- Open Access
Divergence of transcriptional landscape occurs early in B cell activation
© Fowler et al.; licensee BioMed Central. 2015
- Received: 5 March 2015
- Accepted: 1 May 2015
- Published: 14 May 2015
Signaling via B cell receptor (BCR) and Toll-like receptors (TLRs) results in activation of B cells with distinct physiological outcomes, but transcriptional regulatory mechanisms that drive activation and distinguish these pathways remain unknown.
Two hours after ligand exposure RNA-seq, ChIP-seq and computational methods reveal that BCR- or TLR-mediated activation of primary resting B cells proceeds via a large set of shared and a smaller subset of distinct signal-selective transcriptional responses. BCR stimulation resulted in increased global recruitment of RNA Pol II to promoters that appear to transit slowly to downstream regions. Conversely, lipopolysaccharide (LPS) stimulation involved an enhanced RNA Pol II transition from initiating to elongating mode accompanied by greater H3K4me3 activation markings compared to BCR stimulation. These rapidly diverging transcriptomic landscapes also show distinct repressing (H3K27me3) histone signatures, mutually exclusive transcription factor binding in promoters, and unique miRNA profiles.
Upon examination of genome-wide transcription and regulatory elements, we conclude that the B cell commitment to different activation states occurs much earlier than previously thought and involves a multi-faceted receptor-specific transcriptional landscape.
- Plasma Cell Differentiation
- Transcriptional Landscape
- Transcription Factor Motif
- TLR4 Engagement
- Genome Browser Track
B cell activation, the transition from a naïve to an effector state, is important due to its essential role in immunity. Deregulated activation can have disastrous effects resulting in immune disorders and several B cell malignancies, some of which resemble activated B cell phenotypes [1, 2]. Mature resting splenic B cells maintain a quiescent G0 state with limited proliferative output . Upon encountering antigen, these cells become activated, leading to plasma cell differentiation and participation in immune responses. Activation of B cells can occur through either surface B cell receptor (BCR) [4–6] or various pathogen-associated molecular patterns such as bacterial lipopolysaccharide (LPS), which is mediated by Toll-like receptor (TLR) signaling and NF-κB pathways . Exposure of B cells to LPS via TLR4 can promote plasma cell differentiation [7, 8]. A properly regulated LPS activation appears critical as patients with deficient TLR signaling molecules, exhibit autoimmunity .
Splenic B cell differentiation can begin as early as 4 h and fully develop by 48–72 h . While much is known about signaling cascades during B cell activation at early and late time points [5, 11, 12], transcriptional changes during these times are still being addressed . In particular, a high-resolution picture reflecting the immediate transcriptional and epigenetic changes during early B cell activation, before mature B cells proceed toward proliferation and functional immune responses occur, is not available. Regardless of cell type, initial signaling events lead to rapid induction of primary response genes (PRGs) whose products initiate secondary waves of transcription resulting in egress from the G0 state and subsequently in proliferation and effector function . Regulatory mechanisms for these rapid responses, release of preformed “paused” transcription complexes, RNA polymerase II (Pol II) recruitment via transcription co-factors, and promoter accessibility/repression through histone modifications, are particularly well established [14, 15]. But how these mechanisms operate during BCR and LPS signaling in early activation of B cells is not known.
Activation of resting splenic B cells ex vivo provides a tractable model to explore this transition in a ligand-specific manner . B cells are unique hematopoietic cells because they express both BCR and TLRs. Although stimulation of naïve ex vivo B cells through both receptors elicits activation and proliferation, only LPS stimulation results in plasma cell differentiation [4, 17, 18]. We used this ex vivo model to determine how and when these ligand-specific transcriptional landscapes may diverge. We observe clear differences within 2 h post stimulation. In addition to ligand-selective differences in both protein-coding and non-coding RNAs, several other transcriptional regulatory steps differed between the activation states providing three key findings . Although BCR-induced genes show new recruitment of RNA Pol II that appeared to be paused at promoters, LPS/TLR4-induced genes exhibit enhanced transition of RNA Pol II from initiation to elongation . While the H3K4me3 (activating) mark is increased in both activation states (more so during TLR4 engagement), the appearance of the H3K27me3 (repressive) mark is reduced on BCR-responsive genes but remains relatively unchanged in LPS-responsive genes . Predicted transcription factor binding sites in the promoter proximal regions of genes also differ significantly in a ligand-selective manner. Together, our results show that B cell egress from the resting state involves a large pool of shared/common RNAs, and a small set of signal-selective RNAs that exhibit remarkable transcriptional landscape changes soon after ligand engagement.
Response dependent differential transcription during early activation
Figure 1c shows functional relationships between the different groups of genes with shared and response selective transcription that changed significantly relative to the resting state. c-Myc, which plays an important role in B cell proliferation in response to TLR4 and BCR ligation, was dominantly positioned in the shared response genes and also prevalent with BCR-selective genes (known Myc targets are highlighted in yellow at bottom left). As expected, preferentially increased LPS specific transcripts contained TLR signaling pathway genes and known NF-κB targets (Additional file 2). We conclude that the initial stages of B cell activation involve a large set of shared genes, despite being stimulated by distinct signaling pathways and a small but significant set of ligand-selective genes. These ligand-selective genes induced by BCR showed a predominant Myc signature, while those induced by TLR4 exhibited a prominent NF-κB signature.
We also analyzed 1315 genes whose transcription was not significantly altered by BCR or TLR4 stimulation. These genes, such as Polr2a, Max, ActB, and Dicer1, collectively annotated to biological processes, including maintenance of homeostasis, cell cycle, and apoptosis management (Additional file 1: Figure S2). In addition, transcripts associated with antigen processing, TGF-β signaling, TNF signaling, and MHC1 antigen presentation, were also unchanged (Additional file 1: Figure S2), suggesting that prior to activation, these cells are actively executing significant immune functions.
Response dependent RNA Pol II occupancy during early activation
Response dependent changes in chromatin
Polycomb repressor complexes (PRCs) catalyze repressive chromatin marks [25–28]. In mammals, PRC2 activity depends on the SET domain-containing protein Enhancer of zeste homolog 2 (EZH2) that catalyzes transcriptionally repressive histone H3 methylation at lysine 27 mark (H3K27me3) . H3K27me3 plays an important role in B cell proliferation, and EZH2 expression is low in resting B cells but upregulated in activated B cells [30–32]. Hence, we analyzed H3K27me3 in response to BCR and TLR4 engagement. In genes with increased or unchanged transcription, the level of H3K27me3 at promoters was decreased during BCR activation (Fig. 3c). This decrease was most prominent around the TSS, and while there was a small decrease during LPS activation, this was restricted to an area just upstream of the TSS; otherwise, H3K27me3 in the LPS activation state remained largely unperturbed. BCR activation showed a greater genome-wide decrease in H3K27me3 than LPS (Additional file 1: Figure S5). While the boxplot comparison of median analysis did show some variation, the differences remained statistically significant. We concluded that there was a decrease in H3K27me3 during BCR engagement relative to TLR4 engagement around TSSs. Collectively, analysis of H3K4me3 and H3K27me3 illustrates that distinct chromatin modifications separate BCR and LPS activation states, regardless of whether considering all possible transcripts (Fig. 3) or the ones with the RNA Pol II highest occupancy (Additional file 1: Figure S6) was considered.
Analysis of individual genes
Response selective transcription factor binding motif enrichment
We next examined promoter sequences to gain further insights into the observed differences in RNA Pol II recruitment or regulatory histone marks. Promoters containing “CpG islands” correlate with low nucleosome occupancy and increased RNA Pol II occupancy [33–35]. A majority of transcripts was associated with promoters containing or near (within 200 bps) a predicted CpG island; however, no preference for CpG associated promoters was evident for either response (Additional file 1: Figure S8). Although a connection between H3K27me3 and CpG islands exists , we observed no significant ligand-dependent correlation between CpG island promoters and H3K27me3 (data not shown).
Expression of non-coding RNAs
Deregulation of B cell activation can result in autoimmune disorders, chronic inflammation, and lymphoma. Although B cells express both BCR and TLR4, the functional consequences to these stimuli are distinct, at least under ex vivo conditions. While BCR engagement (triggered by anti-IgM stimulation) leads to proliferative expansion of activated B cells, engagement of TLR4 (triggered by LPS stimulation) leads to proliferation and eventual production of plasma cells. However, the transcriptional signatures and molecular mechanisms that distinguish these responses are relatively unknown. Here, we employed ex vivo activation of resting murine splenic B cells to examine these molecular signatures and define the transcriptional and regulatory landscape during early activation by high-resolution RNA- and ChIP-seq. We observe that at 2 h post stimulation, most genes (~90 %) induced by the two pathways are shared, despite being triggered through distinct receptors. This observation is consistent with previous studies [44, 45]. However, the rest of the transcriptome (10 %) exhibit signal-selective transcriptional programs.
While genes induced by both responses (shared) are greatly dominated by Myc targets and Myc-associated processes, groups of genes preferentially induced by LPS, as expected, show a strong correlation with NF-κB (Fig. 1). Surprisingly, BCR-induced genes showed little relationship to processes associated with the shared and LPS specific induced genes; they only overlapped with the shared-induced genes at the level of Myc and Arnt targets. Myc expression in normal lymphoid tissue is present in both activated and resting B cells in all phases of the cell cycle . We were surprised to find that the Myc co-factor Max transcription levels remained constant during activation despite a 15- to 30-fold increase in Myc, suggesting other co-factors were required to drive such a strong Myc-dominated signature. However, another Myc regulator, Mnt , was increased by BCR activation to a small degree. These data further underscore Myc’s dominant but enigmatic role in B cell activation [48–50].
Given the distinct gene sets noted in each of the responses, we examined mechanisms that could drive these observed differences. While preexisting, paused, RNA Pol II is associated with a large number of genes in diverse cell types, new RNA Pol II recruitment indicates transcriptional activity . Although preexisting RNA Pol II was found globally in resting B cells, RNA Pol II occupancy at the TSS was broadly increased during the BCR response and increased further at BCR-responsive gene promoters. This scenario suggests two general BCR-dependent mechanisms are in play , a global recruitment of Pol II to promoters and  a signal-specific guidance to intensify this general recruitment of Pol II to BCR-responsive promoters, particularly around the TSS. Conversely, LPS activation appeared to involve maintenance of steady-state RNA Pol II occupancy at the promoter relative to the resting cells. An increase in downstream RNA Pol II, possibly reflecting elongating Pol II, was noticeable, although this promoter associated RNA Pol II peak can be due to other mechanisms . To better understand this phenomenon, we took a closer look at downstream regions and calculated traveling ratios of polymerase occupancy in promoter versus downstream/coding sequences (Fig. 2c). These analyses revealed that while the BCR signal resulted in enhanced RNA Pol II at promoters, the transition to downstream region (elongation) was less/slower. In contrast, although LPS stimulation did not result in additional RNA Pol II recruitment, transition to downstream regions was greater/faster than that observed with BCR. It remains possible that although the total recruitment of RNA Pol II under two stimulations is very similar, the difference in promoter versus downstream region associated RNA Pol II reflects the rate at which the enzyme transits from initiation to elongation mode. Our preliminary analysis indicates that there are differences in the complexity of gene structure (e.g., number of exons) between the BCR- and LPS-responsive genes. Whether the difference in RNA Pol II movement between the two stimuli reflects these differences, a difference in signal strength and/or a fundamental difference in signaling pathways remain to be determined.
Given that helix-loop-helix (HLH) transcription factors play an important role in B cell development and differentiation [59, 60], it is intriguing that HLH TF motifs are strongly represented in BCR-responsive promoters. While much work has been done on the role of E-box binding proteins in early B cell development (reviewed in ), the role of this class of proteins in mature B cell early activation is limited . The idea that enhancer-promoter actions mediated by HLH TFs (e.g. Myc) via E-boxes might partially account for the increased RNA Pol II occupancy at promoters induced by BCR is appealing. A recent report that targeting BET proteins in high-risk acute lymphoblastic leukemia inhibits Myc and Il7r expression, both of which exhibit increased transcription in response to BCR in our experiment, also suggests Myc plays an important role during early phases of B cell activation . The fact that E-box containing promoter sequences are underrepresented in an EZH2 recruitment assay  further suggests that the decrease in H3K27me3 and preponderance of E-box sequences in BCR-induced genes observed in our study are related.
As deregulation of B cell activation is related to malignancies such as B cell lymphomas, our studies may also provide insights into lymphogenesis. Myc and NF-κB are well-established master regulators of initiation of transcriptional programs, but when deregulated, they function as oncogenic drivers in B cell lymphomas. Deregulated and increased Myc and Bcl proteins, such as Bcl2 and Bcl6, are associated with particularly aggressive lymphoma types [64, 65]. Here, we found BCR stimulation decreased Bcl6 expression; in contrast, Bcl6 remained stable during LPS activation. These and other Bcl transcription patterns (Additional file 1: Figure S11) suggest that the proper regulation of Myc and Bcl proteins is required for early activation. Further investigation into this oncogenic driver network might yield interesting relationships.
Targeting of miRNAs is complex with an average miRNA having approximately 100 target sites in addition to non-canonical miRNA binding [66, 67]. Here, we identified differential expression of many miRNAs known to regulate processes involved in B cell activation. That a greater decrease in miRNA expression occurred in the BCR response compared to LPS suggests rapid downregulation of miRNAs is necessary to orchestrate gene expression driving the adaptive immune response. Given the wide activity spectrum predicted for many miRNAs, it is possible they could coordinate some of the separate regulatory mechanisms we observed. For example, a recent report proposes a regulatory loop linking overexpression of Myc, EZH2, and miR26a repression to lymphoma growth . Our BCR activation data showing overexpression of Myc, lowered H3K27me3, and decreased miR26a highlight the multi-factorial nature and cross-dependency of regulatory systems likely to drive complicated responses such as signal-specific B cell activation. Although miRNAs have generally been associated with oncogenic pathways, targeted deletion of miR-17 cluster shows defects in B cell differentiation [39, 69]. Because LPS but not BCR signaling in splenic B cells results in differentiation, it is tempting to speculate that signal-specific regulation in the miR-17 cluster is a way of distinguishing between the two signals. The miR-15 cluster belongs to a very selective group of miRNAs enriched in the nucleus and thus capable of further directly regulating LPS specific transcription . Despite the fact that miRNAs are critical regulators of diverse biological processes, differential regulation of miRNAs to the extent observed in our analysis is very surprising. However, it is currently unknown if these miRNAs are regulated by rapid turnover of miRNAs, regulated at the level of transcription, or both.
BCR responses are slower and presumably more precise [44, 45, 71], therefore, it is tempting to speculate that a tighter regulatory environment is required to orchestrate these lengthy responses (Fig. 7). An increase in global Pol II recruitment that appears to be regulated at the level of pausing and a greater release of miRNA repression could reflect this strict regulation. Conversely, TLR4-mediated signaling is reflective of innate responses, which are generally rapid and transient, and therefore could be manifested by an enhanced transition of RNA Pol II from initiation to elongation together with a global increase in activation marks at TSSs. Additionally, one would expect the derepression (via decreasing H3K27me3) observed during the BCR response to be slower than the sharper increase in preexisting H3K4me3-activating marks observed during LPS/TLR4 signaling. How these different observations are related to each other will be the next challenging phase to understand the regulation of B cell activation. Nevertheless, our observations begin to elucidate the signal-specific signatures involved in early activation of B cells and further suggest key molecular mechanisms (Fig. 7) that govern this important process.
We conclude that the B cell commitment to different activation states is dependent upon rapid regulatory mechanisms and occurs much earlier than previously thought. Different RNA Pol II recruitment and transition from initiation to elongation, distinct activating (H3K4me3) and repressing (H3K27me3) histone signatures, mutually exclusive transcription factor binding in promoters and highly selective miRNA profiles distinguish these responses.
Cells and induction
Naïve resting B cells from splenocytes of 8-week-old male C57BL6 mice were isolated with anti-CD43 beads (Miltenyi), confirmed as 95 % CD19+ by flow cytometry (FACS Calibur), and resuspended in cold media with either 10 ug/ml anti-mouse IgM goat IgG Fab fragments (Jackson Immunology) or 25 ug/ml Salmonella typhimurium typhus LPS (Sigma) were added. The cells were rested on ice for 30 min following a previously published method  and incubated at 37 °C/5 %CO2 for the experimental times. Animal care and use in this study are covered under the “Assurance of Compliance with PHS (USA) Policy on Humane Care and Use of Laboratory animals by Awardee Institutions” and approved by the Institutional Animal Care and Use Committee of Tufts University (Animal Welfare Assurance Number A-3775-01).
Sample preparation was performed using common techniques. In general, single end, 100 bp (initial RNA), and 50 bp (secondary RNA, ChIP and miRNA) reads were mapped against the mm9/ENSEMBL build 67 genome reference using Tophat v2.0.0  and for RNA, bowtie 1.0.0 for ChIP . RNA Pol II ChIP-seq employed antibody against total RNA Pol II (Santa Cruz N-20, sc-816x), H3K4me3 with Abcam antibody ab8580, and H3K27me3 with Abcam antibody ab6002. Mapped read numbers per million and BCR or LPS time points are 120 min unless indicated. RNA , rest 75.3, BCR30 20.9, BCR120 73.6, LPS30 44.7, LPS120 40.5; RNA , rest 77, BCR 61, LPS 68.9; RNA Pol II, rest 18.1, BCR 14.2, LPS 21.3; H3K4me3, rest 13.1, BCR 16.4, LPS 19.1; K3K27me3, rest 19.0, BCR 20.0, LPS 18.1.
Differential expression analysis
Differential expression (DE) was identified by a minimal twofold difference in log ratios of normalized reads generated with Cufflinks v1.3.1. Preferentially induced or reduced genes sets included genes that were either changed by either a single response or when affected by both responses changed only two- to fourfold by one response and were changed by the preferred response at a ratio of at least twofold more than the non-referred response. A spreadsheet of the differential expression list can be found in Additional file 3. Genes were annotated to biological processes with the online Toppfun program. Gene network analysis was carried out using ToppCluster  and visualized by Cytoscape .
After Trizol isolation of RNA, TruSeq Small RNA Sample Preparation Kits were used to produce material for generating 50 bp single end reads which were then analyzed with miRDeep2  using the miRBase reference v14 with standard settings. Mapped miRNAs were confirmed by visual inspection of miRNA structure and UCSC Genome Browser tracks  and inclusion in the Ensembl data base . Differential expression from the resting state was identified by a minimal twofold difference in miRdeep2 normalized reads. Total miRNA data set reads per million are the following: rest 20.2, BCR 120 28.6, and LPS 120 14.8. Total miRdeep2 miRNA reads (per thousand) are the following: rest 55.8, BCR 16.2, and LPS 27.6.
For histograms of TSS coverage, custom R scripts were used to produce bedgraphs from mapped bam files, which were converted to BigWig files with bedgraphToBigwig for UCSC Genome Browser presentation [80, 81]. Reads per million-normalized coverage was computed for the gene sets and regions indicated, and summary statistics were calculated at each base pair, for histograms or by summing total coverage across regions, as shown for boxplots. Traveling ratios (TR) were calculated from the mean of summed transcript RPM means for each transcription group in an area representing the promoter (p) (−0.3/0.3 kbp) and downstream body (b) (0.3/2.25 kbp) of the transcript, TR = (b/p). TRs were then normalized to the resting state (TRactivation/TRrest).
CpG and TF motif analysis
Predicted CpG island locations were from preloaded USCS Genome Browser tracks and produced by common methods . Proximity of TSSs to CpG islands was analyzed with Bedtools’ IntersectBed . Enriched TF binding motifs in the promoters, defined as −1000 to +1000 regions relative to the TSS based on RNA Pol II occupancy (Fig. 2), employed the motif enrichment algorithm implemented in the HOMER tool  supplemented with the mouse TF binding motifs contained in the CisBP database (build 0.90) , resulting in a total of 3812 mouse motifs. Enrichment calculations used promoter sequences of genes whose expression did not change as our background set.
Quantitative PCR-RNA validation
The sequences have been deposited to the GEO database (NCBI/NLM/NIH)—accession number (GSE61608) (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE61608).
We are deeply indebted to Drs. Ali Shilatifard, Deqing Hu, and Xin Gao of the Stowers Institute for performing an independent RNA-seq and ChIP-seq of RNA Pol II as well as their guidance and helpful discussions throughout the course of this work. We thank Xiaoting Chen (CCHMC) for help with the motif enrichment analysis and members of Tufts’ Computational Biology Initiative for analysis assistance. We also thank Dr. Dinah Singer (NCI) and Dr. Harinder Singh (CCHMC) for critically reading the manuscript and for their thoughtful suggestions. This work was supported in part by the Intramural Research Program of the NIH, the National Institute on Aging to RS, and by grants to STS (R01 GM086372) and ALR (AHA 12GRNT12180023).
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