The RNA-binding protein ProQ directly binds and regulates virulence genes in enterohemorrhagic Escherichia coli O157:H7
Bibliographic record
Abstract
ProQ has recently emerged as a major post-transcriptional regulator in bacteria through directly binding to diverse mRNAs and small regulatory RNAs (sRNAs). However, the impact of ProQ in many pathogenic bacteria remains poorly understood. In this study, we investigated the role of ProQ in enterohaemorrhagic Escherichia coli (EHEC), a non-invasive intestinal pathogen. We found that deletion of proQ significantly enhanced cell adherence ability of EHEC and led to upregulation of the locus of enterocyte effacement (LEE) pathogenicity island. This effect was accompanied by reduced expression of genes encoding known LEE regulators, including protein factors (ihfA and hns) and sRNAs (GlmY and GlmZ), resulting from decreased stability of their transcripts in the absence of proQ. Additionally, proQ positively regulated bacterial motility by stabilizing fliC mRNA. We further demonstrated that ProQ directly binds to ihfA, hns, glmY/glmZ and fliC transcripts at secondary structures located near their 3’ ends. Beyond virulence regulation, ProQ also contributed to antibiotic persistence of EHEC and its survival under host-associated stress conditions. Collectively, our findings highlight ProQ as a key gene regulator in EHEC, providing new insights into how this pathogen modulates its virulence at the post-transcriptional level.
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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".