Loss of primary cilia promotes inflammation and carcinogenesis
Bibliographic record
Abstract
Primary cilia (PC) are important signaling hubs, and we here explored their role in colonic pathology. In the colon, PC are mostly present on fibroblasts, and exposure of mice to either chemically induced colitis‐associated colon carcinogenesis (CAC) or dextran sodium sulfate (DSS)‐induced acute colitis decreases PC numbers. We generated conditional knockout mice with reduced numbers of PC on colonic fibroblasts. These mice show increased susceptibility to CAC, as well as DSS‐induced colitis. Secretome and immunohistochemical analyses of DSS‐treated mice display an elevated production of the proinflammatory cytokine IL‐6 in PC‐deficient colons. An inflammatory environment diminishes PC presence in primary fibroblast cultures, which is triggered by IL‐6 as identified by RNA‐seq analysis together with blocking experiments. These findings suggest an activation loop between IL‐6 production and PC loss. An analysis of PC presence on biopsies of patients with ulcerative colitis or colorectal cancer (CRC) reveals decreased numbers of PC on colonic fibroblasts in pathological compared with surrounding normal tissue. Taken together, we provide evidence that a decrease in colonic PC numbers promotes colitis and CRC. Primary cilia are present on colonic fibroblasts and their decrease in number promotes colitis and colon carcinogenesis. In colitis, primary cilia loss appears to be ultimately linked to IL‐6 signaling. Primary cilia are present on colonic fibroblasts and their decrease in number promotes colitis and colon carcinogenesis. In colitis, primary cilia loss appears to be ultimately linked to IL‐6 signaling.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".