A functional screen for ubiquitin regulation identifies an E3 ligase secreted by <i>Pseudomonas aeruginosa</i>
Bibliographic record
Abstract
ABSTRACT Ubiquitin signaling controls many aspects of eukaryotic biology, including targeted protein degradation and immune defense. Remarkably, invading bacterial pathogens have adapted secreted effector proteins that hijack host ubiquitination to gain control over host responses. These ubiquitin-targeted effectors can exhibit, for example, E3 ligase or deubiquitinase activities, often without any sequence or structural homology to eukaryotic ubiquitin regulators. Such convergence in function poses a challenge to the discovery of additional bacterial virulence factors that target ubiquitin. To overcome this, we have developed a workflow to harvest natively secreted bacterial effectors and functionally screen them for ubiquitin regulatory activities. After benchmarking this approach on diverse ligase and deubiquitinase activities from Salmonella Typhimurium, Enteropathogenic Escherichia coli , and Shigella flexneri , we applied it to the identification of a cryptic E3 ligase activity secreted by Pseudomonas aeruginosa . We identified an unreported P. aeruginosa E3 ligase, which we have termed Pseudomonas Ub ligase 1 (PUL-1), that resembles none of the other E3 ligases previously established in or outside of the eukaryotic system. Importantly, in an animal model of P. aeruginosa infection, PUL-1 ligase activity plays an important role in regulating virulence. Thus, our workflow for the functional identification of ubiquitin-targeted effector proteins carries promise for expanding our appreciation of how host ubiquitin regulation contributes to bacterial pathogenesis.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".