Key Laboratory of Computer-Aided Drug Design of Dongguan City, The First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, 523710, China; Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Dongguan, 523808, China.
Key Laboratory of Big Data Mining and Precision Drug Design of Guangdong Medical University, Key Laboratory for Research and Development of Natural Drugs of Guangdong Province, School of Pharmacy, Dongguan, 523808, China.
Comput Biol Med. 2023 Jun;159:106870. doi: 10.1016/j.compbiomed.2023.106870. Epub 2023 Apr 5.
OBJECTIVE: The aim of this study was to illuminate the similarities and differences of two prescriptions as "cold" and "heat" drugs for treating ulcerative colitis (UC) with the simultaneous occurrence of heat and cold syndrome via network pharmacology. METHODS: (1) Active compounds of Fuzi-Lizhong Pill (FLP) and Huangqin Decoction (HQT) were retrieved from the TCMSP database, and their common active compounds were compared using the Venn diagram. (2) Potential proteins targeted to three sets of compounds either (i) shared by FLP and HQT, (ii) unique to FLP or (iii) unique to HQT were screened from the STP, STITCH and TCMSP databases, and three corresponding core compound sets were identified in Herb-Compound-Target (H-C-T) networks. (3) Targets related to UC were identified from the DisGeNET and GeneCards databases and compared with the FLP-HQT common targets to identify potential targets of FLP-HQT compounds related to UC. (4) Three potential target sets were imported into the STRING database for protein‒protein interaction (PPI) analysis, and three core target sets were defined. (5) The binding capabilities and interacting modes between core compounds and key targets were verified by molecular docking via Discovery Studio 2019 and molecular dynamics (MD) simulations via Amber 2018. (6) The target sets were enriched for KEGG pathways using the DAVID database. RESULTS: (1) FLP and HQT included 95 and 113 active compounds, respectively, with 46 common compounds, 49 FLP-specific compounds and 67 HQT-specific compounds. (2) 174 targets of FLP-HQT common compounds, 168 targets of FLP-specific compounds, and 369 targets of HQT-specific compounds were predicted from the STP, STITCH and TCMSP databases; six core compounds specific to FLP and HQT were screened in the FLP-specific and HQT-specific H-C-T networks, respectively. (3) 103 targets overlapped from the 174 predicted targets and the 4749 UC-related targets; two core compounds for FLP-HQT were identified from the FLP-HQT H-C-T network. (4) 103 FLP-HQT-UC common targets, 168 of FLP-specific targets and 369 of HQT-specific targets had shared core targets (AKT1, MAPK3, TNF, JUN and CASP3) based on the PPI network analysis. (5) Molecular docking demonstrated that naringenin, formononetin, luteolin, glycitein, quercetin, kaempferol and baicalein of FLP and HQT play a critical role in treating UC; meanwhile, MD simulations revealed the stability of protein‒ligand interactions. (6) The enriched pathways indicated that most targets were related to anti-inflammatory, immunomodulatory and other pathways. Compared with the pathways identified using traditional methods, FLP-specific pathways included the PPAR signaling pathway and the bile secretion pathway, and HQT-specific pathways included the vascular smooth muscle contraction pathway and the natural killer cell-mediated cytotoxicity pathway etc. CONCLUSION: In this study, we clarified the common mechanisms of FLP and HQT in treating UC and their specific mechanisms in treating cold and heat syndrome in UC through compound, target and pathway distinction and a literature comparison based on network pharmacology; these results provide a new perspective on the detailed mechanism of "multidrugs and single-disease" thought in traditional Chinese medicine.
目的：本研究旨在通过网络药理学阐明同时具有寒证和热证的溃疡性结肠炎（UC）“寒”“热”药二方——附子理中丸（FLP）和黄芩汤（HQT）的相似性和差异性。
方法：（1）从 TCMSP 数据库中检索 FLP 和 HQT 的活性化合物，并使用 Venn 图比较它们的共同活性化合物。（2）从 STP、STITCH 和 TCMSP 数据库中筛选出FLP 和 HQT 共同化合物（i）、FLP 特有化合物（ii）和 HQT 特有化合物（iii）的潜在作用靶点，然后在 Herb-Compound-Target（H-C-T）网络中确定三个相应的核心化合物集。（3）从 DisGeNET 和 GeneCards 数据库中鉴定与 UC 相关的靶点，并与 FLP-HQT 共同靶点进行比较，以鉴定 FLP-HQT 化合物中与 UC 相关的潜在靶点。（4）将三个潜在的靶标集导入 STRING 数据库进行蛋白质相互作用（PPI）分析，并定义三个核心靶标集。（5）通过 Discovery Studio 2019 对核心化合物和关键靶标之间的结合能力和相互作用模式进行分子对接验证，并通过 Amber 2018 进行分子动力学（MD）模拟验证。（6）使用 DAVID 数据库对靶标集进行 KEGG 通路富集分析。
结果：（1）FLP 和 HQT 分别包含 95 种和 113 种活性化合物，其中 46 种为共有化合物，49 种为 FLP 特有化合物，67 种为 HQT 特有化合物。（2）从 STP、STITCH 和 TCMSP 数据库中预测出 174 个 FLP-HQT 共同化合物的靶点、168 个 FLP 特有化合物的靶点和 369 个 HQT 特有化合物的靶点；分别在 FLP 特有和 HQT 特有 H-C-T 网络中筛选出 6 个核心化合物。（3）从预测靶点和 4749 个 UC 相关靶点中鉴定出 103 个重叠靶点；从 FLP-HQT H-C-T 网络中鉴定出 2 个核心化合物。（4）基于 PPI 网络分析，FLP-HQT-UC 共有靶点、FLP 特有靶点和 HQT 特有靶点分别有 103、168 和 369 个共享核心靶点（AKT1、MAPK3、TNF、JUN 和 CASP3）。（5）分子对接表明，FLP 和 HQT 的柚皮素、芒柄花黄素、木犀草素、大豆苷元、槲皮素、山奈酚和黄芩素在治疗 UC 中发挥关键作用；同时，MD 模拟揭示了蛋白-配体相互作用的稳定性。（6）富集的通路表明，大多数靶点与抗炎、免疫调节等通路相关。与传统方法鉴定的通路相比，FLP 特有通路包括 PPAR 信号通路和胆汁分泌通路，HQT 特有通路包括血管平滑肌收缩通路和自然杀伤细胞介导的细胞毒性通路等。
结论：本研究通过基于网络药理学的化合物、靶标和通路区分及文献比较，阐明了 FLP 和 HQT 治疗 UC 的共同机制及其在治疗 UC 寒证和热证的特异机制，为深入研究中药“多药治一病”的思想提供了新的视角。
Zhongguo Zhong Yao Za Zhi. 2021-12
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