The past 15 years opened new avenues for retrovirus and retroelement research. Not surprisingly, they stemmed from essential knowledge collected in the past, which remains the ground of the present and therefore should be remembered. However, a short supplement of new break-through discoveries and ideas should be recollected. Using selected examples of recent works, I tried to extend and supplement my original article published in Folia Biologica (1996). Having an insight into the molecular events governing the central process of retrovirus replication cycle represented by reverse transcription, the question of site specificity of retrovirus DNA integration became the focus of interest. In order to introduce the process of retrovirus integration the main steps involved in its realization are briefly summarized . FurtherThere are clear differences in the affinity of retroviruses to certain cell genomic regions. ALU/ASV tend to integrate in GC-rich, preferentially house-keeping genes ,5. A simUnderstanding the conditions under which retrovirus integration can be directed in defined cell genome regions has a principal significance for construction of safe and long-term expressed vectors. This goal seems to be close, but still requires careful testing.Detailed study of representative retroviruses indicates that two of them represent the best candidates for vector construction. The first are lentiviruses, among which belongs HIV and similar complex viruses isolated from different mammalian species. These viruses are pathogenic but not oncogenic. They are equipped with a series of accessory genes that also enable their integration in non-dividing cells. Not surprisingly, lentiviruses stripped of their pathogenic sequences were successfully employed for construction of vectors. It was reported that they also acted as an efficient tool for gene therapy of adrenoleukodystrophy (ALD) . ADL is The second candidates for safe vectors are provided by ALV/ASV avian retroviruses. These retroviruses do not replicate in mammalian cells, and therefore it is not required to strip them of replicative genes in order to prevent their multiplication. Furthermore, ALV sequences are not homologous to human endogenous retroviral genomes and therefore there is almost no chance of their mutual recombination. However, ALV integrated in mammalian cells are usually underexpressed and prone to silencing by methylation. Nevertheless, retroviral vectors can be protected against silencing by insertion of CpG islands close to their promoter . Such anRetrotransposable elements, which employ reverse transcription for their genesis and even for their spread through the genome, became an important issue of genomics, evolutionary genetics, oncology, and other fields of general interest.We now know that more than 45% of our genome arose by reverse transcription, which includes 8% of proviral sequences and 18% of mobile LINE elements that are involved in spreading the gene sequences and responsible for pseudogene formation. In contrast to retroviruses LINE elements use as primers for reverse transcription stretches of oligo-dT. Such dT rich regions can aneale with with polyA tails of cell RNAs. Therefore in some cases a mRNA substitutes LINE RNA template becomes reverse transcribed to DNA constituting intronless pseudogenes In spite of the fact that LINE are mostly defective, about hundred LINE copies remain active and retrotranspose to our genome.The field of retrotransposable elements had been reviewed several times in the past, but sophisticated cell control over these elements was recently summarized by Goodier and Kazazian, 2008; BlumensThe first example is represented by PIWI elements, which spread in Drosophila and rapidly expanded in mammalian evolution . PIWI spThe second case Figure  documentThe third example Figure  relates The already mentioned three examples of retroelement involvement in cell functions do not cover the full extent of their activities. There is growing evidence that retroelements take part in the control of gene expression. Evidence has been presented showing significantly increased retroelement representation in the promoter region of genes, especially those involved in development, cell differentiation and transcription regulation, and also in additional structures delineating functional genes such as locus control regions (LCR) or insulators acting as a divide of individual enhancer. The sphere of influences was reviewed by King Jordan et al., 2003; Lowe etOf special interest is the evolution of retroelements and their role in the constitution of new important cell functions. The evolutionary role of retroelements is strongly supported by several observations documenting that some of them have been conserved during evolution of tetrapods and therefore they should have served functions not defined so far . Needles6 insertions in individual human genomes worldwide [Good statistical evidence is available demonstrating that the human genome contains more Alus than the chimp genome. More sensitive techniques provide evidence for more extensive representation of retroelements, namely Alu and LINE, estimated as 10orldwide ,24. Of sorldwide . As provorldwide ,26. Receorldwide .Looking at retroelements from a more general point of view, we should consider their ability to spread not only vertically, but also horizontally. This was encountered at first in LTR containing retroelement copies that were shown to be transmitted among different species of Drosophila. Similarly, LINE elements might have been transferred between snakes and mammals  Such horIn summary, retroelements should be taken seriously not only as principal factors reshaping any genome, but also as elements that due to their mobility can cause damage contributing to the occurrence of important diseases.The author declares that he has no competing interests.


Pseudomonas syringae pv. syringae strains which cause apical necrosis of mango trees. Previous studies showed that mangotoxin biosynthesis is governed by the mbo operon. Random mutagenesis led to the identification of two other gene clusters that affect mangotoxin biosynthesis. These are the gacS/gacA genes and mgo operon which harbors the four genes mgoBCAD.The antimetabolite mangotoxin is a key factor in virulence of mgoA resulted in loss of mangotoxin production and reduced virulence on tomato leaves. Transcriptional analyses by qPCR and promoter reporter fusions revealed that mbo expression is regulated by both gacS/gacA and mgo genes. Also, expression of the mgo operon was shown to be regulated by gacS/gacA. Heterologous expression under the native promoter of the mbo operon resulted in mangotoxin production in non-producing P. syringae strains, but not in other Pseudomonas species. Also introduction of the mbo and mgo operons in nonproducing P. protegens Pf-5 did not confer mangotoxin production but did enhance transcription of the mbo promoter.The current study shows that disruption of the nonribosomal peptide synthetase (NRPS) gene mbo and mgo operons are under the control of the gacS/gacA two-component system and that the MgoA product acts as a positive regulator of mangotoxin biosynthesis.From the data obtained in this study, we conclude that both  Pseudomonas syringae is one of the most ubiquitous plant pathogens, causing various economically important diseases − ,40. Q-PCmbo operon expression in different genetic backgrounds, the mbo operon promoter (PmboI) cloned into pMP220 [mgoA, gacA and gacS genes were transformed with plasmid pMP::PmboI which contains the PmboI. The plasmid pLac-mgoBCAD (harboring the mgo operon) was also used to complement the mgoA, gacA and gacS mutants and finally the β-galactosidase activity of PmboI was measured. In order to evaluate the effect of the mgo operon on the activity of PmboI, P. protegens Pf-5 was used due to the absence of the two operons in its genome. First, P. protegens Pf-5 was transformed with the pMP::PmboI and the promoter activity was measured, and secondly to measure the effect on the mbo operon transcription, this strain containing the plasmid pMP::PmboI, was also transformed with the plasmid pLac-mgoBCAD (mgo operon under pLac regulation). As a negative control the β-galactosidase activity was measured for the wild type strain P. syringae pv. syringae UMAF0158 and each strain used in this assay, transformed with empty vector pMP220. β-galactosidase activities were quantified by the Miller method [To study the o pMP220  as previo pMP220  was usedr method . BrieflymgoA gene in the different genomes of Pseudomonas strains, the mgoA gene from P. syringae pv. syringae UMAF0158 was used in BLASTP [Pseudomonas spp. available in the databases. Once the amino acid sequences of all the orthologous mgoA genes were obtained, the putative adenylation domains were identified using the PKS/NRPS Analysis Web-site (http://nrps.igs.umaryland.edu/nrps) [In order to identify the presence of the n BLASTP  comparisdu/nrps) . Other adu/nrps) . Two phydu/nrps) . NeighboP. protegens Pf5, a Student’s t-test was carried out (p = 0.05), using the IBM.SSPS 19 software (IBM® Company).Searches for sequence similarity in the NCBI databases were carried out using BLAST algorithms . Genome P. syringae pv. syringae UMAF0158, were previously obtained and characterized for mangotoxin production (Table mboB) and 5αC5 (mboD) . Growth of the mgoA mutant was shown to be similar to that of the wild type strain, with cell densities of up to 1011 cfu ml-1 in liquid medium after 108 h of growth at 22ºC . Also mboA, mboC, or mboE mutations did not significantly affect transcription of gacS/gacA or mgoA (data not shown). These results indicate that the GacS/GacA two-component regulatory system affects transcription of both the mbo and mgo genes and that the product of the mgo operon influences transcription of the mbo genes. To further study if the GacS/GacA two-component regulatory system could regulate the mgo and mbo genes via RNA repressor binding proteins [mgo and mbo genes were inspected for the presence of the described consensus motif (5′-CANGGANG-3′) previously described in P. protegens CHAO [mbo/mgo genes  displays an adenylation (A) domain responsible for recognition and subsequent activation of an amino acid substrate. It also contains the typical thiolation (T) and condensation (C) domains. Finally, the thioesterase (TE) domain releases the final molecule from the NRPS assembly line. Based on the specific signature sequences described previously for A domains, analysis of MgoA did not allow prediction of the amino acid to be activated. Therefore, a phylogenetic analysis was performed with multiple A domains from NRPSs of which activated amino acids are known and with MgoA from other s Figure .mgoA present in other Pseudomonas species can regulate the mbo operon, reporter constructs pLac-mboABCDEF (mbo operon under its own and under pLac promoter expression) and pLac-mboFEDCBA (mbo operon only under its own promoter expression) were used. Firstly, only specific P. syringae pathovars harbor the mbo operon, and almost all strains from these pathovars produce mangotoxin [mbo operon containing plasmids  and the mgo genes in P. protegens Pf-5, which lacks both the mgo and the mbo operons in its genome. Compared to the promoter activity in the wild-type Pf-5 background, a two-fold increase in ectopic mbo promoter activity was observed when Pf-5 was complemented with the mgo operon , it produces mangotoxin. However, when P. protegens Pf-5 was transformed with pMP-mboFEDCBA (mbo operon under only its own promoter expression) it was not able to produce detectable amounts of mangotoxin, neither in absence nor in presence of the mgo operon of P. syringae pv. syringae UMAF0158 . Transcriptional analysis of the mgo, mbo and gac genes showed that the mbo genes were markedly down-regulated in both the gacA and mgoA mutants. On the other hand, the transcriptional levels of mgoB and mgoA, also showed down-regulation in the gacA mutant, indicating that the mgo operon is also under regulation by the GacS/GacA two-component regulatory system. These data suggest that GacS/GacA is regulating the mbo operon expression via the mgo operon, however direct regulation of the mbo operon by the two-component regulatory system gacS/gacA cannot be excluded , revealed that the product of the mgo operon could acts as positive regulator of mbo transcription. Interestingly, the pvfC gene (homologue of mgoA) is considered a regulator of virulence in P. enthomophila, but appears not to be part of the GacS/GacA regulatory cascade [mgo operon in a gac mutant could not restore mangotoxin production or mbo-promoter activity, suggesting that next to the mgo operon, additional factors are regulated by the gac system that influence mangotoxin production. It is worth noting that P. entomophila and P. syringae pv. syringae harbor two different genetic backgrounds, adapted to different environments. The first is found in diverse environments such as soil, aquatic ecosystems, rhizosphere, and in pathogenic interactions with Drosophila melanogaster[Pseudomonas species. On the other hand, the fact that both PvfC and MgoA are involved in the regulation of virulence could indicate that in other Pseudomonas spp. these factors would be involved in the regulation of virulence and/or secondary metabolite production.Transcriptional analysis with a  cascade . In straanogaster. The secanogaster. TherefoPseudomonas genus. In this context, it is worth noting that the transformation of the mbo operon under the expression of its own promoter only confers mangotoxin production in the P. syringae group and not in the P. fluorescens group. Therefore, it seems that the NRPS MgoA is involved in different signal transduction pathways depending of the Pseudomonas species. In the case of P. syringae, MgoA appears to activate mangotoxin production. It remains to be studied if MgoA is also involved in the regulation and production of other antimetabolites in the P. syringae group, such as tabtoxin and phaseolotoxin. The positive regulation of the mbo operon promoter activity in the presence of the mgo operon in Pf-5, combined with the lack of detectable amounts of mangotoxin suggests that additional factors for mangotoxin biosynthesis or its export are not present in the P. fluorescens group.Phylogenetic analysis of MgoA and the adenylation domains suggested an evolutionary specialization of this protein into the P. syringae pv. syringae UMAF0158, the GacS/GacA two-component system regulates transcription of the mgo and mbo operons and thereby mangotoxin biosynthesis. At the same time, the mgo operon product seems to act as a positive regulator of the mbo operon. The proposed model for mangotoxin biosynthesis is a simplified and initial overview of the interaction between the gac, mgo and mbo gene products based on the results obtained in the current study. This is the first evidence of the interplay between MgoA and the GacS/GacA two-component regulatory system in the regulation of the mangotoxin biosynthesis.In summary, for We the authors hereby declare that the research performed with plants has been conducted in accordance with institutional, national and international guidelines.We the authors hereby declare that there is no conflict of interests concerning this manuscript.VJC, MV, EA, AV, JMR and FMC conceived the study. VJC and EA did all the cloning and genetics of this study. VJC and MV did the Q-PCR experiments and analysis. VJC and JAG did complementation and reporter construct experiments. JMR and AV supported the research. VJC, MV, JMR and FMC wrote the manuscript. VJC, EA, MV, AV, JMR and FMC coordinated and critically revised the manuscript. All authors read and approved the manuscript.Primers used in this study.Click here for fileP. syringae pv. syringae strain UMAF0158 and the derivatives mgoA and gacA mutants. (A) Growth of the wild type strain UMAF0158 and the mgoA (∆mgoA) and gacA (2βB7) mutants at 22ºC in PMS. At each time point, the bacterial density was estimated by serial dilutions and colony counts on plates of selective medium and expressed as log cfu ml-1 of culture. (B) UMAF0158 mangotoxin production at 22ºC in PMS. At each time point, the mangotoxin production was estimated using cell-free filtrate and represented as the previously defined toxic units (T.U.). The dashed line represents the detection limit of the technique. Mean values for three replicates are given; the error bars represent the standard errors of the mean.Growth characteristics of Click here for fileP. syringae pv. syringae UMAF0158 and corresponding derivatives using a detached tomato leaf assay. (A) In planta growth inside the tomato leaflets after H2O2 surface disinfection of the wild type strain UMAF0158, mgoA and mboA mutants, and their respective complemented derivatives. (B) Severity of necrotic symptoms (necrotic area) on tomato leaflets inoculated with wild type strain UMAF0158, the mutants in mboA and mgoA with their respective complemented derivatives. The total necrotic area (mm2) from 30 inoculated points on tomato leaflets was measured 10 days after inoculation and used to compare the severity of necrotic symptoms produced by the different strains. (C) Representative pictures of the necrotic lesions produced by the wild type strain and the different mutants at 10 dpi. Different letters denote statistically significant differences at p = 0.05, according to analysis of variance followed by Fisher’s least significant difference test.Virulence analysis of the wild type strain Click here for filemboACE transcript levels in the wild type strain UMAF0158. Relative expression of the genes involved in the mangotoxin biosynthesis at the different time points during the growth curve. For each time point, mean values of four biological replicates are given; the error bars represent the standard errors of the mean.Click here for filePseudomonas spp. Neighbor-joining tree was constructed with MEGA5 using a partial sequence of MgoA. The boxes indicate the different groups of Pseudomonas and the presence (mbo +) or absence (mbo -) of the mbo operon. The evolutionary history was inferred using the Neighbor-Joining method [Burkholderia cenocepacia J2315 was used as the outgroup. Bootstrap values  are shown on the branches.Phylogenetic analysis of the MgoA of different g method . The evog method  and are g method . BurkholClick here for file


The epithelial-mesenchymal transition (EMT) is a complex change in cell differentiation that allows breast carcinoma cells to acquire invasive properties. EMT involves a cascade of regulatory changes that destabilize the epithelial phenotype and allow mesenchymal features to manifest. As transcription factors (TFs) are upstream effectors of the genome-wide expression changes that result in phenotypic change, understanding the sequential changes in TF activity during EMT provides rich information on the mechanism of this process. Because molecular interactions will vary as cells progress from an epithelial to a mesenchymal differentiation program, dynamic networks are needed to capture the changing context of molecular processes. In this study we applied an emerging high-throughput, dynamic TF activity array to define TF activity network changes in three cell-based models of EMT in breast cancer based on HMLE Twist ER and MCF-7 mammary epithelial cells. The TF array distinguished conserved from model-specific TF activity changes in the three models. Time-dependent data was used to identify pairs of TF activities with significant positive or negative correlation, indicative of interdependent TF activity throughout the six-day study period. Dynamic TF activity patterns were clustered into groups of TFs that change along a time course of gene expression changes and acquisition of invasive capacity. Time-dependent TF activity data was combined with prior knowledge of TF interactions to construct dynamic models of TF activity networks as epithelial cells acquire invasive characteristics. These analyses show EMT from a unique and targetable vantage and may ultimately contribute to diagnosis and therapy. The epithelial-mesenchymal transition (EMT) is a fundamental program of tissue development in which epithelial cells lose apical-basal polarity and cell-cell contacts, gain the ability to traverse the extracellular matrix, and ultimately contribute to tissue outside the boundaries of the original epithelial sheet 2/COX, PKC, Notch, Hedgehog, and Wnt/β-catenin pathways EMT is a multi-step process that involves numerous signaling pathways and alterations in gene expression A technology has emerged aimed at capturing the dynamic activity of signaling pathways through measuring the activity of transcription factors (TFs), which are the downstream targets of many signaling pathways. TF activity is a powerful regulator of cell phenotype In this report, we applied the emerging TF activity array to models of breast cancer EMT in order to define a dynamic TF network as mammary epithelial cells develop invasive properties. The large-scale quantification of TF activity is accomplished through the parallel delivery of TF reporter constructs 4 cells/cm2 except for HMLE Twist ER cells in arrays that were seeded at 4×104 cells/cm2 to account for differences in growth rate when plated in smaller wells. HMLE Twist ER cells were treated with either 5 ng/mL TGF-β1  or 40 nM 4-hydroxytamoxifen (4OHT) . MCF-7 cells were treated with 2 ng/mL TGF-β1.HMLE Twist ER cells st Strand cDNA Synthesis kit  with random hexamer primers. The Applied Biosystems 7900HT Fast Real-Time PCR system and the following Taqman assays were used according to the manufacturers instructions ; each gene name is followed by the assay ID of the probe/primer set used for reference on the Applied Biosystems website (www.appliedbiosystems.com): 18s rRNA ; E-cadherin ; Fibronectin ; GAPDH (Hs03929097_g1); matrix metalloproteinase 1 ; matrix metalloproteinase 14 ; N-cadherin ; Vimentin . Fold change was calculated using the delta delta Ct method using 18s rRNA as an endogenous control after determining that this control in combination with quantification of total RNA yielded the most accurate results. Experiments were repeated at least three times for each data point. Significant differences relative to vehicle control were calculated using an unpaired two-tailed t-test with p<0.05 considered significant.Cells collected for qPCR were trypsinized, then pelleted by centrifugation. Supernatant was removed and cells were snap-frozen in liquid nitrogen and stored at −80°C until use. Total RNA was extracted from thawed samples using the RNeasy Mini Kit  including incubation with DNase I  to remove DNA contamination. RNA concentration was quantified using a Nanodrop spectrophotometer ). An equal amount (20 ng/reaction) of RNA was added to each cDNA synthesis reaction, which was performed using the AccuScript High Fidelity 14 cells/cm2 and cultured for 3 days before application of 5 ng/mL TGF-β1  or vehicle control (ddH2O) daily for six days. After treatment, the culture was fixed in 4% paraformaldehyde  and stained for E-cadherin . Fluorescent imaging was performed using an inverted Eclipse TE2000U microscope  in the Equipment Core of the Institute for BioNanotechnology in Medicine at Northwestern University.HMLE Twist ER cells were plated on 8-well chamber slides (BD Biosciences) at a density of 2×10Following treatment to induce EMT, medium was replaced and a “wound” was made by scratching an “X” in the same orientation across each well with a 1 mL pipette tip. The intersection of each arm of the “X” was photographed immediately following wounding (0 hours) and at 10 hours (for HMLE Twist ER) or 24 hours (for MCF-7) to assess the extent of invasion into the wound. The width of the four arms just outside of the intersection was measured using ImageJ  at 0 hours and at the end of the assay. The average decrease in width was recorded for each well. At least 3 independent repeats for each condition were separately cultured and assayed on different days. Significance was calculated using an unpaired two-tailed t-test with p<0.05 considered significant.4 cells/well in BD BioCoat Matrigel Invasion Chambers . Medium containing serum served as a chemoattractant in the bottom chamber, while medium without serum was added to the bottom chambers of negative control wells. HMLE Twist ER cells were trypsinized, quenched with complete medium, then washed and plated in medium without bovine pituitary extract (BPE) at a density of 1.25×105 cells/well in invasion chambers with complete medium in the bottom chambers as a chemoattractant; negative control bottom chambers contained medium without BPE. Both cell types were allowed to invade for 72 hours. MCF-7 cells treated with TGF-β1 have a decreased growth rate compared to controls, so after 72 hours total MCF-7 cells in invasion chambers were counted again. Noninvaded cells of both types were removed from atop the matrigel layer with a cotton swab dipped in phosphate-buffered saline  and chambers were stained with 0.5% crystal violet  in 60% phosphate-buffered saline/40% ethanol for 1 hour. Four separate fields per membrane were counted and the average number of invading cells in these fields was recorded as the average number of invading cells for that chamber. For MCF-7 assays, average number of invading cells was normalized to average total cells for each chamber. Each chamber contained cells from an independent culture of EMT-induced or vehicle control cells and each experiment was completed at least 3 times on at least 2 different days. Significance was calculated using an unpaired two-tailed t-test with p<0.05 considered significant.Following treatment to induce EMT, MCF-7 cells were serum-starved overnight before being collected with a cell scraper and plated in serum-free medium at 2.5×10www.panomics.com, and prior publications –r is added to indicate that the DNA sequence is a transactivation reporter, not the gene encoding the TF  and incubated for 48 hours (HMLE Twist ER cells) or 96 hours (MCF-7 cells) to allow full expression of reporters before the assay began. To measure TF activity-dependent luciferase production, D-luciferin  was added to wells to a final concentration of 1 mM, which had been previously determined to be well in excess of a limiting concentration. Following a 10-minute equilibration period, luminescence (photons/second) in each well was measured using an IVIS Lumina LTE camera system . Untransduced cells in arrays served as controls for nonenzymatic D-luciferin breakdown. Cells transduced with a TA-FLUC reporter construct without additional TF response elements served as controls for any differences in basal promoter activity between conditions. Following imaging, media were exchanged for fresh media containing TGF-β1, 4-hydroxytamoxifen (4OHT) or respective vehicle and arrays were returned to the cell-culture incubator for 24 h before being imaged again.Transcription factor reporters consist of a specific transcription factor response element cloned upstream of a TA promoter driving the gene for firefly luciferase (FLUC) and are packaged in self-inactivating lentiviral vectors. Increased transactivation of reporters by TFs results in increased luciferase production and a proportional increase in luminescence when an excess of substrate is added during imaging, thus providing a quantitative measure of relative transactivation. For precision, the reporters are referred to by the names of the TF using italicized letters to indicate that data are readouts from DNA sequences (reporter constructs) known to be transactivated by the specific transcription factors for which they are named  in cells treated with treatment Tx on day Dx after adjustment for basal transactivation from control reporter TA is therefore represented by the formulaLuminescence reads for each well on each day were first divided by the read for that well on Day 0, to control for any differences in seeding density or transduction efficiency. Normalized values were then divided by average luminescence from corresponding TA-FLUC control wells to control for differences in basal TA promoter activity. Luminescent reads for a reporter Veh) to correct for TF activity changes due to continued cell growth in arrays that cannot be attributed to the induction of EMT. Fully normalized luminescent readouts can therefore be represented as:Normalized luminescent reads for each treatment on each day were then divided by the average of the normalized read for the respective vehicle control  and TF(J) is defined asBased on the assumption that co-regulation of TFs results in dynamic reporter activity trajectories which are similar (in-phase), a similarity index was defined to compare daily averages of all pairs of TF reporters over the six-day time course of study. This index was designed to identify pairs exhibiting positive correlation (in-phase) and negative correlation . The similarity index TFg from 1  and divides it by the norm of the differences, resulting in a number between −1 and 1. Values near s = 1 indicate high overlap with positive correlation; near s = 0 indicates minimal overlap; and near s = −1 high overlap with negative correlation (anticorrelation). The similarity index is similar to the Pearson correlation coefficient but it differs in one important aspect: It is based on the scalar product of the deviations from the average vehicle control (which is 1) and not on the scalar product of the deviations from the average TF activity data The similarity index calculates the overlap  of the differences of the daily averages TF(I)(d)  = 2 and gTF(J)(d)  = 2.3 for each day. For this example the similarity index will be maximal (sTF(I)TF(J)  = 1) but the correlation vanishes (r TF(I)TF(J)  = 0).Since we are interested in the similarity of the deviations from the vehicle control, we use a different quantity than the correlation coefficient. To illustrate the significance of using the different term, consider two TF-r's TF(I) and TF(J) were consistently up-regulated compared to the VEH control. In an extreme case where gTF(I), TF(J)) a histogram of similarities TF(I)TF(J)(s)P from a bootstrap sampling  of the vehicle control values was determined. The bootstrap ensemble of the vehicle control values was generated by random draws with replacement from the vehicle-treated repetitions of individual days without mixing values from different days. The daily average was then calculated, then the similarity s of the vehicle controls for TF(I) and TF(J), and the distribution of similarities TF(I)TF(J)(s)P was estimated from the histogram of N = 10,000 repetitions. Pairs of reporters (TF(I), TF(J)) whose similarities TF(I)TF(J)s were found outside of the bulk 95% region were considered significantly correlated.To assign statistical significance to similarity indices for pairs of TF reporters, for each pair . Plotted TF icons represent TF activities significantly different from vehicle controls on each day and plotted according to assigned biological categories. Connections in networks represent either (1) TF binding sites near genes for other TFs and known TF-TF interactions catalogued in the TRANSFAC database, or (2) similarity index analysis of experimental data, or connections identified by both (1) and (2) as indicated in the figure legends. For TRANSFAC-derived connections, connections between TF icons indicate either a known protein-protein interaction between TFs or the presence of a binding site for one TF within the regulatory region of the other. Connections representing TF-TF interactions identified as described above were plotted between TF icons if both TF activities were significantly altered relative to vehicle on the same day. Connections representing transcriptional relationships between TFs were plotted if the upstream TF had significantly altered activity relative to vehicle on either the preceding day or the same day as the downstream TF. All connections that met the criteria described here and in the figure legends are shown in networks.Networks of transcription factor reporters showing altered activity at each time point were constructed as follows. TF reporters with altered activity were assigned to broad biological categories as shown in We initially confirmed EMT in three cell-based models of breast cancer. To allow distinction between conserved and model-specific TF activity changes, three models were selected based on distinct treatments and cell types . The firin vivo include increased cell migration and invasion. In a scratch-wound assay, cells had a marked increase in migration with 4OHT or TGF-ß1 treatment compared to vehicle controls ; HIF1-r, ER-r, HSE-r, MEF2-r, and MNX1-r (decreased). NFκB is considered a master regulator of EMT and its increased activity coordinates a web of other master regulators during this process and links inflammation to cancer progression ER-r activity was more pronounced at later time points when the transition to a mesenchymal phenotype was more advanced. HSE-r is the reporter for heat shock factor (HSF), which responds to multiple stressors. In contrast to our results using human mammary epithelial cells, increased heat shock factor activity has been associated with increased EMT in a mouse model HIF1-r activity was decreased in the HMLE Twist ER/4OHT model and had large fluctuations in both TGF-β1 models with significantly increased and decreased activity at different points during the six-day study period. MEF2 was previously identified as a TF hub in EMT networks PAX1-r activity was increased early in all models with biphasic activity in the HMLE Twist ER/4OHT model only, while MNX1-r activity was decreased at later time points in all models.Seven reporters (13%) with activity significantly different from controls had similar behavior across models. Among these TFs were BRACHYURY-r, SRF-r, and HIF1-r, which had dynamic increases and decreases in activity as noted above), while apoptosis-related E2F1-r had decreased activity. Brachyury is crucial for embryonic mesoderm formation and drives EMT in human cancers in vitro EMT model is consistent with a possible physiologic response to wounding that is subverted in cancer cells In both models of TGF-β1 driven EMT, three TF reporters corresponding to TFs with well-known roles in physiologic EMT had increased activation  was also seen in this model. The MCF-7 model showed unique activation patterns for several differentiation-related  and apoptosis-related  reporters. Because the TF activity array data was least robust in the MCF-7 model, further analysis focused on the two HMLE Twist ER models. Despite the many conserved TF activity changes, we next investigated whether the dynamic activity pattern and TF activity networks were similar in the two models, which would suggest a conserved, coordinated EMT program. Alternatively, the dynamic activity pattern may be distinct for each model, which would suggest that EMT is executed with different TF activity dynamics based on the cell context.Multiple TFs were observed to have significant activity changes in only 1 model, and these non-conserved changes may relate to model-specific mechanisms of EMT or to cell type- or treatment-specific responses not related to EMT. Model-specific changes included a large number of differentiation and development-related reporters with decreased activity in the HMLE Twist ER/4OHT model, particularly at later time points . In HMLECluster analyses were performed to identify TFs with similar activity patterns over the six-day time course in the 2 HMLE Twist ER models. TF activities in each model were grouped into ten clusters so that common patterns of TF activity during EMT could be observed . A hieraTime-dependent networks are ultimately necessary to understand changes in cellular differentiation status such as through the induction of EMT NFκB-rBRACHYURY-rAP1-rFOXA-rNFκB-r and BRACHYURY -r correlate negatively with p53-r. The 4OHT network is notable for a core of highly interconnected TF activities related to differentiation and development . This m modules –3 and ap modules , when thIntegration of novel and existing data remains an important challenge to systems biology The similarity index analysis identified significant interactions that have not been previously captured in the TRANSFAC database , which were also represented in dynamic network models  and 8B. For comparison, the networks incorporating prior knowledge of all TF binding sites within the vicinity of other TF genes, as well as TF-TF interactions, were constructed from the TRANSFAC database to indicate possible interactions between TFs significantly altered in each model A and S4AThis study employed an emerging technology for large-scale quantification of TF activity, which was applied to multiple cell-based models of EMT in breast cancer to identify conserved and model-specific mechanisms. The TF activity array identified a set of previously identified EMT-related factors that were significantly altered in all three models of EMT, including an increased activity of NFκB and PAX1 and decreased activity of ER, HIF1, HSE, MEF2, and MNX1. The observed activities of PAX1, MNX1, and HSF per corresponding reporters were similar in the 3 models, displaying activity patterns that have not previously been described in EMT to our knowledge. Overall, about one-half to one-third of significant changes in TF activity were conserved in all models. This partial overlap was consistent with previous reports directly comparing EMT under the control of different master regulators that identified overlapping but nonidentical gene expression programs The large number of non-conserved gene expression Although 47% of TF activity changes during EMT were common to both HMLE Twist ER models, the dynamics of individual TF activities and thus the observed correlations between pairs and groups of activities were distinct. Only 6% of significant correlations between pairs of TF activities were common to both models. The similarity index and cluster analyses identify connections based on similar dynamics throughout culture; which may be rare. The activity dynamics of two functionally interacting TFs reflect integration of overlapping but non-identical sets of upstream signaling cues, which change during the course of EMT. Increasing the number of time points in future studies would be expected to increase the sensitivity of the similarity index to correlations. Prior knowledge of TF interactions can also infer connections; however, this approach likely includes connections that are not functional in the system of interest The TF activity array data suggest that EMT results from distinct dynamic changes, which can also be seen in the component changes that classically define EMT. All three models displayed the typical changes of EMT robustly such as increased invasion, increased mesenchymal characteristics, and decreased epithelial characteristics –3; howevMNX1-r, NOTCH1-r, RAR-r, HNF1A-r, and PAX1-r is seen in both the HMLE Twist ER/4OHT model and the HMLE Twist ER/TGF-β1 model during the second half of culture  had increased activity at early time points, which declined as the activity of many differentiation-related reporters began to drop. The TF activity patterns suggest that initial epithelial destabilization may have occurred along with partial dedifferentiation to a stem-like state, from which mesenchymal reprogramming ensued. This relationship between EMT and stem cell properties has similarly been proposed in vivoOur analysis of large-scale dynamic TF activity suggests novel time-specific mechanistic steps within EMT. A larger number of TF activities were decreased than were increased , which i culture . The deccis regulatory elements in the promoters of regulated genes, which implicated TFs Atf2, Klf10, Sox11, and SP1 in kidney tubule cells undergoing EMT The complexities of cancer biology demand systems biology approaches with an emphasis on function and integrated analysis In conclusion, the unique properties of the TF activity array allowed visualization of dynamic TF activity networks as the epithelial differentiation program is destabilized and cells acquire an invasive phenotype. The TF activity array distinguished conserved from model-specific TF activity changes in three models of breast cancer EMT, demonstrated similarities and differences in behavior of TFs previously described as EMT master regulators, and identified novel factors with conserved roles. Signaling networks in response to different EMT-inducing treatments were found to be very distinct when dynamic activity of TFs was taken into account. Dynamic patterns of TF activities were combined with prior knowledge to begin defining EMT as a process in which phenotypes are driven by TF network changes. A focus on dynamic TF activity networks complements EMT-related gene expression and proteomic studies, contributing to an integrated understanding of this complex and important program.Figure S1Graphs of normalized TF activity data from arrays. Graphs correspond to data in (DOCX)Click here for additional data file.Figure S2Hierarchical cluster analysis of significantly altered TF activity changes in HMLE models showing the relationship between clusters in  (cluster number from  is noted in color beside each TF reporter name). Dendrogram shows the relatedness of groups of TF activities. The outcome of this standard analysis is very similar to the findings of the similarity index (ty index . Reporte(TIFF)Click here for additional data file.Figure S3EMT at the level of dynamic TF activity networks in HMLE Twist ER cells treated with 4OHT incorporating prior knowledge connections only. As in Top panel. Prior knowledge of all TF binding sites in the vicinity (defined by TRANSFAC and variable between genes) of genes for TFs, as well as any known protein TF-TF interactions from the TRANSFAC database are represented as connections between TF icons. If a binding site for a TF is present in the vicinity of another TF, a connection was plotted if the upstream TF showed significantly altered activity relative to vehicle on the same or the preceding day as the downstream TF. For TF-TF interactions, a connection was plotted if both TFs were significantly altered compared to vehicle on the same day. Bottom panel. Plots in the bottom panel were constructed according to the same parameters as in the top panel but with more stringent criteria for the presence of TF binding sites. Only TF binding sites within the genes of TFs are plotted rather than all TF binding sites within the vicinity of the gene. Note that the positions of all TF icons are the same in both panels although the large number of connections in the top panel obscures many icons.(TIFF)Click here for additional data file.Figure S4EMT at the level of dynamic TF activity networks in HMLE Twist ER cells treated with TGF-β1 incorporating prior knowledge connections only. As in Top panel. Prior knowledge of all TF binding sites in the vicinity (defined by TRANSFAC and variable between genes) of genes for TFs, as well as any known protein TF-TF interactions from the TRANSFAC database are represented as connections between TF icons. If a binding site for a TF is present in the vicinity of another TF, a connection was plotted if the upstream TF showed significantly altered activity relative to vehicle on the same or the preceding day as the downstream TF. For TF-TF interactions, a connection was plotted if both TFs were significantly altered compared to vehicle on the same day. Bottom panel. Plots were constructed according to the same parameters as in the top panel but with more stringent criteria for transcriptional regulation relationships based on the presence of TF binding sites. Only TF binding sites within the genes of TFs are plotted rather than all TF binding sites within the vicinity of the gene. Note that the positions of all TF icons are the same in top and bottom panels although the large number of connections in the top panel obscures many icons.(TIFF)Click here for additional data file.Figure S5Cluster results for 4OHT/induced Twist and TGF-β1 HMLE Twist ER models. A. Cluster results for 4OHT-induced Twist HMLE Twist ER model. B. Cluster results for TGF-β1 HMLE Twist ER model.(DOCX)Click here for additional data file.Figure S6Pairwise correlations of dynamic TF activity patterns with significance defined as p≤0.01. A similarity index was defined to quantify the correlation of pairs of TF activities throughout the six-day experimental time course. A and B. Matrix for HMLE Twist ER cells treated with A. 4OHT (induced Twist model) or B. TGF-β1. All 3080 possible pairwise correlations between the 56 TF reporters are plotted on the x- and y-axes with both axes listing all TF reporters as a number between 1 and 56 (only multiples of ten are shown). Red and blue points on the plots indicate significantly correlated pairs . Plots are symmetric along the diagonal between the upper left and lower right. C–D. Network representation of pairs of significantly similar (p≤0.01) activity patterns for HMLE Twist ER cells treated with C. 4OHT or D. TGF-β1. The similarity index was applied to all pairs of TF reporters with significantly altered activity relative to vehicle Click here for additional data file.Figure S7Pairwise correlations of dynamic TF activity patterns with significance defined as p≤0.1. A similarity index was defined to quantify the correlation of pairs of TF activities throughout the six-day experimental time course. A and B. Matrix for HMLE Twist ER cells treated with A. 4OHT or B. TGF-β1. All 3080 possible pairwise correlations between the 56 TF reporters are plotted on the x- and y-axes with both axes listing all TF reporters as a number between 1 and 56 (only multiples of ten are shown). Red and blue points on the plots indicate significantly correlated pairs . Plots are symmetric along the diagonal between the upper left and lower right. C–D. Network representation of pairs of significantly similar (p≤0.1) activity patterns for HMLE Twist ER cells treated with C. 4OHT or D. TGF-β1. The similarity index was applied to all pairs of TF reporters with significantly altered activity relative to vehicle Click here for additional data file.Table S1TF reporters according to biological functional category. Categories are based on the Online Mendelian Inheritance in Man database (http://omim.org) and TRANSFAC database (reference #36). Binding sequences for associated TFs and references for binding sequences are also listed with the PubMed ID number of references indicated by “PMID”.(XLSX)Click here for additional data file.Table S2Normalized TF activity data. Data from six full arrays started on different days with four biological repeats per array was collected. Time points with insufficient data above background are marked ND. A. HMLE Twist ER/4OHT-induced Twist model. B. HMLE Twist ER/TGF-β1 model. C. MCF-7/TGF-β1 model.(DOCX)Click here for additional data file.Table S3Similarity index values for pairs of TF activities. A. Similarity index values for all pairs of significantly correlated TF activities (regardless of significance relative to vehicle) for 4OHT-treated HMLE Twist ER cells. B. Similarity index values for all pairs of significantly correlated TF activities (regardless of significance relative to vehicle controls) for TGF-β1-treated HMLE Twist ER cells. Tables in A and B are diagonally symmetric. C. Similarity index values for pairs of TF activities in HMLE Twist ER cells treated with 4OHT with 1) significant differences relative to vehicle controls and 2) significantly similar activity patterns. D. Similarity index values for pairs of TF activities in HMLE Twist ER cells treated with TGF-β1 with 1) significant differences relative to vehicle controls and 2) significantly similar activity patterns. In C and D bold type indicates a value for a pair of activities corresponding to TFs with a known interaction or transcriptional relationship in the TRANSFAC database. Italic values also correspond to known relationships in the TRANSFAC database, but these connections are not plotted in (XLSX)Click here for additional data file.


Pluripotency is maintained by a complex system that includes the genetic andepigenetic levels. Recent studies have shown that the genetic level closely interacts withthe enzymes and other specific proteins that participate in the formation of thechromatin structure. The interaction between the two systems results in theunique chromatin state observed in pluripotent cells. In this review, theepigenetic features of embryonic stem cells and induced pluripotent stem cellsare considered. Special attention is paid to the interplay of the transcriptionfactors OCT4, SOX2, and NANOG with the Polycomb group proteins and othermolecules involved in the regulation of the chromatin structure. Theparticipation of the transcription factors of the pluripotency system in theinactivation of the X chromosome is discussed. In addition, the epigeneticevents taking place during reprogramming of somatic cells to the pluripotentstate and the problem of “epigenetic memory” are considered. Pluripotency is the property of cells to differentiate into the derivatives of allthree primary germ layers – ectoderm, endoderm, and mesoderm - as well as toform precursor cells of functional gametes during embryonic development. Inner cellmass (ICM) cells and epiblast cells from pre-implantation mammalian embryos arepluripotent . The aduin vitro simulate various diseases. Furthermore, pluripotentcells are a promising source of material for substitutive cellular therapy[5–Embryonic stem cells (ESCs) are obtained from the inner cell mass of pre-implantationembryos 2–. Under oherapy5–. After the first mouse and human ESC lines were obtained, research into the moleculargenetic basis involved in maintenance of the undifferentiated pluripotent state ofESCs started. It is known today that the pluripotent state of the cells ofpre-implantation embryos and ESCs is maintained via a complex system of cell surfaceproteins, their molecular signal pathways, and the transcription factors thatinitiate the transcription of the target genes. The subsystem of the so-called“external regulators of pluripotency” includes several signalingpathways, among which the cascades triggered by the proteins LIF, BMP4, TGFβ,activin A, NODAL, and bFGF (FGF2) are the major ones . ESC pluripotency is also controlled by the subsystem of “internal regulators ofpluripotency” – transcription factors functioning in cell nuclei. Thefactors OCT4, NANOG, and SOX2 are among the key regulators in this subsystem , 9. Cell by a group of Japanese researchers [In 2006, the data on reprogramming of mouse somatic cells into the pluripotent statewere published in earchers . This waearchers . Oct4, Sox2, Klf4, c-Myc, Nanog, and Lin28  genes) [10–in vitro differentiation,iPSCs can form embryoid bodies consisting of the derivatives of all three germlayers. Furthermore, human iPSCs can form teratomes, whereas mouse iPSCs give riseto chimeras and are even capable of forming an entire organism when injected intotetraploid blastocysts [25–The development of the technology for obtaining animal and human induced pluripotentstem cells has opened up a broad range of possibilities for studying the dynamics ofthe epigenetic events occurring upon reprogramming and the features of theepigenomes of pluripotent cells. A large number of well-reproducible methods forobtaining iPSCs from a broad range of somatic cells are known today. Mostresearchers use a certain gene combination for reprogramming; many of these genesencode transcription factors  10–. Furtherenes) 10–, 15. iPSenes) 10–. Thus, ienes) 10–, 23. iPSenes) 10–, 7. In tenes) 10–. In partcysts 25–. It isoRecent studies have demonstrated that transcription factors, signaling pathways, andmiRNA closely interact with the system of enzymes and other specific proteins thatparticipate in the formation of the chromatin structure. The unique state ofchromatin in pluripotent cells is formed by this interplay. The features of the epigenomes of embryonic stem cells and induced pluripotent stemcells are considered in this review. Special attention is focused on the interactionof the transcription factors OCT4, SOX2 and NANOG with Polycomb group proteins andthe other molecules that participate in the regulation of the chromatin structure.The participation of the transcription factors of the system of pluripotencymaintenance during the process of X chromosome inactivation is discussed. Moreover,the epigenetic events occurring upon reprogramming of somatic cells to a pluripotentstate and the problems associated with the “epigenetic memory” areconsidered. Chromatin regions simultaneously enriched in marks of active and inactive chromatin(H3К4me3 and H3К27me3) are known as bivalent domains . These dThe existence of the so-called open chromatin in ESCs and simultaneous reliablerepression of the differentiation genes are provided by the interaction system bothat the protein – DNA and protein – protein levels. The investigationinto the proteome of pluripotent cells and, in particular, the proteins forming themain system of pluripotency maintenance  has shown that proteinsnot only interact with one another, thus regulating the transcription of a number ofgenes, but that they also form a complex interaction network with the othertranscription factors and proteins that participate in chromatin modification andremodeling. The proteins involved in pluripotency maintenance interact with thecomponents of protein complexes, such as PRC1 and 2, BAF, NuRD, etc. 35–. Polycomb group proteins are an evolutionary-conserved family of regulators of thechromatin structure. The role of these proteins is to achieve and maintain thetranscriptional silencing of homeotic genes 39–. Two complexes belonging to the Polycomb family are known in mammals: PRC1 (PolycombRepressive Complex 1) and PRC2, which play an essential role in embryonicdevelopment and in the maintenance of stem cell self-renewal and normaldifferentiation. The mammalian PRC1complex consists of several subunits; homologues of these werefound in Drosophila: CBX1, 2 and 3; MEL18, Bmi1, RING1A (RING1), RING1B (RNF2) andPHC1, 2 and 3. It is considered that the role of PRC1 is to maintain the genes intheir repressed state, which is originally achieved by the PRC2 complex. Thisfunction is activated through the activity of the subunits RING1A and 1B, whichbelong to the E3 ligase family and perform monoubiquitination of H2A histone at K119(H2AK119Ub1) 42–. Mice haFig. 1 ). Five CBX proteinsassociated with PRC1 – Cbx2, Cbx4, Cbx6, Cbx7, and Cbx8 are known in mammals[A new function of CBX proteins, components of the PRC1 complex, in the regulation ofthe self-renewal and differentiation of mouse ESCs has been recently detected , 53. Cbx2, Cbx4 and Cbx8  genepromoters, suppressing their transcription in ESCs [Cbx7  expression, bind to PRC1 during the differentiationprocess [Cbx7  expression in ESCs results inenhanced expression of the  Cbx2, Cbx4 , and Cbx8genes, disruption of the ESC morphology, and spontaneous differentiation. Ectopicincreased Cbx7  expression suppresses differentiation and Xchromosome inactivation in female cells and enhances their self-renewal [miR-125 andmiR-181 participate in the suppression of Cbx7transcription, which supports the fact that miRNAs play a significant role in theregulation of the Polycomb protein function [Fig. 1 ). It has also been demonstrated using a quantitative proteomic analysis that only CBX7co-localizes with H3K27me3 in undifferentiated mouse ESCs, whereas CBX2 and CBX8interact with this histone modification in differentiated cells and fibroblasts. Further in ESCs . Contrarprocess , 53. Sup Eed gene and theabsence of H3K27 methylation [Eed mutantblastocysts can be used to obtain ESCs possessing pluripotency but tending towardsspontaneous differentiation [Suz12  mutants. Despite thefact that the death of  Suz 12  mutant mouse embryos is observed,ESCs can be successfully obtained. Although ESCs obtained from the mutant embryospossess a high level of transcription of the differentiation genes, they do not formneurons during in vitro differentiation and slightly differentiateto endoderm derivatives when embryoid bodies are formed [Ezh2 gene results in nochanges in the properties of ESCs obtained from mutant embryos; this fact can beattributed to the effect of the EZH1 subunit, which also hashistone–methyltransferase activity and mediates the setting of the mark ofinactive chromatin within the PRC2 target genes [The mammalian protein complex PRC2 contains the EED (embryonic ectoderm development),SUZ12 (suppressor of zeste 12), and EZH1 (enhancer of zeste 1) or EZH2 (enhancer ofzeste 2) subunits. EZH2 is the protein with the SET domain, which is attributable tothe proteins functioning as histone methyltransferases and performs di- andtrimethylation of histone H3 at K27 (H3K27me2/3). As opposed to PRC1, gene mutationsof PRC2 subunits cause significant disruptions in embryonic development andembryonic death , 56, 57.hylation . Howeverntiation . A similet genes . Jarid2 knockout or knockdown onthe H3K27me3 level in promoters of the PRC2 target genes. A decrease in the H3K27me3level has been observed in some studies [Oct4 , Sox2 , and Klf4 genes. On the contrary, knockout of the genes encoding JARID2, MTF2, and esPRC2p48considerably suppresses reprogramming [It has been recently found that the JARID2 protein from the family JUMONJI C (JMJ C)is one of the subunits of the PRC2 complex. JUMONJI proteins belong to histonedemethylases; however, JARID2 lacks such activity. It has been shown that JARID2 isrequired to provide efficient binding of PRC1 and PRC2 to the promoters of thetarget genes; the binding pattern of PRC2 and JARID2 to the DNA of the target genesin the scale of the genome of mouse ESCs coincides to over 90% 61–. There i studies ,65, 66, studies  have als studies , 63,66.gramming . Fig. 2 ). This form of RNApolymerase has a phosphorylated serine residue at position 5 (Ser5P-RNAP) ; thepresence of this form is typical of bivalent epigenome domains, which are formedwith the participation of PRC1 and PRC2 [There are several hypotheses on the molecular basis of the effect of JARID2 on cellpluripotency; none of those has been sufficiently supported through experimentaldata . The majand PRC2 ,70. TheThus, it can be concluded that PRC2 plays an essential role in the regulation ofmammalian development and ESC differentiation; however, this complex does not affectthe process of obtaining ESCs and their self-renewal. Abundant experimental evidenceof joint regulation of the target genes by PRC2 and transcription factors OCT4,SOX2, and NANOG, which are the central part of the system of gene transcriptionregulation in mouse and human ESCs, has been obtained. Whole-genome studies havedemonstrated that OCT4, SOX2, NANOG, and the PRC2 subunits colocalize in the genesresponsible for development, intracellular signal transfer, morphogenesis andorganogenesis; hence, they can function jointly , 71, 72.The proteins of the Trithorax complex are among the major regulators of the embryonicdevelopment of both invertebrates and vertebrates . The rolNanog[Numerous studies have shown that ATP-dependent chromatin remodeling protein complexesplay an essential role in the embryonic development of mammals in general and inmaintaining cell pluripotency in particular 79–. Nearly Nanog, 90. AccNanog, 83, 84.GFP transgene in ESCs under thecontrol of the Oct4 promoter [c-Mycoverexpression [The transcription factors OCT4 and NANOG can interact with chromatin remodelingcomplexes via specific proteins , 90. Thupromoter . Interespromoter . Furtherpression , 94. It has recently been demonstrated that the esBAF complex is directly associated withthe activity of the LIF-STAT3 signaling pathway, which is necessary for maintainingpluripotency of mouse ESCs , 96. Theet al. [Fig. 3 ). L. Ho et al.  haveascet al. , whichexhibits ATP-dependent remodeling and histone deacetylase activity, consists of atleast six subunits , 98. NuR animals .Neverthdherin 3. It has ivatives . Gata6  andFoxA2  [. Thus, ESCs contain at least two subfamilies of NuRD complexes that act inopposite directions: 1) MBD3-containing complexes that regulate (inhibit) thetranscription of the pluripotency genes  and are required for ESCdifferentiation into various cellular derivatives and for cell differentiationduring early embryonic development; 2) HDAC1-, HDAC2-, and Mta1-containing complexesinteracting with OCT4 and NANOG and participating in the transcription activation ofthe genes responsible for the maintenance of an undifferentiated state. Mouse ESCs contain a specific subfamily of NuRD complexes known as NODE (NANOG andOCT4 associated deacetylase). The NODE complex consists of histone deacetylasesHDAC1 and HDAC2, as well as Mta1 and 2. However, this complex contains almost noMBD3 or RBBP7 subunits (they are detected in substoichiometric amounts). NODE is ofinterest because it physically interacts with the OCT4 and NANOG transcriptionfactors in mouse ESCs . NODE exdFoxA2   . Thus,invivo ; the binding of MBD3 to promoters is TET1-dependent. Invitro experiments have shown that MBD3 binds to5-hydroxymethylcytosines more efficiently as compared to 5-methylcytosines; knockoutof the  Mbd3 gene mostly affects the transcription of 5hmC-markedgenes, whereas Mbd3 and Brg1 are required to maintain the 5-hydroxymethylation level[It has recently been established that the MBD3-containing NuRD complex is required tomodify H3K27me3 by the PRC2 complex within the promoters of the genes participatingin development and differentiation processes. Thus, NuRD does not simply repressgene transcription; it is also responsible for the equilibrium between H3K27acetylation and methylation in embryonic stem cells . Howeveron level. Tet family, which encode the proteins performing hydroxylationof 5-methylcytosines, disrupts the differentiation and self-renewal ( Tet1 ) of ESCs [105–5-Hydroxymethylation of DNA was believed to be just an intermediate stage during thedemethylation of 5-methylcytosines .HoweverESCs 105–. In addiESCs 105–, 109. AlTip60 andTrrap genes that encode the Tip60-p400 subunits die at thepre-implantation stage [The Tip60-p400 complex exhibits histone acetyltransferase and remodeling activity; itcan act both as an activator and a repressor of transcription , 111. Inon stage , 115. Thon stage . Tcl1 and Nanog , respectively [The transcription factors that make up the system of pluripotency maintenance, inaddition to their interaction with protein complexes, can directly regulate thegenes of chromatin-modifying enzymes. In ESCs, OCT4 activates the demethylase genesJmjd1a/KDM2A and Jmjd2c/KDM4B, which demethylate H3K9me2 and H3K9me3, respectively,whereas KDM2A and KDM4B, in turn, perform the demethylation of the promoter regionof ectively . Chd1  gene promoter [Chd1  repression inmouse ESCs has no effect on the self-renewal of ESCs; however, it tilts the cellstowards neural differentiation [The transcription factors regulating pluripotency interact with the promoters of thegenes whose products participate in the global regulation of the chromatinstructure. Thus, such factors as OCT4, SOX2, NANOG, SMAD1, ZFX, and E2F1 areassociated with the promoter . This gepromoter . The Chdntiation . Utf1  binds selectively to Oct4 and SOX2 [Factor UTF1 (undifferentiated embryonic cell transcription factor 1), which istranscribed at a high level in undifferentiated mouse ESCs, can participate in theformation of the global chromatin structure. This protein is bound to chromatin; itcolocalizes in the regulatory regions of over 1,700 genes, most of which overlapwith the previously identified target genes of the transcription factors Nanog,Oct4, Klf4, c-Myc, and Rex1. Reduced synthesis of UTF1 increases the level ofexpression of most of its target genes and disrupts ESC differentiation. This factindicates that UTF1 mainly represses the transcription of the genes involved in celldifferentiation . It has and SOX2 . Thus, regulators of the chromatin structure (CHD1 and UTF1), whose gene expression isdirectly regulated by the transcription factors that are components of the maininternal system of pluripotency maintenance, have been found in ESCs. In addition to covalent modifications of histones, DNA methylation is the majormechanism that regulates cellular processes in mammals . Today DOct4 andNanog ) are hypomethylated in undifferentiated cells andhypermethylated in stem and somatic trophoblast cells [in vitro  [132–Dnmt1 , Dnmt3a , and Dnmt3b retain their self-renewal ability [Oct4 promoter,recruits the heterochromatin protein HP1 and DNA methyltransferases intothis region [DNA methylation indisputably plays a crucial role in the regulation of theself-renewal and pluripotency of cells . Promotest cells , 131. Dust cells . Knockoutro  132–. However ability . DNA mets region . Cytosine residues in CpG dinucleotides undergo methylation in mammalian genomes. PluripoDNMT3A and DNMT3B  genes in human ESCs drastically reduces the non-CpGmethylation level [It has recently been demonstrated that a significant fraction (up to 25% in humanESCs) of methylated cytosine residues in ESC and iPSC genomes localizes outside CpG142–; non-CpGon level . Oct4 and Nanog )undergo demethylation [Nanog promoter[Oct4 and Nanog  [Numerous experimental data indicate that the reprogramming of somatic cells to thepluripotent state (obtaining iPSCs) is accompanied by a global change in methylometowards the state characteristic of pluripotent cells , 147. Prhylation , 12, 148promoter. Furtherd Nanog  . The facd Nanog  , 150. Xist andTsix genes, which are anti-parallel-transcribed and encodenuclear untranslated RNAs, are considered to be the major regulators [Xist RNA was shown to be transcribedmonoallelically from the inactive X chromosome, to coat it, and to inducemodifications corresponding to inactive chromatin [Tsix gene is a negativeregulator of the Xist  gene; it is transcribed from the active Xchromosome [X chromosome inactivation is a complex process occurring during early mammalianembryogenesis. In mice, imprinted inactivation of the X chromosome inherited fromthe male parent takes place during the first series of zygote divisions. At theblastocyst stage, the X chromosome is reactivated in ICM cells. Random inactivationof one of the two X chromosomes occurs during gastrulation and differentiation ofICM cells 151–. X-inactgulators , 156. Xihromatin . On the romosome . Since XEmbryonic stem cells of female mice derived from pre-implantation blastocysts (3.5days post coitum) retain a number of the properties of ICM cells; in particular,they can maintain two active X chromosomes in a series of mitotic divisions . Random XIST gene is transcribed; however, cells retain all of theirpluripotency features. The third class contains lines with one X chromosome beinginactive; however, the XIST gene is not transcribed even after celldifferentiation. The inactive X chromosomes in the lines of the second class carryinactive chromatin marks, such as H3K27me3, H4K20me, and the histone variantmacroH2A. Interestingly, the lines that belong to the third class carry almost noinactive chromatin marks. Meanwhile, a molecular genetic analysis shows thattranscription of most of the genes of the inactive X chromosomes is repressed [The situation is more complex for human ESCs, which are also derived from blastocysts(5–9 days post coitum) . Alargeepressed . KLF4  in the presence of a combination of inhibitors ofsignaling pathways in human ESCs and iPSCs can also cause reactivation of theinactive X chromosome [The fact that pluripotency is not associated with the epigenetic status of Xchromosomes in human pluripotent stem cells has also been demonstrated for iPSCs.Mouse iPSCs, similar to ESCs, have two active X chromosomes ; one of those undergoes random inactivation after the differentiation isinduced . Howeverromosome . This faXist and Tsix genes has beenrevealed. Thus, the transcription factors NANOG, OCT4, and SOX2 have potentialbinding sites in the first intron of the Xist  gene and are boundto it in undifferentiated mouse ESCs [Fig. 4 ). Knockout of  Oct4 and Nanoginduces activation of Xist transcription. Thus, pluripotencyfactors can inhibit Xist  expression via the Tsix- independent mechanism [Xist transcription by repressing the expression of itsactivator, Rnf12 . However, the removal of the first intron ofXist  does not result in X-inactivation [Fig. 4 ). Despite the fact that the association between the pluripotency of mouse cells and Xchromosome status during embryogenesis and in culture is rather obvious, no directevidence of association between these phenomena at the molecular level had existeduntil recently. However, the association between transcription factors andregulation of the use ESCs  .  Thus, binding of OCT4, SOX2, and KLF4 has been detectedwithin the Xite enhancer; although this interaction has not beenconfirmed in other works [DXPas34 regulatory element. It has been established that REX1 is required mostly for theelongation of Tsix RNA, rather than for the assembly of thetranscription complex. Thus, all the aforementioned studies support the fact thatthe system of pluripotency maintenance is associated with an active status of both Xchromosomes in undifferentiated mouse ESCs. Human ESCs do not exhibit theseregularities. Human XIST transgenes remain active in mouse ESCsdespite the presence of pluripotency-maintaining factors. Different mechanisms seem to participate in the regulation of human XIST and TSIX genes. In mouse ESCs, the Xistpromoter is only partially methylated even at the active X chromosome; thus, genetranscription is presumably repressed by transcription factors. In human ESCs of thefirst type, the XIST promoter is almost completely methylated(100%). In addition, the differences can be attributed to the fact that theproperties of human ESCs  are similar to those of mouse epiblast stem cells, where one X chromosomeis inactivated, despite the expression of pluripotency factors [The factors associated with maintenance of the pluripotency and repression ofcription  . It has been proven that Jhdm1a/1b are needed for the reprogramming andparticipate in the acceleration of the cell cycle and inhibition of cell aging viarepression of the Ink4/Arf  locus . A high cell division rate and inhibitionof the mechanisms of aging and apoptosis are required to provide complete andefficient reprogramming of somatic cells [176–Fig. 5 ). Ascorbic acid is known to considerably enhance (from 3.8 to 8.75%) the efficiency ofreprogramming of fibroblasts and stem cells from adipose tissue; however, itsmechanism of action remained unclear .Histoneells 176–. Furtherells 176–, 15, 174et al. [EHMT1 and SETDB1  encoding H3K9histone methyltransferases. YY1 , SUV39H1 , andDOT1L  were among the genes in which inhibition of mRNAtranslation considerably enhanced the reprogramming efficiency. The YY1 gene encodes a protein acting both as a transcription activator andrepressor, depending on the specific context.  SUV39H1  encodesH3K9 histone methyltransferase; DOT1L  encodesH3K79methyltransferase. More attention has been given to DOT1L . Itturns out that repression of DOT1L  via RNA interference orchemical inhibition of DOT1L can substitute the functions of KLF4and c-MYC in experiments for generating iPSCs from humanfibroblasts. In addition, inhibition of DOT1L at the early stages of reprogrammingresults in the activation of NANOG and LIN28 ,which are also used in the case of human cells. A genome-wide analysis the H3K79me2distribution has demonstrated that the genes associated with epithelial-mesenchymaltransition, whose expression is specific to fibroblasts, lose this histonemodification at the early stages of reprogramming. DOT1L inhibition acceleratesdeletion of H3K79me2 within the genes subjected to transcriptional silencing iniPSCs [T. Onder et al.  havescriniPSCs . All these facts attest to a crucial role played by the system of epigeneticregulators in the reprogramming process. High-performance analysis methods were used to reveal a high degree of similaritybetween iPSCs and ESCs in terms of the gene expression pattern and epigenomic stateboth at the level of DNA methylation and distribution the covalent histonemodifications H3K27me3 and H3K4me3 ,182. Despite significant similarity between iPSCs and ESCs at the molecular level, it hasbeen demonstrated in a series of studies that the transcriptomes and epigenomes ofindividual iPSC lines can possess certain common features and retain a number ofcharacteristics that are intrinsic to the original somatic cells 183–. The pheet al . [OCT4, SOX2, KLF4 , and c-MYC ; the secondline was obtained via lentiviral transduction of lung fibroblasts IMR90 withOCT4, SOX2, NANOG , and LIN28 ; and threelines were obtained from foreskin fibroblasts using non-integrating episomalvectors. Furthermore, two ESC lines and trophoblast derivatives of iPSCs and ESCsdifferentiated using BMP4 were included in the study. The methylation status of75.7–94.5% of all cytosine residues in the genomes of 11 cell lines has beendetermined. It is of interest to note that the authors have focused on not onlymethylation of cytosines within CpG dinucleotides but on non-CpG-methylation as well. It has been shown that at the genomic scale, human iPSCsand ESCs have similar methylation patterns. The genomes of pluripotent cells tend tobe more methylated (on average) than those of somatic cells. Serious differences atthe level of CpH-methylation of DNA have been revealed. Somatic cells, includingadipose tissue stem cells, are characterized by an extremely low level of such typeof methylation; whereas the share of methylated cytosines within CpH dinucleotidesin DNA in iPSCs and ESCs is 20–30% of the total amount of methylated cytosineresidues in the genome. Moreover, enrichment of exons and introns in methylated CpHis observed both in ESCs and iPSCs. The modern methods of molecular genetic analysis allow one to carry outhigh-resolution genome-wide studies of DNA methylation and distribution of covalenthistone modification. The study by R. Lister et al . , which eIt is interesting to mention that despite the general similarities between themethylomes of ESCs and iPSCs, a number of differences between them have beenrevealed, including 1,175 differentially methylated regions (DMRs) with a lengthvarying from 1 to 11 thousand base pairs .No DMRs have been detected between two ESC lines analyzed under the same conditions.Differentially methylated regions in ESCs and iPSCs can be subdivided into twogroups. The first group contains DMRs whose emergence can be attributed toinheritance of the methylation pattern of the somatic precursor cells of iPSCs. The second group contains DMRs whosemethylation pattern is specific to iPSCs . DMRs of this kind make up 51–56% of the totalnumber of detected DMRs. DMR distribution varies in five of the iPSC lines that havebeen analyzed: 62% occur in two lines out of five; 16% occur in all five lines.These regions can be regarded as “hotspots” of epigenetic reprogramming,which require increased attention when obtaining iPSCs. A significant number of DMRs(80%) are associated with CpG islands; 62% localize near the genes or in the genes;29 and 19% lie within 2 thousand bp from the transcription start or terminationsites, respectively. A bioinformatic analysis of the function of the genes localizednear DMRs and occurring in all the iPSCs under analysis showed no markedpredominance of the genes involved in certain cellular processes. This attests tothe fact that methylation disturbance during the reprogramming can affect a largenumber of cellular functions. Another important regularity is the predominance ofhypomethylation in DMRs  in all five lines. The disturbances inmethylome reprogramming when obtaining iPSCs can be attributed to insufficientmethylation. DMRs have also been detected by an attentive analysis and comparison of CpHmethylation in ESCs and iPSCs. A total of 29 regions have been found; they arecharacterized by the extensive length ; the total length of CpH-DMR is 32.4million bp. Most CpH-DMRs in iPSCs are hypomethylated as compared with ESCs; theylocalize near centromeres and telomeres. These regions are enriched in histone H3trimethylated at K9 (H3K9me3) and colocalized with hypermethylated CpG-DMRs. Mostgenes localized in these regions are characterized by an increased level ofmethylation of promoter regions and, therefore, by a reduced transcription level. Itis interesting that the level of the inactive chromatin mark (H3K27me3) is reducedin these regions. Thus, extensive domains associated with the near-centromeric andnear-telomeric regions with aberrant distribution of histone modifications,disturbed patterns of CpG and CpH methylation, and a disturbed level of genetranscription, have been revealed in human iPSCs. These “hotspots” ofepigenomes undoubtedly need to be subjected to a thorough investigation whenobtaining new human iPSC lines . et al . [et al.[et al . determined cytosine methylation in the entire genome.Furthermore, it has been clearly demonstrated [et al . [An investigation into CpG methylation in 22 human iPSC lines derived from varioussomatic cells  has also revealed differencesfrom ESCs . 1,459 det al .  hasreveet al . , most DM [et al. have rep [et al. has analnstrated  that the[et al . used iPSDlk1-Dio3 locus has been observed in certain iPSC clones, including those derivedfrom hematopoietic precursor cells, which are also characterized by a lowtranscription level of this locus. This effect is assumed to be caused by the“epigenetic memory.” Due to the transcriptional disturbance in theDlk1-Dio3  locus iPSCs become incapable of efficient formationof chimera and cannot form the mouse organism via tetraploid complementation. It isinteresting to mention that treatment with valproic acid, the histone deacetylaseinhibitor, leads to transcription activation in the Dlk1-Dio3 locus and restores the iPSC capability of tetraploid complementation and efficientformation of chimeric animals [The surprising similarity between the transcriptomes and epigenomes of these cellsand those of ESCs was emphasized in the early studies devoted to obtaining mouse andhuman iPSCs. Furthermore, it has been demonstrated that the gene transcriptionpattern in somatic cells at the genome-wide level changes to the maximum extent.However, it was established later that iPSCs retain certain (often ratherinsignificant) features of somatic transcriptomes and epigenomes , 188. De animals . A number of interesting studies have been devoted to the investigation of the effectof the origin of iPSCs on their differentiation pattern , 190. ThIn addition to the studies focused on the disturbance of epigenome reprogramming and“epigenetic memory” in mouse iPSCs, several papers have already beenpublished, which have confirmed the fact that a similar phenomenon exists in thereprogramming of human cells. It has been demonstrated that iPSCs derived fromneuronal precursors and β cells of the pancreatic gland and human retinalpigment epithelium can have a non-random differentiation pattern; i.e., thedirection of differentiation is strongly tilted towards the precursor type ofsomatic cells , 192. AbThus, the problem of “epigenetic memory” today remains among the majorhurdles in the derivation and application of induced pluripotent stem cells. It is apressing problem, especially due to the fact that iPSCs display great potential foruse in regenerative medicine and as models for human diseases. Resolution of thisproblem will not only enable efficient usage of human and animal iPSCs forbiomedical purposes, but can also provide new fundamental knowledge on theorganization and role of cell epigenomes in culture and during the embryonicdevelopment of organisms.


Cdx2 gene, where Rfx1 restricts transcriptional activation by FoxA. The Cdx2 shadow enhancer exhibits a subset of regulatory properties of the upstream Cdx2 promoter region. While Cdx2 is ectopically induced in the early metaplastic condition of Barrett's esophagus, its expression is not necessarily present in progressive Barrett's with dysplasia or adenocarcinoma. By contrast, we find that Rfx1 expression in the esophageal epithelium becomes gradually extinguished during progression to cancer, i.e, expression of Rfx1 decreased markedly in dysplasia and adenocarcinoma. We propose that this decreased expression of Rfx1 could be an indicator of progression from Barrett's esophagus to adenocarcinoma and that similar analyses of other transcription factors bound to silent genes can reveal unanticipated regulatory insights into oncogenic progression and cellular reprogramming.Understanding how silent genes can be competent for activation provides insight into development as well as cellular reprogramming and pathogenesis. We performed genomic location analysis of the pioneer transcription factor FoxA in the adult mouse liver and found that about one-third of the FoxA bound sites are near silent genes, including genes without detectable RNA polymerase II. Virtually all of the FoxA-bound silent sites are within conserved sequences, suggesting possible function. Such sites are enriched in motifs for transcriptional repressors, including for Rfx1 and type II nuclear hormone receptors. We found one such target site at a cryptic “shadow” enhancer 7 kilobases (kb) downstream of the  Cdx2 gene shows that it is a cryptic enhancer, in that it functions poorly unless Rfx1 or NHR-II binding is prevented, in which case FoxA1 promotes enhancer activity. Cdx2 encodes a transcription factor that promotes intestinal differentiation; ectopic expression of Cdx2 in the esophagus can help promote metaplasia and cancer. By screening numerous staged samples of human tissues, we show that Rfx1 expression is extinguished during the progression to esophageal adenocarcinoma and thus may serve as a marker of cancer progression. These studies exemplify how the analysis of pioneer factors bound to silent genes can reveal a basis for the competence of cells to deregulate gene expression and undergo transitions to cancer.FoxA transcriptional regulatory proteins are “pioneer factors” that engage silent genes, helping to endow the competence for activation. About a third of the DNA sites we found to be occupied by FoxA in the adult liver are at genes that are silent. Analysis of transcription factor binding motifs near the FoxA sites at silent genes revealed a co-occurrence of motifs for the transcriptional repressors Rfx1 and type II nuclear hormone receptors (NHR-II). Further analysis of one such region downstream of the  The development of a multicellular organism requires the formation of functionally distinct cell types through the differential activation of gene expression. Such gene expression programs are enabled by transcription factors that endow the progenitors with the competence to differentiate under the influence of inductive signals alb1 gene enhancer in undifferentiated mouse endoderm cells where alb1 is transcriptionally silent, and occupancy correlates with the potential of the cells to be activated in response to developmental signals in vitro and open a local domain for other factors to bind FoxA transcription factors help establish developmental competence for the endoderm-derived tissues Cdx2 expression is normally restricted to the mid- and hindgut regions; loss of Cdx2 in the gut leads to expression of an esophageal program Cdx2 in the esophageal foregut epithelium and, together with other molecular changes, promotes metaplasia of the cells to an intestinal phenotype (Barrett's Esophagus) and subsequent adenocarcinoma Cdx2 in endodermal cells of non-intestinal lineages, thereby potentiating the genes' aberrant activation. If such is the case, there may be regulatory mechanisms that normally restrain such activation. Understanding such networks would provide insight into the basis for diverse pathologies.Another context for silent gene activation is typified by metaplastic transitions, where a cell changes from one type to another. These transitions often occur in response to chronic cellular injury, resulting in pathology. Cdx2 is a homeodomain transcription factor expressed in the developing gut, where it mediates the differentiation of intestinal epithelial cells Cdx2 enhancer element in non-intestinal cells. We further investigated the status of Rfx1 in human esophageal epithelium and in different precursor lesions leading to adenocarcinoma. We conclude that FoxA can occupy silent genes whose activation is restricted by locally binding repressors and suggest that perturbation of such networks can help explain cellular changes leading to invasive cancer. Our approach revealed that Rfx1 expression decreases gradually during cellular progression to esophageal adenocarcinoma, indicating that mechanistic and clinically useful insights can emerge from studies of transcription factors bound to silent genes.To better understand how regulatory factors might endow transcriptional competence for silent genes, as well as how other factors might restrain such competence, we analyzed FoxA2 occupancy in the adult mouse liver, where FoxA factors can occupy silent genes To identify FoxA2 occupied sites at active and inactive genes relevant to endoderm-derived tissue in the adult liver, we designed a high-density tiling microarray covering 210 genes related to endoderm, liver, pancreas, lung, gut, signaling, and cancer . Each tiin situde novo motif analysis of FoxA2 occupied sites revealed an extended in vivo derived FoxA consensus sequence that closely matches the TRANSFAC FoxA motif  expression as weak or silent. By this classification system, 56 genes were highly expressed and 30 genes were weak or silent, demonstrating that FoxA2 clearly occupies transcriptionally silent loci  and assessed factor occupancy at a set of FoxA target genes covering a broad range of gene expression levels. At each gene we queried for Pol II occupancy at the FoxA target site and at the gene promoter. These sites were ranked by occupancy of active Pol II and tested for occupancy of both FoxA1 and FoxA2 . We founribution , suggestTo understand how factors co-bound at silent genes could modulate FoxA activity, we performed motif analysis to identify transcription factors which segregate with FoxA at active versus silent genes. This was facilitated by the resolution our tiling analysis, which allowed the direct identification of FoxA binding sites . We screCdx2 as a candidate gene. Cdx2 is transcriptionally silent in the liver both based on its lack of mRNA expression and lack of Pol II occupancy at its promoter   or the Cdx2 +7 kb element with both FoxA sites mutated (LacZ-ΔFoxA). When tested in E12.5 embryos, the LacZ-wt reporter line exhibited punctate nuclear LacZ staining beginning in the colon and extending caudally to the stomach, where scattered cells were positive . In contrast, two independent LacZ-ΔFoxA lines containing the same transgenic sequences, except for clustered point mutations of the FoxA sites, failed to express LacZ in the gut , Barrett's esophagus (n = 11), dysplastic Barrett's (n = 6), and invasive esophageal adenocarcinoma (n = 20) (Inappropriate activation of (n = 20) . The dys(n = 20) . In addiIn the normal glandular epithelium of the gastro-esophageal junction, we found that Rfx1 is expressed in the nuclei of the epithelial cells lining the esophageal lumen  and the deeper secretory portions of the glandular epithelium . We concBy contrast, in samples of Barrett's esophagus with dysplasia, we found a more marked, statistically significant decrease in the percentage of Rfx1 positive nuclei in epithelial cells (p ≤0.0001) , whereasSox2 and cytokeratin CK14Rfx1 gene expression levels (Rfx1 autoregulation (repression) of its own promoter Rfx1 knock-down conditions, we did not observe induction of Cdx2 expression (data not shown), indicating that the 50% loss of Rfx1 is insufficient for Cdx2 activation. Notably, though, upon Rfx1 knock-down we observed a 50% decrease in the CK14 and Sox2 mRNA levels  cell line n levels . The inaA levels . These fCdx2 enhancer. There are a large number of type II nuclear hormone family members which can bind to DR1 motifs, such as those seen at Cdx2  and inefficiency of siRNA knockdown led us to investigate other parameters affecting the activity of the +7 kb  at Cdx2 . Type IITF1 gene . The extmutation . HoweverCdx2 gene as a cryptic FoxA target. This is notable because Cdx2 is activated during esophageal metaplastic changes, thereby facilitating, in a percentage of cases, the progression towards adenocarcinoma development Cdx2. By this approach, we found that the repressor Rfx1 can restrict the activation of the cryptic FoxA target sequence. We suggest that FoxA occupancy of silent genes in differentiated tissues can endow the competence of such genes to be activated aberrantly and may contribute to human disease. Indeed, FoxA1 has been implicated as a tumor promoting factor In contrast to other genomic studies of transcription factor occupancy at active genes, here we focused on the interactions of FoxA with silent target genes. Such an approach allowed us to identify an element near the silent We observed a marked and statistically significant decrease in the levels of Rfx1 expression in Barrett's esophagus with dysplasia and in esophageal adenocarcinoma. Since most Barrett's esophagus lesions do not become dysplastic, it is useful to possess markers that are indicative of the dysplastic transition. We therefore suggest that Rfx1 loss will function as such a marker, and more reliably so than the induction of Cdx2 in earlier stage Barrett's and then its variable down-regulation during progression Further studies are required to assess whether Rfx1 has a direct tumor suppressive role in the esophageal epithelium. Rfx1 functions genetically downstream of ATR and contributes to the DNA damage response and stalled DNA replication Cdx2 enhancer weakly recapitulates part of the developmental activity of the upstream promoter region . A list of the gene regions probed is provided in 20. For locus specific ChIP, enrichment was quantitated by standard curve analysis using SYBR Green QPCR . A list of oligonucleotides used for ChIP-qPCR is provided in Mouse livers from C3H strain, were crosslinked by perfusion through the portal vein and nuclei were isolated as described in w, a score ix is the intensity of each probe in the window, and n is the number of probes in the window. The T-scores follow symmetric and asymptotically a normal distribution, therefore, the negative T-scores were used to estimate the empirical null distribution and p-values. Significant windows that are nearby each other, with a distance smaller than a specified gap size g, were merged, and the best T-score among them and the associated p-value were reported. We optimized the three parameters, window size w and gap size g, and p-value p, using a small subset of positive and negative controls. The reported results were obtained using w = 400 nt, g = 100 nt, and p = 0.0001. A region is called significant if p<0.0001 in the hybridization of either FoxA2 vs. IgG or FoxA2 vs. input DNA. We estimate an FDR of 16%, using the control hybridization of IgG vs. input DNA. To identify the motifs significantly enriched in the ChIP regions, we use 500-nt sequences centered in each ChIP region, and upstream/downstream sequences of the same size as background. We scan all 673 matrices of vertebrate transcription factors from TRANSFAC (Matys 2003) and JASPAR http://rulai.cshl.edu/cread/).To detect FoxA2 occupied regions or ChIP regions, we apply sliding-window method adapted from MAT RNA was isolated using TRIzol reagent (Invitrogen) or RNeasy kit (QIAgen). 1 µg of RNA was converted to cDNA using iScript cDNA Synthesis kit (BIO-RAD.) and gene expression assessed by SYBR green QPCR using iCycler (BIO-RAD) or by TAQman low density arrays on an PRISM machine (Applied Biosystems.) Gene expression determined by the delta Ct method using HPRT as a reference. A list of Taqman probe IDs is provided in Enhancer elements were PCR amplified and TOPO TA cloned (Invitrogen). Binding site mutations were introduced by overlap-extension PCR and constructs were sequence verified. Enhancer elements were subcloned into pGL3-promoter vector (Promega) downstream of the luciferase reporter gene. Cells were cultured in 6-well dishes and co-transfected with 1250 ng pGL3 vector and 125 ng pRL-CMV with Fugene reagent (Invitrogen). 72-hrs after transfection cells were lysed and luciferase activity was measured with the Dual-luciferase kit (Promega) per manufacturer's instructions.We studied a tissue microarray containing 44 one millimeter cores from 22 anonymized esophageal mucosa samples, including 5 normal mucosa samples, from the gastroesophageal junction and 17 Barrett's esophagus lesions (11 Barrett's without dysplasia and 6 Barrett's with dysplasia). In addition, a total of twenty esophageal well differentiated invasive adenocarcinoma samples from the archives of FCCC were used. Paraffin sections were dewaxed using xylenes and hydrated using a series of ethanol. No antigen retrieval methods were necessary. Endogenous peroxidases were quenched with a short treatment of 1% hydrogen peroxide. Sections were incubated overnight with primary antibodies, washed the next day with PBS, incubated with biotinylated secondary antibodies (Vector Labs), incubated with Vecta Elite ABC kit (Vector Labs), developed with a DAB kit (Vector Labs) and counterstained with hematoxylin. Specimens were documented photographically using a Nikon Optiphot microscope, equipped with an Optronics CCD camera. Antibody against Rfx1 (Santa Cruz sc-10652) was used at a dilution of 1/50. Negative controls were incubated in the absence of primary antibodies as well as using a specific blocking peptide (Santa Cruz sc-10652 P) previously incubated together with the primary antibody at a concentration ten times higher than the latter. The percent of positively stained nuclei in normal and abnormal esophageal epithelium was determined by counting directly a total of 300–600 cells per sample (in at least three random fields per sample) using a micrometric eyepiece grid at a magnification of 400X.Smart pools  were added to cells in 6 cm dishes at 50 nM concentration using RNAifect (QIAGEN) per manufacturer's instruction. Cells were harvested for protein and RNA analysis 3 days post-transfection.Microarray data has been deposited to the NCBI GEO database.Figure S1(A) Pie chart representing the primary tissue of expression for the 210 selected genes on tiling array. (B) Schematic of tiling for gene regions on the microarray. Each tiled locus includes the gene coding region and flanking 30 kb upstream and 10 kb downstream sequence tiled at a density of one 50-nucleotide probe every 24 bp. In aggregate the array covers 14 Mb of the mouse genome. (C) Representative gel of chromatin shearing. (D) Comparison of FoxA2 ChIP to known sites in liver versus kidney, the latter being as a negative control that lacks FoxA2, showing the specificity of the FoxA2 antibody for FoxA2 antigen. (E) Bioanalyzer (Agilent) pseudo gel of DNA following LM-PCR, depicts the retention of small size distribution after amplification.(TIF)Click here for additional data file.Figure S2p-value of 0.0001 yielded ChIP-chip enrichment scores that were significantly above those expected from normally distributed data (red arrow). By contrast, the enrichment scores of the IgG ChIP vs input DNA comparison fit normal distributions very well, suggesting no significant ChIP regions (C).  FoxA2 ChIP validation of regions selected by ChIP-chip analysis. Site-specific ChIP-qPCR was performed from 4 mouse livers, and average signals greater than 2-fold enrichment over background were considered positive. (F) Pictogram of TRANSFAC FoxA motif (top) and that of in vivo FoxA binding motif determined by de novo motif analysis using 193 FoxA2 targets (bottom). (G) FoxA2 bound regions have a high degree of sequence conservation. A comparison of evolutionary conservation was performed between the sequences flanking the 193 ChIP-chip defined FoxA site (solid line) and sequences flanking the 2783 unbound predicted FoxA2 sites present in the intervals covered by the endoderm array (dotted line). A 2 kb region was extracted for each locus, and the PhastCons scores were averaged for each set of regions (A–C) Specificity of FoxA2 ChIP-chip. Region scores by sliding window analysis for ChIP-chip data (solid line) compared to randomized data (dotted line) for the three competitive hybridizations performed. Comparisons of FoxA2 with either IgG (A) or input DNA (B) using an empirical (TIF)Click here for additional data file.Figure S34 of albumin expression. Under this classification active FoxA target genes included Alb1, TTR, FoxA2, Gata4, Xbp1, Prox1, and Hex; whereas silent target genes included AFP, Cdx2, SftpB, and Sox17. (B) Gene activity expressed as function of distance between FoxA site and TSS showed that proximal FoxA binding was associated with gene activity.(A) Gene expression of 86 FoxA targets in liver, expressed as fraction of maximal expression. Weakly expressed and silent genes were binned as genes less than 1/10(TIF)Click here for additional data file.Figure S4Serial sections of Rfx1 IHC in a Barrett's esophagus in the presence of Rfx1 antibody (A), or Rfx1 antibody previously incubated with blocking peptide (1 to 10) (B). In (A) arrows indicate Rfx1 positive nuclei in the epithelium; arrowheads indicate Rfx1 positive nuclei in the stroma. Matched cells lacking Rfx1 positive stain are indicated in (B). Epithelial layer indicated by “E”; stromal cells indicated by “S”.(TIF)Click here for additional data file.Figure S5Loss of Rfx1 expression in esophageal adenocarcinoma. Western blots with Rfx1 antibody and GAPDH control, using extracts from H520 lung cancer cell line as a positive control and 6 anonymized samples of esophageal adenocarcinoma. Numbers to right indicate relative molecular masses of standards (not shown) in kD.(JPG)Click here for additional data file.Table S1Endoderm Array Gene Loci.(DOCX)Click here for additional data file.Table S2Liver FoxA Binding Sites.(DOCX)Click here for additional data file.Table S3Primers for ChIP-qPCR.(DOCX)Click here for additional data file.Table S4Taqman Probe Ids.(DOCX)Click here for additional data file.