Biology subjects

Noda, T.

Publications and source records attributed to Noda, T..

3 recordsLinked to original sources

bioRxiv2018

PTGER2-β-Catenin Axis Links High Salt Environments to Autoimmunity by Balancing IFNγ and IL-10 in FoxP3+ Regulatory T cells

Foxp3+ regulatory T cells (Tregs) are the central component of peripheral immune tolerance. While dysregulation of the Treg cytokine signature has been observed in autoimmune diseases such as multiple sclerosis (MS) and type 1 diabetes, the regulatory mechanisms balancing pro- and anti-inflammatory cytokine production are not known. Here, we identify imbalance between IFN{gamma} and IL-10 as a shared Treg signature, present in patients with MS and under high salt conditions. By performing RNA-seq analysis on human Treg subpopulations, we identify {beta}-catenin as a key regulator that controls the expression of IFN{gamma} and IL-10. The activated {beta}-catenin signature is enriched specifically in IFN{gamma}+Tregs in humans, and this was confirmed in vivo with Treg-specific {beta}-catenin-stabilized mice exhibiting lethal autoimmunity with a dysfunctional, IFN{gamma}-producing, Treg phenotype. Moreover, we identify PTGER2 as a major factor balancing IFN{gamma} and IL-10 production in the context of a high salt environment, with skewed activation of the {beta}-catenin/SGK1/Foxo axis in IFN{gamma}+Tregs. These findings identify a novel molecular mechanism underlying inflammatory Tregs in human autoimmune disease and reveal a new role for a PTGER2-{beta}-catenin loop in Tregs linking environmental high salt conditions to autoimmunity.

immunology↗

bioRxiv2018

National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB): Outline and Patient-Matching Technique

BackgroundThe National Database of Health Insurance Claims and Specific Health Checkups of Japan (NDB) is a comprehensive database of health insurance claims data under Japans National Health Insurance system. The NDB uses two types of personal identification variables (referred to in the database as \"ID1\" and \"ID2\") to link the insurance claims of individual patients. However, the information entered against these ID variables is prone to change for several reasons, such as when claimants find or change employment, or due to variations in the spelling of their name. In the present study, we developed a new patient-matching technique that improves upon the existing system of using ID1 and ID2 variables. We also sought to validate a new personal ID variable (ID0) that we propose in order to enhance the efficiency of patient matching in the NDB database.\n\nMethodsOur study targeted data from health insurance claims filed between April 2013 and March 2016 for hospitalization, combined diagnostic procedures, outpatient treatment, and dispensing of prescription medication. We developed a new patient-matching algorithm based on the ID1 and ID2 variables, as well as variables for treatment date and clinical outcome. We then attempted to validate our algorithm by comparing the number of patients identified by patient matching with the current ID1 variable and our proposed ID0 variable against the estimated patient population as of 1 October 2015.\n\nResultsThe numbers of patients in each sex and age group that were identified with the ID0 variable were lower than those identified using the ID1 variable. By using the ID0 variable, we were able to reduce the number of duplicate records for male and female patients by 5.8% and 6.4%, respectively. The numbers of children, adults older than 75 years, and women of reproductive age identified using the ID1 patient-matching variable were all higher than their corresponding estimates. Conversely, the numbers of these patients identified with the ID0 patient-matching variable were all within their corresponding estimates.\n\nConclusionOur findings show that the proposed ID0 variable delivers more precise patient-matching results than the existing ID1 variable. The ID0 variable is currently the best available technique for patient matching in the NDB database. Future patient population estimates should therefore rely on the ID0 variable instead of the ID1 variable.

epidemiology↗

bioRxiv2017

Identifying developmentally important genes with single-cell RNA-seq from an embryo

Single-cell RNA-seq has been established as a reliable and accessible technique enabling new types of analyses, such as identifying cell types and studying spatial and temporal gene expression variation and change at single-cell resolution. Recently, single-cell RNA-seq has been applied to developing embryos, which offers great potential for finding and characterising genes controlling the course of development along with their expression patterns. In this study, we applied single-cell RNA-seq to the 16-cell stage of the Ciona embryo, a marine chordate and performed a computational search for cell-specific gene expression patterns. We recovered many known expression patterns from our single-cell RNA-seq data and despite extensive previous screens, we succeeded in finding new cell-specific patterns, which we validated by in situ and single-cell qPCR.

developmental biology↗