Entamoeba histolytica transcription factor Upstream Regulatory Element 3-Binding Protein (URE3-BP) is a calcium-responsive regulator of two E. histolytica virulence genes, hgl5 and fdx1. URE3-BP was previously identified by a yeast one-hybrid screen of E. histolytica proteins capable of binding to the sequence TATTCTATT (Upstream Regulatory Element 3 (URE3)) in the promoter regions of hgl5 and fdx1. In this work, precise definition of the consensus URE3 element was performed by electrophoretic mobility shift assays (EMSA) using base-substituted oligonucleotides, and the consensus motif validated using episomal reporter constructs. Transcriptome profiling of a strain induced to produce a dominant-positive URE3-BP was then used to identify additional genes regulated by URE3-BP. Fifty modulated transcripts were identified, and of these the EMSA defined motif T[atg]T[tc][cg]T[at][tgc][tg] was found in over half of the promoters (54% p<0.0001). Fifteen of the URE3-BP regulated genes were potential membrane proteins, suggesting that one function of URE3-BP is to remodel the surface of E. histolytica in response to a calcium signal. Induction of URE3-BP leads to an increase in tranwell migration, suggesting a possible role in the regulation of cellular motility.The  Entamoeba histolytica are asymptomatic. However, in a minority of cases, they develop into invasive and even life-threatening amebiasis. We suspect, based on prior studies of invasive amebae, that changes in amebic gene expression enable the transition from asymptomatic to invasive infection. Our long-term goal is to identify the genetic program required to cause amebic colitis. Here, we studied a transcription factor named URE3-BP that controls the expression of two virulence genes, the Galactose and Galactose N- acetyl- galactosamine inhibitable lectin  and ferredoxin. We suspected that this factor might coordinate invasiveness by co-regulating additional virulence factors. The consensus DNA motif that is recognized by URE3-BP was identified by reporter gene assays and by electromobility shift assays. We then inducibly expressed a constitutively active form of the transcription factor, and measured the changes in total amebic gene expression mediated by overexpression of this dominant-positive version of URE3-BP. This analysis allowed for a further definition of the functional URE3 motif. Inducible expression of URE3-BP led to changes in the transcript levels of several novel amebic membrane proteins. In conclusion, this genome-wide analysis of a transcription factor and its cis-acting regulatory sequence in Entamoeba histolytica has identified new transcripts regulated by URE3-BP that may play a role in trophozoite motility within a coordinated virulence-specific gene regulatory network.Most infections with  Entamoeba histolytica is a human parasite that is the etiologic agent of amebic dysentery and liver abscess. Only one of every five infections leads to disease E. histolytica to trophozoites growing in vivo, as well as to that of less virulent strains and recent clinical isolates The early branching eukaryote E. histolytica by investigating further the role of the upstream regulatory element 3-binding protein (URE3-BP) transcription factor.Here we have attempted to study the molecular mechanisms involved in the transcriptional regulation of virulence in hgl5 and ferredoxin 1 (fdx) genes. Mutation of the URE3 motif within the hgl5 and fdx1 promoter led to a four-fold rise and a two-fold drop in gene expression respectively, indicating that URE3 may function as a repressor or activator depending on context URE3-BP is a calcium regulated transcription factor, that is known to bind to the URE3 motif and thereby modulate transcription of both the Gal/GalNAc-inhibitable lectin E. histolytica cDNA encoding a protein (URE3-BP) that recognized the URE3 DNA motif E. histolytica nucleus and cytoplasm with an apparent molecular mass of 22.6 KDa. Two EF-hand motifs were identified in the amino acid sequence of URE3-BP. Binding of URE3-BP to the URE3 motif was inhibited in vitro by addition of calcium. Mutation of the second EF hand motif in URE3-BP resulted in the loss of calcium inhibition of DNA binding, as monitored by an electrophoretic mobility shift assay. Chromatin immunoprecipitation experiments confirmed the calcium-dependent interaction of URE3-BP with both the hgl5 and fdx1 promoter DNA Previously a yeast one hybrid screen was used to identify an E. histolytica it may be coordinately regulated at the transcription level with other virulence genes. In this light, it was intriguing that the mRNA of (URE3-BP) was down regulated two-fold in vivoE. histolytica pathogenesis and help delineate molecular and cellular mechanisms involved in the expression of virulence.Because the Gal/GalNAc inhibitable lectin is an important virulence factor of Position-specific variability in the sequence of transcription factor binding sites renders recognition of valid targets by computational methods alone extremely challenging E. histolytica genome identified homologues of most of the RNA polymerase II subunits E. histolytica core promoter varies from the conventional norm by containing a third regulatory sequence GAAC in addition to the TATA box and INR. This may have an unpredictable impact on the machinery necessary for regulation of transcription et al to correlate potential E. histolytica DNA motifs with high and low gene expression The sequencing of the hgl5 promoter were assessed for promoter activity in an episomal reporter construct. Finally, to identify additional genes regulated by URE3-BP, genome-wide expression profiling of transcripts from strains over-expressing a calcium insensitive URE3-BP mutant was performed.To identify the consensus binding site sequence, a position weight matrix (PWM) of transcription factor binding to the URE3 motif was developed. To test the validity of the matrix, selected mutants within the URE3 motif of the E. histolytica strain HM1:IMSS trophozoites were grown at 37°C in TYI-S-33 medium containing penicillin (100 U/ml) and streptomycin (100 μg/ml) (GIBCO/BRL)4 trophozoites/ml) were used for nuclear extract preparation. Crude nuclear extracts were prepared by the method previously described E. histolytica trophozoites was achieved by use of the previously described lipofection technique 5 amebae per ml) in Medium 199  supplemented with 5.7 mM cysteine, 1 mM ascorbic acid, 25 mM HEPES pH 6.8 (M199s) 3 μg of DNA and 15 μl of Superfect (Qiagen) was added. Treated amebae were left for 3 hours at 37°C, then growth media was added, and incubation at 37°C was continued overnight. The expression of all the recombinant proteins was confirmed by western blotting. Nuclear and cytoplasmic extracts were prepared using standard techniques et al. Briefly trophozoites were washed and suspended in 120 mM KCI, 0.15 mM CaCl2, 10 mM K2HPO4/KH2PO4, pH 7.5, 25 mM HEPES, 2 mM EGTA, 5 mM MgC12, 50 μg/ml of plasmid and 3.1 μg/ml of DEAE-dextran, and electroporated at 500 μF and 500 V/cm  Stable transfection of et al 2001 hgl5 promoter TATTCTATTGAAAATAAAAGAAGTGTTCCAAAAAGATA (hgl5-URE3). The protein-DNA interaction occurred in band shift buffer  to which 0.2 μg of poly(dIdC), 10 fmol of DNA probe, and 2 μg of nuclear extract were added. The reaction mixture was allowed to incubate at room temperature (20°C) for 1 h prior to electrophoresis on a nondenaturing polyacrylamide gel for 2 to 3 h. The gel was then fixed and dried, and the signal from the protein-DNA complex was quantitated after exposure of the gel to a phosphorimage screen as described previously AGAAAGCGTAATAGCTCA was used as an irrelevant control. Experiments were performed in triplicate, gels scanned  and relative density of the EMSA assessed by use of the ImageQuant program (IQMac v1).URE3-BP, has been shown to bind specifically to the TATTCTATT (URE3) DNA motif in Gilchrist E. histolytica hgl5 gene et al. TGCGGATCCAAATGGAACAAAAATTAATTTCAGAAGAAGATTTA-ATGCAACCACCTGTAGCTAATTTCC, and a control generated using an oligonucleotide that incorporated two stop codons directly after the myc tag (ACAATAGCTAACATCCTTGTATTTA). Both amplicons were subcloned into the pCR2.1 TOPO expression vector (Invitrogen) and sequenced to confirm the presence of the desired mutations. The DNAs were then subcloned into the tetracycline-inducible gene expression system.The stable construct (pHTP.luc) contained the luciferase structural gene under the control of the 6 amebae collected by centrifugation at 900 rpm for 5 min and an initial RNA preparation performed according to the manufacturer's directions. RNA greater than 200 nucleotides in length was separated from total RNA by the RNeasy protocol (Qiagen). RNA was isolated from at least two independent cultures on the same day for microarray analysis.One ml of Trizol (Invitrogen) was added to 2×10E. histolytica Genome Sequencing Project  and the Primer3 program  were linked to the quantity of initial DNA after calibration of the effectiveness of the amplifying primer pair lgl1 transcript was used to compensate for the variation in the amount of amebic mRNA isolated.Reverse transcription real time PCR (qRT-PCR) was used to independently measure mRNA abundance in independently transformed amebae. The cDNA was subjected to 40 amplification cycles with HotStarTaq (Qiagen). Primers were designed to amplify 100–300 base pairs using genomic sequences from the  program  [28]. Thhttp://www.affymetrix.com/support/technical/manual/expression_manual.affx). Two micrograms of total RNA was used for cDNA and subsequent biotinylated cRNA synthesis. This labeled RNA probe was hybridized to the Affymetrix custom array designed using information generated from the E. histolytica genome sequencing project release date 12/08/04 as previously described E. histolytica Open Reading Frames (ORF) 8197 http://pathema.tigr.org/). The ORF probe sets were preferentially selected from the 600 bases proximal to the 3′ end of the E. histolytica sequences. The arrays were scanned with an Affymetrix Gene Chip scanner 7G and report files were generated to determine the percentage of present calls of each array. The detection calls  for each probe set were obtained using the GCOS system (http://www.affymetrix.com/products/software/specific/gcos.affx). Only genes with at least one “present” call were used in assessment of the data. Raw data from the arrays were normalized at probe level by the gcRMA algorithm and then log2 transformed Quality control of RNA samples was performed by use of the Agilent Bioanalyser Nano Assay. The standard protocol for hybridization of eukaryotic mRNA to Affymetrix arrays was followed  publicly available at http://pathema.tigr.org (Genebank accession number (AAFB00000000)). The reannotated genome was searched for the URE3 motif with a custom motif search script (h script .http://pathport.vbi.vt.edu/main/microarray-tool.php). The system uses publicly available tools such as Bioconductor E. histolytica promoters using the chi-squared test (InStat 2.03 program (GraphPad Software)).Microarray data analysis was performed using the Array Data Analysis and Management System (VBI)  and incubated at 37°C for 3 h. Inserts and media were removed and fluorescence measured using a SpectraMax M2 fluorescent plate reader. Fluorescence versus concentration for each sample was determined by using a standard curve. Ameba numbers confirmed in selected experiments by microscopic counting and by use of the Techlab E. histolytica II antigen test used according to the manufacturer's directions.Transwell migration assays were performed using 5 mm transwell inserts (8 μm pore size Costar) suspended by the outer rim within individual wells of 24-well plates. Briefly, ameba trophozoites were incubated in serum free growth media containing 2 μg/ml CellTracker Green CMFDA (Molecular Probes) for 1h 1A2T3T4C5T6A7T8T9. was measured. A representative gel showing competition with the motif modified at positions 1  or 4  is shown in 1A2T3T4C5T6A7T8T9 as a result was modified to a consensus motif of T1[atg]2T3[tc]4[cg]5T6[at] 7[tgc]8[tg]9.Electrophoretic mobility shift analysis (EMSA) was used with base substituted oligonucleotides to define the consensus URE3 motif. The impact of adding an excess of a non-radioactive oligonucleotide with a base pair alteration within the URE3 motif Thgl5 promoter URE3 motif were mutated: T4A, T4C T4G and C5A. These mutant promoter sequences were placed upstream of the luciferase reporter gene. Luciferase values from at least three independent experiments with two different DNA preparations were performed (hgl5 promoter context). This included the promoter with the mutation T4C. In the EMSA assay the T4C oligonucleotide affinity for URE3-BP was approximately 50% of the wild type oligonucleotide. We interpreted this as a consequence of the lower sensitivity of the episomal reporter assays, likely due to over-expression of episomal constructs.We tested whether URE3 mutations that prevented competition in EMSAs , also blerformed . De-reprTo further evaluate the physiological relevance of the URE3 matrix, a calcium-insensitive mutant of URE3-BP (EF(2)mutURE3-BP) , and theE. histolytica ORFs. Total RNA (12 μg) was isolated before induction (–Tet) and after 9 h of induction (+Tet) from cells carrying the myc-tagged recombinant URE3-BP mutant or the control construct . The expression of the mRNA encoding the recombinant calcium-insensitive dominant positive mutant URE3-BP was induced 10–15 fold at nine hours post induction as indicated by myc specific qRT-PCR mutURE3-BP transformed trophozoites  was normalized using gcRMA and statistical significance determined by LIMMA statistical analysis  was used to locate the URE3 matrix in putative promoters of URE3-BP responsive genes  or decreased (42) ≥2-fold at 9h post-induction compared to the induced “control stop” strain, which had a stop codon inserted after the sequence encoding the myc tag. To identify the novel URE3-BP regulated genes, the promoters of transcripts significantly modulated by two-fold or greater were scored for the presence or absence of the URE3 matrix. The DNA Pattern Find program .The URE3 matrix was found in 23% of all predicted promoter regions, however the matrix appeared at a statistically greater frequency (54%) in the URE3-BP modulated transcripts predicted by LIMMA (chi-square test p>0.0001). Alternative analysis using the motif frequency or requiring the presence of two or more motifs for the positive designation also confirmed the correlation between the URE3 motif and transcript modulation . The preInterPro was used to scan the open reading frames of the significantly modulated genes to obtain additional information on protein function, TMpred to predict transmembrane regions, big-PI Predictor to identify Glycosylphosphatidylinisotol (GPI) anchored proteins (GPI-anchor) and SignalP to identify signal peptides A gene was assigned to the membrane encoding group on the basis of the annotated GO term, the presence of a signal peptide, a GPI-anchor signal, or transmembrane domain. The majority of the membrane gene promoters contained a URE3 matrix (73% p<0.0001).The encoded membrane proteins were quite distinct at the protein level. However, a subgroup of these proteins had highly similar promoter, and amino- and carboxyl-terminal sequences  . With onMost of the promoters of the small group of genes encoding metabolic enzymes also contained a URE3 matrix (86% p<0.0001). The enzymes encoded by these genes were linked to phospholipid metabolism. The opposing regulation of two enzymes that catalyze the addition of Coenzyme A to fatty acids (EHI_079300 and EHI_185240) might reflect different substrate specificities of these enzymes To determine whether URE3-BP regulated the promigratory effects of trophozoites, transwell migration assays were performed as described in E. histolytica transcripts modulated by inducible expression of URE3-BP. URE3-BP had previously been shown to regulate the expression of two virulence factors in the parasite. The current studies provide a more global picture of its role in control of gene expression. The key experimental approach was the inducible expression of a dominant positive URE3-BP mutant and the subsequent identification of uniquely altered transcripts. The majority (42/50) of transcripts were repressed. Over half (54%) of the modulated genes had a URE3 matrix in the promoter region while the other half was comprised of genes presumably downstream of control by URE3.In this work the DNA consensus motif recognized by the URE3-BP transcription factor was experimentally defined, and then used to identify a subset of E. histolytica trophozoites.The URE3 matrix was present in the 5′ sequences of URE3-BP modulated genes involved in fatty acid metabolism and in potential membrane or secreted proteins. The latter suggests that phenotypic changes due to the expression of the dominant positive URE3-BP mRNA could occur most noticeably at the cell surface of URE3-BP regulated genes, which encoded proteins with an N terminal signal peptide, included the potential virulence factor EhCP-A7, a cysteine protease, an asparagine-rich antigenic surface protein ariel hgl5 and fdx1 genes that contain URE3 in their promoters, which cannot be distinguished from highly related gene family members that lack URE3-containing promoters. The hgl5 gene belongs to a family of five highly similar genes (up to 99%), and ferredoxin is encoded by two identical ORFs, fdx1, and fdx2 . The presence of the URE3 matrix was not much higher than background in the promoters encoding genes involved in either transcription/translation (25% p = 0.035) or cytoskeletal function (25% p = 0.73).A technical limitation of the gene expression analysis was the inability to measure transcript levels of the However while we could not demonstrate changes in the level of the ferredoxin transcript, a URE3 associated Fe-hydrogenase EHI_005060 (EC 1.12.7.2) E. histolytica growth media has no impact on either the URE3-BP transcript or on the genes involved in this pathway, suggesting the lack of feedback inhibition of URE3-BP from the products of this pathway Four of the other six metabolic enzymes identified by inducible expression of URE3-BP could be linked in a phospholipid degradation/ fatty acid assimilation pathway . A potenE. histolytica may indicate that there are other factors not yet identified involved in promoter specific recognition by URE3-BP. Because appreciable levels of wild type URE3-BP were still present, this might have contributed to the failure to observe changes in the roughly 2000 genes with putative URE3-BP binding sites for which no change was seen following induction of EF(2)mutURE3-BP. Because of these issues it is a reasonable conclusion that the 50 changed transcripts are an underestimate of the genes regulated by URE3-BP.A limitation of this study was that the microarray analysis measured the steady state mRNA levels and we therefore may have missed changes in newly transcribed RNA, especially for abundant transcripts. Changes occurring in mRNA stability and/or transcript processing may obscure changes occurring at the level of transcription The URE3 matrix was absent in 23 of the regulated promoters. Amebae were harvested at nine hours after the addition of tetracycline and shortly after appreciable induction of recombinant URE3-BP protein . TherefoIn conclusion, we have identified a group of genes, which appear to be regulated by URE3-BP. These genes and their products may represent a network of interconnected responses to environmental signals. The biological consequences of these changes may impact the ability of the organism to colonize the host, and/or control its invasive behavior.Table S1(0.04 MB DOC)Click here for additional data file.Table S2(7.70 MB XLS)Click here for additional data file.


Type 2 diabetes mellitus (T2DM) is a disorder characterized by both insulin resistance and impaired insulin secretion. Recent transcriptomics studies related to T2DM have revealed changes in expression of a large number of metabolic genes in a variety of tissues. Identification of the molecular mechanisms underlying these transcriptional changes and their impact on the cellular metabolic phenotype is a challenging task due to the complexity of transcriptional regulation and the highly interconnected nature of the metabolic network. In this study we integrate skeletal muscle gene expression datasets with human metabolic network reconstructions to identify key metabolic regulatory features of T2DM. These features include reporter metabolites—metabolites with significant collective transcriptional response in the associated enzyme-coding genes, and transcription factors with significant enrichment of binding sites in the promoter regions of these genes. In addition to metabolites from TCA cycle, oxidative phosphorylation, and lipid metabolism (known to be associated with T2DM), we identified several reporter metabolites representing novel biomarker candidates. For example, the highly connected metabolites NAD+/NADH and ATP/ADP were also identified as reporter metabolites that are potentially contributing to the widespread gene expression changes observed in T2DM. An algorithm based on the analysis of the promoter regions of the genes associated with reporter metabolites revealed a transcription factor regulatory network connecting several parts of metabolism. The identified transcription factors include members of the CREB, NRF1 and PPAR family, among others, and represent regulatory targets for further experimental analysis. Overall, our results provide a holistic picture of key metabolic and regulatory nodes potentially involved in the pathogenesis of T2DM. st century. Recent studies of gene expression levels in human tissue samples have indicated that multiple metabolic pathways are dysregulated in diabetes and in individuals at risk for diabetes; which of these are primary, or central to disease pathogenesis, remains a key question. Cellular metabolic networks are highly interconnected and often tightly regulated; any perturbations at a single node can thus rapidly diffuse to the rest of the network. Such complexity presents a considerable challenge in pinpointing key molecular mechanisms and biomarkers associated with insulin resistance and type 2 diabetes. In this study, we address this problem by using a methodology that integrates gene expression data with the human cellular metabolic network. We demonstrate our approach by analyzing gene expression patterns in skeletal muscle. The analysis identified transcription factors and metabolites that represent potential targets for therapeutic agents and future clinical diagnostics for type 2 diabetes and impaired glucose metabolism. In a broader perspective, the study provides a framework for analysis of gene expression datasets from complex diseases in the context of changes in cellular metabolism.Type 2 diabetes mellitus is a complex metabolic disease recognized as one of the main threats to human health in the 21 Fluxes, in turn, are dependent on metabolite levels, enzyme activities, abundance of effectors and possibly other variables. Measurement of fluxes and metabolite concentrations at the entire metabolic network-scale is, however, a difficult task in humans due to a variety of technological and experimental limitations. By contrast, methods for measurement of expression of genes encoding metabolic enzymes are relatively well-established. Thus, the primary goal of this study is to use informatics approaches to integrate available gene expression data with metabolic networks, in order to predict metabolic phenotypes of skeletal muscle linked to the pathogenesis of type 2 diabetes. Such an approach will help not only to gain insight into the organization of transcriptional regulation in human tissues, but also provide guidance for improved design of experimental strategies for obtaining metabolite and flux data, which can be further integrated into metabolic models.Homo sapiens Recon1 To achieve these goals, we applied an extension of the algorithm described in We further hypothesized that the observed coordinated changes around reporter metabolites can be, at least in some cases, attributed to common transcriptional regulatory mechanisms. Specifically, we hypothesize that the neighbor enzymes of reporter metabolites may share one or more transcription factor binding sites in the promoter regions of the corresponding genes. In order to identify such potential regulatory players, we tested promoter sequences of the genes associated with the reporter metabolites for enrichment of known transcription factor binding motifs . TranscrSince our goal is to identify reporter metabolites and transcription factors potentially involved in diabetes pathogenesis and progression, we analyzed two independent studies of skeletal muscle transcriptomics in individuals with established type 2 diabetes or insulin resistance In present study, we performed reporter metabolite analysis based on pair-wise comparisons within each dataset; differential expression and its significance were assessed with robust multi-array average (RMA) and empirical Bayes testing. Significance of differential expression for each gene was used as a scoring metric . The resviz., T2DM vs NGT, T2DM vs IGT, and IGT vs NGT, revealed significant reporter metabolites  participating in lipid metabolism, TCA cycle, oxidative phosphorylation (OXPHOS) and glycolysis , acetoacetyl-CoA, and the sphingolipid sphinganine. These are interesting, as prior studies vs NGT comparison , with mostly down-regulated expression of the genes encoding their neighboring enzymes. Ubiquinol was found as reporter metabolite representative of electron transfer chain. Several molecules within β-oxidation pathways, such as 3-cis-dodecenoyl-CoA, glutaryl-CoA, trans-3-decenoyl-CoA, 3-methylbutanoyl-CoA and 3-methylcrotonyl-CoA, as well as in amino acid  metabolism were also identified as reporters . Moreovevs FH− and FH+ vs FH−) was calculated based on the overlap of reporter metabolites between the two comparisons. Jaccard distance provides a measure of dissimilarity between two sets of reporter metabolites, and is quantified as the fraction of non-overlapping reporter metabolites between the two sets. While similar clustering patterns were observed  were enriched in seven downregulated enzyme sets including ATP. Tax/CREB motifs were enriched in promoters of downregulated enzymes associated with ATP, ADP and phosphate. Additional down-regulated neighbors of ATP were enriched for the binding sites of NF-κB, MEF-2, UF1-H3β, Pax-9 and NKX6.2, while the NRF-1 motif was enriched in the set of up-regulated enzymes neighboring ADP. Another potential regulatory signature was identified around the down-regulated neighbors of phosphatidylinositol and phosphatidylinositol 4,5-bisphospate , which were significantly enriched for binding sites of p53, PPARγ, SRF, SEF-1, v-Jun, GCNF, AR and many others  and insulin secretion Recognizing these important gaps in our knowledge of diabetes pathophysiology, we have integrated transcriptomic data with metabolic networks to systematically identify, in an unbiased fashion, regulatory hot spots (reporter metabolites and associated transcription factors) associated with insulin resistance and T2DM. Our reporter metabolite results provide evidence for transcriptional dysregulation of multiple metabolic pathways in skeletal muscle. Interestingly, many of the reporter metabolites identified in our analysis have been appreciated in prior experimental studies in animal models . Moreover, reporter analysis revealed lipid metabolites , a lipid signaling molecule known to inversely correlate with insulin sensitivity via respiration. Moreover, significant enrichment of binding motif for NF-κβ in the upregulated ATP neighbors is consistent with the potential role of this transcription factor in mediating oxidative stress responses triggered by by-products of incomplete β-oxidation via PGC1-α and other regulators linked to nuclear-encoded mitochondrial genes  . The dowal genes  [9],[36]via succinate and fumarate, accounts for most of the ATP (and NADH) turnover in a respiring cell. Our results suggest reduction in the activity of both TCA cycle and oxidative phosphorylation, in agreement with recent NMR data demonstrating that mitochondrial ATP synthesis is reduced in humans with insulin resistance The appearance of highly connected metabolites, such as ATP and NADH, among top-ranking reporter metabolites provides a possible link to the observed network-wide transcriptional changes in IGT and T2DM. Cellular levels of these co-factors are usually constrained within relatively narrow ranges to maintain thermodynamic stability. Oxidative phsophorylation, which is connected to TCA cycle flux The concordance between the changes in gene expression levels for glycolysis, TCA cycle and oxidative phosphorylation in IGT and T2DM suggests that transcriptional regulatory mechanisms may be a response to altered levels of ATP/NADH. Such response may achieve two purposes: (1) regulation of metabolism on global scale, as these co-factors are critical components of many metabolic pathways, and (2) regulation of NADH levels may help in reducing excessive  oxidative stress resulting from sustained oxidation of excessive nutrients vs NGT and T2DM vs FH− comparison, is trichloroethanol, a metabolite in the cytochrome P450-mediated pathway derived from trichlorethene Metabolites involved in protein and lipid glycosylation were found as reporters and characterized by down-regulation of neighboring enzymes . AlteratThe identification of reporter metabolites from glycolysis and energy-generation pathways suggests that there may be regulation of certain physiological parameters, such as glucose uptake, at the transcriptional level of the corresponding metabolic pathways. To investigate the extent of such possible regulation, we calculated Pearson correlation coefficients between insulin sensitivity (as measured by either whole-body glucose uptake during the hyperinsulinemic euglycemic clamp or insulin levels achieved during the OGTT) and mean centroid expression levels of genes surrounding reporter metabolites (Swedish dataset) . A signiA key scientific and clinical challenge is to identify molecular markers of diabetes risk, not only to better understand disease pathophysiology, but also to develop novel therapies for prevention and treatment of established diabetes. In this context, it is interesting that our analysis identified both PPARγ and its potential lipid ligands as regulatory molecules, since PPARγ ligand thiazolidinediones are currently employed as effective therapy for diabetes. We hypothesize that some transcriptional pathways identified in the current analysis, including CREB, NRF-1 and SRF, may be additional novel molecular mediators of the transcriptomic phenotype associated with insulin resistance, and thus potential targets for future intervention strategies. Of course, the potential roles of these pathways will require additional testing in cultured cells and animal models, where their impact on metabolic flux and insulin sensitivity can be fully assessed.Similarly, reporter metabolites identified in our analysis represent molecules likely to be involved in human skeletal muscle insulin resistance phenoytpes and also novel candidate biomarkers of insulin resistance and diabetes risk. In support of this hypothesis, several of the identified metabolites have known physiological roles in T2DM  above. ASupplementary A particularly interesting finding from our analysis is the identification of highly connected metabolites as reporters, including ATP/ADP and NAD+/NADH. We hypothesize that diverse environmental and genetic risk factors result in insulin resistance when individuals are unable to mediate appropriate compensatory transcriptional and metabolic responses in other parts of the network connected by these hubs. Our results also suggest that alterations in gene expression linked to the highly connected co-factors are likely to be acquired features of established T2DM. Analysis of the transcriptional activity of CREB in the context of ATP concentrations and TCA cycle activity in skeletal muscle may help to elucidate regulatory mechanisms leading to these changes.vs NGT comparison and 69% for the rest). The details of this analysis, together with relevant statistical considerations, can be found in Reconstructed human metabolic network models are still evolving, incomplete, and subject to error. Well-annotated pathways such as central carbon metabolism are thereby likely to be over-represented in the reporter analysis. In order to partially compensate for this limitation, we used two reconstructions – Recon1 and EHMN. As network reconstructions will become more complete, it will be possible to better assess the extent of this limitation. Another essential input to our algorithm, in addition to metabolic network, is gene expression data for the genes represented in the network. We would like to note that neither EHMN nor Recon1 network genes were fully represented by the microarray chips used in the two case studies . Only 54vs post insulin stimulation biopsies in Swedish study. Furthermore, the age and BMI (Body Mass Index) of the individuals participating in the two studies were different and may contribute to the differences in the observed gene expression patterns. To our knowledge, these two case studies represent the only human skeletal muscle transcriptome datasets that were available at the time of here reported computational analysis. Analysis of new datasets which may become available in the future will be useful in obtaining further insight into molecular physiology of skeletal muscle in the context of T2DM. Moreover, emergence of better or new gene expression analysis tools will help to cover parts of metabolic network that are currently inaccessible due to the lack of data.Although the present analysis identified common metabolic and regulatory signatures across the two studies, there are several differences in the study designs, and therefore the results must be regarded with certain caution. In addition to relatively low number of subjects in Mexican-American study, the differences include fasting state biopsies in Mexican-American study Extension of the analysis to discover more global regulatory patterns by using additional bio-molecular interaction data a priori disease-specific knowledge regarding the involvement of specific pathways or metabolites, thereby making it a robust and unbiased analytical framework for studying diseases linked to perturbations in the cellular metabolic network. Our results identify the highly connected metabolites ATP and NAD+ as reporters and potential mediators of the widespread changes in gene expression linked to insulin resistance in muscle. Moreover, our results extend previous knowledge about T2DM pathogenesis at the gene expression level – by reporting additional potential sites of disruption, e.g., TCA cycle and Kennedy pathway of phospholipid metabolism. Several metabolites from other pathways were also found to display significant differential gene expression of the genes around them and we suggest putative regulatory mechanisms behind these alterations. Our results suggest a framework of metabolic disruption observed with insulin resistance and diabetes, which can be used to test the role of specific pathways in mediating disease pathophysiology, and more practically, for the identification of potential biomarkers for preventive and therapeutic monitoring.We demonstrate the use of a network-guided data integration approach to discover key, physiologically relevant metabolic and regulatory nodes in T2DM pathogenesis. The methodology does not require the use of http://www.diabetesgenome.org). Brief comparison of microarray platforms from the experimental studies http://www.ensembl.org/biomart). The transcriptional start sites (TSSs) were identified based on the annotation of the Ensembl Biomart sequences. Sequences in the −800 to 200 base pairs region of the TSS were deemed as promoter regions for the analysis. Interspersed repeats and low complexity DNA sequences were masked out.Two datasets used in the study were obtained from the Diabetes Genome Anatomy Project website . The probe intensities were obtained and corrected for background by using robust multi-array average method (RMA) affy package rma function. Significance of the differential expression was calculated by using the empirical Bayes test Preprocessing of the gene expression data was carried out by using the statistical software environment – R . Each enzyme was assigned with a p-value for differential expression based on the p-value of the gene encoding for that enzyme. In case of isozymes and enzyme-complexes, genes with most significant expression change were used to score the enzyme   and dividing by the standard deviation (kσ) of the aggregated Z scores derived by sampling 10000 sets of k enzymes from the network: Each metabolite in the metabolic network was scored based on the scores of its e enzyme . P-valuend order background model. A one-tailed Fisher's exact test was used to assess per-sequence over-representation of any known motif, and the threshold used to calculate significance for each TRANSFAC matrix was set to 70% of the highest-scoring sequence motif. The q-value cut-off criteria For all reporter metabolites, we assessed enrichment of known protein-binding sequence motifs in the promoter regions (−800 to 200 base pairs relative to the transcription start site) of the corresponding neighbor genes. In order to obtain robust results, we only considered sets consisting of at least 5 up- or down-regulated genes. For each reporter metabolite, the sequences of all enzyme neighbors were used as the positive sequence set, whereas all other enzymes in the network model were used as the negative (background) set. Known motifs were identified by using position frequency matrices of all known motifs stored in the TRANSFAC database Text S1Supporting text describing scoring methodology and datasets.(0.74 MB PDF)Click here for additional data file.Table S1Reporter metabolites for Swedish male dataset (Recon1).(0.05 MB XLS)Click here for additional data file.Table S2Reporter metabolites for Swedish male dataset (EHMN).(0.07 MB XLS)Click here for additional data file.Table S3Reporter metabolites for Mexican-American dataset (Recon1).(0.03 MB XLS)Click here for additional data file.Table S4Reporter metabolites for Mexican-American dataset (EHMN).(0.05 MB XLS)Click here for additional data file.Table S5Overlapping reporter metabolites between two case studies (Recon1).(0.05 MB XLS)Click here for additional data file.Table S6Overlapping reporter metabolites between two case studies (EHMN).(0.06 MB XLS)Click here for additional data file.Table S7Results of the motif enrichment analysis.(0.03 MB PDF)Click here for additional data file.Table S8Experimentally studies linking metabolite levels to T2DM pathophysiology(0.12 MB PDF)Click here for additional data file.Figure S1Hierarchical clustering of pair-wise comparisons within the Swedish male and Mexican-American datasets based on the overlapping reporter metabolites (EHMN network).(0.21 MB TIF)Click here for additional data file.


Here, we report the development of a database of computationally predicted transcription factor binding sites and related genomic features for a set of over 2000 murine immune genes of interest. Our database, which includes microarray co-expression clusters and a host of web-based query, analysis and visualization facilities, is available freely via the internet. It provides a broad resource to the research community, and a stepping stone towards the delineation of the network of transcriptional regulatory interactions underlying the integrated response of macrophages to pathogens.As part of a National Institute of Allergy and Infectious Diseases funded collaborative project, we have performed over 150 microarray experiments measuring the response of C57/BL6 mouse bone marrow macrophages to toll-like receptor stimuli. These microarray expression profiles are available freely from our project web site We constructed a database indexed on genes and annotations of the immediate surrounding genomic regions. To facilitate both gene-specific and systems biology oriented research, our database provides the means to analyze individual genes or an entire genomic locus. Although our focus to-date has been on mammalian toll-like receptor signaling pathways, our database structure is not limited to this subject, and is intended to be broadly applicable to immunology. By focusing on selected immune-active genes, we were able to perform computationally intensive expression and sequence analyses that would currently be prohibitive if applied to the entire genome. Using six complementary computational algorithms and methodologies, we identified transcription factor binding sites based on the Position Weight Matrices available in TRANSFAC. For one example transcription factor (ATF3) for which experimental data is available, over 50% of our predicted binding sites coincide with genome-wide chromatin immnuopreciptation (ChIP-chip) results. Our database can be interrogated via a web interface. Genomic annotations and binding site predictions can be automatically viewed with a customized version of the Argo genome browser..We present the Innate Immune Database (IIDB) as a community resource for immunologists interested in gene regulatory systems underlying innate responses to pathogens. The database website can be freely accessed at  Extensive transcriptional regulation underlies macrophage responses to toll-like receptor (TLR) signaling . Differecis- regulatory modules, evolutionary conserved regulatory sequences, DNase hypersensitive sites, co-expression clusters, and genome-wide chromatin immunoprecipitation (ChIP-chip) data. For each gene, we have analyzed the sequence from at least 20 kb upstream to at least 10 kb downstream of the predicted transcription end site, including all exons and introns. In addition to regulatory predictions, annotations for exon/intron boundaries, CpG islands, repeats, Affymetrix Microarray Expression probes are included. Users can interactively interrogate IIDB using a web interface. Search results are mapped to the genome sequence. Standard text-based files are created ('.gff2' and '.gff3') and can be explored visually using the web-enabled Argo genome browser [While there are other examples of mouse genome annotation databases and websites ,26, in t browser .IIDB uses the MYSQL relational database system to store, retrieve and manage the data. The web interface between the user and IIDB is coded in PERL/CGI. Initially, the database was populated with a set of 1670 genes differentially regulated in response to LPS. We subsequently added a selection of approximately 400 other genes suspected or recognized to be of importance to the innate immune system. Approved (data curator) users can easily upload additional genes for analysis by providing the Entrez GeneID via the web interface. The uploading validation and annotation process is shown in Additional File To accommodate the needs of our multiple ongoing experimental projects and users, IIDB provides annotations based on both the UCSC mouse genome version mm5  and the 6 random scores per matrix. For each PWM, MotifLocator scores for the true sequence were converted to p-values by comparison to the score distribution for the same PWM on the randomized sequence. Based on this analysis we generated matrix scan datasets with p-values less than 1 × 10-3, 5 × 10-4 and 1 × 10-4.We annotated the genomic sequence of all the genes in our database with each of the 360 mouse-specific individual TRANSFAC  PositioDisplay of all individual putative TFBSs can produce cluttered visualizations. To provide an alternative representation, avoiding presentation of overlapping hits of identical or similar matrices, we grouped the 360 individual TRANSFAC matrices into 67 matrix families and 76 individual matrices. First, we computationally grouped TRANSFAC matrices whose identifiers correspond to the same transcription factor into a single matrix group. For example, AHR_Q5, AHR_01 both identify the Ahr transcription factor. Next, we computationally combined our matrix groups with TRANSFAC matrices whose identifiers indicated that they belong to the same transcription factor family. For example, matrices for MYC_Q2, EBOX_Q6, and MYCMAX_02 were combined. Finally, we hand curated the groups and remaining single matrices to ensure against computational false positives or false negatives. Any matrices not assigned into a group by the above procedure were retained as individual matrices.matrix1 - startmatrix2| <= lengthmatrix1/2). If the neighboring matrix did not satisfy this condition, it was marked as a distinct TFBS. The same algorithm was used to combine identical overlapping single matrix hits  locus.To remove redundant predictions, overlapping hits from the same PWM group were collapsed into a single predictive hit if the two predicted TFBS overlapped by at least half of the length of the 5' matrix (|startcis-regulatory module [-2). Briefly, the algorithm detects clusters of TFBS hits on a DNA regulatory region previously scanned by a library of matrices . A "maximum cluster size" parameter limits the sequence length over which a cluster can extend. We chose 500 bp for this parameter based on typical lengths of known cis-regulatory modules in animal genes [Transcription factors often bind in close proximity of each other within a y module ,26,30. WIIDB includes a catalog of over 26000 conserved human/rat/mouse and dog promoter sequences identified by Xie et al. . To map cis-regulatory regions on DNA [DNase hypersensitive (HS) sites can help identify the location of s on DNA ,33. We us on DNA  to creats on DNA .To demonstrate the ease of integrating additional data into IIDB, we have included data from a genome wide chromatin immunoprecipitation assay, employing a custom Affymetrix oligonucleotide array . This arIL12b), Entrez geneid (e.g. 16160), Refseq (e.g. NM_008352), or a chromosomal location (e.g. chr:11 44019798–44073744). See Fig. The user can interactively interrogate annotated genes using either the UCSC database version mm5 gene map or the ENSEMBL mouse 29e gene map. All interactions are via a web interface. Searches can be performed by providing a common name e.g. IL2b, Entre-3, 5 × 10-4 and 1 × 10-4. The more stringent p-values greatly reduce the occurrence of background (non-significant) matrix hits but may miss some true binding sites. Taking the IL12b gene as an example, we analyzed 54 kbp of sequence. The above methodology reduced the number of predicted TFBS from 15810 hits at a p-value threshold of 10-3 to 1795 at a p-value threshold of 10-4. We selected 10-4 as the most stringent p-value threshold because filtering at this level still allows exact matches to 67 out of 80 (84%) of known  TFBS's in our data set.Users can filter all TFBS predictions at any of three p-values: 1 × 10The results of the user's selections are temporarily stored in '.gff2' and '.gff3' file formats on our server and can be downloaded, saved to the user's computer and exported to other applications. Alternatively, the user can click on a link on the IIDB web page to invoke the Java Web Start enabled genome browser Argo . This fFor greater stringency of results, IIDB allows the user to filter transcription factor binding site predictions in several different ways as listed below:All DNA sequences in our database have been annotated for exon/intron boundaries, repeats, CpG islands and Affymetrix probes. All sequences were scanned using the 360 TRANSFAC mouse-specific PWMs . Each gUsers can query a gene for some or all information available in IIDB. The strand, sequence length, and exon coordinates are provided. Based on this information, the user can limit the search region by specifying 5' upstream and 3' downstream regions Fig. . FurtherA total of 143 PWMs representing 268 transcription factors as 67 transcription factor families and 76 single-factor matrices are stored in our database. A user can search for the binding sites of one or several transcription factor families at once, and compare TFBS locations across multiple genes  for a range of window sizes.Mammalian re TFBSs ,30,38. TThe user can select a set of IIDB annotated genes based on their common GO annotation ,40. The As an example of additional data integration in IIDB, and to allow evaluation of the accuracy of our TFBS predictions, IIDB includes ChIP-chip data for the ATF3 transcription factor . We planAll IIDB search results are color coded in the genome browser view. IIDB maintains a consistent color coding scheme across all gene displays. Within the Argo genome browser, additional specific detail about a particular feature can be displayed by double clicking that feature as shown in Fig. An extensive help menu is available, as indicated by the '?' symbol next to each link in the navigation bar at the top of each web page. We also provide step-by-step examples of how to perform single and multi-gene analyses using IIDB via on-line help web pages (under the link 'How To Use IIDB') and through a downloadable tutorial .IIDB is designed to accommodate both experimental data  and computationally predicted TFBS and genomic annotations. So far, we have only been able to compare the accuracy of our binding site predictions against two datasets. The results are encouraging in both cases. Firstly, there are 80 known TFBS documented within TRANSFAC which map to the IIDB genes. Of these, we capture at least 80%. Second, approximately 50% of the ATF3 ChIP-chip hits coincide with the IIDB predictions, as summarized in Table Unlike other TFBS data and prediction repositories, IIDB is implemented to be specific in that it includes data relating to a specific cell type (macrophages) in a specific strain (C57/BL6) of a specific species (mus musculus). We hope that this specificity will prove useful for the immunology community. IIDB is also structured so that the data it contains will be generally useful to the broader immunology community.We plan to regularly update the transcription factor binding site data and related statistics within IIDB. We are in the process of building an extended version of IIDB which will include TRANSFAC matrix scans for the entire mouse genome. We also plan to add further ChIP-chip data for various transcription factors we are currently analyzing. We are committed to regularly updating our gene coordinate system with new mouse genome builds. An email-based feedback and help link is provided on the IIDB homepage, and users are encouraged to provide suggestions to continue to refine the utility of IIDB. In this way, we hope IIDB will continue to grow, both in content, and also in its usefulness to the immunology community.The current consensus view is that transcription factor binding site prediction based on PWM sequence scans alone is not sufficiently predictive for most systems biology projects. PWM scans generate very high numbers of false positives and numerous overlapping hits. We have used several TFBS prediction algorithms, multi-species conservation information, data on DNase HS sites, and searches based on TFBS meta patterns to reduce the number of hits and increase the predictive power of TFBS predictions. On the basis of available ChIP-chip data, TFBS predictions available via IIDB appear to have a good chance of being confirmed experimentally. We therefore believe IIDB will make a useful contribution to the immunology research community.Our database currently includes predicted binding sites on the promoters of over 2000 mouse macrophage and immune-specific genes. Results from IIDB analyses of new genes, or new analyses of existing IIDB genes, can be automatically integrated into IIDB following curation. IIDB will grow with time and usage. We have customized a web-based genome browser to simultaneously display multiple genes with multiple annotations and TFBS predictions. Thus, IIDB can be used by researchers without specific computational expertise to develop novel gene regulatory hypotheses.Project name: Innate Immune Database (IIBD).Project home page: Operating system(s): Platform independent.Programming languages: Perl/CGI, Java, MySQL.Licence: free open-access to database via web-interface.Restrictions to use by non-academics: none.bp: DNA sequence base pairs , ChIP-chip: Chromatin Immunoprecipitation followed by microarray-based (chip) global identification of ChIP fragments, CCA: Cobalt Clustering Algorithm  to identify statistically significant spatial clusters of TFBS, IIDB: Innate Immune Database, HS: DNase1 Hypersensitive Site, PWM: Position Weight Matrix for a transcription factor, TF: Transcription Factor, TFBS: Transcription Factor Binding Site on DNA, TLR: Toll-like receptorMK designed and implemented the IIDB database. MK and AR jointly implemented the user interface.AR, VT, CB, BL, and DH developed the statistics and algorithms for the transcription factor binding site predictions.JCR conducted and coordinated the selection and mapping of genes and loci.All computational work was carried out under the guidance of HB and IS.KK, JCR, CR, MG, DZ, CB, and BM performed the ChIP-Chip and microarray experiments under the guidance of AA.HB and MK drafted the manuscript. HB and AGR revised it with input from the other authors. All authors read and approved the final version of the manuscript.Gene uploading and validation process diagram. When a user uploads genes for annotation via the web interface the requested genes first pass through an extensive verification process. Only positively identified genes are transmitted to the annotation pipeline. At end of the process a notification is sent to the user detailing the status of his/her request.Click here for fileMatrix family mapping schematic. Matrix hits were collapsed into the same group if the start site of the next hit of the family fell within the first half of length of the previous hit of the family. Only the highest scoring matrix of a family was reported. The other matrix hits in the family are displayed as an attribute of the highest scoring matrix. For matrices without other family members, only the highest scoring matrix was reported if the start site of the next identical matrix fell within the first half of the length of the previous matrix. The scores of the other identical matrices are displayed as an attribute of the highest scoring matrix.Click here for file


Though the Nuclear factor 1 family member NFIX has been strongly implicated in PDGFB-induced glioblastoma, its molecular mechanisms of action remain unknown. HSF1, a heat shock-related transcription factor is also a powerful modifier of carcinogenesis by several factors, including PDGFB. How HSF1 transcription is controlled has remained largely elusive.By combining microarray expression profiling and a yeast-two-hybrid screen, we identified that NFIX and its interactions with CGGBP1 and HMGN1 regulate expression of HSF1. We found that CGGBP1 organizes a bifunctional transcriptional complex at small CGG repeats in the HSF1 promoter. Under chronic heat shock, NFIX uses CGGBP1 and HMGN1 to get recruited to this promoter and in turn affects their binding to DNA. Results show that the interactions of NFIX with CGGBP1 and HMGN1 in the soluble fraction are heat shock sensitive due to preferential localization of CGGBP1 to heterochromatin after heat shock. HSF1 in turn was found to bind to the NFIX promoter and repress its expression in a heat shock sensitive manner.NFIX and HSF1 exert a mutual transcriptional repressive effect on each other which requires CGG repeat in HSF1 promoter and HSF1 binding site in NFIX promoter. We unravel a unique mechanism of heat shock sensitive DNA sequence-directed reciprocal transcriptional regulation between NFIX and HSF1. Our findings provide new insights into mechanisms of transcription regulation under stress. We identify that NFIX exists in a heat sensitive complex with CGGBP1 and HMGN1. CGGBP1 organizes a transcription regulatory complex comprising of NFIX and HMGN1 at a small CGG repeat element in the HSF1 promoter and suppresses its expression. HSF1 was also found to repress NFIX expression by binding to a potential HSF1-binding site in the NFIX promoter. We report a unique DNA sequence-directed reciprocal transcription regulatory mechanism between NFIX and HSF1 involving heat shock-sensitive protein interactions.To identify the functions of NFIX, we suppressed it by transient siRNA transfections. By real time quantitative RTPCR (qRTPCR) we confirmed that NFIA, NFIB and NFIC were not affected (not shown). As an NFIX peptide has never been demonstrated before, we also characterized the NFIX peptide and its intracellular localization. We also found that tyrosine phosphorylation affects intracellular localization of NFIX . Thus weTo detect transcriptional targets of NFIX, total RNA from control- or NFIX-siRNA transfected U-251 MG cells were profiled using cDNA microarrays in six pairs of dye-swap hybridizations representing six different transfection experiments . Using aWhile the genes with altered transcript levels were involved in diverse cellular processes , the larNext we investigated if the transcriptional deregulation observed after NFIX-siRNA treatment is a direct effect of loss of NFIX binding to its target sequences or not. A TransFac search for commonly occurring transcription factor binding sites did not reveal any significant differences between the genes with altered transcript levels and a set of genes on the microarray which did not exhibit altered transcript levels (not shown). Since the NFI-binding site matrix used by TransFac is based on the half NFI-binding sites, we asked if the full palindromic NFI-binding sites existed in the promoter regions of genes with altered transcript levels. 3 Kb regions spanning upstream of the transcription start sites of 91 genes with altered transcript levels and 137 control genes  were extracted and searched for the presence of full NFI-binding sites, with different stringencies. The sequence motifs YGGM(N5)GCCAA, TGGM(N6)CCAA and TGGM(N6)CCA were found in 6.6, 17.6 and 53.8% of genes with altered transcript levels and in 2.2, 10.2 and 42.3% of control genes respectively . Even if7 independent clones and identified two different prey clones corresponding to DNA-binding protein coding genes. These included a high mobility group protein (HMGN1) and the CGG triplet repeat binding protein 1 (CGGBP1)  spanning from −11 to +7 nucleotide bases relative to HSF1 transcription start site. This raised a possibility that CGGBP1 and HMGN1 might mediate transcriptional regulation of HSF1 by NFIX. The deregulation of many stress-response genes by NFIX suppression could thus be routed through HSF1.Since there are no known interacting partners of NFIX which could be used as candidates to address this issue, we performed a yeast-2-hybrid screen of a fetal human brain cDNA library. Using the CTF1 domain of NFIX as bait, we screened approximately 10(CGGBP1) . HMGN1, If the interactions of NFIX with CGGBP1 and HMGN1 are important for its function, then the inhibition of these genes will recapitulate some of the effects observed after NFIX inhibition. With this premise, we tested if NFIX, CGGBP1 and HMGN1 also regulated transcription of NFIX target genes. U-2987 MG cells were cultured at 37°C or 39°C for 48 hours after five different kinds of siRNA transfections: control, CGGBP1, HMGN1, NFIX, CGGBP1 combined with NFIX, and, HMGN1 combined with NFIX. qRTPCRs were performed on RNA samples to check the expression of HSF1 and HSPA1A (heat shock response genes), DDIT3 (a stress response gene unrelated to heat shock response), CGGBP1, HMGN1 and NFIX, using GAPDH as control. siRNA against CGGBP1, HMGN1 and NFIX effectively silenced their respective target genes to extremely low levels similarly in samples incubated at 37°C or 39°C . CGGBP1 All of these genes except NFIX, CGGBP1 and HMGN1 are known stress-related genes and these results show that CGGBP1 and HMGN1 are both transcriptional regulators of heat shock response genes as well as transcriptional targets of heat shock themselves. Even if HSF1 induction was still a common feature of inhibitions of CGGBP1 and HMGN1, unlike CGGBP1-siRNA, HMGN1-siRNA did not induce NFIX expression. Since combining NFIX-siRNA with CGGBP1 or HMGN1-siRNA did not add up the effects of NFIX-siRNA with those of CGGBP1 or HMGN1-siRNA alone, a possibility was raised that transcriptional regulatory activities of CGGBP1 and HMGN1 involve NFIX and removal of either component caused same non-additive effect .Despite some similarities in gene expression regulation by NFIX-, CGGBP1- and HMGN1-siRNA, we found some discrete phenotypes in U-2987 MG cells upon siRNA-mediated CGGBP1 and HMGN1 suppressions; such phenotypes were not observed after NFIX-siRNA treatment. Chronic heat shocking in presence of control-siRNA for 5 days resulted in elongation of cells , but a pThe physical interactions of endogenously expressed NFIX with HMGN1 and CGGBP1 were confirmed by co-immunoprecipitation assays (Co-IPs) and the identities of HMGN1 and CGGBP1 bands were confirmed by using siRNA against them . We thenU-2987 MG cells either cultured normally or heat shocked at 39°C for 48 h were stained with NFIX and CGGBP1 antibodies. Both proteins were predominantly co-expressed in the nuclei both, after or without heat shock. However, in a subset of cells the extra-nuclear expression of both NFIX and CGGBP1 was strongly reduced and nuclear expression increased after heat shock . Very stNext we investigated if and how CGGBP1, HMGN1 and NFIX interacted with the CGG repeat element containing the HSF1 transcription start site. U-2987 MG cells were transfected with control, CGGBP1-, HMGN1- or NFIX-siRNA and DNA-protein interactions at 37°C or 39°C were studied by ChIP-qPCR.CGGBP1-siRNA increased HMGN1 binding on the HSF1 promoter at 39°C and not at 37°C, while in presence of control-siRNA, heat shocking did not affect HMGN1 binding . HMGN1, GAAAAGAAAATGAA” from positions −585 to −572. We then examined if HSF1 indeed bound to this region in the NFIX promoter. Semiquantitative ChIP assays in this region using HSF1 antibody showed that HSF1 indeed bound to the NFIX promoter in U-2987 MG and U-2197 cells under normal culturing conditions and after chronic heat shock, HSF1 occupancy at the NFIX promoter increased , HSF1 binding site was mutated to an  reduced  proving  reduced  and this reduced . ChIP as reduced  and mild reduced  on theirIn this study we have discovered that NFIX gene codes for two different peptides of which the larger peptide is localized in the nucleus and tyrosine phosphorylation affects its intracellular localization. Since tyrosine phosphorylation is one of the first steps in growth factor receptor signaling activated in tumorigenesis To test the function of CGG repeats in the HSF1 promoter, we deleted a 258 bps region from the wild-type promoter, as described in the results section. This deleted region contains 43 CpG dinucleotides which could potentially be methylation-dependent transcriptional regulatory elements for the HSF1 gene. CpG methylation is typically associated with silencing of gene expression HSF1 expression is augmented by stress and accumulation of denatured proteins, but it is not known what mechanisms keep HSF1 transcription under check during normal and induced conditions. Our results show that NFIX, HMGN1 and CGGBP1 are repressors of HSF1 expression out of which HMGN1 and CGGBP1 are induced by heat shock whereas HSF1-regulation of NFIX is heat shock sensitive. It is known that sustained HSF1 expression leads to induction of the unfolded protein response, which culminates in apoptosis. Due to the inherently high proteotoxic stress in the tumor derived cells, inhibition of HSF1 induction might be a way to counteract the induction of unfolded protein response and avoid apoptosis; a feature that might be necessary for the cell survival. NFIX also emerged as a regulator of CGGBP1 and HMGN1 recruitment to the HSF1 promoter. Interestingly then, HSF1 expression in glioma cell line U-343 MG-Cl2:6 with low NFIX expression  was not The regulation of NFIX transcription is enigmatic and has never been addressed before. Our attempts to establish stable NFIX over-expression systems in human glioma cell lines were unsuccessful and in U-251 MG cells, we did observe loss of cell division in cells transfected with NFIX expressing plasmid . Even in inducible expression system for NFIX, established in U-251 MG and U1242 cell lines, we found extremely low levels of induction in several different clones . This suggests that these cells do not tolerate high levels of NFIX and thus its transcription is under a very tight control, even under heat shock conditions. We identify HSF1 as one of the controllers of endogenous NFIX transcription. In the transgenic systems, where non-mammalian promoters drive NFIX expression, it might also be under post-transcriptional control. Interestingly, human NFIX contains a huge 3 prime UTR which is a target of different microRNAs. The mouse NFIX however lacks this long UTR element and it will be interesting to see the stress response in NFIX knock-out mice.We thus report for the first time that heat shock-sensitive interactions between NFIX, CGGBP1 and HMGN1 mediate a DNA sequence directed inhibition of HSF1 transcription and in a unique mechanism of reciprocal transcription regulation, HSF1 also inhibits NFIX expression by using specific DNA sequence motifs.Cells were cultured at 37°C, 5% CO2 in 10% FCS, 1% Glutamine and antibiotics supplemented minimum essential Eagel's medium (SIGMA). siRNA transfections were performed as per manufacturer's instructions using Dharmafect 2 and siRNA from Dharmacon. Sequences for siRNA are available on request. Before heat shocking see , cells wChIP assays were performed with slight modifications to the ChIP protocol accompanying Upstate (Millipore) EZ ChIP reagents. Cells were cultured as required for each assay and fixed with 1% formaldehyde for 10 minutes at 37°C. Formaldehyde containing medium was promptly removed by two ice cold PBS washes, cells were lysed in SDS lysis buffer containing protease inhibitors and sonicated to fragment size ranging between 400 and 150 bp. Input was separated, samples diluted in ChIP dilution buffer and cleared with protein A-sepharpose beads for 1 h. 2 µg specific antibody or 5 µl rabbit serum was added to the samples, incubated overnight at 4°C followed by 1 h with the beads. Beads were washed with increasing salt concentration buffers, chromatin eluted by SDS-bicarbonate buffer and samples were decrosslinked at 65°C in presence of high salt concentration. DNA was purified by phenol-chloroform method, precipitated and used for PCR assays. All samples were processed identically and equal volumes of samples were taken for PCR assays. Since different quantities of DNA can be precipitated by same antibody under different treatments, DNA was not quantitatively equalized for each sample. Input was used as control for amount of chromatin subjected to ChIP.Total RNA was isolated and cDNA was synthesized using reverse transcription kit (New England Biolabs). ChIP DNA  and input DNA were obtained as described above. Equal amounts of cDNA and equal volumes of ChIP DNA were used as template for each assay. GAPDH was used as internal control in all qRTPCRs and ACTB was used an additional internal control in some cases. All reactions were performed using SYBR-green premix (Applied Biosystems) and values calculated using delta-delta Ct method. Melt curve analysis (50°C to 99°C were performed to establish specificity of amplification). Values for serum negative controls are not shown in any graph because the Ct values were too high or not obtained. All primer sequences are mentioned in Other methods are described in Results S1Characterization of molecular weights of NFIX peptides, an antibody against NFIX and tyrosine phosphorylation sites in NFIX.(0.08 MB DOC)Click here for additional data file.Figure S1Different NFIX peptides: (A) C-terminal FLAG tag recognizes an 18 KDa peptide coded by NFIX-C-FLAG, which together with 8 KDa tag is recognized as a 26 KDa band. (B) N-terminal HA tag recognizes a 60 KDa peptide coded by HA-N-NFIX. (C) Rabbit anti-human NFIX antibody directed against C terminus of NFIX peptide recognizes both the peptides in nine different human glioma cell lines. U-2987 MG expresses highest and U-343 MG expresses lowest amounts of the 60 KDa peptide. Comparable protein loading in different lanes was confirmed by Coomassie blue staining (not shown). (D) HA-N-NFIX codes for both peptides as its transfections increase expression of both peptides as recognized by NFIX antibody. (E) NFIX antibody recognizes the 60 KDa peptide but not the 26 KDa peptide coded by NFIX-C-FLAG construct, perhaps due to epitope masking by FLAG tag. (F) NFIX-siRNA downregulates both peptides in U-2987 MG cells, confirming the identity of the two peptides as products of NFIX mRNA. The last lane shows NFIX expression in HEK 293T cells, used in transient transfection experiments. Two lanes for each construct shown in (A), (B), (D) and (E) represent the expression from two independent clones for each construct.(1.33 MB TIF)Click here for additional data file.Figure S2Expression of NFIX peptides in U-251 MG cells. (A) C-terminal FLAG tagged peptide is primarily present out of nucleus (top two panels). Rarely, it was seen strongly in nucleus also (bottom panel). (B) N-terminal HA-tagged peptide is nuclear in most cells (top two panels) and infrequently present in cytoplasm too (bottom panel). (C) NFIX antibody recognized endogenously expressed NFIX as present all over the cells. Staining with no primary antibody and pre-immune serum in case of NFIX antibody were used as negative controls.(2.38 MB TIF)Click here for additional data file.Figure S3Tyrosine phosphorylation of NFIX. (A) Endogenously expressed NFIX is tyrosine phosphorylated in U-251 MG and U-2987 MG cells. (B) Transgenically expressed wild type (WT) and Y151F mutant constructs showed comparable levels of phosphorylation. But Y253F mutation was associated with reduction in phosphorylation levels with comparable levels of expression of NFIX peptide irrespective of the mutations. The HA antibody non-specifically recognized an approximately 90 KDa peptide. IP; immunoprecipitation, pY; phosphotyrosine antibody, Probe; the primary antibodu used in Western analysis following IP. (C) and (D); Y151F mutation does not affect intra-nuclear presence of HA-N-NFIX peptide (top panels of C and D); Y253F mutation leads to extra-nuclear presence of a fraction of HA-N-NFIX (lower panels of C and D). See text for details of the constructs.(1.21 MB TIF)Click here for additional data file.Figure S4Heat-map plot of 12 microarray hybridizations show reproducibility of NFIX-siRNA-induced changes in global gene expression. The hybridizations represented by transfection number 6 are the most different from the remaining 5 for most of the reporters.(1.46 MB TIF)Click here for additional data file.Figure S5Sequence alignment of prey clones identified in the yeast-2-hybrid screen with RefSeq RNA sequences. (A) Two different clones of HMGN1 and (B) one clone of CGGBP1. Stars indicate exact match.(0.02 MB PDF)Click here for additional data file.Figure S6Phenotypes of U-2987 MG cells after chronic heat shock and/or siRNA indicated. CGGBP1-siRNA recapitulates chronic heat shock even at 37Â°C and HMGN1-siRNA leads to enlarged morbid cells. Combining either siRNA with NFIX-siRNA did not worsen the phenotypes.(2.09 MB TIF)Click here for additional data file.Table S1List of 150 deregulated reporters  in all six pairs of hybridization with a very high statistical significance (p<0.01). The Genbank ID, GeneOntology functional categories and other relevant information are provided. High B values show higher probability of being differentially expressed.(0.08 MB XLS)Click here for additional data file.Table S2Results of search for different full NFI binding sites in the 3 Kb upstream sequences from transcription start sites of 97 deregulated genes and 137 control genes. The deregulated genes are those with very high B value  as shown in (0.05 MB XLS)Click here for additional data file.Table S3Sequences of oligonucleotides used for PCR, cloning, site-directed mutagenesis and siRNA experiments.(0.03 MB XLS)Click here for additional data file.


We have previously identified and characterized the phenomenon of ectopic human centromeres, known as neocentromeres. Human neocentromeres form epigenetically at euchromatic chromosomal sites and are structurally and functionally similar to normal human centromeres. Recent studies have indicated that neocentromere formation provides a major mechanism for centromere repositioning, karyotype evolution, and speciation. Using a marker chromosome mardel(10) containing a neocentromere formed at the normal chromosomal 10q25 region, we have previously mapped a 330-kb CENP-A–binding domain and described an increased prevalence of L1 retrotransposons in the underlying DNA sequences of the CENP-A–binding clusters. Here, we investigated the potential role of the L1 retrotransposons in the regulation of neocentromere activity. Determination of the transcriptional activity of a panel of full-length L1s (FL-L1s) across a 6-Mb region spanning the 10q25 neocentromere chromatin identified one of the FL-L1 retrotransposons, designated FL-L1b and residing centrally within the CENP-A–binding clusters, to be transcriptionally active. We demonstrated the direct incorporation of the FL-L1b RNA transcripts into the CENP-A–associated chromatin. RNAi-mediated knockdown of the FL-L1b RNA transcripts led to a reduction in CENP-A binding and an impaired mitotic function of the 10q25 neocentromere. These results indicate that LINE retrotransposon RNA is a previously undescribed essential structural and functional component of the neocentromeric chromatin and that retrotransposable elements may serve as a critical epigenetic determinant in the chromatin remodelling events leading to neocentromere formation. The centromere is an essential chromosomal structure for the correct segregation of chromosomes during cell division. Normal human centromeres comprise a 171-bp α-satellite DNA arranged into tandem and higher-order arrays. Neocentromeres are fully functional centromeres that form epigenetically on noncentromeric regions of the chromosomes, with recent evidence indicating an important role they play in centromere repositioning, karyotype evolution, and speciation. Neocentromeres contain fully definable DNA sequences and provide a tractable system for the molecular analysis of the centromere chromatin. Here, the authors investigate the role of epigenetic determinants in the regulation of neocentromere structure and function. They identify that a retrotransposable DNA element found within the neocentromere domain is actively transcribed and that the transcribed RNA is essential for the structural and functional integrity of the neocentromere. This study defines a previously undescribed epigenetic determinant that regulates the neocentromeric chromatin and provides insight into the mechanism of neocentromere formation and centromere repositioning. Despite the fact that the functional role of the centromere in mitotic and meiotic cell divisions is evolutionarily conserved, the underlying DNA sequences of the centromeres are highly variable across the phylogeny and show no obvious conservation in hitherto non-centromeric genomic sites suggests the involvement of epigenetic mechanisms of formation, it remains possible that the underlying genomic DNA sequences exert a specific role in the establishment and/or maintenance of the functional integrity of the neocentromeric chromatin. For example, such a possibility is suggested by the universal observation of an elevated AT content, an increase in the density of LINEs (Long Interspersed Nuclear Elements), and a decrease in the density of SINEs (Short Interspersed Nuclear Elements) for the six different neocentromeric domains that have been mapped to date The core neocentromeric chromatin is fundamentally defined by the presence of specialized centromere-specific histone H3 variant CENP-A nucleosomes; however, the exact molecular mechanisms involved in the formation of a neocentromere have yet to be defined de novo interstitial pericentric deletion that has removed the presiding centromere of a normal chromosome 10 The first human neocentromere was identified at position 10q25 on the derivative marker chromosome mardel(10) following a in silico sequence analysis indicated that these CENP-A-binding clusters contain a 2.5-fold increase in the prevalence of L1 retrotransposon sequences (which belong to the only active subfamily of LINEs) when compared to the surrounding non-CENP-A-binding regions or the genome average To further define the finer structural organization of the core neocentromeic chromatin, we have recently performed high-resolution chromatin mapping using PCR fragment-array/ChIP analysis. The CENP-A domain was found to be assembled as multiple clusters  along the 10q25 neocentromeric chromatin in silico analysis of the various types of DNA motifs and sequence properties revealed a significant, 2.5-fold, increase in the prevalence of L1 retrotransposons within the CENP-A-binding domain of the 10q25 neocentromere Our previous http://l1base.molgen.mpg.de/) identified six FL-L1s, four of which (L1a–d) residing within or close to the CENP-A-associated clusters, while the remaining two (L1e–f) were located >1.5 Mb away from the CENP-A-associated domain  of FL-L1b RNA in the CENP-A bound fractions was observed, as indicated by a 4 to 5 fold increase in the yield of PCR products  chromosome that had been tagged with a Zeocin resistance gene To further extend the Zeocin kill-curve results, a direct assessment of the loss of the mardel(10) chromosome following FL-L1b knockdown was determined by FISH (Fluorescence In Situ Hybridization) analysis using a mardel(10)-specific BAC probe. The stability of mardel(10) was greatly affected 48 hours post FL-L1b RNAi-knockdown, with a significant reduction from ∼95% to ∼55% in the CHOK1-M10 cell line, and from ∼100% to ∼60% in the mouse-human hybrid cell line ES-M10 . Under sP<0.001) after the FL-L1b transcriptional knockdown using either siRNA#1 or siRNA#2  and a BAC DNA probe (RP11-359H22) that hybridized to the 10q25 neocentromeric region of mardel(10). Cells were harvested at 24 hours following RNAi-knockdown in order to capture the early to intermediate stages of the disruption of neocentromere function prior to the complete loss of the mardel(10) chromosome. The mean fluorescence intensity of the CREST6 signals on the 10q25 neocentromere was reduced by 20 to 30% ( siRNA#2 . In some siRNA#2 . ConsistP<0.05) after the FL-L1b transcriptional knockdown  chromosomal instability was not attributed to a reduction in the level of TRUB1 and/or ATRNL1 transcripts, siRNA oligonucleotide duplexes were designed to target these and two other immediately surrounding genes, KIAA1600 and GFRA1. Approximately 70–90% transcriptional knockdown was achieved for each of these genes in the CHOK1-M10 cells . No signin silico analysis of other neocentromere sites  and Zea mays (maize) We determined the transcriptional status of all six FL-L1s and other non-full-length L1 targets within the 6-Mb genomic window spanning the core neocentromere. Interestingly, only one of them (i.e. FL-L1b) was actively transcribed from the mardel(10) in CHOK1-M10, although all six FL-L1s contained the internal promoter sequences  and GM10926  was identical to that found in CHOK1-M10. The formation of the 10q25 neocentromere did not significantly change the transcription level of FL-L1b, in consistent with our previous finding on the transcription competence of multiple genes located within this region CentC satellite repeats and CRM retrotransposons in Zea mays160B/Athila2 retrotransposon in Arabidopsis thalianaPRAT satellite repeats in Palorus ratzeburgiZea mays and humans independently showed that these centromeric RNA transcripts were associated with the core centromeric chromatin Using RNA-ChIP-qPCR analysis, we showed that FL-L1b single-stranded RNA transcripts were incorporated as part of the ribonucleoprotein component of the CENP-A-associated domain. Interestingly, the presence of long single-stranded centromeric RNA transcripts including The functional role of FL-L1b RNA at the 10q25 neocentromere was determined by RNAi knockdown of FL-L1b in human/mouse and human/hamster monochromosomal hybrid lines. FISH and/or Zeocin kill-curve analysis indicated that FL-L1b knockdown led to a significant reduction (by ∼40–50%) of the mitotic stability of mardel(10) and the compromised structural integrity of the 10q25 neocentromere. These FL-L1b knockdown-mediated mitotic effects at the 10q25 neocentromere were fast and similar to the rapid response previously described in RNAi knockdown or conditional knockout of core centromere proteins, such as CENP-A In addition to the two FL-L1b siRNA duplexes, we have included the analysis of siRNA duplexes that targeted four genes spanning and surrounding the CENP-A-associated region. None of these siRNAs exerted any effect on mardel(10) stability, as indicated by the cell viability assay . More spSchizosaccharomyces pombe, also provides supports toward a role of transcription in defining a centromere state The precise functional role(s) of FL-L1 RNA transcripts at the core neocentromeric chromatin remains to be delineated. Transcription at the FL-L1 locus and/or the L1 transcript itself may act as an early-specification epigenetic signal for the recruitment of CENP-A nucleosomes. Interestingly, the transcriptional knockdown of FL-L1b leads to a more ‘closed’ local chromatin state, as indicated by the reduction in the transcription of two surrounding genes ATRNL1 and TRUB1. At the 10q25 neocentromere, the transcription activity may facilitate the process of histone replacement by partially disassembling the nucleosomes to provide a more ‘open’ chromatin structure Xist RNA in promoting the establishment of a specialized chromatin state such as the incorporation of macroH2A during X-inactivation Rather than the transcriptional activity itself, it is also possible that the FL-L1b RNA transcript may serve as a specific epigenetic signal at the 10q25 neocentromere since by RNAi knockdown several neighbouring genes did not affect the mitotic stability of mardel(10). Although this hypothesis remains to be tested, the underlying process may be similar to the function of long ie. ATRNL1 and TRUB1) following FL-L1b knockdown indicated that the FL-L1b RNA could be regulating a larger genomic domain than that of the CENP-A-associated chromatin. It is unknown how these FL-L1 RNA transcripts mediate such long-range chromosomal effects, however, it is interesting that this extended genomic domain overlaps with a region of high L1 DNA content (using the human genome average as the baseline threshold)  . The incde novo centromere assembly, but will also provide a better understanding of the operating mechanisms for centromere repositioning and karyotype evolution.Increasing evidence now point to neocentromere formation as the underlying mechanism for centromere repositioning that underpins karyotype evolution and speciation The somatic hybrid cell lines were cultured as previously described A time-course experiment was first performed to determine the time duration required to kill the non-resistant CHOK1 cells (CHOK1-N10) at 200 µg/ml of Zeocin. The transcription-knockdowns of FL-L1b and other genes of interest were performed by siRNA transfection of the relevant siRNA oligonucleotide duplexes for 48 hours at 25 nM in CHOK1-M10. Subsequently, cells were incubated with 200 µg/ml of Zeocin for an additional 48 hours following RNAi knockdown. The number of viable cells was determined by staining with Trypan Blue (0.8 mM Trypan Blue in 1× PBS) for 5 minutes at room temperature and counting with a hemocytometer under the microscope. The mitotic stability of the mardel(10) was calculated as the ratio of the percentage of viable cells under Zeocin selection to the number of viable cells without Zeocin selection.http://genome.ucsc.edu.au) May 2004 builds and its in-build RepeatMasker track http://l1base.molgen.mpg.de/) The genomic location of each chromatin domain and the sequence characteristics were determined using the UCSC Genome Browser  that was known to affect the transcriptional and/or translational activity RNA chromatin immunoprecipitation was performed as described in Total RNA was extracted using either the RNeasy Mini Kit (Qiagen) for transcription detection assays or TRIZOL reagent (Invitrogen) for RNA-ChIP. TURBO DNA-free Kit (Ambion) was used to remove possible contaminating DNA. cDNA synthesis was performed using Transcriptor First Strand cDNA Synthesis Kit (Roche). Quantitative RT-PCR was carried out using SYBR Green PCR Master Mix (Applied Biosystems) on 7300 or 7900HT Real-Time PCR System (Applied Biosystems) according to manufacturer's instructions. cDNA equivalent to 10 ng RNA was amplified with 150 nM forward and reverse primers in a 25 µL reaction −CT (control gene/genomic segment)]. The CT value of each test gene/segment was normalized against the CT value of control gene/segment, either 5S  or β-actin  to give the ΔCT value. The ΔΔCT value was calculated as [ΔΔCT = ΔCT(test cell line)−ΔCT(control cell line)] for transcription analysis, or [ΔΔCT = ΔCT(before siRNA knockdown)−ΔCT(after siRNA knockdown)] for transcription knockdown assay, and [ΔΔCT = ΔCT(input)−ΔCT(bound)] for ChIP-qPCR analysis, respectively. Relative fold-difference in transcription activity was expressed as The comparative C4 cells/well, 24 hours prior to siRNA transfection. Transcriptional knockdown was performed by transfecting cells with Stealth siRNA oligonucleotide duplexes (Invitrogen) at a final concentration of 25 nM in DMEM (Dulbecco's Modified Eagle's Medium) using 2.5 ng/µl Lipofectamine 2000 (Invitrogen) for a period of 24 to 48 hours according to the manufacturer's instructions. The effects of RNAi knockdown of FL-L1b and other target genes were assayed by quantitative RT-PCR. Stealth siRNA Negative Control Low GC Duplex (Invitrogen) was also included as control for sequence independent RNAi knockdown effects.Two sets of Stealth siRNA oligonucleotide duplexes targeting FL-L1b were designed using the online BLOCK-iT RNAi Designer software (Invitrogen). In contrast, siRNA oligonucleotide duplexes targeting genes KIAA1600, TRUB1, ATRNL1, and GFRA1 were obtained as pre-designed Stealth Select siRNA (Invitrogen). Sequences of the siRNA oligonucleotide duplexes are listed in Immunofluorescence BK) for each metaphase spread was calculated by the average arm intensity from five chromosomes and subsequently subtracted from the total intensity (I). Average signal intensity of 15–20 endogenous CHO centromeres (ICHO) from each spread was calculated and used as normalization control to correct for the variation in hybridization between spreads. The ratio of CREST6 fluorescence intensities (R) on 10q25 neocentromere to CHO centromeres was calculated using the following equation: R = (IM10−IBK)/(ICHO−IBK). The mean fluorescence intensity of CREST6 (M) on 10q25 neocentromere was calculated using the following equation: M = R×ICHOALL. ICHOALL represents the average intensity for all CHO centromeres (∼750) calculated for each treatment in the RNAi knockdown experiments.The quantification of CREST6 immunofluorescence signals was performed following FL-L1b RNAi knockdown. A circular area of defined size (diameter of 2 µm) was selected around the centromere of interest. Total intensity (I) of each pixel within the delineated area was determined and defined as arbitrary fluorescence unit (a.f.u.). Digital images obtained from immunofluorescence analysis were nonsaturating and auto-corrected for background removal. Non-specific background signal  in CHOK1-M10 when compared to CHOK1-N10, ranging from approximately 4 to 6 fold increase in relative binding . In contrast, none of negative control sequences, 18S, 5S, and hamster HC2sat repeat was enriched in the pull-down fractions, indicating a specific enrichment of L1 sequences in the CENP-A chromatin.DNA-ChIP-qPCR analysis. DNA-ChIP was performed using a specific anti-CENP-A antibody as previously described (2.2 MB TIF)Click here for additional data file.Figure S2RNA-ChIP-qPCR analysis. RNA-ChIP analysis was performed using a specific anti-CENP-A antibody followed by quantitative RT-PCR analysis. Positive control FL-L1b (amplified using L1b.1 primer set) and negative controls 18S, 5S and ACTB were included in the experiment. Test genomic targets included (A) four transcribed genes  and (B) three other FL-L1s  within or surrounding the CENP-A-binding domain. The PCR amplification of the active FL-L1b retrotransposon and the four test genes occurred at much earlier cycles  than the three silent FL-L1s . Relative binding values  on the Y-axis represent the fold-enrichment of the target sequence in CHOK1-M10 compared to that of CHOK1-N10. Except for FL-L1b, none of the other loci tested showed RNA enrichment at the CENP-A-binding domain.(3.0 MB TIF)Click here for additional data file.Figure S3https://rnaidesigner.invitrogen.com/), two sets of oligonucleotide duplexes, FL-L1b siRNA#1 and siRNA#2, were successfully designed each targeting to a specific site within the ORF2 region of FL-L1b. (B) Output from in silico BLAT  analysis showed that each of these siRNA duplexes had only one hit of 100% homology to the human chromosome 10 and no additional homologous sequences could be found in the other mammalian genomes analysed, including Mus musculus (mouse), Rattus norvegicus (rat), and Gallus Gallus (chicken).Design of siRNA sequences for RNAi knockdown of FL-L1b. (A) Using the online Invitrogen RNAi BLOCK-iT algorithm ((0.9 MB TIF)Click here for additional data file.Figure S4P<0.05, indicated by the asterisks). (B) No significant difference in % cell survival  was observed 48 hours post Zeocin selection following gene knockdown compared to the transfection-reagent-only and Stealth siRNA negative controls.Transcriptional knockdown of four different genes within or surrounding the CENP-A-binding domain. (A) Approximately 70–90% transcription knockdown  was achieved for (i) KIAA1600 (ii) TRUB1 (iii) ATRNL1 (iv) GFRA1 genes by the corresponding siRNAs at a final concentration of 25 nM in CHOK1-M10 cells after 48 hours ((4.3 MB TIF)Click here for additional data file.Figure S5L1b knockdown−R control.)/R control×100%].Transcriptional knockdown of FL-L1b and its effect on neocentromere structure and function. CHOK1-M10 cells were transfected with either (A) control siRNA oligonucleotide duplexes or (B) FL-L1b-specific siRNA oligonucleotide duplex FL-L1b-siRNA#2 (B), at a final concentration of 25 nM. Following 24 hours post transfection, the structural integrity of 10q25 neocentromere in CHOK1-M10 cells was investigated first by immunofluorescence analysis using an anti-CENP-A antiserum  followed by FISH analysis using a BAC probe  specific for the 10q25 neocentromeric region of mardel(10) (as indicated by the arrow). The non-specific background signal intensity (IBK) was calculated as the CREST signal intensity on the chromosome arms (from the average of five chromosomes). The CREST6 signal intensities on both CHO endogenous centromeres  and 10q25 neocentromere on mardel(10) (IM10) were determined. The ratio of CREST6 fluorescence intensities (R) on 10q25 neocentromere to CHO centromeres was calculated using the following equation: R = (IM10-IBK)/(ICHO-IBK). After FL-L1b knockdown, the fluorescence intensity of CREST6 at 10q25 neocentromere was reduced by 49.3% [calculated as (R (4.1 MB TIF)Click here for additional data file.Figure S6T method was used for data analysis. The ΔCT value was calculated as [ΔCT = CT (test segment)−CT (control segment)]. The CT value of each test segment  was normalized against the CT value of control segment (either C1 or C2) to give the ΔCT value. The ΔΔCT value was calculated as [ΔΔCT = ΔCT(input)−ΔCT(bound)]. The fold-enrichment in CENP-A binding was expressed as Transcription knockdown of FL-L1b followed by RNA-ChIP-qPCR analysis. RNAi knockdown of FL-L1b was performed by transfecting CHOK1-M10 cells with either control siRNA oligonucleotide duplexes or FL-L1b-specific siRNA oligonucleotide duplexes, (A) FL-L1b-siRNA#1 and (B) siRNA#2, at a final concentration of 25 nM. Following 24 hours post transfection, ChIP was performed using a specific anti-CENP-A antibody. Subsequent quantitative real-time PCR analysis was carried out using three independent primer sets A3.1, A3.2 and A4 each targeting to a genomic fragment of approximately 200 bp to the previously described CENP-A-binding clusters A3 or A4 (third and fourth clusters counting from the left as shown in (4.3 MB TIF)Click here for additional data file.Figure S7Distribution of FL-L1s within and surrounding the CENP-A-binding domain of six different neocentromeres. To date, the CENP-A-binding domain, ranging in size from 131 to 464 kb (red boxes), has been mapped for six different neocentromeres using ChIP-array analysis (2.3 MB TIF)Click here for additional data file.Table S1Sequence characteristics of L1s across a 6-Mb genomic region of the 10q25 neocentromere.(0.02 MB XLS)Click here for additional data file.Table S2Transcription status of FL-L1s within a 6-Mb genomic region of the 10q25 neocentromere.(0.02 MB XLS)Click here for additional data file.Table S3Sequence characteristics and transcription status of L1s within a 6-Mb genomic region of the 10q25 neocentromere.(0.02 MB XLS)Click here for additional data file.Table S4Sequence features of FL-L1s analysed using the online L1Base program.(0.02 MB XLS)Click here for additional data file.Table S5Sequence characteristics of FL-L1s across the 10q25 neocentromere.(0.04 MB XLS)Click here for additional data file.Table S6Oligonucleotide primers and siRNA duplexes.(0.03 MB XLS)Click here for additional data file.