Chemical cues responsible for pathogen virulence in Acute Oak Decline (AOD)

Interactions between insects and bacteria often play a key role in plant disease, and bacteria can produce chemical signals which influence insect behaviour. However, little is known about the impact of insect chemical cues on bacteria in plant pathosystems. Here, we investigated these inter-kingdom interactions in a tree disease called Acute Oak Decline (AOD). AOD is characterised by stem bleeds, bacterial inner bark necrosis and larval galleries of the bark-boring beetle Agilus biguttatus, eventually leading to tree death. The tissue necrosis is mainly due to Brenneria goodwinii, a bacterium known to possess several putative virulence genes. These genes are over-expressed in tree hosts that are invaded by the bark-boring beetle Agrilus biguttatus. In this study, we characterised the chemical cues produced by the beetle larvae using liquid chromatography and gas spectrometry. We then tested their effect on bacterial growth and gene expression using in vitro cultures and RNAseq. Our results show that chemical cues from the beetle larvae are bioactive toward the bacterial pathogen. They increase its growth rate, increase its final density, and we are investigating the modifications in its gene expression profile including putative virulence genes. Our work confirms that this multi-kingdom interaction plays a significant role in the establishment of the disease and may require more attention in our attempts to mitigate AOD. We also show that bacteria can respond to chemical cues from insects, which have rarely been studied and may be important in other plant diseases.

This work is part of the BAC-STOP project.

• Head of the project: Sandra Denman
• Postdoc advisor: James McDonald

Next-Generation Biomonitoring of forest health

This project aims at finding bioindicators of tree drought stress using environmental DNA. We investigated the potential signature of tree water stress in leaf microbiota using single gene community profiling. We then used machine learning techniques to predict tree water stress from their leaf microbiota composition. This work is part of the ANR project NGB.

• Head of the project: David Bohan
• Postdoc advisor: Corinne Vacher

Heterogeneity within infections

This project addresses multiple sources of heterogeneity during a pathogenic infection. We first investigated within-host evolution of an insect pathogen (heterogeneity in the pathogen population) as well as interactions between the pathogen and the microbiota of its host (heterogeneity in the total bacterial population).

• PhD supervisors: Jean-Baptiste Ferdy and Sophie Gaudriault