Hypoxic stress upregulates K<sub>ir</sub>2.1 expression by a pathway including hypoxic-inducible factor-1α and dynamin2 in brain capillary endothelial cells
Bibliographic record
Abstract
Brain capillary endothelial cells (BCECs) play a central role in maintenance of blood-brain barrier (BBB) function and, therefore, are essential for central nervous system homeostasis and integrity. Although brain ischemia damages BCECs and causes disruption of BBB, the related influence of hypoxia on BCECs is not well understood. Hypoxic stress can upregulate functional expression of specific K+ currents in endothelial cells, e.g., Kir2.1 channels without any alterations in the mRNA level, in t-BBEC117, a cell line derived from bovine BCECs. The hyperpolarization of membrane potential due to Kir2.1 channel upregulation significantly facilitates cell proliferation. In the present study, the mechanisms underlying the hypoxia-induced Kir2.1 upregulation was examined. We emphasize the involvement of dynamin2, a protein known to be involved in a number of surface expression pathways. Hypoxic culture upregulated dynamin2 expression in t-BBEC117 cells. The inhibition of dynamin2 by Dynasore canceled hypoxia-induced upregulation of Kir2.1 currents by reducing surface expression. On the contrary, Kir2.1 currents and proteins in t-BBEC117 cultured under normoxia were increased by overexpression of dynamin2, but not by dominant-negative dynamin2. Molecular imaging based on bimolecular fluorescence complementation, double-immunostaining, and coimmunoprecipitation assays revealed that dynamin2 can directly bind to the Kir2.1 channel. Moreover, hypoxic culture downregulated hypoxic-inducible factor-1α (HIF-1α) expression. Knockdown of HIF-1α increased dynamin2 expression in t-BBEC117 cells, in both normoxic and hypoxic culture conditions. In summary, our results demonstrated that hypoxia downregulates HIF-1α, increases dynamin2 expression, and facilitates Kir2.1 surface expression, resulting in hyperpolarization of membrane potential and subsequent increase in Ca2+ influx in BCECs.
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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) | 0.001 | 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".