Efferocytic Defects in Early Atherosclerosis Are Driven by GATA2 Overexpression in Macrophages
Bibliographic record
Abstract
The loss of efferocytosis – the phagocytic clearance of apoptotic cells – is an initiating event in atherosclerotic plaque formation. While the loss of macrophage efferocytosis is a prerequisite for advanced plaque formation, the transcriptional and cellular events in the pre-lesion site that drive these defects are poorly defined. Transcriptomic analysis of macrophages recovered from early-stage human atherosclerotic lesions identified a 50-fold increase in the expression of GATA2, a transcription factor whose expression is normally restricted to the hematopoietic compartment. GATA2 overexpression in vitro recapitulated many of the functional defects reported in patient macrophages, including deficits at multiple stages in the efferocytic process. These findings included defects in the uptake of apoptotic cells, efferosome maturation, and in phagolysosome function. These efferocytic defects were a product of GATA2-driven alterations in the expression of key regulatory proteins, including Src-family kinases, Rab7 and components of both the vacuolar ATPase and NADPH oxidase complexes. In summary, these data identify a mechanism by which efferocytic capacity is lost in the early stages of plaque formation, thus setting the stage for the accumulation of uncleared apoptotic cells that comprise the bulk of atherosclerotic plaques.
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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 | 0.001 | 0.001 |
| 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".