Abstract 277: Nox Isoforms, Oxidative Stress, Inflammation And Fibrosis In Adipose Tissue In Obese Mice: Differential Regulation In Epididymal, Perirenal And Perivascular Depots.
Bibliographic record
Abstract
Adipose tissue inflammation and fibrosis contribute to cardiovascular complications observed in obese patients. Mechanisms related to these deleterious effects are still elusive. Here, we evaluated the oxidative, fibrotic and inflammatory status of obese mice and the putative role of NADPH oxidases. Epididymal (EP), perivascular (PV) and peri-renal (PR) fat were collected from lean (db/m) and obese (db/db) mice. Gene expression was assessed by qPCR, fibrosis by picro Sirius red staining and polarized light microscopy, oxidative stress by Amplex Red (H2O2) and TBARS. In EP fat, white adipose tissue, increased levels of TBARS (56%), CD206 mRNA (macrophage marker - 106%) and fibrosis (300%) were observed in obese mice (p<0.05 vs lean). Similar findings were found in PR adipose tissue, where H2O2 (65%), TBARS (50%), and macrophage infiltration (F4/80 mRNA - 578%) levels were increased (p<0.05 vs lean). In PV fat, mix of white and brown adipose tissue, H2O2 (42%, p<0.05) levels, but not TBARS, were increased. Inflammatory marker mRNA levels such as, CD206 (120%), F4/80 (550%), TNFα (206%), iNOS (46%), as well as collagen 6a (marker of fibrosis), were increased in PV from obese animals (p<0.05 vs lean). Treatment of obese mice with GKT137831 (GKT, Nox1/4 inhibitor, 16 weeks) did not decrease inflammation/fibrosis in EV fat and increased mRNA levels of inflammatory markers in PR adipose depot. GKT decreased mRNA levels of pro-inflammatory markers, followed by an increase in mRNA levels of anti-inflammatory markers, in PV fat. Levels of Nox2 (EP: 1936%, PR: 336%, PV: 358%, p<0.05 vs lean), but not Nox1 or Nox4, were increased in all types of fat; an effect not changed by GKT treatment. In conclusion, oxidative stress is increased in fat from obese animals and may play a role in obesity-associated adipose tissue inflammation and fibrosis. Our data also suggests that Nox1/4 influence adipose inflammation and fibrosis in an adipose tissue-specific manner, with effects being pro-inflammatory in PR fat and anti-inflammatory in PV fat. These phenomena are associated with upregulation of Nox2, possibly due to increased Nox2-containing macrophages. Our findings highlight an important role for Nox isoforms in perivascular adipocyte tissue inflammation/fibrosis in obesity.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".