- 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.
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).
- Carbone A, Gloghini A, Kwong YL, Younes A. Diffuse large B cell lymphoma: using pathologic and molecular biomarkers to define subgroups for novel therapy. Ann Hematol. 2014;93(8):1263–77. Epub 2014/05/30.View ArticlePubMed CentralPubMedGoogle Scholar
- Shaffer 3rd AL, Young RM, Staudt LM. Pathogenesis of human B cell lymphomas. Annu Rev Immunol. 2012;30:565–610. Epub 2012/01/10.View ArticlePubMedGoogle Scholar
- Sprent J. Lifespans of naive, memory and effector lymphocytes. Curr Opin Immunol. 1993;5(3):433–8. Epub 1993/06/01.View ArticlePubMedGoogle Scholar
- DeFranco AL, Raveche ES, Paul WE. Separate control of B lymphocyte early activation and proliferation in response to anti-IgM antibodies. J Immunol. 1985;135(1):87–94. Epub 1985/07/01.PubMedGoogle Scholar
- Harwood NE, Batista FD. Early events in B cell activation. Annu Rev Immunol. 2010;28:185–210. Epub 2010/03/03.View ArticlePubMedGoogle Scholar
- Nutt SL, Tarlinton DM. Germinal center B and follicular helper T cells: siblings, cousins or just good friends? Nat Immunol. 2011;12(6):472–7. Epub 2011/07/09.View ArticlePubMedGoogle Scholar
- Doyle SL, O’Neill LA. Toll-like receptors: from the discovery of NFkappaB to new insights into transcriptional regulations in innate immunity. Biochem Pharmacol. 2006;72(9):1102–13. Epub 2006/08/26.View ArticlePubMedGoogle Scholar
- Pone EJ, Zhang J, Mai T, White CA, Li G, Sakakura JK, et al. BCR-signalling synergizes with TLR-signalling for induction of AID and immunoglobulin class-switching through the non-canonical NF-kappaB pathway. Nat Commun. 2012;3:767. Epub 2012/04/05.View ArticlePubMed CentralPubMedGoogle Scholar
- Rawlings DJ, Schwartz MA, Jackson SW, Meyer-Bahlburg A. Integration of B cell responses through Toll-like receptors and antigen receptors. Nat Rev Immunol. 2012;12(4):282–94. Epub 2012/03/17.View ArticlePubMed CentralPubMedGoogle Scholar
- Dardick I, Sinnott NM, Hall R, Bajenko-Carr TA, Setterfield G. Nuclear morphology and morphometry of B-lymphocyte transformation. Implications for follicular center cell lymphomas. Am J Pathol. 1983;111(1):35–49. Epub 1983/04/01.PubMed CentralPubMedGoogle Scholar
- Kurosaki T, Shinohara H, Baba Y. B cell signaling and fate decision. Annu Rev Immunol. 2010;28:21–55. Epub 2009/10/16.View ArticlePubMedGoogle Scholar
- Niiro H, Clark EA. Regulation of B-cell fate by antigen-receptor signals. Nat Rev Immunol. 2002;2(12):945–56. Epub 2002/12/04.View ArticlePubMedGoogle Scholar
- Kouzine F, Wojtowicz D, Yamane A, Resch W, Kieffer-Kwon KR, Bandle R, et al. Global regulation of promoter melting in naive lymphocytes. Cell. 2013;153(5):988–99. Epub 2013/05/28.View ArticlePubMed CentralPubMedGoogle Scholar
- Fowler T, Sen R, Roy AL. Regulation of primary response genes. Mol Cell. 2011;44(3):348–60. Epub 2011/11/08.View ArticlePubMed CentralPubMedGoogle Scholar
- Rogatsky I, Adelman K. Preparing the first responders: building the inflammatory transcriptome from the ground up. Mol Cell. 2014;54(2):245–54. Epub 2014/04/29.View ArticlePubMed CentralPubMedGoogle Scholar
- Glynne R, Ghandour G, Rayner J, Mack DH, Goodnow CC. B-lymphocyte quiescence, tolerance and activation as viewed by global gene expression profiling on microarrays. Immunol Rev. 2000;176:216–46. Epub 2000/10/24.View ArticlePubMedGoogle Scholar
- Kearney JF, Lawton AR. B lymphocyte differentiation induced by lipopolysaccharide. I Generation of cells synthesizing four major immunoglobulin classes. J Immunol. 1975;115(3):671–6. Epub 1975/09/01.PubMedGoogle Scholar
- Solvason N, Wu WW, Kabra N, Wu X, Lees E, Howard MC. Induction of cell cycle regulatory proteins in anti-immunoglobulin-stimulated mature B lymphocytes. J Exp Med. 1996;184(2):407–17. Epub 1996/08/01.View ArticlePubMedGoogle Scholar
- Corcoran AE, Smart FM, Cowling RJ, Crompton T, Owen MJ, Venkitaraman AR. The interleukin-7 receptor alpha chain transmits distinct signals for proliferation and differentiation during B lymphopoiesis. EMBO J. 1996;15(8):1924–32. Epub 1996/04/15.PubMed CentralPubMedGoogle Scholar
- Bandow K, Kusuyama J, Shamoto M, Kakimoto K, Ohnishi T, Matsuguchi T. LPS-induced chemokine expression in both MyD88-dependent and -independent manners is regulated by Cot/Tpl2-ERK axis in macrophages. FEBS Lett. 2012;586(10):1540–6. Epub 2012/06/08.View ArticlePubMedGoogle Scholar
- Reppas NB, Wade JT, Church GM, Struhl K. The transition between transcriptional initiation and elongation in E. coli is highly variable and often rate limiting. Mol Cell. 2006;24(5):747–57. Epub 2006/12/13.View ArticlePubMedGoogle Scholar
- Wade JT, Struhl K. The transition from transcriptional initiation to elongation. Curr Opin Genet Dev. 2008;18(2):130–6. Epub 2008/02/20.View ArticlePubMed CentralPubMedGoogle Scholar
- Smale ST, Tarakhovsky A, Natoli G. Chromatin contributions to the regulation of innate immunity. Annu Rev Immunol. 2014;32:489–511. Epub 2014/02/22.View ArticlePubMedGoogle Scholar
- Shilatifard A. The COMPASS family of histone H3K4 methylases: mechanisms of regulation in development and disease pathogenesis. Annu Rev Biochem. 2012;81:65–95. Epub 2012/06/06.View ArticlePubMed CentralPubMedGoogle Scholar
- Baxter J, Sauer S, Peters A, John R, Williams R, Caparros ML, et al. Histone hypomethylation is an indicator of epigenetic plasticity in quiescent lymphocytes. EMBO J. 2004;23(22):4462–72. Epub 2004/10/29.View ArticlePubMed CentralPubMedGoogle Scholar
- Boyer LA, Plath K, Zeitlinger J, Brambrink T, Medeiros LA, Lee TI, et al. Polycomb complexes repress developmental regulators in murine embryonic stem cells. Nature. 2006;441(7091):349–53. Epub 2006/04/21.View ArticlePubMedGoogle Scholar
- Landeira D, Fisher AG. Inactive yet indispensable: the tale of Jarid2. Trends Cell Biol. 2011;21(2):74–80. Epub 2010/11/16.View ArticlePubMed CentralPubMedGoogle Scholar
- Pereira CF, Piccolo FM, Tsubouchi T, Sauer S, Ryan NK, Bruno L, et al. ESCs require PRC2 to direct the successful reprogramming of differentiated cells toward pluripotency. Cell Stem Cell. 2010;6(6):547–56. Epub 2010/06/24.View ArticlePubMedGoogle Scholar
- Simon JA, Kingston RE. Mechanisms of polycomb gene silencing: knowns and unknowns. Nat Rev Mol Cell Biol. 2009;10(10):697–708. Epub 2009/09/10.View ArticlePubMedGoogle Scholar
- Cao R, Wang L, Wang H, Xia L, Erdjument-Bromage H, Tempst P, et al. Role of histone H3 lysine 27 methylation in Polycomb-group silencing. Science. 2002;298(5595):1039–43. Epub 2002/09/28.View ArticlePubMedGoogle Scholar
- van Galen JC, Dukers DF, Giroth C, Sewalt RG, Otte AP, Meijer CJ, et al. Distinct expression patterns of polycomb oncoproteins and their binding partners during the germinal center reaction. Eur J Immunol. 2004;34(7):1870–81. Epub 2004/06/24.View ArticlePubMedGoogle Scholar
- Velichutina I, Shaknovich R, Geng H, Johnson NA, Gascoyne RD, Melnick AM, et al. EZH2-mediated epigenetic silencing in germinal center B cells contributes to proliferation and lymphomagenesis. Blood. 2010;116(24):5247–55. Epub 2010/08/26.View ArticlePubMed CentralPubMedGoogle Scholar
- Hargreaves DC, Horng T, Medzhitov R. Control of inducible gene expression by signal-dependent transcriptional elongation. Cell. 2009;138(1):129–45. Epub 2009/07/15.View ArticlePubMed CentralPubMedGoogle Scholar
- Deaton AM, Bird A. CpG islands and the regulation of transcription. Genes Dev. 2011;25(10):1010–22. Epub 2011/05/18.View ArticlePubMed CentralPubMedGoogle Scholar
- Ramirez-Carrozzi VR, Braas D, Bhatt DM, Cheng CS, Hong C, Doty KR, et al. A unifying model for the selective regulation of inducible transcription by CpG islands and nucleosome remodeling. Cell. 2009;138(1):114–28. Epub 2009/07/15.View ArticlePubMed CentralPubMedGoogle Scholar
- Mendenhall EM, Koche RP, Truong T, Zhou VW, Issac B, Chi AS, et al. GC-rich sequence elements recruit PRC2 in mammalian ES cells. PLoS Genet. 2010;6(12), e1001244. Epub 2010/12/21.View ArticlePubMed CentralPubMedGoogle Scholar
- Contreras J, Rao DS. MicroRNAs in inflammation and immune responses. Leukemia. 2012;26(3):404–13. Epub 2011/12/21.View ArticlePubMedGoogle Scholar
- Danger R, Braza F, Giral M, Soulillou JP, Brouard MRNAS. Major Players in B Cells Homeostasis and Function. Front Immunol. 2014;5:98. Epub 2014/03/22.View ArticlePubMed CentralPubMedGoogle Scholar
- Ventura A, Young AG, Winslow MM, Lintault L, Meissner A, Erkeland SJ, et al. Targeted deletion reveals essential and overlapping functions of the miR-17 through 92 family of miRNA clusters. Cell. 2008;132(5):875–86. Epub 2008/03/11.View ArticlePubMed CentralPubMedGoogle Scholar
- Rodriguez A, Vigorito E, Clare S, Warren MV, Couttet P, Soond DR, et al. Requirement of bic/microRNA-155 for normal immune function. Science. 2007;316(5824):608–11. Epub 2007/04/28.View ArticlePubMed CentralPubMedGoogle Scholar
- Enomoto Y, Kitaura J, Hatakeyama K, Watanuki J, Akasaka T, Kato N, et al. Emu/miR-125b transgenic mice develop lethal B-cell malignancies. Leukemia. 2011;25(12):1849–56. Epub 2011/07/09.View ArticlePubMedGoogle Scholar
- Mao YS, Sunwoo H, Zhang B, Spector DL. Direct visualization of the co-transcriptional assembly of a nuclear body by noncoding RNAs. Nat Cell Biol. 2011;13(1):95–101. Epub 2010/12/21.View ArticlePubMed CentralPubMedGoogle Scholar
- Tripathi V, Ellis JD, Shen Z, Song DY, Pan Q, Watt AT, et al. The nuclear-retained noncoding RNA MALAT1 regulates alternative splicing by modulating SR splicing factor phosphorylation. Mol Cell. 2010;39(6):925–38. Epub 2010/08/28.View ArticlePubMed CentralPubMedGoogle Scholar
- Fambrough D, McClure K, Kazlauskas A, Lander ES. Diverse signaling pathways activated by growth factor receptors induce broadly overlapping, rather than independent, sets of genes. Cell. 1999;97(6):727–41. Epub 1999/06/25.View ArticlePubMedGoogle Scholar
- Zhu X, Hart R, Chang MS, Kim JW, Lee SY, Cao YA, et al. Analysis of the major patterns of B cell gene expression changes in response to short-term stimulation with 33 single ligands. J Immunol. 2004;173(12):7141–9. Epub 2004/12/09.View ArticlePubMedGoogle Scholar
- Cattoretti G. MYC expression and distribution in normal mature lymphoid cells. J Pathol. 2013;229(3):430–40. Epub 2012/11/21.View ArticlePubMedGoogle Scholar
- Link JM, Hurlin PJ. The activities of MYC, MNT and the MAX-interactome in lymphocyte proliferation and oncogenesis. Biochimica et biophysica acta. 2014. Epub 2014/04/16.Google Scholar
- Eilers M, Eisenman RN. Myc’s broad reach. Genes Dev. 2008;22(20):2755–66. Epub 2008/10/17.View ArticlePubMed CentralPubMedGoogle Scholar
- Dang CV. MYC on the path to cancer. Cell. 2012;149(1):22–35. Epub 2012/04/03.View ArticlePubMed CentralPubMedGoogle Scholar
- Sabo A, Kress TR, Pelizzola M, de Pretis S, Gorski MM, Tesi A, et al. Selective transcriptional regulation by Myc in cellular growth control and lymphomagenesis. Nature. 2014;511(7510):488–92. Epub 2014/07/22.View ArticlePubMed CentralPubMedGoogle Scholar
- Ehrensberger AH, Kelly GP, Svejstrup JQ. Mechanistic interpretation of promoter-proximal peaks and RNAPII density maps. Cell. 2013;154(4):713–5. Epub 2013/08/21.View ArticlePubMedGoogle Scholar
- Bhatt D, Ghosh S. Regulation of the NF-kappaB-mediated transcription of inflammatory genes. Front Immunol. 2014;5:71. Epub 2014/03/13.View ArticlePubMed CentralPubMedGoogle Scholar
- Smith E, Shilatifard A. The chromatin signaling pathway: diverse mechanisms of recruitment of histone-modifying enzymes and varied biological outcomes. Mol Cell. 2010;40(5):689–701. Epub 2010/12/15.View ArticlePubMed CentralPubMedGoogle Scholar
- Beguelin W, Popovic R, Teater M, Jiang Y, Bunting KL, Rosen M, et al. EZH2 is required for germinal center formation and somatic EZH2 mutations promote lymphoid transformation. Cancer Cell. 2013;23(5):677–92. Epub 2013/05/18.View ArticlePubMed CentralPubMedGoogle Scholar
- Simon JA, Kingston RE. Occupying chromatin: polycomb mechanisms for getting to genomic targets, stopping transcriptional traffic, and staying put. Mol Cell. 2013;49(5):808–24. Epub 2013/03/12.View ArticlePubMed CentralPubMedGoogle Scholar
- Chen S, Ma J, Wu F, Xiong LJ, Ma H, Xu W, et al. The histone H3 Lys 27 demethylase JMJD3 regulates gene expression by impacting transcriptional elongation. Genes Dev. 2012;26(12):1364–75. Epub 2012/06/21.View ArticlePubMed CentralPubMedGoogle Scholar
- De Santa F, Narang V, Yap ZH, Tusi BK, Burgold T, Austenaa L, et al. Jmjd3 contributes to the control of gene expression in LPS-activated macrophages. EMBO J. 2009;28(21):3341–52. Epub 2009/09/26.View ArticlePubMed CentralPubMedGoogle Scholar
- De Santa F, Totaro MG, Prosperini E, Notarbartolo S, Testa G, Natoli G. The histone H3 lysine-27 demethylase Jmjd3 links inflammation to inhibition of polycomb-mediated gene silencing. Cell. 2007;130(6):1083–94. Epub 2007/09/11.View ArticlePubMedGoogle Scholar
- Anantharaman A, Lin IJ, Barrow J, Liang SY, Masannat J, Strouboulis J, et al. Role of helix-loop-helix proteins during differentiation of erythroid cells. Mol Cell Biol. 2011;31(7):1332–43. Epub 2011/02/02.View ArticlePubMed CentralPubMedGoogle Scholar
- Massari ME, Murre C. Helix-loop-helix proteins: regulators of transcription in eucaryotic organisms. Mol Cell Biol. 2000;20(2):429–40. Epub 1999/12/28.View ArticlePubMed CentralPubMedGoogle Scholar
- Miyazaki K, Miyazaki M, Murre C. The establishment of B versus T cell identity. Trends Immunol. 2014;35(5):205–10. Epub 2014/04/01.View ArticlePubMed CentralPubMedGoogle Scholar
- McDonald JJ, Alinikula J, Buerstedde JM, Schatz DG. A critical context-dependent role for E boxes in the targeting of somatic hypermutation. J Immunol. 2013;191(4):1556–66. Epub 2013/07/10.View ArticlePubMed CentralPubMedGoogle Scholar
- Ott CJ, Kopp N, Bird L, Paranal RM, Qi J, Bowman T, et al. BET bromodomain inhibition targets both c-Myc and IL7R in high-risk acute lymphoblastic leukemia. Blood. 2012;120(14):2843–52. Epub 2012/08/21.View ArticlePubMed CentralPubMedGoogle Scholar
- Ott G, Rosenwald A, Campo E. Understanding MYC-driven aggressive B-cell lymphomas: pathogenesis and classification. Hematology Am Soc Hematol Educ Program. 2013;2013:575–83. Epub 2013/12/10.View ArticlePubMedGoogle Scholar
- Tomita N. BCL2 and MYC dual-hit lymphoma/leukemia. J Clin Exp Hematop. 2011;51(1):7–12. Epub 2011/06/02.View ArticlePubMedGoogle Scholar
- Brennecke J, Stark A, Russell RB, Cohen SM. Principles of microRNA-target recognition. PLoS Biol. 2005;3(3), e85. Epub 2005/02/22.View ArticlePubMed CentralPubMedGoogle Scholar
- Loeb GB, Khan AA, Canner D, Hiatt JB, Shendure J, Darnell RB, et al. Transcriptome-wide miR-155 binding map reveals widespread noncanonical microRNA targeting. Mol Cell. 2012;48(5):760–70. Epub 2012/11/13.View ArticlePubMed CentralPubMedGoogle Scholar
- Zhao X, Lwin T, Zhang X, Huang A, Wang J, Marquez VE, et al. Disruption of the MYC-miRNA-EZH2 loop to suppress aggressive B-cell lymphoma survival and clonogenicity. Leukemia. 2013;27(12):2341–50. Epub 2013/03/30.View ArticlePubMed CentralPubMedGoogle Scholar
- Sandhu SK, Volinia S, Costinean S, Galasso M, Neinast R, Santhanam R, et al. miR-155 targets histone deacetylase 4 (HDAC4) and impairs transcriptional activity of B-cell lymphoma 6 (BCL6) in the Emu-miR-155 transgenic mouse model. Proc Natl Acad Sci U S A. 2012;109(49):20047–52.View ArticlePubMed CentralPubMedGoogle Scholar
- Taft RJ, Simons C, Nahkuri S, Oey H, Korbie DJ, Mercer TR, et al. Nuclear-localized tiny RNAs are associated with transcription initiation and splice sites in metazoans. Nat Struct Mol Biol. 2010;17(8):1030–4. Epub 2010/07/14.View ArticlePubMedGoogle Scholar
- Janeway Jr CA, Medzhitov R. Innate immune recognition. Annu Rev Immunol. 2002;20:197–216. Epub 2002/02/28.View ArticlePubMedGoogle Scholar
- Damdinsuren B, Zhang Y, Khalil A, Wood 3rd WH, Becker KG, Shlomchik MJ, et al. Single round of antigen receptor signaling programs naive B cells to receive T cell help. Immunity. 2010;32(3):355–66. Epub 2010/03/17.View ArticlePubMed CentralPubMedGoogle Scholar
- Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25(9):1105–11. Epub 2009/03/18.View ArticlePubMed CentralPubMedGoogle Scholar
- Langmead B, Trapnell C, Pop M, Salzberg SL. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 2009;10(3):R25. Epub 2009/03/06.View ArticlePubMed CentralPubMedGoogle Scholar
- Kaimal V, Bardes EE, Tabar SC, Jegga AG, Aronow BJ. ToppCluster: a multiple gene list feature analyzer for comparative enrichment clustering and network-based dissection of biological systems. Nucleic Acids Res. 2010;38(Web Server issue):W96–102. Epub 2010/05/21.View ArticlePubMed CentralPubMedGoogle Scholar
- Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, et al. Integration of biological networks and gene expression data using Cytoscape. Nat Protoc. 2007;2(10):2366–82. Epub 2007/10/20.View ArticlePubMed CentralPubMedGoogle Scholar
- Friedlander MR, Mackowiak SD, Li N, Chen W, Rajewsky N. miRDeep2 accurately identifies known and hundreds of novel microRNA genes in seven animal clades. Nucleic Acids Res. 2012;40(1):37–52. Epub 2011/09/14.View ArticlePubMed CentralPubMedGoogle Scholar
- Raney BJ, Dreszer TR, Barber GP, Clawson H, Fujita PA, Wang T, et al. Track data hubs enable visualization of user-defined genome-wide annotations on the UCSC Genome Browser. Bioinformatics. 2014;30(7):1003–5. Epub 2013/11/15.View ArticlePubMed CentralPubMedGoogle Scholar
- Flicek P, Ahmed I, Amode MR, Barrell D, Beal K, Brent S, et al. Ensembl 2013. Nucleic Acids Res. 2013;41(Database issue):D48–55. Epub 2012/12/04.View ArticlePubMed CentralPubMedGoogle Scholar
- Quinlan AR, Hall IM. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics. 2010;26(6):841–2. Epub 2010/01/30.View ArticlePubMed CentralPubMedGoogle Scholar
- Zhang Y, Liu T, Meyer CA, Eeckhoute J, Johnson DS, Bernstein BE, et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 2008;9(9):R137. Epub 2008/09/19.View ArticlePubMed CentralPubMedGoogle Scholar
- Gardiner-Garden M, Frommer M. CpG islands in vertebrate genomes. J Mol Biol. 1987;196(2):261–82. Epub 1987/07/20.View ArticlePubMedGoogle Scholar
- Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell. 2010;38(4):576–89. Epub 2010/06/02.View ArticlePubMed CentralPubMedGoogle Scholar
- Weirauch MT, Yang A, Albu M, Cote AG, Montenegro-Montero A, Drewe P, et al. Determination and inference of eukaryotic transcription factor sequence specificity. Cell. 2014;158(6):1431–43. Epub 2014/09/13.View ArticlePubMedGoogle Scholar
- Fowler T, Suh H, Buratowski S, Roy AL. Regulation of primary response genes in B cells. J Biol Chem. 2013;288(21):14906–16. Epub 2013/03/29.View ArticlePubMed CentralPubMedGoogle Scholar
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