The role of LMNA in adipose: a novel mouse model of lipodystrophy based on the Dunnigan-type familial partial lipodystrophy mutation
Bibliographic record
Abstract
We investigated the role of LMNA in adipose tissue by developing a novel mouse model of lipodystrophy. Transgenic mice were generated that express the LMNA mutation that causes familial partial lipodystrophy of the Dunnigan type (FPLD2). The phenotype observed in FPLD-transgenic mice resembles many of the features of human FPLD2, including lack of fat accumulation, insulin resistance, and enlarged, fatty liver. Similar to the human disease, FPLD-transgenic mice appear to develop normally, but after several weeks they are unable to accumulate fat to the same extent as their wild-type littermates. One poorly understood aspect of lipodystrophies is the mechanism of fat loss. To this end, we have examined the effects of the FPLD2 mutation on fat cell function. Contrary to the current literature, which suggests FPLD2 results in a loss of fat, we found that the key mechanism contributing to the lack of fat accumulation involves not a loss, but an apparent inability of the adipose tissue to renew itself. Specifically, preadipocytes are unable to differentiate into mature and fully functional adipocytes. These findings provide insights not only for the treatment of lipodystrophies, but also for the study of adipogenesis, obesity, and insulin resistance.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".