HEB plays a critical role in the installment of IL-17 program in fetal thymic γδ T cells (HEM2P.231)
Bibliographic record
Abstract
Abstract IL-17 producing γδ T cells that arise during fetal development form an integral part of the immune system of various lymphoid and mucosal tissues in mice. Here we aim to better understand the transcriptional networks that drive the functional programming of IL-17 producing γδ T cells. Specifically, we investigated the role of the transcription factor HEB in this process using mice that carry the HEB null allele. To evaluate the role of HEB in fetal γδ T cell development, we utilized fetal thymic organ culture from HEB+/+ and HEB-/- embryos at E14. We found that the absence of HEB did not have a significant impact on γδ T cell numbers, and that all functional subsets of γδ T cells were present as indicated by expression of CCR6, CD27, CD44 and CD62L. However, the fetal thymic γδ T cells from HEB knockout mice exhibited a profound deficiency in their ability to produce IL-17, especially in the CD44+ γδ T cell population. In addition, HEB-deficient fetal γδ T cells expressed significantly lower levels of SOX13 and RORγt, factors associated with IL-17 producing γδ T cells, while expression of IFNγ, Tbet and Egr3 were unchanged. Furthermore, HEB conditional knockout mice on a Vav-Cre background had significantly lower frequencies of IL-17 producing γδ T cells in the spleen, lymph nodes and lungs. Collectively, our work shows for the first time that HEB plays an important role in installing the IL-17 program in γδ T cells.
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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) | 0.005 | 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".