Back

Chromogranin A regulates the dynamics of neurosecretion through its interaction with phosphatidic acid

FERRAND, T.; WOLF, A.; LAGUERRE, F.; RIACHY, L.; SCHLICHTER, A.; JEANDEL, L.; BENARD, M.; BEAUVAIS, B.; SCHAPMAN, D.; CHASSEROT-GOLAZ, S.; ROYER, C.; BERARD, C.; CARTIER, D.; LEE, J.; GRUMOLATO, L.; GALAS, L.; CHARTREL, N.; RENARD, P.-Y.; ANOUAR, Y.; BALIEU, S.; VITALE, N.; MONTERO, M.

2026-02-02 cell biology
10.64898/2026.01.29.699889 bioRxiv
Show abstract

Altered neurosecretion is a common feature of diverse pathophysiological conditions, including central nervous system disorders, hypertension, and tumorigenesis, where chromogranin A (CgA) is widely used as a biomarker, and both phosphatidic acid (PA) synthesis and catecholamine (CA) secretion are dysregulated. Here, we identify a direct interaction between CgA and PA at the plasma membrane of living cells using a newly developed synthetic PA fluorescent probe and Forsters resonance energy transfer (FRET) combined with fluorescence lifetime imaging microscopy (FLIM). Confocal microscopy and transmission electron microscopy (TEM) further reveal that this interaction is spatially confined to exocytic sites. Using Total Internal Reflection Fluorescence microscopy (TIRF-M), we show that expression of a CgA variant lacking the PA-binding domain (PABD) in COS-7 cells increases the frequency of exocytic events and accelerates CgA release kinetics. In chromaffin cells, amperometry and live tracking of exocytosis-endocytosis demonstrate that CgA overexpression enhances granular CA content, extends fusion pore opening, and accelerates exocytosis-endocytosis coupling, effects that are abolished upon expression of CgA variant. Together, these findings unveil a new role of CgA/PA interaction in fine-tuning neurohormone secretion, suggesting unexplored avenues for restoring neurosecretion in disease-relevant contexts.

Matching journals

The top 7 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.