Difference between revisions of "IC4R004-Epigenomic-2012-22778444"

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* '''Transcriptome and methylome interactions in rice hybrids'''
 
* '''Transcriptome and methylome interactions in rice hybrids'''
 
==The Background of This Project==
 
==The Background of This Project==
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* Chromatin immunoprecipitation (ChIP) coupled with high throughput sequencing (ChIP-Seq) has emerged as one of the most promising tools for profiling protein-DNA binding sites and chromatin modifications on a genome-wide scale [1]. The goal of ChIP-Seq studies is to find those genomic DNA fragments that are enriched in immunoprecipitation fractions using antibodies specific for DNA associated proteins of interest. Enriched regions, those with a high density of short DNA reads after immunopre- cipitation and DNA sequencing, are referred to as peaks. Many programs for identification of peaks with ChIP-Seq data have been developed in recent years [2,3,4,5,6,7,8,9,10,11]. The reported algorithms differ in their approaches for identifying potential enriched regions of the genome. Some algorithms, for example MACS [10] and PeakSeq [8], use a simple sliding window and group all reads within each window together. Others use a finer resolution method, either considering each base pair singly as in FindPeaks [4] or defining the windows based on the read locations as represented by USeq [7]. After identifying windows, the algorithms must then determine which windows are the true enriched regions. Methods without a control (FindPeaks) either simply report the number of reads in the windows or make an assumption about the background distribution, such as assuming the reads follow a Poisson distribution (FindPeaks), and calculate significance based on the assumed distribution.
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* DNA methylation is an epigenetic mark that can often lead to the repression of gene expression (1, 2). It is enriched in heterochromatin and, when present at regulatory sites, usually acts as a repressor of expression, most notably in transposons (3). However, it is also found over coding regions, where it likely does not directly affect transcription and is associated with moderately expressed genes (2, 4–6). In plants, DNA methylation occurs in three different contexts: CG, CHG, and CHH (where H is any nucleotide but G). In Arabidopsis, each context is maintained by different enzymes: MET1 for CG sites, CMT3 for CHG sites and DRM2 for CHH sites. CG and CHG sites are symmetric across the two DNA strands, which is thought to be important for the maintenance of methylation at these sites following DNA repli- cation. In contrast, CHH sites are not symmetric, and their methylation is mediated by RNA-directed DNA methylation pathways (RdDM), which use siRNAs to initiate de novo methylation (3). Cellular methylation states tend to persist during cell division, and recent studies in Arabidopsis have also shown that DNA methylation is faithfully inherited across generations (7, 8). Nonetheless, we are only beginning to understand how different methylation patterns from inbred parents may “interact” during the generation of their hybrid progeny (9, 10).
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* Those including a control sample (MACS, PeakSeq) use the control to more accurately model the background distribution of the reads and calculate an empirical False Discovery Rate (FDR) via, for example, a sample swap technique. Distinguishing between multiple small peaks or a single large peak is also challenging. While some algorithms merge overlapping peaks (MACS) or peaks within a user-supplied threshold (USeq, PeakSeq), others (Find-Peaks) compare the height of peaks to the depth of the separating valley to differentiate multiple small peaks from one large peak. Pepke et al. [12] discussed a number of additional peak identification algorithms in a review article. They made distinc- tions among the algorithms, including how the algorithms aggregated the reads, the criteria for significant peak identification, read shifting to account for reading the end of the reads, use of control, and input parameters. Similarly, Barski and Zhao [13] also reviewed a number of algorithms for peak identification. Thus far, however, no program has emerged as the consensus best approach for identifying peaks in histone modification and DNA binding studies. Therefore, it is important to compare these available algorithms and to suggest essential parameters to assist molecular biology laboratories in selecting the best program for their data analysis.
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* In this project, the researchers generated integrative maps of whole-genome cytosine methylation profiles [bisulfite sequencing (BS-seq)] and transcriptional profiles (RNA-seq), to characterize two rice subspecies, Oryza sativa spp japonica [Nipponbare (NPB)] and Oryza sativa spp indica (93–11) and their two reciprocal hybrid off- spring. Using a combination of BS-seq, RNA-seq, and siRNA-seq, we were able to generate allele-specific patterns of methylation and transcription in the hybrids, and thus directly measure the degree to which these are altered between the corresponding parental and F1 chromosomes.
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* In this project, the researchers identified H3K27me3 modification sites within rice (Oryza sativa) young endosperm using the ChIP-Seq approach. Four different peak identification algorithms (PeakSeq, USeq, MACS, and FindPeaks) were used to locate H3K27me3 enrichment sites. ChIP-PCR was used to evaluate the quality of the peaks identified by these algorithms. We also analyzed the relative location of the peaks with respect to gene expression. Finally, we examined the Gene Ontology (GO) annotations [27] of the ChIP enriched genes.
 
  
 
==Labs working on this Project==
 
==Labs working on this Project==

Revision as of 04:31, 22 June 2016

Project Title

  • Transcriptome and methylome interactions in rice hybrids

The Background of This Project

  • DNA methylation is an epigenetic mark that can often lead to the repression of gene expression (1, 2). It is enriched in heterochromatin and, when present at regulatory sites, usually acts as a repressor of expression, most notably in transposons (3). However, it is also found over coding regions, where it likely does not directly affect transcription and is associated with moderately expressed genes (2, 4–6). In plants, DNA methylation occurs in three different contexts: CG, CHG, and CHH (where H is any nucleotide but G). In Arabidopsis, each context is maintained by different enzymes: MET1 for CG sites, CMT3 for CHG sites and DRM2 for CHH sites. CG and CHG sites are symmetric across the two DNA strands, which is thought to be important for the maintenance of methylation at these sites following DNA repli- cation. In contrast, CHH sites are not symmetric, and their methylation is mediated by RNA-directed DNA methylation pathways (RdDM), which use siRNAs to initiate de novo methylation (3). Cellular methylation states tend to persist during cell division, and recent studies in Arabidopsis have also shown that DNA methylation is faithfully inherited across generations (7, 8). Nonetheless, we are only beginning to understand how different methylation patterns from inbred parents may “interact” during the generation of their hybrid progeny (9, 10).
  • In this project, the researchers generated integrative maps of whole-genome cytosine methylation profiles [bisulfite sequencing (BS-seq)] and transcriptional profiles (RNA-seq), to characterize two rice subspecies, Oryza sativa spp japonica [Nipponbare (NPB)] and Oryza sativa spp indica (93–11) and their two reciprocal hybrid off- spring. Using a combination of BS-seq, RNA-seq, and siRNA-seq, we were able to generate allele-specific patterns of methylation and transcription in the hybrids, and thus directly measure the degree to which these are altered between the corresponding parental and F1 chromosomes.

Labs working on this Project

  • Department of Molecular, Cell and Developmental Biology, University of California, Los Angeles, CA 90095; b Howard Hughes Medical Institute,
  • University of California, Los Angeles, CA 90095; f Molecular Biology Institute, University of California, Los Angeles, CA 90095; c Department of Plant
  • Pathology, Ohio State University, Columbus, OH 43210; d Department of Plant and Soil Sciences, Delaware Biotechnology Institute, University of Delaware,
  • Newark, DE 19711; e US Department of Agriculture—Agricultural Research Service Dale Bumpers National Rice Research Center, Stuttgart, AR 72160;
  • Eli and Edythe Broad Center of Regenerative Medicine and Stem Cell Research, University of California, Los Angeles, CA 90095

Corresponding Author

  • Steven E. Jacobsen (E-mail:jacobsen@ucla.edu) & Matteo Pellegrini (E-mail: matteop@mcdb.ucla.edu)
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