Back

Algae bacteria associations provide metabolite-mediated protection against algicidal bacteria in a tripartite plankton community

Siddiqui, S. A.; Zerfass, C.; Nikitashina, V.; Yu, R.; Pohnert, G.

2026-08-28 microbiology
10.64898/2026.08.28.747787 bioRxiv
Show abstract

Microalgal fitness in nature is shaped by interactions within a diverse microbial community, yet most experimental studies have examined algal-bacterial interactions in pairwise systems. It is well established that bacteria can exhibit growth promoting or inhibiting effects on co-existing algae. Comparatively little information is available about how additional partners can alter the outcome of diatom-bacteria interactions. In the present study, we screened the pairwise interaction of the marine diatom Skeletonema marinoi with ten different bacteria. This screening identified Marinobacter adhaerens as a growth promoting and Vibrio cyclitrophicus HSW24 as growth inhibiting partner. Growth inhibition of V. cyclitrophicus was associated with cell lysis, chain fragmentation and altered pigmentation whereas M. adhaerens supported increased chlorophyll a fluorescence, uniform pigmentation, intact chains and healthy cell morphology. In a tripartite community containing both bacteria and the alga, M. adhaerens protected S. marinoi from the inhibitory effect of V. cyclitrophicus in a density dependent manner. Comparative metabolomics revealed distinct metabolic profiles between the pairwise and tripartite interactions. This allowed to identify metabolites that were up-regulated in the tripartite community and therefore candidates for the observed protection. Among these, kynurenic acid and N-acetyltyramine were identified in bioassays as protective molecules, thus clearly highlighting the importance of secondary metabolites in this interaction. The present findings demonstrate that a third bacterial partner can alter the outcome of an antagonistic algal-bacterial interaction by means of chemical mediators. This work has implications for our understanding of microbial community functioning that cannot only be derived from the investigation of pairwise interactions.

Matching journals

The top 6 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.