Oligosaccharides increase the genotoxic effect of colibactin produced by pks+ Escherichia coli strains
Bibliographic record
Abstract
BACKGROUND: Colibactin is a genotoxin that induces DNA double-strand breaks that may lead to carcinogenesis and is produced by Escherichia coli strains harboring the pks island. Human and animal studies have shown that colibactin-producing gut bacteria promote carcinogenesis and enhance the progression of colorectal cancer through cellular senescence and chromosomal abnormalities. In this study, we investigated the impact of prebiotics on the genotoxicity of colibactin-producing E. coli strains Nissle 1917 and NC101. METHODS: Bacteria were grown in medium supplemented with 20, 30 and 40 mg/mL of prebiotics inulin or galacto-oligosaccharide, and with or without 5 μM, 25 μM and 125 μM of ferrous sulfate. Colibactin expression was assessed by luciferase reporter assay for the clbA gene, essential for colibactin production, in E. coli Nissle 1917 and by RT-PCR in E. coli NC101. The human epithelial colorectal adenocarcinoma cell line, Caco-2, was used to assess colibactin-induced megalocytosis by methylene blue binding assay and genotoxicity by γ-H2AX immunofluorescence analysis. RESULTS: Inulin and galacto-oligosaccharide enhanced the expression of clbA in pks+ E. coli. However, the addition of 125 μM of ferrous sulfate inhibited the expression of clbA triggered by oligosaccharides. In the presence of either oligosaccharide, E. coli NC101 increased dysplasia and DNA double-strand breaks in Caco-2 cells compared to untreated cells. CONCLUSION: Our results suggest that, in vitro, prebiotic oligosaccharides exacerbate DNA damage induced by colibactin-producing bacteria. Further studies are necessary to establish whether oligosaccharide supplementation may lead to increased colorectal tumorigenesis in animal models colonized with pks+ E. coli.
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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) | 0.000 | 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) | 0.002 | 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".