Evaluating the practical utility of endangered plant species is crucial for their conservation. Nevertheless, numerous endangered plants, including Sinocalycanthus chinensis, lack historical usage data, leading to a paucity of guidance in traditional pharmacological research. This gap impedes their development and potential utilization. Ultra-high-performance liquid chromatography and Orbitrap high-resolution mass spectrometry were employed to analyze the S. chinensis leaves collected at different harvesting times. Then, the metabolites were automatically annotated by a self-built R script in conjunction with characteristic fragment ions, neutral loss filtering, and feature-based molecular networking. By integrating metabolomics with network medicine analysis, the potential usage and optimal harvest times for S. chinensis were unlocked. A total of 305 metabolites were identified, with 66.8% annotated by self-built R script. A progressive increase in metabolite disparities was observed from May to August, followed by a relatively minor distinction from August to October. Notably diverse metabolites were detected in S. chinensis harvested during different periods, implying potential variations in efficacy. Network medicine analysis indicated possible therapeutic implications of S. chinensis for lung cancer, diabetes, bladder cancer, and Alzheimer's disease. Samples collected in May and September demonstrated exceptional efficacy. Harvesting was strategically conducted during these months based on variations in sample characteristics and metabolite content, tailored to their intended applications for dietary or medicinal purposes. This study developed an efficient methodology for investigating metabolites and exploring the potential applications of S. chinensis in food and herbal medicine. Consequently, it provides technical support for the sustainable conservation of endangered plants with limited clinical application experience.
Keywords: Sinocalycanthus chinensis; feature‐based molecular networking; harvesting times; metabolomics; network medicine analysis.
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