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Role of activity-dependent neuronal metabolic plasticity in brain aging

Applicant Professor Dr. Joachim Kirsch, since 10/2023
Subject Area Molecular Biology and Physiology of Neurons and Glial Cells
Term from 2019 to 2023
Project identifier Deutsche Forschungsgemeinschaft (DFG) - Project number 431144153
 
Final Report Year 2025

Final Report Abstract

Neuronal energy metabolism is crucial for brain function and is impaired in normal brain aging and neurodegenerative diseases. Synaptic activity causes increased gene transcription, i.e. increased synthesis, of several enzymes that control energy metabolism in neurons. This gene program mediates changes in neuronal energy metabolism that are assumed to confer better energy supply and energetic robustness. In this project, we hypothesized that reduced or impaired synaptic activity in the aging brain leads to impaired expression of the metabolic gene program. This in turn would affect neuronal energy metabolism, leading to impaired function and viability of neurons. To address this, we used single cell sequencing and spatial gene expression analyses to determine gene expression changes in the brains of young and aged mice that had been trained in a learning task to induce synaptic activation. In addition, we measured the activity of mitochondria in the brains of young and aged mice. Finally, we used gene transfer to artificially express one of the above-mentioned activity-regulated metabolic genes in the brain of aging mice, aiming to enhance energy metabolism independent of synaptic activity. We then tested if this intervention can preserve mitochondrial activity, synaptic density, and cognitive abilities during aging. Contrary to our expectations, we observed only small aging-related changes in mitochondrial activity and synaptic density, that were only affected partially by our genetic intervention. Contrary to our hypothesis, expression of the metabolic gene caused a decrease, instead of an increase, in memory performance. The detailed single cell and spatial gene expression analyses provided valuable information on the expression of the metabolic gene program and provided additional insight into basic molecular mechanisms of learning-induced gene expression in the mouse brain.

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