A tricyclic pyrrolobenzodiazepine produced by Klebsiella oxytoca is associated with cytotoxicity in antibiotic-associated hemorrhagic colitis
Bibliographic record
Abstract
Cytotoxin-producing Klebsiella oxytoca is the causative agent of antibiotic-associated hemorrhagic colitis (AAHC). Recently, the cytotoxin associated with AAHC was identified as tilivalline, a known pentacyclic pyrrolobenzodiazepine (PBD) metabolite produced by K. oxytoca . Although this assertion of tilivalline's role in AAHC is supported by evidence from animal experiments, some key aspects of this finding appear to be incompatible with toxicity mechanisms of known PBD toxins. We therefore hypothesized that K. oxytoca may produce some other uncharacterized cytotoxins. To address this question, we investigated whether tilivalline alone is indeed necessary and sufficient to induce cytotoxicity or whether K. oxytoca also produces other cytotoxins. LC-MS- and NMR-based metabolomic analyses revealed the presence of an abundant tricyclic PBD, provisionally designated kleboxymycin, in the supernatant of toxigenic K. oxytoca strains. Moreover, by generating multiple mutants with gene deletions affecting tilivalline biosynthesis, we show that a tryptophanase-deficient, tilivalline-negative K. oxytoca mutant induced cytotoxicity in vitro similar to tilivalline-positive K. oxytoca strains. Furthermore, synthetic kleboxymycin exhibited greater than 9-fold higher cytotoxicity than tilivalline in TC 50 cell culture assays. We also found that the biosynthetic pathways for kleboxymycin and tilivalline appear to overlap, as tilivalline is an indole derivative of kleboxymycin. In summary, our results indicate that tilivalline is not essential for inducing cytotoxicity observed in K. oxytoca -associated AAHC and that kleboxymycin is a tilivalline-related bacterial metabolite with even higher cytotoxicity.
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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 | 0.000 |
| 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".