Agpat4/Lpaatδ deficiency highlights the molecular heterogeneity of epididymal and perirenal white adipose depots
Bibliographic record
Abstract
Acylglycerophosphate acyltransferase 4 (AGPAT4)/lysophosphatidic acid acyltransferase delta catalyzes the formation of phosphatidic acid (PA), a precursor of triacylglycerol (TAG). We investigated the effect of Agpat4 gene ablation on white adipose tissue (WAT) after finding consistent expression across depots. Epididymal WAT mass was 40% larger in male Agpat4 −/− mice than wild-type littermates, but unchanged in perirenal, retroperitoneal, and inguinal WAT and subscapular brown adipose tissue. Metabolic changes were identified in epididymal WAT that were not evident in perirenal WAT, which was analyzed for comparison. The total epididymal TAG content doubled, increasing adipocyte cell size without changing markers of differentiation. Enzymes involved in de novo lipogenesis and complex lipid synthesis downstream of phosphatidic acid production were also unchanged. However, total epididymal TAG hydrolase activity was reduced, and there were significant decreases in total ATGL and reduced phosphorylation of hormone-sensitive lipase at the S563 and S660 PKA-activation sites. Analysis of Agpats 1, 2, 3 , and 5 , as well as Gpats 1, 2, 3, and 4, demonstrated compensatory upregulation in perirenal WAT that did not occur in epididymal WAT. Our findings therefore indicate depot-specific differences in the redundancy of Agpat4 and highlight the molecular and metabolic heterogeneity of individual visceral depots.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".