Development of an efficient fungal micro-cultivation workflow integrating metabolomics and innovative bioassays for efficient antimicrobial hits prioritization

This project aims at the development of a screening platform for efficient antimicrobial hits prioritization originating from fungal strains. Given the size of the collection (> 2500) and the resulting number of extracts, a miniaturized approach was developed ([Fig. 1]).

Fig. 1 Workflow overview.

Culture, extraction, bioassays and LC-MS/MS analysis presented here take advantage of the medium-throughput capacity and compatibility across the platforms confered by the 96-well plate format. All generated extracts were enriched by solid phase extraction (SPE) and systematically submitted to antimicrobial bioassays as well as metabolomic profiling. An hyper-permeable Pseudomonas aeruginosa strain, designed to maximize the detection of antibacterial hits, and a wild type Staphylococcus aureus strain were used as target organisms. The MS data of all extracts were incorporated, together with the bioactivity results, into massive and multi-informative molecular networks [1]. Such spectral organization, annotation and visualization tools facilitate chemical and biological exploration of the fungal collection at a molecular level. Furthermore, these data, in combination with dereplication tools based on mass spectrometry, were used to identify active molecules.

The evaluation of the methodology with state-of-the-art metabolomic workflow revealed sufficient metabolite production and reproducibility at this scale and great compatibility with both bioassays and metabolites profiling. This workflow led to the identification of antimicrobials from Alternaria alternata as a proof of concept. The methodologyʼs efficiency, which allows the microculture, extraction and analysis of hundreds of fungal strains in parallel in only a few days, makes possible mutiplexing experiments to induce biological gene clusters. This tool will be used for efficient antimicrobial hit prioritization.

• Reference

• 1 Olivon F, Allard PM, Koval A. et al. Bioactive Natural Products Prioritization Using Massive Multi-Informational Molecular Networks. ACS Chem Biol 2017; 12: 2644-2651

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Artikel online veröffentlicht:
12. Dezember 2022