Biology subjects

Tai, E. S.

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

3 recordsLinked to original sources

bioRxiv2023

Unbiased phosphoproteomics analysis unveils modulation of insulin signaling by extramitotic CDK1 kinase activity in human myotubes

Sensitivity and plasticity of insulin signaling and glucose uptake in skeletal muscle depends on determinants such as genetic variation and obesity. We collected muscle biopsies and isolated myoblasts from a multi-ethnic cohort of lean South Asians (N=10), lean Chinese (N=10), and obese Chinese (N=10), and analysed the proteome and phosphoproteome dynamics in terminally differentiated myotubes after a low-dose insulin stimulation (10nM at 0, 5, 30 min). The myotubes initially responded with increased abundance and phosphorylation level changes along the PI3K/AKT/mTOR axis, decreased abundance of translation initiation factors, and increased phosphorylation levels on proteins involved in mRNA processing at 5 min. After the acute response, protein abundance returned to baseline at 30 min, while phosphorylation changes persisted in proteins including AKT, RPS6 and AS160 (TBC1D4). A joint kinase-substrate statistical analysis revealed that protein abundance changes of AKT, PAK1 and CDK1 showed concordant phosphorylation changes in their respective substrates upon insulin stimulation. We also observed increased phosphorylation of some substrates uniquely in each group, particularly the substrates of CDKs showing stronger changes in South Asians than in Chinese. Pharmacological inhibition and siRNA knockdown of CDK1, a non-myogenic kinase, in terminally differentiated myotubes reduced glucose uptake and desensitized several phosphorylation-mediated signaling on protein translation initiation factors, IRS1, and AS160. Our data suggest that basal extramitotic activity of CDK1 is required for PI3K/AKT/mTORC1 signaling cascade and glucose uptake in insulin-stimulated myotubes. The data also provide a rich resource for studying the role of other kinases in the mechanism of insulin resistance in human myotubes.

physiology↗

bioRxiv2022

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↗

bioRxiv2022

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↗