Probing the informational and regulatory plasticity of a transcription factor DNA-binding domain

The Evolution of Sequence Specificity in a DNA Binding Protein Family

Transcriptional regulation enables bacteria to adjust to its environment. This is driven by transcription factors (TFs), which display DNA site recognition specificity with some flexibility built in. TFs, however, are not considered essential to a minimal cellular life. How did they evolve? It has been hypothesized that TFs evolve by gaining specificity (and other functions) on a background of non-specific chromosome structuring proteins. We used the IHF/HU family of DNA binding proteins, in which IHF binds DNA in a sequence-specific manner, whereas HU binds more indiscriminately, to test this hypothesis. We show that HUβ has been present from the bacterial root, while both IHF subunits emerged much later and diversified in Proteobacteria, with HUα having possibly arisen from transfer events in Gammaproteobacteria. By reconstructing ancestral sequences in-silico on a rooted phylogeny of IHF/HU we show that the common ancestor of this family was probably HU-like and therefore non-specific in binding DNA. IHF evolved from a branch of HU after HU had substantially diverged. Various residues characteristic of IHFα and shown to be involved in specific sequence recognition (at least in E. coli) have likely been co-opted from preexisting residues in HU, while those residues of IHFβ have likely evolved independently, suggesting that each of the IHF subunits has undergone different trajectories to acquire their DNA binding properties.

Evolution of Transcription Factor-containing Superfamilies in Eukaryotes

Regulation of gene expression helps determine various phenotypes in most cellular life forms. It is orchestrated at different levels and at the point of transcription initiation by transcription factors (TFs). TFs bind to DNA through domains that are evolutionarily related, by shared membership of the same superfamilies (TF-SFs), to those found in other nucleic acid binding and protein-binding functions (nTFs for non-TFs). Here we ask how TF DNA binding sequence families in eukaryotes have evolved in relation to their nTF relatives. TF numbers scale by power law with the total number of protein-coding genes differently in different clades, with fungi usually showing sub-linear powers whereas chordates show super-linear scaling. The LECA probably encoded a complex regulatory machinery with both TFs and nTFs, but with an excess of nTFs when compared to the relative distribution of TFs and nTFs in extant organisms. Losses drive the evolution of TFs and nTFs, with the possible exception of TFs in animals for some tree topologies. TFs are highly dynamic in evolution, showing higher gain and loss rates than nTFs in some TF-SFs though both are conserved to similar extents. Gains of TFs and nTFs are driven by the appearance of a large number of new sequence clusters in a small number of nodes, which determine the presence of as many as a third of extant TFs and nTFs as well as the relative presence of TFs and nTFs. Whereas nodes showing explosion of TF numbers belong to multicellular clades, those for nTFs lie among the fungi and the protists.

The role of promiscuous molecular recognition in the evolution of RNase-based self-incompatibility in plants

How do biological networks evolve and expand? We study these questions in the context of the plant collaborative-non-self recognition self-incompatibility system. Self-incompatibility evolved to avoid self-fertilization among hermaphroditic plants. It relies on specific molecular recognition between highly diverse proteins of two families: female and male determinants, such that the combination of genes an individual possesses determines its mating partners. Though highly polymorphic, previous models struggled to pinpoint the evolutionary trajectories by which new specificities evolved. Here, we construct a novel theoretical framework, that crucially affords interaction promiscuity and multiple distinct partners per protein, as is seen in empirical findings disregarded by previous models. We demonstrate spontaneous self-organization of the population into distinct "classes" with full between-class compatibility and a dynamic long-term balance between class emergence and decay. Our work highlights the importance of molecular recognition promiscuity to network evolvability. Promiscuity was found in additional systems suggesting that our framework could be more broadly applicable.

Disparate Dynamics of Gene Body and cis-Regulatory Element Evolution Illustrated for the Senescence-Associated Cysteine Protease Gene SAG12 of Plants

Gene regulation networks precisely orchestrate the expression of genes that are closely associated with defined physiological and developmental processes such as leaf senescence in plants. The Arabidopsis thaliana senescence-associated gene 12 (AtSAG12) encodes a cysteine protease that is (i) involved in the degradation of chloroplast proteins and (ii) almost exclusively expressed during senescence. Transcription factors, such as WRKY53 and WRKY45, bind to W-boxes in the promoter region of AtSAG12 and play key roles in its activation. Other transcription factors, such as bZIPs, might have accessory functions in their gene regulation, as several A-boxes have been identified and appear to be highly overrepresented in the promoter region compared to the whole genome distribution but are not localized within the regulatory regions driving senescence-associated expression. To address whether these two regulatory elements exhibiting these different properties are conserved in other closely related species, we constructed phylogenetic trees of the coding sequences of orthologs of AtSAG12 and screened their respective 2000 bp promoter regions for the presence of conserved cis-regulatory elements, such as bZIP and WRKY binding sites. Interestingly, the functional relevant upstream located W-boxes were absent in plant species as closely related as Arabidopsis lyrata, whereas an A-box cluster appeared to be conserved in the Arabidopsis species but disappeared in Brassica napus. Several orthologs were present in other species, possibly because of local or whole genome duplication events, but with distinct cis-regulatory sites in different locations. However, at least one gene copy in each family analyzed carried one W-box and one A-box in its promoter. These gene differences in SAG12 orthologs are discussed in the framework of cis- and trans-regulatory factors, of promoter and gene evolution, of genetic variation, and of the enhancement of the adaptability of plants to changing environmental conditions.

Experimental determination of evolutionary barriers to horizontal gene transfer

BACKGROUND

Horizontal gene transfer, the acquisition of genes across species boundaries, is a major source of novel phenotypes that enables microbes to rapidly adapt to new environments. How the transferred gene alters the growth - fitness - of the new host affects the success of the horizontal gene transfer event and how rapidly the gene spreads in the population. Several selective barriers - factors that impact the fitness effect of the transferred gene - have been suggested to impede the likelihood of horizontal transmission, however experimental evidence is scarce. The objective of this study was to determine the fitness effects of orthologous genes transferred from Salmonella enterica serovar Typhimurium to Escherichia coli to identify the selective barriers using highly precise experimental measurements.

RESULTS

We found that most gene transfers result in strong fitness costs. Previously identified evolutionary barriers - gene function and the number of protein-protein interactions - did not predict the fitness effects of transferred genes. In contrast, dosage sensitivity, gene length, and the intrinsic protein disorder significantly impact the likelihood of a successful horizontal transfer.

CONCLUSION

While computational approaches have been successful in describing long-term barriers to horizontal gene transfer, our experimental results identified previously underappreciated barriers that determine the fitness effects of newly transferred genes, and hence their short-term eco-evolutionary dynamics.

Promoter Architecture and Promoter Engineering in Saccharomyces cerevisiae

Promoters play an essential role in the regulation of gene expression for fine-tuning genetic circuits and metabolic pathways in Saccharomyces cerevisiae (S. cerevisiae). However, native promoters in S. cerevisiae have several limitations which hinder their applications in metabolic engineering. These limitations include an inadequate number of well-characterized promoters, poor dynamic range, and insufficient orthogonality to endogenous regulations. Therefore, it is necessary to perform promoter engineering to create synthetic promoters with better properties. Here, we review recent advances related to promoter architecture, promoter engineering and synthetic promoter applications in S. cerevisiae. We also provide a perspective of future directions in this field with an emphasis on the recent advances of machine learning based promoter designs.

The relation between crosstalk and gene regulation form revisited

Genes differ in the frequency at which they are expressed and in the form of regulation used to control their activity. In particular, positive or negative regulation can lead to activation of a gene in response to an external signal. Previous works proposed that the form of regulation of a gene correlates with its frequency of usage: positive regulation when the gene is frequently expressed and negative regulation when infrequently expressed. Such network design means that, in the absence of their regulators, the genes are found in their least required activity state, hence regulatory intervention is often necessary. Due to the multitude of genes and regulators, spurious binding and unbinding events, called “crosstalk”, could occur. To determine how the form of regulation affects the global crosstalk in the network, we used a mathematical model that includes multiple regulators and multiple target genes. We found that crosstalk depends non-monotonically on the availability of regulators. Our analysis showed that excess use of regulation entailed by the formerly suggested network design caused high crosstalk levels in a large part of the parameter space. We therefore considered the opposite ‘idle’ design, where the default unregulated state of genes is their frequently required activity state. We found, that ‘idle’ design minimized the use of regulation and thus minimized crosstalk. In addition, we estimated global crosstalk of S. cerevisiae using transcription factors binding data. We demonstrated that even partial network data could suffice to estimate its global crosstalk, suggesting its applicability to additional organisms. We found that S. cerevisiae estimated crosstalk is lower than that of a random network, suggesting that natural selection reduces crosstalk. In summary, our study highlights a new type of protein production cost which is typically overlooked: that of regulatory interference caused by the presence of excess regulators in the cell. It demonstrates the importance of whole-network descriptions, which could show effects missed by single-gene models.

Genes differ in the frequency at which they are expressed and in the form of regulation used to control their activity. The basic level of regulation is mediated by different types of DNA-binding proteins, where each type regulates particular gene(s). We distinguish between two basic forms of regulation: positive—if a gene is activated by the binding of its regulatory protein, and negative—if it is active unless bound by its regulatory protein. Due to the multitude of genes and regulators, spurious binding and unbinding events, called “crosstalk”, could occur. How does the form of regulation, positive or negative, affect the extent of regulatory crosstalk? To address this question, we used a mathematical model integrating many genes and many regulators. As intuition suggests, we found that in most of the parameter space, crosstalk increased with the availability of regulators. We propose, that crosstalk is usually reduced when networks are designed such that minimal regulation is needed, which we call the ‘idle’ design. In other words: a frequently needed gene will use negative regulation and conversely, a scarcely needed gene will employ positive regulation. In both cases, the requirement for the regulators is minimized. In addition, we demonstrate how crosstalk can be calculated from available datasets and discuss the technical challenges in such calculation, specifically data incompleteness.

Recent insights into the genotype-phenotype relationship from massively parallel genetic assays

With the molecular revolution in Biology, a mechanistic understanding of the genotype-phenotype relationship became possible. Recently, advances in DNA synthesis and sequencing have enabled the development of deep mutational scanning assays, capable of scoring comprehensive libraries of genotypes for fitness and a variety of phenotypes in massively parallel fashion. The resulting empirical genotype-fitness maps pave the way to predictive models, potentially accelerating our ability to anticipate the behaviour of pathogen and cancerous cell populations from sequencing data. Besides from cellular fitness, phenotypes of direct application in industry (e.g. enzyme activity) and medicine (e.g. antibody binding) can be quantified and even selected directly by these assays. This review discusses the technological basis of and recent developments in massively parallel genetics, along with the trends it is uncovering in the genotype-phenotype relationship (distribution of mutation effects, epistasis), their possible mechanistic bases and future directions for advancing towards the goal of predictive genetics.

Mapping DNA sequence to transcription factor binding energy in vivo

Despite the central importance of transcriptional regulation in biology, it has proven difficult to determine the regulatory mechanisms of individual genes, let alone entire gene networks. It is particularly difficult to decipher the biophysical mechanisms of transcriptional regulation in living cells and determine the energetic properties of binding sites for transcription factors and RNA polymerase. In this work, we present a strategy for dissecting transcriptional regulatory sequences using in vivo methods (massively parallel reporter assays) to formulate quantitative models that map a transcription factor binding site’s DNA sequence to transcription factor-DNA binding energy. We use these models to predict the binding energies of transcription factor binding sites to within 1 k_BT of their measured values. We further explore how such a sequence-energy mapping relates to the mechanisms of trancriptional regulation in various promoter contexts. Specifically, we show that our models can be used to design specific induction responses, analyze the effects of amino acid mutations on DNA sequence preference, and determine how regulatory context affects a transcription factor’s sequence specificity.

It has been said that we live in the “genomic era,” a time where we can readily sequence full genomes at will. However, it remains difficult to interpret much of the information within a genome. This is especially true of non-coding sequences such as promoters, which contain a number of features such as transcription factor binding sites that determine how genes are regulated. There is no straightforward regulatory “code” that tells us how transcription factor binding sites are organized within a promoter. In this work we examine how DNA sequence determines one of the most important features of a promoter, the strength with which a transcription factor binds to its DNA binding site. We discuss an approach to modeling DNA sequence-specific transcription factor binding energies in vivo using a massively parellel reporter assay. We develop models that allow us to predict the binding energy between a transcription factor and a mutated version of its binding site. We then show that this modeling technique can be used to address a number of scientific and design questions, such as engineering the behavior of genetic circuit elements or examining how transcription factors and their binding sites co-evolve.

Regulatory network structure determines patterns of intermolecular epistasis

Most phenotypes are determined by molecular systems composed of specifically interacting molecules. However, unlike for individual components, little is known about the distributions of mutational effects of molecular systems as a whole. We ask how the distribution of mutational effects of a transcriptional regulatory system differs from the distributions of its components, by first independently, and then simultaneously, mutating a transcription factor and the associated promoter it represses. We find that the system distribution exhibits increased phenotypic variation compared to individual component distributions - an effect arising from intermolecular epistasis between the transcription factor and its DNA-binding site. In large part, this epistasis can be qualitatively attributed to the structure of the transcriptional regulatory system and could therefore be a common feature in prokaryotes. Counter-intuitively, intermolecular epistasis can alleviate the constraints of individual components, thereby increasing phenotypic variation that selection could act on and facilitating adaptive evolution.

Evolution of new regulatory functions on biophysically realistic fitness landscapes

Gene expression is controlled by networks of regulatory proteins that interact specifically with external signals and DNA regulatory sequences. These interactions force the network components to co-evolve so as to continually maintain function. Yet, existing models of evolution mostly focus on isolated genetic elements. In contrast, we study the essential process by which regulatory networks grow: the duplication and subsequent specialization of network components. We synthesize a biophysical model of molecular interactions with the evolutionary framework to find the conditions and pathways by which new regulatory functions emerge. We show that specialization of new network components is usually slow, but can be drastically accelerated in the presence of regulatory crosstalk and mutations that promote promiscuous interactions between network components.Gene networks evolve by transcription factor (TF) duplication and divergence of their binding site specificities, but little is known about the global constraints at play. Here, the authors study the coevolution of TFs and binding sites using a biophysical-evolutionary approach, and show that the emerging complex fitness landscapes strongly influence regulatory evolution with a role for crosstalk.

On the mechanistic nature of epistasis in a canonical cis-regulatory element

Understanding the relation between genotype and phenotype remains a major challenge. The difficulty of predicting individual mutation effects, and particularly the interactions between them, has prevented the development of a comprehensive theory that links genotypic changes to their phenotypic effects. We show that a general thermodynamic framework for gene regulation, based on a biophysical understanding of protein-DNA binding, accurately predicts the sign of epistasis in a canonical cis-regulatory element consisting of overlapping RNA polymerase and repressor binding sites. Sign and magnitude of individual mutation effects are sufficient to predict the sign of epistasis and its environmental dependence. Thus, the thermodynamic model offers the correct null prediction for epistasis between mutations across DNA-binding sites. Our results indicate that a predictive theory for the effects of cis-regulatory mutations is possible from first principles, as long as the essential molecular mechanisms and the constraints these impose on a biological system are accounted for.

DOI:http://dx.doi.org/10.7554/eLife.25192.001

Mutations are changes to DNA that provide the raw material upon which evolution can act. Therefore, to understand evolution, we need to know the effects of mutations, and how those mutations interact with each other (a phenomenon referred to as epistasis). So far, few mathematical models allow scientists to predict the effects of mutations, and even fewer are able to predict epistasis.

Biological systems are complex and consist of many proteins and other molecules. Genes are the sections of DNA that provide the instructions needed to produce these molecules, and some genes encode proteins that can bind to DNA to control whether other genes are switched on or off. Lagator, Paixão et al. have now used mathematical models and experiments to understand how the environment inside the cells of a bacterium known as E. coli, specifically the amount of particular proteins, affects epistasis.

These mathematical models are able to predict interactions between mutations in the most abundant class of DNA-binding sites in proteins. This approach found that the nature of the interaction between mutations can be explained through biophysical laws, combined with the basic knowledge of the logic of how genes regulate each other’s activities. Furthermore, the models allow Lagator, Paixão et al. to predict interactions between mutations in several different environments, such as the presence of a new food source or a toxin, defined by the amounts of relevant DNA-binding proteins in cells.

By providing new ways of understanding how genes are regulated in bacteria, and how gene regulation is affected by mutations, these findings contribute to our understanding of how organisms evolve. In addition, this work may help us to build artificial networks of genes that interact with each other to produce a desired response, such as more efficient production of fuel from ethanol or the break down of hazardous chemicals.

DOI:http://dx.doi.org/10.7554/eLife.25192.002

Quantification of Cooperativity in Heterodimer-DNA Binding Improves the Accuracy of Binding Specificity Models

Many transcription factors (TFs) have the ability to cooperate on DNA elements as heterodimers. Despite the significance of TF heterodimerization for gene regulation, a quantitative understanding of cooperativity between various TF dimer partners and its impact on heterodimer DNA binding specificity models is still lacking. Here, we used a novel integrative approach, combining microfluidics-steered measurements of dimer-DNA assembly with mechanistic modeling of the implicated protein-protein-DNA interactions to quantitatively interrogate the cooperative DNA binding behavior of the adipogenic peroxisome proliferator-activated receptor γ (PPARγ):retinoid X receptor α (RXRα) heterodimer. Using the high throughput MITOMI (mechanically induced trapping of molecular interactions) platform, we derived equilibrium DNA binding data for PPARγ, RXRα, as well as the PPARγ:RXRα heterodimer to more than 300 target DNA sites and variants thereof. We then quantified cooperativity underlying heterodimer-DNA binding and derived an integrative heterodimer DNA binding constant. Using this cooperativity-inclusive constant, we were able to build a heterodimer-DNA binding specificity model that has superior predictive power than the one based on a regular one-site equilibrium. Our data further revealed that individual nucleotide substitutions within the target site affect the extent of cooperativity in PPARγ:RXRα-DNA binding. Our study therefore emphasizes the importance of assessing cooperativity when generating DNA binding specificity models for heterodimers.

When the Scaffold Cannot Be Ignored: The Role of the Hydrophobic Core in Ligand Binding and Specificity

The traditional view of protein-ligand binding treats a protein as comprising distinct binding epitopes on the surface of a degenerate structural scaffold, largely ignoring the impact of a protein's energy landscape. To determine the robustness of this simplification, we compared two small helix-turn-helix transcription factors with different energy landscapes. λ-Repressor is stable and well folded, while MarA appears to be marginally stable with multiple native conformations (molten). While λ-repressor is known to tolerate any hydrophobic mutation in the core, we find MarA drastically less tolerant to core mutation. Moreover, core mutations in MarA (distant from the DNA-binding interface) change the relative affinities of its binding partners, altering ligand specificity. These results can be explained by taking into account the effects of mutations on the entire energy landscape and not just the native state. Thus, for proteins with multiple conformations that are close in energy, such as many intrinsically disordered proteins, residues distant from the active site can alter both binding affinity and specificity.

Mechanically Induced Trapping of Molecular Interactions and Its Applications

Measuring binding affinities and association/dissociation rates of molecular interactions is important for a quantitative understanding of cellular mechanisms. Many low-throughput methods have been developed throughout the years to obtain these parameters. Acquiring data with higher accuracy and throughput is, however, necessary to characterize complex biological networks. Here, we provide an overview of a high-throughput microfluidic method based on mechanically induced trapping of molecular interactions (MITOMI). MITOMI can be used to obtain affinity constants and kinetic rates of hundreds of protein-ligand interactions in parallel. It has been used in dozens of studies to measure binding affinities of transcription factors, map protein interaction networks, identify pharmacological inhibitors, and perform high-throughput, low-cost molecular diagnostics. This article covers the technological aspects of MITOMI and its applications.

Evolution of H3K27me3-marked chromatin is linked to gene expression evolution and to patterns of gene duplication and diversification

Histone modifications are critical for the regulation of gene expression, cell type specification, and differentiation. However, evolutionary patterns of key modifications that regulate gene expression in differentiating organisms have not been examined. Here we mapped the genomic locations of the repressive mark histone 3 lysine 27 trimethylation (H3K27me3) in four species of Drosophila, and compared these patterns to those in C. elegans. We found that patterns of H3K27me3 are highly conserved across species, but conservation is substantially weaker among duplicated genes. We further discovered that retropositions are associated with greater evolutionary changes in H3K27me3 and gene expression than tandem duplications, indicating that local chromatin constraints influence duplicated gene evolution. These changes are also associated with concomitant evolution of gene expression. Our findings reveal the strong conservation of genomic architecture governed by an epigenetic mark across distantly related species and the importance of gene duplication in generating novel H3K27me3 profiles.

Characterization of the transcriptional activation domains of human TEF3-1 (transcription enhancer factor 3 isoform 1)

TEF3-1 (transcription enhancer factor 3 isoform 1) is a human transcriptional factor, which has a N-terminal TEA/ATTS domain supposedly for DNA binding and C-terminal PRD and STY domains for transcriptional activation. Taking advantage of the efficient reporter design of yeast two-hybrid system, we characterized the TEF3-1 domains in activating gene expression. Previously study usually mentioned that the C-terminal domain of TEF3-1 has the transcriptional activity, however, our data shows that the peptides TEF3-11-66 and TEF3-1197-434 functioned as two independent activation domains, suggesting that N-terminal domain of TEF3-1 also has transcriptional activation capacity. Additionally, more deletions of amino acids 197-434 showed that only the peptides TEF3-1197-265 contained the minimum sequences for the C-terminal transcriptional activation domain. The protein structure is predicted to contain a helix-turn-helix structure in TEF3-11-66 and four β sheets in TEF3-1197-265. Finally, after the truncated fragments of TEF3-1 were expressed in HUVEC cells, the whole TEF3-1 and the two activation domains could increase F-actin stress fiber, cell proliferation, migration and targeted gene expression. Further analysis and characterization of the activation domains in TEF3-1 may broaden our understanding of the gene involved in angiogenesis and other pathological processes.

An evolutionary perspective on protein moonlighting

Moonlighting proteins serve one or more novel functions in addition to their canonical roles. Moonlighting functions arise when an adventitious interaction between a protein and a new partner improves fitness of the organism. Selective pressure for improvement in the new function can result in two alternative outcomes. The gene encoding the newly bifunctional protein may duplicate and diverge so as to encode two proteins, each of which serves only one function. Alternatively, genetic changes that minimize adaptive conflict between the two functions and/or improve control over the time and place at which each function is served can lead to a moonlighting protein. Importantly, genetic changes that enhance a moonlighting function can occur in the gene encoding the moonlighting protein itself, in a gene that affects the structure of its new partner or in a gene encoding a transcription factor that controls expression of either partner. The evolutionary history of each moonlighting protein is complex, depending on the stochastic occurrence of genetic changes such as gene duplication and point mutations, and the effects of those changes on fitness. Population effects, particularly loss of promising individuals due to random genetic drift, also play a role in the emergence of a moonlighting protein. The ultimate outcome is not necessarily the 'optimal' solution to the problem of serving two functions, but may be 'good enough' so that fitness becomes limited by some other function.

Hybrid incompatibility arises in a sequence-based bioenergetic model of transcription factor binding

Postzygotic isolation between incipient species results from the accumulation of incompatibilities that arise as a consequence of genetic divergence. When phenotypes are determined by regulatory interactions, hybrid incompatibility can evolve even as a consequence of parallel adaptation in parental populations because interacting genes can produce the same phenotype through incompatible allelic combinations. We explore the evolutionary conditions that promote and constrain hybrid incompatibility in regulatory networks using a bioenergetic model (combining thermodynamics and kinetics) of transcriptional regulation, considering the bioenergetic basis of molecular interactions between transcription factors (TFs) and their binding sites. The bioenergetic parameters consider the free energy of formation of the bond between the TF and its binding site and the availability of TFs in the intracellular environment. Together these determine fractional occupancy of the TF on the promoter site, the degree of subsequent gene expression and in diploids, and the degree of dominance among allelic interactions. This results in a sigmoid genotype-phenotype map and fitness landscape, with the details of the shape determining the degree of bioenergetic evolutionary constraint on hybrid incompatibility. Using individual-based simulations, we subjected two allopatric populations to parallel directional or stabilizing selection. Misregulation of hybrid gene expression occurred under either type of selection, although it evolved faster under directional selection. Under directional selection, the extent of hybrid incompatibility increased with the slope of the genotype-phenotype map near the derived parental expression level. Under stabilizing selection, hybrid incompatibility arose from compensatory mutations and was greater when the bioenergetic properties of the interaction caused the space of nearly neutral genotypes around the stable expression level to be wide. F2's showed higher hybrid incompatibility than F1's to the extent that the bioenergetic properties favored dominant regulatory interactions. The present model is a mechanistically explicit case of the Bateson-Dobzhansky-Muller model, connecting environmental selective pressure to hybrid incompatibility through the molecular mechanism of regulatory divergence. The bioenergetic parameters that determine expression represent measurable properties of transcriptional regulation, providing a predictive framework for empirical studies of how phenotypic evolution results in epistatic incompatibility at the molecular level in hybrids.

iSLIM: a comprehensive approach to mapping and characterizing gene regulatory networks

Mapping gene regulatory networks is a significant challenge in systems biology, yet only a few methods are currently capable of systems-level identification of transcription factors (TFs) that bind a specific regulatory element. We developed a microfluidic method for integrated systems-level interaction mapping of TF-DNA interactions, generating and interrogating an array of 423 full-length Drosophila TFs. With integrated systems-level interaction mapping, it is now possible to rapidly and quantitatively map gene regulatory networks of higher eukaryotes.

Why transcription factor binding sites are ten nucleotides long

Gene expression is controlled primarily by transcription factors, whose DNA binding sites are typically 10 nt long. We develop a population-genetic model to understand how the length and information content of such binding sites evolve. Our analysis is based on an inherent trade-off between specificity, which is greater in long binding sites, and robustness to mutation, which is greater in short binding sites. The evolutionary stable distribution of binding site lengths predicted by the model agrees with the empirical distribution (5-31 nt, with mean 9.9 nt for eukaryotes), and it is remarkably robust to variation in the underlying parameters of population size, mutation rate, number of transcription factor targets, and strength of selection for proper binding and selection against improper binding. In a systematic data set of eukaryotic and prokaryotic transcription factors we also uncover strong relationships between the length of a binding site and its information content per nucleotide, as well as between the number of targets a transcription factor regulates and the information content in its binding sites. Our analysis explains these features as well as the remarkable conservation of binding site characteristics across diverse taxa.