NtrBC Regulates Invasiveness and Virulence of Pseudomonas aeruginosa During High-Density Infection
Bibliographic record
Abstract
Pseudomonas aeruginosa is an opportunistic pathogen that is a major cause of nosocomial and chronic infections contributing to morbidity and mortality in cystic fibrosis patients. One of the reasons for its success as a pathogen is its ability to adapt to a broad range of circumstances. Here, we show the involvement of the general nitrogen regulator NtrBC, which is structurally conserved but functionally diverse across species, in pathogenic and adaptive states of P. aeruginosa. The role of NtrB and NtrC was examined in progressive or chronic infections, which revealed that mutants (dntrB, dntrC and dntrBC) were reduced in their ability to invade and cause damage in a high-density abscess model in vivo. Progressive infections were established with mutants in the highly virulent PA14 genetic background, whereas chronic infections were established with mutants in the less virulent clinical isolate LESB58 genetic background. Characterization of adaptive lifestyles in vitro confirmed that the double dntrBC mutant demonstrated >40% inhibition of biofilm formation, a nearly complete inhibition of swarming motility, and a modest decrease and altered surfing motility colony appearance; with the exception of swarming, single mutants generally had more subtle or no changes. Transcriptional profiles of deletion mutants under swarming conditions were defined using RNA-Seq and unveiled dysregulated expression of hundreds of genes implicated in virulence in PA14 and LESB58 chronic lung infections, as well as carbon and nitrogen metabolism. Thus, transcriptional profiles were validated by testing responsiveness of mutants to several key intermediates of central metabolic pathways. These results indicate that NtrBC is a global regulatory system involved in both pathological and physiological processes relevant to the success of Pseudomonas in high-density infection.
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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 | 0.001 |
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".