Aurora Kinase A Is Involved in Controlling the Localization of Aquaporin-2 in Renal Principal Cells
Bibliographic record
Abstract
The cAMP-dependent aquaporin-2 (AQP2) redistribution from intracellular vesicles into the plasma membrane of renal collecting duct principal cells induces water reabsorption and fine-tunes body water homeostasis. However, the mechanisms controlling the localization of AQP2 are not understood in detail. Using immortalized mouse medullary collecting duct (MCD4) and primary rat inner medullary collecting duct (IMCD) cells as model systems, we here discovered a key regulatory role of Aurora kinase A (AURKA) in the control of AQP2. The AURKA-selective inhibitor Aurora-A inhibitor I and novel derivatives as well as a structurally different inhibitor, Alisertib, prevented the cAMP-induced redistribution of AQP2. Aurora-A inhibitor I led to a depolymerization of actin stress fibers, which serve as tracks for the translocation of AQP2-bearing vesicles to the plasma membrane. The phosphorylation of cofilin-1 (CFL1) inactivates the actin-depolymerizing function of CFL1. Aurora-A inhibitor I decreased the CFL1 phosphorylation, accounting for the removal of the actin stress fibers and the inhibition of the redistribution of AQP2. Surprisingly, Alisertib caused an increase in actin stress fibers and did not affect CFL1 phosphorylation, indicating that AURKA exerts its control over AQP2 through different mechanisms. An involvement of AURKA and CFL1 in the control of the localization of AQP2 was hitherto unknown.
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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".