Back

Large-scale culturing of the tree microbiome enables targeted disease suppression

Ordonez-Ordonez, A.; Cambon, M. C.; Downie, J.; Kajamuhan, A.; Hussain, U.; Crampton, B.; Richardson, M.; Jones, M.; Green, R.; Pettifor, B.; Pyne, E.; Finch, J.; Brady, C.; Denman, S.; McDonald, J. E.

2025-11-18 microbiology
10.1101/2025.11.18.689053 bioRxiv
Show abstract

The tree microbiome is essential for host health and pathogen suppression. Synthetic microbial communities (SynComs) are emerging as important tools to understand microbiome dynamics and engineer microbiomes to harness beneficial properties. However, while the rational design, assembly and application of SynComs requires representative microbiota isolate collections combined with functional information, microbial culture collections from tree species such as oak (Quercus) are critically lacking. Here, we generated an oak microbiota culture collection comprising >30,000 isolates from 150 oak trees across Britain, belonging to key bacterial and fungal taxa that represented 61% of the total bacterial sequences and 87% of total fungal sequences as determined by culture-independent sequencing. Over 22,000 isolates were screened for suppression of bacterial species associated with degradation of live stem tissue in trees affected by Acute Oak Decline (AOD), identifying 341 bacterial isolates that suppressed oak pathogens. In vitro screening of 40 randomly assembled SynComs demonstrated that oak microbiota SynComs can suppress oak pathogenic bacteria associated with AOD. Inoculation of a disease-suppressive SynCom into the stem of oak seedlings and logs prior to pathogen challenge reduced the quantities of the bacteria, Brenneria goodwinii and Gibbsiella quercinecans, by 56% and 87%, respectively, in seedlings, and 71% and 95% in logs. This work demonstrates that the tree microbiome can be engineered using disease suppressive SynComs.

Matching journals

The top 4 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.