Biology subjects

Washio, T.

Publications and source records attributed to Washio, T..

5 recordsLinked to original sources

bioRxiv2026

Topological defects and coherent myocardial chirality shape torsional heart contraction

The efficient pumping of the mammalian heart relies on torsional contractile motion generated by its highly ordered three-dimensional (3D) architecture of myocardial fibres. However, the topological principles governing how its complex geometry translates into contractile mechanics remain elusive. Here, we show that the mammalian heart forms a chiral nematic field, a biological analogue to 3D liquid crystals, whose topological organisation underlies its function. Analysis of 3D imaging data revealed disclination lines, continuous assemblies of topological defects characteristic of nematic systems, within the compact myocardium. Finite-element simulations reveal that these defects are not mere structural irregularities but can locally modulate contractile behaviour and reduce mechanical work. In heterotaxy hearts with reversed global anatomy (situs inversus), myocardial fibres retain a predominantly counter-clockwise twist, similar to that of the normal heart, but with a small clockwise component near the base. This decoupling of tissue-level chirality from systemic left-right patterning suggests that cardiac twist is an intrinsic property of the myocardial fibre. Mechanical simulations of situs inversus heart demonstrate that the coherence of transmural chirality, rather than its specific orientation, is critical for contractile efficiency. Together, these findings establish the heart as a topological material and reveal how organised chiral fields generate robust organ-level mechanical function.

biophysics↗

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↗

bioRxiv2023

Dynamic coordination of the lever-arm swing of human myosin II in thick filaments on actin

Muscle myosins work in motor ensembles and must adapt their power stroke in response to mechanical actions by surrounding motors. Understanding the coordination of power strokes is essential for bridging microscopic molecular functions and macroscopic muscle contractions, but the details of this phenomenon remain elusive. Here we used high-speed atomic force microscopy to visualize the individual dynamics (lever-arm swing) of the myosin head bound to actin in DNA origami-based synthetic thick filaments. We observed spatially local lever-arm coordination, and our three-dimensional numerical model explained how mechanical communication between myosins achieved coordination. In a sarcomere model, the local coordination was spatially periodic and propagated toward the contraction direction. We confirmed that a structural mismatch between myosin head spacing (42.8 nm) and the actin helical pitch (37 nm) caused the coordination while improving contraction speed and energy efficiency. Our findings reveal a key physical basis of efficient muscle contraction.

biophysics↗

bioRxiv2023

High-fat diet in early life triggers both reversible and persistent epigenetic changes in the medaka fish (Oryzias latipes)

The nutritional status during early life can have enduring effects on an animals metabolism, although the mechanisms underlying these long-term effects are still unclear. Epigenetic modifications are considered a prime candidate mechanism for encoding early-life nutritional memories during this critical developmental period. However, the extent to which these epigenetic changes occur and persist over time remains uncertain, in part due to challenges associated with directly stimulating the fetus with specific nutrients in viviparous mammalian systems. In this study, we used medaka as an oviparous vertebrate model to establish an early-life high-fat diet (HFD) model. Larvae were fed with HFD from the hatching stages (one week after fertilization) for six weeks, followed by normal chow (NC) for eight weeks until the adult stage. We examined the changes in the transcriptomic and epigenetic state of the liver over this period. We found that HFD induces fatty liver phenotypes, accompanied by drastic changes in the hepatic transcriptome, chromatin accessibility, and histone modifications, especially in metabolic genes. These changes were largely reversed after the long-term NC, demonstrating the high plasticity of the epigenetic state in hepatocytes. However, we found a certain number of genomic loci showing non-reversible epigenetic changes, especially around genes related to cell signaling, liver fibrosis, and hepatocellular carcinoma, implying persistent changes in the cellular state of the liver triggered by early-life HFD feeding. Our data provide novel insights into the epigenetic mechanism of nutritional programming and a comprehensive atlas of the long-term epigenetic state in an early-life HFD model of non-mammalian vertebrates.

genomics↗

bioRxiv2021

Identification of bacterial drug-resistant cells by the convolutional neural network in transmission electron microscope images

The emergence of bacteria that are resistant to antibiotics is common in areas where antibiotics are used widely. The current standard procedure for detecting bacterial drug resistance is based on bacterial growth under antibiotic treatments. Here we describe the morphological changes in enoxacin-resistant Escherichia coli cells and the computational method used to identify these resistant cells in transmission electron microscopy (TEM) images without using antibiotics. Our approach was to create patches from TEM images of enoxacin-sensitive and enoxacin-resistant E. coli strains, use a convolutional neural network for patch classification, and identify the strains on the basis of the classification results. The proposed method was highly accurate in classifying cells, achieving an accuracy rate of 0.94. Using a gradient-weighted class activation mapping to visualize the region of interest, enoxacin-resistant and enoxacin-sensitive cells were characterized by comparing differences in the envelope. Moreover, Pearsons correlation coefficients suggested that four genes, including lpp, the gene encoding the major outer membrane lipoprotein, were strongly associated with the image features of enoxacin-resistant cells.

systems biology↗