Computationally identifying virulence factors based on KEGG pathways

Weiren Cui; Lei Chen; Tao Huang; Qian Gao; Min Jiang; Ning Zhang; Lulu Zheng; Kaiyan Feng; Yudong Cai; Hongwei Wang

doi:10.1039/C3MB70024K

Computationally identifying virulence factors based on KEGG pathways†

Weiren Cui,‡^ab Lei Chen,‡^c Tao Huang,‡^d Qian Gao,^a Min Jiang,^ef Ning Zhang,^g Lulu Zheng,^h Kaiyan Feng,^h Yudong Cai*ⁱ and Hongwei Wang*^a

Author affiliations

* Corresponding authors

^a Center for Translational Medicine and Jiangsu Key Laboratory of Molecular Medicine, Medical School of Nanjing University, Nanjing 210093, China
E-mail: hwang@nju.edu.cn
Fax: +86 25 83594755
Tel: +86 25 83594755

^b CAS-MPG Partner Institute for Computational Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, China

^c College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China

^d Department of Genetics and Genomic Sciences, Mount Sinai School of Medicine, New York, USA

^e Institute of Health Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences and Shanghai Jiao Tong University School of Medicine, Shanghai 200025, China

^f State Key Laboratory of Medical Genomics, Ruijin Hospital, Shanghai Jiaotong University, Shanghai 200025, China

^g Department of Biomedical Engineering Tianjin University, Tianjin Key Lab of BME Measurement, Tianjin, China

^h Shanghai Center for Bioinformation Technology, Shanghai 200235, China

ⁱ Institute of Systems Biology, Shanghai University, Shanghai 200444, China
E-mail: cai_yud@yahoo.com.cn
Fax: +86-21-66136109
Tel: +86-21-66136132

Abstract

Virulence factors are molecules that play very important roles in enhancing the pathogen’s capability in causing diseases. Many efforts were made to investigate the mechanism of virulence factors using in silico methods. In this study, we present a novel computational method to predict virulence factors by integrating protein–protein interactions in a STRING database and biological pathways in the KEGG. Three specific species were studied according to their records in the VFDB. They are Campylobacter jejuni NCTC 11168, Escherichia coli O6 : K15 : H31 536 (UPEC) and Pseudomonas aeruginosa PAO1. The prediction accuracies reached were 0.9467, 0.9575 and 0.9180, respectively. Metabolism pathways, flagellar assembly and chemotaxis may be of importance for virulence based on the analysis of the optimal feature sets we obtained. We hope this can provide some insight and guidance for related research.

Molecular BioSystems

Computationally identifying virulence factors based on KEGG pathways†

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Computationally identifying virulence factors based on KEGG pathways

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