Proteomic Analysis of Pharmacological Preconditioning
Bibliographic record
Abstract
Ischemic preconditioning is characterized by resistance to ischemia reperfusion injury in response to previous short ischemic episodes, a protective effect that can be mimicked pharmacologically. The underlying mechanism of protection remains controversial and requires greater understanding before it can be fully exploited therapeutically. To investigate the overall effect of preconditioning on the myocardial proteome, isolated rabbit ventricular myocytes were treated with drugs known to induce preconditioning, adenosine or diazoxide (each at 100 micromol/L for 60 minutes). Their protein profiles were then compared with vehicle-treated controls (n=4 animals per treatment) using a multitiered 2D gel electrophoresis approach. Of 28 significantly altered protein spots, 19 nonredundant proteins were identified (5 spots remained unidentified). The majority of these proteins are involved in mitochondrial energetics, including subunits of tricarboxylic acid cycle enzymes and oxidative phosphorylation complexes. These changes were not indiscriminate, with only a small number of enzymes or complex subunits altered, indicating a very specific and targeted affect of these 2 preconditioning mimetics. Among the changes were shifts in the extent of posttranslational modification of 4 proteins. One of these, the adenosine-induced phosphorylation of the ATP synthase beta subunit, was fully characterized with the identification of 5 novel phosphorylation sites. This proteomics approach provides an overall assessment of the cellular response to pharmacological treatment with adenosine and diazoxide and identifies a distinct subset of enzymes and protein complex subunit that may underlie the preconditioned phenotype.
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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".