Biology subjects

Tsutsumi, M.

Publications and source records attributed to Tsutsumi, M..

4 recordsLinked to original sources

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

Ether phospholipids modulate somatosensory responses by tuning multiple receptor functions in Drosophila

Transient receptor potential (TRP) and PIEZO channels are known receptors for physical stimuli, such as temperature and mechanical touch, respectively, in sensory nerves. As these receptors are localized in the plasma membrane, the modulation of sensory receptor activity by plasma membrane lipids has recently attracted attention. In this study, we focused on ether phospholipids (ePLs), which are abundant in neurons, and analyzed their role in somatosensation using Drosophila as a model. Reduced mechanosensory behavior was observed with ePL synthesizing gene knockout or knockdown in mechano-sensitive PIEZO-expressing neurons. The activation of PIEZO channels was significantly augmented in the presence of ePLs. Furthermore, we observed that ePLs modulate the thermosensory behavior and reduce thermal threshold of the thermosensitive TRPA1 channels. Finally, we revealed that ePLs affect membrane tension and lipid order of the plasma membrane in culture cells. Our study identified ePLs as a modulator of multiple somatosensation modalities in Drosophila, which underscore the significance of functional interaction between membrane lipid and sensory channel proteins.

molecular biology↗

bioRxiv2023

Tight junction membrane proteins regulate the mechanical resistance of the apical junctional complex

Epithelia must be able to resist mechanical force to preserve tissue integrity. While intercellular junctions are known to be important for the mechanical resistance of epithelia, the roles of tight junctions (TJs) remain to be established. We previously demonstrated that epithelial cells devoid of the TJ membrane proteins claudins and JAM-A completely lack TJs and exhibit focal breakages of their apical junctions. Here, we demonstrate that apical junctions undergo spontaneous fracture when claudin/JAM-A-deficient cells are exposed to mechanical stress. The junction fracture was accompanied by actin disorganization, and actin polymerization was required for apical junction integrity in the claudin/JAM-A-deficient cells. Further deletion of CAR resulted in the disruption of ZO-1 molecule ordering at cell junctions, accompanied by severe defects in apical junction integrity. These results demonstrate that TJ membrane proteins regulate the mechanical resistance of the apical junctional complex in epithelial cells. SummaryTight junction membrane proteins claudins, JAM, and CAR coordinately regulate the nanometer-scale organization of ZO-1 molecules, and are required for the mechanical resistance of apical junctions in epithelial cells.

cell biology↗

bioRxiv2022

A method for morphological feature extraction based on variational auto-encoder : an application to mandible shape

Shape analysis of biological data is crucial for investigating the morphological variations during development or evolution. However, conventional approaches for quantifying shapes are difficult as exemplified by the ambiguity in the landmark-based method in which anatomically prominent "landmarks" are manually annotated. In this study, a morphological regulated variational autoencoder (Morpho-VAE) is proposed that conducts image-based shape analysis using imaging processing through a deep-learning framework, thereby removing the need for defining landmarks. The proposed architecture comprises a VAE combined with a classifier module. This integration of unsupervised and supervised learning models (i.e., VAE and classifier modules) is designed to reduce dimensionality by focusing on the morphological features in which the differences between data with different labels are best distinguished. The proposed method is applied to the image dataset of the primate mandible to extract morphological features, which allow us to distinguish different families in a low dimensional latent space. Furthermore, the visualization analysis of decision-making of Morpho-VAE clarifies the area of the mandibular joint that is important for family-level classification. The generative nature of the proposed model is also demonstrated to complement a missing image segment based on the remaining structure. Therefore, the proposed method, which flexibly performs landmark-free feature extraction from complete and incomplete image data is a promising tool for analyzing morphological datasets in biology. AUTHOR SUMMARYShape is the most intuitive visual characteristic; however, shape is generally difficult to measure using a small number of variables. Specifically, for biological data, shape is sometimes highly diverse as it has been acquired through a long evolutionary process, adaptation to environmental factors, etc., which limits the straightforward approach to shape measurement. Therefore, a systematic method for quantifying such a variety of shapes using a low-dimensional quantity is needed. To this end, we propose a novel method that extracts low-dimensional features to describe shapes from image data using machine learning. The proposed method is applied to the primate mandible image data to extract morphological features that reflect the characteristics of the groups to which the organisms belong and then those features are visualized. This method also reconstructs a missing image segment from an incomplete image based on the remaining structure. To summarize, this method is applicable to the shape analysis of various organisms and is a useful tool for analyzing a wide variety of image data, even those with a missing segment.

bioinformatics↗