In pursuit of design principles of regulatory sequences

"Hit-and-run": Transcription factors get caught in the act

A key challenge for understanding transcriptional regulation is being able to measure transcription factor (TF)-DNA binding events with sufficient spatial and temporal resolution; that is, when and where TFs occupy their cognate sites. A recent study by Para et al. has highlighted the dynamics underlying the activation of gene expression by a master regulator TF. This study provides concrete evidence for a long-standing hypothesis in biology, the "hit-and-run" mechanism, which was first proposed decades ago. That is, gene expression is dynamically controlled by a TF that transiently binds and activates a target gene, which might stay in a transcriptionally active state after the initial binding event has ended. Importantly, the experimental procedure introduced, TARGET, provides a useful way for identifying multiple target genes transiently bound by their regulators, which can be used in conjunction with other well-established methods to improve our understanding of transcriptional regulatory dynamics.

Transcriptional (ChIP-Chip) Analysis of ELF1, ETS2, RUNX1 and STAT5 in Human Abdominal Aortic Aneurysm

We investigated transcriptional control of gene expression in human abdominal aortic aneurysm (AAA). We previously identified 3274 differentially expressed genes in human AAA tissue compared to non-aneurysmal controls. Four expressed transcription factors (ELF1, ETS2, STAT5 and RUNX1) were selected for genome-wide chromatin immunoprecipitation. Transcription factor binding was enriched in 4760 distinct genes (FDR < 0.05), of which 713 were differentially expressed in AAA. Functional classification using Gene Ontology (GO), KEGG, and Network Analysis revealed enrichment in several biological processes including “leukocyte migration” (FDR = 3.09 × 10⁻⁰⁵) and “intracellular protein kinase cascade” (FDR = 6.48 × 10⁻⁰⁵). In the control aorta, the most significant GO categories differed from those in the AAA samples and included “cytoskeleton organization” (FDR = 1.24 × 10⁻⁰⁶) and “small GTPase mediated signal transduction” (FDR = 1.24 × 10⁻⁰⁶). Genes up-regulated in AAA tissue showed a highly significant enrichment for GO categories “leukocyte migration” (FDR = 1.62 × 10⁻¹¹), “activation of immune response” (FDR = 8.44 × 10⁻¹¹), “T cell activation” (FDR = 4.14 × 10⁻¹⁰) and “regulation of lymphocyte activation” (FDR = 2.45 × 10⁻⁰⁹), whereas the down-regulated genes were enriched in GO categories “cytoskeleton organization” (FDR = 7.84 × 10⁻⁰⁵), “muscle cell development” (FDR = 1.00 × 10⁻⁰⁴), and “organ morphogenesis” (FDR = 3.00 × 10⁻⁰⁴). Quantitative PCR assays confirmed a sub-set of the transcription factor binding sites including those in MTMR11, DUSP10, ITGAM, MARCH1, HDAC8, MMP14, MAGI1, THBD and SPOCK1.

Core promoter sequence in yeast is a major determinant of expression level

The core promoter is the regulatory sequence to which RNA polymerase is recruited and where it acts to initiate transcription. Here, we present the first comprehensive study of yeast core promoters, providing massively parallel measurements of core promoter activity and of TSS locations and relative usage for thousands of native and designed sequences. We found core promoter activity to be highly correlated to the activity of the entire promoter and that sequence variation in different core promoter regions substantially tunes its activity in a predictable way. We also show that location, orientation, and flanking bases critically affect TATA element function, that transcription initiation in highly active core promoters is focused within a narrow region, that poly(dA:dT) orientation has a functional consequence at the 3' end of promoters, and that orthologous core promoters across yeast species have conserved activities. Our results demonstrate the importance of core promoters in the quantitative study of gene regulation.

Investigating the transcriptional control of cardiovascular development

Transcriptional regulation of thousands of genes instructs complex morphogenetic and molecular events for heart development. Cardiac transcription factors choreograph gene expression at each stage of differentiation by interacting with cofactors, including chromatin-modifying enzymes, and by binding to a constellation of regulatory DNA elements. Here, we present salient examples relevant to cardiovascular development and heart disease, and review techniques that can sharpen our understanding of cardiovascular biology. We discuss the interplay between cardiac transcription factors, cis-regulatory elements, and chromatin as dynamic regulatory networks, to orchestrate sequential deployment of the cardiac gene expression program.

A family of transposable elements co-opted into developmental enhancers in the mouse neocortex

The neocortex is a mammalian-specific structure that is responsible for higher functions such as cognition, emotion and perception. To gain insight into its evolution and the gene regulatory codes that pattern it, we studied the overlap of its active developmental enhancers with transposable element (TE) families and compared this overlap to uniformly shuffled enhancers. Here we show a striking enrichment of the MER130 repeat family among active enhancers in the mouse dorsal cerebral wall, which gives rise to the neocortex, at embryonic day 14.5. We show that MER130 instances preserve a common code of transcriptional regulatory logic, function as enhancers and are adjacent to critical neocortical genes. MER130, a nonautonomous interspersed TE, originates in the tetrapod or possibly Sarcopterygii ancestor, which far predates the appearance of the neocortex. Our results show that MER130 elements were recruited, likely through their common regulatory logic, as neocortical enhancers.

Unraveling determinants of transcription factor binding outside the core binding site

Binding of transcription factors (TFs) to regulatory sequences is a pivotal step in the control of gene expression. Despite many advances in the characterization of sequence motifs recognized by TFs, our ability to quantitatively predict TF binding to different regulatory sequences is still limited. Here, we present a novel experimental assay termed BunDLE-seq that provides quantitative measurements of TF binding to thousands of fully designed sequences of 200 bp in length within a single experiment. Applying this binding assay to two yeast TFs, we demonstrate that sequences outside the core TF binding site profoundly affect TF binding. We show that TF-specific models based on the sequence or DNA shape of the regions flanking the core binding site are highly predictive of the measured differential TF binding. We further characterize the dependence of TF binding, accounting for measurements of single and co-occurring binding events, on the number and location of binding sites and on the TF concentration. Finally, by coupling our in vitro TF binding measurements, and another application of our method probing nucleosome formation, to in vivo expression measurements carried out with the same template sequences serving as promoters, we offer insights into mechanisms that may determine the different expression outcomes observed. Our assay thus paves the way to a more comprehensive understanding of TF binding to regulatory sequences and allows the characterization of TF binding determinants within and outside of core binding sites.

Quantitative modeling of transcription factor binding specificities using DNA shape

DNA binding specificities of transcription factors (TFs) are a key component of gene regulatory processes. Underlying mechanisms that explain the highly specific binding of TFs to their genomic target sites are poorly understood. A better understanding of TF-DNA binding requires the ability to quantitatively model TF binding to accessible DNA as its basic step, before additional in vivo components can be considered. Traditionally, these models were built based on nucleotide sequence. Here, we integrated 3D DNA shape information derived with a high-throughput approach into the modeling of TF binding specificities. Using support vector regression, we trained quantitative models of TF binding specificity based on protein binding microarray (PBM) data for 68 mammalian TFs. The evaluation of our models included cross-validation on specific PBM array designs, testing across different PBM array designs, and using PBM-trained models to predict relative binding affinities derived from in vitro selection combined with deep sequencing (SELEX-seq). Our results showed that shape-augmented models compared favorably to sequence-based models. Although both k-mer and DNA shape features can encode interdependencies between nucleotide positions of the binding site, using DNA shape features reduced the dimensionality of the feature space. In addition, analyzing the feature weights of DNA shape-augmented models uncovered TF family-specific structural readout mechanisms that were not revealed by the DNA sequence. As such, this work combines knowledge from structural biology and genomics, and suggests a new path toward understanding TF binding and genome function.

DNA-mediated cooperativity facilitates the co-selection of cryptic enhancer sequences by SOX2 and PAX6 transcription factors

Sox2 and Pax6 are transcription factors that direct cell fate decision during neurogenesis, yet the mechanism behind how they cooperate on enhancer DNA elements and regulate gene expression is unclear. By systematically interrogating Sox2 and Pax6 interaction on minimal enhancer elements, we found that cooperative DNA recognition relies on combinatorial nucleotide switches and precisely spaced, but cryptic composite DNA motifs. Surprisingly, all tested Sox and Pax paralogs have the capacity to cooperate on such enhancer elements. NMR and molecular modeling reveal very few direct protein-protein interactions between Sox2 and Pax6, suggesting that cooperative binding is mediated by allosteric interactions propagating through DNA structure. Furthermore, we detected and validated several novel sites in the human genome targeted cooperatively by Sox2 and Pax6. Collectively, we demonstrate that Sox-Pax partnerships have the potential to substantially alter DNA target specificities and likely enable the pleiotropic and context-specific action of these cell-lineage specifiers.

Evolving insights on how cytosine methylation affects protein-DNA binding

Many anecdotal observations exist of a regulatory effect of DNA methylation on gene expression. However, in general, the underlying mechanisms of this effect are poorly understood. In this review, we summarize what is currently known about how this important, but mysterious, epigenetic mark impacts cellular functions. Cytosine methylation can abrogate or enhance interactions with DNA-binding proteins, or it may have no effect, depending on the context. Despite being only a small chemical change, the addition of a methyl group to cytosine can affect base readout via hydrophobic contacts in the major groove and shape readout via electrostatic contacts in the minor groove. We discuss the recent discovery that CpG methylation increases DNase I cleavage at adjacent positions by an order of magnitude through altering the local 3D DNA shape and the possible implications of this structural insight for understanding the methylation sensitivity of transcription factors (TFs). Additionally, 5-methylcytosines change the stability of nucleosomes and, thus, affect the local chromatin structure and access of TFs to genomic DNA. Given these complexities, it seems unlikely that the influence of DNA methylation on protein-DNA binding can be captured in a small set of general rules. Hence, data-driven approaches may be essential to gain a better understanding of these mechanisms.

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.