Targeted functional experiments, largely based on designable DNA-binding proteins, have begun to define the major architectural proteins required to establish and maintain appropriate genome regulation. Here, we focus on the accessible and well-characterized system of pluripotent cells to review the functional role of chromatin organization in regulating pluripotency, differentiation and reprogramming.Translating the vast amounts of genomic and epigenomic information accumulated on the linear genome into three-dimensional models of nuclear organization is a current major challenge. In response to this challenge, recent technological innovations based on chromosome conformation capture methods in combination with increasingly powerful functional approaches have revealed exciting insights into key aspects of genome regulation. These findings have led to an emerging model where the genome is folded and compartmentalized into highly conserved topological domains that are further divided into functional subdomains containing physical loops that bring  Three-dimensional nuclear structure is established and maintained by complex layers of regulatory information superimposed on the genome. Compartmentalization of functional chromosome domains and their correct spatial distribution within the nucleus are required for several important nuclear functions including transcription, repair and replication . In addiPluripotent cells, which consist of embryonic stem (ES) cells and their reprogrammed counterparts, induced pluripotent stem (iPS) cells, are one of the most well-characterized cellular systems to study the connection between chromatin organization and cell identity . ExtensiHere, we highlight several emerging technologies that enable the study and functional analysis of chromatin organization, with a particular focus on their application to pluripotent cells. We review how these recent findings have contributed to our understanding of genome function and discuss potential future applications of these novel approaches.Microscopy-based techniques were the first to reveal large-scale structural organization in the genome, when Emil Heitz observed differences in chromosome condensation patterns within interphasic nuclei . Subsequin situ hybridization (FISH) against major satellite repeats that occur within pericentromeric heterochromatin [More recent microscopy-based techniques have advanced our understanding of nuclear organization and how this impacts on cell regulation and function. In particular, an unusual chromatin organization has been observed in mouse ES cells and fully reprogrammed iPS cells, which may be linked to their pluripotent status. For example, visualization of chromatin with DNA-binding dyes or indirectly with antibodies against heterochromatin-binding protein HP1 or heterochromatin-associated histone modifications, such as histone H3 lysine 9 trimethylation (H3K9me3), have shown that mouse ES cells have fewer but larger heterochromatin foci , 14. Thehromatin . Furtherhromatin . Excitinhromatin .in vitro and in vivo cell differentiation [cis-regulatory elements by probing the colocalization of long-range enhancers and their target genes [HoxB or uPA) can loop out of their chromosome territories (CT) on gene activation [FISH studies have further revealed sub-megabase changes in chromatin condensation at specific loci that occur on ntiation . Functioet genes . FISH hativation , 23. Inttivation . Despitetivation   are known architectural proteins that promote and stabilize chromatin looping between distal regulatory elements, including enhancers and promoters , 55. TheNanog and Oct4 promoters [Nanog locus [Key pluripotent transcription factors have also been implicated in chromatin looping in ES and iPS cells. Depletion in ES cells of transcription factors OCT4, KLF4 or SOX2 leads to the transcriptional down-regulation of genes associated with super-enhancers, disturbs enhancer–promoter loops and promotes differentiation , 63–65. romoters , 66, andromoters , 67, 68.og locus .Nanog-promoter is re-established during iPS cell reprogramming, and this is dependent on transcription factor and architectural protein occupancy [OCT4, SOX2 and NANOG loci is specific to iPS cells and correlated with complete reprogramming and active transcription of these endogenous loci [Chromatin loops are also associated with changes in chromatin organization that occur on cell differentiation and reprogramming. For example, differentiated cells have distinct interactomes at several key loci when compared with their originating ES cells , suggestccupancy . During ous loci . Furtherous loci , 70. Theous loci .Supporting a model where cell-identity-specific transcription factors play a dominant role in chromatin organization, a study applied ChIA-PET and chromatin immunoprecipitation to identify RNA polymerase II-mediated chromatin interactions in ES cells and B-cells and found a correlation for cell-specific transcription factors in the establishment of chromatin structure . AstoundExperiments aimed at determining the causative role of genome organization in controlling nuclear function and gene regulation have been challenging because many of the available approaches result in genome-wide perturbation, and can therefore lead to difficulties in distinguishing between cause and consequence. Nevertheless, as described above, many landmark studies have successfully used gain and loss of function studies to define the major factors required for genome organization in stem cells and development including architectural proteins, chromatin regulators and pluripotency transcription factors . AlthougThe new classes of DNA-binding factors include proteins within the zinc-finger family, transcription activator-like effectors (TALE) and clustered regularly interspaced short palindromic repeats (CRISPR). All three protein classes have important and diverse biological roles, but their uptake as sequence-specific genome engineering tools has been driven by rapid technological developments that have repurposed their biological function . DNA-binA recent methodological advance is the design of systems that enable live imaging of nuclear organization and sub-nuclear compartments in individual cells, which is attractive for studying dynamic processes such as the cell cycle and chromatin structure. Endogenous repetitive genomic sequences, including centromeres, telomeres and pericentric heterochromatin, can be visualized through sequence-specific TALEs fused to monomeric green fluorescent protein (GFP) A 83, 8484. When A related approach using an optimized CRISPR/Cas system to visualize specific DNA loci in live cells was recently described , 87. HerThe TALE and Cas9 approaches open up an exciting new area of cell biology, enabling real-time experiments to track the movement of chromatin and specific DNA regions, as well as imaging through cell division and other cell processes. The technology is remarkably flexible and customizable, ensuring their wide adoption and use in the field. Several technical challenges remain. One is that the targeting of the TALE and Cas9 proteins must be carefully designed to minimize the likelihood of off-target effects caused by transcriptional changes and/or blocking access of other DNA-binding elements to their regulatory sequences. A second challenge is that some background signal was detected in the TALE and Cas9 studies, presumably owing to unbound TALE-GFP or dCas9-GFP proteins. Further technical developments are therefore required to improve the signal to noise ratio, and this should also lead to improvements in the ability to visualize non-repetitive sequences.As described earlier, the position of gene loci within the nucleus is correlated with transcriptional regulation, with localization at the nuclear periphery associated with transcriptional down-regulation , 88–90. cis-regulatory region, in line with the conclusion that the CTCF sites can form boundaries that insulate genes in one neighbourhood from another [Hox clusters in mouse ES cells [Hox genes on cell differentiation. These results reveal that CTCF helps establish functional domains that control developmental gene regulation. Our understanding of chromosome boundaries is still far from complete, however, as it is not possible to predict which genes will be transcriptionally altered on loss of boundary and other structural elements. Nevertheless, the combination of DNA interaction methods and functional analysis of target regions will ensure exciting progress towards these goals.Gene loops can be identified from genome-wide DNA proximity ligation assays and are often anchored at either end by architectural components such as mediator, cohesin and CTCF , 59–61.  another . A recenES cells . Excitincis-regulatory elements using chromatin signatures, 3C and functional approaches is now well established [Identification of ablished . One emecis-regulatory elements  with the predicted decrease in histone modifications associated with active enhancers. For several targets , transcription of a nearby gene was also altered, suggesting that regulatory function was perturbed. This is an exciting first step in using epigenomic engineering to alter cis-regulatory element function, and further refinements to the technology, such as combinatorial approaches, will provide a powerful platform to push this work forward.In addition to enhancer deletions, another elegant way to test the function of enhancer . The autThe combination of microscopy, 3C technologies and functional approaches has tremendously advanced our knowledge of genome organization. Identification of the general principles that govern chromatin compartmentalization and physical interactions between regulatory elements have led to a better understanding of cell identity and potentially mechanisms to control cell state changes during pluripotent cell differentiation and reprogramming.Despite the exciting progress in this area, several methodological challenges remain to be overcome. First, because interaction data sets are created from static populations of cells, they represent only the topological tendency of the population. Nagano and colleagues have provided the first attempt to bypass this population-biased limitation by developing a single-cell Hi-C technique , and furIn summary, methods to study and perturb genome organization are enabling exciting new insights into nuclear function. Progress in this area will better define how chromosomes are compartmentalized into functional domains, and how physical interactions between regulatory sequences are formed and regulated, which together will further our understanding of the relationship between three-dimensional organization and genome function.Key PointsThree-dimensional nuclear organization contributes to genome folding, chromosome compartmentalization and the formation of gene regulatory interactions, ensuring appropriate genome function.Recent technological innovations including chromosome conformation capture approaches have revealed the presence of major structural and functional domains within the genome.Architectural proteins and master transcription factors coordinate aspects of genome organization to ensure transcriptional states and appropriate cell identity.Designable DNA-binding proteins have been repurposed to enable the visualization and targeted perturbation of genome organization, thereby providing new functional insights.


Dis2 with chromatin, through an interaction with Ppn1, a protein that we identify as the fission yeast homologue of vertebrate PNUTS. We demonstrate that Swd2.2, Ppn1 and PP1Dis2 form an independent module within the CPF, which provides an essential function in the absence of the CPF-associated Ssu72 phosphatase. We show that Ppn1 and Ssu72, like Swd2.2, are also negative regulators of condensin-mediated chromosome condensation. We conclude that Swd2.2 opposes condensin-mediated chromosome condensation by facilitating the function of the two CPF-associated phosphatases PP1 and Ssu72.Functional links connecting gene transcription and condensin-mediated chromosome condensation have been established in species ranging from prokaryotes to vertebrates. However, the exact nature of these links remains misunderstood. Here we show in fission yeast that the 3′ end RNA processing factor Swd2.2, a component of the Cleavage and Polyadenylation Factor (CPF), is a negative regulator of condensin-mediated chromosome condensation. Lack of Swd2.2 does not affect the assembly of the CPF but reduces its association with chromatin. This causes only limited, context-dependent effects on gene expression and transcription termination. However, CPF-associated Swd2.2 is required for the association of Protein Phosphatase 1 PP1 Failure to properly condense chromosomes prior to their segregation in mitosis can lead to genome instability. The evolutionary-conserved condensin complex is key to the condensation process but the molecular mechanisms underlying its localization pattern on chromosomes remain unclear. Previous observations showed that the localization of condensin is intimately linked to regions of high transcription, although, somewhat paradoxically, its association with chromatin is disrupted by a processive polymerase activity. Here we identify several RNA processing factors as negative regulators of condensin in fission yeast. Two of these factors associate with PP1 phosphatase as an independent entity within the Cleavage and Polyadenylation Factor (CPF), a complex key for 3′ end RNA processing. Lack of this module induces only minor and context-dependent effects on gene expression. Our data suggest that this module helps maintaining the proper level of phosphatase activity within the CPF and thereby opposes the function of condensin in mitotic chromosome condensation. Cut14, SMC4Cut3, CAP-D2Cnd1, CAP-GCnd3 and CAP-HCnd2, name of the human protein followed by its name in fission yeast), which together form a protein ring big enough to entrap two chromatids. Condensin exhibits a DNA-dependent ATPase activity and a DNA supercoiling activity but how these enzymatic activities contribute to mitotic condensation remains elusive. Although condensin interacts directly with histones cis-acting elements facilitate the binding of condensin at specific loci, supporting the current view that the underlying mechanisms of condensin recruitment are, to some extent, locus-specific  studies also indicate that condensin localizes preferentially at highly expressed genes, irrespective of the transcription machinery involved  Experimental evidence indicates that there are strong functional relationships between gene transcription and mitotic chromosome condensation. However some observations suggest that the transcription machinery plays a positive role in condensin-mediated chromosome condensation whilst other evidence indicates that gene transcription can inhibit chromosome condensation. On one hand, specific transcription-associated factors and RNA processing factors, such as the RNA Pol III component TFIIIC and the RNA helicase DDX3, have been shown to facilitate the loading of condensin cut3-477 mutant at high temperatures. We identified a number of suppressor mutations by this approach. Here we focus on one of them, swd2.2Δ, the deletion of a non-essential component of the Cleavage and Polyadenylation Factor (CPF), the complex responsible for the 3′end maturation of RNA Pol II transcripts in yeast  improved the growth of cut3-477 cells , indicating that significant RNA processing defects are not sufficient to alleviate the defects caused by cut3-477. For example, lack of either Rtt103/Rhn1 cut3-477 . Furthermore, lack of Swd2.2 did not rescue the rad21-K1 and smc6-74 mutations . Surprisingly, we found that the localization of Cut14 was fully restored at kinetochores (cnt1) and RNA Pol III-transcribed genes, where its enrichment was indistinguishable in cut3+ and cut3-477 swd2.2Δ cells . Western blot analysis showed that the steady-state level of Cut14 was not affected in cells lacking Swd2.2 . The reduced association of Sfc6-13myc with chromatin could probably be explained in part by the fact that both gene expression and the protein stability of Sfc6 were reduced in swd2.2Δ cells . The stability of Rpc25-13myc however remained unaffected (sfc3-1 cells and found the opposite result to what is published sfc3-1 cells (sfc3-1 cells (sfc3-1 cells (swd2.2Δ and sfc3-1 destabilize the loading of TFIIIC. A common feature of both types of suppressors (sfc3-1 and swd2.2Δ) is therefore that the loading of TFIIIC and RNA Pol III is impaired at Pol III genes.Observations made previously in the fission yeast TFIIIC mutant 2Δ cells E. The st-1 cells . We conf-1 cells . All our-1 cells , which ipfs2-11 is a thermo-sensitive mutation of Pfs2, an essential component of the CPF pfs2-11 (Swd2.2 was previously shown to co-purify with the Cleavage and Polyadenylation Factor (CPF)  pfs2-11 , which i pfs2-11 . Note th pfs2-11 .swd2.2Δ and swd2.2+ strains (raw data accessible at GEO GSE38005). Both strains were grown in triplicates for one generation at 34°C, the temperature at which lack of Swd2.2 improves chromosome segregation in cut3-477  and its cofactor PNUTS as part of the PP1/PTW complex in vivo with fission yeast PP1Dis2. This interaction required the three PP1-binding motifs of Ppn1  , where PP1Dis2 was comparatively 5 times more abundant than Ppn1  but not in the absence of the CPF-associated Ssu72 phosphatase  resulted indeed in the increased phosphorylation of Ser10 on histone H3  module. Note that to further illustrate the quality of our affinity purifications, we provide the full list of unique peptides recovered after purifications of Yth1, Swd2.2, Ppn1 and PP1Dis2 ranked by abundance  significantly improved the growth of cut3-477 cells at the restrictive temperature . Note however that lack of the core CPF component Ctf1 had no effect on the growth of cut3-477 cells  had a weaker suppressive effect on cut3-477 than either swd2.2Δ or ppn1Δ . To explain this observation, we speculated that another phosphatase could at least partly substitute for PP1Dis2. Here we present evidence that one such phosphatase could be the CPF-associated Ssu72 phosphatase. First, lack of Ssu72 exhibited a mild suppressive effect on cut3-477 , which lacks CPF-associated phosphatases, is dead .Lack of PP177 cells . Secondl is dead . This evssu72Δ or dps mutants could facilitate the function of condensin in cut3-477 cells. To test this, we weakened chromosome cohesion by mutating two known regulators of cohesin, Swi6 (swi6Δ) and Pds5 (pds5Δ). Neither deletion was able to suppress the growth defect of cut3-477  in this process is(are) still unknown, although we have shown that it is unlikely that it could be phospho-Ser10 on histone H3, cohesin or Aurora B-dependent phosphorylation of Cnd2. However at this stage, our data do not exclude the possibility that DPS and Ssu72 target other phosphorylated sites on condensin.Human PNUTS is a scaffold for the PP1-containing PTW/PP1 complex, where it interacts directly with three proteins, Wdr82, Tox4 and PP1 Sds21 was not able to replace PP1Dis2 within the DPS. Furthermore, the nuclear localization pattern that PP1Dis2 adopted in the absence of Ppn1 was highly similar to the nuclear localization pattern that was reported for PP1Sds21Dis2 but not PP1Sds21 to bind to Ppn1 remain to be elucidated.Interestingly, we note that the other PP1 isoform in fission yeast, PP1in vitroIn the human PTW/PP1 complex, the interaction between hPNUTS, Wdr82 and PP1 is likely to be relevant for chromosome condensation, as the domain of hPNUTS that mediates the interaction with both PP1 and Wdr82 is sufficient to induce chromosome decondensation swd2.2Δ on the recruitment of the Pol III machinery were highly reminiscent of the effects of mutating the TFIIIC component Sfc3 sfc3-1 is, like swd2.2Δ, a suppressor of cut3-477swd2.2Δ and sfc3-1 cells could by itself facilitate the localization of condensin. Our observations are however consistent with the idea put forward previously that the local reduction in RNA polymerase activity can facilitate the association of condensin Our data show that lack of Swd2.2 had a significant impact on the association of the RNA Pol III transcription machinery with chromatin. In our hands, the effects of swd2.2Δ on the recruitment of the Pol III machinery is likely to be indirect, because lack of Swd2.2 had a mild impact on the expression of Sfc6, a component of TFIIIC. On the other hand, we detected a small but significant enrichment of Swd2.2 and Pfs2 at Pol III-transcribed genes, consistent with the idea that it could also play a direct role there. The fact that a factor associated with the RNA Pol II RNA processing machinery such as Swd2.2 could also play a role at Pol III genes is not unprecedented. In budding yeast in particular, the Nrd1/Nab3 complex is involved in processing both Pol II and Pol III transcripts S.cerevisiae is enriched at Pol III genes cut3-477  as a suppressor of cut3-477 -3flag, the C-terminus of Ppn1 was PCR amplified and cloned into pGEMT-easy . Site-directed mutagenesis was then used to mutate the three PP1-binding sites using Quickchange protocols (Stratagene). Overlapping PCR was used to add a 3xFlag tag and a cassette of resistance to nourseothricin (NatR) to the C-terminus of Ppn1. This PCR product was used to transform yeast. Proper integrants were selected by PCR and western blot and were sequenced to verify the presence of the mutations.A list of the strains used in this study is given in 8 cells were frozen in liquid nitrogen and broken open in lysis buffer  using zirconium beads and a Fast-prep machine . Clarified extracts were then incubated for 60 min at 4°C with Protein A-coupled Dynabeads incubated previously with the proper antibody according to the manusfacturer's recommendations. The immunoprecipitated complexes were washed three times with lysis buffer and once with phosphate-buffered saline containing 0.02% Tween 20. Immunoprecipitated complexes were analyzed by immunoblot using a semi-dry transfer protocol. Cnd2 phosphorylation was analysed as described previously 2.102, 1 µg/mL leupeptin, 1 µg/mL pepstatin, 1 µg/mL chymostatin, and 1 mM Pefabloc) and then sonicated twice for 30 seconds on ice (1.2 W per mL). After centrifugation , the supernatant was filtered through a 2.7 µM then a 1.6 µM glass microfiber filter (Whatmann 25 mm GD/X). 25 µL of 50/50 slurry of anti-Flag M2 affinity agarose beads (Sigma) was added and incubated on a rotating wheel at 4°C for 30′. The beads were then washed three times in cold lysis buffer and then twice in 50 mM Hepes-KOH [pH 7.6], 50 mM KCl. Proteins were eluted by two 5′ incubations in 50 µL of 50 mM H3PO4.5 g of cells were broken using a Retsch MM400 mill for 180 s at 30 Hz (repeat three times). The broken cells were then resuspended in lysis buffer  unless otherwise stated. Acetonitrile and water for LC-MS/MS were HPLC grade . Formic acid was Suprapure 98-100%  and trifluoroacetic acid was 99% purity sequencing grade. All HPLC-MS connector fittings were from Upchurch Scientific or Valco .Gel-free samples were prepared and loaded onto strong cation exchange columns, reduced with DTT, alkylated with iodoacetamide, and digested with trypsin as described in http://www.sanger.ac.uk/) with 5022 sequences. Variable methionine oxidation, STY phosphorylation, protein N-terminal acetylation and fixed cysteine carbamidomethylation were used in all searches. Precursor mass tolerance was set to 7 ppm and MS/MS tolerance to 0.4 amu. The significance threshold (p) was set below 0.05 (MudPIT scoring in Mascot). All LC-MS runs were combined using MaxQuant (version 1.0.13.8), assuming a false positive rate of 0.01 MS/MS data were searched using MASCOT Versions 2.3  against a Schizosaccharomyces pombe database downloaded from the Sanger Institute (8 cells according to the procedure described in RNA was extracted from 2.10http://www.profilexpert.fr/) performed the strand-specific tiling arrays and their initial analysis. The data were established in triplicates. RNAs extracted in the lab were purified using the miRNeasy mini kit (Qiagen), dosed using a nanodrop and their quality was assessed using a Bioanalyzer 2100 (Agilent). To prepare cDNAs, 250 ng of RNA were then amplified using the WT Expression Kit (Ambion). The resulting cDNAs were then dosed (Nanodrop) and their quality assessed using a Bioanalyzer 2100 (Agilent). 5.5 µg of cDNAs were then fragmented and labelled using the GeneChIP WT Terminal Labelling Kit (Affymetrix). 5 µg of cDNAs were then hybridized on a GeneChip S.pombe Tiling 1.0 Array (Affymetrix). The data were normalized using Quantile Normalization swd2.2Δ, the median value of the signals recorded for each gene in each replicate was first calculated. These median values were then averaged for the three replicates. For each gene, The Fold-change (FC) and the corresponding p-value were calculated to express variation in expression between swd2.2Δ and swd2.2+ cells. To identify genes with potential transcription termination defects, we proceeded as follows: for every gene whose 3′UTR is annotated in the fission yeast database, we calculated the ratio between the average signal recorded in the 3′UTR and the average signal recorded in the ORF in swd2.2+ and swd2.2Δ cells. We considered that genes had a transcription termination defect in cells lacking Swd2.2 when the ratio obtained for swd2.2Δ was at least 1.5 greater than the ratio obtained in swd2.2+. The data can be accessed on GEO under the accession number GSE38005.ProfilXpert (www.r-project.org). Genomic features annotations were downloaded on the 09/05/2011 from the Sanger centre (ftp://ftp.sanger.ac.uk/pub/yeast/pombe/GFF/). To test for differential expression between gDWN-1 and gDWN genes in the wild-type context, we computed the 47 logratios and tested their departure from zero using a Wilcoxon test. To test if the intergenic distance (IGR) between gDWN-1 and gDWN was particularly short or long compared to any other gene, we compared the median value of the 47 observed IGR to the distribution of median values obtained from 10,000 randomly chosen sets of 47 genes in the genome. The resulting empirical null distribution is shown on gDWN genes was shorter than all simulated median IGRs. The statistical significance for claiming that these IGRs are particularly short is therefore lower than 1/10,000. Similarly, IGRterm distances observed on the set of 780 gTERM genes were computed and their median was compared to the distribution of median values from 10,000 sets of 780 genes picked at random from the genome  at 18°C for 30 mn. After extensive washes with cold PBS, cells were frozen in liquid Nitrogen. Frozen cells were then broken open in cold lysis buffer  using Acid-wash Glass beads (Sigma) and a Fast-Prep machine . The lysats were then sonicated at 4°C using a Diagenode sonicator. Immuno-precipitation was done overnight at (4°C) using ProtA-coupled Dynabeads previously incubated with the anti-GFP A11122 antibody (Invitrogen). Beads were washed successively with (10′ incubation on rotating wheel): Wash I buffer , Wash II buffer  and Wash III buffer . After two additional washes in TE pH 8, the cross-links were reversed by incubation at 65°C (6 hours minimum) with elution buffer . The immuno-precipitated DNA was then purified using the Wizard PCR purification kit (Promega) according to the manusfacturer's instructions. The DNA was then analyzed by qPCR. Primers are available on demand.10Figure S1AB. Serial dilutions of the indicated strains were plated on rich media at the indicated temperatures.Lack of Swd2.2 does not improve the viability of other mutants defective in chromosome architecture. (TIF)Click here for additional data file.Figure S2Lack of Swd2.2 does not alter the stability of Cut14 protein. Western blot analysis of the indicated strains.(TIF)Click here for additional data file.Figure S3cut3-477 and mde4Δ. Serial dilutions of the indicated strains were plated on rich media at the indicated temperatures.Lack of Swd2.2 suppresses the synthetic lethal interaction between (TIF)Click here for additional data file.Figure S4Lack of Swd2.2 does not significantly alter the localization of the wild-type condensin complex. ChIP qPCR of the GFP-tagged condensin sub-unit Cut3 (Cut3-GFP) in the presence or absence of Swd2.2. Strains were grown in cycling conditions in rich medium at 30°C .(TIF)Click here for additional data file.Figure S5Pht1 acetylation. A. Cycling or early mitotic cells (arrested in prometaphase with the cold-sensitive nda3-KM311 mutation) expressing the condensin sub-unit Cnd2 tagged with GFP at its endogenous locus were imaged under the microscope to count the number of mitotic, GFP-positive cells. Protein extracts were prepared from the same cells and GFP-tagged Cnd2 was immuno-precipitated. The immuno-precipitated complexes were analyzed by western blot using an antibody recognising the phospho-modified form of Cnd2, as described previously B. The same experiment as in (A) was repeated with cut3-477 or cut3-477 swd2.2Δ cells expressing GFP-tagged Cnd2 grown at the restrictive temperature of 34°C for one generation (3 hours). As the mitotic index is much smaller in these cells compared to nda3KM311-arrested cells, a diluted “nda3” samples was put on the same gel as control. C. Lack of Swd2.2 has no detectable effect on the acetylation of H2A.zPht1. Protein extracts were prepared from cells expressing HA-tagged Pht1 and analyzed by western blot using an acetyl-specific antibody recognising the acetylated isoform of Pht1 (see Lack of Swd2.2 has no detectable effect on Aurora B-dependent Cnd2- phosphorylation or H2A.zPht1 see . A HA-ta(TIF)Click here for additional data file.Figure S6sfc3-1 cells. A. Genotyping of the sfc3-1 mutation. (left panel) Method: the sfc3-1 mutation creates a BspH I site. For genotyping, a PCR product (fragment 806–1458 bp) is digested with BspH I for 2 hours. PCR products derived from sfc3+ cells remain undigested (right panel). Genotyping of the strains used for the ChIP in B and C. BC. ChIP qPCR of the indicated strains grown in cycling conditions for 2 hours at 36°C, the restrictive temperature of sfc3-1 .The association of Sfc6 and Rpc25 at Pol III-transcribed genes is impaired in (TIF)Click here for additional data file.Figure S7A. Scheme explaining the genomic context of the 47 down-regulated genes. gDWN  =  gene of interest; gDWN-1  =  gene positioned on the same DNA strand directly upstream of g. IGR: Intergenic distance in bp between the end of the 3′UTR of gDWN-1 and the beginning of the 5′UTR of gDWN. Fas Fraction of gDWN covered by an antisense transcript. B. Distribution of expression differences between gDWN and gDWN-1 in the wild-type context. X-axis: log2 ratio of the expression level of gDWN-1 over the expression level of gDWN. Y-axis: gene counts. Sums of all bars  = 47, representing all pairs  for the 47 gDWN genes under-expressed when Swd2.2 is absent. Red vertical line: median value. Green dotted line indicates the expected median if gDWN-1 and gDWN had similar expression levels. C. The median IGR observed for the 47 gDWN genes (red line) is shorter than for other genes of the genome. 10,000 sets of 47 genes were drawn at random from the genome, and the median IGR computed for each set. The black histogram shows the distribution of the resulting 10,000 median values. All of them exceeded the value observed on the actual set of 47 gDWN genes. D.gDWN genes are more often covered by an antisense transcript than other genes of the genome. X-axis: fraction of gene covered by antisense transcription (Fas). The yellow histogram shows the distribution of Fas values for the 47 gDWN genes. For comparison, the blue histogram shows the distribution of Fas values calculated for 10,000 random sets of 47 genes picked in the genome. The two distributions were significantly different . This diagram indicates that the majority of genes in the S.pombe genome are not covered by an antisense transcript , whilst roughly 10% of genes are completely covered by an antisense transcript . For the 47 gDWN genes, the percentage of genes entirely covered by an antisense transcript increases to roughly ∼35% . E. Tiling-array hybridization intensity profiles along two gDWN genes. F. The expression levels of candidate genes were verified by RT-qPCR in the indicated CPF mutants. The values presented are normalized to the expression levels of act1 established concomitantly. Results are the average of 3 biological replicates. Error bars represent standard deviation.Genomic context of the 47 down-regulated genes in cells lacking Swd2.2. (TIF)Click here for additional data file.Figure S8A. Tiling-array hybridization intensity profiles along two genes showing transcription termination defects when Swd2.2 is missing. Black boxes correspond to the coding region of the genes and grey boxes correspond to the UTRs. White boxes correspond to introns. The arrow indicates the orientation of transcription. B. Scheme explaining the genomic context of the genes with transcription termination defects in the absence of Swd2.2. gTERM  =  gene of interest showing no difference of microarray signal in the coding region but a stronger signal in the 3′UTR when Swd2.2 is missing. gTERM+1RV: the first downstream gene in the reverse orientation. IGRterm: IGR regions located between gTERM and gTERM+1RV. C. The size of IGRterm was measured for each gene of interest. Red vertical line  =  median size measured for the 780 gTERM genes. It is negative, showing that most gTERM genes overlap with their immediate downstream reverse gene. Black histogram: distribution of median values obtained on 10,000 random sets of 780 genes.The effect of Swd2.2 on transcription termination is context-dependent. (TIF)Click here for additional data file.Figure S9Enrichment of Swd2.2 established by ChIP at convergent genes exhibiting transcription termination defects when Swd2.2 is missing. Rhp18 and Mde4 are two genes placed in situation of convergence with respectively SPBC1734.07c and Atg7, whose transcription termination is affected in the absence of Swd2.2. Fba1 and Pgk1 suffer no transcription termination defect in the absence of Swd2.2. ChIP analysis showed that Swd2.2 is not more abundant in the 3′UTR of Rhp18 and Mde4 compared to the 3′UTR of Fba1 and Pgk1 .(TIF)Click here for additional data file.Figure S10A), Cdc18 (B), snoR99 (C) and snoR68 (D). Each panel follows the same organization: on the left, the tiling-array hybridization intensity profiles along the gene of interest is displayed for each strand as seen for Swd2.2+ or swd2.2Δ cells; on the right, the result of a qRT-PCR strategy designed to quantify the proportion of RNA that have been transcribed passed the previously-identified site of transcription termination. Termination sites are identified on the figure together with genomic features such as restriction sites. For each gene examined, the PCR product F-R1 served as internal loading control. The histograms show the size-normalized ratio to R1 =  (band intensity/band size)/(R1 band intensity/R1 size). For each, n = 3 biological replicates.Lack of DPS has a moderate effect on transcription termination at snoRNAs. Transcription termination was monitored in deletion mutants of various CPF-associated proteins at Ura4+ ((TIF)Click here for additional data file.Figure S11A. Scheme explaining the domain organization of the PNUTS homologues in Human and S.pombe. The domain showing homology to TFIIS, and the PP1-binding consensus sites are highlighted. B. The PRALINE software  was used to create an alignment between the human and Xenopus PNUTS homologues and the fission yeast SPCC74.02c (Ppn1). The region of human PNUTS found to be sufficient to induce chromosome decondensation in vitroPpn1 shows significant homology with human and xenopus PNUTS. (TIF)Click here for additional data file.Figure S12A. The proteins indicated at the top were affinity purified and the associated proteins identified by MS/MS mass-spectrometry analysis Click here for additional data file.Figure S13nda3KM311 .The CPF sub-unit Pfs2 remains associated with chromatin in early mitotic cells. ChIP-qPCR analysis was performed to monitor the association of GFP-tagged Pfs2 with chromatin in cycling cells and in cells synchronized at the metaphase to anaphase transition using the cold-sensitive tubulin mutation (TIF)Click here for additional data file.Figure S14A. Serial dilutions of the indicated strains were plated on rich media at the indicated temperatures. B. Chromosome segregation in anaphase was visualized after growing cells of the indicated genotypes for one generation at 34°C. Anaphases were scored as defective when lagging chromatin was detected between the two main DNA masses. For each genotype, 3 independent experiments were performed in which a minimum of 100 anaphase cells was scored.The CPF component Ctf1 does not oppose condensin-mediated chromosome condensation. (TIF)Click here for additional data file.Figure S15cut3-477 cells. The indicated strains were grown at 34°C for 3 hours and ChIP-qPCR was performed to analyze the amount of Cut14-GFP cross-linked to chromatin . The enrichments of Cut14 observed in cut3-477 and cut3-477 ssu72Δ cells were not significantly different .Lack of Ssu72 does not restore the localization of condensin in (TIF)Click here for additional data file.Table S1cut3-477 at 34°C.List of the gene deletions identified in our screen for suppressors of the growth defect of (XLSX)Click here for additional data file.Table S2List of the genes whose expression is significantly altered in the absence of Swd2.2.(XLSX)Click here for additional data file.Table S3List of the genes exhibiting a transcription termination defect in the absence of Swd2.2.(XLSX)Click here for additional data file.Table S4Dis2. Numbers correspond to the number of unique peptides identified for each protein. Proteins labeled in black were discarded as not specific as they were also identified after purification of a no-tag control.List of the proteins identified after LC-MS/MS analysis of the affinity purifications of Yth1, Ppn1, Swd2.2 and PP1(XLSX)Click here for additional data file.Table S5List of the yeast strains used in this study.(XLSX)Click here for additional data file.Text S1Analysis of the genomic context surrounding the genes that are mis-expressed in the absence of Swd2.2. We explain how we have identified the genomic features that are common to the 61 genes either under- or over-expressed in the absence of Swd2.2.(DOCX)Click here for additional data file.


Transcriptional activation throughout the eukaryotic lineage has been tightly linked with disruption of nucleosome organization at promoters, enhancers, silencers, insulators and locus control regions due to transcription factor binding. Regulatory DNA thus coincides with open or accessible genomic sites of remodeled chromatin. Current chromatin accessibility assays are used to separate the genome by enzymatic or chemical means and isolate either the accessible or protected locations. The isolated DNA is then quantified using a next-generation sequencing platform. Wide application of these assays has recently focused on the identification of the instrumental epigenetic changes responsible for differential gene expression, cell proliferation, functional diversification and disease development. Here we discuss the limitations and advantages of current genome-wide chromatin accessibility assays with especial attention on experimental precautions and sequence data analysis. We conclude with our perspective on future improvements necessary for moving the field of chromatin profiling forward.The online version of this article (doi:10.1186/1756-8935-7-33) contains supplementary material, which is available to authorized users. The nucbination . Experimbination –18.in vivo[Nucleosome eviction or destabilization at promoters and enhancers results from the binding of specific regulatory factors responsible for transcriptional activation in eukaryotes , 20. Opein vivo. Notablyin vivo–25. Therin vivo have becDrosophila using DNase I and MNase treatment, provided the first demonstration that active chromatin coincides with nuclease hypersensitivity, that is chromatin accessibility [Low-throughput experiments in sibility –30. Currdirectly the effect of chromatin structure modifications on gene transcription, in contrast to histone chromatin immunoprecipitation with NGS (ChIP-seq)  isolate accessible locations of a genome separate from MNase-seq, which indirectly evaluates chromatin accessibility, and present their principal mode of action, examples of application and main experimental considerations (Table Chromatin accessibility approaches measure seq read –33) wherns Table .Table 1MNase is commonly reported as a single-strand-specific endo-exonuclease, although its exonuclease activity appears to be limited to only a few nucleotides on a single strand before cleavage of the antiparallel strand occurs –36. Sincindirectly by unveiling the areas of the genome occupied by nucleosomes and other regulatory factors. Commonly referred to as a nucleosome occupancy assay, it shares same principal mode of action (enzymatic cleavage) and can provide information on TF occupancy as other chromatin accessibility assays. MNase-seq has been implemented in a number of organisms, ranging from yeast to humans, for the mapping of chromatin structure [et al. [MNase-seq thus probes chromatin accessibility tructure –49. In a [et al.  have alsin vivo protein-nucleic acid and protein-protein interactions [Important considerations in the design of MNase-seq experiments include extent of chromatin crosslinking and level of digestion. Traditionally, chromatin accessibility experiments have been conducted with formaldehyde as a crosslinking agent to capture ractions . It has ractions . Also, Mractions –54 and cractions –57. MNasractions , 58, 59.in vivo experimental data of two distinct nucleosome configurations from different MNase-seq technical preparations supports the same conclusions, and underscores the importance of standardized collection of mononucleosomes for accurate and reproducible comparisons [However, the cause of differences in MNase-seq output across differential levels of enzymatic digestion is difficult to assess due to the effect of inter-nucleosomal linker length on the recovered signal . MNase dparisons . Specifiparisons , 55.Overall, MNase-seq is a superior method for probing genome-wide nucleosome distributions and also provides an accurate way for assessing TF occupancy in a range of cell types . HoweverDrosophila and other eukaryotes, supported the conserved observation that chromatin structure is disrupted during gene activation and that DHSs are the primary sites of active chromatin rendering access of trans-factors to regulatory elements [cis-regulatory elements with the combinatorial action of ATP-dependent nucleosome- and histone-remodelers [Historically, open chromatin has been identified by the hypersensitivity of genomic sites to nuclease treatment with MNase and the non-specific double-strand endonuclease DNase I . In a tyelements , 62–65. elements , due to modelers , 66, 67.Traditionally, identification of DHSs has been based on Southern blotting with indirect end-labeling  and invoDNase-seq has been extensively used by the ENCODE consortium  and otheThe main controversy over DNase-seq is the ability for DNase I to introduce cleavage bias , 82, 84,a priori knowledge of additional epigenetic information. Its reliability as a TF footprint detection assay in a temporal scale is questionable and needs to be investigated further in detail.Less concerning limitations of DNase-seq are that it requires many cells and involves many sample preparation and enzyme titration steps. Success of this assay depends on the quality of nuclei preparations and small-scale preliminary experiments are essential to ascertain the exact amount of detergent needed for cell lysis . Also, Din vivo protein-DNA interactions, and subsequent shearing of chromatin with sonication. Following phenol-chloroform extraction, nucleosome-depleted areas of the genome are released to the aqueous phase of the solution due to much higher crosslinking efficiency of histones to DNA, compared to other regulatory factors [One of the easiest methods for directly probing nucleosome-depleted areas of a genome is FAIRE  Figure , althoug factors , 90. The factors , 86 or m factors , 91.Saccharomyces cerevisiae[Initially demonstrated to identify accessible regulatory elements in erevisiae, FAIRE herevisiae, 92, 93.erevisiae. It has erevisiae, 94, 95,erevisiae or allelerevisiae, and to erevisiae, 98.Overall, FAIRE enriches directly for areas of active chromatin, with the added benefit that the nucleosome-depleted regions are not degraded, it can be applied to any type of cells or tissue and that there is no requirement for initial preparation of cells and laborious enzyme titrations , 89, 94.Success of any FAIRE-seq experiment heavily depends on adequate fixation efficiency that can alter depending on cell permeability, composition and a variety of other physiological factors. For most mammalian cells, 5 minutes of fixation time is usually ample . Fungi ain vivo  that can be sequenced per sample. Tag count represents the most instrumental parameter of output sequencing quality. The number of tags that need to be sequenced depends on the goal of the specific experiment, with nucleosome mapping and TF footprinting experiments requiring higher coverage compared to standard chromatin accessibility detection. To obtain a target coverage depth per sample, the researcher should take into account the minimal number of mappable tags delivered by the instrument in use and adjust accordingly the number of multiplexed samples per lane of flow cell (for details read ). A secoThe vast amount of generated sequencing data is subsequently analyzed using a variety of analytical tools, with progressively increased level of difficulty and advanced requirements for computational and genomics expertise. As a result, data analysis along with computing power and storage capacity, are often regarded the current bottleneck in chromatin accessibility experiments. Below we discuss each stage of analysis with separate references to specific chromatin accessibility assays, and more specialized reviews whenever necessary, in an attempt to provide a comprehensive analysis workflow for the novice chromatin accessibility researcher Figure . We mainOverall, most initial data analysis steps are the same for all chromatin accessibility assays discussed above and are normally done by the NGS facility performing the sequencing reactions. These steps include demultiplexing, alignment to a reference genome, tag filtering and measurement of sequencing quality control (QC) Figure . The goahttp://broadinstitute.github.io/picard). For ATAC-seq data specifically, mapped fragments below 38 bp are removed since that is the minimum spacing of transposition events due to steric hindrance [Initially raw sequencing reads are demultiplexed (Step 1) based on index information into FASTQ files with CASAVA (Illumina) and aligned (Step 2) to a user-defined reference genome  . A numbeindrance . Also, AThis is the stage where most researchers begin their analysis and includes assay QC, data visualization, and detection of genomic regions of enrichment . Multiple tools are available for generating composite plots including ArchTEX , DANPOS-A number of publicly available stand-alone genome browser tools , includiindirectly by deciphering areas of the genome that are occluded by nucleosomes  to represent signal components, which improves detection accuracy especially when the signal-to-noise ratio is low or in analysis of complex datasets (for example DNA copy number amplifications). MACS a model-based analysis algorithm with wide applicability for the analysis of ChIP-seq data [ZINBA, is a statistical pipeline characterized by its flexibility to process recovered signals with differential characteristics . Followiseq data –140, hasA recent comparison of the above four peak callers demonstrated that F-Seq and ZINBA have the highest and lowest sensitivity respectively . F-Seq hhttps://github.com/taoliu/MACS/) and performs reliably in identifying genomic regions of open chromatin (Step 10). This application is invoked by using the command macs2 callpeak and can be combined with the options broad, broad cutoff, no model, no lambda (unless a control file is given) and shift size. The algorithm uses default peak calling (q = 0.05) and broad (q = 0.10) cutoff values, but these settings can be adjusted or converted to P-values empirically. Once the peak-calling cutoff is set as a P-value, the broad cutoff value is automatically perceived as P also. The shift size parameter should be set as the midpoint of the average sonication fragment length in the analyzed dataset. In addition, upon availability a matched control sample can be used as input to increase detection confidence. In this case, command line parameters should be adjusted accordingly. FAIRE enrichment can also be detected using ZINBA [For FAIRE-seq data the algorithm MACS  has beenng ZINBA . As mentIdentified FAIRE-seq enriched regions residing in proximity to each other, have been traditionally merged together using BedTools   can be performed also by using ZINBA . AlternaThis stage of analysis involves estimation of various parameters of the epigenomic landscape, including nucleosome spacing, positioning and occupancy , and TF x, and conditional, based on the probability of a nucleosome starting within an extended region with center base pair x[Nucleosome or translational positioning indicates the position of a population of nucleosomes in relation to DNA, and considers a specific reference nucleosome point like its start, dyad or end . Translase pair x. Nucleosse pair x. Nucleosse pair x. A numbese pair x, 144 andse pair x, 145 frose pair x. In addise pair x.Stable binding of TFs in the vicinity of DHSs protects DNA from nuclease cleavage and generates DNase I footprints that at high-sequencing depth can unveil occupancy of TFs with long DNA residence times (for example CTCF and Rap1) , 85. Thusimultaneous probing of TF occupancy, interactions between TFs and nucleosomes and nucleosome occupancy at individual loci, similar to ATAC-seq. The method is based on the concurrent quantitative analysis of different size fragments released from DNase I digestion of genomic DNA, with microfragments (<125 bp) depicting TF occupancy, and larger fragments (126 to 185 bp) representative of nucleosomal elements.The recently reported modified approach DNase I-released fragment-length analysis of hypersensitivity (DNase-FLASH)  allows ssimultaneously at genome-wide resolution similar to DNase-FLASH [Analysis of ATAC-seq paired-end data can reveal indispensable information on nucleosome packing and positioning, patterns of nucleosome-TF spacing, and TF occupancy se-FLASH . Analysise-FLASH   and its sister PyBEDTools represent a versatile suite of utilities for a variety of comparative and exploratory operations on genomic features such as identifying overlap between two datasets, extracting unique features, and merging enriched regions using a predefined distance value [http://genome.ucsc.edu/util.html). Identified chromatin accessible locations can be compared against functional annotations with GREAT, to identify significantly enriched pathways or ontologies and direct future hypotheses [Data annotation and integration represents the final and most informative stage of analysis and requires computational and genomics background on genomic organization and structure (Step 16). After identification of enriched regions and estimation of metrics of nucleosome organization and TF occupancy, it is often desirable to evaluate this data in light of relevant information from other experiments. For example, a researcher can evaluate the overlap or association of the sequence data with genomic features  and ontological entities . For that purpose, BedTools , Matrix Scan [http://www.genomatix.de/online_help/help_matbase/matbase_help.html), JASPAR [de novo discovery within enriched regions.One can also inspect enriched regions of interest for discovery of putative TF binding events using two approaches. The first approach is straightforward and is based on comparing sequence data against a database of known TF motifs. The second type of analysis can be computationally intensive and involves the re , and TF, JASPAR , TRANSFA, JASPAR  and UniP, JASPAR ) can arrFor DNase-seq and ATAC-seq experiments TF footprints can be analyzed with CENTIPEDE . CENTIPEEach of the chromatin accessibility assays discussed here has inherent limitations in identifying regions of enrichment, based on the fragmentation method used and the involvement of any size selection steps. MNase-seq, DNase-seq and ATAC-seq are all based on the double enzymatic cleavage of DNA fragments and are sensitive to the excision-ability of a fragment. As shown in MNase-seq and ATAC-seq experiments, this sensitivity represents an issue only when mapping larger fragments (>100 bp) because the data is heavily biased by the overall nucleosome configuration at the region , 103. Inin vivo/in vitro assays or computationally based de novo motif discovery algorithms. More importantly there is an imminent need to further investigate the applicability of DNase-seq, and ATAC-seq for that matter, to accurately detect factor-chromatin interactions in dynamic cellular settings. It is possible that future footprinting algorithms will be able to accurately identify only a subset of TF binding events based solely on analysis of footprints with high depth , and not on generic analysis of all cleavage profiles.Functional annotation of accessible regions is factor-dependent and relies highly on the availability of accurate TF binding motifs and their relevant information content as well as the spatial and temporal interaction of TFs with DNA , 85. RecCurrently, most researchers compare their chromatin accessibility data to other published datasets. Although, this approach is advantageous when public datasets are available, it does not explain the cause of identified differences. In the absence of a ‘golden standard’, experimental and computational approaches need to be compared against independently generated data. For example, active regulatory regions identified by chromatin segmentation of histone modification ChIP-seq data, can serve as an independent control for experimental and computational accuracy of current chromatin accessibility assays. Finally, development of specialized statistically supported peak-calling algorithms for DNase-seq and ATAC-seq data will be instrumental in the identification of active regulatory elements genome-wide. We foresee that future applications of chromatin accessibility will include the detection of allele-specific effects to identify functionally important SNPs, use of accessibility in eQTL studies to link regulatory regions with disease phenotypes, and assessment of clinical samples for epigenetic biomarkers of disease.MJB is an associate professor at the Department of Biochemistry at the State University of New York at Buffalo, a director of the WNYSTEM Stem Cell Sequencing/Epigenomics Facility, a co-director of the UB Genomics and Bioinformatics Core, and an adjunct faculty for the Cancer Genetics Roswell Park Cancer Institute and the Department of Biomedical Informatics. He has extensive experience with chromatin accessibility assays and bioinformatics analysis of related data. He is currently the head of an active laboratory focused on epigenomic profiling and the detection of key determinants for gene regulation, disease development and progression. MT is a senior research scientist/project leader in MJB’s laboratory involved in a number of studies on epigenetic regulation.


GRXC9, coding for a CC-type glutaredoxin from Arabidopsis, is an SA-responsive gene induced early and transiently by an NPR1-independent pathway. Here, we address the mechanism involved in this SA-dependent pathway, using GRXC9 as a model gene. We first established that GRXC9 expression is induced by UVB exposure through this pathway, validating its activation in a physiological stress condition. GRXC9 promoter analyses indicate that SA controls gene transcription through two activating sequence-1 (as-1)-like elements located in its proximal region. TGA2 and TGA3, but not TGA1, are constitutively bound to this promoter region. Accordingly, the transient recruitment of RNA polymerase II to the GRXC9 promoter, as well as the transient accumulation of gene transcripts detected in SA-treated WT plants, was abolished in a knockout mutant for the TGA class II factors. We conclude that constitutive binding of TGA2 is essential for controlling GRXC9 expression, while binding of TGA3 in a lesser extent contributes to this regulation. Finally, overexpression of GRXC9 indicates that the GRXC9 protein negatively controls its own gene expression, forming part of the complex bound to the as-1-containing promoter region. These findings are integrated in a model that explains how SA controls transcription of GRXC9 in the context of the defense response to stress.Salicylic acid (SA) is a key hormone that mediates gene transcriptional reprogramming in the context of the defense response to stress. The online version of this article (doi:10.1007/s11105-014-0782-5) contains supplementary material, which is available to authorized users. Salicylic acid (SA) is a key plant hormone involved in stress defense responses against a wide range of biotrophic and hemibiotrophic pathogens and abiotic stress conditions such as UV, high light radiation, ozone exposure, salinity, osmotic, and drought stress  and glutathione S-transferases (GST)  production and for regulation of the ascorbate/dehydroascorbate (ASC/DHA) ratio after stress , a second GRX gene identified as an early SAIG  using the reducing power of GSH and NADPH  is mediated by the master coactivator nonexpressor of pathogenesis-related genes 1 (NPR1)  , separated by four base pairs  promoter, as an element that conferred basal expression in root tips  factors of the TGA family, which has 10 members in GRXC9 expression is activated by stress, such as UVB radiation, via an SA-dependent and NPR1-independent pathway. Bioinformatics analysis of the GRXC9 promoter indicates the presence of two putative as-1-like elements in its proximal region. We show that both elements are functionally relevant and required for the SA-mediated induction of the gene. Moreover, our data indicate that TGA2 and TGA3, but not TGA1, are constitutively bound to the GRXC9 promoter in vivo. We also confirm the requirement of TGA class II factors for the recruitment of the RNA polymerase II (Pol II) enzyme to the basal promoter and for the transcriptional induction of the gene upon SA stimuli. Analysis of the transactivation as well as the homodimerization and heterodimerization ability of TGA class II and TGA3 factors gives further insights into the mechanism of induction of antioxidant genes by the SA/TGA2-3/as-1-like pathway. Interestingly, we show evidence indicating that GRXC9 participates in the control of the expression of its own gene, suggesting its involvement in the transient activation of early SAIGs. Physiological and mechanistic implications of these findings are discussed.In this work, we show evidence that GRXC9 gene expression is early and transiently induced by exogenous treatment with SA, via an NPR1-independent pathway , considered as the full GRXC9 promoter (pC9 WT::GUS), or truncated versions of this sequence that include the two as-1-like elements up to −168 bp (pC9-168::GUS), only the proximal as-1-like element up to −112 bp (pC9-112::GUS), or a minimal promoter up to −61 that includes the putative TATA box (pC9-61::GUS) , which indicates that GRXC9 gene expression is effectively activated by SA at the transcriptional level. Surprisingly, a very small promoter region up to −168 retains an important part of the SA responsiveness of the GRXC9 full promoter  with 0.5× MS as control or with 0.5 mM SA for 2.5 h. We then quantified the basal and SA-induced GUS activities in total protein extracts  or in the distal (pC9 MD::GUS) as-1-like elements   assays , GRXC9 expression was early and transiently activated reaching its peak 2.5 h after SA treatment, as in WT plants , compared to WT plants  levels after 2.5 h of SA treatment seen in the triple mutant is no longer observed in the quadruple mutant  or the activation domain (AD) of the yeast Gal4 factor. Interactions between TGA factors were evaluated in yeast by qualitative assays of the β-galactosidase reporter gene, whose expression is controlled by four copies of the Gal4-responsive element. The transactivation ability of the TGA factors was evaluated in assays using the TGA factors fused to the BD-Gal4 and the empty pDEST22 vector. Our results indicate that TGA2, TGA3, TGA5, and TGA6 cannot transactivate the β-galactosidase gene in a yeast system, reflected by the null activity of the reporter enzyme  with high constitutive expression levels of GRXC9 , has been reported by our group and by others . Here, we validate the activation of the GRXC9 gene by this pathway under an abiotic stress condition such as UVB radiation that, like the immune reaction induced by avirulent Pst strains, triggers an SA-mediated defense response  and xenobiotic chemicals like 2,4-D, in the context of the chemical detoxification process. Based on the overlap of some GST and UGT , it was assumed that exogenous treatments with SA unspecifically induced the chemical detoxification process   recognize GRXC9; the other pathway leads to the expression of late SA- and NPR1-dependent genes, such as PR1 . One of these pathways leads to the expression of early SA-dependent and NPR1-independent genes, such as ile Fig. , being eile Fig.  and recrile Fig. . The queGRXC9 in the context of the defense response to stress. According to our model, TGA2 and TGA3  are constitutively bound to the two as-1-like elements of the GRXC9 promoter. Under basal conditions, we propose that an inactive form of a coregulator complex (Co-RI) is bound to the TGA2-3/as-1-like complex, forming a transcriptionally inactive complex. Upon a stress condition, SA levels increase producing the activation of the coregulator complex (switch from Co-RI to Co-RA). According to our results, Co-RA must provide the transactivation activity for recruitment of the Pol II basal machinery (Pol II complex) to initiate transcription. According to our results with the GRXC9 overexpressor lines , switching from a transcriptionally active to a transcriptionally inactive complex. GRXC9 expression is turned off because Co-RI does not have the ability to recruit Pol II basal machinery. This mechanism would allow a rapid transcriptional response to stress signals, through transient changes in the activity of a coregulator complex bound to the TGA2-TGA3/as-1-like platform complex preformed at the GRXC9 promoter.Results showed in this work prompt us to propose a model Fig.  to explanes Fig. , we propg et al. . We propGRXC9 and TGA2 interact in the nucleus and that GRXC9 overexpression reduces the expression of the 2,4-D-inducible CaMVas-1::GUS transgene and the JA-inducible PDF1.2 gene  to TGA9/10 factors and their role in anthers development  or inactive (Co-RI) forms. The existence of a corepressor complex that binds the TGA2-3/as-1-like complex under basal conditions  form part of the basal inactive complex, the presence of MED18 but not of TGA2/TGA3 factors is essential to repress GRXC9 transcription.One of the key questions raised by this model is what is the nature of the proteins that form the coregulator complex, either in its active  that form part of the Co-RA complex that binds to the TGA2-3/as-1-like complex in GRXC9 promoter.Concerning proteins with coactivator function that could be part of the Co-RI to Co-RA), as well as how GRXC9 can promote the inactivation of the coregulator complex (switch from Co-RA to Co-RI). We speculate in our model that a redox change promoted by SA accumulation can be responsible for the activation of the coregulator complex. This idea is supported by evidence indicating that SA promotes a biphasic change in the GSH/GSSG ratio, first an oxidative phase characterized by a decrease in GSH/GSSG ratio and then a reductive phase characterized by increase in GSH/GSSG ratio , npr1-1 . For ChIP and gene expression assays, 15-day-old seedlings were floated on 0.5 mM SA (treatment) or 0.5× MS medium as a control and incubated for the indicated periods of time under continuous light (80 μmol/m2/s). For gene expression assays, whole seedlings were immediately frozen in liquid nitrogen and stored at −70 °C until RNA isolation. For ChIP assays, whole seedlings were processed immediately as described below. For UVB irradiation assays, 15-day-old seedlings were exposed to UVB light (0.07 mW/cm2) in a chamber equipped with two USHIO UVB F8T5.UB-V, UVP 3400401 fluorescent tubes (λ = 306 nm). As a control, we used nonirradiated seedlings.GRXC9 promoter regions including the 5′UTR, −1,849 to +26; −168 to +26; −112 to +26; and −61 to +26, called in the text as pC9 WT, pC9-168, pC9-112, and pC9-61, respectively, were obtained by amplification from genomic DNA, using the oligonucleotides indicated in Online Resource eGFP and β-glucuronidase GUS reporter genes  using the primers described in the Online Resource GRXC9 protein fused to a c-Myc tag controlled by the 35S CaMV promoter. Final constructs were verified by sequencing and introduced into the Agrobacterium tumefaciens C58 strain. Arabidopsis plants were transformed by floral dip method. Transgenic seeds were selected in 0.5× MS solid medium supplemented with 50 μg/ml kanamycin for the GUS reporter lines or 15 μg/ml glufosinate-ammonium for the overexpressor lines. Stable homozygous transgenic lines were used for further analyses.Genetic constructs were generated using the Gateway technology following the manufacturer’s instructions (Invitrogen). The GRXC9 promoter-driven GUS constructs described above. The 4-methylumbelliferyl-d-glucuronide was used as substrate, and the fluorescent product 4-methylumbelliferone was quantified, as previously described  are listed in Online Resource ChIP assays were performed as described  or Clathrin adaptor complex subunit (AT4G24550) genes. Primers used for each gene are listed in Online Resource Total RNA was obtained from frozen samples using the TRIzol® Reagent (Invitrogen) according to the manufacturer’s instructions. cDNA was synthesized from each sample  with an ImProm II Kit (Promega). qPCR was performed with the Stratagene MX3000P® equipment. The expression levels of Gal4RE::β-Gal reporter construct), and qualitative assays for β-Gal activity were performed as described  were cloned into the pDONR201 vector High Resolution Image (TIFF 19063 kb)Online Resource 2(GIF 124 kb)High Resolution Image (TIFF 20513 kb)Online Resource 3(GIF 139 kb)High Resolution Image (TIFF 61163 kb)Online Resource 4(GIF 116 kb)High Resolution Image (TIFF 14914 kb)Online Resource 5(GIF 119 kb)High Resolution Image (TIFF 20470 kb)Online Resource 6(GIF 86 kb)High Resolution Image (TIFF 57136 kb)Online Resource 7(GIF 126 kb)High Resolution Image (TIFF 16613 kb)Online Resource 8(DOCX 21 kb)Below is the link to the electronic supplementary material.