Noncooperative Interactions between Transcription Factors and Clustered DNA Binding Sites Enable Graded Transcriptional Responses to Environmental Inputs

Giorgetti, Luca; Siggers, Trevor; Tiana, Guido; Caprara, Greta; Notarbartolo, Samuele; Corona, Teresa; Pasparakis, Manolis; Milani, Paolo; Bulyk, Martha L.; Natoli, Gioacchino

doi:10.1016/j.molcel.2010.01.016

Cited by 155 publications

(145 citation statements)

References 40 publications

(44 reference statements)

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“…The levels of Prep1 protein that are consistent with the relative reporter expression data lie in a narrow range (Fig. 4C, shaded region) that is consistent with published nuclear TF concentrations (Gregor et al 2007;Giorgetti et al 2010). For this range of Prep1 concentrations, the model predicts that the EE should be highly sensitive to Prep1 levels.…”

Section: Resultssupporting

confidence: 81%

Precise temporal control of the eye regulatory gene Pax6 via enhancer-binding site affinity

Rowan¹,

Siggers²,

Lachke³

et al. 2010

Genes Dev.

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110

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How transcription factors interpret the cis-regulatory logic encoded within enhancers to mediate quantitative changes in spatiotemporally restricted expression patterns during animal development is not well understood. Pax6 is a dosage-sensitive gene essential for eye development. Here, we identify the Prep1 (pKnox1) transcription factor as a critical dose-dependent upstream regulator of Pax6 expression during lens formation. We show that Prep1 activates the Pax6 lens enhancer by binding to two phylogenetically conserved lower-affinity DNA-binding sites. Finally, we describe a mechanism whereby Pax6 levels are determined by transcriptional synergy of Prep1 bound to the two sites, while timing of enhancer activation is determined by binding site affinity. The precise spatiotemporal patterns of metazoan gene expression are controlled by cis-regulatory modules (CRMs), which are regions of DNA, typically a few hundred base pairs long, that comprise binding sites for transcriptional regulators (Yuh et al. 1998;Arnosti and Kulkarni 2005;Davidson and Levine 2008). While current genomic approaches have traditionally focused on identifying the highest-affinity transcription factor (TF)-binding sites, new genome-wide location analyses have demonstrated widespread TF binding in the absence of highaffinity sites, thus forcing a re-evaluation of the scope and regulatory significance of lower-affinity sites (Hollenhorst et al. 2007;Gordan et al. 2009).The affinity of a binding site provides a mechanism to respond to different concentrations of TFs. In development, the ability of CRMs that contain sites of differing affinities to interpret TF gradients has been studied in Drosophilia melanogaster and other organisms for spatial gradients (Driever et al. 1989;Struhl et al. 1989;Jiang and Levine 1993;Scardigli et al. 2003;Segal et al. 2008), and in Caenorhabditis elegans and sea urchin for temporal gradients (Lai et al. 1988;Gaudet and Mango 2002). For example, in C. elegans, altering PHA-4 DNA-binding sites to higher-or lower-affinity sequences altered the onset of pharyngeal gene expression, with higher-affinity sites directing expression earlier in development (Gaudet and Mango 2002). The ability to tune the timing of expression by modulating the affinity of the DNA-binding site(s) for just one TF is conceptually appealing. However, it remains unclear whether such a simple mechanism could operate in the context of a complex vertebrate developmental enhancer.Pax6 is a key regulator of metazoan eye development, and in mammals is required in a dose-dependent fashion for lens induction and specification (Hill et al. 1991;Grindley et al. 1995 . Several groups have molecularly dissected regulatory elements contained within a region of Pax6 that contains the P0 promoter and 3.9 kb of upstream sequence (P0-3.9), and identified CRMs that control expression in the lens (denoted as the Pax6 ectodermal enhancer or EE) and endocrine pancreas (Williams et al. 1998;Kammandel et al. 1999;Zhang et al. 2002Zhang et al. , 2006. Within the EE, t...

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Section: Resultssupporting

confidence: 81%

Precise temporal control of the eye regulatory gene Pax6 via enhancer-binding site affinity

Rowan¹,

Siggers²,

Lachke³

et al. 2010

Genes Dev.

Self Cite

110

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“…Much past modeling work (27)(28)(29)(30)(31) has dealt with this obstacle by considering that each occupied binding site provides an activating or inhibitory signal and that all signals are then added and compared to a threshold: below (respectively above) this threshold, transcription is off (respectively on). However, more recent experimental work and associated modeling (32,33) suggests that transcription rates in vivo can exhibit graded responses involving no cooperative effects. Our work thus follows (17,32,33) by considering continuous transcription rates determined solely by the independent probabilities that binding sites in a regulatory region are occupied.…”

Section: Modelmentioning

confidence: 99%

“…However, more recent experimental work and associated modeling (32,33) suggests that transcription rates in vivo can exhibit graded responses involving no cooperative effects. Our work thus follows (17,32,33) by considering continuous transcription rates determined solely by the independent probabilities that binding sites in a regulatory region are occupied.…”

Section: Modelmentioning

confidence: 99%

Motifs emerge from function in model gene regulatory networks

Burda

Krzywicki

Martin

et al. 2011

Proc. Natl. Acad. Sci. U.S.A.

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Gene regulatory networks allow the control of gene expression patterns in living cells. The study of network topology has revealed that certain subgraphs of interactions or "motifs" appear at anomalously high frequencies. We ask here whether this phenomenon may emerge because of the functions carried out by these networks. Given a framework for describing regulatory interactions and dynamics, we consider in the space of all regulatory networks those that have prescribed functional capabilities. Markov Chain Monte Carlo sampling is then used to determine how these functional networks lead to specific motif statistics in the interactions. In the case where the regulatory networks are constrained to exhibit multistability, we find a high frequency of gene pairs that are mutually inhibitory and self-activating. In contrast, networks constrained to have periodic gene expression patterns (mimicking for instance the cell cycle) have a high frequency of bifan-like motifs involving four genes with at least one activating and one inhibitory interaction.essential interactions | genetic switch | transcription factors E volutionary forces have shaped living organisms since the beginning of life. It is thus not surprising that the framework for understanding features in today's organisms is often based on considering the history of these organisms: a remarkable feature found today may be the trace of an evolutionary trajectory. But one can also take a different perspective, one in which design constraints may play a significant role. The expectation is then that the architecture of living organisms depends not just on their origin, but also on their functional capabilities. In the present context, we are concerned with biological networks. Are the constraints associated with network functionality major determinants of network architecture? We address this question in this paper, albeit using a somewhat narrow notion of functionality based on the output patterns produced by networks.Our focus here is on gene regulatory networks (GRN), the set of interactions between genes. These interactions along with the gene expression machinery allow living cells to control their gene expression patterns. In the last decade, genetic interactions have been measured, modified, engineered, etc., and so quite a lot is known about how any given gene can affect another's expression. Furthermore, small gene networks have been designed to implement simple functions in vivo (1, 2), and much larger sets of interactions have been reconstructed in a number of organisms (3-5). From these large networks it has been possible to show that several "motifs"-subgraphs with given interactions-arise far more often than might be expected (6-9). One of the most studied motif is the so called Feed Forward Loop or FFL, a graph based on three genes where the first regulates the second, and both the first and the second regulate the third. Another example is the bifan motif in which two genes control two others. Biological functions have been proposed for these moti...

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“…Despite the presence of such non-equilibrium mechanisms, the thermodynamic formalism has been widely used to analyse gene regulation in eukaryotes, including yeast [7], flies [8][9][10][11][12][13] and human cells [14], and has been extensively reviewed [15][16][17][18][19]. In most cases, nonequilibrium mechanisms have not been incorporated in these models.…”

Section: Introductionmentioning

confidence: 99%

A framework for modelling gene regulation which accommodates non-equilibrium mechanisms

et al. 2014

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Background: Gene regulation has, for the most part, been quantitatively analysed by assuming that regulatory mechanisms operate at thermodynamic equilibrium. This formalism was originally developed to analyse the binding and unbinding of transcription factors from naked DNA in eubacteria. Although widely used, it has made it difficult to understand the role of energy-dissipating, epigenetic mechanisms, such as DNA methylation, nucleosome remodelling and post-translational modification of histones and co-regulators, which act together with transcription factors to regulate gene expression in eukaryotes.Results: Here, we introduce a graph-based framework that can accommodate non-equilibrium mechanisms. A gene-regulatory system is described as a graph, which specifies the DNA microstates (vertices), the transitions between microstates (edges) and the transition rates (edge labels). The graph yields a stochastic master equation for how microstate probabilities change over time. We show that this framework has broad scope by providing new insights into three very different ad hoc models, of steroid-hormone responsive genes, of inherently bounded chromatin domains and of the yeast PHO5 gene. We find, moreover, surprising complexity in the regulation of PHO5, which has not yet been experimentally explored, and we show that this complexity is an inherent feature of being away from equilibrium. At equilibrium, microstate probabilities do not depend on how a microstate is reached but, away from equilibrium, each path to a microstate can contribute to its steady-state probability. Systems that are far from equilibrium thereby become dependent on history and the resulting complexity is a fundamental challenge. To begin addressing this, we introduce a graph-based concept of independence, which can be applied to sub-systems that are far from equilibrium, and prove that history-dependent complexity can be circumvented when sub-systems operate independently. Conclusions: As epigenomic data become increasingly available, we anticipate that gene function will come to be represented by graphs, as gene structure has been represented by sequences, and that the methods introduced here will provide a broader foundation for understanding how genes work.

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Noncooperative Interactions between Transcription Factors and Clustered DNA Binding Sites Enable Graded Transcriptional Responses to Environmental Inputs

Cited by 155 publications

References 40 publications

Precise temporal control of the eye regulatory gene Pax6 via enhancer-binding site affinity

Precise temporal control of the eye regulatory gene Pax6 via enhancer-binding site affinity

Motifs emerge from function in model gene regulatory networks

A framework for modelling gene regulation which accommodates non-equilibrium mechanisms

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