Opposing Roles of Dendritic Cell Subsets in Experimental GN
Bibliographic record
Abstract
Dendritic cells (DCs) are thought to form a dendritic network across barrier surfaces and throughout organs, including the kidney, to perform an important sentinel function. However, previous studies of DC function used markers, such as CD11c or CX3CR1, that are not unique to DCs. Here, we evaluated the role of DCs in renal inflammation using a CD11c reporter mouse line and two mouse lines with DC-specific reporters, Zbtb46 -GFP and Snx22 -GFP. Multiphoton microscopy of kidney sections confirmed that most of the dendritically shaped CD11c + cells forming a network throughout the renal interstitium expressed macrophage-specific markers. In contrast, DCs marked by Zbtb46 -GFP or Snx22 -GFP were less abundant, concentrated around blood vessels, and round in shape. We confirmed this pattern of localization using imaging mass cytometry. Motility measurements showed that resident macrophages were sessile, whereas DCs were motile before and after inflammation. Although uninflamed glomeruli rarely contained DCs, injury with nephrotoxic antibodies resulted in accumulation of ZBTB46 + cells in the periglomerular region. ZBTB46 identifies all classic DCs, which can be categorized into two functional subsets that express either CD103 or CD11b. Depletion of ZBTB46 + cells attenuated the antibody-induced kidney injury, whereas deficiency of the CD103 + subset accelerated injury through a mechanism that involved increased neutrophil infiltration. RNA sequencing 7 days after nephrotoxic antibody injection showed that CD11b + DCs expressed the neutrophil-attracting cytokine CXCL2, whereas CD103 + DCs expressed high levels of several anti-inflammatory genes. These results provide new insights into the distinct functions of the two major DC subsets in glomerular inflammation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".