Biology subjects

Ishizawa, H.

Publications and source records attributed to Ishizawa, H..

4 recordsLinked to original sources

bioRxiv2026

Evolution of herbicide resistance through co-option of an ethylene-associated nitrilase

Adaptive evolution frequently proceeds through the co-option of pre-existing biological functions, yet the molecular mechanisms by which such co-option generates new adaptive traits over contemporary evolutionary timescales remain poorly understood. Herbicide-resistant weeds provide a powerful model for addressing this question because enhanced herbicide metabolism has repeatedly evolved within only a few decades. Here, we show that resistance to cyhalofop-butyl in Echinochloa crus-galli evolved through the co-option of EcNIT4A2, which encodes a nitrilase that normally functions in ethylene-associated cyanide detoxification. Genetic mapping, transcriptomics and transgenic analyses identified EcNIT4A2 as the major determinant of resistance. A D317G substitution enhanced its herbicide-detoxifying activity. Molecular dynamics simulations indicated that this substitution facilitates substrate access to the catalytic pocket. A promoter structural variant markedly increased EcNIT4A2 expression. Our findings reveal how coding and regulatory changes can jointly drive the co-option and optimization of a conserved metabolic function during rapid adaptation under intense anthropogenic selection.

plant biology↗

bioRxiv2026

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↗

bioRxiv2025

Decoding the emergent properties of microbial community functions through sub-community observations and interpretable machine learning

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; Pearsons 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.

microbiology↗

bioRxiv2023

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↗