Secretory phospholipase A2 group IIA modulates insulin sensitivity and metabolism
Bibliographic record
Abstract
Secretory phospholipase A 2 group IIA (PLA2G2A) is a member of a family of secretory phospholipases that have been implicated in inflammation, atherogenesis, and antibacterial actions. Here, we evaluated the role of PLA2G2A in the metabolic response to a high fat diet. C57BL/6 (BL/6) mice do not express PLA2g2a due to a frameshift mutation. We fed BL/6 mice expressing the human PLA2G2A gene (IIA+ mice) a fat diet and assessed the physiologic response. After 10 weeks on the high fat diet, the BL/6 mice were obese, but the IIA+ mice did not gain weight or accumulate lipid. The lean mass in chow- and high fat-fed IIA+ mice was constant and similar to the BL/6 mice on a chow diet. Surprisingly, the IIA+ mice had an elevated metabolic rate, which was not due to differences in physical activity. The IIA+ mice were more insulin sensitive and glucose tolerant than the BL/6 mice, even when the IIA+ mice were provided the high fat diet. The IIA+ mice had increased expression of uncoupling protein 1 (UCP1), sirtuin 1 (SIRT1), and PPARγ coactivator 1α (PGC-1α) in brown adipose tissue (BAT), suggesting that PLA2G2A activates mitochondrial uncoupling in BAT. Our data indicate that PLA2G2A has a previously undiscovered impact on insulin sensitivity and metabolism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".