RASSF6 Expression in Adipocytes Is Down-Regulated by Interaction with Macrophages
Bibliographic record
Abstract
Macrophage infiltration into adipose tissue is associated with obesity and the crosstalk between adipocytes and infiltrated macrophages has been investigated as an important pathological phenomenon during adipose tissue inflammation. Here, we sought to identify adipocyte mRNAs that are regulated by interaction with infiltrated macrophages in vivo. An anti-inflammatory vitamin, vitamin B6, suppressed macrophage infiltration into white adipose tissue and altered mRNA expression. We identified >3500 genes whose expression is significantly altered during the development of obesity in db/db mice, and compared them to the adipose tissue mRNA expression profile of mice supplemented with vitamin B6. We identified PTX3 and MMP3 as candidate genes regulated by macrophage infiltration. PTX3 and MMP3 mRNA expression in 3T3-L1 adipocytes was up-regulated by activated RAW264.7 cells and these mRNA levels were positively correlated with macrophage number in adipose tissue in vivo. Next, we screened adipose genes down-regulated by the interaction with macrophages, and isolated RASSF6 (Ras association domain family 6). RASSF6 mRNA in adipocytes was decreased by culture medium conditioned by activated RAW264.7 cells, and RASSF6 mRNA level was negatively correlated with macrophage number in adipose tissue, suggesting that adipocyte RASSF6 mRNA expression is down-regulated by infiltrated macrophages in vivo. Finally, this study also showed that decreased RASSF6 expression up-regulates mRNA expression of several genes, such as CD44 and high mobility group protein HMGA2. These data provide novel insights into the biological significance of interactions between adipocytes and macrophages in adipose tissue during the development of obesity.
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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 | 0.001 |
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".