Activation of the pattern recognition receptor NOD1 augments colon cancer metastasis
Bibliographic record
Abstract
While emerging data suggest nucleotide oligomerization domain receptor 1 (NOD1), a cytoplasmic pattern recognition receptor, may play an important and complementary role in the immune response to bacterial infection, its role in cancer metastasis is entirely unknown. Hence, we sought to determine the effects of NOD1 on metastasis. NOD1 expression in paired human primary colon cancer, human and murine colon cancer cells were determined using immunohistochemistry and immunoblotting (WB). Clinical significance of NOD1 was assessed using TCGA survival data. A series of in vitro and in vivo functional assays, including adhesion, migration, and metastasis, was conducted to assess the effect of NOD1. C12-iE-DAP, a highly selective NOD1 ligand derived from gram-negative bacteria, was used to activate NOD1. ML130, a specific NOD1 inhibitor, was used to block C12-iE-DAP stimulation. Stable knockdown (KD) of NOD1 in human colon cancer cells (HT29) was constructed with shRNA lentiviral transduction and the functional assays were thus repeated. Lastly, the predominant signaling pathway of NOD1-activation was identified using WB and functional assays in the presence of specific kinase inhibitors. Our data demonstrate that NOD1 is highly expressed in human colorectal cancer (CRC) and human and murine CRC cell lines. Clinically, we demonstrate that this increased NOD1 expression negatively impacts survival in patients with CRC. Subsequently, we identify NOD1 activation by C12-iE-DAP augments CRC cell adhesion, migration and metastasis. These effects are predominantly mediated via the p38 mitogen activated protein kinase (MAPK) pathway. This is the first study implicating NOD1 in cancer metastasis, and thus identifying this receptor as a putative therapeutic target.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
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".