Differences between common endothelial cell models (primary human aortic endothelial cells and EA.hy926 cells) revealed through transcriptomics, bioinformatics, and functional analysis
Bibliographic record
Abstract
Endothelial cells (ECs) are involved in various physiological process. Both primary human ECs and immortal endothelial cells are used in various studies. Available genomic or transcriptomic information for difference in ECs is deficient. Therefore, in this study we aim to reveal the difference between primary human aortic ECs (HAECs) and immortal EA.hy926 cells. We identified 529 differentially expressed genes (DEGs) between HAECs and EA.hy926 cells. Gene Ontology (GO), KEGG Pathway and GSEA enrichment analysis suggest that DEGs highly expressed in HAECs are distributed in Rap1 signaling pathway and Ras signaling pathway, which are contributing to the endothelial barrier function and endocytosis, among other functions. We also established long non-coding (lncRNA)-miRNA-mRNA ceRNA network, and further set up protein–protein interaction (PPI) network. High-density lipoprotein (HDL) cellular association experiments were verified that HAECs have stronger response to HDL cellular binding and endocytosis compared to EA.hy926 cells. This study identified DEGs between HAECs and EA.hy926 cells, and found enrichment of the Ras signaling pathway and Rap1 signaling pathway in HAECs, established ceRNA network and suggested that HAECs may have a stronger response to endothelial binding and endocytosis compared to EA.hy926 cells. This work provides a genomic basis to choose suitable EC model to reach respective research goals.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".