Article

The generation of macrophages withanti-inflammatory activity in the absence of

STAT6 signalingBryan D. Fleming,* Prabha Chandrasekaran,* Laura A. L. Dillon,*,† Elizabeth Dalby,*

Rahul Suresh,* Arup Sarkar,*,1 Najib M. El-Sayed,*,† and David M. Mosser*,2

*Department of Cell Biology and Molecular Genetics and Maryland Pathogen Research Institute and †Center for Bioinformatics andComputational Biology, University of Maryland, College Park, Maryland, USA

RECEIVED NOVEMBER 24, 2014; REVISED MAY 1, 2015; ACCEPTED MAY 4, 2015. DOI: 10.1189/jlb.2A1114-560R

ABSTRACT

Macrophages readily change their phenotype in re-

sponse to exogenous stimuli. In this work, macro-

phages were stimulated under a variety of

experimental conditions, and phenotypic alterations

were correlated with changes in gene expression. We

identified 3 transcriptionally related populations of

macrophages with immunoregulatory activity. They

were generated by stimulating cells with TLR ligands in

the presence of 3 different "reprogramming" signals:

high-density ICs, PGE2, or Ado. All 3 of these cell

populations produced high levels of transcripts for

IL-10 and growth and angiogenic factors. They also

secreted reduced levels of inflammatory cytokines

IL-1b, IL-6, and IL-12. All 3 macrophage phenotypes

could partially rescue mice from lethal endotoxemia,

and therefore, we consider each to have anti-

inflammatory activity. This ability to regulate innate-

immune responses occurred equally well in

macrophages from STAT6-deficient mice. The lack of

STAT6 did not affect the ability of macrophages to

change cytokine production reciprocally or to rescue

mice from lethal endotoxemia. Furthermore, treatment

of macrophages with IL-4 failed to induce similar

phenotypic or transcriptional alterations. This work

demonstrates that there are multiple ways to generate

macrophages with immunoregulatory activity. These

anti-inflammatory macrophages are transcriptionally

and functionally related to each other and are quite

distinct from macrophages treated with IL-4.

J. Leukoc. Biol. 98: 395–407; 2015.

Introduction

The plasticity of macrophages allows these cells to undergodramatic alterations in their phenotype in response to diverseenvironmental stimuli [1–6]. This phenotypic heterogeneity ofmacrophages has led to a substantial degree of confusion in thefield about how best to name these cells. This is not simplya semantic problem. A better understanding of the phenotypicalterations that macrophages undergo is necessary if weeventually hope to manipulate immune responses at the level ofmacrophages. Studies to improve understanding of heterogene-ity can put us in a better position to generate macrophages withpredictable phenotypes, to deplete one set of macrophages whilepreserving others, or to target drugs to individual subpopulationsof macrophages.The pioneering work of Gordon and colleagues in the 1990s

helped to define 2 paradigmatic populations of macrophages,generally referred to as “classical” versus “alternative” but latertermed M1 versus M2 or M(IFN-g) versus M(IL-4) [5, 7–9].Exposing macrophages to IFN-g and TLR ligands results in anup-regulation of inflammatory cytokines, an increased MHC-IIand costimulatory molecule expression, and the production ofantimicrobial products [7, 10–13]. Cells exposed to IL-4, incontrast, fail to up-regulate costimulatory molecules and MHC-II,are poor APCs, and produce negligible amounts of NO. Thesecells express higher levels of chitinases and lectin-like receptorsand are termed AA-Mf [7, 14, 15]. In the literature, the variouscategories of macrophages sometimes get confused, and all TLR-stimulated macrophages are grouped into the M1 category byvirtue of the inflammatory cytokines that they produce. Incontrast, all noninflammatory macrophages are collectivelyreferred to as M2. This has led to some confusion as to whatactually constitutes an M2 macrophage. The linear M1/M2classification system remained the standard for nomenclatureuntil investigators attempted to include macrophages that weretreated with glucocorticoids, anti-inflammatory cytokines,

1. Current address: Trident School of Biotech Sciences, Odisha, IN

2. Correspondence: Dept. of Cell Biology and Molecular Genetics, 3102Bioscience Research Bldg., University of Maryland, College Park, MD20742, USA. E-mail: dmosser@umd.edu

Abbreviations: AA-Mf = alternatively activated macrophages,

Ado = adenosine, BMDM = bone marrow-derived macrophages, Chi3l3 =

chitinase 3-like protein 3, CT = comparative threshold cycle, flrt3 =

fibronectin leucine-rich transmembrane protein 3, gem = GTP-binding

protein, IC = immune complex, ildr1 = Ig-like domain receptor 1, IPA =

Ingenuity Pathway Analysis, Itgb3 = integrin b3, L+A = LPS + adenosine,

(continued on next page)

The online version of this paper, found at www.jleukbio.org, includessupplemental information.

0741-5400/15/0098-395 © Society for Leukocyte Biology Volume 98, September 2015 Journal of Leukocyte Biology 395

apoptotic cells, ICs, or Ado derivatives, to name a few.These macrophages did not fit into a simple M1/M2classification scheme.A color-wheel scheme was proposed to highlight the plasticity

of macrophages [4, 16]. This model placed an emphasis on thedynamic nature of macrophage activation and proposed thatmacrophages can readily transition from one activation state toanother. In the present study, we examine 5 macrophagepopulations from different segments of the color wheel anddemonstrate that macrophages treated with IL-4 are transcrip-tionally and phenotypically distinct from 3 macrophage popula-tions with anti-inflammatory phenotypes. We also show thatalthough the 3 macrophage populations can be distinguishedfrom each other at the global transcriptome level, they sharea number of characteristics that endow them with immunoreg-ulatory activity, including the reduced production of inflamma-tory cytokines and the secretion of growth and angiogenicfactors. Therefore, we loosely group them together as R-Mf. Wedescribe chemokine and cytokine signatures of R-Mf anddemonstrate their functionality during inflammatory conditions.

MATERIALS AND METHODS

MiceFive-week-old BALB/c or C57BL/6 female mice were purchased from CharlesRiver Laboratories (Frederick, MD, USA). Mice were housed at the Universityof Maryland (College Park, MD, USA) animal facility. All procedures wereapproved by the University of Maryland Institutional Animal Care and UseCommittee.

Murine macrophagesBMDMs were obtained by flushing the femurs and tibiae of BALB/c mice andwere cultured in DMEM/F12 medium with 10% FBS, 1% glutamine, 1%penicillin/streptomycin, and 15% L929 cell conditioned media, as describedpreviously [17]. Peritoneal macrophages were obtained by peritoneal flushwith PBS from female mice. Cells obtained from 10 to 12 mice were pooledand stimulated with LPS, L+I, L+A, or IL-4 for 4 h.

Human macrophage culturePeripheral blood was collected from healthy volunteers, and mononuclearcells were separated by Ficoll-Hypaque density gradient centrifugation.Monocytes were isolated by attachment and were cultured for 1 wk in RPMI-1640 medium with 10% human AB serum (Life Technologies, Grand Island,NY, USA) and 50 ng/ml M-CSF (PeproTech, Rocky Hill, NJ, USA). All studieson human monocyte-derived macrophages were approved by the University ofMaryland Institutional Review Board.

Cell culture and stimulationM1-Mf and AA-Mf macrophages were generated by adding 10 ng/mlUltrapure LPS (InvivoGen, San Diego, CA, USA) and 20 ng/ml mouse IL-4(R&D Systems, Minneapolis, MN, USA), respectively. R-Mf were obtained byadding 10 ng/ml Ultrapure LPS in combination with ICs, generated asdescribed previously [18], 200 nM PGE2 (Cayman Chemical, Ann Arbor, MI,USA), or 200 mM Ado (Sigma-Aldrich, St. Louis, MO, USA). For RNAisolation, 23 106 BMDMs were stimulated for 2–6 h, and for cytokine analyses,

2.5 3 105 BMDMs were stimulated for 12–16 h. For the membrane proteinarray, 2 3 106 BMDMs were stimulated for 12 h. For the bioplex analyses,supernatants were obtained from 5 3 105 human macrophages after 24 hstimulation.

In vivo regulatory inductionAge-matched female BALB/c or C57BL/6 mice were injected with 500 mg LPSalone or a combination of LPS and 400 ml Ova-IC (L+I), 50 mg PGE2

(L+P), 50 mg Ado (L+A), or 50 mg IL-4 intraperitoneally. After 6 h, peritonealcells were isolated by peritoneal lavage and processed for flow cytometry orreal-time PCR.

Lethal endotoxin challengeBALB/c BMDMs stimulated in vitro under various conditions were injectedintraperitoneally into BALB/c mice. Three hours after cell transplantation,mice were challenged with a 10 mg/kg dose of endotoxin (L2630; Sigma-Aldrich). Survival of the mice was recorded for 1 wk. Statistical significance wasdetermined by Mantel-Cox log rank test.

ELISAIL-12/23p40 and IL-10 levels were measured by the sandwich ELISA methodusing antibodies purchased from BD PharMingen (San Diego, CA, USA).Mouse IL-1b and IL-6 and human IL-12/IL-23p40 levels (see Fig. 7F) weremeasured by use of Duoset ELISA kits (R&D Systems). Human IL-10 (see Fig.7A) and human IL-12p40 (see Fig. 7A) were detected by use of ELISA kitspurchased from eBioscience (San Diego, CA, USA).

Membrane protein arrayMouse cytokine antibody array membranes [Proteome Profiler Antibody Array(R&D Systems] were used to assess the relative differences of 40 differentcytokines and chemokines in cell culture supernatants. Chemiluminescencesignal density was quantified by use of LAS-3000 Imaging Systems fromFujifilm (Tokyo, Japan).

Bioplex assayHuman macrophage cytokines/chemokines were measured by use ofa 14-plex magnetic Luminex screening assay (R&D Systems). The sampleswere acquired in Magpix, and data were analyzed with xPONENT software(Luminex, Austin, TX, USA).

RNA isolation, cDNA synthesis, and PCRRNA was isolated by use of the TRIzol-chloroform method. ComplementaryDNA was synthesized with a Thermoscript RT-PCR kit (Invitrogen, GrandIsland, NY, USA). Primer pairs used in this study are listed in SupplementalTable 1. Relative quantification of RNA was done by use of SYBR-Green-basedreal-time PCR. CT value for gapdh was used to calculate relative differences,and fold induction was calculated by use of 2^(2DDCT) [19].

RNA-seq data generation and processingRNA-seq analyses were performed on 3 different sample sets, obtained ondifferent days from female C57BL/6 mice. Poly(A)-enriched cDNAlibraries were generated by use of the TruSeq Sample Preparation Kit(Illumina, San Diego, CA, USA). Paired end reads (100 bp) were obtainedfrom the HiSeq 1500 platform (Illumina). Trimmomatic [20] was used toremove any remaining adapter sequences from reads and to trim off baseswith quality scores below 20. Sequence quality metrics were assessed by useof FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/).Reads were aligned to the Mus musculus genome (v. mm10/GRCm38)obtained from University of California Los Angeles (CA, USA; http://genome.ucsc.edu) by use of TopHat (v 2.0.10) [21]. Reads wereallowed to map only to a single locus. The abundance of reads mapping

(continued from previous page)

L+I = LPS + immune complex, L+P = LPS + PGE2, MHC-II = MHC class II,

PCA = Principal Component Analysis, PEC = peritoneal exudate cell, qRT-

PCR = quantitative RT-PCR, R-Mf = regulatory macrophages, relma =

resistin-like molecule a, RNA-seq = RNA-Sequencing, WT = wild-type

396 Journal of Leukocyte Biology Volume 98, September 2015 www.jleukbio.org

to each gene was determined by use of HTSeq (http://www-huber.embl.de/users/anders/HTSeq/).

Data quality assessment and differentialexpression analysisMultiple approaches were used to evaluate replicates, including PCA andEuclidean distances-based hierarchical clustering. All components of thestatistical pipeline, named cbcbSEQ, can be accessed on GitHub (https://github.com/kokrah/cbcbSEQ/). Nonexpressed and weakly expressed geneswere removed before differential expression analysis, and a quartilenormalization scheme was applied to all samples [22]. Limma (aBioconductor package; http://www.bioconductor.org/packages/release/bioc/html/limma.html) [23] was used to conduct differential expressionanalyses following log2 data transformation and application of the voom [24]method. Experimental batch effects were adjusted for by including batch/experimental date as a covariate in the statistical model [25]. Differentiallyexpressed genes were defined as genes with a Benjamini-Hochberg multiple-testing adjusted P value of ,0.05. The RNA-seq datasets described in thisarticle are available at the National Center for Biotechnology InformationSequence Read Archive under Accession Numbers SRR1918864,SRR1918994, SRR1918999-SRR1919012, and SRR1919014-SRR1919018 (seeSupplemental Table 2).

IPAsIPA software [26] was used to predict "Diseases and Functions" and "CanonicalPathways" associations. Genes that exhibited a ,2-fold difference wereexcluded from the comparisons.

Macrophage metabolismGlucose levels and L-lactate production were assessed by use of the GlucoseAssay Kit (Sigma-Aldrich) and L-Lactate Kit I (Eton Biosciences, San Diego,CA, USA), respectively.

Flow cytometryMouse CCR1 surface expression was detected by PE-conjugated antibody(R&D Systems). Data acquisition was carried out in FACSCanto II (BDBiosciences, Franklin Lakes, NJ, USA), and analyses were done by use ofFlowJo version 10.

StatisticsNonparametric t-tests were performed to calculate the significance of theobserved differences. P, 0.05 was considered to be significant for all analyses.

RESULTS

Macrophages with immunoregulatory activity can offerprotection from lethal endotoxin challengeMacrophages were stimulated in a variety of different ways in vitroand then transferred into the peritoneum of mice before aninjection of bacterial endotoxin (LPS). The macrophages studiedincluded resting macrophages, macrophages treated with IL-4(AA-Mf), and macrophages that were stimulated with LPS in thepresence of 3 different reprogramming stimuli: ICs (RMf-IC), PGE2(RMf-PGE2), or Ado (RMf-Ado). The goal of this work was todetermine whether any of these macrophages could influence theprogression of lethal endotoxemia. The administration of 1 3 106

resting macrophages into mice did not influence the progression oflethal endotoxemia, and 80% of the mice succumbed over the first4 days (Fig. 1A). All 3 of the macrophage populations that were“reprogrammed” provided some level of protection to micereceiving endotoxin. Mice that received RMf-IC showed a 90%survival, whereas mice that received RMf-PGE2 or RMf-Ado showedsurvival rates of 70% and 50%, respectively. Mice receiving macro-phages that were treated with IL-4 (AA-Mf) were not protectedfrom lethal endotoxemia and in fact, did slightly worse than micereceiving resting macrophages (Fig. 1A). The mice that receivedRMf-IC had a 90% survival rate, regardless of whether themacrophages that were transferred were from WT or stat6 knockoutmice (Fig. 1B). Thus, all 3 of the macrophages that werereprogrammed before stimulation provided some level of protectionfrom lethal endotoxemia, and the ability to provide protection fromlethal endotoxin highlights a major functional difference betweenthe R-Mf described herein and IL-4-treated AA-Mf, both of whichhave been considered previously by some to be M2 macrophages.

Macrophages with this immunoregulatory phenotypehave distinct cytokine and chemokine profilesCytokine production from the various macrophage populations wasmeasured. The addition of the various reprogramming signals tomacrophages resulted in dramatic changes in their cytokine andchemokine expression. As expected, M1-Mf stimulated with LPSexhibited an inflammatory phenotype, secreting high IL-12/23p40,IL-1b, and IL-6 but low levels of IL-10 (Fig. 2A–D). Macrophages

Figure 1. Regulatory activation provides protectionfrom lethal endotoxemia. (A) BALB/c mice re-ceived 1 3 106 resting, nonstimulated macrophages(Mf, filled circles) intraperitoneally or macrophagesstimulated in vitro with LPS in combination with Ova-IC (blue squares), PGE2 (red squares), or Ado (greensquares) or macrophages treated with IL-4 (yellowtriangles), 3 h before challenge with a lethal dose ofendotoxin (10 mg/kg). The survival of the mice wasrecorded every 8 h over the next week. (B) A similarsurvival experiment was carried out in WT mice thatreceived 1 3 106 L+I macrophages from WT (bluesquares) or stat62/2 (olive squares) mice beforeendotoxin challenge. Each graph represents data of2 independent experiments with 10 mice/experi-ment for each condition. The data from the controlgroup and WT RMf-IC were shared between the 2graphs. ***P , 0.0001, and **P = 0.0006, Kaplan-Meier estimates obtained for mice treated with restingmacrophages versus other macrophage treatments.

Fleming et al. Characterization of immunoregulatory macrophages

www.jleukbio.org Volume 98, September 2015 Journal of Leukocyte Biology 397

stimulated with LPS and reprogrammed with IC, PGE2, or Adowere substantially less inflammatory. They secreted higher levelsof IL-10 and suppressed the production of IL-12/23p40, IL-1b,and IL-6 (Fig. 2A–D). To test if regulatory functions weredependent on STAT6, macrophages from stat6 knockout micewere stimulated in the presence of IC, PGE2, or Ado. These cells

produced higher levels of IL-10 and reduced levels of in-flammatory cytokines IL-12/23p40, IL-1b, and IL-6, indicatingthat STAT6 signaling is dispensable for generating macrophageswith this immunoregulatory phenotype (Fig. 2E–H). In contrastto R-Mf, AA-Mf produced little or no detectable levels of thetested cytokines (Figure 2A–D). RT-PCR analyses of AA-Mf from

Figure 2. R-Mf induction is independent ofSTAT6 signaling pathway. BALB/c WT and stat62/2

BMDMs were treated with 10 ng/ml LPS aloneor a combination of LPS and 25 ml Ova/anti-OvaICs (L+I), 200 nM PGE2 (L+P), 200 mM Ado(L+A), or 20 ng/ml IL-4 for 16 h. The levels of IL-10(A and E), IL-12/23p40 (B and F), IL-1b (C andG), and IL-6 (D and H) were measured in theirsupernatants by ELISA. Error bars indicatemeans 6 SEM of 3 independent experiments.***P , 0.001, **P , 0.01, and *P , 0.05.Representative RT-PCR analysis of alternate acti-vation markers relma and ym1 from WT or stat62/2

BMDMs stimulated for 4 h with their respectivestimuli before RNA isolation. The gapdh is usedas the internal control (I).

398 Journal of Leukocyte Biology Volume 98, September 2015 www.jleukbio.org

Figure 3. Regulatory activation results in alterations in cytokine/chemokine profiles. (A) Chemokine and cytokine secretion by BMDMs was measured bya proteome profiler membrane antibody array. Supernatants from nonstimulated macrophages were compared with macrophages treated with 10 ng/mlLPS or a combination of LPS and 100 ml Ova-IC (L+I), 200 nM PGE2 (L+P), 200 mM Ado (L+A), or 20 ng/ml IL-4 for 12 h. The proteins that are ofinterest to this study are indicated in circles, and the letters and numbers are provided to identify the position of the analyte in the membrane. Theprofiling was done on pooled supernatants collected from 3 separate experiments from 3 different mice. (B). Mean fold differences in intensity ofthe duplicate samples for relevant analytes are compared with supernatants from nonstimulated. The alphanumeric values within parentheses indicatetheir position in the membrane array. BCA-1, B Cell-attracting chemokine-1; TCA-3, T cell activation-3; KC, keratinocyte-derived chemokine.

Fleming et al. Characterization of immunoregulatory macrophages

www.jleukbio.org Volume 98, September 2015 Journal of Leukocyte Biology 399

Figure 4. Global RNA expression profiles of macrophage samples. RNA-seq was carried out on an Illumina platform, comparing nonstimulated (NS)murine peritoneal macrophages and macrophages exposed to LPS, L+I, L+P, L+A, or IL-4. A Principal Component Analysis (PCA) plot (A) and heatmap of a hierarchical clustering analysis that uses the Euclidean distance metric (B) are shown. (A) In the PCA plot, each symbol represents anexperimental sample with symbol color indicating macrophage treatment condition (NS, gray; LPS, orange; L+I, light blue; L+A, medium blue;

(continued on next page)

400 Journal of Leukocyte Biology Volume 98, September 2015 www.jleukbio.org

WT macrophages revealed high transcription of relma and ym1[14], confirming that our IL-4 treatment had indeed generatedAA-Mf (Fig. 2I). Macrophages from mice genetically deficient instat6 failed to transcribe relma and ym1 in response to IL-4, asexpected (Fig. 2I).Further analyses of cytokines and chemokines by membrane

arrays revealed an increased expression of G-CSF, CXCL13, andCCL1, as well as IL-10 in the R-Mf, relative to LPS or IL-4-treatedmacrophages (Fig. 3A and B). The chemokines CCL2 and CCL3were down-regulated by at least 2-fold in intensity in R-Mf (Fig.3A and B). Most of the tested chemokines and cytokines showedlittle or no expression in AA-Mf, except CCL2 (Fig. 3A and B).Together, the data suggest that there are multiple ways togenerate macrophages with immunoregulatory activity and thatthese R-Mf exhibit unique expression patterns of cytokines andchemokines that are quite distinct from macrophages treatedwith IL-4.

RNA-seq analysis of murine macrophagesTo dissect further differences between the activation states ofprimary macrophages, we used high-throughput RNA-seq tech-nology to assess the transcriptomes of differentially stimulatedperitoneal macrophages. PCA results revealed that AA-Mf

cluster with nonstimulated macrophages, whereas M1-Mf, RMf-IC, RMf-PGE2, and RMf-Ado align together along principalcomponent 1 (the x-axis), which accounts for ;70% of thevariability observed between samples (Fig. 4A). Likewise, whenEuclidean distance heat-map analysis was used to visualize therelationships between the samples, IL-4-treated AA-Mf groupedclosely with nonstimulated cells, and the 3 R-Mf subtypesclustered with LPS-treated cells (Fig. 4B). Thus, macrophageswith immunoregulatory activity were transcriptionally distinctfrom IL-4-treated AA-Mf.Differential expression analysis was used to generate lists of

genes that were .2-fold different between each macrophagepopulation and nonstimulated macrophages (P , 0.05). RMf-ICand RMf-Ado shared a total of 172 genes that were not up-regulated in other macrophage populations, whereas only 15genes were up-regulated in R-Mf and AA-Mf but not M1-Mf

(Fig. 4C). Likewise, 228 genes were down-regulated in R-Mf-ICand RMf-Ado, but only 12 genes were down-regulated in R-Mf

and AA-Mf (Fig. 4D). The gene products that were uniquely up-regulated in R-Mf have the potential to be used as biomarkersfor defining these macrophages and providing further insightsinto their immunoregulatory functions. It should be noted thatthe 2 populations of R-Mf showed substantial transcriptionaldiversity: 325 genes were uniquely up-regulated, and 354 wereuniquely down-regulated in RMf-IC; 268 genes were uniquelyup-regulated, and 308 genes were down-regulated in RMf-Ado

(Fig. 4C and D). The fold induction of regulatory genes followingreprogramming by PGE2 was insufficient to yield a reliabletranscriptional signature.

Differentially expressed genesR-Mf and AA-Mf are often grouped together as M2 macro-phages [8, 27]. However, our functional profiles, PCA, and heatmap analyses revealed that these macrophages are quite distinct.To identify the genes that define each population, we analyzedthe differentially expressed genes in each of the subsets. The top20 genes that were induced in AA-Mf relative to unstimulatedmacrophages are listed in Table 1. All of these genes are induced32-fold or greater in response to IL-4 treatment. Only 2/60 of themost highly up-regulated genes (Itgb3 and Chi3l3) weresignificantly induced in any of the 3 R-Mf populations, and thehighest induction was ,3-fold. Therefore, the transcripts thatdefine the major functions of AA-Mf are largely lacking in R-Mf.Previously defined markers for murine AA-Mf, including ym1 &relma, were confirmed by our RNA-seq analyses [14]. Genes withat least a 2-fold up-regulation and with an adjusted P valueof ,0.01 were considered to be immunoregulatory genes.Table 2 lists 20 genes significantly up-regulated in all 3 R-Mf pop-ulations relative to unstimulated macrophages. As expected, IL-10was high on the list of genes associated with regulatory activation(Table 2). In addition to this list of genes with known function,there were another 26 genes that had no known function thatwere substantially up-regulated in R-Mf (Supplemental Table 3).Studies to address the relevance of these induced genes areongoing.

Activated Diseases and Functions identified byIPA analysisThe IPA platform was used to identify differences in Diseases andFunctions associations between R-Mf and M1-Mf. The attributesmost closely associated with 2 populations of R-Mf were thedevelopment of new blood vessels, the proliferation of cells, andthe development of tumors (Fig. 4E and F). As expected, R-Mf

down-regulated myeloid cell activation, the induction of TH1-associated functions, and antimicrobial and antiviral functions(Fig. 4E and F). The induction of regulatory-associated tran-scripts in PGE2-programmed cells was not robust enough to allowa similar analysis of these cells. The previously reported functionsassociated with LPS stimulation [28–31] were confirmed by ourRNA-seq and IPA analyses (Supplemental Fig. 1A). AA-Mf

appeared to be more closely associated with the maintenance ofconnective tissue and tissue development in general (Supple-mental Fig. 1B), thus agreeing with previously describedphenotypes for these cells [27, 32]. The IPA analysis of these data

L+P, navy blue; and IL-4, green), and symbol shape indicates batch. (B) Colors along the top of the heat map indicate the treatment condition(same color codes as in A), and colors along the left side of the heat map indicate the batch/experimental date. (C and D) Overlap of differentiallyexpressed genes up-regulated (C) or down-regulated (D) by.2-fold relative to nonstimulated macrophages are displayed in Venn diagrams. Each large-colored square represents the treatment condition (same color codes as in A). The changes associated with Diseases and Functions in R-Mf werepredicted by the IPA program. Genes that showed a changed in L+I or L+A of at least 2-fold when compared with LPS were selected to identify pathwaysassociated with regulatory functions (E and F). Fold changes were uploaded to IPA, and the Diseases and Functions predicted to be altered based ona significant Z-score were selected for these graphs. A Z-score above 1.65 (activated) or below 21.65 (inhibited) is considered statistically significant.

Fleming et al. Characterization of immunoregulatory macrophages

www.jleukbio.org Volume 98, September 2015 Journal of Leukocyte Biology 401

clearly demonstrate that R-Mf have functions that are distinctfrom AA-Mf.

Metabolism in regulatory activation is similar to that inLPS-treated cellsSeveral studies have identified metabolic alterations among thevarious macrophage activation states [33–35]. IPA analysis of theRNA-seq data identified several metabolic changes that occurredin M1-Mf and R-Mf but not in AA-Mf (Table 3). Glucoseconsumption by R-Mf was comparable with M1-Mf, and muchhigher than AA-Mf and nonstimulated cells (Fig. 5A). Likewise,the secretion of L-lactate, a metabolic product of the fermenta-tion pathway, was higher in M1-Mf and R-Mf than AA-Mf (Fig.5B). These results suggest that macrophages with an immuno-regulatory phenotype share metabolic similarities with M1-Mf

and are distinct from AA-Mf. Thus, the reprogramming signalsthat so substantially change the phenotype of these cells (Fig. 2)do not alter the metabolic alterations associated with inflamma-tory M1 macrophages.

Identification of candidate biomarkersTo validate the RNA-seq analyses, qRT-PCR was performed toexamine several regulatory and AA-Mf-associated genes.Activation states were confirmed through the examination ofwell-established markers (Supplemental Fig. 2). Amongtested genes, the mRNA levels of il-33, flrt3, and ccr1 wereinduced in all regulatory conditions but not by LPS or IL-4stimulation, and their induction was STAT6 independent(Fig. 6A–F). Thus, these genes represent potential biomarkers forR-Mf. Additionally, some genes were induced in individualregulatory populations but not shared by all 3 R-Mf. For example,mRNAs encoding gem, ildr1, and epithelial membrane protein 1 were

induced in RMf-IC but not in RMf-PGE2/RMf-Ado (Supple-mental Fig. 3). The mRNA expression of il-4i1, earl1, and cd209ewas specifically induced in WT AA-Mf but not in stat62/2

macrophages (Supplemental Fig. 3), reiterating the importance ofSTAT6 in alternative macrophage activation.Chemokine receptors form an important component of

immune responses, and the polarized macrophages subsetsexhibit differences in their surface chemokine receptorexpression [36]. To correlate transcription with surfaceprotein levels, we performed flow cytometric analysis of surfaceexpression of CCR1 on the different macrophage populationsstimulated in vitro in the presence or absence of reprogramming.There was a 3- to 4-fold increase in mean fluorescent intensityobserved in all of the R-Mf, with only a 2-fold induction in theLPS-treated cells and no induction in IL-4-treated cells (Fig. 6Iand J). Thus, CCR1 represents a potential biomarker for R-Mf.A further analysis of CCR1 expression was performed on R-Mf

from mice injected in vivo with LPS intraperitoneally in thepresence or absence of reprogramming signals. Peritonealmacrophages from BALB/c (Fig. 6I–L)and C57BL/6 mice (Fig.6M and N) were analyzed. Similar to the in vitro results reportedabove, all 3 reprogrammers induced IL-10 transcripts from PEC(Fig. 6K), but only IL-4 injections resulted in macrophageexpression of ym-1 (Fig. 6L). CCR1 was analyzed ex vivo by flowcytometry. PEC from BALB/c (Fig. 6I and J) and C57BL/6 (Fig.6M and N) mice up-regulated CCR1 in response to all 3reprogramming signals, indicating that the regulatory phenotypewas readily inducible in vivo.

Cytokine expression from human macrophagesTo extend our observations from mouse to human, we stimulatedhuman monocyte-derived macrophages under the same

TABLE 1. Top 20 genes induced following IL-4 stimulation

Symbol Name IL-4 (log2) R-IC R-Ado R-PGE2

1 chi3l3 Chitinase 3-like 3 8.85 ns 0.94 ns2 cd209e CD209e antigen 6.83 ns ns ns3 itgb3 Integrin b 3 6.80 1.46 ns ns4 serpina3g Serine peptidase inhibitor clade A-3G 6.71 ns 22.19 ns5 flt1 FMS-like tyrosine kinase 1 6.21 21.47 ns ns6 pdcd1lg2 Programmed cell death 1 lig2 5.78 ns ns ns7 slc7a2 Solute carrier family 7 5.69 ns ns ns8 rnase2a Ribonuclease RNase A family 2a 5.62 ns ns ns9 cish Cytokine-inducible Src homology

2-containing protein5.55 ns ns ns

10 tslp Thymic stromal lymphopoietin 5.49 ns ns ns11 chil4 Chitinase-like 4 5.41 ns ns ns12 tmem26 Transmembrane protein 26 5.30 ns ns ns13 il4i1 IL-4-induced 1 5.26 20.73 ns ns14 cdh1 Cadherin 1 5.25 ns ns ns15 apol7c Apolipoprotein L 7c 5.20 ns ns ns16 socs1 Suppressor cytokine signaling 1 5.04 20.94 ns ns17 mrc1 Mannose receptor C type 1 5.02 ns ns ns18 ddx4 DEAD box polypeptide 4 5.02 ns ns ns19 il31ra IL-31R A 5.00 ns ns ns20 insrr Insulin receptor-related receptor 5.00 ns ns ns

Values represent the fold induction (log 2) over nonstimulated macrophages. R-IC, RMf-IC; R-Ado,RMf-Ado; R-PGE2, RMf-PGE2; DEAD (Asp-Glu-Ala-Asp); ns, not significant.

402 Journal of Leukocyte Biology Volume 98, September 2015 www.jleukbio.org

conditions used for mouse BMDMs. The results were generallyconsistent with the murine data, although there was substantiallymore variability in this small patient sample. Most of thehuman macrophages responded to reprogramming with IC byincreasing their secretion of IL-10 production and decreasingIL-12/23p40 secretion (Fig. 7A, B, and F). By bioplex assay, theinflammatory cytokines TNF-a (Fig. 7D), IL-6 (Fig. 7E), andIL-12/23p40 (Fig. 7F) were generally down-regulated in humanR-Mf, but only IL-6 was decreased significantly in all 3 R-Mf

groups (Fig. 7E). The other 2 inflammatory cytokines were down-regulated in ⅔ of the human R-Mf groups. Likewise, IL-10

(Fig. 7A and B) was up-regulated in ⅔ of the human R-Mf

groups. The biggest difference between the murine and humancells was that the levels of IL-1b were unaffected by any of theregulatory signals in human macrophage (Fig. 7G). Theseresults demonstrate that regulatory signals are inducible inhuman and murine macrophages, and when induced, theyproduce unique cytokine signatures that differentiate themfrom M1-Mf and AA-Mf.

DISCUSSION

There remains a substantial degree of confusion regarding whatconstitutes an M2-Mf. The original grouping of all macrophagesthat were not M1-Mf into the M2 category was initiallyinstructional, as it fostered the idea that not all stimulatedmacrophages are the same. However, it has also led to themisconception that all M2-Mf are similar, and this does notappear to be the case. In this work, we describe populations ofmacrophages with potent anti-inflammatory activity anddemonstrate that they can be generated via a STAT6-independent mechanism. We also demonstrate that R-Mf aretranscriptionally and functionally distinct from the originallydescribed AA-Mf.In earlier reports, we demonstrated that macrophages

stimulated with TLR ligands in the presence of high-density ICsassumed immunoregulatory functions by dampening inflamma-tory cytokine production and enhancing IL-10 secretion [18, 37].The induction of R-Mf required 2 concurrent signals: 1 toactivate the transcription factors necessary for cytokine pro-duction and the 2nd to “reprogram” the cell to secrete

TABLE 2. Genes induced during regulatory activation

Symbol Name L+I (log2) L+A (log2) L+P (log2)

1 lif Leukemia inhibitory factor 9.65 9.64 8.212 il10 IL-10 8.73 6.84 5.993 ildr1 Ig-like domain receptor 1 6.59 4.46 2.934 flrt3 Fibronectin leucine-rich

transmembrane protein 36.27 6.40 5.28

5 xcr1 Chemokine (c-motif) receptor-1 6.30 5.07 2.526 il33 IL-33 5.75 9.06 5.217 ckap2l Cytoskeleton-associated protein 2-like 5.51 6.06 3.928 ndrg1 N-myc downstream-regulated gene 1 5.38 5.09 2.979 itga2 Integrin a 2 5.24 4.83 3.9110 gem GTP-binding protein 5.72 4.72 3.1011 mid1 Midline 1 4.81 3.38 1.5012 odc1 Ornithine decarboxylase structural 1 5.16 5.72 3.9813 hephl1 Hephaestin-like 1 4.83 4.87 4.4114 gdnf Glial cell line-derived neurotrophic

factor5.42 6.17 5.18

15 klk9 Kallikrein-related peptidase 9 4.45 6.67 4.7816 dusp14 Dual-specificity phosphatase 14 5.25 8.00 4.0717 gprc5a G protein-coupled receptor family C

member A4.73 4.04 2.80

18 tmem88 Transmembrane protein 88 5.08 5.77 3.3019 hrc Histidine-rich calcium-binding protein 4.56 2.99 1.9420 nptx2 Neuronal pentraxin 2 4.49 7.62 3.14

Values represent induction over the nonstimulated and are expressed as a log2.

TABLE 3. Metabolic pathways identified by IPA analysis (Z-score)

Canonical pathways related toenergy metabolism LPS L+I L+A IL-4

Pentose phosphate pathway(oxidative branch) 2.54 2.37 2.42 0.73

Pentose phosphate pathway 2.43 2.20 2.26 0.44Fatty acid b-oxidation I 1.82 2.46 3.12 0Glycogen degradation II 1.89 1.70 2.53 0Glycogen degradation III 2.19 1.33 2.79 0Gluconeogenesis I 1.66 3.73 1.06 0.21Oleate biosynthesis II (animals) 0.85 1.77 1.82 0Glycolysis I 1.12 3.51 0.97 0

The top energy metabolism-related canonical pathways that showsignificant up-regulation in at least 1 of the 4 conditions analyzed by RNA-seq. Numbers are expressed as the Z-score, which was generated froma Fisher’s exact test when comparing stimulated with nonstimulatedmacrophages. Numbers that had a Z-score of at least 1.65 (P , 0.05) areconsidered significant.

Fleming et al. Characterization of immunoregulatory macrophages

www.jleukbio.org Volume 98, September 2015 Journal of Leukocyte Biology 403

immunoregulatory cytokines. In this study, we show that thereare many potential reprogramming signals that can change thephenotype of stimulated macrophages, including Ado and PGE2.All of these macrophages exhibit common characteristics, such asthe production of higher levels of the immunomodulatorycytokine IL-10 and the secretion of reduced levels of inflamma-tory cytokines, such as IL-1b, IL-6, and IL-12/23. IPA analysisrevealed that R-Mf were associated with increases in cell growthand proliferation. Thus, it is likely that these R-Mf contribute tothe maintenance of homeostasis by dampening immuneresponses and promoting cellular repair.To address whether IL-4 or STAT6 signaling is required for

R-Mf functions, we studied cytokine production from macro-phages from stat62/2 mice stimulated under these immunoreg-ulatory conditions. Signaling through STAT6 was previouslydemonstrated to be important for the anti-inflammatory prop-erties of IL-4 [38, 39] and the generation of AA-Mf [39]. In ourhands, STAT6 signaling was dispensable for the generation ofR-Mf. These cells produced similar amounts of regulatorytranscripts and provided equal degrees of protection of micefrom lethal endotoxemia. Furthermore, the addition of IL-4 toWT macrophages failed to induce transcripts associated withR-Mf and failed to rescue mice from lethal endotoxemia. Thus,these 2 macrophage populations are functionally and transcrip-tionally distinct.The relationship between AA-Mf and R-Mf is fairly complex.

They share some common activities, in that both appear to beinduced in response to tissue injury, and both may contribute towound healing and tissue regeneration. Furthermore, antigenpresentation by R-Mf can give rise to TH2 T cells [40], whichhave the potential to generate AA-Mf. However, there are someclear distinctions between these 2 cell types. AA-Mf areuniquely involved in thermoregulation [41], and they alone areinduced during helminthic infections. The depletion of AA-Mf

can ameliorate pathologic responses in allergic disease andhelminthic infections, suggesting that these cells can alsoregulate immune responses, but clearly, these cells arephenotypically distinct from the R-Mf that we describe here. Inthe lethal endotoxemia model shown in Fig. 1, the 2 cells havethe opposite effect. The increased mortality following theaddition of AA-Mf may be explained by the previousobservations of others that IL-4 treatment of macrophages can

augment their production of IL-12 [42, 43]. From these studies,one may conclude that R-Mf can exert a strong anti-inflammatory influence on innate-immune responses,whereas AA-Mf may influence adaptive immunity. Part of theconfusion pertaining to M2-Mf stems from the fact that IL-4and IL-10 can inhibit IFN-g production and prevent thegeneration of cell-mediated immunity. The immunomodulatoryeffects of IL-10 appear to be most pronounced at the levelof APCs. Therefore, the high levels of IL-10 production from R-Mf suggest that macrophages themselves are the mainregulators of macrophage activation. We suggest that this isa primary function of R-Mf. The results from our studiesclearly demonstrate that R-Mf and AA-Mf have differentfunctions and should not be considered part of the same(M2) class.RNA-seq analysis of various human [44] and mouse [45]

macrophage subsets has been reported previously, and thesestudies highlight differences between macrophage subsets. Most ofthe genes that we report to be up-regulated specifically in AA-Mf

agree with previously published reports [45], but our analysisemphasizes the comparative differences in transcripts by AA-Mf

relative to R-Mf. By our analysis, AA-Mf are predicted to beinvolved in cell differentiation, whereas R-Mf are predicted topromote angiogenesis, cell growth, and repair. In addition toexamining differences in the transcripts produced by AA-Mf

relative to R-Mf, our analysis examined similarities amongdifferent R-Mf populations. This analysis was undertaken toidentify a “core transcriptome” that would define macrophageswith an immunoregulatory phenotype. We identified some182 genes that were uniquely up-regulated in R-Mf relative toother macrophage populations, including resting, M1, and AA-Mf. Studies that use a NanoString platform are under way todetermine whether these transcripts can collectively lead to theidentification of R-Mf in tissue.The identification of individual, stable, and reliable

protein biomarkers for R-Mf proved to be much moredifficult. The plasticity of macrophages poses a particularchallenge to the identification of macrophage biomarkers.We demonstrated recently that M1-Mf gradually transition intoR-Mf following stimulation [46], and this transition makes itdifficult to establish a baseline biomarker expression levelfrom which to compare. The chemokine receptor CCR1 was

Figure 5. Glucose and lactate production in R-Mf.BMDMs were left unstimulated or stimulated witha combination of 10 ng/ml LPS alone or LPS incombination with 25 ml Ova-IC (L+I), 200 nMPGE2 (L+P), 200 mM Ado (L+A), or 20 ng/ml IL-4for 24 h. (A) Glucose consumption was deter-mined 24 h poststimulation by an enzymatic assay,as described in Materials and Methods. (B) L-Lactate concentrations in the supernatants wereobtained 8 h poststimulation by use of the NADH-coupled enzyme reaction that reduces tetrazoliumsalt to formazan, which is measured at anabsorbance of 490 nm. The error bars representmeans 6 SEM values calculated from values of 4separate experiments. ***P , 0.001, **P , 0.01,and *P , 0.05.

404 Journal of Leukocyte Biology Volume 98, September 2015 www.jleukbio.org

up-regulated at mRNA and protein levels in R-Mf, and thisup-regulation occurred independent of STAT6 expression.Thus, CCR1 represents a potential biomarker for R-Mf.Attempts to identify R-Mf in tissue based on CCR1 expressionare under way. This chemokine receptor has been implicated inimmune regulatory functions in inflammatory and infectionmodels [47, 48]. How this receptor affects the migratory patternof R-Mf is of future interest to us.

Metabolic reprogramming has been implicated in thepolarization of macrophages into M1 and M2 phenotypes [49,50]. Metabolic pathways shift to anaerobic glycolysis in M1-Mf

and to oxidative glucose metabolism in M2-Mf [33]. Wedemonstrate that R-Mf undergo enhanced glycolysis andproduce L-lactate similarly to M1-Mf. This observation maynot be surprising, as M1 and R-Mf are stimulated with LPS,but it does reveal that the reprogramming signals that so

Figure 6. Regulatory gene induction is independent of STAT6 signaling. RT-PCR analyses were carried out at indicated time-points on cDNA obtainedfrom BALB/c WT and stat62/2 BMDMs, which were left unstimulated or stimulated with 10 ng/ml LPS alone or a combination of LPS and 100 ml Ova-IC(L+I), 200 nM PGE2 (L+P), 200 mM Ado (L+A), or 20 ng/ml IL-4 (A–F). Each data point represents mean values 6 SEM from duplicate values of 4separate experiments. (G) The relative expression of genes up-regulated after treatment of macrophages with L+I is shown. (H) The relative expressionof genes up-regulated after treatment of macrophages with IL-4 is shown. Each error bar represents mean values 6 SEM from duplicate values of 4separate experiments. Surface expression of CCR1 on cells stimulated under the same conditions was assessed by flow cytometry by use of a PE-conjugated antibody to CCR1, 24 h after stimulation. A representative histogram (I) and the fold induction in CCR1 mean fluorescence intensity (MFI)over nonstimulated cells are represented from 5 separate experiments (J). The mean values 6 SEM are depicted, and *P , 0.05, and **P , 0.01.Regulatory gene induction was also assessed in the peritoneal macrophages (PMf) of Balb/c (I–L) or C57BL/6 mice (M and N) mice injectedintraperitoneally with 500 mg LPS alone or in combination with 400 ml Ova-IC (L+I), 50 mg PGE2 (L+P), 50 mg Ado (L+A), or 50 mg IL-4. Macrophageswere isolated 6 h later, and surface expression of CCR1 on macrophages was assessed by flow cytometry. Cells were gated on their expression of F4/80(allophycocyanin) but not for other cell markers (CD3, CD4, CD19, and FITC). (I and M) The expression of regulatory (ccr1 and IL-10)- or alternate(ym1)-activated gene induction was evaluated by real-time PCR (J–L and N). Data were obtained from injections of 3 different mice. The numbers on theleft side of the histogram denote the percentage of positive cells. *P , 0.05; **P , 0.01.

Fleming et al. Characterization of immunoregulatory macrophages

www.jleukbio.org Volume 98, September 2015 Journal of Leukocyte Biology 405

dramatically alter cytokine production in these cells doextend to alterations in cellular metabolism. R-Mf exhibit anincrease in anaerobic glycolysis, despite their potent anti-inflammatory activity. Our analyses of metabolic pathways inR-Mf further confirm that these macrophages are distinctfrom AA-Mf.In this study, we demonstrate that R-Mf distinguish

themselves from M1-Mf by a relatively small and unique setof transcripts and immunoregulatory functions. Although thereare subtle differences in the gene expression patterns andcytokine/chemokine responses among the differently generatedR-Mf, it should be appreciated that these macrophages arebiochemically and functionally related and distinct from M1-Mf

and AA-Mf. Hence, we recommend the consideration ofR-Mf as a separate macrophage population and not as a subsetof M2-Mf.

AUTHORSHIP

Mouse cytokine experiments, RNA profiles, and endotoxinexperiments were performed by B.D.F. RNA-seq experimentswere performed by L.A.L.D., R.S., and N.M.E.-S. IPA analysis wasperformed by E.D. and B.D.F. Metabolism experiments wereperformed by E.D. Membrane array, bioplex, and in vivoinjection studies were performed by P.C. Flow cytometry analysiswas performed by P.C. and A.S. Cytokine/chemokine membranearray and human cytokine experiments were performed by P.C.and B.D.F. The manuscript was drafted by B.D.F. and D.M.M.,with critical revisions made by P.C. and N.M.E.-S. This study wasconceived of and designed by D.M.M.

ACKNOWLEDGMENTS

This study was funded by grants from the U.S. National Institutesof Health (R01 GM102589-01, U01 AI088650, and R01AI-094773). The authors thank Andrew Stewart for the criticalreview of the manuscript and advice pertaining to statisticalanalysis of qRT-PCR data.

DISCLOSURES

D.M.M. has a financial interest in a company (LeukoSight) that seeks tomanipulate immune responses at the level of macrophages. All other authorsdeclare no competing financial interests.

REFERENCES

1. Sica, A., Mantovani, A. (2012) Macrophage plasticity and polarization: invivo veritas. J. Clin. Invest. 122, 787–795.

2. Hussell, T., Bell, T. J. (2014) Alveolar macrophages: plasticity in a tissue-specific context. Nat. Rev. Immunol. 14, 81–93.

3. Mantovani, A., Biswas, S. K., Galdiero, M. R., Sica, A., Locati, M. (2013)Macrophage plasticity and polarization in tissue repair and remodelling.J. Pathol. 229, 176–185.

4. Mosser, D. M., Edwards, J. P. (2008) Exploring the full spectrum ofmacrophage activation. Nat. Rev. Immunol. 8, 958–969.

5. Murray, P. J., Allen, J. E., Biswas, S. K., Fisher, E. A., Gilroy, D. W., Goerdt,S., Gordon, S., Hamilton, J. A., Ivashkiv, L. B., Lawrence, T., Locati, M.,Mantovani, A., Martinez, F. O., Mege, J. L., Mosser, D. M., Natoli, G.,Saeij, J. P., Schultze, J. L., Shirey, K. A., Sica, A., Suttles, J., Udalova, I., vanGinderachter, J. A., Vogel, S. N., Wynn, T. A. (2014) Macrophageactivation and polarization: nomenclature and experimental guidelines.Immunity 41, 14–20.

6. Wynn, T. A., Chawla, A., Pollard, J. W. (2013) Macrophage biology indevelopment, homeostasis and disease. Nature 496, 445–455.

7. Stein, M., Keshav, S., Harris, N., Gordon, S. (1992) Interleukin 4 potentlyenhances murine macrophage mannose receptor activity: a marker ofalternative immunologic macrophage activation. J. Exp. Med. 176,287–292.

Figure 7. Cytokine profile of human macrophages under regulatory stimulation conditions. Human monocyte-derived macrophages were culturedat the concentration of 5 3 105 cells/500 mL medium and were left unstimulated or stimulated with 30 ng/ml (A) or 10 ng/ml LPS alone ora combination of LPS and 25 ml Ova/anti-Ova ICs (L+I), 200 nM PGE2 (L+P), 200 mM Ado (L+A), or 20 ng/ml IL-4 for 8 (A) or 24 h (B–G). Cytokinesin A were measured by use of a sandwich ELISA kit. The levels of all other indicated cytokines were measured by use of the bioplex assay. The horizontalbar represents the mean value, and the asterisks represent the significance of the observed values compared with LPS-treated cells. ***P , 0.001, **P , 0.01,and *P , 0.05.

406 Journal of Leukocyte Biology Volume 98, September 2015 www.jleukbio.org

8. Martinez, F. O., Gordon, S. (2014) The M1 and M2 paradigm ofmacrophage activation: time for reassessment. F1000Prime Rep. 6, 13.

9. Mills, C. D., Kincaid, K., Alt, J. M., Heilman, M. J., Hill, A. M. (2000) M-1/M-2macrophages and the Th1/Th2 paradigm. J. Immunol. 164, 6166–6173.

10. Blanchard, D. K., Djeu, J. Y., Klein, T. W., Friedman, H., Stewart II, W. E.(1986) Interferon-gamma induction by lipopolysaccharide: dependenceon interleukin 2 and macrophages. J. Immunol. 136, 963–970.

11. Foss, D. L., Zilliox, M. J., Murtaugh, M. P. (1999) Differential regulationof macrophage interleukin-1 (IL-1), IL-12, and CD80-CD86 by twobacterial toxins. Infect. Immun. 67, 5275–5281.

12. Mukhopadhyay, S., Peiser, L., Gordon, S. (2004) Activation of murinemacrophages by Neisseria meningitidis and IFN-gamma in vitro: distinctroles of class A scavenger and Toll-like pattern recognition receptors inselective modulation of surface phenotype. J. Leukoc. Biol. 76, 577–584.

13. Canton, J. (2014) Phagosome maturation in polarized macrophages. J.Leukoc. Biol. 96, 729–738.

14. Raes, G., De Baetselier, P., Noel, W., Beschin, A., Brombacher, F.,Hassanzadeh Gh, G. (2002) Differential expression of FIZZ1 and Ym1 inalternatively versus classically activated macrophages. J. Leukoc. Biol. 71,597–602.

15. Murray, P. J., Wynn, T. A. (2011) Protective and pathogenic functions ofmacrophage subsets. Nat. Rev. Immunol. 11, 723–737.

16. Fleming, B. D., Mosser, D. M. (2011) Regulatory macrophages: settingthe threshold for therapy. Eur. J. Immunol. 41, 2498–2502.

17. Mosser, D. M., Zhang, X. (2008) Activation of murine macrophages. Curr.Protoc. Immunol. 83, 14.2.1–14.2.8.

18. Anderson, C. F., Mosser, D. M. (2002) Cutting edge: biasing immuneresponses by directing antigen to macrophage Fc gamma receptors. J.Immunol. 168, 3697–3701.

19. Livak, K. J., Schmittgen, T. D. (2001) Analysis of relative gene expressiondata using real-time quantitative PCR and the 2(2Delta Delta C(T))method. Methods 25, 402–408.

20. Bolger, A. M., Lohse, M., Usadel, B. (2014) Trimmomatic: a flexibletrimmer for Illumina sequence data. Bioinformatics 30, 2114–2120.

21. Trapnell, C., Pachter, L., Salzberg, S. L. (2009) TopHat: discoveringsplice junctions with RNA-Seq. Bioinformatics 25, 1105–1111.

22. Bolstad, B. M., Irizarry, R. A., Astrand, M., Speed, T. P. (2003) Acomparison of normalization methods for high densityoligonucleotide array data based on variance and bias. Bioinformatics19, 185–193.

23. Smyth, G. K. (2004) Linear models and empirical bayes methods forassessing differential expression in microarray experiments. Stat. Appl.Genet. Mol. Biol. 3, 1–25.

24. Law, C. W., Chen, Y., Shi, W., Smyth, G. K. (2014) voom: precisionweights unlock linear model analysis tools for RNA-seq read counts.Genome Biol. 15, R29.

25. Leek, J. T., Scharpf, R. B., Bravo, H. C., Simcha, D., Langmead, B.,Johnson, W. E., Geman, D., Baggerly, K., Irizarry, R. A. (2010) Tacklingthe widespread and critical impact of batch effects in high-throughputdata. Nat. Rev. Genet. 11, 733–739.

26. Kramer, A., Green, J., Pollard, Jr., J., Tugendreich, S. (2014) Causalanalysis approaches in Ingenuity Pathway Analysis. Bioinformatics 30,523–530.

27. Martinez, F. O., Helming, L., Gordon, S. (2009) Alternative activation ofmacrophages: an immunologic functional perspective. Annu. Rev.Immunol. 27, 451–483.

28. Edwards, J. P., Zhang, X., Frauwirth, K. A., Mosser, D. M. (2006)Biochemical and functional characterization of three activatedmacrophage populations. J. Leukoc. Biol. 80, 1298–1307.

29. Gao, J. J., Diesl, V., Wittmann, T., Morrison, D. C., Ryan, J. L., Vogel,S. N., Follettie, M. T. (2002) Regulation of gene expression in mousemacrophages stimulated with bacterial CpG-DNA andlipopolysaccharide. J. Leukoc. Biol. 72, 1234–1245.

30. Gao, J. J., Diesl, V., Wittmann, T., Morrison, D. C., Ryan, J. L., Vogel,S. N., Follettie, M. T. (2003) Bacterial LPS and CpG DNA differentiallyinduce gene expression profiles in mouse macrophages. J. Endotoxin Res.9, 237–243.

31. Palaga, T., Buranaruk, C., Rengpipat, S., Fauq, A. H., Golde, T. E.,Kaufmann, S. H., Osborne, B. A. (2008) Notch signaling is activated byTLR stimulation and regulates macrophage functions. Eur. J. Immunol.38, 174–183.

32. Gordon, S., Martinez, F. O. (2010) Alternative activation of macrophages:mechanism and functions. Immunity 32, 593–604.

33. Rodrıguez-Prados, J. C., Traves, P. G., Cuenca, J., Rico, D., Aragones, J.,Martın-Sanz, P., Cascante, M., Bosca, L. (2010) Substrate fate in activatedmacrophages: a comparison between innate, classic, and alternativeactivation. J. Immunol. 185, 605–614.

34. Odegaard, J. I., Chawla, A. (2011) Alternative macrophage activation andmetabolism. Annu. Rev. Pathol. 6, 275–297.

35. Colegio, O. R., Chu, N. Q., Szabo, A. L., Chu, T., Rhebergen, A. M.,Jairam, V., Cyrus, N., Brokowski, C. E., Eisenbarth, S. C., Phillips, G. M.,Cline, G. W., Phillips, A. J., Medzhitov, R. (2014) Functional polarizationof tumour-associated macrophages by tumour-derived lactic acid. Nature513, 559–563.

36. Mantovani, A., Sica, A., Sozzani, S., Allavena, P., Vecchi, A., Locati, M.(2004) The chemokine system in diverse forms of macrophage activationand polarization. Trends Immunol. 25, 677–686.

37. Gallo, P., Gonçalves, R., Mosser, D. M. (2010) The influence of IgGdensity and macrophage Fc (gamma) receptor cross-linking onphagocytosis and IL-10 production. Immunol. Lett. 133, 70–77.

38. Ohmori, Y., Hamilton, T. A. (1998) STAT6 is required for the anti-inflammatory activity of interleukin-4 in mouse peritoneal macrophages.J. Biol. Chem. 273, 29202–29209.

39. Huber, S., Hoffmann, R., Muskens, F., Voehringer, D. (2010)Alternatively activated macrophages inhibit T-cell proliferation by Stat6-dependent expression of PD-L2. Blood 116, 3311–3320.

40. Anderson, C. F., Mosser, D. M. (2002) A novel phenotype for an activatedmacrophage: the type 2 activated macrophage. J. Leukoc. Biol. 72,101–106.

41. Nguyen, K. D., Qiu, Y., Cui, X., Goh, Y. P., Mwangi, J., David, T.,Mukundan, L., Brombacher, F., Locksley, R. M., Chawla, A. (2011)Alternatively activated macrophages produce catecholamines to sustainadaptive thermogenesis. Nature 480, 104–108.

42. Yao, Y., Li, W., Kaplan, M. H., Chang, C. H. (2005) Interleukin (IL)-4inhibits IL-10 to promote IL-12 production by dendritic cells. J. Exp. Med.201, 1899–1903.

43. Bullens, D. M., Kasran, A., Thielemans, K., Bakkus, M., Ceuppens, J. L.(2001) CD40L-induced IL-12 production is further enhanced by the Th2cytokines IL-4 and IL-13. Scand. J. Immunol. 53, 455–463.

44. Xue, J., Schmidt, S. V., Sander, J., Draffehn, A., Krebs, W., Quester, I., DeNardo, D., Gohel, T. D., Emde, M., Schmidleithner, L., Ganesan, H.,Nino-Castro, A., Mallmann, M. R., Labzin, L., Theis, H., Kraut, M., Beyer,M., Latz, E., Freeman, T. C., Ulas, T., Schultze, J. L. (2014)Transcriptome-based network analysis reveals a spectrum model ofhuman macrophage activation. Immunity 40, 274–288.

45. Gundra, U. M., Girgis, N. M., Ruckerl, D., Jenkins, S., Ward, L. N., Kurtz,Z. D., Wiens, K. E., Tang, M. S., Basu-Roy, U., Mansukhani, A., Allen, J. E.,Loke, P. (2014) Alternatively activated macrophages derived frommonocytes and tissue macrophages are phenotypically and functionallydistinct. Blood 123, e110–e122.

46. Cohen, H. B., Briggs, K. T., Marino, J. P., Ravid, K., Robson, S. C., Mosser,D. M. (2013) TLR stimulation initiates a CD39-based autoregulatorymechanism that limits macrophage inflammatory responses. Blood 122,1935–1945.

47. Seki, E., De Minicis, S., Gwak, G. Y., Kluwe, J., Inokuchi, S., Bursill,C. A., Llovet, J. M., Brenner, D. A., Schwabe, R. F. (2009) CCR1and CCR5 promote hepatic fibrosis in mice. J. Clin. Invest. 119,1858–1870.

48. Furuichi, K., Gao, J. L., Horuk, R., Wada, T., Kaneko, S., Murphy, P. M.(2008) Chemokine receptor CCR1 regulates inflammatory cellinfiltration after renal ischemia-reperfusion injury. J. Immunol. 181,8670–8676.

49. Haschemi, A., Kosma, P., Gille, L., Evans, C. R., Burant, C. F., Starkl, P.,Knapp, B., Haas, R., Schmid, J. A., Jandl, C., Amir, S., Lubec, G., Park, J.,Esterbauer, H., Bilban, M., Brizuela, L., Pospisilik, J. A., Otterbein, L. E.,Wagner, O. (2012) The sedoheptulose kinase CARKL directsmacrophage polarization through control of glucose metabolism. CellMetab. 15, 813–826.

50. Biswas, S. K., Mantovani, A. (2012) Orchestration of metabolism bymacrophages. Cell Metab. 15, 432–437.

KEY WORDS:

regulatory • RNA-seq • endotoxemia • alternatively activated •

transcriptomics

Fleming et al. Characterization of immunoregulatory macrophages

www.jleukbio.org Volume 98, September 2015 Journal of Leukocyte Biology 407