Transcriptional regulation is tightly coupled with chromosomal positioning and three-dimensional chromatin architecture. However, it is unclear what proportion of transcriptional activity is reflecting such organisation, how much can be informed by RNA expression alone and how this impacts disease. Here, we develop a computational transcriptional decomposition approach separating the proportion of expression associated with genome organisation from independent effects not directly related to genomic positioning. We show that positionally attributable expression accounts for a considerable proportion of total levels and is highly informative of topological associating domain activities and organisation, revealing boundaries and chromatin compartments. Furthermore, expression data alone accurately predict individual enhancer–promoter interactions, drawing features from expression strength, stabilities, insulation and distance. We characterise predictions in 76 human cell types, observing extensive sharing of domains, yet highly cell-type-specific enhancer–promoter interactions and strong enrichments in relevant trait-associated variants. Overall, our work demonstrates a close relationship between transcription and chromatin architecture. Transcriptional regulation is coupled with chromosomal positioning and chromatin architecture. Here the authors develop a transcriptional decomposition approach to separate expression associated with genome structure from independent effects not directly associated with genomic positioning. On a global level, transcriptional activation or repression is often accompanied by nuclear relocation of chromatin in a cell-type-specific manner, forming chromatin compartments3 of coordinated gene transcription6 or silencing7. Locally, chromatin forms sub-mega base pair domains of self-contained chromatin proximity, commonly referred to as topologically associating domains (TADs)9. TADs frequently encompass interactions between regulatory elements, such as between promoters and enhancers13, as well as between co-regulated genes9, which reflects cell-type-restricted transcriptional programmes14. In contrast, ubiquitously expressed promoters are enriched close to domain boundaries8 and co-localise in the nucleus15. A tight coupling between transcriptional activity and chromosomal positioning is further supported by positional clustering of co-expressed eukaryotic genes17, a phenomenon that is preserved across taxa18. In addition, neighbouring gene expression correlation co-evolves and is particularly evident at distances below a mega base pair19. Furthermore, facultative heterochromatin may cover mega base pair regions20 and thus affect several neighbouring genes. These observations are in line with coordinated gene expression and chromatin states within TADs21, suggesting that the coupling between gene expression and chromatin architecture is, at least partly, linked to chromosomal positioning.The three-dimensional organisation of a genome within a nucleus is strongly associated with cell-specific transcriptional activity22 or regional chromatin states23. While this suggests a key role for chromatin structure in correct transcriptional activity, a general directional cause and effect between chromatin architecture and transcriptional activity is unclear. On one hand, TAD boundaries are to a large extent invariant between cell types9 and have been suggested to be mainly formed by architectural proteins26 independent of transcription15. On the other hand, transcription seems to play a major role in the maintenance of regulatory organisation within TADs27. In addition, chromatin compartments are not affected by architectural protein depletion26. Rather, nuclear three-dimensional co-localisation of genes seems to be driven to a large degree by transcriptional activity30.Genetic disruption or chromosomal rearrangements of TAD boundaries may result in aberrant gene transcription as a consequence of altered regulatory activities11 and, inversely, co-expression is indicative of enhancer–promoter (EP) interactions31. In addition, RNA expression was placed among the top-ranked features for predicting EP interactions in a recent machine-learning approach32. However, it is unclear what proportion of expression is associated with chromatin topology and chromosomal positioning and what proportion is reflecting regulatory programmes independent of the former. A way to systematically extract and separate these components from expression data could lead to new insights into chromatin topology and cell-type-specific transcriptional regulation. This is of high importance since genome-wide profiling of chromatin interactions using chromatin conformation capture techniques such as HiC33 is largely intractable due to several limitations including low spatial resolution, high cost and requirements of large abundant cellular material.The strong relationships between transcription, chromatin state, chromosomal positioning and three-dimensional chromatin organisation can be exploited for inference of one feature from another. Compartments of transcriptional activity can be deduced from genome-wide chromatin interaction dataIn this study, we establish a transcriptional decomposition approach to investigate and model the coupling between transcriptional activity, chromosomal positioning and chromatin architecture. Via modelling of expression similarities between neighbouring genomic loci, we show that the proportion of expression related to genome architecture can be separated from effects independent of genomic positioning, and that both components are strongly represented with contributions depending on cell type and location. We demonstrate that the positionally dependent (PD) component is highly reflective of chromatin organisation, revealing chromatin compartments and structures of transcriptionally active TADs. We further demonstrate how transcriptional components can be used to infer cell-type-specific chromatin interactions and find informative transcriptional features, including enhancer expression strength, which distinguish long-distance interacting from non-interacting EP pairs. We demonstrate the accuracy of our approach in well-established cell lines and then decompose expression data from 76 human cell types in order to investigate their active chromatin architectures. Our results indicate extensive sharing of expression-associated domain structures across human cells, reminiscent of that observed for active TADs. Promoter-localised, positionally independent (PI) events as well as EP interactions are, on the other hand, highly cell-type specific. Finally, we demonstrate how transcriptional components and predicted EP interactions can be used to gain insights into the genetic consequences of complex diseases. We observe variable enrichments of genetic variants in expression components across cell types and traits and find several EP interactions in which disease-associated SNPs at enhancers may cause aberrant expression of distal genes. Taken together, we observe a tight coupling between transcriptional activity and three-dimensional chromatin architecture and suggest that regulatory topologies, domain structures and their activities may be inferred by RNA expression data and chromosomal position alone.34 and utilises replicate measurements to decompose normalised aggregated RNA expression read counts in genomic bins into two components (PD and PI) and some constant intercept (Methods). The expression associated with each genomic bin was modelled as a sum of the two transcriptional components (random effects model), as illustrated in Fig. We posited that a proportion of steady-state RNA expression is reflecting three-dimensional chromatin organisation. We reasoned that a transcription unit (TU) is likely to be more similar in terms of expression to its proximal TUs than to distal loci, which are likely to be associated with different domains of chromatin interactions. Thus, coordination of expression is related between positional and chromatin neighbourhoods Fig. . However35 data36, measuring transcription initiation sites and steady-state abundances of capped RNAs from GM12878, HeLa-S3 and HepG2 cells. In the modelling, we used 10-kb genomic bins to capture large-scale positional effects. We first asked whether the information content of the two components contained different biological interpretations as posited. Upon close inspection ion Fig. , the PD nes Fig. . For ins11 in GM12878 cells. We observed that the PD signal in regions of active chromatin was higher than in those of facultative or constitutive chromatin, while constitutive chromatin states had the lowest signal , with an average of 93% of expressed genes and 70% of genes overall located in positive PD regions. Overlaying the PD component on HiC compartment boundaries also showed clear shifts, which were many magnitudes stronger than what could be detected using the PI component , each associated with features of  active regulatory elements38. The resulting models allowed us to generate a probability distribution for each mark given each transcriptional component. For all tested marks, we observed a clear bias with stronger predictive power from the PI component than the PD component . Furthermore, joint XAD and TAD boundaries displayed a higher HiC11 chromatin interaction directionality8 than DHS-positive TAD boundaries distal to XAD boundaries  can be used to infer chromatin topology in active chromatin compartments.We next assessed the occurrence of DHSs, ChIP-seq binding site peaks for architectural proteins CTCF and Rad21 (a subunit of cohesin) and histone modifications H3K36me3, H3K27me3 and H3K27ac around GM12878 XAD boundaries Fig. . We obseSince positionally attributable RNA expression was strongly associated with structures of transcriptionally active TADs, we questioned the utility of expression data alone in reflecting individual proximity-based interactions. Namely, what does it mean to be proximal in the nucleus, from a transcriptional perspective?12 (Methods). To enable a direct assessment of the relationships between transcriptional component-derived features and chromatin interactions, we mapped CHiC data at 10-kb resolution. We used a lenient interaction score threshold to define positively interacting bin–bin pairs  ratio, similarly to a previous method32, but also on raw (unmodified) NP ratio. Thus, in each cross-validation round, we fully balanced the training data and predicted on held-out data at 20:1 or raw NP ratios.To this end, we collected a total of 25 features in GM12878 that may be derived from expression data sets alone  on raw NP ratios for lenient interaction thresholds Fig. . Using a4 on raw Next, we asked what properties of expression data explained chromatin interactions. High transcriptional activity at the target or bait was ranked among the top features for predicting chromatin interactions Fig. . In addiTDAP2A was predicted to interact with an ~100-kb downstream enhancer specifically in HeLa-S3 cells, supported by HeLa-S3 RNAPII ChIA-PET interaction data38  can be used to accurately infer boundaries of active TADs, as well as differences in chromatin compartments and EP interactions between well-established and biologically distal cell lines. We continued with exploring what insights could be gained by transcriptional decomposition of primary cell expression data, for cell types for which chromatin topology is to a large degree unknown. We applied transcriptional decomposition to replicated primary cell CAGE data46, reflecting the cell-type-specific behaviour of features around XAD boundaries , we observed largely variable preferences between traits and transcriptional components  and independent (PI) components along chromosomes. We show that positionally attributable expression, according to the PD component, closely reflects chromatin compartments and domain architecture, and accounts for a sizeable overall fraction of TU expression levels. This points to the existence of large constraints on genomic organisation, suggesting that the global maintenance of chromatin formation is crucial for correct and precise transcriptional output. On the other hand, the PI component carries information about regulatory element-localised and expression-level-associated effects. We suggest that PI effects may be the result of cell-type-specific programmes involving, for instance, the differential binding or effect of TFs, potentially based on a layer of localised regulation not necessarily attributable to higher-order three-dimensional chromatin. However, we observe that the level of positional dependency of expression data varies between loci and cell types, suggesting locus- and cell-dependent effects of topological organisation and activity, as well as varying the impact of individual gene regulatory programmes. This suggests that genes strongly associated with non-positional expression may be more resistant to perturbations in chromatin domains, such as deletions of TAD boundaries, particularly in the context of a certain disease.46. This is likely connected to the enrichment of promoters for actively transcribed housekeeping genes at the locations of TAD boundaries15, whose expression levels remain stable across cell types. Our predictions also suggest that active transcription at open chromatin loci is linked with stronger TAD boundaries, according to the observation that chromatin interaction directionality scores were stronger at TAD boundaries proximal to XAD boundaries compared to those distal from XAD boundaries, which were associated with closed chromatin. These results suggest that the presence of active transcription aids in the reinforcement of the insulation properties associated with boundary formation, but may also point to different regulatory mechanisms or roles involved in boundaries according to their link to active transcription.The usage of expression data to infer topological chromatin organisation is limited by the inability to inform on transcriptionally silenced, closed or poised states. However, by focussing only on expressed TUs, such as those observed with CAGE, and their relative relationships, we can attempt to understand patterns that are highly relevant to various cell types of interest. For example, the majority of detected XAD boundaries were proximal to TAD boundaries associated with an open chromatin site in GM12878 cells. Interestingly, XAD boundary locations were seen to be strongly informed by the presence of a stable PD signal across cell types, reflected in their high degree of sharing, which closely corresponds to the nature of TADs, whose locations appear to be largely cell-type invariant32. Crucially, we bring up the question of what proximity within the nucleus means from the standpoint of transcriptional initiation, and our results suggest the two to be tightly coupled. Analysis of predicted features points to a model whereby a strongly expressed TU with stable positionally attributable expression is the key predictor of within-domain interactions in a given cell type, and whereby strength of target expression becomes more important for predictions over long distances. Interestingly, of all predicted chromatin interactions, those restricted to EP interactions showed the strongest predictive power, were biased towards the cell types in which they are actively transcribed and target enhancer RNA expression levels rapidly increased with the distance between the bait and the target.We have shown that expression data alone are predictive of fine-scale chromatin interactions, based on predictive models incorporating expression component information, expression strength, stability and distance. Our results concur with, and significantly extend, the previous finding that CAGE is an informative predictor of interactions36 or GTEx54. We have applied our models to 76 cell types from the FANTOM5 consortium, generating predicted components, XAD boundaries and EP interactions. This resource allows a deeper understanding of the dynamic regulation at key identity genes in a wide diversity of cellular states not yet subjected to methods informing on their higher-order structures. Comparison across cell types revealed extensive sharing of positionally attributable expression, while PI effects and, in particular EP interactions, were to a large degree cell-type specific, particularly at long distances, and separated cells into groups of similar type. These results indicate that chromatin compartments are mostly invariant across cells and that fine-tuning of cell-type-specific expression levels is mainly mediated by promoter-localised, position-independent effects or enhancer regulation. The differences in cell-type specificity also indicate that positionally attributable expression, on its own, is not sufficient to inform on EP interactions. While positionally attributable expression seems to reflect the overall large-scale chromatin state in which a TU resides, and informs on the architectural compatibility between regulatory elements, many EP interactions are cell-type specific and associated with punctuated elevated enhancer and promoter expression levels. These results argue for RNAPII-centred chromatin architectures56, whereby transcriptional regulation is associated with cell-type-specific tethering of EP pairs to RNAPII foci, leading to coordinated expression increases.Since transcriptional decomposition is broadly scalable across large numbers of samples and applicable to different expression-sequencing assays, including CAGE and RNA-seq, it potentially paves the way for large-scale cost- and time-effective computational analyses across atlases of high-quality expression data sets, such as FANTOM557. Other traits were associated with cell-type-specific enrichments of variants in the PI component or at enhancers in cell-type-specific EP interactions, likely to cause altered gene expression levels. Our generated resource of regulatory interactions enabled us to identify several cases of predicted EP interactions for which enhancer overlap with disease-associated SNPs was linked with promoters of genes often associated with the disease itself. Overall, we expect that our transcriptional decomposition approach and resource will have large implications for future interpretations of genetic variants associated with disease in cell types that are otherwise largely intractable.While many complex diseases have clear cell-type-specific effects and that positionally attributable expression was chiefly shared between cells, we found an unexpected preference for enrichments of trait-associated genetic variants in the PD component. This suggests that the aetiology of certain diseases may be coupled with the disruption or alteration of TAD structures or association with chromatin compartments, which finds support in the literature36. The data mapped to hg38 and CTSSs (CAGE transcription start sites) were clustered into tag clusters (TCs) according to decomposition peak identification (DPI) generation as per FANTOM558. Only samples with more than 0.5 million tags mapping within the TCs were included in the analysis.CAGE data were produced by the FANTOM5 project31. Enhancers were only identified distal to known exons (±100-bp region from boundaries) and transcription start sites ±300 bp defined by GENCODE v24 annotation. In total, 63,285 enhancers were identified across 1829 libraries. Due to varying noise levels across CAGE libraries and the intrinsic low expression levels of transcribed enhancers, library-specific noise levels were estimated to define a robust set of active (transcribed) enhancers in each sample. For each library, expression was quantified in randomly sampled mappable genomic regions distal to assembly gaps, DNase hypersensitive sites (ENCODE), known exons and gene TSSs (GENCODE) to create a genomic background expression distribution. For each library, we called an enhancer active (used) if its expression was above the 99.9th quantile of the library’s genomic background expression distribution. The resulting robust set consists of 60,215 enhancers over 1829 libraries, being significantly expressed above the background in at least one library. The expression was quantified and TPM normalised according to the total number of mapped reads within the full set of TCs (tags per million (TPM)).Enhancers were called based on bidirectional balanced RNA signatures as per the FANTOM5 consortiumCTSS files containing positions of raw CAGE counts from libraries for the ENCODE tier 1 cell lines  were intersected with non-overlapping 10-kb regions across the genome. For each chromosome (chr1–chr22 and chrX), regions were defined from coordinate 1 (1-based) and in consecutive complete 10-kb blocks, up to two blocks after the last bin containing a single CAGE tag across the set of libraries. Region sizes of 40 kb and 100 kb were also considered, but 10 kb was exclusively used in the analyses for comparability to high-resolution chromatin capture data sets.i, Yi is the bin count, pi represents the zero-probability parameter, μi represents the mean and k the size or dispersion parameter. The mean is given by pi.Due to the sparsity of CAGE tags in eterochromatic regions and regions poorly mapped, including the case of libraries with poor sequencing depth, a zero-inflated negative binomial distribution was assumed to hold for the counts across the 10-kb bins on each chromosome, given by, for bin a is the intercept, PDi and PIi are random effects for the PD and PI components, respectively and vi is the linear predictor given by E where log(E) is the offset term. The PD component is modelled as a first-order random walk τrw is the precision of the normally distributed differences, with mean 0. The PI component assumes that bins are represented by a vector of independent and Gaussian-distributed random variables with precision τiid.The transcriptional decomposition models the mean log count as a combination of an intercept and two random effects, set up as 59 package R-INLA34 which implicitly assumes a Gaussian field on the parameter space and uses a Laplacian approximation to allow for fast and deterministic convergence of parameters. Hyperparameters were defined for the size and zero-probability parameters  of the negative binomial distribution, and precision parameters τrw and τiid (log-gamma-distributed priors) for the PD and PI components, respectively. Three replicates for each of the ENCODE tier 1 cell lines were included, assuming library depths equal to the sum of the tags in their respective CTSS files. ENCODE cell lines were modelled concurrently for each chromosome, assuming a common distribution for the hyperparameters.The model was fit in the form of a Bayesian mixed model using the RIn order to allow for efficient prior estimations, we scaled the random walk components such that the average variance  is equal to 1. In order to achieve efficient convergence and avoid precisely defining priors beforehand, the option diagonal = 1 was set within the INLA call . The posteriors based on the converged model were then fed into a second model specifying diagonal = 0 in order to achieve more accurate estimates.https://www.encodeproject.org/experiments/ENCSR843RJV/)38. The libraries were mapped to hg38 using HISAT60 with default parameters. Multimappers were removed using samtools61 and reads were binned at 10-kb resolution (based on bins defined previously for CAGE) using bamCoverage62. Transcriptional decomposition was applied to the resulting counts, assuming library depth to be the total of the genome-wide bin counts. A range of hyper-parameters for the PD and PI components was tested  and the combination was selected, per chromosome, whereby the PD component correlated the most strongly with the PD component in GM12878 CAGE (ignoring regions not within 25 bins of a TU in RNA-seq).To see if chromosomal transcriptional decomposition is broadly applicable to RNA data sets, we applied the same modelling procedure to deeply sequenced GM12878 RNA-seq samples . For each of these bins, the fraction Genes, according to the GENCODE v24 annotations, were deemed to be expressed in GM12878 cells if they were associated with at least five tags at their TSS across the three GM12878 CAGE libraries and compared to the sign of the PD signal in GM12878 for their containing bins. The full set of genes was based on those associated with at least one FANTOM5 hg38 library.11 were lifted over from hg19 to hg38 using liftOver tool with default settings. For simplicity of interpretation, the five compartment types from Rao et al.11 were associated with active , facultative (B1) and constitutive  chromatin environments. Overlaps between the 10-kb bin regions and compartment regions were used to assign bins to chromatin environments, removing bins not having a corresponding overlap (genomic overlaps were identified using the R package GenomicRanges63). Bins representing shifts between two different chromatin environments were deemed to be boundary bins and for sets corresponding to each possible shift (e.g. active to constitutive), the mean of the PD component signal was calculated for bins at an increasing distance on either side of the boundary bins, in steps of up to 10 kb and including 500 kb. Cases at a given distance whereby another boundary bin was encountered were removed. For each set, a random sampling was used to generate a list of random boundary bins of length equal to the set size. Background sets were each generated 100 times to form distributions.Compartment coordinates for GM12878 cellsIntra-chromosomal bin pairs which overlapped with an annotated compartment were assigned an integer according to how many compartment boundary bins (see above) were between them (boundary insulation). For all bin pairs, both the absolute first-order difference in the PD signal and the correlation between the four ENCODE cell lines were calculated. The differences and the correlations were averaged using the median either at each distance from 10 kb apart to 2 Mb apart or across all distances, separately for each possible boundary insulation.37. For each mark , 10-kb bin regions were overlapped with binding locations to give the presence or absence of the mark in each bin. For each of the PI and PD components, the bin estimate, first-order difference between bin estimates, standard deviation of the bin estimate and stability of the bin estimate (standard deviation across cell-type PD component standard deviations) were calculated. For the two components separately, a random forest model was trained with the mark presence or absence as the response. Out of the bag probabilities, for the models based on the PD data and the PI data were compared directly between the two components.ChIP-seq and DNaseI-seq GRCh38 bigwig and peak data were downloaded from Ensembl FTP and Ensembl biomart based on Ensembl regulatory evidence v 84p values from z-scores were generated in order to produce standardised scores for the differences. A Benjamini–Hochberg correction was applied according to the number of bins containing an active TU (such that there were ≥10 tags across the three replicates in at least one of HeLa-S3 or GM12878), and using an FDR < 0.01 to generate a list of significant DE bins in each component. H3K27me3 and H3K36me3 ENCODE Broad Institute bigwig data (from Ensembl Regulatory Build v 84) were quantified in 10-kb genomic bins. The aggregated signal values in each bin were TPM normalised . The TPM values for H3K27me3 and H3K36me3 were then inspected at DE bins between GM12878 and HeLa-S3 cells.Posterior estimates and standard deviations of the linear combinations representing the difference in the PD or PI component between GM12878 and HeLa-S3 equivalent bins were generated from the transcriptional decomposition models (described above). Since posterior distributions of the estimates are approximately Gaussian, approximate 64. Active regions were defined as expressed FANTOM5 DPI TCs58, requiring >1 TPM in at least two of the six libraries (three GM12878 and three HeLa-S3). DPI TCs were extended to regions of −500/+100 around TSSs. DE regions of the PI components with upregulation in HeLa-S3 or in GM12878 , and DE regions of the PI components with upregulation in HeLa-S3 or in GM12878 , were tested individually against the universe of expressed TC regions. The analyses were performed using the human database of TF-binding motifs imported from the Cis-BP database65, restricted to motifs recognised by TFs expressed in the six libraries of interest, measured using functional data profiling gene expression levels as specified in RTFBSDB. Significantly enriched known motifs (FDR < 0.0001) were selected in each of the four tests and plotted together in a heatmap using the R package pheatmap (version 1.0.8) with clustering of TFs based on Euclidean distance.Active regions within the DE PD and PI bins were tested for TF-binding enrichment using RTFBSDB11 and lifted over to hg38, requiring a 1:1 correspondence between regions defined in each of the two builds. Boundaries were assigned to 10-kb bins based on overlaps. Adjacent bins with boundaries were dealt with by assigning the boundary to the bin with the largest overlap, so that no two adjacent bins contained boundaries, resulting in a total of 14,799 distinct bin-sized regions containing TAD boundaries.TAD regions based on 1-kb resolution HIC data in GM12878 were downloadedFeatures were generated per bin according to those listed in Supplementary Table 66 package in R. The predictors were scaled before modelling for generating scores of relative importance, using the caret package (version 6.0-73) in R. To test the performance of the model at predicting boundary regions, a 2-fold cross-validation was applied, where the data were randomly split into two equal-sized parts and the total performance was assessed according to the combined predictions on the halves of the data which were held out of the modelling after the corresponding half had been trained on. ROC and precision-recall statistics were generated on a per-chromosomal basis, and for all of the chromosomes together, using the ROCR package67 in R and plotted using custom functions.In order to assess the features which might distinguish bins containing or not containing boundaries, a logistic regression model was fit using the glmnetdiff) and the PD component stability (PDstab). Based on these three features, the following algorithm was implemented for the detection of XAD boundaries:Calculate X and rank in order of largest to smallest values of X.Calculate the local maxima of k, calculate the proportion of bins of positive PD, (pk) and take the top Nk of the ranked maxima such that N is the total target number of XAD boundaries.For each chromosome diff, respectively.Split the boundaries into ‘up jumps’ and ‘down jumps’ according to positive and negative values of PDdiff should be called the bound). Leave the ‘down jumps’ as is.Shift the ‘up jumps’ to the right by one bin .Repeat the above for the ENCODE cell lines.For the final set of bounds in GM12878, choose the set such that the bound is in GM12878 and in at least one other ENCODE cell line (and similarly for the set in the other cell types).The top three features from the generalised linear model outlined in the previous section were selected, namely the PD component (PD), the difference in the PD component . To generate enrichment of ChIP-seq marks around XAD boundaries, only those with a positive change were considered, in order to avoid biases from signal averaging. The results for the negative-gradient boundaries were similar or opposing to those of the positive-gradient boundaries.For each of CTCF, DNaseI, H3K36me4, H3K27me3 and H3K27ac, the list of 10-kb bins was overlapped with significantly bound sites to determine how many sites appeared in each bin. The mean number of sites overlapping the bins containing the predicted (positive change) XAD boundaries was calculated, then for bins one away from the boundary and so forth in steps of 10 kb up to a distance of 500 kb. Cases at a given distance whereby another boundary bin was encountered were removed to avoid contaminated signal. For the background set, a set of random XAD boundaries of equal cardinality to the real boundaries were generated, under the conditions of a positive gradient and within the set of bins plausible for being predicted as boundaries as previously defined. The same analysis was performed for CTCF:Rad21, which was based on the number of CTCF sites multiplied by the number of Rad21 sites which overlapped a given bin.To calculate the overlap between the XAD set and the TAD set, regions around the XAD set were extended by five bins (50 kb) on either side and then overlapped with the set of 10-kb regions deemed to contain TAD boundaries. The number of TAD boundaries associated with a DHS which fell within these regions were then counted, together with the number of predicted boundaries which fell in the vicinity of a DHS-associated TAD boundary bin (defined as at least one DHS overlapping the TAD boundary bin or one or more of the two adjacent bins).To calculate the enrichment of XAD boundaries at the locations of boundary bins overlapping TAD boundaries, the proportion of bins with predicted boundaries which overlapped the TAD boundary bin set was calculated and divided by the same value generated from a random set of boundaries . The randomisation step was repeated 100 times andthe mean enrichment and standard deviation were calculated. The same analysis was repeated according to where the TAD boundary was DHS associated and where the TAD boundary was not associated with a DHS site (defined in the same way as with the overlaps above).11 were downloaded and normalised, according to supplied recommendations, using the KR method. The Hi-C directionality score8 was implemented and applied to locations of TAD boundaries whose corresponding liftovers in hg38 were either supported or not supported by XAD boundaries (within ±5 bins of the boundary). The directionality score was calculated for ±25 bins around the location of the boundary bins and for a jump size of 50 bins to calculate the direction bias of interactions. Means were plotted to obtain patterns of global directionality bias.Processed intra-chromosomal HIC data for GM12878 at 10-kb resolution69, specifying bowtie270 for the mapping and GRCh38 for the genome (downloaded from ftp://ftp.ensembl.org/pub/release-85/). CHiCAGO42 was then applied to the remapped BAM files to call significant interactions against each bait, using default settings except for specifying 10-kb binning and with the baits defined as the 10-kb bin containing the defined sequences from the Capture HIC protocol12 lifted over from hg19 to hg38. Score cut-offs of ≥3 or ≥5 were applied to the final output in order to determine which bait–target pairs were considered as interacting, with the rest assigned as non-interacting.Capture HIC data for GM12878 were downloaded and fastq files were extracted using the SRA toolkit. The data were mapped and corrected using HICUPS12, lifted over to hg38. For each such ‘bait bin’, potential targets within 2 Mb (200 bins) were assigned and their numbers reduced according to the presence of CAGE tags in more than one replicate in at least one of the ENCODE cell lines. For analyses based on enhancer-associated targets, targets were considered based on overlap with at least one CAGE-defined enhancer which was active (more than one tag in more than one replicate) in at least one of the ENCODE cell lines. Only bait–target pairs which fell into the distance range of between 6 and 200 bins were considered (corresponding to >50 kb and up to 2 Mb).Binned 10-kb regions were overlapped with Capture HIC protocol defined baits31 and assigning a value of 0 where no tags were present. The boundary insulation between the bait and the target was calculated according to the number of XAD boundaries observed in the intervening bins. Boundary insulation was defined up to 3; all values >3 were given a score of 3. The peaks detected were the number of CAGE peaks overlapping the bait or target bin, according to the hg38 DPI set from FANTOM5, which was transcribed in that cell type (at least one tag in at least one replicate). The cross-correlation for the PI and PD components was calculated per chromosome by correlating the respective PI and PD profiles over four ENCODE cell lines between the bait and target bins, specifying Kendall for the correlation method. The first eigenvector was calculated from the cross-correlation matrices for each chromosome and its value was supplied for the bait and target bins. Distance was defined as the number of 10-kb bins separating the bait and the target.The full list of features considered in the interaction modelling are listed in Supplementary Table To assess model performance, two data sets from GM12878 were analysed. For assessing the applicability of the model trained in GM12878 for predicting interactions in other cell types, the data set as generated was kept in its current format (termed ‘raw’ format). In addition to this, since the number of positive interactions decayed sharply with larger distances, a second data set was generated where the ratio of negative-to-positive cases over distance was balanced by randomly sampling 20 negatives to each positive in the data set, with replacement to account for cases where the negative rows were not at least 20-fold in number to the positive rows.44, as implemented in the unbalanced package (version 2.0) in R, was applied to the data, specifying parameters percOver = 200 and percUnder = 150 to generate new positives together with under-sampling the negatives to achieve a balance of 1:1 in the data set. In order to balance the data set most fairly over distances, SMOTE was applied separately across each possible bait–target separation.To generate a balanced data set for training, SMOTEThe model training, predictions and validations were performed in R using the randomForest package supplemented with foreach (version 1.4.3) and doParallel (version 1.0.10) to run on multiple cores. A 10-fold cross-validation was used, whereby the data set was split randomly into 10 equal-sized pieces and a single piece was held out as a testing set on each of the 10 runs of the model which was trained on the remaining 90%. All training was carried out based on a ChiCAGO score cut-off of 3. All performance statistics and probability estimates in GM12878 were based on predictions made across the held-out runs over the full data set.To assess whether there are different features which are important for specific distances without the bias of most examples being weighted towards short distances, the above analysis outline was repeated for the same data set but restricted to three possible distances: ,  and . For each set of distances, the mean decrease accuracy (MDA) was averaged across the 10 runs in order to obtain a final feature performance.To find the optimal probability cut-off for calling a predicted interaction, the value for which the F1 statistic was maximised was calculated using the optim package in R, according to the desired score cut-off. Since the most efficient cut-off is not fixed according to distance, the F1 statistic was maximised separately for five sets of bait–target distances: , , ,  and , and performance was analysed for predictions generated using the resulting cut-offs. To calculate the effect of the CHiCAGO score cut-off on precision and recall, we optimised the F1 statistic separately for the five sets based on a range of score cut-offs .71 package in R was used to generate AUC statistics and the caret package (version 6.0-73) in R was used to generate the precision, recall and F1 statistics. Plots were generated using custom functions based on statistics generated from the ROCR67 package.The pROCThe data set for GM12878 was trained as described above against a score of ≥3 and was used to predict on the equivalent set of bin pairs in HeLa-S3 and HepG2, which were subsequently reduced to those with enhancer targets only (using the same criteria as described above). Final probabilities were calculated based on the mean of probabilities over the 10 runs. The distance-based F1-maximising cut-offs were applied to obtain a final list of interactions. EP interaction sharing between GM12878, HeLa-S3 and HepG2 was calculated based on whether the interactions were present in 1, 2 or 3 of the cell types and venn diagrams were generated using the R package VennDiagram (version 1.6.17).38 and start and end regions were lifted over to hg38, removing those without a 1:1 correspondence between the two builds. Bait–target pairs for predicted EP interactions in HeLa-S3 were selected according to a probability of at least 0.6 in HeLa-S3 and a probability of less than 0.4 in GM12878. Both ends were intersected with the lifted over start and end regions in the ChIA-PET data in order to generate a list of candidate examples. We selected an example on chromosome 6 due to that chromosome’s high-performance statistics from the modelling described above. The R package Sushi (version 1.10.0) was used to generate plots of the resulting loops and lines together with annotations and enhancer locations for the data sets in the example region.Processed Pol II ChIA-PET interaction data for HeLa-S3 were downloaded from ENCODEA total of 249 FANTOM5 CAGE libraries were selected according to the availability of sample replicates. Transcriptional decomposition was applied to generate PD and PI components for a total of 76 cell types  and hierarchical clustering using hclust (complete linkage method) was applied in R. The ordering from the clustering was used to guide the row and column ordering in the heatmaps for Fig. Raw binned data at 10-kb resolution were normalised into tags per million (TPM) and the mean was taken across replicates to obtain a matrix of 76 columns against the total genomic bin count . Only regions potentially transcribed in the given set of CAGE libraries were considered by asking for bins which had more than one cell type containing tags. The matrix was transformed into logL1 norm between each pair of cell types to calculate a similarity matrix. The same metric was used for calculating similarity matrices representing cell-type boundary sharing and cell-type EP sharing, with methods described in the sections below. The R package pheatmap (version 1.0.8) was used to generate cell-type group-annotated heatmaps directly from the similarity matrices, using the cell-type ordering from the hierarchical clustering in the raw data.A common set of bins  was derived for comparison between cell types of the PD and the PI components by choosing the set of bins where the sign of the PI component was positive in more than one cell type. To generate similarity matrices, the PD and PI signals were converted into binary values according to whether the signal was positive (1) or negative (0) . The resultant matrix of 76 columns and rows according to the common bin set was used to calculate 1–For all cell types, XAD boundaries were calculated from the algorithm described above for ENCODE data sets, supplying the stability scores across the full set of cell types. To calculate boundary sharing across all cell types, non-overlapping bins of size of 100 kb were generated and calculated for each expanded bin in the number of cell types within which a boundary was found. In order to cover all possible windows, the expanded bins were shifted by a 10-kb bin 10 times and the final number of shared boundaries was calculated according to an average of 10.Data sets with features were generated as described for the ENCODE cell lines for all cell types, with the bin selection also extended to the full set, thus creating a common bin set which is larger than that for the ENCODE cell lines alone. Features non-specific to a cell type were also calculated more broadly with consideration to the 76 cell types. The model was trained as above using the GM12878 data set and the broader bin set. Similar model performance was noted or this model when testing on the raw data set using 10-fold cross-validation. The held-out set predicted probabilities were used for GM12878 and the predicted probabilities for the other 75 cell types were generated by averaging over 10 trained models across the whole data set .The data sets for the 76 cell types were reduced according to whether at least one data set had an active CAGE enhancer annotated to it, in order to obtain a list of potential EP interactions. To generate lists of predicted interactions, we generated distance-based probability cut-offs in GM12878, using the same method for the ENCODE data sets above, using score cut-offs of ≥3 and ≥5.L1 norm) between columns of the binary (1 predicted/0 not predicted) matrix with cell types as columns and interaction set as rows. This generated a similarity matrix which was plotted using the R package pheatmap (version 1.0.8) based on the cell-type ordering from the hierarchical clustering in the raw data.To calculate EP interaction-sharing statistics, the ≥3-score cut-off was used and for each possible number of cell types (from 1 to 76), the number of significant interactions which were present in exactly that number of cell types was calculated. To generate the EP-sharing heatmap, all interactions which were present in more than one cell type were considered and pairwise cell-type similarity was calculated from  in at least half of the cell types labelled as ‘blood cells’ . For the PD and PI components, the foreground was assigned as the set of bins with a positive component sign in the given cell type, and the non-foreground was assigned as the set of bins with a positive component sign in at least one cell type, but not the given cell type. For the EP interactions, the foreground was assigned as the set of target enhancers within the given cell type and the non-foreground based on the target enhancers within any other cell type. For the XAD boundaries, the foreground was assigned as the boundary bins ±2 bins in the given cell type against a non-foreground of all boundary bins ±2 bins in any cell type, not including the given cell type. The odds ratios were calculated based on 2 × 2 contingency tables including the number of trait SNPs within the foreground/non-foreground and the number of SNPs for all other trait SNPs within the foreground/non-foreground. For each component, the p values from the total set of χ2 tests were corrected to an FDR with the Benjamini–Hochberg correction.GWAS trait-associated SNPs and those in LD were retrieved from the traseR R package. SNP hg19 coordinates were lifted to hg38. For each trait with at least 50 assigned SNPs, an The R package pheatmap (version 1.0.8) was used to generate heatmaps of the resulting trait enrichments and the R package Sushi (version 1.10.0) was used to generate interaction plots for the given EP interaction examples. To generate the heatmaps for the number of cell types with significant trait enrichments, the number of cell types with FDR<0.01 and an odds of >1.25 were counted per trait, per component. To calculate the component preference by trait, the resulting data were normalised per trait before generating the heatmap.72).Values for the PD and PI components, together with locations of XAD boundaries and predicted EP interactions were saved out as BED files, using UCSC zero-based coordinates, and supplied as supplementary files at Zenodo . FANTOM5 hg38 enhancers are available at 10.5281/zenodo.556775 .Transcriptional decomposition data and downstream results are available at 10.5281/zenodo.556727 (Rennie et al.Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4Supplementary Data 5Supplementary Data 6


TRanscription and EXport (TREX) is a conserved multisubunit complex essential for embryogenesis, organogenesis and cellular differentiation throughout life. By linking transcription, mRNA processing and export together, it exerts a physiologically vital role in the gene expression pathway. In addition, this complex prevents DNA damage and regulates the cell cycle by ensuring optimal gene expression. As the extent of TREX activity in viral infections, amyotrophic lateral sclerosis and cancer emerges, the need for a greater understanding of TREX function becomes evident. A complete elucidation of the composition, function and interactions of the complex will provide the framework for understanding the molecular basis for a variety of diseases. This review details the known composition of TREX, how it is regulated and its cellular functions with an emphasis on mammalian systems. During and/or after their synthesis, these precursors undergo a series of three main processing steps. Their 5′ end receives an mturation . The intturation . The 3′-From its site of synthesis until it reaches the nuclear pore, the future mRNA associates with numerous proteins to form a messenger ribonucleoprotein (mRNP). Throughout its nuclear odyssey, the mRNP composition is modified by the various remodeling events that accompany each maturation stage . The comSaccharomyces cerevisiae, Drosophila, Arabidopsis, Xenopus and humans [A key player in mRNP biogenesis and maturation is the TRanscription and EXport (TREX) complex. TREX is conserved across a wide range of organisms including d humans –10, indid humans –13 , widely known as U2AF65-associated protein 56 (UAP56), is the metazoan ortholog of the yeast Sub2p protein. First discovered as an essential factor for pre-mRNA splicing through its association with U2AF2 , this RNin vitro with a molecular weight of 460 kDa corresponding to 14 ALYREF monomers [in vitro and mutations in the N- and C-terminal arginine-rich regions alter this binding. However, whether this association is physiologically important remains unclear since the mutations that alter phosphoinositide binding, leading to altered ALYREF localization in vivo, may also have altered its RNA-binding activity [ALYREF was originally described as a transcriptional coactivator of the T-cell receptor α gene by binding LEF-1 and acute myeloid leukemia (AML)-1 transcription factors to form an enhancer-stimulating complex . It was monomers . ALYREF monomers   was first identified using a BLAST search with the ALYREF UBM and, although it harbors this motif, it has relatively little additional homology with ALYREF and other mammalian proteins outside this peptide. UIF functions in a redundant manner with ALYREF and knockdown of both proteins leads to a strong mRNA export block in mammalian cells. Knockdown of ALYREF also leads to a large up-regulation of UIF levels in cells, which probably accounts for the modest block in mRNA export seen following ALYREF RNAi . Althougin vivo. While the expression of LUZP4 is normally restricted to testis, it is up-regulated in melanoma cancer cells where it is required for their survival [LUZP4 was originally characterized as a cancer/testis antigen , which rsurvival .in vivo [CHTOP was originally identified as an RNA-binding protein that regulates the cell cycle . It was in vivo .hpr1 mutants [SARNP  was first identified as an EPO-stimulated cytokine-induced protein involved in cell cycle progression . The sma mutants , forming mutants ,73. SARN mutants . Moreove mutants ,74. The  mutants .+ RNAs in nuclear speckles [POLDIP3 was identified as an S6 kinase substrate with homology to ALYREF, which contributes to cell growth regulation . It locaspeckles .ZC3H11A co-purifies with multiple other TREX subunits  and receUsing immunoprecipitation with antibodies to THOC2, SARNP and DDX39A/B, a group of core proteins that associate with all three TREX subunits were identified using mass spectrometry. These included CBP80, CBP20, SRRT and NCBP3 (ELG), which all associate with the 5′ cap on mRNA and ERH . ERH hasDrosophila and Caenorhabditis elegans. LUZP4 is conserved in vertebrates but absent in other metazoans. These important species differences, with an expansion of TREX subunits during evolution, probably reflect the diverse biological roles that TREX plays particularly in multicellular organisms.The two major systems which have been used to study TREX are yeast and mammalian. While TREX is clearly conserved between these organisms , there aAs a dynamic complex, TREX components are recruited to the developing mRNP at various stages of biogenesis. Chromatin remodeling and subsequent transcription form the first phase of mRNA generation. UIF is recruited to the transcription complex and the nascent RNA by association with the SSRP1 subunit of FACT , a histoThe yeast TREX complex associates directly with the Pol II CTD; however, the interaction is partially dependent on the presence of nascent RNA ,82. ThisThe Prp19 complex, which functions in splicing, transcription and transcription-coupled DNA damage repair , is alsoThe NCBP1 (CBP80) and NCBP2 (CBP20) heterodimer forms the CBC, which associates with the 5′ cap. TREX is recruited to the 5′ cap by direct NCBP1–ALYREF and NCBP1–THO interactions ,15. Intein vitro splicing reactions, it was shown that recruitment of human TREX is splicing-dependent [It has long been known that the presence on an intron enhances gene expression from an otherwise intronless cDNA and that splicing enhances mRNA export . Using iependent ,59. The ependent . Therefoependent in vitro occurs in the absence of transcription and 3′-end cleavage/polyadenylation [in vivo on the periphery of nuclear speckles [The ability of TREX to be loaded onto spliced mRNA nylation . Furthernylation , and so speckles , suggestspeckles  and postspeckles .in vitro [in vivo remains to be seen. In yeast, this appears to be the case, with TREX subunits bound along the body of mRNAs [Following splicing, the EJC is loaded onto the mRNA at a canonical position 24 bases upstream of the splice junction , furtherin vitro . The invin vitro   . These ss 61–140 . The pres 61–140 , though s 61–140 . The NXF in vivo , establi in vivo ,121,130. unclear .ALYREF binds to the N-terminal region of NXF1, including the RBD and RRM domains  Figure . As ALYRTHOC5 is not the only co-adaptor protein that binds the NTF2L domain of NXF1; CHTOP, RBM15 and CPSF6 also bind this domain ,136,137.An interesting feature of two of the known NXF1 co-adaptors, THOC5 and CPSF6, is their involvement in 3′-end processing and polyadenylation. Furthermore, NXF1 loss in cells leads to hyperadenylation of mRNA and loss of Mex67 in yeast leads to retention of the 3′-end processing factors Rna14 and Rna15 on the mRNA . A furthChironomus tentans have shown that ALYREF and DDX39B dissociate from the mRNP on the nuclear side or during translocation through the nuclear pore [Once NXF1 has been delivered to the mRNP by TREX, the mRNP is escorted to the nuclear pore . Studiesear pore . This isear pore –142. NXFWhile the majority of work on TREX has thus far focused on its role in mRNA export, recent studies indicate that it has additional roles in piRNA biogenesis. piRNAs are short RNA molecules produced in germ line cells with a major function in suppressing transposons during development. The first indication that TREX may be involved in piRNA biogenesis came from the observation that DDX39A/B co-localized at piRNA clusters with Rhino, which is required for transposon silencing . MoreoveDuring transcription, the nascent RNA can hybridize with the single-stranded template DNA present within the transcription bubble. This structure is known as an R-loop and it leads to the opposing DNA strand being left single-stranded, making it susceptible to cleavage which can eventually lead to various forms of DNA damage in cells ,146. TREGiven the importance of TREX in maintaining genome stability, it is unsurprising that TREX has been implicated in many forms of cancer. CHTOP is recruited to the pS2 promoter, inducing the expression of estrogen target genes in breast cancer cells ,36 and tTREX seems to be directly involved in regulating the cell cycle, consistent with a role in proliferation and differentiation, and this activity probably correlates with the aberrant expression of TREX subunits in cancer cells. During mitosis, the cell moves through characteristic stages and TREX may function at the intermediate checkpoints. THOC1 and THOC2 influence the rate of proliferation, with THOC1 playing a more specific role in the G2-M checkpoint ,54. ALYRThe numerous associations of TREX in cancer development could lead to new specific therapeutics. The accumulating evidence that cancerous tissues are more dependent on TREX to aid their survival and progression suggests that drugs to deplete TREX levels and/or activity could be a potential target for cancer therapy. The marked reduction in metastatic capacity and cancer cell proliferation following ALYREF , THOC1 ,153 and In addition to elevated TREX dependence in cancerous tissue, ES cells show a greater susceptibility to TREX loss. ES cells are pluripotent and derived from the inner cell mass (ICM) of the pre-implantation embryo at the blastocyst stage. Depletion of THO components at this stage results in embryonic lethality as the blastocyst cannot be effectively formed due to increased apoptosis of the ICM ,57,167. The differentiation and development of specific tissues show a greater demand for TREX. The levels of THOC5 and SARNP are greater in the human fetal liver, where hematopoietic cells differentiate , and SARFrom studies in cancer, embryogenesis and tissue differentiation, the dependence on TREX for cellular survival seems to be increased in more rapidly proliferating and differentiating cells. Proliferating cells will have a higher rate of replication, transcription and therefore processing, export and translation to maintain cell division and induce differentiation. This increased burden on the gene expression machinery probably dictates the dependency on TREX that is central to this pathway.THOC2 gene cause syndromic intellectual disability [de novo translocation event in the vicinity of THOC2 on the X-chromosome has been described in a child with cerebellar hypoplasia, ataxia and retardation [THOC2 are likely to be hugely influential in causing this disease, corroborated by C. elegans knockout studies showing impaired movement [THOC6 mutations have also been associated with intellectual disability, consistent with its high expression levels in the developing zebrafish brain [THOC6 is the likely cause of a disease with clinical features such as intellectual disability, brain malformation and renal and heart defects [The role of TREX in neuronal disorders can be divided into two groups: direct mutations in TREX subunits, which trigger neuronal disease, and mutations in other genes, which lead to a potential dependence on TREX during the disease state. In the first class, missense mutations in the sability . Additioardation . The tramovement . THOC6 msh brain . The THO defects .C9ORF72 gene. Following transcription, the repeat expanded pre-mRNA is exported to the cytoplasm where it forms RNA foci. Thus, the transcript overrides the normal nuclear retention mechanisms that keep pre-mRNA in the nucleus. The repeat expansion can also be translated in multiple reading frames in an ATG start codon-independent manner, leading to the production of toxic dipeptides , associated with death of motor neurons. The most common familial form of ALS involves a GGGGCC repeat expansion within an intron of the  in ref. ,177). A  in ref. . A Drosoneration . This scneration . TherefoViruses replicate by utilizing host cell machineries, which are essential for them to infect more cells and organisms. One such machinery is the NXF1–NXT1 mRNA export pathway that is employed to export viral mRNA, under the guise of a host mRNA. This ‘hijacking’ is conserved among viral species and subfamilies as it is used in Influenza ,180,181,Unsurprisingly, TREX seems to play a role in other RNA pathways. The existence and general function of TREX seems conserved in plants ,192,193,The conserved TREX complex is highly integrated into cellular function and homeostasis , functioALS, amyotrophic lateral sclerosis; AML, acute myeloid leukemia; CBC, cap-binding complex; CTD, carboxy terminal domain; CTEs, constitutive transport elements; DDX39B, DEAD-box protein 39B; EJC, exon junction complex; ES, embryonic stem; EPO, erythropoietin; ICM, inner cell mass; MLL, mixed lineage leukemia; mRNP, messenger ribonucleoprotein; NEXT, nuclear exosome targeting complex; NMD, nonsense mediated decay; NTF2L, NTF2-like; NXF1, nuclear export factor 1; Pol II, polymerase II; poly(A), polyadenosine A; pRB, retinoblastoma protein; PRMT1, protein arginine methyltransferase 1; RBD, RNA-binding domain; RNAi, RNA interference; RRM, RNA recognition motif; TAP, tip-associated protein; TAR, transcription-associated recombination; Tip, tyrosine kinase interacting protein; TREX, TRanscription and EXport; UAP56, U2AF65-associated protein 56; UBM, UAP56-binding motif.


Retroviridae, and provide new information about antisense transcriptional regulation through a comparison of Human Immunodeficiency Virus (HIV), Human T-cell Lymphotrophic Virus (HTLV-1) and endogenous retrovirus-K (ERVK) long terminal repeats (LTRs). We will attempt to demonstrate that the potential for antisense transcription is more widespread within retroviruses than has been previously appreciated, with this feature being the rule, rather than the exception.Retroviruses are known to rely extensively on the expression of viral proteins from the sense proviral genomic strand. Yet, the production of regulatory retroviral proteins from antisense-encoded viral genes is gaining research attention, due to their clinical significance. This report will discuss what is known about antisense transcription in  Accessory genes are encoded in ways unique to each viral species. The majority of viral protein products stem from the translation of sense-strand RNA transcripts. Until recently, retroviral antisense transcription has been largely overlooked as a source of viral RNA and proteins. However, there is accumulating evidence of antisense transcription in numerous exogenous retroviral genera, including lentiviruses, deltaretroviruses, gammaretroviruses and betaretroviruses. Thus, the expression of antisense proteins may be a broad phenomenon occurring across Retroviridae, suggesting that antisense encoded genes are an integral part of the viral genome. This report contributes to our understanding of antisense transcription by characterizing exogenous and endogenous retroviral 3ʹ (antisense) promoters. Our results highlight that antisense transcription may be more widespread than previously appreciated, with endogenous retroviruses (ERVs) incapable of antisense transcription being the exception, rather than the rule.Retroviruses share a common genomic organization in which the 5′ long terminal repeat (LTR) is followed by the Antisense transcription is much better understood in exogenous retroviruses, as compared to their endogenous counterparts. Human Immunodeficiency Virus (HIV) and Human T-cell Lymphotrophic Virus (HTLV) are the characterized retroviruses exhibiting this phenomenon. The proteins encoded by their antisense strands serve important functions, including control of viral sense transcription, as well as viral latency, pathology, and spread –4.asp-1, was identified in the region complementary to the env gene in many HIV-1 strains [in vitro in various HIV-expressing cell types, including monocyte-derived macrophages, dendritic cells, and T cells [env gene have also been identified in HIV+ patients [. Recently, ASP-1 has been shown to induce autophagy, which may explain its low abundance in HIV-1 infected cells and the inherent difficulty in detecting this protein [asp have been shown to inhibit HIV-1 replication in vitro in acutely and chronically infected cell lines, as well as in HIV-1 infected human PBMCs [in vivo remain to be clearly elucidated.The ability of HIV-1 to encode an antisense protein was suspected as early as 1988, when a conserved ORF, later named  strains . Since t T cells –10. Antipatients . Recentl protein . ASP-1 h protein . In contan PBMCs . NonetheThe 3′ LTR promoter is crucial for driving antisense transcription in HIV-1. It has been experimentally demonstrated that HIV-1 antisense transcription initiates at multiple positions in the U3 region of the 3′ LTR, as well as at other downstream regions within the antisense strand . Other sIn comparison to that of its sense transcription, the regulation of HIV-1 antisense transcription is not well understood. Nonetheless, several host transcription factors have been shown to play key roles in inducing transcription from the antisense strand of HIV-1, including Specificity Protein-1 (Sp1) , 16 Upstin vitro; instead, gain of GABP binding to this mutated NF-κB site enhances Tat-mediated HIV-1 gene expression in several cell types, as well as improves virus replication in SuPT1 cell line [In addition, there is evidence of single nucleotide polymorphisms in transcription factor binding sites within the LTRs of different HIV-1 subtypes –21. Thisell line . Thus, vFurther, retroviral proteins can also modulate proviral antisense transcription. Conflicting results have been reported on the potential role of the HIV-1 accessory protein Tat in regulating antisense transcription. Tat has been reported to enhance HIV-1 antisense transcription in cell lines co-transfected with a Tat expression vector and luciferase reporter plasmids containing HIV-1 3′ LTR but not 5′ LTR . Howeverin vitro, HBZ protein is mainly detected in CD4+ T cells in vivo [A large portion of our knowledge on retroviral antisense transcription stems from studies of Human T-Lymphotropic Viruses (HTLV), particularly HTLV-1. The HTLV-1 antisense genomic strand encodes a basic leucine zipper (bZIP)-containing protein, designated HBZ. Although HTLV-1 is capable of infecting different cell types  in vivo , 24, 25. in vivo , 26). Fo in vivo . HBZ als in vivo . HBZ als in vivo ). Simila in vivo –30.hbz is transcribed starting from the 3′ LTR of the HTLV-1 provirus [hbz relies heavily on three Sp1 sites in the U5 region of the proviral 3′ LTR [hbz transcription and HTLV-1 3′ LTR activation in luciferase assays [Despite the extensive research focused on deciphering the role of HTLV-encoded antisense proteins in disease pathogenesis, there are a limited number of studies aimed at understanding the regulation of HTLV antisense transcription at the level of the proviral 3′ LTR. It has been demonstrated that HTLV-1 provirus –33. Initprovirus . Like HIprovirus , 34, 35.l 3′ LTR , 34, 36.l 3′ LTR . Thus, te assays . In lineThe transcription of HTLV-1 proviruses is further modulated by the antisense-encoded HBZ protein. HBZ binding to the 5´ LTR of HTLV-1 promotes viral latency by suppressing sense transcription . ConversAs suggested for HIV-1 Tat interaction with its 3´ LTR, the HTLV-1 accessory protein Tax can also up-regulate the proviral antisense transcription. Overexpression of Tax has been shown to markedly enhance luciferase activity from transiently expressed, as well as stably integrated, HTLV-1 3′ LTR reporter constructs in human cell lines . Tax resAntisense transcription is not exclusive to HIV and HTLV, and has also been reported in the deltaretroviruses bovine leukemia virus (BLV)  and simiAntisense transcription has been emerging as a common, but generally underappreciated, feature of ERV gene expression patterns. Several human ERVs, particularly ERV9 and ERVK loci, exhibit transcription from the antisense strand. Above and beyond the potential of antisense products to modulate endogenous retrovirus expression patterns, the impact of antisense viral products on human biology is becoming apparent. Most notably, antisense transcription of ERVs may play important roles in the regulation of human gene expression or modulation of cellular pathways.Among human endogenous retroviruses, antisense transcriptional regulation of ERV9 loci is the best understood. Several cellular transcription factors are known to induce the expression of antisense RNA from the U3 region (referred to as the U3 AS RNA) of the ERV9 LTR. This includes CREB, glucocorticoid receptors (GR), IRF, signal transducers and activators of transcription (STAT), and activating protein 2 (AP-2) . InteresThe human genome is ubiquitously populated with ERVK sequences including solitary LTRs and partial proviral sequences, as well as full-length proviruses. Solitary LTRs are the most abundant ERVK elements within the human genome, and are estimated to number over 25,000 . They arRecently, several ERVK loci present outside human intronic regions have been demonstrated to exhibit transcription of the proviral antisense strand in prostate cancer cell lines. These include ERVK(I), ERVK-106, an un-named ERVK within locus 7q34, and multiple loci of solo LTRs . When inAs a first step to better understand putative antisense ERVK transcription from its 3′ LTR, we performed an extensive bioinformatics analysis of 92 full-length ERVK HML-2 sequences, and predicted intact and conserved binding sites for numerous human transcription factors within 3′ LTRs of human-specific ERVK HML-2 proviruses Fig. . SimilarIt is noteworthy that we identified multiple Sp1 binding sites in the ERVK 3′ LTR, as they are critical for inducing transcription from TATA-less promoters. This is due to Sp1 recruitment of transcription factor II-D (TFII-D) which promotes the formation of transcription initiation complexes . Since SIn addition, the ERVK 3′ LTR contains multiple putative NF-κB binding sites , estrogen (ER), glucocorticoid (GR), and progesterone (PR) receptors  , potentiUnderstanding the activity of the ERVK 3′ LTR promoter may be the key to elucidating the basis of antisense transcription of endogenous proviruses; however, it should be noted that not all ERVK LTRs are equally intact. Evaluation of human-specific and older 3′ ERVK LTRs in the HML-2 family reveals conserved, alternative and unique TF binding site profiles, when comparing recent and older provirus LTRs (data not shown). Therefore, developing an understanding of ERVK antisense transcription, especially in the context of specific genomic loci, is an area of research that clearly requires more investigation.hbz in HTLV-1, aph-2 in HTLV-2, and asp in HIV-1 [Since the ERVK antisense promoter contains conserved putative enhancer elements and consensus binding sites for numerous human transcription factors, it puts forth the question as to whether the ERVK antisense genomic strand contains open reading frames (ORFs) for putative antisense proteins. We have been able to identify conserved regions of ERVK antisense genome that resemble motifs found within glycosyltransferases (GTs) and thioredoxin/thioredoxin reductase (TRX) complexes (data not shown). Interestingly, one of these conserved motifs is located in a region complementary to the sense strand of ERVK env – a position similar to that of the open reading frames for in HIV-1 . Interesin HIV-1 , and morin HIV-1 –65.However, due to several limitations, it is currently difficult to predict with confidence whether the ERVK genome encodes antisense products. Notably, the primary structures of GTs and TRXs are extremely diverse and lack signature features , 67. ThiRetroviridae as a whole.In the light of this report, further research on antisense transcription in endogenous retroviruses is warranted. We have shown that the exogenous and endogenous antisense LTRs share many regulatory similarities. Thus, it would be interesting to examine whether regulatory and pathological processes associated with exogenous retroviral antisense transcription are also applicable to ERVs. The presence of potentially new antisense-encoded transcripts and proteins would provide a more complete understanding of the biology of endogenous retroviruses, such as ERVK, and their roles in health and disease. A reconsideration of the nature of exogenous, as well as endogenous, retroviral transcription is required for a better understanding of http://alggen.lsi.upc.es/cgi-bin/promo_v3/promo/promoinit.cgi?dirDB=TF_8.3. The search parameters used were: factor’s species – Homo sapiens, and site’s species – Homo sapiens. Each binding site for a given transcription factor was compared to the sequence of its known consensus binding site (listed in Table The sequences of antisense promoters (3′ LTR) of exogenous retroviruses (HIV-1 and HTLV-1) and endogenous retrovirus-K (HML2) were obtained from GenBank, and reverse complemented in Geneious . For HIV


Aqp5). In this study, we found that histone deacetylase (HDAC) inhibitor suberoylanilide hydroxamic acid (SAHA) increased AQP5 expression and Sp1-mediated transcription of p358P/E. HDAC3 overexpression inhibited Sp1-mediated Aqp5 activation, while HDAC3 knockdown augmented AQP5 protein expression. Knockdown of GATA6 or transcriptional co-activator/histone acetyltransferase p300 decreased AQP5 expression, while p300 overexpression enhanced p358P/E activation by GATA6 and Sp1. GATA6 overexpression, SAHA treatment or HDAC3 knockdown increased histone H3 (H3) but not histone H4 (H4) acetylation within the homologous p358P/E region of mouse Aqp5. HDAC3 binds to Sp1 and HDAC3 knockdown increased interaction of GATA6/Sp1, GATA6/p300 and Sp1/p300. These results indicate that GATA6 and HDAC3 control Aqp5 transcription via modulation of H3 acetylation/deacetylation, respectively, through competition for binding to Sp1, and suggest that p300 modulates acetylation and/or interacts with GATA6/Sp1 to regulate Aqp5 transcription. Cooperative interactions among transcription factors and histone modifications regulate Aqp5 expression during alveolar epithelial cell transdifferentiation, suggesting that HDAC inhibitors may enhance repair by promoting acquisition of AT1 cell phenotype.Epigenetic regulation of differentiation-related genes is poorly understood. We previously reported that transcription factors GATA6 and Sp1 interact with and activate the rat proximal 358-bp promoter/enhancer (p358P/E) of lung alveolar epithelial type I (AT1) cell-specific gene aquaporin-5 ( AT2 cells are cuboidal, synthesize surfactant proteins  and express the homeodomain transcription factor NKX2.1, while AT1 cells are squamous, cover the majority of the gas exchange surface of the lung and are identified by expression of specific phenotypic markers , podoplanin/T1α, caveolin and receptor for advanced glycation end products (RAGE)). Studies in injury models4, analyses of in vitro primary AT2 cell cultures6 and lineage tracing experiments9 have provided strong evidence that AT2 cells both self-renew and transdifferentiate into AT1 cells during alveolar epithelial maintenance and following injury. In vitro studies have further demonstrated that AT2 cells in primary culture that have already acquired AT1 cell-like phenotypic properties can re-express AT2 cell markers under certain culture conditions14, while in vivo permanently labeled HOPX-expressing AT1 cells are able to express AT2 cell markers in response to partial pneumonectomy15, indicating considerable phenotypic plasticity. Elucidation of molecular mechanisms that regulate cell-specific gene expression accompanying phenotypic transitions between AT2 and AT1 cells is important to understand how alveolar epithelial cells (AEC) are maintained and regenerate following injury.The alveolar epithelium lining the internal surface of the lung is comprised of two cell types, alveolar epithelial type I (AT1) and type II (AT2) cells, which have distinct morphology, molecular phenotypes and functionsSftpb promoter increased Sftpb expression, which correlated with increased H3K4 trimethylation. These observations suggest that NKX2.1 cooperates with proteins mediating both DNA methylation and histone modification to regulate Sftpb16. In fetal lung, Sftpa activation by cyclic AMP (cAMP) and cytokines ) is mediated by increased NKX2.1, CREB (cAMP-response element-binding protein)-binding protein (CBP) and NF-κB binding to the TTF-1-binding element (TBE) of the Sftpa promoter, which is correlated with increased H3K9 acetylation, decreased dimethylation of H3K9 and decreased histone deacetylase (HDAC) binding to the TBE. In contrast, Sftpa inhibition by glucocorticoids is associated with enhanced glucocorticoid receptor (GR) and HDAC binding to the promoter and decreased H3 acetylation18. Additionally, activation of T1α is regulated by methylation at a key Sp1 site in the proximal promoter19.Epigenetic modulation has been implicated in transcriptional regulation of tissue-specific gene expression and cell differentiation in eukaryotic cells, but evaluation of these mechanisms in the context of AT2 and AT1 cell-specific gene expression has been limited. In this regard, interaction of the homeodomain transcription factor NKX2.1 ) with ATP-dependent chromatin remodeling protein BRG1 at the unmethylated 21. In the rat lung, AQP5 is exclusively expressed on the apical membrane of AT1 (and not AT2) cells22. AQP5 expression in human salivary gland is regulated by acetylation of histone H423 and DNA methylation24. In rat lung, activation of Aqp5 in AT1 cells is associated with increased Sp1 binding to the hypomethylated proximal Aqp5 promoter25. However, a role for histone modifications in regulation of Aqp5 expression in AEC has not been reported. We previously showed that AQP5 expression is controlled by the zinc-finger transcription factor GATA6, involving interactions with Sp1 at the rat 358 bp proximal promoter/enhancer region (p358P/E)26. In the current study, we examined the contributions of histone modifications to GATA6/Sp1-mediated Aqp5 transactivation. Our results demonstrate that GATA6 increases H3 acetylation, while HDAC3 causes H3 deacetylation at the proximal Aqp5 promoter/enhancer, leading to activation and repression of Aqp5 transcription, respectively. These data suggest that epigenetic regulation of Aqp5 transcription involves competition of GATA6 and HDAC3 for binding to Sp1. Furthermore, we demonstrate a role for p300 in GATA6/Sp1 activation of Aqp5 transcription, possibly via modulation of H3 acetylation and/or GATA6 and Sp1 activity.AQP5 is a water channel protein that is highly expressed in lung, salivary gland and lacrimal glandAqp5 promoter/enhancer fragment, p358P/E (−358 bp relative to the transcription start site (TSS)), containing multiple putative Sp1 sites, and showed that GATA6 activates this region via interactions with Sp126. Since Sp1 binds to both p300, which has histone acetylase activity, and HDACs29, we hypothesized that modification of histone acetylation is involved in GATA6/Sp1-mediated Aqp5 gene expression. To test this hypothesis, mouse lung epithelial (MLE-15) cells were treated with HDAC inhibitor SAHA. As shown in a representative western blot  in MLE-15 cells treated with SAHA using antibodies against the N-terminal portion of acetylated H3 and H4. There was enrichment of H3 acetylation  at the proximal Aqp5 promoter/enhancer region.We previously identified a rat proximal lot Fig. , SAHA  or a serine/threonine protein phosphatase, PP433. We then knocked down HDAC2 and HDAC3 alone or in combination  and Hdac3 short hairpin RNA (shRNA). Knockdown of HDAC3 , independently of its deacetylase activity34 which might in turn affect translation and therefore levels of AQP5 protein, resulting in no net change in AQP5 protein expression. To examine whether HDAC3 inhibition of Aqp5 transcription is associated with H3 deacetylation at the −358 bp proximal Aqp5 promoter/enhancer region, ChIP was performed using chromatin harvested from MLE-15 cells with/without HDAC3 knockdown, followed by pull-down with anti-acetyl-H3 antibody (Ab). qPCR following ChIP demonstrates enrichment of H3 acetylation at the Aqp5 promoter/enhancer region in cells with HDAC3 knockdown compared to control shRNA  activity  may modulate acetylation.To further investigate the role of GATA6 in regulation of ta6 Fig.  significRNA Fig.  and AQP5RNA Fig. . Additiorol Fig. . These d36. It has been reported that p300/CBP increase H3 acetylation at promoter binding sites for Sp1 in transforming growth factor-β (TGFβ) target genes p21 and plasminogen activator inhibitor-1 (PAI-1) to activate transcription in mesangial cells37. Transient transfections were performed in NIH3T3 cells to determine if p300 or CBP are involved in modulation of Aqp5 transcription by GATA6/Sp1. Neither p300 nor CBP alone had an effect on p358P/E activity  was performed using protein lysates from MLE-15 cells with/without HDAC3 knockdown. As shown in Fig. 40. The role of epigenetic mechanisms, especially histone modifications, in regulation of AT1 cell-specific expression of Aqp5 is not completely understood. In this study, we found that HDAC inhibitor SAHA increases expression of AQP5 and that HDAC3 inhibits Sp1-mediated transcriptional activation of Aqp5, which is associated with H3 acetylation/deacetylation, respectively, at the proximal promoter/enhancer. We demonstrated that GATA6 activation of Aqp5 transcription involves H3 acetylation at the proximal promoter/enhancer and that p300 augments Aqp5 activation by GATA6 and Sp1. We further provide evidence that HDAC3 binds to Sp1 and competes with GATA6/Sp1 binding, and that HDAC3 inhibition of Aqp5 transcription is associated with H3 deacetylation. These data suggest that H3 acetylation/deacetylation is involved in regulation of differentiation-related Aqp5 gene expression concomitant with GATA6/Sp1/p300 and/or Sp1/HDAC binding at the proximal promoter/enhancer. Although SAHA decreased H4 acetylation at the Aqp5 promotor and a decrease in H4 acetylation is known to be associated with gene activation42, the mechanisms underlying decreased H4 acetylation as a result of SAHA treatment and how this might contribute to increased Aqp5 gene expression remain unknown and will require future studies. Nevertheless, our data are consistent with previous findings that the Sp family of transcription factors  regulates cell-specific gene expression by recruiting HDACs or proteins with HAT activity  to target gene promoters45.In the distal lung, AQP5 is specifically expressed in AT1 cells and plays an important role in alveolar homeostasis46. GATA6 is the only GATA family member expressed in the distal epithelium of the developing lung, where it plays an important role in lung branching and AEC differentiation47. GATA6 regulates expression of AT2 cell-specific genes  while also regulating activity of the promoter/enhancer of the AT1 cell-specific gene Aqp551. Overexpression of dominant negative GATA6 in transgenic mice led to impaired AT1 cell development and reduced AQP5 expression in vivo51. In our in vitro rat AEC culture model, expression of GATA6 increases concurrent with increases in AQP5 during AT2 to AT1 cell transdifferentiation. We have previously shown that GATA6 mediates activation of Aqp5, largely through interactions with Sp1 at the proximal promoter/enhancer region, further supporting a role for GATA6 in regulation of AT1 cell differentiation26. In this study, we demonstrate that GATA6 regulation of the AT1 cell-specific gene Aqp5 is associated with increased histone acetylation. Our data suggest that GATA6/Sp1 interaction alters acetylation by interfering with HDAC3/Sp1 binding and recruiting p30044.GATA6 is a key transcription factor that regulates organogenesis and epithelial cell differentiation in the lung52. Additionally, the HAT domain of p300 catalyzes acetylation of promoter-bound histones, leading to chromatin opening and gene activation36. p300 can also acetylate transcription factors such as NF-κB53, Smad37, p5354 and Sp155, as well as GATA family members GATA156 and GATA457, to modulate their transcriptional activity via changes in DNA binding activity, protein stability and interactions with other transcription factors. It will be important to elucidate if p300 mediates activation of Aqp5 transcription by GATA6/Sp1 through histone acetylation, direct acetylation of GATA6/Sp1 or a combination thereof or independent of its acetylation activity. We propose a model , penicillin  and streptomycin . MLE-15 cells  were cultivated in HITES medium .4 cells/well in 24-well plates one day before transfection. To examine the effect of SAHA on Aqp5 transcription, MLE-15 or NIH3T3 cells were transfected with a 358-bp rat Aqp5-luciferase reporter (-358-Aqp5-Luc) construct26 using Superfect reagent . To examine the effect of SAHA on GATA6 and/or Sp1-mediated Aqp5 transcription, -358-Aqp5-Luc was transfected into NIH3T3 cells, together with pCMV/Sp1 and/or pCDNA3/GATA6 expression vectors (or empty vector controls), individually or in combination. Four hours (h) after transfection, cells were treated with HDAC inhibitor suberoylanilide hydroxamic acid (SAHA)  or DMSO as control. Cells were harvested after treatment with SAHA for 18 h for measurement of firefly luciferase activity  that was normalized to protein concentration. To examine effects of HDAC1, HDAC2 and HDAC3 on Aqp5 transcription, NIH3T3 cells were transfected with -358-Aqp5-Luc, together with varying amounts of pCDNA3/HDAC1, pCDNA3/HDAC2 or pCDNA3/HDAC3 expression vector60. Cells were harvested for luciferase assays 48 h after transfection. To examine effects of p300 and CBP on GATA6- and Sp1-mediated Aqp5 transcription, NIH3T3 cells were transfected with -358-Aqp5-Luc, together with varying amounts of pCMVβ-p300-myc 61 or pRc/RSV-m CBP-HA 62. Cells were harvested for luciferase assays 48 h after transfection.MLE-15 or NIH3T3 cells were seeded at 6 × 10Cytotoxicity of SAHA in MLE-15 and NIH3T3 cells was analyzed using WST-1 cell viability assay  according to the manufacturer’s instructions.Hdac3 (TRCN0000039389), Gata6 shRNA (short hairpin RNA) (TRCN000085589) or non-silencing shRNA control was purchased from Sigma-Aldrich and GE Dharmacon . The lentivirus backbone vector PGIPZ expressing p300 shRNA (V2LMN_102133 and V2LMN_90586) or non-silencing control shRNA was obtained from GE Dharmacon. Mouse Gata6 cDNA was cloned into the lentivirus backbone vector pRRL.hCMV.Sin.IRES.GFP63. Infectious lentivirus was created by co-transfection of lentivirus vectors expressing Hdac3, Gata6 and p300 shRNAs and Gata6 or control vectors with pCMVΔR8.91 and pMD.G into human 293 T cells. The infection mixture was added dropwise to 293 T cells plated on 100-mm culture dishes and incubated at 37 °C overnight. Virus was harvested 48 h or 72 h after transfection, concentrated with PEG-it virus precipitation solution  and titered with HIV p24 ELISA .The lentivirus backbone vector pLK0.1-puro, engineered to express Proteins were resolved by SDS-PAGE and electrophoretically blotted onto Immun-Blot PVDF membranes , followed by blocking in 5% nonfat milk. Primary antibodies (Abs) used included rabbit polyclonal anti-AQP5 , -p300 , -HDAC2  and -HDAC3 . Protein was normalized to eukaryotic initiation factor 2α (eIF-2α), β-actin or GAPDH using rabbit anti-eIF2α  polyclonal Ab and mouse anti-β-actin  and anti-GAPDH  monoclonal Abs, respectively. Blots were incubated with horseradish peroxidase-linked anti-IgG conjugates (Promega) for 45 min at room temperature (RT). Complexes were visualized by enhanced chemiluminescence (ECL)  with a FluorChem 8900 Imaging System .Hdac3 or non-silencing shRNA at MOI = 2. Two days later, cells were transfected with siRNA targeting mouse Hdac2 (sequence: 5′-GAAUCCGGAUGACUCAUAATT-3′) or control GFP siRNA (Thermo Fisher Scientific) (75 pmoles) using Targefect . Protein was harvested for western analysis three days after the second transfection. For Gata6 and p300 knockdown, MLE-15 cells grown on 24-well-plates were transduced with lentivirus expressing Gata6, p300 or control shRNA at MOI = 2. Cell lysates were harvested three days after transduction.One day after plating, MLE-15 cells grown on 24-well-plates were transduced with lentivirus expressing Gata6, Aqp5 and p300 expression, cDNA was synthesized using SuperScript III reverse transcriptase  and qRT-PCR was performed using the following primers: Gata6 forward 5′-ACGCCTCTGCACGCTTTCCC-3′ and reverse 5′-GCCGCCACCTCCACTCACAC-3′; p300 forward 5′-CCAGCCACCCATGGGAGCAA-3′ and reverse 5′-AGCCAATTCCTTGGGGCTGCT-3′; Aqp5 forward 5′-CGCTCAGCAACAACACAACACC-3′ and reverse 5′-GACCGACAAGCCAATGGATAAG-3′; 18S forward 5′-CTTTGGTCGCTCGCTCCTC-3′ and reverse 5′-CTGACCGGGTTGGTTTTGAT-3′ using SYBR-Green PCR with the 7900-HT Fast Real-Time PCR System (Applied Biosystems). The amplification protocol was set as follows: 95 °C denaturation for 10 min followed by 40 cycles of 15-s denaturation at 95 °C and 1 min of annealing/extension at 60 °C.To quantify Aqp5 promoter were amplified using SYBR-Green PCR  in a 20 µl reaction volume supplemented with 0.25 µl of 5 Prime HotMaster Taq DNA polymerase  and betaine  with the 7900-HT Fast Real-Time PCR System (Applied Biosystems). Primer pairs were as follows: forward 5′-CGCTGAGCGCACGATGCG-3′ and reverse 5′-CGGCTCCCGCGCCCAGAG-3′. The amplification protocol was set as follows: 96 °C denaturation for 5 min followed by 40 cycles of 1 min denaturation at 96 °C and 80-s of annealing/extension at 70 °C.Chromatin from MLE-15 cells with SAHA treatment, GATA6 overexpression or HDAC3 knockdown as well as corresponding controls, was cross-linked with 1% formaldehyde according to the manufacturer’s instructions . For ChIP, ~100 µg of soluble chromatin, 20 µl of protein A/G plus-agarose beads, 20 µl of ChIP dilution buffer , 1 µl of protease inhibitors and 2 µg of anti-acetyl-histone 3 (H3)  or anti-acetyl-histone 4 (H4) , or control Ab, were incubated in 200 µl at 4 °C overnight. After washing with a combination of ChIP dilution buffer, radioimmunoprecipitation assay buffer (RIPA) /LiCl and Tris-EDTA Buffer (TE), beads were resuspended in 100 µl of 10% Chelex-100 resin (Bio-Rad) and boiled for 10 min, followed by addition of 1 µl of proteinase K and boiling for another 10 min. Following centrifugation, supernatants were collected. DNA fragments encompassing putative enhancer regions homologous to the proximal 358-bp of the rat Protein A/G Sepharose beads  were incubated with 2 μg of HDAC3 , Sp1 , GATA6  Ab or corresponding IgG overnight at 4 °C after equilibration with dilution buffer  in PBS). After washing with dilution buffer, beads were buffered in 0.2 M triethanolamine (pH 9) and Abs were cross-linked by incubating beads with a final concentration of 6.5 mg/ml of dimethyl pimelimidate (DMP)  in 0.2 M triethanolamine (pH 9) for 30 min. Cross-linking was quenched using 0.5 ml quench buffer (50 mM ethanolamine in PBS for 2 hr at room temperature). Excess (unlinked) Ab was removed by washing with 1 M glycine (pH 3). Beads were then washed with RIPA buffer.Hdac3 or non-silencing shRNA was used for each IP. Thirty µl of cross-linked HDAC3, Sp1, GATA6 Ab or corresponding IgG was added to each sample and incubated in a rotator overnight at 4 °C. After washes in RIPA buffer, immunoprecipitated proteins were eluted in Laemmli sample loading buffer and boiled for 15 min. Samples were then subjected to western analysis for HDAC3, GATA6, Sp1 and p300 .Total protein lysate (70 µg in 200 µl RIPA buffer) from MLE-15 cells transduced for 48 hours with lentivirus expressing Data are shown as mean ± SEM, where (n) is the number of observations. We performed two-way ANOVA, t-tests and z-tests for ratiometric data to determine significance. P < 0.05 was considered significant.