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Horiuchi, R.

Publications and source records attributed to Horiuchi, R..

2 recordsLinked to original sources

Accessibility of the unstructured α-tubulin C-terminal tail is controlled by microtubule lattice conformation

Microtubules are cytoskeletal filaments that self-assemble from the protein tubulin, a heterodimer of -tubulin and {beta}-tubulin, and are important for cell mechanics, migration, and division. Much work has focused on how the nucleotide state of {beta}-tubulin regulates the structure and dynamics of microtubules. In contrast, less is known about the structure and function of the C-terminal tails (CTTs) of - and {beta}-tubulin which are thought to freely protrude from the surface of the microtubule. To study the CTT of -tubulin, we developed three different biosensors that bind the tyrosinated -tubulin CTT (Y-CTT). Surprisingly, live imaging of the probes indicates that the Y-CTT is minimally accessible along the microtubule lattice under normal cellular conditions. Lattice binding of the Y-CTT probes can be increased by three different ways of changing the tubulin conformational state: the drug Taxol, expression of microtubule-associated proteins (MAPs) that recognize or promote an expanded tubulin conformation, or expression of tubulin that cannot hydrolyze GTP. Molecular dynamics simulations indicate that the Y-CTT undergoes numerous transient interactions with the bodies of -tubulin and {beta}-tubulin in the lattice, and that the frequency of these interactions is regulated by the tubulin nucleotide state. These findings suggest that accessibility of the Y-CTT is locally governed by nucleotide- and MAP-dependent conformational changes to tubulin subunits within the microtubule lattice.

cell biology↗

Deep learning-based cytoskeleton segmentation for accurate high-throughput measurement of cytoskeleton density

Microscopic analyses of cytoskeleton organization are crucial for understanding various cellular activities, including cell proliferation and environmental responses in plants. Traditionally, assessments of cytoskeleton dynamics have been qualitative, relying on microscopy-assisted visual inspection. However, the transition to quantitative digital microscopy has introduced new technical challenges, with segmentation of cytoskeleton structures proving particularly demanding. In this study, we examined the utility of a deep learning-based segmentation method for accurate quantitative evaluation of cytoskeleton organization using confocal microscopic images of the cortical microtubules in tobacco BY-2 cells. The results showed that, although conventional methods sufficed for measurement of cytoskeleton angles and parallelness, the deep learning-based method significantly improved the accuracy of density measurements. To assess the versatility of the method, we extended our analysis to physiologically significant models in the context of changes in cytoskeleton density, namely Arabidopsis thaliana guard cells and zygotes. The deep learning-based method successfully improved the accuracy of cytoskeleton density measurements for quantitative evaluations of physiological changes in both stomatal movement in guard cells and intracellular polarization in elongating zygotes, confirming its utility in these applications. The results demonstrate the effectiveness of deep learning-based segmentation in providing precise and high-throughput measurements of cytoskeleton density, and has the potential to automate and expedite analyses of large-scale image datasets.

cell biology↗