- Research article
- Open Access
Combined ChIP-Seq and transcriptome analysis identifies AP-1/JunD as a primary regulator of oxidative stress and IL-1β synthesis in macrophages
© Hull et al.; licensee BioMed Central Ltd. 2013
- Received: 13 October 2012
- Accepted: 1 February 2013
- Published: 11 February 2013
The oxidative burst is one of the major antimicrobial mechanisms adopted by macrophages. The WKY rat strain is uniquely susceptible to experimentally induced macrophage-dependent crescentic glomerulonephritis (Crgn). We previously identified the AP-1 transcription factor JunD as a determinant of macrophage activation in WKY bone marrow-derived macrophages (BMDMs). JunD is over-expressed in WKY BMDMs and its silencing reduces Fc receptor-mediated oxidative burst in these cells.
Here we combined Jund RNA interference with microarray analyses alongside ChIP-sequencing (ChIP-Seq) analyses in WKY BMDMs to investigate JunD-mediated control of macrophage activation in basal and lipopolysaccharide (LPS) stimulated cells. Microarray analysis following Jund silencing showed that Jund activates and represses gene expression with marked differential expression (>3 fold) for genes linked with oxidative stress and IL-1β expression. These results were complemented by comparing whole genome expression in WKY BMDMs with Jund congenic strain (WKY.LCrgn2) BMDMs which express lower levels of JunD. ChIP-Seq analyses demonstrated that the increased expression of JunD resulted in an increased number of binding events in WKY BMDMs compared to WKY.LCrgn2 BMDMs. Combined ChIP-Seq and microarray analysis revealed a set of primary JunD-targets through which JunD exerts its effect on oxidative stress and IL-1β synthesis in basal and LPS-stimulated macrophages.
These findings demonstrate how genetically determined levels of a transcription factor affect its binding sites in primary cells and identify JunD as a key regulator of oxidative stress and IL-1β synthesis in primary macrophages, which may play a role in susceptibility to Crgn.
- Crescentic Glomerulonephritis
- Primary Macrophage
- Sequence Detection Software
- Nephritic Glomerulus
- Congenic Interval
Macrophages are efficient phagocytes of the immune system that produce reactive oxygen species (ROS) during the phagocytosis of pathogens, considered as a marker of cell activation. The well-established classical pathway of macrophage activation induced by interferon (IFN)-γ and/or lipopolysaccharide (LPS) is known to play a vital role in host defence during inflammation. Macrophages activated in this manner express high levels of proinflammatory cytokines and reactive oxygen and nitrogen intermediates that are crucial in the defence against intracellular pathogens [1, 2]. The AP-1 transcription factor plays a key role in regulating cell growth and environmental stress responses [3–5]. In classically activated (M1) macrophages, AP-1 plays a central role together with NF-κB in signal-dependant gene expression that is crucial for innate immunity . JunD is a member of AP-1 that is constitutively expressed and has been previously shown to protect cells from oxidative stress and to reduce tumour angiogenesis by limiting the production of ROS . The chronic oxidative stress generated by the inactivation of JunD, has been shown to promote aging and increase tumour development [8, 9]. In various tissues, including the kidney, the absence of JunD led to the over-expression of hypoxia inducible factor (HIF)-target genes in podocytes, most likely as a result of increased oxidative stress .
Wistar Kyoto (WKY) rats are uniquely susceptible to nephrotoxic nephritis (NTN), a rat model of crescentic glomerulonephritis (Crgn) . The macrophages of this strain show a 20-fold increase in Jund mRNA expression as well as increased specific JunD protein binding to AP-1 consensus sequence nucleotides (5′-TGAGTCA-3′) when compared with the NTN-resistant LEW strain . In addition WKY BMDMs show greater superoxide anion production when stimulated with PMA (unpublished observations) and significantly increased NOS2 expression  when stimulated with LPS, suggesting that the macrophages of this strain have a genetically determined pro-inflammatory phenotype characterised by increased oxidative stress. We have previously shown that JunD is a determinant of the macrophage oxidative burst associated with crescentic glomerulonephritis. In a genome-wide linkage analysis and haplotype analysis for NTN-related phenotypes in WKY and LEW rats, we delineated a minimal genomic region of 130 kb on rat chromosome 16 where Jund was the only markedly over-expressed transcript. The functional role of JunD was established by siRNA knock-down of Jund in WKY BMDMs  which resulted in reduced Fc receptor mediated oxidative burst confirming the previously reported antioxidant role of JunD in other tissues [7, 9]. Furthermore, the role of JunD in TLR4-induced primary human macrophage activation was established. siRNA knockdown of JUND in these cells resulted in a significantly reduced secretion of TNFα, IL-6 and IL-10 . One possible mechanism for this was suggested by Smolinska and colleagues who showed that Hck kinase mediates TLR4-induced transcription of both TNF and IL-6 through binding of AP-1 heterodimers composed of c-Fos and JunD . Based on these results, we hypothesised that JunD controls respiratory burst and the related oxidative stress in basal and classically activated (LPS/TLR4, M1) macrophages.
To identify genes and pathways regulated by JunD-mediated macrophage activation in WKY BMDMs, we have carried out microarray-based gene expression studies following siRNA knock down of Jund in basal and LPS-stimulated conditions. ChIP-Seq analysis was performed on basal and LPS-stimulated WKY BMDMs and used to complement the microarray results in order to identify primary JunD targets. ChIP-Seq and microarray analyses were also carried out in a Jund congenic strain (WKY.LCrgn2) known to have reduced JunD mRNA and protein levels . In this strain, the Jund locus was transferred from the Lewis strain into the WKY strain by back-crossing over nine generations. Genome-wide integration of all datasets identified primary JunD-target genes and a regulatory network involved in oxidative stress and IL-1β expression in macrophages leading to increases in mature IL-1β production in BMDMs and glomeruli from the WKY strain.
Jund regulates macrophage gene expression that controls primarily oxidative stress and IL-1β synthesis
JunD levels and macrophage oxidative burst in rat strains used for the combined ChIP-Seq and transcriptome approach
JunD levels in macrophages*
Microarray results of transcripts demonstrating greater than three-fold difference in expression between Jund siRNA and scrambled control siRNA transfected WKY BMDMs
killer cell lectin-like receptor subfamily B member 1B
IL-1β synthesis 
Oxidative stress 
interleukin 1 beta
chemokine (C-X-C motif) ligand 9
Oxidative stress 
Oxidative stress 
NLR family, pyrin domain containing 3
IL-1β synthesis 
DOT1-like, histone H3 methyltransferase (S. cerevisiae)
heat shock protein 1
Oxidative stress 
protein kinase C, alpha
Oxidative stress 
LPS stimulated condition
killer cell lectin-like receptor subfamily B member 1B
IL-1β synthesis 
heat shock protein 1
Oxidative stress 
Gene ontology analysis for genes demonstrating differential expression over the eight hour LPS timecourse between WKY and WKY.L Crgn2 BMDMs
Gene ontology term (BP_FAT or KEGG pathway)
Bonferroni corrected P-Value
GO:0010033~response to organic substance
GO:0009725~response to hormone stimulus
GO:0009611~response to wounding
GO:0009719~response to endogenous stimulus
GO:0043434~response to peptide hormone stimulus
GO:0050865~regulation of cell activation
GO:0002237~response to molecule of bacterial origin
GO:0002694~regulation of leukocyte activation
GO:0032496~response to lipopolysaccharide
GO:0045767~regulation of anti-apoptosis
GO:0001817~regulation of cytokine production
GO:0009617~response to bacterium
GO:0048545~response to steroid hormone stimulus
GO:0043067~regulation of programmed cell death
GO:0010941~regulation of cell death
GO:0031667~response to nutrient levels
GO:0042981~regulation of apoptosis
GO:0009991~response to extracellular stimulus
GO:0002684~positive regulation of immune system process
GO:0051249~regulation of lymphocyte activation
GO:0045768~positive regulation of anti-apoptosis
GO:0014070~response to organic cyclic substance
JunD expression levels determine the extent of the JunD cistromes
JunD binding events in WKY and WKY.L Crgn2 BMDMs
Total number of peaks identified
Number of peaks linked to a gene
Number of genes containing at least one JunD peak
Integration of microarray and ChIP-Seq datasets identifies primary JunD targets in macrophages
JunD expression levels determine active IL-1β secretion in primary macrophages and nephritic glomeruli
The aim of the present study was to investigate genes regulated by JunD mediating macrophage oxidative burst and pro-inflammatory cytokine production leading to enhanced cell activation in the WKY rat. We used combined microarray and JunD/AP1 ChIP-Seq analyses in primary BMDMs from WKY (NTN-susceptible, high JunD levels, enhanced macrophage oxidative burst) and congenic WKY.LCrgn2 (reduced NTN, reduced JunD levels, reduced macrophage oxidative burst) rats. Microarray analysis was performed in two experimental settings: following Jund siRNA knockdown using lipid-based transfection in WKY BMDMs and between WKY and WKY.LCrgn2 primary macrophages. ChIP-Seq analysis was also performed between WKY and WKY.LCrgn2 BMDMs in order to identify primary JunD targets. Microarray and ChIP-Seq experiments were also performed in macrophages activated with LPS to assess the role of JunD in macrophages activated through LPS/TLR4.
JunD reduces tumour angiogenesis by limiting Ras-mediated production of reactive oxygen species (ROS) implicated in the pathophysiology of various diseases, including cancer , regulates genes involved in antioxidant defence and enhances the transcription of VEGF-A, a potent proangiogenic factor [7, 9]. In addition JunD deficient mice display persistent hyperinsulinaemia resulting from enhanced pancreatic islet vascularization owing to chronic oxidative stress . In crescentic glomerulonephritis JunD deficiency may cause increased oxidative stress in the glomerular podocytes, leading to altered VEGFA expression and subsequent glomerular injury . In the rat NTN model of Crgn, reduced JunD expression in the congenic WKY.LCrgn2 strain is associated with 11% reduction in glomerular crescent formation [12, 13]. We have carried out a combined ChIP-Seq and transcriptome approach in macrophages, the main effector cells of Crgn, in order to identify JunD targets that may explain its modulatory role. Alongside a general effect by JunD to alter the immune response to LPS in the overall gene sets, the functions of strongly dysregulated (>3 fold) expression changes suggested that primary gene targets of JunD are key effectors in mediating protection from oxidative stress and IL-1β synthesis. Interestingly, this approach identified genes regulating oxidative stress that were previously identified to be under the regulation of JunD in fibroblasts (i.e. cysteine dioxygenase ) suggesting that JunD may have common targets in the oxidative stress pathway in different cell types. JunD regulates IL-1β secretion in rat BMDMs and pro-inflammatory cytokine secretion in human monocyte-derived macrophages  suggesting that its role of the regulation of the M1 macrophage activation is conserved across the species. Taken together these observations suggest that JunD regulates oxidative stress various diseases with both common and cell specific targets.
Current understanding of JunD function on a genome wide scale has been limited by studies performed on a candidate gene or promoter basis [24–26]. The Encyclopaedia of DNA elements (ENCODE) project has carried out ChIP-Seq for JunD in human transformed cell lines though not in a macrophage or monocyte cell lines [27–29]. JunD has been categorised as a middle-level transcription factor and such factors regulate information-flow bottlenecks and may be the best therapeutic targets for strongly affecting the flow of information through regulatory circuits . The cistrome of JunD was also found to be highly context and cell type specific [29, 30]. We used primary macrophages from two inbred rat strains expressing different amounts of JunD in a comparative ChIP-Seq analysis to identify the genomic regions uniquely bound by JunD. The overall landscape of JunD binding in the BMDMs from WKY and WKY.LCrgn2 BMDMs was comparable to that of other transcription factors studied in primary macrophages stimulated by LPS [31, 32]. We found that after LPS stimulation in WKY BMDMs, there was enrichment for genes involved in multiple immune processes linked with responses to multiple different stimuli. In combination with the gene expression data, these findings suggest that genetically determined up-regulation of JunD expression resulted in enhanced macrophage activation in the WKY strain.
De novo motif analysis identified additional transcription factor motifs that were unique to the WKY strain suggesting that the increased levels of JunD expression facilitated new partnerships with other transcription factors that did not occur in WKY.LCrgn2 BMDMs. Key findings included the CACCC-binding domain which binds Krüppel-like family (KLF) transcription factors and are regulators of signalling following activation of macrophages [33–35] and the Ets-1 motif, a factor controlling the expression of cytokine and chemokine genes in a wide variety of cells  in LPS stimulated WKY BMDMS. In basal WKY BMDMS a REST-NRSF motif was identified consistent with the functional findings of neuron development and differentiation in unique core JunD-bound genes in WKY BMDMS and potential roles for JunD in excitoxic neuronal cell death and ischaemic injury [37, 38].
The main goal of the study was to identify primary JunD target genes responsible for the macrophage activation seen in Crgn-susceptible WKY rat. The siRNA knockdown experiments identified that genes with the greatest changes in expression were associated with IL-1β synthesis. The transcriptional control of Il1b expression by JunD was further confirmed by investigating IL-1β secretion upon inflammasome activation in the WKY and reciprocal Jund congenic strain BMDMs and nephritic glomeruli (WKY.LCrgn2 and LEW.WCrgn2). Our integrative analysis identified primary JunD targets through which JunD could primarily regulate macrophage activation. In the basal state multiple targets have links with oxidative stress including Trpv4, Vav2[40–42], Ifi30, Nqo2 and P2ry2. This was also seen after LPS stimulation with transcripts such as Ctnnb1 and Bcl2l11 highlighting the key role of JunD in the regulation of oxidative stress in WKY BMDMs. Moreover, the transcription factor Runx1 (a target in the LPS stimulation group) has been identified as a primary JunD target suggesting that novel transcription factor interactions in macrophages may underlie some JunD-mediated macrophage activation.
Taken together our data show that genetically determined differences in physiological levels of JunD affect its genome-wide binding patterns in basal and LPS-stimulated primary macrophages. These results identified transcriptional programs underlying JunD-mediated oxidative stress and IL-1β synthesis in primary macrophages which may play a role in susceptibility to Crgn.
WKY (WKY/NCrl) and LEW (LEW/Crl) rats were purchased from Charles River (Margate, UK). Single congenic rats were generated by introgressing the Crgn2 QTL from chromosome 16 from a LEW donor onto a WKY recipient background and vice versa as previously described . All procedures were performed in accordance with the United Kingdom Animals (Scientific Procedures) Act.
BMDMs were prepared from the femurs of parental and congenic strains using previously described methods . Femurs from adult (8-10 weeks) rats were isolated and flushed with Hanks buffer (Life Technologies). Total bone marrow derived cells were plated and cultured for 5 days in Dulbecco’s modified Eagle’s medium (Life Technologies) containing 25 mM Hepes (Sigma), 25% L929 conditioned medium, 25% decomplemented fetal bovine serum (Biosera), penicillin (100 U/ml, Invitrogen), streptomycin (100 μg/ml, Invitrogen) and L-glutamine (2 mM Invitrogen). The cells were characterised as macrophages by ED-1 staining. Basal macrophages were left unstimulated whilst stimulated cells were stimulated with 100 ng/ml lipopolysaccharide (Sigma). Following stimulation, cells for gene expression analysis were homogenized in TRIzol (Invitrogen) and stored at -80°C.
siRNA inhibition of Jund expression
siRNA knockdown was carried out as previously described . Briefly, on day 5 of culture, WKY BMDMs were replated in six-well plates (1x106 cells per well) in DMEM (Invitrogen) overnight and transfected for 48 hours with siGENOME SMARTpool for Jund (100 nM, Dharmacon) or siGENOME non-targeting siRNA pool as the scrambled control siRNA using Dharmafect 1 (1:50, Dharmacon) as a transfection reagent in OPTIMEM medium (Invitrogen). The siRNA sequences used in the siGENOME SMARTpool for Jund are listed in Additional file 2: Table S7. siRNA knockdown was confirmed with quantitative PCR (detailed below) and Western blotting (Figure 1).
RNA extraction and microarray preparation
Total RNA was extracted using the TRIzol method and purified using RNeasy Plus spin columns (Qiagen). 100ng of RNA was amplified, labelled and hybridised to Rat Gene 1.0 ST arrays (Affymetrix, Santa Clara, CA, USA) using the Ambion WT Expression Kit (Life Technologies) as per manufacturer’s instructions. For timecourse expression analysis, four BMDM preparations from four biological replicates were used for each timepoint and condition. For siRNA expression analysis, four BMDM preparations from at least two biological replicates were used for each timepoint and condition. The microarray data is available in MIAME-compliant (minimum information about a microarray experiment) format at the Array Express database (http://www.ebi.ac.uk/arrayexpress) under accession code E-MEXP-3469.
Microarray data analysis
CEL intensity files were produced using GeneChip Operating Software version 1.4 (Affymetrix) and quality tested using the Affymetrix Expression Console v1.1.2. All 32 files in the timecourse data set and 16 files in the siRNA dataset were suitable for further analysis. Probe-level data was normalised using robust multichip average (RMA) [48, 49]. A custom definition file was created using up-to-date probe information  and filtered to exclude probes containing the 2,520,602 single nucleotide polymorphisms present between the WKY and LEW genomes (Santosh Atanur, MRC Clinical sciences centre, personal communication). The moderated T test with 40,000 permutations implemented in Statistical Analysis of Microarrays (SAM) version 3.0 was used to identify differentially expressed genes at an FDR threshold of 5% and timecourse analysis was performed using EDGE with 40,000 permutations and a 5% FDR threshold . Hierarchical clustering analysis was performed using MultiExperiment Viewer (MeV) v4.8 [52, 53] with the Euclidean distance measure. Gene ontology analysis was carried out using the functional annotation tools within DAVID, the Database for Annotation, Visualisation and Integrated Discovery v6.7 [54, 55].
All qPCRs were performed with an ABI 7900 Sequence Detection System (Applied Biosystems, Warrington, UK). A two-step protocol was used as previously described  beginning with cDNA synthesis with iScript select (Bio-Rad) followed by PCR using SYBR Green Jumpstart Taq Ready Mix (Sigma). A total of 10ng of cDNA per sample was used. All samples were amplified using a set of 4 biological replicates with three technical replicates used per sample in the PCR. Sequence detection software (SDS) version (Applied Biosystems) was used to obtain the Ct values. Results were analysed using the comparative Ct method and each sample was normalised to the reference gene Hprt, to account for any cDNA loading differences. The primer sequences used for the qRT-PCR validation of microarray data are listed in Additional file 2: Table S8.
Chromatin immunoprecipitation (ChIP)
BMDMs were left in the basal condition or stimulated for 2 hours with 100ng/ml LPS. Cells were fixed for 10 minutes with 1% formaldehyde and ChIP performed using ChIP-IT Express as per manufacturer’s instructions with some modifications. Sonication was carried out using Covaris S2 (Woburn, Massachusetts, USA) in a volume of 300μl sonication buffer with the following settings; 20% duty cycle, intensity 8, 200 cycles/burst, cycle length 30 s for 28 to 30 cycles dependant on cell count. The ChIP lysate was immunoprecipitated using 2 μg of JunD antibody (Santa Cruz sc74-X) or negative IgG control (sc-2026) overnight. Cross links in the immunoprecipitated chromatin and control input chromatin were reversed by heating the samples at 65°C for 5 hours followed by proteinase K digestion for 1 hour. Samples were purified using Qiagen MinElute columns as per manufacturer’s instructions prior to downstream analysis.
High throughput sequencing
Single read library preparation and high throughput single read sequencing for 36 cycles was carried out on an Illumina Genome Analyser IIx according to the manufacturer’s protocols (Illumina) with some modifications. Each immunoprecipitated sample for library preparation was the product of five separate technical replicates of immunoprecipitation pooled together and purified using Qiagen MinElute columns and quantified using the hsDNA Qubit assay (Invitrogen, Life Technologies, California, USA). A 1:20 dilution of Illumina adaptors was used at the adaptor ligation step to avoid adaptor dimer formation and a 1:2 primer dilution used to prevent dimerization during the PCR amplification stage. The samples were quantified using the hsDNA Qubit assay (Invitrogen, Life Technologies, California, USA) and the size range analysed using a HS DNA chip on a 2100 Bioanalyser (Agilent Technologies, West Lothian, UK) prior to submission for qPCR analysis and cluster generation and sequencing by the CSC/IC Genome Core facility.
qPCR of ChIP enriched DNA
Immunoprecipitated DNA fragments were analysed by real-time PCR. Primers used are listed in Additional file 2: Table S9. All samples were amplified using a set of 3 biological replicates with three technical replicates used per sample. After an initial denaturation step of 94°C for 2mins, the samples were cycled 40 times at 94°C for 15s, 60°C for 1 min and 72°C for 1 min with data collection performed during the 72°C elongation step. Sequence detection software (SDS) version 2.3 (Applied Biosystems) was used to obtain the Ct values and each sample was analysed with reference to Ct values for matched control ‘Input’ non-immunoprecipitated chromatin. A standard curve of 1:5 dilutions of Input DNA was used to calculate the % Input level of the transcription factor binding at the investigated locus. The standard curve was constructed from the Input DNA sample for the appropriate strain and condition and analysed within the SDS software v2.3. For each test gene the % Input levels were then determined using %total = 2ΔCt × (% of input sample used) where Δ = Ct (Input) - Ct (sample). Fold change over IgG was expressed using 2-ΔCt where ΔCt= ΔCtJund – ΔCtIgG.
ChIP-Seq data analysis
Sequencing of the ChIP-Seq libraries was carried out on the high throughput Illumina Genome Analyzer II. Initial data processing was performed using Illumina Real Time Analysis (RTA) v1.6.32 software (equivalent to Illumina Consensus Assessment of Sequence and Variation, CASAVA 1.6) with default filter and quality settings. Quality filtered reads were then realigned to the reference rat genome (RGSC3.4) using the Burrows Wheeler Alignment tool v0.5.9 (BWA) . Read ends were trimmed if Phred-scaled base quality scores dropped below 20. Reads that uniquely mapped to the reference genome were used to detect areas of enrichment with BayesPeak v1.1.3 [22, 23] using a posterior probability threshold of 0.9. A stringent posterior probability threshold of 0.9 was used to filter all bins passing the threshold to form the final contiguous peak regions to produce a more accurate reflection of true peak calls. An over fitting diagnostic was performed using λ1 < 0.7 and score < -2.25 to filter out regions which showed no enrichment but had a high enough background for the algorithm to call peaks in. Peak regions were annotated using the gene intervals annotator (GIN) implemented in CARPET using a gene priority approach to give the associated transcript I.D., the associated gene feature and the distance of the peak to the nearest transcriptional start site . HOMER was used to predict motif occurrence within peaks [32, 59] with default settings for a maximum motif length of 12 base pairs. The outputs of the peak calling algorithms were visualised in the Integrative Genomics Viewer  using custom WIG files generated from the output data generated by BayesPeak. The latter were generated by extending each mapped read by 200 bp and then by using 10 bp bins the overlapping tag count was generated.
Integrated data analysis and identification of primary JunD targets
JunD gene expression patterns in WKY and WKY.LCrgn2 BMDMs over the LPS time course were used for Spearman correlation analysis with the rest of the transcripts on the microarrays (P < 0.001 cut off). In order to integrate the three different datasets, the list of significantly correlated set of transcripts was filtered and annotated according to two criteria: significant differential expression following Jund siRNA knockdown (both basal and after LPS stimulation) and the presence of the JunD ChIP-Seq peaks in WKY BMDMs (both basal and after LPS stimulation). Two separate networks (basal and LPS) were built with Cytoscape version 2.8.3 showing primary JunD targets.
Isolation and culture of rat nephritic glomeruli and detection of IL-1β by Western Blot and ELISA
Glomeruli were isolated as previously described . After 48 hours of incubation at 37°C, nephritic glomeruli supernatants were collected and stored at -20°C for IL-1β sandwich ELISA analysis. For IL-1β detection in BMDMs by Western Blotting, cells were primed with LPS (1 μg/ml, 3hours) and stimulated with ATP (5 mM) for 30 minutes. BMDMs were then scraped and both cells and supernatant were collected, filtered using Amicon ultra centrifugal filters for protein purification and concentration (10 kDa cut-off, Millipore, UK) and the concentrated samples were diluted with 5× sample buffer containing 200 mM Tris-HCl, 6% SDS, 2mM EDTA, 4% 2- Mercaptoethanol, 10% glycerol and boiled for 10 minutes. The samples were then resolved by SDS polyacrylamide gel electrophoresis (PAGE) and transferred to an Immobilon-P Transfer Membrane (Millipore). Rabbit polyclonal anti IL-1β (New England BioLabs, UK) was used to detect the mature IL-1β. To assess secreted IL-1β in BMDMs and nephritic glomeruli, cell supernatants were subjected to sandwich ELISA and secreted IL-1β amounts were determined using a standard curve with rat recombinant IL-1β according to manufacturer’s instructions (R and D Systems).
For the confirmation of siRNA knockdown of Jund, WKY BMDMs were plated into six-well plates at a density of 1 × 106 cells per well and treated with Jund specific siRNA or a scrambled oligonucleotide for 48 hours before total protein was extracted for Western Blot analysis using the above technique. For JunD detection a specific rabbit polyclonal anti-Jund antibody from Santa Cruz Biotechnology (USA) was used. This blot is representative of 4 different experiments performed with 4 biological replicates.
We thank Ailsa Chiu and CSC/IC Genome Core facility for their excellent technical assistance. This 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).
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