Transcription Factor Of Interest Research Articles

Targeted degradation of transcription factors by TRAFTACs: TRAnscription Factor TArgeting Chimeras

Many diseases, including cancer, stem from aberrant activation or overexpression of oncoproteins that are associated with multiple signaling pathways. Although proteins with catalytic activity can be successfully drugged, the majority of other protein families, such as transcription factors, remain intractable due to their lack of ligandable sites. In this study, we report the development of TRAnscription Factor TArgeting Chimeras (TRAFTACs) as a generalizable strategy for targeted transcription factor degradation. We show that TRAFTACs, which consist of a chimeric oligonucleotide that simultaneously binds to the transcription factor of interest (TOI) and to HaloTag-fused dCas9 protein, can induce degradation of the former via the proteasomal pathway. Application of TRAFTACs to two oncogenic TOIs, NF-κB and brachyury, suggests that TRAFTACs can be successfully employed for the targeted degradation of other DNA-binding proteins. Thus, TRAFTAC technology is potentially a generalizable strategy to induce degradation of other transcription factors both invitro and invivo.

Improved linking of motifs to their TFs using domain information.

MotivationA central aim of molecular biology is to identify mechanisms of transcriptional regulation. Transcription factors (TFs), which are DNA-binding proteins, are highly involved in these processes, thus a crucial information is to know where TFs interact with DNA and to be aware of the TFs’ DNA-binding motifs. For that reason, computational tools exist that link DNA-binding motifs to TFs either without sequence information or based on TF-associated sequences, e.g. identified via a chromatin immunoprecipitation followed by sequencing (ChIP-seq) experiment.In this paper, we present MASSIF, a novel method to improve the performance of existing tools that link motifs to TFs relying on TF-associated sequences. MASSIF is based on the idea that a DNA-binding motif, which is correctly linked to a TF, should be assigned to a DNA-binding domain (DBD) similar to that of the mapped TF. Because DNA-binding motifs are in general not linked to DBDs, it is not possible to compare the DBD of a TF and the motif directly. Instead we created a DBD collection, which consist of TFs with a known DBD and an associated motif. This collection enables us to evaluate how likely it is that a linked motif and a TF of interest are associated to the same DBD. We named this similarity measure domain score, and represent it as a P-value. We developed two different ways to improve the performance of existing tools that link motifs to TFs based on TF-associated sequences: (i) using meta-analysis to combine P-values from one or several of these tools with the P-value of the domain score and (ii) filter unlikely motifs based on the domain score.ResultsWe demonstrate the functionality of MASSIF on several human ChIP-seq datasets, using either motifs from the HOCOMOCO database or de novo identified ones as input motifs. In addition, we show that both variants of our method improve the performance of tools that link motifs to TFs based on TF-associated sequences significantly independent of the considered DBD type.Availability and implementation MASSIF is freely available online at https://github.com/SchulzLab/MASSIF.Supplementary information Supplementary data are available at Bioinformatics online.

Identification of essential transcription factors for adequate DNA damage response after benzo(a)pyrene and aflatoxin B1 exposure by combining transcriptomics with functional genomics.

DNA damage mediates widespread changes in transcription through activation or repression of transcription factors (TFs). However, the consequences of regulating specific TFs for the outcome of the DNA repair process remain incompletely understood. Here, we combined transcriptomics and TF binding prediction with functional genomics to identify TFs essential for adequate DNA repair in HepG2 liver cells after a non-cytotoxic dose of carcinogens benzo(a)pyrene (BaP) (2μM) and aflatoxin B1 (AFB1) (5μM). BaP and AFB1 induced a largely common transcriptional response, mediated by similar TFs. A lentiviral shRNA screen knocking down the top31 identified TFs, was performed to determine their effect on DNA repair by assessing phosphorylation of H2AX (γ-H2AX). In addition to the top candidate p53, we identified several other interesting TFs that modulated γ-H2AX after BaP and AFB1 treatment. Validation studies confirmed the role of p53 in reducing γ-H2AX formation and DNA breaks measured by COMET assay after BaP and AFB1 exposure. Expression of the cell cycle inhibitor p21 was profoundly impaired upon p53 knock-down. In addition, the expression of 2 genes involved in nucleotide exchange repair, DDB2 and XPC was significantly reduced in p53 knock-down cells. Although p63 knock-down affected DNA damage upon BaP treatment this was not associated with altered expression of DDB2 or XPC. Finally, knock-down of ARNT reduced γ-H2AX in response to BaP, which was associated with reduced CYP1A1 expression. Importantly, our results suggest a new role for ARNT and its dimerization partner AHR in the occurrence of H2AX phosphorylation after AFB1 treatment. These data show that modulation of TF activity impacts on the repair of BaP- and AFB1-induced DNA damage. Our study also demonstrates the potential of combining functional genomics with genome-wide expression analysis to identify yet unknown causal relationships, thereby aiding in the interpretation of complex biological systems.

Improving analysis of transcription factor binding sites within ChIP-Seq data based on topological motif enrichment.

BackgroundChromatin immunoprecipitation (ChIP) coupled to high-throughput sequencing (ChIP-Seq) techniques can reveal DNA regions bound by transcription factors (TF). Analysis of the ChIP-Seq regions is now a central component in gene regulation studies. The need remains strong for methods to improve the interpretation of ChIP-Seq data and the study of specific TF binding sites (TFBS).ResultsWe introduce a set of methods to improve the interpretation of ChIP-Seq data, including the inference of mediating TFs based on TFBS motif over-representation analysis and the subsequent study of spatial distribution of TFBSs. TFBS over-representation analysis applied to ChIP-Seq data is used to detect which TFBSs arise more frequently than expected by chance. Visualization of over-representation analysis results with new composition-bias plots reveals systematic bias in over-representation scores. We introduce the BiasAway background generating software to resolve the problem. A heuristic procedure based on topological motif enrichment relative to the ChIP-Seq peaks’ local maximums highlights peaks likely to be directly bound by a TF of interest. The results suggest that on average two-thirds of a ChIP-Seq dataset’s peaks are bound by the ChIP’d TF; the origin of the remaining peaks remaining undetermined. Additional visualization methods allow for the study of both inter-TFBS spatial relationships and motif-flanking sequence properties, as demonstrated in case studies for TBP and ZNF143/THAP11.ConclusionsTopological properties of TFBS within ChIP-Seq datasets can be harnessed to better interpret regulatory sequences. Using GC content corrected TFBS over-representation analysis, combined with visualization techniques and analysis of the topological distribution of TFBS, we can distinguish peaks likely to be directly bound by a TF. The new methods will empower researchers for exploration of gene regulation and TF binding.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-472) contains supplementary material, which is available to authorized users.

Global analysis of transcription factor-binding sites in yeast using ChIP-Seq.

Transcription factors influence gene expression through their ability to bind DNA at specific regulatory elements. Specific DNA-protein interactions can be isolated through the chromatin immunoprecipitation (ChIP) procedure, in which DNA fragments bound by the protein of interest are recovered. ChIP is followed by high-throughput DNA sequencing (Seq) to determine the genomic provenance of ChIP DNA fragments and their relative abundance in the sample. This chapter describes a ChIP-Seq strategy adapted for budding yeast to enable the genome-wide characterization of binding sites of transcription factors (TFs) and other DNA-binding proteins in an efficient and cost-effective way.Yeast strains with epitope-tagged TFs are most commonly used for ChIP-Seq, along with their matching untagged control strains. The initial step of ChIP involves the cross-linking of DNA and proteins. Next, yeast cells are lysed and sonicated to shear chromatin into smaller fragments. An antibody against an epitope-tagged TF is used to pull down chromatin complexes containing DNA and the TF of interest. DNA is then purified and proteins degraded. Specific barcoded adapters for multiplex DNA sequencing are ligated to ChIP DNA. Short DNA sequence reads (28-36 base pairs) are parsed according to the barcode and aligned against the yeast reference genome, thus generating a nucleotide-resolution map of transcription factor-binding sites and their occupancy.

ChIP-Array: combinatory analysis of ChIP-seq/chip and microarray gene expression data to discover direct/indirect targets of a transcription factor

Chromatin immunoprecipitation (ChIP) coupled with high-throughput techniques (ChIP-X), such as next generation sequencing (ChIP-Seq) and microarray (ChIP–chip), has been successfully used to map active transcription factor binding sites (TFBS) of a transcription factor (TF). The targeted genes can be activated or suppressed by the TF, or are unresponsive to the TF. Microarray technology has been used to measure the actual expression changes of thousands of genes under the perturbation of a TF, but is unable to determine if the affected genes are direct or indirect targets of the TF. Furthermore, both ChIP-X and microarray methods produce a large number of false positives. Combining microarray expression profiling and ChIP-X data allows more effective TFBS analysis for studying the function of a TF. However, current web servers only provide tools to analyze either ChIP-X or expression data, but not both. Here, we present ChIP-Array, a web server that integrates ChIP-X and expression data from human, mouse, yeast, fruit fly and Arabidopsis. This server will assist biologists to detect direct and indirect target genes regulated by a TF of interest and to aid in the functional characterization of the TF. ChIP-Array is available at http://jjwanglab.hku.hk/ChIP-Array, with free access to academic users.