• Login
    View Item 
    •   Home
    • Research
    • Articles
    • View Item
    •   Home
    • Research
    • Articles
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of KAUSTCommunitiesIssue DateSubmit DateThis CollectionIssue DateSubmit Date

    My Account

    Login

    Quick Links

    Open Access PolicyORCID LibguideTheses and Dissertations LibguideSubmit an Item

    Statistics

    Display statistics

    An RNA polymerase II-and AGO4-associated protein acts in RNA-directed DNA methylation

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    Article-Nature-An_RNA_pol-2010.pdf
    Size:
    990.1Kb
    Format:
    PDF
    Description:
    Article - Full Text
    Download
    Thumbnail
    Name:
    Supplement_1_-_Nature-An_RNA_pol-2010.NIHMS190934-supplement-1.doc
    Size:
    31Kb
    Format:
    Microsoft Word
    Description:
    Supplemental File 1
    Download
    Type
    Article
    Authors
    Gao, Zhihuan
    Liu, Hai-Liang
    Daxinger, Lucia
    Pontes, Olga
    He, Xinjian
    Qian, Weiqiang
    Lin, Huixin
    Xie, Mingtang
    Lorkovic, Zdravko J.
    Zhang, ShouDong
    Miki, Daisuke
    Zhan, Xiangqiang
    Pontier, Dominique
    Lagrange, Thierry
    Jin, Hailing
    Matzke, Antonius J.
    Matzke, Marjori
    Pikaard, Craig S.
    Zhu, Jian-Kang cc
    KAUST Department
    Academic Affairs
    Biological and Environmental Science and Engineering (BESE) Division
    Center for Desert Agriculture
    Office of the VP
    Plant Stress Genomics Research Lab
    Date
    2010-04-21
    Online Publication Date
    2010-04-21
    Print Publication Date
    2010-05
    Permanent link to this record
    http://hdl.handle.net/10754/325291
    
    Metadata
    Show full item record
    Abstract
    DNA methylation is an important epigenetic mark in many eukaryotes. In plants, 24-nucleotide small interfering RNAs (siRNAs) bound to the effector protein, Argonaute 4 (AGO4), can direct de novo DNA methylation by the methyltransferase DRM2 (refs 2, 4-6). Here we report a new regulator of RNA-directed DNA methylation (RdDM) in Arabidopsis: RDM1. Loss-of-function mutations in the RDM1 gene impair the accumulation of 24-nucleotide siRNAs, reduce DNA methylation, and release transcriptional gene silencing at RdDM target loci. RDM1 encodes a small protein that seems to bind single-stranded methyl DNA, and associates and co-localizes with RNA polymerase II (Pol II, also known as NRPB), AGO4 and DRM2 in the nucleus. Our results indicate that RDM1 is a component of the RdDM effector complex and may have a role in linking siRNA production with pre-existing or de novo cytosine methylation. Our results also indicate that, although RDM1 and Pol V (also known as NRPE) may function together at some RdDM target sites in the peri-nucleolar siRNA processing centre, Pol II rather than Pol V is associated with the RdDM effector complex at target sites in the nucleoplasm. © 2010 Macmillan Publishers Limited. All rights reserved.
    Citation
    Gao Z, Liu H-L, Daxinger L, Pontes O, He X, et al. (2010) An RNA polymerase II- and AGO4-associated protein acts in RNA-directed DNA methylation. Nature 465: 106-109. doi:10.1038/nature09025.
    Publisher
    Springer Nature
    Journal
    Nature
    DOI
    10.1038/nature09025
    PubMed ID
    20410883
    PubMed Central ID
    PMC2865564
    ae974a485f413a2113503eed53cd6c53
    10.1038/nature09025
    Scopus Count
    Collections
    Articles; Biological and Environmental Science and Engineering (BESE) Division; Center for Desert Agriculture

    entitlement

    Related articles

    • The ability to form homodimers is essential for RDM1 to function in RNA-directed DNA methylation.
    • Authors: Sasaki T, Lorković ZJ, Liang SC, Matzke AJ, Matzke M
    • Issue date: 2014
    • A protein complex required for polymerase V transcripts and RNA- directed DNA methylation in Arabidopsis.
    • Authors: Law JA, Ausin I, Johnson LM, Vashisht AA, Zhu JK, Wohlschlegel JA, Jacobsen SE
    • Issue date: 2010 May 25
    • AGO4 is specifically required for heterochromatic siRNA accumulation at Pol V-dependent loci in Arabidopsis thaliana.
    • Authors: Wang F, Axtell MJ
    • Issue date: 2017 Apr
    • DTF1 is a core component of RNA-directed DNA methylation and may assist in the recruitment of Pol IV.
    • Authors: Zhang H, Ma ZY, Zeng L, Tanaka K, Zhang CJ, Ma J, Bai G, Wang P, Zhang SW, Liu ZW, Cai T, Tang K, Liu R, Shi X, He XJ, Zhu JK
    • Issue date: 2013 May 14
    • NRPD4, a protein related to the RPB4 subunit of RNA polymerase II, is a component of RNA polymerases IV and V and is required for RNA-directed DNA methylation.
    • Authors: He XJ, Hsu YF, Pontes O, Zhu J, Lu J, Bressan RA, Pikaard C, Wang CS, Zhu JK
    • Issue date: 2009 Feb 1

    Related items

    Showing items related by title, author, creator and subject.

    • Thumbnail

      ProDis-ContSHC: Learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrieval

      Wang, Jim Jing-Yan; Gao, Xin; Wang, Quanquan; Li, Yongping (BMC Bioinformatics, Springer Nature, 2012-05-08) [Article]
      Background: The need to retrieve or classify protein molecules using structure or sequence-based similarity measures underlies a wide range of biomedical applications. Traditional protein search methods rely on a pairwise dissimilarity/similarity measure for comparing a pair of proteins. This kind of pairwise measures suffer from the limitation of neglecting the distribution of other proteins and thus cannot satisfy the need for high accuracy of the retrieval systems. Recent work in the machine learning community has shown that exploiting the global structure of the database and learning the contextual dissimilarity/similarity measures can improve the retrieval performance significantly. However, most existing contextual dissimilarity/similarity learning algorithms work in an unsupervised manner, which does not utilize the information of the known class labels of proteins in the database.Results: In this paper, we propose a novel protein-protein dissimilarity learning algorithm, ProDis-ContSHC. ProDis-ContSHC regularizes an existing dissimilarity measure dij by considering the contextual information of the proteins. The context of a protein is defined by its neighboring proteins. The basic idea is, for a pair of proteins (i, j), if their context N (i) and N (j) is similar to each other, the two proteins should also have a high similarity. We implement this idea by regularizing dij by a factor learned from the context N (i) and N (j). Moreover, we divide the context to hierarchial sub-context and get the contextual dissimilarity vector for each protein pair. Using the class label information of the proteins, we select the relevant (a pair of proteins that has the same class labels) and irrelevant (with different labels) protein pairs, and train an SVM model to distinguish between their contextual dissimilarity vectors. The SVM model is further used to learn a supervised regularizing factor. Finally, with the new Supervised learned Dissimilarity measure, we update the Protein Hierarchial Context Coherently in an iterative algorithm--ProDis-ContSHC.We test the performance of ProDis-ContSHC on two benchmark sets, i.e., the ASTRAL 1.73 database and the FSSP/DALI database. Experimental results demonstrate that plugging our supervised contextual dissimilarity measures into the retrieval systems significantly outperforms the context-free dissimilarity/similarity measures and other unsupervised contextual dissimilarity measures that do not use the class label information.Conclusions: Using the contextual proteins with their class labels in the database, we can improve the accuracy of the pairwise dissimilarity/similarity measures dramatically for the protein retrieval tasks. In this work, for the first time, we propose the idea of supervised contextual dissimilarity learning, resulting in the ProDis-ContSHC algorithm. Among different contextual dissimilarity learning approaches that can be used to compare a pair of proteins, ProDis-ContSHC provides the highest accuracy. Finally, ProDis-ContSHC compares favorably with other methods reported in the recent literature. 2012 Wang et al.; licensee BioMed Central Ltd.
    • Thumbnail

      The human interactome knowledge base (hint-kb): An integrative human protein interaction database enriched with predicted protein–protein interaction scores using a novel hybrid technique

      Theofilatos, Konstantinos A.; Dimitrakopoulos, Christos M.; Likothanassis, Spiridon D.; Kleftogiannis, Dimitrios A.; Moschopoulos, Charalampos N.; Alexakos, Christos; Papadimitriou, Stergios; Mavroudi, Seferina P. (Artificial Intelligence Review, Springer Nature, 2013-07-12) [Article]
      Proteins are the functional components of many cellular processes and the identification of their physical protein–protein interactions (PPIs) is an area of mature academic research. Various databases have been developed containing information about experimentally and computationally detected human PPIs as well as their corresponding annotation data. However, these databases contain many false positive interactions, are partial and only a few of them incorporate data from various sources. To overcome these limitations, we have developed HINT-KB (http://biotools.ceid.upatras.gr/hint-kb/), a knowledge base that integrates data from various sources, provides a user-friendly interface for their retrieval, cal-culatesasetoffeaturesofinterest and computesaconfidence score for every candidate protein interaction. This confidence score is essential for filtering the false positive interactions which are present in existing databases, predicting new protein interactions and measuring the frequency of each true protein interaction. For this reason, a novel machine learning hybrid methodology, called (Evolutionary Kalman Mathematical Modelling—EvoKalMaModel), was used to achieve an accurate and interpretable scoring methodology. The experimental results indicated that the proposed scoring scheme outperforms existing computational methods for the prediction of PPIs.
    • Thumbnail

      CMsearch: simultaneous exploration of protein sequence space and structure space improves not only protein homology detection but also protein structure prediction

      Cui, Xuefeng; Lu, Zhiwu; wang, sheng; Wang, Jim Jing-Yan; Gao, Xin (Bioinformatics, Oxford University Press (OUP), 2016-06-15) [Article]
      Motivation: Protein homology detection, a fundamental problem in computational biology, is an indispensable step toward predicting protein structures and understanding protein functions. Despite the advances in recent decades on sequence alignment, threading and alignment-free methods, protein homology detection remains a challenging open problem. Recently, network methods that try to find transitive paths in the protein structure space demonstrate the importance of incorporating network information of the structure space. Yet, current methods merge the sequence space and the structure space into a single space, and thus introduce inconsistency in combining different sources of information. Method: We present a novel network-based protein homology detection method, CMsearch, based on cross-modal learning. Instead of exploring a single network built from the mixture of sequence and structure space information, CMsearch builds two separate networks to represent the sequence space and the structure space. It then learns sequence–structure correlation by simultaneously taking sequence information, structure information, sequence space information and structure space information into consideration. Results: We tested CMsearch on two challenging tasks, protein homology detection and protein structure prediction, by querying all 8332 PDB40 proteins. Our results demonstrate that CMsearch is insensitive to the similarity metrics used to define the sequence and the structure spaces. By using HMM–HMM alignment as the sequence similarity metric, CMsearch clearly outperforms state-of-the-art homology detection methods and the CASP-winning template-based protein structure prediction methods.
    DSpace software copyright © 2002-2022  DuraSpace
    Quick Guide | Contact Us | KAUST University Library
    Open Repository is a service hosted by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items. For anonymous users the allowed maximum amount is 50 search results.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.