To map the LPS-regulated SerpinB2 promoter regions, we transfected reporter constructs driven by the ∼5 kb 5'-flanking region of the murine SerpinB2 gene and several deletion mutants into murine macrophages. In addition, we compared the DNA sequence of the murine 5′ flanking sequence with the sequence of the human gene for homologous functional regulatory elements and identified several regulatory cis-acting elements in the human SERPINB2 promoter conserved in the mouse. Mutation analyses revealed that a CCAAT enhancer binding (C/EBP) element, a cyclic AMP response element (CRE) and two activator protein 1 (AP-1) response elements in the murine SerpinB2 proximal promoter are essential for optimal LPS-inducibility. Electrophoretic mobility shift (EMSA) and chromatin immunoprecipitation (ChIP) assays demonstrated that LPS induces the formation of C/EBP-β containing complexes with the SerpinB2 promoter. Importantly, both constitutive and LPS-induced SerpinB2 expression was severely abrogated in C/EBP-β-null mouse embryonic fibroblasts (MEFs) and primary C/EBP-β-deficient peritoneal macrophages. Together, these data provide new insight into C/EBP-β-dependent regulation of inflammation-associated SerpinB2 expression.SerpinB2 or plasminogen activator inhibitor type 2 (PAI-2) is highly induced in macrophages in response to inflammatory stimuli and is linked to the modulation of innate immunity, macrophage survival, and inhibition of plasminogen activators. Lipopolysaccharide (LPS), a potent bacterial endotoxin, can induce SerpinB2 expression via the  The inflammatory response is a double-edged sword. Properly orchestrated, it results in the clearing of foreign molecules and invading pathogens from the body. Uncontrolled, it may lead to organ damage, sepsis, and even cancer SerpinB2 gene is highly regulated in a cell type specific manner analogous to that of cytokines and oncogenes toll-like receptor 4 (TLR4) is responsible for the recognition of LPS and other microbial products and plays a central role in the initiation of innate immune responses, including cytokine release. The binding of LPS to TLR4 on the surface of macrophages leads to the recruitment of adaptor molecules and the activation of protein kinases, generating signals to the nuclear factor-κB (NF-κB), mitogen-activated protein kinase (MAPK) and/or phosphoinositide 3(PI3)-kinase pathways The SerpinB2 is distinctly regulated from other LPS-inducible genes in terms of kinetics, LPS dose response and sensitivity to IFN-γ co-stimulation cis-acting elements in the SerpinB2 promoter responsible for LPS-dependent transcription in macrophages and the specific LPS-responsive transcription factors that bind the SerpinB2 promoter were not defined. Here we show that LPS induction of SerpinB2 is dependent upon cis-acting regulatory sequences in the region between nucleotides −189 and −539 of the murine SerpinB2 promoter, and is critically dependent upon a C/EBP binding site at −203/−195. C/EBP-β directly bound to this site in vivo and its deficiency abrogated constitutive SerpinB2 expression and SerpinB2 induction by LPS. Importantly, a C/EBP-β phospho-acceptor site was found to negatively regulate LPS-induced SerpinB2 promoter activity. Together, these findings provide new insight into the transcriptional regulation of the SerpinB2 gene.In studies aimed at identifying LPS-inducible pro-survival factors downstream of p38 MAPK, SerpinB2 was identified as a factor whose expression was upregulated by cooperation of the IKKβ/NF-κB and p38 MAPK/CREB pathways 2 and 95% humidified air atmosphere at 37°C. Wild-type (+/+Cebpb) and knockout (−/−Cebpb) mouse embryonic fibroblasts (MEFs) Salmonella minnesota Strain Re595) was obtained from Sigma.Murine macrophage RAW 264.7 cells (ATCC TIB-71) were maintained in RPMI 1640 media (Gibco BRL), supplemented with 2 mM L-glutamine (Gibco BRL), 10% serum supreme (BioWhittaker), 200 µg/ml penicillin, 100 µg/ml streptomycin, 25 mM N-2-hydroxyethylpiperazine-N-2-ethane sulphonic acid (HEPES) and 25 mM sodium bicarbonate, in 5% COThe RNeasy Mini Kit (Qiagen) was used to isolate total RNA from cells. For cDNA synthesis, 1 µg of total RNA was reverse transcribed using TaqMan® Reverse Transcription Reagents (Applied Biosystems). qPCR was performed using TaqMan® Gene Expression 20X primers for Serpinb2/SerpinB2 (Mm00440905_m1), Cebpb (Mm00843434_s1) and β-actin (Mm00607939_s1) (Applied Biosystems).SerpinB2 promoter was determined by sequencing the pUC-based plasmids pDB9406, pDB9402-41 and pDB9402-42, in addition to plasmids prepared from pDB9402-41 and pDB9402-42 containing deletions introduced by exonuclease III digestion. Plasmids pDB9406, pDB9402-41 and pDB9402-42 containing genomic DNA isolated from a λFIXII (Stratagene) genomic library prepared from a 129 mouse strain, were kindly provided by Dr. Dominique Belin, University of Geneva. pDB9406 contains a 4.4 kb EcoRI/SpeI genomic fragment spanning the transcription initiation site; pDB9402-41 and pDB9402-42 contain a 1.2 kb EcoRI genomic fragment, located immediately upstream of the pDB9406 EcoRI fragment, cloned in opposite orientations. Subcloned inserts were verified by restriction enzyme digestion and DNA sequencing. The nucleotide sequence of the 4480 bp murine SerpinB2 gene 5′ flanking region was determined using the ABI PRISM dye terminator cycle sequencing ready reaction kit (Perkin-Elmer) and a PE 373A sequencer (Perkin-Elmer). This sequence was deposited in GenBank/EMBL/DDBJ Data Bank with Accession No. AF339731.The DNA sequence of the murine E. coli as in Whole cell lysates were prepared in RIPA buffer , proteins were separated on 4–12% Bis-Tris NuPAGE gels (Invitrogen), and transferred to PVDF membranes. Membranes were subsequently blocked with 5% milk in PBS-T , and incubated with primary antibodies overnight. Affinity purified rabbit anti-mouse SerpinB2 antibodies were prepared after immunization with a purified recombinant GST-murine SerpinB2 fusion protein produced in Kpn I restriction site, and DW3’LUC, containing an Xho I restriction site, were used to PCR amplify and clone the SerpinB2 promoter (−3261 to +92) from pDB9406 into the Kpn I/Xho I polylinker restriction sites of pGL3 Basic (Promega) to produce pGLmP-3261. The EcoR I insert of pDB9402-42 was sub-cloned immediately upstream of the SerpinB2 promoter EcoR I site (−3261) of pGLmP-3261 to produce pGLmP-4480. Additional murine SerpinB2 luciferase reporter constructs  were generated by digesting pGLmP-3261 with EcoR I and a second restriction enzyme , blunt ending the resultant 5′ or 3′ overhangs with T4 DNA polymerase (NEB) and re-ligating the vector ends with T4 DNA ligase. The PCR primers BSPCR2 and DW3’LUC were used to subclone the murine SerpinB2 promoter regions −87 to +92 into the Kpn I/Xho I polylinker restriction sites of pGL3 Basic to produce pGLmP-87. The control empty vector was pGL3 Basic (Promega).The PCR primers DW5’LUC, containing a SerpinB2 promoter LPS-responsive regions E box, PU.1, Oct-1 and C/EBP  were generated as described pGLmP-539mEbox: mPAI2mEboxa and mPAI2mEboxb; pGLmP-539mPU.1: mPAI2mPU.1a and mPAI2mPU.1b; pGLmP-539mOct: mPAI2mOct-2a and mPAI2mOct-2b; pGLmP-539mC/EBP: mPAI2mCEBPa and mPAI2mCEBPb. The oligonucleotide PCR primers used to generate mutants, pGLmP-539mCRE, pGLmP-539mAP-1a, and pGLmP-539mAP-1b were reported previously EcoR I and Xho I and cloned between the EcoR I and Xho I restriction sites of pGLmP-539 in place of the −539/+92 region of the wild-type murine SerpinB2 promoter. Sequence verified constructs were used in the experiments.Luciferase reporter constructs containing mutations in the 7) growing in log phase were transfected with the indicated luciferase reporter plasmid (20 µg) along with the pRL-thymidine kinase (TK) (Promega) internal control reporter plasmid (2 µg) by electroporation using a Bio-Rad Gene Pulser with a Capacitance Extender . pGL3 control plasmid which encodes the SV40 promoter and enhancer was included as a positive control for transfection efficiency, and as an internal standard for promoter and enhancer activities. Transfected cells were transferred to 10 ml of pre-warmed media in 6-well tissue culture plates, divided into two identical cell pools and incubated 16 hrs in a 5% CO2 and 95% humidified air atmosphere at 37°C either in the presence or absence of 100 ng/ml LPS. Luciferase activity was measured using a Dual-Luciferase Reporter Assay System kit (Promega). Measurements represent the results of at least three independent experiments. Promoter activity is expressed as the number of firefly luciferase light units normalized either to pRL-TK renilla luciferase light units or to cellular protein concentration (where co-transfected C/EBP-β or LPS affected pRL-TK activity). Protein concentration was determined using the Bio-Rad protein microassay reagent.RAW 264.7 cells  specific for human and mouse Cebpb−/− MEFs (1×105) 188A, T217A, S64A) 32P]-ATP. RAW 264.7 nuclear extracts were prepared by the detergent lysis method Radiolabelled, double-stranded oligonucleotide probes for gel shift assays were prepared using T4 polynucleotide kinase and [γ-SerpinB2 proximal promoter: forward (−338/−315) 5′AAGACTCCCACAGATGGTGGCTGT3’; reverse (−5/+19) 5′TTCTTGGAAAGCTGGCACTGTGTG3’.ChIP assays were performed using a commercially available Magna-ChIP™ kit (Millipore), as recommended by the manufacturer, with minor modifications. Briefly, after crosslinking the chromatin with 1% formaldehyde at room temperature for 10 min and neutralizing with glycine for 5 min at room temperature, cells were washed with cold PBS, scraped and collected on ice. Cells extracts were prepared using a commercially available kit (Millipore). Nuclear lysates were sonicated 5 times for 15 sec with 1 min intervals on ice using a Sonic Dismembrator (Fisher). An equal amount of chromatin was immunoprecipitated at 4°C overnight with at least 1 µg of the following antibodies: C/EBP-β (sc-150X), p-C/EBP-β (T217) (sc-16993X), normal rabbit IgG (sc-2027)(Santa Cruz Biotechnologies) and RNA polymerase II (Clone CTD4H8)(Millipore). Immunoprecipitated products were collected after incubation with Protein G coated magnetic beads (Millipore). The beads were washed, the bound chromatin was eluted in ChIP Elution Buffer (Millipore) and the proteins were digested with Proteinase K for 2 hrs at 62°C. The DNA was then purified using the QIAquick PCR Purification Kit (Qiagen). DNA was amplified by semi-quantitative PCR or by qPCR using the SYBR green method and primers specific for the Data are presented as mean ± SEM per group. Results were analyzed using the analysis of variance (ANOVA) or Student’s t test where relevant. P-values <0.05 were considered significant.SerpinB2 mRNA was detectable as early as 30 min following LPS challenge, reaching maximal levels at 24 hrs   . The repcis-acting elements that might mediate transcription of the murine SerpinB2 gene and the response to LPS, we aligned the murine and human SerpinB2 5′ flanking regions. We reasoned that the presence of evolutionarily conserved, potential transcription factor binding sites in this region might play a role in the regulation of SerpinB2 gene expression cis-acting regulatory elements defined in the human SerpinB2 proximal promoter are conserved in the murine SerpinB2 promoter , (2) a mutant SerpinB2 element containing the same mutation as in pGLmP-539mC/EBP and (3) the rat albumin promoter distal element 1 (DEI) region containing a high affinity C/EBP binding site. Four bands of different mobilities, representing DNA-nuclear protein complexes were detected (lanes 4 and 5) or the sequence of a known C/EBP binding site from the rat albumin promoter (SerpinB2 C/EBP site at −203/−192 binds a LPS-inducible complex that is likely to contain a member of the C/EBP family of transcription factors.Nuclear factor binding to the putative C/EBP element (−203/−192) was investigated by electrophoretic mobility shift assay (EMSA) using nuclear extracts from untreated and LPS-treated RAW 264.7 macrophages. Three different double stranded oligonucleotide probes were used for EMSA, representing (1) the putative detected . Three odetected  while thThe C/EBP family of basic leucine zipper transcription factors are known for their roles in cellular differentiation and inflammation SerpinB2 mRNA expression in response to LPS, we utilized C/EBP-β-null (Cebpb−/−) and wild-type MEFs (+/+Cebpb), since RAW264.7 cells constitutively express endogenous C/EBP-β. Wild-type MEFs express low levels of endogenous SerpinB2 and the absence of C/EBP-β attenuated endogenous SerpinB2 mRNA expression. LPS stimulated an increase in SerpinB2 mRNA expression in wild-type MEFs . RAW 264.7 cells were stimulated with LPS for up to 8 hrs, soluble chromatin was immunoprecipitated with antibodies against DNA binding proteins, and the enriched DNA amplified by both semi-quantitative and qPCR using SerpinB2 proximal promoter specific primers , which has been associated with cell survival T217 was constitutively bound to the SerpinB2 proximal promoter and also present after 1 hr of LPS stimulation . These dSerpinB2 proximal promoter in an LPS-inducible manner both in vitro and in vivo, we wanted to address the question of whether C/EBP-β was an essential factor for driving transcription from the SerpinB2 promoter in cells in response to LPS. The ability of endogenous C/EBP-β to direct transcription from the SerpinB2 proximal promoter was examined by transfection of the pGLmP-539 murine SerpinB2 luciferase reporter construct into +/+Cebpb and −/−Cebpb MEFs. We found that LPS-stimulated SerpinB2 promoter activity was significantly increased in +/+Cebpb MEFs and abrogated in −/−Cebpb MEFs  and AP-1 The murine SerpinB2 gene transcription. In a previous microarray expression profiling study, SerpinB2 was identified as gene whose induction in C/EBP-β-deficient peritoneal macrophages by LPS and IFNγ was severely impaired compared to wild-type macrophages SerpinB2 mRNA expression is significantly abrogated in C/EBP-β-shRNA transduced peritoneal macrophages, emphasizing the link between C/EBP-β and SerpinB2 gene transcription. While C/EBP proteins can act as either homodimers or heterodimers SerpinB2 C/EBP response element, suggesting a predominant role for C/EBP-β in LPS-induced SerpinB2 gene expression. C/EBP-β, like SerpinB2, plays an important role in inflammation, as it is upregulated by LPS and a host of other inflammatory cytokines In this study, C/EBP-β was found to be a major requirement for both constitutive and LPS-induced 217 has been reported to rescue macrophages from apoptosis induced by Bacillus anthracis lethal toxin (LT) T217A, in −/−Cebpb MEFs did not increase SerpinB2 proximal promoter activity over that of wild-type C/EBP-β, even though recruitment of C/EBP-βT217 to the SerpinB2 promoter was observed to decrease following LPS stimulation of RAW264.7 cells. These data indicate that the T217 phospho-acceptor site is not important for the regulation of SerpinB2 gene expression. Similarly, expression of the C/EBP-β phospho-acceptor site mutant, C/EBP-βT188A, did not affect SerpinB2 promoter activity differently from that of wild-type. In contrast, expression of C/EBP-βS64A significantly enhanced LPS-induced SerpinB2 promoter activity, indicating that phosphorylation of C/EBP-β at S64 negatively regulates LPS-induced SerpinB2 promoter activity. Since C/EBP-β S64 is constitutively phosphorylated in both RAW264.7 cells and MEFs SerpinB2 to increase its promoter activity. Roy and colleagues demonstrated that Mixed lineage kinase-3 (MLK3)-driven dephosphorylation of C/EBP-β S64 was important for IFNγ-regulated signaling pathways 64 may also be involved in LPS-signaling pathways.Phosphorylation of C/EBP-β at several different amino acid residues has been shown to modulate transactivation of its target genes SerpinB2 gene, which may have implications for the inflammatory phenotype of infiltrating immune cells in the tumor microenvironment.In recent years it has become apparent that persistent infection is integrally linked to chronic inflammation and cancer, and immune cells such as macrophages can either promote or attenuate cancer progression SerpinB2 proximal promoter is necessary to support both constitutive and LPS-induced SerpinB2 gene expression. Importantly, we were able to uncover a previously unknown role for C/EBP-βS64 in negatively regulating SerpinB2 promoter activity. Taken together these data provide new insight into the regulation of inflammation-associated SerpinB2 gene expression.In summary, our studies show that the C/EBP site (−203/−195) in the murine Figure S1PCR primer sequences used to generate murine SerpinB2 reporter constructs.(top) PCR primer sequences used to subclone SerpinB2 promoter sequences into pGL3 Basic luciferase reporter plasmid and generate deletion constructs as indicated in (bottom) mutant oligonucleotide PCR primers used to mutate LPS-responsive regions in the SerpinB2 proximal promoter. Mutated nucleotides are shown in bold.(TIF)Click here for additional data file.Figure S2Conserved response elements in the murine and human SerpinB2 promoter. Location and nucleotide sequences of the cis-acting elements conserved between the human and murine SerpinB2 promoters are listed as indicated.(TIF)Click here for additional data file.


TNFSF4 are associated with myocardial infarction (MI) in women. This prompted functional studies of TNFSF4 expression.The TNFSF4/TNFRSF4 system, along with several other receptor-ligand pairs, is involved in the recruitment and activation of T-cells and is therefore tentatively implicated in atherosclerosis and acute coronary syndromes. We have previously shown that genetic variants in TNFSF4 genomic region, a promoter polymorphism (rs45454293) and a haplotype were identified, conceivably involved in gene regulation. The rs45454293T-allele, in agreement with the linked rs3850641G-allele, proved to be associated with increased risk of MI in women. Haplotype-specific chromatin immunoprecipitation of activated polymerase II, as a measure of transcriptional activity in vivo, suggested that the haplotype including the rs45454293 and rs3850641 polymorphisms is functionally important, the rs45454293T- and rs3850641G-alleles being associated with lower transcriptional activity in cells heterozygous for both polymorphisms. The functional role of rs45454293 on transcriptional levels of TNFSF4 was clarified by luciferase reporter assays, where the rs45454293T-allele decreased gene expression when compared with the rs45454293C-allele, while the rs3850641 SNP did not have any effect on TNFSF4 promoter activity. Electromobility shift assay showed that the rs45454293 polymorphism, but not rs3850641, affects the binding of nuclear factors, thus suggesting that the lower transcriptional activity is attributed to binding of one or more transcriptional repressor(s) to the T-allele.Based on a screening of the TNFSF4 rs45454293T-allele is associated with lower TNFSF4 expression and increased risk of MI.Our data indicate that the  TNFSF4 , the ligand of the TNFRSF4 (OX40 receptor), is a member of the tumor necrosis factor (TNF) superfamily. TNFSF4 is a T-cell activator that seems to promote the survival (and perhaps prolong the immune response) of CD4+ T-cells at sites of inflammation TNFSF4 gene contributes to the risk of developing myocardial infarction (MI) TNFSF4 (rs3850641) and haplotypes including this SNP were found to be associated with risk of MI in women in two independent populations and with angiographically assessed severity of CAD. TNFSF4 haplotypes were also associated with variation in some risk factors for CAD; carriers of a specific haplotype had significantly higher plasma concentrations of HDL cholesterol and serum amyloid A (SAA) than did carriers of other haplotypes. Furthermore, gene targeting of Tnfsf4 in mice showed that deficient animals had significantly smaller atherosclerotic lesions and higher levels of plasma total cholesterol and HDL cholesterol than controls, while mice over-expressing Tnfsf4 had significantly larger atherosclerotic lesions when compared to controls, indicating that Tnfsf4 is a gene influencing atherosclerosis susceptibility in mice. Further support for a role of TNFSF4 in atherosclerosis was obtained in a study where blockage of the TNFSF4/TNFRSF4 interaction reduced lesion formation in low-density lipoprotein (LDL) receptor-deficient mice TNFSF4 and MI Based on a mouse atherosclerosis model TNFSF4 and risk of MI has not been defined, nor is it known whether high or low expression of TNFSF4 is associated with MI in man. Since the observed association with MI The functional polymorphism responsible for the association between TNFSF4 genomic region. New genetic variants were subsequently examined in one of the clinical studies previously used to establish the association between TNFSF4 and MI. A promoter polymorphism was identified which is in linkage disequilibrium with the rs3850641 SNP and associated with MI in women. Functional analyses of the transcriptional activity of these genetic variants were then performed both in vivo, using haplotype-specific chromatin immunoprecipitation of activated polymerase II (haploChIP assay) in vitro with assays of luciferase reporter gene expression; electromobility shift assay (EMSA) was employed to analyze allele-specific binding of transcription factors in vitro.In an attempt to identify the functional SNP(s) or a minimal haplotype suitable for further functional studies, we performed a detailed screening of the The biobank and database of the Stockholm Coronary Atherosclerosis Risk Factor (SCARF) study were used for the genotype-phenotype association studies. DNA samples from a total of 387 individuals with MI before age 60 and 387 age- and sex-matched population-based control subjects were examined in the present study. Genotyping was successfully performed in 376 patients with MI and 385 controls, while complete data were obtained from 359 patients and 382 controls. Recruitment procedures, inclusion and exclusion criteria, investigation program and basic clinical characteristics have been described in detail in silico were validated by sequencing genomic DNA from whole blood of 20 healthy subjects. Parts of the regulatory region from 2449 bp upstream to 374 bp downstream of the TNFSF4 transcription start site  and parts of intron 1 were PCR amplified and sequenced using a standard cycle-sequencing system . Primer sequences are provided in Polymorphisms detected Genotyping was performed using TaqMan® assays . Post-PCR allelic discrimination was carried out measuring allele-specific fluorescence on the ABI Prism® 7000 Sequence Detection System  using the Sequence Detection System software version 1.01.THP-1 For the haploChIP analyses, nine established Epstein-Barr virus (EBV)-transformed human B cell lines For the luciferase assay HEK293T cells (obtained from ATCC) were grown in Dulbecco's Modified Eagle's medium supplemented with 10% FBS, 2mM glutamine, penicillin (100 U/ml), and streptomycin (100 µg/ml).Total RNA was prepared with RNeasy kit  and reverse-transcribed into cDNA by using oligo dT primers and Superscript II  according to the manufacturer's instructions. Real-time RT-PCR was performed on ABI Prism Sequence Detection System 7000  and the results were normalized to the house keeping gene RPLP0. cDNA (3 µL) was amplified by real-time PCR with 1xTaqMan Universal PCR Master Mix and Assay-on-Demand Hs00967195_m1  according to the manufacturer's instructions. Each sample was analyzed in duplicate under the following conditions: 2 min at 50°C, 10 min at 95°C, 0.15 min at 95°C, and 1 min at 60°C. The PCR amplification was related to a standard curve. Reactions were performed in MicroAmp Optical 96-Well Reaction Plates . The following primers and probe were used for the amplification of the RPLP0 gene: RPLP0-F-5′-CCA TTC TAT CAT CAA CGG GTA CAA-3′; RPLP0-R-5′-AGC AAG TGG GAA GGT GTA ATC C-3′and probe RPLP0-5′-TCT CCA CAG ACA AGG CCA GGA CTC GT-3′.8 cells grown in suspension culture were harvested by centrifugation and fixed using volumes adjusted to the amount of cells; all the incubation steps were followed by 5 min centrifugation. Nuclei containing pellets were resuspended in half of the original volume to increase chromatin concentration and sheared using a Branson 450 Sonifier with 12 pulses of 20 sec each  to an optimal length of 1500 bp. Samples were cooled on ice for 30 sec between pulses. For chromatin immunoprecipitation, specific antibodies against phosphorylated serine residues of the c-terminal domain (CTD) of RNA polymerase II (Pol II)  were used. An antibody against total Pol II  was used as a positive control, while anti-SV40 T Antigen pAb101  was used as mock antibody control. Recovery of antibody bound protein/DNA complexes, washing, collection of immunoprecipitated DNA, reversion of cross-links and DNA purification were performed according to the manufacturer's instructions. Allele-specific loading of Pol II was evaluated by Pyrosequencing™ as described The chromatin immunoprecipitation assay was performed as described TNFSF4 transcription start site (TSS) (from nt -1392 to -6) was amplified with primers 5′-TGTTCTCCTAATGCAAGGCATA and 5′-CAATCTGGGTAGAGGGAAGAT from individuals homozygous for either one or the other allelic variant and cloned into pCRII ; this construct was subsequently digested with KpnI and XhoI to generate a fragment spanning the promoter region from nt -973 to -6 (including the rs45454293 SNP) and transferred to pGL3-Basic expression vector (Promega). Double-stranded DNA fragments (nt 476-492 from TSS) harbouring intron 1 rs3850641 SNP flanked by KpnI and XhoI restriction sites were generated by annealing of oligonucleotides carrying either one or the other allelic variant (CTCGAGCTATCACAATGGGTAGAGGTACCGACT5′-GACT). The polymorphic site is underlined while the restrictions sites are in italics. Double-stranded oligonucleotides were cloned into pGL3-Promoter expression vector (Promega) upstream of the SV40 endogenous promoter. Luciferase assay was carried out using the Luciferase Assay System (Promega) according to the manufacturer's protocol. Cells were transfected with 0.1, 0.25 or 0.4 µg of either one of the constructs and 0.25 µg of the -galactosidase CMV-lacZ plasmid as an internal control for transfection efficiency using FuGene6 transfection reagent . 48 h after transfection cells were lysed with Reporter Lysis Buffer (Promega), and β-galactosidase and luciferase activity were measured using a Vmax kinetic microplate reader  and a dual injection luminometer , respectively. Each experiment was independently repeated three times and each sample was studied in duplicate.The genomic region upstream of the CGTGGCTGGCCTC5′-TTTCTTTGAGGT and rs3850641, ATGGGTAGACCAG5′-ATTACTATCACA. The polymorphic sites are underlined. Nuclear extracts from human THP-1 and U937 cells were prepared according to Alksnis et al. N,N′-methylene-bisacrylamide weight ratio), whereafter electrophoresis was performed in 22.5 mM Tris/22.5 mM boric acid/0.5 mM EDTA buffer for 2 h at 200 V. Non-radioactive competitor DNAs, either identical, of the opposite allelic variant or of non-specific origin, were added to the labelled DNA.The sequences of the double-stranded oligonucleotides used in electrophoretic mobility shift assays (EMSAs) were as follows: rs45454293, http://www.genomatix.de/) was used to identify in silico potential transcription factor binding sites that were lost or generated due to a SNP in TNFSF4. Transcription factor binding sites altered by variants in the TNFSF4 gene were also evaluated by browsing the TRANSFAC public database 7.0 (http://www.gene-regulation.com/pub/databases.html#transfac) with the web-based softwares TESS (http://www.cbil.upenn.edu/tess) and PATCH (http://www.gene-regulation.com/cgi-bin/pub/programs/patch/bin/patch.cgi).Genetic variants affecting potential regulatory regions were identified using RAVEN, a web-based application http://genome.ucsc.edu/cgi-bin/hgTracks?db=hg18).The ENCODE2) for polymorphisms within the TNFSF4 locus were calculated with the EMLD program developed by Qiqing Huang (http://epi.mdanderson.org/~qhuang/Software/pub.htm) and visualized with the Haploview program (version 3.0) developed by Barret & Maller (http://www.broad.mit.edu/mpg/haploview/) using both our own data and data available from the HapMap Project (http://www.hapmap.org/). Haplotype frequencies were estimated using the PHASE© program (version 2.1) Statistical analyses in SCARF were performed using the StatView software version 5.0 . Allele frequencies were estimated by gene counting and tested for deviation from Hardy-Weinberg equilibrium. The chi-square test was used to compare the distribution of genotypes between cases and controls. Differences in continuous variables between groups were tested by analysis of variance (ANOVA) with the Scheffe F-test as a post-hoc test. Pairwise linkage disequilibrium coefficients  in 20 healthy subjects verified the presence of three variants  previously reported in the Ensembl Human Genome Browser. In addition, a genetic variant previously described in Japanese individuals To identify the physiological variant(s) responsible for the previously reported associations between Since one of the aims was to refine linkage disequilibrium blocks described earlier 2) values were calculated, the two blocks appeared to be smaller, linkage disequilibrium being maintained only between rs45454293 and rs3850641 within the first block, and between rs10912564, rs10912558 and rs3861950 within the second one , or homozygous for both SNPs .The haploChIP method was used to analyze allele-specific promoter activity, i.e. the loading status of phosphorylated active Pol II to the TNFSF4 gene was expressed in these cells . There was an allele-specific difference in loading of phosphorylated Pol II to the TNFSF4 gene , B and T lymphocytes and HUVEC. It revealed that potential regulatory elements are located mostly elsewhere upstream of the TSS in the large intron 2. Though, intronic SNP rs3850641, located at position 173175832, co-localises with a region associated with histone modifications (H3K36me3: Histone H3 (tri-methyl K36)) between 173175736 and 173175930 bp, while promoter SNP rs45454293, located at position 173177236, is located just a few base pairs away from two regions also associated with histone modifications (CTCF zinc finger transcription factor) between 173177077 and 173177207 bp, and 173177297 and 173177323 bp. In addition to these locations several other regions were found that are predicted to include transcription factor binding sites and DNaseI hypersensitive sites, which map approximately 100 to 1000 bp away from the rs45454293 and rs3850641, but it is not clear to what extent the two SNPs can affect binding of these regulatory factors.A thorough analysis of the region that harbours both polymorphisms of interest to look for regulatory elements  within the ENCODE project was performed in cell lines relevant to The possibility that the rs45454293 and/or rs3850641 SNPs affect the binding of nuclear proteins was analyzed by EMSAs. Allele-specific binding of nuclear proteins was observed when nuclear extracts derived from U937 cells were incubated with oligonucleotide probes spanning the rs45454293 polymorphism . The rs4In silico analysis using the TRANSFAC database indicated that the rs45454293T-allele induces a binding site for the transcription factor AML1a, a protein affecting granulocyte differentiation and proliferation  responsible for the perturbation of gene expression/activity, we used the haploChIP method to investigate whether the putative regulatory rs3850641 and rs45454293 SNPs influence Pol II loading, an indirect measure of allele-specific gene expression in vivo in the presence of a natural chromatin structure. We selected these two specific SNPs for functional analyses because they were the only ones found to be associated with MI. Differences between the two alleles were observed for both SNPs (ratio of 1.13 and 1.38), indicating that the functional significance resides in the haplotype defined by these polymorphisms. Specifically, the haplotype carrying the T-allele of the rs45454293 SNP and the G-allele of the rs3850641 SNP was associated with decreased loading of activated polymerase II, i.e. with lower transcriptional activity. Needless to say, the effect is small and this result is only based on EBV transformed B-cells and should therefore be interpreted with caution. However, the results of the transient transfection studies in HEK293T cells provide further support for a functional role of the rs45454293 polymorphism. It should be emphasized in this context that the lack of endogenous promoter elements for rs3850641 may influence the results for this SNP, as transcription might be affected by interaction of transcription factors binding at various sites of the promoter. Nevertheless, the results obtained allow us to determine which of the two minor alleles (rs45454293T or rs3850641G) indicated by the haploChIP experiment to be associated with lower transcriptional activity, has a functional effect on transcription.In order to dissect the mechanism behind the observed association between TNFSF4 expression.An important question is whether the apparently small variation in gene activity, ranging between 10% and 40%, can have a physiologically significant effect. Importantly, the extent of this effect can be more substantial considering that measurements were performed under basal conditions, i.e. in cells that were not stimulated. Indeed, the gender-specific effect observed suggests that there might be environmental effects, including sex hormones, amplifying the allele/haplotype-specific differences in transcriptional activity. In fact, the importance of such small variation is indicated by the transfection studies which confirm that the risk T-allele of the rs45454293 polymorphism negatively affects transcriptional regulation, providing a mechanistic insight for the observed allele-specific decrease in TNFSF4 expression is associated with increased risk of MI. The role of this SNP in inflammation and thromboembolic disease was suggested by a study of Malarstig et al. TNFSF4 is associated with inflammatory markers and MI in women in two independent cohorts TNFSF4 is also present in mice, female mice being more susceptible to atherosclerosis than male mice TNFSF4 genetic variation.The rs45454293T-allele was shown to be associated with MI in women, thus suggesting that lowered It is notable that the rs3850641 SNP did not influence the binding of nuclear proteins as shown by our EMSA studies. In contrast, these experiments suggested that the rs45454293 polymorphism affects the binding of nuclear factors, protein complexes showing differential binding to the rs45454293T-allele. Taken together, the results of the haploChIP, transient transfections and EMSA studies suggest that rs45454293 is the functional polymorphism and that the lower transcriptional activity associated with the rs45454293T-allele is due to binding of one or more transcriptional repressor(s) to the T-allele.TNFSF4 SNPs examined in the present study have not come up as genome-wide significant in the hitherto published genome-wide association studies (GWAS) for MI, it is likely that TNFSF4 does not belong to the group of major coronary artery disease susceptibility genes that survive the fairly conservative adjustments for multiple-testing applied on the hypothesis-free, high-density, high-coverage SNP genotyping in GWAS. However, the region containing the TNFSF4 gene has been found to be associated with celiac disease, a chronic inflammatory disease with a strong immune component Since the two Tnfsf4 contributes to atheroma formation using both knockout and transgenic mice Tnfsf4 promoter region that affected gene activity TNFSF4 promotes a pro-inflammatory state in humans that destabilizes the atherosclerotic plaque, making it particularly prone to rupture. For as yet unknown reasons, this effect seems to be gender-specific, being confined to women.We have previously shown that The fact that TNFSF4 is expressed by several cell types suggests that TNFSF4 has more functions than the originally reported involvement in T-cell activation TNFSF4 expression is associated with an increased risk of MI. Further analyses are now needed to precisely determine the function of the TNFSF4 protein in MI. Specifically, the gender difference needs to be evaluated on a molecular level.In conclusion, the present work suggests that lowered Figure S1Representation of degree of linkage disequlibrium, in terms of D′ and LOD scores, across the TNFSF4 gene based on 9 variants genotyped in the SCARF cohort. Each square is generated by the intersection between two SNPs and indicates the corresponding D′ value; empty square means D′ = 1.(TIF)Click here for additional data file.Figure S2Expression levels of TNFSF4 in EBV-transformed human B cell lines. Standards are depicted in blue and Ct values from real-time RT-PCR are presented for TNFSF4 expression when cells were kept at basal conditions and upon various stimuli .(TIF)Click here for additional data file.Table S1PCR primers.(DOC)Click here for additional data file.Table S2Nested sequencing primers.(DOC)Click here for additional data file.Table S3Pyrosequencing primers.(DOC)Click here for additional data file.Table S4Genotype distributions and Hardy-Weinberg equilibrium.(DOC)Click here for additional data file.Table S5Distribution of TNFSF4 haplotypes in patients and control subjects .(DOC)Click here for additional data file.


The past decade has seen dramatic changes in our understanding of the scale and complexity of eukaryotic transcriptome owing to the discovery of diverse types of short and long non-protein-coding RNAs (ncRNAs). While short ncRNA-mediated gene regulation has been extensively studied and the mechanisms well understood, the function of long ncRNAs remains largely unexplored, especially in plants. Nevertheless, functional insights generated in recent studies with mammalian systems have indicated that long ncRNAs are key regulators of a variety of biological processes. They have been shown to act as transcriptional regulators and competing endogenous RNAs (ceRNAs), to serve as molecular cargos for protein re-localization and as modular scaffolds to recruit the assembly of multiple protein complexes for chromatin modifications. Some of these functions have been found to be conserved in plants. Here, we review our current understanding of long ncRNA functions in plants and discuss the challenges in functional characterization of plant long ncRNAs. Arabidopsis [Whole-genome tiling array and RNA sequencing (RNA-seq) have revealed that the transcription landscape in eukaryotes is much more complex than had been expected, with a high proportion of novel transcripts generated from intergenic regions and promoters of annotated genes . Meanwhibidopsis . Althougbidopsis , the ENCbidopsis , suggestncRNAs are arbitrarily grouped into short <200 nt) and long ncRNAs . The importance of short ncRNAs, including siRNAs, miRNAs and piRNAs, in transcriptional and posttranscriptional regulation of gene expression has been well recognized and the molecular mechanisms of short ncRNA-mediated regulation have been well understood ,9. In co0 nt and in silico identification of lncRNAs is that they can be distinguished from protein-coding mRNAs based on the absence of discernible open reading frames (ORFs). The starting data for in silico identification can be sequences of cDNAs or Expressed Sequence Tags (ESTs) deposited in public databases or novel transcripts generated by full-length cDNA cloning, tiling arrays and RNA sequencing (see below). Usually cDNAs or EST sequences are first compared with genomic sequences to remove those overlapping with protein-coding genes; the remaining sequences are then subjected to ORF prediction. The threshold of ORF length is usually 70–100 amino acids, i.e., RNAs with a predicted ORF of <70–100 amino acids would be treated as lncRNAs. Existing ORF prediction programs include GeneMark.hmm [in silico approach has been successfully applied to identifying lncRNAs in both plants [The rationale for Mark.hmm , GenScanMark.hmm , ESTScanMark.hmm , ANGLE [Mark.hmm  and ORF-Mark.hmm . More soMark.hmm , DIANA-EMark.hmm , CSTmineMark.hmm , CONC [3Mark.hmm , Coding Mark.hmm , integraMark.hmm  and RNAcMark.hmm . The in h plants  and animh plants ,40,41.Arabidopsis [Arabidopsis FLC (FLOWERING LOCUS C) locus and its 50-kb surrounding region uncovered a number of non-coding transcripts antisense to FLC [Full-length cDNA sequencing is the gold standard for determining exonic structure and coding or non-coding potential of a transcript; however, this approach is time-consuming and expensive. Furthermore, RNAs with low-level expression, a characteristic of most lncRNAs, would be hard to uncover using traditional cDNA cloning strategies. Tiling DNA microarray, designed for genome-wide high-resolution transcriptome analysis, provides an alternative for detection of lncRNAs and their expression. Using this approach, a large number of uniquely transcribed intergenic regions and stress-induced novel transcripts were found in rice  and Arabbidopsis ,44, respe to FLC . HoweverHOTAIR were identified, and lncRNAs expressed in only a small subpopulation of the cells sampled could be detected [Drosophila melanogaster transcriptome using tiling arrays in combination with RNA-seq. Approximately two thirds of these novel transcripts have an ORF less than 100 amino acids, including a multi-exon lncRNA in the well-studied Bithorax complex, which is expressed in embryos and adult males but not in females [Some of the drawbacks with tiling arrays can be circumvented by RNA-seq, which has emerged as a new technology for tackling the complexity of eukaryotic transcriptomes in an unbiased manner ,45,46. Rdetected . In anot females . When co females ,50.i.e., an active promoter marked by H3K4me3 (trimethylation of lysine 4 of histone H3) in combination with a transcribed region marked by H3K36me3 (trimethylation of lysine 36 of histone H3). By searching K4-K36 domains in intergenic regions in the human and mouse genomes, a large number of lncRNAs, named as long intergenic ncRNAs (lincRNAs), were found in these two species. A significant number of these lincRNAs are conserved between human and mouse [Arabidopsis [An actively transcribed region is usually defined by a K4-K36 domain, nd mouse ,51, suggnd mouse . This apbidopsis .PHO2, a target of miR399 and encoding an E2 ubiquintin conjugase-related enzyme (UBC24), is repressed due to miR399-mediated mRNA cleavage [PHO2 activity leads to enhanced expression levels of two root-specific phosphate transporter genes, Pht1;8 and Pht1;9 [Induced by Phosphate Starvation1 (IPS1), a member of the TPS1/Mt4 gene family that was first identified in tomato and Medicago truncatula [Arabidopsis [IPS1 does not encode a protein, and only a 23-nt long sequence motif is conserved among the members from different plant species [IPS1 and hence inhibits miR399-mediated cleavage of IPS1. This observation leads to the hypothesis that IPS1 functions as a non-cleavable target mimic of miR399 to sequester miR399 which in turn attenuates miR399-mediated repression of PHO2 [IPS1 increased the transcript and protein levels of PHO2, whereas transgenic plants overexpressing a cleavable IPS1 did not [IPS1 under phosphate starvation appears to counter-balance the effect of increased miR399 accumulation under the same condition, resulting in fine tuning of PHO2 expression and phosphate uptake [Phosphate is an essential macronutrient for plant growth and development. Plants must not only absorb considerable amounts of phosphate from the soil but must also have a sophisticated regulatory mechanism to maintain phosphate homeostasis throughout the plant to meet the growth and metabolic requirements of each tissue. miRNAs have been shown to be an essential component of this complex regulatory system ,55,56,57cleavage ,54,55,56d Pht1;9 ,54, resuuncatula ,59 and tuncatula  and Arabbidopsis ,62, is a species ,63,64. T of PHO2 . Indeed, did not . Thus, tSuch inhibition of miRNA activity by an endogenous noncleavable ncRNA target has been termed as target mimicry . Recent PTENP1, which is related to the tumor suppressor gene PTEN and produces a naturally occurring ncRNA. Both PTEN and PTENP1 contain many conserved miRNA binding sites in their 3' untranslated regions (UTRs). PTENP1 was found to regulate the expression of PTEN by acting as a decoy for miRNAs that bind to the common sites in the 3' UTRs of PTENP1 and PTEN [linc-MD1, has been shown to regulate the expression of MAML1 and MEF2C by sequestration of miR-133 and miR-135 that target the two genes. MAML1 and MEF2C are two transcription factors that activate muscle-specific gene expression, controlling the timing of muscle differentiation. Consistently, downregulation or overexpression of linc-MD1 resulted in a decreased or increased accumulation of myogenic marker genes in mouse myoblasts, which leads to retardation or acceleration of the muscle differentiation program, respectively [The first example of ceRNA is the human pseudogene and PTEN ,16,18. Mectively . Apart fectively . These rectively ,65.MIRNA genes and the existence of multiple, highly conserved members in most plant miRNA families, it has been extremely laborious and time-consuming to obtain a corresponding null mutant plant line of a MIRNA gene [MIRNA genes [Besides its biological significance, target mimicry has provided an alternative approach for functional characterization of miRNAs. In plants, characterization of gene function has relied largely on the use of genetic knockout mutants caused by T-DNA or transposon insertion. However, because of the small size of RNA gene . Target NA genes ,67. In aNA genes ,69. In aAir, HOTAIR, Xist and Kcnq1ot1, have been shown to target repressive histone-modifying activities and direct epigenetic silencing through a molecular interaction with specific chromatin domains in animals and human [FLC locus in Arabidopsis [Studies in animals and plants have demonstrated that chromatin modifications are important for tissue-specific gene expression and for genome reprogramming during development ,71. Chrond human ,75,76,77nd human . In planbidopsis .FLC acts as a floral repressor that confers a requirement for vernalization, a process by which certain plants acquire competence to flowering in spring by sensing prolonged exposure to winter cold [FLC expression [FLC [FLC locus. The level of PRC2 occupancy at FLC is correlated with the level of H3K27me3 and consequently the degree of repression of FLC [FLC chromatin is necessary for the stable maintenance of vernalization-induced FLC repression. PRC2 is a conserved repressive chromatin modifier [HOTAIR, an lncRNA generated from the HOXC locus, has been shown to mediate epigenetic changes at the HOXD locus in trans by recruiting PRC2 [FLC locus and if they play a role in the repression of FLC expression.ter cold ,79. Molepression . Vernaliion [FLC ,82 throun of FLC ,82. Incrmodifier . In humaing PRC2 . Furthering PRC2 . These fFLC locus. The first class is COOLAIR, including long and short versions of lncRNAs that are transcribed in antisense orientation relative to FLC by a promoter located downstream of FLC. The expression levels of COOLAIR increase during vernalization, and induction of COOLAIR by vernalization coincides with a reduction of FLC but is earlier than the onset of other vernalization makers, such as VIN3 [COOLAIR is involved in early, cold-dependent transcriptional silencing of FLC [COOLAIR and FLC and that the long version of COOLAIR transcripts extend beyond the transcriptional start site of FLC suggests a possible role of COOLAIR through transcriptional interference [FLC and COOLAIR, showed that the transcription of COOLAIR is not required for the initial repression of FLC; instead the promoter and the first exon of the FLC gene are sufficient to initiate FLC repression during vernalization [COOLAIR does not physically interact with PRC2 [Two classes of lncRNAs are identified from the  as VIN3 . This obg of FLC . The natrference . Howeverlization . In addiith PRC2 .COLDAIR that was uncovered by tiling RT-PCR, are transcribed from the first intron of FLC in the same direction as FLC [COOLAIR, COLDAIR is also transiently induced by vernalization, but its peak expression time point is observed later than that of COOLAIR. The COLDAIR transcript interacts directly with CURLY LEAF (CLF), one of the components of PRC2, and can be co-purified with PRC2, indicating a direct role of COLDAIR in the recruitment of PRC2 to FLC. Recruitment and deposition of PRC2 at FLC increase the level of H3K27me3 at FLC chromatin after vernalization [COLDAIR using RNAi compromises cold-mediated H3K27me3 enrichment and the vernalization response. In addition, the vernalization-induced repression of FLC is not maintained once plants return to warm conditions in the COLDAIR knockdown lines. These results together with the observation that the repression of FLC cannot be maintained in PRC2 component mutants suggest that COLDAIR is required for establishment and maintenance of the stable silencing state of FLC [The second class of lncRNAs, n as FLC . Similarlization . Knockdoe of FLC ,86. These of FLC .A growing body of evidence supports the notion that lncRNAs are key regulators of chromatin state through interacting and recruiting chromatin remodelling complexes to specific genomic loci. Several models, by which lncRNAs tether or guide chromatin modifying complexes to their specific destinations, have been proposed ,84,87. MEnod40, first identified in soybean and Medicago sativa ssp. varia [Enod40 is rapidly induced by rhizobia in the root pericycle and in the dividing cortical cells of the nodule primordium during the symbiotic interaction [Enod40 in nodulation [Enod40 is highly conserved among legumes and is also present in various non-legume species, such as rice [Enod40 transcript lacks long open reading frames, but encodes two short peptides  [Enod40 in M. truncatula [Enod40 in the regulation of sucrose utilization in nodules [Enod40 transcript suggest that the general mechanism of action of Enod40 may be achieved through its RNA molecule rather than the short peptides. Firstly, the Enod40 RNA is highly structured and contains a highly stable RNA secondary structure. Analysis of Enod40 transcripts from numerous leguminous species revealed five conserved domains [Enod40 homologues [Enod40 RNA is more conserved than the ORFs encoding short peptides [The early nodulin gene p. varia ,91, is ap. varia . Enod40 eraction . Transgedulation . Enod40  as rice ,96. The ncatula) ,98. Tran nodules . However domains  and at lmologues . Secondlmologues . In addipeptides .Enod40 was demonstrated in M. truncatula. Plants transformed with an altered Enod40, in which the RNA structural elements were deleted while the proper translation of short peptides was retained, decrease its role in stimulation of cortical cell division and formation of nodules [Enod40 has been shown to directly interact with MtRBP1 (Medicago truncatula RNA binding protein 1), a constitutively expressed RNA-binding protein identified by yeast three-hybrid screening, and play a role in re-localization of MtRBP1 from nuclear speckles into cytoplasmic granules during nodulation in M. truncatula [Enod40-expressing plant cells and was not affected by impaired activity of peptide translation [Enod40 RNA rather than the Enod40-encoded short peptides is important for the MtRBP1 re-localization. This study demonstrated that Enod40, like Mei2p in the fission yeast, is part of the nucleocytoplasmic trafficking machinery [The importance of the secondary structure of  nodules . More imuncatula . This renslation , suggestachinery .Medicago truncatula small nodulin acidic RNA-binding protein 1) and MtSNARP2, were also identified to interact with Enod40 in M. truncatula [Enod40 RNA and synthetic RNA oligos as well. In addition, the exact binding sites in Enod40 RNA have not yet been determined although the 5' and 3' regions of the Enod40 transcripts are important for its interaction with MtRBP1 [Recently, two small nodulin acidic RNA-binding proteins, MtSNARP1  that has potential to encode short peptides [vice versa [Currently, distinguishing lncRNAs from protein-coding mRNAs depends solely on ORF prediction. However, several points need to be considered. First, the accuracy of ORF prediction relies on the completeness of the full-length status of the input sequences. Second, some transcripts such as peptides ,98. For peptides . Third, peptides . Thus, wce versa .Despite a number of lncRNAs have been demonstrated to function in guiding chromatin modifying complexes to specific genomic loci, the exact nature of the chromatin binding sites is yet to be determined. A new technique, Chromatin Isolation by RNA Purification (ChIRP)-seq, is suitable for genome-wide characterization of chromatin-binding sites . Applyinin silico or de novo approaches. While functional characterization of plant lncRNAs is still in its infancy, studies so far suggest that they function via similar mechanisms to animal lncRNAs. It can be anticipated that a diverse array of new molecular functions will emerge for plant lncRNAs with increased numbers of new plant lncRNAs being identified and characterized.A large number of lncRNAs have been identified in animals. Sequence conservation and tissue-specific expression patterns strongly suggest that these lncRNAs are more than just transcriptional noise. With the increased cases of confirmed functional lncRNAs, it is becoming increasingly evident that lncRNAs play important roles in diverse cellular processes. However, for the majority of lncRNAs their biological functions remain unknown, and their precise mechanisms of action are yet to be determined. In plants, a number of lncRNAs have also been identified by


Marek’s disease (MD) is a neoplastic disease in chickens caused by the MD virus (MDV). Successful vaccine development against MD has resulted in increased virulence of MDV and the understanding of genetic resistance to the disease is, therefore, crucial to long-term control strategies. Also, epigenetic factors are believed to be one of the major determinants of disease response.MX1 and CTLA-4 and also on genes reported in other cancers including IGF2BP1 and GAL. We detected bivalent domains on immune-related transcriptional regulators BCL6, CITED2 and EGR1, which underwent dynamic changes in both lines as a result of MDV infection. In addition, putative roles for GAL in the mechanism of MD progression were revealed.Here, we carried out comprehensive analyses of the epigenetic landscape induced by MDV, utilizing genome-wide histone H3 lysine 4 and lysine 27 trimethylation maps from chicken lines with varying resistance to MD. Differential chromatin marks were observed on genes previously implicated in the disease such as GAL system that has known anti-proliferative effects in other cancers is also revealed to be potentially involved in MD progression. Our study provides further insight into the mechanisms of MD progression while revealing a complex landscape of epigenetic regulatory mechanisms that varies depending on host factors.Our results confirm the presence of widespread epigenetic differences induced by MD in chicken lines with different levels of genetic resistance. A majority of observed epigenetic changes were indicative of increased levels of viral infection in the susceptible line symptomatic of lowered immunocompetence in these birds caused by early cytolytic infection. The  Rapid advances in epigenetics have led to the discovery of complex mechanisms of gene regulation involving phenomena such as DNA methylation and chromatin modifications. Methylation of particular histone residues has been found to correlate with specific and often opposing cellular functions, e.g. trimethylation of histone H3 lysine 4 (H3K4me3) is associated with transcriptional start sites (TSSs) of active genes while trimethylation of histone H3 lysine 27 (H3K27me3) is found to mark broad genomic regions for repression. Recent studies have also suggested that characteristic combinations of histone modifications or ‘chromatin states’ define functional elements of the genome and determine their contribution to transcriptional regulation-3. MoreoMarek’s disease (MD) is a highly contagious disease caused by an oncogenic α-herpesvirus MD virus (MDV) and characterized by T-cell lymphomas in chickens. Major l3 and 72 developed at the Avian Disease and Oncology Laboratory  that we used in this study, fall into the latter category. These lines share a high degree of genetic similarity but have divergent responses to MDV infection completely independent of the MHC. Several studies have attempted to pinpoint factors responsible for conferring resistance[Natural resistance to MDV can be divided into two categories: major histocompatibility complex (MHC)-associated resistance, wherein different MHC haplotypes at the B blood group locus confer varying levels of resistance and non-MHC associated resistance in which birds having the same MHC haplotype exhibit vastly different responses to MDV infection. Inbred lines 6sistance-16, but 3 is highly resistant to MD, while line 72 is highly susceptible – and compared the epigenetic effects of MD. Genome-wide maps of H3K4me3 and H3K27me3 in thymus tissues of birds from these chicken lines at the latent stage of MDV infection were generated. We carried out systematic analyses to find differential chromatin marks induced by MDV infection. We also investigated co-localization patterns of the two chromatin modifications to detect putative bivalent domains and the effect of MDV on such domains. The results of our study confirm that Marek’s disease has far-reaching effects on the epigenetic landscape of chicken lines with diverse responses to the virus and, thus, furthers our understanding of this complex disease.We utilized the above population of inbred chickens – line 63 and 72 to investigate the epigenetic effects of MDV infection. More than 76 million reads from eight samples were mapped to the chicken genome yielding 14418 and 24950 significantly enriched regions (SERs) for H3K4me3 and H3K27me3, respectively  compared to line 72 (4494). However, upon closer examination, most of the line-specific and condition-specific SERs were revealed to have low read counts , exons, introns and 3’ UTR – and the distribution of SERs across these elements was probed FigureA. We fouTo analyze the relationship between histone modifications and gene expression, histone modification profiles surrounding the TSS and gene body were plotted for genes ranked by their expression level  although mRNA levels were much lower.nfection althoughCTLA-4 is a cell surface glycoprotein expressed on CD4+ and CD8+ T lymphocytes that is a powerful negative regulator of T-cell activation[reg cells that suppress immune response[CTLA-4 expression after MDV infection[2 as a result of MDV infection (p = 0.0003) and there was a similar trend in line 63  and a small increase in line 63  while there was no such change in line 72  is an RNA-binding factor that regulates the translation of mRNAs produced by certain genes like IGF2 and ACTB. Increased expression of IGF2BP1 has been implicated in the development and progression of cancers of various organs, e.g. lung, brain, breast and colon[2   is a testosterone regulated apoptosis inducer and tumor suppressor. Inactivation of EAF2 has been shown to lead to tumors in multiple organs[3 (p = 0.0414) while among uninfected chickens these levels were markedly higher in line 72  but showed only a small decrease in line 63  is a neuropeptide that modulates various physiological functions, such as, inhibition of insulin secretion and stimulation of growth hormone secretion. Three galanin receptors are known : the expression of GALR1 has anti-proliferative effects while GALR2 can be both anti- or pro-proliferative in function. Therefore, the GAL system is considered to be a promising candidate for detection and treatment of various cancers[GAL after infection in both lines  while there was a similar trend in line 63 . This filtering process yielded a list of 99 putative bivalent genes  is a zinc finger protein that functions as a transcriptional repressor which was downregulated at 15 dpi in spleen tissues from F1 progeny (15I5 X 71) of MD-susceptible chickens[EGR1 belongs to a group of early response genes induced by a variety of signaling molecules such as growth factors, hormones and neurotransmitters that is believed to play a major role in cell proliferation and apoptosis[EGR1 promotes tumor growth in kidney cells[f a gene. B-cell chickens. EGR1 bepoptosis. Overexpey cells but suppey cells.3 chickens. However, in line 72, there was a definite increase in H3K27me3 marks but no change in H3K4me3 , although this was likely due to lower precision of the peak caller for broad chromatin marks. Besides, most of the SERs detected in a subset of samples corresponded to regions of low enrichment, which may also be the reason behind the relatively low number of differential SERs detected in our study.MX1, CTLA-4, EAF2 and GAL) or line-specific (IGF2BP1 and MMP2). The increase in H3K4me3 marks around the TSS of MX1, a gene with known antiviral properties, appeared to be correlated with upregulated expression in both lines in response to MDV infection. However, lowered overall mRNA levels in the resistant line suggest additional factors could be involved in the regulation of this gene. Similarly, increased mRNA levels of the lymphocyte surface marker CTLA4 is possibly due to the presence of larger numbers of T cells in MDV infected birds as a result of higher levels of infection. EAF2 functions as an apoptosis inducer in addition to being a tumor suppressor, and therefore, its downregulation could contribute to higher tumor incidence rates in line 72. However, it is not clear why a significant increase in H3K27me3 levels did not have any effect on transcript levels in the resistant line.Genes that have been previously associated with MD and various other cancers showed differential marks that are either MD-induced  or susceptible (72) to MD were hatched, reared and maintained in Avian Disease and Oncology Laboratory . Four chickens from each line were injected intra-abdominally with a partially attenuated very virulent plus strain of MDV (648A passage 40) at 5 days after hatch with a viral dosage of 500 plaque-forming units (PFU). Infected and control chickens from both lines (n = 4) were terminated at 10dpi to collect thymus tissues. All procedures followed the standard animal ethics and use guidelines of ADOL.Two specific-pathogen-free inbred lines of White Leghorn either resistant [Vim are as follows: Forward: 5’-CAGCCACAGAGTAGGGTAGTC-3’; Reverse: 5’-GAATAGGGAAGAACAGGAAAT-3’.The MDV gene escribed. Quantitimentin). The pri3) and digested with MNase to obtain mononucleosomes. PNK and Klenow enzymes  were used to repair the ChIP DNA ends pulled down by the specific antibody. A 3′ adenine was then added using Taq polymerase and a pair of Solexa adaptors  ligated to the repaired ends. Seventeen cycles of PCR was performed on ChIP DNA using the adaptor primers and fragments with a length of about 190 bp (mononucleosome + adaptors) were isolated from agarose gel. Subsequently, cluster generation and ChIP-sequencing (ChIP-Seq) using the purified DNA was performed on the Solexa 1G Genome Analyzer  following manufacturer protocols. The antibodies used and the total number of reads obtained for each sample is listed in Additional fileChromatin immunoprecipitation (ChIP) was carried out using thymus samples from MDV infected and controls birds. About 3Sequence reads obtained from the Illumina 1G Genome Analyzer were aligned to the May 2006 version of the chicken genome  using Maq version 0.7.1. DefaultM, we searched for overlapping SERs in the next sample. In the case of an overlap between M and a significant region, S, the merged region was updated to include both M and S. This procedure was iterated over all samples to obtain a consolidated list of merged SERs.Summarized read counts were subjected to peak calling with SICER. The souRefSeq and Ensembl gene annotations were downloaded from UCSC genome browser. As therGenes were divided into 10 sets based on their absolute expression and representative sets corresponding to high, medium, low and no expression were chosen for visualization. We defined the gene body as the region between the transcription start site (TSS) and the transcription termination site (TTS). Histone modification profiles surrounding the gene body were calculated in 3 distinct regions: 5000 bp upstream of the 5’ end, gene body and 5000 bp downstream of the 3’ end of the gene. For reads falling within the gene body, read counts were obtained in bins 5% of the gene length while 1000 bp windows were used for the 5’ and 3’ flanking regions. The read counts in all cases were normalized to the total number of genes in the categories and total number of reads in the sample. We also compared gene expression to histone modification levels by plotting normalized microarray data  against reads mapping to (i) TSS ± 500 bp and (ii) the gene body for each gene.3 and 72 using DESeq[Reads mapping to merged SERs were tested for epigenetic changes induced by MDV infection in lines 6ng DESeq. We usedng DESeq. Statisthttp://frodo.wi.mit.edu/primer3/) was used to design the primers and four biological and four technical replicates were performed. The primer sequences are shown in Additional fileQuantitative real-time RT-PCR was used to validate the quality of the ChIP and gene transcript levels on the iCycler iQ PCR system . The real-time RT-PCR reactions were performed with a QuantiTect SYBR Green PCR Kit  according to the manufacturer’s instructions. An online primer system (http://www.ncbi.nlm.nih.gov/geo/) under Series accession number GSE24017.Raw and processed ChIP-Seq data discussed in this manuscript were deposited in the NCBI Gene Expression Omnibus (GEO)  and the associated genes. P-values from three contrasts are displayed as follows: 63: 63I vs 63N, 72: 72I vs 72N, 63v72N: 63N vs 72N. 63I: line 63 infected, 63N: line 63 control, 72I: line 72 infected, 72N: line 72 control.Click here for fileDifferential H3K27me3 marks.Genome-wide differential H3K27me3 marks produced by DESeq (FDR < 0.4) and the associated genes. P-values from three contrasts are displayed as follows: 63: 63I vs 63N, 72: 72I vs 72N, 63v72N: 63N vs 72N. 63I: line 63 infected, 63N: line 63 control, 72I: line 72 infected, 72N: line 72 control.Click here for filePutative bivalent genes from colocalization analysis of H3K4me3 and H3K27me3.Click here for fileFunctional annotation clustering of bivalent genes using DAVID.Click here for fileSequencing results showing raw and mapped reads for each sample.Click here for filePrimers used for quantitative PCR validation.Click here for file


As a consequence of acute kidney injury (AKI), proximal tubular cells hyperrespond to endotoxin  by exaggerated renal Tnf-α Production. This LPS hyperresponsiveness is transcriptionally mediated. The epigenetic pathways that control these responses are unknown.We applied multiplex chromatin immunoprecipitation platform (Matrix ChIP) to explore epigenetic pathways that underlie endotoxin hyperresponsiveness in the setting of preceding unilateral renal ischemia/reperfusion (I/R) in mouse AKI model. Endotoxin exposure after I/R resulted in enhanced transcription, manifested by hyperresponsive recruitment of RNA polymerase II (Pol II) at the Tnf-α gene. At this locus, LPS but not I/R increased levels of Pol II C-terminal domain (CTD) phosho-serine2 &5 and induced dephosphorylation of the transcription-repressive histone H4 phospho-serine-1. In contrast, I/R but not LPS increased the transcription-permissive histone phosphorylation  at the Tnf-α gene. In agreement with these observations, I/R but not LPS increased activity of cognate kinases  at the Tnf-α locus. Cross-talk of histone phosphorylation and acetylation synergize to active gene expression. I/R and LPS increased histone acetylation. . Levels of some histone acetyltransferases at this gene (PCAF and MOF) were increased by I/R but not by LPS, while others were induced by either I/R or LPS and exhibited endotoxin hyperresponsive patterns . The adaptor protein 14-3-3 couples histone phosphorylation with acetylation, and tethers chromatin modifiers/transcription elongation factors to target genes. Both I/R and LPS increased levels of 14-3-3 and several chromatin/transcription modifiers  at the Tnf-α gene, all exhibiting endotoxin hyperresponsive recruitment patterns similar to Pol II.Our results suggest that I/R and LPS differentially trigger phosphorylation (Pol II and histone) and acetylation (histone) epigenetic pathways that interact at the Tnf-α gene to generate endotoxin hyperresponse in AKI. Classical studies from several laboratories, including Nath et al Epigenetic processes play a critical role in transcriptional regulation. These processes are driven by changes in covalent modifications of DNA and associated proteins, alterations in chromatin structure, and recruitment of a diversity of signal responsive transcription factors and enzymes. The molecular mechanisms controlling chromatin and transcription are remarkably conserved in organisms as diverse as yeast, flies and mammals Guided by other model systems To study mechanisms of renal endotoxin hyperresponsiveness, we chose an I/R model RT-PCR was used to assess Tnf-α mRNA levels. Cyclophilin A (CypA) gene was used for comparison http://genome.ucsc.edu/), thus it is appropriate to use this gene site for comparison with the Tnf-α gene. Injury-induced increases in Pol II levels were seen at both the 5′ and 3′ ends of the Tnf-α locus, indicating that transcriptional initiation plays a role in the transcriptional hyperresponse to endotoxin in this AKI model.Measurement of Pol II levels along a locus provides means to assess rates of transcription. Analysis of Pol II binding at the Tnf-α gene, estimated by antibodies to the CTD and N-terminal domains, revealed increased levels of the polymerase following either I/R or LPS treatment . Importa1S2P3T4S5P6S7), which are sites for modifications that define the state of the polymerase Mammalian Pol II CTD contains 52 heptapeptide repeats . I/R, but not LPS, also increased H3pSer10 at the CypA gene. The histone variant H3.3 is enriched at actively transcribed genes row 3). There was little or no change in H4pSer1 at the CypA site.Phosphorylation of H3 serine 10 (H3pSer10) is thought to act synergistically with histone acetylation in bringing p-TEFb elongation complex to transcribed genes Higher levels of H3K36m3 have been correlated with enhanced transcription elongation rates Analysis of H3K9m2, H3K9m3, H3K27m3 and H4K20m3 repressive marks The above results  togetherGiven that Pol II and histones are phosphorylated in our model, protein kinase cascades may play a prominent role in AKI- and LPS-induced transcription changes. Different classes of kinases are being discovered bound to genes in situMsk1/2, activated by Erk1/2, is one of the enzymes that phosphorylate serine 10 of histone H3 Next we tested binding of two other H3pSer10/H3.3pS31 kinases, Aurora and IκB kinase α (IKKα). While the Aurora A pattern was similar to H3pSer10 and H3.3pSer31 , the IKKI/R and LPS increased histone acetylation of all four histones at the Tnf-α gene . These PHistone H3 acetylation and phosphorylation were both induced at Tnf-α by renal injury &4, consiThe 14-3-3 family of proteins brings several chromatin factors to transcribed loci, including bromodomain containing factors involved in transcriptional elongation We have previously shown that BRG1 binds to the Tnf-α gene and activates its expression in renal ischemia BRG1 interacts with heterochromatin proteins (HP) that regulate chromatin structure We set out to define transcription and chromatin bases and factors to explain augmented expression of endotoxin-inducible Tnf-α gene in AKI. Herein, we uncovered previously unknown genomic events associated with renal Tnf-α gene transcription induced by endotoxin and I/R. PTMs, Pol II and chromatin modifiers that bind to Tnf-α gene can be approximately grouped into five synchronous recruitment patterns , and bacterial lipopeptides signal through TLR2 Ramesh et.al. has previously shown that treatment with nephrotoxic cisplatin but not LPS activated MAPK pathways in cultured mouse proximal tubule cells I/R but not LPS increased H3pSer10 and H3.3pSer31 . In agrerow 2 and row2). Different TLR4 agonists can differentially activate Tnf-α expression in the same cell We also found changes that were induced more strongly by LPS than by I/R including Pol II phosphorylation of CTD serine 2 and 5 . HistoneIn AKI, proximal tubules are a major source of cytokine production contributing to renal injury row 3) could also be also contributing to the endotoxin hyperresponse, We suggest that combination of histone phosphorylation and acetylation changes induced by I/R (histone/CTD phosphorylation and acetylation) and LPS (CTD phosphorylation histone acetylation and H4 serine-1 dephosphorylation)  and cause recruitment of Pol II to the Tnf-α promoter ylation) , 5–7 couTnf-α mRNA stability is tightly regulated Endotoxin hyperresponsiveness of cytokine production in AKI has been known for nearly a decade 3VO4, Na2MoO4 and β-glycerophosphate were from Sigma. The antibodies were commercially available and are listed in Bovine serum albumin (BSA), phosphate buffered saline (PBS), salmon sperm DNA, and protein A were from Sigma, and proteinase K was from Invitrogen. Matrix ChIP-MeDIP 96-well polypropylene plates were from Bioexpress. Formaldehyde, ethanol, NaCl, EDTA, Triton X-100, NP-40, Tris, leupeptin, PMSF, p-nitrophenyl phosphate, NaF, NaThe experimental protocol is shown in RNA was extracted from tissue fragments using Trizol reagent as per the manufacturer’s protocol. To synthesize cDNA, 400 ng of Trizol-extracted total RNA was reverse transcribed with 200 units MMLV reverse transcriptase (Invitrogen) and random hexamer primers in 20 µl reactions. RT reactions were diluted 100-fold prior to running qPCR The multiplex microplate Matrix ChIP method was previously described To acquire, store and analyze large data sets generated by the high throughput Matrix ChIP platform, we developed a novel graphical method (GraphGrid). Pair-wise statistically significant differences are represented by the size of a circle for each comparison made; p<0.05 by small circle, p<0.01 by large circle and no circle indicating non-significance. The tool automatically adjusts the computed pairwise p-value (significance t-test) using a Bonferroni correction factor –C.Additional information about the methods and data are available upon request.Figure S1Ischemia-reperfusion and endotoxin acute renal injury model and data analysis. A. Model. Mice were anesthetized and subjected to a midline abdominal incision under sterile conditions and, after 30 min of unilateral renal artery occlusion, the clamp was released . Twenty-four hours later, I/R injury mice received a tail vein injection of either lipopolysaccharide (LPS) or saline B.GraphGrid analysis. Results of statistical analysis of endotoxin and I/R responses are shown. Graph bars represent mean values±SEM. Solid yellow circles positioned at line intersections below the graph designate significant differences between given pairs of means. Bar above the circle represents one of the paired means. The second of the paired means is located above the very left end of horizontal line crossing the yellow circle. Five different paired statistical comparisons are done as shown with the numbered circles (right panel). C. Statistical analysis is done using Bonferroni correction. Statistical differences between two means  are shown by the size of the solid yellow circle. : p<0.05 by small circle, p<0.01 by large circle, and no circle indicating the differences are not statistically significant.(TIF)Click here for additional data file.Figure S2Matrix ChIP analysis of repressive histone lysine methylated marks at the TNF-α genes following unilateral kidney I/R and LPS injection. Sheared cross-linked renal cortex chromatin from mice were assayed using antibodies to histone H3 and H4 lysine methylated residues. ChIP DNA were analyzed at the Tnf-α first and last exon and CypA first exon in real-time PCR. Data represent mean ± SEM , expressed as fraction of input.(TIF)Click here for additional data file.Figure S3Synchronized epigenetic changes grouped into different types of responses to I/R and LPS treatment in AKI.Type I, I/R and LPS co-responsive/hyperresponsive; Type II, I/R only responsive; Type III, I/R and LPS co-responsive at saturation; and Type IV and V, LPS only responsive.(TIF)Click here for additional data file.Table S1Sequences of primers used in ChIP-qPCR primers.(DOCX)Click here for additional data file.Table S2Antibodies used in Matrix ChIP assays.(DOCX)Click here for additional data file.


Motivation: Although chromatin immunoprecipitation coupled withhigh-throughput sequencing (ChIP-seq) or tiling array hybridization (ChIP-chip) isincreasingly used to map genome-wide–binding sites of transcription factors (TFs),it still remains difficult to generate a quality ChIPx (i.e. ChIP-seq or ChIP-chip)dataset because of the tremendous amount of effort required to develop effectiveantibodies and efficient protocols. Moreover, most laboratories are unable to easilyobtain ChIPx data for one or more TF(s) in more than a handful of biological contexts.Thus, standard ChIPx analyses primarily focus on analyzing data from one experiment, andthe discoveries are restricted to a specific biological context.Results: We propose to enrich this existing data analysis paradigm bydeveloping a novel approach, ChIP-PED, which superimposes ChIPx data on large amounts ofpublicly available human and mouse gene expression data containing a diverse collection ofcell types, tissues and disease conditions to discover new biological contexts withpotential TF regulatory activities. We demonstrate ChIP-PED using a number of examples,including a novel discovery that MYC, a human TF, plays an importantfunctional role in pediatric Ewing sarcoma cell lines. These examples show that ChIP-PEDincreases the value of ChIPx data by allowing one to expand the scope of possiblediscoveries made from a ChIPx experiment.Availability:http://www.biostat.jhsph.edu/∼gewu/ChIPPED/Contact:hji@jhsph.eduSupplementary information:Supplementary data are available at Bioinformaticsonline. ChIPx experiments, including ChIP-seq (Supplementary Method 1.1). This is largely because of the prohibitively highlabor and time costs to perform each experiment. To resolve this limitation, we investigatewhether publicly available gene expression data (PED) in the Gene Expression Omnibus .ChIP-PED relies on two large compendiums of gene expression profiles, consisting of 13182 human gene expression samples generated from Affymetrix Human U133A (GPL96) and 9643mouse samples generated from Affymetrix Mouse 430 2.0 (GPL1261) arrays  are genes that areboth TF-bound in the ChIPx experiments and differentially expressed in corresponding geneexpression data in which the expression of the TF is perturbed. The latter are from TFperturbation experiments comparing wild-type with TF-knockout, control with TF-knockdownor control with TF-overexpression and so forth. Users will need to provide and analyzetheir own ChIPx and TF perturbation experiments to define the input target genes.TFE, is defined as asimple average of the normalized probeset intensities, p: TF is the set ofprobesets that measure the expression of the TF, and TFn isthe number of probesets for the TF. TG activity, TGA, isdefined as: ChIP-PED first measures the TF expression and TG activity in each microarray sample inour PED compendiums. TF expression, TG is the set of target genes of the TF,TGn is the number of target genes,gn is the number of probesets for a specific target gene,g, and gs is 1 or −1 depending onwhether gene g is activated (positively regulated) or repressed(negatively regulated), respectively. gs is included toaccount for TFs that are capable of both activating and repressing different target genes.TGA is designed to describe the regulatory activity of aTF through its target genes, rather than measure the raw expression of the target genes.For example, if a TF acts mainly as a repressor in a biological context in which it isfunctionally active, we would observe low expression of its target genes, but highTGA because of the multipliergs = −1 . Examples of the distributions of TFE andTGA for real TF ChIPx data are shown in Supplementary Figure S3.Here, 1 ∼ c4c to define (i) high-TF expression(‘TF > c1E’), (ii) low-TFexpression (‘TF <c2E’), (iii) high-TG activity(‘TG >c3A’) and (iv) low-TG activity(‘TG <c4A’), denoted by TF+, TF−,TG+ and TG−, respectively. By default, 1 ∼c4c are chosen to be values corresponding to a one-sidedP-value of 0.1 based on fitted normal distributions forTFE or TGA across all samples,with 1c and 3c taking values abovethe mean, and 2c and 4c takingvalues below the mean . For eachcontext c, it counts (i) K, the total number of samplesin the compendium that exhibit the pattern, (ii) Cn, the totalnumber of samples in context c, and (iii) Ck,the number of samples in context c that exhibit the pattern.Fisher’s exact test is then applied to the quadruplet  to test the association betweenc and the regulatory pattern of interest (i.e. whetherCk is significantly larger than random expectation). Toaccount for testing multiple contexts, the P-values are adjusted usingthe Bonferroni correction. The final output of ChIP-PED is a ranked table of statisticallysignificant biological contexts at a default Bonferroni corrected P-valuecut-off of 0.05 (Supplementary Method 1.5).Then given a compendium of TFE andTGA values for the set of contexts found, (iii) calculate,sort and plot the mean and standard deviation of the TFE andTGA values for each context and (iv) performt-tests between all pairwise combinations of the contexts forsignificant differences in mean TFE orTGA. See Supplementary Methods 1.6–1.7 for details and Section 3.3 for anexample analysis.After the initial ChIP-PED analysis, ChIP-PED can perform the following analyses tofurther explore each predicted context: (i) search for related contexts in the compendiumbased on user-specified keyword(s), (ii) extract the Oct4, Gata1and Jarid2 in mice and MYC, STAT1 andESR1 in human—using the datasets listed in Supplementary Table S1. The TF target genes were constructed by intersectingTF-bound genes predicted from ChIPx data with differentially expressed genes [falsediscovery rate (FDR) ≤ 10%] in TF perturbation data. TF-bound genes weredefined as genes with a significant peak (FDR ≤ 10%) overlapping with the−10- to +5-kb region around the transcription start site of the gene. Detailsare provided in Supplementary Method 1.2, and full target gene lists can be found inSupplementary Tables S2–S7.We evaluated ChIP-PED by applying it to multiple TFs—Supplementary Tables S2–S7. We also experimentally validated a novelChIP-PED functional connection between MYC and Ewing sarcoma (Supplementary Method 1.8).Predictions were verified by a thorough search of existing literature to identify whethereach prediction was functionally validated or suggested in previous experiments.‘Functional’ validations required previous experimental data from thepredicted biological context demonstrating observable changes in phenotype when theexpression of the TF is perturbed or TF binding coupled with transcriptional responses toTF binding of target genes. ‘Suggested’ predictions must be supported by otherlines of indirect evidence, such as experimentally observed high-TF protein levels in thepredicted context. All supporting references are recorded in Supplementary Fig. S4 andSupplementary Method 1.9).We first investigated whether it was appropriate to compare gene expression acrossthousands of heterogeneous microarray samples generated by different laboratories. To thisend, we asked whether laboratory and batch effects were a significant and detrimentalsource of variation , and thaTFE)and TG activity (TGA) for multiple TFs, including mouseOct4 and Gata1 and human MYC andSTAT1. We reasoned that if there were strong laboratory or batcheffects that overwhelmed the biological signal, we would observe weak to zero correlationbetween TFE and TGA across theheterogeneous samples. Instead, we found significant correlation between TF expression andTG activity; the Pearson correlation coefficients between TFEand TGA for Oct4, MYC, Gata1and STAT1 were 0.679, 0.418, 0.303 and 0.699, respectively and thebiological contexts enriched with a TF+TG+ regulatory pattern were biologicallymeaningful. In this regard, we performed and evaluated ChIP-PED analyses of six TFs: mouseOct4, Gata1 and Jarid2 and humanMYC, STAT1 and ESR1.After verifying that it is meaningful to compare Oct4 is a master regulator in mouse embryonic stem cells (ESCs). Weobtained 519 activated and 337 repressed Oct4 target genes by combiningChIP-seq data from mouse ESCs with gene expression data from ESCs in whichOct4 was knocked down via siRNA (Supplementary Tables S1 and S2). Using these target genes as input, Oct4 target geneactivity was plotted against Oct4 expression after excluding the PEDsamples used to construct the target genes (Oct4 in ESCs and its decrease in expression whenESCs differentiate (et genes A. We fouOct4-expressing (+Oct4) biologicalcontexts—most commonly, undifferentiated ESCs of the TF+TG+ samples were differentiating ESCs or EBs, and the remaining12.7% (61/480) were other contexts, such as embryos, mouse embryonic fibroblasts(MEFs) and so forth. The observation that a large proportion (30.8% =18.1% + 12.7%) of TF+TG+ samples were biological contexts notknown to express Oct4 (-Oct4) shows the noisy nature ofPED. This makes it challenging to correctly predict whether an individual sample trulyexhibits functional TF activity. However, the primary goal of ChIP-PED is not to correctlyidentify TF-active samples, but to identify TF-active biological contexts. Thus, ChIP-PEDtakes advantage of the fact that each biological context has multiple samples in the PEDcompendium to predict TF-active biological contexts by reporting the contexts with astatistically significant proportion of TF+TG+ samples.Of the 9643 mouse samples in the compendium, 480 were labeled as TF+TG+ usingthe default cut-offs. Among them, 69.2% (332/480) were knownted ESCs —coveringP-value cut-off of 0.05 (Supplementary Table S2). Among these, 89.3% (25/28) were different+Oct4 contexts, and 10.7% (3/28) were-Oct4 contexts related to differentiating ESCs and EBs. The 28statistically enriched contexts covered 47.9% (230/480) of the TF+TG+samples. These samples were from multiple laboratories , confirming that the observed enrichment in ESCs was unlikely to be causedby experimental artifacts or laboratory or batch effects. More importantly, ChIP-PEDfiltered out most -Oct4 biological contexts: 30.8% (148/480)TF+TG+ samples were from -Oct4 contexts, whereas only10.7% (3/28) of the TF+TG+-enriched contexts were from-Oct4 contexts, and among the samples of the 28TF+TG+-enriched contexts, only 8.7% (20/230) were -Oct4samples. Therefore, by integrating information from multiple samples, predictions made atthe context level are more accurate than at the sample level.In total, ChIP-PED predicted 28 biological contexts were enriched with TF+TG+activity at a Bonferroni-corrected MYC, a TF known to be involved in multipletumors (Supplementary Tables S1 and S3). The target genes were required to be differentially expressed in thesame direction in ≥50% of the TF perturbation datasets andMYC-bound in ≥50% of the ChIPx datasets. The aim was toidentify the core MYC regulatory target genes that were cell-typeindependent across multiple MYC-active contexts. As expected,MYC regulatory activity was significantly enriched in numerous tumortypes (Supplementary Table S3): 74.7% of the 521 TF+TG+ sampleswere tumors, which was significantly higher than the background percentage of 46.0%for all 13 182 samples in the human PED compendium . Among these samples, ChIP-PED predicted 33TF+TG+-enriched biological contexts. Many of the predictions were found to becorrect, such as B-cell lymphomas, which have been shown to have functionally active MYCprotein . ChIP-PED found enriched Gata1 expression andtarget gene activity in expected biological contexts related to erythrocyte andmegakaryocyte development, such as in fetal liver, common myeloid progenitor cells andmurine erythroleukemia cells  infected with hepatitis C and malaria, consistent with current knowledge ofSTAT1 regulatory functions . To systematically evaluateChIP-PED prediction accuracy, we examined all predictions through a survey of existingliterature. We found that 90 of 178 (50.6%) biological contexts predicted to beenriched with TF+TG+ activity were functionally validated in previousexperiments (see Section 2). For example in the Oct4 analysis, 20 of the28 predictions were functionally validated, even though 25 of the 28 predictions wereknown to express Oct4 RNA (i.e. +Oct4 as describedearlier). This is because functional experiments demonstrating changes in phenotype afterperturbing Oct4 or showing TF binding with associated transcriptionalresponse of target genes could only be found for 20 predictions; therefore, we onlycounted those 20 predictions as functionally validated. The 50.6% accuracy rate isa conservative estimate, as the remaining predictions may not necessarily be falsepositives, but instead may represent unknown/novel functional relationships. Altogether,these results demonstrate that given the target genes of a TF defined from ChIPx and TFperturbation data from one or a few biological contexts, ChIP-PED is capable ofdiscovering TF-active contexts from a broad spectrum of PED samples.For the six TFs analyzed, ChIP-PED made 178 TF+TG+ predictions listed inSupplementary Tables S2–S7). Conversely, searching for only TG+samples resulted in only 34.0% (67/197) functionally validated TG+ predictions(Supplementary Tables S2–S7). Thus, it is useful to check both TFexpression and target gene activity of each context to identify TF+ and TG+samples when predicting TF-active biological contexts.Searching through PED only for TF+ samples or only for TG+ samples, rather thanTF+ and TG+ samples, may result in substantially decreased ChIP-PED predictionaccuracy and number of functionally validated predictions. For instance, when we modifiedChIP-PED to search only for TF+ samples, we found that only 40.0% (62/155) ofthe predicted TF+ contexts were functionally validated in previous experimentscompared with 50.6% (90/178) when using ChIP-PED to search for TF+ andTG+ samples .TF target genes can vary from one cell type to another. If two known TF-active contextsdo not share any target genes, then ChIP-PED will not be able to predict either contextusing target genes constructed from the other context. To test whether ChIP-PED can stillbe effective when only a minority of the target genes are shared, we used ChIP-PED toanalyze Supplementary Tables S2–S7).ChIP-PED would not be useful if the predicted biological contexts were always closelyrelated to the context in which the experimental data were generated. Our results indicateotherwise: among the 90 of 178 (50.6%) predictions supported by previous functionalexperiments, 40 (44.4%) are in contexts unrelated to the context(s) in which theexperimental data used to construct the TF target genes were obtained . To examine STAT1 functionalactivity in hepatitis C-infected PBMCs in more detail, we searched for all contexts inGSE7123 and also found healthy PBMCs along with the predicted hepatitis C-infected PBMCs.We then used ChIP-PED to compare TF expression and TG activity in each context and foundthat TFE and TGA values weresignificantly different between healthy and hepatitis C-infected PBMCs, with a gradualdecrease in TFE and TGA values aspatients recovered from infection . When reviewing both of the original publications,the STAT1 ChIPx study  biological contexts were not supported by functional experiments thatdemonstrated MYC functional activity (Supplementary Table S3). One of the non-functionally validated contexts wasA673 cells . Furthermore, xenograft of control and shMYC TC71 Ewing sarcoma cellsinto immunodeficient mice (NOD/SCID/IL-2γ null) resulted in a significant decreasein volume and weight for the MYC knockdown tumors after 6 weeks of growth. Thus, these predictions are likely torepresent previously unknown functional predictions between each TF regulatory pathway andcontext, which further demonstrates that ChIP-PED can discover known and unknown TF-activebiological contexts. In total, 141 of 178 (79.2%) TF+TG+ predictions forthe six TFs analyzed were either directly supported by functional evidence (90 of 178) orindirectly supported in existing literature (51 of 178).When studying the 88 functionally unverified predictions across the six TFs analyzed, wefound that 51 of the 88 (58.0%) predictions were supported by other lines ofindirect evidence in existing literature, such as experimentally observed high-TF proteinlevel in the predicted context (Supplementary Method 1.12 and Supplementary Table S7). We also analyzed all six TFs by using multipleannotation window sizes to annotate ChIPx peaks. Different window sizes producedcomparable prediction accuracies at the default significance cut-off. However, the−10- to +5-kb window size produced the largest number (i.e. highest power) offunctionally validated and/or indirectly supported predictions (Supplementary Method 1.12 and Supplementary Tables S9 and S10). Thus, our results suggest that the −10- to +5-kb windowrepresents a reasonable choice as a default annotation region.Many TFs regulate a subset of their target genes through distal enhancers. Recent tools,such as GREAT . In addition, we checked whether predicted biologicalcontexts with more samples in the compendium were more or less accurate than predictedcontexts with fewer samples. Our results were unable to find a clear monotone relationshipbetween sample count for a given biological context and prediction accuracy (Supplementary Method 1.14 and Supplementary Table S11).We also compared how well a median, rather than mean, target gene activity measure wouldperform and found the predictions and prediction accuracy to be almost the same; acrossall six TFs, 171 predictions were identical between the two measures accounting for98.8% (171/173) of the median-based predictions and 96.1% (171/178) of themean-based predictions .We view ChIP-PED as an exploratory tool for fast and cost-effective hypothesis generationand screening. In this respect, the default cut-offs that define high- or low-TF expressionor TG activity should be primarily used for initial exploration or first-pass automatichypothesis screening, rather than as strict optimal cut-offs that apply to all TFs. Based onour real data analysis experience, we found it difficult to set a single consistent cut-offthat was optimal across all TFs, as TFs can vary greatly in terms of regulatory behavior. We, theChIP-PED acts primarily as a guide to highlight biological contexts that would be goodleads for experimental investigation. As such, we do not expect all ChIP-PED predictions tobe correct nor for ChIP-PED to recover all TF-active biological contexts. This, however,does not prevent ChIP-PED from being a useful and unique tool: our analyses have shown thatit can predict many known and new TF-active contexts with reasonable accuracy, and therecurrently exists no other computational method for analyzing ChIPx data that performs asimilar task.Although we have shown that ChIP-PED is able to capture pertinent biological information inPED, better statistical models are still needed to address technical biases and variationsbecause of laboratory and batch effects. A natural extension of ChIP-PED would be to analyzemultiple TFs and their TGs together to better connect cooperative TF regulatory pathways tocell types and diseases. Similarly, more work is also needed to understand how homologousTFs or other TFs with similar regulatory functions impact the regulatory activity of the TFof interest in different contexts.Our study is not necessarily the best or only way to integrate ChIPx and PED; however, tothe best of our knowledge, this is the first systematic study of using PED to enhance ChIPxanalyses in human and mouse. We hope that ChIP-PED will inspire new computational approachesthat continue to maximize the value of ChIP-seq and ChIP-chip experiments.Funding: National Institutes of Health(R01HG005220 and R01HG006282 to R.A.I andH.J.); National Institutes of Health training(T32GM074906 to G.W.); St. Baldrick’s CareerDevelopment Award (to J.T.Y.).Conflict of Interest: none declared.