17β-Estradiol Modulates Mechanical Strain-induced MAPK Activation in Mesangial Cells
Bibliographic record
Abstract
Gender is an important determinant of clinical outcome across a broad spectrum of kidney diseases, but the mechanism(s) responsible for the protective effect of female gender have not been fully elucidated. Remnant kidney glomerular injury is limited in female rats compared with male rats despite similar elevations in glomerular capillary pressure. In vitro, mechanical strain leads to the activation of p44/42 mitogen-activated kinase (p44/42 MAPK) and Jun N-terminal kinase/stress-activated protein kinase (SAPK) in glomerular mesangial cells (MC). Accordingly, we studied the effect of 17beta-estradiol on mechanical strain-induced signal transduction in MC. Exposure of MC to mechanical strain increased p44/42 MAPK activation (3-fold) and SAPK activation (2.5-fold), and kinase activation was inhibited by pretreatment with 17beta-estradiol (10(minus sign8) to 10(minus sign11) m) for 24 h in a dose-dependent manner. Mechanical strain-induced nuclear translocation of p44/42 MAPK and SAPK and nuclear protein binding to AP-1 were also attenuated by 17beta-estradiol. The inhibitory effects of 17beta-estradiol were not reproduced by the cell-impermeable estrogen, BSA/17beta-estradiol, nor did preincubation with 17beta-estradiol lead to actin cytoskeleton disassembly or impaired stress fiber formation. However, 17beta-estradiol did increase base-line levels of the dual specificity phosphatase MKP-1. The inhibitory effects of 17beta-estradiol on p44/42 MAPK activation and SAPK activation, translocation, and AP-1 binding were all abrogated by the estrogen receptor antagonist, ICI-182,780. We conclude that attenuation of mechanical strain-induced MAPK activation by 17beta-estradiol is dependent on intracellular estrogen receptor. The attenuation of stretch-induced kinase activation may be due, at least in part, to an effect of 17beta-estradiol on MKP-1 expression. Together, these findings add insight into the protective effect of gender on renal disease progression.
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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".