David A. Hume Tom C. Freeman

Transcriptomic analysis of mononuclear phagocyte differentiation and activation

Authors’ address David A. Hume1, Tom C. Freeman1 1 The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, UK.

Summary: Monocytes and macrophages differentiate from progenitor cells under the influence of colony-stimulating factors. Genome-scale data have enabled the identification of the sets of genes that are associated with specific functions and the mechanisms by which thousands of genes are regulated in response to pathogen challenge. In large datasets, it is possible to identify large sets of genes that are coregulated with the transcription factors that regulate them. They include macrophage-specific genes, interferon-responsive genes, early inflammatory genes, and those associated with endocytosis. Such analyses can also extract macrophage-associated signatures from large cancer tissue datasets. However, cluster analysis provides no support for a signature that distinguishes macrophages from antigen-presenting dendritic cells, nor the classification of macrophage activation states as classical versus alternative, or M1 versus M2. Although there has been a focus on a small subset of lineage-enriched transcription factors, such as PU.1, more than half of the transcription factors in the genome can be expressed in macrophage lineage cells under some state of activation, and they interact in a complex network. The network architecture is conserved across species, but many of the target genes evolve rapidly and differ between mouse and human. The data and publication deluge related to macrophage biology require the development of new analytical tools and ways of presenting information in an accessible form.

Correspondence to: David A. Hume The University of Edinburgh – The Roslin Institute Easter Bush Midlothian, Scotland EH25 9RG, UK Tel.: +1 44 131 6519100 Fax: +44 131 6519105 e-mail: [email protected] Acknowledgement The Roslin Institute is supported by Institute Strategic Programme Grants from BBSRC. The authors have no conflicts of interest to declare.

This article is part of a series of reviews covering Monocytes and Macrophages appearing in Volume 262 of Immunological Reviews.

Keywords: transcriptome, macrophage, dendritic cell, CSF1

The cells of the mononuclear phagocyte system

Immunological Reviews 2014 Vol. 262: 74–84 Printed in Singapore. All rights reserved

© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd

Immunological Reviews 0105-2896

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The mononuclear phagocyte system (MPS) was defined as a family of cells of the innate immune system derived from hematopoietic progenitor cells under the influence of specific growth factors. Differentiated cells of the MPS, monocytes and macrophages, are major effectors of innate immunity, engulfing and killing pathogens. They are also needed for tissue repair and resolution of inflammation and for the generation of an appropriate acquired immune response. Their biology and differentiation has been reviewed by a number of authors (1–6). The original definition of the MPS considered an essentially linear sequence from pluripotent progenitors, through committed myeloid progenitors shared with granulocytes, to promonocytes and © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd Immunological Reviews 262/2014

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blood monocytes, and thence to tissue macrophages (1–6). The number of monocyte-derived macrophages in tissues was considered to increase by recruitment in response to a wide range of inflammatory challenges, and the function of these recruited macrophages varied depending upon the nature of the stimulus. Challenges to the unified concept of an MPS have been discussed elsewhere (3, 4, 7). The clear separation from polymorphonuclear cells implied by the term mononuclear is no longer clear. At least in vitro, inflammatory granulocytes (polymorphs) can trans-differentiate into macrophages (8). The most recent challenge to the MPS concept relates to whether some, if not all, populations of tissue macrophages, notably the microglia of the brain (9) and the epidermal macrophages (Langerhans cells) of the skin (10, 11) do not derive from blood monocytes in the steady state. Instead, they may be seeded from yolk sac or other primitive progenitors during embryonic development and are thereafter renewed by local proliferation. This view has recently been extended to the concept that blood monocytes do not make a significant contribution to any tissue macrophage population, other than that of the gastrointestinal tract (12–14). A critical review of these data has been presented elsewhere (7). It is clear that monocytes can give rise to tissue macrophages and also that tissue macrophages can self-renew (15, 16). However, all of the published studies on the relationship between the two mechanisms probably disturb the steady state and the true role of monocytes in tissue macrophage renewal remains a subject of debate (7). The numbers of blood monocytes and tissue macrophages are controlled homeostatically by the macrophage colonystimulating factor receptor (CSF1R), which is activated by two ligands, CSF1 and IL34 (5, 17). The two ligand, one receptor system is conserved through evolution (18). The two ligands interact in distinct modes with the same domains of the receptor (19, 20). The CSF1-deficient osteopetrotic mouse and toothless rat both have gross deficiencies of tissue macrophages, and many pleiotropic consequences of that deficiency (21). In the brain at least, it appears that the two ligands may exert region-specific functions on microglia (22). CSF1R signaling in the brain has come into the spotlight with the recent observation that dominant negative mutations in the receptor underlie a human hereditary leukoencephalopathy (23). Blockade of the CSF1R with a monoclonal antibody depletes most tissue macrophage populations in adult mice, but does not deplete monocytes (24). Even prolonged treatment is surprisingly well tolerated, and the effects suggest that many of the impacts of the © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd Immunological Reviews 262/2014

op/op mutation reflect essential roles of macrophages in development (25). The transcription control elements of the mouse csf1r locus have been studied in great detail, and the information has been to generate transgenic MacGreen mice in which all of the tissue macrophages express an EGFP reporter gene (26), a tool that illustrates the very large numbers of these cells within tissues. Myeloid-specific transgenes have been utilized in many functional studies of macrophage cell biology (reviewed in 27). The Csf1r gene is also expressed by placental trophoblasts, via a distinct promoter. In an effort to remove this expression in a transgene construct, we removed the trophoblastspecific transcription start site and created the MacBlue mouse (28, 29). The MacBlue transgene was expressed on macrophages as they appear in the yolk sac, and provided a striking picture of their abundance during embryonic development (28, 29). Unexpectedly, the deletion removed expression in the majority of tissue macrophages, whereas expression was retained in blood monocytes and their progenitors, and in the IL34-dependent populations, the microglia and Langerhans cells in the skin (25). The transgene permits live imaging of monocyte trafficking (30). The promoter deletion in the MacBlue transgene removed a highly conserved AP1 site. Exactly why this removes expression in mature tissue macrophages is unclear. Even in the gut, where the macrophage population clearly derives from continuous renewal from monocytes (31), the ECFP transgene is extinguished. Expression of the Csf1r-driven reporter genes requires the activity of a intronic enhancer referred to as FIRE that is functionally conserved across mammalian species (32). We have recently recognized that this enhancer is conserved also in the Csf1r locus of birds and have produced transgenic chickens (33) and transgenic sheep (manuscript in preparation) in which all monocytes and tissue macrophages express reporter gene. The principal of guilt-by-association and genome annotation The set of genes required for any cellular function or pathway frequently share transcriptional regulation, so that their products are available in the correct place at the right time. One can therefore infer the likely function of genes from the transcriptional company that they keep. The potential utility of this information for the identification of candidate genes in human genetics has been emphasized elsewhere (34–36). The completion of genome sequences, the advent of whole genome transcriptomic technologies, such as microarrays and RNA-seq, and advances in bioinformatic

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tools have revolutionized the ability to generate coexpression networks. The information content of such networks depends upon their size: the more states that are sampled, the more stringently one can state that a pair of genes shares strict coexpression. Since the pioneering efforts of Su et al. (37) to generate the Symatlas (now BioGPS, www.biogps. org) from sets of microarray data from mouse and human tissues, there has been an explosion of gene expression ‘atlases’ across multiple tissues and within tissues across cell types and developmental time. The network tool BioLayout Express3D was developed to allow the visualization of coexpression relationships in large datasets (38, 39). We have used this tool to identify coregulated genes in the mouse BioGPS dataset (35) and subsequently carried out a metaanalysis of publically available mouse (40) and human data (41) relating to hematopoietic differentiation and macrophage biology. We also designed a comprehensive cDNA microarray platform for the domestic pig, and produced a preliminary expression atlas (42). The largest expression datasets have recently been released by the FANTOM Consortium, which utilized genome-scale 5’-RACE tag sequencing to analyze promoter utilization across hundreds of tissues, primary cells, and cell lines in mouse and human (43, 44). The principal of guilt-by-association has been confirmed many times in analysis of these large datasets. There are clear regulons. Within the BioGPS dataset, each major hematopoietic cell type expressed a small number of lineage-specific transcription factors and an array of known lineage-specific genes (35). By inference, genes with the same expression pattern are likely to have cell lineage-specific functions, e.g. a T cell-specific gene is likely to function in acquired immunity. Our analysis of mouse cell types (35) also revealed process-specific clusters, for example known genes encoding the components of the cell cycle, RNA and protein synthesis, the extracellular matrix, and mitochondrial respiration form clusters and genes of unknown function within those clusters are likely to have roles in the same pathways. Among the largest clusters was a set of genes that is shared by all of the phagocytes (including osteoclasts). This cluster was strongly internally validated by the presence of the large majority of known genes associated with lysosomes including all of the components of the vacuolar ATPase H+ pump and lysosomal hydrolases. The cluster included the transcription factors such as PU.1 and CEBP that likely contribute to transcriptional control and indeed can drive phagocyte differentiation when expressed in fibroblasts (45). We analyzed the promoters of these phagocyte-restricted genes and in common with a

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study from humans of the promoters of known lysosomal proteins (46), we identified, in addition to purine-rich motifs, the recognition motifs for basic helix–loop–helix transcription factors of the microphthalmia transcription factor family: MITF, TFEB, TFEC, and TFE3. We had previously shown that all four MITF family members are expressed in macrophages, that TFEC is a macrophage-specific transcription factor and a PU.1 target gene (47), that MITF interacts both physically and genetically with PU.1 (48), and that MITF can trans-activate the promoter of the ACP5 lysosomal enzyme (48). Transcriptomic analysis of the relationship between macrophages and dendritic cells The activation of T lymphocytes requires the presentation of the antigen on MHC molecules on the surface of an antigen-presenting cell (APC). Dendritic cells (DCs) are specialized APCs, originally described by Steinman and Cohn. They have been divided into numerous subclasses based upon surface markers including the integrins CD103, CD11c, and CD11b (49–51). The term ‘dendritic cell’ has become almost synonymous with APC, but it may group together cells of quite different function and origin (4, 7, 51–53). Active APCs, referred to by some as DCs, can be generated in vitro by cultivation of monocytes or bone marrow cells in GM-CSF. The relationship between these cells and ‘classical DCs’ or ‘conventional DCs’, which express the growth factor receptor Flt3 and can be expanded in response to Flt3L in vitro and in vivo, remains unclear (49–51). The immunological genome consortium (ImmGen) generated very large datasets comparing mouse macrophages and cells referred to as DCs from multiple sources. They identified a putative DC signature (54) and a set of markers including Cd64 and Mertk that were said to define macrophages (55). Both ‘signatures’ were derived based upon a comparison of a set of DCs with prototypical macrophages from peritoneum, lung, brain (microglia), and splenic red pulp. In effect, macrophages were defined a priori based upon their lack of class II MHC expression and presumed inability to present antigen. An alternative way of looking at these large datasets is to determine whether there are sets of coexpressed genes (especially surface markers) that define either APC function or cell lineage. Our previous cluster analysis of the mouse BioGPS data identified genes associated specifically with plasmacytoid DCs, including the regulators Irf8 and Tcf4, and the surface marker SiglecH (35). However, there was no cluster exclusively associated with classical Flt3+ DCs isolated © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd Immunological Reviews 262/2014

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from spleen, and the surface markers commonly used to separate ‘DCs’, including CD11c (Itgax), DEC205 (Clec13b), DC-HIL (Gpnmb), and Clec9a did not fall with clusters that could be linked in any way either to APC activity or DC identity. We reanalyzed the entire ImmGen dataset and did not identify any cluster of coexpressed genes that correlated with the prior definition of cells as macrophages or DCs (53). The microarray platforms are now reproducible and the processing methods standardized, so that one can actually integrate datasets from independent laboratories and it is not necessary to perform all studies in one laboratory. In the meta-analysis of large microarray datasets from mouse and human (40, 41), the cells annotated as DCs clearly segregated into two clusters. Cells grown in GM-CSF, referred to as monocyte-derived DCs (in humans) or bone marrowderived DCs (in mice), as well as CD11b-positive tissue ‘DCs’, clearly shared more in common with macrophages than with the classical DCs isolated from lymphoid organs. Even recently, these two quite different cell populations have been conflated in published methods for the preparation of DCs (56). The data generated by the FANTOM5 consortium enable promoter-based clustering of coexpression networks. Fig. 1 shows the sample-to-sample coexpression matrix for myeloid samples. Again, the data clearly segregate the monocyte-derived macrophages (grown in CSF1) and DCs (grown

in GM-CSF) from both monocytes and immature or migratory Langerhans cells (LCs), classical DCs. The FANTOM5 data also permit comparison of various stages of myeloid differentiation, from multipotent progenitors through CMP to GMP and promyelocytes, each forming distinct expression clusters. Across this entire dataset, the Csf1r and Flt3 genes appear to vary inversely the monocyte-derived DCs are Csf1r+ and the migratory LC are Flt3+. The data indicate that during lineage commitment, Flt3 is expressed first on CD34+ multipotent progenitors and CMPs, whereas Csf1r is first expressed detectably on the GMP. The same pattern is evidence in the mouse data on BioGPS. These patterns are consistent with a model that places the common DC progenitor upstream of a shared macrophage-DC progenitor (7). The expression cluster that divides all macrophage/DCrelated networks into two clear classes is the lysosomal/ endocytosis cluster described above. The Flt3-positive classical DCs have very low expression of all of the genes in this cluster and of the putative regulators (PU.1, CEBP, MITF family), whereas there is a separate class of cells that includes monocyte and bone marrow-derived DCs and many class II MHC-positive myeloid cells in tissues, which are clearly active phagocytes (35, 40, 53). This conclusion is entirely consistent with the original descriptions of the classical DCs by Steinman and Cohn, which ‘unlike macrophages, do not appear to engage in active endocytosis’ (57).

Fig. 1. Cluster analysis of myeloid sample in the FANTOM5 data. Selected datasets, comprising normalized CAGE Tag frequencies associated with 180,000 high confidence promoters, obtained from the cell types indicated, were downloaded from the RIKEN Zenbu browser (fantom.gsc.riken.jp/zenbu). A sample-to-sample correlation matrix was generated using Biolayout Express3D as described elsewhere (40, 41). Note that this is a two dimensional image of a 3D graph. The samples are described in detail on the browser site. © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd Immunological Reviews 262/2014

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An unambiguous nomenclature would restrict the term classical DC to these non-phagocytic, Flt3-expressing cells. There is a class of APC which are macrophages by every current definition (CSF1-dependence, active endocytic and phagocytic activity, expression of markers). They are clearly distinct from classical DCs, and it is not currently clear that there is any pathway of interconversion. From an unbiased analysis, the only transcriptional signature associated with APC activity in both macrophages and ‘DCs’ is a small one, including class II MHC, CD74, and the regulator CIITA. Several recent reviews (49, 51, 58) have considered the transcriptional control in the development of classical DCs, and the BioGPS and ImmGen data support/predict some of the biology. It is notable that the transcription factors that define classical DC, such as Batf3 and Xbtb46, are transcriptional repressors, and one might argue that their main function is to block macrophage differentiation and expression of endocytic function (59). The ImmGen and BioGPS datasets and much of the literature on antigen presentation and DC/macrophage divergence derives from the C57Bl/6 mouse. In mice, many tissue macrophages and cells grown in CSF1 lack class II MHC, whereas in most other species including humans and pigs, monocytes and monocyte-derived macrophages are strongly class II MHC positive. Furthermore, the C57Bl/6 mouse has only one class II MHC locus (H2-Ea is absent); H2-Ea is inducible by LPS in BALB/c macrophages. C57Bl/6 macrophages do not express cathepsin E, which is needed for antigen processing (60). Some 65 genes distinguish C57Bl/6 macrophages from BALB/c (60), including the C1q components advocated as markers for microglia (61). So, a view of the MPS based upon the biology of the C57Bl/6 mouse may not be entirely generalizable. Differentiation of monocytes Monocytes in peripheral blood have been subdivided into subsets based upon certain surface markers (62). The subsets have been referred to as ‘classical’ and ‘non-classical’ (63). The definition of these subsets relies upon a rather arbitrary positioning of gates on a FACS profile, and they are probably much more of a continuum of maturation with time in response to CSF1 (24) with the less mature cells (ly6C+ in mouse, CD14hi/CD16lo in human) more likely to be recruited in inflammation, and replenishing tissue macrophage populations such as those of the GI tract (7, 64). The more mature populations may perform a patrolling function in the circulation and sense nucleic acids and viruses (65).

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Comparative array profiling of human and mouse monocyte ‘subsets’ defined by different markers supported the idea that they were functionally equivalent across species, although there were many species-specific expression profiles (66). Despite the apparently continuous and inverse spectrum of expression of the markers (CD14 and CD16 in humans), a so-called ‘intermediate’ population (CD14+, CD16+) has been identified and attributed specialized functions (63). We recently analyzed the profiles of pig peripheral blood monocytes, which were more human-like, but appeared to lack a genuine ‘non-classical’ population (67). Within the FANTOM5 project, the three human monocyte populations were subjected to extensive promoter-based expression profiling, alongside detailed chromatin analysis (68). The CAGE-based methodology also permitted identification of regulated enhancer activity, enabling, for example, the identification of active enhancers responsible for the differential expression of CD14. Interestingly, in human monocytes bATF3, the mouse DC regulator mentioned above, is expressed selectively in the CD16+ monocyte subset and not in classical DCs. The data supported the view that the monocytes are a differentiation series, with the intermediate monocytes showing intermediate expression of the large majority of genes studied. The analysis identified the coregulation of genes required for glucose metabolism and the metabolic burst in the classical monocytes (68). In mice, the generation of the ly6C, or non-classical, monocyte subset depends upon the CSF1-responsive early response gene Nr4a1 (Nur77) (69). The precise function of this orphan nuclear receptor is not known, but in the human FANTOM5 data, the gene is weakly expressed in progenitors (CMP, GMP), expressed highly in all monocyte subsets, and ablated in monocyte-derived macrophages grown in CSF1. So, it is likely that Nr4a1 also performs some function in maturation in human monocytes. Macrophage activation: tissue and tumor-associated macrophages Every tissue in the body contains an abundant population of resident tissue macrophages. Each population adapts specifically to the environment in which it finds itself. Sample-tosample comparison of the extensive ImmGen dataset for the mouse suggested that each tissue macrophage population was very distinct (53). A recent study focused on the differentiation of microglia, the macrophages of the brain, and identified a putative marker set that distinguished these from © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd Immunological Reviews 262/2014

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blood monocytes and from tissue macrophages isolated from other locations (61). The BioGPS data, the FANTOM5 data, and the ImmGen data all include strongly validated microglial datasets. The former two confirm known microglial-enriched genes, including Iba1, and the scavenger receptors Fcrls and MARCO, but they do not support others such as C1q and Csf1r, which are highly expressed in other macrophage populations. Indeed, as discussed above, a Csf1r–EGFP transgene has been used as a marker for all tissue macrophages (26). The number of macrophages in most organs is sufficiently high that we can extract a macrophage signature without isolating the cells. Within the pig gene expression atlas, we profiled lung alveolar macrophages, blood monocytes, and bone marrow-derived macrophages alongside many different tissues (42). Comparative analysis revealed that alveolar macrophages express exceptionally high levels of a wide diversity of C-type lectin receptors, presumably to deal with inhaled particles. By contrast, in the wall of the gut, we could detect a clear signature of the abundant lamina propria macrophage population, but the C-type lectins were absent. The apparent plasticity of tissue macrophages reflects, in part, their responsiveness to numerous different stimuli. The generic term ‘activation’ was originally applied to the ability of recruited macrophages to acquire microbicidal and tumoricidal activity. A number of groups have advocated subclassification of the activation states seen in recruited macrophages, broadly into M1 and M2, or classically activated and alternatively activated (2, 70–72). The M1 and M2 nomenclature links the state of activation of the macrophages to the activation of Th1 and Th2 lymphocytes, which in turn links them to the actions of interferon-c (IFNc) [originally known as macrophage-activating factor (73)], and interleukin-4 (IL4). Broadly speaking, classical activation or M1 phenotypes are associated with the defense against bacterial pathogens and the acquisition of microbicidal activity. Classically activated macrophages also express high levels of class II MHC and present antigen to T lymphocytes (in this context, they are synonymous with inflammatory DCs). M2 or alternatively activated macrophages appear most prevalent in parasite infections and tumors, and are commonly defined through expression of known targets of IL4 signaling (74). The classifications of macrophages invite subtle sub-classification (75), and the terms are used very loosely. For example, macrophages grown in GM-CSF have been said to be M1-like and those grown in CSF-1 to be M2-like (74). Different activation states may be associated with the actions of distinct transcription © 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd Immunological Reviews 262/2014

factors, notably members of the IRF transcription factor family: STAT1 and IRF5 collaborating to induce M1 polarization, while STAT6 and IRF4 interact to polarize toward M2 (74). The very distinct response to macrophages to Toll-like receptor agonists such as lipopolysaccharide (LPS), which has been analyzed in great detail (60, 76–81), is also sometimes referred to as an M1 response. This response involves the sequential induction of hundreds of genes and includes an intrinsic and inducible feedback control network that acts at numerous different levels (82) (see below). Our recent study compared the responses to type 1 and type 2 interferon and the response to LPS (60). Each stimulus produced only partially overlapping responses. Interestingly, the data also confirmed that individual LPS target genes differ in their doseresponsiveness, indicating that the outcome of a stimulus can depend upon both the magnitude, and the nature of the signal. The proposed M1/M2 dichotomy is not strongly supported by genome-scale data. If there were coordinated M1 or M2 ‘regulons’, we would expect to see correlated expression of the classical marker genes for the putative M1 and M2 phenotypes across different cellular states. The metaanalysis of macrophage/DC datasets noted above included macrophage populations polarized with different stimuli, but the clustering failed to identify any set of genes equivalent to the proposed markers of the M1 (classical) or M2 (alternative) activation, suggesting that this classification is also something of an illusion. Just as DC has become synonymous with APC, the ‘alternatively activated’ macrophage in the mouse has come to be conflated with IL4-stimulated macrophage. For example, a very interesting recent study documents the effect of alternatively activated macrophages in adaptive thermogenesis, when they are actually studying IL4-inducible target genes (83). Interestingly, the macrophage-specific transcription factor TFEC appears to have a specific function in the inducible expression of a subset of IL4-response genes (84). The utility of the M1/M2 concept across species is also not clear. The gene expression profiles of activated mouse, pig, and human macrophages are very different (85), with the pig being rather more human-like (86). Mosser and Edwards (87) suggested that macrophage activation may better be described as a spectrum, analogous to a color wheel. Xue et al. (88) confirmed the spectrum model with an extensive comparison of human monocyte-derived macrophages grown in CSF1 or GM-CSF and exposed to numerous distinct stimuli including IFNc, IFNb, IL4/IL13,

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IL10, glucocorticoids, TLR agonists, TNF, and prostaglandin. While there was a broad dichotomy between IFNc (M1) and IL4 (M2) directed states, addition of further stimuli segregated the transcriptional response into many separate modules, with 49 distinct coexpression clusters containing 27 to 884 genes per module. One proposed archetypal M2 macrophage activation state is the tumor-associated macrophage, which has been attributed many roles in tumor progression (6, 21, 89). Because the stromal component of any randomly acquired tumor biopsy varies significantly and the stroma is not itself uniform, it is possible to extract cell type-specific expression profiles by clustering of coexpression networks, or deconvolution as it is sometimes called. We recently demonstrated that there is a common macrophage signature in large datasets of tumors of several different origins (90). This signature contains many known macrophage markers, CD68, CD14, and CSF1R and many lysosomal genes, alongside class II MHC and costimulatory molecules, but does fit with an M1 versus M2 paradigm. What is clearly the case is that there is a very obvious and separate set of interferon-inducible genes in many large datasets, including tumors (90), implying that this is a signature that is not perfectly correlated with macrophage A Sample to sample clustering

content. As noted above, we have defined the type 1 and type 2 interferon responses in mice (60) and also identified the interferon signature in the response of pig macrophages to LPS, which varies among pig breeds (86, 91). Another study (92) compared the responses of large set human monocytes to LPS at two time points and to IFNc. Fig. 2 shows a sample-to-sample cluster analysis of the dataset. The samples cluster perfectly based upon the stimulus encountered. Despite this high level of concordance, there was an underlying significant variation in expression of individual genes, with eQTLs associated with around 80% of loci. When we consider the gene-based clustering, the three largest clusters are produced in response to prolonged LPS treatment (largely macrophage differentiation-associated genes), brief LPS treatment (the classical inflammatory cytokines), and c-interferon (class II MHC and a subset of known IFN response genes). The authors of this work identified a SNP associated with variable expression of interferon (IFNB1) after 2 h that was associated in trans with the level of expression of IFN responsive genes including IRF7 and IRF9 and many putative targets after 24 h of LPS treatment. In our analysis, IRF7 and IRF9 form part of a small cluster, which includes many of the same targets (Fig. 2B), and several other antiviral effectors B The type1 IFN targets

IRF7

IRF9

Adar1 Cmpk2 Hsh2d Ifitm2 Oas1 Patl1 Rsad2 Trim5

C220rf28 Ddx60 Ifi44l Ifitm3 Oas2 Phf11 Siglec1 Zbp1

CCl8 HesX1 Ifi6 Ly6e Oas3 Pml Ssb

Ifit1 NcoA7 Samd9

Mx1 Pnpt1 Usp18

Mx2 Ppm1k Usp41

Fig. 2. Cluster analysis of monocyte expression data. Gene expression data described by Fairfax et al. (92) were obtained from monocytes from multiple individuals, treated in vitro with either lipopolysaccharide (LPS) (2 or 24 h) or IFNc (24 h). The data were downloaded from Array Express (https://www.ebi.ac.uk/arrayexpress). A sample-to-sample correlation matrix was generated using Biolayout Express3D as described elsewhere (40, 41). (A) Despite the widespread evidence of eQTL, the three treatments segregated the samples into four very tight and distinct clusters; the green is the controls, the pink 2 h with LPS, the blue IFNc, and the red 24 h with LPS. Note that the 24-h LPS treatment produces the most dispersed cluster. The gene–gene clustering available is presented in detail on the website www.macrophages.com. (B) The variation among LPS-stimulated samples is mainly attributable to two clusters of genes (separately colored). They include the transcription factors IRF7 and IRF9, and putative downstream targets (Oas1,2, and 3, Ifit1, Mx1, Ccl8, and Ly6E), which are subject to impacts of variation at the IFNb1 locus (92).

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including IFITM2 and IFITM3, implicated in genetic susceptibility to influenza (93). We might infer that the variable expression of IFN response genes in the tumor samples noted above could also have a genetic basis. The interferon signature has also been observed in gene expression profiles in tissues and blood in a wide range of human infections and chronic inflammatory diseases and has been implicated in severity, prognosis, and progression (94–97). Dynamic networks in macrophage differentiation and activation Macrophage activation by any agonist involves a massive change in gene expression during the transition from one steady state to another. System-wide views of transcriptional regulation and state change have been greatly expedited by the advent of genome-scale technologies. There are a number of features shared by many regulatory pathways. (i) There is a sequential cascade of gene regulation. We have recently analyzed this cascade in detail in mouse bone marrowderived macrophages (60). In the case of macrophage activation by LPS, for example, the early response genes, which include a number of classical inflammatory cytokines such as TNFa, have been shown to be subject to regulation primarily at the level of transcription elongation from poised RNA pol II complexes (98, 99). Later response genes are regulated by autocrine factors, including TNF and IFNB1, and by numerous regulated transcription factors (76–81). (ii) The numbers and magnitudes of regulation/expression of induced genes are balanced by transcriptional repression of other genes. In a sense, this is obvious, because the total amount of mRNA per cell does not change radically, but the overall mRNA abundance profile is maintained. In fact, analysis of very large numbers of CAGE tag libraries [genome-scale 50 RACE (100, 101)] demonstrated that individual transcript abundance follows a power law relationship with an exponent that is relatively constant during state changes, and even between cell types (102). Relatively few genes in any cell are very highly expressed at any time, so high-level gene induction must be balanced by repression. This is not simply a matter of attrition. Among the repressed genes are numerous transcriptional repressors that would otherwise block the response to LPS and genes involved in other pathways. In the case of the response to LPS, a large cluster of downregulated genes is involved in the response to the growth factor CSF1 and the cell cycle (60). (iii) Many of the early response genes, both induced and repressed, return to the prestimulation level with time; the response is in some measure self© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd Immunological Reviews 262/2014

limiting even in the continued presence of the agonist. Among the LPS-inducible genes are numerous additional feedback-regulators, which we have referred to as ‘inflammation suppressor genes’ (60, 82). The signaling pathway from the LPS receptor, TLR4, leading to transcriptional activation, has been described in considerable detail (103). Motif over-representation among LPS-inducible promoters led to identification of the transcription factor ATF3 as a key inducible feedback regulator of the response to LPS (78). They also identified the stress response factors NRF1 and NRF2 as candidate regulators, based upon the presence of binding site motifs in active promoters (80). But most importantly, the data revealed that some two-thirds of annotated transcription factors can be expressed in primary macrophages, and if one assesses their potential interactions, the system has the characteristics of a scale-free or small-world network (80). Relatively few expressed transcription factors are very highly connected to others and serve as hubs, whereas others occupy peripheral niches within the network with relatively few direct connections. Amit et al. (104) confirmed this prediction by systematically perturbing candidate regulators using lentiviral transduction of short hairpin RNAs. This study also identified a number of regulators, including circadian clock genes, that could not inferred from motif analysis. The study of regulatory networks of human and mouse macrophages responding to LPS was expedited by the availability in both species of CAGE data that precisely defined transcription start sites, so that we were able to look at DNA sequence motif over-representation in the promoters of LPS-inducible genes in both species, including the numerous genes that were induced in one species and not the other (85). The list of motifs over-represented in the promoters and their relative over-representation were identical between the two species, suggesting that in both species, the promoters sample a common transcriptional milieu. In essence, the network architecture is conserved. However, a pairwise comparison of individual promoters revealed that there was very little conservation of individual elements between the species, even though we did not specify direct alignment. This is consistent with a model in which gain and loss of individual motifs, including the TATA box, is a significant driver of evolution, and few individual motifs/binding sites have indispensible functions. Those data also support the model in which each transcription factor-binding site contributes independently to the probability of transcription (105). Genome-scale technologies were employed in a tour de force study of a model system of macrophage differentiation,

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using the induced differentiation of the THP-1 human cell line in response to phorbol ester (106). The study combined the use of CAGE to identify all of the active promoters and their dynamic regulation across time, qRT-PCR of all predicted transcription factors, and knockdown of a substantial set of candidate regulators. Of a curated list of around 1300 candidate transcription factors, 610 were detected by both CAGE and microarray at some time point during differentiation. Around 100 distinguished the immature from the mature cells, and another 100 were regulated transiently during the state transition. So, as with the activation of macrophages by LPS, this system involved the dynamic regulation of numerous transcription factors in a sequential cascade. Although PU.1 occupied a central position in the network, it was clearly not the only driver, and it was also evident that subsets of macrophage-expressed genes required different combinations of transcriptional regulators. Again in common with the response of macrophages to LPS, there were also many genes downregulated during the differentiation of THP-1 cells, including many transcription factors. To assess the importance of individual factors and especially their downregulation, 52 of the factors were knocked down using siRNA. Knockdowns of myb, HoxA9, CEBPG, GFI1, CEBPA, FLI1, and MLLT3 were each sufficient of themselves to cause partial differentiation of the THP-1 cells. Additional double knockdowns indicated that this was not due to the factors operating in a common pathway; each had an independent function. Several of these factors have well-documented repressive roles. The function of myb, in particular, reflects its downregulation from high levels in progenitor cells (www.biogps.org), and its ability to repress macrophage differentiation and to directly repress expression of the csf1r promoter (107). Myb is most likely also a direct repressor of PU.1 expression, as PU.1 was rapidly induced upon myb knockdown (106).

Databases, websites, and the future The escalating amount of data on mononuclear phagocyte biology coming from genome-scale technologies now taxes the capacity of any individual to access all of the useful information about the regulation of their favorite gene. BioGPS is an example of a new era of more user-friendly portals, as is the Immunological Genome portal (www. immgen.org). We established the web site www.macrophages.com as a community web site for sharing access to macrophage-related genomic and other information (108), including the massive promoter-related datasets arising from the FANTOM projects, in which both mouse and human macrophage systems were heavily analyzed. The site provides access to web-start versions of the Biolayout analysis of several large datasets, including the monocyte data shown in Fig. 2. The latest phase of this project, FANTOM5, provides a comprehensive overview of the human promoterome, and includes mouse and human mononuclear phagocytes and other blood cell types analyzed in hundreds of different states, accessible through a convenient portal (http://fantom.gsc.riken.jp/zenbu/). Macrophages.com also provides macrophage-related pathway annotation data and links to the growing InnateDB (www.innateDB.org) which curates molecular interactions among macrophage-expressed proteins. The web site also contains a curated compendium of major reviews on macrophage biology and transcriptional regulation, many of which provide much more comprehensive coverage of subtopics of this brief oversight. With completed genomes, we are seeing comparable datasets available for other species including the domestic pig, chickens, sheep, and cattle that will underpin more rigorous studies of the evolution of innate immunity, and we have also come to recognize that there is very substantial genetic variation within species that underlies disease susceptibility loci. We are in the era of the data deluge.

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