Biology subjects

Oba, S.

Publications and source records attributed to Oba, S..

3 recordsLinked to original sources

bioRxiv2022

CiDRE+ M2c macrophages hijacked by SARS-CoV-2 cause COVID-19 severity

Infection of the lungs with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) via the angiotensin I converting enzyme 2 (ACE2) receptor induces a type of systemic inflammation known as a cytokine storm. However, the precise mechanisms involved in severe coronavirus disease 2019 (COVID-19) pneumonia are unknown. Here, we show that interleukin-10 (IL-10) changed normal alveolar macrophages into ACE2-expressing M2c-type macrophages that functioned as spreading vectors for SARS-CoV-2 infection. The depletion of alveolar macrophages and blockade of IL-10 attenuated SARS-CoV-2 pathogenicity. Furthermore, genome-wide association and quantitative trait locus analyses identified novel mRNA transcripts in human patients, COVID-19 infectivity enhancing dual receptor (CiDRE), which has unique synergistic effects within the IL-10-ACE2 system in M2c-type macrophages. Our results demonstrate that alveolar macrophages stimulated by IL-10 are key players in severe COVID-19. Collectively, CiDRE expression levels are potential risk factors that predict COVID-19 severity, and CiDRE inhibitors might be useful as COVID-19 therapies. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=195 HEIGHT=200 SRC="FIGDIR/small/510331v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@1e96c82org.highwire.dtl.DTLVardef@1d2dc32org.highwire.dtl.DTLVardef@7689d1org.highwire.dtl.DTLVardef@520d17_HPS_FORMAT_FIGEXP M_FIG C_FIG

immunology↗

bioRxiv2019

UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images

Recently, there has been a rapid expansion in the field of micro-connectomics, which targets the three-dimensional (3D) reconstruction of neuronal networks from a stack of two-dimensional (2D) electron microscopic (EM) images. The spatial scale of the 3D reconstruction grows rapidly owing to deep neural networks (DNNs) that enable automated image segmentation. Several research teams have developed their own software pipelines for DNN-based segmentation. However, the complexity of such pipelines makes their use difficult even for computer experts and impossible for non-experts. In this study, we developed a new software program, called UNI-EM, that enables 2D- and 3D-DNN-based segmentation for non-computer experts. UNI-EM is a software collection for DNN-based EM image segmentation, including ground truth generation, training, inference, postprocessing, proofreading, and visualization. UNI-EM comes with a set of 2D DNNs, i.e., U-Net, ResNet, HighwayNet, and DenseNet. We further wrapped flood-filling networks (FFNs) as a representative 3D DNN-based neuron segmentation algorithm. The 2D- and 3D-DNNs are known to show state-of-the-art level segmentation performance. We then provided two-example workflows: mitochondria segmentation using a 2D DNN as well as neuron segmentation using FFNs. Following these example workflows, users can benefit from DNN-based segmentation without any knowledge of Python programming or DNN frameworks.

neuroscience↗

bioRxiv2018

Logical design of oral glucose ingestion pattern minimizing blood glucose in humans

Excessive increase in blood glucose level after eating increases the risk of macroangiopathy, and a method for not increasing the postprandial blood glucose level is desired. However, a logical design method of the dietary ingestion pattern controlling the postprandial blood glucose 2 level has not yet been established. We constructed a mathematical model of blood glucose control by oral glucose ingestion in 3 healthy human subjects, used the model to predict an optimal glucose ingestion pattern, and showed that the optimal ingestion pattern minimized the peak value of blood glucose level. Subjects orally ingested 3 doses of glucose by bolus or over 2 hours, and blood glucose, insulin, C-peptide and incretins were measured for 4 hours. We constructed an ordinary differential equation model that reproduced the time course data of the blood glucose and blood hormone levels. Using the model, we predicted that intermittent ingestion 30 minutes apart was the optimal glucose ingestion patterns that minimized the peak value of blood glucose level. We confirmed with subjects that this intermittent pattern decreased the peak value of blood glucose level. This approach could be applied to design optimal dietary ingestion patterns.\n\nIn BriefAs a forward problem, we measured blood glucose and hormones in three human subjects after oral glucose ingestion and constructed a mathematical model of blood glucose control. As an inverse problem, we used the model to predict the optimal oral glucose ingestion pattern that minimized the peak value of blood glucose level, and validated the pattern with the subjects.\n\nHighlightsO_LIModeling blood glucose concentrations predicts an intermittent ingestion pattern is optimal\nC_LIO_LIHuman validation shows ingestion at 30-minute intervals limits peak blood glucose\nC_LIO_LIWe provide a strategy to design optimal dietary ingestion patterns\nC_LI

systems biology↗