Screening Biomarkers of Astragalus Membranaceus for Hypertensive Ventricular Remodeling Based on Network Pharmacology and Molecular Docking
-
摘要:
目的 基于网络药理学原理和分子对接，探讨黄芪治疗高血压心室重构（ventricular remodeling，VR）的作用机制。 方法 利用在线数据库获得黄芪的有效成分、药物靶点及高血压心室重构的疾病靶点，下载mRNA数据信息，筛选高血压心室重构相关的关键模块及其基因，进而预测高血压心室重构的疾病靶点，再进行GO功能/KEGG通路富集分析，构建关键靶点-功能/通路调控网络及PPI网络并可视化。利用LASSO及Random Forest构建高血压心室重构的诊断模型，分析得到生物标志物后再对生物标记物、药物活性成分绘制中药药理调控网络并可视化。最后通过数据库获取各种成分结构，进行分子对接。 结果 获得黄芪活性成分87个，药物靶点390个，高血压心室重构疾病靶点3281个，差异表达关键模块基因2103个，取交集获得24个关键靶点。分析得到关键靶点基因与生物过程、分子功能和细胞成分Terms分别有288个、15个、29个靶点，共获54条相关信号通路，得到 21个蛋白的互作网络关系。获得4个生物标志物（MAPK1，IL2，CSNK2B，SELE），分子对接结果显示4个标志物的蛋白与小分子之间均存在结合的氢键。 结论 通过筛选获取了黄芪治疗高血压心室重构标志物，验证了黄芪治疗高血压心室重构效果，并且可能成为高血压心室重构诊断和治疗的分子生物标记物。 Abstract:Objective To investigate the mechanisms of Astragalus membranaceus in the treatment of hypertensive ventricular remodeling （VR） based on the principles of network pharmacology and molecular docking. Method The effective components of Astragalus membranaceus, drug targets and disease targets of hypertensive ventricular remodeling were obtained from the online database. The mRNA data were downloaded to screen the key modules and genes related to hypertensive ventricular remodeling, and then the disease targets of hypertensive ventricular remodeling were predicted, followed by GO functional/KEGG pathway enrichment analysis. PPI network were constructed and visualized. LASSO and Random Forest were used to construct the diagnostic model of hypertensive ventricular remodeling. After analyzing the biomarkers, the pharmacological regulatory network of traditional Chinese medicine was drawn and visualized for biomarkers and active components. Finally, the component structures were obtained through the database for molecular docking. Results 87 active ingredients of Astragalus membranaceus, 390 drug targets, 3281 hypertensive ventricular remodeling disease targets and 2103 differentially expressed key module genes were obtained, and 24 key targets were obtained by taking the intersection. There were 288, 15 and 29 targets in terms of key target genes and biological processes, molecular functions and cell components, respectively. A total of 54 related signaling pathways were obtained, and the interaction network relationships of 21 proteins were obtained. Four biomarkers （MAPK1, IL2, CSNK2B, SELE） were obtained, and the molecular docking results showed the existence of binding hydrogen bonds between the proteins and small molecules of all four markers. Conclusion Screening to obtain markers of ventricular remodeling in hypertension by Astragalus membranaceus validated the effect of Astragalus membranaceus in the treatment of hypertensive ventricular remodeling and may become a molecular biomarker for the diagnosis and treatment of hypertensive ventricular remodeling. -
Key words:
- Network pharmacology /
- Astragalus membranaceus /
- Hypertension /
- ventricular remodeling /
- Biomarkers
-
图 1 高血压心室重构关键模块及其关键基因的筛选
A，B：样本聚类及表型热图。分支代表样本，纵坐标代表层次聚类的高度。分支对应红色临床性状代表该样本属于此类性状；C：软阈值筛选。横轴均代表权重参数power值，左图纵轴scale-free fit index，即signed R2，相关系数的平方越高，说明该网络越逼近无尺度分布，右图的纵轴代表对应的基因模块中所有基因邻接函数的均值；D，E：模块合并。F：模块与高血压心室重构相关性热图；G：模块基因与高血压心室重构相关性；H：高血压心室重构/正常样本之间差异表达基因的火山图。横坐标logFC表示差异倍数（高血压心室重构/正常），纵坐标表示可信程度-log10（adj. P-value）。图中每个点代表一个基因，蓝色和橙色的点代表显著差异表达基因。橙色的点表示其基因表达量在高血压心室重构样本中上调，蓝色的点表示基因在高血压心室重构样本中下调。横纵轴虚线分别表示log FC绝对阈值0和P-value阈值0.05。I为差异基因及相关模块基因韦恩图。
Figure 1. Screening of key modules of hypertensive ventricular remodeling and their key genes
图 2 关键靶点筛选及其调控网络
A：差异关键模块基因、药物靶点、疾病靶点取交集的韦恩图，图中黄色代表高血压心室重构的疾病靶点，红色表示药物靶点，绿色代表差异关键基因；B：关键靶点基因的GO富集条形图，纵坐标表示富集的GO Term，条形长短表示该GO Term富集到关键靶点的个数，颜色从蓝到红表示结果的可信度从低到高；C：关键靶点基因的KEGG通路富集气泡图，气泡大小表示通路基因多少，颜色从蓝到红表示结果的可信度从低到高。D，E：关键靶点-功能/通路的调控网络图，黄色六边形代表GO_MF Terms，橙色六边形代表GO_BP Terms，绿色六边形代表GO_CC Terms，蓝色圆点代表关键靶点。粉色六边形代表KEGG Pathways；蓝色圆点代表关键靶点。F：关键靶点的蛋白互作关系网络，其中线条代表它们之间的互作关系；颜色表示他们的degree值，颜色越深表示degree值越高，越处于核心位置。
Figure 2. Screening of key targets and their regulatory networks
图 3 高血压心室重构最佳诊断模型
A:LASSO回归分析筛选特征基因。横坐标deviance表示模型解释的残差的比例，显示了特征基因数量随解释的残差的比例（dev）之间的变化关系，纵坐标为基因系数（左）；横坐标为log（Lambda），纵坐标代表交叉验证的误差（右），实际中笔者希望交叉验证的误差在最小的位置，右图中，左侧虚线位置就是交叉验证误差最小的位置，根据该位置（lambda.min）确定对应的横坐标log（Lambda），上边显示了特征基因的数目，找到最优的log（Lambda）值，就左图中找到对应的基因和它的系数，以及该模型解释的残差的比例；B：ROC曲线对诊断模型的评估及验证；C、D：样本的累积残差分布图及样本残差的箱线图（曲线面积说明整体样本的累积残差值，曲线面积越小说明样本的累积残差值越小）；红点代表残差的均方；E：基因变量在RF模型中的重要性；F：ROC曲线对RF模型的评估。
Figure 3. Best diagnostic model for hypertensive ventricular remodeling
图 4 关键基因的筛选及其表达分析
A：LASSO诊断模型基因与RF模型基因的交集，粉色代表LASSO诊断模型基因，蓝色代表RF模型基因；B：生物标记物的ROC曲线；C：生物标记物的表达，绿色代表正常组，粉色代表高血压心室重构组；D：生物标记物的相关性，蓝色代表负相关，红色代表正相关。
Figure 4. Screening and expression analysis of key genes
图 5 生物标记物的中药药理调控网络
绿色正方形代表中药（Herb）；紫色圆点代表关键靶点；黄绿色菱形代表药物活性成分；红色正方形代表疾病（高血压心室重构）。
Figure 5. TCM pharmacological regulatory network of biomarkers
图 6 分子对接结果图
A：SELE与 Kaempferol 对接结果图，图中绿色双环棍棒模型为活性分子Kaempferol；B：MAPK1与 quercetin对接结果图，图中绿色双环棍棒模型为活性分子quercetin；C：IL2与 quercetin对接结果图，图中绿色双环棍棒模型为活性分子quercetin；D：CSNK2B与 Kumatakenin对接结果图，图中绿色双环棍棒模型为活性分子Kumatakenin。粉色棍棒结构为与活性成分有氢键相互作用的氨基酸残基，黄色虚线为活性成分与氨基酸残基之间形成的氢键。每根黄色的虚线代表一个氢键。
Figure 6. Molecular docking result
表 1 基因在RF模型中的重要性排序
Table 1. Ranking of importance of genes in the RF model
排序 基因 分值 模型 1 full model 0.1366 RF 2 MTHFD1 0.1345 RF 3 COMT 0.1357 RF 4 group 0.1366 RF 5 ATP1A1 0.1366 RF 6 P4HB 0.1366 RF 7 HDAC2 0.1366 RF 8 UBA1 0.1366 RF 9 SOD1 0.1366 RF 10 AKR1B1 0.1366 RF 11 RUVBL2 0.1366 RF 12 GOT1 0.1366 RF 13 CASP3 0.1366 RF 14 PPIA 0.1366 RF 15 CALM1 0.1366 RF 16 PRKACA 0.1366 RF 17 TPI1 0.1366 RF 18 CTSD 0.1366 RF 19 NOS3 0.1366 RF 20 GOT2 0.1366 RF 21 CSNK2B 0.1385 RF 22 MAPK1 0.139 RF 23 GHR 0.14 RF 24 MBL2 0.1432 RF 25 SELE 0.2202 RF 26 IL2 0.3298 RF 
下载: 导出CSV
-
[1] Zhou Y P,Ruan C C,Kong L R,et al. Adenosine A(2A) receptor activation prevents DOCA-salt induced hypertensive cardiac remodeling via iBAT[J]. Biochem Biophys Res Commun,2020,525(1):224-230. doi: 10.1016/j.bbrc.2020.02.035 [2] Zhang X,Dong S,Jia Q,et al. The microRNA in ventricular remodeling: the miR-30 family[J]. Biosci Rep,2019,39(8):1-11. [3] 顾静,郭超,车敏,等. 黄芪对高血压大鼠血管重构中内质网应激反应的影响[J]. 中国实验动物学报,2019,27(1):65-71. [4] 徐银祯,李兰芳,党万军. 黄芪注射液对老年高血压患者血管功能及左心室重塑的影响[J]. 内科,2018,13(6):843-846. [5] 张王宁,高耀,李科,等. 基于网络药理学的黄芪总黄酮治疗肾病综合征的机制研究[J]. 药学学报,2018,53(9):1429-1441. [6] Lan S,Duan J,Zeng N,et al. Network pharmacology-based screening of the active ingredients and mechanisms of Huangqi against aging[J]. Medicine (Baltimore),2021,100(17):e25660. [7] Ritchie M E,Phipson B,Wu D,et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies[J]. Nucleic Acids Res,2015,43(7):e47. doi: 10.1093/nar/gkv007 [8] Langfelder P,Horvath S,Wgcn A. An R package for weighted correlation network analysis[J]. BMC Bioinformatics,2008,9(1):559-559. doi: 10.1186/1471-2105-9-559 [9] Yu G,Wang L G,Han Y,et al. ClusterProfiler: An R package for comparing biological themes among gene clusters[J]. OMICS,2012,16(5):284-287. doi: 10.1089/omi.2011.0118 [10] Shannon P,Markiel A,Ozier O,et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks[J]. Genome Res,2003,13(11):2498-2504. doi: 10.1101/gr.1239303 [11] Jerome Friedman T H,Robert Tibshirani. Regularization Paths for Generalized Linear Models via Coordinate Descent[J]. Journal of Statistical Software,2010,33(1):1-22. [12] Xavier Robin N T,Alexandre Hainard,et al. pROC: an open-source package for R and S+ to analyze and compare ROC curves[J]. BMC Bioinformatics,2011,12(77):1471-2105. [13] Rossier B C,Bochud M,Devuyst O. The Hypertension Pandemic: An Evolutionary Perspective[J]. Physiology (Bethesda),2017,32(2):112-125. [14] Gao Q,Xu L,Cai J. New drug targets for hypertension: A literature review[J]. Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease,2021,1867(3):166037-166037. doi: 10.1016/j.bbadis.2020.166037 [15] 王浩,张振贤. 心室重构发病机制及中医药防治的研究概述[J]. 中医药信息,2021,38(8):70-75. doi: 10.19656/j.cnki.1002-2406.210813 [16] Park S,Lim W,Bazer F W,et al. Quercetin inhibits proliferation of endometriosis regulating cyclin D1 and its target microRNAs in vitro and in vivo[J]. J Nutr Biochem,2019,63:87-100. doi: 10.1016/j.jnutbio.2018.09.024 [17] Kehat I,Davis J,Tiburcy M,et al. Extracellular signal-regulated kinases 1 and 2 regulate the balance between eccentric and concentric cardiac growth[J]. Circ Res,2011,108(2):176-183. doi: 10.1161/CIRCRESAHA.110.231514 [18] Mutlak M,Kehat I. Extracellular signal-regulated kinases 1/2 as regulators of cardiac hypertrophy[J]. Front Pharmacol,2015,6:149-149. [19] Simons K H,De Jong A,Jukema J W,et al. T cell co-stimulation and co-inhibition in cardiovascular disease: a double-edged sword[J]. Nat Rev Cardiol,2019,16(6):325-343. doi: 10.1038/s41569-019-0164-7 [20] Crouch S H,Botha-le roux S,Delles C,et al. Inflammation and hypertension development: A longitudinal analysis of the African-PREDICT study[J]. Int J Cardiol Hypertens,2020,7:100067-100067. doi: 10.1016/j.ijchy.2020.100067 [21] Oparil S,Acelajado M C,Bakris G L,et al. Hypertension[J]. Nat Rev Dis Primers,2018,4:18014-18014. doi: 10.1038/nrdp.2018.14 [22] Abbas A K T E,R Simeonov D,Marson A,et al. Revisiting IL-2: Biology and therapeutic prospects[J]. Sci Immunol,2018,3(25):1482. doi: 10.1126/sciimmunol.aat1482 [23] Zeng Z,Yu K,Chen L,et al. Interleukin-2/Anti-Interleukin-2 Immune Complex Attenuates Cardiac Remodeling after Myocardial Infarction through Expansion of Regulatory T Cells[J]. J Immunol Res,2016,2016:8493767. [24] Li J,Gao K,Cai S,et al. Germline de novo variants in CSNK2B in Chinese patients with epilepsy[J]. Sci Rep,2019,9(1):17909. doi: 10.1038/s41598-019-53484-9 [25] Stengel S T,Fazio A,Lipinski S,et al. Activating transcription factor 6 mediates inflammatory signals in intestinal epithelial cells upon endoplasmic reticulum stress[J]. Gastroenterology,2020,159(4):1357-1374. doi: 10.1053/j.gastro.2020.06.088 [26] Naiel S C R,Lu C,Tat V,et al. Endoplasmic reticulum stress inhibition blunts the development of essential hypertension in the spontaneously hypertensive rat[J]. Am J Physiol Heart Circ Physiol,2019,316(5):H1214-H1223. doi: 10.1152/ajpheart.00523.2018 [27] Liu G,Wu F,Jiang X,et al. Inactivation of Cys(674) in SERCA2 increases BP by inducing endoplasmic reticulum stress and soluble epoxide hydrolase[J]. Br J Pharmacol,2020,177(8):1793-1805. doi: 10.1111/bph.14937 [28] Zhang B,Zhang P,Tan Y,et al. C1q-TNF-related protein-3 attenuates pressure overload-induced cardiac hypertrophy by suppressing the p38/CREB pathway and p38-induced ER stress[J]. Cell Death Dis,2019,10(7):520. doi: 10.1038/s41419-019-1749-0 [29] Blackwood E A,Hofmann C,Santo Domingo M,et al. ATF6 Regulates Cardiac Hypertrophy by Transcriptional Induction of the mTORC1 Activator,Rheb[J]. Circ Res,2019,124(1):79-93. doi: 10.1161/CIRCRESAHA.118.313854 [30] Bartolomaeus H,Balogh A,Yakoub M,et al. Short-chain fatty acid propionate protects from hypertensive cardiovascular damage[J]. Circulation,2019,139(11):1407-1421. doi: 10.1161/CIRCULATIONAHA.118.036652 [31] Li N,Xiao H,Shen J,et al. SELE gene as a characteristic prognostic biomarker of colorectal cancer[J]. J Int Med Res,2021,49(4):3000605211004386. [32] Srivastava K,Chandra S,Narang R,et al. E-selectin gene in essential hypertension: a case-control study[J]. Eur J Clin Invest,2018,48(1):n/a-1. [33] Ouyang Y,Wu H,Tan A,et al. E-selectin gene polymorphism (A561C) and essential hypertension. Meta-analysis in the Chinese population[J]. Herz,2015,40(Suppl 2):197-202. -
点击查看大图
计量
- 文章访问数: 1260
- HTML全文浏览量: 785
- PDF下载量: 6
- 被引次数: 0
