Lymphocyte Activation Gene-3 Regulates Dendritic Cell Metabolic Programing and T Cell Priming Function

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Lymphocyte activation gene-3 (Lag3; mouse Lag3 gene and Lag3 protein) is a member of the Ig superfamily that binds MHC class II (MHC-II) on the surface of APCs (1–3). Lag3 is an immune checkpoint molecule whose mechanism of action differs from that of other checkpoint molecules, including cytotoxic T lymphocyte–associated Ag-4 (CTLA-4), programmed cell death protein-1 (PD-1) and B and T lymphocyte attenuator (BTLA) (4–7). Lag3 is expressed on NK cells, B cells, effector and regulatory T cells, and on dendritic cells (DC) (8). In adaptive cells, Lag3 suppresses homeostatic expansion of effector T cells (9) and is considered a marker of the immunosuppressive Tr1 regulatory T cell subset (10).

We previously reported that LAG3 deficiency in humans was significantly associated with increased cardiovascular disease risk in participants of the Multi-Ethnic Study of Atherosclerosis and that plasma LAG3 provided additional risk prediction with the Framingham risk score (11). In the Multi-Ethnic Study of Atherosclerosis, plasma LAG3 was significantly associated with circulating IL-10 (p < 0.0001) and of borderline significance with hs-CRP (p = 0.087). In vitro studies showed that stimulated LAG3-deficient EBV-transformed human B lymphoblasts secreted significantly more TNF-α and less IL-10 compared with wild-type (WT) cells. Consistent with these findings, Zhu (12) recently reported that LAG3 expression on Tr1 cells was significantly lower in patients with coronary artery disease.

Although much of the literature related to checkpoint molecules focuses on their T cell–intrinsic actions, there is some evidence that they might also play an indirect role in regulating T cell responsiveness through their ability to directly regulate the activity of APCs. For instance, Kobayashi (13) demonstrated that Btla^−/− bone marrow–derived DCs (BMDCs) secreted more inflammatory cytokines, such as TNF-α, in response to certain TLR ligands compared with controls; a pattern similar to LAG3-deficient human B lymphoblasts (11, 13). Thus, in this present study, we examined whether Lag3 expressed on APCs is relevant in the priming of T cell effector function. We found that Lag3^−/− BMDCs exhibited greater TNF-α secretion under basal conditions, as well as an altered cellular metabolism manifested by a shift from oxidative phosphorylation toward glycolytic energy production. Importantly, when WT donor CD4⁺Lag3^+/+ T cells were adoptively transferred into Lag3^−/−-recipient mice and immunized with cognate Ag, they developed a greater capacity to express the Th1 effector cytokine IFN-γ compared with WT recipients. Taken together, these results indicate that, in addition to the well-established role of Lag3 intrinsically regulating T cell responsiveness, Lag3 also controls the capacity of APCs to prime CD4 Th1 cell effector functionality.

Cell culture media was purchased from Thermo Fisher Scientific (Agawam, MA). GM-CSF was used for BMDC differentiation and was purchased from PeproTech (Cranbury, NJ). Recombinant murine IL-10 was purchased from R&D Systems (Minneapolis, MN). Escherichia coli LPS from Salmonella enterica serotype Typhimurium was purchased from Sigma-Aldrich (Milwaukee, WI), and all other reagents were high grade.

Lag3^−/− mice (C57BL/6 background) were kindly provided by Dr. Patrick Murphy (Center for Vascular Biology, UConn Health, Farmington, CT). All mice were maintained in the central animal facility at UConn Health in accordance with National Institutes of Health guidelines. Male 12- to 15-wk-old mice were used in in vitro experiments to generate BMDCs, and age-matched male and female mice were used in all other experiments. Age-matched female OT-II mice were purchased from The Jackson Laboratory (stock no. 004194). WT mice were littermate controls.

BMDCs were harvested from tibias and femurs isolated from WT and Lag3^−/− mice and cultured at 3 × 10⁶ cells per plate (CytoOne dishes of 150 × 20 mm; USA Scientific, Ocala, FL) in RPMI 1640 supplemented with 10% (vol/vol) heat-inactivated FBS, 2 mM l-glutamine, 50 µM 2-ME, 100 U/ml penicillin, and 100 µg/ml streptomycin in the presence of GM-CSF (20 ng/ml) for 6–8 d. BMDCs were enriched by use of a CD11c Positive Selection Kit (8802-6861; Thermo Fisher Scientific, Waltham, MA) and characterized by flow cytometry using the following markers: V500 rat anti-mouse I-A/I-E (M5/114.15.2, 562366; BD Biosciences), PE hamster anti-mouse CD11c (HL3, 561044; BD Biosciences), FITC rat anti-mouse CD40 (3/23, 553790; BD Biosciences), CD64 mAb (X54-5/7.1; Thermo Fisher Scientific), allophycocyanin (eBioscience), PerCP-Cy5.5 hamster anti-mouse CD80 (16-10A1, 560526; BD Biosciences) and allophycocyanin–rat anti-mouse CD86 (GL1, 565479; BD Biosciences) (14). For experimental conditions, replicate dishes of BMDCs were unstimulated and stimulated with LPS (1 µg/ml), IL-10 (100 ng/ml), and LPS plus IL-10 for 24 h. The dose of LPS used for these studies was based on pilot cytokine studies of murine RAW macrophages that were rendered Lag3 deficient based on CRISPR/Cas9 genetic engineering and then stimulated with varying concentrations of LPS. Cells were gated based on forward and side scatter, and dead cells were excluded using LIVE/DEAD Fixable Blue Dead Cell Stain Kit (Thermo Fisher Scientific, Waltham, MA). CD11c⁺ cells were selected and examined for the expression of MHC-II, CD40, CD64, CD80, and CD86 markers. Cells were acquired on a BD LSR II analyzer (BD Biosciences, Franklin Lakes, NJ) using FACSDiva software, then data were analyzed using FlowJo software (FlowJo, Ashland, OR).

At the end of each experiment the culture media was aspirated and centrifuged to pellet nonadherent cells. Aliquots of the media were stored at −80°C for ∼12 wk prior to measurement of cytokines. The media was thawed to room temperature, then levels of IL-12 (IL-12p70), IL-10, TNF-α, IFN-γ, MCP-1, and IL-6 were quantified using the Cytometric Bead Array Kit (BD Biosciences, Sparks, MD) according to the manufacturer’s protocol.

RNA was isolated from three biological replicates of WT and Lag3^−/− BMDCs using the QIAGEN RNeasy Kit, then each sample was individually subjected to sequencing after TruSeq RNA library preparation. The Illumina NextSeq 500 Sequencing Platform (University of Connecticut Center for Genome Innovation, Storrs, CT) was used for sequencing 101-bp paired-end reads for RNA sequencing (RNA-Seq). Replicates were pooled during the analysis for statistical significance.

A Seahorse XF-96 Extracellular Flux Analyzer instrument was used for extracellular acidification rate (ECAR) and oxygen consumption rate (OCR) measurements in BMDCs. For glycolysis tests, a Glycolytic Rate Assay Kit (103344-100; Agilent Technologies, Cedar Creek, TX) was used, and total proton efflux rate (PER) and glycolytic PER (glycoPER), both metrics of the extracellular acidification that accounts for buffer capacity and plate geometry, were calculated as described by the manufacturer. Briefly, BMDCs were plated at a density of 6.5 × 10⁴ cells per well in triplicate for 15 h, then cells were stimulated with and without LPS (1 µg/ml) or IL-10 (100 ng/ml) for 24 h. After that, cells were washed and incubated at 37°C for 45 min in a non-CO₂ incubator in Seahorse assay medium containing 10 mM glucose and 1 mM sodium pyruvate. ECAR and OCR were measured before and after sequential addition of a combination of rotenone and antimycin A (0.5 µM) and 2-deoxyglucose (50 mM). For mitochondrial function, OCR was measured before (basal) and after the sequential addition of 1 µM oligomycin, 1 µM fluorocarbonyl cyanide phenylhydrazone (FCCP) and 1 µM rotenone plus 1 µM antimycin A. These four modulators of respiration are inhibitors of the electron transfer chain and reveal the key parameters of mitochondrial function. For instance, oligomycin inhibits ATP synthase (complex V) and impacts electron flow through the electron transfer chain, resulting in a reduction in mitochondrial respiration. FCCP is an uncoupling agent that collapses the proton gradient and disrupts the mitochondrial membrane potential, and the combination of rotenone (a complex I inhibitor) and antimycin A (a complex III inhibitor) shuts down mitochondrial respiration and enables the calculation of respiration driven by processes outside the mitochondria. For fatty acid oxidation (FAO) metabolism, the manufacturer’s instructions were followed for the Seahorse XF Palmitate Oxidation Stress Kit and FAO Substrate. Briefly, the FAO test was performed over 2 d and involves two pretreatment steps. BMDCs were plated at a density of 6.5 × 10⁴ cells per well in triplicate for 15 h. After 15 h (day 1), the medium was changed to substrate-limited medium containing DMEM supplemented with 0.5 mM of glucose, 1.0 mM GlutaMAX, 0.5 mM carnitine and 1% FBS, and the cells were incubated for 24 h. On day 2, the substrate-limited medium was replaced by FAO medium containing 111 mM sodium chloride, 4.7 mM potassium chloride, 1.25 mM calcium chloride, 2.0 mM magnesium sulfate, and 1.2 mM sodium dihydrogen phosphate supplemented with 2.5 mM glucose, 0.5 mM carnitine, and 5 mM HEPES, and cells were incubated for 45 min in a non-CO₂ incubator. Then, 15 min prior to the assay, 40 µM etomoxir (Eto; an irreversible inhibitor of carnitine palmitoyl transferase-1) was added followed by incubation with either BSA alone [endogenous fatty acid (FA) or palmitate-BSA (exogenous FA) (15)].

To examine interactions between Lag3^−/− DCs and naive CD4⁺ T cells, 5 × 10⁵ naive CD4⁺Lag3^+/+ T cells were first isolated from spleens and mesenteric lymph nodes of OT-II adult female mice and then transferred into recipient adult WT and Lag3^−/− male and female mice by retro-orbital injection. Mice were then immunized by i.p. injection with 100 µg of intact OVA (BioVendor R&D, Asheville, NC) in PBS and costimulatory agonists 50 µg of anti-CD134 (OX-40) and 25 µg of anti-CD137 (4-1BB), each purchased from Bio X Cell (West Lebanon, NH). The Ag control group received rat IgG. Mice were sacrificed 5 d later, and spleens and mesenteric lymph nodes were harvested for ex vivo analysis of T cell activation using flow cytometry. Abs used in the flow panel included allophycocyanin–rat anti-mouse CD4, V500 rat anti-mouse CD8, allophycocyanin–Cy7 rat anti-mouse Vα2 TCR, PE mouse anti-mouse Vβ5.1, β5.2 TCR, allophycocyanin–R700 rat anti-mouse CD44 and PerCP–Cy5.5 rat anti-mouse CD62L. All Abs were purchased from BD Biosciences (Sparks, MD). The flow cytometry gating strategy for T cells was followed as described below. Lymphocytes were first identified by low forward scatter and low side scatter gate. Doublets and dead cells were then excluded, and T cell subsets were identified by CD4 and CD8 expression. The CD4⁺ T cell subpopulation was then gated on TCR Vα2 and TCR Vβ5, and OT-II CD4 T cells were identified as CD4⁺ Vα2⁺ and Vβ5⁺ cells. Cells were then restimulated in vitro to assess intracellular cytokine secretion using PMA and ionomycin in the presence of protein transport inhibitor mixture based on a mixture of brefeldin A and monensin. Briefly, 5 × 10⁶ cells were plated in 24-well plate, and 2 µl/well cell stimulation mixture (500×, 40.5 µM of PMA, and 670 µM ionomycin, 00-4970; eBioscience) and 2 µl/well of protein transport inhibitor mixture (500×, 5.3 mM brefeldin A and 1 mM monensin, 00-4980; eBioscience, Carlsbad, CA) was added to each well. Cells that received only the protein transport inhibitor mixture were used as controls. Cells were incubated for 4 h, and cytokine expression was measured by flow cytometry using FITC rat anti-mouse IFN-γ (BD Biosciences, Sparks, MD) and PE–Cy7 rat anti-mouse TNF-α (eBioscience, Carlsbad, CA) Abs. To evaluate cytokine expression, the gating strategy described above was followed. We also performed an in vitro coculture experiment using BMDCs:OT-II CD4⁺ T cells (1:10) that were stimulated with OVA peptide (0, 2.5, 10 µg protein/ml) for varying time points (24, 48, and 72 h). OT-II CD4 T cells were labeled with 0.5µM ViaFluor 405 (Biotium, San Francisco, CA) prior to the coculture incubation to assess proliferation. At each time point, cells were processed for flow cytometry as previously described.

Lag3^−/− and WT BMDCs were stimulated with and without IL-10, LPS, and LPS plus IL-10 for 24 h prior to the proteomic analysis. Cells were lysed in RIPA buffer supplemented with phosphatase and protease inhibitors (Thermo Fisher Scientific, Atlanta, GA). Whole cell lysates solubilized in RIPA buffer were subjected to protein quantification using the BCA Protein Assay Kit (Thermo Fisher Scientific, Carlsbad, CA). Two biological replicates of Lag3^−/− and three of WT cells were assessed in triplicate per condition. All samples were prepared for proteomic analysis using filter-aided sample preparation, as previously described, with a few modifications (16, 17). Briefly, a 200-µg protein whole cell lysate aliquot was removed and diluted with UA buffer (8 M urea in 0.1 M Tris HCl [pH 8.5]), and protein Cys residues were reduced using 25 mM DTT in UA for 1.5 h at 37°C. Directly afterward, the sample solutions were loaded onto a Microcon YM-10 10 kDa molecular mass cutoff filter (Thermo Fisher Scientific) previously conditioned with intact BSA (Sigma-Aldrich) and washed thoroughly. The reduced protein samples were spun at 14,000 × g for 40 min, washed with another aliquot of 200 µl of UA, and spun again at an identical spin speed and duration. Proteins were resuspended in 100 µl of 50 mM iodoacetamide in UA for 15 min in the dark at 37°C to yield carbamidomethylation of Cys residues. Samples were then spun down at 14,000 × g for 30 min. Following two buffer exchange steps consisting of the addition of a 100 µl of UB buffer (8 M urea in 0.1 M Tris HCl [pH 8.0]) aliquot followed by a 14,000 × g spin at 30 min per addition, the molecular mass cutoff filter filters were washed with 50 µl of UB, and the solution was placed in a clean 1.5-ml Eppendorf Tube. The filter was washed twice more with 50-µl aliquots of 0.1 M ammonium bicarbonate (Thermo Fisher Scientific) and the solutions pooled per sample. Proteolysis was initiated with Endoproteinase Lys (Pierce) at a 1:50 enzyme/protein ratio (w/w) and left for 16 h at 37°C. Samples were then diluted to <1 M urea using 0.1 M ammonium bicarbonate, and sequencing-grade modified trypsin was added at 1:50 (w/w) and incubated for 6 h at 37°C. Formic acid (Optima LC/MS grade; Thermo Fisher Scientific) was added to yield pH 3.0 before peptide desalting using Pierce C18 Peptide Desalting Spin Columns according to manufacturer’s protocol. Desalted tryptic peptides were dried to completion in a Labconco Speedvac Concentrator and frozen at −20°C until further analysis.

Tryptic peptides were resuspended in 0.1% formic acid in water (solvent A), quantified using a NanoDrop spectrophotometer (Thermo Fisher Scientific), and diluted with solvent A to a final concentration of 750 ng/µl for analysis by nanoflow ultra–high-performance liquid chromatography and tandem mass spectrometry (MS/MS). From each sample, 1 µl was injected onto a Waters nanoEase M/Z Peptide ultraperformance liquid chromatography BEH C19 analytical column (75 µm × 250 mm, 130 Å, 1.7 µm particles) using a Dionex Ultimate 3000 RSLCnano instrument (Thermo Fisher Scientific). A 300-nl/min, linear, 300 min reversed-phase binary ultra performance liquid chromatography gradient (solvent B: 0.1% formic acid in acetonitrile) was used to elute peptides directly into a Q Exactive HF mass spectrometer (Thermo Fisher Scientific) via nanoelectrospray ionization. The Q Exactive HF mass spectrometer was operated in positive mode with a 1.5-kV capillary voltage and incorporated a top 15 data-dependent MS/MS acquisition method using the following mass spectrometry parameters: 60,000 resolution, 1e6 AGC target, 60 ms maximum ion time, and 375 to 1800 m/z mass range. MS/MS parameters included the following: 15,000 resolution, 1e5 AGC target, 40 ms maximum ion time, 2.0 m/z isolation window, normalized collision energy of 27, charge state exclusion “on” to exclude ions of unassigned, and +1 and >+8 charge states, and a 30 s dynamic exclusion window was used.

All ultra–high-performance liquid chromatography and MS/MS raw files were searched against the Mus musculus Uniprot proteome database (identification number: UP000000589; updated 05/16/2017) using the Andromeda search engine embedded in MaxQuant v1.6.1.0 (18). The following parameters were used for peptide/protein identification: 4.5 ppm and 20 ppm mass tolerances for precursor and fragment ions, respectively, variable modifications oxidation of Met, deamidation of Asn and Gln, N-terminal Gln to pyro-Glu and protein N-terminal acetylation, fixed modification of carbamidomethyl Cys, trypsin enzyme specificity with up to two missed cleavages, a minimum of 5 aas/peptide, and a 1% false discovery rate (FDR) filter at the protein and peptide spectrum match levels. Label-free quantitation was achieved using the MaxQuant “LFQ” algorithm using unique and razor peptides. All other MaxQuant search parameters were kept at default values. Search results were uploaded into Scaffold Q+S (v4.9; Proteome Software) for data visualization and further analysis.

All experiments were performed with multiple biological replicates and assay replicates. Data were analyzed using GraphPad Prism software and are given as mean ± SD with n indicating the number of experiments or animals as stated in the figure legend. For the proteomic analysis, the average precursor intensity per protein values were used. Data distribution was assessed for normality, then either an unpaired Student t test, two-sided was used to compare two groups, whereas ANOVA was used for multiple group comparisons. Multiple comparison tests were corrected by controlling the FDR using the two-stage linear step-up procedure of Benjamini, Krieger, and Yekutieli, and results were considered significant with p ≤ 0.05. Log-transformed p values associated with individual proteins were plotted against mean difference in abundance between samples. The FDR-adjusted p < 0.05 cutoff for significance was represented. RNA-Seq experiments were performed with three biological replicates and high-depth sequencing (30× coverage), and differential expression was determined using DESeq2, with p ≤ 0.05 considered significant (19). We inputted the RNA and protein lists into GeneAnalytics (https://ga.genecards.org/#input). Based on GeneAnalytics proprietary algorithms, scores that were identified as high (adjusted p ≤ 0.0001) or medium (adjusted p ≥ 0.0001 but ≤ 0.05) were analyzed for pathway analysis.

We had previously shown that, following stimulation, LAG3-deficient human lymphoblasts secreted more TNF-α and less IL-10 compared with control cells (11). In this study, we used Lag3^−/− BMDCs to assess the generalizability of these previous findings. Lag3^−/− and WT BMDCs were stimulated with and without LPS (1µg/ml) for 24 h, then media aliquots were subjected to multiplex cytokine measurements. Unstimulated cells produced very low or undetectable amounts of IL-6, IL-10, IFN-γ, MCP-1, and IL-12p70. However, unstimulated Lag3^−/− BMDCs produced significantly higher amounts of TNF-α compared with WT BMDCs (39.5 ± 14.1 versus 9.1 ± 1.5 pg/ml, p = 0.0052) (Fig. 1A). LPS stimulation of both WT and Lag3^−/− cells resulted in no significant differences of IL-10, IFN-γ, and IL-12p70 compared with unstimulated counterparts (Fig. 1B). However, significantly increased amounts of IL-6 (p = 0.0024), MCP-1 (p = 0.0165), and TNF-α (p = 0.0002) were observed in LPS-stimulated cells as compared with control cells. As an additional assessment of DC activation using flow cytometry, we observed that MHC-II expression was elevated in Lag3^−/− BMDCs as compared with WT (76.3 ± 3.2 versus 67.5 ± 1.7%, p = 0.0421) (Fig. 1C). RNA-Seq heatmap analysis of unstimulated cells showed upregulation of several immunostimulatory chemokines and cytokines in Lag3^−/− BMDCs compared with WT BMDCs (Fig. 1D).

We next examined whether the increased expression of immunostimulatory molecules in Lag3^−/− BMDCs might be associated with alterations in cellular metabolism. We used a number of metabolic Seahorse tests, including glycolytic rate, mitochondrial respiration, and FAO. Total PER was higher in unstimulated Lag3^−/− BMDCs compared with WT cells (Fig. 2A,). Unstimulated Lag3^−/− BMDCs showed higher basal glycolysis compared with WT BMDCs (201.1 ± 6 versus 157.4 ± 9 pmol/min, p = 0.0044) (Fig. 2A, 2B). Cells were then incubated with rotenone and antimycin A to inhibit mitochondrial respiration. The compensatory glycolysis rate in Lag3^−/− BMDCs increased by 46% (p < 0.0001) compared with WT BMDCs (Fig. 2C). The percentage of PER from glycolysis in Lag3^−/− BMDCs was lower compared with WT BMDCs (89.8 ± 1.2 versus 97.3 ± 0.9%, p < 0.0001), suggesting that the contribution of mitochondria CO₂-derived acidification in Lag3^−/− BMDCs was ∼8% (Fig. 2D).

We next assessed mitochondrial function by measuring OCR after sequentially exposing cells to oligomycin, 1 µM FCCP, 2 µM rotenone, and 4 µM antimycin A. Injection of oligomycin decreased OCR in both WT and Lag3^−/− BMDCs (Fig. 2E), and O₂ consumption after FCCP injection was similarly increased in Lag3^−/− and WT cells (Fig. 2E). Overall, mitochondrial respiration parameters calculated from OCR data showed no significant differences between Lag3^−/− and WT BMDCs (Fig. 2F).

Measurement of endogenous and exogenous FAO was done using the Seahorse XF Palmitate-BSA FAO Substrate, Eto, and the XF Cell Mito Stress Test Kit. BMDCs were unstimulated or stimulated with LPS (1 µg/ml) for 24 h and then processed per the manufacturer’s instructions. The ability to oxidize FAs, as indicated by an increase in oxygen consumption, should be influenced by ATP demand, other substrates present in the assay medium (such as glucose), and stores of endogenous substrates (such as glycogen and triglycerides); therefore, cells were starved for 24 h in substrate-limited medium before the assay. On the day of the assay, cells were incubated in FAO medium and in the presence or absence of Eto. In the presence of BSA with or without Eto (Fig. 3A) or the presence of palmitate with or without Eto (Fig. 3B), we did not observe differences in OCR responses in either WT (p = 0.45) or Lag3^−/− BMDCs (p = 0.10) (Fig. 3C). However, there were significant differences between the genotypes. OCR responses during the basal phase prior to oligomycin inhibition were significantly lower in unstimulated Lag3^−/− BMDCs compared with WT BMDCs (mean ± SE; 25.3 ± 3.6 versus 50.2 ± 3.0, pmol/min, p = 0.0002) (Fig. 3D). In comparison with OCR responses in WT BMDCs incubated with BSA and Eto, OCR responses were significantly lower in Lag3^−/− cells (15.4 ± 4.9 versus 47.5 ± 2.7 pmol/min, p = 0.005). In comparison with OCR responses in WT BMDCs incubated with palmitate without Eto, OCR responses were significantly lower in Lag3^−/− cells (16.8 ± 2.8 versus 43.9 ± 1.7 pmol/min, p < 0.0001). In comparison with OCR responses in WT BMDCs incubated with palmitate and Eto, OCR responses were significantly lower in Lag3^−/− cells (11.1 ± 2.2 versus 40.8 ± 4.1 pmol/min, p = 0.0002).

We next assessed OCR responses in LPS-stimulated conditions (Fig. 3E–H). During the basal phase prior to oligomycin inhibition, OCR responses in WT (p = 0.45) and Lag3^−/− (p = 0.34) BMDCs were not different in the presence of BSA with or without Eto (Fig. 3E, 3G). In comparison with WT BMDCs incubated with BSA without Eto, OCR responses were significantly lower in Lag3^−/− cells (42.3 ± 2.1 versus 64.0 ± 2.1 pmol/min, p = 0.0005) (Fig. 3H, endogenous FA). In comparison with WT BMDCs incubated with BSA with Eto, OCR responses were significantly lower in Lag3^−/− cells (38.4 ± 1.9 versus 65.2 ± 1.9 pmol/min, p = 0.0002). In comparison with WT BMDCs incubated with palmitate without Eto, OCR responses were significantly lower in Lag3^−/− cells (34.8 ± 6.4 versus 67.6 ± 6.4 pmol/min, p = 0.007) (Fig. 3H, exogenous FA). In comparison with WT BMDCs incubated with palmitate and Eto, OCR responses were significantly lower in Lag3^−/− cells (29.1 ± 4.4 versus 50.8 ± 4.4 pmol/min, p = 0.008).

We previously found that LAG3-deficient human B lymphoblasts secreted significantly less IL-10 compared with WT (11). Others have identified a role for IL-10 in regulating glycolysis (20, 21), leading us to hypothesize an effect of exogenous IL-10 on glycolysis in Lag3^−/− BMDCs. In the absence of exogenous IL-10, Lag3^−/− BMDCs exhibited a higher glycolytic rate compared with WT cells (179.1 ± 32.1 versus 125.2 ± 16.1 pmol/min, p = 0.0046), whereas both cells responded to LPS stimulation with increased glycoPER (Fig. 4A, 4D, 4E). In the presence of IL-10, Lag3^−/− BMDCs exhibited significantly lower basal glycolysis (132.3 ± 30.9 versus 179.1 ± 32.1 pmol/min, p = 0.0114) as well as compensatory glycolysis (169.2 ± 39.2 versus 44.5 ± 32.7 pmol/min, p = 0.0140) as compared with unstimulated Lag3^−/− cells (Fig. 4B, 4D, 4E). As compared with the LPS-stimulated controls, with the combination of LPS and IL-10, both Lag3^−/− and WT BMDCs showed a significant reduction in basal (335.6 ± 42.7 versus 513.4 ± 33.1 pmol/min, p = 0.0018 in Lag3^−/− and 428.2 ± 34.2 versus 572.8 ± 48.5 pmol/min, p = 0.0023 in WT cells) and compensatory glycolysis (409.7 ± 44.1 versus 645.9 ± 33.8, p = 0.0029 in Lag3^−/− and 463.7 ± 61.9 versus 642.8 ± 51.2 pmol/min, p = 0.0006 in WT cells) (Fig. 4C–E).

Having observed that exogenous IL-10 significantly reduced glycolysis in Lag3^−/− BMDCs as compared with WT BMDCs (Fig. 4), we subjected Lag3^−/− and WT BMDCs to whole cell proteomic analysis by mass spectrometry to evaluate proteomic differences between the genotypes under unstimulated and stimulated conditions, including IL-10, LPS, and LPS plus IL-10. In unstimulated cells, the receptor-interacting protein kinase 3 (Ripk3) and F-box protein 6 (Fbox6) proteins, among others, were significantly upregulated in Lag3^−/− BMDCs, whereas Ras-related GTP binding protein A (Rraga) and TLR 2 (Tlr2) were downregulated (Fig. 5A).

IL-10 treatment was associated with upregulation of Tlr2, Rraga, topoisomerase-1 (Top1), and Ras-like proto-oncogene B (RalB), whereas the antiapoptotic protein myeloid cell leukemia 1 (Mcl1) was downregulated in Lag3^−/− BMDCs (Fig. 5B). IL-10 stimulation induced significantly altered expression patterns for 38 shared proteins in WT and Lag3^−/− BMDCs; at the same time, 168 and 115 proteins were uniquely up- or downregulated (Fig. 6B). Gene Analytics pathway analysis of the 168 unique proteins in WT cells revealed significant matches in chromatin regulation, whereas pathway analysis of the 115 unique proteins for Lag3^−/− BMDCs revealed significant matches in Ag presentation in folding, assembly, and peptide loading of MHC class I molecules (Supplemental Tables I, II).

LPS stimulation of Lag3^−/− and WT BMDCs led to the upregulation of Cd14, Stat1, Acsl1, Sqstm, and Caspase-7 proteins, among others, and the downregulation of Tlr13 and Apoe (Fig. 6C). LPS induced changes in the expression of unique 196 and 140 proteins in WT and Lag3^−/− cells, respectively, whereas they shared 103 proteins (Fig. 6D).

In WT BMDCs treated with LPS plus IL-10, there was increased expression of Ripk3, Trad1, La-related protein 1 (Larp1), and Rl1d1 proteins involved in the cell death signaling pathway. Lag3^−/− BMDCs treated with LPS plus IL-10 showed downregulation of Stat3, Ripk1, Larp1 and Birc6 proteins, among others (Fig. 6F). LPS plus IL-10 treatment led to up/downregulation of 194 proteins in WT cells compared with 248 in Lag3^−/−, whereas they both shared 116 proteins (Fig. 6E). Gene Analytics pathway analysis of the unique proteins identified in WT BMDCs revealed significant matches with PPAR signaling, whereas pathway analysis of the unique proteins identified in Lag3^−/− BMDCs revealed significant matches with apoptosis modulation and signaling (Supplemental Tables III, IV).

A key function of Ag-presenting DCs is to prime naive T cells to expand and undergo effector differentiation (22). To test whether Lag3 expressed on APCs might regulate this process, we adoptively transferred naive OVA-specific OT-II CD4 T cells into Lag3^−/− or WT recipient mice that were immunized with full-length OVA Ag. The OVA-immunized recipient mice were also treated with either a regimen comprising a combination of mAb agonists to the costimulatory receptors CD134 (OX40) plus CD137 (4-1BB) (Ag plus costimulant) that boosts Ag-primed CD4 T cell effector differentiation or control IgG (Ag) (23). Costimulation similarly increased OT-II CD4 T cell expansion in Lag3^−/− and WT recipients (Fig. 7A). Furthermore, following in vitro stimulation with PMA/ionomycin, recovered costimulated Lag3^−/− and WT OT-II CD4 T cells expressed similarly high levels of intracellular TNF-α. In contrast, intracellular IFN-γ expression was significantly greater (64.9 ± 17.7 versus 41.2 ± 13.2%, p = 0.0026) in costimulated Lag3^−/− OT-II CD4 T cells compared with costimulated WT, and there was also a nonsignificant trend toward greater IFN-γ expression in rat IgG-treated Lag3^−/− OT-II CD4 T cells compared with IgG-treated WT (Fig. 7B, 7C). Lag3^−/− mice produced more IFN-γ compared with WT within Ag plus costimulant group (64.9 ± 17.7 versus 41.5 ± 13.2%, p = 0.0029).

We also performed an in vitro DC:T cell coculture experiment to compare proliferation of naive T cells by Lag3^−/− versus WT BMDCs. In this in vitro design, there was no addition of OX40 or 4-1BB agonistic Abs, and BMDCs were incubated with OVA peptide (0–10 µg protein/ml) for 24–72 h. The peak percentage of divided T cells occurred at the 72-h time point with levels 1.6-fold higher in Lag3^−/− versus WT BMDCs (Fig. 7D). Taken altogether, these results are consistent with an intrinsic role for Lag3 in regulating the magnitude by which APCs can prime CD4 T cell effector functionality.

The objective in this study was to examine the effect of Lag3 deficiency in mouse DCs on secretion of inflammatory cytokines, metabolism, and in priming naive T cells to differentiate into Th1 effector cells. Consistent with our previous findings in human B lymphoblasts and in comparison with WT BMDCs, Lag3^−/− BMDCs secreted more TNF-α, exhibited higher glycolysis, and were more effective in priming naive CD4 T cells to differentiate into proliferating IFN-γ–expressing Th1 effectors.

The role of immune checkpoint molecules has been widely studied in T cell metabolism (24). More recently, Previte et al. (25) showed that Lag3 sustains mitochondrial and metabolic quiescence in naive CD4⁺ T cells by limiting oxygen consumption and spare respiratory capacity. These investigators also showed that Lag3 deficiency reduces CD4⁺ T cell's dependence on IL-7 for survival and metabolism because of an enhanced expression of signal transducer and activator of transcription 5 (STAT5). STAT5 activation in Lag3^−/− CD4⁺ T cells leads to higher glycolytic capacity and effector function following activation as compared with their WT counterparts. In agreement with Previte et al. (25), we also identified a borderline significantly increased expression of STAT5a RNA expression in unstimulated Lag3^−/− BMDCs as compared with WT cells (p = 0.09). Other checkpoint molecules, such as CTLA-4, have been shown to preserve the metabolic profile of nonactivated cells by inhibiting glycolysis and the metabolism of glutamine without enhancing FAO (26). In contrast, PD-1 represses glucose metabolism and alters lipid metabolism by suppression of FA synthesis and promoting FAO (26).

DCs also undergo a profound metabolic reprogramming based on their activation state (27–29). In Lag3^−/− BMDCs we have now shown that these cells exhibit a higher glycolytic rate compared with WT as measured by higher basal and compensatory glycolysis. In addition, FAO was also significantly lower in Lag3^−/− BMDCs, although no impairment in mitochondrial function was found. Interestingly, a significant contribution to extracellular acidification in Lag3^−/− BMDCs was, in part, due to mitochondrial activity that leads to CO₂ production. In contrast, under basal conditions, WT BMDCs exhibited the metabolic behavior of quiescence, using FA oxidation over glycolysis (as shown in Figs. 2 and 4). Taken altogether, these findings suggest that, under steady state, Lag3^−/− BMDCs display an activated phenotype characterized by elevated expression levels of MHC-II, proinflammatory cytokines, and other immunostimulatory molecules.

Unlike the role of checkpoint molecules in the metabolism of T cells, little is known about their influence in APCs. DCs are critical for induction of adaptive immunity and tolerance (22). Depending on their maturation/activation status, the molecules expressed on their surface, and the cytokines produced, DCs have been shown to either elicit immune responses through activation of effector T cells or induce tolerance through induction of either T cell anergy, regulatory T cells, or production of regulatory cytokines (30–35). Consistent with Fiebiger et al. (36), we observed higher secretion levels of TNF-α and upregulation of several immunostimulatory molecules in unstimulated Lag3^−/− BMDCs, demonstrating these cells exhibited an immunogenic phenotype associated with a heightened inflammatory state.

Several studies have highlighted the role of IL-10 as a powerful anti-inflammatory cytokine that plays a crucial role in dampening immune responses and preventing chronic inflammation and tissue damage (37). Ip et al. (21) demonstrated the importance of the IL-10–STAT3–DDIT4 axis for the inhibition of the mammalian target of rapamycin complex 1 (mTORC1) and the maintenance of healthy mitochondria in IL-10–deficient bone marrow–derived macrophages. In this study, we used exogenous IL-10 as a modality to restore the Lag3^−/− BMDCs to a WT phenotype. Although Lag3^−/− BMDCs have an altered metabolic profile at steady state, they respond to LPS stimulation in a similar way compared with WT cells. We found that the expression of several proteins was up/downregulated after LPS-mediated DCs maturation. Those proteins belong to pathways involved with inflammation and cytokine signaling in the immune system, IFN signaling, and adaptive immune system. For example, IL-1 receptor antagonist (Il1rn), a protein that inhibits IL-1α and IL-1β activity, was downregulated in Lag3^−/− BMDCs. Furthermore, it is well-known that, after LPS stimulation, DCs undergo changes in cellular morphology, characterized by upregulation of proteins involved in cell movement by regulating actin cytoskeleton homeostasis (38).

Exogenous IL-10 rescued the WT metabolic profile in Lag3^−/− BMDCs by decreasing both basal and compensatory glycolysis. Similar findings reported by Krawczyk et al. (39) revealed that IL-10 may function to inhibit glycolysis and DC activation by preventing TLR-mediated AMPK hypophosphorylation. Proteomic data of unstimulated Lag3^−/− BMDCs treated with IL-10 yielded upregulation of Tlr2, a crucial signaling pathway for the induction of IL-10 production by DCs and macrophages (40, 41). Additionally, IL-10 could impact LPS-stimulated Lag3^−/− BMDCs by downregulating Larp1. Larp1 is an RNA-binding protein that functions downstream of mTORC1 to regulate the translation of 5′-terminal oligopyrimidine tract (TOP) mRNAs, such as those encoding ribosome proteins (42–44). Consistent with the effects of exogenous IL-10 reducing glycolysis, Mangal et al. (45) recently reported the effects of exogenous α-ketoglutarate polymeric microparticles on cell metabolism in BMDCs. The authors reported that exogenous α-ketoglutarate microparticles significantly decreased glycolysis and mitochondrial respiration but did not affect MHC-II and CD86 expression. Taken together, it appears that exogenous agents, such as IL-10 and α-ketoglutarate polymeric microparticles, can be useful in modulating cell metabolism in activated BMDCs.

Given the primary importance of DCs as APCs priming naive T cells, we further investigated the effect of Lag3 deficiency in DCs to prime WT Lag3^+/+ CD4 T cells. Our results showed no differences in the expansion of WT and Lag3^−/− OT-II CD4 T cells following Ag stimulation either in the presence of absence of costimulatory agonists. Nevertheless, IFN-γ expression was increased in Lag3^−/− compared with WT CD4 T cells. Hence, Lag3^−/− APCs are more potent in inducing Th1 effector differentiation, thus revealing that Lag3 expressed on APCs normally limits Th1 responsiveness. Previously, Workman et al. (46) had shown increased T cell proliferation in an adoptive transfer experiment using Lag3^−/− OT-II donor T cells into Thy-1.1⁺B6.PL recipient mice as compared with Lag3^+/+ OT-II donor T cells. With some similarity but with the field continuing to focus on the role of Lag3 on T cell biology, especially in the context of cancer immunology, Lichtenegger et al. (47) observed that Ab blockade of Lag3 enhanced T cell activation by autologous TLR-matured DCs. T cells blocked by anti-Lag3 demonstrated enhanced proliferation and IFN-γ secretion. It should be noted that surface Lag3 expression was not measured in the TLR-matured DCs, so it cannot be determined whether the effect on T cell activation might have a contribution from blocking Lag3 on DCs. Results from our in vitro DC:T cell coculture experiment are consistent in showing enhanced proliferation of T cells in the presence of Lag3^−/− BMDCs.

In conclusion, Lag3 expressed on APCs plays an intrinsic role in limiting both glycolytic metabolism as well as the potency of induced cognate Th1 CD4 T cell effector responses.

A.R. has inventorship rights and is a founder of a startup company. The other authors have no financial conflicts of interest.