The Observation
When I sequenced Pseudomonas aeruginosa isolates from cystic fibrosis patients, I found that lasR mutations - which seemingly cripple quorum sensing and bacterial communication - were remarkably common.
This seems paradoxical. Quorum sensing coordinates virulence, biofilm formation, and resource acquisition. These are essential functions for bacterial survival and infection. Yet these "defective" mutants thrive in the CF lung environment.
Do these mutations confer an advantage of some sort?
My hypothesis is that these mutants become metabolic cheaters, exploiting public goods produced by cooperative neighbors without paying the production costs. Some mutations might also help bacteria evade immune detection by reducing virulence factor expression.
Research Questions
Primary Question
Why do bacteria evolve to lose seemingly essential functions, and how does this become advantageous in the cystic fibrosis lung environment?
Secondary Question
How do different clinically observed lasR mutations alter quorum-sensing behavior and fitness in distinct, mutation-specific ways rather than causing a uniform loss of function?
The A231V mutation sits in the ligand-binding domain, while R216W affects the DNA-binding region. When I analyzed my data, I discovered these mutations produce distinct growth curves, suggesting they impact cellular signaling in fundamentally different ways rather than converging on the same loss-of-function outcome.
Evolution has multiple pathways to similar selective pressures.
The Mutations
I reconstructed three clinically observed lasR mutations in controlled laboratory strains using allelic exchange mutagenesis:
Alanine to Valine substitution at position 231. Affects how the protein binds to its signaling molecule.
Arginine to Tryptophan substitution at position 216. Impacts how the protein interacts with DNA to regulate genes.
Combined mutation with an additional Methionine to Isoleucine change. Creates a compound regulatory effect.
These mutations were chosen because they represent different functional domains and were frequently observed in clinical isolates. By reconstructing them in isogenic backgrounds, I can isolate the effects of each mutation without confounding genetic variation.
Experimental Approach
The project progressed through five main experimental phases:
Engineer Mutations in the Gene
Used allelic exchange mutagenesis to reconstruct the three clinically observed mutations in PAO1 laboratory strains. This involved preparing competent cells, running PCR amplifications, assembling homology arms and selection markers via Gibson assembly, and performing electroporation. Screened 20+ colonies via colony PCR genotyping to identify recombinants and verified through sequencing.
Build Reporter Plasmids
Designed and constructed four fluorescent reporter strains to track expression of different quorum-sensing regulons. Each reporter was built through PCR amplification, Gibson assembly, and sequence verification. These reporters allow real-time visualization of gene expression changes caused by each mutation.
Measure Gene Expression
Independently set up, executed, and analyzed quantitative plate reader assays measuring optical density and fluorescence across 12-24 hour growth curves. Performed data analysis using GraphPad Prism to generate growth curves, normalize fluorescence, and perform statistical comparisons between mutant and wild-type strains.
Test Competitive Advantage
Designed and performed flow cytometry-based competition experiments with marked strains to analyze relative fitness. Testing whether mutant strains gain fitness advantages in co-culture with wild-type strains - the "metabolic cheater" hypothesis where mutants exploit public goods produced by cooperative neighbors.
Measure Virulence Factors
Assess how each mutation affects the production of virulence factors controlled by quorum sensing. This helps understand whether reduced virulence factor expression contributes to immune evasion and survival in the CF lung environment.
Techniques & Methods
This research required integrating molecular techniques with quantitative analysis and evolutionary thinking:
Molecular Cloning
- PCR amplification
- Gibson assembly
- Primer design (Benchling)
- Plasmid minipreps
- Sequence verification
Genetic Engineering
- Allelic exchange mutagenesis
- Electroporation
- Competent cell preparation
- Colony PCR genotyping
- Strain banking (-80°C)
Quantitative Analysis
- Plate reader assays (OD + fluorescence)
- Flow cytometry
- Growth curve analysis
- GraphPad Prism
- Statistical comparisons
Microbiology
- Media preparation (LB, PM, CAA)
- Aseptic technique
- Antibiotic selection
- Competition assays
- Detailed lab notebook
Key Findings
Distinct Regulatory Phenotypes
Rather than converging on identical dysfunction, the mutations produce distinct phenotypes. The A231V mutation in the ligand-binding domain and R216W mutation in the DNA-binding region show fundamentally different impacts on cellular signaling. Bacteria aren't simply losing quorum sensing - they're reconfiguring it in ways specific to each mutation.
Multiple Evolutionary Pathways
Different mutations in the same gene create distinct growth curves and regulatory outcomes. This means evolution has multiple pathways to achieve similar selective pressures. Each mutation represents a different "solution" to the challenge of surviving in the CF lung environment.
Competitive Advantage in Co-culture
Preliminary observations from co-culture experiments suggest that some mutant strains do outcompete wild-type in mixed cultures, supporting the metabolic cheating hypothesis. Once I complete these studies and gather more data, I'll have a clearer picture of how these mutations contribute to bacterial fitness in polymicrobial environments.
This research taught me that evolutionary success isn't about maintaining all functions - it's about optimizing fitness in a specific environment. Loss-of-function mutations can be advantageous when they allow bacteria to exploit cooperative neighbors or evade immune detection.
Why This Matters
Understanding these mutations has implications beyond basic science:
Gene Regulation Principles
We learn how mutations alter the function of transcriptional regulators, revealing general principles about gene regulation networks.
Evolutionary Selection
Understanding why certain mutants are selected helps predict bacterial evolution and adaptation in clinical settings.
Therapeutic Targets
These findings could inform new approaches to treating chronic P. aeruginosa infections by targeting quorum sensing or exploiting evolutionary trade-offs.
CF Disease Progression
Understanding how bacterial populations evolve in the CF lung could help predict disease progression and inform treatment strategies.
The Bigger Picture
This work connects to broader questions about how pathogens evolve resistance and adapt to host environments. Understanding these evolutionary dynamics is essential for developing durable therapies that can stay ahead of bacterial adaptation.
Mentorship
Dr. Ajai Dandekar
Principal Investigator, Dandekar Lab
Dr. Dandekar's lab studies Pseudomonas aeruginosa signaling and evolution through genomic analysis and experimental evolution. He guided me in devising experiments and making strategic decisions about the research direction, while encouraging me to execute all the work independently.