Porphyromonas gingivalis -Stimulated Hyperglycemic Microenvironment Alters the Immunometabolism of Dendritic Cells
Bibliographic record
Abstract
Introduction: Periodontitis in patients with diabetes mellitus results in chronic inflammation, which is the central issue in developing an efficient and consistent treatment plan. Dendritic cells (DC) are antigen presenting cells that initiate the immune inflammatory responses and contribute to the pathogenesis of both diseases. In this study, we investigated the impact of hyperglycemic microenvironment on DC immunometabolism, the cell phenotypes and immunogenic functions. Methodology: Human monocyte differentiated DC and mice bone marrow derived DC were cultured in the presence of 5.5-, 11-, and 25- mM glucose to simulate diabetic microenvironment. Cells were activated with advanced-glycation-end product (AGE) and lipopolysaccharides (LPS) from Porphyromanas gingivalis for 24 hours and processed for transcription, metabolic and microscopic analysis. Expression of activation markers (CD80, CD83, CD86, HLA-DR) and proteins involved in glycolysis (HK2, LDHA, GLUT1) in DC were calculated by qRT-PCR. Lactic acid production and OXPHOS assays, including Seahorse metabolic flux analyzer were utilized to determine the effects on metabolism. Impact on the phagocytic capacity was analyzed using fluorescent microspheres uptake. Cytokine expressions for tumor necrosis factor alpha [TNF- α ], interleukin [IL]-1 β , IL-6, IL-10, and Interferon gamma [IFN- γ ] were evaluated in cell supernatants from DC and DC-T cell coculture. Results: Under simulated hyperglycemic microenvironment an increase in cell dendrite extensions, and activation markers were upregulated in both monocytes differentiated DC and BMDC. There was a significant increase in glycolysis as evident from the gene expression, cell metabolic flux, and lactic acid production. Cell OXPHOS activities was reduced to compensate for the increase in glycolysis. Pro-inflammatory cytokines (TNF- α and IL-1 β ) were significantly increased and this increase was directly proportional to the glucose concentrations. Whereas, phagocytic capability of DC, and their ability to activate T cells decreased with hyperglycemia. Conclusions: Hyperglycemic microenvironment resulted in DC changes with increased expressions of activation markers, glycolytic metabolism, and increased pro-inflammatory cytokines, while impairing phagocytosis and adaptive immunity induction. BMDC and human monocyte differentiated-DC exhibit similar responses toward hyperglycemia, AGE, and LPS. This work emphasizes that diabetes mellitus has an inflammatory impact on DC immunometabolism and immunogenic functions.
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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".