β-1,3-glucan induces natural killer cell memory-like immunity
Bibliographic record
Abstract
Abstract Natural killer (NK) cells are considered innate lymphocytes that are able to kill pathogenic microorganisms, including bacteria, parasites and fungi as well as virus-infected cells and tumor cells without prior stimulation. Recent studies suggest that they possess a memory-like property to previously encountered pathogens (e.g., cytomegalovirus). We showed that NK cells directly kill Cryptococcus and Candida, which are fatal opportunistic fungi often infecting immunocompromised individuals. We determined that NKp30 is the activating pattern recognition receptor that mediates NK cell killing of these fungi, and that β-1,3-glucan is the ligand for NKp30. Interestingly, we found that soluble β-1,3-glucan enhanced NK cell killing of C. neoformans and C. albicans. Since humans and animals are exposed to β-1,3-glucan through inhalation of components of the cell wall of microbes or consumption of dietary grains, we asked whether isolated β-1,3-glucan plays a role in shaping NK cell immunity. To determine whether β-1,3-glucan induces a training or memory effect, YT cells, an NK cell line, were treated for 24 hours with laminarin or Saccharomyces β-1,3-glucan and cultured for various times. After an initial period of enhanced C. neoformans killing, killing was reduced to the untreated level on the second week after the initial treatment. However, when cryptococcal killing was assessed on the fourth week, a much greater killing was observed compared to the enhanced killing. This suggests that a recall immune response was induced. We conclude that β-1,3-glucan may induce NK cell memory- like immunity.
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) | 0.001 | 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".