High Levels of Glucose Stimulate Angiotensinogen Gene Expression Via the P38 Mitogen-Activated Protein Kinase Pathway in Rat Kidney Proximal Tubular Cells
Bibliographic record
Abstract
The present studies investigated whether the effect of high levels of glucose on angiotensinogen (ANG) secretion and gene expression in kidney proximal tubular cells is mediated at least in part via the activation of p38 mitogen-activated protein kinase (p38 MAPK). Rat immortalized renal proximal tubular cells (IRPTCs) were cultured in monolayer. The levels of immunoreactive rat ANG (IR-rANG) secreted into the medium and the levels of cellular ANG messenger RNA were determined by a specific RIA for rat ANG and a RT-PCR assay, respectively. Phosphorylation of cellular p38 MAPK was determined by Western blot analysis using the Phospho Plus p38 MAPK antibody kit. High levels of glucose (i.e. 25 mm) and phorbol 12-myristate 13-acetate (PMA; 10−7m) increased the secretion of IR-rANG and cellular ANG messenger RNA as well as phosphorylation of p38 MAPK in IRPTCs. This stimulatory effect of high levels of glucose and PMA was blocked by SB 203580 (a specific inhibitor of p38 MAPK), but not by SB 202474 (a negative control of SB 203580). High levels of d-sorbitol or 2-deoxy-d-glucose (i.e. ≥35 mm) also stimulated the phosphorylation of p38 MAPK, but did not stimulate ANG secretion or gene expression. GF 109203X (an inhibitor of protein kinase C) blocked the stimulatory effect of high levels of glucose and PMA on ANG gene expression, whereas it did not block the effect of high levels of glucose, sorbitol, or 2-deoxy-d-glucose on p38 MAPK phosphorylation in IRPTCs. These studies demonstrate that the stimulatory effect of a high level of glucose (25 mm) on ANG gene expression in IRPTCS may be mediated at least in part via activation of p38 MAPK signal transduction pathway and is protein kinase C independent.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.002 | 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".