Azithromycin targets the CD27 pathway to modulate CD27hi T-lymphocyte expansion and type-1 effector phenotype
Bibliographic record
Abstract
Macrolide antibiotic azithromycin is widely used in clinical practice to treat respiratory tract infections and inflammatory diseases. However, its mechanism of action is not fully understood. Given the involvement of the CD27 pathway in the pathophysiology of various T-lymphocyte-mediated inflammatory, autoimmune, and lymphoproliferative diseases, we examined the impact of AZM on CD27 regulation and potential consequences on CD4+ and CD8+ T-cell phenotypes. Using cellular immunology approaches on healthy donors' peripheral blood mononuclear cells, we demonstrate AZM-mediated downregulation of surface CD27 expression as well as its extracellular release as soluble CD27. Notably, AZM-exposed CD27high (hi) cells were defective in their ability to expand compared to CD27intermediate (Int) and CD27low (lo) subsets. The defective CD27hi subset expansion was found to be associated with impaired cell proliferation and cell division. At the molecular level, the CD27hi subset exhibited lower mTOR activity than other subsets. Functionally, AZM treatment resulted in marked depletion of helper CD4+ (Th1) and cytotoxic CD8+ T-lymphocyte (Tc1)-associated CXCR3+CD27hi effector cells and inhibition of inflammatory cytokine IFN-γ production. These findings provide mechanistic insights on immunomodulatory features of AZM on T-lymphocyte by altering the CD27 pathway. From a clinical perspective, this study also sheds light on potential clinical benefits observed in patients on prophylactic AZM regimens against various respiratory diseases and opens avenues for future adjunct therapy against Th1- and Tc1-dominated inflammatory and autoimmune diseases.
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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".