Soldatou S(1), Eldjárn GH(2), Ramsay A(2), van der Hooft JJJ(3), Hughes AH(1), Rogers S(2), Duncan KR(1). Author information:
(1)Strathclyde Institute of Pharmacy and Biomedical Sciences, University of
Strathclyde, Glasgow G4 0RE, UK.
(2)School of Computing Science, University of Glasgow, Glasgow G12 8RZ, UK.
(3)Bioinformatics Group, Wageningen University, 6708 PB Wageningen, The
Biosynthetic and chemical datasets are the two major pillars for microbial drug discovery in the omics era. Despite the advancement of analysis tools and platforms for multi-strain metabolomics and genomics, linking these information sources remains a considerable bottleneck in strain prioritisation and natural product discovery. In this study, molecular networking of the 100 metabolite extracts derived from applying the OSMAC approach to 25 Polar bacterial strains, showed growth media specificity and potential chemical novelty was suggested. Moreover, the metabolite extracts were screened for antibacterial activity and promising selective bioactivity against drug-persistent pathogens such as Klebsiella pneumoniae and Acinetobacter baumannii was observed. Genome sequencing data were combined with metabolomics experiments in the recently developed computational approach, NPLinker, which was used to link BGC and molecular features to prioritise strains for further investigation based on biosynthetic and chemical information. Herein, we putatively identified the known metabolites ectoine and chrloramphenicol which, through NPLinker, were linked to their associated BGCs. The metabologenomics approach followed in this study can potentially be applied to any large microbial datasets for accelerating the discovery of new (bioactive) specialised metabolites.
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