bioRxiv preprint doi: https://doi.org/10.1101/437160; this version posted October 7, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

IFN-γ selectively suppresses a subset of TLR4-activated genes and enhancers to potentiate M1-like macrophage polarization

Kyuho Kang1,2, Sung Ho Park1, Keunsoo Kang3 and Lionel B. Ivashkiv1,4

1 Arthritis and Tissue Degeneration Program and the David Z. Rosensweig Genomics Research Center, Hospital for Special Surgery, New York, NY 10021 2 Department of Biology, Chungbuk National University, Cheongju 28644, Republic of Korea 3 Department of Microbiology, Dankook University, Cheonan 31116, Republic of Korea 4 Graduate Program in Immunology and Microbial Pathogenesis, Weill Cornell Graduate School of Medical Sciences, New York, NY 10021

*Correspondence to: Lionel B. Ivashkiv, Hospital for Special Surgery, 535 East 70th Street, New York, NY 10021; Tel: 212-606-1653; Fax: 212-774-2301; Email: [email protected]

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Abstract

Complete polarization of macrophages towards an M1-like proinflammatory and antimicrobial state requires combined action of IFN-γ and LPS. Synergistic activation of canonical inflammatory NF-κB target genes by IFN-γ and LPS is well appreciated, but less is known about whether IFN-γ negatively regulates components of the LPS response, and how this affects polarization. A combined transcriptomic and epigenomic approach revealed that IFN-γ selectively abrogates LPS-induced feedback and select metabolic pathways by suppressing TLR4-mediated activation of gene enhancers. In contrast to superinduction of inflammatory genes via enhancers that harbor IRF sequences and bind STAT1, IFN-γ-mediated repression targeted enhancers with STAT sequences that bound STAT3. TLR4-activated IFN-γ-suppressed enhancers comprised two subsets distinguished by differential regulation of histone acetylation and recruitment of STAT3, CDK8 and cohesin, and were functionally inactivated by IFN-γ. These findings reveal that IFN-γ suppresses feedback inhibitory and metabolic components of the TLR response to achieve full M1 polarization, and provide insights into mechanisms by which IFN-γ selectively inhibits TLR4-induced transcription.

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Introduction

Macrophages are dynamic cells that polarize into diverse states in response to various stimuli1-3. M1 macrophages that are polarized by inflammatory signals, such as interferon-γ (IFN-γ) and lipopolysaccharide (LPS) play an important role in host defense against pathogens as well as the pathogenesis of chronic inflammatory diseases4-7. Although IFN-γ via Jak-STAT1 signaling pathway can induce antigen-presentation molecules and chemokines, the expression of inflammatory cytokines (NF-κB target genes), such as TNF and IL6 are not activated by IFN-γ alone. In addition, LPS alone transiently activates inflammatory genes but pre-exposure to LPS rather induces tolerance and thereby resistance to subsequent Toll-like receptor (TLR) 4 stimulation. To achieve complete M1 polarization, IFN-γ primes macrophages and synergizes with LPS to activate inflammatory programs through several molecular mechanisms including chromatin remodeling and metabolic reprogramming at the level of translation8-10. In the TLR4 response, autocrine IFN-β signals through STAT1–STAT2 and IRF9, which form the interferon-stimulated gene factor 3 (ISGF3) complex that binds to interferon-stimulated response elements (ISREs), to induce a feedforward loop in LPS-induced gene expression that promotes M1-like polarization11. On the other hand, LPS also induces feedback inhibition loops including IL-10-STAT3 anti-inflammatory pathways to prevent excessive inflammation12. However, the importance of over-riding feedback inhibition by IFN-γ and underlying mechanisms are not well understood. Prolonged exposure to various stimuli adjusts the responsiveness of macrophages to secondary stimulation. This feature of innate immune cells, called “innate immune memory”, plays a key role in various immune responses, for example endotoxin tolerance during sepsis, trained immunity after vaccination, and inhibition of anti- microbial responses by parasitic infections13-16. Priming of macrophages by IFN-γ exhibits certain similarities to training10. Most studies of IFN-γ priming have focused on the enhancement of secondary responses to inflammatory challenge, but IFN-γ-mediated attenuation of responses to subsequent stimulation is mostly unexplored. Epigenomic reprogramming of macrophage-specific enhancers by a variety of micro- environmental stimuli contributes to not only the distinct phenotypes of macrophages in different tissues or diseases states, but also to innate immune memory in macrophages17,18. Signal- dependent transcription factors including NF-κB, AP-1, and STATs play a critical role in

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dynamic changes of active enhancer landscapes in macrophages19,20. Our previous work demonstrated that IFN-γ priming mediates genome-wide STAT1 binding with IRF1 at cis- regulatory elements to increase histone acetylation to enhance the transcriptional responsiveness to subsequent LPS stimulation8. Unbiased transcriptome-wide analysis has revealed that environmental signals confer not only the activation of gene expression but also the repression of distinct gene sets21-23. Molecular mechanisms by which the key macrophage-polarization signals, such as IFN-γ or IL-4 suppress gene expression have been studied using epigenomic approaches. For example, IFN-γ can repress basal M2-like gene expression programs by two distinct mechanisms: first, IFN-γ induces the deposition of negative histone mark H3K27me3 at the promoters by EZH2 recruitment24, and second, IFN-γ deactivates and disassembles enhancers by suppressing binding by MAF and lineage-determining transcription factors25. It has also been reported that IL-4 can antagonize IFN-γ-induced transcriptional responses26, and directly suppress LPS-induced inflammatory responses by STAT6-dependent enhancer deactivation27. Despite these efforts to reveal mechanisms of transcriptional repression by IFN-γ, inhibition of LPS-inducible genes by IFN-γ at the transcriptomic and epigenomic level has not been elucidated. In this study, we sought to understand how IFN-γ priming selectively attenuates a component of TLR4-induced genes to fully polarize M1-like macrophages. To address this question, we performed a comprehensive transcriptomic and epigenomic analysis using primary human macrophages, which are physiologically relevant for human inflammatory disease conditions. We found that LPS-induced genes that are repressed by IFN-γ priming are subdivided into at least two subsets: those regulated by an IL-10-STAT3 negative feedback loop, and those that function in metabolic pathways and are regulated independently of IL-10. One mechanism of repression involves deactivation of LPS-induced enhancers, which similarly fall into distinct subsets. Inhibition of one subset of enhancers that harbors STAT motifs occurs via suppression of histone acetylation and recruitment of STAT3, CDK8-Mediator and cohesin. This contrasts with superactivation of TLR4-inducible genes via recruitment of STAT1 to enhancers that harbor IRF motifs. These findings provide insights into mechanisms by which IFN-γ selectively suppresses anti-inflammatory and metabolic components of the TLR response by enhancer deactivation to augment M1-like inflammatory macrophage polarization.

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Results

IFN-γ selectively inhibits components of the LPS-induced transcriptional response

A well-established function of IFN-γ is augmentation of LPS-induced inflammatory gene expression, but little is known about the overall effects of IFN-γ on TLR4-induced transcriptional responses. To examine how IFN-γ alters LPS-induced gene expression, we performed RNA sequencing (RNA-seq) analysis of primary human macrophages cultured with or without IFN-γ for 48 hr, followed by a 3 hr challenge with LPS (Fig. 1a; correlation between biological replicates and principal component analysis is shown in Supplementary Fig. 1a,b). We focused on the 3,909 genes significantly differentially expressed (FDR < 0.01, >2-fold differences in expression) in any pairwise comparison among four conditions. k-means clustering classified the genes into six gene clusters that were distinctly regulated by IFN-γ and LPS (Fig. 1b, c). Although many of the clusters captured known and previously studied patterns of gene regulation by IFN-γ and/or LPS, there was a large subset of genes (cluster IV, n = 770) whose induction by LPS was suppressed by IFN-γ. As the negative regulation of TLR responses by IFN-γ is poorly understood, in this study we focused on class IV genes, and on enhancers that are regulated in a similar manner. As a comparison point and a control, we used class V genes (n = 541) that were synergistically induced by IFN-γ and LPS and comprise canonical inflammatory genes such as IL6, IL23A and CXCL9. Gene ontology (GO) analysis revealed that each cluster was enriched in genes associated with distinct biological functions (Fig. 1d). Cluster I, which contains genes basally expressed in human macrophages and repressed by IFN-γ and LPS, was enriched for genes involved in wound healing and related reparative processes; this extends our previous work showing that IFN-γ suppresses basal expression of genes that are inducible by glucocorticoids and IL-425. In contrast, LPS-inducible genes repressed by IFN-γ (cluster IV) were associated with negative regulation of inflammatory responses, metabolism, and iron transport (Fig. 1d). Closer examination of these genes and comparison to public databases (GSE4370028) revealed that cluster IV contains IL10 and that approximately 30% (239/770) of genes in cluster IV correspond to IL-10-inducible genes (Fig. 1e, left, representative genes are shown, and Supplementary Fig. 1c). These results suggest that IFN-γ broadly interrupts the IL-10-mediated LPS-induced negative feedback loop

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that negatively regulates inflammation, at least in part by suppressing IL10 induction. GO and pathway analysis revealed that the IL-10-inucible genes in cluster IV were associated with anti- inflammatory and heme metabolism pathways, whereas the non-IL-10-inducible genes showed enrichment in distinct pathways related to lipid, purine, tryptophan, and iron metabolism; rRNA processing; and protein stabilization and unfolded protein binding (Fig. 1e, middle, representative genes are shown, Supplementary Fig. 1d-f). The two distinct gene subsets in cluster IV could be partially distinguished by induction mediated by Jak-STAT signaling versus mTORC1 signaling and Myc binding at promoters, which is in accord with a previous report9 (Supplementary Fig. 1f,g). Cluster IV also includes AP-1-dependent genes such as MMPs, which is consistent with previous reports that IFN-γ suppresses AP-1 pathways6,29. In contrast to class IV, class V, which contains genes synergistically induced by IFN-γ and LPS, was enriched in inflammatory cytokine and chemokine genes (Fig. 1c-e). The differential regulation of genes in clusters IV and V by IFN-γ is highlighted in the volcano plot depicted in Fig. 1f. These results identify two groups of LPS-inducible genes that are regulated in opposing directions by IFN-γ and have distinct functions, and identify negative regulation of LPS-induced metabolic genes as a new IFN-γ function.

IFN-γ selectively inhibits a subset of LPS-activated enhancers

We wished to understand mechanisms underlying suppression of LPS-inducible genes by IFN-γ and tested the hypothesis that IFN-γ inhibits a subset of LPS-activated enhancers using an epigenomic approach. We defined active enhancers as regions of “open” chromatin (detected by ATAC-seq) that were located >1 kb away from the TSS, bound lineage-determining transcription factors (TFs) PU.1 and/or C/EBP, and exhibited histone 3 lysine 27 acetylation (H3K27-Ac) in at least one of the four conditions (FDR < 0.05, >2-fold changes in any pairwise comparison; n = 20,782 enhancers, Supplementary Fig. 2b). The enhancer calls were validated relative to DNase-seq and histone 3 monomethylated at lysine 4 (H3K4me1) data for CD14-positive monocytes from ENCODE project (Supplementary Fig. 2c). Similar to the transcriptomic data, enhancers differentially regulated at the H3K27-Ac level sorted into six major clusters (Supplementary Fig. 2a, d and Fig. 2a-c). The patterns of H3K27-Ac regulation in enhancer clusters generally resembled patterns of gene expression in gene clusters. Notably, we detected

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enhancer clusters whose activation (defined by increased H3K27-Ac) by LPS was either blocked (cluster e4, similar pattern to gene cluster IV) or superinduced (cluster e5, similar pattern to gene cluster V) by IFN-γ (Fig. 2a,b, representative gene tracks shown in Fig. 2c). Thus, IFN-γ also differentially regulates LPS-mediated enhancer activation. We next tested the relationship between IFN-γ-mediated changes in LPS-induced enhancer and gene activity. Notably, genes associated with enhancer cluster e4 closely correlated with genes in cluster IV, and genes associated with e5 enhancers correlated with cluster V genes (Fig. 2d). These enhancer- associated genes exhibited the expected pattern of gene expression, namely attenuation (e4- associated genes) or superinduction (e5-associated genes) of the LPS response by IFN-γ (Fig. 2e, f). Overall, these findings suggest that IFN-γ selectively modulates LPS-induced enhancer activation to attenuate or boost expression of different subsets of LPS-inducible genes.

TF expression and binding motif enrichment in distinct enhancer clusters

We reasoned that patterns of TF expression under the four experimental conditions, combined with TF binding motif enrichment in different enhancer clusters, would provide insight into differential regulation of enhancer activity by LPS- and IFN-γ-regulated TFs. IFN-γ and LPS altered the expression of 342 TFs which were partitioned into the six gene clusters defined in Fig. 1b based on pattern of expression (Fig. 3a, b). Cluster IV TFs (IFN-γ attenuates LPS- induced expression) were distinguished from cluster V TFs (IFN-γ synergizes with LPS) by expression of STAT3 and AP-1 family members BATF and FOSL2. A parallel analysis of TF binding motifs enriched under the enhancer peaks defined in Fig. 2b revealed that enhancer clusters e4 and e5 were distinguished by enrichment of AP-1 and STAT motifs in e4 enhancers and IRF motifs in e5 enhancers (Fig. 3c, known motif analysis, Fig. 3d-e, de novo motif analysis, and Supplementary Fig. 3a,b). The enrichment of AP-1 motifs in e4 enhancers is consistent with inhibition of AP-1 signaling by IFN-γ6,30. Given that IFN-γ strongly induces and activates STAT1 (Fig. 3b), which has a predominantly pro-inflammatory function, the absence of STAT motifs in e5 enhancers may appear puzzling. However, this can be explained by previous work from our laboratory and other groups showing that treatment with IFN-γ for the longer times used in this study results in predominant binding of STAT1 to IRF sites8,20,26,31, presumably in a complex with IRF proteins; STAT1 binding measured by ChIP-seq experiments

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is analyzed below. Known motif analysis (Fig. 3c) suggested binding of STAT3 to e4 enhancers under LPS-stimulated conditions, which is consistent with the well-established LPS-induced IL- 10-STAT3 autocrine loop, and was further supported by Factorbook ChIP-seq data from ENCODE project showing co-occupancy of STAT3 with AP-1 at representative cluster e4 enhancers (Fig. 3f). Furthermore, IFN-γ suppressed LPS-induced STAT3 expression (Fig. 3b), and ChIP-qPCR showed that IFN-γ blocked LPS-induced recruitment of STAT3 and the AP-1 protein c-Jun to e4 enhancers at the IL10 locus (Fig. 3g and h). Overall, these data suggested a role for STAT3 in the regulation of e4 enhancers, and motivated further investigation into how the genomic profile of STAT3 binding is affected by LPS and IFN-γ.

e4 enhancers bind STAT3 which is suppressed by IFN-γ

We next performed STAT3 ChIP-seq experiments and analyzed binding of STAT3 and STAT1 (GSE430368) to enhancers in clusters e1-e5 after stimulation of human macrophages with LPS, IFN-γ, or both, under the same conditions as used in Figures 1-3. Interestingly, induction of STAT3 occupancy showed a very restricted pattern, with a substantial increase only in cluster e4 enhancers in cells stimulated by LPS; this increase in binding was strongly blocked by IFN-γ (Fig. 4a-c; representative gene tracks at IL10, TNIP3, and IL4R loci are shown in Fig. 4d). The much weaker recruitment of STAT3 to e5 enhancers was not affected by IFN-γ, which is consistent with the different regulation of e4 and e5 enhancers. In contrast to STAT3, STAT1 occupancy was increased in most enhancer clusters in cells treated with IFN-γ, and was also induced by LPS in clusters e4 and e5 (Fig. 4e-g). As expected8,10, the pattern of STAT1 binding was similar to IRF1 binding (Supplementary Fig. 4b,c). The significance of STAT1 binding to e4 enhancers is not clear, but the increased binding of STAT1 to e5 enhancers in macrophages treated with IFN-γ + LPS is consistent with our previously proposed model of activation of ‘synergy genes’ by concomitant binding of STAT1 to several enhancers at these gene loci8. Overall, the data support a role for IFN-γ-mediated suppression of STAT3 binding at e4 enhancers in the downregulation of expression of associated genes.

IFN-γ suppresses coactivator and CDK8 recruitment to e4 enhancers

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STATs activate gene expression in part by recruiting transcriptional coactivators such as the histone acetyltransferase p300 and Mediator complexes, a subset of which contain the serine kinase CDK832-34. CDK8 in turn potentiates the transcriptional activity of STAT1 and STAT3 by phosphorylating a serine residue in their transactivation domains. We wished to test the idea that IFN-γ suppresses coactivator recruitment to e4 enhancers (LPS-activated enhancers suppressed by IFN-γ). We first examined coactivator and CDK8 recruitment at the prototypical e4 enhancer associated with the class IV gene IL10; as a contrasting control, we used e5 enhancers associated with prototypical class V ‘synergy genes’ genes IL6 and IL23A. IFN-γ strongly suppressed LPS- induced recruitment of p300, the MED1 component of Mediator, and CDK8 to IL10 locus e4 enhancers but not to IL23A and IL6 e5 enhancers (Fig. 5a-c and Supplementary Fig. 5a,b). We followed up these results using ChIP-seq to obtain the genomic profile of CDK8 binding. Strikingly, CDK8 recruitment was most prominent at e4 and e5 enhancers and paralleled their activity (Fig. 5d-f and Supplementary Fig. 5c,d). Namely, LPS-inducible recruitment of CDK8 to e4 enhancers was suppressed by IFN-γ, and to e5 enhancers was increased by IFN-γ. Taken together, the data suggest that IFN-γ suppresses recruitment of STAT3 and coactivators to enhancers concomitant with suppressing expression of associated genes.

e4 enhancers partition into two subsets based on coordinate STAT3 and CDK8 binding

As cluster IV genes partitioned into IL-10-dependent and independent genes, we wondered whether e4 enhancers can be similarly subdivided. Closer examination of STAT3 binding to e4 enhancers, when rank-ordered according to tag counts, revealed that e4 enhancers partitioned into two groups, characterized by either high (52%, n =787) or low (48%, n = 739) if not absent STAT3 binding (Fig. 6a). We wondered whether STAT3hi peaks corresponded to enhancers that are activated by autocrine IL-10, and began to address this question by performing STAT3 ChIP- seq using macrophages stimulated with recombinant IL-10. Interestingly, STAT3hi e4 enhancers (in cells treated with LPS) corresponded closely to enhancers that bound STAT3 after stimulation with IL-10 (Fig. 6b). To determine which additional features distinguished STAT3hi from STAT3lo e4 enhancers, we performed motif analysis, which revealed selective enrichment of the STAT motif only at STAT3hi e4 enhancers (Fig. 6c). This result coincides with selective

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enrichment of the STAT motif in promoters of IL-10-inducible cluster IV genes (Supplementary Fig. 1g), which showed higher STAT3 occupancy than promoters of non-IL-10-inducible cluster IV genes (Supplementary Fig. 6a). Furthermore, STAT3hi e4 enhancers showed higher amounts of CDK8 binding compared to STAT3lo e4 enhancers (Fig. 6d). In contrast, CDK8 occupancy was significantly higher at STAT1hi e5 enhancers in IFN-γ-primed LPS-stimulated macrophages (Supplementary Fig. 6b,c). Thus, consistent with the literature, CDK8 occupancy paralleled STAT recruitment during macrophage activation.

CDK8-Mediator complexes and cohesin co-occupy regulatory elements involved in enhancer-promoter looping during gene activation35-37. We thus wished to investigate the relationship between CDK8 and cohesin occupancy in STAT3hi and STAT3lo e4 enhancers. Similar to CDK8 binding, SMC1 occupancy was higher at STAT3hi e4 enhancers, and this binding was induced by LPS and suppressed by IFN-γ (Fig. 6e,f; representative gene tracks at IL10 and TNIP3). We next tested whether the two distinct groups of e4 enhancers (STAT3hi CDK8hi SMC1hi versus STAT3lo CDK8lo SMC1lo) are associated with the different subsets of cluster IV genes, as defined above in Fig. 1. Genomic Regions Enrichment of Annotations Tool (GREAT) analysis of genes associated with STAT3hi e4 enhancers revealed enrichment for IL- 10-related GO terms and pathways (Fig. 6g, upper panels and 6h), similar to IL-10-inducible cluster IV genes (Fig. 1e and Supplementary Fig. 1e, f). In contrast, genes associated with STAT3lo e4 enhancers showed distinct functional enrichment for lipid metabolism and MAPK signaling pathways (Fig. 6g, lower panels), which partially resembles pathways associated with non-IL-10-inducible cluster IV genes (Supplementary Fig. 1d-f) and is consistent with enrichment of AP-1 motifs in promoters and enhancers of these genes (Supplementary Figs. 1f, g and 6c). Indeed, genes associated with STAT3hi e4 enhancers were highly represented in the IL-10-inducible cluster IV gene set (53 out of 101; Fig. 6h, left), whereas genes associated with STAT3lo e4 enhancers were included in the non-IL-10-inducible cluster IV gene set (18 out of 36; Fig. 6h, right). In addition, genes associated with STAT3hi e4 enhancers were more highly induced by IL-10 in monocytes (Supplementary Fig. 6d). Overall, these data suggest that, similar to cluster IV genes, e4 enhancers partition into two groups: those activated by LPS- induced autocrine IL-10 that bind STAT3 as well as CDK8-Mediator and cohesin, and those possibly regulated by AP-1 or other as yet unknown IL-10-independent mechanisms.

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IFN-γ functionally suppresses LPS-inducible STAT3-binding enhancers

Enhancer activity is associated with recruitment of RNA polymerase II (Pol II) and transcription of enhancer RNAs (eRNAs)38-41. To corroborate the notion that IFN-γ inactivates LPS-induced enhancers, as suggested by the analysis of histone acetylation and CDK8-cohesin recruitment presented above, we more directly measured enhancer activity using ChIP-seq to measure Pol II recruitment. IFN-γ attenuated LPS-induced Pol II recruitment to STAT3hi e4 enhancers (Fig. 7a, b; representative gene tracks at IL10 locus; HSS+6 and HSS-16 mark enhancers). In contrast, IFN-γ increased Pol II recruitment to STAT1hi e5 enhancers (Supplementary Fig. 7a, b; representative gene tracks at IL23A locus). eRNA could not be reliably measured genome-wide by RNA-seq, but IFN-γ-mediated suppression was clearly seen at enhancers of select genes such as IL10 (Fig. 7b, gene tracks 5-8) and was confirmed by qPCR (Fig. 7c and Supplementary Fig. 7c, d). Locked nucleic acid-mediated knockdown of IL10 eRNA at enhancers located 16 kb upstream (HSS-16) or 6 kb downstream (HSS+6) of the IL10 TSS suppressed IL10 mRNA expression, supporting a functional role for eRNA and for IFN-γ-regulated enhancer activity.

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Discussion

Investigation of IFN-γ transcriptional responses has primarily focused on gene activation during ‘M1-like’ macrophage polarization, which is linked to the ‘IFN-γ signature’ observed in autoimmune diseases, and on synergistic activation of inflammatory genes in cooperation with inflammatory factors such as TLR ligands7,10,42,43. However, negative regulation of TLR-induced gene expression, and its potential functional consequences and underlying mechanisms are not well understood. This study used a combined transcriptomic and epigenomic approaches to obtain several new insights into negative regulation of TLR-induced gene expression by IFN-γ. First, IFN-γ selectively represses induction of IL-10-inducible anti-inflammatory genes and also metabolic genes that are regulated by a distinct mechanism. Suppression of IL-10-mediated feedback serves to amplify inflammatory gene activation, whereas suppression of metabolic genes, for example those involved in fatty acid metabolism that is linked with M2 macrophages, may promote M1-like polarization. Second, IFN-γ inhibits TLR4-induced genes by targeting associated TLR4-activated enhancers to suppress histone acetylation. Third, IFN-γ inhibits at least two distinct subsets of enhancers, one of which is characterized by enrichment of STAT binding motifs and TLR-induced recruitment of STAT3, CDK8 and cohesin. Another suppressed enhancer subset is enriched for AP-1 motifs and may regulate metabolic genes. Fourth, suppressed enhancers differ from enhancers associated with superactivated ‘synergy’ genes as the latter harbor IRF instead of STAT motifs and bind STAT1 instead of STAT3. However, TLR-inducible enhancers that are modulated by IFN-γ share the property of CDK8 occupancy, suggestive of binding by a select CDK8-containing Mediator complex. Our findings provide insights into previously unexplored mechanisms that selectively regulate TLR responses to promote inflammatory gene activation and M1-like macrophage polarization.

IFN-γ represses basal gene expression in resting macrophages by two distinct epigenetic mechanisms: (1) induction of the negative histone mark H3K27me3 by recruiting EZH2 to promoters24 and (2) suppression of the active enhancer histone mark H3K27ac by inhibiting key enhancer-occupying transcription factors, such as MAF25. Only a small number of genes is regulated by IFN-γ-mediated direct deposition of H3K27me3, and we did not detect this inhibitory mechanism in the suppression of LPS-induced gene expression. Other groups have

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suggested that the transcription factors induced by different stimuli, such as IL-4-STAT627 and nuclear receptors (PPARs and LXRs) can directly suppress gene transcription44. However, our data did not support a direct repressive role for IFN-γ-induced STAT1 binding at LPS-activated cis-regulatory elements, such as promoters and enhancers. Although we cannot rule out the possibility of STAT1 functioning as a transcriptional repressor, it is more likely that IFN-γ mainly mediates the suppression of gene expression by more indirect mechanisms such as STAT1-induced expression of inhibitors of signaling or transcription. Future work is needed to address whether IFN-γ-induced STAT1 has direct repressive functions in other systems.

In macrophages, it has been shown that IL-10, via STAT3, plays a pivotal role in the induction of anti-inflammatory factors, such as BCL3, IL4R, and TNIP345, and in regulating an mTORC1-mediated metabolic program including DDIT446. Thus, inactivation of the LPS- induced IL-10-STAT3 negative regulatory pathway by IFN-γ can contribute to ‘uncontrolled’ chronic inflammatory responses. Our epigenomic analysis has identified previously uncharacterized LPS-activated enhancer regions (cluster e4), which are selectively inhibited by IFN-γ. Notably, IFN-γ suppressed LPS-inducible STAT3 binding at e4 enhancers that showed loss of active enhancer marks including histone acetylation and of occupancy by CDK8-cohesin and eRNA-related RNA polymerase II. The e4 enhancers with strong recruitment of STAT3 (STAT3hi e4) showed substantial overlap with enhancers that bind STAT3 as part of the canonical IL-10-induced anti-inflammatory program; a similar overlap was observed between e4-associated and IL-10-inducible genes. Given that LPS-inducible STAT3 binding is related to IL-10-induced anti-inflammatory programs, our findings may provide additional insight into the paradoxical effect of Jak inhibitors on the LPS response, namely that they inhibit STAT3- mediated feedback inhibition47. A distinct subset of e4 enhancers with low or minimal STAT3 binding after LPS stimulation (STAT3lo e4) were associated with a distinct IL-10-independent gene set with different functions, such as MAPK signaling pathway and lipid metabolism. We gained new insights into how IFN-γ negatively regulates the expression of LPS-induced autocrine IL-10, which is a major direct inducer of STAT3 activation in response to LPS. Previous work has shown that IFN-γ inhibits TLR-induced autocrine IL-10 production by suppressing AP-1 related pathway, which is related to the differential regulation of MAPK and GSK3 activity48. The current study extends this work to suggest that negative regulation of

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signaling leads to suppression of LPS-induced IL10 expression via epigenetic deactivation of enhancers at the IL10 locus.

In addition to inhibiting the LPS-induced IL-10-STAT3 autocrine feedback loop, IFN-γ suppressed induction of other multiple additional components of the LPS response that are independent of IL-10. In line with previous work9, IFN-γ inhibited transcription of genes that are involved in the ribosomal RNA processing and protein stabilization, and downstream of mTORC1 signaling and Myc. A recent study utilizing co-stimulation of mouse macrophages with IFN-γ and IL-4 suggested that Myc is associated with a component of the IL-4 response that is resistant to suppression by IFN-γ26. Our findings suggest that prolonged exposure to IFN-γ that suppresses Myc may overcome this resistance mechanism. mTORC1 and Myc are major regulators of cellular metabolism, and IFN-γ repressed genes involved in various metabolic processes, such as iron transport, purine synthesis, tryptophan metabolism, and lipid metabolism. Of these, lipid metabolism has been linked with M2-like polarization, tryptophan metabolites in anti-inflammatory responses, and iron transport in host defense. Future work will be needed to determine how regulation of these IL-10-independent TLR4-induced metabolic pathways by IFN-γ contributes to the M1 polarized phenotype49-51. Overall, IFN-γ-mediated inhibition of anti- inflammatory, translational, and metabolic components of the LPS response is likely coordinated to enable a fully ‘classically activated’ macrophage phenotype.

Our findings highlight differences in function and mode of regulatory element binding between STAT1 and STAT3 during macrophage polarization and LPS challenge. Previous work has shown that during IFN-γ-mediated priming/polarization of macrophages, the genomic profile of STAT1 binding changes from STAT sites to IRF sites, which STAT1 can occupy as part of complexes with IRF1 or (after LPS stimulation) as part of the ISGF3 complex8,20. In line with these reports, we found that STAT1 binds to IRF sites coordinately with IRF1 in e5 enhancers, and that joint stimulation with IFN-γ + LPS increased STAT1 binding, which correlated with increased enhancer activity (as assessed by histone acetylation) and superinduction of associated cluster V genes. In contrast, STAT3 bound to e4 enhancers that are enriched for STAT motifs, and joint stimulation with IFN-γ + LPS decreased STAT3 binding, which correlated with decreased enhancer activity and suppression of associated cluster IV genes. These results

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support a model whereby intrinsic DNA sequences (TF binding motifs) in genomic regulatory elements determine whether IFN-γ primes or suppresses activation of an enhancer by LPS. Namely, in IFN-γ-primed macrophages enhancers that contain IRF but not STAT sites (e.g. e5 enhancers) are subject to regulation mostly by STAT1 and exhibit enhanced LPS responses, whereas enhancers that also contain a STAT site are regulated by STAT3 and exhibit attenuated LPS responses. Motif analysis also found that AP-1 motif was highly enriched at STAT3-bound enhancers, which are suppressed by IFN-γ, suggesting that IFN-γ-regulated AP-1 transcription factors including BATF and FOSL2 might serve as ‘auxiliary’ transcription factors and cooperate with STAT3 to regulate e4 enhancers. Identifying additional auxiliary transcription factors that cooperate with STAT1 and STAT3 to drive expression of different LPS-regulated gene sets may provide further insight into STATs-dependent transcriptional regulation in a gene- specific manner.

In summary, this study provides insights about how two stimuli can cooperate at the epigenomic level to achieve differential regulation of gene sets with distinct and even opposing functions. In the case of IFN-γ and LPS, differential regulation of enhancers with different sequence architecture results in decreased expression of anti-inflammatory and metabolic genes, with superinduction of inflammatory genes that may enhance inflammatory responses. The distinct enhancer classes can potentially be targeted to restrain excessive inflammation.

15 bioRxiv preprint doi: https://doi.org/10.1101/437160; this version posted October 7, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Methods

Cell culture. Peripheral blood mononuclear cells were obtained from blood leukocyte preparations purchased from the New York Blood Center by density gradient centrifugation with Ficoll (Thermo Fisher Scientific) using a protocol approved by the Hospital for Special Surgery Institutional Review Board. Primary human CD14+ monocytes were obtained from peripheral blood, using anti-CD14 magnetic beads, as recommended by the manufacturer (Miltenyi Biotec). Monocytes were cultured in RPMI 1640 medium (Invitrogen) supplemented with 10% heat- inactivated defined FBS (HyClone Fisher), penicillin/streptomycin (Invitrogen), L-glutamine (Invitrogen), and 10 ng/ml human macrophage colony-stimulating factor (M-CSF; Peprotech) in the presence or absence of 100 U/ml human IFN-γ (Roche) as indicated; IFN-γ was added at the same time as M-CSF at initiation of cultures. LPS was purchased from Invivogen (tlrl-3pelps).

Analysis of RNA. Total RNA was extracted from cells using RNeasy Mini kit (QIAGEN), and 500 ng of total RNA was reverse transcribed using the RevertAid First Strand cDNA Synthesis kit (Fermentas). Real-time PCR was performed in triplicate with Fast SYBR Green Master Mix and 7500 Fast Real-time PCR system (Applied Biosystems). Primer sequences are provided in the Supplementary Table 1.

RNA-seq. After RNA extraction, libraries for sequencing were prepared using the Illumina TruSeq RNA Library Prep Kit following the manufacturer’s instructions. High throughput sequencing (50 bp, paired-end) was performed at the Genomics Resources Core Facility of Weill Cornell Medicine. On average 100 million reads were obtained per sample. Sequenced reads were mapped to reference human genome (hg19 assembly) using STAR aligner52 with default parameters and Cufflinks version 2.2.153 was used to estimate the abundance of transcripts. The expression levels of genes in each sample were normalized by means of fragments per kilobase of transcript per million mapped reads (FPKM). The concordance between replicates was very high (R2 range, 0.943-0.964).

RNA-seq analysis. Differentially expressed genes (DEGs) were identified using edgeR v3.16.5 54,55. Read counts for edgeR analysis were obtained with featureCounts v1.5.1 56. After eliminating absent features (zero counts), the raw counts were normalized with edgeR, followed by differential expression analysis. Significantly up- or down-regulated genes were defined as

expressed genes with p-value adjusted for multiple testing (FDR < 0.01) and log2 fold-change of at least 1. To generate the heatmap of K-mean clusters, we used GENE-E (Broad Institute) set to global comparison and average-centered. The value of K was chosen at 6 because lower values failed to identify all meaningful clusters and higher values subdivided meaningful clusters. To find the GO terms enriched in differentially regulated genes, we used the DAVID web-tool57 and

16 bioRxiv preprint doi: https://doi.org/10.1101/437160; this version posted October 7, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Gene Set Enrichment Analysis (GSEA) of MSigDB gene sets (allmark, KEGG, and REACTOME).

Analysis of gene expression in IL-10 stimulated human monocytes. Microarray data sets were retrieved from GSE43700. The raw data were normalized by a quantile normalization method using the preprocessCore package in R. Normalized expression levels were averaged within the same condition and fold-change of the average for each gene was calculated.

ChIP and ChIP-seq. Cells were crosslinked for 5 min at room temperature by the addition of one-tenth of the volume of 11% formaldehyde solution (11% formaldehyde, 50 mM HEPES pH 7.5, 100 mM NaCl, 1 mM EDTA pH 8.0, 0.5 mM EGTA pH 8.0) to the growth media followed by 5 min quenching with 100 mM glycine. Cells were pelleted at 4°C and washed with ice-cold PBS. The crosslinked cells were lysed with lysis buffer (50 mM HEPES-KOH pH 7.5, 140 mM NaCl, 1 mM EDTA, 10% glycerol, 0.5% NP-40, and 0.25% Triton X-100) with protease inhibitors on ice for 10 min and washed with washing buffer (10 mM Tris-HCl, pH 8.0, 200 mM NaCl, 1 mM EDTA, 0.5 mM EGTA) for 10 min. The lysis samples were resuspended and sonicated in sonication buffer (10 mM Tris-HCl, pH 8.0, 100 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 0.1% Na-Deoxycholate, 0.5% N-lauroylsarcosine) using a Bioruptor (Diagenode) with 30 sec ON, 30 sec OFF on high power output for 18 cycles. After sonication, samples were centrifuged at 14,000 rpm for 10 minutes at 4°C and 5% of sonicated cell extracts were saved as input. The resulting whole-cell extract was incubated with Protein A Agarose for ChIP (EMD Millipore) for 1 hr at 4°C. Precleared extracts were then incubated with 50 μl (50% v/v) of Protein A Agarose for ChIP (EMD Millipore) with 5 μg of the appropriate antibody overnight at 4°C. ChIP lysates were generated from 2 x 107 to 3 x 107 cells (for PU.1, C/EBPβ, STAT3, c- Jun, and RNA Polymerase II ChIP) or 10 x 107 cells (for p300, MED1, CDK8, and SMC1 ChIP) respectively. ChIP antibodies against PU.1 (sc-352), C/EBPβ (sc-150), STAT3 (sc-482), c-Jun (sc-1694), p300 (sc-585) and CDK8 (sc-1521) were from Santa Cruz Biotechnology. Antibodies against RNA Polymerase II (MMS-126R) were from Covance. Antibodies against MED1 (A300- 793A) and SMC1 (A300-055A) were from Bethyl Laboratories. After overnight incubation, beads were washed twice with sonication buffer, once with sonication buffer with 500 mM NaCl, once with LiCl wash buffer (10 mM Tris-HCl pH 8.0, 1 mM EDTA, 250 mM LiCl, 1% NP-40), and once with TE with 50 mM NaCl. DNA was eluted in freshly prepared elution buffer (1%

SDS, 0.1M NaHCO3). Cross-links were reversed by overnight incubation at 65°C. RNA and protein were digested using RNase A and Proteinase K, respectively and DNA was purified with ChIP DNA Clean & Concentrator™ (Zymo Research). For ChIP-qPCR assays, immunoprecipitated DNA was analyzed by quantitative real-time PCR and normalized relative to input DNA amount. For ChIP-seq experiments, 10 ng of purified ChIP DNA per sample were ligated with adaptors and 100-300 bp DNA fragments were purified to prepare DNA libraries using Illumina TruSeq ChIP Library Prep Kit following the manufacturer’s instructions. ChIP libraries were

17 bioRxiv preprint doi: https://doi.org/10.1101/437160; this version posted October 7, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

sequenced (50 bp single end reads) using an Illumina HiSeq 2500 Sequencer at the Epigenomic Core Facility of Weill Cornell Medicine per manufacturer's recommended protocol. Because of limitations on cell numbers and to decrease variability related to differences among individual donors, chromatin immunoprecipitations were performed using pooled samples from more than two (for STAT3) or four (for CDK8 and SMC1) different donors. For STAT3, a second experiment with pooled samples from several donors was performed and congruence between the replicates was assessed by generating scatter plots and estimating Pearson correlation coefficients (Figure S4A). After ascertaining close correlation between replicates, we performed bioinformatic analysis using replicate 1 and confirmed key results using replicate 2. The H3K27ac, STAT1 and IRF1 data were from GSE43036.

ATAC-seq. ATAC-seq was performed as previously described58. 50,000 cells were centrifuged at 500 g for 5 min at 4°C. Cell pellets were washed once with 1x PBS and cells were pelleted by centrifugation using the previous settings. Cell pellets were resuspended in 25 μl of lysis buffer

(10 mM Tris-HCl pH 7.4, 10 mM NaCl, 3 mM MgCl2, 0.1% IGEPAL CA-630) and centrifuged immediately 500 g for 10 min at 4°C. The cell pellet was resuspended in the transposase reaction mix (25 μL 2× TD buffer (Nextera DNA Sample Preparation Kit), 2.5 μL Illumina Tn5 transposase and 22.5 μL nuclease-free water). The transposition reaction was carried out for 30 min at 37°C. Directly following transposition, the sample was purified using a QIAGEN MinElute Purification Kit. Then, we amplified library fragments using NEBNext 2x PCR master mix and 1.25 M of Nextera PCR primers, using the following PCR conditions: 72 °C for 5 min; 98 °C for 30 s; and thermocycling at 98°C for 10 s, 63°C for 30 s and 72°C for 1 min. The libraries were purified using a QIAGEN PCR purification kit yielding a final library concentration of ~30 nM in 20 μL. Libraries were amplified for a total of 10-13 cycles and were subjected to high-throughput sequencing using the Illumina HiSeq 2500 Sequencer.

ChIP-seq and ATAC-seq analysis. For ChIP-seq and ATAC-seq experiments, sequenced reads were aligned to reference human genome (GRCh37/hg19 assembly) using Bowtie2 version 2.2.6 (Langmead and Salzberg, 2012) with default parameters, and clonal reads were removed from further analysis. A minimum of 20 million uniquely mapped reads were obtained for each condition. We used the makeTagDirectory followed by findPeaks command from HOMER version 4.9.1 (Heinz et al., 2010) to identify peaks of ChIP-seq enrichment over background. A false discovery rate (FDR) threshold of 0.001 was used for all data sets. Regions that overlap with blacklist identified by the ENCODE project were filtered out. The total number of mapped reads in each sample was normalized to ten million mapped reads. ChIP-seq data were visualized by preparing custom tracks for the UCSC Genome browser. For identifying enhancer regions that were differentially acetylated in at least one of four conditions (FDR < 0.05, >2-fold changes in any pairwise comparison), we used the getDifferentialPeaksReplicates.pl with parameters - genome hg19 -balanced -t H3K27ac_L1/ H3K27ac_L2/ -b H3K27ac_R1/ H3K27ac_R2/ -p enhancers.bed from HOMER package and then merged using mergePeaks - size given. For the

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clustering of enhancers, we used GENE-E (Broad Institute) set to global comparison and average-centered. The value of K was chosen at 5 because lower values failed to identify all meaningful clusters and higher values subdivided existing clusters. The third enhancer cluster (C3) in Supplementary Fig. 2c was further classified into two clusters (e2 and e3) in Fig.2a based on the changes in H3K27ac between IFN-γ-stimulated (G) and IFN-γ-primed LPS- stimulated (GL) macrophages. For the distribution plot of ChIP-seq signals in Fig.4c and g, we used the annotatePeaks.pl command with parameters -size 2000 -hist 10 to generate histograms for the average distribution of normalized tag densities. For functional annotations of the enhancers, enriched GO Biological Process and MSigDB Pathways were compiled from the GREAT version 3.0.0 (McLean et al., 2010) on each subset of enhancer-associated genes. GO and MSigDB pathways were ranked based on p-values.

Motif enrichment analysis. de novo and known motif analysis was performed on ±100 bp centered on the ATAC-seq peak that overlapped either PU.1 or C/EBPβ, using command “findMotifsGenome.pl” from HOMER package. Peak sequences were compared to random genomic fragments of the same size and normalized G+C content to identify motifs enriched in the targeted sequences.

LNA transfection. LNAs (LNATM longRNA GapmeR) were designed and synthesized by Exiqon. Knockdown experiments with LNAs were performed using Human Monocyte Nucleofector buffer (Lonza Cologne) and the AMAXA Nucleofector System program Y001 for human monocytes according to the manufacturer’s instructions. Two different LNATM longRNA GapmeRs (negative control A and B) were used as control. Cells were harvested for RNA analysis 24 h after transfection.

The following LNAs were used for knockdown studies of IL10 eRNAs. Custom LNA for IL10 eRNA (HSS-16): ATAGAGAGGAGATGCA, GCAGTCTAGCTTGGTG. Custom LNA for IL10 eRNA (HSS+6): GGATTTGGCGGGAGTT, TCCTAGTGCCAGAAGC.

Statistical analysis. Statistical tests were selected based on appropriate assumptions with respect to data distribution and variance characteristics. Wilcoxon signed-rank test or two-tailed paired t- test was used for the statistical analysis of two paired samples. Welch's t-test or unpaired Student’s t-test was used for the statistical analysis of two non-paired samples. Statistical significance was defined as p < 0.05. The whiskers of box plots represent the 10–90th percentiles of the data. Statistical analyses were performed using GraphPad Prism 7.

Data availability statement. RNA-seq, ChIP-seq and ATAC-seq data for this project have been deposited at NCBI’s Gene Expression Omnibus (GEO) and GSE number is pending.

19 bioRxiv preprint doi: https://doi.org/10.1101/437160; this version posted October 7, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

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Fig. 1. IFN-γ-mediated reprogramming of TLR4-induced transcriptome in human macrophages. (a) Experimental design: human CD14+ monocyte-derived macrophages underwent one of four different combinations of priming for 48 hr with IFN-γ before stimulation with LPS (or not) for 3 hr: without priming or stimulation (R); primed with IFN-γ without LPS stimulation (G); LPS stimulation only with no priming (L); or primed with IFN-γ and stimulated with LPS (GL). (b) K-means (K = 6) clustering of 3,909 differentially expressed genes in any pairwise comparison among four conditions. Clusters are indicated on the left. (c) Examples of expression of select genes from the six clusters identified in (a). (d) Heat map showing the P value significance of GO term enrichment for genes in each cluster. (e) Heatmap showing the relative expression of representative cluster IV genes that are inducible by IL-10 (left), cluster IV genes that are not inducible by IL-10 (middle), and cluster V genes. Distinct biological functions are indicated on the left. IL-10-inducible genes were obtained from GSE43700. (f) Volcano plot of transcriptomic changes between LPS (L) and IFN-γ-primed LPS-stimulated (GL) macrophages; colored dots correspond to genes with significant (FDR < 0.01) and greater than two-fold expression changes. Data from two independent experiments with different donors are depicted.

Fig. 2. Selective regulation of LPS-activated enhancer landscapes by IFN-γ. (a) K-means clustering (K = 5) of enhancers as shown in Supplementary Fig. 2a further filtered (FDR < 0.05, >2-fold changes) and subdivided into six enhancer clusters. Heatmaps showing H3K27ac ChIP- seq signals at each enhancer cluster. (b) The boxplots indicate normalized tag counts at each enhancer cluster. ****p < 0.0001, paired-samples Wilcoxon signed-rank test. (c) Representative UCSC Genome Browser tracks displaying normalized tag-density profiles at enhancers of IL10 and IL23A in four conditions. Shaded boxes enclose a cluster e4 enhancer (left) and cluster e5 enhancer (right). (d) Heatmap presentation of the percentage of genes in each cluster (Fig. 1a) that overlap with genes associated with the differentially regulated enhancer clusters identified in panel a. (e) Boxplots showing the change in gene expression between LPS and IFN-γ-primed and LPS-stimulated macrophages for the differentially expressed genes nearest (within 100 kb) to cluster e4 or e5 enhancers. ****p < 0.0001 by Welch’s t test. (f) Boxplots showing gene expression in the four indicated conditions for e4- or e5-associated genes. ****p < 0.0001 by paired-samples Wilcoxon signed-rank test. Boxes encompass the 25th to 75th percentile changes. Whiskers extend to the 10th and 90th percentiles. The central horizontal bar indicates the median. Data is representative of two independent experiments.

Fig. 3. TF expression and binding motif enrichment in distinct enhancer subsets. (a) Heatmap of gene expression of 91 transcription factors in the clusters defined in Fig. 1a. (b) Examples of TF gene expression from the six identified clusters identified in (a). (c) Heat map showing the P value significance of known motif enrichment in each cluster (defined as in Fig. 2a) and grouped according to TF families (left). (d) Heat map showing the P value significance of de novo motif enrichment in six enhancer clusters. (e) The most significantly enriched transcription factor (TF) motifs identified by de novo motif analysis using HOMER in cluster e4 (left) and cluster e5 (right) enhancers. (f) UCSC Genome Browser tracks showing normalized tag density of H3K27ac ChIP-seq and ATAC-seq in LPS-stimulated macrophages. Cumulative TF binding at each e4 enhancer from ENCODE project (Factorbook) is shown below the gene tracks. Boxes enclose representative e4 enhancers at IL10 (left) and TNIP3 (right) locus. (g) ChIP-qPCR of STAT3 at the e4 enhancer (HSS+6) of IL10. (h) ChIP-qPCR of c-Jun at the e4

25 bioRxiv preprint doi: https://doi.org/10.1101/437160; this version posted October 7, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

enhancer (HSS+6) of IL10. Data depict experiments with two different donors (a) or are representative of two (b-f) or three (g, h) independent experiments.

Fig. 4. Differential occupancy of STAT3 and STAT1 at e4 and e5 enhancers. (a) Heatmaps showing STAT3 ChIP-seq signals at each enhancer cluster defined in Fig. 2a. (b) Boxplots depicting normalized tag counts at each enhancer cluster. ****p < 0.0001, paired-samples Wilcoxon signed-rank test. (c) Distribution of the average signal of STAT3 ChIP-seq at each enhancer cluster in LPS-stimulated (top) and IFN-γ-primed LPS-stimulated macrophages (bottom). (d) Representative UCSC Genome Browser tracks displaying normalized tag-density profiles at enhancers of IL10, TNIP3, IL4R, and IL23A in the four indicated conditions. Boxes enclose cluster e4 enhancer (blue) and cluster e5 enhancer (red). (e) Heatmaps showing STAT1 ChIP-seq signals at each enhancer cluster. (f) The boxplots indicate normalized tag counts at each enhancer cluster. (g) Distribution of the average signal of STAT1 ChIP-seq at each enhancer cluster in LPS-stimulated (top) and IFN-γ-primed LPS-stimulated macrophages (bottom). Data are representative of two independent experiments each of which included at least two independent donors (a-d) or are from GSE43036 (e-g).

Fig. 5. IFN-γ suppresses coactivator and CDK8 recruitment to e4 enhancers. (a) ChIP-qPCR analysis of p300 occupancy at the e4 enhancer (HSS+6) of IL10. (b) ChIP-qPCR analysis of MED1 occupancy at the e4 enhancer (HSS+6) of IL10. (c) ChIP-qPCR analysis of CDK8 occupancy at e4 enhancers (HSS+6 and HSS-16) of IL10 and e5 enhancer of IL6. (d) Heatmap showing CDK8 ChIP-seq signals at each enhancer cluster defined in Fig. 2a. (e) The boxplot (top) indicates normalized tag counts at e4 enhancer in the 4 indicated conditions. ****p < 0.0001, paired-samples Wilcoxon signed-rank test. The boxplot (bottom) indicates normalized tag counts at each enhancer cluster in LPS-stimulated macrophages. (f) Representative UCSC Genome Browser tracks displaying normalized tag-density profiles at e4 enhancers of IL10 and TNIP3 in the four indicated conditions (CDK8) and the LPS-stimulated condition (STAT3 ChIP- seq and ATAC-seq). Data are representative of three independent experiments (a-c), or depict one ChIP experiment using pooled samples from independent experiments using four different donors (d, e).

Fig. 6. The strength of STAT3 binding divides e4 enhancers into two subgroups. (a) Heatmaps of STAT3 ChIP-seq signals at cluster e4 enhancers in the four indicated conditions. Enhancers were separated into two subsets: STAT3hie4 (n = 787) and STAT3loe4 (n = 739) based upon a cutoff of log2 normalized tag counts = 3). The boxplot (right) depicts normalized tag counts at STAT3hie4 and STAT3loe4 enhancers. (b) Heatmaps of STAT3 ChIP-seq signals at two subsets of e4 enhancers (defined in a) in resting and IL-10-stimulated macrophages. The boxplot (right) indicates normalized tag counts at STAT3hie4 and STAT3loe4 enhancers. (c) The most significantly enriched transcription factor (TF) motifs identified by de novo motif analysis using HOMER at STAT3hie4 (top) and STAT3loe4 (bottom) enhancers. (d) Heatmaps of CDK8 ChIP- seq signals at STAT3hie4 enhancers in the four indicated conditions. The boxplot (right) indicates normalized tag counts at STAT3hie4 enhancers. (e) Heatmaps of SMC1 ChIP-seq signals at STAT3hie4 enhancers in the four indicated conditions. The boxplot (right) indicates normalized tag counts at STAT3hie4 enhancers in four conditions. ****p < 0.0001, paired- samples Wilcoxon signed-rank test. (f) Representative UCSC Genome Browser tracks displaying normalized tag-density profiles at e4 enhancers of IL10 and TNIP3 in the indicated conditions.

26 bioRxiv preprint doi: https://doi.org/10.1101/437160; this version posted October 7, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

(g) Enriched Gene Ontology (GO) and MSigDB pathway categories of genes assigned to STAT3hie4 enhancers (upper panel) or STAT3loe4 enhancers (lower panel). (h) Heatmaps of IL- 10-inducible cluster IV genes that correspond to STAT3hie4-associated genes (left panel) or non- IL-10-inducible cluster IV genes that correspond to STAT3loe4-associated genes (right panel). Data are representative of two independent experiments each of which included at least two independent donors (a, c, g, h) or depict one ChIP experiment using pooled samples from independent experiments using two (b) or four different donors (d, e).

Fig.7. IFN-γ-mediated functional deactivation of STAT3-bound e4 enhancers. (a) Heatmaps of Pol II ChIP-seq signals at STAT3hie4 and STAT3loe4 enhancers (defined in Fig. 6a). The boxplots indicate normalized tag counts at STAT3hie4 enhancers in the four indicated conditions (top) and at STAT3hie4 and STAT3loe4 enhancers in LPS-stimulated macrophages (bottom). ****p < 0.0001, paired-samples Wilcoxon signed-rank test. (b) Representative Genome Browser tracks showing RNA polymerase II (Pol II) occupancy, strand-specific RNA transcripts, and STAT3 occupancy (LPS condition) at enhancers of IL10. Boxes enclose HSS+6 (left) and HSS- 16 (right). (c) RT-qPCR analysis of enhancer RNA (eRNA) expression at two e4 enhancers (top, IL10-HSS+6 and IL-10-HSS-16) and two e5 enhancers (bottom, IL6 and IL23A). Data are representative of three independent experiments. (d) RT–qPCR analysis of IL10 mRNA in resting and LPS-stimulated macrophages transfected with the indicated LNAs (IL10-HSS-16 eRNA and and IL10-HSS+6 eRNA). Data are representative of two independent experiments.

27 bioRxiv preprint doi: https://doi.org/10.1101/437160; this version posted October 7, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Supplementary Fig. 1 Transcriptomic profiling of IFN-γ-primed LPS-stimulated macrophages. (a) Correlation of RNA-seq replicates. (b) Principle Component Analysis (PCA) for RNA-seq replicates among four conditions. (c) Venn diagram showing number of genes in cluster IV (n = 531, blue), genes induced by IL-10 alone (n = 1199, yellow), and the overlap between cluster IV and IL-10- inducible genes (n = 239). (d) Heatmap showing the relative expression of examples of cluster IV genes that are not inducible by IL-10. Distinct biological functions showed on the left. (e) Gene ontology (GO) analysis using DAVID 6.8. (f) Heat map showing the FDR q-value significance of GSEA enrichment for the two classes of cluster IV genes classified in (c). (g) Known motif analysis of promoters. Data is representative of two experiments.

Supplementary Fig. 2 IFN-γ regulates TLR4-activated enhancer landscapes. (a) K-means clustering (K = 5) of H3K27ac ChIP-seq intensity in open chromatin regions (ATAC-seq peaks) that were bound by PU.1 or C/EBPβ. (b) Heatmaps of ATAC-seq, PU.1, and C/EBPβ ChIP-seq signals at 20,782 enhancers differentially regulated at the H3K27-Ac level. (c) Left panels show congruence of ATAC-seq peaks with DNase-seq peaks from ENCODE project. Heatmaps of normalized tag densities for DNase-seq (CD14+ monocytes from ENCODE, GSM1024791) and ATAC-seq (monocyte-derived macrophages used in this project) at the 20,782 enhancers defined and analyzed in this project. Middle panels show heatmaps of normalized tag densities for H3K4me1 ChIP-seq from CD14+ monocytes (left, GSM1102793) and CD4+ T cells (right, GSM1220567) at the 20,782 enhancers. Right panels show heatmaps of normalized tag densities for H3K4me1 ChIP-seq from CD14+ monocytes (left) and H3K4me3 ChIP-seq from CD14+ monocytes (right, GSM945225) at the 20,782 enhancers. Four-kilobase windows are shown centered at the midpoints of the ATAC-seq peak. (d) Representative UCSC Genome Browser tracks displaying normalized tag-density profiles at enhancers of F13A1, IGF1, FDFT1, and DCSTAMP in the four indicated conditions. Boxes enclose cluster e1A, e1B, e2, and e3 enhancers.

Supplementary Fig. 3 Distinct motif enrichment in different enhancer clusters. (a) The most significantly enriched transcription factor (TF) motifs identified by de novo motif analysis using HOMER in cluster e1A (left) and cluster e1B (right) enhancers. (b) The most significantly enriched transcription factor (TF) motifs identified by de novo motif analysis using HOMER at cluster e2 (left) and cluster e3 (right) enhancers.

Supplementary Fig. 4 Asymmetric TF binding at each enhancer cluster. (a) Correlation of STAT3 ChIP-seq replicates in LPS-stimulated (top) and IFN-γ-primed LPS-stimulated macrophages (bottom). (b) Heatmaps showing IRF1 ChIP-seq signals at each enhancer cluster defined in Fig. 2a. (c) The boxplots indicate IRF1 normalized tag counts at each enhancer cluster. ****p < 0.0001, paired- samples Wilcoxon signed-rank test.

Supplementary Fig. 5 IFN-γ priming increases LPS-induced binding of p300, MED1 and CDK8 at e5 enhancers. (a) ChIP-qPCR analysis of p300 occupancy at the e5 enhancer of IL23A. (b) ChIP-qPCR of MED1 at the e5 enhancer of IL6. Data are representative of three independent experiments. (c)

28 bioRxiv preprint doi: https://doi.org/10.1101/437160; this version posted October 7, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

The boxplot (left) indicates CDK8 normalized tag counts at e5 enhancers in the four indicated conditions. ****p < 0.0001, paired-samples Wilcoxon signed-rank test. The boxplot (right) indicates CDK8 normalized tag counts at each enhancer cluster in IFN-γ-primed LPS-stimulated macrophages. (d) Representative UCSC Genome Browser tracks displaying normalized tag- density profiles at e5 enhancers of IL23A and IL6 in the four indicated conditions (CDK8) and the LPS-stimulated condition (STAT1 ChIP-seq and ATAC-seq).

Supplementary Fig. 6 e5 enhancers subdivide into two groups based on coordinate STAT1 and CDK8 occupancy. (a) The boxplot indicates normalized tag counts at promoters of IL-10-inducible cluster IV genes (blue) and non-IL-10-inducible cluster IV genes (black). (b) Heatmap of STAT1 ChIP-seq signals at cluster e5 enhancers in the four indicated conditions. Enhancers were separated into hi lo two subsets: STAT1 e5 (n = 828) and STAT1 e5 (n = 426) based upon a log2 normalized tag count cutoff od 3). The boxplot (right) indicates normalized tag counts at STAT1hie5 and STAT1loe5 enhancers. (c) Heatmaps of CDK8 ChIP-seq signals at the two subsets of e5 enhancers (defined in b). The boxplots indicate normalized tag counts at STAT1hie5 enhancers (left) and at STAT1hie5 and STAT1loe5 enhancers in IFN-γ-primed LPS-stimulated macrophages (right). ****p < 0.0001, paired-samples Wilcoxon signed-rank test. (d) Boxplots of the change in gene expression after IL-10 stimulation of macrophages of the differentially expressed genes nearest (within 100 kb) to STAT3hie4 (blue) or STAT3loe4 (black) enhancers. **p = 0.0032 by Welch’s t test.

Supplementary Fig. 7 IFN-γ-mediated functional activation of STAT1-bound e5 enhancers. (a) Heatmaps of Pol II ChIP-seq signals at STAT1hie5 and STAT1loe5 enhancers (defined in Supplementary Fig. 6b) in the four indicated conditions. The boxplots indicate normalized tag counts at STAT1hie5 enhancers (top) and at STAT1hie5 and STAT1loe5 enhancers in IFN-γ-primed LPS-stimulated macrophages (bottom). ****p < 0.0001, paired-samples Wilcoxon signed-rank test. (b) Representative Genome Browser tracks showing RNA polymerase II (Pol II) occupancy, strand- specific RNA transcripts, and STAT1 occupancy (IFN-γ + LPS condition) at enhancers of IL23A. Box encloses enhancer of IL23A. (c) RT-qPCR analysis of IL10 mRNA expression in macrophages cultured without or with IFN-γ for 48 hr and then stimulated with LPS. 5 μg/mL actinomycin D (Act D) was added 1 hr prior to harvesting. (d) RT-qPCR analysis of IL10 eRNA (HSS+6) expression in macrophages cultured without or with IFN-γ for 48 hr and then stimulated with LPS for the indicated time course. 5 μg/mL actinomycin D (ActD) was added 1 hr prior to harvesting. Data are representative of two independent experiments.

29 bioRxiv preprint doi: https://doi.org/10.1101/437160; this version posted October 7, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission.

Supplementary Table 1. Primers for expression of mRNA or eRNA and ChIP-qPCR.

hIL10_F GACTTTAAGGGTTACCTGGGTTG hIL10_R TCACATGCGCCTTGATGTCTG ChIP_control_F GGTTGTGGTGGTAAGAAGTTGA ChIP_control_R TGAAAACACTCCAAGGCAGA IL10_eRNA_HSS+6_F TTCTTCAGTTCAGGATGGACAG IL10_eRNA_HSS+6_R GCGAAAATTCTGAGATGAAAGG IL10_eRNA_HSS-16_F TTCCTTCCTTGTGACATGGTT IL10_eRNA_HSS-16_R CATAGAACAGCTGCAAAGAGTCA IL23A_eRNA_F GTCTTGTGAATGCGATTTGC IL23A_eRNA_R TTTGTTTCACTTCCTGGTGCT IL6_eRNA_F CCATTGCTTTCTAGCTGTGTCA IL6_eRNA_R GGAATCAATGTGTAGGCCAAA

30 Fig. 1.

M-CSF a R: Resting macrophages M-CSF + IFN-γ + G: IFN-γ-primed macrophages CD14 M-CSF + LPS monocytes L: LPS-stimulated macrophages M-CSF + IFN-γ + LPS GL: IFN-γ-primed LPS-stimulated macrophages 48 h 3 h relative

row min row max b R G L GL c cluster I cluster II cluster III F13A1 IGF1 DHCR7 FDFT1 VCAM1 DCSTAMP Donor 1R_1 2R_0 1G_0 2 G_1 1L_0 2L_1 1GL_1 2GL_0 Gene6 Clusters150 12 15 80 8 12

9 60 6 9 CD36 100 10 F13A1 6 40 4 6 FPKM FPKM FPKM FPKM FPKM FPKM 50 5 IGF1 3 20 2 3 I MERTK 0 0 0 0 0 0 SEPP1 R G L GL R G L GL R G L GL R G L GL R G L GL R G L GL DEPTOR STAB1 cluster IV cluster V cluster VI

IL10 TNIP3 IL23A IL6 USP18 INHBA 500 2000 250 1000 200 150 FDFT1 II 400 200 800 1500 150 100 300 150 600 VCAM1 1000 100 FPKM FPKM FPKM FPKM FPKM ITGB7 200 FPKM 100 400 III global 50 500 50 DCSTAMP 100 50 global200 0 0 0.500 3 0 60 0 11.05 0 R G L GL0 0.50 R G L GL 3 R G L GL 6 R G L GL R G L GL11.05 R G L GL IL10 MAOA d IV TIMP1 TNIP3 cluster I cluster II cluster III cluster IV cluster V cluster VI

cluster I cluster II cluster III cluster IV cluster V cluster VI GOGO Term Term TNF GO Term GO:0005764 Lysosome IL6 GO:0005764 Lysosome V GO:0002576GO:0002576GO:0005764 Platelet degranulationPlateletLysosome degranulation IL23A GO:0030169GO:0030169GO:0002576 Low-density PlateletLow-density lipoprotein degranulation particle lipoprotein binding particle binding GO:0042060GO:0042060GO:0030169 Wound healing WoundLow-density healing lipoprotein particle binding CYLD GO:0006695GO:0006695GO:0042060 Cholesterol CholesterolWound biosynthetic healing process biosynthetic process VI INHBA GO:0055114GO:0055114GO:0006695 Oxidation-reduction CholesterolOxidation-reduction process biosynthetic process process SOCS1 GO:0002479GO:0002479GO:0055114 Antigen processing AntigenOxidation-reduction andprocessing presentation and process presentation USP18 GO:0060333GO:0060333GO:0002479 IFN-γ-mediated AntigenIFN--mediated signaling processing pathway signaling and presentation pathway 11.05 GO:0006364GO:0006364GO:0060333 rRNA processing rRNA IFN--mediated processing signaling pathway relative relative relative GO:0050821GO:0050821GO:0006364 Protein stabilization rRNAProtein processing stabilization row min row max row min row max row min row max GO:0051082GO:0051082GO:0050821 Unfolded ProteinUnfoldedprotein binding stabilization protein binding GO:1903874GO:1903874GO:0051082 Ferrous ironFerrousUnfolded transmembrane iron protein transmembrane transport binding transport 6 e 6 GO:1903874 Ferrous iron transmembrane transport cluster IV, IL-10-inducible cluster IV, non-IL-10-inducible global GO:0050728GO:0050728 Negative Negativeregulation of regulationinflammatory response of inflammatory response R_1 R_0 G_0 G_1 L_0 L_1 GL_1 GL_0 Gene6 Clusters GO:0032695GO:0050728 Negative Negativeregulation of regulationIL-12 production of inflammatory response R_1 R_0 G_0 G_1 L_0 L_1 GL_1 GL_0 R_1 R_0 G_0 G_1 L_0 L_1 GL_1 GL_0 GO:0032695 Negative regulation of IL-12 production Value)

R G L GL R G L GL GO Term GO:0005764 Lysosome GO:0002576 Platelet degranulation GO:0030169 Low-density lipoprotein particle binding GO:0042060 Wound healing GO:0006695 Cholesterol biosynthetic process GO:0055114 Oxidation-reduction process GO:0002479 Antigen processing and presentation GO:0060333 IFN--mediated signaling pathway GO:0006364 rRNA processing GO:0050821 Protein stabilization GO:0051082 Unfolded protein binding GO:1903874 Ferrous iron transmembrane transport GO:0050728 Negative regulation of inflammatory response GO:0032695 Negative regulation of IL-12 production GO:0032735 Positive regulation of IL-12 production GO:0002690 Positive regulation of leukocyte chemotaxis GO:0045071 Negative regulation of viral genome replication GO:0006511 Ubiquitin-dependent protein catabolic process

Gene GeneGene P GO:0032695 Negative regulation of IL-12 production 3

Gene 3 GO:0032735GO:0032735 Positive regulationPositive of regulationIL-12 production of IL-12 production

10 VI cluster

ADAADA BOP1BOP1 V cluster GO:0032735 Positive regulation of IL-12 production

cluster IV cluster GO:0002690GO:0002690 Positive regulationPositive of regulation leukocyte chemotaxis of leukocyte chemotaxis ARNT2ARNT2 DDX21DDX21 III cluster

(-log

GO Enrichment GO

cluster II cluster GO:0045071GO:0045071GO:0002690 Negative Negative Positiveregulation of regulation regulationviral genome ofreplication of leukocyte viral genome chemotaxis replication

0.50 BATFBATF DDX47DDX47 I cluster 0 0 GO:0045071 Negative regulation of viral genome replication BCL3BCL3 DHX37DHX37 relative GO:0006511GO:0006511 Ubiquitin-dependent Ubiquitin-dependent protein catabolic processprotein catabolic process CCL18CCL18 EMG1EMG1 GO:0006511 Ubiquitin-dependent protein catabolic process row min row max CD274CD274 HEATR1HEATR1 DDIT4DDIT4 IMP4IMP4 GADD45BGADD45B MAK16MAK16 cluster V f IL10IL10 MRTO4MRTO4 NAT10 R_1 R_0 G_0 G_1 L_0 L_1 GL_1 GL_0 IL4RIL4R NAT10 R G L GL GeneGene NOL6 IL7IL7 NOL6 APOBEC3AAPOBEC3A MARCO NOLC1NOLC1 relative MARCO metabolic process APOBEC3BAPOBEC3B NOP2 PDGFAPDGFA NOP2 CCR7CCR7

rRNA NOP58 row min row maxPTPN2PTPN2 NOP58 CXCL9CXCL9 PTX3PTX3 relative PES1PES1 Regulationinflammatoryof response GBP5GBP5 PWP2 SBNO2SBNO2 PWP2 IDO2IDO2 row min row maxSRFBP1 SOCS3SOCS3 SRFBP1 IFI27IFI27 TGFA TSR1TSR1 R_1 R_0 G_0 G_1 L_0 L_1 GL_1 GL_0 TGFA IFIT2IFIT2 UTP14AUTP14A TNIP3TNIP3Gene IFNB1IFNB1 UTP20 UTP20 IFNL1 BACH1 R_1 R_0 G_0 G_1 L_0 L_1 GL_1 GL_0 IFNL1 BACH1 WDR43WDR43 HAMPHAMP Gene IL12AIL12A PICALMPICALM CCT2CCT2 IL23AIL23A CCT3 LTALTA metabolism SLC11A2SLC11A2 CCT3 SLC25A37SLC25A37 CCT8CCT8 TNFTNF DNAJB6 Heme SLC39A14SLC39A14 DNAJB6 relative GTPBP4GTPBP4 NAA15 row min row max

Proteinstabilization NAA15

R_1 R_0 G_0 G_1 L_0 L_1 GL_1 GL_0 Gene SERPINB2 IL10 TNIP3 MAOA PTX3 MARCO TGFA PDGFA IRG1 IL7 PTPN2 ARNT2 SOCS3 AMIGO2 TIMP1 CCL18 ADA ENPP2 DUSP1 SBNO2 DDIT4 IL4R Fig. 2.

a b cluster e1A cluster e1B cluster e2 c cluster e4 cluster e5

**** **** **** **** **** **** **** R 80 100 80 **** G

80 L 60 60

H3K27ac GL 60 R 40 40 40 G L 20 20 PU.1 20 GL H3K27ac normalized tag count H3K27ac normalized tag count H3K27ac normalized tag count

0 0 0 R β R G L GL R G L GL R G L GL G L cluster e3 cluster e4 cluster e5 C/EBP GL

**** **** 70 90 **** 60 **** R

60 G

50 L 60 40

ATAC-seq GL 40 R 30 30 20 G 20 L 10 RNA-seq H3K27ac normalized tag count H3K27ac normalized tag count H3K27ac normalized tag count GL 0 0 0 R G L GL R G L GL R G L GL IL10 IL23A

global

0.7299 30 5063.26 **** e 3 cluster e3 f cluster e4 cluster e5 d 2 **** I II III IV V IV **** I II III IV V VI Annotation8 9 **** 8 **** 1 e1A e1A 7 8 7 6 6 e1B e1B 0 6 e2 e2 5 5 4 -1 4 FPKM) FPKM) 4 FPKM) 2 e3 2 2

e3 Fold change in 3 2 (log e4 -2 (log 3 (log e4 gene expression (log2) 2 Gene Expression Gene Expression Gene Expression 2 1 e5 e5 -3 0 1 0 % Geneglobal overlap -1 -4 0 -2 00.7299 3030 5063.2650 e4 R G e5L GL R G L GL R G L GL

I II III IV V IV Annotation e1A e1B e2 e3 e4 e5 relative

row min row max global Fig. 3. 01050 200 822.5

a e1A e1B e2 e3 e4 e5 R_1 R_0 G_0 G_1 L_0 L_1 GL_1 GL_0 b c R G L GL Gene6 Clusterscluster I cluster II TF e1A e1B e2 e3 e4 e5 TF EHF EHF MAF MEF2C SREBF1 BHLHE41 ELF1 ELF1 MAF 40 25 20 30 ETS1 ETS1 MEF2C 20 30 15 20 ETS FLI1 FLI1 I EPAS1 15 GABPA GABPA MXD4 20 10 FPKM FPKM FPKM 10 FPKM 10 PU.1 PU.1 10 5 5 PU.1-IRF PU.1-IRF SREBF1 II 0 0 0 0 C/EBP C/EBP R G L GL R G L GL R G L GL R G L GL STAT1 RUNX1 RUNX1

IRF1 RUNX2 RUNX2TF EHF ELF1 ETS1 FLI1 GABPA PU.1 PU.1-IRF C/EBP RUNX1 RUNX2 MAFA MAFB MAFF MAFK MEF2A MEF2C MEF2D MITF IRF1 IRF2 IRF3 IRF8 STAT1 STAT3 STAT4 ATF3 BACH1 BATF FOSL2 JUN JUNB NFB-p65 cluster IV MAFA MAFA III MITF cluster III MAFB MAFB IRF1 STAT1 BATF STAT3 BACH1 200 1200 500 200 MAF MAFF MAFF MAFK BATF 400 MAFK

150 900 150 MEF2A MEF2A STAT3 300 e5 IV 100 600 100 MEF2C MEF2C FPKM FPKM FPKM FOSL2 200 FPKM MEF2

TF PU.1 C/EBP RUNX MAF MEF2 MITF/TFE IRF STAT AP-1 NFB MEF2D MEF2D

50 300 50

HIF1A 100 MITF MITFe4 822.5 ATF3 0 0 0 0 IRF1 IRF1 R G L GL R G L GL R G L GL R G L GL

MAFF IRF2 IRF2 IRF IRF3 IRF3e3 V FOSL1 cluster V cluster VI NR4A1 IRF8 IRF8

ATF3 NR4A1 XBP1 IRF7 STAT1 STAT1

RUNX2 400 15 600 300 e2 e5 STAT3 STAT3

300 STAT STAT4 STAT4 IRF7 global 10 400 200

IRF8 ATF3 ATF3200 200 e1B

FPKM BACH1 FPKM FPKM

FPKM BACH1

STAT2 5 200 100 200 VI 1022 e4 100 BATF Value) RELA BATF

global FOSL2 P FOSL2 0 0 0 0 10 e1A XBP1 50 R G L GL R G L GL R G L GL R G L GL bZIP JUN JUN

relative 50

050180 1022 (-log

JUNB EnrichmentMotif JUNB e3 10 0 row min row max NFκB-p65 NFB-p650

R_1 R_0 G_0 G_1 L_0 L_1 GL_1 GL_0 e

d e1A e1B e2 e3 e4 e5 e1Ae2 e1B e2 e3 Gene e4 e5 Cluster e4, FDR < 0.05 (n = 1,526) Cluster e5, FDR < 0.05 (n = 1,254) SERPINB2 TF TF IL10 PU.1 PU.1

global TNIP3

MAOA C/EBP C/EBP Motif TF P-value Motif TF P-value e1B PTX3

180 MARCO RUNX RUNX TGFA AP-1 1e-370 PU.1 1e-129 PDGFA MAF MAF

180 IRG1 Value) MEF2 MEF2

IL7 P e1A PU.1 1e-186 C/EBP 1e-62

50 PTPN2 MITF/TFEMITF/TFE 10 ARNT2 SOCS3 IRF IRF 50 (-log AMIGO2 C/EBP 1e-47 IRF 1e-59 Motif EnrichmentMotif STAT STAT 0 0 TIMP1 CCL18 AP-1 AP-1 ADA STAT 1e-29 NFκB 1e-36 ENPP2 NFκB NFB DUSP1 SBNO2 DDIT4 IL4R g h f IL10 TNIP3 STAT3 ChIP c-Jun ChIP

IL10 enhancer (HSS+6) IL10 enhancer (HSS+6) 1.4 H3K27ac LPS 0.8 1.2 0.7 1 0.6 ATAC LPS 0.5 0.8 0.4 % input %input ENCODE 0.6 %input 0.3 0.4 Factorbook 0.2 Motifs 0.2 0.1 0 0 IFN-γ - + - + IFN-γ - + - + LPS - - + + LPS - - + + Fig. 4. a STAT3 b c cluster e1A cluster e1B cluster e2 LPS-stimulated 30 R G L GL 120 120 120 e1A

25 e1B 100 100 100 e2

20 e3 80 80 80 e4 e1A 15 e5 60 60 60 10

40 40 40 5 STAT3 normalized tag density tag normalized STAT3

20 20 20 0 e1B STAT3 normalized tag count STAT3 normalized tag count STAT3 normalized tag count -1000 -500 0 500 1000

0 0 0 Distance from ATAC peak (bp) e2 R G L GL R G L GL R G L GL IFN-γ-primed cluster e3 cluster e4 cluster e5 e3 LPS-stimulated ****

**** 30 120 120 120 e1A

25 e1B 100 100 100 e2

e4 20 e3 80 80 80 e4 15 e5 60 60 60 10

40 40 40 5

e5 density tag normalized STAT3

20 20 20 0 STAT3 normalized tag count STAT3 normalized tag count STAT3 normalized tag count -1000 -500 0 500 1000 0 0 0 Distance from ATAC peak (bp) R G L GL R G L GL R G L GL

d IL10 (e4) TNIP3 (e4) IL4R (e4) IL23A (e5)

R G L STAT3 GL

R G L STAT1 GL

e STAT1 f g cluster e1A cluster e1B cluster e2 LPS-stimulated

40 40 40 12 R G L GL e1A

10 e1B 30 30 30 e2

8 e3 e4 e1A 20 20 20 6 e5 4 10 10 10 2 STAT1 normalized tag count STAT1 normalized tag count STAT1 normalized tag count STAT1 normalized tag density tag normalized STAT1

e1B 0 0 0 0 R G L GL R G L GL R G L GL -1000 -500 0 500 1000 Distance from ATAC peak (bp) e2 cluster e3 cluster e4 cluster e5 40 40 40 e3 IFN-γ-primed LPS-stimulated 30 30 30 12 e1A

20 20 20 10 e1B e4 e2 8 e3 10 10 10 e4 6

STAT1 normalized tag count STAT1 normalized tag count STAT1 normalized tag count e5

0 0 0 4 e5 R G L GL R G L GL R G L GL 2 STAT1 normalized tag density tag normalized STAT1 0 -1000 -500 0 500 1000 Distance from ATAC peak (bp) Fig. 5.

a p300 ChIP b MED1 ChIP c CDK8 ChIP

IL10 enhancer (HSS+6) IL10 enhancer (HSS+6) IL10 enhancer (HSS+6) IL10 enhancer (HSS-16) 0.6 0.6 0.8 0.7 0.5 0.5 0.7 0.6 0.6 0.5 0.4 0.4 0.5 0.4 0.3 0.3 0.4 input % % input %input

% input %input 0.3 % input %input 0.3 0.2 0.2 0.2 0.2 0.1 0.1 0.1 0.1 0 0 0 0 IFN-γ - + - + IFN-γ - + - + IFN-γ - + - + IFN-γ - + - + LPS - - + + LPS - - + + LPS - - + + LPS - - + + IL6 enhancer control 1.2 0.7

1 0.6 d e 0.5 CDK8 cluster e4 0.8 0.4 **** 7 **** 0.6 R G L GL %input %input 0.3 6 0.4 0.2 5 0.2 0.1 e1A 4 0 0 3 IFN-γ - + - + IFN-γ - + - + LPS - - + + 2 LPS - - + +

1

e1B CDK8 normalized tag count 0 f e2 R G L GL IL10 TNIP3 LPS-stimulated e3 R 7

6 G e4 5 L CDK8 4 GL 3 e5 2 STAT3 L 1

CDK8 normalized tag count ATAC L 0 e1A e1B e2 e3 e4 e5 Fig. 6.

a STAT3 b STAT3 c STAT3hie4 (n = 787) R G L GL R IL-10 Motif TF P-value ****

200 ****

e4 150 e4 hi

hi AP-1 1e-207

150 120 (n = 787)(n= (n = 787)(n= STAT3

STAT3 PU.1 1e-91 90 100 60 STAT 1e-55 Clustere4 e4 e4

lo 30 50 lo C/EBP 1e-39 FDR < 0.051,526)FDR< (n= STAT3 normalized tag count STAT3 normalized tag count 0 (n = 739)(n= (n = 739)(n= STAT3 0 STAT3 STAT3 STAT3 High e4 Low e4 lo High Low STAT3 e4 (n = 739)

d hi e Motif TF P-value CDK8 STAT3 e4 SMC1 STAT3hi e4

10 **** 30 **** R G L GL **** R G L GL **** AP-1 1e-165 8 25

20 6 PU.1 1e-100 e4 e4 hi

hi 15 4 10 CTCF 1e-26 (n = 787)(n= (n = 787)(n= STAT3 2 STAT3 5 CDK8 normalized tag count SMC1 normalized tag count 0 0 relativerelative R G L GL R G L GL relativerelative

rowrow min min rowrow max max rowrow min min rowrow max max e4 e4 lo lo h f IL10 TNIP3 STAT3hie4, cluster IV STAT3loe4, cluster IV R_1 R_0 R_1 G_0 R_0 G_1 G_0 L_0 G_1 L_1 L_0 GL_1 L_1 GL_0 GL_1 GL_0 R_1 R_0 R_1 G_0 R_0 G_1 G_0 L_0 G_1 L_1 L_0 GL_1 L_1 GL_0 GL_1 GL_0 (n = 739)(n= (n = 739)(n= STAT3 STAT3 R G L GL GeneGene R G L GL GeneGene R ADAM19ADAM19 ALCAMALCAM ARHGAP24ARHGAP24 ANO6ANO6 G BACH1BACH1 BAALCBAALC BATFBATF CCL23CCL23 BATF3BATF3 SMC1 L CTNNB1CTNNB1 BCL3BCL3 FOXO3FOXO3 GL BCL6BCL6 FOXP1FOXP1 C1orf21C1orf21 GREM1GREM1 CARM1CARM1 INSIG1INSIG1 L CD274CD274 JAK1JAK1 CD59CD59 MAOAMAOA CHSY1CHSY1 STAT3 STAT3 GL METMET DENND2DDENND2D NEDD4LNEDD4L DSEDSE PDHXPDHX L ETS1ETS1 FKBP5FKBP5 SLC1A3SLC1A3 STEAP1STEAP1 STAT1 STAT1 GL FNIP2FNIP2 FPR1FPR1 TNIP1TNIP1 FURINFURIN TOM1TOM1 GPR137BGPR137B g GTDC1GTDC1 STAT3hie4 IL10IL10 IL21RIL21R IL4RIL4R GO Biological Process MSigDB Pathway IL7RIL7R ITGAVITGAV Negative regulation of Adipocytokine signaling KCNA3KCNA3 relative immune effector process pathway LILRB1LILRB1 LMNB1LMNB1 row min row max Regulation of defense Jak-STAT signaling LRP12LRP12 response pathway MARCKSMARCKS MARCOMARCO Negative regulation of Cytokine-cytokine receptor MYO10MYO10

immune response interaction NAMPTNAMPT R_1 R_0 G_0 G_1 L_0 L_1 GL_1 GL_0 NRP1NRP1 Gene 0 5 10 15 20 0 2 4 6 8 10 PDPNPDPN SERPINB2 PFKFB3PFKFB3 Binomial P value (-log ) Binomial P value (-log ) IL10 10 10 PGS1PGS1 TNIP3 lo PTPN1PTPN1 STAT3 e4 RASSF4RASSF4 MAOA SEMA4BSEMA4B PTX3 SH3PXD2BSH3PXD2B MARCO Immune response- HDL mediated lipid SLASLA TGFA regulating signaling transport SLC16A10SLC16A10 PDGFA SLC16A7SLC16A7 IRG1 Wound healing MAPK signaling pathway SLC41A2SLC41A2 IL7 SMAD7SMAD7 SMIM3SMIM3 PTPN2 Regulation of MAP kinase Lipoprotein metabolism SOCS3SOCS3 ARNT2 activity TMEM2TMEM2 SOCS3 TNFRSF1ATNFRSF1A AMIGO2 0 5 10 15 0 2 4 6 8 TNIP3TNIP3 TIMP1 TRIB1TRIB1 Binomial P value (-log10) Binomial P value (-log10) CCL18 ADA ENPP2 DUSP1 SBNO2 DDIT4 IL4R Fig. 7. lo hi a LNA HSS-16

LNA HSS+6 STAT3 e4 STAT3 e4

d (n = 739) (n = 787) LNA Ctrl % GAPDH R GL GL 10 15 20 25 30 35 40 45 0 5 + - - Pol2 IL10 + - - mRNA + - - LPS Resting + + - Pol2 normalized tag count Pol2 normalized tag count 10 15 20 10 15 20 0 5 0 5 High e4 R STAT3 **** G **** L Low e4 STAT3 **** GL STAT3 - LPS b RNA-seq Pol2 GL GL G L R G R L HSS+6 IL10 HSS-16 c

IFN- % of GAPDH IFN-

LPS % of GAPDH LPS 0.2 0.4 0.6 0.8 1.2 1.4 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0 1 0 γ γ - - - - IL10 IL6

eRNA + + - - eRNA (HSS+6) (HSS+6) + + - -

+ + + +

% of GAPDH IFN-

IFN- % of GAPDH LPS 0.05 0.15 0.25 LPS 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.1 0.2 0 0 γ γ - - - - IL10 eRNA IL23 + + - - eRNA (HSS-16) (HSS-16) + - + - + + + + Supplementary Supplementary a g d Tryptophan Replicate 2 cluster IV,cluster IL-10-inducible promoters MMPs metabolism Purine metabolism Iron metabolism row min row min row min row min

R_1 R_1 R_1 R GL GL R_1 R_0 R_0 R_0 R_0 G_0 G_0 G_0 G_0 relative Motif relative relative

G_1 G_1 G_1 G_1 relative L_0 L_0 L_0 L_0 cluster IVcluster row max L_1 row max L_1 L_1 L_1 GL_1 GL_1 row max GL_1 GL_1 Replicate 1 Replicate GL_0 GL_0 GL_0 GL_0 row max MMP19 MMP10 MMP7 MMP1 MAOA KMO CYP1B1 KYNU PNPT1 PNP PDE8A PAICS AMPD3 AK4 ADSS TFRC STEAP3 STEAP1 SLC25A28 FOXO3 CYP7B1 CYP27B1 CYP1B1 BMP6 Gene , non-IL-10-inducible MAOA KMO CYP1B1 KYNU Gene TFRC STEAP3 STEAP1 SLC25A28 FOXO3 CYP7B1 CYP27B1 CYP1B1 BMP6 Gene PNPT1 PNP PDE8A PAICS AMPD3 AK4 ADSS Gene MMP19 MMP10 MMP7 MMP1 Gene NF STAT AP-1 AP-1 ETS Sp1 Sp1 TF κ B Fig. 1. P-value P-value

1e-2 1e-2 1e-3 1e-4 1e-6 1e-8 mTORC1 signaling Lipids metabolism row min row min

R_1 R GL GL R_1 R_0 R_0

cluster IV,cluster G_0 G_0 relative G_1 relative G_1 L_0 L_0 row max

L_1 row max L_1 GL_1 GL_1 Motif non-IL-10-inducible promoters GL_0 GL_0 b TFRC SDF2L1 PPA1 PNP INSIG1 EIF2S2 DAPP1 AK4 ABCF2 TNFRSF21 TGS1 SGPP2 PI4K2B MGLL ELOVL7 CYP7B1 CYP27B1 BMP1 AGPAT6 ACSL5 ABCA1 Gene TFRC SDF2L1 PPA1 PNP INSIG1 EIF2S2 DAPP1 AK4 ABCF2 Gene TNFRSF21 TGS1 SGPP2 PI4K2B MGLL ELOVL7 CYP7B1 CYP27B1 BMP1 AGPAT6 ACSL5 ABCA1 Gene NF AP-1 AP-1 MYC MYC ETS IRF TF κ B e P-value P-value cluster IVcluster IV,cluster IL-10-inducible genes (n = 239) Unfolded protein binding (GO:0051082) binding protein Unfolded (GO:0050821) stabilization Protein rRNA (GO:0006364) processing GO term (GO:0032695) production of interleukin-12 regulation Negative (GO:0050728) response of inflammatory regulation Negative (GO:1903874) transport transmembrane iron Ferrous GO term 1e-10 1e-10 1e-11 1e-11 1e-8 1e-8 1e-4 1e-4 f , non-IL-10-inducible genes (n = 531) (-log

GSEA Enrichment cluster IV 0 0 0

IL-10-inducible 10

Not IL-10-inducible FDR q IL-10-inducible

REACTOME KEGG KEGG HALLMARK HALLMARK KEGG KEGG KEGG KEGG Pathway IL-10-inducible 5 5

- Non-IL-10-inducible Value)

METABOLISM_OF_PROTEINS PURINE_METABOLISM MAPK_SIGNALING_PATHWAY MTORC1_SIGNALING MYC_TARGETS FC_GAMMA_R_MEDIATED_PHAGOCYTOSIS ADIPOCYTOKINE_SIGNALING_PATHWAY CHEMOKINE_SIGNALING_PATHWAY JAK_STAT_SIGNALING_PATHWAY Gene SetName Not IL-10-inducible global 10 10 REACTOME KEGG KEGG HALLMARK HALLMARK KEGG KEGG KEGG KEGG MSigDB REACTOME KEGG KEGG HALLMARK HALLMARK KEGG KEGG KEGG KEGG Pathway 20.97 5 2.43E-04 1.04E-04 8.59E-12 c METABOLISM_OF_PROTEINS PURINE_METABOLISM MAPK_SIGNALING_PATHWAY MTORC1_SIGNALING MYC_TARGETS FC_GAMMA_R_MEDIATED_PHAGOCYTOSIS ADIPOCYTOKINE_SIGNALING_PATHWAY CHEMOKINE_SIGNALING_PATHWAY JAK_STAT_SIGNALING_PATHWAY Pathway PValue METABOLISM_OF_PROTEINS PURINE_METABOLISM MAPK_SIGNALING_PATHWAY MTORC1_SIGNALING MYC_TARGETS FC_GAMMA_R_MEDIATED_PHAGOCYTOSIS ADIPOCYTOKINE_SIGNALING_PATHWAY CHEMOKINE_SIGNALING_PATHWAY JAK_STAT_SIGNALING_PATHWAY Gene SetName global 1.02E-02 3.64E-03 1.64E-03 10 PValue 20.97 Supplementary Fig. 2.

a b ATAC-seq PU.1 C/EBPβ R G L GL R G L GL R G L GL R G L GL Donor 1 2 1 2 1 2 1 2

C1

C2 20,782 enhancers

-2 0 2

H3K4me1 C3 c DNase ATAC CD14+ CD4+ Th H3K4me1 H3K4me3

C4

C5 20,782 enhancers

-2 0 2 -2 0 2 -2 0 2

d cluster e1A cluster e1B cluster e2 cluster e3

R G L

H3K27ac GL R G

PU.1 L GL R β G L C/EBP GL R G L

ATAC-seq GL R G L

RNA-seq GL

F13A1 IGF1 FDFT1 DCSTAMP Supplementary Fig. 3.

a Cluster e1A, FDR < 0.05 (n = 1,610) Cluster e1B, FDR < 0.05 (n = 1,146)

Motif TF P-value Motif TF P-value

PU.1 1e-443 PU.1 1e-310

MAF 1e-69 C/EBP 1e-36

AP-1 1e-55 RUNX 1e-21

RUNX 1e-26 MITF 1e-20

MEF2 1e-24 MAF 1e-16

C/EBP 1e-18 IRF 1e-13

b Cluster e2, FDR < 0.05 (n = 668) Cluster e3, FDR < 0.05 (n = 866)

Motif TF P-value Motif TF P-value

PU.1 1e-127 IRF 1e-160

C/EBP 1e-29 PU.1 1e-62

TFE 1e-26 C/EBP 1e-30

RUNX 1e-14 NFκB 1e-23

MITF 1e-21 0 50 100 150 200 250 300 400 300

SupplementaryL1 Fig. 4. 0.88 200 a b 100 c

LPS 0 IRF1 cluster e1A cluster e1B cluster e2 12 25 100 100 ●

300 r = 0.88 ● R G L GL 12 25 100 100 10 20 80 80

250 0 2 4 6 8 10 12 10 ● 8 20 80 80 ●

200 ● 15 60 60 ● 80 ● 8

y 6 ● L2

150 ● 15 60 60 ● ● e1A ● ● ● ● ● 10 40 40 ● ● ● ● ● 6 ● ● 60 ● ● ● ● ● ● 4 ●

100 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 10 40 40 ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●●● ● ● ● ● ●● ● ● ● ●●● ● ●● ●● ● ● ●●● ● 4 ● ● ● ● 5 20 20 ● ●● ● ●● ● ●● ● ● ● ● ● IRF1 normalized tag count IRF1 normalized tag count ● ● ● ● ● ● IRF1 normalized tag count IRF1 normalized tag count 50 ● ● ● ● ● GL1 ● ●● ● ● ●●● ● 2 ● ● ● ● ● ● ● ● ●● ● ●● ● ● ●● ● ● 40 ●●●●●● ●● ● ● ● ● ● ●● ●●● ●●●● ●● ●●● ●● ● ●●●● ● ● ● ● ●●●●●●● ●●●● ●● ●●● ● ●● ●●●●●●●●●●●●●● ● ●●● ●● ● ● ●●● ●●●●●● ●●● ● ● ●●●●●●●●●●●●●●●● ● ●●●● ● ●●●●●●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●● ●●●●● ● 5 ● ● ● 20 20 ●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●

●●●● IRF1 normalized tag count IRF1 normalized tag count ●●●●●●●●●●●●●●●●●●● IRF1 normalized tag count IRF1 normalized tag count ●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●● 2 0 0.85e1B 0 0 0 0

0 100 200 300 400 20 R G L GL R G L GL R G L GL R G L GL 0 0 0 0 x R G L GL R G L GL R G L GL R G L GL

IFN-γ + LPS 0 e2 **** cluster**** e3 cluster e4 cluster e5 ● ●

●

12 12 30 ●

Replicate2 ******** **** r = 0.85● ●

● 12 25 100 100 12 30 **** 10 ● 10 25 ● e3 ● ● ● ● ● ● ● ● ● ● ● 10 25

8 10 ● ● ● 20 80 808 20

● ● ● ● ● ●

y ● ● ● ● ● 6 ● 8 GL2 8 20 ● ● ● ● ● 6 15 ● ● ● ● ●● ●● ● ● ● ● ● 15 60 60 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ●●●● ● ● ● ● ● ●

4 ● ● ●●●● ● ● ● ●● ●● ● ●● ● ● 6 ● e4 ● ● ● 15 ● 6 ● ● ● ● ●● ● ● ● ● ● ● ●●●● ● ● ●●●●●● ● 4 ●● ● ● ● ● ● ● ● ● ●● 10 ● ●● ●●● ● ●● ●●●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ●●●●●●●●●●● ● ● ●●●● ●●● ●●● ● ● ● ●●● ●●● ● ● ● ●● ●● ●● ●●●●●●● ● ● ● ● ●● ●●● ●●●● ●● ● ●●●●●● ● ● ● ●●●●●●●●●● ●●●● ● 10 ● ● 40 40 ● ● ● ●●● ●●●●●●●●●●●●●● ●●● ● ●●●●●●●●●●●●● ●● ●●●●●● ● ●● ● ●●●●●●●●●●●●●●●●●●●●●●● ● ● ● ● 2 ● ● ●●● ● ●●●●●●●●●●●●●●●●●●●●● ●●● ● ●●●●●●●● ●●●●●●●● ●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ● 4 ● ●●●●●●●●●●●●●●●●●●● ●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● 10 ● ●● ●●●●●●● 4 ●●●●●●●●●●●●●●●●●●●● ●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●● ●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● IRF1 normalized tag count 2 IRF1 normalized tag count ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ● 5 ● ●●●● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●● ●●●●●●●●●●●●●●●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●●●●●● ● ● ●●●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●●●●●●●● ●●●●●●●●●●●●● 0 5 20 20 IRF1 normalized tag count IRF1 normalized tag count IRF1 normalized tag count IRF1 normalized tag count IRF1 normalized tag count IRF1 normalized tag count 2 2 0 5 0 20 40 60 80 0 R G L GL R G L GL x 0 e5 0 0 0 0 0 Replicate 1 R G L GL R G L GL R G L GL R G L GL R G L GL R G L GL

**** ****

12 30 ****

10 25

8 20

6 15

4 10

IRF1 normalized tag count 2 IRF1 normalized tag count 5

0 0 R G L GL R G L GL Supplementary Fig. 5. a b p300 ChIP MED1 ChIP

IL23A enhancer control IL6 enhancer control 0.3 0.3 0.6 0.6

0.5 0.5

0.2 0.2 0.4 0.4

0.3 0.3 % input %input %input % input %input %input 0.1 0.1 0.2 0.2

0.1 0.1

0 0 0 0 IFN-γ - + - + IFN-γ - + - + IFN-γ - + - + IFN-γ - + - + LPS - - + + LPS - - + + LPS - - + + LPS - - + +

c IFN-γ-primed d IL23A IL6 cluster e5 LPS-stimulated 9 **** 8 **** R 6 G 6

4 CDK8 L

3 GL 2 STAT1 GL CDK8 normalized tag count CDK8 normalized tag count 0 0 R G L GL e1A e1B e2 e3 e4 e5 ATAC GL Supplementary Fig. 6.

a b STAT1 Promoters 350 R G L GL

300 **** 40

250 e5 200 hi 30

150 828)(n= STAT1 100 20

50 Clustere5

STAT3 normalized tag count 10 e5 FDR < 0.051,254)FDR< (n= 0 lo STAT1 normalized tag count cluster IV cluster IV IL-10 non-IL-10 0 (n = 426)(n=

STAT1 High Low

c CDK8 d IL-10-stimulated R G L GL IFN-γ-primed monocytes STAT1hi e5 LPS-stimulated ** **** 3 ****

10 10 **** e5

hi 2 8 8 (n = 828)(n= STAT1 6 6 1

4 4 Fold change in 0

2 2 gene expression (log2) e5 lo CDK8 normalized tag count CDK8 normalized tag count 0 0 -1

(n = 426)(n= R G L GL STAT1 STAT1 hi lo STAT1 STAT3 e4 STAT3 e4 High Low Supplementary Fig. 7.

Pol2 a b c IL10 mRNA 10 IL23A enhancer R G L GL **** **** 8 R 70 60 6 G 50 4 e5

Pol2 40 hi 2 L

Pol2 normalized tag count 30

0 %GAPDH

(n = 828)(n= R G L GL GL 20 STAT1 10 **** 10

8 0 R 0h 0.5h 1h 2h 4h IFN-γ 0h 2h 6 ActD ActD

4 e5 lo 2

Pol2 normalized tag count G 0 (n = 426)(n= d

STAT1 STAT1 STAT1 IL10 eRNA (HSS+6) High e5 Low e5 0.9 0.8

RNA-seq L 0.7 0.6 0.5 0.4 0.3 GL %GAPDH 0.2 0.1 0 STAT1 - GL 0h 0.5h 1h 2h 4h IFN-γ 0h 2h ActD ActD IL23A