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

AQRNA-seq를 사용한 tRNA 역학 분석

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.

저자 스포트라이트: AQRNA-seq의 Role in Mapping Small RNAs and Unraveling Protein Translation Mechanisms (작은 RNA 매핑 및 단백질 번역 메커니즘 규명에서 AQRNA-seq 역할)

small RNA 정량을 위한 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.

RNA 염기서열분석 준비에서 AlkB Demethylase 및 후속 RNA-cDNA 하이브리드 가수분해를 사용한 RNA의 전사 후 메틸화 제거

시작하려면 10ml의 2X AlkB 반응 완충액을 준비하고 0.2미크론 주사기 필터를 통해 멸균합니다. 멸균 PCR 튜브에 링커 1-결찰 RNA 20마이크로리터, 2X AlkB 반응 완충액 50마이크로리터, AlkB 탈메틸효소 2마이크로리터, RNA 억제제 1마이크로리터 및 RNA 활성수 27마이크로리터를 첨가하여 AlkB 분해 반응을 준비합니다. AlkB 분해 혼합물을 실온에서 2시간 동안 배양하여 RNA에서 전사 후 메틸화를 제거합니다.깨끗한 상 분리를 위해 RNA가 없는 물 50마이크로리터를 AlkB 반응에 첨가합니다. 그런 다음 100마이크로리터의 페놀, 클로로포름 및 이소아밀 알코올 혼합물을 추가합니다. 튜브를 10초 동안 흔듭니다.16, 10 분 동안 000G에 AlkB 혼합물을 분리한다. 약 140 마이크로리터의 상부 수성층 함유 RNA를 멸균된 1.5 밀리리터 튜브에 전달합니다. 잔류 페놀을 제거하려면 추출된 RNA에 클로로포름 100마이크로리터를 넣고 튜브를 흔듭니다.16, 10 분 동안 000G에 혼합물을 분리하고 메마른 1.5 밀리리터 관으로 최고 수성 층의 대략 120 마이크로리터를 옮긴다. 역전사 반응 후, 1 마이크로리터의 5몰 수산화나트륨을 RNA cDNA 하이브리드에 첨가합니다. RNA cDNA 하이브리드의 RNA 가닥을 가수분해하기 위해 섭씨 93도에서 3분 동안 배양합니다.그런 다음 0.77 마이크로 리터의 5 몰 염산을 첨가하여 반응을 중화시킵니다. TAE 완충액에 3%아가로스 겔을 준비한다. 1 마이크로 리터의 6X 로딩 염료를 5 마이크로 리터의 PCR 제품에 혼합하고 혼합물로 겔을로드합니다.50 또는 100 염기쌍 DNA 사다리 5마이크로리터를 첫 번째 샘플 앞의 웰과 마지막 샘플 뒤의 웰에 로드합니다. PCR 산물을 찾기 위해 75분 동안 120볼트 및 400밀리암페어에서 겔 전기영동을 실행합니다. 전기 영동 후 겔을 상자에 넣고 겔이 완전히 잠길 때까지 탈이온수로 채웁니다.10마이크로리터의 에티듐 브로마이드를 물에 넣습니다. 상자를 호일로 싸서 셰이커로 30분 동안 얼룩을 냅니다. 브롬화 에티듐 함유 물을 흄 후드에 넣은 폐기물 병에 버리십시오.탈이온수로 젤을 한 번 헹구고 물을 쓰레기통에 버립니다. 탈이온수로 겔을 10분 동안 세척한 후 겔 이미저를 사용하여 밴드를 시각화하고 겔의 고해상도 이미지를 획득합니다.

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 ...

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