Modulation of <i>Pseudomonas aeruginosa</i> gene expression by host microflora through interspecies communication
Bibliographic record
Abstract
The change in gene expression patterns in response to host environments is a prerequisite for bacterial infection. Bacterial diseases often occur as an outcome of the complex interactions between pathogens and the host. The indigenous, usually non-pathogenic microflora is a ubiquitous constituent of the host. In order to understand the interactions between pathogens and the resident microflora and how they affect the gene expression patterns of the pathogens and contribute to bacterial diseases, the interactions between pathogenic Pseudomonas aeruginosa and avirulent oropharyngeal flora (OF) strains isolated from sputum samples of cystic fibrosis (CF) patients were investigated. Animal experiments using a rat lung infection model indicate that the presence of OF bacteria enhanced lung damage caused by P. aeruginosa. Genome-wide transcriptional analysis with a lux reporter-based promoter library demonstrated that approximately 4% of genes in the genome responded to the presence of OF strains using an in vitro system. Characterization of a subset of the regulated genes indicates that they fall into seven functional classes, and large portions of the upregulated genes are genes important for P. aeruginosa pathogenesis. Autoinducer-2 (AI-2)-mediated quorum sensing, a proposed interspecies signalling system, accounted for some, but not all, of the gene regulation. A substantial amount of AI-2 was detected directly in sputum samples from CF patients and in cultures of most non-pseudomonad bacteria isolated from the sputa. Transcriptional profiling of a set of defined P. aeruginosa virulence factor promoters revealed that OF and exogenous AI-2 could upregulate overlapping subsets of these genes. These results suggest important contributions of the host microflora to P. aeruginosa infection by modulating gene expression via interspecies communications.
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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".