Biology subjects

Ozuru, R.

Publications and source records attributed to Ozuru, R..

6 recordsLinked to original sources

bioRxiv2026

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↗

bioRxiv2026

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↗

bioRxiv2024

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↗

bioRxiv2021

Light-dependent synthesis of a nucleotide second messenger controls bacterial motility

Nucleotide second messengers are universally crucial factors for the signal transduction of various organisms. In prokaryotes, cyclic nucleotide messengers are involved in the bacterial life cycle and function, such as virulence, biofilm formation, and others mainly via gene regulation. Here we show that the swimming motility of a soil bacterium is rapidly modulated by cyclic adenosine monophosphate (cAMP) synthesized upon light exposure. Analysis of a loss-of-photoresponsivity mutant obtained by transposon random mutagenesis determined the novel sensory gene, and its expression in Escherichia coli through codon optimization revealed the light-dependent synthesis of cAMP. GFP labeling showed the localization of the photoresponsive enzyme at the cell poles where flagellar motors reside. The present findings highlight the new role of cAMP that rapidly controls the flagella-dependent bacterial motility and the global distribution of the discovered photoactivated cyclase among diverse microbial species.

microbiology↗

bioRxiv2021

Stx2 Induces Differential Gene Expression by Activating Several Pathways and Disturbs Circadian Rhythm Genes in the Proximal Tubule.

(1) BackgroundShiga toxin-producing Escherichia coli (STEC) causes proximal tubular defects in the kidney. However, factors altered by Shiga toxin (Stx) within the proximal tubules are yet to be shown. (2) MethodsWe determined Stx receptor Gb3 in murine and human kidneys and confirmed the receptor expression in the proximal tubules. Stx2-injected mouse kidney tissues and Stx2-treated human primary renal proximal tubular epithelial cell (RPTEC) were collected, and microarray analysis was performed. (3) ResultsWe compared murine kidney and RPTEC arrays and selected common 58 genes that are differentially expressed vs. control (0 h, no toxin-treated). We found that the most highly expressed gene was GDF15, which may be involved in Stx2-induced weight loss. Genes associated with previously reported Stx2 activities such as src kinase Yes phosphorylation pathway activation, unfolded protein response (UPR) and ribotoxic stress response (RSR) showed differential expressions. Moreover, circadian clock genes were differentially expressed suggesting Stx2-induced renal circadian rhythm disturbance. Circadian rhythm-regulated proximal tubular Na+-glucose transporter SGLT1(SLC5A1) was down-regulated, indicating proximal tubular functional deterioration, and mice developed glucosuria confirming proximal tubular dysfunction. (4) ConclusionsStx2 alters gene expression in murine and human proximal tubules through known activities and newly investigated circadian rhythm disturbance resulting in proximal tubular dysfunctions.

pathology↗

bioRxiv2020

A Machine Learning Approach Towards Standardizing Microscopic Agglutination Test for Diagnosis of Leptospirosis

Leptospirosis is a zoonosis caused by the pathogenic bacterium Leptospira. The Microscopic Agglutination Test (MAT) is widely used as the gold standard for diagnosis of leptospirosis. In this method, diluted patient serum is mixed with serotype-determined Leptospiras, and the presence or absence of aggregation is determined under a dark-field microscope to calculate the antibody titer. Problems of the current MAT method are 1) a requirement of examining many specimens per sample, and 2) a need of distinguishing contaminants from true aggregates to accurately identify positivity. Therefore, increasing efficiency and accuracy are the key to refine MAT. It is possible to achieve efficiency and standardize accuracy at the same time by automating the decision making process. In this study, we built an automatic identification algorithm of MAT using a machine learning method to determine aggregation within microscopic images. The machine learned the features from 316 positive and 230 negative MAT images created with sera of Leptospira- infected (positive) and non-infected (negative) hamsters, respectively. In addition to the acquired original images, wavelet-transformed images were also considered as features. We utilized a support vector machine (SVM) as a proposed decision method. We validated the trained SVMs with 210 positive and 154 negative images. When the features were obtained from original or wavelet-transformed images, all negative images were misjudged as positive, and the classification performance was very low with sensitivity of 1 and specificity of 0. In contrast, when the histograms of wavelet coefficients were used as features, the performance was greatly improved with sensitivity of 0.99 and specificity of 0.99. We confirmed that the current algorithm judges the positive or negative of agglutinations in MAT images and gives the further possibility of automatizing MAT procedure.

microbiology↗