Role of Endoplasmic Reticulum Calcium Disequilibria in the Mechanism of Homocysteine-Induced ER Stress
Bibliographic record
Abstract
Our laboratory demonstrated that hyperhomocysteinemia accelerates atherosclerosis in mouse models through ER stress and activation of the unfolded protein response (UPR). In this study, we tested the hypothesis that homocysteine-induced ER stress may arise from ER-Ca(2+) disequilibria. We found that homocysteine-induced cytosolic Ca(2+) transients in T24/83 cells and human aortic smooth muscle cells (HASMCs). These calcium effects occurred at concentrations of homocysteine in the external medium (1-5 mM) that increase intracellular homocysteine in these cell types. Prolonged homocysteine treatment (5 h) at these exogenous concentrations reduced ER-Ca(2+) emptying evoked by thapsigargin. However, these homocysteine-induced effects on ER-Ca(2+) emptying were of a much smaller magnitude than those evoked by A23187 or thapsigargin (ER stressors known to induce ER stress through ER-Ca(2+) depletion). T24/83 cells stably overexpressing the Ca(2+)-binding ER chaperone GRP78 showed diminished cytosolic Ca(2+) transients induced by homocysteine and reduced ER-Ca(2+) emptying evoked by thapsigargin. Prevention of the homocysteine-induced UPR by cycloheximide pretreatment normalized GRP78 expression and ER-Ca(2+) emptying evoked by thapsigargin. These results are inconsistent with a mechanism of ER stress induction by homocysteine through ER-Ca(2+) depletion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".