Recombinant Legionella pneumophila subsp. pneumophila Methionine--tRNA ligase (metG), partial

What is the function of Methionine--tRNA ligase (metG) in Legionella pneumophila?

Methionine--tRNA ligase (metG) in L. pneumophila is an essential aminoacyl-tRNA synthetase that catalyzes the attachment of methionine to its cognate tRNA molecule. This reaction is critical for protein biosynthesis as it ensures the correct incorporation of methionine during translation. The enzyme participates in both initiation and elongation phases of protein synthesis, as methionine serves as the first amino acid in prokaryotic protein synthesis. In L. pneumophila, functioning protein synthesis machinery is particularly important during intracellular replication within host cells, where the bacterium must rapidly multiply to establish infection .

The gene is relatively conserved across bacterial species, though specific sequence variations exist that may affect substrate specificity or enzymatic efficiency. In the context of L. pneumophila's highly recombinogenic genome, metG shows less variability compared to outer membrane proteins or virulence factors, consistent with its essential housekeeping function .

How is recombinant metG typically expressed and purified for research purposes?

Recombinant metG from L. pneumophila subsp. pneumophila is typically expressed in Escherichia coli expression systems using vectors that allow for inducible expression. The methodology involves:

• Cloning Strategy: The partial or complete metG gene is PCR-amplified from L. pneumophila genomic DNA using primers that incorporate appropriate restriction sites. The amplified product is then cloned into an expression vector (commonly pET series vectors) containing an affinity tag (His-tag, GST, etc.) for purification.

• Expression Conditions: Optimal expression is achieved in E. coli strains such as BL21(DE3) or Rosetta, induced with IPTG (typically 0.5-1.0 mM) at lower temperatures (16-25°C) to enhance protein solubility.

• Purification Protocol: A typical purification workflow includes:

  • Cell lysis using sonication or pressure-based methods in a buffer containing 50 mM Tris-HCl (pH 8.0), 300 mM NaCl, and 10 mM imidazole

  • Immobilized metal affinity chromatography (IMAC) using Ni-NTA resin for His-tagged proteins

  • Further purification by ion exchange chromatography and/or size exclusion chromatography

  • Buffer exchange to a storage buffer containing 20 mM Tris-HCl (pH 7.5), 100 mM NaCl, 5 mM DTT, and 50% glycerol

Protein purity is typically assessed by SDS-PAGE, and functionality can be verified through aminoacylation activity assays measuring the attachment of methionine to tRNA substrates.

What are the common challenges in expressing functional recombinant metG from L. pneumophila?

Researchers commonly encounter several challenges when expressing recombinant metG from L. pneumophila:

• Protein Solubility: MetG often forms inclusion bodies in heterologous expression systems. This can be addressed by:

  • Lowering induction temperature (16-18°C)

  • Reducing IPTG concentration (0.1-0.3 mM)

  • Co-expression with chaperones (GroEL/GroES system)

  • Using solubility-enhancing fusion tags (SUMO, MBP)

• Protein Stability: The enzyme may show reduced stability in purified form. Solutions include:

  • Addition of stabilizing agents (glycerol 10-20%, reducing agents like DTT)

  • Optimization of buffer conditions (pH, ionic strength)

  • Storage at -80°C in small aliquots to avoid freeze-thaw cycles

• Codon Usage Bias: L. pneumophila has different codon preferences compared to E. coli, potentially leading to translational stalling. Using Rosetta or other strains that supply rare tRNAs can help overcome this issue.

• Contamination with E. coli MetG: Endogenous E. coli MetG may co-purify with the recombinant protein. Stringent purification protocols and specific activity assays that can distinguish between L. pneumophila and E. coli enzymes are necessary.

The genetic diversity arising from L. pneumophila's high recombination rates (>96% of SNPs derived from recombination events) can also impact protein expression, as different strains may contain variant metG alleles with different expression characteristics .

How does homologous recombination affect the metG gene in different L. pneumophila sequence types?

Homologous recombination plays a significant role in shaping L. pneumophila genome evolution, with over 96% of SNPs in major disease-associated sequence types (STs) being attributed to recombination events . Regarding the metG gene specifically:

• Recombination Frequency: Analysis of six major disease-associated STs (ST1, ST23, ST37, ST42, ST62, and ST578) shows that metG is not located in a recombination "hotspot" region, consistent with its essential function. Essential genes typically show lower recombination rates compared to genes encoding surface-exposed proteins or virulence factors .

• Sequence Conservation: Compared to outer membrane proteins or Dot/Icm effectors, the metG gene shows higher sequence conservation across different STs. This conservation reflects selective pressure to maintain aminoacyl-tRNA synthetase activity.

• Impact on Protein Function: When recombination does occur in metG, it typically involves exchange with closely related donors (often from the same clade), minimizing functional disruption. This pattern contrasts with lipopolysaccharide (LPS) genes or outer membrane proteins where more diverse recombination events are tolerated .

The limited recombination in metG contrasts with genomic "hotspots" that include regions containing outer membrane proteins, the lipopolysaccharide (LPS) region, and Dot/Icm effectors, which are under stronger diversifying selection pressures from host immune systems .

What experimental approaches can be used to study the role of metG in L. pneumophila pathogenesis?

Advanced methodologies to investigate metG's role in L. pneumophila pathogenesis include:

• CRISPR/Cas9-based Genetic Manipulation:

  • Generate conditional metG mutants using inducible promoters (as complete deletion may be lethal)

  • Create point mutations in catalytic domains to study structure-function relationships

  • Employ CRISPR interference (CRISPRi) for partial knockdown of metG expression

This approach leverages techniques similar to those used in studies of TRIF and MyD88 knockout macrophages responding to L. pneumophila infection .

• Cell Infection Models:

  • Human macrophage-like cell lines (U937, THP-1)

  • Primary alveolar macrophages

  • Amoeba models (Acanthamoeba castellanii, Vermamoeba vermiformis)

These models can be used to assess how metG alterations affect intracellular replication, measured by colony-forming unit (CFU) assays at various time points post-infection.

• Temporal Transcriptomics and Proteomics:

  • RNA-seq analysis of metG expression during different stages of infection

  • Ribosome profiling to assess translation efficiency

  • Comparative proteomics of wild-type vs. metG-altered strains

• MetG Inhibitor Studies:

  • Evaluate selective inhibitors of bacterial MetG in infection models

  • Assess bacterial survival and host immune response modulation

• Host-Pathogen Interaction Assays:

  • Measure cytokine production (IL-6, TNFα, IL-1β, IL-10, IL-8) in response to L. pneumophila with altered metG

  • Assess activation of pathogen recognition receptors like Toll-like receptors

  • Evaluate inflammasome activation and pyroptosis induction

Data from such studies can be analyzed using frameworks similar to those employed in studies of macrophage responses to L. pneumophila, where cytokine production was measured by ELISA at multiple time points (0, 1, 3, 6, 9, 12, 24, and 48 hours post-infection) .

How do sequence variations in metG correlate with virulence across different L. pneumophila isolates?

The correlation between metG sequence variations and virulence across L. pneumophila isolates remains an active area of research. Current evidence suggests:

• Sequence Type Associations: While certain sequence types (STs) such as ST213 and ST222 show increased prevalence in clinical settings, with ST222 linked to at least five Legionnaires' disease outbreaks in the U.S. between 2002 and 2021, direct correlations between metG variations and these epidemiological patterns have not been firmly established .

• Geographic Distribution Patterns: The expanded geographic distribution of specific STs (e.g., ST213 and ST222 in Michigan, New York, Minnesota, and Ohio) suggests potential adaptive advantages, but comprehensive analysis of metG sequence variations across these isolates is needed to determine if metG plays a role in this expansion .

• Methodological Approach for Correlation Analysis:

  • Whole genome sequencing of clinical and environmental isolates

  • Phylogenetic analysis of metG sequences across different STs

  • Statistical correlation of specific metG variants with clinical outcomes

  • Experimental validation using recombinant strains with metG variants

• Functional Implications of Variations: Some amino acid substitutions in MetG could potentially affect:

  • Catalytic efficiency

  • Substrate specificity

  • Protein stability under stress conditions

  • Interaction with other cellular components

A comprehensive analysis would require:

While the high nucleotide identity within sequence types (99.92% for both ST222 and ST213) suggests limited genetic diversity, subtle variations in essential genes like metG could still contribute to functional differences affecting virulence or environmental persistence .

What techniques can be used to study the interaction between recombinant MetG and the L. pneumophila type IV secretion system?

Investigating potential interactions between MetG and the Dot/Icm type IV secretion system (T4SS) of L. pneumophila requires sophisticated methodological approaches:

• Protein-Protein Interaction Assays:

  • Bacterial two-hybrid screening to identify T4SS components that interact with MetG

  • Co-immunoprecipitation with tagged MetG to pull down interacting partners

  • Proximity labeling approaches (BioID, APEX) to identify proteins in close proximity to MetG in vivo

  • Surface plasmon resonance (SPR) or microscale thermophoresis (MST) for quantitative binding analysis

• Functional Secretion Assays:

  • Translocation assays using β-lactamase or adenylate cyclase fusions with MetG

  • CyaA reporter system to detect potential secretion of MetG into host cells

  • Immunofluorescence microscopy to track MetG localization during infection

• Structural Biology Approaches:

  • Cryo-electron microscopy of T4SS complexes in the presence of MetG

  • X-ray crystallography of MetG in complex with T4SS components

  • Hydrogen-deuterium exchange mass spectrometry to map interaction interfaces

• Genetic Manipulation Strategies:

  • Construction of dot/icm mutants to assess impact on MetG function

  • Site-directed mutagenesis of potential binding sites on MetG

  • CRISPR/Cas9-based screening to identify genetic dependencies

The importance of the Dot/Icm system in L. pneumophila pathogenesis is well-established, and it serves as a critical conduit for delivering bacterial effectors to host cells. While traditional aminoacyl-tRNA synthetases like MetG are not typically considered T4SS substrates, moonlighting functions have been identified for some aminoacyl-tRNA synthetases in other pathogens, warranting investigation of potential non-canonical roles for MetG .

What are the optimal conditions for assaying MetG enzymatic activity in vitro?

Establishing optimal conditions for assaying L. pneumophila MetG enzymatic activity requires careful optimization of multiple parameters:

• Aminoacylation Assay Components:

  • Purified recombinant L. pneumophila MetG (typically 50-200 nM)

  • tRNAᴹᵉᵗ substrate (either purified from L. pneumophila or transcribed in vitro, 1-5 μM)

  • L-methionine substrate (typically 50-200 μM)

  • ATP (2-5 mM)

  • Magnesium chloride (5-10 mM)

  • Potassium chloride (50-100 mM)

  • Buffer system (commonly HEPES or Tris-HCl, pH 7.5-8.0)

  • DTT or β-mercaptoethanol (1-5 mM) as reducing agent

• Reaction Conditions:

  • Temperature: 30-37°C (optimal temperature for L. pneumophila proteins)

  • Reaction time: Typically 10-30 minutes with time points collected at regular intervals

  • pH optimum: Generally pH 7.5-8.0, though this should be empirically determined

• Detection Methods:

  • Radioactive assay: Using ³H-labeled or ¹⁴C-labeled methionine with filter binding and scintillation counting

  • Colorimetric pyrophosphate detection: Measuring the release of pyrophosphate during aminoacylation

  • HPLC analysis: Separating charged from uncharged tRNA

  • Mass spectrometry: Detecting methionylated tRNA products

• Control Reactions:

  • No enzyme control

  • No tRNA control

  • No ATP control

  • Heat-inactivated enzyme control

  • E. coli MetG comparison (to benchmark activity)

Kinetic parameters (Kₘ and kcat) should be determined for both methionine and tRNAᴹᵉᵗ substrates to fully characterize the enzyme. These parameters may vary between different sequence types of L. pneumophila, potentially reflecting adaptations to different environmental niches or host interactions.

How can researchers differentiate between the impacts of homologous recombination and point mutations when studying metG evolution?

Distinguishing between homologous recombination and point mutations in metG evolution requires sophisticated bioinformatic and experimental approaches:

• Computational Detection Methods:

  • Sequence-based approaches: Tools like Gubbins and BRATNextGen can identify recombination events based on SNP density patterns. Gubbins detects regions with elevated SNP density, while BRATNextGen focuses on sequence similarity patterns .

  • Phylogenetic incongruence methods: Comparing phylogenetic trees constructed from different regions of the genome to identify sections with discordant evolutionary histories.

  • Population genetics statistics: Calculating metrics such as linkage disequilibrium, Tajima's D, or the four-gamete test across the metG gene region.

• Characteristic Signatures:

  • Homologous recombination: Results in clusters of multiple SNPs, often with a specific pattern matching a potential donor strain. In L. pneumophila, recombination fragments typically range from 5,613bp to 12,757bp with some events spanning up to 94,790bp .

  • Point mutations: Appear as isolated SNPs distributed according to a molecular clock model, without clustering.

• Experimental Validation Approaches:

  • Transformation assays: Testing the natural transformation frequency of metG variants

  • Mutation rate studies: Measuring the baseline mutation rate in metG using fluctuation tests

  • Selective pressure experiments: Subjecting L. pneumophila to various selection pressures and monitoring changes in metG

• Statistical Analysis Framework:

  • Calculate the ratio of SNPs attributed to recombination versus mutation (r/m ratio)

  • For L. pneumophila, the r/m ratio exceeds 96% in major disease-associated STs, indicating recombination is the dominant evolutionary force

  • Apply Bayesian statistical methods to quantify confidence in recombination versus mutation assignments

• Visualization and Interpretation:

  • Generate recombination maps showing the distribution of events across the genome

  • Compare metG recombination patterns to known recombination hotspots such as LPS regions and outer membrane protein genes

  • Assess whether metG recombination events cross subspecies boundaries (e.g., between L. pneumophila subsp. pneumophila and L. pneumophila subsp. fraseri)

When analyzing metG specifically, researchers should note that in L. pneumophila, most recombination events occur between isolates from the same clade, though occasional inter-clade transfers within the same subspecies do occur. Recombination across subspecies boundaries (e.g., with L. pneumophila subsp. fraseri) is rare, suggesting potential recombination barriers or ongoing speciation .

What experimental systems are most appropriate for studying metG function in the context of host-pathogen interactions?

Several experimental systems can be employed to study metG function during host-pathogen interactions, each with specific advantages and limitations:

• Cell Culture Models:

  • Human Macrophage Cell Lines: U937 or THP-1 cells differentiated into macrophage-like cells provide a reproducible system for studying L. pneumophila infection. These models have been successfully used to study pathogen recognition receptors and cytokine responses to L. pneumophila .

  • Primary Human Alveolar Macrophages: These represent the natural target cell for L. pneumophila in human lungs, providing physiologically relevant conditions, though they are more variable and less readily available.

  • CRISPR-Modified Host Cells: Cell lines with specific immune pathway components knocked out (e.g., TRIF KO, MyD88 KO) allow for detailed dissection of host response mechanisms .

• Amoeba Infection Models:

  • Acanthamoeba castellanii: A natural environmental host for L. pneumophila, useful for studying evolutionary adaptations.

  • Dictyostelium discoideum: A genetically tractable amoeba model with well-characterized molecular tools.

  • Vermamoeba vermiformis: Another natural amoeba host with different intracellular conditions.

• Animal Models:

  • A/J Mice: Susceptible to L. pneumophila infection due to a permissive Naip5 allele.

  • Guinea Pigs: Develop pneumonia similar to human disease.

  • Zebrafish Embryos: Transparent system for real-time visualization of infection.

• Ex Vivo Systems:

  • Human Lung Tissue Explants: Maintain the complex cellular architecture of human lungs.

  • Precision-Cut Lung Slices: Preserve lung microanatomy while allowing controlled experimental conditions.

• Advanced Culture Systems:

  • 3D Tissue Models: Lung-on-chip or other organoid systems that recapitulate aspects of lung tissue architecture.

  • Co-culture Systems: Combining epithelial cells, macrophages, and neutrophils to model complex interactions.

For studying metG specifically, conditional expression systems or carefully designed point mutations would be necessary, as complete deletion of this essential gene would likely be lethal. Inducible expression systems or temperature-sensitive mutants could help elucidate the role of metG during different stages of infection .

What strategies can overcome the challenges of generating site-directed mutations in the metG gene of L. pneumophila?

Creating precise genetic modifications in the metG gene presents several technical challenges, but recent advances offer effective solutions:

• Natural Transformation Limitations:

  • Challenge: L. pneumophila has strain-dependent natural competence, and some strains possess transformation-silencing mechanisms like the pLPL plasmid (found in 38/102 ST213 isolates and 10/120 ST222 isolates) .

  • Solution:

    • Screen for naturally competent strains lacking the pLPL plasmid

    • Use longer homology arms (1-2 kb) for increased recombination efficiency

    • Perform transformations during early exponential growth phase when competence is highest

• Allelic Exchange Approaches:

  • Challenge: Traditional methods using suicide vectors have low efficiency in L. pneumophila.

  • Solution:

    • Two-step allelic exchange using counter-selectable markers (sacB, rpsL)

    • Integration of a selection marker flanked by FRT sites, followed by FLP recombinase-mediated excision

    • Design of specialized suicide vectors with L. pneumophila origin of transfer

• CRISPR/Cas9-Based Genome Editing:

  • Challenge: Optimizing CRISPR/Cas9 systems for L. pneumophila.

  • Solution:

    • Use L. pneumophila codon-optimized Cas9

    • Deliver Cas9 and sgRNA on separate plasmids

    • Employ Cas12a (Cpf1) as an alternative that may work better in some strains

    • Supply repair templates with extended homology arms (>500 bp)

• Essential Gene Modification:

  • Challenge: metG is likely essential, making direct knockouts lethal.

  • Solution:

    • Create merodiploid strains with a second copy of metG before modifying the native copy

    • Use inducible promoters to control expression of mutant alleles

    • Employ CRISPRi for partial knockdown instead of complete knockout

    • Use temperature-sensitive mutants for conditional phenotypes

• Verification Strategies:

  • Challenge: Confirming precise modifications without introducing unwanted mutations.

  • Solution:

    • PCR screening followed by Sanger sequencing of the entire modified region

    • Whole genome sequencing to confirm the absence of off-target mutations

    • Functional complementation tests to verify phenotype specificity

    • Protein expression analysis by Western blot

The high recombination rate observed in L. pneumophila (responsible for >96% of SNPs in major disease-associated STs) suggests that homologous recombination machinery is highly active in this bacterium . This characteristic can potentially be leveraged to increase the efficiency of homology-directed repair during genome editing.

How can researchers address the issue of genetic heterogeneity when studying metG across different L. pneumophila isolates?

Genetic heterogeneity presents significant challenges when studying metG across L. pneumophila isolates, particularly due to the bacterium's high recombination rates. Researchers can employ several strategies to address this:

• Comprehensive Sequence Analysis:

  • Challenge: Sequence diversity complicates comparative studies.

  • Solution:

    • Perform whole-genome sequencing of multiple isolates from diverse sources

    • Construct phylogenetic trees specifically for metG and compare to whole-genome phylogenies

    • Identify conserved regions as targets for universal primers or antibodies

    • Map recombination breakpoints around the metG locus using tools like Gubbins and BRATNextGen

• Reference Strain Selection:

  • Challenge: No single strain is representative of the species diversity.

  • Solution:

    • Use multiple reference strains representing major sequence types (STs)

    • Include representatives from both clinical and environmental sources

    • Select strains from different geographic regions

    • Consider the six major disease-associated STs (ST1, ST23, ST37, ST42, ST62, and ST578)

• Molecular Tools Development:

  • Challenge: Tools optimized for one strain may not work in others.

  • Solution:

    • Design degenerate primers targeting conserved regions of metG

    • Develop antibodies against highly conserved epitopes of MetG

    • Create a panel of strain-specific tools for comparative studies

    • Validate molecular tools across multiple sequence types

• Population Structure Analysis:

  • Challenge: Recombination confounds traditional phylogenetic approaches.

  • Solution:

    • Apply population genetics methods that account for recombination

    • Use ClonalFrameML or similar tools to reconstruct clonal relationships

    • Calculate the r/m ratio specifically for the metG genomic region

    • Employ Bayesian analysis to estimate the true evolutionary history

• Data Integration Approaches:

  • Challenge: Diverse datasets are difficult to compare directly.

  • Solution:

    • Develop standardized protocols for metG characterization

    • Create centralized databases of metG sequences and functional data

    • Use machine learning to identify patterns across heterogeneous datasets

    • Employ meta-analysis techniques to synthesize results from multiple studies

Analysis of large datasets, such as the 230 ST213/222 isolates examined in recent studies , can provide the statistical power needed to detect meaningful patterns despite underlying genetic heterogeneity.

What emerging technologies could advance the study of metG function in L. pneumophila pathogenesis?

Several cutting-edge technologies show promise for deepening our understanding of metG function in L. pneumophila pathogenesis:

• Single-Cell Techniques:

  • Single-cell RNA-seq: Reveals heterogeneity in bacterial gene expression during infection

  • Single-cell proteomics: Detects variability in MetG protein levels across bacterial populations

  • Single-bacterium transcriptomics: Captures metG expression dynamics in individual bacteria during infection

• Advanced Imaging Technologies:

  • Super-resolution microscopy: Visualizes MetG localization at nanoscale resolution

  • Live-cell imaging with tagged MetG: Tracks protein dynamics during infection

  • Correlative light and electron microscopy (CLEM): Combines functional and structural imaging

  • Expansion microscopy: Physically enlarges samples for enhanced resolution

• CRISPR-Based Technologies:

  • CRISPRi for fine-tuned regulation: Enables partial and reversible knockdown of metG

  • CRISPR-Cas13 RNA targeting: Allows post-transcriptional regulation of metG

  • Base editing: Introduces precise point mutations without double-strand breaks

  • Prime editing: Enables installation of specific mutations with high precision

• Structural Biology Approaches:

  • Cryo-electron microscopy: Determines MetG structure at near-atomic resolution

  • Integrative structural biology: Combines multiple techniques (X-ray, NMR, cryo-EM)

  • Time-resolved structural studies: Captures conformational changes during catalysis

  • Hydrogen-deuterium exchange mass spectrometry: Maps protein interaction surfaces

• Systems Biology and Computational Approaches:

  • Multi-omics integration: Combines transcriptomics, proteomics, and metabolomics

  • Machine learning algorithms: Identifies patterns in complex host-pathogen interactions

  • Network analysis: Maps MetG interactions within bacterial and host systems

  • Molecular dynamics simulations: Models MetG function in atomic detail

• In Situ Technologies:

  • Spatial transcriptomics: Maps metG expression in infected tissue contexts

  • Proximity labeling: Identifies proteins interacting with MetG in living cells

  • MERFISH or seqFISH: Provides spatial resolution of gene expression

  • in situ CRISPR screening: Evaluates gene function in native tissue environments

These technologies can address critical questions about metG function, potentially revealing unexpected roles beyond its canonical aminoacyl-tRNA synthetase activity, similar to how studies have revealed the diverse roles of pathogen recognition receptors in recognizing L. pneumophila .

How might comparative studies of metG across Legionella species inform our understanding of pathogen evolution?

Comparative studies of metG across Legionella species offer valuable insights into pathogen evolution and adaptation:

• Evolutionary Trajectory Analysis:

  • Pan-genus metG phylogeny: Reconstructs the evolutionary history of metG across Legionella species

  • Selection pressure mapping: Identifies conserved vs. variable regions under different selective forces

  • Recombination analysis: Determines if metG has undergone inter-species horizontal gene transfer

  • Molecular clock studies: Estimates divergence times for metG across the Legionella genus

The frequency of horizontal gene transfer varies across Legionella species. While L. pneumophila shows extensive within-species recombination (>96% of SNPs), cross-species exchange appears more limited, with rare transfer events documented between subspecies like L. pneumophila subsp. pneumophila and L. pneumophila subsp. fraseri .

• Structure-Function Relationship Studies:

  • Comparative protein modeling: Identifies species-specific structural features

  • Enzyme kinetics comparisons: Measures functional differences in MetG activity

  • Domain conservation analysis: Maps evolutionary constraints on protein domains

  • Substrate specificity studies: Determines if MetG enzymes from different species have altered preferences

• Host-Range Correlation:

  • MetG sequence vs. host range: Analyzes whether metG sequences correlate with host specificity

  • Amoeba adaptation signatures: Identifies metG features associated with different amoeba hosts

  • Mammalian pathogenicity markers: Determines if metG variants correlate with human disease potential

  • Environmental adaptation indicators: Links metG sequence features to ecological niches

• Methodological Approach:

  • Whole genome sequencing: Of diverse Legionella species from various sources

  • Recombinant protein expression: Of metG variants from multiple species

  • Biochemical characterization: Of enzymatic properties across species

  • Heterologous complementation: Testing functional interchangeability between species

The expanded geographic distribution observed for certain L. pneumophila sequence types (ST213 and ST222) in the United States suggests these strains may possess adaptations conferring advantages in both environmental persistence and human infection . Comparing their metG genes with those of other Legionella species could reveal whether metG plays any role in this ecological success.