SGK1) gene is a target of the glucocorticoid receptor (GR) and is central to the stress response in many human tissues. Because environmental stress varies across habitats, we hypothesized that natural selection shaped the geographic distribution of genetic variants regulating the level of SGK1 expression following GR activation. By combining population genetics and molecular biology methods, we identified a variant (rs9493857) with marked allele frequency differences between populations of African and European ancestry and with a strong correlation between allele frequency and latitude in worldwide population samples. This SNP is located in a GR-binding region upstream of SGK1 that was identified using a GR ChIP-chip. SNP rs9493857 also lies within a predicted binding site for Oct1, a transcription factor known to cooperate with the GR in the transactivation of target genes. Using ChIP assays, we show that both GR and Oct1 bind to this region and that the ancestral allele at rs9493857 binds the GR-Oct1 complex more efficiently than the derived allele. Finally, using a reporter gene assay, we demonstrate that the ancestral allele is associated with increased glucocorticoid-dependent gene expression when compared to the derived allele. Our results suggest a novel paradigm in which hormonal responsiveness is modulated by sequence variation in the regulatory regions of nuclear receptor target genes. Identifying such functional variants may shed light on the mechanisms underlying inter-individual variation in response to environmental stressors and to hormonal therapy, as well as in the susceptibility to hormone-dependent diseases.The Serum and Glucocorticoid-regulated Kinase1  in a distant SGK1 GR-binding region with unusually large differences in allele frequency between populations of European and African ancestry. Furthermore, rs9493857 shows a strong correlation between allele frequency and distance from the equator, a pattern consistent with a varying selective advantage across environments. Indeed, the ancestral allele at rs9493857 results in increased GR-binding and glucocorticoid-regulated gene expression, suggesting that an increased stress response  was advantageous in ancestral human populations. We speculate that, in modern times, such variation could favor the negative effects of a heightened glucocorticoid response, potentially predisposing individuals to chronic diseases such as metabolic syndrome and hypertension.Susceptibility to many common human diseases including hypertension, heart disease, and the metabolic syndrome is associated with increased neuroendocrine signaling in response to environmental stressors. A key component of the human stress response involves increased systemic glucocorticoid secretion that in turn leads to glucocorticoid receptor (GR) activation. As a result, a variety of GR-expressing cell types undergo gene expression changes, thereby providing an integrated physiological response to stress. The  Substantial genetic and paleontological evidence supports the idea that humans originated in Sub-Saharan Africa and from there expanded across the globe  and subsequent regulation of target gene expression SGK1 gene were targets of local selective pressures, we searched for genetic variants that influence SGK1 expression in response to stress. To this end, we combined population genetics, comparative genomics and molecular biology approaches to identify variants in candidate regulatory region and then tested them by means of functional assays. We found several variants approximately 30 kb upstream of the transcriptional start site (TSS) of SGK1 that show unusually large differences in allele frequencies between populations and that are strongly correlated with both latitude and climate variables. One of these variants lies within a predicted binding site for Oct1, a transcription factor known to cooperate with the GR Under the assumption that the stress response pathway and, in particular, the SGK1 gene. One was to quantify the difference in allele frequency between pairs of populations by means of the FST summary statistic SGK1 region to that of variants from large genome-wide data sets.We used two approaches to search for signatures of adaptation to local environments in the genomic region surrounding the ST value between CEPH Europeans and Yoruba for the SNPs in a region of 105.6 kb spanning and upstream of the SGK1 gene. As shown in ST values for seven out of 82 SNPs in this region fall in the top 5% of the empirical distribution for the >2 M HapMap SNPs. In particular, only 0.2% of the HapMap SNPs have an FST value higher than SNP rs9493857  in the same panel. Derived allele frequencies for the 25 SNPs analyzed in the HGDP populations are reported in SGK1 SNPs genotyped in the HGDP panel are significantly (p<0.05) correlated with at least one of the climate principal components or with latitude alone . The immunoprecipitated DNA was hybridized onto the Affymetrix GeneChip Human Tiling 2.0R A Array (which covers chromosomes 1 and 6). GR binding regions (GBRs) were then identified using the MAT software −3, ChIP-chip identified six GBRs in the SGK1 region. Among these six GBRs, three nonoverlapping GBRs have a MAT p-value<10−5 and also contain two predicted GREs, three ECRs and SNP rs9493857. Of the remaining GBRs, one is located in a region spanning intron 4 to intron 7 and contains a SNP previously implicated in risk to Type II Diabetes and hypertension To further prioritize these three candidate regions , we performed a GR ChIP-chip assay in MCF10A-Myc mammary epithelial cells treated with the synthetic glucocorticoid (GC) dexamethasone  were re-sequenced in a panel of 28 individuals  to determine whether additional variants with large allele frequency differences between Africans and Europeans exist see . We idenSGK1 gene expression.To validate the results of the GR ChIP-chip assay, we treated MCF10A-Myc breast epithelial cells with either dexamethasone or vehicle and performed a conventional GR-ChIP assay followed by quantitative real-time PCR of the region containing SNP rs9493857. Two independent GR-ChIP experiments showed a significant dexamethasone-dependent enrichment for the region containing rs9493857 . The resTo quantify allele-specific DNA occupancy by the GR-Oct1 complex, we next employed the HaploChIP technique, which allows the direct comparison of two alleles within the same heterozygous sample and the same experiment SGK1 can in fact act as a GC-dependent enhancer whose activity depends upon the particular allele within the Oct1 binding site at rs9493857. In summary, rs9493857 is located within a functional GR enhancer; the ancestral allele at rs9493857 demonstrates both increased GR-Oct1 binding and glucocorticoid-driven gene expression.To test whether the newly identified GR-Oct1 binding site is indeed a GC-dependent enhancer region for which the rs9493857 ancestral versus derived alleles convey differential transcriptional activity, we performed luciferase reporter assays in SK-BR-3 breast cancer cells. A 3.8 kb segment encompassing rs9493857 was cloned 5′ to the SV40 promoter driving expression of the luciferase gene . BecauseSGK1, a gene that plays a key role in the stress response and that has been clearly implicated in cell survival, water re-absorption and the insulin response. Because SGK1 expression is induced by the neuroendocrine stress response (through GR and MR activation), we hypothesized that SGK1 regulatory variation was a target of adaptation to environmental stress. Consistent with this hypothesis, we identified a noncoding variant (rs9493857) with marked allele frequency differences between populations and a strong correlation with climate variables. This SNP is located within a binding site for Oct1, a known GR cooperating transcription factor. Using ChIP-chip, conventional ChIP, HaploChIP and gene reporter assays, we show that the ancestral allele at rs9493857 permits more efficient binding of the GR-Oct1 complex to the enhancer region and induces gene expression at higher levels compared to the derived allele. More broadly, our results show that population genetics approaches may complement computational and traditional molecular biology methods for the identification of regulatory variants in genes involved in the stress response. These variants are expected to contribute to inter-individual differences in hormone  responsiveness, and therefore, could contribute to individual susceptibility to common hormone-dependent diseases, such as cancer and the metabolic syndrome.We have used a combination of population genetics and molecular biology methods to identify regulatory variants of Variation in gene regulation has long been hypothesized to be a major mechanism in the phenotypic divergence within and between species Many studies aimed at the identification of variation in regulatory sequences have focused exclusively on the proximal promoter region of a gene . Consistent with the idea that regulatory variation lies at or near the promoter, genome-wide mapping studies of variation in gene expression found that most expression quantitative trait loci (eQTL) lie in proximity of the TSS SGK1 expression. We used computational predictions of GR binding sites and conserved sequence elements to generate a map of candidate regulatory elements in a >100 kb region encompassing the SGK1 gene. This map was compared to one generated by GR ChIP-chip analysis. ChIP-chip mapping tends to identify a smaller number of candidate regulatory elements compared to computational methods; however, these regions are still likely to contain a nontrivial portion of false positives In the present study, we have leveraged both molecular biology and population genetics methods to identify a common variant influencing GR-mediated induction of SGK1 expression levels in response to cortisol could vary greatly across populations with different ancestry. Recent eQTL mapping studies performed on the HapMap LCLs have identified a large fraction of loci with significant differences in mean expression levels among human populations SGK1 mRNA levels, we have inspected the results of genome-wide eQTL studies in which association data are available for all SNPs examined. We did not find a significant association between rs9493857 genotype and SGK1 mRNA levels Given the large between-population differences in allele frequency at rs9493857, our results imply that SGK1 expression levels in response to physiological stress; this allele is also the most common allele present in populations at lower latitudes. This finding suggests that increased stress-induced SGK1 gene expression may have been advantageous in ancestral, and perhaps current, human populations living in equatorial environments. Increased SGK1 expression is consistent with SGK1's role in mediating sodium retention; however, SGK1 expression is also known to enhance tumor cell survival in breast and prostate cancer cells SGK1 expression could contribute to susceptibility to triple negative breast cancers The results of our functional studies imply that the ancestral allele at rs9493857 will result in higher SGK1 expression might contribute to the development of hypertension SGK1 polymorphisms (located in both Intron 6 and Exon 8) have recently been found to be associated with type 2 diabetes in Romanian and German cohorts SGK1 raises the possibility that inter-individual differences in susceptibility to common diseases may be influenced by differential sensitivities to GR signaling. In other words, individuals harboring alleles resulting in increased cortisol-mediated gene expression may, as a result, be at increased risk of some hormone-dependent diseases such as triple negative breast cancer, prostate cancer, and the metabolic syndrome.In addition to triple negative breast cancer, African Americans, especially African American women, have a relatively high prevalence of the metabolic syndrome, which includes elevated blood pressure, obesity, and type 2 diabetes 2 ranging from 0.8 to 0.6 in Europeans) reaches genome-wide significance in the published studies. However, two of the proxy SNPs, rs4896028 (r2 = 0.811) and rs1009840 (r2 = 0.616), reach nominal levels of significance for adult BMI (p = 0.027) and glycosylated hemoglobin levels (p = 0.038) , attention deficit hyperactivity disorder , and systemic lupus erythematosus   . FurtherSGK1, the target of natural selection appears to be the response to a stress-induced hormonal stimulus. This raises the possibility that a signature of local adaptation, and therefore large inter-population differences, may also be found in a global analysis of genes comprising nuclear receptor gene networks. Further studies are necessary to determine whether additional GR target genes show similar inter-population differences in the frequency of ancestral versus derived regulatory alleles.Additional human traits and biological processes that show large inter-population differences include skin pigmentation and energy metabolism GREs were computationally predicted by using NUBIScan 2.0 ECRs between human and mouse, dog or opossum were identified using the ECR Browser tool ST values between pairs of populations were calculated according to ST can vary between 0 and 1, with FST = 0 indicating no difference in allele frequencies and FST = 1 indicating that alternative alleles are fixed in the two populations. Spearman rank correlation coefficients between allele frequency and environmental variables were calculated using an in house program. The Bayesian geographic analysis described in Hancock et al. (2008) SGK1 SNPs to assess the evidence for genetic adaptation to varying environments. With both methods, significance was assessed by comparing the value of the test statistic for each SNP to the empirical distribution of the same statistic for the SNPs in the Illumina Infinium HumanHap 650Y chip typed in the HGDP panel SGK1 SNP was assessed against the distribution of the test statistic for SNPs matched by allele frequency and by panel.For each SNP, Fhttp://genapps.uchicago.edu/slider/index.html). To estimate the significance of Tajima's D and Fay and Wu's H, we performed 1,000 neutral simulations for each population sample separately using the program MS Neutrality tests and summary statistics of genetic variation for the re-sequenced regions were calculated using the program SLIDER  and the resulting sequences were scored using the software Polyphred version 6.11 The human breast cancer cell line SK-BR-3 was cultured in DMEM supplemented with 10% FBS and 1% Penicillin/Streptomycin. The human breast epithelial cell line MCF10A-Myc was cultured in DMEM-F12 media supplemented with growth factors as described previously −6 M Dexamethasone or vehicle (ethanol) alone. Results are given as ratios of luciferase over β-galactosidase activity. Four independent experiments were performed and statistical significance between dexamethasone and ethanol treated samples was evaluated by means of a paired one-tailed t-test.A 3.8 kb sequence segment upstream of the SGK1 TSS from an Italian individual bearing the derived allele at SNP rs9493857 was cloned in pGL3-Promoter vector (Promega) using the restriction sites KpnI and XhoI. The resulting construct, referred to as “DERIVED,” was subjected to site-directed mutagenesis at the same SNP in order to obtain a construct, referred to as “ANCESTRAL,” which is identical to DERIVED except for the nucleotide at rs9493857. Site-directed mutagenesis was performed according to the manufacturer protocol using the QuikChange II Site-directed Mutagenesis Kit (Stratagene). All constructs were verified by Sanger sequencing and did not contain any artifactual mutations. DNA was prepared using the Qiagen Miniprep and Maxiprep kits and transfected into SK-BR-3 cells using the Polyfect Transfection Reagent (Qiagen). Luciferase and β-galactosidase activity were measured according to standard protocols  following 6, 12, and 24 hours of treatment with either 106) and LCLs (∼20×106) were serum starved for 48 hours and then treated with dexamethasone 10−6 M or ethanol for 1 hour. After treatment, cells were cross-linked for 20 minutes with formaldehyde  followed by addition of glycine to a final concentration of 125 mM for 5 minutes to arrest the cross-linking. ChIP experiments were performed according to a standard protocol . Rabbit polyclonal anti-GR (E-20) and anti-Oct1 (C-21) antibodies were obtained from Santa Cruz Laboratories. The immunoprecipitated protein/chromatin complexes were either used to perform a Western-Blot or treated to reverse the crosslinks according to the manufacturer's instructions. Specifically, the Western blot was performed on protein/chromatin complexes obtained from MCF10A-Myc cells to confirm that GR and Oct1 were immunoprecipitated only in the presence of their specific antibodies. The DNA obtained after reversing the crosslinks was either analyzed by quantitative RT-PCR to assess for enrichment of the GR and Oct1 binding regions of interest (DNA from MCF10A-Myc cells) or used to perform the HaploChIP experiments (DNA from LCLs).MCF10A-Myc cells (4–5×10−6 M) for 1 hour. Following standard ChIP, the immunoprecipitated and the input DNA were amplified, fragmented, and labeled for hybridization according to the Affymetrix ChIP Protocol. These samples were then hybridized to the Affymetrix Human Tiling Array 2.0R A (chromosome 1 and 6) and scanned at the University of Chicago Functional Genomics Core Facility. Probe signals from two independent biological experiments were analyzed using the Model-based Analysis of Tiling-array (MAT) software −3 was used to identify regions in chromosome 1 and 6 that are occupied by the GR.MCF10A-Myc cells were serum-starved for 48 hours and then treated with dexamethasone  panel Figure S1−6 M) treatment in MCF10A-Myc cells. Each measurement is the average of three RT-PCR technical replicates normalized to the input DNA. The error bar represents the standard error. The results of two independent biological replicates are plotted.Oct1 does not bind to a negative control region following dexamethasone (10(3.04 MB TIF)Click here for additional data file.Table S1SGK1 SNPs genotyped in the HGDP.Derived allele frequencies of the (0.24 MB DOC)Click here for additional data file.Table S2SGK1 SNPs genotyped in the HGDP.Spearman rank correlation coefficients for the (0.09 MB DOC)Click here for additional data file.Table S3SGK1 SNPs genotyped in the HGDP.Spearman rank correlation coefficients' empirical p values for the (0.12 MB DOC)Click here for additional data file.Table S4SGK1 SNPs genotyped in the HGDP.Bayes Factors for the (0.08 MB DOC)Click here for additional data file.Table S5SGK1 SNPs genotyped in the HGDP.Bayes Factors empirical p values for the (0.09 MB DOC)Click here for additional data file.Table S6ST values in Hausa vs. Italians for the SNPs identified by re-sequencing.F(0.06 MB DOC)Click here for additional data file.Table S7Summary statistics for the six regions resequenced in Hausa (A), Italians (B) and the overall (C) sample.(0.07 MB DOC)Click here for additional data file.Table S8Association p values for rs9493857 and proxy SNPs from publicly available datasets.(0.08 MB DOC)Click here for additional data file.


Drosophila embryo shows that these results are quantitatively consistent with the predicted signatures of input noise, and we discuss the experiments needed to test the importance of input noise more generally.Gene expression levels fluctuate even under constant external conditions. Much emphasis has usually been placed on the components of this noise that are due to randomness in transcription and translation. Here we focus on the role of noise associated with the inputs to transcriptional regulation; in particular, we analyze the effects of random arrival times and binding of transcription factors to their target sites along the genome. This contribution to the total noise sets a fundamental physical limit to the reliability of genetic control, and has clear signatures, but we show that these are easily obscured by experimental limitations and even by conventional methods for plotting the variance vs. mean expression level. We argue that simple, universal models of noise dominated by transcription and translation are inconsistent with the embedding of gene expression in a network of regulatory interactions. Analysis of recent experiments on transcriptional control in the early  A number of recent experiments have focused attention on noise in gene expression b∼103 is the burst size, found to be approximately constant for all genes After demonstrating that one can observe noise in gene expression, most investigators have concentrated on the mechanistic implications of this noise. Working backward from the observation of protein concentrations, one can try to find the components of noise that derive from the translation of mRNA into protein, or the components that arise from noise in the transcription and degradation of the mRNA itself. At least in some organisms, a single mRNA transcript can give rise to many protein molecules, and this ‘burst’ both amplifies the fluctuations in the protein copy number and changes their statistics, so that even if the number of mRNA copies obeys the Poisson distribution the number of protein molecules will not The mechanistic focus on noise in transcription vs translation perhaps misses the functional role of gene expression as part of a regulatory network. Almost all genes are subject to transcriptional regulation, and hence the expression level of a particular protein can be viewed as the cell's response to the concentration of the relevant transcription factors. Seen in this way, transcription and translation are at the ‘output’ side of the response, and the binding of transcription factors to their targets along the genome is at the ‘input’ side . Noise cDrosophila embryo In this paper we revisit the relative contributions of input and output noise. Input noise has a clear signature, namely that its impact on the output protein concentration peaks at an intermediate value of the input transcription factor concentration. The analogous signature was essential, for example, in identifying the noise from random opening and closing of individual ion channels in neurons We begin by asking if a universal noise model given by Eq (1) can be used simultaneously and in a consistent way for both transcription factors and their targets. Our goal for this section is to show that this is not the case, and that, furthermore, one can be led to models of the form of Eq (1) whenever the input noise is neglected in the analysis. These two findings will motivate the following sections in which we will reexamine the theoretical properties of the input noise and look for its signatures in experimental data of Ref g which is regulated by several transcription factors μg, μ = 1, …, K. In the simplest model, the dynamics for the expression of gene g can be written as:f is the production function that depends on the concentrations of the regulators μg, and −g accounts for the gene's degradation with a time constant gτ. A more complete model would include the dynamics of mRNA and binding site occupancy. This level of detail is omitted here because it does not influence the arguments made in this section; in Sources of noise section, we will, however, treat all of these processes explicitly for the case of a single gene regulated by a single transcription factor.Let us consider a gene g in the cell will be a function of the mean copy numbers of all the relevant transcription factors. When the noise is small, we should be able to write:μg fluctuate around the mean value: μg(t) = 〈μg〉+μδg(t). When we speak about the ‘noise’ in TF levels, we are referring precisely to fluctuations μδg, which can be characterized by two parameters: their strength, or variance, g, we propagate them through the input/output relation fδg)2〉0 due to stochastic transcription and translation of gene g; this intrinsic contribution is present even at fixed transcription factor levels or in constitutively expressed genes.In steady state, the mean number of proteins of gene δg)2, where the effect of the fluctuation μδg in the regulator μ on the output gene g is determined by the ‘susceptibility’ ∂f/∂μgμν, which reminds us that Eq (3) is really a result of the dynamical system described by Eq (2), and that noise propagation depends not only on the noise magnitudes, but also fluctuation correlation times f is given  looks much like computing a first order term in a Taylor expansion of Eq (3) around the steady state and squaring it to get  to giveμ represent transcription factors, usually present at low concentrations, and the protein g is a regulated gene—such as a structural or metabolic protein—but not a transcription factor itself, one expects that 〈g〉/〈μg〉?1. If the dynamics of gene expression for both the regulated gene and its transcription factors happen on the same timescale, we have b, and hence doesn't depend on whether the noise is dominated by transcription or translation.Since the proteins labeled by the indices From Eq (12) we conclude that the original hypothesis about the simple noise model, Eq (1), should be re-examined. Alternatively, if this hypothesis were correct, either the downstream gene's expression would have to change on a timescale which is much slower than those of its regulators (and consequently Φ→0), or there would have to exist subtle correlations among all the protein copy number fluctuations of the different transcription factors. However, since the gene expression machinery is shared between transcription factors and their targets, there is no particular reason to expect very different dynamics for various genes. If, conversely, the assumption of Eq (5) is wrong and the correlations between protein levels provide consistency, they would have to take on a very special form—different transcription factors regulating a single gene would have to be correlated in a way that matches their impact on the expression of that gene—which seems implausible but would be very interesting if it were true.a single TF and work out carefully the noise in this small system.The previous section points to severe difficulties for the universal noise model of Eq (1) when it is applied simultaneously to transcription factors and their target, as in Eq (2). To make this general problem more specific, we now focus on a simple case of a gene regulated by Global consistency section). Notice that variations in cell size, protein sorting in cell division, fluctuations in RNA polymerase and ribosome concentrations, and all other extrinsic contributions to the noise are neglected.All of the different processes described above and schematized in g0, which is reached at high concentrations of the transcriptional activator. In these units, we findg̅ = 〈g〉/g0 is the protein copy number expressed as a fraction of its maximal value, c is the concentration of the transcription factor, and other parameters are as explained in The variance in protein copy number gτeR, i.e. the burst size or the number of proteins produced per mRNA b = 1+gτeR.The first term in Eq (13) is the output noise and has a Poisson–like behavior, with variance proportional to the mean, but the proportionality constant differs from 1 by k− that are fast compared to the protein lifetime, but might be large for factors that take a long time to equilibrate or that form energetically stable complexes on their promoters.The second term in Eq (13) originates from binomial “switching” as the transcription factor binding site occupation fluctuates, and is most closely analogous to the noise from random opening and closing of ion channels. This term will be small for unbinding rates c; in effect the receptor site “counts” the number of molecules arriving into its vicinity during a time window , and this number is of the order ∼gDacτ. This argument is conceptually the same as that for the limits to chemoattractant detection in chemotaxis, as discussed by Berg and Purcell a to which the molecules are transported by diffusion cσ using the relationThe third term in Eq (13) arises because the diffusive flux of transcription factor molecules to the binding site fluctuates at low input concentration : Cooperative binding of transcription factors and Ref : Cooperative binding of transcription factors). We expect that cooperative effects among h transcription factors generate a sigmoidal dependence of expression on the transcription factor concentration, so thath is called the Hill coefficient, and dK is the concentration required for half maximal activation. We can invert this relationship to write the concentration c, which is relevant for the diffusive noise, as a function of the mean fractional expression level g̅. Substituting back into Eq (13), and neglecting the switching noise, we obtainα and β are combinations of parameters that measure the strength of the output and diffusion noise, respectively. If we express the variance in fractional terms, this becomesβ = 0 (no input noise) and b = αg0. β/α) and illustrates how the monotonic relationship between the noise and the mean changes when input noise becomes the dominant contribution.Although cooperativity does not change the effective concentration noise due to diffusion, it does reduce the relative significance of the switching noise; see h→∞ to obtaing̅≪1), then the diffusion noise just adds a constant β to the fractional variance. Because a constant contribution to For very highly cooperative, essentially switch–like systems, we can take the limit Input noise arises from fluctuations in the occupancy of the transcription factor binding sites. These fluctuations must vanish at very high transcription factor concentrations, where all sites are fully occupied, or at very low concentrations, where the sites are never occupied. In the case of a transcriptional activator, full and zero occupancy correspond to maximal and minimal expression levels, respectively. Hence a key signature of input noise is a peak at some intermediate expression level as shown in b. There is, of course, a difference between input and output noise, even when plotted as The claim that many genes have expression noise levels which fit the universal output noise model of Eq (1) would seem to contradict the prediction of a peak in the noise as a function of the mean. But if we plot the predictions of the model with input noise as a fractional variance vs mean, the prominent peak disappears inset to . In factDrosophila embryo provide us with an opportunity to search for the signatures of input noise hunchback. Looking along the anterior–posterior axis of the embryo one thus has an array of nuclei that experience a graded range of transcription factor concentrations. Using antibody staining and image processing methods, it is thus possible to collect thousands of points on a scatter plot of input (Bicoid concentration) vs. output (Hunchback protein concentration); since even in a single embryo there are many nuclei that have the same Bcd concentration, one can examine both the mean Hunchback (Hb) response and its variance. In Recent experiments on the precision of gene expression in the early h = 5 α≈1/380) much smaller than that from input noise (β≈1/2). We also consider the same model with h→∞, and this fully switch–like model, although formally still within error bars, systematically deviates from the data. Finally we consider a model in which diffusion noise is absent, but we include the switching noise from Eq (13), which generalizes to the case of cooperative binding (see : Cooperative binding of transcription factors). Interestingly, this model has the same number of parameters as the diffusion noise model, but does a significantly poorer job of fitting the data. While the fit can be improved further by adding a small background to the noise, we emphasize that Eq (16) correctly captures the non–trivial shape of the noise curve with only two parameters. Because input noise falls to zero at maximal expression, the sole remaining noise at that point is the output noise, and this uniquely determines the parameter α. The strength of the input noise (β) then is determined by the height of the noise peak, and there is no further room for adjustment. The shape of the peak is predicted by the theory with no additional parameters, and the different curves in The mean response of Hb to Bcd is fit reasonably well by Eq (15) with a Hill coefficient α and β that fit the Bcd/Hb data biologically reasonable? The fact that diffusive noise dominates at intermediate levels of expression (β≫α) means that the Hunchback expression level provides a readout of Bcd concentration with a reliability that is close to the physical limit set by diffusional noise, as was argued in Ref β /g0 measures the strength of the output noise and thus depends on the absolute number of Hb molecules and on the number of proteins produced per mRNA transcript. If this burst size is in the range b = gτeR∼1–10, then our fit predicts the maximum expression level of Hb corresponds to g0 = 700–4000 molecules in the nucleus. Given the volume of the nuclei at this stage of development  expression level of regulated genes acts as a sensor for the (input) concentration of transcription factors. The performance of this sensor, and hence the regulatory power of the system, is limited by noise. While changes in the parameters of the transcriptional and translational apparatus can change the level of output noise, the input noise is determined by the physical properties of the transcription factor and its interactions with the target sites along the genome. Ultimately, there is a lower bound on this input noise level set by the noise in random arrival of the transcription factors at their targets, in much the same way that any imaging process ultimately is limited by the random arrival of photons.ζ = 1.04 being very similar to the prediction of the universal noise model (ζ = 1) in Eq (1). The departures from power–law behavior are easily obscured by global noise sources, experimental error, or by technical limitations that lead to the exclusion of data at the very highest expression levels, as in Ref Input and output noise seem to be so different that it is hard to imagine that they could be confused experimentally. Some of the difficulty, however, can be illustrated by plotting the results from the Bcd/Hb experiments of Ref The present analysis of the Bicoid/Hunchback data shows that the signatures of the input noise are surprisingly subtle. In this system, the behavior near half maximal expression is biologically most relevant, since it is at this very concentration that the system has to ‘decide’ where to draw a fairly sharp gene expression boundary as one of the first steps in constructing a spatial pattern. While in other systems the behavior of noise at the intermediate gene expression levels might not be of so high a biological importance for the organism, it is only in this region that different sources of noise are qualitatively distinguishable, as is clear from Drosophila embryo is an attractive model system precisely because the organism itself generates a broad range of transcription factor concentrations, and conveniently arranges these different samples along the major axes of the embryo. A caveat to our analysis is the possibility that, in the concentration range of interest, Hunchback is controlled by factors other than Bicoid The early x to generate an effect of similar size, the factor would have to have a susceptibility (i.e. ∂g̅ /∂x) at the major axis midpoint comparable in strength to Bicoid's [Eq (2)]. One would thus need to justify a presence of such a strong alternative regulator at that position as well as the absence of the predicted noise contribution from Bicoid at the same position.First, Bicoid is a known and probably the dominant factor controlling Hunchback expression at midpoint of the anterior-posterior axis. With an absolute measurement of Bicoid concentration – the equivalent of which did not exist in case of Elowitz et al shape of the noise vs the mean expression in g̅h2−1/(1−g̅)h2+1/. These exponents will be different for a non-diffusional extrinsic noise contribution, yet the data of Ref h = 5. We note that despite these arguments it is possible that some, but presumably not the majority, of the noise at intermediate expressions is contributed by unknown extrinsic factors.Second, we emphasize that the matching between noise measurements and theoretical predictions is not just qualitative. Concretely, the detailed Another caveat is that since we don't directly control the transcription factor concentration, we have to measure it. In particular, in order to measure the variance of the output  we have to find many nuclei that all have the same input transcription factor (Bicoid) concentration. Because the mean output is a steep function of the input, errors in the measurement of transcription factor concentration can simulate the effects of input noise, as discussed in Ref proportional to the mean. If the dominant noise source is a single step in transcription, then the number of mRNA transcripts will obey the Poisson distribution, and the variance of protein copy numbers still will be proportional to the mean, but the proportionality constant will be enhanced by the burst size. The same reasoning, however, can be pushed further back: if, far from maximal expression, the dominant source of noise is the infrequent binding of a transcriptional activator (or dissociation of a repressor) to its target site, then the variance in protein copy number still will be proportional to the mean. Thus, the proportionality of variance to mean implies that there is some single rare event that dominates the noise, and by itself doesn't distinguish the nature of this event.Why are the different sources of noise so easily confused? If noise is dominated by randomness in a single step of the translation process, then the number of protein molecules will obey the Poisson distribution, and the variance in copy number will be equal to the mean. But if we can't actually turn measurements of protein level into molecule counts, then all we can say is that the variance will be If noise is dominated by regulatory events, then the number of mRNA transcripts should be drawn from a distribution broader than Poisson. In effect the idea of bursting, which amplifies protein relative to mRNA number variance, applies here too, amplifying the variance of transcript number above the expectations from the Poisson distribution. Transcriptional bursting has in fact been observed directly The main findings of this paper are as follows:Universal and simple noise model of Eq (1), in which the noise scales in inverse proportion to the mean expression for all genes, is inconsistent with the embedding of gene expression in a regulatory network. One possible cause of this inconsistency stems from assigning all of the noise to the processes on the ‘output’ side of transcriptional regulation, and ignoring the contribution of input noise. In particular, previous theoretical work has shown that the contribution of diffusive input noise to the observed variance of gene expression could be significant Despite appearing straightforward in theory, in real data the signatures of input noise can be surprisingly subtle to detect: inability to probe the whole physiological range of transcription factor concentrations, experimental noise, the difficulties in measuring absolute concentrations of both the regulator and controlled gene and established ways of plotting and analyzing data can act, among others, as confounding factors when noise measurements are used to learn about microscopic processes of transcriptional regulation.Drosophila provides an example in which input noise is dominant, and furthermore, that the detailed form of the variance vs mean quantitatively supports a dominant role for diffusion rather than switching noise.We argue that the Bicoid/Hunchback system of the fruit fly Although there are caveats, these conclusions are consistent with the idea that, as with other critical biological processes x0; δ(x−x0) denotes the Dirac-delta function. These transcription factors are produced at sources c. Equation (21) describes the dynamics of the binding site occupancy; binding occurs with a second order rate constant k+ and unbinding with a first order rate constant k−, and the dissociation constant of the site is dK = k−/k+. The Langevin term nξ induces stochastic  switching between occupied and empty states of the site. Equations (22) and (23) describe the production and degradation of mRNA and protein, respectively, and include Langevin noise terms associated with these birth and death processes.We consider a simplified model of regulated gene expression, as schematized in This seems a good place to note that, while conventional, the assumption that transcription and translation are simple one step processes seems a bit strong. We hope to return to this point at another time.Our goal is to compute the variance in protein copy number, n,e,g} to the various Langevin forces {nξ,eξ,gξ}. In the linear approximation, the steady states are also the mean values:g0 = eτeRgτgR is the maximum mean expression level. Notice that what we have called g̅ = 〈g〉/g0 in the text is just the mean occupancy, 〈n〉, of the transcription factor binding site.Our basic strategy is to find the steady state solution of the model, and then linearize around this to compute the response of the variables {μ = n,e,g.Small departures from steady state are written in a Fourier representation:δc that we need to substitute into Eq (21) for the binding site occupancy:As a first step we use the Fourier representation to solve Eq (20) for k in Eq (34) is divergent at large |k| (ultraviolet). This arises, as explained in Ref k|≤maxk = 2π/a, where a is the linear size of the binding site.The integral over δc̃ from Eq (35), we findk+(1−〈n〉/(πDa). The linearization of Eqs (22) and (23) takes the formω is correlated only with the component at −ω, defining the noise power spectrum μ = n,e,g, where δ(ω−ω′) is the Dirac-delta function. Solving the three linear equations, Eqs (39–41), we can find the power spectrum of the protein copy number fluctuations,cτ = k+c̅+k−. This form has a very intuitive interpretation: each Langevin term represents a noise source; as this noise propagates from the point where it enters the dynamical system to the output, it is subjected both to gain of each successive stage (prefactors R), and to filtering by factors of Linearizing Eq (21) for the dynamics of the site occupancy, we haveeN(ω) = 2eR〈n〉 and gN(ω) = 2gR〈e〉, respectively. The spectrum nN(ω) is more subtle. One way to derive it is to realize that since there is only one binding site and this site is either occupied or empty, the total variance of δn must be given by the binomial formula,nN. Alternatively, we can use the fact that binding and unbinding come to equilibrium, and hence the fluctuations in n are a form of thermal noise, like Brownian motion or Johnson noise. The spectrum nN is therefore determined by the fluctuation–dissipation theorem The total variance in protein copy number is given by an integral over the spectrum,gτ is long compared with all other time scales in the problem. Then we can approximate Eq (42) asμN and doing the integral over ω [Eq (43)], we find the variance in protein copy numbergτgR〈e〉, which is just the mean number of proteins 〈g〉 from Eq (27). The second term isgτeR)〈g〉 to the variance, and this corresponds to the output noise term in Eq (16).For simplicity we consider the case where the protein lifetime k+/k− in terms of 〈n〉. Using Eq (50) and Eq (57) together to simplify the output and input noise terms in the equation for total noise, Eq (47), we finally findThe third term in Eq (47) contains the contribution of input noise to the variance in protein copy number. To simplify this term we note that the steady state of Eq (21) is equivalent toGlobal consistency section, in particular with Eq (4), we first point out that the derivation presented here is more detailed, as it includes the dynamics of mRNA and binding site occupancy in addition to the dynamics of the protein levels g. This allows us to capture all the relevant noise sources – especially the input noise that we claim is important – and compute their variances . It is nevertheless instructive to rewrite the output and diffusion input noise terms of Eq (58) as follows:Dτ = a2/πD is approximately the time that a molecule needs to clear out of a region of dimension a by diffusion, and V = a3. To see why the latter is true, consider a small volume V that on average contains N̅ = c̅V molecules. The variance in the number of molecules is Poisson, and therefore cσ = Nσ/V, and hence To establish a connection between this result and the discussion in the g is the transcription factor c. Then the filtering term Φ = Dτ/(Dτ+gτ)≈Dτ/gτ, because diffusion fluctuations happen on timescales much shorter than gτ ; and the susceptibility, ∂〈g〉/∂c̅, can be computed from the steady state 〈g〉 = g0g̅ = g0c̅/(c̅+dK). To summarize: in this section we computed the noise arising from a particular set of microscopic processes, summarized by Eqs (20–23), using the Langevin formalism, and have concluded by showing how the result fits into a general framework of noise propagation of Eq (2).After these rearrangements, Eq (60) is clearly of the form presented in Eq (4), if the only regulator of gene To generalize this analysis of noise to cooperative interactions among transcription factors it is useful to think more intuitively about the two terms in Eq (57), corresponding to switching and diffusion noise. Consider first the switching noise.n such that the number of proteins is g0n. The total variance in n must be 〈(δn)2〉 = 〈n〉(1−〈n〉) [Eq (44)]. This noise fluctuates on a time scale cτ, so during the lifetime of the protein we see sN = gτ/cτ independent samples. The current protein concentration is effectively an average over these samples, so the effective variance is reduced tog0 that converts n into g, this is the first term in Eq (57).We are looking at a binary variable h transcription factors bind cooperatively, we can still have two states, one in which transcription is possible and one in which it is blocked. For the case of activation, which we are considering here, the active state corresponds to all binding sites being filled, and so the rate at which the system leaves this state, k−, should not depend on the concentration of the transcription factors. The rate at which the system enters the active state does depend on concentration, but this does not matter, because with only two states we must always have an analog of Eq (51), which allows us to eliminate the “on rate” in favor of k− and 〈n〉. The conclusion is that the first term in Eq (57), corresponding to switching noise, is unchanged by cooperativity as long as the system is still well approximated as having just two states of transcriptional activity that depend on the potentially many more states of binding site occupancy.Now if c, with a variancen to changes in the concentration, should contribute a noise varianceg0) the second term in Eq (57). Now the generalization to cooperative interactions is straightforward. If we haveh2.For the diffusion noise term we use the ideas of Refs h2. When written as a function of the mean copy number and the transcription factor concentration, this leaves the functional form of the variance fixed, only changing the coefficients. The overall effect is to make the contribution of diffusion noise more important. One way to say this is that, when we refer the noise in copy number back to the input, cooperativity causes the equivalent concentration noise to become closer to the limit Eq (62) set by diffusive noise Thus, in the expression [Eq (16)] for the variance of protein copy number, cooperativity has no effect on the switching noise but actually increases the diffusion noise by a factor of α and β. For example, averaging for a time gτ over N nuclei is equivalent to having one nucleus with an averaging time gNτ.Reference


NEDD4L that generates isoforms that contain or lack a C2 domain.Ubiquitination serves multiple cellular functions, including proteasomal degradation and the control of stability, function, and intracellular localization of a wide variety of proteins. NEDD4L is a member of the HECT class of E3 ubiquitin ligases. A defining feature of NEDD4L protein isoforms is the presence or absence of an amino-terminal C2 domain, a class of subcellular, calcium-dependent targeting domains. We previously identified a common variant in human NEDD4L common variant on NEDD4L subcellular localization, NEDD4L isoforms that either contained or lacked a C2 domain were tagged with enhanced green fluorescent protein, transfected into Xenopus laevis kidney epithelial cells, and imaged by performing confocal microscopy on live cells. We report that the presence or absence of this C2 domain exerts differential effects on the subcellular distribution of NEDD4L, the ability of C2 containing and lacking NEDD4L isoforms to mobilize in response to a calcium stimulus, and the intracellular transport of subunits of the NEDD4L substrate, ENaC. Furthermore, the ability of the C2-containing isoform to influence β-ENaC mobilization from intracellular pools involves the NEDD4L active site for ubiquitination. We propose a model to account for the potential impact of this common genetic variant on protein function at the cellular level.To address the potential functional significance of the NEDD4L isoforms that contain or lack a C2 domain target different intracellular locations. Additionally, whereas the C2-containing NEDD4L isoform is capable of shuttling between the plasma membrane and intracellular compartments in response to calcium stimulus the C2-lacking isoform can not. The C2-containing isoform differentially affects the mobilization of ENaC subunits from intracellular pools and this trafficking step requires NEDD4L ubiquitin ligase activity. This observation suggests a new mechanism for the requirement for the PY motif in cAMP-mediated exocytosis of ENaC. We have elucidated how a common genetic variant can underlie significant functional diversity in NEDD4L at the cellular level. We propose a model that describes how that functional variation may influence blood pressure. Moreover, our observations regarding differential function of the NEDD4L isoforms may impact other aspects of physiology that involve this ubiquitin ligase. Despi ligases ,5, and U ligases . Whereas ligases .neural precursor cell-expressed developmentally down-regulated 4like) is a member of the Nedd4-like family of E3 ubiquitin ligases. The nine members of the Nedd4-like family in humans share a common domain organization that is evolutionarily conserved in the S. cerevisiae E3 ligase ortholog, Rsp5 [NEDD4L  is a heteromultimeric transmembrane ion channel consisting of alpha, beta and gamma  subunits that is a critical mediator of sodium absorption in the epithelium of multiple tissues. ENaC expression at the apical side of the epithelium lining the lumen of the aldosterone-sensitive distal nephron (ASDN) positions ENaC as the ultimate determinant of renal sodium excretion . It is tNEDD4L [NEDD4L that alters exon 1 splice donor site selection [Essential hypertension, a major contributor to cardiovascular morbidity and mortality , involveNEDD4L ,36. We pelection   and NEDD4L-C2(-) in X. laevis kidney epithelial (A6) cells in the absence and presence of a calcium stimulus. Published evidence regarding the functional interactions between NEDD4L and ENaC [The implication of NEDD4L in Liddle syndrome, genetic linkage of and ENaC -44 and tand ENaC , provideX. laevis kidney epithelial) cells, and imaged by using live cell confocal microscopy. EGFP-NEDD4L-C2(+) exhibited a diffuse cytoplasmic localization, similar to EGFP alone  and NEDD4L-C2(-) were tagged with enhanced green fluorescent protein (EGFP), transfected into A6  C2 domain fused to EGFP served as positive control (EGFP-PKCα-C2) for calcium-dependent plasma membrane targeting [2+ and ionomycin, EGFP remained in the cytoplasm while EGFP-PKCα-C2 moved to the plasma membrane , Golgi (BODIPY® TR C5-ceramide), mitochondria (Mitotracker® Red), endoplasmic reticulum (ER-Tracker™ Red), early endosomes (Transferrin Texas Red®), or autophagosomes (mCherry-LC3)  to labeled early endosomal compartments, the organelle in which EGFP-NEDD4L-C2(-) partially localized  and EGFP-NEDD4L-C2(-), incubated in the presence of Caargeting . Prior te Figure . EGFP-NE+ Figure . In mark+ Figure . Repeati+ Figure . The sam+ Figure . However2+ treatment was monitored by time-lapse confocal microscopy. In cells stably expressing α-ENaC-EGFP, mCherry-NEDD4L-C2(+) mobilization occurred in three distinct steps  in response to changes in intracellular calcium raised the hypothesis that NEDD4L-C2(+) may affect the intracellular trafficking of its substrate ENaC. This hypothesis was tested by employing two A6 cell lines that stably expressed either the α- or the β-subunit of ENaC fused to EGFP . Becauses Figure . First, s Figure . Second,s Figure . Third, s Figure . Despites Figure . In cellMutation of the HECT domain catalytic cysteine residue to alanine abolishes NEDD4L-mediated ubiquitination of α and β-ENaC . To testThe availability of two clonal A6 cell lines that stably overexpress functional α- or β-ENaC subunits as EGFP fusion proteins, provided a relevant cellular model to test the effect(s) of potential functional differences between NEDD4L-C2(+) and Neddd4l-C2(-) on the NEDD4L substrates α- or β-ENaC. Furthermore, using the A6 cell culture system allowed functional comparisons in the presence and absence of ENaC subunit overexpression. We report that NEDD4L-C2(+) and NEDD4L-C2(-) exhibit different subcellular localizations and distinct mobilization responses to a calcium stimulus. Additionally, we report that NEDD4L-C2(+) differentially affects short term trafficking of α- and β-ENaC subunits after a calcium stimulus. Moreover, the effect of NEDD4L-C2(+) on a segment of β-ENaC trafficking is ubiquitin dependent.We observed that the presence or absence of the C2 domain affected the subcellular localization of NEDD4L. NEDD4L-C2(+) and NEDD4L-C2(-) were labeled with EGFP and transfected into A6 cells. Whereas EGFP-NEDD4L-C2(+) localized to the cytoplasm, EGFP-NEDD4L-C2(-) localized to the cytoplasm and early endosome. Although it is unclear what functional significance NEDD4L-C2(-) localization to the early endosome may have, it is plausible that the absence of the C2 domain prevents or hinders NEDD4L-C2(-) targeting to intracellular sites beyond the early endosome.2+ and imaged immediately after the addition of 10 μm ionomycin. The Ca2+ concentration is within typical ranges of Ca2+ concentrations that are used in studies that characterize intracellular C2 domain translocation [2+ stimulus, EGFP-NEDD4L-C2(+) mobilized from the cytoplasm to the plasma membrane and then to small intracellular compartments. These compartments were not targeted by Transferrin Texas Red®, a marker of the early endosome, or other subcellular organelle markers used  and EGFP-NEDD4L-C2(-) following an increase in intracellular calcium. A6 cells that had been transfected with either fusion protein were incubated in 1.97 mM Calocation ,49. WhilThe availability of distinct A6 cell lines that stably express α- or β-ENaC, combined with the observation that like NEDD4L-C2(+), ENaC localizes in this overexpression system to unidentified intracellular compartments , provideUbiquitination is intimately involved in several aspects of the intracellular trafficking of various proteins . We therThe paradigm of E3 enzyme function within the ubiquitin system implies that E3s down-regulate or sequester their substrate targets. Ubiquitination results in substrate transport to the proteasome and lysosome (for degradation) or to endosomal compartments where proteins remain intact but sequestered until needed and recycled for future use . It is nThe observation here that neither catalytically inactive NEDD4L (mCherry-NEDD4L-C2(+)-C943A) nor β-ENaC-EGFP mobilize to the plasma membrane suggests that ubiquitination is necessary for wild-type NEDD4L (mCherry-NEDD4L-C2(+)) and β-ENaC-EGFP co-transit to the plasma membrane. This result is surprising considering that ubiquitination of ENaC as well as myriad other proteins typically leads to internalization and functional down-regulation ,51. HoweThe observation that mCherry-NEDD4L-C2(+) mobilizes to the plasma membrane independently in A6 cells that stably express α-ENaC Figure  suggestsThe differential trafficking effect of NEDD4L-C2(+) on α- and β-ENaC subunits is consistent with differential subunit trafficking observed in other contexts. In A6 cells an increase in the density of β-ENaC subunits, but not α- or γ-ENaC subunits, at the cell membrane in response to aldosterone and vasopressin stimuli has been observed . AldosteThe differential regulation of intracellular trafficking as a function of the presence or absence of a C2 domain may extend to other substrates of NEDD4L, including the dopamine transporter  , and to Our data support the hypothesis that the G/A variant impacts ENaC-dependent sodium handling in the ASDN by affecting the relative synthesis of NEDD4L-C2(+) and NEDD4L-C2(-). Individual differences in the regulation of sodium balance mediated by this genotype may contribute modest but definite individual differences in the liability to develop essential hypertension through a sodium dependent mechanism. Our cellular observations suggest a mechanism accounting for recently reported clinical correlations between the genotype of the G/A variant and responses to acute sodium loading as well as changes in blood pressure . GreaterNEDD4L affecting relative expression of C2-defined isoforms, subcellular trafficking and regulation of ENaC subunits, and whether and how such genetic variation can contribute to individual susceptibility to develop essential hypertension. It is of particular interest whether our observations relate to ENaC activity. Electrophysiological experiments that would investigate amiloride sensitive sodium current in a similar tissue culture system would be a logical approach to resolve this issue. While we exploited published evidence regarding interaction between ENaC and NEDD4L as well as the availability of a cellular model system [The cellular observations provide a framework for future investigations of the potential relationship between genetic variation in l system , the funl system ,63 and rl system .Nedd4l cDNAs were amplified by PCR from mouse hippocampal cDNA and ligated into the TOPO®XL TA cloning vector . The following primers that were used for amplification are oriented 5' to 3': GCTCCATGGCGACCGGGC and CCTGTAGCGTGATTAATTCCA ): CCGACAGAAGATCCAACCATGGAG and CCTGTAGCGTGATTAATTCCA ): ATGACAGAGAAGAGGGGGCGG and TTCTGGAATGGGCACGTTGTA ): GCTCCATGGCGACCGGGC and GCCTAAATTGTCCACTTTCTC (NEDD4L C2 domain (C2 only)). Amplified sequences were ligated into the EcoRI site of pEGFP or pmCherry .® II XL Site-Directed Mutagenesis Kit . The site directed mutagenesis was performed on the mCherry-NEDD4L-C2(+) plasmid with the following primers: AAACTACCCAGAGCTCATACAGCCTTTAATCGCCTTGATTTACC and GGTAAATCAAGGCGATTAAAGGCTGTATGAGCTCTGGGTAGTTT. The mutation was verified by sequencing.Alteration of cysteine 943 to alanine in NEDD4L-C2(+) was performed by using the QuikChange2 in 1.7 cm2 chambers . For transfection, the growth medium was removed, the cells were incubated at 27°C in a solution containing 10 μl Lipofectamine 2000  and 1.14 pmol of DNA in a final volume of 300 μl Optimem® I . Five hours post transfection, the transfection medium was replaced with growth medium and the cells were incubated for 24 hours at 27°C prior to imaging.A6 cells (including those that stably express either α- or β-ENaC) were cultured as recommended  and as previously described . 24 hour® Red , was added 2 hours prior to imaging and was washed once with growth medium immediately before imaging. The Golgi marker BODIPY® TR C5-ceramide  was added 30 minutes prior to imaging. Cells were washed 3 times with growth medium immediately before imaging. The mitochondrial marker, Mitotracker® Red  was added 45 minutes prior to imaging. The endoplasmic reticulum marker, ER-Tracker™ Red  was added 2 hours prior to imaging. The early endosome marker, Transferrin Texas Red®  was added 1 hour prior to imaging. The autophagosome marker, LC-3, was fused to mCherry  and was co-transfected with EGFP-NEDD4L-C2(+) 24 hours prior to imaging at a quantity of 0.57 pmol each.The following cellular organelle markers were utilized in colocalization studies under the following conditions: the dsDNA binding stain Hoechst 33342  was added 30 minutes prior to imaging. The lysosome marker, Lysotracker2+. Ionomycin was added at a working concentration of 10 μm, and the time lapse confocal microscopy was performed immediately.In the mobilization experiments, A6 cells that had been transfected as above were incubated in growth medium that contained 1.97 mM CaImages were acquired using a Fluoview™ FV1000 confocal microscope  and the FV10-ASW software. Single plane, xy scans and time lapse, xyt scans were used to image live, transfected A6 cells at 27°C in growth medium. The wavelengths of the excitation lasers were 405 nm and 488 nm for Hoechst 33342 and EGFP respectively. The 543 nm laser was used to excite mCherry as well as the red colocalization fluorophores applying the manufacturers' recommended excitation and emission wavelengths.2 culture chambers. Cells were isolated by scraping and centrifuging at 14,000 RPM for 5 minutes. The growth medium was then replaced with 80 μl of 1× SDS loading buffer (Laemmli buffer). Following four minutes in a boiling water bath, cells were further homogenized using a Dounce homogenizer. Following a brief centrifugation, 25 μl homogenate was loaded onto a 4–15% Tris-HCl gradient gel . Electrophoresis was performed for two hours at 90 V. Proteins were transferred to a nitrocellulose membrane  and incubated with the monoclonal mouse anti-EGFP, JL-8 antibody . A horseradish peroxidase based secondary detection system  enabled visualization of bands via chemiluminescence.A6 cells were transfected as described, proportionally scaling up all reagents for 3.8 cmNFG performed all experiments and analyses and participated in the design of the study and manuscript preparation. BBY provided the stably transformed A6 cell lines and assistance with manuscript preparation. RBW, JML and AR participated in the design of the study, experimental analyses and preparation of the manuscript. All authors read and approved the manuscript.EGFP-NEDD4L-C2(-) does not colocalize with markers of mitochondria, the endoplasmic reticulum (ER), the Golgi complex or lysosome. Confocal images of live A6 cells that were transiently transfected with EGFP-NEDD4L-C2(-) (A-L) and incubated in the presence of MitoTracker® Red (A-C), ER-Tracker™ Red (D-F), BODIPY® TR C5-ceramide complexed to BSA (Golgi marker) (G-I) or Lysotracker® Red (J-L). Blue and green channel overlay . Blue and red channel overlay . Blue, green and red channel overlay . Scale bars are equivalent to 10 μm.Click here for file2+ stimulus, EGFP-NEDD4L-C2(+) does not target the endoplasmic reticulum, lysosome, Golgi complex, mitochondria, early endosome or autophagosomeIn response to a Ca. Confocal images of live A6 cells transiently transfected with EGFP-NEDD4L-C2(+), treated with 10 uM ionomycin and 1.97 mM Ca2+, and incubated in the presence of ER-Tracker™ Red (A), Lysotracker® Red (B), BODIPY® TR C5-ceramide complexed to BSA (Golgi marker) (C), MitoTracker® Red (D), or Transferrin-Texas Red® (E). A confocal image of an A6 cell that was transiently cotransfected with EGFP-NEDD4L-C2(+) and mCherry-LC3 (autophagosome marker) after a Ca2+ stimulus (F). Blue, green and red channel overlay (A-F). Scale bars are equivalent to 10 μm.Click here for file2+Subcellular localization of EGFP tagged α-ENaC and mCherry-NEDD4L-C2(+) in response to ionomycin treatment in the presence of Ca. The movie, taken at 1 frame every 30 seconds, is of an A6 cell that stably expresses α-ENaC-EGFP, was transiently transfected with mCherry-NEDD4L-C2(+), and was incubated in 1.97 mM Ca2+ and 10 μM ionomycin. It is the same A6 cell that is shown in Figure Click here for file2+Subcellular localization of EGFP tagged β-ENaC and mCherry-NEDD4L-C2(+) in response to ionomycin treatment in the presence of Ca. The movie, taken at 1 frame every 2 seconds, is of an A6 cell that stably expresses EGFP-β-ENaC, was transiently transfected with mCherry-NEDD4L-C2(+), and was incubated in 1.97 mM Ca2+ and 10 μM ionomycin. It is the same A6 cell that is shown in Figure Click here for file2+Subcellular localization of EGFP tagged β-ENaC and mCherry-NEDD4L-C2(+)-C943A in response to ionomycin treatment in the presence of Ca. The movie, taken at 1 frame every 10 seconds, is of an A6 cell that stably expresses EGFP-β-ENaC, was transiently transfected with mCherry-NEDD4L-C2(+)-C943A, and was incubated in 1.97 mM Ca2+ and 10 μM ionomycin. It is the same A6 cell that is shown in Figure Click here for file


Gene activation is thought to occur through a series of temporally defined regulatory steps. However, this process has not been completely evaluated in single living mammalian cells.To investigate the timing and coordination of gene activation events, we tracked the recruitment of GCN5 (histone acetyltransferase), RNA polymerase II, Brd2 and Brd4 (acetyl-lysine binding proteins), in relation to a VP16-transcriptional activator, to a transcription site that can be visualized in single living cells. All accumulated rapidly with the VP16 activator as did the transcribed RNA. RNA was also detected at significantly more transcription sites in cells expressing the VP16-activator compared to a p53-activator. After α-amanitin pre-treatment, the VP16-activator, GCN5, and Brd2 are still recruited to the transcription site but the chromatin does not decondense.This study demonstrates that a strong activator can rapidly overcome the condensed chromatin structure of an inactive transcription site and supercede the expected requirement for regulatory events to proceed in a temporally defined order. Additionally, activator strength determines the number of cells in which transcription is induced as well as the extent of chromatin decondensation. As chromatin decondensation is significantly reduced after α-amanitin pre-treatment, despite the recruitment of transcriptional activation factors, this provides further evidence that transcription drives large-scale chromatin decondensation. During transcriptional activation, gene specific activators bind to their response elements and recruit the regulatory factors needed to initiate efficient RNA synthesis. The basic steps in this process include chromatin decondensation, pre-initiation complex assembly, and RNA polymerase II (RNA pol II) elongation through the gene facilitates chromatin transcription), composed of the HMG-1-like protein, SSRP1 and p140/Spt16, transiently binds and removes H2A/H2B dimers from nucleosomes However, in all cases, transcriptional activators must overcome the significant impediment that chromatin structure imposes on transcription. In order for the pre-initiation complex to form and RNA synthesis to proceed, the DNA duplex must be opened, which requires decondensation of high-order chromatin structure and nucleosome disassembly Enzymes that add and remove protein post-translational modifications (PTMs) are also important transcriptional regulators because they create and eliminate regulatory factor binding sites bromodomains and extraterminal) protein family, found in yeast and animals, contain tandem BDs and are of great interest because of their roles in regulating early events in transcriptional activation Members of the BET  of the mouse mammary tumor virus/Harvey viral ras (MMTV/v-Ha-ras) reporter has been used to show that the glucocorticoid receptor (GR) associates dynamically with its binding sites at a natural promoter To address these issues, arrays of bacterial and bacteriophage sequence element repeats have been introduced into inducible transgenes. Fusion of their requisite binding proteins to auto-fluorescent proteins allows these DNA and RNA elements to be used to directly visualize gene activity in single mammalian cells In this study, we use such a system In order to study the dynamics of gene expression in living cells, we previously developed a cell line (2-6-3) in which we can simultaneously visualize a transcription site and the RNA and protein produced from it in real time in single cells Transcription is induced using the Tet-On/Tet-Off system. When tetracycline-regulated activators bind to the tetracycline response elements , transcription is driven from the CMV minimal promoter . In prevIn the inactive state, the transcription site is highly condensed and, as previously reported, is enriched for the heterochromatic histone modification, H3 tri-meK9, and proteins (HP1s) The transcribed RNA encodes cyan fluorescent protein (CFP) fused to a peroxisomal targeting signal (SKL), 24 repeats of the MS2 bacteriophage translational operator and a splicing cassette and the 3′ UTR from the rabbit β-globin gene. This RNA can be visualized by expressing the YFP-MS2 binding protein, which binds to the stem loop structure of the MS2 translational operator as a dimer . 3 hoursTo determine the timing of transcriptional activation, we monitored the recruitment of Cherry-tTA-ER to the transcription site using time-lapse imaging . For thiTo evaluate the changes that occur to the transcription site chromatin during activation, we immunostained inactive and transcriptionally activated cells using antibodies against histone H4 acetyl-K5, histone H4 acetyl-K12, and histone H3 acetyl-K9, which are binding sites for the acetyl-lysine binding, Brd2 and Brd4 proteins Since we detected strong accumulation of histone lysine acetylation at the active site , we alsoYFP-GCN5 was enriched at the transcription site in 79.5±4.5% of activator-expressing cells  and S1B.We next evaluated the localization of RNA pol II and the FACT subunit, p140/Spt16, at the transcription site. Using immunofluorescence (IF), we found that neither co-localized with the inactive site although accumulations were detected around its periphery . SimilarIn order to determine the timing of RNA pol II recruitment, we collected time-lapse images of cells co-expressing YFP-RNA pol II and Cherry-tTA-ER during activation. Cherry-tTA-ER accumulated 6.3±1.8 min and YFP-RNA-pol II accumulated 6.5±2.1 min after tamoxifen addition, indicating that RNA pol II is rapidly recruited upon activator binding .In order to determine when mRNA synthesis begins, we measured YFP-MS2 levels (6.7±1.3 min) at the transcription site in relation to Cherry-tTA-ER (6.6±1.1 min) . As the As the active transcription site is enriched with histone lysine acetylation and GCN5 rapidly accumulates upon activation, we also wanted to evaluate the recruitment of acetyl-lysine binding factors with roles in regulating transcriptional activation. Brd4  and S1B We next evaluated the recruitment timing of Brd4 and Brd2 in relation to Cherry-tTA-ER. Brd4 accumulated rapidly with the activator  but, intInterestingly, Brd2 decreased the timing of activator recruitment by ∼2 min . This isAs RNA levels increase simultaneously with Cherry-tTA-ER , and BrdTo distinguish between these two possibilities, we compared cells expressing Cherry-tTA-ER (VP16 activator) to cells expressing Cherry-TetR-p53-ER, a construct in which the VP16 acidic activation domain was replaced by the first 70 amino acids of p53, the p53 transactivation domain (p53TAD) [47]. WeIn cells activated for 3 hours, Cherry-tTA-ER and YFP-MS2 accumulated at the transcription site in 87.6±3.4% and 84.7±4.2% of cells, respectively . In contInterestingly, the number of cells activated by VP16 was approximately the same at both the 30 min and 3 hour time points suggesting that the decision to activate is made early and that the number does not increase over time . The lowAdditionally, the VP16 activator induced a significantly higher degree of chromatin decondensation than the p53-activator, as determined by measuring the pixel area of the transcription site , which iTo evaluate the role of transcription in chromatin decondensation and regulatory factor recruitment, we pre-treated cells expressing the VP16-activator and YFP-tagged regulatory factors with α-amanitin, added tamoxifen to the media, and evaluated factor recruitment to the transcription site 3 hr later. The lack of YFP-MS2 enrichment confirmed that transcription was inhibited . InteresIn this study, we evaluated the recruitment of transcriptional activation factors to a transcription site composed of a multi-copy array of an inducible reporter construct, which allows DNA, RNA and protein to be simultaneously visualized in single living cells. Transcription is initiated when a tetracycline-regulated activator binds to TRE repeats upstream of the CMV minimal promoter in the transgene. In order to track factor recruitment in relation to the activator, we fused tTA (pTet-Off) to both Cherry and the ER hormone binding domain hsp 70 loci 5 minutes after heat shock Cherry-tTA-ER accumulates at the site ∼7 min after activation. This reflects the time required for it to enter the nucleus, diffuse through the nucleoplasm, and gain access to its binding sites. It is also consistent with previous reports that the glucocorticoid receptor, which also resides in the cytoplasm prior to activation, is detected at the MMTV array within 10 minutes of hormone addition Previously, we showed that this transcription site is heterochromatic in the inactive state To investigate the timing of the events regulating transcriptional activation, we tracked the recruitment of GCN5, RNA pol II, Brd4 and Brd2 in relation to the VP16 activator, Cherry-tTA-ER. We wanted to determine whether these factors are recruited in a specific order and how their recruitment correlates with the initiation of RNA synthesis. GCN5, RNA pol II and Brd4 accumulated coincidently with the activator. Interestingly, Brd2 lagged behind Cherry-tTA-ER by ∼2 min. Since Brd2 with mutated BDs does not associate with the site, the timing of its recruitment provides a measure of the time required for the site to become hyper-acetylated. RNA levels also increase simultaneously with the activator suggesting that when activated by the VP16 activator, transcription can proceed before the site is hyper-acetylated.In general, this assay utilizes transient expression of auto-fluorescently tagged regulatory factors to analyze transcriptional dynamics, although in this study, YFP-RNA pol II and YFP-MS2 were stably expressed. Factor over expression can sometimes introduce effects into a system that can skew results. However, in this study, only YFP-Brd2 detectably increased the rate of activator accumulation at the transcription site . This suImportantly, this analysis also allowed us to identify a difference between Brd2 and Brd4, which likely reflects their differential functions. Although highly homologous in their BDs, they are less conserved in their other domains and have been found in different biochemical complexes −1Overall, our results indicate that the VP16 activator, Cherry-tTA-ER, induces rapid transcriptional activation and that most of the examined factors accumulate simultaneously with it. As most nuclear factors move rapidly throughout the nucleus by diffusion, this suggests a mechanism for how co-accumulation occurs It is also possible that transcription begins so quickly because VP16 is a very strong activator. Unlike many cellular activators, which initiate transcription through a cascade of regulatory factor recruitment events regulated by the progressive addition of histone PTMs Transcriptional activation rates are also strongly influenced by the number of activator binding sites in a reporter. Increasing the number of NF-kB enhancers bypasses the need for histone acetylation and induces the direct recruitment of transcriptional regulatory complexes As most investigations of transcriptional activation have relied on techniques such as chromatin immunoprecipitation (ChIP), nuclear run-on and RNase-protection, in which measurements are taken at widely spaced time points and effects are averaged in cell populations, it has been difficult to determine how transcription is regulated in single cells. Our results show that the number of VP16-activated cells is approximately the same at both the 30 minutes and 3 hours time points indicating that the decision to activate is made early. Although the number of activated cells increases between these time points in p53-activated cells, this is most likely due to detection limits at the early time points. This is also suggested by the fact that the RNA, but not the activator, can be detected 15 min after p53 mediated activation demonstrating that it is the more sensitive detector. This sensitivity difference could also explain why the RNA is detected so rapidly after VP16-activator recruitment in the time-course imaging .Our evaluation of transcriptional activation factor recruitment and the condensation state of the transcription site in cells pre-treatment with α-amanitin provides further evidence for the essential role of transcription in driving large-scale chromatin decondensation Much of what we know about how genes are activated comes from genomic, biochemical and genetic analyses. However, chromatin is a highly dynamic regulator of gene expression. As changes in chromatin architecture occur within minutes of activation, it is difficult to truly understand the nature of these events when measurements are taken at widely spaced time points and by averaging effects in cell populations. Live cell imaging now makes it possible to monitor transcriptional activation in single cells. In this study, we present one of the first delineations of the timing of transcriptional activation events in single mammalian cells at the earliest stages of activation. Future analyses will allow us to place other regulatory events into this temporal context and to further explore how activation events are coordinately regulated.Monomeric YFP (mYFP) and monomeric Cherry (mCherry) The U2OS derived cell line, 2-6-3 2 with freshly added protease inhibitors) for 5–10 min and then fixed in 3% formaldehyde in 1× PBS for 15 min, both steps at room temperature (RT). Cells were blocked with 3% BSA in 1× PBS for 1 hour at RT and incubated with 1° Abs diluted in blocking buffer. The following Abs were used: RNA pol II 4H8 , FACTp140 , histone H4 acetyl-K12 , histone H4 acetyl-K5 , histone H3 acetyl-K9  and Brd4CA For IF, 2-6-3 cells were transfected overnight with SV2-Cherry-lac repressor or Cherry-tTA-ER. Cells were first pre-extracted with 0.5% Triton X-100 in CSK buffer  and transfected overnight with Cherry-tTA-ER alone or co-transfected with YFP-tagged factors; Cherry-tTA-ER was transfected overnight into 2-6-3 cells stably expressing YFP-RNA pol II and YFP-MS2. Coverslips were placed in an FCS2 live-cell chamber  with Leibovitz's L-15 medium (Invitrogen) and activation was induced by prefusing ∼3 ml of medium containing 1 µM tamoxifen into the chamber. The chamber and objective lens were maintained at 37°C using heating units . The microscope was enclosed in the Incubator BL connected to heating and temperature control units  maintained at 37°C. Images were acquired using a Leica DMI 6000 B inverted automated microscope with HCX PL APO 100x/1.40−0.70 oil objective lens using a 457/488/514nm 30mW Argon-Ion laser for YFP, a 561nm/25mW diode laser for mCherry and a 442nm/70mW diode laser for CFP imaging. To avoid cellular toxicity, the laser powers were decreased to 25% for YFP imaging and 20% for mCherry imaging. In time-course experiments, images were captured using a Hamamatsu electron multiplier CCD digital camera with a 512×512 chip. A Marzhauser point-visiting stage and a Z-drive controlled by COMPIX SimplePCI software were used to collect images at multiple X-Y points. A Yokogawa CSU-10 real-time spinning disk confocal attachment with Nipkow and microlens disks was used to collect stacks of 6 images (0.5 µm increments) every 1 or 1.5 min. Exposure time and gain settings were maintained at specific values for each factor studied . Cells with intensity levels registering in the middle of the dynamic range of the camera were selected for imaging in order to ensure that the cells to be analyzed expressed equivalent levels of each factor. For every factor, time course imaging was done on multiple days and on multiple coverslips with 3 cells typically tracked on each coverslip. Cells which went our of focus during imaging, developed a saturated signal at the transcription site, bleached, or showed any kind of aberrant physiological features were not included in the computational analysis. On average, 25–30 cells were imagined for a given factor and ∼10 could be included in the final analysis. Graphs were obtained according to the procedure described below only for cells showing a clear signal to noise ratio of factor accumulation at the transcription site.For fixed images, cells were plated on 1.5 coverslips, transfected overnight, activated as indicated, and fixed for 15 min in 3% PFA in 1× PBS. Coverslips were mounted in antifade fluorescence mounting medium  Image analysis was done using custom software written in Matlab (The Mathworks). In each time-lapse series, the transcription site was manually selected in the frame when the accumulation of the activator could first be clearly detected (∼10 min after tamoxifen addition). The intensity (arbitrary units) at a point inside the transcription site was compared to the intensity of an outside but nearby point and a threshold, calculated from the intensity difference, was used to define the transcription site boundary. At each time point, activator and factor levels were determined by summing the intensities of all pixels within this boundary and subtracting the background intensity levels. The values in images acquired at earlier time points (before ∼10 min) was determined by applying this first defined boundary to the site. The site was tracked and its location, size, and intensity values were recalculated in all later images. For analysis of YFP-RNA pol II expressing cells, a gamma correction of 2 was applied to each image in both the red and green series. Error bars on the graphed data sets represent one standard deviation across at least three sets of normalized data. The intensity data were fit by performing a nonlinear least-squares regression using the Levenberg-Marquart algorithm in MATLAB with the “lsqcurvefit” function to a logistic model:Intensity = (A1–A2)/(1+(x/x0)∧p) + A2,A1 = initial intensityA2 = final intensityx0 = the center p = powerThe timing of the initial accumulations of factors and the activator at the transcription site were determined from the logistic fit. The accumulation start point was defined as the time at which the intensity increased 5% of the difference between the initial and final values. The error estimates of the initial accumulation represent a 90% confidence interval on the estimates of the four model parameters using the function nlparci in MATLAB.To determine the area of the locus, a point inside and a point outside but nearby the transcription site was manually selected. A threshold calculated from the intensity (in arbitrary units) difference between the two points was used to define the boundary of the transcription site. The “imreconstruct” function in Matlab was used to obtain the region of the transcription site with pixels above the threshold value. The number of pixels identified in the region was then summed for the total locus area of the transcription site.rR) values with background correction for region of interest (ROI) were calculated. Values from 0.5–1.0 indicate strong colocalization; values from −1.0−0.5 indicate a lack of co-localization. 2D intensity profiles across the transcription site were also obtained for some images.Quantitative colocalization analysis of fixed images was performed using SimplePCI software. The transcription site was selected manually and Pearson's correlation coefficient (t method −dCt = 2−(Ct(IP)−Ct(In)).For ChIP experiments, cells were infected with the lentiviral expression plasmid, pLU-YFP-tTA-ER for 24 hrs then activated (1 µM tamoxifen) for 3 hrs. ChIP assays were performed using the standard Upstate protocol. The following ChIP grade rabbit polyclonal Abs were used: H4 acetyl-K12 (Abcam), H3 acetyl-K9 (Abcam), histone H4acetyl-K5 (Upstate) and normal rabbit IgG (Abcam). DNA samples from input (In) and antibody-bound (IP) were purified using QIAquick PCR purification kit (Qiagen) and analyzed by real-time PCR using the TaqMan Fast Universal PCR Master Mix (Applied Biosystems) and Applied Biosystems 7500 Fast Real-Time PCR System. Primers and TaqMan probes were designed using Applied Biosystems Primer Express 3.0 software (sequences available upon request). Individual PCR reactions were carried out in triplicates and experiments were repeated three times. Data quantification was performed by applying the modified comparative CFigure S1Association of the histone acetyl-transferases with the inactive and active transcription site and expression levels of expressed constructs. (A) YFP-GCN5 (panels a–c), YFP-PCAF (panels d–f), and YFP-p300 (panels g–i), are not enriched at the inactive transcription site marked by Cherry-lac repressor. Scale bar represents 5 µm. Scale bar in the enlarged inset represents 1 µm. (B) Western blot showing the levels of the transiently expressed factors used for time lapse imaging: YFP-Brd4, YFP-Brd2 and YFP-GCN5. The lower level of Brd4 compared to Brd2 a result of the less efficient transfer of this higher molecular weight protein.(0.70 MB TIF)Click here for additional data file.Figure S2Association of YFP-RNA pol II with the inactive and active transcription site. Cells stably expressing YFP-RNA pol II were transfected with Cherry-lac repressor, to mark the inactive transcription site (panels a–c). Cherry-tTA-ER marks the transcription site 3 hrs after activation induced by tamoxifen (panels e–g). Intensity profile shows that YFP-RNA pol II (green line) surrounds but does not co-localize with the inactive site (red line) (panel d). YFP-RNA pol II significantly co-localizes with Cherry-tTA-ER (panel h). Yellow lines in enlarged insets in c and g show the path starting at the asterisk through which the red and green intensities were measured (panels d and h). Scale bar represents 5 µm. Scale bar in the enlarged inset represents 1 µm.(2.89 MB TIF)Click here for additional data file.Table S1Analysis of factor co-localization with the transcription site.(0.05 MB DOC)Click here for additional data file.Table S2Summary of the recruitment time analyses from time series images of activator and regulatory factor accumulation at the transcription site during activation. The gray shaded column is the 5% accumulation threshold, which is marked by arrows in the graphs in the figures.(0.03 MB DOC)Click here for additional data file.Movie S1Cherry-tTA-ER was transiently transfected into 2-6-3 cells and transcription was induced by the addition of tamoxifen. Frames were collected every minute for ∼40 min. Movie display rate is 8 frames per second. Still images from this movie are shown in (3.41 MB AVI)Click here for additional data file.Movie S2Cherry-tTA-ER and YFP-GNC5 were transiently transfected into 2-6-3 cells and transcription was induced by the addition of tamoxifen. Frames were collected every 1.5 min for ∼40 min. Movie display rate is 8 frames per second.(0.91 MB AVI)Click here for additional data file.Movie S3Cherry-tTA-ER was transiently transfected into a 2-6-3 cell line stably expressing YFP-RNA pol II. Transcription was induced by the addition of tamoxifen. Frames were collected every 1.5 min for ∼40 min. Movie display rate is 8 frames per second.(1.20 MB AVI)Click here for additional data file.Movie S4Cherry-tTA-ER was transiently transfected into a 2-6-3 cell line stably expressing YFP-MS2. Transcription was induced by the addition of tamoxifen. Frames were collected every 1.5 min for ∼40 min. Movie display rate is 8 frames per second. Still images from this movie are shown in (1.04 MB AVI)Click here for additional data file.Movie S5Cherry-tTA-ER and YFP-Brd4 were transiently transfected into 2-6-3 cells and transcription was induced by the addition of tamoxifen. Frames were collected every 1.5 min for ∼40 min. Movie display rate is 8 frames per second.(13.33 MB AVI)Click here for additional data file.Movie S6Cherry-tTA-ER and YFP-Brd2 were transiently transfected into 2-6-3 cells and transcription was induced by the addition of tamoxifen. Frames were collected every 1.5 min for ∼40 min. Movie display rate is 8 frames per second.(0.75 MB AVI)Click here for additional data file.