A270 THE PREBIOTIC, INULIN, IMPACTS TUMORIGENESIS PROMOTION BY COLIBACTIN-PRODUCING
Bibliographic record
Abstract
Abstract Background The prebiotic inulin has previously shown both protective and tumor-promoting effects in colorectal cancer. These discrepancies may be due to polyketide synthase-positive (pks+) Escherichia coli promoting carcinogenesis through the production of colibactin, a genotoxin that induces double-strand DNA breaks (DSBs). Purpose In this study we investigated the impact of inulin on the genotoxicity of colibactin-producing bacteria. Method E. coli strains Nissle (EcN), and NC101 (EcNC101) were grown in medium supplemented with inulin. Colibactin expression was assessed by luciferase reporter gene expression and Caco2 cells were used to assess colibactin-induced genotoxicity by γ-H2AX immunofluorescence analysis. ApcMin/+ mice received 2% dextran sodium sulfate (DSS) followed by oral gavage with EcNC101 and were fed a diet supplemented with 10% cellulose (control diet) or 10% inulin for four weeks. Result(s) Inulin enhanced EcNC101-induced expression of colibactin and DSB levels in Caco2 cells. Inulin supplementation in ApcMin/+ mice led to enhanced EcNC101 colonization and tumor progression. Conclusion(s) The presence of colibactin producing E. coli in the gut influences the outcome of inulin supplementation in CRC progression. Further studies are needed to investigate the interaction between dietary supplements and cancer-promoting bacteria. Please acknowledge all funding agencies by checking the applicable boxes below CIHR, Other Please indicate your source of funding; NSERC, Institut du cancer de montréal Disclosure of Interest None Declared
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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) | 0.003 | 0.001 |
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".