Stimulation of FPR by bacterial N formyl peptides induces ba

Stimulation of FPR by bacterial N-formyl peptides induces basophil and mast ivacaftor release of immunogenic compounds such as histamine [21], [31]. Furthermore, we demonstrated that histamine plays a role in F-MITs-induced sepsis-like symptoms since cimetidine, a histamine H2-receptor antagonist, completely abolished cardiovascular collapse induced by F-MITs infusion [11]. It is well known that histamine causes the contraction of airway smooth muscle [20]. Further, in response to allergens, the release of histamine by mast cells induces a severe airway obstruction and, in some cases, can lead to anaphylaxis. Here, we observed that mast cell degranulation, using compound 48/80, also decreased F-MITs-induced contraction, probably through blocking histamine release. Collectively, our findings provide a new and different way of considering the role of F-MITs in lung injury and airway contraction following trauma. As such, this pathway could be considered a putative target for the treatment of respiratory failure and sterile inflammation (Fig. 5).
Disclosure
Acknowledgments This work was supported by grants from American Heart Association (14POST20490292 and #13PRE14080019) and National Institutes of Health.
Introduction
Materials and methods The multiple homology models of FPR2 were generated based on the antagonist-bound μOR opioid receptor crystal structure (PDB code: 4DKL) and antagonist-bound chemokine receptor CXCR4 crystal structure (PDB code: 3ODU) using our GPCRM server. Multiple alignment (Fig. S1) was also generated by this server. For subsequent docking studies, generated by the GPCRM server, ten FPR2 models were selected based on docking results of fMLFK. Docking was performed in Glide using the SP-peptide protocol with enhanced peptide sampling and scoring. The best group of models was selected based on reproducibility of experimental constraints in the binding mode of fMLFK. Then, the best model was selected from this group based on the docking scores. The non-peptide compounds for docking were selected to have different scaffolds. Most of them are agonists and two are antagonists. They were docked using the Glide SP-protocol, with a 4×4×4 nm box, centered on residues Arg261.32, Asp1063.33, Arg2055.42×43, and Asp2817.32×31 (these residues are suggested to bind fMLFK). The above residues are located on the bottom (Asp1063.33 and Arg2055.42×43) and on the top (Arg261.32 and Asp2817.32×31 on extracellular ends of TM helices) of the binding site for extended conformation of the ligand fMLFK. So the potential area containing residues for binding of non-peptide ligands was as large as possible. All ligands were treated as flexible molecules. The protein and ligands were prepared and visualized using Maestro v.9.5. For each ligand we generated 1000 poses. These poses were clustered using the pose_entropy script in the Schrödinger suite of programs. The script calculates entropy for each cluster, after which it modifies the docking scores, based on the number of poses in each energy well. The pose with the best score was used for further analysis. The residue numbering in transmembrane helices used in this paper is based mainly on Ballesteros-Weinstein numbering but also on GPCRDB (GPCR database) numbering scheme. GPCRDB numbers are distinguished by a separator x and may be used alone, for example, 5x47, or together with one of the sequence-based schemes, for example, 5.46x47. Here, we used this double numbering in case of differences between them. The GPCRDB scheme relies on the crystal structures and introduces corrections for helix bulges and constrictions. A bulge residue is assigned the same number as the preceding residue followed by a 1, for example, 551 for a bulge following position 55. A constriction generates a break in sequential numbering, for example, 4.56x57 after residue 4.55x55.
Results and discussion