Interleukin‐21 Receptor Blockade Inhibits Secondary Humoral Responses and Halts the Progression of Preestablished Disease in the (NZB × NZW)F1 Systemic Lupus Erythematosus Model
Bibliographic record
Abstract
OBJECTIVE: Systemic lupus erythematosus (SLE) is a complex autoimmune disease that is driven in part by chronic B and T lymphocyte hyperresponsiveness to self antigens. A deficiency of interleukin-21 (IL-21) or IL-21 receptor (IL-21R) in mice dramatically reduces inflammation and B and T cell activation in models of autoimmunity, including SLE. However, whether IL-21 is essential for the maintenance and amplification of preestablished inflammation has not been widely examined in various animal models. The purpose of this study was to examine the impact of novel mouse IL-21R neutralizing antibodies on recall responses to antigen challenge and on disease progression in the (NZB × NZW)F1 (NZB/NZW) mouse model of SLE. METHODS: Humoral and cellular immune responses to immunization with sheep red blood cells (SRBCs) were measured in mice dosed with IL-21R blocking antibodies. Progression of nephritis and markers of immune activation was monitored in NZB/NZW mice following different anti-IL-21R treatment regimens. RESULTS: IL-21R blockade specifically inhibited secondary IgG responses to SRBC immunization. In NZB/NZW mice, IL-21R blockade completely inhibited the onset of nephritis, which was associated with dramatic reductions in splenomegaly and in B cell and T cell activation. When administered to mice with preexisting disease, anti-IL-21R antibody halted the disease progression and mortality and reversed the nephritis in a subset of mice. Furthermore, treatment cessation was not followed by rapid reemergence of disease. CONCLUSION: Our results highlight the importance of IL-21 in promoting humoral recall responses and in sustaining autoimmune inflammation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 0.002 |
| 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".