Characterization of Dedifferentiating Human Mature Adipocytes from the Visceral and Subcutaneous Fat Compartments: Fibroblast-Activation Protein Alpha and Dipeptidyl Peptidase 4 as Major Components of Matrix Remodeling
Bibliographic record
Abstract
Mature adipocytes can reverse their phenotype to become fibroblast-like cells. This is achieved by ceiling culture and the resulting cells, called dedifferentiated fat (DFAT) cells, are multipotent. Beyond the potential value of these cells for regenerative medicine, the dedifferentiation process itself raises many questions about cellular plasticity and the pathways implicated in cell behavior. This work has been performed with the objective of obtaining new information on adipocyte dedifferentiation, especially pertaining to new targets that may be involved in cellular fate changes. To do so, omental and subcutaneous mature adipocytes sampled from severely obese subjects have been dedifferentiated by ceiling culture. An experimental design with various time points along the dedifferentiation process has been utilized to better understand this process. Cell size, gene and protein expression as well as cytokine secretion were investigated. Il-6, IL-8, SerpinE1 and VEGF secretion were increased during dedifferentiation, whereas MIF-1 secretion was transiently increased. A marked decrease in expression of mature adipocyte transcripts (PPARγ2, C/EBPα, LPL and Adiponectin) was detected early in the process. In addition, some matrix remodeling transcripts (FAP, DPP4, MMP1 and TGFβ1) were rapidly and strongly up-regulated. FAP and DPP4 proteins were simultaneously induced in dedifferentiating mature adipocytes supporting a potential role for these enzymes in adipose tissue remodeling and cell plasticity.
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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) | 0.000 | 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".