CTCF controls three-dimensional enhancer network underlying the inflammatory response of bone marrow-derived dendritic cells

Yang, Bobae; Kim, Sueun; Jung, Woong-Jae; Kim, Kyungwoo; Kim, Sugyung; Kim, Yong-Jin; Kim, Tae-Gyun; Lee, Eun-Chong; Joo, Jung-Sik; Park, Chae Gyu; Oh, Sumin; Yoo, Kyung Hyun; Kim, Hyoung-Pyo

doi:10.1038/s41467-023-36948-5

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Published: 08 March 2023

CTCF controls three-dimensional enhancer network underlying the inflammatory response of bone marrow-derived dendritic cells

Bobae Yang^1,2,
Sueun Kim ORCID: orcid.org/0000-0003-2753-9551^1,2,
Woong-Jae Jung¹,
Kyungwoo Kim ORCID: orcid.org/0000-0001-8126-2624^1,2,
Sugyung Kim^1,2,
Yong-Jin Kim^1,2,
Tae-Gyun Kim ORCID: orcid.org/0000-0002-2116-4579^1,2,
Eun-Chong Lee¹,
Jung-Sik Joo^1,2,
Chae Gyu Park^2,3,
Sumin Oh⁴,
Kyung Hyun Yoo⁴ &
…
Hyoung-Pyo Kim ORCID: orcid.org/0000-0003-1441-8822^1,2,5

Nature Communications volume 14, Article number: 1277 (2023) Cite this article

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Abstract

Dendritic cells are antigen-presenting cells orchestrating innate and adaptive immunity. The crucial role of transcription factors and histone modifications in the transcriptional regulation of dendritic cells has been extensively studied. However, it is not been well understood whether and how three-dimensional chromatin folding controls gene expression in dendritic cells. Here we demonstrate that activation of bone marrow-derived dendritic cells induces extensive reprogramming of chromatin looping as well as enhancer activity, both of which are implicated in the dynamic changes in gene expression. Interestingly, depletion of CTCF attenuates GM-CSF-mediated JAK2/STAT5 signaling, resulting in defective NF-κB activation. Moreover, CTCF is necessary for establishing NF-κB-dependent chromatin interactions and maximal expression of pro-inflammatory cytokines, which prime Th1 and Th17 cell differentiation. Collectively, our study provides mechanistic insights into how three-dimensional enhancer networks control gene expression during bone marrow-derived dendritic cells activation, and offers an integrative view of the complex activities of CTCF in the inflammatory response of bone marrow-derived dendritic cells.

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Introduction

Gene expression in eukaryotic cells is controlled by DNA sequence-specific transcription factors, which act by binding to gene regulatory elements such as promoters, enhancers, and boundary elements. Enhancers often exist at a considerable distance from their target genes, and chromatin looping provides a mechanism for distal enhancers to interact with their target promoters^1,2,3. Due to technological advancements, recent studies on the structural organization of the genome have revealed that the gene regulatory mechanism is tightly linked to the multi-layered chromatin conformation^4,5,6. Mammalian genomes are folded into a complex three-dimensional (3D) chromatin architecture comprising compartments, topologically associating domains (TADs), and chromatin loops⁷. Individual chromosomes segregate into active “A” or inactive “B” compartments, which may arise as a consequence of homotypic interactions between genomic regions with similar transcription and chromatin states⁷. TADs divide the mammalian genome at the submegabase level and display preferential intra-domain chromatin interactions^4,5. CTCF is a well-known transcription factor with 11 zinc finger domains that defines TADs by binding to boundary elements and promotes the formation of chromatin loops along with cohesin proteins^8,9. The spatial insulation imposed by CTCF and TAD boundaries has been proposed to influence gene expression by facilitating proper intra-domain enhancer-promoter communications while inhibiting cross-boundary enhancer action to prevent inappropriate gene activation^{3,10,11,12,13}.

Dendritic cells (DCs) are professional antigen-presenting cells that provide critical signals for eliciting innate and adaptive immune responses¹⁴. Upon detecting invading pathogens or other inflammatory stimuli, DCs respond with robust transcriptional and epigenetic reprogramming, directing a series of maturation events such as antigen uptake, antigen processing, and migration to specialized lymphoid organs^15,16,17. Thereafter, activated DCs modulate lymphocyte activation and differentiation by upregulating the expression of major histocompatibility complexes, co-stimulatory molecules, and a variety of pro-inflammatory cytokines^18,19. These responses are initiated through a set of innate pattern recognition receptors, such as toll-like receptors (TLRs), which discriminate harmful foreign materials from ourselves and are propagated through well-characterized signaling pathways^20,21. However, an aberrant activation of DCs can also lead to diverse autoimmune and immune-mediated inflammatory diseases such as multiple sclerosis^22,23. Therefore, understanding the molecular mechanisms underlying the activation of DCs is important for developing strategies against inflammatory disorders.

Previously, we reported that CTCF is required for the homeostasis of epidermal Langerhans cells and bone marrow (BM) primitive hematopoietic stem cells^24,25. However, the mechanism through which CTCF regulates the complex molecular events during DC activation has not yet been comprehensively investigated. Here, we analyze the global transcriptional, epigenetic, and topological changes occurring in mouse bone marrow-derived DCs (BMDCs) in response to pathogenic stimuli and demonstrate that CTCF is essential in shaping the three-dimensional enhancer networks for the optimal immune function of BMDCs.

Results

Disrupted occupancies of CTCF and SMC1 due to CTCF depletion in in vitro-generated GM-CSF dendritic cells

To decipher the role of CTCF in the gene expression and immunomodulatory function of the primary mouse DCs, we used common in vitro DC differentiation models, where cultures of naïve bone marrow (BM) in the presence of GM-CSF leads to differentiation of precursors into a monocyte-derived DC state²⁶ (denoted from here on as BMDC). We added 4-hydroxytamoxifen (4-OHT) to the BM cultures on the first day of differentiation to remove loxP-flanked Ctcf alleles for Ctcf conditional knockout cells (CreER;CTCF^fl/fl) but not for WT cells (CreER;CTCF^wt/wt). Flow cytometry analysis demonstrated that GM-CSF BM cultures comprised mainly CD11c⁺MHCII^highCD11b^intCD115⁻CD135⁺ GM-DCs along with a minor fraction of CD11c⁺MHCII^intCD11b^high cells that might have potential to differentiate into GM-DCs (Supplementary Fig. 1)^27,28,29. Following in vitro differentiation, BMDCs were activated using the bacterial cell wall component LPS to trigger an inducible gene expression program (Fig. 1a).

**Fig. 1: Disrupted occupancies of CTCF and SMC1 due to CTCF depletion in BMDCs.**

Depletion of endogenous CTCF in the BMDCs was confirmed at the mRNA and protein levels (Fig. 1b, c). Genome-wide CTCF binding patterns, measured by ChIP-seq, in untreated wild-type BMDCs (WT) were mostly similar to those in LPS-stimulated wild-type BMDCs (WTL), indicating that LPS treatment by itself does not affect overall CTCF binding (Fig. 1d, e, Supplementary Fig. 2a). In contrast, CTCF occupancy was lost or severely reduced at all of its binding sites in CTCF-deficient BMDCs, in both unstimulated and LPS-stimulated cells (KO and KOL, respectively) (Fig. 1d, e, Supplementary Fig. 2a). Genome-wide occupancy of SMC1, a subunit of the cohesin complex functionally associated with CTCF to mediate long-range chromatin interaction, demonstrated a similar pattern to that of CTCF; it was not largely affected by LPS stimulation but was lost or considerably reduced due to CTCF depletion (Fig. 1d, e, Supplementary Fig. 2b).

During GM-CSF-mediated differentiation, compared with WT BM, CTCF-deficient BM demonstrated a similar apoptosis profile but had slightly reduced proliferation (Supplementary Fig. 3a and b). The percentage of CD11b⁺CD11c⁺ DCs after 6 days of GM-CSF-supplemented culturing was comparable between WT and CTCF-deficient BMs (Supplementary Fig. 1 and 3c). However, the resultant number of CTCF-deficient BMDCs decreased by 50% compared with that of WT BMDCs (Supplementary Fig. 3d), possibly due to a slower growth rate in CTCF-deficient BMDCs. Moreover, CTCF-deficient BMDCs exhibited equivalent capacity for antigen uptake and induction of T cell proliferation, and relatively higher LPS-stimulated activation of costimulatory molecules compared with WT BMDCs (Supplementary Fig. 3e–h).

TAD insulation was compromised by CTCF depletion but not by LPS stimulation

To investigate the extent to which LPS stimulation affects higher-order chromatin architecture of BMDCs in the presence or absence of CTCF, we performed in situ Hi-C, high-throughput 3C-based experiments quantifying the genome-wide DNA contacts³. Contact maps (Supplementary Fig. 4a) and compartment signals (Fig. 2a and Supplementary Fig. 4b) demonstrated that segregation of active and inactive chromosome domains into A and B compartments was not significantly affected by LPS stimulation (WT vs. WTL; r² = 0.99) or CTCF depletion (WT vs. KO; r² = 0.93 when untreated, and WTL vs. KOL; r² = 0.94 when LPS-stimulated). We next defined TADs using insulation scores and found that the number and size of TADs were not affected by LPS stimulation; however, fewer and larger TADs were observed in CTCF-deficient BMDCs compared with WT BMDCs (Fig. 2b, c). Moreover, the capacity of preventing inter-TAD interactions, represented by the TAD boundary strength, was significantly reduced by CTCF depletion, but not by LPS stimulation (Fig. 2d). The data suggest that CTCF is dispensable for higher order chromatin compartment but essential for TAD organization, which was consistent with previous reports^30,31,32, and that the activation of BMDCs by LPS stimulation has little effect on the three-dimensional genome organization at the resolution of compartment and TAD.

**Fig. 2: Insulation of TAD and enhancer-promoter looping were reduced by CTCF depletion in BMDCs.**

CTCF is required for the maintenance of enhancer-centric chromatin interactions

Further, we explored intra-TAD chromatin looping, focusing on enhancer-promoter interactions across the genome, by performing H3K27ac HiChIP to generate high-resolution contact maps of active enhancers and target genes in BMDCs. Statistically significant H3K27ac-based chromatin interactions were called using FitHiChIP at a resolution of 10 kb, with FDR < 10⁻⁵, a minimum genomic distance of 20 kb and a maximum genomic distance of 2 Mb. For untreated (WT) and LPS-stimulated (WTL) BMDCs, we identified comparable number of high-confidence H3K27ac HiChIP loops (70612 in WT versus 62373 in WTL) (Fig. 2e), most of which enriched CTCF and SMC1 at least at one of the loop anchors (Supplementary Fig. 5a and b). In contrast, CTCF depletion dramatically decreased the number of H3K27ac HiChIP loops (10466 in KO and 5088 in KOL), suggesting a critical role of CTCF in the maintenance of enhancer-centric chromatin interactions at a sub-TAD scale (Fig. 2e). Further analysis of chromatin interactions demonstrated that the major fraction of the H3K27ac HiChIP loops (~50% in WT and WTL, ~75% in KO and KOL) represented chromatin interactions connecting two gene regulatory elements, such as promoter–promoter, promoter–enhancer, or enhancer-enhancer (Fig. 2f). In addition, each promoter can interact with a single enhancer or multiple enhancers acting in concert (Supplementary Fig. 5c), while multiple promoters can be regulated by a single enhancer (Supplementary Fig. 5d). Moreover, a significant fraction of the chromatin interactions (5% in WT and WTL; 15% in KO and KOL) was found between promoter pairs and many promoters can interact with multiple other promoters (Fig. 2f and Supplementary Fig. 5e), suggesting a potential regulatory function in distal gene regulation.

Dynamic changes in loop strength and enhancer activity correlate with gene expression

A previous report demonstrated that LPS stimulation induced changes in the chromatin state of promoters and enhancers to control gene expression in mouse DCs¹⁷. Indeed, H3K27ac ChIP-seq analysis of untreated and LPS-stimulated BMDCs allowed us to characterize activation-inducible, activation-repressed, and constitutive enhancers on the basis of a differential H3K27ac level, which serves as a proxy for enhancer activity, at the 10 kb-long loop anchors (Fig. 3a). Many enhancers are located quite far from the genes they regulate, and it is proposed that close physical proximity between distal enhancers (DE) and their target genes plays a critical role in the control of proper gene expression³³. Thus, we investigated whether LPS stimulation mediates any dynamic changes in spatial proximity between enhancers and their cognate target promoters. Using differential loop analyses for H3K27ac HiChIP, we defined gain of loop, loss of loop, and preformed constant loop following LPS stimulation (Fig. 3b). Thereafter, we considered six possible regulatory modes controlling LPS-stimulated gene expression, categorized by different combinations of dynamic changes in loop strength and distal enhancer (DE) activity: (i) gain of loop to activation-inducible enhancer; (ii) preformed loop with activation-inducible enhancer; (iii) gain of loop to constitutive enhancer; (iv) loss of loop from constitutive enhancer; (v) preformed loop with activation-repressed enhancer; and (vi) loss of loop from activation-repressed enhancer (Fig. 3c). Further, we performed RNA-seq to map changes in transcript abundance (Fig. 3d) and examined whether each regulatory mode shows any correlation with LPS-induced gene activation or repression (Fig. 3e). Compared to the control genes connected to constitutive distal enhancers by preformed constant loops, the genes connected to activation-inducible enhancers by gained loops (mode (i)) exhibited significantly upregulated gene expression, while genes connected to activation-repressed enhancers by lost loops (mode (vi)) demonstrated significantly downregulated gene expression (Fig. 3e). In addition, activation-inducible (mode (ii)) or activation-repressed enhancers (mode (v)) could significantly increase or decrease RNA expression, respectively, when these distal enhancers were connected to their target promoters by preformed constant loops (Fig. 3e). Moreover, acquisition (mode (iii)) or disruption (mode (iv)) of promoter interaction could activate or repress their target genes, respectively, even though LPS stimulation did not significantly change the enhancer activities at distal ends of the loops (Fig. 3e). Examples of dynamic changes in loop strength and enhancer activities corresponding to each regulatory mode and the resultant differential RNA expression of the target genes are shown in Fig. 3f, g. Overall, the results suggest that both spatial enhancer-promoter proximity and chromatin state of distal enhancer provide crucial regulatory mechanisms for the fine tuning of gene expression in response to LPS stimulation.

**Fig. 3: Dynamic changes in loop strength and distal enhancer activity control LPS-stimulated gene expression.**

Chromatin insulation imposed by CTCF constrains enhancer action to prevent aberrant gene expression

Further, we explored the effect of CTCF depletion on the gene expression profile during the GM-CSF-mediated DC differentiation. Differential expression analysis of RNA-seq between WT and KO BMDCs revealed 4435 deregulated genes (FDR < 0.05, FC > 1.3), of which 2248 and 2187 were upregulated and downregulated, respectively, by CTCF depletion (Fig. 4a). Gene ontology analysis of upregulated genes in KO BMDCs revealed significant enrichment of genes known to be associated with metabolic processes, transport, and cell adhesion, while downregulated genes in KO BMDCs showed enrichment of genes associated with cell cycle, cell division, and immune system processes (Supplementary Fig. 6a and b).

**Fig. 4: Disruption of TAD boundary in CTCF-deficient BMDCs leads to rewiring of enhancer-promoter interactions and dysregulation of RNA expression.**

Given that CTCF depletion weakened insulation capacity at the WT TAD boundaries but did not affect insulation capacity at the KO TAD boundaries (Fig. 4b, c), we examined whether any enhancer-promoter interaction, prevented by strong TAD boundaries in WT BMDCs, could be established de novo within the same TADs in the CTCF-deficient BMDCs (Fig. 4d and Supplementary Fig. 6c). Indeed, 34% of the loops gained in CTCF-deficient BMDCs were observed across the WT boundaries (Fig. 4e); the expression of their target genes more likely upregulated, possibly due to the augmented spatial proximity between enhancer and promoter (123 upregulated vs. 53 downregulated; Fig. 4f and Supplementary Table 5).

The Aldh1a2 gene, encoding aldehyde dehydrogenase 1A2 enzyme that catalyzes synthesis of retinoic acid from retinaldehyde³⁴, was one of the best examples for the upregulated gene expression caused by impaired TAD boundary integrity (Fig. 4f, g). The modest level of Aldh1a2 gene expression in WT BMDCs was consistent with the observation that the promoter of Aldh1a2 gene did not interact with a nearby super-enhancer (SE; 20–60 kb upstream) due to the strong insulation exerted by the TAD boundary between them. However, it interacted with distant CTCF binding regions (~300 kb downstream) which demonstrated very low levels of active H3K4me1 and H3K27ac markers but high enrichment of the repressive marker H3K27me3. Chromatin interaction between Aldh1a2 promoter and distant CTCF-binding regions was not maintained in CTCF-deficient BMDCs (Fig. 4g). Instead, CTCF depletion allowed the Aldh1a2 promoter to interact with the nearby SE as well as far upstream enhancers (denoted as E1 and E2) due to the disruption of a TAD boundary (Fig. 4g). Although H3K27ac levels of these newly-connected enhancers (E1, E2, and SE) did not changed significantly due to CTCF depletion, acquisition of close physical proximity between the Aldh1a2 promoter and these constitutively active enhancers across WT TAD boundary seemed to be sufficient to drive increased Aldh1a2 gene expression in CTCF-deficient BMDCs (Fig. 4g). The upregulated expression of Aldh1a2 gene was further validated by higher H3K4me3 levels at the promoter (Fig. 4g) and increased mRNA and protein levels and enzyme activity in KO BMDCs (Fig. 4h–j). Given the critical role of retinoic acid in the induction and suppression of Treg and Th17 differentiation^35,36,37, respectively, enhanced expression of Aldh1a2 in CTCF-deficient BMDCs might provide favorable conditions for the development of T cell tolerance.

CTCF could also insulate intra-TAD chromatin interactions and prevent inappropriate gene activation, given that 66% of gained loops after CTCF depletion were observed within WT TAD boundaries (Fig. 4e) and the expression of their target genes were more likely upregulated (300 upregulated vs. 125 downregulated; Supplementary Fig. 6d). For example, CTCF depletion resulted in the upregulation of Chst3 gene expression due to the loss of CTCF-mediated insulation and thereby erroneously-established intra-TAD loops between constitutive distal enhancers and the Chst3 promoter (Supplementary Fig. 6e and f). These results clearly demonstrated that CTCF plays a critical role in chromatin insulation and constrains enhancer action to prevent aberrant gene expression in mouse BMDCs.

Attenuated JAK2/STAT5 signaling in CTCF-deficient BMDCs

Given the importance of GM-CSF in the differentiation, proliferation, and survival of BMDCs³⁸, we evaluated whether CTCF depletion has any effect on the JAK/STAT pathway, one of the major signaling pathways triggered by the GM-CSF receptor. Western blot analysis demonstrated that GM-CSF induced phosphorylation of JAK2 and STAT5 in WT BMDCs, which was much weaker in KO BMDCs (Fig. 5a, b). In addition, ChIP-seq analysis with anti-STAT5 antibodies demonstrated that global enrichment of STAT5 was mostly lower in CTCF-deficient BMDCs than in WT BMDCs (Fig. 5c). Subsequently, we identified specific STAT5 peaks lost in KO BMDCs (n = 6714; Fig. 5d) and overlapped them with promoters and/or distal enhancers to determine the candidate genes whose expression could be influenced by defective JAK2/STAT5 signaling (n = 2403; Fig. 5e). Differential RNA expression analysis of the genes associated with lost STAT5 binding revealed 971 genes deregulated due to CTCF depletion (FDR < 0.05, FC > 1.3), of which 495 and 476 were upregulated and downregulated, respectively (Fig. 5f). Interestingly, gene ontology (GO) analysis revealed that downregulated STAT5 target genes (n = 476) were principally enriched in the regulation of NF-κB activity, as well as other terms such as response to cytokines, transcription, and intracellular signal transduction (Fig. 5g). Particularly, CTCF depletion in BMDCs led to a decrease in expression of NF-κB pathway genes, such as Trim25, Irak2, and Myd88, due to the attenuation of the JAK2/STAT5 signaling pathway (Fig. 5h).

**Fig. 5: Defective JAK2/STAT5 signaling in CTCF-deficient BMDCs leads to downregulation of NF-κB pathway component genes.**

Chromatin binding of STAT5 at the promoter region of the Trim25 gene was clearly detected in WT BMDCs but was abrogated by CTCF depletion (Fig. 5i, left panel). A recent report found that TRIM25, identified as an E3-ubiquitin ligase, positively regulates NF-κB signaling by promoting K63-linked ubiquitination of TRAF2 and by enhancing the interaction between TRAF2 and TAK1 or IKKB³⁹. Interestingly, comparable enrichment of STAT5 between WT and KO BMDCs was demonstrated at a distal enhancer located 30 kb downstream from the Trim25 promoter. However, chromatin interaction between the Trim25 promoter and STAT5-binding distal enhancer was significantly decreased in KO BMDCs (Fig. 5i, left panel). As a result, close physical proximity between the promoter and STAT5-dependent enhancer was not secured; this might lead to decreased expression of the Trim25 gene in KO BMDCs. Another example of the NF-κB pathway genes being downregulated by defective JAK2/STAT5 signaling is provided by Irak2, which is reported to lead to TRAF6 ubiquitination, an event critical for TLR-mediated NF-κB activation⁴⁰. Chromatin binding of STAT5 was clearly detected at distal enhancers but not at the promoter of the Irak2 gene in WT BMDCs. CTCF depletion in BMDCs decreased STAT5 occupancy at the distal enhancers located in the intron of the Irak2 gene and weakened chromatin interactions between the Irak2 gene promoter and the STAT5-dependent distal enhancers, potentially resulting in the downregulation of the Irak2 gene (Fig. 5i, right panel). The decreased expression of Trim25 and Irak2 could be further validated by lower H3K4me3 levels at their promoters in the CTCF-deficient BMDCs than in WT BMDCs (Fig. 5i). Noteworthy, rescue of STAT5 activity in CTCF-deficient BMDCs did not increase the expression of Trim25 and Irak2 (Supplementary Fig. 7). These results suggested that downregulation of these STAT5 target genes in CTCF-deficient BMDCs was not simply due to attenuated STAT5 signaling, but also caused by disrupted looping between enhancers and their target genes.

Defective NF-κB activation in CTCF-deficient BMDCs

TLR stimulation in DCs principally induces an activation of the NF-κB pathway through signaling cascades downstream of TLR4 which phosphorylates the inhibitor of κB (IκB) for proteasomal degradation and subsequently releases NF-κB transcription factors^41,42. As CTCF-deficient BMDCs demonstrated a diminished expression of NF-κB signaling components, we next examined whether the TLR4/NF-κB signaling pathways were dysregulated by CTCF depletion. Phosphorylation of IκBα after LPS stimulation was dramatically induced in WT BMDCs but not in KO BMDCs, while the expression of the RelA subunit of the NF-κB complex was not changed by CTCF depletion (Fig. 6a and Supplementary Fig. 8a). In addition, immunofluorescence and western blot analysis demonstrated that the nuclear translocation of RelA following LPS stimulation was considerably reduced by CTCF depletion (Fig. 6b–d, and Supplementary Fig. 8b). Furthermore, ChIP-seq analysis revealed that LPS stimulation potently induced genome-wide chromatin binding of RelA in WT BMDCs but not in KO BMDCs (Fig. 6e–g). These results indicated that NF-κB activation following TLR4 stimulation is severely compromised in CTCF-deficient BMDCs.

**Fig. 6: Attenuation of LPS-induced NF-κB activation in CTCF-deficient BMDCs.**

Notably, the chromatin binding of RelA was accompanied by the induced enrichments of SMC1 and H3K27ac following LPS stimulation in WT BMDCs but not in KO BMDCs (Fig. 6h, i). Given that SMC1 and H3K27ac are known to be tightly associated with loop formation and enhancer activation, respectively, these results suggested that dynamic NF-κB binding may contribute to the changes in loop formation and enhancer activity. Indeed, LOLA enrichment analysis of the H3K27ac HiChIP loops in untreated and LPS-stimulated WT BMDCs (WT vs. WTL) demonstrated that sites bound by RelA were strongly represented at the anchors associated with “gain of loop to activation-inducible enhancer by LPS stimulation” (Fig. 6j). Interestingly, RelA binding was also significantly represented at the anchors associated with “weaker loop from less active enhancer by CTCF depletion”, when H3K27 HiChIP loops were compared between WT and KO BMDCs following LPS stimulation (WTL vs. KOL) (Fig. 6k). Furthermore, the number of H3K27ac HiChIP loops overlapped with RelA ChIP-seq peaks at least at one of the loop anchors was dramatically increased by LPS stimulation in WT BMDCs but not in KO BMDCs (Fig. 6l). These results clearly suggested that LPS stimulation induces NF-κB binding which may facilitate stronger enhancer-promoter interactions as well as higher enhancer activity in WT BMDCs but not in CTCF-deficient BMDCs.

RelA-mediated long-range chromatin interactions in LPS-stimulated BMDCs

To further gain insight into the impact of NF-κB activation on the higher order genome organization, we performed RelA HiChIP for the LPS-stimulated WT BMDCs and identified ~20,000 statistically significant RelA-mediated long-range chromatin interactions using FitHiChIP at a resolution of 10 kb with an FDR < 10⁻² (Fig. 7a). The majority (~40%) of RelA HiChIP loops occurred between enhancers, whereas about 20% occurred between promoters and enhancers (Supplementary Fig. 9a). Given that more than 76% (4153/5465) of RelA binding sites in LPS-stimulated WT BMDCs were located distally from promoters (Supplementary Fig. 9b), we utilized RelA HiChIP loops to map direct RelA target genes. While 846 (499 plus 347) genes were identified by overlapping promoters with RelA ChIP-seq peaks, a much greater number of direct RelA target genes (1539) were found where RelA binds to distal enhancers but not to promoters (Supplementary Fig. 9c). The example of three different types of RelA target genes classified by the overlap feature of RelA peaks with promoters and distal enhancers are provided in Supplementary Fig. 9d. While binding of RelA to the promoters was observed at both the Il1f6 and Acod1 genes, only the latter gene showed additional regulation by RelA by forming loops between its promoter and upstream enhancer regions (Supplementary Fig. 9d, left and middle panels). Furthermore, we could identify the Ch25h gene as the direct RelA target gene, even though very little RelA binding was observed at the promoter region of this gene, since several distal enhancer regions with RelA binding simultaneously interacted with the Ch25h promoter (Supplementary Fig. 9d, right panels).

**Fig. 7: LPS-stimulated expression of pro-inflammatory cytokines was diminished in CTCF-deficient BMDCs.**

Compromised expression of RelA-dependent pro-inflammatory cytokines in CTCF-deficient BMDCs

We then analyzed RNA-seq data to explore whether LPS-induced gene expression was affected by CTCF depletion. Out of 1579 upregulated genes by LPS stimulation in WT BMDCs (WTL vs. WT) (Fig. 3d), 403 were significantly downregulated by CTCF depletion after LPS stimulation (KOL vs. WTL) (Supplementary Fig. 9e). Gene ontology analysis showed that these 403 genes were enriched in positive regulation of cytokine production and inflammatory-related pathways (Supplementary Fig. 9f).

Given that LPS-induced NF-κB activation was defective in CTCF-deficient BMDCs, we next analyzed transcriptomic data and identified the RelA target genes which exhibited upregulation by LPS stimulation (WT vs. WTL) but downregulation by CTCF depletion (WTL vs. KOL). Gene ontology (GO) analysis showed that these CTCF-dependent RelA target genes (n = 169, lower right of Fig. 7b) were enriched in inflammation-related pathways, including cytokine production, nitric oxide (NO) biosynthetic process, inflammatory response, and chemotaxis (Fig. 7c). Particularly, CTCF-deficient BMDCs failed to produce pro-inflammatory cytokines, including Il6, Il12a, and Il12b, when activated by LPS stimulation (Fig. 7d).

Activation of WT BMDCs by LPS treatment led to a dramatic increase of the RelA binding at the Il6 promoter as well as at the upstream SE region spanning ~200 kb, where enrichment of SMC1 and H3K27ac were also upregulated (Fig. 7e). V4C plot for H3K27ac HiChIP loop with the Il6 promoter region as the anchor clearly demonstrated that LPS stimulation leads to a dramatic increase in the loop strength between the Il6 promoter with multiple RelA binding sites located at the SE (Fig. 7e). In contrast to the Il6 gene, the role of RelA in the LPS-induced expression of the Il12a gene was mediated mostly by distal enhancers but not by a promoter, since we could observe very little RelA binding to the Il12a promoter in LPS-stimulated WT BMDCs (Fig. 7f). RelA binding to the distal enhancers following LPS stimulation was accompanied by the establishment of an active SE, which showed a dramatic increase in the loop formation with the Il12a promoter (Fig. 7f). RelA HiChIP loops in the Il6 or Il12a loci could validate the important role of NF-κB in the establishment of chromatin interactions between multiple regulatory elements within SEs as well as between SE and promoters (Figs. 7e, f). The NF-κB-centric regulatory loops required for the upregulation of the Il6 and Il12a genes, however, were abrogated by CTCF depletion, given that LPS stimulation of CTCF-deficient BMDCs displayed very little RelA binding, aborted activation of SEs, and very weak interaction between the SE and promoter (Fig. 7e, f). The 3D clique analysis of the Il6 and Il12a loci also demonstrated that LPS stimulation drives the formation of the highly interacting spatial clusters between activation-inducible enhancers and promoters in WT BMDCs but not in CTCF-deficient BMDCs (Fig. 7g, h). The results clearly indicate that CTCF is essential in establishing the NF-κB-dependent 3D enhancer networks required for optimal expression of pro-inflammatory cytokine genes such as Il6 and Il12a.

To further validate the role of NF-κB activation in facilitating enhancer activation and loop formation, we blocked the NF-κB signals using JSH-23, which prevents the nuclear translocation of NF-κB RelA⁴³ while leaving CTCF intact (Fig. 8a, b). First, H3K27ac ChIP-seq analysis demonstrated that enrichment of H3K27ac at RelA binding sites induced by LPS stimulation was almost completely abrogated by JSH-23 pre-treatment (Fig. 8c). Next, the RelA-mediated chromatin interactions (shown in Fig. 7a) were subject to differential H3K27ac HiChIP loop analysis, which revealed 346 gained loops and 295 lost loops due to LPS stimulation (Supplementary Fig. 10a). Interestingly, these dynamic changes in RelA-mediated chromatin interactions induced by LPS stimulation were significantly attenuated by pre-treatment of the BMDCs with JSH-23 (Fig. 8d). Further, RelA target genes (shown in Supplementary Fig. 9c) were analyzed by RNA-seq, which revealed 853 upregulated and 407 downregulated genes in response to LPS stimulation (Supplementary Fig. 10b). The extent of changes in the transcript abundance of these differentially expressed RelA target genes was significantly diminished by pre-treatment of the BMDCs with JSH-23 before LPS stimulation (Fig. 8e). The best examples showing the effect of NF-κB inhibition on enhancer activation, loop formation, and the resultant RNA expression profile were provided by Il6 and Il12a (Fig. 8f). LPS stimulation resulted in increased mRNA expression accompanied by enhanced H3K27ac levels upstream of Il6 and Il12a promoters, which were severely abrogated by pre-treatment of the BMDCs with JSH-23 (Fig. 8f). Moreover, V4C plot for H3K27ac HiChIP loop with the Il6 and Il12a promoter regions as anchor demonstrated that loop formation induced by LPS stimulation between promoters and RelA binding sites were also decreased by JSH-23 pre-treatment (Fig. 8f). Taken together, these results indicated that NF-κB signaling can control spatial enhancer-promoter proximity, as well as distal enhancer activities for the optimal expression of its target genes.

**Fig. 8: NF-κB signaling controls enhancer activation and enhancer-promoter interactions for optimal expression of its target genes.**

Differentiation of Th1 and Th17 cells is attenuated in CTCF-deficient DCs

Impaired production of IL-6 and IL-12 in CTCF-deficient BMDCs was further validated at both the mRNA and protein levels (Fig. 9a, b). Given that IL-12 and IL-6 are well-known cytokines capable of driving Th1 and Th17 differentiation, respectively, we examined the capacity of CTCF-deficient BMDCs for T cell differentiation. When naïve CD4⁺ T cells were cultured with BMDCs under the Th1- and Th17-driving conditions, CTCF-deficient BMDCs showed significantly lower activities to mediate expression of IFN-γ and IL-17A, respectively, in CD4⁺ T cells (Fig. 9c, d). The reduced T cell differentiation was largely attributed to the defective cytokine production in the CTCF-deficient BMDCs; treatment of exogenous IL-12 and IL-6 significantly rescued Th1 and Th17 differentiation by CTCF-deficient BMDCs, respectively (Fig. 9c, d). These data indicate that CTCF is essential for the maximal expression of IL-12 and IL-6 from BMDCs to prime Th1 and Th17 cell differentiation, respectively.

**Fig. 9: Compromised capacity for inducing Th1 and Th17 CD4⁺ T cells by CTCF-deficient BMDCs.**

Discussion

In this study, we performed an integrated analysis of the three-dimensional genome organization, genome-wide enhancer profile, and their relationship with gene expression. The results revealed that both enhancer-promoter communication and the enhancer landscape are dynamically reprogrammed when BMDCs establish a pro-inflammatory transcriptional program in response to pathogenic stimuli, although LPS stimulation did not induce changes in global genome organization at the compartment and TAD levels. Interestingly, the stimulus-induced enhancer activation can be instructive for specific enhancer-promoter interactions, given that spatial proximity for some enhancers with their target promoters were increased as the activities of enhancers were strengthened by LPS stimulation. In contrast, weakened enhancer activities following BMDC activation can lead to diminished chromatin interactions with their target promoters. Alteration in both enhancer activities and enhancer-promoter interactions exhibited positive correlations with changes in the transcriptional activity of the gene they control. However, formation or disruption of enhancer-promoter interactions can alter transcriptional activity, even without concomitant changes in the chromatin state of enhancers during BMDC activation. The results indicate that transcriptional regulation of a significant number of BMDC genes depends on the rewiring of chromatin interactions between constitutively active enhancers and their target genes following LPS stimulation. In contrast, spatial proximity between enhancer and promoter does not automatically induce gene activation, given that quite a few enhancer-promoter interactions seem to be established before LPS stimulation. Such pre-formed enhancer-promoter contacts may ‘prime’ some genes for transcriptional activation, until an additional trigger mediated by LPS stimulation boosts transcription for maximal gene expression. Taken together, the results indicate that LPS stimulation lead to dramatic changes in spatial enhancer-promoter proximity and chromatin state of distal enhancers, both of which contribute to the diverse mode of regulatory mechanisms for the accurate control of gene expression during BMDC activation.

As reported in other cellular models^30,31,32,44, CTCF depletion in BMDCs does not perturb the A/B compartment, but disrupts TAD boundary integrity. Moreover, we observed that enhancer-centric chromatin interactions were significantly decreased in CTCF-deficient BMDCs, while much weaker insulation capacity caused by CTCF depletion led to aberrant enhancer-promoter interactions and dysregulated RNA expression. Remarkably, CTCF-deficient BMDCs demonstrated a profound defect in producing pro-inflammatory cytokines such as IL-6 and IL-12 in response to LPS, suggesting that CTCF is essential for the terminal differentiation of BMDCs and their effective immune responses. GM-CSF depends on the JAK2/STAT5 signaling pathway to efficiently promote DC differentiation from hematopoietic progenitor cells^38,45,46. Interestingly, we could reveal that JAK2/STAT5 signaling was attenuated by CTCF depletion during the GM-CSF-mediated DC differentiation, since our western blot and ChIP-seq analysis demonstrated that phosphorylation of JAK2 and STAT5 by GM-CSF as well as genome-wide STAT5 occupancy was significantly decreased in CTCF-deficient BMDCs. Moreover, combined analysis of H3K27ac HiChIP, STAT5 ChIP-seq, and RNA-seq allowed us to identify the STAT5 direct target genes whose expressions were affected by attenuated JAK2/STAT5 signaling. Surprisingly, the expression of various signaling molecules involved in TLR4 downstream signaling cascades was commonly downregulated in CTCF-deficient BMDCs, thereby having an attenuated NF-κB activation and defective pro-inflammatory cytokine production in response to LPS stimulation. These results demonstrated that the ability of BMDCs to respond to TLR4 signaling cues is hardwired into the 3D genome architecture during GM-CSF-mediated differentiation, which permits a rapid and accurate NF-κB-centric transcriptional response.

The transcription factor NF-κB regulates multiple aspects of TLR-mediated activation of DCs and serves as a vital mediator of inflammatory responses^41,42. The molecular mechanism through which NF-κB regulates the inducible expression of inflammatory genes has been extensively studied^41,42. Integrated analysis with genome-wide binding of multiple TFs and histone modifications revealed that dynamic alterations of the H3K9K14ac levels following LPS stimulation were tightly linked with stimulus-induced binding of NF-κB at inducible promoters and their enhancers¹⁷. Moreover, emerging works demonstrated that the binding of NF-κB to regulatory elements can induce changes in enhancer-promoter interactions in response to inflammatory stimuli⁴⁷. For example, binding of NF-κB to a distal enhancer facilitated chromatin looping in the OPN promoter to drive LPS-stimulated OPN expression in murine macrophages⁴⁷. Consistent with these reports, we could observe that augmented binding of RelA induced by LPS stimulation plays a crucial role in establishing elevated enhancer activities as well as favorable 3D chromatin structure for maximal expression of target genes. Among the various pro-inflammatory cytokines and chemokines induced by NF-κB signaling, IL-12 and IL-6 are known to be the key drivers for the differentiation of naïve CD4⁺ T lymphocytes to IFN-γ-producing Th1 and IL-17-producing Th17 effectors, respectively⁴⁸. Interestingly, our H3K27ac HiChIP analysis revealed that stimulus-induced binding of NF-κB to SEs located far from promoters facilitated the formation of highly interacting enhancer-promoter spatial clusters in the Il6 and Il12a genes. However, such hyperconnected 3D cliques were abrogated in CTCF-depleted BMDCs possibly due to defective NF-κB signaling as well as weakened NF-κB-mediated chromatin looping, which then resulted in compromised expression of RelA-dependent pro-inflammatory cytokines and attenuated differentiation of Th1 and Th17 cells. These results suggested that non-permissive chromatin topology that prevents close spatial proximity between distal enhancer regions and target genes can function as a regulatory barrier that must be resolved for proper gene expression.

In conclusion, CTCF is essential in establishing the NF-κB-dependent three-dimensional enhancer network underlying inflammatory responses of BMDC, and dysregulated CTCF can contribute to the pathogenesis of a number of inflammatory diseases.

Methods

Mice and cells

All mouse experimental procedures were approved by the Department of Laboratory Animal Resources Committee of Yonsei University College of Medicine. C57BL/6 mice carrying a conditional Ctcf allele (Ctcf^fl/fl) were crossed with Rosa26-CreER (CreER) C57BL/6 mice to generate a tamoxifen-inducible Ctcf conditional knockout strain (CreER;CTCF^fl/fl) as described in our previous study²⁴. Male and female 8-12 weeks old mice that were bred in specific pathogen-free facilities at Yonsei University College of Medicine were used for all experiments with 12-hour light/dark cycles. Room temperature was maintained at 23 ± 1 °C and humidity level was controlled between 40–60%. Age- and sex-matched CreER littermate mice were used as wild-type (WT) controls throughout the study. BM cells prepared from CreER;CTCF^wt/wt or CreER;CTCF^fl/fl mice were cultured in RPMI 1640 (HyClone, SH30027.01) supplemented with 10% fetal bovine serum (HyClone, SH30071.03), 100 U/mL penicillin, 100 μg/mL streptomycin (HyClone, SV30010), and 50 μM 2-mercaptoethanol (Gibco, 21985-023) in the presence of murine granulocyte/monocyte colony-stimulating factor (GM-CSF, provided by Chae Gyu Park, Yonsei University College of Medicine) for 6 days. The medium was changed every 2 days. For the deletion of the Ctcf allele in vitro, 4-OH-tamoxifen (Sigma-Aldrich, H7904) dissolved in 100% ethanol was added on the first day of culture (final 0.5 μM). Floating cells were harvested and CD11c⁺ cells were positively selected as BM-derived dendritic cells (BMDCs) using the CD11c microbead (Miltenyi Biotec, 130-125-835). The cells were stimulated with LPS (Invivogen, E. coli K12 strain) at a concentration of 100 ng/mL for the indicated times.

RNA extraction and quantitative real-time polymerase chain reaction (RT-qPCR)

One million BMDCs were stimulated with LPS for 3 h and subjected to RNA extraction using Hybrid-R Total RNA kit (GeneAll Biotechnology, 305-101) according to the manufacturer’s instruction. RNAs were reverse transcribed using PrimeScript^TM RT Master Mix (Takara Bio, RR036A). The resulting cDNAs were subjected to qRT-PCR using the ABI StepOnePlus real-time PCR system (Applied Biosystems) and QuantStudio3 (Applied Biosystems); the synthesis of double-stranded DNA was monitored during various PCR cycles using QuantiNova SYBR Green PCR Kit (Qiagen, 208052). For each sample, duplicate test reactions were analyzed for the expression of the gene of interest, and the results were normalized to Rpl7 mRNA levels. The primer sequences are listed in Supplementary Table 1. Three biological replicates were performed for each condition.

Western blotting

BMDCs were lysed in T-PER^TM Tissue Protein Extraction Reagent (Thermo Fisher Scientific, 78510) with a protease and phosphate inhibitor cocktail (Thermo Fisher Scientific, 78440). Proteins were separated using sodium dodecyl sulfate-polyacrylamide gel electrophoresis and transferred onto a polyvinylidene fluoride membrane. After blocking with 5% skim milk, the membrane was incubated with primary antibodies with 1:1000 dilution: β-actin (sc-47778) and α-tubulin (sc32293) from Santa Cruz; CTCF (2899) p-JAK2 (3771), JAK2 (3230) p-STAT5 (9351), STAT5 (94205), p-IKKα/β (2078), IKKα (2682), p-IκBα (2859), IκBα (9242), and RelA (8242) from Cell Signaling Technology; ALDH1A2 (ab156019) and Lamin B1 (ab133741) from Abcam, followed by incubation with horseradish peroxidase-conjugated secondary antibody with 1:2000 dilution: HPR-linked anti-Rabbit IgG (7074), and HPR-linked anti-Mouse IgG (7076) from Cell Signaling Technology. The target proteins were visualized using Pierce Western Blotting Substrate (Thermo Fisher Scientific, 32132) and Image Quant LAS 4000 (GE Healthcare). Three biological replicates were performed for each condition.

Flow cytometry

Fluorochrome-conjugated anti-mouse CD4 (RM4-5), CD8a (53-6.7), CD11b (M1/70), CD11c (N418), CD45.1 (A20), CD45.2 (104), CD80 (16-10A1), CD86 (GL1), I-A/E (M5/114.15.2), IFN-γ (XMG1.2), and IL-17A (eBio17B7), and CD135 (A2F10) were obtained from eBioscience and CD115 (T38-320) was obtained from BD Biosciences. All antibody dilutions were 1:200. Cell death and apoptosis were analyzed using an Annexin V/Propidium iodide (PI) staining kit (eBioscience, 88-8007-74) according to the manufacturer’s protocol. Cell proliferation was determined using CFSE staining (Molecular Probes, C1157). Aldehyde dehydrogenase (ALDH) activity was measured in the BMDCs using an ALDEFLUOR Kit (STEMCELL Technologies, 01700) according to the manufacturer’s protocol. For the intracellular detection of IFN-γ and IL-17A, cells were fixed and permeabilized with Foxp3/Transcription Factor Staining Buffer Set (eBioscience, 00-5523-00) and incubated for 30 min on ice with the relevant antibodies diluted in Permeabilization buffer (eBioscience). Cytokines secreted into culture media were quantified using the CBA Mouse Inflammation Kit (BD Biosciences, 552364) and the CBA Mouse Th1/Th2/Th17 Cytokine Kit (BD Biosciences, 560485). Stained cells or samples were analyzed using flow cytometry with the FACSVerse system (BD Biosciences) and a FACS LSR Fortessa flow cytometer (BD Biosciences). All flow cytometry data collected by FACSuite (BD Biosciences) and FACSDiva (BD Biosciences) were analyzed using the FlowJo software (Treestar). CBA data were analyzed using FCAP Array software (BD Biosciences). Three biological replicates were performed for each condition.

Antigen uptake assay

BMDCs were washed with 1× PBS and incubated on ice for 1 min to maintain normal cell conditions. Subsequently, the cells were incubated with or without the Alexa-647 conjugated OVA protein (Invitrogen, O34784) at 4 °C or 37 °C for 30 min. Stained cells were analyzed using the FACSVerse system (BD Biosciences). Three biological replicates were performed for each condition.

T cell proliferation assay

CD4⁺ T cells were isolated from spleen of OTII mouse and purified using the MagniSort Mouse CD4 positive selection Kit (eBioscience, 8802-6841-74). Purified T cells were stained with CFSE and co-cultured with OVA323-339 peptide (Sigma, O1641) pre-educated CD11c⁺ BMDCs for 3 days. Three biological replicates were performed for each condition.

Helper T cell differentiation in vitro

CD4⁺ T cell in vitro differentiation with BMDCs was performed as previously described⁴⁹. In brief, CD4⁺ naive T cells were isolated from the spleen of C57BL6 mice using the MagniSort Mouse CD4⁺ Naïve T cell Enrichment Kit (eBioscience, 8804-6824-74). Naive CD4⁺ T cells (1 × 10⁵ cells/well) and CD11c⁺ BMDCs (1 × 10⁴ cells/well) were co-cultured in the presence of soluble anti-CD3ε (0.15 μg/mL; BioLegend, 100331) for 4 days. For Th1 differentiation, BMDCs were treated with LPS (100 ng/mL). For Th17 differentiation, BMDCs were stimulated with LPS (100 ng/mL) and TGF-β (3 ng/mL; Peprotech, 100-21 C). In vitro differentiated CD4⁺ T cells were incubated with 50 μg/mL PMA (Sigma, P1585) and 1 μg/mL ionomycin (Sigma, I3909) in the presence of brefeldin A (eBioscience, B6542) for 4 h before intracellular cytokine staining analysis. Three biological replicates were performed for each condition.

Immunofluorescence assay

After LPS stimulation, BMDCs were fixed with methanol for 10 min at −20 °C, and permeabilized with 0.5% Triton X-100 for 10 min at RT on slide glass. Cells were incubated overnight at 4 °C with RelA primary antibody (Cell Signaling Technology, 8242) with 1:100 dilution followed by 2 h incubation at RT with Alexa Fluor 594 goat anti-rabbit IgG secondary antibody (Invitrogen, A-11037). DNA was stained with DAPI included in the mounting medium (VECTOR Laboratories, H-1200). Images were acquired using a LSM 700 confocal microscope (Zeiss) and processed with ZEN software (Zeiss). Fluorescence intensity of nucleus and cytoplasm was analyzed using the ImageJ software (National Institutes of Health). Three biological replicates were performed for each condition.

Retroviral constructs and virus production

Constitutively active STAT5a mutant cDNA was amplified by PCR from pBABE-Stat5a1*6 vector⁵⁰ (addgene, 130668) and inserted into the retroviral vector MIT (MSCV-IRES-Thy1.1⁵¹, provided by June-Yong Lee, Yonsei University College of Medicine). High-titer virus was generated by transient transfection of 293FT cell line with retroviral and packaging vectors using TurboFect Transfection Reagent (Thermo Fisher Scientific, R0531). Viral supernatants were harvested at 48 and 72 h after transfection, filtered through a 0.45 μm filter unit (Millipore, SLHVR33RB), and concentrated by centrifugation using Amicon Ultra-15 Centrifugal Filter Unit (Merk, UFC901024).

BMDC transduction

BMDCs were harvested on day 4, plated (3 × 10⁶ cells/well) in 6-well plates, and spin-infected with viral supernatants supplemented with 8 μg/ml polybrene at 1250 × g for 90 min at 32 °C. After the spin, the supernatant was removed and replaced with complete culture medium in the presence of GM-CSF. BMDCs were harvested 48 h post infection and transduction efficiency was examined by analyzing Thy1.1 using flow cytometry.

RNA sequencing

Strand-specific libraries were generated using the TruSeq PolyA Stranded mRNA sample preparation kit (Ilumina, FC-122-1001) according to the manufacturer’s protocol. Barcoded libraries were pooled and sequenced on the Illumina HiSeq2500 generating 100 bp paired-end reads. Three biological replicates were performed for each condition.

Chromatin immunoprecipitation sequencing (ChIP-seq)

ChIP-seq was performed as previously described⁵². Briefly, chromatin samples prepared using the appropriate number of fixed cells (3 × 10⁵ for histone modifications and 5 × 10⁶ for transcription factors) were sonicated and subsequently immunoprecipitated with each antibody recognizing 5 μg of CTCF (Cell Signaling Technology, 2899), 5 μg of SMC1 (Bethyl lab, A300-055A), 5 μg of RelA (Cell Signaling Technology, 8242), 5 μg of STAT5 (Cell Signaling Technology, 9351), 1 μg of H3K27ac (Abcam, ab4729), 1 μg of H3K4me1 (Abcam, ab8895), 1 μg of H3K4me3 (Abcam, ab8580), or 1 μg of H3K27me3 (Abcam, ab6002). Antibody-chromatin complexes were captured with protein A and G Dynabeads (Invitrogen, 100.02D/100.04D), washed with Low Salt Wash Buffer, High Salt Wash Buffer, and LiCl Wash Buffer. Chromatin-antibody immobilized on magnetic beads were then subjected to tagmentation. Eluted DNA was purified using SPRI Ampure XP beads (Beckman Coulter, A63881) and amplified for 8–12 cycles using Nextera PCR primers. Libraries were purified using dual (0.5x–2.0x) SPRI Ampure XP beads and paired-end sequenced (100 bp) on the Illumina HiSeq2500 platform. Two biological replicates were performed for each condition.

In situ Hi-C

In situ Hi-C was performed as previously described³. In brief, 2 × 10⁶ BMDCs were crosslinked with 1% formaldehyde (Sigma, F8775) for 10 min and subsequently quenched with 0.125 M glycine (Duchefa Biochemie, G0709). Chromatin was digested using MboI restriction enzyme (New England Biolabs, R0147), followed by biotin incorporation with Biotin-14-dATP (Jena bioscience, NU-835-BIO14-S). After de-crosslinking, ligated DNA was purified and sheared to 200–300 bp. DNA was purified using the MinElute PCR Purification Kit (Qiagen, 28004) and quantified using the Qubit dsDNA HS Assay Kit (Invitrogen, Q32854). Subsequently, 150 ng DNA was used for capture with Dynabeads MyOne Streptavidin C-1 (Invitrogen, 65001), and an appropriate amount of Tn5 enzyme (Illumina, 20034198) was added to the captured DNA to generate the sequencing library. Each library was paired-end sequenced (100 bp) on Illumina NovaSeq6000 platform. Two biological replicates were performed for each condition.

HiChIP

HiChIP was performed as previously described⁵³, using antibodies against 1 μg of H3K27ac (Abcam) or 5 μg of RelA (Cell Signaling Technology). Briefly, cells (2 × 10⁶ for H3K27Ac, 1 × 10⁷ for RelA) were crosslinked with 1% formaldehyde (Sigma) for 10 min and subsequently quenched with 0.125 M glycine (Invitrogen). Chromatin was digested using MboI restriction enzyme (NEB), followed by biotin incorporation with Biotin-14-dATP (Jena bioscience) in end-repair step, ligation, and sonication. Sheared chromatin was then incubated with antibodies recognizing H3K27Ac or RelA at 4 °C overnight. Chromatin-antibody complexes were captured by Protein A and G magnetic bead (Invitrogen) and subsequently washed with Low Salt Wash Buffer, High Salt Wash Buffer, and LiCl Wash Buffers before being eluted. DNA was purified with the MinElute PCR Purification Kit (Qiagen) and quantified using the Qubit dsDNA HS Assay Kit (Invitrogen). Subsequently, 50–150 ng was used for capture with Dynabeads MyOne Streptavidin C-1 (Invitrogen) and an appropriate amount of Tn5 enzyme (Illumina) was added to captured DNA to generate sequencing library. Each library was paired-end sequenced (100 bp) on Illumina NovaSeq6000 platform. Two biological replicates were performed for each condition.

RNA-seq data processing

Paired end sequencing reads were trimmed using Trim Galore with command-line settings “trim_galore -q 20–trim1–paired” and subsequently aligned to the mouse mm10 genome assembly using STAR⁵⁴ with default parameters. The gene expressions were quantified using RSEM⁵⁵ and differentially expressed genes were determined using DEseq2⁵⁶, with an adjusted p-value threshold of 0.05. The DAVID database⁵⁷ was used for pathway and biological process enrichment analysis of differentially expressed genes.

ChIP-seq data processing

Paired-end sequencing reads were trimmed using trim galore with default parameter settings and subsequently aligned to the mouse mm10 genome assembly using bwa (version 0.7.17)⁵⁸ with default parameter settings. Duplicate reads were removed using picard tools (version 2.9.2). Peaks were identified for each sample and biological replicate using MACS2 (version 2.2.7.1)⁵⁹ with command line options “macs2 callpeak -g mm -f BAMPE–nomodel”. Uniquely mapped reads were normalized using deeptools (version 3.3.0)⁶⁰ with command line options “–normalizeUsing CPM–binSize 1” to visualize ChIP-seq signals at specific genomic loci by IGV (version 2.8.2)⁶¹. ChIP-seq heatmaps were generated with deepTools to show normalized read counts at the peak center ±2 kb.

In situ Hi-C data analysis

Paired-end reads were aligned to mm10 genomes using the HiC-Pro pipeline (version 2.11.1)⁶². Default settings were used to remove duplicate reads, assign reads to MboI restriction fragments, filter for valid interactions, and generate binned interaction matrices. Replicates data were first processed separately. After confirmation of good reproducibility by HiCSpector⁶³, we merged the replicates and re-processed the data as combined results (Supplementary Table 2). The data were visualized using Juicebox (version 1.9.8)⁶⁴. The validated contact pairs were transformed to Juicer.hic files with hicpro2juicebox. To segregate A and B compartments, the eigenvector of each chromosome from each sample was generated from the.hic file using Juicer tools (version 1.19.02)⁶⁵ ‘eigenvector’ command, with KR normalization at 100 kb resolution. Topological domain boundaries were identified at a 10 kb resolution based on insulation scores using command-line script “matrix2insulation.pl –b 500000 –ids 200000 –im mean –nt 0.1 –bmoe 3” as previously described⁶⁶.

HiChIP data analysis

Paired-end reads were aligned to mm10 genomes using the HiC-Pro pipeline (version 2.11.1)⁶². Default settings were used to remove duplicate reads, assign reads to MboI restriction fragments, filter for valid interactions, and generate binned interaction matrices. Each replicate data was first processed separately. After confirmation of good reproducibility by HiCSpector, we merged the replicates and re-processed the data as combined results (Supplementary Table 3 and 4). The data were visualized using Juicebox. Loop-calling for the HiChIP experiment was performed using FitHiChIP⁶⁷ with 10 kb bin sizes, bias correction by coverage, false discovery rate (FDR) < 0.01 (RelA HiChIP) or 10⁻⁵ (H3K27ac HiChIP), a minimum genomic distance of 20 kb and a maximum genomic distance of 2 Mb.

Definition of regulatory elements for annotating HiChIP loop anchors

Promoters were defined as ±2.5 kb from the transcription start site (TSS) of each expressed gene. The longest transcript (ENST) from each Ensembl gene id (ENSG) was used to assign unique transcriptional start sites and gene positions. Enhancers were defined as regions with an H3K27ac peak as determined by ChIP-seq. H3K27ac peaks that overlapped a gene promoter were removed from this list. Super-enhancers were defined by applying the ROSE algorithm to H3K27ac peaks with the default stitching size of 12.5 kb⁶⁸. The presence of one or more promoter was considered a promoter HiChIP anchor. The absence of any promoter but presence of enhancer constituted an enhancer HiChIP anchor. The presence of at least one CTCF ChIP-seq but absence of any promoter or enhancer was considered as a CTCF HiChIP anchor.

Differential analysis of HiChIP loops

For identification of loops with differential strength of chromatin interaction, we considered all H3K27ac HiChIP loops with q < 10⁻⁵ in at least one of the two conditions being compared. DESeq2 (version 1.24.0)⁵⁶ was applied to identify differential loops of H3K27ac HiChIP data using contact counts of each replicate. We selected constant H3K27ac HiChIP loops using a P > 0.5 and absolute log2[FC] < 0.378512. Gained and lost H3K27ac HiChIP loops were selected using a P < 0.1 and log2[FC] > 0.584963 and log2[FC] < −0.584963, respectively. To identify the loop anchors with differential enhancer activity, we considered all 10 kb-long anchors connecting the significant H3K27ac HiChIP loops with q < 10⁻⁵ in at least one of the two conditions being compared. DESeq2 (version 1.24.0)⁵⁶ was applied to identify differential enhancers using the sum of H3K27ac ChIP-seq peaks normalized by input within each 10 kb-long anchor. For constitutive enhancer, we selected anchors using a P > 0.5 and absolute log2[FC] < 0.378512. Activation-inducible and activation-repressed enhancers were selected using a P < 0.1 and log2[FC] > 0.378512 and log2[FC] < −0.378512, respectively.

LOLA enrichment analysis

Anchors of each differential loop were analyzed using LOLA (version 1.8.0)⁶⁹ and compared against the LOLA region databases (regionDB) for mm10 to identify enrichment of experimentally derived transcription factor binding locations.

3D clique analysis

3D clique analysis was performed following the same procedure as previously reported⁷⁰. In brief, an undirected graph of enhancer-centric chromatin interaction was constructed from H3K27ac HiChIP data where each vertex was a loop anchor and each edge was a significant H3K27ac HiChIP loop. “3D Cliques” were defined by spectral clustering of the H3K27ac-mediated chromatin interaction using cluster_louvain function in igraph R package with default parameters. A 3D clique connectivity was defined as the number of edges connecting vertices within the clique.

Quantification and statistical analysis

The statistical significance of differences between measurements was determined by Wilcoxon rank sum using the R package, unless otherwise stated. Statistical details of experiments can be found in the figure legends.

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

All RNA-seq, ChIP-seq, in situ Hi-C, and HiChIP data generated in this study have been deposited in the GEO under accession number GSE185884. Source data are provided with this paper.

References

Furlong, E. E. M. & Levine, M. Developmental enhancers and chromosome topology. Science 361, 1341–1345 (2018).
Article ADS CAS PubMed PubMed Central Google Scholar
Long, H. K., Prescott, S. L. & Wysocka, J. Ever-changing landscapes: transcriptional enhancers in development and evolution. Cell 167, 1170–1187 (2016).
Article CAS PubMed PubMed Central Google Scholar
Rao, S. S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).
Article CAS PubMed PubMed Central Google Scholar
Dixon, J. R. et al. Topological domains in mammalian genomes identified by analysis of chromatin interactions. Nature 485, 376–380 (2012).
Article ADS CAS PubMed PubMed Central Google Scholar
Nora, E. P. et al. Spatial partitioning of the regulatory landscape of the X-inactivation centre. Nature 485, 381–385 (2012).
Article ADS CAS PubMed PubMed Central Google Scholar
Wang, S. et al. Spatial organization of chromatin domains and compartments in single chromosomes. Science 353, 598–602 (2016).
Article ADS CAS PubMed PubMed Central Google Scholar
Lieberman-Aiden, E. et al. Comprehensive mapping of long-range interactions reveals folding principles of the human genome. Science 326, 289–293 (2009).
Article ADS CAS PubMed PubMed Central Google Scholar
Handoko, L. et al. CTCF-mediated functional chromatin interactome in pluripotent cells. Nat. Genet. 43, 630–638 (2011).
Article CAS PubMed PubMed Central Google Scholar
Ong, C. T. & Corces, V. G. CTCF: an architectural protein bridging genome topology and function. Nat. Rev. Genet. 15, 234–246 (2014).
Article CAS PubMed PubMed Central Google Scholar
Merkenschlager, M. & Nora, E. P. CTCF and cohesin in genome folding and transcriptional gene regulation. Annu. Rev. Genomics Hum. Genet. 17, 17–43 (2016).
Article CAS PubMed Google Scholar
Ren, G. et al. CTCF-mediated enhancer-promoter interaction is a critical regulator of cell-to-cell variation of gene expression. Mol. Cell 67, 1049–1058.e1046 (2017).
Article CAS PubMed PubMed Central Google Scholar
Zuin, J. et al. Cohesin and CTCF differentially affect chromatin architecture and gene expression in human cells. Proc. Natl Acad. Sci. USA 111, 996–1001 (2014).
Article ADS CAS PubMed Google Scholar
Rosa-Garrido, M. et al. High-resolution mapping of chromatin conformation in cardiac myocytes reveals structural remodeling of the epigenome in heart failure. Circulation 136, 1613–1625 (2017).
Article CAS PubMed PubMed Central Google Scholar
Steinman, R. M. Decisions about dendritic cells: past, present, and future. Annu. Rev. Immunol. 30, 1–22 (2012).
Article CAS PubMed Google Scholar
Belz, G. T. & Nutt, S. L. Transcriptional programming of the dendritic cell network. Nat. Rev. Immunol. 12, 101–113 (2012).
Article CAS PubMed Google Scholar
Garber, M. et al. A high-throughput chromatin immunoprecipitation approach reveals principles of dynamic gene regulation in mammals. Mol. Cell 47, 810–822 (2012).
Article CAS PubMed Google Scholar
Vandenbon, A., Kumagai, Y., Lin, M., Suzuki, Y. & Nakai, K. Waves of chromatin modifications in mouse dendritic cells in response to LPS stimulation. Genome Biol. 19, 138 (2018).
Article PubMed PubMed Central Google Scholar
Dalod, M., Chelbi, R., Malissen, B. & Lawrence, T. Dendritic cell maturation: functional specialization through signaling specificity and transcriptional programming. EMBO J. 33, 1104–1116 (2014).
Article CAS PubMed PubMed Central Google Scholar
Mellman, I. & Steinman, R. M. Dendritic cells: specialized and regulated antigen processing machines. Cell 106, 255–258 (2001).
Article CAS PubMed Google Scholar
Kawai, T. & Akira, S. The role of pattern-recognition receptors in innate immunity: update on Toll-like receptors. Nat. Immunol. 11, 373–384 (2010).
Article CAS PubMed Google Scholar
Reis e Sousa, C. Dendritic cells as sensors of infection. Immunity 14, 495–498 (2001).
Article CAS PubMed Google Scholar
Ganguly, D., Haak, S., Sisirak, V. & Reizis, B. The role of dendritic cells in autoimmunity. Nat. Rev. Immunol. 13, 566–577 (2013).
Article CAS PubMed PubMed Central Google Scholar
Stasiolek, M. et al. Impaired maturation and altered regulatory function of plasmacytoid dendritic cells in multiple sclerosis. Brain 129, 1293–1305 (2006).
Article PubMed Google Scholar
Kim, T. G. et al. CCCTC-binding factor controls the homeostatic maintenance and migration of Langerhans cells. J. Allergy Clin. Immunol. 136, 713–724 (2015).
Article CAS PubMed Google Scholar
Kim, T. G. et al. CCCTC-binding factor is essential to the maintenance and quiescence of hematopoietic stem cells in mice. Exp. Mol. Med. 49, e371 (2017).
Article CAS PubMed PubMed Central Google Scholar
Mildner, A., Yona, S. & Jung, S. A close encounter of the third kind: monocyte-derived cells. Adv. Immunol. 120, 69–103 (2013).
Article CAS PubMed Google Scholar
Helft, J. et al. GM-CSF mouse bone marrow cultures comprise a heterogeneous population of CD11c+MHCII+ macrophages and dendritic cells. Immunity 42, 1197–1211 (2015).
Article CAS PubMed Google Scholar
Lutz, Manfred, B., Inaba, K., Schuler, G. & Romani, N. Still alive and kicking: in-vitro-generated GM-CSF dendritic cells. Immunity 44, 1–2 (2016).
Article CAS PubMed Google Scholar
Menges, M. et al. IL-4 supports the generation of a dendritic cell subset from murine bone marrow with altered endocytosis capacity. J Leukoc. Biol. 77, 535–543 (2005).
Article CAS PubMed Google Scholar
Nora, E. P. et al. Targeted degradation of CTCF decouples local insulation of chromosome domains from genomic compartmentalization. Cell 169, 930–944.e922 (2017).
Article CAS PubMed PubMed Central Google Scholar
Hyle, J. et al. Acute depletion of CTCF directly affects MYC regulation through loss of enhancer-promoter looping. Nucleic Acids Res. 47, 6699–6713 (2019).
Article CAS PubMed PubMed Central Google Scholar
Lee, R. et al. CTCF-mediated chromatin looping provides a topological framework for the formation of phase-separated transcriptional condensates. Nucleic Acids Res. 50, 207–226 (2022).
Article CAS PubMed Google Scholar
Hnisz, D. et al. Activation of proto-oncogenes by disruption of chromosome neighborhoods. Science 351, 1454–1458 (2016).
Article ADS CAS PubMed PubMed Central Google Scholar
Zhao, D. et al. Molecular identification of a major retinoic-acid-synthesizing enzyme, a retinaldehyde-specific dehydrogenase. Eur. J. Biochem. 240, 15–22 (1996).
Article CAS PubMed Google Scholar
Coombes, J. L. et al. A functionally specialized population of mucosal CD103+ DCs induces Foxp3+ regulatory T cells via a TGF-beta and retinoic acid-dependent mechanism. J. Exp. Med. 204, 1757–1764 (2007).
Article CAS PubMed PubMed Central Google Scholar
Kwok, S.-K. et al. Retinoic acid attenuates rheumatoid inflammation in mice. J. Immunol. 189, 1062 (2012).
Article CAS PubMed Google Scholar
Sun, C. M. et al. Small intestine lamina propria dendritic cells promote de novo generation of Foxp3 T reg cells via retinoic acid. J. Exp. Med. 204, 1775–1785 (2007).
Article CAS PubMed PubMed Central Google Scholar
van de Laar, L., Coffer, P. J. & Woltman, A. M. Regulation of dendritic cell development by GM-CSF: molecular control and implications for immune homeostasis and therapy. Blood 119, 3383–3393 (2012).
Article PubMed Google Scholar
Liu, Y. et al. TRIM25 promotes TNF-alpha-induced NF-kappaB activation through potentiating the K63-linked ubiquitination of TRAF2. J. Immunol. 204, 1499–1507 (2020).
Article CAS PubMed Google Scholar
Keating, S. E., Maloney, G. M., Moran, E. M. & Bowie, A. G. IRAK-2 participates in multiple toll-like receptor signaling pathways to NFkappaB via activation of TRAF6 ubiquitination. J. Biol. Chem. 282, 33435–33443 (2007).
Article CAS PubMed Google Scholar
Kawasaki, T. & Kawai, T. Toll-like receptor signaling pathways. Front. Immunol. 5, 461 (2014).
Article PubMed PubMed Central Google Scholar
Hayden, M. S. & Ghosh, S. NF-kappaB in immunobiology. Cell Res. 21, 223–244 (2011).
Article CAS PubMed PubMed Central Google Scholar
Shin, H.-M. et al. Inhibitory action of novel aromatic diamine compound on lipopolysaccharide-induced nuclear translocation of NF-κB without affecting IκB degradation. FEBS Lett. 571, 50–54 (2004).
Article CAS PubMed Google Scholar
Stik, G. et al. CTCF is dispensable for immune cell transdifferentiation but facilitates an acute inflammatory response. Nat. Genet. 52, 655–661 (2020).
Article CAS PubMed Google Scholar
Zhong, J. et al. Loss of Jak2 selectively suppresses DC-mediated innate immune response and protects mice from lethal dose of LPS-induced septic shock. PLoS ONE 5, e9593 (2010).
Article ADS PubMed PubMed Central Google Scholar
Esashi, E. et al. The signal transducer STAT5 inhibits plasmacytoid dendritic cell development by suppressing transcription factor IRF8. Immunity 28, 509–520 (2008).
Article CAS PubMed PubMed Central Google Scholar
Zhao, W. et al. NF-kappaB- and AP-1-mediated DNA looping regulates osteopontin transcription in endotoxin-stimulated murine macrophages. J. Immunol. 186, 3173–3179 (2011).
Article CAS PubMed Google Scholar
Zhu, J. & Paul, W. E. Peripheral CD4+ T-cell differentiation regulated by networks of cytokines and transcription factors. Immunol. Rev. 238, 247–262 (2010).
Article CAS PubMed PubMed Central Google Scholar
Jeon, Y. T., Na, H., Ryu, H. & Chung, Y. Modulation of dendritic cell activation and subsequent Th1 cell polarization by lidocaine. PLoS ONE 10, e0139845 (2015).
Article PubMed PubMed Central Google Scholar
Mallette, F. A., Gaumont-Leclerc, M. F., Huot, G. & Ferbeyre, G. Myc down-regulation as a mechanism to activate the Rb pathway in STAT5A-induced senescence. J. Biol. Chem. 282, 34938–34944 (2007).
Article CAS PubMed Google Scholar
Wu, Y. et al. FOXP3 controls regulatory T cell function through cooperation with NFAT. Cell 126, 375–387 (2006).
Article CAS PubMed Google Scholar
Schmidl, C., Rendeiro, A. F., Sheffield, N. C. & Bock, C. ChIPmentation: fast, robust, low-input ChIP-seq for histones and transcription factors. Nat. Methods 12, 963–965 (2015).
Article CAS PubMed PubMed Central Google Scholar
Mumbach, M. R. et al. HiChIP: efficient and sensitive analysis of protein-directed genome architecture. Nat. Methods 13, 919–922 (2016).
Article CAS PubMed PubMed Central Google Scholar
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
Article CAS PubMed Google Scholar
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).
Article CAS PubMed PubMed Central Google Scholar
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
Article PubMed PubMed Central Google Scholar
Dennis, G. Jr. et al. DAVID: database for annotation, visualization, and integrated discovery. Genome Biol 4, P3 (2003).
Article PubMed Google Scholar
Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760 (2009).
Article CAS PubMed PubMed Central Google Scholar
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, R137 (2008). .
Ramirez, F., Dundar, F., Diehl, S., Gruning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).
Article CAS PubMed PubMed Central Google Scholar
Thorvaldsdottir, H., Robinson, J. T. & Mesirov, J. P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief Bioinform. 14, 178–192 (2013).
Article CAS PubMed Google Scholar
Servant, N. et al. HiC-Pro: an optimized and flexible pipeline for Hi-C data processing. Genome Biol. 16, 259 (2015).
Article PubMed PubMed Central Google Scholar
Yan, K. K., Yardimci, G. G., Yan, C., Noble, W. S. & Gerstein, M. HiC-spector: a matrix library for spectral and reproducibility analysis of Hi-C contact maps. Bioinformatics 33, 2199–2201 (2017).
Article CAS PubMed PubMed Central Google Scholar
Durand, N. C. et al. Juicebox provides a visualization system for Hi-C contact maps with unlimited zoom. Cell Syst. 3, 99–101 (2016).
Article CAS PubMed PubMed Central Google Scholar
Durand, N. C. et al. Juicer provides a one-click system for analyzing loop-resolution Hi-C experiments. Cell Syst. 3, 95–98 (2016).
Article CAS PubMed PubMed Central Google Scholar
Crane, E. et al. Condensin-driven remodelling of X chromosome topology during dosage compensation. Nature 523, 240–244 (2015).
Article ADS CAS PubMed PubMed Central Google Scholar
Bhattacharyya, S., Chandra, V., Vijayanand, P. & Ay, F. Identification of significant chromatin contacts from HiChIP data by FitHiChIP. Nat. Commun. 10, 4221 (2019).
Article ADS PubMed PubMed Central Google Scholar
Whyte, W. A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013).
Article CAS PubMed PubMed Central Google Scholar
Sheffield, N. C. & Bock, C. LOLA: enrichment analysis for genomic region sets and regulatory elements in R and Bioconductor. Bioinformatics 32, 587–589 (2016).
Article CAS PubMed Google Scholar
Petrovic, J. et al. Oncogenic Notch promotes long-range regulatory interactions within hyperconnected 3D cliques. Mol Cell 73, 1174–1190.e1112 (2019).
Article CAS PubMed PubMed Central Google Scholar

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIP) (2018M3A9D3079290, 2020R1A2C2013258, and 2022M3A9B6017424 to H.-P.K.) as well as by Yonsei University College of Medicine Faculty Research Grant (6-2017-0079 to H.-P.K.).

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Department of Environmental Medical Biology, Institute of Tropical Medicine, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
Bobae Yang, Sueun Kim, Woong-Jae Jung, Kyungwoo Kim, Sugyung Kim, Yong-Jin Kim, Tae-Gyun Kim, Eun-Chong Lee, Jung-Sik Joo & Hyoung-Pyo Kim
Brain Korea 21 PLUS Project for Medical Science, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
Bobae Yang, Sueun Kim, Kyungwoo Kim, Sugyung Kim, Yong-Jin Kim, Tae-Gyun Kim, Jung-Sik Joo, Chae Gyu Park & Hyoung-Pyo Kim
Laboratory of Immunology, Severance Biomedical Science Institute, Yonsei University College of Medicine, 50-1 Yonsei-ro, Seodaemun-gu, Seoul, 03722, Korea
Chae Gyu Park
Laboratory of Biomedical Genomics, Department of Biological Sciences, Research Institute of Women’s Health, Sookmyung Women’s University, Seoul, 04310, Korea
Sumin Oh & Kyung Hyun Yoo
Division of biology, Pohang University of Science and Technology, Pohang, 37673, Korea
Hyoung-Pyo Kim

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Contributions

H.K. conceived and supervised the project, oversaw the experimental strategy, computational analysis, and interpretation of data and held overall responsibility for the study. B.Y., E.L., and J.J. conducted the in situ Hi-C, H3K27ac HiChIP. RelA HiChIP, ChIP-Seq, RNA-seq, Immunofluorescence, western experiments, and generated data. W.J., K.K., S.K.#, Y.K., S.O., and K.H.Y. performed bioinformatic data analysis and interpretation. B.Y., S.K.*, T.K., and C.G.P. performed immunological phenotype analysis and interpretation. H.K. and B.Y. wrote the manuscript, with input from all other authors. (S.K.#: Sugyung Kim, S.K.*: Sueun Kim).

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Correspondence to Hyoung-Pyo Kim.

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Yang, B., Kim, S., Jung, WJ. et al. CTCF controls three-dimensional enhancer network underlying the inflammatory response of bone marrow-derived dendritic cells. Nat Commun 14, 1277 (2023). https://doi.org/10.1038/s41467-023-36948-5

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Received: 24 November 2021
Accepted: 24 February 2023
Published: 08 March 2023
DOI: https://doi.org/10.1038/s41467-023-36948-5

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