<i>Citrobacter rodentium</i> Infection Alters Murine Colonic microRNA Signature
Bibliographic record
Abstract
MicroRNAs (miRs) have been suggested to play a part in the interaction between pathogenic bacteria and host cells. Citrobacter rodentium is a murine pathogen causing transmissible colonic hyperplasia and colitis with similar pathogenicity as the foodborne enterohaemorrhagic Escherichia coli O157:H7 in humans. This study aimed to examine if colonic microRNA signature is altered during C. rodentium infection. C57Bl6/J male mice were randomized to C. rodentium ‐infected or control group, and sacrificed at the peak of infection (10 days post‐infection). Crypt hyperplasia and intestinal inflammation were confirmed by histology and in vivo permeability test. Colonic RNA was used to profile 578 miRs by NanoString technology. Statistics and hierarchical clustering were performed in R. Gene targets of the differentially expressed miRs were identified in silico by prediction algorithms and cross‐matching with experimentally verified targets databases. Ninety‐four miRs were differentially expressed (p<0.05), with 42 downregulated and 52 upregulated vs control (0.2‐8.6 fold), and infected samples clustered together and separately from the controls based on their expression profile. Many of these miRs, including miR‐21, miR‐148a, and miR‐152, are known to regulate barrier function, cell proliferation and inflammatory pathways. Moreover, prediction analysis revealed that globally gene targets are mainly involved in cell cycle and immune response. This study shows that C. rodentium infection alters the colonic microRNA signature; differentially expressed miRs may be involved in the pathogenicity mechanism and serve as targets of nutritional or therapeutic interventions. Funding: NSERC, JP Bickell Foundation, and NSERC Alexander Graham Bell Canada Graduate Scholarship
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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 | 0.001 |
| 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".