Genome- and MS-based mining of antibacterial chlorinated chromones and xanthones from the phytopathogenic fungus Bipolaris sorokiniana strain 11134.
Jianying HanJingyu ZhangZhijun SongMiaomiao LiuJiansen HuChengjian HouGuoliang ZhuLan JiangXuekui XiaRonald J QuinnYunjiang FengJiaqian CaoTom HsiangXueting LiuPublished in: Applied microbiology and biotechnology (2019)
Halogen substituents are important for biological activity in many compounds. Genome-based mining of halogenase along with its biosynthetic gene cluster provided an efficient approach for the discovery of naturally occurring organohalogen compounds. Analysis of the genome sequence of a phytopathogenic fungus Bipolaris sorokiniana 11134 revealed a polyketide gene cluster adjacent to a flavin-dependent halogenase capable of encoding halogenated polyketides, which are rarely reported in phytopathogenic fungi. Furthermore, MS- and UV-guided isolation and purification led to the identification of five chlorine-containing natural products together with seven other chromones and xanthones. Two of the chlorinated compounds and four chromones are new compounds. Their structures were elucidated by NMR spectroscopic analysis and HRESIMS data. The biosynthetic gene clusters of isolated compounds and their putative biosynthetic pathway are also proposed. One new chlorinated compound showed activity against Staphylococcus aureus, methicillin-resistant S. aureus, and three clinical-resistant S. aureus strains with a shared minimum inhibitory concentration (MIC) of 12.5 μg/mL. Genome-based mining of halogenases combined with high-resolution MS- and UV-guided identification provides an efficient approach to discover new halogenated natural products from microorganisms.
Keyphrases
- high resolution
- staphylococcus aureus
- genome wide
- mass spectrometry
- multiple sclerosis
- copy number
- ms ms
- escherichia coli
- dna methylation
- magnetic resonance
- genome wide identification
- small molecule
- molecular docking
- machine learning
- drinking water
- high throughput
- gene expression
- methicillin resistant staphylococcus aureus
- single cell
- electronic health record
- biofilm formation
- liquid chromatography
- artificial intelligence
- simultaneous determination