Cofactor binding evokes latent differences in DNA binding specificity between Hox proteins.

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.

Transcriptional control of human p53-regulated genes.

The p53 protein regulates the transcription of many different genes in response to a wide variety of stress signals. Following DNA damage, p53 regulates key processes, including DNA repair, cell-cycle arrest, senescence and apoptosis, in order to suppress cancer. This Analysis article provides an overview of the current knowledge of p53-regulated genes in these pathways and others, and the mechanisms of their regulation. In addition, we present the most comprehensive list so far of human p53-regulated genes and their experimentally validated, functional binding sites that confer p53 regulation.

ModEx: A text mining system for extracting mode of regulation of transcription factor-gene regulatory interaction.

BACKGROUND: Transcription factors (TFs) are proteins that are fundamental to transcription and regulation of gene expression. Each TF may regulate multiple genes and each gene may be regulated by multiple TFs. TFs can act as either activator or repressor of gene expression. This complex network of interactions between TFs and genes underlies many developmental and biological processes and is implicated in several human diseases such as cancer. Hence deciphering the network of TF-gene interactions with information on mode of regulation (activation vs. repression) is an important step toward understanding the regulatory pathways that underlie complex traits. There are many experimental, computational, and manually curated databases of TF-gene interactions. In particular, high-throughput ChIP-Seq datasets provide a large-scale map or transcriptional regulatory interactions. However, these interactions are not annotated with information on context and mode of regulation. Such information is crucial to gain a global picture of gene regulatory mechanisms and can aid in developing machine learning models for applications such as biomarker discovery, prediction of response to therapy, and precision medicine. METHODS: In this work, we introduce a text-mining system to annotate ChIP-Seq derived interaction with such meta data through mining PubMed articles. We evaluate the performance of our system using gold standard small scale manually curated databases. RESULTS: Our results show that the method is able to accurately extract mode of regulation with F-score 0.77 on TRRUST curated interaction and F-score 0.96 on intersection of TRUSST and ChIP-network. We provide a HTTP REST API for our code to facilitate usage. Availibility: Source code and datasets are available for download on GitHub: https://github.com/samanfrm/modex.

Nonalcoholic Fatty Liver Disease Demonstrates a Pre-fibrotic and Premalignant Molecular Signature.

BACKGROUND: Metabolic syndrome contributing to nonalcoholic fatty liver disease (NAFLD) can lead to hepatic dysfunction, steatohepatitis, cirrhosis, and hepatocellular carcinoma. AIMS: In this study, we tested whether diet-induced fatty liver in a mouse model physiologically mimicked human NAFLD, and whether transcriptional alterations in mouse fatty liver signified risk for the development of hepatitis, cirrhosis, and/or hepatocellular carcinoma. METHODS: SAMP6 strain mice were fed a low-fat diet or high-fat diet (HFD) for 6 months. Mouse livers were isolated and subjected to histology, immunohistochemistry, and whole transcriptome RNA sequencing. Sequences were aligned to the mouse reference genome, and gene expression signatures were analyzed using bioinformatics tools including Cufflinks, Pathview, Cytoscape, ClueGO, and GOstats. RESULTS: Consistent with NAFLD, livers from HFD-fed mice demonstrated steatosis, high levels of inflammation, an up-regulation of genes encoding proteins associated with the complement pathway and immune responses, and down-regulation of those associated with metabolic processes. These livers also showed an up-regulation of genes associated with fibrosis and malignant transformation but no histological evidence of either pathobiology or DNA damage. CONCLUSIONS: HFD-fed mice exhibited NAFLD that had incompletely transitioned from fatty liver to NASH. Importantly, bioinformatics approaches identified pre-fibrotic and premalignant signatures, suggesting that the pathogenesis of both fibrosis and cancer may initiate in fatty livers well before associated histological changes are evident.

Probing DNA shape and methylation state on a genomic scale with DNase I.

DNA binding proteins find their cognate sequences within genomic DNA through recognition of specific chemical and structural features. Here we demonstrate that high-resolution DNase I cleavage profiles can provide detailed information about the shape and chemical modification status of genomic DNA. Analyzing millions of DNA backbone hydrolysis events on naked genomic DNA, we show that the intrinsic rate of cleavage by DNase I closely tracks the width of the minor groove. Integration of these DNase I cleavage data with bisulfite sequencing data for the same cell type's genome reveals that cleavage directly adjacent to cytosine-phosphate-guanine (CpG) dinucleotides is enhanced at least eightfold by cytosine methylation. This phenomenon we show to be attributable to methylation-induced narrowing of the minor groove. Furthermore, we demonstrate that it enables simultaneous mapping of DNase I hypersensitivity and regional DNA methylation levels using dense in vivo cleavage data. Taken together, our results suggest a general mechanism by which CpG methylation can modulate protein-DNA interaction strength via the remodeling of DNA shape.

TLS/FUS (translocated in liposarcoma/fused in sarcoma) regulates target gene transcription via single-stranded DNA response elements.

TLS/FUS (TLS) is a multifunctional protein implicated in a wide range of cellular processes, including transcription and mRNA processing, as well as in both cancer and neurological disease. However, little is currently known about TLS target genes and how they are recognized. Here, we used ChIP and promoter microarrays to identify genes potentially regulated by TLS. Among these genes, we detected a number that correlate with previously known functions of TLS, and confirmed TLS occupancy at several of them by ChIP. We also detected changes in mRNA levels of these target genes in cells where TLS levels were altered, indicative of both activation and repression. Next, we used data from the microarray and computational methods to determine whether specific sequences were enriched in DNA fragments bound by TLS. This analysis suggested the existence of TLS response elements, and we show that purified TLS indeed binds these sequences with specificity in vitro. Remarkably, however, TLS binds only single-strand versions of the sequences. Taken together, our results indicate that TLS regulates expression of specific target genes, likely via recognition of specific single-stranded DNA sequences located within their promoter regions.

The p53HMM algorithm: using profile hidden markov models to detect p53-responsive genes.

BACKGROUND: A computational method (called p53HMM) is presented that utilizes Profile Hidden Markov Models (PHMMs) to estimate the relative binding affinities of putative p53 response elements (REs), both p53 single-sites and cluster-sites. These models incorporate a novel "Corresponded Baum-Welch" training algorithm that provides increased predictive power by exploiting the redundancy of information found in the repeated, palindromic p53-binding motif. The predictive accuracy of these new models are compared against other predictive models, including position specific score matrices (PSSMs, or weight matrices). We also present a new dynamic acceptance threshold, dependent upon a putative binding site's distance from the Transcription Start Site (TSS) and its estimated binding affinity. This new criteria for classifying putative p53-binding sites increases predictive accuracy by reducing the false positive rate. RESULTS: Training a Profile Hidden Markov Model with corresponding positions matching a combined-palindromic p53-binding motif creates the best p53-RE predictive model. The p53HMM algorithm is available on-line: (http://tools.csb.ias.edu). CONCLUSION: Using Profile Hidden Markov Models with training methods that exploit the redundant information of the homotetramer p53 binding site provides better predictive models than weight matrices (PSSMs). These methods may also boost performance when applied to other transcription factor binding sites.

CXCL12/CXCR4-Mediated Procollagen Secretion Is Coupled To Cullin-RING Ubiquitin Ligase Activation.

Tissue fibrosis is mediated by the actions of multiple pro-fibrotic proteins that can induce myofibroblast phenoconversion through diverse signaling pathways coupled predominantly to Smads or MEK/Erk proteins. The TGFß/TGFßR and CXCL12/CXCR4 axes induce myofibroblast phenoconversion independently through Smads and MEK/Erk proteins, respectively. To investigate these mechanisms at the genetic level, we have now elucidated the TGFß/TGFßR and CXCL12/CXCR4 transcriptomes in human fibroblasts. These transcriptomes are largely convergent, and up-regulate transcripts encoding proteins known to promote myofibroblast phenoconversion. These studies also revealed a molecular signature unique to CXCL12/CXCR4 axis activation for COPII vesicle formation, ubiquitination, and Golgi/ER localization/targeting. In particular, both CUL3 and KLHL12, key members of the Cullin-RING (CRL) ubiquitin ligase family of proteins involved in procollagen transport from the ER to the Golgi, were highly up-regulated in CXCL12-, but repressed in TGFß-, treated cells. Up-regulation of CUL3 and KLHL12 was correlated with higher procollagen secretion by CXCL12-treated cells, and this affect was ablated upon treatment with inhibitors specific for CXCR4 or CUL3 and repressed by TGFß/TGFßR axis activation. The results of these studies show that activation of the CXCL12/CXCR4 axis uniquely facilitates procollagen I secretion through a COPII-vesicle mediated mechanism to promote production of the ECM characteristic of fibrosis.

Building accurate sequence-to-affinity models from high-throughput in vitro protein-DNA binding data using FeatureREDUCE.

Transcription factors are crucial regulators of gene expression. Accurate quantitative definition of their intrinsic DNA binding preferences is critical to understanding their biological function. High-throughput in vitro technology has recently been used to deeply probe the DNA binding specificity of hundreds of eukaryotic transcription factors, yet algorithms for analyzing such data have not yet fully matured. Here, we present a general framework (FeatureREDUCE) for building sequence-to-affinity models based on a biophysically interpretable and extensible model of protein-DNA interaction that can account for dependencies between nucleotides within the binding interface or multiple modes of binding. When training on protein binding microarray (PBM) data, we use robust regression and modeling of technology-specific biases to infer specificity models of unprecedented accuracy and precision. We provide quantitative validation of our results by comparing to gold-standard data when available.

SELEX-seq: a method for characterizing the complete repertoire of binding site preferences for transcription factor complexes.

The closely related members of the Hox family of homeodomain transcription factors have similar DNA-binding preferences as monomers, yet carry out distinct 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 a new experimental and computational platform, termed SELEX-seq, to characterize DNA-binding specificities of Hox-based multiprotein complexes. We found that complex formation with the same cofactor reveals latent specificities that are not observed for monomeric Hox factors. The findings from this in vitro platform are consistent with in vivo data, and the "latent specificity" concept serves as a precedent for how the specificities of similar transcription factors might be distinguished in vivo. Importantly, the SELEX-seq platform is flexible and can be used to determine the relative affinities to any DNA sequence for any transcription factor or multiprotein complex.

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.

The regulation of the endosomal compartment by p53 the tumor suppressor gene.

The endosomal compartment of the cell is involved in a number of functions including: (a) internalizing membrane proteins to multivesicular bodies and lysosomes; (b) producing vesicles that are secreted from the cell (exosomes); and (c) generating autophagic vesicles that, especially in times of nutrient deprivation, supply cytoplasmic components to the lysosome for degradation and recycling of nutrients. The p53 protein responds to various stress signals by initiating a transcriptional program that restores cellular homeostasis and prevents the accumulation of errors in a cell. As part of this process, p53 regulates the transcription of a set of genes encoding proteins that populate the endosomal compartment and impact upon each of these endosomal functions. Here, we demonstrate that p53 regulates transcription of the genes TSAP6 and CHMP4C, which enhance exosome production, and CAV1 and CHMP4C, which produce a more rapid endosomal clearance of the epidermal growth factor receptor from the plasma membrane. Each of these p53-regulated endosomal functions results in the slowing of cell growth and division, the utilization of catabolic resources and cell-to-cell communication by exosomes after a stress signal is detected by the p53 protein. These processes avoid errors during stress and restore homeostasis once the stress is resolved.