Back

Physiological bias governs neutrophil inflammatory threat perception

Pajonczyk, D.; Pauli, L.; Puent, C.; Sternschulte, M. F.; Fehler, O.; Vogl, T.; Soehnlein, O.; Bermudez, M.; Raabe, C. A.; Rescher, U.

2024-05-24 systems biology
10.1101/2024.04.05.588243 bioRxiv
Show abstract

Background and PurposeThe functional G protein-coupled receptor (GPCR) signalling unit consists of an agonist acting on a receptor that is coupled to a G protein that transduces the signals to effectors within a complex cellular environment. While much attention is given to GPCR-agonist or GPCR-transducer relationships, the contribution of the cellular environment remains significantly unexplored. Experimental ApproachHere, we juxtaposed the signalling responses triggered by the activation of two GPCR pattern recognition receptors, Formyl peptide receptor 1 and Formyl peptide receptor 2, in a recombinant cell system against their signalling dynamics in the native neutrophilic environment. Key resultsWe observed that agonist activation leads to cell context-dependent substantial differences in the receptor signalling texture. While the impact of receptor activation on de novo cAMP formation varied depending on the cell type, MAPK activation was similar in both systems. This physiological bias was conserved across species. Expression analysis unveiled the absence of the Gi-sensitive adenylyl cyclases ADCY5 and ADCY6 in neutrophils, implying that cAMP de novo synthesis cannot be inhibited by Gi-coupled receptors. The signalling behaviour of the Gi-coupled LTB4 high-affinity receptor BLT1 in neutrophils corroborated our findings. Conclusion and ImplicationsOur data underscore the profound impact of the specific cellular environment on GPCR signalling, causing physiological bias in GPCR signalling, thereby affecting drug efficacy and therapeutic targeting.

Matching journals

The top 8 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.