Local and dynamic regulation of neuronal glycolysis in vivo

• 成果类型: Article
• 署名作者: Wolfe, Aaron D.; Koberstein, John N.; Smith, Chadwick B.; Stewart, Melissa L.; Gonzaleza, Ian J.; Hammarlund, Marc; Hyman, Anthony A.; Stork, Philip J. S.; Goodman, Richard H.; Colon-Ramosa, Daniel A.
• 署名单位: Yale University; Yale University; Oregon Health & Science University; Max Planck Society; Yale University
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-8904
• DOI: 10.1073/pnas.2314699121
• 发表日期: 2024-01-16
• 关键词: glucose-utilization; aerobic glycolysis; energy-metabolism; nervous-system; astrocytes; mitochondria

摘要

Energy metabolism supports neuronal function. While it is well established that changes in energy metabolism underpin brain plasticity and function, less is known about how individual neurons modulate their metabolic states to meet varying energy demands. This is because most approaches used to examine metabolism in living organisms lack the resolution to visualize energy metabolism within individual circuits, cells, or subcellular regions. Here, we adapted a biosensor for glycolysis, HYlight, for use in Caenorhabditis elegans to image dynamic changes in glycolysis within individual neurons and in vivo. We determined that neurons cell-autonomously perform glycolysis and modulate glycolytic states upon energy stress. By examining glycolysis in specific neurons, we documented a neuronal energy landscape comprising three general observations: 1) glycolytic states in neurons are diverse across individual cell types; 2) for a given condition, glycolytic states within individual neurons are reproducible across animals; and 3) for varying conditions of energy stress, glycolytic states are plastic and adapt to energy demands. Through genetic analyses, we uncovered roles for regulatory enzymes and mitochondrial localization in the cellular and subcellular dynamic regulation of glycolysis. Our study demonstrates the use of a single-cell glycolytic biosensor to examine how energy metabolism is distributed across cells and coupled to dynamic states of neuronal function and uncovers unique relationships between neuronal identities and metabolic landscapes in vivo