Microarray analysis of oxidized low-density lipoprotein (ox-LDL)-regulated genes in human coronary artery smooth muscle cells
Bibliographic record
Abstract
Recent studies suggest that circulating LDL (low-density lipoproteins) play a central role in the pathogenesis of atherosclerosis, and the oxidized form (ox-LDL) is highly atherogenic. Deposits of ox-LDL have been found in atherosclerotic plaques, and ox-LDL has been shown to promote monocyte recruitment, foam cell formation and the transition of quiescent and contractile vascular SMCs (smooth muscle cells) to the migratory and proliferative phenotype. SMC phenotype transition and hyperplasia are the pivotal events in the pathogenesis of atherosclerosis. To comprehend the complex molecular mechanisms involved in ox-LDL-mediated SMC phenotype transition, we have compared the differential gene expression profiles of cultured quiescent human coronary artery SMCs with cells induced with ox-LDL for 3 and 21 h using Affymetrix HG-133UA cDNA microarray chips. Assignment of the regulated genes into functional groups indicated that several genes involved in metabolism, membrane transport, cell-cell interactions, signal transduction, transcription, translation, cell migration, proliferation and apoptosis were differentially expressed. Our data suggests that the interaction of ox-LDL with its cognate receptors on SMCs modulates the induction of several growth factors and cytokines, which activate a variety of intracellular signalling mechanisms (including PI3K, MAPK, Jak/STAT, sphingosine, Rho kinase pathways) that contribute to SMC transition from the quiescent and contractile phenotype to the proliferative and migratory phenotype. Our study has also identified several genes (including CDC27, cyclin A1, cyclin G2, glypican 1, MINOR, p15 and apolipoprotein) not previously implicated in ox-LDL-induced SMC phenotype transition and substantially extends the list of potential candidate genes involved in atherogenesis.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".