Construction of gene/protein interaction networks and enrichment pathway analysis for paroxysmal nocturnal hemoglobinuria and aplastic anemia

Background:To develop a protein-protein interaction network of Paroxysmal nocturnal hemoglobinuria(PNH)and Aplastic anemia(AA)based on genetic genes and to predict pathways underlying the molecular complexes in the network.Methods:In this research,the PNH and AA-related genes were screened through Online Mendelian Inheritance in Man(OMIM).The plugins and Cytoscape were used to search literature and build a protein-protein interaction network.Results:The protein-protein interaction network contains two molecular complexes that are five higher than the correlation integral values.The target genes of this study were obtained:CD59,STAT3,TERC,TNF,AKT1,C5AR1,EPO,IL6,IL10 and so on.We also found that many factors regulate biological behaviors:neutrophils,macrophages,vascular endothelial growth factor,immunoglobulin,interleukin,cytokine receptor,interleukin-6 receptor,tumor necrosis factor,and so on.This research provides a bioinformatics foundation for further explaining the mechanism of common development of both.Conclusion:This indicates that the PNH and AA is a complex process regulated by many cellular pathways and multiple genes.

Prediction of the anti-inflammatory mechanisms of curcumin by module-based protein interaction network analysis

Curcumin,the medically active component from Curcuma longa(Turmeric),is widely used to treat inflammatory diseases.Protein interaction network(PIN) analysis was used to predict its mechanisms of molecular action.Targets of curcumin were obtained based on ChE MBL and STITCH databases.Protein–protein interactions(PPIs) were extracted from the String database.The PIN of curcumin was constructed by Cytoscape and the function modules identified by gene ontology(GO) enrichment analysis based on molecular complex detection(MCODE).A PIN of curcumin with 482 nodes and 1688 interactions was constructed,which has scale-free,small world and modular properties.Based on analysis of these function modules,the mechanism of curcumin is proposed.Two modules were found to be intimately associated with inflammation.With function modules analysis,the anti-inflammatory effects of curcumin were related to SMAD,ERG and mediation by the TLR family.TLR9 may be a potential target of curcumin to treat inflammation.

Protein interaction networks:centrality,modularity,dynamics,and applications

In the post-genomic era,proteomics has achieved significant theoretical and practical advances with the development of high-throughput technologies.Especially the rapid accumulation of protein-protein interactions(PPIs)provides a foundation for constructing protein interaction networks(PINs),which can furnish a new perspective for understanding cellular organizations,processes,and functions at network level.In this paper,we present a comprehensive survey on three main characteristics of PINs:centrality,modularity,and dynamics.1)Different centrality measures,which are used to calculate the importance of proteins,are summarized based on the structural characteristics of PINs or on the basis of its integrated biological information;2)Different modularity definitions and various clustering algorithms for predicting protein complexes or identifying functional modules are introduced;3)The dynamics of proteins,PPIs and sub-networks are discussed,respectively.Finally,the main applications of PINs in the complex diseases are reviewed,and the challenges and future research directions are also discussed.

Essential proteins identification method based on four-order distances and subcellular localization information

Essential proteins are inseparable in cell growth and survival. The study of essential proteins is important for understanding cellular functions and biological mechanisms. Therefore, various computable methods have been proposed to identify essential proteins. Unfortunately, most methods based on network topology only consider the interactions between a protein and its neighboring proteins, and not the interactions with its higher-order distance proteins. In this paper, we propose the DSEP algorithm in which we integrated network topology properties and subcellular localization information in protein–protein interaction(PPI) networks based on four-order distances, and then used random walks to identify the essential proteins. We also propose a method to calculate the finite-order distance of the network, which can greatly reduce the time complexity of our algorithm. We conducted a comprehensive comparison of the DSEP algorithm with 11 existing classical algorithms to identify essential proteins with multiple evaluation methods. The results show that DSEP is superior to these 11 methods.

HPC-Atlas:Computationally Constructing A Comprehensive Atlas of Human Protein Complexes

A fundamental principle of biology is that proteins tend to form complexes to play important roles in the core functions of cells.For a complete understanding of human cellular functions,it is crucial to have a comprehensive atlas of human protein complexes.Unfortunately,we still lack such a comprehensive atlas of experimentally validated protein complexes,which prevents us from gaining a complete understanding of the compositions and functions of human protein complexes,as well as the underlying biological mechanisms.To fill this gap,we built Human Protein Complexes Atlas(HPC-Atlas),as far as we know,the most accurate and comprehensive atlas of human protein complexes available to date.We integrated two latest protein interaction networks,and developed a novel computational method to identify nearly 9000 protein complexes,including many previously uncharacterized complexes.Compared with the existing methods,our method achieved outstanding performance on both testing and independent datasets.Furthermore,with HPC-Atlas we identified 751 severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)-affected human protein complexes,and 456 multifunctional proteins that contain many potential moonlighting proteins.These results suggest that HPC-Atlas can serve as not only a computing framework to effectively identify biologically meaningful protein complexes by integrating multiple protein data sources,but also a valuable resource for exploring new biological findings.The HPCAtlas webserver is freely available at http://www.yulpan.top/HPC-Atlas.

Design of new traditional Chinese medicine herbal formulae for treatment of type 2 diabetes mellitus based on network pharmacology

In the present study, 28 Chinese medicinal herbs belonging to traditional Chinese medicine(TCM) for the treatment of type 2 diabetes were selected to explore the application of network pharmacology in developing new Chinese herbal medicine formulae for the treatment of type 2 diabetes mellitus(T2DM). These herbs have the highest appearance rate in the literature, and their compounds are listed. The human protein–protein interaction network and the T2DM disease protein interaction network were constructed. Then, the related algorithm for network topology was used to perform interventions on the interaction network of disease proteins and normal human proteins to test different Chinese herbal medicine compound combinations, according to the information on the interaction of compounds–targets in two databases, namely TarN et and the Medicinal Plants Database. Results of the intervention scores indicate that the method proposed in this study can provide new effective combinations of Chinese herbal medicines for T2DM. Network pharmacology can effectively promote the modernization and development of TCM.

PBNA: An Improved Probabilistic Biological Network Alignment Method

Biological network alignment is an important research topic in the field of bioinformatics. Nowadays almost every existing alignment method is designed to solve the deterministic biological network alignment problem.However, it is worth noting that interactions in biological networks, like many other processes in the biological realm,are probabilistic events. Therefore, more accurate and better results can be obtained if biological networks are characterized by probabilistic graphs. This probabilistic information, however, increases difficulties in analyzing networks and only few methods can handle the probabilistic information. Therefore, in this paper, an improved Probabilistic Biological Network Alignment（PBNA） is proposed. Based on Iso Rank, PBNA is able to use the probabilistic information. Furthermore, PBNA takes advantages of Contributor and Probability Generating Function（PGF） to improve the accuracy of node similarity value and reduce the computational complexity of random variables in similarity matrix. Experimental results on dataset of the Protein-Protein Interaction（PPI） networks provided by Todor demonstrate that PBNA can produce some alignment results that ignored by the deterministic methods, and produce more biologically meaningful alignment results than Iso Rank does in most of the cases based on the Gene Ontology Consistency（GOC） measure. Compared with Prob method, which is designed exactly to solve the probabilistic alignment problem, PBNA can obtain more biologically meaningful mappings in less time.

Prognostic biomarkers and immune cell infiltration characteristics in small cell lung cancer

Background Small cell lung cancer(SCLC)is a highly malignant and aggressive neuroendocrine tumor.With the rise of immunotherapy,it has provided a new direction for SCLC.However,due to the lack of prognostic biomarkers,the median overall survival of SCLC is still to be improved.This study aimed to explore novel biomarkers and tumor-infiltrating immune cell characteristics that may serve as potential diagnostic and prognostic markers in SCLC.Methods Gene expression profiles from patients with SCLC were downloaded from the Gene Expression Omnibus(GEO)database,and tumor microenvironment(TME)infiltration profile data were obtained using CIBERSORT.The robust rank aggregation(RRA)method was utilized to integrate three SCLC microarray datasets downloaded from the GEO database and identify robust differentially expressed genes(DEGs)between normal and tumor tissue samples.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analyses were performed to explore the functions of the robust DEGs.Subsequently,protein-protein interaction networks and key modules were constructed by Cytoscape,and hub genes were selected from the whole network using the plugin cytoHubba.Survival analysis of hub genes was performed by Kaplan-Meier plotter in 18 patients with extensive-stage SCLC.Results A total of 312 robust DEGs,including 55 upregulated and 257 downregulated genes,were screened from 129 SCLC tissue samples and 44 normal tissue samples.GO and KEGG enrichment analyses revealed that the robust DEGs were predominantly involved in human T-cell leukemia virus 1 infection,focal adhesion,complement and coagulation cascades,tumor necrosis factor(TNF)signaling pathway,and ECM-receptor interaction,which are closely associated with the development and progression of SCLC.Subsequently,three DEGs modules and six hub genes(ITGA10,DUSP12,PTGS2,FOS,TGFBR2,and ICAM1)were identified through screening with the Cytoscape plugins MCODE and cytoHubba,respectively.Immune cell infiltration analysis by the CIBERSORT algorithm revealed that resting memory CD4+T cells were the predominant infiltrating immune cells in SCLC.In addition,Kaplan-Meier plotter revealed that the gene prostaglandin-endoperoxide synthase 2(PTGS2)was a potential prognostic biomarker of SCLC.Conclusions Hub genes and tumor-infiltrating immune cells may be the molecular mechanisms underlying the development of SCLC,and this finding could contribute to the formulation of individualized immunotherapy strategies for SCLC.