Three new serine-protease autotransporters of <i>Enterobacteriaceae</i> (SPATEs) from extra-intestinal pathogenic <i>Escherichia coli</i> and combined role of SPATEs for cytotoxicity and colonization of the mouse kidney
Bibliographic record
Abstract
Serine protease autotransporters of Enterobacteriaceae (SPATEs) are secreted proteins that contribute to virulence and function as proteases, toxins, adhesins, and/or immunomodulators. An extra-intestinal pathogenic E. coli (ExPEC) O1:K1 strain, QT598, isolated from a turkey, was shown to contain vat, tsh, and three uncharacterized SPATE-encoding genes. Uncharacterized SPATEs: Sha (Serine-protease hemagglutinin autotransporter), TagB and TagC (tandem autotransporter genes B and C) were tested for activities including hemagglutination, autoaggregation, and cytotoxicity when expressed in E. coli K-12. Sha and TagB conferred autoaggregation and hemagglutination activities. TagB, TagC, and Sha all exhibited cytopathic effects on a bladder epithelial cell line. In QT598, tagB and tagC are tandemly encoded on a genomic island, and were present in 10% of UTI isolates and 4.7% of avian E. coli. Sha is encoded on a virulence plasmid and was present in 1% of UTI isolates and 20% of avian E. coli. To specifically examine the role of SPATEs for infection, the 5 SPATE genes were deleted from strain QT598 and tested for cytotoxicity. Loss of all five SPATEs abrogated the cytopathic effect on bladder epithelial cells, although derivatives producing any of the 5 SPATEs retained cytopathic activity. In mouse infections, sha gene-expression was up-regulated a mean of sixfold in the bladder compared to growth in vitro. Loss of either tagBC or sha did not reduce urinary tract colonization. Deletion of all 5 SPATEs, however, significantly reduced competitive colonization of the kidney supporting a cumulative role of SPATEs for QT598 in the mouse UTI model.
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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 | 0.001 |
| 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".