B7-H7 (HHLA2) inhibits T-cell activation and proliferation in the presence of TCR and CD28 signaling
Bibliographic record
Abstract
Modulation of T-cell responses has played a key role in treating cancers and autoimmune diseases. Therefore, understanding how different receptors on T cells impact functional outcomes is crucial. The influence of B7-H7 (HHLA2) and CD28H (TMIGD2) on T-cell activation remains controversial. Here we examined global transcriptomic changes in human T cells induced by B7-H7. Stimulation through TCR with OKT3 and B7-H7 resulted in modest fold changes in the expression of select genes; however, these fold changes were significantly lower than those induced by OKT3 and B7-1 stimulation. The transcriptional changes induced by OKT3 and B7-H7 were insufficient to provide functional stimulation as measured by evaluating T-cell proliferation and cytokine production. Interestingly, B7-H7 was coinhibitory when simultaneously combined with TCR and CD28 stimulation. This inhibitory activity was comparable to that observed with PD-L1. Finally, in physiological assays using T cells and APCs, blockade of B7-H7 enhanced T-cell activation and proliferation, demonstrating that this ligand acts as a break signal. Our work defines that the transcriptomic changes induced by B7-H7 are insufficient to support full costimulation with TCR signaling and, instead, B7-H7 inhibits T-cell activation and proliferation in the presence of TCR and CD28 signaling.
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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".