Transthyretin Aggregates Induce Production of Reactive Nitrogen Species
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Misfolded and aggregated transthyretins (agTTR) contribute to neurodegenerative amyloid diseases such as familial amyloid polyneuropathy and senile systemic amyloidosis. The neurotoxicity mechanisms of agTTR, however, are not well understood. In the current study, the possible contribution of reactive nitrogen species (RNS) to such mechanisms was investigated by examining agTTR-mediated changes in cellular RNS levels. METHODS AND RESULTS: The production of RNS was assessed through nitrate and nitrite assays in two human cell lines after exposure to agTTR (2.4 µM pre-aggregation concentration). In both epidermoid (A431) and schwannoma (sNF94.3) cell lines, agTTR induced significant increases in RNS (p < 0.05 relative to the same concentration of normal TTR, or no-TTR controls). Redox modulators such as apocynin (1-(4-hydroxy-3-methoxy-phenyl)ethanone) and L-NMMA (N(G)-monomethyl-L-arginine) were tested for their effects on RNS production. These modulators decreased RNS production in both cell lines; although the effects of L-NMMA were statistically significant only in the schwannoma cells. Moreover, cells treated with agTTR exhibited decreases in metabolic activity relative to TTR- or non-TTR-treated cells (p < 0.05) as assessed by reduction of MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide). CONCLUSION: The results provide novel evidence for involvement of RNS in pro-oxidative effects of agTTR in two different human cell lines, and show that agTTR can induce more generalized changes in cellular metabolic activity.
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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 | 0.001 | 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".