Biology subjects

Hashemiaghdam, A.

Publications and source records attributed to Hashemiaghdam, A..

3 recordsLinked to original sources

bioRxiv2024

Mitochondrial pyruvate transport regulates presynaptic metabolism and neurotransmission

Glucose has long been considered the primary fuel source for the brain. However, glucose levels fluctuate in the brain during sleep, intense circuit activity, or dietary restrictions, posing significant metabolic stress. Here, we demonstrate that the mammalian brain utilizes pyruvate as a fuel source, and pyruvate can support neuronal viability in the absence of glucose. Nerve terminals are sites of metabolic vulnerability within a neuron and we show that mitochondrial pyruvate uptake is a critical step in oxidative ATP production in hippocampal terminals. We find that the mitochondrial pyruvate carrier is post-translationally modified by lysine acetylation which in turn modulates mitochondrial pyruvate uptake. Importantly, our data reveal that the mitochondrial pyruvate carrier regulates distinct steps in synaptic transmission, namely, the spatiotemporal pattern of synaptic vesicle release and the efficiency of vesicle retrieval, functions that have profound implications for synaptic plasticity. In summary, we identify pyruvate as a potent neuronal fuel and mitochondrial pyruvate uptake as a critical node for the metabolic control of synaptic transmission in hippocampal terminals. HIGHLIGHTSO_LISerum pyruvate is taken up by the brain and efficiently oxidized in the TCA cycle. C_LIO_LIThe mitochondrial pyruvate carrier (MPC) is essential for presynaptic energy metabolism. C_LIO_LIAcetylation of the MPC complex modulates mitochondrial pyruvate uptake. C_LIO_LIMPC activity regulates the release and retrieval of synaptic vesicles in nerve terminals. C_LI

neuroscience↗

bioRxiv2023

Sirtuin3 ensures the metabolic plasticity of neurotransmission during glucose deprivation

Neurotransmission is an energetically expensive process that underlies cognition. During intense electrical activity or dietary restrictions, glucose levels in the brain plummet, forcing neurons to utilize alternative fuels. However, the molecular mechanisms of neuronal metabolic plasticity remain poorly understood. Here, we demonstrate that glucose-deprived neurons activate the CREB and PGC1 transcriptional program that induces the expression of the mitochondrial deacetylase Sirtuin 3 (Sirt3) both in vitro and in vivo. We show that Sirt3 localizes to axonal mitochondria and stimulates mitochondrial oxidative capacity in hippocampal nerve terminals. Sirt3 plays an essential role in sustaining synaptic transmission in the absence of glucose by powering the retrieval of synaptic vesicles after release. These results demonstrate that the transcriptional induction of Sirt3 ensures the metabolic plasticity of synaptic transmission. HighlightsO_LIGlucose deprivation drives transcriptional reprogramming of neuronal metabolism via CREB and PGC1. C_LIO_LIGlucose or food deprivation trigger the neuronal expression of mitochondrial deacetylase sirtuin 3 (Sirt3) both in vitro and in vivo. C_LIO_LISirt3 stimulates oxidative ATP synthesis in nerve terminals. C_LIO_LISirt3 sustains the synaptic vesicle cycle in the absence of glucose. C_LI

neuroscience↗

bioRxiv2021

Semi-automated analysis of an optical ATP indicator in neural synapses

SignificanceThe firefly enzyme luciferase has been used in a wide range of biological assays, including bioluminescence imaging of ATP. The biosensor Syn-ATP utilizes subcellular targeting of luciferase to nerve terminals for optical measurement of ATP in this compartment. Manual analysis of Syn-ATP signals is challenging due to signal heterogeneity and cellular motion in long imaging sessions. Here, we have leveraged machine learning tools to develop a method for analysis of bioluminescence images. AimOur goal was to create a semi-automated pipeline for analysis of bioluminescence imaging to improve measurements of ATP content in nerve terminals. ApproachWe developed an image analysis pipeline that applies machine learning toolkits to distinguish neurons from background signals, and excludes neural cell bodies, while also incorporating user input. ResultsSide-by-side comparison of manual and semi-automated image analysis demonstrated that the latter improves precision and accuracy of ATP measurements. ConclusionsOur method streamlines data analysis and reduces user-introduced bias, thus enhancing the reproducibility and reliability of quantitative ATP imaging in nerve terminals.

neuroscience↗