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Title: Quantitative Models of the Mechanisms That Control Genome-Wide Patterns of Transcription Factor Binding during Early Drosophila Development}
Article:
Abstract:
Transcription factors that drive complex patterns of gene expression during animal development bind to thousands of genomic regions, with quantitative differences in binding across bound regions mediating their activity. While we now have tools to characterize the DNA affinities of these proteins and to precisely measure their genome-wide distribution in vivo, our understanding of the forces that determine where, when, and to what extent they bind remains primitive. Here we use a thermodynamic model of transcription factor binding to evaluate the contribution of different biophysical forces to the binding of five regulators of early embryonic anterior-posterior patterning in Drosophila melanogaster. Predictions based on DNA sequence and in vitro protein-DNA affinities alone achieve a correlation of ∼0. 4 with experimental measurements of in vivo binding. Incorporating cooperativity and competition among the five factors, and accounting for spatial patterning by modeling binding in every nucleus independently, had little effect on prediction accuracy. A major source of error was the prediction of binding events that do not occur in vivo, which we hypothesized reflected reduced accessibility of chromatin. To test this, we incorporated experimental measurements of genome-wide DNA accessibility into our model, effectively restricting predicted binding to regions of open chromatin. This dramatically improved our predictions to a correlation of 0. 6–0. 9 for various factors across known target genes. Finally, we used our model to quantify the roles of DNA sequence, accessibility, and binding competition and cooperativity. Our results show that, in regions of open chromatin, binding can be predicted almost exclusively by the sequence specificity of individual factors, with a minimal role for protein interactions. We suggest that a combination of experimentally determined chromatin accessibility data and simple computational models of transcription factor binding may be used to predict the binding landscape of any animal transcription factor with significant precision.
Introduction:
In vivo crosslinking studies show that animal transcription factors each bind to many thousands of DNA regions throughout the genome( e. g.[1]–[18]). While many of the strongest binding events are at functional cis regulatory modules and are evolutionary conserved, many thousands of other genomic regions that are bound at lower levels do not appear to be functional targets[2],[13],[14],[16],[18],[19]. In addition, factors with unrelated biochemical or functional properties bind to the same genomic regions with surprisingly high frequency[2],[16],[20], while the biological specificities of factors appear to be determined in part by quantitative differences in occupancy between proteins at these commonly bound sites[2],[16],[21],[22]. It is a fundamental challenge to determine the biochemical mechanisms that direct these complex, quantitative patterns of factor occupancy. Animal transcription factors bind to short( 5–12 bp) sequences of DNA that occur with high frequency throughout the genome[23], yet most occurrences of these recognition sequences are not detectably bound in vivo[2],[5],[13],[24],[25]. This discrepancy between predicted and observed factor binding has been attributed to several mechanisms that could alter the simple behavior of a factor, including:( 1) competitive inhibition of binding at those DNA regions that overlap sites occupied either by other sequence specific factors[26],[27] or by nucleosomes and chromatin associated proteins[28]–[33].( 2) Direct and indirect cooperative interactions between factors bound at physically proximal sites that increase their affinity at those sites[34]–[42]. The relative influence that each of these biochemical mechanisms on the overall pattern of factor binding in vivo, however, is currently a matter of debate. The anterior-posterior( A-P) patterning system in early Drosophila melanogaster development offers an excellent system for addressing the question of transcription factor targeting in an animal regulatory network. Prior to gastrulation, the Drosophila embryo is a syncytium of ∼6, 000 nuclei distributed around its periphery[43]. Extensive genetic and molecular analyses have established that a simple regulatory cascade involving the morphogens Bicoid( BCD) and Caudal( CAD), and the gap transcription factors Hunchback( HB), Giant( GT), Kruppel( KR) and Knirps( KNI) directs the expression of numerous target genes in complex three dimensional patterns across the embryo, which in turn establish the segmental body plan in the trunk of the developing fly[44]–[46]. To analyze this network at a systems level, we have previously established a complementary set of quantitative datasets describing key aspects of the network. We have measured the widespread, overlapping in vivo binding of these and other early regulatory factors genome wide[13],[16],[18], In addition, we have characterized DNA affinities of these proteins from in vitro selection( SELEX) experiments( Berkeley Drosophila Transcription Network Project, unpublished data), measured the relative concentration of each of these transcription factors in every embryonic nucleus[47], and determined the accessibility of DNA regions throughout the genome. Here we incorporate this series of datasets into quantitative models of transcription factor binding to measure the impact of different biophysical forces on occupancy levels. Many previous studies have used computational approaches to predict the locations of genomic regions bound by transcription factors[48]–[68]. While these studies have discovered a number of important principles, they have not sought to quantify the contribution of various factor-targeting forces in the context of the highly overlapping, widespread binding seen in animal systems. For example, many have produced qualitative predictions of which genomic regions might be occupied, and thus do not provide information on the levels of expected factor occupancy, which have been shown to be critical for relating binding patterns to biological function[2],[16],[21],[22]. In addition, while some studies have focused on determining the biochemical mechanisms targeting factors to DNA[57],[58],[60], many others have used additional biological information such as microarray expression data or DNA recognition site conservation to predict only those sites that are functional[59],[63],[65],[67],[68], and thus do not, strictly speaking, attempt to predict binding per se. Finally, when predictions of quantitative occupancy levels have been made, these were either part of more complex models of transcriptional output patterns that did not compare the binding models to any experimental in vivo binding data[66],[69]–[71], or were made to coarse in vivo binding information from yeast[58],[62]. Therefore, we have set out to establish a computational framework that predicts the levels of factor occupancy in a way that allows the effect of various proposed biophysical mechanisms influencing factor binding to be quantified. Our results support a long standing model that suggest that animal transcription factors are expressed at sufficiently high concentrations in cells that they can bind to their recognition sites in accessible parts of the genome without the aid of direct cooperative interactions with other proteins[31],[72],[73]. In this view, the dominant force in cells that modifies the intrinsic DNA specificity of transcription factors is the inhibition of DNA binding by chromatin structure. Because of the high frequency of factor recognition sites in most short accessible regions of the genome, this model explains why animal factors show such widespread, overlapping binding.
Discussion:
We have used a thermodynamic computational framework to investigate the biochemical mechanisms that direct the widespread, quantitative patterns of binding by five developmental regulatory factors in the Drosophila blastoderm embryo. Our most striking finding is that a very simple thermodynamic model does a good job of quantitatively predicting the occupancy of transcription factors based on DNA sequence, in vitro DNA binding affinities and DNase I accessibility data. We find no evidence that either competition between factors for binding sites or direct cooperative interactions between proteins show a significant effect on determining the overall pattern of binding in vivo. In the case of competitive binding, we showed that the reason why these do not dominantly affect net occupancy patterns embryo wide, is because high affinity sites for pairs of factors co-expressed in the same cells rarely overlap in the genome. Because our modeling focused on five early A-P segmentation regulators, it is possible that either competition or direct cooperative interactions with other transcription factors could play some role in shaping binding. The good predictive power of our model, however, sets an upper limit on the degree of this role. Also, it is striking that little evidence was found for positive heteromeric interactions among a set of five proteins that are known to cooperate on common sets of cis regulatory targets. The relationship between chromatin accessibility and transcription factor binding has been previously described. But there is little agreement on the extent to which transcription factors bind regions of accessible chromatin, or if chromatin becomes accessible as a result of transcription factor binding. Our results are more consistent with the former, and fit well with a long-standing model for transcription factor targeting in which sequence specific transcription factors are expressed in cells at sufficient concentration to occupy their high and moderate affinity sites along accessible parts of the genome, without the aid of heteromeric cooperative interactions with other proteins[1],[2],[72]. Many other studies developed computational algorithms for predicting in vivo binding. Crudely, these studies fall into two categories. Qualitative models that aim at identifying statistically significant appearances of binding sites[49],[50],[53],[55]–[57],[59],[61],[63],[65],[67],[68],[78],[79],[102], and quantitative models that estimate the occupancy( or binding probability) at various sites[58],[60],[62],[66]. While the former group is generally more useful to identify putative cis regulatory modules and to unfold the transcriptional regulatory map, the latter approach is more suitable for modeling the continuous quantitative landscape of binding. An additional advantage of the quantitative approach is in its natural probabilistic settings, which allows for an easy integration of external data. For example, we used high-resolution measurements of DNA accessibility using DNase I hypersensitivity assays as an a-priori estimation of protein binding. Alternatively, previous work used various histone modification data as a proxy for DNA accessibility and to highlight regulatory regions[59],[63],[65],[68]. Nonetheless, our goal was to understand the biophysical forces that shape binding per se. For this, we turned to direct in vivo measurements of DNA accessibility and based our predictions on high-resolution quantitative measurements of in vivo binding data. And, unlike these earlier studies, examined the effects of different biophysical phenomena on our ability to predict binding, thereby revealing aspects of transcription factor biochemistry while providing a means to predict where, how, and to what extent transcription factor bind. Finally, we wish to refer to a growing body of parallel work that models gene expression. Some of these studies use similar statistical models to ours, yet their modeling of transcription factor binding is intrinsic, limited to short regions of the genome with known regulatory activity, and has not examined the role of chromatin accessibility. Moreover, these models are optimized to recapitulate the patterns of expression for various genes, and do not focus on understanding the mechanisms of protein-DNA targeting[66],[69]–[71],[103]. One could argue that our use of DNA accessibility to eliminate regions where transcription factors are not bound is cheating. And, indeed, to the extent that our ultimate goal is to predict transcription factor binding from first principles it surely is. We have not established what determines the genome-wide landscape of DNA accessibility. But we have taken advantage of these direct in vivo measurements of DNA accessibility, and, in doing so, provide both a practical method for predicting binding, and a platform on which to better understand the forces that shape quantitative variation in the binding of individual factors to regions of open chromatin. This success suggests a streamlined strategy for estimating of transcription factor binding for large numbers of factors involving in vivo measurement of DNA accessibility and in vitro determination of factor affinities for DNA. While this approach should not be viewed as a substitute for systematic experimental measurement of transcription factor binding in vivo, we believe our predictions are good enough to be useful when such experimental data are unavailable or impractical to obtain. Although our predictions are surprisingly good, they are by no means perfect. We are obviously interested in improving these predictions, primarily by incorporating better data on binding specificities and more realistic models of protein-DNA and protein-protein interactions.
During early stages of development , regulatory proteins bind DNA and control the expression of nearby genes , thereby driving spatial and temporal patterns of gene expression during development . But the biochemical forces that determine where these regulatory proteins bind are poorly understood . We gathered experimental data on the activities of several key regulators of early development of the fruit fly ( Drosophila melanogaster ) and developed a computational method to predict where and how strongly they will bind . We find that competition , cooperativity , and other interactions among individual regulatory proteins have a limited effect on their binding , while the global accessibility of DNA to protein binding has a significant impact on the binding of all factors . Our results suggest a practical method for predicting regulatory binding by combining experimental DNA accessibility assays with computational algorithms to determine where will binding occur among the accessible regions of the genome .