目的 基于液相色譜-質(zhì)譜聯(lián)用（LC-MS）分析當(dāng)歸雞血藤湯（DJD）的化學(xué)成分，通過(guò)網(wǎng)絡(luò)藥理學(xué)探討DJD治療再生障礙性貧血（AA）的作用機(jī)制，并預(yù)測(cè)其潛在的質(zhì)量標(biāo)志物（Q-Marker）。方法 通過(guò)LC-MS分析DJD化學(xué)成分，利用Swiss Target Prediction預(yù)測(cè)化學(xué)成分對(duì)應(yīng)靶點(diǎn)，再通過(guò)GeneCards、OMIM、PharmGKB和TTD等數(shù)據(jù)庫(kù)查詢AA靶點(diǎn)，利用Cytoscape 3.9.1繪制“成分-靶點(diǎn)”及蛋白質(zhì)-蛋白質(zhì)相互作用（PPI）網(wǎng)絡(luò)?；赗語(yǔ)言與微生信得到基因本體（GO）注釋及京都基因與基因組百科全書（KEGG）通路富集分析可視化圖，最后使用分子對(duì)接對(duì)網(wǎng)絡(luò)藥理學(xué)預(yù)測(cè)結(jié)果進(jìn)行初步驗(yàn)證。結(jié)果 經(jīng)LC-MS鑒定出DJD中38個(gè)化合物，通過(guò)Swiss Target Prediction預(yù)測(cè)得到536個(gè)成分靶點(diǎn)，藥物與疾病共有靶點(diǎn)196個(gè)，運(yùn)用Cytoscape 3.9.1篩選出主要活性成分為白芍苷R₁、芍藥內(nèi)酯苷、striatisporolide A、紫鉚花素、黃芩素、高車前素、柚皮素、染料木苷、東莨菪內(nèi)酯和隱丹參酮；經(jīng)PPI分析得出前10名核心蛋白分別為磷酸甘油醛脫氫酶（GAPDH）、蛋白激酶B （Akt1）、白細(xì)胞介素-6 （IL6）、腫瘤壞死因子（TNF）、Caspase-3、表皮生長(zhǎng)因子受體（EGFR）、信號(hào)傳導(dǎo)子及轉(zhuǎn)錄激活子3（STAT3）、熱休克蛋白90A（HSP90AA1）、非受體酪氨酸激酶（SRC）和B細(xì)胞淋巴瘤/白血病-2蛋白（BCL2）；經(jīng)GO富集分析得2 718條目，KEGG富集分析得到包含磷脂酰肌醇-3/蛋白激酶B（PI3K/Akt）等161條通路；分子對(duì)接結(jié)果顯示關(guān)鍵靶點(diǎn)與活性成分結(jié)合效能較低，具較好的親和力。結(jié)論 基于DJD化學(xué)成分識(shí)別，結(jié)合網(wǎng)絡(luò)藥理學(xué)與分子對(duì)接驗(yàn)證，預(yù)測(cè)隱丹參酮、白芍苷R₁、染料木苷及芍藥內(nèi)酯苷可以作為DJD治療AA的候選Q-Markers。

Objective To analyze the chemical constituents of Danggui Jixueteng decoction (DJD) based on LC-MS, and explore the mechanism of DJD in treating aplastic anemia (AA) through network pharmacology, and identify its potential quality markers (QMarker). Methods The chemical components of DJD were analyzed by LC-MS, and the corresponding targets were predicted by Swiss Target Prediction. Then AA targets were obtained by GeneCards, OMIM, PharmGKB and TTD datebase, and the "componenttarget" and protein-protein interaction (PPI) maps were drawn by Cytoscape 3.9.1. The visualization of GO and KEGG enrichment analysis was obtained by R language and micro-information. Finally, the content of network pharmacology prediction was preliminarily verified by molecular docking. Results 38 compounds were identified by LC-MS, 536 targets were predicted by Swiss Target Prediction, and there were 196 common targets. The main active compounds were identified by Cytoscape3.9.1 as paeoniflorin R₁, albiflorin, striatisporolide A, butein, baicalein, hispidulin, naringenin, genistin, scopolitin and cryptotanshinone; By PPI analysis, the top ten core proteins were glyceraldehyde-phosphate dehydrogenase (GAPDH), protein kinase B (AKT1), interleukin-6 (IL-6), tumor necrosis factor (TNF), Caspase-3, epidermal growth factor receptor (EGFR), signal transducer and activator of transcription 3 (STAT3), heat shock protein 90A (HSP90AA1), non-receptor tyrosine kinase (SRC protein, SRC) and Bcell lymphoma/leukemia-2 protein (BCL-2); 2 718 items were obtained by GO enrichment analysis, and 161 pathways such as PI3K/ Akt were obtained by KEGG enrichment analysis. Molecular docking results showed that the binding efficiency of key targets and active components is low and they have good affinity. Conclusion Based on the identification of DJD chemical constituents, combined with network pharmacology and molecular docking verification, it was predicted that cryptotanshinone, paeoniflorin R₁, genistin and genistin can be used as Q-Marker for DJD to treat AA.

R285.5

國(guó)家自然科學(xué)基金資助項(xiàng)目（81774314）;無(wú)錫市中醫(yī)藥管理局項(xiàng)目（ZYYB29）