Dataset Information

Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding

ABSTRACT: Transcription factors (TFs) are primary regulators of gene expression in cells, where they bind specific genomic target sites to control transcription. Quantitative measurements of TF-DNA binding energies can improve the accuracy of predictions of TF occupancy and downstream gene expression in vivo and shed light on how transcriptional networks are rewired throughout evolution. Here, we present a novel sequencing-based TF binding assay and analysis pipeline (BET-seq, for Binding Energy Topography by sequencing) capable of providing quantitative estimates of binding energies for more than one million DNA sequences in parallel at high energetic resolution. Using this platform, we measured the binding energies associated with all possible combinations of 10 nucleotides flanking the known consensus DNA target for two model yeast TFs, Pho4 and Cbf1. A large fraction of these flanking mutations change overall binding energies by an amount equal to or greater than consensus site mutations, suggesting that current definitions of TF binding sites may be too restrictive. By systematically comparing estimates of binding energies output by deep neural networks (NN) and biophysical models trained on these data, we establish that dinucleotide specificities are sufficient to explain essentially all variance in observed binding behavior, with Cbf1 binding exhibiting significantly more non-additivity than Pho4. NN-derived binding energies agree with orthogonal biochemical measurements and reveal that dynamically occupied sites in vivo are both energetically and mutationally distant from the highest-affinity sites.

ORGANISM(S): synthetic construct

PROVIDER: GSE111936 | GEO | 2018/03/17

REPOSITORIES: GEO

ACCESS DATA

Similar Datasets

Project description:Binding of transcription factors to DNA is a key regulatory step in the control of gene expression. DNA sequences with high affinity for transcription factors occur more frequently in the genome than instances of genes bound or regulated by these factors. How specific gene regulation is achieved by transcription factors remains unclear. We used genome-wide approaches to study how trans factors shape the binding and regulatory landscape of Pho4, a budding yeast transcription factor that activates gene expression in response to phosphate limitation. In no phosphate (0mM phosphate) medium, Pho4 is transported into the nucleus and activates transcription of a set of genes (PHO) that are necessary for cell survival in phosphate limited environment. In high phosphate (10mM phosphate) medium, Pho4 is transported outside of the nucleus and the transcription program of PHO genes are turned off. Here we examined the transcription profiling of S. cerevisiae (W303) in various strains treated in media with 10mM or 0mM inorganic phosphate, to study the the transcription activation by Pho4 its cooperative binding factor Pho2, and its competitive binding factor Cbf1. (I) To infer how much Pho4 itself, Pho2 itself, and the interaction between Pho2 and Pho4 contribute to transcriptional activation in response to Pi starvation, we applied mutant cycle analysis to compare wild type, pho2Δ, pho4Δ and pho2Δ pho4Δ strains in both 10 mM and 0 mM Pi conditions. Mutant cycle analysis is a cyclic comparison among all various mutants. For example, by comparing pho2Δ and pho2Δ pho4Δ, we can directly infer the contribution from Pho4 alone to gene activation. And by comparing wild type and pho2Δ, we can infer the contribution from Pho2 alone together with that from the interaction between Pho2 and Pho4. (II) Compare two strains or one strain in two treatments in multiple replicates with dye-swap method. wt no vs high Pi, to identify genes that are induced in response to Pi starvation. cbf1Δ vs wt in high Pi, to identify the influence of Cbf1 on gene expression in high Pi condition. cbf1Δpho4Δ vs wt in high Pi, to identify the influence of Cbf1 on gene expression that is mediated through Pho4, in high Pi condition. rtg3Δtye7Δ vs wt in high Pi, to identify the influence of Rtg3 and Tye7 on gene expression in high Pi condition. pho80Δ vs pho80Δpho4Δ, to identify the genes that are activated Pho4 when Pho4 is fully nuclear localized. pho80Δcbf1Δ vs pho80Δcbf1Δpho4Δ, to identify the influence of Cbf1 on gene expression that is mediated through Pho4, when Pho4 is fully nuclear localized.

Project description:DNA sequence is a major determinant of the binding specificity of transcription factors (TFs) for their genomic targets. However, eukaryotic cells often express, at the same time, TFs with highly similar DNA binding motifs but distinct in vivo targets. Currently, it is not well understood how TFs with seemingly identical DNA motifs achieve unique specificities in vivo. Here, we used custom protein binding microarrays to analyze TF specificity for putative binding sites in their genomic sequence context. Using yeast TFs Cbf1 and Tye7 as our case study, we found that binding sites of these bHLH TFs (i.e., E-boxes) are bound differently in vitro and in vivo, depending on their genomic context. Computational analyses suggest that nucleotides outside E-box binding sites contribute to specificity by influencing the 3D structure of DNA binding sites. Thus, local shape of target sites might play a widespread role in achieving regulatory specificity within TF families. Three protein binding microarray (PBM) experiments of Saccharomyces cerevisiae transcription factors were performed. Briefly, the PBMs involved binding GST-tagged yeast transcription factors Cbf1 and Tye7 to double-stranded 44K Agilent microarrays in order to determine their binding specificity for putative DNA binding sites in native genomic context. Briefly, we represent three categories of 30-bp genomic sequences: 1) ChIP-chip bound probes, 2) ChIP-chip unbound probes, and 3) negative control probes. ChIP-chip bound probes corresponded to genomic regions bound in vivo by Cbf1 or Tye7 (ChIP-chip P < 0.005 in rich medium (YPD) (Harbison et al., Nature 2004, PMID 15343339)) contained at least two consecutive 8-mers with universal PBM E-score > 0.35 (Zhu et al., Genome Research 2009, PMID 19158363). All putative binding sites occurred at the same position within the probes on the array. M-bM-^@M-^\ChIP-chip unboundM-bM-^@M-^] probes corresponded to genomic regions with ChIP-chip P > 0.5 and at least two consecutive 8-mers at a more stringent universal PBM E-score threshold of 0.4. Negative control probes corresponded to S. cerevisiae intergenic regions with a maximum 8-mer E-score < 0.3. We also designed probes that contain, within constant flanking regions, all 10-bp sequences that occur within the M-bM-^@M-^\ChIP-chip boundM-bM-^@M-^] probes and contain the E-box CACGTG, but are flanked by synthetic rather than native genomic sequence. Each DNA sequence represented on the array is present in 4 replicate spots. We report the PBM signal intensity for each spot. The PBM protocol is described in Berger et al., Nature Biotechnology 2006 (PMID 16998473).

Dataset Information

Comprehensive, high-resolution binding energy landscapes reveal context dependencies of transcription factor binding

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure