Back

Cooperative siderophore use stabilizes a protective leaf microbiome

Stincone, P.; Braun, L. M.; Bagci, C.; Navarro-Diaz, M.; Perez-Lorente, A. I.; Farrell, S. P.; Gomez-Perez, D.; Bode, J.; Steuer-Lodd, K.; Mahmoudi, M.; Chaudhry, V.; Romero, D.; Aron, A. T.; Ziemert, N.; Molina-Santiago, C.; Kemen, E.; Petras, D.

2026-03-18 microbiology
10.64898/2026.03.18.712463 bioRxiv
Show abstract

Plant-associated microbial communities provide crucial protection against pathogens. Specialized metabolites play key roles in plant-microbe and microbe-microbe interactions and, ultimately, in plant health; however, the molecular mechanisms underlying their plant-protecting properties remain largely unknown. Nutrient deficiency (e.g., iron) on leaf surfaces creates intense competition among microbes, driving both antagonism and cooperation. Using a gnotobiotic Arabidopsis thaliana model and a synthetic leaf microbial community, we show that community stability and plant protection depend on cooperative siderophore exchange between the basidiomycete yeast Rhodotorula kratochvilovae and commensal Pseudomonas species. Removal of Pseudomonas caused a strong shift in the community metabolome and accumulation of the yeast siderophore rhodotorulic acid (RA). RA selectively promoted the growth of commensal Pseudomonas via TonB-dependent transporters, which are absent in pathogenic Pseudomonas strains. Inactivation of these transporter genes abolished RA uptake, destabilized the synthetic community, and eliminated protection against Pseudomonas syringae infection. RA and Rhodotorula also induced host iron-deficiency and jasmonate-related defense metabolites, linking microbial cooperation to plant stress responses. These findings reveal that microbial siderophore exchange acts as a key mechanism that maintains stability in the phyllosphere microbiome. Rather than solely promoting competition, iron-binding compounds can serve as cooperative currencies that align microbial fitness with host protection.

Matching journals

The top 2 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.