Monitoring and dynamically controlling glucose uptake rate and central metabolism
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Autophagy regulator ATG5 preserves cerebellar function by safeguarding its glycolytic activity
Dysfunctions in autophagy, a cellular mechanism for breaking down components within lysosomes, often lead to neurodegeneration. The specific mechanisms underlying neuronal vulnerability due to autophagy dysfunction remain elusive. Here we show that autophagy contributes to cerebellar Purkinje cell (PC) survival by safeguarding their glycolytic activity. Outside the conventional housekeeping role, autophagy is also involved in the ATG5-mediated regulation of glucose transporter 2 (GLUT2) levels during cerebellar maturation. Autophagy-deficient PCs exhibit GLUT2 accumulation on the plasma membrane, along with increased glucose uptake and alterations in glycolysis. We identify lysophosphatidic acid and serine as glycolytic intermediates that trigger PC death and demonstrate that the deletion of GLUT2 in ATG5-deficient mice mitigates PC neurodegeneration and rescues their ataxic gait. Taken together, this work reveals a mechanism for regulating GLUT2 levels in neurons and provides insights into the neuroprotective role of autophagy by controlling glucose homeostasis in the brain.
Variable bioenergetic sensitivity of neurons and astrocytes to insulin and extracellular glucose
Energy flow within cellular elements of the brain is a well-orchestrated, tightly regulated process, however, details underlying these functions at the single-cell level are still poorly understood. Studying hypometabolism in aging and neurodegenerative diseases may benefit from experimentation on unicellular bioenergetics. Here, we examined energy status in neurons and astrocytes using mixed hippocampal cultures and PercevalHR, an ATP:ADP nanosensor. We assessed exposures of several compounds including KCl, glutamate, FCCP, insulin, and glucose. A mitochondrial stress test was performed, and PercevalHR’s fluorescence was corrected for pH using pHrodo. Results demonstrate that PercevalHR can reliably report on the energetic status of two cell types that communicate in a mixed-culture setting. While KCl, glutamate, and FCCP showed clear changes in PercevalHR fluorescence, insulin and glucose responses were found to be more subtle and sensitive to extracellular glucose. These results may highlight mechanisms that mediate insulin sensitivity in the brain.
A blood glucose fluctuation-responsive delivery system promotes bone regeneration and the repair function of Smpd3-reprogrammed BMSC-derived exosomes
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Consensus on the key characteristics of metabolism disruptors
Metabolism-disrupting agents (MDAs) are chemical, infectious or physical agents that increase the risk of metabolic disorders. Examples include pharmaceuticals, such as antidepressants, and environmental agents, such as bisphenol A. Various types of studies can provide evidence to identify MDAs, yet a systematic method is needed to integrate these data to help to identify such hazards. Inspired by work to improve hazard identification of carcinogens using key characteristics (KCs), we developed 12 KCs of MDAs based on our knowledge of processes underlying metabolic diseases and the effects of their causal agents: (1) alters function of the endocrine pancreas; (2) impairs function of adipose tissue; (3) alters nervous system control of metabolic function; (4) promotes insulin resistance; (5) disrupts metabolic signalling pathways; (6) alters development and fate of metabolic cell types; (7) alters energy homeostasis; (8) causes inappropriate nutrient handling and partitioning; (9) promotes chronic inflammation and immune dysregulation in metabolic tissues; (10) disrupts gastrointestinal tract function; (11) induces cellular stress pathways; and (12) disrupts circadian rhythms. In this Consensus Statement, we present the logic that revealed the KCs of MDAs and highlight evidence that supports the identification of KCs. We use chemical, infectious and physical agents as examples to illustrate how the KCs can be used to organize and use mechanistic data to help to identify MDAs.
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