Functional targeting of ILC2s and ILC3s reveals selective roles in intestinal fibrosis and homeostasis
Bibliographic record
Abstract
Innate lymphoid cells (ILCs) are long-lived, tissue-resident cell analogs to T helper subsets that lack antigen-specific receptors. Understanding the roles of specific ILCs in chronic inflammation and fibrosis has been limited by inadequate tools for selective targeting. Here, we used Il17rb-CreERT2-eGFP and Rorc-Cre strains to selectively delete RORα in ILC2s and ILC3/Th17 cells, respectively. RORα deletion in ILC2s caused significant loss of gastrointestinal ILC2s, increased ILC3 abundance, elevated Th17-type responses, and heightened susceptibility to Crohn's disease-like fibrosis. Conversely, RORα deletion in ILC3/Th17 cells reduced IL-17 production, protecting against fibrosis. Using isolithocholic acid (isoLCA), a microbial secondary bile acid and RORγt inverse agonist, we confirmed the role of ILC3s/Th17 cells in fibrosis. In RORγt reporter and Th17-deficient Rag1-/- mice, isoLCA reduced IL-17 production by ILC3s and attenuated intestinal fibrosis by dampening RORγt-dependent ILC3/Th17 responses. These findings reveal a novel interplay between ILC2s and ILC3s in gut homeostasis and demonstrate the therapeutic potential of targeting RORγt in ILC3s as a strategy for preventing fibrosis.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".