A Potent Tartrate Resistant Acid Phosphatase Inhibitor to Study the Function of TRAP in Alveolar Macrophages
Bibliographic record
Abstract
Abstract The enzyme tartrate resistant acid phosphatase (TRAP, two isoforms 5a and 5b) is highly expressed in alveolar macrophages, but its function there is unclear and potent selective inhibitors of TRAP are required to assess functional aspects of the protein. We found higher TRAP activity/expression in lungs of patients with chronic obstructive pulmonary disease (COPD) and asthma compared to controls and more TRAP activity in lungs of mice with experimental COPD or asthma. Stimuli related to asthma and/or COPD were tested for their capacity to induce TRAP. Receptor activator of NF-κb ligand (RANKL) and Xanthine/Xanthine Oxidase induced TRAP mRNA expression in mouse macrophages, but only RANKL also induced TRAP activity in mouse lung slices. Several Au(III) coordination compounds were tested for their ability to inhibit TRAP activity and [Au(4,4′-dimethoxy-2,2′-bipyridine)Cl2][PF6] (AubipyOMe) was found to be the most potent inhibitor of TRAP5a and 5b activity reported to date (IC50 1.3 and 1.8 μM respectively). AubipyOMe also inhibited TRAP activity in murine macrophage and human lung tissue extracts. In a functional assay with physiological TRAP substrate osteopontin, AubipyOMe inhibited mouse macrophage migration over osteopontin-coated membranes. In conclusion, higher TRAP expression/activity are associated with COPD and asthma and TRAP is involved in regulating macrophage migration.
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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".