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N⁶-methyladenosine (m⁶A) is the most prevalent modification in eukaryotic mRNA, playing a pivotal role in regulating various aspects of mRNA metabolism, including splicing, processing, degradation, and translation. This review provides a comprehensive overview of computational strategies employed in m⁶A research, with an emphasis on data-driven methodologies for the prediction of m⁶A sites and molecular dynamics simulations for deciphering m⁶A-associated biological mechanisms. The article first discusses the evolution of m⁶A detection technologies, outlines the corresponding data processing methods, and summarizes publicly available datasets that serve as essential resources for constructing computational models. Subsequently, we highlight research advancements in machine learning and deep learning models for m⁶A site prediction. Finally, we demonstrate the contributions of molecular dynamics simulations in unravelling m⁶A-related molecular mechanisms, illustrating how computational methods facilitate the understanding of this complex epigenetic regulation. By systematically synthesizing relevant content, this review further discusses the latest research progress and application values of computational methods in m⁶A modification, offering new perspectives and insights for in-depth investigations.

Nucleic acid drugs can function at the gene level, and have the advantages of simple synthesis, easy modification and high specificity. However, there are many obstacles in transfection and in vivo delivery due to their negative charge, high molecular weight, and hydrophilicity. Lipid nanoparticles (LNPs) can encapsulate siRNA or mRNA through electrostatic interactions and five related drugs have been approved as of April 2025. However, due to the inevitable immunogenicity and hepatosplenic toxicity, most LNP-encapsulated nucleic acid drugs were terminated in the early clinical stage. Nucleos(t)idyl lipids are a class of amphiphilic molecules composed of nucleobases or nucleos(t)idyl heads, linkers and lipid tail chains, which can bind with the bases of nucleic acid drugs through hydrogen bonding and π-π stacking and self-assemble to form nanoparticles or micelles with broad application prospects. In this review, we summarize the research progress in delivery systems of nucleic acid drugs based on nucleos(t)idyl lipids and peptidyl lipids, and discuss their differences with LNP-encapsulated nucleic acid drugs, including structural characterization, molecular dynamics simulation, in vivo distribution, as well as efficacy and safety, so as to provide new ideas for improving the targeting delivery of nucleic acid drugs.

Plant small RNAs (sRNAs) are essential regulators of gene expression and genome stability in plants. Based on their biogenesis and mechanisms of action, they are primarily classified into two major categories: microRNAs (miRNAs) and small interfering RNAs (siRNAs). These sRNAs rely on distinct processing proteins for their production and effector proteins to execute their functions, playing pivotal roles in diverse developmental processes and environmental responses. Recent advances in next-generation sequencing have identified numerous novel sRNAs across multiple plant species, while studies in Arabidopsis thaliana and various crops have significantly enhanced our understanding of their biogenesis, regulatory networks, and biological functions. In this review, we systematically summarize the research progress on different classes of plant sRNAs, focusing on their biosynthetic pathways, molecular mechanisms, and biological function. Furthermore, we discuss the potential applications of plant sRNAs in agriculture, including their prospects as next-generation RNA pesticides, supported by current technological developments. This review aims to provide a theoretical foundation for further research on plant sRNAs and their agricultural applications.

RNA editing is one of the important research directions in the field of epigenetics. With further research, scientists have discovered that the CRISPR/Cas system can target not only DNA but also RNA, thereby achieving precise gene editing at the transcriptional level. Moreover, using the CRISPR/Cas system for RNA editing can also avoid damage to genome. At present, a variety of derivative technologies based on RNA-targeting CRISPR systems have been developed, including RNA knockdown and editing, nucleic acid detection and imaging, and RNA tracking. The emergence of these derivative technologies provides powerful tools for deciphering biological genetic mechanisms and disease treatment. In this review, we summarize the structure, function, mechanisms, and derived technologies of RNA-targeting CRISPR/Cas systems, aiming to enrich people's understanding of CRISPR/Cas-mediated RNA editing.

RNA interference (RNAi) is a gene silencing mechanism mediated by small RNAs derived from double-stranded RNA (dsRNA), capable of silencing specific genes. Following viral invasion, the dsRNA produced during viral replication is cleaved by the host cell's Dicer protein, generating virus-derived small interfering RNAs (virus-derived small interference RNAs, vsiRNA). These vsiRNAs then guide the cleavage and degradation of viral RNA via the RNAi pathway, exerting an antiviral effect. Therefore, RNAi is also recognized as an efficient antiviral immune pathway during viral infection. However, through long-term evolution, viruses have developed various strategies to counteract RNAi. For instance, they encode specific viral suppressors of RNAi (VSRs) that target and antagonize key molecules in this pathway. Research indicates that designing drugs to specifically target VSRs can "unlock" the antiviral function of RNAi within host cells, demonstrating highly potent and relatively broad-spectrum antiviral activity. Furthermore, viral infection can also be regulated by host- or virus-derived microRNAs (miRNAs). The role of miRNAs in viral infection provides new targets for antiviral therapy. In this review, we summarize the mechanism of RNAi in antiviral immunity, recent research advances, and its application prospects in antiviral therapy, aiming to provide theoretical support for antiviral immunity research and treatment.

The discovery of fluorescent proteins has revolutionized cell biology research. By fusing fluorescent proteins with target proteins, fluorescent biosensors can be constructed to enable real-time monitoring of dynamic cellular events in live cells and organisms. The unique physicochemical properties of fluorescent proteins, such as spectral range, chromophore maturation speed, pH sensitivity, and stability, offer diverse options for probe design. Researchers have developed various fluorescent biosensors based on these properties to monitor distinct molecular events. This review systematically summarizes the main strategies for designing fluorescent protein probes and highlights their typical applications in biological research. It provides a reference for developing more efficient and specialized fluorescent probes to address complex biological questions.

The genus Streptomyces provides a wealth of natural products with diverse structures, accounting for more than two-thirds of natural antibiotics. However, the original Streptomyces producers of desired natural products are technically challenging due to their harsh cultivation conditions, slow growth rate and intricate genetic backgrounds. Meanwhile, the metabolites of Streptomyces can also cause interference with the extraction of the target natural products. Thus, there is an urgent need to develop an ideal Streptomyces chassis with characterization of clean metabolic background, fluent genetic manipulation, and higher success rate of biosynthetic gene cluster expressions, which can largely increase yield and reduce production costs of natural products. In this review, we summarize the recent development of the chassis in Streptomyces and its important roles in the excavation and production of natural products, with focus on its potential for discovery and synthesis of natural products.

Resistance to anoikis, a crucial factor in cancer cell survival, drives the development and progression of numerous malignancies. Hepatocellular carcinoma (HCC) is a malignant liver tumor characterized by high rates of recurrence and metastasis. However, the role of anoikis in HCC remains poorly understood. In this study, we identify 74 anoikis-related genes (ARGs) differentially expressed in HCC using the transcriptional data from The Cancer Genome Atlas (TCGA). Then, we develop a prognostic model incorporating 9 of these genes through LASSO-Cox regression analysis, and confirm the model's independent prognostic significance for overall survival in HCC patients by using multivariable Cox proportional hazards analysis. Furthermore, we observe significant enrichment of activated proliferation-related pathways, increased infiltration of immunosuppressive cells and resistance to anti-PD-L1 therapy in the high-risk group defined by this model. These findings suggest that the ARG model may serve as a novel prognostic indicator for HCC patients and underscore the critical role of anoikis in HCC progression.

MicroRNAs (miRNAs) are a class of endogenous, single-stranded, noncoding RNAs ranging from 20~24 nucleotides in length. Traditionally, miRNAs are considered to inhibit mRNA translation or promote mRNA degradation, thereby exerting a negative regulatory effect on gene expression by binding to the 3' untranslated region (UTR) in the cytoplasm. However, this framework cannot fully explain the observed positive roles of certain miRNAs in gene regulation. Recent studies have identified mechanisms such as enhancer- and promoter-mediated transcriptional activation, as well as UTR-mediated translational activation, as key processes underlying the positive regulation of gene expression by miRNAs. Additionally, the subcellular distribution of miRNAs and specific physiological conditions can influence their functions. In this review, we summarize the biological characteristics and functions of miRNAs, with the goal of providing insights for future research in this field.

The elongation of the rice mesocotyl is a crucial determinant influencing seedling emergence and early vigor in dry-direct seeding rice, offering significant theoretical and practical implications for direct-seeding research. Mesocotyl elongation is a complex process that involves the regulation of genetic factors, plant hormones, signaling molecules, and environmental influences. These factors interact and jointly determine the elongation of the mesoderm. In this review, we summarize recent advances in the research of long mesocotyl rice germplasm resources mining, the mapped and cloned mesocotyl elongation genes/QTLs, and the genetic mechanism and influencing factors of rice mesocotyl growth. Finally, the application of long-mesocotyl rice germplasm and its associated issues are examined and predicted to establish a foundation for rice genetic breeding and production applications.