The <i>L. pneumophila</i> effector PieF modulates mRNA stability through association with eukaryotic CCR4-NOT
Bibliographic record
Abstract
ABSTRACT The eukaryotic CCR4-NOT deadenylase complex is a highly conserved regulator of mRNA metabolism that influences the expression of the complete transcriptome, representing a prime target for a generalist bacterial pathogen. We show that a translocated bacterial effector protein, PieF (Lpg1972) of L. pneumophila Str. Philadelphia-1, interacts specifically with the CNOT7/8 nuclease module of CCR4-NOT, with a dissociation constant in the low nanomolar range. PieF inhibits the catalytic deadenylase subunit CNOT7 of the CCR4-NOT complex in a stoichiometric, dose-dependent manner in vitro . In transfected cells, PieF can silence reporter gene expression and reduce mRNA steady-state levels when artificially tethered. PieF demonstrates molecular similarities to another family of CNOT7-associated factors but demonstrates divergence concerning the interaction interface with CNOT7. In addition, we show that PieF overexpression changes the subcellular localization of CNOT7 and displaces the CNOT6/6L nucleases from CCR4-NOT. Finally, PieF expression phenocopies knockout of the CNOT7 ortholog in S. cerevisiae , resulting in 6-azauracil sensitivity. Collectively, this work suggests that L. pneumophila targets host mRNA stability and expression through a highly conserved host pathway not previously associated with Legionella 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 | 0.000 |
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".