FANCA, B, C, E, F, G, L and M) that compose the FA core complex. The promoters of these genes show the characteristic attributes of housekeeping genes, such as a high GC content and CpG islands, a lack of TATA boxes and a low conservation. The promoters functioned in a monodirectional way and were, in their most active regions, comparable in strength to the SV40 promoter in our reporter plasmids. They were also marked by a distinctive transcriptional start site (TSS). In the 5′ region of each promoter, we identified a region that was able to negatively regulate the promoter activity in HeLa and HEK 293 cells in isolation. The central and 3′ regions of the promoter sequences harbor binding sites for several common and rare transcription factors, including STAT, SMAD, E2F, AP1 and YY1, which indicates that there may be cross-connections to several established regulatory pathways. Electrophoretic mobility shift assays and siRNA experiments confirmed the shared regulatory responses between the prominent members of the TGF-β and JAK/STAT pathways and members of the FA core complex. Although the promoters are not well conserved, they share region and sequence specific regulatory motifs and transcription factor binding sites (TBFs), and we identified a bi-partite nature to these promoters. These results support a hypothesis based on the co-evolution of the FA core complex genes that was expanded to include their promoters.The Fanconi anemia (FA) gene family is a recent addition to the complex network of proteins that respond to and repair certain types of DNA damage in the human genome. Since little is known about the regulation of this novel group of genes at the DNA level, we characterized the promoters of the eight genes ( The Fanconi anemia  family of genes is an important component of a multi-member DNA damage defense network that protects the human genome from the detrimental consequences of interstrand DNA crosslinks and stalled replication forks in vivo.The TSS information was derived from the database of transcriptional start sites (DBTSS), where experimentally confirmed TSSs are annotated The sequence information from the FA core complex genes and their flanking sequences were used as guides for the primer design, and several fragments were cloned from human genomic DNA. The identities of the genomic DNA fragments were determined by sequencing. We used three sets of constructs  that were designated according to their different lengths. The first construct (L1) contained the longest sequence, which was approximately 1 kb upstream of the TSS, and this is a typical length for several promoters that have been described in the literature renilla luciferase constructs in HeLa and HEK 293 cells. All of the 5′-flanking regions of the FA core complex genes had significant promoter activities in the transiently transfected HeLa and HEK293 cells  and were upstream of the firefly luciferase gene. To determine the region in these promoters that was required for maximal activity, they were transiently transfected into HeLa and HEK 293 cells. To validate our results, we compared the luciferase activities obtained with the FA gene promoters to those obtained with two known promoter sequences. We tested the SV40 promoter as a strong promoter that was inserted into the pGL SV40 plasmid, and we used the minimal promoter of the human GLI3 gene  as an example of a relatively weak promoter The L1 region accounted for 20% to 50% of the activity of the SV40 promoter . A high FANCA- and FANCM-derived L2 constructs showed 20% higher activity than the SV40 promoter  compared to the SV40 promoter alone, which was consistent with the idea that all of these sequences may provide negative activity. The effect of the reverse complement inserted sequences was marginal in both HeLa and HEK293 cells, with a luciferase activity comparable to the control . Our results suggest that the regions upstream of the 5′ end of the L2 sequences act as strand specific silencing elements or silencers of transcription initiation. In all our experiments we did not observe a significant or systematic difference in the results between the HeLa and HEK293 cell lines in their normal or reverse orientations.To further characterize the activity within the 5′ portions of the FA core complex gene promoters, we amplified the regions from the 5′ end of L1 to the 5′ end of L2. One was amplified in the sense direction and one in the reverse-complement orientation, and the products were cloned into the pGL3 SV40 vector . As a coer alone . The conFAAP100 promoter and determine similarities, we divided the putative promoter region (approximately 1 kb) into five segments with lengths of approximately 200 bp. We then cloned the corresponding PCR products into the pGL3 basic plasmid. We measured each of these fragments using the dual luciferase assay in comparison to FANCA in the HEK293 line. We did not detect any significant activity in the 5′ region of the promoter sequence of FAAP100 within −1000 to −601 nt of the TSS, which was divided into 200 bp fragments  is a recently described member of the core complex and the FANCB-FANCL-FAAP100 subcomplex ragments . The secWith the help of the Multiple Em for Motif Elicitation (MEME) software, we identified several DNA sequence motifs that were present in nearly all of the promoter regions of the FA core complex genes. These motifs consisted of short sequences that are distributed throughout the entire tested region . Howeverin silico approaches (PROMO and the Genomatix suite), we identified a large number and variety of TBSs. Therefore, we wanted to identify the TBSs with different sets of consensus similarities: 70% and 85%. However, the overall set of TBSs was similar in all of the L1 promoter fragments. Some binding sites were shared by all of the FA core complex gene promoters, including those for the TFII-I, TFIID, E2F, STAT4, YY1, c-Jun and IRF1 proteins, and an additional group of shared, but not ubiquitously present, binding sites were found  varied from 27% to 3% of their original expression . The results of the SMAD4 knockdown studies in the HeLa cells are of special note because a direct regulatory interaction between SMAD4 and FANCA has been previously reported in mice The results of our siRNA experiments support the notion of shared regulatory functions between the FA core complex gene promoters and prominent cellular pathways, such as TGF-β and JAK/STAT. Following siRNA knockdown of romoters . A similpression . These robserved . The siRSMAD4 downregulation, FANCA showed the strongest decrease in expression, which was 10% (0.1-fold) of the original transcript level . Control western blots showed an adequate knockdown of the transcription factors . Inactivating SMAD4, STAT1 and STAT4 caused FANCA downregulation in both the wild-type and the mutated cells  and compared these results to patient-derived cultured fibroblasts carrying biallelic mutations in ed cells . There wed cells . Almost nockdown , lane 2.nockdown .The aim of our study was to determine the structural and functional features of the regulatory machinery that drives the FA core complex genes. This included the common and rare transcription factor binding sites and their distribution. Similarities in all of the promoters might show a co-evolution in the promoters of FA core complex genes.TATA or CCAAT regulatory sequences, and the presence of several transcription factor binding sites, including YY1, STAT1, AP2 and SP1/GC box cis-elements FANCB, these genes generally do not overlap with other genes on a large scale. In the present study, FANCB was the only gene with a slight bidirectional, basal promoter activity in the dual luciferase assay  gene. Cell-specific effects on the measurements were excluded because the results differed marginally between the cell lines.The cloned FA core complex gene promoter regions have characteristics that are typical of housekeeping genes. These characteristics include the following: a high GC content and CpG islands, a lack of obvious se assay . This miFANCF, all of the core complex gene promoters feature one or both of these two motifs, albeit in different numbers. Similar to what has been reported for FANCD2, the presence of binding sites for E2F in these motifs suggests that the E2F/Rb pathway also may be involved in regulating the FA core complex The transcriptional regulation of genes that participate in protein complex formation is often marked by sequence similarities in silico data indicated that all of the FA core complex gene promoters featured one or more of the STAT1/4/5 binding motifs in combination with SP1, which is a cofactor for STAT1. Independent but closely spaced DNA binding sites for STAT and other transcription factors seem to be required for maximal transcriptional activity FANCG and FANCA promoters and, with a lower consensus conservation, in FANCB, FANCF and FANCL. This suggests that SMAD participates in a regulatory network that includes TGF-β GTCTAGAC, is a palindromic sequence with two copies of GTCT, with its reverse compliment AGAC on the opposite strand. Tandem repeats of this sequence have been shown to promote TGF-β-inducible transcriptional activation TGAGTCAGAC, an AP1 binding site (TGAGTCA) that overlaps with an AGAC-containing SMAD-binding sequence HNSCC), which is a tumor that is frequently encountered in FA patients Regulation of the JAK/STAT pathway by the FA/BRCA pathway has been noted previously FANCA and FANCC) and the TGF-β and JAK/STAT pathways There are only single reports concerning the possible cross-talk between two of the FANC core complex genes ” appear to function as regulatory elements for the Notch signaling pathway FANCE.The regulation of the FA core complex genes is unlikely to occur only through their promoters at the genomic level. Regulation at the transcript level has been recognized as an important aspect in the control of expression for many genes. In Collectively, our study provides the first approximation of the features that govern the activity of the FA core complex group of genes. Based on the similarities, our analysis also adds support to the hypothesis of co-evolved promoters within this group. Given the importance of DNA damage recognition and repair for the prevention of premature cancer and aging, it seems reasonable for evolution to have provided additional protection for long-living, warm-blooded species. These protections include a certain degree of regulatory and functional redundancy, which was found in the FA core complex genes.The used fibroblast cell line MAKE derived from a Fanconi anemia patient, which is unknown to me, during the routine diagnostic process. The cell line was neither used for clinical studies nor for other studies and was processed anonymously. It is used only as a positive control cell line in complete accordance with the German gene diagnostic law that require the consent of the patient and/or their parents. For this type of use of patient material no approval of an ethics committee is required in German law. A written informed consent was obtained for research on this patient sample.www.frodo.wi.mit.edu). Phusion DNA Polymerase (Finzymes) was used primarily, but the CG-rich PCR kit (Roche) was used for difficult templates, such as those with CG-rich regions. All the PCR experiments were performed in a volume of 50 µl and a primer concentration of 10 pmol. With the exception of the FAAP100 primers, all the primers were generated with a KpnI restriction site at the forward strand and a HindIII site at the reverse strand. For FAAP100, we used a MluI site on the forward strand because there was an internal KpnI site in the sequence. The GLI3 promoter is a reference promoter with relatively weak activity, and it was amplified with KpnI/HindIII restriction sites.Three products were amplified that were 5′ to the putative transcription start site  for each of the core complex genes. L1, L2, and L3 were approximately 1 kb, 500 bp, and 200 bp upstream of the TSS, respectively. Primer sequences were generated using the Primer3 program (Escherichia coli TOP10 (Invitrogen) or Turbo (NEB) cells according to the manufacturer's instructions. The integrity and direction of all the inserts were confirmed by sequence analyses using the RVprimer3 (forward) and the GLprimer2 (reverse) sequencing primers (Promega), which bind directly up- and downstream of the multiple cloning sites. Primer sequences are available upon request.The amplified PCR products were digested with their respective enzymes and ligated into the pGL3-Basic Vector (Promega), which contains the gene for firefly luciferase. To test the putative silencing properties of the 5′ FA promoter regions, we used the pGL3 SV40 vector. The constructs were transformed into competent 2/95% air). The transfections were performed with Effectene transfection reagent (Qiagen) according to the manufacturer's protocol. Briefly, 24 h before transfection, cells were split into 12-well plates  at a confluence level between 50 and 80%. The transfections required DNA (500 ng), the pGL basic vector (firefly) with our inserts, the pRL null vector (renilla) as an internal control, and 87 µl of transfection reagent . At 48 h after transfection, the cells were washed with PBS and lysed with passive lysis buffer (Promega) to perform a conventional dual luciferase assay (DLR). Lysate (20 µl) was placed into each well of a white 96-well plate and measured with a Mithras Luminometer (Berthold Technologies). The DLR was performed according to the manufacturer's instructions, except that the amount of substrate was reduced to 50 µl per aliquot.HeLa and HEK 293 cells (DSMZ-German Collection of Microorganisms and Cell Cultures) were grown in MEM containing 10% FCS (Gibco) and 1% penicillin/streptomycin (Gibco) under standard cell culture conditions  (50 ng/µl), 0.05% Nonidet P-40, 5 mm MgCl2, 10 mm EDTA, and 2.5% glycerol in 1× binding buffer  and biotin-end-labeled target DNA (20 fmol) and nuclear extract (4 µg). Unlabeled target DNA (4 pmol) was added per 20 µl of binding reaction where indicated. After a pre-electrophoretic run for 30 min at 100 V in 0.5× Tris borate/EDTA, aliquots were loaded onto 6% DNA retardation gels (Invitrogen) and electrophoresed for 50 min at 100 V. The gels were then transferred onto a positively charged nylon membrane  in 0.5× Tris borate/EDTA at 380 mA for 50 min. The samples were cross-linked to the membrane for 15 min on a transilluminator equipped with 312 nm bulbs. Detection was performed using horseradish peroxidase-conjugated streptavidin in combination with the chemiluminescent substrate (LightShift chemiluminescent EMSA kit) according to the manufacturer's instructions.We performed a non radioactive, biotin labeled chemiluminescent EMSA FANCA wild-type  or which had compound, heterozygous FANCA mutations . We used ON-TARGETplus siRNA (Dharmacon) against STAT1, SMAD1, STAT4 and SMAD4 and a DharmaFECT transfection reagent. Twenty-four h before transfection, the cells were split into 12-well plates  at a confluence level between 50% and 80%. The transfections used siRNA solutions (5 µM) in 1× siRNA buffer (Dharmacon). The transfection reactions were performed according to the manufacturer's instructions. After 48 h, the cells were harvested for mRNA analyses.Knockdown experiments were performed in HeLa cells, non-FA human fibroblasts  and in a patient derived, primary fibroblast cell lines that were either 18 primer (2.5 µM) and reverse transcriptase (10 U) from the transcriptor high fidelity cDNA synthesis kit (Roche) were used for the first strand synthesis according to the manufacturer's suggestions. The cDNA was stored at −20°C.Total RNA was obtained with the RNeasy Kit (Qiagen), and it was used for DNase digestion and cDNA synthesis. Up to 10 µg of RNA was treated with the TURBO DNA-free kit (Ambion) at 37°C for 30 min to remove contaminating DNA. Total RNA (1 µg), anchored oligo(dT)STAT1, STAT4, SMAD1, SMAD4 and FANCA were determined using Primer Design™. Each of the samples was examined in triplicate and subjected to qRT-PCR using PerfeCTa SYBR Green SuperMix (Quanta). GAPDH, ACTB and UBC probes were used as internal controls. The relative RNA expression levels were determined by normalizing them to internal controls, and the values were calculated using the comparative Ct method. Statistical differences between 2 groups of data were analyzed using the 2-tailed Student's t test. P values less than 0.05 were considered significant.The qRT-PCR primers for After the knockdown experiments were completed, immunoblots were performed with the samples containing total protein (40 µg) and 7% NuPage Tris-acetate polyacrylamide gels (Invitrogen). The membranes were probed with rabbit polyclonal anti-FANCA . The secondary antibody was a horseradish peroxidase-linked donkey anti-rabbit IgG , and it was detected by the chemiluminescence technique using the ECL system (Amersham). For a loading control, we used a mouse monoclonal anti-p84 . The secondary antibody was a horseradish peroxidase-linked goat anti-mouse IgG .in silico using the promoter prediction and gene2promoter programs (www.genomatix.de) and the UCSC genome browser. The TSSs were taken from the DTSS database where the TSS data were generated by massively sequencing the full-length cDNAs in humans and mice (www.dbtss.hgc.jp). The putative promoter sequences were analyzed for transcription factor binding sites with two software tools: Mat Inspector (Genomatix), and the PROMO tool/database with two sets of similarity (70% and 85%) (Transfac 8.3)  http://meme.sdsc.edu/meme4_5_0/intro.html.The promoter regions were predicted


Arabidopsis thaliana Pol IV-dependent short interfering (p4-si)RNAs are imprinted and accumulate specifically from maternal chromosomes in the developing seeds. Imprinted expression of protein-coding genes is controlled by differential DNA or histone methylation placed in gametes. To identify epigenetic factors required for maternal-specific expression of p4-siRNAs we analyzed the effect of a series of candidate mutations, including those required for genomic imprinting of protein-coding genes, on uniparental expression of a representative p4-siRNA locus.Small RNAs generated by RNA polymerase IV (Pol IV) are the most abundant class of small RNAs in flowering plants. In Paternal alleles of imprinted genes are marked by DNA or histone methylation placed by DNA METHYLTRANSFERASE 1 or the Polycomb Repressive Complex 2. Here we demonstrate that repression of paternal p4-siRNA expression at locus 08002 is not controlled by either of these mechanisms. Similarly, loss of several chromatin modification enzymes, including a histone acetyltransferase, a histone methyltransferase, and two nucleosome remodeling proteins, does not affect maternal expression of locus 08002. Maternal alleles of imprinted genes are hypomethylated by DEMETER DNA glycosylase, yet expression of p4-siRNAs occurs irrespective of demethylation by DEMETER or related glycosylases.Differential DNA methylation and other chromatin modifications associated with epigenetic silencing are not required for maternal-specific expression of p4-siRNAs at locus 08002. These data indicate that there is an as yet unknown epigenetic mechanism causing maternal-specific p4-siRNA expression that is distinct from the well-characterized mechanisms associated with DNA methylation or the Polycomb Repressive Complex 2. Mendelian laws of inheritance state that a genetic element behaves identically when transmitted through maternal or paternal gametes. Genetic elements that break this law by exhibiting preferential or exclusive expression when inherited from one parent are genomically imprinted. Genomic imprinting is well described only in placental mammals and flowering plants, although a number of parent-of-origin-dependent effects are observed in other organisms Flowering plants are characterized by double fertilization, whereby two identical haploid sperm cells in the pollen grain fertilize two cells in the female gametophyte. Fertilization of the haploid egg cell generates the diploid embryo while fertilization of the diploid central cell generates the triploid endosperm. The endosperm is functionally analogous to mammalian placenta, acting as a conduit between maternal somatic tissues and the growing embryo but not contributing genetically to the next generation. Endosperm makes up the bulk of grains such as rice, wheat, and maize, making it a critical tissue for human nutrition. With a single exception in maize Arabidopsis thaliana differential DNA methylation is established by the opposing actions of DNA METHYLTRANSFERASE 1 (MET1) in the paternal gametophyte and the DNA glycosylase DEMETER (DME) in the central cell of the female gametophyte MET1 activates the normally silent paternal allele of FLOWERING WAGENINGEN (FWA), FERTILIZATION INDEPENDENT ENDOPSERM 2 (FIS2) and MATERNALLY EXPRESSED PAB C-TERMINAL (MPC), and reduces expression of the paternal-specific imprinted gene PHERES (PHE) FIS2 and MPCDME outside of the central cell is sufficient to induce expression of another maternal-specific gene, MEDEA (MEA) In plants, imprinted genes are associated with hypomethylated maternal DNA regardless of which allele is expressed. In Arabidopsis are imprinted FWA, MPC, and MEAOver 125 genes in To examine the mechanism of p4-siRNA imprinting we investigated the genetic requirements for maternal expression and paternal silencing of p4-siRNAs at a representative locus. Here we show that differential DNA methylation does not explain uniparental expression of p4-siRNAs at locus 08002 and that various histone modifications, including Histone H3 Lysine 27 methylation (H3K27me), do not establish maternal-specific expression at this site. Furthermore, demethylation of maternal chromosomes by DME is dispensable for p4-siRNA expression in the endosperm.Arabidopsis endosperm Arabidopsis and is responsible for maintenance of CG dinucleotide methylation cmt3 or drm mutants and wild-type plants of a differing ecotype. Five days after fertilization, when p4-siRNA accumulation is highest, we extracted RNA from crossed fruits and performed allele-specific northern blots to determine the parental origin of p4-siRNAs at locus 08002 (Loss of DNA METHYLTRANSFERASE 1 (MET1) does not alter maternal-specific expression of p4-siRNAs at locus 08002 in us 08002 . DemethySUPPRESSOR OF drm1 drm2 cmt3drm1 drm2 cmt3 triple mutant (ddc) as maternal or paternal parent in inter-ecotype crosses. Maternal-specific expression of locus 08002 p4-siRNAs was maintained even when the pollen donor lacked both CHG and CHH methylation  is exceptional because, unlike other imprinted genes, paternal expression is not repressed by MET1-mediated DNA methylation, but rather by histone H3 lysine 27 methylation (H3K27me) placed by the Polycomb Repressive Complex 2 (PRC2). Loss of PRC2 in the pollen or female gametophyte triggers biparental expression of MEA in developing endosperm fertilization independent endosperm (fie). FIE is the only Extra Sex Combs homolog in Arabidopsis and this mutation lacks all potential PRC2 complexes fie is transmitted through pollen (from a heterozygous pollen donor) paternal MEA accumulates in the developing seeds The HISTONE DEACETYLASE 6 (HDA6) is associated with silencing of transposable elements KRYPTONITE (KYP) encodes a histone methyltransferase that catalyzes dimethylation at lysine 9 of histone H3 (H3K9me), the canonical mark of silent chromatin DECREASE IN DNA METHYLATION 1 (DDM1) and MORPHEUS' MOLECULE 1 (MOM1) have similarity to SWI2/SNF2 ATPases and encode presumed nucleosome remodeling proteins. Mutations in DDM1 eliminate DNA methylation and transcriptional silencing from transposable elements MOM1 causes transcriptional reactivation of transgenes and repeated sequences without changes in DNA methylation To determine if other chromatin modifications might repress paternal expression of p4-siRNAs at locus 08002, we tested several candidate genes as above. MEA, FWA, and FIS2MPCDME expression is also sufficient for expression of at least MEA, as ectopic expression of DME triggers MEA accumulation in vegetative tissue and from paternal alleles Arabidopsis including REPRESSOR OF SILENCING (ROS1), a protein implicated in maintaining the expression of transgenes Loci generating p4-siRNAs undergo extensive DME-mediated DNA demethylation in the central cell, leading to differential methylation in endosperm 35S promoter . Unexpectedly, p4-siRNA accumulation in dme seeds was higher than in wild-type siblings  drm2-2 (SALK_150863) cmt3-11 (SALK_148381) hda6-9 , ddm1-2mom1-2 (SALK_141293) fie (GABI 362D08); Landsberg erecta ecotype: cmt3-7kyp-2drm1-1drm2-1dme-1 and dme-2 mutations were isolated in Columbia and backcrossed to Landsberg erectadrm1 drm2 double mutant contained drm1-1 and drm2-1; the ddc triple mutant contained drm1-2, drm2-2, and cmt3-11. Mutations were confirmed using molecular markers or visible phenotypes. Wassilewskijia and C24 contain the Columbia-0 allele at locus 08002  and the resulting entry vectors were recombined into pEARLEYGATE 202 Total RNA from wild-type Columbia-0 leaf tissue was used to reverse transcribe and amplify full-length cDNAs of DML2 and DML3 with the following primers: DML2: 5′-ATTAAGGATTTCCTAGAACG-3′ and 5′-ATCCTAACTGCTATCCTTCC-3′; MEA 5′-GCTAATCGTGAATGCGATCC-3′ and 5′-AGAGAGTCCCATGTAAATGC-3′; ROS1 5′-GGGATGAACCATAAACTTGC-3′ and 5′-CAACTGGAAAGGCAAGATGG-3′; DML2 5′-GCTTGCCGAAAGAATCAAGG-3′ and 5′-CCGACATTCGTGTCAACAGG-3′; DML3 5′-GAATGGCTTCGAAATGCTCC-3′ and 5′-GGTACTCGAATAGTTGATGC-3′; GAPDH 5′-CTCCCTTGGAAGGAGCTAGG-3′ and 5′-GATGCATTGCTGATGATAGG-3′ (Overexpression of DME-family glycosylases was verified with quantitative reverse transcription-PCR using QuantiFast SYBR Green One-Step RT-PCR Kit (Qiagen) and the following primers: DME ATAGG-3′ .RNA was extracted from leaves using TRI® Reagent (Sigma-Aldrich) according to the manufacturer's protocol. RNA from crossed siliques or dissected seeds was extracted as follows: 5–6 siliques were frozen in liquid nitrogen and ground to a fine powder. 500 µL of room temperature extraction buffer  was added and once thawed, samples were further homogenized and placed on ice. Lysates were extracted once with cold Tris-saturated phenol (pH 8.0), twice with cold 25∶24∶1 Tris-saturated phenol∶chloroform∶isoamyl alcohol, and once with cold 24∶1 chloroform∶isoamyl alcohol before precipitation with sodium acetate and ethanol.32P]-ATP and T4 polynucleotide kinase and purified over an illustra MicroSpin G-25 column (GE/Amersham). After overnight hybridization with labeled oligonucleotides in UltraHyb Oligo buffer membranes were washed twice in 2X SSC, 0.1% SDS. Hybridization and washing was at 35°C. Membranes were exposed to phosphor-storage screens for detection of siRNAs.Small RNA was enriched from 30–50 µg total RNA with mirVana miRNA isolation columns (Ambion) according to the manufacturer's protocol. Small RNAs were resolved on a 7M urea/1X TBE/15% acrylamide gel (19∶1 acrylamide∶bisacrylamide) and transferred to Hybond N+ membrane (GE/Amersham). Membranes were UV-crosslinked before pre-hybridization in UltraHyb Oligo buffer (Ambion). Oligonucleotides were labeled with [γ-met5′-TCGAACTCTCGACCTCAGGAT-3′; 08002.L1 5′-CCCATGGTCTCAAACACATCCTCG-3′; 08002.Ler 5′-TCAAGTGAATCTTTAGCGTATGCT-3′; 08002.Col 5′-AGTGAATCTAGAGATTTAGCGTAT-3′; 00687 5′-GTTCCTCGTTCTACCCTCATACCT-3′; 02815 5′-CCATGTCATTCCACCCATCAAAAG-3′; siRNA02 5′-GTTGACCAGTCCGCCAGCCGAT-3′; AtRep2 5′-GCGGGACGGGTTTGGCAGGACGTTACTTAAT-3′; Simplehat 5′-TGGGTTACCCATTTTGACACCCCTA-3′; siRNA1003 5′-ATGCCAAGTTTGGCCTCACGGTCT-3′. All experiments were replicated with independent biological samples.Probe sequences are as follows (underlined bases are LNA): tRNAFigure S1Characterization of DEMETER family overexpression lines. Transgenic lines expressing the four members of the DEMETER family behind the nearly constitutive 35S promoter were assayed for transcript accumulation in leaves by quantitiative reverse transcription-PCR. Overexpression of REPRESSOR OF SILENCING (ROS1) is in the C24 ecotype 35S::DME and 35S::ROS1 lines are homozygous; 35S::DML2 and 35S::DML3 are pooled samples of homozygous and hemizygous T2 individuals. Overexpression of DEMETER (DME) is weak, but sufficient to induce expression of MEDEA (MEA) in leaves (pink bars).(EPS)Click here for additional data file.Figure S2The 08002 polymorphism in various Arabidopsis ecotypes. The p4-siRNA locus 08002 contains a six nucleotide indel between Arabidopsis ecotypes Columbia (Col) and Landsberg erecta (Ler). This polymorphism is the basis of the allele-specific probes 08002.Col and 08002.Ler (hybridizing to the region in bold type). To determine if these probes would also bind siRNAs from other ecotypes, the 08002 region from Wassilewskijia (WS) and C24 was sequenced. These ecotypes are (Col)-like for the indel, but they also differ from Col at a single nucleotide (in red). However, this SNP does not appear to affect hybridization of the Col probe to C24 and WS siRNAs.(TIF)Click here for additional data file.


Cohesin is a well-known mediator of sister chromatid cohesion, but it also influences gene expression and development. These non-canonical roles of cohesin are not well understood, but are vital: gene expression and development are altered by modest changes in cohesin function that do not disrupt chromatid cohesion. To clarify cohesin's roles in transcription, we measured how cohesin controls RNA polymerase II (Pol II) activity by genome-wide chromatin immunoprecipitation and precision global run-on sequencing. On average, cohesin-binding genes have more transcriptionally active Pol II and promoter-proximal Pol II pausing than non-binding genes, and are more efficient, producing higher steady state levels of mRNA per transcribing Pol II complex. Cohesin depletion frequently decreases gene body transcription but increases pausing at cohesin-binding genes, indicating that cohesin often facilitates transition of paused Pol II to elongation. In many cases, this likely reflects a role for cohesin in transcriptional enhancer function. Strikingly, more than 95% of predicted extragenic enhancers bind cohesin, and cohesin depletion can reduce their association with Pol II, indicating that cohesin facilitates enhancer-promoter contact. Cohesin depletion decreases the levels of transcriptionally engaged Pol II at the promoters of most genes that don't bind cohesin, suggesting that cohesin controls expression of one or more broadly acting general transcription factors. The multiple transcriptional roles of cohesin revealed by these studies likely underlie the growth and developmental deficits caused by minor changes in cohesin activity. Drosophila cells. We find that genes that bind cohesin have higher average levels of RNA polymerase and produce more final processed RNA per RNA polymerase than genes that don't bind cohesin. We also find that cohesin binds nearly all DNA sequences located outside of genes that are predicted to regulate gene expression. Reducing cohesin affects RNA polymerase levels at many genes and the predicted regulatory sequences, indicating that cohesin facilitates communication between regulatory sequences and genes. Our data also show that cohesin affects transcription of most genes that don't bind cohesin, likely by controlling transcription of broadly acting transcription factors that regulate many genes.The cohesin protein complex binds to chromosomes and helps ensure that chromosomes are divided equally into the daughter cells when a cell divides. Cohesin also affects how genes are expressed. Small changes in cohesin alter gene expression and development, causing Cornelia de Lange syndrome, a genetic disease. Cohesin influences the amount of RNA produced by many genes, but the reasons are poorly understood. We investigated this question by measuring how changes in cohesin levels affect the level of RNA polymerase, the enzyme that transcribes genes to make RNA, at all genes in  Cohesin is a large protein ring that topologically encircles DNA and participates in several chromosome functions, including sister chromatid cohesion, chromosome segregation, DNA repair, and gene expression  which is associated with significant physical and intellectual deficits , P-TEFb, and Cdk12 in Drosophila cells derived from central nervous system. Ser2P Pol II is actively elongating and formed by the action of the P-TEFb and Cdk12 kinases. We also measured the effects of cohesin and kollerin depletion on transcriptionally-engaged Pol II by precision global run-on sequencing (PRO-seq). We deduce that cohesin directly promotes the transition of promoter-proximal paused Pol II to elongation at many genes that it binds from comparing the changes in Pol II occupancy and activity in control and cohesin-depleted cells. The evidence indicates, that in many cases, cohesin likely facilitates this transition by supporting long-range enhancer-promoter interactions, but also has other roles directly at the promoter. Surprisingly, we also find that cohesin influences Pol II activity at most genes that don't bind cohesin, possibly through control of broadly-acting transcription factors.To directly assess the influence of cohesin on gene transcription, we compared the genome-wide occupancy of Pol II and Pol II kinases relative to cohesin binding, and measured the effects of cohesin depletion on Pol II and kinase occupancy. We used genome-wide chromatin immunoprecipitation with tiling microarrays (ChIP-chip) to measure the genome-wide binding of Pol II, the Cyclin T (CycT) subunit of the P-TEFb complex, and the Cdk12 Pol II kinase in ML-DmBG3 (BG3) Drosophila cells derived from larval central nervous system. We used antibodies against the Rpb3 subunit of Pol II diminutive, the Drosophila myc gene , especially on gene bodies . Pol II ChIP does not determine if Pol II is transcriptionally engaged, or the direction it is transcribing. We thus used precision global run-on sequencing PRO-seq; , a variaThe number of PRO-seq reads was quantified for nearly 17,000 annotated transcription units, and after normalization for the total number of reads, the genome-wide correlations between the two biological replicates were 0.98 for all three groups . We seleCohesin-binding genes have more Pol II on average than non-binding active genes as measured by both PRO-seq and ChIP-chip. When active genes are subdivided into four groups  from lowA prior report indicated that cohesin preferentially binds genes with promoter-proximal paused polymerase, based in part on genome-wide overlap of cohesin with the Negative Elongation Factor (NELF) pausing factor, and the higher levels of short promoter-proximal transcripts produced by cohesin-binding genes cut (FlyBase FBgn0004198) and Ultrabithorax (FlyBase FBgn0003944) genes by remote tissue-specific enhancers The Drosophila Nipped-B kollerin subunit was discovered in a genetic screen for factors that control long-range activation of the The average cohesin occupancy of the extragenic CRMs is higher than that for all active promoters, while the Pol II occupancy of active promoters is higher than that of the CRMs . PRO-seqcut, in a region without genes, and which produces no mRNA. The surrounding region contains several enhancers that regulate the cut gene throughout development. The wing margin enhancer whose function is sensitive to Nipped-B dosage in vivo is 12 kb upstream of these putative CRMs, and several other tissue-specific enhancers are downstream cut gene as measured by Ser2P Pol II ChIP  depletion substantially reduces the level of elongating Pol II on the  II ChIP , and the II ChIP . Cohesinmyc (dm) gene that promotes cell growth Stable topological binding of cohesin to chromosomes requires loading by kollerin. Thus, depletion of cohesin and kollerin would be expected to have comparable genome-wide effects on Pol II if topologically-bound cohesin is the form that influences transcription. We compared PRO-seq measurements in mock-treated control BG3 cells to cells in which the Rad21 cohesin subunit or the Nipped-B kollerin subunit were depleted by approximately 80% using RNAi. Under these depletion conditions, there are no measurable defects in sister chromatid cohesion or chromosome segregation, and a modest decrease in the rate of cell division, which may reflect decreased expression of the Drosophila The effects of Rad21 and Nipped-B depletion on the PRO-seq signals in the promoter regions and gene bodies of the PRO-seq active genes are remarkably similar. The maximal changes included increases and decreases approaching 15-fold at promoters , and somCohesin and kollerin depletion also had very similar effects on the pause index, which measures the efficiency with which paused Pol II enters into elongation. Upon Rad21 or Nipped-B depletion, genes with high cohesin levels showed increased and decreased pausing at similar frequencies . Thus, dinvected and engrailed, are simultaneously targeted by Polycomb silencing proteins, and increase dramatically in expression upon cohesin depletion. PRO-seq confirms that such genes are among those that show the largest pausing decreases  mice The key suspects for general factors controlled by cohesin are general basal transcription factors, or possibly Diminutive (Dm), the Drosophila Myc protein . CohesinBased on their higher Pol II occupancy, we expected that cohesin-binding genes would produce more mRNA on average, in proportion to the Pol Il levels. We observed, however, that they produced disproportionately more steady-state mRNA per transcriptionally-engaged Pol II complex, with the genes that have high cohesin levels being twice as efficient as the genes that lack cohesin. Cohesin depletion did not reduce the efficiency, indicating that these genes have other features that make them more efficient. Prior studies show that cohesin-binding genes lack the H3K36me3 histone modification, which is found on other active genes, and is mediated by the Set2 protein that travels with the phosphorylated C terminal domain of the Rpb1 Pol II subunit Culture of ML-DmBG3-c2 (BG3) cells and RNAi depletion of Nipped-B and Rad21 were conducted as previously described Genomic chromatin immunoprecipitation of RNAi-treated and mock-treated BG3 cells was performed using Affymetrix Drosophila 2.0R genome tiling arrays as previously described http://bdtnp.lbl.gov/TiMAT/). MAT is the optimal algorithm for analysis of Affymetrix array ChIP-chip, and provides peak detection sensitivity equivalent to ChIP-seq performed at a density of one read per genome base pair MAT software 8 control or RNAi-treated BG3 cells were collected by centrifugation (1000 g for 5 min), suspended in 5 to 10 mL Phosphate Buffered Saline (PBS) pH 7.0, collected by centrifugation, suspended in 5 mL Buffer W , and collected by centrifugation. The cells were suspended in 5 mL Buffer P , the suspension was adjusted to 0.14% NP-40, and then incubated on ice for 3 min. The nuclei were washed twice in 5 mL Buffer W, suspended in 1 mL Buffer W, and transferred to a siliconized 1.5 mL microcentrifuge tube. The nuclei were collected by centrifugation at 1000 g for 5 min, suspended in 0.5 mL Buffer F , and counted using a hemacytometer. The nuclei were then suspended in Buffer F to concentration of 40 to 50×105 per microliter, distributed into 100 microliter aliquots in siliconized 1.5 mL tubes, snap frozen in liquid nitrogen, and stored at −80°. The PRO-seq data has been deposited in the GEO database (accession no. GSE42399).Precision global run-on sequencing for control cells, and cells depleted for Rad21 and Nipped-B, was conducted as described elsewhere http://www.R-project.org), and custom programs. After confirming high correlations between the duplicate samples , bed files showing binding of Rpb3, Ser2P Pol II and CycT at p≤10http://www.affymetrix.com/partners_programs/programs/developer/tools/download_igb.affx) was used to generate bed files indicating where the Rad21 RNAi sample enrichment differs from the enrichment in control cells by at least two standard deviations from the median genome-wide difference over at least 105 bp  for Pol II or Pol II kinases were calculated at each of the nearly 2.8 million points measured across the genome. The distributions of the differences, means, medians and standard deviations of the differences were determined using R. In all cases, there was minimal skew in the distribution of differences, and both the mean and median differences were nearly identical and close to zero. The thresholding tool of the Affymetrix Integrated Genome Browser  Cohesin-binding groups of active genes used in (TIF)Click here for additional data file.Figure S2Cohesin binding correlates with the H3K27ac and H3K4me1 histone modifications at extragenic cis regulatory modules (CRMs). The box plots in the upper left panel show the distributions of the cohesin, H3K27ac and H3K4me1 ChIP signals at 557 extragenic CRMs . The rem(TIF)Click here for additional data file.Figure S3arm, bab2 and pnt, and 4000 for aop. The Δ tracks below the ChIP tracks show the difference in MAT score between the Rad21 depleted and control cells. The bars above and below the Δ tracks show where increases and decreases are ≥2 σ for regions ≥105 bp. (C) The left panel shows the Rad21 (cohesin) levels for the genes used in this analysis. They are three of the four groups shown in Cohesin depletion increases Cdk12 occupancy in gene bodies that have decreased phosphorylated Pol II. (A) Overlap of cohesin-Cdk12 binding genes with decreases in Ser2P Pol II in the gene body and increases in Cdk12. “Other” indicates no significant changes, an increase in Ser2P Pol II, or a decrease in Cdk12. (B) Examples of genes with Ser2P Pol II decreases in the gene body and an increase in Cdk12 and/or CycT. The top tracks show the PRO-seq reads in the control cells. The PRO-seq scale is 1000 for (TIF)Click here for additional data file.Figure S4Validation of Ser2P Pol II antibody. The panels show a western blot of whole cell extracts of BG3 cells mock treated or treated with 1 µM flavopiridol, a P-TEFb inhibitor. The top panel shows the signal obtained with Ser2P Pol II antibody (Abcam ab5095) diluted 1∶1000. The middle panel is the same western reprobed with the 8WG16 antibody that recognizes primarily non-phosphorylated Rpb1, and the bottom panel shows the same blot probed with anti-actin as an internal standard. The asterisk (*) indicates a band that co-migrates with non-phosphorylated Rpb1, indicating that ab5095 antibody may slightly cross-react with non-phosphorylated Rpb1.(TIF)Click here for additional data file.Figure S55′-TAATACGACTCACTATAGGGAGACTCTTCCCAATGAGCCTCTG-3′ and 5′-TAATACGACTCACTATAGGGAGACATGGATGGTGGTACAGCAG-3′, and for dsRNA 2 5′-TAATACGACTCACTATAGGGAGACAAGCTAAATAGCCATCCGC-3′ and 5′-TAATACGACTCACTATAGGGAGAGGCGTGTGTTTCTCCTCAT-3′. Proteins were extracted from cells with buffer (10 microliters per ∼5×105 cells) containing 40 mM Tris-HCl pH 7.4, 8 M urea, and 1% NP-40. After SDS-PAGE (∼2.5×105 cells per lane) on a 4–20% gradient gel (Biorad TGX), proteins were electrotransferred to Immobilon-P membrane in buffer contain 100 mM CAPS pH 10.8 and 10% methanol. The western blot was probed with a 1∶1000 dilution of the CycT rabbit antiserum Validation of CycT antibody. BG3 cells were mock treated (M) or treated with one of two different dsRNAs (1 and 2) against with CycT for 3 days. Templates for synthesis of dsRNA were made by PCR from genomic DNA and dsRNA was prepared as previously described (TIF)Click here for additional data file.Table S1PRO-seq sequencing statistics.(DOCX)Click here for additional data file.Table S2Average PRO-seq promoter (P) and gene body read densities in annotated transcription units.(XLSX)Click here for additional data file.Table S3Predicted extragenic cis-regulatory modules (CRMs).(XLSX)Click here for additional data file.


Two major terminal oxidases are involved in this process - Cyo has a relatively low affinity for O2 but is able to pump protons and hence is energetically efficient; Cyd has a high affinity for O2 but does not pump protons. When E. coli encounters environments with different O2 availabilities, the expression of the genes encoding the alternative terminal oxidases, the cydAB and cyoABCDE operons, are regulated by two O2-responsive transcription factors, ArcA (an indirect O2 sensor) and FNR (a direct O2 sensor). It has been suggested that O2-consumption by the terminal oxidases located at the cytoplasmic membrane significantly affects the activities of ArcA and FNR in the bacterial nucleoid. In this study, an agent-based modeling approach has been taken to spatially simulate the uptake and consumption of O2 by E. coli and the consequent modulation of ArcA and FNR activities based on experimental data obtained from highly controlled chemostat cultures. The molecules of O2, transcription factors and terminal oxidases are treated as individual agents and their behaviors and interactions are imitated in a simulated 3-D E. coli cell. The model implies that there are two barriers that dampen the response of FNR to O2, i.e. consumption of O2 at the membrane by the terminal oxidases and reaction of O2 with cytoplasmic FNR. Analysis of FNR variants suggested that the monomer-dimer transition is the key step in FNR-mediated repression of gene expression.In the presence of oxygen  availabilities. It has two well-characterized terminal oxidases, Cyd and Cyo. Cyd has a very high affinity for O2, whereas Cyo has a lower affinity, but is energetically more efficient. Expression of the genes encoding Cyd and Cyo is controlled by two O2-responsive regulators, ArcBA and FNR. However, it is not clear how O2 molecules enter the E. coli cell and how the locations of the terminal oxidases and the regulators influence the system. An agent-based model is presented that simulates the interactions of O2 with the regulators and the oxidases in an E. coli cell. The model suggests that O2 consumption by the oxidases at the cytoplasmic membrane and by FNR in the cytoplasm protects FNR bound to DNA in the nucleoid from inactivation and that dimerization of FNR in response to O2 depletion is the key step in FNR-mediated repression. Thus, the focus of the agent-based model on spatial events provides information and new insight, allowing the effects of dysregulation of system components to be explored by facile addition or removal of agents.The model bacterium  Escherichia coli is a widely used model organism to study bacterial adaptation to environmental change. As an enteric bacterium, E. coli has to cope with an O2-starved niche in the host and an O2-rich environment when excreted. In order to exploit the energetic benefits that are conferred by aerobic respiration, E. coli has two major terminal oxidases: cytochrome bd-I (Cyd) and cytochrome bo′ (Cyo) that are encoded by the cydAB and cyoABCDE operons, respectively 2 and is induced at low O2 concentrations (micro-aerobic conditions), whereas Cyo has a relatively low affinity for O2 and is predominant at high O2 concentrations (aerobic conditions) 2 allows the bacterium to maintain aerobic respiration at nanomolar concentrations of O2, thereby maintaining aerobic respiratory activity rather than other, less favorable, metabolic modes The bacterium cydAB and cyoABCDE expression in response to O2 supply 2 in the cytoplasm 2 the FNR iron-sulfur cluster is stable and the protein forms dimers that are competent for site-specific DNA-binding and regulation of gene expression 2 in such a way that the DNA-binding dimeric form of FNR is converted into a non-DNA-binding monomeric species E. colicydAB and cyoABCDE operons, which are repressed by FNR when the O2 supply is restricted cydAB and cyoABCDE is relieved and Cyd and Cyo proteins are synthesized 2 availability indirectly via the membrane-bound sensor ArcB. In the absence of O2 ArcB responds to changes in the redox state of the electron transport chain and the presence of fermentation products by autophosphorylating E. coli genome cydAB and cyoABCDE to enhance synthesis of Cyd and inhibit production of Cyo 2 at the cell membrane, a feedback loop is formed that links the activities of the oxidases to the regulatory activities of ArcA and FNR  and maximal Cyo content when O2 is abundant (aerobic conditions) The transcription factors, ArcA and FNR, regulate  and FNR . These fcydAB and cyoABCDE), the regulators (ArcBA and FNR) and the oxidases (Cyd and Cyo) are likely to be fundamentally important in the regulation of this system, the potential significance of this spatial organization has not been investigated. Therefore, a detailed agent-based model was developed to simulate the interaction between O2 molecules and the electron transport chain components, Cyd and Cyo, and the regulators, FNR and ArcBA, to shed new light on individual events within local spatial regions that could prove to be important in regulating this core component of the E. coli respiratory process.Although the cellular locations of the relevant genes  was adopted to investigate the effects of low O2 concentrations on bacteria 2 input that results in undetectable excretion of the fermentation product acetate under carbon-limiting conditions is defined as 100% aerobiosis (100% AU). As the amount of O2 supplied to cultures decreases the specific rate of acetate production (qacetate) increases to a maximum under anaerobic conditions (0% AU). Between these limits (0–100% AU) lies the micro-aerobic range, defined by the linear decrease in qacetate as the O2 transfer rate increases, i.e. there is an inverse correlation between qacetate and aerobiosis 2 supply exceeds the minimum required to abolish acetate excretion the AU value extends beyond 100%, reaching 217% when the culture medium is O2 saturated. This physiological measurement of O2 availability is reliable for cultures grown at values as low as 4% AU The starting point for this work was the suggestion that spatial effects play an important role in controlling the response of the 2 molecules, terminal oxidases, (Cyd and Cyo) and regulators (FNR and ArcBA), which interact with O2 directly or indirectly. As shown in E. coli two-component system, PhoBR E. coli cell. From the initial state, the agents start moving randomly within their 3-D activity space, and interact with each other according to a set of pre-defined interaction rules and interaction radii . Under these conditions, the FNR molecules are present as dimers, all ArcB molecules are phosphorylated and the ArcA is octameric. The DNA binding sites for ArcA (120 in the model) and FNR (350 in the model) in the nucleoid are fully occupied. The number of ArcA sites was chosen from the data reported by Liu and De Wulf 2 into anaerobic steady-state chemostat cultures ∼5 min was required to inactivate ArcA-mediated transcription ½ is ∼45 sec, which is equivalent to ∼0.3% ArcA∼P leaving the DNA per iteration , but fully aerobic metabolism is supported when the O2 supply exceeds 1,000 O2 molecules per cell. The profiles of five repetitive simulations for each agent in the model are presented in 2 was supplied at a constant value of ∼6,500 molecules per cell such that the total number of O2 molecules entering the cell increased linearly; when the O2 supply was stopped (5000 to 15000 and 20000 to 30000 iterations) no more O2 entered the cell and thus the number of O2 molecules that had entered the cell remained unchanged during these periods (2 became available to the cell (from iteration 1), the sensor ArcB was de-phosphorylated and started to de-phosphorylate ArcA. Consequently, the number of ArcA octamers bound at their cognate sites in the nucleoid decreased rapidly. The ArcA tetramers and dimers produced during de-phosphorylation of the ArcA octamer were transformed to inactive (de-phosphorylated) ArcA dimers, (2 supply was stopped (from iteration 5001), the number of inactive ArcA dimers decreased rapidly as shown in 2 withdrawal was slower than the rate of ArcA inactivation  FNR was activated over a similar timeframe to ArcA  decreased when Oe system , but the to ArcA , which wdy-state . These rlacZ reporter, the ArcBA system agents were removed from the model by setting their agent numbers to zero. The ArcBA system is an indirect O2 sensor and does not consume O2, hence the FNR system was not affected by withdrawing ArcBA from the model, but this simplification increased simulation speed.In order to validate the model with biological measurements of FNR DNA-binding activity estimated using an FNR-dependent 2 step length and other model parameters were estimated using the experimental data obtained at 31% AU. Using the estimated O2 step length at 31% AU and defining the step length of O2 molecule, 2 at other AU levels, where k = 2.1 and 2 concentration at different AU levels  under aerobic and anaerobic conditions was tested 2 = 5 nm; 2 to 7 nm for all the FNR proteins considered here. The results of the simulations for both FNR variants now matched the experimental data well . To achieve rapid FNR response times it has been suggested that minimizing the range of O2 concentrations that constitute a micro-aerobic environment, from the viewpoint of FNR, is advantageous 2 environments. Consumption of O2 by terminal oxidases at the cytoplasmic membrane and reaction of O2 with the iron-sulfur clusters of FNR in the cytoplasm present two barriers to inactivation of FNR bound to DNA in the nucleoid, thereby minimizing exposure of FNR to micro-aerobic conditions by maintaining an essentially anaerobic cytoplasm for AU values up to ∼85%. It is suggested that this buffering of FNR response makes the regulatory system more robust by preventing large amplitude fluctuations in FNR activity when the bacteria are exposed to micro-aerobic conditions or experience environments in which they encounter short pulses of low O2 concentrations. Furthermore, investigation of FNR variants with altered oligomerization properties suggested that the monomer-dimer transition, mediated by iron-sulfur cluster acquisition, is the primary regulatory step in FNR-mediated repression of gene expression. It is expected that the current model will act as a foundation for future investigations, e.g. predicting the effects of adding or removing a class of agent to identify the significant regulatory components of the system.The FNR switch has been the subject of several attempts to integrate extensive experimental data into coherent models that account for changes in FNR activity and target gene regulation in response to O2 supply, E. coli cells was required in order to simulate the response of the regulators of cydAB and cyoABCDE to different O2 availabilities. Therefore, un-inoculated chemostat vessels were used to measure dissolved O2 concentrations, 2 in the input gas, iP, in the absence of bacteria. This allowed the rate at which O2 dissolves in the culture medium to be calculated from the equation: 2 molecules distributed to a single bacterial cell was then calculated from the following equation: AN is the Avogadro constant (6.022×1023), cellV is the volume of E. coli cell (0.3925 µm3) and as a constant for this equation, n (3.3×10−9) includes the unit transformations, min to sec (60−1) and µmol to mol (10−6), and the time unit represented by an iteration (0.2 sec).Knowledge of the rate of O2) are able to move and interact within the confines of their respective locations in a 3-D-cylinder representing the E. coli cell. To control the velocity of agents, the maximal distances they can move in 3-D space during one iteration (step length) were pre-defined . As, thus far, no accurate biological data is available for ArcBA system, the radii 5…11r were arbitrarily defined and were refined by training the model to match current biological expectations.Interactions between agents depend upon the biological rules governing their properties and being in close enough proximity to react. The interaction radius of an agent encapsulates the 3-D space within which reactions occur. As the interaction radii cannot be measured, they were first estimated on the basis of known biological properties. For the radii  and r13 , arbitra and r13 . The mod and r13  during mE. coli cell was modeled as a cylinder (500 nm×2000 nm) 2) and the rules for ArcBA and FNR are provided in , http://www.flame.ac.uk) acted as the framework to enable the simulation The rod-shaped fnr ligated into pBR322 E. coli JRG4642 (an fnr lac mutant strain) containing a pRW50-based reporter plasmid carrying the lac-operon under the control of the FFgalΔ4 promoter Plasmids encoding the FNR variants were constructed by site-directed mutagenesis  of pGS196, which contains a 5.65 kb fragment of wild-type 600 = 0.35) before assaying for β-galactosidase activity.β-Galactosidase assays were carried out as described previously on strains grown in LBK medium at pH 7.2 containing 20 mM glucose Figure S12 molecules.Stategraph for O In order to describe the model clearly, every agent is given a formal description to illustrate its states, memory, functions, and relevant messages that it sends out or receives from other agents (see ents see . The sta(TIFF)Click here for additional data file.Figure S2Building a FLAME simulation file. The agent definition (written in XMML) is parsed by a FLAME model parser, called xparser, which generates the simulation code. In the GCC environment, the code is compiled with the message board library, libmboard. The initial agent population settings are set in 0.xml file as the starting status of the model.(TIFF)Click here for additional data file.Table S12 molecule.Agent description for the O(DOCX)Click here for additional data file.Text S1Additional description of interaction rules for the regulatory systems, ArcBA and FNR.(DOCX)Click here for additional data file.Text S2The agent-based modeling framework: FLAME.(DOCX)Click here for additional data file.Video S12 over two 0–217% AU cycles.Simulation of ArcBA and FNR activities in response to O(MP4)Click here for additional data file.


In addition to protein coding genes a substantial proportion of mammalian genomes are transcribed. However, most transcriptome studies investigate steady-state mRNA levels, ignoring a considerable fraction of the transcribed genome. In addition, steady-state mRNA levels are influenced by both transcriptional and posttranscriptional mechanisms, and thus do not provide a clear picture of transcriptional output. Here, using deep sequencing of nuclear RNAs (nucRNA-Seq) in parallel with chromatin immunoprecipitation sequencing (ChIP-Seq) of active RNA polymerase II, we compared the nuclear transcriptome of mouse anemic spleen erythroid cells with polymerase occupancy on a genome-wide scale. We demonstrate that unspliced transcripts quantified by nucRNA-seq correlate with primary transcript frequencies measured by RNA FISH, but differ from steady-state mRNA levels measured by poly(A)-enriched RNA-seq. Highly expressed protein coding genes showed good correlation between RNAPII occupancy and transcriptional output; however, genome-wide we observed a poor correlation between transcriptional output and RNAPII association. This poor correlation is due to intergenic regions associated with RNAPII which correspond with transcription factor bound regulatory regions and a group of stable, nuclear-retained long non-coding transcripts. In conclusion, sequencing the nuclear transcriptome provides an opportunity to investigate the transcriptional landscape in a given cell type through quantification of unspliced primary transcripts and the identification of nuclear-retained long non-coding RNAs. While the full complexity of mammalian transcriptomes has yet to be characterized, it is clear that far more transcription occurs than can be accounted for by protein-coding genes alone Hbb), tissue-specific chromatin loops form between expressed genes and the locus control region (LCR) located approximately 50 kb upstream Hba), Th2, MHC, IgH, Igκ, HoxB1, CFTR, and olfactory receptor genes A study of global genome folding revealed that the active and inactive portions of the genome are individually segregated The multitude of processes that influence regulation of transcription impose challenges on the analysis of the transcriptome. In this study we analyse transcriptional output and RNAPII association in adult mouse anemic spleen erythroid cells by generating genome-wide nuclear RNA and RNAPII chromatin-association Hba, Slc4a1 and Uros, known erythroid-expressed genes were represented, while a non-transcribed region of the IgH locus (VH16) was not detected  positive RNA isolated from intact cells and thus reflect the steady-state levels of mRNA present in the cell population detected . To assedetected . FurtherHba-a1, Hba-a2) and β-like globin genes (Hbb-b1 and Hbb-b2), the erythrocyte membrane protein band 3 (Scl4a1) and hydroxymethylbilane synthase  had lower coverage, while the silent, brain-specific gene Nefm was not enriched above the surrounding intergenic background coverage.We generated a nuclear transcriptome for adult mouse anemic spleen erythroid cells by sequencing our validated nuclear RNA (nucRNA-Seq) using the Illumina paired-end sequencing protocol . We obtaWe calculated the number of reads per kilobase of gene length per million mapped reads (RPKM) at mouse Ensembl genes for our three replicate nucRNA-Seq libraries  [32]. A Gata1-null mouse ES cells Gata1 expression. This is achieved upon treatment with estradiol (E2) in the G1E-ER4 subline which contains an estrogen-activated Gata1-estrogen receptor transgene To confirm that nucRNA-Seq is indicative of raw transcriptional output, we compared exonic and intronic coverage to demonstrate that the sequence data was derived mainly from unprocessed, immature transcripts. In a hypothetical, completely unspliced transcript, we would expect the mean exonic and intronic coverage depths to match, with removal of introns resulting in an exonic bias (high coverage of exons relative to introns). As a proof of concept for this hypothesis, we first compared our nucRNA-Seq dataset to a poly(A) enriched RNA-Seq dataset from a similar cell type. The G1E cells are committed erythroid progenitor cells derived from s = 0.850, 95% CI , p<0.01). Dividing each gene into 5′, body, and 3′ thirds, we observed the association between exonic and intronic coverage levels to increase in a 5′ to 3′ direction , with 5′ regions displaying a slight exonic bias . This indicates that nucRNA quantity is reflective of the frequency at which primary transcription occurs in the cell population for erythroid-expressed protein-coding genes  positive mRNA. To do this we compared RPKM coverage over the first exon in our nucRNA-Seq data to RPKM coverage over the first exon in the G1E and G1E-ER4+E2 RNA-Seq data  of the large subunit of the polymerase complex (RPB1) phosphorylated at serine 5 (S5) of the carboxy-terminal domain (CTD). The RPB1 CTD consists of a heptapeptide repeat of the consensus sequence YSPTSPS As our nucRNA-Seq data correlated with Slc4a1, Slc4a1 transcription unit by qPCR (Sequencing of RNAPII ChIP (ChIP-Seq) using the Illumina paired-end sequencing protocol produced more than six million paired, aligned 36 bp sequences from each of the immuno-purified and genomic input fractions. As expected, and similar to the nucRNA-Seq coverage, RNAPII was associated with erythroid-expressed genes . As for  by qPCR .s = 0.683, 95% CI , p<0.01) and for average coverage depth   .We next investigated the relationship between RNAPII association examined by RNAPII ChIP-Seq and transcriptional output assayed by nucRNA-Seq. Using a 10 kb window, we compared RNAPII ChIP-Seq and nucRNA-Seq coverage depth throughout the genome. We identified the highly enriched outliers in each dataset using the boxplot method (thresholds set as Q3+(1.5×IQR), where Q3 is the upper quartile limit and IQR the interquartile range). Using these thresholds we defined four classes of genomic sequences in our data; regions that were highly RNAPII-bound and transcribed (BT), bound by RNAPII but not highly transcribed (B), transcribed but not highly RNAPII-associated (T), and regions that were not highly RNAPII-associated or transcribed loBT, . RegionsTo explore the relationship between polymerase association and nascent transcription for genic regions, we re-defined the BT, B, T, and loBT classes in our data based on coverage of all Ensembl genes NCBIM37,  rather tNext we calculated the ratio of nucRNA-Seq: RNAPII ChIP-Seq maximum coverage depth as a measure of polymerase transcription efficiency. BT genes display a more efficient ratio of 0.936 compared to 0.394 for loBT genes (5% trimmed mean for each category) suggesting that the observed differences in transcriptional output are not simply explained by different levels of RNAPII association. Instead, it appears that polymerase associated with BT genes is producing RNA more efficiently. The T group displays the most efficient average nucRNA-Seq: RNAPII ChIP-Seq ratio of 12.7.Terc (vertebrate telomerase RNA), small nucleolar RNAs, signal recognition particle RNAs, micro RNAs, and small Cajal body-specific RNAs in the T group of genes suggesting this group is enriched for stable functional and structural RNAs. We later use this signature to identify intergenic regions encoding stable nuclear-retained transcripts (described below). Also included in this group were rRNAs and 7SK RNAs, which are transcribed by RNAPI and RNAPIII respectively and are therefore not expected to associate with RNAPII. B genes seem to be involved in stress responses (though the enrichments were not significant following multiple testing correction) and housekeeping functions , which may point to “poising” of certain genes in readiness for anticipated functions.To probe whether there were any functional relationships behind the differences we observed in transcriptional behaviour, we compared the Gene Ontology (GO) term enrichments between the BT, T and B groups using the DAVID  of the locus control region  algorithm which was originally designed to identify transcription factor binding sites Taking the We next investigated these RNAPII+/nucRNA- regions for additional features associated with regulatory regions. To this end, we examined the evolutionary sequence conservation around our candidates, which has been shown to improve identification of regulatory modules Hbb LCR, a TF-bound validated enhancer upstream of the Lmo2 gene Pim1 and Klf3 genes  In addition to the RNAPII-associated intergenic regions we also noticed that a number of intergenic regions are transcribed above background levels. Many of these transcribed intergenic regions appeared to be several kilobases in size, potentially representing long non-coding RNAs (lncRNAs). lncRNAs are emerging as mediators in the regulation of genome function, alongside and in combination with epigenetic and transcription factor-based mechanisms To obtain as large an initial candidate cohort as possible, we used the ‘Contig Probe Generator’ feature of SeqMonk Malat1, 5830416P10Rik, A130040M12Rik, Gm1995 and Neat1, 17 (5.5%) overlapped lncRNAs identified by Guttman et al. 2009 and 36 (12%) overlapped erythroid expressed lncRNA identified by Hu et al. 2011 Neat1 transcript  is a lncRNA shown to be a post-transcriptional regulator of transcription in synaptogenesis Candidate 1, which is by far the most highly expressed of all the candidates, corresponds to The recent explosion in the number of genome-wide datasets has greatly increased our appreciation of transcriptome complexity and regulation, particularly the role of polymerase distribution, intergenic regulatory elements and non-coding RNAs. Here we study transcriptional output in erythroid cells by sequencing nuclear RNA and chromatin bound by active RNA polymerase II. We show that nucRNA-Seq identifies mainly unspliced primary transcripts and is significantly different than poly(A)-enriched RNA-Seq. Then, we investigated the relationship between RNAPII occupancy and nucRNA output, identified intergenic regions of the genome associated with RNAPII which have characteristics of regulatory regions and identified novel, stable, nuclear-retained lncRNAs expressed in adult erythroid cells.S. cerevisiae, identifying the 3′ ends of nascent transcripts, using the NET-Seq (native elongating transcript sequencing) technique, identified numerous pause sites within genes Our observations show that a generalized level of RNAPII occupancy is a poor predictor of expression levels for most transcription units, with only very highly expressed RNAPII-transcribed genes showing a correlation between RNAPII association and transcriptional output. These results suggest that polymerase occupancy is just one of potentially many factors influencing the level of transcription of chromatin templates. Peaks of RNAPII found in promoter-proximal regions have been suggested to represent paused polymerase and correlate with lower expression S. cerevisiae have been associated with more rapid induction of transcription We also found that accumulation of polymerase at the 5′ end of genes is not always associated with lower expression. In particular, genes featuring both 5′ and 3′ RNAPII peaks are more efficiently transcribed than genes with either peak alone. These peaks of RNAPII located within both the 5′ and 3′ regions of the “double RNAPII peak” genes may reflect a point of chromatin-chromatin interaction between these two regions allowing both locations to be captured in the RNAPII pull-down. Gene loop interactions between the promoter and 3′ end of inducible genes in HBB LCR has promoter activity and the entire LCR region is transcribed Hbb gene, synthesis of short-lived LCR ncRNA, or both. A previous study has identified LCR transcripts and shown that RNAPII is present at the LCR in mouse embryonic stem cells which do not express any of the Hbb genes suggesting the LCR recruits RNAPII independently of and prior to Hbb gene transcription Long range chromatin interactions are known to occur between regulatory regions and active genes Kcnq1ot1 transcript being one example  5 days after treating them with phenylhydrazine RNAPII ChIP was carried out as described in Mitchell and Fraser 2008 2, 0.1 M sucrose, 0.1% Triton X-100 and 0.5 mM DTT, a single cell suspension was homogenised with a B-type Dounce. Intact nuclei were then separated from cytoplasmic debris through a 5 mM MgCl2, 10 mM Tris pH 8.0, 0.5 mM DTT, 0.33 M sucrose cushion at 300 g and re-suspended in 10 mM Tris-HCl pH 7.5, 10 mM NaCl, 3 mM MgCl2. RNA was purified from nuclear and cytoplasmic fractions using Trizol LS (Invitrogen) according to the supplier's instructions. Purified RNA was treated with 10 U of DNaseI (Roche) for 20 min at 28°C. RNA quality was verified on a Bioanalyzer (Agilent). Reverse transcription was performed using Superscript II (Invitrogen) and 10 µg random hexamer primers (Roche) per 500 ng RNA. Second strand synthesis was performed using E. coli RNase H (Ambion) and E. coli DNA Polymerase I (NEB) as described in Sambrook and Russell 2001 Genome-wide transcriptional output was characterized by sequencing a double-stranded cDNA library constructed from nuclear RNA (nucRNA). Following a hyper-osmotic swell in 10 mM Tris-HCl pH 7.5, 10 mM NaCl, 3 mM MgClet al 2008, with all reactions scaled according to starting DNA quantity http://www.ebi.ac.uk/ena/data/view/ERP000702). Sequences were aligned using Bowtie Library preparation was performed according to Illumina PE genomic protocol, incorporating improvements suggested in Quail Tissue was obtained and disrupted as described above, cultured for 1 or 4 hours in Dulbecco's Modified Eagle Medium (Gibco) supplemented with 10% fetal bovine serum and 10 µg/ml Actinomycin D (Sigma) with gentle mixing. Nuclear RNA and cDNA were prepared as detailed above. This cDNA was then used to assess the transcript stability in the absence of active transcription (primer sequences listed in All RT-qPCR was carried out using SYBR Green on an ABI 7000 detection system (both Applied Biosystems). Primer sequences listed in RNA FISH was carried out as detailed in Figure S1Reproducibility of nucRNA-Seq coverage. RPKM values of nucRNA-Seq coverage in three biological replicate nucRNA-Seq libraries  are highly correlated . Scales represent log2 RPKM values taken for Ensembl genes (genome version NCBIM37), *** indicates p<0.0001, correlation coefficients represent Spearman's rho.(PDF)Click here for additional data file.Figure S2RT-qPCR Validation of nucRNA-Seq coverage for 48 amplicons. Observed coverage in our sequence data for 48 randomly selected nucRNA-enriched regions was validated. For these regions, we assayed RNA levels by RT-qPCR in two independent nuclear RNA preparations. We observed a significant association between both the maximum nucRNA-Seq coverage depth (Spearman's rho (rs) = 0.761, 95% CI , p<0.01) and average coverage depth .(PDF)Click here for additional data file.Figure S3Normalising data coverage to input genomic DNA coverage. A) A SeqMonk screenshot of a 0.5 Mb region around the Hjurp locus is depicted. Each track contains individual reads  and bars representing quantitated average coverage depth, non-normalised to input levels, for a 5 kb sliding window (1 kb step size). False positive enrichment of both nucRNA-Seq and RNAPII ChIP-Seq coverage can be observed around the Hjurp locus, in the area where input coverage is abnormally high. The need for normalisation is demonstrated by the fact that while clearly the Hjurp gene  is RNAPII bound and transcribed, it is not bound or transcribed at the levels indicated by non-normalised measures of coverage. (B and C) Shows a comparison of non-normalised RNAPII ChIP-Seq (B) and nucRNA-Seq (C) average coverage depth against the average input gDNA coverage depth for all annotated genes (NCBIM37), the middle panel shows a histogram of average coverage depth for annotated genes. The right histogram shows the same coverage normalised to the corresponding input value (fold enrichment over input).(PDF)Click here for additional data file.Figure S4Nuclear RNA-Seq data compared to RNA-Seq data. RPKM values for exon 1 were compared between erythroid nucRNA-Seq and two erythroid RNA-Seq (G1E and G1e_ER4_E2). The two RNA-Seq libraries are highly correlated (Spearman's rho 0.88) while the nucRNA-Seq library is less well correlated (Spearman's rho 0.25 and 0.30). Scales represent log2 RPKM values taken for Ensembl genes (genome version NCBIM37), *** indicates p<0.0001.(PDF)Click here for additional data file.Figure S5Real-time PCR validation of RNAPII ChIP material. Fold enrichment relative to input was determined for specific gene regions by real-time PCR. We detected reproducibly high levels of enrichment at erythroid-expressed genes  while non-expressed genes (Nefm and VH16) were not enriched above background binding relative to the IgG control material or in relation to the input material. Error bars represent SEM calculated for 3 technical replicates.(PDF)Click here for additional data file.Figure S6Nuclear RNA and RNAPII ChIP sequencing tag density at erythroid-expressed genes. Sequence coverage at the A) Hba and B) Slc4a1 genes.(PDF)Click here for additional data file.Figure S7Validation of RNAPII ChIP-Seq coverage for 48 amplicons. Observed coverage in our sequence data was validated for the same 48 randomly selected nucRNA-enriched regions used in s = 0.683, 95% CI , p<0.01) and for average coverage depth .(PDF)Click here for additional data file.Figure S8Stalling categories. We compared promoter proximal and terminator proximal stalling, identifying 300 genes with promoter stalling, 300 genes with terminator (3′ end) stalling and 60 genes with both promoter and terminator (3′ end) stalling (thresholds set at the 95th percentile for each category).(PDF)Click here for additional data file.Figure S9RNAPII ChIP-Seq coverage at genes in the promoter-proximal, 3′ end and double RNAPII peak categories. A) Calm1 displays a promoter-proximal RNAPII peak, B) Sec14l2 displays a 3′ end RNAPII peak, C) Pttg1ip displays an RNAPII peak in both the promoter-proximal and 3′end region. Sequenced tags are depicted in black, fold enrichment over input in the promoter-proximal region (+/−300 bp), 3′ end (+/−300 bp) and gene body is shown by grey boxes with numbers indicating the fold enrichment value in each region. Image exported from SeqMonk.(PDF)Click here for additional data file.Figure S10Putative regulatory regions upstream of erythroid expressed genes. A) Two intergenic RNAPII peaks upstream of the Pim1 gene overlap several TF binding sites. B) One RNAPII peak upstream of the Klf3 gene overlaps several TF binding sites.(TIF)Click here for additional data file.Figure S11Stable ncRNA candidates expressed in erythroid cells. Mouse chr19 is depicted from 5758468–5875817 (117 kbp) with annotated coding mRNA shown in red (forward) and blue (reverse) depending on the transcript direction. Candidate ncRNAs identified by Guttman et al 2009 are indicated by dark grey boxes. Candidate ncRNAs identified in our study are indicated by light grey boxes. NucRNA sequences are depicted below the ncRNA candidates. Image exported from SeqMonk.(TIF)Click here for additional data file.Figure S12The Kcnq1ot1 ncRNA is detected by nucRNA-Seq. Mouse chr7 is depicted from 150293116–150612579 (319.46 kbp). Kcnq1 transcripts are depicted with the nucRNA sequences mapped to this region depicted below. The region of increased nucRNA levels corresponds to the antisense Kcnq1ot1 trasncript. Image exported from SeqMonk.(TIF)Click here for additional data file.Table S1Number of reads per kilobase of gene length per million mapped reads (RPKM) in nucRNA-Seq replicates.(XLSX)Click here for additional data file.Table S2Transcription frequency determined by RNA FISH.(DOC)Click here for additional data file.Table S3Gene Ontology term enrichments for B, T and BT gene classes.(XLSX)Click here for additional data file.Table S4RNAPII binding patters, promoter peak, terminator peak and double peaks.(XLSX)Click here for additional data file.Table S5RNAPII+/nucRNA- peaks.(XLSX)Click here for additional data file.Table S6Transcription factor ChIP-Seq data used.(DOC)Click here for additional data file.Table S7Overlap between ChIP-Seq peaks. Using 1 kb bins across the genome overlapping regions of RNAPII+/nucRNA- and all erythroid transcription factors (TFs) or p300 were investigated. Log odds ratios and P values were calculated for peaks in each of the indicated regions of the genome.(DOC)Click here for additional data file.Table S8Validation of supervised motif analysis; indentified motifs for RNAPII+/nucRNA- candidates overlapped by TF binding sites identified through publicly available ChIP-Seq data.(DOC)Click here for additional data file.Table S9Supervised motif analysis for RNAPII+/nucRNA- candidates not overlapped by TF binding sites identified through publicly available ChIP-Seq data.(DOC)Click here for additional data file.Table S10Predicted ncRNA candidates.(XLSX)Click here for additional data file.Table S11Selected long ncRNA candidate regions.(DOC)Click here for additional data file.Table S12RNAPII ChIP-seq and nuRNA-seq validation primers.(DOC)Click here for additional data file.