Biology subjects

Tominaga, K.

Publications and source records attributed to Tominaga, K..

22 records · Page 2Linked to original sources

bioRxiv2021

Year-round dynamics of amplicon sequence variant communities differ among eukaryotes, Mimiviridae, and prokaryotes in a coastal ecosystem

Coastal seawater is the habitat of different microbial communities. These communities are affected by seasonal environmental changes and fluctuating nutrient availability, as well as competitive and cooperative interspecific interactions. In this work, we investigated the seasonal dynamics of communities of eukaryotes, a major group of double-stranded DNA viruses infecting eukaryotes (i.e. Mimiviridae), as well as prokaryotes in the Uranouchi Inlet, Kochi, Japan. We performed metabarcoding using ribosomal RNA genes and the Mimiviridae polB gene as marker genes in 43 seawater samples collected during 20 months. Communities characterized by the compositions of amplicon sequence variants (ASVs) showed synchronic seasonal cycles for eukaryotes, Mimiviridae, and prokaryotes. However, the community dynamics showed intriguing differences in several aspects such as the recovery rate after a year. We further show that the differences in the community dynamics can be explained by differences in the recurrence/persistence levels of individual ASVs among eukaryotes, Mimiviridae, and prokaryotes. Mimiviridae ASVs were less persistent than eukaryotic ASVs, and prokaryotic ASVs were the most persistent. We argue that the differences in the specificity of interactions (i.e. virus-eukaryote vs prokaryote-eukaryote) as well as the survival strategies are at the origin of the distinct community dynamics among eukaryotes, Mimiviridae, and prokaryotes. One sentence summaryA one year observation of coastal microbial communities revealed similar but different community dynamics for eukaryotes, a group of large viruses, and prokaryotes.

microbiology↗

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↗

bioRxiv2020

MCM10 compensates for Myc-induced DNA replication stress in breast cancer stem-like cells

Cancer stem-like cells (CSCs) are responsible for the drug resistance of tumors and recurrence while they experience DNA replication stress. However, the underlying mechanisms that cause DNA replication stress in CSCs and how they compensate for this stress remain unclear. Here we provide evidence that upregulated c-Myc expression induces stronger DNA replication stress in patient-derived breast CSCs than in differentiated cancer cells. Our results suggest critical roles for mini-chromosome maintenance protein 10 (MCM10), which is a firing (activating) factor of the DNA replication origins, to compensate for the DNA replication stress. Expression levels of MCM10 are upregulated in CSCs and maintained by c-Myc. c-Myc-dependent collisions may take place between RNA transcription and DNA replication machinery in nuclei, thereby causing DNA replication stress. MCM10 may activate dormant replication origins close to the collisions to ensure replication progression. Moreover, patient-derived breast CSCs were dependent on MCM10 for their maintenance even after enrichment for CSCs that were resistant to paclitaxel, the standard chemotherapeutic agent. In addition, MCM10 depletion decreased the growth of cancer cells but not normal cells. Therefore, MCM10 is likely to robustly compensate for DNA replication stress and facilitate genome duplication in the S-phase in cancer cells, which is more pronounced in CSCs. We provide a preclinical rationale to target the c-Myc-MCM10 axis to prevent drug resistance and recurrence.

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↗