Introduction
Tuberculosis (TB) remains a major global health problem, responsible for approximately 1.6 million deaths annually. The causative agent of TB,
Rapid, directed migration of DCs toward secondary lymphoid organs requires essential changes at the cellular and molecular levels (Currivan et al., 2022). Relatedly, the metabolic state of DCs is complex and varies according to cell origin, differentiation, and maturation states, as well as local microenvironment, among other factors (Basit et al., 2018; Wculek et al., 2019; Du et al., 2018; Møller et al., 2022). Studies have reported that upon pathogen sensing the transcription factor hypoxia-inducible factor-1α (HIF1A) increases glycolysis, which promotes immunogenic functions of DCs, such as IL-12 production, costimulatory marker expression (Everts and Pearce, 2014), and cell migration (Guak et al., 2018; Liu et al., 2019; Everts et al., 2014). By contrast, it was shown that HIF1A represses the proinflammatory output of LPS-stimulated DCs and can inhibit DC-induced T-cell responses in other settings (Lawless et al., 2017). To reconcile these disparate roles for HIF1A, it has been proposed that the impact of metabolic pathway activation on DC functions varies among DC subsets (Wculek et al., 2019). To this point, most prior studies have been conducted using murine conventional DCs and plasmacytoid DCs (Du et al., 2018). Recently, with the implementation of high-dimensional techniques, it was demonstrated that distinct metabolic wiring is associated with individual differentiation and maturation stages of DCs (Adamik et al., 2022), highlighting the importance of defining the metabolic profile of specific subsets of DCs under physiological or pathological conditions (Møller et al., 2022). Given the key role of DCs in the host response to TB, it is thus crucial to investigate DC metabolism in the context of Mtb infection (Kumar et al., 2019).
We previously demonstrated that the TB-associated microenvironment, as conferred by the acellular fraction of TB patient pleural effusions, inhibits HIF1A activity, leading to a reduction in glycolytic and microbicidal phenotypes in macrophages (Marín Franco et al., 2020). Moreover, activation of HIF1A enhances Mtb control at early times post-infection in mouse models (Baay-Guzman et al., 2018), and this effect was associated with a metabolic switch of alveolar macrophages toward an M1-like profile (Marín Franco et al., 2020). Given that HIF1A activation promotes protection at early stages of Mtb infection and given its role as a key regulator of DC migration and inflammation (Liu et al., 2021), we hypothesized that HIF1A could affect the functionality of DCs in regulating the initiation and orchestration of the adaptive immune response to Mtb, a process known to be delayed upon Mtb infection (Lai et al., 2018; Urdahl et al., 2011). Here, we show that HIF1A-mediated glycolysis promotes DC activation and migration in the context of TB. Importantly, we report active glycolysis in monocytes from TB patients, which leads to poor glycolytic induction and migratory capacities of monocyte-derived DCs.
Results
Mtb impacts metabolism in human monocyte-derived DCs
To determine the impact of Mtb on the metabolism of human monocyte-derived DCs (Mo-DCs), we assessed metabolic parameters associated with glycolysis and mitochondrial changes upon Mtb stimulation or infection. Cells undergoing aerobic glycolysis are characterized by increased consumption of glucose and the production and release of lactate. We measured lactate release and glucose consumption in Mo-DCs stimulated for 24 hr with equivalent doses of either irradiated (iMtb) or viable Mtb. DCs treated with either iMtb or viable Mtb released increased levels of lactate and consumed more glucose than untreated DCs (Figure 1A and B). Consistently, both iMtb treatment and Mtb infection resulted in an increase in expression of the key glycolysis-activating regulator HIF1A at both mRNA and protein levels (Figure 1C and D). Expression of the gene encoding the glycolytic enzyme lactate dehydrogenase A (
Figure 1.
Mo-DCs were stimulated with viable or irradiated Mtb (iMtb) at two multiplicities of infection (1 or 2 Mtb per DC) for 24 hr. Glycolysis was measured as (A) lactate release in culture supernatants (N = 8); (B) Glucose uptake measured in culture supernatants (N = 7); (C) relative expression of
Mtb exposure shifts DCs to a glycolytic profile over oxidative phosphorylation
To further characterize the metabolic profile of DCs upon iMtb stimulation or Mtb infection, we next evaluated the metabolism of DCs at single-cell level using the SCENITH technology (Argüello et al., 2020). This method is based on a decrease in ATP levels that is tightly coupled with a decrease in protein synthesis and displays similar kinetics (Argüello et al., 2020). By treating the cells with glucose or mitochondrial respiration inhibitors, and measuring their impact on protein synthesis by puromycin incorporation via flow cytometry, glucose and mitochondrial dependences can be quantified. Two additional derived parameters such as ‘glycolytic capacity’ and ‘fatty acid and amino acid oxidation (FAO and AAO) capacity’ were also calculated. SCENITH technology revealed a lower reliance on oxidative phosphorylation (OXPHOS) in parallel with an increase in the glycolytic capacity of iMtb-stimulated (Figure 2A and B), Mtb-infected DCs and even bystander DCs (those cells that are not directly infected but stand nearby) (Figure 2C and D). Since bystander DCs are not in direct association with Mtb (Mtb-RFP-DCs), soluble mediators induced in response to infection may be sufficient to trigger glycolysis even in uninfected cells. No differences were observed for glucose dependence and FAO and AAO capacity (Figure 2A–D). Additionally, we found no changes between the FAO dependency in Mtb-stimulated DCs in comparison to control cells when the FAO inhibitor (etomoxir) was used (Figure 2—figure supplement 1). For the case of iMtb-stimulated DCs, we also assessed the intracellular rates of glycolytic and mitochondrial ATP production using Seahorse technology. Bioenergetic profiles revealed that iMtb increased the rate of protons extruded over time, or proton efflux rate (PER), as well as the basal oxygen consumption rate (OCR) in Mo-DCs (Figure 2E). The measurements of basal extracellular acidification rate (ECAR) and OCR were used to calculate ATP production rate from glycolysis (GlycoATP) and mitochondrial OXPHOS (MitoATP). The ATP production rates from both glycolysis and mitochondrial respiration were augmented upon iMtb stimulation (Figure 2F). Similar to SCENITH results, the relative contribution of GlycoATP to overall ATP production was increased, while MitoATP contribution was decreased in iMtb-treated cells compared to untreated cells (Figure 2G). These results confirmed the change in DC metabolism induced by Mtb, with an increase in the relative glycolytic contribution to overall metabolism at the expense of the OXPHOS pathway. Together, metabolic profiling indicates that a metabolic switch toward aerobic glycolysis occurs in Mo-DCs exposed to Mtb.
Figure 2.
Monocyte-derived DCs (Mo-DCs) were stimulated with irradiated Mtb (iMtb) or infected with Mtb expressing red fluorescent protein (Mtb-RFP, panel C). (A) Representative histograms showing the translation level after puromycin (Puro) incorporation and staining with a monoclonal anti-Puro (anti-Puro mean fluorescence intensity [MFI]) in response to inhibitor treatment (C, control; DG, 2-deoxy-
Figure 2—figure supplement 1.
Contribution of the fatty acid oxidation (FAO) to dendritic cell (DC) metabolism in response to irradiated
Relative contributions of mitochondrial and FAO dependences to overall DC metabolism analyzed with SCENITH in DCs exposed or not to iMtb (N = 6). Paired
Mtb triggers the glycolytic pathway through TLR2 ligation
Since Mtb is sensed by Toll-like receptors (TLR)-2 and -4 (Quesniaux et al., 2004), we investigated the contribution of these receptors to glycolysis activation in Mo-DCs upon Mtb stimulation. Using specific neutralizing antibodies for these receptors, we found that TLR2 ligation, but not that of TLR4, was required to trigger the glycolytic pathway, as reflected by a decrease in lactate release, glucose consumption, and HIF1A expression in iMtb-stimulated DCs treated with an anti-TLR2 antibody (Figure 3A–C). As a control, and as expected given the reliance on TLR4 for LPS sensing (Chow et al., 1999), lactate release and glucose consumption were abolished in LPS-stimulated DCs in the presence of neutralizing antibodies against TLR4 but not TLR2 (Figure 3—figure supplement 1A and B). Moreover, blockade of TLR2 also diminished glycolytic ATP production in iMtb-stimulated DCs without altering OXPHOS-associated ATP production (Figure 3D) or the size and morphology of mitochondria (Figure 3—figure supplement 1C), suggesting that TLR2 engagement by iMtb is required for the induction of glycolysis but not mitochondrial respiration. Interestingly, TLR2 ligation was also necessary for lactate release and HIF1A upregulation triggered by viable Mtb, (Figure 3—figure supplement 1C and D). To further confirm the involvement of TLR2 in the induction of glycolysis, we tested the effect of synthetic (Pam3CSK4) or mycobacterial (peptidoglycans, PTG) TLR2 agonists (Underhill et al., 1999; Schwandner et al., 1999) and found that both ligands induced lactate release and glucose consumption in DCs (Figure 3E and F), without affecting cell viability (Figure 3—figure supplement 1F). Thus, our data indicate that Mtb induces glycolysis in Mo-DCs through TLR2 engagement.
Figure 3.
Mo-DCs were stimulated with irradiated Mtb (iMtb) in the presence of neutralizing antibodies against either TLR2 (aTLR2), TLR4 (aTLR4), or their respective isotype controls. (A) Lactate release as measured in supernatant (N = 7). (B) Glucose uptake as measured in supernatant (N = 7). (C) Mean fluorescence intensity (MFI) of HIF1A as measured by flow cytometry (N = 4). (D) Kinetic profile of proton efflux rate (PER) and oxygen consumption rate (OCR) measurements (left panels). Metabolic flux analysis showing quantification of mitochondrial ATP production and glycolytic ATP production (right panel) (N = 5). (E, F) Mo-DCs were stimulated with Pam3Cys or Mtb peptidoglycan (PTG) at the indicated concentrations. (E) Lactate release as measured in supernatant (N = 5). (F) Glucose uptake as measured in supernatant (N = 5). Statistics in (A–B, E–F) are two-way ANOVA followed by Tukey’s multiple-comparisons test (*p<0.05; **p<0.01; ****p<0.0001). Statistics in (C, D) are from paired
Figure 3—figure supplement 1.
TLR2 ligation triggers glycolysis in monocyte-derived dendritic cells (Mo-DCs).
(A, B) Mo-DCs were stimulated or not with LPS in the presence of neutralizing antibodies against either TLR2 (aTLR2) or TLR4 (aTLR4). (A) Lactate release measured in supernatant (N = 6). (B) Glucose uptake measured in supernatant (N = 6). (C) Morphometric analysis of mitochondria of Mo-DCs stimulated or not with irradiated
HIF1A is required for DC maturation upon iMtb stimulation but not for CD4+ T lymphocyte polarization
To determine the impact of glycolysis on DC maturation and the capacity to activate T cells, we inhibited HIF1A activity in iMtb-stimulated DCs employing two HIF1A inhibitors that display different mechanisms of action. The first is PX-478 (PX) that lowers HIF1A levels by inhibiting HIF-1α deubiquitination, decreases
Figure 4.
HIF1A is required for dendritic cell (DC) maturation upon irradiated
(A–C) Monocyte-derived DCs (Mo-DCs) were stimulated with iMtb in the presence or absence of the HIF1A inhibitor PX-478 (PX). (A) Metabolic flux analysis showing quantification of mitochondrial ATP production and glycolytic ATP production, as in Figure 2G (N = 4). (B) Mean fluorescence intensity (MFI) of CD83, CD86, and PD-L1 as measured by flow cytometry (N = 6). (C) TNF-α and IL-10 production by Mo-DCs measured by ELISA (N = 4–8). (D, E) Monocytes from PPD+ healthy donors were differentiated toward DCs, challenged or not with iMtb in the presence or absence of PX for 24 hr, washed, and co-cultured with autologous CD4+ T cells for 5 days. (D) Extracellular secretion of IFN-γ and IL-17 as measured by ELISA (N = 6). (E) Absolute abundance of Th1, Th17, Th2, and Th1/Th17 CD4+ T cells after coculture with DCs (N = 6). When indicated, lymphocytes without DCs were cultured (Ly). Statistical significance based on two-way ANOVA followed by Tukey’s multiple-comparison test (*p<0.05; **p<0.01). The data are represented as scatter plots, with each circle representing a single individual, means ± SEM are shown.
Figure 4—figure supplement 1.
HIF1A activity is required to trigger the glycolytic pathway in irradiated
Monocyte-derived DCs (Mo-DCs) were stimulated with iMtb in the presence of PX-478 (PX, A–C) or echinomycin (Ech, D–F), both HIF1A inhibitors. (A, D) Lactate release measured in supernatant (N = 6–8). (B, E) Glucose uptake measured in supernatant (N = 6–8). (C, F) Percentage of viable cells relative to untreated DCs (N = 4). (G) Kinetic profile of proton efflux rate (PER) and oxygen consumption rate (OCR) measurements in iMtb-stimulated DCs and PX-iMtb-stimulated DCs. (H) Relative contributions of glycolytic and fatty acid and amino acid oxidation (FAO and AAO) capacities and glucose and mitochondrial dependences to overall DC metabolism analyzed with SCENITH in DCs exposed to iMtb in the presence or not of Ech (N = 4). (I) Uptake of Mtb-FITC by DCs treated or not with PX-478 in the presence or not of cytochalasin D (Cyt D), a potent phagocytosis inhibitor that interferes with actin polymerization. Representative dot plots and quantifications are shown (N = 4). (A–G, I) Two-way ANOVA followed by Tukey’s multiple-comparisons test (*p<0.05), as depicted by lines. Values are expressed as means ± SEM. (H) Paired
Figure 4—figure supplement 2.
HIF1A is required to adopt a mature phenotype in irradiated
Monocyte-derived DCs (Mo-DCs) were stimulated with iMtb in the presence or not of echinomycin (Ech), an HIF1A inhibitor. Mean fluorescence intensity (MFI) of CD83, CD86, and PD-L1 (N = 10). Two-way ANOVA followed by Tukey’s multiple-comparisons test (*p<0.05; **p<0.01; ***p<0.001; ****p<0.0001), compared to control cells or as depicted by lines. The data are represented as scatter plots, with each circle representing a single individual, means ± SEM are shown.
Figure 4—figure supplement 3.
Gating strategy to define CD4+ T cells in response to
Monocytes from PPD+ healthy donors were differentiated toward dendritic cells (DCs), challenged or not with irradiated Mtb (iMtb) in the presence or not of PX-478, and co-cultured with autologous CD4+ T cells for 5 days. Gating strategy to define Th1 (CXCR3+CCR4−CCR6− cells), Th17 (CXCR3−CCR4+CCR6+ cells), Th2 (CXCR3−CCR4+CCR6− cells), and Th1/Th17 (CXCR3+CCR4−CCR6+ cells or Th1*) CD4+ T populations by FACS.
HIF1A-mediated glycolysis triggers the motility of DCs upon iMtb stimulation
Since DC migration to lymph nodes is essential to initiate an adaptive immune response and glycolytic activity has been reported to control DC migration upon stimulation (Guak et al., 2018; Liu et al., 2019), we evaluated the migratory properties of iMtb-stimulated DCs in the presence of inhibitors of HIF1A and LDH which catalyzes the interconversion of pyruvate and lactate. First, we confirmed that PX and oxamate (OX), a well-established LDH inhibitor, diminished the glycolytic activity of iMtb-stimulated human Mo-DCs, as demonstrated by reduced lactate release (Figure 5A, Figure 4—figure supplement 1A). Next, using a transwell migration assay, we found that PX and OX treatment significantly diminished the chemotactic activity of iMtb-stimulated human Mo-DCs in response to CCL21 (Figure 5B), a CCR7 ligand responsible for the migration of DCs into lymphoid organs. We also assessed the three-dimensional (3D) migration capacity of iMtb-stimulated DCs through a collagen matrix in which DCs use an amoeboid migration mode (Cougoule et al., 2018) and found that 3D migration was significantly impaired upon HIF1A or glycolysis inhibition (Figure 5C). The role of glycolysis in the migration of iMtb-stimulated Mo-DCs was further confirmed using an additional LDHA inhibitor, GSK2837808A, which reduced both the release of lactate by iMtb-stimulated Mo-DCs and their migration in response to CCL21 (Figure 5—figure supplement 1A and B). Attenuation of cell migration through collagen induced by OX and PX was also confirmed in Mtb-infected DCs (Figure 5D). To further investigate the effects of glycolysis on cell migration, we turned to an in vivo model. Murine bone marrow-derived DCs (BMDCs) isolated and stimulated with iMtb in the presence or absence of PX or OX were labeled with CFSE and transferred into naïve mice (Figure 5E). Similar to human Mo-DCs, iMtb stimulation increased glycolysis in BMDCs, which was inhibited by PX and OX treatment in vitro (Figure 5—figure supplement 1C). Three hours after the transfer of BMDCs into recipient mice, nearby lymph nodes were collected for DC quantification (Figure 5E). A higher number of adoptively transferred DCs (CFSE-labeled CD11c+ cells) were detected in lymph nodes from mice that received iMtb-stimulated BMDCs compared to mice that received untreated BMDCs or iMtb-BMDCs treated with either PX or OX (Figure 5F, Figure 5—figure supplement 1D). Of note, we verified that CCR7 expression on iMtb-stimulated BMDCs was not affected by OX or PX treatment, so the effect could not be ascribed to downregulation of the chemokine receptor (Figure 5—figure supplement 1E). Therefore, we conclude that HIF1A-mediated glycolysis is required for the successful migration of iMtb-stimulated DCs into lymph nodes.
Figure 5.
HIF1A-mediated-glycolysis is required to trigger migratory activity in irradiated
Monocyte-derived DCs (Mo-DCs) were treated (or not) with HIF1A inhibitor PX-478 (PX) or LDH inhibitor oxamate (OX) and stimulated with iMtb for 24 hr. (A) Lactate release as measured in supernatants in DCs stimulated or not with iMtb in the presence of OX (N = 5). (B) Percentage of migrated cells toward CCL21 relative to the number of initial cells per condition (N = 6). (C, D) Three-dimensional amoeboid migration of DCs through a collagen matrix after 24 hr. Cells within the matrix were fixed and stained with DAPI. Images of the membrane of each insert were taken and the percentage of cells per field were counted. (C) Mo-DCs stimulated with iMtb for 24 hr (N = 5). (D) Mo-DCs infected with Mtb for 24 hr (N = 4). The data are represented as scatter plots, where each circle represents a microphotograph sourced from either five (C) or four (D) independent donors, with each experiment typically including between 5 to 10 microphotographs. (E) Representative schematic of the experimental setup for in vivo migration assays. (F) Percentages of migrating bone marrow-derived DCs (BMDCs) (CFSE-labeled among CD11c+) recovered from inguinal lymph nodes (N = 3). Statistical significance assessed by (A, B) ANOVA followed by Dunnett’s multiple-comparisons test (*p<0.05; **p<0.01); (C, D) Nested ANOVA followed by Dunnett’s multiple-comparisons test (*p<0.05; **p<0.01); (F) ANOVA followed by Holm–Sidak’s multiple-comparisons test (*p<0.05).
Figure 5—figure supplement 1.
Glycolysis is required to trigger the migratory activity in irradiated
(A, B) Monocyte-derived DCs (Mo-DCs) were treated or not with the glycolysis inhibitor GSK2837808A and stimulated with iMtb. (A) Lactate release (N = 5). (B) Chemotactic activity toward CCL21 (N = 5). (C, D) Murine bone marrow-derived DCs (BMDCs) were treated or not with PX-478 or oxamate and stimulated with iMtb for 24 hr. (C) Lactate release (N = 3). (D) Representative dot blots showing the percentages of migrating BMDCs (CD11c+, CFSE-labeled) determined from inguinal lymph nodes. (E) Mo-DCs were stimulated with iMtb in the presence of either PX-478 or oxamate and CCR7 expression was measured by FACS (N = 6). Two-way ANOVA followed by Tukey’s multiple-comparisons test (*p<0.05; **p<0.01; ***p<0.001), as depicted by lines. The data are represented as scatter plots, with each circle representing a single individual, means ± SEM are shown.
Stabilization of HIF1A promotes migration of tolerogenic DCs and DCs derived from TB patient monocytes
Since DC differentiation is skewed, at least partially, toward a tolerogenic phenotype during TB (Balboa et al., 2013; Parlato et al., 2018; Sakhno et al., 2015), we investigated whether tolerogenic DCs can be reprogrammed into immunogenic DCs by modulating their glycolytic pathway after iMtb stimulation. To this end, we generated tolerogenic Mo-DCs by adding dexamethasone (Dx) before stimulation with iMtb in the presence or absence of dimethyloxalylglycine (DMOG), which stabilizes the expression of HIF1A. HIF1A expression is tightly regulated by prolyl hydroxylase domain containing proteins, which facilitate the recruitment of the von Hippel-Lindau (VHL) protein, leading to ubiquitination and degradation of HIF1A by the proteasomes (McGettrick and O’Neill, 2020). DMOG inhibits the prolyl hydroxylase domain-containing proteins. Acquisition of the tolerogenic phenotype was confirmed by the lack of upregulation of costimulatory markers CD83 and CD86, as well as by increased PD-L1 expression in iMtb-DCs treated with Dx compared to control iMtb-DCs (Figure 6—figure supplement 1A). Moreover, Dx-treated DCs did not exhibit an increase in lactate release, consumption of glucose, or induction of HIF1A expression in response to iMtb, showing a high consumption of levels of glucose under basal conditions (Figure 6A and B). Of note, HIF1A stabilization using DMOG restored the HIF1A expression and lactate production in response to iMtb in Dx-treated DCs and increased the consumption of glucose (Figure 6A and B). Activation of HIF1A also improved 3D amoeboid migration, as well as 2D migration capacity of DCs toward CCL21 of iMtb-stimulated Dx-treated DCs (Figure 6C and D, Figure 6—figure supplement 1B). Confirming the relevance of these findings to human TB patients, we found that iMtb-stimulated Mo-DCs from TB patients were deficient in their capacity to migrate toward CCL21 (Figure 6E) and in glycolytic activity compared to Mo-DCs from healthy subjects (Figure 6F and G). Strikingly, stabilizing HIF1A expression using DMOG in Mo-DCs from TB patients restored their chemotactic activity in response to iMtb (Figure 6H). These data indicate that the impaired migratory capacity of iMtb-stimulated tolerogenic DCs or TB patient-derived DCs can be restored via HIF1A stabilization; thus, glycolysis is critical for DC function during TB in both murine and human contexts.
Figure 6.
Stabilization of HIF1A promotes migration of tolerogenic dendritic cells (DCs) and monocyte-derived DCs (Mo-DCs) from tuberculosis (TB) patients.
Tolerogenic Mo-DCs were generated by dexamethasone (Dx) treatment and were stimulated (or not) with irradiated
Figure 6—figure supplement 1.
Profile of tolerogenic dendritic cells (DCs) induced by dexamethasone (Dx).
Tolerogenic monocyte-derived DCs (Mo-DCs) were generated in the presence or not of Dx and stimulated with iMtb. (A) Mean fluorescence intensity (MFI) of CD83, CD86, and PD-L1 (N = 9). The data are represented as scatter plots, with each circle representing a single individual. Values are expressed as means ± SEM (B) Representative schemes of migrating cells through a collagen matrix. Two-way ANOVA followed by Tukey’s multiple-comparisons test (*p<0.05; **p<0.01), as depicted by lines.
CD16+ monocytes from TB patients show increased glycolytic capacity
Since we observed differences in the metabolic activity of DCs derived from monocytes of TB patients when compared to healthy donors, we next focused on evaluating the release of lactate by DC precursors from both subject groups during the first hours of DC differentiation with IL-4/GM-CSF. We found a high release of lactate by monocytes from TB patients compared to healthy donors after 1 hr of differentiation (Figure 7A). Lactate accumulation increased in both subject groups after 24 hr with IL-4/GM-CSF (Figure 7A). Based on these differential glycolytic activities displayed by DC precursors from both subject groups at very early stages of the differentiation process, we decided to evaluate the ex vivo metabolic profile of monocytes using SCENITH. To this end, we assessed the baseline glycolytic capacity of the three main populations of monocytes: classical (CD14+CD16−), intermediate (CD14+CD16+), and non-classical (CD14dimCD16+) monocytes. We found that both populations of CD16+ monocytes from TB patients had a higher glycolytic capacity than monocytes from healthy donors (Figure 7B). Moreover, the glycolytic capacity of CD16+ monocytes (CD14+CD16+ and CD14dimCD16+) correlates with time since the onset of TB-related symptoms (Figure 7C), with no association to the extent or severity of lung disease (unilateral/bilateral lesions and with/without cavities, Figure 7—figure supplement 1). To further expand the metabolic characterization of monocyte subsets from TB patients, we used previously published transcriptomic data (GEO accession number: GSE185372) of CD14+CD16-, CD14+CD16+, and CD14dimCD16+ monocytes isolated from individuals with active TB, latent TB (IGRA+), as well as from TB-negative healthy controls (IGRA-) (Hillman et al., 2022). Within this framework, we performed high-throughput GeneSet Enrichment Analysis (GSEA) using the BubbleMap module of BubbleGUM, which includes a multiple testing correction step to allow comparisons between the three monocyte subsets (Spinelli et al., 2015). As expected, this approach reveals enrichments in genes associated with interferon responses (alpha and gamma) in patients with active TB compared to healthy donors (either IGRA- or latent TB) for all three monocyte subsets (Figure 7D). Consistent with our findings, glycolysis increases in active TB in both CD14+CD16+ and CD14dimCD16+ monocytes (albeit not significant), while it appears to decrease in classical CD14+CD16- monocytes (Figure 7D). Unlike CD14+CD16- cells, the inflammatory response is notably enriched in CD14+CD16+ and CD14dimCD16+ monocytes from patients with active TB compared those with latent TB or healthy subjects (Figure 7D), suggesting that their glycolytic profile correlates with a higher inflammatory state. Finally, no significant enrichment of oxidative phosphorylation-associated genes was found in any of the performed comparisons (Figure 7D). Taken together, these results demonstrate that TB disease is associated with an increased activation and glycolytic profile of circulating CD16+ monocytes.
Figure 7.
CD16+ monocytes from tuberculosis (TB) patients show increased glycolytic capacity.
(A) Monocytes from TB patients or healthy subjects (HS) were isolated and cultured with IL-4 and GM-CSF for 24 hr. Accumulation of lactate in culture supernatants were measured at 1 and 24 hr of differentiation (N = 5). (B) Glycolytic capacity measured using SCENITH of monocyte subsets as defined by their CD14 and CD16 expression from HS and TB patients (N = 7). (C) Correlation analysis between the baseline glycolytic capacity and the evolution time of TB symptoms for each monocyte subset (CD14+CD16-, CD14+CD16+, and CD14dimCD16+, N = 14). Linear regression lines are shown. Spearman’s rank test. The data are represented as scatter plots, with each circle representing a single individual, means ± SEM are shown. (D) BubbleMap analysis, a high-throughput extension of GeneSet Enrichment Analysis (GSEA), on the pairwise comparisons of monocytes from HS or donors with latent TB (LTB) vs. patients with active TB (TB), for each monocyte subset (CD14+CD16-, CD14+CD16+, and CD14dimCD16+). The gene sets shown come from the Hallmark (H) collection of the Molecular Signature Database (MSigDB). The colors of the BubbleMap correspond to the population from the pairwise comparison in which the geneset is enriched (red if geneset is enriched in TB). The bubble area is proportional to the GSEA normalized enrichment score (NES). The intensity of the color corresponds to the statistical significance of the enrichment, derived by computing the multiple testing-adjusted permutation-based p-value using the Benjamini–Yekutieli correction. Enrichments with a statistical significance above 0.30 are represented by empty circles. Statistical significance was assessed by (A) paired
Figure 7—figure supplement 1.
Association between baseline glycolytic status of monocytes and the severity of lung disease.
The glycolytic capacity was assessed in monocyte subsets from tuberculosis (TB) patients with bilateral versus unilateral disease, reflecting the extent of disease (upper panels), or with cavitary versus non-cavitary disease, reflecting the disease severity (lower panels) (N = 4–11). The data are represented as scatter plots, with each circle representing a single individual. p-Values were calculated using the Kruskal–Wallis test with Dunn’s correction for multiple comparisons.
HIF1A activation in CD16+ monocytes from TB patients leads to differentiated DCs with a poor migration capacity
Since circulating CD16+ monocytes from TB patients are highly glycolytic, we evaluated the expression of HIF1A among the populations. We found that CD16+ monocytes from TB patients exhibited a higher expression of HIF1A than from healthy donors (Figure 8A). As we previously demonstrated that CD16+ monocytes from TB patients generate aberrant DCs (Balboa et al., 2013), we hypothesized that the different metabolic profile of this monocyte subset could yield DCs with some sort of exhausted glycolytic capacity and thus lower migration activity upon Mtb exposure. To test this hypothesis, we treated with DMOG to increase the activity of HIF1A during the first 24 hr of monocyte differentiation from healthy donors, leading to an exacerbated increase in lactate release at early stages of the differentiation (Figure 8B). Such early addition of DMOG to healthy monocytes resulted in the generation of DCs (6 days with IL-4/GM-CSF) characterized by equivalent levels of CD1a as control DCs, with a significant decrease in the expression of DC-SIGN (Figure 8—figure supplement 1A). In terms of activation marker expression, DCs differentiated from DMOG-pretreated cells responded to iMtb by upregulating CD86 at higher levels compared to control cells, with an accompanying trend toward reduced upregulation of CD83 (Figure 8—figure supplement 1B). We also observed that DCs from DMOG pretreated cells exhibited a lower migratory capacity in response to iMtb (Figure 8C), reminiscent of the 2D migration capacities of Mo-DCs from TB patients (Figure 8—figure supplement 2). Altogether, our data suggest that the activated glycolytic status of monocytes from TB patients leads to the generation of DCs with low motility in response to Mtb.
Figure 8.
HIF1A activation in CD16+ monocytes from tuberculosis (TB) patients leads to dendritic cells (DCs) with poor migration capacity.
(A) Ex vivo determination of HIF1A expression by monocytes from healthy subjects (HS) or TB patients (TB) for each monocyte subset (CD14+CD16-, CD14+CD16+, and CD14dimCD16+) (N = 6). (B, C) Monocytes from HS were treated with dimethyloxalylglycine (DMOG) during the first 24 hr of differentiation with IL-4/GM-CSF (earlyDMOG) and removed afterward. On day 6 of differentiation, cells were stimulated (or not) with irradiated
Figure 8—figure supplement 1.
Impact of premature activation of HIF1A in monocytes on the generated dendritic cells (DCs).
Monocytes from healthy subjects (HS) were treated with dimethyloxalylglycine (DMOG) during the first 24 hr of differentiation with IL-4/GM-CSF (earlyDMOG) and removed afterward. On day 6 of differentiation, cells were stimulated or not with iMtb. (A) Percentage of cells expressing CD1a and DC-SIGN after full 6 days of differentiation (N = 5). (B) Mean fluorescence intensity (MFI) of CD83 and CD86 on day 6 differentiated cells stimulated or not with iMtb for further 24 hr (N = 5). The data are represented as scatter plots, with each circle representing a single individual. Statistical significance was assessed by (A) paired
Figure 8—figure supplement 2.
Dual role of the glycolysis/HIF1A axis on dendritic cell (DC) migration in tuberculosis (TB).
Working model showing that DCs enhance their glycolytic activity upon encountering
Discussion
In this study, we provide evidence for the role of HIF1A-mediated glycolysis in promoting the migratory capacity of DCs upon encounter with iMtb. Our approach to quantify the ex vivo metabolism of monocytes shows that CD16+ monocytes from TB patients display an exacerbated glycolytic activity that may result in the generation of DCs with poor migratory capacities in response to iMtb. Our results suggest that under extensive chronic inflammatory conditions, such as those found in TB patients, circulating monocytes may be metabolically preconditioned to differentiate into DCs with low migratory potential (Figure 8—figure supplement 2).
Upon Mtb infection of naive mice, initial accumulation of activated CD4+ T cells in the lung is delayed, occurring between 2–3 weeks post-infection (Wolf et al., 2007; Reiley et al., 2008). The absence of sterilizing immunity induced by TB vaccines, such as BCG, has been proposed to result from delayed activation of DCs and the resulting delay in antigen presentation and activation of vaccine-induced CD4+ T-cell responses (Griffiths et al., 2016). In this context, it was demonstrated that Mtb-infected Mo-DCs recruited to the site of infection exhibit low CCR7 expression and impaired migration to lymph nodes compared to uninfected Mo-DCs (Harding et al., 2015). Additionally, Mo-DCs have been found to play a key role in transporting Mtb antigens from the lung to the draining lymph node, where conventional DCs present antigens to naive T cells (Samstein et al., 2013). The migratory capacity of responding DCs is thus of paramount importance to the host response to Mtb infection.
Here, we found that Mtb exposure triggers glycolysis in Mo-DCs from healthy donors, which promotes their migration capacity in an HIF1A-dependent manner. Recently, it was shown that glycolysis was required for CCR7-triggered murine DC migration in response to LPS (Guak et al., 2018; Liu et al., 2019; Everts et al., 2014). Glycolysis was also reported to be required for the migration of other immune cells such as macrophages (Semba et al., 2016) and regulatory T cells (Kishore et al., 2017). Consistently, we show that inhibition of HIF1A-dependent glycolysis impairs human Mo-DC migration upon Mtb stimulation. The link between cellular metabolism and migratory behavior is supported by studies that have elucidated how glycolysis can be mechanically regulated by changes in the architecture of the cytoskeleton, ultimately impacting the activity of glycolytic enzymes (Park et al., 2020; Fernie et al., 2020). In addition, interesting links between cellular mechanics and metabolism have been previously described for DCs, highlighting the potential to alter DC mechanics to control DC trafficking and consequently T cell priming (Currivan et al., 2022). However, studies focused on the molecular mechanisms by which metabolic pathways impact the machinery responsible for cell movement in the context of TB infection will be required to better understand and design therapeutic manipulation.
Our research indicates that DCs exhibit upregulated glycolysis following stimulation or infection by Mtb. This metabolic shift is crucial for facilitating cell migration to the draining lymph nodes, an essential step in mounting an effective immune response. Yet, it remains uncertain whether this glycolytic induction reaches a threshold conducive to generating a protective immune response, a matter that our findings do not definitively address. In addition, we demonstrated that tolerogenic DCs induced by DX as well as DCs derived from TB patient monocytes exhibit lower lactate release and impaired trafficking toward CCL21 upon Mtb stimulation; both phenotypes could be rescued by stabilization of HIF1A expression. To our knowledge, this is the first study to address how the metabolic status of monocytes from TB patients influences the migratory activity of further differentiated DCs. According to our findings, the activation status of the glycolysis/HIF1A axis in monocytes would be a predictor of refractoriness to differentiation into migratory DCs in TB. With respect to the metabolism of tolerogenic DCs broadly, our results are consistent with reported data showing that DC tolerance can be induced by drugs promoting OXPHOS, such as vitamin D and DX (Ferreira et al., 2012; Ferreira et al., 2009; Basit and de Vries, 2019). It was interesting to note that, although migration of tolerogenic DCs did not increase upon Mtb stimulation, it was increased under basal conditions, which agrees with previous data showing a high steady-state migration capacity of putatively tolerogenic DCs (Ohl et al., 2004).
It has been widely demonstrated that immune cells can switch to glycolysis following engagement of TLRs (Krawczyk et al., 2010). Our work showed that TLR2 ligation by either viable or irradiated Mtb was necessary to trigger glycolysis in DCs, at least at early times post-stimulation. In fact, even bystander DCs increased their glycolytic activity in Mtb-infected cultures, suggesting that mycobacterial antigens or bacterial debris present in the microenvironment may be sufficient to trigger TLR-dependent glycolysis. In the context of natural infection in vivo, we foresee that DC with different levels of infection will coexist, some with low bacillary load that, according to our data, may be able to trigger glycolysis and migrate, while others highly infected DCs would more likely die (Ryan et al., 2011). It remains to be elucidated whether persistent interaction between DCs and Mtb might lead to an attenuation in glycolysis over time, as has been reported for macrophages (Hackett et al., 2020). In this regard, our data demonstrates that chronic Mtb infection leads to monocytes bearing an exacerbated glycolytic status likely tied to prolonged and/or excessive stimulation of membrane-bound TLRs in circulation, which results in DCs with an exhausted glycolytic capacity. Although DCs stimulated with iMtb in the presence of an HIF1A inhibitor exhibited differences in activation markers and cytokine profile, we found that they were still able to activate CD4+ T cells from PPD+ donors in response to iMtb. These findings complement previous evidence showing that LPS-induced mature DCs inhibit T-cell responses through HIF1A activation in the presence of glucose, leading to greater T cell activation capacity in low glucose contexts such as at the interface between DCs and T cells (Lawless et al., 2017). In this work, we did not detect an impact on T cell activation upon HIF1A inhibition in DCs, but we observed a clear reduction in their migration capacity that may limit or delay DC encounters with T cells in vivo, leading to poor T cell activation in the lymph nodes. In this regard, mouse studies have shown that DC migration directly correlates with T cell proliferation (MartIn-Fontecha et al., 2003). However, we cannot rule out the possibility that other CD4+ T cell subsets (such as regulatory T cells), CD1-restricted T cells, and/or CD8+ T cell subsets could be differentially activated by iMtb-stimulated DCs lacking HIF1A activity.
Three different populations of human monocytes have been identified: classical (CD14+, CD16−), intermediate (CD14+, CD16+), and non-classical (CD14dim, CD16+) monocytes (Ziegler-Heitbrock et al., 2010). These monocyte subsets are phenotypically and functionally distinct. Classical monocytes readily extravasate into tissues in response to inflammation, where they can differentiate into macrophage-like or DC-like cells Ginhoux and Jung, 2014; intermediate monocytes are well-suited for antigen presentation, cytokine secretion, and differentiation; and non-classical monocytes are involved in complement and Fc gamma-mediated phagocytosis and their main function is cell adhesion (Wong et al., 2011; Cros et al., 2010). Unlike non-classical monocytes, the two CD14+ monocyte populations are known to extravasate into tissues and thus are likely to act as precursors capable of giving rise to Mo-DCs in inflamed tissues. However, the DC differentiation capacity of the intermediate population is still not well defined. We previously demonstrated that monocytes from TB patients generate aberrant DCs, and that CD16+ monocytes generate aberrant DCs upon treatment with GM-CSF and IL-4 (Cougoule et al., 2018). Here, we demonstrated that glycolysis seems to play a dual role during DC differentiation from monocytes, on the one side, being required for fully differentiated-DC migration to lymph nodes in response to Mtb and, on the other side, leading to DCs with poor iMtb-responsive migratory capacity if activated during the onset of DC differentiation (Figure 8—figure supplement 2). In this regard, DCs from healthy subjects respond to iMtb by inducing a glycolytic and migratory profile, while monocytes isolated from TB patients exhibit an unusual early glycolytic state that results in the ulterior generation of DCs with low glycolytic and migratory activities in response to Mtb. Similarly, we found that CD16+ cells from TB patients display an activated glycolytic status, as well as elevated HIF1A expression levels compared to their healthy counterparts. Additionally, we showed that monocytes from TB patients are not only enriched in CD16+ cells, but also display an altered chemokine receptor expression profile (Balboa et al., 2011), demonstrating that both phenotype and function of a given monocyte subset may differ under pathological conditions. While it is difficult to determine whether the heightened glycolytic profile of monocytes may limit their differentiation into DCs in vivo, we provided evidence that an increase in HIF1A-mediated glycolysis in precursors leads to the generation of cells with poor ability to migrate in response to CCL21 in vitro. In line with this observation, a recent study revealed a significant increase in the glycolytic capacity occurs during the first 24 hr of monocyte differentiation towards a tolerogenic DC phenotype, as induced by vitamin D3 (Everts and Pearce, 2014), highlighting the detrimental role of an early activated inflammatory profile in DC precursors. A possible explanation for these effects may be found in lactate accumulation in monocytes during DC differentiation. Lactate signaling in immune cells leads to metabolic alterations in DCs that program them to a regulatory state (Manoharan et al., 2021), and lactate has also been shown to suppress DC differentiation and maturation (Wculek et al., 2019); thus, excessive precursor glycolytic activity may result in DCs biased toward regulatory functions.
Taken together, our data offer new insights into the immunometabolic pathways involved in the trafficking of DCs to the lymph nodes. These insights may have various implications depending on factors such as timing, cell type, and location induction of the HIF1A/glycolysis axis. On the one hand, nurturing HIF1A-mediated glycolytic activity in DCs during the early stages of infection could potentially enhance the effectiveness of preventive strategies for TB. Particularly noteworthy is the significant impact revealed in studies where the number of DCs reaching the lymph node proved to be a crucial factor in determining the success of DC-based vaccination (MartIn-Fontecha et al., 2003). On the other hand, premature activation of glycolysis in precursors, as observed in CD16+ monocytes from severe TB patients, could disrupt the delicate balance necessary for an optimal immune response. This variability is consistent with the paradigm of ‘too much, too little’, as demonstrated by the dual roles of IFN-γ (Kumar, 2017) and TNF-α (Mootoo et al., 2009) in the context of TB. It also underscores the vital importance of maintaining an equilibrium in inflammatory responses. This study lays the foundation for further exploration into the potential systemic impact of the HIF1A/glycolysis axis within the realm of chronic inflammation contrasting with its role in a local setting during the acute phase of infection. By enhancing our understanding, these findings aim to guide the development of innovative preventive and therapeutic strategies for TB.
Materials and methods
Key resources table
| Reagent type (species) or resource | Designation | Source or reference | Identifiers | Additional information |
|---|---|---|---|---|
| Antibody | Anti-human TLR2 | BioLegend | Cat# 309717 | |
| Antibody | Anti-human TLR4 | BioLegend | Cat# 312813 | |
| Antibody | Anti-human CD1a | eBioscience | RRID:AB_467039 | |
| Antibody | Anti-human DC-SIGN | R&D System | Cat# MAB161 | |
| Antibody | Anti-human CD14 | BD Biosciences | Cat# 557154 | |
| Antibody | Anti-human CD86 | BioLegend | Cat# 374216 | |
| Antibody | Anti-human CD83 | eBioscience | Cat# 14-0839-82 | |
| Antibody | Anti-human CD274 (B7-H1, PD-L1) | BD Pharmingen | Cat# 557924 | |
| Antibody | Anti-human Glut1 | R&D System | Cat# MAB1418 | |
| Antibody | Anti-human HIF1A | BioLegend | Cat# 359704 | |
| Antibody | Anti-human CD4 | BioLegend | Cat# 357402 | |
| Antibody | Anti-human CXCR3 | BioLegend | Cat# 353719 | |
| Antibody | Anti-human CCR4 | BD Biosciences | Cat# 560726 | |
| Antibody | Anti-human CCR6 | BD Biosciences | Cat# 560619 | |
| Antibody | Anti-human CD3 | BioLegend | Cat# 300317 | |
| Antibody | Anti-human CD16 | BioLegend | Cat# 302008 | |
| Antibody | Anti-mouse CD11c | BD Pharmingen | Cat# 561044 | |
| Biological sample ( | N/A | N/A | ||
| Biological sample ( | Tuberculosis γ-irradiated H37Rv | BEI Resource | Cat# NR-49098 | |
| Biological sample | Patients-derived blood | Hospital F.J.Muñiz (Buenos Aires, Argentina) | N/A | |
| Biological sample | Buffy coats from healthy donors | Centro Regional de Hemoterapia Garrahan (Buenos Aires, Argentina) | N/A | |
| Biological sample | Blood from PPD+ healthy donors | N/A | N/A | |
| Peptide, recombinant protein | Recombinant human GM-CSF | Peprotech | Cat# 300-03 | |
| Peptide, recombinant protein | Recombinant human IL-4 | BioLegend | Cat# 430307 | |
| Peptide, recombinant protein | Recombinant mouse GM-CSF | BioLegend | Cat# 576304 | |
| Peptide, recombinant protein | Recombinant mouse IL-4 | BioLegend | Cat# 574302 | |
| Chemical compound, drug | Lipopolysaccharides from | Sigma-Aldrich | Cat# 93572-42-0 | |
| Chemical compound, drug | Dexamethasone | Sidus | Cat# 229197-1 | |
| Chemical compound, drug | PX-478 2HCl | Selleck Chemicals | Cat# S7612 | |
| Chemical compound, drug | DMOG | Bertin Technologies | Cat# 300-02 | |
| Chemical compound, drug | GSK2837808A | Cayman Chemical | Cat# 1445879-21-9 | |
| Chemical compound, drug | Echinomycin | Cayman Chemical | Cat# 512-64-1 | |
| Chemical compound, drug | Sodium oxamate | Cayman Chemical | Cat# 565-73-1 | |
| Peptide, recombinant protein | Recombinant Human Exodus-2 (CCL21) | Peprotech | Cat# 300-35A | |
| Chemical compound, drug | Collagen from calf skin | Sigma-Aldrich | Cat# C9791-10MG | |
| Commercial assay or kit | Lactate Kit | Wiener | Cat# 1999795 | |
| Commercial assay or kit | Glicemia Enzimática AA Kit | Wiener | Cat# 1009803 | |
| Commercial assay or kit | Perm2 solution | BD Biosciences | Cat# 340973 | |
| Commercial assay or kit | Trizol reagent | Thermo Fisher Scientific | Cat# 15596026 | |
| Commercial assay or kit | MitoSpy Green FM | BioLegend | Cat# 424805 | |
| Commercial assay or kit | TNF alpha Human ELISA Kit | eBiosciences | Cat# BMS223-4 | |
| Commercial assay or kit | IL-10 Human ELISA Kit | eBiosciences | Cat# BMS215-2 | |
| Commercial assay or kit | IL-17A Human ELISA Kit | eBiosciences | Cat# 88-7176-22 | |
| Commercial assay or kit | IFN gamma Human ELISA Kit | eBiosciences | Cat# BMS228 | |
| Commercial assay or kit | Zombie Violet Fixable Viability Kit | BioLegend | Cat# 423113 | |
| Commercial assay or kit | SCENITH | Gifted by Rafael Argüello | N/A | |
| Sequence-based reagent | Primer: | Marín Franco et al., 2020 | N/A | |
| Sequence-based reagent | Primer: | Marín Franco et al., 2020 | N/A | |
| Sequence-based reagent | Primer: | Marín Franco et al., 2020 | N/A | |
| Sequence-based reagent | Primer: | Marín Franco et al., 2020 | N/A | |
| Sequence-based reagent | Primer: | Marín Franco et al., 2020 | N/A | |
| Sequence-based reagent | Primer: | Marín Franco et al., 2020 | N/A | |
| Software, algorithm | ImageJ | ImageJ | https://imagej.nih.gov/ij/ | |
| Software, algorithm | Prism (v5) | GraphPad | https://www.graphpad.com/ | |
| Software, algorithm | FlowJo 7.6.5 | TreeStar | https://www.flowjo.com/ | |
| Software, algorithm | FCS Express V3 | DeNovo Software | https://www.denovosoftware.com/ | |
| Software, algorithm | Seahorse Wave | Agilent | https://www.agilent.com/ | |
| Software, algorithm | CFX Maestro | Bio-Rad | https://www.bio-rad.com/ | |
| Software, algorithm | Metamorph | Molecular Devices | https://www.moleculardevices.com/ |
Chemical reagents
LPS from
Bacterial strain and antigens
Mtb H37Rv strain was grown at 37°C in Middlebrook 7H9 medium supplemented with 10% albumin-dextrose-catalase (both from Becton Dickinson, NJ) and 0.05% Tween-80 (Sigma-Aldrich). The Mtb γ-irradiated H37Rv strain (NR-49098) was obtained from BEI Resource (NIAID, NIH, USA). The RFP-expressing Mtb strain was gently provided by Dr. Fabiana Bigi (INTA, Castelar, Argentina).
Preparation of monocyte-derived DCs
Buffy coats from healthy donors were prepared at Centro Regional de Hemoterapia Garrahan (Buenos Aires, Argentina) according to institutional guidelines (resolution number CEIANM-664/07). Informed consent was obtained from each donor before blood collection. Monocytes were purified by centrifugation on a discontinuous Percoll gradient (Amersham, Little Chalfont, UK) as previously described (Genoula et al., 2018). Then, monocytes were allowed to adhere to 24-well plates at 5 × 105 cells/well for 1 hr at 37°C in warm RPMI-1640 medium (Thermo Fisher Scientific, Waltham, MA). The mean purity of adherent monocytes was 85% (range: 80–92%). The medium was then supplemented to a final concentration of 10% fetal bovine serum (FBS, Sigma-Aldrich), human recombinant granulocyte-macrophage colony-stimulating factor (10 ng/ml, GM-CSF, Peprotech, NJ), and IL-4 (20 ng/ml, BioLegend, San Diego, USA). Cells were allowed to differentiate for 5–7 days (DC-SIGN+ cells in the culture >90%).
DC stimulation
DCs were stimulated with either iMtb or viable Mtb at equivalent OD600 doses for 24 hr at 37°C. The cells were washed three times, and their phenotype and functionality were evaluated together with survival of activated cells; cell number and viability were determined by either trypan blue exclusion assays or MTT. Infections were performed in the biosafety level 3 (BSL-3) laboratory at the Unidad Operativa Centro de Contención Biológica (UOCCB), ANLIS-MALBRAN (Buenos Aires), according to the biosafety institutional guidelines.
DC treatments
When indicated, neutralizing monoclonal antibodies (mAb), or their corresponding isotype antibodies as mock controls, were added 30 min prior to DC stimulation to inhibit TLR2 (309717, BioLegend) or TLR4 (312813, BioLegend). In addition, DCs were incubated with PX-478 (20 µM) or echinomycin (1 nM) with the purpose of inhibiting HIF1A activity, DMOG (50 µM) to stabilize HIF1A, and oxamate (20 mM) or GSK2837808A (20 µM) to inhibit LDH. DC stimulation with iMtb occurred 30 min after treatment without drug washout.
In Figure 6 and Figure 6—figure supplement 1, Dx-induced tolerogenic dendritic cells (Dx-DC) were generated by incubating DCs with 0.1 µM of Dx for 1 hr. Thereafter, cells were washed, and ‘complete medium’ was added. Tolerogenic Dx-DCs were then stimulated (or not) with iMtb in the presence or not of DMOG (50 µM).
Determination of metabolite concentrations
Lactate production and glucose concentrations in the culture medium was measured using the spectrophotometric assays Lactate Kit and Glicemia Enzimática AA Kit both from Wiener (Argentina), which are based on the oxidation of lactate or glucose, respectively, and the subsequent production of hydrogen peroxide (Barham and Trinder, 1972). The consumption of glucose was determined by assessing the reduction in glucose levels in culture supernatants in comparison with RPMI 10% FBS. The absorbance was read using a Biochrom Asys UVM 340 Microplate Reader microplate reader and software.
Quantitative RT-PCR
Total RNA was extracted with Trizol reagent (Thermo Fisher Scientific) and cDNA was reverse transcribed using the Moloney murine leukemia virus reverse transcriptase and random hexamer oligonucleotides for priming (Life Technologies, CA). The expression of the genes
Immunofluorescence analysis
FITC-, PE-, or PerCP.Cy5.5-labeled mAbs were used for phenotypic analysis of the following cell-surface receptor repertoires: FITC-anti-CD1a (clone HI149, eBioscience), PE-anti-DC-SIGN (clone 120507, R&D System), PerCP.Cy5.5-anti-CD86 (clone 374216, BioLegend), FITC-anti-CD83 (clone HB15e, eBioscience), PE-anti-PD-L1 (clone MIH1, BD Pharmingen), and in parallel, with the corresponding isotype control antibody. Approximately 5 × 105 cells were seeded into tubes and washed once with PBS. Cells were stained for 30 min at 4°C and washed twice. Additionally, cells were stained for 40 min at 4°C with fluorophore-conjugated antibodies PE-anti-Glut1 (clone 202915, R&D Systems, MN) and in parallel, with the corresponding isotype control antibody. For HIF1A determination, DCs were permeabilized with methanol and incubated with PE-anti-HIF1A (clone 546-16, BioLegend). Stained populations were gated according to forward scatter (FSC) and side scatter (SSC) analyzed on FACScan (Becton Dickinson). Isotype matched controls were used to determine autofluorescence and nonspecific staining. Analysis was performed using the FCS Express (De Novo Software), and results were expressed as median fluorescence intensity (MFI) or percentage of positive cells.
Soluble cytokines determinations
Supernatants from DC populations or DC-T cell cocultures were harvested and assessment of TNF-α, IL-10, IL-17A, or IFN-γ production was measured by ELISA, according to manufacturer’s instructions (eBioscience). The detection limit was 3 pg/ml for TNF-α and IL-17A, 6 pg/ml for IFN-γ, and 8 pg/ml for IL-10.
CD4+ T cell activation assay
Specific lymphocyte activation (recall) assays were carried out in cells from tuberculin purified protein derivative-positive skin test (PPD+) healthy donors by culturing DC populations and autologous T cells at a ratio of 10T cells to 1 DC in round bottom 96-well culture plates for 5 days as detailed previously (Balboa et al., 2016). The numbers of DCs were adjusted to live cells before the start of the co-cultures. After 5 days, CD4+ T cell subsets were identified by immunolabeling according to the differential expression of CCR4, CXCR3, and CCR6 as previously reported (Acosta-Rodriguez et al., 2007). CXCR3+CCR4−CCR6− (Th1), CXCR3−CCR4+CCR6− (Th2), CXCR3−CCR4+CCR6+ (Th17), and CXCR3+CCR4−CCR6+ (Th1* or Th1/Th17). The fluorochrome-conjugated antibodies used for flow cytometry analysis were CD4-FITC (clone A161A1, BioLegend), CXCR3-PE-Cy7 (clone G025H7, BioLegend), CCR4-PerCPCy5.5 (clone 1G1, BD Bioscience), CCR6-APC (clone 11A9, BD Bioscience), and CD3-APC-Cy7 (clone HIT3a, BioLegend). A viability dye, Zombie Violet (BioLegend), was used to exclude dead cells. Fluorescence Minus One (FMO) control was used to set proper gating for CXCR3-PE-Cy7, CCR4-PerCPCy5.5, and CCR6-APC detection. Cells were analyzed by fluorescence-activated cell sorting (FACS), using the BD FACSCANTO cytometer and FlowJo Software (BD Life Sciences).
Chemotactic activity of DCs
Each DC population (4 × 105 cells in 75 µl) was placed on the upper chamber of a transwell insert (5 µm pore size, 96-well plate; Corning), and 230 µl of media (RPMI with 0.5% FCS) with human recombinant CCL21 (200 ng/ml) (Peprotech) was placed in the lower chamber. After 3 hr, cells that had migrated to the lower chamber were removed and analyzed. The relative number of cells migrating was determined on a flow cytometer using Calibrite beads (BD Biosciences), where a fixed number of beads was included in each sample and the number of cells per 1000 beads was evaluated. Data were normalized to the number of initial cells.
In vivo migration assay
DCs were differentiated from bone marrow precursors obtained from female 8-week naïve BALB/c mice in the presence of murine GM-CSF (10 ng/ml) and IL-4 (10 ng/ml) both from BioLegend for 7 days. After differentiation, DCs were treated with oxamate (20 mM) or PX-478 (10 uM) and stimulated with iMtb. After 24 hr, DCs were stained with CFSE (5 µM) and inoculated intradermally in the inguinal zone of naïve female 8-week BALB/c mice. Three hours post-injection, inguinal lymph nodes close to the site of inoculation were harvested and cells were stained with fluorophore-conjugated antibody PE-anti-CD11c (clone HL3, BD Pharmingen). Analysis was performed using the FlowJo Software, and results were expressed as the percentage of CFSE+/CD11c+ cells.
3D migration assay
0.5 × 105 DCs were seeded on top of fibrillar collagen matrices polymerized from Nutragen 2 mg/ml, 10% v/v MEM 10X (MEM invitrogen, Carlsbad, CA), UltraPure distilled water and 4–6% v/v bicarbonate buffer (pH = 9) 7.5%. After 24 hr, cellular migration was quantified by taking images using an inverted microscope (Leica DMIRB, Leica Microsystems, Deerfield, IL) and the software Metamorph, as described previously (Van Goethem et al., 2010). Alternatively, in a similar manner, matrices were polymerized using Collagen (Sigma-Aldrich, C9791-10MG) in Figure 5C and D. After 24 hr of cellular migration, matrices were fixed with paraformaldehyde (PFA) 4% during 30 min at room temperature and stained with DAPI (Cell Signaling). Collagen was removed and membranes were mounted with DAKO. Images were taken using confocal microscopy (FluoView FV 1000), and cells were counted per field.
Measurement of cell respiration with Seahorse flux analyzer
Bioenergetics were determined using a Seahorse XFe24 analyzer. ATP production rates and relative contribution from the glycolysis and the OXPHOS were measured by the Seahorse XF Real-Time ATP Rate Assay kit. DCs (2 × 105 cells/well) were cultured in 3 wells per condition. The assay was performed in XF Assay Modified DMEM. Three consecutive measurements were performed under basal conditions and after the sequential addition of oligomycin and rotenone/antimycin (Agilent, USA). ECAR and OCR were measured. Mitochondrial ATP production rate was determined by the decrease in the OCR after oligomycin addition. On the other hand, the complete inhibition of mitochondrial respiration with rotenone plus antimycin A allows accounting for mitochondrial-associated acidification, and when combined with PER data, allows calculation of glycolysis ATP production rate. All OCR and ECAR values were normalized. Briefly, before the assay, brightfield imaging was performed. Cellular area per condition was calculated using ImageJ software and imported into Wave (Agilent) using the normalization function.
SCENITH assay
SCENITH experiments were performed as previously described (Argüello et al., 2020) using the SCENITH kit containing all reagents and anti-puromycin antibodies (https://www.scenith.com/). Briefly, DCs or PBMCs were treated for 40 min at 37°C in the presence of the indicated inhibitors of various metabolic pathways and puromycin. After the incubation, puromycin was stained using a fluorescently labeled anti-puromycin monoclonal antibody (clone R4743L-E8) with Alexa Fluor 647 or Alexa Fluor 488, and analyzed by flow cytometry. For metabolic analysis of monocyte subsets, PBMCs were labeled with PE-anti-CD16 (clone 3G8, BioLegend) and PECy7-anti-CD14 (clone HCD14, BioLegend) mAbs. The impact of the various metabolic inhibitors was quantitated as described (Argüello et al., 2020).
Transmission electron microscopy
DCs were fixed in 2.5% glutaraldehyde/2% PFA (EMS, Delta-Microscopies) dissolved in 0.1 M Sorensen buffer (pH 7.2) for 1 hr at room temperature, and then preserved in 1% PFA dissolved in Sorensen buffer. Adherent cells were treated for 1 hr with 1% aqueous uranyl acetate then dehydrated in a graded ethanol series and embedded in Epon. Sections were cut on a Leica Ultracut microtome and ultrathin sections were mounted on 200 mesh onto Formvar carbon-coated copper grids. Finally, thin sections were stained with 1% uranyl acetate and lead citrate and examined with a transmission electron microscope (Jeol JEM-1400) at 80 kV. Images were acquired using a digital camera (Gatan Orius). For mitochondrial morphometric analysis, TEM images were quantified with the ImageJ ‘analyze particles’ plugin in thresholded images, with size (μm2) settings from 0.001 to infinite. For quantification, 8–10 cells of random fields (1000× magnification) per condition were analyzed.
Changes of mitochondrial mass
Mitochondrial mass was determined in DCs by fixing the cells with PFA 4% and labeling them with the probe MitoSpy Green FM (BioLegend). Green fluorescence was analyzed by flow cytometry (FACScan, BD Biosciences).
GSEA of human monocytes
BubbleMap analysis was performed with 1000 geneset-based permutations, and with ‘Signal2Noise’ as a metric for ranking the genes. The results are displayed as a BubbleMap, where each bubble is a GSEA result and summarizes the information from the corresponding enrichment plot. The color of the Bubble corresponds to the population from the pairwise comparison in which the geneset is enriched. The bubble area is proportional to the GSEA normalized enrichment score (NES). The intensity of the color corresponds to the statistical significance of the enrichment, derived by computing the multiple testing-adjusted permutation-based p-value using the Benjamini–Yekutieli correction. Enrichments with a statistical significance above 0.30 are represented by empty circles.
Patient blood donors
TB patients were diagnosed at the División Tisioneumonología, Hospital F.J.Muñiz (Buenos Aires, Argentina) by the presence of recent clinical respiratory symptoms, abnormal chest radiography, and positive culture of sputum or positive sputum smear test for acid-fast-bacilli. Written, informed consent was obtained according to the Ethics Committee from the Hospital Institutional Ethics Review Committee. Exclusion criteria included HIV-positive patients and the presence of concurrent infectious diseases or comorbidities. Blood samples were collected during the first 15 days after commencement of treatment. All tuberculous patients had pulmonary TB (Table 1). The term symptoms evolution refers to the time period during which a patient experiences cough and phlegm for more than 2–3 weeks, with (or without) sputum that may (or not) be bloody, accompanied by symptoms of constitutional illness (e.g
Table 1.
Demographic and clinical characteristics of tuberculosis (TB) patients.
| Age, years (range) | 36 (19–67) |
|---|---|
| Gender, % (number/total) | M, 81% (31/38) |
| Nationality, % (number/total) | Argentina, 76.31% (29/38) |
| TB disease localization, % (number/total) | Pulmonary, 94% (36/38) |
| AFB* in sputum, % (number/total) | 3+, 21% (8/38) |
| Leukocyte count, mean ± SEM, cell/µl | 8483 ± 509 |
| Lymphocyte mean ± SEM, % | 19 ± 2 |
| Monocyte mean ± SEM, % | 7 ± 0.5 |
*
Acid-fast-bacilli (AFB) in sputum: -, 1+, 2+, 3+ are defined according to the International Union Against Tuberculosis and Lung Disease (IUATLD)/World Health Organization (WHO) quantification scale.
Statistics
All values are presented as the median ± SEM of 3–13 independent experiments. Each independent experiment corresponds to one donor. Each assay, which included human-derived DCs, was performed with a number of donors specified in each figure legend at a rate of two donors per time. For Seahorse assays, OCR and PER values are shown as mean ± SD. Comparisons between unpaired experimental conditions were made using either ANOVA for parametric data or Friedman test for nonparametric data followed by Dunn’s multiple-comparison test. Comparisons between paired experimental conditions were made using the two-tailed Wilcoxon signed-rank test for nonparametric data or
Study approval
Human specimens
The study design was reviewed and approved by the Ethics Committees of the Academia Nacional de Medicina (49/20/CEIANM) and the Muñiz Hospital, Buenos Aires, Argentina (NI #1346/21). All participants voluntarily enrolled in the study by signing an informed consent form after receiving detailed information about the research study.
Mouse studies
All experimental protocols were approved by the Institutional Animal Care and Use of the Experimentation Animals Committee (CICUAL number 090/2021) of the Institute of Experimental Medicine (IMEX, Buenos Aires).
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Abstract
During tuberculosis (TB), migration of dendritic cells (DCs) from the site of infection to the draining lymph nodes is known to be impaired, hindering the rapid development of protective T-cell-mediated immunity. However, the mechanisms involved in the delayed migration of DCs during TB are still poorly defined. Here, we found that infection of DCs with
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Neither ProQuest nor its licensors make any representations or warranties with respect to the translations. The translations are automatically generated "AS IS" and "AS AVAILABLE" and are not retained in our systems. PROQUEST AND ITS LICENSORS SPECIFICALLY DISCLAIM ANY AND ALL EXPRESS OR IMPLIED WARRANTIES, INCLUDING WITHOUT LIMITATION, ANY WARRANTIES FOR AVAILABILITY, ACCURACY, TIMELINESS, COMPLETENESS, NON-INFRINGMENT, MERCHANTABILITY OR FITNESS FOR A PARTICULAR PURPOSE. Your use of the translations is subject to all use restrictions contained in your Electronic Products License Agreement and by using the translation functionality you agree to forgo any and all claims against ProQuest or its licensors for your use of the translation functionality and any output derived there from. Hide full disclaimer