Improved detection of colibactin-induced mutations by genotoxic E. coli in organoids and colorectal cancer
Bibliographic record
Abstract
Co-culture of intestinal organoids with a colibactin-producing pks + E. coli strain (EcC) revealed mutational signatures also found in colorectal cancer (CRC). E. coli Nissle 1917 (EcN) remains a commonly used probiotic, despite harboring the pks operon and inducing double strand DNA breaks. We determine the mutagenicity of EcN and three CRC-derived pks + E. coli strains with an analytical framework based on sequence characteristic of colibactin-induced mutations. All strains, including EcN, display varying levels of mutagenic activity. Furthermore, a machine learning approach attributing individual mutations to colibactin reveals that patients with colibactin-induced mutations are diagnosed at a younger age and that colibactin can induce a specific APC mutation. These approaches allow the sensitive detection of colibactin-induced mutations in ∼12% of CRC genomes and even in whole exome sequencing data, representing a crucial step toward pinpointing the mutagenic activity of distinct pks + E. coli strains.
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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 | 0.001 | 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".