Arabidopsis SWI/SNF class chromatin remodeling ATPase SPLAYED (SYD) is required for the expression of selected genes downstream of the jasmonate (JA) and ethylene (ET) signaling pathways. SYD is also directly recruited to the promoters of several of these genes. Furthermore, we show that SYD is required for resistance against the necrotrophic pathogen Botrytis cinerea but not the biotrophic pathogen Pseudomonas syringae. These findings demonstrate not only that chromatin remodeling is required for selective pathogen resistance, but also that chromatin remodelers such as SYD can regulate specific pathways within biotic stress signaling networks.Organisms are continuously exposed to a myriad of environmental stresses. Central to an organism's survival is the ability to mount a robust transcriptional response to the imposed stress. An emerging mechanism of transcriptional control involves dynamic changes in chromatin structure. Alterations in chromatin structure are brought about by a number of different mechanisms, including chromatin modifications, which covalently modify histone proteins; incorporation of histone variants; and chromatin remodeling, which utilizes ATP hydrolysis to alter histone-DNA contacts. While considerable insight into the mechanisms of chromatin remodeling has been gained, the biological role of chromatin remodeling complexes beyond their function as regulators of cellular differentiation and development has remained poorly understood. Here, we provide genetic, biochemical, and biological evidence for the critical role of chromatin remodeling in mediating plant defense against specific biotic stresses. We found that the  Arabidopsis. We found that a chromatin remodeling protein, SPLAYED, is required for gene expression within specific biotic stress signaling networks. Consistent with this observation, loss of SPLAYED chromatin-remodeling activity resulted in increased susceptibility to a fungal pathogen, Botrytis cinerea, but not to a bacterial pathogen, Pseudomonas syringae. These results demonstrate that reduced stress tolerance in a chromatin-remodeling mutant plant can be stress specific, and is not simply due to a decrease in overall fitness as a result of non-specific global mis-regulation of gene expression.In eukaryotes, genomic DNA is organized into a complex DNA-protein structure termed chromatin. The organization of chromatin serves to compact DNA within the nucleus and plays a central role in regulating transcription by controlling the access of DNA to transcriptional machinery. Chromatin structure can be altered through several mechanisms, one of which is chromatin remodeling, a process that disrupts DNA–protein interactions resulting in altered accessibility of specific DNA regions to regulatory proteins in the transcriptional machinery. In this study, we investigated the biological role of chromatin remodeling in defense responses to biotic stresses using the model plant  In eukaryotic organisms genomic DNA is packaged into chromatin, which can repress transcription by blocking the access of regulatory proteins to DNA. Dynamic changes in chromatin structure are now recognized as a robust mechanism of transcriptional control Arabidopsis has served as a valuable model due to the fact that mutants in genes encoding a number of chromatin remodeling complex proteins are viable. One of the most well characterized chromatin remodeling complex proteins in Arabidopsis is the SWI/SNF class chromatin remodeling ATPase SPLAYED (SYD). Loss of SYD activity causes defects in many different developmental pathways including stem cell maintenance, patterning, developmental transitions and growth Significant advances have been made in understanding the mechanism of ATP-dependent chromatin remodeling complex action The biological role of altering chromatin structure in response to stress via chromatin modifications and incorporation of histone variants has been documented B. cinerea but not the biotrophic pathogen P. syringae. These findings demonstrate not only that chromatin remodeling is required for selective pathogen resistance, but also that chromatin remodelers, such as SYD can regulate specific pathways within biotic stress signaling networks.In this study we examine the role chromatin remodeling plays in biotic stress responses. We found that SYD is required for expression of specific genes within biotic stress signaling networks. This requirement is likely both direct and indirect as SYD is recruited to the promoter of some, but not all, of the genes for which it is required for expression. We show that SYD is required for resistance against the necrotrophic pathogen Arabidopsis, a multicellular organism where viable chromatin remodeling null mutants exist SYD transcript is upregulated rapidly following mechanical wounding SYD in response to wounding suggests that SYD may be recruited to remodel promoters within stress signaling networks. To begin delineating the placement of SYD in stress signaling we first examined whether SYD is required for expression of other transcripts upregulated rapidly in response to wounding. This demonstrated that SYD is not required for the expression of the rapid wound response genes ETHYLENE RESPONSE FACTOR #18 (ERF#18) or CCR4 ASSOCIATED FACTOR 1-like (CAF1-like)  PATHOGENESIS-RELATED1 (PR1), a marker for intact SA signaling, is maintained in syd-2 plants  . We nextF1-like)  [22],[232 plants . These dPDF1.2a but not PR1 expression in non-stressed syd-2 plants suggests that SYD impacts specific stress signaling pathways. To explore the role of SYD under inductive stress treatments we inoculated plants with the necrotrophic pathogen Botrytis cinerea and the virulent biotrophic pathogen Pseudomonas syringae. As resistance to B. cinerea requires ET and JA signaling, whereas resistance to P. syringae is predominantly mediated by SA signaling B. cinerea treatment. The expression of the transcription factor ETHYLENE RESPONSE FACTOR1 (ERF1), which requires both ET and JA for induction syd-2 plants , is enhanced in syd-2 plants. The apparent lack of detectable enhancement in PR1 expression levels by RT-PCR . This suggests that SYD affects B. cinerea resistance through ET/JA signaling independent of camalexin production. To determine if reduced resistance was specific to B. cinerea we inoculated syd mutants with virulent P. syringae, for which resistance is predominantly mediated via SA signaling B. cinerea, syd mutants and WT plants show similar resistance to P. syringae  assays. Wounding is therefore advantageous as it enables better synchronization of the stress stimulus and is a feasible treatment for the large amount of tissue required for ChIP. We first monitored the expression of ALLENE OXIDE SYNTHASE, which is involved in JA biosynthesis, and found its transcription to be similar in WT and syd-2 plants before and after wounding (syd-2 plants (B. cinerea treatment (ERF1 is similar in WT and syd-2 in response to wounding (ERF2), which when overexpressed results in enhanced PDF1.2a levels syd-2 , a gene in the JA signaling pathway which requires MYC2 for expression syd-2 mutants  assays using the SYD specific antibody, which was previously used in ChIP experiments to show that SYD binds the WUSCHEL promoter to regulate stem cell fate OLEOSIN1 and AT2S3, which are subject to repressive histone H3 lysine 27 trimethylation and are not expressed in Arabidopsis leaf tissue syd-2 tissue wounded for 12 h to further ensure that the results are SYD specific  to enable transcription of PDF1.2a. Additionally, SYD is bound to the MYC2 promoter, which is consistent with the reduced expression of MYC2 in syd-2. The direct recruitment of SYD to the MYC2 promoter may also help explain the reduced transcript levels of VSP2, in non-wounded syd-2, even though SYD binding to the VSP2 promoter region was only detected following wounding. Altogether these data suggest that the altered expression of ET and JA responsive genes in syd-2 is likely a result of the loss of SYD acting both directly and/or indirectly on their promoters to regulate transcription.Based on our findings we propose a model  that sumin vivo. Loss of chromatin remodeling activity also results in increased susceptibility to B. cinerea but not P. syringae. These results provide biological evidence that chromatin remodeling complexes, which are evolutionarily conserved within eukaryotes, are required for stress tolerance not only in yeast but also multicellular organisms. Furthermore, the requirement of ATP-dependent chromatin remodeling complexes is pathogen-specific and not a result of a general reduction in fitness.Our results show that ATP-dependent chromatin remodeling is required for expression of specific genes within stress signaling networks. Additionally, this requirement is likely both direct and indirect as the chromatin remodeling ATPase SYD binds several, but not all, of the stress responsive promoters examined Arabidopsis thaliana plants were grown in a 16 h light/8 h dark photoperiod at 22°C; except plants for pathogen treatments, which were grown in a 12 h light/12 h dark photoperiod. Wounding was performed as previously described B. cinerea isolates DN, Grape, B05.10 and 83-2 Arabidopsis leaves were inoculated with 5 µl of spores at a concentration of 50,000 spores/ml P. syringae bacterial growth assays Arabidopsis leaves were inoculated with 2×104 CFU/ml P. syringae pv. tomato (Pst) DC3000 by hand injection.2, 0.2 mM each dNTP, 0.05 µM each primer, and 1 U Choice-Taq Blue  and amplified for 29 cycles except for At4g34270 and At4g26410 previously described were used for transcript normalization Total RNA from rosette leaves was isolated by TRIzol extraction  and treated with DNAaseI to control for DNA contamination. RNA was reverse transcribed using Superscript III . PCR for RT-PCR were conducted in 25 µl reactions containing 20 ng cDNA, 1.5 mM MgClExtraction of JAs (MeJA and JA) were carried out as previously described B. cinerea inoculation as previously described 20. Extractions were performed largely as previously described, but using centrifugation rather than vacuum to remove liquid from the Sephadex columns Camalexin and glucosinolates were measured 72 h after mock or t method using the SuperArray ChIP-qPCR Data Analysis Template  according to the SuperArray manual, as described t) to account for chromatin sample preparation differences. Input normalized SYD and POLII ChIP fractions were then adjusted for the normalized non-specific background (IgG) giving the ΔΔCt value. Fold differences relative to the IgG reference were then calculated by raising 2 to the ΔΔCt power. The primers used in this study are listed in ChIP-qPCR assays were performed as previously described syd t-tests were performed using Sigma Stat v3.5 . For comparison of WT versus myc2/jin1 factorial ANOVA performed within SAS  was used to analyze the effects of genotype and treatment on measured phenotypes, with significance of differences determined via t-tests of pre-selected comparisons.To determine statistical significance of treatment effects comparing WT versus PR1: At2g14610, PDF1.2a: At5g44420, UBQ10: At4g05320, ERF1: At3g23240, PAD4: At3g52430, ICS1: At1g74710, NPR1: At1g64280, WRKY70: At3g56400, ERF#18: At1g74930, CAF1-like: At3g44260, AOS: At5g42650, ERF2: At5g47220, MYC2: At1g32640, VSP2: At5t24770, Oleo1: At4g25140, AT2S3: At4g27160Figure S1syd-2. (A) RT-PCR analysis of select rapid wound response genes in Ler and syd-2 plants in response to wounding (B) RT-PCR analysis of the JA biosynthesis gene AOS and the ET/JA responsive ERF2 in response to wounding.Expression of wound responsive transcripts in (0.65 MB EPS)Click here for additional data file.Figure S2P. syringae.RT-PCR expression analysis of genes involved in SA biosynthesis and signaling in response to (0.71 MB EPS)Click here for additional data file.Figure S3B. cinerea. (A) Lesion size 3 d after spot inoculation with B. cinerea isolate 83-2. (B) Camalexin levels of detached rosette leaves either mock  or B. cinerea isolate 83-2  treated for 3 d. (A and B) Two alleles were tested, jin1-8 and jin1-9. No significant differences between alleles were detected, therefore the two alleles were pooled (myc2/jin1). Data are means of 8 (Col) or 16 (myc2/jin1) independent biological replicates ±SEM. Experiments were repeated with similar results. (C) Lesion size 3 d after spot inoculation of Col and jin1-9 plants with B. cinerea isolate B05.10. Data are means of 8 independent biological replicates ±SEM. (D) Camalexin levels of detached rosette leaves treated with B. cinerea isolate B05.10 for 3 d. Data are means of 8 independent biological replicates ±SEM. (E) Camalexin levels of detached rosette leaves treated with 5 mM AgNO3 for 3 d. Data are means of 8 independent biological replicates ±SEM.Role of MYC2 in response to (0.62 MB EPS)Click here for additional data file.Figure S4PDF1.2a or ERF1. ChIP-qPCR analysis of SYD  and POLII  recruitment to the promoter of (A) PDF1.2a (−836 to −604) and (B) ERF1 (−1035 to −942). Data presented are normalized to input DNA and expressed as fold enrichment of SYD or POLII relative to IgG. ChIP-qPCR was performed on non-wounded and wounded Ler plants. Data are means of 3 or 4 independent biological replicates ±SEM. The dashed line represents the mean SYD background fold enrichment assayed at the promoters of OLEOSIN1 and AT2S3.SYD does not bind the promoter of (0.57 MB EPS)Click here for additional data file.Figure S5syd-2 plants. ChIP-qPCR analysis of SYD recruitment to the promoters of PDF1.2a (−323 to −151), ERF1 (−1035 to −942), MYC2 (−320 to −222), and VSP2 (−226 to −147). Data are expressed as fold enrichment of SYD relative to IgG. ChIP-qPCR was performed on 12 h wounded Ler and syd-2 plants. Data are means of 3 or 4 independent biological replicates ±SEM.Background levels of SYD ChIP-qPCR signal in (0.47 MB EPS)Click here for additional data file.Table S1myc2/jin1 plants.Glucosinolate levels in (0.03 MB XLS)Click here for additional data file.Table S2Primers used for expression and ChIP-qPCR analysis.(0.02 MB XLS)Click here for additional data file.


Cellular interactions are subject to random fluctuations (noise) in quantities of interacting molecules. Noise presents a major challenge for the robust function of natural and engineered cellular networks. Past studies have analyzed how noise is regulated at the intracellular level. Cell–cell communication, however, may provide a complementary strategy to achieve robust gene expression by enabling the coupling of a cell with its environment and other cells. To gain insight into this issue, we have examined noise regulation by quorum sensing (QS), a mechanism by which many bacteria communicate through production and sensing of small diffusible signals. Using a stochastic model, we analyze a minimal QS motif in Gram-negative bacteria. Our analysis shows that diffusion of the QS signal, together with fast turnover of its transcriptional regulator, attenuates low-frequency components of extrinsic noise. We term this unique mechanism “diffusional dissipation” to emphasize the importance of fast signal turnover (or dissipation) by diffusion. We further show that this noise attenuation is a property of a more generic regulatory motif, of which QS is an implementation. Our results suggest that, in a QS system, an unstable transcriptional regulator may be favored for regulating expression of costly proteins that generate public goods. Quorum sensing (QS) is a mechanism by which many bacteria regulate gene expression via the synthesis and detection of small, diffusible signals. Since its discovery, QS has been shown to control diverse physiological functions in numerous types of bacteria. It provides an elegant strategy for bacteria to sense their density and to achieve coordinated population behavior. By stochastic modeling, we show that QS can effectively reduce variability (“noise”) in the expression of its target genes. Surprisingly, the noise reduction does not significantly depend on the number of bacteria but rather results from the coupling of a bacterium and its environment through signal diffusion. Diffusion enables fast signal turnover, which, together with fast intracellular turnover of the cognate receptor of the signal, leads to noise reduction. Our work suggests a unique role of QS in achieving robust gene regulation, which is distinct from noise-regulation mechanisms that act at the intracellular level. As such, it offers novel insights into evolution of QS as well as its application in construction of synthetic gene circuits. Bacillus subtilisCellular processes are subject to random fluctuations (or noise) in quantities of interacting molecules. Cells may take advantage of noise to achieve diverse functions However, noise often presents a major challenge for reliable cellular function. To this end, cells use specific biochemical networks or motifs to minimize deleterious effects of noise These noise regulation mechanisms all operate at the intracellular level. At the population level, cell–cell communication may play an important role in achieving robust gene expression dynamics. For example, it has been shown to be important for the proper function of many rhythmic processes in physiology lux system from the marine bacterium Vibrio fischeri , a mechanism by which many bacteria broadcast and sense their density fischeri . This syVibrio fischeriAgrobacterium tumefaciensSerratia proteamaculans (SprI/R system) and Erwinia carotovora  is approximately constitutive In this study, we use mathematical modeling to analyze noise regulation in QS-mediated gene expression. Recently, stochastic simulations of QS-mediated dynamics have been carried out for We find that QS can serve as an effective noise-reduction mechanism. In particular, diffusion of the QS signal and fast decay of the transcriptional regulator can reduce noise by synergistically attenuating low-frequency components of extrinsic noise. We term this noise reduction mechanism “diffusional dissipation,” as its defining feature is fast dissipation of signal molecules through diffusion. Further analysis indicates that this noise attenuation is the result of a more generic regulatory motif—bimolecular interaction—of which QS is an implementation. Our results further suggest a connection between QS-regulated functions and the decay of the QS transcriptional regulator. An unstable transcriptional regulator may be favored for regulating expression of costly proteins that generate public goods, as QS-mediated noise reduction in the target protein can increase the average population fitness, thus providing a selection advantage.A) and the LuxR protein (R), which interact to form a complex (C). The complex further forms homodimers to activate a target gene (X) controlled by the lux promoter. We omit the dimerization process and focused on the fluctuations of the complex at steady state, although incorporation of the dimerization does not seem to change our conclusion  intrinsic noise source, which arises from the stochastic nature of chemical reactions in the QS system ; and (2)as noise .C by varying the diffusion rate constant (P) from 0 (no diffusion) to 2×10−11 L min−1. The diffusion rate constants of glucose and lactose through the outer membrane of wild type E. coli have been experimentally estimated to be 3.6×10−12 and 1.8×10−13 L min−1, respectively Vibrio fischeri, is estimated to be ∼2×10−12 L min−1. Different QS modules may use different AHLs that have different diffusion rate constants. Thus we set the fastest diffusion rate constant as 2×10−11 L min−1, which is 10-fold larger than the estimated value. We then modulate the production rate constant of A to “balance” changes in the diffusion rate constant, in order to maintain the same steady-state level of C. By doing so, we aim to reveal noise modulation in C due to different diffusion rate constants that is otherwise masked by changes in average protein levels.We first examine the effect of diffusion on noise in C−13 L min−1 |, P = 2×10−13 L min−1), the gain of low-frequency components (f<0.02 min−1) decreases by about 8-fold (f>7.9 min−1). In essence, diffusion effectively reduces extrinsic noise by reducing the transmission of fluctuating signals (including noise) in the low-frequency domain.Frequency analysis provides further insights into the noise reduction mechanism. The transfer function of the extrinsic noise source . Again, the steady-state level of C was maintained by modulating the production of R.In a R decay results in much more reduction in the extrinsic noise of C than in its intrinsic noise in the presence of diffusion . However, the noise reduction becomes negligible (<2%) in the absence of diffusion (dotted black line). This result indicates a synergistic coupling between signal diffusion and faster decay of R in reducing cellular noise. That is, the noise reduction by increased Rγ is enhanced by larger P and vice versa. In contrast, intrinsic noise is not significantly affected (<1%) with or without diffusion (red solid or dotted line).We find that faster iffusion . For P =Rγ. |ξH(f)| shows the similar tendency to the case of diffusion (R decay (Rγ = 2 min−1) reduces the gain of low-frequency components by ∼20-fold compared with the case with slow R decay (Rγ = 0.02 min−1). Similar to fast diffusion . As expected, fast turnover of A and R, which corresponds to fast dissipation of A and R, synergistically reduces extrinsic noise in C when the condition is satisfied (P<2×10−14 L min−1). Also, we show analytically that the DC component (or the low-frequency components whose behavior can be approximated by the DC component) of the transfer function of extrinsic noise sources decreases monotonically as the turnover of A and R becomes faster (P>2×10−14 L min−1), As shown in ve decay . This sis faster . InteresiA (or A in the simplified model), R and C). However, this assumption may not always hold in a real system. In an extreme case, when the extrinsic noise completely arises from R, increasing turnover of A results in an increase in the extrinsic noise in C . As actual magnitudes of extrinsic noise sources to each species are unclear, we cannot exclude the possibility of unbalanced extrinsic noise sources exemplified above. However, the framework of our analysis is still able to account for these alternative scenarios.Overall, our results are insensitive to variations in the base parameters, except for those that characterize extrinsic noise sources . We haveise in C . In factC by fast diffusion and R decay , which, upon dimerization, leads to activation of downstream genes. We find that fast diffusion and fast R decay can synergistically reduce the extrinsic noise in C but has relatively little impact on its intrinsic noise. The noise reduction is achieved by decreasing the gain of low-frequency fluctuations in the extrinsic noise.Extensive studies have been carried out to define characteristics of noise generation A and R), which together form a heterodimer (C), reduces extrinsic noise by suppressing low frequency components  is similar to that by a single cell  by increasing the total number of molecules, this reduction is unlikely to impact downstream gene expression, as the noise in Ae is already much smaller than that in other species even for a single cell  works as a low-pass filter whose critical frequency is largely determined by the decay rate constant of the output protein C will be filtered out by downstream genes. This makes modulation of low-frequency fluctuations in C particularly relevant. In fact, only reduction in low-frequency fluctuations can effectively affect downstream gene expression. Also, given that dimerization of C confers cooperativity to the downstream gene expression, fluctuation of C around the intermediate level could have a significant effect on gene expression C can thus effectively attenuate noise in downstream gene expression.How does the noise reduction in lux-type QS system, an R protein is often highly unstable in the absence of its cognate signal In a Ai, Ae, R, and C are numbers of the intracellular signal, the extracellular signal, the R protein, and the complex, respectively; Cγ are their decay rate constants; Ak and Rk are production rate constants of iA and R; P is diffusion rate constant of the signal across the cell membrane; Ve is the average extracellular volume per cell; and Vi is an intracellular volume. Parameters for the base case are either obtained or estimated from literature . Also, extrinsic noise sources may be rather band limited than white We assume that extrinsic noise sources are fully correlated with the same magnitude C around its steady state. To obtain the steady-state fluctuation, we first linearize the equations (Equation 1) and calculate the power spectral density (PSD) of noise in each species by solving them in the Fourier domain. The PSD of the noise in C (S(f)) is:f is frequency; ξS are PSD of intrinsic and extrinsic noise sources contributing to noise in C; ξH are transfer functions of corresponding noise sources and they are inherent properties of the reaction network. Each noise source is individually processed by its corresponding transfer function, and transmitted to C (S(f)) over the entire frequencies allows us to calculate variance of each species in the temporal domain The system is monostable in the deterministic domain and does not exhibit noise-induced bistability  as obserted to C . In otheR decay rate constants (P = 0 L min−1 (no diffusion). In this case, the distribution of C becomes wide and skewed, resulting in deviation from the small-noise regime and a discrepancy between its average and the corresponding deterministic steady state level .Our analytical approach is based on the linearization of the system. The underlying assumption of linearization is that the noise is small enough so that the distribution of each molecule is sufficiently tight around the point of the linearization (corresponding to its steady state in a deterministic model). To test this assumption, we perform numerical simulations with the base parameter set and different diffusion rate constants and onstants . We findte level . HoweverP and Rγ .When only considering the intrinsic noise, we also note that the Langevin approach can recover the same PSD and related statistics as the chemical master equation approach for linear or linearized systems even when numbers of interacting molecules are small, as long as linearization can be justified Text S1(0.23 MB PDF)Click here for additional data file.Figure S1D are calculated using the analytical approach with the base parameter set . (B) The gain of extrinsic noise sources decreases as Rγ increases (from the black line (Rγ = 0.02 min−1) to the red line (Rγ = 2 min−1)).Impact of the dimerization reaction. Noise and PSD in eter set . (A) Ext(0.06 MB PDF)Click here for additional data file.Figure S2Ai, Ae, R, and C under different coupling conditions: (1) each cell has its own microenvironment (e.g. no coupling). (2) 100 cells are divided into 10 populations each of which contains 10 cells coupled to one another via their environment, and (3) 100 cells form 1 population of 100 coupled cells. The conditions are indicated on the x-axis. The simulation is carried out as in P = 2×10−13 L min−1.Simulation results of noise in (0.02 MB PDF)Click here for additional data file.Figure S3ζ1, ζ2, …, ζ9). P is changed from 0 (black line) to 2×10−13 L min−1 (blue line). Accordingly, the PSDs of ζ1, ζ3, ζ4, ζ7, and ζ8 increase, while those of the other noise sources decrease. Note that the PSDs of ζ5 and ζ6 are 0 for P = 0.PSD of each intrinsic noise arising from the corresponding intrinsic noise source ((0.06 MB PDF)Click here for additional data file.Figure S4Ak, Rk, iγA, eγA, γR, γC, C1k, and C2k are individually decreased or increased by 10-fold (Ak is only increased) and the dependence of noise in C (ηT2: (A), ηE2: (B) and ηI2: (C)) on P and γR is examined.Qualitative behavior of the system is insensitive to the parameter values. The base values of (0.21 MB PDF)Click here for additional data file.Figure S5AX+RX+CX, (B) ARY, (C) ACY, and (D) RCY on P and γR. As defined in Equation 10  ation 10 , XA+XR+Xeter set  is used (0.09 MB PDF)Click here for additional data file.Figure S6σ). Parameter values are and n = 10,000, μ = 1, λ = 0.2, ε = 0.02, M = 1.8, and F0 = 1. Note that when iX≥M or iF<0, we set iF = 0.Cell-cell variability affects population fitness. Population fitness is calculated by Monte Carlo simulation with different levels of cell-cell variability ((0.02 MB PDF)Click here for additional data file.Figure S7C calculated from numerical simulations (Equation 1). Time series of C is obtained over a time span of 400,000 min with sampling frequency of 10 min−1. Numerical simulation is implemented as in C (ηT2) is calculated from the time series (red dots). The blue line indicates ηT2 calculated by the analytical approach. For these calculations, γR = 0.2 min−1. (B) The PSDs are calculated by taking absolute values of fast Fourier transformation of the time series. For these calculations, P = 2×10−13 L min−1, γR = 0.02 (blue), 0.2 (green), or 2 min−1 (red). The black lines indicate PSD calculated by the analytical approach.Representative results of noise and PSDs of (0.06 MB PDF)Click here for additional data file.Figure S8C using Equation 1 for varying P and γR. A red line indicates the steady-state value of C calculated by the deterministic version of Equation 1, whereas a yellow line indicates the mean value of the corresponding distribution. Numerical simulation is implemented as in Simulated histograms of (0.52 MB PDF)Click here for additional data file.Table S1Noise sources and corresponding reactions.(0.03 MB PDF)Click here for additional data file.Table S2Base parameter values.(0.05 MB PDF)Click here for additional data file.


Stochasticity in gene expression affects many cellular processes and is a source of phenotypic diversity between genetically identical individuals. Events in elongation, particularly RNA polymerase pausing, are a source of this noise. Since the rate and duration of pausing are sequence-dependent, this regulatory mechanism of transcriptional dynamics is evolvable. The dependency of pause propensity on regulatory molecules makes pausing a response mechanism to external stress. Using a delayed stochastic model of bacterial transcription at the single nucleotide level that includes the promoter open complex formation, pausing, arrest, misincorporation and editing, pyrophosphorolysis, and premature termination, we investigate how RNA polymerase pausing affects a gene's transcriptional dynamics and gene networks. We show that pauses' duration and rate of occurrence affect the bursting in RNA production, transcriptional and translational noise, and the transient to reach mean RNA and protein levels. In a genetic repressilator, increasing the pausing rate and the duration of pausing events increases the period length but does not affect the robustness of the periodicity. We conclude that RNA polymerase pausing might be an important evolvable feature of genetic networks. Investigation on how phenotypic diversity of genetically identical organisms is generated and regulated has focused on noise in gene expression. It is unknown to what extent noise in gene expression and genetic networks is evolvable, and by which mechanisms it evolves. The noise has several sources, e.g., noise in transcription initiation and during elongation. We focus on RNA polymerase (RNAP) pausing and show that it can regulate, to some extent, noise in gene expression. RNAP frequently pauses during elongation. The pausing frequency and average duration are sequence-specific, thus evolvable. The dependency of pause propensity on regulatory molecules makes pausing a mechanism adaptable to rapidly changing environments. We study, in a stochastic model of bacterial transcription at the single nucleotide level that includes the promoter open complex formation, pausing, arrest, misincorporation and editing, pyrophosphorolysis, and premature termination, how pausing affects the dynamics of gene expression and gene networks. In a model of a genetic clock, with periodic dynamics, pauses affect the period length but do not disrupt the periodicity. We conclude that RNAP pausing is an important evolvable feature of gene regulatory networks, that can be used by organisms to adapt to changing environments and regulate phenotypic diversity. The stochastic fluctuations in the expression level of a gene under constant environmental conditions Transcription elongation is not a constant forward process. The noisy stepwise progress of RNAP through the DNA template is further affected by pauses, arrests, pyrophosphorolysis, misincorporations and editing Given their high frequency of occurrence, pauses ought to be explicitly included in models of transcription at the single nucleotide level Escherichia coli, suggesting that it is a commonly used regulator of gene expression E. coli, the average rate of pausing is 0.55 s−1  Promoter-proximal pausing has been estimated to occur at above average rates in 10–20% of promoters in While the high propensity of some sites to pauses is sequence-dependent, pause propensity in other sites appears to be regulated by molecules such as GreA and elongin complex that can suppress pausing It should be stressed that not all pauses are sequence-dependent. They can be random in the sense that they can arise solely due to the probabilistic nature of stepwise elongation, or be rare but unavoidable (i.e. certain to occur) such as when due to DNA lesions Recently, a model of transcription in prokaryotes The analysis of the dynamics of this model suggested that pausing is, potentially, one of the major enhancers of the occurrence of collisions between RNA polymerases on the DNA template Using the delayed stochastic model of transcription at the single nucleotide level proposed in First, we describe the model of transcription at the single nucleotide level. Next, we present our results regarding the effects of varying pausing rate and average duration in the transcriptional and translation dynamics of a gene. Finally, we present the effects of RNAP pausing on the dynamics of the 3-gene negative feedback loop; the repressilator Besides the stochasticity, another important feature of the dynamics of gene expression is the time that some steps in transcription and translation take to be completed once initiated. E.g., the promoter open complex formation can take from a few seconds to several minutes stop = stop time, set initial number of molecules, set list of reactions, and create empty waitlist L for delayed events.Step 1) Initialize: set t = 0, t1 and the corresponding occurrence time t+t1.Step 2) Generate an SSA step for reacting events to get the next reacting event R1 with the least time in L, tmin. If t1<tmin or L is empty, set: t = t+t1. Decrement all delays in L by t1. Update the number of molecules by performing R1, adding to L both any delayed products and the time delay for which they have to stay in L..Step 3) Compare t1≥tmin, set t = t+tmin. Update L, by releasing the first element in L and decrement all delays in L by tmin. Update the number of molecules according to the delayed event.Step 4) If L is not empty and if tstop, go to step 2; otherwise stop.Step 5) If t<tt, B is instantaneously produced at t and C placed on a waitlist until it is released, at t+τ seconds. τ can be drawn from a distribution each time the reaction occurs.Delayed events in reactions are represented as, e.g.: A→B+C(τ). When this reaction occurs at moment Transcription is the reading of a gene in the DNA strand by an RNA polymerase (RNAP) and forming it into an RNA molecule. The RNAP unwinds and reads the DNA, producing the RNA by adding matching nucleotides while going through the DNA strand. Transcription has three main phases: initiation, elongation, and termination. In initiation, the RNAP attaches to the promoter and unwinds a portion of the DNA double helix to expose the template DNA strand (promoter open complex formation). After that, the RNAP starts moving on the DNA strand and elongation, forming of the RNA molecule, begins. Behind the region where ribonucleotides are added, the RNA chain is displaced and the DNA double helix is reformed. In termination, a single-stranded RNA molecule is released, ending the transcription process.E. coli, all parameter values in the model are from E. coli.Recent models of transcription at the single nucleotide level were proposed in oc, represented by RNAP.Pro(τoc), meaning that it takes τoc seconds for RNAP.Pro to be produced after the reaction occurs. Each time the reaction occurs, the delay τoc on the promoter release is randomly drawn from a Gaussian distribution with a mean of 40 s and standard deviation of 4 s, according to measurements on an active unrepressed lacZ promoter init is the stochastic rate constant of the reaction which is set to 0.0148 s−1This model of transcription is described in detail in As mentioned, in elongation, at each nucleotide, the forward movement of the RNAP is in constant kinetic competition with other regulatory pathways n  to occupy the next nucleotide, providing there is no steric hindrance from the succeeding RNAP (reaction 2)  move is 150 s−1 (to achieve an average elongation speed 75 nucleotides/s n+1 (and frees nucleotide U(n-Δ)), the most probable pathway is activation (reaction 3), after which the RNAP can again move forward. In this step, a complementary nucleotide is added to the growing RNA act, to 150 nt/s, to attain an elongation rate of 75 nt/s (the sum of kmove and kact) on average E. coliE. coliTranscription stepwise elongation has two stages. First, the RNAP moves from an activated nucleotide Ahis operon. Most “ubiquitous” pauses last less than 10 s pause = 0.55 s−1pause∼kact/136, a pause event occurs, on average, every 136 activation events which, in a template of 2445 nucleotides  = 56 s When the termination sequence is reached, the transcription bubble collapses as the RNA-DNA hybrid disrupts, releasing both the RNAP and the completed RNA molecule (“R”). Reaction (8) models termination. When the last nucleotide is activated and the mature R is released, the RNAP is also released, unoccupying (U) the last 12 nucleotides. The rate for the transcript release, kA note is needed regarding how translation is modeled (reaction 9). We use a multi-delayed reaction from  instead 3) associated to the completion and release of the protein varies from one translation event to the next. Thus, the model copes with variability in the speed of translation and consequent different durations of translation events in normal conditions. However, if many collisions occur between ribosomes the model loses accuracy. One case where, therefore, the model becomes less accurate is if, during translation, long pauses occur. Thus, our model assumes that there are no long pauses in the process of translation or, at least, that these are very rare, in agreement with the measurements from which the mean duration and variability of τ3 were extracted In our model of translation, the delay  is set so as to impose a mean level of ∼5 transcripts.When simulating the model, both RNA and proteins are subject to degradation, modeled as first order reactions. Reactions (10) and (11) model degradation of RNA and proteins, respectively:tsr-venus constructed in E. coliin silico model was shown to match in vivo measurements at the single RNA and protein level pause (rate of occurrence of pauses) and dpause (average pause duration) within a realistic range of values: 0<kpause<10 s−1 and 0<dpause<100 s In all simulations, single genes (and genes in networks) are modeled from the model proposed in We first study the effects of varying pausing rate and duration in all nucleotides. Next, we study the effects of short sequence-specific pauses with long durations (≥30 s), which only occur at specific locations in the DNA sequence The transcription model at the single nucleotide level used here The movement of an RNAP molecule on the strand is stochastic Pauses were shown be to a major enhancer of microbursts –init = 0.0148 s−1E. coliWe measured the interval between consecutive completions of transcription events as we varied the pausing rate of occurrence and average duration. Results are shown in pause and three values of dpause . It is noted that these intervals depend of the value for kinit, of the number of RNAP available at each moment (here kept constant for simplicity), and of τoc. Namely, one expects the mean time between transcription initiation events to be ∼((RNAP*kinit)−1+τoc), which equals, given our parameter values, ∼42.4 s .pause<10, it is visible that the number of microbursts increases with kpause. For a pause to occur, it has to compete with several events such as arrests. The most probable is stepwise elongation (kmove = 150 s−1) and subsequent activation (kact = 150 s−1). Since kpause is much smaller than these rates, it is unlikely that two consecutive RNAPs will both pause. Interestingly, for kpause>10 s−1 the number of microbursts decreases. This is due to kpause having the same order of magnitude as elongation, leading to most RNAPs constantly pausing at each nucleotide, without significant variation in the distance between consecutive RNAPs. Importantly, this suggests that there similarly is a maximum noise level in transcription attainable via selecting for sequences prone to pauses. Also, as the reason for the decrease lies in the relationship of the magnitudes of kpause and kmove, this result, and the value of kpause for which it occurs, is independent of the value of dpause, as the propensity of occurrence and duration of pauses is identical in all nucleotides.From pause on microbursting is different from varying kpause. Confronting the y-axis scales of pause causes significantly more microbursts and that this increase is not limited as when increasing kpause. Notably, the average time between completions does not vary with either kpause or dpause, since increasing the number of microbursts necessarily is accompanied by an increase in the number of consecutive RNAPs separated by longer time intervals  the size of the largest microburst decreases with the increase of kpause. Interestingly, for kpause>50, the maximum size is actually smaller than for kpause<1, meaning that an increased frequency of pauses can, in principle, be used as a means to decrease the occurrence of microbursts.We measured the number of RNAs in the largest microburst in each simulation for each value of kd dpause Figure 3pause and dpause. We ran 100 simulations, each for 5000 s with a sampling rate of 1 s, for each set of parameter values of kpause and dpause described, except that for kpause the maximum value was 10 s−1. The initial transient is defined here as the time it takes for the protein level to be equal or higher, for the first time, than its mean level over the total simulation time. We then averaged the results of the 100 simulations for each set of parameter values of kpause and dpause. The mean RNA level is ∼5 in all simulations and the mean protein level is ∼150. The average transient length with one standard deviation error bars is shown in An important dynamical aspect of gene expression in a genetic network is the time that it takes for a gene, initially repressed, to reach its steady state protein expression level, once activated. This transient time is a measure of the “speed of response” of that gene to either an externally or an internally induced activation or halting of repression. We measured this transient as a function of kpause only affects the transient for values beyond 0.5 s−1. Similarly, the increase in dpause only increases the transient significantly for values beyond 5 s . This effect on the transient has, as shown later, consequences on the dynamics of the repressilator. Notably, the variance of the initial transient does not vary significantly in the range of values tested of kpause, while for dpause it only increases significantly for dpause>20 s explaining why, later on, we observed that the robustness of the genetic repressilator is not significantly affected by varying kpause and dpause.The results suggest that increasing kWe next study the effects of pauses on the noise of the RNA and protein levels of a single gene, given that both RNA and proteins are subject to degradation. Noise is quantified by the coefficient of variation (CV), defined as the standard deviation over the mean level over time.pause from 0 to 10 s−1 causes a 20% increase in RNA noise level  is 0.01 s−1. Importantly, setting kpause = 0.55 s−1 and dpause = 3 s  causes the repressilator to have a period of ∼7.000 s, similar to measurements To investigate how kpause and dpause affect the period length, but not mean protein levels or period robustness , binarized with k-means 7 s sampled every 100 s. Measures of periodicity robustness cannot be chosen according to any fixed criteria, thus, in each case the measure yielding the most plausible results should be selected 1,P2,P3)(t) be the 3-tuple binarized states of the proteins levels at moment t. There are 8 possible states , namely, states  to . From the entire time series, one can assume the probability of being in each state to be, in approximation, the normalized fraction of times that that state occurs. Let i = 1,…,8 be the index of the state and Pri be the probability to be in state i. The 3-tuple information-entropy of the time series of the proteins is then given by (15) pause and dpause tested , indicating that the repressilator is robust to the increase of noise in the temporal levels of each protein. In accordance, in long time scales (107 s), the number of disruptions in the periodic behavior is identical in the three models.Robustness of the periodicity was assessed by the 3-tuple information-entropy (H) of the time series of  kpause = 10 s−1 and dpause = 3 s, and (C) kpause = 0.55 s−1 and dpause = 100 s. As in the case of the individual gene's protein time series, the increase of kpause and dpause cause stronger fluctuations in the protein levels in case (B), and even more in case (C).In 1, P2 and P3 into a single quantity, here referred to as Ptotal, of which we measure the mean and standard deviation of the time series (CV).We also measured the noise level (CV) of the protein time series  of each gene to reach its maximum expression level causes the length of the period of the repressilator to increase.The length of the initial transient of a gene to reach its mean expression level ‘at steady state’ increases with the increase of khis leader pause site showed that the pause it causes has a half-life of 47 seconds and occurs with a probability of 80%, and it was suggested that it facilitates the synchronization of the RNAP and ribosome movements during transcription of the his operon So far, we have focused on the effects of short duration pauses on gene expression. For simplicity, we have assumed an identical probability of pause occurrence and an identical distribution of pause duration at each nucleotide, irrespective of the sequence. In this section, we examine the effects on gene expression of longer pauses, which are known to exist in numerous organisms Interestingly, it has been shown also that, depending on the spacing of the hairpin loop from the RNA 3′ end, and the nature of the intervening RNA sequence, the hairpin can prolong pausing or vary the chance of premature transcriptional termination To study the effects of such long-duration pause sites in transcriptional dynamics, we examine three hypothetical sequences of 400 nucleotides. These, referred to as A, B and C, are in all ways identical except that we introduce, at nucleotide 200, a long-pause in B and C with a 50% probability of occurrence for each RNAP that reaches that nucleotide, and with a mean duration of 60 s. Additionally, in case C, there is a 25% chance of premature termination at nucleotide 200, if a long pause occurs. Note that each RNAP can pause only once at the long-pause site.In When comparing cases A and B, it is apparent that the pause site causes the distribution to convert from Gaussian like (case A) to tri-modal (case B). By introducing a long duration pause with a 50% probability of occurrence two new peaks emerge in the distribution. One is due to microbursting, and the other corresponds to the pairs of consecutive RNAPs separated by long time intervals. This separation of peaks was not observed when examining short duration pauses Figure 1Given the change in the distribution of intervals between completions, one can conclude that, assuming a first order degradation rate of RNA, the existence of the long-pause site causes higher noise in the RNA levels, due to the increase of microbursting.The effects of premature termination (case C) are also of interest. A 25% chance of premature termination following a long pause causes the number of consecutive RNAP pairs separated by short intervals (<5) to decrease significantly but does not affect the number of pairs of consecutive RNAP separated by long intervals . This is due to the fact that a premature termination cannot cause two consecutive RNAPs to shorten the distance between them, but decreases the number of pairs of RNAPs separated by short distances in the template as one of them falls off. Note that As seen, the broadening of the distribution of intervals between transcription completions results in higher noise in RNA levels and, consequently, protein levels. Thus, sequence-specific long-duration pause sites are likely to lead to increasing RNA and protein noise levels.Several recent studies have focused on the stochasticity arising from transcription initiation. Importantly, in elongation there are several events also contributing to transcriptional noise, such as pauses, arrests, or premature terminations. We studied the effect of pauses in elongation on transcriptional dynamics using a delayed stochastic model of transcription at the single nucleotide level that includes the promoter open complex formation, pausing, arrest, misincorporation and editing, pyrophosphorolysis, and premature termination.E. coli of sequence-dependent pauses dynamics suggest that most of these pauses last less than 10 seconds in vitro, and unknown mechanisms may alter some of these pauses lifetime in vivo.Our results show that varying pauses rate of occurrence and duration, within realistic parameter value intervals, affects the dynamics of transcription and protein levels, namely, bursting dynamics and the noise in transcripts and proteins levels. As noise in gene expression is subject to selection Furthermore, there is evidence that cells use stochasticity in gene expression to cope with fluctuating environments Bacillus subtilisDrosophila, by being a source of transcriptional bursts Pause rate of occurrence and duration affect size and number of microbursts in transcription. Size of the largest microburst and number of microbursts might have different and important roles in cellular metabolism. While increasing the number of microbursts increases noise of transcripts levels, increasing the size of the microbursts allows overcoming thresholds in RNA levels otherwise not reachable. The ability of RNAP pausing to regulate microbursting in RNA levels suggests that it might be a regulatory mechanism of cells' sensitivity to external stresses, and of probabilistic decision-making processes such as in cell differentiation and phenotypic variability. Initiation of differentiation usually requires reaching a protein concentration threshold to switch between pathways, as depicted by the French flag model Sequence-specific long pauses were shown here to be an ideal regulatory mechanism of bursts. Not only a single long-pause site can drastically alter the distribution of bursts, but it can do so without changing mean expression levels. Further, combining the long pause site with higher premature termination rate, allows making the distribution between completion of RNA molecules sparser without increasing the number of bursts.There are several evidences that noise in gene expression is subject to selection Our results further suggest that pauses are a likely regulatory mechanism of gene networks dynamics. For example, altering the rate and duration of pauses in the genes composing a repressilator enables tuning the proteins' time series period length. Interestingly, even for rates of pausing exceeding biologically observed values, the robustness of the periodicity was not affected, unlike when using other methods to alter the period length (e.g. decreasing transcription initiation). Also, pausing may be used to, e.g., tune the switching frequency of a genetic switch as switches are noise-driven, due to the effect on individual genes' expression noise.Importantly, both the pausing rate and expected duration are sequence-dependent Finally, while the values of pausing rate and duration tested here are within the range of biologically observed values, extending our studies to values beyond these ranges might provide insights into the potential applications of pausing in synthetically engineered genetic networks.


Here we examine how the regulation of cis-encoded epigenetic switches controls the extent of heterogeneity in gene expression, which is ultimately tied to phenotypic diversity in a population. We show that two copies of the FLO11 locus in Saccharomyces cerevisiae switch between a silenced and competent promoter state in a random and independent fashion, implying that the molecular event leading to the transition occurs locally at the promoter, in cis. We further quantify the effect of trans regulators both on the slow epigenetic transitions between a silenced and competent promoter state and on the fast promoter transitions associated with conventional regulation of FLO11. We find different classes of regulators affect epigenetic, conventional, or both forms of regulation. Distributing kinetic control of epigenetic silencing and conventional gene activation offers cells flexibility in shaping the distribution of gene expression and phenotype within a population.Epigenetic switches encode their state information either locally, often via covalent modification of DNA or histones, or globally, usually in the level of a  FLO11 gene in S. cerevisiae can switch ON and OFF slowly and independently. In addition, we reveal a simple regulatory strategy by which cells can control the proportion of cells in different gene expression states. Because FLO11 encodes a cell-wall protein responsible for mediating cell–cell and cell–surface interactions, this control might literally allow natural populations to have a controllable fraction of cells “stick around” while the other fraction is easily washed away.In an uncertain and changing world, microbial populations with a diverse range of phenotypes may outperform a monolithic population. Over many generations, mutations can lead to genetic diversity in a population. However, microbes have strategies to generate such diversity quickly. For example, if multiple genes switch ON and OFF slowly, randomly, and independently of each other, then a large combination of gene expression states, and hence phenotypes, are possible. The different gene expression states do not involve changes in DNA sequence and are therefore epigenetically inherited. We show that the two copies of the  The molecular basis of local, cis markers involve covalent modifications of DNA or DNA-associated proteins. These include DNA methylation trans markers are often transcription factor activity; the mechanism for stable, slow switching of these levels is positive or double negative feedback loops that generate heritable bistable gene expression states associated with high or low levels of transcription factor activity E. coli the expression of pyelonephritis-associated pili is regulated by an epigenetic switch that maintains its state through both DNA methylation and a positive feedback loop Many epigenetically regulated genes can be considered switches as they have two heritable expression states, “ON” and “OFF.” A stable epigenetic marker maintains each state and can be encoded in The control of phenotypic heterogeneity is arguably as important as its rapid generation. Heterogeneity, or noise, in conventionally regulated gene expression has been well-studied in recent years. Single cell and single molecule studies have revealed that gene activation occurs in random, intermittent transcriptional bursts cis appear to be the dominant source of intrinsic noise in eukaryotes trans factors affecting gene expression: for example, general and gene-specific transcriptional machinery, ribosome number and tRNA availability, or even cell morphology. The two sources can be experimentally distinguished using a dual-reporter assay, where two copies of the same promoter are used to drive distinguishable fluorescent protein variants Noise in gene expression can be partitioned depending on whether its source is intrinsic or extrinsic to the process of gene expression. Intrinsic noise is due to the random nature of chemical transformations, including transcription and translation events. However, the random bursts of transcription thought to be associated with fast promoter fluctuations occurring in How regulators control the kinetics of intrinsic promoter fluctuations dictates the resulting expression heterogeneity. Stochastic models can be used to directly quantify this relationship trans-encoded switches involving feedback loops and associated with bistable gene expression have been studied in detail trans factor that overcome the stability of the two epigenetic states lead to switching cis-encoded switches which must involve changes in the local promoter state. For example, activators could increase population-averaged expression by either stabilizing the competent state or destabilizing the silenced state. The resulting heterogeneity in expression is dictated by the specific kinetic role of the activator.While conventional gene regulation involves fast fluctuations between inactive (competent) and active promoter states, epigenetic silencing of gene expression involves slow fluctuations (<once per cell cycle) between a silenced and competent state. The kinetics of these fluctuations in HMR and HML, in S. cerevisiae. Four distinct expression states were observed in a sir1 background, where SIR-protein dependent silencing of these loci is partially impaired In a diploid organism, an epigenetically regulated gene might exhibit four different expression states if each copy switches independently. With global encoding, both copies respond to the same global factor and must switch in a correlated manner. However, with local encoding, each copy may respond independently if the fluctuation that trips the switch is a molecular event that occurs locally at one copy. In fact, a recent study demonstrated the random and independent switching of two copies of a reporter gene inserted within the canonically silenced mating type loci, cis-encoded epigenetic switches that toggle slowly and randomly could lead a combinatorial explosion of expression states and represent a powerful strategy to generate phenotypic diversity. Is independent switching employed in nature and how are slow fluctuations regulated? The S. cerevisiae Flo11p is a cell-wall adhesin protein and member of the FLO gene family important in mediating cell-to-cell and hydrophobic cell-surface interactions FLO11 occurs in a SIR-protein independent manner and is both promoter and position-specific FLO11, independent switching could represent a fourth mechanism for generating variation.Multiple FLO11 promoter is one of the largest in S. cerevisiae and regulated by many factors (FLO11 is thought to occur through the recruitment of the histone deacetylase Hda1p via the repressor Sfl1p through a yet to be defined mechanism FLO11 through the protein kinase A (PKA) pathway results in phosphorylation of both Sfl1p and Flo8p. While phosphorylation disables Sfl1p binding, it enables Flo8p binding FLO11 expression, a context where silencing does not occur FLO11.At 3.5 kb, the  factors  whose kiFLO11 is indeed a cis-encoded epigenetic switch and identify the kinetic roles of trans factors in the epigenetic and conventional regulation of FLO11. Within a diploid yeast, each locus switches in a slow, random, and independent manner, with switching rates dependent on environmental conditions. Using a stochastic kinetic model, we infer the kinetic role that different regulators have on the slow promoter fluctuations associated with epigenetic transitions between a silenced and competent promoter state and the fast promoter fluctuations associated with conventional gene activation. We find three classes of FLO11 regulators: those that affect the stability of the competent state, affecting slow promoter fluctuations; those that regulate the burst frequency of transcription due to fast promoter fluctuations; and those that have both functions. Moreover, a single synthetic activator can mimic each of these three classes based on the location of its DNA binding site. Because the kinetic role of each regulator defines its impact on expression heterogeneity, this can be controlled by the choice of regulator class. Finally, ethanol controls the extent of gene silencing nearly independently of transcriptional activation through Flo8p, thereby dictating whether FLO11 expression responds in a graded or heterogeneous manner to other signals.Here, we provide evidence that FLO11 is partially silenced in haploid cells and heterogeneous in expression. Members of this population are capable of reversibly transitioning between the OFF (silenced) and ON (competent) state cis or trans fluctuation, we employed the dual-reporter assay, replacing the two copies of a FLO11 ORF in diploid yeast with a distinct fluorescent protein variant (Venus YFP and Cerulean CFP) . Second, we compared fluorescent protein expression in the dual-reporter strain to strains where only one FLO11 allele had been replaced with a fluorescent protein. There was no difference in expression levels  . Importaean CFP) . When grn levels .2p (both ON), (1−p)2 (both OFF), or 2p(1−p) (mixed ON/OFF and OFF/ON), where p is the proportion of cells with a particular allele ON. Note that p is identical for both YFP and CFP expression because the alleles are equivalent. We were able to verify the population's expression profile had reached steady-state  silenced, the predominant source of variation in FLO11 or reporter expression arises from the slow epigenetic transition between silenced to competent states because of (1) the smaller magnitude of the fast intrinsic and extrinsic fluctuations and the fact that (2) the faster fluctuations (<1 cell generation) are more completely time-averaged by the long-lived reporter compared to the slow transition (>1 cell generation). Therefore, we can lump the fast transition rates  into an overall transcription rate μ and ignore extrinsic fluctuations. Gene expression can now be described using the commonly employed two-state model:x is the amount of reporter protein, μ is the (lumped) protein production rate, δ is a protein degradation rate (here the cell growth rate), and f(t) is a “random telegraph process” that takes values of 0 or 1 corresponding to a silenced or active promoter state, with exponentially distributed times between switching events . Epigenetic regulation occurs by definition in the lower left quadrant, when both λ and γ are slower than the division rate (λ∼δ and γ∼δ<1). Expression can turn completely OFF if the active state is destabilized , or the silenced state is stabilized . Opposite changes in λ and γ turn expression completely ON (and can lead to a shift to the upper right quadrant).To obtain transition rates, we fit the measured fluorescence distributions arising from each titration to the Beta distribution, the solution to the simplified two-state model. As described previously, a two-state model which lumps the fast transitions is only strictly applicable when slow epigenetic transitions related to silencing dominate. The four quadrant plot in λ′ increases) and the graded, unimodal distribution of the upper right quadrant. Importantly, it is impossible to distinguish between conventional repression and epigenetic silencing in any population in the lower right quadrant where expression is completely OFF. Furthermore, fitting fluorescence expression distributions generated from a single FLO11 promoter driven reporter that is conventionally regulated does not yield the fast promoter transition rates λ′ and γ′ because here extrinsic fluctuations are significant. The extrinsic noise is due to cell-to-cell variation in factors like morphology, ribosome number, and/or upstream components in the FLO11 regulatory pathway and affects both promoters within the same cell in a correlated fashion. To properly measure the fast transition rates associated with conventional regulation, the intrinsic noise should be analyzed to determine the burst frequency (λ′) and burst size (μ′/γ′)  fluctuations caused by transitions between the competent and active promoter states. For example, a conventionally regulated (but repressed) gene can be OFF and lie in the lower right quadrant. Activation leads to an increased burst frequency (/γ′) see .trans factors were expressed heterologously under the control of a doxycycline-inducible promoter FLO11 expression in a Flo8p-dependent manner (see below). All the trans factor titrations were performed in either SD ura- or SD ura- with ethanol. Titrations of Sfl1p, Flo8p, and Mss11p were done in a sfl1Δ, flo8Δ or mss11Δ background, respectively.To decouple complex upstream signaling events occurring at the promoter , individλ and γ using the two-state model. To summarize the effect of various activators and Sfl1p on the stability of the silenced and competent states, we plot the series of  values determined on the four quadrant plot of FLO11 is OFF, corresponding to the lower right quadrant. Based on the response of the FLO11 promoter  in the sfl1Δ background, as has been shown previously graded fashion  of the promoter.To test this model, we generated a dual-reporter strain in an response . This es SD ura- . Only thhda1Δ background, titration of activators in the presence of high levels of Sfl1p will result in a graded response, since Sfl1p is now functioning as a conventional repressor. In addition, the threshold level of activator required to turn on FLO11 will be lower. We performed these titrations  ethanol concentrations (FLO11 expression is OFF (lower right quadrant), the promoter is actually in the competent state. To test this idea, we titrated Class I and II activators in 1% ethanol. Both classes were capable of increasing FLO11 expression to high levels in a graded manner  the FLO11 promoter is epigenetically regulated URA3 promoter inserted at the mating type loci in S. cerevisiae switch independently in a sir1 background FLO11 gene pool Our demonstration that two copies of the FLO11 is controlled, we took a functional approach and determined the kinetic role of different regulators on both slow and fast promoter fluctuations. Class I regulates fast promoter fluctuations exclusively. Intrinsic noise measurements confirmed these regulators destabilize the competent state and increase the burst frequency, a common theme for regulators in yeast To understand how expression heterogeneity at While the regulator titrations indicate that ethanol desilences the promoter via Flo8p, the pathway(s) by which ethanol activates Flo8p is unknown. The simplest mechanism is that long term growth in ethanol activates PKA , which then activates Flo8p and deactivates Sfl1p. In fact, glucose is not required as the promoter response when Flo8p was titrated in synthetic media with ethanol or glycerol as the sole carbon source was similar to that in SD 1% ethanol (data not shown). However, activation of the PKA pathway via ethanol has not been reported. It is also possible that Flo8p is activated by ethanol via another pathway independent of PKA, although to our knowledge, it is not known to be the target of any other kinase. A third possibility is that ethanol may enable synergistic interactions between Flo8p and other regulators that leads to desilencing, although there is no evidence of Flo8p interacting with any Class I activators. These possibilities could be distinguished by monitoring cyclic AMP levels and the phosphorylation status of Flo8p and Sfl1p in ethanol.FLO11 promoter can largely determine the kinetic role of the transcriptional regulator. However, the mechanistic description of how binding to particular sites affects slow epigenetic regulation and fast conventional regulation and the molecular nature of the silenced and competent promoter states is still unclear. A mechanistic explanation for the dual roles of Sfl1p is likely the clearest. Binding in the −1200 nucleosome-free region governs epigenetic silencing, possibly by recruiting Hda1p via Tup1p/Ssn6p corepressor in vitro. Indeed, preliminary ChIP experiments suggest Sfl1p is bound to this region in vivo .Our results lend support to the idea that binding site position within the FLO11 promoter in a competent, “poised” state whose expression can be controlled by Class I –like regulators. There is evidence, though, that Flo8p can bind to other regions of the promoter Among the activators, the role of Flo8p and its synthetic analog is perhaps the clearest. Flo8p binding and Sfl1p binding at the −1200 region are likely mutually exclusive because of overlapping binding sites, and so Flo8p can prevent the Sfl1p-mediated establishment of silencing but probably not directly affect conventional Sfl1p repression. This would explain the ability of Flo8p and its corresponding synthetic analog to affect the slow epigenetic transition independently of the conventional activation. In fact, binding of tetR, which lacks the VP16 activation domain of rtTA, to the −1160 tetO site is sufficient to abrograte silencing (data not shown), implying steric hindrance is the major mode of action. Furthermore, any weak activation via Flo8p might be through its known role in binding to the promoters of other Class I activators (including the ones tested here) and presumably upregulating their expression. In this manner, Flo8p activation can put the FLO11 promoter. For example, binding site proximity to the transcriptional start site could play a dominant role. Canonical yeast promoters are typically 150–400 bp with transcription factor binding sites are clustered 100–200 bp from the transcriptional start site FLO11 promoter, a region that may be inaccessible to transcription factors and/or transcriptional machinery in the silenced state. However, while the Class I synthetic analog binding site is at −350 in a nucleosome occluded region, not all Class I activators have binding sites in this region Several possibilities exist for the inability of the Class I activators to challenge the silenced state, yet still regulate the burst frequency of the competent in vivo in activating conditions FLO11 promoter is 10 times higher than that of a similarly highly expressed PHO84 promoter (data not shown). However, with either explanation, the inability of Class I activators to challenge the silenced state is not clear. Altered chromatin structure or reduced binding site accessibility could be invoked as Class I activator sites tend to be under nucleosomes. However, other than some nucleosome depletion in the core promoter and the −1300 region, no gross nucleosomal rearrangements seem to occur upon silencing  mechanisms including DNA looping, cryptic transcription, or long range chromatin remodeling. This would provide an explanation for the presence of Class I activator binding sites in these regions that are known to be bound ilencing  althoughagn43 gene in E. coliFLO11 promoter remains unclear FLO11 promoter under various conditions should point toward the biochemical mechanism of the slow promoter transition and will be the focus of future work.While we do not know the biochemical intermediates during the slow promoter transitions, the pseudo-first order rates suggest a single slow step, rather than a distributed control mechanism. This is similar to both the partially silenced mating type loci FLO gene family EPA gene family in the pathogenic yeast C. glabrataEPA gene expression might allow C. glabrata to rapidly colonize new host tissues and evade immune surveillance. Do such genes turn ON and OFF independently, does it depend on the mechanisms of their silencing , their relative chromosomal locations, or the presence of boundary elements? What promoter transitions do trans factors regulate? This understanding will allow the engineering of strains with well-defined levels of phenotypic heterogeneity. Such strains are a prerequisite to quantify what role if any phenotypic heterogeneity has on organismal fitness in natural environments.Our findings have implications for the regulation of various subtelomerically encoded gene families known to be epigenetically regulated. This includes the FLO11 promoter, we replaced the FLO11 ORF in a haploid Σ1278b from the Heitman laboratory FP-KanMX6 or CFP-KanMX6 cassettes by PCR integration, and then mated to create diploids. All strains and plasmids used are provided in To use the dual-reporter assay to study switching of the SD is synthetic defined media with 2% glucose. SD ura- media lacks uracil. SD ura- media with ethanol contains both 2% glucose and a specified amount of ethanol.600 between 0.005 and 0.01, and grown for 15–20 hours in the same media. For the titration experiments, these cultures were treated with serial dilutions of doxycyline (0 to 10000 ng/ml) at 30°C in well-agitated deep well 96-well plates. Cells were harvested in mid-late log phase (OD600 between 0.5 and 1.5), and placed on ice while other samples were being processed. Expression was measured using a Zeiss AxioObserver microscope with filters optimized for yECitrine, mCherry, and Cerulean (Chroma). Metamorph software (Molecular Devices) was used to analyze images and quantify single cell YFP and CFP fluorescence. Between 500 and 1500 cells were imaged for each sample. Fluorescence levels in the RFP channel was used to discard dead cells . Details of data preprocessing and estimation of λ and γ are given in the Cells from overnight cultures grown in SD ura- or SD ura- + ethanol were inoculated at an initial OD−5 µl chambers was constantly replenished at a rate of 10 µl/hr. YFP, CFP, RFP fluorescence and bright field images were obtained every 15 minutes for 20 hours. An example is provided in In timelapse microscopy experiments, cells were loaded onto the ONIX Microfluidic Platform (CellASIC) with initially ∼10 cells trapped in individual chambers. Media in the ∼10600 = 0.8. Lysates from the fixed cells were sonicated to shear the chromatin to an average length of 500 bp, and isolated chromatin was incubated with 2 µl antibodies (Upstate/Millipore) against either histone H3 (Cat. No. 05-928), histone H4 (Cat. No. 05-858), acetylated histone H3 (Cat. No. 07-593) and acetylated histone H4 (Cat. No. 06-866). A sample with no antibodies was also prepared as a control. After reversal of cross-links, DNA from immunoprecipated chromatin was purified and analyzed using quantitative PCR (Applied Biosystems). Primers amplifying the −1.7 to −1.5 kb region of the FLO11 promoter and primers amplifying a telomeric region in the right arm of chromosome VI Chromatin IP's were done based on the method of 600 = 0.5.Micrococcal nuclease assays were performed as in Figure S1Nucleosome occupancy data from (0.46 MB TIF)Click here for additional data file.Figure S2FLO11 expression on solid media - single reporter strains. 10 µL of a mid-log phase culture of various diploid strains with single reporters were spotted on a fresh YPD plate. Labels on the left indicate whether and at which locus the FLO11 ORF was replaced with a particular fluorescent protein variant. Plates were left at room temperature. Cells from all regions of the spot were sampled (see pled see  and CFP (1.26 MB TIF)Click here for additional data file.Figure S3FLO11 expression on solid media - dual reporter strains. As in panel FLO11 expression doesn't feedback and affect its own expression.(1.28 MB TIF)Click here for additional data file.Figure S4Static snapshots of Y45 cells in YP 1% Ethanol, 2% Glycerol maintained in exponential phase by dilution. The null hypothesis that distributions of YFP fluorescence at each time point are equivalent cannot be rejected . Similarly, the null hypothesis that CFP fluorescence distributions at each time point are equivalent cannot be rejected .(0.45 MB TIF)Click here for additional data file.Figure S5CFP expression distribution during timelapse. As in (0.77 MB TIF)Click here for additional data file.Figure S6λ/δ (OFF-ON) = 0.25+0.03 generation−1(pink), γ/δ (ON-OFF) = 0.90+0.17 generation−1 (blue). Error bars correspond to 3 s.d. from the mean calculated by a bootstrap analysis.Switching rates of CFP reporter. As in (0.64 MB TIF)Click here for additional data file.Figure S7FLO11 is not correlated with cell cycle stage. The time at which a cell born OFF switches ON (A) or the time when a cell born ON switches OFF (B) after its most recent division event occurred is shown for each cell observed to switch during the timelapse experiment in OFF(ON)-ON(OFF) switch at (1.23 MB TIF)Click here for additional data file.Figure S8hda1Δ background respectively, the other activators Phd1p and Mss11p also exhibit a graded response in an hda1Δ background (A and B). In the wildtype background, Phd1p (C) like Tec1p is unable to stabilize the ON state enough to enter the bimodal regime, whereas Mss11p (D), like Msn1p (shown in hda1Δ background lowers the threshold level at which Mss11p and Phd1p function (E and F). Error bars represent 3 standard deviations around the mean from bootstrap analysis. Like Tec1p in hda1Δ in λ′) in the absence of silencing, as the square of the intrinsic noise of Phd1p titrated in hda1Δ (G) and Mss11p titrated in hda1Δ (H) scale with the reciprocal of protein abundance. All titrations were done in SD ura-.Loss of Hda1p converts the heterogeneous promoter response to activators Mss11p and Phd1p to a graded response. As in shown in , is able(3.07 MB TIF)Click here for additional data file.Figure S9FLO11 was performed on cells grown in conditions where the promoter was completely silenced (growth of wildtype Y45 in SD complete) in (A) or completely active (growth of an sfl1Δ strain in SD complete plus 2% glucose) in (B). The overall structure agrees well with the in silico predictions in Micrococcal nuclease mapping of (0.55 MB TIF)Click here for additional data file.Table S1Yeast strains used in study.(0.05 MB DOC)Click here for additional data file.Table S2Plasmids used in study.(0.03 MB DOC)Click here for additional data file.Text S1Supplemental discussion.(0.11 MB DOC)Click here for additional data file.Video S1FLO11 promoter are readily seen in this video. Each frame represents 15 minutes elapsed and the total movie represents 20 hours of growth.Movie of cell growth within the microfluidic device. Red and green represent CFP and YFP expression respectively. Both ON to OFF and OFF to ON transitions for each copy of the (3.83 MB AVI)Click here for additional data file.


CHD7, the human counterpart of kis, are associated with CHARGE syndrome, a developmental disorder affecting multiple tissues and organs. To clarify how KIS-L activates gene expression and counteracts Polycomb group silencing, we characterized defects resulting from the loss of KIS-L function in Drosophila. These studies revealed that KIS-L acts downstream of P-TEFb recruitment to stimulate elongation by Pol II. The presence of two chromodomains in KIS-L suggested that its recruitment or function might be regulated by the methylation of histone H3 lysine 4 by the trithorax group proteins ASH1 and TRX. Although we observed significant overlap between the distributions of KIS-L, ASH1, and TRX on polytene chromosomes, KIS-L did not bind methylated histone tails in vitro, and loss of TRX or ASH1 function did not alter the association of KIS-L with chromatin. By contrast, loss of kis function led to a dramatic reduction in the levels of TRX and ASH1 associated with chromatin and was accompanied by increased histone H3 lysine 27 methylation—a modification required for Polycomb group repression. A similar increase in H3 lysine 27 methylation was observed in ash1 and trx mutant larvae. Our findings suggest that KIS-L promotes early elongation and counteracts Polycomb group repression by recruiting the ASH1 and TRX histone methyltransferases to chromatin.Polycomb and trithorax group proteins regulate cellular pluripotency and differentiation by maintaining hereditable states of transcription. Many Polycomb and trithorax group proteins have been implicated in the covalent modification or remodeling of chromatin, but how they interact with each other and the general transcription machinery to regulate transcription is not well understood. The trithorax group protein Kismet-L (KIS-L) is a member of the CHD subfamily of chromatin-remodeling factors that plays a global role in transcription by RNA polymerase II (Pol II). Mutations in  Drosophila. KIS-L also counteracts histone H3 lysine 27 methylation—a covalent modification of chromatin involved in hereditable gene silencing. Our findings provide a plausible explanation for the developmental abnormalities associated with CHARGE syndrome, a serious disorder resulting from mutations in the human counterpart of KIS-L.The transcription of eukaryotic genes involves a highly ordered series of events, including the recruitment of RNA polymerase to promoters, the production of the RNA transcript, and termination. These events are coordinated with changes in chromatin structure that allow regulatory proteins and RNA polymerase to access the DNA template. The recruitment of RNA polymerase II to promoters is rate-limiting for the expression of most eukaryotic genes. However, RNA polymerase often pauses or stalls a short distance downstream of promoters, providing an additional step at which transcription can be regulated. In this study, we present evidence suggesting that a chromatin-remodeling factor, KIS-L, activates transcription by counteracting promoter-proximal pausing in  Drosophila heat-shock genes, allows genes to be rapidly activated in response to cellular or environmental signals Drosophila and humans have shown that paused polymerases are present downstream of the promoters of many silent genes, suggesting that the regulation of early elongation is a relatively widespread phenomenon Eukaryotic transcription involves a highly ordered series of events including the binding of transcription factors to cis-regulatory elements; the assembly of the pre-initiation complex and recruitment of Pol II to promoters; initiation; promoter clearance; elongation and termination Drosophila homeotic (Hox) genes. Hox genes encode homeodomain transcription factors that specify cell fates in Drosophila and other metazoans Hox genes must be regulated precisely, since their inappropriate expression can lead to homeotic transformations and other developmental abnormalities. The initial patterns of Hox transcription are established in response to positional information in the early embryo. During subsequent development, these patterns are maintained by two ubiquitously expressed groups of regulatory proteins: the trithorax group of activators and the Polycomb group of repressors Hox transcription, trithorax and Polycomb group proteins play key roles in the control of cell fate.Numerous factors that regulate elongation or other aspects of transcription by Pol II have been identified in genetic studies of Drosophila; the majority of these genes encode subunits of two protein complexes: PRC1 and PRC2 Hox transcription.Although the detailed mechanism of action of most Polycomb and trithorax group proteins is not well understood, many of these proteins regulate transcription by altering chromatin structure. Roughly a dozen Polycomb group genes have been identified in kis, may regulate elongation by Pol II. Like many other trithorax group genes, kis was identified in a screen for extragenic suppressors of Polycomb (Pc) mutations, suggesting that it directly or indirectly counteracts Polycomb group repression kis function causes homeotic transformations due to the failure to maintain transcription of Hox genes kis encodes a large protein (KIS-L) which is highly related to human CHD7 and other members of the chromodomain-helicase-DNA binding (CHD) subfamily of ATP-dependent chromatin-remodeling factors kis function leads to a dramatic reduction in the levels of elongating Pol II and the elongation factors SPT6 and CHD1 associated with polytene chromosomes A subset of trithorax group members, including Drosophila and mammals, and the trithorax group proteins ASH1 and TRX In addition to highly conserved ATPase domains, KIS-L and other CHD proteins contain chromodomains: a short domain implicated in the binding of methylated histone tails kis function, we found that KIS-L acts downstream of positive transcription elongation factor b (P-TEFb) recruitment to stimulate elongation by Pol II. KIS-L did not bind methylated histone tails in vitro or extensively co-localize with H3K4me on polytene chromosomes, as would be expected if this modification is necessary or sufficient for the recruitment of KIS-L to chromatin. Surprisingly, the loss of kis function led to a dramatic reduction in the levels of TRX and ASH1 associated with chromatin, accompanied by a significant increase in the level of H3K27 methylation. These findings suggest that KIS-L promotes transcription elongation and counteracts Polycomb group repression by recruiting the ASH1 and TRX histone methyltransferases to chromatin.In this study, we sought to clarify the role of KIS-L in transcriptional regulation in vivo. By analyzing defects resulting from the loss of 1S2P3T4S5P6S7 of the C-terminal domain (CTD) of Pol II is highly regulated during the transcription cycle ser5 is enriched in promoter-proximal regions ser2 is associated with the body of actively transcribed genes. Using antibodies sensitive to the phosphorylation state of the CTD, we previously demonstrated that the loss of kis function leads to a dramatic reduction in the level of Pol IIoser2 - but not Pol IIa or Pol IIoser5 - associated with salivary gland polytene chromosomes Previous studies in our laboratory suggested that KIS-L promotes a step in transcription downstream of the recruitment of Pol II to promoters kis mutants, including a reduction in the level of Pol IIoser2, but not Pol IIa or Pol IIoser5, associated with chromatin A key step in early elongation is the recruitment of P-TEFb to promoters k13416kis larvae with an antibody against the CycT subunit of P-TEFb ser2 overlap extensively in wild-type larvae  that recognizes hypo- and hyper-phosphorylated forms of Pol II k13416kis larvae was significantly reduced relative to wild-type  gene for these studies for several reasons. First, fkh is a relatively simple gene that is expressed in the salivary gland at high levels. A single enhancer located 9 kb upstream of the transcription start site activates fkh expression in this tissue fkh expression fkh by immuno-FISH (data not shown).We next examined the relative distributions of H3K4 methylation and KIS-L at higher resolution using chromatin immunoprecipitation (ChIP) assays. We chose the fkh gene by ChIP using chromatin isolated from the salivary glands of wild-type third instar larvae. Consistent with a role in early elongation, KIS-L is associated with the transcriptional start site of the fkh gene  promoter fkh gene and does not precisely mirror the distribution of KIS-L  mutations on the association of KIS-L with chromatin. lid encodes a H3K4me3 demethylase 10424lid allele survive until the third larval instar and display elevated levels of H3K4me3 on their polytene chromosomes lid function has no obvious effect on the level of KIS-L associated with polytene chromosomes  complex, suggesting that it might also physically interact with other trithorax group proteins to regulate transcription by Pol II radients . In contradients . These dK159R and BRMK804R), the expression of high levels of an equivalent form of KIS-L (KIS-LK2060R) had no effect on the viability or development of a wide variety of tissues (data not shown). These findings provide additional, albeit indirect, evidence that KIS-L does not stably interact with other trithorax group proteins as a subunit of a chromatin-remodeling complex.We also employed a genetic approach to investigate whether KIS-L acts as a monomer. Mutations in a conserved lysine residue in the ATP-binding site of chromatin-remodeling factors eliminate their catalytic activity without interfering with their ability to interact with other proteins. For chromatin-remodeling factors that function as subunits of protein complexes (e.g. BRM and ISWI), such catalytically inactive proteins exert strong, dominant-negative effects when expressed at high levels in vivo k13416kis mutant larvae  increase in the level of H3K27me3 on polytene chromosomes (fkh gene of k13416kis mutant larvae by ChIP (We previously noted that the majority (>80%) of PC binding sites in salivary gland polytene chromosomes are adjacent to sites of KIS-L binding omosomes  without omosomes . A simil by ChIP .Drosophilakis mutants is dependent on the action of Polycomb group proteins. Consistent with this view, the chromosomal distributions of PC and H3K27me3 are virtually identical (>90% overlap) in both wild type and k13416kis mutant larvae  is responsible for the majority of H3K27 methylation in t larvae . Althougic sites . These fash1 function in the haltere discs of third instar larvae results in the spread of H3K27me3 into the coding region of the actively transcribed Ubx gene ash1 mutant larvae. As observed in kis mutants, the level of H3K27me3 is dramatically elevated on the salivary gland polytene chromosomes of 22ash1/17ash1 larvae relative to wild-type  binding protein, cytoplasmic 1 gene in HeLa cells is consistent with a role in promoting transcription elongation Although we observed a dramatic decrease in the level of ASH1 and TRX associated with chromatin in ash1 and trx single mutants  as revealed by genome-wide ChIP assays Drosophila UTX co-localizes with elongating Pol II, suggesting that H3K27 demethylation may be directly coupled to transcription elongation Drosophila TRX How does KIS-L antagonize H3K27 methylation? The steady-state level of H3K27 methylation is determined by multiple factors, including the level and activity of the E(z) methyltransferase; the accessibility of its nucleosome substrate; the frequency of nucleosome eviction; the frequency of histone H3 exchange, and the level and activity of histone H3K27 demethylases. The loss of KIS-L does not alter the distribution or level of PRC1 or PRC2 on polytene chromosomes, suggesting that it acts downstream of the E(Z) methyltransferase to counteract H3K27 methylation. Several H3K27 demethylases have been identified, including JMJD3 and UTX Drosophila Hox genes kis, ash1 and trx mutants. The exchange of histone H3 and H3.3 is also elevated in regions transcribed by Pol II It is also possible that KIS-L counteracts H3K27 methylation by promoting the replacement of histone H3 by the histone variant H3.3. H3.3 displays covalent modifications associated with actively transcribed genes, including elevated H3K4 methylation and low levels of H3K27 methylation KIS-L is a member of a large family of CHD proteins that are conserved from flies to humans. Heterozygosity for loss of function mutations in CHD7, a human counterpart of KIS-L, leads to CHARGE syndrome, a serious developmental disorder affecting approximately one in 8,500 births http://www.flybase.org) unless otherwise indicated. k13416kis is a recessive loss of function allele; homozygotes survive until late larval or early pupal stages, but express undetectable levels of KIS-L in salivary gland nuclei Flies were raised on cornmeal/molasses/yeast/agar medium containing Tegosept and propionic acid. Strains are described in FlyBase (ser2 (H5), RPB1 (CTD4H8) (Covance) and His epitope tag (Anaspec). Salivary gland polytene chromosomes from third instar larvae were fixed for 5 minutes in 45% acetic acid/1.85% formaldehyde and stained with antibodies against ASH1, TRX, RPB1, Pol IIa, Pol IIoser5 and KIS-L. To stain polytene chromosomes with antibodies against KIS-L, CycT, PoI IIoser2, E(Z), PC, H3K4me2, H3K4me3 and H3K27me3, glands were dissected in 0.7% NaCl and fixed in 6 mM MgCl2, 1% citric acid and 1% Triton X-100 for 2 minutes. Secondary antibodies were obtained from Jackson ImmunoResearch Laboratories . Samples were mounted in Vectashield containing DAPI (Vector Laboratories). Images were captured using a Zeiss Axioskop 2 plus microscope equipped with an Axioplan HRm camera and Axiovision 4 software . Merged and split images were generated using Adobe Photoshop CS3 software as previously described Indirect immunofluorescence microscopy was used to examine the distribution of proteins on salivary gland polytene chromosomes kis mutants, polytene chromosomes from wild-type and mutant larvae stained with antibodies against H3K27me3 were photographed using exposure times that yielded images of comparable intensity. The fold increase in H3K27me3 was calculated as a ratio of the average exposure times for the wild-type and mutant samples.The levels of proteins associated with wild-type and mutant polytene chromosomes were compared by processing, capturing and analyzing the samples at the same time under identical conditions as described in Srinivasan et al. (2005). To quantify the increase in H3K27me3 levels in 5′-GGAATTCCATATGCAGGACTTTACTGAAGT-3′ and 5′-CGGGATCCGATTTTGTTAAAGCGCAGGTA-3′. DNA encoding the HP1 chromodomain (amino acids 22–75) was amplified using the primers 5′-GGAATTCCATATGGAGGAGTACGCCGTGGA-3′ and 5′-CGGGATCCCTTGCGGCTCGCCTCGTACTG-3′. The amplified sequences were cloned between the Nde I and BamH I sites of pET-16b (Novagen). A pET-16b construct encoding chromodomains 1 and 2 of human CHD1 (amino acids 268 to 443) was generously provided by Sepidah Khorasanizadeh 2+ affinity chromatography under native conditions using the manufacturer's protocol (Qiagen). The binding of purified chromodomains to biotinylated peptides corresponding to N-terminal histone tails (Upstate) immobilized on strepavidin agarose (Upstate) was assayed as described in Pray-Grant et al. (2005).Standard techniques were used to analyze proteins by SDS-PAGE and Western blotting k13416kis larvae, transferred to an eppendorf tube containing ice-cold 0.7% NaCl, and pelleted by brief centrifugation. After removing the supernatant, the glands were flash frozen in liquid nitrogen. Following the addition of boiling SDS-PAGE loading buffer, proteins were extracted from the glands by grinding with a pestle and analyzed by SDS-PAGE and western blotting using antibodies against ASH1 and TRX.Identical numbers of salivary glands were dissected from third instar wild-type or Drosophila embryos as described previously 2, 1 mM EDTA) at 4°C for 20 hours at 35,000 rpm in an SW41 rotor (Beckman Instruments). 500 µl fractions were collected and analyzed by SDS-PAGE and western blotting using antibodies against BRM, KIS-L and ISWI.Proteins were extracted from 0–16 hour k13416kis larvae Chromatin was isolated from salivary glands of wild-type and Figure S1Distribution of KIS-L and H3K4 methylation on polytene chromosomes. The distributions of H3K4me2  and H3K4me3  are compared to that of KIS-L  on wild-type salivary gland polytene chromosomes by double-label indirect immunofluorescence microscopy. Merged images are shown in C and F. KIS-L is present at many, but not all, sites of H3K4 methylation.(3.3 MB TIF)Click here for additional data file.Figure S2k13416kis mutant larvae were analyzed by SDS-PAGE and western blotting using antibodies against TRX and ASH1. The loss of KIS-L function leads to only a modest reduction in the levels of TRX and ASH1, even though the salivary glands of k13416kis larvae are significantly (greater than two fold) reduced in size relative to those of wild-type larvae.Loss of KIS-L function does not dramatically alter the level of TRX and ASH1 in larval salivary glands. Proteins extracted from equal numbers of salivary glands of wild-type and (0.4 MB TIF)Click here for additional data file.Figure S322/ash117ash1  larvae were detected by indirect immunofluorescence microscopy. The loss of ASH1 function does not cause obvious changes in the overall level or distribution of either H3K4me2 or H3K4me3.Loss of ASH1 function does not dramatically alter H3K4 methylation in vivo. The distribution of H3K4me2 , KIS-L , and H3K4me3  on salivary gland polytene chromosomes isolated from wild-type  and (4.2 MB TIF)Click here for additional data file.Figure S41trx  larvae were detected by indirect immunofluorescence microscopy. The loss of TRX function does not cause obvious changes in the overall level or distribution of either H3K4me2 or H3K4me3.Loss of TRX function does not dramatically alter H3K4 methylation in vivo. The distribution of H3K4me2 , KIS-L , and H3K4me3  on salivary gland polytene chromosomes isolated from wild-type  and (5.7 MB TIF)Click here for additional data file.Figure S5KIS-L is not a subunit of a large protein complex. The native molecular mass of KIS-L was determined by fractionating whole embryo extracts by sedimentation through a sucrose density gradient. Fractions were analyzed by SDS-PAGE and western blotting using antibodies against KIS-L, BRM and ISWI. The denatured molecular masses of KIS-L, BRM and ISWI are shown in kDa. KIS-L has a native molecular weight of slightly more than 0.5 MDa based on its sedimentation relative to the 1 MDa BRM and 0.5 MDa ISWI complexes.(5.0 MB TIF)Click here for additional data file.Figure S6Colocalization of KIS-L and H3K27 methylation. A) The distributions of KIS-L (green) and H3K27me3 (red) on wild-type salivary gland polytene chromosomes were compared by double-label immunofluorescence microscopy (A). B–E) The distributions of H3K27me3 (B), KIS-L (C), merged (D) and split (E) images corresponding to chromosome arm bounded by the white rectangle are shown. Note that KIS-L flanks many sites of H3K27me3 staining on polytene chromosomes.(2.6 MB TIF)Click here for additional data file.Text S1Supplemental material.(0.02 MB DOC)Click here for additional data file.