Biology subjects

Oki, S.

Publications and source records attributed to Oki, S..

13 recordsLinked to original sources

bioRxiv2026

Natriuretic Peptide Augmentation Attenuates Renin Cell Hyperactivation and Afferent Arteriolar Hypertrophy During Long-Term Renin-Angiotensin System Inhibition

BACKGROUNDChronic renin-angiotensin system (RAS) inhibition activates renin cells and induces afferent arteriolar hypertrophy, a maladaptive vascular response that may contribute to nephrosclerosis-like renal injury. Although genetic or cell ablation approaches have shown that renin cells are required for this remodeling, no pharmacological strategy to restrain hyperactivated renin cells while preserving RAS inhibition benefits has been established. Natriuretic peptides (NPs) counteract RAS; however, whether NP signaling modulates renin cell activation and afferent arteriolar remodeling remains unclear. METHODSWe examined direct effects of atrial natriuretic peptide (ANP) on As4.1 renin-producing cells using reverse transcription-quantitative PCR, ELISA, and RNA sequencing (RNA-seq). We established a mouse model of long-term RAS inhibition using valsartan, an angiotensin II receptor blocker (ARB), and compared it with sacubitril/valsartan, an angiotensin receptor-neprilysin inhibitor (ARNI). Valsartan was matched between the ARB and ARNI groups. Renin cell activation, afferent arteriolar remodeling, and renal injury were evaluated using biochemical assays, histology, immunostaining, single-nucleus RNA-seq, and region-specific photo-isolation chemistry RNA-seq of afferent arteriolar/juxtaglomerular regions. RESULTSANP suppressed Ren1 expression and renin secretion in As4.1 cells; this effect was attenuated by a natriuretic peptide receptor A antagonist. RNA-seq demonstrated that ANP induced receptor-dependent remodeling of renin cell gene programs. In mice, long-term ARB treatment induced renin cell hyperactivation, expansion of renin-positive juxtaglomerular regions, afferent arteriolar hypertrophy, renal dysfunction, tubular injury markers, and fibrosis. ARNI increased plasma ANP levels and attenuated these pathological changes, despite a comparable blood pressure reduction. Single-nucleus transcriptomics revealed attenuation of tubular injury-associated cellular states and altered renin cell-associated mesenchymal programs with ARNI. Region-specific transcriptomics further demonstrated distinct molecular states in afferent arteriolar/juxtaglomerular regions between ARB- and ARNI-treated kidneys. Integrated transcriptomic analysis suggested that NP signaling converges on vascular regulatory programs in hyperactivated renin-expressing cells. CONCLUSIONSNP signaling acts as a pharmacologically augmentable modulator of maladaptive renin cell activation. ARNI attenuates renin cell hyperactivation, afferent arteriolar hypertrophy, and renal injury during long-term RAS inhibition, suggesting that neprilysin inhibition may preserve RAS blockade benefits while limiting renin cell-driven renal vascular remodeling.

physiology↗

bioRxiv2026

Akkermansia muciniphila-derived LPS links gut dysbiosis to pathogenic miR-21 signaling in experimental autoimmune encephalomyelitis.

Multiple sclerosis (MS) is a chronic T cell-mediated autoimmune disease characterized by blood-brain barrier (BBB) disruption, neuroinflammation, and demyelination of the central nervous system (CNS). Emerging evidence links gut microbiota to disease pathogenesis, but the microbial factors that regulate pathogenic microRNA (miRNA) programs are largely unknown. Here, using experimental autoimmune encephalomyelitis (EAE, a MS mouse model), we investigated whether gut microbiota exacerbate EAE pathogenesis by modulating host miRNA expression. Antibiotic-induced depletion of the gut microbiota markedly attenuated EAE scores and reduced circulating inflammatory miRNAs, with miR-21 emerging as the dominant pathogenic candidate. Functional inhibition of miR-21 significantly ameliorated disease severity and reduced CNS T-cell infiltration. Mechanistically, miR-21 enhanced IL-17 and GM-CSF production by CD4 T cells and promoted immune-cell entry into the CNS through endothelial activation and blood-brain barrier dysfunction. We identified a transient expansion of Akkermansia muciniphila during the prodromal phase of EAE that positively correlated with circulating miR-21 levels. Colonization of antibiotic-treated mice with A. muciniphila exacerbated EAE and increased serum miR-21, whereas monocolonization of germ-free mice was insufficient to induce systemic miR-21, indicating a requirement for an inflammatory host environment. Further analyses revealed that atypical lipopolysaccharides (LPS) derived from A. muciniphila induce epithelial miR-21 production through coordinated TLR2/TLR4 signaling. Circulating miR-21 subsequently promoted endothelial dysfunction through the TIMP3-ADAM17 pathway, facilitating pathogenic T-cell migration into the CNS. Importantly, circulating miR-21 was also elevated in patients with MS. Collectively, these findings identify a previously unrecognized A. muciniphila-LPS-miR-21 axis linking gut dysbiosis to neuroinflammation and suggest that host-derived miRNAs function as systemic mediators through which microbial signals influence CNS autoimmunity.

immunology↗

bioRxiv2025

Uncertainty-aware transcription factor activity and perturbation inference without additional training

Understanding how transcription factors (TFs) control gene expression is essential for deciphering cellular regulatory programs. However, estimating TF activity remains challenging. Current methods rely on either broad TF-target databases, which increase false positives, or on curated sets with limited coverage. Most methods lack uncertainty quantification. Here, we introduce TFActProfiler, an integrated resource and computational framework that learns signed TF-mRNA effects by pretraining on prior TF-mRNA interaction knowledge and large RNA-seq atlases. Given an expression profile as input, TFActProfiler infers per-sample TF activity together with a confidence score that quantifies agreement between predicted and observed mRNA levels. Across TF knockdown datasets spanning multiple human cell types, TFActProfiler consistently improves TF activity inference over approaches based on unfiltered priors or narrowly curated target sets while retaining broad TF and mRNA coverage. The learned effect map further enables training-free prediction of transcriptome responses to TF perturbations. By coupling TF activity inference with training-free perturbation modeling and confidence estimates, TFActProfiler offers a tool for dissecting regulatory programs across diverse cellular states.

bioinformatics↗

bioRxiv2025

The heterochronic activation of TGF-β signaling drives the diversity of the avian sterna

Adaptive evolution has driven the diversification of vertebrate skeletal morphology, enabling a wide array of locomotor modes and lifestyles. In volant birds (carinates), the sternum develops a ventral keel that provides an attachment site for massive pectoral muscles essential for powered flight. In contrast, flightless ratites have lost both flight capability and the keel, resulting in a striking morphological contrast between their sterna. Here, we interrogated the cellular and molecular basis underlying this morphological divergence by using chicken embryos as a carinate model and emu embryos as a ratite model. Through a series of analyses including spatiotemporal transcriptomics and a spheroid culture system, we found that TGF-{beta} signaling, which promotes proliferation of ventral sternal chondroprogenitors, is activated in both species until the stage when the left and right sternal progenitors meet; however, in chicken this activation persists beyond this stage, driving ventral extension of the keel primordium, whereas in emu it shuts off early, culminating in the absence of a protruding keel. Together, our findings suggest that skeletal morphological changes associated with behavioral transitions can arise from heterochrony in developmental signaling, thereby deepening our understanding of the evolutionary logic shaping skeletal diversification.

developmental biology↗

bioRxiv2025

Self-organized fingering instabilities drive the emergence of tissue morphogenesis in digit organoids.

The emergence of complex anatomical structures -such as the hands- from unstructured tissues remains a fundamental question in developmental biology. Turing-type reaction-diffusion models have provided a molecular explanation for the periodic pre-patterning of digits; however, the physical principles driving 3D morphogenesis remain incompletely understood. To identify the biophysical design principles leading to digit formation, we develop a limb-mesenchymal organoid system that spontaneously forms elongated, digit-like protrusions. Iterations between experiments and agent-based models at the cellular level identify sufficient microscopic mechanisms leading to morphogenesis of digit-like structures: symmetry-breaking and the elongation of digits result from a combination of differential cell adhesion and morphogen-induced chemotaxis and convergent-extension. Lastly, to describe tissue-scale deformations, we perform a coarse-graining analysis of the agent-based model and derive a continuum model that reveals a structural analogy to Cahn-Hilliard-type equations. These equations are typically used to describe fluid phase separation and so-called ''fingering instabilities'' in fluid physics. Here, we show that they also accurately describe organoid morphogenesis. These findings suggest that ''finger'' formation is driven by a mechanical fingering instability acting in concert with chemical patterning, shedding a new light on vertebrate limb morphogenesis.

developmental biology↗

bioRxiv2025

Identification of stem cell marker-positive subpopulations in the vocal fold of the larynx through transcriptomic analyses

Information on the maintenance of tissue homeostasis is important for the development of effective therapeutic methods, however, reports on the cellular composition and tissue stem cells of the larynx were scarce. Therefore, we analyzed mouse laryngeal mucosa using scRNA-seq, and spatial transcriptomics by photo-isolation chemistry, and performed the generation of laryngeal organoid used as an in vitro model from mouse laryngeal mucosa. As a result, we found a SOX9-positive basal cell subpopulation and a Lgr5-positive fibroblast subpopulation in the mouse vocal fold, and obtained three types of epithelial organoids from laryngeal epithelium. We also confirmed the differences in pseudostratified ciliated columnar epithelium composition between the supraglottis and subglottis of the mouse larynx. These findings provide novel insights and valuable tools for future research in laryngology and stem cell biology.

cell biology↗

bioRxiv2025

Extraction of biological terms using large language models enhances the usability of metadata in the BioSample database

BioSample is a comprehensive repository of experimental sample metadata, playing a crucial role in providing a comprehensive archive and enabling experiment searches regardless of type. However, the difficulty in comprehensively defining the rules for describing metadata and limited user awareness of best practices for metadata have resulted in substantial variability depending on the submitter. This inconsistency poses significant challenges to the findability and reusability of the data. Given the vast scale of BioSample, which hosts over 40 million records, manual curation is impractical. Rule-based automatic ontology mapping methods have been proposed to address this issue, but their effectiveness is limited by the heterogeneity of BioSample metadata. Recently, large language models (LLMs) have gained attention in natural language processing and have been expected as promising tools for automating metadata curation. In this study, we evaluated the performance of LLMs in extracting cell line names from BioSample descriptions using a gold-standard dataset derived from ChIP-Atlas, a secondary database of epigenomics experiment data, which manually curates samples. Our results demonstrated that LLM-assisted methods outperformed traditional approaches, achieving higher accuracy and coverage. We further extended this approach to extraction of information about experimentally manipulated genes from metadata where manual curation had not yet been applied in ChIP-Atlas. This also yielded successful results for the usage of the database, which facilitates more precise filtering of data and prevents misinterpretation caused by inclusion of unintended data. These findings underscore the potential of LLMs to improve the findability and reusability of experimental data in general, significantly reducing user workload and enabling more effective scientific data management.

bioinformatics↗

bioRxiv2023

Atf3 controls transitioning in female mitochondrial cardiomyopathy as identified by single-cell transcriptomics

Oxidative phosphorylation defects results in mitochondrial diseases, with cardiac involvement markedly impacting prognosis. However, the mechanisms underlying the transition from compensation to dysfunction in response to metabolic deficiency remain unclear, impeding the development of effective treatments. Here, we employed single-nucleus RNA sequencing (snRNA-seq) on hearts from mitochondrial cardiomyopathy (MCM) mice with cardiac-specific Ndufs6 knockdown (FS6KD). Pseudotime trajectory analysis of cardiomyocytes from early stage of female FS6KD hearts revealed dynamic cellular state transitioning from compensation to severe compromise, coincided with transient upregulation of a critical transcription factor, activating transcription factor 3 (Atf3). Genetic ablation or adeno-associated virus-mediated Atf3 knockdown in FS6KD mice effectively delayed cardiomyopathy progression in a female-specific manner. Notably, human MCM snRNA-seq revealed a similar transition, including the dynamic expression of ATF3. In conclusion, our findings highlight a fate-determining role of Atf3 in female MCM progression, providing a promising therapeutic candidate for the currently intractable disease.

cell biology↗

bioRxiv2023

Elucidating disease-associated mechanisms triggered by pollutants via the epigenetic landscape using large-scale ChIP-Seq data

BackgroundDespite well-documented effects on human health, the action modes of environmental pollutants are incompletely understood. Although transcriptome-based approaches are widely used to predict associations between chemicals and disorders, the molecular cues regulating pollutant-derived gene expression changes remain unclear. Therefore, we developed a data-mining approach, termed "DAR-ChIPEA," to identify transcription factors (TFs) playing pivotal roles in the action modes of pollutants. MethodsLarge-scale public ChIP-Seq data (human, n = 15,155; mouse, n = 13,156) were used to predict TFs that are enriched in the pollutant-induced differentially accessible genomic regions (DARs) obtained from epigenome analyses (ATAC-Seq). The resultant pollutant-TF matrices were then cross-referenced to a repository of TF-disorder associations to account for pollutant modes of action. We subsequently evaluated the performance of the proposed method using a chemical perturbation dataset to compare the outputs of the DAR-ChIPEA and our previously developed differentially expressed gene (DEG)-ChIPEA methods using pollutant-induced DEGs as input. We then adopted the proposed method to predict disease-associated mechanisms triggered by pollutants. ResultsThe proposed approach outperformed other methods using the area under the receiver operating characteristic curve score. The mean score of the proposed DAR-ChIPEA was significantly higher than that of our previously described DEG-ChIPEA (0.7287 vs. 0.7060; Q = 5.278 x 10-42; two-tailed Wilcoxon rank-sum test). The proposed approach further predicted TF-driven modes of action upon pollutant exposure, indicating that (1) TFs regulating Th1/2 cell homeostasis are integral in the pathophysiology of tributyltin-induced allergic disorders; (2) fine particulates (PM2.5) inhibit the binding of C/EBPs, Rela, and Spi1 to the genome, thereby perturbing normal blood cell differentiation and leading to immune dysfunction; and (3) lead induces fatty liver by disrupting the normal regulation of lipid metabolism by altering hepatic circadian rhythms. ConclusionHighlighting genome-wide chromatin change upon pollutant exposure to elucidate the epigenetic landscape of pollutant responses outperformed our previously described method that focuses on gene-adjacent domains only. Our approach has the potential to reveal pivotal TFs that mediate deleterious effects of pollutants, thereby facilitating the development of strategies to mitigate damage from environmental pollution.

pharmacology and toxicology↗

bioRxiv2020

Evolutionary Dynamics of Non-Coding Regions in Pancreatic Ductal Adenocarcinoma

While the non-coding genome appears to play a role, the dynamic nature of noncoding alterations with respect to clonal progression of solid tumors remains unexplored. To address this gap in knowledge we performed multiregional whole genome sequencing and clonal analysis to elucidate the evolutionary dynamics of non-coding regions in pancreatic cancer relative to those of the coding genome. We find that the mutational burden of noncoding DNA is higher than coding DNA. However, when noncoding DNA was segregated into enhancer and non-enhancer regions, enhancers were more similar to coding DNA. Mutational signatures of noncoding and coding DNA further revealed the similar mutational spectra of enhancers to coding DNA whereas the mutational spectra of non-enhancer, noncoding DNA had an entirely different pattern. These findings shed light on the role of noncoding DNA in pancreatic cancer.

cancer biology↗

bioRxiv2020

High resolution spatial transcriptome analysis by photo-isolation chemistry

In multicellular organisms, individual cells are characterized by their gene expression profiles and the spatial interactions among cells enable the elaboration of complex functions. Expression profiling in spatially defined regions is crucial to elucidate cell interactions and functions. Here, we established a transcriptome profiling method coupled with photo-isolation chemistry (PIC) that allows the determination of expression profiles specifically from photo-irradiated regions of whole tissues. PIC uses photo-caged oligodeoxynucleotides for in situ reverse transcription. After photo-irradiation of limited areas, gene expression was detected from at least 10 cells in the tissue sections. PIC transcriptome analysis detected genes specifically expressed in small distinct areas of the mouse embryo. Thus, PIC enables transcriptome profiles to be determined from limited regions at a spatial resolution up to the diffraction limit.

genomics↗

bioRxiv2019

Paternal age affects offspring's behavior possibly via an epigenetic mechanism recruiting a transcriptional repressor REST

Advanced paternal age has deleterious effects on mental health of next generation. Using a mouse model, we have confirmed that offspring derived from aged fathers showed impairments in behavior and abnormalities in the brain structure and activity. Comprehensive target DNA methylome analyses revealed in aged sperm more hypo-methylated genomic regions, in which REST/NRSF binding motif was enriched. Gene set enrichment analyses also identified enrichment of "REST/NRSF target genes", in addition to "Late-fetal genes" and autism spectrum disorder-related "SFARI genes", in up-regulated genes of developing brains from aged father. Indeed, gene sets near hypo-methylated genomic regions with REST/NRSF binding motif were also enriched in up-regulated genes of developing brains. Taken altogether, DNA hypo-methylation due to paternal aging in sperm will induce leaky expression of REST/NRSF target genes in the developing brain, thereby causing neuronal abnormalities and subsequent behavioral alteration in offspring.

neuroscience↗

bioRxiv2018

Integrative analysis of transcription factor occupancy at enhancers and disease risk loci in noncoding genomic regions

Noncoding regions of the human genome possess enhancer activity and harbor risk loci for heritable diseases. Whereas the binding profiles of multiple transcription factors (TFs) have been investigated, integrative analysis with the large body of public data available so as to provide an overview of the function of such noncoding regions has remained a challenge. Here we have fully integrated public ChIP-seq and DNase-seq data (n ~ 70,000), including those for 743 human transcription factors (TFs) with 97 million binding sites, and have devised a data- mining platform --designated ChIP-Atlas--to identify significant TF-genome, TF-gene, and TF-TF interactions. Using this platform, we found that TFs enriched at macrophage or T-cell enhancers also accumulated around risk loci for autoimmune diseases, whereas those enriched at hepatocyte or macrophage enhancers were preferentially detected at loci associated with HDL-cholesterol levels. Of note, we identified \"hotspots\" around such risk loci that accumulated multiple TFs and are therefore candidates for causal variants. Integrative analysis of public chromatin-profiling data is thus able to identify TFs and tissues associated with heritable disorders.

genomics↗