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

Analyse der tRNA-Dynamik mit 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.

Autor im Rampenlicht: Die Rolle von AQRNA-seq bei der Kartierung kleiner RNAs und der Entschlüsselung von Protein-Translationsmechanismen

AQRNA-seq zur Quantifizierung kleiner RNAs

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.

Posttranskriptionelle Methylierungsentfernung in RNAs mit AlkB-Demethylase und anschließender RNA-cDNA-Hybridhydrolyse bei der Vorbereitung der RNA-Sequenzierung

Bereiten Sie zunächst 10 Milliliter 2X AlkB-Reaktionspuffer vor und sterilisieren Sie ihn durch einen 0,2-Mikron-Spritzenfilter. Geben Sie in ein steriles PCR-Röhrchen 20 Mikroliter der Linker-1-ligierten RNAs, 50 Mikroliter des 2X-AlkB-Reaktionspuffers, zwei Mikroliter der AlkB-Demethylase, einen Mikroliter RNA-Inhibitor und 27 Mikroliter RNAs mit freiem Wasser, um die AlkB-Verdauungsreaktion vorzubereiten. Inkubieren Sie das AlkB-Verdauungsgemisch zwei Stunden lang bei Raumtemperatur, um die posttranskriptionelle Methylierung von den RNAs zu entfernen.Für eine saubere Phasentrennung geben Sie 50 Mikroliter RNAs-freies Wasser in die AlkB-Reaktion. Fügen Sie dann 100 Mikroliter Phenol-, Chloroform- und Isoamylalkoholmischung hinzu. Schütteln Sie die Tube 10 Sekunden lang.Das AlkB-Gemisch bei 16.000 g 10 Minuten lang zentrifugieren. Übertragen Sie etwa 140 Mikroliter der oberen wässrigen Schicht, die RNAs enthält, in ein steriles 1,5-Milliliter-Röhrchen. Um restliches Phenol zu entfernen, fügen Sie den extrahierten RNAs 100 Mikroliter Chloroform hinzu und schütteln Sie das Röhrchen.Zentrifugieren Sie die Mischung 10 Minuten lang bei 16.000 g und überführen Sie etwa 120 Mikroliter der oberen wässrigen Schicht in ein steriles 1,5-Milliliter-Röhrchen. Nach der reversen Transkriptionsreaktion fügen Sie einen Mikroliter fünfmolares Natriumhydroxid in den RNA-cDNA-Hybrid hinzu. Inkubieren Sie drei Minuten lang bei 93 Grad Celsius, um den RNA-Strang des RNA-cDNA-Hybrids zu hydrolysieren.Fügen Sie dann 0,77 Mikroliter fünfmolare Salzsäure hinzu, um die Reaktion zu neutralisieren. Bereiten Sie ein 3%iges Agarose-Gel in TAE-Puffer vor. Mischen Sie einen Mikroliter 6X Ladefarbstoff in fünf Mikroliter PCR-Produkt und laden Sie das Gel mit der Mischung.Laden Sie fünf Mikroliter mit 50 oder 100 Basenpaar-DNA-Leitern in die Vertiefung vor der ersten Probe und in die Vertiefung nach der letzten Probe. Führen Sie die Gelelektrophorese bei 120 Volt und 400 Milliampere für 75 Minuten durch, um die PCR-Produkte zu lokalisieren. Legen Sie das Gel nach der Elektrophorese in eine Schachtel und füllen Sie sie mit deionisiertem Wasser, bis das Gel vollständig eingetaucht ist.Geben Sie 10 Mikroliter Ethidiumbromid in das Wasser. Wickeln Sie die Schachtel mit Folie ein und färben Sie sie 30 Minuten lang auf einem Shaker. Entsorgen Sie das mit Ethidiumbromid enthaltende Wasser in eine Abfallflasche, die in einem Abzug platziert ist.Spülen Sie das Gel einmal mit entionisiertem Wasser ab und entsorgen Sie das Wasser in eine Abfallflasche. Nachdem Sie das Gel 10 Minuten lang mit deionisiertem Wasser gewaschen haben, verwenden Sie einen Gel-Imager, um die Banden zu visualisieren und ein hochauflösendes Bild des Gels aufzunehmen.

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 ...

Forschung

Genetik