IL-17A Damages the Blood–Retinal Barrier through Activating the Janus Kinase 1 Pathway
Bibliographic record
Abstract
Blood-retinal barrier (BRB) dysfunction underlies macular oedema in many sight-threatening conditions, including diabetic macular oedema, neovascular age-related macular degeneration and uveoretinitis. Inflammation plays an important role in BRB dysfunction. This study aimed to understand the role of the inflammatory cytokine IL-17A in BRB dysfunction and the mechanism involved. Human retinal pigment epithelial (RPE) cell line ARPE19 and murine brain endothelial line bEnd.3 were cultured on transwell membranes to model the outer BRB and inner BRB, respectively. IL-17A treatment (3 days in bEnd.3 cells and 6 days in ARPE19 cells) disrupted the distribution of claudin-5 in bEnd.3 cells and ZO-1 in ARPE19 cells, reduced the transepithelial/transendothelial electrical resistance (TEER) and increased permeability to FITC-tracers in vitro. Intravitreal (20 ng/1 μL/eye) or intravenous (20 ng/g) injection of recombinant IL-17A induced retinal albumin leakage within 48 h in C57BL/6J mice. Mechanistically, IL-17A induced Janus kinase 1 (JAK1) phosphorylation in bEnd.3 but not ARPE19 cells. Blocking JAK1 with Tofacitinib prevented IL-17A-mediated claudin-5 dysmorphia in bEnd.3 cells and reduced albumin leakage in IL-17A-treated mice. Our results suggest that IL-17A can damage the BRB through the activating the JAK1 signaling pathway, and targeting this pathway may be a novel approach to treat inflammation-induced macular oedema.
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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 | 0.001 |
| 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".