S-Nitrosylation of α1-Antitrypsin Triggers Macrophages Toward Inflammatory Phenotype and Enhances Intra-Cellular Bacteria Elimination
Bibliographic record
Abstract
Background. Human α1-antitrypsin (hAAT) is a circulating anti-inflammatory serine-protease inhibitor that rises during acute phase responses. In vivo, hAAT reduces bacterial load, without directly inhibiting bacterial growth. In conditions of excess nitric-oxide (NO), hAAT undergoes S-nitrosylation (S-NO-hAAT) and gains antibacterial capacity. The impact of S-NO-hAAT on immune cells has yet to be explored. Aim. Study the effects of S-NO-hAAT on immune cells during bacterial infection. Methods. Clinical-grade hAAT was S-nitrosylated and then compared to unmodified hAAT, functionally and structurally. Intracellular bacterial clearance by THP-1 macrophages was assessed using live Salmonella typhi. Murine peritoneal macrophages were examined, and signaling pathways were evaluated. S-NO-hAAT was also investigated after blocking free mambranal cysteine residues on cells. Results. S-NO-hAAT (27.5 uM) enhances intracellular bacteria elimination by immunocytes (up to 1-log reduction). S-NO-hAAT causes resting macrophages to exhibit a pro-inflammatory and antibacterial phenotype, including release of inflammatory cytokines and induction of iNOS and TLR2. These pro-inflammatory effects are dependent upon cell surface thiols and activation of MAPK pathways. Conclusions. hAAT duality appears to be context-specific, involving S-nitrosylation in a nitric oxide rich environment. Our results suggest that S-nitrosylation facilitates the antibacterial activity of hAAT by promoting its ability to activate innate immune cells. This pro-inflammatory effect may involve transferring of nitric oxide from S-NO-hAAT to a free cysteine residue on cellular targets.
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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".