Rapid Remodeling of the Host Epithelial Cell Proteome by the Listeriolysin O (LLO) Pore-forming Toxin
Bibliographic record
Abstract
Bacterial pathogens use various strategies to interfere with host cell functions. Among these strategies, bacteria modulate host gene transcription, thereby modifying the set of proteins synthetized by the infected cell. Bacteria can also target pre-existing host proteins and modulate their post-translational modifications or trigger their degradation. Analysis of protein levels variations in host cells during infection allows to integrate both transcriptional and post-transcriptional regulations induced by pathogens. Here, we focused on host proteome alterations induced by the toxin Listeriolysin O (LLO), secreted by the bacterial pathogen Listeria monocytogenes. We showed that a short-term treatment with LLO remodels the host cell proteome by specifically decreasing the abundance of 149 proteins. The same decrease in host protein levels was observed in different epithelial cell lines but not in macrophages. We show in particular that this proteome remodeling affects several ubiquitin and ubiquitin-like ligases and that LLO leads to major changes in the host ubiquitylome. Strikingly, this toxin-induced proteome remodeling involves only post-transcriptional regulations, as no modification in the transcription levels of the corresponding genes was observed. In addition, we could show that Perfringolysin O, another bacterial pore-forming toxin similar to LLO, also induces host proteome changes. Taken together, our data reveal that different bacterial pore-forming toxins induce important host proteome remodeling, that may impair epithelial cell functions.
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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) | 0.000 | 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".