Role of Oral Microbiota Biofilms in Recurrent Tonsillitis and Sleep‐Disordered Breathing in Children
Bibliographic record
Abstract
Aims & Hypothesis The palatine tonsils are aggregates of lymphoid tissue in the lateral oropharynx. In children, tonsillar enlargement can lead to upper airway obstruction and sleep‐disordered breathing (SDB). The reason for tonsillar enlargement in some children, and not others is unknown. We have investigated the role of the tonsillar microbiota in tonsillar enlargement. The role of bacterial biofilms in RT and SDB was investigated through comparisons of tonsil histopathology, mixed‐species biofilm's microbiota composition and biofilm phenotypes. We hypothesized that the SDB tonsillar phenotype has distinct biofilm forming bacterial communities driving tonsillar hyperplasia compared to recurrent tonsillitis. Methods Tonsils were acquired via tonsillectomy from children with RT or SDB. Tonsils were stained to assess florid reactive lymphoid hyperplasia (FRLH). In situ microbiota characterization was assessed by 16S rRNA gene sequencing and overall bacterial biomass was assessed by 16S qPCR. Tonsillar bacteria were extracted and grown in BHI media for in vitro assays. Oral microbiota biofilms were characterized using the Calgary Biofilm Device and “O’Toole” assay. Biofilm biomass was assessed by spectrophotometry using crystal violet staining. Bacterial adhesion/invasion assays were performed by infecting pharyngeal carcinoma cell lines Det‐562 with tonsil microbiota from RT and SDB groups. Immune profile of Det‐562 cells infected with RT and SDB microbiota (t=3 hours) was assessed by cytokine multiplex analysis. Results Histopathological analysis showed higher FRLH of the enlarged lymph node in the SDB group compared to RT group. Microbiota analysis has shown that the tonsil's microbiota composition and bacterial biomass did not significantly differ between tonsils that were taken out due to SDB and those taken out for recurrent tonsillitis. Calgary Biofilm Device biofilm assays indicated that the SDB multi‐species biofilms have higher biomass compared to RT biofilms. Microbiota from SDB group were more adhesive/invasive to Det‐562 cells compared to RT bacteria after 3 hours incubation. Cytokine profiling showed increased expression of granulocyte‐macrophage colony‐stimulating factor (GM‐CSF), pro‐inflammatory cytokines IL‐1β, IL‐2, IL‐12 and interferon gamma (IFNγ) in Det‐562 cells exposed to SDB microbiota compared to RT. Conclusions Our results indicate that SDB and RT tonsils have distinct histopathological signatures. In situ tonsillar microbiota composition and bacterial load did not differ between SDB and RT groups. In vitro assays showed that SDB tonsils exhibit increases in both multi‐species biofilm mass and bacterial adhesion/invasion to epithelial surfaces. These results also suggest that SDB bacterial biofilms lead to greater inflammation of the tonsils, confirmed by the elevated levels of FRLH and increased expression of pro‐inflammatory mediators. This study sheds light on the role pathobionts released from dysbiotic oral microbiota biofilms in the development of tonsillitis.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".