MiR27a Promotes the Development of Macrophage-like Characteristics in 3T3-L1 Preadipocytes
Bibliographic record
Abstract
Recruitment and polarization of classically activated (M1) macrophages within adipose tissue contribute to chronic low-grade inflammation in obesity. Adipose tissue precursor cells exhibit the capacity to develop macrophage-like characteristics and adipocyte-derived miR27a is known to promote reprogramming of somatic cells. It was unknown whether exogenous addition of miR27a promote the development of macrophage-like characteristics of adipose precursor cells. We examined macrophage surface antigen, phagocytosis and migration ability in 3T3-L1 preadipocytes transfected with miR27a mimics. Transfection of 3T3-L1 preadipocytes with miR27a mimics increased phagocytosis and migration and increased the number of cells expressing the macrophage makers F4/80 and MHC compared to controls. M2 and CD206 macrophage markers were unaltered. In addition, transfection of 3T3-L1 preadipocytes with miR27a mimics reduced PPARγ expression, activated NF-κB and promoted secretion of the inflammatory cytokines MCP-1, TNF-α and IL-1β compared to controls. The level of anti-inflammatory factors Arg-1, IL-10, Ym1 and Fizz1 were unaltered. Secretion of miR27a was increased in conditioned medium prepared from palmitic acid-treated differentiated 3T3-L1 adipocytes compared to controls. Incubation of 3T3-L1 preadipocytes with this conditioned medium increased phagocytosis and migration compared to controls. Finally, conditioned medium prepared from differentiated 3T3-L1 adipocytes transfection with miR27a inhibitors reduced phagocytosis and migration in 3T3-L1 preadipocytes compared to controls. The data indicate that PPARγ agonists may reverse the activation of NF-κB pathway mediated by miR27a overexpression and reduce phagocytosis and migration of adipose precursor cells. In addition, miR27a may promote the development of macrophage-like characteristics in 3T3-L1 preadipocytes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 teacher head, 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".