多维度多组分干预脑缺血药理机制的比较研究
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摘要
研究背景：
脑缺血是涉及一系列生物化学和分子机制变化的多基因、多通路和多靶点的复杂疾病。研究表明,许多基因、蛋白质和信号通路,如MAPK、PUK/Akt、 NF-κB、JAK-STAT和Wnt/beta-catenin等通路都与脑缺血再灌注损伤有关。网络药理学的一般观点认为,复杂疾病的治疗应该瞄准整个疾病网络,在疾病网络中只有同时调节多个靶蛋白才能有效地干扰疾病表型。药物作用于多个靶点,其疗效应该是干预复杂的靶点网络的结果。然而,目前大多数药物治疗脑缺血再灌注损伤的研究都仅局限于单一的靶点或通路,很少有人从多基因、多靶点、多通路和多层面来探索药物作用机制的,尚缺乏分子网络水平的认识。在生物网络中,模块性(modularity)是一种普遍存在的现象,探索模块化结构被认为是理解生物系统复杂性的一个关键因素。因此,要从网络水平研究疾病病理和药物作用机制,最关键一步就是要在疾病或药物相关网络中识别出具有相应生物学功能的功能模块(functional module)。因此,如何有效地识别这些模块成为了网络药理学研究中的热点和难点问题。同时,模块识别也成为模块药理学(modular pharmacology, MP)系统分析框架中的最基础的一个步骤。目前,学者们已经提出了大量的模块识别方法,虽然方法很多,但由于缺乏合理、统一的分类标准,实际应用中还存在很多问题和困难。因此,本研究前期收集了大量文献资料,初步确立了一个理论上较为合理的分类标准对现有的模块识别方法进行分类。此外,由于事先我们并不知道某个给定网络中所包含的功能模块的具体数量,因此,用不同的方法对同一个网络进行模块识别时,常常会产生不同的结果,那么,如何去判断模块识别结果的优劣就成为了一个非常棘手的问题。本研究中,我们应用不同的方法对几个网络进行分解,试图对模块识别结果的优劣进行判断。
另外,我们课题组前期已经开展了一系列关于精制清开灵干预脑缺血再灌注损伤的研究,采用精制清开灵的4种单一组分黄芩苷(baicalin, BA)、栀子苷(jasminoidin, JA)、胆酸(cholic acid, CA)和珍珠母(concha margaritiferausta, CM)以及其组分配伍对脑缺血模型进行药物干预,通过基因芯片技术进行全基因表达谱的检测,来研究单一有效组分及其配伍治疗脑缺血再灌注损伤的药效学和药物作用机制。药效学结果显示,与模型组(vehicle)相比,除CM外,BA、CA和JA均能有效减少脑梗死体积(P<0.05)。在前期研究基础上,本研究拟进一步深入揭示BA、CA和JA三种单一有效组分治疗脑缺血再灌注损伤的药理作用机制,并对实验结果进行分析,以期初步回答上文所述的问题。
研究目的：
本研究的目的是要从信号通路、网络和功能模块三个不同的维度揭示不同的有效组分治疗脑缺血再灌注损伤的药理作用机制的相似性和差异性。
研究方法：
本研究以黄芩苷(BA)、栀子苷(JA)、胆酸(CA)三种单一有效组分干预小鼠大脑中动脉闭塞(middle cerebral artery occlusion, MCAO)模型的基因芯片实验为基础,首先借助Ingenuity Pathway Analysis (IPA)分析系统来寻找这三种组分干预脑缺血所影响到的信号通路和网络。其次,通过整合基因表达数据和蛋白质相互作用数据分别构建出BA, CA、JA相应的药物靶点网络,分别采用仿射传播聚类(Affinity propagation, AP)、马尔可夫聚类算法(Markov Cluster algorithm, MCL)和Molecular Complex Detection (MCODE)三种方法对上述网络进行模块识别,以“最小网络结构熵”为标准对结果进行判断,从而确定该网络的相对最优的模块识别结果。再用DAVID6.7软件对识别出来的模块进行GO功能富集分析。最后采用RT-PCR和Western blot的方法,分别从基因水平和蛋白水平对其中的两个功能模块(Mrml-Gukl-Hrsp12和Fos-Cebpg-Atf2)进行生物学实验验证。
研究结果：
1IPA网络分析在vehicle组与sham组、BA组与vehicle组、CA组与vehicle组、JA组与vehicle组之间分别识别出5个、8个、11个和9个有统计学意义的网络(P<0.05)。
2IPA网络功能分析在vehicle组与sham组、BA组与vehicle组、CA组与vehicle组、JA组与vehicle组之间分别识别出78个、70个、77个和74个有统计学意义的生物学功能(P<0.05)。BA、CA和JA三组共同重叠的功能有60个,它们可分为疾病和紊乱、分子和细胞功能、生理系统发育和功能三大类,各类所占比例均为1/3。
3IPA通路分析在vehicle组与sham组、BA组与vehicle组、CA组与vehicle组、JA组与vehicle组之间分别识别出6条、7条、40条和16条有统计学意义的canonical通路(P<0.05)。BA组与CA组、CA组与JA组共同重叠的通路分别有4条和7条。BA、CA和JA都影响了生长因子相关通路,包括EGF信号、PDGF信号、IGF-1信号。BA、CA和JA都作用于Ca2+依赖的信号级联上,BA主要干预了花生四烯酸(arachidonic acid, AA)代谢；JA主要作用于NRF2介导的氧化应激反应；CA则主要影响了类花生酸(eicosanoid)信号、周期蛋白依赖性蛋白激酶5(CDK5)信号、核转录因子κB(NF-κB)激活、IL-2信号以及TNF-α和受体信号。
4Vehicle组、BA组、CA组和JA组对应的4个蛋白质相互作用网络除了在网络大小上略有差异外,在主要的拓扑参数上均没有显著的差异。
5模块识别结果显示,分别用AP、MCL和MCODE三种不同的方法对同一个网络进行模块识别,最终识别出来的功能模块在数量、尺寸大小、模块性、网络结构熵等方面都有较大的差异。根据最小网络结构熵的判断,用MCODE方法(Parameters3或4)识别出模块之后所具有的网络结构熵值最小。BA组、CA组、JA组和vehicle组对应的最小网络结构熵分别为5.33077、5.3029、5.24152和5.44995。而且BA组、CA组、JA组和vehicle组原始网络的最小网络结构熵值与随机网络相比,差异具有统计学意义(P<0.05)。最后,用MCODE方法在vehicle组、BA组、CA组和JA组对应的网络中分别识别出50个、49个、41个和42个功能模块。
6GO功能富集分析一共涉及到218个功能(即GO生物学过程)。Vehicle组、BA组、CA组和JA组分别富集到185个、153个、147个和192个GO功能。其中,四组共同重叠的功能有116个,BA、CA和JA三组共同重叠的功能只有17个,BA、CA和JA都富集到与神经递质相关的功能。BA组单独富集到L-抗坏血酸运输、神经发生等13个功能,CA组仅富集到阳离子运输1个功能。
7模块的GO功能个数与节点个数、边条数之间没有显著的线性相关。每个节点的连接百分比与其所在模块的GO功能个数之间有显著的线性相关,相关系数为0.18。各组模块之间存在不同程度的重叠。
8生物学实验结果
8.1RT-PCR结果显示,与sham组相比,vehicle组中Mrml mRNA表达水平明显下调(P<0.01)；与vehicle组相比,BA组中Mrml mRNA表达水平明显上调(P<0.01),而CA组中Mrml mRNA表达水平明显下调(P<0.01)。Gukl与Hrsp12mRNA表达水平在各组间均无明显变化(P>0.05)。
8.2Western blot结果显示,与sham组相比,vehicle组中Atf2、Fos和Cebpg蛋白水平均明显上调(P<0.01)；与vehicle组相比,JA、BA、CA三个治疗组中Fos与Cebpg蛋白水平均明显下调(P<0.05),但Atf2蛋白水平则无明显变化(P>0.05)。
8.3STRING9.0分析结果显示,Atf2与Fos之间存在表达(expression)的相互作用关系,且相互作用得分为0.800。Gukl与Hrsp12、Guk1与Mrml之间的相互作用得分分别为0.796和0.571。
结论：
1不同组分干预脑缺血再灌注损伤的药理作用机制存在共性和差异性。BA、CA和JA对脑缺血的治疗效应是通过多靶点、多通路、多网络、多模块、多功能作用实现的。
2对于揭示药物作用机制而言,信号通路、功能模块和网络三个不同维度的分析是相互补充的。在信号通路水平,BA、CA和JA影响了Ca2+依赖的信号级联的不同环节,BA干预花生四烯酸代谢,JA表现为抗氧化,CA涉及抗炎反应；在功能模块水平,BA主要涉及抗氧化和影响神经发生,CA侧重于调节阳离子运输,BA、CA和JA都影响了神经递质相关的功能。
3模块化分析方法适用于多靶点药物治疗复杂疾病的研究,其为研究药理机制提供了一种新的方法。
Background
Cerebral ischemia is a process related to a series of biochemical and molecular mechanisms involving changes of multiple genes, pathways and protein targets. It has been demonstrated that a number of genes, proteins and signaling pathways such as MAPK, PI3K/Akt, NF-κB, JAK-STAT and Wnt/beta-catenin have an impact on cerebral ischemia-reperfusion injury. Network pharmacology suggests that, targeting whole networks is maybe the key behavior of pharmacological intervention. Deletion of individual nodes has little effect on disease networks, modulating multiple proteins may be required to perturb robust phenotypes. Many drugs hit multiple targets, which exist within a complex network. The effects of the drug, both therapeutic actions and adverse events, are a result of perturbation of the complex network. However, most previous studies were limited to a single gene or a single pathway. Therefore, it is extremely important and necessary to identify functional modules in disease-and drug-associated networks accurately. Otherwise, module identification is also an essential step in the framework of Modular Pharmacology (MP) proposed recently. Currently, researchers have proposed a lot of methods or algorithms for module identification, however, there are still many problems for application, which due to lack of systematic and reasonable classification criteria. Therefore, we collected a large number of literatures, and preliminary established reasonable classification criteria to classify existing methods. Notably, we do not know the number of functional modules contained in a given network in advance, therefore, when the decomposition of the same network using different methods, it often leads to different results. Then, how can we determine the results of module identification? Previous studies have shown that BA, CA and JA were effective in reducing the ischemic infarct volume compared with the vehicle group (P<0.05). Our study intends to further reveal the pharmacological mechanisms of different compound treatments on cerebral ischemia.
Objective
The aim of our study was to reveal the overlapping and diverse pharmacological protective mechanisms of different compound treatments on cerebral ischemia at the signaling pathway, functional module and network level.
Methods
Firstly, in order to find signaling pathways and networks from gene expression profiles of hippocampus of ischemic mice treated with baicalin (BA), cholic acid(CA) and jasminoidin (JA), a microarray comprising16,463genes, FDA Arraytrack software and Ingenuity Pathway Analysis, were employed. Secondly, drug-target networks were constructed by integrating gene expression data and protein interaction data, and then functional modules were identified using Affinity propagation (AP), Markov Cluster algorithm (MCL) and Molecular Complex Detection (MCODE), respectively. Then, we could determine the relative optimal module identification result based on the minimum entropy criterion. GO functional enrichment analysis was performed with DAVID6.7software. Finally, both module (Mrml-Gukl-Hrsp12) and modules (Fos-Cebpg-Atf2) could be validated using RT-PCR and Western blot, respectively.
Results
1A total of5,8,11,9IPA networks were found in vehicle (vs sham), BA, CA and JA (vs vehicle), respectively (P<0.05).
2A total of78,70,77,74IPA network functions were found in vehicle (vs sham), BA, CA and JA (vs vehicle), respectively (P<0.05). The total of60overlapping functions could be approximately divided into diseases and disorders, molecular and cellular functions, physiological system development and function as categories with ratio of1:1:1.
3A total of6,7,40,16canonical pathways were found in vehicle ((vs sham), BA, CA and JA (vs vehicle), respectively (P<0.05).4and7overlapping pathways were shared between BA and CA, CA and JA, respectively. BA, CA and JA affected some growth factor signaling pathways, including EGF signaling, PDGF signaling, IGF-1signaling. BA, CA and JA all acted on Ca2+-dependent signaling cascades in diverse links. BA intervened in arachidonic acid metabolism. JA might decrease oxidative damage via NRF2-mediated oxidative stress response. CA mainly affected eicosanoid signaling, cyclin-dependent kinase5(CDK5) signaling, nuclear factor kappa B (NF-κB) activation, IL-2Signaling as well as TNF-a receptor signaling.
4Topological attributes of global networks among vehicle, BA, CA and JA groups were similar to each other, except that there was small difference in network size.
5After the optimization of minimum entropy,50,49,41, and42modules (nodes≥3) were identified from related target networks by MCODE analysis.
6GO functional enrichment analysis revealed218significantly enriched biological processes.185,153,147,192GO biological processes were enriched in vehicle, BA, CA and JA groups, respectively. BA, CA and JA were enriched for neurotransmitter-related functions. BA was enriched for L-ascorbic acid transport, neurogenesis, etc. CA was enriched for cation transport.
7There were no significant linear correlations between the number of GO biological processes and the number of nodes or the number of edges in modules. There was significant linear correlation between the degree of each node and the number of GO biological processes in the same module, and the correlation coefficient value was 0.18. There were different degrees of overlap between modules among groups
8The results of RT-PCR showed that, compared with the sham group, the mRNA level of Mrml was significantly down-regulated in vehicle group (P<0.01). Compared with the vehicle group, Mrml mRNA was significantly up-regulated by BA, while down-regulated by CA (P<0.01). The mRNA levels of Gukl and Hrsp12were not significantly different between groups (P>0.05). The results of Western blot showed that, compared with the sham group, the protein expression levels of Atf2, Fos, Cebpg were significantly up-regulated in vehicle group (P<0.01). Compared with the vehicle group, both Fos and Cebpg protein expression levels were significantly down-regulated by JA, BA and CA (P<0.05). There was no significant difference on Atf2protein expression among four groups (P>0.05).
Conclusion
1There were both overlapping and diverse pharmacological mechanisms of different compounds in the intervention of cerebral ischemia-reperfusion injury. BA, CA and JA exerted therapeutic effects in cerebral ischemia through multiple targets, multiple pathways, and multiple functions.
2Analysis of signaling pathways, functional modules and networks may be complementary paradigms for revealing potential pharmacological mechanisms. At signaling pathway level, BA, CA and JA all acted on Ca2+-dependent signaling cascades in diverse links. At functional module level, BA was involved in antioxidation and neurogenesis, CA focused on regulating cation transport. BA, CA and JA affected neurotransmitter-related functions.
3The method of modular analysis is suitable for multi-targeted drugs treatment on complex diseases, and provides a new method for pharmacological study in future.
脑缺血是涉及一系列生物化学和分子机制变化的多基因、多通路和多靶点的复杂疾病。研究表明,许多基因、蛋白质和信号通路,如MAPK、PUK/Akt、 NF-κB、JAK-STAT和Wnt/beta-catenin等通路都与脑缺血再灌注损伤有关。网络药理学的一般观点认为,复杂疾病的治疗应该瞄准整个疾病网络,在疾病网络中只有同时调节多个靶蛋白才能有效地干扰疾病表型。药物作用于多个靶点,其疗效应该是干预复杂的靶点网络的结果。然而,目前大多数药物治疗脑缺血再灌注损伤的研究都仅局限于单一的靶点或通路,很少有人从多基因、多靶点、多通路和多层面来探索药物作用机制的,尚缺乏分子网络水平的认识。在生物网络中,模块性(modularity)是一种普遍存在的现象,探索模块化结构被认为是理解生物系统复杂性的一个关键因素。因此,要从网络水平研究疾病病理和药物作用机制,最关键一步就是要在疾病或药物相关网络中识别出具有相应生物学功能的功能模块(functional module)。因此,如何有效地识别这些模块成为了网络药理学研究中的热点和难点问题。同时,模块识别也成为模块药理学(modular pharmacology, MP)系统分析框架中的最基础的一个步骤。目前,学者们已经提出了大量的模块识别方法,虽然方法很多,但由于缺乏合理、统一的分类标准,实际应用中还存在很多问题和困难。因此,本研究前期收集了大量文献资料,初步确立了一个理论上较为合理的分类标准对现有的模块识别方法进行分类。此外,由于事先我们并不知道某个给定网络中所包含的功能模块的具体数量,因此,用不同的方法对同一个网络进行模块识别时,常常会产生不同的结果,那么,如何去判断模块识别结果的优劣就成为了一个非常棘手的问题。本研究中,我们应用不同的方法对几个网络进行分解,试图对模块识别结果的优劣进行判断。
另外,我们课题组前期已经开展了一系列关于精制清开灵干预脑缺血再灌注损伤的研究,采用精制清开灵的4种单一组分黄芩苷(baicalin, BA)、栀子苷(jasminoidin, JA)、胆酸(cholic acid, CA)和珍珠母(concha margaritiferausta, CM)以及其组分配伍对脑缺血模型进行药物干预,通过基因芯片技术进行全基因表达谱的检测,来研究单一有效组分及其配伍治疗脑缺血再灌注损伤的药效学和药物作用机制。药效学结果显示,与模型组(vehicle)相比,除CM外,BA、CA和JA均能有效减少脑梗死体积(P<0.05)。在前期研究基础上,本研究拟进一步深入揭示BA、CA和JA三种单一有效组分治疗脑缺血再灌注损伤的药理作用机制,并对实验结果进行分析,以期初步回答上文所述的问题。
研究目的：
本研究的目的是要从信号通路、网络和功能模块三个不同的维度揭示不同的有效组分治疗脑缺血再灌注损伤的药理作用机制的相似性和差异性。
研究方法：
本研究以黄芩苷(BA)、栀子苷(JA)、胆酸(CA)三种单一有效组分干预小鼠大脑中动脉闭塞(middle cerebral artery occlusion, MCAO)模型的基因芯片实验为基础,首先借助Ingenuity Pathway Analysis (IPA)分析系统来寻找这三种组分干预脑缺血所影响到的信号通路和网络。其次,通过整合基因表达数据和蛋白质相互作用数据分别构建出BA, CA、JA相应的药物靶点网络,分别采用仿射传播聚类(Affinity propagation, AP)、马尔可夫聚类算法(Markov Cluster algorithm, MCL)和Molecular Complex Detection (MCODE)三种方法对上述网络进行模块识别,以“最小网络结构熵”为标准对结果进行判断,从而确定该网络的相对最优的模块识别结果。再用DAVID6.7软件对识别出来的模块进行GO功能富集分析。最后采用RT-PCR和Western blot的方法,分别从基因水平和蛋白水平对其中的两个功能模块(Mrml-Gukl-Hrsp12和Fos-Cebpg-Atf2)进行生物学实验验证。
研究结果：
1IPA网络分析在vehicle组与sham组、BA组与vehicle组、CA组与vehicle组、JA组与vehicle组之间分别识别出5个、8个、11个和9个有统计学意义的网络(P<0.05)。
2IPA网络功能分析在vehicle组与sham组、BA组与vehicle组、CA组与vehicle组、JA组与vehicle组之间分别识别出78个、70个、77个和74个有统计学意义的生物学功能(P<0.05)。BA、CA和JA三组共同重叠的功能有60个,它们可分为疾病和紊乱、分子和细胞功能、生理系统发育和功能三大类,各类所占比例均为1/3。
3IPA通路分析在vehicle组与sham组、BA组与vehicle组、CA组与vehicle组、JA组与vehicle组之间分别识别出6条、7条、40条和16条有统计学意义的canonical通路(P<0.05)。BA组与CA组、CA组与JA组共同重叠的通路分别有4条和7条。BA、CA和JA都影响了生长因子相关通路,包括EGF信号、PDGF信号、IGF-1信号。BA、CA和JA都作用于Ca2+依赖的信号级联上,BA主要干预了花生四烯酸(arachidonic acid, AA)代谢；JA主要作用于NRF2介导的氧化应激反应；CA则主要影响了类花生酸(eicosanoid)信号、周期蛋白依赖性蛋白激酶5(CDK5)信号、核转录因子κB(NF-κB)激活、IL-2信号以及TNF-α和受体信号。
4Vehicle组、BA组、CA组和JA组对应的4个蛋白质相互作用网络除了在网络大小上略有差异外,在主要的拓扑参数上均没有显著的差异。
5模块识别结果显示,分别用AP、MCL和MCODE三种不同的方法对同一个网络进行模块识别,最终识别出来的功能模块在数量、尺寸大小、模块性、网络结构熵等方面都有较大的差异。根据最小网络结构熵的判断,用MCODE方法(Parameters3或4)识别出模块之后所具有的网络结构熵值最小。BA组、CA组、JA组和vehicle组对应的最小网络结构熵分别为5.33077、5.3029、5.24152和5.44995。而且BA组、CA组、JA组和vehicle组原始网络的最小网络结构熵值与随机网络相比,差异具有统计学意义(P<0.05)。最后,用MCODE方法在vehicle组、BA组、CA组和JA组对应的网络中分别识别出50个、49个、41个和42个功能模块。
6GO功能富集分析一共涉及到218个功能(即GO生物学过程)。Vehicle组、BA组、CA组和JA组分别富集到185个、153个、147个和192个GO功能。其中,四组共同重叠的功能有116个,BA、CA和JA三组共同重叠的功能只有17个,BA、CA和JA都富集到与神经递质相关的功能。BA组单独富集到L-抗坏血酸运输、神经发生等13个功能,CA组仅富集到阳离子运输1个功能。
7模块的GO功能个数与节点个数、边条数之间没有显著的线性相关。每个节点的连接百分比与其所在模块的GO功能个数之间有显著的线性相关,相关系数为0.18。各组模块之间存在不同程度的重叠。
8生物学实验结果
8.1RT-PCR结果显示,与sham组相比,vehicle组中Mrml mRNA表达水平明显下调(P<0.01)；与vehicle组相比,BA组中Mrml mRNA表达水平明显上调(P<0.01),而CA组中Mrml mRNA表达水平明显下调(P<0.01)。Gukl与Hrsp12mRNA表达水平在各组间均无明显变化(P>0.05)。
8.2Western blot结果显示,与sham组相比,vehicle组中Atf2、Fos和Cebpg蛋白水平均明显上调(P<0.01)；与vehicle组相比,JA、BA、CA三个治疗组中Fos与Cebpg蛋白水平均明显下调(P<0.05),但Atf2蛋白水平则无明显变化(P>0.05)。
8.3STRING9.0分析结果显示,Atf2与Fos之间存在表达(expression)的相互作用关系,且相互作用得分为0.800。Gukl与Hrsp12、Guk1与Mrml之间的相互作用得分分别为0.796和0.571。
结论：
1不同组分干预脑缺血再灌注损伤的药理作用机制存在共性和差异性。BA、CA和JA对脑缺血的治疗效应是通过多靶点、多通路、多网络、多模块、多功能作用实现的。
2对于揭示药物作用机制而言,信号通路、功能模块和网络三个不同维度的分析是相互补充的。在信号通路水平,BA、CA和JA影响了Ca2+依赖的信号级联的不同环节,BA干预花生四烯酸代谢,JA表现为抗氧化,CA涉及抗炎反应；在功能模块水平,BA主要涉及抗氧化和影响神经发生,CA侧重于调节阳离子运输,BA、CA和JA都影响了神经递质相关的功能。
3模块化分析方法适用于多靶点药物治疗复杂疾病的研究,其为研究药理机制提供了一种新的方法。
Background
Cerebral ischemia is a process related to a series of biochemical and molecular mechanisms involving changes of multiple genes, pathways and protein targets. It has been demonstrated that a number of genes, proteins and signaling pathways such as MAPK, PI3K/Akt, NF-κB, JAK-STAT and Wnt/beta-catenin have an impact on cerebral ischemia-reperfusion injury. Network pharmacology suggests that, targeting whole networks is maybe the key behavior of pharmacological intervention. Deletion of individual nodes has little effect on disease networks, modulating multiple proteins may be required to perturb robust phenotypes. Many drugs hit multiple targets, which exist within a complex network. The effects of the drug, both therapeutic actions and adverse events, are a result of perturbation of the complex network. However, most previous studies were limited to a single gene or a single pathway. Therefore, it is extremely important and necessary to identify functional modules in disease-and drug-associated networks accurately. Otherwise, module identification is also an essential step in the framework of Modular Pharmacology (MP) proposed recently. Currently, researchers have proposed a lot of methods or algorithms for module identification, however, there are still many problems for application, which due to lack of systematic and reasonable classification criteria. Therefore, we collected a large number of literatures, and preliminary established reasonable classification criteria to classify existing methods. Notably, we do not know the number of functional modules contained in a given network in advance, therefore, when the decomposition of the same network using different methods, it often leads to different results. Then, how can we determine the results of module identification? Previous studies have shown that BA, CA and JA were effective in reducing the ischemic infarct volume compared with the vehicle group (P<0.05). Our study intends to further reveal the pharmacological mechanisms of different compound treatments on cerebral ischemia.
Objective
The aim of our study was to reveal the overlapping and diverse pharmacological protective mechanisms of different compound treatments on cerebral ischemia at the signaling pathway, functional module and network level.
Methods
Firstly, in order to find signaling pathways and networks from gene expression profiles of hippocampus of ischemic mice treated with baicalin (BA), cholic acid(CA) and jasminoidin (JA), a microarray comprising16,463genes, FDA Arraytrack software and Ingenuity Pathway Analysis, were employed. Secondly, drug-target networks were constructed by integrating gene expression data and protein interaction data, and then functional modules were identified using Affinity propagation (AP), Markov Cluster algorithm (MCL) and Molecular Complex Detection (MCODE), respectively. Then, we could determine the relative optimal module identification result based on the minimum entropy criterion. GO functional enrichment analysis was performed with DAVID6.7software. Finally, both module (Mrml-Gukl-Hrsp12) and modules (Fos-Cebpg-Atf2) could be validated using RT-PCR and Western blot, respectively.
Results
1A total of5,8,11,9IPA networks were found in vehicle (vs sham), BA, CA and JA (vs vehicle), respectively (P<0.05).
2A total of78,70,77,74IPA network functions were found in vehicle (vs sham), BA, CA and JA (vs vehicle), respectively (P<0.05). The total of60overlapping functions could be approximately divided into diseases and disorders, molecular and cellular functions, physiological system development and function as categories with ratio of1:1:1.
3A total of6,7,40,16canonical pathways were found in vehicle ((vs sham), BA, CA and JA (vs vehicle), respectively (P<0.05).4and7overlapping pathways were shared between BA and CA, CA and JA, respectively. BA, CA and JA affected some growth factor signaling pathways, including EGF signaling, PDGF signaling, IGF-1signaling. BA, CA and JA all acted on Ca2+-dependent signaling cascades in diverse links. BA intervened in arachidonic acid metabolism. JA might decrease oxidative damage via NRF2-mediated oxidative stress response. CA mainly affected eicosanoid signaling, cyclin-dependent kinase5(CDK5) signaling, nuclear factor kappa B (NF-κB) activation, IL-2Signaling as well as TNF-a receptor signaling.
4Topological attributes of global networks among vehicle, BA, CA and JA groups were similar to each other, except that there was small difference in network size.
5After the optimization of minimum entropy,50,49,41, and42modules (nodes≥3) were identified from related target networks by MCODE analysis.
6GO functional enrichment analysis revealed218significantly enriched biological processes.185,153,147,192GO biological processes were enriched in vehicle, BA, CA and JA groups, respectively. BA, CA and JA were enriched for neurotransmitter-related functions. BA was enriched for L-ascorbic acid transport, neurogenesis, etc. CA was enriched for cation transport.
7There were no significant linear correlations between the number of GO biological processes and the number of nodes or the number of edges in modules. There was significant linear correlation between the degree of each node and the number of GO biological processes in the same module, and the correlation coefficient value was 0.18. There were different degrees of overlap between modules among groups
8The results of RT-PCR showed that, compared with the sham group, the mRNA level of Mrml was significantly down-regulated in vehicle group (P<0.01). Compared with the vehicle group, Mrml mRNA was significantly up-regulated by BA, while down-regulated by CA (P<0.01). The mRNA levels of Gukl and Hrsp12were not significantly different between groups (P>0.05). The results of Western blot showed that, compared with the sham group, the protein expression levels of Atf2, Fos, Cebpg were significantly up-regulated in vehicle group (P<0.01). Compared with the vehicle group, both Fos and Cebpg protein expression levels were significantly down-regulated by JA, BA and CA (P<0.05). There was no significant difference on Atf2protein expression among four groups (P>0.05).
Conclusion
1There were both overlapping and diverse pharmacological mechanisms of different compounds in the intervention of cerebral ischemia-reperfusion injury. BA, CA and JA exerted therapeutic effects in cerebral ischemia through multiple targets, multiple pathways, and multiple functions.
2Analysis of signaling pathways, functional modules and networks may be complementary paradigms for revealing potential pharmacological mechanisms. At signaling pathway level, BA, CA and JA all acted on Ca2+-dependent signaling cascades in diverse links. At functional module level, BA was involved in antioxidation and neurogenesis, CA focused on regulating cation transport. BA, CA and JA affected neurotransmitter-related functions.
3The method of modular analysis is suitable for multi-targeted drugs treatment on complex diseases, and provides a new method for pharmacological study in future.
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