DPR-MEDIATED H <sub>2</sub> O <sub>2</sub> RESISTANCE CONTRIBUTES TO STREPTOCOCCI SURVIVAL IN A CYSTIC FIBROSIS AIRWAY MODEL SYSTEM
Bibliographic record
Abstract
ABSTRACT The cystic fibrosis (CF) lung environment is conducive to the colonization of bacteria as polymicrobial biofilms, which are associated with poor clinical outcomes for persons with CF (pwCF). Streptococcus spp. is highly prevalent in the CF airway, but its role in the CF lung microbiome is poorly understood. Some studies have shown Streptococcus spp. to be associated with better clinical outcomes for pwCF, while others show that high abundance of Streptococcus spp. is correlated with exacerbations. Our lab previously reported a polymicrobial culture system consisting of four CF-relevant pathogens that can be used to study microbial behavior in a more clinically relevant setting. Here, we use this model system to identify genetic pathways that are important for Streptococcus sanguinis survival in the context of the polymicrobial community. We identified genes related to reactive oxygen species (ROS) as differentially expressed in S. sanguinis monoculture versus growth of this microbe in the mixed community. Genetic studies identified Dpr as important for S. sanguinis survival in the community. We show that Dpr, a DNA binding ferritin-like protein, and PerR, a peroxide-responsive transcriptional regulator of Dpr, are important for protecting S. sanguinis from phenazine-mediated toxicity in co-culture with P. aeruginosa and when exposed to ROS, both of which mimic the CF lung environment. Characterizing such interactions in a clinically relevant model system contributes to our understanding of microbial behavior in the context of polymicrobial biofilm infections. IMPORTANCE Streptococcus spp. is recognized as a highly prevalent pathogen in CF airway infections. However, its role in clinical outcomes for pwCF is poorly understood. Here, we leverage a polymicrobial community system previously developed by our group to model CF airway infections as a tool to investigate a Pseudomonas-Streptococcus interaction involving ROS. We show that protection against ROS is required for S. sanguinis survival in a clinically relevant polymicrobial system. Using this model system to study interspecies interactions contributes to our broader understanding of the complex role of Streptococcus spp. in the CF lung.
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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".