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DNA features beyond the transcription factor binding sites specify target recognition by plant bHLHs

ABSTRACT: This SuperSeries is composed of the SubSeries listed below.

ORGANISM(S): Arabidopsis thaliana Solanum lycopersicum Marchantia polymorpha

PROVIDER: GSE155321 | GEO | 2020/12/01

REPOSITORIES: GEO

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DNA features beyond the transcription factor binding sites specify target recognition by plant bHLHs [GenomeData]

Project description:DNA binding analysis of MYCs using Arabidopsis- and bHLH.specific Protein Binding Microarrays

2020-12-01 | GSE155320 | GEO

DNA features beyond the transcription factor binding sites specify target recognition by plant bHLHs [PromData]

Project description:DNA binding analysis of MYCs using Arabidopsis- and bHLH.specific Protein Binding Microarrays

2020-12-01 | GSE155318 | GEO

DNA features beyond the transcription factor binding sites specify target recognition by plant bHLHs [DAP-seq]

Project description:DNA binding analysis of MYCs using Arabidopsis- and bHLH.specific Protein Binding Microarrays

2020-12-01 | GSE155317 | GEO

DNA features beyond the transcription factor binding site specify target recognition by plant MYC2-related bHLH proteins.

Project description:Transcription factors (TFs) regulate gene expression by binding to cis-regulatory sequences in the promoters of target genes. Recent research is helping to decipher in part the cis-regulatory code in eukaryotes, including plants, but it is not yet fully understood how paralogous TFs select their targets. Here we addressed this question by studying several proteins of the basic helix-loop-helix (bHLH) family of plant TFs, all of which recognize the same DNA motif. We focused on the MYC-related group of bHLHs, that redundantly regulate the jasmonate (JA) signaling pathway, and we observed a high correspondence between DNA-binding profiles in vitro and MYC function in vivo. We demonstrated that A/T-rich modules flanking the MYC-binding motif, conserved from bryophytes to higher plants, are essential for TF recognition. We observed particular DNA-shape features associated with A/T modules, indicating that the DNA shape may contribute to MYC DNA binding. We extended this analysis to 20 additional bHLHs and observed correspondence between in vitro binding and protein function, but it could not be attributed to A/T modules as in MYCs. We conclude that different bHLHs may have their own codes for DNA binding and specific selection of targets that, at least in the case of MYCs, depend on the TF-DNA interplay.

| S-EPMC8577090 | biostudies-literature

DNA features beyond the transcription factor binding sites specify target recognition by plant bHLHs

Project description:DNA features beyond the transcription factor binding sites specify target recognition by plant bHLHs

| PRJNA649303 | ENA

DNA features beyond the transcription factor binding sites specify target recognition by plant bHLHs [PromData]

Project description:DNA features beyond the transcription factor binding sites specify target recognition by plant bHLHs [PromData]

| PRJNA649298 | ENA

DNA features beyond the transcription factor binding sites specify target recognition by plant bHLHs [GenomeData]

Project description:DNA features beyond the transcription factor binding sites specify target recognition by plant bHLHs [GenomeData]

| PRJNA649304 | ENA

DNA features beyond the transcription factor binding sites specify target recognition by plant bHLHs [DAP-seq]

Project description:DNA features beyond the transcription factor binding sites specify target recognition by plant bHLHs [DAP-seq]

| PRJNA649293 | ENA

A structural-based strategy for recognition of transcription factor binding sites.

Project description:Scanning through genomes for potential transcription factor binding sites (TFBSs) is becoming increasingly important in this post-genomic era. The position weight matrix (PWM) is the standard representation of TFBSs utilized when scanning through sequences for potential binding sites. However, many transcription factor (TF) motifs are short and highly degenerate, and methods utilizing PWMs to scan for sites are plagued by false positives. Furthermore, many important TFs do not have well-characterized PWMs, making identification of potential binding sites even more difficult. One approach to the identification of sites for these TFs has been to use the 3D structure of the TF to predict the DNA structure around the TF and then to generate a PWM from the predicted 3D complex structure. However, this approach is dependent on the similarity of the predicted structure to the native structure. We introduce here a novel approach to identify TFBSs utilizing structure information that can be applied to TFs without characterized PWMs, as long as a 3D complex structure (TF/DNA) exists. This approach utilizes an energy function that is uniquely trained on each structure. Our approach leads to increased prediction accuracy and robustness compared with those using a more general energy function. The software is freely available upon request.

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Improved predictions of transcription factor binding sites using physicochemical features of DNA.

Project description:Typical approaches for predicting transcription factor binding sites (TFBSs) involve use of a position-specific weight matrix (PWM) to statistically characterize the sequences of the known sites. Recently, an alternative physicochemical approach, called SiteSleuth, was proposed. In this approach, a linear support vector machine (SVM) classifier is trained to distinguish TFBSs from background sequences based on local chemical and structural features of DNA. SiteSleuth appears to generally perform better than PWM-based methods. Here, we improve the SiteSleuth approach by considering both new physicochemical features and algorithmic modifications. New features are derived from Gibbs energies of amino acid-DNA interactions and hydroxyl radical cleavage profiles of DNA. Algorithmic modifications consist of inclusion of a feature selection step, use of a nonlinear kernel in the SVM classifier, and use of a consensus-based post-processing step for predictions. We also considered SVM classification based on letter features alone to distinguish performance gains from use of SVM-based models versus use of physicochemical features. The accuracy of each of the variant methods considered was assessed by cross validation using data available in the RegulonDB database for 54 Escherichia coli TFs, as well as by experimental validation using published ChIP-chip data available for Fis and Lrp.

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