Development and Application of a Novel Real‐Time PCR Assay for <i>Citrobacter rodentium</i> Quantification
Bibliographic record
Abstract
Citrobacter rodentium is a murine pathogen causing transmissible colonic hyperplasia and used to model intestinal colitis including foodborne Escherichia coli O157:H7 pathogenesis. Several probiotics have been found to alleviate infection and Bifidobacterium bifidum MIMBb75 may be effective because it strongly adheres to colonocytes and modifies the gut microbiota. C. rodentium is typically quantified by classical culturing; the aim of this study was to establish a novel molecular assay based on quantitative real-time PCR. Primers were designed to target the C. rodentium-specific espB gene. Specificity was validated in silico and experimentally with a range of intestinal bacteria. A standard curve was constructed using serially diluted C. rodentium DNA depicting a linearity range (R2 > 0.99) of 102 to 107 cells per PCR reaction (efficiency 96%) and sensitivity of 104 cells/g feces. To assess effects of B. bifidum MIMBb75 on C. rodentium fecal load, C57BL/6J mice were gavaged with 109 cells of B. bifidum MIMBb75 in PBS or PBS alone daily from 7 days before to 10 days post infection (PI) with 109 cells of C. rodentium. Fecal counts of C. rodentium at day 10 PI were quantified with the novel qPCR assay and did not differ significantly between the treatment and control group (6.0 ± 0.4 and 6.4 ± 0.1 log cells/g feces, respectively). This was confirmed by classical culturing. This is the first real-time PCR assay specific for C. rodentium and it can be used to determine its load and distribution along the intestinal tract in response to preventive and therapeutic interventions. Other probiotics were unable to reduce fecal load yet mitigated inflammation thus, further research is warranted. *co-first authors Funding JP Bickell Foundation, NSERC, NSERC USRA
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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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".