Biology subjects

Mizuguchi, K.

Publications and source records attributed to Mizuguchi, K..

9 recordsLinked to original sources

bioRxiv2025

Development of an RNA Aptamer as a Therapeutic Agent for Synucleinopathies

The aggregation of -synuclein (Syn), a 140-mer protein, has been implicated in the pathogenesis of Parkinsons disease, multiple system atrophy, and dementia with Lewy bodies. KTKEGV repeats (KR) of Syn are key mediators of prion-like propagation and neurodegeneration. Despite the availability of symptomatic treatments, no current therapy effectively delays disease progression. Here we report a 77-nucleotide (nt) RNA aptamer (1R6) with potent affinity and selectivity for Syn1-95 (KD = 18 nM) through in vitro selection. 1R6 significantly inhibited Syn oligomerization and {beta}-sheet-rich fibril assembly and promoted disaggregation of preformed fibrils. Additionally, 1R6 suppressed Syn seeding, as determined by FRET-based cellular biosensor cell assay. Cellular studies revealed that 1R6 cotransfection completely prevented Syn-induced cytotoxicity. To assess the protective effects of 1R6 in vivo, we used a Drosophila melanogaster model expressing human Syn in neurons. Flies fed with 1R6 showed improved locomotor defects, reduced photoreceptor degeneration, and decreased Syn levels in the head. Structural characterization through 1H-15N heteronuclear multiple quantum correlation nuclear magnetic resonance experiments demonstrated that 1R6 targets KR motifs, a finding further supported by in silico simulations. Our findings indicate that RNA aptamers, such as 1R6, may represent promising therapeutic candidates for synucleinopathies, thus opening new avenues in the treatment of these diseases.

neuroscience↗

bioRxiv2025

Proteomic and in silico dissection of MetaAggregates in amyotrophic lateral sclerosis brains

RNA-binding proteins (RBPs), key translation regulators, are thought to be involved in the pathogenesis of amyotrophic lateral sclerosis (ALS). The pathological entities associated with ALS are known as "MetaAggregates": heterogeneous coaggregates composed of amyloids, RBPs, and RNA G-quadruplexes (rG4s). In this study, to explore the molecular constituents of ALS-associated MetaAggregates, we developed a proteomic approach using a psoralen-conjugated RBP and crosslinked it with a biotinylated rG4 to enable the isolation of MetaAggregates from ALS brain extracts. Single-cell RNA-seq using in vitro ALS models identified ELAVL4 as a cytoplasmic RBP and revealed the enrichment of an IGFBP2-derived rG4 structure in ALS-specific neurons. Mass spectrometry and amyloidogenicity-based principal component analysis revealed 79 candidate proteins with roles in RNA processing, metabolism, trafficking, and stress responses. Docking simulations highlighted a subset of proteins with potential pro-aggregation characteristics, diverse cytosolic associations and functional links to RNA processing relevant to ALS. Through proteomic and in silico dissection of ALS-associated MetaAggregates, the findings of this study establish a conceptual framework for the exploration of unrecognized amyloidogenic drivers of neurodegeneration.

neuroscience↗

bioRxiv2024

Identification of the differences in molecular networks between idiopathic pulmonaryfibrosis and lung squamous cell carcinoma using machine learning

Idiopathic pulmonary fibrosis (IPF), a form of idiopathic interstitial pneumonia, is an independent risk factor for lung cancer. The prognosis of IPF patients with lung cancer is poorer than that of IPF patients without lung cancer, and preventive measures for lung cancer remain elusive in patients with IPF. To mitigate lung cancer onset in patients with IPF, understanding the distinct mechanisms that induce both diseases is crucial. We developed highly accurate machine learning (ML) models to classify patients with IPF and lung cancer using public RNA sequencing data. To construct the ML models, the random restart technique was applied to five algorithms, namely, k-nearest neighbors, support vector machines (SVM) with radial basis function kernel, SVM with linear kernel, eXtreme gradient boosting, and random forest. To identify differentially expressed genes between IPF and lung cancer, feature importance was calculated in the classification models. Furthermore, we detected somatic mutations impacting gene expression using lung cancer data. The ML models identified VCX2, TMPRSS11B, PRUNE2, PRG4, PZP, SCARA5, DES, HPSE2, HOXD11, S100A7A, and PLA2G2A11 as differentially expressed genes. Somatic mutations were detected in four transcription factors, BHLHE40, MYC, STAT1, and E2F4, that regulate the expression of the abovementioned 11 genes. Furthermore, a molecular network was discovered, comprising the four transcription factors and 11 downstream genes. The newly identified molecular network enhances our understanding of the distinct mechanisms underlying IPF and lung cancer onset, providing new insights into the prevention of lung cancer complications in patients with IPF.

cancer biology↗

bioRxiv2024

Biological age prediction using a novel DNN model based on steroid metabolic pathways

Aging involves the progressive accumulation of cellular damage, leading to systemic decline and age-related diseases. Despite advances in medicine, accurately predicting Biological Age (BA) remains challenging due to the complexity of aging processes and the limitations of current models. This study introduces a novel method for predicting BA using a Deep Neural Network (DNN) based on steroid metabolic pathways. We analyzed 22 steroids from 148 serum samples of individuals aged 20 to 73, using 98 samples for model training and 50 for validation. Our model reflects the often-overlooked fact that aging heterogeneity expands over time and uncovers sex-specific variations in steroid interactions. This study identified key markers, including cortisol (COL), which underscore the role of stress-related and sex-specific steroids in aging. The resulting model establishes a biologically meaningful and robust framework for predicting BA across diverse datasets, supporting more targeted strategies in aging research and disease management.

bioinformatics↗

bioRxiv2023

An attention network for predicting T cell receptor-peptide binding can associate attention with interpretable protein structural properties

Understanding how a T cell receptor (TCR) recognizes its specific ligand peptide is crucial for gaining insight into biological functions and disease mechanisms. Despite its importance, experimentally determining TCR-peptide interactions is expensive and time-consuming. To address this challenge, computational methods have been proposed, but they are typically evaluated by internal retrospective validation only, and few have incorporated and tested an attention layer from language models into structural information. Therefore, in this study, we developed a machine learning model based on a modified version of the Transformer, a source-target-attention neural network, to predict TCR-peptide binding solely from the amino acid sequences of the TCRs complementarity-determining region (CDR) 3 and the peptide. This model achieved competitive performance on a benchmark dataset of TCR-peptide binding, as well as on a truly new external dataset. Additionally, by analyzing the results of binding predictions, we associated the neural network weights with protein structural properties. By classifying the residues into large and small attention groups, we identified statistically significant properties associated with the largely attended residues, such as hydrogen bonds within the CDR3. The dataset that we have created and our models ability to provide an interpretable prediction of TCR-peptide binding should increase our knowledge of molecular recognition and pave the way to designing new therapeutics.

bioinformatics↗

bioRxiv2022

Snaq: A Dynamic Snakemake Pipeline for Microbiome data analysis with QIIME2

Optimizing a protocol for 16S microbiome data analysis with QIIME2 is a challenging task for biologists with no programming experience. It involves a multi-step process, and multiple parameters and options that need to be tested and determined. In this article, we describe Snaq, a snakemake pipeline that helps automate and optimize 16S data analysis using QIIME2. Snaq offers an informative file naming system and automatically performs the analysis of a data set by downloading and installing the required databases and classifiers, all through a single command-line instruction. It works natively on Linux and Mac and on Windows through the use of containers, and is potentially extendable by adding new rules. This pipeline will substantially reduce the efforts in sending commands and prevent the confusion caused by the accumulation of analysis results due to testing multiple parameters.

bioinformatics↗

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↗