Identification of Transcription Factor Binding Sites by Cleavage Under Target and Release Using Nuclease in Zebrafish.
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ABSTRACT:
SUBMITTER: Barakat R
PROVIDER: S-EPMC9246268 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Determining the binding locations of a protein on chromatin is essential for understanding its function and potential regulatory targets. Chromatin Immunoprecipitation (ChIP) has been the gold standard for determining protein localization for over 30 years and is defined by the use of an antibody to pull out the protein of interest from sonicated or enzymatically digested chromatin. More recently, antibody tethering techniques have become popular for assessing protein localization on chromatin due to their increased sensitivity. Cleavage Under Targets & Release Under Nuclease (CUT&RUN) is the genome-wide derivative of Chromatin Immunocleavage (ChIC) and utilizes recombinant Protein A tethered to micrococcal nuclease (pA-MNase) to identify the IgG constant region of the antibody targeting a protein of interest, therefore enabling site-specific cleavage of the DNA flanking the protein of interest. CUT&RUN can be used to profile histone modifications, transcription factors, and other chromatin-binding proteins such as nucleosome remodeling factors. Importantly, CUT&RUN can be used to assess the localization of either euchromatic- or heterochromatic-associated proteins and histone modifications. For these reasons, CUT&RUN is a powerful method for determining the binding profiles of a wide range of proteins. Recently, CUT&RUN has been optimized for transcription factor profiling in low populations of cells and single cells and the optimized protocol has been termed ultra-low input CUT&RUN (uliCUT&RUN). Here, a detailed protocol is presented for single-cell factor profiling using uliCUT&RUN in a manual 96-well format.
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Project description:Despite the prominent roles played by R2R3-MYB transcription factors in the regulation of plant gene expression, little is known about the details of how these proteins interact with their DNA targets. For example, while Arabidopsis thaliana R2R3-MYB protein AtMYB61 is known to alter transcript abundance of a specific set of target genes, little is known about the specific DNA sequences to which AtMYB61 binds. To address this gap in knowledge, DNA sequences bound by AtMYB61 were identified using cyclic amplification and selection of targets (CASTing). The DNA targets identified using this approach corresponded to AC elements, sequences enriched in adenosine and cytosine nucleotides. The preferred target sequence that bound with the greatest affinity to AtMYB61 recombinant protein was ACCTAC, the AC-I element. Mutational analyses based on the AC-I element showed that ACC nucleotides in the AC-I element served as the core recognition motif, critical for AtMYB61 binding. Molecular modelling predicted interactions between AtMYB61 amino acid residues and corresponding nucleotides in the DNA targets. The affinity between AtMYB61 and specific target DNA sequences did not correlate with AtMYB61-driven transcriptional activation with each of the target sequences. CASTing-selected motifs were found in the regulatory regions of genes previously shown to be regulated by AtMYB61. Taken together, these findings are consistent with the hypothesis that AtMYB61 regulates transcription from specific cis-acting AC elements in vivo. The results shed light on the specifics of DNA binding by an important family of plant-specific transcriptional regulators.
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Project description:To represent the sequence specificity of transcription factors, the position weight matrix (PWM) is widely used. In most cases, each element is defined as a log likelihood ratio of a base appearing at a certain position, which is estimated from a finite number of known binding sites. To avoid bias due to this small sample size, a certain numeric value, called a pseudocount, is usually allocated for each position, and its fraction according to the background base composition is added to each element. So far, there has been no consensus on the optimal pseudocount value. In this study, we simulated the sampling process by artificially generating binding sites based on observed nucleotide frequencies in a public PWM database, and then the generated matrix with an added pseudocount value was compared to the original frequency matrix using various measures. Although the results were somewhat different between measures, in many cases, we could find an optimal pseudocount value for each matrix. These optimal values are independent of the sample size and are clearly correlated with the entropy of the original matrices, meaning that larger pseudocount vales are preferable for less conserved binding sites. As a simple representative, we suggest the value of 0.8 for practical uses.
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Project description:Gene-editing nucleases enable targeted modification of DNA sequences in living cells, thereby facilitating efficient knockout and precise editing of endogenous loci. Engineered nucleases also have the potential to introduce mutations at off-target sites of action. Such unintended alterations can confound interpretation of experiments and can have implications for development of therapeutic applications. Recently, two improved methods for identifying the off-target effects of zinc finger nucleases (ZFNs) were described-one using an in vitro cleavage site selection method and the other exploiting the insertion of integration-defective lentiviruses into nuclease-induced double-stranded DNA breaks. However, application of these two methods to a ZFN pair targeted to the human CCR5 gene led to identification of largely non-overlapping off-target sites, raising the possibility that additional off-target sites might exist. Here, we show that in silico abstraction of ZFN cleavage profiles obtained from in vitro cleavage site selections can greatly enhance the ability to identify potential off-target sites in human cells. Our improved method should enable more comprehensive profiling of ZFN specificities.
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Project description:BackgroundStudies of gene regulation often utilize genome-wide predictions of transcription factor (TF) binding sites. Most existing prediction methods are based on sequence information alone, ignoring biological contexts such as developmental stages and tissue types. Experimental methods to study in vivo binding, including ChIP-chip and ChIP-seq, can only study one transcription factor in a single cell type and under a specific condition in each experiment, and therefore cannot scale to determine the full set of regulatory interactions in mammalian transcriptional regulatory networks.ResultsWe developed a new computational approach, PIPES, for predicting tissue-specific TF binding. PIPES integrates in vitro protein binding microarrays (PBMs), sequence conservation and tissue-specific epigenetic (DNase I hypersensitivity) information. We demonstrate that PIPES improves over existing methods on distinguishing between in vivo bound and unbound sequences using ChIP-seq data for 11 mouse TFs. In addition, our predictions are in good agreement with current knowledge of tissue-specific TF regulation.ConclusionsWe provide a systematic map of computationally predicted tissue-specific binding targets for 284 mouse TFs across 55 tissue/cell types. Such comprehensive resource is useful for researchers studying gene regulation.
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