Acylation-stimulating protein/C5L2-neutralizing antibodies alter triglyceride metabolism in vitro and in vivo
Bibliographic record
Abstract
Acylation-stimulating protein (ASP), a lipogenic hormone, stimulates triglyceride (TG) synthesis and glucose transport upon activation of C5L2, a G protein-coupled receptor. ASP-deficient mice have reduced adipose tissue mass due to increased energy expenditure despite increased food intake. The objective of this study was to evaluate the blocking of ASP-C5L2 interaction via neutralizing antibodies (anti-ASP and anti-C5L2-L1 against C5L2 extracellular loop 1). In vitro, anti-ASP and anti-C5L2-L1 blocked ASP binding to C5L2 and efficiently inhibited ASP stimulation of TG synthesis and glucose transport. In vivo, neither anti-ASP nor anti-C5L2-L1 altered body weight, adipose tissue mass, food intake, or hormone levels (insulin, leptin, and adiponectin), but they did induce a significant delay in TG clearance [P < 0.0001, 2-way repeated-measures (RM) ANOVA] and NEFA clearance (P < 0.0001, 2-way RM ANOVA) after a fat load. After treatment with either anti-ASP or anti-C5L2-L1 antibody there was no change in adipose tissue AMPK activity, but neutralizing antibodies decreased perirenal TG mass (-38.4% anti-ASP, -18.8% anti-C5L2, P < 0.01-0.001) and perirenal LPL activity (-75.6% anti-ASP, -72.5% anti-C5L2, P < 0.05). In liver, anti-C5L2-L1 decreased TG mass (-42.8%, P < 0.05), whereas anti-ASP increased AMPK activity (+34.6%, P < 0.001). In the muscle, anti-C5L2-L1 significantly increased TG mass (+128.0%, P < 0.05), LPL activity (+226.1%, P < 0.001), and AMPK activity (+71.1%, P < 0.01). In addition, anti-ASP increased LPL activity (+164.4, P < 0.05) and AMPK activity (+53.9%, P < 0.05) in muscle. ASP/C5L2-neutralizing antibodies effectively block ASP-C5L2 interaction, altering lipid distribution and energy utilization.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".