[P008] Poster

1:00 PM - 1:50 PM JST(4:00 AM - 4:50 AM UTC)

[P04-546] Identification of Unique Environmental Bacteria Using a Metabolic Pathway-Based Genome Analytical Tool

○Suzune Honda¹, Masayoshi Wada¹, Kazuki Toyoda¹, Tetsushi Mori¹ (1. Tokyo University of agriculture and Technology (Japan))

Keywords:

Unique traits,Environmental bacteria,Genome analytical tool,Metabolic pathway

Bacteria inhabit diverse environments from soil and freshwater to marine ecosystems, evolving varied physiological and metabolic functions enabling adaptation to distinct conditions. This diversity makes bacteria valuable genetic and biochemical resources utilized in industrial, environmental, and biomedical applications. Recent advances in next-generation sequencing have made bacterial genome data increasingly accessible, allowing researchers to investigate microbial diversity at unprecedented scales and uncover previously unrecognized taxa and functional capabilities. Various bioinformatics tools have been developed to predict bacterial traits and identify organisms with useful characteristics from genomic information. However, many existing approaches rely on complex computational frameworks or require specialized expertise, limiting practical use when rapid evaluation of bacterial uniqueness is needed. We introduce a genome analysis tool designed to efficiently identify unique and potentially beneficial bacteria through comparative metabolic pathway profiling. Rather than focusing on marker genes, this tool evaluates broader metabolic potential encoded in each genome and summarizes pathway-level characteristics in simplified output format for cross-strain comparison. The central concept is that bacterial uniqueness can be inferred by examining differences in presence, absence, or relative enrichment of metabolic pathways among genomes, enabling global comparison of functional traits. By converting genome annotation data into interpretable metabolic profiles, the tool provides a practical framework for identifying strains with distinctive biological potential. To assess validity, we applied the tool to model bacterial strains with available genomic information. Genome-derived metabolic pathway profiles were analyzed to determine whether the system could capture characteristic traits and functional distinctions. Through comparative analysis among multiple genomes, the tool successfully identified pathway patterns reflecting predicted metabolic differences and highlighted unique features associated with each strain. This evaluation demonstrated that the tool could distinguish bacteria based on broader functional capacities rather than taxonomic identity alone. We subsequently applied the tool to environmental bacterial isolates. Draft genomes were generated using nanopore sequencing and annotated for metabolic pathway analysis. Resulting profiles were compared across isolates to investigate whether the tool could detect functionally distinctive bacteria within environmental collections. Analysis revealed unique metabolic pathways in individual isolates and highlighted functional differences between strains, suggesting effectiveness for screening environmental bacteria with diverse metabolic capabilities. The simplified output system enabled straightforward comparative interpretation of annotated pathways, allowing inference of ecological roles and functional specialization within microbial populations. These results demonstrate our tool's potential as an efficient method for characterizing bacterial diversity functionally. This approach may contribute to microbial ecology studies, support discovery of bacteria with useful industrial properties, and provide practical strategy for prioritizing environmental isolates for investigation.