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JUNB Is a Key Transcriptional Modulator of Macrophage Activation Mary F. Fontana, Alyssa Baccarella, Nidhi Pancholi, Miles A. Pufall, De'Broski R. Herbert and Charles C. Kim This information is current as of October 2, 2021. J Immunol published online 3 December 2014 http://www.jimmunol.org/content/early/2014/12/03/jimmun ol.1401595 Downloaded from Supplementary http://www.jimmunol.org/content/suppl/2014/12/03/jimmunol.140159 Material 5.DCSupplemental

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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 © 2014 by The American Association of Immunologists, Inc. All rights reserved. Print ISSN: 0022-1767 Online ISSN: 1550-6606. Published December 3, 2014, doi:10.4049/jimmunol.1401595 The Journal of Immunology

JUNB Is a Key Transcriptional Modulator of Macrophage Activation

Mary F. Fontana,* Alyssa Baccarella,* Nidhi Pancholi,* Miles A. Pufall,† De’Broski R. Herbert,* and Charles C. Kim*

Activated macrophages are crucial for restriction of microbial infection but may also promote inflammatory pathology in a wide range of both infectious and sterile conditions. The pathways that regulate macrophage activation are therefore of great interest. Recent studies in silico have putatively identified key transcription factors that may control macrophage activation, but experimental validation is lacking. In this study, we generated a macrophage regulatory network from publicly available microarray data, employing steps to enrich for physiologically relevant interactions. Our analysis predicted a novel relationship between the AP-1 family transcription factor Junb and the gene Il1b, encoding the pyrogen IL-1b, which macrophages express upon activation by inflammatory stimuli. Previously, Junb has been characterized primarily as a negative regulator of the cell cycle, Downloaded from whereas AP-1 activity in myeloid inflammatory responses has largely been attributed to c-Jun. We confirmed experimentally that Junb is required for full expression of Il1b, and of additional genes involved in classical inflammation, in macrophages treated with LPS and other immunostimulatory molecules. Furthermore, Junb modulates expression of canonical markers of alternative activation in macrophages treated with IL-4. Our results demonstrate that JUNB is a significant modulator of both classical and alternative macrophage activation. Further, this finding provides experimental validation for our network modeling approach, which will facilitate the future use of gene expression data from open databases to reveal novel, physiologically relevant http://www.jimmunol.org/ regulatory relationships. The Journal of Immunology, 2015, 194: 000–000.

acrophages, tissue-resident phagocytic cells of the in- databases, but such data are generally considered unsuitable for nate immune system, are critical sentinels in the detection network analysis due to the confounding effects of technical variation M and containment of infectious microbes and the initia- resulting from the use of diverse nucleic acid amplification proce- tion of inflammatory type I immune responses. In addition to these dures and expression profiling platforms. In this study, we present the functions, collectively referred to as classical activation, macrophages results of a regulatory network analysis approach that is based on may also undergo alternative activation, resulting in distinct nonin- mutual information and data processing inequality procedures (4–8)

flammatory programs that are important in type II immune responses, applied to strictly standardized and normalized public datasets. We by guest on October 2, 2021 wound healing, and tissue homeostasis (1, 2). Given the central role further improved the power of this approach to identify physiological of macrophages in diverse immune functions, it is important to de- relationships by using existing literature to strengthen predictions in velop a more systematic understanding of the transcriptional net- a series of steps that we term “knowledge-based enrichment.” works that govern their activation and polarization. Our network model led us to examine the AP-1 transcription One recently developed tool that may yield great insight into factor JUNB for its role in myeloid immune activation. Although mechanisms of macrophage activation is regulatory network JUNB has historically been studied primarily in the contexts of cell analysis, a statistical method for identifying components of a dataset cycle regulation and differentiation, several recent bioinformatic that covary across a broad range of samples or conditions (3, 4). studies, like the one presented in this study, have predicted a role for A wealth of macrophage transcriptional data is available in public JUNB in the regulation of myeloid immune responses (3, 9). However, there is currently little experimental evidence to support this prediction. To directly test the importance of JUNB in macro- *Division of Experimental Medicine, Department of Medicine, University of Cali- phage activation, we characterized the transcriptional responses of fornia, San Francisco, San Francisco, CA 94143; and †Department of Biochemistry, University of Iowa, Iowa City, IA 52242 JUNB-deficient macrophages to diverse stimuli. Confirming our Received for publication June 23, 2014. Accepted for publication October 31, 2014. network prediction, we found that JUNB modulates subsets of This work was supported by National Institutes of Health Grant R00 AI085035 (to immune-related genes in macrophages treated with microbial C.C.K.). ligands [referred to as classically activated, or M(LPS), macro- The microarray data presented in this article have been submitted to the National phages] as well as with the cytokine IL-4, which stimulates polar- Center for Biotechnology Information Gene Expression Omnibus (http://www.ncbi. ization of alternatively activated M(IL-4) macrophages (10). To our nlm.nih.gov/geo/) under accession number GSE50542. knowledge, this is one of the first reports of a transcription factor that Address correspondence and reprint requests to Dr. Charles C. Kim, Division of promotes polarization of both M(LPS) and M(IL-4) macrophages. Experimental Medicine, University of California, San Francisco, 1001 Potrero Ave- nue, Building 3, Room 603, Box 1234, San Francisco, CA 94143. E-mail address: Furthermore, this study provides experimental validation for several [email protected] recent predictions made in silico (3, 9), demonstrating the power of The online version of this article contains supplemental material. network analysis to lead to new insights into immune regulation. Abbreviations used in this article: BMDM, bone marrow–derived macrophage; ChIP- Seq, chromatin immunoprecipitation and sequencing; DC, dendritic cell; F, forward; Materials and Methods GEO, Gene Expression Omnibus; qPCR, quantitative PCR; R, reverse; SAM, Sig- Gene Expression Omnibus data preprocessing nificance Analysis for Microarrays; UCSF, University of California, San Francisco. All mouse macrophage microarray datasets warehoused in the Gene Ex- Copyright Ó 2014 by The American Association of Immunologists, Inc. 0022-1767/14/$16.00 pression Omnibus (GEO) or ArrayExpress database as of 2010 were

www.jimmunol.org/cgi/doi/10.4049/jimmunol.1401595 2 JUNB MODULATES MACROPHAGE ACTIVATION

downloaded. Data were log2 transformed, and each experimental sample Derivation and stimulation of macrophages was normalized to a baseline sample (e.g., untreated or time zero) to reduce interdataset technical variation. Each microarray dataset was then Macrophages were derived from bone marrow by culturing for 8 d in RPMI z-scaled to minimize distribution variation. This dataset consisted of 40 1640 supplemented with 10% serum, 10% supernatant from 3T3–M-CSF studies and 243 samples, which were subsequently collapsed by averaging cells, and 1 mM sodium pyruvate, with feeding on day 5. Resident peri- technical and biological replicates to reduce bias from studies that used toneal macrophages were isolated by peritoneal lavage with 10 ml ice- larger sample groups. Author-provided gene identifiers were used to map cold PBS containing 1 mm EDTA and 3% FBS. Unless otherwise indi- 3 5 datasets to one another, and studies that failed to map to at least 40% of cated, macrophages were plated in 12-well dishes at a density of 8 10 m the total gene set were removed, leaving 87 samples from 18 studies cells/well and treated with LPS (100 ng/ml; Sigma-Aldrich), CpG (1.5 g/ml; m (Supplemental Table I). Data were further filtered by removal of genes not ODN1826; Invivogen), imiquimod (5 g/ml; Invivogen), polyinosinic- m present in at least 60% of the remaining arrays, resulting in probes for polycytidylic acid (2.5 g/ml; Sigma-Aldrich), IL-4 (20 ng/ml; Pepro- m 11,816 unique genes. Tech), or transfected cyclic di-GMP (10 g/ml; kind gift of D. Burdette and R. Vance, University of California, Berkeley, Berkely, California). ARACNe network prediction Lipofectamine 2000 (Life Technologies) was used for transfection. A regulatory network model was generated using a mutual information IL-1b ELISA metric with p value thresholding and application of the data processing Macrophages were plated in six-well dishes at a density of 106 cells/well in inequality as implemented in ARACNe (4, 5). Briefly, missing data were 5 ml media. After 3-h LPS stimulation, ATP (Sigma-Aldrich) was added to estimated in R using K-nearest neighbors as implemented in the impute a final concentration of 5 mM. Successive 50 ml aliquots of media were package and pairwise mutual information was calculated using ARACNe. 2 2 removed at indicated time points for analysis using the Ready-Set-Go IL- Thresholds for p values were evaluated from 10 3 to 10 15, and the data 1b ELISA (eBioscience) according to the manufacturer’s protocol, with processing inequality was applied at a tolerance of 0.10. For each p value rIL-1b (eBioscience) as a standard. threshold, a false discovery rate was estimated by generating a background model based on the average number of edge predictions in .1000 random Western blot Downloaded from iterations. Random iterations were based on datasets in which gene expression values were unchanged (to preserve the distribution) but randomly Cell lysates were harvested in RIPA buffer supplemented with 1 mM PMSF ordered for each array. and 13 Complete Protease Inhibitor Cocktail (Roche). Proteins were A p value was chosen for thresholding based on the F-score calculated separated on 10% NuPage bis-Tris gels (Life Technologies), transferred to from precision and recall. Calculation of these parameters is limited by the polyvinylidene membrane, and immunoblotted with Abs to JUNB (Cell lack of availability of a suitably large database of mouse macrophage Signaling Technology) or b-actin (Abcam). transcriptional regulation interactions; the most extensive such database, InnateDB (11), contains ,200 such relationships. To obtain a more robust Phagocytosis assay http://www.jimmunol.org/ estimation of precision and recall, we based our calculations on all human Bone marrow–derived macrophages (BMDM) were incubated with 3-mm and mouse interactions cataloged in BioGRID (12), including physical FITC-labeled beads (ECFP-F1; Spherotech) at a cell/bead ratio of 5:1 or associations, under the assumption that coexpressed genes would be with 1-mm Fluosphere beads (Life Technologies) at 10:1 for 24 h. Cells enriched in these data because a physical association implies some degree were labeled with anti-F4/80 Ab as described below, and uptake of fluo- of coexpression. The maximal F-score was found to occur at p value of rescent beads was measured on an LSRII (BD Biosciences). 1026, which was chosen for thresholding and subsequent data processing inequality application. Flow cytometry Knowledge enrichment BMDM were blocked with anti-CD16/32 (2.4G2; UCSF mAb Core), stained with Abs to myeloid surface markers, and analyzed on an LSRII (BD by guest on October 2, 2021 Knowledge enrichment was performed in two steps. First, triangular reg- Biosciences). Intracellular staining was performed after 4-h incubation (for ulatory subnets were identified by retrieving triangular subnets (i.e., those BMDM) or 2-h incubation (for peritoneal macrophages) with GolgiPlug with three nodes and three edges) from the ARACNe-predicted full net- (BD Biosciences) and monensin (eBioscience), using Cytofix/Cytoperm work, followed by removal of triangular subnets without exactly one (BD Biosciences) according to the manufacturer’s protocols. Results transcription factor as defined by comparison with the RIKEN transcription were analyzed using FACSDiva (BD Biosciences) and FlowJo (Tree Star) factor database (13). Triangular subnets were subjected to knowledge software. Abs to the following were used: Ly6C (clone HK1.4), CD11c enrichment by searching PubMed for each transcription factor-gene (N418), IL-1b (NJTEN3), TNF (TN3-19), and rat IgG1 isotype (eBRG1), combination and only retaining triangular subnets in which at least two all from eBioscience; Ly6G (1A8) and Siglec H (550) (BioLegend); citations were found for one transcription factor-gene combination (a Ly6B.2 (7/4) (Cedarlane Laboratories); CD11b (M1/70), F4/80 (BM8), strong association), the rationale being that these subnets were further and Gr-1 (RB6-8C5), all from UCSF mAb Core; and CD68 (FA-11) (AbD enriched for true biological interactions. Triangular subnets were recom- Serotec). Myeloid populations were delineated as previously described bined into a complete network delineating transcription factor–gene rela- (16): granulocytes (CD11b+Ly6G+), eosinophils (CD11b+Ly6cintSSChi), tionships, but not the originally predicted gene–gene relationships. monocytes (CD11b+F4/80intSSClo Ly6c as indicated), red pulp macro- Testing the degree of enrichment is limited by the absence of a robust phages (F4/80hiCD11b2), conventional dendritic cells (DCs; CD11chi), regulatory interaction reference database. In our case, testing is further and plasmacytoid DCs (CD11cintSiglec H+). limited by the fact that existing databases are based on published literature, which is similarly used to enrich our network predictions. For Junb microarray analysis testing, we therefore eliminated all strong associations and based our enrichment analysis only on weak associations. Using all mouse and Following a 4-h LPS stimulation, RNA was isolated from BMDM using the human interactions (including physical) in InnateDB, as well as all human RNaqueous Micro kit (Ambion). Purified RNA was amplified (Amino Allyl regulatory interactions in the Human Transcriptional Regulation Data- MessageAmp II kit; Life Technologies) with minor modifications to the base (14), allowed testing of weak associations against a reference da- manufacturer’s instructions to generate amino allyl–incorporated amplified tabase. Similar comparisons were performed with BioGRID and other RNA. Amino allyl–incorporated amplified RNA was coupled to Cy3 dye subsets of InnateDB (e.g., mouse-specific interactions or transcriptional (GE Healthcare Life Sciences) and hybridized overnight to a SurePrint G3 3 regulation interactions), but insufficient associations were discovered to Mouse Gene Expression 8 60K microarray, which was washed and perform the analysis. Transcription factor-specific subnets were parsed scanned per the manufacturer’s instructions (Agilent Technologies). Raw using in-house scripts, and network predictions were visualized in intensities were extracted using Feature Extraction software (Agilent Cytoscape (15). All analysis described above was performed using Technologies) and quantile normalized using Limma (17). Differentially customized Perl scripts. expressed genes were identified using Significance Analysis for Micro- arrays (SAM) (18), and data were visualized as heat maps using custom Mice Perl scripts. Genes that were increased by at least 2-fold according to SAM (1% false discovery rate) in LPS-treated Junbfl/fl BMDM were considered Junbfl/fl mice (from E. Passegue´, University of California, San Francisco to be LPS-induced. JUNB-dependent genes were identified using SAM [UCSF]) and Lyz2Cre/Cre mice were crossed in-house to generate Junbfl/fl 3 (10% false discovery rate, 1.5-fold change threshold) to compare LPS- Lyz2Cre/Cre mice. All mouse work was conducted with the approval of the stimulated gene expression values, normalized to average baseline ex- UCSF Institutional Animal Care and Use Committee in strict accordance pression, between Junbfl/fl and JunbDLyz2 macrophages. The set of genes with the guidelines of the Office of Laboratory Animal Welfare. regulated by JUNB in response to LPS was defined as the intersection of The Journal of Immunology 3

LPS-induced genes and JUNB-dependent genes. All data are available in Despite a low false discovery rate, estimated using a background GEO under accession number GSE50542. model comprised of randomly ordered expression values (not Reverse transcription and quantitative PCR shown), this approach yielded a large number of predicted regulatory interactions. However, due to the heterogeneity of our MacrophageRNAwasharvested4hpoststimulationusingtheRNeasykit dataset, we expected that some proportion of these interactions (Qiagen) with on-column Turbo DNase treatment (Ambion). Reverse transcription was performed with Superscript III (Life Technologies) primed with (also known as “edges”) would reflect technical covariation within dT(20)V. Quantitative PCR (qPCR) was performed in a Step One Plus RT subsets of microarrays, rather than biological covariation. To en- PCR System (Applied Biosystems) using PerfeCTa 23 qPCR mix (Quanta). rich for biological relationships, we subjected the network to Transcript levels were normalized to levels of actin mRNA. The following a literature-based refinement process. In this procedure, triangular primer sequences were used: Il1b:forward(F)59-CAACCAACAAGTGA- TATTCTCCATG-39 and reverse (R) 59-GATCCACACTCTCCAGCTGCA- subnets, comprised of a transcription factor connected to two 39;actin:F59-ACCCTAAGGCCAACCGTGAA-39 and R 59-CCGCTCG- genes that are likewise connected to one another, were identified TTGCCAATAGTGA-39; Il12b: F59-ACAAGACTTTCCTGAAGTGTGA- as being candidates for improved biological signal based on their AG-39 and R 59-GCTCTTGATGTTGAACTTCAAGTCC-39; Tnf:F59- multiple routes of connectivity. In a further refinement, we iden- 9 9 GACGTGGAACTGGCAGAAGAG-3 and R 5 -TTGGTGGTTTGTGA- tified knowledge-enriched triangular subnets, in which at least one GTGTGAG-39; Ifnb:F59-CAGCTCCAAGAAAGGACGAAC-39 and R 59- GGCAGTGTAACTCTTCTGCAT-39; Junb:F59-ATCCTGCTGGGAGCG- transcription factor–gene relationship was found to have two or GGGAACTGAGGGAAG-39 and R 59-AGAGTCGTCGTGATAGAAAG- more citations in PubMed, lending credibility to at least one of the GC-39; Arg1:F59-TGGGAGGCCTATCTTACAGA-39 and R 59-CA- predictions. These knowledge-enriched subnets were recombined 9 9 9 TGTGGCGCATTCAC-3 ; Retlna:F5-GGATGCCAACTTTGAATAGG-3 into a new global network (Supplemental Fig. 1). The specificity and R 59-GCACACCCAGTAGCAGTCAT-39; Il4ra:F59-CAAGCCCT- 9 9 9 of the knowledge-enriched predictions was tested by comparison TTGGGGATGAC-3 and R 5 -CAGGACAGCTTTCCAAGGC-3 ;and Downloaded from Mrc1:F59-CTCTGTTCAGCTATTGGACGC-39 and R 59-CGGAATTTC- with reference databases that track regulatory, physical, and other TGGGATTCAGCTTC-39. types of interactions. The refinements improved specificity for biological interactions by several fold, demonstrating the value of Meta-analysis of chromatin immunoprecipitation and the approach in improving the frequency of biologically relevant sequencing data predictions (at the expense of sensitivity) (Fig. 1A). The resultant Raw reads from chromatin immunoprecipitation and sequencing (ChIP- network map is likely to be a rich source of undiscovered regu- Seq) of JUNB from bone marrow–derived DCs were downloaded from latory interactions. http://www.jimmunol.org/ GEO (accession GSE36104) (9). These paired end reads were mapped to version mm10 of the Mus musculus genome using Bowtie2 (19). Mapped A role for the JUNB transcription factor in macrophage reads were sorted and converted from SAM to BAM format using Sam- transcriptional responses to LPS tools (20). Peaks were identified in MACS2 (21) using signal over background. Although we identified all peaks with a q , 0.05, our highest Because we were interested in pathways affecting macrophage , 25 confidence peaks had an adjusted q value of 10 . The closest gene to activation, we focused on network predictions surrounding the gene each JUNB binding peak was identified by calculating the distance to the b b nearest transcriptional start site. Genes were considered “bound” when this Il1b, which encodes the proinflammatory cytokine IL-1 . IL-1 is distance was #5 kb in any of the four time points assayed. Genes bound by a potent pyrogen produced by macrophages in response to a vari- JUNB were intersected with JUNB-dependent and JUNB-independent ety of proinflammatory stimuli. As an internal control, our net- by guest on October 2, 2021 genes (identified by microarray above). work analysis correctly predicted a coregulatory relationship be- Arginase assay tween Il1b and the gene Rel, which encodes a subunit of NF-kB,

5 a transcription factor known to be required for Il1b induction in Macrophages were plated in 96-well plates at a density of 10 cells/well and macrophages (22) (Fig. 1B). treated with IL-4 (20 ng/ml) overnight. After a PBS rinse, cells were lysed in 100 ml PBS plus 0.4% Triton-X100, and the lysate was processed per the The network analysis also predicted a relationship between Il1b manufacturer’s instructions using the QuantiChrom Arginase Assay Kit and Junb, a member of the AP-1 family of transcription factors, (BioAssay Systems). Absorbance was measured at 430 nm. which are activated downstream of MAPK signaling and regulate Cytometric bead array a wide variety of cell processes, including differentiation, proliferation, activation, and apoptosis (23). The dimeric AP-1 is as- Cytokine concentrations were measured in cell culture supernatant using the sembled from subunits of the JUN (c-JUN, JUNB, or JUND), FOS Milliplex MAP Mouse Cytokine/Chemokine Magnetic Bead Panel kit (EMD Millipore) according to the manufacturer’s instructions. Measure- (c-FOS, FOSB, FRA-1, and FRA-2), or ATF protein families. ments were made on a MAGPIX instrument (Luminex). Interestingly, JUNB was recently reported to bind extensively to the chromatin of LPS-treated DCs (9) and was also identified as one of the most highly expressed and extensively connected Results transcription factors in a regulatory network model from human Knowledge-enriched analysis of macrophage regulatory monocyte-derived macrophages (3). However, despite these networks observations, no role has been demonstrated for JUNB in the To identify novel transcriptional relationships that regulate mac- regulation of Il1b, or indeed of any gene expressed in activated rophage activation, we generated a de novo regulatory network macrophages. In fact, in myeloid cells, AP-1 activity in response prediction based on microarray expression data obtained from the to inflammatory stimuli has until recently been attributed almost public GEO database (http://www.ncbi.nlm.nih.gov/geo/). Our initial exclusively to c-JUN/c-FOS heterodimers (24–26), which repre- dataset consisted of 243 microarrays from 40 individual studies, each sent AP-1’s canonical and most well-characterized composition. of which examined the transcriptional responses of mouse macro- Systemic deletion of Junb results in embryonic lethality (27). phages to various stimuli (Supplemental Table I). The data from each Therefore, to test whether JUNB does indeed regulate Il1b in individual study were internally normalized to a baseline condition, macrophages, we crossed mice bearing floxed alleles of Junb to reducing variance inherent in technical differences between studies mice expressing Cre recombinase under control of the Lyz2 pro- and enriching for physiological changes in gene expression. These moter, resulting in excision of Junb in a subset of myeloid lineages data were used to generate a mutual information-based network (monocytes, macrophages, granulocytes, and a small fraction of linking genes for which expression covaried over a range of con- DCs) (28). The resulting mice are healthy, breed as expected, and ditions (see Materials and Methods and Supplemental Fig. 1). exhibit no gross pathology. Previously, conditional deletion of 4 JUNB MODULATES MACROPHAGE ACTIVATION Downloaded from http://www.jimmunol.org/

FIGURE 1. Network analysis predicts a regulatory interaction between Junb and Il1b in macrophages. (A) Gene–gene relationships (edges) identified in the initial expression analysis (ARACNe) were enriched for biological relevance based on known interactions; the graph depicts the percent of edges remaining after each of two sequential refinements (triangular subnets and knowledge-enriched) that also appear in two databases of known biological interactions (InnateDB and HTRI). Numbers above bars indicate fold enrichment from the initial to the final datasets. (B) Regulatory network analysis identified significant covariance between Il1b and 7 transcription factors, including Rel and Junb.(C) Splenocytes from 4-wk-old mice (n = 4 of each by guest on October 2, 2021 genotype) were analyzed by flow cytometry to quantify frequencies of the indicated myeloid cells. (D–F) Macrophages were differentiated from the bone marrow of Junbfl/fl or Junbfl/fl x Lyz2 Cre/Cre (JunbDLyz2) mice. (D) Deletion of Junb was assessed by qPCR on BMDM genomic DNA. Means + SD for three technical replicates are shown. (E) BMDM from the indicated genotypes were stimulated with LPS for 4 h. Expression of JUNB (top panel) and b-actin (bottom panel) was measured in lysates by Western blot. (F) BMDM were labeled with Abs to the indicated myeloid surface markers and analyzed by flow cytometry. Data shown are means + SD averaged from three independent experiments. (G) BMDM were incubated for 24 h with fluorescent beads of the indicated diameter, and phagocytosis was measured by flow cytometry. **p , 0.01 by t test. cDC, conventional DC; pDC, plasmacytoid DC; RPM, red pulp macrophage.

Junb in hematopoietic stem cells was shown to result in a marked duced in peritoneal macrophages from JunbDLyz2 mice stimulated expansion of myelomonocytic progenitors (29); importantly, the with LPS ex vivo, confirming that JUNB deficiency affects cyto- myeloid compartment of Junbfl/fl 3 Lyz2Cre/Cre mice (referred to kine production in a distinct type of macrophage (Fig. 2E). henceforth as JunbDLyz2) is indistinguishable from Junbfl/fl controls Next, we used whole-transcriptome microarrays to assess the (Fig. 1C). We confirmed loss of Junb expression in BMDM pro- global impact of Junb on macrophage responses to LPS. We found duced from JunbDLyz2 mice by both quantitative RT-PCR (Fig. 1D) that ∼34% of LPS-induced genes exhibited significantly dimin- and Western blot (Fig. 1E). JunbDLyz2 BMDM had typical mor- ished upregulation in JUNB-deficient macrophages relative to phology (M.F. Fontana and C.C. Kim, unpublished observations) their expression in JUNB-sufficient macrophages (Fig. 3A; 469 and normal levels of macrophage-specific surface markers (Fig. JUNB-dependent genes, Supplemental Table IIA versus 918 1F), and they phagocytosed fluorescent beads as efficiently as JUNB-independent genes, Supplemental Table IIB). JUNB- JUNB-sufficient macrophages (Fig. 1G). Together, these data in- deficient macrophages were not merely poorly responsive to dicate that myeloid-restricted deletion of Junb does not disrupt LPS in general, as the majority of genes was induced equally in macrophage proliferation, differentiation, or phagocytic function. both JUNB-deficient and JUNB-sufficient macrophages (Fig. 3A, In validation of our network model prediction, we observed Supplemental Table II). The JUNB-dependent genes included significant defects in induction of Il1b mRNA (Fig. 2A) and se- cytokines, chemokines, IFN-associated genes, and other genes cretion of IL-1b protein (Fig. 2B) in JunbDLyz2 BMDM following with established immune functions, several of which were previ- stimulation with LPS or LPS plus ATP, respectively. The defects ously shown to be decreased following knockdown of Junb in DCs were partial, indicating that JUNB is not absolutely required for treated with LPS (30). Expression of a small number of genes, Il1b induction but rather augments its expression. Decreased such as Il12b, which encodes a subunit of the cytokine IL-12, was protein in supernatant was not merely due to a secretion defect, as drastically reduced in the absence of JUNB, consistent with the we also observed decreased expression of pro–IL-1b in intact previous observation that JUNB binds to an enhancer in the Il12b JunbDLyz2 BMDM (Fig. 2C, 2D). IL-1b production was also re- promoter (31). However, ∼70% of affected genes, like Il1b, were The Journal of Immunology 5 Downloaded from http://www.jimmunol.org/

FIGURE 2. JUNB regulates IL-1b production in macrophages treated with LPS. (A) Following 4-h treatment with LPS, Il1b mRNA levels were assessed by RT-qPCR. (B) BMDM were pretreated for 3 h with LPS followed by addition of 5 mM ATP. IL-1b was measured in supernatant by ELISA. (C and D) BMDM were treated for 4 h with LPS, and intracellular pro–IL-1b was detected by flow cytometry. (C) Isotype staining and mock-treated controls were by guest on October 2, 2021 used to establish gates for F4/80+ BMDM. (D) The graph depicts the percent of BMDMs positive for intracellular pro–IL-1b.(E) Peritoneal macrophages were stimulated for 2 h with LPS and stained for intracellular pro–IL-1b as in (C) and (D). Data shown are means + SE (D and E)orSD(A and B) and representative of three (A–D) or two (E) independent experiments. *p , 0.05, **p , 0.01, ***p , 0.001 by t test. SSC, side scatter. reduced by only 2- to 3-fold (Fig. 3B). Together, these data sug- consistent with indirect regulation of these targets by JUNB. In- gest that JUNB is not absolutely required for activation of gene terestingly, 41% of JUNB-independent genes were also bound by expression, but that it fine-tunes gene expression and, by infer- JUNB (376 of 919 examined; Supplemental Table III), suggesting ence, modulates the output of other transcription factors. Quan- that binding of this transcription factor is not sufficient to alter titative RT-PCR was used to validate additional predicted targets gene expression. This observation is consistent with the previous (Il12b, Tnf), confirming their JUNB dependence (Fig. 3C). Ad- report’s suggestion that JUNB likely cooperates with several other ditionally, intracellular cytokine staining revealed a defect in factors to activate transcription (9). Alternatively, these binding production of TNF protein in JUNB-deficient macrophages fol- sites could represent sites at which JUNB binds without impacting lowing LPS stimulation (Fig. 3D, 3E). Thus, JUNB regulates gene expression, as has been observed for other transcription production of multiple cytokines and other immune-related genes factors (33). Although we have not assessed binding of JUNB to in macrophages treated with LPS, affecting levels of both tran- target genes in macrophages in this study, our analysis of ChIP- script and protein. Seq data from a closely related cell type, combined with a previous report of JUNB binding to the Il12b enhancer in macrophages JUNB likely regulates transcription both directly and indirectly (31), provides strong evidence that JUNB modulates transcription To gauge whether JUNB regulates transcription through direct at least in part through direct activation. binding of regulatory elements in target genes (9, 30–32), we cross- In addition to binding directly to regulatory elements, JUNB may referenced our microarray data with a previous study that per- affect transcription indirectly by altering the levels of other tran- formed ChIP-Seq on LPS-treated DCs, which are ontologically scription factors, such as other AP-1 family members. However, we and functionally closely related to macrophages (9). We identified observed no defect in mRNA expression of the AP-1 transcription 178 genes, including Il1b and Il12b, that exhibited JUNB- factors c-Jun, Jund, and Fos in JUNB-deficient macrophages ei- dependent expression in macrophages and were also directly ther at baseline or upon stimulation with LPS (Fig. 4A); in fact, bound by JUNB in DCs (Supplemental Table III). These bound expression of some of these factors was slightly increased in the and regulated genes accounted for 38% of all JUNB-dependent absence of JUNB. Nevertheless, these data do not rule out the genes examined. JUNB binding was not observed proximal to the possibility that JUNB affects transcription indirectly; for example, remaining 291 JUNB-dependent genes (Supplemental Table III), although Jun and Fos transcript levels are intact in JUNB-deficient 6 JUNB MODULATES MACROPHAGE ACTIVATION Downloaded from http://www.jimmunol.org/ by guest on October 2, 2021

FIGURE 3. JUNB modulates expression of a cluster of immune-related genes in LPS-treated macrophages. Junbfl/fl or JunbDLyz2 BMDM were treated for 4 h with LPS. RNA was harvested, amplified, and analyzed by microarray (A and B) or reverse transcribed into cDNA (C). (A) Genes exhibiting ,2-fold difference (black dots) or .2-fold difference (red dots) in Junbfl/fl versus JunbDLyz2 macrophages. Results shown are averages from four technical replicates. (B) Graph of fold-reduction in gene expression for JUNB-dependent genes. (C) Levels of the indicated transcripts were measured by RT-qPCR. Means + SD are shown. Representative results from one of three experiments are shown. (D) Gating strategy for measurement of intracellular TNF. Isotype controls (not shown) were used as described for pro–IL-1b.(E) Induction of intracellular TNF after 4 h of LPS treatment. Bars represent means + SE for percentage of BMDMs positive for TNF and are representative of three independent experiments. **p , 0.01, ***p , 0.001 by t tests. macrophages, it is possible that protein levels or activities are altered of these factors may therefore account for the JUNB-dependent in the absence of JUNB or that other AP-1 family members are regulation of genes we observed in the absence of direct JUNB affected. Furthermore, we identified several other transcription fac- binding. Interestingly, Junb itself is bound by JUNB (9), suggesting tors, including Aff1, Mxd1, Crem,andStat3, for which expression is that its own expression may be subject to feedback. Altogether, our defective in the absence of JUNB in macrophages (Supplemental analyses indicate that JUNB is likely to affect transcription both Table IIA) and for which transcriptional initiation regions are di- directly, through binding of target gene regulatory regions, and in- rectly bound by JUNB in DCs (9). Altered transcription downstream directly, by altering levels of other transcription factors. The Journal of Immunology 7

amined 48 LPS-induced genes that were previously classified as primary or secondary response genes on the basis of their activation kinetics, as well as whether they require new protein synthesis for induction (discussed further below) (34, 35). Nine of these targets, including eight primary response genes and a single secondary response gene (Il12b), were determined to be JUNB- dependent by microarray (Fig. 4B). Although the pool of primary gene targets examined was larger (41 primary genes versus 7 secondary genes), the percentage of JUNB-dependent genes was similar in both categories (16% of primary and 10.5% of secondary genes examined) (Fig. 4B). Finally, we examined whether these 48 genes were tolerizable or nontolerizable according to the classification scheme proposed by Foster et al. (36), in which macrophages previously exposed to LPS will selectively upregulate nontolerizable genes, but not tolerizable genes, upon secondary LPS stimulation. Among the 48 primary/secondary response genes examined in (35), 19 genes were further classifi- able as tolerizable or nontolerizable, including three JUNB- dependent genes (Fig. 4B). Thus, JUNB-dependent genes in- clude both primary and secondary response genes as well as Downloaded from tolerizable genes. Although our analysis did not reveal any nontolerizable genes that are JUNB dependent, this is likely due to the small size of the gene sets that were searched for overlap (i.e., secondary genes versus nontolerizable genes versus JUNB- dependent genes). On the whole, we conclude that JUNB modulates the transcription of a broad range of genes from several http://www.jimmunol.org/ ontological and regulatory categories without obvious bias toward any given category. To further characterize JUNB activity, we examined the kinetics of transcription of Il1b, a primary response gene, and Il12b, a secondary response gene, in macrophages treated with LPS. Primary response genes do not require new protein synthesis for induction, whereas secondary response genes do; furthermore, primary genes tend to be upregulated more swiftly than secondary by guest on October 2, 2021 genes, though exceptions exist (35). Consistent with previously reported activation kinetics, Il1b was markedly induced in wild- type macrophages as early as 1 h poststimulation, whereas robust upregulation of Il12b was not observed until 2 h poststimulation (Fig. 4C). Interestingly, Il1b induction was intact in JUNB- deficient macrophages at early time points, but by 4 h posttreat- ment, it lagged behind JUNB-sufficient controls (Fig. 4C). In contrast, upregulation of Il12b was almost completely abrogated FIGURE 4. JUNB affects late transcription of primary and secondary in JUNB-deficient macrophages at all time points assayed genes. (A) Expression of AP-1 family members was assessed by micro- (Fig. 4C). Thus, JUNB modulates expression of both primary and array in BMDMs with or without LPS stimulation as described in Fig. 3A. secondary genes with delayed kinetics and appears to be most SD from four technical replicates is shown. (B) A subset of JUNB-de- important from 2 to 4 h poststimulation, perhaps reflecting the pendent and JUNB-independent genes was identified by microarray and time needed to upregulate JUNB itself. In the case of the rapidly further classified into primary, secondary, tolerizable, and nontolerizable D induced Il1b, this delayed involvement results in an initially intact categories. (C) Junbfl/fl or Junb Lyz2 BMDMs were treated with LPS, and response followed by a leveling off in JUNB-deficient macro- RNA was harvested at the indicated time points. Transcript of Il1b (left panel)orIl12b (right panel) was measured by RT-qPCR. Graphs depict phages, whereas with the more gradually induced Il12b, dramatic means + SD and are representative of two independent experiments, each JUNB dependency is observed from the time that transcript levels with three technical replicates. +, Junbfl/fl BMDM; D, JunbDLyz2 BMDM. first begin to increase. *p , 0.05, ***p , 0.001. JUNB modulates immune-related gene expression downstream of multiple pattern recognition receptors JUNB affects transcription of both primary and secondary Having established that JUNB contributes to transcriptional transcriptional targets responses to LPS, which signals through TLR4, we wished to test To understand whether JUNB preferentially regulates a specific whether JUNB also modulates the transcriptional output of other category of genes, we attempted to classify the JUNB-dependent pattern recognition receptors. Stimulation of JUNB-deficient genes identified by microarray. Gene ontology analysis did not BMDM with the synthetic ligands polyinosinic-polycytidylic reveal enrichment of specific gene categories among JUNB- acid, imiquimod, or CpG DNA resulted in defective induction of dependent genes; instead, JUNB modulated expression of a rep- the target genes Il1b and Il12b (Fig. 5A), implicating JUNB in resentative subset of LPS-induced gene ontology categories (M.F. transcriptional responses downstream of TLRs 3, 7, and 9, re- Fontana and C.C. Kim, unpublished observations). Next, we ex- spectively. JUNB-deficient macrophages were also defective in 8 JUNB MODULATES MACROPHAGE ACTIVATION their response to transfected cyclic di-GMP (Fig. 5B), which upregulated Il33, a type II cytokine, in a JUNB-dependent manner elicits robust expression of IFN-b (Ifnb) in wild-type macrophages (Figs. 3A, 6A). Because IL-4–driven alternative activation is through the cytosolic sensor STING (37). JUNB-deficient mac- a central feature of type II immunity, we evaluated the induction rophages treated with a variety of TLR ligands produced less of M(IL-4) responses in JUNB-sufficient and JUNB-deficient IL-12, IL-1b, and TNF protein than JUNB-sufficient macrophages, BMDMs treated with IL-4. JUNB-deficient BMDMs exhibited as measured by bead array (Fig. 5C) and intracellular cytokine marked defects both in the transcriptional induction of the M(IL-4) staining (Fig. 5D), indicating defects in macrophage responses at target genes Arg1 and Retnla (Fig. 6B) and in the upregulation the level of both transcript and protein. Taken together, these of arginase activity (Fig. 6C). Importantly, expression of the IL-4R results demonstrate the broad involvement of JUNB in generating was intact in JUNB-deficient macrophages both at baseline and macrophage responses to multiple immunostimulatory ligands. following treatment with IL-4, as was expression of another M(IL-4) target, Mrc1 (Fig. 6D, 6E). These data indicate that JUNB- JUNB regulates alternative macrophage activation deficient macrophages are not generally unresponsive to IL-4 sig- Finally, we sought to determine whether JUNB modulated M(IL-4) naling; rather, JUNB appears to act downstream of the IL-4 receptor macrophage responses in addition to M(LPS) responses. Our to positively regulate some aspects of M(IL-4) polarization. microarray analysis had revealed that LPS-treated macrophages Discussion In this study, regulatory network analysis on community-generated gene expression data led us to predict a role for the AP-1 transcription factor Junb in macrophage activation. Whereas previous Downloaded from studies in silico have suggested such a role without providing experimental validation, we have demonstrated in this study that JUNB regulates macrophage responses to a variety of immunos- http://www.jimmunol.org/ by guest on October 2, 2021

FIGURE 5. JUNB regulates cytokine expression downstream of multi- FIGURE 6. JUNB modulates alternative activation markers in macro- ple pattern recognition receptors. (A–D) Junbfl/fl or JunbDLyz2 BMDM were phages treated with IL-4. (A) BMDM were treated with LPS for 4 h, and treated for 4 h with the indicated ligands. Transcripts of the indicated gene expression was measured on microarrays, as described in Fig. 3A. cytokines were measured by RT-qPCR (A and B), and secreted protein was Statistical significance was as described in the Materials and Methods. measured in the supernatant by cytometric bead array (C). (D) Production BMDM were treated for 4 h (B, D, and E)or18h(C) with IL-4. Levels of of pro–IL-1b and TNF by intracellular cytokine staining. Data are depicted the indicated mRNA targets were measured by RT-qPCR (B, D, and E), and as means + SD and are representative of two (C and D) or at least three arginase enzyme activity in cell lysates was assessed (C). Results shown (A and B) experiments. *p , 0.05, **p , 0.01, ***p , 0.001 by t tests. are means + SD and representative of three (B) or two (C–E) experiments. pI:C, polyinosinic-polycytidylic acid. *p , 0.05, **p , 0.01 by t tests. The Journal of Immunology 9 timulatory ligands. The defects we observed in JUNB-deficient each with its own set of protocols and sample treatments. Our work macrophages included altered transcription of a subset of thus serves to demonstrate that despite the technical challenges immune-related genes, decreased secretion of M(LPS)-associated inherent in starting with heterogeneous data, meaningful biological cytokines in response to TLR ligands, and weakened M(IL-4) interactions can be identified with proper data processing. As in- polarization following treatment with IL-4. It is notable that creasingly extensive and diverse datasets become available, many JUNB positively regulates both M(LPS) and M(IL-4) activation, of which introduce the additional variable of genetic heterogeneity because to our knowledge, virtually all previously described across humans, future studies should not be deterred by these transcription factors with demonstrated roles in macrophage po- limitations, which can be overcome with sufficiently large datasets larization specifically promote either M(LPS) or M(IL-4) activa- and appropriate analysis approaches. tion, with the exception of C/EBPa (38). Importantly, at baseline, The network method described in this study is also notable in that JunbDLyz2 mice exhibit no gross defects in survival or develop- it permits enrichment for physiologically relevant predictions, as ment, suggesting that deletion of JUNB does not disrupt the ho- we have demonstrated through extensive experimental validation of meostatic functions of monocytes and tissue macrophages but one of these predictions. Furthermore, in revealing a novel cell specifically affects activation. This selectivity raises the possibility type–specific function for JUNB in innate immune responses, this that therapeutic targeting of JUNB in macrophages, for example study lays the groundwork for future therapeutic targeting of the through use of liposomes to deliver drugs selectively to phagocytic inflammatory response modulated by this key regulator. cells, might be an effective way to dampen harmful inflammatory responses while minimizing the toxicity associated with systemic Acknowledgments inhibition of the canonical c-JUN–c-FOS AP-1 pathway (39). We thank E. Passegue´ (UCSF) for Junbfl/fl mice, D. Burdette and R. Vance Downloaded from Our results deepen our understanding of the contributions of (University of California, Berkeley) for cyclic di-GMP, and J. DeRisi and individual AP-1 subunits to innate immune responses. Previous D. Poscablo for technical assistance. studies have provided conflicting data as to whether JUNB is a negative regulator of gene expression, a competitive inhibitor of Disclosures JNK-mediated phosphorylation of c-JUN, or a transcriptional The authors have no financial conflicts of interest. activator in its own right. Although c-JUN and JUNB appear to play antagonistic roles in processes such as cell cycle progression, http://www.jimmunol.org/ JUNB can also compensate to transcribe c-JUN target genes in the References absence of c-JUN (23, 40). Furthermore, during CD4+ T cell and 1. Mantovani, A., A. Sica, S. Sozzani, P. Allavena, A. Vecchi, and M. Locati. 2004. The chemokine system in diverse forms of macrophage activation and polari- macrophage activation, JUNB appears to act as a transcriptional zation. Trends Immunol. 25: 677–686. activator even in the presence of c-JUN (32 and this work). Al- 2. Van Dyken, S. J., and R. M. Locksley. 2013. 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1700012B15Rik Uck1 Atad2 Pbk Rfc4 Ucp3 Mtmr7 Bid Rad9 Hadh Zc3h12a Bcl2l11 1700052K11Rik Cd83 Ncaph

Cdca7l Ezh2 Kif23 Rel Cdh13 Nr4a1 Pola1 Phf19 Vldlr Snx7 Fam96a Ripk2 Ccne2 Ncapd2 Pqlc3 A130009I22Rik Niacr1 Peli1 Cenpn Ralgds Dbf4 Trpc6 Gadd45b Dyrk2 Il1b Prim1 Pcna Dnmt1 Ttc30a1 Cflar Fas Tro Tank Rapgef2 Icosl 9930012K11Rik Cd28 Ccdc86 Ube2f Klhl6 Commd8 Ccrl2 D4Wsu53e Tnfaip8l2 Ehd1 Kdm2b Uhrf1 Trim37 Tecrl AI467606 Ilf2 Cobra1 Aurkb Cdc42ep2 Nfkbil2 Appbp2 Nfkbiz Zfp85-rs1 Nlrp3 Zmym5 Il6 Foxo3 Sept3 Ets2 Serpinb2 Smc3 Msh6 Itga5 Gpsm3 Slbp Dennd4c Rffl Trappc1 Mrpl34 Socs3 Ocel1 Tnfaip3 Swap70 1110003O08Rik Junb Pmm1 Cxcl10 Brca1 Acpl2 Coq10b Marcksl1 Map3k10 Braf Tnf Icam1 Pold3 AI462493 Nfkbia Dnmbp Dnajc9 Tnip1 Chfr Trim13 Slc35c2 Rb1 C78513 Limk1 C1qa Fen1 BC088983 Tsc22d3 Tnfsf9 Mapk3 E2f1 Zc3h12c Casp4 Ttf2 Maff Gmnn Zfp715 Mdm2 Mcm7 Irf1 Parp1 Ftsj2 Myo10 Irak2 Mid1ip1 Apeh Rad51c Map3k4 Tk1 E2f7 Tiparp Ptgs2 Spata13 Hmga2-ps1 Wdhd1 Clspn Ubac1 Erp29 Thap4 Dna2 Prkag2 Il1a Rfwd3 Gbp3 Cdca5 3110043O21Rik Clec4e Cxcl11 Speg Tarbp2 Cdk2 Zufsp Cdca2 E2f2

1700054N08Rik Tnfsf10 5730499H23Rik Inf2 C1qb Anapc5 Sco1 Bst2 2010107G23Rik Cnpy4 D10Wsu102e Brip1 H2-D1

Gpd1l Suv420h2 4930579G24Rik Cdc6 Cntnap2 Cyp2b10 Plod2

Irf7 C2 Lsm3 9330175E14Rik Aak1 Ick

Rassf7 Cd22 Cd72 Tap1 Tcn2 Malt1 Uba7 Atp1a2 Ar Tap2 Cd86 Herc5 Heca Atp2a2 Ttc28 Dhrs3 Stat1 Ccdc22 Abca5 Nhlrc2 Ddx58 Gpx1 Dgkd Fbxl7 Lap3 Lama4 Neil1 Enpp4 Tpst1 Cybb Ccne1 Sec24d Fcgr4 Hk3 Rasgrp2 Dcun1d2

Vegfa Ecsit Ddx60 Eif2ak2 Cyp2r1 Gimap4 Fip1l1 Adar Lcp2 Mcm4 1110012J17Rik Sav1 Lrrc20 Sgk2 Nfkb2 BB236558

Nrp2 Birc2 4930558J22Rik Skp2 5830437K03Rik Kif22 Nfkb1 Slc2a6 Traf2 Ccl3 Tbc1d9 Dlx3 4833427F10Rik Lat2 Klrg1 Ddi2 Abtb2 Relb Ptprj Msc 1700049G17Rik Mcm5 Txnrd3 Cd40 D15Ertd55e Jarid2 Jak2 Depdc7 Cxcl5 Epor Foxred2 Stat4

Cebpa Krtap10-4 Ncapg 2210021J22Rik Ch25h Cdt1

Racgap1

Acadm Rnf187 Ftsjd2 Twist2 Mcm3 B3gnt8 Psrc1

Lss Tmem33 Btg2 Gkap1 Mki67 Ralgapa1 Hspa8 Atp8b4 Gm5817 Plk1 Asz1 Rnf157 Tpd52l2 Sc4mol Hiat1 Hccs

Ldlr Stat3 Foxo1 Foxm1 Prepl Herpud2 BC030867 Yy1 Hmgcs1 Mapk1 Timm8b Prkci Idi1 Ermp1 Aga Ado Smad4 Aurka Cyp51 Cdc34 Rnf19b Tprgl Sgol1 Rabep1

Hmgcr Golph3l Nedd8

Hras1

Pold1 Uspl1 Chi3l3 Plin2 Wnt7a Tnfrsf1a P4ha1 Mrp63 Adm

Tipin Hmox1 Stat6 Pparg Msx1 Ifnar2 Hif1a Dbp Inppl1 Eepd1 Nxph3 Acvr1b Slc2a1 Btbd6 Mcm2 Mafg

Nup107 Kti12

Stk10 Haus8 Zim3 Hs6st1 Fbxw4 Xpa Nup43 Slc48a1

Impact Mapkapk2 Apc Dusp1 Set F3 Kdm6b Nhp2l1 Mars Hdac4 Pdgfb Cyfip2 Hmgb1 Bcr Itga2b Cd14 Cc2d1a Gca

Bcl6 Bcl3 Atf2 Zfp36 Taf9 Stat5a Sox2 Ruvbl1 Pdlim4 Ncor1 Myc Max Hmgn2 Hdac5 Gata1 Etv6 Deaf1 Cic

Trub2 Nnmt Phip D13Ertd787e Cct2 9130004C02Rik Trim3 Pdcd6 Sil1 Kdm5b Rasgef1b Mier3 Nap1l1 Itpkb Ambn Pvrl3 Chst14 Gcn1l1

Supplemental Fig. 1. Regulatory network prediction for macrophage transcriptional activation. Predicted regulatory interactions are shown as dashed lines between transcription factors (red ovals) and other genes (blue rectangles), and known interactions are depicted as solid lines. The RIKEN transcription factor database contains a number of genes that are likely or known to play only indirect roles in transcriptional regulation (e.g., modifiers of transcription such as ubiquitin ligases and kinases); these are denoted as grey ovals. Supplementary Table 1. Microarray datasets included in the present study. Database ID Pubmed ID First Author Publication Year Host Tissue Treatment GSE4712 16846716 Comer 2006 Ascites Compound GSE21117 16926395 He 2006 Ascites Bacteria GSE6785 16926388 Reece 2006 Alveolus Helminth GSE7348 17538624 Foster 2007 Bone marrow Compound GSE6376 17312158 Goodridge 2007 Bone marrow Compound GSE8621 18086374 Mages 2007 Bone marrow Compound GSE31378 17890046 Cheung 2008 Bone marrow Compound GSE4288 18230670 Kitamura 2008 Ascites Compound GSE12518 19211757 Bok 2009 Ascites Virus GSE15610 19863805 Carlson 2009 Bone marrow Compound GSE11497 19302708 Fortea 2009 Bone marrow Protozoa GSE14769 19270711 Litvak 2009 Bone marrow Compound GSE18500 20421474 Dietrich 2010 Bone marrow Bacteria E-MEXP-2554 20624948 Nguyen Hoang 2010 Ascites Protozoa GSE22935 20716764 Qualls 2010 Bone marrow Bacteria GSE19374 20123961 Rayamajh 2010 Bone marrow Bacteria GSE23306 20729857 Satoh 2010 Bone marrow Compound GSE20087 20361029 Zhang 2010 Peritoneum Compound