Biology subjects

Emerson, S. E.

Publications and source records attributed to Emerson, S. E..

3 recordsLinked to original sources

bioRxiv2025

Spatial Partitioning of Core Glycolysis Enables Tissue-Specific Metabolic Programs In Vivo

Tissues exhibit metabolic heterogeneity that tailors conserved pathways to distinct physiological demands, yet how this heterogeneity is achieved in vivo remains poorly understood. Here, we use Caenorhabditis elegans to investigate tissue-specific requirements for glucose-6-phosphate isomerase (GPI-1), a conserved reversible enzyme that links glycolysis and the pentose phosphate pathway (PPP). Tissue-specific metabolic-network modeling predicted differential glycolytic and PPP flux potential across adult tissues and identified tissue-specific biases in GPI-1 reaction directionality. Genetic disruption of gpi-1 produced germline defects consistent with impaired PPP-associated anabolic metabolism and somatic defects consistent with impaired glycolysis, indicating that GPI-1 supports distinct metabolic functions across tissues. We further discovered that two GPI-1 isoforms are differentially expressed and localized: GPI-1A is broadly expressed and cytosolic, whereas GPI-1B is enriched in the germline and localizes to endoplasmic reticulum-associated compartments. Isoform-specific perturbations revealed distinct requirements for GPI-1A and GPI-1B in somatic glycolysis and reproductive physiology. These findings implicate isoform-specific subcellular localization as a possible contributor to the partition of the functions of a conserved reversible enzyme, enabling tissue-specific anabolic and catabolic metabolism in vivo.

cell biology↗

bioRxiv2025

Glycogen metabolism acts in neurons to support glycolytic plasticity

Glycogen is the largest energy reserve in the brain, but the specific role of glycogen in supporting neuronal energy metabolism in vivo is not well understood. We established a system in C. elegans to dynamically probe glycolytic states in single cells of living animals via the use of the glycolytic sensor HYlight and determined that neurons can dynamically regulate glycolysis in response to activity or transient hypoxia. We performed an RNAi screen and identified that PYGL-1, an ortholog of the human glycogen phosphorylase, is required in neurons for glycolytic plasticity. We determined that neurons employ at least two mechanisms of glycolytic plasticity: glycogen-dependent glycolytic plasticity (GDGP) and glycogen-independent glycolytic plasticity (GIGP). We uncover that GDGP is employed under conditions of mitochondrial dysfunction, such as transient hypoxia or in mutants for mitochondrial function. We find that the ability of neurons to plastically regulate glycolysis through cell-autonomous GDGP is important for sustaining the synaptic vesicle cycle. Together, our study reveals that, in vivo, neurons can directly use glycogen as a fuel source to sustain glycolytic plasticity and synaptic function.

neuroscience↗

bioRxiv2024

NeuroSCAN: Exploring Neurodevelopment via Spatiotemporal Collation of Anatomical Networks

Volume electron microscopy (vEM) datasets such as those generated for connectome studies allow nanoscale quantifications and comparisons of the cell biological features underpinning circuit architectures. Quantifying cell biological relationships in the connectome yields rich, multidimensional datasets that benefit from data science approaches, including dimensionality reduction and integrated graphical representations of neuronal relationships. We developed NeuroSC (also known as NeuroSCAN) an open source online platform that bridges sophisticated graph analytics from data science approaches with the underlying cell biological features in the connectome. We analyze a series of published C. elegans brain neuropils and demonstrate how these integrated representations of neuronal relationships facilitate comparisons across connectomes, catalyzing new insights into the structure-function relationships of the circuits and their changes during development. NeuroSC is designed for intuitive examination and comparisons across connectomes, enabling synthesis of knowledge from high-level abstractions of neuronal relationships derived from data science techniques to the detailed identification of the cell biological features underpinning these abstractions.

neuroscience↗