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Volume 41 Issue 4
Apr.  2023
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Article Navigation >  Journal of Pharmaceutical Practice and Service  > 2023  >  41(4): 245-251

HU Bingyue, ZHANG Xinkang, CEN Jinfeng, LV Chongning, LU Jincai, XIAO Kai. Discussion on the chemical compositions and treatment mechanism of sepsis of Sonchus arvensis L. by network pharmacology[J]. Journal of Pharmaceutical Practice and Service, 2023, 41(4): 245-251. doi: 10.12206/j.issn.2097-2024.202301001
Citation: HU Bingyue, ZHANG Xinkang, CEN Jinfeng, LV Chongning, LU Jincai, XIAO Kai. Discussion on the chemical compositions and treatment mechanism of sepsis of Sonchus arvensis L. by network pharmacology[J]. Journal of Pharmaceutical Practice and Service, 2023, 41(4): 245-251. doi: 10.12206/j.issn.2097-2024.202301001
shu

Discussion on the chemical compositions and treatment mechanism of sepsis of Sonchus arvensis L. by network pharmacology

doi: 10.12206/j.issn.2097-2024.202301001
  • 1.

    School of Chinese Materia Medica, Shenyang Pharmaceutical University, Shenyang 110016, China

  • 2.

    Lab of Toxicology & Pharmacology, Faculty of Naval Medicine, Naval Medical University, Shanghai 200433, China

  • Received Date: 2023-01-01
  • Rev Recd Date: 2023-02-17
  • Publish Date: 2023-04-25
  •   Objective  To explore the effective constituents from Sonchus arvensis L. and the potential mechanism in treating sepsis by network pharmacology.   Methods  The chemical ingredients reported in the literature were taken as research objects and Swiss Target Prediction database was used to collect the identify the potential targets of those ingredients. The GeneCards, OMIM and TTD databases were applied to screen the sepsis related molecular targets. The cross targets were obtained and used to construct the active ingredient-disease target network. In addition, the targets were also imported into STRING database to construct a PPI network. Finally, GO and KEGG enrichment analysis were performed on the target genes to predict the mechanism via DAVID database.   Results  71 components from S. arvensis L. were obtained, which corresponded to 579 potential drug targets. There were 3437 related targets of sepsis. S. arvensis L. and sepsis shared 272 common targets. The results showed that 1366 terms were found by GO function enrichment, including 245 molecular functions (MF), 1002 biological processes (BP), and 119 cell composition (CC), The KEGG enrichment analysis suggested that 166 signaling pathways were involved.   Conclusion  The study revealed that TNF, AKT1, IL-6, IL-1β, TP53 and some other targets might be affected by potentially active ingredients of S arvensis L. such as linoleic acid, linolenic acid and oleic acid to regulate the expression of steroids, sphingolipids hormones as well as epidermal factors and chemokines in treating sepsis.
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Discussion on the chemical compositions and treatment mechanism of sepsis of Sonchus arvensis L. by network pharmacology

doi: 10.12206/j.issn.2097-2024.202301001
  • 1. School of Chinese Materia Medica, Shenyang Pharmaceutical University, Shenyang 110016, China
  • 2. Lab of Toxicology & Pharmacology, Faculty of Naval Medicine, Naval Medical University, Shanghai 200433, China
  • Received Date: 2023-01-01
  • Rev Recd Date: 2023-02-17
  • Publish Date: 2023-04-25

Abstract:   Objective  To explore the effective constituents from Sonchus arvensis L. and the potential mechanism in treating sepsis by network pharmacology.   Methods  The chemical ingredients reported in the literature were taken as research objects and Swiss Target Prediction database was used to collect the identify the potential targets of those ingredients. The GeneCards, OMIM and TTD databases were applied to screen the sepsis related molecular targets. The cross targets were obtained and used to construct the active ingredient-disease target network. In addition, the targets were also imported into STRING database to construct a PPI network. Finally, GO and KEGG enrichment analysis were performed on the target genes to predict the mechanism via DAVID database.   Results  71 components from S. arvensis L. were obtained, which corresponded to 579 potential drug targets. There were 3437 related targets of sepsis. S. arvensis L. and sepsis shared 272 common targets. The results showed that 1366 terms were found by GO function enrichment, including 245 molecular functions (MF), 1002 biological processes (BP), and 119 cell composition (CC), The KEGG enrichment analysis suggested that 166 signaling pathways were involved.   Conclusion  The study revealed that TNF, AKT1, IL-6, IL-1β, TP53 and some other targets might be affected by potentially active ingredients of S arvensis L. such as linoleic acid, linolenic acid and oleic acid to regulate the expression of steroids, sphingolipids hormones as well as epidermal factors and chemokines in treating sepsis.

HU Bingyue, ZHANG Xinkang, CEN Jinfeng, LV Chongning, LU Jincai, XIAO Kai. Discussion on the chemical compositions and treatment mechanism of sepsis of Sonchus arvensis L. by network pharmacology[J]. Journal of Pharmaceutical Practice and Service, 2023, 41(4): 245-251. doi: 10.12206/j.issn.2097-2024.202301001
Citation: HU Bingyue, ZHANG Xinkang, CEN Jinfeng, LV Chongning, LU Jincai, XIAO Kai. Discussion on the chemical compositions and treatment mechanism of sepsis of Sonchus arvensis L. by network pharmacology[J]. Journal of Pharmaceutical Practice and Service, 2023, 41(4): 245-251. doi: 10.12206/j.issn.2097-2024.202301001
shu

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  • 脓毒血症（sepsis）是指由细菌等病原微生物侵入机体引起的全身炎症反应综合征[1-2]。机体产生脓毒血症时会出现免疫功能失衡的现象，即抗炎反应和促炎反应交替发生，多器官炎症同时爆发最终引起炎症风暴[3-4]。中医上将炎症称为热症或湿热火毒，主要使用清热解毒或清湿热类的药物。目前对于脓毒血症缺乏专门的预防和治疗方法[5]，寻找治疗脓毒血症的新方法及作用机制是医学亟待解决的问题[6]。

    苣荬菜（Sonchus arvensis L.）是菊科苦苣菜属的干燥全草，茎直立，全株有白色乳汁，又称败酱草、曲麻菜，最早记载于《植物名实图考》，性寒，味苦，具有清热解毒，补虚止咳的功效，常用于治疗虚弱咳嗽，咽喉肿痛及菌痢等疾病[7-8]。其嫩茎可食用，属于药食同源的植物。苣荬菜分布范围十分广泛，在西北、华北、东北地区都有生长。苣荬菜中主要含有烷烃类、倍半萜类[8]、三萜类[9-10]、黄酮类[11]等化学成分。研究发现，苣荬菜内化学成分具有抗炎的作用[12-13]，但苣荬菜治疗脓毒血症的原理及机制研究尚未见报道。

    网络药理学作为生物信息学的新型工具，其“多成分、多途径、多靶点”的研究方法可高效预测植物化学成分的作用机制[14]。基于此，本研究利用网络药理学预测苣荬菜中潜在作用靶点及其机制，为改善脓毒血症提供新的思路。

    • 1.   材料与方法

      1.1.   苣荬菜化学成分及靶点获取

    • 以Sonchus arvensis L.为关键词在中国知网、Pubmed和Web of sciences数据库中检索，挖掘苣荬菜的成分信息，结合本实验室的化学分离情况[15]确定苣荬菜总成分。将收集到的成分依次通过 Pubmed 数据库检索，得到成分的Smiles结构和2D结构的SDF格式文件。将获得的苣荬菜成分Smiles结构或2D结构的SDF格式文件导入Swiss Target Prediction数据库，预测其作用靶点，然后使用Uniprot数据库对化合物靶点进行校对。

      • 1.2.   脓毒血症相关基因的查找及药物-疾病交集基因的获取

    • 使用 GeneCards （https://www.genecards.org/）、OMIM（https://omim.org/）、TTD（http://db.idrblab.net/ttd/）数据库，以“sepsis”为关键词进行疾病相关基因查询。将各数据库得到的疾病相关基因进行合并去重以得到疾病的靶基因。将去重后的疾病靶点与化学成分预测的潜在靶点共同输入Venny 2.1.0（https://bioinfogp.cnb.csic.es/tools/venny/index.html）网站中获取成分靶点与疾病靶点的交集，即得到苣荬菜治疗脓毒血症的潜在靶点。

      • 1.3.   化合物靶点网络及蛋白质-蛋白质相互作用（PPI）网络构建

    • 将苣荬菜的成分及潜在靶点导入Cytoscape 3.8.2软件中，绘制“成分-潜在靶点”网络图。将交集靶点导入到STRING数据库（https://cn.string-db.org/），选择物种为“Home sapiens”，最低相互作用阈值为0.4，得到PPI网络图，再运用Cytoscape 3.8.2 软件将PPI网络进行可视化处理。

      • 1.4.   交集靶点富集分析

    • 将成分与疾病的交集靶点输入DAVID（https://david.ncifcrf.gov/）数据库中进行基因本体（GO）功能及京都基因与基因组百科全书（KEGG）通路富集分析。将结果以P值从小到大进行排序，分别筛选前15条绘制气泡图。

    2.   结果

      2.1.   苣荬菜化学成分及潜在靶点筛选

    • 通过文献挖掘及结合本课题组分离实验结果共得到71种化学成分，成分对应的潜在靶点579个。具体信息见表1。

      成分编号成分名称成分编号成分名称
      SA1taraxasterol[10]SA371-tricosanol[16]
      SA2β-Amyrin[10]SA382-vinylnaphthalene[16]
      SA3α-Amyrin[10]SA39methyl laurate[16]
      SA4lupeol[10]SA40linolenic acid[16]
      SA5psi-taraxasterol[10]SA41phytol[16]
      SA6taraxasterol acetate[17]SA42linoleic acid[16]
      SA7β-sitosterol[18]SA43methyl linoleate[16]
      SA8daucosterol[18]SA44rutin[19]
      SA9santamarine[17]SA45stigmasterol[20]
      SA10fraxetin[21]SA46ursolic acid[20]
      SA11fraxetin[21]SA47α-amyrenyl acetate[20]
      SA12apigenin[20]SA48β-amyrin acetate[20]
      SA13apigenin-7-O-glucuronide[19]SA49esculetin[22]
      SA14acacetin[23]SA50emodin[22]
      SA15linarin[10]SA511-heptacosanol[15]
      SA16luteolin[20]SA52β-amyrenone[15]
      SA17luteolin-7- O- glucopyranoside[19]SA53taraxasterone[15]
      SA18luteolin 7-galacturonide[10]SA54squalene[15]
      SA19lonicerin[10]SA55taraxeryl acetate[15]
      SA20chrysoeriol[23]SA56balansenate I[15]
      SA21kaempferide[23]SA57dioctyl phthalate[15]
      SA22isorhamnetin[23]SA58stigmast-4-en-3-one[15]
      SA23isorhamnetin-3-O-galactoside[10]SA592,4-di-tert-butylphenol[15]
      SA24quercetin[19]SA60oleic acid[15]
      SA25hyperoside[10]SA61ethyl 4-hydroxyphenylacetate[15]
      SA26friedelin[9]SA62dihydroreynosin[15]
      SA27taraxerylacetate[9]SA63ethyl2-(3,4-dihydroxyphenyl)acetate[15]
      SA28β-amyrone[9]SA64caffeic acid ethyl ester[15]
      SA29bauerenyl acetate[9]SA65syringaresinol[15]
      SA30oleanane[9]SA66phytenoic acid[15]
      SA31palmitic acid[19]SA671-heneicosanol[15]
      SA32methyl hexadecanoate[16]SA68lauric acid[15]
      SA33chrysanthenone[16]SA69dammarenediol II[15]
      SA34anisole[16]SA7011β,13-dihydro-santamarin[15]
      SA35methyl-14
      methylpentadecanoate[16]      		SA711-hexacosanol[15]
      SA36eugenol[16]

      • 2.2.   苣荬菜成分靶点和疾病相关靶点筛选

    • 将苣荬菜成分的Smiles号或2D结构输入SwissTarget Prediction数据库中，以probability>0为筛选依据，共得到苣荬菜成分潜在靶点579个。

      在OMIM、GeneCards、TTD数据库检索得到脓毒血症相关靶点，合并去重后共得到3437个靶点。将579个成分靶点和3437个疾病靶点绘制韦恩图，共得到272个交集靶点（见图1）。

      • 2.3.   “化学成分-潜在靶点”网络分析

    • 将苣荬菜的化学成分潜在靶点导入Cytoscape 3.8.2软件中进行可视化分析，获取“化学成分-潜在靶点”网络图(见图2)。其中包括71个成分节点，用菱形表示；272个成分潜在靶点，用长方形表示。化学成分根据度值前5个从大到小排序为：亚油酸、亚麻酸、二氢炔诺酸、油酸、14-甲基十五烷酸甲酯。相关靶点根据度值排序前5个分别是TNF（肿瘤坏死因子）、AKT1（丝氨酸/苏氨酸激酶）、IL-6（白介素-6）、IL-1β（白介素-1β）、TP53（肿瘤蛋白P53）。

      • 2.4.   PPI网络分析

    • STRING数据库中得到PPI网络图（图3），共有272个节点、4464条边，平均节点度值为32.8，其中排名前10的靶点为TNF、AKT1、IL-6、IL-1β、TP53、VEGFA（血管内皮生长因子A）、MAPK3（人丝裂原活化蛋白激酶3）、EGFR（人表皮生长因子受体）、SRC（非受体酪氨酸激酶）、STAT3（信号转导及转录激活蛋白3）。

      • 2.5.   GO基因功能富集分析和KEGG富集分析结果

      2.5.1.   GO基因功能富集分析

    • 将苣荬菜化学成分相关靶点和疾病的交集靶点输入DAVID数据库进行GO基因功能富集分析，得到GO富集条目1366个，其中涉及生物过程（BP）有关条目1002条，包括蛋白质磷酸化、炎症反应、胞浆钙离子浓度的正调节、药物反应等多种功能；涉及细胞组成（CC）有关条目119条，包括质膜、胞液、细胞质、高分子复合物、薄膜筏等；涉及分子功能（MF）相关条目245条，包括酶结合位点、蛋白激酶活性、同一蛋白质结合、RNA聚合酶II转录因子活性、配体活化序列特异性DNA结合、跨膜受体蛋白酪氨酸激酶活性等多种功能。各类富集评分值排名前5的通路见图4。

      • 2.5.2.   KEGG通路富集分析

    • 将苣荬菜化学成分相关靶点和疾病的交集靶点输入DAVID数据库进行KEGG基因功能富集分析，得到富集通路166个，对其P值排名前15的通路进行可视化分析，绘制气泡图（见图5）。结果显示靶点主要富集于癌症通路、脂质与动脉粥样硬化、人巨细胞病毒感染、HIF-1信号通路、卡波西肉瘤相关疱疹病毒感染、鞘磷脂信号通路、化学致癌-活性氧、C型凝集素受体信号通路、趋化因子信号通路、类固醇激素生物合成、催乳素信号通路等多条通路上。

    3.   讨论

    • 《黄帝内经·热论》曰：“今夫热病者，皆伤寒之类也”。脓毒血症发病病因是外感或内伤温热毒邪，传于脏腑，属于热病、温病的范畴。在治疗上应该以清热解毒，凉血化瘀为主。脓毒血症在发病过程中炎症因子或内毒素会随血液循环到达各脏器，引起多器官的衰竭及凝血功能障碍，严重时会危及生命。临床病理特征主要表现为TNF、IL-6、IL-1β、COX-2和iNOS等促炎因子的增加。

      本研究在文献数据挖掘的基础上基于网络药理学对苣荬菜中化学成分的相关靶点及脓毒血症相关靶点进行预测，并进行一系列的计算分析。结果发现苣荬菜化学成分有71个，相关靶点579个，疾病靶点3437个，交集靶点272个。其中主要的化学成分是亚油酸、亚麻酸、二氢炔诺酸、油酸等，其主要作用靶点为TNF、AKT1、IL-6、IL-1β等。

      亚油酸可以与一氧化氮和亚硝酸盐衍生物反应生成亲电脂肪酸硝基烯烃衍生物，具有抗炎特性。实验研究发现小鼠全身给药亚油酸硝基化物后降低中性粒细胞和单核细胞对脂多糖的动员反应，减弱5-羟基二十碳烯酸的形成抑制肺损伤[24]。并且亚油酸作为ω-3型的不饱和脂肪酸可以通过下调COX-2、一氧化氮合酶（NOS）和TNF-α的表达来缓解血管内皮紊乱[25]。龙碧莹[26]研究了油酸对脂多糖诱导的小鼠巨噬细胞的炎症作用，利用EDU细胞增殖等实验研究发现，油酸可以通过调控NF-κB通路抑制炎症因子的表达。

      TNF是肿瘤坏死因子家族中一种可以引起细胞死亡或细胞凋亡的细胞因子。在脓毒血症实验中主要研究由巨噬细胞产生的TNF-α。IL-6是一种炎症介质，与细胞膜上的IL-6R和糖蛋白gp130结合激活信号通路。IL-1β是IL-1家族的重要成员，可以上调黏附分子，促进淋巴细胞的聚集，激活免疫细胞，产生强促炎活性。当TNF-α与TNFR1受体结合时会产生促炎作用，与IL-6等炎症因子协同导致炎症反应；同时，TNF-α也会协同IL-1等因子产生炎症风暴，导致组织损伤或心肌阻滞等[27]。

      脓毒血症中宿主的防御反应主要体现在炎症[28]，表现为炎症细胞会分泌细胞因子和免疫趋化因子作用于癌症信号通路，影响肿瘤微环境。NF-κB通路在癌症信号通路中起着重要作用。NF-κB是炎症相关基因的重要转录调节因子，该通路激活后会增加IL-6和TNF-α等细胞因子的分泌，产生病理性损伤[29]。药物可以通过调控NF-κB，减轻脓毒血症大鼠中炎症因子的表达，从而减轻炎症反应[30]。

      另外，在炎症条件下，活性氧、肝素酶和其他蛋白酶会破坏血管内皮细胞中的糖萼结构，使其产生脱落，进而使E-选择素、细胞间黏附分子1和其他黏附分子暴露在血管内皮中，同时召集血小板和中性粒细胞，形成血栓和纤维蛋白[31]。杨汉东等[32]研究证明TNF-α可通过调控丝氨酸/苏氨酸蛋白激酶来促进血管内皮VEGFA的增生，转录过程与脂质和动脉粥样硬化通路相关。

      综上，本研究在收集苣荬菜化学成分的基础上，运用网络药理学的方法预测其抗脓毒血症的靶点及作用机制，发现亚油酸、亚麻酸、二氢炔诺酸、油酸和14-甲基十五烷酸甲酯等抗脓毒血症的活性成分通过作用于TNF、AKT1、IL-6、IL-1β、TP53等潜在关键靶点以及癌症和脂质与动脉粥样硬化等重要的相关通路发挥其抗炎作用，为进一步开展苣荬菜抗脓毒血症的临床应用提供依据。

      本研究依然存在很多不足之处，如苣荬菜化学成分相关报道较少，化学成分相关靶点预测精确度存在局限等，不能完整地反映苣荬菜抗脓毒血症的整体情况，需要进一步的实验验证。

Reference (32)

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