Frontline Science: Endotoxin-induced immunotolerance is associated with loss of monocyte metabolic plasticity and reduction of oxidative burst

Abstract

Secondary infections are a major complication of sepsis and associated with a compromised immune state, called sepsis-induced immunoparalysis. Molecular mechanisms causing immunoparalysis remain unclear; however, changes in cellular metabolism of leukocytes have been linked to immunoparalysis. We investigated the relation of metabolic changes to antimicrobial monocyte functions in endotoxin-induced immunotolerance, as a model for sepsis-induced immunoparalysis. In this study, immunotolerance was induced in healthy males by intravenous endotoxin (2 ng/kg, derived from Escherichia coli O:113) administration. Before and after induction of immunotolerance, circulating CD14⁺ monocytes were isolated and assessed for antimicrobial functions, including cytokine production, oxidative burst, and microbial (Candida albicans) killing capacity, as well metabolic responses to ex vivo stimulation. Next, the effects of altered cellular metabolism on monocyte functions were validated in vitro. Ex vivo lipopolysaccharide stimulation induced an extensive rewiring of metabolism in naive monocytes. In contrast, endotoxin-induced immunotolerant monocytes showed no metabolic plasticity, as they were unable to adapt their metabolism or mount cytokine and oxidative responses. Validation experiments showed that modulation of metabolic pathways, affected by immunotolerance, influenced monocyte cytokine production, oxidative burst, and microbial (C. albicans) killing in naive monocytes. Collectively, these data demonstrate that immunotolerant monocytes are characterized by a loss of metabolic plasticity and these metabolic defects impact antimicrobial monocyte immune functions. Further, these findings support that the changed cellular metabolism of immunotolerant monocytes might reveal novel therapeutic targets to reverse sepsis-induced immunoparalysis.

Keywords: endotoxemia; endotoxin tolerance; immunometabolism; immunoparalysis; monocytes; sepsis.

Figure 1

Endotoxin‐induced monocyte tolerance affects monocyte effector functions. (A) Kinetics of ex vivo LPS‐induced IL‐1β, TNF‐α, IL‐6, IL‐10, and IL‐1Ra levels in culture supernatants of CD14+ monocytes that were isolated from healthy volunteers at baseline (BL), 4 h (4h), and 7 days (7d) after administration of endotoxin (2 ng/kg, red lines, n = 15) or placebo (0.9% saline, black lines, n = 6). Data are presented as (mean ± sem) percentage change compared to baseline measurements. Differences within groups over time were analyzed using the Friedmann test with Dunn's post hoc test for multiple comparisons. **P < 0.01, ***P < 0.001. (B) Kinetics of ex vivo E. coli, S. aureus, or C. albicans induced ROS production (AUC relative light units) in isolated CD14+ monocytes from healthy volunteers for all time points. Red lines present subjects challenged with endotoxin (n = 6); black lines show placebo controls (n = 4). Data are presented as mean ± sem. Means were compared for significance using the Friedmann test with Dunn's post hoc test to illustrate differences between defined time points. *= P < 0.05. (C) Ex vivo intracellular killing of C. albicans phagocytized by CD14+ monocytes isolated at baseline (BL), 4 h (4h) and 7 days (7d) in (n = 5) endotoxin‐challenged subjects, presented for each subject separately. The killing was calculated for duplicates and serial dilutions as 1 – (CFU remained after incubation with microbes/CFU determined before incubation with microbes) × 100%, hence presented as the percentage of Candida killing. Data are shown as the mean ± sem. BL versus 4 h was statistically compared for each subject (*= P < 0.05, **= P < 0.01) with the Mann‐Whitney U test

Figure 2

Cellular metabolism during monocyte hyporesponsiveness. (A) Volcano plots representing the –10log of the corrected P‐value (false discovery rate) and the relative mean log 2 fold change for cellular metabolites in monocytes from 5 human endotoxemia subjects. Each of the panels represents the comparison of ex vivo unstimulated versus ex vivo LPS‐stimulated CD14+ monocytes that were isolated at baseline (BL), 4 h, and 7 days following endotoxemia. Metabolites that demonstrated a mean log2 fold change > 1 or < –1 have been marked in black, and metabolites that additionally demonstrate a false discovery rate < 0.05 are marked in purple. (B) Venn diagram showing differentially regulated metabolites for all time points and their relevant overlap. (C) PCA analysis of the metabolic response to ex vivo LPS stimulation. In 5 endotoxemia subjects log2 fold changes of ex vivo LPS stimulated CD14+ monocytes were compared to unstimulated CD14+ monocytes isolated and analyzed for each time point of cell isolation. (D) Heat map of metabolite pathways that were differentially regulated by ex vivo LPS stimulation compared to ex vivo unstimulated CD14+ monocytes, following in vivo endotoxemia. The monocytes were isolated at different time points from 5 subjects before (BL), and 4 h (4h), or 7 days (7d) after intravenous administration of endotoxin. The mean log2 fold change of each of the groups is plotted for the various time points. For each pathway, different upregulated (red) or down‐regulated (blue) groups of metabolites are presented. *P < 0.05, **P < 0.01, and ***P < 0.001

Figure 3

Modulation of cellular metabolism influences monocyte cytokine responses. (A) Schematic overview of the metabolic pathways that were targeted to study the effects of cellular metabolism on cytokine responses to microbial stimulation. Inhibitors of metabolic pathways (2DG, 6AN, SOX, OLI, C75, ETO, BSO, BPTES) are indicated with red labels and stimuli of metabolic pathways (DCA and NAC) with green labels. (B–D) Percentage change in TNF‐α, IL‐1β, and IL‐6 responses for E. coli (B), S. aureus (C), and C. albicans (D) following inhibition (2DG, 6AN, SOX, OLI, C75, ETO, BSO, BPTES) or stimulation (DCA and NAC) of designated metabolic pathways compared to the vehicle control of the inhibitor. 2DG (glycolysis, n = 19), 6AN (PPP, n = 11), SOX (pyruvate metabolism, n = 15), DCA (TCA cycle, n = 20), OLI (ATP synthesis, n = 10), C75 (fatty acid metabolism, n = 13), ETO (fatty acid metabolism, n = 12), NAC (glutathione synthesis, n = 19), BSO (glutathione synthesis, n = 12), and BPTES (glutaminolysis, n = 12). Data are shown as the median with P‐values of statistical comparison by the Wilcoxon signed rank test (*P < 0.05, **P < 0.01, and ***P < 0.001)

Figure 4

Impact of modulation of metabolic pathways on the production of ROS. (A) In vitro ROS production in PBMCs after 24 h of preincubation with LPS (red bars) compared to vehicle control (black bars) upon stimulation with E. coli, S. aureus, and C. albicans (n = 20). Data are expressed as AUC of emitted light after oxidation of luminescence, presented in mean relative light units with a statistical comparison by the Wilcoxon signed rank test (***P < 0.001). (B) Percentage change in ROS production following inhibition (2DG, ‐PYR 2DG, 6AN, SOX, OLI, C75, ETO, BSO, BPTES, and C968) or stimulation (DCA or NAC; green labels) of designated metabolic pathways compared to the vehicle control of the modulator. 2DG (glycolysis, n = 19), ‐PYR 2DG (glycolysis, n = 8), 6AN (PPP, n = 13), SOX (pyruvate metabolism, n = 14), DCA (TCA cycle, n = 18), OLI (ATP synthesis, n = 10), C75 (fatty acid metabolism, n = 9), ETO (fatty acid metabolism, n = 15), NAC (glutathione synthesis, n = 14), BSO (glutathione synthesis, n = 12), BPTES (glutaminolysis, n = 19), and C968 (glutaminolysis, n = 7). Data are shown as the median with P‐value of statistical comparison by the Wilcoxon Signed Rank Test (*P < 0.05, **P < 0.01, and ***P < 0.001). (C) Simplified schematic overview of the metabolic pathways that were targeted with inhibitor (green labels) or stimulator (red labels) that could lead to increased intracellular NAPDH levels (green arrows) or decreased (red dotted line) NADPH levels. (D) Relative changes in monocyte intracellular NADPH levels measured by colorimetric assay following treatment of the cells for 24 h with various modulators of cellular metabolism (n = 6, red bars). Fold changes are relative to the appropriate vehicle control (black bars; either RPMI, or RPMI without pyruvate [‐PYR], or DMSO). Bars represent the mean ± sem and were compared for significance using the Wilcoxon Signed Rank Test (*P < 0.05)

Figure 5

Intracellular microbial killing in vitro. (A) In vitro microbial killing capacity by PBMCs after incubation with C. albicans (n = 8) with 24 h pre‐exposure (red bars) to LPS compared to media control without LPS (black bars). (B,C) In vitro C. albicans killing in PBMCs after 24 h pre‐exposure to modulators (red bars) of (B) glycolysis (2DG, n = 16, 2DG‐PYR, n = 7) TCA cycle (DCA, n = 14), PPP (6AN, n = 12), (C) glutaminolysis (BPTES, n = 16 or C968, n = 6), glutathione synthesis (NAC, n = 19), or glutamine metabolism (‐glutamine, n = 11, 2xglutamine, n = 8) compared to the unmodulated situation. All data in this figure are presented as the mean ± sem and with a significant P‐value of statistical comparison by the Wilcoxon signed rank test (*P < 0.05, **P < 0.01)

Figure 6

Capacity to mount oxidative responses correlates to microbial killing. (A) Schematic presentation of the hypothesis how metabolic changes can influence the production of ROS (oxidative burst) and monocyte C. albicans killing capacity. (B) Correlation of oxidative burst assessed as the integral of the luminesce signal following luminol conversion in response to C. albicans stimulation and C. albicans killing capacity in the percentage of killed fungal CFUs in PBMCs of healthy donors (n = 6). (C) PBMCs of healthy donors preincubated with vehicle control (DMSO black/gray lines) or DPI (red lines) for 24 h and subsequently stimulated with opsonized live C. albicans or left unstimulated. Oxidative burst generation was expressed as luminescence conversion over measured over time (measured every 145 s, during 60 min). (D) C. albicans killing capacity in PBMCs of the same 6 donors after 24 h pre‐exposure to DPI, compared to the unmodulated situation (vehicle control). All data in this figure are presented as the mean ± sem and with a significant P‐value of statistical comparison by the Wilcoxon signed rank test (*P < 0.05, **P < 0.01)