Kidney dendritic cell activation is required for progression of renal disease in a mouse model of glomerular injury
Bibliographic record
Abstract
The progression of kidney disease to renal failure correlates with infiltration of mononuclear immune cells into the tubulointerstitium. These infiltrates contain macrophages, DCs, and T cells, but the role of each cell type in disease progression is unclear. To investigate the underlying immune mechanisms, we generated transgenic mice that selectively expressed the model antigens ovalbumin and hen egg lysozyme in glomerular podocytes (NOH mice). Coinjection of ovalbumin-specific transgenic CD8+ CTLs and CD4+ Th cells into NOH mice resulted in periglomerular mononuclear infiltrates and inflammation of parietal epithelial cells, similar to lesions frequently observed in human chronic glomerulonephritis. Repetitive T cell injections aggravated infiltration and caused progression to structural and functional kidney damage after 4 weeks. Mechanistic analysis revealed that DCs in renal lymph nodes constitutively cross-presented ovalbumin and activated CTLs. These CTLs released further ovalbumin for CTL activation in the lymph nodes and for simultaneous presentation to Th cells by distinct DC subsets residing in the kidney tubulointerstitium. Crosstalk between tubulointerstitial DCs and Th cells resulted in intrarenal cytokine and chemokine production and in recruitment of more CTLs, monocyte-derived DCs, and macrophages. The importance of DCs was established by the fact that DC depletion rapidly resolved established kidney immunopathology. These findings demonstrate that glomerular antigen-specific CTLs and Th cells can jointly induce renal immunopathology and identify kidney DCs as a mechanistic link between glomerular injury and the progression of kidney disease.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".