FT gene.The flowering repressors SMZ and FLM, members of the AP-2 and MADS domain transcription factor families, unexpectedly work together to regulate flowering time via their effects on expression of the  FLOWERING LOCUS T (FT) locus, plays a central role in the control of flowering. FT is regulated positively by CONSTANS (CO), the output of the photoperiod pathway, and negatively by FLC, which integrates the effects of prolonged cold exposure. Here, we reveal the mechanisms of regulation by the microRNA miR172 target SCHLAFMÜTZE (SMZ), a potent repressor of flowering. Whole-genome mapping of SMZ binding sites demonstrates not only direct regulation of FT, but also of many other flowering time regulators acting both upstream and downstream of FT, indicating an important role of miR172 and its targets in fine tuning the flowering response. A role for the miR172/SMZ module as a rheostat in flowering time is further supported by SMZ binding to several other genes encoding miR172 targets. Finally, we show that the action of SMZ is completely dependent on another floral repressor, FLM, providing the first direct connection between two important classes of flowering time regulators, AP2- and MADS-domain proteins.A small mobile protein, encoded by the  FT expression. Using chromatin immunoprecipitation coupled to genome tiling arrays, we find that SMZ binds directly to the FT genomic locus and to several other key flowering-related loci. Unexpectedly, the ability of SMZ to repress flowering strictly depends on the presence of the MADS-domain transcription factor FLM. In addition, SMZ binds to its own regulatory sequences and those of three closely related genes, providing evidence of strong negative feedback between SMZ and the other AP2-like miRNA172 targets.Flowering is a pivotal event in the life cycle of many plants and is therefore under tight control. The ability to detect the daily photoperiod is of particular importance in many plant species, as it enables them to enter the reproductive phase in response to seasonal changes in day length. When the photoperiod is permissive to flowering, a signal is produced in leaves that is transported to the shoot meristem, where it initiates the formation of flowers. It is now widely accepted that an important component of this long-distance signal is the flowering protein FT. Here, we show that the AP2-like transcription factor SMZ, which represses flowering and is a target of the regulatory miRNA172 microRNA, functions together with related proteins to directly regulate  Throughout their lives, plants progress through distinct developmental phases, from germination and vegetative growth to flowering and, finally, senescence. The transition from vegetative growth to flowering is of particular importance because the correct timing of this switch is mandatory to ensure reproductive success. Plants have therefore evolved an elaborate genetic network that integrates endogenous and environmental signals to guarantee that flowering commences when conditions are most favorable.Arabidopsis thaliana and other plants have identified several distinct genetic pathways that are involved in regulating the floral transition A. thaliana is a facultative long-day plant, which means that it will flower more rapidly when day length exceeds a critical minimum. Interestingly, plants measure photoperiod in the leaves and not at the shoot apex where the new flowers will be formed. It has therefore been long postulated that the light-exposed leaves produce a flower-forming substance to regulate the formation of flowers at the shoot apex Genetic and molecular analyses in Arabidopsis to discriminate between short day (SD) and inductive long day (LD) is the B-box–type zinc finger protein CONSTANS (CO) An important factor that allows FT is the major target of CO in leaves APETALA1 (AP1) and FRUITFULL (FUL) FT is transcribed in leaves but acts at the apex implied that FT can move, either as mRNA or as protein. Later experiments were unable to detect FT mRNA movement but provided evidence that FT protein is able to reach the apex when expressed in the vasculature Several lines of evidence suggest that the protein FLOWERING LOCUS T (FT) acts as a florigen to convey flowering time signals from the leaves to the apex FT expression in the leaf or act downstream of FT to modulate its function at the shoot apex. In particular, MADS-domain transcription factors have been shown to act as repressors of flowering. The most prominent of these is FLOWERING LOCUS C (FLC), which represses flowering in winter annual accessions of Arabidopsis before the plants have been exposed to a prolonged period of cold SUC2 promoter or the meristem-specific KNAT1 promoter, efficiently represses flowering and that these effects are additive. Further, it was demonstrated that FLC directly binds to the regulatory regions of three positive regulators of flowering, FT, FD, and SUPRESSOR OF OVEREXPRESSION OF CONSTANS 1 (SOC1), presumably to repress these genes FLM and SVP act as partners Arabidopsis, and SVP has recently been shown to interact with FLC in a repressor complex FT and SOC1Interestingly, the induction of flowering under LD by CO/FT is counteracted by several factors that either prevent TEMPRANILLO 1 (TEM1) and TEM2, have been shown to redundantly repress flowering TEM gene encodes an AP2 domain as well as a B3-type DNA binding domain. TEM1 is most strongly expressed in leaves, where its expression is regulated in a circadian fashion FTFT, indicating that FT is regulated by different repressors in different regions.More recently, two more transcription factors, toe1 mutants are significantly early flowering, and this effect is enhanced in a toe1 toe2 double mutant toe1 toe2 double mutant, indicating that the other AP2 family members most likely act redundantly with TOE1 and TOE2 to repress flowering Yet another family of six AP2-like transcription factors also act as repressors of flowering. This clade of proteins comprises APETALA 2 (AP2) itself, the three TARGET OF EAT (TOE) proteins , and SCHLAFMÜTZE (SMZ) and its paralog SCHNARCHZAPFEN (SNZ) SMZ and SNZ are bona fide floral repressors and act redundantly with TOE1 and TOE2 to delay flowering specifically under LD conditions. Plants expressing SMZ at high levels are late flowering, which is due to an almost complete block in FT induction. The effects of SMZ on FT expression appear to be direct, as chromatin immunoprecipitation coupled to hybridization to tiling arrays (ChIP-chip) identified FT as a target of SMZ. In addition, several other known regulators of flowering time were identified as SMZ targets. Among them are SMZ itself, SNZ, AP2, and TOE3, suggesting a complex feedback regulation among miR172 targets. Finally, we found that repression of flowering by SMZ is independent of the potent floral repressor FLC, but requires FLM for its function, providing a direct connection between two important classes of flowering time regulators, AP2- and MADS-domain proteins.Here, we show that the miR172 targets smz-D was originally isolated as a dominant late-flowering mutant in an activation-tagging screen under LD conditions. SMZ is expressed in young seedlings and is developmentally regulated, as deduced from microarray data  revealed that SMZ exhibits a diurnal expression with a maximum at Zeitgeber 15 under LD conditions ray data  and confray data . ExpressSMZ functions in respect to the known flowering time pathways, we first investigated the flowering time behavior in this mutant under different day lengths . In contrast, under noninductive SD conditions, smz-D (79.1±2.2 leaves) plants flowered similarly to wild-type (73.0±1.6 leaves), indicating that smz-D represses flowering specifically under inductive LD, but has little effect under SD conditions.To better understand where  lengths . We founSMZ is indeed functioning as a floral repressor, we isolated homozygous SMZ loss-of-function alleles from T-DNA insertion collections. Neither of the individual smz mutant lines displayed any obvious phenotypes; in particular, the total number of leaves did not significantly differ from that of wild-type plants . This result confirms that SMZ and its paralog SNZ are indeed acting as floral repressors redundantly with TOE1 and TOE2. This effect is only apparent in the sensitized toe1 toe2 mutant background, as TOE1 normally masks the effects of smz and snz loss of function.Together with smz, snz, toe1, and toe2 loss-of-function alleles was associated with a reduced number of rosette leaves, whereas the number of cauline leaves remained constant  compared to the CO overexpressing line (5.4±0.3 leaves) . These findings are compatible with the idea that SMZ acts as a repressor of flowering and counteracts the flower-promoting activity of CO.To test the genetic position of promoter . Lines e leaves) . In contNext, we tested the dependence of SMZ on the presence of functional FLC, as FLC is a well-described repressor of flowering, integrating environmental signals such as vernalization and ambient temperature FT gene as well as to the promoters of SUPRESSOR OF OVEREXPRESSION OF CONSTANS 1 (SOC1 = AGL20) and FDsmz-D acts through FLC to repress flowering. When we introduced smz-D into the strong flc-3 deletion mutant background, which lacks part of the 5′UTR and the first exon, and is a genetic null allele of FLC, we observed no difference in flowering time in smzD flc-3 (43.8±1.9 leaves) when compared to smz-D (45.6±1.9 leaves), showing that smz-D represses flowering independently of FLC  that carried silent mutations in the miR172 complementary site, rendering the mRNA resistant to miR172-directed cleavage  and FLOWERING LOCUS M (FLM = MAF1), have been shown to function as floral repressors 35S::SMZ and 35S::rSMZ constructs were transformed into established svp and flm T-DNA mutant lines. Loss of SVP did not affect the SMZ overexpression phenotype, i.e., svp plants carrying either the 35S::SMZ or the 35S::rSMZ transgene flowered just as late as control plants  was no longer able to delay flowering (flm mutants. This is in contrast to the extreme effect of (r)SMZ on flowering in wild-type control transformations  (unpublished data).In contrast, the late flowering, which usually would result from ckground . Even colowering  in flm mrmations . Interes5S::rSMZ , despiteFLM have been shown to delay flowering, we therefore examined the expression of FLM in smz-D by qRT-PCR, but did not find any evidence that FLM levels were increased (unpublished data). Therefore, it does not appear that SMZ is simply up-regulating FLM transcription.Because high levels of FT to exert its function in the photoperiod pathway, it is both necessary and sufficient for this gene to be expressed in leaf phloem companion cells FT expression, misexpression of SMZ in phloem companion cells and the resulting repression of FT in this tissue should be sufficient to recapitulate the late-flowering phenotype observed in smz-D.Our previous results demonstrated that SMZ acts as a floral repressor in the photoperiod pathway. A characteristic of this pathway is the spatial separation of the perception of inductive photoperiod in the leaves and the formation of flowers at the shoot apical meristem. Recently, the FT protein has been shown to be an important component of the signal that transmits flowering time information from the leaves to the apex SMZ from the phloem companion cell-specific SUC2 promoter efficiently delayed flowering and resulted in late-flowering plants that were phenotypically indistinguishable from 35S::SMZ plants  was similar to that in controls, indicating that high levels of SMZ at the shoot apex are not sufficient to delay the onset of flowering. The number of cauline leaves, however, was vastly increased in FD::rSMZ plants (13.8±6.3) compared to wild type (2.9±0.2) (LEAFY (LFY) and AP1, in that flowers were replaced by leaf-like organs, which were frequently subtended by bracts  . Additioy bracts . Taken tFT, which is normally induced in leaves under LD.As we showed earlier, genetic analyses clearly place SMZ in the photoperiod pathway and tissue-specific misexpression of SMZ suggests that regulation of flowering time by SMZ occurs predominantly in the leaves. To analyze the molecular cause for the late flowering of SMZ overexpressing plants, we carried out quantitative RT-PCR on the putative target gene FT mRNA was not detectable in noninductive SD conditions in flc-3 mutants, which served as a background for this experiment, irrespective of the presence or absence of smz-D  up-regulated in smz-D plants at all time points in both tissues investigated (GIGANTEA (GI) and CO, which both act upstream of FT in the photoperiod pathway, remained unchanged in smz-D plants  induced in flc-3 leaves 1 and 3 d after plants were transferred to inductive LD but that FT induction in leaves was completely blocked by smz-D  followed the expression of FT in that it was substantially up-regulated in leaves of flc-3 plants, but was not induced at any other time point in any tissues investigated (FT that was significantly (pfp<0.01) induced in leaves of flc-3 plants in response to inductive photoperiod 1 and 3 d after shift to LD that was not also up-regulated in smz-D. This gene encodes a β-amylase  and has not previously been implicated in the regulation of flowering.Interestingly, stigated . This suFT, and to a lesser extent TSF, are major targets of SMZ in leaves. Such a repression of FT could either be due to a direct effect of SMZ on the FT locus or through the activation of other floral repressors. To investigate the possibility that SMZ is acting indirectly on FT by activating transcription of another floral repressor, we examined our smz-D microarray data. Many of the known repressors of flowering encode MADS-domain transcription factors, but neither FLC, FLM, nor SVP RNA was induced in smz-D flc-3, when compared to flc-3  up-regulated in leaves and shoot meristem samples of smz-D at all time points  down-regulated in leaves of smz-D flc-3 1 and 4 d after plants were shifted to LD  at the apex of flc-3 plants after the shift to LD  induced at the meristem  of 5% or less when compared to a line expressing a nuclear-localized YFP  in order to identify regions in the ized YFP . Of thesp<0.0005 among the list of potential SMZ target genes, demonstrating a functional specificity to binding and a nonrandom distribution of the peaks identified by ChIP-chip. The second biological process found to be overrepresented at a FDR p<0.0005 comprises genes involved in “response to stimuli” (GO:0050896), in particular to water  and jasmonic acid (GO:0009753).We observed several interesting enrichments for particular gene ontology (GO) categories among the 307 of 395 genes for which assignments exist. Indeed, genes associated with flower development (GO:0009908) were significantly overrepresented at a FDR TOE3 . Closer inspection of the list of high-confidence SMZ targets (FDR<5%) revealed that three other miR172 targets were also significantly bound. Among them were SMZ itself, its paralog SNZ, and AP2 . It should be noted that the expression levels of SNZ, AP2, and TOE3 were reduced in leaves of smz-D plants as measured by microarrays  targets of SMZ . An alternative would be that FLM needs to be present at target loci in order to facilitate either SMZ binding or activity, but without physical interaction between the two. The dependence of SMZ on FLM seems to be rather specific, as inactivation of the MADS repressors FLC nor SVP, both of which have been shown to directly repress TEM1, a known repressor of flowering, was also bound by SMZ  was also identified among the genes most strongly bound by SMZ  has the opposite effect Arabidopsis must achieve a careful balance between miR172 and its targets. Negative feedback of SMZ onto the other miR172 targets likely contributes to this regulation. miR172 and its targets are not specific to Arabidopsis, but are conserved in other dicotyledonous as well as monocotyledonous plant species, suggesting that these genes play an important role in plant development in general indeterminate spikelet1 (ids1) have been shown to participate in sex determination and meristem cell fate in maize Our results indicate that the miR172/SMZ module functions as a rheostat in flowering time by SMZ binding to several genes encoding miR172 targets and other flowering time regulators. The importance of this regulatory module is highlighted by the finding that overexpression of miR172 strongly accelerates flowering SOC1 and AP1 are also directly targeted and repressed by SMZ. The intricate regulatory interactions we uncovered by just looking at just one single factor, SMZ, demonstrate how complex regulation of flowering time at the molecular level is. To fully understand this complex trait, a concerted effort of the flowering time community will be required to systematically study the genes and proteins involved in floral transition on a genome-wide scale.In summary, we provide evidence for a complex regulatory feedback mechanism among the miR172 target genes that directly controls the expression of FT. In addition, we show that several other known flowering time regulators such as Sequences of oligonucleotide primers used in this work are given in flc-3, flm-3, svp-31, toe1-2, and toe2-1 have been described before smz-1 and smz-2) and one SNZ loss-of-function allele (snz-1) were isolated as part of this work  accession. All T-DNA insertion mutants used in this work are in Col-0 accession his work . smz-2 w−2 s−1. All light bulbs were of the same age. Long day (LD) is defined as 16 h light, 8 h dark, and short days (SD) as 8 h light, 16 h dark. For flowering time measurements, plants were randomized with the respective controls, and the flowering time phenotype was determined without prior knowledge of the genotype. All flowering time assays were performed at least twice.All plants were grown in growth chambers in a controlled environment . Plants were raised on soil under a mixture of Cool White and Gro-Lux Wide Spectrum fluorescent lights, with a fluence rate of 125 to 175 µmol mPfu DNA polymerase (New England Biolabs), cloned into Gateway entry vectors using T4 DNA ligase and subsequently recombined into Gateway-compatible binary vectors suitable for plant transformation. Constructs for constitutive and tissue-specific expression of SMZ were obtained by amplification of the SMZ ORF using oligonucleotide primers G-3323 and G-5638, cloning the PCR product into pJLSmart, resulting in pJM9, and recombination into Gateway-compatible plant binary vectors, providing promoters for expression in plants, generating pJM34 (35S::SMZ), pJM66 (SUC2::SMZ), and pJM50 (FD::SMZ). To generate the miRNA172-resistant form of SMZ (rSMZ), synonymous mutations were introduced into the miR172 binding site by site-directed mutagenesis using oligonucleotide primers G-2050 and G-2051, resulting in pFK37. Cloning of rSMZ into Gateway-compatible entry and destination vectors was as described above, resulting in pJM36, pJM68, and pJM52 . To separate SMZ from the GFP tag, a Gly-Ser linker was added to the N-terminus of the SMZ ORF in a two-step PCR. In a first PCR, the SMZ ORF was amplified using primers G-16615, which replaced the start codon of SMZ with 30 bases encoding for five Gly-Ser pairs, and G-16616. In a second step, the Gly-Ser linker was extended to its final length of 60 bases, encoding for ten Gly-Ser pairs, using G-18665 and G-16616. The resulting PCR product was cloned into the SmaI site of the pJLSmart Gateway-compatible entry vector by blunt end ligation (pFK478). The 10∶SMZGS ORF was subsequently recombined into a pGREEN-IIS based Gateway-compatible destination vector (pFK247), which provided 35S promoter for expression in plants and an in-frame fusion with an N-terminal eGFP, resulting in pFK480. For the genomic SMZ∶GUS reporter, a 10.6-kb EcoRV-SacI, including the whole SMZ 5′ and 3′ regions, was cut from BAC T15C9 and cloned into the pGREEN-IIS plant binary vector. The GUS ORF, including stop codon, was amplified by PCR using primers G-8237 and G-8238, introducing AgeI sites in the process. The GUS ORF was cloned in frame with the SMZ start codon in an AgeI site present in the first exon of SMZ. All sequences amplified by PCR were confirmed by sequencing. All enzymes used were purchased from Fermentas unless otherwise indicated. Complete sequences of constructs used are available on request. For sequences of the primers used to amplify ORFs, see ORFs were amplified using the Agrobacterium tumefaciens strain ASE by electroporation. Arabidopsis plants of the Col-0 accession were transformed by the floral-dip procedure For plant transformation, constructs were transformed into Total RNA was extracted from plant tissue using either the Plant RNeasy kit (Qiagen) or Trizol reagent (Invitrogen) according to the manufacturer's instructions. 2 µg of total RNA was DNase I-treated and single-stranded cDNA was synthesized using oligo(dT) and the RevertAid First Strand cDNA Synthesis Kit (Fermentas). Quantitative real-time PCR was performed on an Opticon Continuous Fluorescence Detection System (MJR) using the Platinum SYBR Green qPCR Supermix-UDG (Invitrogen). Gene expression was calculated relative to β-Tubulin using the ΔΔCT method. Results are reported for triplicate measurements of one of several biological replicates. For each genotype and replicate, a minimum of 10 seedlings was pooled for RNA extraction. Oligonucleotide primers used for qRT PCR are listed in Arabidopsis flc-3 and smz-D flc-3, plants were grown under SD conditions for 14 days and shifted to LD to induce flowering synchronously. Rosette leaves one to three from 10 plants were collected zero, 1 and 4 d after the plants were shifted to LD in duplicate, and total RNA was extracted using Qiagen Plant RNeasy columns (Qiagen). Biotinylated antisense RNA was prepared from 1 µg of total RNA using the MessageAmp II-Biotin Enhanced Kit (Ambion) according to the manufacturer's instructions. A total of 13.5 µg of fragmented amplified RNA (aRNA) was hybridized to an Arabidopsis ATH1-121501 gene expression array (Affymetrix). Arrays were washed and stained on a GeneChip Fluidics Station 450 (Affymetrix) and scanned on an Affymetrix GeneChip scanner GS300 7G. Analysis of the shoot meristem transcriptome was carried out as described above except that plants were grown for 25 days under SD before transfer to LD. RNA from shoot apices was isolated as described http://www.ebi.ac.uk/arrayexpress; accession numbers: E-MEXP-2040 (leaf samples) and E-MEXP-2041 (apices)). Lists of statistically significantly expressed genes .Tiling array data were processed using the CisGenome suite Dataset S1Probe set identifiers of differentially expressed genes. Excel spreadsheet containing ATH1 probe set identifiers of genes differentially expressed in pairwise comparisons (within genotypes and between geneotypes).(0.12 MB XLS)Click here for additional data file.Dataset S2Genes regulated by SMZ in leaves. Excel spreadsheet containing lists of ATH1 probe set identifiers differentially expressed  between flc-3 and smz-D flc-3 in leaves.(0.05 MB XLS)Click here for additional data file.Dataset S3Genes regulated by SMZ at the shoot apex. Excel spreadsheet containing lists of ATH1 probe set identifiers differentially expressed  between flc-3 and smz-D flc-3 in apices.(0.04 MB XLS)Click here for additional data file.Dataset S4RankProducts results. Compressed folder containing results from all RankProducts pairwise comparisons  within a genotype  or between genotypes at a given time point for leaves and meristems.(0.32 MB ZIP)Click here for additional data file.Dataset S5Genomic loci bound by SMZ. Excel spreadsheet containing information on 434 chromosomal regions that were significantly enriched in 35S::GFP∶SMZ compared to 35S::3xYFP-NLS at a FDR rate of <5%.(0.24 MB XLS)Click here for additional data file.Figure S1Statistical analysis of distribution of flowering time by rosette, cauline, and total leaf number in miR172 loss-of-function lines.(0.29 MB TIF)Click here for additional data file.Figure S2Mapping of miR172 cleavage sites in SMZ and SNZ mRNA.(0.14 MB TIF)Click here for additional data file.Figure S3Diurnal expression of GI and CO in smz-D.(0.55 MB TIF)Click here for additional data file.Figure S4Expression profiles of miR172 target genes in leaves and at the shoot apex.(0.12 MB TIF)Click here for additional data file.Figure S5Subcellular localization of GFP∶SMZ fusion protein.(1.96 MB TIF)Click here for additional data file.Figure S6Expression of flowering time genes in the toe1 toe2 smz snz quadruple mutant.(0.39 MB TIF)Click here for additional data file.Table S1Mutant lines used throughout this work.(0.05 MB DOC)Click here for additional data file.Table S2Oligonucleotides used in this work.(0.13 MB DOC)Click here for additional data file.


Inactive Hnf1α-targets in −/−Hnf1a cells are also preferentially located in peripheral subnuclear domains enriched in trimethylated H3-Lys27, whereas active targets in wild-type cells are positioned in more central domains enriched in methylated H3-Lys4 and RNA polymerase II. We demonstrate that this differential positioning involves the decondensation of target chromatin, and show that it is spatially restricted rather than a reflection of non-specific changes in the nuclear organization of Hnf1a-deficient cells. This study, therefore, provides genetic evidence that a single transcriptional activator can influence the subnuclear location of its endogenous genomic targets in primary cells, and links activator-dependent changes in local chromatin structure to the spatial organization of the genome. We have also revealed a defect in subnuclear gene positioning in a model of a human transcription factor disease.DNA binding transcriptional activators play a central role in gene-selective regulation. In part, this is mediated by targeting local covalent modifications of histone tails. Transcriptional regulation has also been associated with the positioning of genes within the nucleus. We have now examined the role of a transcriptional activator in regulating the positioning of target genes. This was carried out with primary β-cells and hepatocytes freshly isolated from mice lacking Hnf1α, an activator encoded by the most frequently mutated gene in human monogenic diabetes (MODY3). We show that in  All cells in an organism share a common genome, yet distinct subsets of genes are transcribed in different cells. Selectivity of gene transcription is largely determined by transcription factors that bind to target genes and promote local changes in chromatin. Such changes are thought to be instrumental for transcription. Emerging evidence indicates that the position of genes in the 3-dimensional structure of the nucleus may also be important in transcriptional regulation. However, the role of transcription factors in gene positioning, and its possible relationship with chromatin modifications, is poorly understood. To examine this, we employed a genetic approach. We used mice lacking Hnf1α, a transcription factor gene that is mutated in an inherited form of diabetes. We studied genes that are directly bound by Hnf1α, as well as various control genomic regions, and determined their position in nuclear space in liver and insulin-producing β-cells. The results showed that the absence of Hnf1α causes local changes in the chromatin of target genes. At the same time, it modifies the position of target genes in nuclear space. The findings of this study lead us to propose a model whereby transcription factor dependent local chromatin modifications are linked to subnuclear gene positioning. They also revealed abnormal subnuclear positioning in a model of a human transcription factor disease. The recognition of nucleotide sequences in the vicinity of genes by DNA binding factors is central to the regulation of gene-specific transcription Numerous studies have linked different covalent histone modifications with the transcriptional state of gene loci It has recently become apparent that the positioning of gene loci within the three dimensional structure of the nucleus may provide a further level of regulation . Hnf1α (Hepatocyte nuclear factor 1α) is a homeodomain protein encoded by the gene implicated in MODY3 (Maturity-onset diabetes of the young 3), the most common form of human monogenic diabetes Hnf1a-null mutant vs. control mice. This model enabled us to ascribe observed changes to the presence or absence of an activator, in contrast to previous studies comparing the position of a locus among cell-types or developmental stages which potentially differ markedly in their chromosomal configurations In the current study, we explored the relationships between transcriptional activator function, chromatin structure, and subnuclear gene positioning. This was addressed using mice with targeted ablation of the in vivo functions of a transcriptional activator and increase our understanding of the cellular defects underlying a human transcriptional disease.The results provide genetic evidence that a transcriptional activator influences the subnuclear position of its endogenous genomic targets in primary cells. In addition, we present data to support that activator-dependent changes in local histone modifications and chromatin condensation may play a role in regulating the spatial organization of the cell nucleus. Collectively, the results provide novel insights into the −/−Hnf1a hepatocytes  and islets (Kif12), all of which are specifically downregulated in their respective Hnf1α-deficient cell-types , whereas no changes were observed in non Hnf1α-dependent control genes .In contrast, methylated H3-Lys27 was increased in Hnf1α-dependent targets in segments . DimethyCyp2j5 chromatin in −/−Hnf1a vs. +/+Hnf1a hepatocytes, whereas no differences were observed between genotypes for the control gene Actb . In contrast, H3-Lys27me3 was significantly enriched in the outermost zones, compared to more internal regions (−18).We next examined the radial distribution of histone modifications. H3-Lys27me3 was markedly enriched whereas H3-Lys4me2 displayed relative depletion in the immediate vicinity of the inner nuclear membrane, as shown by co-immunostaining of Lamin A/C . ErosionThese results are largely consistent with recent studies describing distinct nuclear patterns of histone modifications in cultured cell lines −/−Hnf1a cells  and islets (Kif12) were not positioned in domains that are enriched in H3-Lys9me3 or TO-PRO-3 as compared to wild-type cells . Thus, Hnf1α-deficiency does not result in repositioning of inactive Hnf1α-targets to pericentromeric heterochromatin clustering at chromocenters enriched in H3-Lys9me3.In several studies it has been observed that gene silencing is associated with relocation to constitutive heterochromatic domains enriched in satellite repeat sequences Cyp2j5 alleles in activator-deficient cells were positioned in nuclear domains that are relatively enriched in H3-Lys27me3    or one cd Nanog) . Furthererase II .Cyp2j5 and Kif12 FISH signals was 8- and 2.3- fold higher in −/−Hnf1a vs. +/+Hnf1a cells, respectively, but remained unaltered at control loci  of nuclear epitope intensity as an alternate threshold to define epitope enrichment . Differences were again not observed in four control genes using similar criteria  exhibited a similar subnuclear compartmentalization with Hnf1α as Cypj5  (Kif12 loci in mutant cells was observed in the interior shell 3 (p = 0.04)  . Convers = 0.04) .Cyp2j5 and Kif12 are positioned in more central nuclear domains enriched in RNA polymerase II and H3-Lys4me2, whereas in the absence of Hnf1α inactive targets are positioned in more peripheral, H3-Lys27me3-rich domains. Interestingly, these subnuclear histone modification enrichment patterns parallel those observed locally in Hnf1α-target nucleosomes.Thus, in the presence of Hnf1α its direct target genes Cyp2j5 locus  did show differential positioning similar to Cyp2j5  , but for the more distal clone 12L1 it was decreased from 0.40±0.03 µm in wild-type cells to 0.29±0.02 µm in −/−Hnf1a cells , Cyp2j5-114C9 , and Cyp2j5-12L1  comparisons  . The disparisons . In contnt cells . Thus, CCyp2j5 with respect to its chromosomal territory. We observed that Hnf1α-deficiency did not affect the position of the nearby 114C9 genomic region relative to its chromosomal territory, whereas Cyp2j5 alleles less frequently extended away from their territory surface in −/−Hnf1a cells versus wild-type cells  subnuclear regions enriched in H3-Lys27me3, H3-Lys4me2, and phosphoserine-5 RNA polymerase II , b) radiAlthough numerous studies have shown a relationship between gene transcription and subnuclear positioning, several variables that are only indirectly related to gene transcription, such as regional gene density or nucleotide composition, also appear to impact the subnuclear location of genomic regions, independent of their actual transcriptional activity Earlier reports have linked the function of sequence specific-DNA binding proteins such as Ikaros and NF-E2p18 with the repositioning of endogenous loci Previous evidence supporting the role of transactivators in gene positioning comes from studies of transgenes. Some of these studies took advantage of a lac repressor-VP16 acidic activation domain fusion protein, which was shown to cause repositioning of targeted multicopy loci away from the nuclear periphery Earlier studies describing correlations between gene silencing and perinuclear positioning were based on the comparisons of different cell types or developmental stages Previous studies have also shown that genes are preferentially transcribed in nuclear subdomains enriched in RNA polymerase II Together with previous findings, our data shows that binding of Hnf1α to target loci promotes local histone tail hyperacetylation, methylation of H3-Lys4, and chromatin decondensation, while preventing methylation at H3-Lys27 Kif12 in H3-Lys4me-rich/H3-Lys27me-poor domains in a β-cell line with normal Hnf1α expression  does not alter the preferential positioning of pression . This suCyp2j5 locus that is reflected by changes in distances between adjacent loci and relative to chromosomal territories. Our findings also show that the histone modification enrichment pattern of Hnf1α-dependent genes in nuclear domains with which they associate coincides with the local post-translational histone modification profile. This raises the possibility that histone modifications may be partly instrumental in gene positioning. Although similar measurements of locus positioning relative to histone modification domains have not been carried out before, two studies previously showed that treatment with histone deacetylase inhibitors causes repositioning of inactive genes away from the nuclear periphery Changes in local chromatin structure represent another potential mechanism. Our results showed that Hnf1α regulates not only local chromatin decompaction, but also the decondensation of the in vivo function of an activator to different levels of regulation, namely the binding to specific target sequences, the local modification of target chromatin, and the positioning of targets in distinct subnuclear domains. This demonstration is provided in a genetic model of human diabetes, indicating cellular defects at multiple regulatory levels in a human transcriptional disease. Thus, our findings provide not only new insights into the complexity of trans-activator functions and transcriptional regulation, but are also important for understanding mechanisms underlying human disease.Emerging evidence indicates that gene transcription is an integrated process involving multiple levels of regulation +/+Hnf1a and −/−Hnf1a mice Hepatocytes and pancreatic islets were isolated from 4–6 week-old RNA isolation, reverse transcription and PCR were carried out as described 6) were resuspended in 10 mL NI buffer , and 10 mL of NI buffer supplemented with 1% NP40 was added for 10 min incubation in ice. Nuclei were collected at 500×g for 3 min, washed in 4 mL NI buffer, resuspended in a final volume of 700 µL NI buffer and distributed in 100 µL aliquots for DNAse I digestion. To each suspension of 100 µL NI buffer, either 0, 10, 20, 30, 40, 50 or 80 µg DNAse I was added for 10 min on ice. The reaction was stopped with Nuclei Lysis Solution from Wizard Genomic DNA purification kit (Promega), and DNA was extracted as indicated by the manufacturer. DNAs were resuspended in 20 µL DNA rehydration solution and 1 µL was used for PCR amplification. Oligonucleotides are presented in Isolated hepatocytes (1/500). Cells were washed and counterstained with TO-PRO-3 (Molecular Probes) and mounted with ProLong Antifade (Amersham).Cyp2j5 or 114C9 digoxigenin-labeled probe denatured at 90°C for 5 min was added for overnight hybridization at 37°C. After sequential washes of 5 min at 45°C in 2×SSC-50% formaldehyde, 1×SSC, PBS-0.2% Triton and PBS, biotin and digoxigenin-labeled probes were detected and processed for confocal image acquisition as described above.Cells were fixed and permeabilized as described above. After permeabilization, hepatocytes were incubated with 100 µg/mL RNase A at 37°C for 30 min. Nuclei were then denatured at 74°C in 2×SSC-70% formamide for 3 min followed by 1 min in 2×SSC-50% formamide. Ten µL of chromosome 4 biotin-labeled probe (Cambio) was denatured at 70°C for 10 min in the supplied buffer (Cambio) and pre-annealed for 20 min at RT. Subsequently 1 µL of either Confocal images for each fluorochrome were acquired sequentially at room temperature with a Leica TCS SL laser scanning confocal spectral microscope, using a 63× oil immersion objective lens (NA 1.32). Focal Check Fluorescent microspheres (Molecular Probes) were used before image capture to align laser lines. Non-saturated, unprocessed images were further analyzed with ImageJ. Contrast-stretch and gamma adjustments were made using Photoshop (Adobe) only for display.www.uhnresearch.ca/wcif). Manders' coefficient calculates, for each channel, the proportion of colocalizing pixels respect to the summed up intensities of all pixels in the nucleus. Comparable results were obtained by subtracting pixels lower than the 50th percentile in each channel, and then applying a modified Mander's coefficient using the Colocalization threshold ImageJ plug-in, that first calculates an automated threshold .This analysis was carried out to determine colocalization between the most intense nuclear signals of each epitope. Ten to twenty nuclei were assessed in each double immunofluorescence experiment, and pixels with values exceeding the 75th percentile in each channel were selected for further analysis. The rationale for this threshold is that nuclear RNA polymerase II and H3-Lys27me3 signal intensities do not adhere to a normal distribution, and the 75th percentile enabled separation of visually evident RNA polymerase II and H3-Lys27me3-enriched domains from most remaining nuclear signals. Signals filtered in this manner were used to calculate Manders' coefficient of colocalization using the appropriate ImageJ plug-in  from at least two independent experiments were analyzed blindly in unprocessed images to quantify signal intensities of histone marks and RNA polymerase II. In H3-Lys27me3 experiments Barr bodies were avoided. The 9-pixel area containing the brightest and most central pixels of each FISH signal was selected by inspection of single color images, and the average signal for each channel in this area was obtained using RGB Measure (ImageJ). Each non-thresholded immunofluorescence signal at a FISH-detected locus was divided by the median value of the entire nucleus in the same cell to correct for cell to cell and inter-assay technical variability. The mean intensity for each channel was also calculated from a broad cytoplasmic area in every stack and used to subtract non-specific background from both FISH and nuclear signals. This background value was similar to the non-specific nuclear signal elicited in control Immuno-FISH experiments in which primary antibodies were omitted. The resulting value was referred to as To classify alleles according to the presence or absence of enrichment in either RNA polymerase II or a histone mark, we used the 75th percentile of nuclear pixel intensities in each channel as the threshold, as described above. The results presented here remained statistically significant with alternate thresholds to define enrichment, such as the nuclear median  between the center of adjacent FISH signals defined by the 9-pixel square area containing the brightest and most central pixels, essentially as described for immuno-FISH analysis. Non-overlapping loci were defined as those with signal center distances exceeding 0.4 µm, thus providing a uniform criteria that is not affected by variable FISH signal intensities and shapes.After identification of locus-specific probes in the same z plane as its chromosome territory, the image background of the chromosome territory was blindly modified until a clear visualization of the territory edge was obtained. The distance (in µm) between the center of the locus-specific FISH signal and the nearest chromosome surface was then measured as described for the two-color DNA FISH analysis. One hundred alleles from each genotype were classified as being located either within a territory and >0.4 µm from the edge, outside and >0.4 µm from the edge, or in contact if they were <0.4 µm from the edge.t-test was used for comparison of ChIP values. Mann-Whitney test was used for comparisons of immuno-FISH and allele distance values, which did not adhere to a normal distribution. ANOVA was used for erosion analysis. Fisher's exact test was used for comparison of qualitative two-color DNA FISH, chromosomal territory, and immuno-FISH results.A two-tailed Student's Figure S1Immunofluorescence analysis of H3-Lys27me3 in hepatocytes. (A) Dual immunofluorescence analysis of RNA polymerase II (green) and H3-Lys27me3 (red) in hepatocytes after the immuno-FISH procedure showing that this process does not alter staining patterns. (B–F) Dual immunofluorescence analysis of hepatocytes with anti-H3-Lys27me3 (green) and either an alternate anti-Lys27me3 antibody (B), or anti-H3-Lys4me2 (C), anti-Lys9me2 (D), anti-Lys27me2 (E) or anti-Lys9me3 (F) antibodies (red). Colocalization analyses are depicted as Venn diagrams on the right side of immunolocalization images and were performed as described in the legend of (7.62 MB TIF)Click here for additional data file.Figure S2Cyp2j5 loci in Hnf1a+/+ (+/+) and −/−Hnf1a (−/−) hepatocytes. Non-thresholded signal intensities of methylated histone marks or TO-PRO-3 were measured at 70-200 FISH alleles. To correct for cell to cell variability each value was divided by its nuclear median value, and is referred to as the normalized signal in the graphs. The graphs depict mean±SEM values. **P<0.01 relative to Hnf1a+/+ cells.Analysis of the correlation between TO-PRO-3 density and histone modification patterns and gene positioning. (A–C) Immunofluorescence analysis of H3-Lys4me2 (A), H3 Lys9me3 (B), and H3-Lys27me3 (C) enrichment (red) compared with the DNA marker TO-PRO-3 (blue). Colocalization was analyzed and depicted with Venn diagrams as described in (4.07 MB TIF)Click here for additional data file.Figure S3−/−Hnf1a hepatocytes fixed with 4% paraformaldehyde (E). Colocalization analysis was performed and Venn diagrams were arranged as described in the legend of Subnuclear distribution of Hnf1α in histone code and RNA polymerase II domains.  Dual confocal immunofluorescence analysis of Hnf1α (red) and H3-Lys4me2 (A) or H3-Lys27me3 (B) (green) in interphase hepatocyte nuclei. (C–E) Dual immunofluorescence analysis of Hnf1α (red) and RNA polymerase II (green) in control hepatocytes fixed with 4% paraformaldehyde (C) or methanol (D) and in (4.12 MB TIF)Click here for additional data file.Figure S4Kif12 activity in islet-cells correlates with differential positioning in RNA polymerase II and histone code domains. (A–D) Representative confocal immuno-FISH analysis in Hnf1a+/+ and −/−Hnf1a islet-cells of the Kif12 locus (red) with RNA polymerase II  and either H3-Lys4me2  or H3-Lys27me3  (blue). The framed regions containing FISH signals of Kif12 are shown at higher magnification on the right of each panel with omission of only blue or green channels. (E–L) Quantitative analysis of histone marks and RNA polymerase II in Kif12 (I–L) and control  loci in Hnf1a+/+ and −/−Hnf1a islets. For each condition, non-thresholded signal intensities were measured at 100–200 FISH signals and each value was divided by the nuclear median intensity in the same channel. The graphs thus depict the average of such normalized signal values±SEM, except in H,L which shows mean±SEM of H3-Lys27me3/RNA polymerase II ratios (mK27/Pol II).  Classification of alleles from Ly9 control (M) and Kif12 (N) in 4 categories according to the simultaneous enrichment (+) or non-enrichment (−) of RNA polymerase II (RNA Pol II) and H3-Lys27me3 (K27me3) in Hnf1a+/+ (black bars) and Hnf1a−/− (white bars) islets as described in Hnf1a+/+ cells using Mann-Whitney or Fisher's exact test as appropriate.Hnf1α-dependent (3.89 MB TIF)Click here for additional data file.Figure S5+/+Hnf1a and −/−Hnf1a hepatocytes. (A–C) Venn diagrams showing the colocalization of H3-Lys4me2 (A), H3-Lys9me3 (B) and H3-Lys27me3 (C) with RNA polymerase II in interphase +/+Hnf1a and −/−Hnf1a hepatocytes. The mean±SEM percentage of colocalizing pixels of 20 nuclei is shown below. (D–G). Erosion analysis of the nuclear distribution of RNA polymerase II , and H3-Lys27me3  in interphase +/+Hnf1a and −/−Hnf1a hepatocytes. Erosion analyses were performed as described in the legend of Histone modification/RNA Polymerase II colocalization and radial distribution patterns are similar in (0.61 MB TIF)Click here for additional data file.Figure S6Hnf1a+/+ and −/−Hnf1a hepatocytes by DNA immuno-FISH. Non-thresholded signal intensities were measured at 100-200 FISH signals and corrected by the nuclear median value  exactly as described in the legend of Hnf1a+/+ cells.Quantitative analysis of histone marks and RNA polymerase II at control genes in (0.35 MB TIF)Click here for additional data file.Figure S7Cyp2j5, at a non Hnf1α-target active control gene (Actb), and at an inactive locus (Ly9) in wild-type hepatocytes. Immunofluorescence signals were normalized as described in Ly9.Active Hnf1α-dependent and independent loci preferentially localize in Hnf1α-rich domains. (A) Quantitative analysis of Hnf1α immunofluorescence signal intensity at the Hnf1α-target locus (0.22 MB TIF)Click here for additional data file.Figure S8Cyp2j5 relative to its chromosome territory. (A–C) Representative confocal images of Cyp2j5 DNA FISH (red) and chromosome 4 paint (green) in hepatocytes counterstained with TO-PRO-3 (blue) showing alleles that are classified as being either in, out or in contact of the chromosome territory. More details on the criteria for classification are described in Cyp2j5 FISH signals are shown in the upper right panels. (D–E) Percentage of Cyp2j5 (D) and 114C9 (E) alleles that are located in, out, or in contact with the chromosome 4 territory in Hnf1a+/+ (black bars) and Hnf1a−/− (white bars) hepatocytes. Significance values for the comparison of allele distributions between Hnf1a+/+ and −Hnf1a/− hepatocytes were obtained by Fisher's exact test.Hnf1α-deficiency causes altered positioning of (1.76 MB TIF)Click here for additional data file.Figure S9Kif12 positioning in RNA polymerase II and histone code domains. Graphs show immuno-DNA FISH quantitation of H3-Lys4me2, H3-Lys27me3, and RNA polymerase II fluorescence at the Kif12 locus in MIN6 beta-cells treated for 4 hr with RNA polymerase II inhibitors α-amanitin (50 µg/mL) or 5,6-dichloro-1-beta-D-ribobenzimidazole (DRB) (20 µg/mL) vs. non-treated cells. Non-thresholded fluorescence signal intensities of histone marks and RNA polymerase II at 160-200 FISH signals were divided by the nuclear median intensity in the same channel, and are referred to as normalized signal in the graphs, essentially as described in Inhibition of transcriptional activity does not modify (0.24 MB TIF)Click here for additional data file.Table S1Oligonucleotide sequences used in chromatin immunoprecipitation analysis.(0.03 MB DOC)Click here for additional data file.Table S2www.ncbi.nlm.nih.gov/entrez). #Cyp2j6 locus is immediately adjacent but not included in the BAC clone 263F12. Note that Hnf1β mRNA is mildly increased in Hnf1a−/− hepatocytes.Bacterial artificial chromosomes used in this study. * Gene expression is represented as −, +, or ++ based on qualitative judgment of expression . ND: not determined. For genes marked in bold blue this information has been obtained experimentally by reverse transcription PCR or microarray expression analysis, while for genes marked in black this information has been collected from the Unigene EST Expression profile viewer ((0.07 MB DOC)Click here for additional data file.


The transcription machineries of Archaea and eukaryotes are similar in many aspects, but little is understood about archaeal chromatin and its role in transcription. Here, we describe the identification in hyperthermophilic Crenarchaeota and a Korarchaeon of an orthologue of the eukaryotic transcription elongation factor Elf1, which has been shown to function in chromatin structure maintenance of actively transcribed templates. Our discovery has implications for the relationship of chromatin and transcription in Archaea and the evolution of these processes in eukaryotes.This article was reviewed by Chris P. Ponting and Eugene V. Koonin. Despite their prokaryotic morphology, Archaea are more similar to eukaryotes in their mechanisms of copying and expression of their genetic information than to bacteria . With thIn eukaryotes, transcription elongation factors assist RNAP in overcoming pausing and arrest on the template . TFIIS rSaccharomyces cerevisiae in a screen for mutations that cause synthetic lethality with mutations in other genes coding for transcription elongation factors [elf1Δ cells and the production of short transcripts in an elf1Δ hir1Δ background strongly suggested that Elf1 acts by maintaining the chromatin structure of active transcription units [Elf1 is a transcription elongation factor that has recently been identified and characterized in  factors . A role  factors . Transcron units .Homo sapiens Elf1 (NP_115753.1) and S.cerevisiae Elf1 (NP_012762.1) was generated using MAFFT [-3 were selected and aligned. The alignment was trimmed to aligned sequence containing no more than 50% gaps and sequences were removed so that no sequence pairs with an identity higher than 95% remained in order to prevent sequence bias. A new hidden-Markov model was built and the whole process was repeated iteratively until no new sequences could be identified [During a sensitive sequence-similarity search for transcription elongation factors in an evolutionary wide range of organisms, we noticed high-scoring hits for Elf1 in a subset of archaeal predicted proteomes. Consequently, a thorough search of more archaeal genomes was initiated. A multiple sequence alignment of ng MAFFT  and trimng MAFFT , which w Elf1 NP_5753.1 anKorarchaeum cryptofilum. It was found in none of the predicted proteomes from Euryarchaeota or in the mesophilic marine group I Cenarchaeum symbiosum and Nitrosopumilus maritimus [In this way, we found 46 sequences from 14 archaeal and 25 eukaryotic organisms Figure . All Arcaritimus -14 , and Candidatus K.cryptofilum (ACB06807.1 and ACB07883.1), we were able to also identify a histone homologue in Caldivirga maquilingensis (ABW02527.1)  and differences from this Pfam family need to be stated explicitly.This manuscript describes a thorough analysis and a compelling argument for the existence of archaeal Elf1 orthologues. Whilst these findings are relatively straightforward to derive using standard database search tools, including PSI-BLAST and the authors' method of choice HMMer, the manuscript provides a detailed case for the evolutionary and functional importance of these findings. One addition that I would recommend is for the authors to acknowledge that Pfam has already collated many or all of these orthology relationships , and also rather unexpected because Elf1 is thought to function in the maintenance of eukaryote-specific chromatin structure. This paper shows that there might be even more functional similarity between the process of transcription in eukaryotes and some archaea  than currently thought.Supplementary tables and figures.Click here for file


E2f4 null mice. Thus, the regulatory mechanisms maintaining quiescence are robust even to complete loss of conserved transcription factor binding events.Maintaining quiescent cells in G0 phase is achieved in part through the multiprotein subunit complex known as DREAM, and in human cell lines the transcription factor E2F4 directs this complex to its cell cycle targets. We found that E2F4 binds a highly overlapping set of human genes among three diverse primary tissues and an asynchronous cell line, which suggests that tissue-specific binding partners and chromatin structure have minimal influence on E2F4 targeting. To investigate the conservation of these transcription factor binding events, we identified the mouse genes bound by E2f4 in seven primary mouse tissues and a cell line. E2f4 bound a set of mouse genes that was common among mouse tissues, but largely distinct from the genes bound in human. The evolutionarily conserved set of E2F4 bound genes is highly enriched for functionally relevant regulatory interactions important for maintaining cellular quiescence. In contrast, we found minimal mRNA expression perturbations in this core set of E2f4 bound genes in the liver, kidney, and testes of  Quiescence of cellular proliferation is crucial for mammalian tissue homeostasis, and aberrant activation of cell cycle programs can lead to cancer Removal of key E2F components of the multisubunit complexes that control the cell cycle can cause aberrant activation of cellular proliferation in specific tissues during development and in adulthood reviewed in . For insE2f4 null mice to determine the transcriptional importance of E2f4 binding in primary mouse tissues.Despite wide fluctuations and evolutionary turnover of transcription factor binding events between mouse and human We identified the proximal promoter regions that E2F4 occupies in three primary human tissues  directly isolated from donor organs, and an asynchronous human cell line (HepG2), using chromatin immunoprecipitation and promoter microarrays representing 13,000 regions in the human genome On our array platform, we found that E2F4 binds approximately 500 to 700 human genes, depending on the tissue. Among all three quiescent primary human tissues and the proliferating HepG2 carcinoma line, we observed overlap greater than 70% and as high as 84% . This ovA substantial majority of genes bound by E2F4 are bound in most tissues; we identified a core set of approximately 450 genes common to all human tissues in our study . These i−4, a core set of approximately 450 genes are bound in common by E2f4 in all mouse tissues  may vary between mouse and human; a variation that may have functional implications for E2f4's role in mouse Second, a feature of the approximately fifty genes where E2F4 binding is conserved is their remarkable enrichment in cell cycle, proliferation, and DNA repair functions. This result is consistent with the hypothesis that when a transcription factor binding event is conserved, this conservation is a good indictor of a functional regulatory connection E2f4 null tissues is nevertheless surprising, despite the largely normal physiology of these mice in adulthood. It has been known since the report of viable E2f4 null mice that the function of most tissues, even those directly impacted during development by absence of E2f4, recovers. Our findings reveal that this recovery extends to the level of gene expression; it appears that the absence of E2f4 can have profound, yet remarkably transient, implications for tissue-specific transcriptional programs. This recovery almost certainly depends on the overlapping roles that other members of the E2f family can play. For instance, it has been previously shown that E2F5 can largely compensate for the absence of E2F4 in vivo We expected that removal of E2f4, as a key member of the DREAM complex would have profound implications for the correct gene expression of the genes bound by E2f4 in vivo. Instead, however, we found that developmental recovery and survival to adulthood corresponds with almost completely normal gene expression in multiple tissues, relative to their phenotypically normal heterozygous littermates. Given the substantial developmental defects described above, the subtle and limited changes in the gene expression profiles of Our results also provide direct support to the hypothesis that transcriptional binding is often neutral in nature We have used comparative genomics approaches to explore the evolutionarily conserved regulatory pathways that E2F4, a key member of the DREAM complex, uses to maintain control of gene expression programs. We find that that most genes bound by E2F4 are common to numerous primary tissues within a species. This suggests that the regulatory architecture controlling quiescence may be similar among tissues that have remarkably different capacities for re-entry into the cell cycle. Understanding this architecture will require further studies to explore how E2f4 acts within specific cell types during development. The striking and specific conservation of E2F4 binding between mouse and human at cell cycle and proliferation genes suggests that only the targets crucial for the function of the DREAM complex are under selective pressure, yet our discovery that complete removal of E2f4 has at best modest effects on gene expression programs dramatically underscores the well-known redundancy within cell cycle regulation. Our study demonstrates the power of using the conservation of transcription factor binding at orthologous mouse-human genes as a tool to identify regulatory connections that appear to be under evolutionary pressure.All chemicals were purchased from Sigma-Aldrich, and used as received unless otherwise noted. E2F4 antisera were obtained from Santa Cruz Biotechnology  and used as described in prior studies Mice used in chromatin immunoprecipitation experiments were F1 males from a B6/C57 male cross with an A/J female (Jackson Laboratories). Islets suitable for ChIP studies were isolated by standard techniques and hand picking at Joslin Diabetes Center on mixed gender mice of the same genetic background (B6/C57xA/J). Other tissues were harvested using standard techniques, soaked or perfused with 1% formaldehyde, and homogenized in neutralization buffer followed by ChIP. For expression studies, E2f4 −/− homozygous and E2f4 +/− heterozygous knock-out mice were derived in a B6/C57 and 129S2/SvPas cross background Primary human hepatocytes were obtained from the Liver Tissue Procurement and Distribution Program (NIDDK contract number N01DK92310) at the University of Pittsburgh. Human pancreatic islets and pancreatic acinar tissues were the kind gifts of Gordon Weir, Abdulkadir Omer (Joslin Diabetes Center) and Nicolas Benshoff (University Minnesota) .Mouse tissues were harvested from two E2f4 −/− and two E2f4 +/− heterozygous littermates limmaaffyaffyPLM package The The procedure for chromatin immunoprecipitation has been reported previously limma package. Quality of the arrays was assessed using array images of the background and foreground intensities and also of the red/green (Cy5/Cy3) ratios to check for spatial artifacts. MA-plots (log-fold change against average log-intensity for each gene) −4). The set of commonly bound genes was defined using the less stringent value of 10−3, to capture binding events falling just under the more stringent threshold in one or two tissues. To find orthologous mouse and human genes on the promoter arrays, gene symbols and accession numbers were linked to the Ensembl IDs for each species to match the two gene lists. The data in −4. The comparison with Litovchick ChIP data was done by comparing their published binding results to the cut-offs described in The statistical analysis was performed using the We determined the enrichment of functional categories among the bound gene sets using the GOstat tool Given the sequences of the promoters on the ChIP array, a Perl script was used to automate BLAST searches of alignment across the genome and screen the BLAST output and BioPerl was used to extract the genomic coordinates of the promoters. To interrogate for the presence of the E2F4 motif, we used the one-kilobase E2F4 bound regions plus an additional 300 bases upstream and downstream to account for the resolution of the ChIP technique limma package within R was used in the genomic microarray data analysis. Quality assessment included boxplots and images of the background and foreground intensities for both red (Cy5) and green (Cy3) channels, as well as MA-plots. All the arrays used in the analysis exhibited good quality. Expression red/green log-ratios were median-normalized, after background subtraction, within each array. E2F4 binding events were empirically assigned as minimum contiguous regions comprising, at least, one probe for which the red/green ratio was greater than 5, or two adjacent probes with ratios greater than 2.5 and a combined (summed) ratio greater than 6.5, or three adjacent probes with ratios greater than 2 and a combined (summed) ratio greater than 9. A Perl script was used to compute the genomic base pair distance between each binding event and the closest transcription start site. Transcriptomic annotation relied on tables downloaded from the UCSC Genome Browser server. The smoothed histograms in density function (default parameters) to the distance distributions and plotting the respective outcomes.Human E2F4 ChIPs in primary human liver were hybridized in duplicate to human whole chromosome 21 microarrays; mouse E2f4 ChIPs in primary mouse liver were hybridized in duplicate to mouse whole chromosome 16 microarrays . The Figure S1(0.05 MB PDF)Click here for additional data file.Figure S2(0.06 MB PDF)Click here for additional data file.Figure S3(0.09 MB XLS)Click here for additional data file.Figure S4(0.17 MB PDF)Click here for additional data file.Figure S5(0.13 MB PDF)Click here for additional data file.Figure S6(0.08 MB PDF)Click here for additional data file.Figure S7(0.14 MB XLS)Click here for additional data file.Figure S8(0.08 MB XLS)Click here for additional data file.


Recent advances in genomic techniques combined with computational analysis of binding modules are discussed in the review with the example of our recent screen for PPREs on human chromosome 19.Peroxisome proliferator-activated receptors (PPARs) have via their large set of target genes a critical impact on numerous diseases including cancer. Cancer development involves numerous regulatory cascades that drive the progression of the malignancy of the cells. On a genomic level, these pathways converge on regulatory modules, some of which contain colocalizing PPAR binding sites (PPREs). We developed an in silico screening method that incorporates experiment- and informatics-derived evidence for a more reliable prediction of PPREs and PPAR target genes. This method is based on DNA-binding data of PPAR subtypes to a panel of DR1-type PPREs and tracking the enrichment of binding sites from multiple species. The ability of PPAR Thetranscriptomes of normal and tumor cells as revealed by microarray analysisshow significant differences  suggestiFI/CIPI , is alreFI/CIPI ,have foFI/CIPI –7 or traFI/CIPI , 9, had FI/CIPI  for visuFI/CIPI .Each individual gene is under the control of a large setof transcription factors that can bind upstream and downstream of itstranscription start site (TSS) . These sPPARs are transcription factors that have the specialproperty to be ligand-inducible, which they share with most other members ofthe nuclear receptor superfamily . This prα (NR1C1), β/δ (NR1C2), and γ (NR1C3) are coexpressed in numerous cell types from either ectodermal, mesodermal, orendodermal origin, although their concentration relative to each other varieswidely [The three PPAR subtypes swidely , 21. Impα is highly expressed in cells that have active fatty acidoxidation capacity including hepatocytes, cardiomyocytes, enterocytes, and theproximal tubule cells of the kidney [α is the molecular target forthe hypolipidemic fibrates, a group of drugs that are prescribed for theirability to lower plasma triacylglycerols and elevate plasma HDL (high-densitylipoprotein) levels.PPARe kidney . This PPβ/δ is expressed ubiquitously and often displays at higher expression levels than PPARα and γ. Itstimulates fatty acid oxidation in both adipose tissue and skeletal muscle,regulates hepatic VLDL (very low-density lipoprotein) production and catabolismand is involved in wound healing by governing keratinocyte differentiation [PPARntiation .γ is expressed predominantly in adipose tissue and theimmune system and exists as two distinct protein forms γ1 and γ2, which ariseby differential TSSs and alternative splicing [γ is the master regulator of adipogenesis and regulates cell-cyclewithdrawal, as well as induction of fat-specific target genes that are involvedin adipocyte metabolism [γ stimulates theexpression of numerous genes that are involved in lipogenesis, including thosefor adipocyte fatty acid-binding protein, lipoprotein lipase, and fatty acidtranslocase (CD36). The general role for PPARγ in the regulation of lipidmetabolism is underlined by the therapeutic utilization of the PPARγ ligandsthiazolidinediones in obesity-linked type II diabetes [PPARsplicing . PPARγ itabolism . PPARγ sdiabetes .An essential prerequisite for the direct modulation oftranscription by PPAR ligands is the location of at least one activated PPARprotein close to the TSS of the respective primary PPAR target gene. This iscommonly achieved through the specific binding of PPARs to a DNA binding site,the so-called PPRE, and DNA-looping towards the TSS . In detaWhen a nuclear receptor, such as PPAR, is bound to PPREsin the regulatory regions of its target genes, it recruits positive and negative coregulatory proteins,referred to as coactivators and coretrefoil factor-1 promoter in breastcancer cells [β/δ to the TSS of the pyruvate dehydrogenase kinase 4 .Understanding the events that lead to the disturbance of such coordinatedaction of regulatory proteins in cancer progression could help finding means toreinitiate the coordinated regulation. Partial restoration of regulation wasdemonstrated on the trefoil factor-1 promoter by removal of methylation in an unresponsive cell line [Repression and activation are more likely achieved by a series of sequential events that are mediated bymultiple enzymatic activities that are promoter and cell-type specific.Transcriptional regulation is a highly dynamic event of rapid association anddissociation of proteins and their modification, including degradation and denovo synthesis. A pattern of recruitment and release of cohorts ofcoregulatory complexes was demonstrated on a single region of the er cells . This stell line .γ in adipocyte differentiation and the suppression of apoptosisin keratinocytes by PPARβ/δ suggest a direct role of PPARs in the control ofcellular growth and death [angiopoietin-like4, lipoprotein lipase, LDL-receptor-related protein 1, and caveolin-1,were described to be involved in the control of tumor cell growth [The rapid growth of tumor cells is highly dependent on the availability of macronutrientsand their metabolism. In their role as master regulators of lipid metabolism,all three PPAR subtypes have at least an indirect function in controllingcellular growth . Moreovend death . As a col growth –42.Furtl growth . In thisl growth  provide β/δ has been implicated in colorectal carcinogenesis [β/δ gene results in a profound loss of tumorigenicity in nudemice [β/δ was found to have anessential role in constraining tumor endothelial cell proliferation to allowthe formation of functional tumor microvessels, that is, the receptor is important for angiogenesis [However,there is also evidence to state that PPARs may in some cases promote cancerprogression. PPARogenesis , its mRNudemice . Moreoveogenesis .As a general argument, we can propose that the main role of PPARs, the control ofmetabolism or inflammation, may also contribute to the regulation of cellulargrowth. How that translates  into interferencein cancer progression or change to a more benign phenotype, may be highlydependent on cancer type and state. In fact, the net effect of the activationof some PPAR target genes may rather result in the stimulation of cellularproliferation than in its inhibition, when examined alone. Data on geneexpression, on regulatory modules, on their accessibility, and on the bindingof PPARs to those modules need to be joined, in order to get a handle on thepleiotropic effects of PPARs in cancer.The specificity of PPARs for their binding sites allowsconstructing a model to describe the PPRE properties that can be used topredict potential binding sites in genomic sequences. For this, the PPARbinding preference, often expressed as position weight matrix (PWM), has to be described on the basis of experimental data,such as series of gel shift assays with a large number of natural binding sites–51. HoweInternet-based software tools, such as TRANSFAC ,screen In effect, these approaches and tools are stillinsufficient and there has to be a focus on the creation of bioinformaticsresources that include more directly the biochemical restrains to regulate genetranscription. One important aspect is thatmost putative transcription factor binding sites are covered by nucleosomes, sothat they are not accessible to the transcription factor. This repressiveenvironment is found in particular for those sequences that are eithercontained within interspersed sequences, are located isolated fromtranscription factor modules, or lie outside of insulator sequences marking theborder of chromatin loops . This peThe PAZARinformation mall  is a terc-myc and N-myc genes, which has implications for organ-specific growthcontrol and tumor-type specificity of oncogenes.The concept of cancer-specific regulatory modules has raised increasing attention recently.Genome-wide prediction of enhancers based on analysis of transcription factorbinding affinity by a computational tool, called enhancer element locator ,was shoAGGTCAAAGGTCA) [Approaches for PPRE predictions have been basedon a collection of disparate binding data. To combine evidence from several publicationsfor an efficient binding model has challenges thus creating a demand for acoherent binding dataset. The recently published classifier method  used theAAGGTCA)  as an exuncouplingprotein 3 (UCP3) gene and showeda deviation of less than 15%  and was only in liver responsive to PPAR ligands [carnitinepalmitoyltransferase1A and angiopoietin-like 4, are PPARresponsive in many tissues .The main advantage, when comparing theclassifier to PWM methods, is a clear separation between weak PPREs and thoseof medium and strong strength . For the ligands . In contThe data added by binding strength analysis and by coveringa larger regulatory region (±10 kB) was examined with all 38 humangenes that are known to be primary PPAR targets together with their mouseortholog. The clustering by predicted bindingstrength and evolutionary conservation of their PPREs resulted in fourgroups . In geneThe clustering analysis indicated some useful features for whole genome PPREscreens. Either the presence of at least one strong PPRE or more than twomedium PPREs within the 20 kB surrounding the annotated TSS of a gene is a strong indication for a PPAR target gene. In this way,28 out of the 38 the human genes would have been identified as PPAR targets.Similarly, for 29 of these 38 genes the analysis of their murine ortholog wouldhave come to the same conclusion. A combination of these two criteria (passingthe threshold in either the human or mouse ortholog) would have identified 37of the 38 genes as PPAR targets.γ prediction) [G1/S-specific cyclin E [p19INK4d [prostate tumor overexpressedgene, serine protease hepsin [p21-activated kinase 4 (PAK4), and homeodomain-interacting protein kinase 4. In addition, the prostate tumor marker kallikrein-3 [kallikrein gene family members were detected. Fromnovel targets, the regulatory regions of a ceramide synthesis regulator, LASS1, were experimentally confirmed [In the paper described above, the gene-dense humanchromosome 19  and its syntenic mouse regions(956 genes have known orthologs) were selected for an in silico screening basedon the above explained criteria; that is, both species were investigated formedium and strong PPREs (based on a PPARdiction) . Interescyclin E , p19INKp19INK4d , prostate hepsin  and the onfirmed . Overexponfirmed .The complete list of putative PPARtarget genes in chromosome 19  offersiThe identification of genes showing aprimary response to PPARs and their ligands, the so-called PPAR regulome, canbe used as a prediction of their therapeutic potential as well as theirpossible side effects. Methods incorporating both experimental- andinformatics-derived evidence to arrive at a more reliable prediction of PPARtargets and binding modules can bring all available data together with the aimto predict outcome in specific context. Taking the chromosome 19 in silico screening trial as an example andextrapolating the results to the whole human genome, we suggest thatapproximately 10% of all human genes (an estimate of 2000 to 2500 genes) havethe potential to be directly regulated by PPARs by their PPRE content within 10kB distance to their TSS. Translated to regulatory modules that colocalize withPPREs, an even larger number of genomic regions could be targeted by PPARs. Inconclusion, in this review we have addressed the identification of directtargets using genomic sequences and binding data. In parallel, we havediscussed the potential of looking for PPREs inside regulatory modulesforeseeing that in future, very likely the emphasis will shift from targetgenes to target regulatory modules to alter a physiological response and fromindividual genes to whole genome response.


S. cerevisiae, presenting a unique opportunity to explore their individual roles in coactivator recruitment, chromatin remodeling, and transcription.Adr1 and Cat8 co-regulate numerous glucose-repressed genes in ADH2 and FBP1, we clarified how these activators achieve this wide range of co-regulation. We find that Adr1 and Cat8 are not intrinsically different in their abilities to recruit coactivators but rather, promoter context appears to dictate which activator is responsible for recruitment to specific genes. These promoter-specific contributions are also apparent in the chromatin remodeling that accompanies derepression: ADH2 requires both Adr1 and Cat8, whereas, at FBP1, significant remodeling occurs with Cat8 alone. Although over-expression of Adr1 can compensate for loss of Cat8 at many genes in terms of both activation and chromatin remodeling, this over-expression cannot complement all of the cat8Δ phenotypes.We determined the individual contributions of Cat8 and Adr1 on the expression of a cohort of glucose-repressed genes and found three broad categories: genes that need both activators for full derepression, genes that rely mostly on Cat8 and genes that require only Adr1. Through combined expression and recruitment data, along with analysis of chromatin remodeling at two of these genes, Thus, at many of the glucose-repressed genes, Cat8 and Adr1 appear to have interchangeable roles and promoter architecture may dictate the roles of these activators. S. cerevisiae large changes in transcription accompany the diauxic shift, as organisms switch from using glucose as the main carbon source to non-fermentable carbon sources ADH2 and ACS1Alteration in gene expression patterns in response to variations in environmental signals is a tightly regulated process. In the yeast MSN4 expression depends on Msn2) Carbon Source Response Element or CSRE) with Sip4, another zinc cluster transcription factor, although evidence suggests that Cat8 is more important for regulation of CSRE-containing genes than Sip4 This overlapping regulation created by this non-redundant pair of activators is a unique system for controlling gene expression. Other pairs of transcription factors in yeast exist, but they do not mimic the situation observed with Adr1 and Cat8. For example, Ace2 and Swi5 are similar because they co-regulate some genes, while acting independently at others, but Ace2 and Swi5 are highly homologous and bind to the same DNA target sequence, unlike Adr1 and Cat8 ADH2 promoter, Cat8-binding is enhanced in the presence of Adr1, but the reverse is not true One mechanism for co-regulation is cooperative binding of transcription factors. This seems to occur at a few promoters regulated by both Adr1 and Cat8. At the HO promoter in yeast is activated by Swi5 and the heterodimer Swi4/Swi6 (SBF). Swi5 binds first and recruits the coactivator Swi/Snf, followed by Swi4/Swi6 binding Another potential avenue for differential contributions to activation is in a transcription factor's ability to recruit coactivators to the promoter. The paradigm for eukaryotic activator-dependent transcription relies on such recruitment: DNA-binding transcription factors provide specificity by binding preferentially to unique sequences, and then recruit a number of coactivators, such as complexes involved in chromatin remodeling and adaptors between activators and polymerase, as well as the actual transcription machinery itself. A number of these coactivators are non-essential and are only recruited to some promoters ADH2, ADY2, FBP1 and JEN1). We found that although Adr1 and Cat8 share a common ability to recruit coactivators, this inherent property may be mediated through promoter architecture, which dictates the factor that will be the dominant recruiter. We also looked at chromatin remodeling of two promoters, ADH2 and FBP1. The effects on chromatin remodeling at these promoters upon derepression caused by deletion of Adr1 and/or Cat8 correlated with expression data. When Adr1 was over-expressed however, the role of Cat8 in remodeling was abolished at the ADH2 promoter, and this again was mirrored in expression levels of this high copy Adr1 cat8Δ strain. These data support the conclusion that for coactivator recruitment and chromatin remodeling, Adr1 and Cat8 are not intrinsically unique, and that the observed differences between them may arise from differences in promoter context.Starting from previous microarray studies adr1Δ strain, a cat8Δ strain and a adr1Δcat8Δ strain , whereas others  were only dependent on Adr1. Many genes, however, showed a dependence on both Adr1 and Cat8 that varied between these extremes. We note that at these extremes, however, loss of the unnecessary factor (i.e. loss of Adr1 at MLS1 or loss of Cat8 at SPG1) resulted in significantly higher than wild-type levels of expression. This may indicate a metabolic requirement for elevated transcription of some genes to compensate for the loss of other gene products resultant in the absence of activator. Of the strongly Adr1-dependent genes, only SPG1 contains a consensus CSRE, making it unlikely that Cat8 is acting as a direct repressor. All genes tested, even those that are strongly Cat8-dependent, contain Adr1 binding sites, and furthermore, binding of Adr1 has been observed at a number of these promoters  we examined this possibility. We narrowed our recruitment studies to four genes, ADH2 ORF yielded the same results). This was true for all of the coactivators we studied  so data was shown only for derepressed conditions. For mutant strains, the results were expressed as the percent of the factor bound compared to 100% binding in the wild-type strain, both in derepressing conditions. Pol II binding, based on three biological samples, was generally consistent with expression data . A 5-fold increase on average over the amount of pol II detected at the promoter in repressing conditions was generally observed upon derepression. The amount of pol II at each of the glucose-regulated promoters in repressing conditions was not elevated compared to a region of the telomere not expected to be bound by pol II , or may represent a more stable association at the promoter, even in the absence of activator. We did not observe either Pol II or TFIIB at any promoter under repressed conditions, as compared to a negative control locus (data not shown).To further confirm the presence of the Pol II machinery, occupancy at the promoter by TFIIB was determined . Again, adr1Δ, cat8Δ or adr1Δcat8Δ strain. We looked at the recruitment of several chromatin remodeling complexes , the Mediator complex, and TFIID  (data not shown). Esa1, the HAT component of NuA4, which mainly acetylates the H4 tail, bound to promoters upon derepression, however in contrast to SAGA, its recruitment was largely activator independent, suggesting it may play a global, rather than targeted role  function of SAGA as well as its non-HAT function ruitment . Gcn5 isted role . SimilarADH2 has been reported in the literature to be both dependent on the ATP-dependent remodeling complex Swi/Snf, and independent of it ADH2 and FBP1. The contribution to its recruitment by Cat8 is reduced but still observable at ADY2 and JEN1. We also looked at the binding of Snf5 and saw a similar pattern of recruitment (data not shown).Regulation of ADH2 and ADH1  conditions, but at a much lower amount than in the promoter. For the genes we assayed, Mediator occupancy was highest at the promoter under activating conditions.Recent reports on the importance of Mediator reached contradictory conclusions, with some data suggesting Mediator is involved at only a small fraction of yeast genes, and then only when cells are under stress and ADH1 . ADH1 isADH2 under derepressed conditions (data not shown). The presence of CycC/Cdk8, commonly thought of as a repressor The analysis of Mediator was extended to look at the involvement of activators using three components , Med14 (Rgr1) and Med17 (Srb4). The patterns of occupancy for these three components were the same and therefore results were averaged together . ActivatADH2 and ADY2. TBP was expressed from a 2 µ plasmid, and the resulting high levels of TBP may contribute to non-specific binding, accounting for the low specific signals.This subset of genes contains TATA-boxes, so TBP occupancy is expected upon activation. ChIP for TBP reflects this requirement, as a small but significant increase in binding occurs upon derepression. It was difficult to assess the role of activators in this recruitment due to the small range of signal . It is cII145) (which was not identified as part of SAGA ACT1ADH2 expression were based primarily on Taf5 (TAFII90) ChIP for TFIID, using Taf1  mutation in Med17, we were able to study the affect of inactivating Mediator. Expression of most of the genes assayed decreased at least 10-fold when Med17 was inactivated . ADH2 wagcn5Δ and med17 ts strains, indicating that although protein levels may be diminished up to 50% compared to a wild-type strain, there is still enough transcription factor to fully occupy the promoter (data not shown).Because genome-wide studies indicated that the defects caused by loss of these coactivators are widespread ADH2 and FBP1 occur upon derepression and these changes do not occur in a adr1cat8 double mutant ADH2 promoter in a wild-type strain upon derepression: there is a reduction in protection at all 3 nucleosome positions as predicted FBP1 promoter with a wild-type strain showed changes in chromatin structure upon derepression–a major reduction of protection at N-1 and N-2 and a shift in position of N-2   for expression of these genes. Furthermore, we found no evidence of Mediator acting as a repressor.In agreement with genome-wide localization studies A recent genome-wide study by Steinmetz et al reported that Pol II occupancy does not strictly correlate with gene activity ADH2 (A and C) and FBP1 (B and D). The fact that Mediator, for example, is recruited largely by Adr1 at ADH2, but by Cat8 at FBP1 argues that protein-protein interactions are not the governing principle, but rather that it is promoter context that dictates which transcription factor dominates these interactions (see below).The possibility that these two activators made unique contributions via their ability to recruit different coactivators is ruled out through our extensive ChIP experiments, demonstrating that both Adr1 and Cat8 are capable of recruiting any of the coactivators we studied. ADH2 and FBP1 (i.e. FBP1), excess Adr1 reduces the need for Cat8, but does not abolish it.Another possible manner in which they may make distinct contributions to regulation is via chromatin remodeling. This is evidenced by the fact that Adr1 and Cat8 are differentially required for nucleosome remodeling at and FBP1 . The nonand FBP1 . At promi.e. ADH2), Cat8 is dispensable when Adr1 is over expressed, despite what is otherwise a strong requirement for Cat8 in terms of expression. Since the binding sites recognized by Adr1 and Cat8 are very dissimilar, the possibility that stable PIC formation requires the DNA to be bound at two positions or by two separate factors at these promoters is ruled out. Additionally, there is no consistent arrangement of the Adr1 and Cat8 binding sites within promoters of co-regulated genes Through mass action, high levels of Adr1 may be able to recruit and stably retain more coactivators than it would at wild-type levels. At promoters where Adr1 primarily recruits coactivators .Interestingly, we observed that multi-copy Adr1 strains lacking Cat8 fail to grow on glycerol, ethanol, acetate and lactate, all carbon sources whose utilization normally require Cat8 (data not shown) . A candidate for such a factor is the promoter architecture, which includes such things as the number and position of nucleosomes, the arrangement of binding sites, and the overall spacing of elements throughout the promoter. For example, it has been demonstrated that the correct helical phasing of Adr1 and Cat8-binding sites is required for wild-type levels of expression of ADH2 Nucleosome positioning is another factor that may be important. For example, ADH2 has a repressive chromatin structure under high glucose conditions, with significant remodeling of the nucleosomes upon activation (unpublished data), Based on our studies with these four genes, it appears that despite an overarching shared regulation, the finer workings of the mechanism of activation remain varied. Although there are some commonalities (a consistent set of coactivators), we conclude that the mechanism of activation must be examined on a promoter by promoter basis, even amongst highly related genes. Additionally, the fact that we see no inherent differences in the abilities of Adr1 and Cat8 to recruit coactivators and yet some genes are more heavily dependent on one than the other further supports the conclusion that promoter architecture is equally important for activated transcription.med17ts strain was constructed by first transforming with a srb4-138–containing URA3 plasmid, followed by deletion of chromosomal Med17 according to med17 ts experiments, cultures were grown overnight at room temperature and then shifted to 37° for 30 minutes before taking the repressed sample; cultures were then spun down in a pre-warmed centrifuge and resuspended in 37° low glucose media and grown for 4 hours. Wild-type strains were grown in the same manner for comparison to eliminate indirect effects of the high temperature. Strains over-expressing Adr1 were created by introducing plasmid YEpNKA1(URA), based on pKD17, which expresses Adr1 from the ADH1 promoter, with only a minor modification from its original form ADR1 with its endogenous promoter All strains used in this study were derived from W303. Deletions of activators and epitope tagging of all protein components were introduced according to previously published work ChIP was performed as previously described, using both dimethyladipimate  and formaldehyde as crosslinking agents and using QPCR instead of standard PCR . We note that the use of an additional cross-linker did make a substantial difference in the observed binding for some proteins. Monoclonal antibodies against c-Myc (9E10)(Santa Cruz Biotechnology sc-40) and HA (F-7)(Santa Cruz Biotechnology sc-7293) epitopes were used for all immunoprecipitations except for Pol II, which was immunoprecipitated using the 8WG16 antibody (Abcam ab817). Primers (IDT) for gene-specific QPCR were designed to cover promoter regions. Sequences are available upon request. QPCR was performed on an MJ Research DNA Engine using Power-SYBR Green (Applied Biosystems). Samples were run in triplicate and relative amounts of DNA were calculated using a standard curve generated from serial dilutions of the input DNA. Standard curves were included for every primer set. Experiments were performed in biological duplicate or triplicate and all results were averaged together. Except where noted, values were calculated as the ratio of the percent immunoprecipitation of ChIP to input at the specific locus to the percent immunoprecipitation at the telomeric region. For repressed samples, this ratio was approximately one. Derepressed values were expressed as the percent of the wild-type value. The associated error results from the standard deviation of the biological replicates. Additional analysis using previously published methods ACT1 levels. Samples were prepared from biological triplicates and quantitated in duplicate.mRNA was isolated from strains grown in either repressing or derepressing media using a hot acidic phenol isolation procedure CEN3 values. The amplicon used for CEN3 covered the region shown previously to have a well-positioned nucleosome ADH2 and FBP1 with amplicons averaging 100 bp in size (sequences available upon request).Two hundred mL cell cultures in either repressing or derepressing conditions were processed using the procedures in the yeast culture, micrococcal nuclease digestion, protein degradation and DNA purification steps as outlined in