Disruption of sphingolipid biosynthetic gene<i>IPT1</i>reduces<i>Candida albicans</i>adhesion and prevents activation of human gingival epithelial cell innate immune defense
Bibliographic record
Abstract
We demonstrated the effect of a Candida albicans sphingolipid biosynthetic gene, IPT1, on the interaction between gingival epithelial and Candida cells using monolayer cultures and engineered human oral mucosa tissue (EHOM). Disrupting the IPT1 gene greatly reduced Candida adhesion to gingival epithelial cells, compared to the wild-type and revertant strains. The yeasts adhesion to epithelial cells may activate toll-like receptors (TLRs). Cell response against Candida infection was thus investigated by evaluating TLR expression and antimicrobial peptide (AMP) production. The wild-type and revertant strains both activated TLR2, TLR4, TLR6, and TLR9 gene expression in the epithelial cells, whereas the Δipt1 mutant Candida strain had no effect on this expression. This finding was supported by an increased AMP expression (human β-defensin HBD-2 and HBD-3) in the EHOM tissue infected with the wild-type and revertant Candida strains, and a decreased expression in the Δipt1 mutant-infected model. HBD protein secretion confirmed the absence of any effect by the Δipt1 on epithelial cell innate defense. This is the first study to demonstrate that a disruption of the IPT1 gene affects Candida-host interaction, thus preventing TLR activation and β-defensin expression.
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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 | 0.000 |
| 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".