Glial <i>swip-10</i> controls systemic mitochondrial function, oxidative stress, and neuronal viability via copper ion homeostasis
Bibliographic record
Abstract
Cuprous copper [Cu(I)] is an essential cofactor for enzymes that support many fundamental cellular functions including mitochondrial respiration and suppression of oxidative stress. Neurons are particularly reliant on mitochondrial production of ATP, with many neurodegenerative diseases, including Parkinson’s disease, associated with diminished mitochondrial function. The gene MBLAC1 encodes a ribonuclease that targets pre-mRNA of replication-dependent histones, proteins recently found in yeast to reduce Cu(II) to Cu(I), and when mutated disrupt ATP production, elevates oxidative stress, and severely impacts cell growth. Whether this process supports neuronal and/or systemic physiology in higher eukaryotes is unknown. Previously, we identified swip-10 , the putative Caenorhabditis elegans ortholog of MBLAC1 , establishing a role for glial swip-10 in limiting dopamine (DA) neuron excitability and sustaining DA neuron viability. Here, we provide evidence from computational modeling that SWIP-10 protein structure mirrors that of MBLAC1 and locates a loss of function coding mutation at a site expected to disrupt histone RNA hydrolysis. Moreover, we find through genetic, biochemical, and pharmacological studies that deletion of swip-10 in worms negatively impacts systemic Cu(I) levels, leading to deficits in mitochondrial respiration and ATP production, increased oxidative stress, and neurodegeneration. These phenotypes can be offset in swip-10 mutants by the Cu(I) enhancing molecule elesclomol and through glial expression of wildtype swip-10 . Together, these studies reveal a glial-expressed pathway that supports systemic mitochondrial function and neuronal health via regulation of Cu(I) homeostasis, a mechanism that may lend itself to therapeutic strategies to treat devastating neurodegenerative diseases.
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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".