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Posted Content•DOI•

Nopp140-chaperoned 2'-O-methylation of small nuclear RNAs in Cajal bodies ensures splicing fidelity

Jonathan Bizarro1, Svetlana Deryusheva2, Ludivine Wacheul3, Varun Gupta1, Felix G.M. Ernst3, Denis L. J. Lafontaine3, Joseph G. Gall2, U. Thomas Meier1 •
Albert Einstein College of Medicine1, Carnegie Institution for Science2, Université libre de Bruxelles3
29 Apr 2021-bioRxiv (Cold Spring Harbor Laboratory)-
TL;DR: In this paper, small Cajal body (CB) specific ribonucleoproteins (scaRNPs) are used to ensure snRNP biogenesis and pre-mRNA splicing.
Abstract: Spliceosomal small nuclear RNAs (snRNAs) are modified by small Cajal body (CB) specific ribonucleoproteins (scaRNPs) to ensure snRNP biogenesis and pre-mRNA splicing. However, the function and subcellular site of snRNA modification are largely unknown. We show that CB localization of the protein Nopp140 is essential for concentration of scaRNPs in that nuclear condensate; and that phosphorylation by casein kinase 2 (CK2) at some 80 serines targets Nopp140 to CBs. Transiting through CBs, snRNAs are apparently modified by scaRNPs. Indeed, Nopp140 knockdown-mediated release of scaRNPs from CBs severely compromises 2'-O-methylation of spliceosomal snRNAs, identifying CBs as the site of scaRNP catalysis. Additionally, alternative splicing patterns change indicating that these modifications in U1, U2, U5, and U12 snRNAs safeguard splicing fidelity. Given the importance of CK2 in this pathway, compromised splicing could underlie the mode of action of small molecule CK2 inhibitors currently considered for therapy in cholangiocarcinoma, hematological malignancies, and COVID-19.

Summary (2 min read)

Jump to: [Introduction] – [RESULTS] – [DISCUSSION] – [MATERIALS AND METHODS] – [RT-PCR] – [FIGURE LEGENDS] – [Table S1] and [Primers for RT-PCR]

Introduction

  • 2 Spliceosomal small nuclear RNAs are modified by small Cajal body (CB) specific ribonucleoproteins to ensure snRNP biogenesis and pre-mRNA splicing.
  • Based on this confluence of the scaRNP enzymes and their target snRNAs in CBs, CBs have long been implicated as the sites of snRNA modification (Darzacq et al., 2002).
  • Nopp140, the chaperone of scaRNPs and small nucleolar RNPs , in addition to nucleoli, also concentrates in CBs (Meier, 2005; Meier and Blobel, 1994).

RESULTS

  • The copyright holder for this preprint (whichthis version posted April 29, 2021.
  • Hence, the authors tested if the CK2 inhibitor prevented only phosphorylation of newly synthesized Nopp140.
  • Finally, methylation at G25 of U2 snRNA seems too vital to be lost and apparently occurs in the nucleoplasm of Nopp140 KD cells.
  • No significant variations in mature 28S and 18S rRNAs and in pre-rRNA processing were detected between the parent and KD cell lines (Fig. 5C).

DISCUSSION

  • The authors took advantage of their ability to separate the bulk of scaRNPs from their target snRNAs in CBs.
  • The authors observed that in Nopp140 KD cells, snRNA pseudouridylation proceeds normally but most sites of 2’-O-methylation were impaired.
  • In contrast, most 2’-O-methyl groups of snRNAs U1, U2, U5, and U12 are lost in Nopp140 KD cells allowing investigation of their role in pre-mRNA splicing.
  • The copyright holder for this preprint (whichthis version posted April 29, 2021.
  • Apparently, phosphorylation of Nopp140 is not sufficient for its interactions with snoRNPs in nucleoli but is for those with scaRNPs in CBs.

MATERIALS AND METHODS

  • Plasmids Plasmids used to generate the stable rescue clones are pNK65 and pJB9 expressing HANopp140-GFP under a CMV promoter or UBC promoter, respectively.
  • The copyright holder for this preprint (whichthis version posted April 29, 2021.
  • Briefly, NAP57 images were used to locate the nucleoli and Cajal bodies around which masks were generated.
  • Cells were then incubated with Nopp140 antibodies (RS8 at 1:1000) in blocking buffer for 2 h followed by secondary antibodies (Rabbit-Alexa488 at 1:500) in blocking buffer for 1 h in the dark.
  • 3 µg total RNA was separated on 1.2% denaturing agarose gel, transferred to a nylon membrane, and hybridized with 32P-labelled oligonucleotide probes specific to all major was not certified by peer review) is the author/funder.

RT-PCR

  • Semiquantitative RT-PCR were performed on 1000ng of DNase RQ1 treated total RNA using SuperScript III One-Step RT-PCR System (Thermo Fisher Scientific) following the manufacturer’s instructions.
  • DNA fragments were separated on 4% for PCR products less than 100bp or 2% agarose gels and bands quantified using Image Studio Lite (LI-COR Biosciences).
  • Electron microscopy Monolayers of cells were fixed with 2.5% glutaraldehyde in 0.1 M sodium cacodylate buffer, postfixed with 1% osmium tetroxide followed by 2% uranyl acetate, and dehydrated through a graded series of ethanol, and the cells were lifted from the monolayer with propylene oxide and was not certified by peer review) is the author/funder.
  • BioRxiv preprint 29 embedded as a loose pellet in LX112 resin (LADD Research Industries, Burlington, VT) in Eppendorf tubes.
  • The copyright holder for this preprint (whichthis version posted April 29, 2021.

FIGURE LEGENDS

  • Figure 1 Effects of Nopp140 knockdown (KD) on nucleoli and Cajal Bodies (CBs) are restored in cells stably re-expressing Nopp140.
  • Note the reduction in 2’-O-methylation at all sites except those in U6 and at Gm12 and Gm25 of U2 snRNA (bold).
  • The copyright holder for this preprint (whichthis version posted April 29, 2021.
  • The detected RNAs are indicated on top of each pair of panels.
  • Parent cells are recognized in the right panels by the nucleolar staining of Nopp140 (yellow because or overlap with green RNAs) and the knockdown cells are labeled (KD).

Table S1

  • The copyright holder for this preprint (whichthis version posted April 29, 2021.
  • Table S2 – RMS scores for ribosomal RNA compiled from available studies was not certified by peer review) is the author/funder.
  • The copyright holder for this preprint (whichthis version posted April 29, 2021.

Primers for RT-PCR

  • U794 GTGCATCAGTGGTTCCTTTGA mgh18S-121 (SNORD4A) U795 GGTGCAGATGATGACACTGTAAAG was not certified by peer review) is the author/funder.
  • The copyright holder for this preprint (whichthis version posted April 29, 2021.

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Citations
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Posted Content•DOI•
SnoRNA guide activities-real and ambiguous

[...]

Svetlana Deryusheva1, Gaëlle J.S. Talross1, Joseph G. Gall1•
Carnegie Institution for Science1
17 Jul 2021-bioRxiv
TL;DR: In this article, the authors used yeast and vertebrate cells to test guide activities predicted for a number of small nucleolar (sno)RNAs, based on their regions of complementarity with rRNAs.
Abstract: In eukaryotes, rRNAs and spliceosomal snRNAs are heavily modified posttranscriptionally. Pseudouridylation and 2-O-methylation are the most abundant types of RNA modifications. They are mediated by modification guide RNAs, also known as small nucleolar (sno)RNAs and small Cajal body-specific (sca)RNAs. We used yeast and vertebrate cells to test guide activities predicted for a number of snoRNAs, based on their regions of complementarity with rRNAs. We showed that human SNORA24 is a genuine guide RNA for 18S-{Psi}609, despite some non-canonical base-pairing with its target. At the same time, we found quite a few snoRNAs that have the ability to base-pair with rRNAs and can induce predicted modifications in artificial substrate RNAs, but do not modify the same target sequence within endogenous rRNA molecules. Furthermore, certain fragments of rRNAs can be modified by the endogenous yeast modification machinery when inserted into an artificial backbone RNA, even though the same sequences are not modified in endogenous yeast rRNAs. In Xenopus cells a guide RNA generated from scaRNA, but not from snoRNA, could induce an additional pseudouridylation of U2 snRNA at position 60; both guide RNAs were equally active on a U2 snRNA-specific substrate in yeast cells. Thus, posttranscriptional modification of functionally important RNAs, such as rRNAs and snRNAs, is highly regulated and more complex than simply strong base-pairing between a guide RNA and substrate RNA. We discuss possible regulatory roles for these unexpected modifications.

6 citations

Journal Article•DOI•
SESNet: sequence-structure feature-integrated deep learning method for data-efficient protein engineering

[...]

Mingchen Li, Liqi Kang, Yi Xiong, Yu Wang, Guisheng Fan, Pan Tan, Liang Hong 
29 Dec 2022-Journal of Cheminformatics
TL;DR: SESNet as mentioned in this paper is a supervised deep learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism, which can achieve strikingly high accuracy in prediction of the fitness of protein mutants, especially for the higher order variants.
Abstract: Deep learning has been widely used for protein engineering. However, it is limited by the lack of sufficient experimental data to train an accurate model for predicting the functional fitness of high-order mutants. Here, we develop SESNet, a supervised deep-learning model to predict the fitness for protein mutants by leveraging both sequence and structure information, and exploiting attention mechanism. Our model integrates local evolutionary context from homologous sequences, the global evolutionary context encoding rich semantic from the universal protein sequence space and the structure information accounting for the microenvironment around each residue in a protein. We show that SESNet outperforms state-of-the-art models for predicting the sequence-function relationship on 26 deep mutational scanning datasets. More importantly, we propose a data augmentation strategy by leveraging the data from unsupervised models to pre-train our model. After that, our model can achieve strikingly high accuracy in prediction of the fitness of protein mutants, especially for the higher order variants (> 4 mutation sites), when finetuned by using only a small number of experimental mutation data (< 50). The strategy proposed is of great practical value as the required experimental effort, i.e., producing a few tens of experimental mutation data on a given protein, is generally affordable by an ordinary biochemical group and can be applied on almost any protein.

3 citations

References
More filters
Journal Article•DOI•
Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

[...]

Michael I. Love1, Michael I. Love2, Wolfgang Huber, Simon Anders•
Harvard University1, Max Planck Society2
05 Dec 2014-Genome Biology
TL;DR: This work presents DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates, which enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression.
Abstract: In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html .

47,038 citations

Journal Article•DOI•
STAR: ultrafast universal RNA-seq aligner

[...]

Alexander Dobin1, Carrie A. Davis1, Felix Schlesinger1, Jorg Drenkow1, Chris Zaleski1, Sonali Jha1, Philippe Batut1, Mark Chaisson1, Thomas R. Gingeras1 •
Cold Spring Harbor Laboratory1
01 Jan 2013-Bioinformatics
TL;DR: The Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure outperforms other aligners by a factor of >50 in mapping speed.
Abstract: Motivation Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. Results To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. Availability and implementation STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.

30,684 citations

Journal Article•DOI•
clusterProfiler: an R Package for Comparing Biological Themes Among Gene Clusters

[...]

Guangchuang Yu1, Li Gen Wang, Yanyan Han, Qing-Yu He•
Jinan University1
03 May 2012-Omics A Journal of Integrative Biology
TL;DR: An R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters and can be easily extended to other species and ontologies is presented.
Abstract: Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters The analysis module and visualization module were combined into a reusable workflow Currently, clusterProfiler supports three species, including humans, mice, and yeast Methods provided in this package can be easily extended to other species and ontologies The clusterProfiler package is released under Artistic-20 License within Bioconductor project The source code and vignette are freely available at http://bioconductororg/packages/release/bioc/html/clusterProfilerhtml

16,644 citations

Journal Article•DOI•
featureCounts: an efficient general-purpose program for assigning sequence reads to genomic features

[...]

Yang Liao1, Gordon K. Smyth1, Wei Shi1•
Walter and Eliza Hall Institute of Medical Research1
01 Apr 2014-Bioinformatics
TL;DR: FeatureCounts as discussed by the authors is a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments, which implements highly efficient chromosome hashing and feature blocking techniques.
Abstract: MOTIVATION: Next-generation sequencing technologies generate millions of short sequence reads, which are usually aligned to a reference genome. In many applications, the key information required for downstream analysis is the number of reads mapping to each genomic feature, for example to each exon or each gene. The process of counting reads is called read summarization. Read summarization is required for a great variety of genomic analyses but has so far received relatively little attention in the literature. RESULTS: We present featureCounts, a read summarization program suitable for counting reads generated from either RNA or genomic DNA sequencing experiments. featureCounts implements highly efficient chromosome hashing and feature blocking techniques. It is considerably faster than existing methods (by an order of magnitude for gene-level summarization) and requires far less computer memory. It works with either single or paired-end reads and provides a wide range of options appropriate for different sequencing applications. AVAILABILITY AND IMPLEMENTATION: featureCounts is available under GNU General Public License as part of the Subread (http://subread.sourceforge.net) or Rsubread (http://www.bioconductor.org) software packages.

14,103 citations

Journal Article•DOI•
Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation

[...]

Cole Trapnell1, Cole Trapnell2, Brian A. Williams3, Geo Pertea1, Ali Mortazavi3, Gordon Kwan3, Marijke J. van Baren4, Steven L. Salzberg1, Barbara J. Wold3, Lior Pachter2 •
University of Maryland, College Park1, University of California, Berkeley2, California Institute of Technology3, Washington University in St. Louis4
01 May 2010-Nature Biotechnology
TL;DR: The results suggest that Cufflinks can illuminate the substantial regulatory flexibility and complexity in even this well-studied model of muscle development and that it can improve transcriptome-based genome annotation.
Abstract: High-throughput mRNA sequencing (RNA-Seq) promises simultaneous transcript discovery and abundance estimation. However, this would require algorithms that are not restricted by prior gene annotations and that account for alternative transcription and splicing. Here we introduce such algorithms in an open-source software program called Cufflinks. To test Cufflinks, we sequenced and analyzed >430 million paired 75-bp RNA-Seq reads from a mouse myoblast cell line over a differentiation time series. We detected 13,692 known transcripts and 3,724 previously unannotated ones, 62% of which are supported by independent expression data or by homologous genes in other species. Over the time series, 330 genes showed complete switches in the dominant transcription start site (TSS) or splice isoform, and we observed more subtle shifts in 1,304 other genes. These results suggest that Cufflinks can illuminate the substantial regulatory flexibility and complexity in even this well-studied model of muscle development and that it can improve transcriptome-based genome annotation.

13,337 citations

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Frequently Asked Questions (11)
Q1. What is the role of La related protein 7?

The La related protein 7 (LARP7) is responsible for bringing together U6 snRNA and a specific subset of C/D snoRNAs required for its modification (Hasler et al., 2020). 

In summary, CK2 phosphorylation of Nopp140 is required for the accumulation of both proteins in CBs and by extension for that of scaRNPs. 

To allow for biochemical and genome-wide approaches of Nopp140 rescue, the authors reintroduced Nopp140 on a plasmid with a selectable marker into the Nopp140 KD2 cells followed by antibiotic resistance selection of single clones to obtain three stable rescue cell lines, Nopp140 R2a, R2b, and R2c. 

these hypomodified residues are generally less important for proper ribosome biogenesis and function and thus more susceptible to minor changes in their cellular environs. 

Given that scaRNPs are lost from CBs concomitantly with Nopp140, the authors tested if phosphorylation of Nopp140 by CK2 is required for CB accumulation. 

inhibition of Nopp140 phosphorylation specifically inhibits scaRNP localization to CBs and with it most 2’- O-methylation of snRNAs resulting in altered splicing fidelity. 

Pseudouridines of yeast U2 snRNA are further important for stimulating the ATPase activity of Prp5 during spliceosome assembly (Wu et al., 2016). 

Out of the 65 robustly modified residues of 28S rRNA in all studies, 2’-O-methylation of 12 residues (18%) was reduced in Nopp140 KD cells (Supplemental Table S2, 28S colored, framed red). 

phosphorylation of Nopp140 is not sufficient for its interactions with snoRNPs in nucleoli but is for those with scaRNPs in CBs. 

In contrast, as outlined above, most scaRNPs are displaced from CBs into the nucleoplasm in KD cells where residual Nopp140 is more dispersed and less detectable. 

as in the case of the two guanosines of U2 snRNA that fail to lose their 2’-O-methyl groups, the loss of those three nucleotides normally modified to the full extent may be the ones to look at for consequences.