Probiotics Affect Virulence-Related Gene Expression in <i>Escherichia coli</i> O157:H7
Bibliographic record
Abstract
The attachment of enterohemorrhagic Escherichia coli O157:H7 (EHEC O157) to host intestinal epithelial cells is essential for the development of hemorrhagic colitis and hemolytic-uremic syndrome in humans. Genes involved in attachment are carried within a pathogenicity island named the locus of enterocyte effacement (LEE), known to be directly activated by quorum sensing (QS). In the present study, we investigated autoinducer-2 (AI-2) production and the expression of several virulence-related genes in EHEC O157 grown in the absence and presence of a Lactobacillus acidophilus-secreted molecule(s). Transcription of important EHEC O157 virulence-related genes was studied by constructing promoter-reporter fusions and reverse transcriptase PCR. Shiga toxin (Stx) production was assayed by an enzyme immunoassay. When EHEC O157 was grown in the presence of chromatographically selected fractions of L. acidophilus La-5 cell-free spent medium, we observed a significant reduction of both extracellular AI-2 concentration and the expression of important virulence-related genes, although no significant difference in Stx production was observed. We show here that L. acidophilus La-5 secretes a molecule(s) that either acts as a QS signal inhibitor or directly interacts with bacterial transcriptional regulators, controlling the transcription of EHEC O157 genes involved in colonization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".