Browsing by Subject "synaptic plasticity"

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    Fluorescent Labeling of Newborn Dentate Granule Cells in GAD67-GFP Transgenic Mice: A Genetic Tool for the Study of Adult Neurogenesis
    (2010) Zhao, Shengli; Zhou, Yang; Gross, Jimmy; Miao, Pei; Qiu, Li; Wang, Dongqing; Chen, Qian; Feng, Guoping
    Neurogenesis in the adult hippocampus is an important form of structural plasticity in the brain. Here we report a line of BAC transgenic mice (GAD67-GFP mice) that selectively and transitorily express GFP in newborn dentate granule cells of the adult hippocampus. These GFP+ cells show a high degree of colocalization with BrdU-labeled nuclei one week after BrdU injection and express the newborn neuron marker doublecortin and PSA-NCAM. Compared to mature dentate granule cells, these newborn neurons show immature morphological features: dendritic beading, fewer dendritic branches and spines. These GFP+ newborn neurons also show immature electrophysiological properties: higher input resistance, more depolarized resting membrane potentials, small and non-typical action potentials. The bright labeling of newborn neurons with GFP makes it possible to visualize the details of dendrites, which reach the outer edge of the molecular layer, and their axon (mossy fiber) terminals, which project to the CA3 region where they form synaptic boutons. GFP expression covers the whole developmental stage of newborn neurons, beginning within the first week of cell division and disappearing as newborn neurons mature, about 4 weeks postmitotic. Thus, the GAD67-GFP transgenic mice provide a useful genetic tool for studying the development and regulation of newborn dentate granule cells.
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    Nuclear Arc Interacts with the Histone Acetyltransferase Tip60 to Modify H4K12 Acetylation(1,2,3).
    (eNeuro, 2014-11) Wee, Caroline L; Teo, Shaun; Oey, Nicodemus E; Wright, Graham D; VanDongen, Hendrika MA; VanDongen, Antonius MJ
    Arc is an immediate-early gene whose genetic ablation selectively abrogates long-term memory, indicating a critical role in memory consolidation. Although Arc protein is found at synapses, it also localizes to the neuronal nucleus, where its function is less understood. Nuclear Arc forms a complex with the β-spectrin isoform βSpIVΣ5 and associates with PML bodies, sites of epigenetic regulation of gene expression. We report here a novel interaction between Arc and Tip60, a histone-acetyltransferase and subunit of a chromatin-remodelling complex, using biochemistry and super-resolution microscopy in primary rat hippocampal neurons. Arc and βSpIVΣ5 are recruited to nuclear Tip60 speckles, and the three proteins form a tight complex that localizes to nuclear perichromatin regions, sites of transcriptional activity. Neuronal activity-induced expression of Arc (1) increases endogenous nuclear Tip60 puncta, (2) recruits Tip60 to PML bodies, and (3) increases histone acetylation of Tip60 substrate H4K12, a learning-induced chromatin modification. These mechanisms point to an epigenetic role for Arc in regulating memory consolidation.
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    The NMDA receptor subunit GluN3A regulates synaptic activity-induced and myocyte enhancer factor 2C (MEF2C)-dependent transcription.
    (The Journal of biological chemistry, 2020-05-11) Chen, Liang-Fu; Lyons, Michelle R; Liu, Fang; Green, Matthew V; Hedrick, Nathan G; Williams, Ashley B; Narayanan, Arthy; Yasuda, Ryohei; West, Anne E
    N-methyl-D-aspartate type glutamate receptors (NMDARs) are key mediators of synaptic activity-regulated gene transcription in neurons, both during development and in the adult brain. Developmental differences in the glutamate receptor ionotropic NMDA 2 (GluN2) subunit composition of NMDARs determines whether they activate the transcription factor cAMP-responsive element-binding protein 1 (CREB). However, whether the developmentally regulated GluN3A subunit also modulates NMDAR-induced transcription is unknown. Here, using an array of techniques, including quantitative real-time PCR, immunostaining, reporter gene assays, RNA sequencing, and two-photon glutamate uncaging with calcium imaging, we show that knocking down GluN3A in rat hippocampal neurons promotes the inducible transcription of a subset of NMDAR-sensitive genes. We found that this enhancement is mediated by the accumulation of phosphorylated p38 mitogen-activated protein (MAP) kinase in the nucleus, which drives the activation of the transcription factor myocyte enhancer factor 2C (MEF2C) and promotes the transcription of a subset of synaptic activity-induced genes, including brain-derived neurotrophic factor (Bdnf) and activity-regulated cytoskeleton-associated protein (Arc). Our evidence that GluN3A regulates MEF2C-dependent transcription reveals a novel mechanism by which NMDAR subunit composition confers specificity to the program of synaptic activity-regulated gene transcription in developing neurons.
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    Unsupervised Learning of Persistent and Sequential Activity.
    (Frontiers in computational neuroscience, 2019-01) Pereira, Ulises; Brunel, Nicolas
    Two strikingly distinct types of activity have been observed in various brain structures during delay periods of delayed response tasks: Persistent activity (PA), in which a sub-population of neurons maintains an elevated firing rate throughout an entire delay period; and Sequential activity (SA), in which sub-populations of neurons are activated sequentially in time. It has been hypothesized that both types of dynamics can be "learned" by the relevant networks from the statistics of their inputs, thanks to mechanisms of synaptic plasticity. However, the necessary conditions for a synaptic plasticity rule and input statistics to learn these two types of dynamics in a stable fashion are still unclear. In particular, it is unclear whether a single learning rule is able to learn both types of activity patterns, depending on the statistics of the inputs driving the network. Here, we first characterize the complete bifurcation diagram of a firing rate model of multiple excitatory populations with an inhibitory mechanism, as a function of the parameters characterizing its connectivity. We then investigate how an unsupervised temporally asymmetric Hebbian plasticity rule shapes the dynamics of the network. Consistent with previous studies, we find that for stable learning of PA and SA, an additional stabilization mechanism is necessary. We show that a generalized version of the standard multiplicative homeostatic plasticity (Renart et al., 2003; Toyoizumi et al., 2014) stabilizes learning by effectively masking excitatory connections during stimulation and unmasking those connections during retrieval. Using the bifurcation diagram derived for fixed connectivity, we study analytically the temporal evolution and the steady state of the learned recurrent architecture as a function of parameters characterizing the external inputs. Slow changing stimuli lead to PA, while fast changing stimuli lead to SA. Our network model shows how a network with plastic synapses can stably and flexibly learn PA and SA in an unsupervised manner.