Stimulator of interferon genes (STING) activation exacerbates experimental colitis in mice
Bibliographic record
Abstract
Abstract Detection of cytoplasmic DNA by the host’s innate immune system is essential for microbial and endogenous pathogen recognition. In mammalian cells, an important sensor is the stimulator of interferon genes (STING) protein, which upon activation by bacterially-derived cyclic dinucleotides (cDNs) or cytosolic dsDNA (dsDNA), triggers type I interferons and pro-inflammatory cytokine production. Given the abundance of bacterially-derived cDNs in the gut, we determined whether STING deletion, or stimulation, acts to modulate the severity of intestinal inflammation in the dextran sodium sulphate (DSS) model of colitis. DSS was administered to Tmem173 gt (STING-mutant) mice and to wild-type mice co-treated with DSS and a STING agonist. Colitis severity was markedly reduced in the DSS-treated Tmem173 gt mice and greatly exacerbated in wild-type mice co-treated with the STING agonist. STING expression levels were also assessed in colonic tissues, murine bone marrow derived macrophages (BMDMs), and human THP-1 cells. M1 and M2 polarized THP-1 and murine BMDMs were also stimulated with STING agonists and ligands to assess their responses. STING expression was increased in both murine and human M1 polarized macrophages and a STING agonist repolarized M2 macrophages towards an M1-like subtype. Our results suggest that STING is involved in the host’s response to acutely-induced colitis.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".