Acylation Stimulating Protein and Triacylglycerol Synthesis: Potential Drug Targets?
Bibliographic record
Abstract
Triacylglycerol storage in adipose tissue is mediated by a host of transporters, enzymes and binding proteins. Additionally, several hormones (both autocrine and endocrine) are known to interact with cell surface receptors and modulate triacylglycerol synthesis (such as acylation stimulating protein, ASP). The many proteins involved contribute to the robustness of the system and, in most cases, deletion of a single gene is not deleterious and adipose tissue is preserved. On the other hand, this does not mean that gene disruption is not without effect, and in fact often results in a leaner, and presumably "healthier" mouse. These insights provide valuable indications for potential drug tools to delay and/or reverse obesity. In this review we examine the potential of ASP as a candidate target. ASP deficiency in mice decreases adipose tissue mass, increases insulin sensitivity and energy expenditure even in obese ob/ob mice, suggesting that partial interference of ASP action could be advantageous. ASP interacts with a specific cell surface receptor present in adipose tissue and certain structural components, such as the tightly folded core region, are implicated in activity. We propose that interference of the ASP-receptor interaction using an antagonist offers future prospect for an anti-obesity target.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".