D-Serine reduces the expression of the cytopathic genotoxin colibactin
Bibliographic record
Abstract
Some Escherichia coli strains harbour the pks island, a 54 kb genomic island en-coding the biosynthesis genes for a genotoxic compound named colibactin. In eukaryotic cells, colibactin can induce DNA damage, cell cycle arrest and chro-mosomal instability. Production of colibactin has been implicated in the devel-opment of colorectal cancer (CRC). In this study, we demonstrate the inhibito-ry effect of D-Serine on the expression of the pks island in both prototypic and clinically-associated colibactin-producing strains and determine the implications for cytopathic effects on host cells. We also tested a comprehensive panel of proteinogenic L-amino acids and corresponding D-enantiomers for their ability to modulate clbB transcription. Whilst several D-amino acids exhibited the abil-ity to inhibit expression of clbB, D-Serine exerted the strongest repressing ac-tivity (>3.8-fold) and thus, we focussed additional experiments on D-Serine. To investigate the cellular effect, we investigated if repression of colibactin by D-Serine could reduce the cytopathic responses normally observed during infec-tion of HeLa cells with pks+ strains. Levels of γ-H2AX (a marker of DNA double strand breaks) were reduced 2.75-fold in cells infected with D-Serine treat-ment. Moreover, exposure of pks+ E. coli to D-Serine during infection caused a reduction in cellular senescence that was observable at 72 h post infection. The recent finding of an association between pks-carrying commensal E. coli and CRC, highlights the necessity for the development of colibactin targeting thera-peutics. Here we show that D-Serine can reduce expression of colibactin, and inhibit downstream cellular cytopathy, illuminating its potential to prevent colibactin-associated disease.
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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".