Network pharmacology analysis of the therapeutic mechanisms of the traditional Chinese herbal formula Lian Hua Qing Wen in Corona virus disease 2019 (COVID-19), gives fundamental support to the clinical use of LHQW

BackgroundThe traditional Chinese Medicine (TCM) herbal formula Lian Hua Qing Wen (LHQW) improves the results of COVID-19 treatment. Three very recent studies analyzed with network pharmacology some working mechanisms of LHQW. However, we used more techniques and also included Angiotensin converting enzyme 2 (ACE2) (a SARS-CoV receptor, possibly the viral entry point in alveolar lung cells) and the immune system, as cytokine storm is essential in the late phase.PurposeExtensive detailed Network Pharmacology analysis of the LHQW- treatment mechanism in COVID-19.MethodsTCM-herb-meridian and protein interaction network (PIN) of LHQW, based on LHQW herbs meridian information and the protein-protein interaction (PPI) information of the LHQW-component targets. Hub and topological property analyses to obtain crucial targets and construct the crucial LHQW-PIN. Functional modules determination using MCODE, GO and KEGG pathway analysis of biological processes and pathway enrichment. Intersection calculations between the LHQW-proteins and ACE2 co-expression-proteins.ResultsLHQW herbs have relationships to Stomach-, Heart-, Liver- and Spleen-systems, but most (10 of the 13 herbs) to the Lung system, indicating specific effects in lung diseases. The crucial LHQW PIN has the scale-free property, contains 2,480 targets, 160,266 PPIs and thirty functional modules. Six modules are enriched in leukocyte-mediated immunity, the interferon-gamma-mediated signaling pathway, immune response regulating signaling pathway, interleukin 23 mediated signaling pathway and Fc gamma receptor-mediated phagocytosis (GO analysis). These 6 are also enriched in cancer, immune system-, and viral infection diseases (KEGG). LHQW shared 189 proteins with ACE2 co-expression proteins.ConclusionsDetailed network analysis shows, that LHQW herbal TCM treatment modulates the inflammatory process, exerts antiviral effects and repairs lung injury. Moreover, it also relieves the “cytokine storm” and improves ACE2-expression-disorder-caused symptoms. These innovative findings give a rational pharmacological basis and support for treating COVID-19 and possibly other diseases with LHQW.     

Insights into the antineoplastic mechanism of Chelidonium majus via systems pharmacology approach

Background: The antineoplastic activity of Chelidonium majus has been reported, but its mechanism of action (MoA) is unsuspected. The emerging theory of systems pharmacology may be a useful approach to analyze the complicated MoA of this multi-ingredient traditional Chinese medicine (TCM). Methods: We collected the ingredients and related compound-target interactions of C. majus from several databases. The bSDTNBI (balanced substructure-drug-target network-based inference) method was applied to predict each ingredient’s targets. Pathway enrichment analysis was subsequently conducted to illustrate the potential MoA, and prognostic genes were identified to predict the certain types of cancers that C. majus might be beneficial in treatment. Bioassays and literature survey were used to validate the in silico results. Results: Systems pharmacology analysis demonstrated that C. majus exerted experimental or putative interactions with 18 cancer-associated pathways, and might specifically act on 13 types of cancers. Chelidonine, sanguinarine, chelerythrine, berberine, and coptisine, which are the predominant components of C. majus, may suppress the cancer genes by regulating cell cycle, inducing cell apoptosis and inhibiting proliferation. Conclusions: The antineoplastic MoA of C. majus was investigated by systems pharmacology approach. C. majus exhibited promising pharmacological effect against cancer, and may consequently be useful material in further drug development. The alkaloids are the key components in C. majus that exhibit anticancer activity.     

Genomic Heterogeneity of Osteosarcoma - Shift from Single Candidates to Functional Modules

Osteosarcoma (OS), a bone tumor, exhibit a complex karyotype. On the genomic level a highly variable degree of alterations in nearly all chromosomal regions and between individual tumors is observable. This hampers the identification of common drivers in OS biology. To identify the common molecular mechanisms involved in the maintenance of OS, we follow the hypothesis that all the copy number-associated differences between the patients are intercepted on the level of the functional modules. The implementation is based on a network approach utilizing copy number associated genes in OS, paired expression data and protein interaction data. The resulting functional modules of tightly connected genes were interpreted regarding their biological functions in OS and their potential prognostic significance. We identified an osteosarcoma network assembling well-known and lesser-known candidates. The derived network shows a significant connectivity and modularity suggesting that the genes affected by the heterogeneous genetic alterations share the same biological context. The network modules participate in several critical aspects of cancer biology like DNA damage response, cell growth, and cell motility which is in line with the hypothesis of specifically deregulated but functional modules in cancer. Further, we could deduce genes with possible prognostic significance in OS for further investigation (e.g. EZR, CDKN2A, MAP3K5). Several of those module genes were located on chromosome 6q. The given systems biological approach provides evidence that heterogeneity on the genomic and expression level is ordered by the biological system on the level of the functional modules. Different genomic aberrations are pointing to the same cellular network vicinity to form vital, but already neoplastically altered, functional modules maintaining OS. This observation, exemplarily now shown for OS, has been under discussion already for a longer time, but often in a hypothetical manner, and can here be exemplified for OS.     

MAE-FMD: Multi-agent evolutionary method for functional module detection in protein-protein interaction networks

Background

Studies of functional modules in a Protein-Protein Interaction (PPI) network contribute greatly to the understanding of biological mechanisms. With the development of computing science, computational approaches have played an important role in detecting functional modules.

Results

We present a new approach using multi-agent evolution for detection of functional modules in PPI networks. The proposed approach consists of two stages: the solution construction for agents in a population and the evolutionary process of computational agents in a lattice environment, where each agent corresponds to a candidate solution to the detection problem of functional modules in a PPI network. First, the approach utilizes a connection-based encoding scheme to model an agent, and employs a random-walk behavior merged topological characteristics with functional information to construct a solution. Next, it applies several evolutionary operators, i.e., competition, crossover, and mutation, to realize information exchange among agents as well as solution evolution. Systematic experiments have been conducted on three benchmark testing sets of yeast networks. Experimental results show that the approach is more effective compared to several other existing algorithms.

Conclusions

The algorithm has the characteristics of outstanding recall, F-measure, sensitivity and accuracy while keeping other competitive performances, so it can be applied to the biological study which requires high accuracy.     

DTI和功能磁共振成像对精神分裂症患者脑功能连接强度的临床研究

摘要 目的：探讨DTI和功能磁共振成像对精神分裂症患者脑功能连接强度的临床研究。方法：选取2017年9月-2021年3月在我院精神科就诊的精神分裂患者96例。将纳入的96例患者作为精神分裂组,选择同时期招募的96例健康志愿者作为对照组。使用FMRIB软件库进行结构数据预处理和脑解剖网络构建,并进行相关的分析。结果：精神分裂组较对照组枕叶模块功能连接强度增强,较对照组皮质模块和额顶叶模块功能连接强度减弱（P<0.05）,精神分裂组与对照组的默认模块和中央模块功能连接强度比较无差异（P>0.05）。精神分裂组与对照组各模块的结构连接强度比较无差异（P>0.05）。精神分裂组较对照组默认模式模块和中央模块的结构-功能连接程度增加,精神分裂组较对照组枕叶模块和皮质模块的结构-功能连接程度降低（P<0.05）,额顶叶模块结构-功能连接程度两组比较无差异（P>0.05）。在精神分裂症组中,枕叶模块的结构-功能连接评分与疾病持续时间（r=-0.528,P=0.006）和PANSS总体症状相关（r=-0.174,P=0.003）,皮质模块的结构-功能连接评分也与PANSS整体症状（r=-0.405,P=0.034）显著负相关。皮质模块的结构-功能连接评分与疾病持续时间之间显著负相关（r=-0.336,P=0.029）。结论：精神分裂症患者的脑功能连接强度（功能模块水平上结构-功能耦合）发生改变,其结构-功能耦合的畸变与精神分裂症的临床特征相关。     

Identification of susceptibility modules for coronary artery disease using a genome wide integrated network analysis

Although recent genome-wide association studies (GWAS) have identified a handful of variants with best significance for coronary artery disease (CAD), it remains a challenge to summarize the underlying biological information from the abundant genotyping data. Here, we propose an integrated network analysis that effectively combines GWAS genotyping dataset, protein–protein interaction (PPI) database, literature and pathway annotation information. This three-step approach was illustrated for a comprehensive network analysis of CAD as the following. First, a network was constructed from PPI database and CAD seed genes mined from the available literatures. Then, susceptibility network modules were captured from the results of gene-based association tests. Finally, susceptibility modules were annotated with potential mechanisms for CAD via the KEGG pathway database. Our network analysis identified four susceptibility modules for CAD including a complex module that consisted of 15 functional inter-connected sub-modules, AGPAT3–AGPAT4–PPAP2B module, ITGA11–ITGB1 module and EMCN–SELL module. MAPK10 and COL4A2 among the top-scored focal adhesion pathway related module were the most significant genes (MAPK10: OR = 32.5, P = 3.5 × 10^− 11; COL4A2: OR = 2.7, P = 2.8 × 10^− 10). The significance of the two genes were further validated by other two gene-based association tests (MAPK10: P = 0.009 and 0.007; COL4A2: P = 0.001 and 0.023) and another independent GWAS dataset (MAPK10: P = 0.001; COL4A2: P = 0.0004). Furthermore, 34 out of 44 previously reported CAD susceptibility genes were captured by our CAD PPI network and 17 of them were also significant genes. The susceptibility modules identified in our study might provide novel clues for the clarification of CAD pathogenesis in the future.     

Prioritizing functional modules mediating genetic perturbations and their phenotypic effects: a global strategy

We have developed a global strategy based on the Bayesian network framework to prioritize the functional modules mediating genetic perturbations and their phenotypic effects among a set of overlapping candidate modules. We take lethality in Saccharomyces cerevisiae and human cancer as two examples to show the effectiveness of this approach. We discovered that lethality is more conserved at the module level than at the gene level and we identified several potentially 'new' cancer-related biological processes.     

Predicting new molecular targets for rhein using network pharmacology

Background

Drugs can influence the whole biological system by targeting interaction reactions. The existence of interactions between drugs and network reactions suggests a potential way to discover targets. The in silico prediction of potential interactions between drugs and target proteins is of core importance for the identification of new drugs or novel targets for existing drugs. However, only a tiny portion of drug-targets in current datasets are validated interactions. This motivates the need for developing computational methods that predict true interaction pairs with high accuracy. Currently, network pharmacology has used in identifying potential drug targets to predicting the spread of drug activity and greatly contributed toward the analysis of biological systems on a much larger scale than ever before.

Methods

In this article, we present a computational method to predict targets for rhein by exploring drug-reaction interactions. We have implemented a computational platform that integrates pathway, protein-protein interaction, differentially expressed genome and literature mining data to result in comprehensive networks for drug-target interaction. We used Cytoscape software for prediction rhein-target interactions, to facilitate the drug discovery pipeline.

Results

Results showed that 3 differentially expressed genes confirmed by Cytoscape as the central nodes of the complicated interaction network (99 nodes, 153 edges). Of note, we further observed that the identified targets were found to encompass a variety of biological processes related to immunity, cellular apoptosis, transport, signal transduction, cell growth and proliferation and metabolism.

Conclusions

Our findings demonstrate that network pharmacology can not only speed the wide identification of drug targets but also find new applications for the existing drugs. It also implies the significant contribution of network pharmacology to predict drug targets.     

ModuleSearch: finding functional modules in a protein–protein interaction network

Many biological processes are performed by a group of proteins rather than by individual proteins. Proteins involved in the same biological process often form a densely connected sub-graph in a protein–protein interaction network. Therefore, finding a dense sub-graph provides useful information to predict the function or protein complex of uncharacterised proteins in the sub-graph. We developed a heuristic algorithm that finds functional modules in a protein–protein interaction network and visualises the modules. The algorithm has been implemented in a platform-independent, standalone program called ModuleSearch. In an interaction network of yeast proteins, ModuleSearch found 366 overlapping modules. Of the modules, 71% have a function shared by more than half the proteins in the module and 58% have a function shared by all proteins in the module. Comparison of ModuleSearch with other programs shows that ModuleSearch finds more sub-graphs than most other programs, yet a higher proportion of the sub-graphs correspond to known functional modules. ModuleSearch and sample data are freely available to academics at http://bclab.inha.ac.kr/ModuleSearch.     

Determining modular organization of protein interaction networks by maximizing modularity density

Background

With ever increasing amount of available data on biological networks, modeling and understanding the structure of these large networks is an important problem with profound biological implications. Cellular functions and biochemical events are coordinately carried out by groups of proteins interacting each other in biological modules. Identifying of such modules in protein interaction networks is very important for understanding the structure and function of these fundamental cellular networks. Therefore, developing an effective computational method to uncover biological modules should be highly challenging and indispensable.

Results

The purpose of this study is to introduce a new quantitative measure modularity density into the field of biomolecular networks and develop new algorithms for detecting functional modules in protein-protein interaction (PPI) networks. Specifically, we adopt the simulated annealing (SA) to maximize the modularity density and evaluate its efficiency on simulated networks. In order to address the computational complexity of SA procedure, we devise a spectral method for optimizing the index and apply it to a yeast PPI network.

Conclusions

Our analysis of detected modules by the present method suggests that most of these modules have well biological significance in context of protein complexes. Comparison with the MCL and the modularity based methods shows the efficiency of our method.

     

Revealing the Hidden Relationship by Sparse Modules in Complex Networks with a Large-Scale Analysis

One of the remarkable features of networks is module that can provide useful insights into not only network organizations but also functional behaviors between their components. Comprehensive efforts have been devoted to investigating cohesive modules in the past decade. However, it is still not clear whether there are important structural characteristics of the nodes that do not belong to any cohesive module. In order to answer this question, we performed a large-scale analysis on 25 complex networks with different types and scales using our recently developed BTS (bintree seeking) algorithm, which is able to detect both cohesive and sparse modules in the network. Our results reveal that the sparse modules composed by the cohesively isolated nodes widely co-exist with the cohesive modules. Detailed analysis shows that both types of modules provide better characterization for the division of a network into functional units than merely cohesive modules, because the sparse modules possibly re-organize the nodes in the so-called cohesive modules, which lack obvious modular significance, into meaningful groups. Compared with cohesive modules, the sizes of sparse ones are generally smaller. Sparse modules are also found to have preferences in social and biological networks than others.     

Prioritizing functional modules mediating genetic perturbations and their phenotypic effects: a global strategy

We have developed a global strategy based on the Bayesian network framework to prioritize the functional modules mediating genetic perturbations and their phenotypic effects among a set of overlapping candidate modules. We take lethality in Saccharomyces cerevisiae and human cancer as two examples to show the effectiveness of this approach. We discovered that lethality is more conserved at the module level than at the gene level and we identified several potentially 'new' cancer-related biological processes.     

生物信息学方法预测蛋白质相互作用网络中的功能模块

蛋白质相互作用是大多数生命过程的基础。随着高通量实验技术和计算机预测方法的发展,在各种生物中已获得了数目十分庞大的蛋白质相互作用数据,如何从中提取出具有生物学意义的数据是一项艰巨的挑战。从蛋白质相互作用数据出发获得相互作用网络进而预测出其中的功能模块,对于蛋白质功能预测、揭示各种生化反应过程的分子机理都有着极大的帮助。我们分类概括了用生物信息学预测蛋白质相互作用功能模块的方法,以及对这些方法的评价,并介绍了蛋白质相互作用网络比较的一些方法。     

Modular co-evolution of metabolic networks

Background  

The architecture of biological networks has been reported to exhibit high level of modularity, and to some extent, topological modules of networks overlap with known functional modules. However, how the modular topology of the molecular network affects the evolution of its member proteins remains unclear.