Biology subjects

Ohnuki, Y.

Publications and source records attributed to Ohnuki, Y..

3 recordsLinked to original sources

bioRxiv2021

Role of TLR4 signaling on Porphyromonas gingivalis LPS-induced cardiac dysfunction in mice

Oral infections, particularly periodontitis, are a well-established risk factor for cardiovascular diseases, although the molecular mechanisms involved remain elusive. The aims of the present study were to investigate the effects of lipopolysaccharide derived from Porphyromonas gingivalis (PG-LPS) on cardiac function in mice, and to elucidate the underlying mechanisms. Mice (C57BL/6) were injected with PG-LPS (0.8 mg/kg/day) with or without an inhibitor of Toll-like receptor 4 (TLR4) signaling (TAK-242, 0.8 mg/kg/day) for 4 weeks. Left ventricular ejection function was significantly decreased at 1 week (from 67 {+/-} 0.5 to 58 {+/-} 1.2 %) and remained low at 4 weeks (57 {+/-} 1.0 %). The number of apoptotic myocytes was increased (approximately 7.4-fold), the area of fibrosis was increased (approximately 3.3-fold) and the number of 8-hydroxydeoxyguanosine-positive myocytes, a sensitive indicator of oxidative DNA damage, was increased (approximately 7.6-fold) at 4 weeks in the heart of PG-LPS treated mice. However, levels of various serum pro-inflammatory cytokines in PG-LPS-treated mice were similar to those in control mice. The impairment of cardiac function in PG-LPS-treated mice appears to involve activation of TLR4-NADPH oxidase (NOX) 4 signaling, leading to abundant production of reactive oxygen species and Ca2+ leakage from sarcoplastic reticulumn induced by calmodulin kinase II (CaMKII)-mediated phosphorylation of phospholamban (at Thr-17) and ryanodine receptor 2 (at Ser-2448). Pharmacological inhibition of TLR4 with TAK-242 attenuated the changes in cardiac function in PG-LPS-treated mice. Our results indicate that TLR4-NOX4 signaling may be a new therapeutic target for treatment of cardiovascular diseases in patients with periodontitis.

physiology↗

bioRxiv2021

Deep learning integration of molecular and interactome data for protein-compound interaction prediction

MotivationVirtual screening, which can computationally predict the presence or absence of protein-compound interactions, has attracted attention as a large-scale, low-cost, and short-term search method for seed compounds. Existing machine learning methods for predicting protein-compound interactions are largely divided into those based on molecular structure data and those based on network data. The former utilize information on proteins and compounds, such as amino acid sequences and chemical structures, while the latter utilize interaction network data, such as data on protein-protein interactions and compound-compound interactions. However, few attempts have been made to combine both types of data in molecular information and interaction networks. ResultsWe developed a deep learning-based method that integrates protein features, compound features, and multiple types of interactome data to predict protein-compound interactions. We designed three benchmark datasets with different difficulties and evaluated the performance on them. The performance evaluations show that our deep learning framework for integrating molecular structure data and interactome data outperforms state-of-the-art machine learning methods for protein-compound interaction prediction tasks. The performance improvement is proven to be statistically significant by the Wilcoxon signed-rank test. This reveals that the multi-interactome captures different perspectives than amino acid sequence homology and chemical structure similarity, and both type of data have a synergistic effect in improving prediction accuracy. Furthermore, experiments on three benchmark datasets show that our method is more robust than existing methods in accurately predicting interactions between proteins and compounds that are unseen in the training samples.

bioinformatics↗

bioRxiv2020

TMEM180 contributes to colorectal cancer proliferation through intracellular metabolic pathways

TMEM180, a novel colon cancer-specific membrane protein with a 12-transmembrane topology, is upregulated at low oxygen. Previously, we established a humanized monoclonal antibody against TMEM180 aimed at clinical trials. Prior to such trials, it is necessary to clarify the function of this molecule in cancer. To compare SW480 human colon cancer cells and their TMEM180-knockdown derivatives, we analyzed proliferation and oxygen consumption, and also performed phosphorylation proteomics, metabolomics, and next-generation sequencing. The results revealed that TMEM180 promoted the growth of colon cancer but had almost no effect on oxygen consumption or expression of phosphorylated proteins. By contrast, glycolysis differed dramatically between SW480 and TMEM180-knockdown cells. TMEM180 promotes nitric oxide synthesis, suggesting that it promotes glucose metabolism and glutamine metabolism, thereby contributing to cancer growth. Overall, the results of this study support the clinical development of an anti-TMEM180 antibody.

cancer biology↗