Summary Members of transcription factor families typically have similar DNA binding specificities yet execute unique functions in vivo. Transcription factors often bind DNA as multiprotein complexes, raising the possibility that complex formation might modify their DNA binding specificities. To test this hypothesis, we developed an experimental and computational platform, SELEX-seq, that can be used to determine the relative affinities to any DNA sequence for any transcription factor complex. Applying this method to all eight Drosophila Hox proteins, we show that they obtain novel recognition properties when they bind DNA with the dimeric cofactor Extradenticle-Homothorax (Exd). Exd-Hox specificities group into three main classes that obey Hox gene collinearity rules and DNA structure predictions suggest that anterior and posterior Hox proteins prefer DNA sequences with distinct minor groove topographies. Together, these data suggest that emergent DNA recognition properties revealed by interactions with cofactors contribute to transcription factor specificities in vivo.
Summary In animals, Hox transcription factors define regional identity in distinct anatomical domains. How Hox genes encode this specificity is a paradox, because different Hox proteins bind with high affinity in vitro to similar DNA sequences. Here, we demonstrate that the Hox protein Ultrabithorax (Ubx) in complex with its cofactor Extradenticle (Exd) bound specifically to clusters of very low affinity sites in enhancers of the shavenbaby gene of Drosophila. These low affinity sites conferred specificity for Ubx binding in vivo, but multiple clustered sites were required for robust expression when embryos developed in variable environments. Although most individual Ubx binding sites are not evolutionarily conserved, the overall enhancer architecture—clusters of low affinity binding sites—is maintained and required for enhancer function. Natural selection therefore works at the level of the enhancer, requiring a particular density of low affinity Ubx sites to confer both specific and robust expression.
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 crossvalidation 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 shapeaugmented 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.protein−DNA recognition | statistical machine learning | support vector regression | protein binding microarray | DNA structure
Although neurons within the peripheral nervous system (PNS) have a remarkable ability to repair themselves after injury, neurons within the central nervous system (CNS) do not spontaneously regenerate. This problem has remained recalcitrant despite a century of research on the reaction of axons to injury. The balance between inhibitory cues present in the environment and the intrinsic growth capacity of the injured neuron determines the extent of axonal regeneration following injury. The cell body of an injured neuron must receive accurate and timely information about the site and extent of axonal damage in order to increase its intrinsic growth capacity and successfully regenerate. One of the mechanisms contributing to this process is retrograde transport of injury signals. For example, molecules activated at the injury site convey information to the cell body leading to the expression of regeneration-associated genes and increased growth capacity of the neuron. Here we discuss recent studies that have begun to dissect the injury-signaling pathways involved in stimulating the intrinsic growth capacity of injured neurons.
Unlike neurons in the central nervous system (CNS), injured neurons in the peripheral nervous system (PNS) can regenerate their axons and reinnervate their targets. However, functional recovery in the PNS often remains suboptimal, especially in cases of severe damage. The lack of regenerative ability of CNS neurons has been linked to down-regulation of the mTOR (mammalian target of rapamycin) pathway. We report here that PNS dorsal root ganglial neurons (DRGs) activate mTOR following damage and that this activity enhances axonal growth capacity. Furthermore, genetic up-regulation of mTOR activity by deletion of tuberous sclerosis complex 2 (TSC2) in DRGs is sufficient to enhance axonal growth capacity in vitro and in vivo. We further show that mTOR activity is linked to the expression of GAP-43, a crucial component of axonal outgrowth. However, although TSC2 deletion in DRGs facilitates axonal regrowth, it leads to defects in target innervation. Thus, whereas manipulation of mTOR activity could provide new strategies to stimulate nerve regeneration in the PNS, fine control of mTOR activity is required for proper target innervation.
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