The microbial metabolite desaminotyrosine is a potent antiobesity agent with potential effects on white adipose tissue remodeling in mice
Bibliographic record
Abstract
OBJECTIVES: White adipose tissue plays a critical role in obesity, as its dysfunction can impair lipid homeostasis. We previously demonstrated that desaminotyrosine (DAT), a microbial metabolite, prevents high-fat diet (HFD)-induced body weight gain in mice, but the role of DAT on white adipocyte is unknown. Here, we investigated the role of DAT in host metabolic health and its therapeutic potentials in treating obesity. METHODS: In this study, we employed a pharmacological approach by administering DAT to mice. These mice were subjected to HFD feeding to establish overweight model, followed by DAT treatment. The effect of DAT on white adipocytes were studied using both in vivo and in vitro models. RESULTS: Our data indicated that DAT is a potent weight loss chemical for obesity treatment. This is related to DAT's dual-function in regulating white adipose tissue remodeling. DAT enhances mature white adipocyte-autonomous fat disposal through sustained lipolysis and augmented expression of carnitine palmitoyltransferase I family protein CPT1A, a critical enzyme facilitating fatty acid oxidation (FAO), especially under lipolytic-inducing conditions. In the meantime, it blocks white adipogenesis via FAO-dependent pathway potentiation. CONCLUSIONS: Collectively, these data demonstrate that DAT is a potent antiobesity agent with potential effects on white adipose tissue remodeling. This study provides a novel pharmacological strategy targeting white adipocyte plasticity for treating metabolic disorders.
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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 | 0.000 | 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) | 0.002 | 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".