Back

Fungal-bacterial interaction selects for quorum sensing mutants and a metabolic shift towards the production of natural antifungal compounds

Albarracin Orio, A. G.; Petras, D.; Tobares, R. A.; Aksenov, A.; Wang, M.; Juncosa, F.; Sayago, P.; Moyano, A. J.; Dorrestein, P. C.; Smania, A. M.; Ducasse, D.

2020-01-16 microbiology
10.1101/376590 bioRxiv
Show abstract

Environmental species of bacteria and fungi coexist and interact showing antagonistic and mutualistic behaviors, mediated by exchange of small diffusible metabolites, driving microbial adaptation to complex communal lifestyles1. Here we show that a wild Bacilus subtilis strain undergoes heritable phenotypic variation following interaction with the soil fungal pathogen Setophoma terrestris (ST) in co-culture. Metabolomics analysis revealed a differential profile in B. subtilis before (pre-ST) and after (post-ST) interacting with the fungus, which paradoxically involved the absence of lipopeptides surfactin and plipastatin and yet acquired antifungal activity in post-ST variants. Metabolic changes were also observed in the profile of volatile compounds, with 2-heptanone and 2-octanone being the most discriminating metabolites present at higher concentrations in post-ST during its interaction with the fungus. Most strikingly, both ketones showed strong antifungal activity against S. terrestris, which was lost with the addition of exogenous surfactin to the medium. Whole-genome analyses showed that mutations in the comA and comP genes of the ComQPXA quorum-sensing system, constituted the genetic bases of post-ST conversion, which allowed the concomitant production of ketones and elimination of surfactin. These findings suggest that mutations in ComQXPA stably rewired B. subtilis metabolism towards the depletion of surfactins and the production of antifungal compounds during its antagonistic interaction with S. terrestris.

Matching journals

The top 5 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.