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Navas-Acien, A.

Publications and source records attributed to Navas-Acien, A..

2 recordsLinked to original sources

Manganese availability determines insulin sensitivity by enhancing Akt activity

Insulin signaling is a critical determinant of metabolic health, and impairments in insulin action contribute to the development of type 2 diabetes. The kinase Akt is a central mediator of insulin signaling and is required for insulin's suppression of hepatic glucose output. Although the regulation of Akt by the insulin receptor-PI3K pathway is well understood, there are instances in which signaling downstream of Akt is dissociated from proximal insulin signaling, for example in insulin resistance. Nonetheless, little is known about PI3K-independent mechanisms of Akt regulation. Here, we discovered that hepatocyte manganese concentrations are a key determinant of PI3K-independent Akt function in vivo. We further demonstrated that manganese increases Akt's catalytic efficiency, and quantitative phosphoproteomics revealed that manganese and insulin act additively to enhance Akt activity. Moreover, we uncovered that hepatic manganese concentrations fluctuate during fasting and feeding via carbohydrate-dependent transcriptional regulation of the manganese efflux transporter Slc30a10. This dynamic metal-signaling axis provides a mechanistic link between nutrient status and Akt activation, and suggests a molecular explanation for the glucose-lowering effects of manganese observed in humans. Our findings establish manganese as a physiologically regulated cofactor for Akt and position metal bioavailability as a previously unrecognized layer of insulin signaling control.

physiology↗

CoxMDS: Multiple Data Splitting for High-dimensional Mediation Analysis with Survival Outcomes in Epigenome-wide Studies

Causal mediation analysis investigates whether the effect of an exposure on an outcome operates through intermediate variables known as mediators. Although progress has been made in high-dimensional mediation analysis, current methods do not reliably control the false discovery rate (FDR) in finite samples, especially when mediators are moderately to highly correlated or follow non-Gaussian distributions. These challenges frequently arise in DNA methylation studies. We introduce CoxMDS, a multiple data splitting method that uses Cox proportional hazards models to identify putative causal mediators for survival outcomes. CoxMDS ensures finite-sample FDR control even in the presence of correlated or non-Gaussian mediators. Through simulations, CoxMDS is shown to maintain FDR control and achieve higher statistical power compared with existing approaches. In applications to DNA methylation data with survival outcomes, CoxMDS identified eight CpG sites in The Cancer Genome Atlas (TCGA) that are consistent with the hypothesis that DNA methylation may mediate the effect of smoking on lung cancer survival, and two CpG sites in the Alzheimers Disease Neuroimaging Initiative (ADNI) that are consistent with the hypothesis that DNA methylation may mediate the effect of smoking on time to Alzheimers disease conversion.

bioinformatics↗