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

使用 AQRNA-seq 分析 tRNA 动力学

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.

作者聚焦：AQRNA-seq 在绘制小 RNA 和解开蛋白质翻译机制中的作用

用于定量小 RNA 的 AQRNA-seq

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.

在 RNA 测序制备中使用 AlkB 去甲基化酶和随后的 RNA-cDNA 杂交水解去除 RNA 中的转录后甲基化

首先，制备 10 mL 2X AlkB 反应缓冲液，并通过 0.2 μm 针式过滤器进行灭菌。在无菌 PCR 管中，加入 20 μL 接头 1 连接 RNA、50 μL 2X AlkB 反应缓冲液、2 μL AlkB 去甲基化酶、1 μL RNA 抑制剂和 27 μL 游离 RNA 的水，以制备 AlkB 消化反应。将 AlkB 消化混合物在室温下孵育 2 小时，以去除 RNA 中的转录后甲基化。对于清洁相分离，在 AlkB 反应中加入 50 μL 不含 RNA 的水。然后，加入 100 微升苯酚、氯仿和异戊醇混合物。摇晃试管 10 秒钟。将 AlkB 混合物以 16， 000G 离心 10 分钟。将大约 140 μL 的顶部含水层的 RNA 转移到无菌的 1.5 mL试管中。要去除残留的苯酚，向提取的 RNA 中加入 100 微升氯仿并摇动试管。将混合物以 16， 000G 离心 10 分钟，然后将大约 120 微升的顶部水层转移到 1.5 毫升无菌管中。逆转录反应后，向 RNA cDNA 杂交体中加入 1 微升 5 摩尔氢氧化钠。在 93 摄氏度下孵育 3 分钟，以水解 RNA cDNA 杂交体的 RNA 链。然后，加入 0.77 微升 5 摩尔盐酸以中和反应。在 TAE 缓冲液中制备 3% 琼脂糖凝胶。将 1 μL 6X 上样染料混合到 5 μL PCR 产物中，然后将混合物上样到凝胶中。在第一个样品之前和最后一个样品之后的孔中，将 5 μL 50 或 100 碱基对 DNA 分子量标准加载到孔中。在 120 V 和 400 mA 的电压下运行凝胶电泳 75 分钟，以定位 PCR 产物。电泳后，将凝胶放入盒子中，并用去离子水填充，直到凝胶完全浸没。将 10 微升溴化乙锭加入水中。用铝箔纸包裹盒子，在摇摇杯上染色 30 分钟。将含溴化乙锭的水倒入放置在通风橱中的废液瓶中。用去离子水冲洗凝胶一次，然后将水倒入废液瓶中。用去离子水洗涤凝胶 10 分钟后，使用凝胶成像仪观察条带并获取凝胶的高分辨率图像。

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 ...

科研

遗传学