Mitochondrial-Dependent Ca<sup>2+</sup>Handling in Huntington's Disease Striatal Cells: Effect of Histone Deacetylase Inhibitors
Bibliographic record
Abstract
Evidence suggests that neuronal dysfunction in Huntington's disease (HD) striatum involves deficits in mitochondrial function and in Ca2+ handling. However, the relationship between mitochondria and Ca2+ handling has been incompletely studied in intact HD striatal cells. Treatment with histone deacetylase (HDAC) inhibitors reduces cell death in HD models, but the effects of this promising therapy on cellular function are mostly unknown. Here, we use real-time functional imaging of intracellular Ca2+ and mitochondrial membrane potential to explore the role of in situ HD mitochondria in Ca2+ handling. Immortalized striatal (STHdh) cells and striatal neurons from transgenic mice, expressing full-length mutant huntingtin (Htt), were used to model HD. We show that (1) active glycolysis in STHdh cells occludes the mitochondrial role in Ca2+ handling as well as the effects of mitochondrial inhibitors, (2) STHdh cells and striatal neurons in the absence of glycolysis are critically dependent on oxidative phosphorylation for energy-dependent Ca2+ handling, (3) expression of full-length mutant Htt is associated with deficits in mitochondrial-dependent Ca2+ handling that can be ameliorated by treatment with HDAC inhibitors (treatment with trichostatin A or sodium butyrate decreases the proportion of STHdh cells losing Ca2+ homeostasis after Ca2+-ionophore challenging, and accelerates the restoration of intracellular Ca2+ in striatal neurons challenged with NMDA), and (4) neurons with different response patterns to NMDA receptor activation exhibit different average somatic areas and are differentially affected by treatment with HDAC inhibitors, suggesting subpopulation or functional state specificity. These findings indicate that neuroprotection induced by HDAC inhibitors involves more efficient Ca2+ handling, thus improving the neuronal ability to cope with excitotoxic stimuli.
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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) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".