Transcriptome Analysis of <i>Porphyromonas gingivalis</i> Lipopolysaccharide‐Induced Early Gene Expression in Human Gingival Keratinocytes
Bibliographic record
Abstract
AIM: Porphyromonas gingivalis lipopolysaccharide (PgLPS) is a significant virulence factor and a driver of early innate immune responses in epithelial cells. The presence of PgLPS in immediate proximity to gingival epithelium induces significant inflammatory responses. In primary human gingival keratinocytes (HGK), we utilized transcriptome analysis to elucidate the change in early gene expression induced by PgLPS. METHODS: HGK cell cultures were treated with PgLPS (4 h), and RNA was extracted and prepared for RNA sequence (RNAseq) analysis. Differentially expressed genes (DEGs) were identified, and potential interactions between these genes were subsequently examined using gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analytic approaches to identify significantly enriched pathways. Expression of genes associated with relevant pathways was evaluated using real-time quantitative reverse-transcription polymerase chain reaction (RT-qPCR). RESULTS: RNAseq analysis identified 25 DEGs, and GO and KEGG analytic approaches showed related genes expressed in two general pathways. First, pathways broadly related to urokinase and coagulation included the genes PLAU, PLAUR, and SerpinB2. In RT-qPCR analysis, these genes were induced by PgLPS over time (4-24 h), and these data were consistent with PgLPS induction of cell migration. Second, interleukin-1 (IL-1) receptor binding and cytokine-activity pathways were also enriched. Genes associated with these pathways included IL36G, IL1B, IL1RN, and CXCL14. RT-qPCR analysis confirmed PgLPS induction of genes associated with the IL-1family. When expression of IL1B and IL36G genes was examined in relation to their respective antagonists, only IL36G gene expression was increased. CXCL14 gene expression was reduced over time, and this was consistent with RNAseq analysis. CONCLUSIONS: Genes associated with significantly enriched GO and KEGG pathways are relevant to aspects of periodontal disease (PDD) pathogenesis. First, PgLPS induced expression of PLAU, PLAUR, and SerpinB2, and these changes were consistent with an increase in cell migration that was found. Second, both IL36G and IL1B gene expression was significantly induced, but only IL36G in relation to its selective antagonist (IL36RN) was increased. These data support that early upregulation of IL36G may serve as an alarmin that can drive early innate immune inflammatory responses in HGK. Further in vivo testing of these findings is ongoing.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".