Temperature-responsive control of <i>Pseudomonas aeruginosa</i> virulence determinants through the stabilization of quorum sensing transcriptional regulator RhlR
Bibliographic record
Abstract
Abstract The versatile bacterium Pseudomonas aeruginosa thrives in diverse environments and is notably recognized for its role as an opportunistic pathogen. In line with its adaptability, P. aeruginosa produces various exoproducts crucial for survival and virulence, several of which regulated through quorum sensing (QS). These factors are also regulated in response to environmental cues, such as temperature changes. As a pathogen, P. aeruginosa is generally thought to activate its virulence factors at temperatures akin to warm-blooded hosts rather than environmental temperatures. Recent studies elucidated the functional structure of the QS transcriptional regulator RhlR, which depends on the stabilizing effects of its cognate autoinducing ligand, N -butanoyl-L-homoserine lactone (C 4 -HSL), and of the moonlighting chaperone PqsE. Given the influence of temperature on biomolecular dynamics, we investigated how it affects RhlR activity using the RhlR-regulated phzA1 promoter as a proxy. Unexpectedly, we found that RhlR activity is higher at 25°C than at 37°C. This temperature-dependent regulation likely stems from altered RhlR turnover, with the presence of PqsE extending RhlR activity tenfold from its basal level at 37°C to that observed at 25°C. This lower, environmental-like temperature promotes increased affinity between RhlR and C 4 -HSL, a trait significantly compromised in the absence of PqsE. These results suggest that this response depends on the structural integrity of the complex, indicating that temperature functions as an additional regulating and stabilizing factor of RhlR function. Accordingly, lower growth temperature fails to increase the activity of a structurally stabilized version of RhlR. The thermoregulation aspect of RhlR activity and signalling impacts the virulence profile of a mutant unable to produce C 4 -HSL, underscoring its significance in bacterial behaviours and potentially conferring an evolutionary advantage. Author Summary Pseudomonas aeruginosa is recognized for its capacity to colonize vastly different environments, thereby encountering a range of temperatures. The bacterium’s ability to adapt to these settings necessitates finely regulated gene expression. Within this regulatory framework lies quorum sensing (QS), the intercellular communication system used by P. aeruginosa to orchestrate the expression of genes responsible for producing diverse exoproducts, including the blue phenazine pyocyanin. RhlR primarily governs the expression of genes required for pyocyanin production, including the phz1 operon. Unlike other QS regulators, RhlR possesses a distinctive characteristic – in addition to its cognate signalling ligand C 4 -HSL, it depends on the presence of the chaperone-like protein PqsE for stability and activity. This intrinsic instability implies that RhlR may be susceptible to external influences that can modulate its function. Indeed, a lower culture temperature, akin to an environmental-like condition, induces the transcription of the phz1 operon, used as a proxy for RhlR activity. Using a combination of genetic approaches, we present evidence that this thermoregulation is due to an impact on the stability of the RhlR/C 4 -HSL/PqsE complex. We further show the biological effect of this regulation mechanism in an infection setting, which could underscore a relevant role for other bacterial behaviours.
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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".