Biology subjects

Kawabe, A.

Publications and source records attributed to Kawabe, A..

3 recordsLinked to original sources

bioRxiv2025

Antagonistic histone H2A variants and autonomous heterochromatin formation shape epigenomic patterns in Arabidopsis

Heterochromatin formation is pivotal in many eukaryotes with repetitive sequences, such as transposable elements (TEs). However, in plants, where the known de novo DNA methylation mechanism (RdDM) targets euchromatin, how heterochromatin is formed in a region-specific manner remains unclear. We previously reported an RdDM-independent de novo establishment of H3K9me and non-CpG methylation, both of which localize in heterochromatin. Here we show that the mutually exclusive histone H2A variants, H2A.W and H2A.Z, function as guides to initiate heterochromatin formation; H2A.W and H2A.Z promotes and inhibits heterochromatin establishment, respectively, especially in chromosomal arm regions with dispersed TEs. In contrast, pericentromeric TEs demonstrate autonomous heterochromatin formation, less dependently on these H2A variants. Furthermore, H2A.Z protects protein-coding genes from ectopic heterochromatin formation, possibly by preventing its spreading. We propose that the genome indexing mechanism driven by H2A variants, as well as the autonomous formation of pericentromeric heterochromatin, shapes proper epigenomic patterns in Arabidopsis.

genomics↗

bioRxiv2020

Deep Texture Representations as a Universal Encoder for Pan-cancer Histology

Cancer histological images contain rich biological and clinical information, but quantitative representation can be problematic and has prevented direct comparison and accumulation of large-scale datasets. Here we show that deep texture representations (DTRs) produced by a bilinear Convolutional Neural Network, express cancer morphology well in an unsupervised manner, and work as a universal encoder for cancer histology. DTRs are useful for content-based image retrieval, enabling quick retrieval of histologically similar images from optimally area selected datasets of 7,175 cases from The Cancer Genome Atlas. Via comprehensive comparison with driver and clinically actionable gene mutations, we have successfully predicted 309 combinations of genomic features and cancer types from hematoxylin and eosin-stained images at high accuracy (AUC > 0.70 and q < 0.02). With its mounting capabilities on accessible devices such as smartphones, DTR-based encoding for cancer histology has a potentially strong impact on global equalization for cancer diagnosis and targeted therapies.

cancer biology↗

bioRxiv2018

Luigi: Large-scale histopathological image retrieval system using deep texture representations

BackgroundAs a large number of digital histopathological images have been accumulated, there is a growing demand of content-based image retrieval (CBIR) in pathology for educational, diagnostic, or research purposes. However, no CBIR systems in digital pathology are publicly available.\n\nResultsWe developed a web application, the Luigi system, which retrieves similar histopathological images from various cancer cases. Using deep texture representations computed with a pre-trained convolutional neural network as an image feature in conjunction with an approximate nearest neighbor search method, the Luigi system provides fast and accurate results for any type of tissue or cell without the need for further training. In addition, users can easily submit query images of an appropriate scale into the Luigi system and view the retrieved results using our smartphone application. The cases stored in the Luigi database are obtained from The Cancer Genome Atlas with rich clinical, pathological, and molecular information. We tested the Luigi system by querying typical cancerous regions from four cancer types, and confirmed successful retrieval of relevant images.\n\nConclusionsThe Luigi system will help students, pathologists, and researchers easily retrieve histopathological images of various cancers similar to those of the query image.

pathology↗