Modulation of expression of Connexins 37, 40 and 43 in endothelial cells in culture
Bibliographic record
Abstract
Connexin (Cx) 37, 40, and 43 are implicated in vascular function, specifically in the electrical coupling of endothelial cells and vascular smooth-muscle cells. In the present study, we investigated whether factors implicated in vascular dysfunction can modulate the gene expression of Cx37, Cx40, and Cx43 and whether this is associated with changes in endothelial layer barrier function in human microvascular endothelial cells (HMEC-1). First, HMEC-1 were subjected to stimuli for 4 and 8 h. We tested their responses to DETA-NONOate, H2O2, high glucose, and angiotensin II, none of which relevantly affected the transcription of the connexin genes. Next, we tested inflammatory factors IL-6, interferon gamma (IFNγ), and TNFα. IFNγ (10 ng/mL) consistently induced Cx40 expression at 4 and 8 h to 10–20-fold when corrected for the control. TNFα and IL-6 resulted in small but significant depressions of Cx37 expression at 4 h. Two JAK inhibitors, epigallocatechin-3-gallate (EGCG) (100–250 μM) and AG490 (100–250 μM), dose-dependently inhibited the induction of Cx40 expression by IFNγ. Subsequently, HMEC-1 were subjected to 10 ng/mL IFNγ for 60 h, and intercellular and transcellular impedance was monitored by electric cell-substrate impedance sensing (ECIS). In response to IFNγ, junctional-barrier impedance increased more than cellular-barrier impedance; this was prevented by AG490 (5 μM). In conclusion, IFNγ can strongly induce Cx40 expression and modify the barrier properties of the endothelial cell membrane through the JAK/STAT pathway. Moreover, the Cx37, Cx40, and Cx43 expression in endothelial cells is stable and, apart from IFNγ, not affected by a number of factors implicated in endothelial dysfunction and vascular diseases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".