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Defining GM-CSF− and -CSF− Dependent Macrophage Responses by In Vitro Models

This information is current as Derek C. Lacey, Adrian Achuthan, Andrew J. Fleetwood, of October 1, 2021. Hang Dinh, John Roiniotis, Glen M. Scholz, Melody W. Chang, Sandra K. Beckman, Andrew D. Cook and John A. Hamilton J Immunol 2012; 188:5752-5765; Prepublished online 30 April 2012; doi: 10.4049/jimmunol.1103426 Downloaded from http://www.jimmunol.org/content/188/11/5752

Supplementary http://www.jimmunol.org/content/suppl/2012/04/30/jimmunol.110342 http://www.jimmunol.org/ Material 6.DC1 References This article cites 50 articles, 20 of which you can access for free at: http://www.jimmunol.org/content/188/11/5752.full#ref-list-1

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The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2012 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. The Journal of Immunology

Defining GM-CSF– and Macrophage-CSF–Dependent Macrophage Responses by In Vitro Models

Derek C. Lacey,1 Adrian Achuthan,1 Andrew J. Fleetwood, Hang Dinh, John Roiniotis, Glen M. Scholz, Melody W. Chang, Sandra K. Beckman, Andrew D. Cook, and John A. Hamilton

GM-CSF and M-CSF (CSF-1) induce different phenotypic changes in macrophage lineage populations. The nature, extent, and generality of these differences were assessed by comparing the responses to these CSFs, either alone or in combination, in various human and murine macrophage lineage populations. The differences between the respective global expression profiles of mac- rophages, derived from human by GM-CSF or M-CSF, were compared with the differences between the respective profiles for , derived from murine bone marrow cells by each CSF. Only 17% of regulated differently by these

CSFs were common across the species. Whether a particular change in relative is by direct action of a CSF can be Downloaded from confounded by endogenous mediators, such as type I IFN, IL-10, and activin A. Time-dependent differences in cytokine gene ex- pression were noted in human monocytes treated with the CSFs; in this system, GM-CSF induced a more dramatic expression of IFN-regulated factor 4 (IRF4) than of IRF5, whereas M-CSF induced IRF5 but not IRF4. In the presence of both CSFs, some evidence of “competition” at the level of gene expression was observed. Care needs to be exercised when drawing definitive conclusions from a particular in vitro system about the roles of GM-CSF and M-CSF in macrophage lineage biology. The

Journal of Immunology, 2012, 188: 5752–5765. http://www.jimmunol.org/

he CSFs, M-CSF (or CSF-1) and GM-CSF, were defined lead to disease alleviation in arthritis and many other indications originally as hemopoietic growth factors (1). M-CSF is (7), prompting heightened interest in their biology. T ubiquitously produced by many tissues and controls Macrophage lineage populations respond differently to M-CSF macrophage numbers in many tissues (2, 3). However, GM-CSF and GM-CSF (7, 10–13), although the nature of the differences is has low basal circulating levels that are often elevated during still not clear nor have the responsible mechanisms been eluci- immune/inflammatory reactions (4); recent evidence implicates it dated. In vitro systems have been developed to attempt to un- in the development of a type of (DC), namely, the derstand CSF-dependent macrophage biology at the molecular by guest on October 1, 2021 “-derived” or “inflammatory” DC (5, 6). These CSFs level; as an example, many studies have highlighted the ability of have different receptors and downstream signaling pathways, and GM-CSF to induce cells with DC (antigen-presenting) properties their receptors are distributed differently on myeloid cell pop- (5, 6, 14, 15). Because macrophages at a site of inflammation ulations (7). One common responding cell lineage is the macro- could be exposed to both M-CSF and GM-CSF (7, 16), a limited phage with both CSFs capable of promoting cell survival/pro- number of studies have included both CSFs in the cultures to at- liferation, differentiation, and activation (8, 9). Partly because of tempt to mimic this situation (11, 17–21). With this approach, their involvement in macrophage and/or DC biology, M-CSF and some evidence has been obtained even for opposing actions of the GM-CSF have been implicated in inflammatory/autoimmune CSFs, and M-CSF has been proposed to be attempting to restore conditions because their blockade or that of their receptors can the macrophage target population to a steady-state as a mechanism for inflammatory reaction resolution (7, 21). M-CSF–treated human blood monocytes are often used to gen- Department of Medicine, Arthritis and Inflammation Research Centre, University of Melbourne, The Royal Melbourne Hospital, Parkville, Victoria 3050, Australia erate, following differentiation, monocyte-derived macrophages (MDM) as a model for tissue macrophages (10, 13, 22, 23). GM- 1D.C.L. and A.A. contributed equally to this work. CSF–treated monocytes are widely used as a model system for DC Received for publication December 1, 2011. Accepted for publication April 2, 2012. development and function, although bioinformatics analysis of This work was supported by grants and a Senior Principal Research Fellowship (to J.A.H.) from the National Health and Medical Research Council of Australia. their transcriptome indicates that they are closer to macrophages than to DCs (24, 25); they will therefore be referred to here as The microarray data presented in this article have been submitted to ArrayExpress Archive (http://www.ebi.ac.uk/arrayexpress/) under accession numbers E-MTAB- GM-CSF monocyte-derived macrophages (GM-MDM). Macro- 790, E-MTAB-791, and E-MTAB-792. phage functions have begun to be defined by different polarization Address correspondence and reprint requests to Prof. John Hamilton, Arthritis and or activation states to define them more clearly. One such cate- Inflammation Research Centre, University of Melbourne, Department of Medicine, gorization is M1 and M2 polarization, with the former state The Royal Melbourne Hospital, Parkville, VIC 3050, Australia. E-mail address: [email protected] (classical activation) resulting from the action of stimuli, such as The online version of this article contains supplemental material. IFN-g, in the absence or presence of a microbial agent such as Abbreviations used in this article: BMM, bone marrow-derived macrophage; GM- LPS, and the latter (alternative activation) after stimulation with BMM, GM-CSF bone marrow-derived macrophage; GM-MDM, GM-CSF monocyte- IL-4 or IL-13 (26–29). Based mainly on expression of certain derived macrophage; GO, ; IGF, -like ; IRF, IFN- cytokines, the population resulting from GM-CSF treatment of regulated factor; MDM, monocyte-derived macrophage; qPCR, quantitative PCR. human monocytes has been referred to as Mw-1 macrophages with Copyright Ó 2012 by The American Association of Immunologists, Inc. 0022-1767/12/$16.00 a “proinflammatory” cytokine profile and with some features of www.jimmunol.org/cgi/doi/10.4049/jimmunol.1103426 The Journal of Immunology 5753

M1 cells, with the M-CSF–generated counterparts being termed RPMI 1640 medium, supplemented with 10% heat-inactivated FCS, 2 mM Mw-2, with an “anti-inflammatory” cytokine repertoire and with GlutaMax-1, 100 U/ml penicillin, and 100 mg/ml streptomycin in the some features of M2 macrophages (13). Along these lines, it has presence of M-CSF (2500 U/ml) or murine GM-CSF (5 ng/ml). At day 4, nonadherent cells were collected and cultured for a further 3 d in M-CSF been reported that MDM express a substantial repertoire of the M2 (2500 U/ml) to derive BMM or in GM-CSF (5 ng/ml) to derive GM-BMM. transcriptome (22), and for murine bone marrow-derived macro- On day 7, adherent cells were harvested. phages (BMM), the differences from M1 and M2 macrophages have been mentioned (30, 31). Recently, the transcription factor, Microarray experiments IFN-regulated factor (IRF5), was proposed to be key for the GM- All microarray experiments were performed following recommended CSF–induced M1 polarization in human monocytes (32). protocols supplied by Agilent Technologies (Santa Clara, CA). RNA was In the mouse, BMM, derived from bone marrow cells cultured in isolated from three independent donors (23). cRNA was hybridized to M-CSF are a frequently used and convenient population to study a whole microarray (Agilent Technologies) containing 43,376 probes corresponding to 41,264 transcripts. Microarrays were then macrophage function and signaling (33). GM-CSF–treated bone scanned using a DNA microarray scanner, Model G2565A; data extraction marrow cells, in contrast, give rise to cells used as a model for was conducted using Feature Extraction software Version 10.5 and then DCs, although they have macrophage markers (11), are excellent analyzed using Genespring GX software, Version 11.5.1 (Agilent Tech- osteoclast precursors (17), and have a transcriptome closer to nologies). Normalization was performed according to the Agilent Feature Extraction One-Color Protocol. Filtering was applied, whereby probes that macrophages than to DCs (31); for this reason, we shall continue, were considered “present” in at least 6 of the 9 samples were included, as before (11, 34), to refer to them as GM-CSF bone marrow- generating a list of 23,672 genes. Genes were analyzed by a Welch t test, derived macrophages (GM-BMM). We recently showed by micro- and differences between groups were determined using Tukey’s honest array analysis that endogenous type I IFN can have a profound significant difference post hoc test and Benjamini–Hochberg multiple

. Downloaded from influence on the transcriptomes of GM-BMM and BMM, partic- testing correction. Genes showing 2-fold differences and statistical sig- nificance with a false discovery rate of 0.05 were identified as being dif- ularly for the latter (34). Based solely on cytokine expression, we ferentially expressed, and these were subjected to Gene Ontology (GO) also showed that GM-BMM and BMM have some properties analysis for biological process and molecular function at levels 3 and 4 similar to M1 and M2 cells, and thus termed their phenotypes as (35). Significant ontologies were calculated using a Fisher exact test, and p “M1-like” and “M2-like,” respectively (11). IRF5 has also been values #0.05 are reported. The data set and technical information com- pliant with minimum information about a microarray experiment (35) can implicated in the M1 polarization attributed to GM-BMM (32). again be found at the ArrayExpress Archive Web site (http://www.ebi.ac. What is needed is a comparative analysis of M-CSF– and GM- uk/arrayexpress/; accession number E-MTAB-791). http://www.jimmunol.org/ CSF–treated human and murine macrophage populations in the Similarly, RNA from BMM and GM-BMM or BMM, treated with GM- one study to gain some idea ultimately of their potential relevance CSF, GM-CSF and M-CSF, or M-CSF alone for 16 h, were processed as as models for the function of these CSFs in human macrophage described earlier. cRNA was hybridized to a whole murine genome microarray (Agilent Technologies) containing 45,220 probes corresponding biology and pathology, in particular, inflammatory diseases. Ex- to 41,543 transcripts. Microarrays were then scanned, data extracted, and pression data for such populations were analyzed and are com- analyzed similarly to earlier description. Filtering was applied, whereby pared later. We also describe a number of factors that need to be probes that were considered “present” in at least 100% of samples in any considered when defining the respective CSF responses in mac- one of the conditions were included, generating a list of 20,873 entities. Genes were considered to be significantly different after a paired t test, rophage lineage populations. with a corrected p value #0.05, using Benjamini–Hochberg for multiple by guest on October 1, 2021 testing correction (11,620 entities). Genes showing .2-fold differences Materials and Methods and statistical significance with a false discovery rate of #0.05 were Mice identified as being differentially expressed (4206 entities), and these genes were subjected to GO analysis as described earlier. Significant ontologies Female C57BL/6 mice (8–12 wk) were from Monash University, Clayton, and p values were as described earlier. The data set and minimum infor- VIC, Australia, and IL-102/2 mice were a gift from N. O’Brien-Simpson mation about a microarray experiment compliance (35) can again be found (The University of Melbourne, Parkville, VIC, Australia). All experiments at the ArrayExpress Archive Web site (http://www.ebi.ac.uk/arrayexpress/; were approved by the Royal Melbourne Hospital Research Foundation accession numbers E-MTAB-790 and E-MTAB-792). Animal Ethics Committee. Quantitative PCR Reagents and Abs Total RNA was extracted using RNeasy mini-kits (Qiagen, Valencia, CA) Reagents included recombinant murine GM-CSF (PeproTech, Princeton, NJ), and reverse transcribed using SuperScript III reverse transcriptase (Invi- human GM-CSF (R&D Systems, Minneapolis, MN) and human M-CSF trogen). Quantitative PCR (qPCR) was performed using an ABI PRISM (Chiron, Emeryville, CA), and LPS (Sigma-Aldrich, St. Louis, MO); mono- 7900HT sequence detection system (Applied Biosystems, Carlsbad, CA) clonal Abs were against IRF4 and IRF5 (Cell Signaling Technology, Danvers, and predeveloped TaqMan probe/primer combinations for human and for MA), IL-10 and activin A (R&D Systems), b-tubulin (Sigma-Aldrich), and murine TNF, IL-1b, IL-6, IL-8, IL-10, IL-12p35, IL-12p40, IL-23p19, CD14 PerCP-Cy5.5 and isotype control (eBioscience, San Diego, CA). CCL2, CCL5, CCL12, CCL17, CXCL10, TGF-b, insulin-like growth Isolation and culture of human monocytes factor 1 (IGF-1), INHBA (activin A), UBC, and 18S rRNA (Applied Biosystems) (11, 34). Custom-designed primer sequences were used for Human monocytes were purified from buffy coats (Red Cross Blood IRF4 and IRF5, STAT1 and STAT2, and UBC using SYBR Green. All Bank, Melbourne, VIC, Australia), using RosetteSep Ab mixture (Stem samples were performed in triplicates. Threshold cycle numbers were Cell Technologies, Vancouver, BC, Canada), which negatively selects transformed to ΔΔcycle threshold values, and the results were expressed CD14+ monocytes, followed by Ficoll-Paque density gradient centrifuga- relative to reference genes 18S rRNA or UBC. tion (23). They were cultured in RPMI 1640, supplemented with 10% heat- inactivated FCS (CSL Biosciences, Parkville, VIC, Australia), 2 mM Cell lysis and Western blotting GlutaMax-1 (Invitrogen, Carlsbad, CA), 100 U/ml penicillin, and 100 mg/ ml streptomycin, and stimulated with M-CSF (2500 U/ml), human GM- Whole-cell extracts were lysed and Western blotted as described previously CSF (5 ng/ml), or both, for appropriate time periods, or cultured for 7 d in (34). concentrations of the samples were determined with a Bio- M-CSF to differentiate them into MDM (23) or in GM-CSF to differentiate Rad protein assay . The whole-cell lysates were run on 10% SDS-PAGE them into GM-MDM, or in both CSFs to differentiate them into (M-CSF + (Invitrogen). The separated were transferred onto a polyvinylidene GM-CSF)-MDM. fluoride membrane and then Western blotted with appropriate Abs. Isolation and culture of bone marrow cells Secreted cytokines BMM and GM-BMM were prepared as previously described (11). In brief, Cytokines in cell supernatants were measured by ELISA (OptEIA ELISA bone marrow cells were isolated from femurs of mice and cultured in kits; BD Pharmingen, Franklin Lakes, NJ). 5754 GM-CSF AND M-CSF REGULATION OF MACROPHAGES

Statistics that were expressed in GM-MDM more highly than in MDM. The Statistical comparisons between groups were performed using unpaired 10 most relatively regulated genes in the categories selected are Student t test (GraphPad Prism 4 software) or Wilcoxon matched pairs test presented in Table I. The overall concordance of the patterns (STATISTICA 5.1 software). The p values #0.05 indicate significance. between different donors can be noted in Fig. 1A. The profile Data were plotted using GraphPad Prism 4.03 software. obtained in the presence of both CSFs is discussed later. Results Murine cells. We assessed the relative gene expression profiles Human and murine macrophage gene expression profiles on for GM-BMM versus BMM by microarray, and the data were ar- differentiation in GM-CSF or M-CSF ranged in the same GO categorization as described earlier. From 3 experiments, 80% of genes (16,667 of 20,873) were similarly We compared at the global level the relative gene expression for expressed (i.e., ratio , 2-fold change) between GM-BMM and human and murine macrophage populations generated in the BMM. For the remaining genes whose expression was differen- presence of GM-CSF or M-CSF. tially expressed (i.e., p # 0.05 and ratio $ 2-fold change; the top Human cells. We first compared gene expression profiles for human 100 genes collated in Supplemental Table I), the GO “heat map” monocytes after their differentiation into GM-MDM or MDM over (Fig. 1B) and the relative numbers of genes upregulated in the a period of 7 d. In total, 23,672 genes were present after detection chosen categories (Fig. 2B) are again shown. As examples, there call and spot assessment analysis (see Materials and Methods). were more genes annotated to the GO classifications, immune From 3 experiments, 87% of the genes were similarly expressed response and extracellular region, that were more highly expressed , (i.e., ratio 2-fold change) between both macrophage pop- in GM-BMM than in BMM, as for the respective human pop- ulations. The remaining genes (3044), whose relative expression ulations. Again, the 10 most relatively regulated genes in the Downloaded from was different (i.e., p # 0.05 and ratio $ 2-fold change; the top 100 categories selected are listed (Table II). genes collated in Supplemental Table I), were categorized on the basis of GO classification and certain key macrophage functions Human versus murine cells. Given that the respective human and as before for MDM (23), such as immune response and endocy- murine cell populations analyzed earlier are widely used in vitro tosis. The data are presented as a GO “heat map” (Fig. 1A); the models for studying CSF biology, as well as the biology of relative numbers of genes upregulated in these categories are macrophages and DCs, it is important to determine how these http://www.jimmunol.org/ shown (Fig. 2A). As examples, there were more genes annotated in vitro macrophage models compare across species, especially to the classifications, immune response and extracellular region, because their sources are quite different. by guest on October 1, 2021

FIGURE 1. GO analysis of differentially ex- pressed genes for M-CSF– and GM-CSF–differ- entiated human and murine macrophages. (A) MDM, GM-MDM, and (M-CSF + GM-CSF)- MDM; (B) BMM and GM-BMM; and (C) BMM were starved for 24 h and then treated with M- CSF, GM-CSF, or both for 16 h. Data mining was per Materials and Methods.(A and B) Shown are six significant and nonredundant classifications at levels 3 and 4 for biological process and molec- ular function. (C) Asterisk indicates immune re- sponse and receptor activity were the only GO categories of these six that were significant. All data are representative of three independent ex- periments. The Journal of Immunology 5755

FIGURE 2. Number of differentially expressed genes (GO analysis) for M-CSF– and GM-CSF– differentiated human and murine macrophages. (A) MDM and GM-MDM; (B) BMM and GM-BMM. Data mining was per Materials and Methods. Shown is the number of genes upregulated by M- CSF or GM-CSF in the six significant and nonre- dundant GO classifications at levels 3 and 4 for Downloaded from biological process and molecular function (see Fig. 1A, 1B). (C) Venn diagram showing the number of genes differentially regulated in only human cells, only murine cells, or cells from both species. All data are representative of three independent experi- ments http://www.jimmunol.org/ by guest on October 1, 2021

As mentioned earlier and as can be seen from Figs. 1, 2A, and a similar manner by the CSFs across both species (false recovery 2B, the relative pattern for the different biological processes was rate, p # 0.05), which represents 41 and 34% of the total cate- similar between the respective human and murine CSF-treated gories regulated differently by the CSFs within the human and populations; however, it should be noted (Table I versus Table murine macrophages, respectively (data not shown). However, II; Supplemental Table I) that the differently expressed genes when the GO categories were ranked, of the 50 GO categories that within each species were not the same, and for those that were the were differentially regulated the most (i.e., lowest p value), 84% extent of their regulation (i.e., relative fold change) were often not were similar across the species. Thus, when a higher level of comparable. stringency is applied to the GO classification, a greater degree of Therefore, we next analyzed further the similarities and dif- similarity is observed. We next conducted a “pathway” analysis ferences between the human and murine macrophage populations using two pathway databases, namely, National Institute- by first comparing in detail individual differentially regulated Nature and BioCarta. Using this approach, of the pathways dif- genes, the global GO categories, and the molecular pathways ferentially regulated by the CSFs in the same direction, 82% of the activated. Of the 4206 genes differentially regulated between the human and 77% of the murine, respectively, were common across murine GM-BMM and BMM, 3045 (72%) had human homologs, the species; the top 50 such “pathways” for each species are listed which we compared with the genes differentially regulated between in Supplemental Table II using the NCI-Nature database. GM-MDM versus MDM. There were 812 of these genes that were The findings from the three approaches used earlier to compare common in both species, with 530 of this common list regulated in the relative gene expression for the respective CSF-generated mac- the same direction in both human and murine populations (Fig. rophage populations between species indicate that when cell func- 2C). Thus, overall, only 17% of the genes regulated differently by tion is considered, that is, using GO classification and “pathway” these CSFs and in the same direction are common between the analysis rather than individual gene expression, more overlap human and murine macrophage systems. For the top 150 genes results. most differentially regulated in the same direction between GM- MDM versus MDM and GM-BMM versus BMM, there were only Contribution of endogenous type I IFN to CSF-induced 11 common to both species. macrophage polarization When the human and murine gene lists were compared at the We showed before with GM-BMM and BMM that endogenous level of GO categories (36), there were 106 relatively regulated in type I IFN had a profound effect on basal gene expression, par- 5756 GM-CSF AND M-CSF REGULATION OF MACROPHAGES

Table I. The top 10 most differentially expressed genes in GM-MDM versus MDM for each GO category

Fold Change: Gene Symbol Description GM versus M Immune response CYP27B1 Cytochrome P450, family 27, subfamily B, polypeptide 1 47.2 CD226 CD226 molecule 11.9 CTSW Cathepsin W 10.5 CCL22 Chemokine (C-C motif) ligand 22 9.7 BNIP3 BCL2/adenovirus E1B 19-kDa interacting protein 3 210.0 IL10 IL-10 210.5 CADM1 Cell adhesion molecule 1 211.8 SECTM1 Secreted and transmembrane 1 215.3 THBS1 Thrombospondin 1 216.2 CD28 CD28 molecule 240.6 Receptor activity TACSTD2 Tumor-associated calcium signal transducer 2 71.8 CCR6 Chemokine (C-C motif) receptor 6 39.4 GUCY1B3 Guanylate cyclase 1, soluble, b 3 32.1 IL22RA2 IL-22Ra2 30.9 IL3RA IL-3Ra 26.3 GPR114 G protein-coupled receptor 114 20.3 GP1BA Ib (), a polypeptide 17.2 Downloaded from TNFRSF8 TNFR superfamily, member 8 217.3 ALK Anaplastic lymphoma kinase 221.1 EDG1 Endothelial differentiation G protein-coupled receptor 1 228.2 Cell adhesion CLDN14 Claudin 14 25.2 SORBS1 Sorbin and SH3 domain containing 1 22.2

CDH17 Cadherin 17 15.7 http://www.jimmunol.org/ PTPRF Protein tyrosine phosphatase, receptor type, F 14.4 PTPRK Protein tyrosine phosphatase, receptor type, K 13.6 RND3 Rho family GTPase 3 212.5 FBLN5 Fibulin 5 219.0 NRG1 1 221.5 CD93 CD93 molecule 240.4 DLL1 d-like 1 2119.8 Locomotion IGFBP5 Insulin-like growth factor binding protein 5 31.4 EDN1 1 28.3

CCL24 Chemokine (C-C motif) ligand 24 28.1 by guest on October 1, 2021 CSF1 CSF1 21.4 PDGFRB Platelet-derived , b polypeptide 12.8 CCL5 Chemokine (C-C motif) ligand 5 12.8 F3 factor III 11.3 CXCR4 Chemokine (C-X-C motif) receptor 4 218.6 EDNRB type B 253.7 IGF1 Insulin-like growth factor 1 256.0 Extracellular region CCL1 Chemokine (C-C motif) ligand 1 129.2 INHBA Inhibin, b A 112.0 IGFBP2 Insulin-like growth factor binding protein 2 17.7 IL1A IL-1a 16.6 FLT1 fms-related 1 14.6 PRG2 Proteoglycan 2 13.8 PRSSL1 Protease, serine-like 1 13.0 DKK2 Dickkopf homolog 2 12.9 CFD Complement factor D 218.6 LOXL1 Lysyl oxidase-like 1 222.2 Endocytosis ADORA1 7.7 SFTPD Surfactant, pulmonary-associated protein D 5.2 GATA2 GATA binding protein 2 4.7 PDLIM7 PDZ and LIM domain 7 4.6 LDLRAP1 Low-density lipoprotein receptor adaptor protein 1 4.6 FOLR1 Folate receptor 1 3.9 CAV1 Caveolin 1 3.5 ADRB2 Adrenergic, b-2-, receptor 23.4 MSR1 Macrophage scavenger receptor 1 24.1 ITSN1 Intersectin-1 (SH3 domain-containing protein 1A) 26.0 ticularly for the latter with its high endogenous levels (34). We now IFN to this difference. Of the total of 569 validated genes that examined how many of these type I IFN genes were in the list of were different in both IFNAR2/2 GM-BMM and BMM from their genes that were differentially expressed in GM-BMM and BMM corresponding wild-type populations, that is, type I IFN dependent as an indication of the possible contribution of endogenous type I (34), 333 of them were also differentially expressed in GM-BMM The Journal of Immunology 5757

Table II. The top 10 most differentially expressed genes in GM-BMM versus BMM for each GO category

Fold Change: GM-BMM Gene Symbol Description versus BMM Immune response Ccr7 Chemokine (C-C motif) receptor 7 4905.4 Thy1 Thymus cell Ag 1, u 341.2 Cacnb3 Calcium channel voltage-dependent, b 3 236.1 Ciita CIITA 226.7 Cfb Complement factor B 215.1 Lta Lymphotoxin A 108.5 H2-Ab1 Histocompatibility 2 class II Ag A b 1 106.8 Tnfsf4 TNF (ligand) superfamily, member 4 86.2 Cfh Complement component factor h 263.6 Cx3cr1 Chemokine (C-X3-C) receptor 1 2128.2 Receptor activity Adra2a , a 2a 1006.0 Klrb1b Killer cell lectin-like receptor subfamily B member 1B 764.5 Gpr120 G protein-coupled receptor 120 417.2 Ramp3 Receptor activity modifying protein 3 352.2 Il28ra IL-28Ra 330.9 Pglyrp1 Peptidoglycan recognition protein 1 209.8 Cd200r3 CD200 receptor 3 185.9 Downloaded from Olr1 Oxidized low-density lipoprotein receptor 1 181.3 Il1r1 IL-1R, type I 151.1 Il1rl2 IL-1R-like 2 137.4 Cell adhesion Cldn1 Claudin 1 213.2 Cdh1 Cadherin 1 154.9

Ddr1 Cak receptor kinase 137.5 http://www.jimmunol.org/ Pkp2 Plakophilin 2 96.1 Clec7a C-type lectin domain family 7, member a 41.8 Vcam1 VCAM 1 40.4 Itga1 a 1 32.1 Itga6 Integrin a 6 220.3 Cadm1 Cell adhesion molecule 1 276.8 S1pr1 Sphingosine-1–phosphate receptor 1 299.8 Locomotion Ear3 Eosinophil-associated, RNase A family, member 3 771.8 Ccl17 Chemokine (C-C motif) ligand 17 380.8

Cxcl3 Chemokine (C-X-C motif) ligand 3 139.7 by guest on October 1, 2021 Six1 Sine oculis-related homeobox 1 homolog 101.3 Pdpn Podoplanin 67.9 Cxcr2 Chemokine (C-X-C motif) receptor 2 63.5 Mmp14 Matrix metallopeptidase 14 62.7 Met Met protooncogene 62.0 Gja1 Gap junction protein, a 1 263.4 Cspg4 Chondroitin sulfate 267.7 Extracellular region Ccdc80 Coiled-coil domain containing 80 490.9 Prss34 Protease, serine, 34 452.4 Edn1 300.1 Rbp4 Retinol binding protein 4, plasma 192.1 Klk1b21 1-related peptidase b21 160.9 Il1b IL-1b 133.1 Klk8 Kallikrein related-peptidase 8 61.6 Gata2 GATA binding protein 2 60.0 Timp3 Tissue inhibitor of metalloproteinase 3 53.9 Col18a1 Collagen, type XVIII, a 1 265.8 Endocytosis Ptx3 Pentraxin-related gene 767.7 Pacsin1 Protein kinase C and casein kinase substrate in neurons 1 107.7 Snph Syntaphilin 104.9 Cav1 Caveolin 1 37.9 C3 Complement component 3 26.0 Hip1r Huntingtin interacting protein 1 related 17.7 Cd24a CD24a Ag 11.5 Cd209b CD209b Ag 11.4 Syp Synaptophysin 211.2 Colec12 Collectin subfamily member 12 217.3 and BMM (the top 50 genes are collated in Table III). These data of these type I IFN-regulated genes (34) were also different be- suggest that endogenous type I IFN can also contribute signifi- tween GM-MDM and MDM (the top 50 genes are collated in cantly to the gene expression differences between GM-BMM and Table III), suggesting that type I IFN may also be contributing to BMM. In the human cells, it was found that the expression of 154 this difference as in the mouse. There were 104 type I IFN- 5758 GM-CSF AND M-CSF REGULATION OF MACROPHAGES

Table III. Top 50 type I IFN-dependent genes differentially expressed higher. Another IRF member, namely, IRF7, was 2-fold lower. In between human GM-MDM versus MDM and murine GM-BMM versus GM-BMM versus BMM, similar findings were made with regard BMM to the relative mRNA expression of IRF5 (2-fold higher), IRF4 (44-fold higher), and IRF7 (8-fold lower), the data for IRF7 Human Murine mRNA being consistent with prior qPCR results (34). For IRF7 Fold Change: Fold Change: mRNA, these differences were modulated by endogenous type I Gene GM-MDM Gene GM-BMM IFN, as judged by comparing the differences found between wild- Symbol versus MDM Symbol versus BMM type GM-BMM and BMM with those found between their cor- SERPINB2 2354.7 Epx 1320.6 responding INFAR2/2 counterparts (34). MAOB 85.3 Klrb1b 764.5 As part of the study implicating IRF5 in M1 macrophage po- EDNRB 253.7 Prss34 452.4 COL23A1 252.3 Flrt3 379.4 larization, it was reported that for human monocytes treated with FCGBP 250.1 Prg2 378.4 GM-CSF over a time course of 48 h, GM-CSF stimulated IRF5 LGMN 242.2 Ramp3 352.2 mRNA and protein expression, but not those of IRF4 (32). Given P2RY14 30.8 Klk1b27 315.9 our microarray findings earlier in both the human and murine CH25H 29.1 Arg2 278.9 CCL24 28.1 Serpina3g 264.8 systems, in particular, the strong relative IRF4 expression in GM- SMPD3 27.9 Pglyrp1 209.8 CSF–derived populations, we carried out a similar time course. In GPRC5B 223.4 Prg3 203.8 contrast with Krausgruber et al. (32), in our hands, GM-CSF CSF1 21.4 Ffar2 193.5 stimulated IRF4 mRNA much more strongly than IRF5 mRNA PACSIN1 21.0 Dnahc2 192.8 with only a trend toward an increase being observed for the latter PLXDC1 219.8 Gpr68 192.2 Downloaded from FBLN5 219.0 Cxcl14 2187.1 (Fig. 3A, 3B); protein levels increased for both IRFs (Fig. 3C, RAP1GAP 18.4 Itgb2l 186.1 3D). Also, in contrast with the literature (32, 38) and with GM- CDKN1C 215.5 Camp 184.6 CSF action, M-CSF did not induce IRF4, although it enhanced 2 F13A1 15.1 Pla1a 156.7 expression of IRF5 to a similar extent as GM-CSF. These data are STAB1 214.9 Chi3l1 153.7 SPIB 14.9 Ltf 144.1 consistent with our earlier microarray data for the relative gene FLT1 14.6 Lrg1 132.3 expression of IRF5 and IRF4 in the CSF-differentiated pop- MS4A3 14.0 Lcn2 130.8 ulations. In the time-course experiment whose data are provided in http://www.jimmunol.org/ PRG2 13.8 Klrk1 118.5 Fig. 3, neither CSF enhanced IRF3 and IRF6 mRNA expression PRSSL1 13.0 Cd200 117.6 nor that of the STAT transcription factors implicated in IFN GGTLA1 12.9 Egfbp2 115.4 CCL5 12.8 Pacsin1 107.7 function, namely, STAT1 and STAT2 (data not shown). 2 PRSS22 12.4 Slco2b1 107.5 Cytokine gene expression changes in GM-CSF– and TAGLN3 11.9 Hpgd 2106.5 MMP10 11.5 Dapk2 97.6 M-CSF–treated human monocytes NFE2 11.4 Pkp2 96.1 Relative gene expression. For the murine GM-BMM and BMM, we ITGA2B 11.4 Klk1b4 94.0 2 previously showed that there was a different relative cytokine

RGL1 11.4 Ccl5 93.2 by guest on October 1, 2021 TMEM119 211.2 S100a8 88.9 expression profile, with the former, as a generalization, expressing CD69 11.0 Itga8 287.5 higher levels of proinflammatory cytokines (e.g., TNF, IL-12, and CORO2B 10.7 Cttn 72.6 IL-23), whereas the reverse was true for the anti-inflammatory 2 GP1BB 10.0 Dppa3 70.0 cytokine, IL-10 (11, 34). In GM-MDM versus MDM, when rel- CTSG 9.5 Ctla2b 268.6 HPGD 9.5 Ngp 67.9 ative basal gene expression of TNF, IL-1b, IL-12p35, IL-12p40, CAMP 9.2 Cspg4 267.7 IL-23p19, IL-10, CCL2, and IL-8 were examined by qPCR, the EPX 9.1 Cfh 263.6 only statistically significant difference observed was for higher 2 DPP4 9.1 Klk8 61.6 IL-10 mRNA and CCL2 mRNA in MDM (Fig. 4A). It can also IGFBP6 9.1 Hdc 59.3 SDC3 29.0 Stab1 257.4 be noted that, apart from IL-10 and CCL2 mRNA expression in SIGLEC1 28.7 Lipg 56.7 MDM, when the values for cytokine gene expression were com- TLR7 27.4 Sema6d 56.6 pared with those in the starting monocytes, they were either OLFML3 27.3 Spib 54.3 similar or lower in both CSF-differentiated macrophage popula- GEM 7.2 Dach1 54.3 tions. SOCS1 7.0 Chi3l4 50.0 GPC4 7.0 Tuba8 49.2 Effect of endogenous IL-10 and activin A. In both GM-MDM and CDKN2A 7.0 Dpp4 48.7 MDM, secreted cytokine levels are often quite low or undetectable unless given another stimulus, such as LPS (13, 39). Data for TNF and IL-10 are presented in Fig. 4B. It can be observed that se- regulated genes common between human and murine, 71 of which creted TNF is not detectable unless LPS is given (GM-MDM . were regulated in the same direction in both human and murine MDM), whereas IL-10 is found in untreated MDM but not in GM- populations. MDM unless LPS is added. It is possible that endogenous IL-10 could be contributing to IRFs and CSF-induced macrophage polarization the time-dependent changes in the human cells particularly for The IRF family of transcription factors is important in controlling M-CSF–treated human monocytes (40, 41). We see in Fig. 5A type I IFN cellular responses, as well as its synthesis. Of particular evidence for a contribution of endogenous IL-10 to cytokine gene relevance to CSF biology, it was recently suggested that IRF5 has expression with neutralizing anti–IL-10 Ab increasing mRNA a critical role in GM-CSF–driven M1 macrophage polarization levels for IL-12 p40, IL-23 p19, and IL-10, with a trend toward (32), whereas IRF4 has been implicated in M-CSF–dependent M2 increased TNF mRNA expression for human monocytes treated polarization (37). When relative IRF expression was monitored with M-CSF for only 16 h. As regards the mouse, for BMM, in GM-MDM versus MDM by microarray analysis, we found, evidence that such endogenous IL-10 (42), possibly controlled, in however, that IRF5 was no higher, whereas IRF4 was 4-fold turn, by endogenous type I IFN (34, 43), can contribute to the The Journal of Immunology 5759 Downloaded from

FIGURE 3. GM-CSF preferentially induces IRF4 in human monocytes. Human monocytes were stimulated with either GM-CSF (5 ng/ml) or M-CSF (2500 U/ml) or in culture medium (negative control) for indicated periods. (A and B) qPCR with UBC mRNA as reference. mRNA expression for IRF4 and IRF5, in triplicate, was plotted relative to that at t = 0, which were given an arbitrary value of 1.0. Results are means 6 SE (n = 4 donors). mRNA expression for cells in culture medium alone did not change over the time course (not shown). Statistical analyses (GM-CSF versus M-CSF) were performed using http://www.jimmunol.org/ Wilcoxon matched pairs test, *p # 0.05. (C) Whole-cell lysates were subjected to Western blotting with anti-IRF4, anti-IRF5, and anti–b-tubulin Abs. (D) IRF4 and IRF5 protein levels (n = 3 donors) were plotted relative to expression at t = 0 as means 6 SE. Statistical analyses (GM-CSF versus M-CSF) were performed using Student t test, *p # 0.05. control of expression of certain cytokines is provided in Fig. 5B CSF did likewise for IL-10 and CCL2 mRNA (Fig. 4C), with there and 5C, respectively, by comparing unstimulated and LPS- being little relative difference in the response to each CSF for stimulated wild-type and IL-102/2 cells. At the mRNA level for IL-12p35 mRNA (there was a trend toward increased relative unstimulated macrophages and for the secreted protein in LPS- IL-1b mRNA for GM-CSF–treated monocytes). Second, during a 2/2 stimulated macrophages, IL-10 BMM had higher expression of time course over 24 h, for TNF, IL-1b, IL-12p40, and IL-23p19 by guest on October 1, 2021 TNF, IL-6, IL-12, and IL-23. Such effects were not observed in mRNA, higher levels are expressed in GM-CSF–treated mono- GM-BMM (data not shown). cytes than in M-CSF–treated cells, with the opposite result ob- Table I shows that the gene for INHBA, which codes for the tained again for IL-10 mRNA (Fig. 6). Thus, the time point chosen inhibin bA subunit of activin A, is one of the most differentially for the CSF treatment of human monocytes can markedly deter- regulated genes between GM-MDM and MDM, with a fold- mine the relative gene expression profiles for this set of cytokine change for GM-MDM versus MDM of 112 (Table I). Recently, genes and presumably others. it was found that neutralizing anti-activin A Ab inhibited the ac- quisition of M1 markers by GM-CSF–treated human monocytes Gene expression in the presence of both GM-CSF and M-CSF (i.e., GM-MDM) as part of the evidence for the proposition that There is evidence that GM-CSF and M-CSF can have some op- activin A contributes to the “proinflammatory” macrophage po- posing effects on monocytes/macrophages with possible implica- larization triggered by GM-CSF and limits the acquisition of the tions for the magnitude and maintenance of inflammatory reactions “anti-inflammatory” phenotype (12). In Fig. 5D, we show that (7, 11, 17, 19, 21). We next explored this possibility further in GM-CSF stimulates inhibin bA (activin A) mRNA dramatically in both the human and murine cell populations under study. human monocytes with higher levels at 16 h than at 7 d (i.e., GM- Human cells. Fig. 1A shows that if GM-CSF and M-CSF are both MDM). Consistent with the data by Sierra-Filardi et al. (12), in- included in the 7-d monocyte cultures, then the microarray profile clusion of neutralizing anti-activin A Ab throughout the genera- resembles more that found for GM-MDM rather than that for tion of GM-MDM elevated IL-10 and IGF-1 mRNA levels, MDM. Of the 3044 genes that were .2-fold differentially regu- indicating that there is a significant downregulation of their ex- lated between GM-MDM and MDM (see earlier), only ∼10% pression by endogenous activin A even in the absence of a second were modulated by CSF coaddition. The top 50 genes relative to stimulus. A slight decrease in the “proinflammatory” IL-12 p35 MDM and to GM-MDM are collated in Supplemental Table III. mRNA can also be noted. This modest effect could be because GM-CSF treatment of human Time-dependent changes in cytokine gene expression. Because monocytes can upregulate M-CSF synthesis and secretion in its cells in the long-term (7 d) cultures used to generate our macro- own right (44), with this endogenous M-CSF possibly contributing phage populations may be subjected to feedback regulation over already to the GM-CSF–induced profile in these 7-d cultures. time by accumulating mediators, such as type I IFN, IL-10, and Consistent with this prior observation, it can be noted in Table I activin A, we examined cytokine gene expression of human that M-CSF (CSF-1) is much more highly expressed (21-fold) at monocytes treated with GM-CSF and M-CSF at earlier time points. the gene level in GM-MDM versus MDM. First, we see that at 16 h, GM-CSF induced more TNF, IL-12p40, As regards the possible contribution of endogenous type I IFN IL-23p19, and IL-8 mRNA (Fig. 4C) than M-CSF did, whereas M- (45) in the cocultures, of the putative endogenous type I IFN- 5760 GM-CSF AND M-CSF REGULATION OF MACROPHAGES Downloaded from http://www.jimmunol.org/ by guest on October 1, 2021

FIGURE 4. Cytokine mRNA levels in GM-MDM and MDM, and in monocytes treated with GM-CSF and M-CSF. (A) GM-MDM, MDM, and fresh monocytes. qPCR with 18S rRNA as reference. mRNA expression, in triplicate, was plotted relative to that at t = 0 (i.e., monocytes), which was given an arbitrary value of 1.0 for each cytokine. Box-and-whisker plots represent mRNA expression from eight donors. Statistical analysis was performed using Wilcoxon matched pairs test; p # 0.05 is indicated for GM-MDM versus MDM. (B) GM-MDM and MDM (n = 5 donors) were left untreated or stimulated with LPS (100 ng/ml) for 24 h. Cytokines in the supernatant were measured. Statistical analysis was performed using Wilcoxon matched pairs test, where p # 0.05 is indicated for GM-MDM versus MDM after LPS stimulation. N.D., not detected. (C) Fresh monocytes (t = 0) and monocytes treated with GM-CSF (5 ng/ml) or M-CSF (2500 U/ml) for 16 h. mRNA expression and statistical analysis as in (A)(n = 8 donors again); p # 0.05 is indicated for GM-CSF versus M-CSF. regulated genes regulated differently in GM-MDM and MDM mRNA between M-CSF–treated, GM-CSF–treated, or coaddition (Table III), 37 of them were modulated in the CSF coaddition cultures (Fig. 7). group (data not shown). For the IRFs, in both short-term (16-h) With respect to the cytokines studied earlier (Figs. 4, 6), over the and long-term (7-d) cultures, M-CSF suppressed the induction of 24-h time course shown earlier for CSF-treated monocytes, M- IRF4 mRNA by GM-CSF with no significant differences in IRF5 CSF coaddition reduced the increased gene expression caused by The Journal of Immunology 5761 Downloaded from http://www.jimmunol.org/ by guest on October 1, 2021

FIGURE 5. Effect of endogenous IL-10 and activin A on cytokine expression. (A) Fresh monocytes (t = 0) and monocytes cultured with M-CSF (2500 U/ml) together with either anti–IL-10 Abs (5 mg/ml) or its isotype control (5 mg/ml) for 16 h. qPCR with 18S rRNA as reference. mRNA expression, in triplicate, was plotted relative to that at t = 0 (i.e., monocytes), which was given an arbitrary value of 1.0 for each cytokine. Statistical analysis was performed using Wilcoxon matched pairs test; p # 0.05 is indicated for anti–IL-10 versus isotype control (n = 5 donors). (B) Wild-type and IL-102/2 BMM. qPCR with 18S rRNA as reference. Basal mRNA expression, in triplicate, from IL-102/2 BMM levels were plotted relative to their expression in wild-type BMM, which was given an arbitrary value of 1.0 for each cytokine. Results are means 6 SE from four independent experiments. (C) Wild-type and IL-102/2 BMM stimulated with LPS (100 ng/ml) for 24 h (6 h for TNF). Cytokines in the supernatant were measured by ELISA. Results are mean values 6 SE from four independent experiments. N.D., not detected. *p # 0.05 (Student t test) for IL-102/2 versus wild-type. (D) Fresh monocytes (t = 0) and monocytes treated with GM-CSF (5 ng/ml) for 16 h and 7 d (i.e., GM-MDM). qPCR with 18S rRNA as reference. mRNA expression in triplicate cultures, plotted relative to that at t = 0 (n = 6 donors). (E) GM-MDM generated with either anti-activin A Ab or its isotype control (100 ng/ml). qPCR with 18S RNA as reference. mRNA expression for triplicate cultures, plotted relative to that of isotype control (n = 6 donors), *p # 0.05 (Student t test) unless indicated as NS.

GM-CSF for TNF and IL-12p40, but not for IL-1b and IL-23p19; CCL2 (11, 32, 34). Conversely, M-CSF suppresses the mRNA in contrast, GM-CSF reduced the IL-10 induction by M-CSF levels of the M1-associated cytokines (TNF, IL-12p40, and IL- (Fig. 6). 23p19) (11) (Fig. 8B). Murine cells. We showed previously that when BMM were To assess the generality of these observations for GM-CSF– “primed” with GM-CSF for a short period (16 h), the cells started treated BMM, we measured by microarray the relative gene ex- to express certain secreted cytokines on LPS stimulation, such as pression profiles for BMM, prestarved overnight of M-CSF, IL-12 and IL-23, that is, started to adopt, to some extent, the after 16-h treatment with the CSFs, either alone or in combina- features of GM-BMM (11). The data for the basal gene expression tion. From 3 experiments, 3065 genes (12%) were differentially of these and other cytokines in BMM and GM-BMM are provided expressed (i.e., p , 0.05 and ratio $ 2-fold change) between in Fig. 8A; it can be seen that GM-CSF addition to BMM BMM treated with GM-CSF or M-CSF (the top 50 genes are increases mRNA levels for certain cytokines that have been as- collated in Supplemental Table IV). The “heat map” is presented sociated with an M1 phenotype (11, 32, 34), namely, TNF, IL-6, in Fig. 1C. The top 50 genes from BMM cotreated with M-CSF IL-1b, IL-12p35, IL-23p19, and CCL17, whereas it decreases and GM-CSF relative to GM-CSF–treated BMM and to M-CSF– those for cytokines that have been linked with an M2 phenotype, treated BMM are collated in Supplemental Table IV. In the namely, IL-10, IFN-b, TGF-b, CCL5, CCL12, CXCL10, and presence of both CSFs, M-CSF addition reversed the expression of 5762 GM-CSF AND M-CSF REGULATION OF MACROPHAGES

FIGURE 6. Time course for cytokine mRNA levels in monocytes treated with GM-CSF, M-CSF, or both. Human monocytes were cultured with GM-CSF Downloaded from (5 ng/ml), M-CSF (2500 U/ml), or both for indicated periods. qPCR with 18S rRNA as reference. mRNA expression, in triplicate, was plotted relative to that at t = 0 (i.e., no growth factor is present), which was given an arbitrary value of 1.0 for each cytokine. mRNA expression for cells in culture medium alone did not change over the time course (not shown). Results are means 6 SE from four donors. Statistical analyses were performed using Wilcoxon matched pairs test, *p # 0.05, GM-CSF versus M-CSF.

1936 (63%) genes regulated by GM-CSF alone; this is reflected in they have basal transcriptomes that are close to those of macro- the “heat map” (Fig. 1C), with the (M-CSF + GM-CSF)–treated phages rather than DCs (24, 25, 31). Within each species, we http://www.jimmunol.org/ BMM being more similar to the M-CSF–treated BMM than to the reported earlier that the GM-CSF–derived populations have basal GM-CSF–treated BMM. Of the GM-CSF–regulated genes re- transcriptome profiles (.80%) similar to those of macrophages, in versed by M-CSF, 222 of these were type I IFN-sensitive genes our case, M-CSF–derived populations. How similar or otherwise (34), suggesting that, for starved BMM in short-term cultures, the relative gene expression profiles of these GM-CSF– and M- M-CSF can have significant modulating effects on the response CSF–generated populations between human and mouse are has to GM-CSF, in part, by modulating endogenous type I IFN levels. not been assessed previously as an indication of how appropriate As for the IRF qPCR data for the human cells (Fig. 7), by micro- the murine populations are as models for the corresponding human array analysis, M-CSF reduced the increase in IRF4 mRNA in cell types. We found that at the individual level, only around 17% by guest on October 1, 2021 GM-CSF–treated BMM (16 h), with there being no significant dif- of the genes in the human GM-CSF versus M-CSF comparison ferences between M-CSF–, GM-CSF–, or (M-CSF + GM-CSF)– had a conserved expression profile to the murine GM-CSF versus treated BMM for IRF5 mRNA (data not shown). M-CSF comparison; this conservation was higher when compar- isons were made across GO categories and molecular pathways. Discussion The degree of overlap found at the individual gene level pre- Both the human GM-MDM and MDM, and the murine GM-BMM sumably reflects the fact that the starting populations are dif- and BMM are widely studied populations as models for GM-CSF ferent, namely, monocytes (human) and bone marrow (murine), and M-CSF function, respectively, but also for DC and/or mac- and also that there are very different degrees of differentiation rophage biology (7, 10–13, 15, 22–25, 33, 46). Most studies on versus proliferation occurring during the generation of the CSF- CSF actions on macrophage lineage populations have usually been dependent populations. It is perhaps not unreasonable that, for with one CSF as for our gene expression analysis comparing hu- both species, GM-CSF–derived populations expressed relatively man monocytes with MDM (23). Bioinformatics analyses on GM- more genes implicated in immune/inflammatory reactions seeing MDM and GM-BMM using public access databases indicate that that GM-CSF tends to be produced during such reactions (7).

FIGURE 7. M-CSF suppresses GM-CSF induction of IRF4 mRNA in human monocytes. Human monocytes were cultured with either GM-CSF (5 ng/ ml), M-CSF (2500 U/ml), or both for 16 h or 7 d (168 h). qPCR with UBC mRNA as reference. mRNA expression for IRF4 and IRF5, in triplicate, was plotted relative to that at t = 0 (i.e., no growth factor is present), which was given an arbitrary value of 1.0. Box-and-whisker plot represents mRNA expression from six donors. Statistical analyses were performed using Wilcoxon matched pairs test, p # 0.05. The Journal of Immunology 5763

For convenience, macrophage populations are being categorized into M1 and M2 polarization (activation) states after short-term treatment with agents such as IFN-g 6 LPS or IL-4 and IL-13, respectively, to help define their function in host defense and inflammation/repair (26–29). However, this M1/M2 classification, although worthwhile, has of necessity to be limited, and additional criteria, such as developmental ones dependent on the CSFs, are needed (48); also, such categorization is made difficult because some of the markers used to define M1 and M2 polarization differ between the mouse and the human (49). We and others have highlighted that, for the CSF-differentiated human and murine cell types studied earlier, GM-CSF gave rise to populations that tended to produce higher levels of “proinflammatory” cytokines (e.g., TNF, IL-23), but lower levels of “anti-inflammatory” cytokines (e.g., IL-10), than M-CSF did (11, 13, 42). With this in mind, we have previously labeled GM-BMM and BMM as “M1-like” and “M2-like,” respectively, based only on this pattern of cytokine expression (11). Likewise, Verreck et al. (13) have labeled GM- MDM and MDM as Mw-1 and Mw-2 macrophages, respectively, based on similar relative cytokine expression data. Even though Downloaded from there is some overlap, we suggest that such “conservative” no- menclature, rather than M1/M2, be used to denote macrophage phenotypes induced in response to GM-CSF or M-CSF because many responses to these cytokines will differ from those resulting from the very different signaling pathways activated by IFN-g, LPS, IL-4, among others. Using specific cytokine expression again http://www.jimmunol.org/ as the readout, we showed previously (11), and also earlier in this article, that short-term treatment of BMM and GM-BMM with the CSF not used to generate them “switched” to some extent the FIGURE 8. Modulation of cytokine gene expression in BMM by GM- target population to the other phenotype. Again, for the expression CSF and in GM-BMM by M-CSF. (A) BMM cultured in GM-CSF (5 ng/ ml) for 16 h; (B) GM-BMM cultured in M-CSF (2500 U/ml) for 16 h. of the cytokines that we have used to define the murine pop- qPCR with 18S rRNA as reference. mRNA expression, in triplicate, was ulations in this way, namely, TNF, IL-23, IL-10, among others plotted relative to basal expression in either BMM or GM-BMM given an (11), the time point of the analysis after CSF exposure can be arbitrary value of 1.0. Results are means 6 SE from four independent critical (Figs. 4, 6), with endogenous mediators again capable of by guest on October 1, 2021 experiments. mRNA expression relative to unstimulated BMM (A) or GM- contributing to the outcome (Fig. 5). BMM (B) is significant (p # 0.05, Student t test) unless indicated as NS. Recently, the transcription factor, IRF5, has been proposed to be critical for the development of the M1 phenotype including that generated by GM-CSF (32, 38). However, our earlier data on the Given the differences observed, caution should be exercised in striking degree of upregulation of IRF4 by GM-CSF compared interpreting the relevance of data obtained from the murine GM- with that of IRF5 suggests that the former may be worth further BMM and BMM for the (developmental) functions of the CSFs on consideration as being critical for GM-CSF–dependent responses. human macrophage lineage cells. In addition, our data on the lack of upregulation of IRF4 (Fig. 3), As described previously (34, 43, 47), endogenous mediators can and indeed its suppression (Fig. 7) by M-CSF, raises questions as influence quite dramatically the gene expression profiles of mac- to its proposed role in M-CSF–induced M2 polarization in mac- rophage populations particularly in long-term cultures. We have rophages (37). extrapolated our earlier findings on the role of endogenous type I For its role in macrophage lineage biology, the ubiquitous IFN in determining the gene expression profiles of BMM and GM- M-CSF is particularly implicated in the steady-state control of BMM individually (34) by now highlighting that type I IFN can tissue macrophage development, whereas GM-CSF is often con- also contribute to the difference in the profiles between BMM and sidered to be making its contribution in this context during im- GM-BMM; we also suggested earlier that a similar contribution mune/inflammatory reactions (7). In the latter reactions, therefore, could be occurring for MDM and GM-MDM. The potential con- macrophage populations are likely to be exposed often to both tribution of at least endogenous type I IFN, as well as IL-10 and CSFs simultaneously (16). There are examples in the literature activin A, should therefore always be taken into account when where M-CSF can have the opposite effects to or even oppose the assessing CSF function. Interestingly, there is evidence for a link responses of monocyte/macrophage populations to GM-CSF (7, in macrophages between these mediators with endogenous type I 11, 17–21, 50, 51). For both human and murine populations, IFN able to promote IL-10 formation (34, 43), but endogenous cultured long term or short term, evidence was given earlier for activin A able to inhibit it (Fig. 5E) (12). Such endogenous such a “competition” at the global gene expression level, as well mediators are likely to accumulate over time in macrophage cul- as for the control of specific cytokine genes. Further experimen- tures, thereby making it important to examine CSF-induced tation is needed to explore the significance of such “competition,” responses at different time points if one is to dissect out the di- particularly because M-CSF has been proposed also to have rect effects of the CSFs. In support of this contention, we showed a possible involvement in the resolution of inflammatory reactions earlier that the time of exposure can be quite critical for the es- in its role as a homeostatic macrophage lineage regulator (7). In timation of CSF-dependent cytokine expression in human mono- this context, for IRF4 mRNA, but not IRF5 mRNA, M-CSF cyte cultures. downregulated its enhancement because of GM-CSF. Obviously 5764 GM-CSF AND M-CSF REGULATION OF MACROPHAGES more thorough analyses of the role of IRFs in CSF-dependent 18. Menetrier-Caux, C., G. Montmain, M. C. Dieu, C. Bain, M. C. Favrot, C. Caux, and J. Y. Blay. 1998. Inhibition of the differentiation of dendritic cells from macrophage lineage biology and of the mechanism of any “com- CD34(+) progenitors by tumor cells: role of -6 and macrophage petition” between the CSFs are warranted. We also demonstrated colony-stimulating factor. Blood 92: 4778–4791. earlier that gene expression data obtained from CSF cocultures 19. Phillips, W. A., and J. A. Hamilton. 1990. Colony stimulating factor-1 is a negative regulator of the macrophage respiratory burst. J. Cell. Physiol. 144: 190–196. may also be influenced by the time period of the dual-exposure 20. Willman, C. L., C. C. Stewart, V. Miller, T. L. Yi, and T. B. Tomasi. 1989. cultures and by the presence of endogenous mediators such as type Regulation of MHC class II gene expression in macrophages by hematopoietic I IFN; care again needs to be exercised when endeavoring to as- colony-stimulating factors (CSF). Induction by granulocyte/macrophage CSF and inhibition by CSF-1. J. Exp. Med. 170: 1559–1567. sess whether a particular gene change is due to a direct effect of 21. Broche´riou, I., S. Maouche, H. Durand, V. Braunersreuther, G. Le Naour, CSF action in the cocultures. A. Gratchev, F. Koskas, F. Mach, J. Kzhyshkowska, and E. Ninio. 2011. An- In conclusion, a number of factors need to be taken into account tagonistic regulation of macrophage phenotype by M-CSF and GM-CSF: im- plication in atherosclerosis. Atherosclerosis 214: 316–324. when drawing definitive conclusions from a particular in vitro 22. Martinez, F. O., S. Gordon, M. Locati, and A. Mantovani. 2006. Transcriptional system about the precise roles of GM-CSF and M-CSF in hu- profiling of the human monocyte-to-macrophage differentiation and polarization: new molecules and patterns of gene expression. J. Immunol. 177: 7303–7311. man macrophage lineage biology. 23. Way, K. J., H. Dinh, M. R. Keene, K. E. White, F. I. Clanchy, P. Lusby, J. Roiniotis, A. D. Cook, A. I. Cassady, D. J. Curtis, and J. A. 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Human Murine Fold change Fold change Gene Symbol GM vs M Gene Symbol GM-BMM vs BMM SERPINB2 -354.7 Ccr7 4905.4 HBB 193.5 Aldh1a2 2530.8 CCL1 129.2 Gm5483 2328.5 SOBP 120.9 Stfa1 1768.5 DLL1 -119.8 Car2 1658.1 INHBA 112.0 Ear7 1373.8 CD2 105.9 Epx 1320.6 CD1B 96.8 Clu 1100.4 MAOB 85.3 Stfa2l1 1075.3 HBG1 83.9 BC100530 1061.0 CDH1 80.8 Adra2a 1006.0 KCNK17 79.0 Pdcd1lg2 964.5 CD163L1 -78.0 Nov 939.8 TACSTD2 71.8 Sema7a 926.6 TMEM130 71.4 Ceacam10 908.9 BC037919 65.9 Adam23 848.9 FA2H 62.9 Apol7c 813.2 NANOS1 61.5 Ear3 771.8 HBD 59.6 Ptx3 767.7 CTTNBP2 -57.9 Klrb1b 764.5 CD1E 57.4 Stfa2 759.2 TCL1A 56.1 Fscn1 732.1 IGF1 -56.0 Ear6 715.4 ALDH1A2 55.3 Ear10 680.7 MRO -54.3 Aqp3 679.4 EDNRB -53.7 Vat1l 640.0 COL23A1 -52.3 Fndc5 638.8 SYTL1 50.7 Ear1 603.9 FCGBP -50.1 Mucl1 601.6 SEPP1 -48.5 AI854517 597.8 CYP27B1 47.2 Stfa3 592.8 SPINK1 43.4 Asprv1 566.4 LGMN -42.2 Cbln1 563.1 C20orf26 -41.4 D630039A03Rik 553.4 CD28 -40.6 Socs2 553.4 LRRC32 40.4 Ear11 540.3 CD93 -40.4 Mgll 533.7 LGI2 -39.8 Asgr2 525.1 CCR6 39.4 Car4 512.6 MGC24103 -38.9 Ccdc80 490.9 TSPAN2 38.3 Klk1b9 484.9 OSTbeta 38.1 P2rx5 461.3 MYLK2 -38.0 Tcea3 456.0 Supplemental Table I Cont’d CACNA2D3 -36.3 Prss34 452.4 TMEM37 -35.9 Cd209d 430.3 C10orf116 -34.2 Hepacam2 424.8 LTB 34.2 Hr 424.6 BC042017 -33.9 Slc6a4 422.0 BEAN1 32.2 Gpr120 417.2 GUCY1B3 32.1 Tnfrsf9 407.0 ANXA3 32.0 Nags 403.3 FBXO2 31.6 Kif1a 384.1 IGFBP5 31.4 Ccl17 380.8 IL22RA2 30.9 Flrt3 379.4 P2RY14 30.8 Prg2 378.4 CH25H 29.1 Ldhc 369.5 SLC16A2 28.9 Foxf2 366.8 TSKU 28.8 Ramp3 352.2 FAIM2 28.7 Perp 348.3 EDN1 28.3 Amica1 344.5 EDG1 -28.2 Thy1 341.2 CCL24 28.1 Mcpt8 340.2 DSP 28.1 Il28ra 330.9 BX647543 -28.0 Mgl2 322.6 RGMA -27.9 Klk1b27 315.9 SMPD3 27.9 Gimap1 312.4 GLYATL1 -27.7 F7 309.7 FST 27.4 Esyt3 300.4 BC004287 -27.2 Anxa8 300.2 ARC 27.2 Slc6a12 300.1 ABLIM3 -27.1 Edn1 300.1 MMP12 26.5 F10 287.6 IL3RA 26.3 Arg2 278.9 PDK4 -26.3 1100001G20Rik 276.8 IGFBP4 -26.2 Gm8221 269.2 CES1 25.9 Retnlg 267.3 XLKD1 -25.3 Serpina3g 264.8 CLDN14 25.2 1600029D21Rik 263.1 CLC 24.5 Enah 256.2 HS3ST1 -24.2 Clec2l 249.7 THBD -23.9 Fabp1 249.6 ECSM2 23.6 Epcam 246.9 CYP3A7 23.5 Hgfac 245.5 GPRC5B -23.4 Il1f9 241.8 ADORA3 -23.3 Cacnb3 236.1 SCN4B 23.2 Itgax 230.2 IQCD -23.2 Rasgrp1 229.8 SV2B -23.1 Ciita 226.7 AQP3 22.9 Cfb 215.1 LONRF2 22.7 Cldn1 213.2 NPR1 22.4 Pglyrp1 209.8 ARMC9 -22.3 Nudt17 205.4 Supplemental Table I Cont’d LOXL1 -22.2 Slc26a10 205.0 STEAP4 22.2 Prg3 203.8 SORBS1 22.2 Cish 203.1 GSDM1 -21.8 Inhba 202.3 C1orf106 21.6 Cpa3 201.2 GALNT12 21.5 Tnfaip8l3 198.3 NRG1 -21.5 Syt7 197.0 CDX1 21.5 Cd209c 195.7 Supplemental Table II: Top 50 common pathways differentially regulated between human GM-MDM vs MDM and murine GM-BMM vs BMM using the NCI-Nature pathway database.

Pathways Biomolecules in Human P-value Biomolecules in Mouse P-value AP-1 transcription factor ATF3, BCL2L11, CCND1, CDKN1B, 5.05E-07 CCND1, CDK1, CDKN2A, COL1A2, CYR61, 3.28E-09 network CDKN2A, EDN1, EGR1, ESR1, DUSP1, EDN1, EGR1, ESR1, FABP4, FOS, GATA2, HIF1A, IL10, IL4, IL6, JUN, FOSL2, GATA2, GJA1, IL10, IL2, IL4, IL5, IL6, MAF, MT2A, MYB, TCF4, TCF7L2, JUN, JUNB, JUND, MAF, MAFG, MMP9, MYB, TIMP1 PLAU, TCF4, TCF7L2, TIMP1

Calcineurin-regulated BATF3, CBLB, EGR1, EGR2, EGR3, 8.11E-07 BATF3, CDK4, DGKA, EGR1, EGR2, EGR3, 5.53E-07 NFAT-dependent GATA3, IL2RA, IL4, IRF4, JUN, FOS, GATA3, GBP3, IL2, IL2RA, IL4, IL5, IRF4, transcription in MAF, PPARG, PTPRK, RNF128, JUN, JUNB, MAF, PRKCQ, PTGS2, PTPN1, lymphocytes TBX21, TNF TLE4

HIF-1-alpha transcription ABCB1, ABCG2, ADM, BHLHE41, 3.85E-06 ADM, BHLHE40, CXCL12, CXCR4, DEC1, 1.29E-04 factor network BNIP3, CXCL12, CXCR4, EDN1, EDN1, EGLN3, FOS, FURIN, GATA2, GCK, EGLN3, GATA2, HIF1A, HMOX1, HK1, HMOX1, ID2, ITGB2, JUN, NT5E, JUN, NDRG1, NT5E, PFKFB3, PFKFB3, SMAD3, TF, VEGFA SERPINE1, TF amb2 Integrin signaling ELANE, FGR, GP1BA, IL6, JAM2, 1.79E-05 CTGF, CYR61, ELANE, FGR, FN1, FYN, 1.07E-13 LAMA2, LAMA3, LAMB1, LAMB2, GP1BA, HP, ICAM1, ICAM2, IL6, ITGB2, JAM2, LRP1, MMP2, MPO, PLAT, PRKCZ, JAM3, LAMA2, LAMA3, LAMA5, LAMB2, TNF, YES1 LAMB3, LPA, LRP1, LYN, MMP2, MMP9, MPO, NFKB1, PLAU, PLAUR, RHOA, SELPLG, SRC, THY1, YES1

Syndecan-4-mediated ADAM12, CCL5, CXCL12, CXCR4, 2.47E-05 ACTN1, CCL5, CXCL12, CXCR4, FGF6, FGFR1, 1.54E-07 signaling events FGFR1, LAMA3, MDK, SDC4, TFPI, FN1, LAMA3, MDK, MMP9, PRKCA, PRKCD, THBS1, TNC RHOA, SDC4, TFPI, THBS1, TNC Supplemental Table II Cont’d C-MYB transcription ADA, ADORA2B, BIRC3, CA1, 3.99E-05 ADORA2B, ANPEP, BIRC3, CASP6, CCNB1, 1.64E-06 factor network CCND1, CD34, CDKN1B, CDKN2A, CCND1, CD34, CDK6, CDKN2A, CEBPB, CEBPD, ELANE, GATA1, GATA3, COL1A2, ELANE, ETS2, GATA1, GATA3, KITLG, LEF1, MAF, MPO, MYB, HIPK2, KIT, KITLG, LEF1, MAF, MPO, MYB, SLC1A5, ZFHX3 PIM1, PRTN3, PTGS2, TFEC, TOM1, WNT1, ZFPM1 Direct p53 effectors ATF3, BCL2A1, BNIP3L, C13orf15, 4.06E-05 BCL2L1, BCL2L14, BID, BTG2, CASP6, CAV1, 1.34E-03 CAV1, COL18A1, CX3CL1, DDIT4, CCNB1, CD82, COL18A1, CX3CL1, DUSP1, DUSP5, HGF, HIC1, JMY, JUN, EGFR, EPHA2, FAS, GADD45A, GDF15, HGF, MET, MMP2, NDRG1, PERP, PLK3, HIC1, IRF5, JUN, MET, MMP2, PERP, PMAIP1, PPM1J, PPP1R13B, PRDM1, RB1, PML, POU4F1, PPM1J, PRDM1, SCN3B, SESN1, SERPINE1, TP53INP1, VCAN, VDR VCAN, VDR IL12-mediated signaling CCL3, CCL4, CD247, EOMES, 5.99E-05 CCL3, CCL4, CCL4L1, CCL4L2, CCR5, CD3D, 2.01E-11 events GADD45B, GZMB, IL18R1, IL1B, CD8A, FOS, GADD45B, GADD45G, IL12A, IL1R1, IL2RA, IL4, MAP2K6, IL12B, IL12RB2, IL18R1, IL18RAP, IL1B, SOCS1, STAT4, TBX21, TRA@ IL1R1, IL2, IL2RA, IL2RG, IL4, JAK2, MAP2K6, NFKB1, NFKB2, RELB, RIPK2, SOCS1, STAT1, STAT5A, STAT6, TYK2 IL1-mediated signaling IL1A, IL1B, IL1R1, IL1R2, IL1RAP, 8.20E-05 IL1A, IL1B, IL1R1, IL1R2, IL1RN, JUN, 3.26E-02 events IL1RN, JUN, MAP2K6, MYD88, MAP2K6, MYD88, NFKB1 PRKCZ, SQSTM1 Arf6 trafficking events ADRB2, ASAP2, CD59, CDH1, CPE, 1.16E-04 ACAP1, ADRB2, ASAP2, CDH1, CLTC, CPE, 5.87E-04 EDNRB, IL2RA, ITGA6, ITGA7, EDNRB, IL2RA, ITGA1, ITGA11, ITGA6, ITGA9, ITGAV, JUP, PLD1 ITGA8, ITGA9, ITGAV, JUP, PIP5K1C Regulation of nuclear CBY1, CCND1, CCND2, CDH1, 1.85E-04 BCL9, CCND1, CDH1, CDKN2A, COX2, CYR61, 1.17E-04 beta catenin signaling CDKN2A, CDX1, CTNNBIP1, ID2, IGF2BP1, JUN, KLF4, LEF1, MDFIC, MITF, and target gene IGF2BP1, JUN, KLF4, KRT1, LEF1, MMP2, MMP9, TBL1XR1, TCF3, TCF4, TCF7, transcription MMP2, SP5, TCF4, TCF7L2, VCAN TCF7L1, TCF7L2, TLE2, TLE4, VCAN Signaling events CAV1, CDH5, EPAS1, FES, FLT1, 5.77E-04 CAV1, CDH5, EPAS1, FLT1, FYN, GRB10, 1.91E-07 mediated by VEGFR1 HIF1A, ITGAV, MAPK11, NOS3, ITGAV, KDR, MAPK11, NEDD4, NOS3, NRP1, and VEGFR2 NRP2, S1PR1, SHB, SHC2, YES1 PDK1, PDPK1, PRKACA, PRKCA, RHOA, S1PR1, SHC1, SHC2, SRC, VEGFA, VEGFB, VEGFC, VHL, YES1 Supplemental Table II Cont’d ATF-2 transcription ATF3, CCND1, COL24A1, CSRP2, 6.17E-04 ARG1, BCL2L1, CCNA2, CCND1, CDK4, 5.71E-05 factor network DUSP10, DUSP5, ESR1, IL6, JDP2, COL24A1, DUSP1, ESR1, FOS, GADD45A, IL6, JUN, MAPK11, MMP2, RB1 JDP2, JUN, JUNB, JUND, MAPK11, MMP2, PLAU, PRKCA, SOCS3 Regulation of CCND1, CCND2, CCND3, CDKN1B, 6.70E-04 ABL1, BGLAP, CCNA2, CCND1, CDK4, CDK6, 5.16E-03 retinoblastoma protein CDKN2A, CEBPD, JUN, MAPK11, CDKN2A, CEBPB, GSC, ID2, JUN, MAPK11, MEF2C, MET, PPARG, RB1, MEF2C, MET, MITF, RUNX2, SKP2 RUNX2, SFTPD IL27-mediated signaling EBI3, GATA3, IL1B, IL27RA, IL6, 7.11E-04 GATA3, IL12A, IL12B, IL12RB2, IL1B, IL2, 6.17E-05 events STAT4, TBX21, TNF IL27RA, IL6, JAK2, STAT1, STAT5A, TYK2 CXCR4-mediated CD247, CXCL12, CXCR4, FGR, 8.79E-04 CD3D, CXCL12, CXCR4, FGR, FYN, ITGA1, 1.78E-05 signaling events FOXO1, GNG2, ITGA6, ITGA7, ITGA11, ITGA6, ITGA8, ITGA9, ITGAV, JAK2, ITGA9, ITGAV, LIMK1, PIK3R3, LYN, MMP9, PDK1, PDPK1, PIK3CD, PIK3R2, PIK3R5, PIK3R6, PLCB1, PRKCZ, PIK3R3, PIK3R6, PLCB2, RHOA, RHOB, RHOC, TRA@, YES1 SRC, STAT1, STAT5A, STAT5B, VAV1, YES1 TNF receptor signaling BIRC3, CAV1, MAP3K5, PRKCZ, 1.11E-03 BIRC2, BIRC3, CAV1, GCK, GCKR, MAP4K2, 7.5E-03 pathway SMPD1, SQSTM1, TNF, TNFAIP3, MAP4K5, NFKB1, RFFL, STAT1, TNFAIP3, TNFRSF1A, TNFRSF1B, TRAF1 TNFRSF1B, TRAF1 IL12 signaling mediated CD247, CD28, ETV5, IL18R1, 1.33E-03 CD28, CD3D, CD80, CD86, ETV5, FOS, IL18R1, 4.36E-04 by STAT4 IL2RA, JUN, STAT4, TBX21, TRA@ IL18RAP, IL2, IL2RA, JUN Nectin adhesion pathway CDH1, CLDN1, F11R, ITGAV, 1.51E-03 CDH1, CLDN1, F11R, ITGAV, MLLT4, 8.83E-04 MLLT4, PDGFRB, PVR, PVRL1 PDGFRB, PIP5K1C, PVRL1, PVRL2, SRC, VAV2 Signaling events CAV1, CDH2, CSF1, FCGR2A, FGR, 1.82E-03 CAV1, CDH2, CSF1, EGFR, FGR, FYN, ITGA2B, 2.61E-07 mediated by PTP1B INSR, ITGA2B, LAT, PDGFRB, JAK2, LAT, LEPR, LYN, PDGFRB, PTPN1, SPRY2, YES1 RHOA, SHC1, SOCS3, SPRY2, SRC, STAT5A, STAT5B, TYK2, YES1 Validated transcriptional CCND1, CDKN2A, HMOX1, IL6, 1.98E-03 ATF4, BGLAP, CCNA2, CCND1, CDKN2A, 1.06E-09 targets of AP1 family JUN, LAMA3, MMP2, NOS3, THBD COL1A2, DCN, FOSL2, GJA1, HMOX1, IL6, members Fra1 and Fra2 JUN, JUNB, JUND, LAMA3, MMP2, MMP9, NOS3, PLAU, PLAUR, THBD a6b1 and a6b4 Integrin CD9, CDH1, IL1A, ITGA6, LAMA2, 2.28E-03 CASP7, CDH1, COL17A1, EGFR, ERBB2, 1.09E-05 signaling LAMA3, LAMB1, LAMB2, MET, ERBB3, IL1A, ITGA6, LAMA2, LAMA3, PMP22 LAMA5, LAMB2, LAMB3, MET, PMP22, PRKCA, RXRA, SHC1 Supplemental Table II Cont’d CD40/CD40L signaling BIRC3, CBLB, CD40, IL4, JUN, 2.34E-03 BCL2L1, BIRC2, BIRC3, IL4, JUN, MAP3K14, 4.29E-04 MAPK11, TNFAIP3, TRAF1 MAPK11, NFKB1, NFKBIA, STAT5A, TNFAIP3, TRAF1 Regulation of RhoA ARAP3, ARHGAP6, ARHGAP8, 2.68E-03 AKAP13, ARAP1, ARAP3, ARHGAP4, 2.75E-08 activity BCR, CDKN1B, DLC1, FARP1, ARHGAP9, ARHGDIG, ARHGEF10, OBSCN, RGNEF, SRGAP1 ARHGEF10L, ARHGEF11, ARHGEF17, ARHGEF18, BCR, ECT2, FARP1, GRLF1, MCF2L, NET1, OPHN1, RHOA, VAV1, VAV2, VAV3 IL6-mediated signaling A2M, CEBPD, FOXO1, GAB2, IL6, 3.12E-03 A2M, BCL2L1, CEBPB, FOS, IL6, JAK2, JUN, 5.26E-06 events JUN, LMO4, MAP2K6, MAPK11, JUNB, LBP, LMO4, MAP2K6, MAPK11, MITF, TIMP1 PRKCD, SOCS3, STAT1, TIMP1, TYK2, VAV1 Class I PI3K signaling ARAP3, FGR, FOXO3, LAT, 3.25E-03 ARAP3, ARHGEF7, CYTH1, DAPP1, FGR, FYN, 1.21E-06 events PIK3R3, PIK3R5, PIK3R6, INPPL1, LAT, LYN, PDK1, PDPK1, PIK3CD, PLEKHA1, PLEKHA2, PREX1, PIK3R2, PIK3R3, PIK3R6, PLEKHA1, PREX1, YES1 RHOA, SRC, TIAM1, VAV1, YES1 Thromboxane A2 FGR, GNG2, MAPK11, NOS3, 3.71E-03 ADRBK2, EGFR, FGR, FYN, GNA12, GNA15, 4.02E-07 receptor signaling PIK3R5, PIK3R6, PRKCZ, PTGIR, ICAM1, LYN, MAPK11, NOS3, PIK3R6, PLCB2, TBXA2R, TGM2, YES1 PRKACA, PRKCA, PRKCD, PRKCE, PRKCH, PRKCQ, PTGIR, RHOA, SRC, VCAM1, YES1 HIF-2-alpha transcription ABCG2, ADORA2A, EFNA1, 4.97E-03 ADORA2A, BHLHE40, DEC1, EFNA1, EGLN3, 1.4E-03 factor network EGLN3, EPAS1, FLT1, FXN, EPAS1, FLT1, FXN, KDR, MMP14, VEGFA, SERPINE1 VHL RhoA signaling pathway CDKN1B, JUN, LIMK1, MAL, 6.19E-03 CIT, CYR61, EZR, F2RL2, FOS, JUN, MAL, 1.3E-04 MAP2K6, MAPK12, PLD1, PRKCZ, MAP2K6, MAPK12, MKL1, MYL2, PIP5K1B, SLC9A1 PIP5K1C, RHOA Syndecan-1-mediated CCL5, HGF, HPSE, MET, MMP7 8.51E-03 CCL5, HGF, LAMA5, MET, MMP9, PRKACA 1.75E-04 signaling events VEGFR1 specific signals CAV1, CD2AP, FLT1, HIF1A, 8.74E-03 CAV1, FLT1, NOS3, NRP1, PDK1, PDPK1, PGF, 4.29E-04 NOS3, NRP2, SHC2 PRKACA, PRKCA, SHC2, VEGFA, VEGFB Regulation of p38-alpha DUSP10, DUSP16, FGR, MAP2K6, 8.74E-03 CCM2, DUSP1, FGR, FYN, LYN, MAP2K6, 5.22E-03 and p38-beta MAP3K12, MAPK11, YES1 MAPK11, OSM, SRC, YES1 Supplemental Table II Cont’d Regulation of nuclear ATF3, ESR1, FOXO1, FOXO3, 9.63E-03 CDK4, CEBPB, CITED1, COL1A2, ESR1, FOS, 3.66E-04 SMAD2/3 signaling GATA3, IL10, IRF7, ITGB5, JUN, GATA3, GSC, IFNB1, IL10, IL5, IRF7, ITGB5, MEF2C, RUNX2, SERPINE1, VDR JUN, LAMC1, MEF2C, RBL1, RUNX2, RUNX3, SMAD3, TCF3, TGIF2, VDR PDGFR-beta signaling ACTA2, CTTN, CYFIP2, DOCK4, 1.03E-02 ABL1, ARAP1, CTTN, EPS8, FGR, FOS, FYN, 1.1E-08 pathway EPS8, FGR, ITGAV, JUN, LRP1, GRB10, GRLF1, ITGAV, JAK2, JUN, JUND, MLLT4, PDGFB, PDGFRB, PIK3R3, LRP1, LYN, MLLT4, NCKAP1, PDGFRB, PIK3R5, PIK3R6, PPP2R2B, S1PR1, PIK3CD, PIK3R2, PIK3R3, PIK3R6, PLA2G4A, YES1 PRKCA, PRKCD, PRKCE, PTPN1, PTPN2, PTPRJ, RAB4A, RHOA, S1PR1, SHC1, SLA, SLC9A3R2, SPHK1, SRC, STAT1, FGF signaling pathway CDH1, CDH2, CTTN, FGFR1, HGF, 1.11E-02 BGLAP, CAMK2A, CDH1, CDH2, CTTN, FGF1, 5.28E-09 JUN, MET, RPS6KA1, RUNX2, FGF18, FGF6, FGFR1, FOS, HGF, JUN, MET, SPRY2 MMP9, NCAM1, PDK1, PDPK1, PLAU, PLAUR, RUNX2, SDC2, SHC1, SPRY2, SRC, STAT1, STAT5B Plasma membrane ESR1, GNG2, HBEGF, IGF1, 1.37E-02 ESR1, GNA15, HBEGF, IGF1, IGF1R, MAPK11, 2.43E-03 estrogen receptor MAPK11, MMP2, NOS3, PLCB1 MMP2, MMP9, NOS3, PLCB2, RHOA, SHC1, signaling SRC RXR and RAR NR1H3, NR4A1, PPARD, PPARG, 1.55E-02 ABCA1, NR1H3, NR1H4, RARA, RARB, RXRA, 1.83E-02 heterodimerization with TNF, VDR THRA, VDR other B reverse CXCR4, EPHB2, FGR, ITGA2B, 1.55E-02 CXCR4, EFNB2, FGR, FYN, ITGA2B, LYN, 6.23E-03 signaling PTPN13, YES1 SRC, TIAM1, YES1 HIV-1 Nef: Negative BIRC3, CD247, CYCS, MAP3K5, 1.59E-02 BID, BIRC3, CASP6, CASP7, FAS, MAP3K14, 2.84E-02 effector of Fas and TNF- TNF, TNFRSF1A, TRAF1 NFKB1, NFKBIA, TRAF1 alpha p75(NTR)-mediated APH1B, BCL2L11, BIRC3, CYCS, 1.64E-02 BEX1, BIRC2, BIRC3, CASP6, FURIN, MAG, 1.6E-03 signaling MMP7, MYD88, NGFRAP1, PRKCZ, MAGED1, MAGEH1, MYD88, NGFRAP1, OMG, RTN4R, SORT1, SQSTM1 PRKACB, RHOA, RHOB, RHOC, RIPK2, RTN4, RTN4R, SHC1 Stabilization and AQP3, CDH1, CYFIP2, EFNA1, 1.73E-02 ACTN1, AQP3, AQP5, CDH1, EFNA1, EGFR, 2.05E-10 expansion of the E- HGF, IGF1, MET, MLLT4 ENAH, EPHA2, HGF, IGF1, IGF1R, KIF3C, cadherin adherens KIFC3, LPP, MET, MLLT4, MYL2, MYO6, junction NCKAP1, PIP5K1C, PVRL2, RHOA, VASP, ZYX Supplemental Table II Cont’d Downstream signaling in CD247, EGR1, EOMES, GZMB, 1.78E-02 CD3D, CD8A, EGR1, FOS, IL2, IL2RA, IL2RG, 2.99E-02 naïve CD8+ T cells IFNAR2, IL2RA, JUN, STAT4, TNF, JUN, JUNB, PRKCA, PRKCE, PRKCQ, TNFRSF4, TRA@ TNFRSF18, TNFRSF4, TNFRSF9 Signaling events ARHGAP26, ASAP1, CCND1, 1.80E-02 ACTN1, ARHGAP26, ARHGEF11, ARHGEF7, 3.52E-03 mediated by focal ITGAV, ITGB5, JUN, KLF8, BMX, CCND1, ELMO1, FYN, GRB7, GRLF1, adhesion kinase PTPN21, RGNEF, YES1 ITGAV, ITGB5, JUN, KLF8, MMP14, RHOA, SH3GL1, SRC, YES1 IL23-mediated signaling CXCL1, IL18R1, IL1B, IL6, MPO, 2.05E-02 CXCL1, CXCL9, IL12B, IL18R1, IL18RAP, IL1B, 2.07E-06 events STAT4, TNF IL2, IL23R, IL6, JAK2, MPO, NFKB1, NFKBIA, SOCS3, STAT1, STAT5A, TYK2 IL4-mediated signaling CCL17, COL1A1, EGR2, FES, IL10, 2.32E-02 ALOX15, ARG1, BCL2L1, CCL17, CEBPB, 2.13E-06 events IL4, IRF4, MYB, PIGR, SOCS1 COL1A1, COL1A2, EGR2, IL10, IL13RA1, IL2RG, IL4, IL5, IRF4, JAK2, LTA, MYB, SHC1, SOCS1, SOCS3, STAT5A, STAT5B, STAT6, THY1 Regulation of RAC1 BCR, DOCK6, EPS8, KALRN, 2.58E-02 ARHGAP9, ARHGEF7, BCR, ELMO1, EPS8, 9.57E-05 activity PREX1, RASGRF1, TIAM2 PREX1, RACGAP1, RALBP1, RASGRF2, SPATA13, TIAM1, TIAM2, VAV1, VAV2, VAV3 Posttranslational CABLES1, CDH1, CDH2, DSP, JUP, 2.62E-02 ABL1, CABLES1, CDH1, CDH2, EGFR, FYN, 7.5E-03 regulation of adherens MET, MMP7, RIN2 GNA12, IGF1R, JUP, MET, PTPN1, SRC, TIAM1 junction stability and dissassembly ErbB receptor signaling AREG, EREG, HBEGF, NRG1 3.04E-02 AREG, AREGB, EGFR, ERBB2, ERBB3, 3.02E-03 network HBEGF, NRG1 Alpha-synuclein FGR, GRK5, MAOB, PLD1, UCHL1, 3.08E-02 FGR, FYN, GRK5, KLK6, LYN, MAOB, PARK2, 5.92E-04 signaling YES1 PLCB2, PRKCD, SLC6A3, SRC, YES1 Fc-epsilon receptor I CBLB, FCER1A, GAB2, JUN, LAT, 3.54E-02 DUSP1, FCER1A, FCGR2B, FOS, FYN, JUN, 2.0E-03 signaling in mast cells MS4A2, PPAP2A, PTPN13, S1PR1 LAT, LAT2, LYN, MS4A2, NFKB1, PLA2G4A, PPAP2A, S1PR1, SHC1, SPHK1, VAV1 Supplemental Table III: Top 50 human genes modulated by M-CSF co-addition with GM-CSF relative to MDM or GM-MDM.

Fold change Fold change M-GM-MDM vs M-GM-MDM vs Gene Symbol MDM Gene Symbol GM-MDM SERPINB2 -343.0 FAP -12.4 HBB 183.6 NEFL -10.1 DLL1 -132.0 LGI2 -7.4 SOBP 83.6 IBSP -7.4 IGF1 -79.0 SPARCL1 -7.4 HBG1 79.0 STEAP4 6.9 TMEM130 78.3 STEAP2 -6.3 CD2 72.9 MRO -5.7 KCNK17 65.5 CMYA1 5.3 CD1B 61.4 CXADR -5.2 INHBA 58.9 SPINK6 -5.1 CDH1 58.6 GFRA2 5.1 CTTNBP2 -58.0 SHC3 -5 FCGBP -54.8 PARVA -4.4 HBD 54.0 TAGLN3 4.3 CD163L1 -52.0 SGPP2 4.2 IGFBP2 49.9 EHD2 -4.1 FOLR2 -49.2 Gcom1 -4.1 TACSTD2 48.0 PDK4 -4 SYTL1 46.0 OSTbeta 4 CCL1 42.6 PPAPDC1A -3.9 MAOB 42.2 UNC13A 3.8 ARNT2 -41.1 MMP10 3.8 FA2H 40.4 TCN1 -3.7 SEPP1 -39.3 LRRC32 3.7 TSPAN2 35.7 ACCN2 -3.7 ECSM2 35.7 ABCB1 3.6 FAIM2 34.4 KCNG1 -3.6 TCL1A 33.7 TCTEX1D1 -3.5 CD28 -33.3 LYZL2 3.5 COL23A1 -32.5 GPR109B 3.5 GPRC5B -30.8 EBI3 3.5 CH25H 29.8 TMEM37 -3.5 IL22RA2 28.9 THBD -3.4 CCR6 28.2 PNPLA1 3.4 NEFL 28.0 THBS2 -3.4 C19orf59 26.0 STC2 -3.4 EDNRB -25.9 TMSB15B 3.4 CACNA2D3 -25.7 CD93 -3.3 RGNEF 25.7 EGR3 3.3 Supplemental Table III Cont’d ARL4C -25.0 CAV1 3.3 LTB 24.8 MYH11 3.3 IL3RA 24.4 C17orf72 -3.3 CYP27B1 24.4 MT1JP 3.2 SMPD3 24.3 CLDN1 3.2 TSKU 24.0 C9orf165 3.2 FBLN5 -23.9 CCL17 3.2 CYP3A7 23.6 PRTFDC1 -3.2 LGMN -23.6 GP1BA 3.2 RGMA -23.2 KRT19 -3.2 Supplemental Table IV: Top 50 Genes differentially expressed between BMM starved for 24hrs then treated for 16hr with M-CSF or GM-CSF or both.

Fold change Fold change Fold change M-CSF M-CSF + GM-CSF M-CSF + GM-CSF Gene Symbol vs GM-CSF Gene Symbol vs GM-CSF Gene Symbol vs M-CSF Tppp3 -77.8 Baiap2l1 81.1 Glod5 41.9 Saa3 -66.6 Ccl7 75.2 Myct1 34.6 Gprc5c -64.0 Saa3 -66.1 D030051J21Rik 28.4 Amica1 -54.8 Mmp10 60.0 Ear10 -26.8 Arg2 -45.9 Gprc5c -54.7 1700037N05Rik 22.0 Il1a -38.3 Cfb -47.1 Dnahc3 20.6 Il6 -37.8 Ffar2 -41.1 Cmah -20.0 Gbp4 -33.8 Emr4 -35.8 Adra2c -19.1 D230002A01Rik -32.6 Gbp8 -34.0 B930063P07Rik 17.3 Adamtsl4 -31.8 Ccl12 33.8 Gast 16.8 Gykl1 31.7 Spic -33.4 Olfr628 16.4 Il1b -30.2 Bmf -32.3 Slc34a3 16.2 Ffar2 -28.7 Sult1a1 -29.9 Car4 -15.5 Src -27.9 8030451A03Rik 29.5 Cd97 -14.9 Klk1b1 -26.4 Ch25h 29.4 Dct 14.2 Rasgrp1 -26.1 Tppp3 -27.9 Gpr128 13.5 Csf3 -23.2 Hs3st1 25.0 8030451A03Rik 13.1 Sectm1a -22.7 Gbp5 -25.0 Mmp12 12.8 Sncaip 22.7 Gbp4 -24.9 2210411G17Rik 12.4 2310014F07Rik -22.4 St3gal6 24.9 Krt6b 12.1 Olfr1454 -20.9 Gm711 -24.5 5730585A16Rik 11.7 Ppm1n -19.9 Arg2 -23.2 Lce1h 11.5 Car4 19.9 Src -21.6 Peg3 11.4 Supplemental Table IV Cont’d Rgs1 19.7 Zfp879 20.5 Cx3cr1 -11.4 Dnahc2 -19.4 Amica1 -20.3 Klhl3 -11.2 Cd300e 19.3 Zfp618 20.3 Mgll -11.1 Hmga2 19.3 Il1b -20.2 Kcnj12 10.8 Casp12 -19.2 Fosl1 20.2 Mbl1 10.6 Fcgrt -19.2 Fcgrt -20.0 Zscan4c -10.1 Pdgfc -18.6 Klk1b1 -19.6 Gal3st4 -10.0 Olfr259 -18.3 Aqp3 -18.9 S100a7a 9.9 Hepacam2 -18.1 2200002K05Rik 18.2 Gm5086 -9.8 A630072L19Rik -17.1 Tm4sf5 -17.6 Meox1 9.8 Zfp354b 17.0 Ambp 17.6 1700072E05Rik 9.8 Tcea3 -16.5 Csf3 -17.6 Mapk8ip2 9.7 Zfp618 16.3 Zmynd15 -17.5 Fam181b -9.6 Bok 15.8 Prss46 17.4 Vwa5b1 9.6 Defa-rs12 -15.5 Sectm1a -16.9 Vmn1r201 9.4 Itga8 15.4 Aqp8 15.9 Susd4 9.3 St3gal6 15.4 Cp -15.7 Olfr554 -9.3 Plau 15.4 Mmp14 -15.7 Scgb1c1 9.3 Serpinb6b 15.2 Ccnd1 15.5 Olfr444 9.2 Dcaf12l2 -15.0 Siglecg -15.1 Gsx1 9.2 Bdh2 14.8 Il1a -14.9 Pacrg -9.0 Cish -14.8 Adamtsl4 -14.7 Ccdc11 9.0 Irf4 -14.5 Ppm1n -14.4 2310057J16Rik -9.0 Acpp -14.4 Vmn1r76 14.4 Ear1 -8.8 Rab44 -14.4 Hepacam2 -13.9 Asb9 8.6 Moxd2 14.3 Ccl3 13.7 Slco2b1 -8.4 Baiap2l1 14.1 Tbx6 -13.5 Pcdhga1 -8.3