Structural basis for MTR4–ZCCHC8 interactions that stimulate the MTR4 helicase in the nuclear exosome-targeting complex

Puno, M. Rhyan; Lima, Christopher D.

doi:10.1073/pnas.1803530115

Cited by 54 publications

(73 citation statements)

References 53 publications

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“…A recent study reported that both NRDE2 and MTR4 negatively affects DNA damage responses in mammalian cells (Richard et al 2018), indicating that NRDE2 could cooperate with MTR4 in some other biological processes that are independent of CBC/ZFC3H1 and the exosome. In line with this, similar to previous finding with ZCCHC8 (Puno and Lima 2018) and Trf4/Air2 (Jia et al 2012), NRDE2 binding enhances in vitro RNA-binding properties and helicase activity of MTR4. How these proteins, including NRDE2, enhance MTR4 helicase activity remains unknown.…”

Section: Nrde2 Ensures Efficient Nuclear Mrna Exportsupporting

confidence: 92%

“…Based on the data that NRDE2 enhances RNA binding affinity and helicase activity of MTR4 by similar fold, we speculate that this might be due to enhanced RNA binding ability. Together with studies reported by Puno and Lima (2018) and Jia et al (2012), we speculate that MTR4 alone exhibiting limited RNA binding ability might be due to the hindrance of the flexible Arch domain. When in complex with ZCCHC8, Trf4/ Air2 or NRDE2, the Arch domain is positioned at more favorable orientations for RNA binding.…”

Section: Nrde2 Ensures Efficient Nuclear Mrna Exportsupporting

confidence: 81%

“…5A,B; Supplemental Fig. S6A; Weir et al 2010;Falk et al 2014Falk et al , 2017Taylor et al 2014;Conrad et al 2016;Puno and Lima 2018;Schuller et al 2018;Weick et al 2018). Specifically, the Arch domain rotates and approaches the helicase core, resulting in an unusually "closed" conformation ( Fig.…”

Section: Nrde2 Ties Mtr4 In An Unusually Closed Conformationmentioning

confidence: 99%

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NRDE2 negatively regulates exosome functions by inhibiting MTR4 recruitment and exosome interaction

Wang

Chen

et al. 2019

Genes Dev.

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The exosome functions in the degradation of diverse RNA species, yet how it is negatively regulated remains largely unknown. Here, we show that NRDE2 forms a 1:1 complex with MTR4, a nuclear exosome cofactor critical for exosome recruitment, via a conserved MTR4-interacting domain (MID). Unexpectedly, NRDE2 mainly localizes in nuclear speckles, where it inhibits MTR4 recruitment and RNA degradation, and thereby ensures efficient mRNA nuclear export. Structural and biochemical data revealed that NRDE2 interacts with MTR4's key residues, locks MTR4 in a closed conformation, and inhibits MTR4 interaction with the exosome as well as proteins important for MTR4 recruitment, such as the cap-binding complex (CBC) and ZFC3H1. Functionally, MID deletion results in the loss of self-renewal of mouse embryonic stem cells. Together, our data pinpoint NRDE2 as a nuclear exosome negative regulator that ensures mRNA stability and nuclear export.

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Section: Nrde2 Ensures Efficient Nuclear Mrna Exportsupporting

confidence: 92%

Section: Nrde2 Ensures Efficient Nuclear Mrna Exportsupporting

confidence: 81%

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NRDE2 negatively regulates exosome functions by inhibiting MTR4 recruitment and exosome interaction

Wang

Chen

et al. 2019

Genes Dev.

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“…This gene has already been shown to be differentially expressed between depressed women and controls [38] (see S4B Fig). Furthermore, SKIV2L2 has been implicated in the stress response and neurodegeneration through the nuclear exosome-targeting (NEXT) complex [39].…”

Section: Qtl Network Analysesmentioning

confidence: 99%

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

et al. 2020

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Genome-wide association studies (GWAS) identify genetic variants associated with traits or diseases. GWAS never directly link variants to regulatory mechanisms. Instead, the functional annotation of variants is typically inferred by post hoc analyses. A specific class of deep learning-based methods allows for the prediction of regulatory effects per variant on several cell type-specific chromatin features. We here describe "DeepWAS", a new approach that integrates these regulatory effect predictions of single variants into a PLOS Computational Biology | https://doi. Data Availability Statement:DeepWAS is an open source collaborative initiative available in the GitHub repository https://github.com/cellmapslab/ DeepWAS. The informed consents given by KORA study participants do not cover data posting in public databases. However, data are available upon request from KORA-gen (https://epi.helmholtzmuenchen.de/). Data requests can be submitted online and are subject to approval by the KORA Board. KKNMS data are available upon approved multivariate GWAS setting. Thereby, single variants associated with a trait or disease are directly coupled to their impact on a chromatin feature in a cell type. Up to 61 regulatory SNPs, called dSNPs, were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals). These variants were mainly non-coding and reached at least nominal significance in classical GWAS. The prediction accuracy was higher for DeepWAS than for classical GWAS models for 91% of the genome-wide significant, MS-specific dSNPs. DSNPs were enriched in public or cohort-matched expression and methylation quantitative trait loci and we demonstrated the potential of DeepWAS to generate testable functional hypotheses based on genotype data alone. DeepWAS is available at https://github.com/ cellmapslab/DeepWAS. Author summaryIn the era of steadily increasing amounts of available genetic data, we still lack novel and innovative ideas on how to improve fine-mapping of regulatory variants identified by genome-wide association studies (GWAS), especially in non-coding regions. Current approaches for the identification of functional variants conduct functional annotation after the GWAS analysis either using position-based overlaps of each variant with regulatory elements or deep-learning-based methods predicting regulatory effects per variant on cell-type-specific chromatin features. We here present DeepWAS, which integrates these regulatory effect predictions of single variants into a multivariate GWAS setting. Our results provide evidence that DeepWAS results directly identify disease/trait-associated SNPs with a common effect on a specific chromatin feature in a relevant tissue. We can show for multiple sclerosis, major depressive disorder, and body height, that the SNPs identified by DeepWAS are at least nominally significant in classical univariate GWAS analysis of the same cohorts or larger published GWAS. By integ...

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“…This gene has already been shown to be differentially expressed between depressed women and controls 51 (see Supplementary Figure 6). Furthermore, SKIV2L2 has been implicated in the stress response and neurodegeneration through the nuclear exosome-targeting (NEXT) complex 52 .…”

Section: Qtl Network Analysesmentioning

confidence: 99%

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

Arloth

Eraslan

Andlauer

et al. 2016

Preprint

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Genome-wide association studies (GWAS) identify genetic variants associated with quantitative traits or disease. Thus, GWAS never directly link variants to regulatory mechanisms, which, in turn, are typically inferred during post-hoc analyses. In parallel, a recent deep learning-based method allows for prediction of regulatory effects per variant on currently up to 1,000 cell type-specific chromatin features. We here describe "DeepWAS", a new approach that directly integrates predictions of these regulatory effects of single variants into a multivariate GWAS setting. As a result, single variants associated with a trait or disease are, by design, coupled to their impact on a chromatin feature in a cell type. Up to 40,000 regulatory single-nucleotide polymorphisms (SNPs) were associated with multiple sclerosis (MS, 4,888 cases and 10,395 controls), major depressive disorder (MDD, 1,475 cases and 2,144 controls), and height (5,974 individuals) to each identify 43-61 regulatory SNPs, called deepSNPs, which are shown to reach at least nominal significance in large GWAS. MS-and height-specific deepSNPs resided in active chromatin and introns, whereas MDD-specific deepSNPs located mostly to intragenic regions and repressive chromatin states. We found deepSNPs to be enriched in public or cohort-matched expression and methylation quantitative trait loci and demonstrate the potential of the DeepWAS method to directly generate testable functional hypotheses based on genotype data alone. DeepWAS is an innovative GWAS approach with the power to identify individual SNPs in non-coding regions with gene regulatory capacity with a joint contribution to disease risk. DeepWAS is available at https://github.com/cellmapslab/DeepWAS.

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Structural basis for MTR4–ZCCHC8 interactions that stimulate the MTR4 helicase in the nuclear exosome-targeting complex

Cited by 54 publications

References 53 publications

NRDE2 negatively regulates exosome functions by inhibiting MTR4 recruitment and exosome interaction

NRDE2 negatively regulates exosome functions by inhibiting MTR4 recruitment and exosome interaction

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

DeepWAS: Multivariate genotype-phenotype associations by directly integrating regulatory information using deep learning

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