NOX5 Promotes Diabetic Kidney Disease by Modulating Redox-Sensitive Pathways
Bibliographic record
Abstract
Background: Enhanced level of reactive oxygen species (ROS) in diabetes is considered a major contributor in aggravating renal injury. We aimed to examine the the role of pro-oxidant enzyme NOX5 and associated redox-sensitive pathways in diabetic kidney disease (DKD). Methods: We examined the expression of NOX5 and associated redox-sensitive factors including NOX4, thioredoxin-interacting protein (TXNIP), a transcription factor, EGR1 (early growth response 1) and a protein kinase, PKC-α as well as ROS production in human kidney biopsies and in human renal cell lines as well as in human kidney organoids. We also assessed the effect of NOX5 expression independent of NOX4 in Nox5 transgenic mice in the presence or absence of diabetes. Results: We identified increased expression of renal NOX5 in diabetic patients in association with upregulation of ROS-sensitive factors including EGR-1, PKC-α and TXNIP. We also observed upregulation of human NOX5 and TXNIP in renal organoids exposed to high glucose. Silencing of Nox5 attenuated high glucose induced gene expression of markers of fibrosis and inflammation as well as downregulation of EGR-1, PKC-α and TXNIP. Our data also suggest that Nox5 is upstream of Nox4 and that Nox5 inhibition also downregulates Nox4, but not vice versa. In vivo, overexpression of Nox5 independent of NOX4 pathways demonstrated an increase in albuminuria, renal fibrosis and inflammation in association with upregulation of EGR-1, PKC-α and TXNIP and enhanced ROS production in comparison to diabetic mice not expressing Nox5. Conclusions: These findings suggest that NOX5 plays a key pathogenic role in renal inflammation and fibrosis, thereby providing impetus for the development of NOX5 specific inhibitor to combat DKD.
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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".