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Biology subjects

Tai, E. S.

Publications and source records attributed to Tai, E. S..

2 recordsLinked to original sources

PAX4 loss of function alters human endocrine cell development and influences diabetes risk

Diabetes is a major chronic disease with an excessive healthcare burden on society1. A coding variant (p.Arg192His) in the transcription factor PAX4 is uniquely and reproducibly associated with an altered risk for type 2 diabetes (T2D) in East Asian populations2-7, whilst rare PAX4 alleles have been proposed to cause monogenic diabetes8. In mice, Pax4 is essential for beta cell formation but neither the role of diabetes-associated variants in PAX4 nor PAX4 itself on human beta cell development and/or function are known. Here, we demonstrate that non-diabetic carriers of either the PAX4 p.Arg192His or a newly identified p.Tyr186X allele exhibit decreased pancreatic beta cell function. In the human beta cell model, EndoC-{beta}H1, PAX4 knockdown led to impaired insulin secretion, reduced total insulin content, and altered hormone gene expression. Deletion of PAX4 in isogenic human induced pluripotent stem cell (hiPSC)-derived beta-like cells resulted in derepression of alpha cell gene expression whilst in vitro differentiation of hiPSCs from carriers of PAX4 p.His192 and p.X186 alleles exhibited increased polyhormonal endocrine cell formation and reduced insulin content. In silico and in vitro studies showed that these PAX4 alleles cause either reduced PAX4 expression or function. Correction of the diabetes-associated PAX4 alleles reversed these phenotypic changes. Together, we demonstrate the role of PAX4 in human endocrine cell development, beta cell function, and its contribution to T2D-risk.

developmental biology↗

An atlas of genetic scores to predict multi-omic traits

Genetically predicted levels of multi-omic traits can uncover the molecular underpinnings of common phenotypes in a highly efficient manner. Here, we utilised a large cohort (INTERVAL; N=50,000 participants) with extensive multi-omic data for plasma proteomics (SomaScan, N=3,175; Olink, N=4,822), plasma metabolomics (Metabolon HD4, N=8,153), serum metabolomics (Nightingale, N=37,359), and whole blood Illumina RNA sequencing (N=4,136). We used machine learning to train genetic scores for 17,227 molecular traits, including 10,521 which reached Bonferroni-adjusted significance. We evaluated genetic score performances in external validation across European, Asian and African American ancestries, and assessed their longitudinal stability within diverse individuals. We demonstrated the utility of these multi-omic genetic scores by quantifying the genetic control of biological pathways and by generating a synthetic multi-omic dataset of UK Biobank to identify disease associations using a phenome-wide scan. Finally, we developed a portal (OmicsPred.org) to facilitate public access to all genetic scores and validation results as well as to serve as a platform for future extensions and enhancements of multi-omic genetic scores.

genomics↗