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Clinical Science Aspects

Population-Specific Metabolic Alterations in Professional Antigen-Presenting Cells Contribute to Sepsis-Associated Immunosuppression

Schenz, Judith*; Tamulyte, Sandra*; Nusshag, Christian; Brenner, Thorsten*; Poschet, Gernot; Weigand, Markus A.*; Uhle, Florian*

Author Information
doi: 10.1097/SHK.0000000000001337

Abstract

INTRODUCTION

Sepsis is a complex and, due to the resulting organ dysfunction, life-threatening host response triggered by an infection (1). Depending on disease severity and comorbidities, the mortality ranges from 20% to more than 50% (2). Within the host response, the patient's immune system is the main pathophysiological driver contributing to the clinical phenotype of the syndrome. Initially, the disease is characterized by a strong systemic inflammatory response syndrome (SIRS), involving high levels of cytokines and expansion of neutrophilic granulocytes. This proinflammatory response is accompanied by a compensatory anti-inflammatory response syndrome (CARS), limiting the inflammation and protecting the host (3). Similar to SIRS, CARS is propagated by humoral factors like cytokines as well as the occurrence of anti-inflammatory cell types together with the functional reprogramming of existing cells. If this compensatory reaction prevails, the resulting immunosuppression renders the host susceptible to secondary infections and viral reactivation (4, 5). Furthermore, this state renders the host incapable of developing an adequate immune response (6) and accounts for a significant proportion of sepsis-associated deaths (7). There is growing evidence that metabolic reprogramming of immune cells plays an essential role in the induction and maintenance of immune tolerance in sepsis. In leucocytes, genes involved in metabolism are differentially expressed, leading to reduced pro-inflammatory response and reduced metabolic activity (8–10). Earlier studies conducted with peripheral blood mononuclear cells (PBMC) isolated from patients with sepsis showed that impaired energy metabolism, especially mitochondrial dysfunction, was associated with disease severity and mortality (11–15). Within the PBMC, monocytes are crucial cells of the innate immune response, capable of phagocytosing micro-organisms and to present corresponding antigens on the cell surface. However, this function is profoundly disturbed during sepsis, hallmarked by a sharp decrease of HLA-DR expression on monocytes and even correlating with the incidence of secondary infection and poor outcome (16). The second group of professional antigen-presenting cells in the blood are B cells. In response to binding of their B cell receptor, they phagocytose antigens (even soluble ones) similarly to monocytes and present it on the cell surface. One result of B cell activation is their maturation to plasma cells, resulting in the production of large amounts of antigen-specific antibodies. In sepsis, substantial changes in the blood immunoglobulin concentrations have been described as well as phenotypical alterations of the B cells (17–19). However, little is known about the metabolic activity of these two types of professional antigen-presenting cells during sepsis over time.

Addressing this knowledge gap, we comprehensively evaluated the metabolic activity as well as function and phenotype of cluster of differentiation (CD) 14+ monocytes and CD19+ B cells freshly isolated from patients with sepsis on different time points and compared it to healthy volunteers.

PATIENTS AND METHODS

Study design and patients

This prospective observational clinical study was conducted in the surgical intensive care unit of the Heidelberg University Hospital, Germany and approved by the ethics committee of the Medical Faculty of the Heidelberg University (trial code: S-594/2016). Written informed consent was obtained from all patients with sepsis and healthy volunteers. In the case of patients being incapable of giving consent, it was obtained from their legal designees.

Ten adult patients with sepsis (two or more SIRS criteria according to the Sepsis–2 Definitions (20) had to be fulfilled; clinical or microbiological proof of infection; additionally: history of major abdominal surgery) were enrolled and blood was drawn at three different time points (sepsis onset (d1), day 4 (d4), and day 8 (d8)). All patients initially depended on circulatory support by catecholamines despite fluid resuscitation and therefore fulfilled the consensus criteria of septic shock. Treatment applied was in accordance with the recent guidelines of the Surviving Sepsis Campaign (21). Healthy volunteers (n = 10) were matched for sex and age and blood was taken once. Exclusion criteria were: sepsis diagnosis >24 h, diabetes mellitus (types I and II), mitochondrial disorders, autoimmune disorders (e.g., Crohn disease, Alzheimer disease, celiac disease), iatrogenic immunosuppression, viral infections, enrollment in an interventional study, or pregnancy.

Whole blood anticoagulated with lithium heparin was processed immediately after drawing. Upon centrifugation (2,000×g, 5 min), the plasmatic fraction was removed and stored in aliquots at −80°C until further analysis.

B cell and monocyte isolation

PBMC were separated using density gradient centrifugation. In brief, the cellular fraction was filled up to 60 mL with phosphate-buffered saline (Thermo Fisher Scientific Inc, Waltham, Mass), transferred into two Leucosep tubes prefilled with separation medium for PBMC isolation (greiner bio-one, Kremsmuenster, Austria) and centrifuged (800×g, 15 min, no brake). The PBMC layer was carefully aspirated, pooled, and washed (250×g, 5 min, 4°C) three times with ice-cold phosphate-buffered saline supplemented with 2 mM ethylenediaminetetra acetic acid (Thermo Fisher Scientific Inc, Waltham, Mass) and 0.5% protease-free bovine serum albumin (Carl Roth, Karlsruhe, Germany).

B cells and monocytes were isolated sequentially by magnetic-activated cell sorting (MACS) (Miltenyi Biotec, Bergisch Gladbach, Germany) according to the manufacturer's instructions. Briefly, PBMC were incubated with CD19 MicroBeads (Miltenyi Biotec, Bergisch Gladbach, Germany). CD19+ B cells were separated by positive selection using the AutoMACS program “Possel-s.” Subsequently, the negative fraction was incubated with CD14 MicroBeads (Miltenyi Biotec, Bergisch Gladbach, Germany). CD14+ monocytes were separated by positive selection using the AutoMACS program “Possel.”

Cells were counted via impedance-based particle detection using a Scepter 2.0 Cell Counter (Merck Millipore, Burlington, Vt).

To determine the purity of the isolation (Supplementary Figure 1, http://links.lww.com/SHK/A854), 2×105 cells from each fraction were stained with 5 μL anti-CD19-V500 (clone: HIB19) and 20 μL anti-CD14-fluorescein isothiocyanate (FITC) (clone: M5E2) (both from BD Biosciences, Franklin Lakes, NJ), respectively. A FACSVerse cytometer (BD Biosciences, Franklin Lakes, NJ) was used for measurement.

Live cell metabolic analysis

Proton efflux rate (PER) and oxygen consumption rate (OCR) were measured using Seahorse technology (Agilent Technologies, Santa Clara, Calif). In brief, monocytes (1.5×105 per well) and B cells (2×105 per well) were seeded into Seahorse XFp Cell Culture Miniplates (Agilent Technologies, Santa Clara, Calif) previously coated with Cell-Tak (Corning, Corning, NY). The plate was centrifuged (300×g, 1 min, no brake) and incubated in a non-CO2 37°C incubator for 1 h prior to the measurement. Seahorse XF Base Medium (without Phenol Red) supplemented with 5 mM HEPES (both from Agilent Technologies, Santa Clara, Calif), 10 mM D(+)Glucose (Sigma-Aldrich, St. Louis, Mo), 1 mM sodium pyruvate, and 2 mM L-glutamine (both from Thermo Fisher Scientific Inc, Waltham, Mass) was used as assay medium. After three baseline measurements (each measuring point comprises 3 min mixing and 3 min measuring) the following inhibitors were added: 2 μM oligomycin, 2 μM Carbonyl cyanide–4 (trifluoromethoxy) phenylhydrazone (FCCP), 1 μM rotenone/antimycin A (all three from Seahorse XFp Cell Mito Stress Test Kit, Agilent, Santa Clara, Calif), and 50 mM 2–Deoxy-D–glucose (2-DG) (Sigma-Aldrich, St. Louis, Mo) (Fig. 2A).

OCR and PER were calculated using the Wave 2.6.0 software and parameters were calculated using Seahorse XF Glycolytic Rate Assay Report Generator 3.21 and Seahorse XF Cell Mito Stress Test Report Generator 3.0.11 (all three from Agilent Technologies, Santa Clara, Calif).

Flow cytometry

100 μL of whole blood was incubated with 5 μL Human TruStain FcX (BioLegend, San Diego, Calif) to block the Fc receptors. Subsequently, the staining antibodies were added and the suspension was incubated for further 30 min at 4°C in the dark. Lysing of erythrocytes was done by adding 2 mL of 1× Lysing solution and incubation for 15 min at room temperature in the dark. The suspension was centrifuged (250×g, 5 min), the supernatant was discarded and the cells were washed (250×g, 5 min) once with 2 mL CellWASH and resuspended in FACSFlow (all three from BD Biosciences, Franklin Lakes, NJ). Measurements were performed using a FACSVerse cytometer.

For the identification of the B cell subsets, the following antibodies were used: 5 μL anti-CD19-V500 (clone: HIB19), 5 μL anti-CD20-V450 (clone: L27), 5 μL anti-IgD-Allophycocyanin (APC)–H7 (clone: IA6–2), 5 μL anti-CD27-FITC (clone: M–T271), 5 μL anti-CD24-peridinin-chlorophyll-protein complex (PerCP)-cyanine 5.5 (Cy5.5) (clone: ML5), 20 μL anti-CD38-phycoerythrin (PE) (clone: HIT2), 5 μL anti-CD21-PE-cyanine 7 (Cy7) (clone: B-ly4) (all from BD Biosciences, Franklin Lakes, NJ). For proper gating of CD27 and IgD-positive cells, fluorescence minus one (FMO) controls were used (Supplementary Figure 2, http://links.lww.com/SHK/A854).

To measure the expression level of different immune checkpoints on monocytes, the following antibodies were used: 20 μL anti-CD14-FITC (clone: M5E2), 2 μL anti-CD274 (Programmed death-ligand 1 (PD-L1))-PE-Cy7 (clone: MIH1) (both from BD Biosciences, Franklin Lakes, NJ), 5 μL anti-CD273 (Programmed death-ligand 2 (PD-L2))-APC (clone: MIH18) (BioLegend, San Diego, Calif), 5 μl anti-V-domain Ig suppressor of T cell activation (VISTA)-PE (clone: #730804) (R&D Systems, Minneapolis, Minn). FMO controls were used to determine background fluorescence levels (Supplementary Figure 2, http://links.lww.com/SHK/A854).

HLA-DR expression on B cells was determined using the following antibodies: 5 μL anti-CD19-V500 (clone: HIB19), 5 μL anti-CD20-V450 (clone: L27) (both from BD Biosciences, Franklin Lakes, NJ), 2 μL anti-HLA-DR-APC (clone: L243) (BioLegend, San Diego, Calif). A FMO control was used to determine background fluorescence levels (Supplementary Figure 3A, http://links.lww.com/SHK/A854).

For quantification of HLA-DR expression on monocytes, 50 μL whole blood was stained with 20 μL anti-HLA-DR/anti-Monocyte PerCP-Cy5.5 reagent (clone: L243/ MϕP9) (BD Biosciences, Franklin Lakes, NJ) for 20 min at 4°C in darkness. For erythrocyte lysis, 450 μL 1× Lysing solution was added. Measurement was done immediately after incubation (20 min, room temperature, in darkness) (Supplementary Figure 3B, http://links.lww.com/SHK/A854). BD Quantibrite PE tubes (BD Biosciences, Franklin Lakes, NJ) were used for quantifying the average number of HLA-DR molecules per monocyte as indicated by the manufacturer.

Cytokine secretion of ex vivo-stimulated whole blood

Whole blood was diluted with an equal amount of Roswell Park Memorial Institute medium medium (Thermo Fisher Scientific Inc, Waltham, Mass), containing GlutaMAX and 10% heat-inactivated fetal bovine serum ultra-low endotoxin (Cell Concepts, Umkirch, Germany). Stimulation was performed with 100 ng/mL ultrapure lipopolysaccharide (LPS) or 100 μg/mL depleted zymosan (both from Invivogen, San Diego, Calif). Controls were incubated without a stimulating agent. Following a 24 h incubation (37°C, 5% CO2), supernatants were collected by centrifugation (1,000×g, 5 min) and interleukin-6 (IL–6) and -8 (IL–8) levels were measured using colorimetric enzyme-linked immunosorbent assay (ELISA) (Human IL–6 and Human IL–8/CXCL8 DuoSet ELISA; both from R&D Systems, Minneapolis, Minn) according to the manufacturer's instruction. Induced cytokine production was calculated as the difference between stimulation and control results.

Plasma immunoglobulin levels

Plasma immunoglobulin levels were quantified using commercially available ELISA Kits (IgA, IgG (Total), and IgM Human uncoated ELISA Kit with Plates; all three from Thermo Fisher Scientific Inc, Waltham, Mass) as stated by the manufacturer.

Plasma tricarboxylic acid cycle (TCA) cycle intermediates

Determination of organic acids was adapted from Uran et al. (22). In brief, plasma was diluted 1 to 10 with ultra-pure water. 450 μL ice-cold methanol was added to 100 μL diluted plasma and incubated on ice for 15 min. After centrifugation (20,000×g, 10 min, 4°C) to remove precipitated proteins, 50 μL of the supernatant was mixed with 25 μL 140 mM 3-Nitrophenylhydrazine hydrochloride (Sigma-Aldrich, St. Louis, Mo), 25 μL methanol and 100 μL 50 mM Ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride (Sigma-Aldrich, St. Louis, Mo) and incubated for 20 min at 60°C. Separation was carried out on an Acquity UPLC H-class system coupled to an Acquity QDa mass detector (Waters, Milford, Conn) using an Acquity HSS T3 column (100 mm × 2.1 mm, 1.8 μm, Waters, Milford, Conn) which was heated to 40°C. Separation of derivates was achieved by increasing the concentration of 0.1% formic acid in acetonitrile (B) in 0.1% formic acid in water (A) at 0.55 mL min−1 as follows: 2 min 15% B, 2.01 min 31% B, 5 min 54% B, 5.01 min 90% B, hold for 2 min, and return to 15% B in 2 min. Mass signals for the following compounds were detected in single ion record (SIR) mode using negative detector polarity and 0.8 kV capillary voltage: Malate (403.3 m/z; 25 V cone voltage), succinate (387.3 m/z; 25 V), fumarate (385.3 m/z; 30 V), citrate (443.3 m/z; 10 V), pyruvate (357.3 m/z; 15 V), and α-ketoglutarate (550.2 m/z; 25 V). Data acquisition and processing were performed with the Empower3 software suite (Waters, Milford, Conn).

Statistical analysis

All statistical analyses were done in GraphPad Prism (V 6.01 for Windows, GraphPad software, La Jolla, Calif). Results are visualized as combined scatter/box plots. Horizontal line within the box marks the median, boxes depict the interquartile ranges (IQR), and whiskers indicate the total range. Statistical comparison between two groups was done using the Mann–Whitney U test. Spearman analysis was performed to assess the correlation between two parameters. Statistical significance was considered with P ≤ 0.05.

RESULTS

Study cohort

In total, 20 individuals thereof 10 patients with sepsis and 10 healthy volunteers were enrolled in this prospective, observational study. The median age was 61 years in the group of patients with sepsis and 59.5 years in the group of healthy subjects (Table 1). In the group of patients with sepsis, the median sequential organ failure assessment score (SOFA score) at admission was 11.5 (range: 6–16), 80% of the cases presented with an abdominal infection focus, whereas in 20% of the cases the infection focus was pulmonary.

Table 1
Table 1:
Study cohort

Lower HLA-DR expression on monocytes and B cells

A decrease in monocytic HLA-DR expression is widely recognized as a biomarker for immunosuppression in sepsis (16). As expected, we found a consistent decrease of HLA-DR expression on monocytes in patients with sepsis compared with healthy controls. This applies throughout the whole observation period of 1 week (Fig. 1A). Similarly, also B cells expressed a significantly lower amount of HLA-DR (Fig. 1B). Spearman correlation analysis revealed a strong correlation between HLA-DR expression levels on monocytes and B cells (r = 0.5968; P < 0.0001) (Fig. 1C). This indicates that in patients with sepsis the lost ability to present antigens is a global phenomenon neither restricted to a single cell population nor cell lineage.

Fig. 1
Fig. 1:
Monocytes and B cells of patients with sepsis express lower level of HLA-DR.HLA-DR expression on circulating (A) monocytes (CD14+) and (B) B cells (CD19+, CD20+) was measured at d1, d4, and d8. Each data point represents an individual patient/healthy control (n = 10 per group). Horizontal line within the box marks the median, boxes depict the IQR, and whiskers indicate the total range. Group comparisons were performed by Mann–Whitney U test (**** P < 0.0001, ** P ≤ 0.01, * P ≤ 0.05). (C) Correlation between HLA-DR expression on monocytes and B cells (n = 40) was analyzed using Spearman analysis. HLA-DR indicates human leukocyte antigen-DR; IQR, interquartile ranges.

Sepsis induces a shift toward glycolysis in antigen-presenting cells

Considering the close connection between cellular function and metabolic state in immune cells, we evaluated both cell types for metabolic changes (Fig. 2A). Monocytes from patients with sepsis turned out to be highly glycolytic. Basal glycolysis is enhanced right from the onset with the most pronounced effect at d4 (median PER: 194.60 pmol/min vs. 125.90 pmol/min; P = 0.0185) and remained high even 1 week after onset of sepsis (Fig. 2B). Glycolytic spare capacity was highly variable among the individual subjects (Supplementary Figure 4A, http://links.lww.com/SHK/A854). Intriguingly, the changes in monocytes’ oxygen consumption were very heterogeneous. Basal respiration was not altered over time (Fig. 2C) and respiratory spare capacity displayed a very dynamic kinetic: Right after the onset of sepsis it was highly variable (OCR range: 14.24 pmol/min–86.42 pmol/min, median: 31.43 pmol/min), subsequently returned to the same level as healthy controls and was decreased after 1 week (median OCR: 37.36 pmol/min vs. 53.59 pmol/min; P = 0.0232) (Fig. 2D). Adenosine triphosphate (ATP) production-linked respiration was not altered (Supplementary Figure 4B, http://links.lww.com/SHK/A854) and proton leak was very heterogeneous (Supplementary Figure 4C, http://links.lww.com/SHK/A854).

Fig. 2
Fig. 2:
Sepsis upregulates glycolytic activity in antigen-presenting cells and enhances respiration in B cells.A, Representative calculation method of the glycolytic and respiratory indices using PER and OCR. (B) Basal glycolysis, (C) basal respiration, and (D) respiratory spare capacity of monocytes (CD14+), and (E) basal glycolysis, (F) basal respiration, (G) ATP-production linked respiration and (H) proton leak of B cells (CD19+) was calculated at d1, d4, and d8. Each data point represents an individual patient/healthy control (n = 10 per group). Horizontal line within the box marks the median, boxes depict the IQR and whiskers indicate the total range. Group comparisons were performed by Mann–Whitney U test (** P ≤ 0.01, * P ≤ 0.05). OCR indicates oxygen consumption rate; PER, proton efflux rate; ATP, adenosine triphosphate; IQR, interquartile ranges.

In contrast, B cells showed a differing metabolic profile. Basal glycolysis raised in the course of the disease (d4—median PER: 37.91 pmol/min vs. 18.97 pmol/min; P = 0.0288) (Fig. 2E). Comparable to our findings in monocytes, glycolytic spare capacity in B cells was highly variable among the individual subjects (Supplementary Figure 4D, http://links.lww.com/SHK/A854). Most strikingly, basal respiration increased over time as well (d8—median OCR: 20.89 pmol/min vs. 11.67 pmol/min; P = 0.0068) (Fig. 2F). ATP production-linked respiration (Fig. 2G) and proton leak (Fig. 2H) displayed the same pattern. Surprisingly, proton leak is strongly correlated not only with basal respiration (r = 0.7291; P < 0.0001) but also with basal glycolysis (r = 0.7130; P < 0.0001). No global changes were found in respiratory spare capacity (Supplementary Figure 4E, http://links.lww.com/SHK/A854).

Taken together, we identified alterations in cellular energy metabolism of monocytes and B cells of patients with sepsis: While monocytes shifted early and sustained toward a highly glycolytic state, B cells displayed a delayed metabolic adaption characterized by an elevation of glycolysis and respiration in the course of the disease. These highly dynamic changes during the progression of the disease suggest a close connection with the concurrent patient's immune status.

Sepsis leads to a selectively impaired monocyte response to secondary inflammatory stimuli

Next, we assessed the ex vivo response to distinct inflammatory stimuli. To this end, we stimulated whole blood with either the Dectin–1 agonist zymosan or the Toll-like receptor 4 agonist LPS. Remarkably, apart from a diminished production of IL-8 (median 7.1 ng/mL vs. 183.1 ng/mL; P = 0.0146) the majority of patients were actually unresponsive to zymosan stimulation (Fig. 3A). Despite no statistical significance, a similar pattern of unresponsiveness was seen for IL-6 production (Fig. 3B). To our surprise, we detected differences between the regulation of IL–6 and IL–8 secretion in response to stimulation with LPS. For IL–8, we observed no differences, but remarkably there was a high variation within the sepsis group and a few patients showed even a higher IL-8 response compared with healthy individuals (Fig. 3C). Less IL–6 was measured, depicting a delayed kinetic with the lowest production at d4 (median: 4.9 ng/mL vs. 17.3 ng/mL; P = 0.0147) (Fig. 3D). Basal respiration in monocytes was negatively correlated with IL–6 production upon LPS stimulation (r = –0.3280, P = 0.0388) and IL–6 (r = −0.4600, P = 0.0028) as well as IL–8 production (r = −0.5410, P = 0.0003) in response to zymosan. Astonishingly, LPS-induced IL–8 production correlated positively with basal glycolysis in monocytes (r = 0.3966, P = 0.0112). Moreover, there were strong positive correlations between HLA-DR expression on monocytes and IL-6 production upon LPS stimulation as well as IL-6 and IL-8 production toward zymosan (Table 2).

Fig. 3
Fig. 3:
Monocytes display selectively impaired responses to distinct inflammatory stimuli and reduced expression of immune checkpoints in sepsis.(A) IL-8 and (B) IL-6 secretion upon ex vivo zymosan stimulation. (C) IL-8 and (D) IL-6 secretion upon ex vivo LPS stimulation. E, Relative percentage of circulating monocytes (CD14+). Expression of (F) PD-L1, (G) PD-L2, and (H) VISTA on circulating monocytes (CD14+). Each data point represents an individual patient/healthy control (n = 10 per group). Horizontal line within the box marks the median, boxes depict the IQR, and whiskers indicate the total range. Group comparisons were performed by Mann–Whitney U test (** P ≤ 0.01, * P ≤ 0.05). IL indicates interleukin; LPS, lipopolysaccharide; PD-L1/2, programmed death-ligand 1/2; VISTA, V-domain Ig suppressor of T cell activation.
Table 2
Table 2:
Correlation between cytokine response upon ex vivo stimulation and monocytic metabolic parameters and HLA-DR, respectively

This reduced reaction to inflammatory stimuli and its correlation with increased monocytic respiration hints toward a sustained impairment of systemic immune response and we took a closer look at the patient's immune phenotype.

Monocytes of patients with sepsis express fewer immune checkpoints

The frequency of circulating monocytes in the blood was not altered in patients with sepsis (Fig. 3E), but the expression of PD-L1 on their surface was decreased. Once again, the most pronounced effect was found at d4 (median ΔMFI 711 vs. 1414; P = 0.0052) (Fig. 3F). In contrast, PD-L2 expression was not altered (Fig. 3G), while VISTA expression displayed a kinetic comparable to PD-L1 (Fig. 3H).

B cell subtypes are shifted toward antibody-producing cell types in response to sepsis

The relative percentage of B cells circulating in the blood was strongly and persistently decreased over the whole observation time (Fig. 4A). Beyond this, we found shifted ratios between the different groups of B cells. The share of memory B cells from all CD19+ B cells was decreased immediately at the onset of sepsis. Low levels of preswitched memory B cells were maintained (Fig. 4B) while memory B cells after class-switch of their B cell receptor recovered rapidly and after 1 week, no substantial difference between the two groups was observed anymore (Fig. 4C). In contrast, the proportion of naive B cells decreased in the meantime (Fig. 4D). These shifts were accompanied by a massive increase of plasmablasts (Fig. 4E). The proportions of immature “transitional” B cells (Supplementary Figure 5A, http://links.lww.com/SHK/A854) and exhausted B cells (Supplementary Figure 5B, http://links.lww.com/SHK/A854) were not changed. To further investigate the relevance of these shifts, we measured plasma levels of different immunoglobulins. Compared with healthy controls no differences were detected for neither plasma immunoglobulin A (IgA) (Supplementary Figure 5C, http://links.lww.com/SHK/A854), plasma immunoglobulin M (IgM) (Supplementary Figure 5D, http://links.lww.com/SHK/A854) nor total plasma immunoglobin G (IgG). But remarkably, IgG levels dropped over time (median: 419.2 mg/dL vs. 172.6 mg/dL; P = 0.0115) (Fig. 4F). Overall, the B cell compartment exhibited an activated state and shifted toward antibody-producing cell types, associated with an early increased level of IgG.

Fig. 4
Fig. 4:
Sepsis changes the proportion of different B cell subsets and increases plasma immunoglobulin levels.Relative percentage of circulating (A) B cells (CD19+). Frequencies of memory B cells, (B) (preswitched) (CD19+, CD20+, CD27+, IgD+), (C) (post-switched) (CD19+, CD20+, CD27+, IgD-), (D) naive B cells (CD19+, CD20+, CD27-, IgD+), and (E) plasmablasts (CD19+, CD20-, CD38+). (F) Plasma concentration of total IgG. Each data point represents an individual patient/healthy control (n = 10 per group). Horizontal line within the box marks the median, boxes depict the interquartile ranges IQR, and whiskers indicate the total range. Group comparisons were performed by Mann–Whitney U test (**** P < 0.0001, *** P ≤ 0.001, ** P ≤ 0.01, *p ≤ 0.05). IgG indicates immunoglobin G.

Sepsis leads to broken TCA cycle

Besides the intracellular metabolic adaptions in monocytes and B cells, plasma levels of TCA cycle intermediates displayed a differentiated regulation throughout the disease. While pyruvate levels (Fig. 5A) were not altered, citrate (Fig. 5B) was persistently decreased. Following the substrate flux through the TCA cycle, α-ketoglutarate (Fig. 5C) and succinate (Fig. 5D) showed no differences compared with healthy controls, while fumarate levels displayed a high heterogeneity at d1 (range: 1.5 μM–10.8 μM; median: 3.6 μM), resulting in a decrease at d4 (Fig. 5E). In contrast, malate was increased immediately at the onset of sepsis (median: 23.8 μM vs. 8.1 μM; P = 0.0039), but declined rapidly (Fig. 5F). Citrate and succinate levels strongly correlated inverse with both basal glycolysis in monocytes and B cells and, in addition, fumarate and malate with basal glycolysis in B cells albite less strongly (Table 3). These findings are indicative for an altered TCA cycle's enzyme activity due to sepsis as well as a differentiated biosynthetic activity in patients with sepsis compared with healthy controls.

Fig. 5
Fig. 5:
Sepsis alters concentration of plasma TCA cycle intermediates.Plasma concentration of (A) pyruvate, (B) citrate, (C) α-ketoglutarate, (D) succinate, (E) fumarate, and (F) malate. Each data point represents an individual patient/healthy control (n = 10 per group). Horizontal line within the box marks the median, boxes depict the IQR, and whiskers indicate the total range. Group comparisons were performed by Mann–Whitney U test (**** P < 0.0001, ** P ≤ 0.01, * P ≤ 0.05). TCA indicates tricarboxylic acid cycle.
Table 3
Table 3:
Correlation between plasma TCA cycle intermediates and metabolic parameters of monocytes and B cells

We found a profound sepsis-induced metabolic adaption toward glycolysis in monocytes and B cells, the two most frequent antigen-presenting immune cell populations circulating in the blood, accompanied by lower HLA-DR expression and impaired cellular function. Concurrently, the altered levels of metabolites in the surrounding plasma suggest an adjusted biosynthesis.

DISCUSSION

Our study is the first to our knowledge investigating the functional metabolic state in distinct immune cell populations—B cells and monocytes—of patients with sepsis using a live cell approach. Former studies using PBMC revealed various aspects of mitochondrial dysfunction in sepsis during the first days after onset: Higher oxygen consumption, but an impaired reserve capacity was observed (12) just like a reduced adenosine diphosphate-induced maximal respiration (13) and a lower spare respiratory capacity, but no changes in basal respiration in pediatric sepsis patients (14). By contrast, Sjövall et al. (15) found an increase in cellular respiration and maximal capacity. Clinical differences in the study cohorts and divergent distribution of immune cell populations within the samples might explain these partly contradictory results. A recent study by Merz et al. used cell lysates of monocytes and compared maximum relative enzyme activity (after admission to ICU up to 48 h later), showing an upregulated mitochondrial enzymatic activity in patients with sepsis (23). In contrast, by using a live cell approach we identified no global changes, but a high variability in the basal respiration of monocytes from patients with sepsis compared with healthy individuals, associated with a decreased respiratory spare capacity 8 days after sepsis onset. Nevertheless, high basal respiration correlated negatively with cytokine responses upon inflammatory stimulation, emphasizing a connection between high mitochondrial activity and early sepsis-associated immunosuppression. Considering the high variability we observed and the immunologic heterogeneity at a very early stage of disease (24), presence of “metabolic endotypes” with different degrees of immunosuppression linked to monocytic mitochondrial activity might be assumed and warrants further research in larger cohorts. To our surprise, respiratory spare capacity, an indicator for the cell's ability to comply with a rapidly increased energy demand, displays a delayed kinetic resulting in a decrease compared with healthy controls 1 week after sepsis outset. Since ATP production-linked respiration and proton leak are not altered at that timepoint, this decline might mirror an exhausted and lowered substrate supply in the long run. Additionally, some patients had a low respiratory spare capacity right after the outset of the disease, too. Together with the concurrent variabilities in basal respiration and proton leak, this might reflect a patient-specific increased ATP demand and/or mitochondrial injury. Besides the sepsis’ inherent variability, the inclusion of patients up to 24 h after diagnosis might add further noise, considering the fast turnover of blood monocytes under inflammatory conditions (25), mounting in the observed interpatient heterogeneity. Studies with closer time intervals are needed for in-depth analysis of early mitochondrial alterations.

Beyond respiration, glycolysis and subsequent conversion of pyruvate to lactate (aerobic glycolysis) is another critical pathway to fulfill an activated cell's energy demand. Increased glucose uptake and subsequent glycolytic substrate flux are a hallmark of activated immune cells (26). We found stably elevated basal glycolysis levels in monocytes of patients with sepsis over 8 days, mimicking the metabolic phenotype of “trained immunity(27, 28). These correlated with an increased production of IL-8 upon LPS stimulation. However, the response was cytokine- and stimuli-dependent, with a simultaneously dampened IL-6 production rather indicating immune tolerance. For technical reasons, we have not distinguished between different monocyte subsets and considering the roughly equal amounts of classical and nonclassical monocytes in the blood after acute systemic inflammation (25), we are not able to assess whether all or just distinct subsets are affected by the reprogramming. Reports of an elevated glycolytic activity in mouse hint that glycolytic reprogramming of monocytes in sepsis is already taking place in the bone marrow niche (29) and support the idea that classical monocytes repopulating the blood might already bear this altered phenotype. Contrastingly, the transient and heterogeneous phenotype of mitochondrial alterations suggests that it is caused by a single event, affecting mature monocytes circulating in the blood. These monocytes circulate only shortly in the blood before extravasation, implicating that invading monocyte-derived macrophages might propagate an immunosuppressive phenotype into the tissues and therefore account for peripheral sepsis-induced vulnerability to infection.

Intriguingly, we found a lower PD-L1 expression on monocytes in patients with sepsis. Although PD-L1 upregulation on monocytes was described as a key player in septic immunosuppression (30) and PD-L1 inhibition is thought to be a promising immunotherapeutic target in sepsis (31, 32), our results demonstrate that upregulation of inhibitory immune checkpoints is not a general phenomenon applying for all sepsis patients. Together with others (33, 34), this finding once again underlines the necessity of further subclassification of the molecular and immunological heterogeneous group of sepsis patients for targeted application of novel therapeutic strategies.

In comparison with monocytes, we found a fundamental different metabolic regulation in B cells. At sepsis onset, the metabolic phenotype matched those of B cells isolated from healthy volunteers, but after 1 week we found an increase in both glycolysis and mitochondrial respiration. Concurrently, a massive expansion of plasmablasts and the recovery of postswitched memory B cell frequency occurred while inversely, naive and preswitched memory B cells diminished, indicative for a robust B cell activation. Metabolic reprogramming is necessary to fulfill the high energy needs of activated and proliferating B cells and to provide intermediates for biosynthesis and antibody production compared with quiescent naive and memory B cells (35, 36). B cell receptor-mediated stimulation has been shown to upregulate the glucose transporter 1 and increases both aerobic glycolysis as well as mitochondrial oxidative phosphorylation. To maintain this increased metabolic activity and prevent activation-induced mitochondrial dysfunction and subsequent cell death, a second signal such as T helper cells or signaling via Toll-like receptor 9 is needed (37). We reported low HLA–DR on B cells suggesting a disrupted binding capability with T helper cells and hence a missing second signal. Besides, we found a strong correlation between elevated proton leak and basal glycolysis as well as respiration, substantiating an induction of mitochondrial dysfunction in temporal proximity to an activation-induced upregulation of metabolic activity. These findings suggest an unsustainable B cell activation in line with Shankar-Hari et al., reporting accelerated apoptosis of memory B cells in patients with sepsis in consequence to cell activation (38). Moreover, this unsustainable B cell activation is supported by several studies using murine sepsis models reporting a reduced antigen-specific humoral immune response in sepsis (39, 40).

Notwithstanding the discussed insufficient B cell activation, we saw a trend toward elevated total IgG plasma levels during the first days declining subsequently. Several studies in patients with sepsis reported a beneficial effect of higher Ig levels on survival (41, 42). The changes in plasma Ig levels and the expansion of plasmablasts behave oppositely. Together with the failure in antigen-specific humoral immune response, this seems to be contradictory to the early elevated immunoglobulin production. Moreover, Schmoeckel et al. (39) linked a late-state overwhelming general antibody load with long-term adaptive immunosuppression and assumed a cellular competition for limited niches as underlying mechanism. The early beneficial antibody production might be part of a protective B cell activation, enhancing the innate immune activation (43–45). Only a persistent elevated general antibody load, as reported by Schmoeckel et al., might negatively impact patients’ outcome as a contributing factor to adaptive immunosuppression. Further studies need to unravel these distinct early and late B cell responses, their chronology and their specific contribution to sepsis-induced immunoparalysis. This knowledge is fundamental for time-dependent therapeutic interventions enhancing or restoring failed B cell functions, e.g., by provoking a sustainable B cell activation and preventing mitochondrial dysfunction via the replacement of the missing second signal.

We observed no changes in the frequency of exhausted B cells. This differs from previously published results showing elevated exhausted B cell frequencies (19, 46). However, due to lack of a standardized phenotyping and divergent gating strategies between the studies, comparison is hampered. We, as well as Gustave et al. and Suzuki et al., used the loss of CD21 expression as a key marker for exhaustion. Again, also differences between the patient cohorts might be responsible, emphasizing the urgent need of biomarkers for patient stratification.

Expanding the framework, we found different plasma levels of TCA cycle intermediates in patients with sepsis. The strong negative correlations between plasma citrate as well as succinate levels with basal glycolysis in both monocytes and B cells bridge the disrupted TCA cycle and glycolysis induction. As discussed, glycolysis and immune activation are closely related in both cell types. Thus, changes in TCA cycle's enzyme activity may promote glycolysis induction and therefore facilitate immune cell activation. Since our Seahorse results revealed transient mitochondrial alterations, contrasting the persistently reduced plasma citrate levels, it might underline a high level of cholesterol and fatty acid synthesis in patients with sepsis using citrate as acetyl-CoA source. Lowered levels of the cholesterol derivative vitamin D (47) and altered steroid hormone synthesis due to adrenal dysfunction (48) in sepsis are the most reasonable cause for a counteracting shift towards higher biosynthesis.

Our study yields valuable findings but also implies limitations. Since it is an exploratory pilot study, we enrolled only a limited number of patients and controls. Nevertheless, these were carefully selected, especially considering influencing comorbidities. Importantly, we only enrolled patients with a recent major abdominal surgery, which has a strong impact on immunity itself. Similarly, many aspects of intensive care medicine influence the immune system. While patients received comparable treatment bundles early after diagnosis, those turned into personalized therapies over time, hampering the clear delimitation of influencing factors. Beside the cell types examined, also other cell types are of high importance for the pathophysiology of sepsis, e.g., T cells or tissue-associated cells as alveolar macrophages and should be addressed in the future. Longitudinal approaches as our study will yield insights, if observed changes are only of transient nature or long-lasting, impacting patients’ health in the long run.

In summary, we can provide evidence for population-specific and dynamic rewiring of intracellular energy pathways contributing to different facets of sepsis-associated immunosuppression. Whereas monocytes are characterized by a sustained glycolysis induction and just transient mitochondrial alterations, B cells’ metabolic adaption characterized by elevated glycolysis and respiration arises over time. Metabolic and immune phenotypes are closely connected. A reduced reaction to inflammatory stimuli and increased monocytic respiration are correlated just as B cells’ metabolic upregulation goes hand in hand with a shift toward antibody-producing subtypes. Our results lay the foundation for a detailed cell-type specific understanding of immunometabolic alterations in the course of sepsis and underline the requirement for a personalized understanding of molecular and immunological variations as the prerequisite for the application of novel targeted therapeutic strategies.

Acknowledgments

The authors thank Sabine Stegmaier and Jan Pfister for outstanding technical support and the Metabolomics Core Technology Platform of the Excellence cluster “CellNetworks” (University of Heidelberg) as well as the Deutsche Forschungsgemeinschaft (grant ZUK 40/2010-3009262) for support with HPLC-based metabolite quantification.

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Keywords:

B cells; glycolysis; HLA-DR; monocytes; OXPHOS; PD-L1; trained immunity; VISTA; Abbreviations; 2-DG; 2–deoxy-D–glucose; APC; allophycocyanin; ATP; adenosine triphosphate; CARS; compensatory anti-inflammatory response syndrome; CD; cluster of differentiation; CXCL8; chemokine (C-X-C motif) ligand 8; Cy5.5/7; cyanine 5.5/7; d1; day 1=sepsis onset; d4; day 4; d8; day 8; ELISA; enzyme-linked immunosorbent assay; FACS; fluorescence-activated cell sorting; FCCP; Carbonyl cyanide–4 (trifluoromethoxy) phenylhydrazone; FITC; fluorescein isothiocyanate; FMO; fluorescence minus one; HLA-DR; human leukocyte antigen-DR; IgA/G/M; immunoglobulin A/G/M; IL-6/-9; interleukin-6/-8; IQR; interquartile ranges; LPS; lipopolysaccharide; MACS; magnetic-activated cell sorting; MFI; mean fluorescence intensity; OCR; oxygen consumption rate; PBMC; peripheral blood mononuclear cells; PD-L1/2; programmed death-ligand 1/2; PE; phycoerythrin; PER; proton efflux rate; PerCP; peridinin-chlorophyll-protein complex; SIRS; systemic inflammatory response syndrome; SOFA score; sequential organ failure assessment score; TCA; tricarboxylic acid cycle; VISTA; V-domain Ig

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