An RNA polymerase II-and AGO4-associated protein acts in RNA-directed DNA methylation
Type
ArticleAuthors
Gao, ZhihuanLiu, Hai-Liang
Daxinger, Lucia
Pontes, Olga
He, Xinjian
Qian, Weiqiang
Lin, Huixin
Xie, Mingtang
Lorkovic, Zdravko J.
Zhang, ShouDong
Miki, Daisuke
Zhan, Xianqiang
Pontier, Dominique
Lagrange, Thierry
Jin, Hailing
Matzke, Antonius J.
Matzke, Marjori
Pikaard, Craig S.
Zhu, Jian-Kang
KAUST Department
Plant Stress Genomics Research LabDate
2010-04-21Permanent link to this record
http://hdl.handle.net/10754/325291Metadata
Show full item recordAbstract
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 NatureJournal
NatureISSN
00280836PubMed ID
20410883PubMed Central ID
PMC2865564Scopus Count
Collections
ArticlesRelated items
Showing items related by title, author, creator and subject.
-
ProDis-ContSHC: Learning protein dissimilarity measures and hierarchical context coherently for protein-protein comparison in protein database retrieval(Springer Nature, 2012-05-08)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.
-
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(Springer Nature, 2013-07-12)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.
-
Auxin efflux by PIN-FORMED proteins is activated by two different protein kinases, D6 PROTEIN KINASE and PINOID(eLife Sciences Organisation, Ltd., 2014-06-19)The development and morphology of vascular plants is critically determined by synthesis and proper distribution of the phytohormone auxin. The directed cell-to-cell distribution of auxin is achieved through a system of auxin influx and efflux transporters. PIN-FORMED (PIN) proteins are proposed auxin efflux transporters, and auxin fluxes can seemingly be predicted based on the-in many cells-asymmetric plasma membrane distribution of PINs. Here, we show in a heterologous Xenopus oocyte system as well as in Arabidopsis thaliana inflorescence stems that PIN-mediated auxin transport is directly activated by D6 PROTEIN KINASE (D6PK) and PINOID (PID)/WAG kinases of the Arabidopsis AGCVIII kinase family. At the same time, we reveal that D6PKs and PID have differential phosphosite preferences. Our study suggests that PIN activation by protein kinases is a crucial component of auxin transport control that must be taken into account to understand auxin distribution within the plant.
