Mapping of Complete Set of Ribose and Base Modifications of Yeast rRNA by RP-HPLC and Mung Bean Nuclease Assay

Yang, Jun; Sharma, Sunny; Watzinger, Peter; Hartmann, Johannes David; Kötter, Peter; Entian, Karl‐Dieter

doi:10.1371/journal.pone.0168873

Cited by 62 publications

(65 citation statements)

References 44 publications

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“…While none of the close to 100 known pseudouridylation events are essential, it is thought these modifications provide an additional layer for fine-tuning ribosome structure, enabling it to equally distinguish between 61 different tRNAs and other ligands (104). Interestingly, quantitative mass spectroscopic analyses have revealed sub-stoichiometric rRNA base modification in normal populations of ribosomes (119,120). That cells may normally harbor mixed populations of ribosomes represents a radical departure from the general concept of ‘ the’ ribosome.…”

Section: Defects In Ribosome Biogenesis Ribosomal Proteins and Ribosmentioning

confidence: 99%

How Ribosomes Translate Cancer

et al. 2017

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A wealth of novel findings, including congenital ribosomal mutations in ribosomopathies and somatic ribosomal mutations in various cancers, have significantly increased our understanding of the relevance of ribosomes in oncogenesis. Here we explore the growing list of mechanisms by which the ribosome is involved in carcinogenesis – from the hijacking of ribosomes by oncogenic factors and dysregulated translational control, to the effects of mutations in ribosomal components on cellular metabolism. Of clinical importance, the recent success of RNA polymerase inhibitors highlights the dependence on “onco-ribosomes” as an Achilles’ heel of cancer cells and a promising target for further therapeutic intervention.

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Section: Defects In Ribosome Biogenesis Ribosomal Proteins and Ribosmentioning

confidence: 99%

How Ribosomes Translate Cancer

et al. 2017

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“…Using ELIGOS we identified 315 loci in yeast rRNAs (25S, 18S, 5S and 5.5S combined) with differential %ESB values. Of these, 67 loci matched known modified bases 17 , covering 106 base positions of the total of 111 described modified bases (95%) which is a statistically significant finding, p-value of 7.2e -84 . Our prediction did not capture five bases described to be modified (their regions did not produce %ESB elevated values; see Supplementary Figure S1).…”

Section: Resultsmentioning

confidence: 84%

“…Yeast rRNA modifications have been extensively studied and well characterized 17 . Using ELIGOS we identified 315 loci in yeast rRNAs (25S, 18S, 5S and 5.5S combined) with differential %ESB values.…”

Section: Resultsmentioning

confidence: 99%

Decoding the Epitranscriptional Landscape from Native RNA Sequences

Wongsurawat

Jenjaroenpun

Wassenaar

et al. 2018

Preprint

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22Sequencing of native RNA and corresponding cDNA was performed using Oxford Nanopore 23Technology. The % Error of Specific Bases (%ESB) was higher for native RNA than for 24 cDNA, which enabled detection of ribonucleotide modification sites. Based on %ESB 25 differences of the two templates, a bioinformatic tool ELIGOS was developed and applied to 26 rRNAs of E. coli, yeast and human cells. ELIGOS captured 91%, 95%, ~75%, respectively, 27 of the known variety of RNA methylation sites in these rRNAs. Yeast transcriptomes from 28 different growth conditions were also compared, which identified an association between 29 metabolic adaptation and inferred RNA modifications. ELIGOS was further applied to human 30 transcriptome datasets, which identified the well-known DRACH motif containing N6-31 methyadenine being located close to 3'-untranslated regions of mRNA. Moreover, the RNA 32 G-quadruplex motif was uncovered by ELIGOS. In summary, we have developed an 33 experimental method coupled with bioinformatic software to uncover native RNA 34 modifications and secondary-structures within transcripts. 35 36 37 MAIN TEXT 38The transcriptome is the collection of all RNA molecules present in a given cell that can be 39 determined by high-throughput techniques, such as microarray analysis or RNA sequencing 40 (RNA-seq) methods 1 . RNA-seq using next-generation sequencing (NGS) techniques has 41 been replacing microarray analysis, since the former is able to detect novel or unknown 42 transcripts. Further, NGS enables transcriptome analysis with a higher dynamic range of 43 expression levels than for microarrays 2 . With improved sample preparation methods and 44 reduced sequencing costs, RNA-seq by NGS has become the method of choice to study 45 transcriptomes. 46The length of sequence reads generated with most NGS platforms range from 35 nt up 47 to about 500 nt, so that single reads rarely cover a complete transcript. Accurate alignment 48 and assembly of such short sequences depends on availability of a reference genome, and the 49 identification of spliced isoforms or gene-fusion transcripts remains a challenge 3 . Further, 50 methods depending on reverse transcription (RT) of RNA and amplification may introduce 51 biases and artifacts 4 . These shortcomings can be overcome by directly sequencing native 52RNA molecules using technologies such as the Oxford Nanopore Technologies (ONT) 53 platform. Direct RNA sequencing without amplification (dRNA-seq) is able to generate long 54 reads, typically covering the full length of a transcript 5 . The method can accurately quantify 55 transcripts in order to analyze differential gene expression with a dynamic range comparable 56 to traditional RNA-seq derived from short read sequencing, while it enables accurate 57 identification of the structure and boundaries of transcripts including spliced products 6 . 58An additional advantage of dRNA-seq is the detection of transcriptional modifications 59 inferred from the current signal as the RNA molecule passes a nanopore: modified RN...

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“…( Supplementary Fig. 6 b) We also examined how the modification frequency correlates with the score, using the frequency data from a previous study [34] ( Supplementary Fig. 7).…”

Section: Detection Of Known Ptms In Rrna and Evaluationmentioning

confidence: 99%

nanoDoc: RNA modification detection using Nanopore raw reads with Deep One-Class Classification

Ueda

2020

Preprint

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Advances in Nanopore single-molecule direct RNA sequencing (DRS) have presented the possibility of detecting comprehensive post-transcriptional modifications (PTMs) as an alternative to experimental approaches combined with high-throughput sequencing. It has been shown that the DRS method can detect the change in the raw electric current signal of a PTM; however, the accuracy and reliability still require improvement. Here, we presented a new software, called nanoDoc, for detecting PTMs from DRS data using a deep neural network. Current signal deviations caused by PTMs are analyzed via Deep One-Class Classification with a convolutional neural network. Using a ribosomal RNA dataset, the software archive displayed an area under the curve (AUC) accuracy of 0.96 for the detection of 23 different kinds of modifications in Escherichia coli and Saccharomyces cerevisiae. We also demonstrated a tentative classification of PTMs using unsupervised clustering. Finally, we applied this software to severe acute respiratory syndrome coronavirus 2 data and identified commonly modified sites among three groups. nanoDoc is open source (GPLv3) and available at https://github.com/uedaLabR/nanoDoc .

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Mapping of Complete Set of Ribose and Base Modifications of Yeast rRNA by RP-HPLC and Mung Bean Nuclease Assay

Cited by 62 publications

References 44 publications

How Ribosomes Translate Cancer

How Ribosomes Translate Cancer

Decoding the Epitranscriptional Landscape from Native RNA Sequences

nanoDoc: RNA modification detection using Nanopore raw reads with Deep One-Class Classification

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