Recombinant Salmonella choleraesuis Monofunctional biosynthetic peptidoglycan transglycosylase (mtgA)

What is the basic structure of Recombinant Salmonella choleraesuis Monofunctional Biosynthetic Peptidoglycan Transglycosylase (mtgA)?

Recombinant Salmonella choleraesuis Monofunctional Biosynthetic Peptidoglycan Transglycosylase (mtgA) is a full-length protein consisting of 242 amino acids (1-242aa). The amino acid sequence is: MSKRRIAPLTFLRRLLLRILAALAVFWGGGIALFSVVPVPFSAVMAERQISAWLGGEFGY VAHSDWVSMADISPWMGLAVIAAEDQKFPEHWGFDVPAIEKALAHNERNESRIRGASTLS QQTAKNLFLWDGRSWLRKGLEAGLTLGIETVWSKKRILTVYLNIAEFGDGIFGVEAAAQR YFHKPASRLSVSEAALLAAVLPNPLRYKANAPSGYVRSRQAWIMRQMRQLGGESFMTRNQ LN. When expressed as a recombinant protein, it typically includes an N-terminal His-tag to facilitate purification and detection protocols .

What are the primary functions of mtgA in bacterial cell wall synthesis?

Monofunctional Biosynthetic Peptidoglycan Transglycosylase (mtgA) plays a critical role in bacterial cell wall synthesis by catalyzing the polymerization of glycan strands within the peptidoglycan layer. This enzyme functions as a glycan polymerase (hence one of its synonyms), facilitating the formation of β-1,4 glycosidic bonds between N-acetylmuramic acid (MurNAc) and N-acetylglucosamine (GlcNAc) subunits. This transglycosylation activity is essential for maintaining cell wall integrity and bacterial survival, making mtgA an important target for antimicrobial research .

How does mtgA differ from bifunctional penicillin-binding proteins (PBPs)?

Unlike bifunctional penicillin-binding proteins (PBPs) that possess both transglycosylase and transpeptidase activities, mtgA is classified as monofunctional because it exclusively performs transglycosylation without transpeptidase activity. This specificity allows mtgA to focus solely on glycan strand formation without participating in peptide cross-linking. When designing experiments involving cell wall synthesis inhibitors, researchers must account for this functional distinction to correctly interpret results. Methodologically, this difference necessitates using specific assays that isolate transglycosylase activity when studying mtgA functions .

What expression systems are commonly used for recombinant mtgA production?

For recombinant mtgA production, E. coli expression systems are most commonly employed due to their high yield, relative simplicity, and cost-effectiveness. The standard methodology involves cloning the mtgA gene (with or without a His-tag sequence) into an appropriate expression vector, transforming it into competent E. coli cells, inducing protein expression (typically with IPTG for T7-based systems), and then purifying the resulting protein. For Salmonella choleraesuis mtgA specifically, protocols frequently use E. coli as the heterologous host for expression, as documented in standard recombinant protein production literature .

What purification strategies yield the highest purity mtgA protein?

High-purity mtgA protein (>90% purity) is typically achieved through a multi-step purification protocol. For His-tagged mtgA, this involves:

• Initial capture using immobilized metal affinity chromatography (IMAC) with Ni-NTA or similar resins

• Intermediate purification using ion exchange chromatography

• Final polishing via size exclusion chromatography

Each batch should be analyzed by SDS-PAGE to confirm purity levels above 90%. Researchers should optimize buffer conditions throughout the purification process to maintain protein stability and activity. For long-term storage, aliquoting the purified protein and storing at -20°C/-80°C in a storage buffer containing Tris/PBS with 6% trehalose at pH 8.0 is recommended to preserve functionality .

How should researchers design experiments to accurately assess mtgA enzymatic activity?

When designing experiments to assess mtgA enzymatic activity, researchers should implement a comprehensive approach that accounts for the specific catalytic properties of this transglycosylase. A methodologically sound experimental design should include:

• Substrate preparation: Utilize lipid II or appropriate synthetic analogues that accurately represent the natural substrate for transglycosylase activity.

• Assay conditions optimization: Establish appropriate buffer compositions (typically containing divalent cations like Mg²⁺), pH ranges (generally 7.5-8.5), and temperature conditions (usually 30-37°C) that maximize enzyme activity while maintaining physiological relevance.

• Activity measurement techniques: Employ one or more of the following methods:

  • HPLC-based glycan chain analysis

  • Fluorescence-based assays using dansylated or fluorescently-labeled lipid II

  • Radiolabeled substrate incorporation assays

  • Mass spectrometry-based product identification

• Control experiments: Include proper negative controls (heat-inactivated enzyme), positive controls (known active transglycosylases), and substrate-only controls to account for potential spontaneous reactions .

It's crucial to design experiments that distinguish the specific activity of mtgA from other cellular components that might influence peptidoglycan synthesis, following the "necessary operation requirement" and "exclusive operations assumption" as established in experimental design literature .

What are the critical considerations when analyzing spatial specificity in mtgA localization studies?

When analyzing spatial specificity in mtgA localization studies, researchers must carefully consider methodological factors that influence spatial resolution and data interpretation. Critical considerations include:

• Covariance estimation methods: As demonstrated in related spatial localization studies, the way covariance estimates are calculated can significantly affect spatial specificity. Researchers should evaluate whether narrowing time-frequency windows improves resolution .

• Data averaging approaches: Consider how data averaging prior to covariance estimation may affect spatial specificity. While averaging can enhance spatial resolution, it often produces ill-conditioned covariance matrices requiring appropriate regularization strategies .

• Signal-to-noise optimization: Implement proper background subtraction and signal enhancement techniques to distinguish true localization signals from artifacts.

• Resolution validation: Use known reference points or multiple complementary visualization techniques to confirm localization patterns.

• Statistical validation: Apply rigorous statistical analysis rather than relying on qualitative "looks fine" assessments, ensuring results fit within appropriate confidence intervals and testing against null hypotheses .

When conducting fluorescence microscopy or other visualization techniques for mtgA localization, these principles help ensure accurate spatial mapping of the enzyme within bacterial cell compartments.

How should researchers address data inconsistencies in mtgA functional studies?

When encountering data inconsistencies in mtgA functional studies, researchers should implement a systematic troubleshooting approach:

• Identify potential sources of variability:

  • Protein quality (verify purity via SDS-PAGE and activity via functional assays)

  • Substrate quality and consistency

  • Experimental conditions (temperature, pH, buffer composition)

  • Instrument calibration and performance

• Apply statistical rigor:

  • Use appropriate statistical tests to determine if inconsistencies fall within expected random variation

  • Calculate confidence intervals to establish boundaries of reliable data

  • Consider whether observed distributions match expected hypergeometric distributions for the system being studied

• Cross-validation strategies:

  • Employ multiple analytical techniques to verify results

  • Use complementary approaches to test the same hypothesis

  • Compare results with published literature on related transglycosylases

• Controlled variable isolation:

  • Systematically modify single variables to identify specific factors contributing to inconsistency

  • Design matrix experiments to evaluate interaction effects between variables

• Documentation and reporting:

  • Maintain detailed records of all experimental conditions

  • Report all data, including outliers, with appropriate statistical context

  • Provide raw data when possible to allow independent analysis

This methodological framework ensures that data inconsistencies are addressed scientifically rather than arbitrarily excluded or ignored.

What computational approaches are most effective for modeling mtgA interaction with peptidoglycan substrates?

For modeling mtgA interactions with peptidoglycan substrates, researchers should employ multi-level computational approaches that integrate structural and functional data:

• Molecular docking simulations:

  • Utilize flexible docking algorithms that account for conformational changes in both enzyme and substrate

  • Incorporate water molecules at the active site to accurately model hydrogen bonding networks

  • Apply scoring functions optimized for glycosyltransferase-substrate interactions

• Molecular dynamics simulations:

  • Perform long-timescale (>100 ns) simulations to capture transient binding events

  • Use appropriate force fields optimized for carbohydrate-protein interactions

  • Analyze trajectory data for binding pocket flexibility and substrate orientation

• Quantum mechanics/molecular mechanics (QM/MM) approaches:

  • Apply QM calculations to the active site region to accurately model transition states

  • Use MM for the remainder of the protein to maintain computational efficiency

  • Validate energy profiles against experimental kinetic data

• Network analysis methods:

  • Analyze allosteric communication pathways between substrate binding and catalytic sites

  • Identify cooperative effects in substrate recognition and processing

• Integration with experimental data:

  • Calibrate computational models using experimental binding constants and reaction rates

  • Validate predictions through site-directed mutagenesis studies

  • Iterate between computational predictions and experimental verification

This comprehensive approach ensures that computational models provide meaningful insights into the mechanistic details of mtgA-substrate interactions.

What are the optimal storage conditions for maintaining mtgA stability and activity?

For optimal storage of Recombinant Salmonella choleraesuis Monofunctional Biosynthetic Peptidoglycan Transglycosylase (mtgA), researchers should follow these evidence-based protocols:

• Short-term storage (up to one week):

  • Store working aliquots at 4°C

  • Maintain in Tris/PBS-based buffer with 6% trehalose at pH 8.0

  • Avoid repeated freeze-thaw cycles which significantly reduce enzyme activity

• Long-term storage:

  • Store at -20°C/-80°C in small aliquots to prevent repeated freeze-thaw cycles

  • Add glycerol to a final concentration of 30-50% as a cryoprotectant

  • Use screw-cap microcentrifuge tubes with O-rings to prevent evaporation

  • Label with preparation date, concentration, and buffer composition

• Reconstitution protocol:

  • Briefly centrifuge lyophilized protein vials before opening

  • Reconstitute to 0.1-1.0 mg/mL using deionized sterile water

  • Allow complete dissolution before use or aliquoting

  • Validate activity after reconstitution using appropriate enzymatic assays

Following these methodological guidelines ensures maximum retention of enzymatic activity over time and minimizes batch-to-batch variation in experimental results.

What quality control measures should be implemented before using mtgA in experiments?

Before using mtgA in experiments, researchers should implement the following comprehensive quality control measures:

• Purity assessment:

  • Perform SDS-PAGE analysis to confirm >90% purity

  • Consider mass spectrometry to verify the exact molecular weight and detect potential truncations or modifications

• Activity verification:

  • Conduct specific transglycosylase activity assays using standard substrates

  • Compare activity to previously established benchmarks or reference standards

  • Calculate specific activity (units/mg) to ensure batch consistency

• Structural integrity:

  • Use circular dichroism (CD) spectroscopy to verify secondary structure content

  • Consider differential scanning fluorimetry (DSF) to assess thermal stability

  • Verify correct folding through tryptophan fluorescence if applicable

• Aggregation analysis:

  • Perform dynamic light scattering (DLS) to detect potential aggregation

  • Use size exclusion chromatography to confirm monomeric state if appropriate

• Endotoxin testing:

  • For experiments sensitive to bacterial endotoxins, perform LAL (Limulus Amebocyte Lysate) assay

  • Ensure endotoxin levels are below thresholds that could interfere with downstream applications

Implementing these quality control measures ensures experimental reproducibility and reliability of results when working with recombinant mtgA protein.

How can researchers accurately measure mtgA transglycosylase activity in vitro?

To accurately measure mtgA transglycosylase activity in vitro, researchers should consider the following methodological approaches:

Table 1: Comparison of Transglycosylase Activity Assay Methods

For optimal results, researchers should:

• Prepare defined substrates:

  • Use chemically defined lipid II molecules with appropriate lipid chains

  • Consider substrate variations to test specificity (e.g., different stem peptide compositions)

• Optimize reaction conditions:

  • Determine optimal pH, temperature, and ionic strength

  • Evaluate divalent cation requirements (typically Mg²⁺ or Mn²⁺)

  • Test detergent types and concentrations for optimal activity

• Establish proper controls:

  • Include known transglycosylase inhibitors (e.g., moenomycin) as negative controls

  • Use heat-inactivated enzyme as background control

  • Consider related enzymes with known activity rates as positive controls

This multi-faceted approach ensures accurate and reproducible measurement of mtgA transglycosylase activity across experimental conditions.

What are the most effective approaches for studying mtgA inhibition kinetics?

For studying mtgA inhibition kinetics, researchers should implement these methodologically rigorous approaches:

• Inhibition mechanism determination:

  • Perform steady-state kinetic analysis using varying substrate and inhibitor concentrations

  • Generate Lineweaver-Burk, Dixon, and Cornish-Bowden plots to distinguish between competitive, non-competitive, uncompetitive, or mixed inhibition

  • Calculate inhibition constants (Ki) under standardized conditions

• Time-dependent inhibition analysis:

  • Pre-incubate enzyme with inhibitor for varying time periods before substrate addition

  • Plot remaining activity versus pre-incubation time to detect slow-binding or irreversible inhibitors

  • Determine kinact and Ki* for two-step inhibition mechanisms

• Structure-activity relationship studies:

  • Systematically test inhibitor analogues with defined structural modifications

  • Correlate structural features with inhibition potency

  • Use computational docking to predict binding modes of inhibitors

• Biophysical interaction analysis:

  • Employ surface plasmon resonance (SPR) or isothermal titration calorimetry (ITC) to directly measure binding parameters

  • Determine association and dissociation rate constants

  • Assess thermodynamic parameters of binding

• Selectivity profiling:

  • Test inhibitors against related transglycosylases and other glycosyltransferases

  • Calculate selectivity indices to identify specific versus broad-spectrum inhibitors

  • Evaluate inhibition against a panel of bacterial versus mammalian enzymes

This comprehensive approach allows for detailed characterization of inhibitor potency, mechanism, and specificity, facilitating rational design of improved inhibitors targeting mtgA.

How does mtgA function as a potential target for novel antimicrobial development?

Recombinant Salmonella choleraesuis Monofunctional Biosynthetic Peptidoglycan Transglycosylase (mtgA) represents a promising antimicrobial target due to several key biological and pharmacological factors:

• Essential function in cell wall biosynthesis:

  • mtgA catalyzes the polymerization of glycan strands, a critical step in peptidoglycan synthesis

  • Inhibition of this activity compromises bacterial cell wall integrity, leading to growth inhibition or cell lysis

  • The absence of functionally redundant enzymes in some bacterial species increases target vulnerability

• Structural uniqueness compared to mammalian enzymes:

  • Transglycosylases have no mammalian homologs, reducing potential off-target effects

  • The catalytic domain contains conserved motifs across bacterial species but distinct structural features from human glycosyltransferases

  • This structural divergence facilitates selective targeting of bacterial enzymes

• Established proof-of-concept through natural product inhibitors:

  • Moenomycin, a natural product antibiotic, specifically inhibits transglycosylases including mtgA

  • The clinical success of glycopeptide antibiotics (which target related steps in cell wall synthesis) validates this pathway for intervention

  • Structure-activity relationships from known inhibitors provide templates for rational drug design

• Methodological approach to target validation:

  • Genetic knockout or depletion studies demonstrate growth defects or lethality

  • In vitro enzymatic assays confirm direct inhibition of activity

  • Phenotypic changes in cell morphology and division upon inhibition provide visible markers of efficacy

The methodological approach to developing mtgA inhibitors should include both target-based screening (using purified recombinant protein) and whole-cell assays to ensure compounds achieve sufficient penetration and target engagement in intact bacterial cells.

What resistance mechanisms might emerge against mtgA-targeted antimicrobials?

Understanding potential resistance mechanisms against mtgA-targeted antimicrobials requires systematic consideration of evolutionary adaptations bacteria might develop:

• Target-based resistance mechanisms:

  • Point mutations in the mtgA gene that alter inhibitor binding without compromising enzymatic function

  • Upregulation of mtgA expression to overcome stoichiometric inhibition

  • Compensatory mutations in interacting proteins that stabilize mtgA function under inhibition

  • Expression of variant mtgA enzymes with reduced inhibitor affinity

• Bypass mechanisms:

  • Upregulation of alternate transglycosylases (bifunctional PBPs) to compensate for mtgA inhibition

  • Modification of peptidoglycan precursors to reduce affinity for inhibitors

  • Altered cell wall architecture that reduces dependence on transglycosylase activity

  • Metabolic adaptations that allow survival with compromised cell wall integrity

• Access-based resistance:

  • Reduced outer membrane permeability to limit inhibitor entry (in Gram-negative bacteria)

  • Enhanced efflux pump activity to actively expel inhibitors from the cell

  • Biofilm formation providing physical barriers to inhibitor access

  • Production of inhibitor-binding proteins that sequester compounds before they reach mtgA

• Methodological approaches to study and combat resistance:

  • Serial passage experiments to identify resistance frequency and mechanisms

  • Whole genome sequencing of resistant mutants to identify genetic alterations

  • Combination therapy targeting multiple steps in cell wall biosynthesis

  • Rational design of dual-targeting inhibitors that simultaneously inhibit mtgA and related enzymes

This comprehensive understanding of potential resistance mechanisms should inform antimicrobial development strategies, including combination approaches and structural modifications to minimize resistance development.

How can advanced structural biology techniques enhance understanding of mtgA function?

Advanced structural biology techniques offer powerful methodological approaches to elucidate mtgA function at molecular resolution:

• Cryo-electron microscopy (Cryo-EM):

  • Enables visualization of mtgA in different conformational states during catalysis

  • Allows study of mtgA in complex with native lipid environments or membrane mimetics

  • Can reveal large assemblies of mtgA with other cell wall synthesis machinery

  • Methodological approach: Sample vitrification, data collection with motion correction, 3D reconstruction, and model building

• X-ray crystallography with serial femtosecond crystallography:

  • Provides atomic-resolution structures of mtgA in different functional states

  • Captures short-lived reaction intermediates using time-resolved approaches

  • Reveals precise substrate and inhibitor binding modes

  • Methodological approach: Microcrystal preparation, XFEL data collection, phase determination, and model refinement

• Nuclear magnetic resonance (NMR) spectroscopy:

  • Characterizes dynamic aspects of mtgA function in solution

  • Maps chemical shift perturbations upon substrate or inhibitor binding

  • Identifies flexible regions critical for catalysis

  • Methodological approach: Isotopic labeling, multidimensional NMR experiments, and dynamic parameter calculation

• Hydrogen-deuterium exchange mass spectrometry (HDX-MS):

  • Probes conformational dynamics and solvent accessibility changes

  • Maps protein-substrate interaction interfaces

  • Identifies allosteric networks within mtgA structure

  • Methodological approach: Time-course deuterium labeling, proteolytic digestion, LC-MS analysis, and differential uptake mapping

• Integrative structural biology approaches:

  • Combines multiple techniques (e.g., SAXS, FRET, crosslinking-MS)

  • Correlates structural information with functional data

  • Builds comprehensive models of mtgA in cellular context

  • Methodological approach: Data integration using computational frameworks like IMP (Integrative Modeling Platform)

These advanced structural biology techniques, when applied systematically, provide complementary information about mtgA function across different spatial and temporal scales, enhancing our understanding of this important enzyme's mechanism of action.

What are the most significant unanswered questions about mtgA function and regulation?

Despite considerable progress in understanding Recombinant Salmonella choleraesuis Monofunctional Biosynthetic Peptidoglycan Transglycosylase (mtgA), several critical knowledge gaps remain that represent significant research opportunities:

Addressing these questions requires interdisciplinary approaches combining molecular biology, biochemistry, structural biology, and computational methods to advance our fundamental understanding of this important bacterial enzyme.

What emerging technologies might accelerate research on mtgA function?

Several emerging technologies hold promise for accelerating research on mtgA function and applications:

• CRISPR-based approaches:

  • CRISPRi/CRISPRa systems for tunable mtgA expression regulation

  • Base editing for precise introduction of point mutations without selection markers

  • CRISPR-scanning to systematically map functional domains in vivo

  • Methodological approach: Design guide RNAs targeting mtgA regulatory regions, deliver with appropriate Cas variants, and assess phenotypic consequences

• Single-molecule techniques:

  • Optical tweezers to measure force generation during glycan strand polymerization

  • Single-molecule FRET to visualize conformational changes during catalysis

  • Super-resolution microscopy to track mtgA localization with nanometer precision

  • Methodological approach: Protein labeling with appropriate fluorophores, optimization of immobilization strategies, and time-resolved data acquisition

• Artificial intelligence and machine learning:

  • Deep learning models to predict inhibitor binding and efficacy

  • Molecular dynamics simulations enhanced by AI for extended timescales

  • Automated image analysis for high-throughput phenotypic screening

  • Methodological approach: Training on existing datasets, validation with experimental benchmarks, and iterative refinement

• Synthetic biology platforms:

  • Cell-free expression systems for rapid mtgA variant screening

  • Minimal cell models to study mtgA function in simplified backgrounds

  • Biosensors that report on transglycosylase activity in real-time

  • Methodological approach: Design of genetic circuits, optimization of expression conditions, and development of reporter systems

• Advanced microfluidics and organ-on-chip systems:

  • Gradient devices to study mtgA inhibition under physiological conditions

  • Co-culture systems to assess host-pathogen interactions during mtgA inhibition

  • Real-time monitoring of bacterial responses to transglycosylase inhibition

  • Methodological approach: Device fabrication, optimization of flow conditions, and integration with live-cell imaging

These emerging technologies, when applied systematically and in combination, have the potential to overcome current methodological limitations and accelerate progress in understanding mtgA function and developing novel antimicrobial strategies.

What specialized reagents and tools are essential for mtgA research?

For comprehensive research on Recombinant Salmonella choleraesuis Monofunctional Biosynthetic Peptidoglycan Transglycosylase (mtgA), the following specialized reagents and tools are essential:

Table 2: Essential Reagents and Tools for mtgA Research

When establishing an mtgA research program, it is methodologically sound to begin with expression and purification optimization, followed by activity assay development, before proceeding to more specialized applications such as inhibitor screening or structural studies .

What are the recommended bioinformatics resources for mtgA research?

For effective bioinformatics analysis in mtgA research, the following specialized resources and tools are recommended:

Table 3: Essential Bioinformatics Resources for mtgA Research

When applying these bioinformatics resources, a methodologically sound approach involves starting with sequence analysis to identify conserved regions, proceeding to structural prediction or analysis, and then utilizing more specialized tools based on specific research questions. Integration of multiple bioinformatics approaches typically yields more robust insights than reliance on any single method .