The Single IgG IL-1–Related Receptor Controls TLR Responses in Differentiated Human Intestinal Epithelial Cells
Bibliographic record
Abstract
Intestinal epithelial cells (IECs) are constantly exposed to enteric microbes. Although IECs express TLRs that recognize bacterial products, the activation of these TLRs is strictly controlled through poorly understood mechanisms, producing a state of hyporesponsiveness and preventing unwanted inflammation. The single IgG IL-1-related receptor (Sigirr) is a negative regulator of TLRs that is expressed by IECs and was recently shown to inhibit experimental colitis. However, the importance of Sigirr in IEC hyporesponsiveness and its distribution within the human colon is unknown. In this study, we investigated the role of Sigirr in regulating epithelial-specific TLR responses and characterized its expression in colonic biopsy specimens. Transformed and nontransformed human IECs were cultured as monolayers. Transient gene silencing and stable overexpression of Sigirr was performed to assess innate IEC responses. Sigirr expression in human colonic biopsy specimens was examined by immunohistochemistry. Bacterial infection of IECs and exposure to flagellin transiently decreased Sigirr protein expression, concurrent with secretion of the neutrophil chemokine IL-8. Sigirr gene silencing augmented chemokine responses to bacterial flagellin, Pam3Cys, and the cytokine IL-1beta. Conversely, stable overexpression of Sigirr diminished NF-kappaB-mediated IL-8 responses to TLR ligands. We also found that Sigirr expression increased as IECs differentiated in culture. This observation was confirmed in biopsy sections, in which Sigirr expression within colonic crypts was prominent in IECs at the apex and diminished at the base. Our findings show that Sigirr broadly regulates innate responses in differentiated human IECs; therefore, it may modulate epithelial involvement in infectious and inflammatory bowel diseases.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
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".