PTP1B negatively regulates <i>Pseudomonas aeruginosa</i> killing by neutrophil through TLR4-STAT1-iNOS signaling
Bibliographic record
Abstract
Abstract Pseudomonas aeruginosa is a major opportunistic pathogen in immune-compromised individuals and chronic obstructive pulmonary diseases (COPD) patients. Mechanisms involved in immune responses to P. aeruginosa infection and bacterial clearance remain incompletely defined. Here we demonstrate that protein tyrosine phosphatase-1B (PTP1B) is a critical negative regulator in P. aeruginosa killing by neutrophil. PTP1B-deficient neutrophil display greatly enhanced bacterial killing capability and TLR4 transcription following P. aeruginosa infection. The negative regulation of PTP1B on the production of nitric oxide by neutrophil is blocked by TLR4 antagonist. Furthermore, the STAT1 activation in neutrophils following P. aeruginosa infection occurs in the downstream of TLR4, and PTP1B deficiency leads to enhanced TLR4 dependent STAT1 activation and iNOS expression by neutrophil. Interestingly, PTP1B deficiency mainly up-regulates the production of pro-inflammatory cytokines by neutrophil, including IL-6, IL-1β and TNFα, but only IL-6 is blocked by TLR4 antagonist. Further studies reveal that PTP1B and STAT1 are physically associated. These findings demonstrate a novel regulatory mechanism of the immune response to P. aeruginosa infection through PTP1B-STAT1 interaction. This novel PTP1B-STAT1-iNOS pathway may have broader implications in Toll-like receptor mediated innate immunity.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".