AlkAniline‐Seq: Profiling of m<sup>7</sup>G and m<sup>3</sup>C RNA Modifications at Single Nucleotide Resolution

Marchand, Virginie; Ayadi, Lilia; Ernst, Felix G.M.; Hertler, Jasmin; Bourguignon‐Igel, Valérie; Galvanin, Adeline; Kötter, Annika; Helm, Mark; Lafontaine, Denis L. J.; Motorin, Yuri

doi:10.1002/anie.201810946

Cited by 140 publications

(84 citation statements)

References 42 publications

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“…Several recent publications have demonstrated the presence of numerous RNA modifications, not only in rather well studied species such as tRNA/ rRNA/sn(sno)RNA, but also in coding RNAs (mRNA), in all living organisms studied to date. Thus, several high-throughput methods for the identification of RNA modifications have been developed and successfully applied for mapping m 5 C, m 6 A, pseudouridines, and (more recently) 2′-O-methylations (2′-O-Me), along with m 1 A and m 7 G/m 3 C/D (Dominissini et al, 2012;Squires et al, 2012;Carlile et al, 2014;Schwartz et al, 2014;Hauenschild et al, 2015;Dai et al, 2017;Marchand et al, 2018;Schwartz, 2018).…”

Section: Introductionmentioning

confidence: 99%

Holistic Optimization of Bioinformatic Analysis Pipeline for Detection and Quantification of 2′-O-Methylations in RNA by RiboMethSeq

et al. 2020

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A major trend in the epitranscriptomics field over the last 5 years has been the highthroughput analysis of RNA modifications by a combination of specific chemical treatment (s), followed by library preparation and deep sequencing. Multiple protocols have been described for several important RNA modifications, such as 5-methylcytosine (m 5 C), pseudouridine (y), 1-methyladenosine (m 1 A), and 2′-O-methylation (Nm). One commonly used method is the alkaline cleavage-based RiboMethSeq protocol, where positions of reads' 5'-ends are used to distinguish nucleotides protected by ribose methylation. This method was successfully applied to detect and quantify Nm residues in various RNA species such as rRNA, tRNA, and snRNA. Such applications require adaptation of the initially published protocol(s), both at the wet bench and in the bioinformatics analysis. In this manuscript, we describe the optimization of RiboMethSeq bioinformatics at the level of initial read treatment, alignment to the reference sequence, counting the 5′-and 3′ends, and calculation of the RiboMethSeq scores, allowing precise detection and quantification of the Nm-related signal. These improvements introduced in the original pipeline permit a more accurate detection of Nm candidates and a more precise quantification of Nm level variations. Applications of the improved RiboMethSeq treatment pipeline for different cellular RNA types are discussed.

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Section: Introductionmentioning

confidence: 99%

Holistic Optimization of Bioinformatic Analysis Pipeline for Detection and Quantification of 2′-O-Methylations in RNA by RiboMethSeq

et al. 2020

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“…To conserve this information throughout reverse transcription, modification specific enzymatic [17,18] or chemical treatments [3,19] are applied. Methods such as PSI-seq [19] or AlkAniline-seq [20] are used. The latter combines alkaline treatment of template RNA with subsequent aniline cleavage and advanced bioinformatical processing, thereby analyzing accumulation of abortive cDNA fragments that end at the former modification sites, allowing detection of m 7 G and m 3 C positions.…”

Section: Introductionmentioning

confidence: 99%

Manganese Ions Individually Alter the Reverse Transcription Signature of Modified Ribonucleosides

Kristen

Plehn

Marchand

et al. 2020

Genes

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Reverse transcription of RNA templates containing modified ribonucleosides transfers modification-related information as misincorporations, arrest or nucleotide skipping events to the newly synthesized cDNA strand. The frequency and proportion of these events, merged from all sequenced cDNAs, yield a so-called RT signature, characteristic for the respective RNA modification and reverse transcriptase (RT). While known for DNA polymerases in so-called error-prone PCR, testing of four different RTs by replacing Mg2+ with Mn2+ in reaction buffer revealed the immense influence of manganese chloride on derived RT signatures, with arrest rates on m1A positions dropping from 82% down to 24%. Additionally, we observed a vast increase in nucleotide skipping events, with single positions rising from 4% to 49%, thus implying an enhanced read-through capability as an effect of Mn2+ on the reverse transcriptase, by promoting nucleotide skipping over synthesis abortion. While modifications such as m1A, m22G, m1G and m3C showed a clear influence of manganese ions on their RT signature, this effect was individual to the polymerase used. In summary, the results imply a supporting effect of Mn2+ on reverse transcription, thus overcoming blockades in the Watson-Crick face of modified ribonucleosides and improving both read-through rate and signal intensity in RT signature analysis.

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“…We find a rather high amount of m 3 C in our mouse liver mRNA (5-fold less m 3 C than m 6 A), while in the mouse brain mRNA the abundance is drastically lower (60-fold less m 3 C than m 6 A). The biological significance and correctness needs validation by tools such as the recently presented AlkAnilin-seq [38]. A sequence-specific localization of m 3 C in large RNAs is crucial to understand the origin of the modification.…”

Section: Resultsmentioning

confidence: 99%

Production and Application of Stable Isotope-Labeled Internal Standards for RNA Modification Analysis

Borland

Diesend

Ito-Kureha

et al. 2019

Genes

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Post-transcriptional RNA modifications have been found to be present in a wide variety of organisms and in different types of RNA. Nucleoside modifications are interesting due to their already known roles in translation fidelity, enzyme recognition, disease progression, and RNA stability. In addition, the abundance of modified nucleosides fluctuates based on growth phase, external stress, or possibly other factors not yet explored. With modifications ever changing, a method to determine absolute quantities for multiple nucleoside modifications is required. Here, we report metabolic isotope labeling to produce isotopically labeled internal standards in bacteria and yeast. These can be used for the quantification of 26 different modified nucleosides. We explain in detail how these internal standards are produced and show their mass spectrometric characterization. We apply our internal standards and quantify the modification content of transfer RNA (tRNA) from bacteria and various eukaryotes. We can show that the origin of the internal standard has no impact on the quantification result. Furthermore, we use our internal standard for the quantification of modified nucleosides in mouse tissue messenger RNA (mRNA), where we find different modification profiles in liver and brain tissue.

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AlkAniline‐Seq: Profiling of m⁷G and m³C RNA Modifications at Single Nucleotide Resolution

Cited by 140 publications

References 42 publications

Holistic Optimization of Bioinformatic Analysis Pipeline for Detection and Quantification of 2′-O-Methylations in RNA by RiboMethSeq

Holistic Optimization of Bioinformatic Analysis Pipeline for Detection and Quantification of 2′-O-Methylations in RNA by RiboMethSeq

Manganese Ions Individually Alter the Reverse Transcription Signature of Modified Ribonucleosides

Production and Application of Stable Isotope-Labeled Internal Standards for RNA Modification Analysis

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AlkAniline‐Seq: Profiling of m7G and m3C RNA Modifications at Single Nucleotide Resolution

Cited by 140 publications

References 42 publications

Holistic Optimization of Bioinformatic Analysis Pipeline for Detection and Quantification of 2′-O-Methylations in RNA by RiboMethSeq

Holistic Optimization of Bioinformatic Analysis Pipeline for Detection and Quantification of 2′-O-Methylations in RNA by RiboMethSeq

Manganese Ions Individually Alter the Reverse Transcription Signature of Modified Ribonucleosides

Production and Application of Stable Isotope-Labeled Internal Standards for RNA Modification Analysis

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AlkAniline‐Seq: Profiling of m⁷G and m³C RNA Modifications at Single Nucleotide Resolution