The H3K9me3 histone modification is often found at promoter regions, where it functions to repress transcription. However, we have previously shown that 3′ exons of zinc finger genes (ZNFs) are marked by high levels of H3K9me3. We have now further investigated this unusual location for H3K9me3 in ZNF genes. Neither bioinformatic nor experimental approaches support the hypothesis that the 3′ exons of ZNFs are promoters. We further characterized the histone modifications at the 3′ ZNF exons and found that these regions also contain H3K36me3, a mark of transcriptional elongation. A genome-wide analysis of ChIP-seq data revealed that ZNFs constitute the majority of genes that have high levels of both H3K9me3 and H3K36me3. These results suggested the possibility that the ZNF genes may be imprinted, with one allele transcribed and one allele repressed. To test the hypothesis that the contradictory modifications are due to imprinting, we used a SNP analysis of RNA-seq data to demonstrate that both alleles of certain ZNF genes having H3K9me3 and H3K36me3 are transcribed. We next analyzed isolated ZNF 3′ exons using stably integrated episomes. We found that although the H3K36me3 mark was lost when the 3′ ZNF exon was removed from its natural genomic location, the isolated ZNF 3′ exons retained the H3K9me3 mark. Thus, the H3K9me3 mark at ZNF 3′ exons does not impede transcription and it is regulated independently of the H3K36me3 mark. Finally, we demonstrate a strong relationship between the number of tandemly repeated domains in the 3′ exons and the H3K9me3 mark. We suggest that the H3K9me3 at ZNF 3′ exons may function to protect the genome from inappropriate recombination rather than to regulate transcription. Six different modified histones have been chosen as the focus of a large amount of research due to their association with specific gene structures. For example, H3K9Ac, H3K4me3, H3K27me3, and H3K9me3 are histone modifications that can be found at promoter regions, whereas other modified histones are associated with enhancer regions (e.g H3K4me1) or with transcribed units (e.g. H3K36me3).It is becoming increasingly clear that understanding human health and disease requires a detailed knowledge of both the genetic and epigenetic variations in the human population. Epigenomes are characterized by methylated DNA and certain modified histones. The patterns of methylated DNA and modified histones can vary greatly from cell type to cell type and large changes within a cell type have also been observed when comparing normal to diseased tissue As indicated above, H3K27me3 and H3K9me3 are often found at promoter regions. In fact, initial studies of H3K27me3 and H3K9me3 using ChIP-chip and promoter arrays identified large sets of promoters that were distinguished by these two marks, often in a cell type-specific pattern The 3′ ends of ZNF genes do not have characteristics of promoter regions. As indicated above, H3K9me3 is often associated with promoter regions but our previous studies showed that it specifically covers the 3′ exons of many ZNF genes. Because H3K9me3 is not generally associated with the 3′ ends of genes, we reasoned that it was possible that the 3′ ends of the ZNF genes may harbor previously unidentified promoters. To investigate this hypothesis, we first performed a bioinformatic analysis. Others http://gerg01.gsc.riken.jp/cage/download/hg17prmtr/chromosomes/). The number of CAGE tags were counted for each region  and normalized for region size. As shown in Of course, it is possible that promoters embedded in ZNF 3′ exons might be regulated by a different set of transcription factors and thus would not show enrichment of the common promoter motifs. Therefore, we next investigated whether we could associate the H3K9me3 binding sites with 5′ ends of transcripts. For these analyses, we obtained all CAGE tags available from the Riken CAGE database  and the promoter regions of the ZNF554 and ZNF440 genes. Eight ZNF 3′ ends were analyzed for promoter activity. It was possible that the ZNF 3′ ends could be promoters oriented such that they produced antisense transcripts relative to that same ZNF gene or they could be alternative upstream promoters of a downstream gene. Therefore, we cloned 5 of the ZNF 3′ ends in both orientations upstream of the luciferase cDNA so that we could analyze transcriptional activity in both directions. To begin, HepG2 and DAOY cells were transfected with the various reporter constructs and luciferase activity was determined. As expected, the DHFR, ZNF554, and ZNF440 promoters were active in both cell types. However, none of the ZNF 3′ ends showed any promoter activity in either orientation  and one allele repressed (and covered by H3K9me3). To test the hypothesis that the contradictory modifications are due to imprinting, we examined levels of RNA corresponding to the ZNF genes in hES cells, using RNA-seq data If only one allele of the ZNF genes is transcribed, one might expect that the H3K9me3 and H3K36me3 peak heights at the dually covered regions would be approximately half the peak heights of the marks at the singly covered sites . However, the average height of the H3K9me3 peaks at 3′ ZNF exons that had both marks was higher than the average height of the H3K9me3 peaks at 3′ ZNF exons that had only H3K9me3. Similarly, the average height of the H3K36me3 peaks at 3′ ZNF exons that had both marks was higher than the average height of the H3K36me3 peaks at 3′ ZNF exons that had only H3K36me3 (data not shown). Although not conclusive, these results suggested that the H3K9me3 and H3K36me3 marks on the dually bound ZNF 3′ exons may not be limited to only one allele. We next used the program ssahaSNP to identify single nucleotide polymorphisms (SNPs), as compared to the reference genome sequence, that were located within genes targeted by both H3K9me3 and H3K36me3 on chromosome 19. We then searched for these SNPs within the RNA-seq dataset. Out of 72 SNPs that were identified, 40 SNPs and 3 wt nts were fixed in the RNA sequence data . This could suggest either that the ZNFs containing these SNPs were expressed in an allele-specific manner or that the SNP was fixed in the ES cell genome. However, we did identify 29 cases where both wt and SNP alleles are present in the RNA-seq tags approximately equally . Since bZNF555, ZNF77, ZNF333, and ZNF426 genes are bound by both H3K9me3 and H3K36me3 and that, as predicted from the hES ChIP-seq results, the 3′end of ZNF556 is bound only by H3K9me3 and not by H3K36me3 . Also, we have found that when ZNF genes are introduced into cultured human cells, they very often become truncated at their 3′ ends . It is possible that the “unprotected” plasmid DNA corresponding to the ZNF 3′ exons serves as a recombination template, resulting in recombination and mutation of the introduced gene. Future studies are required to address the possible role of H3K9me3 (and the enzyme complexes that mediate this histone modification) in suppression of homologous recombination amongst the hundreds of ZNF genes.http://chipseq.genomecenter.ucdavis.edu/cgi-bin/chipseq.cgi.Finally, we note that to accurately compare genomic regions enriched for spreading histone modifications such as H3K9me3 and H3K36me3, we developed a modified peak calling program that takes into account duplicated regions of the genome, biases in sequencing of certain regions, and the jagged profile of regions enriched for histone modifications. This program, Sole-searchv2, is publicly available at ZNF440 or ZNF544 gene promoters and 3′ regions of ZNF genes were cloned using XhoI/HindIII restriction sites. All other ZNF constructs were created by blunt cloning into SmaI restriction site. For luciferase assays, HepG2 (ATCC #HB-8065), DAOY (ATCC #HTB-186), U2OS(ATCC #HTB-96), and HEK293 (ATCC #CRL-1573) cells were grown in DMEM supplemented with 10%FBS, 2 mM L-Glutamine, 100 U/mL Penicillin/Streptomycin. Stable KAP1 knockdown cell lines K928-cI10 (HEK293) and U2OS-K4 (U2OS) were kindly provided by David Schultz Luciferase assays: Luciferase assay constructs were created by ligating PCR products into pGL4-11 (Promega) upstream of the luciferase gene. The pGL4-11b-DHFR control plasmid was provided by SwitchGear Genomics. Plasmids containing http://genomecenter.ucdavis.edu/dna_technologies/) or at the University of British Columbia Genome Science Center. Lists of all the ChIP-seq experiments and information concerning the antibodies used for each histone mark are provided in http://www.roadmapepigenomics.org/) and a small set of loci have been previously analyzed using these datasets ChIP-seq. ChIP-seq was performed as described in ChIP-seq peakcalling. Peaks were identified using version 2 of the Sole-search software. Version 2 of this program has been designed to run more quickly and efficiently on the increasingly large ChIP-seq data sets, to more accurately analyze ChIP-seq data from any species, and to improve peak-calling for proteins and histone modifications which are characterized by spreading. Several changes, as compared to the original Sole-search program GMNN which has been described previously in Pfx Polymerase (Invitrogen) using primers that introduced unique XhoI and HindIII sites immediately 5′ and 3′ to the region of interest, respectively. The target sequences were then cloned into the pFC62 episome using the XhoI and HindIII sites; successful cloning of the various inserts was confirmed by sequencing at the UC Davis Sequencing Facility. The episomal constructs (1 µg) were transfected into HEK293c18 cells stably expressing EBNA-1 in 6-well dishes using FuGene 6 Transfection Reagent (Roche) according to manufacturer's recommendations. Forty-eight hours after transfection, cells were selected in medium containing 500 µg/ml of G418 (Sigma) and 200 ug/ml Hygromycin B (CellGro), and drug resistant colonies were pooled and expanded. Pooled colonies were assayed for the presence of the correctly sized episomal insert using primers that flanked the cloning site and genomic DNA prepared from the stably transfected cells. To perform the eChIP assays, HEK293cl18 cells stably transfected with a particular episome were harvested when the cells were ∼75% confluent. Cells were cross-linked and chromatin was prepared and analyzed using our standard ChIP protocol (http://www.genomecenter.ucdavis.edu/farnham) with a few modifications. Namely, ∼20 ug of chromatin was used for each IP and the amount of antibody was greatly reduced , Anti-H3me3K36 , or non-specific rabbit IgG antibody  was used). Complexes were recovered with 10 uls of StaphA cells for 15 minutes at room temperature. Washes, elution and purification of the ChIP samples were performed according to the standard protocol. PCR reactions were performed using only 1 ul of the immunoprecipitated sample and amplified for only 28 cycles.Episomal ChIPs (eChIPs): All regions to be cloned into the episomal vector Click here for additional data file.Figure S1Major steps implemented in Sole-searchv2. The original Sole-search program has been modified to improve the ability to determine statistically and biologically significant peaks in both transcription factor and histone modification datasets. (A) Input data is smoothed and duplication and deletion events are determined based on fold coverage of these regions compared to average coverage. Next, specific regions of the genome that have a higher sequence coverage than expected by chance, due to experimental method, are determined using a t-statistic. Raw ChIP-seq data is normalized based on duplication event copy number and enrichment of input reads, so that the data to be analyzed will reflect a single copy genome without sequencing bias . (B) The second, optional step smoothes data that spreads over large regions (e.g. data from histone modification ChIP-seq experiments), using a sliding average, so that non-uniform “mountain range” peaks are more easily detectable as broad regions. Previously, these regions would be identified as having many, distinct peaks. Also certain smaller peaks would fail to be detected. This smoothing step allows detection of both broad regions and smaller. (C) The third major step determines a statistically significant peak height cutoff. Tags are randomly sampled from the input dataset to create bins. Tags are then shuffled within the bins. Height cutoff is determined based on a user-defined FDR. The cutoff value increases until the number of peaks found within the randomly generated background is sufficiently low, compared to the number of peaks found in the ChIP-seq dataset at the same height.(PDF)Click here for additional data file.Figure S220744 H3K9me3 peaks and 20744 H3K36me3 peaks were searched for tandem repeats using the program XSTREAM (http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2233649/), identifying sequences that are 50bp or larger, repeated at least 5 times in the human genome, with at least 60% conservation between repeat elements. The percent of each peak that was a repetitive element was then calculated. A greater number of the H3K9me3 peaks had high percentages of repetitive regions. For example, there are ∼17 times more H3K9me3 peaks that consist of 91-100% repetitive elements.(PDF)Click here for additional data file.Table S1Summary table of ChIP-seq data sets.(PDF)Click here for additional data file.Table S2List of primers used in this study.(PDF)Click here for additional data file.Table S3RNA-seq SNP frequencies.(XLSX)Click here for additional data file.


Because few studies exist to describe the unique molecular network regulation behind pig pre-implantation embryonic development (PED), genetic engineering in the pig embryo is limited. Also, this lack of research has hindered derivation and application of porcine embryonic stem cells and porcine induced pluripotent stem cells (iPSCs).in vivo-derived and somatic cell nuclear transferred (SCNT) as well as mouse in vivo-derived pre-implantation embryos at different stages using mRNA deep sequencing. Comparison of the pig embryonic transcriptomes with those of mouse and human pre-implantation embryos revealed unique gene expression patterns during pig PED. Pig zygotic genome activation was confirmed to occur at the 4-cell stage via genome-wide gene expression analysis. This activation was delayed to the 8-cell stage in SCNT embryos. Specific gene expression analysis of the putative inner cell mass (ICM) and the trophectoderm (TE) revealed that pig and mouse pre-implantation embryos share regulatory networks during the first lineage segregation and primitive endoderm differentiation, but not during ectoderm commitment. Also, fatty acid metabolism appears to be a unique characteristic of pig pre-implantation embryonic development. In addition, the global gene expression patterns in the pig SCNT embryos were different from those in in vivo-derived pig embryos.We identified and analyzed the genome wide transcriptomes of pig Our results provide a resource for pluripotent stem cell engineering and for understanding pig development.The online version of this article (doi:10.1186/1471-2164-15-4) contains supplementary material, which is available to authorized users. Pigs are an ideal model for preclinical development and design of therapeutic approaches because their organs are morphologically and functionally similar to humans [in vivo developmental potential as mouse ES cells. Since the 1990s, attempts have been made to derive pig ESCs and the generation of porcine iPSCs has been recently reported. However, during this time, no stable porcine cell lines have been capable of germ-line transmission; thus, they are not authentically pluripotent [in vitro culture conditions that cannot support pluripotency maintenance. This is partly a result of a lack of information regarding unique molecular mechanisms of early pig embryonic development [Embryonic stem cells (ESCs) can differentiate into any cell type of the three germ layers as well as into male and female germ cells , 2. As so humans , 8. For ripotent . These pelopment .The progression from fertilization to implantation among mammals is highly conserved, and the morphologic stages are similar . Few intin vitro culture conditions for the generation of true pig ESCs. Therefore, we compared early pig in vivo fertilized-derived and mouse in vivo fertilized-derived embryo transcriptomes and mapped a putative gene regulation network during pig PED. This work represents a significant step towards characterizing normal and cloned pig early embryos using genome-wide gene expression patterns.Genome-wide transcriptome analysis may reveal unique gene regulation networks during PED that would be useful in the biological studies of undifferentiated ESCs and pre-implantation embryos , 19. Train vivo pig PED, we isolated mRNA for deep sequencing from in vivo porcine (Pnm) and mouse embryos (Ms) and porcine SCNT embryos (Pnt) harvested at different stages during PED  . We firss Figure A left. Is Figure A right. in vivo normal embryos  were used to construct heat-maps of hierarchical clustering . The second event is regulated by Nanog and Gata6, resulting in the segregation of the primitive endoderm and epiblast lineages from the ICM at the blastocyst stage . To evalin vivo-derived embryos and 581 genes in SCNT embryos from the first sample set . We then identified specific genes expressed in porcine ICM cells by comparing transcriptomes of pure TE and ICMTE in pig normal embryos. To validate this method, we identified 1,531 ICM-specific genes through differential expression analysis between ICMTE and TE mRNA sequencing data in mouse embryos  were shared between Pnm and Ms, including Oct4, Tbx3, Gata6 and Smad4. These genes may be involved in regulating second lineage segregation   for the three layers were selected based on previous findings [From our data, we assumed that most of the two lineage segregations during PED were conserved across the three-layer networks of mouse and pig embryos collected findings , 37. Co-Network evaluation revealed that pig and mouse pre-implantation embryos share some hub genes (about 50%) with regulatory networks for lineage segregations. In sub-network analysis, ICM-lineage segregation and primitive endoderm differentiation markers such as Esrrb, Gata6, Pou5f1, Tcl1, Sall4 and Tbx3, and TE commitment makers such as Hand1, Ccdc3, Ets2, Wnt7a and Eomes, were found to be common to pigs and mice  and young adult female Nong Da Xiang pigs  were kept in a 12 h light/dark cycle and given water and food Oocytes were collected from the oviduct of C57 mice 14 h after injection with human chorionic gonadotropin (hCG). Then pre-implantation embryos were collected at various points after hCG injection and mating as depicted in Table Normal pig embryos were washed from the oviduct or uterus using PBS with 5% FBS at the indicated time points Table  after esNext, 4–10 embryos at the same stage were pooled together for each sample and transferred into extraction buffer from the PicoPure RNA Isolation Kit  at 42°C for 30 min. Samples were either stored at −80°C for up to one month or used immediately for analysis. RNA from each sample was extracted according to the manufacturer’s instructions and eluted into 10-μL elution buffer.http://www.appliedbiosystems.com) and the Applied Biosystems SOLiD 4 System Library Preparation Guide (http://www.appliedbiosystems.com). mRNA sequencing data are available from the Lab Archive (http://www.ncbi.nlm.nih.gov/sra) under accession number SRA076823.We performed mRNA sequencing using the Applied Biosystems SOLiD 4 System as follows: RNAs isolated from pre-implantation embryos were used for double-stranded cDNA synthesis and PCR amplification. The procedure for fragment library preparations was performed according to the Library Preparation Protocol for whole transcriptome analysis of a single cell (http://asia.ensembl.org/info/data/ftp/index.html).Transcriptome analysis tools included in the BioScope software package (Applied Biosystems) were used to map the corresponding sequenced reads against the mouse mm9 genome and the pig susScr2 genome. Mouse (NCBIM37.65) and pig (Sscrofa9.65) gene and transcript annotation files were downloaded from the Ensembl database (http://pypi.python.org/pypi/HTSeq). The value for reads per kilobase of coding sequence per million mapped reads (RPKM) was calculated to estimate gene expression under each set of conditions [P-value was less than 0.05 and the fold-change was greater than 2 [Prior to differential gene expression analysis, read count tables were generated from binary sequence alignment/map (BAM) files using HTseq software  [in vivo datasets with human PED datasets.We also downloaded human pre-implantation embryonic datasets from GSE36552, deposited in GEO databases (ov/geo/) . For eacGene ontology (GO) was implemented using the GOseq software package, in which gene length bias was adjusted . The BioICM segregation markers included Pou5f1, Sox2, Klf2, Nanog, Rex1, Utf1, Zfx, Esrrb, Tbx3, Tcl1 and Klf4. Primitive endoderm differentiation markers included Gata6, Gata4, Gdf1, Gdf3, Hnf4a (Nr2a1), Mixl1, Sall4, Sox7 and Sox17. TE commitment markers included Cdx2, Eomes, Hand1, Fgfr2, Ets2, Tcfap2, Elf5, Etv4, Furin, Ccdc3, Pace4, Casq1, Wnt7a, Fgf5, Pax6, and Tead4 , 37. OnlEmbryos were fixed in 4% paraformaldehyde in washing solution (PBS with 0.1% Tween 20 and 0.01% Triton X-100) for 30 min at room temperature. Then embryos were permeabilized in 1% Triton X-100 in PBS for 4 h at 4°C, blocked with 1% BSA in washing solution (blocking solution), and incubated with the following antibodies: Oct4 (Santacruz Sc-8628), Cdx2 (Biogenex MU392A-UC), Nanog (Abnova PAB6837), Sox2 (Santacruz Sc-17320) and Gata6 (Abcam Ab22600) in blocking solution for 1 h at room temperature. After incubation with secondary antibody for 1 h at room temperature, embryos were counterstained with 10 μg/mL Hoechst 3342 (Sigma B2261) in washing solution for 10 min at room temperature.Additional file 1: Table S1: Primary records of samples. (DOC 60 KB)Supplement figures.Figure S1. Splitting of pig blastocyst with an ultra-sharp splitting blade. Figure S2. (A) Unsupervised hierarchical clustering of the expression profiles [Figure S3. Dnmt3b and Dnmt1 expression. Figure S4. Heat map of protein binding-associated transcripts under different conditions. Figure S5. Analysis of the ICM-specific genes in mouse and pig. Figure S6. Heat map of ICM and TE marker gene clustering in human pre-implantation embryos. Figure S7. Heat map clusters of ICM-specific genes in TGF-beta, MAPK, Jak-Stat and Wnt signaling pathways expressed during normal mouse PED and normal pig PED. Figure S8. Immunostaining of the pluripotent markers in pig and mouse pre-implantation embryos. Figure S9. Heat map clusters of ICM-specific genes in TGF-beta, MAPK, Jak-Stat and Wnt signaling pathways expressed during normal pig in vivo and SCNT pig PED. Figure S10. Putative regulatory networks for lineage segregation during PED. (PDF 1 MB)Additional file 2: profiles . Figure Additional file 3: Table S2: Sample correlation. (XLSX 14 KB)Additional file 4: Table S3: Identification of maternal deposition and zygotic gene activation. (XLS 836 KB)Additional file 5: Table S4: Gene expression profiling in putative ICM. (XLS 58 KB)Additional file 6: Table S5: Gene profiling of regulation of lineage commitment during PED. (XLS 836 KB)Additional file 7: Table S6: Unique fatty acid metabolism during pig PED. (XLS 582 KB)Additional file 8: Table S7: TE expression and GO analysis. (XLSX 114 KB)Additional file 9: Table S8: Putative regulation networks during PED. (XLS 582 KB)


FRIGIDA and vernalization. Although each separate pathway has been extensively studied, much less is known about the interactions between them. In this study we have investigated the relationship between the photoperiod/circadian clock gene and FRIGIDA/FLC by characterizing the function of the B-box STO gene family. STO has two B-box Zn-finger domains but lacks the CCT domain. Its expression is controlled by circadian rhythm and is affected by environmental factors and phytohormones. Loss and gain of function mutants show diversiform phenotypes from seed germination to flowering. The sto-1 mutant flowers later than the wild type (WT) under short day growth conditions, while over-expression of STO causes early flowering both in long and short days. STO over-expression not only reduces FLC expression level but it also activates FT and SOC1 expression. It also does not rely on the other B-box gene CO or change the circadian clock system to activate FT and SOC1. Furthermore, the STO activation of FT and SOC1 expression is independent of the repression of FLC; rather STO and FLC compete with each other to regulate downstream genes. Our results indicate that photoperiod and the circadian clock pathway gene STO can affect the key flowering time genes FLC and FT/SOC1 separately, and reveals a novel perspective to the mechanism of flowering regulation.Flowering at the appropriate time is crucial for reproductive success and is strongly influenced by various pathways such as photoperiod, circadian clock,  FLOWERING LOCUS C (FLC), FLOWERING LOCUS T (FT), SUPPRESSOR OF CONSTANS1 (SOC1) and LEAFY (LFY) are all involved in this transition. The MADS box gene FLC is a central repressor of flowering in ArabidopsisVRN1, VRN2, VIN3, VIL1/VRN5, atPRMT5) repress FLC expression by histone modification of FLC during and after the cold treatment There are several key developmental changes during the plant lifecycle. One of these is flowering, the correct timing of which is critical for reproductive success Arabidopsis has two antagonistic pathways that regulate FLC expression. The FRIGIDA (FRI) pathway is a positive regulator, while a group of genes that belong to the autonomous floral-promotion pathway are negative regulators . FRI is a unique plant gene that encodes a nuclear-localized protein with a coiled-coil domain FRI is only found in the winter-annual Arabidopsis, which requires vernalization to flower rapidly in the spring through repression of FLC. In rapid-cycling Arabidopsis there is no functional FRI; FLC expression is kept at low levels and the photoperiod pathway accelerates flowering. FLC directly binds and represses the two important flowering genes FT and SOC1FT is the “florigen” acting as a long distance signal that is transported from leaves to the shoot meristem SOC1 was initially cloned as a suppressor of the CONSTANS1 (CO) overexpressor SOC1 regulation integrates inputs from multiple flowering pathways including photoperiod, vernalization, aging and GA SOC1 can also be considered as a meristem-identity gene because it maintains the meristem in a floral state FT and SOC1 are regulated by different flowering pathways Arabidopsis senses changes in day length by the circadian clock, which in turn regulates the transcription factor CO. CO belongs to a subfamily of the zinc finger protein family that is now known as the B-Box Zinc Finger Family (BBX) CO/COL/TOC1) domain. Group II members are similar to group I, and contain both B1, B2 and CCT domains, but have minor differences in their B2 domains. Proteins from group III only contain a B1 and CCT domain, while group IV members have B-box B1 and B2 domains but lack the CCT domain . Proteins in group V have only a single B1 domain CO on a transcriptional and protein stability level is the most important element of the photoperiod pathway. Under long day conditions (LD), CO transcription is regulated by the circadian clock and accumulates late in the day. FLAVIN-BINDING, KELCH REPEAT, F-BOX 1 (FKF1) combined with GIGANTEA (GI) and CYCLING DOF FACTOR1 (CDF1) not only promote CO expression but also stabilize the CO protein in the afternoon in LD CO, CO protein accumulates at the end of the day and activates the downstream genes such as FT and SOC1 to promote flowering in LD. In contrast, the CO protein is not stably produced under short day (SD) conditions FT and SOC1 are common targets of distinct pathways, but the relationship between the different pathways is still unclear. There is little evidence for a direct connection between FRI/FLC and the photoperiod/circadian clock pathway in flowering time except that they antagonistically regulate common gene targets (FT and SOC1). In this article, we have studied the function of another B-box family gene Salt Tolerance (STO or BBX24), and show that STO (BBX24) links the FRI/FLC and photoperiod/circadian clock pathways.The flowering-time integrators −2 s−1 under both LD and SD conditions. Plants were grown on MS plates, 4 d in dark at 4°C before moved to LD or SD at 22°C. The Columbia (Col-0) ecotype was used. In the STO over-expression studies, phenotypic analysis of all transgenic lines and controls were conducted on plates with MS with addition of 50 µg ml−1 kanamycin for transgenic plant selection. The mutant seeds are listed in Plants were grown in soil under controlled conditions of LD (16 h light/8 h dark) or SD (8 h light/16 h dark) at 22°C. The level of photosynthetic active radiation was 60 µmol photons mSeeds were sterilized in 70% ethanol and 0.01% Triton X-100 for 15 min, followed by 95% ethanol for 10 min. After sterilization, seeds were suspended in 0.1% low-melting-point agarose and spotted on plates containing MS medium (Gibco/BRL) and 0.8% phytagar (Gibco/BRL). Seeds on plates were then stratified in the dark at 4°C for 4 d. Plants were transferred to 22°C. Hypocotyl lengths from 20 seedlings were measured on day 4 with NIH Image 1.62.Flowering time was determined by counting the number of rosette leaves after bolting. Data were reported as mean leaf number (± S.D.) and were measured from homozygous lines.Plants were grown on the 1/2 MS plates for 5 d with 0–1000 nM brassinolide (BL), which is the most biologically active BR before measuring hypocotyl length. For the RNA extractions, all seedlings were grown on 1/2 MS plates and treated with 100 nM BL for 3 h or 4 d prior to harvest, with all samples harvested at the same time.sto-1 seedlings were treated with 0–10 µM ACC (the precursor of ethylene) for 3.5 d. SE values were determined from 20 to 30 seedlings.Dark-grown WT and Seeds were germinated on agar plates for 4 d at 22°C and vernalized for 4 weeks at 4°C under SD (8 h light/16 h dark). Post vernalization samples continued to grow on agar plates at 22°C under SD.STO expression analysis by real-time PCR, all the samples were harvested 6 h into the photoperiod when the peak level of STO expression is reached.Total RNA was isolated from seedlings  or mature rosette leaves using the RNAeasy extraction kit (Qiagen). First-strand cDNA synthesis was performed on 1–2 µg of RNA using the M-MLV System for RT-PCR (Fermentas) followed by PCR amplification with dream Taq DNA Polymerase (Fermentas) or by quantitative PCR (Bio-Rad). Reactions were performed using the primers described in g for 5 min to pellet cell debris. The supernatant was transferred to clean microfuge tubes and 250 µl of chloroform/isoamylalcohol (24∶1) was added to each, and then mixed by inversion. After mixing, tubes were centrifuged at 13000 g for 10 min. The upper aqueous phase containing the DNA was transferred to a clean microfuge tube and placed at −20°C for 1 h after the addition of ethanol to precipitate DNA. The precipitated DNA was then pelleted, washed twice in 70% ethanol and then resuspended in sterile DNase-free water.Plant tissue (200 mg) was ground to a fine paste in 500 µl of CTAB buffer. The CTAB/plant extract mixture was transferred to a microfuge tube and incubated for 15 min at 55°C in a recirculating water bath. After incubation, the CTAB/plant extract mixture was centrifuged at 12000 STO was originally found to increase the tolerance of yeast to both Li and Na ions BBX25) interacts with the WD40 domain of COP1 COP1 repressing the transcription of STO and contributing to STO protein destabilization in etiolated seedlings STO acts as a negative regulator in the early photomorphogenesis response to red, far-red, blue and UV-B light signaling STO, we examined the phenotypes of the sto-1 T-DNA insertion knockout mutant (SALK_067473) and STO over-expression line STO-OE (35s:: STO) at different developmental stages. Early in development, STO appears to repress the rate of seed germination (24 h after transfer to light), with the sto-1 mutant exhibiting faster and the STO-OE line slower germination rates than the WT , but all responded to a higher concentration (1000 nM) (sto-1 had slightly longer hypocotyls than the WT and was less sensitive to low concentrations of ACC (0–0.1 µM) (the precursor of ethylene). However, sto-1 still showed a triple-response phenotype at higher concentrations (STO-OE germination rate was very poor in darkness and ACC treatments). In the adult rosette leaves, sto-1 accumulated much more anthocyanin on the abaxial surface than WT, while STO-OE had much less anthocyanin than WT. In STO-OE, the purple color concentrated in the main vascular tissue, while the remaining leaf blade was much greener than WT. STO affects not only mature rosette leaf pigmentation but also leaf morphology. In sto-1, adult leaves were narrower and more curled than WT, but less serrated, whereas the STO-OE leaves had much deeper serrations than WT (n the WT . Despiteectively . The dif1000 nM) . Under d than WT .STO in both LD and SD conditions , results which were confirmed in this study by qPCR at different time points in etiolated seedlings   . We founpression . At same with WT . These rFRI and FLC, the circadian clock system, photoperiod pathway (CO), and flowering integrators  also play important roles in the transition from vegetative to reproductive phases. The circadian clock consists of at least three interlocked transcriptional feedback loops. The LATE ELONGATED HYPOCOTYL (LHY) and CIRCADIAN CLOCK–ASSOCIATED1 (CCA1) proteins are important components of the circadian clock system and have partially redundant functions LHY and CCA1 not only repress the floral transition under LD and SD conditions, they also accelerate flowering in continuous light by promoting FT expression FT and SOC1 are both CO target genes. CO, FT and SOC1 are deeply affected by the circadian clock system at different levels In addition to STO regulates flowering time, we next examined how it affects expression of the aforementioned genes. Several different time points under the SD condition, where the sto-1 knock-out mutant and STO over-expressor showed significant differences in flowering time were investigated . In the STO-OE line, CO expression level was higher compared to that in Col and sto-1 at the end of the day period (after 6–8 h) but lower during the dark (FT and SOC1 first peaked in the middle of the photoperiod (6 h) and then again in the dark (15 h)  . Since band root .FLC is known to block FT by directly binding to its chromatin STO can promote flowering: that either STO directly represses FLC and activates FT and SOC1 expression simultaneously, or that STO only represses FLC that then leads to the activation of both FT and SOC1. To investigate these two scenarios, we overexpressed STO in different genetic backgrounds , FT and SOC1 expression significantly increased  also flowered earlier under SD conditions (STO-OE/FLC/FRI line (line11) that has high levels of FLC and over-expresses STO, the level of FT expression was only slightly higher than in the FRI line, and SOC1 transcripts were no longer up-regulated . BR signaling could through repressing STO achieve the appropriate expression level of downstream genes. In addition, STO expression was also regulated by environmental factors, such as the photoperiod and cold temperature  STO can up-regulate FT and SOC1 expression, and; (iii) STO competes with FLC in their regulation of FT and SOC1. STO can repress FLC in LD, SD and darkness. However, in the FRI and fld-3 genetic background, over-expressed STO can no longer repress FLC. Since STO lacks the transcriptional repression/activation domain, it could be that it interacts with another complex to regulate FLC expression. FLC is not only a repressor of flowering time, but it is also functional throughout the lifecycle of the plant Ler background line that has a low-expressing FLC allele exhibited high dormancy. A high-expressing FLC allele produced a significantly higher germination rate at cool temperatures (10°C), but only slightly higher at warmer temperature (22°C) STO-OE line which has low FLC expression level also displayed slower germination rates, whereas the sto-1 germination rate was slightly higher than the WT  n the WT . As a coSTO activates FT and SOC1 expression, thereby promoting flowering. Given that STO expression is controlled by photoperiod/circadian rhythm, it would appear that it is a new component within this regulatory pathway. Moreover, STO stimulation of FT and SOC1 expression does not rely on repression of FLC but rather STO competes with FLC to regulate FT and SOC1 expression and thereby promotes flowering. Overall, these characteristics of STO reveal new relationships between FRI/FLC and the photoperiod/circadian clock pathway.We have demonstrated that FLC, FT and SOC1 in flowering-time regulation in Arabidopsis, these findings suggest that the B-box family gene STO, which is without the CCT domain, plays an important role in the control of flowering in Arabidopsis. The behavior of STO in promoting flowering gives us new clues in understanding of how plants integrate environmental and developmental signals. We can therefore expect that the relationship between the different pathways to be more complex than first suspected.The combination of phenotype, expression characteristics and genetic results extends our knowledge on the biological function of the B-box zinc finger family in plant development, especially in the transition from vegetative to reproductive phase. Given the central roles of CO and CO-like COL'S CO and COL genes have been well studied. While resembling STO and STH1-3, they also show the opposite function. Flowering in the CO mutant is delayed in LD while overexpression of CO results in the acceleration of flowering in both LD and SD. Over-expression of COL5 can induce flowering in SD but col5 mutants do not show altered flowering COL9 resulted in delayed flowering, whereas the col9 mutant flowered earlier under LD conditions. COL9 negatively regulates CO expression, but it does not appear to directly affect flowering time CO, COL5 and COL9 influence flowering time mainly through FT and SOC1 and no reports to date have shown that they can affect FLC expression. Interestingly, a recent study has shown that COL1 and -2, which are the most closely-related genes to CO, do not affect flowering time due to the amino acid differences coded for within the first exon COL genes (including STO) have evolved rapidly in the Brassicaceae family STO on flowering time independent of the CCT domain is not a unique case. It will be interesting to investigate if other members that lack the CCT domain within the B-box family are also involved in regulating flowering.The homologous B-box zinc-finger family genes are widely conserved in higher plants Figure S1sto-1 and STO-OE were grown in darkness, with FLC expression level checked at 2, 4 and 6 d. FLC expression levels of the indicated genotypes were checked, and all lines were grown under SD.Confirmation of microarray data. Col, (TIF)Click here for additional data file.Figure S2STO expression levels in different lines. The level of STO expression in the indicated genotypes was checked in four-day-old seedlings grown under SD. Data from three independent replicates are shown, with UBQ10 used as a control.(TIF)Click here for additional data file.Figure S3FRI/flc-3 x STO-OE, with the lines 4, 7, 10 and 15 being flc homozygous. (B) Functional FRI allele's have a BsmFI restriction site. Restriction endonuclease BsmFI was used to test FRI homozygous in F2 FRI/flc-3 x STO-OE, with the lines 4,7,10 and 15 having a functional FRI. (C) RT-PCR test of full-length FLC expression, with the lines 4, 10 and 15 having no FLC mRNA. (D) RT-PCR test of FLD expression level in F2 fld-3 x STO-OE, with UBQ10 as the control. The lines 11, 13, 15, 19 and 21 were fld homozygous. (E) Restriction endonuclease BsmFI was used to test FRI homozygous in F2 FRI x STO-OE. The lines 9, 11, 12, 15 and 17 were FRI homozygous. All lines were grown for two weeks under SD prior to genomic PCR or RT-PCR.Double mutant genomic PCR and RT-PCR confirmation. (A) Genomic PCR test was performed in F2 (TIF)Click here for additional data file.Figure S4STO expression levels in different plant tissues. The level of STO expression in different tissues of Arabidopsis was analyzed in ten-day-old seedlings  and adult plants (rosetta leaf and flower meristem). Data from three or four independent replicates are shown, with UBQ10 used as a control. * means p<0.05 and ** means p<0.01 in TTEST. Plants were grown under LD.Determination of (TIF)Click here for additional data file.Figure S5FT in cotyledons and hypocotyls. The level of FT expression in different tissues  of ten-day-old seedlings of Col, sto-1 and STO-OE. . Data from three independent replicates are shown, with UBQ10 used as a control. * means p<0.05 and ** means p<0.01 in TTEST. Plants were grown under LD.Increased expression of (TIF)Click here for additional data file.Table S1Seed list.(PDF)Click here for additional data file.Table S2Primer list.(PDF)Click here for additional data file.Table S3Rosette leaf number at flowering time for the indicated genotypes.(PDF)Click here for additional data file.


We present chromatin immunoprecipitation evidence for a transcriptional elongation defect in which RNA polymerase that is phosphorylated at Ser5 of the largest subunit’s heptad repeat accumulates over introns and that this defect requires Cus2 protein. A similar accumulation of polymerase was observed when prespliceosome formation was blocked by a mutation in U2 snRNA. These results indicate the existence of a transcriptional elongation checkpoint that is associated with prespliceosome formation during cotranscriptional spliceosome assembly. We propose a role for Cus2p as a potential checkpoint factor in transcription.There is good evidence for functional interactions between splicing and transcription in eukaryotes, but how and why these processes are coupled remain unknown. Prp5 protein (Prp5p) is an RNA-stimulated adenosine triphosphatase (ATPase) required for prespliceosome formation in yeast. We demonstrate through in vivo RNA labeling that, in addition to a splicing defect, the   •Transcriptional elongation is inhibited when prespliceosome formation is blocked•The defect is characterized by RNA polymerase accumulation on introns•PRP5 or U2 snRNAThis checkpoint can be triggered by mutations in either •The U2-associated Cus2 protein is a candidate checkpoint factor In eukaryotes, transcription and splicing are functionally linked, but how or why this happens is unknown. Chathoth et al. show that, in budding yeast, these processes are coupled by one or more transcriptional checkpoints associated with surveillance of the splicing machinery as it assembles cotranscriptionally on nascent transcripts. Saccharomyces cerevisiae, the RNA capping enzymes are recruited cotranscriptionally via phosphorylated Ser5 (pSer5) of the CTD (Transcription and precursor mRNA (pre-mRNA) processing, initially considered to be independent processes, are now known to be intimately linked. There is appreciable evidence that 5′ capping and 3′ end cleavage and polyadenylation generally occur cotranscriptionally, and splicing often does so too, with the carboxy-terminal domain (CTD) of the largest subunit of RNA polymerase II (Pol II) playing a significant role in coupling transcription to these processing events . The CTD the CTD , and 3′  the CTD  and in v the CTD .Certain splicing factors, including some members of the serine- and arginine-rich (SR) family of proteins , and some small nuclear ribonucleoprotein particles (snRNPs) were also shown to have links with Pol II   are required to promote the various conformational rearrangements during the cycle of spliceosome assembly, catalysis, and disassembly . Among tproteins . The prpdirectly . Certainproperty .prp5-1 strain at the restrictive temperature. ChIP sequencing (ChIP-seq) analysis with prp5-1 cells reveals that Pol II is enriched on introns genome wide. Furthermore, we show that Pol II stalling in prp5-1 cells is mediated through the U2 snRNP-associated Cus2p and that reduced nascent transcript production from intron-containing genes in the prp5-1 strain is suppressed by deletion of CUS2. A similar accumulation of Pol II is observed when prespliceosome formation is blocked by a mutation in SNR20 that causes hyperstabilization of the stem IIc form of U2 snRNA. We propose the existence of a transcriptional elongation checkpoint that is associated with prespliceosome formation during cotranscriptional spliceosome assembly and that is also triggered by a branchpoint mutation in the intron. These observations suggest a role for Cus2p as a potential checkpoint factor in transcription.Here, we present chromatin immunoprecipitation (ChIP) evidence for a transcriptional elongation defect in which Pol II that is mainly phosphorylated at Ser5 of the CTD accumulates over introns in a prp5-1 mutant cells. Antibodies  and ies 4F8;  that immand 37°C A. Strikisplicing , indicatP27 gene C and 1D.DBP2 gene in WT cells at both 25°C and 37°C . Looking genome wide at sequences that show at least 2-fold (>2) enrichment of Pol II, we calculated the proportion of enrichment over introns, exons, and intronless genes based on base pair coverage of each of these features in the genome  are enriched >2-fold in the mutant at 37°C , 5′SS, BP, 3′ splice site (3′ SS), or 3′ end of the RNA for all intron-containing genes. First, this shows that for WT cells there is a drop in the number of reads immediately upstream of the TSS, followed by increased reads between the TSS and the 5′SS  than at 25°C (176 genes)  B–S2D. Moanalysis , are fouanalysis .Scaling (binning) the reads relative to a point 100 bp upstream of the 5′SS, the 5′SS itself, 3′SS, and 3′ end shows enrichment at the 5′ ends of genes in mutant strains at both temperatures compared to WT .Prior to prespliceosome formation, Prp5p remodels the U2 snRNA from the stem IIc to the stem IIa conformation, hydrolyses ATP, and displaces Cus2p from the complex. This facilitates U2 snRNA interaction with the intron branchpoint . At the CUS2 is not essential for cell viability, and its absence does not cause any detectable splicing defect or cell growth defect  into newly synthesized RNA during a 2 min period in  at 25°C D. In conlabeling H.DBP2 and APE2 transcripts in cus2Δ and cus2Δ,prp5-1 cells showed similar levels of exon2 at 25°C and 37°C compared to those of WT  phosphorylated on Ser5, but not on Ser2, is consistent with a transcriptional elongation defect due to paused or stalled Pol II , a general transcriptional elongation factor that affects both transcription e.g.,  and alteprp5-1 and snr20-G53A strains, release of the paused Pol II is inhibited due to the defect in prespliceosome formation.In higher eukaryotes, Pol II pausing has been observed to occur near the promoter regions of genes, referred to as promoter proximal pausing. Originally associated with stress response genes, it is now recognized to occur more widely, especially at developmentally regulated genes, and has been proposed as a mechanism for holding Pol II in a poised state, ready for rapid activation (reviewed in prp5-1 mutation, the presence of Cus2p is necessary to trigger our proposed splicing-dependent checkpoint, making Cus2p a candidate checkpoint factor. In a parsimonious model, transcriptional pausing may be triggered by recruitment of Cus2p along with the U2 snRNP to the assembling spliceosome. After remodeling of the U2 snRNA by Prp5p, dissociation of Cus2p from the complex might signal that the prespliceosome has been formed and the checkpoint is satisfied, releasing Pol II from pausing. However, in the prp5-1 strain or the snr20-G53A strain at the restrictive temperature, the checkpoint is triggered, but not satisfied, as Cus2p remains in the complex, resulting in the accumulation of stalled Pol II that is strongly phosphorylated on Ser5. Release of paused Pol II at a checkpoint is predicted to require phosphorylation of Ser2 of the CTD. Therefore, Cus2p might potentially affect the function of the yeast CTD kinase complexes, BUR and CTK. Intriguingly, Tat-SF1 interacts with the mammalian CTD kinase P-TEFb. However, a recent investigation failed to find evidence for interaction between CUS2 and mutations in BUR2, CTK2, DST1, or genes encoding other transcription elongation factors (To explain all of our observations, we propose the existence of a splicing-dependent transcriptional checkpoint associated with prespliceosome formation . The red factors .We previously proposed that transient, splicing-dependent Pol II pausing near intron 3′ ends might indicate the presence of a splicing-coupled transcriptional checkpoint associated with the second step of splicing . This woWe anticipate that, under normal splicing conditions, Pol II pausing at checkpoints is highly transient and that the ability to detect it, as well as the location of the pausing event on the gene, could be gene specific if it depends on the rate of transition through the checkpoint-coupled splicing event. This would, in turn, depend on many factors, including the kinetics of cotranscriptional recruitment of different splicing factors to the intron. It is anticipated that splicing-coupled transcriptional checkpoints could be affected by and might also affect splice-site selection.Pol II that is strongly phosphorylated on Ser5 of the CTD has been observed to accumulate on alternatively spliced exons under circumstances of exon inclusion in mammalian cells . ConsideRNA extraction, RT-qPCR, and ChIP-qPCR were as described . In vivoprp5-1 cells grown at 25°C and 37°C and WT cells at 25°C. Library was prepared adapting the Illumina protocol (S. cerevisiae sacCer3 genome assembly using bwa version 0.6.1. Uniquely mapped reads with mapping quality ≥ 20 were selected for further analysis. Samples were normalized by number of mapped reads in order to account for variable sequencing depth. Subsequent analyses to define polymerase distribution were carried out using R and perl scripts interfaced with a Galaxy server. The scripts are available at https://github.com/swebb1/ktc_paper_2013. For further details see ChIP DNA was extracted from protocol . Library


Rhythms underlie the daily functions of mammals, from sleep-wake cycles to metabolic processes in the liver. The circadian clock has evolved in response to daily changes in temperature and light in the environment. At the root of circadian rhythms are daily fluctuations in gene expression, which occur in part through the process of transcription—the creation of RNA from sequences of DNA. Although past studies have uncovered how changes in transcription states relate to irreversible processes, for example when cells become more specialized, much less is known about how transcription fluctuates in synch with recurring cycles.PLOS Biology, new insights into the dynamic nature of transcription are provided in a study led by Nouria Hernandez of the University of Lausanne and Felix Naef of the Ecole Polytechnique Fédérale de Lausanne. The findings reveal the kinetics by which genes are activated in a rhythmic manner as well as the remarkable impact of daily cycles across the genome.In this issue of In the study, Hernandez and Naef used a combination of experimental and computational methods to study genome-wide changes in transcription in the mouse liver, and how these changes relate to messenger RNA (mRNA) levels. They discovered that transcription in the liver occurs predominantly in morning and evening waves. These waves were accompanied by the rhythmic recruitment of RNA polymerase II (Pol II)—an enzyme that catalyzes transcription—to DNA strands. In addition, there were rhythmic changes in the modifications of histones—proteins that act as a scaffold for DNA and help to regulate gene expression. These rhythmic changes in transcription were driven primarily by the recruitment of Pol II to DNA.The researchers also identified three classes of genes. One class showed both rhythmic transcription and mRNA fluctuations, a second class showed rhythmic transcription but flat mRNA levels, and a third class showed constant transcription but rhythmic mRNA fluctuations. The latter finding—that the levels of some mRNAs oscillated even when transcription remained constant—suggests that transcription alone does not regulate all rhythmic changes in mRNA levels. Instead, other processes, such as daily fluctuations in the rate of mRNA degradation, influence the accumulation of mRNA. These results indicate that molecular events taking place after transcription play a greater role in regulating daily fluctuations in mRNA levels than previously thought.Taken together, the study reveals that many functions in the liver, such as lipid and carbohydrate metabolism as well as detoxification, are under the control of rhythmic changes in transcription. The findings could lead to insights into how the daily cycle influences genomic responses to food intake, eventually paving the way to the development of novel treatment strategies for diabetes and other metabolic diseases.Le Martelot G, Canella D, Symul L, Migliavacca E, Gilardi F, et al. (2012) Genome-Wide RNA Polymerase II Profiles and RNA Accumulation Reveal Kinetics of Transcription and Associated Epigenetic Changes During Diurnal Cycles. doi:10.1371/journal.pbio.1001442


Transcription factors control which information in a genome becomes transcribed to produce RNAs that function in the biological systems of cells and organisms. Reliable and comprehensive information about transcription factors is invaluable for large-scale network-based studies. However, existing transcription factor knowledge bases are still lacking in well-documented functional information.Here, we provide guidelines for a curation strategy, which constitutes a robust framework for using the controlled vocabularies defined by the Gene Ontology Consortium to annotate specific DNA binding transcription factors (DbTFs) based on experimental evidence reported in literature. Our standardized protocol and workflow for annotating specific DNA binding RNA polymerase II transcription factors is designed to document high-quality and decisive evidence from valid experimental methods. Within a collaborative biocuration effort involving the user community, we are now in the process of exhaustively annotating the full repertoire of human, mouse and rat proteins that qualify as DbTFs in as much as they are experimentally documented in the biomedical literature today. The completion of this task will significantly enrich Gene Ontology-based information resources for the research community.Database URL:www.tfcheckpoint.org Specific gene regulation mechanisms determine which part of the genome becomes transcribed to provide the active molecular parts of living organisms in various environmental conditions. Central in these mechanisms are multiprotein complexes present at the regulatory regions of genes that determine the onset and rate of RNA synthesis by regulating RNA polymerase activity , 2. ThesOwing to their selective binding within regulatory regions of distinct genes, the DbTFs play decisive roles in directing the assembly of the multiprotein transcription machinery to a particular subset of genes. This assembly can either be followed by immediate RNAP II-dependent transcription or it can result in promoter-proximal pausing of RNAP II that can subsequently be released into active transcription triggered by either DbTFs or other mechanisms , 6, 7. DCurrent estimates suggest that the human genome contains ∼1900 DbTF-coding genes . With thThe Gene Ontology (GO) provides a common vocabulary for the functional description of genes and gene products and consists of three sub-ontologies: Biological Process (BP), Molecular Function (MF) and Cellular Component . The GenSupplementary Material 1 for additional comments on background and orientation for the reorganization].The reorganization of the transcription factor MF terms generated a more robust ontology structure by improving both textual definitions and relationships between terms in the ontology structure  see . EquallyToday (GO database release on 16 February 2013), the GOC provides annotations that allow for identification of ∼300 human, mouse and rat DbTFs, which is ∼15% of the expected DbTFs . Only ∼2www.tfcheckpoint.org.The above findings suggest that, to date, no single comprehensive information resource for mammalian DbTFs exists with the level of coverage and high-quality annotation that is needed for genome-scale data analysis and interpretation. The GOC has standard procedures for annotating proteins, and their database is authoritative in providing comprehensive annotations to the myriad of tools that use GO information for data analysis. However, the capacity of expert curators at the GOC is presently not scaled for or focused for dedicated efforts to comprehensively annotate one particular functional protein class. Therefore, we have embarked on a collaborative effort involving community users and GOC members to exhaustively curate experimentally documented mammalian DbTFs. Similar to other sub-domain annotation initiatives , 19, ourhttp://www.geneontology.org/GO.format.gaf-2_0.shtml) and the PSI-MI data exchange format used for recording interaction data  binds to specific DNA sequences in gene regulatory regions and (ii) is involved in RNAPII-dependent regulation of transcription.It is evident that to capture these functional aspects accurately and efficiently, the specific GO terms that substantiate these assertions need to be precisely defined. These GO terms must address both ‘sequence-specific DNA binding’ and ‘transcription regulation’ capabilities accurately. In the following sections, we provide a detailed reasoning behind the selection of specific GO terms of different granularity as well as assignment of GO evidence codes and experimental assays that are considered adequate and necessary for creating a DbTF annotation.To capture the capability of a protein to bind to specific DNA sequences, a GO MF term that describes ‘sequence-specific DNA binding’ (e.g. GO:0043565 ‘sequence-specific DNA binding’) should be used. GO:0000976 ‘transcription regulatory region sequence-specific DNA binding’ should be used when it is not possible to identify information stating that the regulatory region containing the DNA sequence specifically bound by the DbTF is part of a gene regulated by RNAP II. Where a gene is known to be transcribed by RNAP II, a more specific term (GO:0000977 ‘RNA polymerase II regulatory region sequence-specific DNA binding’) may be applied. If information exists that indicates whether the protein binds the proximal or distal regulatory regions, this may be indicated by use of either of the terms describing the location of binding  (The involvement of a protein in transcription regulation is well captured by the GO BP terms GO:0006357 (regulation of transcription from RNAP II promoter) or any of its children that specify whether the protein is involved in positive or negative regulation of transcription  or one of its children to appropriate DbTFs .Table 1The child terms of ‘GO: 0000981’ are used to delineate whether the TF exerts its activity by binding to the promoter proximal region or the distal enhancer, i.e. ‘GO:0000982 ’ or ‘GO:0003705 ’, and whether the result of binding is positive or negative regulation of TG transcription, e.g. ‘GO:0001077 ’ and ‘GO:0001205 ’. Genes that have been shown to have both positive and negative regulatory roles should be annotated with both the positive and negative regulation terms as appropriate.Transcriptional regulation mechanisms are complex. Usually many TFs work together in concert to regulate transcription. In instances where the activity of a TF is reported to be dependent on interaction with another protein or multi-subunit complex, the protein–protein interaction (PPI) is annotated using ‘transcription factor binding’ MF GO terms as shown in Once TF binding and transcription regulation are each annotated individually, the GO structure allows for the generation of TF binding TF activity annotations by combining the separate annotations .In instances where the complex is a homodimer or higher order multimer of the same protein, there are no special annotation issues, as all of the activities demonstrated are properties of the same gene product. However, when the functional unit is a heterodimer or other multisubunit complex, then there are some additional considerations for annotation.The ‘contributes_to’ qualifier is specifically intended for the annotation of functions that occur in the context of complexes, rather than being an activity of a single subunit within the complex. In the case of a heterodimer, there are times where one of the two proteins does not bind DNA on its own. However, in some cases, a subunit that does not bind DNA independently can be shown to contribute to the sequence specificity of binding when present within a heterodimer. In this situation, the subunit that does not bind DNA alone could be annotated to appropriate ‘sequence-specific DNA binding’ terms . The DbTF curation guidelines presented in the current work use one of the following GO evidence codes: Inferred from Direct Assay (IDA), Inferred from Physical Interaction (IPI), Inferred from Mutant Phenotype (IMP) or Inferred by Curator (IC).In accordance with the overall guidelines for GO annotations, each DbTF annotation must be qualified with an evidence code indicating how the annotation is supported by experimental evidence (http://www.geneontology.org/cgi-bin/references.cgi#GO_REF:0000036). The IC code, which requires the use of the two GO IDs for the appropriate ‘binding’ and ‘transcription regulation’ terms, indicates that GO annotations based on evidence from two different sources have been combined by a curator to infer the appropriate transcription factor activity term.When a single scientific paper comprises all experimental evidence necessary to support each of the annotations for ‘DNA- or TF-binding’ and ‘Transcription regulation’, the evidence codes for these two annotations are transferred to the composite DbTF annotation to a MF ‘transcription factor activity’ term see . HoweverTo provide for a uniform standard for evaluation of experimental evidence for DbTF annotations, we surveyed several relevant resources defining experimental assays that can document TF function, including ORegAnno , TRRD 22, ReguloIn the following sections, we have compiled sets of selected experimental assays that we deem to be most relevant for annotation of DNA binding, TF binding and transcription regulation. PSI-MI-unique identifiers are given wherever they exist. Augmentation of the PSI-MI vocabulary to span a larger repertoire of TF-defining experiments is ongoing.in vitro binding of a TF to specific DNA sequences present in either cloned TG regulatory regions  or in synthetic DNA sequences representing canonical TF binding sites or specific TG regulatory regions (see in vivo TF–DNA interaction (e.g. the Chromatin ImmunoPrecipitation assay) because it is not possible to ascertain in these assays that the TF in question actually binds directly to DNA, or whether some other component in the in vivo system mediates the TF–DNA association.Experimental data documenting specific DNA binding are obtained from experiments that show ions see . We havein vitro assay that has been most frequently used for documenting sequence-specific binding of TF is the Electrophoretic Mobility Shift Assay (EMSA) (nuclear extract from native tissue or cellsnuclear extracts from cells or tissue with ectopic expression of a TFin vitro translated or purified from cell extract)purified TF (nuclear extract from cells with ectopic expression of a mutated TFin vitro translated or purified from cell extract).purified mutated TF (The y (EMSA) . The mosWhen the TF is presented in any of the variants (ii–v), the EMSA qualifies for annotation of a GO term for ‘specific DNA binding’. In the case where the TF is presented as a nuclear extract from native cells or tissue (i), we require that the specific TF is identified with an additional experimental approach. This may involve specific competition experiments demonstrating that the EMSA gel shift is not abolished by competition with an unlabeled DNA probe with a point mutation in a known TFBS for this specific TF, whereas competition with unlabeled DNA probe containing the wild-type TFBS does abolish the gel shift. Also, the use of a TF-specific antibody, i.e. EMSA supershift, will increase confidence in EMSA assays with nuclear extracts from native tissue or cells; however, these assays must be interpreted with caution, as the DNA–protein complex may be shifted even though a different protein than the one recognized by the antibody provides for the DNA-binding part in the complex. If no additional experimental verification of the TF is reported, nuclear extract-based EMSAs of type (i) do not suffice to qualify DNA binding properties of a TF, and the experiment needs to be dismissed.Similarly, the other assays listed in . These assays mainly fall into two groups: either reporter gene assays measuring the transcriptional regulatory effect of a TF on a regulatory region cloned upstream of a reporter gene , or measurement of expression levels of a TG mRNA . This field can be used to record information regarding specific TGs regulated by the TF that is being annotated. The TG is recorded in the Annotation Extension field for the BP transcription regulation GO term using the ‘has_regulation_target’ relationship combined with the gene identifier(s) for the TG(s).An obvious important biological property of a TF lies in the particular TGs that it regulates. Proper recording of this information is of key importance for the building of gene regulatory networks. In studies of DbTF functionality, often one or several specific TGs will be identified and experimentally documented. The GOC has introduced an Annotation Extension field to capture additional information that provides more biological context to the GO annotation , NCBI’s Entrez Gene (http://www.ncbi.nlm.nih.gov/pubmed/). Each scientific paper is first checked for information providing correct identification of species origin of the TF studied. Because we are focusing on DbTFs from human, mouse and rat studies, only papers allowing identification of a DbTF from one of these species will proceed to further curation. Thus, a number of papers that fail to clearly identify the species of the gene(s) used in their construct(s) have to be omitted from the curation process. Then, the paper is searched for adequate experimental evidence to support one or several DbTF annotations. If either TF species origin or sufficient experimental evidence is not identifiable, the curator returns to the scientific literature corpus to search for other suitable papers. When both criteria are fulfilled, the individual GO annotations (i.e. specific DNA binding and/or TF binding and transcription regulation) are assigned together with a supporting evidence code. Finally, the composite TF activity MF GO term(s) is inferred. TF annotation data are submitted to UniProt-GOA in the form of a gene association file  and will subsequently appear in the GOC database via tools such as AmiGO (http://amigo.geneontology.org/) and QuickGO . The current work aims to provide the foundation to curate this source of information and to record adequate GO annotations in compliance with the standards defined here. Currently (GO database release on 16 February 2013), only 202 human, mouse and rat proteins are annotated as DbTFs with ‘GO:0000981 sequence-specific DNA binding RNA polymerase II transcription factor activity’ (or any of its child terms) supported by experimental evidence, meaning that some 600 DbTFs still need to be processed. We aim to complete this task before the end of 2013. Even though the number of curators involved is small, the efficiency of this focused annotation project is high, as the number of different GO terms and evidence codes is limited and well defined, thus allowing each curator to process a relatively high number of scientific papers .Compilation and in-depth analysis of available information on transcription factors indicate that >800 mammalian DbTFs are experimentally documented in the scientific literature (http://www.tfcheckpoint.org/). This will enable users to select subsets of TFs based on the specific experimental methods used to characterize them.The catalogue of experimental assays that qualify for supporting TF annotations presented here is assembled based on the extensive TF annotation experience in the collaborating organizations. This aspect of the annotation procedure improves the quality of the GO annotations, as it provides a uniform standard for interpretation of evidence strength in published experimental work. As some of the assays presently are not adequately covered by PSI-MI vocabulary , a part Metadata are rarely presented in biomedical publications using formalized knowledge representation. This often makes it difficult for a curator to extract accurate information for ontology- or structured vocabulary-annotation from natural language used in the literature. The GOC provides guidelines for the curation of gene products information from scientific publications and procedures for identification of the type of evidence that supports the curated information. Because of these standardized conventions, literature-curated data in the GO database are deemed to be of high utility and quality. In the present work, we have established a comprehensive and specific curation procedure for TFs of RNAP II, which, similar to other data standardization initiatives, provides details on the requirements to properly record an experimentally verified DbTF.The GOC is centrally involved in efforts to provide annotation guidelines for particular protein functional categories. However, the elaboration of procedures for specific tasks like the curation of distinct functional categories of proteins, or of BP subdomains, is enhanced when experts in the respective fields are involved in the curation process. Moreover, the active participation from domain experts is greatly facilitated by generating detailed curation guidelines as vehicles for productive interactions. With the transcription factor curation effort presented here, we wish to provide not only a greater number of high-quality annotations for DbTFs and their TGs across three mammalian species, but also to exemplify the constructive use of detailed guidelines to facilitate collaborative biocuration efforts across institutions.The Norwegian Cancer Society, The Liaison Committee between the Central Norway Regional Health Authority (RHA), the Norwegian University of Science and Technology (NTNU) and Sør- Trøndelag University College (HisT). K.R.C., J.A.B., D.P.H. and R.B. are funded by NHGRI grant (HG 002273) for the Gene Ontology Consortium. R.H. is funded by NHGRI grant NIH (U41HG002273). Funding for open access charge: Norwegian University of Science and Technology.Conflict of interest. None declared.


CENs show Fun30-dependent changes in flanking nucleosome position and/or CEN core micrococcal nuclease accessibility. Fun30 deletion leads to defects in histone variant Htz1 occupancy genome-wide, including at and around most centromeres. FUN30 genetically interacts with CSE4, coding for the centromere-specific variant of histone H3, and counteracts the detrimental effect of transcription through centromeres on chromosome segregation and suppresses transcriptional noise over centromere CEN3. Previous work has shown a requirement for fission yeast and mammalian homologs of Fun30 in heterochromatin assembly. As centromeres in budding yeast are not embedded in heterochromatin, our findings indicate a direct role of Fun30 in centromere chromatin by promoting correct chromatin architecture.Budding yeast centromeres are sequence-defined point centromeres and are, unlike in many other organisms, not embedded in heterochromatin. Here we show that Fun30, a poorly understood SWI/SNF-like chromatin remodeling factor conserved in humans, promotes point centromere function through the formation of correct chromatin architecture at centromeres. Our determination of the genome-wide binding and nucleosome positioning properties of Fun30 shows that this enzyme is consistently enriched over centromeres and that a majority of  Centromeres are essential to chromatin structures, providing a binding platform for the mitotic spindle. Defects in centromere structure or function can lead to chromosome missegregation or chromosome breakage. This, in turn, can cause cancer in metazoans. Centromeres are defined by specialized chromatin that contains the histone H3 variant CENP-A , and transcription over centromeres is tightly controlled. Budding yeast centromeres are composed of a single nucleosome containing the essential Cse4. Loss of one of these specialized centromeric nucleosomes can lead to chromosome missegregation during mitosis followed by cell death. We provide evidence that energy-dependent chromatin remodeling factor Fun30 supports faithful chromosome segregation, especially when centromere structure is challenged by mutation of Cse4 or by forced transcription through centromeres, which disrupt centromere structure. We show that Fun30 binds to centromeres and that loss of Fun30 leads to various defects in centromere chromatin, suggesting a direct role for Fun30 in promoting normal centromere function. Our analysis shows that Fun30 affects nucleosome positioning at many genomic sites, including centromeres, and is required for normal occupancy of histone variant Htz1. In the absence of Fun30, we detect an increase in transcription through centromeres. We suggest that an important function of Fun30 is to limit transcription over centromeres. Saccharomyces cerevisiae the unique SMARCAD1 homolog Fun30 is required for silencing in heterochromatin loci in vitro and affects chromatin structure in vivoThe functional state of chromatin domains results from the action of multiple determinants, including histone modifications, histone variants, nonhistone proteins and nucleosome remodeling factors. The inclusion of specific histone variants is essential for the organisation of chromatin to delineate specific domains The analysis of centromere establishment and maintenance has provided many important insights into how various chromatin factors cooperate to assemble a very specific and essential chromatin configuration. Centromeres serve as attachment anchors for kinetochore proteins, which, in turn, interact with microtubules of the mitotic spindle  to obtain a genome-wide binding profile for Fun30. We obtained more than 4.8×10 regions . Fun30 i regions .To further explore the binding of Fun30 to intergenic sites, we analyzed its association with genomic features . SeveralΔfun30 deletion and control (‘wildtype’) cells. A ‘global expression profile’ analysis FUN30-deletion affected genes, identifying 255 genes which were downregulated and 573 genes which were upregulated  analysis P<0.05). This analysis did not revealed significant GO terms in the group of downregulated genes. The upregulated gene group showed several genes involved in chromosome segregation  and meiosis   which is required for sister chromatid separation and exit from mitosis P = 0.042) or downregulated genes (P = 0.012). Thus, the upregulation of genes involved in these specific pathways appears to be a cellular response to the absence of Fun30 function. To explore this further, we investigated a quantitative genetic interaction profile database containing 75% of all genes in S. cerevisiaehttp://drygin.ccbr.utoronto.ca/) and found 147 genes  that have a significantly similar genetic interaction profile as Fun30. Interestingly analysis of this list of genes by Gene Ontology also reveals roles in meiosis and chromosome segregation  and kinetochore formation   To further understand how Fun30 might have a role in chromosome segregation and to test direct versus indirect roles, we performed expression profiling of mRNA using RNA-seq in egulated . To inve0 = 1.9) . The delregation . This antscse4-1 mutant with an amino acid substitution Δfun30 single mutant cells did not show growth defects, but growth of the cse4-1 mutant was reduced, as expected . Double deletion of FUN30 and TRF4 increased the amount of detectable transcript even further (Δfun30 Δtrf4).Because Fun30 has been linked to gene silencing, we asked if it might be required to silence transcription within the centromeres. We tested how centromere silencing was affected in the Together, our data suggest that Fun30 promotes faithful chromosome segregation when centromere structure is challenged and this may be linked to Fun30's role in gene silencing.Next, we tried to elucidate if Fun30 promotes chromosome segregation by ensuring a correct centromere chromatin structure, in line with its binding to centromeres.FUN30 deletion on histone H3 occupancy indicates that Fun30 binds preferentially at the terminator region of genes and that its binding there is linked to a loss of histone H3 occupancy at this region -digested chromatin samples from wildtype and Δfun30 mutant cells. Nucleosome-like particle positions were mapped as distributions of the center points of paired-end reads with an end-to-end distance of ∼150 bp. This class of size-selected paired-end reads largely defines the DNA entry- and exit-points on nucleosomes exposed by MNase digestion in the original chromatin sample. The frequency distributions of paired-read centre points, therefore, effectively estimate the frequency of nucleosome dyads, and peaks in the distributions correspond to translationally positioned nucleosome-like chromatin particles in the original genome CEN1, CEN10, CEN11, and CEN12. At these sites both CEN flanking nucleosome positions, and/or the MNase accessibility of the CEN core particles themself are altered in the Δfun30 mutant confirming that Fun30 is required for normal CEN chromatin structure. Δfun30 cells was also observed at the other sites identified by Fun30 ChIP sequencing (ARS elements). These results therefore suggest that Fun30 plays a major role in defining local nucleosome positioning at a variety of structural loci, particularly those with boundary and silencing functions, in a manner similar to its S. pombe ortholog Fft3 Because Fun30 has previously been shown to influence nucleosome positions at the FUN30 deletion led to an increase of Htz1 at telomeric sites and within and around the silent mating locus HMR . Therefore, we tested if FUN30 deletion affected Htz1 occupancy genome-wide, including at centromeres. An analysis of Htz1 occupancy at divergent promoters allows focusing on the effect on promoters as opposed on terminator sites from adjacent genes. This shows a dramatic loss of Htz1 around that 5′ transcription start sites upon FUN30 deletion and a corresponding increase within gene bodies  at several other sites including at sites between convergent genes where we find peaks of Fun30 binding . Given the pronounced Fun30 binding over intergenic regions, especially between convergent genes and its link to loss of histone H3, it will be interesting to examine what is the biological role of Fun30 at these sites. A clue may be given by the fact that Fun30 also binds intergenic sites, tRNA elements, ARS sequences, snoRNA genes and centromeres. All these sites have been shown to also bind cohesin and condensin ISW2 is a nucleosome remodeling factor that prevents noncoding transcription away from promoters and other nucleosome depleted regions, by limiting nucleosome free region size CSE4 promoter into the LEU2 locus of strains KBY4001B and SC110 using BstX1 cleaved integration vector SB500, kind gift from Dr Sue Biggins. Standard budding yeast genetic techniques and media were used according to Guthrie et al.6 cells/ml with sterile H2O, than 1∶5 serial dilutions were performed five times. For the dicentric chromosome assay, strains containing GALCEN3 were plated for single colonies on YP galactose or glucose at 30°C as described in Yeast strains used in this study are listed in 600: 0.7) in YPD media using the hot acidic phenol standard extraction protocol For RT-qPCR analysis, total RNA was extracted from mid-logarithmic phase cells  for histone H3; 2 µg affinity-purified rabbit polyclonal anti-Fun30 for Fun30 ChIP was carried out essentially as described Immunoprecipitated and Input DNAs were analysed by qPCR using the SYBR Green PCR Master Mix (Applied Biosystems). For immunoprecipitated DNA a 8-fold dilution was performed, input DNA was diluted 500 times; primers used are listed in Pfx Platinum polymerase (Invitrogen) for 15 cycles as described by Quail et al. Illumina sequencing was performed using protocols derived from 595, then grown to 0.7 OD595 at 30°C. Total RNA was isolated using the hot acidic phenol method Overnight cultures were grown in YPD at 30°C were diluted to 0.2 ODhttp://www.bioinformatics.bbsrc.ac.uk/projects/seqmonk/). Read quantification of probe regions were designed depending on purpose. For analysis of 5′ Transcription Start Site (5TSS) and 3′ Transcription Termination Site (3′TTS) regions, UTRs length were obtained from 2 signal intensities. Visualization of data was performed using the Affymetrix Integrated Genome Browser (IGB) (http://www.affymetrix.com/) and Mochiview th) base coordinate for each 13-nucleotide probe. High-resolution cluster visualization of 5′TSS and 3′TTS were performed using MultiExperiment Viewer MeV4.5.1 http://www.gatcplatform.nl/vennmapper/) http://www.yeastgenome.org/), genome version SGD01.01 (Dec 2006). For determining gene orientation we also considered noncoding genomic elements, i.e. snoRNAs, snRNAs and tRNAsTo increase the sequence yields, the Illumina sequence reads, carrying custom barcodes at the start, were re-analyzed using bareback-processing S. cerevisiae used for nucleosome sequencing were EUROSCARF collection wild-type reference strain BY4742  and mutant Y10389 . Cells were grown in YPD rich medium  at 29°C to 2.6–2.8×107 nucleated cells per ml (determined by haemocytometry). Chromatin digestion and DNA preparation was performed exactly as described S. cerevisiae reference genome using Bowtie 0.12.7 Δfun30 samples respectively. The paired reads were sorted into a range of classes based on the SAM format ISIZE value (difference between 5′ end of the mate read and the 5′ end of the first mapped read) plus or minus a window value of 0.2 times ISIZE as described Δfun30 mutant cells were given an .sgr file ending and rendered using the Integrated Genome Browser http://www.ebi.ac.uk/arrayexpress/) under accession codes E-MTAB-955, E-MTAB-956 and E-MTAB-759. Raw paired sequence reads of nucleosome mapping are deposited at the NCBI short read archive, accession number SRA039099.2.ChIP-seq and RNA-seq read data has been deposited in the ArrayExpress database  Fun30 shows a pronounced binding at the intergenic 3′ end region and this enrichment is directly correlated with expression levels, shown is the average gene analysis for Fun30 binding in relation to RNA transcript levels (determined by RNA-seq in this study) number of genes (n) in each category is indicated in the figure, error bars: 95% confidence interval. C) left panel: Binding profile of Fun30 relative to 5 Transcriptional Start Sites (5′TSS). The clusters contain 4560 genes where the 5′TSS has been identified fun30-deleted/wildtype cells is shown.Fun30 preferentially binds intergenic regions over coding regions (ORFs). A) Analysis of Fun30 binding to intergenic regions, red line: ratio of 5′ intergenic (TIF)Click here for additional data file.Figure S2CEN1, snR35, ARS315. B) Fun30 binding to CEN3 and surrounding region using primer pairs spanning +/−2 kb, controls as in (A). Level of enrichment is expressed as % DNA precipitated as compared to input. Background binding to beads was substracted, error bars represent the difference of the maximum and minimum values from the mean. Shown are results from 2 biological replicas, each with 2 technical replicas.Fun30 is enriched over centromeric regions. A) Validation of Fun30 binding sites by ChIP following qPCR. Fun30 binding at control regions (Ctrl1–3) where Fun30 did not bind according to our ChIP-seq data, and binding to (TIF)Click here for additional data file.Figure S3Δfun30) versus BY4741/Y00000 (WT) plotted as a function of mRNA levels in WT at 30°C in YPD  of the mRNA transcription level ratios in Y00389 ((TIF)Click here for additional data file.Figure S4GAL1 integrated at centromere CEN3. Analysis of GAL1 promoter induction at CEN3 locus. RNA from SLY806  and SC117  strains was isolated at indicated time points after addition of galactose and analyzed by RT-qPCR using primers specific for CEN3 locus (PM22/PM48).Deletion of Fun30 does not affect promoter activity of (TIF)Click here for additional data file.Figure S5i.e. only divergent or convergent genes - were determined for the histone H3 occupancy profiles in wildtype and Δfun30 cells. As previously shown, histone H3 is mainly present within coding regions whereas in both promoter and terminator specific regions histone H3 is relatively depleted. In Δfun30 mutants there is an increase of histone H3 at promoter and terminator regions. B) Average occupancy analysis for histone H3 for divergent orientation genes relative to the 5′TSS position for wildtype cells (left panel) and ratio Δfun30 versus WT . C) Average occupancy analysis for histone H3 for convergent orientation genes relative to the 3′TTS position of genes for wildtype cells (left panel) and ratio Δfun30 versus WT . D) Venn diagrams showing results of hypergeometric probability tests for 5′IGR and 3′IGR Fun30 targets and changes in histone H3 in Δfun30 mutant. This analysis revealed that the changes in histone H3 occupancy are significant for the 5′ and 3′ intergenic regions. Venn diagram illustrating the overlaps in 5′ intergenic regions (5′IGR) and 3′ intergenic regions (3′IGR) between the genes that display increased Fun30 binding (1.5 fold up enrichment) and increased histone H3 occupancy in the Δfun30 mutant. Hypergeometric probability values are indicated. For promoter (divergent genes) or terminator specific regions (convergent genes) these are respectively P(X = 31) = 1.77×10−9 and P(X = 156) = 5.43×10−8.Fun30 regulates histone H3 occupancy at intergenic regions. A) 3′ regions of genes show the greatest enrichment for Fun30 when compared to promoter or coding regions. Average occupancy of Fun30 for divergent orientation promoters (left panel) and convergent terminators (right panel). The data were binned into three groups  according to the expression level of wildtype cells. The number of genes (n) in each category is indicated. Error bars represent 95% confidence intervals. The genomic region of interest was divided into 40 equally sized bins. The 5′ and 3′ flanking regions have 1250 bp from respectively the beginning and the end site of the genomic elements and divided into a 50 fragments of 50 bases (see (TIF)Click here for additional data file.Figure S6CENs exhibits Fun30-dependent changes in flanking nucleosome position and/or CEN core MNase accessibility. A) Genome browser traces of Fun30 ChIP enrichment and nucleosome dyad frequency centered on and surrounding yeast CEN1; B) CEN2; C) CEN3; D) CEN4; E) CEN5; F) CEN6; G) CEN7; H) CEN8; I) CEN9; J) CEN10; K) CEN11; L) CEN12; M) CEN13; N) CEN14; O) CEN15; P) CEN16. The upper trace of each panel shows log2 Fun30 ChIP enrichment values binned at 10 bp intervals and smoothed with a 3 bin moving average. The wild-type (WT) and Δfun30 mutant nucleosome (nuc) traces were plotted as mirror images in the lower panel. Centre point positions of paired sequence reads with end-to-end distances of 150 bp+/−20% were mapped across the yeast genome for the wild-type and Δfun30 mutant MNase-digested chromatin sequencing samples, binned at 10 bp intervals, and the resulting frequency distributions smoothed by applying a 3 bin moving average. This class of size-selected paired-end sequence reads largely defines the DNA entry- and exit-points on nucleosomes exposed by MNase digestion in the original chromatin sample. The frequency distributions of paired-read center points therefore effectively estimates the frequency of nucleosome dyads CEN core particle is also mapped using this method and can be visualized as a small peak centered on the CEN region marked with a grey box on each browser panel, pink boxes mark the surrounding ORFs. Fun30-dependent changes in the height of a nucleosome dyad or CEN core particle peak are marked with a red asterix. Fun30-dependent changes in the position of a CEN-flanking nucleosome dyad peak are marked with red arrows.The majority of yeast (TIF)Click here for additional data file.Figure S7ARS regions show Fun30-dependent nucleosome positioning surrounding the ARS consensus sequence (ACS). The upper graph shows a plot of the cumulative Log2 Fun30 ChIP enrichment values centered on the yeast ACS elements defined by Nieduszynski et al.Δfun30 mutant (red line) chromatin sequencing data sets. The cumulative nucleosome dyad frequencies in each 15 bp bin were normalized by dividing by the average nucleosome dyad frequency for the whole feature window in order to place “random” nucleosome occupancy at a value of 1. Changes in cumulative distribution between wildtype and the Δfun30 mutant are indicated with asterisks. B. Genome browser trace of nucleosome dyad frequencies at Fun30-dependent ARS202 plotted as described for CEN analyses, ARS which exhibit Fun30-dependent changes in position are marked with arrows.Fun30 is required for normal nucleosome positioning at other sites identified by ChIP-seq. A. (TIF)Click here for additional data file.Figure S8FUN30 deletion. Expression in wildtype cells: dark grey bars, in Δfun30 cells: light grey.RNA-seq analysis shows that histone gene expression is not changed upon (TIF)Click here for additional data file.Figure S9CEN1–16. Shown is Htz1 occupancy from wildtype  and Δfun30 cells  expressed as normalized sequence tag counts corrected for input in linear scale. The change in occupancy of Htz1 is indicated in the lane below as the values from the Δfun30 cells minus the values from wt cells (black). Positions of ORFs and centromeres are indicated in the lowest lane, orange box: centromere, back and grey boxes: ORFs in the sense and antisense direction, respectively. Axis and scales as in Fun30 affects Htz1 occupancy around a majority of centromeres. Effect of Fun30 on Htz1 occupancy 5 kb up- and downstream of (TIF)Click here for additional data file.Figure S10Description of flanking regions annotations for coding gene. A) 5′IGR and 3′IGR region assignment for genes having identified 5′UTR and/or 3′UTR by (TIF)Click here for additional data file.Figure S11Δfun30 yeast strains separated by agarose gel electrophoresis and stained with ethidium bromide. B. Graph of the number of aligned paired-end reads obtained by Illumina GAIIx sequencing of material shown in MNase digested chromatin samples processed for paired-end mode Illumina DNA sequencing. A. DNA from MNase digested chromatin fractions purified from wild-type and (TIF)Click here for additional data file.Table S1fun30-deleted cells, a list of genes that genetically interact with FUN30.This EXCEL spreadsheet based table provides gene lists of up- and down-regulated genes in (XLS)Click here for additional data file.