Immunomodulation of Human Alveolar Macrophage Response to the SARS-CoV-2 S Protein by Oral Microbiota
Bibliographic record
Abstract
Background: The oral microbiota is formed by microorganisms that are normal inhabitants of the oral cavity. Recent studies have shown that these organisms are highly dynamic and not strictly confined to the oral cavity, being found in the lung alveoli where they take residence. The severity of coronavirus disease 2019 (COVID-19), caused by the inflammatory response to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection, can be affected by alterations in the oral microbiota. We aimed to study the relationship between the oral microbiota and the inflammatory response to SARS-CoV-2 S protein by human alveolar macrophages. Methods: Human alveolar macrophages (Daisy cells) transfected with a nuclear factor kappa B (NF-кB) reporter plasmid were first exposed to bacteria belonging to the oral microbiota: Corynebacterium spp., Prevotella oralis , Streptococcus viridans , Veillonella spp., and Fusobacteriu spp., and then stimulated to the SARS-CoV-2 S protein trimer. Results: We observed an overall decrease in the activation of inflammatory transcription factor NF-кB when alveolar macrophages were exposed to oral commensals. Conclusions: These findings demonstrate an immunomodulatory role of the oral microbiota in the response to SARS-CoV-2 by alveolar macrophages, and may offer alternative therapeutic options for treating or preventing severity in COVID-19. Clin Infect Immun. 2024;9(1):16-19 doi: https://doi.org/10.14740/cii167
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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 | 0.003 | 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) | 0.000 | 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".