Sharpening Host Defenses during Infection: Proteases Cut to the Chase
Bibliographic record
Abstract
The human immune system consists of an intricate network of tightly controlled pathways, where proteases are essential instigators and executioners at multiple levels. Invading microbial pathogens also encode proteases that have evolved to manipulate and dysregulate host proteins, including host proteases during the course of disease. The identification of pathogen proteases as well as their substrates and mechanisms of action have empowered significant developments in therapeutics for infectious diseases. Yet for many pathogens, there remains a great deal to be discovered. Recently, proteomic techniques have been developed that can identify proteolytically processed proteins across the proteome. These "degradomics" approaches can identify human substrates of microbial proteases during infection in vivo and expose the molecular-level changes that occur in the human proteome during infection as an operational network to develop hypotheses for further research as well as new therapeutics. This Perspective Article reviews how proteases are utilized during infection by both the human host and invading bacterial pathogens, including archetypal virulence-associated microbial proteases, such as the Clostridia spp. botulinum and tetanus neurotoxins. We highlight the potential knowledge that degradomics studies of host–pathogen interactions would uncover, as well as how degradomics has been successfully applied in similar contexts, including use with a viral protease. We review how microbial proteases have been targeted in current therapeutic approaches and how microbial proteases have shaped and even contributed to human therapeutics beyond infectious disease. Finally, we discuss how, moving forward, degradomics research can greatly contribute to our understanding of how microbial pathogens cause disease in vivo and lead to the identification of novel substrates in vivo , and the development of improved therapeutics to counter these pathogens.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".