Uraemic plasma decreases the expression of ABCA1, ABCG1 and cell-cycle genes in human coronary arterial endothelial cells
Bibliographic record
Abstract
BACKGROUND: Uraemia is associated with endothelial dysfunction, but the effect of uraemic plasma on the gene expression pattern of human coronary arterial endothelial cells (HCAEC) has never been defined. METHODS: HCAECs were exposed for 48 h to a culture medium supplemented with 20% uraemic vs normal plasma. We extracted mRNA and hybridized it onto Affymetrix HG-U133 Plus2 microarrays. We validated our findings for five genes of interest by real-time PCR and performed evaluations of cell proliferation and apoptosis in HCAECs exposed to uraemic vs normal plasma. RESULTS: Six genes involved in the regulation of cell-cycle progression (CDK-1, topoisomerase II, PDZ-binding kinase, CDCA1, protein SDP35, E2F transcription factor 8) and two genes of the cholesterol efflux system (ABCA1 and ABCG1) were down-regulated in HCAECs exposed to uraemic plasma (>1.75-fold change vs normal). Real-time PCR confirmed the down-regulation observed in the microarray experiment. Cell proliferation was significantly decreased in HCAECs exposed to uraemic vs normal plasma for 48 h (86 vs 95% of serum-starved control, P = 0.006). Exposure to uraemic plasma for 48 h was associated with increased apoptosis of HCAEC as compared with normal plasma (7.7 vs 2.8%, P < 0.001), a phenomenon that was further enhanced when oxidized LDLs (150 microg protein/ml) were added to the medium containing uraemic plasma (16.9 vs 7.7%, P < 0.001). CONCLUSIONS: The down-regulation of genes involved in cell-cycle progression and cholesterol efflux from HCAECs exposed to uraemic conditions could contribute to enhancing endothelial dysfunction and atherosclerosis in patients with chronic renal failure.
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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) | 0.002 | 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".