Inulin impacts tumorigenesis promotion by colibactin-producing Escherichia coli in ApcMin/+ mice
Bibliographic record
Abstract
Introduction The prebiotic inulin has previously shown both protective and tumor-promoting effects in colorectal cancer (CRC). These inconsistencies may be due to the gut microbial composition as several bacteria have been associated with CRC. Specifically, polyketide synthase-positive (pks+) Escherichia coli promotes carcinogenesis and facilitates CRC progression through the production of colibactin, a genotoxin that induces double-strand DNA breaks (DSBs). We investigated whether colibactin-producing Escherichia coli changed the protection conferred by inulin against tumor growth and progression using the ApcMin/+ mouse model of CRC. Methods Mice received a 2% dextran sodium sulfate (DSS) solution followed by oral gavage with the murine pks + E. coli strain NC101 (EcNC101) and were fed a diet supplemented with 10% cellulose as control or 10% inulin for 4 weeks. Results Inulin supplementation led to increase EcNC101 colonization compared to mice receiving the control diet. The increased colonization of EcNC101 resulted in more DSBs, tumor burden, and tumor progression in ApcMin/+ mice. The tumorigenic effect of EcN101 in ApcMin/+ mice mediated by inulin was dependent on colibactin production. Pasteurized E. coli Nissle 1917 (EcN), a probiotic, suppressed the inulin-driven EcNC101 expansion and impacted tumor progression. Discussion Our results suggest that the presence of pks + E. coli influences the outcome of inulin supplementation in CRC and that microbiota-targeted interventions may mitigate this effect. Given the prevalence of pks + E. coli in both healthy and CRC populations and the importance of a fiber-rich diet, inulin supplementation in individuals colonized with pks + bacteria should be considered with caution.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".