Human and Mouse Eosinophils Differ in Their Ability to Biosynthesize Eicosanoids, Docosanoids, the Endocannabinoid 2-Arachidonoyl-glycerol and Its Congeners
Bibliographic record
Abstract
High eosinophil (EOS) counts are a key feature of eosinophilic asthma. EOS notably affect asthmatic response by generating several lipid mediators. Mice have been utilized in hopes of defining new pharmacological targets to treat asthma. However, many pinpointed targets in mice did not translate into clinics, underscoring that key differences exist between the two species. In this study, we compared the ability of human (h) and mouse (m) EOS to biosynthesize key bioactive lipids derived from arachidonic acid (AA), eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA). hEOS were isolated from the blood of healthy subjects and mild asthmatics, while mEOSs were differentiated from the bone marrow. EOSs were treated with fatty acids and lipid mediator biosynthesis assessed by LC-MS/MS. We found that hEOS biosynthesized leukotriene (LT) C4 and LTB4 in a 5:1 ratio while mEOS almost exclusively biosynthesized LTB4. The biosynthesis of the 15-lipoxygenase (LO) metabolites 15-HETE and 12-HETE also differed, with a 15-HETE:12-HETE ratio of 6.3 for hEOS and 0.727 for mEOS. EOS biosynthesized some specialized pro-resolving mediators, and the levels from mEOS were 9-times higher than those of hEOS. In contrast, hEOS produced important amounts of the endocannabinoid 2-arachidonoyl-glycerol (2-AG) and its congeners from EPA and DHA, a biosynthetic pathway that was up to ~100-fold less prominent in mEOS. Our data show that hEOS and mEOS biosynthesize the same lipid mediators but in different amounts. Compared to asthmatics, mouse models likely have an amplified involvement of LTB4 and specialized pro-resolving mediators and a diminished impact of the endocannabinoid 2-arachidonoyl-glycerol and its congeners.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".