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Biology subjects

Madsen, D.

Publications and source records attributed to Madsen, D..

2 recordsLinked to original sources

A neuronal relay mediates muscle-adipose communication that drives systemic metabolic adaptation to high-sugar diets

Obesity leads to impaired insulin signaling and tissue sensitivity, which drive the onset of type 2 diabetes. Insulin resistance leads to a reduction in cellular glucose uptake, resulting in elevated blood glucose levels, which consequently cause {beta}-cell dysfunction and development of diabetes. Although improving insulin signaling is a key target for restoring whole-body glucose homeostasis and reversing diabetes, the multi-organ mechanisms that regulate insulin signaling and tissue sensitivity are poorly defined. We screened the secretome and receptome in Drosophila to identify the underlying interorgan hormonal crosstalk affecting diet-induced insulin resistance and obesity. We identified complex interplay between muscle, neuronal, and fat tissues, mediated by the conserved BMP and LGR signaling pathways, which augments insulin signaling and improves dietary sugar tolerance. We found that muscle-derived BMP signaling is induced by sugar and governs neuronal Bursicon signaling. Acting through its LGR-family receptor, Bursicon both enhances insulin secretion and improves insulin sensitivity in adipose tissue, thereby preventing sugar-induced hyperglycemia. Inhibition of Bursicon-LGR signaling in adipose tissue exacerbates sugar-induced insulin resistance, and we discovered that this condition could be alleviated by suppressing NF-{kappa}B signaling. Our findings identify a muscle-neuronal-fat tissue axis that drives metabolic adaptation to high-sugar conditions by modulating insulin secretion and adipocyte insulin sensitivity, highlighting mechanisms that may be exploited for the development of strategies for the treatment and reversal of insulin resistance.

physiology↗

Identifying endogenous peptide receptors bycombining structure and transmembrane topologyprediction

Many secreted endogenous peptides rely on signalling pathways to exert their function in the body. While peptides can be discovered through high throughput technologies, their cognate receptors typically cannot, hindering the understanding of their mode of action. We investigate the use of AlphaFold-Multimer for identifying the cognate receptors of secreted endogenous peptides in human receptor libraries without any prior knowledge about likely candidates. We find that AlphaFolds predicted confidence metrics have strong performance for prioritizing true peptide-receptor interactions. By applying transmembrane topology prediction using DeepTMHMM, we further improve performance by detecting and filtering biologically implausible predicted interactions. In a library of 1112 human receptors, the method ranks true receptors in the top percentile on average for 11 benchmark peptide-receptor pairs.

bioinformatics↗