Conservation and regulatory associations of a wide affinity range of mouse transcription factor binding sites

Cooperative assembly confers regulatory specificity and long-term genetic circuit stability

A ubiquitous feature of eukaryotic transcriptional regulation is cooperative self-assembly between transcription factors (TFs) and DNA cis-regulatory motifs. It is thought that this strategy enables specific regulatory connections to be formed in gene networks between otherwise weakly interacting, low-specificity molecular components. Here, using synthetic gene circuits constructed in yeast, we find that high regulatory specificity can emerge from cooperative, multivalent interactions among artificial zinc-finger-based TFs. We show that circuits "wired" using the strategy of cooperative TF assembly are effectively insulated from aberrant misregulation of the host cell genome. As we demonstrate in experiments and mathematical models, this mechanism is sufficient to rescue circuit-driven fitness defects, resulting in genetic and functional stability of circuits in long-term continuous culture. Our naturally inspired approach offers a simple, generalizable means for building high-fidelity, evolutionarily robust gene circuits that can be scaled to a wide range of host organisms and applications.

The evolution, evolvability and engineering of gene regulatory DNA

Mutations in non-coding regulatory DNA sequences can alter gene expression, organismal phenotype and fitness^1-3. Constructing complete fitness landscapes, in which DNA sequences are mapped to fitness, is a long-standing goal in biology, but has remained elusive because it is challenging to generalize reliably to vast sequence spaces^4-6. Here we build sequence-to-expression models that capture fitness landscapes and use them to decipher principles of regulatory evolution. Using millions of randomly sampled promoter DNA sequences and their measured expression levels in the yeast Saccharomyces cerevisiae, we learn deep neural network models that generalize with excellent prediction performance, and enable sequence design for expression engineering. Using our models, we study expression divergence under genetic drift and strong-selection weak-mutation regimes to find that regulatory evolution is rapid and subject to diminishing returns epistasis; that conflicting expression objectives in different environments constrain expression adaptation; and that stabilizing selection on gene expression leads to the moderation of regulatory complexity. We present an approach for using such models to detect signatures of selection on expression from natural variation in regulatory sequences and use it to discover an instance of convergent regulatory evolution. We assess mutational robustness, finding that regulatory mutation effect sizes follow a power law, characterize regulatory evolvability, visualize promoter fitness landscapes, discover evolvability archetypes and illustrate the mutational robustness of natural regulatory sequence populations. Our work provides a general framework for designing regulatory sequences and addressing fundamental questions in regulatory evolution.

Adaptive gene misregulation

Because gene expression is important for evolutionary adaptation, its misregulation is an important cause of maladaptation. A misregulated gene can be incorrectly silent ("off") when a transcription factor (TF) that is required for its activation does not binds its regulatory region. Conversely, a misregulated gene can be incorrectly active ("on") when a TF not normally involved in its activation binds its regulatory region, a phenomenon also known as regulatory crosstalk. DNA mutations that destroy or create TF binding sites on DNA are an important source of misregulation and crosstalk. Although misregulation reduces fitness in an environment to which an organism is well-adapted, it may become adaptive in a new environment. Here, I derive simple yet general mathematical expressions that delimit the conditions under which misregulation can be adaptive. These expressions depend on the strength of selection against misregulation, on the fraction of DNA sequence space filled with TF binding sites, and on the fraction of genes that must be expressed for optimal adaptation. I then use empirical data from RNA sequencing, protein-binding microarrays, and genome evolution, together with population genetic simulations to ask when these conditions are likely to be met. I show that they can be met under realistic circumstances, but these circumstances may vary among organisms and environments. My analysis provides a framework in which improved theory and data collection can help us demonstrate the role of misregulation in adaptation. It also shows that misregulation, like DNA mutation, is one of life's many imperfections that can help propel Darwinian evolution.

Transcriptional reprogramming by oxidative stress occurs within a predefined chromatin accessibility landscape

Reactive oxygen species (ROS) are important signaling molecules in many physiological processes, yet excess ROS leads to cell damage and can lead to pathology. Accordingly, cells need to maintain tight regulation of ROS levels, and ROS-responsive transcriptional reprogramming is central to this process. Although it has long been recognized that oxidative stress leads to rapid, significant changes in gene expression, the impact of oxidative stress on the underlying chromatin accessibility landscape remained unclear. Here, we asked whether ROS-responsive transcriptional reprogramming is accompanied by reprogramming of the chromatin environment in MCF7 human breast cancer cells. Using a time-course exposure to multiple inducers of oxidative stress, we determined that the widespread ROS-responsive changes in gene expression induced by ROS occur with minimal changes to the chromatin environment. While we did observe changes in chromatin accessibility, these changes were: (1) far less numerous than gene expression changes after oxidative stress, and (2) occur within pre-existing regions of accessible chromatin. Transcription factor (TF) footprinting analysis of our ATAC-seq experiments identified 5 TFs or TF families with evidence for ROS-responsive changes in DNA binding: NRF2, AP-1, p53, NFY, and SP/KLF. Importantly, several of these (AP-1, NF-Y, and SP/KLF factors) have not been previously implicated as widespread regulators in the response to ROS. In summary, we have characterized genome-wide changes in gene expression and chromatin accessibility in response to ROS treatment of MCF7 cells, and we have found that regulation of the large-scale transcriptional response to excess ROS is primarily constrained by the cell's pre-existing chromatin landscape.

Contribution of nonconsensus base pairs within ArsR binding sequences toward ArsR-DNA binding and arsenic-mediated transcriptional induction

Background

A transcriptional reporter is the key component in bacterial biosensors which are employed to monitor the induction or repression of a reporter gene corresponding to environmental change. Interaction of a transcription factor with its consensus sequence generated by using a position weight matrix (PWM) model is crucial for its sensitivity of the reporter. However, recent studies suggest that PWM model based on independent contribution of individual consensus base pairs to protein interaction is often insufficient to explain complex regulation, such as the effect of nonconsensus sequences on the protein-DNA binding affinity. In the present study, we employed a simpler prokaryotic arsenic repressor (ArsR) regulation system to access the protein-DNA recognition. Contribution of nonconsensus base pairs within ArsR binding sequences toward ArsR-DNA binding and arsenic-mediated transcriptional induction was studied.

Results

We constructed a series of arsenic responsive reporters, each comprising two copies of the ArsR binding sequences from different resources. We found that high arsenic-mediated induction specifically requires the binding sequence from Escherichia coli to be placed at the first binding sequence; however, no such preference was observed for the second binding sequence, which could be from Acidithiobacillus ferrooxidans, plasmid R773, Synechococcus, or a core binding sequence of arsR. By creating a series of reporters differed at the nonconsensus base pairs of the second binding sequence, we observed that some constructs bound weakly while others strongly to ArsR. Most interestingly, although a number of these reporters showed similar binding affinity to ArsR, their arsenic-dependent induction differed significantly.

Conclusions

The results indicated that nonconsensus base pairs could have profound influence on protein binding and may also modulate post-binding function. These findings provide new insights into the complex regulation of gene expression and facilitate the development of transcriptional reporter-based biosensors.

Classification of different types of estrogen receptor alpha binding sites in MCF-7 cells

Estrogen Receptor alpha (ERα) is a ligand-activated transcription factor and it has a prominent role in both physiological and pathological processes of the reproductive system. ERα has been investigated extensively in breast cancer and the MCF-7 breast-cancer-derived cell line is a widely used model for the study of its behavior. In this paper we provide a systematic catalog of the possible scenarios of binding to more than 80,000 ERα transcription factor binding sites based on the mechanism of ERα binding to DNA (upon both vehicle and estradiol (E2) treatment). A key feature of the estrogen-driven genetic programs is the presence or absence of the specific response element referred to as the estrogen response element (ERE). While ERα-driven super-enhancers are key components of estrogen-dependent genetic programs, three additional classes of enhancers could be identified: one with the presence of ERE where the ERα bound to the DNA prior of E2-treatment, one where the E2 was required for ERα binding even in the presence of ERE, and one where the ERα binding is established through the response elements of the collaborating factors. Our results suggest that different scenarios of ERα binding result in different genetic programs.

Degenerate Pax2 and Senseless binding motifs improve detection of low-affinity sites required for enhancer specificity

Cells use thousands of regulatory sequences to recruit transcription factors (TFs) and produce specific transcriptional outcomes. Since TFs bind degenerate DNA sequences, discriminating functional TF binding sites (TFBSs) from background sequences represents a significant challenge. Here, we show that a Drosophila regulatory element that activates Epidermal Growth Factor signaling requires overlapping, low-affinity TFBSs for competing TFs (Pax2 and Senseless) to ensure cell- and segment-specific activity. Testing available TF binding models for Pax2 and Senseless, however, revealed variable accuracy in predicting such low-affinity TFBSs. To better define parameters that increase accuracy, we developed a method that systematically selects subsets of TFBSs based on predicted affinity to generate hundreds of position-weight matrices (PWMs). Counterintuitively, we found that degenerate PWMs produced from datasets depleted of high-affinity sequences were more accurate in identifying both low- and high-affinity TFBSs for the Pax2 and Senseless TFs. Taken together, these findings reveal how TFBS arrangement can be constrained by competition rather than cooperativity and that degenerate models of TF binding preferences can improve identification of biologically relevant low affinity TFBSs.

While all cells in an organism share a common genome, each cell type must express the appropriate combination of genes needed for its specific function. Cells activate and repress different parts of the genome using transcription factor proteins that bind regulatory regions known as enhancers. We currently have an incomplete view of how enhancers recruit transcription factors to yield accurate gene activation and repression. This problem is complicated by the fact that most animals contain over a thousand different transcription factors, and each can generally bind multiple DNA sequences. Thus, it is difficult to predict which transcription factors interact with which enhancers. To gain insights into this process, we focused on determining how an enhancer that activates a gene needed to make liver-like cells is regulated in a precise manner in the fruit-fly embryo. We demonstrate that the specific activity of this enhancer depends on weak and overlapping transcription factor binding sites. Furthermore, we demonstrate that computational models that include weak transcription factor interactions yield better predictive accuracy. These results shed light on how DNA sequences determine enhancer activity and the types of strategies that are most useful for predicting transcription factor binding sites in the genome.

Divergence in DNA Specificity among Paralogous Transcription Factors Contributes to Their Differential In Vivo Binding

Paralogous transcription factors (TFs) are oftentimes reported to have identical DNA-binding motifs, despite the fact that they perform distinct regulatory functions. Differential genomic targeting by paralogous TFs is generally assumed to be due to interactions with protein co-factors or the chromatin environment. Using a computational-experimental framework called iMADS (integrative modeling and analysis of differential specificity), we show that, contrary to previous assumptions, paralogous TFs bind differently to genomic target sites even in vitro. We used iMADS to quantify, model, and analyze specificity differences between 11 TFs from 4 protein families. We found that paralogous TFs have diverged mainly at medium- and low-affinity sites, which are poorly captured by current motif models. We identify sequence and shape features differentially preferred by paralogous TFs, and we show that the intrinsic differences in specificity among paralogous TFs contribute to their differential in vivo binding. Thus, our study represents a step forward in deciphering the molecular mechanisms of differential specificity in TF families.

ZFX acts as a transcriptional activator in multiple types of human tumors by binding downstream of transcription start sites at the majority of CpG island promoters

High expression of the transcription factor ZFX is correlated with proliferation, tumorigenesis, and patient survival in multiple types of human cancers. However, the mechanism by which ZFX influences transcriptional regulation has not been determined. We performed ChIP-seq in four cancer cell lines (representing kidney, colon, prostate, and breast cancers) to identify ZFX binding sites throughout the human genome. We identified ~9,000 ZFX binding sites and found that the majority of the sites are in CpG island promoters. Moreover, genes with promoters bound by ZFX are expressed at higher levels than genes with promoters not bound by ZFX. To determine if ZFX contributes to regulation of the promoters to which it is bound, we performed RNA-seq analysis after knockdown of ZFX by siRNA in prostate and breast cancer cells. Many genes with promoters bound by ZFX were downregulated upon ZFX knockdown, supporting the hypothesis that ZFX acts as a transcriptional activator. Surprisingly, ZFX binds at +240 bp downstream of the TSS of the responsive promoters. Using Nucleosome Occupancy and Methylome Sequencing (NOMe-seq), we show that ZFX binds between the open chromatin region at the TSS and the first downstream nucleosome, suggesting that ZFX may play a critical role in promoter architecture. We have also shown that a closely related zinc finger protein ZNF711 has a similar binding pattern at CpG island promoters, but ZNF711 may play a subordinate role to ZFX. This functional characterization of ZFX provides important new insights into transcription, chromatin structure, and the regulation of the cancer transcriptome.

Pan-cancer screen for mutations in non-coding elements with conservation and cancer specificity reveals correlations with expression and survival

Cancer develops by accumulation of somatic driver mutations, which impact cellular function. Mutations in non-coding regulatory regions can now be studied genome-wide and further characterized by correlation with gene expression and clinical outcome to identify driver candidates. Using a new two-stage procedure, called ncDriver, we first screened 507 ICGC whole-genomes from 10 cancer types for non-coding elements, in which mutations are both recurrent and have elevated conservation or cancer specificity. This identified 160 significant non-coding elements, including the TERT promoter, a well-known non-coding driver element, as well as elements associated with known cancer genes and regulatory genes (e.g., PAX5, TOX3, PCF11, MAPRE3). However, in some significant elements, mutations appear to stem from localized mutational processes rather than recurrent positive selection in some cases. To further characterize the driver potential of the identified elements and shortlist candidates, we identified elements where presence of mutations correlated significantly with expression levels (e.g., TERT and CDH10) and survival (e.g., CDH9 and CDH10) in an independent set of 505 TCGA whole-genome samples. In a larger pan-cancer set of 4128 TCGA exomes with expression profiling, we identified mutational correlation with expression for additional elements (e.g., near GATA3, CDC6, ZNF217, and CTCF transcription factor binding sites). Survival analysis further pointed to MIR122, a known marker of poor prognosis in liver cancer. In conclusion, the screen for significant mutation patterns coupled with correlative mutational analysis identified new individual driver candidates and suggest that some non-coding mutations recurrently affect expression and play a role in cancer development.

Mutations in the “non-coding” part of the genome have been identified that could be involved in driving cancer development. Jakob Pedersen, Henrik Hornshøj and colleagues from Aarhus University Hospital in Denmark and MIT in the United States developed a two-stage procedure to identify elements that could be driving cancer development in the part of DNA that does not code for proteins. They conducted statisical analyses on catalogs of tumor genomes to identify recurrent mutations. They then evaluated how specific these mutations were to different cancer types, their predicted functional impact, and their association with gene expression and patient survival. The analyses identified mutations in the non-coding part of cancer genomes that could be driving tumor development, but further analyses on larger sample sets need to be conducted to validate the results, which could provide a basis for biomarker discovery and precision medical treatment.

Mathematical Modeling of Avidity Distribution and Estimating General Binding Properties of Transcription Factors from Genome-Wide Binding Profiles

The shape of the experimental frequency distributions (EFD) of diverse molecular interaction events quantifying genome-wide binding is often skewed to the rare but abundant quantities. Such distributions are systematically deviated from standard power-law functions proposed by scale-free network models suggesting that more explanatory and predictive probabilistic model(s) are needed. Identification of the mechanism-based data-driven statistical distributions that provide an estimation and prediction of binding properties of transcription factors from genome-wide binding profiles is the goal of this analytical survey. Here, we review and develop an analytical framework for modeling, analysis, and prediction of transcription factor (TF) DNA binding properties detected at the genome scale. We introduce a mixture probabilistic model of binding avidity function that includes nonspecific and specific binding events. A method for decomposition of specific and nonspecific TF-DNA binding events is proposed. We show that the Kolmogorov-Waring (KW) probability function (PF), modeling the steady state TF binding-dissociation stochastic process, fits well with the EFD for diverse TF-DNA binding datasets. Furthermore, this distribution predicts total number of TF-DNA binding sites (BSs), estimating specificity and sensitivity as well as other basic statistical features of DNA-TF binding when the experimental datasets are noise-rich and essentially incomplete. The KW distribution fits equally well to TF-DNA binding activity for different TFs including ERE, CREB, STAT1, Nanog, and Oct4. Our analysis reveals that the KW distribution and its generalized form provides the family of power-law-like distributions given in terms of hypergeometric series functions, including standard and generalized Pareto and Waring distributions, providing flexible and common skewed forms of the transcription factor binding site (TFBS) avidity distribution function. We suggest that the skewed binding events may be due to a wide range of evolutionary processes of creating weak avidity TFBS associated with random mutations, while the rare high-avidity binding sites (i.e., high-avidity evolutionarily conserved canonical e-boxes) rarely occurred. These, however, may be positively selected in microevolution.

Quantitative characterization of conformational-specific protein-DNA binding using a dual-spectral interferometric imaging biosensor

DNA-binding proteins play crucial roles in the maintenance and functions of the genome and yet, their specific binding mechanisms are not fully understood. Recently, it was discovered that DNA-binding proteins recognize specific binding sites to carry out their functions through an indirect readout mechanism by recognizing and capturing DNA conformational flexibility and deformation. High-throughput DNA microarray-based methods that provide large-scale protein-DNA binding information have shown effective and comprehensive analysis of protein-DNA binding affinities, but do not provide information of DNA conformational changes in specific protein-DNA complexes. Building on the high-throughput capability of DNA microarrays, we demonstrate a quantitative approach that simultaneously measures the amount of protein binding to DNA and nanometer-scale DNA conformational change induced by protein binding in a microarray format. Both measurements rely on spectral interferometry on a layered substrate using a single optical instrument in two distinct modalities. In the first modality, we quantitate the amount of binding of protein to surface-immobilized DNA in each DNA spot using a label-free spectral reflectivity technique that accurately measures the surface densities of protein and DNA accumulated on the substrate. In the second modality, for each DNA spot, we simultaneously measure DNA conformational change using a fluorescence vertical sectioning technique that determines average axial height of fluorophores tagged to specific nucleotides of the surface-immobilized DNA. The approach presented in this paper, when combined with current high-throughput DNA microarray-based technologies, has the potential to serve as a rapid and simple method for quantitative and large-scale characterization of conformational specific protein-DNA interactions.

The Soft Touch: Low-Affinity Transcription Factor Binding Sites in Development and Evolution

Transcription factor proteins regulate gene expression by binding to specific DNA regions. Most studies of transcription factor binding sites have focused on the highest affinity sites for each factor. There is abundant evidence, however, that binding sites with a range of affinities, including very low affinities, are critical to gene regulation. Here, we present the theoretical and experimental evidence for the importance of low-affinity sites in gene regulation and development. We also discuss the implications of the widespread use of low-affinity sites in eukaryotic genomes for robustness, precision, specificity, and evolution of gene regulation.

Identifying transcriptional cis-regulatory modules in animal genomes

Gene expression is regulated through the activity of transcription factors (TFs) and chromatin-modifying proteins acting on specific DNA sequences, referred to as cis-regulatory elements. These include promoters, located at the transcription initiation sites of genes, and a variety of distal cis-regulatory modules (CRMs), the most common of which are transcriptional enhancers. Because regulated gene expression is fundamental to cell differentiation and acquisition of new cell fates, identifying, characterizing, and understanding the mechanisms of action of CRMs is critical for understanding development. CRM discovery has historically been challenging, as CRMs can be located far from the genes they regulate, have few readily identifiable sequence characteristics, and for many years were not amenable to high-throughput discovery methods. However, the recent availability of complete genome sequences and the development of next-generation sequencing methods have led to an explosion of both computational and empirical methods for CRM discovery in model and nonmodel organisms alike. Experimentally, CRMs can be identified through chromatin immunoprecipitation directed against TFs or histone post-translational modifications, identification of nucleosome-depleted 'open' chromatin regions, or sequencing-based high-throughput functional screening. Computational methods include comparative genomics, clustering of known or predicted TF-binding sites, and supervised machine-learning approaches trained on known CRMs. All of these methods have proven effective for CRM discovery, but each has its own considerations and limitations, and each is subject to a greater or lesser number of false-positive identifications. Experimental confirmation of predictions is essential, although shortcomings in current methods suggest that additional means of validation need to be developed. For further resources related to this article, please visit the WIREs website.

CONFLICT OF INTEREST

The authors have declared no conflicts of interest for this article.

Automated genomic context analysis and experimental validation platform for discovery of prokaryote transcriptional regulator functions

BACKGROUND

The clustering of genes in a pathway and the co-location of functionally related genes is widely recognized in prokaryotes. We used these characteristics to predict the metabolic involvement for a Transcriptional Regulator (TR) of unknown function, identified and confirmed its biological activity.

RESULTS

A software tool that identifies the genes encoded within a defined genomic neighborhood for the subject TR and its homologs was developed. The output lists of genes in the genetic neighborhoods, their annotated functions, the reactants/products, and identifies the metabolic pathway in which the encoded-proteins function. When a set of TRs of known function was analyzed, we observed that their homologs frequently had conserved genomic neighborhoods that co-located the metabolically related genes regulated by the subject TR. We postulate that TR effectors are metabolites in the identified pathways; indeed the known effectors were present. We analyzed Bxe_B3018 from Burkholderia xenovorans, a TR of unknown function and predicted that this TR was related to the glycine, threonine and serine degradation. We tested the binding of metabolites in these pathways and for those that bound, their ability to modulate TR binding to its specific DNA operator sequence. Using rtPCR, we confirmed that methylglyoxal was an effector of Bxe_3018.

CONCLUSION

These studies provide the proof of concept and validation of a systematic approach to the discovery of the biological activity for proteins of unknown function, in this case a TR. Bxe_B3018 is a methylglyoxal responsive TR that controls the expression of an operon composed of a putative efflux system.

Abnormal dosage of ultraconserved elements is highly disfavored in healthy cells but not cancer cells

Ultraconserved elements (UCEs) are strongly depleted from segmental duplications and copy number variations (CNVs) in the human genome, suggesting that deletion or duplication of a UCE can be deleterious to the mammalian cell. Here we address the process by which CNVs become depleted of UCEs. We begin by showing that depletion for UCEs characterizes the most recent large-scale human CNV datasets and then find that even newly formed de novo CNVs, which have passed through meiosis at most once, are significantly depleted for UCEs. In striking contrast, CNVs arising specifically in cancer cells are, as a rule, not depleted for UCEs and can even become significantly enriched. This observation raises the possibility that CNVs that arise somatically and are relatively newly formed are less likely to have established a CNV profile that is depleted for UCEs. Alternatively, lack of depletion for UCEs from cancer CNVs may reflect the diseased state. In support of this latter explanation, somatic CNVs that are not associated with disease are depleted for UCEs. Finally, we show that it is possible to observe the CNVs of induced pluripotent stem (iPS) cells become depleted of UCEs over time, suggesting that depletion may be established through selection against UCE-disrupting CNVs without the requirement for meiotic divisions.

Absence of a simple code: how transcription factors read the genome

Transcription factors (TFs) influence cell fate by interpreting the regulatory DNA within a genome. TFs recognize DNA in a specific manner; the mechanisms underlying this specificity have been identified for many TFs based on 3D structures of protein-DNA complexes. More recently, structural views have been complemented with data from high-throughput in vitro and in vivo explorations of the DNA-binding preferences of many TFs. Together, these approaches have greatly expanded our understanding of TF-DNA interactions. However, the mechanisms by which TFs select in vivo binding sites and alter gene expression remain unclear. Recent work has highlighted the many variables that influence TF-DNA binding, while demonstrating that a biophysical understanding of these many factors will be central to understanding TF function.

An improved predictive recognition model for Cys(2)-His(2) zinc finger proteins

Cys(2)-His(2) zinc finger proteins (ZFPs) are the largest family of transcription factors in higher metazoans. They also represent the most diverse family with regards to the composition of their recognition sequences. Although there are a number of ZFPs with characterized DNA-binding preferences, the specificity of the vast majority of ZFPs is unknown and cannot be directly inferred by homology due to the diversity of recognition residues present within individual fingers. Given the large number of unique zinc fingers and assemblies present across eukaryotes, a comprehensive predictive recognition model that could accurately estimate the DNA-binding specificity of any ZFP based on its amino acid sequence would have great utility. Toward this goal, we have used the DNA-binding specificities of 678 two-finger modules from both natural and artificial sources to construct a random forest-based predictive model for ZFP recognition. We find that our recognition model outperforms previously described determinant-based recognition models for ZFPs, and can successfully estimate the specificity of naturally occurring ZFPs with previously defined specificities.

Protein-DNA binding: complexities and multi-protein codes

Binding of proteins to particular DNA sites across the genome is a primary determinant of specificity in genome maintenance and gene regulation. DNA-binding specificity is encoded at multiple levels, from the detailed biophysical interactions between proteins and DNA, to the assembly of multi-protein complexes. At each level, variation in the mechanisms used to achieve specificity has led to difficulties in constructing and applying simple models of DNA binding. We review the complexities in protein–DNA binding found at multiple levels and discuss how they confound the idea of simple recognition codes. We discuss the impact of new high-throughput technologies for the characterization of protein–DNA binding, and how these technologies are uncovering new complexities in protein–DNA recognition. Finally, we review the concept of multi-protein recognition codes in which new DNA-binding specificities are achieved by the assembly of multi-protein complexes.

Low-affinity transcription factor binding sites shape morphogen responses and enhancer evolution

In the era of functional genomics, the role of transcription factor (TF)-DNA binding affinity is of increasing interest: for example, it has recently been proposed that low-affinity genomic binding events, though frequent, are functionally irrelevant. Here, we investigate the role of binding site affinity in the transcriptional interpretation of Hedgehog (Hh) morphogen gradients. We noted that enhancers of several Hh-responsive Drosophila genes have low predicted affinity for Ci, the Gli family TF that transduces Hh signalling in the fly. Contrary to our initial hypothesis, improving the affinity of Ci/Gli sites in enhancers of dpp, wingless and stripe, by transplanting optimal sites from the patched gene, did not result in ectopic responses to Hh signalling. Instead, we found that these enhancers require low-affinity binding sites for normal activation in regions of relatively low signalling. When Ci/Gli sites in these enhancers were altered to improve their binding affinity, we observed patterning defects in the transcriptional response that are consistent with a switch from Ci-mediated activation to Ci-mediated repression. Synthetic transgenic reporters containing isolated Ci/Gli sites confirmed this finding in imaginal discs. We propose that the requirement for gene activation by Ci in the regions of low-to-moderate Hh signalling results in evolutionary pressure favouring weak binding sites in enhancers of certain Hh target genes.

Epigenetic mechanisms and developmental choice hierarchies in T-lymphocyte development

Three interlocking problems in gene regulation are: how to explain genome-wide targeting of transcription factors in different cell types, how prior transcription factor action can establish an 'epigenetic state' that changes the options for future transcription factor action, and how directly a sequence of developmental decisions can be memorialized in a hierarchy of repression structures applied to key genes of the 'paths not taken'. This review uses the finely staged process of T-cell lineage commitment as a test case in which to examine how changes in developmental status are reflected in changes in transcription factor expression, transcription factor binding distribution across genomic sites, and chromatin modification. These are evaluated in a framework of reciprocal effects of previous chromatin structure features on transcription factor access and of transcription factor binding on other factors and on future chromatin structure.

A primer on thermodynamic-based models for deciphering transcriptional regulatory logic

A rigorous analysis of transcriptional regulation at the DNA level is crucial to the understanding of many biological systems. Mathematical modeling has offered researchers a new approach to understanding this central process. In particular, thermodynamic-based modeling represents the most biophysically informed approach aimed at connecting DNA level regulatory sequences to the expression of specific genes. The goal of this review is to give biologists a thorough description of the steps involved in building, analyzing, and implementing a thermodynamic-based model of transcriptional regulation. The data requirements for this modeling approach are described, the derivation for a specific regulatory region is shown, and the challenges and future directions for the quantitative modeling of gene regulation are discussed.

Global analysis of Drosophila Cys₂-His₂ zinc finger proteins reveals a multitude of novel recognition motifs and binding determinants

Cys₂-His₂ zinc finger proteins (ZFPs) are the largest group of transcription factors in higher metazoans. A complete characterization of these ZFPs and their associated target sequences is pivotal to fully annotate transcriptional regulatory networks in metazoan genomes. As a first step in this process, we have characterized the DNA-binding specificities of 129 zinc finger sets from Drosophila using a bacterial one-hybrid system. This data set contains the DNA-binding specificities for at least one encoded ZFP from 70 unique genes and 23 alternate splice isoforms representing the largest set of characterized ZFPs from any organism described to date. These recognition motifs can be used to predict genomic binding sites for these factors within the fruit fly genome. Subsets of fingers from these ZFPs were characterized to define their orientation and register on their recognition sequences, thereby allowing us to define the recognition diversity within this finger set. We find that the characterized fingers can specify 47 of the 64 possible DNA triplets. To confirm the utility of our finger recognition models, we employed subsets of Drosophila fingers in combination with an existing archive of artificial zinc finger modules to create ZFPs with novel DNA-binding specificity. These hybrids of natural and artificial fingers can be used to create functional zinc finger nucleases for editing vertebrate genomes.

Evaluation of methods for modeling transcription factor sequence specificity

Genomic analyses often involve scanning for potential transcription factor (TF) binding sites using models of the sequence specificity of DNA binding proteins. Many approaches have been developed to model and learn a protein's DNA-binding specificity, but these methods have not been systematically compared. Here we applied 26 such approaches to in vitro protein binding microarray data for 66 mouse TFs belonging to various families. For nine TFs, we also scored the resulting motif models on in vivo data, and found that the best in vitro-derived motifs performed similarly to motifs derived from the in vivo data. Our results indicate that simple models based on mononucleotide position weight matrices trained by the best methods perform similarly to more complex models for most TFs examined, but fall short in specific cases (<10% of the TFs examined here). In addition, the best-performing motifs typically have relatively low information content, consistent with widespread degeneracy in eukaryotic TF sequence preferences.

Evolution of cis-regulatory elements in yeast de novo and duplicated new genes

Background

New genes that originate from non-coding DNA rather than being duplicated from parent genes are called de novo genes. Their short evolution time and lack of parent genes provide a chance to study the evolution of cis-regulatory elements in the initial stage of gene emergence. Although a few reports have discussed cis-regulatory elements in new genes, knowledge of the characteristics of these elements in de novo genes is lacking. Here, we conducted a comprehensive investigation to depict the emergence and establishment of cis-regulatory elements in de novo yeast genes.

Results

In a genome-wide investigation, we found that the number of transcription factor binding sites (TFBSs) in de novo genes of S. cerevisiae increased rapidly and quickly became comparable to the number of TFBSs in established genes. This phenomenon might have resulted from certain characteristics of de novo genes; namely, a relatively frequent gain of TFBSs, an unexpectedly high number of preexisting TFBSs, or lower selection pressure in the promoter regions of the de novo genes. Furthermore, we identified differences in the promoter architecture between de novo genes and duplicated new genes, suggesting that distinct regulatory strategies might be employed by genes of different origin. Finally, our functional analyses of the yeast de novo genes revealed that they might be related to reproduction.

Conclusions

Our observations showed that de novo genes and duplicated new genes possess mutually distinct regulatory characteristics, implying that these two types of genes might have different roles in evolution.

STAT1:DNA sequence-dependent binding modulation by phosphorylation, protein:protein interactions and small-molecule inhibition

The DNA-binding specificity and affinity of the dimeric human transcription factor (TF) STAT1, were assessed by total internal reflectance fluorescence protein-binding microarrays (TIRF-PBM) to evaluate the effects of protein phosphorylation, higher-order polymerization and small-molecule inhibition. Active, phosphorylated STAT1 showed binding preferences consistent with prior characterization, whereas unphosphorylated STAT1 showed a weak-binding preference for one-half of the GAS consensus site, consistent with recent models of STAT1 structure and function in response to phosphorylation. This altered-binding preference was further tested by use of the inhibitor LLL3, which we show to disrupt STAT1 binding in a sequence-dependent fashion. To determine if this sequence-dependence is specific to STAT1 and not a general feature of human TF biology, the TF Myc/Max was analysed and tested with the inhibitor Mycro3. Myc/Max inhibition by Mycro3 is sequence independent, suggesting that the sequence-dependent inhibition of STAT1 may be specific to this system and a useful target for future inhibitor design.

Ancient Pbx-Hox signatures define hundreds of vertebrate developmental enhancers

BACKGROUND

Gene regulation through cis-regulatory elements plays a crucial role in development and disease. A major aim of the post-genomic era is to be able to read the function of cis-regulatory elements through scrutiny of their DNA sequence. Whilst comparative genomics approaches have identified thousands of putative regulatory elements, our knowledge of their mechanism of action is poor and very little progress has been made in systematically de-coding them.

RESULTS

Here, we identify ancient functional signatures within vertebrate conserved non-coding elements (CNEs) through a combination of phylogenetic footprinting and functional assay, using genomic sequence from the sea lamprey as a reference. We uncover a striking enrichment within vertebrate CNEs for conserved binding-site motifs of the Pbx-Hox hetero-dimer. We further show that these predict reporter gene expression in a segment specific manner in the hindbrain and pharyngeal arches during zebrafish development.

CONCLUSIONS

These findings evoke an evolutionary scenario in which many CNEs evolved early in the vertebrate lineage to co-ordinate Hox-dependent gene-regulatory interactions that pattern the vertebrate head. In a broader context, our evolutionary analyses reveal that CNEs are composed of tightly linked transcription-factor binding-sites (TFBSs), which can be systematically identified through phylogenetic footprinting approaches. By placing a large number of ancient vertebrate CNEs into a developmental context, our findings promise to have a significant impact on efforts toward de-coding gene-regulatory elements that underlie vertebrate development, and will facilitate building general models of regulatory element evolution.

An alternative splicing switch regulates embryonic stem cell pluripotency and reprogramming

Alternative splicing (AS) is a key process underlying the expansion of proteomic diversity and the regulation of gene expression. Here, we identify an evolutionarily conserved embryonic stem cell (ESC)-specific AS event that changes the DNA-binding preference of the forkhead family transcription factor FOXP1. We show that the ESC-specific isoform of FOXP1 stimulates the expression of transcription factor genes required for pluripotency, including OCT4, NANOG, NR5A2, and GDF3, while concomitantly repressing genes required for ESC differentiation. This isoform also promotes the maintenance of ESC pluripotency and contributes to efficient reprogramming of somatic cells into induced pluripotent stem cells. These results reveal a pivotal role for an AS event in the regulation of pluripotency through the control of critical ESC-specific transcriptional programs.

Mathematical modeling of gene expression: a guide for the perplexed biologist

The detailed analysis of transcriptional networks holds a key for understanding central biological processes, and interest in this field has exploded due to new large-scale data acquisition techniques. Mathematical modeling can provide essential insights, but the diversity of modeling approaches can be a daunting prospect to investigators new to this area. For those interested in beginning a transcriptional mathematical modeling project, we provide here an overview of major types of models and their applications to transcriptional networks. In this discussion of recent literature on thermodynamic, Boolean, and differential equation models, we focus on considerations critical for choosing and validating a modeling approach that will be useful for quantitative understanding of biological systems.

CpG islands and the regulation of transcription

Vertebrate CpG islands (CGIs) are short interspersed DNA sequences that deviate significantly from the average genomic pattern by being GC-rich, CpG-rich, and predominantly nonmethylated. Most, perhaps all, CGIs are sites of transcription initiation, including thousands that are remote from currently annotated promoters. Shared DNA sequence features adapt CGIs for promoter function by destabilizing nucleosomes and attracting proteins that create a transcriptionally permissive chromatin state. Silencing of CGI promoters is achieved through dense CpG methylation or polycomb recruitment, again using their distinctive DNA sequence composition. CGIs are therefore generically equipped to influence local chromatin structure and simplify regulation of gene activity.

Beyond the balance of activator and repressor

Cell differentiation during development is controlled by extracellular morphogens, which induce responding cells to differentiate into distinct cell fates based on the dose of morphogen they receive. Genes that specify the distinct cell fates are differentially responsive to morphogens, and the extracellular morphogen gradient is converted in responding cells to graded activity of transcription factors. In the case of Hedgehog, the gradient is converted to opposing gradients of transcriptional activator and repressor forms of the transcription factor Cubitus interruptus (Ci). It has been generally assumed that the balance between activator and repressor determines target gene responses within this gradient. However, new evidence shows that enhancers can respond selectively to the activator and repressor forms of Ci, and that this selectivity is determined by the affinity of Ci sites within the enhancers.

Genome-wide computational analysis of dioxin response element location and distribution in the human, mouse, and rat genomes

The aryl hydrocarbon receptor (AhR) mediates responses elicited by 2,3,7,8-tetrachlorodibenzo-p-dioxin by binding to dioxin response elements (DRE) containing the core consensus sequence 5'-GCGTG-3'. The human, mouse, and rat genomes were computationally searched for all DRE cores. Each core was then extended by 7 bp upstream and downstream, and matrix similarity (MS) scores for the resulting 19 bp DRE sequences were calculated using a revised position weight matrix constructed from bona fide functional DREs. In total, 72318 human, 70720 mouse, and 88651 rat high-scoring (MS ≥ 0.8437) putative DREs were identified. Gene-encoding intragenic DNA regions had ∼1.6 times more putative DREs than the noncoding intergenic DNA regions. Furthermore, the promoter region spanning ±1.5 kb of a TSS had the highest density of putative DREs within the genome. Chromosomal analysis found that the putative DRE densities of chromosomes X and Y were significantly lower than the mean chromosomal density. Interestingly, the 10 kb upstream promoter region on chromosome X of the genomes were significantly less dense than the chromosomal mean, while the same region in chromosome Y was the most dense. In addition to providing a detailed genomic map of all DRE cores in the human, mouse, and rat genomes, these data will further aid the elucidation of AhR-mediated signal transduction.

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

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.