Energy Metabolism in Astrocytes and Neurons Treated with Manganese: Relation among Cell-Specific Energy Failure, Glucose Metabolism, and Intercellular Trafficking Using Multinuclear NMR-Spectroscopic Analysis
Bibliographic record
Abstract
A central question in manganese neurotoxicity concerns mitochondrial dysfunction leading to cerebral energy failure. To obtain insight into the underlying mechanism(s), the authors investigated cell-specific pathways of [1-13C]glucose metabolism by high-resolution multinuclear NMR-spectroscopy. Five-day treatment of neurons with 100-micro mol/L MnCl(2) led to 50% and 70% decreases of ATP/ADP and phosphocreatine-creatine ratios, respectively. An impaired flux of [1-13C]glucose through pyruvate dehydrogenase, which was associated with Krebs cycle inhibition and hence depletion of [4-13C]glutamate, [2-13C]GABA, and [13C]glutathione, hindered the ability of neurons to compensate for mitochondrial dysfunction by oxidative glucose metabolism and further aggravated neuronal energy failure. Stimulated glycolysis and oxidative glucose metabolism protected astrocytes against energy failure and oxidative stress, leading to twofold increased de novo synthesis of [3-13C]lactate and fourfold elevated [4-13C]glutamate and [13C]glutathione levels. Manganese, however, inhibited the synthesis and release of glutamine. Comparative NMR data obtained from cocultures showed disturbed astrocytic function and a failure of astrocytes to provide neurons with substrates for energy and neurotransmitter metabolism, leading to deterioration of neuronal antioxidant capacity (decreased glutathione levels) and energy metabolism. The results suggest that, concomitant to impaired neuronal glucose oxidation, changes in astrocytic metabolism may cause a loss of intercellular homeostatic equilibrium, contributing to neuronal dysfunction in manganese neurotoxicity.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".