Siglec-7 Mediates Immunomodulation by Colorectal Cancer-Associated Fusobacterium nucleatum ssp. animalis
Bibliographic record
Abstract
Fusobacterium nucleatum is involved in the development of colorectal cancer (CRC) through innate immune cell modulation. However, the receptors of the interaction between F. nucleatum ssp. and immune cells remain largely undetermined. Here, we showed that F. nucleatum ssp. animalis interacts with Siglecs (sialic acid–binding immunoglobulin-like lectins) expressed on innate immune cells with highest binding to Siglec-7. Binding to Siglec-7 was also observed using F. nucleatum -derived outer membrane vesicles (OMVs) and lipopolysaccharide (LPS). F. nucleatum and its derived OMVs or LPS induced a pro-inflammatory profile in human monocyte-derived dendritic cells (moDCs) and a tumour associated profile in human monocyte-derived macrophages (moMϕs). Siglec-7 silencing in moDCs or CRISPR-cas9 Siglec-7-depletion of U-937 macrophage cells altered F. nucleatum induced cytokine but not marker expression. The molecular interaction between Siglec-7 and the LPS O-antigen purified from F. nucleatum ssp. animalis was further characterised by saturation transfer difference (STD) NMR spectroscopy, revealing novel ligands for Siglec-7. Together, these data support a new role for Siglec-7 in mediating immune modulation by F. nucleatum strains and their OMVs through recognition of LPS on the bacterial cell surface. This opens a new dimension in our understanding of how F. nucleatum promotes CRC progression through the generation of a pro-inflammatory environment and provides a molecular lead for the development of novel cancer therapeutic approaches targeting F. nucleatum -Siglec-7 interaction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".