- Research article
- Open Access
Responsiveness of genes to manipulation of transcription factors in ES cells is associated with histone modifications and tissue specificity
© Sharov et al; licensee BioMed Central Ltd. 2011
- Received: 27 September 2010
- Accepted: 9 February 2011
- Published: 9 February 2011
In addition to determining static states of gene expression (high vs. low), it is important to characterize their dynamic status. For example, genes with H3K27me3 chromatin marks are not only suppressed but also poised for activation. However, the responsiveness of genes to perturbations has never been studied systematically. To distinguish gene responses to specific factors from responsiveness in general, it is necessary to analyze gene expression profiles of cells responding to a large variety of disturbances, and such databases did not exist before.
We estimated the responsiveness of all genes in mouse ES cells using our recently published database on expression change after controlled induction of 53 transcription factors (TFs) and other genes. Responsive genes (N = 4746), which were readily upregulated or downregulated depending on the kind of perturbation, mostly have regulatory functions and a propensity to become tissue-specific upon differentiation. Tissue-specific expression was evaluated on the basis of published (GNF) and our new data for 15 organs and tissues. Non-responsive genes (N = 9562), which did not change their expression much following any perturbation, were enriched in housekeeping functions. We found that TF-responsiveness in ES cells is the best predictor known for tissue-specificity in gene expression. Among genes with CpG islands, high responsiveness is associated with H3K27me3 chromatin marks, and low responsiveness is associated with H3K36me3 chromatin, stronger tri-methylation of H3K4, binding of E2F1, and GABP binding motifs in promoters.
We thus propose the responsiveness of expression to perturbations as a new way to define the dynamic status of genes, which brings new insights into mechanisms of regulation of gene expression and tissue specificity.
- Transcription Start Site
- Responsive Gene
- Chromatin Modification
- Chromatin Mark
- Housekeeping Function
Gene expression is regulated by the interplay of various kinds of factors including transcription factors (TFs) that bind to DNAs in a sequence-specific manner, the chromatin structure [1–4], and the association of genes with the nuclear lamina/matrix [5, 6]. Many TFs directly switch gene expression on or off, whereas others factors may serve as constraints (e.g., by controlling the access of TFs to DNA). Thus, in addition to identifying static states of gene expression (e.g., high vs. low) it is important to characterize the dynamic status, which is the capacity to modify the level of expression. For example, genes with H3K27me3 chromatin marks at promoters are not only suppressed (static state) but also poised for activation (dynamic state) . Responsiveness of genes to perturbations is a dynamic property that was never studied systematically because such studies require the analysis of expression profiles of cells responding to a large variety of disturbances, and such databases did not exist before. The only comparable study used indiscriminately all data for a specific human array platform in the Gene Expression Omnibus (GEO) database, which included both perturbations of the same cell type as well as differences between various cell types, tissues, and organs . Thus, it did not distinguish between responsiveness of genes and their tissue-specific expression.
This paper examines the responsiveness of all genes in mouse ES cells estimated using our recently published database on gene expression changes after controlled induction of 53 transcription factors (TFs) and other genes . In this experiment, we established mouse ES cell lines in which individual transgenic TFs were induced by the removal of doxycycline. Each manipulated TF modifies the expression of many downstream target genes, including other TFs, which can in turn activate or repress genes even farther downstream. Thus, we can quantify responsiveness of not only direct targets of manipulated TFs, but also indirect (i.e., secondary, tertiary) target genes. We show that responsive genes mostly have regulatory functions and a propensity to become tissue-specific upon differentiation, whereas many non-responsive genes have housekeeping functions. To examine the relationship between the responsiveness and tissue-specificity of gene expression, we performed whole-genome expression profiling of 15 mouse adult organs/tissues. Responsiveness of genes in undifferentiated ES cells appears to be a better predictor for tissue-specific gene expression than other known markers (presence of a CpG island and TATA box). Among genes with CpG islands, responsiveness is strongly associated with their epigenetic marks (e.g., histone modifications such as H3K27me3 and H3K36me3), as well as with binding of certain TFs in promoters. These results suggest that TF-responsiveness can be used as a novel indicator of the dynamic status of genes.
Definition of TF-responsiveness
where x ij is the logratio of expression change of i-th gene after perturbation j. Term "responsiveness" is similar to "sensitivity", however the latter term is traditionally applied to a single type of perturbation (as in "sensitivity analysis"). Thus, here we use the term "responsiveness" to describe the combined sensitivity of gene expression to various perturbations.
Data sets used in the study
To analyze the tissue specificity of gene expression, we used two datasets of expression profiles. First, the "NIA Differentiated cells/tissues" set includes expression profiles of 15 mouse adult organs and tissues (new data: GEO accession number GSE19806), as well as published data on gene expression in trophectoderm stem (TS) cells, neural stem (NS) cells, placenta, several lines of fibroblasts, and newborn mice [10, 14]. The "NIA Differentiated cells/tissues" microarray data were newly generated for this work and submitted to the public database (GEO accession number GSE19806). Second, the "GNF Mouse tissues" set includes expression profiles of 51 tissues from the mouse Gene Atlas V2  after excluding expression profiles of oocytes, early embryos, and gonads, as they may include undifferentiated cells.
We also used TF binding data, which was inferred from published ChIP-seq data [9, 16, 17] as well as data on whole-genome chromatin modifications for H3K4me3, H3K27me3, H3K36me3 , and H3K9me3 . Relative expression of genes in ES cells was estimated using RNA-seq data  and transcript coordinates from the NIA Mouse Gene Index . See the Methods section for further detail.
Estimating the TF-responsiveness
Indicators of TF-responsiveness (U i , D i and B i ) were estimated for 18,544 non-redundant genes based on the "NIA ES bank, 53 genes" data set (Additional Files 2, 3). Although the microarray platform we used represented 25,030 non-redundant genes, we analyzed only those genes (N = 18,544) whose expression was determined with sufficient accuracy and whose transcription start sites (TSS) were known (see Methods for further detail). TF-responsiveness for upregulation (U i ) and downregulation (D i ) are positively correlated (r = 0.329 in the log-scale), which indicates that genes upregulated by overexpression of some TFs tend to be downregulated by overexpression of other TFs (Figure 1B). A heatmap also showed that many genes that were strongly upregulated following one perturbation were also strongly downregulated by another kind of perturbation (Figure 1A). The positive correlation between U i and D i was the basis for considering TF-responsiveness as a general dynamic state of a gene that can be applied to both upregulation and downregulation of its expression. To check if measurements of TF-responsiveness were reproducible, we analyzed an independent data set "NIA Other Perturbations." Estimates of TF-responsiveness, B i , from both data sets showed strong correlation (r = 0.614, in the log-scale), indicating a high level of reproducibility and independence from the perturbation type (Additional Files 4, 5).
As the spectrum for TF-responsiveness of genes was continuous, we split all the genes examined for expression profiling into three parts: responsive (top 25% of genes), borderline, and non-responsive (bottom 50% of genes) genes (Figure 1C). Because genes with low expression generally had higher levels of TF-responsiveness than highly-expressed genes, we used floating thresholds to separate these groups of genes (Figure 1C) (see details in Methods). All responsive genes had statistically significant responses to manipulation of TFs, based on ANOVA (FDR < 0.05). Further analysis is focused on the comparison of two extreme groups of genes: responsive (N = 4746) and non-responsive (N = 9562).
Because there is a possibility that genes with high expression show weak response to perturbations simply due to saturation or miscalibration of microarray signals, we tested the sensitivity of the microarray by serial dilution of mRNA . Our results showed that saturation was detected only in 0.2% of genes, and microarray signals were well-calibrated in the full dynamic range of gene expression levels. The difference in TF-responsiveness among genes cannot be attributed to differential mRNA stability because groups of responsive and non-responsive genes had similar distributions of mRNA decay rates (Additional File 6). Data on mRNA degradation was taken from our database (http://lgsun.grc.nia.nih.gov/mRNA) . The mean mRNA decay rate of responsive genes (0.1307 hr-1) was only 15.4% higher than that of non-responsive genes (0.1133 hr-1); this difference is too small to explain a 3-fold gap between averages of TF-responsiveness in these groups of genes (0.5224 vs. 0.1745).
Functional annotation of responsive and non-responsive genes
Gene Ontology (GO) categories over-represented in groups of responsive and non-responsive genes
Group of genes
Responsive genes with CpG islands
Nervous system devel.
Blood vessel devel.
Wnt receptor signaling
Non-responsive genes with CpG islands
Cell cycle phase
TF-responsiveness of genes in ES cells is correlated with tissue-specific expression upon cell differentiation
It is conceivable, however, that the association of TF-responsiveness with tissue-specificity may have resulted from a non-random selection of TFs used in our perturbation experiments. To address this issue, we repeated the analysis after dividing the data to 3 functional subsets of TFs according to their expression in ES cells and differentiated organs and cells, as well as information measure of tissue specificity: (1) ES cell-specific (Dnmt3b, Eed, Gadd45a, Id1, Klf4, Mybl2, Mycn, Nanog, Nr0b1, Nr5a2, Pou5f1, Sall4, Sox2, Tcea3, Whsc2, and Zscan4c), (2) tissue-specific (Ascl1, Ascl2, Cdx2, Dlx3, Eomes, Esx1, Gata3, Mef2c, Msc, Myod1, Otx2, Rhox6, Sfpi1, Sox9, and T), and (3) widely expressed (Aes, Atf3, Cbx8, Ctnnb1, Elf1, Etv3, Foxj2, Myc, Nr2f2, Nrip1, Rxra, Smad1, Smad4, Smad7, Stat3, Suz12, Tcf3, Tcf4, and Zfand3). These subsets of induced TFs yielded highly correlated values of TF-responsiveness (r > 0.7) (Additional File 10, A-C), which were strongly associated with tissue specificity of responding genes for all subsets of manipulated TFs (Additional File 10, D-F). Thus, the association between TF-responsiveness with tissue-specificity is stable and does not seem to be related to the function of manipulated TFs.
TF-responsiveness of genes in ES cells is correlated with histone modifications and binding of TFs
To examine the relationship between TF-responsiveness of genes and their known features, we analyzed available data on chromatin modifications and binding of various TFs to gene promoters in ES cells. First, we searched for possible overrepresented sequence motifs (defined de novo) in promoters of non-responsive and responsive genes using CisFinder  and identified GABP, NRF1, YY1, SREBP1, and MIT008 motifs for non-responsive genes (Figure 3A). Motif SREBP1 was described by  and MIT008 was over-represented in mammalian promoters , although the TF that binds to this motif remains unknown. The TATA box was over-represented in promoters of responsive genes with CpG islands. Next, we estimated the proportion of genes that carried specific histone modifications, binding of TFs based on published ChIP-seq data [7, 16, 18], and putative TF-binding motifs identified above. The most striking differences between non-responsive and responsive genes were observed for genes with CpG islands: responsive genes tended to bear the H3K27me3 and H3K9me3 chromatin marks, whereas non-responsive genes tended to have a H3K36me3 chromatin mark, binding of E2F1, ZFX, MYC, and MYCN within 300 bp from TSS, and binding motifs of GABP, NRF1, and YY1 in promoters (Figure 3B, Additional File 12). Among genes with no CpG islands, responsive genes were enriched in H3K4me3 chromatin marks and binding of several TFs to distal portions of promoters; however the effects of these factors were much weaker than for genes with CpG islands (Additional Files 12, 13).
In addition to qualitative categories of histone methylation, we examined quantitative "strength" of methylation as represented by the number of ChIP-seq tags within 1 Kb distance from TSS for H3K4me3 and 3 Kb distance from TSS for H3K27me3 based on data from . Among genes with CpG islands, H3K4me3 chromatin, and no qualitatively-assigned H3K27me3 peaks, responsive genes had weaker H3K4 tri-methylation levels and stronger residual H3K27 tri-methylation levels than non-responsive genes (Figure 3C, D; Additional File 14).
The strongest association of H3K27me3, H3K36me3, and H4K4me3 chromatin marks with the TF-responsiveness of genes with CpG islands prompted us to examine this relationship in greater detail. The proportion of genes with H3K27me3 marks, measured in groups of 100 genes with a similar level of TF-responsiveness, increased with increasing TF-responsiveness (Bi), whereas the proportion of genes with H3K36me3 marks and the strength of H3K4 tri-methylation decreased with increasing TF-responsiveness (Bi) (Figure 4C). This relationship remained the same even if TF-responsiveness of genes was measured by Ui or Di (Figure 4D,E), indicating that the association is not specific to either upregulated or downregulated genes. Similar relationships with chromatin marks were observed if responsiveness of genes was measured using the alternative data set "NIA Other Perturbations" (Additional File 16). Presence of the H3K27me3 chromatin marks among genes that were upregulated during differentiation of ES cells is consistent with the previous finding that these genes are silent in ES cells but poised for activation . However, as this chromatin mark was also overrepresented among downregulated genes (Figure 4E), many genes with H3K27me3 marks were not fully suppressed in ES cells and were poised for both upregulation and downregulation. Strong downregulation of 5 genes with H3K27me3 chromatin marks, selected on the basis of microarray data, was previously confirmed by PCR . Similarly, the presence of the H3K36me3 chromatin marks and strong tri-methylation of H3K4 among genes that were not upregulated following the induction of TFs is consistent with the notion that these genes are already active and thus cannot be activated further . However, this reasoning does not seem to explain why genes with H3K36me3 chromatin and strong tri-methylation of H3K4 had low TF-responsiveness for downregulation (Figure 4D).
Because the association of H3K27me3, H3K36me3, and H3K4me3 chromatin marks with TF-responsiveness of genes can also be mediated by their effects on expression level, we analyzed the relationship between TF-responsiveness and chromatin status within groups of genes with similar expression levels. For simplicity, we limited the analysis to genes with CpG islands because they had a strong correlation between TF-responsiveness and chromatin status. The analysis revealed that the proportion of genes with H3K27me3 histone marks was consistently higher among responsive genes than among non-responsive genes with the same expression level (Figure 4F). By contrast, the proportion of genes with the H3K36me3 histone mark as well as the strength of H3K4 tri-methylation was lower among responsive genes than among non-responsive genes with the same expression level, except genes with very low expression, which had no H3K36me3 histone marks at all (Figure 4G,H). Taken together, the data indicate that association of H3K27me3, H3K36me3, and H3K4me3 chromatin marks with TF-responsiveness of genes is a novel dynamic feature of chromatin modifications and is not reduced to epigenetic control of stable gene expression levels.
This study provides the assessment of the dynamic status of mammalian genes in ES cells by the analysis of their TF-responsiveness to manipulation of 50 TFs and 3 other genes. Comparison with an independent data set shows that measurements of TF-responsiveness are reproducible. The group of responsive genes, which are readily upregulated or downregulated depending on the kind of perturbation, appears to be enriched in regulatory functions. The group of non-responsive genes with steady expression levels unchanged after various perturbations is enriched in housekeeping functions. Responsive genes in ES cells tend to become tissue-specific upon terminal differentiation. The TF-responsiveness of genes in ES cells appears to be the best predictor of tissue-specificity, which can be used in combination with other predictors (e.g., TATA box and CpG islands). Tissue-specific genes are enriched not only in the group of genes with a TATA box and no CpG island, as was found before , but also among genes with CpG islands that have high TF-responsiveness in ES cells. This is consistent with the previous estimate that 40% of genes with CpG islands show tissue restricted expression [26–28].
TF-responsiveness of genes with CpG islands has a strong association with chromatin modifications and binding of certain TFs to promoters. The proportion of genes with H3K27me3 chromatin marks increases, whereas the proportion of genes with H3K36me3 chromatin marks as well as the strength of H3K4 tri-methylation decreases with increasing TF-responsiveness of genes. It is well known that H3K27me3 marks suppress gene expression, and H3K36me3 marks are indicators of genes with high expression [7, 29]. However, our finding shows that in addition to the effect on gene expression level, these chromatin modifications are associated with the TF-responsiveness of genes. Furthermore, we found that binding of several TFs (E2F1, ZFX, and MYCN) and the presence of TF binding motifs (NRF1, GABP, and YY1) in proximal regulatory regions are associated with low TF-responsiveness. Because these factors correlate negatively with the H3K27me3 chromatin mark , it is possible that they can control the type of chromatin modification (i.e., facilitate H3K36me3 and inhibit H3K27me3), and in this way indirectly reduce the TF-responsiveness of genes. However, linear regression shows that these TFs also have a direct negative effect on TF-responsiveness that is not mediated by chromatin modifications in ES cells.
How is gene TF-responsiveness formed and maintained in ES cells? Low TF-responsiveness can be caused by tightly closed chromatin, absence of TF binding sites, or missing cofactors of transcription regulation. Our finding that promoters of many low responsive genes are occupied by E2F1, ZFX, and MYCN, supports another possibility that they have a very stable transcription-initiation complex that occupies the promoter and prevents binding of other TFs. Alternatively, it is conceivable that low TF-responsiveness of genes can be maintained actively via a stabilizing effect of negative feedback: A slight increase of transcription caused by external perturbation could increase the methylation of H3K36 , which, in turn, increases deacetylation of histones and decreases the level of expression, closing the negative feedback loop. For example, the Eaf3 protein (subunit of the Rpd3S histone deacetylase complex) in yeast binds to H3K36me3 and H3K36me2, causing deacetylation of histones [31–33]. Although the main function of this effect is to suppress cryptic promoters in the coding region , it may also cause some decrease in the rate of normal transcription.
High TF-responsiveness of genes could be explained by the presence of TF binding sites in the promoter, de-condensed chromatin, and the presence of cofactors of transcriptional regulation. However, mechanisms that amplify the stimulating effect of TFs via positive feedback could also be conceived. For example, it is possible to consider H3K27me3 histone modification as a key player, as it is abundant among responsive genes with CpG islands and has the strongest association with TF-responsiveness (Figure 4A). Binding of TFs to promoters marked with H3K27me3 initiates the first round of transcription but subsequently removes the H3K27me3 histone mark because the elongating form of RNA polymerase II is known to associate with UTX demethylase . As the repressive chromatin domain shrinks, the rate of transcription increases, causing further reduction of H3K27me3 marks.
Because this is the first study of dynamic status of gene expression, many questions remain un-answered. It would be interesting to quantify the TF-responsiveness of genes in differentiated cells and check if the same factors are associated with responsive and non-responsive genes. After elucidating the mechanisms of TF-responsiveness, we could create conditions where certain genes would be effectively activated following specific treatments, or maintain stable expression levels in fluctuating environments. Finally, an understanding of dynamic gene expression profiles can help to reconstruct transcription regulatory networks because potential main nodes of this network are limited to the set of responsive genes.
Responsiveness of gene expression to perturbations is a new way to characterize the dynamic status of genes. Responsive genes mostly have regulatory functions and a tendency to become tissue-specific upon differentiation, whereas non-responsive genes are enriched in housekeeping functions. Responsive genes mostly have H3K27me3 chromatin marks at their promoters, and non-responsive genes are associated with H3K36me3 chromatin, stronger tri-methylation of H3K4, binding of E2F1, and GABP binding motifs in promoters.
TF-responsiveness was measured from the published data on the change of gene expression following induced overexpression of 50 TFs and 3 other genes . Expression of a transgene inserted in the ROSA26 locus was induced by doxycycline withdrawal (Dox-), whereas control cells were continuously cultured in a Dox+ condition. The effect of TF manipulation was measured by the logratio of gene expression (Dox-/Dox+) on the 2nd day after induction. We analyzed only those genes whose expression was determined with sufficient accuracy (so that 1.5-fold changes were statistically significant) and whose TSS were known. Thresholds of TF-responsiveness that separate responsive and non-responsive genes (Figure 1C) were estimated as 75-percentile and 50-percentile, respectively, for two groups of genes with log-expression (log10, RNA-seq) from 0.5 to 1.5 and from 2.5 to 3.5; then thresholds were linearly interpolated as a function of gene expression in ES cells.
The following organs from 30-week old male mice of C57BL/6 strain were used for gene expression profiling: brain cortex, cerebellum, eyes, skeletal muscle, heart, bone, liver, kidney, bladder, skin, visceral fat, lung, small intestine, large intestine, and stomach. Mouse husbandry and organ collection were approved by the Institutional Animal Care and Use Committee (ASP# 220-LG-2011). Although comparable data is available from the GNF database , the advantage of our data is that we used in-house designed microarrays that represent a large set of genes (N = 25030), and probes for many genes are more sensitive compared to arrays used in the GNF database. Mice were euthanized by cervical dislocation. Total RNA was isolated by TRIzol (Invitrogen). Cy3-CTP labeled sample targets were prepared with total RNA by Low RNA Input Fluorescent Linear Amplification Kit (Agilent). Cy5-CTP labeled reference target was produced from mixture of Stratagene Universal Mouse Reference RNA and MC1 cells RNA. Samples were collected in 2 replications taken from different animals.
To characterize the effect of inhibitors, which are known to support the pluripotent state of ES cells , we treated B6R(5) mouse ES cells (C57BL/6 strain) with FGFR inhibitor PD173074  (100 μM), MEK inhibitor PD98059  (25 μM), and GSK-3 inhibitor BIO  (2 μM) 24 hr after plating. Cells were grown without feeders on gelatin-coated 6-well plates at 100,000 cells/well (104 cells/cm2) in complete ES medium, which was changed daily. Inhibitors dissolved in DMSO were added 24 hr after plating and cells were harvested 48 hr after treatment (72 hr after plating). Control cells were treated with DMSO. RNA was extracted and processed as described. Gene expression data were analyzed using the NIA Array Analysis .
Whole-genome data on chromatin modifications H3K4me3, H3K27me3, and H3K36me3  were re-mapped to the latest mouse genome (mm9, NCBI/NIH) using the UCSC coordinate conversion tool (http://genome.ucsc.edu/cgi-bin/hgLiftOver). Tri-methylation of H3K4 and H3K27 was counted for a gene if methylation peaks identified using hidden Markov models and sliding windows was within 1 Kb from the TSS, whereas tri-methylation of H3K36 was counted along the entire transcript length. Genes with H3K9me3 marks with 5 Kb of TSS were taken from the reference . Strength of H3K4me3 and H3K27me3 methylation was assessed by the number of ChIP-seq tags within 1 and 3 Kb from TSS, respectively, based on the data from the reference . Expression levels were estimated using published RNA-seq data  and transcript coordinates from the NIA Mouse Gene Index, assembly mm9 , and expressed in log-transformed number of tags per 1 Kb transcript length. The RNA-seq method with random tags is better for comparing expression levels of different genes than microarrays because it does not have biases related to the position of the oligo and its sequence. Obtained gene expression values correlated well with microarray results (r = 0.71, Additional File 17). A few genes (N = 345) had no RNA-seq tags, possibly because tags may have been assigned to a different gene copy in the genome. The expression of these genes was interpolated from microarray data (i.e., gene expression in Dox+ conditions) using linear regression. To plot the relationship between TF-responsiveness and chromatin status, genes were ordered by increasing TF-responsiveness and split into sequential sets of 100 genes. We then estimated the average TF-responsiveness and the proportion of genes with a specific chromatin modification in each group of 100 genes.
The location of the main TSS for 17,412 non-redundant genes was taken from  and was identified using CisView  for 4,981 other genes. CpG islands were identified using CpGProD software  and attributed to genes if they were located within 1 Kb from the TSS. TF binding motifs over-represented in promoters of responsive and non-responsive genes were identified and annotated using CisFinder . We analyzed genes with and without CpG islands separately, and for each group of genes we analyzed 200-bp regions upstream of the TSS and 200-bp regions downstream of the TSS (4 pairs of comparisons in total). Promoters of all genes (from -500 to +500 bp) were searched for the occurrence of TF binding motifs using CisFinder assuming 5 false positive matches per 10 Kb of random sequence. Thresholds for the matching score were further adjusted to a minimum of 2-fold enrichment of motif abundance either in the group of non-responsive genes or responsive genes. In particular, all motif matches were separated into groups according to their orientation ("+" or "-") and position relative to TSS in 100-bp intervals (i.e., from -500 to -400; from -400 to -300,..., and from 400 to 500). The combination of these two criteria yielded 20 groups of motif matches, which were analyzed separately. If the over-representation ratio of motif matches in a specific group was >2 fold, then all matches were counted in this group. However, if the over-representation ratio was <2 fold, then the matching threshold was increased to achieve the 2-fold enrichment. If the 2-fold enrichment was not achieved after any increase of the threshold, then no matches were counted in that group. Because TATA box has a strictly defined location in the promoter, it was handled separately from other TF binding motifs. TATA box was identified using the degenerative pattern KAWWW starting from 40 to 20 bp upstream of the TSS .
We thank Bernard Binder for critical reading of the manuscript, Carole Stagg, Yong Qian, and Dawood Dudekula for providing technical help. This research was supported entirely by the Intramural Research Program of the NIH, National Institute on Aging.
- Hublitz P, Albert M, Peters AH: Mechanisms of transcriptional repression by histone lysine methylation. Int J Dev Biol. 2009, 53 (2-3): 335-354. 10.1387/ijdb.082717ph.PubMedView ArticleGoogle Scholar
- Khorasanizadeh S: The nucleosome: from genomic organization to genomic regulation. Cell. 2004, 116 (2): 259-272. 10.1016/S0092-8674(04)00044-3.PubMedView ArticleGoogle Scholar
- Kornberg RD, Lorch Y: Twenty-five years of the nucleosome, fundamental particle of the eukaryote chromosome. Cell. 1999, 98 (3): 285-294. 10.1016/S0092-8674(00)81958-3.PubMedView ArticleGoogle Scholar
- Narlikar GJ, Fan HY, Kingston RE: Cooperation between complexes that regulate chromatin structure and transcription. Cell. 2002, 108 (4): 475-487. 10.1016/S0092-8674(02)00654-2.PubMedView ArticleGoogle Scholar
- Guelen L, Pagie L, Brasset E, Meuleman W, Faza MB, Talhout W, Eussen BH, de Klein A, Wessels L, de Laat W, et al: Domain organization of human chromosomes revealed by mapping of nuclear lamina interactions. Nature. 2008, 453 (7197): 948-951. 10.1038/nature06947.PubMedView ArticleGoogle Scholar
- Wang TY, Han ZM, Chai YR, Zhang JH: A mini review of MAR-binding proteins. Mol Biol Rep. 2010, 37 (7): 3553-3560. 10.1007/s11033-010-0003-8.PubMedView ArticleGoogle Scholar
- Mikkelsen TS, Ku M, Jaffe DB, Issac B, Lieberman E, Giannoukos G, Alvarez P, Brockman W, Kim TK, Koche RP, et al: Genome-wide maps of chromatin state in pluripotent and lineage-committed cells. Nature. 2007, 448 (7153): 553-560. 10.1038/nature06008.PubMedPubMed CentralView ArticleGoogle Scholar
- Hao P, Zheng S, Ping J, Tu K, Gieger C, Wang-Sattler R, Zhong Y, Li Y: Human gene expression sensitivity according to large scale meta-analysis. BMC Bioinformatics. 2009, 10 (Suppl 1): S56-10.1186/1471-2105-10-S1-S56.PubMedPubMed CentralView ArticleGoogle Scholar
- Nishiyama A, Xin L, Sharov AA, Thomas M, Mowrer G, Meyers E, Piao Y, Mehta S, Yee S, Nakatake Y, et al: Uncovering early response of gene regulatory networks in ES cells by systematic induction of transcription factors. Cell Stem Cells. 2009, 5 (4): 420-433. 10.1016/j.stem.2009.07.012.View ArticleGoogle Scholar
- Aiba K, Nedorezov T, Piao Y, Nishiyama A, Matoba R, Sharova LV, Sharov AA, Yamanaka S, Niwa H, Ko MS: Defining developmental potency and cell lineage trajectories by expression profiling of differentiating mouse embryonic stem cells. DNA Res. 2009, 16 (1): 73-80. 10.1093/dnares/dsn035.PubMedView ArticleGoogle Scholar
- Masui S, Nakatake Y, Toyooka Y, Shimosato D, Yagi R, Takahashi K, Okochi H, Okuda A, Matoba R, Sharov AA, et al: Pluripotency governed by Sox2 via regulation of Oct3/4 expression in mouse embryonic stem cells. Nat Cell Biol. 2007, 9 (6): 625-635. 10.1038/ncb1589.PubMedView ArticleGoogle Scholar
- Matoba R, Niwa H, Masui S, Ohtsuka S, Carter MG, Sharov AA, Ko MS: Dissecting Oct3/4-regulated gene networks in embryonic stem cells by expression profiling. PLoS One. 2006, 1: e26-10.1371/journal.pone.0000026.PubMedPubMed CentralView ArticleGoogle Scholar
- Sharova LV, Sharov AA, Piao Y, Shaik N, Sullivan T, Stewart CL, Hogan BL, Ko MS: Global gene expression profiling reveals similarities and differences among mouse pluripotent stem cells of different origins and strains. Dev Biol. 2007, 307 (2): 446-459. 10.1016/j.ydbio.2007.05.004.PubMedPubMed CentralView ArticleGoogle Scholar
- Sharova LV, Sharov AA, Nedorezov T, Piao Y, Shaik N, Ko MS: Database for mRNA half-life of 19 977 genes obtained by DNA microarray analysis of pluripotent and differentiating mouse embryonic stem cells. DNA Res. 2009, 16 (1): 45-58. 10.1093/dnares/dsn030.PubMedView ArticleGoogle Scholar
- Su AI, Cooke MP, Ching KA, Hakak Y, Walker JR, Wiltshire T, Orth AP, Vega RG, Sapinoso LM, Moqrich A, et al: Large-scale analysis of the human and mouse transcriptomes. Proc Natl Acad Sci USA. 2002, 99 (7): 4465-4470. 10.1073/pnas.012025199.PubMedPubMed CentralView ArticleGoogle Scholar
- Chen X, Xu H, Yuan P, Fang F, Huss M, Vega VB, Wong E, Orlov YL, Zhang W, Jiang J, et al: Integration of external signaling pathways with the core transcriptional network in embryonic stem cells. Cell. 2008, 133 (6): 1106-1117. 10.1016/j.cell.2008.04.043.PubMedView ArticleGoogle Scholar
- Ho L, Jothi R, Ronan JL, Cui K, Zhao K, Crabtree GR: An embryonic stem cell chromatin remodeling complex, esBAF, is an essential component of the core pluripotency transcriptional network. Proc Natl Acad Sci USA. 2009, 106 (13): 5187-5191. 10.1073/pnas.0812888106.PubMedPubMed CentralView ArticleGoogle Scholar
- Bilodeau S, Kagey MH, Frampton GM, Rahl PB, Young RA: SetDB1 contributes to repression of genes encoding developmental regulators and maintenance of ES cell state. Genes Dev. 2009, 23 (21): 2484-2489. 10.1101/gad.1837309.PubMedPubMed CentralView ArticleGoogle Scholar
- Cloonan N, Forrest AR, Kolle G, Gardiner BB, Faulkner GJ, Brown MK, Taylor DF, Steptoe AL, Wani S, Bethel G, et al: Stem cell transcriptome profiling via massive-scale mRNA sequencing. Nat Methods. 2008, 5 (7): 613-619. 10.1038/nmeth.1223.PubMedView ArticleGoogle Scholar
- Sharov AA, Dudekula DB, Ko MS: Genome-wide assembly and analysis of alternative transcripts in mouse. Genome Res. 2005, 15 (5): 748-754. 10.1101/gr.3269805.PubMedPubMed CentralView ArticleGoogle Scholar
- Schug J, Schuller WP, Kappen C, Salbaum JM, Bucan M, Stoeckert CJ: Promoter features related to tissue specificity as measured by Shannon entropy. Genome Biol. 2005, 6 (4): R33-10.1186/gb-2005-6-4-r33.PubMedPubMed CentralView ArticleGoogle Scholar
- Barrera LO, Li Z, Smith AD, Arden KC, Cavenee WK, Zhang MQ, Green RD, Ren B: Genome-wide mapping and analysis of active promoters in mouse embryonic stem cells and adult organs. Genome Res. 2008, 18 (1): 46-59. 10.1101/gr.6654808.PubMedPubMed CentralView ArticleGoogle Scholar
- Sharov AA, Ko MSH: Exhaustive search for over-represented DNA sequence motifs with CisFinder. DNA Research. 2009, 16 (5): 261-273. 10.1093/dnares/dsp014.PubMedPubMed CentralView ArticleGoogle Scholar
- Seo YK, Chong HK, Infante AM, Im SS, Xie X, Osborne TF: Genome-wide analysis of SREBP-1 binding in mouse liver chromatin reveals a preference for promoter proximal binding to a new motif. Proc Natl Acad Sci USA. 2009, 106 (33): 13765-13769. 10.1073/pnas.0904246106.PubMedPubMed CentralView ArticleGoogle Scholar
- Xie X, Lu J, Kulbokas EJ, Golub TR, Mootha V, Lindblad-Toh K, Lander ES, Kellis M: Systematic discovery of regulatory motifs in human promoters and 3' UTRs by comparison of several mammals. Nature. 2005, 434 (7031): 338-345. 10.1038/nature03441.PubMedPubMed CentralView ArticleGoogle Scholar
- Illingworth RS, Bird AP: CpG islands--'a rough guide'. FEBS Lett. 2009, 583 (11): 1713-1720. 10.1016/j.febslet.2009.04.012.PubMedView ArticleGoogle Scholar
- Zhu J, He F, Hu S, Yu J: On the nature of human housekeeping genes. Trends Genet. 2008, 24 (10): 481-484. 10.1016/j.tig.2008.08.004.PubMedView ArticleGoogle Scholar
- Larsen F, Gundersen G, Lopez R, Prydz H: CpG islands as gene markers in the human genome. Genomics. 1992, 13 (4): 1095-1107. 10.1016/0888-7543(92)90024-M.PubMedView ArticleGoogle Scholar
- Ku M, Koche RP, Rheinbay E, Mendenhall EM, Endoh M, Mikkelsen TS, Presser A, Nusbaum C, Xie X, Chi AS, et al: Genomewide analysis of PRC1 and PRC2 occupancy identifies two classes of bivalent domains. PLoS Genet. 2008, 4 (10): e1000242-10.1371/journal.pgen.1000242.PubMedPubMed CentralView ArticleGoogle Scholar
- Li J, Moazed D, Gygi SP: Association of the histone methyltransferase Set2 with RNA polymerase II plays a role in transcription elongation. J Biol Chem. 2002, 277 (51): 49383-49388. 10.1074/jbc.M209294200.PubMedView ArticleGoogle Scholar
- Carrozza MJ, Li B, Florens L, Suganuma T, Swanson SK, Lee KK, Shia WJ, Anderson S, Yates J, Washburn MP, et al: Histone H3 methylation by Set2 directs deacetylation of coding regions by Rpd3S to suppress spurious intragenic transcription. Cell. 2005, 123 (4): 581-592. 10.1016/j.cell.2005.10.023.PubMedView ArticleGoogle Scholar
- Keogh MC, Kurdistani SK, Morris SA, Ahn SH, Podolny V, Collins SR, Schuldiner M, Chin K, Punna T, Thompson NJ, et al: Cotranscriptional set2 methylation of histone H3 lysine 36 recruits a repressive Rpd3 complex. Cell. 2005, 123 (4): 593-605. 10.1016/j.cell.2005.10.025.PubMedView ArticleGoogle Scholar
- Youdell ML, Kizer KO, Kisseleva-Romanova E, Fuchs SM, Duro E, Strahl BD, Mellor J: Roles for Ctk1 and Spt6 in regulating the different methylation states of histone H3 lysine 36. Mol Cell Biol. 2008, 28 (16): 4915-4926. 10.1128/MCB.00001-08.PubMedPubMed CentralView ArticleGoogle Scholar
- Smith ER, Lee MG, Winter B, Droz NM, Eissenberg JC, Shiekhattar R, Shilatifard A: Drosophila UTX is a histone H3 Lys27 demethylase that colocalizes with the elongating form of RNA polymerase II. Mol Cell Biol. 2008, 28 (3): 1041-1046. 10.1128/MCB.01504-07.PubMedView ArticleGoogle Scholar
- Carter MG, Hamatani T, Sharov AA, Carmack CE, Qian Y, Aiba K, Ko NT, Dudekula DB, Brzoska PM, Hwang SS, et al: In situ-synthesized novel microarray optimized for mouse stem cell and early developmental expression profiling. Genome Res. 2003, 13 (5): 1011-1021. 10.1101/gr.878903.PubMedPubMed CentralView ArticleGoogle Scholar
- Ying QL, Wray J, Nichols J, Batlle-Morera L, Doble B, Woodgett J, Cohen P, Smith A: The ground state of embryonic stem cell self-renewal. Nature. 2008, 453 (7194): 519-523. 10.1038/nature06968.PubMedView ArticleGoogle Scholar
- Mohammadi M, Froum S, Hamby JM, Schroeder MC, Panek RL, Lu GH, Eliseenkova AV, Green D, Schlessinger J, Hubbard SR: Crystal structure of an angiogenesis inhibitor bound to the FGF receptor tyrosine kinase domain. EMBO J. 1998, 17 (20): 5896-5904. 10.1093/emboj/17.20.5896.PubMedPubMed CentralView ArticleGoogle Scholar
- Pang L, Sawada T, Decker SJ, Saltiel AR: Inhibition of MAP kinase kinase blocks the differentiation of PC-12 cells induced by nerve growth factor. J Biol Chem. 1995, 270 (23): 13585-13588. 10.1074/jbc.270.23.13585.PubMedView ArticleGoogle Scholar
- Meijer L, Skaltsounis AL, Magiatis P, Polychronopoulos P, Knockaert M, Leost M, Ryan XP, Vonica CA, Brivanlou A, Dajani R, et al: GSK-3-selective inhibitors derived from Tyrian purple indirubins. Chem Biol. 2003, 10 (12): 1255-1266. 10.1016/j.chembiol.2003.11.010.PubMedView ArticleGoogle Scholar
- Sharov AA, Dudekula DB, Ko MS: A web-based tool for principal component and significance analysis of microarray data. Bioinformatics. 2005, 21 (10): 2548-2549. 10.1093/bioinformatics/bti343.PubMedView ArticleGoogle Scholar
- Sharov AA, Dudekula DB, Ko MS: CisView: a browser and database of cis-regulatory modules predicted in the mouse genome. DNA Res. 2006, 13 (3): 123-134. 10.1093/dnares/dsl005.PubMedView ArticleGoogle Scholar
- Ponger L, Mouchiroud D: CpGProD: identifying CpG islands associated with transcription start sites in large genomic mammalian sequences. Bioinformatics. 2002, 18 (4): 631-633. 10.1093/bioinformatics/18.4.631.PubMedView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.