Biology subjects

Katoh, H.

Publications and source records attributed to Katoh, H..

8 recordsLinked to original sources

bioRxiv2025

Hox - Meis-relayed topographical genetic switch underlies cardiopharyngeal neural crest diversification, revealed by multimodal analysis

Neural crest cells (NCCs) are multipotent migratory cells essential for cardiac development, yet the lineage trajectories and gene regulatory networks (GRNs) underlying their differentiation in the cardiopharyngeal region remain unclear. Here, we integrate single-cell RNA-seq, spatial transcriptomics, and multiomic analyses to construct a comprehensive map of NCC lineages in developing mouse cardiopharyngeal tissues. We identify a transition from Hox-positive pharyngeal NCCs to Hox-negative intracardiac populations associated with the outflow tract cushion, accompanied by a shift in Meis transcription factor binding and GRN architecture. By contrast, NCCs forming the aorticopulmonary septum and great vessel smooth muscle retain distinct Hox-codes. A Meis2-Sox9-Scx GRN defines a skeletogenic progenitor-like intermediate state that gives rise to coronary artery smooth muscle and semilunar valves. Our findings suggest that the loss of Hox-dependent regional identity enables pharyngeal NCCs to acquire new fates upon entering the cardiac cushion, providing insight into the developmental origins of coronary and valvular calcification.

developmental biology↗

bioRxiv2025

Evolutionary and structural basis of SLAM utilization in morbilliviruses - Its implications for host range and cross-species transmission

Morbilliviruses, including measles virus (MV), canine distemper virus (CDV), and cetacean morbillivirus, pose a significant threat to humans and animals. While the host range of morbilliviruses is generally well-defined, severe cross-species transmission events also have been reported. Their entry into immune cells, the primary targets of morbilliviruses, relies on the signaling lymphocytic activation molecule (SLAM), a receptor whose species-specific variations influence viral host range. However, the extent to which SLAM diversity is a barrier to cross-species transmission remains poorly understood. In this study, we systematically investigated SLAM-mediated host specificity. We found that most morbilliviruses efficiently utilize SLAM from multiple host species, except for human SLAM. Among the morbilliviruses tested, only MV efficiently utilized human SLAM. Bats are natural reservoirs for many zoonotic viruses. Recent discoveries of novel morbilliviruses in bats provide new insights into morbillivirus evolution. Bats may have played a significant role in morbillivirus evolution because bat (Myotis) SLAM also functioned as an efficient receptor for multiple morbilliviruses. Unlike other morbilliviruses, MV utilized Myotis bat SLAM inefficiently. We conducted an MV adaptation experiment with Myotis bat SLAM to better understand SLAM recognition by morbilliviruses. MV readily adapted to utilize Myotis bat SLAM by acquiring a single N187Y mutation in its hemagglutinin protein, and computational structural modeling and fragment molecular orbital calculations with molecular dynamics simulations revealed key interaction changes that facilitated MVs adaptation to Myotis bat SLAM. These findings highlight the adaptability of morbilliviruses in utilizing diverse animal SLAMs. Notably, hypothetical ancestral SLAMs reconstructed in this study acted as universal receptors for all morbilliviruses. These results reinforced that morbillivirus receptor usage is primarily supported by evolutionarily conserved structural features of SLAM, highlighting a molecular basis that enables morbilliviruses to rapidly adapt to diverse animal SLAMs. Author SummaryOur study explores how viruses in the genus morbillivirus, such as measles virus, canine distemper virus, and rinderpest virus, which are notorious for deadly outbreaks in humans and animals, can jump between species. The host range of morbilliviruses is significantly influenced by a receptor molecule on cell surfaces known as the signaling lymphocytic activating molecule (SLAM). By examining SLAMs from various animals, including humans, dolphins, dogs, seals, and bats, we observed how these viruses can infect or adapt to infect different hosts. We found that in some cases slight differences in SLAM may act as initial barriers to cross-species transmission. However, these viruses rapidly overcome such barriers, showing a remarkable ability to adapt. Our research highlights the importance of monitoring these viruses to predict and prevent potential cross-species infections, which is crucial for protecting public health and animal welfare over time.

microbiology↗

bioRxiv2022

Single-Cell Transcriptome Analyses Reveal the Cell Diversity and Developmental Features of Human Gastric and Metaplastic Mucosa

The stomach is an important digestive organ with a variety of biological functions. However, due to the complexity of its cellular and glandular composition, the precise cellular biology has yet to be elucidated. In this study, we conducted single-cell RNA sequence analysis of the human stomach and constructed a 137,610-cell dataset, the largest cell atlas reported to date. By integrating this single-cell analysis with spatial cellular distribution analysis, we were able to clarify novel aspects of the developmental and tissue homeostatic ecosystems in the human stomach. We identified LEFTY1+ as a potential stem cell marker in both gastric and intestinal metaplastic glands. We also revealed skewed distribution patterns for PDGFRA+BMP4+WNT5A+ fibroblasts that play pivotal roles in, or even precede, the phenotypic changes from gastric to metaplastic mucosa. Our extensive dataset will function as a fundamental resource in investigations of the stomach, including studies on development, aging, and carcinogenesis.

genomics↗

bioRxiv2022

Beyond pathologist-level annotation of large-scale cancer histology for semantic segmentation using immunofluorescence restaining

Numerous cancer histopathology specimens have been collected and digitised as whole slide images over the past few decades. A comprehensive evaluation of the distribution of various cells in a section of tumour tissue can provide valuable information for understanding cancer and making accurate cancer diagnoses. Deep learning is one of the most suitable techniques to achieve these goals; however, the collection of large, unbiased training data has been a barrier to producing accurate segmentation models. Here, we developed a pipeline to generate SegPath, the largest annotation dataset that is over one order of magnitude larger than publicly available annotations, for the segmentation of haematoxylin and eosin (H&E)-stained sections for eight major cell types. The pipeline used H&E-stained sections that were destained and subsequently immunofluorescence-stained with carefully selected antibodies. The results showed that SegPath is comparable to, or significantly outperforms, conventional pathologist annotations. Moreover, we revealed that annotations by pathologists are biased toward typical morphologies; however, the model trained on SegPath can overcome this limitation. Our results provide foundational datasets for the histopathology machine learning community.

pathology↗

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↗

bioRxiv2019

Correlation between increased atrial expression of genes related to fatty acid metabolism and autophag y in patients with chronic atrial fibrillation

Atrial metabolic disturbance contributes to the onset and development of atrial fibrillation (AF). Autophagy plays a role in maintaining the cellular energy balance. We examined whether the altered atrial expression of genes related to fatty acid metabolism is linked to that related to autophagy in chronic AF. Right atrial tissue was obtained during heart surgery from 51 patients with sinus rhythm (SR, n=38) or chronic AF (n=13). Preoperative fasting serum free-fatty-acid levels were significantly higher in the AF patients. The atrial gene expression of fatty acid binding protein 3 (FABP3), which is involved in the cells fatty acid uptake and intracellular fatty acid transport, was significantly increased in AF patients compared to SR patients; in the SR patients it was positively correlated with the right atrial diameter and intra-atrial EMD, parameters of structural and electrical atrial remodeling that was evaluated by an echocardiography. In contrast, the two groups atrial contents of diacylglycerol (DAG), a toxic fatty acid metabolite, were comparable. Importantly, the atrial gene expression of microtubule-associated protein light chain 3 (LC3) was significantly increased in the AF patients, and autophagy-related genes including LC3 were positively correlated with the atrial expression of FABP3. In conclusion, in chronic AF patients, the atrial expression of FABP3 was upregulated in association with autophagy-related genes without altered atrial DAG content. Our findings may support the hypothesis that dysregulated cardiac fatty acid metabolism contributes to the progression of AF and induction of autophagy has a cardioprotective effect against cardiac lipotoxicity in chronic AF.

molecular biology↗

bioRxiv2018

Functional Genomics Screen with Pooled shRNA Library and Gene Expression Profiling with Extracts of Azadirachta indica Identify Potential Pathways for Therapeutic Targets in Head and Neck Squamous Cell Carcinoma

Tumor suppression by the extracts of Azadirachta indica (neem) works via anti-proliferation, cell cycle arrest, and apoptosis, demonstrated previously using cancer cell lines and live animal models. However, very little is known about the molecular targets and pathways that the neem extracts and the associated compounds act through. Here, we address this using a genome-wide functional pooled shRNA screen on head and neck squamous cell carcinoma cell line treated with crude neem leaf extracts, known for their anti-tumorigenic activity. By analyzing differences in global clonal sizes of the shRNA-infected cells cultured under no treatment and treatment with neem leaf extract conditions, assayed using next-generation sequencing, we found 225 genes affected the cancer cell growth in the shRNA-infected cells treated with neem extract. Pathway enrichment analyses of whole-genome gene expression data from cells temporally treated with neem extract revealed important roles played by the TGF-{beta} pathway and HSF-1-related gene network. Our results indicate that neem extract simultaneously affects various important molecular signaling pathways in head and neck cancer cells, some of which may be therapeutic targets for this devastating tumor.

genomics↗

bioRxiv2017

Capturing the Difference in Humoral Immunity between Normal and Tumor Environments from RNA Sequences of B-Cell Receptors Using Supervised Machine Learning

The recent success of immunotherapy in treating tumors has attracted increasing interest in research related to the adaptive immune system in the tumor microenvironment. Recent advances in next-generation sequencing technology enabled the sequencing of whole T-cell receptors (TCRs) and B-cell receptors (BCRs)/immunoglobulins (Igs) in the tumor microenvironment. Since BCRs/Igs in tumor tissues have high affinities for tumor-specific antigens, the patterns of their amino acid sequences and other sequence-independent features such as the number of somatic hypermutations (SHMs) may differ between the normal and tumor microenvironments. However, given the high diversity of BCRs/Igs and the rarity of recurrent sequences among individuals, it is far more difficult to capture such differences in BCR/Ig sequences than in TCR sequences. The aim of this study was to explore the possibility of discriminating BCRs/Igs in tumor and in normal tissues, by capturing these differences using supervised machine learning methods applied to RNA sequences of BCRs/Igs.\n\nRNA sequences of BCRs/Igs were obtained from matched normal and tumor specimens from 90 gastric cancer patients. BCR/Ig-features obtained in Rep-Seq were used to classify individual BCR/Ig sequences into normal or tumor classes. Different machine learning models using various features were constructed as well as gradient boosting machine (GBM) classifier combining these models. The results demonstrated that BCR/Ig sequences between normal and tumor microenvironments exhibit their differences. Next, by using a GBM trained to classify individual BCR/Ig sequences, we tried to classify sets of BCR/Ig sequences into normal or tumor classes. As a result, an area under the curve (AUC) value of 0.826 was achieved, suggesting that BCR/Ig repertoires have distinct sequence-level features in normal and tumor tissues.\n\nTo the best of our knowledge, this is the first study to show that BCR/Ig sequences derived from tumor and normal tissues have globally distinct patterns, and that these tissues can be effectively differentiated using BCR/Ig repertoires.

bioinformatics↗