Biology subjects

Koberstein, J. N.

Publications and source records attributed to Koberstein, J. N..

3 recordsLinked to original sources

bioRxiv2023

Local and dynamic regulation of neuronal glycolysis in vivo

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 C. elegans to image dynamic changes in glycolysis within individual neurons and in vivo. We determined that neurons perform glycolysis cell-autonomously, 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 new relationships between neuronal identities and metabolic landscapes in vivo. Significance statementWhile it is generally accepted that energy metabolism underpins neuronal function, how it is distributed and dynamically regulated in different tissues of the brain to meet varying energy demands is not well understood. Here we utilized a fluorescent biosensor, HYlight, to observe glycolytic metabolism at cellular and subcellular scales in vivo. By leveraging both the stereotyped identities of individual neurons in C. elegans, and genetic tools for manipulating glycolytic metabolism, we determined that neurons perform and dynamically regulate glycolysis to match changing cellular demands for energy. Our findings support a model whereby glycolytic states should be considered distinct and related to individual neuron identities in vivo, and introduce new questions about the interconnected nature of metabolism and neuronal function.

neuroscience↗

bioRxiv2023

Neuronal activity-driven O-GlcNAcylation promotes mitochondrial plasticity

Neuronal activity is an energy-intensive process that is largely sustained by instantaneous fuel utilization and ATP synthesis. However, how neurons couple ATP synthesis rate to fuel availability is largely unknown. Here, we demonstrate that the metabolic sensor enzyme O-GlcNAc transferase regulates neuronal activity-driven mitochondrial bioenergetics. We show that neuronal activity upregulates O-GlcNAcylation mainly in mitochondria. Mitochondrial O-GlcNAcylation is promoted by activity-driven fuel consumption, which allows neurons to compensate for high energy expenditure based on fuel availability. To determine the proteins that are responsible for these adjustments, we mapped the mitochondrial O-GlcNAcome of neurons. Finally, we determine that neurons fail to meet activity-driven metabolic demand when O-GlcNAcylation dynamics are prevented. Our findings suggest that O-GlcNAcylation provides a fuel-dependent feedforward control mechanism in neurons to optimize mitochondrial performance based on neuronal activity. This mechanism thereby couples neuronal metabolism to mitochondrial bioenergetics and plays a key role in sustaining energy homeostasis.

neuroscience↗

bioRxiv2020

A sort-seq approach to the development of single fluorescent protein biosensors

The utility of single fluorescent protein biosensors (SFPBs) in biological research is offset by the difficulty in engineering these tools. SFPBs generally consist of three basic components: a circularly permuted fluorescent protein, a ligand-binding domain, and a pair of linkers connecting the two domains. In the absence of predictive methods for biosensor engineering, most designs combining these three components will fail to produce allosteric coupling between ligand binding and fluorescence emission. Methods to construct libraries of biosensor designs with variations in the site of GFP insertion and linker sequences have been developed, however, our ability to construct new variants has exceeded our ability to test them for function. Here, we address this challenge by applying a massively parallel assay termed "sort-seq" to the characterization of biosensor libraries. Sort-seq combines binned fluorescence-activated cell sorting, next-generation sequencing, and maximum likelihood estimation to quantify the dynamic range of many biosensor variants in parallel. We applied this method to two common biosensor optimization tasks: choice of insertion site and optimization of linker sequences. The sort-seq assay applied to a maltose-binding protein domain-insertion library not only identified previously described high-dynamic-range variants but also discovered new functional insertion-sites with diverse properties. A sort-seq assay performed on a pyruvate biosensor linker library expressed in mammalian cell culture identified linker variants with substantially improved dynamic range. Machine learning models trained on the resulting data can predict dynamic range from linker sequence. This high-throughput approach will accelerate the design and optimization of SFPBs, expanding the biosensor toolbox.

biochemistry↗