Plasmacytoid dendritic cells modulate the production of mucosal IgA in IgA nephropathy
Bibliographic record
Abstract
BACKGROUND AND HYPOTHESIS: Aberrantly glycosylated immunoglobulin A (IgA) plays a central role in the pathogenesis of IgA nephropathy (IgAN). The activation of Toll-like receptor 9 (TLR9) has been shown to induce aberrant glycosylation of IgA via the a proliferation-inducing ligand (APRIL)-mediated pathway. TLR9 is known to be highly expressed in B cells and plasmacytoid dendritic cells (pDCs). Although the stimulation of TLR9 in B cells has been reported to promote the production of aberrantly glycosylated IgA, the effects of TLR9 stimulation on pDCs remain unclear. Therefore, in this study, we focused on the role of mucosal pDCs in the synthesis of aberrantly glycosylated IgA in patients with IgAN. METHODS: We evaluated the distribution of the DC subsets in tonsillar mononuclear cells (MNCs). The synthesis of aberrantly glycosylated IgA in MNCs cultured with or without pDCs was analyzed. We also evaluated the effects of pDC depletion on the production of aberrantly glycosylated IgA in ddY mice, a spontaneous murine model of IgAN, nasally immunized with CpG-oligonucleotide, a ligand for TLR9. RESULTS: The percentage of DCs, especially pDCs, was significantly higher in the palatine tonsils of patients with IgAN than in those of patients with chronic tonsillitis. In patients with IgAN, the abundance of pDCs was significantly correlated with the APRIL and TLR9 expressions in tonsillar MNCs. The levels of aberrantly glycosylated IgA in culture supernatants of tonsillar MNCs were found to be increased in the presence of pDCs. The pDC-depleted ddY mice exhibited significantly lower serum levels of aberrantly glycosylated IgA in vivo. CONCLUSION: Mucosal pDCs contribute to the pathogenesis of IgAN by facilitating the production of aberrantly glycosylated IgA via TLR9 signaling. Our findings demonstrated that pDC may be a novel therapeutic target for IgAN.
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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) | 0.001 | 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".