Shuo Huang’s group reports structural-profiling of low molecular weight (LMW) RNAs by nanopore trapping


RNA is a linear polymer composed of four covalently linked ribonucleotides, but its structure exhibits a lot of diversity. Generally, RNA has three levels of structure: primary, secondary and tertiary. The primary structure refers to the sequence of ribonucleotides. The secondary structure refers to the two-dimensional representation of non-covalent interactions between ribonucleotides. The tertiary structure refers to the three-dimensional morphology formed by the non-covalent interaction between the two dimensional structural units, including double helix, kiss structure, pseudoknot, ribose zipper and the most classic tRNA L-type tertiary structure.Currently, the understanding of RNA structure-function relationships is limited, mainly due to the lack of high-resolution structural information. Classical structural biological methods such as X-Ray, NMR, or EM often require violent sample preprocessing, which tends to destroy the natural tertiary structure of RNA. With the rapid discovery of more new biological functions of RNA, there is an urgent need to develop a new mild method to characterize the natural tertiary structure of RNA.

Biological nanopore has been proved to be an emerging single-molecule sensor.This technique uses the specific fingerprint signals generated when molecules pass through pores to identify single molecules, which can distinguish subtle physical and chemical properties and structural differences, while eliminating the need for any sample pretreatment. So it is expected to directly detect the tertiary structure of RNA in the natural state. However, due to the limitation of pore size (1-2 nm), structured biological macromolecules often need to be unfolded into a one-dimensional linear structure during the process of being detected through nanopores. Therefore, this technology cannot directly detect the nature structure of RNA and other biological macromoleculesat this stage.

Recently, Huang Shuo’s group at the State Key Laboratory of Analytical Chemistry for Life in our school reported a new type of nanopore trapping detection method. By using the wide vestibule of conical Mycobacterium smegmatis porin A (MspA), RNA molecules with complex structures can be captured into the pore nano-cavity and the RNA structure information is reported. Meanwhile, the narrow pore constriction is used to report the RNA unfolding kinetics information. With this strategy, they realized the high-resolution detection and discrimination of the tertiary structure of low molecular weight RNA including miRNA, siRNA, tRNA, and rRNA.

Figure 1: Structural-profiling of low molecular weight (LMW) RNAs by nanopore trapping using Mycobacterium smegmatis porin A (MspA)

Further molecular dynamics simulations found that the L-shaped tertiary structure of tRNA has a variety of pore configurations when captured by MspA, so rich characteristic signals can be reported in nanopore sensing. This indicates that the MspA nanopore trapping has a high resolution and can report abundant RNA structure signals for geometrically asymmetric RNA molecules.

Figure 2:Molecular dynamics simulation reveals the mechanism of tRNA trapping/ translocation 

In response to these rich RNA characteristic signals, a multi-parameter machine learning algorithm was further developed integrating 11 characteristic features.Events

from measurements with a mixture of RNA analytes can be automatically classified, reporting a general accuracy of ~93.4%. With this method, tRNAs from different sources were measured and a high structural conservation across different species was

observed in single molecule. The nanopore trapping using MspA breaks the mindset and technical bottleneck that nanopores cannot detect oversized biomolecules in the field, and provides new ideas for the direct detection of structured DNA, RNA and proteins. This work also provides high-resolution, label-free single-molecule analysis tools for studying RNA-guest molecular interactions, as well as understanding the complex physiological functions of RNA and related RNA research on diseases.

Figure 3: Machine learning assisted RNA identification.

The related paper entitled Structural-profiling of low molecular weight (LMW) RNAs by nanopore trapping/translocation using Mycobacterium smegmatis porin A (MspA) has been published on Nature Communications on June 6, 2021 (DOI10.1038/s41467-021-23764-ypaper link . Postdoctoral fellow Yuqin Wang from our school and Dr. Xiaoyu Guan from Nanjing University of Aeronautics and Astronautics are the co-first authors of this paper. Professor Huang Shuo from our school, Professor Li Wenfei from the School of Physics of Nanjing University, and Professor Zhang Daoqiang from Nanjing University of Aeronautics and Astronautics are the co-corresponding authors of the paper. This project was funded by National Natural Science Foundation of China (Grant No. 31972917, No. 91753108, No. 21675083), Programs for high-level entrepreneurial and innovative talents introduction of Jiangsu Province (individual and group program). Natural Science Foundation of Jiangsu Province (Grant No. BK20200009), Excellent Research Program of Nanjing University (Grant No. ZYJH004), State Key Laboratory of Analytical Chemistry for Life Science (Grant No. 5431ZZXM1902), Technology innovation fund program of Nanjing University and the HPC center of Nanjing University.