Biology subjects

Mizuguchi, K.

Publications and source records attributed to Mizuguchi, K..

3 recordsLinked to original sources

bioRxiv2021

Co-expression analysis to identify key modules and hub genes associated with COVID19 in Platelets

AO_SCPLOWBSTRACTC_SCPLOWThe severe acute respiratory syndrome corona virus 2 (SARS-CoV-2) is a highly contagious virus that causes a severe respiratory disease known as Corona virus disease 2019 (COVID19). Indeed, COVID19 increases the risk of cardiovascular occlusive/thrombotic events and is linked to poor outcomes. The pathophysiological processes underlying COVID19-induced thrombosis are complex, and remain poorly understood. To this end, platelets play important roles in regulating our cardiovascular system, including via contributions to coagulation and inflammation. There is an ample of evidence that circulating platelets are activated in COVID19 patients, which is a primary driver of the thrombotic outcome observed in these patients. However, the comprehensive molecular basis of platelet activation in COVID19 disease remains elusive, which warrants more investigation. Hence, we employed gene co-expression network analysis combined with pathways enrichment analysis to further investigate the aforementioned issues. Our study revealed three important gene clusters/modules that were closely related to COVID19. Furthermore, enrichment analysis showed that these three modules were mostly related to platelet metabolism, protein translation, mitochondrial activity, and oxidative phosphorylation, as well as regulation of megakaryocyte differentiation, and apoptosis, suggesting a hyperactivation status of platelets in COVID19. We identified the three hub genes from each of three key modules according to their intramodular connectivity value ranking, namely: COPE, CDC37, CAPNS1, AURKAIP1, LAMTOR2, GABARAP MT-ND1, MT-ND5, and MTRNR2L12. Collectively, our results offer a new and interesting insight into platelet involvement in COVID19 disease at the molecular level, which might aid in defining new targets for treatment of COVID19-induced thrombosis. key pointsO_LICo-expression analysis of platelet RNAseq from COVID19 patients show distinct clusters of genes (modules) that are highly correlated to COVID19 disease. C_LIO_LIIdentifying these modules might help in understanding the mechanism of thrombosis in COVID19 patients C_LI

systems biology↗

bioRxiv2021

Clomipramine suppresses ACE2-mediated SARS-CoV-2 entry

Myocardial damage caused by the newly emerged coronavirus (SARS-CoV-2) infection is one of key determinants of COVID-19 severity and mortality. SARS-CoV-2 entry to host cells are initiated by binding with its receptor, angiotensin converting enzyme (ACE) 2, and the ACE2 abundance is thought to reflect the susceptibility to infection. Here, we found that clomipramine, a tricyclic antidepressant, potently inhibits SARS-CoV-2 infection and metabolic disorder in human iPS-derived cardiomyocytes. Among 13 approved drugs that we have previously identified as potential inhibitor of doxorubicin-induced cardiotoxicity, clomipramine showed the best potency to inhibit SARS-CoV-2 spike glycoprotein pseudovirus-stimulated ACE2 internalization. Indeed, SARS-CoV-2 infection to human iPS-derived cardiomyocytes (iPS-CMs) and TMPRSS2-expressing VeroE6 cells were dramatically suppressed even after treatment with clomipramine. Furthermore, the combined use of clomipramine and remdesivir was revealed to synergistically suppress SARS-CoV-2 infection. Our results will provide the potentiality of clomipramine for the breakthrough treatment of severe COVID-19.

pharmacology and toxicology↗

bioRxiv2020

Network Modeling and Inference of Peroxisome Proliferator-Activated Receptor Pathway in High fat diet-linked Obesity

Systems biology aims to understand how holistic systems theory can be used to explain the observable living system characteristics, and mathematical modeling tools have been successful in understanding the intricate relationships underlying cellular functions. Lately, researchers have been interested in understanding molecular mechanisms underlying obesity, which is a major health concern worldwide and has been linked to several diseases. Various mechanisms such as peroxisome proliferator-activated receptors (PPARs) are known to modulate obesity-induced inflammation and its consequences. In this study, we have modeled the PPAR pathway using a Bayesian model and inferred the sub-pathways that are potentially responsible for the activation of the output processes that are associated with high fat diet (HFD)-induced obesity. We examined a previously published dataset from a study that compared gene expression profiles of 40 mice maintained on HFD against 40 mice fed with chow diet (CD). Our simulations have highlighted that GPCR and FATCD36 sub-pathways were aberrantly active in HFD mice and are therefore favorable targets for anti-obesity strategies. We further cross-validated our observations with experimental results from the literature. We believe that mathematical models such as those presented in the present study can help in inferring other pathways and deducing significant biological relationships.

bioinformatics↗