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

Analisando a dinâmica do tRNA usando 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 em destaque: Papel do AQRNA-seq no mapeamento de pequenos RNAs e desvendamento de mecanismos de tradução de proteínas

AQRNA-seq para quantificar RNAs pequenos

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.

Remoção de metilação pós-transcricional em RNAs usando AlkB desmetilase e subsequente hidrólise híbrida de RNA-cDNA na preparação de sequenciamento de RNA

Para começar, prepare 10 mililitros de tampão de reação 2X AlkB e esterilize através de um filtro de seringa de 0,2 mícron. Em um tubo de PCR estéril, adicione 20 microlitros de RNAs ligados ao ligante 1, 50 microlitros do tampão de reação 2X AlkB, dois microlitros da AlkB desmetilase, um microlitro de inibidor de RNAs e 27 microlitros de água livre de RNAs para preparar a reação de digestão de AlkB. Incube a mistura de digestão AlkB à temperatura ambiente por duas horas para remover a metilação pós-transcricional dos RNAs.Para uma separação de fase limpa, adicione 50 microlitros de água livre de RNAs na reação AlkB. Em seguida, adicione 100 microlitros de fenol, clorofórmio e mistura de álcool isoamílico. Agite o tubo por 10 segundos.Centrifugue a mistura AlkB a 16.000 G por 10 minutos. Transfira aproximadamente 140 microlitros dos RNAs contendo a camada aquosa superior para um tubo estéril de 1,5 mililitro. Para remover o fenol residual, adicione 100 microlitros de clorofórmio aos RNAs extraídos e agite o tubo.Centrifugue a mistura a 16.000 G por 10 minutos e transfira aproximadamente 120 microlitros da camada aquosa superior para um tubo estéril de 1,5 mililitro. Após a reação de transcrição reversa, adicione um microlitro de hidróxido de sódio de cinco molares ao híbrido de RNA cDNA. Incube a 93 graus Celsius por três minutos para hidrolisar a fita de RNA do híbrido de cDNA de RNA.Em seguida, adicione 0,77 microlitros de ácido clorídrico de cinco molares para neutralizar a reação. Prepare um gel de agarose a 3% em tampão TAE. Misture um microlitro de corante de carga 6X em cinco microlitros de produto PCR e carregue o gel com a mistura.Carregue cinco microlitros de 50 ou 100 escadas de DNA de pares de bases no poço antes da primeira amostra e no poço após a última amostra. Execute a eletroforese em gel a 120 volts e 400 miliamperes por 75 minutos para localizar os produtos de PCR. Após a eletroforese, coloque o gel em uma caixa e encha-a com água deionizada até que o gel esteja completamente imerso.Adicione 10 microlitros de brometo de etídio na água. Embrulhe a caixa com papel alumínio e manche por 30 minutos em uma coqueteleira. Descarte a água contendo brometo de etídio em uma garrafa de lixo colocada em uma capela de exaustão.Enxágue o gel com água deionizada uma vez e descarte a água em um frasco de lixo. Depois de lavar o gel com água deionizada por 10 minutos, use um gerador de imagens de gel para visualizar as bandas e obter uma imagem de alta resolução do 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 ...

Genética