- Research article
- Open Access
JunD/AP1 regulatory network analysis during macrophage activation in a rat model of crescentic glomerulonephritis
© Srivastava et al.; licensee BioMed Central Ltd. 2013
- Received: 11 October 2012
- Accepted: 12 September 2013
- Published: 22 September 2013
Function and efficiency of a transcription factor (TF) are often modulated by interactions with other proteins or TFs to achieve finely tuned regulation of target genes. However, complex TF interactions are often not taken into account to identify functionally active TF-targets and characterize their regulatory network. Here, we have developed a computational framework for integrated analysis of genome-wide ChIP-seq and gene expression data to identify the functional interacting partners of a TF and characterize the TF-driven regulatory network. We have applied this methodology in a rat model of macrophage dependent crescentic glomerulonephritis (Crgn) where we have previously identified JunD as a TF gene responsible for enhanced macrophage activation associated with susceptibility to Crgn in the Wistar-Kyoto (WKY) strain.
To evaluate the regulatory effects of JunD on its target genes, we analysed data from two rat strains (WKY and WKY.LCrgn2) that show 20-fold difference in their JunD expression in macrophages. We identified 36 TFs interacting with JunD/Jun and JunD/ATF complexes (i.e., AP1 complex), which resulted in strain-dependent gene expression regulation of 1,274 target genes in macrophages. After lipopolysaccharide (LPS) stimulation we found that 2.4 fold more JunD/ATF-target genes were up-regulated as compared with JunD/Jun-target genes. The enriched 314 genes up-regulated by AP1 complex during LPS stimulation were most significantly enriched for immune response (P = 6.9 × 10-4) and antigen processing and presentation functions (P = 2.4 × 10-5), suggesting a role for these genes in macrophage LPS-stimulated activation driven by JunD interaction with Jun/ATF.
In summary, our integrated analyses revealed a large network of TFs interacting with JunD and their regulated targets. Our data also suggest a previously unappreciated contribution of the ATF complex to JunD-mediated mechanisms of macrophage activation in a rat model of crescentic glomerulonephritis.
- Transcription Factor Binding Site
- Macrophage Activation
- Differentially Express
- Differentially Express Gene
- Crescentic Glomerulonephritis
Recent development of high throughput profiling methods such as microarray, RNA-seq and chromatin immuno-precipitation followed by microarray (ChIP-chip) or sequencing (ChIP-Seq) have revolutionized the study of protein–DNA interactions and have made information regarding gene expression and transcription factor binding sites (TFBS) readily available. Positional binding of transcription factors (TF) can vary from the basal promoter region to a few kilobase pairs from the transcription start site (TSS). When a TF is bound to the promoter region, experimental evidence suggests an increased probability of the gene being its target, but the functional nature of the TFBS would be difficult to characterise based upon the TFBS profile alone. Therefore, the task of associating TFBS with a gene is not trivial. Traditionally, identified TFBS have been associated with the nearest gene . However, the effect of functional TFBS can be observed in a target’s gene expression, and therefore integration of ChIP-Seq profiles with gene expression is crucial for identification of active binding sites. There have been several previous attempts at dataset integration, some of which have tried to apply regression based models , while some have validated the ChIP-Seq identified targets by transcriptionally silencing the TF and observing the effect on gene expression [3, 4]. Whilst most of the methodology developed for integrating the two datasets considers genomic attributes from the ChIP-seq dataset, none of the methods are based on the TF’s interacting partners or interactome.
The function and efficiency of a TF is often modulated by its interaction with other proteins to achieve finely tuned regulation of gene expression. TF complexes can be inferred by assessing significant enrichment of motif spacing within ChIP-Seq peaks . Such TF complexes are often referred to as cis- regulatory modules. Several studies have tried to develop computational frameworks for the identification of relevant TF complexes for co-expressed genes [6, 7], and it has been previously established that if genes are under a common regulatory mechanism they tend to follow similar expression patterns. In this study, we have developed a computational framework which enables integration of the TF-complex obtained from a ChIP-Seq genome-wide TF binding profile with gene expression profiles.
We have devised and tested the new framework using a rat model for crescentic glomerulonephritis (Crgn). Crgn is an important cause of kidney failure, for which the underlying molecular basis is largely unknown. The Wistar-Kyoto (WKY) rat is uniquely susceptible to experimentally induced Crgn . We previously investigated the genetic basis to Crgn susceptibility in crosses between the Crgn-sensitive WKY and the Crgn-resistant Lewis (LEW) rat strains. Crgn was linked to 7 quantitative trait loci (QTLs) including the chromosome 16 QTL Crgn2, in which the AP-1 transcription factor JunD was identified as a primary determinant of macrophage activation and associated with Crgn susceptibility . WKY bone marrow-derived macrophages (BMDMs) demonstrated marked overexpression of JunD and increased Fc receptor-mediated macrophage activation compared with BMDMs from LEW and from congenic rats (WKY.LCrgn2) in which the LEW Crgn2 QTL was introgressed onto the WKY background. Therefore, characterisation of JunD’s physical and genetic interactions may provide key insights into complex biological systems. In our previous study, we have shown that JunD is a regulator of oxidative stress and IL1 beta synthesis in macrophages .
In this study, by characterising the JunD interactome and modelling gene expression data, we have identified a group of genes that show differential co-expression and enrichment during LPS stimulation between WKY and the WKY.LCrgn2 congenic strain. Our data also suggests that interaction of JunD with specific TFs could be important for the over-activation phenomenon of macrophages in the WKY strain. This work suggests that the JunD complex has modulatory effects on macrophage gene expression which provides the basis for understanding JunD- mediated macrophage activation, and enabling identification of novel targets for modulating macrophage function.
ChIP-Seq and gene expression data
Bone-marrow derived macrophages (BMDM) were isolated from WKY and WKY.LCrgn2. BMDMs were subjected to LPS stimulation (additional details on the efficiency of the LPS treatment can be found in Hull et al. ). Gene expression profiles were generated using Rat Gene 1.0 ST arrays (Affymetrix, Santa Clara, CA, USA) at 0, 2, 4 and 8 hours after LPS stimulation using 4 biological replicates for each time point. Sample pre-processing and hybridisation was performed as per manufacturer’s recommendations. The Affymetrix .CEL files were imported to R statistical software version 2.11, using R Affy package version 1.34. Probe annotation was done using custom chip definition files , and probesets with single nucleotide polymorphisms between Brown Norway (reference genome) rat strains and WKY rat strains were removed. Background correction was performed using robust multichip average (RMA)  and data was normalised using the quantile normalisation method. Differential expression analysis was performed using the Bioconductor package SAMR, which implements SAM (Significance analysis of microarray) statistics in R , and a cut-off of 5% false discovery rate (FDR) was applied.
Genome-wide JunD TF binding profiles were generated for WKY and WKY.LCrgn2 at two initial time-points. ChIP was performed with a JunD antibody (Santa Cruz sc74-X) and a negative IgG control (sc-2026). ChIP-Seq peaks were predicted using BayesPeak version 1.13 , and predicted peaks with a posterior probability greater than 0.9 were considered significant (for experimental details please refer to Hull et al. ).
De novo and known TFBSs were identified using the HOMER software package version 2 . De novo motif analysis was performed using default parameters expecting a 12 bp motif. TFBS were identified using the area underneath the ChIP-Seq peaks using the publically available software Transfac transcription factor matrices version 6 .
Inferences of TF targets using gene expression data
TF complexes were inferred using the methodology described in . The primary focus of Banerjee et al. was TFBS in promoter regions. In this study we have extended this methodology to the area under predicted ChIP-Seq peaks. A TF pair was considered to be co-operativly interacting if the expression correlation scores of genes showing binding of both TFs were significantly greater than any set of genes with binding of either TF alone. We used the proposed model based on the multivariate hypergeometric distribution.
TF targets using ChIP-Seq data
Spaced motif analysis was carried out using the SpaMo software . In brief, the SpaMo algorithm predicts transcription factor interactions by assessing significant enrichment of motif spacings within the ChIP-Seq peaks. Primary and secondary motifs for the motif spacing analysis were retrieved from the public Transfac database . Here we have used a window of 50 bp on either side of the primary motif to look for significant enrichment of motif spacings with a secondary motif. All reported P-values were adjusted for the number of intervals and motifs tested using the Bonferroni correction.
Integration of gene expression with ChIP-Seq profiles
AP-1 complex TF gene expression profiles were modelled based on the Gaussian process differential equations . This methodology was specifically developed to model short time-series datasets and is implemented in tigre, R package . This was used for ranking the expressed set of transcripts on the microarray based on the similarity of the gene expression profiles represented in the form of likelihood scores. The ranked sets of transcripts were subjected to gene set enrichment analysis using likelihood scores based on a pre-ranked list and SpaMo identified TF-pair target genes as datasets [18, 19]. A dataset was considered significant if it had a family wise error rate (FWER) < 0.05.
Identification of JunD-enriched sites by ChIP-Seq and characterisation of targets
Inferring TF interactions with JunD using gene expression data
Predicting known JunD interacting proteins using gene expression data
WKY LPS ChIP-seq peaks interaction P-value
WKY.LCrgn2 ChIP-seq peaks interaction P-value
No binding sites observed underneath the peaks
Inferring genome-wide TF-interactions with JunD using ChIP-Seq data
JunD’s co-operative interacting partners for the WKY LPS datasets were predicted on the basis of co-occurrence of binding site motifs within ChIP-Seq peaks using SpaMo . This was investigated in WKY where we observed a higher number of peaks in response to LPS stimulation as compared with WKY.LCrgn2 (Figure 1B), which had also 20 fold less JunD expression. Where TRE and CRE AP-1 motifs were used as primary motifs, we identified 168 (TRE) and 91 (CRE) secondary transfac motifs respectively, representing 107 and 58 TFs respectively (Additional file 1: Table S1 and Additional file 2: Table S2), including ~50% known interacting TFs and suggesting widespread TF-interactions with JunD.
Combining gene expression profiling with TF-targets identified by ChIP-Seq analysis
Integrative analysis of ChIP-Seq peaks containing TRE motif targets of the Jun family members and their gene expression resulted in the identification of 16 unique gene sets, corresponding to 13 TFs. These were significantly enriched in WKY, but none of them were enriched in WKY.LCrgn2 at 5% FWER (Figure 4). Twelve out of the sixteen gene-sets (75%) were specifically associated with JunD and corresponded to ten interacting TFs.
The CRE motif is the preferred motif for ATF homo and hetero dimers (Figure 4b and Additional file 3: Table S3). The integrative analysis of the ATF family of TFs identified only two gene-sets to be significantly enriched in WKY.LCrgn2 while 32 gene-sets (corresponding to 24 TFs) showed significant enrichment in WKY (FWER < 5%). Potential interaction of the identified TFs with JunD or ATF3 were validated using manual and recently published Protein Interaction information Extraction (PIE)  search and have identified that 74% (17 TFs) of the putative JunD-ATF3 interacting partners were previously known to interact with either JunD or ATF3 (Additional file 4: Table S4). Altogether, these results suggest that the interaction of TFs with JunD and the regulation of target genes upon LPS stimulation are affected by JunD genotype as demonstrated by the reduced JunD expression and impaired TF interactions in WKY.LCrgn2.
Transcriptional response to LPS stimulation
Over-representation analysis for the genes significantly associated after integration with genes that are differentially regulated during LPS stimulation
Control v 2 hrs
Control v 4 hrs
Control v 8 hrs
Control v 2 hrs
Control v 4 hrs
Control v 8 hrs
Transfac matrix ID
Identification of functionally active TF targets in specific cell type can provide insights into crucial biological processes, which might underlie the patho-physiology of disease. A genome-wide TF binding profile can be obtained by ChIP-Seq analysis and, based on the definition of the gene promoter length, ChIP-Seq peaks can be associated with the nearest gene’s TSS . However, association of the TFBS to the nearest gene TSS is not sufficient to establish the downstream gene as a TF target since the TFBS might not be active in a given cell-type or cell-activation status . Hence, the characterization of active TFBS and functionally active TF-target genes in a specific cellular context remains to be elucidated. In this study we aimed to identify functional TFBS by integrating TF targets identified by ChIP-Seq analysis with TF target activity that was predicted using gene expression data. We developed a computational framework for the integrative analysis and implemented it in a rat model for Crgn, where we focus on the JunD TF which has been previously shown to be a primary determinant of macrophage activation . The congenic model (WKY.LCrgn2) has been comprehensively tested in previous studies where they have shown that the JunD expression levels are significantly higher in WKY when compared with the congenic  and by performing TransAM assay it has been shown that the canonical binding of AP-1 is significantly greater in WKY compared to WKY.LCrgn2. The aim of this study is to characterise the direct targets of JunD complexes in WKY and its differential function in WKY.LCrgn2 during macrophage activation.
After LPS stimulation, due to changes in the JunD expression levels between WKY and WKY.LCrgn2 the number of JunD ChIP-Seq peaks as well as interactions of AP-1 with other TF was impaired. These observations suggest that JunD plays an active role during macrophage activation in WKY while its role is diminished in the congenic strain. The AP-1 family has been implicated in macrophage activation [9, 25], and here we were interested in characterising the role of JunD driven AP-1 complexes during macrophage activation. AP-1 complexes can be broadly be classified into Jun and ATF families. JunD is capable of forming homo as well as hetero dimers: when forming hetero dimers with the Jun/Fos family it prefers the TRE motif, while with the ATF family it prefers CRE motifs. However, by sequence analysis of TF binding motifs, it is not possible to sub-classify the members of AP-1 families, since they recognise the same TF binding motifs. So, each member of the Jun/Fos and ATF families was considered separately for investigation of target gene expression using microarrays. We have modelled the gene expression profiles of the TFs and their targets to estimate the likelihood of a transcript to be a target of JunD/AP-1 complexes. Moreover, using spaced motif analysis, JunD interacting partners were predicted, ~50% of which were previously experimentally validated (Figure 3).
Considering the differences in the physiological levels of JunD between WKY and WKY.LCrgn2, it can be anticipated that the ChIP-Seq derived TF target data should be more concordant with the gene expression based TF’s targets in WKY compared to WKY.LCrgn2. The HNF4/DR1 was the only gene-set that showed an enrichment with the 4 hr up-regulated significantly differentially expressed genes during WKY and not in WKY.LCrgn2. Interestingly, the entire Jun family member showed enrichment with HNF4/DR1, suggesting that HNF4 might be important for macrophage activation. HNF4 has been previously linked to chemokine induced inflammatory response  and lipid metabolism.
The ATF family also showed an exclusive enrichment for 29 out of 31 gene-sets in WKY, while only 2 gene-sets were observed to be significantly enriched in WKY.LCrgn2. This is not unexpected, considering that the congenic has lower expression of JunD. Although the gene-set enrichment was not exclusive to WKY, Fisher’s exact test for 27 gene-sets showed exclusive enrichment with WKY up-regulated genes during macrophage activation at the 4 hour LPS time point. This analysis led to the identification of 866 genes that showed an enrichment of GO terms associated with antigen processing and presentation, signalling cascades and nucleic acid metabolic processes. ATF’s interaction with JunD in the context of macrophage activation has not been studied in depth. A previous study has shown that their interaction regulates the chemokine RANTES (regulated and normal T cell expressed and secreted), which is critical for macrophage activation . This suggests that ATF3 and/or B-ATF could be important for WKY macrophage activation.
ATF3 primarily acts as a transcriptional repressor but when it forms a hetero dimer with JunD it acts as an activator of transcription . ATF3 has been previously hypothesised to be a global regulator of macrophage activation . It has been shown before that ATF3 is up-regulated early after LPS induced toll-like receptor (TLR) engagement which together with NF-κB constitutes a negative feedback mechanism to down regulate TLR . Although Gilchrist et al. observed over-representation of ATF3, AP-1 and NF-κB target genes, they primarily focused on the co-operative interaction of ATF3 with NF-κB. Hence, the role of ATF3 in the context of JunD was not studied in detail. With our integrated analysis however, we have identified direct targets of the ATF3-JunD complex that might be important for macrophage activation.
B-ATF is another member of the ATF family that is induced in T cells and natural killer cells upon stimulation and is a proposed to be a therapeutic target for immunotherapies. It has also been shown to be critical for the TH17 cell inflammatory immune response , contrary to the belief that B-ATF is a suppressor of AP-1 gene expression .
The ATF-JunD hetero-dimer was observed to be associated with 22 TFs and their targets were significantly enriched in the 4 hr LPS time-point comparison with the basal state while targets for 7 TFs were observed to be enriched in the 8 hrs time-point comparison. This suggests that hetero-dimer interaction of JunD with ATF family members might be more important during macrophage activation than the hetero-dimers formed with the Jun family.
In this study we have developed a methodology not only to predict direct targets for TFs but also to infer functional co-operative interactions. In this study we have only considered the AP-1 complex, but this can be applied to any TF complex. By combining gene expression data with ChIP-Seq profiles we have identified 1,274 genes which are directly associated with JunD and have shown their involvement during macrophage activation. Considering that JunD is a gene with major differential expression between WKY and WKY.LCrgn2, these 1,274 genes are likely to be direct targets of JunD. Our data suggests interplay of JunD with the ATF family and HNF4 during macrophage activation, which was previously unappreciated. Further study of the interplay between these two TFs will provide the basis for understanding JunD- mediated macrophage activation, enabling identification of novel targets for modulating macrophage function.
We thank CSC/IC Genome Core facility for their excellent technical assistance. We also thank Dr. Maxime O Rotival for helpful discussions. We acknowledge funding from European Community’s Seventh Framework Programme (FP7/2007-2013) under grant agreement no. HEALTH-F4-2010-241504 (EURATRANS) (E.P., T.J.A.); the experimental work was primarily supported by a Wellcome Trust Clinical PhD Fellowship (087182/Z/08/Z to RPH), a Junior Fellowship from Imperial College (to JB), by intramural funding from the MRC Clinical Sciences Centre (to TJA) and by the Wellcome Trust project grant (WT092523MA to JB).
- Ji H, Jiang H, Ma W, Johnson DS, Myers RM, Wong WH: An integrated software system for analyzing ChIP-chip and ChIP-seq data. Nat Biotechnol. 2008, 26 (11): 1293-1300. 10.1038/nbt.1505.PubMedPubMed CentralView ArticleGoogle Scholar
- Ouyang Z, Zhou Q, Wong WH: ChIP-Seq of transcription factors predicts absolute and differential gene expression in embryonic stem cells. Proc Natl Acad Sci USA. 2009, 106 (51): 21521-21526. 10.1073/pnas.0904863106.PubMedPubMed CentralView ArticleGoogle Scholar
- Warnatz HJ, Schmidt D, Manke T, Piccini I, Sultan M, Borodina T, Balzereit D, Wruck W, Soldatov A, Vingron M: The BTB and CNC homology 1 (BACH1) target genes are involved in the oxidative stress response and in control of the cell cycle. J Biol Chem. 2011, 286 (26): 23521-23532. 10.1074/jbc.M111.220178.PubMedPubMed CentralView ArticleGoogle Scholar
- Hull RP, Srivastava PK, D’Souza Z, Atanur SS, Mechta-Grigoriou F, Game L, Petretto E, Cook HT, Aitman TJ, Behmoaras J: Combined ChIP-Seq and transcriptome analysis identifies AP-1/JunD as a primary regulator of oxidative stress and IL-1beta synthesis in macrophages. BMC Genomics. 2013, 14: 92-10.1186/1471-2164-14-92.PubMedPubMed CentralView ArticleGoogle Scholar
- Whitington T, Frith MC, Johnson J, Bailey TL: Inferring transcription factor complexes from ChIP-seq data. Nucleic Acids Res. 2011, 39 (15): e98-10.1093/nar/gkr341.PubMedPubMed CentralView ArticleGoogle Scholar
- Hu J, Hu H, Li X: MOPAT: a graph-based method to predict recurrent cis-regulatory modules from known motifs. Nucleic Acids Res. 2008, 36 (13): 4488-4497. 10.1093/nar/gkn407.PubMedPubMed CentralView ArticleGoogle Scholar
- Sinha S, Liang Y, Siggia E: Stubb: a program for discovery and analysis of cis-regulatory modules. Nucleic Acids Res. 2006, 34 (Web Server issue): W555-W559.PubMedPubMed CentralView ArticleGoogle Scholar
- Aitman TJ, Dong R, Vyse TJ, Norsworthy PJ, Johnson MD, Smith J, Mangion J, Roberton-Lowe C, Marshall AJ, Petretto E: Copy number polymorphism in Fcgr3 predisposes to glomerulonephritis in rats and humans. Nature. 2006, 439 (7078): 851-855. 10.1038/nature04489.PubMedView ArticleGoogle Scholar
- Behmoaras J, Bhangal G, Smith J, McDonald K, Mutch B, Lai PC, Domin J, Game L, Salama A, Foxwell BM: Jund is a determinant of macrophage activation and is associated with glomerulonephritis susceptibility. Nat Genet. 2008, 40 (5): 553-559. 10.1038/ng.137.PubMedPubMed CentralView ArticleGoogle Scholar
- Dai M, Wang P, Boyd AD, Kostov G, Athey B, Jones EG, Bunney WE, Myers RM, Speed TP, Akil H: Evolving gene/transcript definitions significantly alter the interpretation of GeneChip data. Nucleic Acids Res. 2005, 33 (20): e175-10.1093/nar/gni179.PubMedPubMed CentralView ArticleGoogle Scholar
- Bolstad BM, Irizarry RA, Astrand M, Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on variance and bias. Bioinformatics. 2003, 19 (2): 185-193. 10.1093/bioinformatics/19.2.185.PubMedView ArticleGoogle Scholar
- Tusher VG, Tibshirani R, Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA. 2001, 98 (9): 5116-5121. 10.1073/pnas.091062498.PubMedPubMed CentralView ArticleGoogle Scholar
- Spyrou C, Stark R, Lynch AG, Tavare S: BayesPeak: Bayesian analysis of ChIP-seq data. BMC Bioinforma. 2009, 10: 299-10.1186/1471-2105-10-299.View ArticleGoogle Scholar
- Heinz S, Benner C, Spann N, Bertolino E, Lin YC, Laslo P, Cheng JX, Murre C, Singh H, Glass CK: Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol Cell. 2010, 38 (4): 576-589. 10.1016/j.molcel.2010.05.004.PubMedPubMed CentralView ArticleGoogle Scholar
- Matys V, Fricke E, Geffers R, Gossling E, Haubrock M, Hehl R, Hornischer K, Karas D, Kel AE, Kel-Margoulis OV: TRANSFAC: transcriptional regulation, from patterns to profiles. Nucleic Acids Res. 2003, 31 (1): 374-378. 10.1093/nar/gkg108.PubMedPubMed CentralView ArticleGoogle Scholar
- Banerjee N, Zhang MQ: Identifying cooperativity among transcription factors controlling the cell cycle in yeast. Nucleic Acids Res. 2003, 31 (23): 7024-7031. 10.1093/nar/gkg894.PubMedPubMed CentralView ArticleGoogle Scholar
- Honkela A, Girardot C, Gustafson EH, Liu YH, Furlong EE, Lawrence ND, Rattray M: Model-based method for transcription factor target identification with limited data. Proc Natl Acad Sci USA. 2010, 107 (17): 7793-7798. 10.1073/pnas.0914285107.PubMedPubMed CentralView ArticleGoogle Scholar
- Mootha VK, Lindgren CM, Eriksson KF, Subramanian A, Sihag S, Lehar J, Puigserver P, Carlsson E, Ridderstrale M, Laurila E: PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet. 2003, 34 (3): 267-273. 10.1038/ng1180.PubMedView ArticleGoogle Scholar
- Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomeroy SL, Golub TR, Lander ES: Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA. 2005, 102 (43): 15545-15550. 10.1073/pnas.0506580102.PubMedPubMed CentralView ArticleGoogle Scholar
- Karin M, Liu Z, Zandi E: AP-1 function and regulation. Curr Opin Cell Biol. 1997, 9 (2): 240-246. 10.1016/S0955-0674(97)80068-3.PubMedView ArticleGoogle Scholar
- Stark C, Breitkreutz BJ, Reguly T, Boucher L, Breitkreutz A, Tyers M: BioGRID: a general repository for interaction datasets. Nucleic Acids Res. 2006, 34 (Database issue): D535-D539.PubMedPubMed CentralView ArticleGoogle Scholar
- Kim S, Kwon D, Shin SY, Wilbur WJ: PIE the search: searching PubMed literature for protein interaction information. Bioinformatics. 2012, 28 (4): 597-598. 10.1093/bioinformatics/btr702.PubMedPubMed CentralView ArticleGoogle Scholar
- Ramirez-Carrozzi VR, Braas D, Bhatt DM, Cheng CS, Hong C, Doty KR, Black JC, Hoffmann A, Carey M, Smale ST: A unifying model for the selective regulation of inducible transcription by CpG islands and nucleosome remodeling. Cell. 2009, 138 (1): 114-128. 10.1016/j.cell.2009.04.020.PubMedPubMed CentralView ArticleGoogle Scholar
- Hai T, Hartman MG: The molecular biology and nomenclature of the activating transcription factor/cAMP responsive element binding family of transcription factors: activating transcription factor proteins and homeostasis. Gene. 2001, 273 (1): 1-11. 10.1016/S0378-1119(01)00551-0.PubMedView ArticleGoogle Scholar
- Gilchrist M, Thorsson V, Li B, Rust AG, Korb M, Roach JC, Kennedy K, Hai T, Bolouri H, Aderem A: Systems biology approaches identify ATF3 as a negative regulator of Toll-like receptor 4. Nature. 2006, 441 (7090): 173-178. 10.1038/nature04768.PubMedView ArticleGoogle Scholar
- Wang Z, Bishop EP, Burke PA: Expression profile analysis of the inflammatory response regulated by hepatocyte nuclear factor 4alpha. BMC Genomics. 2011, 12: 128-10.1186/1471-2164-12-128.PubMedPubMed CentralView ArticleGoogle Scholar
- Boehlk S, Fessele S, Mojaat A, Miyamoto NG, Werner T, Nelson EL, Schlondorff D, Nelson PJ: ATF and Jun transcription factors, acting through an Ets/CRE promoter module, mediate lipopolysaccharide inducibility of the chemokine RANTES in monocytic Mono Mac 6 cells. Eur J Immunol. 2000, 30 (4): 1102-1112. 10.1002/(SICI)1521-4141(200004)30:4<1102::AID-IMMU1102>3.0.CO;2-X.PubMedView ArticleGoogle Scholar
- Roach JC, Smith KD, Strobe KL, Nissen SM, Haudenschild CD, Zhou D, Vasicek TJ, Held GA, Stolovitzky GA, Hood LE: Transcription factor expression in lipopolysaccharide-activated peripheral-blood-derived mononuclear cells. Proc Natl Acad Sci USA. 2007, 104 (41): 16245-16250. 10.1073/pnas.0707757104.PubMedPubMed CentralView ArticleGoogle Scholar
- Schraml BU, Hildner K, Ise W, Lee WL, Smith WA, Solomon B, Sahota G, Sim J, Mukasa R, Cemerski S: The AP-1 transcription factor Batf controls T(H)17 differentiation. Nature. 2009, 460 (7253): 405-409.PubMedPubMed CentralGoogle Scholar
- Williams KL, Nanda I, Lyons GE, Kuo CT, Schmid M, Leiden JM, Kaplan MH, Taparowsky EJ: Characterization of murine BATF: a negative regulator of activator protein-1 activity in the thymus. Eur J Immunol. 2001, 31 (5): 1620-1627. 10.1002/1521-4141(200105)31:5<1620::AID-IMMU1620>3.0.CO;2-3.PubMedView ArticleGoogle Scholar
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