Host manipulation by bacterial type <scp>III</scp> and type <scp>IV</scp> secretion system effector proteases
Bibliographic record
Abstract
Proteases are powerful enzymes, which cleave peptide bonds, leading most of the time to irreversible fragmentation or degradation of their substrates. Therefore they control many critical cell fate decisions in eukaryotes. Bacterial pathogens exploit this power and deliver protease effectors through specialised secretion systems into host cells. Research over the past years revealed that the functions of protease effectors during infection are diverse, reflecting the lifestyles and adaptations to specific hosts; however, only a small number of peptidase families seem to have given rise to most of these protease virulence factors by the evolution of different substrate-binding specificities, intracellular activation and subcellular targeting mechanisms. Here, we review our current knowledge about the enzymology and function of protease effectors, which Gram-negative bacterial pathogens translocate via type III and IV secretion systems to irreversibly manipulate host processes. We highlight emerging concepts such as signalling by protease cleavage products and effector-triggered immunity, which host cells employ to detect and defend themselves against a protease attack. TAKE AWAY: Proteases irreversibly cleave proteins to control critical cell fate decisions. Gram-negative bacteria use type III and IV secretion systems to inject effectors. Protease effectors are integral weapons for the manipulation of host processes. Effectors evolved from few peptidase families to target diverse substrates. Effector-triggered immunity upon proteolytic attack emerges as host defence.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".