石澤 秀紘

Abstract The functions of microbial communities, including substrate conversion and pathogen suppression, arise not as a simple sum of individual species’ capabilities but through complex interspecies interactions. Understanding how such functions arise from individual species and their interactions remains a major challenge, limiting efforts to rationally understand microbial roles in both natural and engineered ecosystems. Because current holistic (meta-omics) and reductionist (isolation- or single-cell-based) approaches struggle to capture these emergent microbial community functions, this study explores an intermediate strategy: analyzing simple sub-community combinations to enable a bottom-up understanding of community-level functions. To examine the validity of this approach, we used a nine-member synthetic microbial community capable of degrading the environmental pollutant aniline, and systematically generated a dataset of 256 sub-community combinations and their associated functions. Analyses using random forest models revealed that the sub-community combinations of just three to four species enabled the quantitative prediction of functions in larger communities (5–9-member; Pearson’s r = 0.78–0.80). Prediction performance remained robust even with limited sub-community data, suggesting applicability to more diverse microbial communities where exhaustive sub-community observation is infeasible. Moreover, interpreting models trained on these simple sub-community combinations enabled the identification of key species and interspecies interactions that strongly influence the overall community function. These findings provide a methodological framework for mechanistically dissecting complex microbial community functions through sub-community-based analysis.

ABSTRACT Understanding the processes through which plant‐associated microbiomes influence host physiology and fitness is a central goal of plant–microbiome interaction research. While traditional model plants such as Arabidopsis thaliana have provided foundational platforms to examine these processes, alternative model systems may address certain bottlenecks in current research. In recent years, duckweeds (family Lemnacea) have emerged as a unique model plant offering several experimental advantages owing to their small size, simple morphology, aquatic habitat, and two‐dimensional clonal growth. These features facilitate the establishment of highly tractable and reproducible model systems that facilitate robust investigations and high‐throughput screening platforms, enabling multifactorial massive parallel experiments. This review provides an overview of the recent studies that have applied the advantages of using duckweed in the field of plant–microbiome interactions to highlight how duckweed‐based systems have enabled unique experimental approaches that are difficult in conventional systems. We have also discussed the emerging directions in duckweed–microbiome research, including elucidation of the co‐evolutionary processes mediated via metabolic exchange and bottom‐up explanation of community structure and functions using synthetic bacterial communities. Together, this review underscores the potential of duckweed to serve as a distinctive model for advancing plant–microbiome interaction research.