Back

Global metabolome changes induced by environnementaly relevant conditions in a marine-sourced Penicillium restrictum

Le, V.-T.; BERTRAND, S.; Brandolini Bunlon, M.; Gentil, E.; Robiou du Pont, T.; Rabesaotra, V.; Wielgosz Collin, G.; Mossion, A.; Grovel, O.

2023-09-06 microbiology
10.1101/2023.09.06.556477 bioRxiv
Show abstract

Marine fungi have been found in all habitats and are able to adapt to their environmental niche conditions. In this study, a combination of LC-HRMS and GC-MS analytical approaches was used to analyse the whole metabolic changes of a marine sourced Penicillium restrictum strain isolated from a marine shellfish area. The P. restrictum MMS417 strain was grown on seven different media including an ecological one with two different water sources (synthetic sea water and distilled water) conditions following the OSMAC approach. Extracts of all media were analysed by LC-HRMS (lipids and specialised metabolites profiling) and GC-MS (fatty acids profiling). Aquired data were analysed using a multiblock strategy to highlight metabolic modification in regards to water conditions and to environmentally relevant conditions (mussel-based culture medium). This revealed that fatty acid composition of lipids was the most altered part of the explored metabolisms either looking to water effect and to environmentally relevant conditions. In particular, data showed that P. restrictum MMS417 is able to produce lipids that include fatty acids usually produced by the mussel itself. This study also provides insight into the P. restrictum adaptation to marine salinity through fatty acids alteration. and shows that lipid metabolisms if far more altered in an OSMAC approach than the specialized metabolism. This study finally highlights the need for using environnementmimicking culture conditions to reveal the metabolic potentialities of marine microbes.

Matching journals

The top 11 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.