Author

Leyu Li

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Open access Jul 2026

Involvement of ascorbate oxidase in the regulation of the leaf development mediated by mepiquat chloride in cotton seedlings

Mepiquat chloride (MC), a plant growth regulator, is extensively used in agricultural practices. However, the mechanism of action of MC is complex and remains largely unclear. In this study, the effects of MC on the leaf development of cotton seedlings and the underlying regulatory mechanism were investigated by integrating morphological, physiological, transcriptomic analysis and virus-induced gene silencing technology. The results showed that MC treatment caused a reduction in leaf area and an increase in chlorophyll content and photosynthetic efficiency. The ascorbic acid content initially increased and then declined after MC treatment. Transcriptomic data demonstrated that the expression of the ascorbate oxidase (AO) gene was initially upregulated and subsequently downregulated by MC. The top 20 KEGG pathways identified from the differentially expressed genes were mainly enriched in plant–pathogen interaction, the MAPK signaling pathway, ascorbate and aldarate metabolism, plant hormone signal transduction, amino acid metabolism, and secondary metabolism. Silencing GhAO1 enhanced the ascorbic acid concentration, which was accompanied by increased plant height, internode length, and leaf area in cotton seedlings. Silencing GhAO1 reduced the sensitivity of cotton seedlings to MC. This study reveals that MC may primarily serve as an abiotic stressor that regulates the stress response during the early stage of MC treatment and may further enhance chlorophyll content by promoting amino acid and secondary metabolism. AO plays an important role in MC-mediated growth regulation in cotton seedlings.

Ye Tian, Xuanxuan Liu, Haiyan Guan et al. · Frontiers in Plant Science · 0 citations

Open access Aug 2026

Machine Learning Prediction and Experimental Validation of Antimicrobial Peptide Activity Differences against Gram-Positive and Gram-Negative Bacteria

Antimicrobial peptides (AMPs) are primary candidates for addressing bacterial resistance. Although their target spectrum specificity varies significantly between Gram-positive and Gram-negative bacteria, current predictive models generally lack experimental validation. In this study, we constructed various machine learning models based on known sequences to systematically evaluate the performance of k-mer frequencies, physicochemical properties, and hybrid features in distinguishing the AMP target specificity. Results indicated that the random forest model based on eight key physicochemical properties performed best, achieving a test set accuracy of 82.09% with balanced classification and robust generalization. Feature importance analysis revealed that hydrophilicity and isoelectric point (pI) are the core physicochemical factors determining the target spectrum differences. The model was rigorously validated through a dual-track approach: first, via the synthesis and in vitro testing of 18 novel protozoan-derived AMPs (overall accuracy 66.67%) and, second, through a blind test on 55 independent external sequences, achieving a robust accuracy of 81.82%. Furthermore, the framework successfully identified candidates with potent activity against multidrug-resistant pathogens including Pseudomonas aeruginosa and Klebsiella pneumoniae. This experimentally validated predictive framework provides a reliable computational tool for the high-throughput screening and rational design of targeted antimicrobial peptides.

Peicheng Lu, Wenhao Li, Muhammad Zubair et al. · ACS Omega · 0 citations