Glycogen synthase kinase 3α regulates urine concentrating mechanism in mice
Bibliographic record
Abstract
In mammals, glycogen synthase kinase (GSK)3 comprises GSK3α and GSK3β isoforms. GSK3β has been shown to play a role in the ability of kidneys to concentrate urine by regulating vasopressin-mediated water permeability of collecting ducts, whereas the role of GSK3α has yet to be discerned. To investigate the role of GSK3α in urine concentration, we compared GSK3α knockout (GSK3αKO) mice with wild-type (WT) littermates. Under normal conditions, GSK3αKO mice had higher water intake and urine output. GSK3αKO mice also showed reduced urine osmolality and aquaporin-2 levels but higher urinary vasopressin. When water deprived, they failed to concentrate their urine to the same level as WT littermates. The addition of 1-desamino-8-d-arginine vasopressin to isolated inner medullary collecting ducts increased the cAMP response in WT mice, but this response was reduced in GSK3αKO mice, suggesting reduced responsiveness to vasopressin. Gene silencing of GSK3α in mpkCCD cells also reduced forskolin-induced aquaporin-2 expression. When treated with LiCl, an isoform nonselective inhibitor of GSK3 and known inducer of polyuria, WT mice developed significant polyuria within 6 days. However, in GSK3αKO mice, the polyuric response was markedly reduced. This study demonstrates, for the first time, that GSK3α could play a crucial role in renal urine concentration and suggest that GSK3α might be one of the initial targets of Li(+) in LiCl-induced nephrogenic diabetes insipidus.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".