Modulatory Effects of IFN-γ and IL-22 on Inflammatory Signaling and Cellular Responses in Intestinal Epithelial Cells
Bibliographic record
Abstract
Abstract Inflammatory bowel disease (IBD) continues to affect millions worldwide, with an increasing prevalence that highlights the urgent need for deeper understanding of its underlying immune mechanisms. The cytokine interactions, especially those mediated by cells from the TH1 and TH17 lymphocyte subsets, are crucial in orchestrating the immune landscape of IBD. TH1 cells are well known for producing TNF-α and IFNγ, which have been extensively studied for their roles in conjunction with each other within the context of IBD. TH17 cells secrete IL-22 and IL-17, with existing studies primarily focusing on IL-22’s interaction with IL-17 rather than its interplay with other cytokines such as IFNγ. Our study focuses on the co-stimulatory effects of IL-22 and IFNγ using organoids derived from mouse small intestines to model epithelial interactions. We found that IFNγ interferes with the capacity of IL-22 to up-regulate antimicrobial peptides, which is essential in mucosal defense. Additionally, higher concentrations of IL-22 enhance IFNγ’s ability to stimulate TNF-α gene expression and CXCL10 protein production, indicating a dose-dependent relationship. This co-stimulation also led to an increased rate of cell death, influenced partly by TNF-α. These findings suggest that IL-22, typically seen as an anti-inflammatory agent, can assume a pro-inflammatory role when combined with IFNγ, complicating its effects on epithelial cells. This study highlights the need to consider specific cytokine interactions in developing more effective IBD treatments.
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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 | 0.001 | 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".