The Compendium of DNA-Binding Specificities of Transcription Factors in a Pathogenic Bacterium
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ABSTRACT:
PROVIDER: PRJNA611637 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR11278329_1.fastq.gz | Fastqsanger.gz | |||
| SRR11278329_2.fastq.gz | Fastqsanger.gz | |||
| SRR11278330_1.fastq.gz | Fastqsanger.gz | |||
| SRR11278330_2.fastq.gz | Fastqsanger.gz | |||
| SRR11278331_1.fastq.gz | Fastqsanger.gz |
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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.
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Project description:Accurate predictions of the DNA binding specificities of transcription factors (TFs) are necessary for understanding gene regulatory mechanisms. Traditionally, predictive models are built based on nucleotide sequence features. Here, we employed three- dimensional DNA shape information obtained on a high-throughput basis to integrate intuitive DNA structural features into the modeling of TF binding specificities using support vector regression. We performed quantitative predictions of DNA binding specificities, using the DREAM5 dataset for 65 mouse TFs and genomic-context protein binding microarray data for three human basic helix-loop-helix TFs. DNA shape-augmented models compared favorably with sequence-based models for these predictions. Although both k-mer and DNA shape features encoded the interdependencies between nucleotide positions of the binding site, using DNA shape features reduced the dimensionality of the feature space compared to k-mer use. Finally, analyzing the weights of DNA shape-augmented models uncovered TF family- specific structural readout mechanisms that were not obvious from the nucleotide sequence.
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Project description:Pseudomonas syringae is a Gram-negative and model pathogenic bacterium that causes plant diseases worldwide. Here, we set out to identify binding motifs for all 301 annotated transcription factors (TFs) of P. syringae using HT-SELEX. We successfully identify binding motifs for 100 TFs. We map functional interactions between the TFs and their targets in virulence-associated pathways, and validate many of these interactions and functions using additional methods such as ChIP-seq, electrophoretic mobility shift assay (EMSA), RT-qPCR, and reporter assays. Our work identifies 25 virulence-associated master regulators, 14 of which had not been characterized as TFs before.
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