T cell-selective deletion of Oct1 protects animals from autoimmune neuroinflammation while maintaining neurotropic pathogen response
Bibliographic record
Abstract
BACKGROUND: T cells, the transcription factor Oct1/Pou2f1 is a dispensable transcription factor for T cell development and response to primary infection, but promotes expression of target genes, including Il2 and Ifng, under conditions of antigen reencounter. As a result, they are more strongly expressed upon secondary stimulation. Such repeated antigen encounters occur in memory recall responses, in autoimmunity where self-antigen can be recognized multiple times, and in chronic infection where foreign antigen is persistent. Based on these previous findings, we hypothesized that Oct1 loss would protect animals from autoimmunity but maintain normal responses to pathogens in the CNS. OBJECTIVE: We used a conditional mouse Oct1 (Pou2f1) allele and a CD4-Cre driver to determine the effect of T cell-specific Oct1 loss on autoimmune- and viral-induced neuroinflammation using an autoantigen-driven EAE model of autoimmunity and a JHMV model of viral infection. RESULTS: T cells stimulated in vitro showed increased expression of markers associated with T cell anergy, particularly in the absence of co-stimulatory signals. In contrast, anti-viral T cell effector functions are intact in the absence of Oct1, with no changes in neuroinflammation, infiltrating T cells or cytokine production. CONCLUSION: Our findings uncover a significant difference between the effect of Oct1 loss on autoimmune and anti-pathogen responses, which potentially could be exploited for therapeutic benefit.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".