Systematic Identification of C. elegans miRISC Proteins, miRNAs, and mRNA Targets by Their Interactions with GW182 Proteins AIN-1 and AIN-2

Zhang, Liang; Lei, Ding; Cheung, Tom H.; Dong, Meng‐Qiu; Chen, Jun; Sewell, Aileen K.; Liu, Xuedong; Yates, John R.; Han, Min

doi:10.1016/j.molcel.2007.09.014

Cited by 227 publications

(256 citation statements)

References 68 publications

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“…In contrast, alg-2 mutants are superficially wild type, suggesting a partially redundant role in miRNA-mediated processes (10). ALG-1 physically interacts with AIN-1 and AIN-2 (ALG-1 interacting protein), functional orthologs of human GW182 (11,12). AIN-1/2 are required for the localization of ALG-1 to processing bodies (P bodies), major cytoplasmic centers for mRNA catabolism and storage.…”

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confidence: 99%

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Casein kinase II promotes target silencing by miRISC through direct phosphorylation of the DEAD-box RNA helicase CGH-1

Alessi

Khivansara

Han

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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MicroRNAs (miRNAs) play essential, conserved roles in diverse developmental processes through association with the miRNA-induced silencing complex (miRISC). Whereas fundamental insights into the mechanistic framework of miRNA biogenesis and target gene silencing have been established, posttranslational modifications that affect miRISC function are less well understood. Here we report that the conserved serine/threonine kinase, casein kinase II (CK2), promotes miRISC function in Caenorhabditis elegans. CK2 inactivation results in developmental defects that phenocopy loss of miRISC cofactors and enhances the loss of miRNA function in diverse cellular contexts. Whereas CK2 is dispensable for miRNA biogenesis and the stability of miRISC cofactors, it is required for efficient miRISC target mRNA binding and silencing. Importantly, we identify the conserved DEAD-box RNA helicase, CGH-1/DDX6, as a key CK2 substrate within miRISC and demonstrate phosphorylation of a conserved N-terminal serine is required for CGH-1 function in the miRNA pathway.

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confidence: 99%

“…AIN-1/2 are required for the localization of ALG-1 to processing bodies (P bodies), major cytoplasmic centers for mRNA catabolism and storage. Thus, AIN-1/2 functions as a molecular link between target-bound miRISC and downstream machinery required for translational repression and degradation of target mRNAs (11,12).…”

mentioning

confidence: 99%

Casein kinase II promotes target silencing by miRISC through direct phosphorylation of the DEAD-box RNA helicase CGH-1

Alessi

Khivansara

Han

et al. 2015

Proc. Natl. Acad. Sci. U.S.A.

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“…Thanks to its simplicity and robustness, this method has since been employed to determine the RNA targets of more than one hundred RBPs in mammalian cells, fl ies, worms, trypanosomes, and in yeast [a comprehensive list of RBP experiments is given in (28) ]. It was also the fi rst technique that was applied for the experimental exploration of miRNA targets through immunopurifi cation of Ago proteins and other miRISC proteins from human cells (29 -36) , fl ies (37,38) , and worms (39,40) . Of note, RIP allows concomitant identifi cation of the protein components of RNPs and other associated regulatory proteins by mass-spectrometry (MS) (41) .…”

Section: Ribonomics -Global Methods For the Identifi Cation Of Rna-prmentioning

confidence: 99%

RNA regulons and the RNA-protein interaction network

Imig¹,

Kanitz²,

Gerber

2012

BioMolecular Concepts

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The development of genome-wide analysis tools has prompted global investigation of the gene expression program, revealing highly coordinated control mechanisms that ensure proper spatiotemporal activity of a cell ' s macromolecular components. With respect to the regulation of RNA transcripts, the concept of RNA regulons, which -by analogy with DNA regulons in bacteria -refers to the coordinated control of functionally related RNA molecules, has emerged as a unifying theory that describes the logic of regulatory RNA-protein interactions in eukaryotes. Hundreds of RNA-binding proteins and small non-coding RNAs, such as microRNAs, bind to distinct elements in target RNAs, thereby exerting specifi c and concerted control over posttranscriptional events. In this review, we discuss recent reports committed to systematically explore the RNA-protein interaction network and outline some of the principles and recurring features of RNA regulons: the coordination of functionally related mRNAs through RNAbinding proteins or non-coding RNAs, the modular structure of its components, and the dynamic rewiring of RNA-protein interactions upon exposure to internal or external stimuli. We also summarize evidence for robust combinatorial control of mRNAs, which could determine the ultimate fate of each mRNA molecule in a cell. Finally, the compilation and integration of global protein-RNA interaction data has yielded fi rst insights into network structures and provided the hypothesis that RNA regulons may, in part, constitute noise ' buffers ' to handle stochasticity in cellular transcription.

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“…One such alternative involves identification of microRNA targets by virtue of their physical association with miRNAcontaining ribonucleoprotein complexes (19)(20)(21)(22)(23)(24). In ref.…”

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confidence: 99%

Immunopurification of Ago1 miRNPs selects for a distinct class of microRNA targets

Hong

Hammell

Ambros

et al. 2009

Proc. Natl. Acad. Sci. U.S.A.

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microRNAs comprise a few percent of animal genes and have been recognized as important regulators of a diverse range of biological processes. Understanding the biological functions of miRNAs requires effective means to identify their targets. Combined efforts from computational prediction, miRNA over-expression or depletion, and biochemical purification have identified thousands of potential miRNA-target pairs in cells and organisms. Complementarity to the miRNA seed sequence appears to be a common principle in target recognition. Other features, including miRNAtarget duplex stability, binding site accessibility, and local UTR structure might affect target recognition. Yet computational approaches using such contextual features have yielded largely nonoverlapping results and experimental assessment of their impact has been limited. Here, we compare two large sets of miRNA targets: targets identified using an improved Ago1 immunopurification method and targets identified among transcripts upregulated after Ago1 depletion. We found surprisingly limited overlap between these sets. The two sets showed enrichment for target sites with different molecular, structural and functional properties. Intriguingly, we found a strong correlation between UTR length and other contextual features that distinguish the two groups. This finding was extended to all predicted microRNA targets. Distinct repression mechanisms could have evolved to regulate targets with different contextual features. This study reveals a complex relationship among different features in miRNAtarget recognition and poses a new challenge for computational prediction.Argonaute ͉ gene regulation ͉ RISC complex A nimal genomes contain hundreds of microRNA genes (miRBase 13.0). Recent estimates suggest that miRNAs comprise Ϸ1% of genes in Drosophila and Caenorhabditis elegans and 2-3% of genes in mouse and human. To date, functional analysis in vivo has revealed biological roles for only a small fraction of these (1-3). One issue limiting progress in understanding the miRNA functions is identification of the target mRNAs that they regulate. Computational target identification is primarily based on sequence complementarity to the miRNA (reviewed in ref. 2), but many computational strategies also make use of sequence context to predict miRNA targets. Comparisons of different methods show limited overlap among the predicted targets, although those that place more emphasis on pairing to the seed sequence at the 5Ј end of the miRNA tend to produce similar results. Most of these methods identify many possible targets for each miRNA, often hundreds (e.g., refs. 4-9).

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Systematic Identification of C. elegans miRISC Proteins, miRNAs, and mRNA Targets by Their Interactions with GW182 Proteins AIN-1 and AIN-2

Cited by 227 publications

References 68 publications

Casein kinase II promotes target silencing by miRISC through direct phosphorylation of the DEAD-box RNA helicase CGH-1

Casein kinase II promotes target silencing by miRISC through direct phosphorylation of the DEAD-box RNA helicase CGH-1

RNA regulons and the RNA-protein interaction network

Immunopurification of Ago1 miRNPs selects for a distinct class of microRNA targets

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