CD6 – a costimulatory receptor at the nexus of Teff / Treg development
Bibliographic record
Abstract
Abstract The balance of proinflammatory effector T cells (Teff) and anti-inflammatory regulatory T cells (Treg) is associated with autoimmune and inflammatory diseases. CD6 is a costimulatory receptor expressed on T cells with its greatest expression on pathogenic Teff cells, while Tregs are CD6low. Notably T cells from CD6knockout mice fail to differentiate to Th17 cells but were seen to skew to a Treg phenotype more rapidly. Although the role of CD6 in Teff development is well characterized, here we sought to investigate the role of CD6 in the development of Treg cells. Itolizumab, a humanized anti-CD6 monoclonal antibody that modulates cell surface levels of CD6, was used to generate CD6low naïve T cells, after which itolizumab was removed and Tregs were differentiated using a standard protocol. Tregs derived from CD6low cells had greater co-expression of FOXP3 and HELIOS (~2-fold) vs. Tregs derived from isotype-treated CD6high. These Tregs possessed greater suppressive function, with increased inhibition of proliferation and cytokine release by stimulated T responder cells by at least 50% more than Tregs derived from CD6high cells; this was verified in three different donors. Similar observations were made when measuring levels of cytokines. CD6low Tregs were able to suppress T responder’s production of proinflammatory cytokines by 60–90% compared to CD6highTregs. This is the first study to directly characterize the role of CD6 in the development and activity of Treg cells. These data suggest that reduced levels of cell surface CD6 facilitate the development of Treg cells with greater stability and suppressive activity and that modulating the levels of CD6 may decrease the Teff/Treg ratio in patients with autoimmune and inflammatory diseases.
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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) | 0.002 | 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".