Determination of Protein-Bound Palmitate Turnover Rates Using a Three-Compartment Model That Formally Incorporates [<sup>3</sup>H]Palmitate Recycling
Bibliographic record
Abstract
The observation that the palmitoylation state of certain proteins can be biologically modulated led to the proposal that it could, much like phosphorylation, be an important dynamic regulator of protein function. However, based on single-phase exponential decay analysis of data from [(3)H]palmitate pulse/chase experiments, the measured protein-bound palmitate turnover rates were often found to be too slow to account for rapid physiological responses. This paper reports that exponential decay does not adequately describe the results of such experiments because it fails to account for the recycling of [(3)H]palmitate from cellular lipids to palmitoyl CoA. Taking this recycling into account, a three-compartment model was used to deduce the time-dependent changes of cellular [(3)H]palmitoyl CoA and to infer the time course for the incorporation of [(3)H]palmitate into proteins. The validity of the inferences made by the model was checked against data obtained by metabolic labeling of endogenous HEK293 cell proteins. In addition, the model could account for reported anomalies, discrepancies, and apparently paradoxical observations obtained by traditional analysis of data from pulse/chase experiments. Including the recycling of cellular palmitate in the formal description of the system offers a new tool for quantitative assessment of protein-bound palmitate turnover rates. Through the re-evaluation of these rates, the model provides a means for the reassessment of the potential physiological implications of dynamic palmitoylation. The model may also be generally applicable to other areas of research where recycling of tracer is a concern.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".