Architecture of androgen receptor pathways amplifying glucagon-like peptide-1 insulinotropic action in male pancreatic β cells

Male mice lacking the androgen receptor (AR) in pancreatic β cells exhibit blunted glucose-stimulated insulin secretion (GSIS), leading to hyperglycemia. Testosterone activates an extranuclear AR in β cells to amplify glucagon-like peptide-1 (GLP-1) insulinotropic action. Here, we examined the architecture of AR targets that regulate GLP-1 insulinotropic action in male β cells. Testosterone cooperates with GLP-1 to enhance cAMP production at the plasma membrane and endosomes via: (1) increased mitochondrial production of CO2, activating the HCO3--sensitive soluble adenylate cyclase; and (2) increased Gαs recruitment to GLP-1 receptor and AR complexes, activating transmembrane adenylate cyclase. Additionally, testosterone enhances GSIS in human islets via a focal adhesion kinase/SRC/phosphatidylinositol 3-kinase/mammalian target of rapamycin complex 2 actin remodeling cascade. We describe the testosterone-stimulated AR interactome, transcriptome, proteome, and metabolome that contribute to these effects. This study identifies AR genomic and non-genomic actions that enhance GLP-1-stimulated insulin exocytosis in male β cells

Benchmarking Ontologies: Bigger or Better?

A scientific ontology is a formal representation of knowledge within a domain, typically including central concepts, their properties, and relations. With the rise of computers and high-throughput data collection, ontologies have become essential to data mining and sharing across communities in the biomedical sciences. Powerful approaches exist for testing the internal consistency of an ontology, but not for assessing the fidelity of its domain representation. We introduce a family of metrics that describe the breadth and depth with which an ontology represents its knowledge domain. We then test these metrics using (1) four of the most common medical ontologies with respect to a corpus of medical documents and (2) seven of the most popular English thesauri with respect to three corpora that sample language from medicine, news, and novels. Here we show that our approach captures the quality of ontological representation and guides efforts to narrow the breach between ontology and collective discourse within a domain. Our results also demonstrate key features of medical ontologies, English thesauri, and discourse from different domains. Medical ontologies have a small intersection, as do English thesauri. Moreover, dialects characteristic of distinct domains vary strikingly as many of the same words are used quite differently in medicine, news, and novels. As ontologies are intended to mirror the state of knowledge, our methods to tighten the fit between ontology and domain will increase their relevance for new areas of biomedical science and improve the accuracy and power of inferences computed across them