The hematopoietic transcription factor GATA2 is a novel regulator of apoptotic cell clearance by macrophages in atherosclerosis
Bibliographic record
Abstract
Abstract Atherosclerosis is an inflammatory disease involving formation of lipid-rich lesions within the arteries. Macrophages clear apoptotic cells that accumulate within these lesions. Defective apoptotic cell clearance is a hallmark of advanced atherosclerotic disease, yet mechanisms that drive this defect are poorly understood. In this study, we identify the hematopoietic transcription factor GATA2 as a novel regulator of macrophage-mediated apoptotic cell clearance. Macrophages were isolated by laser capture microdissection from atherosclerotic lesions in aortic tissue obtained from patients undergoing open heart surgery. Gene expression profiling was performed on these macrophages by microarray, with macrophages derived from peripheral blood monocytes used as a control. We found approximately 3,000 genes to be differentially expressed in aortic punch macrophages, with enrichment in pathways involved in apoptotic cell clearance. In particular, we identified upregulation of GATA2. Mutations in GATA2 have previously been associated with increased risk of coronary artery disease and we found that overexpression of GATA2 in vitro resulted in decreased ability of macrophages to both internalize and degrade apoptotic cells. Conversely, GATA2 downregulation is sufficient to abrogate oxLDL-induced defective apoptotic cell clearance. Atherosclerotic macrophages exhibit dysregulated expression of genes involved in apoptotic cell clearance and upregulation of GATA2. Recapitulation of GATA2 overexpression is sufficient to impair apoptotic cell clearance in vitro. To our knowledge, we are the first to identify a potential role for GATA2 in driving defective apoptotic cell clearance in atherosclerosis.
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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".