Novel antimicrobial peptide prevents C. rodentium infection in C57BL/6 mice by enhancing acid-induced pathogen killing
Bibliographic record
Abstract
Citrobacter rodentium is a Gram-negative, murine-specific enteric pathogen that infects epithelial cells in the colon. It is closely related to the clinically relevant human pathogen, enterohemorrhagic Escherichia coli (EHEC), a leading cause of haemorrhagic colitis and haemolytic uremic syndrome. We have previously reported that a novel antimicrobial peptide, wrwycr, compromises bacterial DNA repair and significantly reduces the survival of acid-stressed EHEC, suggesting an antimicrobial strategy for targeting the survival of ingested EHEC. This study examines the impact of peptide pretreatment on survival of the closely related murine pathogen, C. rodentium, before and after acid stress, using both in vitro and in vivo investigations. Peptide pretreatment of C. rodentium significantly and dramatically increases acid-stress-induced killing in a peptide-dose-dependent and time-dependent manner. Reduction in survival rates after brief pretreatment with peptide (25-65 µM) followed by 1 h at pH 3.5 ranges from 6 to 8 log fold relative to untreated C. rodentium, with no detectable bacteria after 65 µM peptide-acid treatment. Using a C57BL/6 mouse model of infection, peptide pretreatment of C. rodentium with wrwycr prior to orogastric gavage eliminates evidence of infection based on C. rodentium colonization levels, faecal scores, colonic histology, faecal microbiome and visual observation of overall animal health. These findings provide compelling evidence for the role of the peptide wrwycr as a potential strategy to control the growth and colonization of enteric pathogens.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".