Biology subjects

Kusuhara, H.

Publications and source records attributed to Kusuhara, H..

14 recordsLinked to original sources

bioRxiv2026

Biosensor-enabled single-molecule enzyme activity profiling for function-based molecular counting

Single-molecule enzyme activity analysis enables proteoform-resolved quantification of catalytically active enzyme molecules but remains limited by the need for enzyme-specific fluorogenic substrates, particularly for enzymes involved in central metabolism. Here we establish a biosensor-based framework that integrates genetically encoded fluorescent metabolite biosensors with single-molecule enzyme activity profiling (SEAP), enabling native enzymatic reactions to be monitored without enzyme-specific fluorogenic substrates. By employing different biosensors, the framework was applied to distinct metabolic enzymes, including lactate dehydrogenase and pyruvate kinase. Biosensor-based SEAP enables absolute quantification of active enzyme molecules, transforming conventional activity measurements (U/mL) into molecular abundance (molecules/mL). Applying this approach to single-cell functional proteoform analysis revealed functional heterogeneity in lactate dehydrogenase activity during T-cell activation. In addition, analysis of circulating enzymes showed that much of the plasma lactate dehydrogenase pool could be quantitatively accounted for by physiological erythrocyte turnover. These results establish genetically encoded fluorescent protein-based metabolite biosensors as modular optical transducers for single-molecule functional enzyme analysis across cellular and circulating samples.

biochemistry↗

bioRxiv2026

Single-Molecule Activity Profiling of Glycosidase Proteoforms Using Water-Soluble Fluorogenic Probes

In this study, we report a series of highly water-soluble fluorogenic probes for glycosidases useful for microdevice-based single-molecule enzyme activity profiling (SEAP). The assay was able to detect trace glycosidase activities in blood with high sensitivity and with proteoform resolution. This platform revealed the potential of -mannosidase as sensitive blood-based liquid-biopsy biomarkers for liver injury, highlighting the potential of this approach for activity-based diagnostics.

biochemistry↗

bioRxiv2026

Synthesis based on covalent capture and release (SCCR): a programmable strategy for automated preparation of protease-activatable molecules

Enzyme-activatable chemical tools, including fluorogenic probes and prodrugs, are essential in chemical biology and targeted therapeutics but remain challenging to access in structurally diverse forms because their synthesis is often bespoke and difficult to standardize. Here, we introduce synthesis based on covalent capture and release (SCCR) as a programmable chemical strategy that enables the modular assembly of protease-activatable molecules through specifically designed protecting-group logic. The SCCR framework establishes a standardized capture-elongation- release workflow that decouples molecular diversification from individual synthetic optimization, thereby enabling automated preparation of complex libraries. Using this chemistry, we generated a diverse set of fluorogenic probes and applied them to single-molecule enzyme activity analyses to identify candidate activity-based biomarkers of liver diseases. The generality of the SCCR strategy was further demonstrated by extending the same chemical logic to the preparation of antibody-drug conjugate (ADC) linkers, allowing systematic evaluation of plasma stability and cytotoxic potential. By establishing a programmable capture-release chemistry for the synthesis of enzyme-activatable molecules, this work provides a generalizable chemical foundation for the scalable and automated construction of functional small-molecule tools across biological and translational research.

biochemistry↗

bioRxiv2025

Designing Cell Cell Relation Extraction Models: A Systematic Evaluation of Entity Representation and Pre-training Strategies

Extracting cell-cell relations from biomedical literature is essential for understanding intercellular communication in immunity, inflammation, and tissue biology. However, cell-cell relation extraction has not been established as a standalone biomedical relation extraction task, and no benchmark corpus or systematic evaluation framework currently exists. Fully manual corpus construction is costly and difficult to scale, limiting literature-based analyses of cell-cell communication. Here, we define a sentence-level cell-cell relation extraction task and construct complementary manually annotated corpora under realistic annotation constraints. To enable scalable annotation, rule-based literature mining is used solely as an annotation accelerator to identify candidate sentences, while all relation labels are assigned manually. In addition, an independently annotated PubMed corpus without rule-based filtering is constructed to evaluate robustness on natural sentence distributions. Using these resources, we evaluate representative model configurations involving entity indication strategies, classification architectures, and continued pre-training. Our results show that cell-cell relation extraction remains challenging under realistic conditions. Increasing training data size yields consistent performance gains, and specific combinations of entity-aware architectures and continued pre-training provide modest robustness improvements. Nevertheless, performance on unfiltered PubMed sentences remains in the 70% accuracy range, and error analyses indicate that failures cannot be readily explained by simple surface-level factors. Comparisons with general-purpose large language models further suggest that task complexity, rather than model class, is the primary limiting factor. Together, these findings establish a practical foundation for literature-scale cell-cell relation extraction while clarifying its intrinsic limitations.

bioinformatics↗

bioRxiv2025

Development of single-molecule enzyme activity assay for serine hydrolases using activity-based protein labeling probes

Single-molecule enzyme activity assays have proven their potential in elucidating aberrant protein function at the proteoform level. However, the limited number of targetable enzymes is the major drawback of such assays. Here, we report the development of single-molecule enzyme activity assays utilizing activity-based probes that label active enzymes in an enzyme superfamily-wide manner. A proof-of-principle using fluorophosphonate-based probes was conducted to detect the active form of serine hydrolases such as PSA and granzyme B at the single-molecule level in complex biological systems such as blood. The assay suggested the potential of active granzyme B in blood as a suitable biomarker of liver damage with specific activation of immune cells.

biochemistry↗

bioRxiv2025

Mitorubin, berberrubine-based compounds that improve mitochondrial function, exhibit cardioprotective effects against age-related cardiac dysfunction

Mitochondria play a central role in cellular energy metabolism and homeostasis, and their dysfunction is closely linked to the progression of age-related diseases. The mitochondrial ubiquitin ligase MITOL (also known as MARCHF5) is a key regulator of mitochondrial dynamics and function, and reduced MITOL expression in the mouse heart has been implicated in mitochondrial dysfunction and cardiac aging. In this study, we identified berberrubine as a compound that promotes MITOL expression and activates mitochondria. We further assembled a group of berberrubine-based compounds, including its quinoid form and a newly developed water-soluble derivative, and collectively named them "Mitorubin" as mitochondria-activating compounds with therapeutic potential. While conventional berberrubine has poor water solubility, the addition of acetic acid significantly improved its solubility, enabling formulation as a solution. Mitorubin enhanced MITOL expression in cultured cells, increased mitochondrial DNA content and expression of mitochondrial proteins, and promoted mitochondrial respiration. In a model of age-related cardiac dysfunction, oral administration of Mitorubin restored mitochondrial function, improved cardiac performance, suppressed myocardial hypertrophy, and alleviated pulmonary congestion. Moreover, Mitorubin did not shorten lifespan in aged mice and significantly extended lifespan in high-fat diet-fed mice, suggesting both safety and efficacy under chronic administration. These findings suggest that Mitorubin is a promising mitochondrial activator and may represent a novel therapeutic strategy for age-related diseases.

biochemistry↗

bioRxiv2024

SLC35G1: A highly chloride-sensitive transporter responsible for the basolateral membrane transport in intestinal citrate absorption

The intestinal absorption of essential nutrients, especially those not readily biosynthesized, is a critical physiological process for maintaining homeostasis. Numerous studies have indicated that intestinal absorption is mediated by various membrane transporters. Citrate, a crucial bioactive compound produced as an intermediate in the Krebs cycle, is absorbed in the small intestine through carrier-mediated systems because of its high hydrophilicity. While the luminal absorption of citrate is mediated by Na+-dicarboxylate cotransporter 1 (NaDC1/SLC13A2), the mechanism governing the release of the transported citrate into the bloodstream remains unknown. Here, we explored the transporters responsible for intestinal citrate absorption at the basolateral membrane, focusing on highly expressed orphan transporters in the small intestine as candidates. Consequently, SLC35G1, originally identified as a partner of stromal interaction molecule 1, a cell surface transmembrane glycoprotein, was found to play a role in the intestinal absorption of citrate at the basolateral membrane. Furthermore, our results revealed that SLC35G1-mediated citrate transport was diminished by chloride ions at physiologically relevant extracellular concentrations. This suggests that SLC35G1, to our best knowledge, is the first transporter identified to be extremely sensitive to chloride ions among those functioning on the basolateral membrane of intestinal epithelial cells. This study provides valuable insights into the intestinal absorption of citrate and significantly contributes to elucidating the poorly understood molecular basis of the intestinal absorption system.

physiology↗

bioRxiv2024

Establishment of an easy-to-construct liver injury mouse model for longitudinal analysis by drinking-water administration of 4,4'-methylenedianiline

Background & AimsLongitudinal animal models are essential for understanding the temporal dynamics of liver injury and recovery. While drinking water-based administration is ideal for sustained exposure in high feasibility, available compounds are limited, with thioacetamide (TAA) being the primary option. Here, we aimed to establish a novel drinking water-induced mouse model of cholestatic liver injury using 4,4-methylenedianiline (MDA), and to characterize its pathological trajectory in comparison to the TAA model. MethodsMice were administered MDA via drinking water for 28 Days. To elucidate the early events that give rise to chronic pathological divergence, we conducted a multi-layered analysis comprising plasma biochemical assays, immune cell profiling by flow cytometry, and hepatic transcriptomics at five time points during the early phase. The MDA model was evaluated against the established TAA model. ResultsMDA administration induced sustained ALT elevation, peribiliary fibrosis, and spatially irregular focal hepatocellular necrosis, distinguishing it from the centrilobular injury observed with TAA. Additionally, the MDA model showed significant elevations in ALP, TBIL, and TCHO, indicating cholestatic liver dysfunction. Early-phase analyses revealed model-specific differences in immunological and molecular responses, including increased CD8 T cell populations and enrichment of fibrinolysis-related gene expression in MDA-DW mice. ConclusionsWe present the MDA-DW model as a novel, longitudinally tractable liver injury model that complements existing systems by capturing alternative spatial, immunological, and transcriptional patterns of injury. This model offers a valuable platform for dissecting the temporal dynamics of liver disease progression in experimental settings. Significance StatementWe developed a cost-effective, non-invasive mouse model of cholestatic liver injury using drinking-water administration of 4,4-methylenedianiline (MDA). This model exhibits periportal-predominant damage, peribiliary fibrosis, and spatially irregular focal hepatocellular necrosis, distinct from conventional centrilobular models. Early-phase multi-omics analysis revealed immunological and transcriptomic differences, including increased CD8 T cells and activation of fibrinolysis-related pathways. The low mortality rate and ease of implementation enable long-term studies and cross-sectional comparisons across time points or interventions. This study provides not only a practical model for investigating chronic liver injury, but also a rich time-series, multi-view dataset, offering a valuable resource for advancing research on liver pathophysiology and toxicological mechanisms.

pharmacology and toxicology↗

bioRxiv2024

GLDADec: marker-gene guided LDA modelling for bulk gene expression deconvolution

Inferring cell type proportions from bulk transcriptome data is crucial in immunology and oncology. Here, we introduce GLDADec (Guided LDA Deconvolution), a bulk deconvolution method that guides topics using cell type-specific marker gene names to estimate topic distributions for each sample. Through benchmarking using blood-derived datasets, we demonstrate its high estimation performance and robustness. Moreover, we apply GLDADec to heterogeneous tissue bulk data and perform comprehensive cell type analysis in a data-driven manner. We show that GLDADec outperforms existing methods in estimation performance and evaluate its biological interpretability by examining enrichment of biological processes for topics. Finally, we apply GLDADec to TCGA tumor samples, enabling subtype stratification and survival analysis based on estimated cell type proportions, thus proving its practical utility in clinical settings. This approach, utilizing marker gene names as partial prior information, can be applied to various scenarios for bulk data deconvolution. GLDADec is available as an open-source Python package at https://github.com/mizuno-group/GLDADec.

bioinformatics↗

bioRxiv2023

Investigation of normalization procedures for transcriptome profiles of compounds oriented toward practical study design

The transcriptome profile is a representative phenotype-based descriptor of compounds, widely acknowledged for its ability to effectively capture compound effects. However, the presence of batch differences is inevitable. Despite the existence of sophisticated statistical methods, many of them presume a substantial sample size. How should we design a transcriptome analysis to obtain robust compound profiles, particularly in the context of small datasets frequently encountered in practical scenarios? This study addresses this question by investigating the normalization procedures for transcriptome profiles, focusing on the baseline distribution employed in deriving biological responses as profiles. Firstly, we investigated two large GeneChip datasets, comparing the impact of different normalization procedures. Through an evaluation of the similarity between response profiles of biological replicates within each dataset and the similarity between response profiles of the same compound across datasets, we revealed that the baseline distribution defined by all samples within each batch under batch-corrected condition is a good choice for large datasets. Subsequently, we conducted a simulation to explore the influence of the number of control samples on the robustness of response profiles across datasets. The results offer insights into determining the suitable quantity of control samples for diminutive datasets. It is crucial to acknowledge that these conclusions stem from constrained datasets. Nevertheless, we believe that this study enhances our understanding of how to effectively leverage transcriptome profiles of compounds and promotes the accumulation of essential knowledge for the practical application of such profiles.

bioinformatics↗

bioRxiv2023

Investigation of latent representation of toxicopathological images extracted by CNN model for understanding compound properties in vivo

Toxicopathological images acquired during safety assessment elucidate an individuals biological responses to a given compound, and their numerization can yield valuable insights contributing to the assessment of compound properties. Currently, toxicopathological images are mainly encoded as pathological findings, evaluated by pathologists, which introduces challenges when used as input for modeling, specifically in terms of representation capability and comparability. In this study, we assessed the usefulness of latent representations extracted from toxicopathological images using Convolutional Neural Network (CNN) in estimating compound properties in vivo. Special emphasis was placed on examining the impact of learning pathological findings, the depth of frozen layers during learning, and the selection of the layer for latent representation. Our findings demonstrate that a machine learning model fed with the latent representation as input surpassed the performance of a model directly employing pathological findings as input, particularly in the classification of a compounds Mechanism of Action and in predicting late-phase findings from early-phase images in repeated-dose tests. While learning pathological findings did improve accuracy, the magnitude of improvement was relatively modest. Similarly, the effect of freezing layers during learning was also limited. Notably, the selection of the layer for latent representation had a substantial impact on the accurate estimation of compound properties in vivo.

bioinformatics↗

bioRxiv2023

Rat Deconvolution as Knowledge Miner for Immune Cell Trafficking from Toxicogenomics Databases

Toxicogenomics databases are useful for understanding biological responses in individuals because they include a diverse spectrum of biological responses. Although these databases contain no information regarding immune cells in the liver, which are important in the progression of liver injury, deconvolution that estimates cell-type proportions from bulk transcriptome could extend immune information. However, deconvolution has been mainly applied to humans and mice and less often to rats, which are the main target of toxicogenomics databases. Here, we developed a deconvolution method for rats to retrieve information regarding immune cells from toxicogenomics databases. The rat-specific deconvolution showed high correlations for several types of immune cells between spleen and blood, and between liver treated with toxicants compared with those based on human and mouse data. Additionally, we found 4 clusters of compounds in Open TG-GATEs database based on estimated immune cell trafficking, which are different from those based on transcriptome data itself. The contributions of this work are three-fold. First, we obtained the gene expression profiles of 6 rat immune cells necessary for deconvolution. Second, we clarified the importance of species differences on deconvolution. Third, we retrieved immune cell trafficking from toxicogenomics databases. Accumulated and comparable immune cell profiles of massive data of immune cell trafficking in rats could deepen our understanding of enable us to clarify the relationship between the order and the contribution rate of immune cells, chemokines and cytokines, and pathologies. Ultimately, these findings will lead to the evaluation of organ responses in Adverse Outcome Pathway.

bioinformatics↗

bioRxiv2023

Investigation of the usefulness of liver-specific deconvolution method toward legacy data utilization

BackgroundImmune responses in the liver are related to the development and progression of liver failure, and precise prediction of their behavior is important. Deconvolution is a methodology for estimating the immune cell proportions from the transcriptome, and it is mainly applied to blood-derived samples and tumor tissues. However, the influence of tissue-specific modeling on the estimation results has rarely been investigated. In this study, we constructed a system to evaluate the performance of the deconvolution method on liver transcriptome data. ResultsWe prepared seven mouse liver injury models using small-molecule compounds with known hepatotoxicity and established a dataset with corresponding liver bulk RNA-Seq and immune cell proportions, covering various immune responses. RNA-Seq expression for nine leukocyte subsets and four liver-associated cell types were obtained from the Gene Expression Omnibus (GEO) to provide a reference covering liver component cells. Here, we found that the combination of reference cell sets affects the estimation results of reference-based deconvolution methods. We established a liver tissue-specific deconvolution by optimizing the reference cell set for each cell to be estimated. We applied this model to independent datasets and showed that liver-specific modeling focusing on reference cell sets is highly extrapolatable. ConclusionsWe provide an approach of liver-specific modeling when applying reference-based deconvolution to bulk RNA-Seq data and show its importance. It is expected to enable sophisticated estimation from rich tissue data accumulated in public databases and to obtain information on aggregated immune cell trafficking.

bioinformatics↗

bioRxiv2018

Orthogonal linear separation analysis: an approach to decompose the complex effect of a perturbagen

Drugs have multiple, not single, effects. Decomposition of drug effects into basic components helps us to understand the pharmacological properties of a drug and contributes to drug discovery. We have extended factor analysis and developed a novel profile data analysis method, orthogonal linear separation analysis (OLSA). OLSA contracted 11,911 genes to 118 factors from transcriptome data of MCF7 cells treated with 318 compounds in Connectivity Map. Ontology of the main genes constituting the factors detected significant enrichment of the ontology in 65 of 118 factors and similar results were obtained in two other data sets. One factor discriminated two Hsp90 inhibitors, geldanamycin and radicicol, while clustering analysis could not. Doxorubicin was estimated to inhibit Na+/K+ ATPase, one of the suggested mechanisms of doxorubicin-induced cardiotoxicity. Based on the factor including PI3K/AKT/mTORC1 inhibition activity, 5 compounds were predicted to be novel autophagy inducers, and other analysis including western blotting revealed that 4 of the 5 actually induced autophagy. These findings indicate the potential of OLSA to decompose the effects of a drug and identify its basic components. (<175 words)

bioinformatics↗