The Innate Immune Receptor Nod1 in Reprogramming the Myeloid Compartment: Implications for Colorectal Cancer
Bibliographic record
Abstract
Colorectal cancer (CRC) represents one of the leading causes of morbidity and cancer-related mortality in the world. While the etiology of CRC is believed to arise from genetic mutations, there is increasing evidence that the host microbiota plays a crucial role in CRC development by modulating the tumor microenvironment (TME). Accordingly, the Nod-like innate immune receptor, Nod1, is involved in the detection of invading bacteria and its expression correlates with advancing stages of human CRC. However, the role of Nod1 in regulating inflammation and pathogenesis of CRC remains unclear. Here, I aimed to understand the mechanisms whereby Nod1 signalling might influence the TME during colitis-associated carcinogenesis (CAC). Using a model of peritoneal inflammation, I first demonstrated that Nod1 activation resulted in the alternative activation of macrophages and induction of myeloidderived suppressor cells (MDSCs) that promoted the resolution of inflammation. Importantly, myeloid-intrinsic Nod1 signalling was required to maintain the immunosuppressive potential of recruited monocytic MDSCs by regulating Arginase-1 activity. Supporting the tumor promoting properties of MDSCs, I found that deletion of Nod1 specifically in myeloid cells, but not in intestinal epithelial cells, protected mice from CAC. Myeloid-specific Nod1-deficiency resulted in decreased tumor size and burden, characterized by lower incidence of high-grade dysplasia. Mechanistically, myeloid-intrinsic Nod1 signalling sustained an immunosuppressive TME, which resulted in decreased numbers of intra-tumoral effector T cells and increased regulatory T cells. Nod1 expression was also found to promote spontaneous carcinogenesis in Apcmin/+ mutant mice. Collectively, my findings reveal a myeloid-intrinsic function for Nod1 in shaping the TME immune landscape to support tumor development, which opens the possibility of modulating Nod1 signalling using probiotics or inhibitors to improve the effectiveness of cancer therapy.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".