Synthetic genetic circuit reveals overlapping binding sites underlie TF genomic occupancy
Ontology highlight
ABSTRACT:
ORGANISM(S): synthetic construct
PROVIDER: GSE250601 | GEO | 2024/03/06
REPOSITORIES: GEO
Similar Datasets
Project description:We introduce Affinity Distillation (AD), a method for extracting thermodynamic affinities de-novo from in-vivo immunoprecipitation experiments using deep learning. We show that neural networks modeling base-resolution in-vivo binding profiles of yeast and mammalian TFs can accurately predict energetic impacts of varying underlying DNA sequence on TF binding. Systematic comparisons between Affinity Distillation predictions and other predictive algorithms consistently show that Affinity Distillation more accurately predicts affinities across a wide range of TF structural classes and DNA sequences. Affinity Distillation relies on in-silico marginalization against many sequence backgrounds, resulting in a higher dynamic range and more accurate predictions than motif discovery algorithms. Moreover, we show that Affinity Distillation can learn differential paralog-specific affinities, thereby making it possible to more accurately reconstruct regulatory networks in cells.
2022-06-29 | GSE207001 | GEO
Project description:Motivation: The DNA binding specificity of a transcription factor (TF) is typically represented using a position weight matrix (PWM) model, which implicitly assumes that individual bases in a TF binding site contribute independently to the binding affinity, an assumption that does not always hold. For this reason, more complex models of binding specificity have been developed. However, these models have their own caveats: they typically have a large number of parameters, which makes them hard to learn and interpret. Results: We propose novel regression-based models of TF-DNA binding specificity, trained using high resolution in vitro data from custom protein binding microarray (PBM) experiments. Our PBMs are specifically designed to cover a large number of putative DNA binding sites for the TFs of interest (yeast TFs Cbf1 and Tye7, and human TFs c-Myc, Max, and Mad2) in their native genomic context. These high-throughput, quantitative data are well suited for training complex models that take into account not only independent contributions from individual bases, but also contributions from di- and trinucleotides at various positions within or near the binding sites. To ensure that our models remain interpretable, we use feature selection to identify a small number of sequence features that accurately predict TF-DNA binding specificity. To further illustrate the accuracy of our regression models, we show that even in the case of paralogous TF with highly similar PWMs, our new models can distinguish the specificities of individual factors. Thus, our work represents an important step towards better sequence-based models of individual TF-DNA binding specificity. Four protein binding microarray (PBM) experiments of human transcription factors were performed. Briefly, the PBMs involved binding GST-tagged transcription factors c-Myc, Max, and Mad2(Mxi1) to double-stranded 180K Agilent microarrays in order to determine their binding specificity for putative DNA binding sites in native genomic context. Briefly, we represent three categories of 36-bp sequences: 1) bound probes, 2) unbound probes (or negative controls), and 3) test probes. Bound probes corresponded to genomic regions bound in vivo by c-Myc, Max, or Mad2 (ChIP-seq P < 10^(-10) in HeLaS3 or K562 celld (ENCODE)) that contain at least two consecutive 8-mers with universal PBM E-score > 0.4 (Munteanu and Gordan, LNCS 2013). All putative binding sites occurr at the same position within the probes on the array. M-bM-^@M-^\UnboundM-bM-^@M-^] probes corresponded to genomic regions with ChIP-seq P < 10^(-10) and a maximum 8-mer E-score < 0.2. We also designed test probes that contain, within constant flanking regions, all nnCACGTGnn 10-mers and 18 nnnCACGTGnnn 12-mers (where n = A, C, G, or T). Each DNA sequence represented on the array is present in 6 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).
2013-05-17 | E-GEOD-47026 | biostudies-arrayexpress
Project description:Gene expression is governed by transcription factors (TFs) that commonly recognize short DNA sequence motifs. However, TF recruitment to genomic target sites in chromatin is complex and many parameters that ultimately dictate binding-site specificity are still unknown. Here, we systematically explore the interaction of TFs with synthetic and endogenous G-quadruplexes (G4s) DNA secondary structures. Our findings reveal that certain TFs are directly recruited to promoters by endogenous G4s with binding affinities comparable to canonical B-DNA binding and that endogenous G4-TF interactions can be disrupted with synthetic small molecule ligands. G4s in promoters are bound by a large number of TFs and associated with high transcriptional activity. This discovery of structure-specific recognition expands our understanding of how TFs regulate gene transcription.
2021-03-24 | GSE145090 | GEO
Project description:One goal of human genetics is to understand how the information for precise and dynamic gene expression programs is encoded in the genome. The interactions of transcription factors (TFs) with DNA regulatory elements clearly play an important role in determining gene expression outputs, yet the regulatory logic underlying functional transcription factor binding is poorly understood. Many studies have focused on characterizing the genomic locations of TF binding, yet it is unclear to what extent TF binding at any specific locus has functional consequences with respect to gene expression output. To evaluate the context of functional TF binding we knocked down 59 TFs and chromatin modifiers in one HapMap lymphoblastoid cell line. We then identified genes whose expression was affected by the knockdowns. We intersected the gene expression data with transcription factor binding data (based on ChIP-seq and DNase-seq) within 10 kb of the transcription start sites of expressed genes. This combination of data allowed us to infer functional TF binding. Using this approach, we found that only a small subset of genes bound by a factor were differentially expressed following the knockdown of that factor, suggesting that most interactions between TF and chromatin do not result in measurable changes in gene expression levels of putative target genes. We found that functional TF binding is enriched in regulatory elements that harbor a large number of TF binding sites, at sites with predicted higher binding affinity, and at sites that are enriched in genomic regions annotated as M-bM-^@M-^\active enhancersM-bM-^@M-^]. 201 samples were analyzed - 57 transcription factors were knocked down in triplicate in the same cell line, 2 factors were knocked down in 6 replicates each and an additional 18 control knockdowns (siRNA not targeting any known gene) were analyzed
2014-02-05 | E-GEOD-50588 | biostudies-arrayexpress
Project description:Mammalian transcription factors (TFs) differ broadly in their nuclear mobility and sequence-specific/ non-specific DNA binding affinity. How these properties affect the ability of TFs to occupy their specific binding sites in the genome and modify the epigenetic landscape is unclear. Here we combined live cell quantitative measurements of mitotic chromosome binding (MCB) of 502 TFs, measurements of TF mobility by fluorescence recovery after photobleaching, single molecule imaging of DNA binding in live cells, and genome-wide mapping of TF binding and chromatin accessibility. MCB scaled with interphase properties such as association with DNA-rich compartments, mobility, as well as large differences in genome-wide specific site occupancy that correlated with TF impact on chromatin accessibility. As MCB is largely mediated by electrostatic, non-specific TF-DNA interactions, our data suggests that non-specific DNA binding of TFs enhances their search for specific sites and thereby their impact on the accessible chromatin landscape.
2019-01-25 | GSE119781 | GEO
Project description:Mammalian transcription factors (TFs) differ broadly in their nuclear mobility and sequence-specific/ non-specific DNA binding affinity. How these properties affect the ability of TFs to occupy their specific binding sites in the genome and modify the epigenetic landscape is unclear. Here we combined live cell quantitative measurements of mitotic chromosome binding (MCB) of 502 TFs, measurements of TF mobility by fluorescence recovery after photobleaching, single molecule imaging of DNA binding in live cells, and genome-wide mapping of TF binding and chromatin accessibility. MCB scaled with interphase properties such as association with DNA-rich compartments, mobility, as well as large differences in genome-wide specific site occupancy that correlated with TF impact on chromatin accessibility. As MCB is largely mediated by electrostatic, non-specific TF-DNA interactions, our data suggests that non-specific DNA binding of TFs enhances their search for specific sites and thereby their impact on the accessible chromatin landscape.
2019-01-25 | GSE119782 | GEO
Project description:The binding and contribution of transcription factors (TF) to cell specific gene expression is often deduced from open-chromatin measurements to avoid cost and labour intensive TF ChIP-seq assays.It is important to develop reliable and fast computational methods for accurate TF binding prediction in open-chromatin regions (OCRs). Here, we report a novel segmentation-based method, TEPIC, to predict TF binding by combining sets of OCRs with position weight matrices.TEPIC can be applied to various open-chromatin data, e.g. DNaseI-seq and NOMe-seq, using either peaks or footprints as input.In addition to open-chromatin data, also Histone-Marks (HMs) can be used in TEPIC to identify candidate TF binding sites.TEPIC computes TF affinities and uses open-chromatin/HM signal intensity as quantitative measures of TF binding strength.Using machine learning techniques, we show that incorporating low affinity binding sites improves our ability to explain gene expression variability compared to the standard presence/absence classification of binding sites.Further, we show that both footprints and peaks capture essential TF binding events and lead to a good prediction performance.In our application, gene-based scores computed by TEPIC with one open-chromatin assay nearly reach the quality of several TF ChIP-seq datasets.Finally, we show that these scores correctly predict known transcriptional regulators as illustrated by the application to novel DNaseI-seq and NOMe-seq data for primary human hepatocytes and CD4+ T-cells, respectively.
| EGAS00001002073 | EGA
Project description:Cooperative DNA-binding by transcription factor (TF) proteins is critical for gene expression regulation in eukaryotes. In the human genome, many regulatory regions contain TF binding sites in close proximity to each other, which can facilitate cooperative interactions. However, binding site proximity does not necessarily imply cooperative binding, as two TFs could also bind independently to each of their neighboring target sites. Currently, the rules that drive cooperative versus independent binding of TFs to neighboring sites are not well understood. In addition, it is oftentimes difficult to infer direct cooperativity between TFs from existing DNA-binding data. Here, we show that in vitro binding assays using DNA libraries of a few thousand genomic sequences with putative cooperative TF binding events can be used to develop accurate models of cooperativity and to gain insights into cooperative binding mechanisms. Using human factors ETS1 and RUNX1 as our case study, we show that the distance and orientation between ETS1 sites are critical determinants of cooperative ETS1-ETS1 binding, while cooperative ETS1- RUNX1 interactions show more flexibility in distance and orientation and can be accurately predicted based on binding affinity and the sequence/shape features of the binding sites. The approach described here, combining custom experimental design with machine learning modeling, can be easily applied to study the cooperative DNA-binding patterns of any TFs.
2023-07-30 | GSE171734 | GEO
Project description:Cooperative DNA-binding by transcription factor (TF) proteins is critical for gene expression regulation in eukaryotes. In the human genome, many regulatory regions contain TF binding sites in close proximity to each other, which can facilitate cooperative interactions. However, binding site proximity does not necessarily imply cooperative binding, as two TFs could also bind independently to each of their neighboring target sites. Currently, the rules that drive cooperative versus independent binding of TFs to neighboring sites are not well understood. In addition, it is oftentimes difficult to infer direct cooperativity between TFs from existing DNA-binding data. Here, we show that in vitro binding assays using DNA libraries of a few thousand genomic sequences with putative cooperative TF binding events can be used to develop accurate models of cooperativity and to gain insights into cooperative binding mechanisms. Using human factors ETS1 and RUNX1 as our case study, we show that the distance and orientation between ETS1 sites are critical determinants of cooperative ETS1-ETS1 binding, while cooperative ETS1- RUNX1 interactions show more flexibility in distance and orientation and can be accurately predicted based on binding affinity and the sequence/shape features of the binding sites. The approach described here, combining custom experimental design with machine learning modeling, can be easily applied to study the cooperative DNA-binding patterns of any TFs.
2023-07-30 | GSE171732 | GEO
Project description:In vivo transcription factor binding is regulated by DNA sequence and chromatin features. However, the impact of chromatin context on genome-wide transcription factor binding affinities have not yet been characterized. Here we report the establishment of a quantitative protein-DNA binding assay to determine genome-wide binding affinities to native chromatinized DNA. We use this method to quantify apparent genome-wide binding affinities of both pioneering and non-pioneering transcription factors to uncover the regulatory role of chromatin on transcription factor binding. This revealed that DNA accessibility is the main determinant of transcription factor binding, which restricts nanomolar binding of non-pioneering factors to promoters. DNA binding motifs only appear to be important for very high-affinity binding, but are not essential for nanomolar binding. We uncover numerous biological processes enriched at specific binding affinities, suggesting they are regulated at defined transcription factor concentrations. Importantly, our method adds a new quantitative layer of transcription factor biology, enabling stratification of genomic targets based on transcription factor expression and prediction of transcription factor binding sites under non-physiological conditions, such as disease associated elevated expression of (onco)genes.
2023-02-09 | GSE193553 | GEO