Back

Metabolic STAMP for deciphering GPCR-regulated insulin secretion by pancreatic β cells

Aziz-Zanjani, M. O.; Turn, R. E.; Hang, Y.; Asthana, A.; LaBrie, L. E.; Mobedi, M.; Xu, L. A.; Krawitzky, M.; Kim, S. K.; Jackson, P. K.

2025-10-04 physiology
10.1101/2025.10.03.680349 bioRxiv
Show abstract

Pancreatic {beta} cells integrate glucose and metabolic cues to regulate insulin secretion, a process disrupted in T2D. GPCRs play a critical role in fine-tuning insulin release, yet the mechanisms by which ciliary (e.g., FFAR4) and non-ciliary (e.g., GLP1-R) GPCRs coordinate GSIS remains unclear. In this study, we employed Metabolic-STAMP (Synchronized Temporal-Spatial Analysis via Microscopy and Phosphoproteomics) in both mouse {beta} cells (MIN6) and primary human islets to map the dynamic signaling networks governing GSIS and to link transient phosphorylation events to their functional outcomes. We systematically interrogated GPCR-mediated phosphorylation events through selective pharmacological inhibitors, resolving signaling hierarchies and consensus patterns across multiple pathways. Our multi-modal approach uncovered key insulin-secretion-associated PTMs, linked phosphorylation targets with phenotypic organelle dynamics, and provided mechanistic insights into how GLP1-R versus FFAR4 modulates GSIS through shared and GPCR-specific phospho-signatures. We highlighted key examples of stimulus-specific regulation by high glucose alone versus GPCR stimulation, including context-specific activation of the classic ERK signaling pathway, compartmentalized PKA signaling, pathway specificity in organelle dynamics and inter-organellar contacts, and HDAC6/ATAT-mediated regulation of microtubule acetylation. Collectively, these findings provided a blueprint for deconvolving pathway specificity of {beta} cell GPCR signaling, illuminated regulatory nodes that program insulin release, and offered new therapeutic targets to enhance {beta}-cell function .

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.