Specific T Regulatory Cells Display Broad Suppressive Functions against Experimental Allergic Encephalomyelitis upon Activation with Cognate Antigen
Bibliographic record
Abstract
To date, very few Ag-based regimens have been defined that could expand T regulatory (Treg) cells to reverse autoimmunity. Additional understanding of Treg function with respect to specificity and broad suppression should help overcome these limitations. Ig-proteolipid protein (PLP)1, an Ig carrying a PLP1 peptide corresponding to amino acid residues 139-151 of PLP, displayed potent tolerogenic functions and proved effective against experimental allergic encephalomyelitis (EAE). In this study, we took advantage of the Ig-PLP1 system and the PLP1-specific TCR transgenic 5B6 mouse to define a regimen that could expand Ag-specific Treg cells in vivo and tested for effectiveness against autoimmunity involving diverse T cell specificities. The findings indicate that in vivo exposure to aggregated Ig-PLP1 drives PLP1-specific 5B6 TCR transgenic cells to evolve as Treg cells expressing CD25, CTLA-4, and Foxp3 and producing IL-10. These Treg cells were able to suppress PLP1 peptide-induced EAE in both SJL/J and F(1) (SJL/J x C57BL/6) mice. However, despite being effective against disease induced with a CNS homogenate, the Treg cells were unable to counter EAE induced by a myelin basic protein or a myelin oligodendrocyte glycoprotein peptide. Nevertheless, activation with Ag before transfer into the host mice supports suppression of both myelin oligodendrocyte glycoprotein- and myelin basic protein peptide-induced EAE. Thus, it is suggested that activation of Treg cells by the cognate autoantigen is necessary for operation of broad suppressive functions.
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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 | 0.001 |
| 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".