Tissue Specific Knockout of the Cardiolipin Transacylase Enzyme TAFAZZIN in Both Liver and Pancreatic Beta Cells Protects Mice From Diet-Induced Obesity
Bibliographic record
Abstract
Abstract Mutations in the TAFAZZIN gene result in the X-linked genetic disease Barth Syndrome. The protein product tafazzin is a transacylase enzyme that remodels the phospholipid cardiolipin with fatty acids. Some Barth syndrome boys exhibit a lean phenotype. In addition, whole body knockdown of tafazzin in mice protects them from diet-induced obesity through increased hepatic fatty acid oxidation and reduced basal insulin secretion. We thus hypothesized that tafazzin deficiency in both the liver and beta cells of the pancreas contribute to this lean phenotype. Through a Cre-Lox approach we generated control, liver-specific, pancreatic beta cell-specific and liver and pancreatic beta cell-specific double knockout male mice. The animals were fed a high fat diet for 8 weeks and body weight, liver weight and fat pad weights determined. Liver-specific or pancreatic beta cell-specific male tafazzin knockout mice accumulated weight gain (≍40% increase in body weight) at the same rate as control animals. In contrast, the liver- and beta cell-specific double tafazzin knockout mice exhibited a reduced rate of weight gain by 8 weeks (≍26% increase in body weight) compared to control or the single tafazzin knockout animals. In addition, at 8 weeks the double tafazzin knockout mice exhibited reduced weight gain in tissues known to accumulate fat including the liver, the gonadal, inguinal and perirenal white adipose tissues and the brown adipose tissue. Thus, liver- and pancreatic beta cell-specific double tafazzin knockout male mice are protected from high fat diet induced weight gain and fat accumulation. These results may partially explain why some Barth Syndrome boys exhibit a lean 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".