Literature DB >> 30047117

Identification of drug target candidates of the swine pathogen Actinobacillus pleuropneumoniae by construction of protein-protein interaction network.

Siqi Li1, Zhipeng Su1, Chengjun Zhang1, Zhuofei Xu1,2, Xiaoping Chang1, Jiawen Zhu1,3, Ran Xiao1, Lu Li4,5, Rui Zhou6,7.   

Abstract

Porcine pleuropneumonia caused by Actinobacillus pleuropneumoniae has led to severe economic losses in the pig industry worldwide. A. pleuropneumoniae displays various levels of antimicrobial resistance, leading to the dire need to identify new drug targets. Protein-protein interaction (PPI) network can aid the identification of drug targets by discovering essential proteins during the life of bacteria. The aim of this study is to identify drug target candidates of A. pleuropneumoniae from essential proteins in PPI network. The homologous protein mapping method (HPM) was utilized to construct A. pleuropneumoniae PPI network. Afterwards, the subnetwork centered with H-NS was selected to verify the PPI network using bacterial two-hybrid assays. Drug target candidates were identified from the hub proteins by analyzing the topology of the network using interaction degree and homologous comparison with the pig proteome. An A. pleuropneumoniae PPI network containing 2737 non-redundant interaction pairs among 533 proteins was constructed. These proteins were distributed in 21 COG functional categories and 28 KEGG metabolic pathways. The A. pleuropneumoniae PPI network was scale free and the similar topological tendencies were found when compared with other bacteria PPI network. Furthermore, 56.3% of the H-NS subnetwork interactions were validated. 57 highly connected proteins (hub proteins) were identified from the A. pleuropneumoniae PPI network. Finally, 9 potential drug targets were identified from the hub proteins, with no homologs in swine. This study provides drug target candidates, which are promising for further investigations to explore lead compounds against A. pleuropneumoniae.

Entities:  

Keywords:  Actinobacillus pleuropneumoniae; Drug target; Network; Protein–protein interaction

Mesh:

Year:  2018        PMID: 30047117     DOI: 10.1007/s13258-018-0691-3

Source DB:  PubMed          Journal:  Genes Genomics        ISSN: 1976-9571            Impact factor:   1.839


  56 in total

1.  Antimicrobial susceptibility of Actinobacillus pleuropneumoniae isolated from pigs in Korea using new standardized procedures.

Authors:  B Kim; K Min; C Choi; W S Cho; D S Cheon; D Kwon; J Kim; C Chae
Journal:  J Vet Med Sci       Date:  2001-03       Impact factor: 1.267

Review 2.  Actinobacillus pleuropneumoniae: pathobiology and pathogenesis of infection.

Authors:  Janine T Bossé; Håkan Janson; Brian J Sheehan; Amanda J Beddek; Andrew N Rycroft; J Simon Kroll; Paul R Langford
Journal:  Microbes Infect       Date:  2002-02       Impact factor: 2.700

Review 3.  New insights into transcriptional regulation by H-NS.

Authors:  Ferric C Fang; Sylvie Rimsky
Journal:  Curr Opin Microbiol       Date:  2008-04-02       Impact factor: 7.934

Review 4.  Nucleotide, c-di-GMP, c-di-AMP, cGMP, cAMP, (p)ppGpp signaling in bacteria and implications in pathogenesis.

Authors:  Dimpy Kalia; Gökçe Merey; Shizuka Nakayama; Yue Zheng; Jie Zhou; Yiling Luo; Min Guo; Benjamin T Roembke; Herman O Sintim
Journal:  Chem Soc Rev       Date:  2012-09-28       Impact factor: 54.564

5.  Evidence for network evolution in an Arabidopsis interactome map.

Authors: 
Journal:  Science       Date:  2011-07-29       Impact factor: 47.728

6.  Protein interaction network analysis--approach for potential drug target identification in Mycobacterium tuberculosis.

Authors:  Sandeep K Kushwaha; Madhvi Shakya
Journal:  J Theor Biol       Date:  2009-10-13       Impact factor: 2.691

Review 7.  Virulence factors of Actinobacillus pleuropneumoniae involved in colonization, persistence and induction of lesions in its porcine host.

Authors:  Koen Chiers; Tine De Waele; Frank Pasmans; Richard Ducatelle; Freddy Haesebrouck
Journal:  Vet Res       Date:  2010-06-15       Impact factor: 3.683

8.  clusterMaker: a multi-algorithm clustering plugin for Cytoscape.

Authors:  John H Morris; Leonard Apeltsin; Aaron M Newman; Jan Baumbach; Tobias Wittkop; Gang Su; Gary D Bader; Thomas E Ferrin
Journal:  BMC Bioinformatics       Date:  2011-11-09       Impact factor: 3.307

9.  DrugBank: a comprehensive resource for in silico drug discovery and exploration.

Authors:  David S Wishart; Craig Knox; An Chi Guo; Savita Shrivastava; Murtaza Hassanali; Paul Stothard; Zhan Chang; Jennifer Woolsey
Journal:  Nucleic Acids Res       Date:  2006-01-01       Impact factor: 16.971

10.  Occurrence of antimicrobial resistance among bacterial pathogens and indicator bacteria in pigs in different European countries from year 2002 - 2004: the ARBAO-II study.

Authors:  Rene S Hendriksen; Dik J Mevius; Andreas Schroeter; Christopher Teale; Eric Jouy; Patrick Butaye; Alessia Franco; Andra Utinane; Alice Amado; Miguel Moreno; Christina Greko; Katharina D C Stärk; Christian Berghold; Anna-Liisa Myllyniemi; Andrzej Hoszowski; Marianne Sunde; Frank M Aarestrup
Journal:  Acta Vet Scand       Date:  2008-06-13       Impact factor: 1.695

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  3 in total

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Journal:  mSystems       Date:  2022-03-30       Impact factor: 7.324

2.  Identifying Drug Targets in Pancreatic Ductal Adenocarcinoma Through Machine Learning, Analyzing Biomolecular Networks, and Structural Modeling.

Authors:  Wenying Yan; Xingyi Liu; Yibo Wang; Shuqing Han; Fan Wang; Xin Liu; Fei Xiao; Guang Hu
Journal:  Front Pharmacol       Date:  2020-04-30       Impact factor: 5.810

3.  Common Genes Involved in Autophagy, Cellular Senescence and the Inflammatory Response in AMD and Drug Discovery Identified via Biomedical Databases.

Authors:  Shoubi Wang; Chengxiu Liu; Weijie Ouyang; Ying Liu; Chaoyang Li; Yaqi Cheng; Yaru Su; Chang Liu; Liu Yang; Yurun Liu; Zhichong Wang
Journal:  Transl Vis Sci Technol       Date:  2021-01-08       Impact factor: 3.283

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