A Novel Bioluminescent Biosensor Quantifying Intramolecular Interaction and Levels of Pyroptosis Effector GSDMD
Bibliographic record
Abstract
Gasdermin D (GSDMD) is a key executor of pyroptosis, a form of inflammation-induced programmed cell death. Recently, GSDMD has been shown to play important roles in the development of various inflammatory-related human diseases including heart failure and cancer, suggesting that it is a promising therapeutic target for these diseases. While extensive studies on GSDMD's role in pyroptosis have been reported, it is challenging to study its function due to the lack of enzymatic activity of GSDMD. In this study, we used the NanoBiT technology to develop a novel GSDMD bioluminescent biosensor (GSDMD-BS) that detects the amount of non-cleaved GSDMD. This sensor allows us to quantify GSDMD's intramolecular interactions, the amounts of uncleaved GSDMD after caspase-1 cleavage, and expression levels in living cells. In vitro experiments using purified GSDMD-BS also confirmed the sensor's accuracy in reporting GSDMD levels and cleavage. Moreover, the potential for in vivo application was demonstrated in a xenograft mouse model. In conclusion, we have developed a GSDMD biosensor that is a valuable tool for real-time monitoring of GSDMD dynamics and pyroptosis. This biosensor will significantly expedite pyroptosis research and can be used for high-throughput screening for drugs targeting GSDMD for the therapy of many inflammation-related 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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".