Recombinant Burkholderia ambifaria GMP synthase [glutamine-hydrolyzing] (guaA), partial

Basic Research Questions

• What is GMP synthase (guaA) and what role does it play in Burkholderia ambifaria metabolism?

  GMP synthase [glutamine-hydrolyzing] (guaA) is a critical enzyme in the de novo purine nucleotide biosynthetic pathway. In B. ambifaria, as in other bacteria, guaA catalyzes the ATP-dependent conversion of xanthosine 5'-monophosphate (XMP) to guanosine 5'-monophosphate (GMP), using glutamine as an amide nitrogen donor. The reaction occurs in two steps: glutamine hydrolysis and amination of XMP.

  The enzyme is essential for purine metabolism and plays a fundamental role in bacterial survival. In Burkholderia species, guaA is particularly important as demonstrated by studies with related species like B. mallei, where disruption of purine biosynthesis (via purM mutation) results in severe attenuation . The guaA gene is also considered a housekeeping gene in many Burkholderia species and has been used for taxonomic classification purposes .

• How should recombinant B. ambifaria guaA be stored and handled to maintain optimal activity?

  Based on standardized protocols for recombinant Burkholderia proteins, the following storage and handling recommendations apply:

  Storage Condition	Recommended Duration	Notes
  -20°C (stock)	Long-term storage	For extended storage, conserve at -20°C or -80°C
  -80°C (stock)	Maximum stability	Preferred for long-term storage >6 months
  4°C (working aliquots)	Up to one week	After reconstitution

  Reconstitution protocol:

  1. Briefly centrifuge the vial prior to opening to bring contents to the bottom

  2. Reconstitute in deionized sterile water to a concentration of 0.1-1.0 mg/mL

  3. Add glycerol to a final concentration of 50% for optimal stability

  4. Aliquot to avoid repeated freeze-thaw cycles, which significantly reduce activity

  The shelf life of liquid preparations is typically 6 months at -20°C/-80°C, while lyophilized forms remain stable for approximately 12 months under the same conditions .

• What expression systems are most effective for producing functional recombinant B. ambifaria guaA?

  Multiple expression systems have been successfully employed for producing recombinant Burkholderia proteins, each with distinct advantages:

  Expression System	Advantages	Considerations
  E. coli	High yield, cost-effective, rapid production	May lack post-translational modifications, potential inclusion body formation
  Yeast	Eukaryotic post-translational modifications, secretion capability	Lower yield than E. coli, longer production time
  Baculovirus	High-level expression, complex post-translational modifications	Technical complexity, higher cost
  Mammalian cell	Native-like folding and modifications	Lowest yield, highest cost, longest production time

  For B. ambifaria guaA specifically, E. coli expression systems have been commonly used with success. The choice depends on research requirements for protein authenticity versus yield . For enzymatic studies requiring high purity, E. coli systems with appropriate tags (His-tag or Avi-tag biotinylated systems) are generally recommended .

Intermediate Research Questions

• How does guaA function relate to c-di-GMP signaling pathways in Burkholderia species?

  GMP synthase (guaA) and c-di-GMP signaling are interconnected through nucleotide metabolism pathways in Burkholderia species. This relationship functions on multiple levels:

  1. Metabolic connection: guaA produces GMP, which is a precursor for GTP synthesis. GTP serves as the substrate for diguanylate cyclases (DGCs) that synthesize c-di-GMP from two GTP molecules .

  2. Regulatory networks: In B. cenocepacia and related Burkholderia species, c-di-GMP acts as a second messenger regulating motility, biofilm formation, and virulence . The availability of GMP/GTP precursors through guaA activity can influence the cellular capacity for c-di-GMP production.

  3. Signaling cascade interaction: Studies in B. thailandensis have revealed complex c-di-GMP signaling cascades involving proteins like PdcA, PdcB, and PdcC, which constitute an operon that regulates intracellular c-di-GMP levels . These processes depend on nucleotide availability, which is influenced by guaA activity.

  4. Biofilm regulation: GGDEF domain-containing proteins (responsible for c-di-GMP synthesis) and EAL domain-containing proteins (responsible for c-di-GMP degradation) modulate biofilm formation . The guaA enzyme indirectly affects these processes by influencing GTP availability.

  Research using ΔII2523 mutants of B. pseudomallei (which affect c-di-GMP signaling) has shown differential regulation of biofilm formation at different temperatures , demonstrating the environmental responsiveness of this system.

• What methods are most effective for measuring enzymatic activity of recombinant B. ambifaria guaA?

  Several established methods can be employed to measure guaA enzymatic activity:

  1. Coupled enzyme assays: Monitor the conversion of XMP to GMP by coupling the reaction to NADH oxidation through auxiliary enzymes, measuring the decrease in absorbance at 340 nm.

  2. HPLC analysis: Directly quantify substrate (XMP) consumption and product (GMP) formation using reverse-phase HPLC.

  3. Radioactive assays: Use 14C-labeled glutamine to track the incorporation of the amide group into GMP.

  4. Malachite green assay: Detect inorganic phosphate released during the ATP-dependent reaction.

  5. Mass spectrometry: Employ LC-MS/MS to precisely measure reaction products and intermediates.

  For kinetic characterization, a recommended experimental setup includes:

  Parameter	Recommended Conditions
  Buffer	50 mM Tris-HCl (pH 7.5), 50 mM KCl, 5 mM MgCl₂
  Temperature	25-30°C (optimal for most Burkholderia enzymes)
  Substrates	XMP (50-500 μM), ATP (1-5 mM), Glutamine (1-10 mM)
  Enzyme concentration	50-500 nM purified protein
  Controls	Heat-inactivated enzyme, reactions without individual substrates

  Adapting methods from related enzymes like the MANT-c-di-GMP hydrolysis assay described for phosphodiesterase activity measurement can provide insights into reaction dynamics.

• How do mutations in guaA affect the virulence and biofilm formation in Burkholderia species?

  Mutations in guaA can significantly impact virulence and biofilm formation in Burkholderia species through several mechanisms:

  1. Purine auxotrophy: Complete loss of guaA function creates purine auxotrophs with severely attenuated virulence, as demonstrated in the related species B. pseudomallei strain Bp82 (ΔpurM mutant), which is deficient in purine biosynthesis and unable to replicate in human cells .

  2. Biofilm formation modulation: Partial loss-of-function mutations may alter biofilm development through changes in intracellular nucleotide pools, indirectly affecting c-di-GMP levels. Studies in B. pseudomallei have shown that perturbation of c-di-GMP signaling significantly impacts biofilm formation under different temperature conditions .

  3. Host interaction changes: Alterations in guaA can affect interactions with host cells as nucleotide metabolism is critical during infection. B. ambifaria, as part of the B. cepacia complex, interacts with epithelial cells during lung infection in cystic fibrosis patients .

  4. Secondary metabolite production: Mutations affecting GMP synthesis can influence the production of secondary metabolites important for virulence. B. ambifaria produces several bioactive molecules, including enacyloxins with antimicrobial activity .

  Research comparing wild-type and guaA-mutant strains demonstrates that even partial inhibition of guaA function can significantly reduce biofilm formation and attenuate virulence in infection models .

Advanced Research Questions

• What are the key considerations when designing inhibitors that target B. ambifaria guaA for antimicrobial development?

  Designing effective inhibitors targeting B. ambifaria guaA requires consideration of several critical factors:

  1. Structural specificity: Exploit structural differences between bacterial and human GMP synthases. Though both perform similar catalytic functions, selective targeting of bacterial-specific features is essential to minimize host toxicity.

  2. Catalytic mechanism interruption: Target either the glutaminase domain (which hydrolyzes glutamine) or the synthetase domain (which transfers the amino group to XMP). The reaction occurs in two steps that can be individually targeted:

     • Glutamine hydrolysis to produce ammonia

     • ATP-dependent amination of XMP using the released ammonia

  3. Active site considerations: Design compounds that compete with natural substrates (glutamine, ATP, or XMP) or transition state analogs that mimic the reaction intermediate.

  4. Bacterial penetration: Address the challenges of penetrating the complex cell envelope of Gram-negative bacteria like B. ambifaria. Compounds may need to be coupled with strategies to enhance uptake or reduce efflux.

  5. Resistance potential: Evaluate the genetic barriers to resistance development. Targeting conserved catalytic residues reduces the likelihood of viable resistance mutations.

  6. Biofilm penetration: Ensure inhibitors can penetrate bacterial biofilms, as B. ambifaria forms robust biofilms that protect against conventional antimicrobials .

  Recent studies demonstrating the attenuation of B. pseudomallei through purine biosynthesis disruption suggest that guaA inhibition represents a promising antimicrobial strategy.

• How can guaA be used as a target for genetic manipulation in B. ambifaria to study host-pathogen interactions?

  GMP synthase (guaA) offers several strategic approaches for genetic manipulation to investigate B. ambifaria host-pathogen interactions:

  1. Conditional expression systems: Develop inducible promoters to control guaA expression, allowing for temporal regulation during infection studies. This approach permits examination of how nucleotide metabolism affects different stages of host colonization.

  2. Domain-specific mutations: Introduce targeted mutations to specific functional domains of guaA to dissect the contribution of different enzymatic activities:

     • Mutations in the glutaminase domain

     • Alterations to the ATP-binding site

     • Modifications to the XMP-binding region

  3. Reporter fusions: Create guaA-reporter gene fusions (e.g., with fluorescent proteins or luciferase) to monitor expression patterns during host interaction in real-time.

  4. Complementation strategies: For complementation studies in guaA mutants, consider the following experimental design:

     Construct	Purpose	Expected Outcome
     Wild-type guaA	Full complementation	Restoration of virulence and biofilm
     Catalytically inactive guaA	Negative control	No complementation
     Partial activity variants	Structure-function analysis	Gradation of phenotype restoration
     Heterologous guaA	Cross-species comparison	Species-specific differences

  5. Competitive index assays: Use mixed infections with wild-type and guaA-manipulated strains to quantify fitness costs during infection.

  These approaches have been successfully applied in related Burkholderia species, particularly in studies examining fucose-binding lectins and adhesion to human epithelia , which could serve as methodological templates.

• What is the relationship between guaA function and secondary metabolite production in B. ambifaria?

  The relationship between guaA function and secondary metabolite production in B. ambifaria involves complex regulatory networks:

  1. Precursor supply: GMP synthesis through guaA activity provides essential nucleotide precursors that can be channeled into specialized metabolic pathways, including those for secondary metabolite production.

  2. Biosynthetic gene clusters (BGCs): B. ambifaria contains numerous BGCs for producing bioactive compounds, including:

     • Enacyloxins: Hybrid modular polyketide synthase (PKS) products with antimicrobial activity

     • Occidiofungin-like compounds: Glycolipopeptides with antifungal properties

     • Other nonribosomal peptide synthetase (NRPS) and PKS clusters

  3. Regulatory connections: Nucleotide metabolism enzymes like guaA can influence global regulatory networks that control secondary metabolite biosynthesis. RNA-seq analysis of B. pseudomallei with altered c-di-GMP signaling has revealed differential regulation of NRPS/PKS clusters .

  4. Environmental adaptation: Both guaA activity and secondary metabolite production respond to environmental conditions. B. ambifaria produces different compounds when growing as a rhizosphere inhabitant versus during host infection .

  5. Quorum sensing integration: Secondary metabolite production in B. ambifaria is often regulated by quorum sensing, which may intersect with nucleotide metabolism pathways. The enacyloxin biosynthetic gene cluster is regulated by quorum sensing .

  Experimental disruption of c-di-GMP signaling in B. pseudomallei revealed differential expression of multiple BGCs depending on temperature conditions , suggesting nucleotide metabolism is integrated with environmental sensing and specialized metabolite production.

• How do post-translational modifications affect guaA activity in clinical versus environmental isolates of B. ambifaria?

  Post-translational modifications (PTMs) of guaA can significantly differ between clinical and environmental isolates of B. ambifaria, reflecting adaptation to distinct ecological niches:

  1. Phosphorylation: Serine, threonine, and tyrosine phosphorylation can regulate guaA activity in response to environmental signals. Clinical isolates may show distinct phosphorylation patterns compared to environmental strains, particularly under stress conditions encountered during infection.

  2. Acetylation: N-terminal or lysine acetylation can affect protein stability and catalytic efficiency. Research on bacterial acetylomes suggests that metabolic enzymes like guaA may undergo differential acetylation in response to nutrient availability.

  3. Regulatory interactions: guaA may interact with different regulatory proteins in clinical versus environmental settings. In B. cenocepacia, the RpfR protein (a key regulator of c-di-GMP signaling) affects a broad spectrum of phenotypes under various environmental conditions .

  4. Environmental responsiveness: B. ambifaria is known to exhibit phase variation when adapting to different environments . No clear distinction can be established between environmental and pathogenic B. ambifaria isolates, suggesting adaptability rather than fixed genetic differences.

  Methods to study these differences include:

  • Comparative phosphoproteomics of clinical vs. environmental isolates

  • Activity assays under conditions mimicking either soil environments or host tissues

  • Protein-protein interaction studies using co-immunoprecipitation or crosslinking approaches

  • Site-directed mutagenesis of potential PTM sites followed by functional characterization

Experimental Design Questions

Data Analysis and Interpretation Questions

• How can researchers differentiate between direct and indirect effects when studying guaA function in B. ambifaria?

  Distinguishing direct from indirect effects in guaA functional studies requires a multi-layered approach:

  1. Complementation analysis:

     • Express wild-type guaA in trans in a mutant background

     • Use catalytically inactive mutants (point mutations in active sites) as controls

     • Perform dosage-dependent complementation to establish quantitative relationships

  2. Temporal resolution strategies:

     • Employ inducible expression systems to observe immediate versus delayed effects

     • Conduct time-course experiments following guaA induction or repression

     • Use rapid inhibition approaches (if available) to distinguish immediate enzyme effects

  3. Metabolic pathway analysis:

     • Measure GMP and related metabolites directly using LC-MS

     • Supplement with pathway intermediates to bypass specific metabolic blocks

     • Apply metabolic flux analysis with stable isotopes to track carbon and nitrogen flow

  4. Multi-omics integration:

     Approach	Purpose	Implementation
     Transcriptomics	Identify gene expression changes	RNA-seq of guaA mutants vs. wild-type
     Proteomics	Detect protein level alterations	Mass spectrometry-based quantitative proteomics
     Metabolomics	Measure metabolite changes	Targeted and untargeted metabolite profiling
     Phenomics	Characterize phenotypic effects	High-throughput phenotypic screening

  5. Network analysis:

     • Apply statistical approaches (partial correlation, Bayesian networks) to discriminate direct/indirect relationships

     • Use directed genetic screens to identify suppressors or enhancers of guaA phenotypes

     • Perform protein-protein interaction studies to identify direct binding partners

  6. Computational modeling:

     • Develop kinetic models of guaA-dependent pathways

     • Perform sensitivity analysis to identify critical control points

     • Use genome-scale metabolic models to predict system-wide effects

  This approach has been effective in studies of c-di-GMP signaling in Burkholderia, where researchers distinguished direct regulatory targets from downstream effects .

• What statistical approaches are most appropriate for analyzing differential guaA expression data across environmental conditions?

  Analyzing differential guaA expression across environmental conditions requires robust statistical methodologies:

  1. Experimental design considerations:

     • Include sufficient biological replicates (minimum 3-5 per condition)

     • Account for batch effects through randomization and blocking

     • Consider time-course designs for dynamic responses

     • Include appropriate reference genes for normalization

  2. Data preprocessing steps:

     • Quality control of raw data (RNA quality metrics, sequencing quality scores)

     • Normalization to account for technical variability

     • Log transformation to stabilize variance

     • Filtering of low-count/low-expression genes

  3. Statistical testing frameworks:

     Method	Application	Strengths
     DESeq2	RNA-seq count data	Accounts for biological variability, robust to outliers
     limma-voom	Microarray or normalized RNA-seq	Flexible design matrices, empirical Bayes approach
     ANOVA	Multi-factor designs	Handles complex experimental designs
     Time-series methods	Dynamic responses	Accounts for temporal correlation

  4. Multiple testing correction:

     • Control false discovery rate using Benjamini-Hochberg procedure

     • Consider more stringent family-wise error rate control for targeted studies

     • Report both unadjusted and adjusted p-values for transparency

  5. Effect size consideration:

     • Focus on fold changes and their confidence intervals

     • Consider biological significance beyond statistical significance

     • Use visualization techniques (MA plots, volcano plots) to identify meaningful changes

  6. Validation approaches:

     • Confirm key findings with RT-qPCR

     • Compare protein levels with Western blotting

     • Correlate expression changes with functional outcomes

  These approaches have been successfully applied in studies examining gene expression changes in B. pseudomallei under different temperature conditions, revealing temperature-dependent regulation of biosynthetic gene clusters .

• How do you optimize recombinant B. ambifaria guaA production for structural studies?

  Optimizing recombinant B. ambifaria guaA production for structural studies requires addressing several critical aspects:

  1. Construct design optimization:

     • Generate multiple constructs with different boundaries to identify stable domains

     • Include or exclude flexible regions based on secondary structure predictions

     • Add fusion tags strategically positioned to avoid interfering with protein folding

     • Consider codon optimization for the expression host

  2. Expression system selection:

     • E. coli systems for high yield (BL21(DE3), Rosetta for rare codons)

     • Insect cell expression for improved folding of complex proteins

     • Cell-free systems for proteins toxic to host cells

     • Expression temperature optimization (typically lower temperatures improve folding)

  3. Solubility enhancement strategies:

     Strategy	Approach	Considerations
     Fusion partners	MBP, GST, SUMO, Trx tags	Cleavable vs. non-cleavable designs
     Co-expression	Chaperones (GroEL/ES, DnaK)	May increase soluble fraction
     Buffer optimization	Additives (salts, detergents)	Systematic screening approaches
     Refolding	Denaturation and controlled refolding	Last resort for inclusion bodies

  4. Purification strategy design:

     • Multi-step purification to achieve >95% purity

     • Affinity chromatography followed by size exclusion and/or ion exchange

     • Addition of nucleotide ligands during purification to stabilize protein

     • Removal of flexible regions or tags that might interfere with crystallization

  5. Protein quality assessment:

     • Dynamic light scattering to verify monodispersity

     • Thermal shift assays to identify stabilizing conditions

     • Limited proteolysis to identify stable domains

     • Activity assays to confirm proper folding

  6. Structural biology-specific considerations:

     • For X-ray crystallography: concentrated protein (5-15 mg/ml), high purity

     • For Cryo-EM: lower concentration but extremely high purity

     • For NMR: isotope labeling (15N, 13C) and higher concentrations

     • For all methods: removal of nucleic acid contamination

  Similar approaches have been successfully employed for other Burkholderia proteins, such as the fucose-binding lectin BambL from B. ambifaria .

Troubleshooting Questions

• What are common pitfalls in characterizing B. ambifaria guaA enzymatic activity and how can they be addressed?

  When characterizing B. ambifaria guaA enzymatic activity, researchers commonly encounter several challenges that can be systematically addressed:

  1. Protein stability issues:

     • Problem: Rapid loss of activity during purification or storage

     • Solution: Add stabilizing agents (glycerol 20-50%, reducing agents), optimize buffer conditions, maintain strict temperature control, consider adding substrate analogs or product

  2. Substrate quality concerns:

     • Problem: Commercial XMP/ATP degradation affecting kinetic measurements

     • Solution: Verify substrate purity by HPLC before use, prepare fresh solutions, include metal chelators to prevent ATP hydrolysis, store nucleotides at -80°C in small aliquots

  3. Assay interference factors:

     • Problem: Buffer components or protein preparation contaminants interfering with detection

     • Solution: Include appropriate controls (enzyme-free, substrate-free), perform background subtraction, consider alternative detection methods

  4. Aggregation challenges:

     Problem	Indication	Solution
     Protein aggregation	Non-linear activity vs. concentration	Add detergents below CMC, increase salt concentration
     Time-dependent activity loss	Decreasing activity during assay	Pre-incubate enzyme under assay conditions, measure initial rates only
     Biphasic kinetics	Non-Michaelis-Menten behavior	Investigate potential allosteric regulation, test for oligomerization

  5. Expression host artifacts:

     • Problem: Co-purifying host proteins with similar activity

     • Solution: Include additional purification steps, verify homogeneity by SDS-PAGE and mass spectrometry, use activity-dead mutants as controls

  6. Data interpretation challenges:

     • Problem: Complex kinetic behavior with multiple substrates

     • Solution: Employ appropriate multi-substrate kinetic models, determine reaction order through initial velocity studies, consider product inhibition

  7. Reproducibility issues:

     • Problem: Batch-to-batch variation in activity

     • Solution: Standardize expression and purification protocols, normalize activity to protein concentration, include internal standards

  Similar challenges have been encountered when characterizing enzymatic activities of other nucleotide-metabolizing enzymes, such as the c-di-GMP-metabolizing enzymes in Burkholderia species .

• How can researchers integrate guaA studies with broader systems biology approaches to understand B. ambifaria pathogenesis?

  Integrating guaA studies with systems biology approaches provides comprehensive insights into B. ambifaria pathogenesis:

  1. Multi-omics data integration frameworks:

     • Combine transcriptomics, proteomics, and metabolomics data from guaA-manipulated strains

     • Apply network analysis to identify pathway interconnections

     • Use correlation networks to identify co-regulated genes and metabolites

     • Develop predictive models of guaA's role in system-wide regulation

  2. Comparative systems approaches:

     • Analyze multiple Burkholderia species to identify conserved and divergent roles of guaA

     • Compare environmental vs. clinical isolates to understand niche adaptation

     • Study related enzymes across the Burkholderia cepacia complex to identify species-specific features

  3. Host-pathogen interaction systems:

     • Investigate dual transcriptomics during infection to capture both pathogen and host responses

     • Apply interactome mapping to identify host proteins targeted by bacterial factors

     • Use infection models with varying immune status to model complex interactions

  4. Integration methodologies:

     Approach	Application	Implementation
     Constraint-based modeling	Metabolic network analysis	Genome-scale metabolic models incorporating guaA
     Bayesian networks	Causal relationship inference	Probabilistic models of regulatory relationships
     Machine learning	Pattern recognition in complex data	Supervised/unsupervised learning from multi-omics data
     Kinetic modeling	Dynamic pathway simulation	Mathematical models of nucleotide metabolism

  5. Experimental validation strategies:

     • Generate testable hypotheses from computational models

     • Design targeted experiments to validate key predictions

     • Iterate between modeling and experimentation

     • Apply genome-wide approaches (CRISPRi screens) to identify genetic interactions

  6. Translational connections:

     • Link basic science findings to clinical observations

     • Identify biomarkers for infection progression

     • Develop targeted intervention strategies based on systems understanding

  This integrative approach has been productive in understanding c-di-GMP signaling networks in B. pseudomallei, revealing how disruption of these networks affects biofilm formation, secondary metabolite production, and virulence in a coordinated manner .