<scp>CCL19</scp>‐Positive Lymph Node Stromal Cells Govern the Onset of Inflammatory Arthritis via Tropomyosin Receptor Kinase
Bibliographic record
Abstract
Objective The study objective was to assess the role of CCL19+ lymph node stromal cells of the joint‐draining popliteal lymph node (pLN) for the development of arthritis. Methods CCL19+ lymph node stromal cells were spatiotemporally depleted for five days in the pLN before the onset of collagen‐induced arthritis (CIA) using Ccl19‐Cre × iDTR mice. In addition, therapeutic treatment with recombinant CCL19–immunoglobulin G (IgG), locally injected in the footpad, was used to confirm the results. RNA sequencing of lymph node stromal cells combined with T cell coculture assays using tropomyosin receptor kinase (Trk) family inhibitors together with in vivo local pLN small interfering RNA (siRNA) treatments were used to elucidate the pathway by which CCL19+ lymph node stromal cells initiate the onset of arthritis. Results Spatiotemporal depletion of CCL19+ lymph node stromal cells prevented disease onset in CIA mice. These inhibitory effects could be mimicked by local CCL19‐IgG treatment. The messenger RNA sequencing analyses showed that CCL19+ lymph node stromal cells down‐regulated the expression of the tropomyosin receptor kinase A (TrkA) just before disease onset. Blocking TrkA in lymph node stromal cells led to increased T cell proliferation in in vitro coculture assays. Similar effects were observed with the pan‐Trk inhibitor larotrectinib in cocultures of lymph node stromal cells of patients with rheumatoid arthritis and T cells. Finally, local pLN treatment with TrkA inhibitor and TrkA siRNA led to exacerbated arthritis scores. Conclusion CCL19+ lymph node stromal cells are crucially involved in the development of inflammatory arthritis. Therefore, targeting of CCL19+ lymph node stromal cells via TRK could provide a tool to prevent arthritis.
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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) | 0.003 | 0.001 |
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".