MicroRNAs are small non-coding RNAs involved in post-transcriptional regulation of gene expression. Due to the poor annotation of primary microRNA (pri-microRNA) transcripts, the precise location of promoter regions driving expression of many microRNA genes is enigmatic. This deficiency hinders our understanding of microRNA-mediated regulatory networks. In this study, we develop a computational approach to identify the promoter region and transcription start site (TSS) of pri-microRNAs actively transcribed using genome-wide RNA Polymerase II (RPol II) binding patterns derived from ChIP-seq data. Based upon the assumption that the distribution of RPol II binding patterns around the TSS of microRNA and protein coding genes are similar, we designed a statistical model to mimic RPol II binding patterns around the TSS of highly expressed, well-annotated promoter regions of protein coding genes. We used this model to systematically scan the regions upstream of all intergenic microRNAs for RPol II binding patterns similar to those of TSS from protein coding genes. We validated our findings by examining the conservation, CpG content, and activating histone marks in the identified promoter regions. We applied our model to assess changes in microRNA transcription in steroid hormone-treated breast cancer cells. The results demonstrate many microRNA genes have lost hormone-dependent regulation in tamoxifen-resistant breast cancer cells. MicroRNA promoter identification based upon RPol II binding patterns provides important temporal and spatial measurements regarding the initiation of transcription, and therefore allows comparison of transcription activities between different conditions, such as normal and disease states. MicroRNAs are small (∼22 nucleotides) non-coding RNAs known to regulate the expression of target genes by promoting mRNA degradation and suppressing translation The regulation of microRNA biogenesis consists of three major steps 1) pri-miRNA transcribed by RNA polymerase II and III; 2) microRNA maturation, including nuclear cleavage of the pri-miRNA to precursor microRNA and nucleocytoplasmic export, and 3) RISC (RNA-induced silencing complex) assembly that converts pre-miRNAs to mature microRNAs In the current study, we designed a computational approach using genome-wide RPol II binding patterns to identify the promoter region and transcription start site of pri-miRNAs that are actively transcribed. Because transcriptional regulation of most intronic microRNAs is controlled by promoter sequences of the corresponding host protein-coding genes The goal of this study was to use ChIP-seq derived RPol II binding data to identify promoter regions of microRNAs actively transcrbied. We develop a computational model to assess changes in microRNA transcription and genome-wide RPol II binding patterns in steroid hormone-treated breast cancer cells. Four biological conditions and two breast cancer cell lines were utilized: vehicle-treated (control) hormone-dependent MCF7, the anti-estrogen resistant MCF7 subline MCF7-T , 25 upstream intervals, and 25 downstream intervals. The Poisson parameter λ for each interval was based on the transcription level of the gene being studied and the location of the interval relative to the transcription start site. As shown in Poisson parameter λ: S – the number of RPol II binding fragments in the central interval (location of the TSS); T – the number of RPol II binding fragments in the steady transcript region; B – the number of RPol II binding fragments in the steady background region and; Kp and Kt – decay factors of the number of RPol II binding fragments in the promoter and transcription regions, respectively. These five factors each follow a Gamma distribution genome wide among all the expressed genes; therefore, we assume that the RPol II binding patterns around the TSS of expressed genes were determined by 10 Gamma parameters Φ  for each gene; and Φ represents the ten parameters describing the distribution of the five hidden variables. The optimal estimations for the 10 parameters in four conditions  are shown in p were larger than the expected transcription decay factor Kt, indicating that RPol II binding quantities reached steady state levels more rapidly in the transcript region (downstream of the TSS) than in the promoter (upstream) region. If a higher-than-background RPol II binding implies additional interaction(s) with other transcription factors, the longer regulatory region upstream of the TSS supports the concept that transcription factors initiate transcription by binding regulatory elements upstream of the TSS; this is due to the nature of ChIP-seq experiment, in which both protein-DNA and protein-protein interactions will be cross linked. We also observed that the expected quantities of RPol II in the transcript region (T) were higher than the intergenic region (B), indicating constant transcriptional activity in the expressed genes.For each of the four biological samples , the 10 parameters were identified by maximizing the posterior probability defined as Φ identified from 1002 genes in the training sets, one score was calculated for each gene by comparing the probability that the RPol II binding pattern around its TSS fits expressed genes rather than genome-wide unexpressed regions  and 2134 unexpressed genes (Absent genes) in MCF7 cells. To identify model parameters, we randomly selected ¼ of the expressed genes, and the remaining ¾ of expressed genes and all the unexpressed genes were used as positive and negative control sets, respectively. Based upon the parameters To assess whether the RPol II sequencing depth is adequate in identifying active promoter regions, we performed saturation analysis by analyzing how the prediction power change when only a subset of sequencing reads are used for prediction. The same 4007 expressed genes and 2134 unexpressed genes in MCF7 cells were used for saturation analysis. The AUC score was calculated by randomly selecting 1 million to 6 million reads . The preThe objective of this study was to identify the TSS and promoter regions of pri-microRNAs by searching for RPol II binding patterns similar to those seen in expressed protein-coding genes in the upstream regions of annotated mature microRNAs (see We focused our study on 419 intergenic microRNAs obtained from miRBase microRNA sequence database (version 11.0). The intronic microRNAs, based upon human RefSeq gene annotation , and active transcription of an additional 19 microRNAs was seen after E2 stimulation . Among tWe further classified all 72 microRNAs into the following six categories:This category contains 47 microRNAs that are constitutively transcribed in both MCF7 and MCF7-T cells .This category contains 4 microRNAs  that are transcribed in control and E2-treated MCF7-T but not expressed MCF7 cells.Two microRNAs, hsa-miR-135b and hsa-miR-365-1, were transcribed in both control and E2-treated MCF7 cells but not in either of the MCF7-T groups. Transcription of these two microRNAs is thus suppressed by the tamoxifen treatment and independent of E2 treatment.Two microRNAs, hsa-miR-193b and hsa-miR-301b, were induced by E2 in MCF7 but not in MCF7-T cells.Ten microRNAs, hsa-let-7a-1, hsa-let-7f-1, hsa-miR-148a, hsa-miR-23a, hsa-miR-24-2, hsa-miR-27a, hsa-miR-550-2, hsa-miR-564, hsa-miR-663, and hsa-miR-923, were induced by E2 treatment of MCF7 cells, but showed a similar upregulated level of expression in both vehicle- and E2-treated MCF7-T, suggesting that the acquisition of tamoxifen resistance is associated with constitutive activation of certain microRNAs.We further examined conservation levels of identified TSS and promoter regions. For each microRNA, the conservation scores (PhastCons scores), were retrieved for five genomic regions : the 200Approximately 70% of human promoters contain CpG islands As an independent biological validation, we conducted ChIP-seq experiments on one histone mark, dimethylation of lysine 4 at histone H3 (H3K4Me2). Genome-wide study suggested that this mark localizes around gene promoter and enhancer regions, and forms a bi-peak shape centered at transcription start site It has been reported that most intronic microRNAs are co-transcribed with their host genes, and therefore share common promoter regions. However, several publications also suggested that some intronic microRNAs could be transcribed independently cis-acting element and trans-acting factors that control the microRNA expression levels.High throughput DNA sequencing is rapidly changing the landscape of genomic research Φ that describe 5 Gamma distributions, from which the 5 parameters S, B, T, pK, and tK of every expressed coding genes are selected. These 5 parameters determine a Poisson parameter (ijλ) associated with the distribution of the number of RPol II binding fragments in bin j of gene i. Rather than being fitted for every expressed gene, these 5 parameters were treated as hidden variables and bounded by five Gamma distributions; this effectively characterized their between gene variations.We hypothesized that RPol II binding distribution around the TSS is similar for microRNAs and protein coding genes. To test this assumption, we designed a statistical model to characterize RPol II binding patterns using the signals associated with highly expressed coding genes. Briefly, the RPol II ChIP-seq data was used to determine 10 parameters To predict the genomic loci of microRNA transcription start sites, we applied the model on the RPol II binding patterns in the upstream region of all annotated microRNAs. We further used this model to investigate the transcription of microRNAs in response to hormone treatment of two breast cancer cell lines, estrogen-dependent breast cancer cells (MCF7) and the anti-estrogen (tamoxifen) resistant subline (MCF7-T). Our model identified TSS for 72 microRNAs in at least one of four conditions (treatment of MCF7 or MCF7-T with either vehicle or 17β-estradiol). Our results suggest that microRNA predisposition can contribute to the development of antiestrogen resistance in hormone-dependent breast cancer cells. It should be noted that while comparing the predictions between two conditions, we did not take the RPol II binding intensity into account; only two states, “active” and “inactive” promoters, were considered. This is to avoid the potential bias caused by the conditional differences between samples, such as sequencing depth, library preparation errors, and so on. It is possible that for certain active promoters, RPol II binding intensity changes but the signal in both conditions are higher comparing to the background (active in both conditions). Our model cannot distinguish such differences. In addition, RPol II enrichment at the promoter region does not guarantee the expression of downstream gene; many mechanisms can contribute to such deviation, such as RPol II stalling, RNA binding protein-induced post-transcriptional regulation, and so on.Promoter regions and TSS of non-coding RNAs have recently been identified using strategies based on three types of information: 1) sequence composition upstream of the microRNA, such as GC content, level of conservation, transcription factor binding sites and expressed sequence tags Despites these advantages, RPol II binding patterns around the TSS can only be used to identify regulatory regions of intergenic microRNAs, which account for approximately half of all microRNAs. Current evidence is lacking as to whether intronic microRNAs use their own TSS and promoter sequences or share the same regulatory components with the host gene. Our results suggest that most of the intronic microRNAs share promoter regions with their host genes, with a few exceptions. Similarly, our TSS search focus on 10kb upstream of microRNA annotation. Recent studies suggest that some microRNA promoters are far away from their mature product on the genome; they will not be predicted by the current strategy. Technically, increasing the searching scope is possible; however, the prediction accuracy will be decreased due to the interference with the RPol II signals of surrounding genes. It should also be noted that the model presented here only focuses on the transcriptional regulation in the microRNA biogenesis process; the microRNA expression can also be affected by other steps, including Drosha-involved nuclear processing As shown in Eq. 2, the current model did not incorporate the potential correlation among 5 parameters that characterize genome-wide RPol II binding patterns around active promoters. Neglecting such correlations will potentially affect the likelihood estimation, and therefore result in less than optimal promoter prediction. However, ROC curve on our current model suggested that the AUC has reached ∼0.9 in predicting promoter regions of highly expressed genes . Hence, cis-acting DNA elements sharing certain sequence features. While such algorithms can identify DNA binding sites for highly specific transcription factors, they are not appropriate for identifying binding sites for the general transcriptional machinery, such as RPol II, which typically does not display high sequence specificity. In addition, as RPol II activity likely extends beyond the promoter/transcription start site of active genes, algorithms for assessing long-range RPol II binding are needed. Our data demonstrated that RPol II binding pattern around the gene transcription start site follows distinct patterns  for PoI II  and H3K4me2  was performed as previously described ijX denotes the number of detected fragments in the j-th bin of the i-th gene, and ijλ is the expected RPol II quantity for the same bin. We assumed that the expected RPol II quantity ijλ is determined by the expression levels of the i-th gene, and the relative location of the j-th bin from the transcription start site.iS stands for the expected number of RPol II fragments in the bin that contains the TSS of the i-th gene; while iB and iT denote the expected RPol II fragments in the bins that locate distantly upstream or downstream of the TSS, which represents the expected signals for the intergenic background and stead transcript regions, respectively ; the background model only incorporates hidden value (B) since the gene is assumed not to be expressedΦ is the estimated parameter vector identified from the RPol II binding data for the coding genes; iX represents number of RPol II binding fragments in the 50 upstream and downstream bins that surrounding the j-th bin . αB and βB represents the two parameters that describing the Gamma distribution of genome-wide background signals. See We identified TSS of pri-miRNAs and its regulatory region using the ten parameters http://compbio.iupui.edu/liu/miRpromoter.All the data are made available in the NCBI Gene Expression Omnibus (GEO) database with accession number GSE21068 for the ChIP-seq data for RPol II and H3K4me2, and GSE5840 for the microarray data for MCF7 and MCF7-T with and without E2 treatment. In addition, both the R-code for the promoter identification and ChIP-seq data are available in the project website: Appendix S1Supplementary methods and results.(0.14 MB DOC)Click here for additional data file.Figure S1+ T-cells. Because the gene expression measurements were achieved using different microarray platforms, the expression level for MCF7 and T-cell were sub-classified on different scales.The saturation analysis on (A) E2-treated MCF7cells, (B) vehicle MCF7-T cells, (C) E2-treated MCF7-T cells, and (D) CD4(0.20 MB TIF)Click here for additional data file.Figure S2Congruity between promoter predictions based upon RNA polymerase II and H3K4Me3.(0.13 MB TIF)Click here for additional data file.Table S1The optimal estimations for the 10 parameters in four conditions.(0.02 MB XLS)Click here for additional data file.Table S2The predicted transcription start sites and promoter regions of 72 microRNAs, and their association with CpG islands.(0.04 MB XLS)Click here for additional data file.Table S3Annotations of predicted promoters of intronic microRNAs.(0.05 MB XLS)Click here for additional data file.


Microarray measurements of mRNA abundances is a standard tool for evaluation of transcriptional activity in functional genomics. The methodology underlying these measurements assumes existence of a direct link between transcription levels, that is, gene-specific mRNA copy numbers present in the cell, and transcription rates, that is, the numbers of gene-specific mRNA molecules synthesized per unit of time. In this paper, the question of whether or not such a tight interdependence may exist is examined in the context of nonlinear dynamics of genetic regulatory networks. Using the equations of chemical kinetics, a model has been constructed that is capable of explicitly taking into consideration nonlinear interactions between the genes through the teamwork of transcription factors. Jacobian analysis of stability has shown that steady state equilibrium is impossible in such systems. However, phase space compressibility is found to be negative, thus suggesting that asymptotic stability may exist and assume either the form of limit cycle or of a chaotic attractor. It is argued that in rapidly fluctuating or chaotic systems, direct evaluation of transcription rates through transcription levels is highly problematic. It is also noted that even if a hypothetical steady state did exist, the knowledge of transcription levels alone would not be sufficient for the evaluation of transcription rates; an additional set of parameters, namely the mRNA decay rates, would be required. An overall conclusion of the work is that the measurements of mRNA abundances are not truly representative of the functionality of genes and structural fidelity of the genetic codes. After 15 years of research and development, broad consensus is still lacking concerning best practice not only for experimental design and sample preparation, but also for data acquisition, statistical analysis and interpretation. Though problematic for bench research, lack of resolution of these issues continues to even more seriously hamper translation of microarray technology into the regulatory and clinical settings ... Clearly, microarrays have a long way to go before they can be used to support regulatory decision-making or accurate and consistent prediction of patient outcomes in the clinic.”DNA microarray measurements of mRNA abundances are widely recognized as a powerful tool for obtaining the whole-genome information regarding transcriptional activity. After the introduction of DNA microarraysDespite all these difficulties, there is a reasonable hope that relentless efforts towards improvement of technology, standardization of protocols and increasing cross-platform compatibility sooner or later will produce a desirable result, and the measurements will arrive at the status of satisfactory accuracy and self-consistency. However, it should be noted that many questions of interpretation of microarray measurements are still wide open and require much more analytical work. Among these questions is one of utmost importance: whether the measurements of mRNA abundances may serve at all as a basis for conclusions regarding transcriptional activity of corresponding genes.gene expression profiling, and this term has been used interchangeably with transcription profiling. Such a terminology assumes that through the measurements of intracellular mRNA abundances (i.e. mRNA copy numbers) one can come to some conclusions regarding the status and/or activity of the corresponding genes. However, as is well known, each transcription event is supported by a team of transcription factors (TF), the proteins translated from a whole host of other mRNAs  but in the treatment group it is active? A reasonable answer would be maybe. This is because along with a straightforward yes there is also a possibility that the genes in CG and TG are equally active, but due to some epigenetic reasons the mRNA-X in CG is chemically unstable and gets quickly dissolved in the cytoplasm leaving no traces of transcriptional activity. This example shows that mRNA abundance, apart from activity of the corresponding gene, depends on many other factors that may or may not be gene-specific. There is a constant influx of a large assortment of proteins from the cytoplasm to the nucleus serving as transcription factors (TF). Therefore, abilities of the individual genes to transcribe are also dependent on the availability of those factors, and fidelity of the gene’s genetic code may or may not play a role in maintaining a normal transcription rate. Even under the simplest assumption that ribosomes synthesize only one polypeptide chain from one mRNA molecule, a (large) number of other mRNAs are indirectly involved in the activity of each individual gene.Is it really true that such a direct link between the mRNA concentrations within the cell and the transcription rates of corresponding genes does exist? In order to clarify this question, let us consider the following example. Suppose that a microarray experiment shows that a certain mRNA  is under-expressed  in the control group (CG) as compared to that in the treatment group (TG). Would it necessarily mean that the Gene-X in the control group is rX is the concentration of mRNA-X molecules present in the cell, and drX/dt = [drX/dt]+ – [drX/dr]−, where + and – signs distinguish transcription and degradation rates, respectively. The question we are trying to resolve is whether or not rX and [drX/dt]+ are some-how linked to each other? A simple analogy may help to clarify the difference between rX and [drX/dt]+. It is similar to that between someone’s income and his/her account balance. A frugal spender may have big savings  despite a modest income . An indiscriminate spender can squander his/her multimillion dollar income  and drive the estate to bankruptcy, that is, to a zero or a negative balance . Therefore, even complete knowledge of someone’s account balance generates no information regarding his/her income. In other words, there is no direct relation between rX and [drX/dt]+. Such a relation may become known only after solving the dynamical equation for rX, that is drX/dt = FX . However, due to numerous interactions in the system, the solution would depend on the states of all the interacting variables, therefore [drX/dt]+ cannot depend on the rX only.In more formal mathematical language, the question may be reformulated as follows. Suppose that Escherichia coli found no correlation between the transcript abundance and the transcript stability .30Attempts to formulate complex intracellular behavior using the concepts of nonlinear dynamics have been undertaken in many works.The goal of this paper is to discuss the prerequisites for the very possibility that certain TL-TR relationships could exist. An overall message we intend to convey is that DNA microarray data cannot serve as an unambiguous basis for making judgments regarding the ability of genes to generate transcripts.2.Law of Mass ActionN is the number of protein types present in the system which is considered to be identical with the number of mRNA types; ϕi, ψi are the 2N-vectors of kinetic rates, and Pim, Qim are the 2N × 2N matrices of stoichiometric coefficients in the direct and inverse reactions, respectively.xi} may represent concentrations of various biochemical constituents participating in the process, including individual molecules or their aggregates. The system (1.1) is also known as S-System. Applicability of S-Systems to description of biochemical processes and to fitting microarray data have been extensively discussed in the literature.36Mathematical description of biochemical reactions is deeply rooted in statistical thermodynamics, and its special branch called chemical kinetics. At its very core, a system of chemical reactions may be written in the form of the p, and of the mRNA copy numbers, r (per cell). In order to adapt a general system (1.1) to the specific goals of describing the biochemistry of gene expression, we split it into two parts. We introduce the N-vectors of the protein copy numbers, Ai here is the rate of translation, i.e. the number of proteins of i-th type synthesized by ribosome per unit of time. According to the Central Dogma in molecular biology, only one mRNA type participates in the translation of the i-th protein, and this is the i-th mRNA. Also, with a good approximation, one may surmise that other proteins are not involved in the translation of the i-th protein. This means that Ai = αiri.The first term in the first i-th protein. In our idealized system, such a disappearance occurs only when the protein reaches the designated regulatory site, associates with DNA and becomes a TF. According to the Central Dogma, mRNAs do not participate in this process. We also take into consideration that there are no chemical reactions between the proteins within the regulatory site which could produce new proteins. All this means that Bi = βipi.The second term in the first transcription, that is the synthesis of the mRNA of i-th type. Other mRNAs do not participate in this process. As is well known, transcription requires participation of a large number of proteins, typically from 30 to 100, serving as TFs and as building blocks for the RNA Polymerase, the centerpiece of decoding machinery.Pik, are sparse with the majority of elements being zero and the rest of them being small integers. The number of transcription factors, index of complexity. In general network theory, the parameter, ζ = λN, is also called link density.The first term in the second Di = δiri.The second term in the second Summarizing all these considerations (and slightly simplifying indexing), we rewrite (1.2) asSimilar equations  for describing gene expression kinetics has been proposed in.In the considerations leading to (1.4), the author does not claim any novelty; they are provided solely for keeping the paper self-contained and for highlighting the biological origins of assumptions. 38Ci, which is a polynomial function of very high order in the protein concentrations. The way this problem was dealt with inDespite a fairly innocuous look, ri, may be used for inferring information regarding the transcription rates Ci. For the reasons to be explained later in this paper, a specific form of dependence of transcription rates on protein concentrations, that is iC = iC (p) is not of crucial importance in this consideration; the only prerequisite is just recognition of an evident fact that there are many TFs facilitating each transcription, and that these TFs are predominantly the protein molecules. Four questions should be considered in this context. First, if an equilibrium state in the system described by (1.4) did exist, then would it be possible to express the transcription rates through the transcription levels? Second, does an equilibrium state indeed exist in the system described by (1.4)? Third, if an equilibrium state does exist then can it be stable? Fourth, if an equilibrium state does exist but it is not stable then what kind of dynamical behavior could be reasonably expected of such a system?As stated above, the goal of this paper is to explore whether or not measurement of the transcription levels, Prior to exploring these issues, some conventions regarding vocabulary are introduced.3.Since in different contexts the words stability, equilibrium, steady state and such are often used quite differently, there is a need to introduce the meanings of these words as they are used in this paper.Fixed point is a point in the space of all the system’s variables, i.e. protein and mRNA concentrations (a.k.a. phase space) in which the time derivatives are zero, i.e. ṗ(t) = {dpi/dt} ≡ 0 and ṙ(t) ≡{dri/dt}≡ 0. By this definition, the fixed point does not move in phase space with time. The fixed point is also called the equilibrium point.Stable equilibrium is a state for which small deviations from the fixed point generate internal forces that push the system back towards equilibrium. These forces may occur due to the existence of negative feedback loops. Unstable equilibrium is a state for which small deviations from the fixed point generate internal forces that push the system even further from equilibrium, often causing the system’s collapse. These forces may indicate existence of the positive feedback loops.Equilibrium at a fixed point may be stable or unstable. steady state may refer to both motionless position of the system at the stable fixed point and stationary oscillations in the vicinity of this position. In the latter case, the term steady state may be used interchangeably with the term limit cycle. Experimentally, such a motion is often interpreted as a stationary stochastic process, that is, such a process in which statistical characteristics do not change with time.The term Asymptotic dynamic stability is the property of a dynamical system to approach, with time, a certain domain in phase space and stay within this domain forever. This domain is often called the attractor. Motions within this domain may include stable oscillations or chaotic orbits.The concept of dynamical stability is fundamentally different from the concept of biological robustness. An in-depth discussion of this difference has been given by this author in a number of previous works.4.The fixed point, δi}, are known. This fact has recently transpired in a number of publications } cannot be evaluated through the even in the state of equilibrium.According to a number of sources,ions e.g.,46. As o5.δi}, somehow became available. Would it be sufficient for estimating the transcription levels from transcription rates? To this end, the question of stability at equilibrium comes into focus. This is because it is utterly unlikely that if the equilibrium is unstable then the system would reside in this state for a long time. It would be similar to a pyramid being carefully balanced on its tip and stably residing in this position for a long time despite all kinds of disturbances around. It is illustrative that for the equilibrium of a pyramid the forces should be balanced in two dimensions (two angles of rotation), whereas in a genetic regulatory system the balancing should be applied to the dimensions in hundreds and thousands. If a system departs from the fixed point, then the only link between transcription level and transcription rate expressed by the second equation in Let us suppose now, quite hypothetically, that the mRNA decay rates, {Jim, for the systems described by (1.1) is given in RefA standard way of analyzing stability consists in linearization of dynamics in the vicinity of the fixed point, obtaining the Jacobian matrix and computing the spectrum of its eigenvalues. Presence in the spectrum of eigenvalues with positive real parts is an indicator of instability.Symbolically, the specifications of stoichiometric matrices for gene expression kinetics are as followsɛij, indicate how many proteins of type j serve as a TF for the gene i. Since only a comparatively small fraction of all the protein species comprising the proteome participate in each transcription, the majority of all ɛij are zeros, the majority of non-zeros are ones, and sometimes ɛij may be equal to two. Should more specific data become available, these assumptions may be easily modified. A convenient way to express this property mathematically is to assume that ɛij are randomly drawn from the Poisson or binomial distribution. This is not supposed to mean that the stoichiometric coefficients are indeed random; it is just a technical way of saying that the parameterizations of stoichiometry and kinetics are entirely independent.The only matrix requiring some explanation is We introduce two auxiliary matricesA = −U, D = −V, B = U, C = Vɛ. We need to find the solutions of the characteristic equationAccording to J (λ) toApplying Shur’s formula for the determinants of block matrices,diag (U) and diag (V) by their mean values, which, according to In order to envision a general structure of the spectrum (1.13), we first make some inessential simplification. After that, we demonstrate numerically that this simplification is quite satisfactory and does not distort the results. To this end, we replace 2, we arrive to the characteristic equationDenoting Λ = (1 + λ)1:N are the eigenvalues of ||ɛ||, then the roots of J(λ) = 0 are ɛ||. Since ||ɛ|| is non-negative, then, according to the fundamental Perron-Frobenius theorem,max, which is greater than the moduli of the rest of the eigenvalues. In order to demonstrate instability, it is sufficient to show that Λmax >1. In this case, the λ—spectrum contains at least one eigenvalue with a positive real part, λmax > 0.If Λmax > 0, but also there exists a large set of other eigenvalues with positive real parts. We will refer to this property as massive instability. Second, the eigenvalue spectra are fairly robust with respect to the replacement of actual diag (U) and diag (V) by their means. Therefore, the simplification which led to (1.14)  and diag (V) by their means) is justified. Third, such massive instability takes place under widely varying assumptions regarding distributions of kinetic rates and stoichiometric coefficients. To the best of the author’s knowledge, these results are not yet known and may represent some interest on their own, beyond the context of this paper.We have performed an extensive series of numeric computations of Jacobian spectra under wide ranging assumptions regarding kinetic rates and stoichiometric matrices. Several important findings have resulted from these computations. First, it turns out that not only λinstability index) are identical. This is an illustration of the fact that replacement of the kinetic rates by their averages has little or no impact on the spectrum; this means that the assumption underlying the derivation of (1.14) is well justified.  is about 10. ɛ|| can be rescaled to the form ||ɛ|| = ζ ||Ξ|| where ζ is the link density (introduced in the Section 2) and ||Ξ|| is a stochastic matrix, that is, the one in which all the within-row sums are equal to 1. As known from the matrix theory (e.g.J (0), is simplyThe general structure of the spectra in eory e.g. XIII.6, ½ 1 − 0.41, and the spectrum manifests oscillatory motions with a large assortment of frequencies, attenuations and excitations   molecules may not be quite adequate. To address this concern, simulations have been performed in which stoichiometric matrices were replaced by more general forms rather than simply containing only small positive integers. All the results regarding instability remain generally valid in these cases too. Thus, competitive: dx/dt = F(Px) – G(Qx). In the vicinity of the fixed point this system is representable asMoving to the next level of abstraction, one should recall the result by Tournierkind of chemistry per appropriate definitions of chemical constituents and kinetic rates. This also means that the above results regarding stability may make some sense in a much wider class of nonlinear dynamical systems. In particular, the Jacobian matrix of (1.17) is generally neither symmetric nor anti-symmetric; hence, its eigenvalues are complex numbers with both negative and positive real parts, what would indicate dynamic instability.In a sense, this means that in a competitive system, a large-scale dynamics may be considered, at least locally, as a well stirred systems. In such systems, any chemical constituent produced anywhere in the system is assumed to become immediately available for all the chemical processes throughout the system. To some extent, this assumption is acceptable in prokaryotic cells where the tightly coiled DNA molecule is surrounded by ribosomes in a somewhat random manner and located in close proximity to each other. In eukaryotic cells, things are entirely different. Prior to becoming TFs, the protein molecules have to travel from cytoplasm to nucleus by penetrating the nuclear membrane and then finding the sequence-specific regulatory site. The mechanism of how exactly this happens is a major and largely unresolved mystery in biophysics. In a large body of the literature on the topic, three sourcesdelay intervals for the protein variables. A straightforward way of doing this is replacement of the mRNA production terms ζk} is a set of protein-specific delays. Such a measure would move the problem from the realm of ordinary differential equations to the realm of mathematically much more complex delay equations. It is far beyond the scope of this paper to delve deeper into this problem. However, it is possible to envision some general tendencies associated with the introduction of delays. Assuming that the delay times are small as compared to the characteristic times of chemical reactions, we obtainA major limitation of ordinary differential equations, such as (1.1), in application to intracellular biochemistry is that they are valid only for the As seen from (1.18), the problem is now transformed into a more complex one, but generally remains in the same class of nonlinear problems as in the well stirred systems. A new feature is that the mRNA production term has become dependent on the presence of other mRNAs. Obviously, such a complication is not conducive to more stability because it introduces additional feedbacks into the process of gene expression and additional uncertainty associated with random kinetic rates.7.pawol, failed to report to the designated regulatory site. Obviously, transcription activity of the corresponding gene  will be temporarily halted, and the mRNA-X molecule will not be synthesized. This also means that the protein translated from the mRNA-X, let’s call it protein-Y, a much needed TF for regulation of the gene-Y, will also fail to appear at the scene. These failures will propagate further to other genes. Quite accidentally, it may also happen that on a parallel sequence of events, an mRNA-Z is destroyed prior to reaching a ribosome, thus creating a deficit of the protein-Z followed by its own domino effect of the regulatory failures. It is easy to envision that, since there are 25,000 genes in the genome and each requires from 30 to 100 TFs, such unfortunate coincidences should be quite frequent. Due to massive high order interactions, perturbations that appeared somewhere in the system easily penetrate to other domains of regulation creating unidirectional progression to deregulation. It is obvious that in such an environment, nothing like an orderly protein assembly line may exist. Spontaneous failures like traffic jams, bottlenecks, backlogs, delays, loss of synchronization, etc., are unavoidable circumstances surrounding their functioning. Each successful transcription in a dynamically unstable system may be only thought of as a comparatively rare and sporadic event. Instochastic cooperativity.The mathematical description of instability offered above is a formal representation of a wide class of phenomena frequently observed in networks. Among them are traffic jams, stampedes, instabilities of combat operations, fluctuations in predatorprey populations, blackouts of power grids, stock market panics, and others.bursting with random distribution of intensities and durations. Similarly, it was found in67A number of observations support the view of transcription as a sequence of sporadic events  However, it should be taken into consideration that the vast majority of TFs in a cell are produced within the same cell, and the gene expression machinery is their only source. Therefore, it is a more or less closed supply-demand production cycle, without many leftovers or excesses. The hypothesis of copy number redundancy would require availability of many more proteins than the gene expression network is capable of synthesizing. In a sense, it may be said that there is always a shortage of regulatory proteins, and their availability depends on the efficiency of the gene expression production line itself.Is there any alternative to sporadicity of genetic regulation? A common counterargument is that functional redundancy, may exist. In the context of gene expression, functional redundancy would mean that there are many ways to synthesize the same protein thus providing alternative modes of functioning in spite of sporadicity and instabilities. It should be noted, however, that functional redundancy, if it exists, would not be a way of eliminating or suppressing instabilities; rather, it would be the way of resilience in the face of and coexistence with instabilities.It is also conceivable that yet another type of redundancy, a 8.Z is the similarity matrix  All the eigenvalues may be ordered as followsGiven some initial conditions within a small ɛ-vicinity of the fixed point, min, describes rapid attenuation of the perturbation, x0. The second group, {λmin < Re λ < 0}, describes oscillatory decay of the perturbations with a variety of attenuation times and periods. All the eigenvalues in this group comprise the stable manifold. The third group, Re λ = 0, called the center manifold, contains only stationary periodic oscillations (unless Im λ = 0.) The group {0 < Re λ < λmax}, the unstable manifold, describes oscillatory growth of disturbances and moving the system further from the equilibrium. Finally, the last member in (1.20), λmax, describes rapid, purely exponential growth of the disturbances. As seen from examples max, is substantially greater than the moduli of the rest of the eigenvalues. This means that all the numerous transitory details of behavior manifested in the spectra are comparatively unimportant as they become quickly overridden by the rapid exponential growth of just one term, exp(λmax t). It also means that the initial state, x0, which is generally representable as a linear combination of all eigenvectors, x0 = c1 e1λ +c2 e2λ +... +cNemaxλ, without loss of generality, may be reduced to the last term, Ncemaxλ. As shown above, the largest eigenvalue is dependent only on the complexity, λmax = ζ½ – 1. We come, therefore, to an important conclusion that the characteristic time of existence of the equilibrium state rapidly decreases with increasing complexity. It is also worth mentioning that kinetic rates have a negligible impact on this conclusion; the time of decay is overwhelmingly dependent on the system’s topology, that is, on its link density.The first group containing just one real negative eigenvalue, λ9.linear stability) provides little guidance regarding the patterns of long term behavior of the system. Lessons learned from the studies of low dimensional nonlinear systems show that their behavior may be extremely complex, often manifesting the patterns of deterministic chaos, as vividly demonstrated by classical examples of Lorenz attractor and Lotka-Volterra population dynamics.dense in the set of dynamical systems, the authors conjecture that most large dynamical systems ought to be chaotic. Such a conclusion is in agreement with an earlier argument by Brock that “the larger the dimension of a nonlinear dynamical system, the larger the probability that the system dynamics have a positive Lyapunov exponent.”70Jacobian analysis of stability in the vicinity of the fixed point , but it also may become chaotic with exponentially fast increasing of distances between initially close trajectories. Two questions are of key importance for understanding the long-term behavior. First, one needs to establish the sign of the phase space compressibility. Negative compressibility means that the volume occupied by a bunch of trajectories decreases with time; that would generally indicate asymptotic stability.As mentioned in Section 3, the scenario of asymptotic stability assumes that, starting from some arbitrary initial conditions within the basin of attraction, all the trajectories of a dynamical system converge to a certain domain of the phase space and stay within it forever. However, the behavior of trajectories within that domain may be quite different. It may assume the form of orderly quasi-periodic motion  is easy to calculate. For this purpose we use the following form of the equations of chemical kineticsxi, is not autocatalytic. In the context of gene expression, this means that among the TFs for the gene “i”, there are no proteins translated from the same gene. In this case, phase space compressibility is the divergence of the vector field, f. Thus,Suppose that a constituent, In this scenario, the phase space volume is contracting; this is an indication of asymptotic stability.self-destruction (i.e. the degradations supported by the same constituents as those being degraded) cause It is of interest to examine a more complex case when at least some gene expressions are autocatalytic but require participation of only one protein expressed by the same gene. It is easy to see from general expressions (1.23) and (1.24) that this assumption does not change the sign of phase space compressibility since the corresponding terms simply vanish. In an even more complex, but less likely, scenario when the autocatalytic self-reproduction requires participation of more than one protein expressed from the same gene, the sign of phase space compressibility may vary and becomes dependent on many circumstances. It is quite possible that in some of these cases, the system will lose the property of asymptotic stability because high-order self-reproduction is equivalent to the existence of a strong positive feedback loop. It is also worth mentioning that all the scenarios of Computation of Lyapunov exponents is a daunting task even for comparatively simple systems,7410.When theorists and computational scientists try to capture the transcription-translation mechanism in the language of differential equations, importance of the mRNA decay rates immediately jumps into focus. It is not the goal of this paper to claim that this importance is poorly understood. The point is that in the majority of existing routine microarray protocols, the mRNA decay rates are not measured, remain unknown and are not taken into consideration when making the inferences regarding the states of corresponding genes. There is nobody to blame here. Measurements of the decay rates require special, and in fact very complex, laboratory settings; at this time they are mostly of experimental sort rather than a routine, simple and commonly available component of expression profiling.poor reliability of microarray measurements. The question is deeper. We are making the point that measurements of mRNA abundances are not fully representative of gene activity. It often happens in many areas of science that direct measurements of the quantities of interest are not possible, but instead some proxies or surrogate variables can be observed. A textbook example is that we cannot directly measure the temperature of stars. What can be observed and measured are only the spectra of their electromagnetic radiation. But prior to inferring the temperature of a star from the analysis of its radiation, a theory should exist that relates the temperature to the radiation spectrum. Inferring the quantity of interest (temperature) from the quantities observed (spectrum) constitutes the solution of an inverse problem. It is quite a common situation that inverse problems are either ill-conditioned or not solvable at all. This is because solution of the inverse problem would require a number of additional quantities, which in turn require their own independent measurements and even more comprehensive theories. The situation with the relations between transcription levels and transcription rates is quite similar: transcription rates are the unobservable quantities of interest, whereas transcription levels are the quantities being actually observed. It is true that transcription rates directly impact the transcription levels. But it is also true that many other processes may impact transcription levels. This is why the inverse problem of derivation of transcription rates from the transcription levels is ill-conditioned and may be not solvable.The question raised in this paper is not simply a question of The consequences of simplified thinking and lack of logically self-consistent view on the nature of transcription profiling may have detrimental effect, especially in clinical settings. Relative simplicity of transcription profiling with microarrays makes them an attractive tool for clinical diagnostics. A medical practitioner would not hesitate to jump directly from the observed abnormal mRNA abundances to conclusion regarding abnormality of corresponding genes. The author is trying to convey the idea that there is no such direct link and cannot be due to fundamental reasons lying in the very basis of intracellular biochemistry.Robustness differs from stability in that it deals with maintaining the system’s functions as opposed to the system’s states.dual causality formulated by Palsson.dual causality, inherent dynamical instability represents the “natural laws” and “physicochemical principles” whereas biological robustness is a result of evolutionary history in which this dynamical instability is effectively used for gaining evolutionary advantages and survival.Observed biological robustness of living organisms is not a counterargument to dynamical biochemical instability. 11.A nonlinear dynamical model for the description of gene expression has been introduced. The model is based on the equations of chemical kinetics and explicitly takes into consideration nonlinear gene-to-gene interaction through the teamwork of proteins serving as transcription factors.It has been shown that there is a unique fixed point in this model; Jacobian analysis of stability has been performed for this fixed point. Numeric computation of the eigenvalue spectra revealed a high degree of dynamical instability inherent in such systems. The core element responsible for the emergence of instability is nonlinearity of the equations of chemical kinetics associated with team-work of proteins associated with transcription. Instability occurs every time when the average number of proteins (per gene) serving as transcription factors becomes greater than one. With the typical number of transcription factors from 30 to 100 per gene, genetic regulatory systems lie deeply within the domain of dynamical instability.max, the key quantity characterizing instability, is directly related to the complexity of the system measured by the network’s link density, ζ: λmax = ζ½ – 1. It has been shown that this relation is highly robust and holds under widely varying assumptions regarding probabilistic distributions of kinetic rates and stoichiometric coefficients. It may be claimed with a high degree of certainty that the network’s instability is mainly determined by its link density and largely independent of its kinetics.It has been demonstrated analytically, and confirmed numerically, that a simple rule exists connecting the network’s link density with the degree of its instability. Namely, the largest eigenvalue of the Jacobian spectrum, λIt has been shown by direct analytical calculation that long-term behavior of the regulatory system, under some natural assumption of absence of self-reproducibility, represents a dissipative flow with negative phase space compressibility. This guarantees asymptotic stability of the system and existence of some sort of attractor. The question whether or not this attractor is chaotic or, alternatively, represents a multispectral quasi-periodic limit cycle remains open at this time. This question, however, is of secondary importance in systems with the dimensions in the thousands.Direct relations between transcription levels and transcription rates are only possible in the state of lasting steady state equilibrium. But even in such a hypothetical state, inference of transcription rates from transcription levels is not possible unless an additional set of quantities is measured in the same experimental settings; these quantities are the rates of mRNA degradation.max, bears a simple relationship to the network link density, ζ, that is, λmax = ζ½ – 1, may be of interest beyond the scope of this paper and useful in a wider context of network dynamics.The aforementioned result that the largest Jacobian eigenvalue, λ12.A key assumption underlying microarray measurements is that measuring mRNA abundances provides the basis for definitive statements regarding the functionality and integrity of the corresponding genes. In particular, excess or deficiency in mRNA copy numbers is considered to be an indicator of possible damage to their genetic codes. In formal terms, such reasoning assumes the existence of a direct link between transcription levels and transcription rates. As shown above, such a direct link is only possible in the state of stable equilibrium. Inherent dynamical instability and sporadicity of genetic regulation elucidated in this paper make such a direct link highly questionable. There may be many reasons other then genetic mutation for the mRNA abundances being up-or down-regulated.There is no doubt that the analysis of the transcriptome, along with the proteome and the metabolome, may serve as a valuable diagnostic tool. However, the logical leap from the state of transcriptome to the state of genome is not self-evident and requires much more careful substantiation than is currently accepted in experimental biology. Better understanding of the essence of microarray measurement may help in developing more efficient protocols for use in clinical practice. Alternatively, it is conceivable that such an in-depth analysis may lead to realization that a tool that is inherently unstable is not expected to be a reliable instrument for patient-related decision making.


Gene promoters can be in various epigenetic states and undergo interactions with many molecules in a highly transient, probabilistic and combinatorial way, resulting in a complex global dynamics as observed experimentally. However, models of stochastic gene expression commonly consider promoter activity as a two-state on/off system. We consider here a model of single-gene stochastic expression that can represent arbitrary prokaryotic or eukaryotic promoters, based on the combinatorial interplay between molecules and epigenetic factors, including energy-dependent remodeling and enzymatic activities.We show that, considering the mere molecular interplay at the promoter, a single-gene can demonstrate an elaborate spontaneous stochastic activity (eg. multi-periodic multi-relaxation dynamics), similar to what is known to occur at the gene-network level. Characterizing this generic model with indicators of dynamic and steady-state properties (including power spectra and distributions), we reveal the potential activity of any promoter and its influence on gene expression. In particular, we can reproduce, based on biologically relevant mechanisms, the strongly periodic patterns of promoter occupancy by transcription factors (TF) and chromatin remodeling as observed experimentally on eukaryotic promoters. Moreover, we link several of its characteristics to properties of the underlying biochemical system. The model can also be used to identify behaviors of interest (eg. stochasticity induced by high TF concentration) on minimal systems and to test their relevance in larger and more realistic systems. We finally show that TF concentrations can regulate many aspects of the stochastic activity with a considerable flexibility and complexity.This tight promoter-mediated control of stochasticity may constitute a powerful asset for the cell. Remarkably, a strongly periodic activity that demonstrates a complex TF concentration-dependent control is obtained when molecular interactions have typical characteristics observed on eukaryotic promoters . We also show that this regime results in a direct and indirect energetic cost. Finally, this model can constitute a framework for unifying various experimental approaches. Collectively, our results show that a gene - the basic building block of complex regulatory networks - can itself demonstrate a significantly complex behavior. This provides a new vision of regulatory complexes as highly dynamic structures in constant assembly and disassembly, with alternative functionally redundant pathways of formation and with phenomena occurring concomitantly at different timescales  binds and unbinds on/from its target site with rates [A] kon and koff respectively. However, because of cooperation and competition [kon and koff of any TF actually depend on the combination of all the other TFs present on the promoter. We define the N × 2N matrix k0 summarizing the association and dissociation constants of each of the N TFs and for each of the 2N states. s to state s ⊖ f . Multiplying each association rate by the concentration [f] of the TF that binds, we obtain the N × 2N matrix k describing all the transition rates of the weighted directed graph of promoter states , the model can actually account for these transitions .Although for simplicity this short description of the model as well as the examples in this paper do not consider TFs associating and dissociating simultaneously as a complex  and P(t) respectively) follow classical stochastic birth-and-death processes with instantaneous and first order reactions (cf figure X(t) determines the synthesis rate of RNA molecules which, in turn, degrades with rate γ. Protein level P(t) is driven by the translation of RNA molecules at rate Discussion).As in most models of stochastic gene expression, RNA and protein levels . This representation allows one to predict the equilibrium steady-states (by applying a Boltzmann factor) and has been widely used to investigate the mean aspects of prokaryotic regulation  ∈  nM and [B] = 5 nM), for bimolecular TF-DNA residence times . RNA and protein life-times are 5 and 20 min  ∈ 0-2; 103 A that events influencing transcription (ie. B associations and dissociations) become rare and, as it has been known for a long time [A] increases , butong time ,34-37,40s figure . Thus, tB], an influence of B on A, a dependency of the transcription rate on A ..., figure S1 in Additional file Moreover, this property appears to be quite robust when exploring all sorts of deviations from this ideal case (eg. considering a different concentration for [A) can be so that, when bound, it prevents other TFs (type B) from association/dissociating from the promoter .Figure gulation -91,93,94eriments    on a two-state on/off promoter  and on RNA and protein lifetimes. Our work can be viewed as a deep focus on the causes and consequences of the molecular memory that takes place at the promoter. But the fact that indeed RNA and proteins degradations are not first order , but also that synthesis are not instantaneous  influences the shape of filters in Eq 6 and hence the normalized variance. However, these phenomena only constitute deviations  from the global tendency that our simple description of these steps can capture.Pedraza et al.  describeAB) as well as TFs unbinding as complexes (eg. AB → ø). This is taken into account in the general version of the model  determine the concentration of free TFs  and of complexes  so that varying the amount of one TF potentially results in variation of concentrations of all TFs and complexes. In particular, if on-promoter and off-promoter interactions are different, this results in the coupling of two different dynamics, leading to additional complexity in eigenvalues trajectories and providing even more flexibility for regulating the dynamic activity of promoters.Protein-protein interactions can occur away from the promoter and can result in the binding of complexes to the promoter  carry much more information  or SP(ω), this technique was shown to give mechanistic insight into the underlying system [The greater complexity of eukaryotic promoters certainly moderates the success of the previous approach. Nevertheless their singular dynamics promotes the use of time-dependent measures - as obtained by photobleaching -48,55, ci figure . For insg system ,8,12,95.This generic model can thus constitute a unified theoretical framework for all these very different techniques, making them complementary views of the same system.Overview of derivations) results in a simple and stereotyped concentration control ability  while keeping a coherent periodic dynamics could represent another evolutionary pressure toward stronger energy consumption and larger regulatory structures.In the light of the various capabilities of gene promoters we have demonstrated in this study, it seems that the importance of the single-gene level has to be reconsidered. Indeed, systems biology has set gene networks to the front of the stage, expecting complexity to arise from the interaction of many genes, often considered simple and deterministic. It appears now that single nodes of these networks should be given more attention since their spontaneous stochastic dynamics can be a considerable source of complexity.AC, OG and GB designed the study. AC performed the theoretical derivations of the model and conceived/analysed the different systems presented as examples. AC, OG and GB interpreted the results and wrote the paper. All authors read and approved the final manuscript.Supplementary information. This document contains two supplementary figures, an extensive description of the model in different versions and formulations, details of theoretical derivations and all the parameter values of each system presented as example in the main article.Click here for file


Krüppel-associated box domain-zinc finger proteins (KRAB–ZFPs) are tetrapod-specific transcriptional repressors encoded in the hundreds by the human genome. In order to explore their as yet ill-defined impact on gene expression, we developed an ectopic repressor assay, allowing the study of KRAB–mediated transcriptional regulation at hundreds of different transcriptional units. By targeting a drug-controllable KRAB–containing repressor to gene-trapping lentiviral vectors, we demonstrate that KRAB and its corepressor KAP1 can silence promoters located several tens of kilobases (kb) away from their DNA binding sites, with an efficiency which is generally higher for promoters located within 15 kb or less. Silenced promoters exhibit a loss of histone H3-acetylation, an increase in H3 lysine 9 trimethylation (H3K9me3), and a drop in RNA Pol II recruitment, consistent with a block of transcriptional initiation following the establishment of silencing marks. Furthermore, we reveal that KRAB–mediated repression is established by the long-range spreading of H3K9me3 and heterochromatin protein 1 β (HP1β) between the repressor binding site and the promoter. We confirm the biological relevance of this phenomenon by documenting KAP1–dependent transcriptional repression at an endogenous KRAB–ZFP gene cluster, where KAP1 binds to the 3′ end of genes and mediates propagation of H3K9me3 and HP1β towards their 5′ end. Together, our data support a model in which KRAB/KAP1 recruitment induces long-range repression through the spread of heterochromatin. This finding not only suggests auto-regulatory mechanisms in the control of KRAB–ZFP gene clusters, but also provides important cues for interpreting future genome-wide DNA binding data of KRAB–ZFPs and KAP1. The regulation of gene activity by transcription factors is crucial to the function of all cells. Here, we studied the mechanisms of action of the largest family of gene regulators encoded by the human genome, the so-called KRAB–containing zinc finger proteins (KRAB–ZFPs), which in concert with their universal cofactor KAP1 act as transcriptional repressors. For this, we used two parallel approaches. First, by targeting an ectopic KRAB domain to hundreds of different genes, we found that KRAB/KAP1 can repress promoters located several tens of kilobases from the repressor DNA docking site. We further could show that KRAB induces such long-range effects by mediating the spread of repressive chromatin marks along the body of the gene, resulting in a block of transcriptional initiation at the promoter. In a second set of experiments, we analyzed an endogenous KRAB–ZFP gene cluster, where we could also document KAP1–dependent heterochromatin spreading and transcriptional repression. Together, these results support a model whereby KRAB–ZFPs and KAP1 can mediate long-range transcriptional repression through the spread of silencing chromatin marks. This study thus provides insight into KRAB/KAP1–induced gene regulation at KRAB–ZFP gene clusters, and will further help interpret genome-wide studies of KRAB–ZFPs and KAP1 DNA binding patterns. The proper control of gene expression is paramount to all cellular events, and is orchestrated through a sophisticated balance of activating and repressing influences. Krüppel-associated box domain zinc finger proteins (KRAB-ZFP) constitute the single largest group of transcriptional repressors encoded by the genomes of higher organisms. After appearing in early tetrapods, the KRAB-ZFP family has rapidly expanded and diverged through multiple rounds of gene and segmental duplications, to give rise to more than three hundred and fifty members annotated in both mice and humans de novo DNA methyltransferases KRAB-ZFPs all harbor a so-called KRAB domain situated upstream of an array of two to forty C2H2 zinc fingers, which provide sequence-specific DNA binding ability The phylogenetically conserved family of HP1 proteins is implicated in a variety of nuclear events, such as transcriptional repression and maintenance of chromosome structure To examine the genomic features of KRAB/KAP1-mediated transcriptional regulation, we developed an ectopic repressor assay. In our system, promoterless lentiviral vectors serve as gene traps to drive reporter expression from cellular promoters. Drug-controllable docking of an ectopic KRAB-based repressor then allows an assessment of the effects of KRAB/KAP1 recruitment at these loci. Using this system, we found that KRAB-induced silencing can repress promoters situated several tens of kilobases away from the repressor primary docking site through reduced RNA Pol II binding. Furthermore, we observed that this phenomenon is independent of promoter strength, facilitated by HP1 and associated with spreading of heterochromatin marks between repressor binding site and targeted promoters. Finally, we could document KAP1-mediated transcriptional repression at an endogenous KRAB-ZFP gene cluster by propagation of HP1β and H3K9me3 from the 3′ end of these genes to their transcriptional start site. Our results indicate that KRAB/KAP1 induce long-range repression through the spread of heterochromatin.E. coli tetracycline repressor (tTR), and binds to Tet operator sequences (TetO) in a doxycycline (dox)-controllable fashion TetO repeats and a promoter-less puromycin resistance-GFP fusion reporter (puroR-GFP) downstream of a potent adenoviral splice acceptor. This design predicted that i) reporter expression would occur from the promoters of active genes targeted by the integrants, and ii) dox withdrawal would result in tTRKRAB binding to the TetO sites present in the proviruses, thus exposing the trapped promoters to potential KRAB/KAP1-mediated repression  vectors at a low multiplicity of infection to limit integrants to one per cell. Clones expressing the trap reporter puroR-GFP were then isolated and subjected to FACS analysis in the presence (-dox) or absence (+dox) of tTRKRAB binding. Comparing the mean fluorescence intensity values of GFP (MFI) in both conditions revealed heterogeneous effects of tTRKRAB recruitment, with clones exhibiting either high or low levels of reporter silencing  in more detail using 5′RACE to map the trapped fusion transcripts  and by pTo investigate further the mechanisms of this long-range repression, we measured levels of both H3K9me3 and HP1β enrichment between the promoters and the TrapSil integrants of the loci under study. It was previously demonstrated that all HP1 isoforms can interact with KAP1 Indeed, global levels of H3K9me3 and HP1β increased along the body of trapped genes upon tTRKRAB recruitment, and the magnitude and efficiency of propagation of this phenomenon correlated with its phenotypic outcome . Clone IR487E, V488E) We further explored the role of HP1 recruitment to KAP1 in KRAB/KAP1-mediated long-range repression by performing complementation studies in a KAP1 knockout mouse embryonic fibroblast (MEF) cell line stably expressing tTRKRAB. For this, we isolated two LV TrapSil clones, and used 5′RACE to map the fusion transcripts resulting from promoter trapping. Since both proviruses were intronic, we assigned integration to the middle of the targeted intron. This revealed that clone 1 had integrated in the gene Sestd1 (SEC14 and spectrin domains 1) at a distance of 15 kb from the promoter and that clone 2 had trapped the gene Prcp (prolylcarboxypeptidase) and was situated 25 kb away from its TSS . Both clRecent genome-wide studies have identified KAP1 and HP1β binding sites at the 3′end of KRAB-ZFP genes, suggesting long-range auto-regulatory feedback loops KAP1-specific ChIP identified KAP1 binding sites at the 3′end of ZNF554, ZNF555 and ZNF556, which are located more than 10 kb from the corresponding promoters, as well as within the region of ZNF77-1/2 overlap . We thenIn the present work, we demonstrate that KRAB-ZFPs and their co-repressor KAP1 can induce long-range transcriptional repression through heterochromatin spreading. For this, we studied lentivirally-trapped promoters silenced by an ectopic KRAB-containing repressor, as well as the KAP1-mediated modulation of the chromatin landscape at an endogenous KRAB-ZFP gene cluster. We could document the propagation of HP1β and the silencing mark H3K9me3 from repressor binding sites to targeted promoters, and showed that gene silencing results from a block in transcriptional initiation. Furthermore, we could demonstrate that this silencing process is KAP1-dependent and facilitated by HP1.Our results demonstrate that KRAB/KAP1 have the ability to repress promoters at far greater distances than suspected based on earlier publications. Indeed, it was previously found that recruitment of a regulatable KRAB-based repressor to an array of integrated reporter genes led to transcriptional repression of the promoter adjacent to the repressor binding site, but did not affect a transcriptional unit located 2–3 kb away The large family of KRAB-ZFP genes is the result of a recent evolutionary phenomenon, with iterative gene duplication events that have led to large sequence homologies between its different members. Therefore, it was proposed that the large heterochromatin domains containing HP1β and SUV39h1 at KRAB-ZFP genes act as a protection from deleterious recombination events, that such a homology would enhance Spreading is considered to be an intrinsic property of heterochromatin Remarkably, we documented a decreasing gradient of HP1β and H3K9me3 from the KAP1 docking site to the promoter in both ectopically KRAB-repressed genes and at an endogenous KRAB-ZFP gene cluster. Although it is difficult to compare the binding curves of these different factors due to the non-linear binding kinetics of the antibodies used for their capture, this may suggest that KAP1 nucleates the spread of heterochromatin. Notably, KAP1 is enriched along the bodies of all KRAB-ZFP genes , consistOur analyses of TrapSil-targeted genes and of an endogenous KRAB-ZFP gene cluster indicate not only that in both settings silent chromatin marks spread from sites of KAP1 recruitment, but also reveal that the transcriptional consequences of this event vary. Our finding that KAP1 depletion upregulated several transcripts within a KRAB-ZFP cluster, even in the absence of promoter silencing marks suggests that expression was modulated primarily by chromatin changes over the transcribed region of these genes. Therefore, our results imply broad functions for KRAB/KAP1-mediated long-range repression, encompassing not only transcriptional promoter silencing but also transcriptional dampening, as has previously been described for HP1 binding within the coding regions of genes Our analysis of trapped cellular genes exposed to the tTRKRAB ectopic repressor revealed outcomes largely dictated by whether or not silencing marks reached their promoters. It is likely that the cumulative effect of increased H3K9me3 and decreased H3Ac interferes with binding of transcriptional activator proteins and the ultimate recruitment of RNA Pol II. This hypothesis is not only consistent with our finding of less hypophosphorylated RNA Pol II at trapped and silenced promoters, but was also recently put forward to explain how histone modifications regulate the expression of the L-type pyruvate kinase gene Analysis of trapped populations with different silencing phenotypes showed that the distance between sites of KRAB/KAP1 recruitment and trapped promoters had a strong influence on KRAB-mediated silencing, but was not the sole factor at play. Indeed, some promoters located at great distances from the tTRKRAB-binding site were efficiently shut off, while others situated close-by escaped this process. Several possibilities come to mind to explain this variation. First, distinct subgroups of promoters may exhibit intrinsically different susceptibility to KRAB/KAP1-mediated silencing. Second, counteracting influences may be exerted at some promoters, for instance downregulation of H3K9me3 levels by histone demethylases, as exemplified by the proposed action of JMJD1A and JMJD2C on pluripotency-associated genes in mouse ES cells More generally, there may be genomic loci that exhibit chromatin environments permissive or restrictive to KRAB/KAP1-mediated long-range repression. Examples of both situations in this study include the long-range spreading of heterochromatin detected over a 50 kb long region harboring several KRAB-ZFP genes and the impaired spreading observed in the vicinity of the HNRNPA2B1-CBX3 ubiquitously-acting chromatin opening element (A2UCOE) In a second example we made use of trapped genes containing several TrapSil integrations with distinct silencing phenotypes . One of In some cases, long-range acting elements such as enhancers might be the primary targets of KRAB/KAP1-induced chromatin modifications, with a secondary impact on promoters. More generally, specific cis-acting sequences or chromatin features located between KAP1 recruitment sites and transcriptional start sites may alter the spread of heterochromatin, either positively or negatively. Conversely, while the hereby observed long-range spreading of silencing heterochromatin suggests that some clusters of genes might be co-regulated through common KRAB-ZFP binding sites, it also implies the existence of limiting mechanisms to avoid that it systematically be the case.TetO sequences, could be a useful tool to elucidate functions of interesting regulatory landscapes. Finally, the controlled recruitment of KRAB-containing repressors should facilitate studies on the kinetics of heterochromatin spreading in different chromosomal environments, at distinct developmental stages, and in various physiopathogical conditions, thereby shedding light on the intricacies of transcriptional repression.In conclusion, our results reveal that KRAB-ZFPs, which constitute the largest family of transcription factors encoded by the human genome, mediate long-range repression through the spread of heterochromatin in a process influenced by the genomic context. Therefore, it is tempting to speculate that the long-range epigenetic silencing (LRES) of large chromosomal regions observed in cancer cells HeLa, 293T and HCT116 cells  were cultured using standard methods. Mouse embryonic fibroblasts from homozygous KAP1 floxed mice were derived as described TetO repeats. pLV-tTR-KRAB-Red was described LV- based gene trap vectors Cells were lysed using standard procedures and subjected to SDS-PAGE. Antibodies used were mouse monoclonal anti-PCNA  and mouse monoclonal anti-KAP1 , followed by secondary goat anti-mouse IRDye 800CW antibody (Li-cor). Imaging and quantification of the Western blot were performed on ODYSSEY infrared imager using the machine-specific software (Li-cor).Total RNA was isolated using an RNeasy plus kit (Qiagen).For Northern blot 15 µg of RNA were run on a denaturing agarose gel and transferred onto a nylon membrane, which was pre-hybridized in ULTRAhyb buffer (Ambion) and after addition of a GFP-specific DIG-labelled probe  hybridized overnight at 42°C. The membrane was then subjected to washing and detection procedures as recommended by the manufacturer . For 5′RACE a previously described protocol was used For quantitative Real-Time PCR (qPCR), RNA was converted into cDNA as in 5′RACE, however using random hexamer primers. For each sample a control reaction lacking RT was included. Diluted cDNA was used to assay the expression of each gene with 1x Power Sybr (Applied Biosystems), and a 400 nM concentration of each gene-specific primer. To verify specificity, each PCR was followed by a melting curve analysis. The increase in fluorescence was analysed with the SDS software, version 2.2.2 (Applied Biosystems). For all amplification plots the baseline data were set with the automatic cycle threshold function. A mean quantity was calculated from duplicate or triplicate PCRs for each sample, and a normalization factor was obtained by calculating the geometric mean of the values of the selected housekeeping genes, which was subsequently used to normalize the relative amounts of RNAs of interest Integration sites were mapped by LM-PCR using a previously described protocol http://www.millipore.com/techpublications/tech1/mcproto407). About 107 cells were crosslinked with 1% formaldehyde for 15 min at RT, quenched by adding glycine and rinsed with PBS. Cells were then resuspended in lysis buffer and sheared by sonication with a Branson digital sonicator (model 250) on ice four times for 20 s. 100 µl of sonicated chromatin was directly de-crosslinked and used as the total input (TI) reference in quantitative PCR analysis at a dilution of 1∶350 or 1∶700. 100 µl of sonicated chromatin was used for each ChIP reaction and was diluted in 900 µl dilution buffer and precleared with 80 µl salmon-sperm DNA- proteinA agarose beads (Upstate). Precipitating antibodies H3Ac , H3K9me3 (Abcam ab 8898), total H3 (Abcam ab 1791), HP1β , RNA Pol II 8WG16  and rabbit polyclonal KAP1 (gift from D. Schultz) were added overnight (IP). Chromatin-antibody complexes were then captured, washed and eluted with 100 mM NaHCO3, 1% SDS. Cross-links between DNA and proteins were reversed by addition of NaCl  and incubation at 65°C. DNA was precipitated after incubation with RNase A and Proteinase K and resuspended in 50 µl H2O and subjected to quantitative PCR analysis at a dilution of either 1∶4 or 1∶8. Quantitative PCR was done as stated in the RT-PCR section and primer sequences are available in ChIP was performed according to the Upstate protocol with minor modifications  of KAP1 was quantified by ChIP analyses on the provirus of the irrepressible casein kinase 1d (CSNK1D) gene, the repressible prostaglandin E synthase 3 (PTGES3) gene and at the repressible ephrin receptor B4 (EPHB4) gene. All values are expressed as means +SEM of duplicate experiments.(0.06 MB TIF)Click here for additional data file.Figure S2tTRKRAB mediates silencing of retrovirally trapped cellular promoters in a selected HeLa cell population. (A) Mechanism of promoter trapping and dox-controllable silencing following TrapSil-mediated transduction of tTRKRAB-expressing cells.  tTRKRAB-expressing HeLa cells infected with LV- or MLV-derived TrapSil vectors were selected in puromycin before dox withdrawal and analysis by (B) FACS (GFP positive cells are in upper left quadrant) and (C) Northern blot (using a GFP-specific probe). The lower panels show a longer exposure of the blot and the 28S rRNA loading control.(2.17 MB TIF)Click here for additional data file.Figure S3Trapped genes carrying multiple TrapSil integrants of different silencing phenotypes. (A–F) Graphic depiction of six trapped genes carrying multiple LV-based TrapSil integrants with different silencing phenotypes: (A) calnexin precursor (CANX), (B) nucleophosmin 1 (NPM1), (C) sodium-coupled neutral amino acid transporter A2 (SLC38A2), (D) Y-box binding protein 1 (YBX1), (E) heterogeneous nuclear ribonucleoproteins A2/B1 (HNRNPA2B1), and (F) HNRNP C1/C2 (HNRNPC). The proviruses exhibiting a repressible phenotype (>90% silencing) are depicted as black triangles above the baseline, whereas the irrepressible counterparts (<10% silencing) are depicted as light grey triangles below the baseline.(0.27 MB TIF)Click here for additional data file.Figure S4HP1β and H3K9me3 spread from the KRAB-binding site to the promoter. ChIP analyses quantifying the relative enrichment (% of input) of both HP1β and H3K9me3 were performed for the repressible clone IX in the presence and absence of tTRKRAB binding. The interrogated sequence at the ephrin receptor B4 (EPHB4) locus spanned from the proviral tTRKRAB binding sites (light grey circles) to the trapped promoter. qPCR amplicons are depicted as letters and are not drawn to scale. All values are expressed as means +SEM of triplicate experiments. Fold changes were calculated as ratios of -dox/+dox enrichments, with the ratios of the respective positive controls set as 1. The controls consisted of p53BP2 for H3K9me3 and of ZNF556 for HP1β .(0.12 MB TIF)Click here for additional data file.Figure S5HP1β and H3K9me3 spread along the 50 kb-spanning ZNF77/57 locus upon tTRKRAB binding. ChIP analyses quantifying the relative enrichment (% of input) of both HP1β and H3K9me3 were performed for the repressible clone XI in the presence and absence of dox. The ZNF77-1 promoter drives the expression of the integrated TrapSil provirus (depicted as light grey circles). The interrogated sequence spanned the whole ZNF77/57 locus and the respective qPCR amplicons are depicted as letters and are not drawn to scale. All values are expressed as means +SEM of triplicate experiments. Fold changes were calculated as ratios of -dox/+dox enrichments, with the ratios of the respective positive controls set as 1. The controls consisted of p53BP2 for H3K9me3 and of ZNF556 for HP1β .(0.30 MB TIF)Click here for additional data file.Figure S6ChIP analyses of different TrapSil clones at control loci. (A–D) We ensured that there were similar amounts of ChIP material in the +dox compared to the -dox samples for each TrapSil clone by analyzing HP1β or H3K9me3 relative enrichment levels at control loci. These control loci are: p53BP2, ZNF554, ZNF555, ZNF556, ZNF77-1, and ZNF77-2 and were probed in the TrapSil clones (A) XVI, (B) I, (C) XI, and (D) IX.(0.37 MB TIF)Click here for additional data file.Figure S7Quantification of the KAP1 knockdown efficiency in a stable HeLa cell line. HeLa cells were stably transduced with lentiviruses expressing shRNA targeting either KAP1 or GFP. The levels of knockdown were quantified by using (A) qPCR measurements normalized to EEF1α and (B) western blot analyses for KAP1 levels with PCNA as a loading control. The qPCR values are expressed as means +SEM of triplicate experiments.(0.12 MB TIF)Click here for additional data file.Figure S8ChIP analyses of knockdown HeLa cell lines at control loci. We ensured that there were similar amounts of ChIP material in the shKAP1, compared to the shGFP cell lines, by analyzing HP1β or H3K9me3 relative enrichment levels at the control gene p53BP2 and at the human satellite 2 repeats (Sat2).(0.08 MB TIF)Click here for additional data file.Table S1Characterization and integration site mapping of LV-based TrapSil HeLa and MEF clones.(0.08 MB DOC)Click here for additional data file.Table S2Integration site mapping of the repressible and the irrepressible LV TrapSil populations. After proviral integration site mapping, the position of TrapSil integrants was determined relative to the trapped promoter for both the repressible and the irrepressible populations. More specifically, we computed two gene lists (LV repressible and LV irrepressible), which included all the trapped promoters with a single annotated transcriptional start site (TSS), which were further used to build (0.06 MB XLS)Click here for additional data file.Table S3List of primers used in this study.(0.05 MB XLS)Click here for additional data file.Table S4Fold change values calculated for different TrapSil clones by comparing ChIP enrichments in the presence or absence of tTRKRAB binding.(0.04 MB XLS)Click here for additional data file.


Drosophila melanogaster are specified by quantitative differences in transcription factor occupancy on a common set of bound regions.Distinct developmental fates in  Drosophila bind to overlapping sets of thousands of genomic regions in blastoderm embryos. While regions bound at high levels include known and probable functional targets, more poorly bound regions are preferentially associated with housekeeping genes and/or genes not transcribed in the blastoderm, and are frequently found in protein coding sequences or in less conserved non-coding DNA, suggesting that many are likely non-functional.We previously established that six sequence-specific transcription factors that initiate anterior/posterior patterning in in vivo. Collectively, the 21 regulators show a surprisingly high overlap in the regions they bind given that they belong to 11 DNA binding domain families, specify distinct developmental fates, and can act via different cis-regulatory modules. We demonstrate, however, that quantitative differences in relative levels of binding to shared targets correlate with the known biological and transcriptional regulatory specificities of these factors.Here we show that an additional 15 transcription factors that regulate other aspects of embryo patterning show a similar quantitative continuum of function and binding to thousands of genomic regions in vivo, with specificity achieved by modulating the amount, rather than the identity, of bound factor.It is likely that the overlap in binding of biochemically and functionally unrelated transcription factors arises from the high concentrations of these proteins in nuclei, which, coupled with their broad DNA binding specificities, directs them to regions of open chromatin. We suggest that most animal transcription factors will be found to show a similar broad overlapping pattern of binding  Mostiewed in ,5,6. BecDrosophila Transcription Network Project (BDTNP) has initiated an interdisciplinary analysis of the network controlling transcription in the Drosophila melanogaster blastoderm embryo .-19.16-19even-skipped (eve) gene are each controlled by various combinations of A-P early regulators, such as BCD and Hunchback (HB), and a separate later activated autoregulatory CRM is controlled by A-P pair rule regulators, including EVE and PRD [Tens of functional CRMs have been mapped within the network  and Twist (TWI) specify mesoderm, the pair rule factors EVE and Fushi-Tarazu (FTZ) specify location along the trunk of the A-P axis, and TLL and Huckebein (HKB) specify terminal cell fates.The blastoderm regulators include members of most major animal transcription factor families  to measure binding of the six gap and maternal regulators involved in A-P patterning in developing embryos  . While tWe carried out an extensive series of controls and analyses to validate the antibodies and array data, and to ensure that our array intensities could be interpreted as a quantitative measure of relative transcription factor occupancy on each genomic region, that is, as a measure of the average numbers of molecules of a particular factor occupying each region . Our previously published quantitative PCR analysis of immunoprecipitated chromatin for regions randomly selected from the rank list of bound regions and also control BAC DNA 'spike in' experiments support the FDR estimates, suggest that the false negative rate is very low for all but the most poorly bound regions, and indicate that the array intensity signals correlate with the relative amounts of genomic DNA brought down in the immunoprecipitation .in vivo significantly affects the amount of each DNA region crosslinked and immunoprecipitated in the assay.The enrichment of factor recognition DNA sequences in ChIP/chip peaks shows a modest positive correlation with peak array intensity score. Importantly, this is seen even in the upper portion of the rank list where the percentages of false positives are too few to significantly influence the analysis , Mad (MAD), RUN and SNA, but a number of other factors show a less dramatic but similar trend  the more highly bound are significantly more conserved than those poorly bound , many din vivo crosslinking data by other groups studying animal regulators [Our analysis contrasts with the predominant qualitative interpretation of gulators -46. Manygulators ,44,46, tin vivo DNA binding data is that there is considerable overlap in the genomic regions bound by the 21 factors  statistic , which iin vivo crosslinking studies have also noted significant overlap in binding between some sequence-specific factors in animals [Several recent  animals ,37,44,46 animals , and it eve gene is expressed in a seven stripe pair-rule pattern along the A-P axis and contains four stripe CRMs that are known targets of the A-P early factors .To more fully explore if there is a correlation between the level of factor occupancy on common sequences and functional specificity, we next compared the binding of all 21 factors on the 44 A-P early CRMs and 16 D-V CRMs described earlier.  While most of these CRMs are each bound above the 1% FDR threshold by members of all three of the major regulatory classes Figure , the norInformative as the above analyses are, however, they are restricted to previously identified CRMs. These CRMs were identified experimentally using criteria that could well have excluded some types of functional targets. To explore if levels of occupancy correlate with functional specificity more widely, therefore, we examined binding to genomic regions without regard to any published information on which sequences different factors might act on. In the absence of any prior knowledge, we exploited our observation that, on known CRMs, members of a regulatory class have more similar specificities than members of different classes and used this to provide an expectation of specificity elsewhere in the genome. We used two measures to compare binding for factors within and between classes Figure .P < 9 × 10-15), while that among the A-P pair rule factors and the D-V factors is moderately significant (P < 2 × 10-3) for both; Additional data file 13). The next most highly bound cohort shows reduced preferential co-binding within regulatory classes, with only that among A-P early regulators being significant , and on a per factor basis . They even detect moderate discrimination between the classes in the 301 to 600 cohort .Second, because the above measure only partially takes into account the different levels of occupancy on each bound region, we sought a measure that better captures this information. Scatter plots show that while ChIP/chip scores from experiments using antibodies to distinct portions of the same factor are highly correlated, pair-wise comparisons between factors reveal marked differences in scores, suggesting that correlation coefficients calculated in this way would be a useful measure of binding specificity Figure . TherefoThus, across a broad array of mostly uncharacterized genomic regions the levels of binding of transcription factors correlate with the expectation that factors with more similar functions show more similar binding specificity. Consistent with our previous observation that highly bound regions appear more functionally significant, the distinctions in binding preferences between regulatory classes is larger on the most highly bound regions. Just as on the known CRMs, however, the distinctions between the different classes are relatively modest, suggesting that the regulatory specificity of transcription factors in general may be fuzzier than widely realized and perhaps also suggesting a role for post-DNA-binding events to increase the distinctions between factors.All of the preceding analyses consider binding to short genomic regions. The target genes of blastoderm factors, however, are often found associated with several such regions  term associated with the gene whose transcription start site is closest to the peak of binding. The enrichment of the GO terms associated with the 300 most highly bound peaks and the next most highly bound cohorts of peaks were then plotted as heat maps Figure . This shThe GO terms associated with the 301 to 600 ranked peaks show much less difference between each factor and regulatory class, consistent again with these less highly bound regions playing a lesser role in determining biological specificity and function.Some of the types of gene preferentially bound by the regulatory classes readily fit expectations; for example, the strong association of genes involved in A-P axis specification, patterning by pair rule genes, and trunk and head segmentation with A-P early and A-P pair rule factors. Others are unexpected, such as the preference of D-V regulators for a series of GO terms related to eye development. Most likely the differences between factors revealed in these heat maps reflect differences due to target genes that are strongly patterned along the A-P axis versus those strongly patterned along the D-V axis. Because important effectors of blastoderm regulators' functions are patterned along both body axes, because the early factors both activate or repress target genes, and because GO terms imperfectly capture and categorize the biological function of each gene, this analysis does not provide a complete description of the different specificities of each factor at the target gene level.in vitro), the relatively high concentrations at which they are expressed in nuclei in which they are active, chromatin structure, and the law of mass action.What mechanism, though, drives the extraordinarily extensive, overlapping pattern of binding? We speculate that the pattern is a natural consequence of these factors' intrinsic DNA binding specificities , HRY, SLP1, Daughterless (DA), DL, SNA, and TWI) or portions of the protein . Immunoaffinity purifications were performed using E. coli-expressed purified recombinant His-tagged proteins. The amino acid sequences used  whose 5' end was closest to the array intensity peak in the bound region. To identify the closest transcribed gene, the subset of release 4.3 annotations that completely overlap regions bound by RNA polymerase II in our ChIP-chip experiments was used.The data from the complete set of six arrays from each ChIP/chip experiment were processed using TiMAT  as descrRelative percentages of UV crosslinking to defined restriction fragments  and the Enrichment of recognition sequences, GO terms, distance to transcribed genes, genomic locations and phastcons scores were determined essentially as described in . A statiIn Figure Overlap of bound regions between two transcription factors in Figure To calculate the liklihood z score that overlap occurs by chance Figures , z-scoreIn Figure In Figure Mann-Whitney tests were applied to the binding intensity data of transcription factors to CRMs Figure , overlapIn Figure Drosophila Transcription Network Project; ChIP/chip: chromatin immunoprecipitation followed by microarray analysis; CRM: cis-regulatory module; D-V: dorsal-ventral; FDR: false discovery rate; GO: Gene Ontology; GSC: Genome Structure Correction.A-P: anterior-posterior; BDTNP: Berkeley XL, SM, JL, JBB, PB, MBE and MDB conceived and designed the experiments and analyses and wrote the paper. XL, HCC, and SVEK performed the wet laboratory experiments. SM, XL, JL, JBB, AH, PB, MBE and MDB analyzed the data. LS, XL, SM, MDB and MBE designed the database. AH, LZ, BDG, MS, SVEK, XL, HCC, DWK, MBE and MDB contributed reagents/materials/analysis tools. All authors read and approved the final manuscript.eve gene for both factor and negative control immunoprecipitations for all antibodies shown in Table The following additional data are available with the online version of this paper: further evidence that the antibodies used specifically recognize the transcription factors they were raised against in the embryo (Additional data file The antibodies used specifically recognize the transcription factors they were raised against in the embryo.Click here for fileet al. [See Table et al. , and MatClick here for fileThese are plotted down the ChIP/chip rank list in non-overlapping 200-peak cohorts.Click here for fileThe top 200 ChIP/chip peaks are significantly enriched over all peaks in the 1% FDR set for the values plotted in Figures Click here for fileRelative levels of mean UV crosslinking and mean ChIP/chip scores across a series of highly and poorly bound genomic regions.Click here for fileGenomic coordinates of regions bound by each factor for the 1% FDR data set, locations and scores of peak windows, and the closest gene and closest transcribed gene for each peak.Click here for fileGenomic coordinates of regions bound by each factor for the 25% FDR data set, locations and scores of peak windows, and the closest gene and closest transcribed gene for each peak.Click here for fileFraction of bound regions in different cohorts distinguished by ChIP/chip score and, for some factors, the fraction of those bound regions that overlap known CRMs, using the conventions shown in Figure Click here for fileThese are shown plotted down the ChIP/chip rank list in non-overlapping 200-peak cohorts.Click here for fileThese are plotted down the ChIP/chip rank list in non-overlapping 200-peak cohorts.Click here for fileThese are plotted down the ChIP/chip rank list in non-overlapping 200-peak cohorts.Click here for fileThese are plotted down the ChIP/chip rank list in non-overlapping 200-peak cohorts.Click here for fileValues plotted in the heat maps in Figures Click here for fileeve gene for both factor and negative control immunoprecipitations for all antibodies shown in Table The pattern of ChIP/chip scores on the Click here for file