<i>PPARG</i> F388L, a Transactivation-Deficient Mutant, in Familial Partial Lipodystrophy
Bibliographic record
Abstract
Autosomal dominant familial partial lipodystrophy (FPLD) due to mutant LMNA encoding nuclear lamin A/C is characterized by adipose tissue repartitioning together with multiple metabolic disturbances, including insulin resistance and dyslipidemia. There is emerging evidence that some rare mutations in peroxisome proliferator-activated receptor-gamma (PPAR-gamma), encoded by PPARG, might be associated with human lipodystrophy. We report a three-generation Canadian kindred ascertained based upon partial lipodystrophy, with a normal LMNA gene sequence. Candidate gene sequencing showed that all four affected subjects were heterozygous for a novel T-->A mutation at PPARG nucleotide 1164 in exon 5 that predicted substitution of phenylalanine at codon 388 by leucine (F388L). The mutation was absent from normal family members and normal unrelated subjects, and altered a highly conserved residue within helix 8 of the predicted ligand-binding pocket of PPAR-gamma. The mutant receptor had significantly decreased basal transcriptional activity and impaired stimulation by a synthetic ligand. The germline transmission of a transactivation-deficient mutation in PPARG suggests that autosomal dominant partial lipodystrophy is genetically heterogeneous. Our findings are consistent with the idea that mutant PPARG can underlie the partial lipodystrophy phenotype.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".