Protein-protein regulation of calsequestrin expression in cardiomyocytes
Bibliographic record
Abstract
Heart failure is the leading cause of death in both men and women of Western countries. The pathophysiology of heart failure is associated with abnormalities in intracellular calcium control. Calsequestrin (CSQ2), a calcium storage protein in cardiomyocytes, is negatively regulated by the transcription factor Egr-1 thus altering calcium availability for cardiac contraction/relaxation. Here, we tested the hypothesis that the proteins complexed to Egr-1 and/or their post-translational modifications would affect regulation of CSQ2 expression. Egr-1 and Sp1 compete for binding at the CSQ2 promoter, but also bind one another. In fact, together they form a complex with another ubiquitous transcription factor YBX-1. This complex was identified in vivo and in vitro by a series of co-immunoprecipitations. To test the idea that complex formation and CSQ2 expression could be affected by acetylation, histone acetyltransferase (HAT) inhibitors and histone deacetylase (HDAC) inhibitors were used to respectively decrease or increase acetylation within cells. We found that acetylation did not impact the formation of the Egr-1: Sp1: YBX-1 complex thought to regulate CSQ2 expression. However, changes in CSQ2 expression were observed when acetylation was modified by HAT and HDAC inhibitors. We conclude that acetylation modifies CSQ2 expression although not by means of the Egr-1: Sp1: YBX-1 complex even though Egr-1 is known to be acetylated.
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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) | 0.003 | 0.001 |
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".