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. 2021 Feb 8:11:614697.
doi: 10.3389/fimmu.2020.614697. eCollection 2020.

The Systemic and Cellular Metabolic Phenotype of Infection and Immune Response to Listeria monocytogenes

Robert M Johnson  1 , Adesola C Olatunde  1 , Lauren N Woodie  2 , Michael W Greene  2 , Elizabeth Hiltbold Schwartz  1
Affiliations

The Systemic and Cellular Metabolic Phenotype of Infection and Immune Response to Listeria monocytogenes

Robert M Johnson et al. Front Immunol. .

Abstract

It is widely accepted that infection and immune response incur significant metabolic demands, yet the respective demands of specific immune responses to live pathogens have not been well delineated. It is also established that upon activation, metabolic pathways undergo shifts at the cellular level. However, most studies exploring these issues at the systemic or cellular level have utilized pathogen associated molecular patterns (PAMPs) that model sepsis, or model antigens at isolated time points. Thus, the dynamics of pathogenesis and immune response to a live infection remain largely undocumented. To better quantitate the metabolic demands induced by infection, we utilized a live pathogenic infection model. Mice infected with Listeria monocytogenes were monitored longitudinally over the course of infection through clearance. We measured systemic metabolic phenotype, bacterial load, innate and adaptive immune responses, and cellular metabolic pathways. To further delineate the role of adaptive immunity in the metabolic phenotype, we utilized two doses of bacteria, one that induced both sickness behavior and protective (T cell mediated) immunity, and the other protective immunity alone. We determined that the greatest impact to systemic metabolism occurred during the early immune response, which coincided with the greatest shift in innate cellular metabolism. In contrast, during the time of maximal T cell expansion, systemic metabolism returned to resting state. Taken together, our findings demonstrate that the timing of maximal metabolic demand overlaps with the innate immune response and that when the adaptive response is maximal, the host has returned to relative metabolic homeostasis.

Keywords: Listeria (L.) monocytogenes; immunometabolism; life history theory; metabolic phenotype; sickness behavior.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Infection-induced weight loss and lethargy. Analysis of change in (A) body weight (B) activity, or (C) sleep over the 12-day experimental period. For each parameter, the values were averaged for each individual mouse over the 12-h light/dark cycle. Each data point represents the combined average of the five animal group over a 12-h light/dark cycle. Significance was assessed using one-way ANOVA followed by a Dunnett Test for multiple comparison. Data are represented as mean ± SD. A indicates a significant between High Dose - Control and B indicates a difference between Low Dose - Control at a p =/< 0.05. Control (n = 6), High Dose (n = 5), Low Dose (n = 5).
Figure 2
Figure 2
Infection-induced changes to systemic metabolism. Analysis of change in (A) Metabolic rate (VO2), (B) Energy Expenditure, or (C) Respiratory Exchange Ratio (VCO2/VO2) over the 12-day experimental period. Each data point represents the average for a 12-h light/dark cycle. The Weir Equation was used to calculate EE (kcal/h = 60 × (0.003941 × V̇O2 + 0.001106 × V̇CO2), and an ANCOVA was utilized to adjust EE for bodyweight. Significance was assessed using one-way ANOVA followed by a Dunnett Test for multiple comparison. Data are represented as mean ± SD. A indicates a significant between High Dose - Control and B indicates a difference between Low Dose - Control at a p =/< 0.05. Control (n = 6), High Dose (n = 5), Low Dose (n = 5). (D) The HOMA-IR was used to determine insulin resistance in mice infected the high dose at various time points across the experiment. *p < 0.05.
Figure 3
Figure 3
Linear Regression of Activity and VO2 during the course of the infection. Linear Regression at (A) Pre-infection, (B) Night 4, (C) Night 5, & (D) Night 6 post infection. Dotted regions represent the 95% Confidence interval. Control (n = 6), Infected (n = 5).
Figure 4
Figure 4
Bacterial Enumeration and Listeria-specific T cell response. Bacterial burden at various time points following a Listeria infection in the (A) spleen and (B) liver. LOD indicated limit of detection. Data are represented as mean ± SD. High Dose (n = 5), Low Dose (n = 5). (C) Listeria-specific IFN-γ producing T cells were enumerated at various time points post infection in the spleen following a Listeria infection. Statistical analysis was performed by a Mixed-effect analysis followed by a Dunnett Test for multiple comparison. Data are represented as mean ± SD. * indicates a significant between High Dose (n = 5) - Control (n = 3) and # indicates a difference between Low Dose (n = 5) - Control (n = 3). *p < 0.05, # p < 0.05. v indicates a mouse death in the day 7 post infection low dose group.
Figure 5
Figure 5
Changes in cellular metabolism in Ly6C and Ly6G-expressing cells. (A) Percent of splenocytes expressing Ly6C following Lm infection and (B) Percent of Ly6C+ cells expressing Glut1 over the course of the infection. (C) Percent of Ly6Cint or (D) Ly6Chi cells expressing Glut1. (E) Percent of splenocytes expressing Ly6G following Lm infection and (F) Percent of Ly6G+ cells expressing Glut1 over the course of the infection. Significance was assessed using one-way ANOVA followed by a Dunnett Test for multiple comparison. Data are represented as mean ± SD. High Dose (n = 5) - Control (n = 3). *p < 0.05, **p < 0.01, ***p < 0.005, ****p < 0.0001.
Figure 6
Figure 6
Markers of cellular metabolism of T cells. Percent of splenocytes expressing (A) CD3+ (T cells) and (B) percent of CD3+ cells expressing Glut1 over the course of the infection. (C) Representative flow cytometric plots of CD3 and Glut1 on T cells on uninfected controls and at day 3 and 14 post infection. Significance was assessed using one-way ANOVA followed by a Dunnett Test for multiple comparison. Data are represented as mean ± SD. High Dose (n = 5) - Control (n = 3). *p < 0.05, **p < 0.01, ***p < 0.005.
Figure 7
Figure 7
Summary of metabolic changes against the backdrop of immune response over the course of a primary infection. (A) Illustration of the magnitude of bacterial infection, innate response, and adaptive (T cell) response over time. (B) Summary of changes in systemic and cellular metabolic phenotype over time. Long arrows denote days at which most significant differences were observed in specific readout vs. uninfected control. Shorter arrows indicate direction of change in a specific readout. BW, body weight; EE, energy expenditure; HOMA-IR, Homeostatic Model of Insulin Resistance; RER, Respiratory Exchange Ratio; VO2, metabolic rate.
Figure 8
Figure 8
Hypothetical model of findings within the context of life history theory. Upon infection, immune cells produce cytokines which act on both brain and muscle. The effects on the brain lead to sickness behavior and cause tradeoffs with activity, reproduction and growth. Cytokines acting on muscle leads to insulin resistance and increased systemic lipid utilization, allowing increased circulating glucose to be used by immune cells as they proliferate and develop effector function. Image created in Biorender.com.
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