The effects of agonistic α-4-1BB antibody on natural killer cell function
Bibliographic record
Abstract
Abstract 4-1BB is a member of the Tumor Necrosis Factor Receptor (TNFR) family. The TNFR family regulates a variety of functions including immune response, inflammation, and cell life/death. 4-1BB is a co-stimulatory molecule known best for its ability to increase survival, proliferation, anti-viral/tumor effects on T cells, and thus is currently undergoing clinical trials. However, the effects of 4-1BB stimulation in other cell types, such as natural killer cells (NK), are not well studied. Using a murine cytomegalovirus (MCMV) infected mouse model, we investigated the effects of agonistic α-4-1BB antibody stimulation on NK cell function and its impact on viral resistance. We hypothesized that 4-1BB stimulation will induce robust activation and proliferation of NK cells, resulting in an increased resistance against MCMV. To the contrary, treatment of the α-4-1BB antibody increased viral burden on 4 days post infection (p.i.) with highly increased NK cell proportion in spleens and livers. Further studies showed higher viral burden on day 1.5 p.i. with a reduced NK cell frequency. This suggests that an uncontrolled viral replication due to low NK numbers on early days of MCMV infection allows rapid expansion of residual NK cells by day 4. In vitro, treatment with α-4-1BB along with α-FasL antibodies rescued NK cell reduction, indicating that the NK cell loss is induced by Fas-mediated death signaling. Taken together, our data demonstrates that 4-1BB stimulation induces cell death signaling in the NK cell population, and results in an impaired anti-viral response. Further studies will provide a more complete view of the effects of 4-1BB stimulation as a cancer therapeutic, and also into the mechanism of NK cell life/death through TNFR 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) | 0.002 | 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".