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Biology subjects

Ike, M.

Publications and source records attributed to Ike, M..

2 recordsLinked to original sources

Competition-induced metabolic switching governs the interaction network in plant microbiome assembly

Plants are colonized by characteristic microbiomes that help maintain plant health and function. However, the mechanisms that make these communities assemble in a consistent, repeatable manner remain poorly understood. Here, we show how microbial metabolic strategies for host-derived substrates drive plant microbiome assembly, using a six-member synthetic community that captures the taxonomic diversity of natural duckweed microbiome. Contrary to expectations of metabolic niche partitioning, five of six strains adopt similar metabolic states when colonizing the host alone, consistent with the preferential use of a shared substrate, acetaldehyde. In contrast, during competition with specific community members, these strains consistently switch toward alternative substrates, including sugars and aromatics. Strikingly, this metabolic switching accompanies all negative interspecies interactions observed in the community, indicating that a single class of metabolic response dominates the inhibitory interaction network organizing community structure. By comparison, although metabolite cross-feeding is widespread, its contribution to facilitative interactions depends on the background of resource competition. Together, these findings highlight competition-induced metabolic switching as a primary driver of microbial interaction networks, and provide a cross-scale account of how microbial metabolic strategies underpin the reproducible assembly of plant microbiomes.

microbiology↗

Learning beyond-pairwise interactions enables the bottom-up prediction of microbial community structure

The way to deal with higher-order effects (i.e., modification of pairwise interactions by third-party species) has been a major consideration in community ecology. Ignoring these effects is not in line with reality, yet fully considering them make the situation overly complex. Here, we propose a simple framework incorporating higher-order effects into a bottom-up community modeling, and assessed its validity using a seven-member synthetic bacterial community on a host plant, duckweed. Our findings revealed that actual interspecies interactions in community could not be predicted from pairwise co-culturing results; however, using information from trio combinations allowed for acceptable prediction. In addition, inclusion of four-, five-, and six-member combinations did not markedly enhance the prediction accuracy from trio-based prediction, suggesting that trio combinations, the smallest unit of higher-order effects, provide a reasonable baseline to unravel complex interaction networks. Building on this finding, we developed a prediction rule to estimate the structure of 4 - 7 member communities based on information from [≤] 3-member combinations, which yielded significantly better accuracy (relative mean square percentage errors of 22.7% - 61.2%) than pairwise-based model (53.5% - 185.2%). This highlights the possibility of establishing a quantitative link between the interspecies interactions and community structure, by observing beyond-pairwise combinations.

ecology↗