Obesity and diabetes modulate macrophage activation, often leading to prolonged inflammation and dysfunctional tissue repair. Increasing evidence suggests that the NLRP3 inflammasome plays an important role in obesity-associated inflammation. We have previously shown that activation of the lipotoxic inflammasome by excess fatty acids in macrophages occurs via a lysosome-dependent pathway. However, the mechanisms that link cellular lipid metabolism to altered inflammation remain poorly understood. PPARγ is a nuclear receptor transcription factor expressed by macrophages that is known to alter lipid handling, mitochondrial function, and inflammatory cytokine expression. To undercover novel links between metabolic signaling and lipotoxic inflammasome activation, we investigated mouse primary macrophages deficient in PPARγ. Contrary to our expectation, PPARγ knockout (KO) macrophages released significantly less IL-1β and IL-1α in response to lipotoxic stimulation. The suppression occurred at the transcriptional level and was apparent for multiple activators of the NLRP3 inflammasome. RNA sequencing revealed upregulation of IFN-β in activated PPARγKO macrophages, and this was confirmed at the protein level. A blocking Ab against the type 1 IFNR restored the release of IL-1β to wild type levels in PPARγKO cells, confirming the mechanistic link between these events. Conversely, PPARγ activation with rosiglitazone selectively suppressed IFN-β expression in activated macrophages. Loss of PPARγ also resulted in diminished expression of genes involved in sterol biosynthesis, a pathway known to influence IFN production. Together, these findings demonstrate a cross-talk pathway that influences the interplay between metabolism and inflammation in macrophages.

Macrophage dysfunction is a hallmark of diabetes and contributes to several diabetes complications, including impaired wound healing, postmyocardial infarction heart failure, atherosclerosis, and nonalcoholic steatohepatitis (14). Diabetes is associated with elevated levels of free fatty acids and triglycerides in circulation and tissues (57). The presence of excess lipids in the nutrient microenvironment can modulate macrophage responses to inflammatory stimuli, leading to dysfunction. As an example, we have previously shown that LPS activation of macrophages in the presence of the saturated fatty acids (SFAs), such as palmitate, leads to lysosome damage and cell death (810). The elaboration of proinflammatory cytokines, such as IL-1β, is also increased in this context. In response to excess fatty acids, lysosome damage facilitates activation of the NLRP3 inflammasome, leading to the secretion of IL-1β (11). The combination of premature macrophage cell death and augmented proinflammatory cytokine release likely plays a role in the abnormal inflammatory and reparative responses observed in patients with diabetes.

IL-1–mediated inflammation contributes to pathogenesis of metabolic and cardiovascular disease. Prior studies have shown a strong association between IL-1 levels and the risk of diabetes and atherosclerosis in both preclinical models and humans (1215). In fact, the CANTOS trial recently demonstrated that a neutralizing Ab against IL-1β reduces cardiovascular complications in high-risk patients with coronary artery disease (16). In light of these observations, the inflammasome has emerged as an important target for therapy in cardiometabolic disease. The NLRP3 inflammasome forms as a cytosolic complex that consists of NLRP3, ASC, and caspase 1 (17). There are two signals that are necessary for full activation. Signal 1 is typically generated via a TLR ligation, leading to the activation of NF-κB and the transcriptional upregulation of pro–IL-1β and NLRP3. Signal 2 then stimulates assembly of the inflammasome complex allowing for caspase 1–mediated cleavage of pro–IL-1β. Once cleaved, IL-1β is biologically active and secreted from the cells. Understanding the mechanisms that regulate signal 1 and signal 2 could facilitate the discovery of new approaches to dampen the activity of this potent inflammatory complex.

It has been increasingly recognized that cellular metabolism is an important regulator of macrophage inflammatory and reparative function. PPARγ is a transcription factor that functions as a master regulator of macrophage lipid metabolism and is upregulated in macrophages at sites of inflammation, including atherosclerotic plaques (1820). In addition to its metabolic effects, PPARγ activation also suppresses macrophage cytokine production (2123). The anti-inflammatory action of PPARγ on proinflammatory cytokines is thought to occur via sumoylation of the transcriptional repressor NCOR and/or via the anti-inflammatory nature of oxidative mitochondrial metabolism (21, 24). In models of obesity and atherosclerosis, loss of PPARγ from macrophages results in increased insulin resistance and vascular disease, respectively (21, 2527). These findings support the conclusion that PPARγ in macrophages drives a program that is protective against cardiometabolic disease. Despite these preclinical observations, the use of PPARγ agonists in humans with diabetes has been controversial because of unexpected cardiovascular complications (2830).

Based on our prior studies and the literature, we initially hypothesized that PPARγ would be a potent suppressor of lipotoxic inflammasome activation through effects on both signal 1 and signal 2. We also anticipated that dissecting the mechanism(s) of this response could lead to new insights regarding the interplay between inflammatory signals and lipid metabolism in macrophages. To test this hypothesis, we investigated inflammasome activation in macrophages deficient in PPARγ. Contrary to our expectations, loss of PPARγ from macrophages led to a significant decrease in IL-1β and IL-1α release in response to SFAs and other NLRP3 activators in vitro. This effect occurred at the level of IL-1 mRNA regulation, which resulted in a decrease in pro–IL-1β and pro–IL-1α protein production. RNA sequencing revealed upregulation of type 1 IFN genes in activated PPARγ knockout (KO) macrophages, and treatment with PPARγ agonists repressed the expression of these genes. The suppression of IL-1 expression in PPARγKO macrophages was reversed when type 1 IFN signaling was disrupted. Interestingly, PPARγKO macrophages also displayed a defect in the sterol biosynthesis pathway, which has been linked to enhanced IFN-β production via stimulator of IFN genes (STING). Together, these data support a model in which the loss of PPARγ dampens de novo sterol biosynthesis and augments IFN-β production, which in turn suppresses the transcription of IL-1α and IL-1β.

L-NIL was from Enzo Life Sciences (Farmingdale, NY). T0070907 was from Tocris Bioscience (Minneapolis, MN). Rosiglitazone, actinomycin D, anti-tubulin Ab, anti-actin Ab, and ATP were from Sigma-Aldrich (St. Louis, MO). IL-1β, IL-1α, NLRP3, STAT1, and phospho-STAT1 (no. 14994) Abs were from Cell Signaling Technology (Danvers, MA). IFN-β and the IFN-β ELISA were from PBL Assay Science (Piscataway, NJ). The IL-10R–blocking Ab and PE-conjugated IFN-α/β receptor α chain (IFNAR) Ab were from BioLegend (San Diego, CA). IFNAR-blocking Ab (MAR1-5AE) and control IgG were from Leinco Technologies (St. Louis, MO). The anti-TRIF Ab, IL-10, and DuoSet ELISA kits (IL-1β, IL-1α, TNF-α) were from R&D Systems (Minneapolis, MN). Ultrapure Escherichia coli LPS, cGAMP, poly I:C (PIC), and silica were from InvivoGen (San Diego, CA). Thioglycollate was from BD Difco (Franklin Lakes, NJ). Fatty acids were from Nu-Chek Prep (Waterville, MN). Ultrapure BSA was from Lampire Biological Laboratories (Ottsville, PA) and was tested for TLR ligand contamination prior to use by treating primary macrophages and assaying for TNF-α release.

Peritoneal macrophages (pMACs) were isolated from C57BL/6 or the indicated KO mice 4 d after i.p. injection of 1 ml of 3.85% thioglycollate and plated at a density of 1 × 106 cells/ml in DMEM containing 10% inactivated fetal serum, 50 U/ml penicillin G sodium, and 50 U/ml streptomycin sulfate. Stimulations were performed on the day after harvest. For flow cytometry experiments, peritoneal cells were cultured on low-adherence plates (Greiner Bio-One) to facilitate cell harvest. Cells were removed from low-adherence plates by washing with PBS followed by 10 min with Cell Stripper (Life Technologies) and then 10 min with EDTA/trypsin (Sigma-Aldrich). Growth medium was supplemented with palmitate or stearate complexed to BSA at a 2:1 molar ratio as described previously, and BSA-supplemented medium was used as the control (31). For cell stimulations, PBS or LPS (100 ng/ml) was added to media containing BSA or BSA–free fatty acid complexes. For triggering the NLRP3 inflammasome by nonlipid activators, pMACs were treated with LPS (100 ng/ml) for 16 h, after which they were incubated with ATP (4 mM) for 30 min, silica (150 μg/ml) for 6 h, or alum (150 μg/ml) for 6 h.

Wild type (WT) C57BL/6 mice were bred in our mouse facility; PPARγflox × LysM-Cre and LysM-Cre controls were from Gwen Randolph (Washington University) and bred in our facility; IFNR1flox × LysM-Cre were from Mike Diamond (Washington University). STING knockin (KI)/golden ticket mice were from Jonathon Miner. All lines were in the C57BL/6 background. Mice were maintained in a pathogen-free facility on a standard chow diet ad libitum (6% fat). All animal experiments were conducted in strict accordance with National Institutes of Health guidelines for humane treatment of animals and were reviewed by the Animal Studies Committee of Washington University School of Medicine.

Total cellular RNA was isolated using Qiagen RNeasy columns and reverse transcribed using a high-capacity cDNA reverse transcription kit (Applied Biosystems). Real-time quantitative RT-PCR (qRT-PCR) was performed using SYBR Green reagent (Applied Biosystems) on an ABI 7500 fast thermocycler. Relative gene expression was determined using the delta-delta cycle threshold method normalized to 36B4 expression. Mouse primer sequences were as follows (all 5′-3′): 36B4 (forward, 5′-ATC CCT GAC GCA CCG TGA-3′; reverse, 5′-TGC ATC TGC TTG GAG CCC ACG TT-3′); TNF-α (forward, 5′-CAT CTT CTC AAA ATT CGA GTG ACA A-3′; reverse, 5′-TGG GAG TAG ACA CAA GGT ACA ACC C-3′); IL-1β (forward, 5′-AAG GAG AAC CAA GCA ACG ACA AAA-3′; reverse, 5′-TGG GGA ACT CTG CAG ACT CAA ACT-3′); IL-1α (forward, 5′-TGA GTT TTG GTG TTT CTG GC-3′; reverse, 5′-TCG GGA GAC GAC TCT AA-3′); NLRP3 (forward, 5′-AAA ATG CCT TGG GAG ACT CA-3′; reverse, 5′-AAG TAA GGC CGG AAT TCA CC-3′); CXCL10 (forward, 5′-ATC ATC CCT GCG AGC CTA TCC TG-3′; reverse, 5′-CGG ATT CAG ACA TCT CTG CTC ATC-3′); IFN-β (forward, 5′-GAC GGA GAA GAT GCA GAA GAG TT-3′; reverse, 5′-AGT TCA TCC AGG AGA CGT ACA AC-3′); inducible NO synthase (iNOS) (forward, 5′-ACA TCG ACC CGT CCA CAG TAT-3′; reverse, 5′-CAG AGG GGT AGG CTT GTC TC-3′); MX1 (forward, 5′-CCA GGT CCT GCT CCA CAC-3′; reverse, 5′-TCT GAG GAG AGC CAG ACG AT-3′); ISG15 (forward, 5′-AGC GGA ACA AGT CAC GAA GAC-3′; reverse, 5′-TGG GGC TTT AGG CCA TAC TC-3′); acyl-CoA synthetase 1 (ACSL1) (forward, 5′-ACC ATC AGT GGT ACC CGC TA-3′; reverse, 5′-CGC TCA CCA CCT TCT GGT AT-3′); IL-10 (forward, 5′-TGG CCT TGT AGA CAC CTT GG-3′; reverse, 5′-AGC TGA AGA CCC TCA GGA TG-3′); MVD (forward, 5′-ATG GCC TCA GAA AAG CCT CAG-3′; reverse, 5′-TGG TCG TTT TTA GCT GGT CCT-3′); PMVK (forward, 5′-AAA ATC CGG GAA GGA CTT CGT-3′; reverse, 5′-AGA GCA CAG ATG TTA CCT CCA-3′); MVK (forward, 5′-GGT GTG GTC GGA ACT TCC C-3′; reverse, 5′-CCT TGA GCG GGT TGG AGA C-3′); and FPDS (forward, 5′-GGA GGT CCT AGA GTA CAA TGC C-3′; reverse, 5′-AAG CCT GGA GCA GTT CTA CAC-3′).

The total cellular RNA was also prepared for RNA sequencing with the Clontech SMARTer kit according to the manufacturer’s protocols, ligated with adapters and unique molecular indexes for each sample for every read, and then sequenced on one single-end 50-bp lane on an Illumina HiSeq 3000. RNA-seq reads demultiplexed with Illumina’s bcl2fastq2 were then aligned to the Mus musculus Ensembl release 76 top-level assembly with STAR version 2.0.4b (32). Gene counts were derived from the number of uniquely aligned unambiguous reads by Subread:featureCount version 1.4.5 (33). Sequencing performance was assessed for total number of aligned reads; total number of uniquely aligned reads; genes detected and ribosomal fraction, known junction saturation; and read distribution over known gene models with RSeQC version 2.3 (34).

All gene counts were then imported into the R/Bioconductor package EdgeR and TMM normalization size factors were calculated to adjust for differences in library size across all samples (35, 36). Ribosomal features as well as any feature not expressed in at least two samples above 1 count per million were excluded from further analysis, and TMM size factors were recalculated to create effective TMM size factors. The effective TMM size factors and the matrix of counts were then imported into the R/Bioconductor package limma, and weighted likelihoods based on the observed mean-variance relationship of every gene and sample were then calculated for all samples with the voomWithQualityWeights function (37, 38). Performance of the samples was assessed with a Pearson correlation matrix (Supplemental Fig. 1A) and multidimensional scaling plots (Supplemental Fig. 1B). Generalized linear models were then created to test for differentially expressed genes. The results were then filtered for false discovery rate–adjusted p values ≤ 0.05.

The biological interpretation of the large set of features found in the limma results were then elucidated for global transcriptomic changes in known Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) terms with the R/Bioconductor packages GAGE and Pathview (39, 40). Briefly, GAGE measures for perturbations in GO or KEGG terms based on changes in observed log2 fold changes for the genes within that term versus the background log2 fold changes observed across features not contained in the respective term as reported by limma. For GO terms with a statistical significance of p ≤ 0.05, heatmaps were automatically generated for each respective term to show how genes covary or coexpress across the term in relation to a given biological process or molecular function. In the case of KEGG-curated signaling and metabolism pathways, Pathview was used to generate annotated pathway maps of any perturbed pathway with an unadjusted statistical significance of p ≤ 0.05. The RNA sequencing data were deposited into the Gene Expression Omnibus database with the accession number GSE117115 (https://www.ncbi.nlm.nih.gov/geo/).

WT or myeloid-specific PPARγKO (mPPARγKO) mice were treated with thioglycollate as described above. On day 4, the mice were injected with 10 μg of LPS in a 200-μl volume. After 16 h, the mice were sacrificed, and the peritoneal cavity was flushed with 5 ml of regular DMEM plus serum. The concentration of IL-1β, IL-1α, and TNF-α in the peritoneal fluid was determined via ELISA. At the time, a subset of mice was harvested just prior to LPS injection to evaluate cell recruitment to the peritoneum. Both total cell number and macrophage composition by F480 and CD11b expression were similar between the two genotypes.

Total cellular protein was isolated by lysing cells in 150 mM NaCl, 10 mM Tris (pH 8), triton X-100 1%, 1× Protease Complete, and phosphatase inhibitors (Thermo-Fisher Scientific). Subsequently, 25 μg of protein from each sample was separated on a TGX gradient gel (4–20%; Bio-Rad) and transferred to a nitrocellulose membrane. For blots of pro–IL-1β, pro–IL-1α, phospho- and total STAT1 protein transfer occurred for 1 h on ice.

Supernatants were harvested from macrophage cultures after the indicated stimulations. IL-1β, IL-1α, and TNF-α were quantified using a DuoSet ELISA kit (R&D Systems) according to the manufacturer’s instructions. IFN-β was quantified using an ELISA kit from (PBL Assay Science).

pMACs were removed from a low-adherence plate, and 1 × 106 cells were pelleted in FACS buffer (PBS, 1% BSA) and incubated with Fc block for 5 min on ice, followed by incubation with IFNR1-PE (1:200) for 30 min on ice in the dark. Samples were analyzed on a FACScalibur flow cytometer (BD Biosciences).

Cells were plated into 96-well Seahorse plates at a density of 75,000 cells per well and stimulated as indicated in the text. After stimulation, the cells were washed and placed in XF media (nonbuffered RPMI 1640 containing 25 mM glucose, 2 mM l-glutamine, and 1 mM sodium pyruvate) with 10% FCS. Oxygen consumption rates and extracellular acidification rates were measured under basal conditions and following the addition the following drugs: 1.5 μM flurorcarbonyl cynade phenylhydrazon (FCCP) and 100 nM rotenone + 1 μM antimycin A (all Sigma-Aldrich). Measurements were taken using a 96-well Extracellular Flux Analyzer (Seahorse Bioscience; North Billerica, MA).

Two million pMACs per well were plated in six-well plates. After stimulation, the cells were washed three times with PBS and then were snap frozen and scraped in liquid nitrogen. Intracellular metabolites were quantified by liquid chromotography–tandem mass spectrometry at Sanford Burnham Metabolomics Core, Medical Discovery Institute (Lake Nona, FL).

Macrophages (5 × 105 cells) were grown in 24-well tissue culture plates. After stimulation with BSA-PBS or palm-LPS for 8 h, the cells were washed three times with PBS, and lysates were prepared by scraping cells in 100 μl of ice-cold PBS followed by homogenization by 10 passes through a 26-gauge syringe. The resulting homogenate (50 μl) was mixed 1:1 with methanol for protein precipitation. Oxysterols and free cholesterol listed above were extracted from 50 μl of each macrophage sample with 200 μl of methanol, containing 2 ng of deuterated 24–hydroxycholesterol (HC)–d7, 25-HC-d6, and 27-HC-d7 and 2 μg of cholesterol-d7 as internal standards. All oxysterols and cholesterol as well as their deuterated standards were derivatized with N,N-dimethylglycine (DMG) to increase the mass spectroscopy sensitivity.

The sample analysis was performed with a Shimadzu 20AD HPLC system coupled to a tandem mass spectrometer (API-6500+Qtrap; Applied Biosystems) operated in multiple reaction monitoring mode. The positive ion electrospray ionization mode was used for detection of these analytes. The DMG derivatized samples were injected in duplicate for data averaging. Data processing was conducted with Analyst 1.6.3 (Applied Biosystems).

Statistical analysis was performed using GraphPad Prism software. All results are expressed as means ± SE. Groups were compared by paired Student t test or two-way ANOVA as appropriate. A value of p ≤ 0.05 was considered significant.

We have previously shown that NLRP3 inflammasome activation and IL-1β release are enhanced in lipid-loaded macrophages activated with the TLR4 agonist LPS (10, 11). In macrophages, PPARγ is robustly expressed and is known to be an important regulator of metabolism and inflammatory function. Therefore, we used PPARγ-deficient macrophages as a tool to explore the links between lipid metabolism and the lipotoxic inflammasome. For these experiments, PPARγ-floxed mice were crossed to LysM-Cre animals to generate mPPARγKO (18). As expected, these macrophages had reduced expression of PPARγ gene targets (Supplemental Fig. 2).

To simulate the biology of macrophages encountering an inflammatory stimulus in a high-lipid environment, we incubated macrophages with the SFAs palmitate or stearate in combination with the TLR4 ligand LPS. Using this system, mPPARγKO cells released significantly less IL-1β compared with WT cells. IL-1α is commonly coreleased in response to NLRP3 activators, and its release was also decreased in the KO cells. In contrast, TNF-α release was not diminished in these macrophages, indicating this is not a global inflammatory defect or a derangement of TLR4 signaling (Fig. 1A–C). To determine whether the effects of PPARγ deficiency were specific to lipids, we also assessed nonlipid activators of the NLRP3 inflammasome, including ATP, silica, and aluminum. Surprisingly, mPPARγKO macrophages also released less IL-1β and IL-1α in response to these activators, and, again, TNF-α release was not diminished (Fig. 1D–F). To further confirm that PPARγ was required for maximal IL-1 release, we treated WT macrophages with the PPARγ antagonist T0070907 for 24 h, and then similar stimulations were performed. As shown in Fig. 1, this compound phenocopied the genetic loss of PPARγ.

FIGURE 1.

PPARγ loss of function is associated with impaired IL-1 cytokine release in primary macrophages. (AC) pMACs isolated from WT (open bars) or mPPARγKO mice (filled bars) were treated with control (BSA-LPS), palm (250 μM)–LPS (100 ng), or stearate (150 μM)–LPS for 20 h, and the release levels of (A) IL-1β, (B) IL-1α, and (C) TNF-α were determined by ELISA. (DF) pMACs isolated from WT (open bars) or mPPARγKO mice (filled bars) were treated with LPS and ATP, alum, or silica as described in the 2Materials and Methods, and (D) IL-1β, (E) IL-1α, and (F) TNF-α were determined by ELISA. WT pMACs were treated with veh (open bars) or T0070907 (T007; gray bars) for 24 h, after which they received the indicated stimuli. The release of (G) IL-1β, (H) IL-1α, and (I) TNF-α were determined by ELISA. Bar graphs report the mean ± SE for a minimum of three experiments, each performed in triplicate. *p < 0.05 for WT versus mPPARγKO or veh versus T007.

FIGURE 1.

PPARγ loss of function is associated with impaired IL-1 cytokine release in primary macrophages. (AC) pMACs isolated from WT (open bars) or mPPARγKO mice (filled bars) were treated with control (BSA-LPS), palm (250 μM)–LPS (100 ng), or stearate (150 μM)–LPS for 20 h, and the release levels of (A) IL-1β, (B) IL-1α, and (C) TNF-α were determined by ELISA. (DF) pMACs isolated from WT (open bars) or mPPARγKO mice (filled bars) were treated with LPS and ATP, alum, or silica as described in the 2Materials and Methods, and (D) IL-1β, (E) IL-1α, and (F) TNF-α were determined by ELISA. WT pMACs were treated with veh (open bars) or T0070907 (T007; gray bars) for 24 h, after which they received the indicated stimuli. The release of (G) IL-1β, (H) IL-1α, and (I) TNF-α were determined by ELISA. Bar graphs report the mean ± SE for a minimum of three experiments, each performed in triplicate. *p < 0.05 for WT versus mPPARγKO or veh versus T007.

Close modal

The fact that mPPARγKO cells produced less IL-1β and IL-1α in response to a variety of NLRP3 inflammasome activators argued that PPARγ deficiency was impacting a common part of the pathway, such as production of the precursor cytokines and/or NLRP3 itself (signal 1). To investigate the impact of PPARγ on signal 1 in primary macrophages, we activated pMACs from both genotypes and assessed expression of pro–IL-1β and pro–IL-1α protein. As seen in Fig. 2A, the level of both IL-1 family members was reduced at the protein level in mPPARγKO macrophages. Consistent with a transcriptional mechanism, IL-1β and IL-1α mRNA levels were also decreased in macrophages lacking PPARγ, whereas the level of NLRP3 mRNA was unaffected (Fig. 2B). Kinetic analysis revealed a similar reduction in IL-1β mRNA abundance at all timepoints after palm-LPS treatment. In comparison, TNF-α mRNA levels were similar between the genotypes over the same timeframe (Fig. 2C, 2D). To assess whether this occurred via changes in mRNA stability, WT and mPPARγKO cells were stimulated with LPS for 4 h to induce IL-1β transcription and then actinomycin D was added to suppression ongoing transcription. The decay of the IL-1β transcript was similar between WT and KO cells (Fig. 2E). Thus, macrophages lacking PPARγ produce less IL-1β and IL-1α but have preserved induction of other classical NF-κB regulated cytokines including TNF-α and NLRP3.

FIGURE 2.

PPARγ deficiency leads to decreased levels of IL-1 mRNA and protein levels. (A) WT or mPPARγKO macrophages were stimulated with BSA-PBS veh or palm-LPS for 16 h, and the protein level of pro–IL-1β and pro–IL-1α was assessed by Western blotting. Tubulin (tub) is shown as a loading control. (B) pMACs isolated from WT (open bars) or mPPARγKO mice (filled bars) were treated with veh or palm-LPS for 8 h and mRNA expression of IL-1β, IL-1α, and NLRP3 was assessed by qRT-PCR. (C and D) Kinetic assessment of IL-1β (C) and TNF-α (D) mRNA levels following palm-LPS stimulation in WT and mPPARγKO cells. (E) WT or mPPARγKO cells were treated with palm-LPS for 4 h after actinomycin D was added to the culture. The levels of IL-1β mRNA relative to baseline were performed by qRT-PCR. Each genotype was compared with its own baseline, which was arbitrarily defined as 1. Bar graphs report the mean ± SE for a minimum of three experiments, each performed in triplicate. *p < 0.05 for WT versus mPPARγKO.

FIGURE 2.

PPARγ deficiency leads to decreased levels of IL-1 mRNA and protein levels. (A) WT or mPPARγKO macrophages were stimulated with BSA-PBS veh or palm-LPS for 16 h, and the protein level of pro–IL-1β and pro–IL-1α was assessed by Western blotting. Tubulin (tub) is shown as a loading control. (B) pMACs isolated from WT (open bars) or mPPARγKO mice (filled bars) were treated with veh or palm-LPS for 8 h and mRNA expression of IL-1β, IL-1α, and NLRP3 was assessed by qRT-PCR. (C and D) Kinetic assessment of IL-1β (C) and TNF-α (D) mRNA levels following palm-LPS stimulation in WT and mPPARγKO cells. (E) WT or mPPARγKO cells were treated with palm-LPS for 4 h after actinomycin D was added to the culture. The levels of IL-1β mRNA relative to baseline were performed by qRT-PCR. Each genotype was compared with its own baseline, which was arbitrarily defined as 1. Bar graphs report the mean ± SE for a minimum of three experiments, each performed in triplicate. *p < 0.05 for WT versus mPPARγKO.

Close modal

To determine whether a similar phenotype could be observed in PPARγ-deficient macrophages in vivo, we elicited macrophages to the peritoneum using thioglycollate, and at day 4, when ∼85% of the cells are mature macrophages, we injected 10 μg of LPS i.p. After 16 h, the peritoneum was lavaged with media, and the concentrations of IL-β, IL-1α, and TNF-α were determined. Consistent with our ex vivo data, lavage fluid from mPPARγKO mice had reduced concentrations of IL-1β and IL-1α compared with WT mice, whereas TNF-α levels were similar (Fig. 3A–C). The number of inflammatory cells in the peritoneum was not different between the two genotypes (Fig. 3D).

FIGURE 3.

mPPARγKO macrophages produce less IL-1 cytokines in vivo. (AC) WT or mPPARγKO mice were injected with thioglycollate to induce macrophage recruitment to the peritoneal cavity. At day 4, when ∼90% of the cells in the peritoneum are macrophages, the mice were given an i.p. injection of LPS (10 μg/200 μl), and 16 h later, IL-1β (A), IL-1α (B), and TNF-α (C) levels in the peritoneal fluid were quantified by ELISA. (D) Day 4 peritoneal cell count for WT versus mPPARγKO mice following thioglycollate injection. Bar graphs report the mean ± SE, and the individual dots each represent one mouse. The p values for the comparison of WT versus mPPARγKO mice are shown.

FIGURE 3.

mPPARγKO macrophages produce less IL-1 cytokines in vivo. (AC) WT or mPPARγKO mice were injected with thioglycollate to induce macrophage recruitment to the peritoneal cavity. At day 4, when ∼90% of the cells in the peritoneum are macrophages, the mice were given an i.p. injection of LPS (10 μg/200 μl), and 16 h later, IL-1β (A), IL-1α (B), and TNF-α (C) levels in the peritoneal fluid were quantified by ELISA. (D) Day 4 peritoneal cell count for WT versus mPPARγKO mice following thioglycollate injection. Bar graphs report the mean ± SE, and the individual dots each represent one mouse. The p values for the comparison of WT versus mPPARγKO mice are shown.

Close modal

IL-1β and IL-1α signal via the same receptor and have similar biologic effects. As both family members are relevant to cardiometabolic disease and host defense, we sought to understand the mechanism linking PPARγ to IL-1 suppression. Recently, it has become clear that cellular metabolism can modulate inflammatory cytokine production in macrophages (41, 42). One relevant example is the small molecule succinate, which is an intermediate of the Krebs cycle. Succinate is known to accumulate in LPS-activated macrophages, and it has been linked to increased IL-1β transcription via HIF-1α, although the mechanism is not completely understood (43). Succinate levels increase after LPS in part because of the inhibition of succinate dehydrogenase by itaconate, a product of the enzyme Irg1, which is induced by TLR4 signaling (Supplemental Fig. 3A) (44). However, Irg1 mRNA expression was similar between the genotypes after activation, and the intracellular levels of succinate were slightly increased, rather than decreased, in mPPARγKO cells (Supplemental Fig. 3B, 3C). Inhibition of glycolytic flux has also been associated with reduced IL-1β production (45). However, assessment of lactate/pyruvate ratio by metabolomics or direct measurements of extracellular acidification rate, a surrogate of anaerobic glycolysis, via Seahorse metabolic flux analyzer measurements revealed only a slight reduction of aerobic glycolysis in activated mPPARγKO cells (Supplemental Fig. 3D, 3E). Together, these data argue against a critical role for these described metabolic pathways in the phenotype of PPARγKO macrophages.

PPARγ is known to negatively regulate expression of iNOS in response to LPS in part through a sumoylation-dependent mechanism (24). The production of NO has been shown to suppress inflammasome activation, leading us to investigate this pathway (46). In agreement with published studies, we observed enhanced expression of iNOS transcript in mPPARγKO macrophages compared with WT cells after palm-LPS stimulation (Fig. 4A). NO production was also slightly increased in PPARγKO cells compared with WT in response palm-LPS (Fig. 4B). To determine the biologic impact of the NO in this system, a selective inhibitor of iNOS, L-NIL, was added to the cells at the start of the experiment. As a control, we demonstrated that L-NIL completely blocked NO production induced by 500 U of IFN-β in combination with palm-LPS (Fig. 4B). However, L-NIL only slightly impacted IL-1β release in WT cells and did not restore IL-1 release to WT levels in PPARγKO cells (Fig. 4C).

FIGURE 4.

The suppression of IL-1 release from PPARγKO macrophages is independent of NO and IL-10. (A) iNOS mRNA levels in WT versus PPARγKO pMACs 8 h after the indicated stimulation. (B) WT or mPPARγKO pMACS were treated with veh or palm-LPS, and nitrite levels were quantified via colorimetric assay. At the same time, WT macrophages were treated with palm-LPS together with IFN-β (500 U) in the presence or absence of the iNOS inhibitor L-NIL (10 μM). (C) pMACs from WT or KO mice were treated with palm-LPS in the presence of veh or L-NIL, and IL-1β release was quantified by ELISA. (D) WT macrophages were treated with palm-LPS for 20 h in the presence of veh (open bars) or rIL-10 (20 ng/ml; gray bars) ± anti–IL-10R–blocking Ab (IL-10Rab), and IL-1β release was measured by ELISA. (E) pMACs from WT or mPPARγKO mice were treated with palm-LPS for 20 h in the presence of control Ab or IL-10Rab, and IL-1β release was quantified by ELISA. Bar graphs report the mean ± SE for a minimum of three experiments, each performed in triplicate. *p < 0.05 for WT versus mPPARγKO, #p < 0.05 for veh versus L-NIL or IL-10Rab. ND, not detected.

FIGURE 4.

The suppression of IL-1 release from PPARγKO macrophages is independent of NO and IL-10. (A) iNOS mRNA levels in WT versus PPARγKO pMACs 8 h after the indicated stimulation. (B) WT or mPPARγKO pMACS were treated with veh or palm-LPS, and nitrite levels were quantified via colorimetric assay. At the same time, WT macrophages were treated with palm-LPS together with IFN-β (500 U) in the presence or absence of the iNOS inhibitor L-NIL (10 μM). (C) pMACs from WT or KO mice were treated with palm-LPS in the presence of veh or L-NIL, and IL-1β release was quantified by ELISA. (D) WT macrophages were treated with palm-LPS for 20 h in the presence of veh (open bars) or rIL-10 (20 ng/ml; gray bars) ± anti–IL-10R–blocking Ab (IL-10Rab), and IL-1β release was measured by ELISA. (E) pMACs from WT or mPPARγKO mice were treated with palm-LPS for 20 h in the presence of control Ab or IL-10Rab, and IL-1β release was quantified by ELISA. Bar graphs report the mean ± SE for a minimum of three experiments, each performed in triplicate. *p < 0.05 for WT versus mPPARγKO, #p < 0.05 for veh versus L-NIL or IL-10Rab. ND, not detected.

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The anti-inflammatory cytokine IL-10 has also been linked to the suppression of IL-1 production, and we noted that IL-10 mRNA levels were increased slightly in the mPPARγKO cells (47). In our system, the addition of rIL-10 was able to decrease IL-1β release by ∼50% in activated WT cells, and this effect was prevented by an IL-10R–blocking Ab (Fig. 4D). In contrast, inhibition of IL-10 signaling in mPPARγKO cells did not significantly alter IL-1β release (Fig. 4E). Taken together, these data suggest that the inflammatory phenotype of PPARγKO macrophages occurs independently of changes in NO or IL-10.

PPARγ is a transcription factor that can both activate or repress gene expression based on its binding partners. Prior data have established that PPARγ does not bind to the promoter of IL-1β or directly influence its transcription in macrophages (48). Therefore, to understand how PPARγ deficiency might alter IL-1β mRNA, we performed RNA sequencing on WT and PPARγKO cells at baseline or following activation for 8 h. We compared differences in gene expression between WT and PPARγKO macrophages stimulated with palm-LPS using a cutoff of a 2.0-fold change in expression. GO pathway analysis of the top 20 upregulated pathways in activated PPARγKO compared with WT macrophages revealed an enrichment of pathways involved in antiviral defense and IFN-β. Consistent with this, several pathways related to the release of and response to IFN-β were enhanced in PPARγ-deficient cells (Fig. 5B). As illustrated in Fig. 5B, heatmap analysis of gene expression in the response to IFN-β module revealed a modest but consistent increase in the expression of IFN target genes, including IFN-β itself, in PPARγKO macrophages. These results suggested that loss of PPARγ may engage the type 1 IFN pathway in macrophages.

FIGURE 5.

RNA sequencing reveals type 1 IFN gene signature in activated PPARγKO macrophages. (A and B) WT or PPARγKO pMACs were treated with BSA-PBS or palm-LPS for 8 h, after which RNA was isolated and RNA sequencing was performed. (A) Group summary for pathways related to IFN-β in PPARγ versus WT pMACS treated with LPS. (B) Heatmap expression profile of genes from the cellular response to IFN-β GO biologic processes module. WT and PPARγKO cells are shown under basal conditions (BSA-PBS) and after activation (palm-LPS). The color map for fold change is shown.

FIGURE 5.

RNA sequencing reveals type 1 IFN gene signature in activated PPARγKO macrophages. (A and B) WT or PPARγKO pMACs were treated with BSA-PBS or palm-LPS for 8 h, after which RNA was isolated and RNA sequencing was performed. (A) Group summary for pathways related to IFN-β in PPARγ versus WT pMACS treated with LPS. (B) Heatmap expression profile of genes from the cellular response to IFN-β GO biologic processes module. WT and PPARγKO cells are shown under basal conditions (BSA-PBS) and after activation (palm-LPS). The color map for fold change is shown.

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To validate these findings, we performed quantitative PCR for several type 1 IFN gene targets. In line with the RNA sequencing data, mRNA levels of MX1, CXCL10, ISG15, and CCL2 were increased in PPARγKO macrophages after palm-LPS activation. In contrast, ACSL1 expression, which is induced by LPS via a TRIF-dependent, IFN-independent mechanism, was similar between the genotypes (Fig. 6A) (49). IFNAR signaling leads to phosphorylation of the transcription factor STAT1, which drives IFN-dependent gene expression. STAT1 phosphorylation was also augmented in mPPARγKO macrophages compared with WT cells (Fig. 6B). The expression of IFNAR was similar between WT and mPPARγKO cells (Fig. 6C, 6D). However, mRNA levels and secretion of IFN-β were increased in the KO macrophages (Fig. 6E, 6F). The expression of TRIF was similar between the genotypes (Fig. 6G). Thus, PPARγKO macrophages produce increased IFN-β in response to activation, which results in augmentation activation of the downstream signaling pathways.

FIGURE 6.

Augmented expression and release of IFN-β in PPARγ-deficient macrophages in response to palm-LPS. (A) pMACs isolated from WT (open bars) or mPPARγKO mice (filled bars) were treated with veh or palm-LPS for 8 h, and mRNA expression of the indicated targets was determined by qRT-PCR. (B) Macrophages were treated with veh or palm-LPS for 4 or 8 h, after which cell lysates were isolated and phospho-STAT1 (P-STAT1) (Y701) was assessed by Western blotting. Total (tot) STAT1 and tubulin are shown as controls. (C and D) pMACs from WT or PPARγKO mice were stained with an Ab; the IFNAR and surface expression was assessed by flow cytometry. A representative histogram (C) and grouped mean fluorescence intensity data (D) are shown. (E) mRNA levels of IFN-β were quantified in WT and mPPARγKO cells at 1 h after palm-LPS treatment via qRT-PCR. (F) IFN-β release from WT and KO pMACs 6 h after palm-LPS stimulation. (G) Protein was isolated from WT or PPARγKO macrophages, and TRIF protein expression was assessed by Western blotting. Bar graphs report the mean ± SE for a minimum of three experiments, each performed in triplicate. *p < 0.05 for WT versus mPPARγKO. ns, nonsignificant.

FIGURE 6.

Augmented expression and release of IFN-β in PPARγ-deficient macrophages in response to palm-LPS. (A) pMACs isolated from WT (open bars) or mPPARγKO mice (filled bars) were treated with veh or palm-LPS for 8 h, and mRNA expression of the indicated targets was determined by qRT-PCR. (B) Macrophages were treated with veh or palm-LPS for 4 or 8 h, after which cell lysates were isolated and phospho-STAT1 (P-STAT1) (Y701) was assessed by Western blotting. Total (tot) STAT1 and tubulin are shown as controls. (C and D) pMACs from WT or PPARγKO mice were stained with an Ab; the IFNAR and surface expression was assessed by flow cytometry. A representative histogram (C) and grouped mean fluorescence intensity data (D) are shown. (E) mRNA levels of IFN-β were quantified in WT and mPPARγKO cells at 1 h after palm-LPS treatment via qRT-PCR. (F) IFN-β release from WT and KO pMACs 6 h after palm-LPS stimulation. (G) Protein was isolated from WT or PPARγKO macrophages, and TRIF protein expression was assessed by Western blotting. Bar graphs report the mean ± SE for a minimum of three experiments, each performed in triplicate. *p < 0.05 for WT versus mPPARγKO. ns, nonsignificant.

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Type 1 IFN has been reported to inhibit or activate the NLRP3 inflammasome, depending on the context (50). To determine whether increased IFN-β was responsible for the IL-1 phenotype in mPPARγKO macrophages, we used pharmacologic and genetic approaches. In WT cells, inhibition of type 1 IFN signaling with an IFNAR-blocking Ab led to a modest increase in IL-1β release after palm-LPS stimulation. In contrast, IFNAR blockade completely reversed the phenotype of PPARγKO cells (Fig. 7A). Consistent with these results, the suppression of IL-1β release by the PPARγ antagonist was blunted in IFNARKO macrophages (Fig. 7B). Western blot and mRNA analysis confirmed a similar phenotype at the level of pro–IL-1β expression (Fig. 7C, 7D). To evaluate whether PPARγKO macrophages may also have enhanced sensitivity to type 1 IFN, we stimulated WT and KO cells with rIFN-β and assessed STAT1 phosphorylation and CXCL10 induction. There were no differences between WT and PPARγ-deficient macrophages with either of these assays, further arguing that increased IFN-β release, not enhanced IFNR1 signaling, accounts for the observed phenotype (Fig. 7E, 7F). Thus, increased IFN-β release accounts for the diminished IL-1 production in PPARγKO cells.

FIGURE 7.

Suppression of IL-1 expression occurs via a type 1 IFN–dependent mechanism. (A) WT or PPARγKO pMACs were treated with palm-LPS for 20 h in the presence of an IFNAR-blocking Ab or control Ig, and IL-1β release was quantified by ELISA. (B) WT or IFNAR KO pMACs were treated with veh or the PPARγ antagonist T0070907 24 h prior to stimulation with palm-LPS, and IL-1β release was quantified by ELISA. (C and D) WT or PPARγKO pMACs were treated with palm-LPS for 8 h (mRNA) or 16 h (protein) in the presence of an IFNAR-blocking Ab or control Ig, and IL-1β expression was assessed via qRT-PCR (C) and Western blotting (D). (E) WT or KO pMACs were stimulated with palm-LPS or IFN-β (500 U) for 4 h, and phospho-STAT1 (P-STAT1) was assessed by Western blotting. (F) pMACs from WT or mPPARγKO mice were treated with IFN-β, and mRNA expression of the IFN gene target CXCL10 was assessed via qRT-PCR. Bar graphs report the mean ± SE for a minimum of three experiments, each performed in triplicate. *p < 0.05 for WT versus mPPARγKO. ns, nonsignificant.

FIGURE 7.

Suppression of IL-1 expression occurs via a type 1 IFN–dependent mechanism. (A) WT or PPARγKO pMACs were treated with palm-LPS for 20 h in the presence of an IFNAR-blocking Ab or control Ig, and IL-1β release was quantified by ELISA. (B) WT or IFNAR KO pMACs were treated with veh or the PPARγ antagonist T0070907 24 h prior to stimulation with palm-LPS, and IL-1β release was quantified by ELISA. (C and D) WT or PPARγKO pMACs were treated with palm-LPS for 8 h (mRNA) or 16 h (protein) in the presence of an IFNAR-blocking Ab or control Ig, and IL-1β expression was assessed via qRT-PCR (C) and Western blotting (D). (E) WT or KO pMACs were stimulated with palm-LPS or IFN-β (500 U) for 4 h, and phospho-STAT1 (P-STAT1) was assessed by Western blotting. (F) pMACs from WT or mPPARγKO mice were treated with IFN-β, and mRNA expression of the IFN gene target CXCL10 was assessed via qRT-PCR. Bar graphs report the mean ± SE for a minimum of three experiments, each performed in triplicate. *p < 0.05 for WT versus mPPARγKO. ns, nonsignificant.

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Although IFN-β release was increased in mPPARγKO cells, the absolute amount of type 1 IFN produced was modest. To investigate the impact of low-dose rIFN-β on the expression of IL-1, we treated WT macrophages with rIFN-β in conjunction with palm-LPS and monitored cytokine production. Doses of rIFN-β as low as 25 U significantly suppressed IL-1β release, while having no effect on TNF-α (Fig. 8A, 8B). Similar to what was observed in PPARγKO cells, both mRNA and protein levels of pro–IL-1β were reduced with rIFN-β treatment (Fig. 8C, 8D). Moreover, the suppression by rIFN-β was prevented with a blocking Ab to IFNAR (Fig. 8E). Conversely, pMACs from mice lacking IFNAR had elevated levels of IL-1β mRNA and protein in response to palm-LPS stimulation (Fig. 8F, 8G). In sum, these data demonstrate that low concentrations of type 1 IFN can potently suppress IL-1 cytokine production in a manner that recapitulates the findings obtained with PPARγ-deficient macrophages.

FIGURE 8.

Low-dose rIFN-β phenocopies PPARγ loss of function in primary macrophages. (A and B) WT pMACs were treated with palm-LPS and increasing concentrations of IFN-β, after which IL-1β (A) and TNF-α (B) release was quantified by ELISA. (C) Macrophages were treated with veh or palm-LPS for 16 h in the presence of IFN-β (50 U), and pro–IL-1β and NLRP3 protein levels were assessed by Western blotting. Tubulin is shown as a loading control. (D) pMACS were treated as indicated, and gene expression of IL-1β and CXCL10 was assessed 8 h after stimulation via qRT-PCR. (E) pMACS were stimulated with palm-LPS ± IFN-β in the presence of IFNAR-blocking Ab or control Ig and IL-1β release was determined by ELISA. (F and G) WT (open bar) or IFNAR KO (gray bar) macrophages were stimulated with palm-LPS and IL-1β mRNA (F), or protein (G) levels were assessed at 8 and 16 h, respectively. Bar graphs report the mean ± SE for a minimum of three experiments, each performed in triplicate. *p < 0.05 for veh versus IFN-β or WT versus IFNAR KO. ns, nonsignificant.

FIGURE 8.

Low-dose rIFN-β phenocopies PPARγ loss of function in primary macrophages. (A and B) WT pMACs were treated with palm-LPS and increasing concentrations of IFN-β, after which IL-1β (A) and TNF-α (B) release was quantified by ELISA. (C) Macrophages were treated with veh or palm-LPS for 16 h in the presence of IFN-β (50 U), and pro–IL-1β and NLRP3 protein levels were assessed by Western blotting. Tubulin is shown as a loading control. (D) pMACS were treated as indicated, and gene expression of IL-1β and CXCL10 was assessed 8 h after stimulation via qRT-PCR. (E) pMACS were stimulated with palm-LPS ± IFN-β in the presence of IFNAR-blocking Ab or control Ig and IL-1β release was determined by ELISA. (F and G) WT (open bar) or IFNAR KO (gray bar) macrophages were stimulated with palm-LPS and IL-1β mRNA (F), or protein (G) levels were assessed at 8 and 16 h, respectively. Bar graphs report the mean ± SE for a minimum of three experiments, each performed in triplicate. *p < 0.05 for veh versus IFN-β or WT versus IFNAR KO. ns, nonsignificant.

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PPARγ can be activated by agonists such as rosiglitazone and pioglitazone. To determine whether increasing PPARγ activity could suppress IFN-β production, we investigated the impact of rosiglitazone on the expression of IFN-β mRNA and type 1 IFN gene targets. Primary macrophages were incubated with 1 μM rosiglitazone for 16 h prior to the addition of vehicle (veh) or palm-LPS. As shown previously, PPARγKO cells had elevated levels of IFN-β mRNA early after stimulation with palm-LPS. Rosiglitazone suppressed IFN-β by ∼50% in WT cells, whereas it had no effect on the PPARγKO macrophages (Fig. 9A). A similar pattern was observed for other IFN-regulated genes including iNOS, CXCL10, and MX1 (Fig. 9B–D). In contrast, TNF-α expression was not affected by either rosiglitazone or loss of PPARγ. Interestingly, IL-1β mRNA levels were substantially decreased in PPARγKO macrophages, but rosiglitazone did not increase or decrease expression of this cytokine (Fig. 9E, 9F). When macrophages were treated with a higher concentration of rosiglitazone (10 μM), the expression of multiple IFN gene targets in both WT and PPARγKO macrophages was reduced, demonstrating that at higher doses, PPARγ-independent effects occur (data not shown) (51).

FIGURE 9.

PPARγ activation suppresses IFN-β. (AF) WT or PPARγKO macrophages were treated with BSA-PBS/veh (open bars), palm-LPS/veh (filled bars), or palm-LPS/rosiglitazone (1 μM) (gray bars) for 16 h followed by stimulation with palm-LPS for 8 h in continued presence of the PPARγ agonist. mRNA was isolated from the macrophages, and the expression of IFN-β (A) and several of its gene targets (B–D) was assessed by qRT-PCR. In addition, mRNA expression of the proinflammatory cytokines IL-1β (E) and TNF-α (F) was also determined. (G and H) WT or KO pMACs were treated with rosiglitazone or veh as described above, followed by palm-LPS for 16 h. The release of IFN-β (G) and IL-1β (H) was quantified by ELISA. Bar graphs report the mean ± SE for a minimum of three experiments, each performed in triplicate. *p < 0.05 for veh versus rosiglitazone. ns, nonsignificant.

FIGURE 9.

PPARγ activation suppresses IFN-β. (AF) WT or PPARγKO macrophages were treated with BSA-PBS/veh (open bars), palm-LPS/veh (filled bars), or palm-LPS/rosiglitazone (1 μM) (gray bars) for 16 h followed by stimulation with palm-LPS for 8 h in continued presence of the PPARγ agonist. mRNA was isolated from the macrophages, and the expression of IFN-β (A) and several of its gene targets (B–D) was assessed by qRT-PCR. In addition, mRNA expression of the proinflammatory cytokines IL-1β (E) and TNF-α (F) was also determined. (G and H) WT or KO pMACs were treated with rosiglitazone or veh as described above, followed by palm-LPS for 16 h. The release of IFN-β (G) and IL-1β (H) was quantified by ELISA. Bar graphs report the mean ± SE for a minimum of three experiments, each performed in triplicate. *p < 0.05 for veh versus rosiglitazone. ns, nonsignificant.

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In an effort to understand the mechanism by which PPARγ influences IFN-β production, we returned to our RNA sequencing analysis. Of particular interest, PPARγKO cells had a marked decrease in the expression of several genes involved in the sterol biosynthesis pathway, which has been linked to enhanced IFN production (Fig. 10A, 10B) (52, 53). To confirm these findings, we performed qRT-PCR to assess transcript levels for several enzymes involved in sterol biosynthesis and found that multiple genes in this pathway were significantly downregulated in PPARγKO macrophages (Fig. 10C). Moreover, rosiglitazone increased the expression of these genes in WT macrophages, whereas it failed to do so in KO cells (Fig. 10D).

FIGURE 10.

Expression of genes involved in sterol biosynthesis are suppressed in PPARγ-deficient cells. (A) Heatmap display of gene expression from sterol biosynthesis pathway obtained from RNA sequencing data macrophages treated with BSA-PBS or palm-LPS for 8 h. The brackets indicate genes with particularly low expression in activated PPARγKO cells (blue). (B) Schematic of the metabolic pathway and enzymes (red text) involved in sterol biosynthesis. (C) mRNA expression of the indicated sterol biosynthesis genes was determined via qRT-PCR in WT and PPARγKO cells treated with control or palm-LPS for 8 h. (D) pMACs from WT or PPARγ-deficient macrophages were treated with 1 μM rosiglitazone (rosi) or veh for 16 h, after which they were treated with palm-LPS and the expression of sterol biosynthesis genes was assessed by qRT-PCR. (E) pMACs from WT or PPARγ-deficient macrophages were treated with BSA-PBS or palm-LPS for 8 h, after which cells were lysed, and free cholesterol, 25-HC, and 27-HC were measured by gas chromatography–tandem mass spectrometry. Data are presented as normalized ratio an internal standard. Bar graphs report the mean ± SE for a minimum of three experiments, each performed in triplicate. *p < 0.05 for WT versus mPPARγKO, #p < 0.05 for veh versus rosi. ns, nonsignificant.

FIGURE 10.

Expression of genes involved in sterol biosynthesis are suppressed in PPARγ-deficient cells. (A) Heatmap display of gene expression from sterol biosynthesis pathway obtained from RNA sequencing data macrophages treated with BSA-PBS or palm-LPS for 8 h. The brackets indicate genes with particularly low expression in activated PPARγKO cells (blue). (B) Schematic of the metabolic pathway and enzymes (red text) involved in sterol biosynthesis. (C) mRNA expression of the indicated sterol biosynthesis genes was determined via qRT-PCR in WT and PPARγKO cells treated with control or palm-LPS for 8 h. (D) pMACs from WT or PPARγ-deficient macrophages were treated with 1 μM rosiglitazone (rosi) or veh for 16 h, after which they were treated with palm-LPS and the expression of sterol biosynthesis genes was assessed by qRT-PCR. (E) pMACs from WT or PPARγ-deficient macrophages were treated with BSA-PBS or palm-LPS for 8 h, after which cells were lysed, and free cholesterol, 25-HC, and 27-HC were measured by gas chromatography–tandem mass spectrometry. Data are presented as normalized ratio an internal standard. Bar graphs report the mean ± SE for a minimum of three experiments, each performed in triplicate. *p < 0.05 for WT versus mPPARγKO, #p < 0.05 for veh versus rosi. ns, nonsignificant.

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Based on these observations, we quantified the level of free cholesterol and the enzymatically derived oxysterols 25-HC and 27-HC at baseline and with activation (Fig. 10E). 25-HC levels increased in macrophages of both genotypes, consistent with the known induction of 25-hydroxycholesterolase expression by TLR4. Free cholesterol and 27-HC showed a similar pattern in which levels were slightly higher at baseline in macrophages from PPARγKO mice but dropped more with activation. Consistent with this, the addition of cholesterol did not impact IL-1 release from WT or PPARγKO cells, indicating that cholesterol per se is unlikely to be driver of IFN-β production.

The disruption of sterol biosynthesis in macrophages has been reported to augment IFN-β production through a mechanism that involves enhanced activation of the innate immune receptor STING (53). In contrast to TLR4, STING resides in the endoplasmic reticulum and activates IFN production independent of the adaptor TRIF but still requires the downstream transcription factor IRF3 (54). As shown in Fig. 11, PPARγ cells treated with the STING activator cGAMP also expressed higher levels of IFN target genes. Interestingly, the augmented response to STING activation became less apparent at increasing doses of cGAMP (Fig. 11D, 11E). Thus, PPARγ deficiency modulates the sensitivity of macrophages to STING activation without changing the maximal activation response. To confirm that the cGAMP activation was dependent on STING, we treated macrophages with a KI of an inactive variant of STING with high-dose cGAMP and demonstrated no induction of IFN target genes (Fig. 11F). To investigate whether all IRF3 activation stimuli produce a similar phenotype in PPARγKO macrophages, we also transfected macrophages with PIC to activate the mitochondrial antiviral signaling protein (MAVS). In contrast to STING activation, PPARγKO cells transfected with PIC did not have augmented IFN gene expression. In fact, PPARγKO cells expressed lower levels of IFN gene targets relative to WT macrophages (Fig. 11G–I). As MAVS and STING induce IFN-β through the same signaling pathway, these data suggest that differences in IRF3 activation are unlikely to mediate the PPARγKO phenotype.

FIGURE 11.

PPARγKO macrophages have enhanced sensitivity to STING ligands. (AC) WT or PPARγKO pMACs were stimulated with the STING activator cGAMP (0.5 μg/ml) or palm-LPS, and mRNA was harvested 8 h later. The expression of type 1 IFN gene targets CXCL10 (A), MX1 (B), and iNOS (C) was assessed via qRT-PCR. (D and E) pMACs from WT or PPARγ-deficient macrophages were stimulated with the indicated concentrations of cGAMP, and the expression of CXCL10 (D) and iNOS (E) was assessed by qRT-PCR. (F) WT or STING KI mice were treated with PBS or 10 μg/ml of cGAMP, and the mRNA expression of CXCL10 was determined at 8 h. (GI) WT or PPARγKO pMACs were treated with cGAMP (0.5 μg/ml) or transfected with PIC for 8 h, and the expression of the indicated IFN gene targets was assessed by qRT-PCR. Bar graphs report the mean ± SE for a minimum of three experiments, each performed in triplicate. *p < 0.05 for WT versus mPPARγKO or STING KI. ns, nonsignificant.

FIGURE 11.

PPARγKO macrophages have enhanced sensitivity to STING ligands. (AC) WT or PPARγKO pMACs were stimulated with the STING activator cGAMP (0.5 μg/ml) or palm-LPS, and mRNA was harvested 8 h later. The expression of type 1 IFN gene targets CXCL10 (A), MX1 (B), and iNOS (C) was assessed via qRT-PCR. (D and E) pMACs from WT or PPARγ-deficient macrophages were stimulated with the indicated concentrations of cGAMP, and the expression of CXCL10 (D) and iNOS (E) was assessed by qRT-PCR. (F) WT or STING KI mice were treated with PBS or 10 μg/ml of cGAMP, and the mRNA expression of CXCL10 was determined at 8 h. (GI) WT or PPARγKO pMACs were treated with cGAMP (0.5 μg/ml) or transfected with PIC for 8 h, and the expression of the indicated IFN gene targets was assessed by qRT-PCR. Bar graphs report the mean ± SE for a minimum of three experiments, each performed in triplicate. *p < 0.05 for WT versus mPPARγKO or STING KI. ns, nonsignificant.

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In this study, we unravel an unexpected connection between PPARγ and IL-1 expression that is linked by alterations in type 1 IFN signaling. We initially demonstrated that PPARγKO macrophages produce less IL-1β and IL-1α in response to NLRP3 activators in vitro and in vivo, whereas having a preserved TNF-α response. The suppression of IL-1 cytokines occurred at the transcriptional level and resulted in lower expression of the precursor cytokine proteins. RNA sequencing uncovered a type 1 IFN signature in PPARγKO macrophages that was driven by enhanced production of IFN-β. Using gain and loss-of-function approaches, we established that IFN-β is required for the suppression of IL-1 expression in PPARγKO cells. We also demonstrate that activation of PPARγ with rosiglitazone diminishes IFN-β mRNA levels. Finally, we provide evidence that this phenotype may be driven by altered sterol biosynthesis and enhanced activity of the innate immune receptor STING.

The NLRP3 inflammasome is a tightly regulated inflammatory complex that requires two distinct signals for activation. Importantly, NLRP3 and IL-1β have been implicated in the pathogenesis of several human diseases, including atherosclerosis, diabetes, nonalcoholic steatohepatitis, Alzheimer disease, gout, and heart failure (5559). Relevant to diabetes and vascular disease, we have previously shown that excess fatty acids can cause lysosome damage in macrophages triggering NLRP3 assembly and enhanced IL-1β release (11). In this study, we also demonstrate that IL-1α release is increased under these same conditions. This observation is consistent with previous data showing that the majority of NLRP3 activators also trigger the release of IL-1α (60). As an approach to investigate how lipid metabolism might influence inflammasome activation, we assessed IL-1 cytokine release in macrophages lacking the lipid metabolic regulator PPARγ. Contrary to the notion that PPARγ has general anti-inflammatory activity, we discovered that loss of this transcription factor resulted in diminished IL-1β and IL-1α release, while having no effect on TNF-α secretion. Loss of PPARγ was associated with reduced levels of IL-1β and IL-1α and mRNA and protein, indicating that priming was attenuated in PPARγKO macrophages. Moreover, decreased IL-1 cytokine release occurred in response to nonlipid NLRP3 activators as well, demonstrating that this phenotype is broader than just lipotoxicity.

PPARγ is a transcription factor with both activating and repressing capabilities. Prior studies of PPARγ-mediated repression of inflammatory cytokines have shown that this transcription factor does not directly affect IL-1β transcription, suggesting an indirect link between these events (48). Based on prior studies demonstrating that glycolytic flux and succinate accumulation can modulate IL-1β transcription, we initially hypothesized that alterations in cellular metabolism and/or intracellular metabolites might explain the relationship between PPARγ and IL-1 (43). However, these metabolic pathways were not significantly altered in PPARγ-deficient macrophages. Instead, using an RNA sequencing approach, we uncovered a signature of enhanced IFN-β signaling in PPARγKO cells. Using gain and loss of function approaches, we demonstrated that the enhanced IFN-β response was responsible for the suppression of IL-1 family members in PPARγ-deficient primary macrophages. Our findings of enhanced expression of IFN-β target genes in mPPARγKO macrophages are in line with previous observations made by Welch et al. (51) in which they used a distinct inducible Cre system to delete PPARγ. Although not commented upon in their manuscript, the expression of IL-1β mRNA via Northern blot was decreased in LPS-treated PPARγ-deficient cells. It is unclear how these findings relate to a subsequent study suggesting that IL-1β release is increased in PPARγKO macrophages (61). However, because the link between PPARγ and IL-1 occurs via IFN, this interplay could be context dependent. Using the PPARγ agonist rosiglitazone, we found that the expression of IFN-β mRNA and its downstream targets was suppressed by PPARγ activation. In contrast, TNF-α mRNA expression was not affected. These data are consistent with another study, in which the PPARγ agonist troglitazone was shown to suppress IFN-β transcription (62). In sum, our results add to a growing body of literature, demonstrating an antagonist relationship between PPARγ expression and IFN-mediated inflammatory responses. Also of relevance, PPARγ has been implicated in macrophage alternative activation with IL-4, which is thought to be protective in response to obesity-related inflammation (63, 64). Understanding the links between PPARγ, type 1 IFN, and IL-1 has the potential to shed significant insight into macrophage phenotypes and effector responses in vivo.

To gain insight into the mechanism linking PPARγ loss of function to enhanced release of IFN-β, we returned to our RNA sequencing analysis. PPARγKO cells had more robust downregulation of genes involved in sterol biosynthesis at baseline and with activation compared with WT macrophages. Moreover, PPARγ activation augmented expression of these genes. These findings are of interest as recent data have correlated disruption of flux through the sterol biosynthesis pathway with enhanced IFN-β release from macrophages (53). However, direct quantification of free cholesterol levels in PPARγKO macrophages revealed slightly increased levels in the KO cells. This observation may represent defective cholesterol efflux in PPARγ-deficient cells (27). Upon activation, KO cells had a more dramatic decrease in free cholesterol compared with WT cells, suggesting that sterol pathways are differentially affected; however, the addition of cholesterol to WT or PPARγKO macrophages did not change IFN-β or IL-1 production. Although these data argue against a direct role for cholesterol itself, flux through the de novo sterol biosynthesis pathway also generates other lipid and metabolic intermediates. One such intermediate is the oxysterol 25-HC, which is known to increase with TLR4 activation due to the induction of 25-hydroxycholesterolase. Prior studies have shown that 25-HC can suppress IL-1β by decreasing SREBP activation (52). However, we found that 25-HC levels increased to a similar extent in macrophages from both genotypes in response to palm-LPS. These observations raise the possibility that PPARγ may influence other lipid intermediates/metabolites or directly suppress IRF3.

To gain insight into the role of IRF3 in the IFN phenotype, we compared IFN-β responses in WT and PPARγKO macrophages stimulated with ligands for the intracellular pathogen receptors STING or MAVS. STING resides in the ER, whereas MAVS is located in the mitochondrial outer membrane. Both receptors activate IRF3 via a shared signaling pathway; however, STING is also known to be regulated by changes in membrane lipid composition. Because IFN-β release was augmented in response to STING ligands, but not MAVS ligands, a direct role for PPARγ on IRF3 is less likely. Together, these data, along with our gene expression results, support a model whereby the IFN-β response is linked to alternations in cellular lipids/sterols.

Regulation of the NLRP3 inflammasome by type 1 IFN is complex, with both activating and inhibitory properties described depending on the context. One mechanism by which type 1 IFN can suppress IL-1 production in macrophages is by inducing the expression of IL-10, a potent anti-inflammatory cytokine (65). However, we demonstrated that inhibition of IL-10 signaling did not restore IL-1 release in PPARγKO cells. Moreover, we determined that the stability of IL-1β mRNA was not affected in PPARγKO cells, suggesting that transcription of this cytokine was directly suppressed by IFN signaling. Although the mechanism(s) underlying IFN-β–mediated suppression of IL-1β and IL-1α in PPARγKO macrophages is not clear, this area remains a focus of ongoing research.

The observation that PPARγKO macrophages can overproduce type 1 IFN may also be relevant to other metabolic and autoimmune phenotypes described in mPPARγKO mice. In the setting of high-fat diet, mPPARγKO mice have worsened insulin resistance and increased levels of atherosclerosis, both of which are associated with chronic inflammation (21, 2527). Moreover, these mice are also predisposed to autoimmune diseases (66, 67). Our findings raise the intriguing possibility that an enhanced type 1 IFN response may contribute to some of these observations. The fact that type 1 IFN can promote autoimmunity and atherosclerosis is line with this hypothesis (6870). Future research into this possibility could be of interest as the PPARγ agonists rosiglitazone and pioglitazone are clinically available and thus could potentially be used to suppress IFN-β production in patients with autoimmune disease (7173).

In this study, we demonstrate that PPARγ-deficient macrophages have a defect in IL-1β and IL-1α release in response to treatment with NLRP3 activators. Unexpectedly, we determined that this response was driven by an overproduction of IFN-β in PPARγKO cells. Our findings represent another important example of the complex interplay between metabolic and inflammatory pathways in macrophages that could have relevance to obesity-associated diseases and autoimmunity.

We thank the Genome Technology Access Center in the Department of Genetics at Washington University School of Medicine for help with genomic analysis.

This work was supported by National Institutes of Health Grants R01 DK11003401 (to J.D.S.) and P30 DK020579 (to J.D.S.).

The sequences presented in this article have been submitted to the Gene Expression Omnibus (https://www.ncbi.nlm.nih.gov/geo/) under accession number GSE117115.

The online version of this article contains supplemental material.

Abbreviations used in this article:

GO

Gene Ontology

HC

hydroxycholesterol

IFNAR

IFN-α/β receptor α chain

iNOS

inducible NO synthase

KEGG

Kyoto Encyclopedia of Genes and Genomes

KI

knockin

KO

knockout

MAVS

mitochondrial antiviral signaling protein

mPPARγKO

myeloid-specific PPARγKO

PIC

poly I:C

pMAC

peritoneal macrophage

qRT-PCR

quantitative RT-PCR

SFA

saturated fatty acid

STING

stimulator of IFN gene

veh

vehicle

WT

wild type.

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The authors have no financial conflicts of interest.

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