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- Adenylyl cyclase (1)
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Background/Aims: Signaling of Gs protein-coupled receptors (GsPCRs) is accomplished by stimulation of adenylyl cyclase, causing an increase of the intracellular cAMP concentration, activation of the intracellular cAMP effectors protein kinase A (PKA) and Epac, and an efflux of cAMP, the function of which is still unclear.
Methods: Activation of adenylyl cyclase by GsPCR agonists or cholera toxin was monitored by measurement of the intracellular cAMP concentration by ELISA, anti-phospho-PKA substrate motif phosphorylation by immunoblotting, and an Epac-FRET assay in the presence and absence of adenosine receptor antagonists or ecto-nucleotide phosphodiesterase/pyrophosphatase2 (eNPP2) inhibitors. The production of AMP from cAMP by recombinant eNPP2 was measured by HPLC. Extracellular adenosine was determined by LC-MS/MS, extracellular ATP by luciferase and LC-MS/MS. The expression of eNPP isoenzymes 1-3 was examined by RT-PCR. The expression of multidrug resistance protein 4 was suppressed by siRNA.
Results: Here we show that the activation of GsPCRs and the GsPCRs-independent activation of Gs proteins and adenylyl cyclase by cholera toxin induce stimulation of cell surface adenosine receptors (A2A or A2B adenosine receptors). In PC12 cells stimulation of adenylyl cyclase by GsPCR or cholera toxin caused activation of A2A adenosine receptors by an autocrine signaling pathway involving cAMP efflux through multidrug resistance protein 4 and hydrolysis of released cAMP to AMP by eNPP2. In contrast, in PC3 cells cholera toxin- and GsPCR-induced stimulation of adenylyl cyclase resulted in the activation of A2B adenosine receptors.
Conclusion: Our findings show that stimulation of adenylyl cyclase causes a remarkable activation of cell surface adenosine receptors.
Macrophages are highly versatile cells, which acquire, depending on their microenvironment, pro- (M1-like), or antiinflammatory (M2-like) phenotypes. Here, we studied the role of the G-protein coupled receptor G2A (GPR132), in chemotactic migration and polarization of macrophages, using the zymosan-model of acute inflammation. G2A-deficient mice showed a reduced zymosan-induced thermal hyperalgesia, which was reversed after macrophage depletion. Fittingly, the number of M1-like macrophages was reduced in the inflamed tissue in G2A-deficient mice. However, G2A activation was not sufficient to promote M1-polarization in bone marrow-derived macrophages. While the number of monocyte-derived macrophages in the inflamed paw was not altered, G2A-deficient mice had less macrophages in the direct vicinity of the origin of inflammation, an area marked by the presence of zymosan, neutrophil accumulation and proinflammatory cytokines. Fittingly neutrophil efferocytosis was decreased in G2A-deficient mice and several lipids, which are released by neutrophils and promote G2A-mediated chemotaxis, were increased in the inflamed tissue. Taken together, G2A is necessary to position macrophages in the proinflammatory microenvironment surrounding the center of inflammation. In absence of G2A the macrophages are localized in an antiinflammatory microenvironment and macrophage polarization is shifted toward M2-like macrophages.
Based on increasing evidence suggesting that MS pathology involves alterations in bioactive lipid metabolism, the present analysis was aimed at generating a complex serum lipid-biomarker. Using unsupervised machine-learning, implemented as emergent self-organizing maps of neuronal networks, swarm intelligence and Minimum Curvilinear Embedding, a cluster structure was found in the input data space comprising serum concentrations of d = 43 different lipid-markers of various classes. The structure coincided largely with the clinical diagnosis, indicating that the data provide a basis for the creation of a biomarker (classifier). This was subsequently assessed using supervised machine-learning, implemented as random forests and computed ABC analysis-based feature selection. Bayesian statistics-based biomarker creation was used to map the diagnostic classes of either MS patients (n = 102) or healthy subjects (n = 301). Eight lipid-markers passed the feature selection and comprised GluCerC16, LPA20:4, HETE15S, LacCerC24:1, C16Sphinganine, biopterin and the endocannabinoids PEA and OEA. A complex classifier or biomarker was developed that predicted MS at a sensitivity, specificity and accuracy of approximately 95% in training and test data sets, respectively. The present successful application of serum lipid marker concentrations to MS data is encouraging for further efforts to establish an MS biomarker based on serum lipidomics.