title: Identifying co-targets to fight drug resistance based on a random walk model.
abstract: BACKGROUND
  Drug resistance has now posed more severe and emergent threats to human health and infectious disease treatment. However, wet-lab approaches alone to counter drug resistance have so far still achieved limited success due to less knowledge about the underlying mechanisms of drug resistance. Our approach apply a heuristic search algorithm in order to extract active network under drug treatment and use a random walk model to identify potential co-targets for effective antibacterial drugs.
  
  
  RESULTS
  We use interactome network of Mycobacterium tuberculosis and gene expression data which are treated with two kinds of antibiotic, Isoniazid and Ethionamide as our test data. Our analysis shows that the active drug-treated networks are associated with the trigger of fatty acid metabolism and synthesis and nicotinamide adenine dinucleotide (NADH)-related processes and those results are consistent with the recent experimental findings. Efflux pumps processes appear to be the major mechanisms of resistance but SOS response is significantly up-regulation under Isoniazid treatment. We also successfully identify the potential co-targets with literature confirmed evidences which are related to the glycine-rich membrane, adenosine triphosphate energy and cell wall processes.
  
  
  CONCLUSIONS
  With gene expression and interactome data supported, our study points out possible pathways leading to the emergence of drug resistance under drug treatment. We develop a computational workflow for giving new insights to bacterial drug resistance which can be gained by a systematic and global analysis of the bacterial regulation network. Our study also discovers the potential co-targets with good properties in biological and graph theory aspects to overcome the problem of drug resistance.
authors.affiliation: Institute of Information Systems and Applications, National Tsing Hua University, HsinChu 300, Taiwan.
authors.forename: Liang-Chun
authors.initials: LC
authors.lastname: Chen
authors.forename: Hsiang-Yuan
authors.initials: HY
authors.lastname: Yeh
authors.forename: Cheng-Yu
authors.initials: CY
authors.lastname: Yeh
authors.forename: Carlos Roberto
authors.initials: CR
authors.lastname: Arias
authors.forename: Von-Wun
authors.initials: VW
authors.lastname: Soo
mesh_terms: D000465:Algorithms; D000900:Anti-Bacterial Agents / Q000494:pharmacology*; D019295:Computational Biology / Q000379:methods; D004351:Drug Resistance / Q000235:genetics / Q000502:physiology*; D005000:Ethionamide; D005227:Fatty Acids / Q000378:metabolism; D015964:Gene Expression Regulation, Bacterial / Q000235:genetics*; D006801:Humans; D007538:Isoniazid; D046228:Microarray Analysis; D008962:Models, Theoretical; D009169:Mycobacterium tuberculosis / Q000187:drug effects*; D009243:NAD / Q000378:metabolism; D060066:Protein Interaction Maps; D013014:SOS Response, Genetics / Q000235:genetics / Q000502:physiology; D057386:Search Engine / Q000379:methods*; D013269:Stochastic Processes
chemical_list: D000900:Anti-Bacterial Agents; D005227:Fatty Acids; D009243:NAD; D005000:Ethionamide; D007538:Isoniazid