SIGIRR, a Negative Regulator of TLR/IL-1R Signalling Promotes Microbiota Dependent Resistance to Colonization by Enteric Bacterial Pathogens
Bibliographic record
Abstract
Enteric bacterial pathogens such as enterohemorrhagic E. coli (EHEC) and Salmonella Typhimurium target the intestinal epithelial cells (IEC) lining the mammalian gastrointestinal tract. Despite expressing innate Toll-like receptors (TLRs), IEC are innately hypo-responsive to most bacterial products. This is thought to prevent maladaptive inflammatory responses against commensal bacteria, but it also limits antimicrobial responses by IEC to invading bacterial pathogens, potentially increasing host susceptibility to infection. One reason for the innate hypo-responsiveness of IEC is their expression of Single Ig IL-1 Related Receptor (SIGIRR), a negative regulator of interleukin (IL)-1 and TLR signaling. To address whether SIGIRR expression and the innate hypo-responsiveness of IEC 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 but surprisingly, Sigirr -/- mice proved dramatically more susceptible to infection than wildtype mice. Through haematopoietic transplantation studies, it was determined that SIGIRR expression by non-haematopoietic cells (putative IEC) regulated these responses. Moreover, the exaggerated responses were found to be primarily dependent on IL-1R signaling. Whilst exploring the basis for their susceptibility, Sigirr -/- mice were found to be unusually susceptible to intestinal Salmonella Typhimurium colonization, developing enterocolitis without the typical requirement for antibiotic based removal of competing commensal microbes. Strikingly, the exaggerated antimicrobial responses seen in Sigirr -/- mice were found to cause a rapid and dramatic loss of commensal microbes from the infected intestine. This depletion appears to reduce the ability of the microbiota to compete for space and nutrients (colonization resistance) with the invading pathogens, leaving the intestine highly susceptible to pathogen colonization. Thus, SIGIRR expression by IEC reflects a strategy that sacrifices maximal innate responsiveness by IEC in order to promote commensal microbe based colonization resistance against bacterial 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) | 0.000 | 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".