A pleiotropic role of sialidase in the pathogenicity of <i>Porphyromonas gingivalis</i>
Bibliographic record
Abstract
ABSTRACT As one of the keystone pathogens of periodontitis, the oral bacterium Porphyromonas gingivalis produces an array of virulence factors, including a recently identified sialidase (PG0352). Our previous report involving loss-of-function studies indicated that PG0352 plays an important role in the pathophysiology of P. gingivalis . However, this report had not been corroborated by gain-of-function studies or substantiated in different P. gingivalis strains. To fill these gaps, herein we first confirm the role of PG0352 in cell surface structures (e.g., capsule) and serum resistance using P. gingivalis W83 strain through genetic complementation and then recapitulate these studies using P. gingivalis ATCC33277 strain. We further investigate the role of PG0352 and its counterpart (PGN1608) in ATCC33277 in cell growth, biofilm formation, neutrophil killing, cell invasion, and P. gingivalis -induced inflammation. Our results indicate that PG0352 and PGN1608 are implicated in P. gingivalis cell surface structures, hydrophobicity, biofilm formation, resistance to complement and neutrophil killing, and host immune responses. Possible molecular mechanisms involved are also discussed. In summary, this report underscores the importance of sialidases in the pathophysiology of P. gingivalis and opens an avenue to elucidate their underlying molecular mechanisms.
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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 | 0.000 |
| 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".