Multiplexed identification of the genomic targets of DNA-binding proteins
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ABSTRACT:
ORGANISM(S): Saccharomyces cerevisiae
PROVIDER: GSE27381 | GEO | 2011/02/18
SECONDARY ACCESSION(S): PRJNA137021
REPOSITORIES: GEO
Similar Datasets
Project description:Transcription factors direct gene expression, and so there is much interest in mapping their genome-wide binding locations. M-BM- Current methods do not allow for the multiplexed analysis of TF binding, and this limits their throughput. We describe a novel method for determining the genomic target genes of multiple transcription factors simultaneously. DNA-binding proteins are endowed with the ability to direct transposon insertions into the genome near to where they bind. The transposon becomes a M-bM-^@M-^\Calling CardM-bM-^@M-^] marking the visit of the DNA-binding protein to that location. A unique sequence M-bM-^@M-^\barcodeM-bM-^@M-^] in the transposon matches it to the DNA-binding protein that directed its insertion. The sequences of the DNA flanking the transposon (which reveal where in the genome the transposon landed) and the barcode within the transposon (which identifies the TF that put it there) are determined by massively-parallel DNA sequencing. To demonstrate the methodM-bM-^@M-^Ys feasibility, we determined the genomic targets of eight transcription factors in a single experiment. The Calling Card method promises to significantly reduce the cost and labor needed to determine the genomic targets of many transcription factors in different environmental conditions and genetic backgrounds. These data contain Ty5 insertion sites mapped by an Illumina GAII analyzer in the S. cerevisiae genome for the background strain without any Sir4 present (1 run), in strains expressing Sir4-tagged copies of three well-characterized TFs: Gal4, Leu3, and Gcn4 (1 run each), and a multiplex of eight Sir4-tagged TFs pooled in a single experiment (2 biological replicates), and insertions from the Thi2-Sir4 fusion expressed from its native locus in two conditions (1 run each). The format of each insertions file is [chromosome number] [position of genomic base] [direction of insertion] [number of reads at that position]. Raw sequencing data comes in two varieties. Paired-end data contains a 5 bp barcode at the beginning of read #2. Single-end data contains a 2 bp barcode on the beggining of read #1.
2011-02-18 | E-GEOD-27381 | biostudies-arrayexpress
Project description:Calling cards technology using self-reporting transposons enables the identification of DNA-protein interactions through RNA sequencing . Here, we have drastically reduced the cost and labor requirements of calling card experiments in bulk populations of cells by introducing a DNA barcode into the calling card itself. An additional barcode incorporated during reverse transcription enables simultaneous transcriptome measurement in a facile and affordable protocol. We demonstrate that barcoded self-reporting transposons recover in vitro binding sites for four basic helix-loop-helix transcription factors with important roles in cell fate specification: ASCL1, MYOD1, NEUROD2, and NGN1. Further, simultaneous calling cards and transcriptional profiling during transcription factor overexpression identified both binding sites and gene expression changes for two of these factors. In sum, RNA-based identification of transcription factor binding sites and gene expression through barcoded self-reporting transposon calling cards and transcriptomes is an efficient and powerful method to infer gene regulatory networks in a population of cells.
2022-02-04 | GSE195992 | GEO
Project description:Pseudohyphal growth is a developmental pathway seen in some strains of yeast, in which cells elongate and form multicellular filaments in response to environmental stresses. Regulation of this process is complex and involves multiple signaling pathways and a large number of transcription factors (TFs). We used multiplexed transposon M-bM-^@M-^\Calling CardsM-bM-^@M-^] to simultaneously record the genome-wide binding patterns of 28 TFs in nitrogen-starved yeast. We were able to identify TF targets relevant for pseudohyphal growth, producing a detailed map of the regulatory network that governs this process. Using tools from graph theory, we identified 8 transcription factors that lie at the center of this network, including Flo8, Mss11, and Mfg1, which bind as a complex. Surprisingly, the DNA binding preferences for these key transcription factors were not known. Using Calling Card data, we predicted the in vivo DNA binding motif for the Flo8-Mss11-Mfg1 complex and validated it using a reporter assay. We found that this complex binds several important targets, including FLO11, at both their promoter and termination sequences. We demonstrated that this binding pattern is the result of DNA-looping, which regulates the transcription of these targets and is stabilized by an interaction with the nuclear pore complex. This looping provides yeast cells with a transcriptional memory, enabling them to more rapidly execute the filamentous growth program when nitrogen-starved if they have been previously exposed to this condition. These data contain mapped insertion sites from Ty5 transposon "Calling Cards" in M-NM-#1278b yeast sequenced using an Illumina Hiseq. Other sequencing data present are RNA-seq reads.
2014-02-11 | E-GEOD-54831 | biostudies-arrayexpress
Project description:The ability to chronicle transcription factor binding events throughout the development of an organism would facilitate mapping of transcriptional networks that control cell fate decisions. We describe a method for permanently recording protein-DNA interactions in mammalian cells. We endow transcription factors with the ability to deposit a transposon into the genome near to where they bind. The transposon becomes a “Calling Card” the transcription factor leaves behind to record its visit to the genome. The locations of the Calling Cards can be determined by massively-parallel DNA sequencing. We show that the transcription factor SP1 fused to the piggyBac transposase directs insertion of the piggyBac transposon near SP1 binding sites. The locations of transposon insertions are highly reproducible, and agree with sites of SP1-binding determined by ChIP-seq. Genes bound by SP1 are more likely to be expressed in the HCT116 cell line we used, and SP1-bound CpG islands show a strong preference to be unmethylated. This method has the potential to trace transcription factor binding throughout cellular and organismal development in a way that has heretofore not been possible.
2011-12-31 | GSE34791 | GEO
Project description:Pseudohyphal growth is a developmental pathway seen in some strains of yeast, in which cells elongate and form multicellular filaments in response to environmental stresses. Regulation of this process is complex and involves multiple signaling pathways and a large number of transcription factors (TFs). We used multiplexed transposon “Calling Cards” to simultaneously record the genome-wide binding patterns of 28 TFs in nitrogen-starved yeast. We were able to identify TF targets relevant for pseudohyphal growth, producing a detailed map of the regulatory network that governs this process. Using tools from graph theory, we identified 8 transcription factors that lie at the center of this network, including Flo8, Mss11, and Mfg1, which bind as a complex. Surprisingly, the DNA binding preferences for these key transcription factors were not known. Using Calling Card data, we predicted the in vivo DNA binding motif for the Flo8-Mss11-Mfg1 complex and validated it using a reporter assay. We found that this complex binds several important targets, including FLO11, at both their promoter and termination sequences. We demonstrated that this binding pattern is the result of DNA-looping, which regulates the transcription of these targets and is stabilized by an interaction with the nuclear pore complex. This looping provides yeast cells with a transcriptional memory, enabling them to more rapidly execute the filamentous growth program when nitrogen-starved if they have been previously exposed to this condition.
2014-02-11 | GSE54831 | GEO
Project description:Calling Cards is a platform technology to record a cumulative history of transient protein-DNA interactions in the genome of genetically targeted cell types. The record of these interactions are recovered by next generation sequencing. Compared to other genomic assays, whose readout provides a snapshot at the time of harvest, Calling Cards enables correlation of historical molecular states to eventual outcomes or phenotypes. To achieve this, Calling Cards uses the piggyBac transposase to insert self-reporting transposon (SRT) “Calling Cards” into the genome, leaving permanent marks at interaction sites. Calling Cards can be deployed in a variety of in vitro and in vivo biological systems to study gene regulatory networks involved in development, aging, and disease. Out of the box, it assesses enhancer usage but can be adapted to profile specific transcription factor binding with custom transcription factor (TF)-piggyBac fusion proteins. The Calling Cards workflow has five main stages: delivery of Calling Card reagents, sample preparation, library preparation, sequencing, and data analysis. Here, we first present a comprehensive guide for experimental design, reagent selection, and optional customization of the platform to study additional TFs. Then, we provide an updated protocol for the five steps, using reagents that improve throughput and decrease costs, including an overview of a newly deployed computational pipeline. This protocol is designed for users with basic molecular biology experience to process samples into sequencing libraries in 1-2 days. Familiarity with bioinformatic analysis and command line tools is required to set up the pipeline in a high performance computing environment and to conduct downstream analyses.
2023-02-01 | GSE223926 | GEO
Project description:The nucleosome is a fundamental unit of chromatin in eukaryotes, and generally prevents the binding of transcription factors to genomic DNA. Pioneer transcription factors overcome the nucleosome barrier, and bind their target DNA sequences in chromatin. OCT4 is a representative pioneer transcription factor that plays a role in stem cell pluripotency. In the present study, we biochemically analyzed the nucleosome binding by OCT4. Crosslinking mass spectrometry showed that OCT4 binds the nucleosome.
2020-05-13 | PXD019160 | JPOST Repository
Project description:Binding of transcription factors (TFs) to regulatory sequences is a pivotal step in the control of gene expression. Despite many advances in the characterization of sequence motifs recognized by TFs, our ability to quantitatively predict TF binding to different regulatory sequences is still limited. Here, we present a novel experimental assay termed BunDLE-seq that provides quantitative measurements of TF binding to thousands of fully designed sequences of 200 bp in length within a single experiment. Applying this binding assay to two yeast TFs we demonstrate that sequences outside the core TF binding site profoundly affect TF binding. We show that TF-specific models based on the sequence or DNA shape of the regions flanking the core binding site are highly predictive of the measured differential TF binding. We further characterize the dependence of TF binding, accounting for measurements of single and co-occurring binding events, on the number and location of binding sites and on the TF concentration. Finally, by coupling our in vitro TF binding measurements, and another application of our method probing nucleosome formation, to in vivo expression measurements carried out with the same template sequences now serving and promoters, we offer insights into mechanisms that may determine the different expression outcomes observed. Our assay thus paves the way to a more comprehensive understanding of TF binding to regulatory sequences, and allows the characterization of TF binding determinants within and outside of core binding sites. Binding experiments were carried out with one of two input libraries; Lib1 (also referred to as “main lib”) includes designed sequence variants of length 150bp, and Lib2 (also referred to as “constant flanks lib”) includes designed sequence variants of length 200bp. The sequences of the variants of interest can be found in the second column of the processed data file (BunDLE-seq_data, see 3 excel sheets). EXP1, EXP2 and EXP3 were carried out using Lib1, and EXP4 was carried out using Lib2. Binding experiments were carried out with different proteins (in different concentrations) – DNA was extracted from bound bands that were formed on the gel and amplified with a primer with a unique 5bp upstream tail specifying the identity of the originating band (also referred to as “band barcode”). Data from the following bands was used in the analysis: EXP1_band1, no-protein band – with band barcode “AACGA”, EXP1_band2, single Gcn4 (conc 1:8) bound band* – with band barcode “TCGTA” , EXP1_band3, histones bound band – with band barcode “AGATA”, EXP2_band1, no-protein band – with band barcode “AACGA”, EXP2_band2, single Gcn4 (conc 1:8) bound band* – with band barcode “AGATA”, EXP2_band2, single Gcn4 (conc 1:7) bound band – with band barcode “CACTT”, EXP2_band2, single Gcn4 (conc 1:6) bound band – with band barcode “TCGTA”, EXP2_band2, single Gcn4 (conc 1:5) bound band – with band barcode “CATCA”, EXP2_band2, single Gcn4 (conc 1:4) bound band – with band barcode “TATTA”, EXP2_band2, single Gcn4 (conc 1:3) bound band – with band barcode “AGTCA”, EXP2_band2, single Gcn4 (conc 1:2) bound band – with band barcode “ACGAA”, EXP2_band2, single Gcn4 (conc 1:1) bound band – with band barcode “ACAGA”, EXP2_band2, two Gcn4 (conc 1:1) bound band – barcoded with “GTAGA”, EXP3_band1, no-protein band – with band barcode “GCATA”, EXP3_band2, single Gal4 (conc 1:1) bound band – with band barcode “GATGT”, EXP3_band3, single Gal4 (conc 3:1) bound band – with band barcode “GTTCA”, EXP3_band4, two Gal4 (conc 3:1) bound band – with band barcode “CTCAA”, EXP4_band1, no-protein band – with band barcode “GAGTA”, EXP4_band2, single Gcn4 (conc 1:3) bound band – “TACCA”, EXP4_band3, no-protein band – with band barcode “TCGTT”, EXP4_band3, single Gal4 (conc 3:1) bound band – with band barcode “ACATC”, *biological replicates
2015-03-04 | E-GEOD-66143 | biostudies-arrayexpress
Project description:Here, we examine how six single amino acid variants in the DNA-binding domain of Ste12 – a yeast transcription factor regulating mating and invasion – alter Ste12 genome binding, motif recognition and gene expression to yield markedly different phenotypes. Using a combination of the calling card method, RNA sequencing , we find that variants with dissimilar binding and expression profiles can converge onto similar cellular behaviors.
2020-01-30 | GSE141713 | GEO
Project description:A high-confidence map of the direct, functional targets of each transcription factor (TF) requires convergent evidence from independent sources. Two significant sources of evidence are TF binding locations and the transcriptional responses to direct TF perturbations. Systematic data sets of both types exist for yeast and human. Standard analysis of the genes whose regulatory DNA is bound by a TF, assayed by ChIP-chip/seq, and the genes that respond to a perturbation of that TF, shows that these two data sources rarely converge on a common set of direct, functional targets. Even taking the few genes that are both bound and responsive as direct functional targets is not safe -- when there are many non-functional binding sites and many indirect targets, non-functional sites are expected to occur in the cis-regulatory DNA of indirect targets by chance. To address this problem, we introduce Dual Threshold Optimization, a new method for setting significance thresholds on binding and response data, and show that it improves convergence. It also enables comparison of binding data to perturbation-response data that has been processed by network inference algorithms, which further improves convergence. Next, we analyze a comprehensive new data set measuring the transcriptional response shortly after inducing overexpression of a yeast TF. We also present a new yeast binding location data set obtained by transposon calling cards and compare it to recent ChIP-exo data. The combination of dual threshold optimization and network inference greatly expands the high-confidence TF network map in both yeast and human. In yeast, measuring the response shortly after inducing TF overexpression and measuring binding locations by using transposon calling cards or ChIP-exo improve the network synergistically.
2020-02-04 | GSE144657 | GEO