Standard bioinformatics analysis of ChIP-seq data was performed by BGI (Shenzhen, Guangdong, China) includ- ing primary data filtering to remove adaptor sequences, contamination and low-quality reads from raw reads, read alignment, and genome-wide distribution of ChIP-seq reads (Table 1), general classification of all hits, GO func- tion analysis of peak-related genes, sequence analysis using Motif discovery tool (MEME) and Motif Alignment and Search Tool (MAST). Peaks were identified using peak calling approaches MACS (Model-based Analysis for ChIP-Seq) and SICER (spatial clustering approach for the identification of ChIP-enriched regions) [42–44]. Differen- tial binding peaks were obtained by software MAnorm as described . Functional classification of refined data for peak-related genes was performed using NCBI Gene re- source (Bethesda, MD, USA). All hit classification dia- grams were made with Excel software. Distribution of distances from gene peak reads to the nearest transcrip- tion start site (TSS) were defined according to the posi- tions of RefSeq TSSs using ChIPseek software . Genomic annotation of gene peak positions within inter- genic and genic regions was received using ChIPseek soft- ware. By default, promoter-TSS annotation is defined as the area from – 1 kb to + 100 bp of the TSS position and the transcription termination site area (TTS) is defined from – 100 bp to + 1 kb of the TTS position . DNA- binding site inference for DNA-interacting protein partner of WASp was assessed by applying TRANSFAC® software . Distribution of activating and repressive epigenetic marks and specific TF sites in selected candidate genes re- sulted from ChIP-seq data was examined using the UCSC Genome Browser tool suite. Intra-intronic positions of certain peak reads and distances from peak reads to near- est exon of genes selected from ChIP-seq data were identi- fied with UCSC Genome Browser tool suite.
point mutation of I74 in H2 on subcellular localization and distribution of overexpressed Myb2 (Fig. 7). The mutation to proline, which has the highest propensity to disrupt a helix (37), resulted in the accumulation of Myb2 in the cytoplasm as revealed by IFA (Fig. 7A) and the cytosolic fractions as re- vealed by Western blotting (Fig. 7B) and a more-altered helical content and ternary folding of Myb2 than the mutation to alanine (Fig. 7D and E), which has the lowest propensity to disrupt a helix. It is notable that with even a change in the structure as slight as that of I74A (Fig. 7D and E), nuclear translocation of Myb2 was substantially perturbed with an ap- parent accumulation of Myb2 near the nuclear membrane in a significant fraction of transfected cells (Fig. 7A), underscoring the importance of structural integrity of the NLD of Myb2. A major concern for the nuclear import of a small protein like Myb2 is its ability to freely diffuse into the nucleus and remain there through its binding to chromosomal DNA. Although structural changes due to mutation of I74 correlated with the changes in the DNA-binding activity, nuclear localization of Myb2 did not rely on its DNA-binding activity, as demon- strated by the critical role of K51 in the DNA binding, but not nuclear localization, of Myb2 (Fig. 8). Indeed, F52, K138, and N139, which are crucial for DNA-binding activity (19), had only a slight effect on the nuclear localization of Myb2 (un- published observations). It is conceivable that the structural aspect of Myb2 NLD may only provide itself a suitable con- formation to contact its interacting partner, be it a cargo pro- tein or a karyopherin-like protein. The binding affinity must be strengthened through interactions of specific amino acid resi- dues on the exposed surface of the NLD and its interacting import partner (4).
IkBa degradation and NF-kB nuclear translocation are ter- minal steps in NF-kB activation, which occur downstream of IKK activation. To determine whether digitization of the NF- kB cascade occurs at the level of or upstream of the IKK complex, we examined signaling events known to occur up- stream of IKK activation. Punctate and oligomeric killing or activating domains transducing signals (POLKADOTS) are cytoplasmic foci containing Bcl10, Malt1, and a number of additional NF-kB signaling molecules. These structures form post TCR stimulation in a manner highly correlated with successful signal transmission by Bcl10 and Malt1 (10, 11, 16). The concentration of stimulatory anti-CD3 Ab affected the percentage of responding cells, as higher anti-CD3 Ab concentrations resulted in an increased percentage of cells displaying POLKADOTS and nuclear RelA (Supplemental Fig. 3A, 3B). Indeed, at all tested concentrations of anti-CD3, 100% of the cells that formed POLKADOTS also had RelA nuclear translocation, and the distribution of RelA nuclear/ cytoplasmic intensities was remarkably similar at all three concentrations (Fig. 2C, Supplemental Fig. 3C).
Iron-inducible nuclear import of Myb3. The Myb3 level in the nuclear fraction was lower in cells cultivated under iron deple- tion than repletion (18). Similarly, a much lower level of HA- Myb3 was seen in nuclei of transfected cells depleted of iron than in those cultivated in normal medium (see Fig. 7). Upon iron repletion, the intensity of the nuclear signal increased contigu- ously to a much greater level within an ⬃ 15-min period (Fig. 6A), a phenomenon referred to as iron-inducible nuclear import. The signal intensity had substantially decreased at 22.5 min and was barely detectable at 30 to 60 min after iron repletion. To test whether this phenomenon is unique to Myb3, cells overexpressing 4HA-Myb2 (11) were concurrently examined, and the level of 4HA-Myb2 in nuclei remained unchanged. When iron-depleted cells were pretreated with LMB, a nuclear export inhibitor (Fig. 6B), the signal intensity of HA-Myb3 in nuclei also reached an optimal level at 15 min after iron repletion but changed little thereafter, suggesting that Myb3 that had been imported into nu- clei upon iron repletion was rapidly exported. To test whether iron also affected the nuclear translocation of endogenous Myb3, cell lysates from nontransfected cells were fractionated for Western blotting. In these experiments, Myb1 and Myb3 were enriched in the cytosolic fraction, with only minor amounts in the nuclear fraction, whereas Myb2 was slightly more enriched in the nuclear than in the cytosolic fraction in cells depleted of iron (Fig. 6C). Upon iron repletion for 15 min, the level of nuclear Myb3 in- creased, with a concomitant decrease in cytosolic Myb3. By 30 min, Myb3’s subcellular distribution had returned to the original level. In contrast, the subcellular distributions of Myb1 and Myb2 changed little upon iron repletion. Consistent with the transgenic studies (Fig. 6A and B), these observations suggest that iron spe- cifically induces the nuclear influx of Myb3. Similar results were observed in transfected cells overexpressing HA-Myb3 (see Fig. S2 in the supplemental material).
of the subunits p50 and p65 (RelA), although other complexes have been described (3, 4). In the inactive state the NF- k B dimer is present in the cytosol bound to an inhibitory protein, I k B (5, 6). Activation of NF- k B by a multitude of stimuli, in- cluding inflammatory cytokines, reactive oxygen intermedi- ates, and microorganisms requires the release of the inhibitor I k B from the dimeric complex (3–7). This is followed by an im- mediate translocation of activated NF- k B to the nucleus where the dimer interacts with regulatory k B elements in promoters and enhancers, thereby controlling gene transcription (3, 4, 7). Several lines of evidence indicate that NF- k B/Rel tran- scription factors may play an important role in atherosclerosis (8–11). Activation of NF- k B by inflammatory or proliferative stimuli has been demonstrated in cultures of monocyte/mac- rophages, endothelial cells, smooth muscle cells, and T cells us- ing electrophoretic mobility shift assays (4, 7, 11, 12). All of these cell types have been established as playing a key role in atherogenesis and display a distinct pattern of gene expression in this environment (10, 13). A variety of genes are induced in the atherosclerotic lesion that have been shown to be regu- lated by NF- k B proteins, including the genes encoding TNF- a (4, 10, 14, 15), IL-1 b (10, 16), macrophage colony-stimulating factor (M-CSF) (4, 17, 18), GM-CSF (4, 10), monocyte chemo- tactic protein-1 (4, 19), tissue factor (TF) (20–23), vascular cell adhesion molecule-1 (24–26), intercellular adhesion molecule-1 (ICAM-1) (4, 27) and c-myc (10, 28). Some of these gene prod- ucts such as TNF- a and IL-1 are also able to activate NF- k B in vitro (3, 4). Recently, it has been demonstrated in cell culture experiments that minimally oxidized LDL, a potential etio- logic agent for promoting lesion formation (8, 9) activates NF- k B in endothelial cells (29). Oxidized lipoproteins have also been shown to modulate the expression of several cytokines, growth factors, and LPS-induced molecules which are regu- lated by NF- k B transcriptional proteins (30–35).
The dopaminergic system has been consistently implicated in the regulation of sexual behavior, and studies have looked into the involvement of related monoamines in teleost sex- change (Larson et al., 2003a; Larson et al., 2003b). Additional works have mapped sexually dimorphic neuropeptides and monoamines linked to sexual behavior, including arginine vasotocin (AVT) (Propper et al., 1992; Jurkevich et al., 1995; Godwin et al., 2000), tyrosine hydroxylase (TH) (Meek et al., 1989; Ball et al., 1995; Rink and Wullimann, 2001; Marsh et al., 2006), and the estrogen biosynthesis enzyme aromatase (Marsh et al., 2006; Baillen and Balthazart, 2007), in the hypothalmus and looked into the co-localization, regulation, and roles in behavioral signaling pathways. The bluehead wrasse model allows the study of behavioral roles, neuroanatomical localization, and expression variation across sexual phenotypes, therefore permitting potential functional investigation into zic2, a transcriptionfactor that is well-studied in developmental biology but whose roles in adulthood are poorly understood.
Overexpression of the orphan receptor hepatocyte nuclearfactor 4 (HNF-4), which binds to the NHRRE, dramatically stimulates apoAI gene expression in Caco-2 cells but not in HepG2 cells. Maximal stimulation of transcription by HNF-4 in Caco-2 cells required the presence of both the intestinal specific promoter, the NHRRE, […]
a. Loss of Tel or CtBP2 augments expression of dll4, ve-cadherin and spry genes. Stable primary HUVECs cell lines were derived following their infection with either control shRNA-expressing lentiviruses (Mock) or specific shRNA-expressing lentiviruses to diminish expression of Tel (Teli) or CtBP2 (CtBP2i). Effective knock-down was confirmed by Western blotting. Expression levels of the indicated transcripts were determined by real time qPCR. All values were averaged relative to three different control genes: TATA binding protein (TBP), signal recognition particle receptor (SRPR) and calcium-activated neutral proteinase 1 (CAPNS1). b. Loss of Tel leads to a concomitant increase in the levels of Dll4 and VE-Cadherin. Immunofluorescence was performed on cells (as described in 6b) using the indicated antibodies. The left panel shows double-staining with Tel and Dll4 antibodies. The right panel shows VE-cadherin staining (double-staining was precluded due to the absence of available antibodies). c. The Tel:CtBP regulates dll4 expression in response to VEGFR signaling. Primary endothelial cells were cultured without serum then stimulated with 50 ng/ml VEGF fro the indicated periods. Three assays were performed. The top right panel shows (by Western blotting) the kinetics of MAPK phosphorylation during the indicated time-course. Next to this are images of a P-LISA atop a graphic representation of a quantitative measure of the relative amounts of complex during the same time-course. For the bottom panel, RNA was collected at each time-point, and qPCR was performed (as above) to determine the levels of dll4 expression. d. Tel associates with conserved elements in the dll4 promoter. An alignment of the human and mouse putative dll4 promoter. The presumed transcription start site is highlighted in italics. Conserved core consensus Ets DNA-binding sites are shown in bold. A ChIP analysis was performed on primary endothelial cells incubated with or without 50 ng/ml VEGF for the indicated times. Three different primer sets centered on the illustrated promoter region were used and a single representative is shown (all three gave very similar results). Equivalent amounts of rabbit IgG were used as a control and results are presented as fold changes in recovery (as a fraction of input) relative to the control. The lower panel shows endogenous dll4 expression levels under identical conditions. e. Inhibition of Dll4
FIG. 2. Nuclear localization of yeast Pcl5 is required for Gcn4 degradation. (A) PCL5-GFP-NES is correctly expressed as 54-kDa equivalent to PCL5-GFP. Yeast pcl5 cells (RH3238) were transformed to express either PCL5-GFP (pME2846) or PCL5-GFP-NES (pME2861) from the high-copy plasmids under the control of the MET25 promoter. Cells were grown to early log phase, and the expression of the GFP fusion proteins was analyzed by Western blotting with monoclonal anti-GFP antibodies. (B) Pcl5-GFP-NES is transported out of the nucleus into the cytoplasm. Localization of the fusion proteins Pcl5-GFP (pME2846) and Pcl5-GFP-NES (pME2861) expressed under the control of the MET25 promoter on high-copy (2 m) plasmid was analyzed in a pcl5 mutant strain (RH3238) by fluorescence microscopy (GFP), DAPI staining, and DIC microscopy. The numbers in parentheses represent the total amount of cells investigated (n ⫽ 200) and the respective amount showing the displayed localization. (C) Pcl5-GFP-NES is incapable of suppressing the toxicity of overexpressed GCN4 in the absence of a functional PCL5 gene. Wild-type cells (RH3237) and pcl5 mutant cells (RH3238) expressing GAL1-driven myc 3 -GCN4 from the low-copy plasmid pME2848 alone or
Cayman’s p53 TranscriptionFactor Assay is a non-radioactive, sensitive method for detecting specific transcriptionfactor DNA binding activity in nuclear extracts. A 96-well enzyme-linked immunosorbent assay (ELISA) replaces the cumbersome radioactive electrophoretic mobility shift assay (EMSA). A specific double-stranded DNA (dsDNA) sequence containing the p53 response element is immobilized onto the wells of a 96-well plate (see Figure 1, on page 7). p53 contained in a nuclear extract, binds specifically to the p53 response element. p53 is detected by addition of a specific primary antibody directed against p53. A secondary antibody conjugated to HRP is added to provide a sensitive colorimetric readout at 450 nm.
Galarneau, J.-F. Pare´, D. Allard, D. Hamel, L. Le´vesque, J. D. Tugwood, S. Green, and L. Be´langer, Mol. Cell. Biol. 16:3853–3865, 1996). Here we report a functional analysis of FTF interactions with the hepatitis B virus (HBV) nucleocapsid promoter. DNA-protein-binding assays show that the HBV core promoter contains two high-affinity FTF-binding sites and a third, lower-affinity site shared with other receptors. Transfections in HepG2, Hep3B, and PLC/PRF/5 hepatoma cells using chloramphenicol acetyltransferase reporter genes with the nucleocapsid promoter linked or not linked to enhancer I indicate that FTF is a potent activator of the HBV core promoter, more efficient than HNF4 ␣ , HNF3 ␣ , HNF3 ␤ , or C/EBP ␣ . Steroidogenic factor 1, a close FTF homolog which binds to the same DNA motif and is expressed ectopically in HepG2 cells, seems to be an even stronger inducer than FTF. Point mutations of the FTF-binding sites indicate direct FTF activatory effects on the core promoter and the use of both high-affinity sites for productive interaction between the core promoter and enhancer I. Coexpression assays further indicate that FTF and HNF4 ␣ are the most efficient partners for coactivation of the pregenomic core promoter, which may largely account for the hepatic tropism and the early amplification of HBV infection. Carboxy terminus-truncated FTF behaves as a dominant negative mutant to compete all three FTF sites and strongly deactivate core promoter interactions with enhancer I; this suggests possible new ways to interfere with HBV infection.
7FK506 and CsA also inhibit the activation of other tran- scription factors involved in IL-2 gene expression in T cells such as NF- k B (5, 6). NF- k B is an inducible transcription fac- tor essential for the activation of various cytokines genes, not only of the IL-2 gene but also of several important inflamma- tory cytokine genes such as the IL-6 and IL-8 genes. NF- k B is a heterodimer containing two subunits of 50 and 65 kD termed p50 and p65 and preexists in the cytoplasm in an inactive form complexed with inhibitory proteins termed I k B a . Various stim- uli activate NF- k B through the proteolytic degradation of I k B a (7–9). In T cells, extracellular stimuli and intracellular signal transduction pathways similar to those involving calcineurin and leading to NF-AT activation are also involved in I k B a degradation and NF- k B activation. Calcineurin thus appears to be also a major target for FK506-FKBP12 and CsA-cyclo- philin complexes in inhibiting I k B a degradation and NF- k B acti- vation (5). By contrast, the results of recent studies indicated that another immunosuppressive drug, glucocorticoid, inhibits NF- k B activation through induction of I k B a synthesis (10, 11). Although the immunosuppressive drug FK506 and CsA are widely used in clinical transplantation, various side effects of these drugs, particularly nephrotoxity, limit their usefulness in widespread applications such as the possible treatment of autoimmune diseases (12–14). However, it is not clearly estab- lished yet how these drugs cause renal abnormalities in vivo. We show here that, contrary to the situation in T cells, FK506 induces NF- k B activation in nonlymphoid cells such as renal mesangial cells and fibroblasts. We further show that, as a re- sult of NF- k B activation by FK506, FK506 induces the produc- tion of a pleiotropic inflammatory cytokine, IL-6 (15), in kid- ney. IL-6 has been shown previously to produce renal abnormalities in vivo, such as mesangioproliferative glomeru- lonephritis (16–19). Similar renal abnormalities were also re- ported in FK506-treated animals (20) and are shown in the present study. The results of the present study therefore raise the possibility of a causal relationship between FK506-induced NF- k B activation/IL-6 production and some FK506-induced renal abnormalities.
It is likely that the level of synthesis of the various HBV RNAs is coordinately regulated so that the correct levels of viral products are produced to permit viral biosynthesis to occur efficiently. In this regard, the observation that an impor- tant regulatory element (CpE) of the nucleocapsid promoter (54) also binds HNF3 transcription factors (Fig. 2) (17) sug- gests that this family of liver-enriched transcription factors might also serve a role in coordinately regulating the level of the 3.5- and 2.4-kb viral RNAs. However, it is clear that the magnitude of the effect of HNF3 on the level of transcription from the large surface antigen and nucleocapsid promoters is very different, at least in the transient-transfection analysis in HepG2.1 cells (Table 1). Recently, it has been observed that HBV enhancer I possesses an HNF3-binding site (9, 35). It appears that the HNF3 site in the enhancer I sequence can modulate the level of transcription in the context of the herpes simplex virus thymidine kinase promoter (9). However, in our study, exogenously expressed HNF3 does not stimulate tran- scription from any of the HBV promoters in the context of the complete viral genome by interacting with the enhancer I se- quence, except possibly the nucleocapsid promoter. In con- trast, HNF3 dramatically increases the level of transcription from the large surface antigen promoter in the absence of enhancer I sequences (Table 1; Fig. 3). This suggests that the transcriptional potential of the enhancer I HNF3 site can be observed only in transient-transfection analysis when the en- hancer I sequence is removed from the context of the HBV genome. Despite the fact that the HNF3 transcription factors appear to influence the level of expression from the HBV promoters differentially in transient-transfection analysis, it seems likely that these transcription factors play some role in the coordinate regulation of the level of expression from the enhancer I/X gene, nucleocapsid, and large surface antigen promoters during viral infection.
Many computational tools deal with ab initio discovery of new PFMs on a set of related sequences (Tompa et al., 2005). Since there is no best method, several programs are usually applied resulting in a redundant set of PFMs. Furthermore, the methods might discover PFMs similar to known PFMs. Therefore, either similar PFMs should be removed or merged in to a new PFM. Thus, an appropriate similarity measure for PFMs is required. Most similarity measures consider PFMs as probability distibutions. Hence, the distance between the distributions is used as dissimilarity measure. Due to the position indepen- dence of PFMs, the comparison is done column-by-column which has been shown to work well (Liu et al., 1990). The Pearson correlation coefficient which has been shown to be more effective than other methods (Pietrokovski, 1996) is widely used. Wang and Stormo (2003) describe the average log-likelihood ratio method. Schones et al. (2005) and Kielbasa et al. (2005) calculate the independence of the columns of two PFMs using the χ 2 statistic (Fleiss et al., 2003). The Kullback-Leibler distance is also often used (Roepcke et al., 2005; Aerts et al., 2003). The Tomtom algorithm (Gupta et al., 2007) can use any of these measures to compute a null distribution of similarity scores to obtain p-values. An additional measure described by Kielbasa et al. (2005) does not compute the distance between the PFM distri- butions but the correlation between the scores of the PFMs on a given sequence. The idea to correlate scores on random sequences is proposed in Liefooghe et al. (2006). However, they do not propose a method to summarize the correlations for different overlaps.
Pho85 is required for the cellular response to alkaline con- ditions. Rim101, Pho4, and Crz1 are involved in the cellular response to alkaline conditions (29). Deletions of these tran- scription factors and of Pho85 in various combinations were tested for their effects on yeast growth under alkaline condi- tions (Fig. 4A). The absence of Pho4 or Rim101 conferred alkali sensitivity on yeast cells, as previously reported (Fig. 4A, rows 3 and 5) (6, 13). The absence of Pho85 should allow Rim101 and Pho4 to exert their activities and conceivably stimulate alkali tolerance, but a ⌬ pho85 mutant showed a growth defect even at pH 7.8, where ⌬ rim101 cells could grow almost normally (Fig. 4A, row 2). We assume that the dereg- ulation of Pho4, Rim101, and Crz1 in the absence of Pho85 could disrupt the coordinated expression of alkali-responsive genes needed under alkaline conditions. The introduction of a ⌬ rim101 mutation could suppress the growth defect of ⌬ pho85 cells at pH 7.8 (Fig. 4, rows 2 and 6), suggesting that the mechanisms by which Pho85 participates in the alkali stress response involves the regulation of Rim101. However, this suppression was no longer observed at or above pH 8.0, prob- ably because the presence of Rim101 became crucial for alkali tolerance under high pH conditions. In addition to the dereg- ulation of the transcription factors, the observation that cyto- plasmic pH became high in a ⌬ pho85 mutant, irrespective of its genetic background (Fig. 5), indicates that normal ion ho- meostasis is disturbed in the absence of Pho85. This notion further supports a more general role of Pho85 in the cellular response to alkaline stress.
upon various cues and stresses. As mentioned above, MTFs play critical roles in environmental stress and cel- lular ER stress responses. They help plants to cope with unfavourable growth conditions by regulating gene ex- pression with their transcriptional activity . Extend- ing our understanding of the molecular mechanisms underlying how intracellular movement of MTFs is orga- nized may provide us with an advantage by enabling plants to increase their stress tolerance. Moreover, there are also some MTFs which are involved in the develop- ment and progression of tumours, such as Notch, EGFR and SREBP. Examination of the subcellular locations where they are processed and of their related functional proteins may provide potential molecular targets for de- signing novel therapeutic drugs. Nuclear translocation regulation of MTFs occurs through a series of events. Targeting the modulation of membrane properties, spatial structure of membrane proteins and the corre- sponding processes of proteases could potentially trans- form nuclear transport routes such as endocytosis or ER-Golgi trafficking. This may significantly improve the survival of patients, for example, γ-secretase inhibitors (GSIs) suppress tumour growth in several preclinical cancer models by blocking the cleavage of Notch at the cell membrane, effectively inhibiting the release of the active NICD subunit [57–59]. Studies have shown that patients with high nuclear EGFR levels have poor clinical outcomes in breast cancer , which implies that nu- clear EGFR may benefit tumours by helping to evade cell surface EGFR-targeted small molecule inhibitors and therapeutic antibodies. Therefore, inhibition of EGFR nuclear translocation may increase overall survival of pa- tients. Collectively, targeting intracellular movement pathways of MTFs is valuable for therapeutics, and further research will help us to find more effective treatments.
In this paper, we show how to incorporate time infor- mation in the factor analysis approach. Factor analysis is attractive, since it is on of the most straightforward ways to link hidden transcriptionfactor activities to observed out- puts without knowledge of the connectivity. However, time series information is ignored in all the methods discussed in our previous paper. Here, we explore an extension to factor analysis that integrates time series correlation. Since some data might show very little correlation or none at all, we estimate the posterior distribution of the strength of correlation of TF activities from one time point to the next. This information is useful in several respects as we show for gene expression data for E. coli from  and for yeast from Spellman et al. . Based on these datasets, we highlight some important points: (a) the correlation parameter within the factors reveals whether the time step during experimental sampling is large or small in relation to gene regulatory processes, and what the eﬀect of this choice has on the reconstruction process; (b) our analysis also indicates that data obtained under diﬀerent experimental conditions can show quite diﬀerent dynamics as reflected in the correlation, and caution is required when combining such data sets for joint inference of regulatory relationships.