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Dorrestein, P.

Publications and source records attributed to Dorrestein, P..

3 recordsLinked to original sources

Adaptation of Bacillus subtilis upon interaction with Setophoma terrestris results in loss of surfactin and plipastatin production

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.

microbiology

Did a plant-herbivore arms race drive chemical diversity in Euphorbia?

The genus Euphorbia is among the most diverse and species-rich plant genera on Earth, exhibiting a near-cosmopolitan distribution and extraordinary chemical diversity, especially across highly toxic macro-and polycyclic diterpenoids. However, very little is known about drivers and evolutionary origins of chemical diversity within Euphorbia. Here, we investigate 43 Euphorbia species to understand how geographic separation over evolutionary time has impacted chemical differentiation. We show that the structurally highly diverse Euphorbia diterpenoids are significantly reduced in species native to the Americas, compared to the Eurasian and African continents, where the genus originated. The localization of these compounds to young stems and roots suggest ecological relevance in herbivory defense and immunomodulatory defense mechanisms match diterpenoid levels, indicating chemoevolutionary adaptation to reduced herbivory pressure.\n\nOne Sentence SummaryGlobal chemo-evolutionary adaptation of Euphorbia affected immunomodulatory defense mechanisms.

evolutionary biology

Significance estimation for large scale untargeted metabolomics annotations

The annotation of small molecules in untargeted mass spectrometry relies on the matching of fragment spectra to reference library spectra. While various spectrum-spectrum match scores exist, the field lacks statistical methods for estimating the false discovery rates (FDR) of these annotations. We present empirical Bayes and target-decoy based methods to estimate the false discovery rate. Relying on estimations of false discovery rates, we explore the effect of different spectrum-spectrum match criteria on the number and the nature of the molecules annotated. We show that the spectral matching settings needs to be adjusted for each project. By adjusting the scoring parameters and thresholds, the number of annotations rose, on average, by +139% (ranging from -92% up to +5705%) when compared to a default parameter set available at GNPS. The FDR estimation methods presented will enable a user to define the scoring criteria for large scale analysis of untargeted small molecule data that has been essential in the advancement of large scale proteomics, transcriptomics, and genomics science.

bioinformatics