ANGIOTENSIN II INCREASES FORMATION OF NEUTROPHIL EXTRACELLULAR TRAPS: ROLE IN HYPERTENSION AND DIABETES
Bibliographic record
Abstract
Objective: Endothelial dysfunction plays a central mechanistic role in the pathogenesis of hypertension and diabetes. However, the molecular determinants of endothelial dysfunction are not fully understood. Peptidylarginine deiminases (PADs) irreversibly convert arginine to citrulline in protein, generating neoantigens that are unrecognizable to the immune system. PAD4, an isoform predominantly expressed in neutrophils is upregulated in autoimmune and coronary artery disease. PAD4 activation, in turn, promotes vascular injury through the formation of neutrophil extracellular traps (NETs), networks of chromatin and protein-containing extracellular fibres released to fight infection. Whether PAD4 activity is altered in hypertension and diabetes and whether this contributes to disease pathogenesis is not known. The objective of the present study was to examine the effect of angiotensin II on formation of neutrophil extracellular traps in vitro and in an animal model of hypertension and diabetes. Design and method: Neutrophil-like cells were generated from differentiation of the HL-60 cell line and then treated with angiotensin II (10-7 M) for 24 hours. NET formation was assessed by Western blot analysis of citrullinated histone H3 (a primary component of NETs). We further assessed NET formation in mice expressing human renin under the control of the transthyretin promoter (TTRhRen) intercrossed with OVE26 diabetic mice [i.e. hypertensive-diabetic, HD mice] and compared with wild-type (WT) FVBN/J mice. We examined levels of citrullinated histone H3, neutrophil elastase, and PAD4 expression in the kidney of HD and WT mice by immunohistochemistry. Results: Treatment of neutrophil-like cells with angiotensin-II increased levels of citrullinated histone H3 by ∼2-fold. Similarly, in HD mice we observed a significant increase in citrullinated histone H3 (P < 0.0001), in neutrophil elastase (P < 0.0001), and in PAD4 protein levels (P < 0.0001) compared with WT. Increased PAD4 activity was associated with the presence of albuminuria, renal interstitial fibrosis and reduced glomerular filtration rate. Conclusions: Taken together these data suggest that angiotensin II induces NET formation leading to renal accumulation in hypertension and diabetes. PAD4 may therefore play a role in target organ injury in hypertension and diabetes.
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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 | 0.001 |
| 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".