Porphyromonas gingivalis under palmitate-induced obesogenic microenvironment modulates the inflammatory transcriptional signature of macrophage-like cells
Bibliographic record
Abstract
Metabolic diseases and low-grade chronic inflammation are interconnected: obese persons are at higher risk of developing periodontitis. However, the molecular mechanisms involved in the development and progression of periodontitis in an obesogenic microenvironment in response to periodontopathogens are still lacking. This study aims to investigate the combined effects of palmitate and Porphyromonas gingivalis on the secretion of pro-inflammatory cytokines and on transcriptional landscape modifications in macrophage-like cells. U937 macrophage-like cells were treated with palmitate and stimulated with P. gingivalis for 24h. Cytokines IL-1β, TNF-α and IL-6 were measured by ELISA in the culture medium and cell extracted RNA was submitted to a microarray analysis followed by Gene Ontology analyses. P. gingivalis, in presence of palmitate, potentiated IL-1β and TNF-α secretion in comparison to palmitate alone. Gene Ontology analyses also revealed that the combination palmitate-P. gingivalis potentiated the number of gene molecular functions implicated in the regulation of immune and inflammatory pathways compared to macrophages treated with palmitate alone. Our results provide the first comprehensive mapping of gene interconnections between palmitate and P. gingivalis during inflammatory responses in macrophage-like cells. These data highlight the importance of considering systemic conditions, specifically obesogenic microenvironment, in the management of periodontal disease in obese patients.
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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".