ANOVA followed by a Tukey’s multiple comparison tests to compensate for type I error where indicated. Differences in survival between groups was determined using a Mantel–Cox test. In all analyses, statistical significance was set at p < 0.05.

To determine how obesity might contribute to susceptibility to respiratory infection we first established DIO in mice using a previously described model (25). After 12 wk of feeding animals on the prescribed diets described in the Materials and Methods, mice on a high-fat diet (DIO) had ≥30% higher weights compared with RW controls (C57BL/6 DIO: 47 ± 2 g versus RW: 30 ± 5 g; k18-hACE2 DIO: 47 ± 3 g versus RW: 33 ± 4 g) (Fig. 1A, 1B). Concentrations of leptin in homogenates of lung tissues of mock-infected controls harvested the day after infecting mice were statistically significantly increased in DIO animals (Fig. 1C). With the model of DIO established, we next intranasally infected mice with either 1000 PFU of SARS-CoV-2 or 25 CFU of SchuS4 and monitored mice for endpoint criteria. Similar to other viral infections, obese mice displayed significantly increased susceptibility to SARS-CoV-2 infection compared with RW mice (Fig. 1D) (2, 3). In contrast, animals with DIO were less susceptible to SchuS4 infection in terms of both significantly extended mean time to death (RW: 4.2 ± 0.3 d versus DIO: 5.4 ± 0.5 d) and overall survival compared with RW mice (Fig. 1E). Taken together, these results demonstrate differences in resistance to acute viral and bacterial pulmonary infections among mice with DIO.

Morbidity and mortality following infection is typically associated with an inability to constrain microbial replication. Therefore, we next assessed viral (Fig. 2A) and bacterial (Fig. 2B) burdens over time in RW and DIO mice. DIO mice began to succumb to infection on day 6 after SARS-CoV-2 inoculation, and all RW mice were displaying endpoint criteria by day 5 following SchuS4 inoculation; therefore, viral and bacterial burdens were assessed on days 2, 4, and 5 and days 1, 2, 3, and 4 postinfection with SARS-CoV-2 or SchuS4, respectively. No statistically significant differences in pulmonary burdens of SARS-CoV-2 among RW and DIO mice were detected at any time point assessed (Fig. 2A). Because SchuS4 routinely causes fulminant disseminated infection resulting in high bacterial burdens in peripheral tissues, we also assessed bacterial loads in the spleen and livers of DIO and RW mice in addition to the lungs. Obese mice infected with SchuS4 did have small, but significantly lower bacterial burdens on days 1 and 3 in the lungs and day 3 in the liver compared with RW animals (Fig. 2B). However, there were no differences in bacterial burdens in the spleen throughout the infection, and by day 4 after inoculation there were no statistically significant differences in bacterial loads in any organ (Fig. 2B). These data show that neither increased susceptibility nor resistance to infection among DIO mice was likely due to the differential ability to control microbial replication.

Obesity leads to increased numbers of inflammatory cells in adipose tissue (8). However, cellular composition and potential recruitment of inflammatory cells to the lung among obese animals are not well characterized. It was possible that, similar to adipose tissue, mice with DIO may have had increased numbers of inflammatory cells in the pulmonary compartment that contributed to our observed outcomes described above. There may also have been dysregulation in the recruitment of key innate cells that resulted in either increased susceptibility or resistance to infection. Therefore, we first established that we observed a hyperinflammatory phenotype in terms of cellular composition and cytokine production in adipose tissue of uninfected DIO mice compared with uninfected RW animals, consistent with previous reports. As expected, adipose tissues from DIO animals were enriched with inflammatory macrophages and monocytes (Supplemental Fig. 2A, 2B) (8). Cells isolated from adipose tissue from DIO mice also spontaneously secreted significantly higher concentrations of IL-6 compared with cells isolated from adipose tissue of RW mice (Supplemental Fig. 2C). We then compared cellular composition in the lungs of RW and DIO mice prior to infection and throughout the course of disease. Unlike the adipose tissue, we did not observe any significant differences in overall cellularity or populations of innate cells (e.g., alveolar macrophages, interstitial macrophages, monocytes, or neutrophils) in the lungs of resting DIO mice compared with RW animals (Fig. 3A, 3B). Following infection and regardless of the infecting pathogen, there were no changes in the alveolar macrophage populations over time (Fig. 3A, 3B). There were also no significant differences in neutrophil populations throughout infection with SARS-CoV-2–infected RW and DIO mice (Fig. 3A). By day 5 after SARS-CoV-2 infection both RW and DIO animals had significantly greater numbers of interstitial macrophages compared with mock-treated (day 0) controls, but these numbers were not different between the two diet conditions (Fig. 3A). Similarly, RW animals had significantly higher numbers of monocytes at day 5 postinfection, but these values were not significantly different from DIO animals at the same time point (Fig. 3A). This increase in monocytes was consistent with previous reports in SARS- and SARS-CoV-2–infected k18-hACE2 mice or mice transduced with adenovirus expressing hACE2 (26, 27). SchuS4 infection did not induce changes in interstitial macrophages in either group of mice (Fig. 3B). In contrast, both monocyte and neutrophil populations were significantly increased in both RW and DIO mice on days 3 and 4 after SchuS4 infection compared with mock (day 0) controls (Fig. 3B), which was consistent with previously published work on RW animals (28). However, once again these populations were not significantly different between RW and DIO mice at these time points (Fig. 3B). Thus, fundamental differences in cellular composition at baseline or throughout the course of infection did not explain either enhanced susceptibility or resistance of DIO mice in either infection model.

Although the overall cellular content as quantitated by flow cytometry was not different among RW and DIO mice at baseline or following their respective infections, it was possible that the organization of these cells in lesions might vary between diet conditions. Therefore, we assessed pathological changes following infection that could indicate diet-dependent differences in the control of tissue damage. Mock-infected mice did not demonstrate pulmonary, hepatic, or splenic changes with the exception of vacuolar degeneration in the livers of DIO mice commensurate with consumption of a high-fat diet (Fig. 4). Mice infected with SchuS4 developed lung, liver, and splenic lesions consisting of acute neutrophilic and histiocytic inflammation with necrosis as has been previously described for SchuS4-infected mice (29). Lung lesions, when present, were considered mild due to a low distribution but marked local tissue inflammation and damage. These lesions were characterized by an abundance of viable and degenerate neutrophils and fewer macrophages, which filled bronchioles and obscured alveoli (Fig. 4). Other features included reactive endothelial cells and vasculitis in regionally associated vessels and hyperplasia of the pleural mesothelium over affected foci (Fig. 4). The liver of SchuS4-infected animals developed mild to moderate necrotizing hepatitis consisting of multiple foci of hepatocellular necrosis with inflammation by viable and degenerate neutrophils and macrophages (Fig. 4). Following infection, spleens developed minimal to moderate, multifocal to coalescing foci of necrosis with loss of lymphoid follicles and replacement by moderate numbers of viable and degenerate neutrophils and necrotic debris. When present, similar inflammation occurred in the red pulp (Fig. 4).

Mice infected with SARS-CoV-2 developed lung lesions typical for SARS-CoV-2 interstitial pneumonia in mice as has been previously described (17). Lesions were not evident in lung tissues until day 4 postinfection. At this time point, both RW and DIO animals developed lesions of mild, multifocal interstitial pneumonia (Fig. 4). Of note, lung lesions in both the RW and DIO groups were rare but similar in features with the exception of perivascular inflammation, which was more modestly visibly prevalent in the DIO group at this time point.

Timely initiation and retraction of proinflammatory cytokine and chemokine responses is a central element in both the control and survival of infectious diseases, including those that are initiated in the lung (30). DIO mice have been shown to have heightened production of IL-6 at baseline in the adipose tissue, which we also confirmed in our model (7, 9) (Supplemental Fig. 2). Extension of this dysregulation of production of cytokines in the lungs of DIO mice may help explain their difference in susceptibility or resistance to SARS-CoV-2 and SchuS4, respectively. Therefore, we compared production of cytokines and chemokines commonly associated with antimicrobial responses, for example, TNF-α and IFN-β, as well as development of unconstrained pathology, for example, IL-6 and MCP-1, in RW and DIO mice following SARS-CoV-2 or SchuS4 infection. Among SARS-CoV-2–infected mice, most molecules were below the level of detection on day 2 postinfection, peaked at day 4, and had either completely or partially retracted by day 5 (Fig. 5A). With the exception of TNF-α and IL-6 on day 5 postinfection (in which both cytokines were significantly lower in DIO mice compared with RW mice), there were no significant differences in concentrations of cytokines among these two groups of mice at any time point (Fig. 5A). Similar to SARS-CoV-2–infected animals, cytokines and chemokines were not detected on day 2 after SchuS4 infection. In agreement with previous work, these molecules were readily detected on day 3 after SchuS4 infection, and in RW mice increased production of these molecules was detected on day 4 postinfection (Fig. 5B) (31, 32). On days 3 and 4 after SchuS4 infection DIO mice had significantly lower TNF-α as well as significantly lower IL-6 and CXCL1 on day 4 compared with RW controls (Fig. 5B). This significant reduction in cytokines and chemokines among DIO mice infected with SchuS4 did not correlate with differences in bacterial loads observed in RW and DIO mice. That is, although DIO animals had less production of proinflammatory cytokines and chemokines at these time points, they had similar bacterial loads as RW mice. Thus, while both SARS-CoV-2– and SchuS4-infected DIO mice had some degree of reduction in the production of proinflammatory responses, these did not positively correlate with microbial burden or differences in recruitment of inflammatory cells. These data suggested that blunted production of proinflammatory cytokines was a common feature among DIO animals regardless of infecting agent and therefore could not predict susceptibility to infection. These data also pointed to the possibility that there could be dysregulation of additional pathways in DIO mice that either enhance or decrease their susceptibility to viral and bacterial infection, respectively.

Obesity is a disease state that features dysregulated metabolic homeostasis leading to the development of secondary metabolic diseases such as diabetes and hepatic steatosis (1). This disruption of metabolic homeostasis could also functionally alter the processes that connect metabolism and the immune response. One collection of metabolites that have profound effects on initiation and resolution of inflammation are the oxidized products of polyunsaturated fatty acids collectively referred to as LMs. Obesity is known to affect the resting systemic levels of LMs, and some of these obesity-induced changes persist even in obese individuals that have returned to regular or lean status (33). Furthermore, LMs also exert changes in organ function that are independent of production of cytokines or recruitment of inflammatory cells (34). Studies by our group and others have demonstrated that clinical LM levels closely correlated with disease severity in hospitalized cases of SARS-CoV-2 patients (24, 35). Additionally, members of the PG family of LMs have been shown to be responsive in mouse models of attenuated F. tularensis infection (36, 37). Thus, we hypothesized that varied triggering of LM pathways among DIO and RW mice may contribute to differential outcomes observed in the SARS-CoV-2 and SchuS4 infection models.

Panels of LMs and LM metabolites were assessed by targeted liquid chromatography–tandem mass spectroscopy in lung samples at each time point following infection in each model. Multivariate principal component analysis (PCA) indicated time-dependent events in the pulmonary LM milieu that varied between RW and DIO in both infection models. RW and DIO k18-hACE2 mice exhibited a slight preinfection separation along the first principal component (PC1), with DIO lungs being shifted in the positive direction (Fig. 6A). PC1 was defined by positive loading of most LM species assayed, suggesting elevated levels of multiple polyunsaturated fatty acid (PUFA) and LM species in the DIO lungs in the absence of infection (Fig. 6B). Two temporal events were evident following SARS-CoV-2 infection by PCA. The first was observable as a positive shift along PC3 among RW lungs on day 2 postinfection (Fig. 6A, cyan arrow), which was positively loaded with COX and CYP450 products (Fig. 6B, purple and orange circles). The second was a separation along PC1 in RW lungs on day 6 postinfection (Fig. 6A, red arrow). These results indicated an early COX and CYP450 response and a subsequent late response across all LM species in the RW lungs that was dampened in the DIO lungs.

In the SchuS4 model, preinfection of C57BL/6J DIO lungs also shifted positively along PC1 compared with RW samples, indicating a diet-associated increase in PUFAs, LMs, and LM metabolites consistent with the k18-hACE2 mice (Fig. 6C, 6D). On day 1 postinfection, DIO but not RW lungs shifted along a vector that was positive along PC1 and negative along PC3 (Fig. 6C, cyan arrow). A similar pattern, but to a lesser extent, was observed on day 2. This positive PC1 and negative PC3 vector that marks the day 1 DIO response was most strongly associated with COX products (Fig. 6D, purple circle) but also defined by the coherent behavior of CYP450 products and ALOX12/15 products. Collectively, these multivariate analyses showed an early COX- and CYP450-associated response in both the SARS-CoV-2 and SchuS4 models associated with increased survival. Furthermore, survival of SchuS4 was also associated with increased production of ALOX12/15 products.

These observations from multivariate analysis were further confirmed when LM species were considered individually across each infection course organized by the enzymes responsible for their synthesis. Preinfection elevation of PUFAs was evident in DIO lungs from both models, and various other LM metabolite families also trended upward (Supplemental Fig. 3A, 3B). Among SARS-CoV-2–infected lungs the early (day 2) LM response in RW lungs was clearly dominated by COX products including PGE₂, PGD₂, and PGF_2a and secondary keto derivatives of these PG species (Fig. 7A). Notably, this effect was almost entirely absent in DIO lungs. Among SARS-CoV-2–infected animals the CYP450 contribution to the day 2 pattern observed by PCA was evident to an equal extent in both DIO and RW lungs and thus suggested a non–diet-dependent effect of infection (Supplemental Fig. 3A). The day 6 LM response in these animals observed by PCA was evenly distributed across LM families suggestive of a nonspecific oxidative event in the late stage of the infection.

In the SchuS4 model, DIO lungs also exhibited elevated PUFAs consistent with a diet-induced effect prior to infection (Supplemental Fig. 3B). The LM response observed by PCA from samples at day 1 postinfection was evident in elevated COX-derived PGs and secondary PG metabolites in both DIO and RW mice with the magnitude of the effect considerably larger in DIO lungs (Fig. 7B). Unlike the early SARS-CoV-2 LM pattern in RW lungs, the early induction of LMs in SchuS4-infected DIO lungs also featured contributions from CYP450 and ALOX12/15 LMs and LM metabolites (Supplemental Fig. 3B).

Given the role of PGs as immune regulatory compounds, we speculated that the early response of these compounds may be essential in establishing a protective immune response in pulmonary infection. In both the SARS-CoV-2 and SchuS4 models we observed a positive correlation of early COX activity with increased survival. Specifically, these products were increased in the first 1–2 d in both RW mice infected with SARS-CoV-2 and DIO mice infected with SchuS4 (Fig. 6A, 6B). Synthesis of PGs can be initiated by either COX-1 or COX-2. However, COX-1 is constitutively expressed whereas COX-2 must be induced. Therefore, we next determined whether COX-2 was upregulated in tissues following SchuS4 or SARS-CoV-2 infection. Mock-infected animals presented with COX-2–positive cells primarily associated with the bronchiolar epithelial cells. This staining was similar among C57BL/6 and k18-hACE2 mice (Fig. 8). Following infection, increased COX-2 staining was not evident in SchuS4-infected mice until day 3 with additional staining evident in macrophages and endothelial cells in the foci of pneumonia (Fig. 8). Among SARS-CoV-2–infected animals a similar pattern of COX-2 staining in foci of pneumonia was evident on day 4 postinfection, but to a lesser extent than that found in SchuS4-infected animals (Fig. 8). Therefore, COX-2 production was increased postinfection and included expansion of the cellular source to macrophages but was not qualitatively different among RW and DIO animals.

Although we did not observe increased staining of COX-2 on day 1 or 2 following infection or obvious differences in COX-2 staining among RW and DIO mice, this staining was qualitative at best and may not reveal subtle changes that contribute to generation of downstream lipids. Additionally, there may have been difference in the activity of phospholipase A₂ required to liberate AA or the location of this enzyme and its proximity to the AA substrate that may have contributed to differential production of downstream mediators observed in the LM profile (Fig. 6). For example, if COX-2 expression was increased in a cell type that was not enriched for free AA by either inadequate phospholipase activity or inadequate lipid pools, the response would be limited by substrate transport. Therefore, it was still possible that activity of COX-2 was contributing to survival in DIO and RW mice; therefore, we hypothesized that early inhibition of COX-2 in DIO and RW mice infected with SchuS4 or SARS-CoV-2, respectively, would increase their susceptibility to infection. RW and DIO mice were intranasally infected with either SARS-CoV-2 or SchuS4. RW mice were treated with the COX-2 inhibitor ns-398 on days 1 and 2 postinfection with SARS-CoV-2 whereas DIO mice were treated with this inhibitor on day 1 after SchuS4 infection. In agreement with our hypothesis, early inhibition of COX-2 increased both RW and DIO animals’ susceptibility to infection with either SARS-CoV-2 or SchuS4, respectively, compared with the vehicle-treated controls (Fig. 9). Therefore, early synthesis of COX-2 products is one requirement for survival of acute pulmonary infection regardless of the infecting pathogen. Additionally, this COX-2 response was affected by obesity but resulted in opposing patterns dependent on the infectious agent.

One of the primary contributors to morbidity following Francisella infection is the overproduction of cytokines following widespread organ damage. Attenuation of this response was observed in DIO animals infected with SchuS4. It has been demonstrated that early production of PGs may influence subsequent immune responses, including resolving processes (38). Therefore, we hypothesized that inhibition of COX-2, and thus the resulting PGs, among DIO animals infected with SchuS4 would increase production of cytokines associated with morbidity. RW and DIO mice were infected with SchuS4 and treated with either vehicle or ns-398 at the day postinfection. Because most RW animals show clinical sings of illness that require euthanasia between day 4 and 5 of infection, we assessed bacterial loads and the presence of cytokines at this time point in all mice. As expected from the absence of early COX-2 LM production in RW mice, we did not observe any statistically significant differences in bacterial loads or concentration of cytokines in the lung, liver, or spleen among RW mice treated with ns-398 compared with vehicle controls (Fig. 10 and data not shown). In contrast, bacterial burdens in the lungs and livers of DIO mice treated with ns-398 returned to levels observed in RW animals (Fig. 10). Similarly, ns-398 treatment also resulted in the significant elevation of lung cytokine concentrations in DIO mice infected with SchuS4 to concentrations that were comparable to RW mice infected with SchuS4. Therefore, one mechanism by which early production of COX-2–associated lipids improved survival of SchuS4 in DIO mice is to temper the “cytokine storm” typically associated with morbidity in Francisella infections.

Obesity is a growing epidemic that contributes to premature morbidity and mortality in the world’s population. Although there is clear evidence that obesity exacerbates many disease states, including cancer, diabetes, and heart disease, its influence on the outcome of infectious disease (especially respiratory) is less well described. In this study, we compared the influence of obesity in acute pulmonary infections driven by a virus or bacterium, SARS-CoV-2 and F. tularensis strain SchuS4, respectively. In addition to the acute nature of these infections, a strength of this comparison was that both pathogens initially fail to provoke inflammatory responses in the host as part of their pathogenesis, and it is thought that morbidity is largely driven by dysregulated host responses following pathogen-triggered cell death (15, 16, 39, 40).

In the present study, we made a number of surprising findings. First, in contrast to SARS-CoV-2, DIO mice infected with SchuS4 had a small but statistically significant increase in survival. Among DIO animals that did succumb to SchuS4 infection, we observed a statistically significant increase in mean time to death over RW controls. Second, the relative susceptibility of DIO mice to partially resist or succumb to infection in either model was not attributable to consistent differences in control of pathogen replication, recruitment of inflammatory cells, or overexuberant production of proinflammatory cytokines. In fact, regardless of the infecting agent, DIO mice were observed to have similar or significantly less concentrations of proinflammatory cytokines and chemokines in the lungs compared with RW controls, especially at the later time points of infection. Lastly, we found that generation of COX-2 products early in either infection positively correlated with survival and that blocking this response resulted in increased susceptibility to infection.

Roles for obesity contributing to poor outcomes following infection with attenuated strain of F. tularensis (live vaccine strain [LVS]) and mouse-adapted SARS-CoV-2 have been described (41–43). In a study utilizing LVS, the authors found a positive correlation between obesity and morbidity. However, in contrast to our current findings, the authors not only used an attenuated strain of Francisella but also employed female mice that had only been on a high-fat diet for 9 wk. Among the studies examining the role of obesity in SARS-CoV-2 infection, Zhang et al. (43) also found an increase in morbidity among obese mice as indicated by increased weight loss and higher viral loads. We did not observe differences in these two parameters. Furthermore, Zhang et al. used mouse-adapted SARS-CoV-2, female mice, and the db/db model of obesity. This model is very different from DIO mouse models in that db/db animals lack the ability to respond to leptin, a hormone critical for sensing of satiety, due to a mutation in the leptin receptor that disrupts signaling. Leptin is also an important adipokine that participates in the regulation of inflammatory responses (10). A common feature of both of these reports was the use of female mice. In our hands, we did not observe a difference among wild-type male and female mice to succumb to virulent Francisella or SARS-CoV-2 infection. However, it is possible that under conditions with specific metabolic and/or immunologic perturbations differences in sex may be an important feature. Therefore, in addition to utilization of an attenuated strain of Francisella and the shortened time on a high-fat diet in the first report, and the use of db/db animals in the report focused on SARS-CoV-2, sex of the mice could explain our disparate findings. Finally, in a study by Rai et al. (41) they used a replication-deficient adenovirus vector to generate expression of hACE2 in pulmonary tissue. However, obese mice had increased morbidity following exposure to the adenovirus vector that impeded the ability to assess how obesity might play a role in SARS-CoV-2 infection.

Our data also revealed one mechanism for the differential ability of obese mice to control Francisella and SARS-CoV-2 infection by clearly demonstrating a role for specific, temporally produced LMs in survival of either SchuS4 or SARS-CoV-2. Although the PUFA precursors of COX-2 products were elevated in DIO lungs prior to infection, the intensity of the early phase COX-2 response correlated with improved outcome of infection and not with the presence or absence of obesity. PGs are synthesized enzymatically by a cascade involving the liberation of AA from membrane phospholipids by a phospholipase, conversion of AA to the cyclized epoxide PGH₂ by a cyclooxygenase, and finally conversion of PGH₂ to PGs by a PG synthase. As PGH₂ is an unstable molecule, the proximity of enzymes in this pathway is likely important for the controlled production of target PGs (44). An important distinction between the observed COX-2 response in SARS-CoV-2– versus SchuS4-infected lungs was that the SARS-CoV-2 response was largely limited to PGE₂, PGD₂, and PGF_2a and the corresponding downstream 15-keto degradation products of these molecules. Conversely, the SchuS4 response was widely distributed across COX-2 products including the 6k-PGF_1a metabolite derived from PGI₂, a pathway known to compete catalytically and functionally with PGE₂ synthesis. This contrasting behavior suggested that the SchuS4 response may be regulated at the substrate or COX-2 level but not the PG synthase level, whereas the higher specificity of the SARS-CoV-2 PG response suggested regulation of individual synthases.

Roles for COX-2 products in both SchuS4 and SARS-CoV-2 infections have been described and/or postulated. PGE₂ was found to be triggered in the lungs following infection with attenuated F. tularensis LVS (36). However, PGE₂ did not appear to play a role in the innate response in that model. Rather, the activity of PGE₂ in that model was found to modulate the ensuing T cell response during the adaptive phase of immunity (36). In murine models of SARS infection, PGD₂ was described to contribute to lethality depending on the age of the infected animal (45, 46). However, a broad examination of the generation of LMs and/or a kinetic of LM production, including the PG family, has not been reported. In our current study, we add a critical component to understanding the role of these molecules in pulmonary infection by mapping the kinetic behavior of these COX-2–mediated responses. Specifically, we found that production of COX-2–associated lipids following either SARS-CoV-2 or SchuS4 infection was temporally limited to the first few days of infection and was followed by a subsequent reduction in PGs to baseline or below baseline levels. This suggested that removal of COX-2 products may be important for regulating the pulmonary immune response, and a detailed understanding of disease progression may be necessary to apply therapies targeting COX-2 in pulmonary infection. Regardless, these data point to an essential role for COX-2 products early postinfection in both models.

Although we did establish that COX-2 activity in SchuS4-infected DIO mice was correlated with dampening the cytokine storm associated with morbidity and mortality in this infection model, we were not able to fully define the mechanisms by which early production of PGs were working to improve the outcome of SARS-CoV-2 infection. Given that there were few differences in the kinetics and intensity of the cytokine and chemokine response among RW and DIO mice, we hypothesize that the protective role of PGs may be different in SARS-CoV-2. Recently, we have reported that one of the primary contributors of morbidity following SARS-CoV-2 infection was not an unconstrained inflammatory response, but rather targeted damage of the vasculature via signaling of damage-associated molecular patterns through receptor for advanced glycation end products (RAGE) that contributed to insufficient oxygen exchange and ultimately death (16). COX-2 products have been reported to protect mucosal cells from cell death (47). Therefore, the rapid production of COX-2–derived lipids in RW animals infected with SARS-CoV-2 may have helped to protect the endothelium from damage mediated by RAGE. We did assess lung tissues from SARS-CoV-2–infected RW and DIO mice for expression of RAGE and did not observe any differences in expression of this receptor among the different groups (data not shown). However, there still could be differences in the type or concentration of damage-associated molecular patterns released in RW and DIO animals infected with SARS-CoV-2 that influences outcome. Therefore, additional experimentation to define the mechanisms and specific COX-2 products at work in these models are the subjects of ongoing work.

Lastly, although we demonstrate a clear role for COX-2–derived products in protective immune responses at early phases of infection, we also observed distinct changes in other LM species that correlated with survival. These changes were more pronounced in SchuS4 compared with SARS-CoV-2–infected animals. For example, in addition to COX-2–derived lipids, SchuS4-infected DIO mice also had a notable increase in ALOX-12/15 and CYP450 products. The wide range of changes in lipid species among SchuS4-infected mice may reflect the systemic nature of this disease compared with SARS-CoV-2, which is primarily restricted to the lung. Alternatively, differences in the nature of the activating signal that triggers LM production early in infection may have contributed to differences in LM production observed at late stages of SARS-CoV-2 and SchuS4 infection. Further work is needed to determine the conserved nature of these early pulmonary LM signatures between other bacterial and viral pathogens.

Taken together, our data show that obesity can drive differential outcomes in acute pulmonary infections and that these outcomes are correlated with the intensity of early responses in LMs. The SARS-CoV-2 LM data presented in the present study, which show a protective role for COX-2 products in the RW host, are consistent with our previously published work in hospitalized COVID-19 patients. Specifically, patients requiring admission to the intensive care unit and intubation exhibited decreased peripheral levels of COX-2 products compared with patients with less severe disease (24). This increase of PGs observed in the patients with moderate disease was inversely correlated with obesity. Therefore, our present findings extend those observations by demonstrating that the COX-2 response early postinfection (often prior to detectable production of cytokine or tissue damage) was an essential element for survival of both SARS-CoV-2 and SchuS4. Continued investigation into the role of LMs throughout the course of infection in both RW and the obese host will provide novel targets for host directed therapies.

We thank Drs. Matt Edin and Darryl Zeldin (National Institute on Environmental Health Sciences) for advice for inhibiting COX-2 activity in vivo. We thank Dr. Charles Serhan and the members of the Serhan laboratory (Brigham and Women’s Hospital, Harvard Medical School) for methodological training in LM detection. We thank Drs. Paul Norris (AB Sciex) and Mackenzie Pearson (AB Sciex, currently Eli Lilly & Co.) for further analytical method consultation. We also thank Rebecca Rosenke (Rocky Mountain Veterinary Branch) for processing and staining tissue sections, and Anita Mora and Austin Athman (National Institute of Allergy and Infectious Diseases Research Technologies Branch) for assembling the histology images.

The authors have no financial conflicts of interest.