Recombinant Desulfovibrio vulgaris Serine hydroxymethyltransferase (glyA)

What is the primary function of Serine hydroxymethyltransferase (glyA) in bacterial metabolism?

Serine hydroxymethyltransferase (SHMT) catalyzes the reversible interconversion of serine and glycine using tetrahydrofolate as the one-carbon carrier, playing a central role in one-carbon metabolism. Beyond this primary function, SHMT enzymes demonstrate broad reaction specificity and catalyze other side reactions typical for pyridoxal phosphate (PLP) dependent enzymes, including decarboxylation, transamination, and retroaldol cleavage . In certain bacteria like Chlamydiaceae, GlyA has demonstrated alanine racemase co-activity, allowing for the conversion of L-alanine to D-alanine, which is critical for bacterial cell wall synthesis .

How does glyA expression vary across different growth conditions in anaerobic bacteria?

While specific data on D. vulgaris glyA expression patterns across growth conditions isn't provided in the search results, researchers should consider examining glyA expression using transcriptomic analysis under various conditions including:

• Different carbon and energy sources

• Varying sulfate concentrations (particularly relevant for sulfate-reducing bacteria like D. vulgaris)

• Stress conditions such as oxidative stress, temperature variations, and metal exposure

• Different growth phases (lag, exponential, stationary)

RNA sequencing with biological replicates is recommended, with differentially expressed genes selected using appropriate statistical thresholds (e.g., false-discovery rate q-value ≤0.05 and fold change ≥2), as demonstrated in studies of glyA in other bacterial systems .

What expression system is optimal for producing recombinant D. vulgaris glyA?

For expression of recombinant D. vulgaris glyA, an E. coli-based expression system is typically recommended due to its:

• High expression yields

• Well-established protocols

• Compatibility with anaerobic protein expression

A general methodology includes:

• Cloning the glyA gene into an expression vector with an appropriate tag (Strep-tag has been successfully used for other bacterial GlyA proteins )

• Transforming into a suitable E. coli strain (BL21(DE3) or similar)

• Growing cultures to mid-log phase (OD600 of 0.6-0.8)

• Inducing with IPTG (typically 0.1-1.0 mM)

• Harvesting cells and purifying using affinity chromatography

For optimal activity, ensure the presence of pyridoxal-5'-phosphate (PLP) cofactor during purification and storage .

What are the critical factors affecting solubility and stability of recombinant glyA?

Several factors critically affect the solubility and stability of recombinant glyA:

• Cofactor presence: PLP must be maintained throughout purification (typically 10-50 μM)

• Reducing environment: Include reducing agents (e.g., DTT or β-mercaptoethanol) to prevent oxidation of cysteine residues

• Temperature control: Lower induction temperatures (16-25°C) often improve solubility

• Buffer composition: Optimize pH (typically 7.0-8.0) and include glycerol (10-20%) for stability

• Expression time: Shorter induction periods may reduce inclusion body formation

For long-term storage, flash freezing in liquid nitrogen and storage at -80°C with glycerol is recommended to maintain enzymatic activity.

How can I measure the serine hydroxymethyltransferase activity of recombinant D. vulgaris glyA?

The serine hydroxymethyltransferase activity can be measured using several well-established assays:

• Coupled enzyme assay: Monitoring the formation of methylenetetrahydrofolate by coupling to another enzyme reaction

• Spectrophotometric assay: Measuring the formation of glycine from serine and tetrahydrofolate by monitoring absorbance changes

• Radioactive assay: Using 14C-labeled serine and measuring the conversion to labeled glycine

A standard reaction mixture typically contains:

• Purified recombinant glyA protein (1-10 μg)

• L-serine (1-10 mM)

• Tetrahydrofolate (0.1-1 mM)

• PLP cofactor (50-100 μM)

• Buffer (typically HEPES or phosphate, pH 7.5)

• Reducing agent (1-5 mM DTT)

Control reactions should include enzyme-free and substrate-free samples to account for background activity.

How do I assess the alanine racemase co-activity of glyA?

To assess the alanine racemase co-activity of glyA, a D-amino acid oxidase (DAAO) coupled enzymatic assay can be employed, as demonstrated with GlyA from C. pneumoniae :

• Prepare a reaction mixture containing:

  • Purified recombinant glyA (10-50 μg)

  • L-alanine substrate (10-50 mM)

  • PLP cofactor (100 μM)

  • Appropriate buffer (typically pH 7.5-8.0)

• Incubate at optimal temperature (typically 37°C) for 30-60 minutes

• Add D-amino acid oxidase and its components to detect D-alanine production:

  • D-amino acid oxidase converts D-alanine to pyruvate

  • Pyruvate can be detected colorimetrically

• Include appropriate controls:

  • A known alanine racemase (e.g., from Bacillus stearothermophilus) as a positive control

  • Reaction mixture without glyA as a negative control

This approach allows quantification of the D-alanine produced by glyA's racemase activity .

What experimental design is most appropriate for comparing wild-type and mutant D. vulgaris glyA proteins?

When comparing wild-type and mutant D. vulgaris glyA proteins, a systematic experimental design should include:

• Define your variables clearly:

  • Independent variable: The specific mutation(s) in glyA

  • Dependent variables: Enzyme kinetics parameters (Km, Vmax, kcat), substrate specificity, cofactor binding, stability, etc.

  • Control variables: Expression conditions, purification methods, assay conditions

• Develop a specific, testable hypothesis about how the mutation affects enzyme function

• Design treatments that manipulate only the independent variable:

  • Ensure all proteins are expressed and purified using identical protocols

  • Consider using tags that minimally impact enzyme function

  • Validate protein folding using circular dichroism or thermal shift assays

• Use both between-subjects and within-subjects measurements:

  • Between-subjects: Compare different protein variants

  • Within-subjects: Test each protein across multiple conditions (temperature, pH, substrate concentrations)

• Plan precise measurement of dependent variables:

  • Use standardized assays for activity measurements

  • Include technical replicates (minimum 3) and biological replicates (minimum 3)

  • Employ statistical methods appropriate for the data distribution

How should I design experiments to investigate the role of glyA in antimicrobial resistance?

To investigate glyA's role in antimicrobial resistance, design experiments that address both genetic and functional aspects:

• Genetic approach:

  • Generate glyA knockout mutants using appropriate genetic tools

  • Create complemented strains expressing wild-type glyA

  • Develop site-directed mutants targeting catalytic residues

• Phenotypic characterization:

  • Determine minimum inhibitory concentrations (MICs) of various antibiotics for wild-type, ΔglyA, and complemented strains

  • Test growth under different nutritional conditions (with/without glycine supplementation)

  • Assess cell morphology and cell wall characteristics

• Metabolic analysis:

  • Quantify changes in amino acid pools, particularly glycine, serine, and alanine

  • Measure one-carbon metabolite levels

  • Analyze cell wall composition, especially peptidoglycan precursors

• Molecular mechanism investigation:

  • Perform transcriptomic analysis to identify differentially expressed genes

  • Use proteomics to determine changes in protein expression

  • Investigate specific resistance pathways activated in response to glyA modulation

An example experimental design could follow the approach used in S. aureus studies, where glyA was found to play a key role in lysostaphin resistance through functional genomics and complementation studies .

How does D. vulgaris glyA compare functionally to glyA from other bacterial species?

Comparative analysis of glyA proteins across bacterial species reveals important functional similarities and differences:

• Primary function conservation: All bacterial glyA proteins catalyze the reversible interconversion of serine and glycine using tetrahydrofolate

• Secondary activities: Many glyA proteins exhibit secondary enzymatic activities:

  • Alanine racemase activity: Demonstrated in E. coli and C. pneumoniae glyA

  • Other PLP-dependent reactions: Including decarboxylation, transamination, and retroaldol cleavage

• Metabolic context:

  • In D. vulgaris and other Desulfovibrio species, glyA likely functions within the context of anaerobic metabolism

  • In Chlamydiaceae, glyA's alanine racemase activity may compensate for the absence of dedicated alanine racemases

  • In E. coli, glyA deletion leads to glycine auxotrophy and CycA-dependent glycine assimilation

• Antibiotic resistance connections:

  • In S. aureus, glyA is linked to lysostaphin resistance

  • In E. coli, glyA deletion increases novobiocin sensitivity, which can be reversed by glycine supplementation

When comparing glyA proteins, it's important to consider their genomic context and metabolic networks, as these influence their physiological roles across different bacterial species.

What phylogenetic approaches are most informative for analyzing evolutionary relationships of glyA across bacterial species?

For phylogenetic analysis of glyA across bacterial species, consider the following approaches:

• Sequence selection and alignment:

  • Use complete glyA protein sequences rather than partial sequences

  • Consider including both glyA and related genes for context

  • Employ robust alignment algorithms like MAFFT for accurate sequence alignment

  • Include outgroup sequences to root the phylogenetic tree properly

• Evolutionary model selection:

  • Use software like ProtTest to select the optimal evolutionary model (e.g., LG evolutionary model)

  • Consider rate heterogeneity and invariant sites in your model

• Tree construction methods:

  • Maximum Likelihood method (using PhyML or similar algorithms)

  • Bayesian inference approaches (MrBayes)

  • Maximum Parsimony as a complementary analysis

• Validation approaches:

  • Bootstrap analysis (typically 1000 replicates) to assess branch support

  • Alternative topology testing to evaluate competing evolutionary hypotheses

• Contextual analysis:

  • Combine glyA phylogeny with analysis of genomic context

  • Consider using concatenated sequences of multiple conserved genes (e.g., RpoB and GyrB) for species-level relationships

  • Analyze gene synteny around glyA locus across species

This approach has proven effective in studies of Desulfovibrio species relationships and other bacterial phylogenetic analyses .

How can I identify novel functions or catalytic activities of D. vulgaris glyA?

To identify novel functions or catalytic activities of D. vulgaris glyA, implement a multi-faceted approach:

• Substrate screening:

  • Test activity with structurally similar amino acids beyond serine/glycine

  • Examine non-canonical reactions typical for PLP-dependent enzymes

  • Use high-throughput screening methods to test diverse substrate libraries

• Genetic approaches:

  • Create glyA knockout mutants and perform phenotypic characterization

  • Use RNA-seq to identify genes with altered expression in ΔglyA strains

  • Perform suppressor mutation analysis to identify genetic interactions

• Biochemical approaches:

  • Conduct metabolomic analysis comparing wild-type and ΔglyA strains

  • Perform in vitro activity assays under various conditions (pH, temperature, salt)

  • Test for alanine racemase activity using D-amino acid oxidase coupled assays

• Structural analysis:

  • Generate crystal structures of glyA with various ligands

  • Perform molecular docking with potential substrates

  • Use site-directed mutagenesis to probe specific residues

• Comparative genomics:

  • Analyze genomic context of glyA across Desulfovibrio species

  • Examine co-evolution with other genes

This comprehensive approach may reveal unexpected functions, similar to how alanine racemase activity was discovered in GlyA from E. coli and C. pneumoniae .

What methodologies can resolve contradictory data regarding glyA function or activity?

When facing contradictory data regarding glyA function or activity, employ these methodological approaches to resolve discrepancies:

• Standardize experimental conditions:

  • Systematically test the effect of buffer composition, pH, and temperature

  • Ensure consistent protein preparation methods and enzyme:substrate ratios

  • Standardize cofactor (PLP) concentration and quality

• Employ multiple, independent assay methods:

  • Use both direct and coupled enzyme assays

  • Implement spectrophotometric, fluorometric, and chromatographic detection

  • Consider isotope labeling experiments to trace reaction pathways

• Address protein heterogeneity:

  • Analyze protein by size-exclusion chromatography to detect oligomeric states

  • Employ mass spectrometry to confirm protein integrity and modifications

  • Assess cofactor binding using spectroscopic methods

• Comparative analysis:

  • Test recombinant protein from multiple expression systems

  • Compare native vs. recombinant enzyme properties

  • Examine enzyme from closely related Desulfovibrio species

• Statistical rigor:

  • Increase biological and technical replicates

  • Apply appropriate statistical tests based on data distribution

  • Calculate minimum detectable differences to ensure adequate power

• Meta-analysis approach:

  • Systematically compare methods and results across studies

  • Identify variables that consistently affect outcomes

  • Develop a comprehensive model that explains apparent contradictions

This systematic approach can help distinguish genuine biological variability from technical artifacts or experimental design issues.

What statistical approaches are appropriate for analyzing kinetic data from glyA enzyme assays?

For analyzing kinetic data from glyA enzyme assays, employ these statistical approaches:

• Enzyme kinetics parameter estimation:

  • Non-linear regression for Michaelis-Menten kinetics

  • Lineweaver-Burk, Eadie-Hofstee, or Hanes-Woolf transformations as complementary analyses

  • Bootstrap or jackknife resampling to estimate parameter confidence intervals

• Model selection:

  • Akaike Information Criterion (AIC) to compare different kinetic models

  • F-test for nested models to determine if more complex models are justified

  • Residual analysis to check for systematic deviations from models

• Comparative analysis:

  • ANOVA with post-hoc tests for comparing multiple conditions

  • t-tests (paired or unpaired) for direct comparisons between two conditions

  • Consider non-parametric alternatives if normality assumptions are violated

• Visualization approaches:

  • Diagnostic plots (residuals vs. fitted, Q-Q plots)

  • Enzyme kinetics plots with confidence intervals

  • Heat maps for multi-parameter experiments

• Software recommendations:

  • GraphPad Prism for user-friendly enzyme kinetics analysis

  • R with specialized packages (drc, nlme) for advanced modeling

  • Python with scipy.optimize for custom analysis pipelines

When reporting results, include both best-fit parameters with confidence intervals and goodness-of-fit metrics to enable critical evaluation of the data quality.

How should transcriptomic and proteomic data be integrated to understand the physiological role of glyA?

To effectively integrate transcriptomic and proteomic data for understanding glyA's physiological role:

• Data normalization and quality control:

  • Apply appropriate normalization methods for each data type

  • For transcriptomics: Normalize read counts using DESeq2 or similar methods

  • For proteomics: Use total spectral counts or intensity-based methods

  • Employ principal component analysis (PCA) to assess sample clustering and identify outliers

• Differential expression analysis:

  • Set stringent statistical thresholds (e.g., false-discovery rate q-value ≤0.05 and fold change ≥2)

  • Generate volcano plots to visualize significance and magnitude of changes

  • Create tables of differentially expressed genes/proteins with relevant statistics:

  Gene name	Expression level
  	Wild-type	ΔglyA	log2FC	P value	q value
  glyA	594.7281	3.4385	-7.4343	0.0000	0.0000
  cysK	507.0357	1,725.7782	1.7671	0.0000	0.0000
  cysN	20.4097	135.7450	2.7336	0.0000	0.0000
  cysH	38.9700	271.4150	2.8001	0.0000	0.0018

• Pathway and network analysis:

  • Conduct Gene Ontology (GO) enrichment analysis

  • Perform KEGG pathway analysis to identify affected metabolic pathways

  • Use network analysis to identify hub genes/proteins connected to glyA

• Integration strategies:

  • Calculate correlation between transcript and protein levels

  • Identify concordantly and discordantly regulated genes

  • Apply integrative computational methods (e.g., weighted gene co-expression network analysis)

  • Develop multi-omics visualization of key pathways

• Biological validation:

  • Select key findings for targeted experimental validation

  • Construct and test specific gene knockouts of identified interactors

  • Perform metabolomic analyses to validate predicted metabolic impacts

This integrated approach has been successfully applied to understand the effects of glyA deletion in E. coli, revealing connections to sulfur metabolism and antibiotic susceptibility .

Why might recombinant D. vulgaris glyA show low or no enzymatic activity?

Several factors can contribute to low or no enzymatic activity in recombinant D. vulgaris glyA:

• Cofactor issues:

  • Insufficient PLP incorporation during expression/purification

  • PLP degradation due to light exposure or oxidation

  • Incorrect PLP:enzyme ratio in assays

• Protein misfolding or damage:

  • Expression conditions promoting inclusion body formation

  • Oxidation of critical cysteine residues

  • Improper pH during purification affecting tertiary structure

• Assay conditions:

  • Suboptimal buffer composition or pH

  • Missing essential metal cofactors

  • Inhibitory components in the reaction mixture

  • Temperature outside optimal range

• Technical considerations:

  • Interference with detection method

  • Enzyme concentration too low for detection limits

  • Substrate quality or concentration issues

• Protein modification issues:

  • Inhibitory effects of affinity tags

  • Post-translational modifications affecting activity

  • Proteolytic degradation of critical domains

Troubleshooting approach:

• Verify protein integrity by SDS-PAGE and mass spectrometry

• Test enzyme with excess PLP (100-200 μM) in assay buffer

• Try multiple buffer systems (HEPES, phosphate, Tris) at various pH values

• Include reducing agents (DTT, β-mercaptoethanol) to protect cysteine residues

• Compare activity of protein expressed under different conditions (temperature, induction time)

How can I improve the yield and purity of recombinant D. vulgaris glyA for structural studies?

To improve yield and purity of recombinant D. vulgaris glyA for structural studies:

• Expression optimization:

  • Test multiple E. coli expression strains (BL21(DE3), Rosetta, Arctic Express)

  • Optimize induction conditions (temperature: 16-30°C, IPTG: 0.1-1.0 mM)

  • Consider auto-induction media for higher cell density

  • Test codon-optimized gene synthesis for rare codon issues

  • Evaluate expression with different fusion tags (His, GST, MBP)

• Purification enhancement:

  • Implement multi-step purification strategy:

    1. Affinity chromatography (IMAC, GST, etc.)

    2. Ion exchange chromatography

    3. Size exclusion chromatography

  • Add PLP (50-100 μM) to all purification buffers

  • Include stabilizing agents (glycerol 10%, reducing agents, salt)

  • Consider on-column refolding for proteins in inclusion bodies

• Sample preparation for structural studies:

  • Concentrate protein using centrifugal filters with appropriate MWCO

  • Perform buffer optimization screening (pH, salt, additives)

  • Remove affinity tags if they interfere with structure or activity

  • Verify monodispersity by dynamic light scattering

  • Assess protein stability using thermal shift assays

• Quality control measures:

  • Analytical SEC to confirm oligomeric state

  • Mass spectrometry to verify protein integrity

  • Activity assays to confirm functional protein

  • Circular dichroism to assess secondary structure

• Storage considerations:

  • Identify optimal storage conditions (temperature, buffer composition)

  • Test protein stability after freeze-thaw cycles

  • Consider flash-freezing small aliquots in liquid nitrogen

Following these approaches can significantly improve the quality of recombinant protein preparations for demanding applications like X-ray crystallography or cryo-EM studies.