Large-scale profiling of physiologically relevant naturally occurring rare GPCR variants using the bioSensAll® technology
GPCR signaling can be differentially modulated by specific ligands to selectively promote the engagement of different subsets of signaling pathways, an outcome known as biased signaling. Although dysregulation of GPCR activity has been linked to pathological consequences, the involvement of their signaling pathways toward specific clinical repercussions remain unresolved. Thus, linking the role of GPCR signaling pathways to distinct physiological processes is not only important for understanding GPCR biology, but also essential for successfully targeting these receptors as potential therapeutic avenues. Here, to exploit the potential of naturally occurring GPCR variants emerging from large scale exome and genome sequencing efforts, we assessed the signaling profiles of rare variants across different GPCRs found in human populations with documented clinical phenotypes. Signaling profiles were assessed through an automated platform using our bioSensAll® technology, which measures the activity of various signaling pathways using a panel of 16 selective bioluminescence resonance energy transfer (BRET)-based biosensors that monitor the activation of heterotrimeric G proteins (Gαs, Gαi1, Gαi2, GαoA, GαoB, Gαz, Gαq, Gα11, Gα14, Gα15/16, Gα12, Gα13) or recruitment of β-arrestins to the plasma membrane (β arrestin 1, and β-arrestin 2) upon agonist stimulation. To achieve the large-scale screen in a high throughput system, bioSensAll® assays were first miniaturized and adapted to a 384-well format. Subsequently, HEK293 cells were co-transfected with receptors and biosensors, and a scheduling software was used to coordinate the interaction between various instruments via a robotic microplate mover. Analysis was performed through an automated tool to generate and compile signaling profiles for each variant normalized to the wild-type receptor, revealing gain- or loss-of-function properties induced by the variants across different signaling pathways, thus also allowing us to identify genetic alterations that impart biased signaling profiles. By pairing the multiparametric signaling profiles of variants found in human populations generated through our automated bioSensAll® platform with information on their associated clinical phenotypes, we can uncover associations between altered receptor pharmacology and clinical outcomes to accelerate the discovery of disease-relevant targets and improve translation from bench to the clinic.