IL22 Inhibits Epithelial Stem Cell Expansion in an Ileal Organoid Model
Bibliographic record
Abstract
Background & Aims Crohn's disease is an inflammatory bowel disease that affects the ileum and is associated with increased cytokines. Although interleukin (IL)6, IL17, IL21, and IL22 are increased in Crohn's disease and are associated with disrupted epithelial regeneration, little is known about their effects on the intestinal stem cells (ISCs) that mediate tissue repair. We hypothesized that ILs may target ISCs and reduce ISC-driven epithelial renewal. Methods A screen of IL6, IL17, IL21, or IL22 was performed on ileal mouse organoids. Computational modeling was used to predict microenvironment cytokine concentrations. Organoid size, survival, proliferation, and differentiation were characterized by morphometrics, quantitative reverse-transcription polymerase chain reaction, and immunostaining on whole organoids or isolated ISCs. ISC function was assayed using serial passaging to single cells followed by organoid quantification. Single-cell RNA sequencing was used to assess Il22ra1 expression patterns in ISCs and transit-amplifying (TA) progenitors. An IL22-transgenic mouse was used to confirm the impact of increased IL22 on proliferative cells in vivo. Results High IL22 levels caused decreased ileal organoid survival, however, resistant organoids grew larger and showed increased proliferation over controls. Il22ra1 was expressed on only a subset of ISCs and TA progenitors. IL22-treated ISCs did not show appreciable differentiation defects, but ISC biomarker expression and self-renewal–associated pathway activity was reduced and accompanied by an inhibition of ISC expansion. In vivo, chronically increased IL22 levels, similar to predicted microenvironment levels, showed increases in proliferative cells in the TA zone with no increase in ISCs. Conclusions Increased IL22 limits ISC expansion in favor of increased TA progenitor cell expansion.
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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 | 0.001 | 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) | 0.002 | 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".