Fingerprints of Modified RNA Bases from Deep Sequencing Profiles

Kietrys, Anna M.; Velema, Willem A.; Kool, Eric T.

doi:10.1021/jacs.7b07914

Cited by 33 publications

(26 citation statements)

References 51 publications

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“…These signatures include fragment generation because of RT termination but also read-through misincorporations, i.e. base misincorporation and indels produced from the RT enzyme reading through a modified base (Kietrys et al 2017;Motorin and Helm 2019). Despite these advances, modification indexes have been produced for few species, and there has been limited investigation of tRNA modification profiles of isodecoders (tRNAs that share the same anticodon but have sequence variation at other parts along the tRNA).…”

Section: Introductionmentioning

confidence: 99%

Combining tRNA sequencing methods to characterize plant tRNA expression and post-transcriptional modification

Warren

Salinas‐Giegé

Hummel

et al. 2019

Preprint

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Differences in tRNA expression have been implicated in a remarkable number of biological processes.There is growing evidence that tRNA genes can play dramatically different roles depending on both expression and post-transcriptional modification, yet sequencing tRNAs to measure abundance and detect modifications remains challenging. Their secondary structure and extensive post-transcriptional modifications interfere with RNA-seq library preparation methods and have limited the utility of highthroughput sequencing technologies. Here, we combine two modifications to standard RNA-seq methods by treating with the demethylating enzyme AlkB and ligating with tRNA-specific adapters in order to sequence tRNAs from four species of flowering plants, a group that has been shown to have some of the most extensive rates of post-transcriptional tRNA modifications. This protocol has the advantage of detecting full-length tRNAs and sequence variants that can be used to infer many post-transcriptional modifications. We used the resulting data to produce a modification index of almost all unique reference tRNAs in Arabidopsis thaliana, which exhibited many anciently conserved similarities with humans but also positions that appear to be "hot spots" for modifications in angiosperm tRNAs. We also found evidence based on northern blot analysis and droplet digital PCR that, even after demethylation treatment, tRNA-seq can produce highly biased estimates of absolute expression levels most likely due to biased reverse transcription. Nevertheless, the generation of full-length tRNA sequences with modification data is still promising for assessing differences in relative tRNA expression across treatments, tissues or subcellular fractions and help elucidate the functional roles of tRNA modifications.

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Section: Introductionmentioning

confidence: 99%

Combining tRNA sequencing methods to characterize plant tRNA expression and post-transcriptional modification

Warren

Salinas‐Giegé

Hummel

et al. 2019

Preprint

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“…44 For example, inosine (I) is present at the wobble position of a single tRNA in mycobacteria 41 and tends to pair with C during RT. 42,45 I is thus detected by a near stoichiometric T-to-C sequencing mutation, as illustrated in Fig. 6d for position 34 of BCG tRNA Arg-ACG.…”

Section: Quantitative Mapping Of Rna Modifications and Secondary Strumentioning

confidence: 88%

“…10 In some cases, modifications interfere with the fidelity and processivity of the reverse transcriptase during RNA-seq library preparation, which can be exploited to map modification positions based the resulting sequence data. 15,42,43 AQRNA-seq detects RT defects as read pile ups at specific sites along the RNA sequence. We illustrate this behavior with small RNA isolated from log-growing E. coli, for which there is detailed information about the types and locations of tRNA modifications.…”

Section: Quantitative Mapping Of Rna Modifications and Secondary Strumentioning

confidence: 99%

Sequencing-based quantitative mapping of the cellular small RNA landscape

Yim

Huber

et al. 2019

Preprint

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Current next-generation RNA sequencing methods cannot provide accurate quantification of the population of small RNAs within a sample due to strong sequence-dependent biases in capture, ligation, and amplification during library preparation. We report the development of an RNA sequencing method -AQRNA-seq -that minimizes biases and enables absolute quantification of all small RNA species in a sample mixture. Validation of AQRNA-seq library preparation and data mining algorithms using a 963-member microRNA reference library, RNA oligonucleotide standards of varying lengths, and northern blots demonstrated a direct, linear correlation between sequencing read count and RNA abundance. Application of AQRNA-seq to bacterial tRNA pools, a traditionally hard-to-sequence class of RNAs, revealed 80-fold variation in tRNA isoacceptor copy numbers, patterns of site-specific tRNA fragmentation caused by stress, and quantitative maps of ribonucleoside modifications, all in a single AQRNA-seq experiment.AQRNA-seq thus provides a means to quantitatively map the small RNA landscape in cells and tissues.Next generation RNA sequencing platforms (RNA-seq) have advanced functional genomics by facilitating discoveries of new RNA species, gene annotations, and quantitative analysis of gene expression. 1, 2 RNA-seq has played a central role in unravelling the complexity of RNA function by facilitating quantitative profiling of the transcriptome as a function of cell state.Although a variety of RNA-seq methods provide precise and accurate analysis of changes in transcript abundance between samples or conditions, their major drawback is the inability to accurately quantify small RNA species within a sample. This is partly rooted in biased ligation of sequencing linkers to the 3'-and 5'-ends of RNAs, which varies by 10 3 -fold depending upon on the identity of the terminal nucleotides 3-7 and causes 10 6 -fold artifacts in sequencing read counts. 5,8,9 Highly structured and heavily modified RNA molecules, such as tRNAs, further challenge the quantitative accuracy of RNA-seq. 10, 11 These structural features cause polymerase fall-off during cDNA synthesis, which prevents detection of the transcripts.A variety of specialized RNA-seq methods, many limited specifically to either miRNA or tRNA only, 12-14 attempt to minimize ligation bias using linkers with randomized terminal nucleotides and molecular crowding agents to enhance ligation 4 and to reduce polymerase fall-off during reverse transcription (RT) with two-step ligation 8 and removal of some methyl modifications with AlkB. [15][16][17] Although polymerase fall-off effects are minimized, residual ligation biases persist and lead to "jackpot" sequences. 8 For example, the template-switching reverse transcriptase TGIRT used in DM-tRNA-seq 16 is biased by the identity of the overhanging nucleotide in the adapter strand. 18 The problem with existing RNA-seq techniques is that none have been systematically engineered to optimize ligation and amplification efficiencies or validated for quanti...

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“…Other reports have looked at whether there might be natural or designed RNA aptamer sequences that can predispose an RNA to alkylation by organic electrophiles . How natural post‐transcriptional modifications influence sequencing datasets has recently been explored . The techniques most similar in workflow to what we describe herein are SHAPE sequencing and other types of high‐throughput chemical RNA probing .…”

Section: Figurementioning

confidence: 99%

Profiling the Nucleobase and Structure Selectivity of Anticancer Drugs and other DNA Alkylating Agents by RNA Sequencing

Sauter

Gillingham

2018

ChemBioChem

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Drugs that covalently modify DNA are components of most chemotherapy regimens, often serving as first-line treatments. Classically, the reactivity and selectivity of DNA alkylating agents has been determined in vitro with short oligonucleotides. A statistically sound analysis of sequence preferences of alkylating agents is untenable with serial analysis methods because of the combinatorial explosion of sequence possibilities. Next-generation sequencing (NGS) is ideally suited for the broad characterization of sequence or structure selectivities because it analyzes many sequences at once. Herein, NGS is used to report on the chemoselectivity of alkylating agents on RNA and this technology is applied to the previously uncharacterized alkylating agent trimethylsilyl diazomethane.

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Fingerprints of Modified RNA Bases from Deep Sequencing Profiles

Cited by 33 publications

References 51 publications

Combining tRNA sequencing methods to characterize plant tRNA expression and post-transcriptional modification

Combining tRNA sequencing methods to characterize plant tRNA expression and post-transcriptional modification

Sequencing-based quantitative mapping of the cellular small RNA landscape

Profiling the Nucleobase and Structure Selectivity of Anticancer Drugs and other DNA Alkylating Agents by RNA Sequencing

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