Macrophage polarization is linked to Ca²⁺-independent phospholipase A₂β-derived lipids and cross-cell signaling in mice

Abstract

Phospholipases A₂ (PLA₂s) catalyze hydrolysis of the sn-2 substituent from glycerophospholipids to yield a free fatty acid (i.e., arachidonic acid), which can be metabolized to pro- or anti-inflammatory eicosanoids. Macrophages modulate inflammatory responses and are affected by Ca²⁺-independent phospholipase A₂ (PLA₂)β (iPLA₂β). Here, we assessed the link between iPLA₂β-derived lipids (iDLs) and macrophage polarization. Macrophages from WT and KO (iPLA₂β−/−) mice were classically M1 pro-inflammatory phenotype activated or alternatively M2 anti-inflammatory phenotype activated, and eicosanoid production was determined by ultra-performance LC ESI-MS/MS. As a genotypic control, we performed similar analyses on macrophages from RIP.iPLA₂β.Tg mice with selective iPLA₂β overexpression in β-cells. Compared with WT, generation of select pro-inflammatory prostaglandins (PGs) was lower in iPLA₂β^−/−, and that of a specialized pro-resolving lipid mediator (SPM), resolvin D2, was higher; both changes are consistent with the M2 phenotype. Conversely, macrophages from RIP.iPLA₂β.Tg mice exhibited an opposite landscape, one associated with the M1 phenotype: namely, increased production of pro-inflammatory eicosanoids (6-keto PGF₁α, PGE₂, leukotriene B₄) and decreased ability to generate resolvin D2. These changes were not linked with secretory PLA₂ or cytosolic PLA₂α or with leakage of the transgene. Thus, we report previously unidentified links between select iPLA₂β-derived eicosanoids, an SPM, and macrophage polarization. Importantly, our findings reveal for the first time that β-cell iPLA₂β-derived signaling can predispose macrophage responses. These findings suggest that iDLs play critical roles in macrophage polarization, and we posit that they could be targeted therapeutically to counter inflammation-based disorders.

T1D is a consequence of autoimmune destruction of β-cells, involving activation of cellular immunity and inflammation that lead to infiltration of islets by leukocytes (1). Lipid signaling is recognized as a key modulator of inflammation and immune responses (2). For instance, arachidonic acid (AA) and oxidized lipids [i.e., prostaglandins (PGs) and leukotrienes (LTs)] (3) generated via its metabolism can trigger immune responses leading to β-cell death (4, 5). They are generated via cyclooxygenases (COXs) and lipoxygenases (LOs), subsequent to phospholipase A₂ (PLA₂)-mediated hydrolysis of AA (6–12) from membrane glycerophospholipids.

Among the family of PLA₂s is the Ca²⁺-independent phospholipase A₂β (iPLA₂β), and its activity promotes deleterious outcomes in experimental and clinical diabetes (13–15). Immune cells express iPLA₂β (16–20) and inhibition of iPLA₂β reduces generation of reactive oxygen species (18) and antibody production from B-cells and TNFα from CD4⁺ T-cells (20) and macrophages (21). TNFα acts as a powerful chemoattractant (22) and is produced by CD4⁺ T-cells within inflamed islets during diabetes development (23). TNFα overexpression exacerbates insulitis while the opposite occurs in TNFα-receptor-null mice (24). In this regard, lipids derived from iPLA₂β activation, but not other PLA₂s, promote monocyte chemotaxis (18, 21, 25, 26) and provide migratory directionality to monocytes to inflamed sites (27). Inhibition of iPLA₂β has shown to be effective against diseases related to autoimmunity (28) and inflammation (29–32).

Macrophages are important for innate and adaptive immunity and participate in autoimmune-mediated destruction of β-cells and T1D. In diabetes-prone individuals, immune cells, including macrophages, migrate to pancreatic islets and secrete pro-inflammatory cytokines and reactive oxygen species that result in β-cell death (33). Two different activation states of macrophages have been described; M1 pro-inflammatory macrophages (34), which are classically activated by interferon-γ (IFNγ), lipopolysaccharide (LPS), or TNFα, and M2 macrophages, which are alternatively activated by interleukin (IL)-4 or IL-10 (35). Whereas M1 macrophages are recognized causative factors in T1D development (36), M2 macrophages protect against T1D (37). Pro-inflammatory eicosanoids have been linked to macrophage phagocytosis, adhesion, and apoptosis, and amplifying macrophage-derived eicosanoid release (38–41).

Macrophages express iPLA₂β (18, 42), and our recent studies reveal that activation of iPLA₂β promotes macrophage polarization toward the M1 inflammatory phenotype (42). In contrast, iPLA₂β deficiency favors macrophage polarization toward the M2 anti-inflammatory phenotype. Further, inhibitors of iPLA₂β, COX, and 12-LO reduce M1 inflammatory markers, recapitulating the iPLA₂β ^−/− macrophage phenotype. Collectively, these findings suggest that iPLA₂β-derived lipids (iDLs) modulate immune responses.

Given the importance of macrophages to T1D development and evidence for a role of iPLA₂β in modulating macrophage polarization and function, we sought to identify iDLs generated by activated macrophages, which could be targeted to counter autoimmune/inflammatory-based disorders. To facilitate our assessments, we utilized targeted/quantitative MS analyses of eicosanoids produced by macrophages isolated from WT (MΦ_WT) and iPLA₂β^−/− (KO, MΦ_KO) mice. As a control for the genotype, we performed similar analyses with macrophages isolated from RIP.iPLA₂β.Tg [transgenic (Tg), MΦ_Tg] mice, which selectively overexpress iPLA₂β in β-cells (43, 44).

We found that iPLA₂β deficiency led to an attenuated pro-inflammatory and amplified anti-inflammatory lipid profile that was consistent with a macrophage M2 phenotype. In contrast, MΦ_Tg were found to exhibit an opposite landscape, encompassing an exaggerated pro-inflammatory and attenuated anti-inflammatory lipid profile that was associated with a macrophage M1 phenotype. These findings suggest that iPLA₂β modulates the inflammatory lipid profile and raises the intriguing possibility that increased expression of iPLA₂β in the β-cells can confer increased susceptibility of macrophages to activation.

EXPERIMENTAL PROCEDURES

Animals

Mouse breeders obtained from Dr. John Turk (Washington University School of Medicine, St. Louis, MO) were used to generate colonies of WT, RIP.iPLA₂β.Tg (selectively overexpressing iPLA₂β in β-cells only), and global iPLA₂β-KO mice at the University of Alabama at Birmingham. Tg founders (TG1 line) were mated with WT C57BL/6J mice (Jackson Laboratory) to generate RIP.iPLA₂β.Tg (Tg) mice and WT mice, and male and female iPLA₂β ^+/− pairings were used to generate iPLA₂β ^−/− (KO) and iPLA₂β ^+/+ (WT) mice, as previously described (43, 45). Prior to experimentation, the mice were genotyped, as described (44), utilizing primers described in Table 1. Animal experiments were conducted according to approved IACUC guidelines at the University of Alabama at Birmingham. As our earlier studies suggested differences between the WT generated by the two schemes, data from the KO and Tg genotypes were compared against their corresponding WT littermates.

TABLE 1.

Genotyping primers

Name	Sequence (5′ to 3′)	Target	Product Size (bp)
WT-PLA2G6_F	AGCTTCAGGATCTCATGCCCATC	WT (KO) iPLA2β	1,400
WT-PLA2G6_R	CTCCGCTTCTCGTCCCTCATGGA
KO-Neo_F	TCGCCTTCTATCGCCTTCTTGAC	KO-iPLA2β	400
KO-Ex_R	GGGGCCTCAGACTGGGAATC
WT-PLA2G6_F	CCTCCGGAGAGCAGCGATTAAAAGTGTCAG	WT (Tg) iPLA2β	450
WT-PLA2G6_R	TAGAGCTTTGCCACATCACAGGTCATTCAG
Tg-PLA2G6_F	CTAGGCTCAGACATCATGCTGGACGAGGT	RIP.iPLA2β.Tg	200
Tg-PLA2G6_R	AAGATCTCAGTGGTATTTGTGAGCCAGGG

Isolation and culture of peritoneal macrophages

Mice (7–8 weeks of age) were euthanized by CO₂ inhalation and cervical dislocation. Peritoneal macrophages were obtained by filling the peritoneal cavity with 5 ml of cold PBS containing 2% FBS, massaging gently, and withdrawing the cell-containing solution. Cells were pelleted at 300 g for 5 min and resuspended in growth medium [Eagle’s minimum essential medium (Sigma-Aldrich; M0894), 2.0 mg/ml sodium bicarbonate (Fisher Scientific; BP328-500), 2 mM l-glutamine (Life Technologies; 25030-081), 100 U/ml penicillin-100 μg/ml streptomycin [Life Technologies; 15140-122), and 10% heat-inactivated FBS (Life Technologies; 16000044)] supplemented with 10% L929 cell-conditioned medium (source of M-CSF). Macrophages from a single collection were sufficient to seed six 60 mm nontreated culture dishes. Adherent macrophages appeared after 16 h of culture. All experiments were performed with expanded freshly isolated peritoneal macrophages under classical and alternate activation conditions, as described below. Macrophages isolated from WT, iPLA₂β^−/− (KO), and RIP.iPLA₂β.Tg (Tg) mice are designated as MΦ_WT, MΦ_KO, and MΦ_Tg, respectively.

Macrophage activation

Macrophage activation was accomplished according to previously published methods (42). For classical activation, macrophages were treated with 15 ng/ml recombinant IFNγ (R&D Systems; 485-MI-100) for 8 h in growth medium followed by addition of 10 ng/ml ultrapure LPS (InvivoGen; tlrl-3pelps) and incubated for 16 h at 37°C. For alternative activation, macrophages were treated with 8 ng/ml recombinant IL-4 (R&D Systems; 404-ML-010) in growth medium for 16 h. Naïve macrophages, which received no activation stimuli, were maintained in growth medium with no additional treatment. In some experiments, the macrophages were pretreated for 1 h with either a secretory phospholipase A₂ (sPLA₂) (LY315920, 10 μM; Cayman Chemical) or cytosolic (c)PLA₂α (CAY 10502, 50 nM; Cayman Chemical) inhibitor prior to activation. The inhibitors were present during the entire activation period.

Macrophage mRNA target analyses

Macrophages cultured in 60 mm non-tissue culture-treated dishes were lysed in 1 ml of TRIzol (Life Technologies; 15596-026). Total RNA was prepared and purified using RNeasy mini kits (QIAGEN; 74104), and 1 μg RNA was converted to cDNA using the Superscript III first strand synthesis system (Life Technologies; 18080-051), according to manufacturer’s instructions. The cDNA was diluted 10-fold and used as template in conventional or real-time quantitative PCR (qPCR). cDNA transcripts were amplified with primers (Table 2) designed using NCBI Primer-BLAST (https://www.ncbi.nlm.nih.gov/tools/primer-blast/). Real-time qPCR was carried out using SYBR Select Mastermix (Life Technologies; 4472908) according to the manufacturer’s instructions. Relative gene expression levels were determined using the 2^−ΔΔCt method.

TABLE 2.

Real-time qPCR primers

Name	Sequence (5′ to 3′)	Tm (salt)	Target
msMRC1.F	GTCAGAACAGACTGCGTGGA	60.0	Mrc1
msMRC1.R	AGGGATCGCCTGTTTTCCAG	60.0
msARG2.F	GCAAATTCCTTGCGTCCTGA	60.0	Arg2
msARG2.R	AGGCCCACTGAACGAGGATA	60.0
msPtgs2.F3	TGAGTGGGGTGATGAGCAAC	60.0	PTGS2
msPtgs2.R3	TTCAGAGGCAATGCGGTTCT	60.0
ALOX12.F	GGCTATCCAGATTCAGCCCC	60.0	ALOX-12
ALOX12.R	CCGGCTTCGCGTGTTAATTT	57.1
miPLA2.F	TATGCGTGGTGTGATCTTCCG	57.1	iPLA2β
miPLA2.R	CATGGAGCTCAGGATGAACGC	60.0
msPLA2 GV.F (101)	AGCCTCGATCATGGCCTTT	56.8	GV sPLA2
msPLA2 GV.R (101)	GCCGAATCATTTCCCCAAA	56.8
mcPLA2.F	CCCTGAGTAGTTTGAAGGAAAAGG	55.4	cPLA2
mcPLA2.R	ACACGTGAAGAGAGGCAAAGG	55.4

Eicosanoid preparation

Eicosanoids were extracted using a modified extraction process, as previously described (46, 47). Medium from cells (2 ml) was combined with an internal standard mixture comprised of 10% total volume methanol (200 μl) and glacial acetic acid (10 μl) before spiking with internal standard (20 μl) containing the following deuterated eicosanoids (2 pmol/μl, 40 pmol total) (all standards purchased from Cayman Chemicals): (d₄) 6-keto-PGF₁α, (d₄) PGF₂α, (d₄) PGE₂, (d₄) PGD₂, (d₈) 5-HETE, (d₈) 12-HETE, (d₈) 15-HETE, (d₆) 20-HETE, (d₁₁) 8,9-epoxyeicosatrienoic acid (EET), (d₈) 14,15-EET, (d₈) AA, (d₅) eicosapentaenoic acid, (d₅) DHA, (d₄) PGA₂, (d₄) LTB₄, (d₄) LTC₄, (d₄) LTD₄, (d₄) LTE₄, (d₅) 5(S),6(R)-lipoxin A₄, (d₁₁) 5-iPF2α-VI, (d₄) 8-iso PGF₂α, (d₁₁) (±)14,15-dihydroxyeicosatrienoic acid (DHET), (d₁₁) (±)8,9-DHET, (d₁₁) (±)11,12-DHET, (d₄) PGE₁, (d₄) thromboxane B₂ (TXB₂), (d₆) dihomo-γ linoleic acid, (d₅) resolvin D2, (d₅) resolvin D1, (d₅) maresin2, and (d₅) resolvin D3. Samples and vial rinses (5% methanol; 2 ml) were applied to Strata-X SPE columns (Phenomenex) previously washed with methanol (2 ml) and then dH₂O (2 ml). Eicosanoids eluted with isopropanol (2 ml), were dried in vacuo and reconstituted in ethanol:dH₂O (50:50;100 μl) prior to ultra-performance (UP)LC ESI-MS/MS analysis.

Analysis of eicosanoids by UPLC ESI-MS/MS

Eicosanoids were separated using a Shimadzu Nexera X2 LC-30AD coupled to a SIL-30AC auto injector, coupled to a DGU-20A5R degassing unit in the following way [as previously described by us (47)]. A 14 min reversed phase LC method utilizing an Acentis Express C18 column (150 × 2.1 mm, 2.7 μm) was used to separate the eicosanoids at a 0.5 ml/min flow rate at 40°C as we previously described (47). The column was equilibrated with 100% solvent A [acetonitrile:water:formic acid (20:80:0.02, v/v/v)] for 5 min and then 10 μl of sample were injected. Solvent A (100%) was used for the first 2 min of elution. Solvent B [acetonitrile:isopropanol:formic acid (20:80:0.02, v/v/v)] was increased in a linear gradient to 25% solvent B at 3 min, to 30% at 6 min, to 55% at 6.1 min, to 70% at 10 min, and to 100% at 10.10 min. Solvent B (100%) was held constant until 13.0 min, where it was decreased to 0% solvent B and 100% solvent A from 13.0 min to 13.1 min. From 13.1 min to 14.0 min, solvent A was held constant at 100%. All solvents were purchased from Fischer Scientific.

Eicosanoids were analyzed via MS using an AB Sciex Triple Quad 5500 mass spectrometer, as previously described by us (47). Q1 and Q3 were set to detect distinctive precursor and product ion pairs. Ions were fragmented in Q2 using N₂ gas for collisionally induced dissociation. Analysis used multiple-reaction monitoring in negative-ion mode. Eicosanoids were monitored using precursor → product MRM pairs. The mass spectrometer parameters used were: curtain gas, 20 psi; CAD, medium; ion spray voltage, −4,500 V; temperature, 300°C; gas 1, 40 psi; gas 2, 60 psi (declustering potential, entrance potential, collision energy, and cell exit potential varied per transition). MRM transitions with corresponding declustering potentials, collision energies, entrance potentials, and collision cell exit potentials are shown in supplemental Table S1.

Selectivity of transgene function

As described (43), to generate Tg mice with selective overexpression of iPLA₂β in β-cells only, rat iPLA₂β cDNA was inserted downstream of the rat insulin promoter (RIP) at a site within the rabbit globin gene sequence. As such, transcription of the sequence encoding iPLA₂β is under control of RIP, and transgenic overexpression of iPLA₂β is expected only in cells that express insulin, i.e., pancreatic islet β-cells, but not in other cells (i.e., macrophages). To verify that induction of the transgene was specific to β-cells, PCR analyses were performed using islet and macrophage cDNA as a template with two pairs of primers. One pair amplified the sequence in the internal control fatty acid-binding protein, intestinal (Fabpi) gene, and the primer sequences were (Fabpi 5′) cctccggagagcagcgattaaaagtgtcag and (Fabpi 3′) tagagctttgccacatcacaggtcattcag (expected product size, 450 bp). The other primer pair amplified a sequence that spanned the junction of iPLA₂β and globin cDNA in the TG construct. The primer sequences were (TG 5′) ctaggctcagacatcatgctggacgaggt and (TG 3′) aagatctcagtggtatttgtgagccaggg (expected product size, 200 bp). Subsequently, the islets and macrophages were processed for insulin content and iPLA₂β immunoblotting (1° antibodies: iPLA₂β, E-8, sc-166616; tubulin, TU-02, sc-8035) analyses, as described (48, 49).

Statistical analysis

Lipidomics samples were analyzed via a multivariate approach using SPSS. In experiments with more than two conditions, samples were analyzed by ANOVA on the R platform with a Tukey’s post hoc test. In experiments using two conditions, samples were analyzed using a Student’s t-test. Presented data are mean ± SEM and, where appropriate, as fold-change relative to control (vehicle). Values of P < 0.05 were considered significant.

RESULTS

iPLA₂β^−/− model verification

As previously reported (43, 45), DNA was generated from tail clips and progeny were genotyped by PCR analyses. The primers used for the WT or for the disrupted iPLA₂β^−/− (KO) sequence were expected to yield product sizes of 1,400 or 400 bp, respectively. Consistently, only a 1,400 bp product in WT (lanes 1 and 2) and a 400 bp product in KO (lanes 3 and 4) were evident (Fig. 1). The iPLA₂β^−/− group is referred to as KO and macrophages isolated from these mice as MΦ_KO, and macrophages from WT, MΦ_WT.

Fig. 1.

Genotyping and verification of the iPLA₂β^−/− model. DNA was generated from tail clips and progeny were genotyped by PCR analyses. Reactions were performed in the presence of primers for the WT sequence (sets 1 and 2) or for the disrupted KO sequence (sets 3 and 4) for each mouse. The expected bands for WT (1,400 bp) and KO (400 bp) in two mice each are presented.

Comparison of basal lipid species in WT and KO

The MS protocol identified several lipid species (Table 3), including pro-inflammatory and anti-inflammatory PGs, LTs, HETEs, DHETs, EETs, and specialized pro-resolving lipid mediators (SPMs). Analyses of lipid production under basal conditions revealed no significant differences in the abundances of detected lipids between WT and KO (supplemental Table S2). These findings suggest an overall similar profile in MΦ_WT and MΦ_KO production of lipids.

TABLE 3.

Macrophage lipids identified by UPLC ESI-MS/MS analyses

	Lipid Species
Pro-inflammatory
PGs	6-keto PGF1α, TXB2, PGF2α, 8-iso PGF2α, 5-iPF2α-VI, PGE2, PGD2, PGA2, 15-deoxy-Δ12,14-PGJ2
LTs	LTB4, LTC4, LTD4, LTE4
Dihydoxyeicosatrienoic acids	(±)11,12-DHET, (±)14,15-DHET, (±)8,9-DHET
Hydoxyeicosatrienoic acids	20-HETE, 15-HETE, 12-HETE, 5-HETE
Anti-inflammatory
PGs	PGE1
SPMs	Resolvin D2, resolvin D1, lipoxin A4
EETs	(±)11,12-EET, (±)14,15-EET, (±)8,9-EET

Comparison of pro-inflammatory lipid production from activated MΦ_WT and MΦ_KO

AA, subsequent to its release from membrane glycerophospholipids, can be metabolized by COXs and LOs to generate oxidized bioactive products, designated eicosanoids. Some of these lipids are considered to be pro-inflammatory. They include several PGs, LTs, and HETEs (Table 3). Further, epoxidation of AA by cytochrome P450 epoxygenases leads to the generation of EETs, which are then hydrolyzed by soluble epoxide hydrolases to pro-inflammatory DHETs. Comparison of macrophage pro-inflammatory lipid production in response to classical activation revealed an increase in pro-inflammatory PGs generated by both MΦ_WT and MΦ_KO in response to IFNγ + LPS (Fig. 2A). However, the increase in PGE₂ in the KO group was of lower magnitude relative to the WT group. Overall, a nearly 40% lower production of pro-inflammatory PGs (pool of PGs presented in Fig. 2A) by MΦ_KO relative to MΦ_WT was evident (Fig. 2B). However, production of other pro-inflammatory lipid species, including LTs (Fig. 3), HETEs (supplemental Fig. S1A), or DHETs (±14,15 and ±11,12; supplemental Fig. S2A), by MΦ_KO was similar to MΦ_WT, and the production of (±)8,9-DHET by MΦ_KO was higher relative to MΦ_WT. These observations suggest that iPLA₂β promotes production of select pro-inflammatory PGs in macrophages.

Fig. 2.

Pro-inflammatory PG production by MΦ_WT and MΦ_KO. Peritoneal macrophages isolated from 8-week-old WT and KO mice were treated with vehicle [control (C)] or IFNγ + LPS and the media collected for lipidomics analyses. The data represent activated (A) fold-changes in lipids relative to C. A: Individual PGs. Control (pmol lipid/1e⁺⁰⁶) MΦ_WT and MΦ_KO: 6-keto PGF₁α, 2.512 ± 0.302 and 3.818 ± 0.564; TXB₂, 4.563 ± 0.585 and 4.491 ± 0.538; PGD₂, 0.568 ± 0.083 and 0.881 ± 0.128; 8-Iso PGF₂α, 0.056 ± 0.008 and 0.059 ± 0.013; 5-IPF₂α-VI, 0.263 ± 0.033 and 0.301 ± 0.068; PGE₂, 1.213 ± 0.313 and 1.690 ± 0.349; PGA₂, 0.472 ± 0.035 and 0.525 ± 0.050; 15-deoxyΔ12,14-PGJ₂, 0.253 ± 0.087 and 0.240 ± 0.072; PGF₂α, 0.646 ± 0.055 and 0.591 ± 0.032. B: PG pool. Control (pmol lipid/1e⁺⁰⁶) MΦ_WT and MΦ_KO: 3.804 ± 0.253 and 4.564 ± 0.443. Data are mean ± SEM determined from seven to nine independent experiments. *MΦ_KO significantly different from MΦ_WT, P < 0.05).

Fig. 3.

LT production by MΦ_WT and MΦ_KO. Peritoneal macrophages isolated from 8-week-old WT and KO mice were treated with vehicle [control (C)] or IFNγ + LPS and the media collected for lipidomics analyses. The data represent activated (A) fold-changes in lipids relative to C. Control (pmol lipid/1e⁺⁰⁶) MΦ_WT and MΦ_KO: LTD4, 0.072 ± 0.028 and 0.072 ± 0.027; LTC₄, 0.022 ± 0.005 and 0.019 ± 0.005; LTE₄, 0.101 ± 0.016 and 0.158 ± 0.038; LTB₄, 0.429 ± 0.074 and 0.573 ± 0.118. Data are mean ± SEM determined from seven to nine independent experiments.

Comparison of anti-inflammatory lipid production from activated WT and MΦ_KO

In addition to generation of pro-inflammatory lipids, metabolism of AA can generate anti-inflammatory lipids (Table 3), such as EETs, which have been reported to manifest anti-inflammatory properties (50). Further, dihomo-γ-linolenic acid (DHGLA) can be converted to PGE₁, and SPMs are generated via metabolism of AA (lipoxin A₄) or DHA (resolvins D₁ and D₂). With classical activation, anti-inflammatory PGs increased similarly in the WT and KO (Fig. 4A). In contrast, production of resolvin D₂ by MΦ_KO was significantly higher (Fig. 4B) relative to MΦ_WT; whereas, production of EETs was similar between WT and KO (supplemental Fig. S2B). Further, alternative activation did not promote differential changes in the production of SPMs (data not shown). These observations suggest that iPLA₂β can modulate macrophage production of select SPMs.

Fig. 4.

Production of anti-inflammatory PGE₁ and SPMs by MΦ_WT and MΦ_KO. Peritoneal macrophages isolated from 8-week-old WT and KO mice were treated with vehicle [control (C)] or IFNγ + LPS and the media collected for lipidomics analyses. The data represent activated (A) fold-changes in lipids relative to C. A: PGE₁ ± IFNγ + LPS. Control (pmol lipid/1e⁺⁰⁶) MΦ_WT and MΦ_KO: 0.162 ± 0.020 and 0.240 ± 0.021. B: SPMs. Control (pmol lipid/1e⁺⁰⁶) MΦ_WT and MΦ_KO: resolvin D2, 0.110 ± 0.029 and 0.100 ± 0.032; resolvin D1, 0.100 ± 0.013 and 0.115 ± 0.019; lipoxin A₄, 0.235 ± 0.065 and 0.288 ± 0.109. Data are mean ± SEM determined from seven to nine independent experiments. *MΦ_KO significantly different from MΦ_WT, P < 0.05.

iPLA₂β^−/− (KO) macrophage phenotype

As expected, real-time qPCR analyses (Fig. 5A) confirmed a relative absence of iPLA₂β mRNA in MΦ_KO under both basal and activated conditions. iPLA₂β mRNA in MΦ_WT was modestly, but not significantly, affected by classical activation (approximately 25% increased) and alternative activation (approximately 25% decreased) relative to basal expression. We previously demonstrated that classical activation of macrophages promotes an M1 inflammatory phenotype, whereas alternative activation favors an M2 anti-inflammatory phenotype (42). To confirm that macrophages used in the present lipid analyses exhibited the expected phenotype, they were stimulated with IFNγ + LPS or IL-4 and the macrophages were assessed for expression of markers for M1 [arginase 2 (Arg2)] and M2 [mannose receptor C type 1 (MRC1)], respectively, by real-time qPCR. Activation induced the corresponding markers in MΦ_WT and MΦ_KO; however, relative expression of Arg2 was reduced (Fig. 5B) and MRC1 increased (Fig. 5C) in MΦ_KO in comparison with MΦ_WT. These findings are consistent with the reduced pro-inflammatory lipid profile in the KO. However, induction of Arg2 and MRC1 in MΦ_WT and MΦ_KO by alternative and classical activation, respectively, was similar (supplemental Fig. S3A, B).

Fig. 5.

Induction of M1 (Arg2) and M2 (MRC1) markers and iPLA₂β in MΦ_WT and MΦ_KO. Peritoneal macrophages isolated from 8-week-old WT and KO mice were treated with vehicle [control (Con)], IFNγ + LPS, or IL-4. The cells were harvested and processed for real-time qPCR analyses. A: iPLA₂β . B: Arg2 (MΦ_WT and MΦ_KO Arg2 2^−ΔΔCT, 0.095 ± 0.074 and 1.506 ± 0.558.) C: MRC1 (MΦ_WT and MΦ_KO MRC1 2^−ΔΔCT, 4.273 ± 2.357 and 16.053 ± 10.294). Data are mean ± SEM of fold-change relative to Con determined from four independent experiments. *MΦ_KO significantly different than MΦ_WT group, P < 0.05; ^†MΦ_KO significantly different than MΦ_WT group, P < 0.005.

RIP.iPLA₂β.Tg (Tg) model verification

In view of the observed effects on macrophage lipid production associated with iPLA₂β-deficiency and literature evidence for intercellular communication between inflamed sites and immune cells, we explored the possibility that signals generated by β-cells could manifest effects in macrophages. To address this, we compared lipid production from MΦ_Tg and MΦ_WT littermates. We previously demonstrated that the Tg mice overexpress iPLA₂β selectively in β-cells (44). To assess the genotype of the RIP.iPLA₂β.Tg (Tg), PCR analyses were performed in the presence of two sets of primers expected to yield product sizes of 450 and 200 bp for the WT (Fig. 6, lanes 1–3) and the RIP.iPLA₂β.Tg (Fig. 6, lanes 4–6), respectively. Such analyses (Fig. 6) revealed only a 450 bp band in the WT and a 200 bp in the Tg. The RIP.iPLA₂β.Tg group is referred to as Tg and macrophages isolated from these mice as MΦ_Tg.

Fig. 6.

Genotyping and verification of the RIP.iPLA₂β.Tg model. DNA was generated from tail clips and progeny were genotyped by PCR analyses. Reactions were performed in the presence of two sets of primers and the expected bands for WT (lanes 1–3, 450 bp) and Tg (lanes 4–6, 200 bp) in three mice each are presented.

Comparison of basal lipid species in WT and Tg

Because the KO and Tg mice were derived through different breeding schemes, the Tg mice were compared with their own littermate WT mice. Lipidomics analyses of basal production of lipids revealed no significant differences in the abundances of lipids between MΦ_WT and MΦ_Tg (supplemental Table S3). These findings suggest that similar to MΦ_KO, inherent production of lipids by MΦ_Tg is unaffected.

Comparison of pro-inflammatory lipid production from activated WT and MΦ_Tg

Classical activation of the macrophages promoted higher production of select pro-inflammatory PG lipids (6-ketoPGF₁α and PGE₂) by MΦ_Tg relative to MΦ_WT (Fig. 7A). Though PGD₂ production by MΦ_Tg was decreased, there was an overall 2-fold higher production of pro-inflammatory PGs (pool of PGs presented in Fig. 7A) by MΦ_Tg relative to MΦ_WT (Fig. 7B). Further, while LTD₄, LTC₄, and LTE₄ production was similar in MΦ_WT and MΦ_Tg (Fig. 8), LTB₄ was significantly elevated in MΦ_Tg relative to MΦ_WT. However, production of HETEs (supplemental Fig. S1B) or DHETs (supplemental Fig. S4A) was not significantly different between MΦ_WT and MΦ_Tg. These observations suggest that MΦ_Tg are primed to respond to classical activation.

Fig. 7.

Pro-inflammatory PG production by MΦ_WT and MΦ_Tg. Peritoneal macrophages isolated from 8-week-old WT and Tg mice were treated with vehicle [control (C)] or IFNγ + LPS and the media collected for lipidomics analyses. The data represent activated (A) fold-changes in lipids relative to C. Data are mean ± SEM determined from three to four independent experiments. A: Individual PGs. Control (pmol lipid/1e⁺⁰⁶) MΦ_WT and MΦ_Tg: 6-keto PGF₁α, 0.502 ± 0.179 and 0.462 ± 0.069; TXB₂, 3.725 ± 0.206 and 4.139 ± 0.152; PGD₂, 0.036 ± 0.006 and 0.039 ± 0.008; 8-Iso PGF₂α, 0.131 ± 0.006 and 0.103 ± 0.039; 5-IPF₂α-VI, 0.200 ± 0.082 and 0.162 ± 0.030; PGE₂, 0.147 ± 0.044 and 0.083 ± 0.008; PGA₂, 0.177 ± 0.068 and 0.139 ± 0.018; 15-deoxyΔ12,14-PGJ₂, 0.028 ± 0.006 and 0.037 ± 0.012; PGF₂α, 4.587 ± 0.784 and 3.856 ± 0.448. ^#MΦ_Tg significantly different from MΦ_WT, P < 0.01; *MΦ_Tg significantly different from MΦ_WT, P < 0.05. B: PG pool. Control (pmol lipid/1e⁺⁰⁶) MΦ_WT and MΦ_Tg: 9.533 ± 0.153 and 9.019 ± 0.087. *MΦ_Tg significantly different from MΦ_WT, P < 0.05.

Fig. 8.

LT production by MΦ_WT and MΦ_Tg. Peritoneal macrophages isolated from 8-week-old WT and Tg mice were treated with vehicle [control (C)] or IFNγ + LPS and the media collected for lipidomics analyses. The data represent activated (A) fold-changes in lipids relative to C. Control (pmol lipid/1e⁺⁰⁶) MΦ_WT and MΦ_Tg: LTD₄, 0.0020 ± 0.0001 and 0.0060 ± 0.0001; LTC₄, 0.011 ± 0.002 and 0.016 ± 0.005; LTE₄, 0.009 ± 0.002 and 0.009 ± 0.002; LTB₄, 0.229 ± 0.015 and 0.188 ± 0.015. Data are mean ± SEM determined from four independent experiments. *MΦ_Tg significantly different from MΦ_WT, P < 0.05.

Comparison of anti-inflammatory lipid production from activated WT and MΦ_Tg

Classical activation increased PGE₁ production by MΦ_Tg relative to MΦ_WT (Fig. 9A), but the production of SPMs by MΦ_WT and MΦ_Tg was similar (data not shown). Alternative activation promoted a 3-fold increase in resolvin D2 production by MΦ_WT; however, its production by MΦ_Tg was unchanged from basal production (Fig. 9B). EETs were not significantly different between MΦ_WT and MΦ_Tg (supplemental Fig. S4B). These observations suggest that MΦ_Tg are predisposed to differential generation of PGE₁, derived from DHGLA, and select SPMs.

Fig. 9.

Production of anti-inflammatory PGE₁ and SPMs MΦ_WT and MΦ_Tg. Peritoneal macrophages isolated from 8-week-old WT and Tg mice were treated with vehicle [control (C)], IFNγ + LPS, or IL-4 and the media collected for lipidomics analyses. The data represent activated (A) fold-changes in lipids relative to C. Data are mean ± SEM determined from four independent experiments. A: PGE₁ ± IFNγ + LPS. Control (pmol lipid/1e⁺⁰⁶) MΦ_WT and MΦ_Tg: 0.033 ± 0.011 and 0.017 ± 0.003. B: SPMs ± IL-4. Control (pmol lipid/1e⁺⁰⁶) MΦ_WT and MΦ_Tg: resolvin D2, 0.561 ± 0.073 and 1.074 ± 0.524; resolvin D1, 0.134 ± 0.042 and 0.136 ± 0.039; lipoxin A₄, 0.050 ± 0.008 and 0.050 ± 0.006. *MΦ_Tg significantly different from MΦ_WT, P < 0.05.

Impact of sPLA₂ and cPLA₂α on macrophage lipid profiles

Noting that other PLA₂s may also contribute to the altered lipid profile, we sought to determine whether sPLA₂s or cPLA₂αs contribute to the observed changes in eicosanoid production by MΦ_Tg. The C57BL/J6 macrophages are reported to express GV and GX sPLA₂ (51, 52) and cPLA₂α (16), but not GIIA sPLA₂ (51, 53–56). mRNA analyses revealed that sPLA₂ mRNA in MΦ_Tg was not altered under basal or activated conditions, whereas cPLA₂ mRNA was higher under basal but similar under activated conditions relative to MΦ_WT (Fig. 10). We next determine whether the lipid profiles of MΦ_WT and MΦ_Tg were modulated by inhibitors of sPLA₂ [LY315920 (57), which inhibits several sPLA₂s including GIIA, GV, and GX] or cPLA₂α [CAY 10502 (58)]. Lipidomics analyses revealed that neither basal (Table 4) nor activated (Table 5) production of lipids by MΦ_WT and MΦ_Tg was affected by LY315920 or CAY 10502. Comparisons between MΦ_WT and MΦ_Tg exposed to LY315920 revealed a higher basal production of several pro-inflammatory PGs and LTE₄ by MΦ_Tg relative to MΦ_WT (Table 4). In contrast, basal production of lipids by MΦ_WT and MΦ_Tg exposed to CAY 10502 was unaffected. Classical activation resulted in increases in lipid production by both MΦ_WT and MΦ_Tg (Table 5), with production by MΦ_Tg significantly higher relative to MΦ_WT. Such increases were maintained in the presence of LY315920 or CAY 10502. These findings suggest that GV and GX sPLA₂s and cPLA₂α are not likely contributors to the inflammatory lipid profile in activated MΦ_Tg.

Fig. 10.

Comparison of GV sPLA₂ and cPLA₂α mRNA in MΦ_WT and MΦ_Tg. Peritoneal macrophages isolated from 8-week-old WT and Tg mice were treated with vehicle (Control) or IFNγ + LPS. The cells were harvested and processed for real-time qPCR analyses. A: sPLA₂ GV (Control MΦ_WT 2^−ΔΔCT, 1.01 ± 0.06). B: cPLA₂α (Control MΦ_WT 2^−ΔΔCT, 1.01 ± 0.07). Data are mean ± SEM of fold-change relative to control determined from four independent experiments. *MΦ_Tg significantly different from MΦ_WT, P < 0.01.)

TABLE 4.

Effects of sPLA₂ and cPLA₂ inhibition on basal eicosanoid production from MΦ_WT and MΦ_Tg

Lipid	MΦWT (n = 4) (pmol lipid/1e+06)			MΦTg (n = 3) (pmol lipid/1e+06)
	DMSO	+sPLA2 Inhibitor	+cPLA2 Inhibitor	DMSO	+sPLA2 Inhibitor	+cPLA2 Inhibitor
6-Keto PGF1α	5.555 ± 0.585	4.775 ± 0.599	5.425 ± 1.082	9.418 ± 2.493	9.742 ± 1.732a	9.604 ± 3.708
TXB2	4.619 ± 0.173	4.575 ± 0.190	4.696 ± 0.188	4.949 ± 0.108	4.892 ± 0.294	5.108 ± 0.593
PGE2	2.343 ± 0.297	2.320 ± 0.406	3.944 ± 1.372	3.734 ± 1.053	5.347 ± 1.074a	8.549 ± 5.762
PGA2	1.674 ± 0.114	1.550 ± 0.074	1.858 ± 0.207	1.850 ± 0.176	2.010 ± 0.143a	2.008 ± 0.322
PGD2	0.395 ± 0.058	0.330 ± 0.046	0.419 ± 0.067	0.413 ± 0.027	0.507 ± 0.145	0.705 ± 0.137
PGF2α	0.638 ± 0.043	0.536 ± 0.022	0.579 ± 0.053	0.613 ± 0.021	0.649 ± 0.032a	00.685 ± 0.069
PGE1	0.332 ± 0.059	0.288 ± 0.037	0.633 ± 0.250	0.532 ± 0.105	0.648 ± 0.167	1.239 ± 0.864
Resolvin D1	0.516 ± 0.012	0.509 ± 0.008	0.506 ± 0.012	0.520 ± 0.017	0.514 ± 0.009	0.495 ± 0.006
LTE4	0.928 ± 0.107	0.959 ± 0.151	0.762 ± 0.072	12.560 ± 1.089	1.879 ± 0.285a	1.411 ± 0.600
(±)14,15-DHET	0.671 ± 0.018	0.639 ± 0.017	0.626 ± 0.021	0.664 ± 0.100	0.643 ± 0.026	0.678 ± 0.021
(±)11,12-DHET	0.761 ± 0.017	0.725 ± 0.016	0.757 ± 0.047	0.745 ± 0.010	0.729 ± 0.012	0.788 ± 0.027
(±)8,9-DHET	0.717 ± 0.052	0.655 ± 0.015	0.692 ± 0.052	0.697 ± 0.048	0.713 ± 0.021	0.881 ± 0.102
(±)14(15)-EET	0.458 ± 0.028	0.403 ± 0.009	0.458 ± 0.024	0.479 ± 0.044	0.432 ± 0.026	0.468 ± 0.032
(±)8(9)-EET	0.044 ± 0.014	0.111 ± 0.059	0.123 ± 0.077	0.182 ± 0.067	0.132 ± 0.073	0.048 ± 0.007
20-HETE	48.113 ± 0.951	47.770 ± 1.245	46.796 ± 1.049	54.631 ± 5.502	52.000 ± 3.387	51.376 ± 2.587
15-HETE	8.658 ± 0.492	8.344 ± 0.194	9.514 ± 0.749	9.049 ± 0.390	8.850 ± 0.367	10.759 ± 1.188
12-HETE	26.115 ± 3.799	24.261 ± 1.304	30.636 ± 1.518	30.433 ± 3.028	27.678 ± 0.959	40.769 ± 5.260
5-HETE	15.490 ± 0.906	15.002 ± 0.897	16.287 ± 1.389	16.100 ± 2.337	15.301 ± 1.812	17.798 ± 1.123
EPA	110.120 ± 4.666	108.113 ± 5.052	108.650 ± 6.474	120.756 ± 4.953	116.367 ± 3.977	123.173 ± 3.299
DHA	541.748 ± 25.043	528.699 ± 24.293	531.345 ± 24.191	528.597 ± 16.190	535.621 ± 26.864	539.104 ± 25.425
AA	1,416.140 ± 136.869	1,346.699 ± 115.662	1,413.377 ± 126.250	1,431.009 ± 165.233	1,391.521 ± 170.268	1,547.321 ± 140.321
DHGLA	461.100 ± 37.288	438.201 ± 39.349	457.010 ± 33.448	472.864 ± 54.049	427.581 ± 46.155	486.472 ± 17.667

Media from MΦ_WT and MΦ_Tg treated with vehicle, sPLA₂ inhibitor (LY315920), or cPLA₂ inhibitor (CAY 10502) only were processed for lipidomics analyses. The data are mean ± SEM.

^aSignificantly different from WT-sPLA₂ inhibitor, P < 0.05.

TABLE 5.

Effects of sPLA₂ and cPLA₂ inhibition on eicosanoid production from classically activated MΦ_WT and MΦ_Tg

Lipid	MΦWT (n = 4) (pmol lipid/1e+06)			MΦTg (n = 3) (pmol lipid/1e+06)
	IFNγ + LPS alone	+sPLA2 Inhibitor	+cPLA2 Inhibitor	IFNγ + LPS alone	+sPLA2 Inhibitor	+cPLA2 Inhibitor
6-keto PGF1α	9.561 ± 0.967	9.263 ± 2.300	10.615 ± 2.484	25.550 ± 5.683a	24.711 ± 6.504	30.651 ± 11.155
TXB2	5.483 ± 0.119	5.465 ± 0.425	5.607 ± 0.793	7.213 ± 0.608a	6.680 ± 0.422	7.672 ± 1.082
PGE2	12.345 ± 1.661	11.542 ± 2.797	18.140 ± 5.066	29.945 ± 5.518a	28.940 ± 7.541	43.413 ± 17.386
PGA2	2.745 ± 0.263	2.521 ± 0410	3.436 ± 0.707	5.029 ± 0.923	4.798 ± 1.116	6.769 ± 2.360
PGD2	0.406 ± 0.013	0.372 ± 0.027	0.469 ± 0.101	0.629 ± 0.177	0.688 ± 0.237	1.239 ± 0.386
PGF2α	0.659 ± 0.040	0.595 ± 0.060	0.665 ± 0.137	0.882 ± 0.105	0.865 ± 0.123	1.002 ± 0.226
PGE1	1.862 ± 0.264	1.791 ± 0.509	3.129 ± 1.055	5.253 ± 0.949a	4.707 ± 1.301	7.204 ± 2.719
Resolvin D1	0.497 ± 0.020	0.497 ± 0.012	0.519 ± 0.025	0.519 ± 0.016	0.493 ± 0.006	0.513 ± 0.017
LTE4	0.993 ± 0.182	0.663 ± 0.170	0.631 ± 0.165	1.544 ± 0.471	1.555 ± 0.135*	0.776 ± 0.190
(±)14,15-DHET	0.652 ± 0.022	0.668 ± 0.044	0.602 ± 0.049	0.701 ± 0.033	0.700 ± 0.030	0.693 ± 0.030
(±)11,12-DHET	0.783 ± 0.062	0.792 ± 0.007	0.841 ± 0.057	0.823 ± 0.037	0.852 ± 0.015	0.865 ± 0.037
(±)8,9-DHET	0.683 ± 0.016	0.669 ± 0.049	0.833 ± 0.180	0.716 ± 0.036	0.906 ± 0.132	0.934 ± 0.094
(±)14(15)-EET	0.473 ± 0.012	0.440 ± 0.019	0.525 ± 0.027	0.446 ± 0.019	0.464 ± 0.035	0.573 ± 0.050
(±)8(9)-EET	0.127 ± 0.089	0.063 ± 0.011	0.031 ± 0.009	0.217 ± 0.086	0.049 ± 0.011	0.046 ± 0.009
20-HETE	49.441 ± 1.344	48.211 ± 0.504	51.483 ± 1.518	48.650 ± 3.136	49.563 ± 1.295	53.640 ± 3.342
15-HETE	10.040 ± 0.608	9.840 ± 0.902	10.632 ± 1.832	13.091 ± 1.920	12.933 ± 1.949	14.962 ± 2.682
12-HETE	27.123 ± 2.916	25.808 ± 1.084	30.599 ± 3.665	37.284 ± 8.799	40.913 ± 6.892	49.287 ± 11.085
5-HETE	14.827 ± 1.023	14.756 ± 0.983	15.383 ± 0.506	14.036 ± 0.558	14.717 ± 1.211	17.931 ± 1.451
EPA	97.624 ± 9.624	89.281 ± 6.085	87.666 ± 1.709	92.912 ± 4.441	92.378 ± 5.735	96.929 ± 5.827
DHA	531.386 ± 27.303	512.364 ± 27.212	497.151 ± 34.632	517.238 ± 20.479	497.449 ± 34.805	519.231 ± 29.269
AA	1,326.516 ± 36.127	1,276.119 ± 58.193	1,300.538 ± 27.380	1,241.023 ± 39.727	1,247.448 ± 62.748	1,557.577 ± 174.420
DHGLA	428.036 ± 32.703	395.946 ± 21.708	410.002 ± 20.994	401.953 ± 14.966	392.479 ± 31.565	482.797 ± 49.330

Media from MΦ_WT and MΦ_Tg treated with IFNγ + LPS ± sPLA₂ (LY315920) or cPLA₂ (CAY 10502) inhibitor were processed for lipidomics analyses. The data are mean ± SEM.

^aSignificantly different from WT-FNγ + LPS, P < 0.05.

Phenotype comparison of MΦ_WT and MΦ_Tg

In view of the observations that MΦ_Tg exhibit a higher inflammatory state, we assessed macrophage phenotype with activation. We found that classical activation of MΦ_Tg promoted a greater induction of the M1 marker Arg2 (Fig. 11A), while the M2 marker MRC1 was reduced with alternative activation (Fig. 11B) relative to MΦ_WT. Whereas, induction of Arg2 in MΦ_WT and MΦ_Tg by alternative activation was not different, induction of MRC1 by classical activation was reduced in the MΦ_Tg (supplemental Fig. S3C, D). These findings are consistent with a heightened pro-inflammatory and reduced anti-inflammatory lipid profile in MΦ_Tg. No differences were observed in the expression of ALOX-12 or PTGS2 mRNA, which encode 12-LO and COX2, respectively, in MΦ_WT and MΦ_Tg under basal (Fig. 12A) or activated (Fig. 12B, C) conditions. Under both basal and activated conditions, iPLA₂β mRNA was modestly and similarly increased in MΦ_Tg (Fig. 12D) relative to MΦ_WT. To determine whether the transgene is induced in MΦ_Tg, PCR, insulin content, and iPLA₂β immunoblotting analyses were performed in islets and macrophages isolated from WT and Tg mice. As expected, transgene expression was evident in both islets and macrophages from Tg mice but not from WT mice (Fig. 13A). However, as islet β-cells but not macrophages produce insulin (Fig. 13B), overexpression of iPLA₂β protein was only detected in the islets and not in the MΦ_Tg relative to MΦ_WT (Fig. 13C). These findings suggest that signals generated by iPLA₂β-overexpressing β-cells, upon appropriate stimulation, prime macrophages to adopt an M1 phenotype, independent of transgene leakage.

Fig. 11.

Induction of M1 (Arg2) and M2 (MRC1) markers in MΦ_WT and MΦ_Tg. Peritoneal macrophages isolated from 8-week-old WT and Tg mice were treated with vehicle [control (Con)], IFNγ + LPS, or IL-4. The cells were harvested and processed for real-time qPCR analyses. A: Arg2 (Control MΦ_WT and MΦ_Tg 2^−ΔΔCT, 2.21 ± 0.86 and 2.41 ± 0.39). B: MRC1 (Control MΦ_WT and MΦ_Tg 2^−ΔΔCT, 4.81 ± 2.91 and 15.38 ± 4.28). Data are mean ± SEM of fold-change relative to Con determined from four independent experiments. *MΦ_Tg significantly different from MΦ_WT, P < 0.05.

Fig. 12.

Comparisons of PLA₂, ALOX-12, and PTGS2 mRNA in MΦ_WT and MΦ_Tg. Peritoneal macrophages isolated from 8-week-old WT and Tg mice were treated with vehicle (Control) or activated with IFNγ + LPS or IL-4. The cells were harvested and processed for real-time qPCR analyses for ALOX-12 (A, B) and PGST2 (A, C) ± IFNγ + LPS, and iPLA₂β ± IFNγ + LPS or IL-4 (D). A: Control ALOX-12 and PTGS2 2^−ΔΔCT, 6.46 ± 1.17 and 2.98 ± 1.04. B: Control MΦ_WT 2^−ΔΔCT, 6.46 ± 1.17. C: Control MΦ_WT 2^−ΔΔCT, 2.98 ± 1.04. D: Control MΦ_WT 2^−ΔΔCT, 1.01 ± 0.084. Data are mean ± SEM of fold-change relative to control determined from four independent experiments. *MΦ_Tg significantly different from MΦ_WT, P < 0.05.

Fig. 13.

Transgene verification. Pancreatic islets and peritoneal macrophages were isolated from 8-week-old WT and Tg mice for the following analyses: A: Transgene PCR. Analyses were performed using cDNA from WT or RIP.iPLA₂β.TG (Tg) mice, and primers that amplify sequence that either spans junction between iPLA₂β and globin. cDNA (200 bp product, lower band) or is within internal control Fabpi (450 bp product, upper band). Lane L, molecular weight ladder; lane N, control reaction without template. B: Insulin content was determined by ELISA using acid-extracted samples. C: iPLA₂β and tubulin (loading control) immunoblotting were performed using 30 μg protein. The data were obtained from two independent replicates.

DISCUSSION

Inflammatory processes play critical roles in promoting autoimmune-mediated β-cell death leading to T1D; however, the underlying mechanisms promoting inflammation are not well understood. Eicosanoids are recognized contributors to inflammatory responses (4, 59, 60) and these bioactive lipids are generated via metabolism of AA, following its release from membrane glycerophospholipids by a PLA₂-mediated mechanism. We reported that activation of iPLA₂β participates in β-cell death due to ER stress and pro-inflammatory cytokines (44, 61, 62), which are key contributors to β-cell demise leading to T1D. We also observed that activation of iPLA₂β promotes an M1 inflammatory macrophage phenotype, whereas iPLA₂β deficiency favors an M2 anti-inflammatory phenotype (42). Consistent with these reports, selective inhibition of iPLA₂β was found to markedly reduce insulitis, CD4⁺ T-cell and B-cell responses, and diabetes incidence in the nonobese diabetic (NOD) mouse model, a spontaneous autoimmune diabetes-prone mouse model of T1D (20). Collectively, these findings suggested a prominent role for iDLs in promoting inflammatory responses.

Eicosanoids generated via metabolism of AA by COX and 12-LO are recognized to play significant roles in a variety of inflammatory diseases, including diabetes, cancer, atherosclerosis, osteoporosis, and EAE (3, 5, 41, 60, 63–66). The most potent of the inflammatory lipids include PGs, LTs, HETEs, and DHETs. Roles ascribed to them include promoting CD4 Th1/Th17 differentiation (67), inhibition of Tr1 differentiation (68), inducing NF-κB (69), inhibiting Treg cell differentiation (70), modulating local activation of T-cells (71), inducing NO (72), participating in oxidative stress pathways (73), increasing expression of cytokine genes and monocyte chemoattractant protein 1 (74, 75), and reducing inflammation-resolving processes (76–79). In contrast, other PGs (i.e., PGE₁), SPMs, and EETs are involved in resolving inflammation (50, 80–82).

Our collection of earlier findings suggests that iDLs are key contributors to inflammation, β-cell death, and subsequent development of T1D. While eicosanoids have been linked with inflammation development (46, 83), to date, very little is known about the eicosanoid profile of activated macrophages and whether iDLs contribute to an inflammatory state. Therefore, in view of our findings of a link between iPLA₂β and macrophage polarization and responses, we utilized a targeted lipidomics approach with commercially available eicosanoid standards and WT, iPLA₂β^−/− (KO), and RIP.iPLA₂β.Tg (Tg) mouse models to identify iDLs generated by activated macrophages. Our studies revealed two salient and previously undescribed features: 1) the predominant anti-inflammatory M2 phenotype due to iPLA₂β-deficiency is associated with reduced generation of select pro-inflammatory eicosanoids and increased production of the SPM, resolvin D2; and 2) greater production of select pro-inflammatory eicosanoids and reduced resolvin D2 from MΦ_Tg is associated with macrophage polarization toward the inflammatory M1 phenotype. These findings, for the first time, reveal an association between selective changes in eicosanoids and SPMs with macrophage polarization and, further, that the relevant lipid species are modulated by iPLA₂β activity. Most importantly, our findings unveil the possibility that events triggered in β-cells can modulate macrophage responses. This is supported by observations in the RIP.iPLA₂β.Tg model with a select overexpression of iPLA₂β in β-cells; yet, macrophages from these mice exhibit an opposite spectrum relative to MΦ_KO. To our knowledge, this is the first demonstration of intercellular signaling, in particular iPLA₂β-derived intercellular signaling, originating from β-cells affecting macrophage function.

iPLA₂β deficiency, verified by genotype and real-time qPCR analyses, was associated with a significant reduction in the pool of pro-inflammatory PGs with a selective decrease in the abundance of PGE₂. Other recognized inflammatory lipids, including LTs and DHETs, were not significantly affected by iPLA₂β deficiency. While there were no significant differences in the induction of anti-inflammatory PGs by MΦ_WT and MΦ_KO, production of resolvin D2 by MΦ_KO in response to classical activation was significantly higher relative to MΦ_WT. These lipid changes are associated with decreased Arg2 (M1 marker) and increased MRC1 (M2 marker) in MΦ_KO. Resolvin D₂ arises from the metabolism of DHA, and lipids containing DHA are substrates for iPLA₂β (84). However, sPLA₂s exhibit greater activity toward lipids containing DHA than does iPLA₂β (84), and the group V and IIa sPLA₂s are activated during inflammation (85, 86). Consistent with this premise, it has been suggested that at the onset of inflammation, iPLA₂β activity predominates and leads to increased production of inflammatory lipids (2). However, a subsequent resolution phase is unmasked during which groups IIA and V sPLA₂s are activated. Taken together with our present findings, it is likely that iPLA₂β deficiency is permissive for sPLA₂ activation to affect resolution through generation of resolvin D₂. This latter phase appears to occur in the absence of increased expression of sPLA₂s, at least at the time point studied. Acknowledging that in vitro systems are artificial and limited, we speculate that the in vivo inflammatory landscape gives rise to a more dramatic lipid profile.

Intriguingly, we found that the production of pro-inflammatory lipids by MΦ_Tg was increased. These included 6-keto PGF₁α, PGE₂, and LTB₄. Concurrently, there was increased production of anti-inflammatory PGE₁ but decreased production of PGD₂, suggesting differential regulation of pathways leading to generation of these PGs during inflammation. The increase in PGE₁ most likely reflects an attempt at resolution (87). Interestingly, in contrast to MΦ_KO, resolvin D2 production by MΦ_Tg was not altered with classical activation, but was significantly reduced under alternative activation. This suggests a decrease in the metabolism of DHA, which has been reported to increase susceptibility to inflammation (88, 89). In our earlier study, we observed that the polarization toward M2 macrophage phenotype with iPLA₂β deficiency was not a direct effect on iDL production, but was most likely due to the overall decrease in an inflammatory state, which is permissive for the evolution of anti-inflammatory processes (42). The differential production of resolvin D2 by MΦ_KO and MΦ_Tg supports this possibility; in the absence of iPLA₂β, there is a reduced inflammatory state, which unmasks pathways leading to production of resolvin D2; whereas a heightened inflammatory status in MΦ_Tg mitigates such processes. Overall, our lipidomics analyses suggest a pro-inflammatory status in MΦ_Tg and, consistently, polarization of MΦ_Tg favored an M1 over M2 phenotype.

Examination of potential involvement of other PLA₂s expressed in macrophages revealed an absence in induction of sPLA₂ GV or cPLA₂α in activated MΦ_Tg. Because MΦ_Tg exhibited more profound lipid changes with classical activation, we tested the impact of inhibiting sPLA₂s or cPLA₂α on the lipid profile under this condition. Production of eicosanoids by MΦ_WT or MΦ_Tg under basal or activated conditions were found to be similar following exposure to CAY 10502, which inhibits cPLA₂α, or LY315920, which inhibits mGIB, mGIIA, mGIID, mGIIE, mGV, and mGX sPLA₂ (personal communication). In the presence of LY315920, basal production of pro-inflammatory PGs by MΦ_Tg was found to be higher relative to MΦ_WT, most likely a reflection of modest decreases in their abundances in the WT group. Nevertheless, the inability of either CAY 10502 or LY315920 to mitigate the select lipid changes in MΦ_Tg relative to MΦ_WT suggests that neither sPLA₂s nor cPLA₂α contribute to the pro-inflammatory lipid profile in MΦ_Tg during the activated state during the time course of our studies. Additionally, expression of ALOX-12 and PTGS2 mRNA, which encode 12-LO and COX2 proteins, respectively, were similar in the MΦ_WT and MΦ_Tg, suggesting that they likely are not responsible for the differences between the two groups.

These studies add to a growing body of literature indicating a role for iPLA₂β in inflammatory disease, particularly in T1D. We previously reported that glucose-stimulated insulin secretion (GSIS) is accompanied by iPLA₂β-mediated hydrolysis of AA from β-cell membrane glycerophospholipids and that overexpression of iPLA₂β leads to higher GSIS (6, 7, 9). As reported in those studies, the RIP.iPLA₂β.Tg mice exhibited lower circulating glucose levels and amplified GSIS insulin (43). Further, whereas survival of β-cells (INS-1 or in RIP.iPLA₂β.Tg islets) was unaffected by iPLA₂β overexpression under basal conditions, ER stress and pro-inflammatory cytokines amplified apoptosis of the β-cells (44, 48, 61, 90–92). It is important to note that the impact of iPLA₂β on insulin secretion is evident within 1 h of glucose stimulation and is not associated with an increase in iPLA₂β expression. In contrast, its effect on β-cell survival is manifested after several hours of exposure to ER stressors or pro-inflammatory cytokines and is associated with induction of iPLA₂β (44, 48, 61, 62, 90, 92, 93).

In this regard, it was intriguing to find that iPLA₂β mRNA was modestly higher in MΦ_Tg relative to MΦ_WT. However, this did not translate to greater protein expression or increased production of lipids by resting MΦ_Tg. In contrast, classical activation led to significantly higher production of the select lipids by MΦ_Tg, and this may be a consequence of increased availability of substrates derived through iPLA₂β-mediated hydrolysis of sn-2 fatty acyl substituents for metabolism by downstream enzymes. This is analogous to previous reports of minimal effects of higher iPLA₂β expression in β-cells under basal conditions, but amplified in response to stress (43, 44), suggesting that the iPLA₂β overexpressing β-cells are sensitized to respond to stimulation. Our current observations raise the possibility that there may be cross-talk between β-cells and macrophages in vivo, to the extent that MΦ_Tg are predisposed to exhibiting enhanced responses to classical activation leading to an inflammatory landscape. While transfer of stress-induced signals between cells of different types has been suggested (60, 94, 95), to our knowledge, this is the first demonstration of lipid signaling generated by β-cells having an impact on an immune cell that elicits inflammatory consequences.

While the present study did not discern the intercellular signaling molecules in RIP.iPLA₂β.Tg mice, it is likely that they are derived directly or indirectly through an increase in iDLs, as a consequence of increased iPLA₂β expression. In support of this, recent studies reveal that inhibition of iPLA₂β activity mitigates inflammatory processes in different cell systems (18, 78, 96–98), raising the possibility that bioactive lipids generated through β-cell-iPLA₂β activity or factors arising from the effects of such lipids may serve as potential candidate intercellular signals. For instance, insulin secretion from β-cells is accompanied by a parallel iPLA₂β-mediated hydrolysis of AA from β-cell membranes and generation of PGE₂, which is mitigated by selective inhibition of iPLA₂β (7, 99). Mice with selective overexpression of iPLA₂β in β- cells exhibit lower basal blood glucose (43). Further β-cells in these mice express higher pPERK and generate more ceramides under basal conditions (44). This leads to the possibility that circulating insulin, PGE₂ or other iDLs, ER stress factors, or ceramides originating from β-cells could prime macrophages to respond more robustly to classical activation. This is supported by the greater induction of Arg2 and lower induction of MRC1 in MΦ_Tg, which sets the table for the onset and progression of an inflammatory state. Further detailed studies are needed to identify the specific signals from β-cells that affect macrophages and they are currently underway.

The importance of intercellular signaling originating from β-cells is realized in view of findings that β-cells in the diabetes-prone NOD mice express higher iPLA₂β during the prediabetic phase relative to β-cells in spontaneous diabetes-resistant models (20). This early phase encompasses spontaneous evolution of inflammatory signaling leading to the onset of insulitis. Our findings offer the possibility that higher activation of iPLA₂β in the β-cells of NOD mice during this phase contributes signals that can be transmitted to infiltrating leukocytes to initiate immune responses that work in concert with events in the β-cells to amplify the onset and progression of inflammation that subsequently induces β-cell death and T1D. As discussed in a recent review (100), inflammation and β-cell dysfunction are also associated with the development of insulin resistance, and iPLA₂β has been implicated in this process as well, suggesting that signaling between β-cells and immune cells must also be considered as a contributor to the development of T2D.

In summary, our findings reveal that iPLA₂β modulates macrophage production of select lipids, which collectively are recognized to be associated with an enhanced inflammatory state. The selective nature of the lipid changes suggests that the impact of iPLA₂β is not broad but is specific to certain pathways that lead to the generation of such lipids. We find that induction of an M1 inflammatory macrophage phenotype is associated with increased production of iPLA₂β-derived pro-inflammatory lipids and that responses in macrophages can be modulated by iPLA₂β-derived signals of β-cell origin. These findings raise the importance of assessing more carefully the mechanisms governing modulation of the function of immune cells constituting an inflammatory landscape.