The Transcription Factor NF-κB Orchestrates Nucleosome Remodeling during the Primary Response to Toll-Like Receptor 4 Signaling

An-Chieh Feng; Brandon J Thomas; Prabhat K Purbey; Filipe Menegatti de Melo; Xin Liu; Allison E Daly; Fei Sun; Jerry Hung-Hao Lo; Lijing Cheng; Michael F Carey; Philip O Scumpia; Stephen T Smale

doi:10.1016/j.immuni.2024.02.004

. Author manuscript; available in PMC: 2025 Mar 12.

Published in final edited form as: Immunity. 2024 Mar 1;57(3):462–477.e9. doi: 10.1016/j.immuni.2024.02.004

The Transcription Factor NF-κB Orchestrates Nucleosome Remodeling during the Primary Response to Toll-Like Receptor 4 Signaling

An-Chieh Feng ^1,^2,^6,⁸, Brandon J Thomas ^1,^2,^6,⁹, Prabhat K Purbey ^2,⁴, Filipe Menegatti de Melo ^1,^2,⁵, Xin Liu ^1,², Allison E Daly ^1,², Fei Sun ^2,³, Jerry Hung-Hao Lo ^1,², Lijing Cheng ⁴, Michael F Carey ^2,³, Philip O Scumpia ^2,⁴, Stephen T Smale ^1,^2,^4,^5,^7,^*

PMCID: PMC10984581 NIHMSID: NIHMS1973150 PMID: 38430908

SUMMARY

Inducible nucleosome remodeling at hundreds of latent enhancers and several promoters shapes the transcriptional response to Toll-like receptor 4 (TLR4) signaling in macrophages. We aimed to define the identities of the transcription factors that promote TLR-induced remodeling. An analysis strategy based on ATAC-seq and single-cell ATAC-seq that enriched for genomic regions most likely to undergo remodeling revealed that the transcription factor NF-κB bound to all high-confidence peaks marking remodeling during the primary response to the TLR4 ligand, lipid A. Deletion of NF-κB subunits RelA and c-Rel resulted in the loss of remodeling at high-confidence ATAC-seq peaks and CRISPR-Cas9 mutagenesis of NF-κB binding motifs impaired remodeling. Remodeling selectivity at defined regions was conferred by collaboration with other inducible factors, including IRF3 and MAP kinase-induced factors. Thus, NF-κB is unique among TLR4-activated transcription factors in its broad contribution to inducible nucleosome remodeling, alongside its ability to activate poised enhancers and promoters assembled into open chromatin.

Keywords: macrophages, transcription, chromatin, nucleosome remodeling, NF-κB, IRF-3

Graphical Abstract

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eTOC paragraph

Innate immune cells tailor responses to specific stimuli via the selectively activation of hundreds of genes. Feng et al. examine inducible nucleosome remodeling in response to TLR4 stimulation and reveal a unique requirement for NF-κB. Collaboration between NF-κB and other inducible factors, including IRF3, contributes to remodeling selectivity, with NF-κB-independent primary response genes likely to be activated without inducible remodeling.

INTRODUCTION

Innate immune cells require the selective activation of hundreds of genes to mount a tailored response to diverse stimuli. The selectivity of the response is dictated by sensors that recognize the stimulus, by the downstream signaling pathways, and by the transcriptional and chromatin regulators activated by these pathways.^1-5

The differential responses to Toll-like receptor 4 (TLR4) and TLR2 serve as an example of response selectivity. TLR4 activates both the MyD88 and TRIF signaling pathways, which activate a defined set of transcription factors and target genes. In contrast, TLR2 potently activates only MyD88 and therefore fails to activate many TLR4-induced genes.^6-8 Transcription factors induced solely by post-translational mechanisms regulate the primary response to the stimulus; factors encoded by primary response genes help shape the subsequent secondary response.^9-11

Chromatin helps orchestrate responses via two distinct mechanisms.^12-13 First, the chromatin landscape assembled during cell development dictates which control regions can participate in the response. In quiescent macrophages, the landscape is characterized by hundreds of poised enhancers that exhibit physical accessibility and histone modifications associated with poised but inactive transcriptional states.^14-16 The promoters of most TLR-induced genes are also assembled into accessible chromatin in quiescent macrophages. NF-κB and AP-1 are among the inducible transcription factors that bind poised regions to activate transcription.^14,15,17

Chromatin also contributes directly to the response.^12-13 Hundreds of latent enhancers remain inaccessible until cells encounter a stimulus.^18,19 A small number of TLR4-induced promoters also undergo stimulus-responsive nucleosome remodeling, with remodeling critical for response selectivity.^10,17,20,21 In one example, Ccl5 was the only TLR4-induced primary response gene exhibiting a requirement for the transcription factor IRF3 for inducible remodeling at its promoter.¹⁷ This finding suggests that promoter nucleosomes help prevent expression of the Ccl5 chemokine following exposure to many stimuli, with induction limited to stimuli that induce IRF3.¹⁷

Stimulus-responsive nucleosome modeling is typically catalyzed by ATP-dependent remodeling complexes, including SWI/SNF complexes, which can promote nucleosome eviction, sliding, or conformational transitions.^22,23 However, a key unresolved issue is the identities of the transcription factors that promote TLR-induced remodeling. Comoglio et al.²¹ provided evidence of combinatorial regulation of remodeling by multiple factors through an analysis of TLR-induced chromatin changes combined with modeling and transcription factor ChIP-seq analyses. A computational tool used in this study identified NF-κB as the strongest candidate to induce remodeling.²¹ Further evidence that NF-κB may participate in remodeling emerged from a study in which IκBα deficiency prolonged NF-κB activation in response to tumor necrosis factor α, leading to increased accessibility at several control regions unaffected by transient NF-κB activation.²⁴ AP-1 family proteins are also implicated in the regulation of inducible nucleosome remodeling, but primarily in TLR-independent settings.^25-27

A challenge in uncovering the mechanisms regulating inducible remodeling is that the genomic locations of remodeling events are difficult to define genomewide. Assays like ATAC-seq reveal regions exhibiting changes in accessibility.^21,28 However, most statistically significant physical changes are unlikely to represent true nucleosome remodeling, as changes in transcription factor-DNA interactions within open chromatin and other changes in chromatin structure are also likely to alter accessibility.^28,29 Another challenge is that SWI/SNF complexes bind most open chromatin genomewide,³⁰ making it difficult to use SWI/SNF ChIP-seq to definitively identify locations of inducible remodeling. Moreover, SWI/SNF complexes are broadly required to maintain open chromatin, and their absence often results in cell death over time,^10,30 making loss-of-function results challenging to interpret.

To gain further insight into the regulation of TLR4-induced nucleosome remodeling, we used ATAC-seq profiling in lipid A-stimulated macrophages, concentrating on genomic regions exhibiting the largest and most consistent changes in accessibility rather than employing typical analysis approaches focused on all regions exhibiting statistically significant increases in accessibility. This approach, followed by extensive functional analyses, provides strong evidence that NF-κB plays a broad and possibly universal role in remodeling during the TLR4 primary response and that remodeling selectivity is conferred by collaborations between NF-κB and other TLR-induced factors.

RESULTS

ATAC-seq Profiling Reveals a Broad Spectrum of TLR4-Induced Peaks

ATAC-seq was performed with mouse bone marrow-derived macrophages (BMDMs) stimulated with the TLR4 ligand, lipid A, for 0, 30, 60, or 120 min. Merger of reads from multiple biological replicates revealed 101,448 accessible regions (i.e. statistically significant sequencing read peaks) that were reproducibly observed in at least one time point. By analyzing ATAC-seq reads per kb of DNA per million mapped reads (RPKM), most peaks (72.8%) changed by less than 2-fold among all stimulated time points (Figure 1A), with 2.7% of peaks exhibiting repression of more than 2-fold as their maximum change. In contrast, 19.4% were maximally induced 2-5-fold, with 5.1% (5,178 peaks) induced >5-fold. Most peaks were intronic and intergenic, with 10.1% in promoters (Figure 1B). However, peaks induced >5-fold were scarce within promoters, consistent with prior evidence that promoters rarely exhibit inducible remodeling (Figure 1B).¹⁷ Examples of Integrated Genomics Viewer (IGV)³¹ tracks are shown in Figure 1C. Notably, several genes exhibit accessibility throughout the transcription unit, presumably due to accessibility linked to active transcription (Figure 1C, right).

We next divided the 101,448 peaks into 40 bins based on maximum fold-change during the time course (Figure 1D). Consistent with Figure 1B, promoters represent a large fraction of peaks in bins that exhibit little accessibility change, but only a small fraction in the most induced and repressed bins. Importantly, although most peaks induced >2-fold are statistically significant (p<0.05, data not shown), a large fraction of these peaks are present in both unstimulated and stimulated cells, with the fraction of peaks observed in unstimulated cells declining as the average fold-activation in each bin increases (Figure 1E). For example, in Bin 30, half of the regions exhibit called peaks in unstimulated cells, whereas only a small percentage in Bin 40 are observed prior to stimulation.

The finding that large numbers of ATAC-seq peaks with weak induction have called peaks in unstimulated cells suggests that these regions often contain constitutively open chromatin, with the small induction due to transcription factor binding or smaller changes in chromatin rather than remodeling. We therefore reasoned that the most strongly induced ATAC-seq peaks, where peaks are generally undetectable in unstimulated cells and at which the largest fold inductions are observed, deserve special attention, as they represent the regions most likely to undergo remodeling. (Eliminating peaks that are called in unstimulated cells is undesirable because, in bins with weakly induced peaks, the vast majority of such peaks barely exceed the peak-calling threshold in stimulated cells and are therefore unreliable.) Therefore, for most of the analyses described below, we divided peaks into bins based on their ATAC-seq induction magnitude, allowing us to focus on bins with the most strongly induced peaks. Alternatively, we focused on ATAC-seq peaks induced >5-fold with statistical criteria added to further increase our emphasis on regions most likely to undergo nucleosome remodeling. The term “nucleosome remodeling” is used below to refer to the most strongly and consistently induced ATAC-seq signals, with the caveat that we cannot definitively establish that large physical changes in accessibility in vivo are analogous to ATP-dependent nucleosome remodeling events studied in vitro.

Figure 1F shows tracks for the TLR4-induced Ccl9 gene as an example of the above points. Three peaks show weak induction (1.2-2.2-fold) and are also present in unstimulated cells, whereas a fourth intergenic peak is induced 14-fold and lacks a called peak in unstimulated cells. With our reasoning, the fourth region is the most likely to reflect TLR4-induced remodeling.

ATAC-seq Induction Coincides with Inducible BRG, p300, and H3K27Ac ChIP-seq Peaks

ChIP-seq with the SWI/SNF Brg1 catalytic subunit provided further insight (Figure 1G). Brg1 peaks are detected in unstimulated BMDMs at 20-50% of the ATAC-seq peaks in bins induced by small magnitudes (e.g. bins 3-30), with little change in these percentages following stimulation. In contrast, in bin 40, Brg1 ChIP-seq peaks are observed at only 0.8% of the ATAC-seq peaks in unstimulated cells, but at 37% of the ATAC-seq peaks following stimulation. This result reveals a close correlation between inducible Brg1 binding and inducible ATAC-seq signals, but only in bins containing the most strongly induced ATAC-seq peaks.

Similar results were obtained upon ChIP-seq for p300 (a transcriptional co-activator)¹³ and histone H3K27ac (a histone modification associated with active transcription),³² as the bins containing the most strongly induced ATAC-seq peaks also exhibit the largest increases in the percentages of H3K27ac and p300 peaks (Figure S1A,B). These results support the hypothesis that bins with peaks displaying the strongest ATAC-seq induction are more likely to undergo TLR4-induced Brg1 recruitment and nucleosome remodeling than peaks exhibiting weaker ATAC-seq induction.

scATAC-seq Establishes Distinctions between Strongly and Weakly Induced Peaks

To better understand the significance of strong versus weak induction of ATAC-seq signals, we performed scATAC-seq^33,34 with BMDMs stimulated with lipid A for 0 or 2 hrs. Analysis of the normalized combined scATAC-seq data by UMAP³⁵ reveals five cell clusters (0-4), with almost all cells assigned to clusters 0 or 1 (Figure 2A). A clear separation is observed between unstimulated and stimulated cells (Figure 2B), suggesting a high level of purity of the BMDM cultures.

Figure 2. — (A) UMAP projections are shown after combining scATAC-seq counts from 6362 unstimulated and 8582 stimulated (2 hrs) BMDMs. Five cell clusters (0-4) were identified using the FindClusters function with the Louvain method “algorithm=3” at a resolution = 0.1 in the Signac software package in R.⁴⁵

(B) The locations of unstimulated (blue) and stimulated (red) cells are shown with respect to the UMAP clusters in panel A.

(C) Beginning with the 101,448 bulk ATAC-seq peaks separated into 40 bins (Figure 1E), the percentage of peaks in each bin that overlap with one of the 266,098 peaks identified by pseudo-bulk analysis of the scATAC-seq data are shown for unstimulated (grey) and stimulated (yellow) BMDMs. The high percentages are due to the low peak-calling stringency implemented by the Cell Ranger ATAC software pipeline (10X Genomics) for scATAC-seq.

(D) A peak coinciding with the *Ccl5* promoter (dashed box) provides an example of a strongly induced ATAC-seq peak from Figure 1E, bin 40. Total counts at this location in unstimulated and stimulated cells are shown. Peak locations are indicated by horizontal lines.

(E) *Ccl5* promoter peak counts are indicated by Feature plot analysis.

(F) Normalized counts in each unstimulated and stimulated cell for the *Ccl5* promoter peak are displayed by violin plot using the VlnPlot function in Signac.⁴⁵ No counts are observed in unstimulated cells. Counts are observed in 2914 (34.2%) of stimulated cells.

(G) Similar to panel D, a pseudo-bulk analysis is displayed for a representative region upstream of the *Tmem173* gene showing a 2.7-fold increase in counts in stimulated cells.

(H) Peak counts within the *Tmem173* region are indicated, showing substantial numbers of both unstimulated and stimulated cells with counts.

(I) Normalized *Tmem173* counts in each unstimulated and stimulated cell are displayed as in panel F. Counts are observed in 15% (952) and 22% (1867) of unstimulated and stimulated cells, respectively. The 2.7-fold induction represents the combined impact of the small increase in the percentage of cells with counts and the small increase in mean counts per cell containing counts.

Upon pseudo-bulk analysis of the scATAC-seq data, peaks are apparent at >98% of the 101,448 peaks identified by bulk ATAC-seq, spanning all 40 bins from Figure 1E (Figure 2C). However, in Figure 1E bin 40, scATAC-seq peaks are absent in unstimulated cells at approximately 50% of the peaks (Figure 2C). Thus, the scATAC-seq data largely mirror the bulk ATAC-seq results, with a large difference between unstimulated and stimulated cells observed only in the last of the 40 bins. Importantly, the detection of scATAC-seq peaks in 50% of bin 40 peaks prior to stimulation is largely due to the lower peak-calling stringency of the algorithms used for scATAC-seq data analysis. Specifically, 266,098 total peaks are detected by scATAC-seq in comparison to 101,448 peaks by bulk ATAC-seq.

We focused on the Ccl5 promoter as an example of a strongly induced ATAC-seq peak from bin 40. In the scATAC-seq data, no counts are observed at this location in the 6362 unstimulated cells analyzed, with 4,118 unique counts in the 8528 stimulated cells. Feature plot analysis demonstrates the counts at this location exclusively in the UMAP clusters that align with stimulated cells (Figure 2E). Following normalization, counts are observed in 34.2% of the stimulated cells (2914 of 8528 cells) and in 0% of the unstimulated cells (Figure 2F). The most likely reason for this modest percentage of stimulated cells with counts is that scATAC-seq efficiency is inadequate to generate counts in all cells within the population. Notably, no genomic regions in the BMDMs (including constitutively accessible regions) display counts in 100% of cells and only 0.8% of regions display counts in >50% of cells, with 56% of genomic regions displaying counts in 5-50% of the cells (data not shown). As an additional perspective, 66.6% of the stimulated cells with Ccl5 promoter counts display exactly two unique counts (one count rarely observed due to the use of paired end sequencing)³⁶ (Figure S1C). Thus, at a single-cell level, transposon targeting generates results that can largely be considered as binary.^33,34,36

A peak upstream of the Tmem173 gene provides an example of a more weakly induced peak. The Tmem173 peak, in bin 37 in the bulk analysis (Figure 1G), is induced 2.7-fold in the pseudo-bulk analysis (Figure 2G). Notably, a feature plot analysis (Figure 2H) and a quantitative analysis of counts (Figure 2I) reveal counts in 15% of unstimulated cells, with an increase to only 22% following stimulation. This small increase, combined with a small increase in the mean normalized counts per cell (Figure 2I and S1C), results in the 2.7-fold overall increase in the pseudo-bulk analysis. Similar profiles are observed with several other moderately induced peaks (data not shown)

Although we cannot exclude the possibility that the small number of cells that display Tmem173 counts only in stimulated cells transition from closed to open chromatin upon TLR4 signaling, the scATAC-seq findings are most consistent with the suggestion that moderate, statistically significant increases in ATAC-seq signals result from modest changes in chromatin or transcription factor binding that do not represent bona fide nucleosome remodeling.

Features of Inducible ATAC-seq Peaks at Promoters and Intergenic Regions

Returning to the bulk analysis, our prior study that focused on the promoters of 132 primary response genes potently induced by TLR4 signaling revealed strong ATAC-seq induction at only three promoters: Ccl5, Gbp5, and Irg11.¹⁷ Using our current thresholds and datasets, induced ATAC-seq signals (>5-fold) are observed at these three promoters and four additional primary response promoters (Figure S2A). Induced ATAC-seq signals (>5-fold) are also observed at the promoters of six of our previously defined secondary response genes (Figure S2A).¹⁷ Further analysis across the 2-hr time course of all promoters throughout the genome (−500 to +150) reveals inducible ATAC-seq signals (>5-fold) at only 26 additional promoters (Figure S2A). Twenty are associated with weakly induced genes, with the remaining six showing little or no transcriptional induction. The results confirm that strongly induced ATAC-seq signals at promoters are rare.

We next analyzed intergenic regions by first calculating the distance from each TLR4-induced ATAC-seq intergenic peak to the nearest gene that exhibited transcriptional induction of at least 10-fold.¹⁷ This analysis reveals that strongly induced ATAC-seq peaks (>5-fold) are over-represented near strongly induced genes; weakly induced ATAC-seq peaks are over-represented to a lesser extent, with little over-representation of constitutive peaks (Figure S2B).

K-means cluster analysis reveals that strongly induced intergenic peaks exhibit diverse kinetic profiles (Figure S2C). Distinct subsets of peaks are either resistant to or sensitive to the protein synthesis inhibitor, cycloheximide (CHX), demonstrating that inducible remodeling occurs during both the primary and secondary responses (Figure S2C).

We also stimulated BMDMs with Pam3CSK4 (TLR2 ligand) and poly(I:C) (TLR3 ligand) for 0 and 2 hr to compare ATAC-seq profiles generated by TLR4, TLR2, and TLR3 signaling. We focused on the 2,536 peaks in Bin 40 (Figure 1E). Comparing TLR4 and TLR2, volcano plots reveal about two dozen peaks that are relatively poorly induced by TLR2 in comparison to TLR4, with no peaks preferentially induced by TLR2 (Figure S1D). After assigning each peak to its closest gene, the results suggest that over half of the TLR4 preferential peaks are closest to genes we previously classified as Type I IFN receptor (IFNAR)-dependent secondary response genes (Figure S1D), consistent with expectations based on knowledge that TLR2 poorly induces the TRIF pathway.^6-8 The remaining differentially induced peaks are either near the TRIF-dependent Cxcl10 primary response gene or genes that remained unclassified in our prior analysis.

Comparing TLR4 and TLR3, several of the small number of peaks induced preferentially by TLR3 are near IFNAR-dependent genes (Figure S1E), consistent with TLR3’s preferential induction of TRIF. A larger number of ATAC-seq peaks are preferentially induced by TLR4, with most close to either TLR4-induced primary response genes or genes that were previously unclassified.¹⁷ Thus, the differential induction of high-confidence ATAC-seq peaks reflects known differences in signaling pathways induced by the TLRs.

Unique Profile of NF-κB Motif Enrichment at Primary Response ATAC-seq Peaks

To identify transcription factors that may contribute to TLR4-induced remodeling, motif analysis was performed with the 40 bins from Figure 1E. Several transcription factor families exhibit differential enrichment across the fold-induction range (Figure 3A; motifs assigned to several different members of each family are displayed in separate rows). Motifs for bZIP, ETS, IRF, NF-κB (Rel-homology domain, RHD), and POU/Homeobox family members exhibit the greatest enrichment in the bins containing strongly induced ATAC-seq peaks (Figure 3A). Strikingly, NF-κB motifs stood out in showing a close correlation with the most strongly induced ATAC-seq peak bins in comparison to weakly induced bins (Figure 3B). That is, the percentage of peaks containing an NF-κB motif differs between the strongly (>5-fold) and weakly (2-5-fold) induced bins to a much greater extent than observed with other motifs. In particular, bZIP and IRF motifs display a gradual enrichment across the bins (Figure 3B). This distinctive property of NF-κB provides initial evidence of a close relationship between NF-κB and TLR4-induced remodeling.

Figure 3. — (A) ATAC-seq peaks were separated into 40 bins (see Figure 1). Transcription factor motif enrichment (HOMER) was performed with each bin. Motifs are grouped by transcription factor family, with motifs for different members of each family shown in individual rows. Induction ranges are shown (top).

(B) Line graphs show enrichment of motifs in each of the 40 bins (panel A) quantified as −log(p-value) (y-axis, blue) and as percentage of peaks with the motif (y-axis, orange) for representative motifs in four transcription factor families.

(C) ATAC-seq was performed with lipid A-stimulated macrophages (0, 30, 60, and 120 min) in the absence and presence of CHX. Peaks induced >5-fold were separated into eight bins based on sensitivity of peak induction to CHX, with motif enrichment determined as in panel A. Bins 1 and 8 contain peaks with the greatest (secondary response) and least (primary response) CHX sensitivity, respectively. Arbitrary separations between the primary and secondary responses are shown.

(D) ATAC-seq peaks were separated into 40 bins by fold-induction (blue) or statistical confidence (−log[p-value], orange). The percentage of ATAC-seq peaks in each bin overlapping a RelA ChIP-seq peak (y-axis) is shown. Numerical percentages are indicated for bin 40.

(E) To further examine the prevalence of RelA binding at high-confidence, inducible ATAC-seq peaks, 3,290 primary response peaks displaying an average induction >5-fold were separated into 10 bins on the basis of their statistical significance of induction (−log[p-value]). The percentage of peaks overlapping with a RelA ChIP-seq peak (y-axis) is shown for each bin (x-axis). In bin 40, 82.7% of ATAC-seq peaks overlap with a RelA peaks.

(F) Tracks are shown for a region between the *Tyk2* and *Cdc37* genes containing a strongly induced ATAC-seq peak and a RelA ChIP-seq peak. ATAC-seq tracks are shown from a wild-type macrophage line (tracks 1-5), a clonal line in which substitution mutations were introduced into two NF-κB motifs underlying the ATAC-seq peak (tracks 6-10), and a mixture of lines in which the *Rela* and *Rel* genes were simultaneously inactivated by CRISPR deletion (tracks 11-15). RelA ChIP-seq tracks from wild-type BMDMs are shown (tracks 16-20). Tracks are shown for five time points (noted at left). Black horizontal lines denote locations of constitutive (left) and inducible (right) ATAC-seq peaks.

To separate putative remodeling events associated with the primary versus secondary responses, ATAC-seq was performed in BMDMs stimulated with lipid A for 0, 30, 60, and 120 min in the presence of CHX. Strongly induced peaks (>5-fold) were placed into eight bins on the basis of the extent to which CHX altered the induced ATAC-seq signal. NF-κB motifs are over-represented most strongly in the peaks exhibiting the greatest CHX resistance (primary response), with IRF and POU/Homeobox motifs over-represented in CHX-sensitive peaks (secondary response) (Figure 3C). The over-representation of IRF motifs during the secondary response is consistent with prior evidence that nucleosome remodeling contributes to the Type 1 interferon response, which dominates the TLR4 secondary response.³⁷

The above results show that NF-κB motifs are the only identifiable transcription factor motifs that exhibit a strong, preferential enrichment at ATAC-seq peaks that are strongly induced during the primary response. NF-κB motifs are also enriched at weakly induced peaks, but by a much smaller magnitude. The much broader enrichment of bZIP motifs across the fold-induction spectrum does not rule out a role for bZIP factors in remodeling, but it suggests a different relationship across the spectrum (see Discussion).

NF-κB Binds Broadly to High-Confidence Primary Response ATAC-seq Peaks

To evaluate further the relationship between NF-κB and ATAC-seq peaks, we examined ChIP-seq data for the NF-κB family member, RelA. When examining the 40 ATAC-seq peak bins defined on the basis of fold-induction, RelA binding is observed most frequently in bin 40, with RelA binding to 34% of peaks in this bin (Figure 3D). When the 40 bins were created based on the statistical significance (p-value) of ATAC-seq induction instead of fold-induction, 59% of peaks in the highest confidence bin exhibit RelA binding (Figure 3D).

Because low p-values are not restricted to ATAC-seq peaks exhibiting large fold-inductions (low p-values can result from small fold-changes that are highly reproducible), we focused on 3,289 primary response peaks exhibiting strong induction (>5-fold) and then separated them into ten bins on the basis of their p-value. In this analysis, a remarkable 82.7% (272 of 329) of the peaks in the highest confidence bin (bin 10) coincide with RelA ChIP-seq peaks (Figure 3E). The increasing frequency of RelA binding across the bins, from 10.4% of peaks with RelA binding in bin 1 to 82.7% in bin 10, highlights the value of combining fold-induction and statistical significance criteria.

The above results demonstrate that RelA frequently binds the most strongly and consistently induced ATAC-seq peaks, but they suggest that the remaining 17.3% of peaks in bin 10 that lack RelA ChIP-seq peaks might correspond to RelA-independent enhancers. To address this issue, we individually scrutinized ATAC-seq, RelA ChIP-seq, and RNA-seq tracks at the 57 ATAC-seq peaks in bin 10 that do not coincide with a RelA ChIP-seq peak. The ATAC-seq signals at 24 of these regions appear to reflect read-through transcription from nearby genes rather than discrete peaks representative of enhancers (data not shown; see Figure 4B below). The remaining 33 ATAC-seq peaks exhibit elevated RelA ChIP-seq reads, but not to a sufficient extent to meet our peak-calling criteria (data not shown). Thus, we are unable to conclusively identify strongly induced ATAC-seq peaks with no evidence of RelA binding.

Figure 4. — (A) ATAC-seq was performed with a wild-type macrophage line and two clonal *Rela*^−/− *Rel*^−/− lines. 1001 primary response ATAC-seq peaks induced >5-fold in the wild-type line were separated into ten bins based on statistical significance (−log[p-value]) of maximum induction at the time point at which maximum induction was achieved in two independent experiments. Scatter plots are shown for the four bins with the strongest statistical significance ranges. The scatter plots show ATAC-seq RPKM from wild-type (x-axis) and *Rela*^−/− *Rel*^−/− (y-axis) cells at the time point that yields the maximum difference (p-value). The solid diagonal line represents equal RPKM and the dashed line, average RPKM in the mutant lines that is 33% of the wild-type RPKM. Peaks that are >33% of wild-type in the mutant lines are colored in red. Numbered peaks (1-5) are shown in panel B.

(B) Browser tracks are shown for a representative constitutive ATAC-seq peak and for five inducible peaks (numbered in panel A) across the time-course from wild-type and *Rela*^−/− *Rel*^−/− mutant cells (tracks contained merged data from two wild-type experiments or two mutant lines). Peak 1 is from an *Nfkb1* intron. Peak 2 is downstream of the constitutive *Tgds* gene. Peak 3 is adjacent to the inducible *Ptgs2* gene. Peak 4 appears to result from readthrough transcription downstream of the inducible *Plk2* gene. Peak 5 is from an intron for the inducible *Dcbld2* gene. Scale ranges are consistent among tracks for each peak, but differ from peak to peak.

(C) Scatter plots show Log2 RPKM from mRNA-seq experiments performed with the wild-type transformed line and two *Rela*^−/− *Rel*^−/− mutant lines. The plots show all genes induced 2-5-fold (left) or >5-fold (right) 120-min after stimulation (maximum RPKM >3). Values are averages of two experiments with wild-type cells or with two mutant lines. The solid diagonal line represents equal RPKM in wild-type and mutant and the dashed line represents 33% RPKM in the mutant samples in comparison to wild-type. Grey dots represent genes whose transcript levels are <33% of wild-type.

(D) Tracks are shown for a strongly induced gene that exhibits reduced mRNA levels in two independent *Rela*^−/− *Rel*^−/− lines (*Nfkbiz*), along with tracks for two primary response genes (*Dusp5* and *Ccl7*) that remain strongly induced in the *Rela*^−/− *Rel*^−/− lines.

(E) Scatter plots are shown for the four peak bins examined in panel A. The scatter plots compare average RPKM for each peak in a wild-type macrophage line (x-axis; average of two replicates at 0- and-2 hr time points) and *Nfkb1*^−/− *Nfkb2*^−/− macrophage lines (y-axis; average of three replicates from two mutant lines at 0- and 2-hr time points.). RNA-seq revealed similarly modest impacts on lipid A-induced mRNA levels in the mutant cells (Daly et al., unpublished results).

Important Roles of NF-κB Motifs and the Rela and Rel Genes in ATAC-seq Induction

The unique enrichment pattern of NF-κB motifs (Figure 3B) and RelA’s extensive binding to strongly induced ATAC-seq peaks (Figure 3E) suggest that NF-κB may be a broad regulator of inducible remodeling during the primary response. To test this hypothesis, we first used CRISPR/Cas9 editing with homology-directed repair (CRISPR-HDR) to introduce substitution mutations into two NF-κB motifs underlying a potently induced ATAC-seq peak between the Tyk2 and Cdc37 genes (Figure S3A). We used a J2 virus-transformed mouse macrophage line that displays a robust transcriptional response to lipid A stimulation (data not shown). The inducible ATAC-seq signal at the intergenic site is selectively absent in the mutant line (Figure 3F, tracks 6-10), strongly suggesting that NF-κB binding is critical. An examination of read numbers at local sequences that are not altered by the mutation confirms that the NF-κB motif mutations eliminate ATAC-seq induction (Figure S3A,B). Transcriptional induction of Tyk2 was strongly reduced by the mutations (Figure S3C), demonstrating that Tyk2 is a target of this putative enhancer. Transcription of Cdc37, which is not induced in wild-type cells, was unaffected (data not shown).

To further examine the role of NF-κB, we performed ATAC-seq with lipid A-stimulated macrophages in the presence of the IκB kinase inhibitor, Bay 11-7082. Inhibitor treatment strongly reduced the Tyk2/Cdc27 ATAC-seq peak (data not shown). Because of Bay11’s imperfect specificity,³⁸ we used CRISPR/Cas9 editing to create mutant macrophage lines in which homozygous deletions were introduced into both the Rela and Rel (c-Rel) genes (Rela^−/− Rel^−/− lines; Figure S4). The combined mutations eliminated possible redundancy between RelA and c-Rel. Importantly, the inducible Tyk2/Cdc37 ATAC-seq signal is eliminated in two independent Rela^−/− Rel^−/− clonal lines (Figure 3F, tracks 11-15, and data not shown). This result further strengthens the evidence that NF-κB is essential for inducible nucleosome remodeling at this ATAC-seq peak. In the Rela^−/−Rel^−/− line, Tyk2 mRNA was not induced by lipid A (Figure S3C).

Broad Impact of RelA and c-Rel at High Confidence ATAC-seq Peaks

We next further analyzed the ATAC-seq data obtained with the Rela^−/− Rel^−/− clones following lipid A stimulation for 0, 15, 30, 60, and 120 min (Figure 4A). 1,001 primary response peaks induced >5-fold in the wild-type line were examined, after separating those peaks into 10 bins on the basis of their statistical significance (p-value) of induction using time-courses performed with two independent mutant lines. Figure 4A displays the impacts of the mutations on ATAC-seq RPKM for peaks in the four bins with the greatest statistical significance. In each bin, approximately 90% of the peaks are strongly reduced in the Rela^−/− Rel^−/− clones (<33% of WT). One example of a suppressed peak is shown in Figure 4B (Peak 1). Most of the peaks that are suppressed to a lesser extent (orange dots in Figure 4A) are either, 1. strongly suppressed at early time points, with less suppression at the 120-min time point, suggestive of NF-κB-dependence during the primary response but partial NF-κB-independence during the secondary response (Figure 4A,B, Peaks 2 and 3), or 2. a result of read-through transcription that does not reflect an inducible regulatory region (Figure 4B; see Peak 4, which appears to represent read-through transcription downstream of the inducible Plk2 gene).

We cannot rule out the possibility of NF-κB-independent ATAC-seq induction associated with the primary response at a few peaks (e.g. Figure 4B, Peak 5). However, these peaks are rare and appear to lack ATAC-seq induction at the earliest time points. Therefore, NF-κB may be universally important for nucleosome remodeling during the TLR4 primary response.

RNA-seq analysis of the wild-type and mutant Rela^−/− Rel^−/− lines reveals that the mutations strongly reduced mRNA levels (<33% of wild-type) of 74.5% (70 of 94) of genes induced >5-fold that reach an RPKM threshold of 3 RPKM (Figure 4C, right). In contrast, only 28.1% (38 of 135) of genes induced by 2-5-fold exhibit strongly reduced transcription (Figure 4C, left). Figure 4D displays tracks of representative NF-κB-dependent (Nfkbiz) and - independent (Dusp5 and Ccl7) genes. In our prior study,¹⁷ Dusp5 was classified as a serum response factor (SRF) target gene and Ccl7 exhibited TRIF-dependence but MyD88- and IRF3-independence. A manual inspection of tracks surrounding RelA/c-Rel-independent genes failed to identify strongly induced ATAC-seq peaks (data not shown), suggesting that their induction does not require inducible remodeling.

We also performed ATAC-seq with macrophage lines in which both the Nfkb1 and Nfkb2 genes, which encode the NF-κB p50 and p52 subunits, are disrupted (A. Daly and S.T. Smale, unpublished data). Surprisingly, only a small number of TLR4-induced ATAC-seq peaks in the highest confidence bins exhibit strong decreases in the Nfkb1^−/− Nfkb2^−/− macrophages, with little or no reduction at the vast majority of peaks (Figure 4E). Thus, RelA and c-Rel appear sufficient for most TLR4-induced remodeling events.

Critical Role for IRF3 in Inducible Remodeling at a Small Number of ATAC-seq Peaks

The above results do not address whether NF-κB acts alone to promote remodeling or acts in concert with other transcription factors. As shown above, we found enrichment of motifs for several other factors in bins containing the most strongly induced ATAC-seq peaks, consistent with prior evidence of collaboration.²¹ To examine whether NF-κB collaborates with other inducible factors to promote remodeling, we focused on IRF3, a transcription factor induced during the TLR4 primary response.^39,40

We previously showed that IRF3 plays a dominant role in the activation of only nine of 132 potently induced TLR4 primary response genes.¹⁷ Remarkably, IRF3 appeared to be required for remodeling at only one promoter, the Ccl5 promoter.¹⁷ RelA binding to the Ccl5 promoter was strongly reduced in Irf3^−/− BMDMs, and RelA bound with slower kinetics to the Ccl5 promoter than to other primary response promoters, with RelA binding and Ccl5 induction coinciding with the slow kinetics of IRF3 activation.¹⁷ These results suggested that IRF3 is critical for Ccl5 promoter remodeling.

To further examine the extent to which IRF3 contributes to remodeling during the primary response, we first examined all 40 primary response promoters exhibiting ATAC-seq signals induced >5-fold (see Figure S2A). Consistent with our previous results, the Ccl5 promoter remains unique in its IRF3 requirement (Figure 5A, left).

Figure 5. — (A) ATAC-seq was performed with wild-type and *Irf3*^−/− BMDMs left unstimulated or stimulated for 120 min in the presence of CHX to prevent secondary response proteins from masking IRF3 requirements during the primary response.¹⁷ Induced ATAC-seq signals (RPKM) from wild-type and mutant cells were compared by scatter plot (top) and volcano plot (bottom) for the 40 promoters (left) and 1,494 intergenic (right) ATAC-seq peaks induced >5-fold. Peaks overlapping with a strong (peak score >90) IRF3 ChIP-seq peak (red) are distinguished from peaks with weak (peak score <90) IRF3 peaks (blue) and from peaks lacking IRF3 peaks (black).

(B) IRF and Rel-homology domain (RHD, NF-κB and NFAT) family member motif enrichment is shown for primary and secondary response ATAC-seq bins (see Figure 3C) and for IRF3-dependent primary response peaks (right). Because only 107 IRF3-dependent ATAC-seq peaks were identified, wild-type BMDM ATAC-seq peaks were divided into bins with 107 or 108 peaks each, to allow an accurate comparison between the wild-type and *Irf3*^−/− datasets. Only a subset of the wild-type bins is shown.

Next, to determine how many intergenic primary response ATAC-seq peaks exhibit IRF3 dependence, inducible intergenic ATAC-seq peaks were compared in wild-type and Irf3^−/− BMDMs (stimulated in the presence of CHX to focus on the primary response). In a scatterplot analysis (Figure 5A, right top), only 107 inducible intergenic sites exhibit strong (<33% of WT) dependence on IRF3; 19 of these exhibit strong IRF3 binding by ChIP-seq (red dots in Figure 5A), with another 34 (blue dots) exhibiting weaker binding. A volcano plot (Figure 5A, right bottom) reveals only 22 statistically significant intergenic, IRF3-dependent, primary response ATAC-seq peaks, 13 of which (59%) strongly bind IRF3, with another 6 exhibiting weaker IRF3 binding. Of the three that exhibit no binding, two coincide with readthrough transcription downstream of Ccl4 and Ccl5 (data not shown).

IRF motifs were not enriched during the primary response in our initial ATAC-seq analysis (see Figures 2C). However, motif analysis performed with the IRF3-dependent ATAC-seq peaks reveals a clear enrichment of IRF motifs (Figure 5B). Thus, IRF motifs were not enriched in our initial analysis because IRF3 is important at only a small fraction of inducible peaks.

Transcription factors in the bZIP family, including AP-1, are difficult to functionally examine due to extensive redundancy between the many family members. However, many bZIP proteins are induced by mitogen activated protein kinases (MAPKs). We therefore performed ATAC-seq in the presence of MAPK inhibitors (a combination of ERK and p38 MAPK inhibitors) to gain preliminary insight into the frequency with which strongly induced primary response ATAC-seq peaks require MAPK signaling. This analysis identified six promoters with strongly induced peaks at which ATAC-seq induction is dependent on MAPK signaling (Figure S5). Among 757 primary response intergenic sites that exhibit strongly induced ATAC-seq peaks in this experiment, 166 (21.9%) were strongly reduced in the presence of MAPK inhibitors (Figure S5). Thus, NF-κB appears to collaborate more frequently with MAPK-induced proteins than with IRF3 to promote remodeling. Additional experiments are needed to determine whether NF-κB acts alone or collaborates with other factors at inducible ATAC-seq peaks that lack requirements for IRF3 and MAPKs.

NF-κB and IRF3 are Both Required for Remodeling at the Ccl5 Locus

To further interrogate collaboration between NF-κB and IRF3, we focused on the Ccl5 locus, which contains two of the high-confidence ATAC-seq peaks exhibiting strong IRF3 dependence; at the Ccl5 promoter and at an intergenic site 10 kb upstream of the Ccl5 TSS (Figure 6, tracks 3 and 4, IRF3-dependent peaks highlighted with red and blue shading). Both peaks are resistant to CHX (Figure 6A, tracks 2 and 3), consistent with their classification as primary response peaks, and both are absent in ATAC-seq experiments from Irf3^−/− BMDMs (Figure 6A, track 4). Notably, additional lipid A-induced peaks lacking IRF3 dependence reside upstream of the Ccl5 promoter (Figure 6A, tracks 1-4).

Both IRF3 and RelA bind the two IRF3-dependent peaks (Figure 6A, tracks 18-21; note that IRF3 binding is also detected at other peaks that do not exhibit IRF3-dependence). Furthermore, both peaks have underlying consensus motifs for both IRF3 and NF-κB, with the motifs separated by 55 and 28 bp at the promoter and intergenic region, respectively (Figure 6B).¹⁷

Importantly, all of the inducible ATAC-seq peaks are absent in the Rela^−/− Rel^−/− mutant lines (Figure 6A, tracks 5-12). Ccl5 mRNA induction is also strongly reduced in the Rela^−/− Rel^−/− lines (Figure 6A, tracks 16-18) in addition to Irf3^−/− BMDMs (tracks 13-15). Thus, both IRF3 and NF-κB are required for the inducible ATAC-seq peaks at the two IRF3-dependent sites, confirming that the two factors collaborate to promote remodeling.

Notably, the Ccl5 promoter and distal region represent the clearest examples of IRF3-dependent ATAC-seq peaks. A small subset of the remaining primary response ATAC-seq peaks that exhibit strong IRF3 dependence are in the vicinity of other primary response genes, and a few of these genes exhibit modest IRF3 dependence (data not shown). However, the induction magnitudes for these genes are small (generally 2-8-fold) in comparison to Ccl5 (>800-fold), many do not exhibit IRF3-dependent induction, and the degree of IRF3-dependence of those genes that exhibit dependence is limited (data not shown). Thus, our confidence in the functional significance of most of the IRF3-dependent ATAC-seq peaks remains low.

Inducible ATAC-seq Peak Regions at the Ccl5 Locus Support Looping and Function

To further examine the Ccl5 intergenic site, we performed circularized chromosome conformation capture (4C) and capture Hi-C experiments. We focused on the DNA region corresponding to the intergenic IRF3-dependent ATAC-seq peak as the target. The results reveal a limited number of interactions between the target and distant sites in unstimulated BMDMs, with a substantial increase in interactions following stimulation (Figure 7A). The most extensive, inducible interactions are in the vicinity of the flanking Ccl3 and Ccl4 genes. These genes, like Ccl5, are strongly by lipid A, but with lesser (roughly 2-fold) IRF3 dependence(data not shown). In addition, a higher resolution interaction profile reveals lipid A-enhanced interactions in the vicinity of the Ccl5 promoter and gene, as well as at other nearby genomic regions (Figure S6). Thus, the IRF3-dependent intergenic ATAC-seq peak region participates in distant interactions, consistent with a role in inducible transcription.

Finally, we used CRISPR to delete the genomic region corresponding to the IRF3-dependent ATAC-seq peak (Figure 7B). The Ccl5 promoter was deleted separately as a control (Figure 7B). In three independent clones lacking the Ccl5 promoter, Ccl5 transcription was abolished, as revealed by RT-PCR analysis (Figure 7C, right). Deletion of the intergenic region also decreased Ccl5 transcripts, but by only a small magnitude, (Figure 7C, left). Despite the observed interactions between the intergenic site and the Ccl3 and Ccl4 loci, deletion of the intergenic region had no effect on Ccl3 or Ccl4 transcripts (Figure 7C, bottom). Thus, although this Ccl5 intergenic region contains one of only a small number of IRF3-dependent intergenic ATAC-seq peaks, it may have evolved for the purpose of modulating Ccl5 transcription by only a small magnitude. Alternatively, it may play a larger role in physiological settings we have not examined, or its function may be redundant with other nearby regulatory regions.

DISCUSSION

TLR-induced nucleosome remodeling was shown almost 25 years ago to occur at select transcriptional control regions in macrophages,^20,41 and was shown a decade ago to occur at thousands of enhancers.^16,18,19 However, the transcription factors regulating TLR-induced remodeling have remained elusive. Data presented here strongly suggest that remodeling during the primary response to TLR4 signaling exhibits a surprisingly broad dependence on NF-κB, with remodeling selectivity resulting from collaboration with other inducible factors.

The above findings are dependent on the study’s focus on the most strongly and consistently induced ATAC-seq peaks; a similarly strong association with NF-κB is not observed at peaks induced by small magnitudes. Figures 3D and 3E, combined with the scATAC-seq data, provide a clear view of the importance of combining stringent fold-induction and statistical thresholds. In particular, Figure 3E shows that the extremely high prevalence of RelA binding becomes apparent only when high fold-induction and statistical criteria are combined. Stringent fold-induction criteria alone are inadequate because of variable statistical reproducibility of peaks that meet this criterion. A p-value threshold alone is similarly inadequate because many peaks can reach a strong threshold with only small magnitudes of induction if the weak induction is reproducible.

The difference in the prevalence of NF-κB motifs and NF-κB binding between ATAC-seq peaks that are weakly induced versus strongly induced demonstrates that weak ATAC-seq induction is, on average, fundamentally different from strong ATAC-seq induction. The most likely explanation for this difference is that weak ATAC-seq induction often does not represent inducible remodeling, but rather changes in transcription factor binding and other small changes in chromatin architecture.^28,29 Importantly, although combining stringent fold-induction and statistical criteria is likely to enrich for genomic regions that undergo remodeling, these combined criteria remain imperfect measures and relate only to the relative probability that an inducible ATAC-seq peak represents inducible remodeling.

NF-κB’s uniquely broad role in TLR-induced nucleosome remodeling was unexpected because NF-κB frequently activates transcription by binding promoters and enhancers that possess poised chromatin.^14,15,17 Nevertheless, a recent study found that NF-κB binding correlated more closely with TLR-induced remodeling than did the binding of other factors.²¹ Our data add direct functional evidence of NF-κB’s critical importance for remodeling, and demonstrates the breadth of its involvement.

Many studies have shown that inducible remodeling is orchestrated by the recruitment of SWI/SNF complexes, which possess an ATP-dependent subunit that can catalyze accessibility through the eviction, sliding, or conformational opening of nucleosomes.^12,22,23 However, the mechanisms by which NF-κB and its collaborators contribute to the recruitment and function of SWI/SNF complexes remain to be defined. In fact, the mechanism by which remodeling is promoted by well-studied developmental pioneer factors remains only partially understood,⁴² including the mechanisms by which pioneer factors collaborate with remodeling complexes to access nucleosomal DNA.

Early studies demonstrated that NF-κB can bind stably to nucleosome flanks, but not within the nucleosome interior.⁴³ However, local NF-κB binding within the nucleosome core was reported more recently.⁴⁴ A recent study of the developmental pioneer factor, PU.1, demonstrated that local transcription factor access allows SWI/SNF complex recruitment to compacted chromatin in vitro, with the SWI/SNF complex in turn stabilizing PU.1 binding.⁴² Nevertheless, the mechanistic relationship between nucleosome remodeling directed by pioneer factors during development versus stimulus-directed remodeling remains undefined, and it therefore is premature to refer to NF-κB as a pioneer factor.

Additional mechanistic uncertainty arises from the finding that TLR-induced remodeling requires, in at least some instances, a partnership between NF-κB and other inducible factors. Among the unanswered questions is why NF-κB is so broadly required, with its partners acting at a limited subset of regions? One possibility is that NF-κB possesses a unique ability to transiently access its binding sites in nucleosomal DNA and promote binding of its partners, with domains of both transcription factors subsequently needed for productive SWI/SNF recruitment.

An additional unexpected finding was the rare importance of IRF3 for remodeling during the TLR4 primary response. Although we previously found an important role for IRF3 in remodeling at only one TLR4-induced promoter,¹⁷ a much larger number of IRF3-dependent intergenic remodeling events was expected. Instead, only a small number were identified, most of which are of questionable significance. The strong dependence of Ccl5 transcription on two factors that are both essential for remodeling at the Ccl5 promoter and intergenic region may provide initial insight into the biological relevance of inducible remodeling. Ccl5 is among the most potently induced genes in response to TLR4 signaling.¹⁷ The presence of nucleosomes in resting macrophages is likely to constrain aberrant transcription and allow potent activation only in response to a select subset of stimuli capable of activating both IRF3 and NF-κB.

Finally, our findings suggest that NF-κB plays dual roles in promoting nucleosome remodeling and as a conventional transcriptional activator at control regions pre-assembled into open chromatin. Whether these two functions of NF-κB rely on the same protein domains and whether they are regulated by similar mechanisms remains to be determined. Differences in the regulation of the two functions would open new opportunities for the selective modulation of inflammatory gene transcription.

LIMITATIONS OF THE STUDY

A primary limitation of the study is that ATAC-seq, even when analyzed with the high-stringency approaches described here, cannot conclusively identify genomic regions subject to inducible remodeling. A second limitation is that, despite the frequent recruitment of SWI/SNF complexes to genomic regions displaying strongly induced ATAC-seq peaks, and despite the physical evidence of increased accessibility, we cannot formally conclude that strong ATAC-seq induction is due to nucleosome remodeling events analogous to those extensively studied in vitro.

STAR METHODS

RESOURCE AVAILABILITY

Lead contact

Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Stephen T. Smale (smale@mednet.ucla.edu).

Material availability

J2 virus-transformed mouse macrophage lines and mutant versions of these lines (see key resources table) are available upon request from the lead contact.

KEY RESOURCES TABLE

REAGENT or RESOURCE	SOURCE	IDENTIFIER
Antibodies
Rabbit monoclonal anti-Brg1	Abcam	Cat#ab110641; RRID: AB_10861578
Rabbit monoclonal anti-c-Rel	Cell Signaling	Cat#67489S; RRID: AB_2799726
Rabbit polyclonal anti-H3K27ac	Active Motif	Cat#39133; RRID: AB_2561016
Rabbit monoclonal anti-Irf3	Cell Signaling	Cat#4302; RRID: AB_1904036
Mouse monoclonal anti-P300	Santa Cruz	Cat#48343; RRID: AB_628075
Rabbit monoclonal anti-RelA	Cell Signaling	Cat#8242; RRID: AB_10859369
Mouse monoclonal anti-β-actin	Sigma-Aldrich	Cat#A5441; RRID: AB_476744
Chemicals, Peptides, and Recombinant Proteins
Accutase	Sigma-Aldrich	Cat#A6968
AMPure XP purification beads	Beckman Coulter	Cat# A63881
ATP Solution (100 mM)	Thermo Fisher	Cat#R0441
Bay11-7082	Sigma-Aldrich	Cat#B5556
Biotin-14-dATP	Thermo Fisher	Cat#19524016
BSA	Thermo Fisher	Cat#AM2616
Cas9 2NLS nuclease	Synthego	Cat# SpCas9 2NLS Nuclease (300 pmol)
Choice-Taq^™ DNA Polymerase	Deville	Cat#CB4050-2
CviQI restriction endonuclease	New England Biolabs	Cat#R0639L
Cycloheximide (CHX)	Sigma-Aldrich	Cat#239765
Digitonin	Promega	Cat#G9441
Dimethyl sulfoxide	Sigma-Aldrich	Cat#2438
DNA Polymerase I, large (Klenow) fragment	New England Biolabs	Cat#M0210L
DNaseI	Qiagen	Cat#79254
dNTP Sets	Denville Scientific	Cat#CB4420-6
Dulbecco’s phosphate-buffered saline (DPBS)	ThermoFisher	Cat#14190144
DpnII restriction endonuclease	New England Biolabs	Cat#R0543M
DSG crosslinker	Covachem	Cat#13301-1
DTT	Thermo Fisher	Cat#R0861
Dynabeads MyOne SA T1 beads	Invitrogen	Cat#65601
Dynabeads Protein G	Invitrogen	Cat#1004D
Ethanol	Rossville/UCLA STORE	Cat#43196-115
Formaldehyde	Thermo Fisher	Cat#52622
Igepal	MP Biochemicals	Cat#198596
Linear acrylamide	Invitrogen	Cat#AM9520
MboI	New England Biolabs	Cat#R0147L
Monophosphoryl lipid A	Sigma-Aldrich	Cat#L6895-1MG
NEBuffer^™ 2	New England Biolabs	Cat#B7002S
NEBuffer^™ 3.1	New England Biolabs	Cat#B7203
NEBNext High-Fidelity 2x PCR Master Mix	New England Biolabs	Cat#M0541S
NP-40 Surfact-Amps^™ Detergent Solution	Thermo Fisher	Cat#85124
Pam3CSK4	InvivoGen	Cat#tlrl-pms
Phase Lock Gel Heavy 2 ml	Quantabio	Cat#2302830
Pierce^™ Protease Inhibitor Mini Tablets, EDTA-free	Thermo Fisher	Cat#A32955
Platinum^™ Taq DNA Polymerase High Fidelity	Thermo Fisher	Cat#11304011
Poly(I:C)	InvivoGen	Cat#5936-44-01
Proteinase K, recombinant, PCR grade	Thermo Fisher	Cat#E00492
RNaseA	Thermo Fisher	Cat#EN0531
SDS	Sigma-Aldrich	Cat#L5750-1KG
SuperScript^™ III Reverse Transcriptase	Invitrogen	Cat#18080044
Triton-X100	Sigma-Aldrich	Cat#X100-100ML
TRIzol reagent	Molecular Research Center	Cat#TS 120
T4 DNA ligase	New England Biolabs	Cat#M0202M
T4 DNA Ligase Reaction Buffer	New England Biolabs	Cat#B0202S
UltraPure^™ Glycogen	Thermo Fisher	Cat#10814010
UltraPure^™ Phenol:Chloroform:Isoamyl Alcohol (25:24:1, v/v)	Thermo Fisher	Cat#15593031
20X Nuclei Buffer	10X Genomics	Cat#2000207
Critical Commercial Assays
Chromium Next GEM Single Cell ATAC Reagent Kit v2	10X Genomics	Cat#PN-1000390
GFX^™ PCR DNA and Gel Band Purification Kit	MERCK	Cat#GE28-9034-70
illustra^™ GFX PCR DNA and Gel Band Purification Kit	Sigma-Aldrich	Cat#GE28-9034-66
Kapa Hyper Prep Kit Ilumina Platform	Kapa Biosystems	Cat#KK8502
MinElute PCR Purification Kit	Qiagen	Cat#28004
Neon Transfection System 10ul Kit	ThermoFisher	Cat#MPK1096
Nextera DNA Library Prep Kit	Illumina	Cat#FC-121-1030
NEXTflex^™ ChIP-Seq Barcodes - 12	BIOO Scientific	Cat#514121
QIAquick Gel Extraction Kit	QIAGEN	Cat#28706
QIAquick PCR Purification Kit	QIAGEN	Cat#28104
Qubit dsDNA HS Assay Kit	Invitrogen	Cat#Q32854
Qubit RNA High Sensitivity Assay Kits	Invitrogen	Cat#Q3855
RNeasy Mini Kit (250 rxn)	Qiagen	Cat#74136
SuperScript^™ III First-Strand Synthesis System	Invitrogen	Cat#18080051
SureSelect Target Enrichment Kit, ILM Indexing Hyb Module Box 2	Agilent	Cat#5190-4455
SureSelect XT library Prep Kit ILM	Agilent	Cat#5500-132
TruSeq Dual Index Sequencing Primer Kit (for single end runs)	llumina	Cat#FC-121-1003
TruSeq Stranded mRNA Library Prep Kit	Illumina	Cat#RS-122-2102
Deposited data
ATAC-seq, scATAC-seq, RNA-seq, ChIP-seq data	This paper	Super Series GSE234914
Chromatin-associated RNA-seq data	Tong et al.¹⁷	Super Series GSE67357
Experimental models: Organisms/Strains
Mouse: C57BL/6J	The Jackson Laboratory	RRID: IMSR_JAX:000664
Mouse: Irf3^−/−	Sato et al.⁴⁵	N/A
Experimental models: Cell lines
J2-transformed WT BMDM lines	This paper	N/A
NF-κB motif mutant lines	This paper	N/A
Rela^−/− Rel^−/− mutant lines	This paper	N/A
J2-transformed Nfkb1^−/− BMDM lines	This paper	N/A
Nfkb1^−/−Nfkb2^−/− mutant lines	This paper	N/A
Ccl5 promoter mutant lines	This paper	N/A
Ccl5 upstream region mutant lines	This paper	N/A
Oligonucleotides	Oligonucleotides	Oligonucleotides
See Table S1 for oligonucleotide sequences
Software and Algorithms
Bowtie2	Langmead and Salzberg⁴⁶	http://bowtie-bio.sourceforge.net/bowtie2/index.shtml; RRID: SCR_016368
Cell Ranger ATAC v.2.0.0	10X Genomics	https://support.10xgenomics.com/single-cell-atac/software/overview/welcome
CHiCAGO	Cairns et al. ⁴⁷	http://regulatorygenomicsgroup.org/chicago; RRID: SCR_014941
dplyr	https://CRAN.R-project.org/package=dplyr	RRID: SCR_016708
EnsDb.Mmusculus.v79	Rainer⁴⁸	https://bioconductor.org/packages/EnsDb.Mmusculus.v79/
GenomeInfoDb	Arora et al.⁴⁹	https://bioconductor.org/packages/GenomeInfoDb; RRID: SCR_024235
GenomicRanges	Lawrence et al.⁵⁰	https://bioconductor.org/packages/GenomicRanges; RRID: SCR_017051
GOPHER	Hansen et al.⁵¹	https://thejacksonlaboratory.g ithub.io/Gopher/
HiCUP	Wingett et al.⁵²	https://www.bioinformatics.babraham.ac.uk/projects/hicup/; RRID: SCR_005569
Hisat2	Kim et al.⁵³	http://daehwankimlab.github.io/hisat2/; RRID: SCR_015530
HOMER	Heinz et al.¹⁵	http://homer.ucsd.edu/homer/ ; RRID: SCR_010881
Integrative Genomic Viewer (IGV)	Robinson et al.⁵⁴	https://igv.org
MACS2	Zhang et al.⁵⁵	RRID: SCR_013291
R Version 4.3.1	https://cran.r-project.org/bin/windows/base/old/4.3.1/
R-Studio	https://www.rstudio.com
R.4Cker	Raviram et al.⁵⁶	https://github.com/rr1859/R.4 Cker
Samtools	Danecek et al.⁵⁷	https://github.com/samtools/samtools; RRID: SCR_002105
Signac v.1.10.0	Stuart et al.⁵⁸	https://stuartlab.org/signac/; RRID: SCR_021158
SeqMonk	https://www.bioinformatics.babraham.ac.uk/projects/seqmonk/;	RRID: SCR_001913
Seurat 4.1	Hao et al.⁵⁹	https://github.com/satijalab/seurat; RRID: SCR_016341
UCSC Genome Browser	Kent et al.⁶⁰	https://genome.ucsc.edu : RRID: SCR_005780
WashU Epigenome Browser	http://epigenomegateway.wustl.edu	RRID: SCR_006208
4Cseqpipe	van de Werken et al.⁶¹	https://github.com/changegen e/4Cseqpipe

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Data and code availability

All ATAC-seq, scATAC-seq, RNA-seq and ChIP-seq data have been deposited at GEO and are publicly available. Accession numbers are listed in the key resources table.
This paper does not report original code.
An additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Mice

C57BL/6 mice were purchased from Jackson Laboratory (Bar Harbor, ME), and Irf3^−/− mice⁴⁵ were a gift from Genhong Cheng (UCLA). Sources of the mice are provided in the key resources table. Mice were bred and maintained on the C57BL/6 background in the UCLA vivarium monitored by the UCLA Division of Laboratory Animal Medicine. Experiments were performed under the written approval of the UCLA Chancellor’s Animal Research Committee (ARC) in accordance with all federal, state, and local guidelines.

Primary Cell Culture

BMDMs were prepared from 8-10-week-old C57BL/6 and Irf3^−/− male mice as described previously.^10,62 Specifically, mice were euthanized in a CO₂ chamber followed by cervical dislocation. The euthanized mice were sterilized with 75% ethanol spray and pinned on palms to the dissection board. An incision was made from the lower abdomen along the medial side of lower legs to remove the fur and skin and expose muscle. Lower extremities were isolated by disconnecting the femoral head from the acetabulum. The associated soft tissue, such as muscle and tendons, was removed from the femur and tibia bones. The fibrous capsules were then removed from femoral heads and proximal tibia and the distal ends were cut. By inserting a needle (25 gauge, ⅝ in) attached to a 10 ml syringe to the proximal ends of femur and tibia, cold PBS (phosphate-buffered saline, 20 ml for each mouse) was applied to flush the bone marrow clumps out from the distal ends. The bone marrow clumps were dissociated by pipetting up and down with a 10 ml serological pipette several times and passed through a 70 μm cell strainer to remove bony debris. The single cell suspension was pelleted at 1300 rpm for 10 min and suspended with 1 ml RBC lysis buffer (Sigma, #R7757). After incubating at room temperature for 5 min, 10 ml cold PBS was added and centrifuged at 1300 rpm for 10 min to remove the diluted RBC lysis buffer. The cell pellet was suspended in 10 ml BMDM media (20% FBS, 10% CMG condition medium [containing M-CSF], 1x Pen-Strep [Gibco, #15140-122], 1x L-Glutamine [Gibco, #25030-081], and 0.5 mM sodium pyruvate [Gibco, #11360-070] in Dulbecco’s Modified Eagle Medium). 25 million cells were plated on each 15 cm plate in 30 ml of media. On day 4, cells were scraped and re-seeded on 6-well plates at a cell density of 0.5 million/2 ml media, on 10 cm plates at 5 million/10 ml media, or on 15 cm plates at 20 million/ 20 ml media.

Cell Lines

J2 retrovirus-immortalized macrophage lines prepared from C57BL/6 and Nfkb1^−/− BMDMs were cultured in the same media as BMDMs. The generation of mutant versions of these lines by CRISPR/Cas9 targeting is described below.

METHOD DETAILS

Stimulation

BMDMs or the macrophage cell lines were stimulated with 100ng/mL lipid A, 100ng/mL Pam3CSK, or 100μg/mL poly(I:C) on day 6 (for BMDMs) for the indicated times. When indicated, cells were preincubated with 10μg/mL CHX for 15 min or with 10μM Bay11-7082 for one hr.

ATAC-seq

ATAC-seq libraries were prepared using the Nextera Tn5 Transposase kit (Illumina) using the manufacturer’s instructions with slight modifications. Specifically, approximately 50,000 cells were washed with PBS twice and lysed with a cold lysis buffer (10mM Tris pH 7.4, 10mM NaCl, 3mM MgCl₂, and 0.1% Igepal) followed by centrifugation at 500g to obtain nuclei. Cell nuclei were suspended in a 50μL reaction buffer containing 25μL 2x Tagment DNA buffer, 2.5μL Tagment DNA enzyme (Tn5), and 22.5μL nuclease-free water. The transposase reaction was carried out at 37°C for 30 min on the thermomixer with 700 rpm intermittent mixing. DNA was isolated immediately using a Qiagen MinElute PCR purification kit to generate 20μL transposed DNA. The resulting DNA was PCR amplified for 7 cycles with corresponding barcoding primers and 100-1000-bp fragments were size-selected. Library DNA was purified and quantified by Qubit and Agilent 4200 TapeStation followed by 50-bp single-end sequencing using a HiSeq2000 or a HiSeq3000 sequencing platform.

scATAC-seq

Day 6 bone BMDMs (approximately 1.5 x 10⁶) in a 35mm tissue culture dish were either untreated or stimulated for 2 hr with 100ng/mL lipid A. Both untreated and stimulated cells were washed twice with Dubecco’s phosphate-buffered saline (DPBS) and incubated with 1mL Accutase for 8 min at 37°C and then diluted with an additional 2mL of DPBS + 0.04% BSA. A single cell suspension was prepared by gently dislodging by splashing and pipetting the DPBS (3-4 times). The resuspended cells were pelleted in a 5 mL Eppendorf tube (300g for 5 min at 4 °C) and then washed with 1mL DPBS + 0.04% BSA. The pellet was resuspended in 50μL DPBS + 0.04% BSA and then lysed with 200μL nuclei lysis buffer (10mM Tris-HCl pH 7.4, 10mM NaCl, 3mM MgCl₂, 0.1% Tween 20, 0.1% NP-40 substitute, 0.01% digitonin and 1% BSA by pipetting 8 times, vortexing for 10 sec, and incubating 8 min on ice (with gentle flicking of the cells every 2 min). The pellet was washed with 1ml of chilled wash buffer (10mM Tris-HCl pH 7.4, 10mM NaCl, 3mM MgCl₂, 0.1% Tween 20 and 1% BSA) by pipetting 5-6 times. Nuclei were pelleted at 500g for 5 min at 4 °C, and the supernatant was removed carefully without disrupting the nuclei pellet. Nuclei were resuspended into 60μL of chilled 1X Nuclei Buffer (10X Genomics). We achieved 10,000-12,000 nuclei per μL as determined by counting the nuclei using a Countess II FL automated cell counter (Thermo Fisher). Nuclei were further diluted with 1X Nuclei Buffer to achieve about 5000 nuclei/per μL.

scATAC-seq libraries were prepared according to the Chromium Next GEM Single Cell ATAC Reagent kits v2 user guide (10x Genomics, CG000496 Rev B). Specifically, the transposition reaction was prepared by mixing about 12,000 nuclei (to finally recover about 8000 nuclei) with ATAC Buffer and Enzyme and incubating for 30 min at 37 °C. Nuclei were partitioned into Gel Bead-in-Emulsions (GEMs) by using the Chromium iX instrument (10X Genomics). DNA linear amplification was then performed by incubating the GEMs under the following thermal cycling conditions: 72°C for 5 min, 98°C for 30 sec and 12 cycles of 98°C for 10 sec, 59°C for 30 sec and 72°C for 1 min. GEMs were broken using Recovery Agent (10x Genomics), and the resulting DNA was purified by sequential Dynabeads and SPRIselect reagent beads cleanups. Libraries were indexed by PCR using a Single Index kit (Plate N) and incubating under the following thermal cycling conditions: 98°C for 45 sec and seven cycles of 98°C for 20 sec, 67°C for 30 sec and 72°C for 20 sec with a final extension of 72°C for 1 min. Sequencing libraries were subjected to a final bead cleanup with SPRIselect reagent. Samples were sequenced on a NovaSeq 6000 S1 platform at 100 cycles.

RNA-seq

Total RNA was extracted from BMDMs or macrophage cell lines using TRIzol reagent followed by Qiagen RNAeasy plus mini kit according to the manufacturer's instructions. To eliminate genomic DNA contamination, RNA samples were treated with DNase I at room temperature for 15 min on an RNAeasy column membrane before elution. Total RNA samples were quantified by nanodrop. 500 ng RNA was used for library preparation with the Illumina TruSeq RNA Library Preparation Kit 2.0 and sequenced on an Illumina Hiseq 2000 or 3000 for 50-bp single-end reads.

ChIP-seq

ChIP-seq for RelA, IRF3, Brg1, p300, and H3K27ac was performed as described,⁶³ with optimization for each antibody. ChIP-seq libraries were prepared using the Kapa Hyper Prep Kit, followed by sequencing (Illumina HiSeq 3000). BMDMs and macrophage cells were cultured at 80% confluence and washed with PBS twice. Cells were first crosslinked with 1mM DSG for 45 min with shaking on a table top shaker, followed by 1% formaldehyde crosslinking for 10 min at room temperature and then quenched by 500mM Tris pH8.0 for 15 min. Cells were immediately washed with cold PBS twice and scraped off from the plate in 10ml cold PBS. The cell suspensions were centrifuged at 2000 rpm for 10 min and snap frozen in liquid nitrogen. If not used immediately, the pellet was stored at −80°C.

Crosslinked cells were resuspended in 4ml lysis buffer 1 (50mM HEPES pH 7.5, 140mM NaCl, 1mM EDTA, 10% glycerol, 0.5% NP-40, 0.25% Triton X-100, 1X complete protease inhibitor cocktail) and incubated for 10 min on ice. Cells were centrifuged at 3000 rpm and the cell pellet was resuspended in 4ml lysis buffer 2 (10mM Tris pH 8.0, 200mM NaCl, 1mM EDTA, 0.5 mM EGTA, 1X complete protease inhibitor cocktail) and incubated for 10 min on ice. Cells were centrifuged at 3000 rpm and the cell pellet was resuspended in 1ml lysis buffer 3 and transferred to a Covaris sonication tube (1ml). Cell sonication was performed on a Covaris M220 sonicator with 60% peak incidence power (W), 22% duty factor, and 250 cycles per burst for 25 min at 4°C. Sonicated lysate was centrifuged at maximum speed for 10 min to remove pelleted debris and pre-cleaned with Dynabeads Protein A/G at 4°C for 2 hr to remove nonspecific binding. 5μl of the pre-cleaned lysate was collected as input and stored in −20°C for later use. The remaining lysate was incubated at 4°C with 2~6μl of antibody overnight. The conjugated DNA/Protein-antibody complexes were captured with pre-cleaned Dynabeads Protein A/G at 4°C for 3~5 hr and precipitated using a magnetic stand. Beads were washed with 1ml buffer A (50mM HEPES-KOH pH 7.9, 140mM NaCl, 1mM EDTA, 1% Triton X-100, 0.1% Na-Deoxycholate, 0.1% SDS) twice, buffer B (50mM HEPES-KOH pH 7.9, 500mM NaCl, 1mM EDTA, 1% Triton X-100, 0.1% Na-Deoxycholate, 0.1% SDS) twice, LiCl buffer (20mM Tris-HCl pH 8.0, 250mM LiCl, 1mM EDTA, 0.5% Na-Deoxycholate, 0.5% NP-40) twice and TE buffer (10mM Tris-HCl pH 8.0, 1mM EDTA) twice. Cross-links were reversed and DNA was eluted in 250μl elution buffer (10mM Tris-HCl pH 8.0, 1mM EDTA) overnight. RNA and protein were removed using RNase A and Proteinase K. DNA was purified and extracted by phenol-chloroform and ethanol precipitation. Libraries were prepared with Kapa Hyper Prep Kit for Illumina Platform and sequenced on Illumina Hiseq 2000 or 3000 for 50 base-pair single-end reads.

CRISPR-Cas9 Ribonucleoprotein (cRNP) genome editing

A. Guide RNA Design

Two guide RNAs were designed for each deletion or edit site by using CRISPOR online tool.⁶⁴ The gRNAs were ranked from highest to lowest specificity score⁶⁴ and gRNAs with the highest specificity score were selected. The selected gRNAs were synthesized and purchased from SYNTHEGO (see Table S1)

B. HDR Template Design and Preparation

The HDR template was designed as sense single-stranded DNA with 300 nucleotides flanking the replaced sequence and purchased from Integrated DNA Technologies, Inc. in the amount of 9 μg (see Table S1). The dry DNA oligonucleotide was dissolved in 4μL resuspension buffer R provided with Neon^™ Transfection System 10μL kit immediately before electroporation.

C. Assembly of the cRNP complex

Cas9 2NLS nuclease (S. Pyrogenes) was purchased from SYNTHEGO as a 20μM stock and the dry synthetic gRNAs (sgRNAs) were prepared in a 30μM stock. We assembled cRNP complexes by mixing Cas9 (10pmol) and sgRNAs (90pmol) in 3.5μL of resuspension buffer R. The mixture was then incubated for 10 min at room temperature. For NF-κB motif editing, Cas9 and sgRNAs (see Table S1) were added in 3.5μL of resuspension buffer R containing the HDR templates.

D. Cell Preparation

The J2-tranformed cells were subcultured one day before electroporation to allow 60-70% confluency on the day of transfection. Before electroporation, the cells were scraped off of the 10cm plates and counted to obtain 1.5 x 10⁶ cells, which is sufficient enough for 10 transfections. The cells were washed with 1x PBS and resuspended in 50μL of resuspension buffer R.

E. Electroporation

To prepare the cell-RPN solution, 5μL of cell suspension was added to 7μL RNP mix (total 12μL). Electroporation was performed using the Neon Transfection System at pulse code (10 ms x 4 pulses) using 10μL Neon tips at 1,900V. Immediately following electroporation, cells were pipetted into a 6-well plate containing prewarmed culture media.

F. Single-Cell Colony Expansion

Fresh cell culture media was added to replace the media containing dead cells 24 hr after electroporation. Forty-eight hr after electroporation, cells were scraped off of the plate, counted, and seeded 2 cells/well in 96-well plates. Wells containing single-cell colonies were selected under a microscope and replaced with fresh media every four days until wells turned yellow. Cells were then detached from the wells with trypsin and collected for freezing/storage and DNA extraction.

G. Screening and Sequencing

Enhancer and promoter deletion clones: DNA was extracted from the single-cell colonies and regions containing deleted sites were amplified by PCR and analyzed by gel electrophoresis. Clones with successful deletion were selected based on the size of the amplified fragment and confirmed by Sanger sequencing. For the NF-κB motif edited clones, the regions containing the edited sites were PCR amplified and successfully edited clones were confirmed by Sanger sequencing directly.

qRT-PCR

RNA was extracted using TRIzol reagent, treated with DNase I, and purified using the RNeasy kit. One μg RNA was reverse-transcribed using random hexamers and primers targeting the mRNAs of Ccl5, Ccl3, and Ccl4 and designed to amplify PCR products, which were quantified by SYBR-green-based qPCR (see Table S1).

4C assay

The 4C library was prepared as described.⁶⁵ Specifically, we crosslinked the lipid-A treated BMDMs with 1% formaldehyde for 10 min and quenched them with 2M Tris (0.125M final concentration) for 10 min at room temperature. Cells were lysed for 30 min in 1mL cold lysis buffer (10mM Tris-HCl pH 8.0, 10mM NaCl, and 0.2% NP-40 supplemented with protease inhibitor). Nuclei were pelleted and resuspended in 500μL of the first restriction enzyme buffer with 0.1% SDS and incubated for 1 hr at 37°C at 1200 rpm. Triton-X 100 (final concentration 0.8%) was then added, following incubation for 1 hr. 400 units of the first restriction enzyme (DpnII) was then added to the samples, incubated at 37°C for 6 hr, following by addition of another 400 units of enzyme for overnight digestion. The first restriction enzyme was inactivated by incubating at 65°C for 10 min and samples were placed on ice immediately. Ligation buffer (66mM Tris-HCl pH 7.5, 5mM MgCl₂, 5mM DTT, 1mM ATP) was added to dilute samples to a total volume of 7mL. 2,000 units of T4 DNA ligase was added to each sample and incubated at 16°C overnight to achieve proximal ligation. Reverse crosslinking and RNA digestion were performed and followed by phenol/chloroform DNA extraction. Ligation efficiency was confirmed by running the samples before and after ligation on a 1% agarose gel. The ligated DNA was digested overnight in a volume of 500μL with 200 units of the second restriction enzyme (CviQI). The digested samples were diluted in the ligation buffer into a total volume of 7mL. Proximal ligation was performed by adding 4000 units of T4 DNA ligase, followed by overnight incubation at 16°C. The 4C template DNA was extracted by phenol/chloroform and ethanol precipitation. The extracted DNA was further purified using the QIAquick PCR Purificatioin kit.

To generate the library for subsequent sequencing, reading and non-reading primers were designed for each viewpoint. Choice-Taq^™ DNA Polymerase was used to PCR amplify 4C DNA templates containing the viewpoints in four of 25μL reactions. The PCR products were purified with the illustra^™ GFX PCR DNA and Gel Band Purification Kit to remove primer dimers. Libraries were sequenced for 50-bp single-end reads on the Illumina Hiseq2000 platform.

Promoter capture Hi-C assay

The capture Hi-C (CHi-C) library was prepared as described⁶⁶ with slight modifications. Cells were crosslinked in 1% formaldehyde for 10 min at room temperature and quenched with 2M Tris-HCl pH 8.0 (final 0.125M) for 10 min at room temperature. Cells then were lysed in 300μL of cold lysis buffer (10mM Tris-HCl pH 8.0, 10mM NaCl, 0.2% Igepal supplemented with protease inhibitor). The nuclei were digested in 1x NEBuffer 2 containing 100 units of MboI and incubated for 6 hr at 37 °C following by heat inactivation (65°C for 10 min). DNA polymerase I, Large (Klenow) fragment, and biotin-14-dATP were used to fill in restriction fragment overhangs and biotinylate DNA ends. 4000 units of T4 DNA ligase then added for proximate ligation and incubated at 16°C overnight. After reverse crosslinking, DNA was extracted with phenol/chloroform and ethanol precipitation. Sonication was then performed to make the biotinylated DNA size suitable for high-throughput sequencing (300-500 bp). DNA was dissolved in 130μL 10mM Tris-HCl pH 8.0 and transferred to microTUBE (Covaris, 520045) for sonication (Peak Incidence Power 50W, Duty Factor 10%, 200 Cycle per Burst, 60 sec). Sonicated DNA was size selected for fragments in the range of 300-500 bp by AMPure XP purification beads. The fragments marked with biotin were pulled down with Dynabeads^™ MyOne^™ Streptavidin T1 beads. End repair, A-tailing, and paired-end Agilent adaptor ligation (SureSelect XT Library Prep Kit ILM) were performed and followed by on-bead PCR amplification before hybridizing to the customized probe library (Custom 6-11.9 Mb library for 16 samples). The samples were prepared and hybridized to the capture library according to the protocol provided by Agilent (https://www.agilent.com/cs/library/usermanuals/Public/G7530-90000.pdf). The final PCR amplification was carried out for 9 cycles and purified by using AMPure XP beads. The library was paired-end sequenced for 150 bp on the Illumina Novaseq platform.

QUANTIFICATION AND STATISTICAL ANALYSIS

RNA-seq analysis

Sequencing reads were mapped to the mouse genome (NCBIv37/mm9) using hisat2 software⁵³ with the option –kown-splicesite-infile –rna-strandness R -k 1 by allowing reads to be aligned once with up to two mismatches per read. The aligned reads were quantified against exons of Refseq genes as RPKM using SeqMonk software (RRID: SCR_001913).

For chromatin-associated RNA-seq, normalization of RNA levels across the entire gene body was achieved by computing RPKM. To visualize gene transcription levels on browser tracks, bedgraph files were created using HOMER.¹⁵ Additional parameters used for each experiment are described in the Results or Figure Legends.

ATAC-seq analysis

Sequenced reads were mapped to mouse genome (NCBIv37/mm9) using Bowtie2 software.⁴⁶ Only those reads aligning to a unique position with no more than two mismatches were retained for downstream processing. After mapping, clonal reads in each sample were removed. To call peaks, MACS2 software⁵⁵ was applied with the option –nomodel –keep-dup all -q 0.01 –llocal 10000. The reproducible enriched sites in each treatment time point were merged and used to obtain RPKM using SeqMonk. HOMER software was applied to generate bedgraph files. Additional parameters used for each experiment are described in the Results or Figure Legends.

scATAC-seq data analysis

A. Alignment of scATAC-seq Reads

FASTQ files were generated from binary base call files using Cell Ranger ATAC v.2.0.0 mkfastq command. On FASTQ files, Cell Ranger ATAC (v2.0.0) ‘cellranger-atac count’ command with default settings was used for Read alignment, barcode counting, and peak calling with mm10 reference genome that generated fragments, barcodes metadata, and count matrix files.

B. Quality Control and Preprocessing of scATAC-seq Data

All scATAC-seq data preprocessing was performed with Signac and Seurat package-based pipelines in R.^58,59 Using Signac, we created ChromatinAssay objects using filtered peak barcode matrix and meta data information from CellRanger ATAC V 2.0 generated files 'filtered_peak_bc_matrix.h5', 'fragments.tsv.gz', and mm10 reference genome with minimum thresholds criteria for genomic features observed in at least 10 cells (min.cells = 10) and cells with at least 200 features (min.features=200) using the ‘CreateChromatinAssay()’ function. Next, we created Seurat objects with the ‘CreateSeuratObject()’ function with “ChromatinAssay object” and Cell Ranger ATAC generated meta data CSV file “singlecell.csv”. R Bioconductor Packages “EnsDb.Mmusculus.v79” and “GenomeInfoDb” were used to annotate the genomic features/fragments. For QC and preprocessing, we computed quality control metrics for each sample, including total fragments in peaks (peak_region_fragments), fraction of fragments in peaks (pct_reads_in_peaks), nucleosome banding pattern (nucleosome_signal) and TSS enrichment score (TSS.enrichment). To ensure the inclusion of only high-quality cells for downstream analysis, cells analyzed were limited to those with peak region fragments greater than the 2nd percentile and less than the 98th percentile, a percentage of reads in peaks higher than 2nd percentile, a nucleosome signal below the 98th percentile as high nucleosome signals may arise from poorly accessible chromatin and technical artifacts, and TSS enrichment greater than 2nd percentile. After quality control filtering, 6362 cells for “BMDM_0_control” and 8528 cells for “BMDM_LA_2h” were used for further analysis.

C. Dimensionality Reduction, UMAP and Clustering of sc-ATAC-seq Data with Signac

Both datasets were merged using “merge” command in Seurat. The merged file was used for top feature selection using “FindTopFeatures” function with a quantile-based cutoff set to the lowest non-zero quantile. Dataset was then normalized using the RunTFIDF function, which applies the Term Frequency-Inverse Document Frequency technique to highlight specific regions of interest by reducing the impact of ubiquitously accessible regions. Singular Value Decomposition (SVD) using “RunSVD” was implemented for dimensionality reduction of data to obtain principal components explaining largest variance in the dataset. As a precaution, 1^st principal component was omitted as it may account for difference in sequencing depth. 2-30 principal components were used for data visualization in two-dimensional space by RunUMAP function. “FindNeighbors” and “FindClusters” functions with 0.1 resolution were used to reveal distinct groups of cells with similar chromatin accessibility and was plotted using dimension plot “DimPlot” in R, showing 5 clusters (Figure 2A) and grouped by unstimulated and lipid A 2 hr stimulated samples (Figure 2B).

D. Comparison of scATAC-seq Peaks with Bulk ATAC-seq Peaks

Default cutoff criteria for peak calling in Cell Ranger ATAC V2.0 (peaks were called based on enrichment of 1.5-fold in reads signal over background) generated 266098 peaks from both unstimulated and stimulated cells. A very high percentage (>98%) of previous bulk ATAC peaks defined by MACS2 criteria (peaks are called if 5-fold enrichment in reads signal over local background) overlapped with scATAC-seq called peaks.

As previous bulk ATAC-seq were analyzed using mm9 reference genome, we first converted mm9 (NCBI/mm9) co-ordinates of bulk ATAC peaks (n=101,448) to mm10 (GRCm/mm10) co-ordinates by using liftover tool in the UCSC genome browser webtool (https://genome.ucsc.edu/cgi-bin/hgLiftOver). Packages, such as “GenomicRanges” and “dplyr” in R, were used to retrieve scATAC-seq peaks (from the meta data slots of BM_0h and BM_2h Seurat objects) corresponding to bulk ATAC peaks. These co-ordinates of peaks for BM_0h and BM_2h were used to retrieve counts in each cell from 'filtered_peak_bc_matrix.h5' files using “findOverlaps” function in “GenomicRanges” package. Then, we created a master text file that has information for peaks ID, number of cells with counts, and percent of cells with counts. Next, sc-ATAC-seq peaks were arranged into 40 bins as done for bulk ATAC peaks and the proportion of called peaks in each bin for BM_0h and BM_2h was graphed in Figure 2C. Genomic coverage plots of the selected peaks were generated by using the “CoveragePlot” function in Seurat package for the indicated peaks with 10kb extension at both ends of the peaks. The Ccl5 promoter peak (chr11-83530170-83531095) and Tmem173 enhancer peak (chr18-35745243-35746097) are shown in Figure 2D and 2G, respectively. Feature plots were generated using the “FeaturePlot” function in Seurat package representing cells with counts in BM_0h and BM_2h samples for Ccl5 promoter peak (chr11-83530170-83531095) and Tmem173 enhancer peak (chr18-35745243-35746097) are shown in Figure 2E and 2H, respectively. Violin plots generated using the “Vlnplot” function in Seurat package showing normalized counts for the Ccl5 and Tmem173 peaks in each cell of BM_0h and BM_2h samples are shown in Figure 2F and 2I, respectively. For the bar plot for occurrence of counts in cells (Figure S1E), cells with raw count value were filtered for each peak from the original peak barcode count matrix files ('filtered_peak_bc_matrix.h5') using “which” function of “GenomicRanges” in R. Occurrences of each unique value in the “Count column” were summarized using “table()” function in R, and were plotted as bar graph using ggplot2.

ChIP-seq analysis

ChIP-seq reads were aligned and processed the same as ATAC-seq reads. To call peaks, HOMER software was used to identified IP sites with the option findpeaks -style factor -center -i corresponding input directory -fdr 0.01. HOMER software was applied to generate bedgraph files for visualization. Additional parameters used for each experiment are described in the Results or Figure Legends.

4C analysis

For the analysis employing 4Cseqpipe,⁶¹ the initial step involved converting the fastq file containing all reads into a format compatible with downstream processing, accomplished through the "–fastq2raw" function within the pipeline. Subsequently, de-multiplexing was conducted based on barcodes and reading primer sequences specific to each library. The "–map" function was utilized to map reads to a fragmented mm9 genome, virtually digested by the restriction enzyme. Normalization and graphing were executed using the "–nearcis" function, with default settings employed throughout, except for adjusting the ylim of the trend line to enhance the visualization of promoter-enhancer or promoter-boundary interactions. In the case of analysis using R.4C-Ker,⁵⁶ a reduced genome was constructed via Bowtie2, containing only sequences flanking the primary restriction enzyme sites (51 bp on both sides). Reads were then de-multiplexed, and barcode and primer sequences were trimmed using a custom script. The trimmed reads were mapped to the reduced genome by Bowtie2 with the specified option –p 12 –N 0. To eliminate self-ligated and undigested fragments, the fragments preceding and following the bait fragment were excluded. Near-cis interactions were scrutinized using the "nearBaitAnalysis" function in 4C-Ker, with default settings applied. To visualize the interactions, R package ggplot2 was used to generate the line graphs showing normalized counts across the selected genomic regions.

Promoter/ target Capture Hi-C analysis

Capture Hi-C sequence reads underwent processing using HiCUP⁵² with default settings for mapping, pairing, filtering, and deduplication. The resulting unique and valid read pairs were further analyzed through CHiCAGO.⁴⁷ A chinput file was generated, presenting raw counts for each interaction, and loops were identified with parameters tailored for four-cutter restriction enzyme digestion, as suggested at https://bitbucket.org/chicagoTeam/chicago/src/master. A CHiCAGO score greater than 5 served as the cutoff for designating loops as significant. For the visualization of PCHi-C data in a 4C-like style (virtual 4C), data exclusively associated with an individual promoter were extracted from the chinput file. A bedgraph file was then generated using a custom script that incorporated the coordinates of each nonbait anchor fragment and the normalized PCHi-C counts.

Data visualization and motif enrichment

HOMER tools¹⁵ with provided motif matrices (n=364) were used to identify enriched motifs among targeted genomic regions. The UCSC genome browser⁶⁰ and IGV⁵⁴ were used to visualize genomic data generated from RNA-seq, ATAC-seq and ChIP-seq. The virtual 4C and capture Hi-C data were visualized using the Washington University epigenome browser (RRID: SCR_006208).

Supplementary Material

NIHMS1973150-supplement-2.pdf^{(2.5MB, pdf)}

Highlights.

ATAC-seq studies of nucleosome remodeling are impacted by analysis parameters

NF-κB is broadly required for nucleosome remodeling during the TLR4 primary response

NF-κB collaborates with IRF3 and other factors to promote remodeling specificity

TLR4-induced NF-κB-independent primary response genes may not require remodeling

ACKNOWLEDGMENTS

We thank A. Hoffmann and A. Koh for helpful discussions and critical reading of the manuscript, and L. Smale for the graphical abstract design. This work was supported by R01GM086372, R01AI073868, and P50AR063020 (to S.T.S.) T32GM008042 (to B.J.T.), T32AI007323 and F31AI157267 (to A.E.D.). S.T.S. is a Senior Scientific Officer with the Howard Hughes Medical Institute.

Footnotes

SUPPLEMENTAL INFORMATION

Supplemental information can be found online at…

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DECLARATION OF INTERESTS

The authors declare no competing interests.

REFERENCES

1.Heinz S, Romanoski CE, Benner C, and Glass CK (2015). The selection and function of cell type-specific enhancers. Nat. Rev. Mol. Cell. Biol 16, 144–154. [DOI] [PMC free article] [PubMed] [Google Scholar]
2.Glass CK, and Natoli G (2016). Molecular control of activation and priming in macrophages. Nat. Immunol 17, 26–33. [DOI] [PMC free article] [PubMed] [Google Scholar]
3.Monticelli S, and Natoli G (2017). Transcriptional determination and functional specificity of myeloid cells: making sense of diversity. Nat. Rev. Immunol 17, 595–607. [DOI] [PubMed] [Google Scholar]
4.Natoli G, and Ostuni R (2019). Adaptation and memory in immune responses. Nat. Immunol 20, 783–792. [DOI] [PubMed] [Google Scholar]
5.Sheu K,M, and Hoffmann A (2022). Functional hallmarks of healthy macrophage responses: their regulatory basis and disease relevance. Annu. Rev. Immunol 40, 295–321. [DOI] [PMC free article] [PubMed] [Google Scholar]
6.Toshchakov V, Jones BW, Perera P-Y, Thomas K, Cody MJ, Zhang S, Williams BRG, Major J, Hamilton TA, Fenton MJ, et al. (2002). TLR4, but not TLR2, mediates IFN-beta-induced STAT1alpha/beta-dependent gene expression in macrophages. Nat. Immunol 3, 392–398. [DOI] [PubMed] [Google Scholar]
7.Hoebe K, Du X, Georgel P, Janssen E, Tabeta K, Kim SO, Goode J, Lin P, Mann N, Mudd S, et al. (2003). Identification of Lps2 as a key transducer of MyD88-independent TIR signalling. Nature 424, 743–748. [DOI] [PubMed] [Google Scholar]
8.Yamamoto M, Sato S, Hemmi H, Hoshino K, Kaisho T, Sanjo H, Takeuchi O, Sugiyama M, Okabe M, Takeda K, et al. (2003). Role of adaptor TRIF in the MyD88-independent Toll-like receptor signaling pathway. Science 301, 640–643. [DOI] [PubMed] [Google Scholar]
9.Ramirez-Carrozzi VR, Nazarian AA, Li CC, Gore SL, Sridharan R, Imbalzano AN, and Smale ST (2006). Selective and antagonistic functions of SWI/SNF and Mi-2beta nucleosome remodeling complexes during an inflammatory response. Genes Dev. 20, 282–296. [DOI] [PMC free article] [PubMed] [Google Scholar]
10.Ramirez-Carrozzi VR, Braas D, Bhatt DM, Cheng CS, Hong C, Doty KR, Black JC, Hoffmann A, Carey M, and Smale ST (2009). A unifying model for the selective regulation of inducible transcription by CpG islands and nucleosome remodeling. Cell 138, 114–128. [DOI] [PMC free article] [PubMed] [Google Scholar]
11.Hargreaves DC, Horng T, and Medzhitov R (2009). Control of inducible gene expression by signal-dependent transcriptional elongation. Cell 138, 129–145. [DOI] [PMC free article] [PubMed] [Google Scholar]
12.Gatchalian J, Liao J, Maxwell MB, and Hargreaves DC (2020). Control of stimulus-dependent responses in macrophages by SWI/SNF chromatin remodeling complexes. Trends Immunol. 41, 126–140. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Martínez de Paz A, and Josefowicz SZ (2021). Signaling-to-chromatin pathways in the immune system. Immunol. Rev 300, 37–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.Ghisletti S, Barozzi I, Mietton F, Polletti S, De Santa F, Venturini E, Gregory L, Lonie L, Chew A, Wei C-L, et al. (2010). Identification and characterization of enhancers controlling the inflammatory gene expression program in macrophages. Immunity 32, 317–328. [DOI] [PubMed] [Google Scholar]
15.Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, and Glass CK (2010). Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Barozzi I, Simonatto M, Bonifacio S, Yang L, Rohs R, Ghisletti S, and Natoli G (2014). Coregulation of transcription factor binding and nucleosome occupancy through DNA features of mammalian enhancers. Mol. Cell 54, 844–857. [DOI] [PMC free article] [PubMed] [Google Scholar]
17.Tong AJ, Liu X, Thomas BJ, Lissner MM, Baker MR, Senagolage MD, Allred AL, Barish GD, and Smale ST (2016). A stringent systems approach uncovers gene-specific mechanisms regulating inflammation. Cell 165, 165–179. [DOI] [PMC free article] [PubMed] [Google Scholar]
18.Kaikkonen MU, Spann NJ, Heinz S, Romanoski CE, Allison KA, Stender JD, Chun HB, Tough DF, Prinjha RK, Benner C, et al. (2013). Remodeling of the enhancer landscape during macrophage activation is coupled to enhancer transcription. Mol. Cell 51, 310–325. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Ostuni R, Piccolo V, Barozzi I, Polletti S, Termanini A, Bonifacio S, Curina A, Prosperini E, Ghisletti S, and Natoli G (2013). Latent enhancers activated by stimulation in differentiated cells. Cell 152, 157–171. [DOI] [PubMed] [Google Scholar]
20.Weinmann AS, Plevy SE, and Smale ST (1999). Rapid and selective remodeling of a positioned nucleosome during the induction of IL-12 p40 transcription. Immunity 11, 665–675. [DOI] [PubMed] [Google Scholar]
21.Comoglio F, Simonatto M, Polletti S, Liu X, Smale ST, Barozzi I, and Natoli G (2019). Dissection of acute stimulus-inducible nucleosome remodeling in mammalian cells. Genes Dev. 33, 1159–1174. [DOI] [PMC free article] [PubMed] [Google Scholar]
22.Hargreaves DC, and Crabtree GR (2011). ATP-dependent chromatin remodeling; genetics, genomics and mechanisms. Cell Res. 21, 396–420. [DOI] [PMC free article] [PubMed] [Google Scholar]
23.Clapier CR, Iwasa J, Cairns BR, and Peterson CL (2017). Mechanisms of action and regulation of ATP-dependent chromatin-remodelling complexes. Nat. Rev. Mol. Cell. Biol 18, 407–422. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Cheng QJ, Ohta S, Sheu KM, Spreafico R, Adelaja A, Taylor B, and Hoffmann A (2021). NF-κB dynamics determine the stimulus specificity of epigenomic reprogramming in macrophages. Science 372, 1349–1353. [DOI] [PMC free article] [PubMed] [Google Scholar]
25.Vierbuchen T, Ling E, Cowley CJ, Couch CH, Wang X, Harmin DA, Roberts CWM, and Greenberg ME (2017). AP-1 transcription factors and the BAF complex mediate signal-dependent enhancer selection. Mol. Cell 68, 1067–1082. [DOI] [PMC free article] [PubMed] [Google Scholar]
26.Gao F, Elliott NJ, Ho J, Sharp A, Shokhirev MN, and Hargreaves DC (2019). Heterozygous mutations in SMARCA2 reprogram the enhancer landscape by global retargeting of SMARCA4. Mol. Cell 75, 891–904. [DOI] [PMC free article] [PubMed] [Google Scholar]
27.Wolf BK, Zhao Y, McCray A, Hawk WH, Deary LT, Sugiarto NW, LaCroix IS, Gerber SA, Cheng C, and Wang X (2023). Cooperation of chromatin remodeling SWI/SNF complex and pioneer factor AP-1 shapes 3D enhancer landscapes. Nat. Struct. Mol. Biol 30, 10–21. [DOI] [PMC free article] [PubMed] [Google Scholar]
28.Buenrostro JD, Giresi PG, Zaba LC, Chang HY, and Greenleaf WJ (2013). Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins, and nucleosome position. Nat. Methods 10, 1213–1218. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Li Z, Schulz MH, Look T, Begemann M, Zenke M, and Costa IG (2019). Identification of transcription factor binding sites using ATAC-seq. Genome Biol. 20, 45. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Hargreaves DC (2021). Chromatin openness requires continuous SWI/SNF activity. Nat. Genet 53, 263–264. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, and Mesirov JP (2011). Integrated genomics viewer. Nat. Biotechnol 29, 24–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Creyghton MP, Cheng AW, Welstead GG, Kooistra T, Carey BW, Steine EJ, Hanna J, Lodato MA, Frampton GM, Sharp PA, et al. (2010). Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc. Natl. Acad. Sci 105, 21931–21936. [DOI] [PMC free article] [PubMed] [Google Scholar]
33.Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML Snyder MP, Chang HY, and Greenleaf WJ (2015). Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490. [DOI] [PMC free article] [PubMed] [Google Scholar]
34.Satpathy AT, Granja JM, Yost KE, Qi Y, Meschi F, DcDermott GP, Olsen BN, Mumbach MR, Pierce SE, Corces MR, et al. (2019). Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol 37, 925–936. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Becht E, McInnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG, Ginhoux F, and Newell EW (2018) Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol 37, 38–44. [DOI] [PubMed] [Google Scholar]
36.Martens LD, Fischer DS, Theis FJ, and Gagneur J (2023). Modeling fragment counts improves single-cell1 ATAC-seq analysis. bioRxiv, doi: 10.1101/2202.05.05.490536 [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Liu H, Kang H, Liu R, Chen X, and Zhao K (2002). Maximal induction of a subset of interferon target genes requires the chromatin-remodeling activity of the BAF complex. Mol. Cell. Biol 22, 6471–6479. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Lee J, Rhee MH, Kim E, and Cho JY (2012). BAY11-7082 is a broad-spectrum inhibitor with anti-inflammatory activity against multiple targets. Mediators Inflamm. 2012:416036. [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Tamura T, Yanai H, Savitsky D, and Taniguchi T (2008). The IRF family transcription factors in immunity and oncogenesis. Annu. Rev. Immunol 26, 535–584. [DOI] [PubMed] [Google Scholar]
40.Liu S, Cai X, Wu J, Cong Q, Chen X, Li T, Du F, Ren J, Wu Y-T, Grishin NV, et al. (2015). Phosphorylation of innate immune adaptor proteins MAVS, STING, and TRIF induces IRF3 activation. Science 347, aaa2630. [DOI] [PubMed] [Google Scholar]
41.Agalioti T, Lomvardes S Parekh B, Yie J, Maniatis T, and Thanos D (2000). Ordered recruitment of chromatin modifying and general transcription factors to the IFN-beta promoter. Cell 103, 667–678. [DOI] [PubMed] [Google Scholar]
42.Frederick MA, Williamson KE, Garcia MF, Ferretti MB, McCarthy RL, Donahue G, Monteiro EL, Takenaka N, Reynaga J, Kadoch C, et al. (2023). A pioneer factor locally opens compacted chromatin to enable targeted ATP-dependent nucleosome remodeling. Nat. Struct. Mol. Biol 30, 31–27. [DOI] [PMC free article] [PubMed] [Google Scholar]
43.Lone IN, Shukla MS, Richard JLC, Peshev ZY, Dimitrov S, and Angelov D (2013). Binding of NF-κB to nucleosomes: effect of translational positioning, nucleosome remodeling, and linker histone H1. PLoS Genet. 9, e1003830. [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Stormberg T, Filliaux S, Baughman HER, Komives EA, and Lyubchenko YL (2021). Transcription factor NF-κB unravels nuclesomes. Biochim. Biophys. Acta Gen. Subj 1865, 129934. [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Sato M, Suemori H, Hata N, Asagiri M, Ogasawara K, Nakao K, Takaya T, Katsuki M, Noguchi S, Tanaka N, et al. (2000). Distinct and essential roles of transcription factors IRF-3 and IRF-7 in response to viruses for IFN-alpha/beta gene induction. Immunity 13, 539–548. [DOI] [PubMed] [Google Scholar]
46.Langmead B, Salzberg SL (2012). Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Cairns J, Freire-Pritchett P, Wingett SW, Várnai C, Dimond A, Plagnol V, Zerbino D, Schoenfelder S, Javierre BM, Osborne C, et al. (2016). CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data. Genome Biol. 17, 127. [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Rainer J (2017). EnsDb.Mmusculus.v79: Ensembl based annotation package. R package version 2.99.0. https://bioconductor.org/packages/EnsDb.Mmusculus.v79/ [Google Scholar]
49.Arora S, Morgan M Carlson M Pagès H (2023). GenomeInfoDb: Utilities for manipulating chromosome names, including modifying them to follow a particular naming style. R package version 1.38.5, https://bioconductor.org/packages/GenomeInfoDb. [Google Scholar]
50.Lawrence M, Huber W, Pagès H, Aboyoun P, Carlson M, Gentleman R, Morgan MT, Carey VJ (2013). Software for computing and annotating genomic ranges. PloS Comput. Biol 9, 31003118. [DOI] [PMC free article] [PubMed] [Google Scholar]
51.Hansen P, Ali S, Blau H, Danis D, Hecht J, Kornak U, Lupiáñez DG, Mundlos S, Steinhaus R, Robinson PN (2019). GOPHER: Generator of probes for capture Hi-C experiments at high resolution. BMC Genomics 20, 40. [DOI] [PMC free article] [PubMed] [Google Scholar]
52.Wingett S, Ewels P, Furlan-Magaril M, Nagano T, Schoenfelder S, Fraser P, Andrews S (2015). HiCUP: pipeline for mapping and processing Hi-C data. F1000Res 4, 1310. [DOI] [PMC free article] [PubMed] [Google Scholar]
53.Kim D, Langmead B, Salzberg SL (2015). HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360. [DOI] [PMC free article] [PubMed] [Google Scholar]
54.Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, Mesirov JP (2011). Integrative genomics viewer. Nat. Biotechnol 29, 24–26. [DOI] [PMC free article] [PubMed] [Google Scholar]
55.Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, Liu XS (2008). Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137. [DOI] [PMC free article] [PubMed] [Google Scholar]
56.Raviram R, Rocha PP, Müller CL, Miraldi ER, Badri S, Fu Y, Swanzey E, Proudhon C, Snetkova V, Bonneau R, Skok JA (2016). 4C-ker: A method to reproducibly identify genome-wide interactions captured by 4C-seq experiments. PloS Comput. Biol 12, e1004780. [DOI] [PMC free article] [PubMed] [Google Scholar]
57.Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, et al. (2021). Twelve years of SAMtools and BCFtools. Gigascience 10, giab008. [DOI] [PMC free article] [PubMed] [Google Scholar]
58.Stuart T, Srivastava A, Madad S, Lareau CA, Satija R (2021). Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341. [DOI] [PMC free article] [PubMed] [Google Scholar]
59.Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, Lee MJ, Wilk AJ Darby C, Zager M, et al. (2021). Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29. [DOI] [PMC free article] [PubMed] [Google Scholar]
60.Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D (2002). The human genome browser at UCSC. Genome Res. 12, 996–1006. [DOI] [PMC free article] [PubMed] [Google Scholar]
61.van de Werken HJG, de Vree. PJP, Splinter E, Holwerda SJB, Klous P, de Wit E, de Laat W (2012). 4C technology: protocols and data analysis. Methods Enzymol. 513, 89112. [DOI] [PubMed] [Google Scholar]
62.Bhatt DM, Pandya-Jones A, Tong AJ, Barozzi I, Lissner MM, Natoli G, Black DL, Smale ST (2012). Transcript dynamics of proinflammatory genes revealed by sequence analysis of subcellular RNA fractions. Cell 150, 279–290. [DOI] [PMC free article] [PubMed] [Google Scholar]
63.Barish GD, Yu RT, Karunasiri M, Ocampo CB, Dixon J, Benner C, Dent AL, Tangirala RK, Evans RM (2010). Bcl-6 and NF-kappaB cistromes mediate opposing regulation of the innate immune response. Genes Dev. 24, 2760–2765. [DOI] [PMC free article] [PubMed] [Google Scholar]
64.Concordet JP, Haeussler M (2018). CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens. Nucleic Acids Res. 46(W1), W242–W245. [DOI] [PMC free article] [PubMed] [Google Scholar]
65.Krijger PHL, Geeven G, Bianchi V, Hilvering CRE, de Laat W (2020). 4C-seq from beginning to end: A detailed protocol for sample preparation and data analysis. Methods 170, 17–32. [DOI] [PubMed] [Google Scholar]
66.Schoenfelder S, Javierre BM, Furlan-Magaril M, Wingett SW, Fraser P (2018). Promoter capture Hi-C: high-resolution, genome-wide profiling of promoter interactions. J. Vis. Exp 136, 57320. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

NIHMS1973150-supplement-2.pdf^{(2.5MB, pdf)}

Data Availability Statement

All ATAC-seq, scATAC-seq, RNA-seq and ChIP-seq data have been deposited at GEO and are publicly available. Accession numbers are listed in the key resources table.
This paper does not report original code.
An additional information required to reanalyze the data reported in this paper is available from the lead contact upon request.

[R1] 1.Heinz S, Romanoski CE, Benner C, and Glass CK (2015). The selection and function of cell type-specific enhancers. Nat. Rev. Mol. Cell. Biol 16, 144–154. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R2] 2.Glass CK, and Natoli G (2016). Molecular control of activation and priming in macrophages. Nat. Immunol 17, 26–33. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R3] 3.Monticelli S, and Natoli G (2017). Transcriptional determination and functional specificity of myeloid cells: making sense of diversity. Nat. Rev. Immunol 17, 595–607. [DOI] [PubMed] [Google Scholar]

[R4] 4.Natoli G, and Ostuni R (2019). Adaptation and memory in immune responses. Nat. Immunol 20, 783–792. [DOI] [PubMed] [Google Scholar]

[R5] 5.Sheu K,M, and Hoffmann A (2022). Functional hallmarks of healthy macrophage responses: their regulatory basis and disease relevance. Annu. Rev. Immunol 40, 295–321. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R6] 6.Toshchakov V, Jones BW, Perera P-Y, Thomas K, Cody MJ, Zhang S, Williams BRG, Major J, Hamilton TA, Fenton MJ, et al. (2002). TLR4, but not TLR2, mediates IFN-beta-induced STAT1alpha/beta-dependent gene expression in macrophages. Nat. Immunol 3, 392–398. [DOI] [PubMed] [Google Scholar]

[R7] 7.Hoebe K, Du X, Georgel P, Janssen E, Tabeta K, Kim SO, Goode J, Lin P, Mann N, Mudd S, et al. (2003). Identification of Lps2 as a key transducer of MyD88-independent TIR signalling. Nature 424, 743–748. [DOI] [PubMed] [Google Scholar]

[R8] 8.Yamamoto M, Sato S, Hemmi H, Hoshino K, Kaisho T, Sanjo H, Takeuchi O, Sugiyama M, Okabe M, Takeda K, et al. (2003). Role of adaptor TRIF in the MyD88-independent Toll-like receptor signaling pathway. Science 301, 640–643. [DOI] [PubMed] [Google Scholar]

[R9] 9.Ramirez-Carrozzi VR, Nazarian AA, Li CC, Gore SL, Sridharan R, Imbalzano AN, and Smale ST (2006). Selective and antagonistic functions of SWI/SNF and Mi-2beta nucleosome remodeling complexes during an inflammatory response. Genes Dev. 20, 282–296. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R10] 10.Ramirez-Carrozzi VR, Braas D, Bhatt DM, Cheng CS, Hong C, Doty KR, Black JC, Hoffmann A, Carey M, and Smale ST (2009). A unifying model for the selective regulation of inducible transcription by CpG islands and nucleosome remodeling. Cell 138, 114–128. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R11] 11.Hargreaves DC, Horng T, and Medzhitov R (2009). Control of inducible gene expression by signal-dependent transcriptional elongation. Cell 138, 129–145. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R12] 12.Gatchalian J, Liao J, Maxwell MB, and Hargreaves DC (2020). Control of stimulus-dependent responses in macrophages by SWI/SNF chromatin remodeling complexes. Trends Immunol. 41, 126–140. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R13] 13.Martínez de Paz A, and Josefowicz SZ (2021). Signaling-to-chromatin pathways in the immune system. Immunol. Rev 300, 37–53. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R14] 14.Ghisletti S, Barozzi I, Mietton F, Polletti S, De Santa F, Venturini E, Gregory L, Lonie L, Chew A, Wei C-L, et al. (2010). Identification and characterization of enhancers controlling the inflammatory gene expression program in macrophages. Immunity 32, 317–328. [DOI] [PubMed] [Google Scholar]

[R15] 15.Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, and Glass CK (2010). Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R16] 16.Barozzi I, Simonatto M, Bonifacio S, Yang L, Rohs R, Ghisletti S, and Natoli G (2014). Coregulation of transcription factor binding and nucleosome occupancy through DNA features of mammalian enhancers. Mol. Cell 54, 844–857. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R17] 17.Tong AJ, Liu X, Thomas BJ, Lissner MM, Baker MR, Senagolage MD, Allred AL, Barish GD, and Smale ST (2016). A stringent systems approach uncovers gene-specific mechanisms regulating inflammation. Cell 165, 165–179. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R18] 18.Kaikkonen MU, Spann NJ, Heinz S, Romanoski CE, Allison KA, Stender JD, Chun HB, Tough DF, Prinjha RK, Benner C, et al. (2013). Remodeling of the enhancer landscape during macrophage activation is coupled to enhancer transcription. Mol. Cell 51, 310–325. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R19] 19.Ostuni R, Piccolo V, Barozzi I, Polletti S, Termanini A, Bonifacio S, Curina A, Prosperini E, Ghisletti S, and Natoli G (2013). Latent enhancers activated by stimulation in differentiated cells. Cell 152, 157–171. [DOI] [PubMed] [Google Scholar]

[R20] 20.Weinmann AS, Plevy SE, and Smale ST (1999). Rapid and selective remodeling of a positioned nucleosome during the induction of IL-12 p40 transcription. Immunity 11, 665–675. [DOI] [PubMed] [Google Scholar]

[R21] 21.Comoglio F, Simonatto M, Polletti S, Liu X, Smale ST, Barozzi I, and Natoli G (2019). Dissection of acute stimulus-inducible nucleosome remodeling in mammalian cells. Genes Dev. 33, 1159–1174. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R22] 22.Hargreaves DC, and Crabtree GR (2011). ATP-dependent chromatin remodeling; genetics, genomics and mechanisms. Cell Res. 21, 396–420. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R23] 23.Clapier CR, Iwasa J, Cairns BR, and Peterson CL (2017). Mechanisms of action and regulation of ATP-dependent chromatin-remodelling complexes. Nat. Rev. Mol. Cell. Biol 18, 407–422. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R24] 24.Cheng QJ, Ohta S, Sheu KM, Spreafico R, Adelaja A, Taylor B, and Hoffmann A (2021). NF-κB dynamics determine the stimulus specificity of epigenomic reprogramming in macrophages. Science 372, 1349–1353. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R25] 25.Vierbuchen T, Ling E, Cowley CJ, Couch CH, Wang X, Harmin DA, Roberts CWM, and Greenberg ME (2017). AP-1 transcription factors and the BAF complex mediate signal-dependent enhancer selection. Mol. Cell 68, 1067–1082. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R26] 26.Gao F, Elliott NJ, Ho J, Sharp A, Shokhirev MN, and Hargreaves DC (2019). Heterozygous mutations in SMARCA2 reprogram the enhancer landscape by global retargeting of SMARCA4. Mol. Cell 75, 891–904. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R27] 27.Wolf BK, Zhao Y, McCray A, Hawk WH, Deary LT, Sugiarto NW, LaCroix IS, Gerber SA, Cheng C, and Wang X (2023). Cooperation of chromatin remodeling SWI/SNF complex and pioneer factor AP-1 shapes 3D enhancer landscapes. Nat. Struct. Mol. Biol 30, 10–21. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R28] 28.Buenrostro JD, Giresi PG, Zaba LC, Chang HY, and Greenleaf WJ (2013). Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins, and nucleosome position. Nat. Methods 10, 1213–1218. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R29] 29.Li Z, Schulz MH, Look T, Begemann M, Zenke M, and Costa IG (2019). Identification of transcription factor binding sites using ATAC-seq. Genome Biol. 20, 45. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R30] 30.Hargreaves DC (2021). Chromatin openness requires continuous SWI/SNF activity. Nat. Genet 53, 263–264. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R31] 31.Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, and Mesirov JP (2011). Integrated genomics viewer. Nat. Biotechnol 29, 24–26. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R32] 32.Creyghton MP, Cheng AW, Welstead GG, Kooistra T, Carey BW, Steine EJ, Hanna J, Lodato MA, Frampton GM, Sharp PA, et al. (2010). Histone H3K27ac separates active from poised enhancers and predicts developmental state. Proc. Natl. Acad. Sci 105, 21931–21936. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R33] 33.Buenrostro JD, Wu B, Litzenburger UM, Ruff D, Gonzales ML Snyder MP, Chang HY, and Greenleaf WJ (2015). Single-cell chromatin accessibility reveals principles of regulatory variation. Nature 523, 486–490. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R34] 34.Satpathy AT, Granja JM, Yost KE, Qi Y, Meschi F, DcDermott GP, Olsen BN, Mumbach MR, Pierce SE, Corces MR, et al. (2019). Massively parallel single-cell chromatin landscapes of human immune cell development and intratumoral T cell exhaustion. Nat. Biotechnol 37, 925–936. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R35] 35.Becht E, McInnes L, Healy J, Dutertre CA, Kwok IWH, Ng LG, Ginhoux F, and Newell EW (2018) Dimensionality reduction for visualizing single-cell data using UMAP. Nat. Biotechnol 37, 38–44. [DOI] [PubMed] [Google Scholar]

[R36] 36.Martens LD, Fischer DS, Theis FJ, and Gagneur J (2023). Modeling fragment counts improves single-cell1 ATAC-seq analysis. bioRxiv, doi: 10.1101/2202.05.05.490536 [DOI] [PMC free article] [PubMed] [Google Scholar]

[R37] 37.Liu H, Kang H, Liu R, Chen X, and Zhao K (2002). Maximal induction of a subset of interferon target genes requires the chromatin-remodeling activity of the BAF complex. Mol. Cell. Biol 22, 6471–6479. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R38] 38.Lee J, Rhee MH, Kim E, and Cho JY (2012). BAY11-7082 is a broad-spectrum inhibitor with anti-inflammatory activity against multiple targets. Mediators Inflamm. 2012:416036. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R39] 39.Tamura T, Yanai H, Savitsky D, and Taniguchi T (2008). The IRF family transcription factors in immunity and oncogenesis. Annu. Rev. Immunol 26, 535–584. [DOI] [PubMed] [Google Scholar]

[R40] 40.Liu S, Cai X, Wu J, Cong Q, Chen X, Li T, Du F, Ren J, Wu Y-T, Grishin NV, et al. (2015). Phosphorylation of innate immune adaptor proteins MAVS, STING, and TRIF induces IRF3 activation. Science 347, aaa2630. [DOI] [PubMed] [Google Scholar]

[R41] 41.Agalioti T, Lomvardes S Parekh B, Yie J, Maniatis T, and Thanos D (2000). Ordered recruitment of chromatin modifying and general transcription factors to the IFN-beta promoter. Cell 103, 667–678. [DOI] [PubMed] [Google Scholar]

[R42] 42.Frederick MA, Williamson KE, Garcia MF, Ferretti MB, McCarthy RL, Donahue G, Monteiro EL, Takenaka N, Reynaga J, Kadoch C, et al. (2023). A pioneer factor locally opens compacted chromatin to enable targeted ATP-dependent nucleosome remodeling. Nat. Struct. Mol. Biol 30, 31–27. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R43] 43.Lone IN, Shukla MS, Richard JLC, Peshev ZY, Dimitrov S, and Angelov D (2013). Binding of NF-κB to nucleosomes: effect of translational positioning, nucleosome remodeling, and linker histone H1. PLoS Genet. 9, e1003830. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R44] 44.Stormberg T, Filliaux S, Baughman HER, Komives EA, and Lyubchenko YL (2021). Transcription factor NF-κB unravels nuclesomes. Biochim. Biophys. Acta Gen. Subj 1865, 129934. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R45] 45.Sato M, Suemori H, Hata N, Asagiri M, Ogasawara K, Nakao K, Takaya T, Katsuki M, Noguchi S, Tanaka N, et al. (2000). Distinct and essential roles of transcription factors IRF-3 and IRF-7 in response to viruses for IFN-alpha/beta gene induction. Immunity 13, 539–548. [DOI] [PubMed] [Google Scholar]

[R46] 46.Langmead B, Salzberg SL (2012). Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R47] 47.Cairns J, Freire-Pritchett P, Wingett SW, Várnai C, Dimond A, Plagnol V, Zerbino D, Schoenfelder S, Javierre BM, Osborne C, et al. (2016). CHiCAGO: robust detection of DNA looping interactions in Capture Hi-C data. Genome Biol. 17, 127. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R48] 48.Rainer J (2017). EnsDb.Mmusculus.v79: Ensembl based annotation package. R package version 2.99.0. https://bioconductor.org/packages/EnsDb.Mmusculus.v79/ [Google Scholar]

[R49] 49.Arora S, Morgan M Carlson M Pagès H (2023). GenomeInfoDb: Utilities for manipulating chromosome names, including modifying them to follow a particular naming style. R package version 1.38.5, https://bioconductor.org/packages/GenomeInfoDb. [Google Scholar]

[R50] 50.Lawrence M, Huber W, Pagès H, Aboyoun P, Carlson M, Gentleman R, Morgan MT, Carey VJ (2013). Software for computing and annotating genomic ranges. PloS Comput. Biol 9, 31003118. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R51] 51.Hansen P, Ali S, Blau H, Danis D, Hecht J, Kornak U, Lupiáñez DG, Mundlos S, Steinhaus R, Robinson PN (2019). GOPHER: Generator of probes for capture Hi-C experiments at high resolution. BMC Genomics 20, 40. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R52] 52.Wingett S, Ewels P, Furlan-Magaril M, Nagano T, Schoenfelder S, Fraser P, Andrews S (2015). HiCUP: pipeline for mapping and processing Hi-C data. F1000Res 4, 1310. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R53] 53.Kim D, Langmead B, Salzberg SL (2015). HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R54] 54.Robinson JT, Thorvaldsdóttir H, Winckler W, Guttman M, Lander ES, Getz G, Mesirov JP (2011). Integrative genomics viewer. Nat. Biotechnol 29, 24–26. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R55] 55.Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, Nusbaum C, Myers RM, Brown M, Li W, Liu XS (2008). Model-based analysis of ChIP-seq (MACS). Genome Biol. 9, R137. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R56] 56.Raviram R, Rocha PP, Müller CL, Miraldi ER, Badri S, Fu Y, Swanzey E, Proudhon C, Snetkova V, Bonneau R, Skok JA (2016). 4C-ker: A method to reproducibly identify genome-wide interactions captured by 4C-seq experiments. PloS Comput. Biol 12, e1004780. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R57] 57.Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO, Whitwham A, Keane T, McCarthy SA, Davies RM, et al. (2021). Twelve years of SAMtools and BCFtools. Gigascience 10, giab008. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R58] 58.Stuart T, Srivastava A, Madad S, Lareau CA, Satija R (2021). Single-cell chromatin state analysis with Signac. Nat. Methods 18, 1333–1341. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R59] 59.Hao Y, Hao S, Andersen-Nissen E, Mauck WM 3rd, Zheng S, Butler A, Lee MJ, Wilk AJ Darby C, Zager M, et al. (2021). Integrated analysis of multimodal single-cell data. Cell 184, 3573–3587.e29. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R60] 60.Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D (2002). The human genome browser at UCSC. Genome Res. 12, 996–1006. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R61] 61.van de Werken HJG, de Vree. PJP, Splinter E, Holwerda SJB, Klous P, de Wit E, de Laat W (2012). 4C technology: protocols and data analysis. Methods Enzymol. 513, 89112. [DOI] [PubMed] [Google Scholar]

[R62] 62.Bhatt DM, Pandya-Jones A, Tong AJ, Barozzi I, Lissner MM, Natoli G, Black DL, Smale ST (2012). Transcript dynamics of proinflammatory genes revealed by sequence analysis of subcellular RNA fractions. Cell 150, 279–290. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R63] 63.Barish GD, Yu RT, Karunasiri M, Ocampo CB, Dixon J, Benner C, Dent AL, Tangirala RK, Evans RM (2010). Bcl-6 and NF-kappaB cistromes mediate opposing regulation of the innate immune response. Genes Dev. 24, 2760–2765. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R64] 64.Concordet JP, Haeussler M (2018). CRISPOR: intuitive guide selection for CRISPR/Cas9 genome editing experiments and screens. Nucleic Acids Res. 46(W1), W242–W245. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R65] 65.Krijger PHL, Geeven G, Bianchi V, Hilvering CRE, de Laat W (2020). 4C-seq from beginning to end: A detailed protocol for sample preparation and data analysis. Methods 170, 17–32. [DOI] [PubMed] [Google Scholar]

[R66] 66.Schoenfelder S, Javierre BM, Furlan-Magaril M, Wingett SW, Fraser P (2018). Promoter capture Hi-C: high-resolution, genome-wide profiling of promoter interactions. J. Vis. Exp 136, 57320. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

The Transcription Factor NF-κB Orchestrates Nucleosome Remodeling during the Primary Response to Toll-Like Receptor 4 Signaling

An-Chieh Feng

Brandon J Thomas

Prabhat K Purbey

Filipe Menegatti de Melo

Xin Liu

Allison E Daly

Fei Sun

Jerry Hung-Hao Lo

Lijing Cheng

Michael F Carey

Philip O Scumpia

Stephen T Smale

SUMMARY

Graphical Abstract

eTOC paragraph

INTRODUCTION

RESULTS

ATAC-seq Profiling Reveals a Broad Spectrum of TLR4-Induced Peaks

Figure 1. ATAC-seq profiling of the TLR4 response.

ATAC-seq Induction Coincides with Inducible BRG, p300, and H3K27Ac ChIP-seq Peaks

scATAC-seq Establishes Distinctions between Strongly and Weakly Induced Peaks

Figure 2. scATAC-seq analysis of TLR4-induced BMDMs.

Features of Inducible ATAC-seq Peaks at Promoters and Intergenic Regions

Unique Profile of NF-κB Motif Enrichment at Primary Response ATAC-seq Peaks

Figure 3. Initial evidence of a broad role for NF-κB in TLR4-induced remodeling.

NF-κB Binds Broadly to High-Confidence Primary Response ATAC-seq Peaks

Figure 4. Broad impacts on ATAC-seq and mRNA induction in Rela−/− Rel−/− cells.

Important Roles of NF-κB Motifs and the Rela and Rel Genes in ATAC-seq Induction

Broad Impact of RelA and c-Rel at High Confidence ATAC-seq Peaks

Critical Role for IRF3 in Inducible Remodeling at a Small Number of ATAC-seq Peaks

Figure 5. IRF3-dependence of ATAC-seq peaks.

NF-κB and IRF3 are Both Required for Remodeling at the Ccl5 Locus

Figure 6. NF-κB/IRF3 co-dependence of IRF3-dependent ATAC-seq peaks.

Inducible ATAC-seq Peak Regions at the Ccl5 Locus Support Looping and Function

Figure 7. Analysis of an Ccl5 intergenic NF-κB/IRF3 co-dependent region.

DISCUSSION

LIMITATIONS OF THE STUDY

STAR METHODS

RESOURCE AVAILABILITY

Lead contact

Material availability

Data and code availability

EXPERIMENTAL MODEL AND STUDY PARTICIPANT DETAILS

Mice

Primary Cell Culture

Cell Lines

METHOD DETAILS

Stimulation

ATAC-seq

scATAC-seq

RNA-seq

ChIP-seq

CRISPR-Cas9 Ribonucleoprotein (cRNP) genome editing

A. Guide RNA Design

B. HDR Template Design and Preparation

C. Assembly of the cRNP complex

D. Cell Preparation

E. Electroporation

F. Single-Cell Colony Expansion

G. Screening and Sequencing

qRT-PCR

4C assay

Promoter capture Hi-C assay

QUANTIFICATION AND STATISTICAL ANALYSIS

RNA-seq analysis

ATAC-seq analysis

scATAC-seq data analysis

A. Alignment of scATAC-seq Reads

B. Quality Control and Preprocessing of scATAC-seq Data

C. Dimensionality Reduction, UMAP and Clustering of sc-ATAC-seq Data with Signac

D. Comparison of scATAC-seq Peaks with Bulk ATAC-seq Peaks

ChIP-seq analysis

4C analysis

Promoter/ target Capture Hi-C analysis

Data visualization and motif enrichment

Supplementary Material

Highlights.

ACKNOWLEDGMENTS

Figure 4. Broad impacts on ATAC-seq and mRNA induction in Rela^−/− Rel^−/− cells.