Metabolomic profiling, biological evaluation of Aspergillus awamori, the river Nile-derived fungus using epigenetic and OSMAC approaches

LC-HRMS-based metabolomics approach was applied to the river Nile-derived fungus Aspergillus awamori after its fermentation on four different media and using four epigenetic modifiers as elicitors. Thereafter, a comprehensive multivariate statistical analysis such as PCA, PLS-DA and OPLS-DA were employed to explain the generated metabolomic data (1587 features). PCA showed that the fungus displayed a unique chemical profile in each medium or elicitor. Additionally, PLS-DA results revealed the upregulated metabolites under each of these conditions. Results indicated that both rice and malt dextrose agar were recognized as the best media in terms of secondary metabolites diversity and showed better profiles than the four applied epigenetic modifiers, of which nicotinamide was the best secondary metabolite elicitor. Testing the antibacterial and cytotoxic effects of all A. awamori-derived extracts revealed that using epigenetic modifiers can induce antimicrobial metabolites against S. aureus and E. coli, whereas using rice, malt dextrose or nicotinamide can induce groups of cytotoxic metabolites. OPLS-DA results assisted in the putative identification of the induced metabolites that could be responsible for these observed inhibitory activities. This study highlighted how powerful the OSMAC approach in maximizing of the chemical diversity of a single organism. Furthermore, it revealed the power of metabolomics in tracing, profiling and categorizing such chemical diversity and even targeting the possible bioactive candidates which require further scaling up studies in the future.

Automated summary

The LC-HRMS-based metabolomics approach revealed metabolic differences of Aspergillus awamori derived from the river Nile when fermented on different media and epigenetic modifiers, with nicotinamide being the most effective secondary metabolite elicitor. The study demonstrated the power of OSMAC approach in maximizing chemical diversity and the potential of metabolomics in tracing, profiling, and categorizing chemical diversity for identifying bioactive candidates.