Estrogen‐dependent control and cell‐to‐cell variability of transcriptional bursting
2018; Springer Nature; Volume: 14; Issue: 2 Linguagem: Inglês
10.15252/msb.20177678
ISSN1744-4292
AutoresChristoph Fritzsch, Stephan Baumgärtner, Monika Kuban, Daria Steinshorn, George Reid, Stefan Legewie,
Tópico(s)CRISPR and Genetic Engineering
ResumoArticle23 February 2018Open Access Transparent process Estrogen-dependent control and cell-to-cell variability of transcriptional bursting Christoph Fritzsch Christoph Fritzsch Institute of Molecular Biology, Mainz, Germany Search for more papers by this author Stephan Baumgärtner Stephan Baumgärtner Institute of Molecular Biology, Mainz, Germany Search for more papers by this author Monika Kuban Monika Kuban Institute of Molecular Biology, Mainz, Germany Search for more papers by this author Daria Steinshorn Daria Steinshorn Institute of Molecular Biology, Mainz, Germany Search for more papers by this author George Reid Corresponding Author George Reid [email protected] orcid.org/0000-0002-0298-693X Institute of Molecular Biology, Mainz, Germany European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Stefan Legewie Corresponding Author Stefan Legewie [email protected] orcid.org/0000-0003-4111-0567 Institute of Molecular Biology, Mainz, Germany Search for more papers by this author Christoph Fritzsch Christoph Fritzsch Institute of Molecular Biology, Mainz, Germany Search for more papers by this author Stephan Baumgärtner Stephan Baumgärtner Institute of Molecular Biology, Mainz, Germany Search for more papers by this author Monika Kuban Monika Kuban Institute of Molecular Biology, Mainz, Germany Search for more papers by this author Daria Steinshorn Daria Steinshorn Institute of Molecular Biology, Mainz, Germany Search for more papers by this author George Reid Corresponding Author George Reid [email protected] orcid.org/0000-0002-0298-693X Institute of Molecular Biology, Mainz, Germany European Molecular Biology Laboratory, Heidelberg, Germany Search for more papers by this author Stefan Legewie Corresponding Author Stefan Legewie [email protected] orcid.org/0000-0003-4111-0567 Institute of Molecular Biology, Mainz, Germany Search for more papers by this author Author Information Christoph Fritzsch1,‡, Stephan Baumgärtner1,‡, Monika Kuban1, Daria Steinshorn1, George Reid *,1,2,‡ and Stefan Legewie *,1,‡ 1Institute of Molecular Biology, Mainz, Germany 2European Molecular Biology Laboratory, Heidelberg, Germany ‡These authors contributed equally to this work as first authors ‡These authors contributed equally to this work as senior authors *Corresponding author. Tel: +49 6221 3878936; E-mail: [email protected] *Corresponding author. Tel: +49 6131 3921430; E-mail: [email protected] Molecular Systems Biology (2018)14:e7678https://doi.org/10.15252/msb.20177678 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract Cellular decision-making and environmental adaptation are dependent upon a heterogeneous response of gene expression to external cues. Heterogeneity arises in transcription from random switching between transcriptionally active and inactive states, resulting in bursts of RNA synthesis. Furthermore, the cellular state influences the competency of transcription, thereby globally affecting gene expression in a cell-specific manner. We determined how external stimuli interplay with cellular state to modulate the kinetics of bursting. To this end, single-cell dynamics of nascent transcripts were monitored at the endogenous estrogen-responsive GREB1 locus. Stochastic modeling of gene expression implicated a two-state promoter model in which the estrogen stimulus modulates the frequency of transcriptional bursting. The cellular state affects transcriptional dynamics by altering initiation and elongation kinetics and acts globally, as GREB1 alleles in the same cell correlate in their transcriptional output. Our results suggest that cellular state strongly affects the first step of the central dogma of gene expression, to promote heterogeneity in the transcriptional output of isogenic cells. Synopsis Transcription heterogeneity is studied using live-cell imaging of nascent RNAs of an endogenous, estrogen-sensitive gene. Fitting stochastic models reveals mechanisms of promoter regulation, and distinguishes cell- versus gene-specific determinants of heterogeneity. Gene expression noise is described by a two-state promoter model, in which estrogen stimulation modulates the frequency of transcriptional bursting. Cell-specific rates of transcript initiation and elongation determine RNA output on long time scales. Transcription noise can be decoupled from mean expression level using inhibitors of epigenetic regulation. Introduction Heterogeneity is an essential feature of cellular decision-making. Genetically identical cells frequently respond in different ways to the same external stimulus, leading to differences in differentiation programs (Chang et al, 2008), drug resistance (Sharma et al, 2010; Paek et al, 2016), and viral pathogenesis (Weinberger et al, 2005). Such heterogeneous cellular behavior can be beneficial for the diversification of tissues and was shown to be related to variable expression of key regulators of cellular differentiation programs (Goolam et al, 2016). Variability in protein levels arises because gene expression in single cells is a stochastic process (Harper et al, 2011; Suter et al, 2011; Molina et al, 2013). As a consequence of random, limiting, biochemical interactions, each gene has intrinsic temporal fluctuations in activity. For instance, mammalian transcription involves alternating transcriptionally active and inactive intervals, which are observed as transcriptional bursts (Chubb et al, 2006; Raj et al, 2006). Mathematical models have been developed that capture the stochastic nature of transcription and that interpret single-cell transcription datasets. In such models, promoters randomly switch between active (ON) and inactive (OFF) states (Paulsson, 2005; Suter et al, 2011; Zoller et al, 2015). The number of transcripts produced over time (i.e., the expression level) can be regulated by modulating burst frequency and burst size; that is, how often the promoter is active and by the number of transcripts produced per burst, respectively. In addition to these intrinsic fluctuations, cells differ in their phenotypic state (e.g., cell cycle stage, cell volume, stimulation by extracellular conditions). This class of factors influences gene expression globally and introduces correlated fluctuations in multiple or in all genes. Such differences are referred to as extrinsic noise, and they can influence gene expression at various levels including transcription and translation. A unifying model that quantitatively describes gene regulation and that incorporates noise contributed by intrinsic and extrinsic factors is still lacking. Live-cell microscopy of fluorescently labeled nascent transcripts provides a unique methodology to directly observe temporal fluctuations at the level of gene promoter activity. The PP7 reporter system enables such visualization and is based on the integration of PP7 sequences into a gene of interest. The PP7 sequences fold into stem-loop structures in nascent transcripts, which in turn associate with fluorescently labeled PP7 coat protein (PCP) (Chao et al, 2008). Observing transcription in four dimensions, that is, at distinct loci within multiple single cells over time, permits characterization of intrinsic and extrinsic variability. In this work, we employed CRISPR/Cas9 genome engineering to introduce PP7 sequences into an endogenous GREB1 locus. GREB1 is a central mediator of estrogen-induced cell growth in vitro and is a marker of tumor growth in estrogen-sensitive breast cancers (Rae et al, 2005; Laviolette et al, 2014). Estrogen (17β-estradiol, E2) activates transcription of target genes by binding to the ligand-dependent transcription factor estrogen receptor alpha (ERα). This signaling pathway is a paradigm for the dynamic behavior of chromatin in transcription (Métivier et al, 2003) and is a relevant mammalian system in which to study the adaptation of bursting to external cues. We performed quantitative and time-resolved imaging of nascent GREB1 transcripts to characterize how the dynamics of transcriptional bursting are modulated by E2 and by extrinsic noise sources. We employed a model fitting framework, known as approximate Bayesian computation (ABC), to calibrate stochastic models of transcription based on our data and to discriminate between alternative hypotheses of promoter regulation. We present a unifying model that quantitatively describes GREB1 transcription as a two-state promoter cycle in which E2 regulates the frequency of transcriptional bursts. The cellular state modulates the amount of transcripts that are produced per burst by affecting kinetics of transcriptional initiation and elongation, thereby coordinately affecting multiple GREB1 alleles in the same cell. Furthermore, we report that the relative importance of intrinsic and extrinsic noise sources can be altered by small-molecule inhibitors of histone deacetylases. In conclusion, our work quantifies how noise at different time scales is shaped by the contributions of transcriptional bursting, extrinsic noise, and the additive effects of multiple alleles. Results Direct observation of endogenous estrogen-mediated transcriptional activity We wished to monitor endogenous estrogen-regulated transcription in living cells within a native chromatin environment. To achieve this, we modified a GREB1 locus, using CRISPR/Cas9, in the ERα-positive breast cancer cell line MCF7 and visualized nascent transcripts using the PP7 reporter system. We generated the MCF7-GREB1-PP7 cell line by knocking-in an array of 24 PP7 sequences into exon 2, directly upstream of the start codon within a minimally perturbed GREB1 gene (Fig 1A). Correct knock-in and recombination was confirmed by genomic PCR (Fig EV1A). Stable co-expression of the GFP-labeled PP7 coat protein (PCP-GFP) led to fluorescent labeling of nascent transcripts, with transcription sites visible as bright foci within the nucleus (Fig 1B and C). The presence of GREB1 transcripts at these transcription sites was independently confirmed using single-molecule (sm) RNA fluorescence in-situ hybridization (FISH) with probes against intronic and exonic sequences of GREB1 (Fig EV1D and E). The knock-in allele was transcribed at comparable levels to the two remaining endogenous GREB1 alleles, as judged by exonic smRNA FISH spot intensities (Fig EV1G). Furthermore, the knock-in and wild-type alleles showed similar sensitivity to E2 stimulation in RT–qPCR analyses (Fig EV1B) and smRNA FISH (Fig EV1G). This suggests that the knock-in of PP7 sequences did not significantly perturb GREB1 expression. Figure 1. Knock-in of PP7 stem-loop sequences provides visualization of estrogen-mediated transcription from the endogenous GREB1 locus in living cells (see also Fig EV1 and Movie EV1) Knock-in strategy to integrate PP7 sequences into a GREB1 locus in MCF7 cells. CRISPR/Cas9-mediated knock-in of PP7 sequences, together with a selection cassette, into the 5′ UTR within exon 2 of GREB1 was followed by excision of the selection cassette by Cre recombinase to yield the cell line MCF7-GREB1-PP7 (ERE: estrogen response element, HA: homology arm, pA: polyadenylation site, Puro: puromycin resistance, IRES: internal ribosomal entry site, CMV: promoter of cytomegalovirus). Schematic description of the PP7 system. Binding of GFP-labeled PP7 coat protein (tdGFP-tdPCP) to PP7 stem-loops within nascent transcripts leads to fluorescence accumulation at the transcription site. Spot intensity decreases upon termination and transcript release. A schematic description of the fluorescence signal of a single transcript is shown below, with the 30 min which a transcript is observable estimated from gene length and published Pol II elongation rates. Transcriptional foci in MCF7-GREB1-PP7 cells grown at low and high concentrations of E2. Single fluorescent foci (arrowheads) are observed within nuclei due to nuclear localization of tdGFP-tdPCP. Maximum intensity projections of z-stacks are shown. Scale bar: 10 μm. E2 dose-response. Transcription sites were automatically identified and quantified in images of fixed MCF7-GREB1-PP7 cells. The mean ± standard deviation from three biological replicates of > 3,000 cells per condition is shown along with a fitted Hill function. ICI 182,780 (pure anti-estrogen) and actinomycin D (ActD; transcriptional inhibitor) serve to prevent transcription at 100 pM E2. Endogenous E2-initiated transcription occurs in bursts. MCF7-GREB1-PP7 cells were imaged for 13 h at 10 pM E2. Transcription sites (red cross) were tracked within nuclei (dashed line). A zt-kymograph of the tracked transcription site demonstrates stable focus. Quantified transcript numbers are shown for a transcription site (red) and a control site at the center of the nucleus (gray). Scale bar: 5 μm. Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Validation of knock-in cell line and image calibration (related to Fig 1) Validation of successful genome engineering. Genotyping PCRs were performed on genomic DNA with primers positioned along the transgene as indicated in the scheme above. PCR products confirm successful recombination after Cas9-mediated cleavage on both 3′ and 5′ ends of the construct, as well as successful Cre-mediated recombination between the two loxP sites (wt: wild-type locus, rec: locus after Cre recombination). The estrogen sensitivity of knock-in and wild-type (wt) allele is comparable. E2 dose-response curves were measured by allele-specific RT–qPCR after starving cells of E2 for 3 days followed by induction for 18 h at the indicated E2 concentrations (data points). Hill functions were fitted (lines), and the resulting EC50 is indicated. They confirm unperturbed sensitivity (EC50) for E2 of the knock-in allele with minor differences in maximal RNA levels that most probably result from altered transcript stability. Error bars represent standard deviation from four biological replicates. The fraction of cells with visible transcription sites increases with E2 concentration. Transcription sites were automatically identified and quantified in fixed cells as in Fig 1D. The percentage of nuclei with detectable spots was obtained. Mean and standard deviation from three biological replicates are plotted alongside with the fit of a Hill function. ICI 182,780 (ICI) and actinomycin D (ActD) were applied at 100 pM E2. The single-molecule RNA FISH signal of exonic and intronic signal overlaps with the GFP signal at transcription sites. RNA FISH was performed with probes against exonic (red) and intronic (blue) regions of GREB1. Single RNAs are visualized by exonic probes as diffraction limited spots in the nucleus (dashed line) and cytoplasm of the knock-in cell line. Bright foci in the nucleus correspond to nascent RNA at the transcription sites that are visualized by intronic and exonic probes. One of three nuclear foci (arrowhead and inset) co-localizes with GFP signal from the PP7 system. Scale bars: 5 and 0.5 μm in the inset. Quantification of FISH signals determines a correlation between exonic, intronic, and GFP signal at transcription sites; 87% of bright nuclear exonic foci co-localize (distance < 5 px) with intronic foci, while only 26% of them co-localize with GFP foci, indicating that one in three foci is labeled with GFP. Co-localizing foci also correlate in intensity. Calibration of spot intensities by matching live-cell imaging distributions to absolute smRNA FISH signals. The mean intensity of single RNAs (solid vertical line) was derived from FISH images of GREB1 exons at 100 pM E2. The intensity distribution of bright nuclear foci in smRNA FISH (> 10 RNAs, red) was matched with the intensity distribution of transcription sites from live-cell imaging at 100 pM E2 (green), indicating that an estimated maximum of 150 RNAs occurs within a transcription site. Dose dependence of E2-dependent transcription. Nuclear smRNA FISH signals were quantified at various E2 concentrations. The mean intensity of the two brightest nuclear foci without GFP signal (wild-type alleles) is comparable to the mean intensity of the brightest spot co-localizing with GFP (knock-in allele). Both show an E2-dependent increase that is reduced upon addition of 1 μM ICI 182,780 (ICI). Absolute calibration of spot intensities by counting single PP7-labeled GREB1 RNA molecules. (Left) MCF7-GREB1-PP7 cells were grown in 1,000 pM E2, and images were acquired at maximum light intensity. Single RNAs (red arrowheads) are apparent as dim spots, often in close proximity to the transcription site (blue asterisk). A maximum intensity projection of bandpass-filtered images is shown in the middle. The filtering accentuates particles which are then quantified by fitting to a three-dimensional Gaussian distribution, as seen on the right for the particle marked with the red arrowhead with white border. Scale bars: 5 and 1 μm for the single spot. (Middle) Intensity distribution of single transcripts. The histogram of spot intensities was fitted to a Gaussian function. The mean of the Gaussian function is indicated in the legend. (Right) Transcription sites were imaged at the same stage position under imaging conditions for long-term live-cell imaging (2% light intensity) and conditions for visualization of single transcripts (100% light intensity). Their intensities were quantified and the fitted ratio of intensities used to calculate the equivalent intensity of a single transcript under live-cell imaging conditions. Download figure Download PowerPoint The mean intensity and frequency of occurrence of transcription sites increased in an E2 dose-dependent manner in the cell population (Figs 1D and EV1C and G). In the absence of E2, only a few cells had dim transcription sites, whereas at the saturating E2 concentrations, the GREB1 locus was actively transcribed in around 90% of cells, highlighting an appropriate dynamic range within our experimental system. The pure anti-estrogen ICI 182,780 and the transcriptional inhibitor actinomycin D reduced spot intensities and the number of cells with active transcription, confirming that the occurrence of nuclear foci depends on estrogen signaling and on transcription (Fig 1D). Digital modulation of GREB1 transcription by estrogen Snapshot measurements at particular time points contain limited information about the kinetics of transcriptional bursting. We therefore monitored the temporal fluctuations of GREB1 transcription for 13 h using time-lapse fluorescence microscopy at an imaging interval of 3 min, which is well below the estimated ~30 min residence time of individual, nascent GREB1 RNAs at the locus (Fig 1E). Transcribing foci were detected, tracked within nuclei, and quantified from their 3D image volume (see Materials and Methods). Absolute transcript numbers were derived through calibration to the intensity of single transcripts from images at high excitation intensities (Fig EV1H). This quantification was independently confirmed through exonic smRNA FISH (Fig EV1F). We observed that endogenous GREB1 is transcribed in stochastic bursts with up to ~150 elongating polymerases present on the body of the gene. To evaluate the effect of E2 on burst properties, we recorded single-cell transcription after 3 days of stimulation at eight concentrations of E2, ranging from absence to saturating conditions (Figs 2A–C and EV2A–C, Dataset EV1). We analyzed about 60–90 cells per condition and observed that E2 increased the transcriptional activity of the GREB1 gene in a dose-dependent manner. Dose dependence is also visible in the global intensity histogram over all cells and time points (Figs 2D and EV2D) as a characteristic bimodal distribution, in which transcription is either close to the background intensity or much higher, with intermediate intensities rarely observed. Similar bimodal distributions were also observed in smRNA FISH experiments (Fig EV2D). This suggests that GREB1 exhibits digital ON/OFF-behavior, where increasing E2 increases the duration of time the gene spends in the transcriptionally active state. Higher doses of E2 furthermore gradually shift the right peak in the histogram toward higher intensity values. This suggests either an analog mode of transcription regulation, where more polymerases are recruited per burst, or an overlap in the signal between consecutive bursts after short OFF-times. Figure 2. The transcriptional behavior of GREB1 changes with estrogen dose and exhibits considerable cell-to-cell variation (see also Fig EV2 and Movie EV2) A. Cell-to-cell variation in GREB1 expression at multiple E2 concentrations. Transcription was observed for 13 h in individual MCF7-GREB1-PP7 cells at different E2 concentrations. Trajectories of single cells are represented as color maps and are sorted from lowest (top) to highest (bottom) total RNA output (ΣRNA), calculated as area under the curve divided by the average signal from single transcripts (right). Color denotes the absolute number of nascent RNAs. The squared coefficient of variation (standard deviation2/mean2) of the total RNA output among the population is indicated (right). Dashed lines indicate exemplary cells shown in panels (B and C). B. Representative time traces for low, medium, and high expressing cells. C. Autocorrelation (ACF) curves of the individual time traces in (B). D. Increasing E2 increases the proportion and productivity of transcriptionally active periods. Histograms were generated from the number of RNA molecules at the transcription site from all data points at different E2 concentrations. The distribution of the background signal is shown in gray. E, F. Increasing E2 concentrations decrease the length of inactive periods and increase the burst size. Promoter OFF-times (E) and burst sizes (F) were extracted from the experimental tracks (see Fig EV2E). Exponential functions (red) with the same mean (dashed line) are shown for the OFF-times. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. GREB1 transcription changes with estrogen concentration and exhibits considerable cell-to-cell variation throughout all datasets. Related to Fig 2 A–C. Raw data, example trajectories and autocorrelation functions (ACF) as in Fig 2, are shown for all eight E2 concentrations. D. Histograms of the number of RNA molecules at transcription sites. The distribution of nascent RNAs from all eight live-cell datasets over all cells and time points is plotted (left), with the background signal shown in gray. The intensity distribution from smRNA FISH (right) is comparable across E2 concentrations. E. Extraction of ON- and OFF-times from raw time traces. A simple thresholding strategy was used on the slope of the median-filtered time trace to separate transcriptionally active from inactive periods. The duration of each ON- and OFF-time was calculated. For each ON-time, the burst size was calculated as the increase in the number of nascent RNAs during an ON-time, and the initiation rate was derived as the ratio of burst size and ON-time. F. Distribution of OFF- and ON-times indicates that single rate-limiting steps occur during the transition between promoter states. Burst features were extracted from time traces in panel (A) as described above. The mean value is indicated in the plots (dashed line). Exponential distributions with the same mean are plotted for the OFF- and ON-times (red) and indicate good agreement. Durations in the range of the imaging interval cannot be reliably estimated leading to deviation at short ON-durations. Download figure Download PowerPoint To further characterize the dynamics of E2-dependent regulation of transcriptional behavior, we directly extracted the duration of transcriptionally active and inactive periods from single-cell time courses by assuming that active periods are characterized by positive slopes in the time course (see Materials and Methods). We observed that the average pause duration in between bursts shortens with increasing E2 levels (from 184 to 26 min), while the average burst size increases (from 5 to 17 RNAs/burst) (Figs 2E and F, and EV2E and F). We thereafter employ mathematical modeling to quantitatively describe how burst properties change with the stimulus concentration. The productivity of GREB1 RNA synthesis exhibits considerable cell-to-cell variability We observed that, although all individual cells show stochastic bursting, some cells generate low total amounts of RNA throughout the 13-h observation period, whereas others synthesize much larger total amounts of GREB1 RNA (Fig 2A, right). This results in a considerable spread in the time-integrated intensity, which varies from 3,600 to 32,000 RNA∙min between individual cells at a saturating E2 concentration of 100 pM. Considering the background intensity and that a single RNA contributes to the fluorescence signal for 30 min, this corresponds to a total RNA output of between ~110 and 1,100 GREB1 RNAs within 13 h. The observed cell-to-cell variability is stable over time, as the RNA output during the first half of the movie correlates with the RNA output during the second half (Appendix Fig S1A). Thus, the GREB1 locus exhibits intrinsic stochastic dynamics and experiences more stable (extrinsic) fluctuations that affect long-term RNA production rates. This highlights that stable extrinsic factors, which reflect cellular state, directly impact gene expression on the level of nascent transcription. Furthermore, at low E2 concentrations, we observe cells that do not have any transcriptional activation during the entire imaging period. Such cells can be reliably identified through analysis of their autocorrelation function, which decays instantaneously to zero if a trajectory consists solely of background noise (Figs 2C and EV2C). Based on this criterion, non-responders make up approximately 50% of the population in the absence of stimulation; all cells however respond when the concentration of E2 is above 10 pM. In responding cells, the autocorrelation function decays with slower kinetics (t1/2 ~20–30 min), with a longer decay occurring with increasing E2 concentrations. To investigate why individual cells show stable differences in transcriptional output, we extracted several morphological features from our microscopy images, namely cell area, nuclear shape, and local cell density. We found that none of these features alone correlated strongly and consistently across estrogen doses with overall transcriptional output (Appendix Fig S2). As a weak trend, we observe that cells with higher transcriptional activity tend to exhibit higher nuclear and cytoplasmic areas, supporting previous studies showing that the cell volume contributes to transcriptional output (Kempe et al, 2015; Padovan-Merhar et al, 2015). We consider it unlikely that the cell cycle stage constitutes a major source of extrinsic noise, as we completely discarded cells that show two transcription sites from a replicated allele at any time point during the observation period. Hence, cells that were analyzed never passed through S, G2 or M and were restricted to the G0/G1 phases of the cell cycle. Furthermore, we tracked cells after cell division and observed that stable differences in transcriptional output persist over the subsequent 6 h, when all cells are exclusively in early G1 phase (Appendix Fig S3 and Dataset EV2). Taken together, our dataset exhibits several characteristic features, including a digital-to-analog global intensity histogram, strong cell-to-cell variability in the time-averaged intensity of transcription, and a subpopulation of non-responding cells. The interpretation of these phenomena is not straight-forward, due to the stochastic nature of the single-cell trajectories. We therefore turned to mathematical modeling to infer promoter dynamics from the data, and to understand why individual cells show a different susceptibility to estrogen-induced GREB1 expression. Quantitative stochastic modeling of the dynamics of single-cell transcription We implemented stochastic models that describe GREB1 promoter activity and nascent RNA transcription. These models were fitted to experimental data to discriminate between different hypotheses of promoter regulation and to estimate kinetic parameters. As in previous work, the gene promoter was not modeled in molecular detail, but as an abstract cycle of transcriptionally active (ON) or inactive (OFF) promoter states (Paulsson, 2005; Zoller et al, 2015). As model variants, we considered five different promoter topologies, ranging from a simple two-state model with two rate-limiting steps in gene (de)activation to a 10-state cycle (Fig 3A). A two-state model was sufficient to describe the behavior of other mammalian genes (Harper et al, 2011; Dar et al, 2012; Larson et al, 2013), while more states may better reflect the multiple sequential epigenetic steps reported for estrogen-dependent gene activation (Lemaire et al, 2006). Progression through promoter states was modeled to occur as a series of irreversible reactions with rates kON and kOFF, respectively, and transcription could be initiated from active states with rate kinit. We simulated the temporal evolution of the promoter using the stochastic simulation algorithm (Gillespie, 1977) and modeled polymerase-mediated transcript elongation deterministically with rate kelong. To link model and experiment, we considered how each elongating transcript contributes to the fluorescent signal
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