ERK Is Integral to the IFN-γ–Mediated Activation of STAT1, the Expression of Key Genes Implicated in Atherosclerosis, and the Uptake of Modified Lipoproteins by Human Macrophages
Bibliographic record
Abstract
The proinflammatory cytokine IFN-gamma is a master regulator of atherosclerosis and mediates its cellular actions mainly through STAT1. Unfortunately, the impact of other IFN-gamma inducible pathways on STAT1 activation and the regulation of downstream responses associated with atherosclerosis in human macrophages are poorly understood and were therefore investigated. In this study, we demonstrate that the IFN-gamma-mediated phosphorylation of STAT1 on Ser(727), crucial for its maximal activity, was attenuated in human macrophages by pharmacological inhibition of ERK. In these cells, IFN-gamma induced changes in the expression of several key genes implicated in atherosclerosis, such as MCP-1, through an ERK-dependent mechanism. Additionally, the IFN-gamma-induced activity of STAT1-responsive promoters was attenuated by transfection of dominant-negative forms of ERK and other key components of this pathway. Furthermore, the IFN-gamma-induced uptake of acetylated and oxidized low-density lipoprotein by human macrophages was attenuated by pharmacological inhibition or RNA interference-mediated knockdown of ERK. These studies suggest a critical role for ERK signaling in the IFN-gamma-mediated changes in macrophage cholesterol homeostasis and gene expression during atherosclerosis.
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 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) | 0.002 | 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".