A genetic screen identifies a role for <i>oprF</i> in <i>Pseudomonas aeruginosa</i> biofilm stimulation by subinhibitory antibiotics
Bibliographic record
Abstract
Abstract Biofilms are surface-associated communities of bacteria that grow in a self-produced matrix of polysaccharides, proteins, and extracellular DNA (eDNA). Sub-minimal inhibitory concentrations (sub-MIC) of antibiotics induce biofilm formation, indicating a potential defensive response to antibiotic stress. However, the mechanisms behind sub-MIC antibiotic-induced biofilm formation are unclear. We show that treatment of Pseudomonas aeruginosa with multiple classes of sub-MIC antibiotics with distinct targets induces biofilm formation. Further, addition of exogenous eDNA or cell lysate failed to increase biofilm formation to the same extent as antibiotics, suggesting that the release of cellular contents by antibiotic-driven bacteriolysis is insufficient. Using a genetic screen to find stimulation-deficient mutants, we identified the outer membrane porin OprF and the extracytoplasmic function sigma factor SigX as important for the phenotype. Similarly, loss of OmpA – the Escherichia coli OprF homologue – prevented sub-MIC antibiotic stimulation of E. coli biofilms. The C-terminal PG-binding domain of OprF was dispensable for biofilm stimulation. Our screen also identified the periplasmic disulfide bond-forming enzyme DsbA and a predicted cyclic-di-GMP phosphodiesterase encoded by PA2200 as essential for biofilm stimulation. The phosphodiesterase activity of PA2200 is likely controlled by a disulfide bond in its regulatory domain, and folding of OprF is influenced by disulfide bond formation, connecting the mutant phenotypes. Addition of the reducing agent dithiothreitol prevented sub-MIC antibiotic biofilm stimulation. Finally, we show that activation of a c-di-GMP-responsive promoter follows treatment with sub-MIC antibiotics in the wild-type but not an oprF mutant. Together, these results show that antibiotic-induced biofilm formation is likely driven by a signalling pathway that translates changes in periplasmic redox state into elevated biofilm formation through increases in c-di-GMP. Significance Bacterial biofilms cause significant treatment challenges in clinical settings due to their ability to resist antibiotic killing. Paradoxically, sub-inhibitory levels of a number of chemically distinct antibiotics, with different modes of action, can stimulate biofilm formation of multiple bacterial species. In this work we used the biofilm forming opportunistic pathogen Pseudomonas aeruginosa to look for mutants blind to sub-inhibitory antibiotic exposure, failing to demonstrate biofilm stimulation in response to 3 different antibiotics. We identified key players in this response, including the outer membrane porin OprF and the sigma factor SigX. Notably, the hits from our mutant screen connect changes in periplasmic redox state to elevated biofilm formation via c-di-GMP signaling. By understanding these underlying mechanisms, we can better strategize interventions against biofilm-mediated antibiotic resistance in P. aeruginosa and other bacterial species.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".