Biology subjects

Okada, T.

Publications and source records attributed to Okada, T..

At least 19 recordsLinked to original sources

bioRxiv2026

Adolescent prodromal phenotypes of psychiatric disorders: insights from a novel DISC1-perturbed monkey model

Neurodevelopmental deficits frequently lead to mild but significant adolescent social and cognitive changes, representing prodromal manifestations of adult-onset psychiatric disorders in a cross-disease manner. Although experimental medicine utilizes animal models to address the mechanisms, rodent brains lack critical structures for human higher brain functions such as the dorsolateral prefrontal cortex. To fill this translational gap, we generated monkeys with an impaired DISC1 gene locus, targeting the multifunctional protein essential for neurodevelopment and involved in transdiagnostic mental health risks. Here we analyze the F0 animals by minimizing mosaicism. At ages equivalent to human adolescence, these monkeys exhibited blunted responsiveness to social stimuli and increased internal drives, mirroring altered social behaviors in the prodromal stage in humans. Resting-state functional MRI data indicated disrupted global functional connectivity and reorganization of network topology, marked by a shift in the dominance of brain hubs from prefrontal regions to sensory areas. This primate model provides a unique platform for studying psychiatric disorders, such as schizophrenia, across the developmental trajectory.

neuroscience↗

bioRxiv2026

IL-10 Overexpression Improves Cerebral Microcirculation and Attenuates Cerebral Vasospasm After Experimental SAH

BACKGROUND: Subarachnoid hemorrhage (SAH) induces inflammatory responses and subsequent immune cell activation, which may contribute in cerebral vasospasm, microcirculatory impairment and poor neurological outcomes. Although cerebral vasospasm has traditionally been considered a major cause of delayed cerebral ischemia after SAH, therapies targeting angiographic vasospasm have not consistently improved functional outcomes. Early inflammatory responses may contribute to microcirculatory impairment, cerebral vasospasm, and subsequent neurological injury. Herein, we investigated whether interleukin-10 (IL-10), an anti-inflammatory cytokine, improves these outcomes in an experimental SAH model. METHODS: Mice received intramuscular injections of either an adeno-associated virus encoding IL-10 (AAV/IL-10) vector or an AAV expressing green fluorescent protein (AAV/GFP) vector (control). India ink angiography was performed to assess the diameter of the sphenoidal segment of the middle cerebral artery (MCA), the total length of the visible cortical arteries, and cortical staining intensity, as indices of cerebral vasospasm, microcirculatory impairment, and cerebral perfusion, respectively. Perivascular inflammatory cell infiltration and cytokine levels were assessed using immunohistochemistry and ELISA. We also evaluated the therapeutic efficacy of the AAV/IL-10 vector when administered immediately after SAH induction. RESULTS: IL-10 overexpression significantly improved neurological outcomes after SAH and was associated with attenuated cerebral vasospasm and microcirculatory impairment, as well as preservation of cerebral perfusion. It also significantly reduced neutrophil and macrophage infiltration around the internal carotid artery and attenuated SAH-induced elevations in IL-6 and matrix metalloproteinase-3 levels. Mice treated with the AAV/IL-10 vector immediately after SAH induction showed significant improvements in neurological scores and cerebral perfusion. CONCLUSIONS: AAV-mediated IL-10 overexpression improves neurological outcomes after SAH, likely by attenuating inflammatory responses, cerebral vasospasm, and microcirculatory impairment. These findings suggest that IL-10-based anti-inflammatory therapy is a promising therapeutic strategy for SAH.

pathology↗

bioRxiv2026

ALDH3A2 acts as a metabolic safeguard that regulates sphingolipid metabolism to suppress DNA damage and cell death

Highly reactive aldehydes are generated during metabolic processes in the body, and their detoxification is essential for maintaining cellular homeostasis. Hexadecenal, a long-chain fatty aldehyde, is formed during the sphingolipid degradation pathway from the lipid mediator sphingosine-1-phosphate (S1P). However, the cytotoxicity resulting from dysregulation of hexadecenal metabolism is still unclear. To elucidate the effects of impaired hexadecenal metabolism, we analyzed the function of ALDH3A2, an aldehyde dehydrogenase in humans. Our results revealed that ALDH3A2 enzymatic activity is crucial for the suppression of DNA damage, particularly interstrand DNA crosslinks, upon S1P exposure. Furthermore, we demonstrated that hexadecenal accumulation promotes cell death accompanied by the activation of cellular stress responses and morphological abnormalities in the endoplasmic reticulum. These findings suggest that ALDH3A2 functions as a metabolic safeguard to suppress DNA damage and cell death in response to the enhanced metabolic flux of hexadecenal.

molecular biology↗

bioRxiv2026

Inferring Migration Networks with Time-Lagged F2 Statistics

Major demographic events, such as population bottlenecks, founder effects, range expansions, and admixture events, have left lasting imprints on human genetic diversity. Ancient DNA (aDNA) sequencing now makes it increasingly possible to observe these signals across time, opening new avenues to address long-standing questions in human demographic history. Yet, deciphering this genetic archive of demographic history is challenging due to high levels of noise, and the complex ways in which demographic processes shape genetic variation. Here, leveraging the linear time evolution of the expectation of neutral allele frequencies, we develop a method to uncover systematic patterns of gene flow from metapopulation time-series data. We show that directional migration rates can be inferred via linear regression on time-dependent genetic dissimilarity between populations, quantified by an extended F2 statistic evaluated between successive time points. Despite small sample sizes, the method reliably infers migration rates from simulated data by integrating information across multiple time slices. Applied to aDNA sampled from the last 6000 years, we recover signals of well-documented migrations and infer an ancient pan-European migration network. While complementing existing tools that estimate static ancestry proportions, our framework tracks how ancestry is dynamically redistributed through time.

evolutionary biology↗

bioRxiv2025

Identifying Phenotype-Indicative Molecules from the Structure of Biochemical Reaction Networks

Multistability in chemical reaction networks (CRNs) is a key mechanism underlying the diversity in cellular phenotypes. Identifying the steady state a cell occupies requires measuring molecular concentrations. This in turn raises a fundamental question: which chemical species should be selected to reliably distinguish among multiple steady states? We introduce a network-structural approach for identifying indicator species--a subset of species whose concentrations alone suffice to identify which steady state the system occupies. By decomposing a CRN into subnetworks based on structural criteria and applying topological degree theory, we show that the concentrations within certain subnetworks uniquely determine those of all remaining species. These subnetworks thus provide indicator species that distinguish all possible steady states. Crucially, our method relies only on stoichiometric and regulatory information, without requiring kinetic parameters. Implemented via a computational algorithm, the framework is validated through numerical simulations and applied to biochemical networks. This work provides a principled strategy for steady-state identification in high-dimensional biochemical networks from partial observations, with potential applications in single-cell data analysis and biomarker discovery.

biophysics↗

bioRxiv2025

A Novel MiRNA Detection System Combining TWJ-SDA,Multistep L-TEAM, and CRISPR-Cas3

MicroRNAs (miRNAs) serve critical regulatory roles in gene expression and are valuable biomarkers for early disease detection. However, their inherent low concentration in biological fluids poses significant detection challenges. Although traditional methods like real-time quantitative PCR (RT-qPCR) are highly sensitive, they require thermal cycling, limiting their application in point-of-care testing (POCT). Here, we present an isothermal amplification-based miRNA detection system integrating Three-Way Junction (TWJ) formation, Multistep Low-Temperature Amplification (L-TEAM), and CRISPR-Cas3-mediated signal amplification. The integration of the Multistep L-TEAM with the TWJ method achieves high sensitivity, detecting miRNA at concentrations as low as 10 femtomolar within 50 minutes, and effectively distinguishes single-nucleotide mismatches. When CRISPR-Cas3-mediated reaction was integrated, it still proved effective for confirming the presence of the target, but its quantitative reliability requires further optimization. We developed a predictive model using machine learning to facilitate rational optimization of experimental conditions through contribution analysis and to establish a methodology for designing more favorable sequences. The modular nature of our method permits adaptation to diverse miRNA targets without modifications to the fundamental amplification mechanism.

molecular biology↗

bioRxiv2025

CRISPR/Cas9-mediated genome editing reveals the involvement of a polyphenol oxidase in the shikonin-specific biosynthesis in Lithospermum erythrorhizon

Shikonin, a 1,4-naphthoquinone derivative produced by several Boraginaceae species, exhibits unique pharmacological properties and is used as a natural dye. The regulatory factors of shikonin production have been demonstrated using a cell culture system of Lithospermum erythrorhizon. Among these factors, copper is known to be the strongest enhancer of shikonin production. Although shikonin biosynthesis has been studied for over 40 years, the steps of naphthalene ring formation are still unknown, as is the reason for the effect of copper. In this study, we explored candidate genes associated with shikonin production using a PCR-select subtraction experiment. Polyphenol oxidase (PPO), a dicopper-dependent oxidoreductase, was highlighted because it showed synchronous expression with shikonin production. Transcriptome analysis of hairy roots and cultured cells of this plant revealed that, of the five PPO genes expressed in L. erythrorhizon, only PPO1 showed a strong correlation with shikonin production. Next, we generated genome-edited hairy roots of LePPO1 using CRISPR/Cas9-mediated mutagenesis to analyze its impact on shikonin derivative and other specialized metabolite production. The results showed that shikonin content was markedly reduced in all LePPO1-ge lines. Interestingly, the content of deoxyshikonofuran, a hydroquinone derivative and shunt product that branches after GHQ-3''-OH in the shikonin biosynthetic pathway, remained unaffected in the LePPO1-ge lines. These findings suggest that LePPO1 participates in naphthalene ring formation and explain why a copper ion is crucial for shikonin biosynthesis.

biochemistry↗

bioRxiv2025

Systemic aldehyde storm induced by allyl alcohol exposure results in extensive hepatic ferroptosis in Aldh2*2 knock-in mice

Aldehyde dehydrogenase 2 (ALDH2) is a mitochondrial enzyme that detoxifies multiple aldehyde species in the body. The ALDH2*2 allele (E487K) is one of the most common gene polymorphisms in humans, resulting in dysfunction of its enzyme activity. This study investigated in vivo mechanism of acute liver injury caused by exposure to allyl alcohol (AA) using Aldh2*2 knock-in (KI) mice with the same amino acid replacement as human ALDH2*2. A rapid burst of plasma acrolein as an active metabolite of AA, as well as plasma endogenous reactive aldehydes malondialdehyde (MDA) and formaldehyde, was observed at 10 min after exposure to 75 mg/kg of AA in the Aldh2*2 KI homozygous mice, which was not evident in the wild-type mice. Hepatocellular necrosis was more extensive in the Aldh2*2 KI homozygous mice than in the wild-type mice, which was preceded by hepatic glutathione depletion and was coincident with an accumulation of acrolein, MDA, and 4-hydroxy-2-nonenal adducts and iron deposition, suggesting an involvement of ferroptosis in the exacerbation of hepatic necrosis. Recovery from hepatic glutathione depletion was delayed in the homozygous mice compared with the wild-type mice, with a decreasing tendency of hepatic expression of cystine transporter xCT. These results suggest that increased hepatic glutathione consumption, due to decreased aldehyde detoxification capacity, can sensitize the Aldh2*2 KI homozygous mice to hepatic ferroptosis after the rapid "aldehyde storm". Our study revealed a crosstalk between aldehyde metabolism and ferroptosis pathways and potential health impacts of endogenous reactive aldehydes on ALDH2*2 carriers.

pathology↗

bioRxiv2025

Unsupervised feature computation-based feature selection robustly extracted resting-state functional connectivity patterns related to mental disorders

Research on biomarkers for predicting psychiatric disorders from resting-state functional connectivity (FC) is advancing. While the focus has primarily been on the discriminative performance of biomarkers by machine learning, identification of abnormal FCs in psychiatric disorders has often been treated as a secondary goal. However, it is crucial to investigate the effect size and robustness of the selected FCs because they can be used as potential targets of neurofeedback training or transcranial magnetic stimulation therapy. Here, we incorporated approximately 5,000 runs of resting-state functional magnetic resonance imaging from six datasets, including individuals with three different psychiatric disorders (major depressive disorder [MDD], schizophrenia [SCZ], and autism spectrum disorder [ASD]). We demonstrated that an unsupervised feature-computation-based feature selection method can robustly extract FCs related to psychiatric disorders compared to other conventional supervised feature selection methods. We found that our proposed method robustly extracted FCs with larger effect sizes from the validation dataset compared to different types of feature selection methods based on supervised learning for MDD (Cohens d = 0.40 vs. 0.25), SCZ (0.37 vs. 0.28), and ASD (0.17 vs. 0.16). We found 78, 69, and 81 essential FCs for MDD, SCZ, and ASD, respectively, and these FCs were mainly thalamic and motor network FCs. The current study showed that the unsupervised feature-computation-based feature selection method robustly identified abnormal FCs in psychiatric disorders consistently across datasets. The discovery of such robust FCs will contribute to understanding neural mechanisms as abnormal brain signatures in psychiatric disorders. Furthermore, this finding can aid in developing precise therapeutic interventions, such as neurofeedback training or transcranial magnetic stimulation therapy.

neuroscience↗

bioRxiv2025

Global Identification of Highly Expandable and Shrinkable Proteins via Intramolecular Distance Scoring Analysis

Experimental and analytical characterizations of protein structural changes are essential for understanding various biological functions. It is well known that each protein or protein complex undergoes shape changes in a specific manner and to a certain extent, influenced by factors such as molecular interactions and the solvent environment. To enable quantitative comparisons of shape changes among multiple proteins, we previously introduced an index called the "UnMorphness Factor" (UMF), which is derived using a simple, original method-- intramolecular distance scoring analysis (DSA). Using this approach, we have accumulated data for over 15,000 proteins. Here, we show that among the proteins exhibiting significant structural changes, some are found to undergo substantial isotropic volume expansion or contraction. The uniformity of three-dimensional size changes can be represented by a linear correlation between the average and standard deviation of all intramolecular C-C distances across a proteins structural set. Packing analysis of such proteins reveals that the void volume within the same polypeptide chain can increase by up to 50% relative to the reference state. Our study clearly demonstrates that this type of structural change plays a crucial role in understanding the detailed processes of ligand binding, catalytic reaction cycles, and protein folding pathways.

biophysics↗

bioRxiv2024

Uncovering Bifurcation Behaviors of Biochemical Reaction Systems from Network Topology

The regulation of biological functions is achieved through the modulation of biochemical reaction network dynamics. The diversity of cell states and the transitions between them have been interpreted as bifurcations in these dynamics. However, due to the complexity of networks and limited knowledge of reaction kinetics, bifurcation behaviors in biological systems remain largely underexplored. To address this, we developed a mathematical method, Structural Bifurcation Analysis (SBA), which decomposes the system into substructures and determines important aspects of bifurcation behaviors--such as substructures responsible for bifurcation conditions, bifurcation-inducing parameters, and bifurcating variables--solely from network topology. We establish a direct relationship between SBA and classical bifurcation analysis, enabling the study of systems even in the presence of conserved quantities. Additionally, we provide a step-by-step bifurcation analysis for general use. We applied our method to the macrophage M1/M2 polarization system. Our analysis reveals that the network structure strongly constrains possible patterns of polarization. We also clarify the dependency of the M1/M2 balance on gene expression levels and predict the emergence of intermediate polarization patterns under gene deletions, including SOCS3, which are experimentally testable.

biophysics↗

bioRxiv2024

Comprehensive gene expression analysis of organoid-derived healthy human colonic epithelium and cancer cell line by stimulated with live probiotic bacteria

The large intestine has a dense milieu of indigenous bacteria, generating a complex ecosystem with crosstalk between individual bacteria and host cells. In vitro host cell modeling and bacterial interactions at the anaerobic interphase have elucidated the crosstalk molecular basis. Although classical cell lines derived from patients with colorectal cancer including Caco-2 cells are used, whether they adequately mimic normal colonic epithelial physiology is unclear. To address this, we performed transcriptome profiling of Caco-2 and Monolayer cells derived from healthy Human Colonic Organoid (MHCO) cultured hemi-anaerobically. Coculture with the anaerobic gut bacteria, Bifidobacterium longum subsp. longum differentiated the probiotic effects of test cells from those of physiologically normal intestinal and colorectal cancer cells. We cataloged non- or overlapping gene signatures where gene profiles of Caco-2 cells represented absorptive cells in the small intestinal epithelium, and MHCO cells showed complete colonic epithelium signature, including stem/progenitor, goblet, and enteroendocrine cells colonocytes. Characteristic gene expression changes related to lipid metabolism, inflammation, and cell-cell adhesion were observed in cocultured live Bifidobacterium longum and Caco-2 or MHCO cells. B. longum-stimulated MHCO cells exhibited barrier-enhancing characteristics, as demonstrated in clinical trials. Our data represent a valuable resource for understanding gut microbe and host cell communication.

ecology↗

bioRxiv2024

Computational Mechanisms of Neuroimaging Biomarkers Uncovered by Multicenter Resting-State fMRI Connectivity Variation Profile

Resting-state functional connectivity (rsFC) is increasingly used to develop biomarkers for psychiatric disorders. Despite progress, development of the reliable and practical FC biomarker remains an unmet goal, particularly one that is clinically predictive at the individual level with generalizability, robustness, and accuracy. In this study, we propose a new approach to profile each connectivity from diverse perspective, encompassing not only disorder-related differences but also disorder-unrelated variations attributed to individual difference, within-subject across-runs, imaging protocol, and scanner factors. By leveraging over 1500 runs of 10-minute resting-state data from 84 traveling-subjects across 29 sites and 900 participants of the case-control study with three psychiatric disorders, the disorder-related and disorder-unrelated FC variations were estimated for each individual FC. Using the FC profile information, we evaluated the effects of the disorder-related and disorder-unrelated variations on the output of the multi-connectivity biomarker trained with ensemble sparse classifiers and generalizable to the multicenter data. Our analysis revealed hierarchical variations in individual functional connectivity, ranging from within-subject across-run variations, individual differences, disease effects, inter-scanner discrepancies, and protocol differences, which were drastically inverted by the sparse machine-learning algorithm. We found this inversion mainly attributed to suppression of both individual difference and within-subject across-runs variations relative to the disorder-related difference by weighted-averaging of the selected FCs and ensemble computing. This comprehensive approach will provide an analytical tool to delineate future directions for developing reliable individual-level biomarkers.

neuroscience↗

bioRxiv2024

BayesianSSA: a Bayesian statistical model based on structural sensitivity analysis for predicting responses to enzyme perturbations in metabolic networks

BackgroundChemical bioproduction has attracted attention as a key technology in a decarbonized society. In computational design for chemical bioproduction, it is necessary to predict changes in metabolic fluxes when up-/down-regulating enzymatic reactions, that is, responses of the system to enzyme perturbations. Structural sensitivity analysis (SSA) was previously developed as a method to predict qualitative responses to enzyme perturbations on the basis of the structural information of the reaction network. However, the network structural information can sometimes be insufficient to predict qualitative responses unambiguously, which is a practical issue in bioproduction applications. To address this, in this study, we propose BayesianSSA, a Bayesian statistical model based on SSA. BayesianSSA extracts environmental information from perturbation datasets collected in environments of interest and integrates it into SSA predictions. ResultsWe applied BayesianSSA to synthetic and real datasets of the central metabolic pathway of Escherichia coli. Our result demonstrates that BayesianSSA can successfully integrate environmental information extracted from perturbation data into SSA predictions. In addition, the posterior distribution estimated by BayesianSSA can be associated with the known pathway reported to enhance succinate export flux in previous studies. ConclusionsWe believe that BayesianSSA will accelerate the chemical bioproduction process and contribute to advancements in the field.

bioinformatics↗

bioRxiv2024

PURIFICATION METHOD FOR ADENO-ASSOCIATED VIRAL VECTOR SEROTYPE 9 USING CERAMIC HYDROXYAPATITE CHROMATOGRAPHY AND ITS QUALIFICATION

Adeno-associated virus (AAV) vectors can efficiently transduce exogenous genes into various tissues in vivo. Owing to their convenience, high efficiency, long-term stable gene expression, and minimal side effects, AAV vectors have become one of the gold standards for investigating gene functions in vivo, especially in non-clinical studies. However, challenges persist in efficiently preparing a substantial quantity of high-quality AAV vectors that comprise only genome-packaged full particles. Commercial AAV vectors are typically associated with high costs. Further, in-laboratory production is hindered by the lack of specific laboratory equipment, such as ultracentrifuges. Therefore, a simple, quick, and scalable preparation method for AAV vectors is needed for proof-of-concept experiments. Herein, we present an optimized method for producing and purifying high-quality AAV serotype 9 (AAV9) vectors using standard laboratory equipment and chromatography. Using ceramic hydroxyapatite as a mixed-mode chromatography medium can markedly increase the quality of purified AAV vectors. Basic protocols and optional methods for evaluating purified AAV vectors are also described.

molecular biology↗

bioRxiv2023

Actin dynamics switches two distinct modes of endosomal fusion in yolk sac visceral endoderm cells

Membranes undergo various patterns of deformation during vesicle fusion, but how this membrane deformation is regulated and contributes to fusion remains unknown. In this study, we developed a new method of observing the fusion of individual late endosomes and lysosomes by using yolk sac visceral endoderm cells that have huge endocytic vesicles. We found that there were two distinct fusion modes that were differently regulated. In homotypic fusion, two late endosomes fused quickly, whereas in heterotypic fusion they fused to lysosomes slowly. Mathematical modeling showed that vesicle size is a critical determinant of these fusion types and that membrane fluctuation forces can overcome the vesicle size effects. We found that actin filaments were bound to late endosomes and forces derived from dynamic actin remodeling were necessary for quick fusion during homotypic fusion. Furthermore, cofilin played a role in endocytic fusion by regulating actin turnover. These data suggest that actin promotes vesicle fusion for efficient membrane trafficking in visceral endoderm cells.

cell biology↗

bioRxiv2023

Patterns of change in regulatory modules of chemical reaction systems induced by network modification

Cellular functions are realized through the dynamics of chemical reaction networks formed by thousands of chemical reactions. Numerical studies have empirically demonstrated that small differences in network structures among species or tissues can cause substantial changes in dynamics. However, the existence of a general principle for behavior changes in response to network structure modifications is not known. The chemical reaction system possesses substructures called buffering structures, which are characterized by a certain topological index being zero. It was proven that the steady-state response to modulation of reaction parameters inside a buffering structure is localized in the buffering structure [1, 2, 3]. In this study, we developed a method to systematically identify the loss or creation of buffering structures induced by the addition of a single degradation reaction from network structure alone. This makes it possible to predict the qualitative and macroscopic changes in regulation that will be caused by the network modification. This method was applied to two reaction systems: the central metabolic system and the mitogen-activated protein kinases (MAPK) signal transduction system. Our method enables identification of reactions that are important for biological functions in living systems. Significance StatementDynamics of complex biological systems have been understood using mathematical models[4, 5, 6, 7]; however, various assumptions are necessary to construct models for large networks such as those found in the life sciences, and discussing the relation between the network structure and its behavior is difficult. Our study using structural sensitivity analysis [1, 2, 3] shows that directly discussing changes in the structure and behavior of a reaction network without making assumptions about kinetics is possible. This enabled the prediction of changes in behavior depending on whether a reaction is present or not and identify reactions that are essentially important for biological behavior.

biophysics↗

bioRxiv2023

Finding regulatory modules of chemical reaction systems

Within a cell, numerous chemical reactions form chemical reaction networks (CRNs), which are the origins of cellular functions. We previously developed a theoretical method called structural sensitivity analysis (SSA) [1], which enables us to determine, solely from the network structure, the qualitative changes in the steady-state concentrations of chemicals resulting from the perturbations to a parameter. Notably, if a subnetwork satisfies specific topological conditions, it is referred to as a buffering structure, and the effects of perturbations to the parameter within the subnetwork are localized to the subnetwork (the law of localization) [2, 3]. A buffering structure can be the origin of modularity in the regulation of cellular functions generated from CRNs. However, an efficient method to search for buffering structures in a large CRN has not yet been established. In this study, we proved the "inverse theorem" of the law of localization, which states that a certain subnetwork exhibiting a confined response range is always a buffering structure. In other words, we are able to identify buffering structures in terms of sensitivities rather than the topological conditions. By leveraging this property, we developed an algorithm to enumerate all buffering structures for a given network by calculating the sensitivity. In addition, using the inverse theorem, we demonstrated that the hierarchy among nonzero responses is equivalent to the hierarchy of buffering structures. Our method will be a powerful tool for understanding the regulation of cellular functions generated from CRNs.

systems biology↗