A comparative analysis of genome-wide chromatin immunoprecipitation data for mammalian transcription factors.
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ABSTRACT:
SUBMITTER: Ji H
PROVIDER: S-EPMC1669715 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundChromatin remodeling, histone modifications and other chromatin-related processes play a crucial role in gene regulation. A very useful technique to study these processes is chromatin immunoprecipitation (ChIP). ChIP is widely used for a few model systems, including Arabidopsis, but establishment of the technique for other organisms is still remarkably challenging. Furthermore, quantitative analysis of the precipitated material and normalization of the data is often underestimated, negatively affecting data quality.ResultsWe developed a robust ChIP protocol, using maize (Zea mays) as a model system, and present a general strategy to systematically optimize this protocol for any type of tissue. We propose endogenous controls for active and for repressed chromatin, and discuss various other controls that are essential for successful ChIP experiments. We experienced that the use of quantitative PCR (QPCR) is crucial for obtaining high quality ChIP data and we explain why. The method of data normalization has a major impact on the quality of ChIP analyses. Therefore, we analyzed different normalization strategies, resulting in a thorough discussion of the advantages and drawbacks of the various approaches.ConclusionHere we provide a robust ChIP protocol and strategy to optimize the protocol for any type of tissue; we argue that quantitative real-time PCR (QPCR) is the best method to analyze the precipitates, and present comprehensive insights into data normalization.
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Project description:Numerous studies in plants have shown the vital roles of MYB transcription factors in signal transduction, developmental regulation, biotic/abiotic stress responses and secondary metabolism regulation. However, less is known about the functions of MYBs in Ganoderma In this study, five medicinal macrofungi of genus Ganoderma were subjected to a genome-wide comparative analysis of MYB genes. A total of 75 MYB genes were identified and classified into four types: 1R-MYBs (52), 2R-MYBs (19), 3R-MYBs (2) and 4R-MYBs (2). Gene structure analysis revealed varying exon numbers (3-14) and intron lengths (7-1058 bp), and noncanonical GC-AG introns were detected in G. lucidum and G. sinense In a phylogenetic analysis, 69 out of 75 MYB genes were clustered into 15 subgroups, and both single-copy orthologous genes and duplicated genes were identified. The promoters of the MYB genes harbored multiple cis-elements, and specific genes were co-expressed with the G. lucidum MYB genes, indicating the potential roles of these MYB genes in stress response, development and metabolism. This comprehensive and systematic study of MYB family members provides a reference and solid foundation for further functional analysis of MYB genes in Ganoderma species.
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Project description:We propose a method to predict yeast transcription factor targets by integrating histone modification profiles with transcription factor binding motif information. It shows improved predictive power compared to a binding motif-only method. We find that transcription factors cluster into histone-sensitive and -insensitive classes. The target genes of histone-sensitive transcription factors have stronger histone modification signals than those of histone-insensitive ones. The two classes also differ in tendency to interact with histone modifiers, degree of connectivity in protein-protein interaction networks, position in the transcriptional regulation hierarchy, and in a number of additional features, indicating possible differences in their transcriptional regulation mechanisms.
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Project description:Gene transcription mediated by RNA polymerase II (pol-II) is a key step in gene expression. The dynamics of pol-II moving along the transcribed region influence the rate and timing of gene expression. In this work, we present a probabilistic model of transcription dynamics which is fitted to pol-II occupancy time course data measured using ChIP-Seq. The model can be used to estimate transcription speed and to infer the temporal pol-II activity profile at the gene promoter. Model parameters are estimated using either maximum likelihood estimation or via Bayesian inference using Markov chain Monte Carlo sampling. The Bayesian approach provides confidence intervals for parameter estimates and allows the use of priors that capture domain knowledge, e.g. the expected range of transcription speeds, based on previous experiments. The model describes the movement of pol-II down the gene body and can be used to identify the time of induction for transcriptionally engaged genes. By clustering the inferred promoter activity time profiles, we are able to determine which genes respond quickly to stimuli and group genes that share activity profiles and may therefore be co-regulated. We apply our methodology to biological data obtained using ChIP-seq to measure pol-II occupancy genome-wide when MCF-7 human breast cancer cells are treated with estradiol (E2). The transcription speeds we obtain agree with those obtained previously for smaller numbers of genes with the advantage that our approach can be applied genome-wide. We validate the biological significance of the pol-II promoter activity clusters by investigating cluster-specific transcription factor binding patterns and determining canonical pathway enrichment. We find that rapidly induced genes are enriched for both estrogen receptor alpha (ER?) and FOXA1 binding in their proximal promoter regions.
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Project description:Chromatin immunoprecipitation (ChIP) followed by next generation sequencing (ChIP-Seq) is a powerful technique to study transcriptional regulation. However, the requirement of millions of cells to generate results with high signal-to-noise ratio precludes it in the study of small cell populations. Here, we present a tagmentation-assisted fragmentation ChIP (TAF-ChIP) and sequencing method to generate high-quality histone profiles from low cell numbers. The data obtained from the TAF-ChIP approach are amenable to standard tools for ChIP-Seq analysis, owing to its high signal-to-noise ratio. The epigenetic profiles from TAF-ChIP approach showed high agreement with conventional ChIP-Seq datasets, thereby underlining the utility of this approach.
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Project description:Chromatin immunoprecipitation (ChIP) is used to study interactions between proteins and DNA. Nuclear lysates are prepared, and chromatin is fragmented by sonication. Antibodies are used to purify a protein of interest (e.g., a transcription factor or histone mark) along with any bound DNA. The genomic binding sites can then be mapped by sequencing the bound DNA (ChIP-seq) or by qPCR if binding sites are already known. ChIP requires optimization for each cell type, and success is highly antibody dependent. This protocol can be adapted to other cell lines with careful optimization. For complete details on the use and execution of this protocol, please refer to Holliday et al. (2021).
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Project description:MotivationHistone acetylation (HAc) is associated with open chromatin, and HAc has been shown to facilitate transcription factor (TF) binding in mammalian cells. In the innate immune system context, epigenetic studies strongly implicate HAc in the transcriptional response of activated macrophages. We hypothesized that using data from large-scale sequencing of a HAc chromatin immunoprecipitation assay (ChIP-Seq) would improve the performance of computational prediction of binding locations of TFs mediating the response to a signaling event, namely, macrophage activation.ResultsWe tested this hypothesis using a multi-evidence approach for predicting binding sites. As a training/test dataset, we used ChIP-Seq-derived TF binding site locations for five TFs in activated murine macrophages. Our model combined TF binding site motif scanning with evidence from sequence-based sources and from HAc ChIP-Seq data, using a weighted sum of thresholded scores. We find that using HAc data significantly improves the performance of motif-based TF binding site prediction. Furthermore, we find that within regions of high HAc, local minima of the HAc ChIP-Seq signal are particularly strongly correlated with TF binding locations. Our model, using motif scanning and HAc local minima, improves the sensitivity for TF binding site prediction by approximately 50% over a model based on motif scanning alone, at a false positive rate cutoff of 0.01.AvailabilityThe data and software source code for model training and validation are freely available online at http://magnet.systemsbiology.net/hac.
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