Macrophages Alter the Differentiation‐Dependent Decreases in Fibronectin and Collagen I/III Protein Levels in Human Preadipocytes
Bibliographic record
Abstract
Adipose tissue of obese individuals is characterized by increased fibrosis and macrophage infiltration. Extensive remodeling of the extracellular matrix (ECM) that occurs during adipogenesis can be influenced by macrophages, but it remains unclear how macrophage-secreted factors alter preadipocyte ECM protein expression under non-adipogenic versus adipogenic conditions. Confluent human subcutaneous abdominal preadipocytes were cultured for 14 days, with or without adipogenic inducers, in either control medium, medium conditioned by THP-1 monocytes (THP-1-MonCM), or medium conditioned by THP-1 macrophages (THP-1-MacCM). Under non-adipogenic conditions in THP-1-MacCM, collagen I/III and fibronectin protein levels rose by 40 and 70 %, respectively (p < 0.05, n = 3; compared to control non-adipogenic medium). When preadipocytes were exposed to adipogenic inducers in THP-1-MacCM, collagen I/III levels increased by 50 %, but those of fibronectin fell by 48 %, both compared to non-adipogenic THP-1-MacCM conditions. The rise in collagen I/III levels contrasts with the 51 % decrease in collagen I/III that occurs with induction of differentiation in control medium, whereas, the decrease in fibronectin is more modest, but consistent in THP-1-MacCM (48 %) and control medium (92 %). A similar effect on fibronectin levels occurred using medium conditioned by LPS-treated human monocyte-derived macrophages (MD-MacCM). Our data indicate macrophage-derived factors regulate levels of collagen I/III and fibronectin in preadipocytes under non-adipogenic and adipogenic conditions. Further studies are needed to determine if these changes in these ECM proteins contribute to the anti-adipogenic action of MacCM.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".