Jin Bong Park
Seoul National University · 生化学・遺伝学・分子生物学
研究室紹介
Professor Jin Bong Park's research lab specializes in the neurobiological mechanisms underlying synaptic and extrasynaptic inhibition in the hypothalamic paraventricular nucleus (PVN), with a focus on GABAergic signaling, astrocyte-neuron interactions, and the role of GABAA receptor subtypes—particularly those containing δ subunits—in regulating neuronal excitability and sympathetic outflow. The lab investigates how tonic inhibition, mediated by extrasynaptic GABA release from astrocytes via Best1 channels, contributes to the maintenance of excitation/inhibition balance and its disruption in pathological conditions such as heart failure and epilepsy. Using electrophysiological, imaging, and molecular techniques, the lab explores the functional significance of glial GABA transporters and astrocytic GABA release in autonomic control and neurovascular regulation.
Research Overview
Research Output Trend
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Selected Papers
4Background Genomic studies on fungal species with hydrolytic activity have gained increased attention due to their great biotechnological potential for biomass-based biofuel production. The amylolytic yeast Saccharomycopsis fibuligera has served as a good source of enzymes and genes involved in saccharification. Despite its long history of use in food fermentation and bioethanol production, very little is known about the basic physiology and genomic features of S. fibuligera. Results We performe
Overall, these data demonstrate that transposon-mediated transgenesis can be applied to cattle without being detrimental to their long-term genomic stability or general health. We further suggest that this technology may be usefully applied in other fields, such as the generation of transgenic animal models.
<i>Lentinula edodes</i> is one of the most popular edible mushrooms in the world and contains useful medicinal components such as lentinan. The whole-genome sequence of <i>L. edodes</i> has been determined with the objective of discovering candidate genes associated with agronomic traits, but experimental verification of gene models with correction of gene prediction errors is lacking. To improve the accuracy of gene prediction, we produced 12.6 Gb of long-read transcriptome data of variable len
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