Besides its function as a local mediator of the immune response, histamine can play a role as a neurotransmitter and neuromodulator. Histamine actions are classically mediated through four different G protein-coupled receptor subtypes but non-classical actions were also described, including effects on many ligand-gated ion channels. Previous evidence indicated that histamine acts as a positive modulator on diverse GABA receptor subtypes, such as GABAα1β2γ2, GABAα2β3γ2, GABAα3β3γ2, GABAα4β3γ2 and GABAα5β3γ2. Meanwhile, its effects on GABAρ1 receptors, known to stand for tonic currents in retinal neurons, had not been examined before. The effects of histamine on the function of human homomeric GABAρ1 receptors were studied here, using heterologous expression in Xenopus laevis oocytes followed by the electrophysiological recording of GABA-evoked Cl currents. Histamine inhibited GABAρ1 receptor-mediated responses. Effects were reversible, independent of the membrane potential, and strongly dependent on both histamine and GABA concentration. A rightward parallel shift in the concentration-response curve for GABA was observed in the presence of histamine, without substantial change in the maximal response or the Hill coefficient. Results were compatible with a competitive antagonism of histamine on the GABAρ1 receptors. This is the first report of inhibitory actions exerted by histamine on an ionotropic GABA receptor.
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http://dx.doi.org/10.1016/j.ejphar.2023.175880 | DOI Listing |
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Department of Biotechnology, School of Bioengineering, SRM Institute of Science and Technology, Kattankulathur, India.
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Department of Neurosurgery, Institute of Brain Diseases, Nanfang Hospital of Southern Medical University, Guangzhou, 510515, China.
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The Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, 90069, USA.
Background: With recent advances in single cell technology, high-throughput methods provide unique insight into disease mechanisms and more importantly, cell type origin. Here, we used multi-omics data to understand how genetic variants from genome-wide association studies influence development of disease. We show in principle how to use genetic algorithms with normal, matching pairs of single-nucleus RNA- and ATAC-seq, genome annotations, and protein-protein interaction data to describe the genes and cell types collectively and their contribution to increased risk.
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Department of General Biochemistry, Faculty of Biochemistry, Biophysics and Biotechnology, Jagiellonian University, Krakow, Poland.
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