Mouse polyomavirus (MPyV) lytically infects mouse cells, transforms rat cells in culture, and is highly oncogenic in rodents. We have used deep sequencing to follow MPyV infection of mouse NIH3T6 cells at various times after infection and analyzed both the viral and cellular transcriptomes. Alignment of sequencing reads to the viral genome illustrated the transcriptional profile of the early-to-late switch with both early-strand and late-strand RNAs being transcribed at all time points. A number of novel insights into viral gene expression emerged from these studies, including the demonstration of widespread RNA editing of viral transcripts at late times in infection. By late times in infection, 359 host genes were seen to be significantly upregulated and 857 were downregulated. Gene ontology analysis indicated transcripts involved in translation, metabolism, RNA processing, DNA methylation, and protein turnover were upregulated while transcripts involved in extracellular adhesion, cytoskeleton, zinc finger binding, SH3 domain, and GTPase activation were downregulated. The levels of a number of long noncoding RNAs were also altered. The long noncoding RNA MALAT1, which is involved in splicing speckles and used as a marker in many late-stage cancers, was noticeably downregulated, while several other abundant noncoding RNAs were strongly upregulated. We discuss these results in light of what is currently known about the MPyV life cycle and its effects on host cell growth and metabolism. Mouse polyomavirus (MPyV) is a small 5.3kb circular double-stranded DNA virus capable of causing tumors in a variety of tissues in immunocompromised mice. It has been a subject of study for over 60 years, yielding insights into a number of processes including tumorigenesis, cell cycle signaling, and transformation. This study serves to provide a global view of the MPyV infection by utilizing Illumina sequencing to observe changes in total RNA from both the virus and the host cell as well as applying new methods to more directly confirm the extent of A-to-I editing of viral RNA by host ADAR enzymes. This allows for a simultaneous observation of both host and viral transcriptional changes that occur as a result of early gene expression and the viral switch from early to late genes that occurs coincident with the initiation of DNA replication. MPyV is a small circular double-stranded DNA virus with a life cycle that is divided into distinct early and late phases of infection. During the early phase the virus expresses both early-strand and late-strand transcripts from its bidirectional noncoding control region , but theMPyV Large T antigen is involved in a variety of roles during the course of infection, including altering host protein pathways to promote virus production , blockinMPyV middle T antigen is a highly oncogenic protein that is necessary for viral transformation of cells in culture. Middle T has no catalytic domain but rather acts as a scaffold, which functions like a constitutively active receptor tyrosine kinase to activate signal transduction pathways such as Shc, PI3K, and PLCγ . Middle MPyV small t antigen functions primarily by binding protein phosphatase 2A (PP2A). Like middle T, small t binds PP2A by mimicking the B subunit to bind to the A and C subunits. MPyV small t is able to bind to both the Aα and Aβ subunits at the B binding site, precluding PP2A regulation by the B subunit ,41. SmalAlthough the switch from early to late phase of infection has been thought by some to be regulated primarily at the level of transcription , this haIn order to further characterize MPyV infection we have used deep sequencing to compare changes in the transcriptome of both viral and host transcripts at different times of infection . This alMouse NIH 3T6 cells were chosen for infection for their ability to facilitate lytic infection by wild type MPyV . Cells wIn order to visualize changes in viral transcripts at different time points during infection, we used Bowtie and Top Hat to align sequencing reads to a Py59RA reference genome . At 12 hWhile the relative levels of early-strand and late-strands change dramatically during infection, we noted no significant changes in patterns of RNA splicing, but did observe several interesting and unexpected changes to early-strand RNA accumulation during infection. First, it is clear that, relative to the bulk of early-strand RNAs, there is an apparent elevated alignment to a roughly 200 base pair region just downstream of the early polyadenylation site . This pe). Such a shift from a strict early transcription start site downstream of a canonical TATA box at early times  to variable upstream transcription start sites at late times was suggested previously to explain confounding results obtained by S1 nuclease experiments [The second unexpected change in early-strand RNA alignments during infection is in the early promoter/origin/noncoding control region. We originally noted changes in alignments to the edges of our reference genome. By aligning the reads to a linearized reference genome shifted by about 150 bp, we were better able to visualize the nature of these molecules, which span the replication origin. As infection proceeds, there is a clear increase in the alignment of early-strand reads to the origin of replication and enhancer region, strongly suggesting a potential upstream shift in early-strand transcription start sites during infection, and particularly after the onset of viral DNA replication . We noteeriments ,59. Mapperiments ,59. In oeriments  and for eriments . ImportaUsing TopHat to map the splice junction reads we were able to determine the relative expression levels of the 5 viral isoforms that contain splice junctions . At 12 hOwing to the presence of tandem repeats of late leaders in all three late mRNAs, alternative reference sequences were required to capture reads that span at least one leader-to-leader splice junction . The strDue to the read-through of early and late transcripts at late times, as well as the genomic overlap of the early and late polyadenylation signals, there is the possibility that if complementary sequences accumulate near one another in the nucleus, they might form double stranded RNAs. As nuclear dsRNAs can be promiscuously edited by ADAR enzymes, which deaminate adenosines to inosines , viral dcis. Editing clusters can also be seen in the noncoding control region suggesting that multi-genome length giant transcripts are capable of annealing with one another. While editing has long been predicted due to the long antisense transcripts and poly(A) read-through, this is the first time we have had direct evidence of genomewide editing at late times. We note, however, that while editing is readily and abundantly observed, we cannot conclude from these data whether it is a cause or a consequence of viral gene regulation.As long dsRNA is subject to hyper-editing, where up to 50% of the adenosines are converted to inosines , it is qOur deep sequencing data allowed us to not only examine viral gene expression during infection, but also changes in the host transcriptome that result from the infection. Using Cuffdiff to compare gene expression between MPyV and mock infected samples, we found 1,216 genes were differentially expressed at least 1.5 fold across all three replicates with 359 genes upregulated and 857 genes downregulated compared to the mock infections at the 36 hour time point Tables  and S4.http://david.abcc.ncifcrf.gov/) to determine the gene ontology of the differentially expressed genes that were significantly enriched based on common biological function   to determine common transcription factor binding sites among the differentially expressed genes [Do differentially expressed genes share common patterns of regulation that may help to explain their response to viral infection? In order to determine common transcription factor binding sites upstream of differentially expressed genes we used the Transfind tool  to late times (36 hours post infection) but before extensive cytopathic effects were noted. Although there were several surprises, the observed expression and processing of viral transcripts was generally consistent with previous literature. At early times in infection, early-strand transcripts dominated over late-strand transcripts. This early pattern is even more dramatic when DNA replication is blocked with aphidicolin and the infection allowed to proceed for 48 hours. At early times, most of these transcripts appear to initiate at a defined site and terminate just downstream of the annotated early polyadenylation signal. These are spliced to produce mRNAs for the three viral early proteins, Large, middle and small T antigens. However, by 18 hours, after Large T antigen has promoted the onset of viral DNA replication, the pattern of expression reveals a striking shift toward the late phase, where late-strand RNAs greatly dominate over early-strand RNAs. This early-to-late switch is characterized by several distinct and important changes in the pattern of alignments in our data. First, as late messages accumulate rapidly and genomes replicate, we noted an unexpected shift in the position of the 5’-ends of early-strand transcripts. At late times, early-strand RNAs appear to initiate over a broad region upstream of the early TATA box, and throughout the origin of replication and enhancer region. We do not yet know why this is the case or how it is regulated, but these findings are consistent with an earlier reports in which heterogeneous 5’-ends at late times were mapped using nuclease S1 protection ,59 as weAnother clear change in early-strand expression, and also dependent on DNA replication, was the appearance and accumulation of reads mapping to a region several hundred nucleotides downstream of the major early polyadenylation site. These reads appear to result from poly(A) site read-through and are antisense to VP1. While we do not yet know why or how they accumulate, we considered one possible explanation to be that they represent precursors for viral-encoded microRNA(s). MPyV and SV40 have previously shown viral expression of microRNAs antisense to Large T antigen, which were hypothesized to help avoid a cytotoxic T cell response ,84. UponLate-strand transcription and processing also changes during infection. While accumulating to only low levels before viral replication, or in its absence, these RNAs accumulate rapidly afterwards, and by late times represent the great majority of viral RNAs in the cell. Their processing may also change with time, although we did not note any discernible change in transcription start sites. As we and others have reported before, late transcription can proceed many times around the circular viral genome, leading to a heterogeneous collection of giant transcripts that can serve as precursors to mature late mRNAs –50. ProcLate leader splicing requires inefficient late-strand transcription termination and polyadenylation. We have reported previously that this is the result of particular viral genomic features in the early/late polyadenylation region . These sAlignment of reads from the time course infection to the host genome showed the greatest change between mock and infected samples by 36 hours, allowing us to observe the accumulated effects of the virus on the host transcriptome. We suspect that examination of even later time points might yield more genes changing more than 2 fold up or down, but at time points closer to 72 hours cells would be entering the lysis phase of the viral life cycle and any observed changes would likely be difficult to separate from those associated with host cell death. At 36 hours post infection, we found 359 genes significantly upregulated and 857 genes significantly downregulated. Many of the genes upregulated were involved in predicted processes such as metabolism, ribosome synthesis, mitochondrial proteins, and cell cycle progression. This is consistent with the action of viral T antigens in stimulating the proliferation of host cells. Many downregulated genes were also largely unsurprising, with those involved in cell motility, extracellular matrix, and cell signaling processes prominent on the list. We note that our results are somewhat consistent with those published by the Wintersberger group, where small t and Large T were individually induced in NIH3T3 cells, followed by microarray analysis [Finally, we noted the upregulation or downregulation of several abundant long noncoding RNAs. There are several lncRNAs of particular interest. One is Malat1 (downregulated), which has been associated with metastasis and which may play a role in RNA processing . AnotherHere we have presented for the first time a global analysis of both viral and host transcriptional changes from a mouse polyomavirus infection. The viral transcriptome analysis revealed insights into changes in RNA expression, especially with respect to the early strand at late times and confirmed a number of previous observations of the MPyV lifecycle. This is the most direct evidence of widespread hyper-editing of viral RNA predicted from a model of extensive early and late strand transcription. The observations of changes in host transcription also confirm a number of predictions including the increase in genes involved in cell growth, metabolism, and translation. Future investigation into more precise aspects of the lifecycle including later time points, nuclear and cytoplasmic fractionation, and poly(A) plus and minus samples may yield further insight into the viral-host interactions.NIH 3T6 mouse fibroblast cells were cultured on 10 cm plates at a density of 600Kcells/plate in Gibco Dulbecco’s Modified Eagle Medium (11995–065) supplemented with 100 units of penicillin/streptomycin, 2mM L-Glutamine, and 5% calf serum. Cells were serum starved in 2% calf serum 1 day before infection to synchronize cell cycles. Mouse polyomavirus strain Py59RA was generously provided by Dr. Robert Garcea in 2% Medium at 180 million plaque forming units per ml. In our hands, higher apparent multiplicities were required to ensure maximal infection of our cells in culture. Virus was diluted to 15 million PFU/ml to infect cells at an MOI of 50 using 2 ml of virus per plate. Cells were infected by removing media and adding 2 ml of virus for 2 hours before being supplemented with 8ml of 2% media at time point 0. Mock infections were carried out using 2% medium without filtered virus particles. Total RNA was harvested at 12, 18, 24, and 36 hours after infection by removing media, washing with PBS, and adding 4M guanidinium isothiocyanate. For the aphidicolin treated samples, cells were plated at the same density, treated with the same media, and infected at the same MOI for 40 and 48 hours. Aphidicolin positive cells were treated with media containing 2μg/ml aphidicolin.RNA was isolated by cesium centrifugation through 2 ml of 5.7M cesium chloride at 39000 rpm at 20°C for 18 hours and DNAse treated with DNAse 1 from the Ambion DNAse I Kit. 1 μg of RNA was used from each sample for library preparation with the TrueSeq Stranded Total RNA kit from Illumina. Ribosomal RNA was depleted using rRNA binding beads and the RNA was fragmented and primed with random hexamers. First strand synthesis was done using a mixture containing Superscript II and Actinomycin D to synthesize RNA dependent DNA and inhibit DNA dependent RNA respectively. Second strand synthesis was done using dUTP instead of dTTP to create stranded cDNA. Libraries were then adenylated at the 3’ end and adaptors containing identifier sequences and flow-cell binding sequences were ligated to both ends. Finally the cDNA fragments were amplified for 12 cycles instead of 15. The Agilent Technologies 2200 Tapestation was used to verify library size at ~260bp. Sequencing was done on a HiSeq 2000 by Perkin Elmer.All alignments were made using Tophat v 2.0.9, Bowtie 2.1.0, and Samtools 1.19. Alignments to MPyV were done using the reference genome for the Py59RA strain of mouse polyomavirus for both time course and aphidicolin experiments. Time course experiments consisted of four conditions, for mock infection and four conditions for MPyV infection representing 12, 18, 24, and 36 hour time points with three biological replicates per condition. Aphidicolin experiments consisted of two conditions Aphi+ and Aphi- with two biological replicates.Alignment of time course reads to the host genome was done using the reference genome from Illumina’s IGENOMES collection, which in turn was taken from USCS March 5, 2013. Custom scripts were used to postprocess Tophat output to produce strand-specific bedGraph files suitable for use on the UCSC genome browser.Alignment of reads to the viral origin was done using a custom script that rotated the genome zero position counterclockwise by 149nt to avoid splitting annotated transcripts. The reads were aligned to the “rotated” reference genome using Tophat with a new Bowtie index and transcriptome index. A custom script was then used to convert the new alignments to the original coordinates and the new alignments were added to the original alignments and displayed on the UCSC genome browser.Alignment of reads containing tandem repeats of the late leader exon from late gene mRNAs that would not align to the viral genome directly was accomplished by doing a direct Bowtie1 end-to-end alignment to predefined reference sequences allowing for up to 2 mismatches. Three references contained 42 bases of the 5’ UTR of VP1, VP2, or VP3 followed by the 57 base late leader exon and the first 42 bases of the next late leader. One reference contained the last 42 bases of the late leader followed by a full 57 base late leader followed by the first 42 bases of the transcription start region upstream of the late leader exon. The last reference sequence contained a full 57 base late leader flanked by 42 bases of each late leader on either side. This allowed 100-base reads spanning at least one leader-to-leader splice junction that would not align more than once to the same reference sequence to align.A to G mismatches were identified using custom scripts that split the fastq reads into smaller 25nt fragments and filtered out any that could align to the mouse genome. The remaining fragments were aligned to the Py59RA genome using Bowtie1, allowing for up to 3 mismatches. Another custom postprocessing script was used to interpret Bowtie alignment output format and scan mismatch fields for canonical editing substitutions.The hyperediting scripts from Porath et al.  were dowThe relative expression of host genes between time course Mock and PyV infected samples was determined using Cuffdiff v 2.1.1. Dispersion method pooled and dispersion method per condition were used to compare individual replicates and all replicates of a given condition. Custom scripts were used to post process/filter Cuffdiff output to produce time-series instances of significant log Fold change. Cluster 3.0 was used to produce a k-means clustering of time course gene expression. Genes with a log2 fold change of +/-0.75 representing a 1.5 fold change with an fpkm greater than or equal to 1 were used for gene ontology and transcription factor prediction.http://david.abcc.ncifcrf.gov/home.jsp). Gene lists of upregulated and down regulated genes were each run against a Mus musculus background and gene categories with a false discovery rate below 1E-03 were used with redundant categories removed.Gene ontology analysis was done using the browser-based application DAVID  (http://transfind.sys-bio.net/). The same gene lists of up and down regulated mouse genes were used with “800 upstream … 200 downstream of TSS ” for the promoter set category and with “1000” genes under the “highest predicted factor affinities to take” category. The TRANSFAC (highest info content for each factor) setting was used to represent the factors. The data for this paper have been deposited at GEO (GSE69314). http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE69314Transcription factor binding prediction was done using the browser-based application Transfind Click here for additional data file.S2 FigLibraries used for sequencing were run on Agilent Technologies 2200 Tape Station to verify library size.(TIF)Click here for additional data file.S3 Fig5’ splice sites are indicated in blue. 3’ splice sites are indicated in red. The late leader exon is shown with the black box. The early poly(A) site is shown with a blue box. The complement of the late poly(A) site is shown with a red box.(TIF)Click here for additional data file.S4 FigCells were infected with Py59RA for either 12 or 36 hours, then total RNA was isolated and subjected to 5’-end analysis using the SMARTer RACE 5’/3’ Kit  according to the vendor’s instructions. A gene specific primer (5’-GCCGGTTCCTCCTAGATTCATTCTC) corresponding to positions 370–394 of the Py59RA genome was used to make 5’ RACE cDNA. cDNA was further amplified using another gene specific nested primer (5’-GCAGTGACTGCTGCTTATATGCCTG) corresponding to positions 257–281 of the viral genome. 25 μl aliquots of the PCR reactions were loaded onto a 6% polyacrylamide gel. Lane 1, markers. Lane 2, no template added to the PCR reaction. Lane 3, mouse heart total RNA was used for cDNA synthesis and RACE. Lane 4, 12 hour mock infection RNA. Lane 5, 36 hour mock infected RNA. Lane 6, 12 hour infection RNA. Lane 7, 36 hour infection RNA. Note: the band of approximately 200 bp denotes startsites in the nt 139–144 region of the virus, reported to represent the major startsites for early-strand transcripts in the absence of DNA replication . At 36 h(TIF)Click here for additional data file.S5 FigA. Cells were infected for 12, 24 or 36 hours and RNA isolated and subjected to RT-PCR analysis using primers specific for late leader to VP1 splicing or late leader to VP2 junctions. Bands were resolved by polyacrylamide gel electrophoresis. Note that since leader-VP2 junctions are collinear with viral DNA. The bottom bands in the VP2 lanes are contaminated with signal from residual viral DNA. B. Cells were infected in the presence or absence of 10 μg/ml cytosine arabinoside (AraC), which inhibits DNA replication and RNA isolated at 24 hrs post infection. RT-PCR for VP1 spliced to the late leader was carried out using 5’-32P-labeled RT primers but otherwise as in panel A. Bands were revealed by autoradiography. Note that in the absence of viral replication (AraC treatment), multiple tandem leaders are less frequent than in the presence of viral replication.(TIF)Click here for additional data file.S6 FigReads from the time course run were broken from 100 bases to 25 bases and realigned with a threshold of 3 mismatches allowed per 25 base read. Reads with A-G mismatches were visualized using the UCSC genome browser to indicate regions with potential editing sites. The polyadenylation sites showed the highest number of A-G mismatched reads at late times of infection.(TIF)Click here for additional data file.S7 FigOne noncoding and three coding genes with a high expression and at least 1.5 fold change were selected for validation of upregulated genes by qPCR. FPKM results shown in left column. qRT-PCR results shown in the right column.(TIF)Click here for additional data file.S8 FigOne noncoding and three coding genes with a high expression and at least 1.5 fold change were selected for validation of downregulated genes by qPCR. FPKM results shown in left column. qRT-PCR results shown in the right column.(TIF)Click here for additional data file.S1 TableReads from the three replicates of each time point in the time course. Total reads shown in the left column. Number and percentage of reads aligned to the mouse genome shown in the middle column. Number and percentage of reads aligned to the virus genome shown in the right column.(TIFF)Click here for additional data file.S2 TableExcel spreadsheet listing all base to base mismatches (columns) at each position in the MPyV genome (rows) on either strand found in the 25-base read alignments allowing for up to 3 mismatches per read.(XLSX)Click here for additional data file.S3 TableExcel spreadsheet listing fpkm for all mouse genes at each time point for each replicate.(XLSX)Click here for additional data file.S4 TableExcel spreadsheet listing significant differentially expressed genes between Mock and PyV infected samples for each time point across all three replicates.(XLSX)Click here for additional data file.S5 TableList of noncoding RNAs by name, feature type, and fold change log2 ratio that differs more than 1.5 fold between Mock and PyV infected samples by 36 hours after infection.(TIFF)Click here for additional data file.


Chromatin regulators play an important role in the development of human diseases. In this study, we focused on Plant Homeo Domain Finger protein 8 (PHF8), a chromatin regulator that has attracted special concern recently. PHF8 is a histone lysine demethylase ubiquitously expressed in nuclei. Mutations of PHF8 are associated with X-linked mental retardation. It usually functions as a transcriptional co-activator by associating with H3K4me3 and RNA polymerase II. We found that PHF8 may associate with another regulator, REST/NRSF, predominately at promoter regions via studying several published PHF8 chromatin immunoprecipitation-sequencing (ChIP-Seq) datasets. Our analysis suggested that PHF8 not only activates but may also repress gene expression. Chromatin dynamics are affected by histone modifications. Chromatin regulators can alter histone modifications and thus are extensively linked to gene activation and repressionPHF8 is a JmjC domain-containing protein and erases repressive histone marks including H4K20me1 and H3K9me1/2456810PHF8 are associated with X-linked mental retardation (XLMR) and cleft lip/cleft palate12ZNF711 or PHF8 significantly decreases the expression of ZNF711-PHF8 target genes, including KDM5CNonsense or missense mutations in To investigate PHF8 regulatory functions, a motif-finding analysis for published PHF8 ChIP-seq data was conducted. The canonical REST motif was observed in PHF8 binding sites with high confidence. PHF8 targeting gene promoters were also bound by REST, which indicates that REST might interact with PHF8. Additionally, we detected the E2F1 motif in both PHF8 and REST ChIP-seq data. E2F1 is known to interact with PHF8Several PHF8 ChIP-seq datasets have recently been published in several immortalized human cell lines, including HelaS3, 293T, Hs68, K562 and H114516Since bindings of PHF8 and E2F1 enriched in gene promoters, we deduced that these three factors maybe co-bind at gene promoters. In order to verify our assumption, we separately chose peaks for each factor with fold change greater than ten and calculated their overlaps. Here and in the remainder of this work, we processed data from HeLaS3 cell line because ChIP-Seq data of these three factors are all accessible in this cell line. Cis-regulatory Element Annotation System (CEAS)15RPS3 (p < 2.2e-16 by Student's t-Test)  . We furt t-Test) .To investigate the chromatin state differences between sites bound by PHF8 only and PHF8-REST co-binding, PHF8 peaks were divided into two groups according to whether the peaks are co-occupied by REST or not. We detected 2,780 PHF8 peaks with REST co-binding (PHF8 ensemble peaks) and 10,562 peaks without REST co-binding (PHF8 solo peaks). To make the average binding signal profiles comparable between these two groups, 2,780 peaks were randomly selected from the PHF8 solo peaks. The average binding signal profiles of H3K4me3, PHF8, and E2F1 on the PHF8 solo and ensemble peaks showed that their binding would be stronger with REST co-binding , indicatPHF8 knockdown or over-expression. We used GEO dataset GSE20563PHF8 knockdown. The expression of 261 genes decreased after PHF8 knockdown, while 530 genes increased; we defined these groups as PHF8 up-regulated genes and PHF8 down-regulated genes, respectively. The distributions of PHF8 up- and down-regulated genes on all expressed PHF8 target genes do not show strong pattern, that suggest PHF8 binding strength has no influence on the gene differential expressions  . For both PHF8 down-regulated and up-regulated genes, distances between PHF8 binding sites are significantly smaller than those background genes  , indicatp = 9.29e-155 by one-sided Kolmogorov-Smirnov test) and repressive (p = 1.48e-193 by one-sided Kolmogorov-Smirnov test) gene expression were significantly regulated by PHF8  and 1,000 down-regulated (active) genes. In comparison to all genes, active 5http://encodeproject.org/ENCODE/). Peak calling was performed using MACS2, a new version of MACSq = 0.01. 14,402 and 11,159 peaks are called in PHF8 and E2F1 ChIP-seq data set, respectively; 28,689 peaks are called in REST ChIP-seq data set. Genome-wide peak distributions were generated with CEASGSE20725 and GSE22478 are PHF8 and E2F1, respectively, ChIP-seq datasets from GEO de novo and known DNA motifs from the database to examine motif enrichment among peak regions.DNA motifs were identified using MDSeqpos (X. Shirley Liu laboratory). All binding sites were trimmed or extended to 600 bp and centered at the summit of the peak region identified by MACS2. MDSeqpos searched both A tool developed by Hyunjin Gene Shin (X. Shirley Liu laboratory) was used to investigate whether a transcription factor or a chromatin regulator is a repressor or an activator. This tool draws the cumulative distribution for each gene set and tests if each distribution is identical using the one-sided Kolmogorov-Smirnov test.J.W. and J.H. analyzed the results and wrote the manuscript; J.W., X.Q.L., S.W. and C.F.W. prepared the figures. Q.X.W., X.K.D., C.Y.W., P.L., Q.W. and S.X.L. provided key suggestions. All authors reviewed the manuscript.SUPPLEMENTARY


Genome-wide mapping of transcriptional regulatory elements is an essential tool for understanding the molecular events orchestrating self-renewal, commitment and differentiation of stem cells. We combined high-throughput identification of transcription start sites with genome-wide profiling of histones modifications to map active promoters and enhancers in embryonic stem cells (ESCs) induced to neuroepithelial-like stem cells (NESCs). Our analysis showed that most promoters are active in both cell types while approximately half of the enhancers are cell-specific and account for most of the epigenetic changes occurring during neural induction, and most likely for the modulation of the promoters to generate cell-specific gene expression programs. Interestingly, the majority of the promoters activated or up-regulated during neural induction have a “bivalent” histone modification signature in ESCs, suggesting that developmentally-regulated promoters are already poised for transcription in ESCs, which are apparently pre-committed to neuroectodermal differentiation. Overall, our study provides a collection of differentially used enhancers, promoters, transcription starts sites, protein-coding and non-coding RNAs in human ESCs and ESC-derived NESCs, and a broad, genome-wide description of promoter and enhancer usage and of gene expression programs characterizing the transition from a pluripotent to a neural-restricted cell fate. Several protocols have been developed to obtain neural stem cells from ESCs, attempting to recapitulate in vitro the intermediate stages of neural induction, a process marked by the down-regulation of the pluripotency markers OCT4 and NANOG and up-regulation of neuroectodermal-specific markers such as NESTIN, SOX1 and PAX6. However, the regulatory circuitry driving cell fate restriction, and in particular neural commitment, is still ill-defined. The identification of the transcriptional regulatory elements involved in neural commitment is challenging, due to the difficulty in obtaining a suitable neural stem cell model. A protocol for the derivation of a homogeneous population of long-term self-renewing neuroepithelial-like stem cells (NESCs) from human ESCs has been recently established [ ability  and the  ability , 4.At present, little is known about the differential usage of promoters and enhancers occurring at ESCs restriction towards a neural fate. Specific histone modifications are currently used to define chromatin regions with different regulatory functions. In particular, monomethylation of lysine 4 of histone 3 (H3K4me1) characterizes enhancer regions, whereas its trimethylation (H3K4me3) defines active promoters , 6. WhenIn addition to epigenetic marks, transcription by RNA polymerase-II (Pol-II) from active enhancers was recently described, first in mouse cortical neurons , 12, and+/H3K27me3+) and active in NECs, where they lose H3K27 methylation in favor of acetylation [The epigenetic changes occurring during human ESC induction to neuroectodermal spheres (NECs), a heterogeneous culture system of neural stem/precursor cells, were recently described . Enhancetylation . In thistylation , 17 withThe human Embryonic Stem Cell line H9  was previously published , and was6 cells from triplicate cultures of ESCs and NESCs, transcribed into biotinylated cRNA and hybridized onto GeneChip HG-U133 Plus 2.0 Arrays (Affymetrix) according to the manufacturer protocol . Signals were converted to expression values by the RMA algorithm [Total RNA was extracted from 1–2 10lgorithm  and HG-Ulgorithm . Differelgorithm ) settinglgorithm  thresholwww.ingenuity.com).Gene Ontology (GO) functional annotation of differentially expressed genes was performed by DAVID v6.7. GO-term enrichments were based on a modified one-tail Fisher Exact p-value (EASE score) after Benjamini correction for multiple testing, and considered significant at p<0.05. Networks of genes associated to cell-specific and up- and down- regulated CAGE promoters were made with the Ingenuity Pathway Analysis (IPA) software, with the tool “my pathways” based on the Ingenuity Knowledge Base dataset. For a complete IPA legend refer to IPA website . In CAGE-enhancers, the search for TFBSs was made on a region of 400bp centered on the middle-point of CAGE-enhancer region.The search for transcription factor binding site (TFBS) enrichment in cell-specific promoters, enhancers and CAGE-enhancers was performed by the HOMER tool   genes and transcripts coordinates were downloaded from the UCSC table browser for the hg19 genome assembly. The significance of differential promoter expression was determined by a χ2 test.CAGE-seq was performed by DNAFORM Inc. at RIKEN Omics Science Center on an Illumina Genome Analyzer as previously described  (see S1 6 NESCs following the truChIP Chromatin Sheering Kit standard protocol (Covaris Inc.) and sonicated to obtain DNA fragments averaging 200 bp. 10 ng of DNA immunoprecipitated with anti-H3K4me1 and anti-H3K4me3 antibodies  or control input DNA were used to prepare the sequencing libraries, which were checked by capillary electrophoresis and sequenced in one lane of a single-strand 50 bp GAIIx Illumina Run . Gene expression data highlighted that pluripotency master regulators and reprogramming factors, such as NANOG, POU5F1 (OCT4), MYC and KLF4 , 38, werAnalyzing this list of 2,413 differentially expressed genes by Gene Ontology functional enrichment, we found that the subset of up-regulated genes in ESCs was enriched in several GO terms accounting for functional categories of early embryonic development, such as blastocyst formation, and RNA processing and ribosome biogenesis, comprising many MYC direct target genes  Fig 2C)Fig 2C). 6 raw CAGE tags in ESCs and NESCs, respectively. Transcription Start Sites (TSSs) and CAGE promoter expression values were expressed as tags per million (tpm). TSSs were defined by summing the weighted number of CAGE tags at each genomic position, and clustered into CAGE promoters if closer than 20 bp on the same chromosomal strand and yielding at least 10 tpm  ranging between 1 and 12 , of which 13,730 were found expressed in ESCs  and 13,569 in NESCs . Among the 14,253 CAGE promoters, 684 were expressed exclusively in ESCs and 523 in NESCs , which d1 and 12 . As expe1 and 12 .We assigned each CAGE promoter to the closest RefSeq gene within an arbitrarily defined distance of ≤400 bp between the gene TSS and either end of the promoter on the same strand. Overall, 80% of all CAGE promoters were annotated to RefSeq genes in both cell types. Interestingly, the fraction of un-annotated promoters increased up to 27% and 23% in differentially expressed  and 50% and 44% in ESC- and NESC-specific promoters respectively . NotablyOverall, ~97% of annotated CAGE promoters were associated to protein-coding genes, while the remaining 3% to non-coding RNAs , i.e., aThe number of protein-coding genes associated to CAGE promoters was 9,502 and 9,372 in ESCs and NESCs respectively, where ~11% of the genes used two or more alternative promoters. In particular, we found 4 genes  transcribed from five alternative promoters in both cell types . The corAs expected, we found several coding and non-coding members of the Pluripotency Core Regulatory Network among genes transcribed from ESC-specific and down-regulated promoters , such asLikewise, many genes transcribed by NESC-specific and up-regulated promoters were among the members of the Network, repressed in pluripotent cells. Interestingly, GO categories associated to genes transcribed by ESC-specific or down-regulated promoters were related to generic embryonic developmental processes  , while g–5) TFBS motifs are shown in To understand the regulatory circuitry operating on differentially regulated promoters, we analyzed putative transcription factor binding sites (TFBSs) within a region extending from -300 to +100 bp from the most expressed TSS in cell-specific and up- and down-regulated CAGE promotes by the HOMER tool . Signifi+/me1- or H3K4me3high/me1low epigenetic marks of promoters. As expected, these regions co-mapped with the majority of the CAGE promoters . Genome-wide maps of H3K4me3, H3K4me1 and H3K27me3 for ESCs H9 cells, available from the NIH Roadmap Epigenomics Mapping Consortium, were downloaded and re-analyzed. We identified 13,640 and 13,098 genomic regions in ESCs and NESCs respectively, carrying the H3K4me3romoters . In partthe TSSs , and thethe TSSs .+/me1- or H3K4me3high/me1low islands (69%) overlapped in the two cell types, as observed for the CAGE promoters, and therefore did not change during neural commitment. In ESCs, 21%  of the CAGE promoters associated with the H3K4me3+/me1- or H3K4me3high/me1low modifications was marked also by H3K27me3, an indication of a poised transcriptional status. As expected, the average expression level of the “bivalent” promoters was significantly lower (p ≤0.0001 by t test) than the overall promoter population  showed a promoter epigenetic signature, compared to only about 43% of the un-annotated CAGE promoters (43% in ESCs and 44% in NESCs) . Most ofpulation . These ppulation , definin in ESCs .+/me3- or H3K4me1high/me3low signature of putative enhancers in ESCs and NESCs cells, respectively, 41% and 46% of which co-localized totally or partially, identifying genetic regions stably marked as enhancers during neural induction  were located >2 kb away from CAGE promoters . H3K4me1+/high/H3K4me1-/low) or an enhancer (H3K4me1+/high/H3K4me3-/low) signature  are shown in To identify the circuitry of TFs operating on cell-specific enhancers, we searched for putative TFBSs in the epigenetically-defined enhancer regions by the HOMER tool . The topHigh-throughput approaches are essential for the understanding of transcriptional and epigenetic dynamics orchestrating self-renewal, commitment and differentiation of stem cells. By massively parallel sequencing of Pol-II-transcribed RNAs, we mapped active promoters and quantitatively analyzed their transcriptional activity in a model of human neural commitment, represented by human ESCs induced to differentiate into a relatively well-defined neural stem cell (NESC). We correlated these genome-wide maps of active Pol-II transcription to specifically mapped or publicly available epigenetic annotations of promoters and enhancers, and compared them to discover shared or cell-specific regulatory elements associated to pluripotency and neural lineage restriction.We used CAGE-seq, a high-throughput technique that allows sequencing of capped Pol-II-dependent transcripts and precise mapping of their TSSs, to obtain a detailed and comprehensive collection of active promoters in ESCs and NESCs. CAGE-seq also provides quantitative estimations of the number of transcripts generated by each promoter, allowing defining cell transcriptomes that we compared to classical Affymetrix profiling of cytoplasmic RNAs. As expected, ESCs and NESCs showed two very distinct transcript phenotypes, with ESCs expressing the pluripotency master regulators OCT4, NANOG and SOX2, and NESCs characterized by the absence of OCT4 and NANOG and the expression of SOX2 together with the marker of neural commitment SOX1, PAX6 and NESTIN. The co-expression of SOX2 and SOX1 confirms the multipotent neural stem cell identity of NESCs. Overall, over 2,400 genes were differentially expressed between the two cell lines with a fold-change of at least 2, and >300 showed strong up-or down-regulation (FC>10) during neural differentiation of ESCs. The correlation between Affymetrix and CAGE-Seq data was acceptable in both cell types (Pearson’s coefficients ~0.6), considering the very different nature of the two datasets: microarray measures mature transcripts in a non-quantitative fashion, while CAGE-seq measures promoter activity that does not necessarily result in the generation of mature mRNAs. Importantly, CAGE-seq identified sets of genes transcribed exclusively in ESCs or NESCs, including non-coding RNAs and novel transcripts not detected by microarrays, and provided a more accurate description of the cell-specific gene expression programs.Among the genes associated to ESC-specific promoters and to promoters down-regulated during neural induction, we found the pluripotency master regulators, a set of genes recently described to play a role in pluripotency and cell reprogramming –56, a vaBy CAGE-seq, we mapped around 13,500 promoters in either ESCs or NESCs, a number consistent with previously published data on ESCs and other cell types . The mosAs mentioned above, a significant fraction of the cell-specific and regulated promoters identified by CAGE-Seq are associated to non-coding transcripts . More than 300 annotated ncRNAs were expressed by ESCs and NESCs, and among them, 20 miRNAs. A panel of miRNAs expressed in ESCs, NESCs and their neuronal progeny was recently described .+/high /H3K4me3-/low islands in ESCs and NESCs respectively were cell-specific. These data indicate that most of the relevant genomic changes occurring during neural commitment/differentiation of ESCs are at the level of enhancers rather than promoters. It is therefore a differential enhancer usage that orchestrates qualitative and quantitative changes in the expression of protein-coding and non-coding genes, which most likely determines a pluripotent vs. a neural-restricted stem cell identity. Interestingly, a small fraction of unannotated CAGE promoters, or promoters associated to annotated ncRNAs, co-localized with epigenetically-defined enhancers, and some of them were differentially-expressed. Evidence of Pol-II transcription at enhancers was already described in mammalian cells, generating regulatory eRNAs [To confirm the identification of promoters and to map enhancer regions in both ESCs and NESCs, we correlated the CAGE-Seq data with genome-wide maps of H3K4me3 and H3K4me1 as distinctive epigenetic signatures of promoters and enhancers. Overall, we observed high co-localization of CAGE promoters and H3K4me3-enriched genomic islands, confirming that the vast majority of active promoters are in common between ESCs and NESCs independently from the technique used to identify them. On the contrary, more than 50% of the >80,000 and >70,000 enhancers mapped as H3K4me1ry eRNAs , 14, 68.ry eRNAs , we reanry eRNAs , 58 by a+/H3K27me3+ promoters, an epigenetic signature considered as characteristic of promoters poised for transcription. As expected, most (>60%) of the ESC-specific CAGE promoters, promoters down-regulated during NESC induction, and non-specific promoters were epigenetically marked as active (H3K4me3+/H3K27me3-) in ESCs. Strikingly, however, the vast majority of the NESC-specific promoters (89%) and of those up-regulated during induction (74%) has a bivalent signature in ESCs. This finding is consistent with previous evidence that developmentally-regulated promoters are often poised for transcription in pluripotent cells [The analysis of the epigenetic state of the CAGE promoters in ESCs was particularly interesting. We used publicly available maps of H3K27me3 in ESCs to map bivalent, H3K4me3nt cells , 15, andFinally, to understand the regulatory circuitry operating on differentially regulated promoters and enhancers, we analyzed putative TFBSs within a region extending from -300 to +100 bp from the most expressed TSS in cell-specific and up- and down-regulated CAGE promotes and in the epigenetically defined enhancer regions. As expected, ESC-specific and down-regulated promoters, and ESC-specific enhancers, showed enrichment for binding motifs of the pluripotency TFs OCT4, SOX2, NANOG and KLF4. Conversely, NESC-specific and up-regulated promoters were essentially enriched for the ubiquitous NFY, MAZ and TBP factors and the RFX-family HTH factors. NESC-specific enhancers were enriched for ETS-family, RFX-family and Jun/AP1 factors. These data correlated well with the cell-specific transcriptomes and promoter usage, and provide evidence that neural commitment is accompanied by dramatic changes in the regulatory circuitry operating on a restricted set of promoters and a much larger set of enhancers.S1 FigIn the absence of specific growth factors, NESCs give rise to mature neurons positive for neuronal markers such as MAP2 (A), TUBB3 (B), and for GAD65-67 (C), a GABAergic-specific protein. Bar scale 100 μm.(PDF)Click here for additional data file.S2 FigCAGE-TSSs mapped upstream to NANOG (A) and SOX2 (B) genes in ESCs (red) and NESCs (blue); C) At the bottom, CAGE-TSSs and the corresponding CAGE promoters of ETS1 gene in ESCs and NESCs are shown. The upstream ETS1 promoter was upregulated in NESCs  whereas the downstream one was upregulated in ESCs (red bars). In the upper part of the figure, indicated by the arrow, zoom-in on the CAGE promoter of ETS1 in NESCs.(PDF)Click here for additional data file.S3 FigFor each gene, the capped RNA amount detected by CAGE-seq (x-axis) was correlated to the mRNA amount evaluated by microarray fluorescent intensity (y-axis), in ESCs (A) and NESCs (B); the same correlation was made on the subset of genes associated to significantly differential promoters, in ESCs (C) and NESCs (D). A modest Person correlation was found between promoter activity and mRNAs quantity, slightly higher for genes whose promoter activity is significantly changing during ESCs-neural commitment.(PDF)Click here for additional data file.S4 Fighttp://ingenuity.force.com/ipa/articles/Feature_Description/Legend.Most of the genes are included in the regulatory pathways mastered by OCT4 and NANOG, and ESC pluripotency in general. Purple arrows indicate the connections between genes based on the Ingenuity Knowledge Base dataset (dotted or solid lines for indirect and direct relationships respectively). Genes involved in IPA canonical pathways (CP) are indicated by grey arrows. The shape of the gene symbol indicates the corresponding protein function, while the color (from white to red) represents the CAGE-seq expression level of the promoter associated to the gene. For a complete IPA legend refer to (PDF)Click here for additional data file.S5 Fighttp://ingenuity.force.com/ipa/articles/Feature_Description/LegendMost of the genes are included in the regulatory pathways of ESC pluripotency, signal transduction and epithelial-mesenchymal transition. Purple arrows indicate the connections between genes based on the Ingenuity Knowledge Base dataset (dotted or solid lines for indirect and direct relationships respectively). Genes involved in IPA canonical pathways (CP) are indicated by grey arrows. The shape of the gene symbol indicates the corresponding protein function, while the color (from white to red) represents the ratio of CAGE-seq expression level of the promoter associated to the gene in ESCs and NESCs. For a complete IPA legend refer to (PDF)Click here for additional data file.S6 FigA) Distribution of H3K4me3 peaks around CAGE TSSs (top panels), and the corresponding box-whisker plots (bottom panels). A significant correlation between H3K4me3 intensity and CAGE promoter expression levels was observed. ESC-specific and down-regulated promoters were highly enriched in H3K4me3 in ESCs, compared to NESC-specific and up-regulated promoters. Similarly, NESC-specific and up-regulated promoters showed significantly higher levels of H3K4me3 in NESCs. B) H3K4me1 intensity of total (upper panels) and cell-specific (bottom panels) enhancers close to CAGE promoters (window of ±50 kb). In ESCs H3K4me1 signal of total and cell-specific enhancers is higher around CAGE promoters highly active in ESCs (ESC-specific- and down-regulated promoters) compared to the H3K4me1 intensity around CAGE promoters expressed at lower levels (NESC-specific- and up-regulated promoters) (left panels). Similar results were obtained in NESCs (right panels). Statistical significance was determined by Wilcoxon test with Bonferroni correction .(PDF)Click here for additional data file.S7 FigA) The graph shows the expression level of CAGE promoters (tpm mean with SEM) carrying an epigenetic signature of active or poised promoter, in a window of 2kb. B) Expression level of CAGE promoters associated to active or poised enhancers in a window of 50 kb. CAGE promoters located around poised promoter regions and enhancers were significantly lower expressed than the overall population of CAGE promoters .(PDF)Click here for additional data file.S8 FigIn the previous study a set of 5,118 active and 2,287 poised enhancers were defined in ESCs. Then, 195 poised enhancers in ESCs were defined as active enhancers in ESC-derived neurospheres. We compared the 89,124 ESCs enhancers we mapped with the 5,118 ESCs active (upper left Venn diagram) and 2,287 poised (upper right Venn diagram) enhancers by the previous study. The same comparison was made for the 74,413 NESCs enhancers we mapped and the 195 active enhancers in neurospheres (down).(PDF)Click here for additional data file.S9 Fig+/high/H3K4me1-/low) or an enhancer (H3K4me1+/high/H3K4me3-/low) signature. B and C) Analysis of putative TFBS enrichment within down- (B) and up- (C) regulated CAGE-enhancers, as determined by HOMER tool.A) The graph shows the epigenetic signature of CAGE-enhancers in ESCs and NESCs. Most CAGE-enhancers carried either a promoter (H3K4me3(PDF)Click here for additional data file.S1 Materials and Methods(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.S2 Table(PDF)Click here for additional data file.S3 Table(PDF)Click here for additional data file.S4 Table(PDF)Click here for additional data file.S5 Table(PDF)Click here for additional data file.S6 Table(PDF)Click here for additional data file.


ASH2L is a core subunit of KMT2 methyltransferase complexes that target histone H3 lysine 4 (H3K4), a mark associated with open chromatin. Indeed, MYC associates with H3K4 methyltransferase activity, dependent on the presence of ASH2L. MYC does not regulate this methyltransferase activity but stimulates demethylation and subsequently acetylation of H3K27. KMT2 complexes have been reported to associate with histone H3K27-specific demethylases, while CBP/p300, which interact with MYC, acetylate H3K27. Finally WDR5, another core subunit of KMT2 complexes, also binds directly to MYC and in genome-wide analyses MYC and WDR5 are associated with transcribed promoters. Thus, our findings suggest that MYC and ASH2L–KMT2 complexes cooperate in gene transcription by controlling H3K27 modifications and thereby regulate bivalent chromatin.The appropriate expression of the roughly 30,000 human genes requires multiple layers of control. The oncoprotein MYC, a transcriptional regulator, contributes to many of the identified control mechanisms, including the regulation of chromatin, RNA polymerases, and RNA processing. Moreover, MYC recruits core histone-modifying enzymes to DNA. We identified an additional transcriptional cofactor complex that interacts with MYC and that is important for gene transcription. We found that the trithorax protein ASH2L and MYC interact directly  The oncoprotein MYC functions as a transcriptional regulator. Together with its heterodimerization partner MAX, MYC controls the expression of many genes with broad physiological implications . MYC impproteinaceous center of the nucleosome, are the target of multiple enzymes. Post-translational modifications of core histones and the exchange with histone variants modulate nucleosomal stability and the interaction with numerous proteins. More than 100 different modifications of core histones are presently known. While for some of these modifications functional consequences are described, many have not been studied in great detail. Also the combinatorial effects of these modifications are only partially understood. Methylation of lysine 4 of histone H3 (H3K4) is associated with accessible chromatin  and the SET1A/B methyltransferases (MTase). These MTases have been classified as the KMT2 (lysine-specific methyltransferases) group of lysine-specific MTases  family of proteins, which positively control gene transcription  and H3K4 trimethylation (H3K4me3) serve as marks for enhancer regions and accessible promoters, respectively . H3K4me3We identified ASH2L in a screen for MYC-interacting proteins . We foun2. P493-6 and Jurkat T cells were cultivated in RPMI medium and HEK293, HEK293T, U2OS and HeLa cells were grown in DMEM+Glutamax medium, both supplemented with 10% heat-inactivated fetal calf serum and 1% penicillin/streptomycin (P/S). For growth-arrest and MYC suppression, P493-6 B cells were seeded at 5 × 105/ml and treated with 0.1 μg/ml tetracycline for 72 h. To induce MYC expression, tetracycline was removed by washing the cells twice with phosphate buffered saline (PBS) and adding fresh RPMI medium containing 10% FCS (+MYC). Control cells were maintained in tetracycline-containing medium until harvest (-MYC).All cell lines were incubated in humidified atmosphere at 37°C with 5% COTransfections of plasmids were carried out using the calcium phosphate method ,36. ExpeDharmacon siRNA pools (Thermo Scientific)Control: siGENOME Non-Targeting siRNA Pool #2 (D-001206-14)ASH2L: SMARTpool siGENOME ASH2L siRNA (M-019831-01)MYC: SMARTpool siGENOME MYC siRNA (M-003282-07)Dharmacon Set of 4 single siRNAs (Thermo Scientific)ASH2L: siGENOME ASH2L siRNA (MQ-019831-01)MYC: siGENOME MYC siRNA (MQ-003282-07)The four individual siOligos were tested for knockdown efficiency (data not shown). The two most efficient siOligos were then used in additional control experiments .pcDNA3-Flag-MYC-1-439 (wt), -1-410, -1-350, -1-180, -101-439, -148-439, -178-439, -Δ103-263, -Δ265-329, -Δ265-367, -Δ319-341 and pCGN-HA-MYC-1-439 (wt), -1-366, -1-293, -1-220, -221-439, -294-439, -367-439 were kindly provided by W. Tansey . PlasmidEscherichia coli (E. coli) BL21(DE)pLysS as GST-fusion or His6-fusion proteins using the following constructs pGEX4T3-ASH2L-wt, -1-121, -1-279, -1-394, -1-444, pGEX2T-MYC-1-262 (N262), -1-156, -263-439, pGEX3X-MAX, pGEX2T-YY1, pGEX4T3-H3-N, pQE30-ASH2-1-387, pRSET-His6-WDR5 (Recombinant proteins were produced in is6-WDR5 . TransfoE. coli BL21(DE)pLysS grown in NZC-medium  NaCl, 0.2% (w/v) MgCl2, 0.2% glucose) induced at OD 0.5 with 0.3 mM IPTG at 30°C for 4 h. Cells were lysed in buffer C  Aprotinin), sonicated, and centrifuged at 10,000 x g at 4°C for 30 min. MBP proteins were separated from the cell lysate with amylose-agarose (New England Biolabs) and eluted from the beads with 10 mM maltose in buffer A. Core histones and recombinant histone H3 (New England Biolabs) and GST-Histone H3 N’-terminal-tails (provided by Y. Shinkai)  and MBP  were purShinkai)  were use35S]methionine-labeled, in vitro transcribed and translated proteins in binding buffer or with 3 μg of recombinant His6-fusion proteins  was used with the plasmids pcDNA3-Flag-MYC and pcDNA3-Flag-ASH2L.For binding reactions, 3 μg GST or GST-fusion proteins were bound to glutathione–agarose beads and incubated with [proteins . For in 7 cells were prepared in F-buffer . Co-immunoprecipitated proteins were detected by western blot analysis  at 30°C for 30 min. Modified proteins were visualized by SDS-PAGE and autoradiogaphy. Site specificity and MYC-associated methyltransferase activity was measured with 1 μg recombinant histone H3 in the presence of 100 μM SAM at 30°C for 1 h. Histone H3 methylation was detected by immunoblotting with different H3K4 and H3K9 methyl-specific antibodies.Whole-cell lysates from 3 × 10HeLa cells were seeded onto coverslips, transiently transfected with pSuper-ASH2L and fixed in PBS containing 3.7% paraformaldehyde 72 h after transfection. Cells were permeabilized in PBS with 0.2% Triton X-100 at room temperature for 5 min and stained with the ASH2L-specific rat mAb 4C5 and the H3K4me3-specific rabbit polyclonal antibody ab8898 (abcam) at 37°C for 45 min. Anti-rat-Cy3 and anti-rabbit-Cy2 secondary antibodies were used at 37°C for 30 min. Coverslips were mounted with Moviol (Merck) in PBS containing 2.5% N-propylgallate (Sigma). For the proximity ligation assay (PLA), the DuolinkII fluorescence system was used in conjunction with the Probemaker Plus kit for labeling of the secondary anti-rat antibody according to the manufacturer's instructions (Olink Bioscience) , and western blot: rabbit anti-MYC , rabbit anti-MAX , rat anti-MXD1 (5F4), mouse anti-nucleolin  rabbit anti-ASH2L ), rat anti-ASH2L (4C5 and 4B5 (antigen ASH2L-1-279)), mouse anti-WDR5 , rabbit anti-RbBP5 , rabbit anti-H3 , rabbit anti-H3ac , rabbit anti-H3K9ac , rabbit anti-H3K27ac , rabbit anti-H3K4me3 , rabbit anti-H3K4me2 , rabbit anti-H3K4me1 , rabbit anti-H3K9me3 , rabbit H3K9me2 , rabbit anti-H3K9me1 , rabbit anti-H3K27me3 , rabbit anti-CBP , rabbit anti-p300 , rabbit IgG , mouse anti-actin , rat anti-HA , mouse anti-Flag , rabbit anti-caspase 5 .7 P493-6 cells and 1×107 HEK293T cells) and chromatin was sheared for 15 min using a Bioruptor (Diagenode). Immunoprecipitation was performed with 100 μg chromatin (input) and 2 μg antibody. DNA was purified and the quantitative polymerase chain reaction (qPCR) was performed with the following primer pairs:Ctrl pp4 (for): 5’-GCGTGACTCAAATTGTGTGTGCCT-3’Ctrl pp4 (rev): 5’-ATCAAGCATTCAGCAGCGTTC CA-3’CCND2 prom1 (for): 5’-CCCCTTCCTCCTGGAGTGAAATAC-3’CCND2 prom2 (rev): 5’-CGTGCTCTAACGCATCCTTGAGTC-3’CCND2 Ex1 (for): 5’-GGGAGAGCGAGACCAGTTTTAAG-3’CCND2 Ex1 (rev): 5’-CCTTTGGCTAAATAGGGGGTTTTC-3’ODC E-box (for): 5’-ATCACTTCCAGGTCCCTTGCAC-3’ODC E-box (rev): 5’-TTCCACCTGGCGTTCAGTACCT-3’NCL In1 (for): 5’-TGGGCCGGGAAATGGCGGTA-3’NCL In1 (rev): 5’-TAGGCCACCACGTGCCCGAA-3’ChIP-qPCR assays were performed with the Diagenode OneDay ChIP kit according to the manufacturer's instructions. P493-6 and HEK293T cells were cross-linked with 1% formaldehyde  on a shaking platform for 10 min at room temperature and quenched for 5 min by adding 125 mM glycine. Cells were lysed in 100 μl lysis buffer (2 × 10For Re-ChIP experiments the first IP was performed with 100–200 μg chromatin (input). Chromatin was released from beads using release buffer  for 30 min at 37°C and diluted in 4 volumes ChIP buffer . Then, 2 μg of antibody were added for the second immunoprecipitation, followed by DNA purification and qPCR. PCR was performed as described below.The DNA was purified from one tenth of the input chromatin that was used for the ChIPs, diluted two-fold and used for qPCR amplification. The dilution factor was included in the % input calculation of all the qPCR results.Total RNA was extracted using RNeasy Mini Kit (Qiagen) and DNase digestion (Qiagen) according to the manufacturer's instructions. 1 μg of RNA was reverse transcribed into cDNA using the QuantiTect Reverse Transcription Kit (Qiagen) and analyzed by qPCR with SensiMix SYBR Green Mix (Bioline) in a Rotor-Gene Q (Qiagen) machine.β-glucuronidase (GUS) for: 5’-CTCATTTGGAATTTTGCCGATT-3’,β-glucuronidase (GUS) rev: 5’-CCGAGTGAAGATCCCCTTTTTA-3’The following primer pairs were used for amplification:MYC (Hs_Myc_1_SG), ASH2L (Hs_ASH2L_1_SG), CCND2 (Hs_CCND2_1_SG), ODC (Hs_ODC1_1_SG), NCL (Hs_NCL_1_SG). Relative mRNA expression was calculated by the comparative ΔΔCt method and normalized to the housekeeping gene GUS using Rotor-Gene Q software.QuantiTect primer assays (Qiagen): P493-6 cells were harvested by centrifugation, washed with PBS and fixed by adding 100% methanol at -20°C. After RNase A treatment (20 μg/ml) cells were stained with propidium iodide  for 15 min at room temperature and analyzed using a FACSCanto II (BD Biosciences).p-value of 10−5 and the ChIP-Seq control as input.Public data from ChIP experiments followed by massively parallel DNA sequencing (ChIP-Seq) was used to analyze the occupancy of the factors MYC, WDR5 and POL II and the occurrence of the histone modifications H3K4me3, H3K4me1 and H3K27me3 in normal human epidermal keratinocytes (NHEK). Data from histone modifications, POL II, MYC and controls were obtained from the ENCODE project . WDR5 ChIP-Seq was obtained from published findings  GEO acc. All aliPublicly available data for histone modifications were combined to define regulatory regions in NHEK cells: active promoters ), active enhancers (regions distant from core promoters with peaks of both H3K4me1 and H3K27ac), poised promoters (H3K4me3 and H3K27me3 ), poisedhttps://code.google.com/p/reg-gen/.To analyze the presence of binding sites for MYC and ASH2L inside ChIP-Seq peaks, the summit regions of the Chip-Seq peaks (positions with highest reads within a peak as provided by MACS), extended by 125 bps in each direction, were inspected. This procedure ensures that all peaks possess the same size and avoids biasing the binding site statistics. The choice of peaks with a size of 250 bps is in accordance with previously published work . Note thTo evaluate if regions of ChIP-Seq peaks and NHEK expression data from RNA-Seq experiments are related, the alignment of RNA-Seq reads from the ENCODE project (GSM958736) was analyzed. The summits of peaks of MYC, WDR5 and both MYC and WDR5 were extended by 1000 bps to capture the expression of neighboring genes. The number of aligned reads inside the ChIP-Seq peaks was counted and normalized by the size of the peaks. In addition, the same analysis was performed considering only ChIP-Seq peaks overlapping with the region 200 bps upstream of a TSS.In a previous study, we identified three proteins that interact with MYC . One of In vitro transcribed and translated ASH2L interacted with the C-terminal region of MYC (GST-MYC(263–439)), but not with an N-terminal transactivation domain (GST-MYC(1–262)) or with GST alone  pull-down assays. e Figure . This ine Figure . To map e Figure . These sn Figure . Next, wn Figure , most liin vitro interaction data, in co-IP experiments using tagged proteins the C-terminal fragment of MLL2, WDR5 and ASH2L were detected efficiently, while RbBP5 binding was very weak . This indicates that MYC has the capacity to interact with at least two components of the KMT2 core complex. The main conclusion from these assays was that two distinct regions in MYC contribute to ASH2L binding, i.e. amino acids 263–350 through direct protein–protein interaction and the HLHZip domain by an indirect mechanism . Upon removal of tetracycline, MYC expression, both mRNA and protein, was reactivated and the cells entered the cell cycle . In parallel, the expression of three known MYC target genes, CCND2 (encoding cyclin D2), ornithine decarboxylase (ODC) and NCL (encoding nucleolin), was induced   , demonstCCND2, ODC and NCL was induced upon removal of tetracycline, while no binding was detectable to a control region 22 kb upstream of the CCND2 promoter  and to stimulate acetylation of core histones . Indeed,2 Figure . To expa2 Figure . Thus, oCCND2 E-box region showed a robust signal . Thus, these findings suggest that ASH2L and MYC are involved in controlling the modification of H3K27.In the P493-6 B cell model, the binding of ASH2L was independent of MYC. This was also true in HEK293T cells as the knockdown of MYC, which resulted in reduced MYC loading to the s Figure , did neis Figure  nor H3K4s Figure . Insteads Figure  and E, c3 Figure  and E. W3 Figure , despite3 Figure  and H. ITo further address the interaction of MYC with ASH2L complexes, we used publically available ChIP-Seq data . While lAlthough the available data did not allow the analysis of ASH2L directly, we made use of the recently reported DNA consensus sequence that is recognized by ASH2L . This alFinally, we addressed the question whether the ChIP-Seq peaks of MYC and WDR5, as a surrogate of ASH2L complexes, have an influence on gene expression whenever they are located close to gene promoters, i.e. within 200 base pairs 5’ of the start of transcription (TSS). Using public RNA-Seq data from NHEK cells, the analysis revealed that genes with MYC or MYC/WDR5 ChIP-Seq peaks were significantly higher expressed than genes with WDR5 ChIP-Seq peaks alone Figure . This arODC, NCL and CCND2, studied in HEK293T and in P493-6 B cells. All three genes were expressed and showed POL II binding , which together with other Polycomb group proteins negatively controls gene transcription . EZH2, aMLL1 gene is the translocation partner of more than 100 genes. Moreover in other tumors, some MLLs are amplified (As pointed out above, bivalent chromatin regulates gene transcription also in tumor cells . In suppmplified ,94. Furtmplified ,60. Finamplified ,96. ThusThe mechanism of how bivalent chromatin is controlled rests on the interplay of different enzymes recruited to specific sites in DNA. The acetyltransferases CBP and p300 are recruited by numerous transcriptional regulators , one beiHoxc8 and Fgfr3 (Myf5 expression upon Carm1-dependent methylation, which in turn allows recruitment of an ASH2L complex (The first involves the recruitment of ASH2L complexes by transcriptional regulators to chromatin in a sequence-specific manner. The second indicates that ASH2L–KMT2 complexes interact with chromatin components, for example specifically modified core histones, which allows context-dependent binding. These two possibilities, which are detailed below, are fundamentally different as in the first the information to position the complex is present in the DNA sequence, while in the second the binding occurs to specialized chromatin. Thus the second possibility allows in principle DNA sequence-independent localization at and modification of chromatin. As to the first possibility, several transcription factors interact with ASH2L–KMT2 complexes and thus can potentially recruit H3K4 trimethylase activity to specific sites in chromatin. These include the broadly acting NF-Y , NF-E2 tnd Fgfr3 ,105, and complex ,107. In In addition ASH2L–KMT2 complexes bind to chromatin independent of defined DNA sequences, as outlined as second possibility. It appears that several subunits of ASH2L–KMT2 complexes display distinct activities compatible with binding to chromatin. WDR5 interacts with the N-terminus of histone H3 , a functIn summary our studies provide evidence that MYC, together with ASH2L–KMT2 complexes, controls gene transcription by activating bivalent chromatin Figure . This suSupplementary Data are available at NAR Online.


Prolonged glucocorticoids (GCs) treatment may lead to the formation of posterior subcapsular cataracts. The present study aimed to investigate differential gene expression in lens epithelial cells (LECs) in response to GCs using DNA microarray profiling. The gene expression profile of GSE13040 was downloaded from the Gene Expression Omnibus database, which includes 12 human LECs treated with vehicle or dexamethasone (Dex) for 4 or 16 h with six samples at each time period, of which three samples were treated with vehicle (control group) and three samples were treated with Dex (Dex group) at each time point. The differentially expressed genes (DEGs) were identified between the control group and the Dex group at each time period with the thresholds of P<0.05 and |logFC|>1. The DEGs were further analyzed using bioinformatics methods. Firstly, DEGs were subject to a hierarchical cluster analysis. Subsequently, the functional enrichment analysis was performed for the common DEGs between the two time periods. Finally, the transcription factors and binding sites of DEGs associated with response to GC stimulus were analyzed. A total of 696 and 949 DEGs were identified at 4 h and 16 h, respectively. Hierarchical cluster analysis revealed that DEG expression was higher in the Dex group than in the control group (P<0.05). A total of 13 significant functions were enriched for the 72 common DEGs at the two time periods. Chemokine (C-C motif) ligand 2 (CCL2), dual-specificity phosphatase-1 (DUSP1) and FAS were associated with the response to GC stimulus and the transcription factor c-Jun bound to promoter regulation regions of CCL2, DUSP1 and FAS. In conclusion, the transcription factors and binding sites of DEGs associated with the response of LECs to GCs may provide potential gene targets for designing and developing drugs to protect against GC-induced cataract formation. Glucocorticoid (GCs) steroid hormones are used in the treatment of diseases, including rheumatoid arthritis, asthma and various ocular diseases. It has been widely reported that prolonged treatment with GCs can lead to the formation of posterior subcapsular cataracts –3. AlthoGCs have important roles in numerous biological processes, including regulation of anti-inflammatory activity and immunosuppressive action ,7. GCs ein vivo . The other is dependent on its binding to and interfering with other transcription factors . Transcrin vivo . Thus, iThe present study aimed to identify differentially expressed genes (DEGs) and their common transcription factors in order to gain a novel insight into the mechanism of action of GCs in LECs.http://www.ncbi.nlm.nih.gov/geo/) database, which is based on the GPL96 [HG-U133A] Affymetrix Human Genome U133A Array . There were 12 samples of human LECs treated with vehicle or dexamethasone (Dex) at 4 and 16 h. At each time period, there were six samples, of which three samples were treated with vehicle (control group) and three samples were treated with Dex (Dex group). Freshly isolated human LECs were obtained from capsulorhexis specimens following surgery, these were the original cells used in the GEO  (www.bioconductor.org/packages/release/bioc/html/limma.html)  ,18. The ma.html)  were useGene hierarchical cluster analysis of DEGs was performed using the Pearson correlation coefficient algorithm  in clusthttp://david.abcc.Ncifcrf.gov/) (http://www.geneontology.org/) categories in biological processes based on the hypergeometric distribution. The GO terms with a value of P<0.05 were selected as significantly enriched DEGs.The Database for Annotation, Visualization and Integrated Discovery  , a high-A transcription factor is a protein, which binds to specific DNA sequences. The TRANSFAC database comprising information about transcription factors, target genes and binding sites has been developed . The TRAThe publicly available microarray dataset, GSE3040, was obtained from the GEO database. Student’s t-test was used to identify genes specifically differentially expressed at 4 and 16 h with the cut-off criteria of P<0.05 and fold change >1. The results revealed that 696 and 949 genes at 4 and 16 h, respectively, exhibited significant differential expression.As indicated using hierarchical cluster analysis, the expression levels of DEGs were markedly increased in Veh samples compared with that of the Dex group, at 4 and 16 h .DEGs set at 4 and 16 h were compared and presented as a Venn diagram . There wTo gain further insight into the function of genes in our interaction network, the online biological classification tool DAVID was used. A total of 13 significant GO function enrichment nodes were obtained and the distributions of genes is shown in Using the TRANSFAS database, the transcription factors and binding sites, which were associated with the three GC response genes, CCL2, DUSP1 and FAS, were assessed. As GCs have been used in clinical treatment for decades; however, prolonged GC treatment may lead to the formation of cataracts . In the et al (CCL2 is also known as monocyte chemotactic protein-1 and is secreted by endothelial cells, fibroblasts and monocytes . It has et al  reportedIn the present study, c-Jun was observed to bind the promoter regulatory regions of CCL2, DUSP1 and FAS. The c-Jun gene encodes a basic region-leucine zipper transcription factor implicated in numerous cellular processes. C-Jun regulates gene expression and cell function by being involved in the formation of a variety of dimeric complexes, which exhibit high affinity sequence specific DNA-binding activity . It has In conclusion, the gene expression profiles of LECs following GC treatment were analyzed using bioinformatics analysis and it was found that CCL2, DUSP1 and FAS are involved in the response to GC stimulus. The transcription factor c-Jun, when bound to CCL2, DUSP1 and FAS, may affect their expression. CCL2, DUSP1, FAS and transcription factor c-Jun may be used as specific therapeutic molecular targets in order to treat cataracts induced by GCs. However, further studies are required to confirm the present results.


Summary: Promoters have diverse regulatory architectures and thus activate genes differently. For example, some have a TATA-box, many others do not. Even the ones with it can differ in its position relative to the transcription start site (TSS). No Promoter Left Behind (NPLB) is an efficient, organism-independent method for characterizing such diverse architectures directly from experimentally identified genome-wide TSSs, without relying on known promoter elements. As a test case, we show its application in identifying novel architectures in the fly genome.Availability and implementation: Web-server at http://nplb.ncl.res.in. Standalone also at https://github.com/computationalBiology/NPLB/ (Mac OSX/Linux).Contact:l.narlikar@ncl.res.inSupplementary information:Bioinformatics online. These can miss PEs present only in a small fraction of promoters. Since promoters have diverse mechanisms of activation, most PEs fall in this category  contribute to the diversity in transcriptional regulation is not yet clear. While high-throughput technologies are increasingly used to produce accurate maps of transcription start sites (TSSs) , the subfull set .de novo  is a new method modelled along the lines of unsupervised learning with feature selection that partitions TSS-aligned promoter sequences into distinct promoter architectures (PAs), each characterized by its own set of PEs, all learned de novo . Since iA,C,G,T} at specific positions relative to the TSS. These positions and their distributions are expected to be unique to that PA. All other positions follow a background categorical distribution, common for all PAs. Parameters of models with various numbers of PAs are learned using Gibbs sampling and the best model is decided using cross validation. Key advantages of NPLB are that itWritten in C and Python, NPLB requires a prior installation of gnuplot 4.6+. Weblogo 3.3  or categorize new promoters based on an input PA-model (promoterClassify). Both require a fasta file of promoters, aligned according to the TSS. A typical eukaryotic file would contain DNA sequences ∼50 bp up- and downstream of the TSS. promoterClassify also needs a previously learned model. Various other default settings such as number of PAs to be explored and the number of sampling iterations can be overridden by the user. This is especially useful when the user wants to choose between a quick, approximate solution and a slow, but more accurate characterization. A tab-separated text file with one line per promoter, containing additional characteristics of each TSS such as UTR length, TSS spread, etc. is an optional input. In such a situation, NPLB creates plots that can give insights into functional differences between PAs.NPLB can learn new PAs . This PA was missed in the original analysis possibly since it contains <2% of all promoters. Interestingly, A7–A11, which contain variants of the DPE, but no obvious upstream element, create transcripts with longer 5′ UTRs than other PAs (P < 10−62). This has not been noted before. A more detailed description of the PAs is available in the The characteristic file with the number of tags at each TSS and 5′ UTR length was used to construct two box-plots c and d. Data from new and advanced high-throughput technologies are increasingly making it clear that cells employ diverse mechanisms for transcriptional regulation. NPLB seeks to fulfil the need for an efficient and unbiased method that can identify these mechanisms directly from such data. Although NPLB has been designed for TSS maps, it can be applied to any DNA sequences aligned on the basis of a common genomic event such as splicing, eRNA synthesis or protein–DNA binding and expected to have distinct sequence architectures in the immediate neighbourhood.Supplementary DataClick here for additional data file.


These results strongly suggest a crucial role of Kin28 kinase-dependent gene looping in the termination of transcription in budding yeast.TFIIH is a general transcription factor with kinase and helicase activities. The kinase activity resides in the Kin28 subunit of TFIIH. The role of Kin28 kinase in the early steps of transcription is well established. Here we report a novel role of Kin28 in the termination of transcription. We show that RNAPII reads through a termination signal upon kinase inhibition. Furthermore, the recruitment of termination factors towards the 3′ end of a gene was compromised in the kinase mutant, thus confirming the termination defect. A concomitant decrease in crosslinking of termination factors near the 5′ end of genes was also observed in the kinase-defective mutant. Simultaneous presence of termination factors towards both the ends of a gene is indicative of gene looping; while the loss of termination factor occupancy from the distal ends suggest the abolition of a looped gene conformation. Accordingly, CCC analysis revealed that the looped architecture of genes was severely compromised in the Kin28 kinase mutant. In a looping defective  The kinase subunit of TFIIH, Kin28, phosphorylates carboxy-terminal domain (CTD) of RNAPII at serine-5 and serine-7 residues234671910Our initial understanding of the function of TFIIH kinase under physiological conditions has come from studies with the temperature-sensitive mutants of Kin28 in budding yeast. The shifting of Kin28 mutants to the elevated temperature adversely affected the recruitment of TFIIH complex at the promoter resulting in a dramatic decrease in the CTD-serine-5 phosphorylation, and a concomitant decrease in the level of steady state mRNA level111113512144A number of genes in budding yeast exhibit a physical interaction of the terminator region of the gene with its cognate promoter resulting in the formation of looped gene architecture1718192021222324252627293031321719202223272829To investigate the precise role of Kin28 kinase in transcription by RNAPII, we examined the transcription of a number of inducible and constitutively expressed genes in Kin28-as mutant. Our results suggest that Kin28 kinase is not essential for transcription, but is necessary for the optimal transcription of both inducible and non-inducible genes. Kin28 crosslinked to both the 5′ and 3′ ends of transcriptionally engaged genes. In the absence of kinase activity, localization of Kin28 towards the 3′ end of genes exhibited a dramatic decline. The delocalization of Kin28 from the 3′ end coincided with the polymerase reading through the termination signal with a concomitant loss of looped gene conformation. These results implicate Kin28 kinase activity both in termination of transcription as well as gene looping. We propose that Kin28 kinase-mediated gene looping facilitates termination of transcription in at least a subset of genes in budding yeast.Although CTD-kinase activity of Kin28 is not essential either for transcription or for the survival of yeast cells, growth of cells is severely inhibited in the kinase-defective mutant5121414HXT1, MET16, CHA1, GAL10 and INO1) and six constitutively expressed genes  in Kin28-as mutant in the presence and absence of NA-PP1. The transcription was monitored in terms of steady state mRNA level determined by RT-PCR approach. In the presence of NA-PP1, transcription of both inducible and constitutively expressed genes exhibited a decline by about 2–10 folds in the mutant . No such decrease in RNA level was observed in the isogenic wild type strain in the presence of NA-PP1 or in the Kin28-as strain in the absence of NA-PP1. Our results are in disagreement with another study that did not find a significant decrease in the global mRNA level upon inhibition of Kin28 kinase using the same kin28-as mutantet al.et al.To investigate the precise function of Kin28 kinase in transcription cycle, we examined transcription of five inducible  approach. TRO assay is a better indicator of transcriptional activity of a gene than RNAPII density ChIP as it measures nascent transcription of a gene. The Kin28-as cells were grown to the mid-log phase, and transcription was induced in the presence and absence of NA-PP1 as described in Kanin et al.et al., (2008)CHA1 and HXT1 decreased by about 2 fold in the presence of NA-PP1  signal of both genes under kinase defective condition thereby affirming the termination defect observed using TRO approach . A similar elevated level of serine-7 phosphorylation was observed near the terminator region of both genes . In the presence of NA-PP1, however, a 2-4 fold decrease in phosphorylation of both serine-5 as well as serine-7 was observed for two genes in the analog-sensitive mutant . As expected, no appreciable decrease in the level of serine-2 phosphorylation was observed towards the 3′ end of either CHA1 or ACT1 in the absence of Kin28 kinase activity .To confirm the recruitment of Kin28 towards the 3′ end of genes, we examined the phosphorylation status of CTD-serine-5 and CTD-serine-7 near the 3′ end of The overall conclusion of these results is that the recruitment of Kin28 towards the 3′ end of genes as well as Kin28-mediated termination of transcription are both dependent on its kinase activity in budding yeast.The results described above ruled out the possibility of Kin28 kinase playing an indirect role in termination of transcription by affecting CTD-serine-2 phosphorylation. Since inactivation of Kin28 kinase resulted in lowering of both CTD-serine-5 and serine-7 phosphorylation near the 3′ end of genes, the possibility of serine-5 and serine-7 phosphorylation playing a role in transcription termination cannot be ruled out. These results, however, raised the possibility of a direct role of Kin28 in transcription termination by interacting with the CF1 and CPF complexes and facilitating their recruitment towards the 3′ end of genes. We therefore examined the physical interaction of Kin28 with the CF1 and CPF complexes by coimmunoprecipitation approach. The TAP-tagged Kin28 was pulled down using IgG-Sepharose beads, and the presence of either Rna15 or Ssu72 was detected in the immunoprecipitated fraction. Our results show that Kin28 interacts with both CF1 subunit Rna15 and CPF subunit Ssu72 in the absence of NA-PP1 , lane 2.CHA1, HXT1, ACT1 and ASC1 in Kin28-as cells in the presence and absence of NA-PP1. We have previously used this approach to demonstrate looping of genes in a transcription-dependent mannerCHA1, HXT1, ACT1 and ASC1 assume a looped gene conformation during transcription in the Kin28-as mutant as indicated by a strong P1T1 PCR signal for all four genes . No such read-through was observed in the isogenic wild type strain . This termination defect occurred even in the presence of enzymatically active Kin28. Furthermore, crosslinking of Kin28 towards the 3′ end of genes was severely compromised in sua7-1 cells . Strains SAM99, SAM107 and ZD3, which contain C-terminal TAP-tagged TFIIH subunits Kin28, Ssl2, and Tfb4 respectively, were constructed by transforming the Kin28as mutant yFR763 with DNA that was PCR amplified from plasmid pBS1479 (TRP marker). The C-terminal Myc-tagged strains SAM93, SAM51, SAM94, SAM103, SAM101, SAM102, SAM104 and SAM105 which contain Myc tagged Rna15 or Ssu72, respectively, were created by transforming either wild type or Kin28as cells with DNA that was PCR amplified from plasmid pFA6-13Myc-HIS3mx6. Strain SAM106, which contains a C-terminal Myc tagged Rpb4 was created by transforming Kin28as cells with DNA that was PCR amplified from plasmid pFA6-13Myc-HIS3mx6. Strain AS1 was derived from BY4733 as described inThe yeast strains used in this study are listed in supplemental A600 0.6 as previously describedA600 0.6 and then grown in the presence and absence of NA-PP1 as described above.Kin28-as mutant cells were grown in uracil-depleted media to an CCC experiments were performed as described previouslyChIP experiments were performed as described previouslyTranscription analysis was performed by the RT-PCR approach as described previouslyet al.The strand specific ‘Transcription Run-On’ (TRO) assay was performed by the modification of method described in Core 2, 100 mM NaCl) and resuspended in 940 μl of DEPC (Diethylpyrocarbonate)-treated ice cold water. Cells were permeabilized by the incubating with 60 μl 10% sarkosyl at 4 °C for 25 minutes with gentle shaking. Permeabilized ells were spun down by centrifugation at 1.2×g for 6 minutes at 4 °C. The cells were resuspended in 150 μl of transcription elongation reaction buffer  and incubated at 30 °C for 5 minutes. Cells were immediately lysed by addition of 500 μl ice cold Trizol and 250 μl acid-washed glass beads followed by vigorous shaking at 4 °C for 20 minutes. The lysate was transferred to a new 1.5 ml microfuge tube containing 500 μl of Trizol and 200 μl of chloroform. The tubes were shaken vigorously and centrifuged at 14000 rpm at 25 °C for 20 minutes. The supernatant was extracted with phenol-chloroform three times. Total RNA was precipitated by incubating with NaCl overnight at −20 °C. The RNA pellet was washed once with ice cold 70% ethanol and resuspended in 100 μl of DEPC-treated water. RNA was further purified using the QIAGEN RNA Easy kit and eluted twice with 50 μl of DEPC treated water.Cells were grown as described above. The cell pellet was washed with 10 ml of ice cold TMN buffer . Beads were blocked using 500 μl of blocking buffer  by gently shaking for 1 hour at 4 °C. Beads were washed two times with 500 μl of binding buffer and then 400 μl of binding buffer was added to the beads and placed on ice.The RNA from step I was incubated at 65 °C water bath for 5 minutes and immediately placed on ice for 2 minutes. The RNA was then incubated with the processed anti-BrdU beads with gentle shaking for 2 hours at 4 °C. Beads were then washed sequentially with 500 μl of binding buffer, 500 μl of low salt buffer  500 μl of high salt buffer  and 500 μl of TET buffer . RNA was eluted two times with 150 μl of elution buffer  by incubating at 42 °C for 5 minutes followed by a final elution with 200 μl of elution buffer at 42 °C for 5 minutes. After phenol-chloroform extraction, RNA was precipitated overnight at -20 °C in the presence of NaCl. RNA pellet was resuspended in 26 μl of DEPC-treated water and quantified using a Nanodrop spectrophotometer. RNA concentration was adjusted to 50 ng/μl.et al., (2011).The cDNA synthesis was performed with Protoscript II (NEB) using the strand specific primers following to manufacturer’s recommendation. PCR amplification was performed using either Advantage polymerase or Taq polymerase depending on amplicon size. Quantification was done as described in Medler CO-IP was performed as described previouslyWestern blotting was performed as described previouslyThe data shown in figures is the result of at least four biological replicates. The data was quantified and statistical analysis was performed as described inHow to cite this article: Medler, S. and Ansari, A. Gene looping facilitates TFIIH kinase-mediated termination of transcription. Sci. Rep.5, 12586; doi: 10.1038/srep12586 (2015).


The Na,K-ATPase classically serves as an ion pump creating an electrochemical gradient across the plasma membrane that is essential for transepithelial transport, nutrient uptake and membrane potential. In addition, Na,K-ATPase also functions as a receptor, a signal transducer and a cell adhesion molecule. With such diverse roles, it is understandable that the Na,K-ATPase subunits, the catalytic α-subunit, the β-subunit and the FXYD proteins, are controlled extensively during development and to accommodate physiological needs. The spatial and temporal expression of Na,K-ATPase is partially regulated at the transcriptional level. Numerous transcription factors, hormones, growth factors, lipids, and extracellular stimuli modulate the transcription of the Na,K-ATPase subunits. Moreover, epigenetic mechanisms also contribute to the regulation of Na,K-ATPase expression. With the ever growing knowledge about diseases associated with the malfunction of Na,K-ATPase, this review aims at summarizing the best-characterized transcription regulators that modulate Na,K-ATPase subunit levels. As abnormal expression of Na,K-ATPase subunits has been observed in many carcinoma, we will also discuss transcription factors that are associated with epithelial-mesenchymal transition, a crucial step in the progression of many tumors to malignant disease. This function is crucial to maintaining the ion gradient across the membrane and is critical for the resting membrane potential, electrical activity of muscle and nerve, Na+-coupled transport, and osmotic balance and cell volume regulation  and three isoforms of β-subunit (β1–β3) have been identified so far, which are encoded by different genes , which show 43 ~ 51% identity in their amino acid sequence.The Na,K-ATPase subunits are highly conserved across species and among isoforms. Four isoforms of α-subunit  and Zinc finger E-box-binding homeobox 1 (ZEB1). Recently, it has been shown that hypermethylation in promoter regions of human ATP1B1 gene is associated with reduced expression of β1-subunit  and a Sp1 binding site , a Spl binding consensus sequence (−123 to −118 bp) and a Sp1 binding site , two CAAT boxes, four GC boxes, five half-site TREs, several GRE and calcium and serum response elements , a putative CAAT box (−137 to −142 bp), and a GC box  and two Sp1-like elements (−323 to −315 bp and −312 to −304 bp). Moreover, the human ATP1B3 also has four identical 32-nucleotide-long repeats from position −760 to −633 bp. The sequence from −1 to −328 bp on the 5′ flanking region of human and mouse Atp1b3 shows high homology. The mouse Atp1b3 promoter has one Sp1-like sequence (−319 to −310 bp), but does not have any TATA-like sequence. No CAAT or GC boxes are present at the promoter region of human or mouse Atp1b3 ; a DNA-binding domain (DBD) and a ligand-binding domain  also belongs to the nuclear receptor family. PR's sequence and architecture is very similar to MR and GR  is a member of the nuclear receptor superfamily and is activated by thyroid hormone, triiodothyronine (T3) and its prohormone, thyroxine (T4). Although TR alone can bind as monomer or homodimer to specific DNA sequences in the gene promoter regions known as thyroid hormone response elements (TREs), it preferentially forms heterodimers with retinoid X receptor (RXR) and then binds to TREs, thus enhancing or inhibiting the expression of genes that are involved in development, homeostasis, cellular proliferation and differentiation  and 3T3-L1 cells  significantly reduced the promoter activity  is a small family of zinc-finger transcription factors with four members: Sp1, Sp2, Sp3, and Sp4. Sp1, Sp2, and Sp3 are ubiquitously expressed, while Sp4 is expressed mainly in neurons and testes. The Sp family members contain three zinc fingers close to the C-terminus, which is DBD, and preferentially bind to the GC box [(G/T)(G/A)GGC(G/T)(G/A)(G/A)(G/T)] on the promoter regions of target genes to enhance gene expression. Sp1, Sp3, and Sp4 have a highly conserved DBDs and they recognize the same consensus GC rich element with identical affinities. However, Sp2, in which the important histidine residue within the first zinc finger is replaced by a leucine residue, does not bind to the GC box but instead binds to a GT rich element Suske, . PotentiATP1B1 gene. Snail1 binds to the non-canonical E-box located at position −71 to −66 bp and selectively repressed the expression of β1-subunit without affecting the α1-subunit level  domain and recruits the transcription repressor complex (such as sin3A/HDAC). The Snail family functions as a transcriptional repressor by binding to the E-box (CAGGTG) on the promoter regions of target genes which are important for embryonic development as well as tumor progression Nieto, . Four E-1-subunit but not β1-subunit  family, which bind to DNA as dimers and function as activators. The ATF/CREB family is composed of different ATFs, CREB, CREM (cAMP response element modulator) and related proteins. This family contains an N-terminal DBD and a C-terminal bZIP domain that binds other bZIP transcription factors to form homo- and heterodimers. Both ATF and CREB bind to the cAMP response element (CRE: GTGACGT A/C A/G). Phosphorylation of ATF-1 and CREB by multiple kinases enhances the binding to CRE and activates the transcription of target genes which play an important role in cell survival and cell growth  binding sequence and Areb6 (ZEB1) had been shown to activate gene transcription in a cell-specific manner  and ZEB2. They contain five major domains: two zinc-finger clusters separated by a central homeodomain, a Smad interacting domain (SID) and a CtBP interacting domain (CID). Each zinc finger cluster contains 3 or 4 zinc fingers and is responsible for DNA binding. ZEB binds to E-boxes (CACCT and CACCTG) on the promoter of target genes and recruits co-activators (PCAF or p300), thus activating gene transcription  isoforms , which are essential for optimal transcriptional activation of target genes  and Smad3  is a secreted protein that regulates numerous physiological processes, including cell differentiation, proliferation, development and survival. TGF-β has three isoforms in mammals: TGF-β1- and β1-subunit mRNA levels in VSMC and rat liver cells and induced Atp1a1 and Atp1b1 promoter activity, suggesting that serum regulates Na,K-ATPase gene expression at the transcriptional level  control a wide range of biological functions and regulate cellular proliferation, survival, migration, and differentiation. FGFs are a family of glyocoproteins that are generally sequestered by the extracellular matrix and cell surface heparan sulfate proteoglycans. Activation of FGF signaling occurs upon release of the factor and binding to one of the four highly conserved FGF receptors, FGR1, FGR2, FGR3, and FGR4 ; −226 to −216 bp (PGRE2: GTCCCTCA); and −92 to −100 bp  cells  is a member of the prostaglandin family, which are lipid mediators produced from arachidonic acid by cyclooxygenase and prostaglandin synthases. Prostaglandins exert their effects by activating G protein-coupled receptors and four PGE receptors have been reported: EP1 (E prostanoid receptor 1), EP2, EP3, and EP4  and a GC box motif (at −57 to −48 bp). Mutations in the CRE/ATF site or GC box substantially reduced low K+-mediated Atp1a1 promoter activity. Low K+ increased the expression of Sp1, Sp3 and CREB-1 and enhanced the binding of these transcription factors to the GC box and, to a lesser extent, to the CRE/ATF site on rat Atp1a1 promoter , ERK1/2, and histone deacetylase (HDAC) were required for up-regulation of α1-subunit transcription, whereas the transcription of β1-subunit was dependent on protein kinase C (PKC), c-Jun-N-terminal kinase (JNK) and p38 mitogen-activated protein kinase , a voltage-dependent Na+ channel blocker, significantly reduced α1-, α3-, and β1-subunit transcripts in murine neurons , a common transcriptional modulator. MeCP2 directly bound to methylated CpG in the fxyd1 promoter and repressed fxyd1 transcription  signaling pathway . EMT is a switch in which epithelial cells undergo a shift from a well-differentiated polarized epithelial phenotype to a fibroblastic, mesenchymal phenotype and is a natural developmental process in tissue and organ formation. EMT can also be activated during wound healing, in tissue fibrosis, and is a major factor in cancer developing into a malignant disease (Kalluri and Weinberg, The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.