Zinc induces motor neuron death via a selective inhibition of brain-derived neurotrophic factor activity
Bibliographic record
Abstract
Amyotrophic lateral sclerosis is a debilitating disease that results from the deterioration and loss of motor neurons. The neurotoxic potential of Zn(2+), both in vitro and in vivo, has been well established; however, the mechanism(s) of zinc's toxicity remain unclear. Our laboratory has demonstrated that Zn(2+)-mediated inhibition of neurotrophins can induce cell death. The present study investigates the neurotoxic mechanism(s) of this metal ion by assessing zinc's selectivity in altering the neurotrophin BDNF, but not the neural cytokine CNTF, with respect to motor neuron survival. Embryonic day 15 rat spinal motor neuron cultures were maintained in either BDNF or CNTF. Terminal deoxynucleotidyl transferase-mediated dUTP-digoxigenin nick end-labelling (TUNEL) staining showed that exposure to 100microM Zn(2+) significantly increased the number of pro-apoptotic neurons in cultures maintained with BDNF, while these conditions had no effect on cultures maintained with CNTF. We also demonstrate that BDNF protomer cross-linking efficiency and TrkB receptor cross-linking to BDNF are significantly inhibited by Zn(2+), suggesting that a Zn(2+)-induced change in BDNF conformation inhibits receptor-binding activity. This study reveals a mechanism by which zinc toxicity is mediated via a selective loss in neurotrophin activity resulting in motor neuron death.
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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) | 0.001 | 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".