Nanog locus undergoes the large scale reorganization in formation/dissolution of transcription apparatus, which should have the timescale similar to the cell cycle period. With this slow transcriptional switching of Nanog, the simulated ESCs fluctuate among multiple transient states, which can trigger the differentiation into the lineage-specific cell states. From the simulated transitions among cell states, the epigenetic landscape underlying transitions is calculated. The slow Nanog switching gives rise to the wide basin of ESC states in the landscape. The bimodal Nanog distribution arising from the kinetic flow running through this ESC basin prevents transdifferentiation and promotes the definite decision of the cell fate. These results show that the distribution of timescales of the regulatory processes is decisively important to characterize the fluctuation of cells and their differentiation process. The analyses through the epigenetic landscape and the kinetic flow on the landscape should provide a guideline to engineer cell differentiation.A remarkable feature of the self-renewing population of embryonic stem cells (ESCs) is their phenotypic heterogeneity: Nanog and other marker proteins of ESCs show large cell-to-cell variation in their expression level, which should significantly influence the differentiation process of individual cells. The molecular mechanism and biological implication of this heterogeneity, however, still remain elusive. We address this problem by constructing a model of the core gene-network of mouse ESCs. The model takes account of processes of binding/unbinding of transcription factors, formation/dissolution of transcription apparatus, and modification of histone code at each locus of genes in the network. These processes are hierarchically interrelated to each other forming the dynamical feedback loops. By simulating stochastic dynamics of this model, we show that the phenotypic heterogeneity of ESCs can be explained when the chromatin at the  Nanog locus explains the observed large fluctuation of ESCs. The epigenetic landscapes are calculated based on the stochastic simulation, and the role of the phenotypic fluctuation in the differentiation process is analyzed through the landscape picture.Embryonic stem cells (ESCs) can proliferate indefinitely by keeping pluripotency, i.e., the ability to differentiate into any cell-lineage. ESCs, therefore, have been the focus of intense biological and medical interests. A remarkable feature of ESCs is their phenotypic heterogeneity: ESCs show large cell-to-cell fluctuation in the expression level of Nanog, which is a key factor to maintain pluripotency. Since Nanog regulates many genes in ESCs, this fluctuation should seriously affect individual cells when they start differentiation. In this paper we analyze this phenotypic fluctuation by simulating the stochastic dynamics of gene network in ESCs. The model takes account of the mutually interrelated processes of gene regulation such as binding/unbinding of transcription factors, formation/dissolution of transcription apparatus, and histone-code modification. We show the distribution of timescales of these processes is decisively important to characterize the dynamical behavior of the gene network, and that the slow formation/dissolution of transcription apparatus at the  Embryonic stem cells (ESCs) are pluripotent having the ability to differentiate into a variety of lineages, while in suitable culture conditions they proliferate indefinitely by maintaining pluripotency. These self-renewing ESCs are distinguished by the marker proteins including Sox2, Oct4 and Nanog (SON) NanogNanogOct4 is activated by the reception of Lif and the Oct4 expression is maintained through mutually activating interactions among SON. Contrary to this expectation, the observed expression of Oct4 is rather homogeneous Oct4 locus suppress fluctuation of the Oct4 expression One can figure out, at a glance, several scenarios which may explain the phenotypic heterogeneity. A simple scenario relies on the possible enhancement of fluctuation of the signal received by a cell: Since the reception of factors such as leukemia inhibitory factor (Lif) by a cell is stochastic, it necessarily bears fluctuation, which might be enhanced through the signal cascade to stochastically activate Bacillus subtilis into the competence period should be due to the non-adiabatic gene switching in the excitatory self-activating gene network For modeling the gene regulatory dynamics, not only the topological wiring diagram among genes but also the rates of reactions in the regulatory network should be quantified. These estimated rates, however, have very different values depending on the type of reactions, and hence it is strongly desired to develop the theoretical framework to treat effects of coexistence of the distributed timescales Many marker genes of ESCs have been identified Gene network and epigenetic dynamics. Coexistence of multiple timescales in the eukaryotic gene dynamics is the focus of the present study.Before starting the explanation of the simulated results, we briefly explain the interaction network among genes considered in the present model and discuss the dynamics of each gene in the network in subsection Nanog: Though both Sox2 and Oct4 loci have the Nanog binding sites Sox2 and Oct4 by Nanog is reasonable Sox2 and Oct4. We show that even with such assumptions, the weak correlation of Sox2 and Oct4 to the heterogeneous Nanog expression can be explained when epigenetic dynamics is considered explicitly.Interactions among genes have been inferred from observations on how the expressions of genes are correlated with each other and how one factor binds to the genetic locus of the other factor. It is not straightforward, however, to identify each elementary interaction embedded in the complex web of regulation. Indeed, consensus has not been obtained on the role of Cdx2 and Gata6Inference of the network from experimental observations in section In the model of Nanog is active in ESCs cultivated with Lif though two alleles can be active for other genes Shown in The rate of binding of the de novo methylation in the developmental process, the distribution pattern of methylated regions of DNA often lasts through the lifetime of the organism, showing its timescale is very long These different processes should have different timescales; From the observed rate of decrease of the amount of Oct4 Nanog locus.The turnover time of TF binding/unbinding is Here, we define “adiabaticity” as the ratio of the rate of the process to the rate of protein copy-number change; adiabaticity is larger  than 1 when the process is adiabatic (non-adiabatic). We use three sets of adiabaticity parameters, Parameters subsection of As in the previous works c-MycWe first discuss ESCs in media containing Lif and other agents. Lif activates First simulated is the case that the formation/dissolution of TA is adiabatic with Nanog locus in an ESC-specific manner Nanog in ESCs. For such complex and spatially extended processes for TA at the Nanog locus, it should be reasonable to assume that the timescale of TA formation/dissolution is as long as the cell cycle period. To find the plausible values for the rate of TA formation  synthesized from one gene directly regulates the other gene. The Level A assumptions give the backbone of the present model of the regulatory network.Oct4, Sox2 and Nanog loci has the Oct/Sox enhancer region Oct4, Sox2, or Nanogbefore they bind to DNA, or Oct4 and Sox2 bind to the adjacent sites of DNA to form the complex after binding. These two ways of binding are different in their cooperativity in the binding process. However, since the cooperativity of binding is masked by the cooperative formation/dissolution of transcription apparatus (TA) in the present model, these two ways of binding do not give significant difference in the switching behavior. We use, for simplicity, the latter assumption of forming complex after binding to DNA, but represent the effects of complex formation by assuming that the binding of either one of Oct4 or Sox2 is not enough but the binding of both two factors are needed for forming TA (We assume that the formation of the Oct4-Nanog complex is another route to form TA).1. Each of Oct4 and Nanog loci to repress them 2. Gcnf binds to the Oct4 and Cdx23. The Oct4-Cdx2 complex represses both Gata6 locus to repress it 4. Nanog binds directly to the Nanog locus is necessary for forming the higher order structure of chromatin at the Nanog locus Nanog locus Nanog.5. Because the binding of Oct4 to the Oct4 locus Oct4 locus Oct4 locus.6. Nanog promotes the expression of Oct4 Sox2 locus Sox2 locus Sox2 locus.7. Nanog is suggested to promote expression of Sox2 Level B. Genetic experimental data showed the correlation or anti-correlation between expression levels of two genes, but the direct evidence for the physical interactions between two genes are not yet obtained. In this case, the interactions can be indirect through the other unidentified factors. Even in that case, we may assume the hypothetical direct interaction between two genes in the model. Such assumption is reasonable in the coarse-grained model, in which the multiple detailed molecular processes are summarized into one process.Nanog locus has multiple binding sites of Oct4 and the occupation of the part of those sites is necessary for the formation of TA, but the occupation of all sites increases the rate for making the histone-code repressive.8. Excess expression of Oct4 reduces the expression level of Nanog 9. The Nanog-null ESCs differentiate into cells similar to those induced by Gata6-positive cells Gcnf is positively regulated by Gata6 and Cdx2 Gcnf is activated directly by Gata6 and Cdx2 in the model.10. Level C. No precise genetic data is available on the correlation or anti-correlation of gene activities, but from functional or biological observations, it is reasonable to assume the relation between two genes. The assumed interaction on such phenomenological basis might be a summary of the action of the larger network, but its representation as a single hypothetical process is useful to make the model behavior biologically reasonable.Gata6, Cdx2 and Gcnf. These auto-activating interactions may be phenomenological or hypothetical interactions.11. Upon the removal of Lif or other agents from the culture, ESCs start differentiation. Then, each cluster of differentiated cells do not return to ESCs spontaneously. This stabilization of the differentiated cells may be enhanced by the positive feedback among the lineage-specific genes as was assumed by 12. The cluster of cells differentiated into one lineage do not spontaneously transdifferentiate into the other lineages. This inhibition of transdifferentiation may arise from the reception of the external factor that is secreted or exhibited by the neighbor cells in the cluster. Such effect of the cell-cell communication is phenomenologically represented in the model by the mutual repression of genes specific to the different lineages.Others. Other biochemical or biophysical data showed the existence of interactions.13. Nanog dimerization is essential for the self-renewal of ESCs As assumed in the above argument, each locus of gene has multiple binding sites of TFs. From The state of the allele The  rate  asFrom 2, w protein . The binThe binding of activator TFs triggers the formation of TA; starting from es as(8)In 9 the We next explain how the rates defined in Gcnf and Sox2, we do not consider the repressor explicitly, so the rule is simplified asThe rates of TA formation and dissolution depend on how TFs are bound on the locus. Since TFs can be either of repressor or activator when they bind on the particular locus, we distinguish the binding sites Oct4 and Nanog, the histone-code activation is promoted by binding the Oct4-Sox2 or Oct4-Nanog complex. To represent cooperativity due to this complex formation, we modify the rule of Sox2, we haveGcnf or Sox2 locus in the model, so that for Sox2 we have the rule,The rates of the histone-code modification, Oct4, and Nanog andGata6, and Gcnf. Starting from this initial condition, the simulation for Gata6, and Gcnf to reach the steady ESC state. This first Gata6, and Gcnf to simulate ESCs or by turning Gata6, and Gcnf to simulate the differentiation process.Model and The model has parameters, A basic timescale for the description of cellular processes is the period of cell cycle. The period of cell cycle of ESCs is Though the cell cycle period is a convenient measure of biological processes, it is not explicitly used in the model. Alternatively, we use the lifetime of proteins, or the rate constant of protein degradation Another important quantity is the typical copy number of proteins in a cell. It has been observed that this copy number is The rate of TF binding/unbinding may not be much different from that in bacteria, so that The Nanog dimerization constant is Oct4 in ESCs is several days, which is approximately The rate to change the chemical state of a nucleosome may be as large as The timescale of TA formation/dissolution is not known, but we assume it to be slower than TF binding/unbinding and faster than the cell cycle. The latter assumption of the larger rate of TA formation/dissolution than the cell-cycle frequency should be reasonable because the TA structure should be reset and resolved in the large scale reorganisation of chromatin structure at the time of cell division. The former assumption of the slower rate than the TF binding/unbinding is also reasonable when we consider that TA formation is the much more complex process including assembly of many factors, looping of chromatin, and chemical modification of RNA polymerase. Therefore, we examined the wide range of From the above consideration, we can estimate the orders of magnitude of parameters, which are summarized in Cdx2 in ESCs.The parameters used in Nanog locus, which is a main feature of the present model of epigenetic dynamics as explained in Sox2 or Oct4 as shown in The smaller values of From Parameters subsection, we impose the constraints An important undetermined set of parameters are We generated about 1,000 parameter sets scattered on the two-dimensional plane of In this way, Fig. S1, which shows that The other undetermined set of parameters in Fig. S4 that the small number of states appear much more frequently than the other many states. We disregard the rarely appearing states and draw the transition diagrams among the states whose The total time length, Then, the time of the trajectory needed for the transition from the  are 200 , 600 Click here for additional data file.Figure S2Simulated distributions of the copy number of protein factors which appeared in trajectories of the differentiation process from the ESC states to the Gata6-dominant state. Distributions are divided to define the cell states by introducing thresholds designated by arrows. The abbreviations used to refer the cell states in (TIF)Click here for additional data file.Figure S3Distribution of expression level of Nanog in the case that either of two alleles ofNanogis not silenced through the allelic regulation. (A) (TIF)Click here for additional data file.Figure S4Probability  of the appearance of the cell state  in the 10,000 simulated trajectories are shown in the rank order of .(TIF)Click here for additional data file.


Macrophages play a critical role in innate immunity, and the expression of early response genes orchestrate much of the initial response of the immune system. Macrophages undergo extensive transcriptional reprogramming in response to inflammatory stimuli such as Lipopolysaccharide (LPS).TNF), was characterized by H3Ac, high CpG content and lack of TATA boxes. The second group, containing inflammatory mediators (interleukins and CCL chemokines), was up-regulated upon LPS stimulation despite lacking H3Ac in their annotated promoters, which were low in CpG content but did contain TATA boxes. Genome-wide analysis showed that few H3Ac peaks were unique to either +/−LPS condition. However, within these, an unpacking/expansion of already existing H3Ac peaks was observed upon LPS stimulation. In contrast, a significant proportion of S5P RNAPII peaks (approx 40%) was unique to either condition. Furthermore, data indicated a large portion of previously unannotated TSSs, particularly in LPS-stimulated macrophages, where only 28% of unique S5P RNAPII peaks overlap annotated promoters. The regulation of the inflammatory response appears to occur in a very specific manner at the chromatin level for specific genes and this study highlights the level of fine-tuning that occurs in the immune response.To identify gene transcription regulation patterns involved in early innate immune responses, we used two genome-wide approaches - gene expression profiling and chromatin immunoprecipitation-sequencing (ChIP-seq) analysis. We examined the effect of 2 hrs LPS stimulation on early gene expression and its relation to chromatin remodeling  and promoter binding of Sp1 and RNA polymerase II phosphorylated at serine 5 (S5P RNAPII), which is a marker for transcriptional initiation. Our results indicate novel and alternative gene regulatory mechanisms for certain proinflammatory genes. We identified two groups of up-regulated inflammatory genes with respect to chromatin modification and promoter features. One group, including highly up-regulated genes such as tumor necrosis factor ( Macrophages play a critical role in innate immunity, and recognize ‘microbial non-self’ via receptors known as toll-like receptors (TLRs) that recognize pathogen-associated molecular patterns (PAMPs) The expression and repression of genes is associated with alterations in chromatin structure mediated by enzymatic modifications  of the N-terminal ends of the core histones in nucleosomes The recruitment of the RNA polymerase II (RNAPII) in the core promoter is essential for gene expression. However, there is not necessarily a correlation between the rate of its recruitment and mRNA levels of a gene Studies of gene regulation have advanced rapidly in recent years, and this is due to the development of next generation sequencing techniques. Chromatin immunoprecipitation followed by massively parallel sequencing (ChIP-seq) has become a valuable experimental tool for examining protein-DNA interactions 2. For differentiation of THP-1 cells into macrophages a protocol using conditioned media as previously described by Whatling et al CD11b expression was measured using FACS (THP-1 cells (monocyte cell line)  were maintained in culture in RPMI 1640 medium containing 10% fetal bovine serum, 1 mM sodium pyruvate, 100 units/ml penicillin and 100 µg/ml streptomycin at 37°C with 5% COing FACS  and quaning FACS .Escherichia coli O55:B5 for 2 hours  to induce an acute inflammatory response. To confirm an induction of the inflammatory response in the THP-1 macrophages, TNF mRNA levels were measured in +/−LPS stimulated samples (THP-1 macrophages were then either left unstimulated (−LPS) or stimulated with 1 µg/ml LPS (+LPS) from  samples .6) were incubated for 2 hours  and total mRNA was extracted using TRIZOL® reagent  and purified using the RNeasy Mini Kit following the manufacturer's instructions, including DNase treatment . RNA concentration and quality were analyzed using Nanodrop 1000 Spectrophotometer  and Agilent 2100 Bioanalyzer  respectively. The RNA was then hybridized and scanned on the Affymetrix® Genechip® Human Exon 1.0 ST array  following manufacturer's protocols at the Karolinska Institute microarray core facility. Standard Affymetrix quality controls were followed (http://www.genomatix.de).THP-1 macrophages (0.5×10followed . The subChromatin immunoprecipitation (ChIP) was performed on differentiated THP1 macrophage cells +/−LPS stimulation for 2 hours using a method previously described by Rada-Iglesias et al http://main.g2.bx.psu.edu/) Sequencing was performed on the Illumina Genome Analyzer I . The data is available from gene expression omnibus under accession number GSE32325. All reads that passed mapping quality checks  were anain silico analysis and a matrix similarity value of 0.9 was used as the cut-off point.TATA-box patterns were identified in the promoter sequences of genes downloaded from the UCSC browser. Using GENOMATIX, classical TATA boxes matrices were used for Using the genes identified in the microarray gene expression analysis, a list was generated of those that had peaks that were replicated in the biological replicas. Data for CpG island coordinates were downloaded from the UCSC genome browser CD11b expression using FACS  TSS position, annotated transcription start site (hg19); H3AC common peak: presence (YES) or absence (NO) of a significant H3Ac peaks in promoter (−2 Kb/+1 Kb of TSS) for both +/−LPS conditions.; the effect of LPS, Peak extension; direction of chromatin expansion ; S5P RNAPII & Sp1 peaks: presence of a significant peak in unstimulated condition (−LPS), LPS treated cells (+LPS); common in both conditions (+/−LPS) or absence in either condition (NO) in promoters (−2 Kb/+1 Kb of TSS); CpG island presence or absence in promoter (−2 Kb/+1 Kb of TSS); TATA box in silico finding (−150 bp/+50 bp of TSS). RED BOLD: 105 genes included in MeSH inflammatory category. Tables are divided into gene families and classified groups.(DOC)Click here for additional data file.Table S7MatchInspector in silico TATA Box finding. Tables show the results of in silico TATA box finding in the subset of up- and down-regulated genes upon LPS stimulation in a distance of −150 bp/+50 bp of RefSeq gene annotated TSS. Gene symbol, Position of the TATA box from 5′UTR in base pairs (bp), gene strand direction , promoter sequence (Lowercase DNA sequence letters: promoter area (Upstream 5′UTR), Uppercase DNA sequence: 5′UTR; and Underlined and in bold: TATA box sequence), TATA box category , and matrix similarity (cut off ≥0.9). .(DOCX)Click here for additional data file.


Use of alternative gene promoters that drive widespread cell-type, tissue-type or developmental gene regulation in mammalian genomes is a common phenomenon. Chromatin immunoprecipitation methods coupled with DNA microarray (ChIP-chip) or massive parallel sequencing (ChIP-seq) are enabling genome-wide identification of active promoters in different cellular conditions using antibodies against Pol-II. However, these methods produce enrichment not only near the gene promoters but also inside the genes and other genomic regions due to the non-specificity of the antibodies used in ChIP. Further, the use of these methods is limited by their high cost and strong dependence on cellular type and context.We trained and tested different state-of-art ensemble and meta classification methods for identification of Pol-II enriched promoter and Pol-II enriched non-promoter sequences, each of length 500 bp. The classification models were trained and tested on a bench-mark dataset, using a set of 39 different feature variables that are based on chromatin modification signatures and various DNA sequence features. The best performing model was applied on seven published ChIP-seq Pol-II datasets to provide genome wide annotation of mouse gene promoters.We present a novel algorithm based on supervised learning methods to discriminate promoter associated Pol-II enrichment from enrichment elsewhere in the genome in ChIP-chip/seq profiles. We accumulated a dataset of 11,773 promoter and 46,167 non-promoter sequences, each of length 500 bp, generated from RNA Pol-II ChIP-seq data of five tissues . We evaluated the classification models in building the best predictor and found that Bagging and Random Forest based approaches give the best accuracy. We implemented the algorithm on seven different published ChIP-seq datasets to provide a comprehensive set of promoter annotations for both protein-coding and non-coding genes in the mouse genome. The resulting annotations contain 13,413  protein-coding (non-coding) genes with single promoters and 9,929  protein-coding (non-coding) genes with two or more alternative promoters, and a significant number of unassigned novel promoters.Our new algorithm can successfully predict the promoters from the genome wide profile of Pol-II bound regions. In addition, our algorithm performs significantly better than existing promoter prediction methods and can be applied for genome-wide predictions of Pol-II promoters. Alternative promoter usage is known to affect more than half of all human and mouse genes, and has been proposed as a primary driver of varied transcriptional regulation in different cellular conditions or developmental stages -4. NumerThe development of chromatin immunoprecipitation methods coupled with DNA microarray (ChIP-chip) technology and massively parallel sequencing (ChIP-seq) has enabled genome-wide identification of promoters, using antibody against RNA polymerase II (Pol-II) in different cells or tissues ,7. The cHere, we developed a computational method to discriminate promoter associated RNA Pol-II enriched regions of length 500 bp from the enrichment at other genomic regions, using the rich source of existing promoter data and associated chromatin modification signatures and various DNA sequence features. We prepared a data-set consisting of 11,773 Pol-II enriched promoters and 46,167 Pol-II enriched non-promoter regions from our recent ChIP-seq experiments, using antibody against Pol-II on five mouse tissues. We systematically trained and evaluated recent ensemble classifiers on this data set, using both 10-fold cross validation and testing on independent test set, and selected Bagging and Random Forest classifier for the final algorithm.The training set was generated from RNA Pol-II ChIP-seq data of five mouse tissues  generated by our lab. The RNA Pol-II ChIP-seq data was first processed and Pol-II enrichment peaks were identified at an FDR of 0.001, by assuming that peaks in the random background would follow Poisson statistics. The identified peaks were compared with TSS of non-redundant gene list generated from four different sources: RefSeq, Vega, Ensembl and UCSC. The gene lists were downloaded from UCSC genome browser  for mm9.We tried different state-of-art ensemble and meta classifiers for identification of promoter and non-promoter classes. The WEKA data-mining toolbox  was usedEach sequence record (500 bp window) of the promoter and non-promoter set is represented by a 39-dimensional feature vector. The feature values were calculated based on (a) DNA sequence composition; (b) DNA physico-chemical-structural properties, and (c) experimental data. Most of the conversion tables that are based on DNA physical-chemical-structural properties were downloaded from . The feaS = s1s2s3s4⋯sL-1sL, where si ∈ {A, C, G, T}, 1 ≤ i ≤ L . Let Δ1 ≡ {A, C, G, T}, Δ2 ≡ {AA, AC, AG, AT, CA, CC, CG, CT, GA, GC, GG, GT, TA, TG, TC, TT}, Δ3 ≡ {AAA, AAC, AAG, AAT, ACA, ACC, ⋯, TTT}, Δ4 ≡ {AAAA, AAAC, AAAG, AAAT, AACA, AACC, ⋯, TTTT} represent single, di, tri, tetra nucleotide symbol set respectively.Let a given DNA sequence be: nx represents the total number of times symbol x appeared in S and x ∈ Δ1 for single nucleotide features.We calculate 10 different features in this category. The first 7 features are calculated from single nucleotide composition. Let A_Fraction: nA/L1. C_Fraction: nC/L2. G_Fraction: nG/L3. T_Fraction: nT/L4. PurPyr_Fraction: (nA + nG - nC - nT)/L5. AmKe_Fraction: (nA + nC - nG - nT)/L6. WeSt_Fraction: (nA + nT - nC - nG)/L7. x ∈ Δ2. And remaining 2 features are based on tri-nucleotide composition, where x ∈ Δ3. Similar CpG features were used in [The remaining 3 features are related to CpG island. One of the features is based on di-nucleotide composition, where  used in  for promCpG1: (2*nCG + 2*nGC)/(L-1)8. CpG2: (nACG + nAGC + nCAG + nCCG + nCGA + nCGC + 2* nCGG + nCGT + nCTG + nGAC + nGCA + 2* nGCC + nGCG + nGCT + 2* nGGC + nGTC + nTCG + nTGC)/(L-2)9. CpG3: (4* nCAG + nCCG + nCGG + 4* nCTG + 4* nGAC + nGCC + nGGC + 4* nGTC)/(L-2)10. φp(x) represent a mapping function for a property 'P', where x ∈ Δ1 or x ∈ Δ2 or x ∈ Δ3 or x ∈ Δ4 depending upon given property. The feature value for a given sequence 'S' based on property 'P' is given by nx represents total number of times symbol x has appeared in S. And Δ ≡ Δ1 or Δ ≡ Δ2 or Δ ≡ Δ3 or Δ ≡ Δ4 depending up on the property 'P'.In this category we calculate 22 features. Let P' from which the features are calculated is as follow:The DNA sequence properties '1. A-philicity 2. Base-stacking 3. B-DNA twist 4. DNA bending stiffness 5. Di-nucleotide flexibility energy 6. DNA denaturation ,267. Duplex stability disrupt energy8. Duplex stability free energy 9. Helical rise 10. Helical twist 11. Helical tilt 12. Helical roll 13. Helical shift 14. Helical slide 15. Propeller twist 16. Protein induced deformability 17. Protein-DNA twist 18. Z-DNA stabilizing energy 19. Tri-nucleotide bendability 20. Nucleosome position preference 21. Tetra-nucleotide flexibility  and22. EIIP In this category we calculate 7 features. The feature values are calculated from CAGE tags, RNA Pol II Chip-seq and H3K4me3 Chip-seq data sets. The CAGE tags were downloaded from FANTOM4 project . RNA Pol1. Average Tag count per million (TPM)2. Maximum TPM3. Maximum TPM/average TPMet. al. [The performance of the fitted models and other best performing promoter prediction programs, which are publicly available for download to run on whole chromosomes on a local computer, was tested on an independent un-seen data set. When a predicted promoter overlaps with Pol-II enriched promoter, then the respective record is counted as true positive (TP). And when such case is missed it is termed as false negative (FN). When a predicted promoter overlaps with Pol-II enriched non-promoter, then such case is counted as false positive (FP). And if such case is predicted as non-promoter then it is termed as true negative (TN). The performance of classifier is evaluated based on the promoter prediction metrics suggested by Bajic et. al. : sensitith of the dataset and tested on the remaining 1/4th of the dataset. The performance measures obtained by using 10-fold cross-validation and independent test set are presented in Tables For selecting the best performing classifier, we trained four different ensemble and meta classification models on 3/4While the classification methods used here are considered "black box" methods, with no interpretable classification model, the methods still provide useful information, such as variable importance. One of the measures of variable importance in Random Forest method is the mean decrease in accuracy, calculated using the out-of-bag sample. The difference between the prediction accuracy on the untouched out-of-bag sample and that on the out-of-bag sample permuted on one predictor variable is averaged over all trees in the forest and normalized by the standard error. This gives the mean decrease in accuracy of that particular predictor variable which has been permuted. Thus, the importance of the predictor variables can be ranked by their mean decrease in accuracy. Figure We compared our algorithm with some of the existing best performing promoter prediction methods : EP3 1919, EponiAlthough extensive promoter annotations are available from the EBI and UCSC genome servers, most annotations do not contain information about tissue or cell-type information from experimental data. To demonstrate the efficacy of our new algorithm in finding promoters and to provide the annotation of potential novel promoters, we used the Pol-II enrichment peaks obtained from seven ChIP-seq datasets available on in vitro adipocyte differentiation of mouse 3T3-L1 cells and mouse ES cells, and the results are presented in Table We will use this program to annotate human gene Pol-II promoters by running on all the publicly available ChIP-seq Pol-II enrichment profiles. Our method successfully predicts 500 bp promoter regions (-300 bp to + 200 bp) and to better localize the core-promoter regions within the predicted promoters, we will apply recently developed CoreBoost_HM program published by Zhang Laboratory at CSHL [Chromatin modification and transcription factor binding profiles in the mammalian genomes is rapidly accumulating with the advent of next generation sequencing approaches. However, computational methods to effectively integrate these profiles to identify and annotate the promoter usage in specific cell/tissue types or developmental stages, are still limited. Recently, machine learning strategies have been applied to combine some of the wealth of published ChIP-seq data sets, such as chromatin modification signatures, to predict core promoter regions . A logicIn conclusion, we have developed a novel algorithm based on Bagging and Random Forest based classification methods to predict Pol-II bound promoters from ChIP-seq profiles. The present algorithm will help the discovery of novel promoters and ongoing annotation of alternative promoters of human and mouse genes from different ChIP-seq experiments.Supplementary material is available at http://bioinfo.wistar.upenn.edu/promoterprediction/.The authors declare that they have no competing interests.RG designed the computational methods and performed the statistical analyses. PW performed some computational analysis, AB and FP performed database design and updates to MPromDb website. SP performed the biological experiments. RD formulated and directed the design of the study. All authors read and approved the final manuscript.


Transcriptome dynamics is governed by two opposing processes, mRNA production and degradation. Recent studies found that changes in these processes are frequently coordinated and that the relationship between them shapes transcriptome kinetics. Specifically, when transcription changes are counter-acted with changes in mRNA stability, transient fast-relaxing transcriptome kinetics is observed. A possible molecular mechanism underlying such coordinated regulation might lay in two RNA polymerase (Pol II) subunits, Rpb4 and Rpb7, which are recruited to mRNAs during transcription and later affect their degradation in the cytoplasm. Here we used a yeast strain carrying a mutant Pol II which poorly recruits these subunits. We show that this mutant strain is impaired in its ability to modulate mRNA stability in response to stress. The normal negative coordinated regulation is lost in the mutant, resulting in abnormal transcriptome profiles both with respect to magnitude and kinetics of responses. These results reveal an important role for Pol II, in regulation of both mRNA synthesis and degradation, and also in coordinating between them. We propose a simple model for production-degradation coupling that accounts for our observations. The model shows how a simple manipulation of the rates of co-transcriptional mRNA imprinting by Pol II may govern genome-wide transcriptome kinetics in response to environmental changes. Organisms alter genes expression programs in response to changes in their environment. Such programs can specify fast induction, slow relaxation, oscillations, etc. Conceivably these kinetic outputs may depend on proper orchestration of the various phases of gene expression, including transcription, translation, and mRNA decay. In particular, in the transcriptomes of a broad range of species, fast mRNA “spikes” appear to result from surprisingly “pressing the gas and the brakes” together, i.e. by activating both transcription and degradation of same transcripts. A recently discovered molecular mechanism, in which subunits of RNA polymerase II (Pol II) associate to mRNAs during transcription and control their decay, could explain how such transcription-decay counter-action works. Yet, how such potential coupling responds to physiological conditions and how it shapes transcriptome kinetics remain unknown. Here we used a minimalist mutation in yeast RNA Pol II that is defective in the above mechanism in order to show that Pol II governs the ability of the cell to modulate mRNA decay in stress and, most importantly, that Pol II is essential for appropriate coupling between mRNA production and degradation. We further show that this transcription-decay coupling is responsible for shaping the transcriptome kinetic profiles under changing environmental conditions. The dynamics of the transcriptome in response to environmental changes is chiefly governed by two opposing processes – RNA production, namely transcription, and RNA degradation. Despite this fact, most of the attention has been given to the study of transcription. Recently genome-wide techniques have been established that allow to measure separately the contribution of mRNA degradation Saccharomyces cerevisiaeSaccharomyces pombeYet the emerging picture from many of these studies is that in addition to heavy regulation on both levels of production and degradation, there is often a correlation between the regulation of the two levels. In particular several studies have shown a “counter-action” mode of coupling between the two levels of control. In this mode of coupling genes that are induced at a given situation undergo, somewhat surprisingly, de-stabilization. The outcome of such type of coupling appears to be a fast transient change in mRNA abundance. This notion was demonstrated in the yeast S. cerevisiae. It was recently shown that, in addition to their genome wide role in transcription in vitro studies, using proteins extracted from human cells, demonstrated that hsRpb7p interacts with the transcript as it emerges from Pol II A major question is thus – is there a molecular mechanism in cells which ensures the coupling between transcription and mRNA decay. A potential mechanistic basis for such coupling can be suggested based on detailed biochemical analysis of individual genes in the yeast Here we used this minimalist perturbation to reveal a role of Pol II in affecting mRNA degradation and coordinating this process with transcription on a genome wide level. For that we compared gene expression, and decay rates in optimal growth conditions and in stress in the rpb6 mutant and a wild-type strain that is otherwise 100% identical to the mutant throughout the genome. We found that the rpb6 mutant is compromised in its ability to module mRNA decay rates, in addition to impaired transcription, in response to stress. As a result, while the wild-type features a counter-action coupling between production and degradation of mRNA, in the mutant this coupling is lost. This loss of negative correlation between changes in mRNA abundance to changes in stability in response to stress, results in an impaired temporal mRNA abundance response both in magnitude and kinetics. We thus conclude that in addition to its prime role in transcription, Pol II, also affects mRNA decay genome-wide, in a way that provides coupling of the two processes. This coupling appears to shape the kinetics of the transcriptome in response to stress. We propose a simple model which explains this coupling and its effect on the transcriptome.Q100R mutant previously described Q100R mutant strain we applied oxidative stress to two cultures, one for a conventional mRNA abundance profiling and one in which transcription was inhibited using 1,10-phenanthroline To check whether Rpb4/7 has a role in the previously observed counter-action coupling of mRNA production and degradation in stress In another stress condition, the DNA-damaging drug MMS, which we have tuned before to invoke a qualitatively different transcriptome kinetics compared to the response to the current oxidative stress We computed for each gene in each strain the mRNA abundance maximal fold change in response to stress, a measure that can be affected both by changes in transcription and degradation. In parallel, to isolate response to stress at the degradation level, we characterize each gene in each strain by the ratio between its half-life in oxidative stress to its half-life in the reference condition. These results are an indication that the counter-action mode of coupling may indeed require coordination between transcription and degradation and they further suggest that recruitment of Rpb4/7 to Pol II is important for the coupling mechanism.We suggested earlier that the counter-action mode of coupling between production and degradation may be responsible for the spiked and fast relaxing dynamics on both induced and repressed genes in response to oxidative stress To show the effect of the mutation on the entire genome and on different groups of genes we clustered concatenated wild-type and mutant mRNA abundance profiles for the stress-responsive genes see . As showIt is important to mention, that actually the fastest responding and relaxing induced genes show the least degree of change due to the mutation  in the two strains where normalized using spiked-in RNA internal standard. A unique normalization procedure is required here because standard microarray normalization methods assume a constant global distribution of intensity values between samples preserving ranking differences between genes but eliminating global differences see . While gWe next asked how the difference in steady state mRNA abundance relates to differences in stability by plotting the two parameters against each other. We thus conclude from this plot that the genes which are most affected by the mutation at optimal conditions are the genes which show a decrease in mRNA abundance coupled with stabilization. We find that those genes are highly enriched with ribosome biogenesis GO functionalities and other specific functional groups , in linePrevious studies of Rbp4/7 proposed for the first time a biochemical mechanism in which Pol II affects mRNA degradation by imprinting mRNA transcripts with two of the polymerase subunits that escort mRNAs to the cytoplasm To explain how Rpb4/7 has a genome-wide effect on mRNA stability, and in opposite directions for induced and repressed genes, we propose a simple molecular model, described schematically in Now let us assume that in stress the probability of imprinting by Rpb4/7 is increased (e.g. p = 0.8) per individual transcript molecule. This can be easily achieved by increasing the concentration of any cofactor contributing to Rpb4/7 imprinting. Following this change, genes which are induced due to the stress will quickly increase the percentage of imprinted mRNA molecules, thus gaining a higher affinity to general decay machinery which will result in faster decay for these genes  . Two types of experiments were conducted: experiments measuring mRNA abundance and experiments measuring mRNA decay. Both types of experiments were carried out for both strains  (Frutarom LTD.), or 0.1% MMS (Sigma-Aldrich) (data not shown). Survival experiments were also performed resulting in similar survival rates for both strains. For mRNA abundance measurements, aliquots (15 ml) were removed following each treatment, in the following time points: 0, 5, 10, 15, 25, 35 and 45 minutes and frozen in liquid nitrogen. RNA was extracted using MasterPure (EPICENTER Biotechnologies). The quality of the RNA was assessed using the BIOANALYZER 2100 platform (AGILENT); the samples were then processed and hybridized to Affymetrix yeast 2.0 microarrays using the Affymetrix GeneChip system according to manufacture instructions. For measuring mRNA decay a similar protocol was applied albeit with the following modifications: transcription was inhibited by adding 1,10-phenanthroline (before each experiment we prepared a fresh stock of 40 mg/ml in ethanol which was then diluted to a working concentration of 100 ug/ml) to the media 7 minutes following the addition of Hydrogen peroxide. Then aliquots were removed in the following time points: 0, 5, 10, 20, 30, 40 and 50 and were processed and hybridized as previously described. In addition we added a complete biological replicate for all mutant measurements, including half-lives in reference and oxidative stress and mRNA abundance changes in response to oxidative stress. The replicate reproduces well the presented results . The qualitative results presented remained robust to different response and R-square cutoffs.For clustering the responsive mRNA abundance profiles the wild type and mutant profiles were concatenated and clustered. Distance matrix between genes was calculated using spearman rank correlation which was followed by average linkage clustering. Separation to the three clusters was done by eye using the presented dendrogram (MATLAB implementation).All data is available at the GEO public data base under accession GSE26829.Standard mRNA abundance preprocessing algorithms assume a constant global distribution of mRNA levels across samples, thus any global differences between samples, e.g. an increase in the genome mean mRNA level, would not be detected. In order to compare basal mRNA abundance levels between the two strains, we normalized two zero time points, of the reference decay time courses, using spiked in RNA which followed the standard RMA normalization. We used the same normalization method described previously to normalize decay experimentsFigure S1Standard deviation normalized mRNA abundance profiles of cluster 1. mRNA abundance profiles from cluster 1 of (TIF)Click here for additional data file.Figure S2Biological replicate for (TIF)Click here for additional data file.Figure S3Repetition of (TIF)Click here for additional data file.Figure S4Repetition of (TIF)Click here for additional data file.Figure S5Mutant half-life comparisons between repeats. Correlation between the half-live measurements between the two mutant biological replicates.(TIF)Click here for additional data file.Table S1GO enrichment analysis for clusters in (XLS)Click here for additional data file.


B3galtl gene is subject to transcriptional silencing via IAP-induced heterochromatin. Hence, although rare, IAP-induced local heterochromatin spreading into nearby genes may influence expression and, in turn, host fitness.The “arms race” relationship between transposable elements (TEs) and their host has promoted a series of epigenetic silencing mechanisms directed against TEs. Retrotransposons, a class of TEs, are often located in repressed regions and are thought to induce heterochromatin formation and spreading. However, direct evidence for TE–induced local heterochromatin in mammals is surprisingly scarce. To examine this phenomenon, we chose two mouse embryonic stem (ES) cell lines that possess insertionally polymorphic retrotransposons  at specific loci in one cell line but not the other. Employing ChIP-seq data for these cell lines, we show that IAP elements robustly induce H3K9me3 and H4K20me3 marks in flanking genomic DNA. In contrast, such heterochromatin is not induced by LINE copies and only by a minority of polymorphic ETn/MusD copies. DNA methylation is independent of the presence of IAP copies, since it is present in flanking regions of both full and empty sites. Finally, such spreading into genes appears to be rare, since the transcriptional start sites of very few genes are less than one Kb from an IAP. However, the  Transposable elements (TEs) are often thought to be harmful because of their potential to spread heterochromatin (repressive chromatin) into nearby sequences. However, there are few examples of spreading of heterochromatin caused by TEs, even though they are often found within repressive chromatin. We exploited natural variation in TE integrations to study heterochromatin induction. Specifically, we compared chromatin states of two mouse embryonic stem cell lines harboring polymorphic retrotransposons of three families, such that one line possesses a particular TE copy (full site) while the other does not (empty site). Nearly all IAP copies, a family of retroviral-like elements, are able to strongly induce repressive chromatin surrounding their insertion sites, with repressive histone modifications extending at least one kb from the IAP. This heterochromatin induction was not observed for the LINE family of non-viral retrotransposons and for only a minority of copies of the ETn/MusD retroviral-like family. We found only one gene that was partly silenced by IAP-induced chromatin. Therefore, while induction of repressive chromatin occurs after IAP insertion, measurable impacts on host gene expression are rare. Nonetheless, this phenomenon may play a role in rapid change in gene expression and therefore in host adaptive potential. Transposable elements (TEs) are major constituents of eukaryotic genomes and are important catalysts of evolution Intracisternal A-type Particle elements), long terminal repeats (LTR) retrotransposons ), are highly DNA methylated and the disruption of enzymes responsible for such methylation (DNA methyltransferases Dnmt1 and Dnmt3L) causes global derepression of IAP copies Arabidopsis thaliana, TEs are strictly silenced by DNA methylation, which is often guided by small RNAs Drosophila melanogaster, rasiRNAs and piRNAs  are responsible for the silencing of many TE copies The well-known arms-race between TEs and the host genome D. melanogaster mitotic chromosome analysis and the genome sequencing projects have also shown that TEs are abundant in heterochromatic regions Drosophila, HeT-A, TAHRE and TART elements are found in telomeres but never in other heterochromatic regions, such as centromeres or interspersed heterochromatin, suggesting therefore an inclination for telomere specific sequences and not heterochromatin TEs are therefore often observed in regions associated with repressive histone marks and hence trapped into local heterochromatin. The analysis of euchromatin/heterochromatin boundaries has shown that an increase in TE density is co-localized with heterochromatin delimitation Early transposons), is much more variable. We found that transcription of one gene is impacted by the spread of IAP-induced heterochromatin in ES cells, but these effects on genes are likely rare, as such insertions are likely subject to strong negative selection.While genome-wide studies on histone post-translational modifications have revealed the repressive chromatin environment of several TE types in mouse ES cells Long interspersed nuclear elements) We chose three different families of TEs for this study based on the data available on insertionally polymorphic copies, namely IAPs, ETn/MusD elements and LINEs  originating from different mouse backgrounds  Total average density of H3K9me3 was first calculated in regions flanking specific LINE, ETn/MusD and IAP copies present in both ES cell lines (common copies) . It is iTo determine if such tendency was a general phenomenon and not the result of only a few very enriched regions in TT2 cells (full site), we calculated the RPKM asymmetry  lacking H3K9me3 To better characterize the chromatin induced by IAPs, we chose five copies in neighborhoods devoid of genes and if possible other repeats in order to observe an unbiased environment far from potential selective pressures . We usedAs described above, silencing of TEs is often associated with DNA methylation. It is important to note that 70–80% of all CpGs in the mouse genome are methylated, half in repeats The presence of IAP copies is therefore not necessary for DNA methylation of the flanking regions analyzed. Also, together with many other reports Drosophila melanogaster as a phenomenon called “position effect variegation” (PEV) where a transgene may be silenced if near heterochromatin  where H3K9me3 was detected at the IAP copy but also at the associated gene promoter. We required the gene promoter/TSS near the empty site not to be enriched in H3K9me3. Using RNA-seq data for both ES cell lines B3galtl, a beta 1,3-galactosyltransferase-like gene, is differentially expressed in these two ES cell lines and this gene appears to be affected by heterochromatin spreading according to the criteria outlined above  but not in either allele of TT2 cells (C57BL/6 x CBA F1 hybrid) (The  hybrid) . We stud hybrid) . Further hybrid) . There i hybrid) . In bothB3Galtl, we wondered if any expression differences could be observed between both strains as suggested by the RNA-seq data. Indeed, the presence of the IAP insertion is associated with a decrease in the RNA expression of B3galtl , 20 mM HEPES, 0.1 mM nonessential amino acids, 0.1 mM 2-mercaptoethanol, 100 units/ml penicillin, 0.05 mM streptomycin, leukemia inhibitory factor (LIF) and 2mM glutamine on gelatinized plates.Common and polymorphic copies of ETn/MusD and IAP were obtained from our previous analysis of different strains of mouse Methods and details on H3K9me3 ChIP-seq can be found in Karimi et al. iix following the manufacturer's recommended protocol . The resulting sequence reads were aligned using BWA v0.5.7 MeDIP-seq libraries were constructed as described in Harris et al. To compare total average density of H3K9me3 between two cell lines we used only full-length elements including flanking LTRs. For example out of 7,666 IAPE elements annotated in UCSC (mm9), only 1,318 (945 common and 373 polymorphic) satisfied the length selection criteria  and were used in the analysis. Using the strand information, for every copy we identified the 5′ and 3′ 5 kb flanking regions and calculated coverage profiles in these regions for BWA aligned H3K9me3 reads directionally extended by 150 bp. To calculate total average density we agglomerated 5′ profiles and 3′ profiles for all elements for a given family and normalized them by 1) total number of copies in the family and 2) relative number of aligned reads in the library between TT2 and J1 cell lines. Reads were filtered by the BWA alignment quality (QC> = 7). If there were more than one sequenced read aligned to the same location, it was considered only once. Reads mapped to multiple locations were ignored.Reads per Kilobase per million mapped reads  were calculated using GraphPad Prism version 5.00 for Windows, GraphPad Software, San Diego California USA.To plot heat maps of H3K9me3, we used the same coverage profiles as described above. Color indicates coverage at every base in the flank of individual copies. Rows corresponding to individual IAP elements were sorted according to the total coverage of H3K9me3 in the 5′ flank of the copies present in TT2 in the descending order. Same ordering of elements was preserved in the heat map showing H3K9me3 in J1.http://genome.ucsc.eduTo evaluate a mappability of a given genomic region we averaged genomic mappability (CRG Alignability) profiles that we downloaded in a form of bedGraph from the UCSC browser  and disrupted using a p1000 tip and a syringe. Micrococcal nuclease digestion (150 U/ml) was performed for 7 min at 37°C and stopped with 10 mM EDTA. Correct digestion of the chromatin was verified by gel electrophoresis. Further lysis of the digested chromatin was done by incubating it 1 hour on ice with 1 ml of hypotonic lysis buffer  and vortexing every 10 min. The digested chromatin was centrifuged at 3000g for 5 min and the supernatant was pre-cleared at 4°C for 2 hours in a rotating wheel with 100 µl of 50% solution of pre-blocked Protein A beads. Pre-cleared chromatin was aliquoted into 7 fractions (6 IP and 1 Input) and all IP fraction volumes were brought to 325 µl using IP Buffer . We used antibodies recognizing total Histone H3 , and histone modifications as H3K4me3 , H3K27me3 , H3K9me3 , H4K20me3  and also IgG (12–370) as a negative control. After 1 hour at 4°C in a rotating wheel we added 20 µl of 50% solution of pre-blocked Protein A beads and rotated at 4°C overnight. Washes were held the next day by adding 400 µl of washing buffer to the beads , rotating the samples for 3 min at 4°C and spinning at 4000 rpm. The amount of NaCl was increased in the second wash step (500 mM NaCl) and performed as described above. The INPUT and IP fractions were eluted with 100 µl of 100 mM NaHCO3 - 1% SDS. RNase treatment was performed for 2 hours at 68°C with gentle vortexing. The IP samples were spun at 4000 rpm for 2 min and the supernatant recovered (twice). All samples were purified with the Qiaquick PCR purification kit (Qiagen) and quantified by using the PicoGreen system from Invitrogen. 0.05 ng/µl of ChIP material was analyzed in technical duplicates through quantitative PCR (Fast SYBR Green Master Mix from Applied Biosystems) by comparing the amplification of Input DNA relative to immunoprecipitated DNA (IP) using the formula “Efficiency of primers∧(CtInput – CtIP)” where the efficiency is calculated through serial dilutions of Input DNA . ChIP enrichment for each specific antibody was tested with control regions for each antibody used for both ES cells ; chosen as the most stable genes out of 6 reference genes tested using the GeNorm method Total RNA was extracted  with the All Prep DNA/RNA mini kit from Qiagen. RNA was treated with the Turbo DNA-free kit from Ambion in order to remove DNA. One µg of total RNA extracts was reverse transcribed with SuperScript II reverse transcriptase system (Invitrogen). We synthesized two different cDNAs : a control reaction with no reverse transcriptase to test DNA contamination, and a pool of total cDNA synthesized with random primers. The cDNA samples were diluted 10 fold, and PCR was carried out using Fast SYBR Green Master Mix (Applied Biosystems) using specific primers for each gene analyzed. Primers were chosen surrounding introns in order to amplify 100-250 bp fragments of cDNA. Quantitative PCR cycling conditions were 20 s at 95°C (1 cycle), and then 3 s at 95°C, followed by 30 s at 60°C (45cycles). Reactions were done in duplicate, and standard curves were calculated from serial dilutions of cDNA. The quantity of the transcripts was estimated relative to the expression of For determining allele-specific expression, RNA extraction and cDNA synthesis was carried as described above. Allelic expression was determined with the PeakPicker software Western blot was done with biological triplicates. Cells were washed in PBS and solubilised in RIPA buffer  for 20 min at 4°C. For performing Western blot analysis, total cell extracts (40 µg/lane) were size-fractionated by NuPAGE 4-12% Bis-Tris Gel (Invitrogen) and transferred to polyvinylidene difluoride membranes. The membranes were blocked for one hour and then incubated with first antibodies against B3GALTL (Santa Cruz sc-67610) and ACTIN (Sigma A2066) overnight. After washing, blots were incubated with the secondary antibodies for one hour (1:8000 Peroxidase rabbit anti-goat (Sigma A5420) and 1∶10000 goat anti-rabbit (Sigma A0545), and specifically bound antibodies were detected with the ECL (Thermo Scientific). The films were then scanned and the quantification of B3GALTL protein was performed using Adobe Photoshop  with three biological replicates as previously described Figure S1Distribution of common and polymorphic copies of IAP and ETn/MusD families and of repeat masker annotated L1Md copies. A. Ensembl view of all common and polymorphic copies. Each arrow represents an insertion, but since the number of TE copies is high, some loci show superposed arrows. B. Distance to genes for IAPs and ETn/MusD. The distance was grouped as in (PDF)Click here for additional data file.Figure S2Chromatin immunoprecipitation and sequencing chart protocol.(TIF)Click here for additional data file.Figure S3A. RNA-seq and H3K9me3-seq enrichment per copy on ERV and LINE families studied. Asterisks denote the qualitative strength of H3K9me3 spreading as in (PDF)Click here for additional data file.Figure S4H3K9me3 RPKM of flanking regions for J1 and TT2 cell lines. Black lines represent equal distribution of J1 and TT2 H3K9me3 RPKM (y = x).(PDF)Click here for additional data file.Figure S5RPKM asymmetry of three random sets of 69 IAP copies. Skewing towards higher H3K9me3 in full sites is still observed with IAP copies even when fewer copies are analyzed.(PDF)Click here for additional data file.Figure S6H3K9me3 RPKM asymmetry for full-length and solo-LTR ETn/MusD copies. Full-length copies were chosen empirically larger than 4 Kb. Solo-LTRs were chosen as being ±20% of a typical solo-LTR size (320 bp). The percentage of LTR identity was calculated by retrieving the LTR sequences with a typical LTR probe and aligning both LTRs of the same full-length copy with blast2 (PDF)Click here for additional data file.Figure S7Genome browser view of the five IAP copies chosen to characterize IAP-induced heterochromatin.(PDF)Click here for additional data file.Figure S8H3K9me3 RPKM and ChIP for the five copies analyzed in both ES cell lines. TT2 Left and TT2 Right correspond to the upstream and downstream adjacent regions to the IAP insertion.(PDF)Click here for additional data file.Figure S9H4K20me3 enrichment of the five IAP copies studied in (TIF)Click here for additional data file.Figure S10ChIP-qPCR positive and negative controls. H4K20me3 region was chosen with the Mikkelsen et al. data set (PDF)Click here for additional data file.Figure S11Bisulfite sequencing of full and empty sites of five IAP copies. Statistical analysis of methylation of flanking regions is shown. A star illustrates a significant increase in DNA methylation in the region analyzed.(PDF)Click here for additional data file.Figure S12Heatmap of H3K9me3 spreading in the 3′ flanking sequences of both common and polymorphic copies (for the 5′ region see (PDF)Click here for additional data file.Figure S13Genome Browser view of IAP copies showing H3K9me3 spreading for several kb.(PDF)Click here for additional data file.Figure S14B3galtl. The polymorphic ERV track shows a black bar for the IAP insertion in the 129 (J1) genome. H3K9me3 ChIP-seq data represented is from Karimi et al. M. castaneus F1; male). The IAP insertion is present in the 129 allele but is absent from the M. castaneus allele. Hence, enrichment observed for histone marks obtained in these hybrid ES cells should be viewed with caution.UCSC Genome Browser view of (PDF)Click here for additional data file.Figure S15B3galtl locus.Bisulfite analysis of DNA from hybrid B6/129 ES cells of the (PDF)Click here for additional data file.Figure S16Western blot on three biological replicates of TT2 and J1 cell lines with anti- B3GALTL and -ACTIN.(PDF)Click here for additional data file.Table S1Primers for ChIP-qPCR and RT-qPCR.(PDF)Click here for additional data file.Text S1ETn/MusD elements are variably regulated by epigenetic mechanisms. References cited are (PDF)Click here for additional data file.Text S2New putative IAP copies in ES cell lines. References cited are (PDF)Click here for additional data file.


Escherichia coli have shown that negative autoregulation confers both rapid response times and reduced intrinsic noise, which is reflected in the fact that almost half of Escherichia coli transcription factors are negatively autoregulated. However, negative autoregulation is rare amongst the transcription factors of Saccharomyces cerevisiae. This difference is surprising because E. coli and S. cerevisiae otherwise have similar profiles of network motifs. In this study we investigate regulatory interactions amongst the transcription factors of Drosophila melanogaster and humans, and show that they have a similar dearth of negative autoregulation to that seen in S. cerevisiae. We then present a model demonstrating that this stiking difference in the noise reduction strategies used amongst species can be explained by constraints on the evolution of negative autoregulation in diploids. We show that regulatory interactions between pairs of homologous genes within the same cell can lead to under-dominance — mutations which result in stronger autoregulation, and decrease noise in homozygotes, paradoxically can cause increased noise in heterozygotes. This severely limits a diploid's ability to evolve negative autoregulation as a noise reduction mechanism. Our work offers a simple and general explanation for a previously unexplained difference between the regulatory architectures of E. coli and yeast, Drosophila and humans. It also demonstrates that the effects of diploidy in gene networks can have counter-intuitive consequences that may profoundly influence the course of evolution.Regulatory networks have evolved to allow gene expression to rapidly track changes in the environment as well as to buffer perturbations and maintain cellular homeostasis in the absence of change. Theoretical work and empirical investigation in  E. coli but, we find, occurs much more rarely in S. cerevisiae, D. melanogaster and humans. Whilst there are a great many important differences in the genetic architectures of these organisms, they tend to share, with the exception of negative autoregulation, similar profiles of network motifs. This makes the discrepancy in the degree of negative autoregulation all the more striking, as it lacks any obvious explanation. Our study presents a potential explanation, by comparing the evolvability of negative autoregulation as a noise reduction mechanism in haploids and diploids. We show that, in diploids, mutations that increase the strength of negative autoregulation at one gene copy often increase overall noise in gene expression. This results in under-dominance, in which heterozygotes are less fit than homozygotes. The result is that the evolution of negative autoregulation in diploids is significantly constrained. We verify our results using a combination of detailed molecular simulations and evolutionary simulationsAll genes have to deal with intrinsic noise, and a variety of mechanisms have evolved to reduce it. One important mechanism of noise reduction for transcription factors is negative autoregulation, in which a gene product represses its own rate of transcription. Negative auotregulation occurs frequently in  E. coli have confirmed these predictions E. coli, close to 50% of transcription factors (82 out of 182) S. cerevisiae (3 out of 169) Negative autoregulation is a network motif in which a transcription factor inhibits its own expression. Theoretical work has shown that this type of regulation reduces intrinsic noise and quickens the response time to environmental perturbations D. melanogaster and humans and found a similar pattern to that observed in yeast: in D. melanogaster 3 out of 87 How can we account for this discrepancy? In order to answer this, we looked at the extent to which negative autoregulation is used in other species. We interrogated systematic datasets on the regulatory interactions amongst the known transcription factors of Previous theoretical work on the dynamics of gene expression under negative autoregulation has considered single genes and so is implicitly haploid We model negative autoregulation in a diploid using a set of ordinary differential equations that track changes in the mRNA and protein concentrations for each of a pair of alleles  constant background rate As in previous work In the absence of negative autoregulation . We use To study the evolution of negative autoregulation for homeostasis, we turn to stochastic simulations of negatively autoregulating genes, which allow us to assess the amount of intrinsic noise associated with gene expression. Previous work has shown that negative autoregulation can help maintain homeostasis in gene expression by reducing the amount of intrinsic noise in negatively autoregulating genes, compared to other genes tion see .We first compare the response times of two homogozyotes whose alleles are identical in every respect except for the dissociation constant. One homozygote carries two copies of a resident allele with dissociation constant e with , . This isThe results above depend on comparing homozygotes for alleles with different dissociation constants, Numerical analysis of se times , resultiTo better understand why under-dominance arises in this system, we calculated the eigenvalues associated with as(2)see  where  ium,  see . Eq. 2 sWe can understand why increasing the difference in allelic expression results in increased response time by considering the contribution of the individual alleles to the response time of the gene pair . The levUnder-dominance for response time occurs across a wide range of parameter values, but can be avoided if mutations have small effects on binding site strength . To deteTranscription factor binding sites in eukaryotes vary between Based on these properties, we performed simulations of the evolution of an autoregulatory binding site under selection for decreased response time. These took into account the empirical distribution of binding site length in model eukaryotes and the variation in contributions to binding strength ence see . The valThe results  confirm In order to investigate the evolution of negative autoregulation as a mecahnism to reduce intrinsic noise in diploids, we turned to stochastic simulations. Intrinsic noise in gene expression occurs because transcription and translation are inherently noisy processes: all genes experience constant fluctuations in their mRNA and protein levels. The greater intrinsic noise associated with a particular gene, the higher the variance in its expression level relative to the mean. Therefore, a natural way to characterise the amount of intrinsic noise associated with a gene is to measure the ratio of the variance to the mean expression level at equilibrium (known as the Fano factor) nsidered . TherefoTo test the generality of our findings, we also considered variation in other parameters see SI . We firse see SI . TherefoWe also considered variation in the rates of mRNA and protein degradation Click here for additional data file.Figure S2Increasing the Hill coefficient leads to slower response times unless binding strength is weak. The x-axis shows the binding strength in the resident allele, in units of (PDF)Click here for additional data file.Figure S3Changing the background rate of transcription does not substantially alter the impact of under-dominance on mutations of size (PDF)Click here for additional data file.Figure S4Changing degradation rates changes response times but does not allow autoregulation to escape under-dominance. The figure shows results for the response time of autoregulating genes, to return to (PDF)Click here for additional data file.Figure S5Invasibility of autoregulatory binding sites. The response time of mutant (left) homozygotes and (right) heterozygotes are shown. Different values of the binding strength of the resident allele, in units of (PDF)Click here for additional data file.Table S1Saccharomyces cerevisiaeAutoregulation in (PDF)Click here for additional data file.Table S2Autoregulation in Humans (PDF)Click here for additional data file.Table S3DrosophilaAutoregulation in (PDF)Click here for additional data file.Text S1The supporting information text describes the methods used in constructing (PDF)Click here for additional data file.


A link forming the last sentence of the "RNA-seq Data Analysis" section of the Materials and Methods was incorrectly inserted. The correct last sentence is: "Enrichment analysis was performed using the functional categories with a custom python skript (Fisher’s exact test) correcting for multiple testing using the FDR."In addition, a link was incorrectly inserted into the last sentence of the first paragraph of the "Differentially Expressed Genes Involved in Transcription and Replication" section of the Results and Discussion. The correct sentence is: "The ATP-dependent RNA helicase-encoding genes (rhlB), whose transcription was enhanced (1.9-fold) after temperature downshift, are known to be involved in the resolving of the cold-induced structuring of mRNA [27]."


Different histone post-translational modifications (PTMs) are crucial in the regulation of chromatin, including methylations of H3 at Lysine 4 by the MLL complex. A relevant issue is how this is causally correlated to the binding of specific transcription factors (TFs) in regulatory regions. NF-Y is a TF that regulates 30% of mammalian promoters containing the widespread CCAAT element. We and others established that the presence of H3K4me3 is dependent upon the binding of NF-Y. Here, we investigate the mechanisms of H3K4me3 deposition by NF-Y.We employed Chromatin Immunoprecipitation in cells in which Ash2L and NF-Y subunits were knocked down by RNAi, to monitor the presence of histones PTMs and components of the MLL complex. We performed gene expression profiling of Ash2L-knocked down cells and analyzed the regulated genes. We performed ChIPs in leukemic cells in which MLL1 is devoid of the methyltransferase domain and fused to the AF4 gene.in vivo. Analysis of the promoters of Ash2L regulated genes, identified by transcriptional profiling, suggests that a handful TF binding sites are moderately enriched, among which the CCAAT box. Finally, leukemic cells carrying the MLL-AF4 translocation show a decrease of H3K4me3, absence of Ash2L and increase in H3K79me2, while NF-Y binding was not significantly affected.Knock down of the Ash2L subunit of MLL leads to a decrease in global H3K4me3 with a concomitant increase in H3K79me2. Knock down of NF-Y subunits prevents promoter association of Ash2L, but not MLL1, nor WDR5, and H3K4me3 drops dramatically. Endogenous NF-Y and Ash2L specifically interact Three types of conclusions are reached: (i) H3K4 methylation is not absolutely required for NF-Y promoter association. (ii) NF-Y acts upstream of H3K4me3 deposition by recruiting Ash2L. (iii) There is a general cross-talk between H3K4me3 and H3K79me2 which is independent from the presence of MLL oncogenic fusions. Drosophila Trithorax, assembled in a complex that includes Menin, Ash2L, WDR5, RbBP5, DPY30 and HCFs loci in aggressive myeloid and lymphoid leukemias Histone post-translational modifications -PTMs- are marks of chromatin environments. Some of them are associated with accessible chromatin, others with heterochromatin, either constitutive or facultative +/+ and myc−/− cells- left the H3K4 pattern intact at target sites, whereas H4 acetylations were substantially ablated. Therefore, it was concluded that these marks are required for MYC binding to E boxes In general, a key question is how histone modifying complexes are selectively and timely recruited to promoters, and binding of sequence-specific transcription factors offers a convenient explanation To study the role of H3K4me3 in NF-Y promoter association, we knocked down Ash2L by siRNA in HCT116 cells: Removal of the NF-Y trimer from cell cycle promoters by an NF-YA dominant negative mutant led to a substantial decrease of H3K4 tri-methylation in vivo, we performed immunoprecipitations with NF-Y and Ash2L antibodies with HCT116 nuclear extracts, followed by Western blot analysis. in vivo, and that other subunits of the MLL complex are recruited independently from Ash2L.To ascertain whether there is a NF-Y-Ash2L interaction DNA and RNA Metabolism being dominant in the Biological Process and Molecular Function categories . Note that in MLL-AF10 regulated genes, this is compensated by higher levels of H3K79me2 A relevant question in chromatin studies is what determines the location of histone marks on genomes. The binding of TFs and cofactors to promoters are hallmarks of expression, by signalling to the Pol II machinery the appropriate positional coordinates. In general, it seems plausible that TFs are instrumental in determining the positions of specific histone PTMs. However, it is becoming increasingly clear that certain TFs do require a particular set of histone TFs to access regulatory regions: MYC binding, for example, happens only in sites in which H3K4 and H3K79 methylations are abundant The presence of the CCAAT box at the top of the list of the Ash2L regulated genes, together with few other TFBS with similar scores, is in line with the data. However, the relatively low statistical enrichments in TFBS analysis among Ash2L-regulated promoters indicate that the preference is not absolute, and many other TFs can recruit Ash2L, as indicated by genetic screenings for interactors of AP2, Mef2c, PAX7 and Tbx1 MLL is a complex and genetically heterogeneous disease in which MLL1 fusion proteins alter gene expression; this is, in part, due to the partners, some of which -AF4, AF9 and ENL- have transcription activation domains required for transformation de novo motifs discovery in leukemias pointed at three sites: the expected E2F-NF-Y duo and p53 via alteration of the local histone PTMs. Further biochemical and in vivo ChIP work is required to shed light on this hypothesis.In 23 acute lymphoblastic leukemias with MLL translocations, two signatures correlating with prognosis were found HCT116 cells were cultured in DMEM supplemented with 10% FCS, 1% penicillin and streptomycin, and L-glutamine. All transfections were carried out using Lipofectamine 2000 . The Ash2L siRNA was purchased by . Scrambled siRNA was used as a negative control . NF-YA and NF-YB shRNA vectors were purchased from Sigma; the control was a similar vector expressing shRNA against GFP.Total RNAs were extracted using an RNA-Easy kit . 1µg of each RNA was retrotranscribed . Normalization of the cDNAs were performed with GAPDH control. The RT-PCR primers used are listed in 2, 10 mM KCl, 0.5 mM DTT, 1.5 mM PMSF); Perchloric acid 0.2 M was added and cells were kept on ice for 30 minutes, followed by centrifugation for 10 minutes at 11000 g at 4°C; the supernatants were stocked at −80°C. 15 µg of nuclear and acid extracts were used in 12% SDS-PAGE. Proteins were transferred to nitrocellulose membranes and immunoblotted using the antibodies of interest. The protein-antibody complexes were detected using horseradish peroxidase-conjugated secondary antibodies  and the chemioluminescence system .Nuclear extracts were prepared according to standard procedures (20). Acid extracts were prepared collecting cells in 5–10 volumes of Lysis Buffer H . Immunoprecipitations were performed with ProtG-Sepharose  and 3µg of the following antibodies: NF-YB ; H3 (Abcam 1791); H4K4me3 ; H4K4me2 (Abcam 7766); H4K4me1 (Abcam 8895); H3K79me2 (Abcam 3594), Ash2L (Active Motif 39099). The MLL antibody was a kind gift of E. Canaani . The ChIP-PCR primers are listed in Figure S1List of Ash2L-regulated genes in HCT116 cells. Genes whose expression is altered upon Ash2L knock down.(DOC)Click here for additional data file.Figure S2Transcription Factor Binding Site (TFBS) analysis of the promoters  of the Ash2L-regulated genes derived from the profiling experiments.(DOC)Click here for additional data file.Figure S3List of primers used in q-RT-PCR and ChIPs.(PDF)Click here for additional data file.