Biology subjects

Hatakeyama, S.

Publications and source records attributed to Hatakeyama, S..

4 recordsLinked to original sources

bioRxiv2023

Complete de novo assembly of Tricholoma bakamatsutake chromosomes revealed the structural divergence and differentiation of Tricholoma genomes

Tricholoma bakamatsutake, which is an edible ectomycorrhizal fungus associated with Fagaceae trees, may have diverged before the other species in Tricholoma section Caligata. We generated a highly contiguous whole-genome sequence for T. bakamatsutake SF-Tf05 isolated in an oak (Quercus salicina) forest in Japan. The assembly of high-fidelity long reads, with a median read length of 12.3 kb, resulted in 13 chromosome-sized contigs comprising 142,068,211 bases with an average GC content of 43.94%. The 13 chromosomes were predicted to encode 11,060 genes. A contig (122,566 bases) presumably containing the whole circular mitochondrial genome was also recovered. The chromosome-wide comparison of T. bakamatsutake and T. matsutake (TMA_r1.0) indicated that the basic number of chromosomes (13) was conserved, but the structures of the corresponding chromosomes diverged, with multiple inversions and translocations. Gene conservation and cluster analyses revealed at least three groups in Tricholoma. Specifically, all T. bakamatsutake strains belonged to the "bakamatsutake" clade, which is most proximal to the "caligatum" clade consisting of T. caligatum and T. fulvocastaneum. The constructed highly contiguous telomere-to-telomere genome sequence of a T. bakamatsutake isolate will serve as a fundamental resource for future research on the evolution and differentiation of Tricholoma species.

genomics↗

bioRxiv2022

In silico labeling enables kinetic myelination assay in brightfield

Recent advances with deep neural networks have shown the feasibility of acquiring brightfield images with transmitted light and applying in-silico labeling to predict fluorescent images. We have developed a novel in-silico labeling method based on a generative adversarial network and outperforms the state-of-the-art Unet method in generating realistic fluorescent images and quantitatively recapitulating real staining signals, as demonstrated in a complex co-culture myelination assay. Furthermore, we have performed the assay in live mode with multiple kinetic points, applied in-silico labeling to predict fluorescent images from brightfield and quantified the kinetic phenotypic changes. Thus, the proposed approach provides a potential tool to study the kinetics of cellular phenotypic changes with brightfield imaging.

cell biology↗

bioRxiv2021

On the application of BERT models for nanopore methylation detection

MotivationDNA methylation is a common epigenetic modification, which is widely associated with various biological processes, such as gene expression, aging, and disease. Nanopore sequencing provides a promising methylation detection approach through monitoring abnormal signal shifts for detecting modified bases in target motif regions. Recently, model-based approaches, especially those with deep learning models, have achieved significant performance improvements on nanopore methylation detection. In this work, we explore using bidirectional encoder representations from transformers (BERT) for doing the task, which can provide non-recurrent neural structures for fast parallel computation. ResultsWe find original BERT architecture does not work as well as the bidirectional recurrent neural network (biRNN) on the nanopore methylation prediction task. Through further analysis, we observe recurrent patterns of positional-signal-shift in the context window surrounding target 5-methylcytosine (5mC) and N6-methyladenine (6mA) motifs. We propose a refined BERT with relative position representation and center hidden units concatenation, which takes account of task-specific characters into modeling. We perform systematic evaluations in-sample and cross-sample. The experiment results show that the refined BERT model can achieve competitive or even better results than the state-of-the-art biRNN model, while the model inference speed is about 6x faster. Besides, on the cross-sample evaluation of datasets from the different research groups, BERT models demonstrate a good generalization performance. AvailabilityThe source code and data are available at https://github.com/yaozhong/methBERT Contactyaozhong@ims.u-tokyo.ac.jp

bioinformatics↗

bioRxiv2018

mitoNEET Regulates Mitochondrial Iron Homeostasis Interacting with Transferrin Receptor

Iron is an essential trace element for regulation of redox and mitochondrial function, and then mitochondrial iron content is tightly regulated in mammals. We focused on a novel protein localized at the outer mitochondrial membrane. Immunoelectron microscopy revealed transferrin receptor (TfR) displayed an intimate relationship with the mitochondria, and mass spectrometry analysis also revealed mitoNEET interacted with TfR in vitro. Moreover, mitoNEET was endogenously coprecipitated with TfR in the heart, which indicates that mitoNEET also interacts with TfR in vivo. We generated mice with cardiac-specific deletion of mitoNEET (mitoNEET-knockout). Iron contents in isolated mitochondria were significantly increased in mitoNEET-knockout mice compared to control mice. Mitochondrial reactive oxygen species (ROS) were higher, and mitochondrial maximal capacity and reserve capacity were significantly decreased in mitoNEET-knockout mice, which was consistent with cardiac dysfunction evaluated by echocardiography. The complex formation of mitoNEET with TfR may regulate mitochondrial iron contents via an influx of iron. A disruption of mitoNEET could thus be involved in mitochondrial ROS production by iron overload in the heart.

cell biology↗