HIF-1α is a key regulator in potentiating suppressor activity and limiting the microbicidal capacity of MDSC-like cells during visceral leishmaniasis

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Abstract

Leishmania donovani is known to induce myelopoiesis and to dramatically increase extramedullary myelopoiesis. This results in splenomegaly, which is then accompanied by disruption of the splenic microarchitecture, a chronic inflammatory environment, and immunosuppression. Chronically inflamed tissues are typically hypoxic. The role of hypoxia on myeloid cell functions during visceral leishmaniasis has not yet been studied. Here we show that L. donovani promotes the output from the bone marrow of monocytes with a regulatory phenotype that function as safe targets for the parasite. We also demonstrate that splenic myeloid cells acquire MDSC-like function in a HIF-1α-dependent manner. HIF-1α is also involved in driving the polarization towards M2-like macrophages and rendering intermediate stage monocytes more susceptible to L. donovani infection. Our results suggest that HIF-1α is a major player in the establishment of chronic Leishmania infection and is crucial for enhancing immunosuppressive functions and lowering leishmanicidal capacity of myeloid cells.

Author summary

The protozoan parasite Leishmania donovani causes chronic infection in the spleen, which is accompanied by a chronic inflammatory environment, an enlargement of the organ, and immunosuppression. The environment of chronically inflamed tissues is characterized by low oxygen levels and tissue disruption, which induce the expression of the transcription factor HIF-1α in all cells. The kinetics of monocyte production and differentiation in the bone marrow and the spleen, and the role of hypoxia in myeloid cell functions during visceral leishmaniasis have not yet been studied. Here we show that L. donovani promotes the output from the bone marrow of monocytes with a regulatory phenotype that function as safe targets for the parasite. We also demonstrate that HIF-1α potentiates inhibitory functions of myeloid cells and is involved in driving the polarization towards M2-like macrophages and rendering them more susceptible to L. donovani infection. Our results suggest that HIF-1α is a major player in the establishment of chronic Leishmania infection and is crucial for enhancing immunosuppressive functions and lowering leishmanicidal capacity of myeloid cells.

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HIF-1α regulates function and differentiation of myeloid-derived suppressor cells in the tumor microenvironment

Cesar A. Corzo, Thomas Condamine, Lily Lu … (2010)

Myeloid-derived suppressor cells (MDSCs) are one of the major components of the immune-suppressive network responsible for T cell defects in cancer. These cells also contribute to tumor progression via regulation of angiogenesis and tumor cell motility. MDSC is a large group of myeloid cells consisting of immature macrophages (MΦ), granulocytes, and DCs, as well as myeloid cells at earlier stages of differentiation (Sica and Bronte, 2007; Talmadge, 2007; Gabrilovich and Nagaraj, 2009; Peranzoni et al., 2010). In mice, MDSCs express both the myeloid lineage differentiation antigen Gr-1 (Ly6G and Ly6C) and αM integrin CD11b. Two major groups of MDSCs are now identified: cells with granulocytic (CD11b+Ly6G+Ly6Clow) and monocytic (CD11b+Ly6G−Ly6Chigh) phenotype (Movahedi et al., 2008; Youn et al., 2008). In humans, MDSCs are generally defined as cells that express CD11b, the common myeloid marker CD33, but lack the expression of markers of mature myeloid and lymphoid cells and the MHC class II molecule HLA-DR (Almand et al., 2001; Zea et al., 2005; Diaz-Montero et al., 2009; Nagaraj et al., 2010). In tumor-free mice, MDSCs represent ∼30% of the normal BM cells and 20% of all splenocytes, and MDSCs are easily detectable in tumors and lymph nodes (Kusmartsev and Gabrilovich, 2006; Rabinovich et al., 2007; Sica and Bronte, 2007; Gabrilovich and Nagaraj, 2009). Similar expansion, albeit to a lesser degree, is observed in patients with cancer. In the presence of appropriate cytokines in vitro and after adoptive transfer in vivo, MDSC can differentiate into mature myeloid cells (Kusmartsev and Gabrilovich, 2003). This differentiation is blocked, however, in the presence of tumor cell–conditioned media or in tumor-bearing hosts (Kusmartsev and Gabrilovich, 2003; Talmadge, 2007). Extensive studies in recent years suggested several mechanisms of MDSC-mediated immune suppression that involve arginine (Bronte and Zanovello, 2005; Rodríguez and Ochoa, 2008) and cysteine (Srivastava et al., 2010) metabolism, expression of some surface molecules (Pan et al., 2010), up-regulation of reactive oxygen species (ROS), and production of different cytokines (Talmadge, 2007; Gabrilovich and Nagaraj, 2009). Practically all these studies were performed with MDSC isolated from peripheral lymphoid organs (mostly spleen). Although an important role of MDSC in tumor-associated immune suppression is well established in recent years, its nature remains unclear. One of the major unresolved questions is the role of MDSCs in peripheral lymphoid organs and tumor tissues as well as their relationship with MΦ and DCs. The main paradox is that, despite the presence of a large number of MDSCs in spleens and lymph nodes of tumor-bearing mice and in the peripheral blood of cancer patients with advanced disease, T cells mostly retain the ability to respond to different tumor-nonspecific stimuli including viruses, lectins, costimulatory molecules, IL-2, and stimulation with CD3- and CD28-specific antibodies (Fricke et al., 2007; Monu and Frey, 2007; Nagaraj et al., 2007). In a sharp contrast, T cells directly isolated from tumors display profound defects in their ability to respond to those stimuli (Rabinowich et al., 1996; Lopez et al., 1998; Reichert et al., 2002). Some evidence may suggest that MDSC-mediated immune suppression in peripheral lymphoid organs could be mainly tumor antigen specific. MDSCs mediate antigen-specific T cell tolerance by taking up soluble antigens, including tumor-associated antigens, processing them, and presenting them to CD8+ T cells in the context of MHC class I (Kusmartsev et al., 2005; Movahedi et al., 2008). The role of tumor-associated MDSC remains largely obscure. Despite the fact that the cells with the phenotype of MDSC are abundantly present in tumor tissues but are often referred to as monocytes, MΦ, or granulocytes, the relationship (or lack thereof) between MDSC and tumor-associated macrophages (TAMs) is unclear. This confusion is derived from the difficulties in clearly defining the phenotype of myeloid cells in the tumor site. This creates a convoluted, rather incoherent picture of the various functions of different myeloid cell subsets within the tumor. Ambiguity of the biological role of and the relationship between different myeloid cell populations within the tumor site severely limits our understanding of the biology of tumor progression and the development of targeted therapeutics. In this study, we addressed this issue by investigating the function and differentiation of MDSC at the tumor site using an experimental model where tumors were generated as ascites. This allowed for a rapid isolation of cells and enabled direct experiments with adoptive transfer of MDSC directly into the tumor microenvironment. In this paper, we report that MDSCs in tumor-bearing mice display a biological dichotomy regulated by the tumor microenvironment. In peripheral lymphoid organs, MDSC primarily caused antigen-specific T cell nonresponsiveness, which was mediated by ROS. In contrast, within tumor microenvironment, MDSC with the same phenotype and morphology had low ROS levels but dramatically up-regulated NO production and arginase activity, which caused suppression of antigen-nonspecific T cell functions. In contrast to MDSC from the spleen, MDSC in the tumor microenvironment rapidly differentiated primarily into TAM. This process was mediated primarily by hypoxia via hypoxia-inducible factor (HIF) 1α. Our data thus help to explain the differences in the nature of T cell suppression between tumor and peripheral lymphoid organs in tumor-bearing hosts and suggest a regulatory pathway of myeloid cell function in the tumor microenvironment. RESULTS Phenotype and function of MDSC in the tumor site To directly compare MDSC from the tumor site to MDSC from peripheral lymphoid organs (spleen), we have developed a model where EL-4 tumor grows as an ascites in C57BL/6 mice. The dose of EL-4 cells was selected to form an ascites within 3 wk after tumor injection. MDSCs in mice are broadly defined as Gr-1+CD11b+ cells. These markers were used to sort MDSC from within the spleen and tumor of the same mouse (Fig. 1 A). MDSC from both sites expressed the same level of Gr-1 and CD11b molecules and had similar mixed granulocytic and monocytic cell morphology (Fig. 1 B). Granulocytic MDSCs (CD11b+Ly6G+Ly6Clow) were a predominant type of MDSC in both sites. There was no difference between the ratio of granulocytic and monocytic (CD11b+Ly6G−Ly6Chigh) MDSC isolated from tumor or spleens (Fig. 1 C). The CD11c marker specific for DCs was not expressed on either spleen or tumor MDSC, and 0.05; Fig. 1 D). We compared the ability of tumor and spleen MDSC to suppress antigen-specific and -nonspecific T cell responses. Two basic functions of T cells were evaluated: IFN-γ production and T cell proliferation. The antigen-specific response to MHC class I–restricted SIYRYYGL (SIY) peptide was measured in 2C transgenic CD8+ T cells. The antigen-nonspecific T cell response was evaluated after stimulation with anti-CD3/CD28 antibodies. MDSCs were isolated from the ascites and spleens of the same mice and were used under the same experimental conditions. Gr-1+CD11b+ immature myeloid cells (IMCs) from spleens of naive tumor-free mice were used as a control. As was demonstrated in previous studies, IMCs lack immunosuppressive activity (Kusmartsev et al., 2004, 2005; Zhou et al., 2007; Yamamoto et al., 2008). Both spleen and tumor MDSCs effectively suppressed the antigen-specific T cell response, although the level of suppression was significantly (P 70%) in the spleen and tumor site still retained the MDSC phenotype (Gr-1+CD11b+; Fig. 4 D and Fig. S4). Those donor cells that lost Gr-1 expression were represented in the spleens evenly by CD11c+ DCs and CD11b+F4/80+ MΦ (Fig. 4 E). In sharp contrast, most of the Gr-1− donor cells in tumor site were CD11b+F4/80+ MΦ (Fig. 4 E and Fig. S5). After 48 h, the proportion of Gr-1+CD11b+ MDSC among donor cells in spleens remained about the same (>60%). However, in the tumor site it was substantially decreased to 30% of these cells were CD11c+ (Fig. 4 E). 3 d after the transfer, all Gr-1− donor cells in the tumor site remained F4/80+CD11b+ MΦ, whereas MΦ represented 95%. Within 24 h after the transfer of either WT or HIF-1α–deficient MDSC, ∼70% of donor (CD45.2+CD11b+) cells lost the expression of Gr-1 (unpublished data). Half of HIF-1α WT Gr-1− donor cells acquired the F4/80 marker of MΦ and 20% became CD11c+ DCs (Fig. 7 A). These results were similar to those described in Fig. 4 E demonstrating preferential differentiation of MDSC to MΦ in tumor microenvironment. In contrast, HIF-1α–deficient MDSC acquired CD11c marker, with only 20% of cells expressing F4/80 (Fig. 7 A). As a result, the proportion of MΦ differentiated in tumor site from HIF-1α WT MDSC was more than twofold higher than the proportion of cells differentiated from HIF-1α–deficient MDSC (P 5%, hydroxylation of the proline residues 402 and 564 in the ODD of HIF-1α enables binding of the ubiquitination ligase von Hippel-Lindau tumor suppressor protein, which leads to degradation of HIF-1α by the proteosome. In contrast, at oxygen levels 60% of MDSC in the spleen retained an immature phenotype, whereas the rest of the cells differentiated evenly into MΦ and DCs. In contrast, MDSC transferred into tumor site differentiated much more rapidly, with most of the cells acquiring the phenotype of MΦ. Experiments with MDSC culture in hypoxic conditions recapitulated these findings. Stabilization of HIF-1α with DFO reproduced this effect, suggesting that HIF-1α could be an important factor regulating the differentiation of MDSC to TAM. MDSC lacking HIF-1α did not differentiate into TAM within the tumor microenvironment or hypoxia but instead acquired markers of DCs. Experiments with vaccination of HIF-1α–deficient tumor-bearing mice support an important role of HIF-1α in antitumor responses. Even without vaccination, mice that received HIF-1α–deficient BM cells demonstrated a significant delay in tumor progression. This effect was observed only 4–5 wk after tumor inoculation and could be possibly explained by the reconstitution of the lymphoid compartment by that time (6–7 wk after BM transfer). Stronger antitumor responses were not necessarily the result of improved function of myeloid cells because HIF-1α is known to negatively affect the function of T cells as well. Deletion of HIF-1α in T cells resulted in their activation in vitro and in vivo (Lukashev et al., 2006; Thiel et al., 2007). However, experiments with adoptive transfer of antigen-specific Pmel-1 T cells (which are HIF-1α+/+) and vaccination of mice 1 wk after tumor inoculation allowed for better interpretation of the results. Our experiments indicated that mice that received HIF-1α–deficient BM developed stronger antitumor response than mice with WT HIF-1α BM. It is important to point out that those experiments, although suggestive, cannot definitively address the specific effect of HIF-1α deletion in MDSC because lack of HIF-1α in other myeloid cells (DC and MΦ) may also impact antitumor responses. More specific depletion experiments will be necessary to clarify this question. Our study may suggest a model of MDSC differentiation and function in cancer. Expansion of IMCs in BM of tumor-bearing hosts, which is governed in large part by up-regulation of STAT3 (Nefedova et al., 2004; Cheng et al., 2008; Kujawski et al., 2008), results in an accumulation of MDSC in peripheral lymphoid organs and in the tumor site. In lymphoid organs, MDSCs retain a high level of NOX2 and increased ROS levels. This is associated with a little increase in NO production and arginase I activity. As a result, these MDSCs produce peroxynitrite and exert their effect only via close cell–cell contact with activated antigen-specific T cells, which induce antigen-specific T cell tolerance. At the same time, these MDSCs fail to suppress antigen-nonspecific activation of T cells. In contrast, at the tumor site, MDSCs, as a result of the effect of hypoxia via HIF-1α, dramatically up-regulate expression of inos and argI, which is associated with down-regulation of both NOX2 expression and ROS production. Because of these changes, MDSCs acquire the ability to suppress antigen-nonspecific T cell functions, which contribute to the profound immune suppression observed within the tumor microenvironment. In addition, hypoxia via HIF-1α promotes differentiation of MDSC to immune suppressive TAM, which further supports the immune-suppressive network (Fig. S8). Elucidation of this dual role of MDSC may not only help to understand the biology of tumor-associated immune suppression but also suggest that any therapeutic interventions should take into account the effect of microenvironment on the function of these cells. MATERIALS AND METHODS Mice and tumor models All experiments with mice were approved by University of South Florida Animal Care and Use Committee. C57BL/6 and BALB/c female mice (6–8 wk of age) were obtained from the National Cancer Institute. Mice were kept in pathogen-free conditions. CD45.1+ congenic mice (B6.SJL-PtrcaPep3b/BoyJ), gp91phox−/− (B6.129S6-Cybbtm1Din), HIF-1αflox/flox (B6.129-Hif1atm3Rsjo/JE), and Mx1-Cre+/− (C57BL/6J-Tg(Mx1-cre)1Cgn/J) were purchased from The Jackson Laboratory. 2C TCR transgenic mice have been described previously (Nagaraj et al., 2007). EL4 thymoma was obtained from American Type Culture Collection. To establish s.c. tumors, 5 × 105 EL-4 tumor cells were injected into C57BL/6 mice. This number of cells formed a tumor with a 1.5-cm diameter within 2–3 wk of injection. EL-4 ascitic tumor was generated by injecting 3 × 105 tumor cells i.p. into C57BL/6 mice. To harvest cells from ascitic tumors, mice were sacrificed and the peritoneum was washed with 10 ml of ice-cold PBS. Cells were then aspirated and placed on ice immediately. The CT26 colon carcinoma model used in some in vitro experiments was established by injecting 5 × 105 CT26 tumor cells s.c. into BALB/c mice. The mCC10TAg transgene model of lung cancer was described previously (Magdaleno et al., 1997). Patients 14 patients (47–78 yr old) with resectable T3 or T4 and N2b stage of HNC were enrolled in the study after signing University of South Florida IRB-approved consent. Patients did not receive radiation or chemotherapy for at least 3 mo before sample collection. Peripheral blood and tumor tissues were collected at the time of surgery from all patients. To obtain single cell suspensions from tumors, solid tissue was subjected to 1 h of enzymatic digestion using 0.1 mg/ml hyaluronidase (Sigma-Aldrich), 2 mg/ml collagenase (Sigma-Aldrich), 600 U/ml DNase (Sigma-Aldrich), and 0.2 mg/ml protease (Sigma-Aldrich) in RPMI 1640. The digested tissue was passed through a 70-µm mesh, and erythrocytes were removed by hypotonic lysis and washed thoroughly to remove debris. Mononuclear cell suspensions were obtained from whole blood using density gradient centrifugation. All cell samples were analyzed within 3 h after collection. Cells were loaded with DCFDA. To identify live MDSC, mononuclear cells were first labeled with PerCP-Cy5.5–conjugated anti-CD14, APC-conjugated anti-CD11b, and PE-Cy-7–conjugated anti-CD33. Antibody-labeled cells were then finally resuspended in DAPI buffer to identify viable cells before data collection. To detect iNOS in MDSCs after surface staining with PerCP-Cy5.5–conjugated anti-CD14, APC-conjugated anti-CD11b and PE-Cy-7–conjugated anti-CD33 antibodies were fixed, permeabilized using the Cytofix/Cytoperm Fixation/Permeabilization kit (BD), and analyzed using an LSR II flow cytometer (BD). At least 100,000 cells were collected for each parameter to obtain reliable data. Analysis of the samples was performed essentially as described elsewhere (Mirza et al., 2006). Cell isolation To collect splenocytes, single cell suspensions were prepared from spleens, and red cells were removed using ammonium chloride lysis buffer. MDSCs were isolated by cell sorting on a FACSAria (BD) after cell staining with APC-conjugated anti–Gr-1 antibody and PE-conjugated anti-CD11b antibodies. To harvest peritoneal macrophages, mice were injected i.p. with 1 ml thioglycollate (BD). 3 d later, peritoneal cells were obtained by peritoneal lavage with 10 ml of ice-cold PBS. Peritoneal macrophages were harvested using magnetic beads and biotinylated anti-F4/80 antibody. Reagents Arginase inhibitor NW-hydroxyl-nor-l-arginine (nor-NOHA) and iNOS inhibitor NG-monomethyl-l-arginine (L-NMMA) were obtained from EMD. 2C-specific (H-2Kb, SIYRYYGL) and control (H-2Kb RAHYNIVTF) peptides were obtained from American Peptide Company. DCFDA was purchased from Invitrogen. Antibodies against p47phox were purchased from Santa Cruz Biotechnology, Inc., anti–HIF-1α antibody from R&D Systems, and biotinylated anti-F4/80 antibody from AbD Serotec. All other antibodies used for flow cytometry were purchased from BD. MDSC isolation from solid tumors Tumors were dissected and digested with 0.7 mg/ml collagenase XI (Sigma-Aldrich) and 30 mg/ml of type IV bovine pancreatic DNase (Sigma-Aldrich) for 45 min at 37°C. Remaining red cells were lysed by ACK and dead cells were removed by centrifugation with Lympholyte M. Gr-1+ cells were isolated by using biotinylated anti–Gr-1 antibody and streptavidin microbeads with MiniMACS columns (Miltenyi Biotec). Cell culture and hypoxic conditions MDSCs were cultured in complete RPMI media containing 10 ng/ml GM-CSF. A hypoxic environment (1% O2 with 5% CO2) was created and maintained using a C-Chamber Hypoxic Incubator Chamber (BioSpherix). ROS detection, arginase activity and NO production The oxidation-sensitive dye DCFDA was used to measure ROS production by MDSC. Cells were incubated at 37°C in RPMI in the presence of 2.5 µM DCFDA for 30 min. For PMA-induced activation, cells were simultaneously cultured, along with DCFDA, with 30 ng/ml PMA (Sigma-Aldrich). Cells were then labeled with anti–Gr-1 and anti-CD11b antibodies on ice and evaluated by flow cytometry. Arginase activity was measured in cell lysates, as previously described (Kusmartsev and Gabrilovich, 2005). In brief, cells were lysed for 30 min with 0.1% Triton X-100. To 100 µl of protein lysate (25 µg/ml), 100 µl of 25 mM Tris-HCl and 10 µl of 10 mM MnCl2 were added, and the enzyme was activated by heating for 10 min at 56°C. Arginine hydrolysis was conducted by incubating the lysate with 100 µl of 0.5 M L-arginine, pH 9.7, at 37°C for 120 min. The reaction was stopped with 900 µl H2SO4 (96%)/H3PO4 (85%)/H2O (1/3/7, vol/vol/vol). The urea concentration was measured at 540 nm after addition of 40 µl β-isonitrosopropiophenone (dissolved in 100% ethanol), followed by heating at 95°C for 30 min. One unit of enzyme activity is defined as the amount of enzyme that catalyzed the formation of 1 µmol urea per min. To detect nitrites, equal volumes of culture supernatants (100 µl) were mixed with Greiss reagent. After a 10-min incubation at room temperature, the absorbance at 550 nm was measured using a microplate plate reader (Bio-Rad Laboratories). Nitrite concentrations were determined by comparing the absorbance values for the test samples to a standard curve generated by serial dilution of 0.25 mM sodium nitrite. qRT-PCR RNA was extracted with Trizol (Invitrogen). cDNA was synthesized and used for the evaluation of gene expression as described previously (Nefedova et al., 2007). To detect arg1, inos, gp91phox , and p47phox , PCR was performed with 2 µl cDNA, TaqMan Universal PCR Master Mix (Applied Biosystems), and target gene assay mix containing sequence-specific primers and 6-carboxyfluorescein dye–labeled TaqMan minor groove binder probe (Applied Biosystems). Amplification with 18S endogenous control assay mix was used for controls. PCR was performed in triplicate for each sample. Data quantitation was performed using the relative standard curve method. Expression levels of the genes were normalized by 18S mRNA. To detect expression of cytokines and β-actin, PCR was performed with 12.5 µl SYBR Master Mixture (Applied Biosystems) and the following primers: sense+ IL-6, 5′-ATCCAGTTGCCTTCTTGGGACTGA-3′; IL-12, 5′-ATGCAGCAAGTGGGCATGTGTT-3′; TGF-β, 5′-TACGTCAGACATTCCGGGAAGCAGT-3′; IL-10, 5′-TACCAAAGCCACAAAGCAGCCT-3′; and β-actin, 5′-ACCGCTCGTTGCCAATAGTGATGA-3′. The expression of IL-6, TGF-β, IL-12, and IL-10 were normalized to β-actin. Western blotting Cells were lysed in TNE buffer (20 mM Tris-HCl, pH 7.5, 150 mM NaCl, and 1 mM EDTA) containing 1% NP-40 in the presence of protease and phosphatase inhibitors. Whole-cell lysates were subjected to 8% SDS-PAGE and transferred to PVDF membranes. Membranes were probed with appropriate primary antibodies overnight at 4°C. Membranes were washed and incubated overnight at 4°C with secondary antibody conjugated with peroxidase. Results were visualized by chemiluminescence detection using a commercial kit (GE Healthcare). To confirm equal loading, membranes were stripped and reprobed with antibody against β-actin (Santa Cruz Biotechnology, Inc.) Evaluation of T cell function Proliferation. Splenocytes from 2C transgenic mice, depleted of red cells, were placed in triplicates into U-bottom 96-well plates (2 × 105/well). For antigen-specific responses, splenocytes were cultured in the presence of cognate antigen (2C-specific peptide SIYRYYGL) and cultured for 72 h. For anti-CD3/CD28 antibody-induced T cell proliferation, splenocytes were cultured in the presence of 1 µg/ml anti-CD3 antibody and 5 µg/ml anti-CD28 antibody. 18 h before harvesting, cells were pulsed with 3H-thymidine (1 µCi/well; GE Healthcare). 3H-thymidine uptake was counted using a liquid scintillation counter and expressed as cpm. IFN-γ production. The number of IFN-γ–producing cells in response to cognate antigens or CD3/CD28 antibodies were evaluated in an ELISPOT assay as previously described (Nagaraj et al., 2007). Each well contained 105 splenocytes. The number of spots was counted in triplicates and calculated by an automatic ELISPOT counter (Cellular Technology). Treatment of tumor-bearing mice Mice were inoculated s.c. with 3 × 105 B16 tumor cells, and 6 d later the mice received 3 × 106 splenocytes from Pmel-1 T cell receptor transgenic mice. 1 d later, the mice were immunized with 200 µg Hugp10025-33 peptide (KVPRNQDWL) mixed with 2 toll-like receptor agonists (50 µg poly-IC and 100 µg CpG-1826). Poly-IC (Hiltonol, a clinical grade stabilized formulation containing poly-l-lysine and carboxymethyl cellulose) was provided by A. Salazar (Oncovir, Inc., Washington, DC). CpG-1826 was prepared by the Mayo Clinic Molecular Core Facility. Tumor growth was monitored every 3–4 d in individually tagged mice by measuring two opposing diameters with a set of calipers. Evaluation of the presence of myeloid cells in the areas of hypoxia in tumor To detect hypoxia, we used pimonidazole HCl, which is activated in hypoxic cells and forms stable covalent adducts with thiol groups in proteins, peptides, and amino acids. The antibody reagent binds to these adducts, allowing their detection by immunochemical means. 60 mg/kg pimonidazole HCl (Hypoxyprobe) was injected i.v. into EL4 tumor-bearing mice. 30 min later, tumor tissues were collected and processed for immunohistochemical staining with MAb1 against hypoxia and anti-Gr1 or anti-F4/80 antibodies (BD). The secondary biotinylated antibodies and the color development systems VECTASTAIN ABC kits and substrate kits (Vector Laboratories) were used for detection. Statistics Statistical analysis was performed using a two-tailed Mann-Whitney U or Wilcoxon nonparametric test and Prism 5 software (GraphPad Software, Inc.), with significance determined at P < 0.05. Online supplemental material Fig. S1 shows the effect of MDSC isolated from spleens and tumors of CC10 transgenic mice on T cell proliferation. Fig. S2 shows iNOS in MDSC from tumor and spleens of EL-4 tumor-bearing mice. Fig. S3 shows ROS in MDSC from tumor and spleens of EL-4 tumor-bearing mice. Fig. S4 shows the effect of the tumor microenvironment on MDSC function after adoptive transfer. Fig. S5 shows a typical example of MDSC differentiation in tumor site and spleens of EL-4 tumor-bearing mice. Fig. S6 shows the effect of hypoxia on the phenotype of MDSC. Fig. S7 shows the effect of hypoxia on differentiation of MΦ with M1 and M2 phenotypes. Fig. S8 shows a schematic of MDSC function and differentiation in tumor-bearing host. Online supplemental material is available at http://www.jem.org/cgi/content/full/jem.20100587/DC1.

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Metabolic Reprograming in Macrophage Polarization

Silvia Galván-Peña, Luke A J O'Neill (2014)

Studying the metabolism of immune cells in recent years has emphasized the tight link existing between the metabolic state and the phenotype of these cells. Macrophages in particular are a good example of this phenomenon. Whether the macrophage obtains its energy through glycolysis or through oxidative metabolism can give rise to different phenotypes. Classically activated or M1 macrophages are key players of the first line of defense against bacterial infections and are known to obtain energy through glycolysis. Alternatively activated or M2 macrophages on the other hand are involved in tissue repair and wound healing and use oxidative metabolism to fuel their longer-term functions. Metabolic intermediates, however, are not just a source of energy but can be directly implicated in a particular macrophage phenotype. In M1 macrophages, the Krebs cycle intermediate succinate regulates HIF1α, which is responsible for driving the sustained production of the pro-inflammatory cytokine IL1β. In M2 macrophages, the sedoheptulose kinase carbohydrate kinase-like protein is critical for regulating the pentose phosphate pathway. The potential to target these events and impact on disease is an exciting prospect.

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Cd8+ but Not Cd8− Dendritic Cells Cross-Prime Cytotoxic T Cells in Vivo

Joke M.M. den Haan, Sophie M. Lehar, Michael J. Bevan (2000)

Bone marrow–derived antigen-presenting cells (APCs) take up cell-associated antigens and present them in the context of major histocompatibility complex (MHC) class I molecules to CD8+ T cells in a process referred to as cross-priming. Cross-priming is essential for the induction of CD8+ T cell responses directed towards antigens not expressed in professional APCs. Although in vitro experiments have shown that dendritic cells (DCs) and macrophages are capable of presenting exogenous antigens in association with MHC class I, the cross-presenting cell in vivo has not been identified. We have isolated splenic DCs after in vivo priming with ovalbumin-loaded β2-microglobulin–deficient splenocytes and show that they indeed present cell-associated antigens in the context of MHC class I molecules. This process is transporter associated with antigen presentation (TAP) dependent, suggesting an endosome to cytosol transport. To determine whether a specific subset of splenic DCs is involved in this cross-presentation, we negatively and positively selected for CD8− and CD8+ DCs. Only the CD8+, and not the CD8−, DC subset demonstrates cross-priming ability. FACS® studies after injection of splenocytes loaded with fluorescent beads showed that 1 and 0.6% of the CD8+ and the CD8− DC subsets, respectively, had one or more associated beads. These results indicate that CD8+ DCs play an important role in the generation of cytotoxic T lymphocyte responses specific for cell-associated antigens.

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Author and article information

Contributors

Akil Hammami: Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: Writing – review & editing

Belma Melda Abidin:

ORCID: http://orcid.org/0000-0002-1406-1344

Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: Methodology

Tania Charpentier: Role: Data curationRole: Formal analysisRole: InvestigationRole: Methodology

Aymeric Fabié: Role: Formal analysisRole: Investigation

Annie-Pier Duguay:

ORCID: http://orcid.org/0000-0003-2984-1818

Role: Formal analysisRole: Investigation

Krista M. Heinonen: Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: MethodologyRole: ResourcesRole: Writing – review & editing

Simona Stäger:

ORCID: http://orcid.org/0000-0001-5508-9565

Role: ConceptualizationRole: Data curationRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: Writing – original draft

David Sacks: Role: Editor

Journal

Journal ID (nlm-ta): PLoS Pathog

Journal ID (iso-abbrev): PLoS Pathog

Journal ID (publisher-id): plos

Journal ID (pmc): plospath

Title: PLoS Pathogens

Publisher: Public Library of Science (San Francisco, CA USA )

ISSN (Print): 1553-7366

ISSN (Electronic): 1553-7374

Publication date (Electronic): 11 September 2017

Publication date Collection: September 2017

Volume: 13

Issue: 9

Electronic Location Identifier: e1006616

Affiliations

[001]INRS-Institut Armand-Frappier and Center for Host-Parasite interactions, 531 Boulevard des Prairies, Laval (QC), Canada

National Institute of Health, UNITED STATES

Author notes

The authors have declared that no competing interests exist.

* E-mail: simona.stager@ 123456iaf.inrs.ca

Author information

Belma Melda Abidin http://orcid.org/0000-0002-1406-1344

Annie-Pier Duguay http://orcid.org/0000-0003-2984-1818

Simona Stäger http://orcid.org/0000-0001-5508-9565

Article

Publisher ID: PPATHOGENS-D-17-01878

DOI: 10.1371/journal.ppat.1006616

PMC ID: 5608422

PubMed ID: 28892492

SO-VID: 6f1b5a65-d9d3-4493-b501-3cbdd2a6c0e6

License:

This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

History

Date received : 25 August 2017

Date accepted : 29 August 2017

Page count

Figures: 9, Tables: 1, Pages: 27

Funding

Funded by: funder-id http://dx.doi.org/10.13039/501100000032, Institute of Infection and Immunity;

Award ID: MOP-123293

Award Recipient :

ORCID: http://orcid.org/0000-0001-5508-9565

Simona Stäger

Funded by: funder-id http://dx.doi.org/10.13039/501100000038, Natural Sciences and Engineering Research Council of Canada;

Award ID: 419226-2012

Award Recipient : Krista M. Heinonen

Funded by: funder-id http://dx.doi.org/10.13039/501100000156, Fonds de Recherche du Québec - Santé;

Award ID: 32598

Award Recipient : Krista M. Heinonen

Funded by: funder-id http://dx.doi.org/10.13039/501100000196, Canada Foundation for Innovation;

Award ID: 31377

Award Recipient :

ORCID: http://orcid.org/0000-0001-5508-9565

Simona Stäger

Funded by: funder-id http://dx.doi.org/10.13039/501100000196, Canada Foundation for Innovation;

Award ID: 31377

Award Recipient : Krista M. Heinonen

This work was supported by the Canadian Institute of Health Research grant MOP-123293 (to SS) and PJT-148614 (to KMH), the Fonds de recherche du Québec – Santé grant #32598 (to KMH), and the Canada Foundation for Innovation John Evans Leader Fund grant #31377 (to KMH and SS). KMH is Chercheur-Boursier (Junior 1) of the Fonds de recherche du Québec – Santé. AH was partly supported by an Imperial Tobacco scholarship from the Fondation Universitaire Armand-Frappier Institut National de la Recherche Scientifique. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

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PLOS Publication Stage vor-update-to-uncorrected-proof

Publication Update 2017-09-21

Data Availability All relevant data are within the paper and its Supporting Information files.

ScienceOpen disciplines: Infectious disease & Microbiology

Data availability:

ScienceOpen disciplines: Infectious disease & Microbiology

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