Overexpression of <i>Escherichia coli yaiX</i> Confers Multidrug Resistance and Enhances Virulence in the Silkworm Infection Model
Bibliographic record
Abstract
Abstract The emergence of bacteria with both antimicrobial resistance and high virulence has become a global health concern, underscoring the urgent need to elucidate the molecular basis underlying these traits. Here, we employed the silkworm ( Bombyx mori ) infection model, which is suitable for high-throughput screening, together with an Escherichia coli library containing plasmid clones of all genes from strain W3110, to identify genes whose overexpression enhances virulence. We found that overexpression of the uncharacterized protein YaiX promoted bacterial proliferation in silkworms and increased host lethality. Compared with the empty-vector control, the YaiX-overexpressing strain exhibited resistance to multiple antimicrobial agents with diverse mechanisms of action, including β-lactams, tetracyclines, fluoroquinolones, aminoglycosides, cationic surfactants, and hydrogen peroxide. Sequence analysis revealed that amino acids 18–52 of YaiX contain a transferase hexapeptide domain predicted to form a left-handed parallel β-helix. Overexpression of YaiX mutants lacking regions outside this domain conferred ampicillin resistance, whereas deletion of the hexapeptide domain abolished this phenotype. RNA sequencing and GO enrichment analyses further indicated that YaiX overexpression altered the expression of genes encoding RNA-binding proteins and porins. These findings suggest that YaiX overexpression, through its hexapeptide domain, modulates gene expression and contributes to both multidrug resistance and enhanced virulence in E. coli .
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".