IFN-γ is reciprocally involved in the concurrent development of organ-specific autoimmunity in the liver and stomach
Bibliographic record
Abstract
Interferon (IFN)-γ acts as a critical proinflammatory mediator in autoimmune processes, whereas it exerts regulatory functions to limit tissue damage associated with inflammation. However, a detailed understanding of the complex roles of IFN-γ in the development of organ-specific autoimmunity is still lacking. Recently, we found that programmed cell death 1-deficient mice thymectomized 3 days after birth (NTx-PD-1(-/-) mice) concurrently developed autoimmune hepatitis (AIH) and autoimmune gastritis (AIG). In this study, we investigated the roles of IFN-γ in the development of AIH and AIG in this mouse model. In NTx-PD-1(-/-) mice, serum levels of IFN-γ were markedly elevated. Neutralization of IFN-γ prevented the development of AIG. However, the same treatment exacerbated hepatic T-cell infiltration in AIH. Because of the loss of anti-proliferative effects by IFN-γ, neutralization of IFN-γ increased T-cell proliferation in the spleen and liver, resulting in exacerbated T-cell infiltration in the liver. On the other hand, in the development of AIG, CD4+ T-cell migration into the gastric mucosa is essential for induction. CCL20 expression was up-regulated in the gastric mucosa, and anti-CCL20 suppressed CD4+ T-cell infiltration into the gastric mucosa. Importantly, anti-IFN-γ suppressed CCL20 expression and infiltration of CD4+ T cells in the gastric mucosa, whereas in vivo injection of recombinant IFN-γ up-regulated CCL20 expression in the stomach, suggesting that IFN-γ is critically involved in CD4+ T-cell accumulation in AIG by up-regulating local CCL20 expression. In conclusion, IFN-γ is involved differently in the development of AIH and of AIG. IFN-γ negatively regulates T-cell proliferation in fatal AIH, whereas it initiates development of AIG. These findings imply that increased production of IFN-γ induced by an organ-specific autoimmunity may trigger the concurrent development of another organ-specific autoimmune disease.
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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) | 0.000 | 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".