Nishioka, Y. | bioRxiv Science

bioRxiv2021

Implications of cholesterol and sphingomyelin in STING phosphorylation by TBK1

Stimulator of interferon genes (STING) is essential for the type I interferon response induced by microbial DNA from virus or self-DNA from mitochondria/nuclei. In response to emergence of such DNAs in the cytosol, STING translocates from the endoplasmic reticulum (ER) to the Golgi, and activates TANK-binding kinase 1 (TBK1) at the trans-Golgi network (TGN). Activated TBK1 then phosphorylates STING at Ser365, generating an interferon regulatory factor 3 (IRF3)-docking site on STING. How this reaction proceeds specifically at the TGN remains poorly understood. Here we report a cell-free reaction in which endogenous STING is phosphorylated by TBK1. The reaction utilizes microsomal membrane fraction prepared from TBK1-knockout (KO) cells and recombinant TBK1. We observed agonist-, TBK1-, "ER-to-Golgi" traffic-, and palmitoylation-dependent phosphorylation of STING at Ser365, mirroring the nature of STING phosphorylation in vivo. Treating the microsomal membrane fraction with sphingomyelinase or methyl-{beta}-cyclodextrin, an agent to extract cholesterol from membranes, suppressed the phosphorylation of STING by TBK1. Given the enrichment of sphingomyelin and cholesterol in the TGN, these results may provide the molecular basis underlying the specific phosphorylation reaction of STING at the TGN.

cell biology↗

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↗