Elevated Levels of Interferon‐α Act Directly on B Cells to Breach Multiple Tolerance Mechanisms Promoting Autoantibody Production
Bibliographic record
Abstract
OBJECTIVE: Elevated levels of serum interferon-α (IFNα) and the disruption of B cell tolerance are central to systemic lupus erythematosus (SLE) immunopathogenesis; however, the relationship between these 2 processes remains unclear. The purpose of this study was to investigate the impact of elevated IFNα levels on B cell tolerance mechanisms in vivo and determine whether any changes observed were due to the direct effect of IFNα on B cells. METHODS: Two classical mouse models of B cell tolerance were used in conjunction with an adenoviral vector encoding IFNα to mimic the sustained elevations of IFNα seen in SLE. The role of B cell IFNα signaling, T cells, and Myd88 signaling was determined using B cell-specific IFNα receptor-knockout, CD4+ T cell-depleted, or Myd88-knockout mice, respectively. Flow cytometry, enzyme-linked immunosorbent assay, real-time quantitative polymerase chain reaction, and cell cultures were used to study the effects of elevated IFNα on the immunologic phenotype. RESULTS: Elevation of serum IFNα disrupts multiple B cell tolerance mechanisms and leads to autoantibody production. This disruption was dependent upon B cell expression of IFNα receptor. Many of the IFNα-mediated alterations also required the presence of CD4+ T cells as well as Myd88, suggesting that IFNα acts directly on B cells to modify their response to Myd88 signaling and their ability to interact with T cells. CONCLUSION: The results provide evidence that elevated IFNα levels act directly on B cells to facilitate autoantibody production and further highlight the importance of IFN signaling as a potential therapeutic target in SLE.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.002 | 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".