Drosophila embryo, maternal morphogen gradients establish gap gene expression domain patterning along the anterior-posterior axis, through linkage with an elaborate gene network. To understand the evolution and behaviour of such systems better, it is important to establish the minimal determinants required for patterning. We have therefore engineered artificial transcription-translation networks that generate simple patterns, crudely analogous to the Drosophila gap gene system. The Drosophila syncytium was modelled using DNA-coated paramagnetic beads fixed by magnets in an artificial chamber, forming a gene expression network. Transient expression domain patterns were generated using various levels of network connectivity. Generally, adding more transcription repression interactions increased the “sharpness” of the pattern while reducing overall expression levels. An accompanying computer model for our system allowed us to search for parameter sets compatible with patterning. While it is clear that the Drosophila embryo is far more complex than our simplified model, several features of interest emerge. For example, the model suggests that simple diffusion may be too rapid for Drosophila-scale patterning, implying that sublocalisation, or “trapping,” is required. Second, we find that for pattern formation to occur under the conditions of our in vitro reaction-diffusion system, the activator molecules must propagate faster than the inhibitors. Third, adding controlled protease degradation to the system stabilizes pattern formation over time. We have reconstituted transcriptional pattern formation from purified substances, including phage RNA polymerases, ribonucleotides, and an eukaryotic translation extract. We anticipate that the system described here will be generally applicable to the study of any biological network with a spatial component.Pattern formation is essential in the development of higher eukaryotes. For example, in the  Drosophila embryoTo understand how patterns are established during early development, these authors have created an artificial system to mimic aspects of the early  Engineering a system to emulate a particular behaviour can be an extremely informative approach to systems biology ,2,3,4,5.In this work, our primary aim was the development of an in vitro system that allows the careful buildup of complex networks under controlled conditions. To demonstrate the usefulness of such a system, we decided to reconstruct a developmental pattern-formation program based on the formation of a gradient of a transcription activator—a “morphogen”—and to link it to a network of transcription repressors. In a sense, we set out to design a patterning system similar to chemical reaction-diffusion systems (see below). However, the use of components such as transcription activators and repressors in an in vitro transcription-translation system made the system significantly closer to a biological system, albeit highly simplified when compared to the complexity of eukaryotic transcription .Fucus, see  in boiling water and cooled to 30 °C); and TNT Coupled Wheat Germ Extract System , which comprised 20 μl of TNT wheat germ extract, 1.2 μl of TNT reaction buffer, 0.6 μl of amino acid mixture (1 mM), 1.2 μl of RNasin (not included in TNT kit), and 0.5 μl of SP6 polymerase. 54 μl of this mixture was dispensed per chamber.For degradation experiments, 2.25 units of Factor Xa  were added per chamber. Coated Dynabeads were injected at appropriate positions over the magnetic array: typically, 100 fmol of gene A (1 μl), 5 fmol of gene B, and 3.5 fmol of gene C (0.5 μl). T7 polymerase  was immediately injected at the chamber edges. After timed incubations at 25 °C, chambers were transferred to 4 °C for 35 min, to form a gel. Gel slices were cut with a razor blade (guided by the printed template) and aspirated with a P10 Gilson pipette. Samples were mixed with 10 μl of SDS-loading buffer and analysed by SDS-PAGE, Western blotting, and ECL, with anti-M2 FLAG antibody . Further details on this step can be found in Drosophila scale, chamber size was scaled down, and only simple diffusion was allowed for all components; for simplicity, transcription-translation rates were not varied .Click here for additional data file.Protocol S2This file contains the default parameters for the computer model in a format that can be read by the Perl script.(5 KB DOC).Click here for additional data file.Protocol S3This text file is a Perl script to run the computer simulations described in the manuscript.(22 KB DOC).Click here for additional data file.http://www.ncbi.nlm.nih.gov/LocusLink/, or GeneID , caudal (35341), giant (31227), huckebein (40549), hunchback (41032), knirps (40287), Krüppel (38012), Nanos (42297), tailless (43656), Torso (35717), and zerknüllt (40828).The Locuslink (


Transcription factors can regulate the expression of a suite of anti-stress genes, many of which encode enzymes functioning to counteract the perturbed variables. The objective of this study was to explore, using control theory and computational approaches, the theoretical basis that underlies the steady-state dose response relationship between cellular stressors and intracellular biochemical species  in these gene regulatory networks. Our work indicated that the shape of dose response curves  depends on changes in the specific values of local response coefficients (gains) distributed in the feedback loop. Multimerization of anti-stress enzymes and transcription factors into homodimers, homotrimers, or even higher-order multimers, play a significant role in maintaining robust homeostasis. Moreover, our simulation noted that dose response curves for the controlled variables can transition sequentially through four distinct phases as stressor level increases: initial superlinear with lesser control, superlinear more highly controlled, linear uncontrolled, and sublinear catastrophic. Each phase relies on specific gain-changing events that come into play as stressor level increases. The low-dose region is intrinsically nonlinear, and depending on the level of local gains, presence of gain-changing events, and degree of feedforward gene activation, this region can appear as superlinear, sublinear, or even J-shaped. The general dose response transition proposed here was further examined in a complex anti-electrophilic stress pathway, which involves multiple genes, enzymes, and metabolic reactions. This work would help biologists and especially toxicologists to better assess and predict the cellular impact brought about by biological stressors.To maintain a stable intracellular environment, cells utilize complex and specialized defense systems against a variety of external perturbations, such as electrophilic stress, heat shock, and hypoxia, etc. Irrespective of the type of stress, many adaptive mechanisms contributing to cellular homeostasis appear to operate through gene regulatory networks that are organized into negative feedback loops. In general, the degree of deviation of the controlled variables, such as electrophiles, misfolded proteins, and O To maintain a stable intracellular environment, cells are equipped with multiple specialized defense programs that are launched in response to various external chemical and physical stressors. These anti-stress mechanisms comprise primarily gene regulatory networks, and like many manmade control devices, such as thermostats and automobile cruise controls, they are often organized into negative feedback circuits. A quantitative understanding of how these control circuits operate in the cell can help us to assess and predict more accurately the cellular impacts brought about by perturbing stressors, such as environmental toxicants. Using control theory and computer simulations, we explored nature's design principle for anti-stress gene regulatory networks, and the manner in which cells respond and adapt to perturbations. We showed that cells can exploit multiple mechanisms, such as protein homodimerization, cooperative binding, and auto-regulation, to enhance the feedback loop gain, which, according to control theory, is a basic principle for effective perturbation resistance. We also illustrated that the steady-state dose response curve is likely to transition through multiple phases as stressor level increases, and that the low-dose region is inherently nonlinear. Our results challenge the common practice of linear extrapolation for evaluating the low-dose effect, and would lead to improved human health risk assessment for exposures to environmental toxicants. When external stressors cause these molecules to deviate from their basal operating concentrations for an extended period of time, normal cell functions become disrupted, and cell cycle arrest and apoptosis may ensue [Cells in vivo must maintain a relatively stable intracellular milieu in an extracellular environment that is constantly changing and is potentially unpredictable. Notably, many intracellular biomolecules need to be held within closely regulated ranges of concentrations for normal cell functions. Examples of these biochemical species, which could be detrimental and/or beneficial to cellular health, are electrophiles, reactive oxygen species (ROS), DNA adducts, misfolded proteins, Oay ensue . Homeostay ensue . As withControl and dynamic system theory has benefited applied fields such as electronic and mechanical engineering for many decades, and in recent years increasing efforts have been made to apply similar concepts to biological systems including adaptive responses –14. Our If a signal molecule X controls a target molecule Y, either directly or indirectly, then the steady-state transfer function from X to Y can be quantitatively described as the ratio of the fractional change in Y over the fractional change in X, i.e.,k,k the intercept  where S0 is the background stress level at the basal condition, and Se is the stress level introduced by external stressors. The total stress level S is expressed as multiples of S0. Levels of Y, T, and G are also normalized to their respective levels at the basal condition where S = S0. For simplicity, we first considered the circumstance where all local gains are independent of each other and remain constant as the value of S is varied.In this section, we set out to investigate the steady-state dose response relationships for the generalized negative feedback control scheme –S3. We dAccording to signal transfer/modular response analysis –24, the loopR = |r1r2r3| is the loop gain. This formalism, conforming to that originally derived for intracellular signal propagations with feedback [After rearranging the feedback control graph in feedback ,20,22,24r0 = 1, i.e., the controlled variable Y increases linearly with S in the absence of the feedback. The shapes of dose response curves, i.e., linear, superlinear, or sublinear, for the control circuit in loopR ≥ 0 . Therefore, the Y versus S dose response curve is superlinear or at best linear. The smaller loopR as large, hence According to loopR can be achieved by increasing local gain r1, r2, and r3, either alone or in combination. Along the feedback loop—from activation of transcription factor, to gene induction, to enzyme formation, to enzymatic reaction—a variety of local interactions can operate in an ultrasensitive manner to provide high local gains . For instance, in response to stress, some transcription factors involved form homodimers or homotrimers to become transcriptionally active [r1 a characteristic value of 2 and 3, respectively. Simulation results indicated that dimerization or trimerization of T increasingly suppresses the Y versus S dose response curve, consistently matching the analytic results . Lastly, cells are likely to use combinations of ultrasensitive steps described above to achieve a large loop gain for robust homeostasis. For instance, dimerization of T, coupled with cooperative binding and dimerization of G, gives rise to a very insensitive, almost horizontal Y versus S dose response curve , a charr1 and r2, similar increases in r1 and r2 tend to linearize the G versus S dose response curve . Local gains in other steps in the feedback loop may undergo a similar recovery from repression owing to constitutive activities. Such slow increases in local gains in approaching their respective characteristic values lead to a similar sluggish increase in the loop gain loopR. As a result, the systems-level gain Y = y0S for very small S to a superlinear function of G = g0, through a transient sublinear stage, to function In a feedback control scheme such as in n for Y B begins d), the percentage binding will increase little even if the transcription factor continues to rise in concentration. In the process of approaching saturation of activation, local gains fall from their characteristic values toward zero. Accordingly, the loop gain loopR decreases from an initial value to zero, as well. For the controlled variable Y, the systems-level gain Y = y2S for large S, interposed with a transitional sublinear segment. In the process of approaching saturation, the systems-level gain After recovering from gain repression by constitutive activation or in its absence, each local step operates at its characteristic gain value. However, it is unlikely that local gains would remain at these values for very high input of S. The saturable nature of biochemical interactions sets an upper limit for the degree of activation. For instance, if the transcription factor is activated by phosphorylation, then the abundance of the phosphorylated form cannot be greater than the total amount of that transcription factor. With respect to promoter binding, once the concentration of the specific transcription factor becomes much greater than the dissociation constant . Then, the local gainThe steady-state concentration of the controlled variable Y is determined by its production and clearance rates. In the generalized control scheme, Y is produced at a rate of r0 = −r3 ≈ 1 for small S. As S increases, r0 rises from unity to infinity because the enzymatic activity of G is eventually saturated by Y. In the absence of negative feedback, the controlled variable Y will first increase linearly with S, then sublinearly with an accelerated gain  for small S and 1/r1r2 for large S. Correspondingly, the Y versus S dose response will migrate from a superlinear to a less superlinear curve , given the fact that in most cases an enzyme's elasticity is unity [Thus, as long as is unity .loopR very small, hence Above, we have discussed how individual gain-changing events associated with constitutive activation, saturation of gene activation, and saturation of G-catalyzed reaction, generate different types of curvature changes in dose response relationships. In a homeostatic gene regulatory network, it is unlikely that these events take place exclusively of one another. Rather, these processes could come into play in combinations and in orders as the stressor level increases, steering the dose response curve through distinct phases. A very possible scenario which can fully manifest all the effects of these events is as follows . In the au phase C.d value for transcription factor T binding to the gene promoter, shortens the second phase . The underlying assumption is that the dose response behaves consistently in a linear fashion from high- through low-dose regions. Yet in the absence of the knowledge of low-dose curvature and its relationship with high-dose responses, linear extrapolation is unlikely to accurately represent low-dose risks. As we demonstrated above, in a homeostatic cellular defense system the low-dose region is intrinsically nonlinear. Notably, the dose response curve for Y versus external stress Se in the low-dose region is composed primarily of the superlinear phases and the transitional sublinear segment linking phase 2 and 3 . The primary curvature in the low-dose region depends on the relative influences from the superlinear phase and sublinear segment. Simulation results demonstrated that under conditions where the pre-transcriptional local gain (for instance r1) is relatively small, the effect of constitutive activation is not negligible, and gene activation does not saturate early, a superlinear appearance occupies the low-dose region  enzymatically or in some cases non-enzymatically. The anti-electrophilic stress gene regulatory network in mammalian cells can be decomposed, from an engineer's perspective, into three functional units as seen in a classical control system, i.e., transducer, controller, and biochemical plant . The tra2) [Our simulation indicated that the anti-electrophilic defense system launches a typical adaptive response when challenged continuously with electrophilic stresses A. The si2) ,42. The 2) [The steady-state dose response curve for electrophile X, which is a controlled variable here, transitions from an initial superlinear controlled phase, through a minimal linear phase, to a sublinear catastrophic phase B. This t2) . InteresAs far as homeostatic feedback control is concerned, the mammalian anti-electrophilic system, as at least in the current model, is complex in the following aspects. First, control of detoxification of electrophile X is a concerted action by multiple genes through a series of metabolic reactions, including co-substrate GSH synthesis by GCL and GS, conjugation of X by GST, and extrusion of GSX by MRP. Second, there are several places in the feedback loop where local gains can be enhanced for effective homeostatic control. These include formation of GS, GST, and MRP homodimers by their respective monomeric subunits –46, formGclc, Gclm, and Gs, does not affect the controllability at low stressor levels significantly, as X level follows very closely that in the complete system  and lack of MRP functioning as a homodimer (green line) both unfavorably affect the homeostatic performance, with the former being more damaging. Thus, through product inhibition, MRP, the enzyme located last in the detoxification chain, plays a significant role in the homeostatic control of X. In supporting this role, it has been experimentally demonstrated that MRP2 can potentiate GST A1–1 mediated cellular resistance to electrophilic agents [The GSH conjugates of many electrophiles can exert an inhibitory action on the catalytic activity of GST –51. Withic phase C. As menc agents ,53. All Gclc and Gclm genes, as well as inhibition of GCL activity, affect the homeostatic control in anti-electrophilic defense. Because mouse homozygous Gclc(−/−) knockout is embryonically lethal, it is not discussed here. While basal GSH level is 5 mM in the complete model system, it decreases to 4, 3.6, and 1.2 mM in our models, equivalent to Gclc(+/−), Gclm(+/−), and Gclm(−/−) knockouts, respectively. These values are in line with or close to changes in GSH levels reported experimentally for respective knockout mice, which are 80%, 43%~83%, and 9%~16% of that in the wild-type [Gclc(+/−) and Gclm(+/−) models produce a dose response curve for X that is almost identical to that with the complete system (Gclm(−/−) homozygous knockout, results in a slightly elevated superlinear controlled phase followed by an earlier onset of the catastrophic phase (Gclm(−/−) knockout mice and cells depleted of GSH with GCL-inhibiting agent BSO are more sensitive to electrophilic/oxidative damage [The intracellular GSH level has been experimentally manipulated via both genetic and pharmacological means to study the role of GSH in anti-oxidant/electrophilic response –56. Hereild-type ,55. Our e system D. This ue damage ,56.While most electrophilic compounds are detoxified through conjugating with GSH enzymatically, certain chemicals, such as the electrophilic intermediates of vinylidene chloride (VDC), can efficiently react with GSH to form conjugates without GST . Thus, iIn summary, we have demonstrated that the dose response transition derived from the generalized control scheme holds well in an anti-stress gene regulatory network as complex as the system defending electrophilic stresses, which involves multiple genes, enzymes, and metabolic reactions. The transition profile is even retained in highly impaired circumstances, such as gene knockout and deregulation. The feedback regulation of multiple genes not only enhances the system's controllability, but also makes the system less vulnerable to functional disruptions of individual genes.Cells do not remain passive when confronted with environmental challenges. To maintain a relatively stable intracellular milieu, they are equipped with a suite of specialized defense programs that are launched in response to various external stressors –5. Theser0 ≈ 1, the systems-level gain, or sensitivity of Y to S, loopR as large, as possible. The larger loopR is, the more superlinear the Y versus S dose response curve becomes, and cells are more resistant to perturbations. Since loopR is the product of individual local gains sequentially distributed along the feedback loop, increases in any individual local gain will augment loopR, leading to more robust homeostasis.Analogous to the I/O relationship in a manmade control system implemented via proportional negative feedback, the steady-state systems-level gain in a gene regulatory network functioning to resist perturbations abides by a similar transfer principle. The systems-level gain for the controlled variable Y, transcription factor T, and gene product G can be generically described by their respective open-loop gain and the loop gain , a formaan unity . This reHsp82 promoter contains three copies of HSE in adjacency, HSF binds the promoter in a profound cooperative fashion to induce Hsp82 gene expression [2O2 and lipid peroxide, exist largely as homotetramers [Nrf2 gene promoters, and Nrf2 can transcriptionally upregulate its own gene expression [Delving into the molecular details of many anti-stress gene regulatory networks readily reveals that local gain enhancement appears to be a common strategy cells utilize for robust homeostasis. In this regard, cells are furnished with many biochemical reactions/interactions or functional modules that can transfer signals in an ultrasensitive, or even switch-like manner, and thereby enhance local gains. It is not uncommon that many transcription factors specifically involved in stress responses must homodimerize or homotrimerize to become transcriptionally active. For instance, in response to heat shock, heat shock transcription factor 1 (HSF1) monomers multimerize into homotrimers to gain affinity to bind the heat shock element (HSE) . Ideallypression . Similarpression ,62. Subspression ,64. Moreetramers ,65. It ipression ,48. Lastpression . Specifipression  and was pression . Cells apression .2O2 is used to mediate intracellular insulin signaling, it would be less desirable for an insulin-induced H2O2 signal to frequently trigger anti-oxidant gene expression, which will otherwise dampen H2O2 as a second messenger [Local gains do not remain characteristically constant as the feedback network is increasingly activated by external stressors. This stress level–dependent variation suggests that a dose response curve could undergo multiple phases that cannot be represented by a simple function. The slow recovery of local gains from repression , owing tessenger .The second phase of the Y versus S dose response curve is superlinear in appearance and characterized with the highest loop gain. In this phase the homeostatic mechanism operates at full capacity so that cells are best able to effectively resist external disturbance. With a large loop gain, the controlled variable could change very little in response to a wide range of stress levels. But ultimately, activation of gene expression would approach saturation, and the superlinear controlled phase will transition either into the linear phase or directly into the catastrophic sublinear phase, depending on the degree of enzyme saturation by that point. In the two latter phases, the system loses active controls, and persistent elevation of the controlled variable may lead to cell death. Since the feedback regulation in a homeostatic gene network is transcriptionally mediated, it can take hours or longer for the system to settle at a new steady state, as demonstrated in the electrophilic stress response. Before reaching the steady state, the controlled variable may be at very high levels. If cells cannot tolerate such a short-term spike of the controlled variable internally, programs such as apoptosis may be initiated, and the steady-state response for high doses would not be achieved.The homeostatic benefit of a high loop gain is obvious—it increases the resistance of the cell to external perturbations at low doses, and extends the resistance to higher doses by delaying catastrophic rises in the levels of controlled variables. A high loop gain can be obtained either by concentrating it in one or two local steps or by distributing it more evenly throughout the feedback loop. Overly concentrated loop gains may be less preferred since they may impose special biochemical or energy challenges to the specific reactions/interactions involved. Even if allocating the loop gain more evenly is a better design, some locations within the feedback loop may be preferred over others for gain placement. Among three of the ultrasensitive steps discussed here where local gains can be enhanced—transcription factor multimerization, cooperative binding of transcription factor to response elements, and enzyme multimerization—the latter is probably preferred over the former two when a choice has to be made about gain placement. Although a high loop gain achieved at the two pre-transcriptional locations can definitely enhance the local superlinearity of the controlled phase, thus boosting resistance to perturbations by relatively low-dose stressors, this increased resistance eventually has to succumb to the limiting effect of gene saturation, with the response converging invariably into a similar linear and eventually catastrophic phase A. In con32 is a mechanism for suppressing stochastic fluctuation in the heat shock gene regulatory network [Although the present study is concerned with the steady-state behaviors of anti-stress gene regulatory networks, it is important to note that gene expression is intrinsically stochastic and may fluctuate to a great extent around the steady state in both simple and complex gene networks –86. Give network . In this network ,99,100. A critically important issue in toxicological research and risk assessment is how to estimate low-dose effect from experimental data obtained for high doses. Although linear extrapolation from the high-dose region to the basal point has been a popular practice, in many situations the assumption that dose response relationships in the low-dose region behave linearly does not have theoretical basis. The present study revealed that for cells capable of anti-stress homeostatic regulation, the low-dose region has various nonlinear characteristics. The nature of negative feedback regulation determines that the low-dose region for the controlled variable is basically superlinear, and the stronger the feedback is, the more superlinear it becomes. However, in the presence of diminishing gains for gene activation owing to saturation, the superlinear phase gradually reverses its curvature to become sublinear during its course to join the subsequent linear phase. Therefore, the primary curvature in the low-dose region depends, by and large, on the relative influence from the superlinear controlled phase and the sublinear segment that immediately follows. In conditions where the effect of constitutive activation is insignificant, pre-transcriptional gain is high, and saturation of gene activation occurs early, the superlinear controlled phase appears only transiently, leaving the low-dose region dominated by the sublinear segment. In the presence of feedforward gene activation, the sublinear appearance is even more prominent, and eventually a J-shaped curve could arise. It has long been hypothesized that low-dose effects including hormesis are of homeostatic and adaptive nature ,102; ourThe present study represents our initial effort to achieve a quantitative understanding of the adaptive cellular response for homeostasis. We realize that the generalized control scheme we studied is a simplification of realistic biological networks that are more complex. But as we showed for the anti-electrophilic defense system, complex feedback networks involving multiple genes, enzymes, and biochemical reactions may observe similar transitions in their controllability. Although the dose response transition we proposed remains to be extensively examined, experimental studies by others on the formation of DNA adducts and protein conjugates ,31 have http://www.innetics.com) and then exported into MatLab . All the dose response simulation results were obtained by running the models to steady state in MatLab. Models in the format of MatLab are available for downloading in File Collection S1.Details of the model structures, reactions, and parameter values are provided under Supporting Information, which contains references and rationale for the choice of parameter values. For the generalized control scheme, the parameter values were kept dimensionless and chosen to assist in visualizing the analytical results –S3. For Figure S1(98 KB PDF)Click here for additional data file.Figure S2(911 KB PDF)Click here for additional data file.Figure S3(89 KB PDF)Click here for additional data file.Figure S4(78 KB PDF)Click here for additional data file.File Collection S1(7 KB ZIP)Click here for additional data file.Table S1(83 KB PDF)Click here for additional data file.Table S2(80 KB PDF)Click here for additional data file.Table S3(85 KB PDF)Click here for additional data file.Table S4(164 KB PDF)Click here for additional data file.Table S5(118 KB PDF)Click here for additional data file.Text S1(81 KB PDF)Click here for additional data file.Text S2(97 KB PDF)Click here for additional data file.Text S3(82 KB PDF)Click here for additional data file.Text S4(58 KB PDF)Click here for additional data file.Text S5(96 KB PDF)Click here for additional data file.


The mechanisms by which sRNAs modulate expression of its targets are diverse. In common to most is the possibility that interference with the translation of mRNA targets may also alter the abundance of functional sRNAs. Aiming to understand the unique role played by sRNAs in gene regulation, we studied examples from two distinct classes of bacterial sRNAs in Escherichia coli using a quantitative approach combining experiment and theory. Our results demonstrate that sRNA provides a novel mode of gene regulation, with characteristics distinct from those of protein-mediated gene regulation. These include a threshold-linear response with a tunable threshold, a robust noise resistance characteristic, and a built-in capability for hierarchical cross-talk. Knowledge of these special features of sRNA-mediated regulation may be crucial toward understanding the subtle functions that sRNAs can play in coordinating various stress-relief pathways. Our results may also help guide the design of synthetic genetic circuits that have properties difficult to attain with protein regulators alone.An increasing number of small RNAs (sRNAs) have been shown to regulate critical pathways in prokaryotes and eukaryotes. In bacteria, regulation by  Escherichia coli. We have demonstrated that small RNAs can tightly repress their target genes when their synthesis rate is smaller than some threshold, but have little or no effect when the synthesis rate is much larger than that threshold. Importantly, the threshold level is set by the synthesis rate of the small RNA itself and can be dynamically tuned. The effect of biochemical properties—such as the binding affinity of the two RNA molecules, which can only be altered on evolutionary time scales—is limited to setting a hierarchical order among different targets of a small RNA, facilitating in principle a global coordination of stress response.The activation of stress response programs, while crucial for the survival of a bacterial cell under stressful conditions, is costly in terms of energy and substrates and risky to the normal functions of the cell. Stress response is therefore tightly regulated. A recently discovered layer of regulation involves small RNA molecules, which bind the mRNA transcripts of their targets, inhibit their translation, and promote their cleavage. To understand the role that small RNA plays in regulation, we have studied the quantitative aspects of small RNA regulation by integrating mathematical modeling and quantitative experiments in  In bacteria, small RNAs can regulate the expression of genes at the translational level. The many advantages of this type of control include a tuneable threshold response and resistance to biochemical noise.  High expression of the plasmid target effectively rescues the chromosomal targets from repression ([IPTG] = 0.5 mM).Further evidences for cross-talk between different targets of RyhB are given in and fumA ,60), as (geneR) that is regulated by the same sRNA as another target gene (geneT). We denote the transcription rates of the two genes by αR and αT, respectively, and their binding rates to the sRNA by kR and kT. The predicted dependence of geneR mRNA level on the ratio between transcription rates of the two genes (αT/αR ), and the ratio between the two binding constants (kT/kR), is displayed in geneR mRNA level is measured in units of its level in the absence of the sRNA). In this figure, transcription rate of geneR is chosen to be 5 times smaller than that of the sRNA. Therefore, in the absence of geneT (αT = 0), expression of geneR is strongly suppressed by the sRNA.These data suggest that the cross-talk between different targets may allow for one target to relieve sRNA-mediated repression of another target. To explore this possibility, we used our model  is highly affected by another target that is more strongly interacting ; see the large kT/kR region of geneT (increasing αT/αR) indirectly activates geneR by relieving the sRNA repression. Conversely, a strongly interacting target  is expected to be much less affected by a weakly interacting one . Thus, in the small kT/kR region of geneR remains suppressed even when geneT is highly expressed. Interestingly, our calculation predicts that for large kT/kR, the response of geneR to changes in the transcription rate of geneT may be very sharp. For example, the data of kT/kR ≈ 2. Thus, the sensitivity of the sRNA-mediated repression may be translated into sensitivity in the indirect interaction between its targets.The “standard model” of gene regulation in bacteria primarily involves transcriptional initiation control by one or more regulatory proteins. Solid understanding of the key mechanistic ingredients of transcriptional regulation , stemmedAnalysis of a simple model of protein-mediated gene regulation  predictsThis is, however, not the case for the threshold-linear mode that characterizes sRNA-mediated regulation. Here the fold-change depends not only on the presence of the repressor, but also on the transcription of the target A, arrows2+ ions and negatively regulates transcription of RyhB, which in turn regulates targets whose expressions are required when Fe2+ is abundant in the cytoplasm [2+ continuously (through rapid equilibration between Fur and Fur–Fe2+), we predict that targets of RyhB will only be expressed when the Fe2+ level crosses some threshold. This threshold can be set dynamically for each target by regulators controlling its transcription.In a number of cases studied, a sRNA serves as a node in a regulatory cascade. Expression of the sRNA may be controlled by protein regulator that senses (directly or indirectly) an environmental signal. For example, the Ferric Uptake Regulator (Fur) is activated by free Feytoplasm ,60. Our Recently it has been suggested that targets of microRNA regulation in eukaryotes may be classified as “switch,” “tuning,” and “neutral” targets, depending on their response to microRNA level ,76. In tgeneA is silenced by a sRNA, and geneP is repressed transcriptionally by a protein regulator. In general, we predict a much-reduced variance in protein level for sRNA-mediated regulation , in a double-log plot. From the solution  can also be accomplished via, e.g., DNA looping [sRNA-mediated regulation was predicted to be ultrasensitive to small changes in sRNA expression near the threshold . A commosolution , we find looping . On the  looping .(geneR) is completely repressed by the sRNA when another, stronger target  is not expressed (geneR to be translated into proteins (geneR may not attract enough sRNA to affect the expression of geneR (unpublished data).Our data demonstrate how the activity of a strong target of RyhB may influence the expression of another target. In particular, we show that over-expression of a plasmid-borne target relieves completely the strong sRNA repression of its chromosomal target. Generalizing our kinetic model offers a simple intuitive picture . A weak xpressed A. Expresproteins B. On theIn the context of a single target, our model predicts that the strength of the sRNA–target interaction influences only the smoothness of the transition, but not the threshold value of the threshold-linear expression pattern. However, when multiple targets are expressed simultaneously, the different mRNA species are expected to compete for association with the same pool of sRNA, and the relative interaction strength becomes a key determinant of the complex interactions that ensue. The interaction strength of the different targets sets their relative position in the cross-talk hierarchy, where targets of a given binding strength affect—but are not affected by—targets of lower binding strength.Through quantitative characterization of gene regulation for two distinct classes of sRNA regulators, we have shown that sRNA-mediated regulation has many functional properties that are fundamentally different from the classical, protein-mediated mode of gene regulations. Analysis of our model suggests that sRNAs may offer tight regulation below the threshold (repressing the average expression and reducing fluctuations) accompanied by derepression away from the threshold. Taken together, this suggests that sRNAs working in the threshold-linear mode may be particularly suitable for a “stress-relief” mechanism, where no action is elicited until a tolerance threshold is exceeded. Knowledge of these properties is essential to an integrated understanding of gene regulatory systems, and may inspire the design and synthesis of novel genetic circuits  with proE. coli K-12 BW25113 [r-lacI-tetRsp cassette from DH5α-ZI cells [lacI and tetR genes [ryhB and/or sodB were deleted from BW-RI [All experiments were performed with BW-RI cells derived from  BW25113 , with thZI cells  by phagetR genes . For somom BW-RI . These sluc, whose copy number has been estimated at 50–70 copies [EcoRI site was created by adding GAAT immediately downstream of +1 of the PLlac-O1 promoter. The region between the newly created EcoRI site and the resident EcoRI site 6 bp upstream of RBS was then deleted by EcoRI digestion and subsequent religation, yielding pZE12-lucM. The KpnI-XbaI flanking luc gene in pZE12-lucM was replaced by the gfpmut3b structure gene [Llac-O1:gfpmut3b construct with a 5′-UTR defined by an EcoRI site immediately downstream of +1 and a KpnI site immediately upstream of the translation start of gfpmut3b. The 15-base sequence sandwiched by the EcoRI and KpnI sites, ATTAAAGAGGAGAAA, contains an RBS indicated by the underlined bases. The 5′-UTR from the control region of sodB  was cloned into the EcoRI and KpnI sites of pZE12G, yielding pZE12S. pZE12S therefore contains the ColE1 ori, the PLlac-O1 promoter [crsodB fused to the coding sequence of the gfpmut3b gene. Similarly, the control region of is10in (from +1 to +36) was substituted for crsodB in pZE12S, yielding pZE12IS. To improve the expression level, the RBS in the is10in control region was modified by changing TC (+16 to +17) to GG.pZE12-0 copies , was useure gene . This yipromoter , and crsluc plasmid, which has been estimated to maintain at 20–30 copies per cell [ori and is marked by chloramphenicol resistance. First, a NdeI site was added immediately downstream the +1 of the luc gene by inserting ATG between +2 and +3, and a BamHI site was added downstream of luc by inserting ATC between the 1,772th and 1,773th nucleotides, yielding pZA31-lucNB. For pZA31R, the ryhB gene (from +1 to +96 cloned directly from E. coli K-12) was ligated into the NdeI/BamHI sites of pZA31-lucNB, replacing the luc gene. For pZA30R, the PLtet-O1 promoter and the luc gene of pZA31-lucNB were replaced by PryhB:ryhB (from −62 to +96 cloned directly from E. coli K-12 MG1655), which contains the ryhB gene and its native promoter. Finally, for pZA31O, the is10out gene (from +1 to +103) was substituted for the luc gene in pZA31-lucNB.Three sRNA-source plasmids , were derived from the pZA31-per cell . Each cocrsodB-gfp to the attP site of strain ZZS00 (ryhB−) chromosome using the method of Diederich et al. [SalI/BamHI-flanked PLlac-O1: crsodB-gfpmut3b containing the downstream terminator was cloned into the same sites of pLDR10 containing the attachment site attP and encoding the chloramphenicol (Cm) resistance. The recombinant plasmid was digested with NotI and the larger portions of the plasmids containing the fragment of interest but not the ori were religated. The circular DNA molecules were transformed into ZZS00 cells expressing the int gene contained in pLDR8, a helper plasmid bearing a temperature-sensitive ori and encoding the kanamycin (Km) resistance. The transformations were applied on LB+Ap plates that were incubated at 42 °C. The transformants were tested for sensitivity to Cm and Km. The ampicillin (Ap)-resistant but Cm- and Km-sensitive transformants were identified as the clones that carry the DNA fragment of interest at the attP site of E. coli chromosome.In addition, we transferred the target h et al. . Briefly600 ≈ 0.002) containing the appropriate antibiotics as well as varying amounts of the inducers  in the wells of 48-well plates. The plates were incubated with shaking at 37 °C and taken for OD600 and fluorescence measurements every hour for up to 12 h  using a TECAN Genios-Pro plate reader (http://www.tecan.com). Each measurement was repeated 3–6 times and the data were analyzed as discussed below.BW-RI strains each containing the target and/or source plasmids were grown in M63 minimal media with 0.5% glucose, and standard concentrations of the appropriate antibiotics. The overnight cultures were diluted into fresh M63 media . When OD600 of these cultures reached 0.3–0.5, approximately 109 cells of each culture were harvested in a microcentrifuge at 4 °C, treated with 10 mg/ml lysozyme in TE buffer (pH = 8.0) and total RNA was collected using an Absolutely RNA miniprep kit . The prepared samples were then treated with Turbo DNA-free DNase , and PCR controls were performed on each sample to verify the absence of contaminating DNA. cDNA was prepared with 1 μg of RNA from each sample using Superscript III First Strand Synthesis system . Dilutions of the resulting samples were then used as the template in PCR reactions using iQ SYBR Green Supermix  in a Smart Cycler thermal cycler .For RT-PCR measurements, overnight cultures were used to inoculate M63 medium with 0.5% glucose, standard concentrations of the appropriate antibiotics, and various concentrations of inducers to an initial OD600 = 0.001, and grown in a 12-well plate with 3 ml of culture in each well, with appropriate antibiotics and inducers. To determine the growth rate, OD600 was measured every 60 min. Cultures were grown at 37 °C with constant shaking until they reach OD600 = 0.3, at which time 1.7 ml of each culture was spun down and resuspended in 1 ml phosphate buffer solution (PBS). GFP fluorescence was measured using a Becton-Dickinson FACSCalibur flow cytometer with a 488-nm argon excitation laser and a 515- to 545-nm emission filter (FL1) at a low flow rate. Photomultiplier tube (PMT) voltage was set to 950 V, and a linear amplifier was set at 9.5×. Forward scatter and fluorescence values were collected for 50,000 cells.To measure expression from a chromosomal target, cells were grown overnight in minimal media with antibiotics. Cultures were then diluted to OD2(OD600) versus time for each strain and condition; this yielded a doubling time of ∼2 h for most strains. Next, for all of the time points concerning each strain and condition, we plotted the average fluorescence versus average OD600 on linear-linear plot and extracted the slope (f). In 600 for the ryhB− strain (ZZS21), together with the fitted slopes. Each slope gave the average fluorescence per growing cell (in unit of relative fluorescence units (RFU)/OD) for that strain and the corresponding growth condition. The raw fluorescence production rate per cell was computed as fμ(1 + μτ) [Llac-O1, and is referred to as the GFP expression. The results were plotted in Ltet-O1 promoter controlled by the amount of aTc in the growth medium.To obtain gene expression patterns for the different strains, we averaged (for each time point) the data obtained from the different repeats for each combination of strain and inducers. First, the cell doubling rate (μ) was obtained as the slope of a linear fit of log(1 + μτ) , upon ta(1 + μτ) . We thenm, the steady-state mRNA level, i.e., GFP expression = bm, where b reflects the rate of GFP translation and maturation. Then, f using a standard Levenberg-Marquardt algorithm implemented in MATLAB , with the least-square error defined asTo fit the experimental data with the steady-state solution , we assuThe values of the best-fit parameters obtained are given in s ∼ 1/50 min−1. Similarly, from the half-life of ∼6 min for sodB mRNA [m ∼ 1/10 min−1 . Moreover, DNA microarray experiments [sdhCDAB and sodB mRNA in rich medium (where iron is abundant and RyhB is expected to be repressed). This suggests a target transcription rate (αm) of ∼ 1 nM/min in the state where mRNA is expressed. In general, αm is controlled by various cellular signals  and can typically vary ∼10-fold.  On the other hand, the activity of the RyhB promoter has a broad range, since it is strongly regulated by Fur-Fe2+ whose concentration can vary over 1000-fold [s to take on the range from 0.1/min to 10/min. Finally the coupled degradation rate k can also be deduced from the results of Masse et al. [p of order 1). Because RyhB is shown to disappear in the presence of its targets within 3 min, then by using an estimated target mRNA concentration of 20 nM, we find 1/50 (nM min) −1, which is close to the diffusion-limited association rate for typical small proteins [fhlA [hok/sok pair [The values of the model parameters can be estimated from various experiments. Consider first RyhB and its targets ,42. In teriments ,89 indicg et al.  showed a000-fold . We modeproteins ,91 and ins [fhlA , as wellsok pair .is10in. RNA-OUT itself is extremely stable, with a half-life dictated by dilution due to growth βs ∼ 0.02 min−1 [is10in mRNA is typical to bacterial mRNA  −1. The pOUT promoter is a typical promoter, and we assume that αs is not very different from that of RyhB [pIN promoter, on the other hand, is atypically weak, and is only enhanced 10-fold upon methylation [Finally, we consider RNA-OUT and its target, the mRNA of 02 min−1 , while tβm ∼ 0.3 ). Bindin of RyhB . The pINhylation –67.Values of the model parameters are summarized in Figure S1geneT is not expressed, the sRNA silences the expression of geneR. When geneT is expressed, most sRNA molecules bind and degrade with mRNAs of geneT, allowing mRNAs of geneR to be translated into proteins.When (63 KB PDF)Click here for additional data file.Figure S2600 for the RyhB-less strain (ZZS21) containing the plasmid borne PLlac-O1:crsodB-gfp reporter. Lines are given by a linear fit. The slope of each line was used to define the GFP expression.GFP fluorescence is plotted against OD(55 KB PDF)Click here for additional data file.Figure S3Ltet-O1:ryhB on a plasmid, in addition to the PLlac-O1:crsodB-gfp reporter. The IPTG dependence of GFP expression (defined from plots such as GFP expression for strains (ZZS23) harboring P(46 KB PDF)Click here for additional data file.Figure S4Llac−O1:crsodB-gfp, was measured as in ryhB) and ZZS23 (plasmid-borne ryhB). The fold of repression  is defined as the ratio between the two. The repression effect of RyhB is diminished at higher levels of IPTG, corresponding to higher transcription rates of the target.The fluorescence levels of cells carrying a plasmid coding for the target, P(45 KB PDF)Click here for additional data file.Table S1See Material and Methods for a detailed description of the fitting procedure.(13 KB PDF)Click here for additional data file.Table S2See Material and Methods for a detailed description of the fitting procedure.(13 KB PDF)Click here for additional data file.Table S3(13 KB PDF)Click here for additional data file.Text S1(120 KB PDF)Click here for additional data file.http://www.ncbi.nlm.nih.gov/Genbank/) accession numbers for the genes and gene products discussed in this paper are ryhB (GeneID: 2847761), sodB (GeneID: 944953), fumA (GeneID: 2955664), fur (GeneID: 945295), hfq (GeneID: 948689), oxyS (GeneID: 2847701), micC (GeneID: 2847713), micF (GeneID: 2847742), rprA (GeneID: 2847671), dsrA (GeneID: 946470), rpoS (GeneID: 947210), gadY (GeneID: 2847729).The GenBank (


There are a number of methods  currently in use that quantify codon usage in genes. These measures are often influenced by other sequence properties, such as length. This can introduce strong methodological bias into measurements; therefore we attempted to develop a method free from such dependencies. One of the common applications of codon usage analyses is to quantitatively predict gene expressivity.We compared the performance of several commonly used measures and a novel method we introduce in this paper – Measure Independent of Length and Composition (MILC). Large, randomly generated sequence sets were used to test for dependence on (i) sequence length, (ii) overall amount of codon bias and (iii) codon bias discrepancy in the sequences. A derivative of the method, named MELP (MILC-based Expression Level Predictor) can be used to quantitatively predict gene expression levels from genomic data. It was compared to other similar predictors by examining their correlation with actual, experimentally obtained mRNA or protein abundances.We have established that MILC is a generally applicable measure, being resistant to changes in gene length and overall nucleotide composition, and introducing little noise into measurements. Other methods, however, may also be appropriate in certain applications. Our efforts to quantitatively predict gene expression levels in several prokaryotes and unicellular eukaryotes met with varying levels of success, depending on the experimental dataset and predictor used. Out of all methods, MELP and Rainer Merkl's GCB method had the most consistent behaviour. A 'reference set' containing known ribosomal protein genes appears to be a valid starting point for a codon usage-based expressivity prediction. As the numbers of sequenced genes grew, it became evident that synonymous codons are not used equally -3. CodonMoreover, the relative influence of each of these factors varies from genome to genome, and from gene to gene. For example, selection for translation efficiency shapes codon usage more in fast-growing microbes  than in Many researchers in this field formulated their own measures, which led to a large number of available methods ,18 for cOur primary motivation in developing this novel method was to correct for possible artefacts due to sequence length variability. The measure should be able to quantify the distance in codon usage between a gene and some expected distribution of codons. The codon distribution could either be calculated from the background nucleotide composition, or derived from a single gene or a gene group. Therefore, MILC is conceptually similar to Karlin and Mrazek's B , NovembrG-test for goodness-of-fit. This methodology yields numerically similar results to the more commonly used χ2 test, but may hold theoretical advantages over it in statistical analyses op [1], Eop  and GCB op . Insteadop .The agreement of predicted and actual protein/transcript levels varied greatly between all examined combinations of prediction method and dataset. The cause may lie in the quality of experimental data; for instance, mRNA abundances and protein 2D-PAGE data have been shown not to agree well in certain cases ; 2D-PAGEBacillus harvested at OD600 ≅ 0.4 – 0.6; an analogous dataset [see 600 ≅ 1.1 does not correlate so well with predicted expression levels . In addition, the growth conditions should match the organism's natural habitat. For instance, E. coli grown in a rich medium has gene expression levels closer to the predicted values than E. coli in a defined medium; should the data in Eco-2 dataset be replaced with data from MOPS+glucose grown cells [see E. coli in the Eco-3 dataset reduces the correlation with predicted values (data not shown). Such connections between codon usage and gene expression under different conditions can be used to hypothesize about the exact 'natural' environment of a microbe [Transcript and/or protein levels in a cell are normally subject to regulation, as opposed to codon usage patterns, which are 'hard-coded' in the genome sequence. If we suppose the major force shaping gene-specific codon usage patterns in microbes is selection for translation efficiency, which operates in periods of fast competitive growth, it follows that codon usage will be 'optimised' for genes highly expressed in such periods. For that reason we chose datasets of organisms harvested in exponential growth phase, and without severe nutritional restrictions in the medium. For instance, the Bsu-2 datasets describes aset see  for bactells see , the Pea microbe .E. coli; Karlin and Mrazek [Synechocystis and histones in P. falciparum; such functional assignments for reference set genes were not unexpected. Under the assumption that the major trend is due to translational selection, the change in reference set composition should have theoretically resulted in improved prediction. However, the outcome was highly dependent on the genome examined, and the predictor used  which are expected to differ significantly in codon usage and/or exhibit bias discrepancy; or MCB, when comparing sequences of varying lengths but relatively similar in codon preferences. We have also evaluated the methods' ability to estimate gene expression levels by comparing them to actual mRNA/protein abundance data from several species. Out of the tested predictors, GCB and MELP exhibit the most consistent behaviour. A reference set defined simply by including ribosomal protein genes appears to be a valid starting point for expression level predictions in examined prokaryotes and unicellular eukaryotes, although one should be cautious when interpreting the results of such estimations. The MILC and MELP methods have been implemented in the version 2 of the INCA software, available from the bioinfo-hr.org website .The measures of codon usage ENC, B, MCB, ENC' and SCUO were computed as in ,21,22,19Values in Figures The codon frequency tables used to generate sequences, derived from the None, Low, Med and High nucleotide compositions, are available in the accompanying materials see , as wellop, a codon with a relative adaptiveness (codon frequency divided by the frequency of the most frequent codon) larger than 0.9 was considered optimal. Experimental datasets used to investigate the performance of the predictors are listed in Table Sce-1, 2, 3, and Eco-4 were used 'as-is' from the respective sources. Eco-1 dataset was created by combining molar abundances (column "N-abd") from Tables a1, a2 and a3 in [Eco-2 dataset was created from the E. coli Gene-Protein Database [Eco-3 dataset was created by averaging the "PHNppm", "PSppm" and "NSppm" (control groups for phosphorus and nitrogen starvation experiments), and by dividing by the "MWc" column. Ecj-6, Bsu-1, 2, 3, Syn-1 and Syn-2 datasets were downloaded from the KEGG expression data repository [6 per experiment. Finally, for each spot/gene a median value over all experiments in a dataset was calculated. The Pfa-1 dataset was created by averaging the sequence coverage of a protein over all four life stages; if a protein was not detected in a P. falciparum stage, its sequence coverage was assumed to equal 0. To create the Pfa-2 dataset, the columns I, K, AB and AD were averaged to obtain an mRNA abundance for the trophozoite, Q and AJ for the merozoite; column AO provided values for the gametocyte, and column AQ for the sporozoite. The final abundance values were again obtained by averaging the four life stages. Files containing coding regions of genes were downloaded from the NCBI ftp site [The expression level predictors CAI, E, and GCB were computed as in ,36 and , respectnd a3 in ; if a geDatabase  by multipository  and wereftp site  for the ftp site  for the FS devised, tested and implemented the MILC and MELP methods. KV supervised the project and contributed in biological expertise. Both authors read and approved the final manuscript.Rationale behind the length correction of the MILC method, codon frequencies used for testing of codon usage measures, and performance of the expression level predictors. Sheets 1a, 1b and 1c demonstrate, by example, how the chi-square and G scores for amino acids of different degeneracy classes behave when the observed codon counts are small. Sheet 2 contains the codon frequency tables used in testing of the codon usage measures. Sheet 3 describes the performance of the expression level predictors, expressed as Spearman (rank) and Pearson (linear) correlation coefficients of the predicted values and experimentally obtained mRNA/protein abundance data sets.Click here for file


Drosophila is a component of an expandable extrachromosomal domain of the nucleus. To better understand its function, we studied the dynamics and localization of GFP-tagged EAST. In live larval salivary glands, EAST-GFP is highly mobile and localizes to the extrachromosomal nucleoplasm. When these cells are permeabilized, EAST-GFP rapidly associated with polytene chromosomes. The affinity to chromatin increases and mobility decreases with decreasing salt concentration. Deleting the C-terminal residues 1535 to 2301 of EAST strongly reduces the affinity to polytene chromosomes. The bulk of EAST-GFP co-localizes with heterochromatin and is absent from transcriptionally active chromosomal regions. The predominantly chromosomal localization of EAST-GFP can be detected in non-detergent treated salivary glands of pupae as they undergo apoptosis, however not in earlier stages of development. Consistent with this chromosomal pattern of localization, genetic evidence indicates a role for EAST in the repression of gene expression, since a lethal east mutation is allelic to the viable mutation suppressor of white-spotted. We propose that EAST acts as an ion sensor that modulates gene expression in response to changing intracellular ion concentrations.The EAST protein of  Chro, Skeletor and east has been shown to lead to defects in mitosis and meiosis Chro affect the structure and organization of larval polytene chromosomes The nuclear matrix as an internal non-chromosomal scaffold of the cell nucleus remains a controversial concept in eukaryotic cell biology In this report, we describe that the predominantly extrachromosomal EAST protein has the ability to associate with chromosomes and that this behavior is linked to a negative role in gene regulation.rd instar larval salivary glands and performed fluorescence recovery after photo bleaching (FRAP). An extremely fast recovery demonstrated that EAST-GFP is highly mobile, suggesting that EAST does not associate with a static internal nucleoskeleton  skeleton . For simTo evaluate if EAST might associate with an ‘elusive’ structure termed the nuclear matrix, salivary glands expressing EAST-GFP were subjected to the first step of the Nuclear Matrix extraction protocol whereby cells are treated with detergent buffer. The permeabilization using saponin or Triton-X-100 results in a rapid re-localization of EAST-GFP onto polytene chromosomes . An incueasthop7 mutants .To investigate the relationship between transcription and the binding of EAST to chromosomes, we performed a run-on transcription assay by including BrUTP into detergent buffer. Transcriptionally active regions that incorporate high levels of BrUTP contain low levels of EAST-GFP , while D mutants . Loss of+ or K+ ions) in the detergent buffer. At 100 mM or lower concentrations, the bulk of EAST-GFP associates with polytene chromosomes  and C-terminally deleted EASTΔC-GFP (residues 1-1520) at 50 mM salt. The truncated version of EAST was able to associate with polytene chromosomes, yet the banding patterns was less pronounced . Moreoveeast was identical to that of a gene called suppressor of white-spotted or su(wsp) . The su(wsp) mutation suppresses the reduction of eye pigmentation caused by 4 spotted alleles of the white gene (wsp1-4), which are characterized by lesions in the upstream region of weast suppresses the wsp phenotype. Like the viable mutation su(wsp)1, the pupal lethal allele easthop7 restores eye pigmentation to almost wildtype levels (easthop7 and su(wsp)1 is indistinguishable from homozygous su(wsp)1 in terms of eye pigmentation (sp)su are allelic. To understand the molecular nature of the su(wsp)1 mutation, we sequenced the coding region of the genomic DNA of the east locus. At codon 1956, we found a point mutation that introduces a premature stop codon, reducing the length of EAST protein from 2301 to 1955 amino acids. In summary, as has already been inferred from the genetic interactions between spw and su(wsp), the product of the east/su(wsp) gene might act as a negative regulator of gene expression that binds to the upstream regulatory region of the w gene.We learned that the sequence of e levels . The traentation . Heterozsmid-GAL4 UAS-eastFL-GFP pupae contained two types of cells: small cells lacking EAST-GFP with fragmented and condensed chromosomes and larger cells maintaining high levels of EAST-GFP expression, in which polytene chromosomes appeared to have escaped from apoptotic fragmentation. As has been previously reported, cell death in salivary glands is preceded by changes in the levels and localization of filamentous actin and nuclear lamin The intra-nuclear localization of EAST-GFP depends on the developmental stage. The fusion protein shows a mainly extrachromosomal pattern throughout larval and prepupal development . During su(wsp)/east would inhibit white by interacting with its upstream regulatory sequences In this study we show that the nuclear EAST protein has the intrinsic ability to associate with chromosomes and that this behavior is linked to an involvement in gene regulation. A previous study postulated that the gene product of Because the characterization of EAST protein was carried out in permeabilized cells under non-physiological conditions it might be argued that the association with polytene chromosomes is an experimental artifact. However, we were able to observe co-localization with DNA in untreated cells while they undergo apoptosis. Moreover, despite permeabilization, cells were still able to perform transcription and replication, showing that chromatin binding of EAST did not occur under conditions that caused a general block of nuclear functions.The affinity to polytene chromosomes is sensitive to the salt concentration of the detergent buffer. We observed co-localization of the bulk of EAST-GFP with polytene chromosomes in permeabilized cells at concentrations at and below 100 mM salt, which is lower than the estimated physiological concentration of 150 mM. The release of EAST-GFP from chromosomes with increased salt concentrations (150 mM and above) is consistent with the observation that the fusion protein does not display a discernible chromosomal localization pattern in intact cells.su(wsp)1 allele, suggesting that this region is required to mediate the suppression of pigmentation seen in the wsp alleles. The C-terminus therefore might promote binding to chromatin or recruit other factors involved in repression. Although the N-terminal residues 1-1955 seem to be deficient in gene silencing, they are sufficient for viability.The C-terminal residues 1520 to 2301 promote affinity to chromatin but are dispensable for nuclear import. This domain is also involved in tethering EAST to a nuclear remnant during mitosis white transcripts do not differ between wildtype and the allele wsp despite the lesions in the promoter regions white transcription between su(wsp)1wsp1 and wsp1 adults, confirming the conclusion of the two earlier studies that altered white transcription does not appear to be responsible for the reduction in eye pigmentation . To test if sequence variations in white transcripts might explain the difference in pigmentation we sequenced cDNAs corresponding to white transcripts derived from wsp1 and su(wsp) wsp1 adults. However, we were unable to detect any evidence for genomic mutations or RNA editing.On the molecular level, the exact role of EAST in gene regulation remains a mystery. Two previous studies showed that the expression levels of east mutants Taken together, our results suggest that EAST could act as an ion sensor that fine-tunes the expression of target genes in response to fluctuating intra-cellular salt concentrations . Assumineast function leads to an acceleration of cell death in muscles, while overexpression of EAST-GFP in salivary glands attenuates apoptosis . These two opposite phenotypes could be explained by EAST counteracting the loss of K+ ions during apoptosis.In a previous study we proposed that the increase in nuclear volume in response to EAST overexpression was caused by an expansion of a non-chromosomal nucleoskeleton east alleles used have been previously described UAS-eastFL-GFP and UAS-eastΔC-GFP have been described ftz-GAL4 and smid-GAL4wsp1 and y2sc1su(wsp)1wsp1 were obtained from the Bloomington Drosophila stock center.The 2, 1 mM EGTA, 2 mM DTT, 50 µg/ml saponin, 20 µ/ml of 1 Roche EDTA free protease inhibitor cocktail dissolved in 1 ml water) that was based on the cytoskeleton extraction buffer Permeabilization of larval salivary glands was performed using detergent buffer  rd instar larval salivary glands was carried out as previously described Drosophila lamin Dm0 . Images of fluorescently labeled samples were acquired by laser scanning confocal miscroscopy using the Zeiss LSM 510 Meta or Zeiss LSM 510 upright microscopes. Image processing was carried out using Adobe Photoshop, Zeiss LSM 5 image browser and Metamorph (Molecular Devices).Antibody staining of whole-mount 3


MAGIC's main novelty lies in its biologically intuitive clustering approach, which aims towards both calculating reorder-free segments and identifying orthologous segments. In the process, MAGIC efficiently handles ambiguities resulting from duplications that occurred before the speciation of the considered organisms from their most recent common ancestor. We demonstrate both MAGIC's robustness and scalability: the former is asserted with respect to its initial input and with respect to its parameters' values. The latter is asserted by applying MAGIC to distantly related organisms and to large genomes. We compare MAGIC to other comparative mapping methods and provide detailed analysis of the differences between them. Our improvements allow a comprehensive study of the diversity of genetic repertoires resulting from large-scale mutations, such as indels and duplications, including explicitly transposable and phagic elements. The strength of our method is demonstrated by detailed statistics computed for each type of these large-scale mutations. MAGIC enabled us to conduct a comprehensive analysis of the different forces shaping prokaryotic genomes from different clades, and to quantify the importance of novel gene content introduced by horizontal gene transfer relative to gene duplication in bacterial genome evolution. We use these results to investigate the breakpoint distribution in several prokaryotic genomes.We present MAGIC, an integrative and accurate method for comparative genome mapping. Our method consists of two phases:  Comparative genomics is an important discipline with applications in evolutionary, genetic, and genome rearrangement studies. When comparing genomes, one is usually interested in investigating the relation between the genomic segments to establish their evolutionary origin: are the segments orthologous, and hence inherited from their most recent common ancestor? Are they paralogs, and hence duplicated from an ancestral segment? Did the segments undergo reordering? Were the segments deleted or inserted and—if so—how ?In this paper, Swidan et al. present MAGIC, a new approach for comparative genome mapping. The main novelty of this approach is the biologically intuitive clustering step, which aims towards both calculating reorder-free segments and identifying orthologous segments. The authors demonstrate MAGIC's robustness, relative to both its initial input and to its parameters' values. MAGIC's scalability is demonstrated by running it on distantly related organisms and on large genomes. In addition, Swidan et al. provide a detailed analysis of the differences between MAGIC and other comparative mapping methods.Applying MAGIC to several prokaryotic pairs enabled the authors to address the aforementioned questions and to quantitatively study the different evolutionary forces shaping the prokaryotic genome as well as to investigate their breakpoint distribution. However, their corresponding segments in S. flexneri 2457t are different. Since these two entries are not collinear to any other entry, one cannot infer positional ortholog and inparalog relations based on length considerations. Hence, these two entries are considered a conflict.The cyan and the gray entries correspond to the same segment in (30 KB PDF)Click here for additional data file.Figure S2Entry 2 (green) corresponds to K00357. By comparing it with Entry 1 and Entry 3, it is easy to see that Entry 2 corresponds to a false anchor and is a nuisance cross-overlap.(21 KB PDF)Click here for additional data file.Table S1(101 KB PDF)Click here for additional data file.Table S2(98 KB PDF)Click here for additional data file.Table S3(109 KB PDF)Click here for additional data file.Table S4(117 KB PDF)Click here for additional data file.Table S5(37 KB PDF)Click here for additional data file.Text S1(40 KB PDF)Click here for additional data file.


Drosophila CTCF has only been characterised recently. To date only one endogenous binding location for CTCF has been identified in the Drosophila genome, the Fab-8 insulator in the Abdominal-B locus in the Bithorax complex (BX-C). We carried out chromatin immunopurification coupled with genomic microarray analysis to identify CTCF binding sites within representative regions of the Drosophila genome, including the 3-Mb Adh region, the BX-C, and the Antennapedia complex. Location of in vivo CTCF binding within these regions enabled us to construct a robust CTCF binding-site consensus sequence. CTCF binding sites identified in the BX-C map precisely to the known insulator elements Mcp, Fab-6, and Fab-8. Other CTCF binding sites correlate with boundaries of regulatory domains allowing us to locate three additional presumptive insulator elements; “Fab-2,” “Fab-3,” and “Fab-4.” With the exception of Fab-7, our data indicate that CTCF is directly associated with all known or predicted insulators in the BX-C, suggesting that the functioning of these insulators involves a common CTCF-dependent mechanism. Comparison of the locations of the CTCF sites with characterised Polycomb target sites and histone modification provides support for the domain model of BX-C regulation.Insulator or enhancer-blocking elements are proposed to play an important role in the regulation of transcription by preventing inappropriate enhancer/promoter interaction. The zinc-finger protein CTCF is well studied in vertebrates as an enhancer blocking factor, but  Drosophila genome. In particular, we have focussed on the Hox gene cluster in the Bithorax complex; a region whose regulation has been extensively characterised. Previous investigations have identified independent regulatory domains that control the expression of Bithorax complex genes in different segments of the fly, however the molecular nature of the domain boundaries is unclear. Our major result is that we find CTCF binding sites precisely located at the boundaries of these regulatory domains, giving a common molecular basis for these boundaries. This provides a clear example of the link between the positioning of insulators and the organisation of gene regulation in the Drosophila genome.There is still much to learn about the organisation of regulatory elements that control where, when, and how much individual genes in the genome are transcribed. Several types of regulatory element have been identified; some, such as enhancers, act over large genomic distances. This creates a problem: how do such long-range elements only regulate their appropriate target genes? Insulator elements have been proposed to act as barriers within the genome, confining the effects of long-range regulatory elements. Here we have mapped the locations of one insulator-binding protein, CTCF, in several regions of the  Insulator elements are DNA sequences that regulate interactions between promoters and enhancers. By preventing inappropriate enhancer/promoter communication, insulators are believed to play a key role in the genomic organisation of transcriptional regulation. Their mode of action is still unclear but may involve the formation of chromatin loops that partition the genome into separate regulatory domains –5.Drosophila CTCF has been identified , abdominal-A [abd-A], and Abdominal-B [Abd-B]) into distinct enhancer domains ) or normal rabbit serum (control IP). The immunopurified DNA preparations were labelled with either Cy3 or Cy5 and hybridised to a 1-kb tiling-path genomic microarray covering the 3-Mb Adh region together with other selected genomic regions including the BX-C, the Antennapedia complex (ANT-C), and the achaete-scute region. As a positive control, the immunopurification reactions were assessed using specific PCR primers to amplify a 378-bp fragment from the Fab-8 region, containing characterised CTCF binding sites ) in TAE-buffer. Electrophoresis was performed at 4 °C with a field strength of 12 V/cm for 3 h.Radiolabelled DNA probes (150–250 bp) were generated by PCR with eviously . The binDataset S1t-value, and p-value derived by CyberT from the ChIP-array data.Table shows the Array Spot ID, chromosome coordinates, Fragment ID, the values for the four biological replicate ratios, number of observations, Mn, standard deviation, (483 KB TDS)Click here for additional data file.Dataset S2t-value, and p-value for the selected fragments with Mn > 0.45 and p < 0.05.Table shows Fragment ID, start coordinate, stop coordinate, CyberT Mn, (13 KB PDF)Click here for additional data file.Figure S1Drosophila S2 cells as in Fab-8 is the positive control, and the negative controls were pre-immune serum or a nonbinding sequence (Fab-8 5′-control). Fab-7 shows significant enrichment, although less enrichment than Fab-8.ChIP was performed with chromatin from (54 KB PDF)Click here for additional data file.Table S1(29 KB DOC)Click here for additional data file.Table S2(12 KB PDF)Click here for additional data file.http://www.ncbi.nlm.nih.gov/geo) accession number for the genomic tiling array is GEO Platform GPL5028 XC003 and for the ChIP data is series GSE7351.The Gene Expression Omnibus (GEO) (http://www.ncbi.nlm.nih.gov) accession numbers of the genes discussed in this paper are: CTCF human, 10664 and Ctcf mouse, 13018.The Entrez Gene (http://flybase.bio.indiana.edu) accession numbers of the genes and gene products discussed in this paper are: abdominal-A (abd-A), FBgn0000014; Abdominal-B (Abd-B), FBgn0000015; achaete (ac), FBgn0000022; Alcohol dehydrogenase (Adh), FBgn0000055; Antennapedia (Antp), FBgn0000095; BEAF32, FBgn0015602; cactus (cact), FBgn0000250; CTCF, FBgn0035769; Cyclin E (CycE), FBgn0010382; Enhancer of zeste (E(z)), FBgn0000629; kuzbanian (kuz), FBgn0015954; lethal of scute (l(1)sc), FBgn0002561; outspread (osp), FBgn0003016; Polycomb (Pc), FBgn0003042; Posterior sex combs (Psc), FBgn0005624; scute (sc), FBgn0004170; smell-impaired 35A (smi35A), FBgn0016930; suppressor of Hairy wing (su(Hw)), FBgn0003567; Su(var)3–9, FBgn0003600; Ultrabithorax (Ubx), FBgn0003944; and Zw5, FBgn0000520.The Flybase (


Systematic chromatin immunoprecipitation (chIP-chip) experiments have become a central technique for mapping transcriptional interactions in model organisms and humans. However, measurement of chromatin binding does not necessarily imply regulation, and binding may be difficult to detect if it is condition or cofactor dependent. To address these challenges, we present an approach for reliably assigning transcription factors (TFs) to target genes that integrates many lines of direct and indirect evidence into a single probabilistic model. Using this approach, we analyze publicly available chIP-chip binding profiles measured for yeast TFs in standard conditions, showing that our model interprets these data with significantly higher accuracy than previous methods. Pooling the high-confidence interactions reveals a large network containing 363 significant sets of factors (TF modules) that cooperate to regulate common target genes. In addition, the method predicts 980 novel binding interactions with high confidence that are likely to occur in so-far untested conditions. Indeed, using new chIP-chip experiments we show that predicted interactions for the factors Rpn4p and Pdr1p are observed only after treatment of cells with methyl-methanesulfonate, a DNA-damaging agent. We outline the first approach for consistently integrating all available evidences for TF–target interactions and we comprehensively identify the resulting TF module hierarchy. Prioritizing experimental conditions for each factor will be especially important as increasing numbers of chIP-chip assays are performed in complex organisms such as humans, for which “standard conditions” are ill defined. Saccharomyces cerevisiae using seven distinct sources of evidences to score all possible TF–target interactions in this organism. Subsequently, the authors employ another newly developed algorithm to reveal TF modules based on the top 5,000 TF–target interactions, yielding more than 300 TF modules. The new scoring scheme for TF–target interactions allows predicting the binding of TFs under so-far untested conditions, which is demonstrated by experimentally verifying interactions for two TFs . Importantly, the new methods (scoring of TF–target interactions and TF module identification) are scalable to much larger datasets, making them applicable to future studies in humans, which are thought to have substantially larger numbers of TF–target interactions.Transcription factors (TFs) bind close to their target genes for regulating transcript levels depending on cellular conditions. Each gene may be regulated differently from others through the binding of specific groups of TFs (TF modules). Recently, a wide variety of large-scale measurements about transcriptional networks has become available. Here the authors present a framework for consistently integrating all of this evidence to systematically determine the precise set of genes directly regulated by each TF . The framework is applied to the yeast  Combinatorial transcriptional regulation is an important means of achieving highly specific expression of individual genes using small groups of transcription factors (TFs) –7. TheseRecently, measurement of TF–target binding relationships has become much more systematic through the technique of chromatin immunoprecipitation coupled with microarray chips (chIP-chip) –11. By tTherefore, to correctly interpret measurements of TF–target binding, there is a need for computational methods that (1) identify which binding interactions have a regulatory function; (2) provide insight into new TF–target relationships that are likely to be condition-specific; and 3) perform an efficient yet exhaustive identification of TF modules, including quantification of their statistical significance. Existing bioinformatic approaches for assigning TFs to target genes rely on stepwise integration of one or a few lines of evidence, such as combining chIP-chip data  with TF  perform Here, we implement a Bayesian approach that integrates all available types of genome-scale evidence to construct accurate transcriptional regulatory networks. In addition to measurement of direct promoter binding and detection of DNA binding motifs, we find that evidence of gene fusion and shared phylogenetic profiles  is surprisingly informative for predicting true regulatory interactions. High-confidence interactions are used to identify TF modules . Application of this procedure to integrate genome-scale data for yeast reveals a large hierarchical network of regulatory relationships and predicts many new condition-specific transcriptional interactions. We validate several of these interactions through new chIP-chip experiments for Rpn4p and Pdr1p, two transcription factors predicted to bind many new targets in response to chemical stress. Incorporation of these new binding data into modules reveals cross-talk between TFs involved in the response to stress, histone regulation, and regulation of the cell cycle.p-value threshold) and accounts for the relative predictive power among the input data types.To permit the construction of accurate transcriptional networks, we developed an integrative framework to quantify the likelihood of direct regulatory interaction between a TF and each of its possible target genes . The TF–p-values of significance to these overlaps [Given a method for assessing interaction reliability, we also sought to organize high-confidence interactions into TF modules . For this purpose, we applied an algorithm that identified all TF combinations regulating common targets and assigned overlaps . This meoverlaps . Given aB), TF binding motifs in S. cerevisiae (S) and four other sensu stricto species (O), coexpression information (E), physical protein–protein interactions [(P), gene pairs with shared phylogentic profiles [(Y), and pairs of genes that were fused together in other species [G). Initially, we found that only B, S, and O were directly predictive of known transcriptional interactions , genprofiles  (Y), and species  (G). Iniractions A–2C. Howractions  but, as (X → Y) was always measured by TF binding (evidence B), and the second hop (Y ↔ Z) was supported by evidences E, P, Y, or G, giving 2hops BE, BP, BY, and BG, respectively ((BE) or those that encode interacting proteins (BP) tend to be coregulated [(BG) or shared phylogenetic profiles (BY) can indicate coregulation are potentially less intuitive. In this case, phylogenetic information (G and Y) is employed for the identification of functionally related genes in S. cerevisiae [C, see Four types of 2hops were examined, in which the first hop ectively D–2G. Whiegulated ,15,26,27revisiae ,29. SuchB, S, O, C, and 2hops) yielded a total of 7,817 high-confidence interactions with integrated LLS > 5  and one evidence for functional interaction  have an LLS > 0.5 , each of which contained two to 13 distinct TFs. Examples of identified modules are shown in p < 10−4) overlaps between target gene sets and coexpression clusters.Pooling high-confidence TF–target interactions revealed a total of 363 significant TF modules (We next compared the integrative approach (“Bayes”) to two previous methods, one based on a chIP-chip binding measurement alone (“binding only”) ,11, and Saccharomyces Genome Database (SGD) [p-values are functionally more homogeneous and more likely to be coexpressed than modules with higher p-values. Hence, the statistical significance expressed by the module p-value (pmod) can be interpreted as a biological significance.While the receiver operator characteristic (ROC) curves imply better coverage of our approach, we also wanted to assess the quality of these predictions. If several target genes are regulated by the same TF, one might expect these genes to be coexpressed and to have similar cellular functions. This notion provided a means to benchmark the integrative Bayes classifier versus the other methods for TF–target assignment. se (SGD)  (similarse (SGD) ; Figure Beyond assigning confidences to raw interaction measurements, we investigated whether an integrative approach could predict interactions that had not yet been observed experimentally. Overall, our high-confidence set of 5,124 TF–target pairs included 980 interactions that were based on multiple lines of evidence but were not supported by direct chIP binding (LLS for binding < 0.05). We hypothesized that for many of these TF–target pairs, direct binding might indeed occur but in conditions that had not been previously measured. Although the available chIP binding data included profiles for most TFs in nominal conditions (YPD media), few of these factors had been examined in more than one to two other conditions .To test our hypothesis, we applied a cross-validation procedure in which LLS values were recalculated using only chIP data from nominal conditions, and the resulting TF–target pairs with high LLSs were compared with the available binding measurements from other growth conditions. As shown in Encouraged by the above cross-validation results, we sought to experimentally verify several of the interactions predicted to operate under new conditions. Rpn4p and Pdr1p exhibit significant transcriptional reprogramming under oxidative stress . This imp-values of binding under MMS . Accordingly, Of the 104 predicted interactions (LLS > 4) for Rpn4p and Pdr1p that did not have prior chIP-chip binding evidence, 19 had significant Relating the new MMS binding data to TF modules suggested that, although both TFs respond to DNA damage, they regulate distinct sets of genes in a nonredundant manner. First, Pdr1p and Rpn4p were never present in the same module (they had no common targets at LLS > 5). Instead, Pdr1p formed a TF module with Pdr3p , reflectRpn4p and Pdr1p exhibit distinct stress response schemes. While Rpn4p primarily binds under stress conditions but not under nominal conditions, Pdr1p binds a large fraction of its targets under nominal conditions. These binding sites are released by Pdr1p under stress (clusters b-ii and b-iii). A second group of Pdr1p targets comprises genes that are unbound under any of the tested conditions (cluster b-i) or just weakly bound under MMS stress (cluster b-iv). Binding of Pdr1p was not observed for any of its significant (LLS > 5) targets under oxidative stress. The distinct regulatory patterns are at least partially explained by cofactors that act in concert with Rpn4p or Pdr1 in a modular fashion. For instance, clusters b-i and b-iv are regulated by Pdr1 and Pdr3p together, whereas clusters b-ii and b-iii contain no targets of Pdr3p. A consistent pattern emerged indicating that genes regulated by Pdr1p but not by Pdr3p are bound under nominal conditions, whereas those regulated by the Pdr1p/Pdr3p complex are not. In support of previous speculations our findings suggest that dimer composition affects binding site specificity of Pdr1p and Pdr3p .In summary, we have developed an approach for assigning likelihood scores to transcriptional interactions based on integration across eight types of direct and indirect evidence. The integration of different lines of evidence serves two major purposes: first, if binding was already observed in chIP-chip experiments, additional evidence helps reduce the number of false positive predictions by verifying that the interaction between a TF and its target gene is functional. Secondly, if no binding has been observed, other evidences may reduce false negative predictions and suggest that interactions may occur under so-far untested conditions. Based on the latter, we were able to experimentally confirm 19 new transcriptional interactions that are active during damage-related stress. We have also explored how high-confidence TF–target interactions can be used to infer the TF module hierarchy underlying transcriptional gene regulation. In this regard, our analysis of modules involving Pdr1p, Rpn4p, Hir1p, and Hir2p suggested how cells achieve a high degree of specificity by combining generic factors with other more specific factors into complex regulatory units. Although we have focused on yeast, the framework is general and may be especially relevant as large-scale transcriptional mapping projects get under way in humans.http://www.incyte.com), which is based on a curated, literature-derived dataset. In order to obtain negative control data, five sets of random TF–gene associations were generated, where each set contained > 9,000 interactions. Cocitation criteria [p < 0.1), whereas interactions in the negative control set were required not to have a cocitation link.A set of 484 high-confidence TF–target interactions was created as a positive control by extracting regulatory interactions from the Incyte YPD Database (criteria  were appP(L) is the prior probability of observing a true TF–target interaction, P(L̄) is the prior probability of not observing a true TF–target interaction, P(L)/P(L̄) is the prior likelihood ratio for observing a true interaction, and P(L|E)/P(L̄|E) is the posterior likelihood ratio after observing the evidence E. Input evidences were binned ensuring that bins for one evidence type always have the same size and LLSs were calculated for each bin based on the positive and negative training sets. The best regression (maximizing R2) through the resulting LLSs was used for predicting LLSs of unknown TF–gene pairs  [D = 1). Filtering positive from negative interactions was improved by additionally requiring that at least one of the two evidences (B or S) and one of the remaining evidence types have LLS > 0.5. If written as pseudocode, this rule reads: SELECT IF( (B > 0.5 OR S > 0.5) AND (O > 0.5 OR TE > 0.5 OR…OR C > 0.5) ). The rationale for this grouping of evidences is that B and S provide evidence for (possible) upstream binding, but they do not imply a true regulatory interaction. The remaining evidences, on the other hand, functionally link the TF to its target gene. Note that evidence O implies that the binding site is conserved upstream of the respective gene in a significant number of species, which also suggests a functional interaction. Different LLS thresholds were tested in steps of ΔLLS = 0.05 to maximize the MCC. The threshold at 0.5 was found to maximize the MCC. The final predictions were based on the sum of all evidences   to judgevidences A and thevidences . The maxp < 10−4, hypergeometric distribution) between a target gene set and a coexpression cluster was taken as evidence that every gene in this cluster was regulated by the corresponding TF (or TF module). The cluster membership values of significant clusters were used as additional evidence for TF–target interaction and were translated into LLSs accordingly. If a gene was the member of several clusters regulated by the same TF, the maximum membership value was selected for computing the LLS. This new line of evidence, C, was added to the LLSs determined in the first step.The score for TF–target interactions was computed over two steps. In the first step, an initial set of target genes (LLS > 6) and TF modules was identified with high confidence. In the second step, these high-confidence target sets were used to search for additional targets that were coexpressed with the existing ones, in a manner similar to Bar-Joseph et al.  For the −4.TF binding site motifs were defined as position-specific weight matrices (PWMs) . PWMs weS. cerevisiae  and of the promoters of homologous genes in four other sensu stricto species  were obtained from SGD [O (“binding motif in other yeast species”).The genome sequences of promoter regions in from SGD  .m Let F be the total number of all TF–target interactions and if be the number of interactions from im to its target genes in the entire network. We then compute the relative frequency of im as iφ = if/F. A random set of n TFs has n! different permutations and thus, the probability of finding M in a random set of n transcription factors is n! × Πiφ. Note that this implicitly assumes that the probability of drawing a TF i is independent of the other TFs in the module. This assumption may be violated for small numbers of TFs, because the probability of drawing one TF would then depend on the TFs that have already been withdrawn from the “pool.” In our case we have >100 different TFs. We compared the direct estimation of the p-values with random permutations of the TF–target interactions to verify that the pool size does not affect the p-values. We observed no significant deviations between the two schemes (data not shown). Next, it is possible to calculate the probability, M,p of finding M in the set of k ≥ n TFs that regulate a given gene. This Mp can be computed as 1 minus the probability of not finding M in TF modules were determined using our previously described method , yieldinMp are calculated for all set sizes k appearing in the original (observed) data and a weighed sum MP is calculated askN is the number of sets of size k and N is the total number of sets ≥ n . Equation 4 gives the average probability of finding M in one random set. A binomial distribution is assumed for estimating the probability pmod of module occurrence .Note that a TF occurs at most once in every set. The p-value to every TF module based on the number of target genes.Apart from being scalable, our approach has a number of advantages in comparison to existing algorithms for finding TF modules ,18,40–42The importance of some of these aspects has been discussed previously ,42. Exis2-fold change > 0.45) [(k) and the cluster membership of each gene. Within each step, clustering is performed using the fuzzy c-means algorithm [k was set to the largest value allowed for the given dataset (3 times the number of profiles); all other parameters were set to default values. Genes with membership values less than 0.2 were removed from the respective clusters. We define “centrality” as the average membership value of a subset CS of a cluster C normalized by the average of all memberships for cluster C.Microarray mRNA expression data were taken from the literature ,43–45 (2 > 0.45) . Gene cllgorithm , which erpn4 or pdr1 tagged with the cMyc epitope were obtained from the laboratory of Dr. Richard A. Young at the Whitehead Institute for Biomedical Research . Cells were grown to log-phase in YPD media at 30 °C, then treated with 0.03% MMS for 1 h. Protein-DNA binding locations were assayed using a chIP-chip protocol previously described [p-values of TF binding for each promoter region. Unlike for gene expression analysis, in which both increases and decreases in fluorescent intensity are of interest, DNA binding is indicated for increases only, representing increased promoter binding in the IP-enriched versus IP-unenriched sample. We therefore modified the VERA likelihood ratio test to use a one-sided statistic by forcing xμ > yμ in the denominator of Equation 5 of Ideker et al. [p-values from the log-likelihood ratio statistic, we indexed values on the cumulative distribution for a negative control experiment: IP-unenriched versus IP-unenriched over three replicate microarrays.Haploid W303-derived strains harboring either escribed  with corescribed  to generr et al. . To derihttp://www.fli-leibniz.de/tsb/tfb.Supplementary data files can also be downloaded from the accompanying Web site at Figure S1(A) Using selections at specificity = 0.995 . (B) Using selections at specificity = 0.997 . See (571 KB PNG)Click here for additional data file.Figure S2p-values .Sliding window average of LLS is shown. Horizontal lines are average LLSs over all genes. LLSs were determined without MMS binding data, but using all data from Harbison et al. . LLSs ar(218 KB PNG)Click here for additional data file.Figure S3(240 KB PNG)Click here for additional data file.Table S1http://www.fli-leibniz.de/tsb/tfb.First column contains TF name, second column contains target gene, and columns 3 to 11 contain the respective evidences expressed as LLS. The last column contains the sum of the individual LLS. An extended table including TF–target pairs at lower thresholds can be downloaded from (539 KB PDF)Click here for additional data file.Table S2pmod. An extended table including the target genes can be downloaded from http://www.fli-leibniz.de/tsb/tfb.Module names are followed by module size, number of target genes, and (553 KB EPS)Click here for additional data file.Table S3pmod. An extended table including the target genes can be downloaded from http://www.fli-leibniz.de/tsb/tfb.Module names are followed by module size, number of target genes and (272 KB EPS)Click here for additional data file.Table S4Target gene sets are targets of either individual TFs or TF modules. All TF–target interactions have an LLS > 5. Clusters were determined with fuzzy c-means see . Genes w(46 KB PDF)Click here for additional data file.Table S5http://www.fli-leibniz.de/tsb/tfb.The negative control sets can be downloaded from (7 KB PDF)Click here for additional data file.