FimH Can Directly Activate Human and Murine Natural Killer Cells via TLR4
Bibliographic record
Abstract
Although the importance of natural killer (NK) cells in innate immune responses against tumors or viral infections are well documented, their ability to directly recognize pathogens is less well defined. We have recently reported FimH, a bacterial fimbrial protein, as a novel Toll-like receptor (TLR)4 ligand that potently induces antiviral responses. Here, we investigated whether FimH either directly or indirectly can activate human and murine NK cells. We demonstrate that FimH potently activates both human and murine NK cells in vitro to induce cytokines [interferon (IFN)-gamma and tumor necrosis factor (TNF)-alpha] and cytotoxicity. Importantly, NK cells directly recognize FimH-expressing pathogens as FimH(+), but not FimH(-), bacteria were able to activate human NK cells. FimH activation of NK cells required TLR4 and MyD88 signaling, as NK cells from both TLR4(-/-) and MyD88(-/-) mice as well as human NK-92 cells, which lack TLR4, were all unresponsive to FimH. In addition, TLR4 neutralization significantly abrogated the response of human NK cells to FimH. Activation of purified NK cells by FimH was independent of lipopolysaccharide (LPS) or other bacterial contaminations. These data demonstrate for the first time that highly purified NK cells directly recognize and respond to FimH via TLR4-MyD88 pathways to aid innate protection against cancer or microbial infections.
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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) | 0.001 | 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) | 0.005 | 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".