Quorum sensing inhibitory activity of the metabolome from endophytic Kwoniella sp. PY016: characterization and hybrid model-based optimization

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作者
Abhirup Mookherjee
Ramalingam Dineshkumar
Nithya N. Kutty
Tarun Agarwal
Ramkrishna Sen
Adinpunya Mitra
Tapas Kumar Maiti
Mrinal Kumar Maiti
机构
[1] Indian Institute of Technology Kharagpur,Department of Biotechnology
[2] SRM Institute of Science and Technology,Department of Genetic Engineering, School of Bio
[3] Indian Institute of Technology Kharagpur,Engineering
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关键词
Artificial neural network; Endophyte; Genetic algorithm; sp.; Metabolome; Quorum sensing inhibitor;
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摘要
Quorum sensing, the microbial communication system, is gaining importance as a therapeutic target against pathogens. The two key reasons for the rising demand of quorum sensing (QS) inhibitory molecules are low selective pressure to develop resistance by pathogens and possibility of more species-specific effects. Due to complex interactions in a unique niche of live plant tissues, endophytes, as a survival mechanism, potentially produce various bioactive compounds such as QS inhibitors. We report the isolation of an endophytic fungus Kwoniella sp. PY016 from the medicinal plant “Bahera” (Terminalia bellirica), which exhibits substantial quorum sensing inhibition and anti-biofilm activities against the standard test organism, Chromobacterium violaceum. Sugar, sugar alcohol, carboxylic acid, lipid, and phenolic classes of metabolites (predominantly xylitol) are responsible components of the metabolome for the desired bioactivity. A judicious combination of single-factor-at-a-time strategy and artificial neural network modeling combined with genetic algorithm was employed for the selection and optimization of the critical process and medium parameters. Through this newly adopted hybrid model-based optimization, the quorum sensing inhibitory activity of the endophytic metabolome was increased by ~ 30%. This is the first report on optimization of QS inhibitory activity from any fungal endophyte using such a hybrid advanced approach.
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页码:7389 / 7406
页数:17
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