Elongation factor P mediates a novel post-transcriptional regulatory pathway critical for bacterial virulence
Bibliographic record
Abstract
Bacterial pathogens detect and integrate multiple environmental signals to coordinate appropriate changes in gene expression including the selective expression of virulence factors, changes to metabolism and the activation of stress response systems. Mutations that abolish the ability of the pathogen to respond to external cues are typically attenuating. Here we discuss our recent discovery of a novel post-transcriptional regulatory pathway critical for Salmonella virulence and stress resistance. The enzymes PoxA and YjeK coordinately attach a unique beta-amino acid onto a highly conserved lysine residue in the translation factor elongation factor P (EF-P). Strains in which EF-P is unmodified due to the absence of PoxA or YjeK are attenuated for virulence and display highly pleiotropic phenotypes, including hypersusceptibility to a wide range of unrelated antimicrobial compounds. Work from our laboratory and others now suggests that EF-P, previously thought to be essential, instead plays an ancillary role in translation by regulating the synthesis of a relatively limited subset of proteins. Other observations suggest that the eukaryotic homolog of EF-P, eIF5A, may illicit similar changes in the translation machinery during stress adaptation, indicating that the role of these factors in physiology may be broadly conserved.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".