Subinhibitory concentrations of tetracyclines induce <scp>lipopolysaccharide</scp> shedding by <i>Porphyromonas gingivalis</i> and modulate the host inflammatory response
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Antibiotics at below minimal inhibitory concentrations (MICs) may induce various biological responses in bacteria. In this study, we hypothesized that subinhibitory concentrations (subICs) of tetracycline and doxycycline induce the shedding of lipopolysaccharide (LPS) by Porphyromonas gingivalis and, as a consequence, may contribute to enhancing the host inflammatory response associated with periodontitis. MATERIAL AND METHODS: A polymyxin-based enzyme-linked immunosorbent assay was used to quantify LPS shedding by P. gingivalis grown in the presence of subICs of tetracycline and doxycycline. A macrophage model was used to show that tetracycline- and doxycycline-mediated LPS shedding by P. gingivalis can induce cytokine secretion. The secretion of interleukin (IL)-1β, IL-8, and tumor necrosis factor-α was quantified by enzyme-linked immunosorbent assay. RESULTS: LPS was shed spontaneously in a time-dependent way by P. gingivalis during growth. LPS shedding was significantly increased by growth in the presence of subICs of tetracycline and doxycycline corresponding to 1/20 of their MICs (0.025 μg/mL for tetracycline and 0.0125 μg/mL for doxycycline). This shedding was not associated with an increased rate of bacterial cell lysis. Stimulating macrophages with a P. gingivalis culture supernatant induced the secretion of IL-1β, IL-8 and tumor necrosis factor-α when the bacteria were grown in the presence of 1/20 MIC of the antibiotics. CONCLUSION: Our study showed that growing P. gingivalis in the presence of subICs of either tetracycline or doxycycline induces LPS shedding. Shed LPS may in turn increase cytokine secretion in a macrophage model.
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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".