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Hiromatsu, K.

Publications and source records attributed to Hiromatsu, K..

3 recordsLinked to original sources

Host-like Temperature Unlocks Glucose Uptake and Metabolism in Pathogenic Leptospira

Pathogenic Leptospira transitions between environmental reservoirs and mammalian hosts, expose the bacterium to distinct temperature conditions. Although Leptospira is generally considered to rely its growth primarily on long-chain fatty acids and to have limited capacity to use glucose, whether host-like temperature alters glucose-associated metabolic capacity remains unclear. Here, we used transcriptome-integrated genome-scale metabolic modeling to examine temperature-dependent metabolic states in pathogenic Leptospira. Models contextualized with transcriptomic data from cultures at 37{degrees}C predicted increased flux through glucose transport, glucose phosphorylation, and downstream glucose-associated reactions than it did at 29-30{degrees}C. Similar temperature-dependent predictions were obtained for another pathogenic Leptospira species under in vitro conditions. Consistent with these findings, analysis of leptospires cultivated intraperitoneally in dialysis membrane chambers also predicted enhanced glucose uptake and metabolism under host-temperature conditions. In vitro validation experiments showed increased bacterial uptake or accumulation of a fluorescent glucose analogue, elevated expression of candidate glucose transporter genes, and glucose-dependent enhancement of bacterial growth at 37{degrees}C. Treatment with a glucose degradation pathway inhibitor, 2-deoxy-D-glucose further supported a contribution of glucose-associated processes to proliferation at 37{degrees}C. Together, these findings indicate that pathogenic Leptospira displays condition-dependent glucose uptake and glucose-associated metabolic activity that become apparent at host-like temperature, opposed to glucose-incompetent in an environmental-like temperature. This prediction-driven framework refines the conventional view of Leptospira carbon metabolism and provides a basis for future studies of glucose-associated metabolic capacity during mammalian infection. ImportanceEnvironmental reservoirs play a central role in the transmission of many bacterial pathogens, yet the metabolic mechanisms that enable adaptation to both environmental and host-associated niches remain largely unknown. Using a prediction-driven framework that combines contextualized genome-scale metabolic modeling with multi-omics analyses and experimental validation, we discovered that pathogenic Leptospira activates glucose uptake and metabolism only under host-like conditions. This finding resolves a long-standing misconception arising from studies performed under conventional laboratory conditions and fundamentally revises our understanding of Leptospira physiology. More importantly, our study establishes an integrative systems biology strategy for uncovering condition-dependent metabolic traits that would be difficult to identify experimentally alone, with broad applicability to diverse environmentally transmitted pathogens.

microbiology↗

Soil bacterium Massilia secretes metabolites that promote Leptospira growth

Understanding pathogen metabolism is critical for identifying key functions for drug targeting, establishing effective in vitro experimental systems, etc., particularly for metabolically unique organisms such as Leptospira. Pathogenic Leptospira are thought to infect humans from environmental sources; however, direct isolation from environmental samples remains technically challenging and is not yet well established. Here, we report that a ubiquitous environmental bacterium, Massilia sp., produces metabolites that promote the growth of Leptospira interrogans, encountered through an incidental contamination event, and identified in this study. Gas chromatography-tandem mass spectrometry (GC-MS/MS) analysis showed demonstrated that cultivating of Massilia sp. in R2A medium resulted in the accumulation of metabolites, including branched-chain amino acid (BCAA) intermediates, compared to fresh medium. By combining genome-scale metabolic modeling with experimental validation using cell-free culture supernatant supplementation assays, we demonstrate that BCAA intermediates, particularly 2-ketoisocaproic acid (4-methyl-2-oxopentanoate; 4MOP), a leucine biosynthetic intermediate produced by Massilia sp., enhance Leptospira growth. To investigate the metabolic role of 4MOP, we incorporated transcriptomic data into a genome-scale metabolic network model to generate condition-specific models. Resulted flux distributions indicated that Leptospira catabolized imported 4MOP to produce acetyl-CoA. Our results reveal a previously unrecognized metabolic interaction where metabolites produced by environmental bacteria support the growth of pathogenic Leptospira, offering mechanistic insight into its metabolic requirement. These findings have implications to understand the environmental persistence of Leptospira through its metabolic dependencies on coexisting microbes, and they also help develop better strategies for this pathogen. ImportancePathogenic Leptospira persist in environmental reservoirs, yet the mechanisms supporting their growth remain poorly defined. Here, we find that metabolites produced by common environmental bacteria, Massilia sp., can promote Leptospira growth, suggesting a previously unrecognized metabolic dependency on coexisting microbes. Importantly, this study indicates that combining genome-scale metabolic modeling with experimental validation provides a useful framework for identifying metabolic interactions that are otherwise difficult to resolve using conventional culture-based approaches. Current strategy may facilitate the systematic identification of growth-supporting metabolites and provide a basis for improving selective cultivation for uncultured or difficult to culture organisms. Determination of growth promoting metabolites advances our understanding of pathogen persistence in natural environments and offers a generalized framework to study metabolically dependent microorganisms.

microbiology↗

A Deep Learning-Based Evaluation for Microscopic Agglutination Test

The Microscopic Agglutination Test (MAT) is widely recognized as the gold standard for diagnosing zoonosis leptospirosis. However, its reliance on examiners subjective evaluations often leads to inconsistent results. To address this limitation, we propose a deep neural network replicating the agglutination rate estimation in MAT. By leveraging a pre-trained DenseNet121, the network parameters are optimized efficiently during training. We validated our approach using an in-house dataset, and experimental results show that the proposed network achieves accurate agglutination rate estimates. In addition, we employed a standard visualization technique to elucidate the decision-making process, revealing that the network captures image features indicative of leptospire abundance. Overall, our findings suggest that deep learning effectively estimates agglutination rates and that enhancing interpretability supports medical experts in understanding the underlying functionality of deep learning models.

microbiology↗