LinearAlifold: Linear-Time Consensus Structure Prediction for RNA Alignments.

Predicting the consensus structure of a set of aligned RNA homologs is a convenient method to find conserved structures in an RNA genome, which has many applications including viral diagnostics and therapeutics. However, the most commonly used tool for this task, RNAalifold, is prohibitively slow for long sequences, due to a cubic scaling with the sequence length, taking over a day on 400 SARS-CoV-2 and SARS-related genomes (∼30,000nt). We present LinearAlifold, a much faster alternative that scales linearly with both the sequence length and the number of sequences, based on our work LinearFold that folds a single RNA in linear time. Our work is orders of magnitude faster than RNAalifold (0.7 hours on the above 400 genomes, or ∼36× speedup) and achieves higher accuracies when compared to a database of known structures. More interestingly, LinearAlifold's prediction on SARS-CoV-2 correlates well with experimentally determined structures, substantially outperforming RNAalifold. Finally, LinearAlifold supports two energy models (Vienna and BL*) and four modes: minimum free energy (MFE), maximum expected accuracy (MEA), ThreshKnot, and stochastic sampling, each of which takes under an hour for hundreds of SARS-CoV variants. Our resource is at: https://github.com/LinearFold/LinearAlifold (code) and http://linearfold.org/linear-alifold (server).

CodonBert large language model for mRNA vaccines.

mRNA-based vaccines and therapeutics are gaining popularity and usage across a wide range of conditions. One of the critical issues when designing such mRNAs is sequence optimization. Even small proteins or peptides can be encoded by an enormously large number of mRNAs. The actual mRNA sequence can have a large impact on several properties including expression, stability, immunogenicity, and more. To enable the selection of an optimal sequence, we developed CodonBERT, a large language model (LLM) for mRNAs. Unlike prior models, CodonBERT uses codons as inputs which enables it to learn better representations. CodonBERT was trained using more than 10 million mRNA sequences from a diverse set of organisms. The resulting model captures important biological concepts. CodonBERT can also be extended to perform prediction tasks for various mRNA properties. CodonBERT outperforms previous mRNA prediction methods including on a new flu vaccine dataset.

Representations of lipid nanoparticles using large language models for transfection efficiency prediction.

MOTIVATION: Lipid nanoparticles (LNPs) are the most widely used vehicles for mRNA vaccine delivery. The structure of the lipids composing the LNPs can have a major impact on the effectiveness of the mRNA payload. Several properties should be optimized to improve delivery and expression including biodegradability, synthetic accessibility, and transfection efficiency. RESULTS: To optimize LNPs, we developed and tested models that enable the virtual screening of LNPs with high transfection efficiency. Our best method uses the lipid Simplified Molecular-Input Line-Entry System (SMILES) as inputs to a large language model. Large language model-generated embeddings are then used by a downstream gradient-boosting classifier. As we show, our method can more accurately predict lipid properties, which could lead to higher efficiency and reduced experimental time and costs. AVAILABILITY AND IMPLEMENTATION: Code and data links available at: https://github.com/Sanofi-Public/LipoBART.

Impact of primary glaucoma on health-related quality of life in China: the handan eye study.

BACKGROUND: We assessed health-related quality of life (HRQOL) and its determinants among rural glaucoma participants compared to age-matched normal controls in the population-based Handan Eye Study (HES), in rural Yongnian County, northern China. METHODS: We enrolled 99 adults with glaucoma (mean age 63.0 ± 11.0 years), including primary open-angle glaucoma (POAG, n = 67) and primary angle-closure glaucoma (PACG, n = 32) and 102 controls (mean age 58.5 ± 5.3 years) with normal visual acuity and visual field and no history of glaucoma. Results of ophthalmic examinations and socioeconomic data were recorded. HRQOL was measured using the EQ-5D (converted to utility valves, UVs), and visual function (VF) and vision-related quality of life (VRQOL) were evaluated using the visual function-quality of life (VF-QOL) instrument. PRIMARY AND SECONDARY OUTCOME MEASURES: EQ-5D and VF-QOL scores. RESULTS: The mean UVs, VF, and VRQOL scores for glaucoma cases were 0.98 ± 0.04, 87.9 ± 15.2, and 95.5 ± 12.8, respectively, significantly worse than VF (94.4 ± 4.4) and VRQOL (100.0 ± 0.0) among controls, even after adjusting for age, gender, educational level, and family income (P = 0.015, P = 0.033). UVs were significantly lower among glaucoma participants with impaired VRQOL (55.4 ± 11.5) compared to those with normal VRQOL scores (99.1 ± 2.8) (UVs: 0.92 ± 0.08 vs. 0.99 ± 0.03, P = 0.036), also after adjustment for age and family income (P = 0.006). Participants with PACG had significantly lower VF and VRQOL scores compared to POAG (77.8 ± 21.4 vs. 92.9 ± 6.8, P < 0.001; 89.0 ± 18.1 vs. 98.7 ± 7.5, P < 0.001). CONCLUSION: Participants with glaucoma have worse visual function and related quality of life compared to age-matched normal population controls. Participants with PACG have lower VF and VRQOL compared to those with POAG. UVs can be used for cost-effectiveness research and to support public health strategies for glaucoma in rural China.

LinearCoFold and LinearCoPartition: linear-time algorithms for secondary structure prediction of interacting RNA molecules.

Many RNAs function through RNA-RNA interactions. Fast and reliable RNA structure prediction with consideration of RNA-RNA interaction is useful, however, existing tools are either too simplistic or too slow. To address this issue, we present LinearCoFold, which approximates the complete minimum free energy structure of two strands in linear time, and LinearCoPartition, which approximates the cofolding partition function and base pairing probabilities in linear time. LinearCoFold and LinearCoPartition are orders of magnitude faster than RNAcofold. For example, on a sequence pair with combined length of 26,190 nt, LinearCoFold is 86.8× faster than RNAcofold MFE mode, and LinearCoPartition is 642.3× faster than RNAcofold partition function mode. Surprisingly, LinearCoFold and LinearCoPartition's predictions have higher PPV and sensitivity of intermolecular base pairs. Furthermore, we apply LinearCoFold to predict the RNA-RNA interaction between SARS-CoV-2 genomic RNA (gRNA) and human U4 small nuclear RNA (snRNA), which has been experimentally studied, and observe that LinearCoFold's prediction correlates better with the wet lab results than RNAcofold's.

RNA design via structure-aware multifrontier ensemble optimization.

MOTIVATION: RNA design is the search for a sequence or set of sequences that will fold to desired structure, also known as the inverse problem of RNA folding. However, the sequences designed by existing algorithms often suffer from low ensemble stability, which worsens for long sequence design. Additionally, for many methods only a small number of sequences satisfying the MFE criterion can be found by each run of design. These drawbacks limit their use cases. RESULTS: We propose an innovative optimization paradigm, SAMFEO, which optimizes ensemble objectives (equilibrium probability or ensemble defect) by iterative search and yields a very large number of successfully designed RNA sequences as byproducts. We develop a search method which leverages structure level and ensemble level information at different stages of the optimization: initialization, sampling, mutation, and updating. Our work, while being less complicated than others, is the first algorithm that is able to design thousands of RNA sequences for the puzzles from the Eterna100 benchmark. In addition, our algorithm solves the most Eterna100 puzzles among all the general optimization based methods in our study. The only baseline solving more puzzles than our work is dependent on handcrafted heuristics designed for a specific folding model. Surprisingly, our approach shows superiority on designing long sequences for structures adapted from the database of 16S Ribosomal RNAs. AVAILABILITY AND IMPLEMENTATION: Our source code and data used in this article is available at https://github.com/shanry/SAMFEO.

A CRISPR-Cas9 mutation in sox9b long intergenic noncoding RNA (slincR) affects zebrafish development, behavior, and regeneration.

The role of long noncoding RNAs (lncRNAs) regulators of toxicological responses to environmental chemicals is gaining prominence. Previously, our laboratory discovered an lncRNA, sox9b long intergenic noncoding RNA (slincR), that is activated by multiple ligands of aryl hydrocarbon receptor (AHR). Within this study, we designed a CRISPR-Cas9-mediated slincR zebrafish mutant line to better understand its biological function in presence or absence of a model AHR ligand, 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD). The slincRosu3 line contains an 18 bp insertion within the slincR sequence that changes its predicted mRNA secondary structure. Toxicological profiling showed that slincRosu3 is equally or more sensitive to TCDD for morphological and behavioral phenotypes. Embryonic mRNA-sequencing showed differential responses of 499 or 908 genes in slincRosu3 in absence or presence of TCDD Specifically, unexposed slincRosu3 embryos showed disruptions in metabolic pathways, suggesting an endogenous role for slincR. slincRosu3 embryos also had repressed mRNA levels of sox9b-a transcription factor that slincR is known to negatively regulate. Hence, we studied cartilage development and regenerative capacity-both processes partially regulated by sox9b. Cartilage development was disrupted in slincRosu3 embryos both in presence and absence of TCDD. slincRosu3 embryos also displayed a lack of regenerative capacity of amputated tail fins, accompanied by a lack of cell proliferation. In summary, using a novel slincR mutant line, we show that a mutation in slincR can have widespread impacts on gene expression and structural development endogenously and limited, but significant impacts in presence of AHR induction that further highlights its importance in the developmental process.

Association of n-3 polyunsaturated fatty acid intakes with juvenile myopia: A cross-sectional study based on the NHANES database.

Aim: Inflammation is involved in the development of myopia. n-3 polyunsaturated fatty acids (n-3 PUFAs) have vasodilating and anti-inflammatory effects, which may be involved in controlling myopia. It is of great significance to explore the relationship between n-3 PUFA intakes and juvenile myopia in order to control and alleviate myopia among teenagers through dietary intervention. Methods: Sociodemographic data, information of nutrient intakes, cotinine, PUFAs, and eye refractive status of 1,128 juveniles were extracted from the National Health and Nutrition Examination Survey (NHANES) database in this cross-sectional study. PUFAs contained total polyunsaturated fatty acid (TPFAs), alpha-linolenic acid, octadecatetraenoic acid, eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), and docosahexaenoic acid (DHA). Covariates were screened by comparison among groups of normal vision, low myopia, and high myopia. The association between n-3 PUFA intakes and the risk of juvenile myopia was evaluated using univariate and multivariate logistic regression analyses with odds ratios (ORs) and 95% confidence intervals (CIs). Results: Among the juveniles, 788 (70.68%) had normal vision, 299 (25.80%) had low myopia, and 41 (3.52%) had high myopia. There were significant differences in average EPA and DHA intakes among the three groups, and mean DPA and DHA intakes in the normal vision group were lower than those in the low myopia group (P < 0.05). After adjustment for age, gender, TPFAs, and cotinine, a high dietary intake of EPA (≥11 mg/1,000 kcal) in juveniles seemed to be associated with the risk of high myopia (OR = 0.39, 95% CI: 0.18-0.85), while no significant associations were identified between n-3 PUFA intakes and the risk of low myopia. Conclusion: A high dietary intake of EPA may be associated with a decreased risk of high myopia among juveniles. A further prospective study is needed to validate this observation.

Generation and Functional Analysis of Defective Viral Genomes during SARS-CoV-2 Infection.

Defective viral genomes (DVGs) have been identified in many RNA viruses as a major factor influencing antiviral immune response and viral pathogenesis. However, the generation and function of DVGs in SARS-CoV-2 infection are less known. In this study, we elucidated DVG generation in SARS-CoV-2 and its relationship with host antiviral immune response. We observed DVGs ubiquitously from transcriptome sequencing (RNA-seq) data sets of in vitro infections and autopsy lung tissues of COVID-19 patients. Four genomic hot spots were identified for DVG recombination, and RNA secondary structures were suggested to mediate DVG formation. Functionally, bulk and single-cell RNA-seq analysis indicated the interferon (IFN) stimulation of SARS-CoV-2 DVGs. We further applied our criteria to the next-generation sequencing (NGS) data set from a published cohort study and observed a significantly higher amount and frequency of DVG in symptomatic patients than those in asymptomatic patients. Finally, we observed exceptionally diverse DVG populations in one immunosuppressive patient up to 140 days after the first positive test of COVID-19, suggesting for the first time an association between DVGs and persistent viral infections in SARS-CoV-2. Together, our findings strongly suggest a critical role of DVGs in modulating host IFN responses and symptom development, calling for further inquiry into the mechanisms of DVG generation and into how DVGs modulate host responses and infection outcome during SARS-CoV-2 infection. IMPORTANCE Defective viral genomes (DVGs) are generated ubiquitously in many RNA viruses, including SARS-CoV-2. Their interference activity to full-length viruses and IFN stimulation provide the potential for them to be used in novel antiviral therapies and vaccine development. SARS-CoV-2 DVGs are generated through the recombination of two discontinuous genomic fragments by viral polymerase complex, and this recombination is also one of the major mechanisms for the emergence of new coronaviruses. Focusing on the generation and function of SARS-CoV-2 DVGs, these studies identify new hot spots for nonhomologous recombination and strongly suggest that the secondary structures within viral genomes mediate the recombination. Furthermore, these studies provide the first evidence for IFN stimulation activity of de novo DVGs during natural SARS-CoV-2 infection. These findings set up the foundation for further mechanism studies of SARS-CoV-2 recombination and provide evidence to harness the immunostimulatory potential of DVGs in the development of a vaccine and antivirals for SARS-CoV-2.

Linear-Time Algorithms for RNA Structure Prediction.

RNA secondary structure prediction is widely used to understand RNA function. Existing dynamic programming-based algorithms, both the classical minimum free energy (MFE) methods and partition function methods, suffer from a major limitation: their runtimes scale cubically with the RNA length, and this slowness limits their use in genome-wide applications. Inspired by incremental parsing for context-free grammars in computational linguistics, we designed linear-time heuristic algorithms, LinearFold and LinearPartition, to approximate the MFE structure, partition function and base pairing probabilities. These programs are orders of magnitude faster than Vienna RNAfold and CONTRAfold on long sequences. More interestingly, LinearFold and LinearPartition lead to more accurate predictions on the longest sequence families for which the structures are well established (16S and 23S Ribosomal RNAs), as well as improved accuracies for long-range base pairs (500 + nucleotides apart). This chapter provides protocols for using LinearFold and LinearPartition for secondary structure prediction.

LazySampling and LinearSampling: fast stochastic sampling of RNA secondary structure with applications to SARS-CoV-2.

Many RNAs fold into multiple structures at equilibrium, and there is a need to sample these structures according to their probabilities in the ensemble. The conventional sampling algorithm suffers from two limitations: (i) the sampling phase is slow due to many repeated calculations; and (ii) the end-to-end runtime scales cubically with the sequence length. These issues make it difficult to be applied to long RNAs, such as the full genomes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). To address these problems, we devise a new sampling algorithm, LazySampling, which eliminates redundant work via on-demand caching. Based on LazySampling, we further derive LinearSampling, an end-to-end linear time sampling algorithm. Benchmarking on nine diverse RNA families, the sampled structures from LinearSampling correlate better with the well-established secondary structures than Vienna RNAsubopt and RNAplfold. More importantly, LinearSampling is orders of magnitude faster than standard tools, being 428× faster (72 s versus 8.6 h) than RNAsubopt on the full genome of SARS-CoV-2 (29 903 nt). The resulting sample landscape correlates well with the experimentally guided secondary structure models, and is closer to the alternative conformations revealed by experimentally driven analysis. Finally, LinearSampling finds 23 regions of 15 nt with high accessibilities in the SARS-CoV-2 genome, which are potential targets for COVID-19 diagnostics and therapeutics.

Generation and functional analysis of defective viral genomes during SARS-CoV-2 infection.

Defective viral genomes (DVGs) have been identified in many RNA viruses as a major factor influencing antiviral immune response and viral pathogenesis. However, the generation and function of DVGs in SARS-CoV-2 infection are less known. In this study, we elucidated DVG generation in SARS-CoV-2 and its relationship with host antiviral immune response. We observed DVGs ubiquitously from RNA-seq datasets of in vitro infections and autopsy lung tissues of COVID-19 patients. Four genomic hotspots were identified for DVG recombination and RNA secondary structures were suggested to mediate DVG formation. Functionally, bulk and single cell RNA-seq analysis indicated the IFN stimulation of SARS-CoV-2 DVGs. We further applied our criteria to the NGS dataset from a published cohort study and observed significantly higher DVG amount and frequency in symptomatic patients than that in asymptomatic patients. Finally, we observed unusually high DVG frequency in one immunosuppressive patient up to 140 days after admitted to hospital due to COVID-19, first-time suggesting an association between DVGs and persistent viral infections in SARS-CoV-2. Together, our findings strongly suggest a critical role of DVGs in modulating host IFN responses and symptom development, calling for further inquiry into the mechanisms of DVG generation and how DVGs modulate host responses and infection outcome during SARS-CoV-2 infection. Importance: Defective viral genomes (DVGs) are ubiquitously generated in many RNA viruses, including SARS-CoV-2. Their interference activity to full-length viruses and IFN stimulation provide them the potential for novel antiviral therapies and vaccine development. SARS-CoV-2 DVGs are generated through the recombination of two discontinuous genomic fragments by viral polymerase complex and the recombination is also one of the major mechanisms for the emergence of new coronaviruses. Focusing on the generation and function of SARS-CoV-2 DVGs, these studies identify new hotspots for non-homologous recombination and strongly suggest that the secondary structures within viral genomes mediate the recombination. Furthermore, these studies provide the first evidence for IFN stimulation activity of de novo DVGs during natural SARS-CoV-2 infection. These findings set up the foundation for further mechanism studies of SARS-CoV-2 recombination and provide the evidence to harness DVGs’ immunostimulatory potential in the development of vaccine and antivirals for SARS-CoV-2.

LinearAlifold: Linear-Time Consensus Structure Prediction for RNA Alignments.

Predicting the consensus structure of a set of aligned RNA homologs is a convenient method to find conserved structures in an RNA genome, which has applications in SARS-CoV-2 diagnostics and therapeutics. However, the state-of-the-art algorithm for this task, RNAalifold, is prohibitively slow for long sequences, due to a cubic scaling with the sequence length, and even slower when analyzing many such sequences, due to a superlinear scaling with the number of homologs, taking 4 days on 200 SARS-CoV variants. We present LinearAlifold, an efficient algorithm for folding aligned RNA homologs that scales linearly with both the sequence length and the number of sequences, based on our recent work LinearFold that folds a single RNA in linear time. Our work is orders of magnitude faster than RNAalifold (e.g., 0.5 hours on the above 200 sequences or 316 times speedup) and achieves comparable accuracies compared to a database of known structures. More interestingly, LinearAlifold's prediction on SARS-CoV-2 correlates well with experimentally determined structures, outperforming RNAalifold. Finally, LinearAlifold supports three modes: minimum free energy (MFE), partition function, and stochastic sampling, each of which takes under an hour for hundreds of SARS-CoV variants, while only the MFE mode of RNAalifold works for them, taking days or weeks.

Gastrodin protects retinal ganglion cells from ischemic injury by activating phosphatidylinositol 3-kinase/protein kinase B/nuclear factor erythroid 2-related factor 2 (PI3K/AKT/Nrf2) signaling pathway.

Glaucoma is a progressive optic neuropathy and improper treatment may cause irreversible damage to visual function. Gastrodin is an effective active substance extracted from Gastrodia elata and possesses antioxidant as well as anti-inflammatory properties. However, the therapeutic potential of gastrodin for retinal ischemia/reperfusion (I/R) injury remains unclear. We adopted oxygen and glucose deprivation/reoxygenation (OGD/R) to induce R28 cells with the aim of simulating glaucomatous neurodegeneration. CCK-8 analysis and TUNEL were applied for examining cell proliferation and apoptosis . In addition, RT-qPCR and ELISA were performed to test the releases of inflammatory factors in cells . Related indicators of intracellular oxidative stress and ROS production were detected by corresponding kits. Moreover, western blot was applied to assay the expressions of PI3K/AKT/Nrf2 pathway-related proteins. OGD/R induction contributed to the decreased cell viability and reduced Bcl-2 protein expression, while the protein contents of Bax, Cyto-C, c-caspase 9 and c-PARP as well as ROS production were ascended. The co-treatment of hypoxia and gastrodin greatly improved R28 cell viability but effectively suppressed cell apoptosis, ROS level and the releases of OGD/R-induced inflammatory factors as well as oxidative stress. In addition, OGD/R stimulation reduced Nrf2, accompanied by a decrease in the phosphorylation levels of PI3K and AKT. Gastrodin significantly promoted the activation of PI3K/AKT/Nrf2 signaling pathway in R28 cells, which was then counteracted by PI3K/AKT inhibitors. In conclusion, the present study suggested that gastrodin has a protective effect on OGD/R-induced R28 cell injury, which is achieved through the activation of the PI3K/AKT/Nrf2 signaling pathway.

Trimetazidine mitigates high glucose-induced retinal endothelial dysfunction by inhibiting PI3K/Akt/mTOR pathway-mediated autophagy.

Trimetazidine (TMZ), as a metabolic regulator, has been widely testified to exhibit positive therapeutic effects on various disease models, but its role in diabetic retinopathy has not been reported. Therefore, this study was designed with the purpose of exploring the effects of TMZ on high-glucose (HG)-induced retinal endothelial dysfunction and its underlying mechanism. To establish DR model in vitro, 30 mM glucose was applied to induce human retinal endothelial cells (HRECs). Cell proliferation, invasion, and migration were examined by means of Cell Counting Kit-8, transwell, and wound healing assays, respectively. The tubule formation experiment was used to test the tubulogenesis ability and fluorescein isothiocyanate (FITC)-albumin was utilized to measure the permeability of monolayer HRECs. In addition, immunofluorescence and Western blot were employed to detect protein expression. Compared with the HG-induced group, TMZ concentration dependently inhibited the proliferation, migration, and angiogenesis of HG-induced HRECs, decreased the permeability of monolayer HRECs, and increased the protein expression levels of Claudin-5 and VE-cadherin. In addition, TMZ intervention increased the expression of p-PI3K, p-AKT, and p-mTOR but decreased the expression of LC3I, LC3II, and Beclin 1, which were then partially reversed by P13 K inhibitor (LY294002). Moreover, the autophagy agonist rapamycin (RAPA) was also testified to reverse the inhibitory effects of TMZ on the proliferation, migration, and angiogenesis of HG-induced HRECs. In summary, TMZ inhibited excessive autophagy by activating PI3K/Akt/mTOR pathway, thereby improving retinal endothelial dysfunction induced by HG.

LinearTurboFold: Linear-time global prediction of conserved structures for RNA homologs with applications to SARS-CoV-2.

The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) genomes as potential targets for variant-proof diagnostics and therapeutics. However, the algorithms to predict these conserved structures, which simultaneously fold and align multiple RNA homologs, scale at best cubically with sequence length and are thus infeasible for coronaviruses, which possess the longest genomes (∼30,000 nt) among RNA viruses. As a result, existing efforts on modeling SARS-CoV-2 structures resort to single-sequence folding as well as local folding methods with short window sizes, which inevitably neglect long-range interactions that are crucial in RNA functions. Here we present LinearTurboFold, an efficient algorithm for folding RNA homologs that scales linearly with sequence length, enabling unprecedented global structural analysis on SARS-CoV-2. Surprisingly, on a group of SARS-CoV-2 and SARS-related genomes, LinearTurboFold's purely in silico prediction not only is close to experimentally guided models for local structures, but also goes far beyond them by capturing the end-to-end pairs between 5' and 3' untranslated regions (UTRs) (∼29,800 nt apart) that match perfectly with a purely experimental work. Furthermore, LinearTurboFold identifies undiscovered conserved structures and conserved accessible regions as potential targets for designing efficient and mutation-insensitive small-molecule drugs, antisense oligonucleotides, small interfering RNAs (siRNAs), CRISPR-Cas13 guide RNAs, and RT-PCR primers. LinearTurboFold is a general technique that can also be applied to other RNA viruses and full-length genome studies and will be a useful tool in fighting the current and future pandemics.

LinearTurboFold: Linear-Time Global Prediction of Conserved Structures for RNA Homologs with Applications to SARS-CoV-2.

The constant emergence of COVID-19 variants reduces the effectiveness of existing vaccines and test kits. Therefore, it is critical to identify conserved structures in SARS-CoV-2 genomes as potential targets for variant-proof diagnostics and therapeutics. However, the algorithms to predict these conserved structures, which simultaneously fold and align multiple RNA homologs, scale at best cubically with sequence length, and are thus infeasible for coronaviruses, which possess the longest genomes (∼30,000 nt ) among RNA viruses. As a result, existing efforts on modeling SARS-CoV-2 structures resort to single sequence folding as well as local folding methods with short window sizes, which inevitably neglect long-range interactions that are crucial in RNA functions. Here we present LinearTurboFold, an efficient algorithm for folding RNA homologs that scales linearly with sequence length, enabling unprecedented global structural analysis on SARS-CoV-2. Surprisingly, on a group of SARS-CoV-2 and SARS-related genomes, LinearTurbo-Fold's purely in silico prediction not only is close to experimentally-guided models for local structures, but also goes far beyond them by capturing the end-to-end pairs between 5' and 3' UTRs (∼29,800 nt apart) that match perfectly with a purely experimental work. Furthermore, LinearTurboFold identifies novel conserved structures and conserved accessible regions as potential targets for designing efficient and mutation-insensitive small-molecule drugs, antisense oligonucleotides, siRNAs, CRISPR-Cas13 guide RNAs and RT-PCR primers. LinearTurboFold is a general technique that can also be applied to other RNA viruses and full-length genome studies, and will be a useful tool in fighting the current and future pandemics. SIGNIFICANCE STATEMENT: Conserved RNA structures are critical for designing diagnostic and therapeutic tools for many diseases including COVID-19. However, existing algorithms are much too slow to model the global structures of full-length RNA viral genomes. We present LinearTurboFold, a linear-time algorithm that is orders of magnitude faster, making it the first method to simultaneously fold and align whole genomes of SARS-CoV-2 variants, the longest known RNA virus (∼30 kilobases). Our work enables unprecedented global structural analysis and captures long-range interactions that are out of reach for existing algorithms but crucial for RNA functions. LinearTurboFold is a general technique for full-length genome studies and can help fight the current and future pandemics.

MicroRNA-145-5p targeting of TRIM2 mediates the apoptosis of retinal ganglion cells via the PI3K/AKT signaling pathway in glaucoma.

BACKGROUND: There is accumulating evidence to suggest that microRNAs (miRNAs) are associated with the progressive optic neuropathy including glaucoma. Apoptosis of retinal ganglion cells (RGCs) is a hallmark of glaucoma. The present study focused on the effects of miR-145-5p on RGC apoptosis in glaucoma. METHODS: We established a glaucoma rat model by intraocular injection of N-methyl-d-aspartic acid (NMDA). RGCs were isolated from newborn rats and treated with NMDA. Hematoxylin and eosin staining was performed to detect morphological changes in the retinas of rats. The expression of miR-145-5p and tripartite motif-containing 2 (TRIM2) in RGCs was measured by RT-qPCR. The viability of RGCs was measured by MTT assay. Flow cytometry analysis and TUNEL assays were conducted to assess the apoptosis of RGCs. The interaction between miR-145-5p and TRIM2 was investigated using a luciferase reporter assay. RESULTS: Rats injected with NMDA showed a thinner ganglion cell layer (GCL) and inner plexiform layer (IPL) as well as increased expression of miR-145-5p. Silencing of miR-145-5p significantly increased the GCL and IPL in the glaucoma rat model. Moreover, miR-145-5p expression was upregulated in RGCs ex vivo in response to NMDA. Silencing of miR-145-5p promoted cell viability and suppressed apoptosis in NMDA-treated RGCs. Mechanistically, miR-145-5p targeted the TRIM2 3' untranslated region to suppress its expression. TRIM2 was upregulated in NMDA-treated RGCs and protected RGCs against NMDA-induced apoptosis. Furthermore, miR-145-5p suppressed the PI3K/AKT pathway by downregulating TRIM2 in NMDA-treated RGCs. CONCLUSIONS: Suppression of miR-145-5p inhibited the apoptosis of RGCs via TRIM2-mediated activation of the PI3K/AKT signaling pathway in NMDA-induced glaucoma.

Genetic Association Study Revealed Three Loci Were Associated Risk of Myopia Among Minors.

BACKGROUND: Myopia has raised a predominant public concern among minors. A recent genome-wide association study (GWAS) identified six novel loci in Asian adults. Whether these genetic loci works for myopia in minors remains unknown and worthy of exploration. METHODS: In order to validate the findings, here we performed a case-control study (600 myopia minors, 110 high myopia (HM) minors, and 800 non-myopia minors as controls) utilizing the TaqMan single nucleotide polymorphism (SNP) genotyping assays. Adjusted odds ratios (ORs) and 95% confidence intervals (CIs) was adopted. RESULTS: The median ages in controls, myopia, and HM were 15.1, 15.0, and 15.1, respectively, while the means ± standard deviations for them were 0.32±0.41, - 3.2 ±1.6, and -9.8±2.2, respectively. We found rs2246661 (allelic OR: 1.29; 95% CI: 1.09-1.52; P =0.003), rs74633073 (allelic OR: 1.41; 95% CI: 1.12-1.78; P =0.004), and rs76903431 (allelic OR: 1.42; 95% CI: 1.11-1.81; P =0.005) were significantly associated with increased risk of myopia. Rs2246661 was also significantly associated with increased risk of HM in minors (OR: 1.37; 95% CI: 1.02-1.84; P =0.035). CONCLUSION: We identified three loci contributed to myopia in minors and these findings gave new insight into the genetic susceptibility mechanisms of myopia at the molecular level.

SNHG3 cooperates with ELAVL2 to modulate cell apoptosis and extracellular matrix accumulation by stabilizing SNAI2 in human trabecular meshwork cells under oxidative stress.

Glaucoma is the main reason for irreversible blindness, and pathological increased intraocular pressure is the leading risk factor for glaucoma. It is reported that trabecular meshwork cell injury is closely associated with the elevated intraocular pressure. The current study aimed to investigate the role of small nucleolar RNA host gene 3 (SNHG3) in human trabecular meshwork (HTM) cells under oxidative stress. A series of experiments including real-time quantitative polymerase chain reaction, subcellular fractionation assay, western blot analysis, cell counting kit-8 assay, RNA pull down, flow cytometry analysis, and RNA immunoprecipitation assay were used to explore the biological function and regulatory mechanism of SNHG3 in HTM cells under oxidative stress. First, we observed that H2 O2 induced SNHG3 upregulation in HTM cells. Then, we found that SNHG3 silencing alleviated H2 O2 -induced oxidative damage in HTM cells. Moreover, snail family transcriptional repressor 2 (SNAI2) knockdown alleviated the oxidative damage induced by H2 O2 in HTM cells. Mechanistically, SNHG3 bound with ELAV like RNA binding protein 2 (ELAVL2) to stabilize SNAI2. Finally, SNAI2 overexpression counteracted the effect of SNHG3 silencing on H2 O2 -treated HTM cells. In conclusion, our results demonstrated that SNHG3 cooperated with ELAVL2 to modulate cell apoptosis and extracellular matrix accumulation by stabilizing SNAI2 in HTM cells under oxidative stress.