Identification of Phosphoribosyl-AMP cyclohydrolase, as drug target and its inhibitors in Brucella melitensis bv. 1 16M using metabolic pathway analysis

Characterization of medicinal plant-associated biocontrol Bacillus subtilis (SSL2) by liquid chromatography-mass spectrometry and evaluation of compounds by in silico and in vitro methods

This study explores the antimicrobial properties of bioactive secondary metabolites extracted from the medicinal plant (Solanum surattense)-associated Bacillus subtilis strain SSL2. The secondary metabolites were extracted from B. subtilis (SSL2) using ethyl acetate, acetone, butanol, chloroform and methanol solvents. The crude extract was tested against two wilt causing pathogens: Ralstonia solanacearum and Fusarium oxysporum. The results revealed that the ethyl acetate extract has maximum inhibition against both the pathogens tested in this study. Furthermore, liquid chromatography-mass spectrometry (LC-MS) analysis of ethyl acetate extract identified 80 different compounds based on mass-to-charge ratio, database difference, resolution of mass spectrum and so on. Among the 80 compounds, citrulline (m/z = 158.0917), chloramphenicol (m/z = 195.075) and carnitine (m/z 162.11) were further selected based on m/z ratio for in silico and in vitro analyses. The in silico analysis revealed that citrulline, chloramphenicol and carnitine inhibited the virulent genes phcA (R. solanacearum) and ste12 (F. oxysporum). Further, under in vitro condition, citrulline and chloramphenicol were found to inhibit the growth of R. solanacearum and F. oxysporum. On the basis of the biocontrol activity of B. subtilis (SSL2) in in silico and in vitro conditions, the bacteria could be used as a biocontrol agent against both bacterial and fungal wilt-causing pathogens. However, this needs to be tested in pot studies or field conditions before being used as biocontrol agents.Communicated by Ramaswamy H. Sarma.

Computational Systems Biology of Metabolism in Infection

A systems approach to elucidate the effect of infection on cell metabolism provides several opportunities from a better understanding of molecular mechanisms to the identification of potential biomarkers and drug targets. This is obvious from the fact that we have witnessed the accelerated use of computational systems biology in the last five years to study metabolic changes in pathogen and/or host cells in response to infection. In this chapter, we aim to present a comprehensive review of the recent research by focusing on genome-scale metabolic network models of pathogen-host systems and genome-wide metabolomics and fluxomics analysis of infected cells.