Toll-Like Receptors are critical in controlling colonic inflammation and cancer
Bibliographic record
Abstract
Despite the presence of large number and diverse populations of commensal microbes, gut mucosa has evolved to maintain “microbial-tolerance”, which is critically regulated by well-controlled Toll-like receptor (TLR) signaling. Deregulated TLR signaling has been linked to the pathogenesis of inflammatory bowel disease and colon cancer; however, the underlying mechanisms need to be further defined. In this study, we uncovered that lack of SIGIRR, a negative regulator for TLR and IL-1R signaling, led to increased genetic instability and LOH of Apc, resulting in spontaneous colonic polyposis in Apcmin/+/Sigirr-/- mice. Importantly, elevated colonic tumorigenesis in Apcmin/+/Sigirr-/- mice is dependent on the presence of commensal microbes in gut, implicating a critical role for TLR signaling in tumorigenesis. Furthermore, we demonstrated that SIGIRR-modulated TLR-mediated tumor initiation is mainly through the activation of the Akt-mTOR axis, which promotes cell cycle progression through its impact on posttranscriptional control of the key cell cycle regulators (Cyclins, c-Myc and cdk2). Moreover, abrogation of mTOR pathway by rapamycin prevented microadenoma and polyps formation in Apcmin/+/Sigirr-/- mice, providing new insights into treating human cancers. In addition, augmented production of proinflammatory cytokines, such as IL-6 and IL-23, further promoted tumor growth in Apcmin/+/Sigirr-/- mice. Epithelium specific re-expression of SIGIRR in Apcmin/+/Sigirr-/- mice ameliorated intestinal tumorigenesis. In summary, this study indicates that SIGIRR is a critical tumor suppressor that controls tumorigenesis by inhibiting TLR-induced mTOR and NFkB pathways in colonic epithelium.
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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 | 0.000 | 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".