Changes in the spatial distribution of regulatory protein binding elements relative to gene coding sequences is sufficient to change gene expression patterns from graded to switch-like. Bistability in signaling networks is frequently employed to promote stochastic switch-like transitions between cellular differentiation states. Differentiation can also be triggered by antagonism of activators and repressors mediated by epigenetic processes that constitute regulatory circuits anchored to the chromosome. Their regulatory logic has remained unclear. A reaction–diffusion model reveals that the same reaction mechanism can support both graded monostable and switch-like bistable gene expression, depending on whether recruited repressor proteins generate a single silencing gradient or two interacting gradients that flank a gene. Our experiments confirm that chromosomal recruitment of activator and repressor proteins permits a plastic form of control; the stability of gene expression is determined by the spatial distribution of silencing nucleation sites along the chromosome. The unveiled regulatory principles will help to understand the mechanisms of variegated gene expression, to design synthetic genetic networks that combine transcriptional regulatory motifs with chromatin-based epigenetic effects, and to control cellular differentiation. In the simplest scenario, a gene is expressed when an activator protein binds to its regulatory sequence, and is silenced when the regulatory sequence is bound by a repressor. Many genes are regulated by both activators and repressors, with the response determined by the combined influence of both factors. When the response is monostable graded, expression is finely tuned to a level that reflects the proportion of the bound activator to the bound repressor. Monostable graded systems allow cells to respond precisely to stimuli. If the response is bistable, the response of each cell depends on whether the activator or the repressor wins. Bistable regulation results in the same gene being expressed in some cells and silenced in others, an outcome that promotes cellular differentiation. It remains unclear, however, how different genetic regulatory structures code for monostable graded and bistable responses. We modeled mathematically the behavior of repressors as they bind to and spread their inhibitory effect along genes and found that the spatial distribution of the binding sites determines which response is chosen. If repressors bind both upstream and downstream of the coding sequence, the response is bistable. If they bind only to one side of the coding sequence, the response is monostable. We confirmed our theoretical findings using synthetic genetic constructs in yeast. These findings help to explain how variations in the location of regulatory elements can lead to cellular differentiation and adaption to varying environments. Graded and switch-like responses reflect fundamental aspects of the functioning of regulatory networks. A graded, monostable response enables the faithful propagation of a signal, and it is often the default response of simple pathways, but regulatory mechanisms can improve the linearity and the dynamic range of the graded response long-range repressors, silencing proteins, and silencers in different systems and organisms In contrast, inhomogeneously distributed regulatory components are frequently observed in eukaryotic transcriptional regulation. Binding of eukaryotic transcriptional factors—activators and repressors—to the DNA can lead to recruitment of enzymes and structural proteins of opposing functions, that induce structural changes and covalent modifications of chromatin, exemplified by acetylation and methylation Genes exposed to the antagonism of activators and repressors or silenced chromosomal regions have been frequently observed to display binary response We examined whether the spatial distribution of activator and repressor binding sites influences gene expression to become monostable or bistable. We examined long-range interactions between these sites. Since long intervening DNA sequences can receive signals from endogenous cellular pathways, we used heterologous synthetic gene expression systems precluding pleiotropic cellular effects. Synthetic networks have been instrumental in reconstituting nonmonotonous responses and in revealing the basic principles of binary response and bistability in transcriptional regulatory networks based on feedback or competition of activators and repressors Saccharomyces cerevisiae. When recruited to these dual recruitment constructs, Sir3p evoked a variegated GFP expression at intermediate levels of gene activation (GA) with a bimodal distribution of cellular fluorescence , are governed by source s(x), reaction r(c), and nonlinear diffusion terms s(x), represents the recruitment of the silencing proteins, and it is a rectangular function. Its width, sw, is proportional to the number of tet operators, while the height of the rectangle, sh, is proportional to the amount of the silencing proteins recruited to the operators. Thus, sh is a function of the doxycycline concentration. The constant nucleation of silencing proteins is necessary for the establishment of steady-state concentration profiles of silencing proteins around the nucleation sites , itself is a variable because the silencing proteins, in particular Sir3, can bridge neighboring DNA segments and condense the chromatin in a concentration-dependent manner, leading the heterochromatin formation D was approximated by AcD, so that the diffusion term was expressed as Silencing proteins and their cofactors spread along the chromosome, whereby nonspecific protein DNA interactions can facilitate their sliding, a process described by one-dimensional diffusion The reaction term represents an autocatalytic loop based on processes encompassing the cooperative binding of Sir3p and Sir4p, mutual binding of Sir2p, Sir3p, and Sir4p, deacetylation of chromatin by Sir2p creating higher affinity sites for Sir3p and Sir4p, and polymerization of Sir3p proteins b. The former is represented by a Hill function, where L stands for the maximal association rate in the limit of c → ∞. The dissociation of the silencing proteins is a linear process, and occurs at a rate of kd.It is assumed that the autocatalytic association of the silencing proteins is superimposed onto a basal, nonspecific association, occurring at a rate of Initial conditions with uniformly distributed low and high concentrations were used to reflect biochemical fluctuations in the initial accumulation of the silencing proteins . The simGA . Fold inhibition was equated with the concentration of silencing proteins at the gene regulatory region, assuming a linear relation between them. Since repression from the upstream and downstream sites interact multiplicatively xu and xd correspond to the positions −0.38 kb and 0 kb, respectively   . The bifresponse  and 2C.GA levels since the diffusion of silencing proteins is further diminished -inducible transcriptional activator, when bound to the GALUAS, and was repressed by tetR fusion proteins (tet operators in the presence of doxycycline (d), and repression was relieved at d = 2 µM.The expression of GFP from chromosomally integrated constructs was activated by GEV, an estradiol .CF is the background fluorescence of the cells. Fold inhibition is the ratio of the unrepressed expression to the repressed expression , at a given degree of activation. GA and fold inhibition at given concentrations of estradiol and doxycycline.Thus, normalized gene expression is the product of Typically, the OFF cells had fluorescence levels very close to the cellular fluorescence background, which implies that the values of fold inhibition − 1 calculated for the OFF cells after histogram fitting are associated with large measurement errors. For this reason, we calculated fold inhibition − 1 for the entire cell population, which has a higher fluorescence value.GA = . Error bars represent standard deviations calculated from three experiments.The inhibition strength is the average value for fold inhibition − 1 in the interval The logarithmic cellular fluorescence intensities of more than 30,000 cells were extracted from list mode files. The data were subjected to an adaptive binning algorithm Finally, the data were transformed from the log space into the linear space.μM = 0, and standard deviation of 1, σM = 1, and then subjected to binning and regression, as previously described. Subsequently, we considered three metapopulations for the further analysis. The first metapopulation corresponded to the measured events (M), with μM = 0 and σM = 1, since the distribution had been normalized. The population size was normalized to 100. The two remaining metapopulations, denoted A and B, represented the two fitted Gaussian components (Equation 3) with the mean and variance parameters  resulting from the nonlinear regression, whereas the respective population sizes na and nb resulted from the normalization of the coefficients a and b to the sum of 100, na + nb = 100. Thus, the sample sizes of M, A, and B were set empirically to correspond to percentages. Next, we performed statistical comparisons between the means of the metapopulations using two-sample t-test with unequal variance. When the difference, μM − μ1  was significant (α = 10−4), the distribution m(x) was considered bimodal.To systematically detect bimodality in a distribution, we performed the following procedure. The fluorescence distribution was first normalized to a mean of zero, Figure S1Simulated evolution of concentration of silencing proteins in the absence of persistent nucleation, sh = 0.c = 6 within the segment −0.6< x <0.6 kb. DA = 0.64. The initiated accumulation of silencing proteins dissipates after around 15 time units, indicating that a constant source of silencing proteins is needed for the maintenance of concentration profiles in the range of parameter values used in our simulations.An initial pulse was provided in the form of (0.12 MB TIF)Click here for additional data file.Figure S2Simulated concentration distribution of silencing proteins along a DNA segment with coarse spatial discretization.D0 = 0.5, sh = 4; sw had to be extended to 0.16 kb, because this is the minimal nucleation width using the coarse spatial discretization. The steady-state concentration profiles were obtained by extending the data points to lines (as with the zero-order hold procedure) to better illustrate the coarseness of the space resolution.To account for the compartmental nature of chromatin, we employed the method of finite difference to simulate the model (Equation 1). For the Euler discretization of space and time, the space steps were sized according to the length of the nucleosome (0.16 kb) to ensure the numerical stability of the procedure, the time step was considerably smaller than the space step. The simulation ran to reach 200 time units, similar to the simulations employing the FEM. The concentration profiles are comparable to those in (0.19 MB TIF)Click here for additional data file.Figure S3Parameter dependence of the switch-like transition.L, K, and n were varied in the range , , and L, K, n) that give rise to bistability. Note, that for n = 1 (lack of cooperativity), bistability did not occur.The surface represents the bistable region, which separates the ON and OFF expression states. (0.24 MB TIF)Click here for additional data file.Figure S42-GFP-GALUAS-[tetO]4 construct repressed by tetR-Sir3.Cellular fluorescence distributions due to the expression of the [tetO]The cells (PRY524.1) were induced by 2.1, 4.1, 8, 16, and 200 nM estradiol in the absence of doxycycline.(0.19 MB TIF)Click here for additional data file.Figure S52-Gene-[O]4 setting.Comparison of the concentration profiles with uniform and nonuniform diffusivities within the [O]GA reduces the spreading of the silencing proteins, which can be mediated by histone acetylation, and by the activator-induced transcription that disrupts heterochromatin. The former process is expected to reduce diffusivity around the activator binding sites, whereas the latter reduces diffusivity along the entire gene. In the main simulations, the diffusion coefficient was reduced uniformly in the segment flanked by the nucleation sites to imitate reduction of diffusivity along the entire gene . For comparison, we simulated concentration profiles when the diffusivity was reduced nonuniformly, around the activator binding sites . The results are comparable using the two approaches.DA was reduced uniformly as GA was increased in-between the nucleation sites, whereas outside of this region, D0 = 0.64. Curves represent the functions DA = 0.52, 0.36, and 0.24.(A) DA(x) = D0•)−1 where Ν  denotes the Gaussian distribution with mean μ and variance σ2. μ was set to −0.38 kb, which corresponds to the activator binding site, while σ equals the internucleation distance divided by four. D0 = 0.64. GA was increased by setting f to 1.5, 6, and 12.(B) The nonuniform distribution is given by (C and D) The red dashed and gray continuous lines represent the solutions initiated with low and high starting concentrations. The internucleation distance was 1.2 kb.(E and F) Simulations as performed in (C) and (D), but the internucleation distance was increased to 1.5 kb. Consequently, the synergistic interaction between the two gradients was abolished.(0.55 MB TIF)Click here for additional data file.Figure S62-GFP-[tetO]4 construct repressed by Sum1p or Sum1-1p.Long-term changes in the cellular fluorescence distributions due to the expression of the [tetO]The cells were induced by 0, 8, 11.3, 22, and 200 nM estradiol , in the absence of doxycycline. Cells were grown exponentially for the period (8 h or 16 h) indicated. Bimodal expression can be seen 16 h after induction by 11.3 nM estradiol due to silencing by Sum1-1p.(0.45 MB TIF)Click here for additional data file.Figure S7Synergy of repression by Sum1-1p.2-GFP (DHS43), GFP-[tetO]4 (DHS44), and [tetO]2-GFP-[tetO]4 (DHS45) constructs. The gray dashed line represents calculated multiplicative interaction of repression from upstream and downstream sites. Fold inhibition − 1 at GA = 0.2 was 13.1 times higher for the dual recruitment construct in comparison to the multiplicative effect, indicating a very strong synergy Click here for additional data file.Figure S8The I silencer alone does not repress the reporter gene.4 construct  was not lower than that at the GFP-tetO4 construct (YJK15), in nonrepressive conditions (tetR-Sum1p and tetR-Sir3p do not repress expression in the presence of 2 µM doxycycline). Thus, the I silencer alone does not repress the reporter gene; it has rather a weak activatory potential.The expression induced by GEV at the I silencer-GFP-[tetO](0.23 MB TIF)Click here for additional data file.Figure S9Cellular fluorescence distributions due to the expression of the I-silencer-GFP-tetO construct repressed by tetR-Sir3.The cells (PRY544.1) were induced by 1.5, 5.8, 8, 11, and 200 nM estradiol, in the absence of doxycycline.(0.19 MB TIF)Click here for additional data file.Figure S10Collapse of bimodal expression as the distance between the recruitment sites for tetR-Sum1 is increased.2, GFP-T-YFP, GFP-T-lacZ integrated within the respective strains: YJKD-16, −3.4, −3.5, −3.6). The relative inhibition denotes the inhibition strength  Sum1p was recruited to the dual recruitment constructs enclosing reporter genes of varying lengths  and [tetO]2-GFP-T-lacZ-[tetO]4 (C) constructs repressed by Sum1p. The cells were induced by 0, 4.1, 5.8, 16, and 200 nM estradiol, in the absence of doxycycline. No bimodal response was detected for the [tetO]2-GFP-T-lacZ-[tetO]4 construct.(B and C) Cellular fluorescence distributions due to the expression of the [tetO](0.36 MB TIF)Click here for additional data file.Figure S11Cellular fluorescence distributions when expression is repressed by Sum1p.The cells (YJKD21.2.2 and YJK16) were induced by 3.75, 7.5, 15, 30, and 200 nM estradiol, in the absence of doxycycline.(0.24 MB TIF)Click here for additional data file.Figure S12Monostable concentration profiles arise when cooperativity in the positive feedback loop is small.n = 1.5. The following parameters were used for the simulations: sh = 6, L = 5, K = 5, b = 0.01, and dk = 1. The internucleation distance was 1.2 kb for the [O]2-Gene-[O]2 setting.The Hill coefficient was reduced from 2 to GA.(A) The red dashed and gray continuous lines represent the solutions initiated with low and high starting concentrations. The blue lines delimit the nucleation sites. When the two concentration profiles overlap red–gray dashed lines are visible. Monostable concentration profiles were obtained even at intermediate GA, a sigmoidal change in fold inhibition can be seen in this range.(B) Inhibition of gene expression, expressed as fold inhibition − 1, was calculated from the values of the silencing concentration gradients. Even though there is no bistability at intermediate (0.25 MB TIF)Click here for additional data file.Figure S13Bistable concentration profiles are confined to the proximity of the nucleating segment when diffusivity is low relative to the nucleation width.sh = 0.3, L = 5, K = 7, b = 0.01, and kd = 1 for a [O]20-Gene setting. DA was set to the indicated values uniformly between the boundaries of the simulation. The blue lines delimit the nucleation segment, sw = 0.741 kb. The widening of the nucleation segment and reduction of the diffusivity renders the spatial aspect of the reaction–diffusion system less pronounced. Consequently, the behavior of the systems approximates that of a simple  positive feedback loop that generates bistability. The yellow dots denote the concentrations at −0.38 and 0 kb, which determine the level of GA.The following parameters were used for the simulations: DA = 0.2 (A) and 0.6 (B). Bistable solution is obtained for lower diffusivity, DA = 0.2. It is evident that the silencing proteins do not propagate to long distances relative to the width of the nucleation segment and the concentrations of the silencing proteins at the gene regulatory region (yellow dots) are low even for the high-concentration profile. Thus, they have an effect on gene expression only in the vicinity of the nucleating segment.(A and B) The red dashed and gray continuous lines represent the solutions initiated with low and high starting concentrations with DA = 0.2 (thin line) and 0.6 (thick line). It is evident that the concentration profile obtained for the lower diffusivity is more square-like.(C) The magnified version of the low-concentration profiles is displayed for (0.29 MB TIF)Click here for additional data file.Table S1Constants used in the equations.(0.04 MB DOC)Click here for additional data file.Table S2Strains.(0.06 MB DOC)Click here for additional data file.Table S3Plasmids.(0.05 MB DOC)Click here for additional data file.Text S1Supporting text and references.(0.05 MB DOC)Click here for additional data file.


The Set2-Rpd3S pathway is important for the control of transcription memory. Mutation of components of this pathway results in cryptic transcription initiation within the coding region of approximately 30% of yeast genes. Specifically, deletion of the Set2 histone methyltransferase or Rco1, a component of the Rpd3S histone deacetylase complex leads to hyperacetylation of certain open reading frames (ORFs). We used this mutant as a system to study the role of histone modifications and co-activator recruitment in preinitiation complex (PIC) formation. Specifically, we looked at the dependence of promoters on the bromodomain-containing RSC complex and the Bdf1 protein. We found that the dependence of cryptic promoters for these proteins varied. Overall, our data indicate that cryptic promoters are independently regulated, and their activation is dependent on factors that govern gene activation at canonical promoters. Transcription by RNA polymerase II (Pol II) is a complex process that requires co-ordination of many factors, including chromatin modifying and remodeling enzymes, in order to elongate through the nucleosome barrier , is an essential ATP-dependent chromatin remodeling complex Saccharomyces cerevisiae RSC complex contain multiple bromodomains, which recognize acetyl lysine residues on histones and other proteins. For instance, the N-terminus of the Rsc4 subunit contains tandem bromodomains, one of which participates in binding acetylated Histone H3 lysine 14 (H3 K14Ac) In vitro, purified RSC complex was shown to stimulate Pol II transcription through a nucleosome template; an event that was enhanced by NuA4 or SAGA-mediated histone acetylation S. cerevisiae, Taf1 does not have bromodomains; rather it pairs with Bdf1 as part of the TFIID complex Another bromodomain-containing protein that is important for the early recruitment of general transcription factors is Bdf1. This tandem bromodomain-containing protein has been shown to interact with the TFIID general transcription factor rco1Δ, which has an ORF hyperacetylation phenotype. This mutant was ideal for our studies because it could be used to determine the affect of histone acetylation on recruitment and activity of bromodomain-containing proteins and the role that these proteins play in cryptic promoter-formation.To investigate the role of the Rsc and Bdf1 bromodomain-containing proteins in activation of cryptic internal transcription, we exploited a mutant of the Rpd3S histone deacetylase complex, In addition to the full-length transcript, mutants in components of the Set2-Rpd3S pathway have cryptic internally initiated transcripts rco1Δ, which does not have HDAC function rco1Δ mutants have hyperacetylated ORFs STE11, which has two cryptic transcripts  and H4 (AcH4), and tri-methylated histone H3, lysine four (H3K4me3), are associated with the promoter and 5′ ORF region of actively transcribed genes  at Pol II-transcribed promoters RSC1 or RSC2 individually results in cells that are viable, but show growth defects due to the loss of transcription at sporulation-specific genes Bromodomain-containing proteins are important for recruitment of chromatin remodeling complexes to acetylated histones (reviewed in RSC1 or RSC2 on acetylated H4 at genes that show hyperacetylation in rco1Δ mutants (2 ratios of immunoprecipitated (IP) AcH4 versus input were subjected to a modified average gene analysis rco1Δ mutants versus wild type, we identified genes that grouped into three clusters based on their enrichment patterns  was composed mainly of longer, less frequently transcribed genes e strain . When th or PCA1 , which wrco1Δ mutants. Specifically, ChIP was performed using myc-tagged Rsc1 and Rsc2 in wild type rco1Δ strains, followed by PCR with primers spanning the STE11 ORF (rco1Δ mutant compared to the wild type strain. We do not see a direct association of the Rpd3S and RSC1 complexes by mass spectrometry (data not shown). Therefore, given that Rsc1 played a role in repression of acetylation at promoter regions in a subset of genes .We wanted to know if the occupancy profiles of either Rsc1 or Rsc2 changed in TE11 ORF . There wof genes , it is prco1Δ background should suppress cryptic transcription. Due to difficulties with making either rsc1Δrco1Δ, or rsc2Δrco1Δ double mutants, we created an Rco1-degron strain (Rco1-deg), using the system described in Kanemaki et al. STE11 gene beginning at 40 minutes post-induction  and small (+1790) cryptic promoter regions was comparable to that of a probe located in a region that does not contain a cryptic promoter (+390). This high baseline of Ada2 occupancy is likely to contribute to the increased levels of acetylation across the ORF as shown in Genes that rely on Bdf1 for TFIID recruitment are generally not associated with SAGA nscripts , we comp11 locus . In the Overall, cryptic promoters are independently regulated by a variety of co-activators. In this sense, they resemble canonical gene promoters, which may explain why the location of these cryptic transcription start sites does not vary like the levels of expression.STE11 ORF, including AcH4, AcH3K14, and H3K4me3. These histone modifications gave the entire STE11 ORF promoter-like characteristics. Yet, cryptic transcript initiation did not randomly occur throughout the ORF; instead it initiated from two distinct positions that could be mapped by 5′-RACE (data not shown). Thus, histone modifications alone do not dictate the position of the cryptic transcription start site. Specific locations for cryptic initiation could be due to a number of additional factors that affect transcription from canonical promoters including the availability of binding sites for co-transcriptional activators, and the presence or absence of an exposed DNA element such as a TATA box.Disruption of Rpd3S function resulted in hyperacetylation at about 30% of yeast genes genome-wide. Locally, we showed that several promoter-associated histone modifications increased across the rsc1Δ mutants produce aberrant asci which could not be rescued by RSC2 overexpression Rsc1 and Rsc2 are present in two distinct complexes RSC1 alone results in an increase in 5′ histone H4 acetylation compared to wild type that is not seen in RSC2 mutants. At the STE11 locus, deletion of RSC1 in an Rco1-degron background resulted in a partial suppression of the small cryptic transcript. When RSC2 was deleted in the Rco1-degron strain, the intensity of the small cryptic transcript actually increased. These data provide further evidence that the RSC1 and RSC2 complexes have different functions. It also shows that the cryptic and full-length promoters are differentially regulated.Our genome-wide data shows that deletion of STE11 cryptic transcript is sensitive to RSC1 deletion. It is possible that in the absence of the RSC1 complex, certain promoters are not able to maintain an NFR and therefore have greater histone density at the promoter region. Future studies could examine the role of RSC1 complex versus RSC2 complex in the establishment and maintenance of Pol II promoter NFRs.At Pol II promoters, RSC activity generally involves single nucleosome events that help to form and maintain the NFR BDF1 in the Rco1-degron background completely suppresses only the small cryptic transcript further confirms this finding. It also suggests that DNA elements are important for initiation of cryptic transcription since Bdf1 is known to affect a very specific subset of promoters. At the PHO5 promoter, when the TATA box was obstructed by a re-positioned nucleosome, gene expression became entirely dependent on the presence of Bdf1 STE11 full-length or cryptic promoters, we did note the same increase of the FLO8 full-length and cryptic transcripts, both of which are known have TATA boxes rco1Δ mutants.The differential role that the RSC1 and RSC2 complexes play in transcription from cryptic promoters indicates that co-activator dependence varies just as it does with canonical promoters. Our data showing that deletion of bdf1Δ Rco1-deg strain has full-length STE11 transcript, so the full-length promoter is not dependent on Bdf1 for TFIID recruitment. Expression of STE11 is inhibited 2.2 fold compared to wild type cells when the catalytic histone acetyltransferase subunit of SAGA, Gcn5, is deleted STE11 expression is SAGA-dependent. Also, the full-length and large cryptic transcripts increase in intensity following deletion of Bdf1, which is thought to inhibit SAGA-dominated promoters STE11 promoters. In contrast, the small cryptic promoter lacked SAGA occupancy and was completely dependent on the presence of Bdf1 for transcription activation.In contrast to the housekeeping genes, stress-induced genes characteristically have promoters that do not require Bdf1, have a TATA box, and show a strong correlation with SAGA activity Overall, cryptic transcription in yeast mutant Rpd3S strains provides an excellent system in which to study the role of various regulatory factors in preinitiation complex formation. We showed that bromodomain-containing proteins are important for transcription activation at acetylated promoters, and that cryptic promoters are regulated as independent units that follow a process of activation similar to canonical promoters.See ChIP assays were performed using the following antibodies: Anti-hyperacetylated Histone H4 (Penta), Upstate #06-866; Anti-c-myc, Roche Applied Science #11667149001; Histone H3 (tri methyl K36), Abcam #ab9050; Anti-dimethyl-Histone H3 (lys4), Millipore #07-030; Anti-acetyl-Histone H3 (lys14), Millipore #07-353; Anti-Ada2 Chromatin immunoprecipitation (ChIP) assays were performed as previously described STE11, PCA1, FLO8 and SCR1 were generated using primers described in Total RNA was extracted as described in STE11 ORF 18, In vitro transcription was then performed using the Ampliscribe T7 kit (Epicentre Biotechnologies). Amplified RNA (aRNA) was purified using the RNeasy Mini Kit (Qiagen) and quantified on a Nanodrop 2000 Spectrophotometer (Thermo Scientific). For the second round amplification, 50–150 ng of aRNA was reverse transcribed using Superscript III reverse transcriptase (Invitrogen), followed by reaction cleanup with the MinElute Reaction Cleanup Kit (Qiagen). A fill reaction followed by in vitro transcription was performed as described above, except amino allyl-UTP (Ambion) was added to the reaction. Final reaction cleanup was performed with the RNeasy Mini Kit (Qiagen). For the labeling reactions, 4–6 ug amino allyl-incorporated aRNA in a 5 uL volume of 0.1 M Carbonate Buffer, pH 8.7, was mixed with 5 uL (0.01 nmol) monofunctional NHS-ester Cy3 or Cy5 dye in DMSO (Sigma) and incubated at 22°C for 2 hours. Reactions were quenched with 5 uL 4 M hydroxylamine at 22°C for 15 minutes, cleaned with an RNeasy MinElute Cleanup Kit (Qiagen), and the efficiency of dye incorporation measured using the Nanodrop 2000 spectrophotometer (Thermo Scientific). Samples with a label incorporation efficiency of 2–4% were used for microarray hybridization.The double T7 linear amplification protocol was adapted from Input was labeled with Cy3 dye, while immunoprecipitated samples were labeled with Cy5 dye. Samples were combined 1∶1 based on quantity (ng). Before hybridization, labeled aRNA samples were fragmented  according to manufacturer's instructions. The hybridization mixture was set up for the Agilent yeast 4x44K platform (Agilent Technologies) according to manufacturer's instructions with the addition of 20 ug of T7 blocking oligo . Microarrco1Δ mutants versus wild type were divided into 6 bins Final datasets from ChIP-chip experiments were pipelined into a modified average gene analysis based on the frame work originated by the Young laboratory Table S1List of yeast strains used in this study.(0.14 MB DOCX)Click here for additional data file.Table S2List of primers used in this study.(0.15 MB DOCX)Click here for additional data file.


ChIP-chip data are routinely used to identify transcription factor binding targets. However, the presence of false positives and false negatives in ChIP-chip data complicates and hinders analyses, especially when the binding targets for a specific transcription factor are compared across conditions or species.We propose an Expectation Maximization based approach to infer the underlying true counts of "positives" and "negatives" from the observed counts. Based on this approach, we study the effect of false positives and false negatives on inferences related to transcription regulation.Our results indicate that if there is a significant degree of association among the binding targets across conditions/species (log odds ratio > 4), moderate values of false positive and false negative rates (0.005 and 0.4 respectively) would not change our inference qualitatively (i.e. the presence or absence of conservation) based on the observed experimental data despite a significant change in the observed counts. However, if the underlying association is marginal, with odds ratios close to 1, moderate to large values of false positive and false negative rates (0.01 and 0.2 respectively) could mask the underlying association. Transcription factors play an important role in gene regulation by binding to specific DNA sequences in the regulatory regions of their targets. Accurate identification of the binding targets of the transcription factors is paramount to the understanding of the regulatory mechanism. Chromatin immunoprecipitation (ChIP) experiments are commonly used to identify the regulatory targets in prokaryotes and eukaryotes. ChIP-chip experiments provide us with information about the binding targets of a particular regulator at the genome level -4.The output of ChIP-chip experiments are often summarized in binary forms. Using replicate data, the statistical evidence for a gene being the binding target of a transcription factor is typically summarized as a p-value. A threshold for the p-value, e.g. 0.001 is then chosen, and genes with p-values less than the threshold are considered the binding targets for the transcription factor. Thus, for a transcription factor, we can enumerate a list of genes which are "positives", i.e. binding targets and a list of genes which are "negatives", i.e. non-binding targets. If the threshold is set at a very stringent level to control the number of false positives, this will be achieved at the expense of high false negatives. A more relaxed threshold will reduce the number of false negatives, but will end up with more false positive results. Over the past few years, ChIP-chip data has formed the basis of many transcription regulatory mechanism studies. Several groups have compared the binding of a regulator across multiple experimental conditions to determine condition dependence of binding -7. SimilAs discussed above, the output of ChIP-chip experiments is typically summarized into binary forms and results across different experiments for the same transcription factor can be crosstabulated into a contingency table. A common question asked is whether a transcription factor has similar binding targets across conditions, and this is reflected as the dependency of outcome among the conditions. In the following, we give a brief discussion on two statistical measures that we will use to summarize the degree of dependency in a contingency table.Odds Ratio and Positive specific agreement [For the sake of clarity, we will focus on ChIP-chip experiments involving two different conditions or two species. The number of target genes in the two conditions/species can be cross tabulated into a 2 by 2 contingency table. We use two metrics to summarize such contingency tables – greement ,10.odds ratio is a commonly used measure to quantify association among the categorical variables. An odds is defined as the ratio of the frequency of being in one category and the frequency of not being in that category. For example, from Table c + d)/(a + b). This odds is called marginal odds, obtained from the total frequencies in one margin of the table, disregarding the effects of the other variable. Conditional odds are the chances of the transcription factor binding relative to not binding in one experimental condition, given a particular level (binding state) in the other experimental condition. The variables are deemed to be unassociated if the conditional odds are equal or close to each other, and hence equal to the marginal odds. To compare directly the two conditional odds, a single summary statistic, obtained by dividing the first conditional odds by the second is called odds ratio. Thus, for the data in Table a/c)/(b/d) = ad/bc. Odds ratio takes only positive values and has no upper limit. An odds ratio of 1 indicates no relationship among the variables. In addition to the odds ratio, its logarithm is also commonly used. Logarithmic transformation of data has a number of advantages – the variation of log transformed data tends to be less dependent on the magnitude of values, while taking logs also reduces the skewness of the distributions. After log transformation, data tends to be spread out more evenly, also making it easier to examine visually.Table simple agreement, which is defined as the proportion of cases for which both raters agree, or (a + d)/(a + b + c + d). However, if a is large, this would approach 1 regardless of the performance on positive cases. Positive specific agreement provides insight when the positive cases are rare. It estimates the conditional probability that one rater will agree that a case is positive given the other one rated it positive, where the role of the two raters is selected randomly. Positive specific agreement, ppos is defined as: ppos = 2d/(2d + b + c). Both (log) odds ratio and positive specific agreement will be considered in our following discussion.Other measures of dependency are also often used in psychological and medical research. For example, the problem can be formulated as follows: Suppose two raters classify each subject in a sample from some target population according to the presence or absence of some characteristic of interest. The resulting data can then be summarized into a 2 by 2 table. The agreement between raters can be quantified by the metric p0 denote the proportion of true negatives, while p1 be the proportion of true positives. We denote p = t as the vector of true proportions. Due to false positives and false negatives, the observed proportions likely differ from the true proportions. Let p and E can be written as:where M-1 gives us:Thus, for different values of false positive and false negative rates, different observed proportions will be obtained based on Equation (2). If the false positive and false negative rates are known, the true proportions may be inferred based on the observed experimental proportions. Multiplying both sides of Equation (2) by p obtained through this approach based on the observed However, due to chance variations, p = t. Here p00 denotes the proportion of genes which are not targets of the regulator in either condition, p01 denotes the proportion of genes which are targets of the regulator in the second condition but not in the first, p10 denotes the proportion of genes which are targets of the regulator in the first condition but not in the second, while p11 denotes the proportion of genes which are targets of the regulator in both conditions. Similarly, the vector for the observed proportions can be denoted as Often, we are interested in the analysis of the binding of a particular transcription factor in multiple experimental conditions or across different species. In either case, we are interested in counts of similarity of binding across conditions or organisms. This would correspond to an extension of Equation (2) into a higher dimension. For simplicity, we present our analysis for a 2-dimensional case. For example, if we consider the binding targets of a transcription factor across two experimental conditions, the vector of true proportions can be represented as n-dimensional case, the new transformation matrix would simply be obtained by taking the tensor product of M with itself n times. Here we assume that the false positive and false negative rates to be the same across two conditions. In general that may not be the case. In such a scenario, for a 2-dimensional case, Equation 4 takes the general form:If we consider Equation (2) to correspond to a 1-dimensional case, for the M1 and M2 are the transformation matrices for the first and second conditions respectively.where Given a vector of observed proportions which we obtain from experimental output, for different values of false positive rates and false negative rates, we aim to infer the true proportions. This would give us an idea about how the observed and true proportions differ for different levels of noise in the form of false positives and false negatives. We infer the true proportions from the observed proportions using an EM based approach which we now discuss in detail.c1 and c2. We define the vector for the true binary binding pattern of a particular gene G as b = , where b1 and b2 take binary value 1 or 0 depending on whether the gene is a true binding target for the transcription factor in c1 and c2 respectively. Thus, for the experimental conditions c1 and c2, this binary binding pattern vector can take four possible values, {, , , }. For example, a binary binding pattern vector equal to  indicates that the gene is a binding target for the transcription factor in both c1 and c2. We aim to infer this true binary binding pattern for all the genes and thus obtain the true binary counts. Due to experimental errors, we have the observed counts as the experimental output.Let us consider the binding patterns for a transcription factor in experimental conditions g = , where each component is either 0 or 1 denoting whether the gene is observed to be the binding target of the particular transcription factor in c1 and c2 respectively, based on the experimental output. Thus, the vector g represents the observed data. The probability of the observed binding data is then given byWe denote the observed binding pattern for a particular gene as N genes, the probability of the observed data isThus, for P(b) to maximize P using the EM algorithm, by treating b as the missing data as follows.In this article, we propose to estimate the E-Step: In the Expectation step, the conditional distribution of the missing data given the observed data is evaluated. We evaluate the posterior probabilities of each true binding state given the observed binding pattern. Thus, for every gene G with observed binding pattern g, we estimate:bm) (is the estimate of the true binding state b probability at the m-th step. Since bm)  can be expanded as:where bm)  results from the independence assumption for the data from two separate ChIP-chip experiments. Thus, the probability of observing P(bm) and obtain estimates of the posterior probabilities P(bm)(|g) for each gene at the E-step.Thus, for each gene, we start with a set of estimates M-Step: In the Maximization step, the parameters P(b) are re-estimated to maximize the likelihood of the complete data. After obtaining P(bm)(|g) for each gene, we cross-tabulate a two-way contingency table, with the "count" for each of the four values {, , , } being the sum of the probabilities for that particular value across all the genes. These counts are then used to obtain updated P estimates for P(b). For example, P(bm) - P(bm-1)| < 10-12.We iterate between the E-Step and the M-Step until convergence. The convergence criterion was set as: |In this section, we study the effect of false positives and false negatives on inferring regulatory target conservation across conditions/species through both simulations and real data analyses.p01 and p10, we repeated this simulation for differing values of p01 and p10. We observed similar trends of decreasing observed odds ratios for increasing false positive rate for a fixed false negative rate and a fixed true odds ratio.We consider an experiment involving the binding of a transcription factor in two different conditions with a total of 1000 genes. We consider the odds ratio as our metric of interest. For fixed true odds ratios, and different values of false positive and false negative rates, we plot the surface of the observed odds ratio in Figure In the following, we give an analytical proof for the reduction in the observed odds ratio for increasing false positive rates, with the false negative rate being fixed. Equation 4 can be expanded as:The observed odds ratio is:where from Equation 11 we get,s < 1/2 and s + t < 1, ∂ (OOR)/∂s is negative. These are reasonable assumptions for real data, where the false positives are low and false negatives are not very high. The denominator of ∂ (OOR)/∂s is always positive as it is a squared number. The numerator can be written as:We show that for a true odds ratio greater than 1 and for F = F1 * F2 * F3where,F1 = (p01p10 - p00p11)(-1 + s + t),F2 = (1 - s)(p01 + p10 + 2p00s) + (-p01 - p10 + 2p11 + 2(p01 + p10)s)t - 2p11t2,F3 = (1 - p00)(-1 + t)t + p00(-1 + t - s(-2 + s + 2t)).Let us consider each term separately.s + t < 1, then p01p10 <p00p11 and (-1 + s + t) < 0. Thus, we have F1 > 0. F2 can be simplified as:If the true odds ratio is greater than 1 and F2 = (1 - s)(p01 + p10 + 2p00s) + (p01 + p10)(-1 + 2s)t + 2p11t(1 - t).s) < 0, i.e. s < 1/2, then all the three product terms in F2 are positive. Thus, for s < 1/2, F2 > 0. F3 can be simplified as:Thus, if (-1 + 2F3 = (-1 + t)(t(1 - p00) + p00) - p00s(-2 + s + 2t) = -p00s2 + 2p00s(1 - t) + (-1 + t)(t(1 - p00) + p00).F3 is a quadratic function of s. Since the coefficient of s2 is negative, if the discriminant of the quadratic is negative, F3 is always negative. The discriminant D is given by:Thus, F is negative for true odds ratio greater than 1, s < 1/2 and s + t < 1.Simplifying, we get, t. We fixed the false positive rate to 0.01 and the false negative rate to 0.2. The vector of inferred true counts was determined to be t. The EM algorithm was initialized by giving equal weights to each possible true binding pattern for each gene. We used the chi-squared test and counted the number of times the null hypothesis was rejected for the inferred true data at the same level of significance. We repeated this analysis for different values of the true odds ratio and different values of false positive and false negative rates. Figure To study the effect of false positives and false negatives on statistical inference of dependence between two conditions, we consider a similar setting in which the binding of a regulator in two conditions is studied for 1000 genes. We simulated data for a fixed true odds ratio, and fixed the false positive and false negative rates. We randomly added false positives and false negatives to the data based on the false positive rate and false negative rate. This manifests itself as the observed data, and we repeated this 1000 times. We performed a chi-squared test for independence between the two conditions and counted the number of times the null hypothesis was rejected at a significance level of 0.001. Further, for each observed dataset, we inferred back the true data using our EM algorithm. The inferred true counts are almost equal to the true counts before false positives and false negatives were randomly added. This is because the false positives and false negatives were randomly added based on the fixed false positive rate and false negative rate. These fixed rates are used in our EM algorithm to obtain the inferred true counts. For example, for a true odds ratio of 2, the vector of true counts was et al. [S. Cerevisiae in Rich medium, and 84 of these transcription factors were also profiled in at least one other experimental condition. In their study, transcription factors were selected for profiling in a particular environment if they were essential for growth in that environment, or if there was other evidence suggesting their role in gene regulation in that environment. Borneman et al. [S. cerevisiae, S. mikatae and S. bayanus under pseudohyphal conditions. They listed genes which showed differing degrees of conservation across the three species, i.e. genes which were targets in only one species, the targets in two species, and the targets all three species.We considered two ChIP-chip datasets. Harbison et al.  describeet al. [For ChIP-chip data from Harbison et al. , we focup = t. For a false positive rate of 0.001 and a false negative rate of 0.2, multiplying p by the inverse of the transformation matrix results in the vector of the inferred true proportions t. However, for a false positive rate of 0.002 and a false negative rate of 0.3, the vector of the inferred true proportions is t. Similarly, for a false positive rate of 0.004 and a false negative rate of 0.4, the vector of the inferred true proportions is t. Thus, the inferred true proportions obtained by simply multiplying the observed proportions with the inverse of the transformation matrix could contain negative components.In the section describing the model setup, we stated that multiplication of the vector of the observed proportions with the inverse of the transformation matrix could lead to inferred true proportions with negative components. Here we illustrate the scenario. For the regulator Ste12, in Rich Medium and Mating inducing condition the vector of the observed proportions is t. Table et al. reported that the false discovery rate in their data was likely to be approximately 4%, while the false negative rate was around 24% for a p-value threshold of 0.001. For binding data from Harbison et al., typically the number of "negatives" was close to 6000, while the number of "positives" was about 100 to 200 at a p-value threshold of 0.001. Thus, the false positive rate was close to 0.001. For our analysis, we studied the variation of observed and true outcomes by varying the false positive rate from 0.001 to 0.005, and the false negative rate from 0.2 to 0.4. Thus, our range of false positive rates would correspond to about 6 to 30 false positives, and about 20 (100 * 0.2) to 80 (200 * 0.4) false negatives, which appears to be quite reasonable. From Table Table et al. [ to categorize the genes into broad functional categories. For the top categories which contained the largest number of genes, we cross-tabulated the genes and created a 2 by 2 contingency table based on counts of the genes which are binding targets  were the binding targets in both species. Of the remaining 9 genes, 5  were targets in only S. cerevisiae, while 4  were targets in only S. mikatae.We also analyzed the results of ChIP-chip experiments performed by Borneman et al. . We obtas Tables , 10, 11.In this article, we have studied the effect of false positives and false negatives in the analysis and interpretation of ChIP-chip data. We have derived a relationship between the observed and the underlying true binary outcomes. Given the observed binary outcome of an experiment, we have developed an EM based approach to infer the underlying true binary outcome for given values of false positive and false negative rates.A common limitation with finding binding targets from ChIP-chip data is that typically an arbitrary threshold, e.g. 0.001, is applied to the data, and all genes with p-values less than this threshold are considered binding targets. The false positive rate and false negative rate for the binding data change with the threshold applied . Datta aOdds Ratio and Positive specific agreement. Both these metrics are widely used to study dependency among categorical variables. Since we are interested in quantifying association among two categorial variables, i.e. whether there is association across two different conditions/species, the Odds Ratio and Positive specific agreement are appropriate metrics of interest. In our simulation, we used the chi-squared test of independence to test the null hypothesis that the binding targets are independent. Instead of using the chi-squared test, we could also use the Fisher's exact test to test the independence assumption on the two-way contingency table. The resulting p-values from both tests indicate the statistical evidence against the independence assumption. However, they do not provide a meaningful summary of the degree of dependence as they are also dependent on the sample size.To summarize data in contingency tables we utilized two commonly used metrics – In general, for independently performed real world experiments, such as two separate ChIP-chip experiments, the independence assumption of equation (9) should hold. This is because we can assume that data points in a particular experiment are independent identically distributed random variables. However, for experiments with closely associated results, it is possible that the false positive and false negative data points for the experiments are not entirely independent. This could result in an under-estimation of the underlying association after the EM procedure.Due to the limited degrees of freedom of the data, our EM algorithm cannot be used to estimate the false positive rate and false negative rate in experimental data. At each step in the EM algorithm, we estimate three parameters, and we have three equations to solve for them. If we also wish to estimate the false positive and false negative rates, we would have two additional parameters, but the number of equations would still be three. This would lead to an identifiability problem.l(b|g) be continuous and differentiable in the parameter space. Unfortunately, the M-step of our algorithm does not have a closed form. Hence, it is difficult to evaluate the gradient of the log likelihood function.We initialized the EM algorithm by different initial estimates of the parameters. For each initial estimate of the parameters, the algorithm converged. The convergence criteria for the EM algorithm require that the log likelihood of the parameters et al. performed their ChIP-chip experiments using microarrays consisting of spotted polymerase chain reaction (PCR) products representing all the intergenic regions of Saccharomyces cerevisiae. To obtain the binding targets a p-value threshold was applied to the binding intensities associated with the probes. One of the drawbacks of PCR based arrays is the low resolution of the DNA elements in the microarray chip. For PCR arrays designed for Yeast, the typical resolution achieved is less than 1 kb. In recent years, high density oligonucleotide arrays, comprising of large numbers  of short oligonucleotides have been utilized for ChIP-chip studies [et al. used high density oligonucleotide arrays to perform their experiment. The binding targets were obtained using Tilescope [Harbison  studies -16. A nu studies -20. Bornilescope . Since tet al. and Borneman et al., we observe that for different values of the false positive and false negative rate, the observed and true metrics for the binary data can differ quite dramatically. However, we notice that when the true log odds ratio is greater than 4, i.e. there is a significant degree of association among the binding targets across conditions/species, such differences in the observed and true metrics would not change our inference. On the other hand, our simulation results indicate that when the true odds ratio is close to 1, i.e. for cases when the underlying association is marginal, moderate values of false positive and false negative rates (0.01 and 0.2 respectively) may not be able to provide conclusive evidence of any underlying association or independence.Our analysis can be applied to any experimental setting with binary outcomes. However, for the sake of simplicity, we have illustrated its application for ChIP-chip experiments. By applying our algorithm to ChIP-chip data from Harbison DD performed data analysis and drafted the manuscript. HZ conceived and guided the study. Both authors read and approved the final manuscript.


Genome-wide mapping of the human TFIIB and NC2 transcription factor binding sites reveals insights into the formation of the pre-initiation complex. in vivo.The general transcription factor TFIIB and its antagonist negative cofactor 2 (NC2) are hallmarks of RNA polymerase II (RNAPII) transcription. Both factors bind TATA box-binding protein (TBP) at promoters in a mutually exclusive manner. Dissociation of NC2 is thought to be followed by TFIIB association and subsequent preinitiation complex formation. TFIIB dissociates upon RNAPII promoter clearance, thereby providing a specific measure for steady-state preinitiation complex levels. As yet, genome-scale promoter mapping of human TFIIB has not been reported. It thus remains elusive how human core promoters contribute to preinitiation complex formation in vitro defined TFIIB recognition elements, are enriched in some 4 to 5% of the genes. NC2 binds to a highly related target gene set. Nonetheless, subpopulations show strong variations in factor ratios: whereas high TFIIB/NC2 ratios select for promoters with focused start sites and conserved core elements, high NC2/TFIIB ratios correlate to multiple start-site promoters lacking defined core elements.We compare target genes of TFIIB and NC2 in human B cells and analyze associated core promoter architectures. TFIIB occupancy is positively correlated with gene expression, with the vast majority of promoters being GC-rich and lacking defined core promoter elements. TATA elements, but not the previously TFIIB and NC2 are global players that occupy active genes. Preinitiation complex formation is independent of core elements at the majority of genes. TATA and TATA-like elements dictate TFIIB occupancy at a subset of genes. Biochemical data support a model in which preinitiation complex but not TBP-NC2 complex formation is regulated. The core region of metazoan promoters shows various architectures and can harbor several distinct motifs, termed TATA box (TATA) , initiatin vitro selection procedure employing the TATA-containing Adenovirus major late (AdML) promoter [in vitro. BREu and BREd suppressed basal transcription of the AdML core promoter [The general initiation factor TFIIB is absolutely required for transcription initiation by RNA polymerase II (RNAPII) . TFIIB apromoter . The corpromoter . Both elpromoter ; howeverpromoter . Broadlyin vivo, nor has TFIIB occupancy so far been correlated with gene activity. Prevalence of BREs in active genes remains subject to controversy. A computational study based on statistical analysis of curated promoter sets concluded that up to 25% of human core promoters contain a potential BREu. The motif was found to be enriched in CpG promoters (>30% frequency) but depleted in CpG-less promoters (<10% frequency) [The function of TFIIB has not been investigated equency) . In contequency) . The preGenome-wide binding studies on general initiation factors have been extensively performed in yeast and include maps of TBP, TFIID and SAGA ,17, GTFsr = 0.92; Figure HIST1 histone gene cluster and the adjoining BTN butyrophilin gene cluster on chromosome 6 -chip analysis of TFIIB in two biological replicates from human B cell line LCL721 with promoter arrays covering roughly 24,000 TSS regions. Following binding site determination, an excellent overall correlation of the TFIIB ChIP-chip duplicates was observed  was scanned by ChIP-qPCR with eight primer pairs scattered throughout the locus . TFIIB did not bind the 3' region of GAPDH or other genes [A subset of target genes was validated by quantitative ChIP-quantitative PCR (ChIP-qPCR) using a third independent B cell-derived chromatin sample. A total of 29 promoters were interrogated that represent high occupancy , mid-to-low occupancy , or no TFIIB occupancy  as determined by ChIP-chip Figure . Non-TSSs Figure . Bindinger genes  as was rer genes .r = 0.97). Moreover, a disproportionately high number of the most strongly expressed genes bear high TFIIB levels, as revealed by the skewed distribution of expression quantiles  and/or specific positioning in the core region could be identified. Most notably, MEME uncover neither BREu nor BREd from these TFIIB-bound promoters.General features of high-TFIIB promoters (upper 5th percentile) were compared to low-TFIIB promoters (45th to 50th percentile) and no-TFIIB promoters (lower 5th percentile). For each group the core promoter sequences from position -50 to +50 were extracted, aligned at the major TSS and represented in a nucleotide frequency plot  Additio. High- aram MEME  in the tin vitro  versus PIC inhibition  Drosophila. In contrast, alignment of NC2-dominated genes did not reveal recognizable core elements. Instead, the core regions of these genes were enriched for G and C, which were the most frequent bases at every single position from -50 to +50  by running a Wilcoxon-Mann-Whitney test on the positions of TFIIB and NC2.Average occupancy profiles of TFIIB and NC2 at promoters of genes with high or low TFIIB/NC2 ratios  showed similar factor profiles at the former group, with peak maxima coinciding at position -50 Figure , left. Iin vitro PIC formation assay in which transcription complexes were assembled on a Gal4-responsive heterologous promoter template containing either a wild-type or mutant TATA box, both in the presence and absence of the model activator Gal4-VP16  association correlates with TATA or is independent of core elements altogether, whereas NC2 association is largely independent of the underlying core promoter structure.TFIIB and NC2 act globally and are present at active genes. We report a strong positive correlation of TFIIB with gene expression levels at a genome-wide scale Figure , which iOur data reason against a positive influence of core elements on NC2 promoter association. For example, NC2-dominated genes with high NC2/TFIIB ratios were enriched for GC but depleted for core promoter elements, in particular TATA, BREu and DPE Figure . AttemptNC2 occupancy and activity appear in a distinct light if compared with TFIIB. A generally positive correlation of binding with the presence of TATA turns into a negative correlation relative to the competing GTF TFIIB. Related to this, NC2 occupancy positively correlates with gene expression, yet TFIIB correlation with it is more pronounced. Indeed, TFIIB/NC2 ratios increase especially in the most strongly expressed 5% of the B cell genes Figure . Our datTATA, although a rather infrequent motif, is positively correlated with the binding of TFIIB Figure . SomewhaDrosophila consensus RGWYVT [Drosophila, DPE presence is strongly linked to TATA in this subset of human promoters  seem not to employ core elements to facilitate or stabilize GTF-core promoter interactions. TATA consensus is found with a frequency below 5%, TATA-like elements reach 29% Figure . The DPEs RGWYVT . In conts Figure .in vivo and in vitro. While this will undoubtedly uncover different scenarios in directing PIC formation, we have initially taken a reductionist biochemical approach using one model activator together with prototypic  promoters  and non-specific IgG serum (sc-2027) were purchased from Santa Cruz Biotechnology . Anti-NC2 alpha (DRAP1) antibody 4G7 has been previously described [5 and non2.LCL721 cells were grown in RPMI 1640 medium supplemented with 10% (v/v) heat-inactivated fetal bovine serum, 5 mM L-glutamine and 100 units/ml penicillin-streptomycin  in a humidified incubator at 37°C and 5% CO8 cells (0.4 × 106 cells/ml) by centrifugation  and washed them with PBS. The cell pellet was resuspended in 36 ml of PBS. Cells were fixed by adding 4 ml of a freshly prepared 10% formaldehyde solution (10% (v/v) formaldehyde , 140 mM NaCl, 1 mM EDTA, 0.5 mM EGTA, 50 mM Hepes-KOH pH 8.0). Cross-linking was done for 9 minutes at room temperature, followed by quenching with 125 mM glycine, with immediate transfer of cells to ice followed by 5 minutes incubation on ice. Cells were washed twice with ice-cold PBS and sequentially lysed by resuspending the cell pellet in 5 ml of ice-cold ChIP lysis buffer 1  glycerol, 0.5% (v/v) Igepal CA-630 , 0.25% Triton X-100 (Sigma-Aldrich), and freshly added 1× protease inhibitor cocktail ) and 10 minutes rotation at 4°C. Cells were collected by centrifugation , followed by resuspension in 5 ml of ice-cold ChIP lysis buffer 2  and 10 minutes rotation at 4°C. After centrifugation , the pellet was resuspended in 3 ml of ice-cold ChIP lysis buffer 3 , 0.1% sodium deoxycholate (Sigma-Aldrich), and 1× protease inhibitor cocktail). Acid-washed glass beads  were added, and the cross-linked chromatin was sheared to an average size of 300 bp by 6 minutes sonication  in an ice-water bath using a Branson 250-D sonicator and a microtip. After sonication, Triton X-100 was added to 0.5% as final concentration, and the lysate was centrifuged  to remove cell debris. The chromatin extract was pre-cleared with 100 μl blocked (pre-absorbed with PBS/0.5% (w/v) BSA (Sigma-Aldrich)) protein A/G sepharose FF beads  for 2 h at 4°C, quantified in a UV spectrophotometer and diluted to 1 mg/ml and 0.25% N-lauryl sarcosine. We used 500 μl of the chromatin extract per single ChIP reaction in lubricated tubes in a total volume of 1 ml. The extract was incubated overnight at 4°C with 50 μl blocked protein A/G sepharose beads that had been pre-adsorbed with 10 μg of antibody. Immune complexes were collected by centrifugation  and washed six times with 1 ml of ice-cold ChIP wash buffer  and one time with 1× TE  containing 50 mM NaCl. The protein-DNA complexes were eluted from the beads by adding 200 μl ChIP elution buffer ) and incubation at 65°C under constant agitation for 10 minutes. After removal of beads by centrifugation  the supernatant was incubated at 65°C overnight to revert the cross-links. Then, the sample was diluted to 400 μl with 1× TE. DNAse-free RNAse A  was added and the sample was incubated for 1 h at 37°C, followed by addition of proteinase K  to 250 μg/ml and digestion for 2 h at 55°C. Genomic DNA was isolated from the precipitated material as well as from the sheared chromatin input  by phenol extraction and ethanol precipitation.We pelleted 1 × 10ChIP and input DNA was end-polished using T4 DNA polymerase  and 200 μM dNTPs for 20 minutes at 12°C. After phenol extraction and ethanol precipitation, blunted DNA was ligated to 100 pmol of annealed linker  using T4 DNA ligase (New England Biolabs) overnight at 16°C. DNA was ethanol precipitated and amplified by ligation-mediated PCR in a total volume of 55 μl containing 250 μM dNTPs, 50 pmol of oligo-25, 5 units of Taq polymerase (New England Biolabs) and 0.025 units of Pfu Turbo polymerase  for one initial cycle consisting of 2 minutes at 55° (during which polymerase was added), 5 minutes at 72°C and 2 minutes at 94°C, followed by 22 cycles of 0.5 minutes at 94°C, 0.5 minutes at 60°C and 2 minutes at 72°C, and a final 4-minute extension at 72°C. Amplicons were purified with a PCR purification kit . At least 5 μg of amplified ChIP and input DNA were labeled and hybridized to human promoter arrays (NimbleGen HGS17 human 1.5 K promoter chip) containing 24,134 human promoter regions, each represented by a probe set of 15 tiling 50-mer oligonucleotide probes covering 1.5 kb DNA around TSSs. Slides were scanned by NimbleScan and raw data were processed by NimbleGen according to standard procedures . BrieflyChIP-qPCR analysis was done with Power SYBR green PCR master mix in an ABI StepOne Plus thermocycler . Triplicate reactions were carried out in a total volume of 10 μl containing 4 pmol of forward and reverse primers. Reactions containing serially diluted input DNA were used as standard curve to quantify ChIP DNA reactions. Melting curve analysis was used to determine the specificity of all reactions. Primer sequences are available upon request.2 signal ratios  plus either 1, 2, or 2.5 standard deviations  were matched with corresponding Affymetrix probeset IDs  to generate the gene expression correlation analysis of TFIIB  [Peak finding was done using the Mpeak program . Peaks wns Table  and 6b. B Figure  or TFIIBB Figure . Degree B Figure . The hyp Sekhon) , for the Sekhon) . Similar Sekhon) .in vitro transcription were performed as described [2, 10 mM dithiothreitol , 0.025% Igepal CA-630, 0.2 mM phenylmethanesulfonyl fluoride ) containing 5 mg/ml BSA and 5 mg/ml polyvinylpyrrolidone (Sigma-Aldrich) and incubated for 15 minutes at room temperature. Afterwards, beads were washed three times with buffer A. PIC assembly was conducted in a total volume of 200 μl with 1,050 ng pGL2-MRG5 promoter template coupled to 70 μg beads, 2 μg poly(dG:dC) competitor DNA, 100 to 200 μg Jurkat nuclear extract and, if indicated, 200 ng Gal4-VP16 . PIC assembly buffer was composed of 20 mM Hepes-KOH pH 8.2, 5 mM MgCl2, 10 mM DTT, 0.025% Igepal CA-630, 0.5 mg/ml BSA (Roche), 10% (w/v) glycerol, 0.1 mg/ml PEG 8000 and 0.2 mM PMSF. After 45 minutes incubation at 30°C the template-bound complexes were concentrated with a magnet and washed three times with 200 μl buffer A. PICs were either eluted with Laemmli buffer and analyzed by immunoblot or probed in an in vitro transcripton reaction (see below). Immunoblots were scanned and signal intensities quantified using the ImageJ program [in vitro transcription was performed. PICs were formed as above, washed and resuspended in transcription buffer  glycerol, 0.1 mg/ml PEG 8000, 4 units RNAsin (Promega), 0.2 mM PMSF). Transcription was initiated by addition of the NTP mix supplemented with 1 μl alpha-32P UTP . Final NTP concentrations were 100 μM ATP, CTP and GTP each, and 5 μM UTP. Transcription reactions were incubated at 30°C for 30 minutes and stopped by addition of 400 μl transcription stop buffer . Reactions were extracted with phenol/chloroform, RNA precipitated with isopropanol and analyzed by autoradiography.Immobilized template assays and escribed . The pGLThe raw and processed ChIP-chip data have been deposited at the Gene Expression Omnibus (GEO) public repository  and are AdML: Adenovirus major late; bp: base pair; BREd: downstream TFIIB recognition element; BREu: upstream TFIIB recognition element; BSA: bovine serum albumin; CAGE: cap analysis gene expression; ChIP: chromatin immunoprecipitation; ChIP-chip: ChIP with detection by microarrays; DPE: downstream promoter element; DTT: dithiothreitol; GAPDH: glyceraldehyde 3-phosphate dehydrogenase; GTF: general transcription factor; INR: initiator; NC2: negative cofactor 2 (DR1/DRAP1); PBS: phosphate-buffered saline; PEG: polyethylene glycol; PIC: preinitiation complex; PMSF: phenylmethanesulfonyl fluoride; qPCR: quantitative PCR; RNAPII: RNA polymerase II; TATA: TATA box; TBP: TATA box-binding protein; TFIIB: general transcription factor IIB (GTF2B); TSS: transcription start site.TKA and MM conceptualized the study; TKA and SB performed the experiments; TKA, KG and MM conducted the analyses; TKA and MM wrote the manuscript.Aligned core promoter regions of TFIIB target genes. Nucleotide frequency plots of TSS-aligned core promoter regions from high-, low- and no-TFIIB genes.Click here for fileData set of TFIIB and NC2 ChIP-chip signals. Log2-scaled ChIP/input ratios of probeset signals from TFIIB and NC2 ChIP-chip experiments using two biological samples  on a NimbleGen human (HG17) promoter array. The data set includes the median, mean and standard deviation of 24,134 probesets, each composed of 15 individual 50-oligomer probes.Click here for file


Candida maltosa is able to ferment glucose as well as xylose to produce a significant amount of ethanol. Here we report the ADH system in C. maltosa composed of three microbial group I ADH genes , mainly focusing on its metabolic regulation and physiological function.The alcohol dehydrogenase (ADH) system plays a critical role in sugar metabolism involving in not only ethanol formation and consumption but also the general “cofactor balance” mechanism. CmADH2A and CmADH2B tandemly located on the chromosome could be derived from tandem gene duplication. In vitro characterization of enzymatic properties revealed that all the three CmADHs had broad substrate specificities. Homo- and heterotetramers of CmADH1 and CmADH2A were demonstrated by zymogram analysis, and their expression profiles and physiological functions were different with respect to carbon sources and growth phases. Fermentation studies of ADH2A-deficient mutant showed that CmADH2A was directly related to NAD regeneration during xylose metabolism since CmADH2A deficiency resulted in a significant accumulation of glycerol.Genetic analysis indicated that CmADH1 was responsible for ethanol formation during glucose metabolism, whereas CmADH2A was glucose-repressed and functioned to convert the accumulated ethanol to acetaldehyde. To our knowledge, this is the first demonstration of function separation and glucose repression of ADH genes in xylose-fermenting yeasts. On the other hand, CmADH1 and CmADH2A were both involved in ethanol formation with NAD regeneration to maintain NADH/NAD ratio in favor of producing xylitol from xylose. In contrast, CmADH2B was expressed at a much lower level than the other two CmADH genes, and its function is to be further confirmed.Our results revealed that  Alcohol dehydrogenase (ADH), which catalyzes the interconversion between acetaldehyde and ethanol, plays a central role in ethanol production and assimilation. Moreover, as NAD(H) or NADP(H) takes part in the reaction, ADH is involved in the general “cofactor balance” mechanism Saccharomyces cerevisiae, ScADH1 encodes the classical fermentative enzyme responsible for ethanol generation, and is expressed in large amounts in the presence of glucose ScADH2 encodes the enzyme that converts ethanol to acetaldehyde, and is negatively regulated by glucose et al.ADH1 and ADH2 in S. cerevisiae, ADH1Pichia stipitis, a natural xylose-fermenting yeast which is well studied for ethanol production, encodes the principal ADH with both fermentative and assimilatory functions, and is induced by oxygen limitation. PsADH2PsADH1 is disrupted.In P. stipitis, the dual cofactor (NADPH and NADH) specificity of XR Candida tropicalis and Candida guilliermondii, xylitol is largely accumulated due to the cofactor imbalance between NADPH-dependent XR and NAD-dependent XDH C. maltosa accumulated xylitol with high substrate consumption rates and product yields in the batch fermentation under oxygen-limited conditions. XR of C. maltosa was exclusively NADPH-dependent, but NADP-dependent XDH activities were detected, which leaded to a significant accumulation of ethanol. Furthermore, C. maltosa showed a strong ability to produce ethanol from glucose similar to that of S. cerevisiae under aerobic conditions. However, the ADH system related to ethanol production of C. maltosa has not yet been studied in detail.In xylose-fermenting yeasts, D-xylose is first reduced to xylitol and sequentially oxidized to D-xylulose by xylose reductase (XR) and xylitol dehydrogenase (XDH), respectively C. maltosa and its physiological function during glucose or xylose metabolism. As a consequence, the investigation would contribute to a better understanding of regulatory properties of fermenting both glucose and xylose to produce ethanol and other high-valued bio-products, e.g. xylitol, in natural xylose-utilizing yeasts.Hence, the objective of this study was to identify, characterize and elucidate composition and regulation of the ADH system in Candida albicansC. tropicalisC. maltosa ADH genes were successfully obtained  and C. tropicalis ADH1 (87.3%), and therefore this ORF was designated CmADH1. The other DNA fragment of 4660 bp was interestingly found to contain two tandem 1050-bp long ORFs that were both similar to C. albicans ADH2. Thus, the upstream and downstream ORFs were designated CmADH2A and CmADH2B, respectively  by PCR cloning  independently in their respective evolutionary history, resulting in their modern ADH systems encoded by more than two ADH genes, but none of their ADH genes were adjacent (genomic contexts of ADH genes other than ADH1 and ADH2 were not shown).To better understand the phenomenon of tandem adjacent ectively . FollowiThe recombinant CmADH proteins with His-tags were purified and used for subsequent characterization of enzymatic properties as described in Km(ethanol) values to those reported for ScADH1(17–24 mM) than for ScADH2 (0.6–0.8 mM) ADH activities of CmADHs were tested with NAD or NADP as cofactor to determine cofactor preference. The specific activity of CmADH1, CmADH2A and CmADH2B with NAD was 24, 31 and 11 times higher than those with NADP , respectADH activities of CmADHs towards nineteen alcohols, including aliphatic, aromatic and unsaturated alcohols, were measured to characterize their substrate specificities . All theC. maltosa produced multiple forms of ADH isozymes  did not reveal the similar patterns (data not shown). Therefore, the fastest and the slowest moving isozymes could be the CmADH1 homotetramer and the CmADH2A homotetramer, respectively, and three middle migrating bands should represent heterotetramers formed between the CmADH1 and CmADH2A gene products with different ratio   . SimilarCmADHs, protein profiles of CmADH isozymes were investigated with respect to carbon sources  and the heterotetramers of CmADH1 and CmADH2A, while the CmADH2A homotetramer  was undetectable in the presence of glucose or faintly detectable in the presence of xylose. However, when C. maltosa cells were grown in the presence of ethanol as a sole carbon source (lane 2), the CmADH2A homotetramer was the only detectable CmADH isozyme. These results have implicated that CmADH1 could be expressed during fermentative metabolism to reduce acetaldehyde to ethanol, while CmADH2A should be expressed under respiratory conditions to serve ethanol assimilation.To examine the distinctive expression modes of  sources . When C.CmADH1 and CmADH2A during sugar metabolism, expression profiles of CmADH isozymes were analyzed  decreased by approximately half in ADH2A-deficient mutant M3-400, while CmADH2A deficiency had little effect on the maximum specific growth rates (μmax) or the biomass yields (Yx/s). At the same time, CmADH2A deficiency had almost no effect on the ethanol yields  from glucose, but resulted in lower ethanol yields and significant accumulations of glycerol from xylose. Intriguingly, the xylitol yield  clearly decreased in ADH2A-deficient mutant M3-400 under oxygen-limited conditions. Furthermore, M3-400 accumulated more glycerol under oxygen-limited conditions than under aerobic conditions. Glycerol has been reported to serve as a redox sink by oxidizing the excess NADH to NAD in S. cerevisiaeCmADH2A would facilitate ethanol production from xylose and seemed to be important for xylitol production of C. maltosa under oxygen-limited conditions.ADH2A-deficient mutant M3-400 was compared to wild type strain Xu316 for the same sugar fermentation (glucose or xylose) under the same aeration conditions (aerobic or oxygen-limited) . CompareC. maltosa shows great potential to utilize xylose C. maltosa has not yet been determined. Here we have cloned and sequenced three distinct structural ADH genes  from C. maltosa. Intriguingly, CmADH2A and CmADH2B were tandem adjacent genes, and this is the first reported phenomenon in yeast, although the ADH genes are often arranged in tandem in some other organisms, such as human and mouse In vitro characterization of enzymatic properties showed that all the three CmADHs were NAD-dependent, and had broad substrate specificities similar to ScADH2 K. lactisCandida utilis ADH1 C. guilliermondiiCmADH1 was induced in presence of glucose, while CmADH2A was glucose-repressed and largely expressed after glucose was consumed .During glucose metabolism, consumed  and 6A.  glucose . Hence, CmADH1 and CmADH2A were both expressed and involved into ethanol formation during xylose metabolism. This has implicated that the ADH system of C. maltosa, a xylitol-producing yeast, is different from that of the ethanol-producing yeast P. stipitis, in which PsADH1 is the critical gene responsible for ethanol production C. tropicalis and C. guilliermondii can regenerate NAD by accumulation of ethanol and glycerol and by glycerol accumulation C. maltosa seemed to regenerate NAD by ethanol accumulation to maintain NADH/NAD ratio in favor of producing xylitol from xylose.Based on the expression analysis of CmADH isozymes and fermentation studies of ADH2A-deficient mutant, it was found that CmADH1 and CmADH2A, CmADH2B was expressed at a very low level in wild type strain and ADH2A-deficient mutant, although the expression was up-regulated to some extent with CmADH2A deficiency. In other yeasts, there are some similar ADH genes, e.g. ADH5 in S. cerevisiae, which are not expressed under normal physiological conditions. However, ScADH5 can produce ethanol in an ADH1/ADH3 double null mutant S. cerevisiae strain capable of anaerobic growth on xylose, which is not normally utilized by yeasts in the absence of oxygen CmADH2B was significantly up-regulated in ADH2A-deficient mutant grown on xylose, but its precise function remains to be investigated.In contrast to C. maltosa, CmADH1, CmADH2A and CmADH2B, were cloned and characterized. CmADH1 had the similar physiological function to ADH1 in S. cerevisiae and P. stipitis. However, the expression regulation and the physiological function of CmADH2A were more similar to ScADH2 than to PsADH2, although CmADH2A were the ortholog of PsADH2 according to comparative analysis of genomic contexts of ADH homologs. Furthermore, CmADH2A did not show a significantly low Km for ethanol like ScADH2 CmADH2A might be due to the acquirement of glucose repression instead of high affinity to ethanol. In terms of xylose metabolism in natural xylose-fermenting yeasts, previous studies mainly focus on metabolic reconstruction and optimization of xylose to xylulose-5-phosphate, which are catalyzed by XR, XDH and xylulokinase. We are now investigating regulation factors of CmADH2A to reveal the mechanism of glucose repression in C. maltosa. From an evolutionary standpoint, glucose repression of CmADH2A is presumably acquired after the speciation of C. maltosa and P. stipitis. Moreover, the regulation mechanism of glucose repression in C. maltosa might be different from that in S. cerevisiae, which is thought to basically derive from the whole genome duplication event C. maltosa should be a potential model for studying yeast sugar metabolism, and a significant candidate for the future utilization of glucose and xylose from renewable lignocelluloses to produce fuel ethanol and other high-valued bio-products.In summary, three microbial group I ADH genes of C. maltosa Xu316, a wild type strain from our laboratory collection, was used as a source for the ADH genes. C. maltosa ATCC 28140 and AS 2.1386 from China General Microbiological Culture Collection Center (CGMCC) were used as reference strains. C. maltosa M3-400, a spontaneous mutant deficient in ADH2A, was derived from Xu316 and isolated by using allyl alcohol as a select agent as reported for C. guilliermondiiE. coli DH5α was used for cloning, and BL21(DE3) for overproduction of the recombinant C. maltosa ADH proteins.600 = 0.2. In non-controlled shake flask fermentation, aerobic and oxygen-limited conditions were achieved by varying the volume of medium and the speed of shaking as described earlier Yeast cells were grown at 30°C in YP  medium containing different carbon sources for different times as specified in the text, and collected for ADH expression analysis using zymogram analysis or quantitative real-time RT-PCR. Inocula were grown in flask for 12 h, and initial cells were adjusted to ODC. maltosa ADH genes were obtained by degenerate PCR and cassette-mediated PCR following with nested PCR , the GeneDB (Candida dubliniensis), the Map View of NCBI (P. stipitis and P. guilliermondii), and the Yeast Gene Order Browser (some yeast species in the Saccharomyces complex).The complete fragments of sted PCR . PCR reaCmADH1, CmADH2A and CmADH2B were subcloned into the NdeI and XhoI sites of the pET28a(+) vector or the pET21a(+) vector (Invitrogen), respectively  vector were not His-tagged, and cell extracts of the corresponding E. coli BL21(DE3) transformant were used for zymogram analysis. Protein concentrations were determined by the method of Bradford The ORF fragments of ectively . All theKm) values. To determine the substrate specificities of CmADHs towards a variety of alcohol substrates, ADH activity was assayed in the oxidative direction in the presence of the alcohol substrate (at 100 mM) with NAD as cofactor.ADH activity was assayed as previously described C. maltosa were prepared by vortexing with glass beads as described previously ADH activity was visualized by native PAGE ΔCT− and the 2ΔΔCT− method Real-time PCR primers  were desTable S1Nde I and Xho I restriction sites are underlined. *Primer set ADH2Aup/ADH2Bdn was used to clone the DNA fragment containing CmADH2A and CmADH2B from the reference strains C. maltosa ATCC 28140 and AS 2.1386.Primers used in this study. (0.06 MB DOC)Click here for additional data file.Table S2Cofactor preference of CmADH1, CmADH2A and CmADH2B. Shown are mean and S.E. (n = 3).(0.04 MB DOC)Click here for additional data file.Figure S1C. maltosa ADH genes. Firstly, a 2565-bp DNA fragment containing the upstream sequence and the 5′ coding region of CmADH1 and a 737-bp DNA fragment harboring partial sequence of CmADH2 were amplified with degenerate primers ADH1-3/ADH1-4 and P1/P2  anchor synthesis. And ADH1-4 was prepared in highly conserved regions of C. albicans ADH1 and C. tropicalis ADH1. PCR primers P1 and P2 were designed according to C. albicans ADH2. Then, the remaining upstream and downstream sequences were obtained through several cassette-mediated PCRs using TaKaRa LA PCR™ in vitro Cloning Kit (TaKaRa), a PCR-based DNA walking method. The restriction endonucleases and genome-specific primers Click here for additional data file.Figure S2Alignment of the deduced amino acid sequences of CmADHs with those of other yeast ADHs. Numbering of amino acid corresponds to ScADH1. Residues that are involved in catalyses, are headed by letters: a, adenine binding pocket; r, adenosine ribose binding; p, pyrophosphate binding; n, binding of nicotinamide or nicotinamide ribose; s, substrate binding pocket; b, poton relay system; z, ligands of the active site zinc atom or ligands of the structural zinc atom. m, five more strictly conserved residues among the microbial ADHs. Asterisks indicate conserved amino acid residues, eight glycine residues and one valine of ADHs from divergent sources. Box I, mitochondrial targeting region; Box II, zinc-binding consensus; Box III, NAD(P)-binding motif. Reversed letters indicate the residues involved in cofactor binding, substrate binding or catalysis which are not conserved in three CmADHs and ScADH1. Accession numbers (from CaADH1 to SpADH1): X81694, XM_712556, AF008245, AF008244, V01292, Z49212, AY692988, XM_456023, X64397, X62766, X62767, AL032681.(2.49 MB TIF)Click here for additional data file.Figure S3ADH1 (A) or ADH2 (B) homologs from species of the CTG clade are drawn according to their genomic sequences. Species names and gene identifiers are shown in each box. Orthologous gene boxes are represented in the same color. Arrows indicate directions of gene transcription and are not to scale. Connectors join nearby genes: a solid bar for adjacent genes, two gray bars for loci less than five genes apart and one gray bar for loci <20 genes apart. Genomic contexts in square brackets are conversed compared with C. maltosa. The species of the CTG clade exhibited highly conserved gene order around ADH1. In case of ADH2 region, C. maltosa had the same genomic context as C. tropicalis except that another ADH existed in C. maltosa, and the other species of the CTG clade had transketolase gene TKL1 instead of squalene epoxidase gene ERG1. In general, two ADH genes, ADH1 and ADH2, existed in all the species of the CTG clade except for Pichia (Candida) guilliermondii in this study, although the ADH loci were dispersed in Candida parapsilosis and Lodderomyces elongisporus.Genomic contexts of (1.34 MB TIF)Click here for additional data file.Figure S4C. maltosa by zymogram analysis. Cells were grown in YP medium containing 20 g/l glucose and harvested in the mid-exponential phase for zymogram analysis. Lane 1, wild-type strain Xu316. Lane 2–7, ADH-deficient mutants M3-360, M3-400, M1-400, M11-400, M12-400 and M15-400.Characterization of ADH-deficient strain of (0.22 MB TIF)Click here for additional data file.