Toso controls encephalitogenic immune responses by dendritic cells and regulatory T cells
Bibliographic record
Abstract
The ability to mount a strong immune response against pathogens is crucial for mammalian survival. However, excessive and uncontrolled immune reactions can lead to autoimmunity. Unraveling how the reactive versus tolerogenic state is controlled might point toward novel therapeutic strategies to treat autoimmune diseases. The surface receptor Toso/Faim3 has been linked to apoptosis, IgM binding, and innate immune responses. In this study, we used Toso-deficient mice to investigate the importance of Toso in tolerance and autoimmunity. We found that Toso(-/-) mice do not develop severe experimental autoimmune encephalomyelitis (EAE), a mouse model for the human disease multiple sclerosis. Toso(-/-) dendritic cells were less sensitive to Toll-like receptor stimulation and induced significantly lower levels of disease-associated inflammatory T-cell responses. Consistent with this observation, the transfer of Toso(-/-) dendritic cells did not induce autoimmune diabetes, indicating their tolerogenic potential. In Toso(-/-) mice subjected to EAE induction, we found increased numbers of regulatory T cells and decreased encephalitogenic cellular infiltrates in the brain. Finally, inhibition of Toso activity in vivo at either an early or late stage of EAE induction prevented further disease progression. Taken together, our data identify Toso as a unique regulator of inflammatory autoimmune responses and an attractive target for therapeutic intervention.
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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 | 0.000 |
| 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".