With the prevalence of epidemics, disposable face masks have been used in large quantities and has caused global environmental pollution concern. The gut microbiome of Zophobas atratus larvae showed great potential for plastic degradation. In a preliminary study, the larval gut microbiome could degrade masks, which has not been previously reported. This study validated the ability of the gut microbiome to degrade masks. Functional microbiomes and metabolic pathways associated with the degradation of masks were also analyzed. Our findings confirmed that the larvae have high masks-degrading ability with a consumption of 60 ± 0.04 mg/d (dry mass by per 50 larvae), which is gut microbiome-dependent. At the genus level, Hafnia and Corynebaterium were highly abundant and contributed to masks degradation. The degrading metabolites were then identified, of which 46 were significantly upregulated. Steroid hormone biosynthesis and the cytochrome P450 pathway may be linked to DFM (PP) oxidation and degradation. Finally, Stenotrophomonas sp. strain M212 with a masks-degrading ability was screened from these functional microorganisms, further establishing the role of the gut microbiome.

Nannochloropsis is an industrially relevant marine microalga with exceptional potential as a chassis for sunlight-driven CO₂ valorization. However, its broad application in synthetic biology has been constrained by the lack of a standardized and modular genetic toolbox. Here, we report the development of a comprehensive Modular Cloning (MoClo) toolkit for Nannochloropsis, based on Golden Gate assembly and a standard syntax. The toolkit comprises 91 domesticated genetic parts spanning promoters, signal peptides, selectable markers, reporter genes, tags and terminators. A large subset of these parts, including several not previously evaluated in Nannochloropsis, was functionally validated, enabling convenient and reliable transformant selection, immunodetection, and subcellular localization. To demonstrate the utility of the toolkit for multi-gene pathway engineering, modularly assembled keto-carotenoid biosynthetic pathways were introduced into Nannochloropsis, leading to substantial accumulation of canthaxanthin (4.5 mg g⁻¹) or astaxanthin (2.8 mg g⁻¹). Collectively, this flexible and expandable MoClo toolkit establishes a standardized foundation for synthetic biology in Nannochloropsis, enables rapid design-build-test cycles for multi-gene constructs, and advances the use of industrial microalga for sustainable, CO₂-based production of value-added biochemicals.

Inflammatory bowel disease (IBD), encompassing Crohn’s disease (CD) and ulcerative colitis (UC), is a chronic inflammatory disorder of the gastrointestinal tract. Autophagy, an essential intracellular homeostatic process, plays a pivotal role in the pathogenesis and progression of IBD. This review systematically examines recent advances in understanding the involvement of autophagy in IBD, with a particular focus on the regulatory mechanisms governing its sequential phases—initiation, elongation, and termination—and their respective contributions to intestinal inflammation. We highlight how dysregulation of core autophagy components, including the ULK1 complex, Beclin 1 complex, and ATG16L1, influences inflammatory responses. Furthermore, this article delves into the context-dependent roles of selective autophagy pathways such as mitophagy, ER-phagy, and xenophagy in IBD, as well as the emerging significance of non-autophagic functions exerted by autophagy-related genes. By integrating these multifaceted aspects, this review aims to provide a theoretical foundation and identify potential targets for future precision therapeutics targeting autophagy in IBD.

Modern agriculture currently demands higher standards for the simultaneous improvement of crop yield, quality and stress resistance. However, traditional crop breeding methods can no longer meet the needs of modern agricultural development. Improving a single trait is no longer sufficient to meet the multifaceted demands of modern agricultural production and consumer expectations. Multiple traits breeding has increasingly become a key objective in current crop breeding. Over the past decade, CRISPR/Cas9-based multiplex genome editing (MGE) has enabled efficient pyramiding and precise regulation of multiple traits via targeted editing of multiple gene loci, revolutionizing crop breeding. In this review, we briefly describe the core CRISPR/Cas-based MGE strategies and technical workflows, and thoroughly discuss the practical outcomes of MGE applications in various fields, such as enhancing crop stress resistance, increasing yield and improving quality. This review aims to provide a summary and theoretical reference for crop breeding, as well as open up new ideas for achieving different breeding goals.

Extracellular vesicles (EVs) mediate tumor–host communication and represent a promising liquid biopsy source for metastasis risk assessment, yet quantitative detection of low-abundance, epitope-defined EV subpopulations in plasma remains technically challenging. Here, we establish a nanoscale flow cytometry workflow on the CytoFLEX platform for sensitive single-EV phenotyping by optimizing violet side scatter (VSSC) triggering, defining an acquisition window that minimizes coincidence or “swarm” effects, and applying fluorescence-based analysis with stringent background controls. Using this framework, we quantified EV-associated tumor necrosis factor-related apoptosis-inducing ligand (TRAIL) at the single particle level, with good inter-assay reproducibility (CV ~ 11–13%), and resolved low-abundance TRAIL⁺ EVs at approximately 1% abundance within total EV events. Due to the low abundance of EV-associated TRAIL in pancreatic ductal adenocarcinoma (PDAC) plasma, ELISA lacked sufficient analytical sensitivity to accurately reflect EV-associated TRAIL levels, whereas flow-based enumeration preserved quantitative resolution. Clinically, plasma EV-associated TRAIL was significantly elevated in PDAC patients with liver metastasis and demonstrated predictive utility for postoperative liver metastatic recurrence (AUC = 0.766). These results support nanoscale flow cytometry as a robust platform for plasma EV biomarker profiling and identify EV-associated TRAIL as an informative indicator of liver metastatic risk in PDAC.

N⁶-methyladenosine (m⁶A), the most prevalent internal mRNA modification, regulates plant development and stress responses through modulating various mRNA metabolic processes and epigenetic effects. Although well studied in animals, its roles in plant–virus interactions have only recently begun to be elucidated. Multiple plant viruses carry m⁶A modifications on their RNAs, validated by MeRIP-seq, LC–MS/MS, and direct RNA sequencing. Viral RNAs acquire m⁶A through the recruitment or relocalization of host methyltransferase complexes, which is often mediated by viral proteins. Functionally, m⁶A can restrict infection by promoting viral RNA decay via YTH-domain readers and RNA surveillance pathways, or alternatively stabilize viral RNAs to enhance replication and systemic spread. In turn, viruses disrupt the functionality of host m⁶A machinery to promote infection. Moreover, viral infection reprograms host m⁶A homeostasis, altering methylation landscapes in immune and hormone pathways. These findings establish m⁶A as a dynamic epitranscriptomic switch in plant-virus interactions, with promising implications for antiviral strategies and crop improvement.

How do plants, lacking a central nervous system, translate environmental stimuli into physiological actions within milliseconds? Vesicular trafficking acts as a cellular core signal and material transport hub that facilitates this rapid adaptation, yet its dynamic nature has long remained a "black box". Traditional imaging approaches have struggled not only with optical resolution (the "unseen"), but critically with a lack of quantitative precision (the "immeasurable") and the inability to track molecular history (the "unknown age"). This review synthesizes a new paradigm that unlocks this black box by integrating advanced chemical biology with deep learning computational analysis. We detail how multimodal strategies combining pH-sensitive probes (e.g., pHluorin), covalent tags (HaloTag), and fluorescent timers visualize molecular events with unprecedented fidelity. Furthermore, we explore how integrating next generation FRAP/FCS variants (DeepFRAP, FCSNet) with deep learning allows for the rigorous mathematical modeling of vesicle kinetics. By resolving long-standing controversies such as endocytic stoichiometry and secretory sorting logic, this quantitative framework maps nanoscale membrane dynamics to organismal phenotypes, ultimately refining our understanding of plant stress resilience and signal transduction.

Recent advancements in sequencing technologies have transformed the characterization of genomic and transcriptomic complexity. In this review, we present a comprehensive overview of Oxford Nanopore Technologies (ONT), emphasizing its unique capability for real-time, long-read, and direct RNA sequencing. We begin by outlining the core ONT analytical workflow—base calling, alignment, re-squiggling, and quality control—and summarize the major computational tools applied at each stage. Then extensive illustrations of various RNA modification detection techniques are provided, spanning from statistical models, machine learning and deep learning frameworks to advanced strategies incorporating large language models. To assess methodological performance, additional benchmark analyses of m6A and pseudouridine (Ψ) are carried out across two publicly available datasets. These results demonstrate substantial variability across different tools, underscoring the inherent difficulties in reliably detecting modifications from ONT signals. We further examine the biological roles of key RNA modifications and contrast ONT-based approaches with conventional detection technologies. Finally, we discuss persistent limitations such as sequencing error rates, data and computational demands, and the complexity of multi-modification inference, and further propose future directions aimed at improving accuracy, robustness, and biological interpretability in ONT-based epitranscriptomic research.

Traditional chemotherapy and radiotherapy for glioma are challenging due to the hypoxia in tumor microenvironment and the inability of chemotherapeutic agents to enter into tumor cells. Phototherapy is a novel therapeutic approach against various tumors in recent years. When combined with chemotherapy, the antitumor efficacy of phototherapy is superior than each alone. However, the combination of chemotherapy and phototherapy is still hampered by the hypoxic tumor microenvironment which upregulates the expression of hypoxia-inducible factor 1 (HIF-1) and its downstream pathways, as well as the thermoresistance caused by the overexpression of heat shock proteins (HSPs). To solve this, the biocompatible albumin-based nanoparticles (NPs) are developed to co-deliver IR780 iodine (IR780) and paclitaxel (PTX) simultaneously at an optimized ratio (IR780-PTX NPs) for synergistic photochemotherapy. Moreover, acriflavine (ACF), a chemical inhibitor of HIF-1, is formulated into intratumorally formed hydrogels to reinforce synergistic photochemotherapy. The continuously released ACF from hydrogel not only relieves the impact of photodynamic therapy-exacerbated tumor hypoxia by suppressing HIF-1 activity, but also efficiently attenuates HSP70 upregulation. The collaboration between IR780-PTX NPs and ACF hydrogels leads to an extraordinary antitumor effect in vitro and in vivo. The reinforced synergistic photochemotherapy via a single molecule by overcoming HIF-1 activity and HSP overexpression provides an effective therapeutic example to treat tumors, especially in those undergone severe hypoxia and/or therapy-induced thermoresistance.

Traditional Chinese Medicine (TCM) offers valuable therapeutic strategies for chronic and infectious diseases, yet the inherent complexity of its multi-component, multi-target formulations and synergistic effects presents substantial challenges to pharmacological mechanistic understanding. Structural pharmacology of Chinese Medicine has emerged as a transformative discipline, integrating structural biology, computational chemistry, and pharmacology to elucidate the precise mechanisms underlying TCM efficacy. This review synthesizes technological advancements that enable the characterization of synergistic mechanisms and dynamic molecular interactions in TCM. Key advancements include high-resolution structural techniques such as X-ray crystallography, cryo-electron microscopy, sophisticated computational approaches such as AI-driven predictive modeling, and advanced analytical platforms. We critically examine persistent technical hurdles, such as capturing transient binding events and modeling complex multi-component system dynamics. Finally, we outline future research trajectories to establish a predictive and adaptable scientific foundation for TCM modernization, facilitating its evidence-based global integration and application in precision medicine.