RNA Dynamics Research Articles

Gene and transcript expression patterns, coupled with isoform switching and long non-coding RNA dynamics in adipose tissue, underlie the longevity of Ames dwarf mice.

Our study investigates gene expression in adipose tissue of Ames dwarf (df/df) mice, whose deficiency in growth hormone is linked to health and extended lifespan. Recognizing adipose tissue influence on metabolism, aging, and related diseases, we aim to understand its contribution to the health and longevity of df/df mice. We have identified gene and transcript expression patterns associated with critical biological functions, including metabolism, stress response, and resistance to cancer. Intriguingly, we identified genes that,despite maintaining unchanged expression levels, switch between different isoforms, impacting essential cellular functions such as tumor suppression, oncogenic activity, ATP transport, and lipid biosynthesis and storage. The isoform switching is associated with changes in protein domains, retention of introns, initiation of nonsense-mediated decay, and emergence of intrinsically disordered regions. Moreover, we detected various alternative splicing events that may drive these structural alterations. We also found changes in the expression of long non-coding RNAs (lncRNAs) that may be involved in the aging process and disease resistance by regulating crucial genes in survival and metabolism. Through weighted gene co-expression network analysis, we have linked four lncRNAs with 29 genes, which contribute to protein complexes such as the Mili-Tdrd1-Tdrd12 complex. Beyond safeguarding DNA integrity, this complex also has a wider impact on gene regulation, chromatin structure, and metabolic control. Our detailed investigation provides insight into the molecular foundations of the remarkable health and longevity of df/df mice, emphasizing the significance of adipose tissue in aging and identifying new avenues for health-promoting therapeutic strategies.

NAT10 Phase Separation Regulates YTHDF1 Splicing to Promote Gastric Cancer Progression.

Gastric cancer is an aggressive malignancy with poor patient outcomes. N-Acetyltransferase 10 (NAT10) is an acetyltransferase that has been reported to contribute to gastric cancer progression. In-depth investigation into the underlying molecular mechanisms driven by NAT10 could help identify therapeutic targets to improve gastric cancer treatment. In this study, we found that NAT10 forms condensates to regulate RNA dynamics and promote gastric cancer progression. In samples of patients with gastric cancer, elevated NAT10 expression correlated with an unfavorable prognosis, advanced disease stage, and metastasis. NAT10 enhanced the proliferation, migration, and invasion of gastric cancer cells; supported the growth of patient-derived organoids; and accelerated tumor development. A C-terminal intrinsically disordered region-mediated liquid-liquid phase separation of NAT10 and was essential for its tumor-promoting function in gastric cancer. Moreover, NAT10 interacted with the splicing factor serine/arginine-rich splicing factor 2 (SRSF2), leading to its acetylation and increased stability. Acetylated SRSF2 directly bound to the pre-mRNA of the m6A reader YTHDF1, resulting in enhanced YTHDF1 exon 4 skipping and upregulation of a short YTHDF1 transcript that could stimulate gastric cancer cell proliferation and migration. Furthermore, YTHDF1 exon 4 skipping correlated with NAT10 and SRSF2 expression and was associated with a more aggressive phenotype in samples of patients with gastric cancer. Together, this study uncovers the role of NAT10 liquid-liquid phase separation in modulating YTHDF1 splicing through SRSF2 acetylation to drive gastric cancer progression, providing insights into the oncogenic mechanism of NAT10. Significance: Phase separation of NAT10 enables acetylation of SRSF2 that enhances YTHDF1 exon 4 skipping, which is a tumor-promoting axis in gastric cancer that represents potential therapeutic targets and prognostic biomarkers.

Magnesium Regulates RNA Ring Dynamics and Folding in Subgenomic Flaviviral RNA.

Mosquito-borne flaviviruses including dengue, Zika, yellow fever, and regional encephalitis produce a large amount of short subgenomic flaviviral RNAs during infection. A segment of these RNAs named as xrRNA1 features a multi-pseudoknot (PK)-associated structure, which resists the host cell enzyme (XRN1) from degrading the viral RNA. We investigate how this long-range RNA PK folds in the presence of counterions, specifically in a mix of monovalent (K+) and divalent (Mg2+) salts at physiological concentrations. In this study, we use extensive explicit solvent molecular dynamics (MD) simulations to characterize the RNA ion environment of the folded RNA conformation, as determined by the crystal structure. This allowed us to identify the precise locations of various coordinated RNA-Mg2+ interactions, including inner-sphere/chelated and outer-sphere coordinated Mg2+. Given that RNA folding involves large-scale conformational changes, making it challenging to explore through classical MD simulations, we investigate the folding mechanism of xrRNA1 using an all-atom structure-based RNA model with a hybrid implicit-explicit treatment of the ion environment via the dynamic counterion condensation model, both with and without physiological Mg2+ concentration. The study reveals potential folding pathways for this xrRNA1, which is consistent with the results obtained from optical tweezer experiments. The equilibrium and free energy simulations both capture a dynamic equilibrium between the ring-open and ring-close states of the RNA, driven by a long-range PK interaction. Free energy calculations reveal that with the addition of Mg2+ ions, the equilibrium shifts more toward the ring-close state. A detailed analysis of the free energy pathways and ion-mediated contact probability map highlights the critical role of Mg2+ in bridging G50 and A33. This Mg2+-mediated connection helps form the long-range PK which in turn controls the transition between the ring-open and ring-close states. The study underscores the critical role of Mg2+ in the RNA folding transition, highlighting specific locations of Mg2+ contributing to the stabilization of long-range PK connections likely to enhance the robustness of Xrn1 resistance of flaviviral xrRNAs.

GRNAde: A Geometric Deep Learning Pipeline for 3D RNA Inverse Design.

Fundamental to the diverse biological functions of RNA are its 3D structure and conformational flexibility, which enable single sequences to adopt a variety of distinct 3D states. Currently, computational RNA design tasks are often posed as inverse problems, where sequences are designed based on adopting a single desired secondary structure without considering 3D geometry and conformational diversity. In this tutorial, we present gRNAde, a geometric RNA design pipeline operating on sets of 3D RNA backbone structures to design sequences that explicitly account for RNA 3D structure and dynamics. gRNAde is a graph neural network that uses an SE (3) equivariant encoder-decoder framework for generating RNA sequences conditioned on backbone structures where the identities of the bases are unknown. We demonstrate the utility of gRNAde for fixed-backbone re-design of existing RNA structures of interest from the PDB, including riboswitches, aptamers, and ribozymes. gRNAde is more accurate in terms of native sequence recovery while being significantly faster compared to existing physics-based tools for 3D RNA inverse design, such as Rosetta.

Zooming in and out: Exploring RNA Viral Infections with Multiscale Microscopic Methods.

RNA viruses, being submicroscopic organisms, have intriguing biological makeups and substantially impact human health. Microscopic methods have been utilized for studying RNA viruses at a variety of scales. In order of observation scale from large to small, fluorescence microscopy, cryo-soft X-ray tomography (cryo-SXT), serial cryo-focused ion beam/scanning electron microscopy (cryo-FIB/SEM) volume imaging, cryo-electron tomography (cryo-ET), and cryo-electron microscopy (cryo-EM) single-particle analysis (SPA) have been employed, enabling researchers to explore the intricate world of RNA viruses, their ultrastructure, dynamics, and interactions with host cells. These methods evolve to be combined to achieve a wide resolution range from atomic to sub-nano resolutions, making correlative microscopy an emerging trend. The developments in microscopic methods provide multi-fold and spatial information, advancing our understanding of viral infections and providing critical tools for developing novel antiviral strategies and rapid responses to emerging viral threats.

RNAfcg: RNA Flexibility Prediction Based on Topological Centrality and Global Features.

The dynamics of RNAs are related intimately to their functions. Molecular flexibility, as a starting point for understanding their dynamics, has been utilized to predict many characteristics associated with their functions. Since the experimental measurement methods are time-consuming and labor-intensive, it is urgently needed to develop reliable theoretical methods to predict RNA flexibility. In this work, we develop an effective machine learning method, RNAfcg, to predict RNA flexibility, where the Random Forest (RF) is trained by features including the topological centralities, flexibility-rigidity index, and global characteristics first introduced by us, as well as some traditional sequence and structural features. The analyses show that the three types of features introduced first have significant contributions to RNA flexibility prediction, among which the topological type contributes the most, which indicates the importance of structural topology in determining RNA flexibility. The performance comparison indicates that RNAfcg outperforms the state-of-the-art machine learning methods and the commonly used Gaussian Network Model (GNM) models, achieving a much higher Pearson correlation coefficient (PCC) of 0.6619 on the test data set. This work is helpful for understanding RNA dynamics and can be used to predict RNA function information. The source code is available at https://github.com/ChunhuaLab/RNAfcg/.

Nanodynamo quantifies subcellular RNA dynamics revealing extensive coupling between steps of the RNA life cycle

The coordinated action of transcriptional and post-transcriptional machineries shapes gene expression programs at steady state and determines their concerted response to perturbations. We have developed Nanodynamo, an experimental and computational workflow for quantifying the kinetic rates of nuclear and cytoplasmic steps of the RNA life cycle. Nanodynamo is based on mathematical modelling following sequencing of native RNA from cellular fractions and polysomes. We have applied this workflow to triple-negative breast cancer cells, revealing widespread post-transcriptional RNA processing that is mutually exclusive with its co-transcriptional counterpart. We used Nanodynamo to unravel the coupling between transcription, processing, export, decay and translation machineries. We have identified a number of coupling interactions within and between the nucleus and cytoplasm that largely contribute to coordinating how cells respond to perturbations that affect gene expression programs. Nanodynamo will be instrumental in unravelling the determinants and regulatory processes involved in the coordination of gene expression responses.

Programming Fast DNA Amplifier Circuits with Versatile Toehold Exchange Pathway.

DNA amplifier circuits establish powerful tools to dynamically control molecular assembly for computation, sensing, and biological applications. However, the slow reaction speed remains a major barrier to their practical utility. Here, diverse fast DNA amplifier circuits termed toehold exchange polymerization (TEP) and toehold exchange catalysis (TEC) using toehold exchange-mediated assembly as a fundamental mechanism are built. Both TEP and TEC with a duplex and a hairpin can respond within minutes to diverse nucleic acid inputs with high fidelity. In addition, the circuits can amplify live-cell signals for fluorescence imaging target RNA dynamics and discriminating different cell lines. Compared with existing DNA circuits that involve time scales of hours for transducing small signals, TEP and TEC exhibit much faster dynamics, simpler design, and comparable sensitivity. These features make TEP and TEC promising platforms to develop programmable nucleic acid tools and devices and to create fast sensing and processing systems, amenable to wide practical applications.

Studying RNA dynamics from single-cell RNA sequencing snapshots.

Studying RNA dynamics from single-cell RNA sequencing snapshots.

Synthetic antibodies for accelerated RNA crystallography.

RNA structure is crucial to a wide range of cellular processes. The intimate relationship between macromolecular structure and function necessitates the determination of high-resolution structures of functional RNA molecules. X-ray crystallography is the predominant technique used for macromolecular structure determination; however, solving RNA structures has been more challenging than their protein counterparts, as reflected in their poor representation in the Protein Data Bank (<1%). Antibody-assisted RNA crystallography is a relatively new technique that promises to accelerate RNA structure determination by employing synthetic antibodies (Fabs) as crystallization chaperones that are specifically raised against target RNAs. Antibody chaperones facilitate the formation of ordered crystal lattices by minimizing RNA flexibility and replacing unfavorable RNA-RNA contacts with contacts between chaperone molecules. Atomic coordinates of these antibody fragments can also be used as search models to obtain phase information during structure determination. Antibody-assisted RNA crystallography has enabled the structure determination of 15 unique RNA targets, including 11 in the last 6 years. In this review, I cover the historical development of antibody fragments as crystallization chaperones and their application to diverse RNA targets. I discuss how the first structures of antibody-RNA complexes informed the design of second-generation antibodies and led to the development of portable crystallization modules that have greatly reduced the uncertainties associated with RNA crystallography. Finally, I outline unexplored avenues that can increase the impact of this technology in structural biology research and discuss potential applications of antibodies as affinity reagents for interrogating RNA biology outside of their use in crystallography. This article is categorized under: RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry RNA Interactions with Proteins and Other Molecules > Protein-RNA Recognition RNA Interactions with Proteins and Other Molecules > RNA-Protein Complexes.

Isoform and pathway-specific regulation of post-transcriptional RNA processing in human cells.

Steady-state levels of RNA transcripts are controlled by their rates of synthesis and degradation. Here we used nascent RNA Bru-seq and BruChase-seq to profile RNA dynamics across 16 human cell lines as part of ENCODE4 Deeply Profiled Cell Lines collection. We show that RNA turnover dynamics differ widely between transcripts of different genes and between different classes of RNA. Gene set enrichment analysis (GSEA) revealed that transcripts encoding proteins belonging to the same pathway often show similar turnover dynamics. Furthermore, transcript isoforms show distinct dynamics suggesting that RNA turnover is important in regulating mRNA isoform choice. Finally, splicing across newly made transcripts appears to be cooperative with either all or none type splicing. These data sets generated as part of ENCODE4 illustrate the intricate and coordinated regulation of RNA dynamics in controlling gene expression to allow for the precise coordination of cellular functions.

Valine aminoacyl-tRNA synthetase promotes therapy resistance in melanoma

Transfer RNA dynamics contribute to cancer development through regulation of codon-specific messenger RNA translation. Specific aminoacyl-tRNA synthetases can either promote or suppress tumourigenesis. Here we show that valine aminoacyl-tRNA synthetase (VARS) is a key player in the codon-biased translation reprogramming induced by resistance to targeted (MAPK) therapy in melanoma. The proteome rewiring in patient-derived MAPK therapy-resistant melanoma is biased towards the usage of valine and coincides with the upregulation of valine cognate tRNAs and of VARS expression and activity. Strikingly, VARS knockdown re-sensitizes MAPK-therapy-resistant patient-derived melanoma in vitro and in vivo. Mechanistically, VARS regulates the messenger RNA translation of valine-enriched transcripts, among which hydroxyacyl-CoA dehydrogenase mRNA encodes for a key enzyme in fatty acid oxidation. Resistant melanoma cultures rely on fatty acid oxidation and hydroxyacyl-CoA dehydrogenase for their survival upon MAPK treatment. Together, our data demonstrate that VARS may represent an attractive therapeutic target for the treatment of therapy-resistant melanoma.

Nanopore Translocation Reveals Electrophoretic Force on Noncanonical RNA:DNA Double Helix.

Electrophoretic transport plays a pivotal role in advancing sensing technologies. So far, systematic studies have focused on the translocation of canonical B-form or A-form nucleic acids, while direct RNA analysis is emerging as the new frontier for nanopore sensing and sequencing. Here, we compare the less-explored dynamics of noncanonical RNA:DNA hybrids in electrophoretic transport to the well-researched transport of B-form DNA. Using DNA/RNA nanotechnology and solid-state nanopores, the translocation of RNA:DNA (RD) and DNA:DNA (DD) duplexes was examined. Notably, RD duplexes were found to translocate through nanopores faster than DD duplexes, despite containing the same number of base pairs. Our experiments reveal that RD duplexes present a noncanonical helix, with distinct transport properties from B-form DD molecules. We find that RD and DD molecules, with the same contour length, move with comparable velocity through nanopores. We examined the physical characteristics of both duplex forms using atomic force microscopy, atomistic molecular dynamics simulations, agarose gel electrophoresis, and dynamic light scattering measurements. With the help of coarse-grained and molecular dynamics simulations, we find the effective force per unit length applied by the electric field to a fragment of RD or DD duplex in nanopores with various geometries or shapes to be approximately the same. Our results shed light on the significance of helical form in nucleic acid translocation, with implications for RNA sensing, sequencing, and the molecular understanding of electrophoretic transport.

Utility of SARS-CoV-2 Subgenomic RNA in Kidney Transplant Recipients Receiving Remdesivir.

There is no reliable microbiological marker to guide responses to antiviral treatment in kidney transplant recipients (KTR) with COVID-19. We aimed to evaluate the dynamics of subgenomic RNA (sgRNA) RT-PCR before and after receiving treatment with remdesivir compared with genomic RNA (gRNA) RT-PCR and its use as a surrogate marker of viral replication. We analyzed gRNA and sgRNA at baseline and after remdesivir treatment in KTR who received remdesivir for SARS-CoV-2 infection from November 2021 to February 2022. Thirty-four KTR received remdesivir for SARS-CoV-2 infection. The median time since transplantation was 80months (IQR 3-321) and 75% of patients had previously received 3 doses of a mRNA SARS-CoV-2 vaccine. Three patients (8%) were classified with mild, 25 (73%) with moderate, and 6 (17%) with severe SARS-CoV-2 infection. Thirty-two (94%) patients received 5 doses of remdesivir and two patients received 2 doses. The median time between symptom onset to remdesivir treatment was 5days (IQR 3-8.5). The median days of hospitalization were 6 (IQR 2-112). gRNA was positive in all patients at baseline and after remdesivir. Five (15%) patients had negative sgRNA at baseline and 20 (59%) after receiving remdesivir. Patients presenting with negative sgRNA at baseline were discharged from hospital in≤6days without complications. Moreover, those with negative sgRNA after remdesivir therapy did not require ICU admission and had favorable outcomes. Nevertheless, patients with positive sgRNA after antiviral treatment presented worse outcomes, with 47% requiring ICU admission and the three (9%) recorded deaths in the study were in this group. Based on these data, we hypothesize that sgRNA may have clinical utility to help monitor virologic response more accurately than gRNA in KTR who receive remdesivir. Moreover, patients with negative sgRNA at baseline may not require antiviral treatment and others presenting positive sgRNA at day 5 could benefit from prolonged or combined therapies.

Development of a SARS-CoV-2 viral dynamic model for patients with COVID-19 based on the amount of viral RNA and viral titer.

The target-cell limited model, which is one of the mathematical modeling approaches providing a quantitative understanding of viral dynamics, has been applied to describe viral RNA profiles of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in previous studies. However, these models have been developed mainly using patient data from the early phase of the pandemic. Furthermore, no reports focused on the profiles of the viral titer. In this study, the dynamics of both viral RNA and viral titer were characterized using data reflecting the current clinical situation in which the Omicron variant has become epidemic and vaccines for SARS-CoV-2 have become available. Consecutive data for 5212 viral RNA levels and 5216 viral titers were obtained from 720 patients with coronavirus disease 2019 (COVID-19) in a phase II/III study for ensitrelvir. Our model assumed that productively infected cells would produce only infectious viruses, which could be transformed into non-infectious viruses, and has been used to describe the dynamics of both viral RNA levels and viral titer. The time from infection to symptom onset (tinf) of unvaccinated patients was estimated to be 3.0 days, which was shorter than that of the vaccinated patients. The immune-related parameter as a power function for the vaccinated patients was 1.1 times stronger than that for the unvaccinated patients. Our model allows the prediction of the viral dynamics in patients with COVID-19 from the time of infection to symptom onset. Vaccination status was identified as a factor influencing tinf and the immune function.

Imaging the dynamics of messenger RNA with a bright and stable green fluorescent RNA.

Fluorescent RNAs (FRs) provide an attractive approach to visualizing RNAs in live cells. Although the color palette of FRs has been greatly expanded recently, a green FR with high cellular brightness and photostability is still highly desired. Here we develop a fluorogenic RNA aptamer, termed Okra, that can bind and activate the fluorophore ligand ACE to emit bright green fluorescence. Okra has an order of magnitude enhanced cellular brightness than currently available green FRs, allowing the robust imaging of messenger RNA in both live bacterial and mammalian cells. We further demonstrate the usefulness of Okra for time-resolved measurements of ACTB mRNA trafficking to stress granules, as well as live-cell dual-color superresolution imaging of RNA in combination with Pepper620, revealing nonuniform and distinct distributions of different RNAs throughout the granules. The favorable properties of Okra make it a versatile tool for the study of RNA dynamics and subcellular localization.

Exploring Biomolecular Conformational Dynamics with Polarizable Force Field AMOEBA and Enhanced Sampling Method Milestoning.

Conformational dynamics play a crucial role in determining the behavior of the biomolecules. Polarizable force fields, such as AMOEBA, can accurately capture electrostatic interactions underlying the conformational space. However, applying a polarizable force field in molecular dynamics (MD) simulations can be computationally expensive, especially in studying long-time-scale dynamics. To overcome this challenge, we incorporated the AMOEBA potential with Milestoning, an enhanced sampling method in this work. This integration allows us to efficiently sample the rare and important conformational states of a biomolecule by using many short and independent molecular dynamics trajectories with the AMOEBA force field. We applied this method to investigate the conformational dynamics of alanine dipeptide, DNA, and RNA A-B form conversion. Well-converged thermodynamic and kinetic properties were obtained, including the free energy difference, mean first passage time, and critical transitions between states. Our results demonstrate the power of integrating polarizable force fields with enhanced sampling methods in quantifying the thermodynamic and kinetic properties of biomolecules at the atomic level.

Predictive role of early treatment dynamics of HBV RNA and HBcrAg for HBeAg seroconversion in children with chronic hepatitis B.

This study aimed to assess the predictive capacity of emerging serological markers, serum HBV RNA and HBcrAg, for HBeAg seroconversion in children with HBeAg-positive chronic hepatitis B (CHB). Treatment-naïve HBeAg-positive CHB children who admitted to the Liver Disease Center of Hunan Children's Hospital between April 2021 and September 2022 and received treatment with the combined entecavir and interferon-alpha treatment were recruited. Serum HBV RNA and HBcrAg were measured at baseline and Weeks 12, 24, and 48 of treatment. Our study showed that serum HBV RNA (HR = 0.71, 95% CI: 0.56-0.91, p = 0.006), HBcrAg (HR = 0.60, 95% CI: 0.43-0.84, p = 0.003), and HBsAg (HR = 0.49, 95%CI: 0.36-0.69, p < 0.001) at Week 12 were independent predictors of HBeAg seroconversion. ROC curve analysis presented that serum HBV RNA decline value (ΔHBV RNA) at Week 36 and HBcrAg decline value (ΔHBcrAg) at Week 12 (AUC = 0.871, p = 0.003 and AUC = 0.810, p = 0.003, respectively) could effectively predict HBeAg seroconversion. Furthermore, the optimal critical values were determined and the children with ΔHBV RNA > 3.759 log10 copies/mL at Week 36 or ΔHBcrAg >0.350 log10 U/mL at Week 12 more likely to achieve HBeAg seroconversion. The serum HBV RNA and HBcrAg provide new insights into the treatment of CHB in children. Early assessment of serum HBV RNA and HBcrAg during treatment can assist clinical decision-making and optimize individualized therapeutic approaches.

Energy landscapes of homopolymeric RNAs revealed by deep unsupervised learning

Conformational dynamics of RNA plays important roles in a variety of cellular functions such as transcriptional regulation, catalysis, scaffolding, and sensing. Recently, RNAs with low-complexity sequences have been shown to phase separate and form condensate phases similar to lowcomplexity protein domains. The affinity for phase separation and the material characteristics of RNA condensates are strongly dependent on sequence composition and patterning. We hypothesize that differences in the affinities for RNA phase separation can be uncovered by studying sequence-dependent conformational dynamics of single RNA chains. To this end, we have employed atomistic simulations and deep dimensionality reduction techniques to map temperature-dependent conformational free energy landscapes for 20 base-long homopolymeric RNA sequences: poly(U), poly(G), poly(C), and poly(A). The energy landscapes of homopolymeric RNAs reveal a plethora of metastable states with qualitatively different populations stemming from differences in base chemistry. Through detailed analysis of base, phosphate, and sugar interactions, we show that experimentally observed temperature-driven shifts in metastable state populations align with experiments on RNA phase transitions. Specifically, we find that the thermodynamics of unfolding of homopolymeric RNA follows the poly(G) > poly(A) > poly(C) > poly(U) order of stability, mirroring the propensity of RNA to form condensates. To conclude, this work shows that at least for homopolymeric RNA sequences the single-chain conformational dynamics contains sufficient information for predicting and quantifying condensate forming affinities of RNAs. Thus, we anticipate that atomically detailed studies of temeprature -dependent energy landscapes of RNAs will be a useful guide for understanding the propensity of various RNA molecules to form condensates.

Na+-Mg2+ ion effects on conformation and translocation dynamics of single-stranded RNA: Cooperation and competition

The nanopore-based translocation of a single-stranded RNA (ssRNA) in mixed salt solution has garnered increasing interest for its biological and technological significance. However, it is challenging to comprehensively understand the effects of the mixed ion species on the translocation dynamics due to their cooperation and competition, which can be directly reflected by the ion screening and neutralizing effects, respectively. In this study, Langevin dynamics simulation is employed to investigate the properties of ssRNA conformation and translocation in mixed Na+-Mg2+ ion environments. Simulation results reveal that the ion screening effect dominates the change in the ssRNA conformational size, the ion neutralizing effect controls the capture rate of the ssRNA by the nanopore, and both of them take charge of the different changes in translocation time of the ssRNA under various mixed ion environments. Under high Na+ ion concentration, as Mg2+ concentration increases, the ion neutralizing effect strengthens, weakening the driving force inside the nanopore, leading to longer translocation time. Conversely, at low Na+ concentration, an increase in Mg2+ concentration enhances the ion screening effect, aiding in faster translocation. Furthermore, these simulation results will be explained by quantitative analysis, advancing a deeper understanding of the complicated effects of the mixed Na+-Mg2+ ions.