Elevated infiltration of immunosuppressive alternatively polarized (M2) macrophages is associated with poor prognosis in patients with cancer. The tumor microenvironment remarkably orchestrates molecular mechanisms that program these macrophages. Here we identify a novel role for oncogenic Hedgehog (Hh) signaling in programming signature metabolic circuitries that regulate alternative polarization of tumor-associated macrophages. Two immunocompetent orthotopic mouse models of mammary tumors were used to test the effect of inhibiting Hh signaling on tumor-associated macrophages. Treatment with the pharmacologic Hh inhibitor vismodegib induced a significant shift in the profile of tumor-infiltrating macrophages. Mass spectrometry-based metabolomic analysis showed Hh inhibition induced significant alterations in metabolic processes, including metabolic sensing, mitochondrial adaptations, and lipid metabolism. In particular, inhibition of Hh in M2 macrophages reduced flux through the UDP-GlcNAc biosynthesis pathway. Consequently, O-GlcNAc-modification of STAT6 decreased, mitigating the immune-suppressive program of M2 macrophages, and the metabolically demanding M2 macrophages shifted their metabolism and bioenergetics from fatty acid oxidation to glycolysis. M2 macrophages enriched from vismodegib-treated mammary tumors showed characteristically decreased O-GlcNAcylation and altered mitochondrial dynamics. These Hh-inhibited macrophages are reminiscent of inflammatory (M1) macrophages, phenotypically characterized by fragmented mitochondria. This is the first report highlighting the relevance of Hh signaling in controlling a complex metabolic network in immune cells. These data describe a novel immunometabolic function of Hh signaling that can be clinically exploited.

Significance:

These findings illustrate that Hh activity regulates a metabolic and bioenergetic regulatory program in tumor-associated macrophages that promotes their immune-suppressive polarization.

In the tumor microenvironment (TME) tumor cells and stromal cells coexist in a metabolically complex milieu. Immune cells are responsive to their surrounding environment and can readily react to perturbations by altering their metabolic profiles. Changes in the metabolic profile are noted in macrophages during the early phase of tumor evolution. Macrophages exist in a continuum of functional states, although, they are simplistically characterized as classically activated, pro-inflammatory (M1) macrophages or alternatively activated, immunosuppressive (M2) macrophages. Although these represent the polar extremes of their functions, tumor-associated macrophages (TAM) exist in a spectrum of intermediate activation states in response to the heterogeneous conditions they are exposed to in the TME (1).

Cytokines in the TME, including IL12, TNFα, and IFNγ, promote macrophage polarization to an M1 state, whereas IL4, IL10, and IL13 promote M2 macrophage polarization. These two states have distinct metabolic circuitries that contribute to survival, and subtype-specific functions (2). The metabolism of M1 macrophages is predominantly governed by glycolysis, whereas M2 macrophages utilize oxidative phosphorylation (OXPHOS; ref. 3). The mechanisms controlling these bioenergetic fates are unclear but are likely due to responses to signaling cues from the TME. Complex crosstalk between tumor and immune cells programs macrophages to a tumor-fostering M2 state. As such, the feed-forward loop between tumor and immune cells circumvents normalizing cues from the TME and evolves over time to sustain the tumor (2).

The Hedgehog (Hh) signaling pathway is aberrantly activated in multiple cancer types, including breast cancer, and promotes tumor invasion, metastasis, and multidrug resistance (4, 5, 6). Hh signaling cascade is initiated through the binding of Hh ligands to Patched1 (PTCH1), relieving PTCH1's inhibitory effect on the signal transducer, Smoothened (SMO). The ensuing regulatory circuit permits transcriptional activation by GLI transcription factors (7, 8, 9).

Although Hh signaling in tumor cells has been studied extensively, the impact of Hh signaling on the immune TME is now being realized. We previously characterized a determinative role for Hh signaling in significantly altering the immune components of the TME (10). Since then, it has been confirmed that SHH promotes M2 polarization and reduces effector CD8+ T-cell recruitment to the tumor (11, 12). A high percentage of M2 TAMs is associated with poor patient outcomes (13). Approaches previously employed to ablate TAMs in breast and lung cancer have yielded little success (14, 15). This is, in part, due to the fact that immunotherapeutic strategies have been successfully employed for highly heterogeneous tumor types with high T-cell infiltration but remain less effective for tumor types with limited T-cell infiltration.

Thus, inhibition of Hh signaling could present with the dual benefit of directly targeting tumor cells and reconfiguring the tumor immune microenvironment to an immune-activated state. We investigated the possibility that Hh signaling orchestrates changes in macrophage metabolism that enable the immunosuppressive M2 polarization state. We demonstrate that Hh signaling critically controls metabolic regulation of M2 TAMs by impinging upon the hexosamine biosynthetic pathway (HBP) and altering metabolic messaging that converges upon overlapping circuits of fatty acid oxidation (FAO), bioenergetics, and mitochondrial dysfunction.

Supplementary Table S1 includes a comprehensive list of all reagents used throughout this study.

Cell culture

RAW 264.7 cells were cultured in DMEM (Thermo Fisher Scientific) supplemented with 10% heat-inactivated FBS (Thermo Fisher Scientific). The 4T1 murine mammary carcinoma cell line was cultured in RPMI1640 medium (Thermo Fisher Scientific) supplemented with 5% heat-inactivated FBS and 100 μg/mL G418 (Thermo Fisher Scientific). EMT6 murine mammary carcinoma cell line was cultured in DMEM supplemented with 5% heat-inactivated FBS, 1% Glutamax (Life Technologies), 1% sodium pyruvate (Thermo Fisher Scientific) and 1% nonessential amino acids (Thermo Fisher Scientific). RAW264.7 and 4T1 cells were obtained from ATCC, whereas the EMT6 cells were a gift from Dr. Sophia Ran, Southern Illinois University. 4T1 cells have been validated by STR profiling from ATCC. Cell lines are typically used in their early passages. We expand cells at low passages and freeze multiple stocks. After about 10 to 15 passages, a new vial is thawed. All cell lines used are routinely tested for Mycoplasma.

Bone marrow–derived macrophage isolation

Bone marrow from femurs and tibias of 6- to 8-week-old mice was flushed in MEMα medium (Life Technologies) supplemented with 10% FBS and penicillin-streptomycin. MCSF (25 ng/mL) was added to cells for 7 days to differentiate monocytes into macrophages (BMDM).

In vivo experiments

Luciferase-expressing 4T1 cells (5 × 105) or luciferase expressing EMT6 cells (2 × 105) were suspended in Hanks' Balanced Salt Solution and injected into the inguinal mammary fat pad of 8-week-old female Balb/c mice. Tumor growth was documented by caliper measurements and bioluminescent imaging (BLI; Xenogen Corp.). Once tumors were palpable, mice were orally gavaged with 100 μL (2 mg/mouse) of vismodegib or DMSO as a vehicle control thrice weekly for 3 weeks. Animal studies were conducted in accordance with University of Alabama at Birmingham Institutional Animal Care and Use Committee.

Library preparation with polyA selection and HiSeq sequencing for transcriptomics profiling

RNA library preparations and sequencing were conducted at GENEWIZ, LLC. Libraries were prepared using the NEBNext Ultra RNA Library Prep Kit using manufacturer's instructions. The sequencing library was validated on the Agilent TapeStation and quantified using Qubit 2.0 Fluorometer and quantitative PCR. Samples were sequenced using a 2 × 150bp Paired End (PE) configuration. Image analysis and base calling were conducted by the HiSeq Control Software (HCS). FASTQ files were generated using Illumina's bcl2fastq 2.17 software. One mismatch was allowed for index sequence identification.

RNA-seq data analysis

All raw data from sequencing in FASTq format were processed by removing adaptor sequences using Trimmomatic tool, alignment with HISAT2 algorithm, and removal of PCR replicates using SAMtools before expression level quantification. The resulting BAM or SAM files were analyzed through a workflow of Partek Genomics Suite. RPKM normalization was performed before any statistical analysis to select genes by fold changes and statistical P values. The resulting gene lists were used for pathway analyses using IPA, GESTALT (15–18) NetworkAnalyst (19–21), and GSEA (22, 23). Accession numbers for sequencing data are GSE171168 (whole tumor) and GSE146223 (RAW 264.7 Cells).

Chromatin immunoprecipitation

RAW 264.7 cells were pretreated with 100 nmol/L recombinant SHH (R&D systems) in the presence of 20 ng/mL MCSF. Next, cells were treated with 10 μmol/L GANT61 and 20 ng/mL IL4. Cells were processed using the SimpleChip Plus Enzymatic Kit. Ten micrograms of cross-linked chromatin was IP with 5 μg of anti-Gli1 (Novus Biologicals) or isotype control antibody. Chromatin was eluted, cross-links were reversed, and purified DNA was subjected to qRT-PCR. CT values of input DNA were used to calculate percent IP. Each reaction was done in triplicate using Applied Biosystems Step One Plus.

Confocal imaging

Cells (BMDMs or RAW 264.7) were plated on poly-l-lysine coated coverslips and polarized to M0, M2 + DMSO (10 μmol/L), M2 + GANT61 (10 μmol/L), M2 + vismodegib (20 μmol/L), or M2 + BMS-833923 (3 μmol/L) for 24 hours and stained with 100 ng of Mitotracker Green for 25 minutes. After fixation, nuclei were stained with DAPI and imaged (×100 magnification) using Nikon A1R Confocal Microscope. Quantification was done using Nikon Elements software.

Flow cytometry and FACS sorting

Tumors were dissociated with the heated Miltenyi gentleMACS Octo Dissociator (Miltenyi Biotec). Cells were stained with viability dye eFluor 450 (Thermo Fisher Scientific), incubated with primary conjugated antibody cocktail (Biolegend), and processed using a BD LSRII Analyzer for flow cytometric analysis or ARIA Analyzer for sorting. FMO controls were utilized. Analysis was done using FlowJo v 10.

Gating strategy - M1 macrophages: Live cells, Cd11b+, F4/80+, Ly6C, CD80+ CD86+; M2 macrophages: Live cells, Cd11b+, F4/80+, Ly6C, Cd206+, Arg1+; and pan macrophages: Live, Cd11b+, F4/80+.

Fluorescence imaging of tumor sections

Primary antibodies [anti-F4/80, anti-Cd206, or anti-Patched-1 (Novus Biologicals)] were applied to tumor sections (4.5 μm). After washing with 1× PBS, secondary antibodies [Alexa Fluro 488-labeled goat anti-rabbit IgG antibody or Alexa Fluor 594-labeled donkey anti-rat antibody or Alexa Fluor 488-labeled donkey anti-goat antibody or Alexa Fluor 594-labeled goat anti-rabbit antibody] were applied to the sections. Autofluorescence was quenched using TrueVIEW Autofluorescence Quenching Kit with DAPI (Vector Labs). F4/80 Cd206 double-positive staining signified M2 macrophages, and Cd206 Ptch1 double-positive staining marked M2 macrophages expressing Ptch1. Images were captured using a Nikon Eclipse Ti-U microscope (Nikon) and analyzed using NIS Elements.

Western blotting

BMDMs were polarized to M0, M2 + DMSO (10), M2 + GANT61 (10), or M2 + SHH (100 nmol/L) for 24 hours. Protein lysates were immunoblotted for MFN, OPA1, DRP1, P-DRP1, STAT6, P-STAT6, RL2, CTD110.6, Cd206, OGA, or OGT and imaged using chemiluminescence. For Dot Blots, 0.1 μg of protein lysate was spotted on a nitrocellulose membrane, allowed to dry, and immunoblotted with RL2. β-Actin was used as control.

Immunoprecipitation

RAW 264.7 cells were polarized using 20 ng/mL IL4 and 20 ng/mL MCSF with 20 μmol/L GANT61 or DMSO for 24 hours. One milligram of protein lysate was precleared using Protein A/G Plus Agarose bead slurry (50 μL; Santa Cruz Biotechnology) for 1 hour at 4°C. Precleared lysates were incubated with 2 μg RL2 antibody for 2 hours at 4°C and 50 μL of Protein A/G Plus Agarose bead slurry was added at 4°C overnight. Beads were resuspended in 2× Laemmli Buffer and heated at 95°C; supernatant was resolved on SDS PAGE gel.

qRT-PCR

Real-time PCR was performed using TaqMan Fast Advanced Master Mix and Taqman gene expression assay probes for Arginase 1, Mrc1, Acadl, Acadm, Cpt1b, Pparg, Ppargc1b, Ogt, Mgea5, Fasn, and Acaca (Thermo Fisher Scientific). Actb served as an endorse control gene. Each reaction was done in triplicate using an Applied Biosystems Step One Plus Kit.

Luciferase assay

Cells (RAW 264.7) were plated in triplicate and transfected with PPRE-X3-TK-luc (100 ng) or p4xSTAT6-Luc2P (100 ng) using Lipofectamine 2,000 for 6 hours. Cells were polarized to M0, M2 + DMSO, M2 + GANT61 (10 μmol/L), M2 + vismodegib (20 μmol/L), or M2 + OSMI-1 (5, 10, or 20 μmol/L). Luminescence was quantified using a luminometer (Promega) and normalized to total protein concentration.

Phagocytosis assay

Cells (BMDM) were polarized to M0, M1, or M2, followed by treatment with Hh inhibitors. Media was aspirated and 100 μL of reconstituted pHrodo Red E. coli BioParticles were added to each dish in MEM alpha. Two hours later, cells were washed and imaged using the Nikon Eclipse Ti-U microscope and quantified using ImageJ.

JC1 staining

Differentiated BMDMs were plated in the presence of 25 ng/mL of MCSF. Cells were polarized to M2, then treated with DMSO, GANT61 (10 μmol/L), or BMS-833923 (3 μmol/L) for 24 hours. BMDMs were stained with JC1 (2 μmol/L) for 30 minutes at 37°C. Data were acquired using a BD LSRII Analyzer and analyzed with FlowJo v10.

ATP quantification

RAW 264.7 cells were polarized to M0, M1, or M2 followed by treatment with DMSO or GANT61 (10 μmol/L). ATP was assessed using the ATPLite 1 step Assay Kit (Promega).

Oil Red O staining

Cells were polarized to M0, M1, or M2 with DMSO or GANT61 (10 μmol/L), and assessed for lipid accumulation using Oil Red O Staining following manufacturer's protocol. ImageJ was used to quantify accumulated lipid droplets.

Metabolomics data analysis

Metabolite extraction was performed as described in previous study (16). LC/MS peak extraction and integration were performed using commercially available software Sieve 2.2 (Thermo Fisher Scientific). The peak area was used to represent the relative abundance of each metabolite. The missing values were handled as described in previous study (17).

FAO seahorse palmitate oxidation assay

BMDMs polarized to M2 or M2 + GANT61 [10 μmol/L] (5 × 104) were plated in MEM alpha with inhibitors for 24 hours. Media was changed to DMEM supplemented with 2 mmol/L glucose and 0.5 mmol/L carnitine pH 7.4. Cells were pretreated with etomoxir (40 μmol/L) 15 minutes prior to assay. Immediately before assay, BSA or BSA/Palmitate was added to plates. Respiration was determined using the XF96 Seahorse extracellular flux analyzer (Seahorse Bioscience). BMDMs were sequentially treated with inhibitors: 1.5 μg/mL oligomycin, 3 μmol/L FCCP, 10 μmol/L antimycin A, and 1 μmol/L rotenone. Protein concentration was determined to be equal across all wells.

Metabolic extracellular flux analyzer

BMDMs were polarized to M0, M1, or M2 followed by treatment with DMSO or Hh inhibitors for 12 hours and incubated in unbuffered DMEM containing 5.55 mmol/L glucose, 2 mmol/L l-glutamine, and 1 mmol/L pyruvate at pH 7.4 for 1 hour at 37°C in a non-CO2 incubator. BMDMs were sequentially treated with inhibitors at a final port concentration of 0.8 μmol/L oligomycin, 0.45 μmol/L carbonyl cyanide 4-(trifluoromethoxy) phenylhydrazone (FCCP), and 0.4 μmol/L antimycin A. OCR and ECAR analyses were performed according to the Seahorse Biosciences manual.

Statistical analysis

We used the Student t test or ANOVA analysis, as appropriate, and plotted using GraphPad Prism 8 software. Statistical significance was determined for P ≤ 0.05 (indicated with a *). All data shown as mean ± SEM.

Hedgehog signaling blockade alters metabolic processes in macrophages

To elucidate the effect of Hh activity on macrophage polarization, we utilized two immunocompetent mammary tumor mouse models (Fig. 1A): the 4T1-Balb/c model and the EMT6-Balb/c model. We targeted Hh signaling using the pharmacologic FDA-approved, orally available inhibitor vismodegib (Smo-i) that targets the signal transducer, SMO, of the Hh pathway. We initiated administration of Smo-i once the tumors were nearly 4 mm in diameter. Smo-i did not significantly impact tumor growth (Supplementary Fig. S1A) or the portfolio of chemokines that direct macrophage recruitment (Fig. 1B). However, Smo-i–treated tumors had significantly reduced levels of IL4 and IL13 cytokines that are instrumental in M2 macrophage polarization with a concomitant increase in IFNγ, the M1 programming cytokine (Fig. 1B). Although there were no appreciable changes in the tumor infiltrating pan-macrophage population (Cd11bhi F4/80hi; Supplementary Fig. S1B), we registered a significant decrease in the abundance of tumor infiltrating M2 macrophages concurrent with increased M1 macrophage infiltration (Figs. 1CF; Supplementary Figs. S1C and S1D). We also evaluated mammary tumor sections from both models for PTCH1. Tumor-infiltrating Cd206-positive macrophages express the Hh receptor PTCH1 (Supplementary Figs. S1E–S1H), which is expectedly decreased in Smo-i–treated tumors. M2 polarization integrally involves activation of STAT6, which potentiates M2-specific polarization by transcriptionally upregulating arginase 1 (Arg1), mannose receptor 1 (Mrc1, Cd206), and resistin-like α (Fizz1). To determine the effect of Hh on STAT6 activity within the M2 TAMs, we evaluated the activation status of STAT6 in F4/80hi Cd206hi M2 TAMs. There was a significant reduction in active p-STAT6 in M2 TAMs from Smo-i tumors, indicating that Hh blockade mitigates STAT6 activity within the M2 TAMs (Supplementary Fig. S1I). To evaluate a direct role for the activity of the Gli transcription factors, we used the small molecule inhibitor, GANT61, which directly targets and disables DNA binding of Gli transcription factors at the terminal end of the Hh pathway. To understand the functional ramifications of Hh signaling on macrophages, we evaluated their phagocytic activity. Although activation of Hh with exogenous recombinant SHH decreased the phagocytic capability of M2-polarized macrophages, inhibiting Gli activity remarkably elevated their phagocytic activity, indicating that blockade of Hh signaling promotes the effector functions of immunosuppressive macrophages (Supplementary Fig. S1J).

We analyzed the transcriptome of bulk tumors resected from vehicle control or Smo-i–treated mice. Enrichment plots from RNA-seq data indicate that genes involved in macrophage activation are enriched in Smo-i–treated tumors (Fig. 1G). Analysis of RNA-sequencing data based on Kyoto Encyclopedia of Genes and Genomes (KEGG), Wikipathways, Panther (Fig. 1H), NetworkAnalyst, and Ingenuity Pathway Analysis (IPA) reveal remarkable alterations in metabolic processes with Smo-i treatment, and out of the top metabolic pathways that are significantly altered, carbohydrate and lipid metabolism present with high significance (Fig. 1I; Supplementary Fig. S1K). These changes are also captured in RNA seq analysis of M2 macrophages (skewed from RAW264.7 cells, Supplementary Fig. S1L) inhibited for Hh signaling, highlighting alterations in FAO, PPAR signaling, O-linked glycosylation, and glycolysis (Supplementary Figs. S1M and S1N).

Following this lead, we adopted a mass spectrometry-based untargeted metabolomics analysis approach with BMDMs skewed to M0, M2, or M2 inhibited for Gli activity (Supplementary Fig. S2A). This analysis revealed that 98 metabolites were significantly altered by a fold change of two or greater (Fig. 2A). IPA of metabolomics presented the UDP-N-acetyl-d-glucosamine Biosynthesis II pathway as a top significantly altered pathway in M2 macrophages inhibited for Hh signaling (Fig. 2B). One metabolite that is enhanced in M2 macrophages and possibly contributes to their immune-suppressive phenotype is uridine diphosphate N-acetyl-alpha-d-glucosamine (UDP-GlcNAc; ref. 5), an end product of the HBP. Relative to M0 macrophages, UDP-GlcNAc levels are significantly increased in M2 macrophages (Supplementary Fig. S2B). Inhibiting Hh activity significantly reduced the levels of UDP-GlcNAc and the majority of metabolites in the UDP-GlcNAc synthesis pathway (Fig. 2C; Supplementary Fig. S2C). Thus, Hh signaling blockade alters the UDP-GlcNAc metabolic signature in M2 macrophages.

Hedgehog signaling enhances O-GlcNAcylation to potentiate the M2 phenotype

UDP-GlcNAc is the substrate for O-GlcNAcylation, a posttranslational modification that culminates in the addition of O-GlcNAc moieties to serine and threonine residues on proteins (Fig. 3A). Elevated O-GlcNAcylation in the TME contributes to increased tumor growth and invasion (18, 19). Hh blockade diminished the cellular pool of O-GlcNAcylated proteins (Fig. 3B; Supplementary Figs. S3A–S3C). This is accompanied by a significant decrease in the levels of O-GlcNAc Transferase (OGT), the enzyme that adds O-GlcNAc to target proteins (Fig. 3C; Supplementary Fig. S3D). No changes in transcript or protein levels were evident in O-GlcNAcase (OGA, Mgea5), the enzyme that removes O-GlcNAc residues (Supplementary Figs. S3E and S3F).

We next explored if Hh/Gli activity transcriptionally regulates Ogt. We identified three putative Gli1 binding sites in the promoter sequence of Ogt. By employing a chromatin immunoprecipitation (ChIP) assay, we registered that Gli1 binding to all three predicted sites is significantly enriched in M2 skewed macrophages compared with resting (M0) macrophages (Fig. 3D). GANT61 caused a significant decrease in Gli1 binding to the Ogt promoter (Fig. 3D). Furthermore, exogenous SHH ligand elevated cellular O-GlcNAcylation, establishing a heretofore unknown direct link between Hh signaling and O-GlcNAcylation in M2-polarized macrophages (Supplementary Fig. S3G).

To evaluate the translational relevance of this finding, we assessed O-GlcNAcylation in M2 macrophages (F4/80hi Cd206hi) sorted from mammary tumors (Supplementary Fig. S3H). F4/80hi Cd206hi macrophages from tumors of Smo-i–treated mice demonstrate an overall decrease in the landscape of O-GlcNAcylated proteins and reduced OGT protein levels (Fig. 3E and F; Supplementary Fig. S3I). Aligned with this, we registered decreased O-GlcNAcylation in the F4/80hi TAM population sorted from Smo-i mammary tumors (Supplementary Fig. S3J), indicating that even though the overall abundance of F4/80hi TAMs is unchanged (Fig. 1B; Supplementary Fig. S1C), their O-GlcNAc landscape is altered. Cumulatively these data establish a role for Hh signaling in directing metabolic changes and altering cellular O-GlcNAcylation in M2 TAMs.

Cd206 is a critical marker of M2-macrophages that is modified by posttranslational N-glycosylation (20, 21). Inhibiting Hh notably decreased Cd206 protein levels in M2 macrophages (Supplementary Fig. S4A). We explored if Hh activity might impact its O-GlcNAcylation, based on predictions of several possible O-GlcNAc sites in Cd206 protein (Supplementary Fig. S4B). Hh blockade remarkably decreased the pool of O-GlcNAc-modified Cd206 (Supplementary Fig. S4C). This likely reflects a cumulative decrease in Cd206 and its O-GlcNAcylation.

STAT6 has a prime role in orchestrating the M2 transcriptional program, in part by transcriptionally regulating Cd206. In addition to phosphorylation, O-GlcNAc-modification of STAT6 enhances its transcriptional activity (22, 23). The Yin-O-Yang1.2 engine predicts three robust sites of O-GlcNAcylation in STAT6 protein (Fig. 4A). Inhibiting Hh activity significantly decreased O-GlcNAc modification of STAT6 without impacting total STAT6 levels (Fig. 4B and C; Supplementary Fig. S4D). To test the impact of O-GlcNAc-modification on the transcriptional activity of STAT6, we inhibited OGT using the small molecule inhibitor, OSMI-1, in M2 macrophages. OGT blockade diminished STAT6 activity in a dose-dependent manner (Fig. 4D) and significantly reduced the expression of STAT6 transcriptional targets, Arg1 and Mrc1, suggesting that O-GlcNAc-modification of STAT6 critically impacts M2 gene expression (Fig. 4E; Supplementary Fig. S4E). These outcomes collectively substantiate a role for Hh signaling in impacting STAT6 activity through enhancing its O-GlcNAcylation.

As an essential aspect of immunosuppressive programming of macrophages, STAT6 acts as a cofactor for peroxisome proliferator-activator receptor gamma coactivator 1-beta (PGC1β) to activate transcription of genes that regulate lipid metabolism. Accordingly, we tested the effects of O-GlcNAc manipulation on the expression of these genes. Inhibition of OGT significantly decreased expression of Ppargc1b, and of FAO genes Cpt1b, Acadm, and Acadl (Fig. 4F). Collectively, these findings support a role for Hh/Gli signaling in intersecting with the UDP-GlcNAc biosynthesis pathway impinging upon critical metabolic programs that regulate alternative polarization of macrophages.

Hedgehog signaling alters FAO in immunosuppressive macrophages

In agreement with STAT6 regulating genes critical for FAO, global transcriptomics analyses of the bulk mammary tumor and skewed M2 macrophages, and our metabolomics analysis, independently underscore a role for Hh/Gli signaling in orchestrating lipid oxidation in macrophages (Figs. 5AD). Blockade of Hh in mammary tumors diminished several fatty acid metabolism signatures (Fig. 5A). Inhibiting Hh signaling in skewed M2 macrophages resulted in a metabolome signature of lipid oxidation of short, long, and branched chain fatty acids (Fig. 5B), and highlighted signatures of lipid metabolic processes within the bulk tumor mass (KEGG pathways; Fig. 5C; Supplementary Tables S2–S5). GSEA from macrophage RNA-seq highlights alterations consistent with fatty acid metabolic processes and nuclear receptor activity (Fig. 5D). STAT6 functions as a cofactor with peroxisome proliferator-activated receptor gamma (PPARγ) and promotes lipid uptake, and enhances FAO as a means for generating energy for the metabolically demanding M2 macrophages, potentiating a switch to OXPHOS (24). Hh inhibition in M2 macrophages reduced the transcript levels of Pparg and Ppargcb1 (Fig. 5E; Supplementary Figs. S5A and S5B) and blunted the activity of PPARγ (Fig. 5F; Supplementary Fig. S5C). Concordantly, we registered decreased expression of genes involved in FAO (Fig. 5G; Supplementary Figs. S5D and S5E) with a simultaneous increase in fatty acid synthesis genes (Supplementary Fig. S5F). Silencing Gli1 expression phenocopied the effects of GANT61 on these alterations in FAO (Supplementary Fig. S5G).

To assign functional relevance to transcriptional alterations in lipid metabolism, we utilized the Seahorse XF Palmitate-BSA FAO assay to quantify oxidation of exogenous fatty acids. In brief, M2 macrophages were pretreated with etomoxir, BSA, and/or BSA-Palmitate prior to the assay. Oligomycin was introduced first to inhibit ATP synthase, followed by FCCP to uncouple the mitochondrial inner membrane, and finally antimycin A/rotenone to inhibit complex III of the electron transport chain (Fig. 6A). Etomoxir was used as a FAO inhibitor and elicited effective inhibition of FAO. Concordant with the transcriptional FAO profile, GANT61 significantly reduced FAO at basal and maximal levels in M2 polarized macrophages (Fig. 6B and C). At the cellular level, this manifested as increased accumulation of lipid droplets (Fig. 6D; Supplementary Fig. S6A). These observations are consistent with findings linking the family of nuclear receptors to induce a metabolic shift contributing to M2 polarization (25). Such metabolic signatures involve activation of PPARγ in controlling homeostasis by regulating FAO (Fig. 5A and B; Supplementary Tables S2–S5) to ensure a supply of ATP to sustain cellular functions. As such, reduced FAO in Hh inhibited M2 macrophages is reflected in significantly diminished levels of ATP (Fig. 6E; Supplementary Fig. S6B). Collectively, our studies establish an important role for Hh signaling in altering FAO in immunosuppressive macrophages.

Inhibition of Hh/Gli signaling alters mitochondrial bioenergetic activity and morphology

Mitochondria are the main site for FAO and function of nuclear receptors PGC1β and PPARγ (26). We surmised that changes in ATP levels reflect a switch in the bioenergetic profile of the macrophages. M2 macrophages principally rely on OXPHOS, whereas M1 macrophages depend on glycolysis for energy production (5, 18, 19, 27). For M1 macrophages, this reliance on glycolysis results in enhanced Krebs cycle intermediates, such as succinate, which activate HIF1α to stimulate the production of inflammation-related genes. For M2 macrophages, their reliance on oxidative metabolism is important for prolonged energy generation to maintain wound healing and tissue repair functions (28). Our untargeted metabolomics assessment has identified significant changes in OXPHOS, mitochondrial dysfunction, and glycolysis (Fig. 2B), suggestive of a bioenergetic re-calibration when Hh signaling is impeded in macrophages.

We evaluated mitochondrial respiration in macrophages using the Seahorse extracellular flux analyzer (Fig. 7A) to assess changes in the mitochondrial oxygen consumption rate (OCR) and extracellular acidification rate (ECAR). Relative to M1 macrophages, the M2 macrophages show robust respiration characterized by elevated basal and maximal reserve capacity. Inhibiting Hh significantly decreased basal and maximal respiration of M2 macrophages (Fig. 7BD; Supplementary Figs. 7A and S7B). In contrast to OCR, the ECAR of M2 macrophages is significantly reduced relative to that of M1 macrophages. Inhibiting Hh/Gli signaling results in a significant increase in ECAR, reminiscent of M1 macrophages (Fig. 7E; Supplementary Fig. S7C). In agreement with this, mitochondria from M2 macrophages displayed high membrane potential as indicated by greater numbers of macrophages with JC-1 stained red fluorescent aggregates. In contrast, Hh inhibited macrophages had increased proportion mitochondria with low membrane potential (Fig. 7F).

Cellular bioenergetics is also intricately associated with distinct mitochondrial morphology (29). Cells utilizing OXPHOS have mitochondria with a fused morphology, whereas cells reliant on upregulated glycolysis have characteristically fragmented mitochondria due to increased electron leakage (30). We utilized confocal imaging to visualize mitochondrial morphology of skewed macrophages inhibited for Hh signaling (Fig. 8A; Supplementary Fig. S8A). In contrast to M1 macrophages with fragmented mitochondria, M2 macrophages demonstrate a fused mitochondrial phenotype that is disrupted upon Hh inhibition. Moreover, mitochondria from macrophages inhibited for Hh/Gli signaling resemble those of M1 macrophages (Fig. 8A; Supplementary Fig. S8A). Such morphological changes are orchestrated by proteins including MFN1, OPA1, and p-DRP that regulate fusion (MFN1 and OPA1) or fission (p-DRP). Agreeably, inhibition of Hh/Gli signaling registers as diminished expression of MFN1 and OPA1. There is concurrent upregulation in the expression of p-DRP1 (Fig. 8B; Supplementary Figs. S8B–S8D).

To explore pharmacological relevance of these leads, we employed the 4T1-Balb/c and EMT6-Balb/c mammary tumor models (Fig. 1A) and sorted tumor-infiltrating F4/80hi Cd206hi (M2) macrophages (Supplementary Fig. S3H). M2 macrophages enriched from Smo-i-treated primary tumors demonstrate decreased expression of OPA1 and MFN1 and upregulated expression of p-DRP1 (Fig. 8C; Supplementary Figs. S8E and S8F). Inhibiting Hh activity did not significantly impact mitochondrial DNA (mtDNA) abundance (Supplementary Fig. S8G). Taken together, our data indicate that Hh signaling directs major alterations in metabolic and bioenergetic programs that collectively impact alternative polarization of macrophages (Fig. 8D).

Hedgehog signaling is essential for effective metabolic reprogramming in immunosuppressive macrophages

Utilizing two immunocompetent orthotopic models of mammary tumors treated with the pharmacologic Hh inhibitor, vismodegib, we registered a remarkable shift in the portfolio of tumor-infiltrating macrophages. Untargeted metabolomics identified that Hh inhibition significantly reduced flux through the UDP-GlcNAc biosynthesis pathway in M2 macrophages. Relative to M1 macrophages, M2 macrophages have elevated UDP-GlcNAc, which serves as a substrate for N- or O-linked glycosylation (18, 19). Although the relevance of N-Glycosylation in the M2 macrophage state is appreciated, the role of O-linked glycosylation in M2 macrophages remains largely associative and enigmatic. Elevated O-GlcNAcylation in the TME contributes to increased tumor growth, potentially through decreased inflammatory cytokine production by TAMs (31). O-GlcNAcylation of STAT6 augmented its activity and expression of its transcriptional targets, Arg1 and Mrc1, critically contributing to the immune-suppressive state of macrophages. STAT6 functions as a cofactor for PGC1β to bind the promoters of genes responsible for upregulating lipid uptake and FAO. PGC1β, in concert with PPARγ and STAT6, promotes lipid uptake and enhances FAO as a means for generating energy for the metabolically demanding M2 macrophages. Blocking Hh reduced the transcript levels of both Pparg and Ppargc1b, and attenuated FAO. In medulloblastoma, Hh signaling activates PPARγ and enhances the proliferative capacity and progression of tumor cells (32). As such, our work establishes a novel role for Hh signaling in orchestrating O-linked glycosylation that impinges upon STAT6-regulated FAO and consequently governing the M2 state of macrophages.

Hedgehog signaling regulates bioenergetics in immunosuppressive macrophages

Metabolic adaptations that drive the bioenergetic profiles associated with M1 and M2 macrophages are fundamental to macrophage activation and effector function (2). Although M2 macrophages require an abundance of “fuel,” they employ programs driving OXPHOS and lipid oxidation; M1 macrophages principally employ glycolysis, yielding less ATP. Uncoupling Hh activity in M2 macrophages prompted a decrease in mitochondrial function reflected in compromised basal and maximal respiration. In contrast with our findings in macrophages, paracrine Hh signaling in hepatocellular carcinoma stimulates glycolysis in surrounding myofibroblasts resulting in increased lactate production providing an additional energy source to fuel tumor cell proliferation (33). Moreover, in brown fat and muscle cells, Hh signaling drives glycolysis by reducing OCR and increasing ECAR (34). Interestingly, Hh signaling regulates bioenergetics in a cell type specific manner that ultimately supports tumor growth. By upregulating glycolysis in tumor cells, Hh signaling helps contribute to the availability of growth-promoting molecular intermediates through the Warburg effect; whereas in macrophages, Hh signaling supports OXPHOS to promote their M2 phenotype. Collectively, such findings cement the integral role of Hh signaling in regulating cellular metabolism, in both immune cells and tumor cells, to collectively foster tumor survival, thus providing a multifaceted role for targeting Hh signaling within the TME.

These changes in cellular metabolism and bioenergetics are enabled by mitochondrial functional adaptations (29). A continuous mitochondrial network formed by mitochondrial fusion is required for FAO and OXPHOS. Consequently, cellular supplementation with lipids results in reduced mitochondrial fusion (35), leading to an accumulation of triacylglycerols and the export of free fatty acids out of the cell (36). When cells utilize glycolysis predominately for energy production, which is indicative of a state of nutrient abundance, their mitochondria rapidly undergo fission diminishing their cellular fitness (30). Zheng and colleagues have established that hypoxia provokes mitochondrial fragmentation and diminishes the function of natural killer cells within the TME (37). Studies such as this demonstrate a close connection between hypoxia and changes in cellular metabolic profiles, suggesting that many complex aspects of TME can contribute to these alterations. Our findings indicate that Hh inhibition elicits a markedly altered bioenergetic program that mitigates the health and functionality of M2 macrophages in the TME.

Hh signaling is uniquely positioned at the nexus of several transcriptional networks. In the context of macrophages, this work shows that Hh establishes a circuit with STAT6 via its O-GlcNAcylation, resulting in a metabolic and bioenergetic portfolio that is reminiscent of inflammatory macrophages. As such, interference with the homeostasis of macrophage metabolism readily primes macrophages for phenotype reversal. This is the first time that Hh activity has been shown to regulate metabolic and bioenergetic programming of TAMs. Further, our work provides evidence for modulating Hh activity, and consequently macrophage metabolism, to sculpt the plasticity of macrophages.

Current approaches to target TAMs involve depleting M2 macrophages or re-polarizing them to M1-like macrophages. The allure of targeting Hh signaling stems from the availability of several FDA-approved inhibitors already in use in the clinic, making it applicable to different tumor types. Our work supports the possibility that Hh blockade is multidimensional in its effect on recalibrating the metabolism and consequently the functions of immunosuppressive macrophages. Therefore, inhibition of Hh activity could present a dual-pronged approach that reduces the abundance of M2 TAMs and perhaps, promotes M2 to M1 transdifferentiation by inherently changing their metabolic status, thus promoting a robust immunogenic response to cancer.

J.C. Rathmell reports grants and personal fees from Sitryx; personal fees from Caribou, Nirogy, Merck, and Mitobrige; and grants from Incyte and Tempest outside the submitted work. No disclosures were reported by the other authors.

D.C. Hinshaw: Investigation, visualization, methodology, writing–original draft, writing–review and editing. A. Hanna: Conceptualization, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. T. Lama-Sherpa: Methodology, writing–original draft, writing–review and editing. B. Metge: Formal analysis, validation, investigation, methodology, writing–review and editing. S.C. Kammerud: Investigation, methodology, writing–original draft. G.A. Benavides: Formal analysis, validation, investigation, methodology, writing–review and editing. A. Kumar: Investigation, writing–original draft. H. Alsheikh: Formal analysis, writing–review and editing. M. Mota: Investigation, methodology. D. Chen: Formal analysis, writing–review and editing. S.W. Ballinger: Investigation. J.C. Rathmell: Validation, investigation. S. Ponnazhagan: Investigation, methodology. V. Darley-Usmar: Methodology. R.S. Samant: Validation, investigation, writing–review and editing. L.A. Shevde: Conceptualization, resources, data curation, formal analysis, supervision, funding acquisition, validation, investigation, writing-original draft, project administration, writing–review and editing.

The authors acknowledge funding from the following sources: Department of Defense (W81XWH-14-1-0516, W81XWH-18-1-0036, W81XWH-10-1-0755), NCI R01CA169202, O'Neal Invests, and The Breast Cancer Research Foundation of Alabama (BCRFA), all awarded to L.A. Shevde. The work was supported in part by BXAl3374 (VA) and CA194048 (NCI/NIH) to R.S. Samant. The authors would like to thank Melissa J. Sammy for her technical assistance with experimental procedures; the UAB Bio-Analytical Redox Biology (BARB) Core, the UAB High Resolution Imaging Facility, the UAB Comprehensive Flow Cytometry Core supported by NIH Grants P30 AR048311 and P30 AI027667, and UAB Comprehensive Cancer Center's Preclinical Imaging Shared Facility supported by NIH grant P30 CA013148. The authors thank the Juan Liu and the Jason Locasale laboratory at Duke University for their assistance with the metabolomics analysis.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

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