Recombinant Burkholderia vietnamiensis Phosphoserine aminotransferase (serC)

What is Burkholderia vietnamiensis and why is it significant for research?

Burkholderia vietnamiensis is a gram-negative bacterium belonging to the Burkholderia cepacia complex (BCC). Unlike other BCC species, B. vietnamiensis is notably susceptible to aminoglycosides while maintaining resistance to cationic antimicrobial peptides and polymyxin B. This unique susceptibility pattern makes it an important model organism for studying antibiotic resistance mechanisms and potential therapeutic targets. B. vietnamiensis has been detected in environmental samples and can be isolated from nasal swabs of small ruminants such as goats, as demonstrated in research from Nueva Ecija, Philippines where a strain showed 97.86% homology to B. vietnamiensis strain G4 . This bacterium is significant both as an opportunistic pathogen in cystic fibrosis patients and for its environmental roles in nitrogen fixation and bioremediation .

What is phosphoserine aminotransferase (serC) and what role does it play in bacterial metabolism?

Phosphoserine aminotransferase (serC) is a key enzyme in the serine biosynthesis pathway. It catalyzes the conversion of 3-phosphohydroxypyruvate to 3-phosphoserine, which is a crucial step in the synthesis of the amino acid L-serine. In bacterial metabolism, serC plays essential roles in:

• Amino acid biosynthesis

• Protein synthesis

• Cell wall development

• One-carbon metabolism

• Purine and pyrimidine synthesis

This enzyme is particularly important in B. vietnamiensis as serine metabolism influences various cellular processes including bacterial growth, virulence factor production, and potentially antibiotic resistance mechanisms. The recombinant form of this enzyme allows researchers to investigate its structure, function, and potential as a therapeutic target.

What are the recommended methods for cloning and expressing recombinant B. vietnamiensis phosphoserine aminotransferase?

Effective cloning and expression of recombinant B. vietnamiensis phosphoserine aminotransferase requires careful optimization of several parameters:

• Strain selection: Use B. vietnamiensis strain G4 as a reference strain, which has been well-characterized molecularly .

• Gene isolation: Amplify the serC gene using PCR with primers designed against conserved regions of the gene.

• Expression system selection: E. coli BL21(DE3) is typically preferred for initial expression studies.

• Vector selection: pET expression systems with histidine tags facilitate purification.

• Codon optimization: Consider codon usage bias between B. vietnamiensis and the expression host.

• Expression conditions: Optimize temperature (typically 18-25°C), IPTG concentration (0.1-1.0 mM), and induction duration (4-16 hours).

• Solubility enhancement: Include solubility tags (MBP, SUMO) if initial expression yields insoluble protein.

For molecular validation of the clone, both 16S rDNA analysis and gene-specific amplification of the Tat-domain protein can be used, following approaches similar to those used for identifying B. vietnamiensis in environmental samples .

What purification strategies yield the highest activity for recombinant phosphoserine aminotransferase from B. vietnamiensis?

Optimal purification of recombinant B. vietnamiensis phosphoserine aminotransferase requires a multi-step approach to maintain enzyme activity:

• Initial capture: Immobilized metal affinity chromatography (IMAC) using Ni-NTA resin with His-tagged protein

• Buffer optimization:

  • pH range: 7.5-8.0

  • Salt concentration: 150-300 mM NaCl

  • Inclusion of 10% glycerol and 1-5 mM β-mercaptoethanol to maintain stability

• Intermediate purification: Ion exchange chromatography

• Polishing step: Size exclusion chromatography

• Activity preservation: Include the cofactor pyridoxal-5'-phosphate (PLP) at 0.1-0.2 mM in all buffers

• Storage conditions: 50% glycerol at -20°C or flash-frozen aliquots at -80°C

Typical yields range from 10-15 mg of pure protein per liter of bacterial culture, with specific activity measurements using 3-phosphohydroxypyruvate as substrate.

How can researchers accurately measure phosphoserine aminotransferase activity in recombinant B. vietnamiensis preparations?

Accurate measurement of phosphoserine aminotransferase activity requires specific assay conditions and controls:

• Spectrophotometric coupled assay:

  • Monitor formation of 3-phosphoserine by coupling to a secondary reaction

  • Measure at 340 nm to track NADH oxidation when coupled with a dehydrogenase

  • Maintain temperature at 25°C with pH 7.6-8.0

• Direct product quantification:

  • HPLC separation and quantification of 3-phosphoserine

  • LC-MS/MS for higher sensitivity and specificity

• Kinetic parameters determination:

  • Measure initial velocities across substrate concentration range (0.1-10 mM)

  • Generate Lineweaver-Burk plots to determine Km and Vmax

  • Calculate kcat and kcat/Km for catalytic efficiency

• Activity controls:

  • Perform heat-inactivated enzyme controls

  • Include no-substrate controls

  • Use commercially available phosphoserine aminotransferase (if available) as reference standard

• Data analysis considerations:

  • Apply appropriate statistical tests for replicate measurements

  • Consider using contradiction pattern analysis (α, β, θ) for complex data sets with potential inconsistencies

  • Calculate standard deviation and coefficient of variation for all measurements

How does the structure-function relationship of phosphoserine aminotransferase in B. vietnamiensis compare to other bacterial species?

The structure-function relationship of phosphoserine aminotransferase in B. vietnamiensis exhibits both conserved features and unique characteristics compared to other bacterial species:

• Conserved features:

  • PLP-binding site with conserved lysine residue forming Schiff base

  • Dimeric quaternary structure

  • α/β fold typical of fold type I aminotransferases

  • Catalytic residues involved in substrate binding and transition state stabilization

• Unique characteristics of B. vietnamiensis serC:

  • Substrate specificity range may differ from other bacterial serC enzymes

  • Potential adaptations related to metabolic requirements in diverse environments

  • Possible influence on antibiotic resistance mechanisms, as B. vietnamiensis shows unique aminoglycoside susceptibility patterns

• Structural comparison approaches:

  • Homology modeling based on crystal structures from related organisms

  • Analysis of substrate binding pocket residues

  • Examination of oligomeric interfaces

  • Investigation of potential allosteric sites

• Functional implications:

  • Role in serine biosynthesis pathway regulation

  • Connection to aminoglycoside resistance mechanisms (aminoglycoside susceptibility is a unique feature of B. vietnamiensis among BCC bacteria)

  • Potential involvement in adaptation to diverse environments including soil and mammalian hosts

How can researchers investigate the role of phosphoserine aminotransferase in B. vietnamiensis antibiotic resistance mechanisms?

Investigating the connection between phosphoserine aminotransferase and B. vietnamiensis antibiotic resistance requires multi-faceted approaches:

• Gene knockout and complementation studies:

  • Create serC deletion mutants using CRISPR-Cas9 or homologous recombination

  • Complement with wild-type and mutated serC genes

  • Assess changes in aminoglycoside susceptibility profiles

• Expression analysis:

  • Measure serC expression levels in susceptible vs. resistant isolates

  • Analyze expression changes under aminoglycoside pressure

  • Use RT-qPCR to quantify transcriptional responses

• Association with efflux mechanisms:

  • B. vietnamiensis aminoglycoside resistance appears to involve decreased cellular accumulation due to active efflux

  • Investigate potential metabolic connections between serC activity and efflux pump function

  • Combine serC modulation with efflux pump inhibitors (EPIs) to assess synergistic effects

• Clinical isolate comparative analysis:

  • Compare serC sequence and expression in isolates with different resistance profiles

  • Analyze serC mutations in isolates that acquired resistance during chronic infection

  • Build a correlation table between MIC values and serC variants:

• Metabolomic impact assessment:

  • Profile serine pathway metabolites in susceptible vs. resistant strains

  • Identify metabolic signatures associated with aminoglycoside resistance

  • Test if serine supplementation affects resistance profiles

What are the methodological considerations for analyzing contradictions in phosphoserine aminotransferase activity data across different experimental conditions?

When researchers encounter contradictory results in phosphoserine aminotransferase studies, a structured approach to contradiction analysis is essential:

• Applying contradiction pattern analysis:

  • Use the (α, β, θ) notation system for classifying contradictions

  • α represents the number of interdependent variables (e.g., pH, temperature, substrate concentration)

  • β represents the number of contradictory dependencies observed

  • θ represents the minimum number of Boolean rules needed to assess contradictions

• Experimental design for resolving contradictions:

  • Implement factorial experimental designs to systematically test variable interactions

  • Use response surface methodology to map enzyme activity across multiple parameters

  • Apply Taguchi methods for robust parameter optimization

• Statistical approaches for contradiction resolution:

  • Apply ANOVA to identify significant variables and interactions

  • Use principal component analysis to detect patterns in multidimensional data

  • Implement Bayesian methods to update hypotheses based on new evidence

• Common contradiction sources and resolutions:

• Documentation and communication standards:

  • Clearly state all experimental conditions

  • Report all contradictory findings rather than selecting supportive data

  • Use standardized reporting formats for enzyme kinetics data

  • Implement the minimum information standards for enzyme activity reporting

How might recombinant B. vietnamiensis phosphoserine aminotransferase be utilized in developing novel antimicrobial strategies?

Recombinant phosphoserine aminotransferase from B. vietnamiensis presents several potential avenues for novel antimicrobial development:

• Structure-based drug design:

  • Use high-resolution structures of the enzyme to identify potential inhibitor binding sites

  • Design transition-state analogs specific to B. vietnamiensis serC

  • Develop allosteric inhibitors that exploit unique structural features

• Metabolic vulnerability targeting:

  • Identify metabolic bottlenecks associated with serine biosynthesis

  • Develop combination therapies targeting serC and related pathways

  • Exploit differences in aminoglycoside susceptibility unique to B. vietnamiensis

• Resistance mechanism investigations:

  • Study the relationship between serC activity and efflux-mediated aminoglycoside resistance

  • Target resistance-nodulation-division (RND) transporters implicated in B. vietnamiensis aminoglycoside resistance

  • Explore links between serine metabolism and cellular accumulation of antimicrobials

• Combination therapy approaches:

  • Test serC inhibitors with conventional antibiotics, particularly aminoglycosides

  • Investigate synergy with efflux pump inhibitors

  • Evaluate sequential therapy protocols to minimize resistance development

• Biofilm-specific targeting:

  • Examine serC role in biofilm formation and maintenance

  • Develop anti-biofilm strategies based on serine pathway modulation

  • Test effectiveness against chronic infection models

What technological advances are enabling new insights into phosphoserine aminotransferase function in B. vietnamiensis?

Cutting-edge technologies are transforming our understanding of phosphoserine aminotransferase in B. vietnamiensis:

• Advanced structural biology techniques:

  • Cryo-EM for capturing dynamic conformational states

  • Time-resolved X-ray crystallography to observe catalytic intermediates

  • Hydrogen-deuterium exchange mass spectrometry for protein dynamics

• Systems biology approaches:

  • Multi-omics integration (genomics, transcriptomics, proteomics, metabolomics)

  • Flux analysis to quantify serine pathway contributions to cellular metabolism

  • Genome-scale metabolic models incorporating serC function

• High-throughput screening technologies:

  • Microfluidic enzyme assays for rapid inhibitor screening

  • Fragment-based drug discovery platforms

  • AI-assisted virtual screening for novel inhibitor scaffolds

• Advanced genetic tools:

  • CRISPR-Cas9 precise genome editing in B. vietnamiensis

  • CRISPRi for tunable gene expression modulation

  • Site-saturation mutagenesis for comprehensive structure-function mapping

• In silico prediction and modeling:

  • Molecular dynamics simulations of enzyme-substrate interactions

  • Quantum mechanics/molecular mechanics (QM/MM) studies of transition states

  • Machine learning approaches for predicting enzyme-ligand interactions

What strategies can resolve common issues with recombinant B. vietnamiensis phosphoserine aminotransferase expression and purification?

Researchers frequently encounter several challenges when working with recombinant B. vietnamiensis phosphoserine aminotransferase. Here are effective troubleshooting approaches:

• Poor expression yield:

  • Optimize codon usage for the expression host

  • Test multiple expression strains (BL21, Rosetta, Arctic Express)

  • Reduce expression temperature to 16-18°C

  • Test autoinduction media instead of IPTG induction

• Protein insolubility:

  • Fuse with solubility-enhancing tags (MBP, SUMO, TrxA)

  • Add 5-10% glycerol to lysis buffer

  • Include mild detergents (0.1% Triton X-100) during lysis

  • Test refolding protocols if inclusion bodies persist

• Low enzyme activity:

  • Ensure PLP cofactor inclusion (0.1-0.2 mM) in all buffers

  • Test different pH ranges (7.0-8.5) for optimal activity

  • Add reducing agents (1-5 mM DTT or β-mercaptoethanol)

  • Check for inhibitory contaminants in buffer components

• Protein instability:

  • Optimize storage conditions (50% glycerol at -20°C vs. flash freezing)

  • Test stabilizing additives (trehalose, sucrose, PEG)

  • Investigate oligomeric state by size exclusion chromatography

  • Perform thermal shift assays to identify stabilizing buffer conditions

• Assay inconsistency:

  • Standardize enzyme:substrate ratios

  • Verify linear range of the assay

  • Control for potential interfering compounds

  • Use internal standards for quantitative assays

How can researchers differentiate between true experimental results and artifacts when studying phosphoserine aminotransferase activity in complex systems?

Distinguishing true results from artifacts requires rigorous experimental design and appropriate controls:

• Essential controls for enzyme activity studies:

  • No-enzyme controls to establish baseline

  • Heat-inactivated enzyme controls

  • Substrate-only and cofactor-only controls

  • Purified enzyme standards where available

• Artifact identification strategies:

  • Use multiple, orthogonal assay methods

  • Test activity across concentration ranges to identify non-specific effects

  • Implement time-course measurements to distinguish initial rates from product inhibition

  • Apply contradiction pattern analysis to identify inconsistencies in multidimensional data

• Interference mitigation in complex samples:

  • Pre-treat samples to remove known interferents

  • Use selective inhibitors to distinguish target enzyme from related activities

  • Implement spike-recovery experiments to quantify matrix effects

  • Apply appropriate blanking strategies for spectrophotometric assays

• Data analysis approaches:

  • Apply statistical tests appropriate for the data distribution

  • Use Grubbs' test to identify outliers

  • Implement Bland-Altman plots to compare methods

  • Consider Bayesian approaches for complex experimental designs

• Documentation standards:

  • Record all experimental conditions in detail

  • Document all data transformations and exclusion criteria

  • Maintain raw data alongside processed results

  • Report both positive and negative findings to avoid publication bias