Creatine kinase B regulates glycolysis and de novo lipogenesis pathways to control lipid accumulation during adipogenesis
Bibliographic record
Abstract
White adipocyte differentiation or adipogenesis requires coordination of metabolic sensing and transcriptional modifications to orchestrate lipid storage. Creatine and its kinases are implicated in adipose energy buffering, but the roles of cytosolic (CKB) and mitochondrial (CKMT2) creatine kinases in adipogenesis are unclear. We find that both CKB and CKMT2 are progressively upregulated during differentiation. Functional studies show that CKB restrains de novo lipogenesis (DNL) by limiting activation of carbohydrate-responsive element-binding protein (ChREBP), a key regulator of lipogenic genes. Mechanistically, CKB interacts with AKT and regulates its activation in response to insulin. Loss of CKB causes persistent AKT-mTORC1 signaling, increases glycolytic flux, and enhances ChREBP activation, thereby promoting glucose-derived lipid synthesis. Thus, CKB acts as a metabolic rheostat linking creatine-kinase activity to insulin signaling and nutrient-responsive transcription. We propose a CKB-AKT-ChREBP regulatory axis that contributes to metabolic remodeling and lipid homeostasis during adipocyte differentiation.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".