Skip to main content
  • Loading metrics

Analysis of the human monocyte-derived macrophage transcriptome and response to lipopolysaccharide provides new insights into genetic aetiology of inflammatory bowel disease


The FANTOM5 consortium utilised cap analysis of gene expression (CAGE) to provide an unprecedented insight into transcriptional regulation in human cells and tissues. In the current study, we have used CAGE-based transcriptional profiling on an extended dense time course of the response of human monocyte-derived macrophages grown in macrophage colony-stimulating factor (CSF1) to bacterial lipopolysaccharide (LPS). We propose that this system provides a model for the differentiation and adaptation of monocytes entering the intestinal lamina propria. The response to LPS is shown to be a cascade of successive waves of transient gene expression extending over at least 48 hours, with hundreds of positive and negative regulatory loops. Promoter analysis using motif activity response analysis (MARA) identified some of the transcription factors likely to be responsible for the temporal profile of transcriptional activation. Each LPS-inducible locus was associated with multiple inducible enhancers, and in each case, transient eRNA transcription at multiple sites detected by CAGE preceded the appearance of promoter-associated transcripts. LPS-inducible long non-coding RNAs were commonly associated with clusters of inducible enhancers. We used these data to re-examine the hundreds of loci associated with susceptibility to inflammatory bowel disease (IBD) in genome-wide association studies. Loci associated with IBD were strongly and specifically (relative to rheumatoid arthritis and unrelated traits) enriched for promoters that were regulated in monocyte differentiation or activation. Amongst previously-identified IBD susceptibility loci, the vast majority contained at least one promoter that was regulated in CSF1-dependent monocyte-macrophage transitions and/or in response to LPS. On this basis, we concluded that IBD loci are strongly-enriched for monocyte-specific genes, and identified at least 134 additional candidate genes associated with IBD susceptibility from reanalysis of published GWA studies. We propose that dysregulation of monocyte adaptation to the environment of the gastrointestinal mucosa is the key process leading to inflammatory bowel disease.

Author summary

Macrophages are immune cells that form the first line of defense against pathogens, but also mediate tissue damage in inflammatory disease. Macrophages initiate inflammation by recognising and responding to components of bacterial cells. Macrophages of the wall of the gut are constantly replenished from the blood. Upon entering the intestine, newly-arrived cells modulate their response to stimuli derived from the bacteria in the wall of the gut. This process fails in chronic inflammatory bowel diseases (IBD). Both the major forms of IBD, Crohn’s disease and ulcerative colitis, run in families. The inheritance is complex, involving more than 200 different regions of the genome. We hypothesised that the genetic risk of IBD is associated specifically with altered regulation of genes that control the development of macrophages. In this study, we used the comprehensive transcriptome dataset produced by the FANTOM5 consortium to identify the sets of promoters and enhancers that are involved in adaptation of macrophages to the gut wall, their response to bacterial stimuli, and how their functions are integrated. A reanalysis of published genome-wide association data based upon regulated genes in monocytes as candidates strongly supports the view that susceptibility to IBD arises from a primary defect in macrophage differentiation.


Inflammatory bowel disease (IBD) comprises a group of complex syndromes that arise from a dysfunctional interaction between the microbiota of the intestinal lumen and the immune system [1]. Loci associated with the heritability of susceptibility to IBD are shared in some measure with other chronic inflammatory diseases [2,3]. Extensive genome-wide association studies (GWAS) have identified more than 200 risk loci for IBD, with significant overlaps between the two major forms, Crohn’s disease and ulcerative colitis [46]. The analysis of candidate genes within susceptibility loci based upon apparently shared biological function has emphasised three major pathways: the activation of Th17 T cells, autophagy, and the response to mycobacteria [7,8]. The emphasis on T cell activation derives in part from a clear association of susceptibility with certain MHC haplotypes, and the observed activation of T cells in inflamed mucosa [9]. An alternative view is that IBD is primarily initiated by functional dysregulation in cells of the macrophage lineage [10,11] and prioritisation of candidates on that basis may be informative [12].

By contrast to other tissue macrophage populations which have a significant capacity for self-renewal, lamina propria macrophages of the gut are renewed continuously from the circulating monocyte pool [13,14]. The proliferation and differentiation of the monocyte-macrophage lineage is controlled by the growth factor, macrophage colony-stimulating factor (CSF1), which signals through a tyrosine kinase receptor, CSF1R [1517]. The replenishment of the resident cells of the lamina propria requires the continuous exposure to CSF1 and lamina propria macrophages are rapidly depleted in mice treated with a blocking anti-CSF1R antibody [18,19]. Human macrophages are commonly generated by cultivation of isolated CD14+ monocytes for 4–5 days in CSF1 [20,21]. Several groups have published detailed transcriptomic analysis of the differentiation of monocytes in CSF1 [2023]. Based upon the known biology (derivation from monocytes, dependence upon CSF1), we suggest that the monocyte-derived macrophage actually approximates an in vitro model of the differentiation of intestinal macrophages from incoming monocytes.

Alongside CSF1, monocytes entering the lamina propria of the lower GI tract are immediately exposed to microbial products of which the archetype is lipopolysaccharide (LPS) from gram-negative bacteria. Monocytes must rapidly down-modulate their response to bacteria in the lumen, to avoid initiating an inflammatory response in the gut wall [18]. In response to LPS, macrophages initiate a complex feed-forward and feed-back cascade of induction and repression of transcription factors and autocrine regulators [24,25] leading to a new steady state. The response of mouse macrophages to LPS has been studied in detail at many levels from the mechanisms of signaling [26,27] through transcriptional networks [2830] to the underlying alterations in chromatin structure [3133]. Dense time course data are required to enable inference of the sequence of transcriptional events in this response.

The FANTOM consortium established tag sequencing of genome-scale 5’-RACE (CAGE) as an expression profiling tool and used the approach to create a comprehensive human promoter-based expression atlas [34]. Deep sequencing of CAGE libraries also detected the bidirectional transcripts (eRNAs) derived from active enhancers enabling genome-wide quantification of enhancer activity [35]. CAGE-based delineation of transcription start sites was used in the comparison of the promoters of LPS-inducible genes in mouse and human macrophages [21] as well as a recent detailed analyses of monocyte subsets [36]. Extensive analysis of cells undergoing state change showed a clear temporal pattern in induction of expression; enhancers are expressed first, then transcription factor genes, and finally the genes they regulate [37].

In the current study, we analysed CAGE data generated within FANTOM5 to dissect the transcriptional changes that occur in human macrophages grown in CSF1, and their subsequent response to LPS, as a model for events that occur when monocytes adapt to the gut environment. We use these data, combined with extensive data on the gene expression of blood monocytes generated by the FANTOM5 consortium, to reassess the known candidate intervals associated with IBD. Our analysis supports the hypothesis that IBD is primarily a macrophage-initiated pathology and provides the basis for identification of alternative candidate genes within many IBD susceptibility loci that have been identified by genome-wide analysis (GWA).


Description of the datasets

The aim of this study was to examine the hypothesis that dysregulation of intestinal macrophage responses to bacterial antigens is an important component of susceptibility to IBD. Initially, as part of the FANTOM5 project, we produced a detailed quantitative analysis of promoter utilization and gene expression across a dense time course of the LPS response of human monocyte-derived macrophages (MDM) from three separate donors. The time course focused on very early events (at 15 minute intervals from initiation of the response), as well as the later change in differentiation state up to 48 hours after stimulation. For comparison, the FANTOM5 dataset contains three additional unstimulated MDM samples from different donors, obtained commercially, three separate CD14+ monocyte populations, each in triplicate, and cultured monocytes stimulated for 2 hours with a range of agonists including gamma interferon (IFNγ) LPS and live salmonella. The monocytes differed in their method of isolation. One set, also purchased commercially, was clearly “activated” and expressed many inflammatory cytokines. Another set was divided into the three known monocyte sub-populations based upon relative expression of CD14 and CD16, as described in detail elsewhere [36,38]. The entire FANTOM5 dataset, including an extensive mouse CAGE-based transcriptomic dataset is available on the ZENBU browser (see below). All references in the text to the level of expression, or the temporal expression profiles of individual genes within the text can be confirmed by accessing this browser and entering the gene name.

Transcriptional landscape of the macrophage response to LPS

Previous studies of mouse macrophage response to LPS revealed sequential induction and repression of numerous transcription factors [30]. To gain an overview of the transcription regulatory cascade of human macrophage response to LPS, we first utilized the data visualisation tool Biolayout Express3D [39] to generate a pairwise correlation matrix based upon aggregated expression of the promoter activity of annotated transcription factors identified previously [34]. The sample-to-sample graph in Fig 1, based solely upon the set of transcription factors, clearly shows that the response to LPS can be visualized as a progressive and profound change in transcription factor milieu. Gene-to-gene analysis showed that this involves at least 200 distinct transcription factors. Fig 1 shows the averaged expression profiles of the transcription factor genes within selected clusters, identified by the gene-to-gene analysis, emphasizing that the transcription factor genes can be classified based upon the peak time of induction and whether or not induction was sustained. The detailed list of the transcription factor genes within all of the co-regulated clusters is provided in S1 Table. Many of these transcription factors are discussed below.

Fig 1. Transcription factor gene expression during stimulation of human monocyte derived macrophages by LPS.

A. Sample-based network of transcription factor gene expression. Gene-based expression levels for all of the annotated transcription factors in the human genome [34] were extracted from the FANTOM5 data and averaged. Only transcription factor genes where at least one value (averaged across three replicates) was ≥ 20 TPM were included in the analysis. The results were entered into BioLayout Express3D to create a sample-to-sample network layout where the nodes (spheres) represent a single time point after stimulation with LPS (averaged over three replicates). Edges (lines between nodes) represent sample-to-sample correlations in overall expression pattern of transcription factor genes of R ≥ 0.90. The time points are group and color-coded as indicated, showing the clear progressive change in the transcriptional profile with time. B. Representative expression profiles for genes expressed at the different time points. Clusters of co-regulated transcription factor genes were identified based upon gene-to-gene network analysis with BioLayout Express3D (R ≥ 0.75; MCL inflation value 2.2) as described in Methods. The X axis shows the time points (in the order 0 min, 15 min, 30 min, 45 min, 1 hour, 1 hour 20 min, 1 hour 40 min, 2 hours, 2 hours 30min, 3 hours, 3 hours 30 min, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 10 hours, 12 hours, 14 hours, 16 hours, 18 hours, 20 hours, 22 hours, 24 hours, 36 hours, 48 hours). Y axis shows the mean TPM value for the set of genes with that profile (averaged across three replicates). Note that each cluster has a characteristic temporal profile. The complete set of clusters and the names of the transcription factor genes within them is provided in S2 Table.

CAGE is essentially genome-scale 5’ RACE. Unlike microarrays, CAGE tag sequencing resolves the separate and independent utilization of alternative promoters for the same gene, and is significantly more sensitive across a large dynamic range [34]. For example, the CAGE data reveal that SERPINA1 has at least 4 promoters. Macrophages and other myeloid cells profiles by the FANTOM5 consortium selectively utilize individual promoters p2, p3 and p4 of the SERPINA1 gene, whereas p1 is liver-specific. Because it sequences only the 5’ ends, CAGE does not require normalisation for the length of the transcript, which is necessary for RNA sequencing (RNAseq) data, and is considerably more cost-effective than RNAseq. Extensive validation of the CAGE methodology has been published elsewhere, showing the strong correlation of CAGE tag peaks with tissue-specific chromatin marks and DNase hypersensitive sites, and the precise colocation of the 5’ ends of the CAGE tags with the 5’ ends of full length transcripts and promoter-associated motifs such as the TATA box [3436]

We created a pairwise correlation network of individual promoters (defined as cluster of transcription start sites, or CTSS) of all expressed genes (Fig 2). Because most genes have several alternative promoters, the same gene can appear in multiple coexpression clusters. The gene-to-gene (or more correctly, promoter-to-promoter) clusters, with graphical displays of their average expression profiles can be accessed at, and the gene names within selected clusters discussed in the text are also provided in S2 Table, together with a summary of the pattern of expression. In keeping with the regulation of transcription factors in Fig 1, the analysis revealed clusters of coexpressed promoters that have peak activity at different times during the time course. The complex pattern is shown in a network graph produced with the software Biolayout Express3D in S1 Fig. The network graph groups sets of transcripts with related temporal profiles. The positions of clusters on the graph progressively diverge from each other as sets of transcripts are induced or repressed compared to the initial state. The clusters characterized by either early or late expression profiles are the most similar to the unstimulated state. So, the network graph forms a circle. The data are presented in more conventional heat map form in Fig 2. The heat map highlights the cascade of transient regulation of gene expression with time in response to LPS, and the fact that no new steady state is reached even after 48 hours. Within the first 15–30 minutes, there was a set of genes that was rapidly induced and another set that was repressed. Thereafter, there were waves of transient induction of thousands of promoters, each of which peaks at different time points, ranging from 30–45 mins until 36–48 hours. In addition to those clusters that were induced transiently, there were others that were induced in a sustained manner, so that at the 48 hour time point, the transcriptional state of the stimulated cells was still changing.

Fig 2. Transcript expression in human macrophages exposed to LPS.

Heatmaps of transcript expression in human macrophages exposed to LPS depicting change from baseline, normalized to the maximum expression of each transcript. Red = increase from baseline, green = decrease from baseline. Only nodes included in a coexpression cluster are shown here. (A) All clustered transcripts in LPS-treated human monocyte-derived macrophages. Clusters containing more than 50 transcripts are labeled. (B) Transcripts contained within the clusters shown in (A) that initiate near a putative Crohn’s disease-associated SNP (p<10e-6, window size to transcription start site = 2000 bases).

These coexpressed promoter clusters have been numbered in order based upon the number of annotated genes they contain. The largest clusters, are slowly-repressed (Cluster 0) or slowly-induced (Clusters 1, 2, 3, 4) peaking at different times late in the time course. Cluster 0 contains many known cell cycle genes, and presumably reflects in part the cessation of cell division in a subset of cells growing in CSF1. Clusters 1–4 are all enriched for innate immune genes. Together, these slow-response clusters paint a picture of the progressive differentiation of the cells, which parallels the progressive change in transcription factor milieu shown in Fig 1.

Induction of immediate early genes by LPS

Immediate early genes are a set of transcriptional regulators induced in many different cell types as they transition from one cellular state to another. Cluster 5 (S1 Fig, Fig 2, S2 Table) contains promoters with detectable increased expression within 15 mins, peaking at 45 mins, and declining rapidly thereafter. This cluster of the earliest inducible promoters contains genes encoding many immediate early transcription factors (EGR1, EGR2, EGR3, FOS, FOSB, JUN) [37]. One of the most inducible, and the most highly-expressed, transcriptional regulators in the LPS response is NFKBIZ, which encodes IκB-ζ. NFKBIZ has two promoters, both of which were LPS-inducible. Induction of IκB-ζ is likely to be critical for subsequent events in the transcriptional cascade [40]. In mouse macrophages, it interacts with AKIRIN2, to bridge NFκB and the chromatin remodeling SWI/SNF complex on proinflammatory genes including IL6 and IL12B in macrophages [41]. The early-inducible transcription factors included genes encoding factors more commonly associated with lymphocyte functions; NFIL3, IKZF1, KLF2 and PRDM1.

Two smaller clusters, 30 and 42 (S2 Table), were induced marginally later, peaking around 1hr 20 minutes. This group contained IFNB1, a target of NFκB signaling which is known to act in an autocrine manner to induce the interferon target genes (see below). Other inducible feed-forward activators include the gene encoding the adaptor protein TRAF1, which interacts directly with signaling molecule MAP3K5 (also known as ASK1) which in turn is involved in LPS signaling [42] and the gene encoding EGR4, which along with EGR3, can interact with NFκB and probably contribute to activation of NFκB target genes [43].

The inflammatory cytokine clusters

Genes for the well-known inflammatory cytokines (IL1B, TNF, IL6, IL10) and chemokines that are the hallmark of the response to LPS form parts of Clusters 6 and 7 (S2 Table). A previously unreported member of this group was TNFSF9, or CD137L, which was massively-induced by LPS and has been proposed as a growth and differentiation factor for myeloid cells [44]. Expression of the proinflammatory genes differed from immediate early response genes in terms of the duration of the expression. For example, TNF mRNA detected by CAGE was induced 10-fold within 15 mins, but was still somewhat elevated after 5–6 hours. The small MAF transcription factor gene, MAFF, was strongly induced alongside the inflammatory cytokines. The other members of this family (MAFG, MAFK) were also induced by LPS, but later in the time course. The small MAF transcription factors form heterodimers with many other transcription factors, notably the NFE2 family (NFE2, NFE2L1, NFE2L2, NFE2L3 genes) [45] all of which are induced by LPS later in the time course. Hence, induction of MAFF is likely to be part of a feed forward cascade.

Interferon-response genes

Interferons (IFNs) and their targets are a key part of innate immune defence mechanisms, and chronic over-expression of IFN target genes is a feature of many inflammatory diseases [4649]. The immediate early induction of IFNB1 in response to LPS initiates an autocrine loop that leads in turn to downstream induction of the targets of IFN signaling [27,33,47,50]. Many known IFN targets were induced by LPS and formed parts of coexpressed clusters, but distinct clusters containing these genes followed distinct temporal profiles. Based upon a search of the Interferome database [51], a total of 123 genes within Clusters 2 and 3 (the large late-response clusters noted above) have previously been shown to respond directly to IFN signaling in human monocytes or monocyte-derived macrophages. [51]. Several smaller clusters of known IFN-response genes were induced significantly earlier. One, Cluster 10 (S2 Table), peaked at 2–3 hours, and included the IFIT2, 3 and 5 genes that derive from a single genomic region as well as genes for known virus/IFN-induced chemokines, CCL3 and 4 and CXCL10 [52]. Other clusters showing an early peak in expression contained genes for the known inducible feedback regulators of IFN-induced JAK-STAT signaling, SOCS1 and SOCS3 [53,54]. Cluster 11 peaked around 1 hour later than Cluster 10, and Cluster 17 peaked slightly later again, and declined more slowly, similar to Clusters 33 and 49. [53,54].

The diversity of responses of IFN-inducible genes probably reflects the complex regulation of the transcription factors that bind to interferon-response elements (IRFs). The canonical MyD88-independent, TRIF/TRAM (TICAM1/2)-dependent pathway of IFN regulation determined from studies of mice involves the interactions of TRAF3 and the kinase TBK1 to phosphorylate IRF3 [55]. However, this pathway is not conserved in humans [21]. In the human MDM, IRF3 mRNA was almost undetectable, and both TRAF3 and TBK1 were very low. Genes encoding the adaptor molecule TICAM1, and the downstream RIK kinases (RIPK1, RIPK2) were induced by LPS, peaking after 2–3 hours with Cluster 13.

The most-rapidly-induced IRF family member in the LPS-stimulated human MDM was IRF1. This observation is consistent with recent direct evidence of LPS-induced IRF1 binding to promoters and enhancers of inflammatory cytokine genes (TNF, IL6, IL12B) in human MDM, and its role in the priming response to IFNγ [56]. IRF1 mRNA was induced detectably within 15 mins, peaked around 2 hours and was sustained at greatly elevated levels for the remainder of the time course. IRF4 was part of cluster 17, maximally induced at 3–4 hours and declining thereafter. IRF7 was induced marginally later and remained elevated throughout, IRF8 was induced transiently, peaking at 3 hours and gone by 12 hours, IRF2 and IRF9 were induced in late response clusters. Other components of the IFN signaling pathway, notably STAT1, STAT2 and STAT4, and JAK2 and JAK3 were also induced transiently. It is likely that each of these factors contributes specifically to transcription regulation of the clusters in which they are coexpressed possibly by interacting with other induced transcription factors. For example, IRF8 is known to bind uniquely to a specific subset of IRF recognition sequences [29].

Inducible autocrine regulatory loops in the LPS response

Members of the FANTOM Consortium have produced a cellular interactome linking coexpressed ligands and receptors [57]. The progressive response of MDM to LPS emphasized in Figs 1 and 2 and S1 and S2 Tables is based upon part upon numerous autocrine loops in which induction of ligands is followed by induction of the receptors. The most obvious is the response to endogenous IFNB1; the genes for receptors for type 1 interferon, IFNAR1 and IFNAR2, were expressed in MDM and further induced starting around 5–6 hours. TNF has also been shown to initiate an autocrine loop in mouse macrophages responding to LPS, so that induction of some late response genes is ablated in TNF knockout mice [58]. Both TNF receptor genes TNFR1 (TNFRSF1A) and TNFR2 (TNFRSF1B) were induced later in the response to LPS. Aside from TNF, five other members of the TNF superfamily, TNFSF15, TNFSF8 (encoding CD30L), TNFSF10 (TRAIL), TNFSF14 (LIGHT) and TNFSF9 (CD137L) were each induced ahead of induction of their respective receptors TNFRSF6B, TNFRSF8, TNFRSF10A and 10B and TNFRSF9 (CD137). The rapid induction of prostanoid synthesis is a hallmark of LPS signaling, associated with the early induction of PTGS2 (COX2)[26]. PGE receptors (PTGER3,4) and the prostacyclin receptor (PTGIR) were strongly induced at later time points in the response. The gene for the vitamin D3-generating enzyme, CYP27B1, was strongly-induced by LPS in MDM and the vitamin D3 receptor gene (VDR) was induced later in the response to LPS.

Another set of potential autocrine loops was generated through the induction by LPS of CSF1, GMCSF (CSF2) and GCSF (CSF3), commencing around 2–3 hours after LPS treatment. Their receptors, CSF1R and CSF3R, were expressed constitutively and CSF2RB was strongly-induced by LPS around 4–5 hours. The cytokine IL15 was induced substantially in MDM by 2 hours, whilst IL15RA was induced from undetectable levels starting at 2–3 hours. Genes for both IL27 subunits (IL27 and EBI3) were strongly induced, and MDM expressed IL27RA constitutively. Finally, many of the inducible CC chemokines probably produce autocrine signals through CC receptors; some were expressed constitutively, and some (e.g. CCR1, CCRL2, CCR5, CCR7) were themselves LPS-inducible with distinct individual time courses.

Inducible feedback inhibition of the LPS response

One striking feature of the response to LPS is the transience of each successive wave of inducible gene expression (S1 Fig, Fig 2). Each wave probably contains the seeds of its own destruction, inducible repressors that promote decay of the signal and degradation of the induced transcripts and proteins. For example, the set of induced autocrine loops elicited by LPS includes progressive accumulation of the repressive cytokine IL10, alongside its receptor, IL10RB. The LPS time course data supports many known and suggests several novel feedback loops. Amongst the earliest inducible transcription factors in Cluster 5, PRDM1 (also known as BLIMP1) was the most highly-expressed. In mice, the PRDM1 protein has been attributed functions as a transcriptional repressor of cytokine induction [59]. IKZF1 (ikaros) has also been attributed roles in feedback repression of LPS signaling in mice [60] and was associated with a trans-acting expression quantitative trait locus (eQTL) in a large survey of gene expression in human peripheral blood leukocytes [61]. NR4A1 (encoded by NR4A1) has also been implicated as a transcriptional repressor of NFκB signaling in mouse macrophages [62]. Alongside these inducible feedback repressors of transcription, Cluster 5 also contains promoters for feedback regulators of MAP kinase signaling, DUSP1 and DUSP2 [63].

All of the inhibitors of NFκB (NFKBIA, NFKBIB, NFKBIE, NFKBIZ, BCL3) were rapidly induced by LPS, as were genes for molecules such as TNFAIP3 (A20) which mediates feedback inhibition of NFκB through regulated ubiquitination. OTUD1 encodes a novel inducible deubiquitinase (DUB) enzyme, in the same structural class as TNFAIP3 but not previously implicated in control of the LPS response. Other induced genes for feedback repressors of the initial signaling cascade include the protein tyrosine kinase LYN, multiple members of the TRIM family of E3 ubiquitin ligases which promote degradation of signaling molecules (TRIM5, TRIM10, TRIM25, TRIM35, TRIM36, TRIM38), the TRAF inhibitor TANK, multiple inhibitor microRNAs (notably miR146A, miR155, miR21, miR3648, miR4741), IER3, each of the GADD45 family members, which amongst many other targets, probably inhibit p38 MAP kinases [64], the caspase inhibitor TNFAIP8 and the transcription factor ATF3 [28]. PPP1R15A (also known as GADD34), a regulator of protein phosphatase 1 is identified as a feedback regulator of TLR-induced phosphorylation of TAK1 [65]. One gene of unknown function that was clearly induced was ZBTB10 (RINZF), most likely also encoding a transcriptional repressor [66].

Many proinflammatory genes encode short-lived mRNAs with AU-rich elements in their 3’UTR, subject to degradation by the ZFP36 (tristetraprolin) gene product [62,67]. Two related genes, ZFP36L1 and ZFP36L2, have also been implicated in control of mRNA stability in LPS-stimulated mouse macrophages [68]. However, by contrast to mouse macrophages, in human MDM, ZFP36 is repressed by CSF1 (compared to high levels in monocytes), and none of the ZFP36 family genes was highly-inducible by LPS. Instead, ZC3H12A, another gene encoding a novel ribonuclease that controls stability of other inflammatory cytokine mRNAs, notably IL6 and IL12p40 [69], was highly-induced by LPS, within a similar time course to TNF, but remained elevated even after 48 hours. Interestingly, PARP14, encoding a member of a family of intracellular proteins that generate ADP-ribose posttranslational adducts, was strongly induced by LPS, commencing around 2 hours, and peaking at 12 hours (within Cluster 3). PARP14 forms a complex with ZFP36 and the AU-rich element in the mRNA 3' untranslated region of the tissue factor (TF) gene [70]. The neighbouring PARP15 and PARP9 genes were also induced by LPS. PARP9 shares a shares bidirectional promoter with the E3 ubiquitin ligase gene DTX3L, and the two proteins interact to control, amongst other things, the function and expression of IRF1 [71]. Other members of the PARP family, PARP7, PARP10 and PARP12, which control protein translation [72] and/or feedback inhibit NFκB signaling [73] were also LPS-inducible at later time points. Based upon the function in mice, the inducible expression of the signaling molecule gene, IRAK2 [74] and the regulator of intracellular trafficking, optineurin [75] probably contributes positively to the sustained induction of inflammatory genes.

HCAR2 and HCAR3 (also known as GPR109A and GPR109B) are neighbouring duplicated genes in the human genome, and encode receptors for butyrate and niacin. Both genes were induced in parallel by LPS. In mice, GPR109A has been associated with feedback regulation of the LPS response and suppression of macrophage reactivity to gut luminal contents [76]. PELI1 (Pellino1) encodes an E3 ubiquitin ligase that is required for TRIF-dependent signaling from TLR3 and TLR4 [77] and probably enables subsequent induction of the IFN target genes. XBP1 lies downstream of the gene for the ER stress sensor kinase, IRE1alpha (ERN1), and in mice XBP1 was found to be required for optimal and sustained cytokine production by macrophages responding to LPS [78]. The gene for another target of ER stress, PPP1R15B [79] was induced in parallel with XBP1. MSC, encoding musculin or activated B cell factor-1 (ABF-1) [80], a repressor of bHLH transcription factors in muscle and B cells which has not previously been reported in macrophages, was also within this cluster.

One novel finding was the marked induction of PDSS2. PDSS2 encodes prenyl (decaprenyl) diphosphate synthase, subunit 1, an enzyme involved in the synthesis of coenzymeQ (CoQ). It is not known whether the activity of this enzyme limits flux through the pathway, but heterozygous mutation in another gene in the CoQ pathway, Mclk1 (Cog7) in mice, produced increased levels of TNF in macrophages, and hypersensitivity to LPS [81]. Hence, this gene probably also contributes to feedback inhibition of the LPS response.

The autocrine interferon response induces its own set of feedback regulators, notably the obvious suppressors of cytokine signaling (SOCS1, SOCS3 and SOCS6) which were each induced with distinct time courses (S2 Table). PLEKHF2 is linked to control of interferon production [82]. LPS-inducible GPR183 (also known as EBI2, or EB virus induced 2) is a feedback regulator of type 1 interferon pathways [83]. The ligand for EBI2 is 7α,25-dihydrocholesterol and the enzyme that synthesises it, CYP7B1, was also induced strongly by LPS.

Finally, the induction of the metal ion responsive transcription factor, MT1, is linked to the recently described role of zinc in feedback regulation of NFκB activation and inflammatory transcription [84]. Indeed, genes for the zinc transporters, SLC39A8 (ZIP8), SLC39A14 (ZIP14) and SLC30A1 (ZNC1), were each strongly induced by LPS, commencing from around 4–5 hours after induction, and rising continuously.

The activation of enhancer-associated transcription in response to LPS precedes target gene activation

LPS stimulation of mouse macrophages was reported to induce transcription from some 3000 enhancer loci, preceding modification of histone methylation [31,85]. eRNAs are relatively unstable, and are degraded by the RNA-exosome complex [86]. However, CAGE enables their quantitative detection, and the activation of eRNA transcription can be correlated with subsequent activation of promoter activity in putative target genes in the chromosomal vicinity [35]. Many enhancers described by the FANTOM5 consortium came from stimulated monocytes. The genes encoding limiting exosome components, EXOSC3 and EXOSC10 were low in MDM, and repressed transiently between 1 and 2 hours after LPS stimulation. CAGE tags derived from the genes encoding several other exosome components, EXOSC1, EXOSC2, EXOSC5 and EXOSC7, were almost undetectable in monocytes in culture. The relative lack of the exosome complex may facilitate the detection of active enhancers in monocytes and macrophages.

S2 Fig shows profiles of enhancer activation at a selected subset of inducible genes in which there was robust activation of transcription that clearly preceded the appearance of transcripts from associated promoters. In every case, the enhancers that have been identified in the time series are robustly supported by evidence of inducible bi-directional transcription in the LPS-stimulated monocytes. The data strongly support the view that enhancer transcription precedes activation of target promoters [35].

In many of these cases there were numerous individual regulated enhancers apparently associated with a single inducible promoter. For example, the IL6 locus has recently been dissected in detail in human monocyte-derived macrophages responding to LPS [56], showing the roles of STAT1 and IRF1 in establishing permissive chromatin architecture in the vicinity of the gene, and identifying sites up to 50kb upstream of the transcription start site. The FANTOM5 data for the LPS induction series revealed LPS-inducible bidirectional transcription up to 150 kb upstream of IL6. Fig 3 shows a time course of detection of transcription of each of these elements; nine separate elements had detectable activity in LPS-stimulated MDM in advance of the peak of IL6 promoter activity. These elements were more readily detectable in LPS-stimulated monocytes, where almost all of the 25 detected enhancers had significant transcriptional activity.

Fig 3. The time course of activation of enhancers and promoter at the IL6 locus.

The core panel shows a genome browser view of the IL6 locus with the locations of FANTOM5 enhancers. The upper right panel shows the time course of induction of IL6 mRNA, detected by CAGE, which peaks around 3–4 hours and declines by 12 hours. The lower panels show the transient activity of the enhancers indicated, the majority of which peak around 1–2 hours and decline rapidly. Data are expressed at TPM, and are the average of the three replicates.

The chemokine genes CCL3 and CCL4 are coregulated by LPS. Here again, there was bidirectional promoter activity associated with enhancers outside, and between, the two inducible genes, more readily detected in the LPS-stimulated human monocytes (Fig 4). Two enhancers downstream of CCL4 (to the right of the panel) were induced ahead of coordinated induction of CCL3 and CCL4 promoter activity. By contrast, the neighbouring CCL18 gene was much more slowly induced, and four enhancers appeared to show more prolonged activation. The entire region shows evidence of bidirectional promoter activity in LPS-stimulated monocytes. Finally, the region surrounding the TNFAIP3 locus contained an array of enhancers extending over 400kb, at least 25 of which had detectable induction of transcription in advance of the activation of the promoter (Fig 5).

Fig 4. The time course of activation of enhancers and promoters at the CCL3/CLL4/CCL18 locus.

The core panel shows a genome browser view of the locus with the locations of FANTOM5 enhancers. The upper panel show the time course of induction of each of the mRNAs, detected by CAGE. Whereas CCL3 and CCL4 are coordinately-regulated, CCL18 follows a much slower time course and is still rising at 48 hours. The lower panels show the activity of the enhancers indicated. The lowest track shows the histograms of CAGE tags mapped to the region, with colours indicating direction of transcription; green to the right and purple to the left. Note that the entire regions shows evidence of bidirectional transcription initiation. Data are expressed as TPM, and are the average of the three replicates.

Fig 5. The time course of activation of enhancers and promoter at the TNFAIP3 locus.

The core panel shows a genome browser view of the TNFAIP3 locus with the locations of FANTOM5 enhancers. The lower right panel (p1@TNFAIP3) shows the time course of induction of TNFAIP3 mRNA, detected by CAGE, which peaks around 2 hours and declines to a new, elevated steady state by 8 hours. Other panels show the transient activity of the enhancers indicated, the majority of which peak around 1–2 hours and decline rapidly. Panel at bottom right shows the activity of the enhancer containing the SNP originally associated with CD susceptibility, 185kb upstream of the TNFAIP3 locus [2]. Data are expressed as TPM, and are the average of the three replicates.

A recent study identified around 120 long non-coding RNA (lncRNA) that were induced by LPS in human monocytes based upon RNAseq [87]. We reexamined these loci individually in the FANTOM5 data. All but one of the top 20 most-inducible candidate lncRNA were contained within large clusters of enhancers (so-called super-enhancers) identified by the FANTOM5 data, and were actually associated with bidirectional transcription in LPS-stimulated monocytes. The potential target loci identified included IL6 and TNFAIP3 (discussed above), as well as IDO1, miR155, ACSL1, IRF2, HS3ST3B1, TNFSF8, DDX58, CD38 and SLAMF7. The only exception is NONCO3094, which is driven by a strongly-LPS-induced promoter that is antisense to IL7 (which is not itself expressed in monocytes/macrophages). Based upon the extensive linked CTSS associated with enhancers in these regions, there is some question as to whether the proposed lncRNA could actually be artificially concatenated over-lapping short RNAs.

Global analysis of the transcriptional cascade from motif activity response analysis

When sets of co-regulated genes share a particular motif in their promoters, we can infer that DNA binding protein(s) that recognize the motif regulate that set of genes. We utilized Motif Activity Response Analysis (MARA) [88] to identify transcription factor binding motifs associated with the response of the MDM to LPS. Fig 6 shows the motif activities for the most active motifs. There was a temporal separation of motif activities associated with enhancers (red lines in Fig 6), which peaked before the activation of promoters through the same motifs (blue lines in Fig 6). The exception to this pattern was the macrophage-specific transcription factor, SPI1 (PU.1), which showed early motif activity for enhancers, but no subsequent activation on promoters. This finding is consistent with the proposed role for PU.1 as a pioneer transcription factor that defines “latent enhancers” that are subsequently bound by other factors in response to agents such as LPS [32,89].

Fig 6. Motif activity response analysis (MARA) of the response to LPS

MARA analysis of promoters and enhancers was carried out as described in Materials and Methods. MARA averages the expression of all promoters (blue) and enhancers (red) that contain the consensus transcription factor binding motif. Activities are plotted as Z-scores (averages of activity divided by standard deviation) in each time point, with error bars representing the standard deviation of Z-scores.

The earliest response detected by MARA was associated with the Fos/Jun, or AP1 motif, consistent also with these factors being amongst the earliest targets of LPS activation (see above). Subsequently, the increased activity of the NFKB/REL/RELA motif peaked around 2 hours, but declined only slowly (Fig 6). An initial burst of activity probably derives from the translocation of a preexisting complex subsequent to activation by IKB kinases [27]. Subsequently, from around 2 hours, genes encoding all of the members of the NFκB family, REL (c-rel), RELA (p65), RELB, NFKB1 and NFKB2, were induced and presumably contributed to both replacement of the cytoplasmic pool of NFκB and transcriptional regulation of a distinct late-response set of target promoters containing the NFKB/REL/RELA motif. The progressive increase in motif activity of IRF1/2, IRF7 and STAT2,4,6 was initiated later in the time course, and is entirely consistent with the induction of IFNB1, and of the IRFs themselves, as discussed above.

The MARA analysis reinforces the likely importance of the stress response in LPS action. NFE2 (also known as NRF) sites were previously identified as active motifs in LPS-stimulated mouse macrophages [30]. The stress response factors, NFE2L1 (NRF1) and NFE2L2 (NRF2) are the likely occupiers of the NRF sites, being expressed constitutively in MDM but further inducible by LPS. The motif annotated as NFATC (1…3) is most likely bound by NFATC1, which was induced by LPS, peaking at 2 hours. NFATC1 has two promoters encoding alternative 5’ exons in both mouse and human, as discussed in a recent review [90]. The more distal promoter was stimulated by LPS. NFATC1 is itself a downstream target of both AP1 and NFκB factors [90].

MARA also implies a function for the MEF2 family. MEF2A, MEF2C and MEF2D, which recognise the MEF2 motif, were all expressed constitutively in MDM, as described by others [91] and the latter two were down-regulated by LPS late in the time course. Like NFκB these proteins undergo regulated nuclear translocation [92]. MEF2 most likely underlies the motif activity attributed to the serum response factor SRF (another MADS box protein). Although there have been reports of the activity of SRF in mouse macrophages [93] SRF was expressed at barely-detectable levels in MDM, similar to the Ets components of the serum-response element complex (ELK1, ELK3, ELK4).

The absence of detectable motif activity based upon MARA does not constitute evidence that any particular factor does not contribute to transcriptional regulation. The GGAA/T core motif bound by ETS family factors is relatively uninformative, and overlaps with the purine-rich PU.1 motif found in many myeloid-specific promoters [16]. Genes encoding ETS factors with related binding motifs, including ETS2, ELF4, FLI1, ETV3, ETV5 and ETV7 (TEL) were each up-regulated by LPS with distinct kinetics. Sweet et al. reported previously that ETS2 is not only induced, but is phosphorylated on the pointed domain, required for effective trans-activation, in response to LPS [94].

MARA is also rather insensitive for the detection of activity of factors that bind GC-rich motifs within CpG island promoters. For example, the “KLF4” motif (CaCaCCC) showed little change in activity across the LPS time course. However, as evident from the clusters in Fig 1, multiple members of the KLF family were regulated in response to LPS. KLF2 was one of the immediate early genes. KLF7 has two promoters, one induced by 2 hours, another induced later in the time course. KLF5 was induced even later (around 3–4 hours, cluster 43). KLF3 also has two promoters, one induced at 7–8 hours, one at 20 hours. Each of these KLF family members could have a distinct function and distinct target genes. In mice, KLF4 was suggested to be a feedback regulator, and to polarise macrophages towards M2 phenotype [95]. In humans, KLF4 was constitutively expressed in monocytes and almost completely repressed in MDM. It was transiently, but relatively weakly-induced by LPS. KLF6 did not cluster in the coexpression analysis, having an idiosyncratic expression pattern. It was induced rapidly, peaking by around 80 mins in all samples, but unlike most early response genes, it declined slowly and remained somewhat elevated even after 48 hours. KLF6 is in a rather gene poor-region of the genome, and is surrounded by numerous enhancers identified in the FANTOM5 dataset. Of these, at least 4 were detectably and transiently-induced ahead of the peak of accumulation of KLF6. KLF6 is required for optimal LPS-induced gene expression in mice [96].

Macrophage-specific gene expression and inflammatory bowel disease susceptibility

Based on the observations described above, we hypothesised that IBD-associated genes, of which NOD2 is an archetype, are likely to be specifically-inducible in monocytes in response to inflammatory stimuli AND down-regulated during differentiation to macrophages. In order to test this hypothesis, we quantified the evidence for disease association in the regions surrounding promoters meeting these criteria. 2413 promoters exhibited this expression pattern, out of a total of 201,801 promoters in the FANTOM5 dataset. After correcting for linkage disequilibrium, regional co-regulation, and the genomic distribution of variants (see Methods), we found that variants associated with Crohn’s disease by GWA were very strongly enriched in the regions surrounding this set of promoters (Fig 7). Less enrichment was seen for genes putatively associated with ulcerative colitis, and no significant enrichment was observed for control sets of loci/SNPs associated by GWA with the other traits (rheumatoid arthritis (RA), LDL cholesterol, and height). The association between CD loci and regulated transcription in macrophages can also be visualized by examining the expression profiles of promoters that lie within 2kb of a putative Crohn’s disease-associated SNP (p<10e-6). This subset is displayed in Fig 2B.

Fig 7. Enrichment of macrophage-expressed or regulated genes in genomic regions associated with IBD susceptibility

Each panel shows the enrichment of GWAS-associated variants in vicinity of genes meeting the expression criteria (specifically inducible in monocytes AND down regulated during differentiation to macrophages) for each of the traits/diseases shown. For 1000 bins spanning a region of 1Mb above and below all TSS meeting expression criteria, observed:expected ratios were calculated as the ratio of the absolute count of variants with p<1e06 for association with each trait, to the absolute count of all SNPs genotyped in the same study. p-values for enrichment were calculated for the whole set using PASCAL (see Methods).

To enable visualization of the results of the CAGE data with the location of disease-associated SNPs, we created a genome viewer that displays locations of FANTOM5 promoters and enhancers together with the P values for association with CD and UC and other traits (; available for review; this resource is currently being transferred to a permanent server, which will be available at the time of publication. We also provide a direct link to visualize the association data on the ZENBU browser (see Methods).

The most extensive meta-analysis of IBD GWA data to date [4] identified 163 loci that met genome-wide significance thresholds for CD, UC or IBD. A subsequent study identified an additional 38 loci that underlie shared genetic risk across different ethnicities [5]. Most dissections of candidate genes in GWAS focus on the nearest gene to the peak of association, or the genes within the genomic interval of association, and assume that actual causal variants are in high LD with the SNP(s). The genomic intervals are not always precise, and of course they may contain enhancers that alter transcription of genes outside that interval.

The criteria we tested (specifically inducible in monocytes; down-regulated during differentiation to macrophages) identify plausible alternative candidates in many known IBD loci. For example, the MUC19 gene on Chr12 is annotated by Jostins et al. [4] as the only gene with the IBD GWA candidate interval defined by SNP rs11564258. However, Lacour et al. [97] identified an extended haplotype that includes the neighbouring LRRK2 gene, a clear candidate for which there is already functional evidence of a role in IBD [98]. LRRK2 is identified by our criteria, and shares transcriptional regulation with NOD2, being expression and further induced by IFNγ in monocytes and ablated by CSF1 in monocyte-derived macrophages.

S3 Table provides a detailed manual curation of genes within >200 genomic intervals from published IBD datasets, inspected using the viewer described above, and gene expression derived from the FANTOM5 data. All but 12 of the intervals within the boundaries of significant GWA peaks for IBD contained promoters/genes for which expression was strongly monocyte-macrophage-enriched and/or regulated by CSF1, LPS or both. For some regions identified by GWA, no candidate gene had been proposed but the CAGE data implicate a novel candidate that was expressed and/or regulated specifically in monocytes or macrophages or both. For others, our analysis identified alternative candidates to those that are currently emphasized. For example, NOD2 was regulated in parallel with two flanking genes, SNX20 and CYLD (cylindromatosis). These three genes are close to monocyte-specific, IFNγ -inducible enhancers, detected by CAGE, located in the interval between NOD2 and SNX20. All three genes were expressed highly in monocytes and almost absent from MDM grown in CSF1. SNX20 has been implicated in control of P-selectin location, and might therefore also contribute to extravasation of monocytes [99]. CYLD encodes a deubiquitinating enzyme that exerts feedback control on both NFκB and MAP kinase pathways and has itself been implicated in inflammation control [100]. So, even in this archetypal CD susceptibility locus, NOD2, the gene that has been most studied, is not necessarily the best or only candidate, notwithstanding evidence of protein-coding variation.

Another well-studied candidate IBD susceptibility gene, ATG16L1, was expressed ubiquitously in the FANTOM5 atlas, providing no support for a gut-specific or inflammatory pathology. By contrast, the neighbouring gene, INPPP5D (inositol polyphosphate 5-phosphatase, also known as SHIP1), shares tight co-regulation with NOD2, being high in monocytes, induced by IFNγ, down-regulated in MDM, and further ablated by LPS. Mice deficient in SHIP1 have chronic inflammatory disease, including a Crohn’s-like colitis [101]. A recent detailed analysis of the ATG16L1 association with IBD revealed that the commonly-studied SNP is in linkage disequibilibrium with SNPs across an extended haplotype and additional variants could implicate any region of the locus [102]. The CAGE data also identified at least four monocyte-specific, regulated enhancers in the 40kb interval between ATG16L1 and INPPP5D, including one only 2.5 kb upstream of the ATG16L1 locus (Chr2:234156397). Immediately downstream of ATG16L1, another candidate within the genomic interval identified by linage to CD is DGKD, diacylglycerol kinase delta, also expressed highly in monocytes and ablated completely in MDM. The hypothetical involvement of dysregulated autophagy in IBD [4], is based largely on association with ATG16L1 and is cast into doubt if this gene is not the only, or even the most likely, candidate gene in the region.

The association of IBD susceptibility with autophagy also rests in part upon genetic linkage to IRGM, which has been linked in turn to the functions of the large family of inducible GTPases in the mouse. The original study of human IRGM [103] refers to the gene as an ortholog of mouse Irgm1, but the location of human IRGM in the genome is actually not syntenic with mouse ( Humans have lost the inducible GTPase family as a mechanism of host defense, and IRGM was shown not to be interferon-inducible in humans [104]. The evidence that the human IRGM locus actually encodes an expressed transcript or a functional protein that is detected in any cell type is equivocal [105]. Strongly indicating that it does not, the FANTOM5 CAGE data detects no expression of IRGM mRNA in any cell population or tissue in any state of activation. The IRGM SNP associated with IBD susceptibility is in strict LD with a 20kb deletion polymorphism, 2.5kb upstream of the putative TSS of IRGM [106]. One alternative explanation for the association is that the IRGM region contains distal enhancers for macrophage-expressed genes. Chromatin immunoprecipitation sequencing (ChIPseq) data derived from monocytes and macrophages suggests this is the case, with clear peaks for binding of PU.1 and enhancer marks [20] corresponding to a cluster of enhancers identified by CAGE, which can be visualised on the ZENBU browser. Amongst the neighbouring genes, SMIM3, encodes a small integral membrane protein about which little is known, but it is up-regulated in MDM and further induced by LPS. The most likely candidate is TNIP1, encoding a regulator of NFKB signalling, which has been implicated in genetic susceptibility to other inflammatory diseases [107].

The association of the NKX2.3 locus on chromosome 10 with IBD susceptibility has been widely-reported and replicated, but no function of the gene product associated with inflammation has been described. Within the same region, SLC25A28 encodes a mitochondrial iron transporter (also known as mitoferrin 2) which showed the same pattern of regulation as NOD2, down-regulated in MDM compared to monocytes, but very strongly-induced by LPS. Similarly, no mechanistic link has been made between the SNP associated with the IL23R locus and IBD susceptibility, but there has been considerable emphasis in the literature on the central role of Th17 T cells, activated by IL23, in disease pathogenesis [4]. There is an alternative myeloid-expressed candidate gene, MIER, within the linked candidate interval surrounding IL23R.

Previous data have revealed that promoters and enhancers identified by CAGE are strongly enriched for informative SNPs in GWAS [35]. One specific example is the IL12B locus, where the SNP rs6871626, associated with CD susceptibility, is located within one of a cluster of LPS-inducible upstream enhancers. Interestingly, susceptibility to tuberculosis in an African cohort has been associated with a separate SNP cluster 3’ of the IL12B locus and of the neighbouring UBLCP1 gene ([108]). The UBLCP1 gene is expressed ubiquitously, but these remote enhancers were activated by LPS in both monocytes and MDM. Similarly, Jiang et al [109] identified multiple allelic variants in the PTGER4 locus on chromosome 5, that associated independently with CD. The FANTOM5 data revealed around 50 LPS-inducible enhancers in the 0.5Mb upstream of PTGER4. Confirming this pattern, the subset of CTSS within 2kb of a putative CD-associated promoter has a distinct pattern in the LPS time course data.

Prior to the adoption of population GWAS, family-based studies identified six IBD loci with very high relative risks amongst related individuals. IBD1 contained NOD2, and IBD3, the well-known association with the complex HLA locus. IBD5, located around chromosome 5q31, was located to a missense mutation in the organic cation transporter, SLC22A4 [110] and a promoter variant in the neighbouring SLC22A5 gene. The combined haplotype conferred a 7.5 fold risk of Crohn’s disease [111]. Consistent with our hypothesis, SLC22A4 was highly-expressed in CD14+ monocytes, and completely down-regulated by CSF1. The IBD6 locus was located on Chromosome 19p13, by analysis of patients/families that did not possess NOD2 variants [112]. Subsequent analysis revealed multiple variants associated with the linked EMR1, EMR2, EMR3 and CD97 cluster of genes that contributed to the overall IBD susceptibility. All four of these genes were expressed at high levels in monocytes, and strongly down-regulated by CSF1. The strong expression of EMR1 contradicts previous report claimed that human EMR1 is expressed only in eosinophils [113]. Emr1 in mice encodes the F4/80 antigen, widely used as a macrophage marker [113]. The expression of EMR1 in humans is clearly different in mice, where the mRNA and protein are actually induced by CSF1 and are retained on tissue macrophages. However the phenotype of a knockout of the gene in mice implies a function in oral and peripheral tolerance [114].

To further explore candidate loci, we considered one individual component of the criteria described above that is central to our hypothesis: the large set of promoters that is expressed in freshly-isolated blood monocytes (excluding those that were isolated by adherence and culture in vitro) and either up or down-regulated at least 5-fold in monocyte-derived macrophages grown in CSF1. These promoters and their expression values in all samples analysed are shown in S4 Table; in many cases we identified multiple promoters from the same locus, as well as distal enhancers that have not been ascribed the gene name, but may be connected based upon their apparent strict coexpression [35]. Excluding existing candidate genes such as NOD2/SNX20, amongst the promoters down-regulated by CSF1 in MDM, and associated with specific gene names, at least 88 genes were identified as functional candidates lying within 200kb intervals with a peak p value of at least 10−6. These are annotated in S4 Table. Amongst these new candidates, several transcription factors (JUN, FOS, FOSL2, JUND, ETS1, ETS2, NFKB1, NFKBIZ, NFKBIA, CREM, SMAD3, BATF3, NFAT5, NR1D1, RARA, NFACTC4, FOXP1, PRDM1, NFIL3, KLF3) are of particular interest since they are likely to produce trans-acting impacts. Note that CSF1 strongly down-regulates many of the non-MHC genes within the HLA region on chromosome 6 (and to a lesser extent, most Class II MHC (HLA-D) transcripts) providing an alternative explanation for some of the well-documented HLA association with CD. Amongst the CSF1-inducible genes annotated in S4 Table there were a further 46 candidates. This set did not include any transcription factors, and in the large majority of cases, the association was specific to either CD or UC. In passing, we noted that the CSF1-inducible set separately identifies many genes, including CSF1 itself, with loci associated with lipid traits (e.g. LDL cholesterol), and as previously noted [115], CSF1 induced many genes associated with lipid metabolism. The IBD loci identified by Jostins et al. [4] do not include any on the X chromosome, which is consistently under-represented in GWAS [116]. Nevertheless, there is evidence for X-linked IBD susceptibility [117]. Amongst the genes on the X chromosome, TLR8 is strongly monocyte-specific, and is part of an extended haplotype associated with IBD [118]. ARHGEF6, also linked to CD [116], is also strongly monocyte-enriched and expression was abolished in MDM.

In summary, on the sole basis of regulation in monocytes by CSF1 as a biological prior, we identify a further 134 candidate genes associated with IBD susceptibility, in addition to novel candidates within IBD loci identified from traditional linkage analysis. S4 Table contains many additional regions that are not associated with a gene name, and the larger set of 2400 promoters analysed in Fig 7 (S5 Table) contains 392 named genes that will undoubtedly expand the list of candidate functional loci still further.


The monocyte-derived macrophage response to LPS as a model for intestinal macrophage differentiation

We analysed the response of human macrophages, differentiated in CSF1, to the TLR4 agonist LPS in order to determine whether this mirrors the development of gut macrophages and hence whether gut macrophages might be involved in the development of IBD. The direct relevance of this system to IBD is reinforced by the recent identification of a functional missense variant in the LPS receptor, TLR4, associated with Crohn’s disease [2].

We have produced an unprecedented analysis of the transcriptional events during the response of macrophages to LPS. Figs 1 and 2 and S1 Fig overview the complex transcriptional cascade of feed-forward and feedback regulation of sets of genes so that the transcriptome is not stable even after 48 hours. The earliest events detected were the induced transcription of enhancers ([37] (S2 Fig). As a class these regions are opened by binding of pioneer transcription factors. In the case of macrophages, the dominant pioneer is PU.1 [20,89,119,120]. The observation that enhancer transcription precedes activation of target promoters is known from other systems, and indeed one might use this correlation to infer the likely connection between distal enhancers and promoters [35]. What is less obvious in all of the examples shown in Figs 3, 4, 5 and S2, is that the induction of enhancer activity appears transient, even when the putative target gene is induced relatively stably. The transcripts are presumably rapidly degraded by the exosome complex. This suggests that the act of transcription of an enhancer is associated with its activation, but the transcript per se is less likely to be functional.

Subsequent to early enhancer activation, wave after wave of transcription factors was induced (Fig 1) followed by their putative target genes (Fig 2) which were identified by enrichment of target motifs in their promoters (Fig 6). Genes for the inducible transcription factors, such as the members of the IRF family, formed clusters with their likely target genes, which contained binding site motifs detected by MARA. The temporal data provide indications of distinct and specific roles. For example, distinct members of IRF family were found in different clusters whereas in a previous analysis of monocyte eQTL data, IRF7 and IRF9 clustered together [47].

The endpoint of differentiation of mucosa-associated macrophages is a population that is longer responsive to restimulation with LPS or other microbial challenges [121]. The FANTOM5 data demonstrate that MDM grown in CSF1, in common with mucosal macrophages, strongly down-regulated most pathogen recognition molecules expressed at high levels in monocytes, notably TLR1, 2, 4 and 6, NOD2 and other Nod-like receptors (NLR) and non-NLR inflammasome activators (NLRC3, NLRC5, NLRP1, NLRP3, NRLP6, MEFV, PYCARD), C-type lectins CLEC7A (dectin1), CLEC5A, CLEC4D, CLEC4E and CLEC12B; SIGLECs 3, 5, 9, 10 and 14, formyl peptide receptors FPR1 and FPR2, recently described cytoplasmic LPS receptors CASP4 and CASP5 [36] and the nucleic acid detector CGAS. There is clearly the potential for dysregulation of any of these individual pattern recognition receptors, which could explain why it has been difficult to link IBD incidence to particular microbial challenges common to all patients [1,122].

Compared to monocytes, MDM also shared with human gut macrophages the suppression of CD14 (the LPS coreceptor), and adaptor and signaling molecules such as MYD88, TRIF, TRAF6 and IRAK1. So, although they clearly do respond to LPS, the response of MDM is greatly attenuated compared to the response of blood monocytes. The FANTOM5 data assays the blood monocyte response to LPS at only a single time point, but based upon the quantitative measure of CAGE tag frequency, the classical inflammatory cytokines, IL1B and IL6 were each induced by LPS to levels 10–100 fold greater in monocytes than at any time point in the MDM time course. By contrast, the monocyte attractants, CCL2, CCL3 and CCL4 were induced to comparable levels in MDM and monocytes. We suggest that once inflammation has been initiated through the activation of hyper-responsive incoming monocytes, or resident macrophages, accelerated recruitment of further monocytes would induce a self-perpetuating hyperinflammatory state. TGFB1 has also been implicated in mucosal macrophage differentiation, and strongly implicated in IBD [123]. The modulated response of MDM to LPS probably involves this factor. The MDM are most likely autocrine for TGFB1 signaling, since they express high levels of TGFB1 mRNA, and both receptors, TGFBR1 and TGFBR2.

Extensive feedback control of the LPS response

The rigorous feedback control that is documented here has previously been seen as a way to produce robustness and stringent control [24]. If this were true, one might expect the system to be resilient in the face of varied expression or function of individual components. This is clearly not the case. Most of the literature on the function of inducible repressors derives from studies of inbred mice, in which knockout of any one these regulators produces dysregulated LPS responses [24,25,83]. Parnas et al. [124] reported a genome-wide CRISPR screen that revealed hundreds of genes apparently required for the production of TNF by mouse macrophages in response to LPS. The structure of the screen did not reveal as many genes involved in negative regulation. To gain further insight into the scale of feedback control of the response, we searched PubMed to identify mouse knockouts that impact the response to LPS. S6 Table identifies >180 additional mouse loci where a null mutation is viable but produces a global change in macrophage responsiveness to LPS in vitro and/or in vivo. The list does not include all of the inflammatory cytokines and chemokines, and essential signaling molecules. Almost 2/3 of these mutations produced hypersensitivity to LPS administration or sepsis in vivo, and many also produced spontaneous colitis. The set of LPS susceptibility genes in S6 Table is annotated with the expression from the FANTOM5 data, and excludes the obvious signaling molecules and inflammatory cytokines and chemokines in the KEGG Toll-like receptor signaling pathway (hsa04620). In every case, the FANTOM5 data confirms that the genes are also expressed/enriched in human monocytes and/or regulated by CSF1, LPS, or both. All of this discussion indicates that there may be hundreds of different genomic variants that can each contribute to an increased susceptibility to IBD.

The functional genomics of IBD susceptibility

A major limitation of GWAS is the assumption that common variants underlie the risk of disease in different families. The hypothesis-free GWA approach can drive understanding of new areas of biology. Conversely, we have shown clearly in Fig 7 that a biological prior, based upon transcriptional co-expression, in this case based upon expression and regulation in monocytes, can focus analysis on a subset of genomic regions and specific candidate genes within those regions, thereby reducing the false discovery rate and the genome-wide significance threshold. Our hypothesis reduced the number of candidate genes/promoters analysed by two orders of magnitude (around 2000 of 200,000 promoters). A previous study aimed at identifying gene features linked to CD susceptibility noted the differential expression of genes associated with “M1” versus “M2” macrophage activation states as a criterion [12]. The definition of those activation states is somewhat problematic [125,126]. We have taken a more nuanced view, with a much more extensive dataset, to emphasise the specific importance of CSF1 and monocyte differentiation.

NOD2 is amongst the most studied of loci underlying Crohn’s disease susceptibility, and there are several protein-coding variants apparently linked to disease. However, there is little consensus about NOD2 function in the GI tract and the mechanistic link between sequence variants and disease susceptibility [127]. NOD2 mRNA is undetectable in either colon or small intestine in the CAGE data, supporting the view that it is rapidly down-regulated during monocyte differentiation to macrophages. NOD2 is believed to be expressed in Paneth cells, and to influence their functions including production of defensins. If NOD2 is expressed in Paneth cells, it is likely to be at a very low level, since Paneth cell markers such as DEFA6, and stem cell markers such as LGR5, were easily detected in the total intestine mRNA. Our data suggest that NOD2 is more likely to have a function in monocyte differentiation. Furthermore, we suggest that NOD2 is not necessarily responsible for the genetic linkage on 16q, since the neighbouring genes, CYLD and SNX20 are tightly co-regulated and lie within the interval of maximal association. The linkage to NOD2 is seen even in families where there is no coding variation in NOD2, and an extended haplotype includes CYLD and the shared enhancer/promoter region between SNX20/NOD2 [128]. Similarly, the case for biological roles of autophagy in Crohn’s disease is also based upon linkage to a coding variant, T300A, in a key gene, ATG16L1, but in this case also the link to function has been elusive [129] and the expression and functional data suggest a more likely candidate in the neighbouring INPPP5D (SHIP1) gene.

If macrophage differentiation in response to CSF1 is a key event in mucosal macrophage anergy to intestinal flora, CSF1 itself, the alternate CSF1R ligand, IL34, and the receptor CSF1R, would also be obvious candidate genes for IBD susceptibility. Two recent studies have focused on elevated IL34 expression in IBD [130,131]. Both claim that IL34 is expressed in uninflamed intestinal mucosa, but the FANTOM5 data indicate that the levels are very low. The IL34 gene has two promoters, one expressed highly in skin and spleen, and the other in brain, consistent with evidence from the mouse IL34 knockout that IL34 controls the development of Langerhans cells and microglia [132]. Based upon the data herein, the elevated expression of IL34 mRNA and protein [130,131] in inflamed mucosa might actually be part of a feedback control to dampen inflammation and initiate repair.

As noted above, CSF1R lies within 0.5MB of the IRGM locus linked with CD susceptibility by GWA, and might potentially share upstream regulatory elements. Heterozygous mutation in Csf1r has been shown to protect against pathology in a colitis model in mice [133]. There is no evidence of association with IBD in the more immediate vicinity of CSF1R in the GWA data, but one report based upon direct sequencing in a Acadian American population indicated linkage to an intron 11 SNP [134]. Whole genome sequence data from 1000 genomes and other sources (, and the recent human exome paper [135] also reveal the existence of numerous likely loss-of-function alleles, with allele frequencies of 1/1000 or more, within the intracellular tyrosine kinase domain of CSF1R. So, there may be rare/private mutations in CSF1R that impact on IBD susceptibility. CSF1 was strongly induced in MDM compared to monocytes, suggesting that these cells become autocrine. Variation in the vicinity of an upstream enhancer at the CSF1 locus is very strongly linked to Paget’s disease [136], and there is some evidence of association with UC at the same interval (S4 Table).

Given the complexity of the transcriptional regulation in monocytes and macrophages, and the lack of robustness discussed above, it is not at all surprising that there are hundreds of ways in which genetic variation can alter the sensitivity to environmental challenge, on the one hand underlying infectious disease resistance, and on the other producing susceptibility to IBD. Some of the genes discussed herein are expressed in both T cells and macrophages. For some, it is clear that expression in macrophages is most relevant to the gut. Macrophage-specific conditional deletion of at least two IBD susceptibility loci, Il10ra [137] and Stat3 knockout [138] has been shown to generate spontaneous colitis in mice. By contrast, in the case of Ship1 (Inppp5d), conditional deletion studies in mice suggest that ileitis present in the constitutive knockout involves both myeloid and T cell dysfunction [101]. Many of the genes implicated in Mendelian very early onset inflammatory bowel diseases (VEOIBD) (reviewed in [139,140]) such as all the components of the phagocyte NADPH oxidase system, are also highly-expressed in blood monocytes. They include some, such as TTC7A, XIAP, BTK and MEFV that are down-regulated by CSF1 and therefore meet our criteria. The FANTOM5 data provide a novel insight into some VEOIBD loci. For example, both STXBP2 and DOCK8 genes contain distinct monocyte-specific promoters, and the latter also shows evidence of linkage to CD.

Current GWA hits still account for only a small proportion of the heritability in IBD. There are likely to be epistatic interactions between susceptibility loci, which could explain some of the apparently missing heritability [141]. On the other hand, rare coding variants that are specific to particular populations, that might have much larger effect, are invisible to GWA. A recent example is a frame-shift mutation in the CSFR2B gene [142,143], also highly-expressed in monocytes and regulated by CSF1 and LPS, associated with Crohn’s disease in Ashkenazi Jews. Several groups have sought evidence of rare protein-coding variation within larger populations, with success limited in large measure by the lack of family-based inheritance data and the lack of a biological hypothesis to prioritise variants [144146] (reviewed in [6])

Identification of candidate genes within genomic intervals identified by GWA or direct sequencing currently relies on programs such as GRAIL (Gene Relationships Across Implicated Loci). Van Limbergen et al. [8] in a recent review, suggested that knowledge of the genetic architecture of IBD has uncovered biological processes involved in IBD pathogenesis, but that new insights would require knowledge of regulation of transcription of key genes and cell-type specific experiments. Our detailed reanalysis of all of the susceptibility loci thus far identified in GWA support the view that the most relevant cell type to study is the monocyte-macrophage and the prevalent candidate mechanism underlying specific IBD susceptibility is a dysregulation of their differentiation in response to CSF1 and their subsequent response to TLR agonists. Westra et al [61] identified candidate genes based upon an eQTL analysis of blood transcriptional profiles. This is somewhat less sensitive than our approach because monocytes are only a subset of the total blood cells. Nevertheless, supporting our hypothesis, 15/16 separate genes identified as both eQTL and associated with IBD by these authors were also repressed in MDM compared to monocytes and/or induced by LPS in our dataset. Similarly, Fairfax et al. [47] described an eQTL analysis of the response of human monocytes to LPS or IFNγ in which >80% of genes showed genetic association under some condition, and associated a subset of those eQTL to CD susceptibility loci; notably CARD9 (which is completely repressed by CSF1). As noted in analysis of the entire FANTOM5 dataset [34,35], SNPs associated with promoters and enhancers are strongly enriched for disease associations. The dataset we have created therefore provides a resource for identification and prioritization of SNPs that are likely to have direct effects on gene expression and causal links to disease.


This monocyte-macrophage dysregulation hypothesis for IBD is based upon the specific premise that the differentiation of monocytes in response to CSF1, and their subsequent response to LPS, is unique to the gut and the response to the gut microbiome. Clearly, there are also aspects of T cell differentiation that are specific to the gut, for example the Th17 pathway, but there is currently limited direct evidence that any variation in these functions is causally linked to IBD susceptibility. Some of the T cell-associated genes and pathways are controlled by macrophages and their products, including the many cytokines discussed above, so that there is clearly the potential for pleiotropy. In general, variants that affect T cell activation would seem likely to influence multi-system inflammatory disease susceptibility. Similarly, not all variants that affect monocyte differentiation need impact solely on the gut. The obvious role of monocytes in all forms of inflammation could provide an explanation for shared susceptibility loci between IBD and other chronic inflammatory diseases [2], but Fig 7 shows clearly that the CD-specific enrichment is not shared with RA. Our data indicate that the specific association with differentiation in response to CSF1 and/or TLR ligands exemplified by LPS provides an explanation for specific association with inflammation in the unique environment of the GI tract. Of course, the advantage of this hypothesis is that is testable by comparing the transcriptional profiles of MDM from patients and unaffected siblings. That will be a future direction.


Cell culture

A full description of CAGE protocols was provided previously [34]. Ethical approval for the isolation of cells from anonymous donors was granted by the University of Edinburgh Research Ethics Committee. This study was reviewed by the University of Edinburgh College of Medicine Ethics Committee (2009/01) and subsequently renewed by the Lothian Research Ethics Committee (11/AL/0168). Written informed consent was received from all volunteers participating in the study. Blood samples from three donors were used. Peripheral blood mononuclear cells were isolated from a single donation of 320ml of whole blood anticoagulated with acid-citrate-dextrose, by Ficoll density gradient centrifugation. Monocytes were extracted using selection for CD14-positive cells using antibodies on magnetic beads (MACS, Miltenyi Biotech). Monocytes were plated in 6-well plates at 800,000 cells per well in complete media (RPMI containing 10% FCS, 20U/ml penicillin, 20 μg/ml streptomycin, and 2mM l-glutamine (Invitrogen)) with 100ng/ml recombinant human CSF-1 (a gift from Chiron Corp, Emeryville, CA) on tissue culture plastic for 7 days. Supplemental medium (50% of the volume in each well, containing 300ng/ml CSF-1) was added at day 5, and cells were used on day 7. Cells were treated with 10ng/ml bacterial lipopolysaccharide (LPS) from salmonella Minnesota R595 as described previously [21], and then harvested at time points from 15 minutes to 48 hours after treatment by cell lysis, before RNA extraction using the Qiagen RNeasy kit (Qiagen, UK).

Gene and promoter expression analysis

CAGE library preparation and sequencing was carried out on each RNA sample, and clusters of transcription start sites (CTSS) were identified by decomposition-based peak identification as described [34]. The tag counts associated with CTSS meeting the FANTOM5 ‘robust’ criteria (>200,000 CTSS) were quantified and normalized as described in [34]. As discussed previously, many human genes have multiple promoters, and they are numbered in the FANTOM5 dataset in order of relative abundance of detected CAGE tags. The majority of the analysis herein is based upon quantification of individual promoters. Primary access to all of the FANTOM5 data, including comparable mouse data, is available at

All of the primary data, including the normalized tag counts for each individual replicate at every time point, can be visualized in Table form by accessing the ZENBU browser for the human data directly at:;loc=hg19::chr17:34413143..34438026+

The link opens a specific locus in a genome browser. A search window can be used to move to other loci based upon gene names or genomic interval. One track shows only the LPS time course data. Another shows the entire FANTOM5 data set. The expression of individual promoters can be highlighted and entire set of expression profiles across the primary FANTOM5 data is available for download from the site.

LPS time course analysis

In order to reduce noise arising from technical variation and biological differences between cells from different volunteers, the expression value for each CTSS was compared to the expression values of time points immediately before or after it, and the corresponding values from the other two donors. Values deviating widely (>3SD) from the mean of this pool of 8 values were replaced with the average of the pool. An average expression value for each CTSS from the three volunteers at each time point was then calculated. CTSS with a minimum expression level of 10 tags per million in at least one timepoint, and with a coefficient of variation > 0.5, were included in expression analysis.

A pairwise coexpression network was generated using the Pearson correlation between expression profiles for each CAGE-defined CTSS across the LPS timecourse. Correlations were included where r ≥ 0.9. Coexpressed clusters were detected using the clustering algorithm, MCL (MCLtools,; inflation value = 1.6). Heatmaps and line graphs of expression data were created using the matplotlib library for Python 2.7.2. For the set of transcription factor genes identified previously [34], total expression associated with all the promoters was aggregated, the three replicates were averaged and only samples that had at least one time point with ≥ 20 tags per million (TPM) were included in the analysis. Gene-based expression data were clustered using BioLayout Express3D ( The network was again based on correlations of r ≥ 0.9 between genes. A sample-to-sample Pearson correlation for the pattern of transcription factor expression was calculated, and the resultant graph with correlations of r ≥ 0.95 and an MCL inflation value of 2.2 was also displayed using Biolayout Express3D

Expression profiles of transcripts of named genes arising near genetic variants significantly associated with Crohn’s disease were examined manually and are described in the text. Following the observation that many of these named transcripts shared a similar pattern of expression with NOD2, numerical criteria were determined, to describe the characteristics of this pattern. Subsets of cell types were defined according to the groups in S2 Table. The following criteria were determined a priori to describe the expression pattern observed:

  • downregulated during monocyte differentiation to macrophage ('unstimulated_monocyte>unstimulated_MDM')
  • specifically induced in interferon-stimulated monocytes ('IFN_monocyte>everythingelse')
  • specifically induced in monocytes after other inflammatory stimuli ('inflammatory_monocyte>everythingelse')

To assess the association of these candidates with IBD susceptibility, Crohn’s disease summary p-values were obtained from the International Inflammatory Bowel Disease Genetics Consortium ( For comparison, we applied the same criteria to loci putatively associated with ulcerative colitis [ ucmeta-sumstats.txt.gz], rheumatoid arthritis [] [147] and to a comparable size set of genes associated with unrelated pathologies: the level of LDL cholesterol in blood [ public/lipids2013/] [148], height [] [149]. The ZENBU Browser cited above also shows individual tracks for the p values for association of SNPs with each of the chosen traits, so that they can be precisely aligned with enhancers and promoters within chosen loci.

For each of these studies, GWAS variants with p-values stronger than a permissive threshold (p<1e-6) were counted in 1000 equally-sized bins above and below all TSS in the input set (observed counts), and compared to the absolute count of all variants genotyped in the same study in the same distance bins from all TSS in the input set (expected counts under the null hypothesis that no enrichment exists). For Crohn's disease, ulcerative colitis and the mixed phenotype of inflammatory bowel disease, linkage disequilibrium score regression analyses reveal strong evidence of polygenicity, supporting our decision to draw inferences from variants that do not meet stringent criteria for genome-wide significance. These analyses were conducted by Bulik-Sullivan et al [150] using the same summary p-value data that we use here. The ratio of observed:expected ratio was plotted for each bin (Fig 7). Summary p-values for this set of loci were computed using PASCAL [151] to calculate the total “burden of significance” within a range of +/-100kb from each TSS region, for each GWAS study, and then to quantify the probability of such a signal arising by chance using the PASCAL pathway enrichment feature to compensate for linkage disequilibrium.

Supporting information

S1 Fig. Clusters of coexpressed promoters regulated during the response of monocyte-derived macrophages to LPS.


S2 Fig. Time course of regulation of selected LPS-inducible promoters and enhancers in their immediate genomic vicinity.


S1 Table. Gene names and profiles of coexpressed transcription factor clusters described in Fig 1.


S2 Table. Names of genes associated with clusters shown in Fig 2.


S3 Table. Detailed curation of genomic regions associated with susceptibility to IBD


S4 Table. Genes differentially repressed or induced by CSF1, and annotation of association with IBD susceptibility loci.


S5 Table. The set of promoters regulated in monocytes by activation stimuli and down-regulated during differentiation.


S6 Table. Literature survey of mutations in mice associated with altered responses to LPS in vitro or in vivo.


S7 Table. Membership of the FANTOM Consortium.



We thank Lynsey Fairbairn and Malcolm Fisher for technical assistance.

Author Contributions

  1. Conceptualization: JKB DAH YH PC ARRF CAW MR PP KMS.
  2. Data curation: JKB EA CD MDH MI HK TL PC ARRF.
  3. Formal analysis: JKB KMS DAH.
  4. Funding acquisition: DAH YH.
  5. Investigation: JKB EA GJF CAW MR.
  6. Methodology: JKB EA.
  7. Project administration: DAH YH PC.
  8. Resources: JKB DAH CAW MR.
  9. Supervision: DAH YH PC ARRF.
  10. Validation: JKB EA.
  11. Writing – original draft: DAH JKB KMS MR PP.


  1. 1. Kostic AD, Xavier RJ, Gevers D (2014) The microbiome in inflammatory bowel disease: current status and the future ahead. Gastroenterology 146: 1489–1499. pmid:24560869
  2. 2. Ellinghaus D, Jostins L, Spain SL, Cortes A, Bethune J, et al. (2016) Analysis of five chronic inflammatory diseases identifies 27 new associations and highlights disease-specific patterns at shared loci. Nat Genet 48: 510–518. pmid:26974007
  3. 3. Richard-Miceli C, Criswell LA (2012) Emerging patterns of genetic overlap across autoimmune disorders. Genome Med 4: 6. pmid:22284131
  4. 4. Jostins L, Ripke S, Weersma RK, Duerr RH, McGovern DP, et al. (2012) Host-microbe interactions have shaped the genetic architecture of inflammatory bowel disease. Nature 491: 119–124. pmid:23128233
  5. 5. Liu JZ, van Sommeren S, Huang H, Ng SC, Alberts R, et al. (2015) Association analyses identify 38 susceptibility loci for inflammatory bowel disease and highlight shared genetic risk across populations. Nat Genet 47: 979–986. pmid:26192919
  6. 6. McGovern DP, Kugathasan S, Cho JH (2015) Genetics of Inflammatory Bowel Diseases. Gastroenterology 149: 1163–1176 e1162. pmid:26255561
  7. 7. Boyapati R, Satsangi J, Ho GT (2015) Pathogenesis of Crohn's disease. F1000Prime Rep 7: 44. pmid:26097717
  8. 8. Van Limbergen J, Radford-Smith G, Satsangi J (2014) Advances in IBD genetics. Nat Rev Gastroenterol Hepatol 11: 372–385. pmid:24614343
  9. 9. Goyette P, Boucher G, Mallon D, Ellinghaus E, Jostins L, et al. (2015) High-density mapping of the MHC identifies a shared role for HLA-DRB1*01:03 in inflammatory bowel diseases and heterozygous advantage in ulcerative colitis. Nat Genet 47: 172–179. pmid:25559196
  10. 10. Farache J, Zigmond E, Shakhar G, Jung S (2013) Contributions of dendritic cells and macrophages to intestinal homeostasis and immune defense. Immunol Cell Biol 91: 232–239. pmid:23399695
  11. 11. Weber B, Saurer L, Mueller C (2009) Intestinal macrophages: differentiation and involvement in intestinal immunopathologies. Semin Immunopathol 31: 171–184. pmid:19533135
  12. 12. Ning K, Gettler K, Zhang W, Ng SM, Bowen BM, et al. (2015) Improved integrative framework combining association data with gene expression features to prioritize Crohn's disease genes. Hum Mol Genet 24: 4147–4157. pmid:25935003
  13. 13. Bain CC, Mowat AM (2014) Macrophages in intestinal homeostasis and inflammation. Immunol Rev 260: 102–117. pmid:24942685
  14. 14. Zigmond E, Jung S (2013) Intestinal macrophages: well educated exceptions from the rule. Trends Immunol 34: 162–168. pmid:23477922
  15. 15. Chitu V, Stanley ER (2006) Colony-stimulating factor-1 in immunity and inflammation. Curr Opin Immunol 18: 39–48. pmid:16337366
  16. 16. Hume DA (2008) Differentiation and heterogeneity in the mononuclear phagocyte system. Mucosal Immunol 1: 432–441. pmid:19079210
  17. 17. Jenkins SJ, Hume DA (2014) Homeostasis in the mononuclear phagocyte system. Trends in Immunology In press.
  18. 18. Bain CC, Mowat AM (2014) The monocyte-macrophage axis in the intestine. Cell Immunol 291: 41–48. pmid:24726741
  19. 19. MacDonald KP, Palmer JS, Cronau S, Seppanen E, Olver S, 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 116: 3955–3963. pmid:20682855
  20. 20. Pham TH, Benner C, Lichtinger M, Schwarzfischer L, Hu Y, et al. (2012) Dynamic epigenetic enhancer signatures reveal key transcription factors associated with monocytic differentiation states. Blood 119: e161–171. pmid:22550342
  21. 21. Schroder K, Irvine KM, Taylor MS, Bokil NJ, Cao K-AL, et al. (2012) Conservation and divergence in toll-like receptor 4-regulated gene expression in primary human versus mouse macrophages. Proc Natl AcadSci (USA) 109: E944–953.
  22. 22. Martinez FO, Gordon S, Locati M, Mantovani A (2006) Transcriptional profiling of the human monocyte-to-macrophage differentiation and polarization: new molecules and patterns of gene expression. J Immunol 177: 7303–7311. pmid:17082649
  23. 23. Saeed S, Quintin J, Kerstens HH, Rao NA, Aghajanirefah A, et al. (2014) Epigenetic programming of monocyte-to-macrophage differentiation and trained innate immunity. Science 345: 1251086. pmid:25258085
  24. 24. Kondo T, Kawai T, Akira S (2012) Dissecting negative regulation of Toll-like receptor signaling. Trends Immunol 33: 449–458. pmid:22721918
  25. 25. Wells CA, Ravasi T, Hume DA (2005) Inflammation suppressor genes: please switch out all the lights. J Leukoc Biol 78: 9–13. pmid:15774547
  26. 26. Sweet MJ, Hume DA (1996) Endotoxin signal transduction in macrophages. J Leukoc Biol 60: 8–26. pmid:8699127
  27. 27. Takeuchi O, Akira S (2010) Pattern recognition receptors and inflammation. Cell 140: 805–820. pmid:20303872
  28. 28. Gilchrist M, Thorsson V, Li B, Rust AG, Korb M, et al. (2006) Systems biology approaches identify ATF3 as a negative regulator of Toll-like receptor 4. Nature 441: 173–178. pmid:16688168
  29. 29. Mancino A, Termanini A, Barozzi I, Ghisletti S, Ostuni R, et al. (2015) A dual cis-regulatory code links IRF8 to constitutive and inducible gene expression in macrophages. Genes Dev 29: 394–408. pmid:25637355
  30. 30. Nilsson R, Bajic VB, Suzuki H, di Bernardo D, Bjorkegren J, et al. (2006) Transcriptional network dynamics in macrophage activation. Genomics 88: 133–142. pmid:16698233
  31. 31. Kaikkonen MU, Spann NJ, Heinz S, Romanoski CE, Allison KA, et al. (2013) Remodeling of the enhancer landscape during macrophage activation is coupled to enhancer transcription. Mol Cell 51: 310–325. pmid:23932714
  32. 32. Ostuni R, Piccolo V, Barozzi I, Polletti S, Termanini A, et al. (2013) Latent enhancers activated by stimulation in differentiated cells. Cell 152: 157–171. pmid:23332752
  33. 33. Smale ST, Natoli G (2014) Transcriptional control of inflammatory responses. Cold Spring Harb Perspect Biol 6: a016261. pmid:25213094
  34. 34. The FANTOM Consortium, Forrest A.R.R. et al. (2014) A human gene expression atlas based upon promoter activity. Nature 507:462–470 pmid:24670764
  35. 35. Andersson R, Gebhard C, Miguel-Escalada I, Hoof I, Bornholdt J, et al. (2014) An atlas of active enhancers across human cell types and tissues. Nature 507: 455–461. pmid:24670763
  36. 36. Schmidl C, Renner K, Peter K, Eder R, Lassmann T, et al. (2014) Transcription and enhancer profiling in human monocyte subsets. Blood 123: e90–99. pmid:24671955
  37. 37. Arner E, Daub CO, Vitting-Seerup K, Andersson R, Lilje B, et al. (2015) Gene regulation. Transcribed enhancers lead waves of coordinated transcription in transitioning mammalian cells. Science 347: 1010–1014. pmid:25678556
  38. 38. Joshi A, Pooley C, Freeman TC, Lennartsson A, Babina M, et al. (2015) Transcription factor, promoter, and enhancer utilization in human myeloid cells. J Leukoc Biol. 97:985–995
  39. 39. Theocharidis A, van Dongen S, Enright AJ, Freeman TC (2009) Network visualization and analysis of gene expression data using BioLayout Express(3D). Nat Protoc 4: 1535–1550. pmid:19798086
  40. 40. Yamamoto M, Yamazaki S, Uematsu S, Sato S, Hemmi H, et al. (2004) Regulation of Toll/IL-1-receptor-mediated gene expression by the inducible nuclear protein IkappaBzeta. Nature 430: 218–222. pmid:15241416
  41. 41. Tartey S, Matsushita K, Vandenbon A, Ori D, Imamura T, et al. (2014) Akirin2 is critical for inducing inflammatory genes by bridging IkappaB-zeta and the SWI/SNF complex. EMBO J 33: 2332–2348. pmid:25107474
  42. 42. Chiang E, Dang O, Anderson K, Matsuzawa A, Ichijo H, et al. (2006) Cutting edge: apoptosis-regulating signal kinase 1 is required for reactive oxygen species-mediated activation of IFN regulatory factor 3 by lipopolysaccharide. J Immunol 176: 5720–5724. pmid:16670275
  43. 43. Wieland GD, Nehmann N, Muller D, Eibel H, Siebenlist U, et al. (2005) Early growth response proteins EGR-4 and EGR-3 interact with immune inflammatory mediators NF-kappaB p50 and p65. J Cell Sci 118: 3203–3212. pmid:16014385
  44. 44. Jiang D, Chen Y, Schwarz H (2008) CD137 induces proliferation of murine hematopoietic progenitor cells and differentiation to macrophages. J Immunol 181: 3923–3932. pmid:18768847
  45. 45. Kannan MB, Solovieva V, Blank V (2012) The small MAF transcription factors MAFF, MAFG and MAFK: current knowledge and perspectives. Biochim Biophys Acta 1823: 1841–1846. pmid:22721719
  46. 46. Chaussabel D, Allman W, Mejias A, Chung W, Bennett L, et al. (2005) Analysis of significance patterns identifies ubiquitous and disease-specific gene-expression signatures in patient peripheral blood leukocytes. Ann N Y Acad Sci 1062: 146–154. pmid:16461797
  47. 47. Fairfax BP, Humburg P, Makino S, Naranbhai V, Wong D, et al. (2014) Innate immune activity conditions the effect of regulatory variants upon monocyte gene expression. Science 343: 1246949. pmid:24604202
  48. 48. Ramilo O, Allman W, Chung W, Mejias A, Ardura M, et al. (2007) Gene expression patterns in blood leukocytes discriminate patients with acute infections. Blood 109: 2066–2077. pmid:17105821
  49. 49. Ronnblom L, Eloranta ML (2013) The interferon signature in autoimmune diseases. Curr Opin Rheumatol 25: 248–253. pmid:23249830
  50. 50. Sheikh F, Dickensheets H, Gamero AM, Vogel SN, Donnelly RP (2014) An essential role for IFN-beta in the induction of IFN-stimulated gene expression by LPS in macrophages. J Leukoc Biol 96: 591–600. pmid:25024400
  51. 51. Rusinova I, Forster S, Yu S, Kannan A, Masse M, et al. (2013) Interferome v2.0: an updated database of annotated interferon-regulated genes. Nucleic Acids Res 41: D1040–1046. pmid:23203888
  52. 52. Al-Afif A, Alyazidi R, Oldford SA, Huang YY, King CA, et al. (2015) Respiratory syncytial virus infection of primary human mast cells induces the selective production of type I interferons, CXCL10, and CCL4. J Allergy Clin Immunol. 136:1346–1354 pmid:25819983
  53. 53. Kinjyo I, Hanada T, Inagaki-Ohara K, Mori H, Aki D, et al. (2002) SOCS1/JAB is a negative regulator of LPS-induced macrophage activation. Immunity 17: 583–591. pmid:12433365
  54. 54. Piganis RA, De Weerd NA, Gould JA, Schindler CW, Mansell A, et al. (2011) Suppressor of cytokine signaling (SOCS) 1 inhibits type I interferon (IFN) signaling via the interferon alpha receptor (IFNAR1)-associated tyrosine kinase Tyk2. J Biol Chem 286: 33811–33818. pmid:21757742
  55. 55. Pandey S, Kawai T, Akira S (2015) Microbial sensing by Toll-like receptors and intracellular nucleic acid sensors. Cold Spring Harb Perspect Biol 7: a016246.
  56. 56. Qiao Y, Giannopoulou EG, Chan CH, Park SH, Gong S, et al. (2013) Synergistic activation of inflammatory cytokine genes by interferon-gamma-induced chromatin remodeling and toll-like receptor signaling. Immunity 39: 454–469. pmid:24012417
  57. 57. Ramilowski JA, Goldberg T, Harshbarger J, Kloppman E, Lizio M, et al. (2015) A draft network of ligand-receptor-mediated multicellular signalling in human. Nat Commun 6: 7866. pmid:26198319
  58. 58. Yarilina A, Park-Min KH, Antoniv T, Hu X, Ivashkiv LB (2008) TNF activates an IRF1-dependent autocrine loop leading to sustained expression of chemokines and STAT1-dependent type I interferon-response genes. Nat Immunol 9: 378–387. pmid:18345002
  59. 59. Severa M, Islam SA, Waggoner SN, Jiang Z, Kim ND, et al. (2014) The transcriptional repressor BLIMP1 curbs host defenses by suppressing expression of the chemokine CCL8. J Immunol 192: 2291–2304. pmid:24477914
  60. 60. Sung MH, Li N, Lao Q, Gottschalk RA, Hager GL, et al. (2014) Switching of the relative dominance between feedback mechanisms in lipopolysaccharide-induced NF-kappaB signaling. Sci Signal 7: ra6. pmid:24425788
  61. 61. Westra HJ, Peters MJ, Esko T, Yaghootkar H, Schurmann C, et al. (2013) Systematic identification of trans eQTLs as putative drivers of known disease associations. Nat Genet 45: 1238–1243. pmid:24013639
  62. 62. Schott J, Reitter S, Philipp J, Haneke K, Schafer H, et al. (2014) Translational regulation of specific mRNAs controls feedback inhibition and survival during macrophage activation. PLoS Genet 10: e1004368. pmid:24945926
  63. 63. Lang R, Hammer M, Mages J (2006) DUSP meet immunology: dual specificity MAPK phosphatases in control of the inflammatory response. J Immunol 177: 7497–7504. pmid:17114416
  64. 64. Schmitz I (2013) Gadd45 proteins in immunity. Adv Exp Med Biol 793: 51–68. pmid:24104473
  65. 65. Gu M, Ouyang C, Lin W, Zhang T, Cao X, et al. (2014) Phosphatase holoenzyme PP1/GADD34 negatively regulates TLR response by inhibiting TAK1 serine 412 phosphorylation. J Immunol 192: 2846–2856. pmid:24534530
  66. 66. Scott GK, Mattie MD, Berger CE, Benz SC, Benz CC (2006) Rapid alteration of microRNA levels by histone deacetylase inhibition. Cancer Res 66: 1277–1281. pmid:16452179
  67. 67. Hao S, Baltimore D (2009) The stability of mRNA influences the temporal order of the induction of genes encoding inflammatory molecules. Nat Immunol 10: 281–288. pmid:19198593
  68. 68. Wang KT, Wang HH, Wu YY, Su YL, Chiang PY, et al. (2015) Functional regulation of Zfp36l1 and Zfp36l2 in response to lipopolysaccharide in mouse RAW264.7 macrophages. J Inflamm (Lond) 12: 42.
  69. 69. Matsushita K, Takeuchi O, Standley DM, Kumagai Y, Kawagoe T, et al. (2009) Zc3h12a is an RNase essential for controlling immune responses by regulating mRNA decay. Nature 458: 1185–1190. pmid:19322177
  70. 70. Iqbal MB, Johns M, Cao J, Liu Y, Yu SC, et al. (2014) PARP-14 combines with tristetraprolin in the selective posttranscriptional control of macrophage tissue factor expression. Blood 124: 3646–3655. pmid:25293769
  71. 71. Bachmann SB, Frommel SC, Camicia R, Winkler HC, Santoro R, et al. (2014) DTX3L and ARTD9 inhibit IRF1 expression and mediate in cooperation with ARTD8 survival and proliferation of metastatic prostate cancer cells. Mol Cancer 13: 125. pmid:24886089
  72. 72. Atasheva S, Frolova EI, Frolov I (2014) Interferon-stimulated poly(ADP-Ribose) polymerases are potent inhibitors of cellular translation and virus replication. J Virol 88: 2116–2130. pmid:24335297
  73. 73. Verheugd P, Forst AH, Milke L, Herzog N, Feijs KL, et al. (2013) Regulation of NF-kappaB signalling by the mono-ADP-ribosyltransferase ARTD10. Nat Commun 4: 1683. pmid:23575687
  74. 74. Pauls E, Nanda SK, Smith H, Toth R, Arthur JS, et al. (2013) Two phases of inflammatory mediator production defined by the study of IRAK2 and IRAK1 knock-in mice. J Immunol 191: 2717–2730. pmid:23918981
  75. 75. Chew TS, O'Shea NR, Sewell GW, Oehlers SH, Mulvey CM, et al. (2015) Optineurin deficiency in mice contributes to impaired cytokine secretion and neutrophil recruitment in bacteria-driven colitis. Dis Model Mech 8: 817–829. pmid:26044960
  76. 76. Singh N, Gurav A, Sivaprakasam S, Brady E, Padia R, et al. (2014) Activation of Gpr109a, receptor for niacin and the commensal metabolite butyrate, suppresses colonic inflammation and carcinogenesis. Immunity 40: 128–139. pmid:24412617
  77. 77. Chang M, Jin W, Sun SC (2009) Peli1 facilitates TRIF-dependent Toll-like receptor signaling and proinflammatory cytokine production. Nat Immunol 10: 1089–1095. pmid:19734906
  78. 78. Martinon F, Chen X, Lee AH, Glimcher LH (2010) TLR activation of the transcription factor XBP1 regulates innate immune responses in macrophages. Nat Immunol 11: 411–418. pmid:20351694
  79. 79. Tsaytler P, Harding HP, Ron D, Bertolotti A (2011) Selective inhibition of a regulatory subunit of protein phosphatase 1 restores proteostasis. Science 332: 91–94. pmid:21385720
  80. 80. Massari ME, Rivera RR, Voland JR, Quong MW, Breit TM, et al. (1998) Characterization of ABF-1, a novel basic helix-loop-helix transcription factor expressed in activated B lymphocytes. Mol Cell Biol 18: 3130–3139. pmid:9584154
  81. 81. Wang D, Malo D, Hekimi S (2010) Elevated mitochondrial reactive oxygen species generation affects the immune response via hypoxia-inducible factor-1alpha in long-lived Mclk1+/- mouse mutants. J Immunol 184: 582–590. pmid:20007531
  82. 82. Kariuki SN, Ghodke-Puranik Y, Dorschner JM, Chrabot BS, Kelly JA, et al. (2015) Genetic analysis of the pathogenic molecular sub-phenotype interferon-alpha identifies multiple novel loci involved in systemic lupus erythematosus. Genes Immun 16: 15–23. pmid:25338677
  83. 83. Chiang EY, Johnston RJ, Grogan JL (2013) EBI2 is a negative regulator of type I interferons in plasmacytoid and myeloid dendritic cells. PLoS One 8: e83457. pmid:24386204
  84. 84. Liu MJ, Bao S, Galvez-Peralta M, Pyle CJ, Rudawsky AC, et al. (2013) ZIP8 regulates host defense through zinc-mediated inhibition of NF-kappaB. Cell Rep 3: 386–400. pmid:23403290
  85. 85. Lam MT, Li W, Rosenfeld MG, Glass CK (2014) Enhancer RNAs and regulated transcriptional programs. Trends Biochem Sci 39: 170–182. pmid:24674738
  86. 86. Pefanis E, Wang J, Rothschild G, Lim J, Kazadi D, et al. (2015) RNA exosome-regulated long non-coding RNA transcription controls super-enhancer activity. Cell 161: 774–789. pmid:25957685
  87. 87. NE II, Heward JA, Roux B, Tsitsiou E, Fenwick PS, et al. (2014) Long non-coding RNAs and enhancer RNAs regulate the lipopolysaccharide-induced inflammatory response in human monocytes. Nat Commun 5: 3979. pmid:24909122
  88. 88. Suzuki H, Forrest AR, van Nimwegen E, Daub CO, Balwierz PJ, et al. (2009) The transcriptional network that controls growth arrest and differentiation in a human myeloid leukemia cell line. Nat Genet 41: 553–562. pmid:19377474
  89. 89. Gosselin D, Link VM, Romanoski CE, Fonseca GJ, Eichenfield DZ, et al. (2014) Environment drives selection and function of enhancers controlling tissue-specific macrophage identities. Cell 159: 1327–1340. pmid:25480297
  90. 90. Rudolf R, Busch R, Patra AK, Muhammad K, Avots A, et al. (2014) Architecture and expression of the nfatc1 gene in lymphocytes. Front Immunol 5: 21. pmid:24550910
  91. 91. Aude-Garcia C, Collin-Faure V, Bausinger H, Hanau D, Rabilloud T, et al. (2010) Dual roles for MEF2A and MEF2D during human macrophage terminal differentiation and c-Jun expression. Biochem J 430: 237–244. pmid:20590529
  92. 92. Bai XL, Zhang Q, Ye LY, Liang F, Sun X, et al. (2015) Myocyte enhancer factor 2C regulation of hepatocellular carcinoma via vascular endothelial growth factor and Wnt/beta-catenin signaling. Oncogene 34: 4089–4097. pmid:25328135
  93. 93. Sullivan AL, Benner C, Heinz S, Huang W, Xie L, et al. (2011) Serum response factor utilizes distinct promoter- and enhancer-based mechanisms to regulate cytoskeletal gene expression in macrophages. Mol Cell Biol 31: 861–875. pmid:21135125
  94. 94. Sweet MJ, Stacey KJ, Ross IL, Ostrowski MC, Hume DA (1998) Involvement of Ets, rel and Sp1-like proteins in lipopolysaccharide-mediated activation of the HIV-1 LTR in macrophages. J Inflamm 48: 67–83. pmid:9656143
  95. 95. Liao X, Sharma N, Kapadia F, Zhou G, Lu Y, et al. (2011) Kruppel-like factor 4 regulates macrophage polarization. J Clin Invest 121: 2736–2749. pmid:21670502
  96. 96. Date D, Das R, Narla G, Simon DI, Jain MK, et al. (2014) Kruppel-like transcription factor 6 regulates inflammatory macrophage polarization. J Biol Chem 289: 10318–10329. pmid:24385430
  97. 97. Lacour A, Ellinghaus D, Schreiber S, Franke A, Becker T (2016) Haplotype synthesis analysis reveals functional variants underlying known genome-wide associated susceptibility loci. Bioinformatics 32: 2136–2142. pmid:27153721
  98. 98. Vora P, McGovern DP (2012) LRRK2 as a negative regulator of NFAT: implications for the pathogenesis of inflammatory bowel disease. Expert Rev Clin Immunol 8: 227–229. pmid:22390486
  99. 99. Schaff UY, Shih HH, Lorenz M, Sako D, Kriz R, et al. (2008) SLIC-1/sorting nexin 20: a novel sorting nexin that directs subcellular distribution of PSGL-1. Eur J Immunol 38: 550–564. pmid:18196517
  100. 100. Mathis BJ, Lai Y, Qu C, Janicki JS, Cui T (2015) CYLD-mediated signaling and diseases. Curr Drug Targets 16: 284–294. pmid:25342597
  101. 101. Maxwell MJ, Srivastava N, Park MY, Tsantikos E, Engelman RW, et al. (2014) SHIP-1 deficiency in the myeloid compartment is insufficient to induce myeloid expansion or chronic inflammation. Genes Immun 15: 233–240. pmid:24598798
  102. 102. Van Limbergen J, Kabakchiev B, Stempak JM, Schumm P, Xu W, et al. (2013) Hypothesis-free analysis of ATG16L1 demonstrates gene-wide extent of association with Crohn's disease susceptibility. Gut 62: 331–333. pmid:22833393
  103. 103. Singh SB, Davis AS, Taylor GA, Deretic V (2006) Human IRGM induces autophagy to eliminate intracellular mycobacteria. Science 313: 1438–1441. pmid:16888103
  104. 104. Bekpen C, Hunn JP, Rohde C, Parvanova I, Guethlein L, et al. (2005) The interferon-inducible p47 (IRG) GTPases in vertebrates: loss of the cell autonomous resistance mechanism in the human lineage. Genome Biol 6: R92. pmid:16277747
  105. 105. Huett A, McCarroll SA, Daly MJ, Xavier RJ (2009) On the level: IRGM gene function is all about expression. Autophagy 5: 96–99. pmid:19029815
  106. 106. McCarroll SA, Huett A, Kuballa P, Chilewski SD, Landry A, et al. (2008) Deletion polymorphism upstream of IRGM associated with altered IRGM expression and Crohn's disease. Nat Genet 40: 1107–1112. pmid:19165925
  107. 107. Adrianto I, Wang S, Wiley GB, Lessard CJ, Kelly JA, et al. (2012) Association of two independent functional risk haplotypes in TNIP1 with systemic lupus erythematosus. Arthritis Rheum 64: 3695–3705. pmid:22833143
  108. 108. Sobota RS, Stein CM, Kodaman N, Scheinfeldt LB, Maro I, et al. (2016) A Locus at 5q33.3 Confers Resistance to Tuberculosis in Highly Susceptible Individuals. Am J Hum Genet 98: 514–524. pmid:26942285
  109. 109. Jiang D, Zhong S, McPeek MS (2016) Retrospective Binary-Trait Association Test Elucidates Genetic Architecture of Crohn Disease. Am J Hum Genet 98: 243–255. pmid:26833331
  110. 110. Peltekova VD, Wintle RF, Rubin LA, Amos CI, Huang Q, et al. (2004) Functional variants of OCTN cation transporter genes are associated with Crohn disease. Nat Genet 36: 471–475. pmid:15107849
  111. 111. Newman B, Gu X, Wintle R, Cescon D, Yazdanpanah M, et al. (2005) A risk haplotype in the Solute Carrier Family 22A4/22A5 gene cluster influences phenotypic expression of Crohn's disease. Gastroenterology 128: 260–269. pmid:15685536
  112. 112. van Heel DA, Dechairo BM, Dawson G, McGovern DP, Negoro K, et al. (2003) The IBD6 Crohn's disease locus demonstrates complex interactions with CARD15 and IBD5 disease-associated variants. Hum Mol Genet 12: 2569–2575. pmid:12928481
  113. 113. Hamann J, Koning N, Pouwels W, Ulfman LH, van Eijk M, et al. (2007) EMR1, the human homolog of F4/80, is an eosinophil-specific receptor. Eur J Immunol 37: 2797–2802. pmid:17823986
  114. 114. Lin HH, Stacey M, Stein-Streilein J, Gordon S (2010) F4/80: the macrophage-specific adhesion-GPCR and its role in immunoregulation. Adv Exp Med Biol 706: 149–156. pmid:21618834
  115. 115. Irvine KM, Andrews MR, Fernandez-Rojo MA, Schroder K, Burns CJ, et al. (2009) Colony-stimulating factor-1 (CSF-1) delivers a proatherogenic signal to human macrophages. J Leukoc Biol 85: 278–288. pmid:19004987
  116. 116. Chang D, Gao F, Slavney A, Ma L, Waldman YY, et al. (2014) Accounting for eXentricities: analysis of the X chromosome in GWAS reveals X-linked genes implicated in autoimmune diseases. PLoS One 9: e113684. pmid:25479423
  117. 117. Vermeire S, Satsangi J, Peeters M, Parkes M, Jewell DP, et al. (2001) Evidence for inflammatory bowel disease of a susceptibility locus on the X chromosome. Gastroenterology 120: 834–840. pmid:11231937
  118. 118. Ortiz-Fernandez L, Garcia-Lozano JR, Montes-Cano MA, Conde-Jaldon M, Leo E, et al. (2015) Association of haplotypes of the TLR8 locus with susceptibility to Crohn's and Behcet's diseases. Clin Exp Rheumatol 33: S117–122.
  119. 119. Barozzi I, Simonatto M, Bonifacio S, Yang L, Rohs R, et al. (2014) Coregulation of transcription factor binding and nucleosome occupancy through DNA features of mammalian enhancers. Mol Cell 54: 844–857. pmid:24813947
  120. 120. Heinz S, Benner C, Spann N, Bertolino E, Lin YC, et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell 38: 576–589. pmid:20513432
  121. 121. Mowat AM, Bain CC (2011) Mucosal macrophages in intestinal homeostasis and inflammation. J Innate Immun 3: 550–564. pmid:22025201
  122. 122. Knights D, Lassen KG, Xavier RJ (2013) Advances in inflammatory bowel disease pathogenesis: linking host genetics and the microbiome. Gut 62: 1505–1510. pmid:24037875
  123. 123. Sedda S, Marafini I, Dinallo V, Di Fusco D, Monteleone G (2015) The TGF-beta/Smad System in IBD Pathogenesis. Inflamm Bowel Dis.
  124. 124. Parnas O, Jovanovic M, Eisenhaure TM, Herbst RH, Dixit A, et al. (2015) A Genome-wide CRISPR Screen in Primary Immune Cells to Dissect Regulatory Networks. Cell 162: 675–686. pmid:26189680
  125. 125. Hume DA, Freeman TC (2014) Transcriptomic analysis of mononuclear phagocyte differentiation and activation. Immunol Rev 262: 74–84. pmid:25319328
  126. 126. Mabbott NA, Baillie JK, Brown H, Freeman TC, Hume DA (2013) An expression atlas of human primary cells: inference of gene function from coexpression networks. BMC Genomics 14: 632. pmid:24053356
  127. 127. Caruso R, Warner N, Inohara N, Nunez G (2014) NOD1 and NOD2: signaling, host defense, and inflammatory disease. Immunity 41: 898–908. pmid:25526305
  128. 128. Elding H, Lau W, Swallow DM, Maniatis N (2011) Dissecting the genetics of complex inheritance: linkage disequilibrium mapping provides insight into Crohn disease. Am J Hum Genet 89: 798–805. pmid:22152681
  129. 129. Salem M, Ammitzboell M, Nys K, Seidelin JB, Nielsen OH (2015) ATG16L1: A multifunctional susceptibility factor in Crohn disease. Autophagy 11: 585–594. pmid:25906181
  130. 130. Franze E, Monteleone I, Cupi ML, Mancia P, Caprioli F, et al. (2015) Interleukin-34 sustains inflammatory pathways in the gut. Clin Sci (Lond) 129: 271–280.
  131. 131. Zwicker S, Martinez GL, Bosma M, Gerling M, Clark R, et al. (2015) Interleukin 34: a new modulator of human and experimental inflammatory bowel disease. Clin Sci (Lond) 129: 281–290.
  132. 132. Wang Y, Szretter KJ, Vermi W, Gilfillan S, Rossini C, et al. (2012) IL-34 is a tissue-restricted ligand of CSF1R required for the development of Langerhans cells and microglia. Nat Immunol 13: 753–760. pmid:22729249
  133. 133. Huynh D, Akcora D, Malaterre J, Chan CK, Dai XM, et al. (2013) CSF-1 receptor-dependent colon development, homeostasis and inflammatory stress response. PLoS One 8: e56951. pmid:23451116
  134. 134. Zapata-Velandia A, Ng SS, Brennan RF, Simonsen NR, Gastanaduy M, et al. (2004) Association of the T allele of an intronic single nucleotide polymorphism in the colony stimulating factor 1 receptor with Crohn's disease: a case-control study. J Immune Based Ther Vaccines 2: 6. pmid:15144560
  135. 135. Lek M, Karczewski KJ, Minikel EV, Samocha KE, Banks E, et al. (2016) Analysis of protein-coding genetic variation in 60,706 humans. Nature 536: 285–291. pmid:27535533
  136. 136. Albagha OM, Visconti MR, Alonso N, Langston AL, Cundy T, et al. (2010) Genome-wide association study identifies variants at CSF1, OPTN and TNFRSF11A as genetic risk factors for Paget's disease of bone. Nat Genet 42: 520–524. pmid:20436471
  137. 137. Zigmond E, Bernshtein B, Friedlander G, Walker CR, Yona S, et al. (2014) Macrophage-restricted interleukin-10 receptor deficiency, but not IL-10 deficiency, causes severe spontaneous colitis. Immunity 40: 720–733. pmid:24792913
  138. 138. Deng L, Zhou JF, Sellers RS, Li JF, Nguyen AV, et al. A novel mouse model of inflammatory bowel disease links mammalian target of rapamycin-dependent hyperproliferation of colonic epithelium to inflammation-associated tumorigenesis. Am J Pathol 176: 952–967. pmid:20042677
  139. 139. Liu TC, Stappenbeck TS (2016) Genetics and Pathogenesis of Inflammatory Bowel Disease. Annu Rev Pathol 11: 127–148. pmid:26907531
  140. 140. Uhlig HH, Schwerd T, Koletzko S, Shah N, Kammermeier J, et al. (2014) The diagnostic approach to monogenic very early onset inflammatory bowel disease. Gastroenterology 147: 990–1007 e1003. pmid:25058236
  141. 141. Wang MH, Fiocchi C, Zhu X, Ripke S, Kamboh MI, et al. (2014) Gene-gene and gene-environment interactions in ulcerative colitis. Hum Genet 133: 547–558. pmid:24241240
  142. 142. Chuang LS, Villaverde N, Hui KY, Mortha A, Rahman A, et al. (2016) A Frameshift in CSF2RB Predominant Among Ashkenazi Jews Increases Risk for Crohn's Disease and Reduces Monocyte Signaling via GM-CSF. Gastroenterology. 151:710–723 pmid:27377463
  143. 143. Levine AP, Pontikos N, Schiff ER, Jostins L, Speed D, et al. (2016) Genetic Complexity of Crohn's Disease in Two Large Ashkenazi Jewish Families. Gastroenterology. 151:698–709 pmid:27373512
  144. 144. Beaudoin M, Goyette P, Boucher G, Lo KS, Rivas MA, et al. (2013) Deep resequencing of GWAS loci identifies rare variants in CARD9, IL23R and RNF186 that are associated with ulcerative colitis. PLoS Genet 9: e1003723. pmid:24068945
  145. 145. Ellinghaus D, Zhang H, Zeissig S, Lipinski S, Till A, et al. (2013) Association between variants of PRDM1 and NDP52 and Crohn's disease, based on exome sequencing and functional studies. Gastroenterology 145: 339–347. pmid:23624108
  146. 146. Prescott NJ, Lehne B, Stone K, Lee JC, Taylor K, et al. (2015) Pooled sequencing of 531 genes in inflammatory bowel disease identifies an associated rare variant in BTNL2 and implicates other immune related genes. PLoS Genet 11: e1004955. pmid:25671699
  147. 147. Okada Y, Wu D, Trynka G, Raj T, Terao C, et al. (2014) Genetics of rheumatoid arthritis contributes to biology and drug discovery. Nature 506: 376–381. pmid:24390342
  148. 148. Global Lipids Genetics Consortium, Willer CJ, Schmidt EM, Sengupta S, Peloso GM, et al. (2013) Discovery and refinement of loci associated with lipid levels. Nat Genet 45: 1274–1283. pmid:24097068
  149. 149. Lango Allen H, Estrada K, Lettre G, Berndt SI, Weedon MN, et al. (2010) Hundreds of variants clustered in genomic loci and biological pathways affect human height. Nature 467: 832–838. pmid:20881960
  150. 150. Bulik-Sullivan BK, Loh P-R, Finucane HK, Ripke S, Yang J, Schizophrenia Working Group of the Psychiatric Genomics Consortium, Patterson N, Daly MJ, Price AL, Neale N. (2015) Nat Genet 47: 291–295 pmid:25642630
  151. 151. Lamparter D, Marbach D, Rueedi R, Kutalik Z, Bergmann S (2016) Fast and Rigorous Computation of Gene and Pathway Scores from SNP-Based Summary Statistics. PLoS Comput Biol 12: e1004714. pmid:26808494