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Review ARTICLE

Front. Immunol., 22 July 2015 | https://doi.org/10.3389/fimmu.2015.00370

The many alternative faces of macrophage activation

  • The Roslin Institute and Royal (Dick) School of Veterinary Studies, University of Edinburgh, Edinburgh, UK

Monocytes and macrophages provide the first line of defense against pathogens. They also initiate acquired immunity by processing and presenting antigens and provide the downstream effector functions. Analysis of large gene expression datasets from multiple cells and tissues reveals sets of genes that are co-regulated with the transcription factors that regulate them. In macrophages, the gene clusters include lineage-specific genes, interferon-responsive genes, early inflammatory genes, and genes required for endocytosis and lysosome function. Macrophages enter tissues and alter their function to deal with a wide range of challenges related to development and organogenesis, tissue injury, malignancy, sterile, or pathogenic inflammatory stimuli. These stimuli alter the gene expression to produce “activated macrophages” that are better equipped to eliminate the cause of their influx and to restore homeostasis. Activation or polarization states of macrophages have been classified as “classical” and “alternative” or M1 and M2. These proposed states of cells are not supported by large-scale transcriptomic data, including macrophage-associated signatures from large cancer tissue datasets, where the supposed markers do not correlate with other. Individual macrophage cells differ markedly from each other, and change their functions in response to doses and combinations of agonists and time. The most studied macrophage activation response is the transcriptional cascade initiated by the TLR4 agonist lipopolysaccharide. This response is reviewed herein. The network topology is conserved across species, but genes within the transcriptional network evolve rapidly and differ between mouse and human. There is also considerable divergence in the sets of target genes between mouse strains, between individuals, and in other species such as pigs. The deluge of complex information related to macrophage activation can be accessed with new analytical tools and new databases that provide access for the non-expert.

The Cells of the Mononuclear Phagocyte System

The mononuclear phagocyte system (MPS) (1) is a family of professional phagocytes derived from hematopoietic progenitor cells under the influence of specific growth factors. They are the front line innate defense against pathogens, drive appropriate acquired immune responses, initiate inflammation, and promote resolution and repair. (27). The MPS has been viewed as a linear sequence from pluripotent progenitors, through committed myeloid progenitors shared with granulocytes, to promonocytes and blood monocytes, finally giving rise to tissue macrophages (27).

Challenges to the unified concept of an MPS have been discussed elsewhere (4, 5, 8). Some authors claim that macrophages, notably the microglia of the brain (9) and the epidermal macrophages (Langerhans cells) of the skin (10, 11) are seeded entirely during embryonic development and are renewed by local proliferation (1214). A critical review of the experimental basis for this conclusion has been published elsewhere (8). Monocytes obviously can give rise to tissue macrophages, and tissue macrophages can also self-renew (15). An assessment of the relationship between the two mechanisms depends upon the interpretation of lineage-trace studies in mice that are open to criticism (8).

The abundance of monocytes and tissue macrophages is controlled through the macrophage colony-stimulating factor receptor (CSF1R), which is activated by two ligands, CSF1 and IL34 (6, 1620). Treatment of mice with monoclonal antibody against CSF1R depletes most tissue macrophage populations in adult mice (21). Csf1r is itself a macrophage marker gene. The promoter of the mouse gene was used to generate transgenic MacGreen mice in which all of the tissue macrophages express an EGFP reporter gene (22). Myeloid-specific transgenes, using this and other tissue-restricted promoters, have been used in many studies of macrophage cell biology [reviewed in Ref. (23)].

How Do We Define a Macrophage?

The network tool BioLayout Express3D supports user-friendly visualization of co-expression relationships in large datasets (24, 25). We used this tool to identify co-regulated genes in the mouse BioGPS dataset (26) and in other mouse (27), human (28), and pig gene expression data (29). Macrophages as recognized by Metchnikoff are first and foremost professional phagocytes. To carry out that function, they must express all of the genes required to internalize particles and to degrade those particles in lysosomes. To identify those genes, we can compare the expression profiles of macrophages with cells that are not phagocytic. The underlying principal of guilt-by-association was recently extended to RNAseq data including long non-coding RNAs in the networks (30). Within the BioGPS dataset, different hematopoietic cell types expressed lineage-specific transcription factors alongside known lineage-specific genes (26). One obvious functional set of genes shared by all of the phagocytes (including bone-resorbing osteoclasts) contains the components of lysosomes, such as vacuolar ATPase H+ pump and lysosomal hydrolases. The significant numbers of genes within this set that possess little associated annotation are therefore likely to also participate in phagocytosis. The phagocyte-enriched gene set also contains a set of transcription factors that are linked in turn to their likely binding sites in phagocyte-restricted promoters. For example, the promoters of these phagocyte-restricted genes contain purine-rich motifs (binding sites for the macrophage-specific transcription factor, PU.1) alongside the motifs bound by basic helix–loop–helix transcription factors of the microphthalmia transcription factor family; MITF, TFEB, TFEC, and TFE3. All four MITF family members are expressed in macrophages. TFEC is a macrophage-specific transcription factor and itself a PU.1 target gene (31). MITF interacts both physically and genetically with PU.1 (32) and is able to transactivate the promoter of the ACP5 lysosomal enzyme (32). So, taken together, the data provide a picture of the transcriptional network required to become a professional phagocyte and provide a signature that distinguishes macrophages from other cell types.

The most controversial distinction in this respect is between macrophages and dendritic cells (DC). In the minds of some, the term “dendritic cell” has become almost synonymous with antigen-presenting cell (APC). However, the term clearly groups together cells of quite different function and origin (5, 8, 3335). Active APC grow in vitro by cultivation of monocytes (in humans) or bone marrow cells (in mice) in GM-CSF. These cultured-derived APCs are quite distinct from “classical DC” or “conventional DC,” which express the growth factor receptor, Flt3, and differentiate in response to Flt3L in vitro and in vivo (3537). The immunological genome consortium (ImmGen) produced datasets comparing mouse macrophages and “DC” from multiple sources. They claimed to have identified a DC signature (38) as well as markers (Cd64 and Mertk) that defined macrophages as a separate cell type (39). They based the analysis upon comparison of DC with “prototypical” macrophages from peritoneum, lung, brain (microglia), and splenic red pulp that were known already to lack class II MHC. We could not confirm the validity of the signatures of either cell type (34). Instead, the APC could be separated based upon their expression of the phagocyte gene cluster. The microarray platforms are now sufficiently reproducible and the processing methods standardized, that one can actually integrate datasets from independent laboratories, so it is not necessary to perform all studies in one laboratory. In microarray datasets from mouse and human (27, 28), cells annotated as DC form two distinct clusters. Cells grown in GM-CSF segregate with the macrophages in a separate class from classical “DC” isolated from lymphoid organs. The FANTOM5 consortium produced data that enable promoter-based clustering of co-expression networks, which support the same distinction between two different classes of APC (40, 41). The same data supported a study of the process of macrophage differentiation from pluripotent progenitors through monocytes to macrophages (40, 41). Overall, all the data are consistent with the original definition of the classical DC by Steinman and Cohn; which “unlike macrophages, do not appear to engage in active endocytosis” (42). So, I take the view that antigen presentation is a function, not a cell type, and prefer to restrict the use of the term DC to APCs that depend up Flt3L.

Macrophage Activation

Macrophages are abundant in every organ of the body, but each tissue macrophage population is distinct (34). For example, microglia, the macrophages of the brain, are quite different from blood monocytes and tissue macrophages isolated from other locations (43). Indeed, as discussed above, a Csf1r-EGFP transgene has been used as a marker for all tissue macrophages (22). In most organs, there are so many macrophages that we can infer the macrophage signature from analysis of total mRNA. In pigs, for example, we profiled lung macrophages, blood monocytes, and bone marrow-derived macrophages alongside most major organs (29). Alveolar macrophages expressed many different C-type lectin receptors, presumably to recognize and engulf inhaled microorganisms. In mRNA from the wall of the gut, macrophage-specific transcripts derived from the abundant lamina propria macrophage population were easily detected, but the C-type lectins were undetectable. We infer that recognition of gut luminal microorganisms by macrophages in the lamina propria would be undesirable.

Macrophages change their gene expression in response to many different stimuli. Aside from driving tissue-resident macrophage heterogeneity, this plasticity enables an appropriate response to pathogen challenge. The term “activation” applied originally to recruited macrophages that acquired tumoricidal and microbicidal activity. Activation occurred in two phases, a priming event initiated by T cell products and a triggering event generated by lipopolysaccharide (LPS) (44, 45). The activation states in recruited macrophages have since been classified as M1 and M2, or classically activated and alternatively activated (3, 4649). M1 and M2 activation of macrophages is associated with Th1 and Th2 lymphocytes, and in turn with interferon-gamma (IFNγ) (45) and interleukin 4 (IL4) as the mediator. Classical or M1 activation mediates defense against bacterial pathogens. Classically activated macrophages also express class II MHC, and present antigen to T lymphocytes. M2 or alternatively activated macrophages are produced in parasite infections and tumors and express the targets of IL4 signaling (5052). The house of cards built on this foundation included the identification of M2 markers, such as Arg1, Retnla, Chi3l3 (53).

The nomenclature has become increasingly confused. Macrophages grown in GM-CSF are said to be M1-like (when they are not called DC), and those grown in CSF1 to be M2-like (50). Different activation states require distinct transcription factors, such as the IRF transcription factor family; STAT1 and IRF5 induce M1 polarization where STAT6 and IRF4 interact to polarize toward M2 (50). The quite different response to macrophages to toll-like receptor agonists such as LPS (5459) has also been called an M1 response. This response involves the sequential induction of hundreds of genes and includes a complex intrinsic and inducible feedback control network [see below, Ref. (60)]. We recently compared responses to IFNγ and IFNβ and the response to LPS (61). There was limited overlap. Interestingly, the LPS target genes differ in their dose-responsiveness, so even the response to a single agent cannot be classified.

The problem with nomenclature in macrophage activation prompted a group of researchers in the field to try and establish a common framework (53). They noted that the origins of the M1/M2 concept came from the significant biases in macrophage polarization between C57Bl/6 (M1) and BALB/c (M2) mice. Macrophages from different mouse strains differ substantially in their gene expression profiles (58, 61, 62). As discussed further below, these differences can be related to gain and loss of transcription factor-binding sites (58) and are also associated with allele-specific DNA methylation patterns in macrophages from F1 crosses between strains (63). Some of the sequence changes between strains produce clear strain-specific variation that is completely idiosyncratic. For example, the C57Bl/6 mouse has a deficiency in cathepsin E expression, due to loss of a PU.1 site in the promoter, and is therefore deficient in antigen processing (64).

The attempt by Murray et al. (53) to establish a consensus view of activation nomenclature still favors the use of combinations of markers to describe macrophage polarization states. Unfortunately, there is absolutely no support for the usefulness of markers in genome-scale data. There is no set of genes that is co-expressed in large datasets that could be used as a signature of a macrophage activation state. Just as DC has been conflated with APC, the “alternatively activated” macrophage is often a synonym for IL4-stimulated. For example, the implied role of “alternatively activated macrophages” in adaptive thermogenesis was no more than a study of the function of IL4-inducible target genes (65). Interestingly, the macrophage-restricted transcription factor, TFEC, appears to have a specific function in the inducible expression of a subset of IL4-response genes (66). The M1/M2 concept does not translate well across species. The gene expression profiles of “activated” mouse, pig, and human macrophages are quite distinct (67), with the pig more human-like (68, 69). Comparative analysis of IL4-stimulated mouse and human macrophages identified transglutaminase 2 as a “conserved M2 marker” (52). However, this did not appear in any co-expression clusters from large mouse or human datasets.

As discussed elsewhere (70), I personally support Mosser and Edwards (71) suggestion that macrophage activation at a population level at least is analogous to a color wheel. The extremes of polarization of macrophage function may be seen as red, blue, or yellow, but macrophages exist in every hue and intensity in between. Xue et al. (72) confirmed the spectrum model based upon analysis of human monocyte-derived macrophages grown in CSF1 or GM-CSF and exposed to stimuli including IFNγ, IFNβ, IL4/IL13, IL10, glucocorticoids, TLR agonists, TNF, and prostaglandin. While there was some separation between IFNγ (M1) and IL4 (M2) directed states, the transcriptional response could be divided into 49 distinct co-expression clusters with 27–884 genes per module. Even these modules are clearly a simplification based upon synchronous stimulation with individual agents or combinations. As they enter an inflammatory site in vivo, macrophages are exposed to a complex gemisch of signals and the cellular phenotype changes with time. As we move toward single cell profiling in diseased tissues, we will inevitably come to the conclusion that every macrophage is unique.

The tumor-associated macrophage has been considered the archetype of the M2 phenotype (7, 73). Because the stromal component of a tumor biopsy is not uniform, each sample differs in its relative macrophage content. For that reason, we can extract a common macrophage co-expression signature from the expression profile of solid tumors (74). This signature includes macrophage markers, CD68, CD14, and CSF1R, many lysosomal genes, class II MHC, co-stimulatory molecules, and markers of both the M1 and M2 phenotypes. There is a separate set of interferon-inducible genes in many datasets, including tumors (74) implying that these genes are regulated independently of macrophage content. As noted above, we have defined the interferon responses in mice (61) and the interferon signature in the response of pig macrophages to LPS, which differs between breeds (69, 75). Another study (76) compared the responses of human monocytes from several hundred donors to IFNg and LPS. Each stimulus produced a stereotypical response (77), but there was also variation in expression of individual genes, with eQTLs associated with the majority of loci. The authors (76) identified an SNP associated with the level of expression of interferon (IFNβ1) after 2 h; linked in trans to expression of IFN responsive genes after 24 h of LPS treatment. The targets include many anti-viral effectors including IFITM2 and IFITM3, implicated in genetic susceptibility to influenza (78). On this basis, the variation in IFN-response genes between patient samples in many large datasets probably reflects the underlying genetic predisposition rather than the disease state. The interferon signature is present in gene expression profiles in tissues and blood in human infections and chronic inflammatory diseases, and implicated in severity, prognosis, and progression (7882).

Dynamic Networks in Macrophage Differentiation and Activation

Macrophage activation by any agonist alters gene expression radically during the transition from one steady state to another. Features of the regulatory cascade are exemplified by the detailed analysis of the response to LPS.

(1) There is a sequential cascade of gene regulation. This cascade has been analyzed in great detail in mouse bone marrow-derived macrophages (54, 55, 61). The early response genes, including classical inflammatory cytokines, such as TNFα, are controlled at the level of transcription elongation from poised RNA pol II complexes (83, 84). Later response genes are regulated by autocrine factors, including TNF and IFNβ1, and by numerous induced and repressed transcription factors regulated downstream of the initial signal (5459).

(2) The response of individual target genes to LPS requires the interaction between promoters and poised enhancers. In the macrophage lineage, a critical “pioneer” factor that binds to these elements is PU.1. PU.1 mRNA is expressed in both myeloid and lymphoid cells, but the level of PU.1 protein in the nucleus is vastly higher in macrophages (85, 86). These high levels permit PU.1 binding to relatively low affinity sites, to support both constitutive and stimulus-inducible transcriptional regulation (56, 8790). Macrophage-specific transcriptional enhancers involve the interaction between PU.1 and multiple other transcription factors, including the MITF family, IRF8, AP1, Klf, and Rel (NFκB) family members (56, 8790). These factors are themselves regulated by LPS. A recent study separated the characteristics of IRF8 binding to constitutive and inducible enhancers. IRF8–PU.1 cooperated to support basal expression of macrophage-specific genes. Conversely, binding of IRF8 in the vicinity of inducible genes required cooperation with other inducible transcription factors, including AP1, IRF, and STAT family members (91).

(3) The numbers and magnitudes of regulation/expression of induced genes are balanced by transcriptional repression of other genes. Analysis of CAGE tag libraries [genome-scale 5′-RACE (92, 93)] revealed that individual transcript abundance follows a power law relationship with a constant exponent (94). So, gene induction of some genes must be balanced by repression of others. In LPS-stimulated macrophages, the repressed genes include transcriptional repressors that can block the response to LPS, as well as genes involved in other pathways. For example, LPS produced growth arrest in macrophages, so the LPS-downregulated genes include those required for the response to the growth factor, CSF1, and the cell cycle (61).

(4) The response to LPS is self-limiting even in the continued presence of the agonist. The LPS-inducible genes are numerous additional feedback-regulators (60, 61, 95) described as “inflammation suppressor genes” (60, 61). They include many inducible splice variants that encode competitive inhibitors of signaling (96). These inducible feedback regulators act at every level of the TLR signaling cascade. The intuitive explanation for this complex feedback regulation is that they represent an “effective self-control mechanism to ensure that sufficient levels of innate and adaptive immune response are induced to combat pathogens but to avoid “over-heating” the system once this has been achieved” (95). However, the feedback is not robust, since even heterozygous knockouts of many of the feedback repressors produce severe pathology, and many are polymorphic within populations (60). The alternative view is that the complex feedback regulation produces population heterogeneity between macrophages and between individuals. In essence, when dealing with an ever-changing pathogen landscape, it is desirable not to have all the macrophage eggs in one basket.

(5) Enhancer activation precedes the activation of specific target promoters. The activation of enhancers can be detected in LPS-stimulated macrophages through the production of so-called eRNAs, transcribed by RNApolII (97, 98). The use of genome-scale 5′-RACE (CAGE) by the FANTOM5 consortium enabled the quantitative analysis of these eRNAs, which are generally transcribed equally in both directions from active enhancers, with transcription start sites (TSS) around 150–200 bp apart. The same technology detects active promoters in the genomic vicinity, and one can infer the likely relationship between active promoters and enhancers that show the same expression pattern across a large cell and tissue atlas (99). This association is more powerful when one has access to detailed time course data. As part of the FANTOM5 project, we produced a detailed time course of the response of human monocyte-derived macrophages to LPS (100) (Ms in preparation). Figures 1 and 2 show examples of the power of this data. Figures 1A,B shows the well-studied SERPINA1 (alpha-1-antitrypsin) locus. The data reveal that there are two promoters, one used in liver and the other in myeloid cells, as previously shown (101) and confirmed in the mouse (93). The SERPINA1 gene is expressed constitutively in monocytes and granulocytes, repressed in monocyte-derived macrophages grown in CSF1, and strongly induced as a late-response genes upon addition of LPS. As shown in Figure 1A, the actual TSS in macrophages form a broad cluster, typical of myeloid promoters, around 50 bp upstream of the TSS originally identified, but downstream of the EntrezGene transcript. Within the promoter region, there are four copies of the CAGGAA core recognized by Ets family transcription factors, and it is likely that the induction of multiple members of the family by LPS, revealed in the same data set, contributes to regulation. As shown in Figure 1B, induction by LPS is preceded by increased transcription of multiple enhancers. The MAK kinase phosphatase, DUSP1, is one of the key inducible feedback regulators that is induced rapidly in response to LPS. As shown in Figure 2, the DUSP1 gene was induced massively by LPS, with an initial peak at 2–2.5 h, and a secondary peak at 7–8 h. At least eight enhancers upstream and downstream of TSS were detectably induced by LPS, with peaks around 30–60 min prior to peak induction of DUSP1 transcripts, and some evidence of secondary peaks.

(6) Population-level analyses assay the average behavior of cells in a population and obscure the massive underlying heterogeneity. At a single cell level, there is essentially bimodal variation; genes are either induced by LPS or they are not (102). One consequence is that the autocrine loops mediated by inducible cytokines are, in fact, paracrine and the response to LPS in closed systems, in cell culture or in defined inflammatory sites, can vary with cell density. Although some of this variation could arise through covariance of transcription factor expression, the major driver of heterogeneity is the intrinsic probabilistic nature of transcriptional regulation (103). Variation occurs even at the single allele level. Indeed, the LPS receptor, TLR4, is expressed from only one allele in individual cells, with an allele-counting mechanism similar to that of the X chromosome (104). This finding explains the semidominance of the Tlr4 mutation in C3H/HeJ mice, since in heterozygotes, 50% of cells express the non-functional protein. When one allele is deleted, all cells express Tlr4. Furthermore, because of the complex feedback loops in stimulated cells, in which LPS rapidly induces inhibitors that block signaling and degrade induced mRNAs and proteins (60), individual cells show an oscillating response with relatively short time frames, eventually reaching a new steady state (105).

FIGURE 1
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Figure 1. Transcriptional regulation of SERPINA1 in human macrophages. The FANTOM5 analysis across hundreds of cells and tissues revealed the existence of multiple transcription start site (TSS) clusters in the vicinity of the SERPINA1 gene, as well as at least six enhancers in the genomic facility. At top left, (A) summarizes the fact that existing annotated upstream TSS in GenCode contributes only 20% of the TSS detected across the entire dataset. The majority of transcripts derive from two “intragenic” regions. The expanded genomic view above links the TSS to the expression profile. Note that the most abundant TSS, p1@serpinA1, was detected most highly in liver and in primary hepatocyte libraries, and much less in the relatively de-differentiated HepG2 cells. The second most abundant TSS, p3@serpinA1, was constitutively active in granulocytes. At top right, we see that three of the distal promoters were induced by LPS in human monocyte-derived macrophages, starting around 3–4 h after stimulation. The lower part of the panel (B) shows the location, and the time course of induction, of four separate enhancers, upstream, downstream, and within the SERPINA1 gene. (C) shows a close-up view of the distal TSS region on the ZENBU viewer, showing that the TSS identified by CAGE do align with known transcripts, but none supports the most distal 5′ end annotated by Entrez Gene. The primary data summarized in this image are derived from Arner et al. (100) and may be freely downloaded. The CAGE data were extracted and the diagram produced by Albin Sandelin and Erik Arner. The original data were produced in collaboration with Kenneth Baillie.

FIGURE 2
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Figure 2. Transcriptional regulation of DUSP1 in human macrophages. The FANTOM5 analysis across hundreds of cells and tissues revealed the existence of multiple transcription start site (TSS) clusters in the vicinity of a single dominant TSS for the DUSP1 gene, with a classical TATA-box architecture typical of highly inducible genes, as well as at least 14 enhancers in the genomic facility. Upper left panel shows the induction of transcription from this TSS when human monocyte-derived macrophages were treated with LPS. The lower part of the panel illustrates the fact that at least eight of these enhancers showed detectable activation of production of eRNAs detected by CAGE tags. Note that the enhancers were activated around 1–1.5 h, where the peak of DUSP1 transcripts was detected around 2.5 h. The primary data summarized in this image are derived from Arner et al. (100) and may be freely downloaded. The CAGE data were extracted and the diagram produced by Albin Sandelin and Erik Arner. The original data were produced in collaboration with Kenneth Baillie.

The signaling pathway from the LPS receptor, TLR4, leading to transcriptional activation, has been reviewed in detail by others (106, 107). The target genes of the signaling cascade are bound by a set of regulated transcription factors. The most obvious, of course, are NFkB and the IRFs, mentioned above. Genome-scale analysis revealed others. For example, motif over-representation among LPS-inducible promoters identified ATF3 as a novel inducible feedback regulator of the response to LPS (54). They also identified the stress response factors, NRF1 and NRF2, as candidate regulators, based upon the presence of binding site motifs in active promoters (55). We compared the motif over-representation in the promoters of LPS-inducible macrophage genes in humans and mice (67). The motifs over-represented in the promoters, and their relative over-representation, was conserved suggesting that in both species the promoters sample a common transcriptional milieu. However, there was a little conservation of individual elements between the species. So, the 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 (108). But the “big names” of transcription are not the only players in the LPS-inducible transcriptional network. Around two-thirds of all transcription factors can be detected in primary macrophages, and they form a scale-free or small-world interaction network (55). Amit et al. (109) disrupted the candidate regulators using lentiviral transduction of short hairpin RNAs and identified many novel regulators, including circadian clock genes, that could not inferred from motif analysis.

Transcriptional network analysis reached a pinnacle in a study of a model system of human macrophage differentiation (110). Nearly half of all transcription factors were detected at some time point during differentiation and around 200 were dynamically regulated. Although the well-known macrophage lineage marker, PU.1, was crucial to differentiation, subsets of macrophage-expressed genes required different combinations of transcriptional regulators. Many transcription factors were rapidly repressed during the differentiation of THP-1 cells. Fifty-two of these factors were artificially repressed using siRNA. The knockdowns of myb, HoxA9, CEBPG, GFI1, CEBPA, FLI1, and MLLT3 were each sufficient to cause partial differentiation of the THP-1 cells. Additional double knockdowns proved that each contributed independently to the differentiation process. Several of these factors have well-documented repressive roles. The function of myb, in particular, correlates with its known downregulation as progenitor cells differentiate and exit the cell cycle1, and its ability to repress macrophage differentiation and to directly repress csf1r transcription (111). Myb is most likely also a direct repressor of PU.1 expression, since PU.1 was rapidly induced upon myb knockdown (110).

Databases, Web Sites, and the Future

BioGPS is one example of a new era of emerging user-friendly portals (see text footnote 1) that enable the analysis of complex transcriptomic data. Although we disagree with the published analysis, the gene expression data produced by the ImmGen is an important resource for mouse functional genomics2. The web site, www.macrophages.com, was established as a community web site for sharing access to macrophage-related genomic and other information including the promoter-related datasets arising from the FANTOM projects (112). The site provides access to web-start versions of the Biolayout analysis of large macrophage datasets. The FANTOM5 consortium produced a comprehensive overview of the human and mouse promoterome, including mouse and human mononuclear phagocytes and other blood cell types analyzed in hundreds of different states, accessible through a convenient portal3. We developed a specialized portal for the myeloid data at www.myeloidome.roslin.ed.ac.uk. The PrESSTo system provides an intuitive promoter and enhancer selection interface on top of the FANTOM data. It permits the selection of promoters expressed in specific sets of tissues or cells based on slider thresholding, where the number of promoter adhering to a given combination of expression thresholds for cells or tissue can be updated in real time4. With completed genomes, comparable datasets are becoming available species such as the domestic pig, chickens, sheep, and cattle. As we have already seen, mice are not small humans when it comes to macrophage biology. Studies of the evolution of macrophage transcriptional networks will also reveal the points of weakness that are exploited by pathogens in specific species, and which may also be the points for therapeutic intervention.

Conflict of Interest Statement

The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Acknowledgments

The Roslin Institute is supported by Institute Strategic Programme Grants from BBSRC.

Footnotes

References

1. van Furth R, Cohn ZA. The origin and kinetics of mononuclear phagocytes. J Exp Med (1968) 128:415–35. doi:10.1084/jem.128.3.415

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Geissmann F, Manz MG, Jung S, Sieweke MH, Merad M, Ley K. Development of monocytes, macrophages, and dendritic cells. Science (2010) 327:656–61. doi:10.1126/science.1178331

CrossRef Full Text | Google Scholar

3. Gordon S, Taylor PR. Monocyte and macrophage heterogeneity. Nat Rev Immunol (2005) 5:953–64. doi:10.1038/nri1733

CrossRef Full Text | Google Scholar

4. Hume DA. The mononuclear phagocyte system. Curr Opin Immunol (2006) 18:49–53. doi:10.1016/j.coi.2005.11.008

CrossRef Full Text | Google Scholar

5. Hume DA. Differentiation and heterogeneity in the mononuclear phagocyte system. Mucosal Immunol (2008) 1:432–41. doi:10.1038/mi.2008.36

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Hume DA, MacDonald KP. Therapeutic applications of macrophage colony-stimulating factor-1 (CSF-1) and antagonists of CSF-1 receptor (CSF-1R) signaling. Blood (2012) 119:1810–20. doi:10.1182/blood-2011-09-379214

PubMed Abstract | CrossRef Full Text | Google Scholar

7. Wynn TA, Chawla A, Pollard JW. Macrophage biology in development, homeostasis and disease. Nature (2013) 496:445–55. doi:10.1038/nature12034

CrossRef Full Text | Google Scholar

8. Jenkins SJ, Hume DA. Homeostasis in the mononuclear phagocyte system. Trends Immunol (2014) 35(8):358–67. doi:10.1016/j.it.2014.06.006

CrossRef Full Text | Google Scholar

9. Ginhoux F, Greter M, Leboeuf M, Nandi S, See P, Gokhan S, et al. Fate mapping analysis reveals that adult microglia derive from primitive macrophages. Science (2010) 330:841–5. doi:10.1126/science.1194637

PubMed Abstract | CrossRef Full Text | Google Scholar

10. Chorro L, Sarde A, Li M, Woollard KJ, Chambon P, Malissen B, et al. Langerhans cell (LC) proliferation mediates neonatal development, homeostasis, and inflammation-associated expansion of the epidermal LC network. J Exp Med (2009) 206:3089–100. doi:10.1084/jem.20091586

PubMed Abstract | CrossRef Full Text | Google Scholar

11. Hoeffel G, Wang Y, Greter M, See P, Teo P, Malleret B, et al. Adult Langerhans cells derive predominantly from embryonic fetal liver monocytes with a minor contribution of yolk sac-derived macrophages. J Exp Med (2012) 209:1167–81. doi:10.1084/jem.20120340

PubMed Abstract | CrossRef Full Text | Google Scholar

12. Schulz C, Gomez Perdiguero E, Chorro L, Szabo-Rogers H, Cagnard N, Kierdorf K, et al. A lineage of myeloid cells independent of Myb and hematopoietic stem cells. Science (2012) 336:86–90. doi:10.1126/science.1219179

PubMed Abstract | CrossRef Full Text | Google Scholar

13. Hashimoto D, Chow A, Noizat C, Teo P, Beasley MB, Leboeuf M, et al. Tissue-resident macrophages self-maintain locally throughout adult life with minimal contribution from circulating monocytes. Immunity (2013) 38:792–804. doi:10.1016/j.immuni.2013.04.004

PubMed Abstract | CrossRef Full Text | Google Scholar

14. Yona S, Kim KW, Wolf Y, Mildner A, Varol D, Breker M, et al. Fate mapping reveals origins and dynamics of monocytes and tissue macrophages under homeostasis. Immunity (2013) 38:79–91. doi:10.1016/j.immuni.2012.12.001

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Davies LC, Jenkins SJ, Allen JE, Taylor PR. Tissue-resident macrophages. Nat Immunol (2013) 14:986–95. doi:10.1038/ni.2705

PubMed Abstract | CrossRef Full Text | Google Scholar

16. Liu H, Leo C, Chen X, Wong BR, Williams LT, Lin H, et al. The mechanism of shared but distinct CSF-1R signaling by the non-homologous cytokines IL-34 and CSF-1. Biochim Biophys Acta (2012) 1824(7):938–45. doi:10.1016/j.bbapap.2012.04.012

PubMed Abstract | CrossRef Full Text | Google Scholar

17. Ma X, Lin WY, Chen Y, Stawicki S, Mukhyala K, Wu Y, et al. Structural basis for the dual recognition of helical cytokines IL-34 and CSF-1 by CSF-1R. Structure (2012) 20:676–87. doi:10.1016/j.str.2012.02.010

PubMed Abstract | CrossRef Full Text | Google Scholar

18. Garceau V, Smith J, Paton IR, Davey M, Fares MA, Sester DP, et al. Pivotal advance: avian colony-stimulating factor 1 (CSF-1), interleukin-34 (IL-34), and CSF-1 receptor genes and gene products. J Leukoc Biol (2010) 87:753–64. doi:10.1189/jlb.0909624

PubMed Abstract | CrossRef Full Text | Google Scholar

19. Nandi S, Gokhan S, Dai XM, Wei S, Enikolopov G, Lin H, et al. The CSF-1 receptor ligands IL-34 and CSF-1 exhibit distinct developmental brain expression patterns and regulate neural progenitor cell maintenance and maturation. Dev Biol (2012) 367:100–13. doi:10.1016/j.ydbio.2012.03.026

PubMed Abstract | CrossRef Full Text | Google Scholar

20. Nakamichi Y, Udagawa N, Takahashi N. IL-34 and CSF-1: similarities and differences. J Bone Miner Metab (2013) 31:486–95. doi:10.1007/s00774-013-0476-3

PubMed Abstract | CrossRef Full Text | Google Scholar

21. MacDonald KP, Palmer JS, Cronau S, Seppanen E, Olver S, Raffelt NC, et al. An antibody against the colony-stimulating factor 1 receptor depletes the resident subset of monocytes and tissue- and tumor-associated macrophages but does not inhibit inflammation. Blood (2010) 116:3955–63. doi:10.1182/blood-2010-02-266296

PubMed Abstract | CrossRef Full Text | Google Scholar

22. Sasmono RT, Oceandy D, Pollard JW, Tong W, Pavli P, Wainwright BJ, et al. A macrophage colony-stimulating factor receptor-green fluorescent protein transgene is expressed throughout the mononuclear phagocyte system of the mouse. Blood (2003) 101:1155–63. doi:10.1182/blood-2002-02-0569

PubMed Abstract | CrossRef Full Text | Google Scholar

23. Hume DA. Applications of myeloid-specific promoters in transgenic mice support in vivo imaging and functional genomics but do not support the concept of distinct macrophage and dendritic cell lineages or roles in immunity. J Leukoc Biol (2011) 89:525–38. doi:10.1189/jlb.0810472

PubMed Abstract | CrossRef Full Text | Google Scholar

24. Freeman TC, Goldovsky L, Brosch M, Van Dongen S, Maziere P, Grocock RJ, et al. Construction, visualisation, and clustering of transcription networks from microarray expression data. PLoS Comput Biol (2007) 3:2032–42. doi:10.1371/journal.pcbi.0030206

PubMed Abstract | CrossRef Full Text | Google Scholar

25. Theocharidis A, Van Dongen S, Enright AJ, Freeman TC. Network visualization and analysis of gene expression data using BioLayout express (3D). Nat Protoc (2009) 4:1535–50. doi:10.1038/nprot.2009.177

PubMed Abstract | CrossRef Full Text | Google Scholar

26. Hume DA, Summers KM, Raza S, Baillie JK, Freeman TC. Functional clustering and lineage markers: insights into cellular differentiation and gene function from large-scale microarray studies of purified primary cell populations. Genomics (2010) 95:328–38. doi:10.1016/j.ygeno.2010.03.002

PubMed Abstract | CrossRef Full Text | Google Scholar

27. Mabbott NA, Kenneth Baillie J, Hume DA, Freeman TC. Meta-analysis of lineage-specific gene expression signatures in mouse leukocyte populations. Immunobiology (2010) 215:724–36. doi:10.1016/j.imbio.2010.05.012

PubMed Abstract | CrossRef Full Text | Google Scholar

28. Mabbott NA, Baillie JK, Brown H, Freeman TC, Hume DA. An expression atlas of human primary cells: inference of gene function from coexpression networks. BMC Genomics (2013) 14:632. doi:10.1186/1471-2164-14-632

PubMed Abstract | CrossRef Full Text | Google Scholar

29. Freeman TC, Ivens A, Baillie JK, Beraldi D, Barnett MW, Dorward D, et al. A gene expression atlas of the domestic pig. BMC Biol (2012) 10:90. doi:10.1186/1741-7007-10-90

PubMed Abstract | CrossRef Full Text | Google Scholar

30. van Dam S, Craig T, De Magalhaes JP. GeneFriends: a human RNA-seq-based gene and transcript co-expression database. Nucleic Acids Res (2015) 43:D1124–32. doi:10.1093/nar/gku1042

PubMed Abstract | CrossRef Full Text | Google Scholar

31. Rehli M, Lichanska A, Cassady AI, Ostrowski MC, Hume DA. TFEC is a macrophage-restricted member of the microphthalmia-TFE subfamily of basic helix-loop-helix leucine zipper transcription factors. J Immunol (1999) 162:1559–65.

PubMed Abstract | Google Scholar

32. Luchin A, Suchting S, Merson T, Rosol TJ, Hume DA, Cassady AI, et al. Genetic and physical interactions between microphthalmia transcription factor and PU.1 are necessary for osteoclast gene expression and differentiation. J Biol Chem (2001) 276:36703–10. doi:10.1074/jbc.M106418200

PubMed Abstract | CrossRef Full Text | Google Scholar

33. Hume DA. Macrophages as APC and the dendritic cell myth. J Immunol (2008) 181:5829–35. doi:10.4049/jimmunol.181.9.5829

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Hume DA, Mabbott N, Raza S, Freeman TC. Can DCs be distinguished from macrophages by molecular signatures? Nat Immunol (2013) 14:187–9. doi:10.1038/ni.2516

CrossRef Full Text | Google Scholar

35. Mildner A, Jung S. Development and function of dendritic cell subsets. Immunity (2014) 40:642–56. doi:10.1016/j.immuni.2014.04.016

CrossRef Full Text | Google Scholar

36. Liu K, Nussenzweig MC. Origin and development of dendritic cells. Immunol Rev (2010) 234:45–54. doi:10.1111/j.0105-2896.2009.00879.x

CrossRef Full Text | Google Scholar

37. Merad M, Sathe P, Helft J, Miller J, Mortha A. The dendritic cell lineage: ontogeny and function of dendritic cells and their subsets in the steady state and the inflamed setting. Annu Rev Immunol (2013) 31:563–604. doi:10.1146/annurev-immunol-020711-074950

PubMed Abstract | CrossRef Full Text | Google Scholar

38. Miller JC, Brown BD, Shay T, Gautier EL, Jojic V, Cohain A, et al. Deciphering the transcriptional network of the dendritic cell lineage. Nat Immunol (2012) 13:888–99. doi:10.1038/ni.2370

PubMed Abstract | CrossRef Full Text | Google Scholar

39. Gautier EL, Shay T, Miller J, Greter M, Jakubzick C, Ivanov S, et al. Gene-expression profiles and transcriptional regulatory pathways that underlie the identity and diversity of mouse tissue macrophages. Nat Immunol (2012) 13:1118–28. doi:10.1038/ni.2419

PubMed Abstract | CrossRef Full Text | Google Scholar

40. Consortium (The FANTOM Consortium), Forrest AR, Kawaji H, Rehli M, Baillie JK, De Hoon MJ, et al. A promoter-level mammalian expression atlas. Nature (2014) 507:462–70. doi:10.1038/nature13182

PubMed Abstract | CrossRef Full Text | Google Scholar

41. Joshi A, Pooley C, Freeman TC, Lennartsson A, Babina M, Schmidl C, et al. Technical advance: transcription factor, promoter, and enhancer utilization in human myeloid cells. J Leukoc Biol (2015) 97:985–95. doi:10.1189/jlb.6TA1014-477RR

PubMed Abstract | CrossRef Full Text | Google Scholar

42. Steinman RM, Cohn ZA. Identification of a novel cell type in peripheral lymphoid organs of mice. I. Morphology, quantitation, tissue distribution. J Exp Med (1973) 137:1142–62. doi:10.1084/jem.137.5.1142

CrossRef Full Text | Google Scholar

43. Butovsky O, Jedrychowski MP, Moore CS, Cialic R, Lanser AJ, Gabriely G, et al. Identification of a unique TGF-beta-dependent molecular and functional signature in microglia. Nat Neurosci (2014) 17:131–43. doi:10.1038/nn.3599

PubMed Abstract | CrossRef Full Text | Google Scholar

44. Adams DO, Hamilton TA. The cell biology of macrophage activation. Annu Rev Immunol (1984) 2:283–318. doi:10.1146/annurev.iy.02.040184.001435

CrossRef Full Text | Google Scholar

45. Schroder K, Hertzog PJ, Ravasi T, Hume DA. Interferon-gamma: an overview of signals, mechanisms and functions. J Leukoc Biol (2004) 75:163–89. doi:10.1189/jlb.0603252

PubMed Abstract | CrossRef Full Text | Google Scholar

46. Taylor PR, Gordon S. Monocyte heterogeneity and innate immunity. Immunity (2003) 19:2–4. doi:10.1016/S1074-7613(03)00178-X

CrossRef Full Text | Google Scholar

47. Sica A, Larghi P, Mancino A, Rubino L, Porta C, Totaro MG, et al. Macrophage polarization in tumour progression. Semin Cancer Biol (2008) 18:349–55. doi:10.1016/j.semcancer.2008.03.004

CrossRef Full Text | Google Scholar

48. Gordon S, Martinez FO. Alternative activation of macrophages: mechanism and functions. Immunity (2010) 32:593–604. doi:10.1016/j.immuni.2010.05.007

PubMed Abstract | CrossRef Full Text | Google Scholar

49. Sica A, Mantovani A. Macrophage plasticity and polarization: in vivo veritas. J Clin Invest (2012) 122:787–95. doi:10.1172/JCI59643

PubMed Abstract | CrossRef Full Text | Google Scholar

50. Lawrence T, Natoli G. Transcriptional regulation of macrophage polarization: enabling diversity with identity. Nat Rev Immunol (2011) 11:750–61. doi:10.1038/nri3088

PubMed Abstract | CrossRef Full Text | Google Scholar

51. Martinez FO, Sica A, Mantovani A, Locati M. Macrophage activation and polarization. Front Biosci (2008) 13:453–61. doi:10.2741/2692

PubMed Abstract | CrossRef Full Text | Google Scholar

52. Martinez FO, Helming L, Milde R, Varin A, Melgert BN, Draijer C, et al. Genetic programs expressed in resting and IL-4 alternatively activated mouse and human macrophages: similarities and differences. Blood (2013) 121:e57–69. doi:10.1182/blood-2012-06-436212

PubMed Abstract | CrossRef Full Text | Google Scholar

53. Murray PJ, Allen JE, Biswas SK, Fisher EA, Gilroy DW, Goerdt S, et al. Macrophage activation and polarization: nomenclature and experimental guidelines. Immunity (2014) 41:14–20. doi:10.1016/j.immuni.2014.06.008

PubMed Abstract | CrossRef Full Text | Google Scholar

54. Gilchrist M, Thorsson V, Li B, Rust AG, Korb M, Roach JC, et al. Systems biology approaches identify ATF3 as a negative regulator of toll-like receptor 4. Nature (2006) 441:173–8. doi:10.1038/nature04768

PubMed Abstract | CrossRef Full Text | Google Scholar

55. Nilsson R, Bajic VB, Suzuki H, Di Bernardo D, Bjorkegren J, Katayama S, et al. Transcriptional network dynamics in macrophage activation. Genomics (2006) 88:133–42. doi:10.1016/j.ygeno.2006.03.022

PubMed Abstract | CrossRef Full Text | Google Scholar

56. Ghisletti S, Barozzi I, Mietton F, Polletti S, De Santa F, Venturini E, et al. Identification and characterization of enhancers controlling the inflammatory gene expression program in macrophages. Immunity (2010) 32:317–28. doi:10.1016/j.immuni.2010.02.008

PubMed Abstract | CrossRef Full Text | Google Scholar

57. Bhatt DM, Pandya-Jones A, Tong AJ, Barozzi I, Lissner MM, Natoli G, et al. Transcript dynamics of proinflammatory genes revealed by sequence analysis of subcellular RNA fractions. Cell (2012) 150:279–90. doi:10.1016/j.cell.2012.05.043

PubMed Abstract | CrossRef Full Text | Google Scholar

58. Heinz S, Romanoski CE, Benner C, Allison KA, Kaikkonen MU, Orozco LD, et al. Effect of natural genetic variation on enhancer selection and function. Nature (2013) 503:487–92. doi:10.1038/nature12615

PubMed Abstract | CrossRef Full Text | Google Scholar

59. Ostuni R, Piccolo V, Barozzi I, Polletti S, Termanini A, Bonifacio S, et al. Latent enhancers activated by stimulation in differentiated cells. Cell (2013) 152:157–71. doi:10.1016/j.cell.2012.12.018

PubMed Abstract | CrossRef Full Text | Google Scholar

60. Wells CA, Ravasi T, Hume DA. Inflammation suppressor genes: please switch out all the lights. J Leukoc Biol (2005) 78:9–13. doi:10.1189/jlb.1204710

PubMed Abstract | CrossRef Full Text | Google Scholar

61. Raza S, Barnett MW, Barnett-Itzhaki Z, Amit I, Hume DA, Freeman TC. Analysis of the transcriptional networks underpinning the activation of murine macrophages by inflammatory mediators. J Leukoc Biol (2014) 96(2):167–83. doi:10.1189/jlb.6HI0313-169R

PubMed Abstract | CrossRef Full Text | Google Scholar

62. Wells CA, Ravasi T, Faulkner GJ, Carninci P, Okazaki Y, Hayashizaki Y, et al. Genetic control of the innate immune response. BMC Immunol (2003) 4:5. doi:10.1186/1471-2172-4-5

CrossRef Full Text | Google Scholar

63. Schilling E, El Chartouni C, Rehli M. Allele-specific DNA methylation in mouse strains is mainly determined by cis-acting sequences. Genome Res (2009) 19:2028–35. doi:10.1101/gr.095562.109

PubMed Abstract | CrossRef Full Text | Google Scholar

64. Tulone C, Tsang J, Prokopowicz Z, Grosvenor N, Chain B. Natural cathepsin E deficiency in the immune system of C57BL/6J mice. Immunogenetics (2007) 59:927–35. doi:10.1007/s00251-007-0256-0

PubMed Abstract | CrossRef Full Text | Google Scholar

65. Nguyen KD, Qiu Y, Cui X, Goh YP, Mwangi J, David T, et al. Alternatively activated macrophages produce catecholamines to sustain adaptive thermogenesis. Nature (2011) 480:104–8. doi:10.1038/nature10653

PubMed Abstract | CrossRef Full Text | Google Scholar

66. Rehli M, Sulzbacher S, Pape S, Ravasi T, Wells CA, Heinz S, et al. Transcription factor Tfec contributes to the IL-4-inducible expression of a small group of genes in mouse macrophages including the granulocyte colony-stimulating factor receptor. J Immunol (2005) 174:7111–22. doi:10.4049/jimmunol.174.11.7111

PubMed Abstract | CrossRef Full Text | Google Scholar

67. Schroder K, Irvine KM, Taylor MS, Bokil NJ, Le Cao KA, Masterman KA, et al. Conservation and divergence in toll-like receptor 4-regulated gene expression in primary human versus mouse macrophages. Proc Natl Acad Sci U S A (2012) 109:E944–53. doi:10.1073/pnas.1110156109

PubMed Abstract | CrossRef Full Text | Google Scholar

68. Fairbairn L, Kapetanovic R, Sester DP, Hume DA. The mononuclear phagocyte system of the pig as a model for understanding human innate immunity and disease. J Leukoc Biol (2011) 89:855–71. doi:10.1189/jlb.1110607

PubMed Abstract | CrossRef Full Text | Google Scholar

69. Kapetanovic R, Fairbairn L, Beraldi D, Sester DP, Archibald AL, Tuggle CK, et al. Pig bone marrow-derived macrophages resemble human macrophages in their response to bacterial lipopolysaccharide. J Immunol (2012) 188:3382–94. doi:10.4049/jimmunol.1102649

PubMed Abstract | CrossRef Full Text | Google Scholar

70. Schultze JL, Freeman T, Hume DA, Latz E. A transcriptional perspective on human macrophage biology. Semin Immunol (2015) 27:44–50. doi:10.1016/j.smim.2015.02.001

PubMed Abstract | CrossRef Full Text | Google Scholar

71. Mosser DM, Edwards JP. Exploring the full spectrum of macrophage activation. Nat Rev Immunol (2008) 8:958–69. doi:10.1038/nri2448

PubMed Abstract | CrossRef Full Text | Google Scholar

72. Xue J, Schmidt SV, Sander J, Draffehn A, Krebs W, Quester I, et al. Transcriptome-based network analysis reveals a spectrum model of human macrophage activation. Immunity (2014) 40:274–88. doi:10.1016/j.immuni.2014.01.006

PubMed Abstract | CrossRef Full Text | Google Scholar

73. Pollard JW. Trophic macrophages in development and disease. Nat Rev Immunol (2009) 9:259–70. doi:10.1038/nri2528

CrossRef Full Text | Google Scholar

74. Doig TN, Hume DA, Theocharidis T, Goodlad JR, Gregory CD, Freeman TC. Coexpression analysis of large cancer datasets provides insight into the cellular phenotypes of the tumour microenvironment. BMC Genomics (2013) 14:469. doi:10.1186/1471-2164-14-469

PubMed Abstract | CrossRef Full Text | Google Scholar

75. Kapetanovic R, Fairbairn L, Downing A, Beraldi D, Sester DP, Freeman TC, et al. The impact of breed and tissue compartment on the response of pig macrophages to lipopolysaccharide. BMC Genomics (2013) 14:581. doi:10.1186/1471-2164-14-581

PubMed Abstract | CrossRef Full Text | Google Scholar

76. Fairfax BP, Humburg P, Makino S, Naranbhai V, Wong D, Lau E, et al. Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science (2014) 343:1246949. doi:10.1126/science.1246949

PubMed Abstract | CrossRef Full Text | Google Scholar

77. Hume DA, Freeman TC. Transcriptomic analysis of mononuclear phagocyte differentiation and activation. Immunol Rev (2014) 262:74–84. doi:10.1111/imr.12211

PubMed Abstract | CrossRef Full Text | Google Scholar

78. Everitt AR, Clare S, Pertel T, John SP, Wash RS, Smith SE, et al. IFITM3 restricts the morbidity and mortality associated with influenza. Nature (2012) 484:519–23. doi:10.1038/nature10921

PubMed Abstract | CrossRef Full Text | Google Scholar

79. Ramilo O, Allman W, Chung W, Mejias A, Ardura M, Glaser C, et al. Gene expression patterns in blood leukocytes discriminate patients with acute infections. Blood (2007) 109:2066–77. doi:10.1182/blood-2006-02-002477

PubMed Abstract | CrossRef Full Text | Google Scholar

80. Li H, Ice JA, Lessard CJ, Sivils KL. Interferons in Sjogren’s syndrome: genes, mechanisms, and effects. Front Immunol (2013) 4:290. doi:10.3389/fimmu.2013.00290

PubMed Abstract | CrossRef Full Text | Google Scholar

81. Ronnblom L, Eloranta ML. The interferon signature in autoimmune diseases. Curr Opin Rheumatol (2013) 25:248–53. doi:10.1097/BOR.0b013e32835c7e32

CrossRef Full Text | Google Scholar

82. Chiche L, Jourde-Chiche N, Whalen E, Presnell S, Gersuk V, Dang K, et al. Modular transcriptional repertoire analyses of adults with systemic lupus erythematosus reveal distinct type I and type II interferon signatures. Arthritis Rheumatol (2014) 66(6):1583–95. doi:10.1002/art.38628

PubMed Abstract | CrossRef Full Text | Google Scholar

83. Hargreaves DC, Horng T, Medzhitov R. Control of inducible gene expression by signal-dependent transcriptional elongation. Cell (2009) 138:129–45. doi:10.1016/j.cell.2009.05.047

PubMed Abstract | CrossRef Full Text | Google Scholar

84. Medzhitov R, Horng T. Transcriptional control of the inflammatory response. Nat Rev Immunol (2009) 9:692–703. doi:10.1038/nri2634

CrossRef Full Text | Google Scholar

85. Ross IL, Dunn TL, Yue X, Roy S, Barnett CJ, Hume DA. Comparison of the expression and function of the transcription factor PU.1 (Spi-1 proto-oncogene) between murine macrophages and B lymphocytes. Oncogene (1994) 9:121–32.

PubMed Abstract | Google Scholar

86. Kueh HY, Champhekar A, Nutt SL, Elowitz MB, Rothenberg EV. Positive feedback between PU.1 and the cell cycle controls myeloid differentiation. Science (2013) 341:670–3. doi:10.1126/science.1240831

PubMed Abstract | CrossRef Full Text | Google Scholar

87. Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell (2010) 38:576–89. doi:10.1016/j.molcel.2010.05.004

PubMed Abstract | CrossRef Full Text | Google Scholar

88. Heinz S, Glass CK. Roles of lineage-determining transcription factors in establishing open chromatin: lessons from high-throughput studies. Curr Top Microbiol Immunol (2012) 356:1–15. doi:10.1007/82_2011_142

PubMed Abstract | CrossRef Full Text | Google Scholar

89. Barozzi I, Simonatto M, Bonifacio S, Yang L, Rohs R, Ghisletti S, et al. Coregulation of transcription factor binding and nucleosome occupancy through DNA features of mammalian enhancers. Mol Cell (2014) 54:844–57. doi:10.1016/j.molcel.2014.04.006

PubMed Abstract | CrossRef Full Text | Google Scholar

90. Gosselin D, Glass CK. Epigenomics of macrophages. Immunol Rev (2014) 262:96–112. doi:10.1111/imr.12213

CrossRef Full Text | Google Scholar

91. Mancino A, Termanini A, Barozzi I, Ghisletti S, Ostuni R, Prosperini E, et al. A dual cis-regulatory code links IRF8 to constitutive and inducible gene expression in macrophages. Genes Dev (2015) 29:394–408. doi:10.1101/gad.257592.114

PubMed Abstract | CrossRef Full Text | Google Scholar

92. Carninci P, Kasukawa T, Katayama S, Gough J, Frith MC, Maeda N, et al. The transcriptional landscape of the mammalian genome. Science (2005) 309:1559–63. doi:10.1126/science.1112014

PubMed Abstract | CrossRef Full Text | Google Scholar

93. Carninci P, Sandelin A, Lenhard B, Katayama S, Shimokawa K, Ponjavic J, et al. Genome-wide analysis of mammalian promoter architecture and evolution. Nat Genet (2006) 38:626–35. doi:10.1038/ng1789

PubMed Abstract | CrossRef Full Text | Google Scholar

94. Balwierz PJ, Carninci P, Daub CO, Kawai J, Hayashizaki Y, Van Belle W, et al. Methods for analyzing deep sequencing expression data: constructing the human and mouse promoterome with deepCAGE data. Genome Biol (2009) 10:R79. doi:10.1186/gb-2009-10-7-r79

PubMed Abstract | CrossRef Full Text | Google Scholar

95. Liew FY, Xu D, Brint EK, O’Neill LA. Negative regulation of toll-like receptor-mediated immune responses. Nat Rev Immunol (2005) 5:446–58. doi:10.1038/nri1630

PubMed Abstract | CrossRef Full Text | Google Scholar

96. Wells CA, Chalk AM, Forrest A, Taylor D, Waddell N, Schroder K, et al. Alternate transcription of the toll-like receptor signaling cascade. Genome Biol (2006) 7:R10. doi:10.1186/gb-2006-7-2-r10

PubMed Abstract | CrossRef Full Text | Google Scholar

97. Kaikkonen MU, Spann NJ, Heinz S, Romanoski CE, Allison KA, Stender JD, et al. Remodeling of the enhancer landscape during macrophage activation is coupled to enhancer transcription. Mol Cell (2013) 51:310–25. doi:10.1016/j.molcel.2013.07.010

PubMed Abstract | CrossRef Full Text | Google Scholar

98. Lam MT, Li W, Rosenfeld MG, Glass CK. Enhancer RNAs and regulated transcriptional programs. Trends Biochem Sci (2014) 39:170–82. doi:10.1016/j.tibs.2014.02.007

PubMed Abstract | CrossRef Full Text | Google Scholar

99. Andersson R, Gebhard C, Miguel-Escalada I, Hoof I, Bornholdt J, Boyd M, et al. An atlas of active enhancers across human cell types and tissues. Nature (2014) 507:455–61. doi:10.1038/nature12787

PubMed Abstract | CrossRef Full Text | Google Scholar

100. Arner E, Daub CO, Vitting-Seerup K, Andersson R, Lilje B, Drablos F, et al. Gene regulation. Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells. Science (2015) 347:1010–4. doi:10.1126/science.1259418

PubMed Abstract | CrossRef Full Text | Google Scholar

101. Perlino E, Cortese R, Ciliberto G. The human alpha 1-antitrypsin gene is transcribed from two different promoters in macrophages and hepatocytes. EMBO J (1987) 6:2767–71.

PubMed Abstract | Google Scholar

102. Shalek AK, Satija R, Adiconis X, Gertner RS, Gaublomme JT, Raychowdhury R, et al. Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells. Nature (2013) 498:236–40. doi:10.1038/nature12172

PubMed Abstract | CrossRef Full Text | Google Scholar

103. Ravasi T, Wells C, Forest A, Underhill DM, Wainwright BJ, Aderem A, et al. Generation of diversity in the innate immune system: macrophage heterogeneity arises from gene-autonomous transcriptional probability of individual inducible genes. J Immunol (2002) 168:44–50. doi:10.4049/jimmunol.168.1.44

PubMed Abstract | CrossRef Full Text | Google Scholar

104. Pereira JP, Girard R, Chaby R, Cumano A, Vieira P. Monoallelic expression of the murine gene encoding toll-like receptor 4. Nat Immunol (2003) 4:464–70. doi:10.1038/ni917

PubMed Abstract | CrossRef Full Text | Google Scholar

105. Lee TK, Covert MW. High-throughput, single-cell NF-kappaB dynamics. Curr Opin Genet Dev (2010) 20:677–83. doi:10.1016/j.gde.2010.08.005

PubMed Abstract | CrossRef Full Text | Google Scholar

106. Kawai T, Akira S. The role of pattern-recognition receptors in innate immunity: update on toll-like receptors. Nat Immunol (2010) 11:373–84. doi:10.1038/ni.1863

PubMed Abstract | CrossRef Full Text | Google Scholar

107. Takeuchi O, Akira S. Pattern recognition receptors and inflammation. Cell (2010) 140:805–20. doi:10.1016/j.cell.2010.01.022

CrossRef Full Text | Google Scholar

108. Hume DA. Probability in transcriptional regulation and its implications for leukocyte differentiation and inducible gene expression. Blood (2000) 96:2323–8.

PubMed Abstract | Google Scholar

109. Amit I, Garber M, Chevrier N, Leite AP, Donner Y, Eisenhaure T, et al. Unbiased reconstruction of a mammalian transcriptional network mediating pathogen responses. Science (2009) 326:257–63. doi:10.1126/science.1179050

PubMed Abstract | CrossRef Full Text | Google Scholar

110. Suzuki H, Forrest AR, Van Nimwegen E, Daub CO, Balwierz PJ, Irvine KM, et al. The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line. Nat Genet (2009) 41:553–62. doi:10.1038/ng.375

PubMed Abstract | CrossRef Full Text | Google Scholar

111. Reddy MA, Yang BS, Yue X, Barnett CJ, Ross IL, Sweet MJ, et al. Opposing actions of c-ets/PU.1 and c-myb protooncogene products in regulating the macrophage-specific promoters of the human and mouse colony-stimulating factor-1 receptor (c-fms) genes. J Exp Med (1994) 180:2309–19. doi:10.1084/jem.180.6.2309

PubMed Abstract | CrossRef Full Text | Google Scholar

112. Robert C, Lu X, Law A, Freeman TC, Hume DA. Macrophages.com: an on-line community resource for innate immunity research. Immunobiology (2011) 216:1203–11. doi:10.1016/j.imbio.2011.07.025

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: macrophage, transcriptomics, activation, colony-stimulating factor, lipopolysaccharide

Citation: Hume DA (2015) The many alternative faces of macrophage activation. Front. Immunol. 6:370. doi: 10.3389/fimmu.2015.00370

Received: 22 May 2015; Accepted: 06 July 2015;
Published: 22 July 2015

Edited by:

Dawn M. E. Bowdish, McMaster University, Canada

Reviewed by:

Albert Descoteaux, INRS-Institut Armand-Frappier, Canada
Karim Christian El Kasmi, University of Colorado Denver, USA

Copyright: © 2015 Hume. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: David A. Hume, The Roslin Institute, Royal (Dick) School of Veterinary Studies, University of Edinburgh, Easter Bush, Midlothian, Scotland EH25 9RG, UK, david.hume@roslin.ed.ac.uk