Abstract

The multicellular islet of Langerhans is made up primarily of insulin-secreting beta cells, which play a key role in glucose homeostasis. Thus, mechanistic understanding of intracellular signaling pathways within the beta cells is needed for the development of new and better treatments for metabolic diseases, including diabetes. Glucose-stimulated insulin secretion is driven by the closure of ATP-sensitive K+ channels and subsequent membrane depolarization, which activates L-type Ca2+ channels and leads to exocytosis. Beta cell insulin secretion can also be stimulated or inhibited by numerous autocrine, paracrine, and juxtracrine factors via multiple signaling pathways. Despite the heterogeneity of signals that regulate beta cell exocytosis, glucose stimulation robustly leads to insulin secretion from healthy cells. The relative pathological roles of the non-glucose-dependent pathways are not well understood, which makes novel therapeutic interventions challenging to identify.Information theory is a rising tool in systems biology to quantitatively approach questions in signal transduction. Here, we utilize Shannon entropy and mutual information to measure the mutual dependence between second messenger signaling pathways involved in insulin secretion. Using a suite of biosensors targeted to signaling molecules including cAMP, Ca2+, and PIP3, we collect time-dependent information on the transmission of signals in pancreatic beta cells. Using a snapshot hyperspectral imaging system, we can measure several overlapping biosensors simultaneously, allowing us to collect second messenger activity from multiple pathways. Measured outputs also include insulin secretion and phosphorylation of signaling targets. Pharmacological perturbations (stimulants/inhibitors) and/or genetic modifications provide a means of creating conditional dependence among the input pathways that modulate the signaling molecules. These studies should help identify mechanisms by which beta cells discriminate between multiple extracellular signals.

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