Timing of gene expression in a cell‐fate decision system
2018; Springer Nature; Volume: 14; Issue: 4 Linguagem: Inglês
10.15252/msb.20178024
ISSN1744-4292
AutoresDelphine Aymoz, Carme Solé, J Talbot Pierre, Marta Schmitt, Eulàlia de Nadal, Francesc Posas, Serge Pelet,
Tópico(s)Cell Image Analysis Techniques
ResumoArticle25 April 2018Open Access Transparent process Timing of gene expression in a cell-fate decision system Delphine Aymoz Delphine Aymoz orcid.org/0000-0002-4903-6229 Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland Search for more papers by this author Carme Solé Carme Solé orcid.org/0000-0003-4543-7401 Cell Signaling Research Group, Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain Search for more papers by this author Jean-Jerrold Pierre Jean-Jerrold Pierre orcid.org/0000-0002-6209-5717 Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland Search for more papers by this author Marta Schmitt Marta Schmitt Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland Search for more papers by this author Eulàlia de Nadal Eulàlia de Nadal orcid.org/0000-0003-0039-5607 Cell Signaling Research Group, Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain Search for more papers by this author Francesc Posas Francesc Posas orcid.org/0000-0002-4164-7076 Cell Signaling Research Group, Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain Search for more papers by this author Serge Pelet Corresponding Author Serge Pelet [email protected] orcid.org/0000-0002-0245-049X Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland Search for more papers by this author Delphine Aymoz Delphine Aymoz orcid.org/0000-0002-4903-6229 Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland Search for more papers by this author Carme Solé Carme Solé orcid.org/0000-0003-4543-7401 Cell Signaling Research Group, Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain Search for more papers by this author Jean-Jerrold Pierre Jean-Jerrold Pierre orcid.org/0000-0002-6209-5717 Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland Search for more papers by this author Marta Schmitt Marta Schmitt Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland Search for more papers by this author Eulàlia de Nadal Eulàlia de Nadal orcid.org/0000-0003-0039-5607 Cell Signaling Research Group, Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain Search for more papers by this author Francesc Posas Francesc Posas orcid.org/0000-0002-4164-7076 Cell Signaling Research Group, Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain Search for more papers by this author Serge Pelet Corresponding Author Serge Pelet [email protected] orcid.org/0000-0002-0245-049X Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland Search for more papers by this author Author Information Delphine Aymoz1, Carme Solé2, Jean-Jerrold Pierre1, Marta Schmitt1, Eulàlia de Nadal2, Francesc Posas2 and Serge Pelet *,1 1Department of Fundamental Microbiology, University of Lausanne, Lausanne, Switzerland 2Cell Signaling Research Group, Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, Barcelona, Spain *Corresponding author. Tel: +41 21 692 5621; E-mail: [email protected] Molecular Systems Biology (2018)14:e8024https://doi.org/10.15252/msb.20178024 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 During development, morphogens provide extracellular cues allowing cells to select a specific fate by inducing complex transcriptional programs. The mating pathway in budding yeast offers simplified settings to understand this process. Pheromone secreted by the mating partner triggers the activity of a MAPK pathway, which results in the expression of hundreds of genes. Using a dynamic expression reporter, we quantified the kinetics of gene expression in single cells upon exogenous pheromone stimulation and in the physiological context of mating. In both conditions, we observed striking differences in the timing of induction of mating-responsive promoters. Biochemical analyses and generation of synthetic promoter variants demonstrated how the interplay between transcription factor binding and nucleosomes contributes to determine the kinetics of transcription in a simplified cell-fate decision system. Synopsis Quantitative and dynamic single cell measurements uncover a complex temporal orchestration of gene expression events in the yeast mating response. The promoter architecture controls the timing of gene expression relative to the time of fusion. Exogenous stimulations of yeast cells with pheromone have allowed to classify mating promoters in three categories, based on their gene expression dynamics. The number and affinity of transcription factor binding sites in nucleosome depleted regions regulate the timing of gene induction. In presence of mating partners, early genes are expressed during the sensing phase, while late genes are induced shortly before fusion. Introduction Cell-fate decisions play a key role in crucial processes such as tissue repair, immune response, or embryonic development. In order to make choices, cells integrate cues from neighboring cells as well as from morphogens. Signal transduction cascades relay this information inside the cell to translate these extracellular signals into defined biological responses. The cellular output includes the induction of complex transcriptional programs where specific genes are expressed to different levels and at various times (Gurdon et al, 1995; Ashe et al, 2000). Ultimately, these different expression programs will determine the fate of individual cells. The mating pathway in budding yeast has often been considered as a simplified cell-fate decision system, where each cell can either continue to cycle in the haploid state or decide to mate with a neighboring cell of opposing mating type. This decision results in an arrest of the cell cycle and formation of a mating projection and ultimately leads to the fusion with the partner to form a diploid zygote (Bardwell, 2005; Atay & Skotheim, 2017). Haploid budding yeast senses the presence of potential mating partners by detecting pheromone in the medium. This small peptide elicits the activation of a mitogen-activated protein kinase (MAPK) cascade (Appendix Fig S1), which can integrate multiple cues such as stresses, cell cycle stage, or nutrient inputs (Strickfaden et al, 2007; Doncic et al, 2011; Nagiec & Dohlman, 2012; Clement et al, 2013). Once the MAPKs Fus3 and Kss1 are activated, they phosphorylate a large number of substrates and induce a new transcriptional program. Ste12 is the major transcription factor (TF) implicated in this response and controls the induction of more than 200 genes (Roberts et al, 2000). Under normal growth conditions, this TF is repressed by Dig1 and Dig2. Phosphorylation by active Fus3 and Kss1 relieves this inhibition, such that Ste12 can recruit the transcriptional machinery (Tedford et al, 1997). Ste12 associates with the DNA via well-established binding sites located in promoters called pheromone response elements (PRE), with the consensus sequence ATGAAACA (Kronstad et al, 1987; Hagen et al, 1991). Although PREs are found upstream of the vast majority of pheromone-induced genes (Chou et al, 2006), the number of binding sites, their orientation, and their position relative to the transcription start site vary widely from one gene to the next (Chou et al, 2006; Su et al, 2010). Promoter sequences are primary determinants of the strength and kinetics of gene expression. Unfortunately, the basic rules governing transcription regulation remain poorly understood. Libraries of synthetic promoter sequences have allowed establishing a few rules in the control of the expression level and the noise of a promoter sequence (Sharon et al, 2012; Levo & Segal, 2014; Hansen & O'Shea, 2015). However, the slow maturation time of fluorescent proteins (FP) precluded thorough investigations of gene expression kinetics. In a previous paper, we developed the dPSTR, a fluorescent relocation reporter that converts the expression of a promoter into a signal of relocation of a fluorescent protein (Aymoz et al, 2016). In this study, we use these dynamic gene expression reporters to characterize the induction dynamics of a set of promoters activated in response to yeast mating pheromone. We have identified different classes of promoters based on the kinetics of their expression. Deeper analysis of early and late promoters highlighted the interplay between TF binding and nucleosome positioning as a major determinant of the expression dynamics. In addition, we demonstrate that under physiological mating conditions, the induction of the target genes follows a precise chronology and they are sequentially expressed until fusion occurs. Results Interplay between kinase activity and expression dynamics In multiple MAPK pathways, MAPK activity has been shown to be tightly linked to the transcriptional process by phosphorylating TFs, contributing to the recruitment of remodeling complexes, and participating in the elongation complex (de Nadal et al, 2011). Therefore, we wanted to measure, in the mating pathway, how kinase activity and gene expression were temporally correlated. Using fluorescent relocation sensors that we previously engineered, we are able to quantify, in real-time and at the single-cell level, both MAPK activity and gene expression upon stimulation of MATa cells with synthetic pheromone (α-factor, 1 μM; Durandau et al, 2015; Aymoz et al, 2016). Signaling activity was quantified using a Ste7DS-SKARSY, which exits the nucleus when the mating MAPKs Fus3 and Kss1 phosphorylate specific residues in the vicinity of a nuclear localization sequence (NLS) (Fig 1A; Appendix Fig S2A). In the same cells, a dynamic protein expression reporter pFIG1-dPSTRR was integrated. FIG1 displays the largest fold induction upon pheromone stimulation (Roberts et al, 2000). In this assay, the FIG1 promoter drives the expression of a small peptide, which interacts with a fluorescent protein and promotes its recruitment in the nucleus (Fig 1A, Appendix Fig S2B, Aymoz et al, 2016). Upon stimulation, the cells activate the mating MAPKs a few minutes after stimulation, as previously described (Yu et al, 2008; Nagiec & Dohlman, 2012; Durandau et al, 2015). Despite this fast signal transduction, the resulting pFIG1 expression occurs 30 min later (Fig 1A and C). Individual yeast cells are known to possess a large diversity in signaling capacity (Colman-Lerner et al, 2005; Strickfaden et al, 2007). However, the expression dynamics of pFIG1 is still highly variable within the sub-population of cells that activate the MAPK within the 10 min following stimulation, suggesting that the heterogeneity in pFIG1 expression does not result from various kinetics of MAPK activation (Appendix Fig S3A). This finding suggests an absence of temporal correlation between kinase activity and the downstream transcriptional response. Figure 1. Interplay between kinase activity and promoter induction in the mating pathway A, B. Microscopy images of cells stimulated with a saturating pheromone concentration (1 μM) at time 0 min. The cells bear a histone tagged with CFP, a yellow SKARS reporting on Fus3p and Kss1p activities, and a red dPSTR reporting on pFIG1 (A) or pAGA1 (B) induction. For all experiments, unless stated otherwise, the stimulation was performed by addition of 1 μM α-factor at time 0 min. C, D. Quantifications of the kinase activity (green, left axis), measured by the ratio of cytoplasmic to nuclear YFP, and of the pFIG1 (C) and pAGA1 (D) expressions, measured by the difference between nuclear and cytoplasmic fluorescence of the dPSTR (right axis). For all similar graphs, the solid line is the median response and the shaded area represents the 25th–75th percentiles of the population. E. Microscopy images of a strain carrying pFIG1-dPSTRR and pAGA1-dPSTRY. F. Quantification of the response time of pFIG1 and pAGA1 reporters (see Materials and Methods). The inset is the difference response time between the pAGA1-dPSTRY and the pFIG1-dPSTRR, for all cells expressing both promoters. The red shaded area represents cells expressing pAGA1 before pFIG1 (87%). G. Correlation of normalized dPSTR nuclear enrichments from all single cells of a representative experiment at different time points after stimulation. H. Northern blot detection of mRNAs from AGA1 and FIG1 after stimulation of the cells with mating pheromone. See also Appendix Fig S15. Data information: All scale bars on microscopy images represent 2.5 μm. Download figure Download PowerPoint This surprising result led us to test the expression kinetics of multiple mating-responsive promoters. Among them was AGA1, a gene reported to be strongly induced upon pheromone stimulation (Roberts et al, 2000; McCullagh et al, 2010). The pAGA1-dPSTRR begins to enrich in the nucleus of cells 15 min after stimulation (Fig 1B and D). Thus, the induction of gene expression from this promoter is much faster than for pFIG1. In addition, the induction of pAGA1 in signaling-competent cells is less variable with the vast majority of the cells inducing the reporter within 30 min following the stimulus (Appendix Fig S3A). This raises the question of how the activation of these two promoters is related in a same cell. Direct comparison of two dynamic expression reporters We used a second protein expression reporter, the dPSTRY, which is orthogonal to the dPSTRR, to quantify pAGA1 and pFIG1 expression dynamics in the same strain (Aymoz et al, 2016; Fig 1E and Appendix Fig S3B). In all expressing cells, the response time for each promoter was determined based on the time at which the dPSTR nuclear enrichment reached 20% of its maximum (Fig 1F, see Materials and Methods and Appendix Fig S4). pAGA1 expression is relatively homogeneous between cells, with 83% of the cells inducing the promoter within the first 30 min following stimulation. In comparison, pFIG1 expression is highly variable from cell to cell. In cells inducing both promoters, the difference in response times can be measured (Fig 1F, inset). In 87% of cells, the pAGA1-dPTSRY is activated prior to the pFIG1-dPSTRR, which on average is delayed by 23 min. These different dynamics of induction are also well illustrated by the absence of correlation between the dPSTR enrichment seen at early time points (Fig 1G). The cell population becomes first pAGA1 expressing, as denoted by a shift along the x-axis. Later, a shift of the cell population is observed along the y-axis, illustrating the delay in the induction of pFIG1. This delay is not an artifact from the dPSTRs, since the same results can be obtained when exchanging the promoters on the dPSTRs (Appendix Figs S5 and S6). In parallel, we have also verified that mRNA production dynamics from these two promoters correlate well with the expression dynamics we measured with the dPSTR (Fig 1H, Appendix Fig S7). Together, these data demonstrate that although the MAPK activity rises quickly in response to pheromone sensing, it does not lead to a fast and simultaneous transcriptional activation of all mating genes. Characterization of mating-induced promoters Having established that the two promoters pAGA1 and pFIG1 are induced with different kinetics following pheromone stimulation, we tested when other mating-induced genes were induced with respect to pAGA1. Fourteen mating-responsive promoters, previously described in the literature, were characterized using a dPSTRR (Fig EV1; Roberts et al, 2000; Chou et al, 2006; Su et al, 2010). We quantified for each of them their expression output, by measuring the maximal variation of the nuclear enrichment of the dPSTRR upon stimulation (see Materials and Methods, Appendix Fig S4). These promoters display a large variability both in the level of induction and in the timing of expression (Fig 2A, Dataset EV1). Some genes are expressed early as AGA1 (FUS1, FAR1, STE12, etc…); others are late responders similar to FIG1 (PRM3, KAR3). Click here to expand this figure. Figure EV1. Dynamics and expression level of mating-dependent promoters A–N. Population median (solid line) of the nuclear enrichment of the red dPSTRR (left axis) or the pAGA1-dPSTRY (right axis, yellow curves) for the 14 promoters of the study. Panels (A–F): early promoters, panels (G–K): intermediate promoters, panels (L–N): late promoters. Note that the scale of the dPSTRY is identical for all graphs, whereas dPSTRR scales are different. The basal level and induced level can vary according to the measured promoter. For instance, pFAR1 has a high basal level, due to its cell cycle-dependent induction. O–Q. Similar graphs for strains carrying different combinations of dPSTRs. Data information: In all graphs, the solid line is the median response and the shaded areas represent the 25th–75th percentiles of the single-cell responses. The curves are one representative experiment from at least three replicates. Download figure Download PowerPoint Figure 2. Dynamics of induction of mating promoters after pheromone stimulation A. Response time versus mean expression output for the 14 mating-dependent promoters. Dots represent the median response times of the cell population, and lines represent the 25th and 75th percentiles. All promoters were measured with the dPSTRR. The strains also bear the pAGA1-dPSTRY for direct comparison of the dynamics of promoter induction. The dashed line represents the detection sensitivity of the dPSTRR reporter. B. Distributions of the differences in the response times between the pAGA1-dPSTRY and the dPSTRR in the same cell for pFUS1, pFUS2, and pFIG1. C. Correlation of the population-averaged normalized nuclear enrichment of pAGA1-dPSTRY and a selected set of promoters measured with the dPSTRR at all time points of the experiments. The curves show the evolution in course of the experiment, from the bottom left to upper right corner, of the expression levels of the two measured promoters. The dots represent the P-value (10−3 > P > 10−6 for small dots and P < 10−6 for large dots) of the t-test comparing the offset of the measured promoter relative to the x = y line with the offset of the reference promoter pAGA1. D, E. Correlation of normalized dPSTR nuclear enrichments of single cells of at different time points after stimulation in a strain with pFUS1-dPSTRR and pAGA1-dPSTRY (D) or pFIG1-dPSTRR and pKAR3-dPSTRY (E). F. Evolution of the correlative promoter variability (CPV) in course of time, for various pairs of promoters. The curve represents the mean of three replicates, and the error bar represents the standard deviation between replicates. A low CPV corresponds to a similar expression between two promoters in the same cell (see Materials and Methods). Download figure Download PowerPoint In order to better characterize the dynamics of expression of the 14 promoters, they were compared to the same internal control, a pAGA1-dPSTRY. The difference in response time relative to pAGA1 induction was calculated (Figs 2B and EV2). In addition, the comparison of the overall dynamics of induction was visualized by plotting the mean nuclear enrichment of the yellow and red dPSTRs, normalized between their basal and maximal expression levels (Figs 2C and EV2). Each curve represents the correlation of the normalized expression levels of the two measured promoters and its evolution in course of the time-lapse, going from the bottom left to the upper right corner. Promoters which are induced with similar dynamics as pAGA1 will remain close to the x = y diagonal (dashed line). Any difference in induction dynamics will cause a deviation from this line. Based on these measurements, we defined three classes of promoters: early, intermediate, and late. The early promoters, with kinetics similar to pAGA1, display a difference in response time centered around zero and a correlation aligned on the x = y diagonal (Fig EV2). Late promoters, which behave similarly to pFIG1, have a response time delayed by at least 15 min and a correlation strongly deviating from the diagonal. Between these two clearly identifiable groups, a set of promoters display intermediate kinetics, where the response time is slightly delayed and/or where the dynamic correlation with pAGA1 is significantly deviating from the pAGA1/pAGA1 correlation at many time points. Click here to expand this figure. Figure EV2. Characterization of promoters relative to pAGA1 Distribution of the difference between the response time for pAGA1-dPSTRY and the specified promoter measured with dPSTRR for one representative experiment. NC > 100 cells (see Materials and Methods). A sign test was performed to assess distribution centered around 0 (*10−20 < P < 10−5; **P < 10−20). Correlation of the average normalized nuclear enrichment of pAGA1-dPSTRY and specified promoters measured with dPSTRR at all time points of the experiments. The dotted line is the x = y line and indicates the time direction (from bottom left to upper right). Each curve starts at 0 at the beginning of the experiment. The dots represent the P-value (10−3 < P < 10−6 for small dots and P < 10−6 for big dots) of the t-test comparing the offset of the measured promoter from the x = y line to the offset of the reference promoter pAGA1 (red curve, upper panel). Quantification of the correlative promoter variability between the indicated promoter measured by dPSTRR and the pAGA1-dPSTRY (see Materials and Methods). Each curve represents the average of the CPV calculated for at least three biological replicates with the standard deviation of the three experiments. The dotted line is pAGA1-dPSTRR curve for comparison. Download figure Download PowerPoint The basal level of expression before stimulus (Appendix Fig S8) or the maximal expression level reached after pheromone induction (Fig 2A) does not allow to predict whether a promoter will be fast or slow. For instance, the STE12 promoter belongs to the early genes group, but possesses one of the lowest induction levels. However, there is a clear link between the ability to respond at low pheromone concentration and the dynamics of promoter induction (Fig EV3, Appendix Fig S9). pAGA1 and other promoters from this category display a graded response as α-factor concentration increases. In comparison, late promoters behave in a more switch-like manner (Hill coefficient close to 3), where gene expression occurs only at high concentrations of α-factor (300 nM). Click here to expand this figure. Figure EV3. Dose response of pAGA1 and pFIG1 induction A, B. Mean expression output for pAGA1 (A) and pFIG1 (B) in response to different pheromone concentrations, in a WT or bar1∆ (shaded) background. The expression output is defined as the maximal dPSTR nuclear enrichment following stimulation, for all cells of the experiment. Error bars represent the standard deviation of three replicates. Note that the induction of pAGA1 gradually increases with the pheromone concentration, whereas pFIG1 displays a switch-like response in WT. Note that in a bar1∆ background, the expression occurs at lower concentrations and with higher level for both promoters. The first dot is the non-induced control. C. Percentage of cells expressing pAGA1 (red) or pFIG1 (blue) in a WT (upper panel) or bar1∆ (lower panel) background, at various pheromone concentrations for one representative experiment. Note that only at high concentrations, a significant proportion of the population expresses pFIG1. D. Median nuclear enrichment of the pAGA1-dPSTRY (red, left axis) and of the pFIG1-dPSTRR (blue, right axis) in course of time, for the different pheromone concentration, in the WT background, for one representative experiment. The solid line is the median, and the shaded area represents the 25th–75th percentile. The reference curves at 1 μM are represented in dashed line for comparison. Note that the scale of the pFIG1-dPSTRR is different between the 1 μM and the other concentrations. Download figure Download PowerPoint Variability in gene expression When focusing on the single-cell responses, a remarkable correlation between the expressions of the fast promoters at various time points can be observed (pAGA1/pFUS1: Fig 2D and other pairs in Appendix Figs S10 and S11). This tight correlation can be explained by the low noise present in the mating pathway and the expression variability being mostly governed by extrinsic variables such as the cell cycle stage and the expression capacity (Colman-Lerner et al, 2005). More striking is the fact that two late promoters in the same cell are also induced with a good correlation. This implies that despite the fact that the induction of these late genes can occur from 30 to 80 min after the stimulus, these two promoters are activated synchronously within a given cell (Fig 2E and Appendix Figs S6 and S11). These data also allow to rule out the presence of a slow stochastic activation of the late genes and rather argue in favor of a specific commitment point that the cells reach when they start to induce the late promoters. In order to illustrate this better, we defined the correlative promoter variability (CPV), which allows to quantify the deviation in the induction of two promoters measured in the same cell, relative to the overall noise in expression (Fig 2F, Appendix Fig S6 and Fig EV2, see Materials and Methods). For two promoters well correlated like pAGA1 and pFUS1 (Oehlen et al, 1996), the CPV starts below 50% and tends to further decrease upon pheromone-dependent induction. Among fast promoters, there can be different types of behavior, depending mostly on the pre-stimulus levels of the reporter. The variability between pFAR1 and pAGA1 is a good illustration of this (Fig EV2). The CPV is high in basal conditions, because pAGA1 and pFAR1 are both transiently expressed during the cell cycle, although in different phases (Appendix Figs S8 and S10, Oehlen et al, 1996). However, following stimulation with pheromone, the variability decreases quickly as the two promoters are simultaneously induced. In comparison, the CPV between the late FIG1 promoter and the early pAGA1 increases during the first 20 min following induction, due to an asynchronous induction of pAGA1 and pFIG1. Upon activation of the late promoter, the variability decreases. The CPV value comparing the two slow promoters pFIG1 and pKAR3 is around 60% before stimulation (Fig 2F, blue curve). This value is higher than 50% because of the cell cycle driven induction of pKAR3, leading to various basal levels of this promoter, whereas pFIG1 is not expressed in absence of stimulation (Kurihara et al, 1996; Appendix Fig S8). After stimulus, this CPV level is maintained for roughly 30 min, during which none of these two promoters are induced and then drops. Overall, these measurements demonstrate that each mating-induced promoter is expressed with specific dynamics and expression level. Some cells will induce the early genes few minutes after the stimulus, while in the same cell, other genes can be expressed up to 40 min after the first wave of gene expression. Remarkably, the tight co-regulation of early and late genes within their group strongly suggests that a shared mechanism exists that regulates the early promoters, which is different from the one controlling the activation of the late promoters. Architecture of mating promoters In order to understand how the timing of induction is regulated, we have mapped all putative Ste12 binding sites in the sequences of the fourteen promoters (Appendix Fig S12). We defined consensus PREs as nTGAAACn, as it was reported that these six core nucleotides were the most important to promote Ste12 binding in vitro (Su et al, 2010). We also identified several non-consensus PREs that carry additional mutations within the six core nucleotides. These putative binding sites possess a decreased affinity for Ste12, but can contribute to Ste12-mediated expression (Su et al, 2010). As reported previously, there is a large variability in the number, orientation, spacing, and sequences of PREs among all promoters (Chou et al, 2006; Su et al, 2010). Therefore, there is no obvious rule that would allow to predict whether a gene is early- or late-induced, or expressed at low or high levels. Interestingly, pAGA1 and pFIG1 possess three consensus PREs with relatively similar dispositions and orientations and respectively four and five non-consensus PREs (Fig 3A and B). Despite these similarities, we have observed drastic differences in their expression kinetics. Therefore, we decided to use pAGA1 and pFIG1 as model promoters of their categories and decipher their mode of regulation. Figure 3. Influence of promoter architecture on expression dynamics A, B. Maps of the two promoters pAGA1 and pFIG1. The filled arrows represent the location and orientation of consensus Ste12-binding sites (nTGAAACn). The open arrows symbolize the non-consensus binding sites that possess mutations within the six core nucleotides of the PREs. The sequences of each binding sites are detailed above, with capital nucleotides matching the consensus sequences and small nucleotides being mutations from the consensus. The numbers between si
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