AI Article Synopsis

  • * The study utilized transcriptional data and a genome-scale metabolic model (GSMM) to identify potential new anti-targets for treating BB, focusing on changes in metabolic processes before and after a specific treatment.
  • * Glutathione oxidoreductase (GSR) was identified as a promising target, and the compound DB12411 showed potential as a new antibacterial agent, demonstrating the effectiveness of combining metabolic data with drug discovery.

Article Abstract

Bacterial blight (BB) of rice caused by pathovar oryzae () is a serious global rice disease. Due to increasing bactericide resistance, developing new inhibitors is urgent. Drug repositioning offers a potential strategy to address this issue. In this study, we integrated transcriptional data into a genome-scale metabolic model (GSMM) to screen novel anti- targets. Two RNA-seq datasets (before and after bismerthiazol treatment) were used to constrain the GSMM and simulate metabolic processes. Metabolic fluxes were calculated using parsimonious flux balance analysis (pFBA) identifying reactions with significant changes for target screening. Glutathione oxidoreductase (GSR) was selected as a potential anti- target and validated through antibacterial experiments. Virtual screening based on the target identified DB12411 as a lead compound with the potential for new antibacterial agents. This approach demonstrates that integrating metabolic networks and transcriptional data can aid in both understanding antibacterial mechanisms and discovering novel drug targets.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11594844PMC
http://dx.doi.org/10.3390/ijms252212236DOI Listing

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