Biology subjects

Hara, S.

Publications and source records attributed to Hara, S..

8 recordsLinked to original sources

bioRxiv2026

High-dimensional spatial proteomics and novel machine learning pipeline identifies disease specific renal damage states

Lupus nephritis (LuN) and renal allograft rejection (RAR) manifest inflammation and fibrosis that ultimately lead to kidney failure. To quantitatively assess spatial injury patterns, we collected high dimensional spatial proteomics data from 23 LuN, 33 RAR, and 8 kidney control (KC) biopsies. We developed a computational pipeline to segment and classify tubules, capillaries, and glomeruli in whole-slide images using three trained neural networks (Renal Damage diagnosis, RDDx). RDDx achieved high accuracy and generalizability, reliably identifying small capillaries and differentiating tubular and vascular inflammation in kidney tissues. Both LuN and RAR showed reduced tubular and capillary areas with expanded interstitial space. LuN displayed patchy clusters of stressed and inflamed tubules, whereas RAR exhibited diffuse injury. Within RAR, T cell-mediated rejection (TCMR) showed intense tubulitis while antibody-mediated rejection (ABMR) featured proliferating and inflamed capillaries near atrophic tubules. RDDx quantitative metric outputs correlated with histopathological scores, highlighting their reproducibility and clinical relevance. Stressed tubules in mildly inflamed LuN biopsies suggested they were a sensitive injury marker, while proliferating capillaries revealed microvascular remodeling in ABMR. These findings indicated RDDx can identify and quantify damage mechanisms specific to each renal disease thus facilitating future mechanistic studies and therapeutic target discovery.

immunology↗

bioRxiv2025

VEGF-C-mediated Cardiac Lymphangiogenesis Promotes Inflammation Resolution in Autoimmune Acute Myocarditis in Mice

BACKGROUNDAcute myocarditis is an immune-mediated inflammatory disease characterized by myocardial inflammation and edema. Cardiac lymphatic vessels are essential for fluid clearance and immune regulation; however, their role in modulating inflammation in autoimmune myocarditis remains largely unclear. METHODSWe investigated the impact of cardiac lymphangiogenesis using a murine model of experimental autoimmune myocarditis induced by cardiac myosin peptide immunization. Human autopsy samples were analyzed for lymphatic expansion. Mice were treated with VEGF-C C156S, a VEGFR3-specific agonist, beginning one week after immunization. Cardiac lymphangiogenesis, myocardial edema, inflammatory infiltration, fibrosis, and cardiac function were evaluated by immunohistochemistry, echocardiography, gene expression analysis, and water content measurements. RESULTSThe VEGF-C treatment accelerated cardiac lymphangiogenesis, enhanced lymphatic drainage, reduced myocardial edema, and attenuated inflammatory cell infiltration and fibrosis. Echocardiography showed the preservation of left ventricular function. VEGF-C selectively decreased the accumulation of iNOS inflammatory macrophages without broadly suppressing T cells or reparative macrophages. Bulk RNA sequencing confirmed the down-regulation of inflammatory gene signatures associated with macrophage activation. CONCLUSIONSThe early stimulation of cardiac lymphangiogenesis by VEGF-C promotes the resolution of inflammation, reduces myocardial injury, and preserves cardiac function in autoimmune myocarditis. Targeting the cardiac lymphatic system may represent a promising therapeutic strategy for acute myocarditis.

pathology↗

bioRxiv2025

Resolution of in situ inflammation in human lupus nephritis into principal immune cell trajectories

In human lupus nephritis (LuN), tubulointerstitial inflammation (TII) is prognostically more important than glomerular inflammation. However, a comprehensive understanding of both TII complexity and heterogeneity is lacking. Herein, we used high-dimensional confocal microscopy and specialized computer vision techniques to quantify immune cell populations and localize these within normal and diseased renal cortex structures. With these tools, we compared LuN to renal allograft rejection (RAR) and normal kidney. In both LuN and RAR, the 33 characterized immune cell populations formed discrete subgroups whose constituents co-varied in prevalence across biopsies. In both diseases, these co-variant immune cell subgroups organized into the same unique niches. Therefore, inflammation could be resolved into trajectories representing the relative prevalence and density of cardinal immune cell members of each co-variant subgroup. Indeed, in any one biopsy, the inflammatory state could be characterized by quantifying constituent immune cell trajectories. Remarkably, LuN heterogeneity could be captured by quantifying a few myeloid immune cell trajectories while RAR was more complex with additional T cell trajectories. Our studies identify rules governing renal inflammation and thus provide an approach for resolving LuN into discrete mechanistic categories. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/638655v1_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@109dd33org.highwire.dtl.DTLVardef@995037org.highwire.dtl.DTLVardef@161525dorg.highwire.dtl.DTLVardef@6692d8_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical AbstractC_FLOATNO C_FIG

immunology↗

bioRxiv2025

Comparative Extraction of Cellular Features from High-Resolution Volume Imaging

High-resolution volume imaging techniques, such as lattice light-sheet microscopy (LLSM), generate vast and complex datasets that demand advanced analytical approaches to uncover biologically meaningful insights. While LLSMs high spatial and temporal resolution provides critical data for understanding cellular processes, distinguishing subtle differences between cells in distinct states remains challenging. Here, using an adaptive, human-interpretable kernel-based calculation strategy, we developed Machine-Learning-Based Visual Extraction of Structural Features (M-VEST), a method designed to identify and interpret structural differences with high precision. By applying M-VEST to mitotic cells, we uncovered novel functions of the oncogene Aurora kinase A, demonstrating its utility in revealing previously undetected features. Validated using LLSM datasets, M-VEST offers a scalable framework for analyzing large and complex imaging data, advancing insights into cellular dynamics and beyond.

bioinformatics↗

bioRxiv2025

Single-cell genomics of single soil aggregates: methodological assessment and potential implications with a focus on nitrogen metabolism

Soil particles in plant rooting zone are largely clustered to form complex porous structural unit called aggregates where highly diverse microbes coexist and drive biogeochemical cycling. The complete extraction of microbial cells and DNA from soil is a substantial task as certain microbes exhibit strong adhesion to soil surfaces and/or inhabit deep within aggregates. Yet, the degree of aggregate dispersion and the efficacy of extraction have rarely been examined, and thus adequate cell extraction method from soil remain unclear. We aimed to develop an optimal method of cell extraction for single-cell genomics (SCG) analysis of single soil aggregates by focusing on water-stable macroaggregates (diameter: 5.6-8.2 mm) isolated from a topsoil of cultivated Acrisol. Using the same six individual aggregates, we performed both SCG sequencing and amplicon analysis. While both bead-vortexing and sonication dispersion methods improved the extractability of bacterial cells compared to previous studies, the latter yielded higher number and more diverse microbes compared to the former. The analyses of nitrogen-cycling and exopolysaccharides-related genes suggested that the sonication-assisted extraction led to greater recovery of microbes strongly attached to soil particles and/or inhabited the aggregate subunits that were more physically stable (e.g., aggregate core). Further SCG analysis revealed that all six aggregates held intact microbes having the genes (i.e., potentials) to convert nitrate into all possible nitrogen forms while some low-abundance genes showed inter-aggregate heterogeneity. In addition, all six aggregates studied showed overall similarity in pore characteristics, phylum-level composition, and the microbial functional redundancy. Together, these results suggest that water-stable macroaggregates may act as a functional unit in soil and show potential as a useful experimental unit in soil microbial ecology. Our study also suggest that conventional methods employed for the extraction of cell and DNA may not be optimal. The current findings underscore the necessity to advance extraction methodologies, thereby facilitating a more comprehensive understanding of the microbial diversity and functioning within soil environments.

microbiology↗

bioRxiv2024

Identification of the sequences responsible for maternal H19-ICR hypermethylation with Beckwith-Wiedemann syndrome-like overgrowth in mice

Beckwith-Wiedemann syndrome (BWS) is caused by a gain of methylation (GOM) at the imprinting control region within the Igf2-H19 domain on the maternal allele (H19-ICR GOM). Mutations in the binding sites of several transcription factors are involved in H19-ICR GOM and BWS. However, the responsible sequence(s) for H19-ICR GOM with BWS-like overgrowth has not been identified in mice. Here, we report that a mutation in the SOX-OCT binding site (SOBS) causes partial H19-ICR GOM, which does not extend beyond CTCF binding site 3 (CTS3). Moreover, simultaneously mutating both SOBS and CTS3 causes complete GOM of the entire H19-ICR, leading to the misexpression of the imprinted genes, and frequent BWS-like overgrowth. In addition, CTS3 is critical for CTCF/cohesin-mediated chromatin conformation. These results indicate that SOBS and CTS3 are the sequences responsible for H19-ICR GOM leading to BWS-like overgrowth and are essential for maintaining the unmethylated state of maternal H19-ICR.

genetics↗

bioRxiv2021

Early Dynamics of Chronic Myeloid Leukemia on Nilotinib Predicts Deep Molecular Response

Chronic myeloid leukemia (CML) is a myeloproliferative disorder caused by the BCR-ABL1 tyrosine kinase1,2. ABL1-selective tyrosine kinase inhibitors (TKIs) including nilotinib have dramatically improved the prognosis of patients with CML3-7. The ultimate goal of CML treatment is likely to be TKI-free maintenance of deep molecular response (DMR), which is defined by quantitative measurement of BCR-ABL1 transcripts on the international scale (IS)8, and durable DMR is a prerequisite to reach this goal9. Thus, an algorithm to enable the early prediction of DMR achievement on TKI therapy is quite valuable. Here, we show that our mathematical framework based on a clinical trial dataset10 can accurately predict the response to frontline nilotinib. We found that our simple dynamical model can predict nilotinib response by using two common laboratory findings (observation values): IS11,12 and white blood cell (WBC) count. Furthermore, our proposed method identified patients who failed to achieve DMR with high fidelity according to the data collected only at three initial time points during nilotinib therapy. Since our model relies on the general properties of TKI response, our framework would be applicable to CML patients who receive frontline nilotinib or other TKIs in clinical practice. Significance StatementChronic myeloid leukemia (CML) is a myeloproliferative disorder caused by the BCR-ABL1 tyrosine kinase. The goal of this treatment is the sequential achievement of deep molecular response (DMR). Tyrosine kinase inhibitors (TKIs) are effective in the reduction because they inhibit CML cell proliferation. However, because of individual differences in the TKI efficacy, some patients are unable to achieve DMR, so that early prediction of DMR reachability is necessary for personalized medicine. By combining time series analysis and mathematical modeling, we developed a mathematical method that accurately predicts patients who do not achieve DMR in the early stages of TKI administration. Our prediction method gives a basis of effective personalized treatments for CML patients.

systems biology↗

bioRxiv2020

Bacteroides uniformis enhances endurance exercise performance through gluconeogenesis

Athletes require high levels of energy to exercise under extreme conditions. Gut microbiota supplies energy to the host; however, the mechanism how gut microbiota contribute in the athlete is unclear. In this study, we determined that gut microbiota of Japanese long-distance runners differed from that of non-athletes, and the Bacteroides uniformis cell number in the feces correlated with 3,000-m race time. Mice administrated with B. unformis extended the swimming time to exhaustion. Furthermore, acetate and propionate concentrations in the cecum increased in B. uniformis-administered mice subjected to weekly exercise. Expression levels of carnitine palmitoyl transferase la and phosphoenolpyruvate carboxykinase genes were elevated in the liver, suggesting that acetate and propionate produced by B. uniformis improve endurance exercise performance, at least in part, through enhancing gluconeogenesis. In addition, -cyclodextrin administration increased B. uniformis and improved the performance in humans and mice, thus it is a candidate substance enhancing exercise performance through modification of gut microbiota.

microbiology↗