SIGIRR limits colitic and epithelial homeostatic responses, but promotes microbiota dependent colonization resistance to enteric bacterial pathogens (P3067)
Bibliographic record
Abstract
Abstract Enteric bacterial pathogens such as Enterohemorrhagic E. coli (EHEC) and Salmonella typhimurium target the intestinal epithelial cells (IEC) lining the gastrointestinal (GI) tract. Despite expressing Toll like receptors (TLRs), IEC are generally hypo-responsive to invading bacteria and their products. One reason is Single Ig IL-1 Related Receptor (SIGIRR), a negative regulator of interleukin (IL)-1R/ TLR signaling expressed by IEC. To address whether SIGIRR expression impacts on enteric host defense, Sigirr deficient (-/-) mice were infected with the EHEC related pathogen Citrobacter rodentium. Sigirr -/- mice responded with accelerated IEC proliferation and strong pro-inflammatory and antimicrobial responses that were primarily IL-1R signaling dependent. Surprisingly the Sigirr -/- mice were highly susceptible to infection, carrying 100-1000x heavier pathogen burden at Day (D) 6 and D10 post infection (pi). Sigirr -/- mice were also found to be unusually susceptible to intestinal S. typhimurium colonization, developing enterocolitis without the requirement for antibiotic based removal of commensal microbes. Strikingly, the exaggerated antimicrobial responses in the Sigirr -/- mice led to the rapid loss of competing commensal microbes (~80%) from the infected intestine, reducing colonization resistance as early as D1 pi. Thus, despite limiting IEC responses to infection, SIGIRR aids host defense by promoting commensal based resistance to pathogen colonization of the GI tract.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".