Loss of <scp>VSTM2A</scp> promotes adipocyte hypertrophy and disrupts metabolic homeostasis
Bibliographic record
Abstract
OBJECTIVE: Adipose tissue expands through hyperplasia and hypertrophy to store excess lipids, a process that is essential for the maintenance of metabolic homeostasis. The mechanisms regulating adipocyte recruitment from progenitors remain unclear. We have previously identified V-set and transmembrane domain-containing protein 2A (VSTM2A) as a factor promoting fat cell development in vitro. Whether VSTM2A impacts adipose tissue and systemic metabolism in vivo is still unknown. METHODS: ) using clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) and fed them either a chow or high-fat diet. These mice were evaluated for body weight, adiposity, blood parameters, and glucose homeostasis. RESULTS: mice had larger adipocytes, an effect linked to inflammation, ectopic lipid deposition, and impaired glucose and lipid metabolism. Transcriptomic analysis revealed that VSTM2A loss affects the expression of several genes in adipose tissue, including some related to the lysosome. Interestingly, acute lysosomal inhibition early in life is sufficient to cause adipocyte hypertrophy in adults. CONCLUSIONS: VSTM2A is dispensable for adipose tissue formation, but its loss causes adipocyte hypertrophy and impairs glucose and lipid homeostasis. Our study also underscores a critical role of the lysosome in initiating adipogenesis.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".