A compendium of DNA-binding specificities of transcription factors in Pseudomonas syringae.
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ABSTRACT:
SUBMITTER: Fan L
PROVIDER: S-EPMC7532196 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The regulation of transcription is primarily exerted through transcription factors (TFs) binding to genomic DNA. Although molecular mechanisms of TFs have been studied individually for decades, a complete picture of binding profiles of all TFs and their precise targets in the genome are still lacking in the model pathogen Pseudomonas syringae. To this end, we performed a high-throughput systematic evolution of ligands by exponential enrichment (HT-SELEX) approach on all 301 annotated TFs in P. syringae. Robust enrichment of specific sequences was deduced to 118 SELEX motifs. We identified 12,464 interactions between 100 TFs and their target genes in the genome, for an individual TF ranging from 6 to 1481 sites. It showed that 90% TF binding was of dimeric site type, in which 85% with the head-to-head palindromic binding preference. To further explore the pathogenic mechanism of TFs in P. syringae, we mapped intricate networks of these TFs and their targets in the virulence-associated pathways, many of which were verified by orthologous methods such as ChIP-seq, EMSA, RT-qPCR and a reporter assay of promoter activity. By checking the enrichment of binding sites in pathways, we identified 25 virulence-associated master regulators of which 14 had never been characterized as TFs before. Overall, the present study provides a valuable resource for TF binding specificities in P. syringae and demonstrates a novel and an integrative analysis for seeking the virulence-associated TFs and its target genes. We expect that the results will significantly benefit future studies on the transcriptional regulation in P. syringae, and facilitate the design of drug targets to protect plants from attacks by relevant pathogens.
2020-08-18 | GSE146697 | GEO
Project description:Today, annotated amino acid sequences of more and more transcription factors (TFs) are readily available. Quantitative information about their DNA-binding specificities, however, are hard to obtain. Position frequency matrices (PFMs), the most widely used models to represent binding specificities, are experimentally characterized only for a small fraction of all TFs. Even for some of the most intensively studied eukaryotic organisms (i.e., human, rat and mouse), roughly one-sixth of all proteins with annotated DNA-binding domain have been characterized experimentally. Here, we present a new method based on support vector regression for predicting quantitative DNA-binding specificities of TFs in different eukaryotic species. This approach estimates a quantitative measure for the PFM similarity of two proteins, based on various features derived from their protein sequences. The method is trained and tested on a dataset containing 1 239 TFs with known DNA-binding specificity, and used to predict specific DNA target motifs for 645 TFs with high accuracy.
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Project description:We developed a new DNA microarray-based technology, called protein binding microarrays (PBMs), that allows rapid, high-throughput characterization of the in vitro DNA binding-site sequence specificities of transcription factors in a single day. Using PBMs, we identified the DNA binding-site sequence specificities of the yeast transcription factors Abf1, Rap1 and Mig1. Comparison of these proteins' in vitro binding sites with their in vivo binding sites indicates that PBM-derived sequence specificities can accurately reflect in vivo DNA sequence specificities. In addition to previously identified targets, Abf1, Rap1 and Mig1 bound to 107, 90 and 75 putative new target intergenic regions, respectively, many of which were upstream of previously uncharacterized open reading frames. Comparative sequence analysis indicated that many of these newly identified sites are highly conserved across five sequenced sensu stricto yeast species and, therefore, are probably functional in vivo binding sites that may be used in a condition-specific manner. Similar PBM experiments should be useful in identifying new cis regulatory elements and transcriptional regulatory networks in various genomes.
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Project description:The majority of CpG dinucleotides in the human genome are methylated at cytosine bases. However, active gene regulatory elements are generally hypomethylated relative to their flanking regions, and the binding of some transcription factors (TFs) is diminished by methylation of their target sequences. By analysis of 542 human TFs with methylation-sensitive SELEX (systematic evolution of ligands by exponential enrichment), we found that there are also many TFs that prefer CpG-methylated sequences. Most of these are in the extended homeodomain family. Structural analysis showed that homeodomain specificity for methylcytosine depends on direct hydrophobic interactions with the methylcytosine 5-methyl group. This study provides a systematic examination of the effect of an epigenetic DNA modification on human TF binding specificity and reveals that many developmentally important proteins display preference for mCpG-containing sequences.
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Project description:Transcription factors (TFs) regulate gene expression through binding to cis-regulatory specific sequences in the promoters of their target genes. In contrast to the genetic code, the transcriptional regulatory code is far from being deciphered and is determined by sequence specificity of TFs, combinatorial cooperation between TFs and chromatin competence. Here we addressed one of these determinants by characterizing the target sequence specificity of 63 plant TFs representing 25 families, using protein-binding microarrays. Remarkably, almost half of these TFs recognized secondary motifs, which in some cases were completely unrelated to the primary element. Analyses of coregulated genes and transcriptomic data from TFs mutants showed the functional significance of over 80% of all identified sequences and of at least one target sequence per TF. Moreover, combining the target sequence information with coexpression analysis we could predict the function of a TF as activator or repressor through a particular DNA sequence. Our data support the correlation between cis-regulatory elements and the sequence determined in vitro using the protein-binding microarray and provides a framework to explore regulatory networks in plants.
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Project description:Hypoxia-inducible factor (HIF) is the major transcriptional regulator of cellular responses to hypoxia. The two principal HIF-α isoforms, HIF-1α and HIF-2α, are progressively stabilized in response to hypoxia and form heterodimers with HIF-1β to activate a broad range of transcriptional responses. Here, we report on the pan-genomic distribution of isoform-specific HIF binding in response to hypoxia of varying severity and duration, and in response to genetic ablation of each HIF-α isoform. Our findings reveal that, despite an identical consensus recognition sequence in DNA, each HIF heterodimer loads progressively at a distinct repertoire of cell-type-specific sites across the genome, with little evidence of redistribution under any of the conditions examined. Marked biases towards promoter-proximal binding of HIF-1 and promoter-distant binding of HIF-2 were observed under all conditions and were consistent in multiple cell type. The findings imply that each HIF isoform has an inherent property that determines its binding distribution across the genome, which might be exploited to therapeutically target the specific transcriptional output of each isoform independently.
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Project description:The majority of CpG dinucleotides in the human genome are methylated at cytosine bases. The methylation is heritable across cell divisions, and contributes to the epigenetic memory that maintains the differentiated state of distinct cell types. Active gene regulatory elements are generally undermethylated relative to their flanking regions, and the undermethylation is thought to be important for their activity. Consistently with this model, the binding of some transcription factors (TFs) is diminished by methylation of their target sequences. By systematic analysis of 542 human TFs using methylation sensitive SELEX, we find here that a large number of TFs also prefer to bind to CpG methylated sequences. Most of these represent the extended homeodomain family. Structural analysis of HOXB13, CDX1, CDX2 and LHX4 revealed that the specificity of homeodomain proteins towards methylcytosine depends on amino-acids that form direct hydrophobic interactions with the 5-methyl group of methylcytosine. Our results provide a systematic examination of the effect of an epigenetic DNA modification on human TF binding specificity, and reveal that many developmentally important homeodomain proteins display strong preference for binding to mCpG containing sequences. Genome-scale analysis reveals that binding of most transcription factors is affected by CpG methylation, and that many developmentally important homeodomain TFs directly recognize the 5-methyl group of methylcytosine.
2017-05-05 | GSE94634 | GEO
Project description:A high-throughput systematic evolution of ligands by exponential enrichment assay was applied to 371 putative TFs in Pseudomonas aeruginosa, which resulted in the robust enrichment of 199 unique sequence motifs describing the binding specificities of 182 TFs. By scanning the genome, we predicted in total 33,709 significant interactions between TFs and their target loci, which were more than 11-fold enriched in the intergenic regions but depleted in the gene body regions. To further explore and delineate the physiological and pathogenic roles of TFs in P. aeruginosa, we constructed regulatory networks for nine major virulence-associated pathways and found that 51 TFs were potentially significantly associated with these virulence pathways, 32 of which had not been characterized before, and some were even involved in multiple pathways. These results will significantly facilitate future studies on transcriptional regulation in P. aeruginosa and other relevant pathogens, and accelerate to discover effective treatment and prevention strategies for the associated infectious diseases.
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Project description: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.
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