WO2018049793A1 - 一种用于定量分析药物干预前后生物分子网络中模块变化的方法 - Google Patents

一种用于定量分析药物干预前后生物分子网络中模块变化的方法 Download PDF

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WO2018049793A1
WO2018049793A1 PCT/CN2017/075604 CN2017075604W WO2018049793A1 WO 2018049793 A1 WO2018049793 A1 WO 2018049793A1 CN 2017075604 W CN2017075604 W CN 2017075604W WO 2018049793 A1 WO2018049793 A1 WO 2018049793A1
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module
network
change
modules
biomolecular
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王�忠
于亚南
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王�忠
于亚南
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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B5/00—ICT specially adapted for modelling or simulations in systems biology, e.g. gene-regulatory networks, protein interaction networks or metabolic networks

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  • the invention belongs to the field of bioinformatics.
  • the present invention relates to the quantification of modules in a complex biomolecular network such as a protein interaction network, a gene expression regulatory network, a drug metabolism, and a drug target, from a conformation to another conformation after drug intervention. method.
  • Network structure analysis is the process of expressing a system of interest as a network and then using quantitative indicators to study the topology characteristics of the network.
  • the module is a medium granularity of the network structure hierarchy and represents the local characteristics and network composition of the network. Therefore, network topology parameter metrics can be used to describe the topology characteristics of the module.
  • Network pharmacology believes that when the biological system is in a steady state and in an equilibrium state, the body is in a healthy state.
  • the balance (health state) of a biological system biological network
  • the therapeutic effect of the drug on the disease can be seen as the response of the biological system or biological network to the external disturbance information, that is, the action of the effective drug will shift the balance in a direction that can attenuate the change, and the balance or balance of the reconstructed biological system is The extent of the damage.
  • whether the impact of drugs can be effectively reflected in the global changes of the network is still debatable.
  • the present invention provides a new method capable of quantitatively analyzing the degree of change of the overall conformation of a module in a biomolecular network, which integrates topological parameters of different dimensions (ie, attributes) of the module, constructs a comprehensive evaluation system for the module, and can quantitatively analyze the drug.
  • the degree of change in the overall conformation of the module before and after the intervention is a new method capable of quantitatively analyzing the degree of change of the overall conformation of a module in a biomolecular network, which integrates topological parameters of different dimensions (ie, attributes) of the module, constructs a comprehensive evaluation system for the module, and can quantitatively analyze the drug.
  • the degree of change in the overall conformation of the module before and after the intervention.
  • biomolecular network refers to the existence of different organizational forms in biological systems. In a biological system network, it consists of nodes representing various biomolecules and edges representing the interaction between the biomolecules. Common biomolecular networks include protein interaction networks, gene co-expression networks, gene transcriptional regulatory networks, biological metabolic networks, epigenetic networks, phenotypic networks, and signaling networks.
  • node refers to different individual biomolecules in a biomolecular network.
  • nodes in the network represent individual proteins.
  • edge refers to a specific relationship between different individual biomolecules (nodes) in a biomolecular network in which the relationship between two individual biomolecules is represented by edges.
  • edges In a protein interaction network, the relationships between proteins (nodes) that are related to each other and interact with each other are represented by the edges between them.
  • module refers to an entity in a biomolecular network that is composed of at least three different biomolecules (nodes) that are relatively functionally and morphologically independent. In the network topology, the node connections inside the module appear to be relatively dense, and the node connections between the module and the module appear relatively sparse. Modules have independent functions derived from the interactions between the individual biomolecules (nodes such as proteins, DNA, RNA, and small molecules) that make up them.
  • module change refers to a change (eg, splitting, merging, shrinking, growth, etc.) of a module due to external disturbances (eg, drug intervention) in different time spaces (eg, before and after drug intervention).
  • (module) dimension refers to different attributes of the module topology, which can be described by parameters such as neighbor nodes, network density, network centrality, and median centrality. In this article, “dimensions” and “attributes” are used interchangeably.
  • change module pair refers to a module pair consisting of a module from a biomolecular network before drug intervention and a module of a biomolecular network after drug intervention, the two modules having at least one node. ( ⁇ 1) overlap.
  • effective change module pair refers to a pair of varying modules whose degree of change exceeds a certain threshold before and after drug intervention.
  • non-effective drug refers to a drug that does not achieve the desired improvement effect after application in a specific target pathological state.
  • the present invention provides a method for quantitatively analyzing module changes in a biomolecular network before and after drug intervention, the method comprising the steps of:
  • the method of performing module identification is selected from one or more of the following: MCODE, MCL, CFinder, CPM, SPC, G-N algorithm, ModuLand, WGCNA, DME, MINE, SVD, and the like.
  • the method of module identification in the method of the invention is MCODE or WGCNA.
  • the biomolecular network is a protein interaction network, a gene co-expression network, a gene transcription regulatory network, a biological metabolic network, an epigenetic network, a phenotypic network, a signaling network, and the like. More preferably, the biomolecular network is a protein interaction network or a gene co-expression network.
  • step (2) Matching a set of modules of the biomolecular network after drug intervention identified by step (1) with a set of modules of the biomolecular network before drug intervention to obtain one or more pairs of change modules.
  • the overall degree of change k is used to quantify the degree of change of the module, wherein the overall comprehensive index k value is determined by the topological parameters and their weights describing different dimensions. Calculated, the formula is as follows:
  • n is the number of selected topological parameters
  • i is an integer from 1 to n
  • a i is the spatial vector feature of the i-th topological parameter of the module before the drug intervention in the module group
  • b i is in the module group
  • di is the distance of the spatial vector feature of the i-th topological parameter before and after the drug intervention
  • w i is the weight of each topology parameter.
  • the different dimensional topological parameters of the overall comprehensive index of the quantitative module change in the step (3) of the method of the present invention may be selected from Table 1:
  • Characteristic path length Charateric path length Average neighbor node Average number of neighbors density Network density Centrality Network centralization Heterogeneity Heterogeneity Aggregation coefficient Clustering coefficient Topological coefficient Topological coefficient Median centrality Betweenness centrality Close to centrality Closeness centrality Stress centrality Stress centrality Shortest path Shortest path Weights Weight Connection degree Connectivity
  • the analytic hierarchy process the Delphi method, the factor analysis weight method, the information weight method, the principal component analysis method, the entropy weight method, the superior sequence method, the standard deviation method, etc. can be used to determine the weight.
  • the weights are determined using a hierarchical analysis.
  • the invention provides a method for identifying pairs of effective changes in a biomolecular network before and after a drug intervention, the method comprising the steps of:
  • step (1) replacing the drug in step (1) with a substance other than the effective drug, and repeating steps (1) to (3) of the above method to obtain an overall comprehensive index indicating the degree of change of the module in one or more change module pairs.
  • k value select the maximum k value as the threshold;
  • step (C) Comparing the value of k obtained in step (A) with the threshold obtained in step (B), and if the value of k is ⁇ the threshold, the pair of varying modules having the value of k is identified as a pair of valid variations.
  • the module variation quantification method of the invention can be used in the fields of biological network analysis, new drug research and development design, pharmacological mechanism research and the like.
  • a mouse cerebral ischemia model is intervened by using five effective components of refined Qingkailing to obtain a separate biomolecular network in a disease state and after drug intervention, by the method of the present invention.
  • FIG. 1 is a result of network construction of each group in Embodiment 1.
  • a node in a network represents a gene or protein, and a line between nodes represents an interaction between proteins.
  • FIG. 2 is a result of module identification of each network by using the MCODE method in Embodiment 1.
  • Example 3 is a calculation formula of the overall comprehensive index k value of the module change before the drug intervention (A state) to the drug intervention (B state) in Example 1.
  • FIG. 5 is a pair of effective change modules of the Vehicle-JU group in Embodiment 1.
  • Embodiment 6 is a k value of a pair of Vehicle-black and JA-cyan variation modules in Embodiment 4.
  • Figure 8 is a technical road map of the present invention.
  • the object of the present invention is to quantify the degree of change in modules in different states (eg, before drug intervention (possibly disease state) and after drug intervention) to identify effective change modules that contribute to drug intervention.
  • the object of the present invention is to quantify the degree of change in modules in different states (eg, before drug intervention (possibly disease state) and after drug intervention) to identify effective change modules that contribute to drug intervention.
  • the following examples demonstrate the effectiveness and feasibility of the method of the invention. These embodiments are non-limiting and the methods of the present invention may also be applied to other types of networks.
  • Example 1 Intervention of a mouse model of cerebral ischemia using Qingkailing effective components, quantification of module changes in the protein interaction network, and identification of effective change module pairs (10 topological parameter fusions)
  • the data of this example is derived from: using the five effective components of refined Qingkailing, namely baicalin (BA), geniposide (JA), cholic acid (UA), baicalin + geniposide (BJ; BA + JA)
  • baicalin geniposide
  • JA geniposide
  • UA cholic acid
  • BJ baicalin + geniposide
  • CM inactive component mother-of-pearl
  • IPA Ingenuity Pathway Analysis
  • the model group contains 149 statistically significant genes, 74 in the BA group, 121 in the JA group, 104 in the UA group, 70 in the BJ group, 107 in the JU group, and 40 in the CM group.
  • the genes of these seven groups were mapped as target genes to the global background (global mouse gene and protein interaction data).
  • model group was used as the data before the drug intervention, and the BJ group with the additive effect and the JU group with the synergistic effect were used as the data after the drug intervention.
  • the protein interaction network of the cerebral ischemia model group (Vehicle group) (Fig. 1-a) consists of 3750 nodes and 9162 edges;
  • the protein interaction network of the BJ group (Fig. 1-b) consists of 2968 nodes and 6273 edges;
  • the protein interaction network of the JU group (Fig. 1-c) consists of 3429 nodes and 8111 edges.
  • step 1 the MCODE method (the number of module nodes ⁇ 3) is used to identify each network.
  • the result is shown in Figure 2.
  • the module identified by the protein interaction network of the cerebral ischemia model group (Vehicle) is shown in Fig. 2a.
  • the module identified by the protein interaction network of the BJ group is shown in Figure 2b, and the module identified by the protein interaction network of the JU group is shown in Figure 2c.
  • Step 2 Track the changes of the network module before and after the drug intervention.
  • the drug group modules (BJ, JU) are respectively matched with the Vehicle group module, and at least one node overlap is defined as a change module pair (modular) Reconstructed pairs).
  • the matching results of the BJ, JU and Vehicle group change modules are shown in Tables 2 and 3:
  • Step 3 Apply the overall comprehensive index of the quantitative module change (the fusion of multiple indicators, and combine the weights indicating the importance degree of the specific topological parameters to form a comprehensive module topology parameter index) to quantitatively analyze the degree of change of the module.
  • 10 non-overlapping nodes, non-overlapping edges, overlapping nodes, overlapping edges, average neighbor nodes, network density, network centrality, average median centrality, network average weight, and shortest path are used.
  • the topological parameters of the dimension are fused (other parameters can be selected based on the exclusion of collinearity between different parameters).
  • the structure of module A is set to a, for its topological state, the 10 parameters (different attribute variables) are applied to describe its spatial vector features as a 1 , a 2 , a 3 ...a 10 ;
  • the topology of module A changes to module B, and the corresponding topology b is described as b 1 , b 2 , b 3 ... b 10 .
  • the corresponding weights (AHP) representing the importance of different parameters are combined, as shown in Table-4.
  • the change in the overall topology parameters of the module from A to B is represented by the k value, as shown in Figure 3.
  • the value of k is between 0-1. The smaller the smaller, the smaller the overall difference between the two modules. Conversely, the larger the value of k, the greater the difference between the two modules.
  • Table-4 variables represent parameters and weights (10 parameters)
  • the maximum k value of the vehicle-BJ change module pair is 0.587 (Vehicle4-BJ7), and the minimum k value is 0.180 (Vehicle2--BJ2), with an average of 0.355.
  • the k value is mostly between 0.3 and 0.5.
  • the maximum k value of the vehicle--JU change module pair is 0.556 (Vehicle12--JU15), and the minimum k value is 0.137 (Vehicle16--JU4), with an average of 0.326.
  • Step 4 in this embodiment, based on the quantitative analysis of the degree of change of the Vehicle--BJ and Vehicle-JU modules in step 3, using the threshold k 0 to identify an effective contribution to the drug intervention in the disease network.
  • Change module. The threshold k 0 is obtained as follows:
  • the maximal k value of the mother-of-pearl module change was 0.456 compared with the model group, which was used as the threshold value, and any change in Vehicle--BJ, Vehicle--JU If the module pair ki ⁇ 0.456, it is considered to be a valid change module pair.
  • the three pairs of effective change modules determined by the Vehicle--BJ group are: a.Vehicle4--BJ2, Vehicle4--BJ7; b.Vehicle11--BJ16.
  • three pairs of effective change modules determined by the Vehicle-JU group are: a.Vehicle12--JU15; b.Vehicle16--JU19; c.Vehicle20--JU13.
  • steps 1 and 2 are the same as in the first embodiment.
  • Step 3 using eight parameters, that is, non-overlapping nodes, non-overlapping edges, overlapping nodes, overlapping edges, network density, network centrality, network average weight, and shortest path, the topological parameters of different dimensions of the eight representative modules are merged, and simultaneously The weights of the parameters are shown in Table 8.
  • the degree of change in the state of the JU group from the disease state to the drug intervention is expressed as follows:
  • Table-8 variables represent parameters and weights (8 parameters)
  • the same method is used to obtain a threshold value k 0 using the Qingkailing component ineffective drug group-nacre (CM), and an effective change module pair capable of contributing to the drug intervention in the disease network is identified.
  • CM Qingkailing component ineffective drug group-nacre
  • an effective change module pair capable of contributing to the drug intervention in the disease network is identified.
  • the maximum k value of the module change after CM intervention in the cerebral ischemia network is 0.535 (Table 10)
  • the arbitrary change module in Vehicle-JU is considered to be effective for ki ⁇ 0.535.
  • the change module pair is used to obtain a threshold value k 0 using the Qingkailing component ineffective drug group-nacre (CM), and an effective change module pair capable of contributing to the drug intervention in the disease network.
  • the effective change module pairs are the same.
  • Example 3 Intervention of mouse cerebral ischemia model with Qingkailing effective component, quantification of module changes in protein interaction network, and identification of effective change module pairs (five topological parameter fusion)
  • steps 1 and 2 are the same as in the first embodiment.
  • Step 3 using five parameters, that is, non-overlapping nodes, non-overlapping edges, overlapping nodes, overlapping edges,
  • the network centrality is the fusion of the topological parameters of the different dimensions of the five representative modules, and the weights of the parameters are shown in Table 11.
  • the degree of change in the state of the BJ group from the disease state to the drug intervention is expressed as follows:
  • Table-11 shows the parameters and weights (5 parameters)
  • the same method is used to obtain a threshold value k 0 using the Qingkailing component-ineffective drug group-nacre (CM), and an effective change module pair capable of generating a contributing response to the drug intervention disease network is identified.
  • CM Qingkailing component-ineffective drug group-nacre
  • an effective change module pair capable of generating a contributing response to the drug intervention disease network is identified.
  • the maximum k value of the module change after CM intervention in the cerebral ischemia network was 0.722 (Table 13), and the arbitrary change module in Vehicle-JU was considered to be effective for ki ⁇ 0.722. Change module pair.
  • the effective change module determined in this embodiment is the same as the effective change module pair determined in Embodiment 1, except that the order of k values of the three valid module pairs is slightly different:
  • the three effective change modules k value are from The sequence of Vehicle4-BJ2, Vehicle11--BJ16, Vehicle4-BJ7 in the order of size is larger; in this embodiment, the value of k of the Vehicle11--BJ16 module pair is greater than Vehicle4--BJ7.
  • Example 4 Intervention of mouse cerebral ischemia model with Qingkailing effective component to quantify the changes of modules in gene co-expression network
  • the gene expression profile data of the cerebral ischemia model of the mouse Qingqiling effective component jasminoside (JA) was used as an example.
  • the specific implementation process is as follows:
  • the model group gene expression profile data was used as the data before drug intervention, and the JA group gene expression profile data was used as the data after drug intervention.
  • Each group of gene expression profile data consists of 374 genes from 12 samples (Tbp, Zeb1, Pou2f1, Foxb1, Creb1, Camk2g, Csf1, F5, Hspd1, Matn2, Mt1, Adamts1, Klf6, Dffa, Rgs18, Rhoa, Kcnmb1) , Pdcd11, Pdpk1, Casp8ap2, Mogat1, Rps26, Ak1, Csnk2a2, Dkk2, Ppm1e, Tnfrsf22, Trp53i11, Smpd3, Grin1, Cdk5, Jund, E2f1, Apoe, Ilb, Prkar1b, Il7r, Ngfb, Rela, Ifnar1, Adcy6, Bak1 , Fzd6, Prkch, Rgs4, Actg1, Gck, Rgs
  • the gene co-expression network (the number of nodes is 374) of the model group (Vehicle) and JA group data was constructed by the weighted co-expression network analysis (WGCNA) tool and the module was divided (the minimum module was set to three nodes).
  • the Vehicle group gets 48 modules, and the JA group gets 42 modules.
  • Application of the fusion of 10 topological parameters in Embodiment 1 The quantitative tracking of the changes, the corresponding values of the four change modules are shown in Table 14, Figure 7, and Figure 8 (the nodes highlighted by the dashed circle are the nodes of the change module pair).

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Abstract

一种定量分析方法，所述方法用于量化比较药物干预前后生物分子网络中模块的变化程度。所述方法整合模块不同维度(属性)的拓扑参数，构建量化模块变化的整体综合指标，定量分析比较药物干预前后生物分子网络中模块的变化程度。所述方法包括：1)分别构建药物干预前和药物干预后的生物分子网络，并分别进行模块的识别；2)匹配药物干预前和药物干预后的生物分子网络中变化的模块，确定变化模块对；3)整合表示模块不同维度的拓扑参数，结合所述拓扑参数的权值，构建模块综合评价体系，计算药物干预前后生物分子网络中模块整体构象的改变程度。可应用于生物网络分析、新药研发设计、药理机制研究等领域。

Description

一种用于定量分析药物干预前后生物分子网络中模块变化的方法 技术领域
本发明属于生物信息技术领域。具体而言，本发明涉及复杂生物分子网络如蛋白质相互作用网络、基因表达调控网络、药物代谢和药物靶点等网络中的模块在经药物干预后由一种构象转变为另一种构象的量化方法。
背景技术
网络结构分析是将一个感兴趣的系统表示成网络，进而采用量化指标来研究该网络的拓扑结构特性的过程。模块作为网络结构层次的中等粒度，代表了网络的局部特征和网络构成。因此，可以将网络拓扑参数度量方法用于对模块拓扑特征的描述。
网络药理学认为当生物系统为稳态和平衡态时，机体处于健康状态。当生物系统(生物网络)的平衡(健康状态)被扰乱或破坏时，则会导致病理或疾病状态。而药物对疾病的治疗作用可以看作生物系统或生物网络对外部扰动信息的应答，即有效药物的作用将使平衡向能够减弱这种改变的方向移动，在于重建生物系统的平衡或减轻平衡被破坏的程度。然而，药物的影响是否能有效地体现在网络的全局改变上还值得商榷，有研究认为聚焦于网络局部及片段(模块)的改变可能会更直接有效地评估药物的影响。
既往研究对模块改变程度的测量多从某一个或几个拓扑参数分别进行量化分析，忽略了对模块整体(多维)构象改变程度的综合定量分析。因此目前本领域仍然需要提供能够综合量化模块整体构象变化程度的新方法。
发明内容
针对上述技术问题，本发明提供一种能够定量分析生物分子网络中模块整体构象变化程度的新方法，该方法整合模块不同维度(即属性)的拓扑参数，构建模块综合评价体系，能够定量分析药物干预前后模块整体构象的变化程度。
定义
本文采用的术语“生物分子网络”是指在生物系统中以不同组织形式存 在的生物系统网络，其由代表各种生物分子的节点与代表该生物分子之间的相互作用关系的边组成。常见的生物分子网络包括蛋白质相互作用网络、基因共表达网络、基因转录调控网络、生物代谢网络、表观遗传网络、表型网络、信号传导网络等。
本文采用的术语“节点”是指生物分子网络中不同的个体生物分子。如蛋白质相互作用网络中，网络中的节点表示个体蛋白质。
本文采用的术语“边”是指生物分子网络中不同的个体生物分子(节点)之间具有的某种特定的关系，在生物分子网络中两个个体生物分子之间的关系用边表示。如蛋白质相互作用网络中，蛋白质(节点)之间相互关联、相互影响的关系通过它们之间的边来表示。
本文采用的术语“模块”是指生物分子网络中由至少三个不同的生物分子(节点)组成的，在功能上和形态上相对独立的实体。在网络拓扑结构上，模块内部的节点连接表现为相对稠密，而模块与模块之间的节点连接表现为相对稀疏。模块具有独立的功能，这些功能来源于组成它们的个体生物分子(节点，例如蛋白质、DNA、RNA和小分子)之间的相互作用。
本文采用的术语“模块变化”是指在不同的时空间(如药物干预前后)，模块由于外界扰动(例如药物干预)而进行的改变(如分裂、合并、减缩、增长等)。
本文采用的术语“(模块的)维度”是指模块拓扑结构的不同属性，可以用参数如邻居节点、网络密度、网络中心性、介数中心性等进行描述。在本文中，“维度”与“属性”可互换使用。
本文采用的术语“变化模块对”是指由分别来自药物干预前的生物分子网络的一个模块及药物干预后的生物分子网络的一个模块共同组成的模块对，所述两个模块至少有一个节点(≥1)重叠。
本文采用的术语“有效变化模块对”是指药物干预前后其变化程度超过一定阈值的变化模块对。
本文采用的术语“非有效药物”是指在具体目标病理状态下，应用后未达到预期改善效果的药物。
本发明的具体技术方案如下。
本发明提供了一种用于定量分析药物干预前后生物分子网络中模块变化的方法，所述方法包括以下步骤：
(1)以节点数目≥3为标准，分别对药物干预前和药物干预后的生物分子网络进行模块识别，以获得药物干预前的生物分子网络的一组模块和药物干预后的生物分子网络的一组模块；
优选地，进行模块识别的方法选自下述中的一种或多种：MCODE、MCL、CFinder、CPM、SPC、G-N algorithm、ModuLand、WGCNA、DME、MINE、SVD等。根据本发明的具体实施方式，在本发明的方法中进行模块识别的方法为MCODE或WGCNA。
优选地，所述生物分子网络为蛋白质相互作用网络、基因共表达网络、基因转录调控网络、生物代谢网络、表观遗传网络、表型网络、信号传导网络等。更优选地，所述生物分子网络为蛋白质相互作用网络或基因共表达网络。
(2)将经步骤(1)识别的药物干预后的生物分子网络的一组模块与药物干预前的生物分子网络的一组模块相匹配，获得一个或多个变化模块对。
(3)对于经步骤(2)获得的一个或多个变化模块对，采用整体综合指标k值对模块变化程度进行量化，其中，整体综合指标k值由描述不同维度的拓扑参数及其权值计算得到，计算公式如下：
Figure PCTCN2017075604-appb-000001
Figure PCTCN2017075604-appb-000002
所述公式中n为选择的拓扑参数的个数，i为1至n的整数，ai为模块组中药物干预前的模块的第i个拓扑参数的空间向量特征，bi为模块组中药物干预后的模块的第i个拓扑参数的空间向量特征，di为第i个拓扑参数的空间向量特征在药物干预前后的距离，wi为每个拓扑参数的权值。
其中，本发明方法的步骤(3)中构建量化模块变化的整体综合指标的不同维度拓扑参数可以选自表1：
表1：模块不同维度拓扑参数
节点(重叠/非重叠) Note
边(重叠/非重叠) Edge
特征路径长度 Charateric path length
平均邻居节点 Average number of neighbors
密度 Network density
中心性 Network centralization
异质性 Heterogeneity
聚集系数 Clustering coefficient
拓扑系数 Topological coefficient
介数中心性 Betweenness centrality
接近中心性 Closeness centrality
压力中心性 Stress centrality
最短路径 Shortest path
权重 Weight
连接度 Connectivity
优选地，n≥5，优选n≥8，更优选n≥10。
其中，可以用层次分析法、特尔斐法法、因子分析权数法、信息量权数法、主成分分析法、熵权法、优序图法、标准离差法等确定权值。根据本发明的具体实施方式，用分层次分析法确定权值。
上述方法的技术路线图见图8。
另一方面，本发明提供一种用于鉴定药物干预前后生物分子网络中有效变化模块对的方法，所述方法包括以下步骤：
(A)执行上述方法的步骤(1)至步骤(3)，以获得表示模块变化程度的整体综合指标k值；
(B)采用非有效药物的物质替换步骤(1)中的药物，重复上述方法的步骤(1)至步骤(3)，以获得一个或多个变化模块对中表示模块变化程度的整体综合指标k值，选择最大k值作为阈值；
(C)将步骤(A)中获得的k值与步骤(B)中获得的阈值相比较，如果k值≥阈值，则将具有所述k值的变化模块对鉴定为有效变化模块对。
本发明的模块变化量化方法可以用于生物网络分析、新药研发设计、药理机制研究等领域。例如根据本发明的具体实施方式，采用精制清开灵的五个有效组分干预小鼠脑缺血模型，可以获得疾病状态下与药物干预后的分别的生物分子网络，通过本发明的方法进行模块识别、匹配、模块变化的量化， 以及与非有效药物珍珠母进行比较，可以获得药物干预前后的变化模块对与有效变化模块对，为药物开发与药理机制研究提供相关信息。
附图说明
以下，结合附图来详细说明本发明的实施方案，其中：
图1为实施例1中各组网络构建结果。网络中节点表示基因或蛋白质，节点之间的连线表示蛋白质之间的相互作用。a.Vehicle组；b.BJ组；c.JU组。
图2为实施例1中采用MCODE方法对各个网络进行模块识别的结果。a.Vehicle组；b.BJ组；c.JU组。
图3为实施例1中药物干预前(A状态)到药物干预后(B状态)模块变化的整体综合指标k值的计算公式。
图4为实施例1中Vehicle-BJ组有效变化模块对。
图5为实施例1中Vehicle-JU组有效变化模块对。
图6为实施例4中Vehicle-black与JA-cyan变化模块对的k值。
图7为实施例4中Vehicle-violet与JA-lightyellow、Vehicle-violet与JA-red、Vehicle-violet与JA-yellow变化模块对的k值。
图8为本发明的技术路线图。
实施发明的最佳方式
以下参照具体的实施例来说明本发明。本领域技术人员能够理解，这些实施例仅用于说明本发明，其不以任何方式限制本发明的范围。
本发明的目的是量化不同状态下(如药物干预前(有可能为疾病状态)和药物干预后的生物分子网络)模块的变化程度，从而识别出对药物干预起到贡献性响应的有效变化模块，为指导疾病治疗和药物研发提供依据。以下的实施例证明了本发明方法的有效性和可行性。这些实施例是非限制性的，本发明的方法还可以应用其他类型的网络。
下述实施例中的实验方法，如无特殊说明，均为常规方法。下述实施例中所用的药材原料、试剂材料等，如无特殊说明，均为市售购买产品。
实施例1采用清开灵有效组分干预小鼠脑缺血模型，量化蛋白质相互作 用网络中模块的变化，以及识别有效变化模块对(10个拓扑参数融合)
数据来源
本实施例数据来源于：利用精制清开灵五个有效组分即黄芩苷(BA)、栀子苷(JA)、胆酸(UA)、黄芩苷+栀子苷(BJ；BA+JA)、栀子苷+胆酸(JU；JA+UA)及无效组分珍珠母(CM)对小鼠脑缺血再灌注损伤模型进行干预后，采用Ingenuity Pathway Analysis(IPA)对其显著差异表达基因进行分析，提取IPA生物功能富集结果中与生物功能注释相关的有统计学意义的基因集。其中模型组(Vehicle)包含149个有统计学意义的基因，BA组74个，JA组121个，UA组104个，BJ组70个，JU组107个，CM组40个。将这7个组的基因作为目标基因分别映射于全局背景(全局的小鼠基因和蛋白质相互作用数据)。
本实施例用模型组作为药物干预前的数据，具有加合效应的BJ组与具有协同效应的JU组作为药物干预后的数据。
参见图1，脑缺血模型组(Vehicle组)的蛋白质相互作用网络(图1-a)，由3750个节点和9162条边组成；
BJ组的蛋白质相互作用网络(图1-b)，由2968个节点和6273条边组成；
JU组的蛋白质相互作用网络(图1-c)，由3429个节点和8111条边组成。
药物干预前后模块变化的定量分析过程如下：
步骤1，采用MCODE方法(模块节点数目≥3)对各个网络进行模块识别，结果如图2所示：由脑缺血模型组(Vehicle)的蛋白质相互作用网络识别的模块见图2a。由BJ组的蛋白质相互作用网络识别的模块见图2b，由JU组的蛋白质相互作用网络识别的模块见图2c。
步骤2，追踪药物干预前后网络模块的变化情况，在本实施例中，将药物组模块(BJ、JU)分别与Vehicle组模块相匹配，至少有一个节点的重叠定义为一个变化模块对(modular reconstructional pairs)。最终，BJ、JU与Vehicle组变化模块对匹配结果如表2、3所示：
表2-Vehicle与BJ变化模块对匹配
Figure PCTCN2017075604-appb-000003
Figure PCTCN2017075604-appb-000004
表3-Vehicle与JU变化模块对匹配
Figure PCTCN2017075604-appb-000005
Figure PCTCN2017075604-appb-000006
步骤3，应用量化模块变化的整体综合指标(多个指标的融合，同时组合表示具体拓扑参数的重要程度的权重，形成综合的模块拓扑参数指标)对模块的变化程度进行量化分析。在本实施例中，采用非重叠节点、非重叠边、重叠节点、重叠边、平均邻居节点、网络密度、网络中心性、平均介数中心性、网络平均权重、最短路径这10个代表模块不同维度的拓扑参数进行融合(在排除不同参数间共线性的基础上还可以选择其他参数)。将在药物干预前(Vehicle组)，模块A的结构设置为a，对于它的拓扑状态，应用这10个参数(不同属性变量)将它的空间向量特征描述为a1、a2、a3……a10；药物干预后(BJ或JU组)，模块A的拓扑结构发生变化，转变为模块B，相对应的拓扑结构b描述为b1、b2、b3……b10。同时组合表示不同参数的重要程度的相应的权值(层次分析法)，如表-4所示。将模块从A到B的综合拓扑参数变化用k值表示，如图3所示。k取值在0-1之间，越小证明两个模块的整体差异越小；反之，k值越大证明两个模块间的差异越大。
表-4各变量代表参数及权值(10个参数)
Figure PCTCN2017075604-appb-000007
Figure PCTCN2017075604-appb-000008
应用图3描述的k值方法，对模块从药物干预前的疾病状态到药物干预后状态的变化程度表述如下，见表5(Vehicle--BJ)、表6(Vehicle--JU)
表-5Vehicle--BJ变化模块对k值(10个参数)
Figure PCTCN2017075604-appb-000009
Figure PCTCN2017075604-appb-000010
可以看出，Vehicle—BJ各变化模块对中最大k值为0.587(Vehicle4-BJ7)，最小k值为0.180(Vehicle2--BJ2)，平均0.355。k值大部分在0.3-0.5之间。
表-6Vehicle--JU变化模块对k值(10个参数)
Figure PCTCN2017075604-appb-000011
Figure PCTCN2017075604-appb-000012
Vehicle--JU各变化模块对中最大k值为0.556(Vehicle12--JU15)，最小k值为0.137(Vehicle16--JU4)，平均0.326。
步骤4，本实施例中，在经步骤3对Vehicle--BJ、Vehicle--JU模块变化程度量化分析的基础上，利用阈值k0来识别出能够对药物干预疾病网络产生贡献性响应的有效变化模块。其中阈值k0通过如下方式获得：
利用无效药物珍珠母(CM)干预脑缺血网络后，与模型组相比，珍珠母组模块变化的最大k值为0.456，将其作为阈值，Vehicle--BJ、Vehicle--JU中任意变化模块对ki≥0.456，则认为是有效变化模块对。
表-7Vehicle--CM变化模块对k值(10个参数)
Figure PCTCN2017075604-appb-000013
图4中为Vehicle--BJ组确定的3对有效变化模块对，分别是:a.Vehicle4--BJ2、Vehicle4--BJ7；b.Vehicle11--BJ16。图5中为Vehicle--JU组确定的3对有效变化模块对，分别是：a.Vehicle12--JU15；b.Vehicle16--JU19；c.Vehicle20--JU13。
实施例2采用清开灵有效组分干预小鼠脑缺血模型，量化蛋白质相互作用网络中模块的变化，以及识别有效变化模块对(8个拓扑参数融合)
采用与实施例1相同的网络数据。在本实施例中，步骤1和步骤2与实施例1相同。
步骤3，采用8个参数，即非重叠节点、非重叠边、重叠节点、重叠边、网络密度、网络中心性、网络平均权重、最短路径这8个代表模块不同维度的拓扑参数进行融合，同时各参数的权值见表8。应用实施例1中描述的k值方法，对模块从疾病状态到药物干预后JU组状态的变化程度表述如下：
表-8各变量代表参数及权值(8个参数)
Figure PCTCN2017075604-appb-000014
表-9Vehicle--JU变化模块对k值(8个参数)
Figure PCTCN2017075604-appb-000015
发现Vehicle-JU各变化模块对中拥有最大k值的仍为Vehicle12--JU15；拥有最小k值仍为Vehicle16--JU4，与实施例1相同，平均0.355。
本实施例中，采用相同方法利用清开灵组分无效药物组-珍珠母(CM) 得到阈值k0，识别出能够对药物干预疾病网络产生贡献性响应的有效变化模块对。在8个拓扑参数标融合的量化指标中，CM组干预脑缺血网络后模块变化的最大k值为0.535(表10)，Vehicle--JU中任意变化模块对ki≥0.535，则认为是有效的变化模块对。
表-10Vehicle--CM变化模块对k值(8个参数)
Figure PCTCN2017075604-appb-000016
Vehicle--JU组确定的3对有效变化模块对分别是Vehicle12--JU15(k＝0.640)、Vehicle16--JU19(k＝0.543)、Vehicle20--JU13(k＝0.573)，与实施例1确定的有效变化模块对相同。
实施例3采用清开灵有效组分干预小鼠脑缺血模型，量化蛋白质相互作用网络中模块的变化，以及识别有效变化模块对(5个拓扑参数融合)
采用与实施例1相同的网络数据。在本实施例中，步骤1和步骤2与实施例1相同。
步骤3，采用5个参数，即非重叠节点、非重叠边、重叠节点、重叠边、 网络中心性这5个代表模块不同维度的拓扑参数进行融合，同时各参数的权值见表11。应用实施例1中描述的k值方法，对模块从疾病状态到药物干预后BJ组状态的变化程度表述如下：
表-11各变量代表参数及权值(5个参数)
Figure PCTCN2017075604-appb-000017
表-12Vehicle--BJ变化模块对k值(5个参数)
Figure PCTCN2017075604-appb-000018
Figure PCTCN2017075604-appb-000019
发现Vehicle-BJ各变化模块对最大k值为0.858(Vehicle11--BJ16)，最小k值为0.255(Vehicle2--BJ7)，平均0.496。
本实施例中，采用相同方法利用清开灵组分无效药物组-珍珠母(CM)得到阈值k0，识别出能够对药物干预疾病网络产生贡献性响应的有效变化模块对。在5个拓扑参数标融合的量化指标中，CM组干预脑缺血网络后模块变化的最大k值为0.722(表13)，Vehicle-JU中任意变化模块对ki≥0.722，则认为是有效的变化模块对。
表-13Vehicle--CM变化模块对k值(5个参数)
Figure PCTCN2017075604-appb-000020
Figure PCTCN2017075604-appb-000021
Vehicle-BJ组确定的3对有效变化模块对分别是Vehicle4--BJ2(k＝0.733)、Vehicle4--BJ7(k＝0.850)、Vehicle11--BJ16(k＝0.858)。本实施例确定的有效变化模块对与实施例1确定的有效变化模块对相同，只是三个有效模块对的k值大小排序稍有差别：在实施例1中，三个有效变化模块k值从大到小依次为Vehicle4--BJ2、Vehicle11--BJ16、Vehicle4--BJ7；而本实施例中Vehicle11--BJ16模块对的k值大于Vehicle4--BJ7。
实施例4采用清开灵有效组分干预小鼠脑缺血模型，量化基因共表达网络中模块的变化
本实施例以精制清开灵有效组分栀子苷(JA)干预小鼠脑缺血模型的基因表达谱数据为例，具体实施过程如下：
1.数据来源：
将模型组基因表达谱数据作为药物干预前的数据，JA组基因表达谱数据作为药物干预后的数据。每组基因表达谱数据都由12个样本的374个基因组成(Tbp、Zeb1、Pou2f1、Foxb1、Creb1、Camk2g、Csf1、F5、Hspd1、Matn2、Mt1、Adamts1、Klf6、Dffa、Rgs18、Rhoa、Kcnmb1、Pdcd11、Pdpk1、Casp8ap2、Mogat1、Rps26、Ak1、Csnk2a2、Dkk2、Ppm1e、Tnfrsf22、Trp53i11、Smpd3、Grin1、Cdk5、Jund、E2f1、Apoe、Il1b、Prkar1b、Il7r、Ngfb、Rela、Ifnar1、Adcy6、Bak1、Fzd6、Prkch、Rgs4、Actg1、Gck、Rgs9、Sox9、Rgs1、Dgke、Rgs20、Map2k2、Pin1、Prkcn、Dgkz、Csnk1g1、Dusp4、Il11、Grb2、Shc1、Syk、Sim2、Ywhah、Fgf13、Bid、Gstm2、Rarg、Pou3f1、Camk2b、Mapkapk2、Tcf4、Sos1、Stat5a、Vegfb、Bad、Etv3、Id1、Lcat、Nf1、Gsn、Bbc3、Clu、Capn9、Ercc5、Comt、Ctsl、Amph、Vegfc、Bax、Cyp51、Sox10、Nfyc、Gata2、Id3、Lef1、Pou6f1、6330503C03Rik、Ech1、Ccl4、Itm2a、Hspa1a、Cbx3、Klf10、Idh3g、Gpx2、Map2k5、Daxx、E2f3、Fgf12、Ikbkg、Btrc、Ikbkap、Ifnar2、Cdk5、Psmb1、Sufu、Gab1、Sox30、Pxn、Pygo2、Ctnnb1、Grin2a、Il5ra、Cdk4、Bcl2l1、Actb、Myb、Prkca、Csf2rb2、Gnaq、B-raf、Wnt6、Adcy7、Cacna1b、Fzd7、Prkcm、Rock1、Adcy8、Prkcc、Sub1、 Tuba1b、Rgs6、Plcb1、Mknk1、Diablo、Mef2c、Lrp1b、Dgkg、Rgs12、Serpina5、Hspb1、Ppm1b、Dlk1、Cdc42、Fadd、Mdfi、Fgf11、Map3k4、Klk1b3、Il6ra、Tgfb2、Wnt11、Ccna1、Map2k6、Htr1f、Zmat3、Bnip3、Tsg101、Vim、Srf、D14Abb1e、Cdh11、Vdac2、Tfdp1、Gak、Ccna2、Vegfa、Vegfa、Hdac1、Srebf1、Stch、E2f1、Nfatc1、Gna12、Gna13、Cacnb3、Zic1、Pou4f3、Tcf12、Ldb1、Capns1、Fxyd2、Gcgr、LOC100304588、Syt11、Gadd45a、Pbx2、Ier3、Mapk9、Ctnnbip1、Fgf15、Smad3、Nlk、Mecp2、Sigirr、Rgs18、Ptk2b、Sap30bp、Pcmt1、Tcf3、Braf、Ankrd6、Rgs5、Rap1gap、Adcy1、Grin2b、Gap43、Map2k1、Mapk10、Tgfb1、Lta、Rps6ka1、Wnt3、Rara、Prkcd、Atf4、Adcyap1r1、Cycs、Hint1、Rdx、Src、Adcy9、Prkce、Shcbp1、Elk3、Rgs14、Rgs17、Dusp10、Tubb3、Cyc1、Dusp16、Plcg2、Fzd10、Dgkd、Stat3、Mapk14、Map2k4、Htr1a、Map3k2、Frat1、Casp7、Eef2k、Thbd、Rarb、Camk4、Htr2c、E2f5、Met、Htr7、Camk2b、Stat6、Sod1、Efna4、Vdac3、Adora1、Bmp1、Vdac1、Grb2、Igfbp2、Top2b、Rpl35、Bdnf、Ppp3cb、Raf1、Cpe、Cacnb3、0610007C21Rik、Gna14、Gna11、Tuba1a、Zic3、Mlx、Id4、Ldb2、Sepp1、Prodh、S100a9、Pgam2、Rcan1、Abcc5、Ccr5、Ap1m1、Map3k5、Csnk1e、Axin1、Freq、Sh2b1、Rps6ka4、Wif1、Nkd1、Pam、Crem、Tgm2、Barhl1、Tradd、Plcd4、Ppp2r4、Otud7b、Rgs7、Casp2、Junb、Il2rg、Bad、Il1a、Egr1、Pdgfa、Gapdh、Eif4e、Apc、Prkcz、Parp1、Egfr、Prkcb1、Rgs2、Traf2、Ccr3、Rgs16、Smpd1、Tbp、Dgka、Mos、B230120H23Rik、Eif4e2、Rgs19、Adcy3、Creb5、Taf7、Pik3ca、Stat1、Il15、Atf3、Dvl3、Map3k3、Casp4、Kcnq1、Ptp4a3、fosB、Wnt3a、Calm1、Htr3a、Crkl、Casp3、Lhx1、Camk4、Selenbp2、Tcfe2a、Scg5、Pold3、Mmp2、Farp2、Pold2、Pold1、Gpx4、App、Mlh3、Rbl2、Tpp2、Cdh3、Fmo2、Pold4、Arf1、Sox1、Arhgef1)
2.网络构建和模块划分
用加权共表达网络分析(WGCNA)工具分别构建模型组(Vehicle)及JA组数据的基因共表达网络(节点数目均为374个)并划分模块(将最小模块设定为三个节点)。Vehicle组得到48个模块，JA组得到42个模块。选择模型组和JA组的四个变化模块对进行k值分析，分别是：Vehicle-black与JA-cyan；Vehicle-violet与JA-lightyellow；Vehicle-violet与JA-red；Vehicle-violet与JA-yellow。应用实施例1中10个拓扑参数的融合进行模块 变化的量化追踪，四个变化模块对相应的k值如表14、图7和图8所示(虚线圆形强调的节点为变化模块对重叠的节点)。
表-14Vehicle-JA部分变化模块对k值(基因共表达网络)
Figure PCTCN2017075604-appb-000022
以上对本发明具体实施方式的描述并不限制本发明，本领域技术人员可以根据本发明作出各种改变或变形，只要不脱离本发明的精神，均应属于本发明所附权利要求的范围。

Claims (7)

  1. 一种用于定量分析药物干预前后生物分子网络中模块变化的方法，所述方法包括以下步骤：
    (1)以节点数目≥3为标准，分别对药物干预前和药物干预后的生物分子网络进行模块识别，以获得药物干预前的生物分子网络的一组模块和药物干预后的生物分子网络的一组模块；
    (2)将经步骤(1)识别的药物干预后的生物分子网络的一组模块与药物干预前的生物分子网络的一组模块相匹配，确定一个或多个变化模块对；
    (3)对于经步骤(2)获得的一个或多个变化模块对，采用整体综合指标k值对模块变化程度进行量化，其中，整体综合指标k值由描述不同维度的拓扑参数及其权值计算得到，计算公式如下：
    Figure PCTCN2017075604-appb-100001
    Figure PCTCN2017075604-appb-100002
    所述公式中n为选择的拓扑参数的个数，i为1至n的整数，ai为模块组中药物干预前的模块的第i个拓扑参数的空间向量特征，bi为模块组中药物干预后的模块的第i个拓扑参数的空间向量特征，di为第i个拓扑参数的空间向量特征在药物干预前后的距离，wi为每个拓扑参数的权值。
  2. 根据权利要求1所述的方法，其特征在于，所述步骤(1)中，所述生物分子网络为蛋白质相互作用网络、基因共表达网络、基因转录调控网络、生物代谢网络、表观遗传网络、表型网络、信号传导网络等；
    优选地，所述生物分子网络为蛋白质相互作用网络或基因共表达网络。
  3. 根据权利要求1或2所述的方法，其特征在于，所述步骤(1)中进行模块识别的方法选自下述中的一种或多种：MCODE、MCL、CFinder、CPM、SPC、G-N algorithm、ModuLand、WGCNA、DME、MINE、SVD等；
    优选地，进行模块识别的方法为MCODE或WGCNA。
  4. 根据权利要求1至3中任一项所述的方法，其特征在于，所述步骤(3)中，所述拓扑参数选自下述中的一种或多种：节点(重叠/非重叠)、边(重 叠/非重叠)、特征路径长度、平均邻居节点、密度、中心性、异质性、聚集系数、拓扑系数、介数中心性、接近中心性、压力中心性、最短路径、边权重、连接度。
  5. 根据权利要求1至4中任一项所述的方法，其特征在于，所述步骤(3)中，n≥5，优选n≥8，更优选n≥10。
  6. 根据权利要求1至5中任一项所述的方法，其特征在于，所述步骤(3)中，用层次分析法、特尔斐法法、因子分析权数法、信息量权数法、主成分分析法、熵权法、优序图法、标准离差法等确定权值；
    优选地，用分层次分析法确定权值。
  7. 一种用于鉴定药物干预前后生物分子网络中有效变化模块对的方法，所述方法包括以下步骤：
    (A)执行上述方法的步骤(1)至步骤(3)，以获得表示模块变化程度的整体综合指标k值；
    (B)采用非有效药物的物质替换步骤(1)中的药物，重复上述方法的步骤(1)至步骤(3)，以获得一个或多个变化模块对中表示模块变化程度的整体综合指标k值，选择最大k值作为阈值；
    (C)将步骤(A)中获得的k值与步骤(B)中获得的阈值相比较，如果k值≥阈值，则将具有所述k值的变化模块对鉴定为有效变化模块对。
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