Macrophage‐conditioned medium inhibits the activation of cyclin‐dependent kinase 2 by adipogenic inducers in 3T3‐L1 preadipocytes
Bibliographic record
Abstract
Macrophage infiltration into adipose tissue, associated with obesity, is thought to contribute to abnormal adipose tissue remodeling, low-grade inflammation, and insulin resistance. Medium conditioned by macrophages (MacCM) inhibits 3T3-L1 and human adipocyte differentiation, as well as early adipogenic cell cycle events including MCE and retinoblastoma protein (Rb) phosphorylation. Our objective was to determine if the inhibition of Rb phosphorylation was linked to changes in cell cycle-related proteins. We treated 3T3-L1 preadipocytes with adipogenic inducers for 24 h in control medium versus J774A.1-MacCM. The differentiation-induced mRNA and protein expression of cyclin A, an activator of cyclin-dependent kinase (cdk) 2 which phosphorylates Rb, was inhibited by 82% and 73%, respectively, by J774A.1-MacCM; adipogenic expression of Myc, a transcriptional regulator of cyclin A, was also suppressed significantly. Consistent with the reduction in cyclin A levels, the activation of cdk2 by adipogenic inducers was inhibited by 75% by J774A.1-MacCM. J774A.1-MacCM also lowered levels of cyclins D1 and D2. Inhibition studies demonstrated that platelet-derived growth factor, an anti-adipogenic factor found in J774A.1-MacCM, was not responsible for the inhibitory effect on differentiation. The anti-adipogenic effect of J774A.1-MacCM was resistant to proteinase K and heat treatment, and was present in a <3 kDa fraction. Our data indicate that J774A.1-MacCM interferes with the upregulation of cyclin A levels and cdk2 activity that are required for Rb phosphorylation and MCE in 3T3-L1 adipogenesis.
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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) | 0.001 | 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) | 0.001 | 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".