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Abstract：
Network pharmacology is an ideal tool to explore the effects of therapeutic components derived from plants on human metabolic diseases that are linked to inflammation. This study investigated the antioxidant effects of ginger leaves (GLs) and predicted targets for antioxidant activity. Quantitative and free radical scavenging analyses were performed to detect the main bioactive compounds of GLs and evaluate their antioxidant activities. Chemical diversity and network pharmacology approaches were used to predict key antioxidant components of GLs and their molecular targets. Nine major bioactive compounds of GLs were quantified using an internal standard method, and the antioxidant activity was evaluated using the DPPH and ABTS free radical scavenging methods. We first built the compound-gene-pathways and protein-protein interaction networks of GLs-related antioxidant targets and then conducted gene ontology and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment analyses. Molecular docking results show that astragalin, a compound isolated from GLs, had the highest level of connectivity in the compound-target network and was involved in inflammation-related biosynthesis by directly impacting cytokine gene expression and PTGS2 inhibition markers. These findings not only suggest that the compounds isolated from GLs can be developed as potential antioxidants, but also demonstrate the applicability of network pharmacology to assess the potential of foods for disease treatment.
摘要：
网络药理学是探索源自植物的治疗成分对与炎症相关的人类代谢性疾病的影响的理想工具。这项研究调查了姜叶（GL）的抗氧化作用，并预测了抗氧化活性的目标。进行了定量和自由基清除分析，以检测GL的主要生物活性化合物并评估其抗氧化活性。使用化学多样性和网络药理学方法来预测GL的关键抗氧化剂成分及其分子靶标。使用内标方法对9种主要生物活性化合物进行了定量，并使用DPPH和ABTS自由基清除方法评估抗氧化活性。我们首先建立了与GL相关的抗氧化靶标的复合基因途径和蛋白质-蛋白质相互作用网络，然后进行了基因本体论和京都基因和基因组百科全书（KEGG）途径富集分析。分子对接结果表明,黄芪,从GL中分离出的一种化合物，在化合物-靶标网络中具有最高水平的连通性,并且通过直接影响细胞因子基因表达和PTGS2抑制标志物而参与炎症相关的生物合成。这些发现不仅表明从GL中分离出的化合物可以被开发为潜在的抗氧化剂，同时也证明了网络药理学在评估食品用于疾病治疗的潜力方面的适用性。
