The quantitative prediction of transcriptional activity of genes using promoter sequence is fundamental to the engineering of biological systems for industrial purposes and understanding the natural variation in gene expression. To catalyze the development of new algorithms for this purpose, the Dialogue on Reverse Engineering Assessment and Methods (DREAM) organized a community challenge seeking predictive models of promoter activity given normalized promoter activity data for 90 ribosomal protein promoters driving expression of a fluorescent reporter gene. By developing an unbiased modeling approach that performs an iterative search for predictive DNA sequence features using the frequencies of various k-mers, inferred DNA mechanical properties and spatial positions of promoter sequences, we achieved the best performer status in this challenge. The specific predictive features used in the model included the frequency of the nucleotide G, the length of polymeric tracts of T and TA, the frequencies of 6 distinct trinucleotides and 12 tetranucleotides, and the predicted protein deformability of the DNA sequence. Our method accurately predicted the activity of 20 natural variants of ribosomal protein promoters (Spearman correlation r = 0.73) as compared to 33 laboratory-mutated variants of the promoters (r = 0.57) in a test set that was hidden from participants. Notably, our model differed substantially from the rest in 2 main ways: i) it did not explicitly utilize transcription factor binding information implying that subtle DNA sequence features are highly associated with gene expression, and ii) it was entirely based on features extracted exclusively from the 100 bp region upstream from the translational start site demonstrating that this region encodes much of the overall promoter activity. The findings from this study have important implications for the engineering of predictable gene expression systems and the evolution of gene expression in naturally occurring biological systems. While transcriptional regulation is one of the most highly studied areas in biology, the ability to quantitatively predict gene expression from DNA sequence remains inadequate10. Knowledge of transcription factors and their cognate binding sites continues to grow and has enhanced our ability to make qualitative predictions about gene expression. For example, a number of transcription factors are now well known to be involved in differentiation of stem cells into specific cell types, leading to potentially clinically useful applications such as induced pluripotent stem cells11. Inspite of this progress, only limited quantitative predictions of gene expression are possible13. Knowledge that promoter sequences of genes encode both qualitative (e.g. when to switch a gene on and off) and quantitative properties (e.g. precise levels and noise) of gene expression is implied by the heritable nature of these attributes14. It is becoming increasingly clear that while transcription factors are critical in gene regulation, regulatory outputs are ultimately determined by co-operation between regulators in complex circuits17 and with chromatin states21. In particular, transcription factors compete for DNA binding sites with nucleosomes23. The information for nucleosome binding is largely encoded in the DNA sequence27, even thoughin vivo nucleosome occupancy is highly dynamic29. Quantitative models of gene expression, therefore, benefit from the integration of nucleosome and transcription factor binding data30.Transcription is a fundamental step in the decoding of information encoded in DNA into phenotypes. Therefore, knowledge of transcriptional regulation is crucial for understanding the natural variation of gene expression31 and sequence dependent read-depth coverage32, respectively. To overcome these limitations, approaches based on promoters fused to fluorescent reporters have been developed to generate direct, real-time measurement of promoter activity with high accuracy33. This has been applied in large libraries of synthetic bacterial promoters thereby generating new insights on combinatorial cis-regulation8. It was not until recently that the first large-scale library of naturally occurring promoters of any eukaryote fused to yellow fluorescent protein (YFP) became available30. 110 yeast ribosomal protein (RP) promoters were fused to YFP and integrated into a different strain at a fixed genomic location, hence alleviating both post-translational and genomic context related effects30. Consequently, this data set is very well poised for the computational modeling of the relationship between promoter sequence and transcription activity of a eukaryotic promoter.A key barrier to quantitative modeling of gene expression using promoter sequence has been the lack of experimental methods for accurately measuring transcript levels. DNA microarrays and RNA-seq are the most widely-used systems for measuring transcript abundance, but this measurement can reflect many effects including promoter sequence, genomic position of a gene and post-transcriptional regulation of mRNA levels by processes like mRNA degradation. In addition, microarray and RNA-seq can be affected by systematic biases arising from sequence dependent hybridization kinetics30. Participants were provided with the activities of 90 promoters and their corresponding promoter sequences and challenged to predict the activity of 53 promoters whose activities were known only to the organizers of the challenge (P = 0.002). Our approach was built upon three key propositions: i) transcription factor binding and nucleosome binding, as well as other regulatory signals are encoded in DNA27, ii) if i) is true, then explicit prior knowledge of transcription factor and nucleosome binding is not a mandatory prerequisite for prediction of promoter activity if training data is available. That is, an unbiased approach that explores the associations between DNA sequence patterns and promoter activity should be able to rediscover patterns that relate to the observed activity. To do this, we used machine learning methods to iteratively explore the association between promoter activity and DNA sequence patterns in 100 bp windows of promoter sequence. We considered sequence patterns such as k-mers (k = 1 to k = 5), homopolymer stretches, nucleosome binding and three mechanical properties of DNA . Based on iterative exploration of different machine learning models, we established that a support vector machine (SVM) was the most predictive of promoter activity based on specific sequence patterns in the 100 bp upstream of the translation start site (TrSS). Our model outperformed those which applied transcription factor binding sites of known RP promoters34, implying that other sequence patterns besides transcription factor binding sites can help in fine-tuning gene expression. Indeed, among the predictive features employed by our model were poly(dT-dA) tracts that occlude nucleosomes; these have since been applied to fine-tune gene expression beyond resolutions attainable by transcription factor site mutations38. Our study expands the understanding of sequence patterns that could potentially be useful in engineering fine-tuned gene expression.To provide a fair assessment of the relationship between promoter sequence and quantitative transcript levels, the Dialogue for Reverse Engineering Assessments and Methods (DREAM) organized an open community challenge in 2011 . Details of promoter construction are available from Zeeviet al. 201130 and the DREAM website. Briefly, the organizers considered the promoter region as the sequence 1200 bp upstream of a gene or until the nearest gene. Each promoter was linked to a URA3 selection marker and inserted into the same fixed genomic location of a master yeast strain containing theYFP gene. In total, 110 natural RP promoter strains and 33 strains with synthetically mutated RP promoters were constructed. As a control for experimental variation, all these strains contained a control promoter (TEF2) driving the expression of red fluorescent protein (mCherry). ThemCherry,TEF2,URA3,RP promoter andYFP were all a single contiguous DNA sequence arranged in that order. Measurements of themCherry expression levels and replicates of promoters had very low variation, enabling the distinction between any two promoters with activities differing by as little as ~ 8%. The promoter activity was determined as the amount of YFP fluorescence produced during the exponential growth phase, divided by the integral of the OD during the same period. The promoter activity measures the average amount of YFP produced from each promoter, per cell, per second during the exponential phase.The training data composed of DNA sequence for 90 yeast RP promoters with known activities and a test data set of 53 promoters was downloaded from the DREAM challenge website (53 and BioMoby web-services (accessed in August 2011) imported from the Molecular Modeling and Bioinformatics Group, Barcelona, Spain54. Bendability was estimated based on trinucleotide parameters obtained from DNase I digestion and nucleosome binding data35. Deformability was based on parameters from the analysis of protein-DNA crystallography structures36. Bending stiffness was based on bending free energy using the near-neighbor model37. Nucleosome binding was based on trinucleotide preferences55.Each promoter sequence was divided into 100 bp non-overlapping windows. The full promoter sequence was considered as another window. To extract information from each of the windows, we considered the frequencies of specific sequences in k-mers (k = 1 to 5), length of homopolymeric stretches DNA, mechanical properties  and nucleosome binding. K-mer counts were performed using custom scripts. DNA mechanical properties were computed using workflows constructed in the Taverna Workbench version 2.2.056 to select feature combinations that are most predictive of promoter activity. Performance of feature combinations was tested using 5- and 10-fold cross validation.For each window, feature selection was performed using a linear regression wrapper in the WEKA machine learning toolkit version 3.456 were considered: SVM regression using sequential minimal optimization (SMO), linear regression and regression trees. Models were trained using 66% of the data and tested using 34%, and included only the features that were selected as important by the linear regression wrapper. Performance was determined using Pearson correlation between model predictions and actual promoter activities computed in R version 2.11.1. The SVM model was selected for refinement based on high performance compared to the other models.Three models implemented in the WEKA toolkitPromoter activities were not available to the participants of the challenge. We applied the ensemble of 501 SVMs built from 500 different training/test sets in which 80% of the data was used in training and 20% in testing and a single SVM validated by 66% training set and 34% testing sets. Each SVM model utilized the 24 features selected by a linear regression wrapper as most predictive of promoter activity. To predict activities of the DREAM6 test set, the 24 features were extracted from the upstream 100 bp sequence for each promoter. Predictions were then made using each of the SVM models and averaged to obtain the final predictions.P-values to evaluate the performance on the test set34. Details of the equations used for these statistics have been published separately by the DREAM6 Promoter Prediction Consortium34.Predictions from the SVM ensemble were submitted through the DREAM website to the organizers for a blinded evaluation on the test set. The DREAM organizers used four statistics and corresponding1.P-value for observing a Pearson correlation coefficient of the same magnitude or smaller than that of a given participant, a null distribution was generated by randomly sampling predictions from other teams and repeating this 10,000 times34.Pearson correlation between predicted and observed activities for each model submitted: To generate a2.P-value was then generated using a null distribution obtained from randomly sampling the predictions made by the other participants. The process was repeated 10,000 times34.Spearman correlation for participant between ranks of the predicted and actual ranks of promoter activities: A3.P-value for observing a chi-square distance metric of the same magnitude or smaller than that of a given model submission, a null distribution was generated by randomly sampling predictions from other teams and repeating this 10,000 times34.Chi-square distance metric measuring the distance between predicted and actual promoter activities: To generate a4.P-value was generated from a null distribution obtained by randomly sampling predicted ranks from other teams, repeating this 10,000 times.A rank distance metric measuring the difference in ranks between predicted ranks and actual ranks of promoter activities. AP-values34. All these scores were computed using R version 2.11.1.The overall score was defined as the product of the fouret al. 2016Raw data for 'Prediction of fine-tuned promoter activity from DNA sequence’, SiwoREADME.txt contains a description of the files.Click here for additional data file.Copyright: © 2016 Siwo G et al.2016Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).30. We first partitioned the promoter sequences into 100 bp non-overlapping windows, extracted specific DNA features from each window and considered the full promoter sequence as its own window  and another set of 53 promoters whose activity was withheld from participants (test set) window . The fea window . The per window  selectedhttps://www.synapse.org//#!Synapse:syn2820426/wiki/71013). Predictions from our team had a Spearman correlation of 0.65 , chi-squared (χ2) distance metric of 52.62 (P = 0.508) andR2 statistic measuring the difference in ranks between predicted and actual promoter activities of 35.85 (P = 0.004). TheP-values were generated from the probability of obtaining a comparable or lower performance using a null distribution in which predictions were made by randomly choosing an activity for each promoter amongst all the 21 participating teams. A combined score based on the negative logarithm (base 10) of the geometric mean of theP-values for all the 4 scores ranked our team first34  and poly(dT-dA) tracts which influence the rigidity of DNA26, thereby directly impacting nucleosome binding. Furthermore, insertion of poly(dT-dA) sequences into promoters can be used to regulate gene expression to a finer degree and at more gradual intervals than could be attained by transcription factor binding site mutations38. Some transcription factors are also highly dependent on the ability of DNA to bend43. In particular, TATA binding protein (TBP), which binds to the TATA box, is important for regulating the activity of RP promoters45. Another directly biologically relevant feature identified by the SVM was the deformability of DNA46. Promoters of low activity had more deformable DNA than those of high activity (P = 0.008). This was particularly evident at 40 to 60 bp from the TrSS when comparing the top 20 promoters with the highest versus those with the lowest activity  and poly(dA-dT) tracts . The relctivity .Finally, some of the features may affect mRNA stability, especially given their potential location downstream of the transcription start sites (TSS). Besides sequence features in the 5’UTR that are close to the TSS could affect transcription, translation and mRNA stability.Rap1, Fhl andSfp1, as well as introduction of nucleosome disfavoring sequences and random mutations. At the time of the challenge, participants were not aware of these mutations. The performance of our model on the set of natural promoters was much higher  compared to that for the mutated promoters . The prediction error was significantly less for natural promoters versus the mutated promoters  so the correlation between activity and prediction error may be a consequence of the low predictability of synthetic promoters. Thus, future models may benefit from data on activities of mutated promoters, which could enable a more accurate modeling of the impact of mutation on specific transcription factor binding sites.Understanding the biases in prediction accuracy could provide biological insights into promoter classes and allow for refinement of models. Therefore, we investigated relationships between the nature of the test promoters and the magnitude of prediction error made by our model. Among the 53 test promoters provided by the DREAM challenge, 20 were natural yeast RP promoters while 33 were variants of these promoters with specific synthetic mutations introduced. These mutations included changes in the binding sites of the TBP,30, the challenge posed was more targeted towards DNA sequence patterns that fine-tune gene expression rather than simply determine the ‘on/off’ expression status. RP transcription regulation occurs in a highly coordinated manner and is critical for growth, allowing cells to adjust their protein synthesis capacity to physiological needs48. This is especially crucial as RP gene expression accounts for 50% of transcripts produced by RNA polymerase II49 and their dysregulation leads to reduced fitness48. The yeast genome contains 137 RP genes, of which 19 encode a unique RP and 59 are duplicated. The proper functioning of ribosomes requires that all the ribosome components be expressed in equimolar concentrations50 while simultaneously remaining responsive to physiological needs52. This is potentially challenging given the copy-number differences between the RP genes because high copy number genes generally show increased expression. The regulatory mechanisms underlying this fine-tuned regulation are not known. By accurately predicting the activity of the RP genes using the promoter sequences, we demonstrate that a considerable amount of this information is encoded in the DNA sequence.The quantitative modeling of gene expression has the potential to enhance our understanding of how gene regulation is fine-tuned in natural populations and has implications for the design of predictable gene expression systems. The DREAM6 challenge data set for promoter activity prediction was a unique opportunity to evaluate the predictability of gene expression from its promoter sequence. Given that all promoters were derived from natural yeast RP promoters that are expressed in the exponential phase38. Cooperation between transcription factors or competition among them17, and with nucleosomes23, provides an additional mechanism for fine-tuned gene expression. RP promoters with high activity have not only more nucleosome disfavoring sequences but also characteristic spatial organization of the binding sites forRap1,Sfp1 andFhl130. The low performance of our model on synthetic promoters containing targeted mutations in transcription factor binding sites and nucleosome disfavoring sequences reinforces the importance of these factors. Consistent with this, the combination of our model and the mechanistically driven model involving transcription factors and nucleosome binding30 was more predictive of promoter activity34. Our findings have implications for understanding the fine-tuned regulation of RP genes and engineering desirable activity in synthetic promoters.It is intriguing that our model did not explicitly use transcription factor binding site information and focused only on the 100 bp upstream region. Some of the features identified by our model may influence transcription factor binding or nucleosomes indirectly, and could even affect mRNA translation. Transcription factors are critical for gene regulation. Their empirically identified binding sites are 6 to 8 bp, theoretically putting an upper bound on the level of regulatory flexibility that can be attained by mutating positions at these sitesThe data referenced by this article are under copyright with the following copyright statement: Copyright: © 2016 Siwo G et al.Data associated with the article are available under the terms of the Creative Commons Zero "No rights reserved" data waiver (CC0 1.0 Public domain dedication).F1000Research: Dataset 1. Raw data for ‘Prediction of fine-tuned promoter activity from DNA sequence’, Siwoet al. 2016,10.5256/f1000research.7485.d11351657 The authors present FIrST, an approach for predicting promoter activity from sequence, which won one of the DREAM6 challenges. FIrST is using only simple sequence features in a limited range (100 bp) upstream of the translation start site for making its predictions, which distinguishes it from several other approaches in this field. Prediction results are convincing and the method appears to be sound. However, currently the method is not described detailed enough. In addition, I have a few further major and minor concerns regarding the current version of the manuscript:In the list of features described in section "Feature extraction", some seem redundant to me. For instance, the trinucleotide parameters for bendability are just computed from the k-mers for k=3. Also nucleosome binding prediction was based on trinucleotide preference. Please explain why it may be useful to also include those 3-mer-derived features in addition to the 3-mers themselves.The description of methods in section "Machine learning model exploration" is too coarse. Please provide more detail on the SVMs, linear regression, and regression trees employed. It also remains unclear if the scales of features are normalized somehow, before their values are provided to the SVM.No details are given on the selected 3-mers and 4-mers (Table 1). Please provide a list of the specific k-mers selected by FIrST. It may also be reasonable to discuss potential biological reasons for their importance (as partly covered for TATA-boxes on page 6).Considering Fig. 3, I wondered if the difference in deformability may be related to transcription initiation. Or, stated differently, might we observe an ever clearer signal if all sequences (and their deformability profiles) would be aligned by the transcription start site (TSS) instead of the TrSS? One idea in the same direction, which could contribute to the novelty of the manuscript, would be to evaluate similar profiles  for all features found to be informative by FIrST. For instance, one could expect to see something like general fluctuations of G/C content, or the TATA-box in 4-mer profiles as a spike approx. 35 bp before the TSS. From my perspective, this might improve the novelty of the manuscripts and the interpretation of features. Major comments:The data from the DREAM6 challenge only consider a special subset of genes  and only in yeast. It is unclear if the features derived by the authors' method would also be informative for higher eukaryotes. I understand that this question cannot be finally answered from the DREAM6 data, but the authors might comment on this issue.Figure 1B remains a bit unclear. In the caption and the main text, the authors explain that they use non-overlapping 100bp sub-sequences. However, from the figure it rather seems that they consider upstream sequences of 300 bp, 200 bp and 100 bp (and the full promoter sequence) relative to the translation start site. Please clarify.In section "DREAM6 challenge data" of "Methods", the authors refer to "the sequence 1200 bp upstream of a gene", where "upstream of the translation start site" (as in the remainder of the text) would be more specific.In section "Feature extraction", the authors explain that "each promoter sequence was divided into 100 bp non-overlapping windows", while in the previous section they explain that the full 1200 bp sequences do not extend over the nearest gene. From my understanding, this may result in some of the sequences being shorter than 1200 bp, and their length might not be dividable by 100. Please explain how such cases are handled.10 of the geometric mean of the p-values was used as the overall measure. Although bot definition are equivalent with respect to the resulting ranking, I would suggest to provide one consistent definition of the overall score.At the end of section "Validation of model by DREAM6 consortium", the authors explain that "the overall score was defined as the product of the four P-values", whereas later they explain that -logFrom the manuscript it did not become fully clear if the TA-tracts  tracts in some parts of the manuscript) are tracts of poly "A or T" or tracts of poly "AT"-dinucleotides.In section "Error profile of SVM promoter activity model", the authors explain that natural promoters had (slightly) lower activity than synthetic promoters and that the prediction error of the SVM is lower for natural promoters. However, I did not get the idea, why this should explain that low activity genes had larger prediction errors.In section "Error profile of SVM promoter activity model", the authors explain that one reason why FIrST did not perform well for synthetic promoters is that most mutations had been introduced outside the 100 bp range considered by FIrST. However, this reasoning partly contradicts the claim of the authors that most of the transcriptional activity may be explained from the sequence in that 100 bp window. If this would truly be the case, mutations outside this range should have only minor effects.In the Discussion, the authors mention that TF binding motifs are 6 to 8 bp in length. While this may be true for several yeast TFs, it is not correct for eukaryotes in general and motifs may be wider than 10 bp. Minor comments:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. et al., 2013), this article provides more details of the winning method and some additional analysis of the predictive model, which may lead to better understanding of the predictability of gene transcription. While its contribution is undoubtable, this article should be revised to address several issues:This article describes the winning method of the DREAM6 promoter activity prediction challenge. While a meta analysis of the competing methods participated in the challenge has already been published (MeyerThe 23 features utilized by the SVM model (as well as their coefficients in the model) is not provided explicitly in the main text nor in the supplement file. Table 1 in the main text shows that 6 trinucleotides and 12 tetranucleotides are important features, but it is nowhere to be found which tri- and tetra-nucleotides they are. For lengths of T or TA-tracts, the supplement file shows several different values, including mean, median and stdev. It is unclear which one is actually used by the SVM model. Similarly, supplement file shows 79 values for deformability and it is unknown which one is used. In the case of ranking the features by their SVM coefficients, the authors need to clarify if the feature values were normalized prior to model building, as these features are on very different scales and if not normalized the ranking of the coefficients are not very meaningful.The main conclusion in the subsection "Error profile of SVM promoter activity model" do not seem to make sense. First, promoters of low activity had larger prediction error. Then the authors stated that natural promoters had lower activity. This seems to contradict with their observation that the prediction error was significantly less for natural promoters than for mutated promoters.Major issues:Authors only mentioned that feature selection was done in WEKA with wrapper. More details need to be given. For example, what was the selection strategy used by the wrapper, e.g., exhaustive search, greedy forward search, backward search, or other types of heuristics? What is the purpose of first training 1000 SVM classifiers using 66% of data as training and 34% as testing, and then another 500 SVM classifiers using 80% as training and 20% as testing?Minor issues:I have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. et al. give a detailed report of their entry to the DREAM promoter activity prediction assessment, conducted in 2011. The paper describing the results of the assessment appeared in 2013 , and the entry from Siwoet al. (“FiRST”) was the top-performer overall. Meyeret al. gives few details about the specific methods, mentioning only that the FiRST entry used an SVM and did not use TF binding site motif information. Here it is clarified that FiRST is a simple method that uses only part of the sequence and the most prominent features were about nucleotide content.Siwoet al. but it could be fleshed out further to get at what is going on. My suggestions for revisions are to give more detail about the properties of the sequences used and the relationship to performance.Because it is perhaps a little eye-opening  that the best method in the assessment is so simple, this paper is an important footnote to Meyeret al. in the methods section), and that their predictions were dominated by the effect of a simple measure of G content. Siwoet al. report that they did worse at predicting the synthetically mutated promoters . In Meyeret al., adding tf binding information to FiRST improved performance.FiRST predicts from only the 100 bases of sequence upstream of the translation start  are not in the 100 bp window used. That is, because in most cases these synthetic sequences were (as I understand it) identical in features to other examples while having different activities, for the purposes of FiRST, they could only introduce prediction errors. In light of this fact the rest of the speculation about why performance varied in this way seems extraneous.The authors mention this, but the most important reason that FiRST does poorly at predicting the synthetic mutations seems to be that most of the mutations , and the prediction error in each case. How well does one predict using G content alone? This might all be reconstructed from the data supplement helpfully provided, but the authors should consider providing the analysis. It also seems reasonable to ask for more details about the performance of other sequence windows.The main other missing piece from this paper is any discussion or evidence that the method works beyond the narrow confines of the DREAM setup. Even for the RP genes, does it make a useful prediction, that increasing the G content of RP promoters in that 100 bp window will decrease promoter activity? I am fine with leaving this as “future work” but it would be worth mentioning.et al., except FiRST is not marked . The authors should clearly cite Meyeret al. in the figure caption as the source of the data for this figure, or simply point the readers to Meyeret al., or else explain where the data came from if not from Meyeret al.Figure 2B is apparently the same as part of Figure 1E from MeyerI have read this submission. I believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard.


Expression of the adenovirus E1A N-terminal transcription repression domain alone (E1A 1-80) represses transcription from specific promoters such as HER2 , 4. E1A  The first gene expressed after adenovirus (Ad) infection is early gene 1A (E1A). Group C Ad E1A transcribes two highly related mRNAs which code for two regulatory proteins (E1A 243R and E1A 289R). Discrete domains of E1A interact with key regulatory proteins and confer diverse functions including transcriptional activation, transcriptional repression, induction of cellular DNA synthesis, cell immortalization, cell transformation, as well as inhibition of metastasis and cell differentiation -11. E1A An important function encoded in the E1A 243R protein N-terminus is transcriptional repression of cellular genes involved in cell proliferation and differentiation , 15. We E1A 1-80 can repress some promoters of clinical significance such as the HER2 proto-oncogene [A major target of the E1A repression function, p300 and its close homolog CBP are co-activators targeted by many transcription factors involved in regulation of many cellular genes that function in various metabolic pathways -21. SponIt has been reported that Ad E1A 243R globally inhibits p300-mediated acetylation of histone H3K18 in vivo, but has no effect on acetylation of H3K9 , we askeSince transcription and histone acetylation occur in the context of chromatin, we examined the effect of E1A 1-80 on histone acetylation in reconstituted chromatin. Chromatin was assembled using purified core histone, ATP-dependent chromatin assembly factors, and the transcription template pG5MLT , 28. TheTranscriptional activators, through binding to either enhancers or promoter-proximal regions, can recruit histone modifiers and chromatin remodelers to induce changes in the nucleosome occupancy/positioning around the promoter, and consequently, changes in gene expression , 30. TheUsing conditions of strong H3K18 acetylation when both p300 and Gal4-VP16 were present, we examined the effects of increasing amounts of purified E1A 1-80, and two mutants, C6A and L20A, that are defective in transcriptional repression and do not effectively interact with p300 . BecauseE1A 1-80 inhibition of H3K18 acetylation by p300 could simply be due to competition between E1A 1-80 and histone H3 for access to p300. Alternatively, E1A 1-80 interaction with p300 may alter its conformation and reduce its catalytic activity towards histone H3 in chromatin. p300 has been shown to autoacetylate, which may impact p300 structure and function and its targeting of the preinitiation complex (PIC) , 26. ThiIn vitro acetylation reactions were performed with p300, Gal4-VP16, E1A 1-80 and its C6A mutant in different combinations in the absence of chromatin Figure . ReactioWe next determined whether p300 autoacetylation and inhibition of H3K18 acetylation in chromatin are intrinsic properties of the E1A repression domain. A dose-response analysis with E1A 1-80 during in vitro acetylation of p300 was performed with reconstituted chromatin. Increasing amounts of E1A 1-80 resulted in increased levels of p300 autoacetylation . Our data demonstrate strong inhibition by E1A 1-80 of p300-mediated acetylation of H3K18 in reconstituted chromatin. Since H3K18 is one of the histone lysine residues favored by p300 , 34, theA potentially important observation is that p300 autoacetylation is enhanced by E1A 1-80. Previous studies have shown that p300 autoacetylation affects its structure and HAT enzymatic activity , 35. E1AHow does E1A 1-80 enhance p300 autoacetylation and concurrently inhibit H3K18 acetylation? One possibility is that E1A 1-80 induces structural changes in p300 that increases p300 autoacetylation and reduces p300's ability to acetylate H3K18. This could be due to reduced p300 binding to chromatin or diminished p300 HAT activity caused by autoacetylation-induced conformational changes in p300 or by interaction with E1A 1-80. Alternatively, enhanced autoacetylation of p300 by E1A 1-80 may result in increased dissociation of p300 from the promoter . Our resThomas and Chiang  demonstrWe previously identified TFIID as well as p300 as important targets for E1A transcriptional repression and proposed a model in which a p300/E1A 1-80 complex bound upstream of the promoter causes dissociation of TFIID from the TATA box . The resIt has been reported that autoacetylated p300 dissociates more efficiently from the promoter in reconstituted chromatin thus facilitating recruitment of TFIID and subsequent PIC formation resulting in transcription . Perhapsp300 autoacetylation and HAT activity in vivo may be dynamically regulated by various factors and play important roles in gene regulation. For example, targeted down-regulation of the histone deacetylase SIRT2 with shRNA resulted in hyperacetylated p300 which was less capable of activating a p300-responsive promoter in vivo . The enhAdenovirus vectors expressing E1A 1-80 or lacZ were purified by CsCl ultra-centrifugation as described  and usedIn order to assemble chromatin sufficient for one in vitro acetylation assay, 63.5 ng of the in vitro transcription template pG5MLT was combined with 49 ng of core histone, as well as histone chaperones and chromatin remodeling factors in a total volume of 6 μl essentially as described , except Purified p300, Gal4-VP16 and E1A 1-80 were combined on ice in 14 μl. Expression and purification of the recombinant proteins were described previously , 28. Aft


Long non-coding RNAs (lncRNAs) are non-protein coding transcripts longer than 200 nucleotides. The post-transcriptional regulation is influenced by these lncRNAs by interfering with the microRNA pathways, involving in diverse cellular processes. The regulation of gene expression by lncRNAs at the epigenetic level, transcriptional and post-transcriptional level have been well known and widely studied. Recent recognition that lncRNAs make effects in many biological and pathological processes such as stem cell pluripotency, neurogenesis, oncogenesis and etc. This review will focus on the functional roles of lncRNAs in epigenetics and related research progress will be summarized. Messenger RNA (mRNA) is the RNA that carries information from DNA to the ribosome for protein synthesis. And yet, many RNAs do not code for proteins in eukaryotes. There are RNAs that lack an apparent open reading frame (ORF) of 300 nt or longer. They do not encode a protein product thus classified as putative noncoding RNAs-3. Long It has been reported that about 20% of transcription progress across the human genome would be associated with protein-coding genes. The facRNA is an information encoding molecule with high flexibility and high-fidelity. It has also been characterized with easy activation, modification, transportation, and degrasion. Thus, RNA is considered as an integrative character of both the digital lexicon of DNA and the analog language of proteins. It is also a dynamic participant of DNA and protein molecules in performing cellular activities.According to taxonomic, non-coding RNAs (ncRNAs) are composite of the familial "housekeeping" RNAs and regulatory RNAs in recent intensive studies. There are many different sizes of NcRNAs and for this reason they have been divided into small and long classes: small ncRNAs (sncRNA) being less than 200 nt and lncRNA greater than 200 nt to over 100 kb in length. The curIt is predicted that there are thousands of lncRNAs in the mammalian transcriptome,14-17. NEpigenetics associated with a gene activity state that may be stable over long periods of time, persist through many cell divisions, or even be inherited through several generations and all without any variations to the primary DNA sequence-27. WhatLncRNAs were considered as non-functional junk initially. And now, their presence and significance have still being debated-30. It iStudies have shown that lncRNAs play critical regulatory roles in diverse cellular processes such as chromatin remodeling, transcription, post-transcriptional processing and intracellular trafficking,31,36-40cis[Imprinting has been a significant process in identifying special nucleic acid and protein, such as DNA methylation and histone modification. LncRNAs have been found to participate in imprinting processes. It means lncRNAs influence the monoallelic expression of a gene according to its parents of origin. More than 200 lncRNAs were found to participate in imprinting processes. Depending on their parental origins, differentially methylated regions unmethylated DNA imprinting control regions (ICRs), resulting in specific expression of nearby lncRNAs and suppressing neighboring genes in cis.The H19 lncRNA-MBD1 complex could interact with histone lysine methyltransferases. Therefore, it could work by bringing repressive histone marks on the differentially methylated regions of the three direct targets of the H19 gene.cis by interacting with repressive chromatin modifying complexes[Airn and Kcnq1ot1/LIT1  are examples of lncRNAs that cause suppression of paternally inherited genes. In particular, Kcnq1ot1/LIT1 is involved in the repression of several protein-coding genes in omplexes,45. Kcnqomplexes. Kcnq1otomplexes-49.LncRNAs have played crucial roles in controlling gene expression during both developmental and differentiation processes. Furthermore, the number of lncRNA species will be higher in genomes of developmentally complex organisms. It highlights the significance of RNA-based levels of control in the evolution of multi-cellular organisms. ExpressMEG3 (Gtl2) was a lncRNAs in human with the length of about 1.6 kb. There are a number of splice isoforms in MEG3 and it retains introns creating longer transcripts,53. ReceMore and more evidences have proved that lncRNAs play critical roles in various biological processes. The mutations and dys-regulations of these lncRNAs contribute to the development of many complex diseases, such as virus infection and carcinogenesis. Beta2.7 is the popular one being studied in virus infection. Generally, beta2.7 specifically binds and prevents the re-localization of essential complex I subunit GRIM-19 . In response to apoptotic stimuli, beta2.7 is responsible for stabilizing the mitochondrial membrane potential and mitochondrial ATP production. It also prevents metabolic dys-function, which will be essential for completing the virus’ life cycle,58. The LncRNAPCAT-1, a target gene of polycomb repressive complex 2, has been implicated in disease progression by promoting cell proliferation. The up-Compared with DNA or protein, RNA molecules are considered to be more efficiently couple bioenergetic requirements with information storage and processing. Therefocis. While, some lncRNAs, transcribed by RNA polymerase II, are able to recruit transcriptional repressive complexes including PcGs and G9a to silence specific genomic regions, both in cis (top) and in trans (bottom)[There are many different functions of lncRNA has been explored in latest few years. Besides of transcription regulation, there are also several versatile lncRNAs that have been evidenced, such as Kcnq1ot1, Airn, Xist and HOTAIR. Their function have mainly focused on regulating transcription of multiple target genes through epigenetic modifications,88-90. F(bottom),96,97  in CD14 (+) and CD34 (+) cells, RNA4.9 interacts with components of the polycomb repression complex (PRC) as well as the MIE promoter region where the enrichment of the repressive H3K27me3 mark. It will also disclose the repression function of lncRNA on transcription.in vitro and in vivo[Berghoff EG and his colleague have shown that Evf2 (Dlx6as) lncRNA antisense transcription, Evf2-dependent balanced recruitment of activator and repressor proteins enabled differentially transcriptional control of adjacent genes with shared DNA regulatory elements. Researcd in vivo.The lncRNA-LET has been reduced by hypoxia-induced histone deacetylase 3 by reducing the associated histone acetylation-mediated modulation of the lncRNA-LET promoter region. And the down-regulation of lncRNA-LET was found to be a key step in the stabilization of nuclear factor 90 protein. It leads to hypoxia-induced cancer cell invasion. lncRNA-TNFα expression is regulated by the long non-coding RNA THRIL (TNFα and hnRNPL related immunoregulatory LincRNA: large intergenic non-coding RNAs) through its interaction with hnRNPL (heterogeneous nuclear ribonucleoprotein L). Both acThe dynamics of miRNA regulatory network mediated by RNA editing is implicated in stroke. LncRNAs-151 is found to be unregulated after middle cerebral artery occlusion. The immature form of lncRNAs-151 is subject to RNA editing that influences the primary lncRNAs processing into mature lncRNAs within the CNS. IntriguEnhancer-associated (elncRNA) and promoter-associated (plncRNA) elements play different roles in the chromatin status at intergenic lncRNAs transcription. ExpressLncRNAs are dynamically expressed during erythropoiesis with epigenetic regulation. And they are targeted by key erythroid transcription factors such as GATA1, TAL1 and KLF1. After exploring 12 candidate lncRNAs, they were nuclear-localized, exhibiting complex developmental expression patterns. Depleting them severely impaired erythrocyte maturation, inhibiting cell size reduction and subsequent enucleation. lncRNA-EC7 is transcribed from an enhancer and is specifically needed for activation of the neighboring gene encoding BAND 3.Recently, researchers have identified a translational regulatory lncRNA (trlncRNA) through genome-wide computational analysis. Furthermore, they found trlncRNA was upregulated in paired clinical breast cancer primary and lymph-node metastasis samples. Tumor invasion and metastasis will be stimulated by its expression in vitro and in vivo, respectively. In addition to this, trlncRNA is involved in the down-regulation of the epithelial marker E-cadherin by suppressing the translation of its mRNA.cis and in trans by inducing heterochromatin formation without changing DNA methylation in a Dicer-independent manner[cis in contrast to lincRNAs which can operate both in cis and in trans[Cellular reprogramming is known to accompany cell type-specific epigenetic alterations of the genome. It is the conversion of one specific cell type to another. Chromatin structure and dynamics can be influenced by epigenetic factors such as covalent histone modifications, histone variants, DNA methylation, ncRNAs and etc. Chromatin remodeling complex may play an important role in cell fate decision. It has t manner-166. It t manner. The rolt manner,169. Onet manner. Antisen in trans,172.The human body is composed of hundreds of distinct cell types. There is a specific position for each cell within the body and each cell performs a specific function. Since all the cells within a multi-cellular organism contain the same genome, the information that inducing cells to establish their identity is likely to be coded in their epigenome. The epicis will be repressed[Similarly, several large lncRNAs transcribed antisense of protein-coding genes and they can also interact with chromatin modifying complexes and affect the landscape of chromatin,182. Forepressed,97 (FiguThe mRNA of BCL2 will be negatively regulated by the miR-15a/16-1 group. As an aLncRNAs play critical roles in epigenetic modulation of chromatin structure by regulating key genes in specific cancerous cells,187. DisThe significance of epigenetic regulation of lncRNAs in human melanoma cells is increasing with more evidence,192. LnclncRNAs function make effects in many biological and pathological processes such as stem cell pluripotency, neurogenesis, oncogenesis and etc. In this review, it has focused on the functional roles of lncRNAs in epigenetics and summarized related research progress.Reasoning, primary ncRNA precursor chains have a high frequency of nonsense codons in their short and highly interrupted ‘reading frames’. In addition, they will never be translated into proteins because they are too short. However, they may be associated with proteins that detect nonsense codons within a reading frame.Thus, distinct forms of chromatin proteins may make effects through protein–protein contacts via the nonsense-mediated decay complex proteins. It might be able to organize in genes encoding ncRNA or lncRNAs. Chromatin will also be associated with a variety of other RNA binding proteins. In principle, ncRNAs could exert regulatory effects on the chromatin through their association with any of these proteins. Thus, much more exploratory work is needed in these fields.It was indicated that several ncRNAs are functional and not just ‘transcriptional noise’ as has been previously speculated. To the early geneticists, a ‘gene’ was a very abstract entity. It was only considered to reflect the way phenotypes were observed when transmitted between generations. Today, however, it is dispensable to re-evaluate the way for classifying ‘gene’ and genomic regions of apparently ‘gene poor’. It may produce important transcripts. All these will need to be tested with various methods for proving its clinical linkage to diseases.Anril: Antisense non-coding RNA in the ink4 locus; DNMT: DNA methylation transferase; ElncRNA: Enhancer-associated Long non-coding RNA; FANTOM: Functional Annotation of Mammalian cDNA; GRIM–19: Genes associated with retinoid/interferon-induced mortality-19; ICRs: Imprinting control regions; Kcnq1: Potassium voltage-gated channel subfamily Q member 1; LIT1: Long QT intronic transcript 1; LncRNA: Long non-coding RNA; MBD: Methyl-DNA-binding domain proteins; MBD1: Methyl-DNA-binding domain proteins; MEG3: Maternally expressed 3; mRNA: Messenger RNA; ncRNA: Non-coding RNA; ORF: Open reading frame; PlncRNA: Promoter-associated Long non-coding RNA; PRC: Polycomb repressive complex; PRINS: Psoriasis susceptibility-related RNA Gene Induced by Stress; sncRNA: Small non-coding RNAs; SncRNA: Small non-coding RNAs.The author’s declare that he has no competing interests.


Journal of Cellular Biochemistry published by Wiley Periodicals, Inc.The fibrillar collagen scaffold of the extracellular matrix provides a structural framework for cells in tissues and regulates intercellular communication; its disregulation has been associated with tumour development and progression. Previous work has shown that expression of type I collagen, the most abundant mammalian extracellular matrix protein, is decreased in chemically or virally transformed cells. This negative regulation could be mapped to a proximal COL1A2 promoter element spanning a CME (Collagen Modulating Element) site in SV40‐transformed human fibroblasts (SV‐WI38) that binds an unknown repressing protein. By magnetic bead pull‐down, we observed a multi‐protein complex bound to the CME with preference for single‐stranded over conventional double‐stranded DNA. MALDI‐TOF mass spectrometry of the CME‐binding protein complex revealed involvement of nuclear annexin A2 (AnxA2) which was increased in SV40‐transformed cells. Further EMSA analysis demonstrated that AnxA2 did not directly bind to the DNA but stabilised the complex and led to an increase in protein binding to the CME in SV‐WI38 but not untransformed WI38 cells. Knockdown of AnxA2 by siRNA increased type I collagen production in both WI38 and SV‐WI38 cells; however, these effects were not mediated at the transcriptional level. Rather, our data indicate a novel functional role of AnxA2 in the negative post‐transcriptional regulation of type I collagen synthesis in human fibroblasts. In SV40‐transformed cells, AnxA2 is accumulated at the proximal COL1A2 promoter region, suggesting close association with the transcriptional machinery that possibly facilitates binding to the emerging mRNA, eventually contributing to overall repression of type I collagen protein synthesis. J. Cell. Biochem. 116: 408–417, 2015. © 2014 The Authors.  AnxA2annexin A2CMEcollagen modulating elementCo‐IPcoimmunoprecipitationCOL1A2α2(1) collagen chainEMSAelectrophoretic mobility shift assaySV40 T AgSV40 large T antigenSV‐WI38SV40 transformed WI38 cellsTCEPtris (2‐carboxylethyl) phosphine hydrochlorideType I collagen is the most abundant mammalian extracellular matrix protein and consists of a heterotrimer of two α1(1) and one α2(1) chains. Its expression is regulated by both transcriptional and translational events, as this is a prerequisite for the normal functioning of the connective tissue , this binding was probably not directly through AnxA2 but in complex with other proteins as we observed in this study. We therefore hypothesise that AnxA2 stabilises nuclear protein complexes at the CME region as the anti‐AnxA2 antibody disrupted protein‐DNA binding to some extent while addition of recombinant AnxA2 protein increased complex formation. These effects were more pronounced with SV‐WI38 nuclear proteins compared to WI38 extracts, the latter expressing nuclear AnxA2 to a lower extent, suggesting a role of AnxA2 in the differential regulation of the COL1A2 gene in these two cell lines. Interestingly, we did not detect differences in the sequence of the AnxA2 transcript in the two cell lines (data not shown), suggesting that SV40 transformation did not cause mutations in the AnxA2 gene that could be responsible for the differential type I collagen expression [Lubbe et al., We confirmed the involvement of AnxA2 in CME binding by EMSA studies with a specific antibody against AnxA2 and observed that the antibody did not completely disrupt complex formation to the probe supporting the hypothesis of a large multi‐protein complex interacting with the CME. We further could show that AnxA2 itself did not directly bind to the single‐stranded COL1A2 promoter region (−100/−61)Functional studies confirmed the involvement of AnxA2 in type I collagen expression as altered AnxA2 expression had a substantial influence: siRNA knockdown of AnxA2 was accompanied by significantly elevated type I collagen levels in both WI38 and SV‐WI38 cells. Although this effect was more pronounced in SV‐WI38 cells, type I collagen expression could not be rescued to the level of the untransformed WI38 cells. Conversely, transient overexpression of AnxA2 slightly decreased type I collagen expression in WI38 cells which did not reach the low amounts seen in SV40‐transformed cells (data not shown), suggesting additional repressive mechanisms upon malignant transformation. Surprisingly, alterations of the amount of available AnxA2 did not affect transcription of the COL1A2 gene, suggesting that AnxA2 primarily acts via post‐transcriptional regulation of the gene. Indeed, it has been observed previously that AnxA2 can bind to cis‐acting elements in the 3′‐UTR of specific genes [Vedeler et al., Although we demonstrated a novel role in the negative regulation of type I collagen gene expression, the presence of AnxA2 alone does not explain the observed substantial overall decrease of COL1A2 mRNA and protein upon SV40 transformation. AnxA2 acting at the post‐transcriptional level contributes to type I collagen down‐regulation; however, transcriptional mechanisms do certainly play a significant role in the overall negative regulation. The question of the identity of the proteins that bind to the CME resulting in inhibition of COL1A2 transcription in SV‐WI38 cells still remains. A possible candidate that has been shown to directly interact with AnxA2 is the transcription factor STAT6, leading to enhanced transcriptional activity [Das et al., The role of AnxA2 in the carcinogenesis of human cancers is still debated as it has been shown to be overexpressed in some cancers and downregulated in others, both of which have been identified as beneficial to cancer cells depending on location (Liu et al., AnxA2 is known to be associated with diverse cellular processes including functions in ECM degradation through intercellular and cell‐ECM interactions. Here, we describe a novel function for nuclear AnxA2 as part of a post‐transcriptional regulatory protein complex involved in the regulation of type I collagen. Its regulatory function was more pronounced upon transformation with the SV40 tumour virus which led to nuclear AnxA2 accumulation. We speculate that, under normal conditions, AnxA2 is involved in physiological COL1A2 regulation to prevent excessive deposition of type I collagen which is characteristic of many fibrotic disorders such as systemic sclerosis, hepatic cirrhosis and pulmonary fibrosis [Varga and Jimenez, Supporting Information.Click here for additional data file.


The background information required for the construction of such networks is often dispersed across a multitude of knowledge bases in a variety of formats. The seamless integration of this information is one of the main challenges in bioinformatics. The Semantic Web offers powerful technologies for the assembly of integrated knowledge bases that are computationally comprehensible, thereby providing a potentially powerful resource for constructing biological networks and network-based analysis.Network-based approaches for the analysis of large-scale genomics data have become well established. Biological networks provide a knowledge scaffold against which the patterns and dynamics of ‘We have developed the Gene eXpression Knowledge Base (GeXKB), a semantic web technology based resource that contains integrated knowledge about gene expression regulation. To affirm the utility of GeXKB we demonstrate how this resource can be exploited for the identification of candidate regulatory network proteins. We present four use cases that were designed from a biological perspective in order to find candidate members relevant for the gastrin hormone signaling network model. We show how a combination of specific query definitions and additional selection criteria derived from gene expression data and prior knowledge concerning candidate proteins can be used to retrieve a set of proteins that constitute valid candidates for regulatory network extensions.Semantic web technologies provide the means for processing and integrating various heterogeneous information sources. The GeXKB offers biologists such an integrated knowledge resource, allowing them to address complex biological questions pertaining to gene expression. This work illustrates how GeXKB can be used in combination with gene expression results and literature information to identify new potential candidates that may be considered for extending a gene regulatory network.The online version of this article (doi:10.1186/s12859-014-0386-y) contains supplementary material, which is available to authorized users. Cellular signaling cascades support the transmission of information from external signals (e.g. hormones) to distinct cellular responses, for instance changes in gene expression. Gene expression is controlled by a network of highly interconnected proteins known as transcription regulators ,2. ThereIn general, the formulation and assessment of biological hypotheses against prior knowledge fundamentally relies on efficient knowledge integration that interlinks information and knowledge at various levels in standardized formats, after which the best-supported hypotheses can be selected for testing in wet-lab experiments. Therefore, the development of technologies for knowledge integration and representation has evolved into a major research area ,6.In recent years the Semantic Web has emerged as one of the most promising solutions to high scale integration of distributed resources. The Semantic Web initiative  essentiaOntologies, though introduced to the field of knowledge management long before the advent of the Semantic Web, have become an indispensable tool for practical implementations of semantic web technologies by providing a common understanding for people and computers alike, and may be regarded as part of the toolbox of the Semantic Web. In the field of biomedical research, the Open Biomedical Ontologies (OBO) Foundry  providesEven though SPARQL supports federated querying, this mode presents an additional hurdle for a biologist.Querying distributed and typically very large resources takes long execution times.The currently available reasoners are still too sluggish to be deployed on very large graphs, in particular when rule chaining is involved.The resources necessary for adequately answering specific questions are not always found in the available triple stores.We are currently witnessing a growing use of semantic web technologies for the management of biological concepts and for providing a scaffold for integrating concepts and data from disparate biological databases -17. In tGeXKB accommodates the field of gene expression regulation by seamlessly integrating the most relevant ontologies and databases, using semantic web technologies (preliminary results appeared in a conference paper ). GeXKB Several biological questions were formulated in the context of the gastrin response pathways. Gastrin is a gastrointestinal peptide hormone, which, similar to many other extracellular signals such as e.g. growth factors, plays a crucial role in both normal and pathological processes. After binding to the Cholecystokinin 2 receptor (CCK2R), gastrin triggers the activation of multiple intracellular signaling pathways and transcription regulation networks culminating in the regulation of numerous genes. We previously performed an extensive genome-wide gene expression time-series experiment on gastrin-treated rat AR42J cells  the Arr section The cAMP response element binding protein 1 (CREB1) is a specific DNA binding transcription factor. It is known to be under the control exerted by multifarious regulator complexes that include DbTFs, co-factors and kinases. We were interested in retrieving an exhaustive overview of possible regulators of CREB1.NFκB1 and RELA are members of the NFκB transcription factor family known to be involved in regulating apoptosis, proliferation, and immune responses . GastrinDbTFs are implicated in different cellular processes in the gastrin response signaling cascade. TCF7L2 plays a central role in gastrin mediated cellular migration , whereasDbTFs are central to the regulation of gene transcription, which in turn plays a key role in determining gene expression levels. Often, several DbTFs act together in the regulation of transcription of a specific gene. To enhance our understanding of mechanisms involved in gastrin mediated cellular responses we were interested in retrieving shared target genes of CREB1, NFKB1, TCF7L2 and the regulators of these DbTFs.GeXKB was conceived as an easily extensible knowledge base consisting of a core to which any number of optional resources could be easily added (See The construction involves 1) the development of three application ontologies that form the core of GeXKB, 2) conversion of optional resources to RDF, 3) uploading the ontologies and the optional resources to a triple store to make them accessible through a SPARQL endpoint, 4) inferring and adding to the store new triples supported by the explicitly asserted ones to increase the power and flexibility in querying. The 4 steps in detail:Step 1: The GeXKB ontologies are generated by an automated data integration pipeline Figure  that relnameSpace:ID in the OBO files and nameSpace_ID in the RDF files. Original IDs are used throughout if available. IDs for modified residues are constructed by replacing spaces with underscores in the corresponding names. Original name spaces are used for the imported ontological terms. The only ontological terms constructed specifically for this project are GeXO:0000001, ReXO:0000001 and ReTO:0000001. These three terms are modelled by analogy with the term ‘cell cycle process’ in GO. The name spaces used for other term types are as follows: ‘UniProtKB’ for protein terms, ‘KEGG’ for pathway terms, ‘NCBIGene’ for gene terms, ‘NCBITaxon’ for taxon terms, ‘SSB’ for modified residue terms and ‘intact’ for protein-protein interactions terms. Apart from the generic subsumption and partonomy, 10 more specific relation types are used to construct GeXKB ontologies  the PAZAR database , an openStep 3: All the RDF files are uploaded to an instance of the OpenLink Virtuoso data storage engine  as separStep 4: The inference process is performed by using the SPARQL update language (SPARUL)  as descrHomo sapiens information in GeXKB.All biological questions for the use cases (see section: positive regulation of CREB transcription factor activity (GO:0032793) and cAMP response element binding protein binding (GO:0008140) were included in the query. These terms suggest direct association with the process of regulating CREB1. Additionally, the term direct interaction (MI:0407) was included in the query to retrieve proteins that interact directly with CREB1. Then, to widen the breadth of the query, the broader GO term positive regulation of sequence-specific DNA binding transcription factor activity (GO:0051091) was included. However, in this case only proteins that have a physical association (MI:0914) with the CREB1 protein were considered, thus reducing the number of false positives  and negative regulation of sequence-specific DNA binding transcription factor activity (GO:0043433) were used.Similarly, Q2 retrieves proteins involved in the repression of CREB1 protein. For this query, proteins associated with biological process terms histone acetyltransferase (GO:0004402) and histone deacetylase (GO:0004407) activity that are involved in the biological process regulation of sequence-specific DNA binding transcription factor activity (GO:0051090), and are interacting with the CREB1 protein. Other than providing putative network components, these queries also serve to demonstrate the utility of targeting relations obtained through the inferencing process. By using the ReTO-tc graph, we were able to include implicit knowledge statements in the query output, meaning ontology term relationships not directly annotated to proteins, but linked to them through the inferencing process (see section: The query Q3 specifies chromatin modifiers that are involved in the regulation of CREB1. It retrieves the union of proteins associated with the molecular function terms negative regulation of NFκB transcription factor activity (GO:0032088) was chosen as the central term, as this would retrieve all proteins annotated as negative regulators of NFκB1 and RELA. Next, GeXKB was explored to identify terms that suggested an involvement with proteasomal degradation. Several terms were identified: ubiquitin ligase complex (cellular component: GO:0000151), ubiquitin binding (molecular function: GO:0043130), ubiquitination reaction (interaction type: MI:0220), and ubiquitin mediated proteolysis (KEGG pathway: ko04120). The SPARQL union construct was used to formulate a combination of the central term and the additional set of terms.Use case II is represented by Q4, which was constructed similar to the previous queries by using a combination of terms. First, the GO term negative regulation of sequence-specific DNA binding transcription factor activity (GO:0043433), and positive regulation of sequence-specific DNA binding transcription factor activity (GO:0051091).Query Q5 represents use case III, but for this query no terms specifically suggesting negative regulation of TCF7L2 were found . Hence, Q5 was formulated by using generic terms that indicated a dual role of proteins. Consequentially, Q5 retrieves proteins that interact with the TCF7L2 protein (UniProt accession: Q9NQB0) and are further annotated with the terms Use case IV was investigated by first identifying DbTFs among the results obtained for queries Q1, Q2, Q4 and Q5. This was done by extending these queries and using the TFcheckpoint graph for DbTF identification.Next, Q6 was formulated to retrieve from the TFactS, PAZAR and HTRIdb graphs target genes shared between the query DbTFs  and the DbTFs identified above.Homo sapiens, Mus musculus and Rattus norvegicus. Currently GeXKB is composed of three application ontologies integrating only primary resources which are regularly updated; four secondary resources containing DbTF-target gene relations (not necessarily up to date); and ID mappings to support querying.GeXKB utilizes the knowledge representation features offered by RDF and builds on previous efforts to use semantic web technologies for the integration of knowledge -51. GeXKThe knowledge base is hosted by a triple store and can be queried with SPARQL.To satisfy the requirements of end users, three nested application ontologies , which is built ‘on the fly’. It is not available as an independent artifact in contrast with upper level ontologies like BFO, and it solely serves to ‘glue’ together the various components within an application ontology Figure . The ULOThe GeXKB ontologies are protein-centric, and they are populated with proteins from GOA, IntAct, KEGG, and orthology relations by the filtering and aggregation procedure described in the Although RDF is efficient in integrating data, it has limited expressivity and it was not conceived to perform inferencing tasks. In GeXKB this limitation is partially overcome by the use of a semi-automated reasoning approach developed in . This aphttp://www.semantic-systems-biology.org) for all URIs. This solution combines the benefits of faster query execution and familiarity of the IDs for users. For instance, GeXKB can be queried using NCBI Gene IDs or UniProt accessions to retrieve information pertaining to a gene or protein of interest.A major effort of the Semantic Web community aspires to make resources available as part of the Linked Data cloud . We haveb1 below, was identified on the basis of transcriptomic data from a 14h time series gastrin response data set [b1 two disjoint subsets were defined – proteins known to be responsive to stimuli other than gastrin, and those not known, designated b1i and b1j respectively. The purpose of this classification was to prioritize the putative components. For instance, b1i proteins were given higher priority as new putative members of the gastrin response network members due to the available evidence from literature, whereas proteins in category b1j are still potentially interesting for future laboratory work, but with a lower priority. Finally, in use case IV the results returned for Q6 were assessed based on whether the genes regulated by the DbTFs in the query are expressed in the AR42J cell line and whether their expression changed in response to gastrin stimulation  are all expressed in AR42J cells. Five of these proteins  were considered as high priority mainly due to the available literature evidence. However, we believe that further investigation of proteins classified under the b1j category will certainly enhance the identification of novel candidates important for regulating gastrin activated DbTFs.In use cases I-III, protein candidates that show evidence for gastrin induced regulation in the AR42J cell line model system and other model systems : gene knock-out experiments conducted on CRTC1 and COMD1 implicate them as a potential regulator of transcription of CREB1 (ArrayExpress accession: E-GEOD-12209) and NFkB1 (ArrayExpress accession: E-MEXP-832), respectively.The results presented in this paper demonstrate that GeXKB can facilitate the identification of potential novel regulators of gastrin activated DbTFs. Based on our results with gastrin-mediated gene regulation reported in the present paper, we believe that GeXKB can be of equal use in any other experimental system as well. Obviously, the more specific biological roles of the regulators and target genes identified through GeXKB require further experimental validation. Observations made through small scale experiments such as RNAi mediated knock down of the novel regulators or large-scale studies on knock out model organisms should greatly enhance our current understanding of transcription regulation and subsequent cellular outcomes. Information contained in gene expression databases such as ArrayExpress may provide clues as to the role of genes and products thereof. We therefore searched for gene knockout experiments concerning the gastrin regulation network candidates in ArrayExpress and found evidence for candidate regulators CRTC1 and COMD1 (see Figure Our work demonstrates the level of knowledge discovery that can be achieved when information from a broad range of GO annotations and experimental evidence is semantically integrated. Interlinking various data sets using RDF provides the much needed homogeneity and extensibility for advanced data analysis. Additionally, we have shown the benefits of using computational inferencing in building the knowledge base, as this approach allows the retrieval of information that would otherwise have remained implicit and hidden from querying. Our efforts have involved a close collaboration between Semantic Web specialists and biological domain experts, resulting in novel ways for generating hypotheses and an initial assessment of these hypotheses against the current understanding of a regulatory network.The utility of GeXKB is expected to grow with its further development. The goal for future releases will be to expand the knowledge base with additional high quality datasets which will include relations between DbTFs and other interactors from curated texts, partially based on our current work on checking the full repertoire of transcription factors of human, mouse and rat, and their respective target genes.


Dendritic cells (DC) are professional antigen presenting cells that develop from hematopoietic stem cells through successive steps of lineage commitment and differentiation. Multipotent progenitors (MPP) are committed to DC restricted common DC progenitors (CDP), which differentiate into specific DC subsets, classical DC (cDC) and plasmacytoid DC (pDC). To determine epigenetic states and regulatory circuitries during DC differentiation, we measured consecutive changes of genome-wide gene expression, histone modification and transcription factor occupancy during the sequel MPP-CDP-cDC/pDC. Specific histone marks in CDP reveal a DC-primed epigenetic signature, which is maintained and reinforced during DC differentiation. Epigenetic marks and transcription factor PU.1 occupancy increasingly coincide upon DC differentiation. By integrating PU.1 occupancy and gene expression we devised a transcription factor regulatory circuitry for DC commitment and subset specification. The circuitry provides the transcription factor hierarchy that drives the sequel MPP-CDP-cDC/pDC, including Irf4, Irf8, Tcf4, Spib and Stat factors. The circuitry also includes feedback loops inferred for individual or multiple factors, which stabilize distinct stages of DC development and DC subsets. In summary, here we describe the basic regulatory circuitry of transcription factors that drives DC development. Dendritic cells (DC) represent specialized immune cells that develop from hematopoietic stem cells ,2. DC arDC development from hematopoietic stem cells comprises two critical steps: DC commitment and DC subset specification ,2,5. FirGenome-wide gene expression and gene knockout studies in mice identified several critical regulators for DC commitment and subset specification, such as Flt3, Stat3, Id2, Irf8 and Tcf4 ,3,6–13. Epigenetic mechanisms regulate cell development, identity and function. This occurs by positioning specific histone modifications at promoter and enhancer sequences that impact on transcription factor binding and thus gene expression ,17. HistEpigenetic modifications and transcription factors also regulate hematopoiesis, the development of hematopoietic stem cells into all cells in blood and blood-borne lymphoid organs ,23. HencHere, we determined how DC transcription factors are wired to drive DC lineage commitment and subset specification. First, we generated high resolution genome-wide maps of gene expression, histone modification and transcription factor occupancy in MPP, CDP, cDC and pDC. Second, we developed an integrative computational approach by combining differential transcription factor binding, gene expression data and motif enrichment analysis to reverse engineer a DC regulatory circuitry for DC commitment and subset specification. The circuitry was further validated and provides the transcription factor hierarchy that drives the sequel MPP-CDP-cDC/pDC and includes several feedback loops that stabilize distinct stages of DC development and DC subsets.Culture of progenitor cells from mouse bone marrow and their differentiation into DC were done as previously described . MPP, CDet al. . The PU.1 ChIP-seq data are from GSE21953, GSE31233, GSE21621 and GSE36104. The ChIP-seq data of Irf4, Irf8 and Stat3 in cDC and Irf8 and Tcf4 in pDC are from GSE36104, GSE53311, GSE27161, GSE62702, GSE43876 and GSE66899  using Bowtie. The aligned ChIP-seq data sets of transcription factor PU.1 and the enhancer mark H3K4me1 were then processed to identify genomic regions with sequence enrichment described as peaks using MACS software .An integrative approach was designed to detect transcription factor motifs that are highly enriched in PU.1 peaks. Briefly, we focused on differentially expressed transcription factors upon DC development and collected their sequence motifs from public databases. We then performed motif search around PU.1 differential peaks to determine the enrichment of transcription factor binding sites for each cell type and to identify cell-specific PU.1 co-binding partners. PU.1 co-binding transcription factors and selected key DC regulators from the literature ,39–45 weet al. . This gein vitro into CDP (DC commitment) and further into cDC and pDC . An increasing number of genes was found to be differentially regulated during DC commitment (429 genes) and DC subset specification  and funThe MPP cluster contains Gata2, Gfi1 and Tal1, which are down-regulated from MPP to CDP and have low or no expression in DC. This reflects the gradual restriction of development from early hematopoietic progenitors toward DC committed progenitors and DC Figure . The panNext we determined how histone marks  and PU.1 binding relate to stage-specific gene clusters in DC development by ChIP-seq for MPP, CDP, cDC and pDC Figure  and C. TTo investigate DC epigenetic priming in CDP, we focused on genes differentially expressed during DC commitment Figure . Upon MPThe DC-primed epigenetic signatures in CDP are enhanced upon CDP-cDC or CDP-pDC transition, which is particularly prominent for H3K4me1 and PU.1 occupancy . For example, the cDC gene Cd83 shows an increase in H3K4me1, H3K4me3 and PU.1 occupancy during CDP-cDC transition and is repressed in pDC due to bivalent modifications . The epigenetic profile of the pDC gene Siglec-H during CDP-pDC transition changed accordingly . Collectively, CDP acquire DC lineage-specific epigenetic signatures during DC commitment. These lineage-primed H3K4me1, H3K4me3 and PU.1 marks initiate the transcriptional program, and thus drive DC commitment and subset specification, while H3K27me3 mark restricts alternative developmental options.Next, we focused on the analysis of individual representative genes, including Gfi1, Flt3, Id2 and Irf8 for progenitor, pan-DC, cDC and pDC affiliated genes, respectively Figures  and 2. AThe progenitor gene Gfi1 has prominent H3K4me3 signals at the promoter in MPP and acquires H3K27me3 upon DC differentiation, thus forming a bivalent modification  represents a master regulator in hematopoiesis with a prominent role in multiple cell fate decisions, including DC development ,14. PU.1Genome-wide analysis of PU.1 ChIP-seq data reveals a preference for PU.1 binding to gene bodies and intergenic regions in MPP, CDP, cDC and pDC . This is consistent with previous studies and PU.1 function as an enhancer factor ,26. AddiNext, we analyzed the consecutive changes of PU.1 peaks during DC development and the associated PU.1 motifs Figure . A totalTranscription factor networks control hematopoietic cell differentiation, including DC development ,11,54,55In total, 23 transcription factors (represented by 28 motifs) are significantly enriched at different stages of DC development Figure . TranscrUpon DC commitment a panel of transcription factors, such as Irf1/4/5/8, Stat1, Egr1, Klf4, and Runx1, are enriched in CDP (Cluster II; Figure Upon DC subset specification, PU.1 is predicted to collaborate with distinct sets of transcription factors to restrict the developmental program toward to either cDC or pDC, such as Rel/Nfkb1 for cDC and Tcf4 for pDC (Clusters III, V and VI; Figure Frequently, PU.1 peaks harbor predicted Irf8 binding sites. Thus, to validate co-binding of PU.1 and Irf8, we generated Irf8 ChIP-seq data of cDC and pDC. We also included a recently published Irf8 ChIP-seq data set by Grajales-Reyes et al. . Irf8 ChNext we examined co-localization of PU.1 peaks and H3K4me1 footprints, which represent enhancer regions with potential transcription factor binding ,60. We fWe proceeded to construct cell-specific transcription factor regulatory networks Figure . In eachDifferent topologies and connectivity densities of the four stage-specific networks reflect how consecutive recruitment of PU.1 co-binding factors drives DC development Figure . In CDP,Auto-regulatory feedback loops are important building blocks of transcriptional regulatory networks ,62. PU.1To confirm predicted network connections, we performed functional tests for Irf8 and Stat5 Figure –E. The pWe proceeded to integrate the four stage-specific transcription factor networks in one DC regulatory circuitry Figure . Each reDuring MPP–CDP transition, PU.1 is connected with Flt3, Stat1/3/5, Irf5/8, Klf4 and Egr1, suggesting that it induces in concert with these factors a genetic program to initiate and establish DC lineage fate Figure . FollowiIn the DC circuitry, the cDC marker Irf4 is predicted to be regulated by multiple transcription factors, including PU.1, Irf8, Stat3 and Irf4 itself Figures  and 7. CMultiple feedback loops were observed between pDC factors, such as Irf1, Ets1, Spib and Tcf4 (pDC sub-network in Figure Here, we generated high-resolution genome-wide maps of H3K4me1, H3K4me3, H3K27me3 and PU.1 for DC commitment and subset specification. We demonstrate that consecutive changes of stage-specific expression for key DC regulators, including PU.1, Irf1, Irf8, Batf3, Spib and Tcf4, are associated with specific histone modifications in promoter and enhancer sequences. Genome-wide analysis led us to devise a regulatory circuitry, which provides the basic architecture of how DC transcription factors are wired to drive DC development.Genes that are increasingly expressed during DC development show an increase in H3K4me1 and H3K4me3 marks and a decrease in H3K27me3, as expected. Interestingly, gain in H3K4me1 or H3K4me3 marks on DC genes activates a DC-primed gene expression profile in CDP, which results in DC lineage commitment. These results are in line with our data on the DC-primed transcriptional signature in CDP . AdditioPU.1 represents a pioneer transcription factor in hematopoietic cell development that acts in concert with other lineage specific factors ,26,49,67et al. (Previous work proposed a layered transcription factor network for inflammatory DC stimulated with lipopolysaccharide . PU.1 waet al. , our regHere, we addressed an emerging need for network construction from gene expression and ChIP-seq data  and deveImportantly, several auto-regulatory feedback loops were identified for key DC genes, such as Irf4, Irf8, Klf4, Tcf4 and Spib. ChIP-seq data demonstrate co-localization of PU.1, Irf8, Stat3 and Irf4 at upstream region of Irf4 gene. This supports the predicted gene-target connections in DC transcription factor circuitry, including the auto-regulatory loop of Irf4 inferred from the prediction. Such auto-regulatory loops reinforce the expression of stage-specific transcription factors and lock cells in specific differentiation stages, thereby leading to the overall stabilization of the network. Additionally, in keeping with cDC being considered as the default DC developmental pathway (In summary, here we describe the basic architecture of the DC transcriptional regulatory program, which drives commitment of hematopoietic stem cells and their differentiation into DC. We provide a comprehensive characterization of the interplay between individual key DC regulators at specific stages of DC development, which is expected to pave the way for specific tailoring of DC development and function.


Peg3  imprinted domain is predicted to be regulated through a large number of evolutionarily conserved regions (ECRs) that are localized within its middle 200-kb region. In the current study, we characterized these potential cis-regulatory regions using phylogenetic and epigenetic approaches. According to the results, the majority of these ECRs are potential enhancers for the transcription of the Peg3 domain. Also, these potential enhancers can be divided into two groups based on their histone modification and DNA methylation patterns: ubiquitous and tissue-specific enhancers. Phylogenetic and bioinformatic analyses further revealed that several cis-regulatory motifs are frequently associated with the ECRs, such as the E box, PITX2, NF-κB and RFX1 motifs. A series of subsequent ChIP experiments demonstrated that the trans factor MYOD indeed binds to the E box of several ECRs, further suggesting that MYOD may play significant roles in the transcriptional control of the Peg3 domain. Overall, the current study identifies, for the first time, a set of cis-regulatory motifs and corresponding trans factors that may be critical for the transcriptional regulation of the Peg3 domain.The  Peg3  is an imprinted gene identified from human chromosome 19q13.4/mouse proximal chromosome 7 [Peg3 is highly expressed in brain, testis and ovary [Peg3 has also been known as Pw1 as a DNA-binding transcription factor involved in myogenesis [Peg3 as a transcriptional repressor controlling various downstream genes [PEG3 has been often identified as a potential tumor suppressor based on the observation that its promoter is usually methylated in ovarian and breast cancers [PEG3 appears to be one of the most epigenetically unstable imprinted genes during tumorigenesis [in vitro experiments demonstrated that the PEG3 protein has the potential to stop cell division in ovarian cancer cell lines [Peg3 are also dynamically fluctuated in response to various intrinsic and environmental cues, including nutritional starvation and hypoxic conditions [Peg3 is regulated to cope with various needs and challenges at the cellular and organism levels.mosome 7 –4. Consind ovary ,6. Peg3 am genes . In huma cancers . Accordiigenesis ,13. In fll lines . The expnditions ,16. NevePeg3 is the first imprinted gene identified from the 500-kb genomic interval that harbors 6 additional imprinted genes [Peg3 domains are well conserved in terms of gene content, orientation and distance [Peg3 domain [Peg3 domain [Peg3 domain.ed genes . The 500distance  Fig 1)..Peg3 is distance ,19. Acco3 domain . Given tPeg3 domain are also associated with several cis-regulatory motifs, including the E box, PITX2, NF-κB and RFX1 motifs. A series of ChIP analyses further demonstrated that MYOD, one of the E box binding factors, indeed binds to the ECRs of the Peg3 domain. More detailed information has been described below.As part of ongoing efforts, these ECRs were characterized in the current study using a series of phylogenetic and epigenetic approaches. According to the results, these ECRs have been well conserved during mammalian evolution. These putative enhancers can be further divided into two different types, ubiquitous and tissue-specific enhancers, based on their epigenetic profiles. Interestingly, two ubiquitous enhancers, ECR5 and ECR18, are shown to be epigenetically unstable during tumorigenesis. The ECRs of the cis-regulatory regions, termed ECRs (Evolutionarily Conserved Regions), which are localized within the middle 200-kb interval of the Peg3 domain  and an ECR from the H19/Igf2 imprinted domain (mH19-ECR1) [Peg3 (mPeg3-Pro) showed about 50% methylation levels in all 4 tissues, consistent with the fact that this region is methyated in an allele-specific manner [S2 Fig). As expected, the results demonstrated no major bias during PCR amplification and restriction enzyme digestion. The 6 ECRs can be divided into two groups based on their DNA methylation patterns among the tested tissues. Four ECRs, ECR2, ECR4, ECR6, ECR8, displayed variable DNA methylation levels among the individual tissues. Yet, all of these ECRs tend to show lower levels of DNA methylation in cerebellum and hypothalamus than in liver and thymus. This agrees with the hypomethylation patterns observed from the data set of the Epigenome consortium  . The proc manner ,17. We hnsortium . On the nsortium .Peg3 domain. This indicated that the changes observed from the ECRs of the Peg3 domain may be an outcome of specific events, but not of global DNA hypo or hypermethylation during tumorigenesis.The DNA methylation analyses were also performed on a set of DNA derived from different-stage thymic tumor samples, which had been prepared through a breeding experiment involving MMTV-Cre and KrasG12D models  (http://www.dcode.org/). According to the prediction, two sets of transcription factors can bind to ECR18. The first set of transcription factors include AP4, MYOD, E47 and Myogenin, which all share the E box motif as their core DNA-binding sites. This E box motif (CAGCTG) is well conserved among all the mammalian sequences of ECR18, as shown in the top section of GGGATTA) found within ECR18. This motif is also well conserved among ECR18 of all the mammals. This series of analyses were performed on all the remaining ECRs, and the results were summarized in the following manner  . This seign2seq) . These sg manner . The freCAGCTG), PITX2 (GGGATTA/TAATCCC), NF-κB (GGAATTTT/AAAATTCC), and RFX1 (CCATGG). Each of these motifs has been identified as a conserved motif from more than three ECRs . As expected, we were able to detect the enrichment of the anti-MYOD antibody-immunoprecipitated DNA at this control locus only from the MEF cells, but not from the neonatal brain, confirming the feasibility and specificity of our ChIP experiments  of the Peg3 domain. First, the orthologous sequences of the 18 ECRs have been successfully identified from the majority of the mammals . Thus, this structural conservation strongly suggests that these ECRs are very critical for the function of the Peg3 domain. Second, histone modification profiles indicated that the majority of ECRs are most likely enhancers for Pol II transcription , protocol #13–061.Peg3 domains with the following criteria: any region longer than 50 bp in length and also showing greater than 75% sequence identity between human and mouse [http://genome.ucsc.edu/cgi-bin/hgBlat). The outcome of each ECR’s screening has been summarized as a table with its raw score from the BLAT search . A subset of ECR sequences was further used for predicting transcription factors using the dcode program (http://www.dcode.org/). The individual sequence and corresponding alignment files are available . The genomic interval of each ECR was individually intersected with histone modification peaks and also with DNA hypomethylated regions. This series of scoring has been initially summarized as a table, and subsequently presented as The histone and DNA modification profiles associated with the ECRs were obtained from the publically available data sets of the UCSC genome browser 4Mam-LineD) and KrasG12D strains (Stock No. 08179 B6.129-Krastm4Tyj /Nci (LSL-KrasG12D)) . The thrEach DNA was treated with the bisulfite conversion protocol , and theThe chromatin was prepared from neonatal brains and MEF (Mouse Embryonic Fibroblast) cells according to the protocol previously described . The curS1 File(RTF)Click here for additional data file.S2 File(PPTX)Click here for additional data file.S3 File(PDF)Click here for additional data file.S4 File(PPTX)Click here for additional data file.S1 Table(XLSX)Click here for additional data file.S2 Table(XLSX)Click here for additional data file.