武尾 正弘

BACKGROUND: Microbial communities play fundamental roles in industrial processes and ecosystem stability. However, understanding how individual members and their interactions give rise to community-level function remains challenging because such functions emerge from complex interactions among diverse members. RESULTS: In this study, we developed SubCom analysis, a subcommunity-based experimental-computational workflow for inferring candidate taxon-specific contributions and interaction contexts underlying microbial community function. Using an aniline-degrading microbial community, we generated paired composition-function data from 558 randomly assembled, low-complexity subcommunities constructed using a dilution-and-dispense strategy. We then trained decision-tree-based models to predict community function from composition, achieving high predictive performance (r = 0.77-0.89). Interpretation of the learned decision rules identified taxa with consistent functional association: specific Pseudomonas and Acinetobacter taxa were associated with increased community-level aniline utilization, whereas an Achromobacter taxon exhibited a negative association despite its presumed role in downstream metabolism. The models further suggested potential functional interactions, including attenuation of the positive contributions of Pseudomonas and Acinetobacter in the presence of a Corynebacterium taxon, highlighting functional relationships that are not readily inferred from genome-based approaches alone. An augmentation assay using representative isolates supported the predicted direction of several effects and enabled targeted improvement of community function. CONCLUSIONS: These results demonstrate the potential of SubCom analysis as a practical framework for inferring taxon-specific contributions and interaction contexts in complex, nonsynthetic microbial communities. Video Abstract.

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 We report the complete genome assembly of a hydroquinonesulfonate-assimilating bacterium, Delftia lacustris strain HQS1. This strain contains one circular chromosome (6,979,964 bp) and one circular plasmid (39,999 bp). The chromosomal sequence contained 6,359 coding sequences and a gene cluster involved in the degradation of gentisate, which is structurally similar to hydroquinonesulfonate.