CD161 is a marker of all human IL‐17‐producing T‐cell subsets and is induced by RORC
Bibliographic record
Abstract
We have previously shown that human Th17 lymphocytes are characterized by the selective expression of IL-23 receptor (IL-23R), CCR6, CD161, and the transcription factor retinoic acid-related orphan receptor C (RORC), and originate from a CD161(+)CD4(+) naïve T-cell precursor in response to the combined activity of IL-1β and IL-23. We show here that not only CD4(+)TCRαβ(+), but also CD8(+)TCRαβ(+), CD4(-)CD8(-) TCRαβ(+), and CD4(-)CD8(-) TCRγδ(+) circulating lymphocytes that produce IL-17 express the distinctive marker CD161 on their surface. In addition, we demonstrate that CD161 expression identifies CD8(+) and CD4(-)CD8(-) umbilical cord blood T cells that already express RORC and IL-23R mRNA and that can be induced to differentiate into IL-17-producing cells in the presence of IL-1β and IL-23. Finally, we provide evidence that umbilical cord blood naïve CD4(+)CD161(-) T cells, upon lentivirus-mediated transduction with RORC2 can acquire the ability to express IL-23R, IL-1RI, and CD161, as well as to produce IL-17. Taken together, these data allow to conclude that T-cell subsets able to produce IL-17, as well as precursors of IL-17-producing T cells, exhibit surface expression of CD161, and that this feature is at least in part RORC2-dependent.
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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 | 0.001 | 0.001 |
| 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) | 0.008 | 0.002 |
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".