Gap Junctions in the Brain: Hardwired but Functionally Versatile

Role of O-GlcNAcylation in Central Nervous System Development and Injuries: A Systematic Review

The development of central nervous system (CNS) can form perceptual, memory, and cognitive functions, while injuries to CNS often lead to severe neurological dysfunction and even death. As one of the prevalent post-translational modifications (PTMs), O-GlcNAcylation has recently attracted great attentions due to its functions in regulating the activity, subcellular localization, and stability of target proteins. It has been indicated that O-GlcNAcylation could interact with phosphorylation, ubiquitination, and methylation to jointly regulate the function and activity of proteins. Furthermore, a growing number of studies have suggested that O-GlcNAcylation played an important role in the CNS. During development, O-GlcNAcylation participated in the neurogenesis, neuronal development, and neuronal function. In addition, O-GlcNAcylation was involved in the progress of CNS injuries including ischemic stroke, subarachnoid hemorrhage (SAH), and intracerebral hemorrhage (ICH) and played a crucial role in the improvement of brain damage such as attenuating cognitive impairment, inhibiting neuroinflammation, suppressing endoplasmic reticulum (ER) stress, and maintaining blood-brain barrier (BBB) integrity. Therefore, O-GlcNAcylation showed great promise as a potential target in CNS development and injuries. In this article, we presented a review highlighting the role of O-GlcNAcylation in CNS development and injuries. Hence, on the basis of these properties and effects, intervention with O-GlcNAcylation may be developed as therapeutic agents for CNS diseases.

Learning with sparse reward in a gap junction network inspired by the insect mushroom body

Animals can learn in real-life scenarios where rewards are often only available when a goal is achieved. This 'distal' or 'sparse' reward problem remains a challenge for conventional reinforcement learning algorithms. Here we investigate an algorithm for learning in such scenarios, inspired by the possibility that axo-axonal gap junction connections, observed in neural circuits with parallel fibres such as the insect mushroom body, could form a resistive network. In such a network, an active node represents the task state, connections between nodes represent state transitions and their connection to actions, and current flow to a target state can guide decision making. Building on evidence that gap junction weights are adaptive, we propose that experience of a task can modulate the connections to form a graph encoding the task structure. We demonstrate that the approach can be used for efficient reinforcement learning under sparse rewards, and discuss whether it is plausible as an account of the insect mushroom body.