The Hepatokine TSK does not affect brown fat thermogenic capacity, body weight gain, and glucose homeostasis
Bibliographic record
Abstract
OBJECTIVES: Hepatokines are proteins secreted by the liver that impact the functions of the liver and various tissues through autocrine, paracrine, and endocrine signaling. Recently, Tsukushi (TSK) was identified as a new hepatokine that is induced by obesity and cold exposure. It was proposed that TSK controls sympathetic innervation and thermogenesis in brown adipose tissue (BAT) and that loss of TSK protects against diet-induced obesity and improves glucose homeostasis. Here we report the impact of deleting and/or overexpressing TSK on BAT thermogenic capacity, body weight regulation, and glucose homeostasis. METHODS: We measured the expression of thermogenic genes and markers of BAT innervation and activation in TSK-null and TSK-overexpressing mice. Body weight, body temperature, and parameters of glucose homeostasis were also assessed in the context of TSK loss and overexpression. RESULTS: The loss of TSK did not affect the thermogenic activation of BAT. We found that TSK-null mice were not protected against the development of obesity and did not show improvement in glucose tolerance. The overexpression of TSK also failed to modulate thermogenesis, body weight gain, and glucose homeostasis in mice. CONCLUSIONS: TSK is not a significant regulator of BAT thermogenesis and is unlikely to represent an effective target to prevent obesity and improve glucose homeostasis.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".