Endoplasmic reticulum stress-related deficits in calcium clearance promote neuronal dysfunction that is prevented by SERCA2 gene augmentation
Bibliographic record
Abstract
Disruption of calcium (Ca 2+ ) homeostasis in neurons is a hallmark of neurodegenerative diseases. Here, we investigate the mechanisms leading to Ca 2+ dysregulation and ask whether altered Ca 2+ dynamics impinge on neuronal stress and circuit dysfunction. Using two-photon microscopy, we show that ocular hypertension, a major risk factor in glaucoma, and optic nerve crush injury disrupt the capacity of retinal neurons to clear cytosolic Ca 2+ leading to impaired light-evoked responses. Gene- and protein expression analysis reveal the loss of the sarco-endoplasmic reticulum (ER) Ca 2+ -ATPase2 pump (SERCA2/ ATP2A2 ) in injured retinal neurons from mice and patients with primary open-angle glaucoma. Pharmacological activation or neuron-specific gene delivery of SERCA2 is sufficient to rescue single-cell Ca 2+ dynamics and promote robust survival of damaged neurons. Furthermore, SERCA2 gene supplementation reduces ER stress, reestablishes circuit balance, and restores visual behaviors. Our findings reveal that enhancing the Ca 2+ clearance capacity of vulnerable neurons alleviates organelle stress and promotes neurorecovery. • Calcium (Ca 2+ ) clearance capacity is compromised in injured retinal neurons • Ocular hypertension induces SERCA2 downregulation and ER stress • Increased SERCA2 activity restores Ca 2+ homeostasis and mitigates ER stress • SERCA2 gene augmentation promotes neuronal survival and restores visual function Using two-photon microscopy, Shiga et al. show that impaired calcium clearance and ER stress are hallmarks of neuronal dysfunction in preclinical models of glaucoma and traumatic optic neuropathy. SERCA2 gene therapy restores calcium homeostasis, alleviates organelles stress, and promotes neurorecovery of vulnerable neurons.
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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) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".