MDSCs enhance Th17 differentiation and disease progression through arginase-1 production in patients with systemic lupus erythematosus
Bibliographic record
Abstract
Abstract Expansion of myeloid-derived suppressor cells (MDSCs) has been documented in some murine models and patients with autoimmune diseases, but the exact role of MDSCs in this process remains largely unknown. Although MDSCs are supposed to be immunosuppressive, previous studies have shown that these cells may also enhance Th17 responses via an IL-1-dependent mechanism in murine autoimmune models, such as experimental autoimmune encephalomyelitis (EAE) and collagen-induced arthritis (CIA). However, the role of MDSCs in the development of autoimmune responses in human remains largely unknown. Current study investigates this question in patients with systemic lupus erythematosus (SLE). We found that patients with active SLE (n=32) had a significant increase in HLA-DR−CD11b+CD33+ MDSCs, in the peripheral blood compared to healthy controls (HCs; n=25). The frequency of MDSCs was positively correlated with the level of serum arginase-1 (Arg-1) activity, Th17 responses and disease severity in SLE patients. Consistently, in comparison with MDSCs from HCs, MDSCs from SLE patients exhibited significantly elevated Arg-1 production, and increased potential to promote Th17 differentiation in vitro in an Arg-1-dependent manner that is likely mediated by multiple mechanisms involving RORgt, RORa and mTOR. Moreover, in a humanized SLE model, MDSCs were essential for the induction of Th17 responses and the associated renal injuries and the effect of MDSCs was Arg-1-dependent. Our data provide direct evidence demonstrating a pathogenic role for MDSCs in human SLE, and suggests that targeting MDSCs or Arg-1 may offer novel therapeutic strategies for the treatment of SLE and other Th17 cell-mediated autoimmune diseases.
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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 | 0.001 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".