Repository Community: null
http://hdl.handle.net/20.500.11750/13550
2024-04-10T21:12:11Z
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Gradual decorrelation of CA3 ensembles associated with contextual discrimination learning is impaired by Kv1.2 insufficiency
http://hdl.handle.net/20.500.11750/17293
Title: Gradual decorrelation of CA3 ensembles associated with contextual discrimination learning is impaired by Kv1.2 insufficiency
Author(s): Eom, Kisang; Lee, Hyoung Ro; Hyun, Jung Ho; An, Hyunhoe; Lee, Yong-Seok; Ho, Won-Kyung; Lee, Suk-Ho
Abstract: The associative network of hippocampal CA3 is thought to contribute to rapid formation of contextual memory from one-trial learning, but the network mechanisms underlying decorrelation of neuronal ensembles in CA3 is largely unknown. Kv1.2 expressions in rodent CA3 pyramidal cells (CA3-PCs) are polarized to distal apical dendrites, and its downregulation specifically enhances dendritic responses to perforant pathway (PP) synaptic inputs. We found that haploinsufficiency of Kv1.2 (Kcna2+/-) in CA3-PCs, but not Kv1.1 (Kcna1+/-), lowers the threshold for long-term potentiation (LTP) at PP-CA3 synapses, and that the Kcna2+/- mice are normal in discrimination of distinct contexts but impaired in discrimination of similar but slightly distinct contexts. We further examined the neuronal ensembles in CA3 and dentate gyrus (DG), which represent the two similar contexts using in situ hybridization of immediate early genes, Homer1a and Arc. The size and overlap of CA3 ensembles activated by the first visit to the similar contexts were not different between wild type and Kcna2+/- mice, but these ensemble parameters diverged over training days between genotypes, suggesting that abnormal plastic changes at PP-CA3 synapses of Kcna2+/- mice is responsible for the impaired pattern separation. Unlike CA3, DG ensembles were not different between two genotype mice. The DG ensembles were already separated on the first day, and their overlap did not further evolve. Eventually, the Kcna2+/- mice exhibited larger CA3 ensemble size and overlap upon retrieval of two contexts, compared to wild type or Kcna1+/- mice. These results suggest that sparse LTP at PP-CA3 synapse probably supervised by mossy fiber inputs is essential for gradual decorrelation of CA3 ensembles. © 2021 Wiley Periodicals LLC.
2022-02-28T15:00:00Z
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Tagging active neurons by soma-targeted Cal-Light
http://hdl.handle.net/20.500.11750/17238
Title: Tagging active neurons by soma-targeted Cal-Light
Author(s): Hyun, Jung Ho; Nagahama, Kenichiro; Namkung, Ho; Mignocchi, Neymi; Roh, Seung-Eon; Hannan, Patrick; Krüssel, Sarah; Kwak, Chuljung; McElroy, Abigail; Liu, Bian; Cui, Mingguang; Lee, Seunghwan; Lee, Dongmin; Huganir, Richard L.; Worley, Paul F.; Sawa, Akira; Kwon, Hyung-Bae
Abstract: Verifying causal effects of neural circuits is essential for proving a direct circuit-behavior relationship. However, techniques for tagging only active neurons with high spatiotemporal precision remain at the beginning stages. Here we develop the soma-targeted Cal-Light (ST-Cal-Light) which selectively converts somatic calcium rise triggered by action potentials into gene expression. Such modification simultaneously increases the signal-to-noise ratio of reporter gene expression and reduces the light requirement for successful labeling. Because of the enhanced efficacy, the ST-Cal-Light enables the tagging of functionally engaged neurons in various forms of behaviors, including context-dependent fear conditioning, lever-pressing choice behavior, and social interaction behaviors. We also target kainic acid-sensitive neuronal populations in the hippocampus which subsequently suppress seizure symptoms, suggesting ST-Cal-Light’s applicability in controlling disease-related neurons. Furthermore, the generation of a conditional ST-Cal-Light knock-in mouse provides an opportunity to tag active neurons in a region- or cell-type specific manner via crossing with other Cre-driver lines. Thus, the versatile ST-Cal-Light system links somatic action potentials to behaviors with high temporal precision, and ultimately allows functional circuit dissection at a single cell resolution. © 2022, The Author(s).
2022-11-30T15:00:00Z