post-transcriptional methylation removal in rnas using alkb demethylase and subsequent rna-cdna hybrid hydrolysis in rna sequencing preparation

Analyse de la dynamique de l’ARNt à l’aide de l’AQRNA-seq

Next-generation sequencing (NGS), also known as massively parallel sequencing, is a DNA sequencing technology that involves DNA fragmentation, ligation of adaptor oligonucleotides, polymerase chain reaction (PCR)-based amplification, sequencing of the DNA, and reassembly of the fragment sequences into a genome. The adaptation of NGS to sequence RNA (RNA-seq) is a powerful approach to identify and quantify RNA transcripts and their variants1. Innovative developments in RNA library preparation workflows and bioinformatic analysis pipelines, coupled with advancements in laboratory instrumentation, have expanded the repertoire of RNA-seq applications, progressing beyond exome sequencing into advanced functional omics like non-coding RNA profiling2, single cell analysis3, spatial transcriptomics4,5, alternative splicing analysis6, among others. These advanced RNA-seq methods reveal complex RNA functions through quantitative analysis of the transcriptome in normal and diseased cells and tissue.
Despite these advances in RNA-seq, several key technical features limit the quantitative power of the method. While most RNA-seq methods allow precise and accurate quantification of changes in the levels of RNAs between experimental variables (i.e., biological samples and/or physiological states), they cannot provide quantitative comparisons of the levels of RNA molecules within a sample. For example, most RNA-seq methods cannot accurately quantify the relative number of copies of individual tRNA isoacceptor molecules in a cellular pool of expressed tRNAs. As highlighted in the companion publication7, this limitation to RNA-seq arises from several features of RNA structure and the biochemistry of library preparation. For example, the activity of the ligation enzymes used to attach the 3&#39;- and 5&#39;-end sequencing linkers to RNA molecules is strongly influenced by the identity of the terminal nucleotides of the RNA and the sequencing linkers. This leads to large variations in efficiencies of linker ligations and profound artifactual increases in sequencing reads8,9,10.
A second set of limitations arise from the inherent structural properties of RNA molecules. Specifically, RNA secondary structure formation and dynamic changes in the dozens of post-transcriptional RNA modifications of the epitranscriptome&#160;can cause polymerase fall-off or mutation during reverse transcription. These errors result in incomplete or truncated cDNA synthesis or altered RNA sequence. While both of these phenomena can be exploited to map secondary structures or some modifications, they degrade the quantitative accuracy of RNA-seq if subsequent library preparation steps fail to capture truncated cDNAs or if data processing throws out mutated sequences not matching a reference dataset11,12. Furthermore, the immense chemical, length, and structural diversity of RNA transcripts, as well as the lack of tools to uniformly fragment long RNAs, diminishes the applicability of most RNA-seq methods to all RNA species13.
The AQRNA-seq (absolute quantification RNA sequencing) method has been developed to remove several of these technical and biological constraints that limit quantitative accuracy7. By minimizing sequence-dependent biases in capture, ligation, and amplification during RNA sequencing library preparation, AQRNA-seq achieves superior linearity compared to other methods, accurately quantifying 75% of a reference library of 963 miRNAs within 2-fold accuracy. This linear correlation of sequencing read count and RNA abundance is also observed in an analysis of a variable length pool of RNA oligonucleotide standards and in reference to orthogonal methods like northern blotting. Establishing linearity between sequencing read count and RNA abundance enables AQRNA-seq to achieve accurate, absolute quantification of all RNA species within a sample.
Here is a description of the protocol for AQRNA-seq library preparation workflow and the accompanying downstream data analytics pipeline. The method was applied to elucidate the dynamics of tRNA abundance during starvation-induced dormancy and subsequent resuscitation in&#160;the Mycobacterium bovis bacilli de Calmette et Gu&#233;rin (BCG) model of tuberculosis. Results were presented for the exploratory visualization of the sequencing data, along with subsequent clustering and differential expression analyses that unveiled discernable patterns in tRNA abundance associated with various phenotypes.

Pleins feux sur l’auteur : Le rôle d’AQRNA-seq dans la cartographie des petits ARN et l’élucidation des mécanismes de traduction des protéines

AQRNA-seq pour la quantification de petits ARN

AQRNA sequencing quantitatively maps small RNAs such as microRNAs, tRNAs, and tRNA fragments in virtually any cell or tissue sample. It can be used to map some of the 170 known tRNA modifications, but there are other methods that are more specific for mapping. You can use AQRNA-seq to discover disease biomarkers in the RNome and explore protein translation mechanisms at the systems level.Most RNA-seq methods can't accurately quantify the abundance of individual RNA molecules in a sample. This is caused by both structural properties of the RNA, such as secondary structures, post transcriptional modifications, as well as the biochemistry of library preparation such as ligation biases of a thousand fold induced by the terminal nucleotides on the RNAs. AQRNA-seq was developed to overcome several technical and biological challenges, limiting the accurate quantification of RNA abundance in the sample.Compared to other matters, it achieves linearity between recounts and copy numbers of RNA molecules, accurately, it quantifies 75%of a reference library, compressing 963 microRNAs with twofold accuracy. AQRNA-seq is unique in providing absolute quantification of small RNAs. This links the sequencing recounts directly to the number of copies of an RNA molecule in the sample.This level of accuracy is crucial for comparing dozens to hundreds of tRNAs in a sample, and it's unlike regular small RNA-seq, which only permits relative quantification across conditions and samples. We designed AQRNA-seq for an unmet need in understanding protein translation. We found that cells reprogrammed dozens of tRNA modifications in the number of copies of the modified tRNAs, to allow selective translation of messenger RNAs that are enriched with codons matching those tRNAs.AQRNA-seq allows us to quantify changes in the tRNA pool as part of this mechanism. We've used it extensively to validate this new model for protein translation.

Élimination de la méthylation post-transcriptionnelle dans les ARN à l’aide de l’AlkB déméthylase et hydrolyse ultérieure de l’hybride ARN-ADNc dans la préparation du séquençage de l’ARN

Pour commencer, préparez 10 millilitres de tampon de réaction 2X AlkB et stérilisez à travers un filtre à seringue de 0,2 micron. Dans un tube PCR stérile, ajoutez 20 microlitres d’ARN ligaturés 1 de liaison, 50 microlitres de tampon de réaction AlkB 2X, deux microlitres de déméthylase AlkB, un microlitre d’inhibiteur d’ARN et 27 microlitres d’eau libre d’ARN pour préparer la réaction de digestion d’AlkB. Incuber le mélange de digestion AlkB à température ambiante pendant deux heures pour éliminer la méthylation post-transcriptionnelle des ARN.Pour une séparation de phase propre, ajoutez 50 microlitres d’eau sans ARN dans la réaction AlkB. Ensuite, ajoutez 100 microlitres de mélange de phénol, de chloroforme et d’alcool isoamylique. Secouez le tube pendant 10 secondes.Centrifugez le mélange AlkB à 16 000 G pendant 10 minutes. Transférez environ 140 microlitres de la couche aqueuse supérieure contenant des ARN dans un tube stérile de 1,5 millilitre. Pour éliminer le phénol résiduel, ajoutez 100 microlitres de chloroforme aux ARN extraits et secouez le tube.Centrifugez le mélange à 16 000 G pendant 10 minutes et transférez environ 120 microlitres de la couche aqueuse supérieure dans un tube stérile de 1,5 millilitre. Après la réaction de transcription inverse, ajoutez un microlitre de hydroxyde de sodium à cinq molaires dans l’hybride d’ADNc d’ARN. Incuber à 93 degrés Celsius pendant trois minutes pour hydrolyser le brin d’ARN de l’hybride d’ADNc.Ensuite, ajoutez 0,77 microlitre d’acide chlorhydrique à cinq molaires pour neutraliser la réaction. Préparez un gel d’agarose à 3 % dans un tampon TAE. Mélangez un microlitre de colorant de chargement 6X dans cinq microlitres de produit PCR et chargez le gel avec le mélange.Chargez cinq microlitres d’échelles d’ADN de 50 ou 100 paires de bases dans le puits avant le premier échantillon et le puits après le dernier échantillon. Exécutez l’électrophorèse sur gel à 120 volts et 400 milliampères pendant 75 minutes pour localiser les produits PCR. Après l’électrophorèse, placez le gel dans une boîte et remplissez-le d’eau déminéralisée jusqu’à ce que le gel soit complètement immergé.Ajoutez 10 microlitres de bromure d’éthidium dans l’eau. Enveloppez la boîte avec du papier d’aluminium et teignez pendant 30 minutes sur un shaker. Jetez l’eau contenant du bromure d’éthidium dans une bouteille usagée placée dans une hotte.Rincez une fois le gel à l’eau déminéralisée et jetez l’eau dans une bouteille à déchets. Après avoir lavé le gel avec de l’eau déminéralisée pendant 10 minutes, utilisez un imageur de gel pour visualiser les bandes et acquérir une image haute résolution du gel.

Research

JoVE Journal

Genetics

Post-Transcriptional Methylation Removal in RNAs Using AlkB Demethylase and Subsequent RNA-cDNA Hybrid Hydrolysis in RNA Sequencing Preparation

Analyzing tRNA Dynamics Using AQRNA-seq

AQRNA-seq for Quantifying Small RNAs

Author Spotlight: AQRNA-seq Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms

AQRNA-seq provides a direct linear relationship between sequencing read counts and small RNA copy numbers in a biological sample, thus enabling accurate ...

Recherche

Génétique