Development of Enhanced RNA G-Quadruplex Structure Sequencing Method and Application to Other RNA Structural Studies

Abstract

Guanine (G)-rich sequences in RNA can self-associate into stacks of G-quartets to form complex motifs known as RNA G-quadruplexes (rG4s). This non-canonical RNA secondary structure could be stabilized by Hoogsteen hydrogen bonding and monovalent cations such as K⁺ and Na⁺, while destabilized under Li⁺ condition. Over the past years, rG4s have attracted great attentions in the field of chemistry and biology due to their diverse molecular structures, as well as their involvement in fundamental biological processes including transcriptional control, RNA processing and translational regulation, which provides important mechanisms for controlling gene expression and genome stability. Thus, it is of great importance to develop methods to identify rG4 structure. Previously, biophysical methods such as circular dichroism have been invented and widely applied to detect rG4 structure. Then other low-throughput techniques including reverse transcriptase footprinting assays and ligand-binding assays are developed in succession. However, these methods rely on putative G-quadruplex sequence (PQS) prediction and bottleneck the discovery rate of new rG4 structures, and thus degrades the capabilities of the factors to fully describe the rG4 formation propensity. To overcome this problem, developing high-throughput rG4 mapping methods is necessary and significant to profile rG4s at a transcriptome-wide scope without requiring PQS input from rG4 prediction methods.

Taking advantage of the rapid development of next generation sequencing (NGS), we previously developed RNA G-quadruplex structure sequencing (rG4-seq) and identified thousands of in vitro rG4 formation in the human transcriptome, providing a useful resource for further in vivo rG4 structural and functional characterization. However, the rG4-seq protocol needs to be improved due to high RNA input and the lengthy library preparation time, which against the rG4 mapping with low RNA abundance. Moreover, it also suffers from complicated gel purification step and limited PCR product yield. Therefore, in Chapter 2, an extensive assessment on the 5 key procedures of the experimental pipeline of rG4-seq was performed to identify better reaction conditions and/or enzymes. When applying the optimized methods to human cell, the result showed reduced RNA input requirement, lower transcript abundance variations between biological replicates and lower transcript coverage bias.

Although, we obtained some improvements from the optimized rG4-seq protocol, it still suffers from complicated gel purification step and low PCR efficiency, leading to the substantial sample loss, long experimental processing time and increasing PCR duplicates, which will further result in less useful reads for data analysis or require a high RNA input amount to compensate for. Consequently, in Chapter 3, we next developed an improved rG4-seq method (rG4-seq 2.0) by introducing a new 5’ adapter containing deoxyuridine (dU) in the library preparation and we got enhanced library quality with reduced gel purification steps, higher yield of PCR products by comparing with old method. the following benchmarked rG4-seq 2.0 using polyadenylated RNA varying from 500 ng to 10 ng produced high quality of cDNA libraries with reliable and reproducible rG4 identification. In addition, we found that rG4-seq 2.0 required 5-fold less RNA input to achieve same rG4 sensitivity, and could adapt to input levels as low as 10 ng. Furthermore, rG4-seq 2.0 also improved the rG4-seq calling outcome and nucleotide bias in rG4 detection persistent in rG4-seq 1.0. In summary, our new method can improve the identification and study of rG4s in low abundance transcripts, and our findings can provide insights to optimize cDNA library preparation in other related methods.

DHX36 protein is a well-known helicase that has been showed to bind and unwind RNA G-quadruplex (rG4) structures with high affinity and specificity. However, the RNA structure dynamic induced by DHX36 and how the structural changes subsequently influence RNA fate remains unknown. Thus, in Chapter 4, we applied the new strategy from rG4-seq 2.0 to structure-seq technology with experimental modification and captured in vivo RNA structural landscape to investigate the structure change of DHX36-bound mRNAs by incorporating with other public data. We found that DHX36 induces structure remodeling across the entire mRNA transcript region especially in 3’UTR of more accessible structures, which are correlated with post-transcriptional mRNA decrease. Furthermore, we demonstrated that DHX36 binding sites are enriched for m⁶A modification and YTHDF1 binding. Finally, we experimentally validate that DHX36 loss-induced structure enhancement could hinder YTHDF1 binding, and the increasing mRNA structure stability without YTHDF1 binding could explain the DHX36 loss-induced mRNA increase.

In summary, our experimental and bioinformatic analysis has improved the mapping of rG4s in low abundance transcripts and can also apply to other RNA structural study such as structure sequencing technology, providing insights to the further development of simple and efficient cDNA library preparation for different biological application. Moreover, our new findings uncover the effect of DHX36 on mRNA structure in the human transcriptome and explain how RNA secondary structure change involves in post-transcriptional regulation through orchestrating YTHDF1 binding. rG4-seq 2.0 could apply to ribosomal RNA (rRNA)-depleted RNA to reveal more functional rG4 candidates such as non-coding rG4s. Meanwhile, similar strategy of cDNA library preparation could also be used to uncover other important RBP-RNA interactions and the structural change-induced regulatory functions by incorporating the structure probing techniques. Furthermore, it could also be used to discover the in vivo RNA structurome of other species. 

Date of Award	15 Aug 2022
Original language	English
Awarding Institution	City University of Hong Kong
Supervisor	Chun Kit KWOK (Supervisor)