Discovery of a caspase cleavage motif antibody reveals insights into noncanonical inflammasome function
Bibliographic record
Abstract
Inflammasomes sense a number of pathogen and host damage signals to initiate a signaling cascade that triggers inflammatory cell death, termed pyroptosis. The inflammatory caspases (1/4/5/11) are the key effectors of this process through cleavage and activation of the pore-forming protein gasdermin D. Caspase-1 also activates proinflammatory interleukins, IL-1β and IL-18, via proteolysis. However, compared to the well-studied apoptotic caspases, the identity of substrates and therefore biological functions of the inflammatory caspases remain limited. Here, we construct, validate, and apply an antibody toolset for direct detection of neo-C termini generated by inflammatory caspase proteolysis. By combining rabbit immune phage display with a set of degenerate and defined target peptides, we discovered two monoclonal antibodies that bind peptides with a similar degenerate recognition motif as the inflammatory caspases without recognizing the canonical apoptotic caspase recognition motif. Crystal structure analyses revealed the molecular basis of this strong yet paradoxical degenerate mode of peptide recognition. One antibody selectively immunoprecipitated cleaved forms of known and unknown inflammatory caspase substrates, allowing the identification of over 300 putative substrates of the caspase-4 noncanonical inflammasome, including caspase-7. This dataset will provide a path toward developing blood-based biomarkers of inflammasome activation. Overall, our study establishes tools to discover and detect inflammatory caspase substrates and functions, provides a workflow for designing antibody reagents to study cell signaling, and extends the growing evidence of biological cross talk between the apoptotic and inflammatory caspases.
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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) | 0.000 | 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 | 0.001 | 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".