Neutrophil Transintestinal Epithelial Migration to CXCR2 Ligands Is Regulated by Adenosine
Bibliographic record
Abstract
OBJECTIVES: Polymorphonuclear leukocytes (PMN) feature prominently in the mucosa, including in crypt abscesses, of patients with inflammatory bowel disease, yet the mediators that are responsible for this migration are unknown. We discovered that CXCR2 chemokines (reportedly elevated in the mucosa) have reduced potency recruiting PMN across epithelial cell monolayers versus acellular filters, so the objective was to determine what molecules modify transepithelial PMN migration to CXCR2 chemokines. METHODS: Transwells with T84 colon carcinoma monolayers or no epithelium were used with adolescent patient peripheral blood PMN and CXCL8 (interleukin-8 [IL-8], binds CXCR1 and CXCR2), CXCL5 (epithelial-derived neutrophil chemoattractant-78 [ENA-78]), or CXCL1 (Gro-α, both bind CXCR2) as chemoattractants. RESULTS: IL-8 was equally potent at recruiting PMN across filters and T84 monolayers growing on the filters. In contrast, ENA-78 and Gro-α were significantly less potent at recruiting PMN across monolayers than across bare filters. Blocking CXCR1 reduced PMN migration across monolayers to IL-8. We ruled out superoxide radicals possibly enhancing migration to IL-8 by using PMN from a patient with chronic granulomatous disease. PMN constitutively produce adenosine, so we added adenosine deaminase to the transwell assays and observed increased migration to ENA-78 across T84 monolayers. The level of migration was further enhanced by pretreating PMN with adenosine before adding the cells to the assay in the presence of the deaminase. CONCLUSIONS: PMN migration mediated by CXCR2 through the epithelium is regulated by adenosine. Adenosine appears to reduce transepithelial migration by influencing β2 integrin use on the PMN.
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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) | 0.002 | 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".